suvadityamuk/huggingface-transformers-code-dataset

# Troubleshooting This is a document explaining how to deal with various issues on Circle-CI. The entries may include actual solutions or pointers to Issues that cover those. ## Circle CI * pytest worker runs out of resident RAM and gets killed by `cgroups`: https://github.com/huggingface/transformers/issues/11408

transformers/.circleci/TROUBLESHOOT.md/0

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0

FROM rocm/dev-ubuntu-20.04:5.6 # rocm/pytorch has no version with 2.1.0 LABEL maintainer="Hugging Face" ARG DEBIAN_FRONTEND=noninteractive ARG PYTORCH='2.1.0' ARG TORCH_VISION='0.16.0' ARG TORCH_AUDIO='2.1.0' ARG ROCM='5.6' RUN apt update && \ apt install -y --no-install-recommends git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-dev python3-pip ffmpeg && \ apt clean && \ rm -rf /var/lib/apt/lists/* RUN python3 -m pip install --no-cache-dir --upgrade pip RUN python3 -m pip install torch==$PYTORCH torchvision==$TORCH_VISION torchaudio==$TORCH_AUDIO --index-url https://download.pytorch.org/whl/rocm$ROCM RUN python3 -m pip install --no-cache-dir --upgrade pip setuptools ninja git+https://github.com/facebookresearch/detectron2.git pytesseract "itsdangerous<2.1.0" ARG REF=main WORKDIR / # Invalidate docker cache from here if new commit is available. ADD https://api.github.com/repos/huggingface/transformers/git/refs/heads/main version.json RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch,testing,video] RUN python3 -m pip uninstall -y tensorflow flax # When installing in editable mode, `transformers` is not recognized as a package. # this line must be added in order for python to be aware of transformers. RUN cd transformers && python3 setup.py develop

transformers/docker/transformers-pytorch-amd-gpu/Dockerfile/0

{ "file_path": "transformers/docker/transformers-pytorch-amd-gpu/Dockerfile", "repo_id": "transformers", "token_count": 516 }

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# Wie kann ich ein Modell zu 🤗 Transformers hinzufügen? Die 🤗 Transformers-Bibliothek ist dank der Beiträge der Community oft in der Lage, neue Modelle anzubieten. Aber das kann ein anspruchsvolles Projekt sein und erfordert eine eingehende Kenntnis der 🤗 Transformers-Bibliothek und des zu implementierenden Modells. Bei Hugging Face versuchen wir, mehr Mitgliedern der Community die Möglichkeit zu geben, aktiv Modelle hinzuzufügen, und wir haben diese Anleitung zusammengestellt, die Sie durch den Prozess des Hinzufügens eines PyTorch-Modells führt (stellen Sie sicher, dass Sie [PyTorch installiert haben](https://pytorch.org/get-started/locally/)). <Tip> Wenn Sie daran interessiert sind, ein TensorFlow-Modell zu implementieren, werfen Sie einen Blick in die Anleitung [How to convert a 🤗 Transformers model to TensorFlow](add_tensorflow_model)! </Tip> Auf dem Weg dorthin, werden Sie: - Einblicke in bewährte Open-Source-Verfahren erhalten - die Konstruktionsprinzipien hinter einer der beliebtesten Deep-Learning-Bibliotheken verstehen - lernen Sie, wie Sie große Modelle effizient testen können - lernen Sie, wie Sie Python-Hilfsprogramme wie `black`, `ruff` und `make fix-copies` integrieren, um sauberen und lesbaren Code zu gewährleisten Ein Mitglied des Hugging Face-Teams wird Ihnen dabei zur Seite stehen, damit Sie nicht alleine sind. 🤗 ❤️ Um loszulegen, öffnen Sie eine [New model addition](https://github.com/huggingface/transformers/issues/new?assignees=&labels=New+model&template=new-model-addition.yml) Ausgabe für das Modell, das Sie in 🤗 Transformers sehen möchten. Wenn Sie nicht besonders wählerisch sind, wenn es darum geht, ein bestimmtes Modell beizusteuern, können Sie nach dem [New model label](https://github.com/huggingface/transformers/labels/New%20model) filtern, um zu sehen, ob es noch unbeanspruchte Modellanfragen gibt, und daran arbeiten. Sobald Sie eine neue Modellanfrage eröffnet haben, sollten Sie sich zunächst mit 🤗 Transformers vertraut machen, falls Sie das noch nicht sind! ## Allgemeiner Überblick über 🤗 Transformers Zunächst sollten Sie sich einen allgemeinen Überblick über 🤗 Transformers verschaffen. 🤗 Transformers ist eine sehr meinungsfreudige Bibliothek, es ist also möglich, dass Es besteht also die Möglichkeit, dass Sie mit einigen der Philosophien oder Designentscheidungen der Bibliothek nicht einverstanden sind. Aus unserer Erfahrung heraus haben wir jedoch dass die grundlegenden Designentscheidungen und Philosophien der Bibliothek entscheidend sind, um 🤗 Transformers effizient zu skalieren. Transformatoren zu skalieren und gleichzeitig die Wartungskosten auf einem vernünftigen Niveau zu halten. Ein guter erster Ansatzpunkt, um die Bibliothek besser zu verstehen, ist die Lektüre der [Dokumentation unserer Philosophie](Philosophie). Als Ergebnis unserer Arbeitsweise gibt es einige Entscheidungen, die wir versuchen, auf alle Modelle anzuwenden: - Komposition wird im Allgemeinen gegenüber Abstraktion bevorzugt - Die Duplizierung von Code ist nicht immer schlecht, wenn sie die Lesbarkeit oder Zugänglichkeit eines Modells stark verbessert - Modelldateien sind so in sich geschlossen wie möglich, so dass Sie, wenn Sie den Code eines bestimmten Modells lesen, idealerweise nur in die entsprechende Datei `modeling_....py` schauen müssen. Unserer Meinung nach ist der Code der Bibliothek nicht nur ein Mittel, um ein Produkt bereitzustellen, *z.B.* die Möglichkeit, BERT für Inferenz zu verwenden, sondern auch als das Produkt selbst, das wir verbessern wollen. Wenn Sie also ein Modell hinzufügen, ist der Benutzer nicht nur die Person, die Ihr Modell verwenden wird, sondern auch jeder, der Ihren Code liest, zu verstehen versucht und ihn möglicherweise verbessert. Lassen Sie uns daher ein wenig tiefer in das allgemeine Design der Bibliothek einsteigen. ### Überblick über die Modelle Um ein Modell erfolgreich hinzuzufügen, ist es wichtig, die Interaktion zwischen Ihrem Modell und seiner Konfiguration zu verstehen, [`PreTrainedModel`] und [`PretrainedConfig`]. Als Beispiel werden wir das Modell, das zu 🤗 Transformers hinzugefügt werden soll, `BrandNewBert` nennen. Schauen wir uns das mal an: <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers_overview.png"/> Wie Sie sehen, machen wir in 🤗 Transformers von der Vererbung Gebrauch, aber wir beschränken die Abstraktionsebene auf ein absolutes Minimum. Minimum. Es gibt nie mehr als zwei Abstraktionsebenen für ein Modell in der Bibliothek. `BrandNewBertModel` erbt von `BrandNewBertPreTrainedModel`, das wiederum von [`PreTrainedModel`] erbt und das war's. In der Regel wollen wir sicherstellen, dass ein neues Modell nur von [`PreTrainedModel`] abhängt. Die wichtigen Funktionalitäten, die jedem neuen Modell automatisch zur Verfügung gestellt werden, sind Modell automatisch bereitgestellt werden, sind [`~PreTrainedModel.from_pretrained`] und [`~PreTrainedModel.save_pretrained`], die für die Serialisierung und Deserialisierung verwendet werden. Alle anderen wichtigen Funktionalitäten, wie `BrandNewBertModel.forward` sollten vollständig in der neuen Skript `modeling_brand_new_bert.py` definiert werden. Als nächstes wollen wir sicherstellen, dass ein Modell mit einer bestimmten Kopfebene, wie z.B. `BrandNewBertForMaskedLM` nicht von `BrandNewBertModel` erbt, sondern `BrandNewBertModel` verwendet als Komponente, die im Forward Pass aufgerufen werden kann, um die Abstraktionsebene niedrig zu halten. Jedes neue Modell erfordert eine Konfigurationsklasse, genannt `BrandNewBertConfig`. Diese Konfiguration wird immer als ein Attribut in [PreTrainedModel] gespeichert und kann daher über das Attribut `config` für alle Klassen aufgerufen werden die von `BrandNewBertPreTrainedModel` erben: ```python model = BrandNewBertModel.from_pretrained("brandy/brand_new_bert") model.config # model has access to its config ``` Ähnlich wie das Modell erbt die Konfiguration grundlegende Serialisierungs- und Deserialisierungsfunktionalitäten von [`PretrainedConfig`]. Beachten Sie, dass die Konfiguration und das Modell immer in zwei verschiedene Formate serialisiert werden unterschiedliche Formate serialisiert werden - das Modell in eine *pytorch_model.bin* Datei und die Konfiguration in eine *config.json* Datei. Aufruf von [`~PreTrainedModel.save_pretrained`] wird automatisch [`~PretrainedConfig.save_pretrained`] auf, so dass sowohl das Modell als auch die Konfiguration gespeichert werden. ### Code-Stil Wenn Sie Ihr neues Modell kodieren, sollten Sie daran denken, dass Transformers eine Bibliothek mit vielen Meinungen ist und dass wir selbst ein paar Macken haben wie der Code geschrieben werden sollte :-) 1. Der Vorwärtsdurchlauf Ihres Modells sollte vollständig in die Modellierungsdatei geschrieben werden und dabei völlig unabhängig von anderen Modellen in der Bibliothek. Wenn Sie einen Block aus einem anderen Modell wiederverwenden möchten, kopieren Sie den Code und fügen ihn mit einem `# Kopiert von` ein (siehe [hier](https://github.com/huggingface/transformers/blob/v4.17.0/src/transformers/models/roberta/modeling_roberta.py#L160) für ein gutes Beispiel und [hier](pr_checks#check-copies) für weitere Dokumentation zu Copied from). 2. Der Code sollte vollständig verständlich sein, auch für einen Nicht-Muttersprachler. Das heißt, Sie sollten beschreibende Variablennamen wählen und Abkürzungen vermeiden. Ein Beispiel: `activation` ist `act` vorzuziehen. Von Variablennamen mit nur einem Buchstaben wird dringend abgeraten, es sei denn, es handelt sich um einen Index in einer for-Schleife. 3. Generell ziehen wir längeren expliziten Code einem kurzen magischen Code vor. 4. Vermeiden Sie die Unterklassifizierung von `nn.Sequential` in PyTorch, sondern unterklassifizieren Sie `nn.Module` und schreiben Sie den Vorwärtspass, so dass jeder so dass jeder, der Ihren Code verwendet, ihn schnell debuggen kann, indem er Druckanweisungen oder Haltepunkte hinzufügt. 5. Ihre Funktionssignatur sollte mit einer Typ-Annotation versehen sein. Im Übrigen sind gute Variablennamen viel lesbarer und verständlicher verständlicher als Typ-Anmerkungen. ### Übersicht der Tokenizer Noch nicht ganz fertig :-( Dieser Abschnitt wird bald hinzugefügt! ## Schritt-für-Schritt-Rezept zum Hinzufügen eines Modells zu 🤗 Transformers Jeder hat andere Vorlieben, was die Portierung eines Modells angeht. Daher kann es sehr hilfreich sein, wenn Sie sich Zusammenfassungen ansehen wie andere Mitwirkende Modelle auf Hugging Face portiert haben. Hier ist eine Liste von Blogbeiträgen aus der Community, wie man ein Modell portiert: 1. [Portierung eines GPT2-Modells](https://medium.com/huggingface/from-tensorflow-to-pytorch-265f40ef2a28) von [Thomas](https://huggingface.co/thomwolf) 2. [Portierung des WMT19 MT-Modells](https://huggingface.co/blog/porting-fsmt) von [Stas](https://huggingface.co/stas) Aus Erfahrung können wir Ihnen sagen, dass die wichtigsten Dinge, die Sie beim Hinzufügen eines Modells beachten müssen, sind: - Erfinden Sie das Rad nicht neu! Die meisten Teile des Codes, den Sie für das neue 🤗 Transformers-Modell hinzufügen werden, existieren bereits irgendwo in 🤗 Transformers. Nehmen Sie sich etwas Zeit, um ähnliche, bereits vorhandene Modelle und Tokenizer zu finden, die Sie kopieren können von. [grep](https://www.gnu.org/software/grep/) und [rg](https://github.com/BurntSushi/ripgrep) sind Ihre Freunde. Beachten Sie, dass es sehr gut möglich ist, dass der Tokenizer Ihres Modells auf einer Modellimplementierung basiert und und der Modellierungscode Ihres Modells auf einer anderen. *Z.B.* Der Modellierungscode von FSMT basiert auf BART, während der Tokenizer-Code von FSMT auf XLM basiert. - Es handelt sich eher um eine technische als um eine wissenschaftliche Herausforderung. Sie sollten mehr Zeit auf die Schaffung einer eine effiziente Debugging-Umgebung zu schaffen, als zu versuchen, alle theoretischen Aspekte des Modells in dem Papier zu verstehen. - Bitten Sie um Hilfe, wenn Sie nicht weiterkommen! Modelle sind der Kernbestandteil von 🤗 Transformers, so dass wir bei Hugging Face mehr als mehr als glücklich, Ihnen bei jedem Schritt zu helfen, um Ihr Modell hinzuzufügen. Zögern Sie nicht zu fragen, wenn Sie merken, dass Sie nicht weiterkommen. Fortschritte machen. Im Folgenden versuchen wir, Ihnen ein allgemeines Rezept an die Hand zu geben, das uns bei der Portierung eines Modells auf 🤗 Transformers am nützlichsten erschien. Die folgende Liste ist eine Zusammenfassung all dessen, was getan werden muss, um ein Modell hinzuzufügen und kann von Ihnen als To-Do verwendet werden Liste verwenden: ☐ (Optional) Verstehen der theoretischen Aspekte des Modells<br> ☐ Vorbereiten der 🤗 Transformers-Entwicklungsumgebung<br> ☐ Debugging-Umgebung des ursprünglichen Repositorys eingerichtet<br> ☐ Skript erstellt, das den Durchlauf `forward()` unter Verwendung des ursprünglichen Repositorys und des Checkpoints erfolgreich durchführt<br> ☐ Erfolgreich das Modellskelett zu 🤗 Transformers hinzugefügt<br> ☐ Erfolgreiche Umwandlung des ursprünglichen Prüfpunkts in den 🤗 Transformers-Prüfpunkt<br> ☐ Erfolgreich den Durchlauf `forward()` in 🤗 Transformers ausgeführt, der eine identische Ausgabe wie der ursprüngliche Prüfpunkt liefert<br> ☐ Modell-Tests in 🤗 Transformers abgeschlossen<br> ☐ Erfolgreich Tokenizer in 🤗 Transformers hinzugefügt<br> ☐ End-to-End-Integrationstests ausgeführt<br> ☐ Docs fertiggestellt<br> ☐ Modellgewichte in den Hub hochgeladen<br> ☐ Die Pull-Anfrage eingereicht<br> ☐ (Optional) Hinzufügen eines Demo-Notizbuchs Für den Anfang empfehlen wir in der Regel, mit einem guten theoretischen Verständnis von `BrandNewBert` zu beginnen. Wie auch immer, wenn Sie es vorziehen, die theoretischen Aspekte des Modells *on-the-job* zu verstehen, dann ist es völlig in Ordnung, direkt in die in die Code-Basis von `BrandNewBert` einzutauchen. Diese Option könnte für Sie besser geeignet sein, wenn Ihre technischen Fähigkeiten besser sind als als Ihre theoretischen Fähigkeiten, wenn Sie Schwierigkeiten haben, die Arbeit von `BrandNewBert` zu verstehen, oder wenn Sie einfach Spaß am Programmieren mehr Spaß am Programmieren haben als am Lesen wissenschaftlicher Abhandlungen. ### 1. (Optional) Theoretische Aspekte von BrandNewBert Sie sollten sich etwas Zeit nehmen, um die Abhandlung von *BrandNewBert* zu lesen, falls eine solche Beschreibung existiert. Möglicherweise gibt es große Abschnitte des Papiers, die schwer zu verstehen sind. Wenn das der Fall ist, ist das in Ordnung - machen Sie sich keine Sorgen! Das Ziel ist ist es nicht, ein tiefes theoretisches Verständnis des Papiers zu erlangen, sondern die notwendigen Informationen zu extrahieren, um das Modell effektiv in 🤗 Transformers zu implementieren. Das heißt, Sie müssen nicht zu viel Zeit auf die theoretischen Aspekten verbringen, sondern sich lieber auf die praktischen Aspekte konzentrieren, nämlich: - Welche Art von Modell ist *brand_new_bert*? BERT-ähnliches Modell nur für den Encoder? GPT2-ähnliches reines Decoder-Modell? BART-ähnliches Encoder-Decoder-Modell? Sehen Sie sich die [model_summary](model_summary) an, wenn Sie mit den Unterschieden zwischen diesen Modellen nicht vertraut sind. - Was sind die Anwendungen von *brand_new_bert*? Textklassifizierung? Texterzeugung? Seq2Seq-Aufgaben, *z.B.,* Zusammenfassungen? - Was ist die neue Eigenschaft des Modells, die es von BERT/GPT-2/BART unterscheidet? - Welches der bereits existierenden [🤗 Transformers-Modelle](https://huggingface.co/transformers/#contents) ist am ähnlichsten ähnlich wie *brand_new_bert*? - Welche Art von Tokenizer wird verwendet? Ein Satzteil-Tokenisierer? Ein Wortstück-Tokenisierer? Ist es derselbe Tokenisierer, der für für BERT oder BART? Nachdem Sie das Gefühl haben, einen guten Überblick über die Architektur des Modells erhalten zu haben, können Sie dem Hugging Face Team schreiben und Ihre Fragen stellen. Dazu können Fragen zur Architektur des Modells gehören, seiner Aufmerksamkeitsebene usw. Wir werden Ihnen gerne weiterhelfen. ### 2. Bereiten Sie als nächstes Ihre Umgebung vor 1. Forken Sie das [Repository](https://github.com/huggingface/transformers), indem Sie auf der Seite des Repositorys auf die Schaltfläche 'Fork' klicken. Seite des Repositorys klicken. Dadurch wird eine Kopie des Codes unter Ihrem GitHub-Benutzerkonto erstellt. 2. Klonen Sie Ihren `transformers` Fork auf Ihre lokale Festplatte und fügen Sie das Basis-Repository als Remote hinzu: ```bash git clone https://github.com/[your Github handle]/transformers.git cd transformers git remote add upstream https://github.com/huggingface/transformers.git ``` 3. Richten Sie eine Entwicklungsumgebung ein, indem Sie z.B. den folgenden Befehl ausführen: ```bash python -m venv .env source .env/bin/activate pip install -e ".[dev]" ``` Abhängig von Ihrem Betriebssystem und da die Anzahl der optionalen Abhängigkeiten von Transformers wächst, kann es sein, dass Sie bei diesem Befehl einen Fehler mit diesem Befehl. Stellen Sie in diesem Fall sicher, dass Sie das Deep Learning Framework, mit dem Sie arbeiten, installieren (PyTorch, TensorFlow und/oder Flax) und führen Sie es aus: ```bash pip install -e ".[quality]" ``` was für die meisten Anwendungsfälle ausreichend sein sollte. Sie können dann zum übergeordneten Verzeichnis zurückkehren ```bash cd .. ``` 4. Wir empfehlen, die PyTorch-Version von *brand_new_bert* zu Transformers hinzuzufügen. Um PyTorch zu installieren, folgen Sie bitte den Anweisungen auf https://pytorch.org/get-started/locally/. **Anmerkung:** Sie müssen CUDA nicht installiert haben. Es reicht aus, das neue Modell auf der CPU zum Laufen zu bringen. 5. Um *brand_new_bert* zu portieren, benötigen Sie außerdem Zugriff auf das Original-Repository: ```bash git clone https://github.com/org_that_created_brand_new_bert_org/brand_new_bert.git cd brand_new_bert pip install -e . ``` Jetzt haben Sie eine Entwicklungsumgebung eingerichtet, um *brand_new_bert* auf 🤗 Transformers zu portieren. ### 3.-4. Führen Sie einen Pre-Training-Checkpoint mit dem Original-Repository durch Zunächst werden Sie mit dem ursprünglichen *brand_new_bert* Repository arbeiten. Oft ist die ursprüngliche Implementierung sehr "forschungslastig". Das bedeutet, dass es an Dokumentation mangeln kann und der Code schwer zu verstehen sein kann. Aber das sollte genau Ihre Motivation sein, *brand_new_bert* neu zu implementieren. Eines unserer Hauptziele bei Hugging Face ist es, *die Menschen dazu zu bringen auf den Schultern von Giganten zu stehen*, was sich hier sehr gut darin ausdrückt, dass wir ein funktionierendes Modell nehmen und es umschreiben, um es so es so **zugänglich, benutzerfreundlich und schön** wie möglich zu machen. Dies ist die wichtigste Motivation für die Neuimplementierung von Modelle in 🤗 Transformers umzuwandeln - der Versuch, komplexe neue NLP-Technologie für **jeden** zugänglich zu machen. Sie sollten damit beginnen, indem Sie in das Original-Repository eintauchen. Die erfolgreiche Ausführung des offiziellen Pre-Trainingsmodells im Original-Repository ist oft **der schwierigste** Schritt. Unserer Erfahrung nach ist es sehr wichtig, dass Sie einige Zeit damit verbringen, sich mit der ursprünglichen Code-Basis vertraut zu machen. Sie müssen das Folgende herausfinden: - Wo finden Sie die vortrainierten Gewichte? - Wie lädt man die vorab trainierten Gewichte in das entsprechende Modell? - Wie kann der Tokenizer unabhängig vom Modell ausgeführt werden? - Verfolgen Sie einen Forward Pass, damit Sie wissen, welche Klassen und Funktionen für einen einfachen Forward Pass erforderlich sind. Normalerweise, müssen Sie nur diese Funktionen reimplementieren. - Sie müssen in der Lage sein, die wichtigen Komponenten des Modells zu finden: Wo befindet sich die Klasse des Modells? Gibt es Unterklassen des Modells, *z.B.* EncoderModel, DecoderModel? Wo befindet sich die Selbstaufmerksamkeitsschicht? Gibt es mehrere verschiedene Aufmerksamkeitsebenen, *z.B.* *Selbstaufmerksamkeit*, *Kreuzaufmerksamkeit*...? - Wie können Sie das Modell in der ursprünglichen Umgebung des Repo debuggen? Müssen Sie *print* Anweisungen hinzufügen, können Sie mit einem interaktiven Debugger wie *ipdb* arbeiten oder sollten Sie eine effiziente IDE zum Debuggen des Modells verwenden, wie z.B. PyCharm? Es ist sehr wichtig, dass Sie, bevor Sie mit der Portierung beginnen, den Code im Original-Repository **effizient** debuggen können Repository können! Denken Sie auch daran, dass Sie mit einer Open-Source-Bibliothek arbeiten, also zögern Sie nicht, ein Problem oder oder sogar eine Pull-Anfrage im Original-Repository zu stellen. Die Betreuer dieses Repositorys sind wahrscheinlich sehr froh darüber dass jemand in ihren Code schaut! An diesem Punkt liegt es wirklich an Ihnen, welche Debugging-Umgebung und Strategie Sie zum Debuggen des ursprünglichen Modell zu debuggen. Wir raten dringend davon ab, eine kostspielige GPU-Umgebung einzurichten, sondern arbeiten Sie einfach auf einer CPU, sowohl wenn Sie mit dem in das ursprüngliche Repository einzutauchen und auch, wenn Sie beginnen, die 🤗 Transformers-Implementierung des Modells zu schreiben. Nur ganz am Ende, wenn das Modell bereits erfolgreich auf 🤗 Transformers portiert wurde, sollte man überprüfen, ob das Modell auch auf der GPU wie erwartet funktioniert. Im Allgemeinen gibt es zwei mögliche Debugging-Umgebungen für die Ausführung des Originalmodells - [Jupyter notebooks](https://jupyter.org/) / [google colab](https://colab.research.google.com/notebooks/intro.ipynb) - Lokale Python-Skripte. Jupyter-Notebooks haben den Vorteil, dass sie eine zellenweise Ausführung ermöglichen, was hilfreich sein kann, um logische Komponenten besser voneinander zu trennen und logische Komponenten voneinander zu trennen und schnellere Debugging-Zyklen zu haben, da Zwischenergebnisse gespeichert werden können. Außerdem, Außerdem lassen sich Notebooks oft leichter mit anderen Mitwirkenden teilen, was sehr hilfreich sein kann, wenn Sie das Hugging Face Team um Hilfe bitten möchten. Face Team um Hilfe bitten. Wenn Sie mit Jupyter-Notizbüchern vertraut sind, empfehlen wir Ihnen dringend, mit ihnen zu arbeiten. Der offensichtliche Nachteil von Jupyter-Notizbüchern ist, dass Sie, wenn Sie nicht daran gewöhnt sind, mit ihnen zu arbeiten, einige Zeit damit verbringen müssen einige Zeit damit verbringen müssen, sich an die neue Programmierumgebung zu gewöhnen, und dass Sie möglicherweise Ihre bekannten Debugging-Tools nicht mehr verwenden können wie z.B. `ipdb` nicht mehr verwenden können. Für jede Codebasis ist es immer ein guter erster Schritt, einen **kleinen** vortrainierten Checkpoint zu laden und in der Lage zu sein, einen einzelnen Vorwärtsdurchlauf mit einem Dummy-Integer-Vektor von Eingabe-IDs als Eingabe zu reproduzieren. Ein solches Skript könnte wie folgt aussehen (in Pseudocode): ```python model = BrandNewBertModel.load_pretrained_checkpoint("/path/to/checkpoint/") input_ids = [0, 4, 5, 2, 3, 7, 9] # vector of input ids original_output = model.predict(input_ids) ``` Was die Debugging-Strategie anbelangt, so können Sie im Allgemeinen aus mehreren Strategien wählen: - Zerlegen Sie das ursprüngliche Modell in viele kleine testbare Komponenten und führen Sie für jede dieser Komponenten einen Vorwärtsdurchlauf zur Überprüfung - Zerlegen Sie das ursprüngliche Modell nur in den ursprünglichen *Tokenizer* und das ursprüngliche *Modell*, führen Sie einen Vorwärtsdurchlauf für diese Komponenten durch und verwenden Sie dazwischenliegende Druckanweisungen oder Haltepunkte zur Überprüfung. Auch hier bleibt es Ihnen überlassen, welche Strategie Sie wählen. Oft ist die eine oder die andere Strategie vorteilhaft, je nach der ursprünglichen Codebasis Basis. Wenn die ursprüngliche Codebasis es Ihnen erlaubt, das Modell in kleinere Teilkomponenten zu zerlegen, *z.B.* wenn die ursprüngliche Code-Basis problemlos im Eager-Modus ausgeführt werden kann, lohnt es sich in der Regel, dies zu tun. Es gibt einige wichtige Vorteile am Anfang den schwierigeren Weg zu gehen: - Wenn Sie später das ursprüngliche Modell mit der Hugging Face-Implementierung vergleichen, können Sie automatisch überprüfen, ob für jede Komponente einzeln überprüfen, ob die entsprechende Komponente der 🤗 Transformers-Implementierung übereinstimmt, anstatt sich auf anstatt sich auf den visuellen Vergleich über Druckanweisungen zu verlassen - können Sie das große Problem der Portierung eines Modells in kleinere Probleme der Portierung einzelner Komponenten zerlegen einzelnen Komponenten zu zerlegen und so Ihre Arbeit besser zu strukturieren - Die Aufteilung des Modells in logisch sinnvolle Komponenten hilft Ihnen, einen besseren Überblick über das Design des Modells zu bekommen und somit das Modell besser zu verstehen - In einem späteren Stadium helfen Ihnen diese komponentenweisen Tests dabei, sicherzustellen, dass keine Regressionen auftreten, während Sie fortfahren Ihren Code ändern [Lysandre's](https://gist.github.com/LysandreJik/db4c948f6b4483960de5cbac598ad4ed) Integrationstests für ELECTRA gibt ein schönes Beispiel dafür, wie dies geschehen kann. Wenn die ursprüngliche Codebasis jedoch sehr komplex ist oder nur die Ausführung von Zwischenkomponenten in einem kompilierten Modus erlaubt, könnte es zu zeitaufwändig oder sogar unmöglich sein, das Modell in kleinere testbare Teilkomponenten zu zerlegen. Ein gutes Beispiel ist die [T5's MeshTensorFlow](https://github.com/tensorflow/mesh/tree/master/mesh_tensorflow) Bibliothek, die sehr komplex ist sehr komplex ist und keine einfache Möglichkeit bietet, das Modell in seine Unterkomponenten zu zerlegen. Bei solchen Bibliotheken ist man oft auf die Überprüfung von Druckanweisungen angewiesen. Unabhängig davon, welche Strategie Sie wählen, ist die empfohlene Vorgehensweise oft die gleiche, nämlich dass Sie mit der Fehlersuche in den die Anfangsebenen zuerst und die Endebenen zuletzt debuggen. Es wird empfohlen, dass Sie die Ausgaben der folgenden Ebenen abrufen, entweder durch Druckanweisungen oder Unterkomponentenfunktionen Schichten in der folgenden Reihenfolge abrufen: 1. Rufen Sie die Eingabe-IDs ab, die an das Modell übergeben wurden 2. Rufen Sie die Worteinbettungen ab 3. Rufen Sie die Eingabe der ersten Transformer-Schicht ab 4. Rufen Sie die Ausgabe der ersten Transformer-Schicht ab 5. Rufen Sie die Ausgabe der folgenden n - 1 Transformer-Schichten ab 6. Rufen Sie die Ausgabe des gesamten BrandNewBert Modells ab Die Eingabe-IDs sollten dabei aus einem Array von Ganzzahlen bestehen, *z.B.* `input_ids = [0, 4, 4, 3, 2, 4, 1, 7, 19]` Die Ausgaben der folgenden Schichten bestehen oft aus mehrdimensionalen Float-Arrays und können wie folgt aussehen: ``` [[ [-0.1465, -0.6501, 0.1993, ..., 0.1451, 0.3430, 0.6024], [-0.4417, -0.5920, 0.3450, ..., -0.3062, 0.6182, 0.7132], [-0.5009, -0.7122, 0.4548, ..., -0.3662, 0.6091, 0.7648], ..., [-0.5613, -0.6332, 0.4324, ..., -0.3792, 0.7372, 0.9288], [-0.5416, -0.6345, 0.4180, ..., -0.3564, 0.6992, 0.9191], [-0.5334, -0.6403, 0.4271, ..., -0.3339, 0.6533, 0.8694]]], ``` Wir erwarten, dass jedes zu 🤗 Transformers hinzugefügte Modell eine Reihe von Integrationstests besteht, was bedeutet, dass das ursprüngliche Modell und die neu implementierte Version in 🤗 Transformers exakt dieselbe Ausgabe liefern müssen, und zwar mit einer Genauigkeit von 0,001! Da es normal ist, dass das exakt gleiche Modell, das in verschiedenen Bibliotheken geschrieben wurde, je nach Bibliotheksrahmen eine leicht unterschiedliche Ausgabe liefern kann eine leicht unterschiedliche Ausgabe liefern kann, akzeptieren wir eine Fehlertoleranz von 1e-3 (0,001). Es reicht nicht aus, wenn das Modell fast das gleiche Ergebnis liefert, sie müssen fast identisch sein. Daher werden Sie sicherlich die Zwischenergebnisse Zwischenergebnisse der 🤗 Transformers-Version mehrfach mit den Zwischenergebnissen der ursprünglichen Implementierung von *brand_new_bert* vergleichen. In diesem Fall ist eine **effiziente** Debugging-Umgebung des ursprünglichen Repositorys absolut wichtig ist. Hier sind einige Ratschläge, um Ihre Debugging-Umgebung so effizient wie möglich zu gestalten. - Finden Sie den besten Weg, um Zwischenergebnisse zu debuggen. Ist das ursprüngliche Repository in PyTorch geschrieben? Dann sollten Sie dann sollten Sie sich wahrscheinlich die Zeit nehmen, ein längeres Skript zu schreiben, das das ursprüngliche Modell in kleinere Unterkomponenten zerlegt, um Zwischenwerte abzurufen. Ist das ursprüngliche Repository in Tensorflow 1 geschrieben? Dann müssen Sie sich möglicherweise auf die TensorFlow Druckoperationen wie [tf.print](https://www.tensorflow.org/api_docs/python/tf/print) verlassen, um die Zwischenwerte auszugeben. Ist das ursprüngliche Repository in Jax geschrieben? Dann stellen Sie sicher, dass das Modell **nicht jitted** ist, wenn wenn Sie den Vorwärtsdurchlauf ausführen, *z.B.* schauen Sie sich [dieser Link](https://github.com/google/jax/issues/196) an. - Verwenden Sie den kleinsten vortrainierten Prüfpunkt, den Sie finden können. Je kleiner der Prüfpunkt ist, desto schneller wird Ihr Debugging-Zyklus wird. Es ist nicht effizient, wenn Ihr vorab trainiertes Modell so groß ist, dass Ihr Vorwärtsdurchlauf mehr als 10 Sekunden dauert. Falls nur sehr große Checkpoints verfügbar sind, kann es sinnvoller sein, ein Dummy-Modell in der neuen Umgebung mit zufällig initialisierten Gewichten zu erstellen und diese Gewichte zum Vergleich mit der 🤗 Transformers-Version Ihres Modells - Vergewissern Sie sich, dass Sie den einfachsten Weg wählen, um einen Forward Pass im ursprünglichen Repository aufzurufen. Idealerweise sollten Sie die Funktion im originalen Repository finden, die **nur** einen einzigen Vorwärtspass aufruft, *d.h.* die oft aufgerufen wird Vorhersagen", "Auswerten", "Vorwärts" oder "Aufruf" genannt wird. Sie wollen keine Funktion debuggen, die `forward` aufruft mehrfach aufruft, *z.B.* um Text zu erzeugen, wie `autoregressive_sample`, `generate`. - Versuchen Sie, die Tokenisierung vom *Forward*-Pass des Modells zu trennen. Wenn das Original-Repository Beispiele zeigt, bei denen Sie eine Zeichenkette eingeben müssen, dann versuchen Sie herauszufinden, an welcher Stelle im Vorwärtsaufruf die Zeichenketteneingabe in Eingabe-IDs geändert wird geändert wird und beginnen Sie an dieser Stelle. Das könnte bedeuten, dass Sie möglicherweise selbst ein kleines Skript schreiben oder den Originalcode so ändern müssen, dass Sie die ids direkt eingeben können, anstatt eine Zeichenkette einzugeben. - Vergewissern Sie sich, dass sich das Modell in Ihrem Debugging-Setup **nicht** im Trainingsmodus befindet, der oft dazu führt, dass das Modell Dies führt häufig zu zufälligen Ergebnissen, da das Modell mehrere Dropout-Schichten enthält. Stellen Sie sicher, dass der Vorwärtsdurchlauf in Ihrer Debugging Umgebung **deterministisch** ist, damit die Dropout-Schichten nicht verwendet werden. Oder verwenden Sie *transformers.utils.set_seed*. wenn sich die alte und die neue Implementierung im selben Framework befinden. Im folgenden Abschnitt finden Sie genauere Details/Tipps, wie Sie dies für *brand_new_bert* tun können. ### 5.-14. Portierung von BrandNewBert auf 🤗 Transformatoren Als nächstes können Sie endlich damit beginnen, neuen Code zu 🤗 Transformers hinzuzufügen. Gehen Sie in den Klon Ihres 🤗 Transformers Forks: ```bash cd transformers ``` In dem speziellen Fall, dass Sie ein Modell hinzufügen, dessen Architektur genau mit der Modellarchitektur eines Modells übereinstimmt, müssen Sie nur ein Konvertierungsskript hinzufügen, wie in [diesem Abschnitt](#write-a-conversion-script) beschrieben. In diesem Fall können Sie einfach die gesamte Modellarchitektur des bereits vorhandenen Modells wiederverwenden. Andernfalls beginnen wir mit der Erstellung eines neuen Modells. Sie haben hier zwei Möglichkeiten: - `transformers-cli add-new-model-like`, um ein neues Modell wie ein bestehendes hinzuzufügen - `transformers-cli add-new-model`, um ein neues Modell aus unserer Vorlage hinzuzufügen (sieht dann aus wie BERT oder Bart, je nachdem, welche Art von Modell Sie wählen) In beiden Fällen werden Sie mit einem Fragebogen aufgefordert, die grundlegenden Informationen zu Ihrem Modell auszufüllen. Für den zweiten Befehl müssen Sie `cookiecutter` installieren, weitere Informationen dazu finden Sie [hier](https://github.com/huggingface/transformers/tree/main/templates/adding_a_new_model). **Eröffnen Sie einen Pull Request auf dem Haupt-Repositorium huggingface/transformers** Bevor Sie mit der Anpassung des automatisch generierten Codes beginnen, ist es nun an der Zeit, einen "Work in progress (WIP)" Pull Anfrage, *z.B.* "[WIP] Add *brand_new_bert*", in 🤗 Transformers zu öffnen, damit Sie und das Hugging Face Team Seite an Seite an der Integration des Modells in 🤗 Transformers arbeiten können. Sie sollten Folgendes tun: 1. Erstellen Sie eine Verzweigung mit einem beschreibenden Namen von Ihrer Hauptverzweigung ```bash git checkout -b add_brand_new_bert ``` 2. Bestätigen Sie den automatisch generierten Code: ```bash git add . git commit ``` 3. Abrufen und zurücksetzen auf die aktuelle Haupt ```bash git fetch upstream git rebase upstream/main ``` 4. Übertragen Sie die Änderungen auf Ihr Konto mit: ```bash git push -u origin a-descriptive-name-for-my-changes ``` 5. Wenn Sie zufrieden sind, gehen Sie auf die Webseite Ihrer Abspaltung auf GitHub. Klicken Sie auf "Pull request". Stellen Sie sicher, dass Sie das GitHub-Handle einiger Mitglieder des Hugging Face-Teams als Reviewer hinzuzufügen, damit das Hugging Face-Team über zukünftige Änderungen informiert wird. zukünftige Änderungen benachrichtigt wird. 6. Ändern Sie den PR in einen Entwurf, indem Sie auf der rechten Seite der GitHub-Pull-Request-Webseite auf "In Entwurf umwandeln" klicken. Vergessen Sie im Folgenden nicht, wenn Sie Fortschritte gemacht haben, Ihre Arbeit zu committen und in Ihr Konto zu pushen, damit sie in der Pull-Anfrage erscheint. damit sie in der Pull-Anfrage angezeigt wird. Außerdem sollten Sie darauf achten, dass Sie Ihre Arbeit von Zeit zu Zeit mit dem aktuellen main von Zeit zu Zeit zu aktualisieren, indem Sie dies tun: ```bash git fetch upstream git merge upstream/main ``` Generell sollten Sie alle Fragen, die Sie in Bezug auf das Modell oder Ihre Implementierung haben, in Ihrem PR stellen und in der PR diskutiert/gelöst werden. Auf diese Weise wird das Hugging Face Team immer benachrichtigt, wenn Sie neuen Code einreichen oder wenn Sie eine Frage haben. Es ist oft sehr hilfreich, das Hugging Face-Team auf Ihren hinzugefügten Code hinzuweisen, damit das Hugging Face-Team Ihr Problem oder Ihre Frage besser verstehen kann. Face-Team Ihr Problem oder Ihre Frage besser verstehen kann. Gehen Sie dazu auf die Registerkarte "Geänderte Dateien", auf der Sie alle Ihre Änderungen sehen, gehen Sie zu einer Zeile, zu der Sie eine Frage stellen möchten eine Frage stellen möchten, und klicken Sie auf das "+"-Symbol, um einen Kommentar hinzuzufügen. Wenn eine Frage oder ein Problem gelöst wurde, können Sie auf die Schaltfläche "Lösen" des erstellten Kommentars klicken. Auf dieselbe Weise wird das Hugging Face-Team Kommentare öffnen, wenn es Ihren Code überprüft. Wir empfehlen, die meisten Fragen auf GitHub in Ihrem PR zu stellen. Für einige sehr allgemeine Fragen, die für die Öffentlichkeit nicht sehr nützlich sind, können Sie das Hugging Face Team per Slack oder E-Mail zu stellen. **5. Passen Sie den Code der generierten Modelle für brand_new_bert** an. Zunächst werden wir uns nur auf das Modell selbst konzentrieren und uns nicht um den Tokenizer kümmern. Den gesamten relevanten Code sollten Sie finden Sie in den generierten Dateien `src/transformers/models/brand_new_bert/modeling_brand_new_bert.py` und `src/transformers/models/brand_new_bert/configuration_brand_new_bert.py`. Jetzt können Sie endlich mit dem Programmieren beginnen :). Der generierte Code in `src/transformers/models/brand_new_bert/modeling_brand_new_bert.py` wird entweder die gleiche Architektur wie BERT haben, wenn wenn es sich um ein reines Encoder-Modell handelt oder BART, wenn es sich um ein Encoder-Decoder-Modell handelt. An diesem Punkt sollten Sie sich daran erinnern, was was Sie am Anfang über die theoretischen Aspekte des Modells gelernt haben: *Wie unterscheidet sich das Modell von BERT oder BART?*". Implementieren Sie diese Änderungen, was oft bedeutet, dass Sie die *Selbstaufmerksamkeitsschicht*, die Reihenfolge der Normalisierungsschicht usw. ändern müssen. Schicht usw... Auch hier ist es oft nützlich, sich die ähnliche Architektur bereits bestehender Modelle in Transformers anzusehen, um ein besseres Gefühl dafür zu bekommen ein besseres Gefühl dafür zu bekommen, wie Ihr Modell implementiert werden sollte. **Beachten Sie**, dass Sie an diesem Punkt nicht sehr sicher sein müssen, dass Ihr Code völlig korrekt oder sauber ist. Vielmehr ist es Sie sollten vielmehr eine erste *unbereinigte*, kopierte Version des ursprünglichen Codes in src/transformers/models/brand_new_bert/modeling_brand_new_bert.py" hinzuzufügen, bis Sie das Gefühl haben, dass der gesamte notwendige Code hinzugefügt wurde. Unserer Erfahrung nach ist es viel effizienter, schnell eine erste Version des erforderlichen Codes hinzuzufügen und den Code iterativ mit dem Konvertierungsskript zu verbessern/korrigieren, wie im nächsten Abschnitt beschrieben. Das einzige, was zu diesem Zeitpunkt funktionieren muss, ist, dass Sie die 🤗 Transformers-Implementierung von *brand_new_bert* instanziieren können, *d.h.* der folgende Befehl sollte funktionieren: ```python from transformers import BrandNewBertModel, BrandNewBertConfig model = BrandNewBertModel(BrandNewBertConfig()) ``` Der obige Befehl erstellt ein Modell gemäß den Standardparametern, die in `BrandNewBertConfig()` definiert sind, mit zufälligen Gewichten und stellt damit sicher, dass die `init()` Methoden aller Komponenten funktionieren. Beachten Sie, dass alle zufälligen Initialisierungen in der Methode `_init_weights` Ihres `BrandnewBertPreTrainedModel` stattfinden sollten. Klasse erfolgen sollte. Sie sollte alle Blattmodule in Abhängigkeit von den Variablen der Konfiguration initialisieren. Hier ist ein Beispiel mit der BERT `_init_weights` Methode: ```py def _init_weights(self, module): """Initialize the weights""" if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) ``` Sie können weitere benutzerdefinierte Schemata verwenden, wenn Sie eine spezielle Initialisierung für einige Module benötigen. Zum Beispiel in `Wav2Vec2ForPreTraining` müssen die letzten beiden linearen Schichten die Initialisierung des regulären PyTorch `nn.Linear` haben. aber alle anderen sollten eine Initialisierung wie oben verwenden. Dies ist wie folgt kodiert: ```py def _init_weights(self, module): """Initialize the weights""" if isinstance(module, Wav2Vec2ForPreTraining): module.project_hid.reset_parameters() module.project_q.reset_parameters() module.project_hid._is_hf_initialized = True module.project_q._is_hf_initialized = True elif isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() ``` Das Flag `_is_hf_initialized` wird intern verwendet, um sicherzustellen, dass wir ein Submodul nur einmal initialisieren. Wenn Sie es auf `True` für `module.project_q` und `module.project_hid` setzen, stellen wir sicher, dass die benutzerdefinierte Initialisierung, die wir vorgenommen haben, später nicht überschrieben wird, die Funktion `_init_weights` nicht auf sie angewendet wird. **6. Schreiben Sie ein Konvertierungsskript** Als nächstes sollten Sie ein Konvertierungsskript schreiben, mit dem Sie den Checkpoint, den Sie zum Debuggen von *brand_new_bert* im im ursprünglichen Repository in einen Prüfpunkt konvertieren, der mit Ihrer gerade erstellten 🤗 Transformers-Implementierung von *brand_new_bert*. Es ist nicht ratsam, das Konvertierungsskript von Grund auf neu zu schreiben, sondern die bereits bestehenden Konvertierungsskripten in 🤗 Transformers nach einem Skript zu suchen, das für die Konvertierung eines ähnlichen Modells verwendet wurde, das im demselben Framework wie *brand_new_bert* geschrieben wurde. Normalerweise reicht es aus, ein bereits vorhandenes Konvertierungsskript zu kopieren und es für Ihren Anwendungsfall leicht anzupassen. Zögern Sie nicht, das Hugging Face Team zu bitten, Sie auf ein ähnliches, bereits vorhandenes Konvertierungsskript für Ihr Modell zu finden. - Wenn Sie ein Modell von TensorFlow nach PyTorch portieren, ist ein guter Ausgangspunkt das Konvertierungsskript von BERT [hier](https://github.com/huggingface/transformers/blob/7acfa95afb8194f8f9c1f4d2c6028224dbed35a2/src/transformers/models/bert/modeling_bert.py#L91) - Wenn Sie ein Modell von PyTorch nach PyTorch portieren, ist ein guter Ausgangspunkt das Konvertierungsskript von BART [hier](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bart/convert_bart_original_pytorch_checkpoint_to_pytorch.py) Im Folgenden werden wir kurz erklären, wie PyTorch-Modelle Ebenengewichte speichern und Ebenennamen definieren. In PyTorch wird der Name einer Ebene durch den Namen des Klassenattributs definiert, das Sie der Ebene geben. Lassen Sie uns ein Dummy-Modell in PyTorch, das wir `SimpleModel` nennen, wie folgt: ```python from torch import nn class SimpleModel(nn.Module): def __init__(self): super().__init__() self.dense = nn.Linear(10, 10) self.intermediate = nn.Linear(10, 10) self.layer_norm = nn.LayerNorm(10) ``` Jetzt können wir eine Instanz dieser Modelldefinition erstellen, die alle Gewichte ausfüllt: `dense`, `intermediate`, `layer_norm` mit zufälligen Gewichten. Wir können das Modell ausdrucken, um seine Architektur zu sehen ```python model = SimpleModel() print(model) ``` Dies gibt folgendes aus: ``` SimpleModel( (dense): Linear(in_features=10, out_features=10, bias=True) (intermediate): Linear(in_features=10, out_features=10, bias=True) (layer_norm): LayerNorm((10,), eps=1e-05, elementwise_affine=True) ) ``` Wir können sehen, dass die Ebenennamen durch den Namen des Klassenattributs in PyTorch definiert sind. Sie können die Gewichtswerte Werte einer bestimmten Ebene anzeigen lassen: ```python print(model.dense.weight.data) ``` um zu sehen, dass die Gewichte zufällig initialisiert wurden ``` tensor([[-0.0818, 0.2207, -0.0749, -0.0030, 0.0045, -0.1569, -0.1598, 0.0212, -0.2077, 0.2157], [ 0.1044, 0.0201, 0.0990, 0.2482, 0.3116, 0.2509, 0.2866, -0.2190, 0.2166, -0.0212], [-0.2000, 0.1107, -0.1999, -0.3119, 0.1559, 0.0993, 0.1776, -0.1950, -0.1023, -0.0447], [-0.0888, -0.1092, 0.2281, 0.0336, 0.1817, -0.0115, 0.2096, 0.1415, -0.1876, -0.2467], [ 0.2208, -0.2352, -0.1426, -0.2636, -0.2889, -0.2061, -0.2849, -0.0465, 0.2577, 0.0402], [ 0.1502, 0.2465, 0.2566, 0.0693, 0.2352, -0.0530, 0.1859, -0.0604, 0.2132, 0.1680], [ 0.1733, -0.2407, -0.1721, 0.1484, 0.0358, -0.0633, -0.0721, -0.0090, 0.2707, -0.2509], [-0.1173, 0.1561, 0.2945, 0.0595, -0.1996, 0.2988, -0.0802, 0.0407, 0.1829, -0.1568], [-0.1164, -0.2228, -0.0403, 0.0428, 0.1339, 0.0047, 0.1967, 0.2923, 0.0333, -0.0536], [-0.1492, -0.1616, 0.1057, 0.1950, -0.2807, -0.2710, -0.1586, 0.0739, 0.2220, 0.2358]]). ``` Im Konvertierungsskript sollten Sie diese zufällig initialisierten Gewichte mit den genauen Gewichten der entsprechenden Ebene im Kontrollpunkt. *Z.B.* ```python # retrieve matching layer weights, e.g. by # recursive algorithm layer_name = "dense" pretrained_weight = array_of_dense_layer model_pointer = getattr(model, "dense") model_pointer.weight.data = torch.from_numpy(pretrained_weight) ``` Dabei müssen Sie sicherstellen, dass jedes zufällig initialisierte Gewicht Ihres PyTorch-Modells und sein entsprechendes Checkpoint-Gewicht in **Form und Name** genau übereinstimmen. Zu diesem Zweck ist es **notwendig**, assert Anweisungen für die Form hinzuzufügen und die Namen der Checkpoint-Gewichte auszugeben. Sie sollten z.B. Anweisungen hinzufügen wie: ```python assert ( model_pointer.weight.shape == pretrained_weight.shape ), f"Pointer shape of random weight {model_pointer.shape} and array shape of checkpoint weight {pretrained_weight.shape} mismatched" ``` Außerdem sollten Sie die Namen der beiden Gewichte ausdrucken, um sicherzustellen, dass sie übereinstimmen, *z.B.*. ```python logger.info(f"Initialize PyTorch weight {layer_name} from {pretrained_weight.name}") ``` Wenn entweder die Form oder der Name nicht übereinstimmt, haben Sie wahrscheinlich das falsche Kontrollpunktgewicht einer zufällig Ebene der 🤗 Transformers-Implementierung zugewiesen. Eine falsche Form ist höchstwahrscheinlich auf eine falsche Einstellung der Konfigurationsparameter in `BrandNewBertConfig()` zurückzuführen, die nicht genau mit denen übereinstimmen, die für den zu konvertierenden Prüfpunkt verwendet wurden. Es könnte aber auch sein, dass die PyTorch-Implementierung eines Layers erfordert, dass das Gewicht vorher transponiert wird. Schließlich sollten Sie auch überprüfen, ob **alle** erforderlichen Gewichte initialisiert sind und alle Checkpoint-Gewichte ausgeben, die die nicht zur Initialisierung verwendet wurden, um sicherzustellen, dass das Modell korrekt konvertiert wurde. Es ist völlig normal, dass die Konvertierungsversuche entweder mit einer falschen Shape-Anweisung oder einer falschen Namenszuweisung fehlschlagen. Das liegt höchstwahrscheinlich daran, dass entweder Sie haben falsche Parameter in `BrandNewBertConfig()` verwendet, haben eine falsche Architektur in der 🤗 Transformers Implementierung, Sie haben einen Fehler in den `init()` Funktionen einer der Komponenten der 🤗 Transformers Implementierung oder Sie müssen eine der Kontrollpunktgewichte transponieren. Dieser Schritt sollte mit dem vorherigen Schritt wiederholt werden, bis alle Gewichte des Kontrollpunkts korrekt in das Transformers-Modell geladen sind. Nachdem Sie den Prüfpunkt korrekt in die 🤗 Transformers-Implementierung geladen haben, können Sie das Modell das Modell unter einem Ordner Ihrer Wahl `/path/to/converted/checkpoint/folder` speichern, der dann sowohl ein Datei `pytorch_model.bin` und eine Datei `config.json` enthalten sollte: ```python model.save_pretrained("/path/to/converted/checkpoint/folder") ``` **7. Implementieren Sie den Vorwärtspass** Nachdem es Ihnen gelungen ist, die trainierten Gewichte korrekt in die 🤗 Transformers-Implementierung zu laden, sollten Sie nun dafür sorgen sicherstellen, dass der Forward Pass korrekt implementiert ist. In [Machen Sie sich mit dem ursprünglichen Repository vertraut](#3-4-führen-sie-einen-pre-training-checkpoint-mit-dem-original-repository-durch) haben Sie bereits ein Skript erstellt, das einen Forward Pass Durchlauf des Modells unter Verwendung des Original-Repositorys durchführt. Jetzt sollten Sie ein analoges Skript schreiben, das die 🤗 Transformers Implementierung anstelle der Originalimplementierung verwenden. Es sollte wie folgt aussehen: ```python model = BrandNewBertModel.from_pretrained("/path/to/converted/checkpoint/folder") input_ids = [0, 4, 4, 3, 2, 4, 1, 7, 19] output = model(input_ids).last_hidden_states ``` Es ist sehr wahrscheinlich, dass die 🤗 Transformers-Implementierung und die ursprüngliche Modell-Implementierung nicht genau die gleiche Ausgabe liefern. beim ersten Mal nicht die gleiche Ausgabe liefern oder dass der Vorwärtsdurchlauf einen Fehler auslöst. Seien Sie nicht enttäuscht - das ist zu erwarten! Erstens, sollten Sie sicherstellen, dass der Vorwärtsdurchlauf keine Fehler auslöst. Es passiert oft, dass die falschen Dimensionen verwendet werden verwendet werden, was zu einem *Dimensionality mismatch* Fehler führt oder dass der falsche Datentyp verwendet wird, *z.B.* `torch.long` anstelle von `torch.float32`. Zögern Sie nicht, das Hugging Face Team um Hilfe zu bitten, wenn Sie bestimmte Fehler nicht lösen können. bestimmte Fehler nicht lösen können. Um sicherzustellen, dass die Implementierung von 🤗 Transformers korrekt funktioniert, müssen Sie sicherstellen, dass die Ausgaben einer Genauigkeit von `1e-3` entsprechen. Zunächst sollten Sie sicherstellen, dass die Ausgabeformen identisch sind, *d.h.*. Die Ausgabeform *outputs.shape* sollte für das Skript der 🤗 Transformers-Implementierung und die ursprüngliche Implementierung ergeben. Als nächstes sollten Sie sicherstellen, dass auch die Ausgabewerte identisch sind. Dies ist einer der schwierigsten Teile des Hinzufügens eines neuen Modells. Häufige Fehler, warum die Ausgaben nicht identisch sind, sind: - Einige Ebenen wurden nicht hinzugefügt, *d.h.* eine *Aktivierungsebene* wurde nicht hinzugefügt, oder die Restverbindung wurde vergessen - Die Worteinbettungsmatrix wurde nicht gebunden - Es werden die falschen Positionseinbettungen verwendet, da die ursprüngliche Implementierung einen Offset verwendet - Dropout wird während des Vorwärtsdurchlaufs angewendet. Um dies zu beheben, stellen Sie sicher, dass *model.training auf False* steht und dass keine Dropout Schicht während des Vorwärtsdurchlaufs fälschlicherweise aktiviert wird, *d.h.* übergeben Sie *self.training* an [PyTorch's functional dropout](https://pytorch.org/docs/stable/nn.functional.html?highlight=dropout#torch.nn.functional.dropout) Der beste Weg, das Problem zu beheben, besteht normalerweise darin, sich den Vorwärtsdurchlauf der ursprünglichen Implementierung und die 🤗 Transformers-Implementierung nebeneinander zu sehen und zu prüfen, ob es Unterschiede gibt. Idealerweise sollten Sie die Zwischenergebnisse beider Implementierungen des Vorwärtsdurchlaufs debuggen/ausdrucken, um die genaue Position im Netzwerk zu finden, an der die 🤗 Transformers-Implementierung eine andere Ausgabe zeigt als die ursprüngliche Implementierung. Stellen Sie zunächst sicher, dass die hartcodierten `input_ids` in beiden Skripten identisch sind. Überprüfen Sie dann, ob die Ausgaben der ersten Transformation von der `input_ids` (normalerweise die Worteinbettungen) identisch sind. Und dann arbeiten Sie sich bis zur allerletzten Schicht des Netzwerks. Irgendwann werden Sie einen Unterschied zwischen den beiden Implementierungen feststellen, der Sie auf den Fehler in der Implementierung von 🤗 Transformers hinweist. Unserer Erfahrung nach ist ein einfacher und effizienter Weg, viele Druckanweisungen hinzuzufügen sowohl in der Original-Implementierung als auch in der 🤗 Transformers-Implementierung an den gleichen Stellen im Netzwerk hinzuzufügen und nacheinander Druckanweisungen zu entfernen, die dieselben Werte für Zwischenpräsentationen anzeigen. Wenn Sie sicher sind, dass beide Implementierungen die gleiche Ausgabe liefern, überprüfen Sie die Ausgaben mit `torch.allclose(original_output, output, atol=1e-3)` überprüfen, haben Sie den schwierigsten Teil hinter sich! Herzlichen Glückwunsch - die Arbeit, die noch zu erledigen ist, sollte ein Kinderspiel sein 😊. **8. Hinzufügen aller notwendigen Modelltests** An diesem Punkt haben Sie erfolgreich ein neues Modell hinzugefügt. Es ist jedoch sehr gut möglich, dass das Modell noch nicht noch nicht vollständig mit dem erforderlichen Design übereinstimmt. Um sicherzustellen, dass die Implementierung vollständig kompatibel mit 🤗 Transformers ist, sollten alle gemeinsamen Tests bestehen. Der Cookiecutter sollte automatisch eine Testdatei für Ihr Modell hinzugefügt haben, wahrscheinlich unter demselben `tests/models/brand_new_bert/test_modeling_brand_new_bert.py`. Führen Sie diese Testdatei aus, um zu überprüfen, ob alle gängigen Tests bestehen: ```bash pytest tests/models/brand_new_bert/test_modeling_brand_new_bert.py ``` Nachdem Sie alle allgemeinen Tests festgelegt haben, müssen Sie nun sicherstellen, dass all die schöne Arbeit, die Sie geleistet haben, gut getestet ist, damit - a) die Community Ihre Arbeit leicht nachvollziehen kann, indem sie sich spezifische Tests von *brand_new_bert* ansieht - b) zukünftige Änderungen an Ihrem Modell keine wichtigen Funktionen des Modells zerstören. Als erstes sollten Sie Integrationstests hinzufügen. Diese Integrationstests tun im Wesentlichen dasselbe wie die Debugging-Skripte die Sie zuvor zur Implementierung des Modells in 🤗 Transformers verwendet haben. Eine Vorlage für diese Modelltests wurde bereits von dem Cookiecutter hinzugefügt, die `BrandNewBertModelIntegrationTests` heißt und nur noch von Ihnen ausgefüllt werden muss. Um sicherzustellen, dass diese Tests erfolgreich sind, führen Sie ```bash RUN_SLOW=1 pytest -sv tests/models/brand_new_bert/test_modeling_brand_new_bert.py::BrandNewBertModelIntegrationTests ``` <Tip> Falls Sie Windows verwenden, sollten Sie `RUN_SLOW=1` durch `SET RUN_SLOW=1` ersetzen. </Tip> Zweitens sollten alle Funktionen, die speziell für *brand_new_bert* sind, zusätzlich in einem separaten Test getestet werden unter `BrandNewBertModelTester`/`BrandNewBertModelTest`. Dieser Teil wird oft vergessen, ist aber in zweierlei Hinsicht äußerst nützlich Weise: - Er hilft dabei, das Wissen, das Sie während der Modellerweiterung erworben haben, an die Community weiterzugeben, indem er zeigt, wie die speziellen Funktionen von *brand_new_bert* funktionieren sollten. - Künftige Mitwirkende können Änderungen am Modell schnell testen, indem sie diese speziellen Tests ausführen. **9. Implementieren Sie den Tokenizer** Als nächstes sollten wir den Tokenizer von *brand_new_bert* hinzufügen. Normalerweise ist der Tokenizer äquivalent oder sehr ähnlich zu einem bereits vorhandenen Tokenizer von 🤗 Transformers. Es ist sehr wichtig, die ursprüngliche Tokenizer-Datei zu finden/extrahieren und es zu schaffen, diese Datei in die 🤗 Transformers Implementierung des Tokenizers zu laden. Um sicherzustellen, dass der Tokenizer korrekt funktioniert, empfiehlt es sich, zunächst ein Skript im ursprünglichen Repository zu erstellen zu erstellen, das eine Zeichenkette eingibt und die `input_ids` zurückgibt. Es könnte etwa so aussehen (in Pseudocode): ```python input_str = "This is a long example input string containing special characters .$?-, numbers 2872 234 12 and words." model = BrandNewBertModel.load_pretrained_checkpoint("/path/to/checkpoint/") input_ids = model.tokenize(input_str) ``` Möglicherweise müssen Sie noch einmal einen Blick in das ursprüngliche Repository werfen, um die richtige Tokenizer-Funktion zu finden, oder Sie müssen Sie müssen vielleicht sogar Änderungen an Ihrem Klon des Original-Repositorys vornehmen, um nur die `input_ids` auszugeben. Nach dem Schreiben ein funktionierendes Tokenisierungsskript geschrieben, das das ursprüngliche Repository verwendet, sollten Sie ein analoges Skript für 🤗 Transformers erstellt werden. Es sollte ähnlich wie dieses aussehen: ```python from transformers import BrandNewBertTokenizer input_str = "This is a long example input string containing special characters .$?-, numbers 2872 234 12 and words." tokenizer = BrandNewBertTokenizer.from_pretrained("/path/to/tokenizer/folder/") input_ids = tokenizer(input_str).input_ids ``` Wenn beide `input_ids` die gleichen Werte ergeben, sollte als letzter Schritt auch eine Tokenizer-Testdatei hinzugefügt werden. Analog zu den Modellierungstestdateien von *brand_new_bert* sollten auch die Tokenisierungs-Testdateien von *brand_new_bert* eine Reihe von fest kodierten Integrationstests enthalten. **10. Führen Sie End-to-End-Integrationstests aus** Nachdem Sie den Tokenizer hinzugefügt haben, sollten Sie auch ein paar End-to-End-Integrationstests, die sowohl das Modell als auch den Tokenizer zu `tests/models/brand_new_bert/test_modeling_brand_new_bert.py` in 🤗 Transformers. Ein solcher Test sollte bei einem aussagekräftigen Text-zu-Text-Beispiel zeigen, dass die Implementierung von 🤗 Transformers wie erwartet funktioniert. Ein aussagekräftiges Text-zu-Text-Beispiel kann z.B. *ein Quell-zu-Ziel-Übersetzungspaar, ein Artikel-zu-Zusammenfassung-Paar, ein Frage-zu-Antwort-Paar, usw... Wenn keiner der der portierten Prüfpunkte in einer nachgelagerten Aufgabe feinabgestimmt wurde, genügt es, sich einfach auf die Modelltests zu verlassen. In einem letzten Schritt, um sicherzustellen, dass das Modell voll funktionsfähig ist, sollten Sie alle Tests auch auf der GPU durchführen. Es kann Es kann vorkommen, dass Sie vergessen haben, einige `.to(self.device)` Anweisungen zu internen Tensoren des Modells hinzuzufügen, was in einem solchen Test zu einem Fehler führen würde. Falls Sie keinen Zugang zu einem Grafikprozessor haben, kann das Hugging Face Team diese Tests für Sie durchführen. Tests für Sie übernehmen. **11. Docstring hinzufügen** Nun sind alle notwendigen Funktionen für *brand_new_bert* hinzugefügt - Sie sind fast fertig! Das Einzige, was Sie noch hinzufügen müssen, ist ein schöner Docstring und eine Doku-Seite. Der Cookiecutter sollte eine Vorlagendatei namens `docs/source/model_doc/brand_new_bert.md` hinzugefügt haben, die Sie ausfüllen sollten. Die Benutzer Ihres Modells werden in der Regel zuerst einen Blick auf diese Seite ansehen, bevor sie Ihr Modell verwenden. Daher muss die Dokumentation verständlich und prägnant sein. Es ist sehr nützlich für die Gemeinschaft, einige *Tipps* hinzuzufügen, um zu zeigen, wie das Modell verwendet werden sollte. Zögern Sie nicht, das Hugging Face-Team anzupingen bezüglich der Docstrings. Stellen Sie als nächstes sicher, dass der zu `src/transformers/models/brand_new_bert/modeling_brand_new_bert.py` hinzugefügte docstring korrekt ist und alle erforderlichen Eingaben und Ausgaben enthält. Wir haben eine ausführliche Anleitung zum Schreiben von Dokumentationen und unserem Docstring-Format [hier](writing-documentation). Es ist immer gut, sich daran zu erinnern, dass die Dokumentation mindestens so sorgfältig behandelt werden sollte wie der Code in 🤗 Transformers, denn die Dokumentation ist in der Regel der erste Kontaktpunkt der Berührungspunkt der Community mit dem Modell ist. **Code refactor** Großartig, jetzt haben Sie den gesamten erforderlichen Code für *brand_new_bert* hinzugefügt. An diesem Punkt sollten Sie einige mögliche falschen Codestil korrigieren, indem Sie ausführen: ```bash make style ``` und überprüfen Sie, ob Ihr Kodierungsstil die Qualitätsprüfung besteht: ```bash make quality ``` Es gibt noch ein paar andere sehr strenge Designtests in 🤗 Transformers, die möglicherweise noch fehlschlagen, was sich in den den Tests Ihres Pull Requests. Dies liegt oft an fehlenden Informationen im Docstring oder an einer falschen Benennung. Das Hugging Face Team wird Ihnen sicherlich helfen, wenn Sie hier nicht weiterkommen. Und schließlich ist es immer eine gute Idee, den eigenen Code zu refaktorisieren, nachdem man sichergestellt hat, dass er korrekt funktioniert. Wenn alle Tests bestanden haben, ist es nun an der Zeit, den hinzugefügten Code noch einmal durchzugehen und einige Überarbeitungen vorzunehmen. Sie haben nun den Codierungsteil abgeschlossen, herzlichen Glückwunsch! 🎉 Sie sind großartig! 😎 **12. Laden Sie die Modelle in den Model Hub hoch** In diesem letzten Teil sollten Sie alle Checkpoints konvertieren und in den Modell-Hub hochladen und eine Modellkarte für jeden hochgeladenen Modell-Kontrollpunkt. Sie können sich mit den Hub-Funktionen vertraut machen, indem Sie unsere [Model sharing and uploading Page](model_sharing) lesen. Hier sollten Sie mit dem Hugging Face-Team zusammenarbeiten, um einen passenden Namen für jeden Checkpoint festzulegen und die erforderlichen Zugriffsrechte zu erhalten, um das Modell unter der Organisation des Autors *brand_new_bert* hochladen zu können. *brand_new_bert*. Die Methode `push_to_hub`, die in allen Modellen in `transformers` vorhanden ist, ist ein schneller und effizienter Weg, Ihren Checkpoint in den Hub zu pushen. Ein kleines Snippet ist unten eingefügt: ```python brand_new_bert.push_to_hub("brand_new_bert") # Uncomment the following line to push to an organization. # brand_new_bert.push_to_hub("<organization>/brand_new_bert") ``` Es lohnt sich, etwas Zeit darauf zu verwenden, für jeden Kontrollpunkt passende Musterkarten zu erstellen. Die Modellkarten sollten die spezifischen Merkmale dieses bestimmten Prüfpunkts hervorheben, * z.B.* auf welchem Datensatz wurde der Prüfpunkt vortrainiert/abgestimmt? Für welche nachgelagerte Aufgabe sollte das Modell verwendet werden? Und fügen Sie auch etwas Code bei, wie Sie wie das Modell korrekt verwendet wird. **13. (Optional) Notizbuch hinzufügen** Es ist sehr hilfreich, ein Notizbuch hinzuzufügen, in dem im Detail gezeigt wird, wie *brand_new_bert* für Schlussfolgerungen verwendet werden kann und/oder bei einer nachgelagerten Aufgabe feinabgestimmt wird. Dies ist nicht zwingend erforderlich, um Ihren PR zusammenzuführen, aber sehr nützlich für die Gemeinschaft. **14. Reichen Sie Ihren fertigen PR ein** Sie sind jetzt mit der Programmierung fertig und können zum letzten Schritt übergehen, nämlich der Zusammenführung Ihres PR mit main. Normalerweise hat das Hugging Face Team Ihnen an diesem Punkt bereits geholfen haben, aber es lohnt sich, sich etwas Zeit zu nehmen, um Ihrem fertigen PR eine schöne Beschreibung zu geben und eventuell Kommentare zu Ihrem Code hinzuzufügen, wenn Sie Ihren Gutachter auf bestimmte Designentscheidungen hinweisen wollen. Gutachter hinweisen wollen. ### Teilen Sie Ihre Arbeit!! Jetzt ist es an der Zeit, von der Community Anerkennung für Ihre Arbeit zu bekommen! Die Fertigstellung einer Modellergänzung ist ein wichtiger Beitrag zu Transformers und der gesamten NLP-Gemeinschaft. Ihr Code und die portierten vortrainierten Modelle werden sicherlich von Hunderten und vielleicht sogar Tausenden von Entwicklern und Forschern genutzt werden. Sie sollten stolz auf Ihre Arbeit sein und Ihre Ihre Leistung mit der Gemeinschaft teilen. **Sie haben ein weiteres Modell erstellt, das für jeden in der Community super einfach zugänglich ist! 🤯**

transformers/docs/source/de/add_new_model.md/0

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2

# Contribute to 🤗 Transformers Everyone is welcome to contribute, and we value everybody's contribution. Code contributions are not the only way to help the community. Answering questions, helping others, and improving the documentation are also immensely valuable. It also helps us if you spread the word! Reference the library in blog posts about the awesome projects it made possible, shout out on Twitter every time it has helped you, or simply ⭐️ the repository to say thank you. However you choose to contribute, please be mindful and respect our [code of conduct](https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md). **This guide was heavily inspired by the awesome [scikit-learn guide to contributing](https://github.com/scikit-learn/scikit-learn/blob/main/CONTRIBUTING.md).** ## Ways to contribute There are several ways you can contribute to 🤗 Transformers: * Fix outstanding issues with the existing code. * Submit issues related to bugs or desired new features. * Implement new models. * Contribute to the examples or to the documentation. If you don't know where to start, there is a special [Good First Issue](https://github.com/huggingface/transformers/contribute) listing. It will give you a list of open issues that are beginner-friendly and help you start contributing to open-source. The best way to do that is to open a Pull Request and link it to the issue that you'd like to work on. We try to give priority to opened PRs as we can easily track the progress of the fix, and if the contributor does not have time anymore, someone else can take the PR over. For something slightly more challenging, you can also take a look at the [Good Second Issue](https://github.com/huggingface/transformers/labels/Good%20Second%20Issue) list. In general though, if you feel like you know what you're doing, go for it and we'll help you get there! 🚀 > All contributions are equally valuable to the community. 🥰 ## Fixing outstanding issues If you notice an issue with the existing code and have a fix in mind, feel free to [start contributing](#create-a-pull-request) and open a Pull Request! ## Submitting a bug-related issue or feature request Do your best to follow these guidelines when submitting a bug-related issue or a feature request. It will make it easier for us to come back to you quickly and with good feedback. ### Did you find a bug? The 🤗 Transformers library is robust and reliable thanks to users who report the problems they encounter. Before you report an issue, we would really appreciate it if you could **make sure the bug was not already reported** (use the search bar on GitHub under Issues). Your issue should also be related to bugs in the library itself, and not your code. If you're unsure whether the bug is in your code or the library, please ask in the [forum](https://discuss.huggingface.co/) first. This helps us respond quicker to fixing issues related to the library versus general questions. Once you've confirmed the bug hasn't already been reported, please include the following information in your issue so we can quickly resolve it: * Your **OS type and version** and **Python**, **PyTorch** and **TensorFlow** versions when applicable. * A short, self-contained, code snippet that allows us to reproduce the bug in less than 30s. * The *full* traceback if an exception is raised. * Attach any other additional information, like screenshots, you think may help. To get the OS and software versions automatically, run the following command: ```bash transformers-cli env ``` You can also run the same command from the root of the repository: ```bash python src/transformers/commands/transformers_cli.py env ``` ### Do you want a new feature? If there is a new feature you'd like to see in 🤗 Transformers, please open an issue and describe: 1. What is the *motivation* behind this feature? Is it related to a problem or frustration with the library? Is it a feature related to something you need for a project? Is it something you worked on and think it could benefit the community? Whatever it is, we'd love to hear about it! 2. Describe your requested feature in as much detail as possible. The more you can tell us about it, the better we'll be able to help you. 3. Provide a *code snippet* that demonstrates the features usage. 4. If the feature is related to a paper, please include a link. If your issue is well written we're already 80% of the way there by the time you create it. We have added [templates](https://github.com/huggingface/transformers/tree/main/templates) to help you get started with your issue. ## Do you want to implement a new model? New models are constantly released and if you want to implement a new model, please provide the following information: * A short description of the model and a link to the paper. * Link to the implementation if it is open-sourced. * Link to the model weights if they are available. If you are willing to contribute the model yourself, let us know so we can help you add it to 🤗 Transformers! We have added a [detailed guide and templates](https://github.com/huggingface/transformers/tree/main/templates) to help you get started with adding a new model, and we also have a more technical guide for [how to add a model to 🤗 Transformers](https://huggingface.co/docs/transformers/add_new_model). ## Do you want to add documentation? We're always looking for improvements to the documentation that make it more clear and accurate. Please let us know how the documentation can be improved such as typos and any content that is missing, unclear or inaccurate. We'll be happy to make the changes or help you make a contribution if you're interested! For more details about how to generate, build, and write the documentation, take a look at the documentation [README](https://github.com/huggingface/transformers/tree/main/docs). ## Create a Pull Request Before writing any code, we strongly advise you to search through the existing PRs or issues to make sure nobody is already working on the same thing. If you are unsure, it is always a good idea to open an issue to get some feedback. You will need basic `git` proficiency to contribute to 🤗 Transformers. While `git` is not the easiest tool to use, it has the greatest manual. Type `git --help` in a shell and enjoy! If you prefer books, [Pro Git](https://git-scm.com/book/en/v2) is a very good reference. You'll need **[Python 3.8](https://github.com/huggingface/transformers/blob/main/setup.py#L426)** or above to contribute to 🤗 Transformers. Follow the steps below to start contributing: 1. Fork the [repository](https://github.com/huggingface/transformers) by clicking on the **[Fork](https://github.com/huggingface/transformers/fork)** button on the repository's page. This creates a copy of the code under your GitHub user account. 2. Clone your fork to your local disk, and add the base repository as a remote: ```bash git clone git@github.com:<your Github handle>/transformers.git cd transformers git remote add upstream https://github.com/huggingface/transformers.git ``` 3. Create a new branch to hold your development changes: ```bash git checkout -b a-descriptive-name-for-my-changes ``` 🚨 **Do not** work on the `main` branch! 4. Set up a development environment by running the following command in a virtual environment: ```bash pip install -e ".[dev]" ``` If 🤗 Transformers was already installed in the virtual environment, remove it with `pip uninstall transformers` before reinstalling it in editable mode with the `-e` flag. Depending on your OS, and since the number of optional dependencies of Transformers is growing, you might get a failure with this command. If that's the case make sure to install the Deep Learning framework you are working with (PyTorch, TensorFlow and/or Flax) then do: ```bash pip install -e ".[quality]" ``` which should be enough for most use cases. 5. Develop the features in your branch. As you work on your code, you should make sure the test suite passes. Run the tests impacted by your changes like this: ```bash pytest tests/<TEST_TO_RUN>.py ``` For more information about tests, check out the [Testing](https://huggingface.co/docs/transformers/testing) guide. 🤗 Transformers relies on `black` and `ruff` to format its source code consistently. After you make changes, apply automatic style corrections and code verifications that can't be automated in one go with: ```bash make fixup ``` This target is also optimized to only work with files modified by the PR you're working on. If you prefer to run the checks one after the other, the following command applies the style corrections: ```bash make style ``` 🤗 Transformers also uses `ruff` and a few custom scripts to check for coding mistakes. Quality controls are run by the CI, but you can run the same checks with: ```bash make quality ``` Finally, we have a lot of scripts to make sure we don't forget to update some files when adding a new model. You can run these scripts with: ```bash make repo-consistency ``` To learn more about those checks and how to fix any issues with them, check out the [Checks on a Pull Request](https://huggingface.co/docs/transformers/pr_checks) guide. If you're modifying documents under the `docs/source` directory, make sure the documentation can still be built. This check will also run in the CI when you open a pull request. To run a local check make sure you install the documentation builder: ```bash pip install ".[docs]" ``` Run the following command from the root of the repository: ```bash doc-builder build transformers docs/source/en --build_dir ~/tmp/test-build ``` This will build the documentation in the `~/tmp/test-build` folder where you can inspect the generated Markdown files with your favorite editor. You can also preview the docs on GitHub when you open a pull request. Once you're happy with your changes, add the changed files with `git add` and record your changes locally with `git commit`: ```bash git add modified_file.py git commit ``` Please remember to write [good commit messages](https://chris.beams.io/posts/git-commit/) to clearly communicate the changes you made! To keep your copy of the code up to date with the original repository, rebase your branch on `upstream/branch` *before* you open a pull request or if requested by a maintainer: ```bash git fetch upstream git rebase upstream/main ``` Push your changes to your branch: ```bash git push -u origin a-descriptive-name-for-my-changes ``` If you've already opened a pull request, you'll need to force push with the `--force` flag. Otherwise, if the pull request hasn't been opened yet, you can just push your changes normally. 6. Now you can go to your fork of the repository on GitHub and click on **Pull Request** to open a pull request. Make sure you tick off all the boxes on our [checklist](#pull-request-checklist) below. When you're ready, you can send your changes to the project maintainers for review. 7. It's ok if maintainers request changes, it happens to our core contributors too! So everyone can see the changes in the pull request, work in your local branch and push the changes to your fork. They will automatically appear in the pull request. ### Pull request checklist ☐ The pull request title should summarize your contribution.<br> ☐ If your pull request addresses an issue, please mention the issue number in the pull request description to make sure they are linked (and people viewing the issue know you are working on it).<br> ☐ To indicate a work in progress please prefix the title with `[WIP]`. These are useful to avoid duplicated work, and to differentiate it from PRs ready to be merged.<br> ☐ Make sure existing tests pass.<br> ☐ If adding a new feature, also add tests for it.<br> - If you are adding a new model, make sure you use `ModelTester.all_model_classes = (MyModel, MyModelWithLMHead,...)` to trigger the common tests. - If you are adding new `@slow` tests, make sure they pass using `RUN_SLOW=1 python -m pytest tests/models/my_new_model/test_my_new_model.py`. - If you are adding a new tokenizer, write tests and make sure `RUN_SLOW=1 python -m pytest tests/models/{your_model_name}/test_tokenization_{your_model_name}.py` passes. - CircleCI does not run the slow tests, but GitHub Actions does every night!<br> ☐ All public methods must have informative docstrings (see [`modeling_bert.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py) for an example).<br> ☐ Due to the rapidly growing repository, don't add any images, videos and other non-text files that'll significantly weigh down the repository. Instead, use a Hub repository such as [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) to host these files and reference them by URL. We recommend placing documentation related images in the following repository: [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images). You can open a PR on this dataset repository and ask a Hugging Face member to merge it. For more information about the checks run on a pull request, take a look at our [Checks on a Pull Request](https://huggingface.co/docs/transformers/pr_checks) guide. ### Tests An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the [tests](https://github.com/huggingface/transformers/tree/main/tests) folder and examples tests in the [examples](https://github.com/huggingface/transformers/tree/main/examples) folder. We like `pytest` and `pytest-xdist` because it's faster. From the root of the repository, specify a *path to a subfolder or a test file* to run the test: ```bash python -m pytest -n auto --dist=loadfile -s -v ./tests/models/my_new_model ``` Similarly, for the `examples` directory, specify a *path to a subfolder or test file* to run the test. For example, the following command tests the text classification subfolder in the PyTorch `examples` directory: ```bash pip install -r examples/xxx/requirements.txt # only needed the first time python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/text-classification ``` In fact, this is actually how our `make test` and `make test-examples` commands are implemented (not including the `pip install`)! You can also specify a smaller set of tests in order to test only the feature you're working on. By default, slow tests are skipped but you can set the `RUN_SLOW` environment variable to `yes` to run them. This will download many gigabytes of models so make sure you have enough disk space, a good internet connection or a lot of patience! <Tip warning={true}> Remember to specify a *path to a subfolder or a test file* to run the test. Otherwise, you'll run all the tests in the `tests` or `examples` folder, which will take a very long time! </Tip> ```bash RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/models/my_new_model RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/text-classification ``` Like the slow tests, there are other environment variables available which not enabled by default during testing: - `RUN_CUSTOM_TOKENIZERS`: Enables tests for custom tokenizers. - `RUN_PT_FLAX_CROSS_TESTS`: Enables tests for PyTorch + Flax integration. - `RUN_PT_TF_CROSS_TESTS`: Enables tests for TensorFlow + PyTorch integration. More environment variables and additional information can be found in the [testing_utils.py](src/transformers/testing_utils.py). 🤗 Transformers uses `pytest` as a test runner only. It doesn't use any `pytest`-specific features in the test suite itself. This means `unittest` is fully supported. Here's how to run tests with `unittest`: ```bash python -m unittest discover -s tests -t . -v python -m unittest discover -s examples -t examples -v ``` ### Style guide For documentation strings, 🤗 Transformers follows the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html). Check our [documentation writing guide](https://github.com/huggingface/transformers/tree/main/docs#writing-documentation---specification) for more information. ### Develop on Windows On Windows (unless you're working in [Windows Subsystem for Linux](https://learn.microsoft.com/en-us/windows/wsl/) or WSL), you need to configure git to transform Windows `CRLF` line endings to Linux `LF` line endings: ```bash git config core.autocrlf input ``` One way to run the `make` command on Windows is with MSYS2: 1. [Download MSYS2](https://www.msys2.org/), and we assume it's installed in `C:\msys64`. 2. Open the command line `C:\msys64\msys2.exe` (it should be available from the **Start** menu). 3. Run in the shell: `pacman -Syu` and install `make` with `pacman -S make`. 4. Add `C:\msys64\usr\bin` to your PATH environment variable. You can now use `make` from any terminal (PowerShell, cmd.exe, etc.)! 🎉 ### Sync a forked repository with upstream main (the Hugging Face repository) When updating the main branch of a forked repository, please follow these steps to avoid pinging the upstream repository which adds reference notes to each upstream PR, and sends unnecessary notifications to the developers involved in these PRs. 1. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead, merge directly into the forked main. 2. If a PR is absolutely necessary, use the following steps after checking out your branch: ```bash git checkout -b your-branch-for-syncing git pull --squash --no-commit upstream main git commit -m '<your message without GitHub references>' git push --set-upstream origin your-branch-for-syncing ```

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# Utilities for Generation This page lists all the utility functions used by [`~generation.GenerationMixin.generate`], [`~generation.GenerationMixin.greedy_search`], [`~generation.GenerationMixin.contrastive_search`], [`~generation.GenerationMixin.sample`], [`~generation.GenerationMixin.beam_search`], [`~generation.GenerationMixin.beam_sample`], [`~generation.GenerationMixin.group_beam_search`], and [`~generation.GenerationMixin.constrained_beam_search`]. Most of those are only useful if you are studying the code of the generate methods in the library. ## Generate Outputs The output of [`~generation.GenerationMixin.generate`] is an instance of a subclass of [`~utils.ModelOutput`]. This output is a data structure containing all the information returned by [`~generation.GenerationMixin.generate`], but that can also be used as tuple or dictionary. Here's an example: ```python from transformers import GPT2Tokenizer, GPT2LMHeadModel tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2") model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2") inputs = tokenizer("Hello, my dog is cute and ", return_tensors="pt") generation_output = model.generate(**inputs, return_dict_in_generate=True, output_scores=True) ``` The `generation_output` object is a [`~generation.GenerateDecoderOnlyOutput`], as we can see in the documentation of that class below, it means it has the following attributes: - `sequences`: the generated sequences of tokens - `scores` (optional): the prediction scores of the language modelling head, for each generation step - `hidden_states` (optional): the hidden states of the model, for each generation step - `attentions` (optional): the attention weights of the model, for each generation step Here we have the `scores` since we passed along `output_scores=True`, but we don't have `hidden_states` and `attentions` because we didn't pass `output_hidden_states=True` or `output_attentions=True`. You can access each attribute as you would usually do, and if that attribute has not been returned by the model, you will get `None`. Here for instance `generation_output.scores` are all the generated prediction scores of the language modeling head, and `generation_output.attentions` is `None`. When using our `generation_output` object as a tuple, it only keeps the attributes that don't have `None` values. Here, for instance, it has two elements, `loss` then `logits`, so ```python generation_output[:2] ``` will return the tuple `(generation_output.sequences, generation_output.scores)` for instance. When using our `generation_output` object as a dictionary, it only keeps the attributes that don't have `None` values. Here, for instance, it has two keys that are `sequences` and `scores`. We document here all output types. ### PyTorch [[autodoc]] generation.GenerateDecoderOnlyOutput [[autodoc]] generation.GenerateEncoderDecoderOutput [[autodoc]] generation.GenerateBeamDecoderOnlyOutput [[autodoc]] generation.GenerateBeamEncoderDecoderOutput ### TensorFlow [[autodoc]] generation.TFGreedySearchEncoderDecoderOutput [[autodoc]] generation.TFGreedySearchDecoderOnlyOutput [[autodoc]] generation.TFSampleEncoderDecoderOutput [[autodoc]] generation.TFSampleDecoderOnlyOutput [[autodoc]] generation.TFBeamSearchEncoderDecoderOutput [[autodoc]] generation.TFBeamSearchDecoderOnlyOutput [[autodoc]] generation.TFBeamSampleEncoderDecoderOutput [[autodoc]] generation.TFBeamSampleDecoderOnlyOutput [[autodoc]] generation.TFContrastiveSearchEncoderDecoderOutput [[autodoc]] generation.TFContrastiveSearchDecoderOnlyOutput ### FLAX [[autodoc]] generation.FlaxSampleOutput [[autodoc]] generation.FlaxGreedySearchOutput [[autodoc]] generation.FlaxBeamSearchOutput ## LogitsProcessor A [`LogitsProcessor`] can be used to modify the prediction scores of a language model head for generation. ### PyTorch [[autodoc]] AlternatingCodebooksLogitsProcessor - __call__ [[autodoc]] ClassifierFreeGuidanceLogitsProcessor - __call__ [[autodoc]] EncoderNoRepeatNGramLogitsProcessor - __call__ [[autodoc]] EncoderRepetitionPenaltyLogitsProcessor - __call__ [[autodoc]] EpsilonLogitsWarper - __call__ [[autodoc]] EtaLogitsWarper - __call__ [[autodoc]] ExponentialDecayLengthPenalty - __call__ [[autodoc]] ForcedBOSTokenLogitsProcessor - __call__ [[autodoc]] ForcedEOSTokenLogitsProcessor - __call__ [[autodoc]] ForceTokensLogitsProcessor - __call__ [[autodoc]] HammingDiversityLogitsProcessor - __call__ [[autodoc]] InfNanRemoveLogitsProcessor - __call__ [[autodoc]] LogitNormalization - __call__ [[autodoc]] LogitsProcessor - __call__ [[autodoc]] LogitsProcessorList - __call__ [[autodoc]] LogitsWarper - __call__ [[autodoc]] MinLengthLogitsProcessor - __call__ [[autodoc]] MinNewTokensLengthLogitsProcessor - __call__ [[autodoc]] NoBadWordsLogitsProcessor - __call__ [[autodoc]] NoRepeatNGramLogitsProcessor - __call__ [[autodoc]] PrefixConstrainedLogitsProcessor - __call__ [[autodoc]] RepetitionPenaltyLogitsProcessor - __call__ [[autodoc]] SequenceBiasLogitsProcessor - __call__ [[autodoc]] SuppressTokensAtBeginLogitsProcessor - __call__ [[autodoc]] SuppressTokensLogitsProcessor - __call__ [[autodoc]] TemperatureLogitsWarper - __call__ [[autodoc]] TopKLogitsWarper - __call__ [[autodoc]] TopPLogitsWarper - __call__ [[autodoc]] TypicalLogitsWarper - __call__ [[autodoc]] UnbatchedClassifierFreeGuidanceLogitsProcessor - __call__ [[autodoc]] WhisperTimeStampLogitsProcessor - __call__ ### TensorFlow [[autodoc]] TFForcedBOSTokenLogitsProcessor - __call__ [[autodoc]] TFForcedEOSTokenLogitsProcessor - __call__ [[autodoc]] TFForceTokensLogitsProcessor - __call__ [[autodoc]] TFLogitsProcessor - __call__ [[autodoc]] TFLogitsProcessorList - __call__ [[autodoc]] TFLogitsWarper - __call__ [[autodoc]] TFMinLengthLogitsProcessor - __call__ [[autodoc]] TFNoBadWordsLogitsProcessor - __call__ [[autodoc]] TFNoRepeatNGramLogitsProcessor - __call__ [[autodoc]] TFRepetitionPenaltyLogitsProcessor - __call__ [[autodoc]] TFSuppressTokensAtBeginLogitsProcessor - __call__ [[autodoc]] TFSuppressTokensLogitsProcessor - __call__ [[autodoc]] TFTemperatureLogitsWarper - __call__ [[autodoc]] TFTopKLogitsWarper - __call__ [[autodoc]] TFTopPLogitsWarper - __call__ ### FLAX [[autodoc]] FlaxForcedBOSTokenLogitsProcessor - __call__ [[autodoc]] FlaxForcedEOSTokenLogitsProcessor - __call__ [[autodoc]] FlaxForceTokensLogitsProcessor - __call__ [[autodoc]] FlaxLogitsProcessor - __call__ [[autodoc]] FlaxLogitsProcessorList - __call__ [[autodoc]] FlaxLogitsWarper - __call__ [[autodoc]] FlaxMinLengthLogitsProcessor - __call__ [[autodoc]] FlaxSuppressTokensAtBeginLogitsProcessor - __call__ [[autodoc]] FlaxSuppressTokensLogitsProcessor - __call__ [[autodoc]] FlaxTemperatureLogitsWarper - __call__ [[autodoc]] FlaxTopKLogitsWarper - __call__ [[autodoc]] FlaxTopPLogitsWarper - __call__ [[autodoc]] FlaxWhisperTimeStampLogitsProcessor - __call__ ## StoppingCriteria A [`StoppingCriteria`] can be used to change when to stop generation (other than EOS token). Please note that this is exclusively available to our PyTorch implementations. [[autodoc]] StoppingCriteria - __call__ [[autodoc]] StoppingCriteriaList - __call__ [[autodoc]] MaxLengthCriteria - __call__ [[autodoc]] MaxTimeCriteria - __call__ ## Constraints A [`Constraint`] can be used to force the generation to include specific tokens or sequences in the output. Please note that this is exclusively available to our PyTorch implementations. [[autodoc]] Constraint [[autodoc]] PhrasalConstraint [[autodoc]] DisjunctiveConstraint [[autodoc]] ConstraintListState ## BeamSearch [[autodoc]] BeamScorer - process - finalize [[autodoc]] BeamSearchScorer - process - finalize [[autodoc]] ConstrainedBeamSearchScorer - process - finalize ## Utilities [[autodoc]] top_k_top_p_filtering [[autodoc]] tf_top_k_top_p_filtering ## Streamers [[autodoc]] TextStreamer [[autodoc]] TextIteratorStreamer ## Caches [[autodoc]] Cache - update [[autodoc]] DynamicCache - update - get_seq_length - reorder_cache - to_legacy_cache - from_legacy_cache [[autodoc]] SinkCache - update - get_seq_length - reorder_cache [[autodoc]] StaticCache - update - get_seq_length

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# Image Processor An image processor is in charge of preparing input features for vision models and post processing their outputs. This includes transformations such as resizing, normalization, and conversion to PyTorch, TensorFlow, Flax and Numpy tensors. It may also include model specific post-processing such as converting logits to segmentation masks. ## ImageProcessingMixin [[autodoc]] image_processing_utils.ImageProcessingMixin - from_pretrained - save_pretrained ## BatchFeature [[autodoc]] BatchFeature ## BaseImageProcessor [[autodoc]] image_processing_utils.BaseImageProcessor

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# Audio Spectrogram Transformer ## Overview The Audio Spectrogram Transformer model was proposed in [AST: Audio Spectrogram Transformer](https://arxiv.org/abs/2104.01778) by Yuan Gong, Yu-An Chung, James Glass. The Audio Spectrogram Transformer applies a [Vision Transformer](vit) to audio, by turning audio into an image (spectrogram). The model obtains state-of-the-art results for audio classification. The abstract from the paper is the following: *In the past decade, convolutional neural networks (CNNs) have been widely adopted as the main building block for end-to-end audio classification models, which aim to learn a direct mapping from audio spectrograms to corresponding labels. To better capture long-range global context, a recent trend is to add a self-attention mechanism on top of the CNN, forming a CNN-attention hybrid model. However, it is unclear whether the reliance on a CNN is necessary, and if neural networks purely based on attention are sufficient to obtain good performance in audio classification. In this paper, we answer the question by introducing the Audio Spectrogram Transformer (AST), the first convolution-free, purely attention-based model for audio classification. We evaluate AST on various audio classification benchmarks, where it achieves new state-of-the-art results of 0.485 mAP on AudioSet, 95.6% accuracy on ESC-50, and 98.1% accuracy on Speech Commands V2.* <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/audio_spectogram_transformer_architecture.png" alt="drawing" width="600"/> <small> Audio Spectrogram Transformer architecture. Taken from the <a href="https://arxiv.org/abs/2104.01778">original paper</a>.</small> This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/YuanGongND/ast). ## Usage tips - When fine-tuning the Audio Spectrogram Transformer (AST) on your own dataset, it's recommended to take care of the input normalization (to make sure the input has mean of 0 and std of 0.5). [`ASTFeatureExtractor`] takes care of this. Note that it uses the AudioSet mean and std by default. You can check [`ast/src/get_norm_stats.py`](https://github.com/YuanGongND/ast/blob/master/src/get_norm_stats.py) to see how the authors compute the stats for a downstream dataset. - Note that the AST needs a low learning rate (the authors use a 10 times smaller learning rate compared to their CNN model proposed in the [PSLA paper](https://arxiv.org/abs/2102.01243)) and converges quickly, so please search for a suitable learning rate and learning rate scheduler for your task. ## Resources A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with the Audio Spectrogram Transformer. <PipelineTag pipeline="audio-classification"/> - A notebook illustrating inference with AST for audio classification can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/AST). - [`ASTForAudioClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/audio-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/audio_classification.ipynb). - See also: [Audio classification](../tasks/audio_classification). If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource. ## ASTConfig [[autodoc]] ASTConfig ## ASTFeatureExtractor [[autodoc]] ASTFeatureExtractor - __call__ ## ASTModel [[autodoc]] ASTModel - forward ## ASTForAudioClassification [[autodoc]] ASTForAudioClassification - forward

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# Blenderbot Small Note that [`BlenderbotSmallModel`] and [`BlenderbotSmallForConditionalGeneration`] are only used in combination with the checkpoint [facebook/blenderbot-90M](https://huggingface.co/facebook/blenderbot-90M). Larger Blenderbot checkpoints should instead be used with [`BlenderbotModel`] and [`BlenderbotForConditionalGeneration`] ## Overview The Blender chatbot model was proposed in [Recipes for building an open-domain chatbot](https://arxiv.org/pdf/2004.13637.pdf) Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston on 30 Apr 2020. The abstract of the paper is the following: *Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, and displaying knowledge, empathy and personality appropriately, while maintaining a consistent persona. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.* This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten). The authors' code can be found [here](https://github.com/facebookresearch/ParlAI). ## Usage tips Blenderbot Small is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than the left. ## Resources - [Causal language modeling task guide](../tasks/language_modeling) - [Translation task guide](../tasks/translation) - [Summarization task guide](../tasks/summarization) ## BlenderbotSmallConfig [[autodoc]] BlenderbotSmallConfig ## BlenderbotSmallTokenizer [[autodoc]] BlenderbotSmallTokenizer - build_inputs_with_special_tokens - get_special_tokens_mask - create_token_type_ids_from_sequences - save_vocabulary ## BlenderbotSmallTokenizerFast [[autodoc]] BlenderbotSmallTokenizerFast <frameworkcontent> <pt> ## BlenderbotSmallModel [[autodoc]] BlenderbotSmallModel - forward ## BlenderbotSmallForConditionalGeneration [[autodoc]] BlenderbotSmallForConditionalGeneration - forward ## BlenderbotSmallForCausalLM [[autodoc]] BlenderbotSmallForCausalLM - forward </pt> <tf> ## TFBlenderbotSmallModel [[autodoc]] TFBlenderbotSmallModel - call ## TFBlenderbotSmallForConditionalGeneration [[autodoc]] TFBlenderbotSmallForConditionalGeneration - call </tf> <jax> ## FlaxBlenderbotSmallModel [[autodoc]] FlaxBlenderbotSmallModel - __call__ - encode - decode ## FlaxBlenderbotForConditionalGeneration [[autodoc]] FlaxBlenderbotSmallForConditionalGeneration - __call__ - encode - decode </jax> </frameworkcontent>

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# Encoder Decoder Models ## Overview The [`EncoderDecoderModel`] can be used to initialize a sequence-to-sequence model with any pretrained autoencoding model as the encoder and any pretrained autoregressive model as the decoder. The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation tasks was shown in [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. After such an [`EncoderDecoderModel`] has been trained/fine-tuned, it can be saved/loaded just like any other models (see the examples for more information). An application of this architecture could be to leverage two pretrained [`BertModel`] as the encoder and decoder for a summarization model as was shown in: [Text Summarization with Pretrained Encoders](https://arxiv.org/abs/1908.08345) by Yang Liu and Mirella Lapata. ## Randomly initializing `EncoderDecoderModel` from model configurations. [`EncoderDecoderModel`] can be randomly initialized from an encoder and a decoder config. In the following example, we show how to do this using the default [`BertModel`] configuration for the encoder and the default [`BertForCausalLM`] configuration for the decoder. ```python >>> from transformers import BertConfig, EncoderDecoderConfig, EncoderDecoderModel >>> config_encoder = BertConfig() >>> config_decoder = BertConfig() >>> config = EncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder) >>> model = EncoderDecoderModel(config=config) ``` ## Initialising `EncoderDecoderModel` from a pretrained encoder and a pretrained decoder. [`EncoderDecoderModel`] can be initialized from a pretrained encoder checkpoint and a pretrained decoder checkpoint. Note that any pretrained auto-encoding model, *e.g.* BERT, can serve as the encoder and both pretrained auto-encoding models, *e.g.* BERT, pretrained causal language models, *e.g.* GPT2, as well as the pretrained decoder part of sequence-to-sequence models, *e.g.* decoder of BART, can be used as the decoder. Depending on which architecture you choose as the decoder, the cross-attention layers might be randomly initialized. Initializing [`EncoderDecoderModel`] from a pretrained encoder and decoder checkpoint requires the model to be fine-tuned on a downstream task, as has been shown in [the *Warm-starting-encoder-decoder blog post*](https://huggingface.co/blog/warm-starting-encoder-decoder). To do so, the `EncoderDecoderModel` class provides a [`EncoderDecoderModel.from_encoder_decoder_pretrained`] method. ```python >>> from transformers import EncoderDecoderModel, BertTokenizer >>> tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = EncoderDecoderModel.from_encoder_decoder_pretrained("google-bert/bert-base-uncased", "google-bert/bert-base-uncased") ``` ## Loading an existing `EncoderDecoderModel` checkpoint and perform inference. To load fine-tuned checkpoints of the `EncoderDecoderModel` class, [`EncoderDecoderModel`] provides the `from_pretrained(...)` method just like any other model architecture in Transformers. To perform inference, one uses the [`generate`] method, which allows to autoregressively generate text. This method supports various forms of decoding, such as greedy, beam search and multinomial sampling. ```python >>> from transformers import AutoTokenizer, EncoderDecoderModel >>> # load a fine-tuned seq2seq model and corresponding tokenizer >>> model = EncoderDecoderModel.from_pretrained("patrickvonplaten/bert2bert_cnn_daily_mail") >>> tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/bert2bert_cnn_daily_mail") >>> # let's perform inference on a long piece of text >>> ARTICLE_TO_SUMMARIZE = ( ... "PG&E stated it scheduled the blackouts in response to forecasts for high winds " ... "amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were " ... "scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow." ... ) >>> input_ids = tokenizer(ARTICLE_TO_SUMMARIZE, return_tensors="pt").input_ids >>> # autoregressively generate summary (uses greedy decoding by default) >>> generated_ids = model.generate(input_ids) >>> generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] >>> print(generated_text) nearly 800 thousand customers were affected by the shutoffs. the aim is to reduce the risk of wildfires. nearly 800, 000 customers were expected to be affected by high winds amid dry conditions. pg & e said it scheduled the blackouts to last through at least midday tomorrow. ``` ## Loading a PyTorch checkpoint into `TFEncoderDecoderModel`. [`TFEncoderDecoderModel.from_pretrained`] currently doesn't support initializing the model from a pytorch checkpoint. Passing `from_pt=True` to this method will throw an exception. If there are only pytorch checkpoints for a particular encoder-decoder model, a workaround is: ```python >>> # a workaround to load from pytorch checkpoint >>> from transformers import EncoderDecoderModel, TFEncoderDecoderModel >>> _model = EncoderDecoderModel.from_pretrained("patrickvonplaten/bert2bert-cnn_dailymail-fp16") >>> _model.encoder.save_pretrained("./encoder") >>> _model.decoder.save_pretrained("./decoder") >>> model = TFEncoderDecoderModel.from_encoder_decoder_pretrained( ... "./encoder", "./decoder", encoder_from_pt=True, decoder_from_pt=True ... ) >>> # This is only for copying some specific attributes of this particular model. >>> model.config = _model.config ``` ## Training Once the model is created, it can be fine-tuned similar to BART, T5 or any other encoder-decoder model. As you can see, only 2 inputs are required for the model in order to compute a loss: `input_ids` (which are the `input_ids` of the encoded input sequence) and `labels` (which are the `input_ids` of the encoded target sequence). ```python >>> from transformers import BertTokenizer, EncoderDecoderModel >>> tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = EncoderDecoderModel.from_encoder_decoder_pretrained("google-bert/bert-base-uncased", "google-bert/bert-base-uncased") >>> model.config.decoder_start_token_id = tokenizer.cls_token_id >>> model.config.pad_token_id = tokenizer.pad_token_id >>> input_ids = tokenizer( ... "The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side.During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft).Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct.", ... return_tensors="pt", ... ).input_ids >>> labels = tokenizer( ... "the eiffel tower surpassed the washington monument to become the tallest structure in the world. it was the first structure to reach a height of 300 metres in paris in 1930. it is now taller than the chrysler building by 5. 2 metres ( 17 ft ) and is the second tallest free - standing structure in paris.", ... return_tensors="pt", ... ).input_ids >>> # the forward function automatically creates the correct decoder_input_ids >>> loss = model(input_ids=input_ids, labels=labels).loss ``` Detailed [colab](https://colab.research.google.com/drive/1WIk2bxglElfZewOHboPFNj8H44_VAyKE?usp=sharing#scrollTo=ZwQIEhKOrJpl) for training. This model was contributed by [thomwolf](https://github.com/thomwolf). This model's TensorFlow and Flax versions were contributed by [ydshieh](https://github.com/ydshieh). ## EncoderDecoderConfig [[autodoc]] EncoderDecoderConfig <frameworkcontent> <pt> ## EncoderDecoderModel [[autodoc]] EncoderDecoderModel - forward - from_encoder_decoder_pretrained </pt> <tf> ## TFEncoderDecoderModel [[autodoc]] TFEncoderDecoderModel - call - from_encoder_decoder_pretrained </tf> <jax> ## FlaxEncoderDecoderModel [[autodoc]] FlaxEncoderDecoderModel - __call__ - from_encoder_decoder_pretrained </jax> </frameworkcontent>

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# ImageGPT ## Overview The ImageGPT model was proposed in [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt) by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever. ImageGPT (iGPT) is a GPT-2-like model trained to predict the next pixel value, allowing for both unconditional and conditional image generation. The abstract from the paper is the following: *Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models can learn useful representations for images. We train a sequence Transformer to auto-regressively predict pixels, without incorporating knowledge of the 2D input structure. Despite training on low-resolution ImageNet without labels, we find that a GPT-2 scale model learns strong image representations as measured by linear probing, fine-tuning, and low-data classification. On CIFAR-10, we achieve 96.3% accuracy with a linear probe, outperforming a supervised Wide ResNet, and 99.0% accuracy with full fine-tuning, matching the top supervised pre-trained models. We are also competitive with self-supervised benchmarks on ImageNet when substituting pixels for a VQVAE encoding, achieving 69.0% top-1 accuracy on a linear probe of our features.* <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/imagegpt_architecture.png" alt="drawing" width="600"/> <small> Summary of the approach. Taken from the [original paper](https://cdn.openai.com/papers/Generative_Pretraining_from_Pixels_V2.pdf). </small> This model was contributed by [nielsr](https://huggingface.co/nielsr), based on [this issue](https://github.com/openai/image-gpt/issues/7). The original code can be found [here](https://github.com/openai/image-gpt). ## Usage tips - ImageGPT is almost exactly the same as [GPT-2](gpt2), with the exception that a different activation function is used (namely "quick gelu"), and the layer normalization layers don't mean center the inputs. ImageGPT also doesn't have tied input- and output embeddings. - As the time- and memory requirements of the attention mechanism of Transformers scales quadratically in the sequence length, the authors pre-trained ImageGPT on smaller input resolutions, such as 32x32 and 64x64. However, feeding a sequence of 32x32x3=3072 tokens from 0..255 into a Transformer is still prohibitively large. Therefore, the authors applied k-means clustering to the (R,G,B) pixel values with k=512. This way, we only have a 32*32 = 1024-long sequence, but now of integers in the range 0..511. So we are shrinking the sequence length at the cost of a bigger embedding matrix. In other words, the vocabulary size of ImageGPT is 512, + 1 for a special "start of sentence" (SOS) token, used at the beginning of every sequence. One can use [`ImageGPTImageProcessor`] to prepare images for the model. - Despite being pre-trained entirely unsupervised (i.e. without the use of any labels), ImageGPT produces fairly performant image features useful for downstream tasks, such as image classification. The authors showed that the features in the middle of the network are the most performant, and can be used as-is to train a linear model (such as a sklearn logistic regression model for example). This is also referred to as "linear probing". Features can be easily obtained by first forwarding the image through the model, then specifying `output_hidden_states=True`, and then average-pool the hidden states at whatever layer you like. - Alternatively, one can further fine-tune the entire model on a downstream dataset, similar to BERT. For this, you can use [`ImageGPTForImageClassification`]. - ImageGPT comes in different sizes: there's ImageGPT-small, ImageGPT-medium and ImageGPT-large. The authors did also train an XL variant, which they didn't release. The differences in size are summarized in the following table: | **Model variant** | **Depths** | **Hidden sizes** | **Decoder hidden size** | **Params (M)** | **ImageNet-1k Top 1** | |---|---|---|---|---|---| | MiT-b0 | [2, 2, 2, 2] | [32, 64, 160, 256] | 256 | 3.7 | 70.5 | | MiT-b1 | [2, 2, 2, 2] | [64, 128, 320, 512] | 256 | 14.0 | 78.7 | | MiT-b2 | [3, 4, 6, 3] | [64, 128, 320, 512] | 768 | 25.4 | 81.6 | | MiT-b3 | [3, 4, 18, 3] | [64, 128, 320, 512] | 768 | 45.2 | 83.1 | | MiT-b4 | [3, 8, 27, 3] | [64, 128, 320, 512] | 768 | 62.6 | 83.6 | | MiT-b5 | [3, 6, 40, 3] | [64, 128, 320, 512] | 768 | 82.0 | 83.8 | ## Resources A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ImageGPT. <PipelineTag pipeline="image-classification"/> - Demo notebooks for ImageGPT can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/ImageGPT). - [`ImageGPTForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb). - See also: [Image classification task guide](../tasks/image_classification) If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource. ## ImageGPTConfig [[autodoc]] ImageGPTConfig ## ImageGPTFeatureExtractor [[autodoc]] ImageGPTFeatureExtractor - __call__ ## ImageGPTImageProcessor [[autodoc]] ImageGPTImageProcessor - preprocess ## ImageGPTModel [[autodoc]] ImageGPTModel - forward ## ImageGPTForCausalImageModeling [[autodoc]] ImageGPTForCausalImageModeling - forward ## ImageGPTForImageClassification [[autodoc]] ImageGPTForImageClassification - forward

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# LongT5 ## Overview The LongT5 model was proposed in [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/abs/2112.07916) by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung and Yinfei Yang. It's an encoder-decoder transformer pre-trained in a text-to-text denoising generative setting. LongT5 model is an extension of T5 model, and it enables using one of the two different efficient attention mechanisms - (1) Local attention, or (2) Transient-Global attention. The abstract from the paper is the following: *Recent work has shown that either (1) increasing the input length or (2) increasing model size can improve the performance of Transformer-based neural models. In this paper, we present a new model, called LongT5, with which we explore the effects of scaling both the input length and model size at the same time. Specifically, we integrated attention ideas from long-input transformers (ETC), and adopted pre-training strategies from summarization pre-training (PEGASUS) into the scalable T5 architecture. The result is a new attention mechanism we call {\em Transient Global} (TGlobal), which mimics ETC's local/global attention mechanism, but without requiring additional side-inputs. We are able to achieve state-of-the-art results on several summarization tasks and outperform the original T5 models on question answering tasks.* This model was contributed by [stancld](https://huggingface.co/stancld). The original code can be found [here](https://github.com/google-research/longt5). ## Usage tips - [`LongT5ForConditionalGeneration`] is an extension of [`T5ForConditionalGeneration`] exchanging the traditional encoder *self-attention* layer with efficient either *local* attention or *transient-global* (*tglobal*) attention. - Unlike the T5 model, LongT5 does not use a task prefix. Furthermore, it uses a different pre-training objective inspired by the pre-training of [`PegasusForConditionalGeneration`]. - LongT5 model is designed to work efficiently and very well on long-range *sequence-to-sequence* tasks where the input sequence exceeds commonly used 512 tokens. It is capable of handling input sequences of a length up to 16,384 tokens. - For *Local Attention*, the sparse sliding-window local attention operation allows a given token to attend only `r` tokens to the left and right of it (with `r=127` by default). *Local Attention* does not introduce any new parameters to the model. The complexity of the mechanism is linear in input sequence length `l`: `O(l*r)`. - *Transient Global Attention* is an extension of the *Local Attention*. It, furthermore, allows each input token to interact with all other tokens in the layer. This is achieved via splitting an input sequence into blocks of a fixed length `k` (with a default `k=16`). Then, a global token for such a block is obtained via summing and normalizing the embeddings of every token in the block. Thanks to this, the attention allows each token to attend to both nearby tokens like in Local attention, and also every global token like in the case of standard global attention (*transient* represents the fact the global tokens are constructed dynamically within each attention operation). As a consequence, *TGlobal* attention introduces a few new parameters -- global relative position biases and a layer normalization for global token's embedding. The complexity of this mechanism is `O(l(r + l/k))`. - An example showing how to evaluate a fine-tuned LongT5 model on the [pubmed dataset](https://huggingface.co/datasets/scientific_papers) is below. ```python >>> import evaluate >>> from datasets import load_dataset >>> from transformers import AutoTokenizer, LongT5ForConditionalGeneration >>> dataset = load_dataset("scientific_papers", "pubmed", split="validation") >>> model = ( ... LongT5ForConditionalGeneration.from_pretrained("Stancld/longt5-tglobal-large-16384-pubmed-3k_steps") ... .to("cuda") ... .half() ... ) >>> tokenizer = AutoTokenizer.from_pretrained("Stancld/longt5-tglobal-large-16384-pubmed-3k_steps") >>> def generate_answers(batch): ... inputs_dict = tokenizer( ... batch["article"], max_length=16384, padding="max_length", truncation=True, return_tensors="pt" ... ) ... input_ids = inputs_dict.input_ids.to("cuda") ... attention_mask = inputs_dict.attention_mask.to("cuda") ... output_ids = model.generate(input_ids, attention_mask=attention_mask, max_length=512, num_beams=2) ... batch["predicted_abstract"] = tokenizer.batch_decode(output_ids, skip_special_tokens=True) ... return batch >>> result = dataset.map(generate_answer, batched=True, batch_size=2) >>> rouge = evaluate.load("rouge") >>> rouge.compute(predictions=result["predicted_abstract"], references=result["abstract"]) ``` ## Resources - [Translation task guide](../tasks/translation) - [Summarization task guide](../tasks/summarization) ## LongT5Config [[autodoc]] LongT5Config <frameworkcontent> <pt> ## LongT5Model [[autodoc]] LongT5Model - forward ## LongT5ForConditionalGeneration [[autodoc]] LongT5ForConditionalGeneration - forward ## LongT5EncoderModel [[autodoc]] LongT5EncoderModel - forward </pt> <jax> ## FlaxLongT5Model [[autodoc]] FlaxLongT5Model - __call__ - encode - decode ## FlaxLongT5ForConditionalGeneration [[autodoc]] FlaxLongT5ForConditionalGeneration - __call__ - encode - decode </jax> </frameworkcontent>

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# Mistral ## Overview Mistral was introduced in the [this blogpost](https://mistral.ai/news/announcing-mistral-7b/) by Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed. The introduction of the blog post says: *Mistral AI team is proud to release Mistral 7B, the most powerful language model for its size to date.* Mistral-7B is the first large language model (LLM) released by [mistral.ai](https://mistral.ai/). ### Architectural details Mistral-7B is a decoder-only Transformer with the following architectural choices: - Sliding Window Attention - Trained with 8k context length and fixed cache size, with a theoretical attention span of 128K tokens - GQA (Grouped Query Attention) - allowing faster inference and lower cache size. - Byte-fallback BPE tokenizer - ensures that characters are never mapped to out of vocabulary tokens. For more details refer to the [release blog post](https://mistral.ai/news/announcing-mistral-7b/). ### License `Mistral-7B` is released under the Apache 2.0 license. ## Usage tips The Mistral team has released 3 checkpoints: - a base model, [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1), which has been pre-trained to predict the next token on internet-scale data. - an instruction tuned model, [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1), which is the base model optimized for chat purposes using supervised fine-tuning (SFT) and direct preference optimization (DPO). - an improved instruction tuned model, [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2), which improves upon v1. The base model can be used as follows: ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer >>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", device_map="auto") >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1") >>> prompt = "My favourite condiment is" >>> model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda") >>> model.to(device) >>> generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True) >>> tokenizer.batch_decode(generated_ids)[0] "My favourite condiment is to ..." ``` The instruction tuned model can be used as follows: ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer >>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2", device_map="auto") >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2") >>> messages = [ ... {"role": "user", "content": "What is your favourite condiment?"}, ... {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, ... {"role": "user", "content": "Do you have mayonnaise recipes?"} ... ] >>> model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda") >>> generated_ids = model.generate(model_inputs, max_new_tokens=100, do_sample=True) >>> tokenizer.batch_decode(generated_ids)[0] "Mayonnaise can be made as follows: (...)" ``` As can be seen, the instruction-tuned model requires a [chat template](../chat_templating) to be applied to make sure the inputs are prepared in the right format. ## Speeding up Mistral by using Flash Attention The code snippets above showcase inference without any optimization tricks. However, one can drastically speed up the model by leveraging [Flash Attention](../perf_train_gpu_one.md#flash-attention-2), which is a faster implementation of the attention mechanism used inside the model. First, make sure to install the latest version of Flash Attention 2 to include the sliding window attention feature. ```bash pip install -U flash-attn --no-build-isolation ``` Make also sure that you have a hardware that is compatible with Flash-Attention 2. Read more about it in the official documentation of the [flash attention repository](https://github.com/Dao-AILab/flash-attention). Make also sure to load your model in half-precision (e.g. `torch.float16`) To load and run a model using Flash Attention-2, refer to the snippet below: ```python >>> import torch >>> from transformers import AutoModelForCausalLM, AutoTokenizer >>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", torch_dtype=torch.float16, attn_implementation="flash_attention_2", device_map="auto") >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1") >>> prompt = "My favourite condiment is" >>> model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda") >>> model.to(device) >>> generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True) >>> tokenizer.batch_decode(generated_ids)[0] "My favourite condiment is to (...)" ``` ### Expected speedups Below is a expected speedup diagram that compares pure inference time between the native implementation in transformers using `mistralai/Mistral-7B-v0.1` checkpoint and the Flash Attention 2 version of the model. <div style="text-align: center"> <img src="https://huggingface.co/datasets/ybelkada/documentation-images/resolve/main/mistral-7b-inference-large-seqlen.png"> </div> ### Sliding window Attention The current implementation supports the sliding window attention mechanism and memory efficient cache management. To enable sliding window attention, just make sure to have a `flash-attn` version that is compatible with sliding window attention (`>=2.3.0`). The Flash Attention-2 model uses also a more memory efficient cache slicing mechanism - as recommended per the official implementation of Mistral model that use rolling cache mechanism we keep the cache size fixed (`self.config.sliding_window`), support batched generation only for `padding_side="left"` and use the absolute position of the current token to compute the positional embedding. ## Shrinking down Mistral using quantization As the Mistral model has 7 billion parameters, that would require about 14GB of GPU RAM in half precision (float16), since each parameter is stored in 2 bytes. However, one can shrink down the size of the model using [quantization](../quantization.md). If the model is quantized to 4 bits (or half a byte per parameter),that requires only about 3.5GB of RAM. Quantizing a model is as simple as passing a `quantization_config` to the model. Below, we'll leverage the BitsAndyBytes quantization (but refer to [this page](../quantization.md) for other quantization methods): ```python >>> import torch >>> from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig >>> # specify how to quantize the model >>> quantization_config = BitsAndBytesConfig( ... load_in_4bit=True, ... bnb_4bit_quant_type="nf4", ... bnb_4bit_compute_dtype="torch.float16", ... ) >>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2", quantization_config=True, device_map="auto") >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2") >>> prompt = "My favourite condiment is" >>> messages = [ ... {"role": "user", "content": "What is your favourite condiment?"}, ... {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, ... {"role": "user", "content": "Do you have mayonnaise recipes?"} ... ] >>> model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda") >>> generated_ids = model.generate(model_inputs, max_new_tokens=100, do_sample=True) >>> tokenizer.batch_decode(generated_ids)[0] "The expected output" ``` This model was contributed by [Younes Belkada](https://huggingface.co/ybelkada) and [Arthur Zucker](https://huggingface.co/ArthurZ) . The original code can be found [here](https://github.com/mistralai/mistral-src). ## Resources A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Mistral. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource. <PipelineTag pipeline="text-generation"/> - A demo notebook to perform supervised fine-tuning (SFT) of Mistral-7B can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/Mistral/Supervised_fine_tuning_(SFT)_of_an_LLM_using_Hugging_Face_tooling.ipynb). 🌎 - A [blog post](https://www.philschmid.de/fine-tune-llms-in-2024-with-trl) on how to fine-tune LLMs in 2024 using Hugging Face tooling. 🌎 - The [Alignment Handbook](https://github.com/huggingface/alignment-handbook) by Hugging Face includes scripts and recipes to perform supervised fine-tuning (SFT) and direct preference optimization with Mistral-7B. This includes scripts for full fine-tuning, QLoRa on a single GPU as well as multi-GPU fine-tuning. - [Causal language modeling task guide](../tasks/language_modeling) ## MistralConfig [[autodoc]] MistralConfig ## MistralModel [[autodoc]] MistralModel - forward ## MistralForCausalLM [[autodoc]] MistralForCausalLM - forward ## MistralForSequenceClassification [[autodoc]] MistralForSequenceClassification - forward ## FlaxMistralModel [[autodoc]] FlaxMistralModel - __call__ ## FlaxMistralForCausalLM [[autodoc]] FlaxMistralForCausalLM - __call__

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# Nezha ## Overview The Nezha model was proposed in [NEZHA: Neural Contextualized Representation for Chinese Language Understanding](https://arxiv.org/abs/1909.00204) by Junqiu Wei et al. The abstract from the paper is the following: *The pre-trained language models have achieved great successes in various natural language understanding (NLU) tasks due to its capacity to capture the deep contextualized information in text by pre-training on large-scale corpora. In this technical report, we present our practice of pre-training language models named NEZHA (NEural contextualiZed representation for CHinese lAnguage understanding) on Chinese corpora and finetuning for the Chinese NLU tasks. The current version of NEZHA is based on BERT with a collection of proven improvements, which include Functional Relative Positional Encoding as an effective positional encoding scheme, Whole Word Masking strategy, Mixed Precision Training and the LAMB Optimizer in training the models. The experimental results show that NEZHA achieves the state-of-the-art performances when finetuned on several representative Chinese tasks, including named entity recognition (People's Daily NER), sentence matching (LCQMC), Chinese sentiment classification (ChnSenti) and natural language inference (XNLI).* This model was contributed by [sijunhe](https://huggingface.co/sijunhe). The original code can be found [here](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/NEZHA-PyTorch). ## Resources - [Text classification task guide](../tasks/sequence_classification) - [Token classification task guide](../tasks/token_classification) - [Question answering task guide](../tasks/question_answering) - [Masked language modeling task guide](../tasks/masked_language_modeling) - [Multiple choice task guide](../tasks/multiple_choice) ## NezhaConfig [[autodoc]] NezhaConfig ## NezhaModel [[autodoc]] NezhaModel - forward ## NezhaForPreTraining [[autodoc]] NezhaForPreTraining - forward ## NezhaForMaskedLM [[autodoc]] NezhaForMaskedLM - forward ## NezhaForNextSentencePrediction [[autodoc]] NezhaForNextSentencePrediction - forward ## NezhaForSequenceClassification [[autodoc]] NezhaForSequenceClassification - forward ## NezhaForMultipleChoice [[autodoc]] NezhaForMultipleChoice - forward ## NezhaForTokenClassification [[autodoc]] NezhaForTokenClassification - forward ## NezhaForQuestionAnswering [[autodoc]] NezhaForQuestionAnswering - forward

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# Persimmon ## Overview The Persimmon model was created by [ADEPT](https://www.adept.ai/blog/persimmon-8b), and authored by Erich Elsen, Augustus Odena, Maxwell Nye, Sağnak Taşırlar, Tri Dao, Curtis Hawthorne, Deepak Moparthi, Arushi Somani. The authors introduced Persimmon-8B, a decoder model based on the classic transformers architecture, with query and key normalization. Persimmon-8B is a fully permissively-licensed model with approximately 8 billion parameters, released under the Apache license. Some of the key attributes of Persimmon-8B are long context size (16K), performance, and capabilities for multimodal extensions. The authors showcase their approach to model evaluation, focusing on practical text generation, mirroring how users interact with language models. The work also includes a comparative analysis, pitting Persimmon-8B against other prominent models (MPT 7B Instruct and Llama 2 Base 7B 1-Shot), across various evaluation tasks. The results demonstrate Persimmon-8B's competitive performance, even with limited training data. In terms of model details, the work outlines the architecture and training methodology of Persimmon-8B, providing insights into its design choices, sequence length, and dataset composition. The authors present a fast inference code that outperforms traditional implementations through operator fusion and CUDA graph utilization while maintaining code coherence. They express their anticipation of how the community will leverage this contribution to drive innovation, hinting at further upcoming releases as part of an ongoing series of developments. This model was contributed by [ArthurZ](https://huggingface.co/ArthurZ). The original code can be found [here](https://github.com/persimmon-ai-labs/adept-inference). ## Usage tips <Tip warning={true}> The `Persimmon` models were trained using `bfloat16`, but the original inference uses `float16` The checkpoints uploaded on the hub use `torch_dtype = 'float16'` which will be used by the `AutoModel` API to cast the checkpoints from `torch.float32` to `torch.float16`. The `dtype` of the online weights is mostly irrelevant, unless you are using `torch_dtype="auto"` when initializing a model using `model = AutoModelForCausalLM.from_pretrained("path", torch_dtype = "auto")`. The reason is that the model will first be downloaded ( using the `dtype` of the checkpoints online) then it will be cast to the default `dtype` of `torch` (becomes `torch.float32`). Users should specify the `torch_dtype` they want, and if they don't it will be `torch.float32`. Finetuning the model in `float16` is not recommended and known to produce `nan`, as such the model should be fine-tuned in `bfloat16`. </Tip> Tips: - To convert the model, you need to clone the original repository using `git clone https://github.com/persimmon-ai-labs/adept-inference`, then get the checkpoints: ```bash git clone https://github.com/persimmon-ai-labs/adept-inference wget https://axtkn4xl5cip.objectstorage.us-phoenix-1.oci.customer-oci.com/n/axtkn4xl5cip/b/adept-public-data/o/8b_base_model_release.tar tar -xvf 8b_base_model_release.tar python src/transformers/models/persimmon/convert_persimmon_weights_to_hf.py --input_dir /path/to/downloaded/persimmon/weights/ --output_dir /output/path \ --pt_model_path /path/to/8b_chat_model_release/iter_0001251/mp_rank_00/model_optim_rng.pt --ada_lib_path /path/to/adept-inference ``` For the chat model: ```bash wget https://axtkn4xl5cip.objectstorage.us-phoenix-1.oci.customer-oci.com/n/axtkn4xl5cip/b/adept-public-data/o/8b_chat_model_release.tar tar -xvf 8b_base_model_release.tar ``` Thereafter, models can be loaded via: ```py from transformers import PersimmonForCausalLM, PersimmonTokenizer model = PersimmonForCausalLM.from_pretrained("/output/path") tokenizer = PersimmonTokenizer.from_pretrained("/output/path") ``` - Perismmon uses a `sentencepiece` based tokenizer, with a `Unigram` model. It supports bytefallback, which is only available in `tokenizers==0.14.0` for the fast tokenizer. The `LlamaTokenizer` is used as it is a standard wrapper around sentencepiece. The `chat` template will be updated with the templating functions in a follow up PR! - The authors suggest to use the following prompt format for the chat mode: `f"human: {prompt}\n\nadept:"` ## PersimmonConfig [[autodoc]] PersimmonConfig ## PersimmonModel [[autodoc]] PersimmonModel - forward ## PersimmonForCausalLM [[autodoc]] PersimmonForCausalLM - forward ## PersimmonForSequenceClassification [[autodoc]] PersimmonForSequenceClassification - forward

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# ResNet ## Overview The ResNet model was proposed in [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun. Our implementation follows the small changes made by [Nvidia](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/resnet_50_v1_5_for_pytorch), we apply the `stride=2` for downsampling in bottleneck's `3x3` conv and not in the first `1x1`. This is generally known as "ResNet v1.5". ResNet introduced residual connections, they allow to train networks with an unseen number of layers (up to 1000). ResNet won the 2015 ILSVRC & COCO competition, one important milestone in deep computer vision. The abstract from the paper is the following: *Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.* The figure below illustrates the architecture of ResNet. Taken from the [original paper](https://arxiv.org/abs/1512.03385). <img width="600" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/resnet_architecture.png"/> This model was contributed by [Francesco](https://huggingface.co/Francesco). The TensorFlow version of this model was added by [amyeroberts](https://huggingface.co/amyeroberts). The original code can be found [here](https://github.com/KaimingHe/deep-residual-networks). ## Resources A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ResNet. <PipelineTag pipeline="image-classification"/> - [`ResNetForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb). - See also: [Image classification task guide](../tasks/image_classification) If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource. ## ResNetConfig [[autodoc]] ResNetConfig <frameworkcontent> <pt> ## ResNetModel [[autodoc]] ResNetModel - forward ## ResNetForImageClassification [[autodoc]] ResNetForImageClassification - forward </pt> <tf> ## TFResNetModel [[autodoc]] TFResNetModel - call ## TFResNetForImageClassification [[autodoc]] TFResNetForImageClassification - call </tf> <jax> ## FlaxResNetModel [[autodoc]] FlaxResNetModel - __call__ ## FlaxResNetForImageClassification [[autodoc]] FlaxResNetForImageClassification - __call__ </jax> </frameworkcontent>

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# Speech2Text2 ## Overview The Speech2Text2 model is used together with [Wav2Vec2](wav2vec2) for Speech Translation models proposed in [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau. Speech2Text2 is a *decoder-only* transformer model that can be used with any speech *encoder-only*, such as [Wav2Vec2](wav2vec2) or [HuBERT](hubert) for Speech-to-Text tasks. Please refer to the [SpeechEncoderDecoder](speech-encoder-decoder) class on how to combine Speech2Text2 with any speech *encoder-only* model. This model was contributed by [Patrick von Platen](https://huggingface.co/patrickvonplaten). The original code can be found [here](https://github.com/pytorch/fairseq/blob/1f7ef9ed1e1061f8c7f88f8b94c7186834398690/fairseq/models/wav2vec/wav2vec2_asr.py#L266). ## Usage tips - Speech2Text2 achieves state-of-the-art results on the CoVoST Speech Translation dataset. For more information, see the [official models](https://huggingface.co/models?other=speech2text2) . - Speech2Text2 is always used within the [SpeechEncoderDecoder](speech-encoder-decoder) framework. - Speech2Text2's tokenizer is based on [fastBPE](https://github.com/glample/fastBPE). ## Inference Speech2Text2's [`SpeechEncoderDecoderModel`] model accepts raw waveform input values from speech and makes use of [`~generation.GenerationMixin.generate`] to translate the input speech autoregressively to the target language. The [`Wav2Vec2FeatureExtractor`] class is responsible for preprocessing the input speech and [`Speech2Text2Tokenizer`] decodes the generated target tokens to the target string. The [`Speech2Text2Processor`] wraps [`Wav2Vec2FeatureExtractor`] and [`Speech2Text2Tokenizer`] into a single instance to both extract the input features and decode the predicted token ids. - Step-by-step Speech Translation ```python >>> import torch >>> from transformers import Speech2Text2Processor, SpeechEncoderDecoderModel >>> from datasets import load_dataset >>> import soundfile as sf >>> model = SpeechEncoderDecoderModel.from_pretrained("facebook/s2t-wav2vec2-large-en-de") >>> processor = Speech2Text2Processor.from_pretrained("facebook/s2t-wav2vec2-large-en-de") >>> def map_to_array(batch): ... speech, _ = sf.read(batch["file"]) ... batch["speech"] = speech ... return batch >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> ds = ds.map(map_to_array) >>> inputs = processor(ds["speech"][0], sampling_rate=16_000, return_tensors="pt") >>> generated_ids = model.generate(inputs=inputs["input_values"], attention_mask=inputs["attention_mask"]) >>> transcription = processor.batch_decode(generated_ids) ``` - Speech Translation via Pipelines The automatic speech recognition pipeline can also be used to translate speech in just a couple lines of code ```python >>> from datasets import load_dataset >>> from transformers import pipeline >>> librispeech_en = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> asr = pipeline( ... "automatic-speech-recognition", ... model="facebook/s2t-wav2vec2-large-en-de", ... feature_extractor="facebook/s2t-wav2vec2-large-en-de", ... ) >>> translation_de = asr(librispeech_en[0]["file"]) ``` See [model hub](https://huggingface.co/models?filter=speech2text2) to look for Speech2Text2 checkpoints. ## Resources - [Causal language modeling task guide](../tasks/language_modeling) ## Speech2Text2Config [[autodoc]] Speech2Text2Config ## Speech2TextTokenizer [[autodoc]] Speech2Text2Tokenizer - batch_decode - decode - save_vocabulary ## Speech2Text2Processor [[autodoc]] Speech2Text2Processor - __call__ - from_pretrained - save_pretrained - batch_decode - decode ## Speech2Text2ForCausalLM [[autodoc]] Speech2Text2ForCausalLM - forward

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# Vision Encoder Decoder Models ## Overview The [`VisionEncoderDecoderModel`] can be used to initialize an image-to-text model with any pretrained Transformer-based vision model as the encoder (*e.g.* [ViT](vit), [BEiT](beit), [DeiT](deit), [Swin](swin)) and any pretrained language model as the decoder (*e.g.* [RoBERTa](roberta), [GPT2](gpt2), [BERT](bert), [DistilBERT](distilbert)). The effectiveness of initializing image-to-text-sequence models with pretrained checkpoints has been shown in (for example) [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei. After such a [`VisionEncoderDecoderModel`] has been trained/fine-tuned, it can be saved/loaded just like any other models (see the examples below for more information). An example application is image captioning, in which the encoder is used to encode the image, after which an autoregressive language model generates the caption. Another example is optical character recognition. Refer to [TrOCR](trocr), which is an instance of [`VisionEncoderDecoderModel`]. ## Randomly initializing `VisionEncoderDecoderModel` from model configurations. [`VisionEncoderDecoderModel`] can be randomly initialized from an encoder and a decoder config. In the following example, we show how to do this using the default [`ViTModel`] configuration for the encoder and the default [`BertForCausalLM`] configuration for the decoder. ```python >>> from transformers import BertConfig, ViTConfig, VisionEncoderDecoderConfig, VisionEncoderDecoderModel >>> config_encoder = ViTConfig() >>> config_decoder = BertConfig() >>> config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder) >>> model = VisionEncoderDecoderModel(config=config) ``` ## Initialising `VisionEncoderDecoderModel` from a pretrained encoder and a pretrained decoder. [`VisionEncoderDecoderModel`] can be initialized from a pretrained encoder checkpoint and a pretrained decoder checkpoint. Note that any pretrained Transformer-based vision model, *e.g.* [Swin](swin), can serve as the encoder and both pretrained auto-encoding models, *e.g.* BERT, pretrained causal language models, *e.g.* GPT2, as well as the pretrained decoder part of sequence-to-sequence models, *e.g.* decoder of BART, can be used as the decoder. Depending on which architecture you choose as the decoder, the cross-attention layers might be randomly initialized. Initializing [`VisionEncoderDecoderModel`] from a pretrained encoder and decoder checkpoint requires the model to be fine-tuned on a downstream task, as has been shown in [the *Warm-starting-encoder-decoder blog post*](https://huggingface.co/blog/warm-starting-encoder-decoder). To do so, the `VisionEncoderDecoderModel` class provides a [`VisionEncoderDecoderModel.from_encoder_decoder_pretrained`] method. ```python >>> from transformers import VisionEncoderDecoderModel >>> model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained( ... "microsoft/swin-base-patch4-window7-224-in22k", "google-bert/bert-base-uncased" ... ) ``` ## Loading an existing `VisionEncoderDecoderModel` checkpoint and perform inference. To load fine-tuned checkpoints of the `VisionEncoderDecoderModel` class, [`VisionEncoderDecoderModel`] provides the `from_pretrained(...)` method just like any other model architecture in Transformers. To perform inference, one uses the [`generate`] method, which allows to autoregressively generate text. This method supports various forms of decoding, such as greedy, beam search and multinomial sampling. ```python >>> import requests >>> from PIL import Image >>> from transformers import GPT2TokenizerFast, ViTImageProcessor, VisionEncoderDecoderModel >>> # load a fine-tuned image captioning model and corresponding tokenizer and image processor >>> model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning") >>> tokenizer = GPT2TokenizerFast.from_pretrained("nlpconnect/vit-gpt2-image-captioning") >>> image_processor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning") >>> # let's perform inference on an image >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> pixel_values = image_processor(image, return_tensors="pt").pixel_values >>> # autoregressively generate caption (uses greedy decoding by default) >>> generated_ids = model.generate(pixel_values) >>> generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] >>> print(generated_text) a cat laying on a blanket next to a cat laying on a bed ``` ## Loading a PyTorch checkpoint into `TFVisionEncoderDecoderModel`. [`TFVisionEncoderDecoderModel.from_pretrained`] currently doesn't support initializing the model from a PyTorch checkpoint. Passing `from_pt=True` to this method will throw an exception. If there are only PyTorch checkpoints for a particular vision encoder-decoder model, a workaround is: ```python >>> from transformers import VisionEncoderDecoderModel, TFVisionEncoderDecoderModel >>> _model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning") >>> _model.encoder.save_pretrained("./encoder") >>> _model.decoder.save_pretrained("./decoder") >>> model = TFVisionEncoderDecoderModel.from_encoder_decoder_pretrained( ... "./encoder", "./decoder", encoder_from_pt=True, decoder_from_pt=True ... ) >>> # This is only for copying some specific attributes of this particular model. >>> model.config = _model.config ``` ## Training Once the model is created, it can be fine-tuned similar to BART, T5 or any other encoder-decoder model on a dataset of (image, text) pairs. As you can see, only 2 inputs are required for the model in order to compute a loss: `pixel_values` (which are the images) and `labels` (which are the `input_ids` of the encoded target sequence). ```python >>> from transformers import ViTImageProcessor, BertTokenizer, VisionEncoderDecoderModel >>> from datasets import load_dataset >>> image_processor = ViTImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k") >>> tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained( ... "google/vit-base-patch16-224-in21k", "google-bert/bert-base-uncased" ... ) >>> model.config.decoder_start_token_id = tokenizer.cls_token_id >>> model.config.pad_token_id = tokenizer.pad_token_id >>> dataset = load_dataset("huggingface/cats-image") >>> image = dataset["test"]["image"][0] >>> pixel_values = image_processor(image, return_tensors="pt").pixel_values >>> labels = tokenizer( ... "an image of two cats chilling on a couch", ... return_tensors="pt", ... ).input_ids >>> # the forward function automatically creates the correct decoder_input_ids >>> loss = model(pixel_values=pixel_values, labels=labels).loss ``` This model was contributed by [nielsr](https://github.com/nielsrogge). This model's TensorFlow and Flax versions were contributed by [ydshieh](https://github.com/ydshieh). ## VisionEncoderDecoderConfig [[autodoc]] VisionEncoderDecoderConfig <frameworkcontent> <pt> ## VisionEncoderDecoderModel [[autodoc]] VisionEncoderDecoderModel - forward - from_encoder_decoder_pretrained </pt> <tf> ## TFVisionEncoderDecoderModel [[autodoc]] TFVisionEncoderDecoderModel - call - from_encoder_decoder_pretrained </tf> <jax> ## FlaxVisionEncoderDecoderModel [[autodoc]] FlaxVisionEncoderDecoderModel - __call__ - from_encoder_decoder_pretrained </jax> </frameworkcontent>

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# Whisper ## Overview The Whisper model was proposed in [Robust Speech Recognition via Large-Scale Weak Supervision](https://cdn.openai.com/papers/whisper.pdf) by Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever. The abstract from the paper is the following: *We study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet. When scaled to 680,000 hours of multilingual and multitask supervision, the resulting models generalize well to standard benchmarks and are often competitive with prior fully supervised results but in a zeroshot transfer setting without the need for any finetuning. When compared to humans, the models approach their accuracy and robustness. We are releasing models and inference code to serve as a foundation for further work on robust speech processing.* This model was contributed by [Arthur Zucker](https://huggingface.co/ArthurZ). The Tensorflow version of this model was contributed by [amyeroberts](https://huggingface.co/amyeroberts). The original code can be found [here](https://github.com/openai/whisper). ## Usage tips - The model usually performs well without requiring any finetuning. - The architecture follows a classic encoder-decoder architecture, which means that it relies on the [`~generation.GenerationMixin.generate`] function for inference. - One can use [`WhisperProcessor`] to prepare audio for the model, and decode the predicted ID's back into text. - To convert the model and the processor, we recommend using the following: ```bash python src/transformers/models/whisper/convert_openai_to_hf.py --checkpoint_path "" --pytorch_dump_folder_path "Arthur/whisper-3" --convert_preprocessor True ``` The script will automatically determine all necessary parameters from the OpenAI checkpoint. A `tiktoken` library needs to be installed to perform the conversion of the OpenAI tokenizer to the `tokenizers` version. ## Inference Here is a step-by-step guide to transcribing an audio sample using a pre-trained Whisper model: ```python >>> from datasets import load_dataset >>> from transformers import WhisperProcessor, WhisperForConditionalGeneration >>> # Select an audio file and read it: >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> audio_sample = ds[0]["audio"] >>> waveform = audio_sample["array"] >>> sampling_rate = audio_sample["sampling_rate"] >>> # Load the Whisper model in Hugging Face format: >>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en") >>> # Use the model and processor to transcribe the audio: >>> input_features = processor( ... waveform, sampling_rate=sampling_rate, return_tensors="pt" ... ).input_features >>> # Generate token ids >>> predicted_ids = model.generate(input_features) >>> # Decode token ids to text >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) >>> transcription[0] ' Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.' ``` ## Resources A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Whisper. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource. - A fork with a script to [convert a Whisper model in Hugging Face format to OpenAI format](https://github.com/zuazo-forks/transformers/blob/convert_hf_to_openai/src/transformers/models/whisper/convert_hf_to_openai.py). 🌎 Usage example: ```bash pip install -U openai-whisper python convert_hf_to_openai.py \ --checkpoint openai/whisper-tiny \ --whisper_dump_path whisper-tiny-openai.pt ``` ## WhisperConfig [[autodoc]] WhisperConfig ## WhisperTokenizer [[autodoc]] WhisperTokenizer - set_prefix_tokens - build_inputs_with_special_tokens - get_special_tokens_mask - create_token_type_ids_from_sequences - save_vocabulary - batch_decode - decode - basic_normalize - normalize ## WhisperTokenizerFast [[autodoc]] WhisperTokenizerFast - set_prefix_tokens - build_inputs_with_special_tokens - get_special_tokens_mask - create_token_type_ids_from_sequences - save_vocabulary - batch_decode - decode - basic_normalize - normalize ## WhisperFeatureExtractor [[autodoc]] WhisperFeatureExtractor - __call__ ## WhisperProcessor [[autodoc]] WhisperProcessor - __call__ - from_pretrained - save_pretrained - batch_decode - decode <frameworkcontent> <pt> ## WhisperModel [[autodoc]] WhisperModel - forward - _mask_input_features ## WhisperForConditionalGeneration [[autodoc]] WhisperForConditionalGeneration - forward - generate ## WhisperForCausalLM [[autodoc]] WhisperForCausalLM - forward ## WhisperForAudioClassification [[autodoc]] WhisperForAudioClassification - forward </pt> <tf> ## TFWhisperModel [[autodoc]] TFWhisperModel - call ## TFWhisperForConditionalGeneration [[autodoc]] TFWhisperForConditionalGeneration - call </tf> <jax> ## FlaxWhisperModel [[autodoc]] FlaxWhisperModel - __call__ ## FlaxWhisperForConditionalGeneration [[autodoc]] FlaxWhisperForConditionalGeneration - __call__ ## FlaxWhisperForAudioClassification [[autodoc]] FlaxWhisperForAudioClassification - __call__ </jax> </frameworkcontent>

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# Perplexity of fixed-length models [[open-in-colab]] Perplexity (PPL) is one of the most common metrics for evaluating language models. Before diving in, we should note that the metric applies specifically to classical language models (sometimes called autoregressive or causal language models) and is not well defined for masked language models like BERT (see [summary of the models](model_summary)). Perplexity is defined as the exponentiated average negative log-likelihood of a sequence. If we have a tokenized sequence \$X = (x_0, x_1, \dots, x_t)\$, then the perplexity of \$X\$ is, $$\text{PPL}(X) = \exp \left\{ {-\frac{1}{t}\sum_i^t \log p_\theta (x_i|x_{<i}) } \right\}$$ where \$\log p_\theta (x_i|x_{<i})\$ is the log-likelihood of the ith token conditioned on the preceding tokens \$x_{<i}\$ according to our model. Intuitively, it can be thought of as an evaluation of the model's ability to predict uniformly among the set of specified tokens in a corpus. Importantly, this means that the tokenization procedure has a direct impact on a model's perplexity which should always be taken into consideration when comparing different models. This is also equivalent to the exponentiation of the cross-entropy between the data and model predictions. For more intuition about perplexity and its relationship to Bits Per Character (BPC) and data compression, check out this [fantastic blog post on The Gradient](https://thegradient.pub/understanding-evaluation-metrics-for-language-models/). ## Calculating PPL with fixed-length models If we weren't limited by a model's context size, we would evaluate the model's perplexity by autoregressively factorizing a sequence and conditioning on the entire preceding subsequence at each step, as shown below. <img width="600" alt="Full decomposition of a sequence with unlimited context length" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/ppl_full.gif"/> When working with approximate models, however, we typically have a constraint on the number of tokens the model can process. The largest version of [GPT-2](model_doc/gpt2), for example, has a fixed length of 1024 tokens, so we cannot calculate \$p_\theta(x_t|x_{<t})\$ directly when \$t\$ is greater than 1024. Instead, the sequence is typically broken into subsequences equal to the model's maximum input size. If a model's max input size is \$k\$, we then approximate the likelihood of a token \$x_t\$ by conditioning only on the \$k-1\$ tokens that precede it rather than the entire context. When evaluating the model's perplexity of a sequence, a tempting but suboptimal approach is to break the sequence into disjoint chunks and add up the decomposed log-likelihoods of each segment independently. <img width="600" alt="Suboptimal PPL not taking advantage of full available context" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/ppl_chunked.gif"/> This is quick to compute since the perplexity of each segment can be computed in one forward pass, but serves as a poor approximation of the fully-factorized perplexity and will typically yield a higher (worse) PPL because the model will have less context at most of the prediction steps. Instead, the PPL of fixed-length models should be evaluated with a sliding-window strategy. This involves repeatedly sliding the context window so that the model has more context when making each prediction. <img width="600" alt="Sliding window PPL taking advantage of all available context" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/ppl_sliding.gif"/> This is a closer approximation to the true decomposition of the sequence probability and will typically yield a more favorable score. The downside is that it requires a separate forward pass for each token in the corpus. A good practical compromise is to employ a strided sliding window, moving the context by larger strides rather than sliding by 1 token a time. This allows computation to proceed much faster while still giving the model a large context to make predictions at each step. ## Example: Calculating perplexity with GPT-2 in 🤗 Transformers Let's demonstrate this process with GPT-2. ```python from transformers import GPT2LMHeadModel, GPT2TokenizerFast device = "cuda" model_id = "openai-community/gpt2-large" model = GPT2LMHeadModel.from_pretrained(model_id).to(device) tokenizer = GPT2TokenizerFast.from_pretrained(model_id) ``` We'll load in the WikiText-2 dataset and evaluate the perplexity using a few different sliding-window strategies. Since this dataset is small and we're just doing one forward pass over the set, we can just load and encode the entire dataset in memory. ```python from datasets import load_dataset test = load_dataset("wikitext", "wikitext-2-raw-v1", split="test") encodings = tokenizer("\n\n".join(test["text"]), return_tensors="pt") ``` With 🤗 Transformers, we can simply pass the `input_ids` as the `labels` to our model, and the average negative log-likelihood for each token is returned as the loss. With our sliding window approach, however, there is overlap in the tokens we pass to the model at each iteration. We don't want the log-likelihood for the tokens we're just treating as context to be included in our loss, so we can set these targets to `-100` so that they are ignored. The following is an example of how we could do this with a stride of `512`. This means that the model will have at least 512 tokens for context when calculating the conditional likelihood of any one token (provided there are 512 preceding tokens available to condition on). ```python import torch from tqdm import tqdm max_length = model.config.n_positions stride = 512 seq_len = encodings.input_ids.size(1) nlls = [] prev_end_loc = 0 for begin_loc in tqdm(range(0, seq_len, stride)): end_loc = min(begin_loc + max_length, seq_len) trg_len = end_loc - prev_end_loc # may be different from stride on last loop input_ids = encodings.input_ids[:, begin_loc:end_loc].to(device) target_ids = input_ids.clone() target_ids[:, :-trg_len] = -100 with torch.no_grad(): outputs = model(input_ids, labels=target_ids) # loss is calculated using CrossEntropyLoss which averages over valid labels # N.B. the model only calculates loss over trg_len - 1 labels, because it internally shifts the labels # to the left by 1. neg_log_likelihood = outputs.loss nlls.append(neg_log_likelihood) prev_end_loc = end_loc if end_loc == seq_len: break ppl = torch.exp(torch.stack(nlls).mean()) ``` Running this with the stride length equal to the max input length is equivalent to the suboptimal, non-sliding-window strategy we discussed above. The smaller the stride, the more context the model will have in making each prediction, and the better the reported perplexity will typically be. When we run the above with `stride = 1024`, i.e. no overlap, the resulting PPL is `19.44`, which is about the same as the `19.93` reported in the GPT-2 paper. By using `stride = 512` and thereby employing our striding window strategy, this jumps down to `16.45`. This is not only a more favorable score, but is calculated in a way that is closer to the true autoregressive decomposition of a sequence likelihood.

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# Image captioning [[open-in-colab]] Image captioning is the task of predicting a caption for a given image. Common real world applications of it include aiding visually impaired people that can help them navigate through different situations. Therefore, image captioning helps to improve content accessibility for people by describing images to them. This guide will show you how to: * Fine-tune an image captioning model. * Use the fine-tuned model for inference. Before you begin, make sure you have all the necessary libraries installed: ```bash pip install transformers datasets evaluate -q pip install jiwer -q ``` We encourage you to log in to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to log in: ```python from huggingface_hub import notebook_login notebook_login() ``` ## Load the Pokémon BLIP captions dataset Use the 🤗 Dataset library to load a dataset that consists of {image-caption} pairs. To create your own image captioning dataset in PyTorch, you can follow [this notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/GIT/Fine_tune_GIT_on_an_image_captioning_dataset.ipynb). ```python from datasets import load_dataset ds = load_dataset("lambdalabs/pokemon-blip-captions") ds ``` ```bash DatasetDict({ train: Dataset({ features: ['image', 'text'], num_rows: 833 }) }) ``` The dataset has two features, `image` and `text`. <Tip> Many image captioning datasets contain multiple captions per image. In those cases, a common strategy is to randomly sample a caption amongst the available ones during training. </Tip> Split the dataset’s train split into a train and test set with the [`~datasets.Dataset.train_test_split`] method: ```python ds = ds["train"].train_test_split(test_size=0.1) train_ds = ds["train"] test_ds = ds["test"] ``` Let's visualize a couple of samples from the training set. ```python from textwrap import wrap import matplotlib.pyplot as plt import numpy as np def plot_images(images, captions): plt.figure(figsize=(20, 20)) for i in range(len(images)): ax = plt.subplot(1, len(images), i + 1) caption = captions[i] caption = "\n".join(wrap(caption, 12)) plt.title(caption) plt.imshow(images[i]) plt.axis("off") sample_images_to_visualize = [np.array(train_ds[i]["image"]) for i in range(5)] sample_captions = [train_ds[i]["text"] for i in range(5)] plot_images(sample_images_to_visualize, sample_captions) ``` <div class="flex justify-center"> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/sample_training_images_image_cap.png" alt="Sample training images"/> </div> ## Preprocess the dataset Since the dataset has two modalities (image and text), the pre-processing pipeline will preprocess images and the captions. To do so, load the processor class associated with the model you are about to fine-tune. ```python from transformers import AutoProcessor checkpoint = "microsoft/git-base" processor = AutoProcessor.from_pretrained(checkpoint) ``` The processor will internally pre-process the image (which includes resizing, and pixel scaling) and tokenize the caption. ```python def transforms(example_batch): images = [x for x in example_batch["image"]] captions = [x for x in example_batch["text"]] inputs = processor(images=images, text=captions, padding="max_length") inputs.update({"labels": inputs["input_ids"]}) return inputs train_ds.set_transform(transforms) test_ds.set_transform(transforms) ``` With the dataset ready, you can now set up the model for fine-tuning. ## Load a base model Load the ["microsoft/git-base"](https://huggingface.co/microsoft/git-base) into a [`AutoModelForCausalLM`](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForCausalLM) object. ```python from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained(checkpoint) ``` ## Evaluate Image captioning models are typically evaluated with the [Rouge Score](https://huggingface.co/spaces/evaluate-metric/rouge) or [Word Error Rate](https://huggingface.co/spaces/evaluate-metric/wer). For this guide, you will use the Word Error Rate (WER). We use the 🤗 Evaluate library to do so. For potential limitations and other gotchas of the WER, refer to [this guide](https://huggingface.co/spaces/evaluate-metric/wer). ```python from evaluate import load import torch wer = load("wer") def compute_metrics(eval_pred): logits, labels = eval_pred predicted = logits.argmax(-1) decoded_labels = processor.batch_decode(labels, skip_special_tokens=True) decoded_predictions = processor.batch_decode(predicted, skip_special_tokens=True) wer_score = wer.compute(predictions=decoded_predictions, references=decoded_labels) return {"wer_score": wer_score} ``` ## Train! Now, you are ready to start fine-tuning the model. You will use the 🤗 [`Trainer`] for this. First, define the training arguments using [`TrainingArguments`]. ```python from transformers import TrainingArguments, Trainer model_name = checkpoint.split("/")[1] training_args = TrainingArguments( output_dir=f"{model_name}-pokemon", learning_rate=5e-5, num_train_epochs=50, fp16=True, per_device_train_batch_size=32, per_device_eval_batch_size=32, gradient_accumulation_steps=2, save_total_limit=3, evaluation_strategy="steps", eval_steps=50, save_strategy="steps", save_steps=50, logging_steps=50, remove_unused_columns=False, push_to_hub=True, label_names=["labels"], load_best_model_at_end=True, ) ``` Then pass them along with the datasets and the model to 🤗 Trainer. ```python trainer = Trainer( model=model, args=training_args, train_dataset=train_ds, eval_dataset=test_ds, compute_metrics=compute_metrics, ) ``` To start training, simply call [`~Trainer.train`] on the [`Trainer`] object. ```python trainer.train() ``` You should see the training loss drop smoothly as training progresses. Once training is completed, share your model to the Hub with the [`~Trainer.push_to_hub`] method so everyone can use your model: ```python trainer.push_to_hub() ``` ## Inference Take a sample image from `test_ds` to test the model. ```python from PIL import Image import requests url = "https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png" image = Image.open(requests.get(url, stream=True).raw) image ``` <div class="flex justify-center"> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/test_image_image_cap.png" alt="Test image"/> </div> Prepare image for the model. ```python device = "cuda" if torch.cuda.is_available() else "cpu" inputs = processor(images=image, return_tensors="pt").to(device) pixel_values = inputs.pixel_values ``` Call [`generate`] and decode the predictions. ```python generated_ids = model.generate(pixel_values=pixel_values, max_length=50) generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] print(generated_caption) ``` ```bash a drawing of a pink and blue pokemon ``` Looks like the fine-tuned model generated a pretty good caption!

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# docstyle-ignore INSTALL_CONTENT = """ # Transformers installation ! pip install transformers datasets # To install from source instead of the last release, comment the command above and uncomment the following one. # ! pip install git+https://github.com/huggingface/transformers.git """ notebook_first_cells = [{"type": "code", "content": INSTALL_CONTENT}] black_avoid_patterns = { "{processor_class}": "FakeProcessorClass", "{model_class}": "FakeModelClass", "{object_class}": "FakeObjectClass", }

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# Modelos multilingües para inferencia [[open-in-colab]] Existen varios modelos multilingües en 🤗 Transformers y su uso para inferencia difiere de los modelos monolingües. Sin embargo, no *todos* los usos de los modelos multilingües son diferentes. Algunos modelos, como [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased), pueden utilizarse igual que un modelo monolingüe. Esta guía te enseñará cómo utilizar modelos multilingües cuyo uso difiere en la inferencia. ## XLM XLM tiene diez checkpoints diferentes de los cuales solo uno es monolingüe. Los nueve checkpoints restantes del modelo pueden dividirse en dos categorías: los checkpoints que utilizan language embeddings y los que no. ### XLM con language embeddings Los siguientes modelos XLM usan language embeddings para especificar el lenguaje utilizado en la inferencia: - `FacebookAI/xlm-mlm-ende-1024` (Masked language modeling, English-German) - `FacebookAI/xlm-mlm-enfr-1024` (Masked language modeling, English-French) - `FacebookAI/xlm-mlm-enro-1024` (Masked language modeling, English-Romanian) - `FacebookAI/xlm-mlm-xnli15-1024` (Masked language modeling, XNLI languages) - `FacebookAI/xlm-mlm-tlm-xnli15-1024` (Masked language modeling + translation, XNLI languages) - `FacebookAI/xlm-clm-enfr-1024` (Causal language modeling, English-French) - `FacebookAI/xlm-clm-ende-1024` (Causal language modeling, English-German) Los language embeddings son representados como un tensor de la mismas dimensiones que los `input_ids` pasados al modelo. Los valores de estos tensores dependen del idioma utilizado y se identifican mediante los atributos `lang2id` y `id2lang` del tokenizador. En este ejemplo, carga el checkpoint `FacebookAI/xlm-clm-enfr-1024` (Causal language modeling, English-French): ```py >>> import torch >>> from transformers import XLMTokenizer, XLMWithLMHeadModel >>> tokenizer = XLMTokenizer.from_pretrained("FacebookAI/xlm-clm-enfr-1024") >>> model = XLMWithLMHeadModel.from_pretrained("FacebookAI/xlm-clm-enfr-1024") ``` El atributo `lang2id` del tokenizador muestra los idiomas de este modelo y sus ids: ```py >>> print(tokenizer.lang2id) {'en': 0, 'fr': 1} ``` A continuación, crea un input de ejemplo: ```py >>> input_ids = torch.tensor([tokenizer.encode("Wikipedia was used to")]) # batch size of 1 ``` Establece el id del idioma, por ejemplo `"en"`, y utilízalo para definir el language embedding. El language embedding es un tensor lleno de `0` ya que es el id del idioma para inglés. Este tensor debe ser del mismo tamaño que `input_ids`. ```py >>> language_id = tokenizer.lang2id["en"] # 0 >>> langs = torch.tensor([language_id] * input_ids.shape[1]) # torch.tensor([0, 0, 0, ..., 0]) >>> # We reshape it to be of size (batch_size, sequence_length) >>> langs = langs.view(1, -1) # is now of shape [1, sequence_length] (we have a batch size of 1) ``` Ahora puedes pasar los `input_ids` y el language embedding al modelo: ```py >>> outputs = model(input_ids, langs=langs) ``` El script [run_generation.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-generation/run_generation.py) puede generar texto con language embeddings utilizando los checkpoints `xlm-clm`. ### XLM sin language embeddings Los siguientes modelos XLM no requieren language embeddings durante la inferencia: - `FacebookAI/xlm-mlm-17-1280` (modelado de lenguaje enmascarado, 17 idiomas) - `FacebookAI/xlm-mlm-100-1280` (modelado de lenguaje enmascarado, 100 idiomas) Estos modelos se utilizan para representaciones genéricas de frases a diferencia de los anteriores checkpoints XLM. ## BERT Los siguientes modelos de BERT pueden utilizarse para tareas multilingües: - `google-bert/bert-base-multilingual-uncased` (modelado de lenguaje enmascarado + predicción de la siguiente oración, 102 idiomas) - `google-bert/bert-base-multilingual-cased` (modelado de lenguaje enmascarado + predicción de la siguiente oración, 104 idiomas) Estos modelos no requieren language embeddings durante la inferencia. Deben identificar la lengua a partir del contexto e inferir en consecuencia. ## XLM-RoBERTa Los siguientes modelos de XLM-RoBERTa pueden utilizarse para tareas multilingües: - `FacebookAI/xlm-roberta-base` (modelado de lenguaje enmascarado, 100 idiomas) - `FacebookAI/xlm-roberta-large` (Modelado de lenguaje enmascarado, 100 idiomas) XLM-RoBERTa se entrenó con 2,5 TB de datos CommonCrawl recién creados y depurados en 100 idiomas. Proporciona fuertes ventajas sobre los modelos multilingües publicados anteriormente como mBERT o XLM en tareas posteriores como la clasificación, el etiquetado de secuencias y la respuesta a preguntas. ## M2M100 Los siguientes modelos de M2M100 pueden utilizarse para traducción multilingüe: - `facebook/m2m100_418M` (traducción) - `facebook/m2m100_1.2B` (traducción) En este ejemplo, carga el checkpoint `facebook/m2m100_418M` para traducir del chino al inglés. Puedes establecer el idioma de origen en el tokenizador: ```py >>> from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer >>> en_text = "Do not meddle in the affairs of wizards, for they are subtle and quick to anger." >>> chinese_text = "不要插手巫師的事務, 因為他們是微妙的, 很快就會發怒." >>> tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M", src_lang="zh") >>> model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M") ``` Tokeniza el texto: ```py >>> encoded_zh = tokenizer(chinese_text, return_tensors="pt") ``` M2M100 fuerza el id del idioma de destino como el primer token generado para traducir al idioma de destino.. Establece el `forced_bos_token_id` a `en` en el método `generate` para traducir al inglés: ```py >>> generated_tokens = model.generate(**encoded_zh, forced_bos_token_id=tokenizer.get_lang_id("en")) >>> tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) 'Do not interfere with the matters of the witches, because they are delicate and will soon be angry.' ``` ## MBart Los siguientes modelos de MBart pueden utilizarse para traducción multilingüe: - `facebook/mbart-large-50-one-to-many-mmt` (traducción automática multilingüe de uno a muchos, 50 idiomas) - `facebook/mbart-large-50-many-to-many-mmt` (traducción automática multilingüe de muchos a muchos, 50 idiomas) - `facebook/mbart-large-50-many-to-one-mmt` (traducción automática multilingüe muchos a uno, 50 idiomas) - `facebook/mbart-large-50` (traducción multilingüe, 50 idiomas) - `facebook/mbart-large-cc25` En este ejemplo, carga el checkpoint `facebook/mbart-large-50-many-to-many-mmt` para traducir del finlandés al inglés. Puedes establecer el idioma de origen en el tokenizador: ```py >>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM >>> en_text = "Do not meddle in the affairs of wizards, for they are subtle and quick to anger." >>> fi_text = "Älä sekaannu velhojen asioihin, sillä ne ovat hienovaraisia ja nopeasti vihaisia." >>> tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-50-many-to-many-mmt", src_lang="fi_FI") >>> model = AutoModelForSeq2SeqLM.from_pretrained("facebook/mbart-large-50-many-to-many-mmt") ``` Tokeniza el texto: ```py >>> encoded_en = tokenizer(en_text, return_tensors="pt") ``` MBart fuerza el id del idioma de destino como el primer token generado para traducirlo. Establece el `forced_bos_token_id` a `en` en el método `generate` para traducir al inglés: ```py >>> generated_tokens = model.generate(**encoded_en, forced_bos_token_id=tokenizer.lang_code_to_id("en_XX")) >>> tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) "Don't interfere with the wizard's affairs, because they are subtle, will soon get angry." ``` Si estás usando el checkpoint `facebook/mbart-large-50-many-to-one-mmt` no necesitas forzar el id del idioma de destino como el primer token generado, de lo contrario el uso es el mismo.

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# Modelado de lenguaje El modelado de lenguaje predice palabras en un enunciado. Hay dos formas de modelado de lenguaje. <Youtube id="Vpjb1lu0MDk"/> El modelado de lenguaje causal predice el siguiente token en una secuencia de tokens, y el modelo solo puede considerar los tokens a la izquierda. <Youtube id="mqElG5QJWUg"/> El modelado de lenguaje por enmascaramiento predice un token enmascarado en una secuencia, y el modelo puede considerar los tokens bidireccionalmente. Esta guía te mostrará cómo realizar fine-tuning [DistilGPT2](https://huggingface.co/distilbert/distilgpt2) para modelos de lenguaje causales y [DistilRoBERTa](https://huggingface.co/distilbert/distilroberta-base) para modelos de lenguaje por enmascaramiento en el [r/askscience](https://www.reddit.com/r/askscience/) subdataset [ELI5](https://huggingface.co/datasets/eli5). <Tip> Puedes realizar fine-tuning a otras arquitecturas para modelos de lenguaje como [GPT-Neo](https://huggingface.co/EleutherAI/gpt-neo-125M), [GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B) y [BERT](https://huggingface.co/google-bert/bert-base-uncased) siguiendo los mismos pasos presentados en esta guía! Mira la [página de tarea](https://huggingface.co/tasks/text-generation) para generación de texto y la [página de tarea](https://huggingface.co/tasks/fill-mask) para modelos de lenguajes por enmascaramiento para obtener más información sobre los modelos, datasets, y métricas asociadas. </Tip> ## Carga el dataset ELI5 Carga solo los primeros 5000 registros desde la biblioteca 🤗 Datasets, dado que es bastante grande: ```py >>> from datasets import load_dataset >>> eli5 = load_dataset("eli5", split="train_asks[:5000]") ``` Divide este dataset en subdatasets para el entrenamiento y el test: ```py eli5 = eli5.train_test_split(test_size=0.2) ``` Luego observa un ejemplo: ```py >>> eli5["train"][0] {'answers': {'a_id': ['c3d1aib', 'c3d4lya'], 'score': [6, 3], 'text': ["The velocity needed to remain in orbit is equal to the square root of Newton's constant times the mass of earth divided by the distance from the center of the earth. I don't know the altitude of that specific mission, but they're usually around 300 km. That means he's going 7-8 km/s.\n\nIn space there are no other forces acting on either the shuttle or the guy, so they stay in the same position relative to each other. If he were to become unable to return to the ship, he would presumably run out of oxygen, or slowly fall into the atmosphere and burn up.", "Hope you don't mind me asking another question, but why aren't there any stars visible in this photo?"]}, 'answers_urls': {'url': []}, 'document': '', 'q_id': 'nyxfp', 'selftext': '_URL_0_\n\nThis was on the front page earlier and I have a few questions about it. Is it possible to calculate how fast the astronaut would be orbiting the earth? Also how does he stay close to the shuttle so that he can return safely, i.e is he orbiting at the same speed and can therefore stay next to it? And finally if his propulsion system failed, would he eventually re-enter the atmosphere and presumably die?', 'selftext_urls': {'url': ['http://apod.nasa.gov/apod/image/1201/freeflyer_nasa_3000.jpg']}, 'subreddit': 'askscience', 'title': 'Few questions about this space walk photograph.', 'title_urls': {'url': []}} ``` Observa que `text` es un subcampo anidado dentro del diccionario `answers`. Cuando preproceses el dataset, deberás extraer el subcampo `text` en una columna aparte. ## Preprocesamiento <Youtube id="ma1TrR7gE7I"/> Para modelados de lenguaje causales carga el tokenizador DistilGPT2 para procesar el subcampo `text`: ```py >>> from transformers import AutoTokenizer >>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilgpt2") ``` <Youtube id="8PmhEIXhBvI"/> Para modelados de lenguaje por enmascaramiento carga el tokenizador DistilRoBERTa, en lugar de DistilGPT2: ```py >>> from transformers import AutoTokenizer >>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilroberta-base") ``` Extrae el subcampo `text` desde su estructura anidado con el método [`flatten`](https://huggingface.co/docs/datasets/process#flatten): ```py >>> eli5 = eli5.flatten() >>> eli5["train"][0] {'answers.a_id': ['c3d1aib', 'c3d4lya'], 'answers.score': [6, 3], 'answers.text': ["The velocity needed to remain in orbit is equal to the square root of Newton's constant times the mass of earth divided by the distance from the center of the earth. I don't know the altitude of that specific mission, but they're usually around 300 km. That means he's going 7-8 km/s.\n\nIn space there are no other forces acting on either the shuttle or the guy, so they stay in the same position relative to each other. If he were to become unable to return to the ship, he would presumably run out of oxygen, or slowly fall into the atmosphere and burn up.", "Hope you don't mind me asking another question, but why aren't there any stars visible in this photo?"], 'answers_urls.url': [], 'document': '', 'q_id': 'nyxfp', 'selftext': '_URL_0_\n\nThis was on the front page earlier and I have a few questions about it. Is it possible to calculate how fast the astronaut would be orbiting the earth? Also how does he stay close to the shuttle so that he can return safely, i.e is he orbiting at the same speed and can therefore stay next to it? And finally if his propulsion system failed, would he eventually re-enter the atmosphere and presumably die?', 'selftext_urls.url': ['http://apod.nasa.gov/apod/image/1201/freeflyer_nasa_3000.jpg'], 'subreddit': 'askscience', 'title': 'Few questions about this space walk photograph.', 'title_urls.url': []} ``` Cada subcampo es ahora una columna separada, como lo indica el prefijo `answers`. Observa que `answers.text` es una lista. En lugar de tokenizar cada enunciado por separado, convierte la lista en un string para tokenizarlos conjuntamente. Así es como puedes crear una función de preprocesamiento para convertir la lista en una cadena y truncar las secuencias para que no superen la longitud máxima de input de DistilGPT2: ```py >>> def preprocess_function(examples): ... return tokenizer([" ".join(x) for x in examples["answers.text"]], truncation=True) ``` Usa de 🤗 Datasets la función [`map`](https://huggingface.co/docs/datasets/process#map) para aplicar la función de preprocesamiento sobre el dataset en su totalidad. Puedes acelerar la función `map` configurando el argumento `batched=True` para procesar múltiples elementos del dataset a la vez y aumentar la cantidad de procesos con `num_proc`. Elimina las columnas que no necesitas: ```py >>> tokenized_eli5 = eli5.map( ... preprocess_function, ... batched=True, ... num_proc=4, ... remove_columns=eli5["train"].column_names, ... ) ``` Ahora necesitas una segunda función de preprocesamiento para capturar el texto truncado de cualquier ejemplo demasiado largo para evitar cualquier pérdida de información. Esta función de preprocesamiento debería: - Concatenar todo el texto. - Dividir el texto concatenado en trozos más pequeños definidos por un `block_size`. ```py >>> block_size = 128 >>> def group_texts(examples): ... concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()} ... total_length = len(concatenated_examples[list(examples.keys())[0]]) ... total_length = (total_length // block_size) * block_size ... result = { ... k: [t[i : i + block_size] for i in range(0, total_length, block_size)] ... for k, t in concatenated_examples.items() ... } ... result["labels"] = result["input_ids"].copy() ... return result ``` Aplica la función `group_texts` sobre todo el dataset: ```py >>> lm_dataset = tokenized_eli5.map(group_texts, batched=True, num_proc=4) ``` Para modelados de lenguaje causales, usa [`DataCollatorForLanguageModeling`] para crear un lote de ejemplos. Esto también *rellenará dinámicamente* tu texto a la dimensión del elemento más largo del lote para que de esta manera tengan largo uniforme. Si bien es posible rellenar tu texto en la función `tokenizer` mediante el argumento `padding=True`, el rellenado dinámico es más eficiente. <frameworkcontent> <pt> Puedes usar el token de final de secuencia como el token de relleno y asignar `mlm=False`. Esto usará los inputs como etiquetas movidas un elemento hacia la derecha: ```py >>> from transformers import DataCollatorForLanguageModeling >>> tokenizer.pad_token = tokenizer.eos_token >>> data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False) ``` Para modelados de lenguaje por enmascaramiento usa el mismo [`DataCollatorForLanguageModeling`] excepto que deberás especificar `mlm_probability` para enmascarar tokens aleatoriamente cada vez que iteras sobre los datos. ```py >>> from transformers import DataCollatorForLanguageModeling >>> tokenizer.pad_token = tokenizer.eos_token >>> data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15) ``` </pt> <tf> Puedes usar el token de final de secuencia como el token de relleno y asignar `mlm=False`. Esto usará los inputs como etiquetas movidas un elemento hacia la derecha: ```py >>> from transformers import DataCollatorForLanguageModeling >>> data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, return_tensors="tf") ``` Para modelados de lenguajes por enmascaramiento usa el mismo [`DataCollatorForLanguageModeling`] excepto que deberás especificar `mlm_probability` para enmascarar tokens aleatoriamente cada vez que iteras sobre los datos. ```py >>> from transformers import DataCollatorForLanguageModeling >>> data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, return_tensors="tf") ``` </tf> </frameworkcontent> ## Modelado de lenguaje causal El modelado de lenguaje causal es frecuentemente utilizado para generación de texto. Esta sección te muestra cómo realizar fine-tuning a [DistilGPT2](https://huggingface.co/distilbert/distilgpt2) para generar nuevo texto. ### Entrenamiento <frameworkcontent> <pt> Carga DistilGPT2 con [`AutoModelForCausalLM`]: ```py >>> from transformers import AutoModelForCausalLM, TrainingArguments, Trainer >>> model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2") ``` <Tip> Si no estás familiarizado con el proceso de realizar fine-tuning sobre un modelo con [`Trainer`], considera el tutorial básico [aquí](../training#finetune-with-trainer)! </Tip> A este punto, solo faltan tres pasos: 1. Definir tus hiperparámetros de entrenamiento en [`TrainingArguments`]. 2. Pasarle los argumentos de entrenamiento a [`Trainer`] junto con el modelo, dataset, y el data collator. 3. Realiza la llamada [`~Trainer.train`] para realizar el fine-tuning sobre tu modelo. ```py >>> training_args = TrainingArguments( ... output_dir="./results", ... evaluation_strategy="epoch", ... learning_rate=2e-5, ... weight_decay=0.01, ... ) >>> trainer = Trainer( ... model=model, ... args=training_args, ... train_dataset=lm_dataset["train"], ... eval_dataset=lm_dataset["test"], ... data_collator=data_collator, ... ) >>> trainer.train() ``` </pt> <tf> Para realizar el fine-tuning de un modelo en TensorFlow, comienza por convertir tus datasets al formato `tf.data.Dataset` con [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.to_tf_dataset). Especifica los inputs y etiquetas en `columns`, ya sea para mezclar el dataset, tamaño de lote, y el data collator: ```py >>> tf_train_set = lm_dataset["train"].to_tf_dataset( ... columns=["attention_mask", "input_ids", "labels"], ... dummy_labels=True, ... shuffle=True, ... batch_size=16, ... collate_fn=data_collator, ... ) >>> tf_test_set = lm_dataset["test"].to_tf_dataset( ... columns=["attention_mask", "input_ids", "labels"], ... dummy_labels=True, ... shuffle=False, ... batch_size=16, ... collate_fn=data_collator, ... ) ``` <Tip> Si no estás familiarizado con realizar fine-tuning de tus modelos con Keras, considera el tutorial básico [aquí](training#finetune-with-keras)! </Tip> Crea la función optimizadora, la tasa de aprendizaje, y algunos hiperparámetros de entrenamiento: ```py >>> from transformers import create_optimizer, AdamWeightDecay >>> optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01) ``` Carga DistilGPT2 con [`TFAutoModelForCausalLM`]: ```py >>> from transformers import TFAutoModelForCausalLM >>> model = TFAutoModelForCausalLM.from_pretrained("distilbert/distilgpt2") ``` Configura el modelo para entrenamiento con [`compile`](https://keras.io/api/models/model_training_apis/#compile-method): ```py >>> import tensorflow as tf >>> model.compile(optimizer=optimizer) ``` Llama a [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) para realizar el fine-tuning del modelo: ```py >>> model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3) ``` </tf> </frameworkcontent> ## Modelado de lenguaje por enmascaramiento El modelado de lenguaje por enmascaramiento es también conocido como una tarea de rellenar la máscara, pues predice un token enmascarado dada una secuencia. Los modelos de lenguaje por enmascaramiento requieren una buena comprensión del contexto de una secuencia entera, en lugar de solo el contexto a la izquierda. Esta sección te enseña como realizar el fine-tuning de [DistilRoBERTa](https://huggingface.co/distilbert/distilroberta-base) para predecir una palabra enmascarada. ### Entrenamiento <frameworkcontent> <pt> Carga DistilRoBERTa con [`AutoModelForMaskedlM`]: ```py >>> from transformers import AutoModelForMaskedLM >>> model = AutoModelForMaskedLM.from_pretrained("distilbert/distilroberta-base") ``` <Tip> Si no estás familiarizado con el proceso de realizar fine-tuning sobre un modelo con [`Trainer`], considera el tutorial básico [aquí](../training#finetune-with-trainer)! </Tip> A este punto, solo faltan tres pasos: 1. Definir tus hiperparámetros de entrenamiento en [`TrainingArguments`]. 2. Pasarle los argumentos de entrenamiento a [`Trainer`] junto con el modelo, dataset, y el data collator. 3. Realiza la llamada [`~Trainer.train`] para realizar el fine-tuning de tu modelo. ```py >>> training_args = TrainingArguments( ... output_dir="./results", ... evaluation_strategy="epoch", ... learning_rate=2e-5, ... num_train_epochs=3, ... weight_decay=0.01, ... ) >>> trainer = Trainer( ... model=model, ... args=training_args, ... train_dataset=lm_dataset["train"], ... eval_dataset=lm_dataset["test"], ... data_collator=data_collator, ... ) >>> trainer.train() ``` </pt> <tf> Para realizar el fine-tuning de un modelo en TensorFlow, comienza por convertir tus datasets al formato `tf.data.Dataset` con [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.to_tf_dataset). Especifica los inputs y etiquetas en `columns`, ya sea para mezclar el dataset, tamaño de lote, y el data collator: ```py >>> tf_train_set = lm_dataset["train"].to_tf_dataset( ... columns=["attention_mask", "input_ids", "labels"], ... dummy_labels=True, ... shuffle=True, ... batch_size=16, ... collate_fn=data_collator, ... ) >>> tf_test_set = lm_dataset["test"].to_tf_dataset( ... columns=["attention_mask", "input_ids", "labels"], ... dummy_labels=True, ... shuffle=False, ... batch_size=16, ... collate_fn=data_collator, ... ) ``` <Tip> Si no estás familiarizado con realizar fine-tuning de tus modelos con Keras, considera el tutorial básico [aquí](training#finetune-with-keras)! </Tip> Crea la función optimizadora, la tasa de aprendizaje, y algunos hiperparámetros de entrenamiento: ```py >>> from transformers import create_optimizer, AdamWeightDecay >>> optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01) ``` Carga DistilRoBERTa con [`TFAutoModelForMaskedLM`]: ```py >>> from transformers import TFAutoModelForMaskedLM >>> model = TFAutoModelForCausalLM.from_pretrained("distilbert/distilroberta-base") ``` Configura el modelo para entrenamiento con [`compile`](https://keras.io/api/models/model_training_apis/#compile-method): ```py >>> import tensorflow as tf >>> model.compile(optimizer=optimizer) ``` Llama a [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) para realizar el fine-tuning del modelo: ```py >>> model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3) ``` </tf> </frameworkcontent> <Tip> Para un ejemplo más profundo sobre cómo realizar el fine-tuning sobre un modelo de lenguaje causal, considera [PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb) o [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb). </Tip>

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# Allenamento distribuito con 🤗 Accelerate La parallelizzazione è emersa come strategia per allenare modelli sempre più grandi su hardware limitato e accelerarne la velocità di allenamento di diversi ordini di magnitudine. In Hugging Face, abbiamo creato la libreria [🤗 Accelerate](https://huggingface.co/docs/accelerate) per aiutarti ad allenare in modo semplice un modello 🤗 Transformers su qualsiasi tipo di configurazione distribuita, sia che si tratti di più GPU su una sola macchina o di più GPU su più macchine. In questo tutorial, imparerai come personalizzare il training loop nativo di PyTorch per consentire l'addestramento in un ambiente distribuito. ## Configurazione Inizia installando 🤗 Accelerate: ```bash pip install accelerate ``` Poi importa e crea un oggetto [`Accelerator`](https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator). `Accelerator` rileverà automaticamente il tuo setup distribuito e inizializzerà tutte le componenti necessarie per l'allenamento. Non dovrai allocare esplicitamente il tuo modello su un device. ```py >>> from accelerate import Accelerator >>> accelerator = Accelerator() ``` ## Preparati ad accelerare Il prossimo passo è quello di passare tutti gli oggetti rilevanti per l'allenamento al metodo [`prepare`](https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.prepare). Questo include i tuoi DataLoaders per l'allenamento e per la valutazione, un modello e un ottimizzatore: ```py >>> train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare( ... train_dataloader, eval_dataloader, model, optimizer ... ) ``` ## Backward Infine, sostituisci il tipico metodo `loss.backward()` nel tuo loop di allenamento con il metodo [`backward`](https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.backward) di 🤗 Accelerate: ```py >>> for epoch in range(num_epochs): ... for batch in train_dataloader: ... outputs = model(**batch) ... loss = outputs.loss ... accelerator.backward(loss) ... optimizer.step() ... lr_scheduler.step() ... optimizer.zero_grad() ... progress_bar.update(1) ``` Come puoi vedere nel seguente codice, hai solo bisogno di aggiungere quattro righe in più di codice al tuo training loop per abilitare l'allenamento distribuito! ```diff + from accelerate import Accelerator from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler + accelerator = Accelerator() model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2) optimizer = AdamW(model.parameters(), lr=3e-5) - device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") - model.to(device) + train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare( + train_dataloader, eval_dataloader, model, optimizer + ) num_epochs = 3 num_training_steps = num_epochs * len(train_dataloader) lr_scheduler = get_scheduler( "linear", optimizer=optimizer, num_warmup_steps=0, num_training_steps=num_training_steps ) progress_bar = tqdm(range(num_training_steps)) model.train() for epoch in range(num_epochs): for batch in train_dataloader: - batch = {k: v.to(device) for k, v in batch.items()} outputs = model(**batch) loss = outputs.loss - loss.backward() + accelerator.backward(loss) optimizer.step() lr_scheduler.step() optimizer.zero_grad() progress_bar.update(1) ``` ## Allenamento Una volta che hai aggiunto le righe di codice rilevanti, lancia il tuo allenamento in uno script o in un notebook come Colaboratory. ### Allenamento con uno script Se stai eseguendo il tuo allenamento da uno script, esegui il comando seguente per creare e salvare un file di configurazione: ```bash accelerate config ``` Poi lancia il tuo allenamento con: ```bash accelerate launch train.py ``` ### Allenamento con un notebook La libreria 🤗 Accelerate può anche essere utilizzata in un notebook se stai pianificando di utilizzare le TPU di Colaboratory. Inserisci tutto il codice legato all'allenamento in una funzione, e passala al `notebook_launcher`: ```py >>> from accelerate import notebook_launcher >>> notebook_launcher(training_function) ``` Per maggiori informazioni relative a 🤗 Accelerate e le sue numerose funzionalità, fai riferimento alla [documentazione](https://huggingface.co/docs/accelerate).

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# Inferenza Efficiente su CPU Questa guida si concentra sull'inferenza di modelli di grandi dimensioni in modo efficiente sulla CPU. ## `BetterTransformer` per inferenza più rapida Abbiamo integrato di recente `BetterTransformer` per fare inferenza più rapidamente con modelli per testi, immagini e audio. Visualizza la documentazione sull'integrazione [qui](https://huggingface.co/docs/optimum/bettertransformer/overview) per maggiori dettagli. ## PyTorch JIT-mode (TorchScript) TorchScript è un modo di creare modelli serializzabili e ottimizzabili da codice PyTorch. Ogni programmma TorchScript può esere salvato da un processo Python e caricato in un processo dove non ci sono dipendenze Python. Comparandolo con l'eager mode di default, jit mode in PyTorch normalmente fornisce prestazioni migliori per l'inferenza del modello da parte di metodologie di ottimizzazione come la operator fusion. Per una prima introduzione a TorchScript, vedi la Introduction to [PyTorch TorchScript tutorial](https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html#tracing-modules). ### IPEX Graph Optimization con JIT-mode Intel® Extension per PyTorch fornnisce ulteriori ottimizzazioni in jit mode per i modelli della serie Transformers. Consigliamo vivamente agli utenti di usufruire dei vantaggi di Intel® Extension per PyTorch con jit mode. Alcuni operator patterns usati fequentemente dai modelli Transformers models sono già supportati in Intel® Extension per PyTorch con jit mode fusions. Questi fusion patterns come Multi-head-attention fusion, Concat Linear, Linear+Add, Linear+Gelu, Add+LayerNorm fusion and etc. sono abilitati e hanno buone performance. I benefici della fusion è fornito agli utenti in modo trasparente. In base alle analisi, il ~70% dei problemi più popolari in NLP question-answering, text-classification, and token-classification possono avere benefici sulle performance grazie ai fusion patterns sia per Float32 precision che per BFloat16 Mixed precision. Vedi maggiori informazioni per [IPEX Graph Optimization](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/features/graph_optimization.html). #### Installazione di IPEX I rilasci di IPEX seguono PyTorch, verifica i vari approcci per [IPEX installation](https://intel.github.io/intel-extension-for-pytorch/). ### Utilizzo del JIT-mode Per abilitare JIT-mode in Trainer per evaluation e prediction, devi aggiungere `jit_mode_eval` negli argomenti di Trainer. <Tip warning={true}> per PyTorch >= 1.14.0. JIT-mode potrebe giovare a qualsiasi modello di prediction e evaluaion visto che il dict input è supportato in jit.trace per PyTorch < 1.14.0. JIT-mode potrebbe giovare ai modelli il cui ordine dei parametri corrisponde all'ordine delle tuple in ingresso in jit.trace, come i modelli per question-answering. Nel caso in cui l'ordine dei parametri seguenti non corrisponda all'ordine delle tuple in ingresso in jit.trace, come nei modelli di text-classification, jit.trace fallirà e lo cattureremo con una eccezione al fine di renderlo un fallback. Il logging è usato per notificare gli utenti. </Tip> Trovi un esempo con caso d'uso in [Transformers question-answering](https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering) - Inference using jit mode on CPU: <pre>python run_qa.py \ --model_name_or_path csarron/bert-base-uncased-squad-v1 \ --dataset_name squad \ --do_eval \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir /tmp/ \ --no_cuda \ <b>--jit_mode_eval </b></pre> - Inference with IPEX using jit mode on CPU: <pre>python run_qa.py \ --model_name_or_path csarron/bert-base-uncased-squad-v1 \ --dataset_name squad \ --do_eval \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir /tmp/ \ --no_cuda \ <b>--use_ipex \</b> <b>--jit_mode_eval</b></pre>

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# 🤗 Accelerate を用いた分散学習モデルが大きくなるにつれて、限られたハードウェアでより大きなモデルを訓練し、訓練速度を大幅に上昇させるための方法として並列処理が浮上してきました。1台のマシンに複数のGPUがあっても、複数のマシンにまたがる複数のGPUがあっても、あらゆるタイプの分散処理セットアップ上でユーザーが簡単に 🤗 Transformers モデルを訓練できるように、 Hugging Face では [🤗 Accelerate](https://huggingface.co/docs/accelerate) ライブラリを作成しました。このチュートリアルでは、PyTorch の訓練ループをカスタマイズして、分散処理環境での訓練を可能にする方法について学びます。 ## セットアップはじめに 🤗 Accelerate をインストールしましょう: ```bash pip install accelerate ``` そしたらインポートして [`~accelerate.Accelerator`] オブジェクトを作成しましょう。[`~accelerate.Accelerator`] は分散処理セットアップを自動的に検出し、訓練のために必要な全てのコンポーネントを初期化します。モデルをデバイスに明示的に配置する必要はありません。 ```py >>> from accelerate import Accelerator >>> accelerator = Accelerator() ``` ## Accelerate する準備をしましょう次に、関連する全ての訓練オブジェクトを [`~accelerate.Accelerator.prepare`] メソッドに渡します。これには、訓練と評価それぞれのDataloader、モデル、optimizer が含まれます: ```py >>> train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare( ... train_dataloader, eval_dataloader, model, optimizer ... ) ``` ## Backward 最後に訓練ループ内の `loss.backward()` を 🤗 Accelerate の [`~accelerate.Accelerator.backward`] メソッドで置き換えます： ```py >>> for epoch in range(num_epochs): ... for batch in train_dataloader: ... outputs = model(**batch) ... loss = outputs.loss ... accelerator.backward(loss) ... optimizer.step() ... lr_scheduler.step() ... optimizer.zero_grad() ... progress_bar.update(1) ``` 以下のコードで確認できる通り、訓練ループに4行のコードを追加するだけで分散学習が可能です！ ```diff + from accelerate import Accelerator from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler + accelerator = Accelerator() model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2) optimizer = AdamW(model.parameters(), lr=3e-5) - device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") - model.to(device) + train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare( + train_dataloader, eval_dataloader, model, optimizer + ) num_epochs = 3 num_training_steps = num_epochs * len(train_dataloader) lr_scheduler = get_scheduler( "linear", optimizer=optimizer, num_warmup_steps=0, num_training_steps=num_training_steps ) progress_bar = tqdm(range(num_training_steps)) model.train() for epoch in range(num_epochs): for batch in train_dataloader: - batch = {k: v.to(device) for k, v in batch.items()} outputs = model(**batch) loss = outputs.loss - loss.backward() + accelerator.backward(loss) optimizer.step() lr_scheduler.step() optimizer.zero_grad() progress_bar.update(1) ``` ## 訓練する関連するコードを追加したら、スクリプトまたは Colaboratory などのノートブックで訓練を開始します。 ### スクリプトで訓練するスクリプトから訓練をしている場合は、設定ファイルを作成・保存するために以下のコマンドを実行してください: ```bash accelerate config ``` そして次のようにして訓練を開始します: ```bash accelerate launch train.py ``` ### ノートブックで訓練する Colaboratory の TPU の利用をお考えの場合、🤗 Accelerate はノートブック上で実行することもできます。訓練に必要な全てのコードを関数に含め、[`~accelerate.notebook_launcher`] に渡してください: ```py >>> from accelerate import notebook_launcher >>> notebook_launcher(training_function) ``` 🤗 Accelerate と豊富な機能についてもっと知りたい方は[ドキュメント](https://huggingface.co/docs/accelerate)を参照してください。

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# Hyperparameter Search using Trainer API 🤗 Transformersは、🤗 Transformersモデルのトレーニングを最適化する[`Trainer`]クラスを提供し、独自のトレーニングループを手動で記述せずにトレーニングを開始するのが簡単になります。[`Trainer`]はハイパーパラメーター検索のAPIも提供しています。このドキュメントでは、それを例示します。 ## Hyperparameter Search backend [`Trainer`]は現在、4つのハイパーパラメーター検索バックエンドをサポートしています： [optuna](https://optuna.org/)、[sigopt](https://sigopt.com/)、[raytune](https://docs.ray.io/en/latest/tune/index.html)、および[wandb](https://wandb.ai/site/sweeps)。これらを使用する前に、ハイパーパラメーター検索バックエンドをインストールする必要があります。 ```bash pip install optuna/sigopt/wandb/ray[tune] ``` ## How to enable Hyperparameter search in example ハイパーパラメータの検索スペースを定義し、異なるバックエンドには異なるフォーマットが必要です。 Sigoptの場合、sigopt [object_parameter](https://docs.sigopt.com/ai-module-api-references/api_reference/objects/object_parameter) を参照してください。それは以下のようなものです： ```py >>> def sigopt_hp_space(trial): ... return [ ... {"bounds": {"min": 1e-6, "max": 1e-4}, "name": "learning_rate", "type": "double"}, ... { ... "categorical_values": ["16", "32", "64", "128"], ... "name": "per_device_train_batch_size", ... "type": "categorical", ... }, ... ] ``` Optunaに関しては、[object_parameter](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/002_configurations.html#sphx-glr-tutorial-10-key-features-002-configurations-py)をご覧ください。以下のようになります： ```py >>> def optuna_hp_space(trial): ... return { ... "learning_rate": trial.suggest_float("learning_rate", 1e-6, 1e-4, log=True), ... "per_device_train_batch_size": trial.suggest_categorical("per_device_train_batch_size", [16, 32, 64, 128]), ... } ``` Optunaは、多目的のハイパーパラメータ最適化（HPO）を提供しています。 `hyperparameter_search` で `direction` を渡し、複数の目的関数値を返すための独自の `compute_objective` を定義することができます。 Pareto Front（`List[BestRun]`）は `hyperparameter_search` で返され、[test_trainer](https://github.com/huggingface/transformers/blob/main/tests/trainer/test_trainer.py) のテストケース `TrainerHyperParameterMultiObjectOptunaIntegrationTest` を参照する必要があります。これは以下のようになります。 ```py >>> best_trials = trainer.hyperparameter_search( ... direction=["minimize", "maximize"], ... backend="optuna", ... hp_space=optuna_hp_space, ... n_trials=20, ... compute_objective=compute_objective, ... ) ``` Ray Tuneに関して、[object_parameter](https://docs.ray.io/en/latest/tune/api/search_space.html)を参照してください。以下のようになります： ```py >>> def ray_hp_space(trial): ... return { ... "learning_rate": tune.loguniform(1e-6, 1e-4), ... "per_device_train_batch_size": tune.choice([16, 32, 64, 128]), ... } ``` Wandbについては、[object_parameter](https://docs.wandb.ai/guides/sweeps/configuration)をご覧ください。これは以下のようになります： ```py >>> def wandb_hp_space(trial): ... return { ... "method": "random", ... "metric": {"name": "objective", "goal": "minimize"}, ... "parameters": { ... "learning_rate": {"distribution": "uniform", "min": 1e-6, "max": 1e-4}, ... "per_device_train_batch_size": {"values": [16, 32, 64, 128]}, ... }, ... } ``` `model_init` 関数を定義し、それを [`Trainer`] に渡す例を示します： ```py >>> def model_init(trial): ... return AutoModelForSequenceClassification.from_pretrained( ... model_args.model_name_or_path, ... from_tf=bool(".ckpt" in model_args.model_name_or_path), ... config=config, ... cache_dir=model_args.cache_dir, ... revision=model_args.model_revision, ... token=True if model_args.use_auth_token else None, ... ) ``` [`Trainer`] を `model_init` 関数、トレーニング引数、トレーニングデータセット、テストデータセット、および評価関数と共に作成してください: ```py >>> trainer = Trainer( ... model=None, ... args=training_args, ... train_dataset=small_train_dataset, ... eval_dataset=small_eval_dataset, ... compute_metrics=compute_metrics, ... tokenizer=tokenizer, ... model_init=model_init, ... data_collator=data_collator, ... ) ``` ハイパーパラメーターの探索を呼び出し、最良のトライアルパラメーターを取得します。バックエンドは `"optuna"` / `"sigopt"` / `"wandb"` / `"ray"` である可能性があります。方向は `"minimize"` または `"maximize"` であり、目標をより大きくするか小さくするかを示します。 `compute_objective` 関数を独自に定義することもできます。定義されていない場合、デフォルトの `compute_objective` が呼び出され、F1などの評価メトリックの合計が目標値として返されます。 ```py >>> best_trial = trainer.hyperparameter_search( ... direction="maximize", ... backend="optuna", ... hp_space=optuna_hp_space, ... n_trials=20, ... compute_objective=compute_objective, ... ) ``` ## Hyperparameter search For DDP finetune 現在、DDP（Distributed Data Parallel）のためのハイパーパラメーター検索は、Optuna と SigOpt に対して有効になっています。ランクゼロプロセスのみが検索トライアルを生成し、他のランクに引数を渡します。

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# ALBERT <div class="flex flex-wrap space-x-1"> <a href="https://huggingface.co/models?filter=albert"> <img alt="Models" src="https://img.shields.io/badge/All_model_pages-albert-blueviolet"> </a> <a href="https://huggingface.co/spaces/docs-demos/albert-base-v2"> <img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"> </a> </div> ## 概要 ALBERTモデルは、「[ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942)」という論文でZhenzhong Lan、Mingda Chen、Sebastian Goodman、Kevin Gimpel、Piyush Sharma、Radu Soricutによって提案されました。BERTのメモリ消費を減らしトレーニングを高速化するためのパラメータ削減技術を2つ示しています： - 埋め込み行列を2つの小さな行列に分割する。 - グループ間で分割された繰り返し層を使用する。論文の要旨は以下の通りです： *自然言語表現の事前学習時にモデルのサイズを増やすと、下流タスクのパフォーマンスが向上することがしばしばあります。しかし、ある時点でさらなるモデルの増大は、GPU/TPUのメモリ制限、長い訓練時間、予期せぬモデルの劣化といった問題のために困難になります。これらの問題に対処するために、我々はBERTのメモリ消費を低減し、訓練速度を高めるための2つのパラメータ削減技術を提案します。包括的な実証的証拠は、我々の提案方法が元のBERTに比べてはるかによくスケールするモデルを生み出すことを示しています。また、文間の一貫性をモデリングに焦点を当てた自己教師あり損失を使用し、複数の文が含まれる下流タスクに一貫して助けとなることを示します。その結果、我々の最良のモデルは、BERT-largeに比べてパラメータが少ないにもかかわらず、GLUE、RACE、SQuADベンチマークで新たな最先端の結果を確立します。* このモデルは[lysandre](https://huggingface.co/lysandre)により提供されました。このモデルのjaxバージョンは[kamalkraj](https://huggingface.co/kamalkraj)により提供されました。オリジナルのコードは[こちら](https://github.com/google-research/ALBERT)で見ることができます。 ## 使用上のヒント - ALBERTは絶対位置埋め込みを使用するモデルなので、通常、入力を左側ではなく右側にパディングすることが推奨されます。 - ALBERTは繰り返し層を使用するためメモリ使用量は小さくなりますが、同じ数の（繰り返し）層を反復しなければならないため、隠れ層の数が同じであればBERTのようなアーキテクチャと同様の計算コストがかかります。 - 埋め込みサイズEは隠れサイズHと異なりますが、これは埋め込みが文脈に依存しない（一つの埋め込みベクトルが一つのトークンを表す）のに対し、隠れ状態は文脈に依存する（1つの隠れ状態がトークン系列を表す）ため、H >> Eとすることがより論理的です。また、埋め込み行列のサイズはV x Eと大きいです（Vは語彙サイズ）。E < Hであれば、パラメータは少なくなります。 - 層はパラメータを共有するグループに分割されています（メモリ節約のため）。次文予測（NSP: Next Sentence Prediction）は文の順序予測に置き換えられます：入力では、2つの文AとB（それらは連続している）があり、Aに続いてBを与えるか、Bに続いてAを与えます。モデルはそれらが入れ替わっているかどうかを予測する必要があります。 ## 参考資料 - [テキスト分類タスクガイド](../tasks/sequence_classification) - [トークン分類タスクガイド](../tasks/token_classification) - [質問応答タスクガイド](../tasks/question_answering) - [マスクされた言語モデルタスクガイド](../tasks/masked_language_modeling) - [多肢選択タスクガイド](../tasks/multiple_choice) ## AlbertConfig [[autodoc]] AlbertConfig ## AlbertTokenizer [[autodoc]] AlbertTokenizer - build_inputs_with_special_tokens - get_special_tokens_mask - create_token_type_ids_from_sequences - save_vocabulary ## AlbertTokenizerFast [[autodoc]] AlbertTokenizerFast ## Albert specific outputs [[autodoc]] models.albert.modeling_albert.AlbertForPreTrainingOutput [[autodoc]] models.albert.modeling_tf_albert.TFAlbertForPreTrainingOutput <frameworkcontent> <pt> ## AlbertModel [[autodoc]] AlbertModel - forward ## AlbertForPreTraining [[autodoc]] AlbertForPreTraining - forward ## AlbertForMaskedLM [[autodoc]] AlbertForMaskedLM - forward ## AlbertForSequenceClassification [[autodoc]] AlbertForSequenceClassification - forward ## AlbertForMultipleChoice [[autodoc]] AlbertForMultipleChoice ## AlbertForTokenClassification [[autodoc]] AlbertForTokenClassification - forward ## AlbertForQuestionAnswering [[autodoc]] AlbertForQuestionAnswering - forward </pt> <tf> ## TFAlbertModel [[autodoc]] TFAlbertModel - call ## TFAlbertForPreTraining [[autodoc]] TFAlbertForPreTraining - call ## TFAlbertForMaskedLM [[autodoc]] TFAlbertForMaskedLM - call ## TFAlbertForSequenceClassification [[autodoc]] TFAlbertForSequenceClassification - call ## TFAlbertForMultipleChoice [[autodoc]] TFAlbertForMultipleChoice - call ## TFAlbertForTokenClassification [[autodoc]] TFAlbertForTokenClassification - call ## TFAlbertForQuestionAnswering [[autodoc]] TFAlbertForQuestionAnswering - call </tf> <jax> ## FlaxAlbertModel [[autodoc]] FlaxAlbertModel - __call__ ## FlaxAlbertForPreTraining [[autodoc]] FlaxAlbertForPreTraining - __call__ ## FlaxAlbertForMaskedLM [[autodoc]] FlaxAlbertForMaskedLM - __call__ ## FlaxAlbertForSequenceClassification [[autodoc]] FlaxAlbertForSequenceClassification - __call__ ## FlaxAlbertForMultipleChoice [[autodoc]] FlaxAlbertForMultipleChoice - __call__ ## FlaxAlbertForTokenClassification [[autodoc]] FlaxAlbertForTokenClassification - __call__ ## FlaxAlbertForQuestionAnswering [[autodoc]] FlaxAlbertForQuestionAnswering - __call__ </jax> </frameworkcontent>

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# BigBirdPegasus ## Overview BigBird モデルは、[Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) で提案されました。ザヒール、マンジルとグルガネシュ、グルとダベイ、クマール・アヴィナヴァとエインズリー、ジョシュアとアルベルティ、クリスとオンタノン、サンティアゴとファム、フィリップとラブラ、アニルードとワン、キーファンとヤン、リーなど。 BigBird は注目度が低い BERT などの Transformer ベースのモデルをさらに長いシーケンスに拡張する、Transformer ベースのモデル。まばらに加えてアテンションと同様に、BigBird は入力シーケンスにランダムアテンションだけでなくグローバルアテンションも適用します。理論的には、まばらで全体的でランダムな注意を適用すると、完全な注意に近づくことが示されていますが、長いシーケンスでは計算効率が大幅に向上します。より長いコンテキストを処理できる機能の結果として、 BigBird は、質問応答や BERT または RoBERTa と比較した要約。論文の要約は次のとおりです。 *BERT などのトランスフォーマーベースのモデルは、NLP で最も成功した深層学習モデルの 1 つです。残念ながら、それらの中核的な制限の 1 つは、シーケンスに対する二次依存性 (主にメモリに関する) です。完全な注意メカニズムによる長さです。これを解決するために、BigBird は、まばらな注意メカニズムを提案します。この二次依存関係を線形に削減します。 BigBird がシーケンス関数の汎用近似器であることを示します。チューリングは完全であるため、二次完全注意モデルのこれらの特性が保存されます。途中、私たちの理論分析により、O(1) 個のグローバルトークン (CLS など) を持つ利点の一部が明らかになり、スパース注意メカニズムの一部としてのシーケンス。提案されたスパースアテンションは、次の長さのシーケンスを処理できます。同様のハードウェアを使用して以前に可能であったものの 8 倍。より長いコンテキストを処理できる機能の結果として、 BigBird は、質問応答や要約などのさまざまな NLP タスクのパフォーマンスを大幅に向上させます。私達もゲノミクスデータへの新しいアプリケーションを提案します。* ## Usage tips - BigBird の注意がどのように機能するかについての詳細な説明については、[このブログ投稿](https://huggingface.co/blog/big-bird) を参照してください。 - BigBird には、**original_full** と **block_sparse** の 2 つの実装が付属しています。シーケンス長が 1024 未満の場合、次を使用します。 **block_sparse** を使用してもメリットがないため、**original_full** を使用することをお勧めします。 - コードは現在、3 ブロックと 2 グローバルブロックのウィンドウサイズを使用しています。 - シーケンスの長さはブロックサイズで割り切れる必要があります。 - 現在の実装では **ITC** のみがサポートされています。 - 現在の実装では **num_random_blocks = 0** はサポートされていません。 - BigBirdPegasus は [PegasusTokenizer](https://github.com/huggingface/transformers/blob/main/src/transformers/models/pegasus/tokenization_pegasus.py) を使用します。 - BigBird は絶対位置埋め込みを備えたモデルであるため、通常は入力を右側にパディングすることをお勧めします。左。元のコードは [こちら](https://github.com/google-research/bigbird) にあります。 ## ドキュメントリソース - [テキスト分類タスクガイド](../tasks/sequence_classification) - [質問回答タスクガイド](../tasks/question_answering) - [因果言語モデリングタスクガイド](../tasks/language_modeling) - [翻訳タスクガイド](../tasks/translation) - [要約タスクガイド](../tasks/summarization) ## BigBirdPegasusConfig [[autodoc]] BigBirdPegasusConfig - all ## BigBirdPegasusModel [[autodoc]] BigBirdPegasusModel - forward ## BigBirdPegasusForConditionalGeneration [[autodoc]] BigBirdPegasusForConditionalGeneration - forward ## BigBirdPegasusForSequenceClassification [[autodoc]] BigBirdPegasusForSequenceClassification - forward ## BigBirdPegasusForQuestionAnswering [[autodoc]] BigBirdPegasusForQuestionAnswering - forward ## BigBirdPegasusForCausalLM [[autodoc]] BigBirdPegasusForCausalLM - forward

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# CLIP ## Overview CLIP モデルは、Alec Radford、Jong Wook Kim、Chris Hallacy、Aditya Ramesh、Gabriel Goh Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) で提案されました。サンディニ・アガルワル、ギリッシュ・サストリー、アマンダ・アスケル、パメラ・ミシュキン、ジャック・クラーク、グレッチェン・クルーガー、イリヤ・サツケヴァー。クリップ (Contrastive Language-Image Pre-Training) は、さまざまな (画像、テキスト) ペアでトレーニングされたニューラルネットワークです。かもね直接最適化することなく、与えられた画像から最も関連性の高いテキストスニペットを予測するように自然言語で指示されます。 GPT-2 および 3 のゼロショット機能と同様に、タスクに対して。論文の要約は次のとおりです。 *最先端のコンピュータービジョンシステムは、あらかじめ定められたオブジェクトカテゴリの固定セットを予測するようにトレーニングされています。これ制限された形式の監視では、指定するために追加のラベル付きデータが必要となるため、一般性と使いやすさが制限されます。その他の視覚的なコンセプト。画像に関する生のテキストから直接学習することは、より広範な監督源。どのキャプションが表示されるかを予測するという単純な事前トレーニングタスクが有効であることを示します。 400 のデータセットで SOTA 画像表現を最初から学習するための効率的かつスケーラブルな方法はどの画像ですかインターネットから収集された数百万の（画像、テキスト）ペア。事前トレーニング後、自然言語を使用して参照します。視覚的な概念を学習し（または新しい概念を説明し）、下流のタスクへのモデルのゼロショット転送を可能にします。私たちは勉強します 30 を超えるさまざまな既存のコンピュータービジョンデータセットでタスクをまたがってベンチマークを行うことにより、このアプローチのパフォーマンスを評価します。 OCR、ビデオ内のアクション認識、地理的位置特定、およびさまざまな種類のきめ細かいオブジェクト分類など。のモデルはほとんどのタスクに簡単に移行でき、多くの場合、必要がなくても完全に監視されたベースラインと競合します。データセット固有のトレーニングに適しています。たとえば、ImageNet ゼロショットではオリジナルの ResNet-50 の精度と一致します。トレーニングに使用された 128 万のトレーニングサンプルを使用する必要はありません。コードをリリースし、事前トレーニング済みモデルの重みはこの https URL で確認できます。* このモデルは [valhalla](https://huggingface.co/valhalla) によって提供されました。元のコードは [ここ](https://github.com/openai/CLIP) にあります。 ## Usage tips and example CLIP は、マルチモーダルなビジョンおよび言語モデルです。画像とテキストの類似性やゼロショット画像に使用できます。分類。 CLIP は、ViT のようなトランスフォーマーを使用して視覚的特徴を取得し、因果言語モデルを使用してテキストを取得します特徴。次に、テキストと視覚の両方の特徴が、同じ次元の潜在空間に投影されます。ドット投影された画像とテキストの特徴間の積が同様のスコアとして使用されます。画像を Transformer エンコーダに供給するために、各画像は固定サイズの重複しないパッチのシーケンスに分割されます。これらは線形に埋め込まれます。 [CLS] トークンは、イメージ全体の表現として機能するために追加されます。作家たちまた、絶対位置埋め込みを追加し、結果として得られるベクトルのシーケンスを標準の Transformer エンコーダに供給します。 [`CLIPImageProcessor`] を使用して、モデルの画像のサイズ変更 (または再スケール) および正規化を行うことができます。 [`CLIPTokenizer`] はテキストのエンコードに使用されます。 [`CLIPProcessor`] はラップします [`CLIPImageProcessor`] と [`CLIPTokenizer`] を両方の単一インスタンスに統合テキストをエンコードして画像を準備します。次の例は、次のメソッドを使用して画像とテキストの類似性スコアを取得する方法を示しています。 [`CLIPProcessor`] と [`CLIPModel`]。 ```python >>> from PIL import Image >>> import requests >>> from transformers import CLIPProcessor, CLIPModel >>> model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") >>> processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True) >>> outputs = model(**inputs) >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score >>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities ``` ## Resources CLIP を使い始めるのに役立つ公式 Hugging Face およびコミュニティ (🌎 で示されている) リソースのリスト。 - [リモートセンシング (衛星) 画像とキャプションを使用した CLIP の微調整](https://huggingface.co/blog/fine-tune-clip-rsicd)、[RSICD データセット] を使用して CLIP を微調整する方法に関するブログ投稿(https://github.com/201528014227051/RSICD_optimal) と、データ拡張によるパフォーマンスの変化の比較。 - この [サンプルスクリプト](https://github.com/huggingface/transformers/tree/main/examples/pytorch/contrastive-image-text) は、プレ- [COCO データセット](https://cocodataset.org/#home) を使用してトレーニングされたビジョンおよびテキストエンコーダー。 <PipelineTag pipeline="image-to-text"/> - 画像キャプションのビーム検索による推論に事前トレーニング済み CLIP を使用する方法に関する [ノートブック](https://colab.research.google.com/drive/1tuoAC5F4sC7qid56Z0ap-stR3rwdk0ZV?usp=sharing)。 🌎 **画像検索** - 事前トレーニングされた CLIP を使用した画像検索と MRR (平均相互ランク) スコアの計算に関する [ノートブック](https://colab.research.google.com/drive/1bLVwVKpAndpEDHqjzxVPr_9nGrSbuOQd?usp=sharing)。 🌎 - 画像の取得と類似性スコアの表示に関する [ノートブック](https://colab.research.google.com/github/deep-diver/image_search_with_natural_language/blob/main/notebooks/Image_Search_CLIP.ipynb)。 🌎 - 多言語 CLIP を使用して画像とテキストを同じベクトル空間にマッピングする方法に関する [ノートブック](https://colab.research.google.com/drive/1xO-wC_m_GNzgjIBQ4a4znvQkvDoZJvH4?usp=sharing)。 🌎 - を使用してセマンティックイメージ検索で CLIP を実行する方法に関する [ノートブック](https://colab.research.google.com/github/vivien000/clip-demo/blob/master/clip.ipynb#scrollTo=uzdFhRGqiWkR) [Unsplash](https://unsplash.com) および [TMDB](https://www.themoviedb.org/) データセット。 🌎 **説明可能性** - 入力トークンと画像セグメントの類似性を視覚化する方法に関する [ノートブック](https://colab.research.google.com/github/hila-chefer/Transformer-MM-Explainability/blob/main/CLIP_explainability.ipynb)。 🌎 ここに含めるリソースの送信に興味がある場合は、お気軽にプルリクエストを開いてください。審査させていただきます。リソースは、既存のリソースを複製するのではなく、何か新しいものを示すことが理想的です。 ## CLIPConfig [[autodoc]] CLIPConfig - from_text_vision_configs ## CLIPTextConfig [[autodoc]] CLIPTextConfig ## CLIPVisionConfig [[autodoc]] CLIPVisionConfig ## CLIPTokenizer [[autodoc]] CLIPTokenizer - build_inputs_with_special_tokens - get_special_tokens_mask - create_token_type_ids_from_sequences - save_vocabulary ## CLIPTokenizerFast [[autodoc]] CLIPTokenizerFast ## CLIPImageProcessor [[autodoc]] CLIPImageProcessor - preprocess ## CLIPFeatureExtractor [[autodoc]] CLIPFeatureExtractor ## CLIPProcessor [[autodoc]] CLIPProcessor <frameworkcontent> <pt> ## CLIPModel [[autodoc]] CLIPModel - forward - get_text_features - get_image_features ## CLIPTextModel [[autodoc]] CLIPTextModel - forward ## CLIPTextModelWithProjection [[autodoc]] CLIPTextModelWithProjection - forward ## CLIPVisionModelWithProjection [[autodoc]] CLIPVisionModelWithProjection - forward ## CLIPVisionModel [[autodoc]] CLIPVisionModel - forward </pt> <tf> ## TFCLIPModel [[autodoc]] TFCLIPModel - call - get_text_features - get_image_features ## TFCLIPTextModel [[autodoc]] TFCLIPTextModel - call ## TFCLIPVisionModel [[autodoc]] TFCLIPVisionModel - call </tf> <jax> ## FlaxCLIPModel [[autodoc]] FlaxCLIPModel - __call__ - get_text_features - get_image_features ## FlaxCLIPTextModel [[autodoc]] FlaxCLIPTextModel - __call__ ## FlaxCLIPTextModelWithProjection [[autodoc]] FlaxCLIPTextModelWithProjection - __call__ ## FlaxCLIPVisionModel [[autodoc]] FlaxCLIPVisionModel - __call__ </jax> </frameworkcontent>

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# Decision Transformer ## Overview Decision Transformer モデルは、[Decision Transformer: Reinforcement Learning via Sequence Modeling](https://arxiv.org/abs/2106.01345) で提案されました。 Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch. 論文の要約は次のとおりです。 *強化学習（RL）をシーケンスモデリング問題として抽象化するフレームワークを紹介します。これにより、Transformer アーキテクチャのシンプルさとスケーラビリティ、および関連する進歩を活用できるようになります。 GPT-x や BERT などの言語モデリングで。特に、Decision Transformer というアーキテクチャを紹介します。 RL の問題を条件付きシーケンスモデリングとして投げかけます。値関数に適合する以前の RL アプローチとは異なり、ポリシー勾配を計算すると、Decision Transformer は因果的にマスクされたアルゴリズムを利用して最適なアクションを出力するだけです。変成器。望ましいリターン (報酬)、過去の状態、アクションに基づいて自己回帰モデルを条件付けすることにより、 Decision Transformer モデルは、望ましいリターンを達成する将来のアクションを生成できます。そのシンプルさにも関わらず、 Decision Transformer は、最先端のモデルフリーのオフライン RL ベースラインのパフォーマンスと同等、またはそれを超えています。 Atari、OpenAI Gym、Key-to-Door タスク* このバージョンのモデルは、状態がベクトルであるタスク用です。このモデルは、[edbeeching](https://huggingface.co/edbeeching) によって提供されました。元のコードは [ここ](https://github.com/kzl/decion-transformer) にあります。 ## DecisionTransformerConfig [[autodoc]] DecisionTransformerConfig ## DecisionTransformerGPT2Model [[autodoc]] DecisionTransformerGPT2Model - forward ## DecisionTransformerModel [[autodoc]] DecisionTransformerModel - forward

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# Efficient Inference on a Multiple GPUs この文書には、複数のGPUで効率的に推論を行う方法に関する情報が含まれています。 <Tip> 注意: 複数のGPUセットアップは、[単一のGPUセクション](./perf_infer_gpu_one)で説明されているほとんどの戦略を使用できます。ただし、より良い使用法のために使用できる簡単なテクニックについても認識しておく必要があります。 </Tip> ## Flash Attention 2 Flash Attention 2の統合は、複数のGPUセットアップでも機能します。詳細については、[単一のGPUセクション](./perf_infer_gpu_one#Flash-Attention-2)の適切なセクションをご覧ください。 ## BetterTransformer [BetterTransformer](https://huggingface.co/docs/optimum/bettertransformer/overview)は、🤗 TransformersモデルをPyTorchネイティブの高速実行パスを使用するように変換し、その下でFlash Attentionなどの最適化されたカーネルを呼び出します。 BetterTransformerは、テキスト、画像、音声モデルの単一GPUおよび複数GPUでの高速推論もサポートしています。 <Tip> Flash Attentionは、fp16またはbf16 dtypeを使用しているモデルにのみ使用できます。BetterTransformerを使用する前に、モデルを適切なdtypeにキャストしてください。 </Tip> ### Decoder models テキストモデル、特にデコーダーベースのモデル（GPT、T5、Llamaなど）の場合、BetterTransformer APIはすべての注意操作を[`torch.nn.functional.scaled_dot_product_attention`オペレーター](https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention)（SDPA）を使用するように変換します。これはPyTorch 2.0以降でのみ使用可能です。モデルをBetterTransformerに変換するには： ```python from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m") # convert the model to BetterTransformer model.to_bettertransformer() # Use it for training or inference ``` SDPAは、ハードウェアや問題のサイズなどの特定の設定で[Flash Attention](https://arxiv.org/abs/2205.14135)カーネルを呼び出すこともできます。Flash Attentionを有効にするか、特定の設定（ハードウェア、問題のサイズ）で利用可能かを確認するには、[`torch.backends.cuda.sdp_kernel`](https://pytorch.org/docs/master/backends.html#torch.backends.cuda.sdp_kernel)をコンテキストマネージャとして使用します。 ```diff import torch from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m").to("cuda") # convert the model to BetterTransformer model.to_bettertransformer() input_text = "Hello my dog is cute and" inputs = tokenizer(input_text, return_tensors="pt").to("cuda") + with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False): outputs = model.generate(**inputs) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` もしトレースバックで次のようなエラーメッセージが表示された場合： ```bash RuntimeError: No available kernel. Aborting execution. ``` 当日、Flash Attentionのカバレッジが広範囲である可能性があるPyTorch Nightlyバージョンを試すようにお勧めします。 ```bash pip3 install -U --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu118 ``` [このブログ投稿](https://pytorch.org/blog/out-of-the-box-acceleration/)をチェックして、BetterTransformer + SDPA APIで可能なことについて詳しく学びましょう。 ### Encoder Models 推論中のエンコーダーモデルでは、BetterTransformerはエンコーダーレイヤーのforward呼び出しを、エンコーダーレイヤーの[`torch.nn.TransformerEncoderLayer`](https://pytorch.org/docs/stable/generated/torch.nn.TransformerEncoderLayer.html)の相当するものにディスパッチします。これにより、エンコーダーレイヤーの高速実装が実行されます。 `torch.nn.TransformerEncoderLayer`の高速実装はトレーニングをサポートしていないため、代わりに`torch.nn.functional.scaled_dot_product_attention`にディスパッチされます。これにより、ネストされたテンソルを活用しないFlash AttentionまたはMemory-Efficient Attentionの融合カーネルを使用できます。 BetterTransformerのパフォーマンスの詳細については、この[ブログ投稿](https://medium.com/pytorch/bettertransformer-out-of-the-box-performance-for-huggingface-transformers-3fbe27d50ab2)をご覧いただけます。また、エンコーダーモデル用のBetterTransformerについては、この[ブログ](https://pytorch.org/blog/a-better-transformer-for-fast-transformer-encoder-inference/)で詳しく学ぶことができます。 ## Advanced usage: mixing FP4 (or Int8) and BetterTransformer モデルの最良のパフォーマンスを得るために、上記で説明した異なる方法を組み合わせることができます。例えば、FP4ミックスプレシジョン推論+Flash Attentionを使用したBetterTransformerを組み合わせることができます。 ```py import torch from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig quantization_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16 ) tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m", quantization_config=quantization_config) input_text = "Hello my dog is cute and" inputs = tokenizer(input_text, return_tensors="pt").to("cuda") with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False): outputs = model.generate(**inputs) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ```

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# Checks on a Pull Request 🤗 Transformersリポジトリでプルリクエストを開くと、追加しているパッチが既存のものを壊していないことを確認するために、かなりの数のチェックが実行されます。これらのチェックには、次の4つのタイプがあります： - 通常のテスト - ドキュメンテーションのビルド - コードとドキュメンテーションのスタイル - リポジトリ全体の一貫性このドキュメントでは、これらのさまざまなチェックとその背後にある理由、そしてそれらのいずれかがあなたのプルリクエストで失敗した場合のローカルでのデバッグ方法について説明します。なお、理想的には、開発者用のインストールが必要です： ```bash pip install transformers[dev] ``` または編集可能なインストールの場合： ```bash pip install -e .[dev] ``` トランスフォーマーズのリポジトリ内で作業しています。トランスフォーマーズのオプションの依存関係の数が増えたため、すべてを取得できない可能性があります。開発用インストールが失敗した場合、作業しているディープラーニングフレームワーク（PyTorch、TensorFlow、および/またはFlax）をインストールし、次の手順を実行してください。 ```bash pip install transformers[quality] ``` または編集可能なインストールの場合： ```bash pip install -e .[quality] ``` ## Tests `ci/circleci: run_tests_` で始まるすべてのジョブは、Transformersのテストスイートの一部を実行します。これらのジョブは、特定の環境でライブラリの一部に焦点を当てて実行されます。たとえば、`ci/circleci: run_tests_pipelines_tf` は、TensorFlowのみがインストールされた環境でパイプラインのテストを実行します。テストスイートの一部のみが実行されるように注意してください。テストスイートは、変更前と変更後のPRのライブラリの違いを決定し、その違いに影響を受けるテストを選択するためのユーティリティが実行されます。このユーティリティは、ローカルで以下のコマンドを実行して実行できます： ```bash python utils/tests_fetcher.py ``` 1. リポジトリのルートからスクリプトを実行します。これは次のステップを実行します： 1. 差分内の各ファイルをチェックし、変更がコード内にあるか、コメントやドキュメンテーション文字列のみにあるかを確認します。実際のコード変更があるファイルのみを保持します。 2. 内部のマップを構築します。このマップは、ライブラリのソースコードの各ファイルが再帰的に影響を与えるすべてのファイルを提供します。モジュールAがモジュールBに影響を与えるとは、モジュールBがモジュールAをインポートする場合を指します。再帰的な影響を得るには、モジュールAからモジュールBへのモジュールのチェーンが必要で、各モジュールは前のモジュールをインポートする必要があります。 3. このマップをステップ1で収集したファイルに適用します。これにより、PRに影響を受けるモデルファイルのリストが得られます。 4. これらのファイルをそれに対応するテストファイルにマップし、実行するテストのリストを取得します。 2. スクリプトをローカルで実行する場合、ステップ1、3、および4の結果が表示され、実行するテストがわかります。スクリプトはまた、`test_list.txt` という名前のファイルを作成します。このファイルには実行するテストのリストが含まれており、次のコマンドでローカルで実行できます： ```bash python -m pytest -n 8 --dist=loadfile -rA -s $(cat test_list.txt) ``` ## Documentation build `build_pr_documentation` ジョブは、ドキュメンテーションのビルドを行い、あなたのPRがマージされた後にすべてが正常に表示されることを確認します。ボットがプレビューのドキュメンテーションへのリンクをPRに追加します。PRに対する変更は、プレビューに自動的に反映されます。ドキュメンテーションのビルドに失敗した場合、失敗したジョブの隣にある「詳細」をクリックして、何が問題になっているかを確認できます。多くの場合、問題は`toctree`内のファイルが不足しているなど、単純なものです。ドキュメンテーションをローカルでビルドまたはプレビューしたい場合は、[docsフォルダ内の`README.md`](https://github.com/huggingface/transformers/tree/main/docs)をご覧ください。 ## Code and documentation style すべてのソースファイル、例、テストには、`black`と`ruff`を使用してコードのフォーマットが適用されます。また、ドックストリングと`rst`ファイルのフォーマット、Transformersの`__init__.py`ファイルで実行される遅延インポートの順序についてもカスタムツールが存在します（`utils/style_doc.py`と`utils/custom_init_isort.py`）。これらすべては、以下を実行することで起動できます。 ```bash make style ``` CIは、`ci/circleci: check_code_quality` チェック内でこれらのチェックが適用されていることを確認します。また、`ruff` を実行し、未定義の変数や使用されていない変数がある場合にエラーを報告します。このチェックをローカルで実行するには、以下のコマンドを使用してください。 ```bash make quality ``` 時間がかかることがあります。したがって、現在のブランチで変更したファイルのみで同じことを実行するには、次のコマンドを実行します。 ```bash make fixup ``` この最後のコマンドは、リポジトリの整合性のためのすべての追加のチェックも実行します。それらを詳しく見てみましょう。 ## Repository consistency これには、あなたのPRがリポジトリを適切な状態に保ったままであることを確認するためのすべてのテストが含まれており、ci/`circleci: check_repository_consistency` チェックによって実行されます。ローカルでこのチェックを実行するには、以下を実行します。 ```bash make repo-consistency ``` これを確認します： - `utils/check_repo.py` によって実行される、init に追加されたすべてのオブジェクトが文書化されています。 - `utils/check_inits.py` によって実行される、すべての `__init__.py` ファイルがその2つのセクションで同じ内容を持っています。 - `utils/check_copies.py` によって実行される、他のモジュールからのコピーとして識別されたすべてのコードが元のコードと一致しています。 - `utils/check_config_docstrings.py` によって実行される、すべての設定クラスには少なくとも1つの有効なチェックポイントがドキュメント文字列に記載されています。 - `utils/check_config_attributes.py` によって実行される、すべての設定クラスには、対応するモデリングファイルで使用されている属性のみが含まれています。 - `utils/check_copies.py` によって実行される、README とドキュメントのインデックスの翻訳が、メインのREADME と同じモデルリストを持っています。 - `utils/check_table.py` によって実行される、ドキュメンテーションの自動生成テーブルが最新であることを確認します。 - `utils/check_dummies.py` によって実行される、すべてのオブジェクトが利用可能であり、オプションの依存関係がすべてインストールされていなくても問題ありません。このチェックが失敗する場合、最初の2つの項目は手動で修正する必要があり、最後の4つはコマンドを実行して自動的に修正できます。 ```bash make fix-copies ``` 追加のチェックポイントは、新しいモデルを追加するPull Request（PR）に関連しています。主に次の点を確認します： - すべての追加されたモデルは、Auto-mapping（`utils/check_repo.py`で実行）に含まれています。  - すべてのモデルが適切にテストされています（`utils/check_repo.py`で実行）。  ### Check copies Transformersライブラリは、モデルコードに関して非常に意見があるため、各モデルは他のモデルに依存せずに完全に1つのファイルに実装する必要があります。したがって、特定のモデルのコードのコピーが元のコードと一貫しているかどうかを確認する仕組みを追加しました。これにより、バグ修正がある場合、他の影響を受けるモデルをすべて確認し、変更を伝達するかコピーを破棄するかを選択できます。 <Tip> ファイルが別のファイルの完全なコピーである場合、それを`utils/check_copies.py`の`FULL_COPIES`定数に登録する必要があります。 </Tip> この仕組みは、`# Copied from xxx`という形式のコメントに依存しています。`xxx`は、コピーされているクラスまたは関数の完全なパスを含む必要があります。例えば、`RobertaSelfOutput`は`BertSelfOutput`クラスの直接のコピーですので、[こちら](https://github.com/huggingface/transformers/blob/2bd7a27a671fd1d98059124024f580f8f5c0f3b5/src/transformers/models/roberta/modeling_roberta.py#L289)にコメントがあります。 ```py # Copied from transformers.models.bert.modeling_bert.BertSelfOutput ``` 注意点として、これをクラス全体に適用する代わりに、コピー元の関連メソッドに適用できます。たとえば、[こちら](https://github.com/huggingface/transformers/blob/2bd7a27a671fd1d98059124024f580f8f5c0f3b5/src/transformers/models/roberta/modeling_roberta.py#L598)では、`RobertaPreTrainedModel._init_weights` が `BertPreTrainedModel` からコピーされており、以下のコメントがあります： ```py # Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta ``` 注：矢印の周りにはスペースが含まれていてはいけません（もちろん、そのスペースが置換パターンの一部である場合を除きます）。カンマで区切られた複数のパターンを追加できます。例えば、ここでは `CamemberForMaskedLM` は `RobertaForMaskedLM` の直接のコピーで、2つの置換があります： `Roberta` から `Camembert` へ、そして `ROBERTA` から `CAMEMBERT` へと置換されます。[こちら](https://github.com/huggingface/transformers/blob/15082a9dc6950ecae63a0d3e5060b2fc7f15050a/src/transformers/models/camembert/modeling_camembert.py#L929)で、この作業はコメント付きで行われています。 ```py # Copied from transformers.models.roberta.modeling_roberta.RobertaForMaskedLM with Roberta->Camembert, ROBERTA->CAMEMBERT ``` もし順序が重要な場合（以前の置換と競合する可能性があるため）、置換は左から右に実行されます。 <Tip> もし置換がフォーマットを変更する場合（たとえば、短い名前を非常に長い名前に置き換える場合など）、自動フォーマッタを適用した後にコピーが確認されます。 </Tip> パターンが同じ置換の異なるケース（大文字と小文字のバリアントがある）の場合、オプションとして `all-casing` を追加するだけの別の方法もあります。[こちら](https://github.com/huggingface/transformers/blob/15082a9dc6950ecae63a0d3e5060b2fc7f15050a/src/transformers/models/mobilebert/modeling_mobilebert.py#L1237)は、`MobileBertForSequenceClassification` 内の例で、コメントがついています。 ```py # Copied from transformers.models.bert.modeling_bert.BertForSequenceClassification with Bert->MobileBert all-casing ``` この場合、コードは「BertForSequenceClassification」からコピーされ、次のように置換されます： - `Bert` を `MobileBert` に置き換える（例：`init`で `MobileBertModel` を使用する場合） - `bert` を `mobilebert` に置き換える（例：`self.mobilebert` を定義する場合） - `BERT` を `MOBILEBERT` に置き換える（定数 `MOBILEBERT_INPUTS_DOCSTRING` 内で）

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# Monocular depth estimation 単眼奥行き推定は、シーンの奥行き情報を画像から予測することを含むコンピュータービジョンタスクです。単一の画像。言い換えれば、シーン内のオブジェクトの距離を距離から推定するプロセスです。単一カメラの視点。単眼奥行き推定には、3D 再構築、拡張現実、自動運転、そしてロボット工学。モデルがオブジェクト間の複雑な関係を理解する必要があるため、これは困難な作業です。シーンとそれに対応する深度情報（照明条件などの要因の影響を受ける可能性があります）オクルージョンとテクスチャ。 <Tip> このチュートリアルで説明するタスクは、次のモデルアーキテクチャでサポートされています。  [DPT](../model_doc/dpt), [GLPN](../model_doc/glpn)  </Tip> このガイドでは、次の方法を学びます。 * 深度推定パイプラインを作成する * 手動で深度推定推論を実行します始める前に、必要なライブラリがすべてインストールされていることを確認してください。 ```bash pip install -q transformers ``` ## Depth estimation pipeline 深度推定をサポートするモデルで推論を試す最も簡単な方法は、対応する [`pipeline`] を使用することです。 [Hugging Face Hub のチェックポイント](https://huggingface.co/models?pipeline_tag=Depth-estimation&sort=downloads) からパイプラインをインスタンス化します。 ```py >>> from transformers import pipeline >>> checkpoint = "vinvino02/glpn-nyu" >>> depth_estimator = pipeline("depth-estimation", model=checkpoint) ``` 次に、分析する画像を選択します。 ```py >>> from PIL import Image >>> import requests >>> url = "https://unsplash.com/photos/HwBAsSbPBDU/download?ixid=MnwxMjA3fDB8MXxzZWFyY2h8MzR8fGNhciUyMGluJTIwdGhlJTIwc3RyZWV0fGVufDB8MHx8fDE2Nzg5MDEwODg&force=true&w=640" >>> image = Image.open(requests.get(url, stream=True).raw) >>> image ``` <div class="flex justify-center"> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/depth-estimation-example.jpg" alt="Photo of a busy street"/> </div> 画像をパイプラインに渡します。 ```py >>> predictions = depth_estimator(image) ``` パイプラインは 2 つのエントリを含む辞書を返します。最初のものは`predicted_ Depth`と呼ばれ、次の値を持つテンソルです。深さは各ピクセルのメートル単位で表されます。 2 番目の`depth`は、深度推定結果を視覚化する PIL 画像です。視覚化された結果を見てみましょう。 ```py >>> predictions["depth"] ``` <div class="flex justify-center"> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/depth-visualization.png" alt="Depth estimation visualization"/> </div> ## Depth estimation inference by hand 深度推定パイプラインの使用方法を理解したので、同じ結果を手動で複製する方法を見てみましょう。まず、[Hugging Face Hub のチェックポイント](https://huggingface.co/models?pipeline_tag=Depth-estimation&sort=downloads) からモデルと関連プロセッサをロードします。ここでは、前と同じチェックポイントを使用します。 ```py >>> from transformers import AutoImageProcessor, AutoModelForDepthEstimation >>> checkpoint = "vinvino02/glpn-nyu" >>> image_processor = AutoImageProcessor.from_pretrained(checkpoint) >>> model = AutoModelForDepthEstimation.from_pretrained(checkpoint) ``` 必要な画像変換を処理する`image_processor`を使用して、モデルの画像入力を準備します。サイズ変更や正規化など: ```py >>> pixel_values = image_processor(image, return_tensors="pt").pixel_values ``` 準備された入力をモデルに渡します。 ```py >>> import torch >>> with torch.no_grad(): ... outputs = model(pixel_values) ... predicted_depth = outputs.predicted_depth ``` 結果を視覚化します。 ```py >>> import numpy as np >>> # interpolate to original size >>> prediction = torch.nn.functional.interpolate( ... predicted_depth.unsqueeze(1), ... size=image.size[::-1], ... mode="bicubic", ... align_corners=False, ... ).squeeze() >>> output = prediction.numpy() >>> formatted = (output * 255 / np.max(output)).astype("uint8") >>> depth = Image.fromarray(formatted) >>> depth ``` <div class="flex justify-center"> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/depth-visualization.png" alt="Depth estimation visualization"/> </div>

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# Testing 🤗 Transformersモデルがどのようにテストされ、新しいテストを書いて既存のテストを改善できるかを見てみましょう。このリポジトリには2つのテストスイートがあります： 1. `tests` -- 一般的なAPI用のテスト 2. `examples` -- APIの一部ではないさまざまなアプリケーション用のテスト ## How transformers are tested 1. PRが提出されると、9つのCircleCiジョブでテストされます。PRへの新しいコミットごとに再テストされます。これらのジョブは、[この設定ファイル](https://github.com/huggingface/transformers/tree/main/.circleci/config.yml)で定義されており、必要な場合は同じ環境を自分のマシンで再現できます。これらのCIジョブは `@slow` テストを実行しません。 2. [GitHub Actions](https://github.com/huggingface/transformers/actions)によって実行される3つのジョブがあります： - [torch hub integration](https://github.com/huggingface/transformers/tree/main/.github/workflows/github-torch-hub.yml): torch hubの統合が動作するかどうかを確認します。 - [self-hosted (push)](https://github.com/huggingface/transformers/tree/main/.github/workflows/self-push.yml): `main` にコミットが行われた場合に、GPUで高速テストを実行します。このジョブは、`main` でのコミットが以下のフォルダーのコードを更新した場合にのみ実行されます：`src`、`tests`、`.github`（追加されたモデルカード、ノートブックなどの実行を防ぐため）。 - [self-hosted runner](https://github.com/huggingface/transformers/tree/main/.github/workflows/self-scheduled.yml): GPUで `tests` と `examples` の通常のテストと遅いテストを実行します。 ```bash RUN_SLOW=1 pytest tests/ RUN_SLOW=1 pytest examples/ ``` 結果は[here](https://github.com/huggingface/transformers/actions)で観察できます。 ## Running tests ### Choosing which tests to run このドキュメントは、テストを実行する方法の多くの詳細について説明しています。すべてを読んだ後でも、さらに詳細が必要な場合は、[こちら](https://docs.pytest.org/en/latest/usage.html)で見つけることができます。以下は、テストを実行するためのいくつかの最も便利な方法です。すべて実行します: ```console pytest ``` または： ```bash make test ``` 後者は次のように定義されることに注意してください。 ```bash python -m pytest -n auto --dist=loadfile -s -v ./tests/ ``` 以下は、pytestに渡す設定情報です。 - テストプロセスをCPUコアの数と同じだけ実行するように指示します。ただし、RAMが十分でない場合は注意が必要です。 - 同じファイルからのすべてのテストは、同じテストプロセスで実行されるようにします。 - 出力のキャプチャを行いません。 - 冗長モードで実行します。 ### Getting the list of all tests テストスイートのすべてのテスト： ```bash pytest --collect-only -q ``` 指定されたテストファイルのすべてのテスト: ```bash pytest tests/test_optimization.py --collect-only -q ``` ### Run a specific test module 個別のテストモジュールを実行するには: ```bash pytest tests/utils/test_logging.py ``` ### Run specific tests ほとんどのテストでunittestが使用されているため、特定のサブテストを実行するには、それらのテストを含むunittestクラスの名前を知っている必要があります。例えば、それは次のようになるかもしれません： ```bash pytest tests/test_optimization.py::OptimizationTest::test_adam_w ``` テストの実行方法: テストファイル: `tests/test_optimization.py` クラス名: `OptimizationTest` テスト関数の名前: `test_adam_w` ファイルに複数のクラスが含まれている場合は、特定のクラスのテストのみを実行することを選択できます。例えば： ```bash pytest tests/test_optimization.py::OptimizationTest ``` テストクラス内のすべてのテストを実行します。前述の通り、`OptimizationTest` クラスに含まれるテストを実行するには、次のコマンドを実行できます： ```bash pytest tests/test_optimization.py::OptimizationTest --collect-only -q ``` キーワード式を使用してテストを実行できます。名前に `adam` が含まれるテストのみを実行するには： ```bash pytest -k adam tests/test_optimization.py ``` `and`および`or`は、すべてのキーワードが一致するか、いずれかを示すために使用できます。`not`は否定するために使用できます。 `adam`という名前を含むテストを除いてすべてのテストを実行するには： ```bash pytest -k "not adam" tests/test_optimization.py ``` 以下は、提供されたテキストの日本語訳です。 ```bash pytest -k "ada and not adam" tests/test_optimization.py ``` たとえば、`test_adafactor`と`test_adam_w`の両方を実行するには、以下のコマンドを使用できます: ```bash pytest -k "test_adam_w or test_adam_w" tests/test_optimization.py ``` 注意: ここでは、`or` を使用しています。キーワードのいずれか一つが一致すれば、両方を含めるためです。両方のパターンを含むテストのみを含めたい場合は、`and` を使用してください。 ```bash pytest -k "test and ada" tests/test_optimization.py ``` ### Run `accelerate` tests 時々、モデルに対して `accelerate` テストを実行する必要があります。たとえば、`OPT` 実行に対してこれらのテストを実行したい場合、コマンドに `-m accelerate_tests` を追加するだけで済みます： ```bash RUN_SLOW=1 pytest -m accelerate_tests tests/models/opt/test_modeling_opt.py ``` ### Run documentation tests ドキュメンテーションの例が正しいかどうかをテストするには、`doctests` が合格しているかを確認する必要があります。例として、[`WhisperModel.forward` のドックストリング](https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/modeling_whisper.py#L1017-L1035)を使用しましょう。 ```python r""" Returns: Example: ```python >>> import torch >>> from transformers import WhisperModel, WhisperFeatureExtractor >>> from datasets import load_dataset >>> model = WhisperModel.from_pretrained("openai/whisper-base") >>> feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-base") >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt") >>> input_features = inputs.input_features >>> decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id >>> last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state >>> list(last_hidden_state.shape) [1, 2, 512] ```""" ``` 指定したファイル内のすべてのドックストリング例を自動的にテストするために、以下の行を実行してください： ```bash pytest --doctest-modules <path_to_file_or_dir> ``` ファイルにマークダウン拡張子がある場合は、`--doctest-glob="*.md"`引数を追加する必要があります。 ### Run only modified tests [pytest-picked](https://github.com/anapaulagomes/pytest-picked)を使用すると、未ステージングのファイルまたは現在のブランチ（Gitに従って）に関連するテストを実行できます。これは、変更内容に関連するテストのみ実行されるため、変更が何も壊れていないことを迅速に確認する素晴らしい方法です。変更されていないファイルに関連するテストは実行されません。 ```bash pip install pytest-picked ``` ```bash pytest --picked ``` すべてのテストは、変更されたがまだコミットされていないファイルとフォルダから実行されます。 ### Automatically rerun failed tests on source modification [pytest-xdist](https://github.com/pytest-dev/pytest-xdist)は、非常に便利な機能を提供しており、すべての失敗したテストを検出し、ファイルを修正する間にそれらの失敗したテストを連続して再実行することができます。そのため、修正を行った後にpytestを再起動する必要がありません。すべてのテストが合格するまで繰り返され、その後再度フルランが実行されます。 ```bash pip install pytest-xdist ``` モードに入るには： `pytest -f`または`pytest --looponfail` ファイルの変更は、`looponfailroots`ルートディレクトリとその内容全体（再帰的に）を見て検出されます。この値のデフォルトが機能しない場合、`setup.cfg`で設定オプションを変更してプロジェクト内で変更できます。 ```ini [tool:pytest] looponfailroots = transformers tests ``` または `pytest.ini`/`tox.ini` ファイル: ```ini [pytest] looponfailroots = transformers tests ``` ファイルの変更を探すことは、iniファイルのディレクトリを基準にして指定されたディレクトリ内でのみ行われます。 [pytest-watch](https://github.com/joeyespo/pytest-watch) は、この機能の代替実装です。 ### Skip a test module 特定のテストモジュールを除外してすべてのテストモジュールを実行したい場合、実行するテストの明示的なリストを指定することができます。例えば、`test_modeling_*.py` テストを除外してすべてを実行するには次のようにします： ```bash pytest *ls -1 tests/*py | grep -v test_modeling* ``` ### Clearing state CIビルドおよび速度に対する隔離が重要な場合（キャッシュに対して）、キャッシュをクリアする必要があります： ```bash pytest --cache-clear tests ``` ### Running tests in parallel 前述のように、`make test` は `pytest-xdist` プラグインを介してテストを並列実行します（`-n X` 引数、例: `-n 2` で2つの並列ジョブを実行）。 `pytest-xdist` の `--dist=` オプションを使用すると、テストがどのようにグループ化されるかを制御できます。`--dist=loadfile` は同じファイルにあるテストを同じプロセスに配置します。テストの実行順序が異なり予測不可能であるため、`pytest-xdist` を使用してテストスイートを実行すると失敗が発生する場合（つまり、いくつかの未検出の連動テストがある場合）、[pytest-replay](https://github.com/ESSS/pytest-replay) を使用してテストを同じ順序で再生し、その後、失敗するシーケンスを最小限にするのに役立ちます。 ### Test order and repetition 潜在的な相互依存性や状態に関連するバグ（ティアダウン）を検出するために、テストを複数回、連続して、ランダムに、またはセットで繰り返すことは有用です。そして、単純な複数回の繰り返しは、DLのランダム性によって明らかになるいくつかの問題を検出するのに役立ちます。 #### Repeat tests - [pytest-flakefinder](https://github.com/dropbox/pytest-flakefinder): ```bash pip install pytest-flakefinder ``` そして、すべてのテストを複数回実行します (デフォルトでは 50 回)。 ```bash pytest --flake-finder --flake-runs=5 tests/test_failing_test.py ``` <Tip> このプラグインは、`pytest-xdist` の `-n` フラグでは動作しません。 </Tip> <Tip> 別のプラグイン `pytest-repeat` もありますが、これは `unittest` では動作しません。 </Tip> #### Run tests in a random order ```bash pip install pytest-random-order ``` 重要: `pytest-random-order` が存在すると、テストは自動的にランダム化されます。設定の変更や変更は必要ありません。コマンドラインオプションは必須です。前に説明したように、これにより、結合されたテスト (1 つのテストの状態が別のテストの状態に影響を与える) の検出が可能になります。いつ `pytest-random-order` がインストールされていると、そのセッションに使用されたランダムシードが出力されます。例: ```bash pytest tests [...] Using --random-order-bucket=module Using --random-order-seed=573663 ``` そのため、指定された特定のシーケンスが失敗した場合、その正確なシードを追加することでそれを再現できます。例: ```bash pytest --random-order-seed=573663 [...] Using --random-order-bucket=module Using --random-order-seed=573663 ``` 特定のテストのリストを使用しない場合、またはまったくリストを使用しない場合、同じテストの正確な順序を再現します。テストのリストを手動で絞り込み始めると、シードに依存せず、テストが失敗した正確な順序で手動でリストを指定する必要があります。これには、`--random-order-bucket=none` を使用してランダム化を無効にするようpytestに指示する必要があります。例えば、次のようにします： ```bash pytest --random-order-bucket=none tests/test_a.py tests/test_c.py tests/test_b.py ``` すべてのテストのシャッフルを無効にするには: ```bash pytest --random-order-bucket=none ``` デフォルトでは、`--random-order-bucket=module` が暗黙的に適用され、モジュールレベルでファイルをシャッフルします。また、`class`、`package`、`global`、および`none` レベルでシャッフルすることもできます。詳細については、その[ドキュメンテーション](https://github.com/jbasko/pytest-random-order)を参照してください。別のランダム化の代替手段は、[`pytest-randomly`](https://github.com/pytest-dev/pytest-randomly) です。このモジュールは非常に似た機能/インターフェースを持っていますが、`pytest-random-order` で利用可能なバケットモードを持っていません。インストール後に自動的に有効になるという同じ問題があります。 ### Look and feel variations #### pytest-sugar [pytest-sugar](https://github.com/Frozenball/pytest-sugar) は、外観と操作性を向上させ、プログレスバーを追加し、即座に失敗したテストとアサーションを表示するプラグインです。インストール後に自動的にアクティブ化されます。 ```bash pip install pytest-sugar ``` これを使用せずにテストを実行するには、次を実行します。 ```bash pytest -p no:sugar ``` またはアンインストールします。 #### Report each sub-test name and its progress `pytest` による単一またはグループのテストの場合 (`pip install pytest-pspec` の後): ```bash pytest --pspec tests/test_optimization.py ``` #### Instantly shows failed tests [pytest-instafail](https://github.com/pytest-dev/pytest-instafail) では、失敗とエラーが即座に表示されます。テストセッションが終了するまで待機します。 ```bash pip install pytest-instafail ``` ```bash pytest --instafail ``` ### To GPU or not to GPU GPU が有効な設定で、CPU のみモードでテストするには、`CUDA_VISIBLE_DEVICES=""`を追加します。 ```bash CUDA_VISIBLE_DEVICES="" pytest tests/utils/test_logging.py ``` または、複数の GPU がある場合は、`pytest` でどれを使用するかを指定できます。たとえば、 2 番目の GPU GPU `0` と `1` がある場合は、次を実行できます。 ```bash CUDA_VISIBLE_DEVICES="1" pytest tests/utils/test_logging.py ``` これは、異なるGPUで異なるタスクを実行したい場合に便利です。一部のテストはCPUのみで実行する必要があり、他のテストはCPU、GPU、またはTPUで実行する必要があり、また別のテストは複数のGPUで実行する必要があります。次のスキップデコレーターは、テストのCPU/GPU/TPUに関する要件を設定するために使用されます： - `require_torch` - このテストはtorchの下でのみ実行されます。 - `require_torch_gpu` - `require_torch` に加えて、少なくとも1つのGPUが必要です。 - `require_torch_multi_gpu` - `require_torch` に加えて、少なくとも2つのGPUが必要です。 - `require_torch_non_multi_gpu` - `require_torch` に加えて、0または1つのGPUが必要です。 - `require_torch_up_to_2_gpus` - `require_torch` に加えて、0、1、または2つのGPUが必要です。 - `require_torch_tpu` - `require_torch` に加えて、少なくとも1つのTPUが必要です。以下の表にGPUの要件を示します： | n gpus | decorator | |--------+--------------------------------| | `>= 0` | `@require_torch` | | `>= 1` | `@require_torch_gpu` | | `>= 2` | `@require_torch_multi_gpu` | | `< 2` | `@require_torch_non_multi_gpu` | | `< 3` | `@require_torch_up_to_2_gpus` | たとえば、使用可能な GPU が 2 つ以上あり、pytorch がインストールされている場合にのみ実行する必要があるテストを次に示します。 ```python no-style @require_torch_multi_gpu def test_example_with_multi_gpu(): ``` テストに `tensorflow` が必要な場合は、`require_tf` デコレータを使用します。例えば： ```python no-style @require_tf def test_tf_thing_with_tensorflow(): ``` これらのデコレータは積み重ねることができます。たとえば、テストが遅く、pytorch で少なくとも 1 つの GPU が必要な場合は、次のようになります。設定方法: ```python no-style @require_torch_gpu @slow def test_example_slow_on_gpu(): ``` `@parametrized` のような一部のデコレータはテスト名を書き換えるため、`@require_*` スキップデコレータをリストする必要があります。最後にそれらが正しく動作するようにします。正しい使用例は次のとおりです ```python no-style @parameterized.expand(...) @require_torch_multi_gpu def test_integration_foo(): ``` この順序の問題は `@pytest.mark.parametrize` には存在しません。最初または最後に配置しても、それでも問題は解決されます。仕事。ただし、それは非単体テストでのみ機能します。内部テスト: - 利用可能な GPU の数: ```python from transformers.testing_utils import get_gpu_count n_gpu = get_gpu_count() # works with torch and tf ``` ### Testing with a specific PyTorch backend or device 特定のtorchデバイスでテストスイートを実行するには、`TRANSFORMERS_TEST_DEVICE="$device"` を追加します。ここで `$device` は対象のバックエンドです。例えば、CPUでテストするには以下のようにします： ```bash TRANSFORMERS_TEST_DEVICE="cpu" pytest tests/utils/test_logging.py ``` この変数は、`mps`などのカスタムまたはあまり一般的ではない PyTorch バックエンドをテストするのに役立ちます。また、特定の GPU をターゲットにしたり、CPU 専用モードでテストしたりすることで、`CUDA_VISIBLE_DEVICES`と同じ効果を達成するために使用することもできます。特定のデバイスでは、初めて「torch」をインポートした後、追加のインポートが必要になります。これは、環境変数 `TRANSFORMERS_TEST_BACKEND` を使用して指定できます。 ```bash TRANSFORMERS_TEST_BACKEND="torch_npu" pytest tests/utils/test_logging.py ``` ### Distributed training `pytest` は直接的に分散トレーニングを処理することはできません。試みると、サブプロセスは正しい処理を行わず、自分自身が `pytest` であると思い込んでテストスイートをループで実行し続けます。ただし、通常のプロセスを生成し、それから複数のワーカーを生成し、IOパイプを管理するプロセスを生成すれば機能します。これを使用するいくつかのテストがあります： - [test_trainer_distributed.py](https://github.com/huggingface/transformers/tree/main/tests/trainer/test_trainer_distributed.py) - [test_deepspeed.py](https://github.com/huggingface/transformers/tree/main/tests/deepspeed/test_deepspeed.py) 実行ポイントにすぐに移動するには、これらのテスト内で `execute_subprocess_async` 呼び出しを検索してください。これらのテストを実行するには、少なくとも2つのGPUが必要です： ```bash CUDA_VISIBLE_DEVICES=0,1 RUN_SLOW=1 pytest -sv tests/test_trainer_distributed.py ``` ### Output capture テストの実行中に、`stdout` および `stderr` に送信された出力はキャプチャされます。テストまたはセットアップメソッドが失敗した場合、通常、それに対応するキャプチャされた出力が失敗のトレースバックと共に表示されます。出力のキャプチャを無効にし、`stdout` と `stderr` を通常通りに取得するには、`-s` または `--capture=no` を使用してください：これらのテストを実行するには少なくとも2つのGPUが必要です： ```bash pytest -s tests/utils/test_logging.py ``` テスト結果を JUnit 形式の出力に送信するには: ```bash py.test tests --junitxml=result.xml ``` ### Color control 色を持たないようにする（例：黄色のテキストを白い背景に表示すると読みにくいです）： ```bash pytest --color=no tests/utils/test_logging.py ``` ### Sending test report to online pastebin service テスト失敗ごとに URL を作成します。 ```bash pytest --pastebin=failed tests/utils/test_logging.py ``` これにより、テスト実行情報がリモートのPasteサービスに送信され、各エラーに対してURLが提供されます。通常通りテストを選択するか、たとえば特定のエラーのみを送信したい場合は `-x` を追加で指定できます。テストセッション全体のログに対するURLを作成する方法： ```bash pytest --pastebin=all tests/utils/test_logging.py ``` ## Writing tests 🤗 transformersのテストは `unittest` を基にしていますが、 `pytest` で実行されるため、ほとんどの場合、両方のシステムの機能を使用できます。 [こちら](https://docs.pytest.org/en/stable/unittest.html)でサポートされている機能を読むことができますが、重要なことは、ほとんどの `pytest` のフィクスチャが動作しないことです。パラメータ化も同様ですが、似たような方法で動作する `parameterized` モジュールを使用しています。 ### Parametrization 同じテストを異なる引数で複数回実行する必要があることがよくあります。これはテスト内部から行うこともできますが、その場合、そのテストを単一の引数セットで実行する方法はありません。 ```python # test_this1.py import unittest from parameterized import parameterized class TestMathUnitTest(unittest.TestCase): @parameterized.expand( [ ("negative", -1.5, -2.0), ("integer", 1, 1.0), ("large fraction", 1.6, 1), ] ) def test_floor(self, name, input, expected): assert_equal(math.floor(input), expected) ``` デフォルトでは、このテストは3回実行され、それぞれの実行で `test_floor` の最後の3つの引数がパラメータリストの対応する引数に割り当てられます。そして、`negative` と `integer` パラメータのセットのみを実行することもできます: ```bash pytest -k "negative and integer" tests/test_mytest.py ``` または、`Negative`のサブテストを除くすべての場合、次のようになります。 ```bash pytest -k "not negative" tests/test_mytest.py ``` `-k` フィルターを使用することに加えて、各サブテストの正確な名前を調べ、その正確な名前を使用して任意のサブテストまたはすべてのサブテストを実行することができます。 ```bash pytest test_this1.py --collect-only -q ``` すると次のものがリストされます: ```bash test_this1.py::TestMathUnitTest::test_floor_0_negative test_this1.py::TestMathUnitTest::test_floor_1_integer test_this1.py::TestMathUnitTest::test_floor_2_large_fraction ``` したがって、2 つの特定のサブテストのみを実行できるようになりました。 ```bash pytest test_this1.py::TestMathUnitTest::test_floor_0_negative test_this1.py::TestMathUnitTest::test_floor_1_integer ``` `transformers`の開発者依存関係にすでに含まれているモジュール[parameterized](https://pypi.org/project/parameterized/) は、`unittests` と `pytest` テストの両方で機能します。ただし、テストが `unittest` でない場合、`pytest.mark.parametrize` を使用することができます（または既存のテストのいくつかで、主に `examples` の下で使用されているのを見ることができます）。次に、同じ例を示しますが、今度は `pytest` の `parametrize` マーカーを使用しています： ```python # test_this2.py import pytest @pytest.mark.parametrize( "name, input, expected", [ ("negative", -1.5, -2.0), ("integer", 1, 1.0), ("large fraction", 1.6, 1), ], ) def test_floor(name, input, expected): assert_equal(math.floor(input), expected) ``` `parameterized` と同様に、`pytest.mark.parametrize` を使用すると、`-k` フィルタが役立たない場合でも、サブテストの実行を細かく制御できます。ただし、このパラメータ化関数はサブテストの名前をわずかに異なるものにします。以下にその例を示します： ```bash pytest test_this2.py --collect-only -q ``` すると次のものがリストされます: ```bash test_this2.py::test_floor[integer-1-1.0] test_this2.py::test_floor[negative--1.5--2.0] test_this2.py::test_floor[large fraction-1.6-1] ``` これで、特定のテストのみを実行できるようになりました。 ```bash pytest test_this2.py::test_floor[negative--1.5--2.0] test_this2.py::test_floor[integer-1-1.0] ``` 前の例と同様に。 ### Files and directories テストの中で、現在のテストファイルからの相対位置を知る必要があることがよくあります。しかし、これは簡単なことではありません。なぜなら、テストは複数のディレクトリから呼び出されるか、異なる深さのサブディレクトリに存在することがあるからです。`transformers.test_utils.TestCasePlus` というヘルパークラスは、すべての基本パスを整理し、簡単にアクセスできるようにすることで、この問題を解決します。 - `pathlib` オブジェクト（すべて完全に解決されたもの）： - `test_file_path` - 現在のテストファイルのパス、つまり `__file__` - `test_file_dir` - 現在のテストファイルを含むディレクトリ - `tests_dir` - `tests` テストスイートのディレクトリ - `examples_dir` - `examples` テストスイートのディレクトリ - `repo_root_dir` - リポジトリのディレクトリ - `src_dir` - `transformers` サブディレクトリが存在する場所 - パスの文字列表現――上記と同じですが、これらは `pathlib` オブジェクトではなく文字列としてパスを返します： - `test_file_path_str` - `test_file_dir_str` - `tests_dir_str` - `examples_dir_str` - `repo_root_dir_str` - `src_dir_str` これらを使用し始めるには、テストが `transformers.test_utils.TestCasePlus` のサブクラスに存在することを確認するだけです。例： ```python from transformers.testing_utils import TestCasePlus class PathExampleTest(TestCasePlus): def test_something_involving_local_locations(self): data_dir = self.tests_dir / "fixtures/tests_samples/wmt_en_ro" ``` もし、`pathlib` を介してパスを操作する必要がない場合、または単に文字列としてパスが必要な場合は、`pathlib` オブジェクトに `str()` を呼び出すか、`_str` で終わるアクセサを使用できます。例： ```python from transformers.testing_utils import TestCasePlus class PathExampleTest(TestCasePlus): def test_something_involving_stringified_locations(self): examples_dir = self.examples_dir_str ``` ### Temporary files and directories 一意の一時ファイルとディレクトリの使用は、並列テストの実行には欠かせません。これにより、テストがお互いのデータを上書きしないようにします。また、これらを作成した各テストの終了時に一時ファイルとディレクトリが削除されることを望みます。そのため、これらのニーズを満たすパッケージである `tempfile` のようなパッケージの使用は重要です。しかし、テストのデバッグ時には、一時ファイルやディレクトリに何が格納されているかを確認できる必要があり、テストを再実行するたびにランダムに変更されないその正確なパスを知りたいと思います。 `transformers.test_utils.TestCasePlus` というヘルパークラスは、このような目的に最適です。これは `unittest.TestCase` のサブクラスであるため、テストモジュールで簡単に継承することができます。以下はその使用例です： ```python from transformers.testing_utils import TestCasePlus class ExamplesTests(TestCasePlus): def test_whatever(self): tmp_dir = self.get_auto_remove_tmp_dir() ``` このコードはユニークな一時ディレクトリを作成し、`tmp_dir` をその場所に設定します。 - ユニークな一時ディレクトリを作成します： ```python def test_whatever(self): tmp_dir = self.get_auto_remove_tmp_dir() ``` `tmp_dir` には、作成された一時ディレクトリへのパスが含まれます。期間終了後は自動的に削除されますテスト。 - 任意の一時ディレクトリを作成し、テストの開始前にそれが空であることを確認し、テスト後には空にしないでください。 ```python def test_whatever(self): tmp_dir = self.get_auto_remove_tmp_dir("./xxx") ``` これは、特定のディレクトリを監視し、前のテストがそこにデータを残さないことを確認したい場合に、デバッグに役立ちます。 - `before` と `after` 引数を直接オーバーライドすることで、デフォルトの動作をオーバーライドできます。以下のいずれかの動作に導きます： - `before=True`：テストの開始時に常に一時ディレクトリがクリアされます。 - `before=False`：一時ディレクトリが既に存在する場合、既存のファイルはそのままになります。 - `after=True`：テストの終了時に常に一時ディレクトリが削除されます。 - `after=False`：テストの終了時に常に一時ディレクトリはそのままになります。 <Tip> `rm -r`の相当を安全に実行するために、明示的な `tmp_dir` が使用される場合、プロジェクトリポジトリのチェックアウトのサブディレクトリのみが許可されます。誤って `/tmp` などのファイルシステムの重要な部分が削除されないように、常に `./` から始まるパスを渡してください。 </Tip> <Tip> 各テストは複数の一時ディレクトリを登録でき、要求がない限りすべて自動で削除されます。 </Tip> ### Temporary sys.path override 別のテストからインポートするために一時的に `sys.path` をオーバーライドする必要がある場合、`ExtendSysPath` コンテキストマネージャを使用できます。例： ```python import os from transformers.testing_utils import ExtendSysPath bindir = os.path.abspath(os.path.dirname(__file__)) with ExtendSysPath(f"{bindir}/.."): from test_trainer import TrainerIntegrationCommon # noqa ``` ### Skipping tests これは、バグが見つかり、新しいテストが作成された場合であっても、バグがまだ修正されていない場合に役立ちます。メインリポジトリにコミットできるようにするには、`make test` の実行中にそれをスキップする必要があります。メソッド： - **skip** は、テストが特定の条件が満たされた場合にのみパスすることを期待しており、それ以外の場合は pytest がテストの実行をスキップします。一般的な例は、Windows専用のテストを非Windowsプラットフォームでスキップする場合、または現在利用できない外部リソースに依存するテストをスキップする場合です（例: データベースが利用できない場合）。 - **xfail** は、何らかの理由でテストが失敗することを期待しています。一般的な例は、まだ実装されていない機能のテストや、まだ修正されていないバグのテストです。テストが予想される失敗にもかかわらずパスした場合（pytest.mark.xfailでマークされたテスト）、それはxpassとしてテストサマリーに報告されます。これらの2つの間の重要な違いの1つは、`skip` はテストを実行しない点であり、`xfail` は実行します。したがって、バグのあるコードが他のテストに影響を与える場合は、`xfail` を使用しないでください。 #### Implementation - テスト全体を無条件にスキップする方法は次のとおりです： ```python no-style @unittest.skip("this bug needs to be fixed") def test_feature_x(): ``` または pytest 経由: ```python no-style @pytest.mark.skip(reason="this bug needs to be fixed") ``` または `xfail` の方法: ```python no-style @pytest.mark.xfail def test_feature_x(): ``` - テスト内の内部チェックに基づいてテストをスキップする方法は次のとおりです。 ```python def test_feature_x(): if not has_something(): pytest.skip("unsupported configuration") ``` またはモジュール全体: ```python import pytest if not pytest.config.getoption("--custom-flag"): pytest.skip("--custom-flag is missing, skipping tests", allow_module_level=True) ``` または `xfail` の方法: ```python def test_feature_x(): pytest.xfail("expected to fail until bug XYZ is fixed") ``` - 一部のインポートが欠落している場合にモジュール内のすべてのテストをスキップする方法は次のとおりです。 ```python docutils = pytest.importorskip("docutils", minversion="0.3") ``` - 条件に基づいてテストをスキップします。 ```python no-style @pytest.mark.skipif(sys.version_info < (3,6), reason="requires python3.6 or higher") def test_feature_x(): ``` または： ```python no-style @unittest.skipIf(torch_device == "cpu", "Can't do half precision") def test_feature_x(): ``` またはモジュール全体をスキップします。 ```python no-style @pytest.mark.skipif(sys.platform == 'win32', reason="does not run on windows") class TestClass(): def test_feature_x(self): ``` 詳細、例、および方法についての詳細は[こちら](https://docs.pytest.org/en/latest/skipping.html)を参照してください。 ### Slow tests テストライブラリは着実に成長しており、テストの一部は数分かかります。そのため、CIでテストスイートの完了を待つのは1時間待つ余裕がないことがあります。したがって、いくつかの例外を除いて、遅いテストは以下の例のようにマークすべきです： ```python no-style from transformers.testing_utils import slow @slow def test_integration_foo(): ``` テストが`@slow`としてマークされたら、そのようなテストを実行するには、環境変数 `RUN_SLOW=1`を設定します。例: ```bash RUN_SLOW=1 pytest tests ``` `@parameterized` のようなデコレータはテスト名を書き換えるため、`@slow` および他のスキップデコレータ `@require_*` は正しく動作するためには、最後にリストアップする必要があります。以下は正しい使用例の一例です： ```python no-style @parameterized.expand(...) @slow def test_integration_foo(): ``` このドキュメントの冒頭で説明したように、遅いテストは定期的なスケジュールに従って実行され、PRのCIチェックでは実行されません。そのため、一部の問題がPRの提出時に見落とされ、マージされる可能性があります。そのような問題は次回のスケジュールされたCIジョブで検出されます。しかし、それはまた、PRを提出する前に自分のマシンで遅いテストを実行する重要性を意味しています。どのテストを遅いテストとしてマークすべきかを選択するための、おおまかな意思決定メカニズムが次に示されています： - テストがライブラリの内部コンポーネントの1つに焦点を当てている場合（例: モデリングファイル、トークン化ファイル、パイプライン）、そのテストは遅いテストスイートで実行する必要があります。それがライブラリの他の側面、たとえばドキュメンテーションや例に焦点を当てている場合、それらのテストは遅いテストスイートで実行する必要があります。そして、このアプローチを洗練させるために例外を設ける必要があります。 - 重いウェイトセットや約50MB以上のデータセットをダウンロードする必要があるすべてのテスト（例: モデル統合テスト、トークナイザ統合テスト、パイプライン統合テスト）は遅いテストとして設定する必要があります。新しいモデルを追加する場合、統合テスト用にランダムなウェイトを持つ小さなバージョンを作成し、ハブにアップロードする必要があります。これについては以下の段落で詳しく説明します。 - 特に高速化されていないトレーニングを行う必要があるすべてのテストは遅いテストとして設定する必要があります。 - 一部の「遅い」であるべきでないテストが非常に遅い場合、およびそれらを `@slow` として設定する必要がある場合には例外を導入できます。大容量のファイルをディスクに保存および読み込みする自動モデリングテストは、`@slow` としてマークされたテストの良い例です。 - CIで1秒未満でテストが完了する場合（ダウンロードを含む）、それは通常のテストであるべきです。すべての非遅いテストは、さまざまな内部要素を完全にカバーする必要がありますが、高速である必要があります。たとえば、特別に作成された小さなモデル（レイヤー数が最小限で、語彙サイズが小さいなど）を使用して、かなりのカバレッジを実現できます。その後、`@slow` テストでは大規模な遅いモデルを使用して質的なテストを実行できます。これらを使用するには、以下のように *tiny* モデルを探してください： ```bash grep tiny tests examples ``` [スクリプトの例](https://github.com/huggingface/transformers/tree/main/scripts/fsmt/fsmt-make-tiny-model.py)があり、これにより tiny-wmt19-en-de のような小さなモデルが作成されます。特定のモデルのアーキテクチャに簡単に調整できます。実行時間を誤って測定することが簡単です。たとえば、巨大なモデルのダウンロードに関するオーバーヘッドがある場合、ローカルでテストするとダウンロードされたファイルがキャッシュされ、ダウンロード時間が計測されなくなります。したがって、CIログの実行速度レポート（`pytest --durations=0 tests` の出力）を確認してください。このレポートは、遅いテストとしてマークされていない遅い外れ値や、高速に書き直す必要があるテストを見つけるのにも役立ちます。テストスイートがCIで遅くなり始めた場合、このレポートのトップリストには最も遅いテストが表示されます。 ### Testing the stdout/stderr output `stdout` および/または `stderr` に書き込む関数をテストするために、テストは `pytest` の [capsys システム](https://docs.pytest.org/en/latest/capture.html) を使用してこれらのストリームにアクセスできます。以下はその方法です： ```python import sys def print_to_stdout(s): print(s) def print_to_stderr(s): sys.stderr.write(s) def test_result_and_stdout(capsys): msg = "Hello" print_to_stdout(msg) print_to_stderr(msg) out, err = capsys.readouterr() # consume the captured output streams # optional: if you want to replay the consumed streams: sys.stdout.write(out) sys.stderr.write(err) # test: assert msg in out assert msg in err ``` そしてもちろん、ほとんどの場合、`stderr`は例外の一部として提供されるため、そのような場合には try/excel を使用する必要があります。ケース： ```python def raise_exception(msg): raise ValueError(msg) def test_something_exception(): msg = "Not a good value" error = "" try: raise_exception(msg) except Exception as e: error = str(e) assert msg in error, f"{msg} is in the exception:\n{error}" ``` stdout をキャプチャするもう 1 つのアプローチは、`contextlib.redirect_stdout`を使用することです。 ```python from io import StringIO from contextlib import redirect_stdout def print_to_stdout(s): print(s) def test_result_and_stdout(): msg = "Hello" buffer = StringIO() with redirect_stdout(buffer): print_to_stdout(msg) out = buffer.getvalue() # optional: if you want to replay the consumed streams: sys.stdout.write(out) # test: assert msg in out ``` stdout をキャプチャする際の重要な潜在的な問題は、通常の `print` でこれまでに出力された内容をリセットする可能性がある `\r` 文字が含まれている可能性があることです。`pytest` 自体には問題はありませんが、`pytest -s` ではこれらの文字がバッファに含まれるため、`-s` ありとなしでテストを実行できるようにするには、`re.sub(r'~.*\r', '', buf, 0, re.M)` を使用してキャプチャされた出力に対して追加のクリーンアップを行う必要があります。しかし、その後、`\r` が含まれているかどうかにかかわらず、すべての操作を自動的に処理するヘルパーコンテキストマネージャラッパーがあります。したがって、次のように簡単に行えます： ```python from transformers.testing_utils import CaptureStdout with CaptureStdout() as cs: function_that_writes_to_stdout() print(cs.out) ``` 完全なテスト例は次のとおりです。 ```python from transformers.testing_utils import CaptureStdout msg = "Secret message\r" final = "Hello World" with CaptureStdout() as cs: print(msg + final) assert cs.out == final + "\n", f"captured: {cs.out}, expecting {final}" ``` `stderr` をキャプチャしたい場合は、代わりに `CaptureStderr` クラスを使用してください。 ```python from transformers.testing_utils import CaptureStderr with CaptureStderr() as cs: function_that_writes_to_stderr() print(cs.err) ``` 両方のストリームを一度にキャプチャする必要がある場合は、親の `CaptureStd` クラスを使用します。 ```python from transformers.testing_utils import CaptureStd with CaptureStd() as cs: function_that_writes_to_stdout_and_stderr() print(cs.err, cs.out) ``` また、テストの問題のデバッグを支援するために、デフォルトで、これらのコンテキストマネージャーは終了時にキャプチャされたストリームを自動的に再生します。文脈から。 ### Capturing logger stream ロガーの出力を検証する必要がある場合は、`CaptureLogger`を使用できます。 ```python from transformers import logging from transformers.testing_utils import CaptureLogger msg = "Testing 1, 2, 3" logging.set_verbosity_info() logger = logging.get_logger("transformers.models.bart.tokenization_bart") with CaptureLogger(logger) as cl: logger.info(msg) assert cl.out, msg + "\n" ``` ### Testing with environment variables 特定のテストで環境変数の影響をテストしたい場合は、ヘルパーデコレータを使用できます。 `transformers.testing_utils.mockenv` ```python from transformers.testing_utils import mockenv class HfArgumentParserTest(unittest.TestCase): @mockenv(TRANSFORMERS_VERBOSITY="error") def test_env_override(self): env_level_str = os.getenv("TRANSFORMERS_VERBOSITY", None) ``` 場合によっては、外部プログラムを呼び出す必要があるため、`os.environ` に`PYTHONPATH`を設定してインクルードする必要があります。複数のローカルパス。ヘルパークラス `transformers.test_utils.TestCasePlus` が役に立ちます。 ```python from transformers.testing_utils import TestCasePlus class EnvExampleTest(TestCasePlus): def test_external_prog(self): env = self.get_env() # now call the external program, passing `env` to it ``` テストファイルが `tests` テストスイートまたは `examples` のどちらにあるかに応じて `env[PYTHONPATH]` を使用して、これら 2 つのディレクトリのいずれかを含めます。また、テストが確実に行われるようにするための `src` ディレクトリも含めます。現在のリポジトリに対して実行され、最後に、テストが実行される前にすでに設定されていた `env[PYTHONPATH]` を使用して実行されます。何かあれば呼ばれます。このヘルパーメソッドは `os.environ` オブジェクトのコピーを作成するため、元のオブジェクトはそのまま残ります。 ### Getting reproducible results 状況によっては、テストのランダム性を削除したい場合があります。同一の再現可能な結果セットを取得するには、シードを修正する必要があります: ```python seed = 42 # python RNG import random random.seed(seed) # pytorch RNGs import torch torch.manual_seed(seed) torch.backends.cudnn.deterministic = True if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) # numpy RNG import numpy as np np.random.seed(seed) # tf RNG tf.random.set_seed(seed) ``` ### Debugging tests 警告が発生した時点でデバッガーを開始するには、次の手順を実行します。 ```bash pytest tests/utils/test_logging.py -W error::UserWarning --pdb ``` ## Working with github actions workflows セルフプッシュのワークフローCIジョブをトリガーするには、以下の手順を実行する必要があります： 1. `transformers` のリモートリポジトリで新しいブランチを作成します（フォークではなく、元のリポジトリで行います）。 2. ブランチの名前は `ci_` または `ci-` で始まる必要があります（`main` もトリガーしますが、`main` ではPRを作成できません）。また、特定のパスでのみトリガーされます - このドキュメントが書かれた後に変更された場合に備えて、最新の定義は[こちら](https://github.com/huggingface/transformers/blob/main/.github/workflows/self-push.yml)の *push:* にあります。 3. このブランチからPRを作成します。 4. その後、このジョブが[ここ](https://github.com/huggingface/transformers/actions/workflows/self-push.yml)に表示されます。ジョブはバックログがある場合、すぐに実行されないことがあります。 ## Testing Experimental CI Features CI機能のテストは通常のCIの正常な動作に干渉する可能性があるため、新しいCI機能を追加する場合、以下の手順に従う必要があります。 1. テストが必要なものをテストするための新しい専用のジョブを作成します。 2. 新しいジョブは常に成功する必要があるため、常にグリーン ✓（詳細は以下参照）を表示する必要があります。 3. さまざまな種類のPR（ユーザーフォークブランチ、非フォークブランチ、github.com UIから直接ファイルを編集するブランチ、さまざまな強制プッシュなど）が実行されるまでいくつかの日間実行し、実験的なジョブのログを監視します（意図的に常にグリーンになるようになっている全体のジョブの緑ではなく）。 4. すべてが安定していることが明確になったら、新しい変更を既存のジョブに統合します。このように、CI機能自体の実験が通常のワークフローに干渉しないようにできます。では、新しいCI機能が開発中である間、ジョブを常に成功させるにはどうすればいいでしょうか？ TravisCIのような一部のCIは `ignore-step-failure` をサポートし、全体のジョブを成功として報告しますが、この文書が作成された時点ではCircleCIとGithub Actionsはそれをサポートしていません。したがって、以下のワークアラウンドを使用できます： 1. bashスクリプト内で潜在的な失敗を抑制するために実行コマンドの冒頭に `set +euo pipefail` を記述します。 2. 最後のコマンドは成功する必要があります。たとえば `echo "done"` または単に `true` を使用できます。以下は例です： ```yaml - run: name: run CI experiment command: | set +euo pipefail echo "setting run-all-despite-any-errors-mode" this_command_will_fail echo "but bash continues to run" # emulate another failure false # but the last command must be a success echo "during experiment do not remove: reporting success to CI, even if there were failures" ``` 単純なコマンドの場合は、次のようにすることもできます。 ```bash cmd_that_may_fail || true ``` もちろん、結果に満足したら、実験的なステップやジョブを通常のジョブと統合し、`set +euo pipefail` などの追加した要素を削除して、実験的なジョブが通常のCIの動作に干渉しないようにします。このプロセス全体は、実験的なステップに対して `allow-failure` のようなものを設定し、PRの全体のステータスに影響を与えずに失敗させることができれば、はるかに簡単になったでしょう。しかし、前述の通り、現在はCircleCIとGithub Actionsはこの機能をサポートしていません。この機能に関しての投票や、CIに特有のスレッドでその進捗状況を確認できます： - [Github Actions:](https://github.com/actions/toolkit/issues/399) - [CircleCI:](https://ideas.circleci.com/ideas/CCI-I-344)

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# BERTology BERT와 같은 대규모 트랜스포머의 내부 동작을 조사하는 연구 분야가 점점 더 중요해지고 있습니다. 혹자는 "BERTology"라 칭하기도 합니다. 이 분야의 좋은 예시는 다음과 같습니다: - BERT는 고전적인 NLP 파이프라인의 재발견 - Ian Tenney, Dipanjan Das, Ellie Pavlick: https://arxiv.org/abs/1905.05950 - 16개의 헤드가 정말로 1개보다 나은가? - Paul Michel, Omer Levy, Graham Neubig: https://arxiv.org/abs/1905.10650 - BERT는 무엇을 보는가? BERT의 어텐션 분석 - Kevin Clark, Urvashi Khandelwal, Omer Levy, Christopher D. Manning: https://arxiv.org/abs/1906.04341 - CAT-probing: 프로그래밍 언어에 대해 사전훈련된 모델이 어떻게 코드 구조를 보는지 알아보기 위한 메트릭 기반 접근 방법: https://arxiv.org/abs/2210.04633 우리는 이 새로운 연구 분야의 발전을 돕기 위해, BERT/GPT/GPT-2 모델에 내부 표현을 살펴볼 수 있는 몇 가지 기능을 추가했습니다. 이 기능들은 주로 Paul Michel의 훌륭한 작업을 참고하여 개발되었습니다 (https://arxiv.org/abs/1905.10650): - BERT/GPT/GPT-2의 모든 은닉 상태에 접근하기, - BERT/GPT/GPT-2의 각 헤드의 모든 어텐션 가중치에 접근하기, - 헤드의 출력 값과 그래디언트를 검색하여 헤드 중요도 점수를 계산하고 https://arxiv.org/abs/1905.10650에서 설명된 대로 헤드를 제거하는 기능을 제공합니다. 이러한 기능들을 이해하고 직접 사용해볼 수 있도록 [bertology.py](https://github.com/huggingface/transformers/tree/main/examples/research_projects/bertology/run_bertology.py) 예제 스크립트를 추가했습니다. 이 예제 스크립트에서는 GLUE에 대해 사전훈련된 모델에서 정보를 추출하고 모델을 가지치기(prune)해봅니다.

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# Whisper [[whisper]] ## 개요 [[overview]] Whisper 모델은 Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever에 의해 [Robust Speech Recognition via Large-Scale Weak Supervision](https://cdn.openai.com/papers/whisper.pdf)에서 제안되었습니다. 논문의 초록은 다음과 같습니다: *우리는 인터넷에서 대량의 오디오를 글로 옮긴 것을 예측하도록 간단히 훈련된 음성 처리 시스템의 성능을 연구합니다. 68만 시간의 다국어 및 다중 작업 지도(multitask supervision)에 확장했을 때, 결과 모델은 표준 벤치마크에 잘 일반화되며, 미세 조정이 필요 없는 제로샷 전송 설정에서 이전의 완전히 지도된(fully-supervised) 결과와 경쟁할 수 있는 경우가 많습니다. 사람과 비교하면, 이 모델은 사람의 정확도와 견고성에 근접합니다. 우리는 강력한 음성 처리를 위한 추가 작업의 기반이 될 모델과 추론 코드를 공개합니다.* 팁: - 이 모델은 일반적으로 별도의 미세 조정 없이도 잘 작동합니다. - 아키텍처는 고전적인 인코더-디코더 아키텍처를 따르기 때문에, 추론을 위해 [`~generation.GenerationMixin.generate`] 함수를 사용합니다. - 현재 추론은 짧은 형식에만 구현되어 있으며, 오디오는 30초 미만의 세그먼트로 미리 분할되어야 합니다. 타임스탬프를 포함한 긴 형식에 대한 추론은 향후 릴리스에서 구현될 예정입니다. - [`WhisperProcessor`]를 사용하여 모델에 사용할 오디오를 준비하고, 예측된 ID를 텍스트로 디코딩할 수 있습니다. - 모델과 프로세서를 변환하려면 다음을 사용하는 것이 좋습니다: ```bash python src/transformers/models/whisper/convert_openai_to_hf.py --checkpoint_path "" --pytorch_dump_folder_path "Arthur/whisper-3" --convert_preprocessor True ``` 스크립트는 OpenAI 체크포인트에서 필요한 모든 매개변수를 자동으로 결정합니다. OpenAI 변환을 수행하려면 `tiktoken` 라이브러리를 설치해야 합니다. 라이브러리를 설치해야 OpenAI 토큰화기를 `tokenizers` 버전으로 변환할 수 있습니다. 이 모델은 [Arthur Zucker](https://huggingface.co/ArthurZ)에 의해 제공되었습니다. 이 모델의 Tensorflow 버전은 [amyeroberts](https://huggingface.co/amyeroberts)에 의해 제공되었습니다. 원본 코드는 [여기](https://github.com/openai/whisper)에서 찾을 수 있습니다. ## WhisperConfig [[whisperconfig]] [[autodoc]] WhisperConfig ## WhisperTokenizer [[whispertokenizer]] [[autodoc]] WhisperTokenizer - set_prefix_tokens - build_inputs_with_special_tokens - get_special_tokens_mask - create_token_type_ids_from_sequences - save_vocabulary ## WhisperTokenizerFast [[whispertokenizerfast]] [[autodoc]] WhisperTokenizerFast - set_prefix_tokens - build_inputs_with_special_tokens - get_special_tokens_mask - create_token_type_ids_from_sequences - save_vocabulary ## WhisperFeatureExtractor [[whisperfeatureextractor]] [[autodoc]] WhisperFeatureExtractor - __call__ ## WhisperProcessor [[whisperprocessor]] [[autodoc]] WhisperProcessor - __call__ - from_pretrained - save_pretrained - batch_decode - decode ## WhisperModel [[whispermodel]] [[autodoc]] WhisperModel - forward - _mask_input_features ## WhisperForConditionalGeneration [[whisperforconditionalgeneration]] [[autodoc]] WhisperForConditionalGeneration - forward ## WhisperForAudioClassification [[whisperforaudioclassification]] [[autodoc]] WhisperForAudioClassification - forward ## TFWhisperModel [[tfwhispermodel]] [[autodoc]] TFWhisperModel - call ## TFWhisperForConditionalGeneration [[tfwhisperforconditionalgeneration]] [[autodoc]] TFWhisperForConditionalGeneration - call ## FlaxWhisperModel [[flaxwhispermodel]] [[autodoc]] FlaxWhisperModel - __call__ ## FlaxWhisperForConditionalGeneration [[flaxwhisperforconditionalgeneration]] [[autodoc]] FlaxWhisperForConditionalGeneration - __call__ ## FlaxWhisperForAudioClassification [[flaxwhisperforaudioclassification]] [[autodoc]] FlaxWhisperForAudioClassification - __call__

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# 고정 길이 모델의 펄플렉서티(Perplexity)[[perplexity-of-fixedlength-models]] [[open-in-colab]] 펄플렉서티(Perplexity, PPL)는 가장 일반적인 언어 모델 평가지표 중 하나입니다. 자세히 알아보기 전에 이 평가지표는 고전적인 언어 모델(자기회귀 또는 인과적 언어 모델이라고도 함)에만 적용되며 BERT와 같은 마스킹된 언어 모델에는 잘 적용하지 않습니다 (BERT는 [summary of the models](../en/model_summary) 문서를 참고하세요). 펄플렉서티는 시퀀스의 음의 로그 우도(negative log-likelihood, NLL) 값의 평균에 지수(exponentiate)를 취한 값으로 정의됩니다. 토큰화된 시퀀스 \$X = (x_0, x_1, \dots, x_t)\$ 가 있을 때, \$X\$ 의 펄플렉서티는 아래 수식과 같이 구할 수 있습니다. $$\text{PPL}(X) = \exp \left\{ {-\frac{1}{t}\sum_i^t \log p_\theta (x_i|x_{<i}) } \right\}$$ \$\log p_\theta (x_i|x_{<i})\$ 는 모델에 i번째 이전까지 토큰이 주어졌을 때 i번째 토큰의 로그 우도값입니다. 직관적으로 말뭉치에서 지정된 토큰 집합을 균일하게 예측하는 모델의 능력에 대한 평가로 생각할 수 있습니다. 중요한 점은 토큰화 과정이 모델의 펄플렉서티에 직접적인 영향을 미치므로 서로 다른 모델을 비교할 때 항상 이를 고려해야 합니다. 이는 데이터와 모델 예측 간의 cross-entropy 값에 지수를 취한 것과 동일합니다. 펄플렉서티와 문자당 비트 수(BPC) 및 데이터 압축과의 관계에 대해 더 직관적인 이해를 원하신다면 다음 글 [fantastic blog post on The Gradient](https://thegradient.pub/understanding-evaluation-metrics-for-language-models/)을 확인하세요. ## 고정 길이 모델의 펄플렉서티(PPL) 계산하기[[calculating-ppl-with-fixedlength-models]] 모델의 컨텍스트 크기가 정해져있지 않다면, 아래와 같이 시퀀스를 자동 회귀적으로 분해하고 각 단계에서 선행 하는 전체 시퀀스를 조건부 확률에 넣어 모델의 펄플렉서티를 계산할 것입니다. <img width="600" alt="Full decomposition of a sequence with unlimited context length" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/ppl_full.gif"/> 그러나 모델의 근사치를 구할 때는 일반적으로 모델이 처리할 수 있는 토큰 수에 제한이 있습니다. 예를 들어, 가장 큰 버전의 [GPT-2](model_doc/gpt2)는 토큰의 길이가 1024로 고정되어 있습니다. 따라서 \$t\$ 가 1024보다 큰 경우에 \$p_\theta(x_t|x_{<t})\$ 을 계산할 수 없습니다. 대신 시퀀스는 일반적으로 모델의 최대 입력 크기와 동일한 길이는 가지는 부분 시퀀스로 쪼갭니다. 만약 모델의 최대 입력 길이가 \$k\$ 라면, 토큰 \$x_t\$ 의 우도 값을 계산할 때 이전 토큰을 모두 사용하지 않고, \$k-1\$ 토큰까지 사용해 대략적인 우도 값을 추정합니다. 모델의 시퀀스에 대한 펄플렉서티를 계산할 때, 수월하지만 차선책은 시퀀스를 청크로 쪼개고 분해된 각 부분의 로그 우도 값을 독립적으로 합산하는 것입니다. <img width="600" alt="Suboptimal PPL not taking advantage of full available context" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/ppl_chunked.gif"/> 이 방법은 각 부분의 펄플렉서티를 한 번의 포워드 패스로 계산할 수 있어 빠르지만 일반적으로 더 높은(더 나쁜) PPL을 산출합니다. 왜냐하면 대부분의 예측 단계에서 모델의 컨텍스트가 적기 때문입니다. 대신, 고정 길이 모델의 PPL은 슬라이딩 윈도우 전략으로 평가해야 합니다. 이 전략에는 컨텍스트 윈도우을 반복적으로 슬라이딩해 모델이 각 예측을 수행할 때 더 많은 컨텍스트를 갖도록 하는 작업이 포함됩니다. <img width="600" alt="Sliding window PPL taking advantage of all available context" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/ppl_sliding.gif"/> 이는 시퀀스 확률의 실제 분해에 더 가까운 근사치이며 일반적으로 더 유리한 점수를 산출합니다. 단점은 말뭉치의 각 토큰에 대해 별도의 포워드 패스가 필요하다는 것입니다. 현실적으로 좋은 절충안은 한 번에 한 토큰씩 슬라이딩하는 것이 아니라 더 큰 간격으로 컨텍스트를 이동하는 스트라이드가 적용된 슬라이딩 윈도우을 사용하는 것입니다. 이렇게 하면 계산을 훨씬 더 빠르게 진행하면서도 모델에 각 단계에서 예측을 수행할 수 있는 긴 컨텍스트를 제공할 수 있습니다. ## 예제: 🤗 Transformers에서 GPT-2로 펄플렉서티(perplexity) 계산하기[[example-calculating-perplexity-with-gpt2-in-transformers]] 이제 GPT-2로 위의 과정을 시연해 보겠습니다. ```python from transformers import GPT2LMHeadModel, GPT2TokenizerFast device = "cuda" model_id = "openai-community/gpt2-large" model = GPT2LMHeadModel.from_pretrained(model_id).to(device) tokenizer = GPT2TokenizerFast.from_pretrained(model_id) ``` WikiText-2 데이터 세트를 가져오고 몇 가지 슬라이딩 윈도우 전략을 사용해 펄플렉서티를 계산해보겠습니다. 이 데이터 세트는 크기가 작고 포워드 패스 한 번만 수행하기 때문에 전체 데이터 세트를 메모리에 가져오고 인코딩할 수 있습니다. ```python from datasets import load_dataset test = load_dataset("wikitext", "wikitext-2-raw-v1", split="test") encodings = tokenizer("\n\n".join(test["text"]), return_tensors="pt") ``` 🤗 Transformers를 사용하면 모델의 `labels`로 `input_ids`를 전달해 각 토큰에 대한 평균 음의 우도 값을 손실로 반환할 수 있습니다. 하지만 슬라이딩 윈도우 방식을 사용하면 각 반복마다 모델에 전달하는 토큰이 겹칩니다. 컨텍스트로 처리하는 토큰에 대한 로그 우도 값이 손실에 포함되는 것을 원하지 않기 때문에 이러한 토큰의 `input_ids`를 `-100`으로 설정하여 무시할 수 있습니다. 다음은 스트라이드(stride)를 `512`로 사용한 예시입니다. 즉, 모델이 한 토큰의 조건부 우도 값을 계산할 때 컨텍스트에 최소한 512개의 토큰이 포함되어있다는 의미입니다 (해당 토큰 앞에 512개의 토큰이 있는 경우). ```python import torch from tqdm import tqdm max_length = model.config.n_positions stride = 512 seq_len = encodings.input_ids.size(1) nlls = [] prev_end_loc = 0 for begin_loc in tqdm(range(0, seq_len, stride)): end_loc = min(begin_loc + max_length, seq_len) trg_len = end_loc - prev_end_loc # 마지막 루프의 스트라이드 값과 다를 수 있음 input_ids = encodings.input_ids[:, begin_loc:end_loc].to(device) target_ids = input_ids.clone() target_ids[:, :-trg_len] = -100 with torch.no_grad(): outputs = model(input_ids, labels=target_ids) # 손실은 모든 유효한 레이블에 대한 평균값을 구하는 교차 엔트로피(cross entropy)로 계산됩니다. # 나이브 베이지안 모델은 내부적으로 레이블을 왼쪽으로 1개씩 밀기 때문에, (타켓 - 1)개 만큼의 레이블에 대해 손실을 계산합니다. neg_log_likelihood = outputs.loss nlls.append(neg_log_likelihood) prev_end_loc = end_loc if end_loc == seq_len: break ppl = torch.exp(torch.stack(nlls).mean()) ``` 스트라이드를 최대 입력 길이와 동일하게 설정하면 위에서 설명한 차선책인 비슬라이딩 윈도우 전략과 동일합니다. 일반적으로 스트라이드가 작을수록 모델이 각 예측을 할 때 더 많은 컨텍스트를 볼 수 있게 되어 펄플렉서티 값이 좋아집니다. 위의 계산을 토큰이 겹치지 않도록 `stride = 1024`로 설정하면 PPL은 `19.44`로 GPT-2 논문에서 보고된 `19.93`과 거의 동일합니다. `stride = 512`로 슬라이딩 윈도우 전략을 사용하면 PPL은 `16.45`로 떨어집니다. 이는 더 좋은 점수일 뿐만 아니라 시퀀스 확률의 실제 자동 회귀 분해에 더 가까운 방식으로 계산됩니다.

transformers/docs/source/ko/perplexity.md/0

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# 테스트[[testing]] 먼저 🤗 Transformers 모델이 어떻게 테스트되는지 살펴보고, 새로운 테스트를 작성 및 기존 테스트를 개선하는 방법을 알아봅시다. 이 저장소에는 2개의 테스트 스위트가 있습니다: 1. `tests` - 일반 API에 대한 테스트 2. `examples` - API의 일부가 아닌 다양한 응용 프로그램에 대한 테스트 ## Transformers 테스트 방법[[how-transformers-are-tested]] 1. PR이 제출되면 9개의 CircleCi 작업으로 테스트가 진행됩니다. 해당 PR에 대해 새로운 커밋이 생성될 때마다 테스트는 다시 진행됩니다. 이 작업들은 이 [config 파일](https://github.com/huggingface/transformers/tree/main/.circleci/config.yml)에 정의되어 있으므로 필요하다면 사용자의 로컬 환경에서 동일하게 재현해 볼 수 있습니다. 이 CI 작업은 `@slow` 테스트를 실행하지 않습니다. 2. [github actions](https://github.com/huggingface/transformers/actions)에 의해 실행되는 작업은 3개입니다: - [torch hub integration](https://github.com/huggingface/transformers/tree/main/.github/workflows/github-torch-hub.yml): torch hub integration이 작동하는지 확인합니다. - [self-hosted (push)](https://github.com/huggingface/transformers/tree/main/.github/workflows/self-push.yml): `main` 브랜치에서 커밋이 업데이트된 경우에만 GPU를 이용한 빠른 테스트를 실행합니다. 이는 `src`, `tests`, `.github` 폴더 중 하나에 코드가 업데이트된 경우에만 실행됩니다. (model card, notebook, 기타 등등을 추가한 경우 실행되지 않도록 하기 위해서입니다) - [self-hosted runner](https://github.com/huggingface/transformers/tree/main/.github/workflows/self-scheduled.yml): `tests` 및 `examples`에서 GPU를 이용한 일반 테스트, 느린 테스트를 실행합니다. ```bash RUN_SLOW=1 pytest tests/ RUN_SLOW=1 pytest examples/ ``` 결과는 [여기](https://github.com/huggingface/transformers/actions)에서 확인할 수 있습니다. ## 테스트 실행[[running-tests]] ### 실행할 테스트 선택[[choosing-which-tests-to-run]] 이 문서는 테스트를 실행하는 다양한 방법에 대해 자세히 설명합니다. 모든 내용을 읽은 후에도, 더 자세한 내용이 필요하다면 [여기](https://docs.pytest.org/en/latest/usage.html)에서 확인할 수 있습니다. 다음은 가장 유용한 테스트 실행 방법 몇 가지입니다. 모두 실행: ```console pytest ``` 또는: ```bash make test ``` 후자는 다음과 같이 정의됩니다: ```bash python -m pytest -n auto --dist=loadfile -s -v ./tests/ ``` 위의 명령어는 pytest에게 아래의 내용을 전달합니다: - 사용 가능한 CPU 코어 수만큼 테스트 프로세스를 실행합니다. (RAM이 충분하지 않다면, 테스트 프로세스 수가 너무 많을 수 있습니다!) - 동일한 파일의 모든 테스트는 동일한 테스트 프로세스에서 실행되어야 합니다. - 출력을 캡처하지 않습니다. - 자세한 모드로 실행합니다. ### 모든 테스트 목록 가져오기[[getting-the-list-of-all-tests]] 테스트 스위트의 모든 테스트: ```bash pytest --collect-only -q ``` 지정된 테스트 파일의 모든 테스트: ```bash pytest tests/test_optimization.py --collect-only -q ``` ### 특정 테스트 모듈 실행[[run-a-specific-test-module]] 개별 테스트 모듈 실행하기: ```bash pytest tests/utils/test_logging.py ``` ### 특정 테스트 실행[[run-specific-tests]] 대부분의 테스트 내부에서는 unittest가 사용됩니다. 따라서 특정 하위 테스트를 실행하려면 해당 테스트를 포함하는 unittest 클래스의 이름을 알아야 합니다. 예를 들어 다음과 같을 수 있습니다: ```bash pytest tests/test_optimization.py::OptimizationTest::test_adam_w ``` 위의 명령어의 의미는 다음과 같습니다: - `tests/test_optimization.py` - 테스트가 있는 파일 - `OptimizationTest` - 클래스의 이름 - `test_adam_w` - 특정 테스트 함수의 이름 파일에 여러 클래스가 포함된 경우, 특정 클래스의 테스트만 실행할 수도 있습니다. 예를 들어 다음과 같습니다: ```bash pytest tests/test_optimization.py::OptimizationTest ``` 이 명령어는 해당 클래스 내부의 모든 테스트를 실행합니다. 앞에서 언급한 것처럼 `OptimizationTest` 클래스에 포함된 테스트를 확인할 수 있습니다. ```bash pytest tests/test_optimization.py::OptimizationTest --collect-only -q ``` 키워드 표현식을 사용하여 테스트를 실행할 수도 있습니다. `adam`이라는 이름을 포함하는 테스트만 실행하려면 다음과 같습니다: ```bash pytest -k adam tests/test_optimization.py ``` 논리 연산자 `and`와 `or`를 사용하여 모든 키워드가 일치해야 하는지 또는 어느 하나가 일치해야 하는지를 나타낼 수 있습니다. `not`은 부정할 때 사용할 수 있습니다. `adam`이라는 이름을 포함하지 않는 모든 테스트를 실행하려면 다음과 같습니다: ```bash pytest -k "not adam" tests/test_optimization.py ``` 두 가지 패턴을 하나로 결합할 수도 있습니다: ```bash pytest -k "ada and not adam" tests/test_optimization.py ``` 예를 들어 `test_adafactor`와 `test_adam_w`를 모두 실행하려면 다음을 사용할 수 있습니다: ```bash pytest -k "test_adam_w or test_adam_w" tests/test_optimization.py ``` 여기서 `or`를 사용하는 것에 유의하세요. 두 키워드 중 하나가 일치하도록 하기 위한 목적으로 사용하기 때문입니다. 두 패턴이 모두 포함되어야 하는 테스트만 실행하려면, `and`를 사용해야 합니다: ```bash pytest -k "test and ada" tests/test_optimization.py ``` ### `accelerate` 테스트 실행[[run-`accelerate`-tests]] 모델에서 `accelerate` 테스트를 실행해야 할 때가 있습니다. 이를 위해서는 명령어에 `-m accelerate_tests`를 추가하면 됩니다. 예를 들어, `OPT`에서 이러한 테스트를 실행하려면 다음과 같습니다: ```bash RUN_SLOW=1 pytest -m accelerate_tests tests/models/opt/test_modeling_opt.py ``` ### 문서 테스트 실행[[run-documentation-tests]] 예시 문서가 올바른지 테스트하려면 `doctests`가 통과하는지 확인해야 합니다. 예를 들어, [`WhisperModel.forward`'s docstring](https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/modeling_whisper.py#L1017-L1035)를 사용해 봅시다: ```python r""" Returns: Example: ```python >>> import torch >>> from transformers import WhisperModel, WhisperFeatureExtractor >>> from datasets import load_dataset >>> model = WhisperModel.from_pretrained("openai/whisper-base") >>> feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-base") >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt") >>> input_features = inputs.input_features >>> decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id >>> last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state >>> list(last_hidden_state.shape) [1, 2, 512] ```""" ``` 원하는 파일의 모든 docstring 예제를 자동으로 테스트하려면 다음 명령을 실행하면 됩니다: ```bash pytest --doctest-modules <path_to_file_or_dir> ``` 파일의 확장자가 markdown인 경우 `--doctest-glob="*.md"` 인수를 추가해야 합니다. ### 수정된 테스트만 실행[[run-only-modified-tests]] 수정된 파일 또는 현재 브랜치 (Git 기준)와 관련된 테스트를 실행하려면 [pytest-picked](https://github.com/anapaulagomes/pytest-picked)을 사용할 수 있습니다. 이는 변경한 내용이 테스트에 영향을 주지 않았는지 빠르게 확인할 수 있는 좋은 방법입니다. ```bash pip install pytest-picked ``` ```bash pytest --picked ``` 수정되었지만, 아직 커밋되지 않은 모든 파일 및 폴더에서 테스트가 실행됩니다. ### 소스 수정 시 실패한 테스트 자동 재실행[[automatically-rerun-failed-tests-on-source-modification]] [pytest-xdist](https://github.com/pytest-dev/pytest-xdist)는 모든 실패한 테스트를 감지하고, 파일을 수정한 후에 파일을 계속 재실행하여 테스트가 성공할 때까지 기다리는 매우 유용한 기능을 제공합니다. 따라서 수정한 내용을 확인한 후 pytest를 다시 시작할 필요가 없습니다. 모든 테스트가 통과될 때까지 이 과정을 반복한 후 다시 전체 실행이 이루어집니다. ```bash pip install pytest-xdist ``` 재귀적 모드의 사용: `pytest -f` 또는 `pytest --looponfail` 파일의 변경 사항은 `looponfailroots` 루트 디렉터리와 해당 내용을 (재귀적으로) 확인하여 감지됩니다. 이 값의 기본값이 작동하지 않는 경우, `setup.cfg`의 설정 옵션을 변경하여 프로젝트에서 변경할 수 있습니다: ```ini [tool:pytest] looponfailroots = transformers tests ``` 또는 `pytest.ini`/`tox.ini`` 파일: ```ini [pytest] looponfailroots = transformers tests ``` 이렇게 하면 ini-file의 디렉터리를 기준으로 상대적으로 지정된 각 디렉터리에서 파일 변경 사항만 찾게 됩니다. 이 기능을 대체할 수 있는 구현 방법인 [pytest-watch](https://github.com/joeyespo/pytest-watch)도 있습니다. ### 특정 테스트 모듈 건너뛰기[[skip-a-test-module]] 모든 테스트 모듈을 실행하되 특정 모듈을 제외하려면, 실행할 테스트 목록을 명시적으로 지정할 수 있습니다. 예를 들어, `test_modeling_*.py` 테스트를 제외한 모든 테스트를 실행하려면 다음을 사용할 수 있습니다: ```bash pytest *ls -1 tests/*py | grep -v test_modeling* ``` ### 상태 초기화[[clearing state]] CI 빌드 및 (속도에 대한) 격리가 중요한 경우, 캐시를 지워야 합니다: ```bash pytest --cache-clear tests ``` ### 테스트를 병렬로 실행[[running-tests-in-parallel]] 이전에 언급한 것처럼 `make test`는 테스트를 병렬로 실행하기 위해 `pytest-xdist` 플러그인(`-n X` 인수, 예를 들어 `-n 2`를 사용하여 2개의 병렬 작업 실행)을 통해 실행됩니다. `pytest-xdist`의 `--dist=` 옵션을 사용하여 테스트를 어떻게 그룹화할지 제어할 수 있습니다. `--dist=loadfile`은 하나의 파일에 있는 테스트를 동일한 프로세스로 그룹화합니다. 실행된 테스트의 순서가 다르고 예측할 수 없기 때문에, `pytest-xdist`로 테스트 스위트를 실행하면 실패가 발생할 수 있습니다 (검출되지 않은 결합된 테스트가 있는 경우). 이 경우 [pytest-replay](https://github.com/ESSS/pytest-replay)를 사용하면 동일한 순서로 테스트를 다시 실행해서 실패하는 시퀀스를 최소화하는 데에 도움이 됩니다. ### 테스트 순서와 반복[[test-order-and-repetition]] 잠재적인 종속성 및 상태 관련 버그(tear down)를 감지하기 위해 테스트를 여러 번, 연속으로, 무작위로 또는 세트로 반복하는 것이 좋습니다. 그리고 직접적인 여러 번의 반복은 DL의 무작위성에 의해 발견되는 일부 문제를 감지하는 데에도 유용합니다. #### 테스트를 반복[[repeat-tests]] - [pytest-flakefinder](https://github.com/dropbox/pytest-flakefinder): ```bash pip install pytest-flakefinder ``` 모든 테스트를 여러 번 실행합니다(기본값은 50번): ```bash pytest --flake-finder --flake-runs=5 tests/test_failing_test.py ``` <Tip> 이 플러그인은 `pytest-xdist`의 `-n` 플래그와 함께 작동하지 않습니다. </Tip> <Tip> `pytest-repeat`라는 또 다른 플러그인도 있지만 `unittest`와 함께 작동하지 않습니다. </Tip> #### 테스트를 임의의 순서로 실행[[run-tests-in-a-random-order]] ```bash pip install pytest-random-order ``` 중요: `pytest-random-order`가 설치되면 테스트가 자동으로 임의의 순서로 섞입니다. 구성 변경이나 커맨드 라인 옵션이 필요하지 않습니다. 앞서 설명한 것처럼 이를 통해 한 테스트의 상태가 다른 테스트의 상태에 영향을 미치는 결합된 테스트를 감지할 수 있습니다. `pytest-random-order`가 설치되면 해당 세션에서 사용된 랜덤 시드가 출력되며 예를 들어 다음과 같습니다: ```bash pytest tests [...] Using --random-order-bucket=module Using --random-order-seed=573663 ``` 따라서 특정 시퀀스가 실패하는 경우에는 정확한 시드를 추가하여 재현할 수 있습니다. 예를 들어 다음과 같습니다: ```bash pytest --random-order-seed=573663 [...] Using --random-order-bucket=module Using --random-order-seed=573663 ``` 정확히 동일한 테스트 목록(또는 목록이 없음)을 사용하는 경우에만 정확한 순서를 재현합니다. 목록을 수동으로 좁히기 시작하면 더 이상 시드에 의존할 수 없고 실패했던 정확한 순서로 수동으로 목록을 나열해야합니다. 그리고 `--random-order-bucket=none`을 사용하여 pytest에게 순서를 임의로 설정하지 않도록 알려야 합니다. 예를 들어 다음과 같습니다: ```bash pytest --random-order-bucket=none tests/test_a.py tests/test_c.py tests/test_b.py ``` 모든 테스트에 대해 섞기를 비활성화하려면 다음과 같습니다: ```bash pytest --random-order-bucket=none ``` 기본적으로 `--random-order-bucket=module`이 내재되어 있으므로, 모듈 수준에서 파일을 섞습니다. 또한 `class`, `package`, `global` 및 `none` 수준에서도 섞을 수 있습니다. 자세한 내용은 해당 [문서](https://github.com/jbasko/pytest-random-order)를 참조하세요. 또 다른 무작위화의 대안은 [`pytest-randomly`](https://github.com/pytest-dev/pytest-randomly)입니다. 이 모듈은 매우 유사한 기능/인터페이스를 가지고 있지만, `pytest-random-order`에 있는 버킷 모드를 사용할 수는 없습니다. 설치 후에는 자동으로 적용되는 문제도 동일하게 가집니다. ### 외관과 느낌을 변경[[look-and-feel-variations] #### pytest-sugar 사용[[pytest-sugar]] [pytest-sugar](https://github.com/Frozenball/pytest-sugar)는 테스트가 보여지는 형태를 개선하고, 진행 상황 바를 추가하며, 실패한 테스트와 검증을 즉시 표시하는 플러그인입니다. 설치하면 자동으로 활성화됩니다. ```bash pip install pytest-sugar ``` pytest-sugar 없이 테스트를 실행하려면 다음과 같습니다: ```bash pytest -p no:sugar ``` 또는 제거하세요. #### 각 하위 테스트 이름과 진행 상황 보고[[report-each-sub-test-name-and-its-progress]] `pytest`를 통해 단일 또는 그룹의 테스트를 실행하는 경우(`pip install pytest-pspec` 이후): ```bash pytest --pspec tests/test_optimization.py ``` #### 실패한 테스트 즉시 표시[[instantly-shows-failed-tests]] [pytest-instafail](https://github.com/pytest-dev/pytest-instafail)은 테스트 세션의 끝까지 기다리지 않고 실패 및 오류를 즉시 표시합니다. ```bash pip install pytest-instafail ``` ```bash pytest --instafail ``` ### GPU 사용 여부[[to-GPU-or-not-to-GPU]] GPU가 활성화된 환경에서, CPU 전용 모드로 테스트하려면 `CUDA_VISIBLE_DEVICES=""`를 추가합니다: ```bash CUDA_VISIBLE_DEVICES="" pytest tests/utils/test_logging.py ``` 또는 다중 GPU가 있는 경우 `pytest`에서 사용할 GPU를 지정할 수도 있습니다. 예를 들어, GPU `0` 및 `1`이 있는 경우 다음을 실행할 수 있습니다: ```bash CUDA_VISIBLE_DEVICES="1" pytest tests/utils/test_logging.py ``` 이렇게 하면 다른 GPU에서 다른 작업을 실행하려는 경우 유용합니다. 일부 테스트는 반드시 CPU 전용으로 실행해야 하며, 일부는 CPU 또는 GPU 또는 TPU에서 실행해야 하고, 일부는 여러 GPU에서 실행해야 합니다. 다음 스킵 데코레이터는 테스트의 요구 사항을 CPU/GPU/TPU별로 설정하는 데 사용됩니다: - `require_torch` - 이 테스트는 torch에서만 실행됩니다. - `require_torch_gpu` - `require_torch`에 추가로 적어도 1개의 GPU가 필요합니다. - `require_torch_multi_gpu` - `require_torch`에 추가로 적어도 2개의 GPU가 필요합니다. - `require_torch_non_multi_gpu` - `require_torch`에 추가로 0개 또는 1개의 GPU가 필요합니다. - `require_torch_up_to_2_gpus` - `require_torch`에 추가로 0개, 1개 또는 2개의 GPU가 필요합니다. - `require_torch_tpu` - `require_torch`에 추가로 적어도 1개의 TPU가 필요합니다. GPU 요구 사항을 표로 정리하면 아래와 같습니디ㅏ: | n gpus | decorator | |--------+--------------------------------| | `>= 0` | `@require_torch` | | `>= 1` | `@require_torch_gpu` | | `>= 2` | `@require_torch_multi_gpu` | | `< 2` | `@require_torch_non_multi_gpu` | | `< 3` | `@require_torch_up_to_2_gpus` | 예를 들어, 2개 이상의 GPU가 있고 pytorch가 설치되어 있을 때에만 실행되어야 하는 테스트는 다음과 같습니다: ```python no-style @require_torch_multi_gpu def test_example_with_multi_gpu(): ``` `tensorflow`가 필요한 경우 `require_tf` 데코레이터를 사용합니다. 예를 들어 다음과 같습니다: ```python no-style @require_tf def test_tf_thing_with_tensorflow(): ``` 이러한 데코레이터는 중첩될 수 있습니다. 예를 들어, 느린 테스트로 진행되고 pytorch에서 적어도 하나의 GPU가 필요한 경우 다음과 같이 설정할 수 있습니다: ```python no-style @require_torch_gpu @slow def test_example_slow_on_gpu(): ``` `@parametrized`와 같은 일부 데코레이터는 테스트 이름을 다시 작성하기 때문에 `@require_*` 스킵 데코레이터는 올바르게 작동하려면 항상 맨 마지막에 나열되어야 합니다. 다음은 올바른 사용 예입니다: ```python no-style @parameterized.expand(...) @require_torch_multi_gpu def test_integration_foo(): ``` `@pytest.mark.parametrize`에는 이러한 순서 문제는 없으므로 처음 혹은 마지막에 위치시킬 수 있고 이러한 경우에도 잘 작동할 것입니다. 하지만 unittest가 아닌 경우에만 작동합니다. 테스트 내부에서 다음을 사용할 수 있습니다: - 사용 가능한 GPU 수: ```python from transformers.testing_utils import get_gpu_count n_gpu = get_gpu_count() #torch와 tf와 함께 작동 ``` ### 분산 훈련[[distributed-training]] `pytest`는 분산 훈련을 직접적으로 다루지 못합니다. 이를 시도하면 하위 프로세스가 올바른 작업을 수행하지 않고 `pytest`라고 생각하기에 테스트 스위트를 반복해서 실행하게 됩니다. 그러나 일반 프로세스를 생성한 다음 여러 워커를 생성하고 IO 파이프를 관리하도록 하면 동작합니다. 다음은 사용 가능한 테스트입니다: - [test_trainer_distributed.py](https://github.com/huggingface/transformers/tree/main/tests/trainer/test_trainer_distributed.py) - [test_deepspeed.py](https://github.com/huggingface/transformers/tree/main/tests/deepspeed/test_deepspeed.py) 실행 지점으로 바로 이동하려면, 해당 테스트에서 `execute_subprocess_async` 호출을 검색하세요. 이러한 테스트를 실행하려면 적어도 2개의 GPU가 필요합니다. ```bash CUDA_VISIBLE_DEVICES=0,1 RUN_SLOW=1 pytest -sv tests/test_trainer_distributed.py ``` ### 출력 캡처[[output-capture]] 테스트 실행 중 `stdout` 및 `stderr`로 전송된 모든 출력이 캡처됩니다. 테스트나 설정 메소드가 실패하면 캡처된 출력은 일반적으로 실패 추적 정보와 함께 표시됩니다. 출력 캡처를 비활성화하고 `stdout` 및 `stderr`를 정상적으로 받으려면 `-s` 또는 `--capture=no`를 사용하세요: ```bash pytest -s tests/utils/test_logging.py ``` 테스트 결과를 JUnit 형식의 출력으로 보내려면 다음을 사용하세요: ```bash py.test tests --junitxml=result.xml ``` ### 색상 조절[[color-control]] 색상이 없게 하려면 다음과 같이 설정하세요(예를 들어 흰색 배경에 노란색 글씨는 가독성이 좋지 않습니다): ```bash pytest --color=no tests/utils/test_logging.py ``` ### online pastebin service에 테스트 보고서 전송[[sending test report to online pastebin service]] 각 테스트 실패에 대한 URL을 만듭니다: ```bash pytest --pastebin=failed tests/utils/test_logging.py ``` 이렇게 하면 각 실패에 대한 URL을 제공하는 remote Paste service에 테스트 실행 정보를 제출합니다. 일반적인 테스트를 선택할 수도 있고 혹은 특정 실패만 보내려면 `-x`와 같이 추가할 수도 있습니다. 전체 테스트 세션 로그에 대한 URL을 생성합니다: ```bash pytest --pastebin=all tests/utils/test_logging.py ``` ## 테스트 작성[[writing-tests]] 🤗 transformers 테스트는 대부분 `unittest`를 기반으로 하지만, `pytest`에서 실행되므로 대부분의 경우 두 시스템의 기능을 사용할 수 있습니다. 지원되는 기능에 대해 [여기](https://docs.pytest.org/en/stable/unittest.html)에서 확인할 수 있지만, 기억해야 할 중요한 점은 대부분의 `pytest` fixture가 작동하지 않는다는 것입니다. 파라미터화도 작동하지 않지만, 우리는 비슷한 방식으로 작동하는 `parameterized` 모듈을 사용합니다. ### 매개변수화[[parametrization]] 동일한 테스트를 다른 인수로 여러 번 실행해야 하는 경우가 종종 있습니다. 테스트 내에서 이 작업을 수행할 수 있지만, 그렇게 하면 하나의 인수 세트에 대해 테스트를 실행할 수 없습니다. ```python # test_this1.py import unittest from parameterized import parameterized class TestMathUnitTest(unittest.TestCase): @parameterized.expand( [ ("negative", -1.5, -2.0), ("integer", 1, 1.0), ("large fraction", 1.6, 1), ] ) def test_floor(self, name, input, expected): assert_equal(math.floor(input), expected) ``` 이제 기본적으로 이 테스트는 `test_floor`의 마지막 3개 인수가 매개변수 목록의 해당 인수에 할당되는 것으로 3번 실행될 것입니다. 그리고 `negative` 및 `integer` 매개변수 집합만 실행하려면 다음과 같이 실행할 수 있습니다: ```bash pytest -k "negative and integer" tests/test_mytest.py ``` 또는 `negative` 하위 테스트를 제외한 모든 서브 테스트를 다음과 같이 실행할 수 있습니다: ```bash pytest -k "not negative" tests/test_mytest.py ``` 앞에서 언급한 `-k` 필터를 사용하는 것 외에도, 각 서브 테스트의 정확한 이름을 확인한 후에 일부 혹은 전체 서브 테스트를 실행할 수 있습니다. ```bash pytest test_this1.py --collect-only -q ``` 그리고 다음의 내용을 확인할 수 있을 것입니다: ```bash test_this1.py::TestMathUnitTest::test_floor_0_negative test_this1.py::TestMathUnitTest::test_floor_1_integer test_this1.py::TestMathUnitTest::test_floor_2_large_fraction ``` 2개의 특정한 서브 테스트만 실행할 수도 있습니다: ```bash pytest test_this1.py::TestMathUnitTest::test_floor_0_negative test_this1.py::TestMathUnitTest::test_floor_1_integer ``` `transformers`의 개발자 종속성에 이미 있는 [parameterized](https://pypi.org/project/parameterized/) 모듈은 `unittests`와 `pytest` 테스트 모두에서 작동합니다. 그러나 테스트가 `unittest`가 아닌 경우 `pytest.mark.parametrize`를 사용할 수 있습니다(이미 있는 일부 테스트에서 사용되는 경우도 있습니다. 주로 `examples` 하위에 있습니다). 다음은 `pytest`의 `parametrize` 마커를 사용한 동일한 예입니다: ```python # test_this2.py import pytest @pytest.mark.parametrize( "name, input, expected", [ ("negative", -1.5, -2.0), ("integer", 1, 1.0), ("large fraction", 1.6, 1), ], ) def test_floor(name, input, expected): assert_equal(math.floor(input), expected) ``` `parameterized`와 마찬가지로 `pytest.mark.parametrize`를 사용하면 `-k` 필터가 작동하지 않는 경우에도 실행할 서브 테스트를 정확하게 지정할 수 있습니다. 단, 이 매개변수화 함수는 서브 테스트의 이름 집합을 약간 다르게 생성합니다. 다음과 같은 모습입니다: ```bash pytest test_this2.py --collect-only -q ``` 그리고 다음의 내용을 확인할 수 있을 것입니다: ```bash test_this2.py::test_floor[integer-1-1.0] test_this2.py::test_floor[negative--1.5--2.0] test_this2.py::test_floor[large fraction-1.6-1] ``` 특정한 테스트에 대해서만 실행할 수도 있습니다: ```bash pytest test_this2.py::test_floor[negative--1.5--2.0] test_this2.py::test_floor[integer-1-1.0] ``` 이전의 예시와 같이 실행할 수 있습니다. ### 파일 및 디렉터리[[files-and-directories]] 테스트에서 종종 현재 테스트 파일과 관련된 상대적인 위치를 알아야 하는 경우가 있습니다. 테스트가 여러 디렉터리에서 호출되거나 깊이가 다른 하위 디렉터리에 있을 수 있기 때문에 그 위치를 아는 것은 간단하지 않습니다. `transformers.test_utils.TestCasePlus`라는 헬퍼 클래스는 모든 기본 경로를 처리하고 간단한 액세서를 제공하여 이 문제를 해결합니다: - `pathlib` 객체(완전히 정해진 경로) - `test_file_path` - 현재 테스트 파일 경로 (예: `__file__`) - test_file_dir` - 현재 테스트 파일이 포함된 디렉터리 - tests_dir` - `tests` 테스트 스위트의 디렉터리 - examples_dir` - `examples` 테스트 스위트의 디렉터리 - repo_root_dir` - 저장소 디렉터리 - src_dir` - `src`의 디렉터리(예: `transformers` 하위 디렉터리가 있는 곳) - 문자열로 변환된 경로---위와 동일하지만, `pathlib` 객체가 아닌 문자열로 경로를 반환합니다: - `test_file_path_str` - `test_file_dir_str` - `tests_dir_str` - `examples_dir_str` - `repo_root_dir_str` - `src_dir_str` 위의 내용을 사용하려면 테스트가 'transformers.test_utils.TestCasePlus'의 서브클래스에 있는지 확인해야 합니다. 예를 들어 다음과 같습니다: ```python from transformers.testing_utils import TestCasePlus class PathExampleTest(TestCasePlus): def test_something_involving_local_locations(self): data_dir = self.tests_dir / "fixtures/tests_samples/wmt_en_ro" ``` 만약 `pathlib`를 통해 경로를 조작할 필요가 없거나 경로를 문자열로만 필요로 하는 경우에는 `pathlib` 객체에 `str()`을 호출하거나 `_str`로 끝나는 접근자를 사용할 수 있습니다. 예를 들어 다음과 같습니다: ```python from transformers.testing_utils import TestCasePlus class PathExampleTest(TestCasePlus): def test_something_involving_stringified_locations(self): examples_dir = self.examples_dir_str ``` ### 임시 파일 및 디렉터리[[temporary-files-and-directories]] 고유한 임시 파일 및 디렉터리를 사용하는 것은 병렬 테스트 실행에 있어 필수적입니다. 이렇게 함으로써 테스트들이 서로의 데이터를 덮어쓰지 않게 할 수 있습니다. 또한 우리는 생성된 테스트의 종료 단계에서 이러한 임시 파일 및 디렉터리를 제거하고 싶습니다. 따라서 이러한 요구 사항을 충족시켜주는 `tempfile`과 같은 패키지를 사용하는 것이 중요합니다. 그러나 테스트를 디버깅할 때는 임시 파일이나 디렉터리에 들어가는 내용을 확인할 수 있어야 하며, 재실행되는 각 테스트마다 임시 파일이나 디렉터리의 경로에 대해 무작위 값이 아닌 정확한 값을 알고 싶을 것입니다. `transformers.test_utils.TestCasePlus`라는 도우미 클래스는 이러한 목적에 가장 적합합니다. 이 클래스는 `unittest.TestCase`의 하위 클래스이므로, 우리는 이것을 테스트 모듈에서 쉽게 상속할 수 있습니다. 다음은 해당 클래스를 사용하는 예시입니다: ```python from transformers.testing_utils import TestCasePlus class ExamplesTests(TestCasePlus): def test_whatever(self): tmp_dir = self.get_auto_remove_tmp_dir() ``` 이 코드는 고유한 임시 디렉터리를 생성하고 `tmp_dir`을 해당 위치로 설정합니다. - 고유한 임시 디렉터리를 생성합니다: ```python def test_whatever(self): tmp_dir = self.get_auto_remove_tmp_dir() ``` `tmp_dir`에는 생성된 임시 디렉터리의 경로가 포함됩니다. 이는 테스트의 종료 단계에서 자동으로 제거됩니다. - 선택한 경로로 임시 디렉터리 생성 후에 테스트 시작 전에 비어 있는 상태인지 확인하고, 테스트 후에는 비우지 마세요. ```python def test_whatever(self): tmp_dir = self.get_auto_remove_tmp_dir("./xxx") ``` 이것은 디버깅할 때 특정 디렉터리를 모니터링하고, 그 디렉터리에 이전에 실행된 테스트가 데이터를 남기지 않도록 하는 데에 유용합니다. - `before` 및 `after` 인수를 직접 오버라이딩하여 기본 동작을 변경할 수 있으며 다음 중 하나의 동작으로 이어집니다: - `before=True`: 테스트 시작 시 임시 디렉터리가 항상 지워집니다. - `before=False`: 임시 디렉터리가 이미 존재하는 경우 기존 파일은 그대로 남습니다. - `after=True`: 테스트 종료 시 임시 디렉터리가 항상 삭제됩니다. - `after=False`: 테스트 종료 시 임시 디렉터리가 항상 그대로 유지됩니다. <Tip> `rm -r`에 해당하는 명령을 안전하게 실행하기 위해, 명시적인 `tmp_dir`을 사용하는 경우 프로젝트 저장소 체크 아웃의 하위 디렉터리만 허용됩니다. 따라서 실수로 `/tmp`가 아닌 중요한 파일 시스템의 일부가 삭제되지 않도록 항상 `./`로 시작하는 경로를 전달해야 합니다. </Tip> <Tip> 각 테스트는 여러 개의 임시 디렉터리를 등록할 수 있으며, 별도로 요청하지 않는 한 모두 자동으로 제거됩니다. </Tip> ### 임시 sys.path 오버라이드[[temporary-sys.path-override]] `sys.path`를 다른 테스트로 임시로 오버라이드하기 위해 예를 들어 `ExtendSysPath` 컨텍스트 관리자를 사용할 수 있습니다. 예를 들어 다음과 같습니다: ```python import os from transformers.testing_utils import ExtendSysPath bindir = os.path.abspath(os.path.dirname(__file__)) with ExtendSysPath(f"{bindir}/.."): from test_trainer import TrainerIntegrationCommon # noqa ``` ### 테스트 건너뛰기[[skipping-tests]] 이것은 버그가 발견되어 새로운 테스트가 작성되었지만 아직 그 버그가 수정되지 않은 경우에 유용합니다. 이 테스트를 주 저장소에 커밋하려면 `make test` 중에 건너뛰도록 해야 합니다. 방법: - **skip**은 테스트가 일부 조건이 충족될 경우에만 통과될 것으로 예상되고, 그렇지 않으면 pytest가 전체 테스트를 건너뛰어야 함을 의미합니다. 일반적인 예로는 Windows가 아닌 플랫폼에서 Windows 전용 테스트를 건너뛰거나 외부 리소스(예를 들어 데이터베이스)에 의존하는 테스트를 건너뛰는 것이 있습니다. - **xfail**은 테스트가 특정한 이유로 인해 실패할 것으로 예상하는 것을 의미합니다. 일반적인 예로는 아직 구현되지 않은 기능이나 아직 수정되지 않은 버그의 테스트가 있습니다. `xfail`로 표시된 테스트가 예상대로 실패하지 않고 통과된 경우, 이것은 xpass이며 테스트 결과 요약에 기록됩니다. 두 가지 중요한 차이점 중 하나는 `skip`은 테스트를 실행하지 않지만 `xfail`은 실행한다는 것입니다. 따라서 오류가 있는 코드가 일부 테스트에 영향을 미칠 수 있는 경우 `xfail`을 사용하지 마세요. #### 구현[[implementation]] - 전체 테스트를 무조건 건너뛰려면 다음과 같이 할 수 있습니다: ```python no-style @unittest.skip("this bug needs to be fixed") def test_feature_x(): ``` 또는 pytest를 통해: ```python no-style @pytest.mark.skip(reason="this bug needs to be fixed") ``` 또는 `xfail` 방식으로: ```python no-style @pytest.mark.xfail def test_feature_x(): ``` - 테스트 내부에서 내부 확인에 따라 테스트를 건너뛰는 방법은 다음과 같습니다: ```python def test_feature_x(): if not has_something(): pytest.skip("unsupported configuration") ``` 또는 모듈 전체: ```python import pytest if not pytest.config.getoption("--custom-flag"): pytest.skip("--custom-flag is missing, skipping tests", allow_module_level=True) ``` 또는 `xfail` 방식으로: ```python def test_feature_x(): pytest.xfail("expected to fail until bug XYZ is fixed") ``` - import가 missing된 모듈이 있을 때 그 모듈의 모든 테스트를 건너뛰는 방법: ```python docutils = pytest.importorskip("docutils", minversion="0.3") ``` - 조건에 따라 테스트를 건너뛰는 방법: ```python no-style @pytest.mark.skipif(sys.version_info < (3,6), reason="requires python3.6 or higher") def test_feature_x(): ``` 또는: ```python no-style @unittest.skipIf(torch_device == "cpu", "Can't do half precision") def test_feature_x(): ``` 또는 모듈 전체를 건너뛰는 방법: ```python no-style @pytest.mark.skipif(sys.platform == 'win32', reason="does not run on windows") class TestClass(): def test_feature_x(self): ``` 보다 자세한 예제 및 방법은 [여기](https://docs.pytest.org/en/latest/skipping.html)에서 확인할 수 있습니다. ### 느린 테스트[[slow-tests]] 테스트 라이브러리는 지속적으로 확장되고 있으며, 일부 테스트는 실행하는 데 몇 분이 걸립니다. 그리고 우리에게는 테스트 스위트가 CI를 통해 완료되기까지 한 시간을 기다릴 여유가 없습니다. 따라서 필수 테스트를 위한 일부 예외를 제외하고 느린 테스트는 다음과 같이 표시해야 합니다. ```python no-style from transformers.testing_utils import slow @slow def test_integration_foo(): ``` `@slow`로 표시된 테스트를 실행하려면 `RUN_SLOW=1` 환경 변수를 설정하세요. 예를 들어 다음과 같습니다: ```bash RUN_SLOW=1 pytest tests ``` `@parameterized`와 같은 몇 가지 데코레이터는 테스트 이름을 다시 작성합니다. 그러므로 `@slow`와 나머지 건너뛰기 데코레이터 `@require_*`가 올바르게 작동되려면 마지막에 나열되어야 합니다. 다음은 올바른 사용 예입니다. ```python no-style @parameterized.expand(...) @slow def test_integration_foo(): ``` 이 문서의 초반부에 설명된 것처럼 느린 테스트는 PR의 CI 확인이 아닌 예약된 일정 기반으로 실행됩니다. 따라서 PR 제출 중에 일부 문제를 놓친 채로 병합될 수 있습니다. 이러한 문제들은 다음번의 예정된 CI 작업 중에 감지됩니다. 하지만 PR을 제출하기 전에 자신의 컴퓨터에서 느린 테스트를 실행하는 것 또한 중요합니다. 느린 테스트로 표시해야 하는지 여부를 결정하는 대략적인 결정 기준은 다음과 같습니다. 만약 테스트가 라이브러리의 내부 구성 요소 중 하나에 집중되어 있다면(예: 모델링 파일, 토큰화 파일, 파이프라인), 해당 테스트를 느린 테스트 스위트에서 실행해야 합니다. 만약 라이브러리의 다른 측면(예: 문서 또는 예제)에 집중되어 있다면, 해당 테스트를 느린 테스트 스위트에서 실행해야 합니다. 그리고 이 접근 방식을 보완하기 위해 예외를 만들어야 합니다. - 무거운 가중치 세트나 50MB보다 큰 데이터셋을 다운로드해야 하는 모든 테스트(예: 모델 통합 테스트, 토크나이저 통합 테스트, 파이프라인 통합 테스트)를 느린 테스트로 설정해야 합니다. 새로운 모델을 추가하는 경우 통합 테스트용으로 무작위 가중치로 작은 버전을 만들어 허브에 업로드해야 합니다. 이 내용은 아래 단락에서 설명됩니다. - 특별히 빠르게 실행되도록 최적화되지 않은 학습을 수행해야 하는 테스트는 느린 테스트로 설정해야 합니다. - 느리지 않아야 할 테스트 중 일부가 극도로 느린 경우 예외를 도입하고 이를 `@slow`로 설정할 수 있습니다. 대용량 파일을 디스크에 저장하고 불러오는 자동 모델링 테스트는 `@slow`으로 표시된 테스트의 좋은 예입니다. - CI에서 1초 이내에 테스트가 완료되는 경우(다운로드 포함)에는 느린 테스트가 아니어야 합니다. 느린 테스트가 아닌 경우에는 다양한 내부를 완전히 커버하면서 빠르게 유지되어야 합니다. 예를 들어, 무작위 가중치를 사용하여 특별히 생성된 작은 모델로 테스트하면 상당한 커버리지를 얻을 수 있습니다. 이러한 모델은 최소한의 레이어 수(예: 2), 어휘 크기(예: 1000) 등의 요소만 가집니다. 그런 다음 `@slow` 테스트는 대형 느린 모델을 사용하여 정성적인 테스트를 수행할 수 있습니다. 이러한 작은 모델을 사용하는 방법을 확인하려면 다음과 같이 *tiny* 모델을 찾아보세요. ```bash grep tiny tests examples ``` 다음은 작은 모델[stas/tiny-wmt19-en-de](https://huggingface.co/stas/tiny-wmt19-en-de)을 만든 [script](https://github.com/huggingface/transformers/tree/main/scripts/fsmt/fsmt-make-tiny-model.py) 예시입니다. 특정 모델의 아키텍처에 맞게 쉽게 조정할 수 있습니다. 예를 들어 대용량 모델을 다운로드하는 경우 런타임을 잘못 측정하기 쉽지만, 로컬에서 테스트하면 다운로드한 파일이 캐시되어 다운로드 시간이 측정되지 않습니다. 대신 CI 로그의 실행 속도 보고서를 확인하세요(`pytest --durations=0 tests`의 출력). 이 보고서는 느린 이상값으로 표시되지 않거나 빠르게 다시 작성해야 하는 느린 이상값을 찾는 데도 유용합니다. CI에서 테스트 스위트가 느려지기 시작하면 이 보고서의 맨 위 목록에 가장 느린 테스트가 표시됩니다. ### stdout/stderr 출력 테스트[[testing-the-stdout/stderr-output]] `stdout` 및/또는 `stderr`로 쓰는 함수를 테스트하려면 `pytest`의 [capsys 시스템](https://docs.pytest.org/en/latest/capture.html)을 사용하여 해당 스트림에 액세스할 수 있습니다. 다음과 같이 수행할 수 있습니다. ```python import sys def print_to_stdout(s): print(s) def print_to_stderr(s): sys.stderr.write(s) def test_result_and_stdout(capsys): msg = "Hello" print_to_stdout(msg) print_to_stderr(msg) out, err = capsys.readouterr() # 캡처된 출력 스트림 사용 # 선택 사항: 캡처된 스트림 재생성 sys.stdout.write(out) sys.stderr.write(err) # 테스트: assert msg in out assert msg in err ``` 그리고, 물론 대부분의 경우에는 `stderr`는 예외의 일부로 제공됩니다. 그러므로 해당 경우에는 try/except를 사용해야 합니다. ```python def raise_exception(msg): raise ValueError(msg) def test_something_exception(): msg = "Not a good value" error = "" try: raise_exception(msg) except Exception as e: error = str(e) assert msg in error, f"{msg} is in the exception:\n{error}" ``` `stdout`를 캡처하는 또 다른 방법은 `contextlib.redirect_stdout`를 사용하는 것입니다. ```python from io import StringIO from contextlib import redirect_stdout def print_to_stdout(s): print(s) def test_result_and_stdout(): msg = "Hello" buffer = StringIO() with redirect_stdout(buffer): print_to_stdout(msg) out = buffer.getvalue() # 선택 사항: 캡처된 스트림 재생성 sys.stdout.write(out) # 테스트: assert msg in out ``` `stdout` 캡처에 관련된 중요한 문제 중 하나는 보통 `print`에서 이전에 인쇄된 내용을 재설정하는 `\r` 문자가 포함될 수 있다는 것입니다. `pytest`에서는 문제가 없지만 `pytest -s`에서는 이러한 문자가 버퍼에 포함되므로 `-s`가 있거나 없는 상태에서 태스트를 수행할 수 있으려면 캡처된 출력에 대해 추가적인 정리가 필요합니다. 이 경우에는 `re.sub(r'~.*\r', '', buf, 0, re.M)`을 사용할 수 있습니다. 하지만 도우미 컨텍스트 관리자 래퍼를 사용하면 출력에 `\r`이 포함되어 있는지의 여부에 관계없이 모든 것을 자동으로 처리하므로 편리합니다. ```python from transformers.testing_utils import CaptureStdout with CaptureStdout() as cs: function_that_writes_to_stdout() print(cs.out) ``` 다음은 전체 테스트 예제입니다. ```python from transformers.testing_utils import CaptureStdout msg = "Secret message\r" final = "Hello World" with CaptureStdout() as cs: print(msg + final) assert cs.out == final + "\n", f"captured: {cs.out}, expecting {final}" ``` `stderr`를 캡처하고 싶다면, 대신 `CaptureStderr` 클래스를 사용하세요. ```python from transformers.testing_utils import CaptureStderr with CaptureStderr() as cs: function_that_writes_to_stderr() print(cs.err) ``` 두 스트림을 동시에 캡처해야 한다면, 부모 `CaptureStd` 클래스를 사용하세요. ```python from transformers.testing_utils import CaptureStd with CaptureStd() as cs: function_that_writes_to_stdout_and_stderr() print(cs.err, cs.out) ``` 또한, 테스트의 디버깅을 지원하기 위해 이러한 컨텍스트 관리자는 기본적으로 컨텍스트에서 종료할 때 캡처된 스트림을 자동으로 다시 실행합니다. ### 로거 스트림 캡처[[capturing-logger-stream]] 로거 출력을 검증해야 하는 경우 `CaptureLogger`를 사용할 수 있습니다. ```python from transformers import logging from transformers.testing_utils import CaptureLogger msg = "Testing 1, 2, 3" logging.set_verbosity_info() logger = logging.get_logger("transformers.models.bart.tokenization_bart") with CaptureLogger(logger) as cl: logger.info(msg) assert cl.out, msg + "\n" ``` ### 환경 변수를 이용하여 테스트[[testing-with-environment-variables]] 특정 테스트의 환경 변수 영향을 검증하려면 `transformers.testing_utils.mockenv`라는 도우미 데코레이터를 사용할 수 있습니다. ```python from transformers.testing_utils import mockenv class HfArgumentParserTest(unittest.TestCase): @mockenv(TRANSFORMERS_VERBOSITY="error") def test_env_override(self): env_level_str = os.getenv("TRANSFORMERS_VERBOSITY", None) ``` 일부 경우에는 외부 프로그램을 호출해야할 수도 있는데, 이 때에는 여러 개의 로컬 경로를 포함하는 `os.environ`에서 `PYTHONPATH`의 설정이 필요합니다. 헬퍼 클래스 `transformers.test_utils.TestCasePlus`가 도움이 됩니다: ```python from transformers.testing_utils import TestCasePlus class EnvExampleTest(TestCasePlus): def test_external_prog(self): env = self.get_env() # 이제 `env`를 사용하여 외부 프로그램 호출 ``` 테스트 파일이 `tests` 테스트 스위트 또는 `examples`에 있는지에 따라 `env[PYTHONPATH]`가 두 디렉터리 중 하나를 포함하도록 설정되며, 현재 저장소에 대해 테스트가 수행되도록 `src` 디렉터리도 포함됩니다. 테스트 호출 이전에 설정된 경우에는 `env[PYTHONPATH]`를 그대로 사용합니다. 이 헬퍼 메소드는 `os.environ` 객체의 사본을 생성하므로 원본은 그대로 유지됩니다. ### 재현 가능한 결과 얻기[[getting-reproducible-results]] 일부 상황에서 테스트에서 임의성을 제거하여 동일하게 재현 가능한 결과를 얻고 싶을 수 있습니다. 이를 위해서는 다음과 같이 시드를 고정해야 합니다. ```python seed = 42 # 파이썬 RNG import random random.seed(seed) # 파이토치 RNG import torch torch.manual_seed(seed) torch.backends.cudnn.deterministic = True if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) # 넘파이 RNG import numpy as np np.random.seed(seed) # 텐서플로 RNG tf.random.set_seed(seed) ``` ### 테스트 디버깅[[debugging tests]] 경고가 있는 곳에서 디버거를 시작하려면 다음을 수행하세요. ```bash pytest tests/utils/test_logging.py -W error::UserWarning --pdb ``` ## Github Actions 워크플로우 작업 처리[[working-with-github-actions-workflows]] 셀프 푸시 워크플로우 CI 작업을 트리거하려면, 다음을 수행해야 합니다. 1. `transformers` 원본에서 새 브랜치를 만듭니다(포크가 아닙니다!). 2. 브랜치 이름은 `ci_` 또는 `ci-`로 시작해야 합니다(`main`도 트리거하지만 `main`에서는 PR을 할 수 없습니다). 또한 특정 경로에 대해서만 트리거되므로 이 문서가 작성된 후에 변경된 내용은 [여기](https://github.com/huggingface/transformers/blob/main/.github/workflows/self-push.yml)의 *push:*에서 확인할 수 있습니다. 3. 이 브랜치에서 PR을 생성합니다 4. 그런 다음 [여기](https://github.com/huggingface/transformers/actions/workflows/self-push.yml)에서 작업이 나타나는지 확인할 수 있습니다. 백로그가 있는 경우, 바로 실행되지 않을 수도 있습니다. ## 실험적인 CI 기능 테스트[[testing-Experimental-CI-Features]] CI 기능을 테스트하는 것은 일반 CI 작동에 방해가 될 수 있기 때문에 잠재적으로 문제가 발생할 수 있습니다. 따라서 새로운 CI 기능을 추가하는 경우 다음과 같이 수행해야 합니다. 1. 테스트해야 할 내용을 테스트하는 새로운 전용 작업을 생성합니다. 2. 새로운 작업은 항상 성공해야만 녹색 ✓를 받을 수 있습니다(아래에 자세한 내용이 있습니다). 3. 다양한 PR 유형에 대한 확인을 위해 (사용자 포크 브랜치, 포크되지 않은 브랜치, github.com UI 직접 파일 편집에서 생성된 브랜치, 강제 푸시 등 PR의 유형은 아주 다양합니다.) 며칠 동안 실험 작업의 로그를 모니터링하면서 실행해봅니다. (의도적으로 항상 녹색을 표시하므로 작업 전체가 녹색은 아니라는 점에 유의합니다.) 4. 모든 것이 안정적인지 확인한 후, 새로운 변경 사항을 기존 작업에 병합합니다. 이렇게 하면 CI 기능 자체에 대한 실험이 일반 작업 흐름에 방해가 되지 않습니다. 그러나 새로운 CI 기능이 개발 중인 동안, 항상 성공하도록 할 수 있는 방법은 무엇일까요? TravisCI와 같은 일부 CI는 `ignore-step-failure`를 지원하며 전체 작업을 성공한 것으로 보고하지만, 현재 우리가 사용하는 CircleCI와 Github Actions는 이를 지원하지 않습니다. 따라서 다음과 같은 해결책을 사용할 수 있습니다. 1. bash 스크립트에서 가능한 많은 오류를 억제하기 위해 실행 명령의 시작 부분에 `set +euo pipefail`을 추가합니다. 2. 마지막 명령은 반드시 성공해야 합니다. `echo "done"` 또는 `true`를 사용하면 됩니다. 예시는 다음과 같습니다. ```yaml - run: name: run CI experiment command: | set +euo pipefail echo "setting run-all-despite-any-errors-mode" this_command_will_fail echo "but bash continues to run" # emulate another failure false # but the last command must be a success echo "during experiment do not remove: reporting success to CI, even if there were failures" ``` 간단한 명령의 경우 다음과 같이 수행할 수도 있습니다. ```bash cmd_that_may_fail || true ``` 결과에 만족한 후에는 물론, 실험적인 단계 또는 작업을 일반 작업의 나머지 부분과 통합하면서 `set +euo pipefail` 또는 기타 추가한 요소를 제거하여 실험 작업이 일반 CI 작동에 방해되지 않도록 해야 합니다. 이 전반적인 과정은 실험 단계가 PR의 전반적인 상태에 영향을 주지 않고 실패하도록 `allow-failure`와 같은 기능을 설정할 수 있다면 훨씬 더 쉬웠을 것입니다. 그러나 앞에서 언급한 바와 같이 CircleCI와 Github Actions는 현재 이러한 기능들 지원하지 않습니다. 이 기능의 지원을 위한 투표에 참여하고 CI 관련 스레드들에서 이러한 상황을 확인할 수도 있습니다. - [Github Actions:](https://github.com/actions/toolkit/issues/399) - [CircleCI:](https://ideas.circleci.com/ideas/CCI-I-344)

transformers/docs/source/ko/testing.md/0

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# Usando os Tokenizers do 🤗 Tokenizers O [`PreTrainedTokenizerFast`] depende da biblioteca [🤗 Tokenizers](https://huggingface.co/docs/tokenizers). O Tokenizer obtido da biblioteca 🤗 Tokenizers pode ser carregado facilmente pelo 🤗 Transformers. Antes de entrar nos detalhes, vamos começar criando um tokenizer fictício em algumas linhas: ```python >>> from tokenizers import Tokenizer >>> from tokenizers.models import BPE >>> from tokenizers.trainers import BpeTrainer >>> from tokenizers.pre_tokenizers import Whitespace >>> tokenizer = Tokenizer(BPE(unk_token="[UNK]")) >>> trainer = BpeTrainer(special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"]) >>> tokenizer.pre_tokenizer = Whitespace() >>> files = [...] >>> tokenizer.train(files, trainer) ``` Agora temos um tokenizer treinado nos arquivos que foram definidos. Nós podemos continuar usando nessa execução ou salvar em um arquivo JSON para re-utilizar no futuro. ## Carregando diretamente de um objeto tokenizer Vamos ver como aproveitar esse objeto tokenizer na biblioteca 🤗 Transformers. A classe [`PreTrainedTokenizerFast`] permite uma instanciação fácil, aceitando o objeto *tokenizer* instanciado como um argumento: ```python >>> from transformers import PreTrainedTokenizerFast >>> fast_tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer) ``` Esse objeto pode ser utilizado com todos os métodos compartilhados pelos tokenizers dos 🤗 Transformers! Vá para [a página do tokenizer](main_classes/tokenizer) para mais informações. ## Carregando de um arquivo JSON Para carregar um tokenizer de um arquivo JSON vamos primeiro começar salvando nosso tokenizer: ```python >>> tokenizer.save("tokenizer.json") ``` A pasta para qual salvamos esse arquivo pode ser passada para o método de inicialização do [`PreTrainedTokenizerFast`] usando o `tokenizer_file` parâmetro: ```python >>> from transformers import PreTrainedTokenizerFast >>> fast_tokenizer = PreTrainedTokenizerFast(tokenizer_file="tokenizer.json") ``` Esse objeto pode ser utilizado com todos os métodos compartilhados pelos tokenizers dos 🤗 Transformers! Vá para [a página do tokenizer](main_classes/tokenizer) para mais informações.

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- sections: - local: index title: 🤗 Transformers 简介 - local: quicktour title: 快速上手 - local: installation title: 安装 title: 开始使用 - sections: - local: pipeline_tutorial title: 使用pipelines进行推理 - local: autoclass_tutorial title: 使用AutoClass编写可移植的代码 - local: preprocessing title: 预处理数据 - local: training title: 微调预训练模型 - local: run_scripts title: 通过脚本训练模型 - local: accelerate title: 使用🤗Accelerate进行分布式训练 - local: peft title: 使用🤗 PEFT加载和训练adapters - local: model_sharing title: 分享您的模型 - local: transformers_agents title: agents教程 - local: llm_tutorial title: 使用LLMs进行生成 title: 教程 - sections: - local: fast_tokenizers title: 使用 🤗 Tokenizers 中的分词器 - local: multilingual title: 使用多语言模型进行推理 - local: create_a_model title: 使用特定于模型的 API - local: custom_models title: 共享自定义模型 - local: serialization title: 导出为 ONNX - local: tflite title: 导出为 TFLite title: 开发者指南 - sections: - local: performance title: 综述 - sections: - local: perf_hardware title: 用于训练的定制硬件 - local: hpo_train title: 使用Trainer API 进行超参数搜索 title: 高效训练技术 - local: big_models title: 实例化大模型 - local: debugging title: 问题定位及解决 - local: tf_xla title: TensorFlow模型的XLA集成 - local: perf_torch_compile title: 使用 `torch.compile()` 优化推理 title: 性能和可扩展性 - sections: - local: contributing title: 如何为 🤗 Transformers 做贡献？ title: 贡献 - sections: - local: task_summary title: 🤗Transformers能做什么 - local: tokenizer_summary title: 分词器的摘要 title: 概念指南 - sections: - sections: - local: main_classes/agent title: Agents和工具 - local: main_classes/callback title: Callbacks - local: main_classes/configuration title: Configuration - local: main_classes/data_collator title: Data Collator - local: main_classes/keras_callbacks title: Keras callbacks - local: main_classes/logging title: Logging - local: main_classes/model title: 模型 - local: main_classes/text_generation title: 文本生成 - local: main_classes/onnx title: ONNX - local: main_classes/optimizer_schedules title: Optimization - local: main_classes/output title: 模型输出 - local: main_classes/pipelines title: Pipelines - local: main_classes/processors title: Processors - local: main_classes/quantization title: Quantization - local: main_classes/tokenizer title: Tokenizer - local: main_classes/trainer title: Trainer - local: main_classes/deepspeed title: DeepSpeed集成 - local: main_classes/feature_extractor title: Feature Extractor - local: main_classes/image_processor title: Image Processor title: 主要类 - sections: - local: internal/modeling_utils title: 自定义层和工具 - local: internal/pipelines_utils title: pipelines工具 - local: internal/tokenization_utils title: Tokenizers工具 - local: internal/trainer_utils title: 训练器工具 - local: internal/generation_utils title: 生成工具 - local: internal/image_processing_utils title: 图像处理工具 - local: internal/audio_utils title: 音频处理工具 - local: internal/file_utils title: 通用工具 - local: internal/time_series_utils title: 时序数据工具 title: 内部辅助工具 title: 应用程序接口 (API)

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# 自定义层和工具此页面列出了库使用的所有自定义层，以及它为模型提供的实用函数。其中大多数只有在您研究库中模型的代码时才有用。 ## Pytorch自定义模块 [[autodoc]] pytorch_utils.Conv1D [[autodoc]] modeling_utils.PoolerStartLogits - forward [[autodoc]] modeling_utils.PoolerEndLogits - forward [[autodoc]] modeling_utils.PoolerAnswerClass - forward [[autodoc]] modeling_utils.SquadHeadOutput [[autodoc]] modeling_utils.SQuADHead - forward [[autodoc]] modeling_utils.SequenceSummary - forward ## PyTorch帮助函数 [[autodoc]] pytorch_utils.apply_chunking_to_forward [[autodoc]] pytorch_utils.find_pruneable_heads_and_indices [[autodoc]] pytorch_utils.prune_layer [[autodoc]] pytorch_utils.prune_conv1d_layer [[autodoc]] pytorch_utils.prune_linear_layer ## TensorFlow自定义层 [[autodoc]] modeling_tf_utils.TFConv1D [[autodoc]] modeling_tf_utils.TFSequenceSummary ## TensorFlow loss 函数 [[autodoc]] modeling_tf_utils.TFCausalLanguageModelingLoss [[autodoc]] modeling_tf_utils.TFMaskedLanguageModelingLoss [[autodoc]] modeling_tf_utils.TFMultipleChoiceLoss [[autodoc]] modeling_tf_utils.TFQuestionAnsweringLoss [[autodoc]] modeling_tf_utils.TFSequenceClassificationLoss [[autodoc]] modeling_tf_utils.TFTokenClassificationLoss ## TensorFlow帮助函数 [[autodoc]] modeling_tf_utils.get_initializer [[autodoc]] modeling_tf_utils.keras_serializable [[autodoc]] modeling_tf_utils.shape_list

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# 预处理 [[open-in-colab]] 在您可以在数据集上训练模型之前，数据需要被预处理为期望的模型输入格式。无论您的数据是文本、图像还是音频，它们都需要被转换并组合成批量的张量。🤗 Transformers 提供了一组预处理类来帮助准备数据以供模型使用。在本教程中，您将了解以下内容： * 对于文本，使用[分词器](./main_classes/tokenizer)(`Tokenizer`)将文本转换为一系列标记(`tokens`)，并创建`tokens`的数字表示，将它们组合成张量。 * 对于语音和音频，使用[特征提取器](./main_classes/feature_extractor)(`Feature extractor`)从音频波形中提取顺序特征并将其转换为张量。 * 图像输入使用[图像处理器](./main_classes/image)(`ImageProcessor`)将图像转换为张量。 * 多模态输入，使用[处理器](./main_classes/processors)(`Processor`)结合了`Tokenizer`和`ImageProcessor`或`Processor`。 <Tip> `AutoProcessor` **始终**有效的自动选择适用于您使用的模型的正确`class`，无论您使用的是`Tokenizer`、`ImageProcessor`、`Feature extractor`还是`Processor`。 </Tip> 在开始之前，请安装🤗 Datasets，以便您可以加载一些数据集来进行实验： ```bash pip install datasets ``` ## 自然语言处理 <Youtube id="Yffk5aydLzg"/> 处理文本数据的主要工具是[Tokenizer](main_classes/tokenizer)。`Tokenizer`根据一组规则将文本拆分为`tokens`。然后将这些`tokens`转换为数字，然后转换为张量，成为模型的输入。模型所需的任何附加输入都由`Tokenizer`添加。 <Tip> 如果您计划使用预训练模型，重要的是使用与之关联的预训练`Tokenizer`。这确保文本的拆分方式与预训练语料库相同，并在预训练期间使用相同的标记-索引的对应关系（通常称为*词汇表*-`vocab`）。 </Tip> 开始使用[`AutoTokenizer.from_pretrained`]方法加载一个预训练`tokenizer`。这将下载模型预训练的`vocab`： ```py >>> from transformers import AutoTokenizer >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased") ``` 然后将您的文本传递给`tokenizer`： ```py >>> encoded_input = tokenizer("Do not meddle in the affairs of wizards, for they are subtle and quick to anger.") >>> print(encoded_input) {'input_ids': [101, 2079, 2025, 19960, 10362, 1999, 1996, 3821, 1997, 16657, 1010, 2005, 2027, 2024, 11259, 1998, 4248, 2000, 4963, 1012, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]} ``` `tokenizer`返回一个包含三个重要对象的字典： * [input_ids](glossary#input-ids) 是与句子中每个`token`对应的索引。 * [attention_mask](glossary#attention-mask) 指示是否应该关注一个`token`。 * [token_type_ids](glossary#token-type-ids) 在存在多个序列时标识一个`token`属于哪个序列。通过解码 `input_ids` 来返回您的输入： ```py >>> tokenizer.decode(encoded_input["input_ids"]) '[CLS] Do not meddle in the affairs of wizards, for they are subtle and quick to anger. [SEP]' ``` 如您所见，`tokenizer`向句子中添加了两个特殊`token` - `CLS` 和 `SEP`（分类器和分隔符）。并非所有模型都需要特殊`token`，但如果需要，`tokenizer`会自动为您添加。如果有多个句子需要预处理，将它们作为列表传递给`tokenizer`： ```py >>> batch_sentences = [ ... "But what about second breakfast?", ... "Don't think he knows about second breakfast, Pip.", ... "What about elevensies?", ... ] >>> encoded_inputs = tokenizer(batch_sentences) >>> print(encoded_inputs) {'input_ids': [[101, 1252, 1184, 1164, 1248, 6462, 136, 102], [101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102], [101, 1327, 1164, 5450, 23434, 136, 102]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1]]} ``` ### 填充句子的长度并不总是相同，这可能会成为一个问题，因为模型输入的张量需要具有统一的形状。填充是一种策略，通过在较短的句子中添加一个特殊的`padding token`，以确保张量是矩形的。将 `padding` 参数设置为 `True`，以使批次中较短的序列填充到与最长序列相匹配的长度： ```py >>> batch_sentences = [ ... "But what about second breakfast?", ... "Don't think he knows about second breakfast, Pip.", ... "What about elevensies?", ... ] >>> encoded_input = tokenizer(batch_sentences, padding=True) >>> print(encoded_input) {'input_ids': [[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0], [101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102], [101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]]} ``` 第一句和第三句因为较短，通过`0`进行填充，。 ### 截断另一方面，有时候一个序列可能对模型来说太长了。在这种情况下，您需要将序列截断为更短的长度。将 `truncation` 参数设置为 `True`，以将序列截断为模型接受的最大长度： ```py >>> batch_sentences = [ ... "But what about second breakfast?", ... "Don't think he knows about second breakfast, Pip.", ... "What about elevensies?", ... ] >>> encoded_input = tokenizer(batch_sentences, padding=True, truncation=True) >>> print(encoded_input) {'input_ids': [[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0], [101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102], [101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]]} ``` <Tip> 查看[填充和截断](./pad_truncation)概念指南，了解更多有关填充和截断参数的信息。 </Tip> ### 构建张量最后，`tokenizer`可以返回实际输入到模型的张量。将 `return_tensors` 参数设置为 `pt`（对于PyTorch）或 `tf`（对于TensorFlow）： <frameworkcontent> <pt> ```py >>> batch_sentences = [ ... "But what about second breakfast?", ... "Don't think he knows about second breakfast, Pip.", ... "What about elevensies?", ... ] >>> encoded_input = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="pt") >>> print(encoded_input) {'input_ids': tensor([[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0], [101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102], [101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]])} ``` </pt> <tf> ```py >>> batch_sentences = [ ... "But what about second breakfast?", ... "Don't think he knows about second breakfast, Pip.", ... "What about elevensies?", ... ] >>> encoded_input = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="tf") >>> print(encoded_input) {'input_ids': <tf.Tensor: shape=(2, 9), dtype=int32, numpy= array([[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0], [101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102], [101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)>, 'token_type_ids': <tf.Tensor: shape=(2, 9), dtype=int32, numpy= array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)>, 'attention_mask': <tf.Tensor: shape=(2, 9), dtype=int32, numpy= array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)>} ``` </tf> </frameworkcontent> ## 音频对于音频任务，您需要[feature extractor](main_classes/feature_extractor)来准备您的数据集以供模型使用。`feature extractor`旨在从原始音频数据中提取特征，并将它们转换为张量。加载[MInDS-14](https://huggingface.co/datasets/PolyAI/minds14)数据集（有关如何加载数据集的更多详细信息，请参阅🤗 [Datasets教程](https://huggingface.co/docs/datasets/load_hub)）以了解如何在音频数据集中使用`feature extractor`： ```py >>> from datasets import load_dataset, Audio >>> dataset = load_dataset("PolyAI/minds14", name="en-US", split="train") ``` 访问 `audio` 列的第一个元素以查看输入。调用 `audio` 列会自动加载和重新采样音频文件： ```py >>> dataset[0]["audio"] {'array': array([ 0. , 0.00024414, -0.00024414, ..., -0.00024414, 0. , 0. ], dtype=float32), 'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav', 'sampling_rate': 8000} ``` 这会返回三个对象： * `array` 是加载的语音信号 - 并在必要时重新采为`1D array`。 * `path` 指向音频文件的位置。 * `sampling_rate` 是每秒测量的语音信号数据点数量。对于本教程，您将使用[Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base)模型。查看模型卡片，您将了解到Wav2Vec2是在16kHz采样的语音音频数据上预训练的。重要的是，您的音频数据的采样率要与用于预训练模型的数据集的采样率匹配。如果您的数据的采样率不同，那么您需要对数据进行重新采样。 1. 使用🤗 Datasets的[`~datasets.Dataset.cast_column`]方法将采样率提升到16kHz： ```py >>> dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000)) ``` 2. 再次调用 `audio` 列以重新采样音频文件： ```py >>> dataset[0]["audio"] {'array': array([ 2.3443763e-05, 2.1729663e-04, 2.2145823e-04, ..., 3.8356509e-05, -7.3497440e-06, -2.1754686e-05], dtype=float32), 'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav', 'sampling_rate': 16000} ``` 接下来，加载一个`feature extractor`以对输入进行标准化和填充。当填充文本数据时，会为较短的序列添加 `0`。相同的理念适用于音频数据。`feature extractor`添加 `0` - 被解释为静音 - 到`array` 。使用 [`AutoFeatureExtractor.from_pretrained`] 加载`feature extractor`： ```py >>> from transformers import AutoFeatureExtractor >>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base") ``` 将音频 `array` 传递给`feature extractor`。我们还建议在`feature extractor`中添加 `sampling_rate` 参数，以更好地调试可能发生的静音错误： ```py >>> audio_input = [dataset[0]["audio"]["array"]] >>> feature_extractor(audio_input, sampling_rate=16000) {'input_values': [array([ 3.8106556e-04, 2.7506407e-03, 2.8015103e-03, ..., 5.6335266e-04, 4.6588284e-06, -1.7142107e-04], dtype=float32)]} ``` 就像`tokenizer`一样，您可以应用填充或截断来处理批次中的可变序列。请查看这两个音频样本的序列长度： ```py >>> dataset[0]["audio"]["array"].shape (173398,) >>> dataset[1]["audio"]["array"].shape (106496,) ``` 创建一个函数来预处理数据集，以使音频样本具有相同的长度。通过指定最大样本长度，`feature extractor`将填充或截断序列以使其匹配： ```py >>> def preprocess_function(examples): ... audio_arrays = [x["array"] for x in examples["audio"]] ... inputs = feature_extractor( ... audio_arrays, ... sampling_rate=16000, ... padding=True, ... max_length=100000, ... truncation=True, ... ) ... return inputs ``` 将`preprocess_function`应用于数据集中的前几个示例： ```py >>> processed_dataset = preprocess_function(dataset[:5]) ``` 现在样本长度是相同的，并且与指定的最大长度匹配。您现在可以将经过处理的数据集传递给模型了！ ```py >>> processed_dataset["input_values"][0].shape (100000,) >>> processed_dataset["input_values"][1].shape (100000,) ``` ## 计算机视觉对于计算机视觉任务，您需要一个[ image processor](main_classes/image_processor)来准备数据集以供模型使用。图像预处理包括多个步骤将图像转换为模型期望输入的格式。这些步骤包括但不限于调整大小、标准化、颜色通道校正以及将图像转换为张量。 <Tip> 图像预处理通常遵循某种形式的图像增强。图像预处理和图像增强都会改变图像数据，但它们有不同的目的： * 图像增强可以帮助防止过拟合并增加模型的鲁棒性。您可以在数据增强方面充分发挥创造性 - 调整亮度和颜色、裁剪、旋转、调整大小、缩放等。但要注意不要改变图像的含义。 * 图像预处理确保图像与模型预期的输入格式匹配。在微调计算机视觉模型时，必须对图像进行与模型训练时相同的预处理。您可以使用任何您喜欢的图像增强库。对于图像预处理，请使用与模型相关联的`ImageProcessor`。 </Tip> 加载[food101](https://huggingface.co/datasets/food101)数据集（有关如何加载数据集的更多详细信息，请参阅🤗 [Datasets教程](https://huggingface.co/docs/datasets/load_hub)）以了解如何在计算机视觉数据集中使用图像处理器： <Tip> 因为数据集相当大，请使用🤗 Datasets的`split`参数加载训练集中的少量样本！ </Tip> ```py >>> from datasets import load_dataset >>> dataset = load_dataset("food101", split="train[:100]") ``` 接下来，使用🤗 Datasets的[`Image`](https://huggingface.co/docs/datasets/package_reference/main_classes?highlight=image#datasets.Image)功能查看图像： ```py >>> dataset[0]["image"] ``` <div class="flex justify-center"> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/vision-preprocess-tutorial.png"/> </div> 使用 [`AutoImageProcessor.from_pretrained`] 加载`image processor`： ```py >>> from transformers import AutoImageProcessor >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224") ``` 首先，让我们进行图像增强。您可以使用任何您喜欢的库，但在本教程中，我们将使用torchvision的[`transforms`](https://pytorch.org/vision/stable/transforms.html)模块。如果您有兴趣使用其他数据增强库，请参阅[Albumentations](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification_albumentations.ipynb)或[Kornia notebooks](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification_kornia.ipynb)中的示例。 1. 在这里，我们使用[`Compose`](https://pytorch.org/vision/master/generated/torchvision.transforms.Compose.html)将[`RandomResizedCrop`](https://pytorch.org/vision/main/generated/torchvision.transforms.RandomResizedCrop.html)和 [`ColorJitter`](https://pytorch.org/vision/main/generated/torchvision.transforms.ColorJitter.html)变换连接在一起。请注意，对于调整大小，我们可以从`image_processor`中获取图像尺寸要求。对于一些模型，精确的高度和宽度需要被定义，对于其他模型只需定义`shortest_edge`。 ```py >>> from torchvision.transforms import RandomResizedCrop, ColorJitter, Compose >>> size = ( ... image_processor.size["shortest_edge"] ... if "shortest_edge" in image_processor.size ... else (image_processor.size["height"], image_processor.size["width"]) ... ) >>> _transforms = Compose([RandomResizedCrop(size), ColorJitter(brightness=0.5, hue=0.5)]) ``` 2. 模型接受 [`pixel_values`](model_doc/visionencoderdecoder#transformers.VisionEncoderDecoderModel.forward.pixel_values) 作为输入。`ImageProcessor` 可以进行图像的标准化，并生成适当的张量。创建一个函数，将图像增强和图像预处理步骤组合起来处理批量图像，并生成 `pixel_values`： ```py >>> def transforms(examples): ... images = [_transforms(img.convert("RGB")) for img in examples["image"]] ... examples["pixel_values"] = image_processor(images, do_resize=False, return_tensors="pt")["pixel_values"] ... return examples ``` <Tip> 在上面的示例中，我们设置`do_resize=False`，因为我们已经在图像增强转换中调整了图像的大小，并利用了适当的`image_processor`的`size`属性。如果您在图像增强期间不调整图像的大小，请将此参数排除在外。默认情况下`ImageProcessor`将处理调整大小。如果希望将图像标准化步骤为图像增强的一部分，请使用`image_processor.image_mean`和`image_processor.image_std`。 </Tip> 3. 然后使用🤗 Datasets的[`set_transform`](https://huggingface.co/docs/datasets/process#format-transform)在运行时应用这些变换： ```py >>> dataset.set_transform(transforms) ``` 4. 现在，当您访问图像时，您将注意到`image processor`已添加了 `pixel_values`。您现在可以将经过处理的数据集传递给模型了！ ```py >>> dataset[0].keys() ``` 这是在应用变换后的图像样子。图像已被随机裁剪，并其颜色属性发生了变化。 ```py >>> import numpy as np >>> import matplotlib.pyplot as plt >>> img = dataset[0]["pixel_values"] >>> plt.imshow(img.permute(1, 2, 0)) ``` <div class="flex justify-center"> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/preprocessed_image.png"/> </div> <Tip> 对于诸如目标检测、语义分割、实例分割和全景分割等任务，`ImageProcessor`提供了训练后处理方法。这些方法将模型的原始输出转换为有意义的预测，如边界框或分割地图。 </Tip> ### 填充在某些情况下，例如，在微调[DETR](./model_doc/detr)时，模型在训练时应用了尺度增强。这可能导致批处理中的图像大小不同。您可以使用[`DetrImageProcessor.pad`]来指定自定义的`collate_fn`将图像批处理在一起。 ```py >>> def collate_fn(batch): ... pixel_values = [item["pixel_values"] for item in batch] ... encoding = image_processor.pad(pixel_values, return_tensors="pt") ... labels = [item["labels"] for item in batch] ... batch = {} ... batch["pixel_values"] = encoding["pixel_values"] ... batch["pixel_mask"] = encoding["pixel_mask"] ... batch["labels"] = labels ... return batch ``` ## 多模态对于涉及多模态输入的任务，您需要[processor](main_classes/processors)来为模型准备数据集。`processor`将两个处理对象-例如`tokenizer`和`feature extractor`-组合在一起。加载[LJ Speech](https://huggingface.co/datasets/lj_speech)数据集（有关如何加载数据集的更多详细信息，请参阅🤗 [Datasets 教程](https://huggingface.co/docs/datasets/load_hub)）以了解如何使用`processor`进行自动语音识别（ASR）： ```py >>> from datasets import load_dataset >>> lj_speech = load_dataset("lj_speech", split="train") ``` 对于ASR（自动语音识别），主要关注`audio`和`text`，因此可以删除其他列： ```py >>> lj_speech = lj_speech.map(remove_columns=["file", "id", "normalized_text"]) ``` 现在查看`audio`和`text`列： ```py >>> lj_speech[0]["audio"] {'array': array([-7.3242188e-04, -7.6293945e-04, -6.4086914e-04, ..., 7.3242188e-04, 2.1362305e-04, 6.1035156e-05], dtype=float32), 'path': '/root/.cache/huggingface/datasets/downloads/extracted/917ece08c95cf0c4115e45294e3cd0dee724a1165b7fc11798369308a465bd26/LJSpeech-1.1/wavs/LJ001-0001.wav', 'sampling_rate': 22050} >>> lj_speech[0]["text"] 'Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition' ``` 请记住，您应始终[重新采样](preprocessing#audio)音频数据集的采样率，以匹配用于预训练模型数据集的采样率！ ```py >>> lj_speech = lj_speech.cast_column("audio", Audio(sampling_rate=16_000)) ``` 使用[`AutoProcessor.from_pretrained`]加载一个`processor`： ```py >>> from transformers import AutoProcessor >>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h") ``` 1. 创建一个函数，用于将包含在 `array` 中的音频数据处理为 `input_values`，并将 `text` 标记为 `labels`。这些将是输入模型的数据： ```py >>> def prepare_dataset(example): ... audio = example["audio"] ... example.update(processor(audio=audio["array"], text=example["text"], sampling_rate=16000)) ... return example ``` 2. 将 `prepare_dataset` 函数应用于一个示例： ```py >>> prepare_dataset(lj_speech[0]) ``` `processor`现在已经添加了 `input_values` 和 `labels`，并且采样率也正确降低为为16kHz。现在可以将处理后的数据集传递给模型！

transformers/docs/source/zh/preprocessing.md/0

{ "file_path": "transformers/docs/source/zh/preprocessing.md", "repo_id": "transformers", "token_count": 12751 }

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#!/usr/bin/env python # coding=utf-8 # Copyright 2022 The HuggingFace Team All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning the library vision-encoder-decoder models for image captioning. """ import json import logging import os import sys import time import warnings from dataclasses import asdict, dataclass, field from enum import Enum from functools import partial from pathlib import Path from typing import Callable, Optional import datasets import evaluate import jax import jax.numpy as jnp import nltk # Here to have a nice missing dependency error message early on import numpy as np import optax from datasets import Dataset, load_dataset from filelock import FileLock from flax import jax_utils, traverse_util from flax.jax_utils import unreplicate from flax.training import train_state from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key from huggingface_hub import Repository, create_repo from PIL import Image from tqdm import tqdm import transformers from transformers import ( AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser, is_tensorboard_available, ) from transformers.utils import is_offline_mode, send_example_telemetry logger = logging.getLogger(__name__) try: nltk.data.find("tokenizers/punkt") except (LookupError, OSError): if is_offline_mode(): raise LookupError( "Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files" ) with FileLock(".lock") as lock: nltk.download("punkt", quiet=True) # Copied from transformers.models.bart.modeling_flax_bart.shift_tokens_right def shift_tokens_right(input_ids: np.ndarray, pad_token_id: int, decoder_start_token_id: int) -> np.ndarray: """ Shift input ids one token to the right. """ shifted_input_ids = np.zeros_like(input_ids) shifted_input_ids[:, 1:] = input_ids[:, :-1] shifted_input_ids[:, 0] = decoder_start_token_id shifted_input_ids = np.where(shifted_input_ids == -100, pad_token_id, shifted_input_ids) return shifted_input_ids @dataclass class TrainingArguments: output_dir: str = field( metadata={"help": "The output directory where the model predictions and checkpoints will be written."}, ) overwrite_output_dir: bool = field( default=False, metadata={ "help": ( "Overwrite the content of the output directory. " "Use this to continue training if output_dir points to a checkpoint directory." ) }, ) do_train: bool = field(default=False, metadata={"help": "Whether to run training."}) do_eval: bool = field(default=False, metadata={"help": "Whether to run eval on the dev set."}) do_predict: bool = field(default=False, metadata={"help": "Whether to run predictions on the test set."}) per_device_train_batch_size: int = field( default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for training."} ) per_device_eval_batch_size: int = field( default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."} ) _block_size_doc = """ The default value `0` will preprocess (tokenization + image processing) the whole dataset before training and cache the results. This uses more disk space, but avoids (repeated) processing time during training. This is a good option if your disk space is large enough to store the whole processed dataset. If a positive value is given, the captions in the dataset will be tokenized before training and the results are cached. During training, it iterates the dataset in chunks of size `block_size`. On each block, images are transformed by the image processor with the results being kept in memory (no cache), and batches of size `batch_size` are yielded before processing the next block. This could avoid the heavy disk usage when the dataset is large. """ block_size: int = field(default=0, metadata={"help": _block_size_doc}) learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for AdamW."}) weight_decay: float = field(default=0.0, metadata={"help": "Weight decay for AdamW if we apply some."}) adam_beta1: float = field(default=0.9, metadata={"help": "Beta1 for AdamW optimizer"}) adam_beta2: float = field(default=0.999, metadata={"help": "Beta2 for AdamW optimizer"}) adam_epsilon: float = field(default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."}) label_smoothing_factor: float = field( default=0.0, metadata={"help": "The label smoothing epsilon to apply (zero means no label smoothing)."} ) num_train_epochs: float = field(default=3.0, metadata={"help": "Total number of training epochs to perform."}) warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."}) logging_steps: int = field(default=500, metadata={"help": "Log every X updates steps."}) eval_steps: int = field(default=None, metadata={"help": "Run an evaluation every X steps."}) seed: int = field(default=42, metadata={"help": "Random seed that will be set at the beginning of training."}) push_to_hub: bool = field( default=False, metadata={"help": "Whether or not to upload the trained model to the model hub after training."} ) hub_model_id: str = field( default=None, metadata={"help": "The name of the repository to keep in sync with the local `output_dir`."} ) hub_token: str = field(default=None, metadata={"help": "The token to use to push to the Model Hub."}) def __post_init__(self): if self.output_dir is not None: self.output_dir = os.path.expanduser(self.output_dir) def to_dict(self): """ Serializes this instance while replace `Enum` by their values (for JSON serialization support). It obfuscates the token values by removing their value. """ d = asdict(self) for k, v in d.items(): if isinstance(v, Enum): d[k] = v.value if isinstance(v, list) and len(v) > 0 and isinstance(v[0], Enum): d[k] = [x.value for x in v] if k.endswith("_token"): d[k] = f"<{k.upper()}>" return d @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. """ model_name_or_path: str = field( metadata={"help": "The model checkpoint for weights initialization."}, ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} ) use_fast_tokenizer: bool = field( default=True, metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, ) dtype: Optional[str] = field( default="float32", metadata={ "help": ( "Floating-point format in which the model weights should be initialized and trained. Choose one of" " `[float32, float16, bfloat16]`." ) }, ) token: str = field( default=None, metadata={ "help": ( "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token " "generated when running `huggingface-cli login` (stored in `~/.huggingface`)." ) }, ) use_auth_token: bool = field( default=None, metadata={ "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead." }, ) trust_remote_code: bool = field( default=False, metadata={ "help": ( "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option " "should only be set to `True` for repositories you trust and in which you have read the code, as it will " "execute code present on the Hub on your local machine." ) }, ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ dataset_name: Optional[str] = field( default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} ) dataset_config_name: Optional[str] = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) data_dir: Optional[str] = field( default=None, metadata={"help": "The data directory of the dataset to use (via the datasets library)."} ) image_column: Optional[str] = field( default=None, metadata={"help": "The name of the column in the datasets containing the full image file paths."}, ) caption_column: Optional[str] = field( default=None, metadata={"help": "The name of the column in the datasets containing the image captions."}, ) train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."}) validation_file: Optional[str] = field( default=None, metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}, ) test_file: Optional[str] = field( default=None, metadata={"help": "An optional input predict data file to do prediction on (a text file)."}, ) max_target_length: Optional[int] = field( default=128, metadata={ "help": ( "The maximum total sequence length for target text after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded." ) }, ) val_max_target_length: Optional[int] = field( default=None, metadata={ "help": ( "The maximum total sequence length for validation target text after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`. " "This argument is also used to override the `max_length` param of `model.generate`, which is used " "during evaluation." ) }, ) max_train_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ) }, ) max_eval_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of evaluation examples to this " "value if set." ) }, ) max_predict_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of prediction examples to this " "value if set." ) }, ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) predict_with_generate: bool = field( default=False, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."} ) num_beams: Optional[int] = field( default=None, metadata={ "help": ( "Number of beams to use for evaluation. This argument will be passed to `model.generate`, " "which is used during evaluation." ) }, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} ) def __post_init__(self): if self.dataset_name is None and self.train_file is None and self.validation_file is None: raise ValueError("Need either a dataset name or a training/validation file.") else: if self.train_file is not None: extension = self.train_file.split(".")[-1] if extension not in ["csv", "json"]: raise ValueError(f"`train_file` should be a csv or a json file, got {extension}.") if self.validation_file is not None: extension = self.validation_file.split(".")[-1] if extension not in ["csv", "json"]: raise ValueError(f"`validation_file` should be a csv or a json file, got {extension}.") if self.val_max_target_length is None: self.val_max_target_length = self.max_target_length image_captioning_name_mapping = { "image_caption_dataset.py": ("image_path", "caption"), } class TrainState(train_state.TrainState): dropout_rng: jnp.ndarray def replicate(self): return jax_utils.replicate(self).replace(dropout_rng=shard_prng_key(self.dropout_rng)) def data_loader(rng: jax.random.PRNGKey, dataset: Dataset, batch_size: int, shuffle: bool = False): """ Returns batches of size `batch_size` from truncated `dataset`, sharded over all local devices. Shuffle batches if `shuffle` is `True`. """ steps = len(dataset) // batch_size # Skip incomplete batch. # We use `numpy.ndarray` to interact with `datasets.Dataset`, since using `jax.numpy.array` to index into a # dataset is significantly slow. Using JAX array at the 1st place is only to keep JAX's PRNGs generation # mechanism, which works differently from NumPy/SciPy. if shuffle: batch_idx = jax.random.permutation(rng, len(dataset)) batch_idx = np.asarray(batch_idx) else: batch_idx = np.arange(len(dataset)) for idx in range(steps): start_idx = batch_size * idx end_idx = batch_size * (idx + 1) selected_indices = batch_idx[start_idx:end_idx] batch = dataset[selected_indices] batch = shard(batch) yield batch def write_metric(summary_writer, metrics, train_time, step, metric_key_prefix="train"): if train_time: summary_writer.scalar("train_time", train_time, step) metrics = get_metrics(metrics) for key, vals in metrics.items(): tag = f"{metric_key_prefix}_{key}" for i, val in enumerate(vals): summary_writer.scalar(tag, val, step - len(vals) + i + 1) else: for metric_name, value in metrics.items(): summary_writer.scalar(f"{metric_key_prefix}_{metric_name}", value, step) def create_learning_rate_fn( train_ds_size: int, train_batch_size: int, num_train_epochs: int, num_warmup_steps: int, learning_rate: float ) -> Callable[[int], jnp.ndarray]: """Returns a linear warmup, linear_decay learning rate function.""" steps_per_epoch = train_ds_size // train_batch_size num_train_steps = steps_per_epoch * num_train_epochs warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps) decay_fn = optax.linear_schedule( init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps ) schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps]) return schedule_fn def main(): # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() if model_args.use_auth_token is not None: warnings.warn( "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.", FutureWarning, ) if model_args.token is not None: raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.") model_args.token = model_args.use_auth_token # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_image_captioning", model_args, data_args, framework="flax") if ( os.path.exists(training_args.output_dir) and os.listdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir ): raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to overcome." ) # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) # Setup logging, we only want one process per machine to log things on the screen. logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR) if jax.process_index() == 0: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # Set the verbosity to info of the Transformers logger (on main process only): logger.info(f"Training/evaluation parameters {training_args}") # Handle the repository creation if training_args.push_to_hub: # Retrieve of infer repo_name repo_name = training_args.hub_model_id if repo_name is None: repo_name = Path(training_args.output_dir).absolute().name # Create repo and retrieve repo_id repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id # Clone repo locally repo = Repository(training_args.output_dir, clone_from=repo_id, token=training_args.hub_token) # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files this script will use the first column for the full image path and the second column for the # captions (unless you specify column names for this with the `image_column` and `caption_column` arguments). # if data_args.dataset_name is not None: # Downloading and loading a dataset from the hub. dataset = load_dataset( data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, keep_in_memory=False, data_dir=data_args.data_dir, token=model_args.token, ) else: data_files = {} if data_args.train_file is not None: data_files["train"] = data_args.train_file extension = data_args.train_file.split(".")[-1] if data_args.validation_file is not None: data_files["validation"] = data_args.validation_file extension = data_args.validation_file.split(".")[-1] if data_args.test_file is not None: data_files["test"] = data_args.test_file extension = data_args.test_file.split(".")[-1] dataset = load_dataset( extension, data_files=data_files, cache_dir=model_args.cache_dir, token=model_args.token, ) # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets. # Load pretrained model and tokenizer model = FlaxVisionEncoderDecoderModel.from_pretrained( model_args.model_name_or_path, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype), token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) image_processor = AutoImageProcessor.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) tokenizer = AutoTokenizer.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) tokenizer.pad_token = tokenizer.convert_ids_to_tokens(model.config.pad_token_id) # Preprocessing the datasets. # We need to tokenize inputs and targets. if training_args.do_train: column_names = dataset["train"].column_names elif training_args.do_eval: column_names = dataset["validation"].column_names elif training_args.do_predict: column_names = dataset["test"].column_names else: logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.") return # Get the column names for input/target. dataset_columns = image_captioning_name_mapping.get(data_args.dataset_name, None) if data_args.image_column is None: if dataset_columns is None: raise ValueError( f"`--dataset_name` {data_args.dataset_name} not found in dataset '{data_args.dataset_name}'. Make sure" " to set `--dataset_name` to the correct dataset name, one of" f" {', '.join(image_captioning_name_mapping.keys())}." ) image_column = dataset_columns[0] else: image_column = data_args.image_column if image_column not in column_names: raise ValueError( f"--image_column' value '{data_args.image_column}' needs to be one of: {', '.join(column_names)}" ) if data_args.caption_column is None: if dataset_columns is None: raise ValueError( f"`--dataset_name` {data_args.dataset_name} not found in dataset '{data_args.dataset_name}'. Make sure" " to set `--dataset_name` to the correct dataset name, one of" f" {', '.join(image_captioning_name_mapping.keys())}." ) caption_column = dataset_columns[1] else: caption_column = data_args.caption_column if caption_column not in column_names: raise ValueError( f"--caption_column' value '{data_args.caption_column}' needs to be one of: {', '.join(column_names)}" ) # In Flax, for seq2seq models we need to pass `decoder_input_ids` # as the Flax models don't accept `labels`, we need to prepare the decoder_input_ids here # for that dynamically import the `shift_tokens_right` function from the model file model_module = __import__(model.__module__, fromlist=["shift_tokens_right"]) shift_tokens_right_fn = getattr(model_module, "shift_tokens_right", shift_tokens_right) def filter_fn(examples): """remove problematic images""" bools = [] for image_file in examples[image_column]: try: image = Image.open(image_file) image_processor(images=image, return_tensors="np") bools.append(True) except Exception: bools.append(False) return bools # Setting padding="max_length" as we need fixed length inputs for jitted functions def tokenization_fn(examples, max_target_length): """Run tokenization on captions.""" captions = [] for caption in examples[caption_column]: captions.append(caption.lower() + " " + tokenizer.eos_token) targets = captions model_inputs = {} labels = tokenizer( text_target=targets, max_length=max_target_length, padding="max_length", truncation=True, return_tensors="np", ) model_inputs["labels"] = labels["input_ids"] decoder_input_ids = shift_tokens_right_fn( labels["input_ids"], model.config.pad_token_id, model.config.decoder_start_token_id ) model_inputs["decoder_input_ids"] = np.asarray(decoder_input_ids) # We need decoder_attention_mask so we can ignore pad tokens from loss model_inputs["decoder_attention_mask"] = labels["attention_mask"] model_inputs[image_column] = examples[image_column] return model_inputs def image_processing_fn(examples, check_image=True): """ Run preprocessing on images If `check_image` is `True`, the examples that fails during `Image.open()` will be caught and discarded. Otherwise, an exception will be thrown. """ model_inputs = {} if check_image: images = [] to_keep = [] for image_file in examples[image_column]: try: img = Image.open(image_file) images.append(img) to_keep.append(True) except Exception: to_keep.append(False) for k, v in examples.items(): if k != image_column: model_inputs[k] = v[to_keep] else: images = [Image.open(image_file) for image_file in examples[image_column]] encoder_inputs = image_processor(images=images, return_tensors="np") model_inputs["pixel_values"] = encoder_inputs.pixel_values return model_inputs def preprocess_fn(examples, max_target_length, check_image=True): """Run tokenization + image processing""" model_inputs = {} # This contains image path column model_inputs.update(tokenization_fn(examples, max_target_length)) model_inputs.update(image_processing_fn(model_inputs, check_image=check_image)) # Remove image path column model_inputs.pop(image_column) return model_inputs features = datasets.Features( { "pixel_values": datasets.Array3D( shape=( getattr(model.config.encoder, "num_channels", 3), model.config.encoder.image_size, model.config.encoder.image_size, ), dtype="float32", ), "labels": datasets.Sequence(feature=datasets.Value(dtype="int32", id=None), length=-1, id=None), "decoder_input_ids": datasets.Sequence(feature=datasets.Value(dtype="int32", id=None), length=-1, id=None), "decoder_attention_mask": datasets.Sequence( feature=datasets.Value(dtype="int32", id=None), length=-1, id=None ), } ) # If `block_size` is `0`, tokenization & image processing is done at the beginning run_img_proc_at_beginning = training_args.block_size == 0 # Used in .map() below function_kwarg = preprocess_fn if run_img_proc_at_beginning else tokenization_fn # `features` is used only for the final preprocessed dataset (for the performance purpose). features_kwarg = features if run_img_proc_at_beginning else None # Keep `image_column` if the image processing is done during training remove_columns_kwarg = [x for x in column_names if x != image_column or run_img_proc_at_beginning] processor_names = "tokenizer and image processor" if run_img_proc_at_beginning else "tokenizer" # Store some constant train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count() eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count() if training_args.block_size % train_batch_size > 0 or training_args.block_size % eval_batch_size > 0: raise ValueError( "`training_args.block_size` needs to be a multiple of the global train/eval batch size. " f"Got {training_args.block_size}, {train_batch_size} and {eval_batch_size} respectively instead." ) if training_args.do_train: if "train" not in dataset: raise ValueError("--do_train requires a train dataset") train_dataset = dataset["train"] if data_args.max_train_samples is not None: max_train_samples = min(len(train_dataset), data_args.max_train_samples) train_dataset = train_dataset.select(range(max_train_samples)) # remove problematic examples # (if image processing is performed at the beginning, the filtering is done during preprocessing below # instead here.) if not run_img_proc_at_beginning: train_dataset = train_dataset.filter(filter_fn, batched=True, num_proc=data_args.preprocessing_num_workers) train_dataset = train_dataset.map( function=function_kwarg, batched=True, num_proc=data_args.preprocessing_num_workers, # kept image paths remove_columns=remove_columns_kwarg, load_from_cache_file=not data_args.overwrite_cache, desc=f"Running {processor_names} on train dataset", fn_kwargs={"max_target_length": data_args.max_target_length}, features=features_kwarg, ) if run_img_proc_at_beginning: # set format (for performance) since the dataset is ready to be used train_dataset = train_dataset.with_format("numpy") steps_per_epoch = len(train_dataset) // train_batch_size num_train_examples_per_epoch = steps_per_epoch * train_batch_size num_epochs = int(training_args.num_train_epochs) total_train_steps = steps_per_epoch * num_epochs else: num_train_examples_per_epoch = 0 if training_args.do_eval: if "validation" not in dataset: raise ValueError("--do_eval requires a validation dataset") eval_dataset = dataset["validation"] if data_args.max_eval_samples is not None: max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples) eval_dataset = eval_dataset.select(range(max_eval_samples)) # remove problematic examples # (if image processing is performed at the beginning, the filtering is done during preprocessing below # instead here.) if not run_img_proc_at_beginning: eval_dataset = eval_dataset.filter(filter_fn, batched=True, num_proc=data_args.preprocessing_num_workers) eval_dataset = eval_dataset.map( function=function_kwarg, batched=True, num_proc=data_args.preprocessing_num_workers, # kept image paths remove_columns=remove_columns_kwarg, load_from_cache_file=not data_args.overwrite_cache, desc=f"Running {processor_names} on validation dataset", fn_kwargs={"max_target_length": data_args.val_max_target_length}, features=features_kwarg, ) if run_img_proc_at_beginning: # set format (for performance) since the dataset is ready to be used eval_dataset = eval_dataset.with_format("numpy") num_eval_examples = len(eval_dataset) eval_steps = num_eval_examples // eval_batch_size if training_args.do_predict: if "test" not in dataset: raise ValueError("--do_predict requires a test dataset") predict_dataset = dataset["test"] if data_args.max_predict_samples is not None: max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples) predict_dataset = predict_dataset.select(range(max_predict_samples)) # remove problematic examples # (if image processing is performed at the beginning, the filtering is done during preprocessing below # instead here.) if not run_img_proc_at_beginning: predict_dataset = predict_dataset.filter( filter_fn, batched=True, num_proc=data_args.preprocessing_num_workers ) predict_dataset = predict_dataset.map( function=function_kwarg, batched=True, num_proc=data_args.preprocessing_num_workers, # kept image paths remove_columns=remove_columns_kwarg, load_from_cache_file=not data_args.overwrite_cache, desc=f"Running {processor_names} on prediction dataset", fn_kwargs={"max_target_length": data_args.val_max_target_length}, features=features_kwarg, ) if run_img_proc_at_beginning: # set format (for performance) since the dataset is ready to be used predict_dataset = predict_dataset.with_format("numpy") num_test_examples = len(predict_dataset) test_steps = num_test_examples // eval_batch_size def blockwise_data_loader( rng: jax.random.PRNGKey, ds: Dataset, block_size: int, batch_size: int, shuffle: bool = False, keep_in_memory: bool = False, split: str = "", ): """ Wrap the simple `data_loader` in a block-wise way if `block_size` > 0, else it's the same as `data_loader`. If `block_size` > 0, it requires `ds` to have a column that gives image paths in order to perform image processing (with the column name being specified by `image_column`). The tokenization should be done before training in this case. """ # We use `numpy.ndarray` to interact with `datasets.Dataset`, since using `jax.numpy.array` to index into a # dataset is significantly slow. Using JAX array at the 1st place is only to keep JAX's PRNGs generation # mechanism, which works differently from NumPy/SciPy. if shuffle: indices = jax.random.permutation(rng, len(ds)) indices = np.asarray(indices) else: indices = np.arange(len(ds)) _block_size = len(ds) if not block_size else block_size steps_per_block = _block_size // batch_size num_examples = len(ds) steps = num_examples // batch_size num_splits = steps // steps_per_block + int(steps % steps_per_block > 0) for idx in range(num_splits): if not block_size: _ds = ds else: start_idx = block_size * idx end_idx = block_size * (idx + 1) selected_indices = indices[start_idx:end_idx] _ds = ds.select(selected_indices) _ds = _ds.map( image_processing_fn, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=[image_column], load_from_cache_file=not data_args.overwrite_cache, features=features, keep_in_memory=keep_in_memory, # The images are already checked either in `.filter()` or in `preprocess_fn()` fn_kwargs={"check_image": False}, desc=f"Running image processing on {split} dataset".replace(" ", " "), ) _ds = _ds.with_format("numpy") # No need to shuffle here loader = data_loader(rng, _ds, batch_size=batch_size, shuffle=False) for batch in loader: yield batch # Metric metric = evaluate.load("rouge", cache_dir=model_args.cache_dir) def postprocess_text(preds, labels): preds = [pred.strip() for pred in preds] labels = [label.strip() for label in labels] # rougeLSum expects newline after each sentence preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds] labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels] return preds, labels def compute_metrics(preds, labels): decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True) decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True) # Some simple post-processing decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels) result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True) # Extract a few results from ROUGE result = {key: value.mid.fmeasure * 100 for key, value in result.items()} prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds] result["gen_len"] = np.mean(prediction_lens) result = {k: round(v, 6) for k, v in result.items()} return result, decoded_preds, decoded_labels # Enable tensorboard only on the master node has_tensorboard = is_tensorboard_available() if has_tensorboard and jax.process_index() == 0: try: from flax.metrics.tensorboard import SummaryWriter summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir)) except ImportError as ie: has_tensorboard = False logger.warning( f"Unable to display metrics through TensorBoard because some package are not installed: {ie}" ) else: logger.warning( "Unable to display metrics through TensorBoard because the package is not installed: " "Please run pip install tensorboard to enable." ) # Initialize our training rng = jax.random.PRNGKey(training_args.seed) rng, dropout_rng = jax.random.split(rng) # Create learning rate schedule linear_decay_lr_schedule_fn = create_learning_rate_fn( num_train_examples_per_epoch, train_batch_size, training_args.num_train_epochs, training_args.warmup_steps, training_args.learning_rate, ) # We use Optax's "masking" functionality to not apply weight decay # to bias and LayerNorm scale parameters. decay_mask_fn returns a # mask boolean with the same structure as the parameters. # The mask is True for parameters that should be decayed. def decay_mask_fn(params): flat_params = traverse_util.flatten_dict(params) # find out all LayerNorm parameters layer_norm_candidates = ["layernorm", "layer_norm", "ln"] layer_norm_named_params = { layer[-2:] for layer_norm_name in layer_norm_candidates for layer in flat_params.keys() if layer_norm_name in "".join(layer).lower() } flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_named_params) for path in flat_params} return traverse_util.unflatten_dict(flat_mask) # create adam optimizer adamw = optax.adamw( learning_rate=linear_decay_lr_schedule_fn, b1=training_args.adam_beta1, b2=training_args.adam_beta2, eps=training_args.adam_epsilon, weight_decay=training_args.weight_decay, mask=decay_mask_fn, ) # Setup train state state = TrainState.create(apply_fn=model.__call__, params=model.params, tx=adamw, dropout_rng=dropout_rng) # label smoothed cross entropy def loss_fn(logits, labels, padding_mask, label_smoothing_factor=0.0): """ The label smoothing implementation is adapted from Flax's official example: https://github.com/google/flax/blob/87a211135c6a377c8f29048a1cac3840e38b9da4/examples/wmt/train.py#L104 """ vocab_size = logits.shape[-1] confidence = 1.0 - label_smoothing_factor low_confidence = (1.0 - confidence) / (vocab_size - 1) normalizing_constant = -( confidence * jnp.log(confidence) + (vocab_size - 1) * low_confidence * jnp.log(low_confidence + 1e-20) ) soft_labels = onehot(labels, vocab_size, on_value=confidence, off_value=low_confidence) loss = optax.softmax_cross_entropy(logits, soft_labels) loss = loss - normalizing_constant # ignore padded tokens from loss loss = loss * padding_mask loss = loss.sum() num_labels = padding_mask.sum() return loss, num_labels # Define gradient update step fn def train_step(state, batch, label_smoothing_factor=0.0): dropout_rng, new_dropout_rng = jax.random.split(state.dropout_rng) def compute_loss(params): labels = batch.pop("labels") logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0] loss, num_labels = loss_fn(logits, labels, batch["decoder_attention_mask"], label_smoothing_factor) return loss, num_labels grad_fn = jax.value_and_grad(compute_loss, has_aux=True) (loss, num_labels), grad = grad_fn(state.params) num_labels = jax.lax.psum(num_labels, "batch") # true loss = total loss / total samples loss = jax.lax.psum(loss, "batch") loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss) # true grad = total grad / total samples grad = jax.lax.psum(grad, "batch") grad = jax.tree_util.tree_map(lambda x: x / num_labels, grad) new_state = state.apply_gradients(grads=grad, dropout_rng=new_dropout_rng) metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)} return new_state, metrics # Define eval fn def eval_step(params, batch, label_smoothing_factor=0.0): labels = batch.pop("labels") logits = model(**batch, params=params, train=False)[0] loss, num_labels = loss_fn(logits, labels, batch["decoder_attention_mask"], label_smoothing_factor) num_labels = jax.lax.psum(num_labels, "batch") # true loss = total loss / total samples loss = jax.lax.psum(loss, "batch") loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss) metrics = {"loss": loss} return metrics # Define generation function max_length = ( data_args.val_max_target_length if data_args.val_max_target_length is not None else model.config.max_length ) num_beams = data_args.num_beams if data_args.num_beams is not None else model.config.num_beams gen_kwargs = {"max_length": max_length, "num_beams": num_beams} def generate_step(params, batch): model.params = params output_ids = model.generate(batch["pixel_values"], **gen_kwargs) return output_ids.sequences # Create parallel version of the train and eval step p_train_step = jax.pmap( partial(train_step, label_smoothing_factor=training_args.label_smoothing_factor), "batch", donate_argnums=(0,) ) p_eval_step = jax.pmap(partial(eval_step, label_smoothing_factor=training_args.label_smoothing_factor), "batch") p_generate_step = jax.pmap(generate_step, "batch") # Replicate the train state on each device state = state.replicate() if training_args.do_train: logger.info("***** Running training *****") logger.info(f" Num train examples = {num_train_examples_per_epoch}") logger.info(f" Num Epochs = {num_epochs}") logger.info(f" Instantaneous train batch size per device = {training_args.per_device_train_batch_size}") logger.info(f" Total train batch size (w. parallel & distributed) = {train_batch_size}") logger.info(f" Optimization steps per epoch = {steps_per_epoch}") logger.info(f" Total optimization steps = {total_train_steps}") if training_args.do_eval: logger.info(f" Num evaluation examples = {num_eval_examples}") logger.info(f" Instantaneous evaluation batch size per device = {training_args.per_device_eval_batch_size}") logger.info(f" Total evaluation batch size (w. parallel & distributed) = {eval_batch_size}") logger.info(f" Evaluation steps = {eval_steps}") if training_args.do_predict: logger.info(f" Num test examples = {num_test_examples}") logger.info(f" Instantaneous test batch size per device = {training_args.per_device_eval_batch_size}") logger.info(f" Total test batch size (w. parallel & distributed) = {eval_batch_size}") logger.info(f" Test steps = {test_steps}") # create output directory if not os.path.isdir(os.path.join(training_args.output_dir)): os.makedirs(os.path.join(training_args.output_dir), exist_ok=True) def save_ckpt(ckpt_dir: str, commit_msg: str = ""): """save checkpoints and push to Hugging Face Hub if specified""" # save checkpoint after each epoch and push checkpoint to the hub if jax.process_index() == 0: params = jax.device_get(jax.tree_util.tree_map(lambda x: x[0], state.params)) model.save_pretrained(os.path.join(training_args.output_dir, ckpt_dir), params=params) tokenizer.save_pretrained(os.path.join(training_args.output_dir, ckpt_dir)) if training_args.push_to_hub: repo.push_to_hub(commit_message=commit_msg, blocking=False) def evaluation_loop( rng: jax.random.PRNGKey, dataset: Dataset, metric_key_prefix: str = "eval", ckpt_dir: str = "", is_prediction=False, ): logger.info(f"*** {'Predict' if is_prediction else 'Evaluate'} ***") metrics = [] preds = [] labels = [] batches = blockwise_data_loader( rng, dataset, block_size=training_args.block_size, batch_size=eval_batch_size, keep_in_memory=False, shuffle=False, split="prediction" if is_prediction else "validation", ) steps = len(dataset) // eval_batch_size for _ in tqdm( range(steps), desc=f"{'Predicting' if is_prediction else 'Evaluating'}...", position=2, leave=False ): # Model forward batch = next(batches) _labels = batch.get("labels", None) if not is_prediction and _labels is None: raise ValueError("Evaluation requires the validation dataset to have `labels`") if _labels is not None: _metrics = p_eval_step(state.params, batch) metrics.append(_metrics) # generation if data_args.predict_with_generate: generated_ids = p_generate_step(state.params, batch) preds.extend(jax.device_get(generated_ids.reshape(-1, gen_kwargs["max_length"]))) if _labels is not None: labels.extend(jax.device_get(_labels.reshape(-1, _labels.shape[-1]))) if metrics: # normalize metrics metrics = get_metrics(metrics) metrics = jax.tree_util.tree_map(jnp.mean, metrics) # compute ROUGE metrics generations = [] rouge_desc = "" if data_args.predict_with_generate: if labels: rouge_metrics, decoded_preds, decoded_labels = compute_metrics(preds, labels) metrics.update(rouge_metrics) rouge_desc = " ".join( [ f"{'Predict' if is_prediction else 'Eval'} {key}: {value} |" for key, value in rouge_metrics.items() ] ) for pred, label in zip(decoded_preds, decoded_labels): pred = pred.replace("\n", " ") label = label.replace("\n", " ") generations.append({"label": label, "pred": pred}) else: decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True) # Some simple post-processing decoded_preds = [pred.strip() for pred in decoded_preds] # rougeLSum expects newline after each sentence decoded_preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in decoded_preds] for pred in decoded_preds: pred = pred.replace("\n", " ") generations.append({"pred": pred}) if metrics: # Print metrics and update progress bar desc = f"{'Predict' if is_prediction else 'Eval'} Loss: {metrics['loss']} | {rouge_desc})" if training_args.do_train and not is_prediction: desc = f"Epoch... ({epoch + 1}/{num_epochs} | Step: {cur_step} | " + desc epochs.write(desc) epochs.desc = desc logger.info(desc) if jax.process_index() == 0: if not os.path.isdir(os.path.join(training_args.output_dir, ckpt_dir)): os.makedirs(os.path.join(training_args.output_dir, ckpt_dir), exist_ok=True) if metrics: # Save metrics (only for the evaluation/prediction being done along with training) if has_tensorboard and training_args.do_train: write_metric( summary_writer, metrics, train_time=None, step=cur_step, metric_key_prefix=metric_key_prefix ) # save final metrics in json metrics = { f"{metric_key_prefix}_{metric_name}": round(value.item(), 6) for metric_name, value in metrics.items() } _path = os.path.join(training_args.output_dir, ckpt_dir, f"{metric_key_prefix}_results.json") with open(_path, "w") as f: json.dump(metrics, f, indent=4, sort_keys=True) # Update report with open(os.path.join(training_args.output_dir, "log"), "a", encoding="UTF-8") as fp: fp.write(desc + "\n") # Save generations if generations: output_file = os.path.join(training_args.output_dir, ckpt_dir, f"{metric_key_prefix}_generation.json") with open(output_file, "w", encoding="UTF-8") as fp: json.dump(generations, fp, ensure_ascii=False, indent=4) def evaluate(rng: jax.random.PRNGKey, dataset: Dataset, ckpt_dir: str = ""): evaluation_loop(rng, dataset, metric_key_prefix="eval", ckpt_dir=ckpt_dir) def predict(rng: jax.random.PRNGKey, dataset: Dataset): evaluation_loop(rng, dataset, metric_key_prefix="test", is_prediction=True) input_rng = None if training_args.do_train: cur_step = 0 train_time = 0 epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0) for epoch in epochs: # ======================== Training ================================ # Create sampling rng rng, input_rng = jax.random.split(rng) train_metrics = [] train_batches = blockwise_data_loader( input_rng, train_dataset, block_size=training_args.block_size, batch_size=train_batch_size, keep_in_memory=True, shuffle=True, split="train", ) # train for batch_idx, _ in enumerate(tqdm(range(steps_per_epoch), desc="Training...", position=1, leave=False)): cur_step += 1 batch = next(train_batches) batch_start = time.time() state, train_metric = p_train_step(state, batch) train_metrics.append(train_metric) train_time += time.time() - batch_start time_per_step = train_time / cur_step # log and save info if training_args.logging_steps > 0 and cur_step % training_args.logging_steps == 0: _train_metric = unreplicate(train_metric) desc = ( f"Epoch... ({epoch + 1}/{num_epochs} | Step: {cur_step} | Loss: {_train_metric['loss']} |" f" Learning Rate: {_train_metric['learning_rate']} | Time per step: {time_per_step})" ) epochs.desc = desc epochs.write(desc) logger.info(desc) with open(os.path.join(training_args.output_dir, "log"), "a", encoding="UTF-8") as fp: fp.write(desc + "\n") # Save metrics if has_tensorboard and jax.process_index() == 0: write_metric( summary_writer, train_metrics, train_time=train_time, step=cur_step, metric_key_prefix="train", ) # ======================== Evaluating (inside an epoch) ============================== if ( training_args.do_eval and (training_args.eval_steps is not None and training_args.eval_steps > 0) and cur_step % training_args.eval_steps == 0 ): ckpt_dir = f"ckpt_epoch_{epoch + 1}_step_{cur_step}" commit_msg = f"Saving weights and logs of epoch {epoch + 1} - step {cur_step}" evaluate(input_rng, eval_dataset, ckpt_dir) save_ckpt(ckpt_dir=ckpt_dir, commit_msg=commit_msg) # ======================== Epoch End ============================== # log and save info if training_args.logging_steps <= 0: logger.info(desc) with open(os.path.join(training_args.output_dir, "log"), "a", encoding="UTF-8") as fp: fp.write(desc + "\n") # Save metrics if has_tensorboard and jax.process_index() == 0: write_metric( summary_writer, train_metrics, train_time=train_time, step=cur_step, metric_key_prefix="train" ) # ======================== Evaluating (after each epoch) ============================== if training_args.do_eval and (training_args.eval_steps is None or training_args.eval_steps <= 0): ckpt_dir = f"ckpt_epoch_{epoch + 1}_step_{cur_step}" commit_msg = f"Saving weights and logs of epoch {epoch + 1} - step {cur_step}" evaluate(input_rng, eval_dataset, ckpt_dir) save_ckpt(ckpt_dir=ckpt_dir, commit_msg=commit_msg) # ======================== Evaluating | Predicting ============================== # Create sampling rng if input_rng is None: rng, input_rng = jax.random.split(rng) # run evaluation without training if training_args.do_eval and not training_args.do_train: evaluate(input_rng, eval_dataset) # run prediction after (or without) training if training_args.do_predict: predict(input_rng, predict_dataset) if __name__ == "__main__": main()

transformers/examples/flax/image-captioning/run_image_captioning_flax.py/0

{ "file_path": "transformers/examples/flax/image-captioning/run_image_captioning_flax.py", "repo_id": "transformers", "token_count": 24465 }

43

#!/usr/bin/env python # coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Benchmarking the library on inference and training """ from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments def main(): parser = HfArgumentParser(PyTorchBenchmarkArguments) try: benchmark_args = parser.parse_args_into_dataclasses()[0] except ValueError as e: arg_error_msg = "Arg --no_{0} is no longer used, please use --no-{0} instead." begin_error_msg = " ".join(str(e).split(" ")[:-1]) full_error_msg = "" depreciated_args = eval(str(e).split(" ")[-1]) wrong_args = [] for arg in depreciated_args: # arg[2:] removes '--' if arg[2:] in PyTorchBenchmarkArguments.deprecated_args: # arg[5:] removes '--no_' full_error_msg += arg_error_msg.format(arg[5:]) else: wrong_args.append(arg) if len(wrong_args) > 0: full_error_msg = full_error_msg + begin_error_msg + str(wrong_args) raise ValueError(full_error_msg) benchmark = PyTorchBenchmark(args=benchmark_args) benchmark.run() if __name__ == "__main__": main()

transformers/examples/legacy/benchmarking/run_benchmark.py/0

{ "file_path": "transformers/examples/legacy/benchmarking/run_benchmark.py", "repo_id": "transformers", "token_count": 699 }

44

#!/usr/bin/env python # coding=utf-8 # Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ OpenAI GPT model fine-tuning script. Adapted from https://github.com/huggingface/pytorch-openai-transformer-lm/blob/master/train.py It self adapted from https://github.com/openai/finetune-transformer-lm/blob/master/train.py This script with default values fine-tunes and evaluate a pretrained OpenAI GPT on the RocStories dataset: python run_openai_gpt.py \ --model_name openai-community/openai-gpt \ --do_train \ --do_eval \ --train_dataset "$ROC_STORIES_DIR/cloze_test_val__spring2016 - cloze_test_ALL_val.csv" \ --eval_dataset "$ROC_STORIES_DIR/cloze_test_test__spring2016 - cloze_test_ALL_test.csv" \ --output_dir ../log \ --train_batch_size 16 \ """ import argparse import csv import logging import os import random import numpy as np import torch from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset from tqdm import tqdm, trange from transformers import ( CONFIG_NAME, WEIGHTS_NAME, AdamW, OpenAIGPTDoubleHeadsModel, OpenAIGPTTokenizer, get_linear_schedule_with_warmup, ) logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO ) logger = logging.getLogger(__name__) def accuracy(out, labels): outputs = np.argmax(out, axis=1) return np.sum(outputs == labels) def load_rocstories_dataset(dataset_path): """Output a list of tuples(story, 1st continuation, 2nd continuation, label)""" with open(dataset_path, encoding="utf_8") as f: f = csv.reader(f) output = [] next(f) # skip the first line for line in tqdm(f): output.append((" ".join(line[1:5]), line[5], line[6], int(line[-1]) - 1)) return output def pre_process_datasets(encoded_datasets, input_len, cap_length, start_token, delimiter_token, clf_token): """Pre-process datasets containing lists of tuples(story, 1st continuation, 2nd continuation, label) To Transformer inputs of shape (n_batch, n_alternative, length) comprising for each batch, continuation: input_ids[batch, alternative, :] = [start_token] + story[:cap_length] + [delimiter_token] + cont1[:cap_length] + [clf_token] """ tensor_datasets = [] for dataset in encoded_datasets: n_batch = len(dataset) input_ids = np.zeros((n_batch, 2, input_len), dtype=np.int64) mc_token_ids = np.zeros((n_batch, 2), dtype=np.int64) lm_labels = np.full((n_batch, 2, input_len), fill_value=-100, dtype=np.int64) mc_labels = np.zeros((n_batch,), dtype=np.int64) for ( i, (story, cont1, cont2, mc_label), ) in enumerate(dataset): with_cont1 = [start_token] + story[:cap_length] + [delimiter_token] + cont1[:cap_length] + [clf_token] with_cont2 = [start_token] + story[:cap_length] + [delimiter_token] + cont2[:cap_length] + [clf_token] input_ids[i, 0, : len(with_cont1)] = with_cont1 input_ids[i, 1, : len(with_cont2)] = with_cont2 mc_token_ids[i, 0] = len(with_cont1) - 1 mc_token_ids[i, 1] = len(with_cont2) - 1 lm_labels[i, 0, : len(with_cont1)] = with_cont1 lm_labels[i, 1, : len(with_cont2)] = with_cont2 mc_labels[i] = mc_label all_inputs = (input_ids, mc_token_ids, lm_labels, mc_labels) tensor_datasets.append(tuple(torch.tensor(t) for t in all_inputs)) return tensor_datasets def main(): parser = argparse.ArgumentParser() parser.add_argument("--model_name", type=str, default="openai-community/openai-gpt", help="pretrained model name") parser.add_argument("--do_train", action="store_true", help="Whether to run training.") parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.") parser.add_argument( "--output_dir", default=None, type=str, required=True, help="The output directory where the model predictions and checkpoints will be written.", ) parser.add_argument("--train_dataset", type=str, default="") parser.add_argument("--eval_dataset", type=str, default="") parser.add_argument("--seed", type=int, default=42) parser.add_argument("--num_train_epochs", type=int, default=3) parser.add_argument("--train_batch_size", type=int, default=8) parser.add_argument("--eval_batch_size", type=int, default=16) parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.") parser.add_argument("--max_grad_norm", type=int, default=1) parser.add_argument( "--max_steps", default=-1, type=int, help=( "If > 0: set total number of training steps to perform. Override num_train_epochs." ), ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument("--learning_rate", type=float, default=6.25e-5) parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.") parser.add_argument("--lr_schedule", type=str, default="warmup_linear") parser.add_argument("--weight_decay", type=float, default=0.01) parser.add_argument("--lm_coef", type=float, default=0.9) parser.add_argument("--n_valid", type=int, default=374) parser.add_argument("--server_ip", type=str, default="", help="Can be used for distant debugging.") parser.add_argument("--server_port", type=str, default="", help="Can be used for distant debugging.") args = parser.parse_args() print(args) if args.server_ip and args.server_port: # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script import ptvsd print("Waiting for debugger attach") ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) ptvsd.wait_for_attach() random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) torch.cuda.manual_seed_all(args.seed) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") n_gpu = torch.cuda.device_count() logger.info("device: {}, n_gpu {}".format(device, n_gpu)) if not args.do_train and not args.do_eval: raise ValueError("At least one of `do_train` or `do_eval` must be True.") if not os.path.exists(args.output_dir): os.makedirs(args.output_dir) # Load tokenizer and model # This loading functions also add new tokens and embeddings called `special tokens` # These new embeddings will be fine-tuned on the RocStories dataset special_tokens = ["_start_", "_delimiter_", "_classify_"] tokenizer = OpenAIGPTTokenizer.from_pretrained(args.model_name) tokenizer.add_tokens(special_tokens) special_tokens_ids = tokenizer.convert_tokens_to_ids(special_tokens) model = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name) model.resize_token_embeddings(len(tokenizer)) model.to(device) # Load and encode the datasets def tokenize_and_encode(obj): """Tokenize and encode a nested object""" if isinstance(obj, str): return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(obj)) elif isinstance(obj, int): return obj return [tokenize_and_encode(o) for o in obj] logger.info("Encoding dataset...") train_dataset = load_rocstories_dataset(args.train_dataset) eval_dataset = load_rocstories_dataset(args.eval_dataset) datasets = (train_dataset, eval_dataset) encoded_datasets = tokenize_and_encode(datasets) # Compute the max input length for the Transformer max_length = model.config.n_positions // 2 - 2 input_length = max( len(story[:max_length]) + max(len(cont1[:max_length]), len(cont2[:max_length])) + 3 for dataset in encoded_datasets for story, cont1, cont2, _ in dataset ) input_length = min(input_length, model.config.n_positions) # Max size of input for the pre-trained model # Prepare inputs tensors and dataloaders tensor_datasets = pre_process_datasets(encoded_datasets, input_length, max_length, *special_tokens_ids) train_tensor_dataset, eval_tensor_dataset = tensor_datasets[0], tensor_datasets[1] train_data = TensorDataset(*train_tensor_dataset) train_sampler = RandomSampler(train_data) train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size) eval_data = TensorDataset(*eval_tensor_dataset) eval_sampler = SequentialSampler(eval_data) eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size) # Prepare optimizer if args.do_train: if args.max_steps > 0: t_total = args.max_steps args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1 else: t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs param_optimizer = list(model.named_parameters()) no_decay = ["bias", "LayerNorm.bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], "weight_decay": args.weight_decay, }, {"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], "weight_decay": 0.0}, ] optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon) scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total ) if args.do_train: nb_tr_steps, tr_loss, exp_average_loss = 0, 0, None model.train() for _ in trange(int(args.num_train_epochs), desc="Epoch"): tr_loss = 0 nb_tr_steps = 0 tqdm_bar = tqdm(train_dataloader, desc="Training") for step, batch in enumerate(tqdm_bar): batch = tuple(t.to(device) for t in batch) input_ids, mc_token_ids, lm_labels, mc_labels = batch losses = model(input_ids, mc_token_ids=mc_token_ids, lm_labels=lm_labels, mc_labels=mc_labels) loss = args.lm_coef * losses[0] + losses[1] loss.backward() optimizer.step() scheduler.step() optimizer.zero_grad() tr_loss += loss.item() exp_average_loss = ( loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item() ) nb_tr_steps += 1 tqdm_bar.desc = "Training loss: {:.2e} lr: {:.2e}".format(exp_average_loss, scheduler.get_lr()[0]) # Save a trained model if args.do_train: # Save a trained model, configuration and tokenizer model_to_save = model.module if hasattr(model, "module") else model # Only save the model itself # If we save using the predefined names, we can load using `from_pretrained` output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME) output_config_file = os.path.join(args.output_dir, CONFIG_NAME) torch.save(model_to_save.state_dict(), output_model_file) model_to_save.config.to_json_file(output_config_file) tokenizer.save_vocabulary(args.output_dir) # Load a trained model and vocabulary that you have fine-tuned model = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir) tokenizer = OpenAIGPTTokenizer.from_pretrained(args.output_dir) model.to(device) if args.do_eval: model.eval() eval_loss, eval_accuracy = 0, 0 nb_eval_steps, nb_eval_examples = 0, 0 for batch in tqdm(eval_dataloader, desc="Evaluating"): batch = tuple(t.to(device) for t in batch) input_ids, mc_token_ids, lm_labels, mc_labels = batch with torch.no_grad(): _, mc_loss, _, mc_logits = model( input_ids, mc_token_ids=mc_token_ids, lm_labels=lm_labels, mc_labels=mc_labels ) mc_logits = mc_logits.detach().cpu().numpy() mc_labels = mc_labels.to("cpu").numpy() tmp_eval_accuracy = accuracy(mc_logits, mc_labels) eval_loss += mc_loss.mean().item() eval_accuracy += tmp_eval_accuracy nb_eval_examples += input_ids.size(0) nb_eval_steps += 1 eval_loss = eval_loss / nb_eval_steps eval_accuracy = eval_accuracy / nb_eval_examples train_loss = tr_loss / nb_tr_steps if args.do_train else None result = {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss} output_eval_file = os.path.join(args.output_dir, "eval_results.txt") with open(output_eval_file, "w") as writer: logger.info("***** Eval results *****") for key in sorted(result.keys()): logger.info(" %s = %s", key, str(result[key])) writer.write("%s = %s\n" % (key, str(result[key]))) if __name__ == "__main__": main()

transformers/examples/legacy/run_openai_gpt.py/0

{ "file_path": "transformers/examples/legacy/run_openai_gpt.py", "repo_id": "transformers", "token_count": 6175 }

45

# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning the library models for named entity recognition on CoNLL-2003. """ import logging import os import sys from dataclasses import dataclass, field from importlib import import_module from typing import Dict, List, Optional, Tuple import numpy as np from seqeval.metrics import accuracy_score, f1_score, precision_score, recall_score from torch import nn from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask import transformers from transformers import ( AutoConfig, AutoModelForTokenClassification, AutoTokenizer, DataCollatorWithPadding, EvalPrediction, HfArgumentParser, Trainer, TrainingArguments, set_seed, ) from transformers.trainer_utils import is_main_process logger = logging.getLogger(__name__) @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. """ model_name_or_path: str = field( metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) task_type: Optional[str] = field( default="NER", metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"} ) tokenizer_name: Optional[str] = field( default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} ) use_fast: bool = field(default=False, metadata={"help": "Set this flag to use fast tokenization."}) # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script, # or just modify its tokenizer_config.json. cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ data_dir: str = field( metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."} ) labels: Optional[str] = field( default=None, metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}, ) max_seq_length: int = field( default=128, metadata={ "help": ( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded." ) }, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} ) def main(): # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() if ( os.path.exists(training_args.output_dir) and os.listdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir ): raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. Use" " --overwrite_output_dir to overcome." ) module = import_module("tasks") try: token_classification_task_clazz = getattr(module, model_args.task_type) token_classification_task: TokenClassificationTask = token_classification_task_clazz() except AttributeError: raise ValueError( f"Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. " f"Available tasks classes are: {TokenClassificationTask.__subclasses__()}" ) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN, ) logger.warning( "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", training_args.local_rank, training_args.device, training_args.n_gpu, bool(training_args.local_rank != -1), training_args.fp16, ) # Set the verbosity to info of the Transformers logger (on main process only): if is_main_process(training_args.local_rank): transformers.utils.logging.set_verbosity_info() transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() logger.info("Training/evaluation parameters %s", training_args) # Set seed set_seed(training_args.seed) # Prepare CONLL-2003 task labels = token_classification_task.get_labels(data_args.labels) label_map: Dict[int, str] = dict(enumerate(labels)) num_labels = len(labels) # Load pretrained model and tokenizer # # Distributed training: # The .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. config = AutoConfig.from_pretrained( model_args.config_name if model_args.config_name else model_args.model_name_or_path, num_labels=num_labels, id2label=label_map, label2id={label: i for i, label in enumerate(labels)}, cache_dir=model_args.cache_dir, ) tokenizer = AutoTokenizer.from_pretrained( model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast, ) model = AutoModelForTokenClassification.from_pretrained( model_args.model_name_or_path, from_tf=bool(".ckpt" in model_args.model_name_or_path), config=config, cache_dir=model_args.cache_dir, ) # Get datasets train_dataset = ( TokenClassificationDataset( token_classification_task=token_classification_task, data_dir=data_args.data_dir, tokenizer=tokenizer, labels=labels, model_type=config.model_type, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.train, ) if training_args.do_train else None ) eval_dataset = ( TokenClassificationDataset( token_classification_task=token_classification_task, data_dir=data_args.data_dir, tokenizer=tokenizer, labels=labels, model_type=config.model_type, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.dev, ) if training_args.do_eval else None ) def align_predictions(predictions: np.ndarray, label_ids: np.ndarray) -> Tuple[List[int], List[int]]: preds = np.argmax(predictions, axis=2) batch_size, seq_len = preds.shape out_label_list = [[] for _ in range(batch_size)] preds_list = [[] for _ in range(batch_size)] for i in range(batch_size): for j in range(seq_len): if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index: out_label_list[i].append(label_map[label_ids[i][j]]) preds_list[i].append(label_map[preds[i][j]]) return preds_list, out_label_list def compute_metrics(p: EvalPrediction) -> Dict: preds_list, out_label_list = align_predictions(p.predictions, p.label_ids) return { "accuracy_score": accuracy_score(out_label_list, preds_list), "precision": precision_score(out_label_list, preds_list), "recall": recall_score(out_label_list, preds_list), "f1": f1_score(out_label_list, preds_list), } # Data collator data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8) if training_args.fp16 else None # Initialize our Trainer trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset, compute_metrics=compute_metrics, data_collator=data_collator, ) # Training if training_args.do_train: trainer.train( model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None ) trainer.save_model() # For convenience, we also re-save the tokenizer to the same directory, # so that you can share your model easily on huggingface.co/models =) if trainer.is_world_process_zero(): tokenizer.save_pretrained(training_args.output_dir) # Evaluation results = {} if training_args.do_eval: logger.info("*** Evaluate ***") result = trainer.evaluate() output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt") if trainer.is_world_process_zero(): with open(output_eval_file, "w") as writer: logger.info("***** Eval results *****") for key, value in result.items(): logger.info(" %s = %s", key, value) writer.write("%s = %s\n" % (key, value)) results.update(result) # Predict if training_args.do_predict: test_dataset = TokenClassificationDataset( token_classification_task=token_classification_task, data_dir=data_args.data_dir, tokenizer=tokenizer, labels=labels, model_type=config.model_type, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.test, ) predictions, label_ids, metrics = trainer.predict(test_dataset) preds_list, _ = align_predictions(predictions, label_ids) output_test_results_file = os.path.join(training_args.output_dir, "test_results.txt") if trainer.is_world_process_zero(): with open(output_test_results_file, "w") as writer: for key, value in metrics.items(): logger.info(" %s = %s", key, value) writer.write("%s = %s\n" % (key, value)) # Save predictions output_test_predictions_file = os.path.join(training_args.output_dir, "test_predictions.txt") if trainer.is_world_process_zero(): with open(output_test_predictions_file, "w") as writer: with open(os.path.join(data_args.data_dir, "test.txt"), "r") as f: token_classification_task.write_predictions_to_file(writer, f, preds_list) return results def _mp_fn(index): # For xla_spawn (TPUs) main() if __name__ == "__main__": main()

transformers/examples/legacy/token-classification/run_ner.py/0

{ "file_path": "transformers/examples/legacy/token-classification/run_ner.py", "repo_id": "transformers", "token_count": 5023 }

46

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and import logging import os import sys import warnings from dataclasses import dataclass, field from typing import Optional import evaluate import numpy as np import torch from datasets import load_dataset from PIL import Image from torchvision.transforms import ( CenterCrop, Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, Resize, ToTensor, ) import transformers from transformers import ( MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, AutoConfig, AutoImageProcessor, AutoModelForImageClassification, HfArgumentParser, Trainer, TrainingArguments, set_seed, ) from transformers.trainer_utils import get_last_checkpoint from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version """ Fine-tuning a 🤗 Transformers model for image classification""" logger = logging.getLogger(__name__) # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.39.0.dev0") require_version("datasets>=2.14.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt") MODEL_CONFIG_CLASSES = list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) def pil_loader(path: str): with open(path, "rb") as f: im = Image.open(f) return im.convert("RGB") @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify them on the command line. """ dataset_name: Optional[str] = field( default=None, metadata={ "help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)." }, ) dataset_config_name: Optional[str] = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) train_dir: Optional[str] = field(default=None, metadata={"help": "A folder containing the training data."}) validation_dir: Optional[str] = field(default=None, metadata={"help": "A folder containing the validation data."}) train_val_split: Optional[float] = field( default=0.15, metadata={"help": "Percent to split off of train for validation."} ) max_train_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ) }, ) max_eval_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of evaluation examples to this " "value if set." ) }, ) image_column_name: str = field( default="image", metadata={"help": "The name of the dataset column containing the image data. Defaults to 'image'."}, ) label_column_name: str = field( default="label", metadata={"help": "The name of the dataset column containing the labels. Defaults to 'label'."}, ) def __post_init__(self): if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None): raise ValueError( "You must specify either a dataset name from the hub or a train and/or validation directory." ) @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. """ model_name_or_path: str = field( default="google/vit-base-patch16-224-in21k", metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}, ) model_type: Optional[str] = field( default=None, metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)}, ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} ) model_revision: str = field( default="main", metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, ) image_processor_name: str = field(default=None, metadata={"help": "Name or path of preprocessor config."}) token: str = field( default=None, metadata={ "help": ( "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token " "generated when running `huggingface-cli login` (stored in `~/.huggingface`)." ) }, ) use_auth_token: bool = field( default=None, metadata={ "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead." }, ) trust_remote_code: bool = field( default=False, metadata={ "help": ( "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option " "should only be set to `True` for repositories you trust and in which you have read the code, as it will " "execute code present on the Hub on your local machine." ) }, ) ignore_mismatched_sizes: bool = field( default=False, metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}, ) def main(): # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() if model_args.use_auth_token is not None: warnings.warn( "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.", FutureWarning, ) if model_args.token is not None: raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.") model_args.token = model_args.use_auth_token # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_image_classification", model_args, data_args) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout)], ) if training_args.should_log: # The default of training_args.log_level is passive, so we set log level at info here to have that default. transformers.utils.logging.set_verbosity_info() log_level = training_args.get_process_log_level() logger.setLevel(log_level) transformers.utils.logging.set_verbosity(log_level) transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Log on each process the small summary: logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, " + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}" ) logger.info(f"Training/evaluation parameters {training_args}") # Detecting last checkpoint. last_checkpoint = None if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: last_checkpoint = get_last_checkpoint(training_args.output_dir) if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to overcome." ) elif last_checkpoint is not None and training_args.resume_from_checkpoint is None: logger.info( f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) # Set seed before initializing model. set_seed(training_args.seed) # Initialize our dataset and prepare it for the 'image-classification' task. if data_args.dataset_name is not None: dataset = load_dataset( data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, token=model_args.token, ) else: data_files = {} if data_args.train_dir is not None: data_files["train"] = os.path.join(data_args.train_dir, "**") if data_args.validation_dir is not None: data_files["validation"] = os.path.join(data_args.validation_dir, "**") dataset = load_dataset( "imagefolder", data_files=data_files, cache_dir=model_args.cache_dir, ) dataset_column_names = dataset["train"].column_names if "train" in dataset else dataset["validation"].column_names if data_args.image_column_name not in dataset_column_names: raise ValueError( f"--image_column_name {data_args.image_column_name} not found in dataset '{data_args.dataset_name}'. " "Make sure to set `--image_column_name` to the correct audio column - one of " f"{', '.join(dataset_column_names)}." ) if data_args.label_column_name not in dataset_column_names: raise ValueError( f"--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. " "Make sure to set `--label_column_name` to the correct text column - one of " f"{', '.join(dataset_column_names)}." ) def collate_fn(examples): pixel_values = torch.stack([example["pixel_values"] for example in examples]) labels = torch.tensor([example[data_args.label_column_name] for example in examples]) return {"pixel_values": pixel_values, "labels": labels} # If we don't have a validation split, split off a percentage of train as validation. data_args.train_val_split = None if "validation" in dataset.keys() else data_args.train_val_split if isinstance(data_args.train_val_split, float) and data_args.train_val_split > 0.0: split = dataset["train"].train_test_split(data_args.train_val_split) dataset["train"] = split["train"] dataset["validation"] = split["test"] # Prepare label mappings. # We'll include these in the model's config to get human readable labels in the Inference API. labels = dataset["train"].features[data_args.label_column_name].names label2id, id2label = {}, {} for i, label in enumerate(labels): label2id[label] = str(i) id2label[str(i)] = label # Load the accuracy metric from the datasets package metric = evaluate.load("accuracy", cache_dir=model_args.cache_dir) # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a # predictions and label_ids field) and has to return a dictionary string to float. def compute_metrics(p): """Computes accuracy on a batch of predictions""" return metric.compute(predictions=np.argmax(p.predictions, axis=1), references=p.label_ids) config = AutoConfig.from_pretrained( model_args.config_name or model_args.model_name_or_path, num_labels=len(labels), label2id=label2id, id2label=id2label, finetuning_task="image-classification", cache_dir=model_args.cache_dir, revision=model_args.model_revision, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) model = AutoModelForImageClassification.from_pretrained( model_args.model_name_or_path, from_tf=bool(".ckpt" in model_args.model_name_or_path), config=config, cache_dir=model_args.cache_dir, revision=model_args.model_revision, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ignore_mismatched_sizes=model_args.ignore_mismatched_sizes, ) image_processor = AutoImageProcessor.from_pretrained( model_args.image_processor_name or model_args.model_name_or_path, cache_dir=model_args.cache_dir, revision=model_args.model_revision, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) # Define torchvision transforms to be applied to each image. if "shortest_edge" in image_processor.size: size = image_processor.size["shortest_edge"] else: size = (image_processor.size["height"], image_processor.size["width"]) normalize = ( Normalize(mean=image_processor.image_mean, std=image_processor.image_std) if hasattr(image_processor, "image_mean") and hasattr(image_processor, "image_std") else Lambda(lambda x: x) ) _train_transforms = Compose( [ RandomResizedCrop(size), RandomHorizontalFlip(), ToTensor(), normalize, ] ) _val_transforms = Compose( [ Resize(size), CenterCrop(size), ToTensor(), normalize, ] ) def train_transforms(example_batch): """Apply _train_transforms across a batch.""" example_batch["pixel_values"] = [ _train_transforms(pil_img.convert("RGB")) for pil_img in example_batch[data_args.image_column_name] ] return example_batch def val_transforms(example_batch): """Apply _val_transforms across a batch.""" example_batch["pixel_values"] = [ _val_transforms(pil_img.convert("RGB")) for pil_img in example_batch[data_args.image_column_name] ] return example_batch if training_args.do_train: if "train" not in dataset: raise ValueError("--do_train requires a train dataset") if data_args.max_train_samples is not None: dataset["train"] = ( dataset["train"].shuffle(seed=training_args.seed).select(range(data_args.max_train_samples)) ) # Set the training transforms dataset["train"].set_transform(train_transforms) if training_args.do_eval: if "validation" not in dataset: raise ValueError("--do_eval requires a validation dataset") if data_args.max_eval_samples is not None: dataset["validation"] = ( dataset["validation"].shuffle(seed=training_args.seed).select(range(data_args.max_eval_samples)) ) # Set the validation transforms dataset["validation"].set_transform(val_transforms) # Initialize our trainer trainer = Trainer( model=model, args=training_args, train_dataset=dataset["train"] if training_args.do_train else None, eval_dataset=dataset["validation"] if training_args.do_eval else None, compute_metrics=compute_metrics, tokenizer=image_processor, data_collator=collate_fn, ) # Training if training_args.do_train: checkpoint = None if training_args.resume_from_checkpoint is not None: checkpoint = training_args.resume_from_checkpoint elif last_checkpoint is not None: checkpoint = last_checkpoint train_result = trainer.train(resume_from_checkpoint=checkpoint) trainer.save_model() trainer.log_metrics("train", train_result.metrics) trainer.save_metrics("train", train_result.metrics) trainer.save_state() # Evaluation if training_args.do_eval: metrics = trainer.evaluate() trainer.log_metrics("eval", metrics) trainer.save_metrics("eval", metrics) # Write model card and (optionally) push to hub kwargs = { "finetuned_from": model_args.model_name_or_path, "tasks": "image-classification", "dataset": data_args.dataset_name, "tags": ["image-classification", "vision"], } if training_args.push_to_hub: trainer.push_to_hub(**kwargs) else: trainer.create_model_card(**kwargs) if __name__ == "__main__": main()

transformers/examples/pytorch/image-classification/run_image_classification.py/0

{ "file_path": "transformers/examples/pytorch/image-classification/run_image_classification.py", "repo_id": "transformers", "token_count": 7153 }

47

#!/usr/bin/env python # coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and import json import logging import os import random import sys import warnings from dataclasses import dataclass, field from typing import Optional import evaluate import numpy as np import torch from datasets import load_dataset from huggingface_hub import hf_hub_download from PIL import Image from torch import nn from torchvision import transforms from torchvision.transforms import functional import transformers from transformers import ( AutoConfig, AutoImageProcessor, AutoModelForSemanticSegmentation, HfArgumentParser, Trainer, TrainingArguments, default_data_collator, ) from transformers.trainer_utils import get_last_checkpoint from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version """ Finetuning any 🤗 Transformers model supported by AutoModelForSemanticSegmentation for semantic segmentation leveraging the Trainer API.""" logger = logging.getLogger(__name__) # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.39.0.dev0") require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/semantic-segmentation/requirements.txt") def pad_if_smaller(img, size, fill=0): size = (size, size) if isinstance(size, int) else size original_width, original_height = img.size pad_height = size[1] - original_height if original_height < size[1] else 0 pad_width = size[0] - original_width if original_width < size[0] else 0 img = functional.pad(img, (0, 0, pad_width, pad_height), fill=fill) return img class Compose: def __init__(self, transforms): self.transforms = transforms def __call__(self, image, target): for t in self.transforms: image, target = t(image, target) return image, target class Identity: def __init__(self): pass def __call__(self, image, target): return image, target class Resize: def __init__(self, size): self.size = size def __call__(self, image, target): image = functional.resize(image, self.size) target = functional.resize(target, self.size, interpolation=transforms.InterpolationMode.NEAREST) return image, target class RandomResize: def __init__(self, min_size, max_size=None): self.min_size = min_size if max_size is None: max_size = min_size self.max_size = max_size def __call__(self, image, target): size = random.randint(self.min_size, self.max_size) image = functional.resize(image, size) target = functional.resize(target, size, interpolation=transforms.InterpolationMode.NEAREST) return image, target class RandomCrop: def __init__(self, size): self.size = size if isinstance(size, tuple) else (size, size) def __call__(self, image, target): image = pad_if_smaller(image, self.size) target = pad_if_smaller(target, self.size, fill=255) crop_params = transforms.RandomCrop.get_params(image, self.size) image = functional.crop(image, *crop_params) target = functional.crop(target, *crop_params) return image, target class RandomHorizontalFlip: def __init__(self, flip_prob): self.flip_prob = flip_prob def __call__(self, image, target): if random.random() < self.flip_prob: image = functional.hflip(image) target = functional.hflip(target) return image, target class PILToTensor: def __call__(self, image, target): image = functional.pil_to_tensor(image) target = torch.as_tensor(np.array(target), dtype=torch.int64) return image, target class ConvertImageDtype: def __init__(self, dtype): self.dtype = dtype def __call__(self, image, target): image = functional.convert_image_dtype(image, self.dtype) return image, target class Normalize: def __init__(self, mean, std): self.mean = mean self.std = std def __call__(self, image, target): image = functional.normalize(image, mean=self.mean, std=self.std) return image, target class ReduceLabels: def __call__(self, image, target): if not isinstance(target, np.ndarray): target = np.array(target).astype(np.uint8) # avoid using underflow conversion target[target == 0] = 255 target = target - 1 target[target == 254] = 255 target = Image.fromarray(target) return image, target @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify them on the command line. """ dataset_name: Optional[str] = field( default="segments/sidewalk-semantic", metadata={ "help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)." }, ) dataset_config_name: Optional[str] = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) train_val_split: Optional[float] = field( default=0.15, metadata={"help": "Percent to split off of train for validation."} ) max_train_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ) }, ) max_eval_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of evaluation examples to this " "value if set." ) }, ) reduce_labels: Optional[bool] = field( default=False, metadata={"help": "Whether or not to reduce all labels by 1 and replace background by 255."}, ) def __post_init__(self): if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None): raise ValueError( "You must specify either a dataset name from the hub or a train and/or validation directory." ) @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. """ model_name_or_path: str = field( default="nvidia/mit-b0", metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}, ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} ) model_revision: str = field( default="main", metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, ) image_processor_name: str = field(default=None, metadata={"help": "Name or path of preprocessor config."}) token: str = field( default=None, metadata={ "help": ( "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token " "generated when running `huggingface-cli login` (stored in `~/.huggingface`)." ) }, ) use_auth_token: bool = field( default=None, metadata={ "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead." }, ) trust_remote_code: bool = field( default=False, metadata={ "help": ( "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option " "should only be set to `True` for repositories you trust and in which you have read the code, as it will " "execute code present on the Hub on your local machine." ) }, ) def main(): # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() if model_args.use_auth_token is not None: warnings.warn( "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.", FutureWarning, ) if model_args.token is not None: raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.") model_args.token = model_args.use_auth_token # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_semantic_segmentation", model_args, data_args) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout)], ) if training_args.should_log: # The default of training_args.log_level is passive, so we set log level at info here to have that default. transformers.utils.logging.set_verbosity_info() log_level = training_args.get_process_log_level() logger.setLevel(log_level) transformers.utils.logging.set_verbosity(log_level) transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Log on each process the small summary: logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, " + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}" ) logger.info(f"Training/evaluation parameters {training_args}") # Detecting last checkpoint. last_checkpoint = None if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: last_checkpoint = get_last_checkpoint(training_args.output_dir) if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to overcome." ) elif last_checkpoint is not None and training_args.resume_from_checkpoint is None: logger.info( f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) # Load dataset # In distributed training, the load_dataset function guarantees that only one local process can concurrently # download the dataset. # TODO support datasets from local folders dataset = load_dataset(data_args.dataset_name, cache_dir=model_args.cache_dir) # Rename column names to standardized names (only "image" and "label" need to be present) if "pixel_values" in dataset["train"].column_names: dataset = dataset.rename_columns({"pixel_values": "image"}) if "annotation" in dataset["train"].column_names: dataset = dataset.rename_columns({"annotation": "label"}) # If we don't have a validation split, split off a percentage of train as validation. data_args.train_val_split = None if "validation" in dataset.keys() else data_args.train_val_split if isinstance(data_args.train_val_split, float) and data_args.train_val_split > 0.0: split = dataset["train"].train_test_split(data_args.train_val_split) dataset["train"] = split["train"] dataset["validation"] = split["test"] # Prepare label mappings. # We'll include these in the model's config to get human readable labels in the Inference API. if data_args.dataset_name == "scene_parse_150": repo_id = "huggingface/label-files" filename = "ade20k-id2label.json" else: repo_id = data_args.dataset_name filename = "id2label.json" id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) id2label = {int(k): v for k, v in id2label.items()} label2id = {v: str(k) for k, v in id2label.items()} # Load the mean IoU metric from the datasets package metric = evaluate.load("mean_iou", cache_dir=model_args.cache_dir) # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a # predictions and label_ids field) and has to return a dictionary string to float. @torch.no_grad() def compute_metrics(eval_pred): logits, labels = eval_pred logits_tensor = torch.from_numpy(logits) # scale the logits to the size of the label logits_tensor = nn.functional.interpolate( logits_tensor, size=labels.shape[-2:], mode="bilinear", align_corners=False, ).argmax(dim=1) pred_labels = logits_tensor.detach().cpu().numpy() metrics = metric.compute( predictions=pred_labels, references=labels, num_labels=len(id2label), ignore_index=0, reduce_labels=image_processor.do_reduce_labels, ) # add per category metrics as individual key-value pairs per_category_accuracy = metrics.pop("per_category_accuracy").tolist() per_category_iou = metrics.pop("per_category_iou").tolist() metrics.update({f"accuracy_{id2label[i]}": v for i, v in enumerate(per_category_accuracy)}) metrics.update({f"iou_{id2label[i]}": v for i, v in enumerate(per_category_iou)}) return metrics config = AutoConfig.from_pretrained( model_args.config_name or model_args.model_name_or_path, label2id=label2id, id2label=id2label, cache_dir=model_args.cache_dir, revision=model_args.model_revision, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) model = AutoModelForSemanticSegmentation.from_pretrained( model_args.model_name_or_path, from_tf=bool(".ckpt" in model_args.model_name_or_path), config=config, cache_dir=model_args.cache_dir, revision=model_args.model_revision, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) image_processor = AutoImageProcessor.from_pretrained( model_args.image_processor_name or model_args.model_name_or_path, cache_dir=model_args.cache_dir, revision=model_args.model_revision, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) # Define torchvision transforms to be applied to each image + target. # Not that straightforward in torchvision: https://github.com/pytorch/vision/issues/9 # Currently based on official torchvision references: https://github.com/pytorch/vision/blob/main/references/segmentation/transforms.py if "shortest_edge" in image_processor.size: # We instead set the target size as (shortest_edge, shortest_edge) to here to ensure all images are batchable. size = (image_processor.size["shortest_edge"], image_processor.size["shortest_edge"]) else: size = (image_processor.size["height"], image_processor.size["width"]) train_transforms = Compose( [ ReduceLabels() if data_args.reduce_labels else Identity(), RandomCrop(size=size), RandomHorizontalFlip(flip_prob=0.5), PILToTensor(), ConvertImageDtype(torch.float), Normalize(mean=image_processor.image_mean, std=image_processor.image_std), ] ) # Define torchvision transform to be applied to each image. # jitter = ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1) val_transforms = Compose( [ ReduceLabels() if data_args.reduce_labels else Identity(), Resize(size=size), PILToTensor(), ConvertImageDtype(torch.float), Normalize(mean=image_processor.image_mean, std=image_processor.image_std), ] ) def preprocess_train(example_batch): pixel_values = [] labels = [] for image, target in zip(example_batch["image"], example_batch["label"]): image, target = train_transforms(image.convert("RGB"), target) pixel_values.append(image) labels.append(target) encoding = {} encoding["pixel_values"] = torch.stack(pixel_values) encoding["labels"] = torch.stack(labels) return encoding def preprocess_val(example_batch): pixel_values = [] labels = [] for image, target in zip(example_batch["image"], example_batch["label"]): image, target = val_transforms(image.convert("RGB"), target) pixel_values.append(image) labels.append(target) encoding = {} encoding["pixel_values"] = torch.stack(pixel_values) encoding["labels"] = torch.stack(labels) return encoding if training_args.do_train: if "train" not in dataset: raise ValueError("--do_train requires a train dataset") if data_args.max_train_samples is not None: dataset["train"] = ( dataset["train"].shuffle(seed=training_args.seed).select(range(data_args.max_train_samples)) ) # Set the training transforms dataset["train"].set_transform(preprocess_train) if training_args.do_eval: if "validation" not in dataset: raise ValueError("--do_eval requires a validation dataset") if data_args.max_eval_samples is not None: dataset["validation"] = ( dataset["validation"].shuffle(seed=training_args.seed).select(range(data_args.max_eval_samples)) ) # Set the validation transforms dataset["validation"].set_transform(preprocess_val) # Initialize our trainer trainer = Trainer( model=model, args=training_args, train_dataset=dataset["train"] if training_args.do_train else None, eval_dataset=dataset["validation"] if training_args.do_eval else None, compute_metrics=compute_metrics, tokenizer=image_processor, data_collator=default_data_collator, ) # Training if training_args.do_train: checkpoint = None if training_args.resume_from_checkpoint is not None: checkpoint = training_args.resume_from_checkpoint elif last_checkpoint is not None: checkpoint = last_checkpoint train_result = trainer.train(resume_from_checkpoint=checkpoint) trainer.save_model() trainer.log_metrics("train", train_result.metrics) trainer.save_metrics("train", train_result.metrics) trainer.save_state() # Evaluation if training_args.do_eval: metrics = trainer.evaluate() trainer.log_metrics("eval", metrics) trainer.save_metrics("eval", metrics) # Write model card and (optionally) push to hub kwargs = { "finetuned_from": model_args.model_name_or_path, "dataset": data_args.dataset_name, "tags": ["image-segmentation", "vision"], } if training_args.push_to_hub: trainer.push_to_hub(**kwargs) else: trainer.create_model_card(**kwargs) if __name__ == "__main__": main()

transformers/examples/pytorch/semantic-segmentation/run_semantic_segmentation.py/0

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# Text classification examples ## GLUE tasks Based on the script [`run_glue.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py). Fine-tuning the library models for sequence classification on the GLUE benchmark: [General Language Understanding Evaluation](https://gluebenchmark.com/). This script can fine-tune any of the models on the [hub](https://huggingface.co/models) and can also be used for a dataset hosted on our [hub](https://huggingface.co/datasets) or your own data in a csv or a JSON file (the script might need some tweaks in that case, refer to the comments inside for help). GLUE is made up of a total of 9 different tasks. Here is how to run the script on one of them: ```bash export TASK_NAME=mrpc python run_glue.py \ --model_name_or_path google-bert/bert-base-cased \ --task_name $TASK_NAME \ --do_train \ --do_eval \ --max_seq_length 128 \ --per_device_train_batch_size 32 \ --learning_rate 2e-5 \ --num_train_epochs 3 \ --output_dir /tmp/$TASK_NAME/ ``` where task name can be one of cola, sst2, mrpc, stsb, qqp, mnli, qnli, rte, wnli. We get the following results on the dev set of the benchmark with the previous commands (with an exception for MRPC and WNLI which are tiny and where we used 5 epochs instead of 3). Trainings are seeded so you should obtain the same results with PyTorch 1.6.0 (and close results with different versions), training times are given for information (a single Titan RTX was used): | Task | Metric | Result | Training time | |-------|------------------------------|-------------|---------------| | CoLA | Matthews corr | 56.53 | 3:17 | | SST-2 | Accuracy | 92.32 | 26:06 | | MRPC | F1/Accuracy | 88.85/84.07 | 2:21 | | STS-B | Pearson/Spearman corr. | 88.64/88.48 | 2:13 | | QQP | Accuracy/F1 | 90.71/87.49 | 2:22:26 | | MNLI | Matched acc./Mismatched acc. | 83.91/84.10 | 2:35:23 | | QNLI | Accuracy | 90.66 | 40:57 | | RTE | Accuracy | 65.70 | 57 | | WNLI | Accuracy | 56.34 | 24 | Some of these results are significantly different from the ones reported on the test set of GLUE benchmark on the website. For QQP and WNLI, please refer to [FAQ #12](https://gluebenchmark.com/faq) on the website. The following example fine-tunes BERT on the `imdb` dataset hosted on our [hub](https://huggingface.co/datasets): ```bash python run_glue.py \ --model_name_or_path google-bert/bert-base-cased \ --dataset_name imdb \ --do_train \ --do_predict \ --max_seq_length 128 \ --per_device_train_batch_size 32 \ --learning_rate 2e-5 \ --num_train_epochs 3 \ --output_dir /tmp/imdb/ ``` > If your model classification head dimensions do not fit the number of labels in the dataset, you can specify `--ignore_mismatched_sizes` to adapt it. ## Text classification As an alternative, we can use the script [`run_classification.py`](./run_classification.py) to fine-tune models on a single/multi-label classification task. The following example fine-tunes BERT on the `en` subset of [`amazon_reviews_multi`](https://huggingface.co/datasets/amazon_reviews_multi) dataset. We can specify the metric, the label column and aso choose which text columns to use jointly for classification. ```bash dataset="amazon_reviews_multi" subset="en" python run_classification.py \ --model_name_or_path google-bert/bert-base-uncased \ --dataset_name ${dataset} \ --dataset_config_name ${subset} \ --shuffle_train_dataset \ --metric_name accuracy \ --text_column_name "review_title,review_body,product_category" \ --text_column_delimiter "\n" \ --label_column_name stars \ --do_train \ --do_eval \ --max_seq_length 512 \ --per_device_train_batch_size 32 \ --learning_rate 2e-5 \ --num_train_epochs 1 \ --output_dir /tmp/${dataset}_${subset}/ ``` Training for 1 epoch results in acc of around 0.5958 for review_body only and 0.659 for title+body+category. The following is a multi-label classification example. It fine-tunes BERT on the `reuters21578` dataset hosted on our [hub](https://huggingface.co/datasets/reuters21578): ```bash dataset="reuters21578" subset="ModApte" python run_classification.py \ --model_name_or_path google-bert/bert-base-uncased \ --dataset_name ${dataset} \ --dataset_config_name ${subset} \ --shuffle_train_dataset \ --remove_splits "unused" \ --metric_name f1 \ --text_column_name text \ --label_column_name topics \ --do_train \ --do_eval \ --max_seq_length 512 \ --per_device_train_batch_size 32 \ --learning_rate 2e-5 \ --num_train_epochs 15 \ --output_dir /tmp/${dataset}_${subset}/ ``` It results in a Micro F1 score of around 0.82 without any text and label filtering. Note that you have to explicitly remove the "unused" split from the dataset, since it is not used for classification. ### Mixed precision training If you have a GPU with mixed precision capabilities (architecture Pascal or more recent), you can use mixed precision training with PyTorch 1.6.0 or latest, or by installing the [Apex](https://github.com/NVIDIA/apex) library for previous versions. Just add the flag `--fp16` to your command launching one of the scripts mentioned above! Using mixed precision training usually results in 2x-speedup for training with the same final results: | Task | Metric | Result | Training time | Result (FP16) | Training time (FP16) | |-------|------------------------------|-------------|---------------|---------------|----------------------| | CoLA | Matthews corr | 56.53 | 3:17 | 56.78 | 1:41 | | SST-2 | Accuracy | 92.32 | 26:06 | 91.74 | 13:11 | | MRPC | F1/Accuracy | 88.85/84.07 | 2:21 | 88.12/83.58 | 1:10 | | STS-B | Pearson/Spearman corr. | 88.64/88.48 | 2:13 | 88.71/88.55 | 1:08 | | QQP | Accuracy/F1 | 90.71/87.49 | 2:22:26 | 90.67/87.43 | 1:11:54 | | MNLI | Matched acc./Mismatched acc. | 83.91/84.10 | 2:35:23 | 84.04/84.06 | 1:17:06 | | QNLI | Accuracy | 90.66 | 40:57 | 90.96 | 20:16 | | RTE | Accuracy | 65.70 | 57 | 65.34 | 29 | | WNLI | Accuracy | 56.34 | 24 | 56.34 | 12 | ## PyTorch version, no Trainer Based on the script [`run_glue_no_trainer.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue_no_trainer.py). Like `run_glue.py`, this script allows you to fine-tune any of the models on the [hub](https://huggingface.co/models) on a text classification task, either a GLUE task or your own data in a csv or a JSON file. The main difference is that this script exposes the bare training loop, to allow you to quickly experiment and add any customization you would like. It offers less options than the script with `Trainer` (for instance you can easily change the options for the optimizer or the dataloaders directly in the script) but still run in a distributed setup, on TPU and supports mixed precision by the mean of the [🤗 `Accelerate`](https://github.com/huggingface/accelerate) library. You can use the script normally after installing it: ```bash pip install git+https://github.com/huggingface/accelerate ``` then ```bash export TASK_NAME=mrpc python run_glue_no_trainer.py \ --model_name_or_path google-bert/bert-base-cased \ --task_name $TASK_NAME \ --max_length 128 \ --per_device_train_batch_size 32 \ --learning_rate 2e-5 \ --num_train_epochs 3 \ --output_dir /tmp/$TASK_NAME/ ``` You can then use your usual launchers to run in it in a distributed environment, but the easiest way is to run ```bash accelerate config ``` and reply to the questions asked. Then ```bash accelerate test ``` that will check everything is ready for training. Finally, you can launch training with ```bash export TASK_NAME=mrpc accelerate launch run_glue_no_trainer.py \ --model_name_or_path google-bert/bert-base-cased \ --task_name $TASK_NAME \ --max_length 128 \ --per_device_train_batch_size 32 \ --learning_rate 2e-5 \ --num_train_epochs 3 \ --output_dir /tmp/$TASK_NAME/ ``` This command is the same and will work for: - a CPU-only setup - a setup with one GPU - a distributed training with several GPUs (single or multi node) - a training on TPUs Note that this library is in alpha release so your feedback is more than welcome if you encounter any problem using it. ## XNLI Based on the script [`run_xnli.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_xnli.py). [XNLI](https://cims.nyu.edu/~sbowman/xnli/) is a crowd-sourced dataset based on [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/). It is an evaluation benchmark for cross-lingual text representations. Pairs of text are labeled with textual entailment annotations for 15 different languages (including both high-resource language such as English and low-resource languages such as Swahili). #### Fine-tuning on XNLI This example code fine-tunes mBERT (multi-lingual BERT) on the XNLI dataset. It runs in 106 mins on a single tesla V100 16GB. ```bash python run_xnli.py \ --model_name_or_path google-bert/bert-base-multilingual-cased \ --language de \ --train_language en \ --do_train \ --do_eval \ --per_device_train_batch_size 32 \ --learning_rate 5e-5 \ --num_train_epochs 2.0 \ --max_seq_length 128 \ --output_dir /tmp/debug_xnli/ \ --save_steps -1 ``` Training with the previously defined hyper-parameters yields the following results on the **test** set: ```bash acc = 0.7093812375249501 ```

transformers/examples/pytorch/text-classification/README.md/0

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## Translation This directory contains examples for finetuning and evaluating transformers on translation tasks. Please tag @patil-suraj with any issues/unexpected behaviors, or send a PR! For deprecated `bertabs` instructions, see [`bertabs/README.md`](https://github.com/huggingface/transformers/blob/main/examples/research_projects/bertabs/README.md). For the old `finetune_trainer.py` and related utils, see [`examples/legacy/seq2seq`](https://github.com/huggingface/transformers/blob/main/examples/legacy/seq2seq). ### Supported Architectures - `BartForConditionalGeneration` - `FSMTForConditionalGeneration` (translation only) - `MBartForConditionalGeneration` - `MarianMTModel` - `PegasusForConditionalGeneration` - `T5ForConditionalGeneration` - `MT5ForConditionalGeneration` `run_translation.py` is a lightweight examples of how to download and preprocess a dataset from the [🤗 Datasets](https://github.com/huggingface/datasets) library or use your own files (jsonlines or csv), then fine-tune one of the architectures above on it. For custom datasets in `jsonlines` format please see: https://huggingface.co/docs/datasets/loading_datasets#json-files and you also will find examples of these below. ## With Trainer Here is an example of a translation fine-tuning with a MarianMT model: ```bash python examples/pytorch/translation/run_translation.py \ --model_name_or_path Helsinki-NLP/opus-mt-en-ro \ --do_train \ --do_eval \ --source_lang en \ --target_lang ro \ --dataset_name wmt16 \ --dataset_config_name ro-en \ --output_dir /tmp/tst-translation \ --per_device_train_batch_size=4 \ --per_device_eval_batch_size=4 \ --overwrite_output_dir \ --predict_with_generate ``` MBart and some T5 models require special handling. T5 models `google-t5/t5-small`, `google-t5/t5-base`, `google-t5/t5-large`, `google-t5/t5-3b` and `google-t5/t5-11b` must use an additional argument: `--source_prefix "translate {source_lang} to {target_lang}"`. For example: ```bash python examples/pytorch/translation/run_translation.py \ --model_name_or_path google-t5/t5-small \ --do_train \ --do_eval \ --source_lang en \ --target_lang ro \ --source_prefix "translate English to Romanian: " \ --dataset_name wmt16 \ --dataset_config_name ro-en \ --output_dir /tmp/tst-translation \ --per_device_train_batch_size=4 \ --per_device_eval_batch_size=4 \ --overwrite_output_dir \ --predict_with_generate ``` If you get a terrible BLEU score, make sure that you didn't forget to use the `--source_prefix` argument. For the aforementioned group of T5 models it's important to remember that if you switch to a different language pair, make sure to adjust the source and target values in all 3 language-specific command line argument: `--source_lang`, `--target_lang` and `--source_prefix`. MBart models require a different format for `--source_lang` and `--target_lang` values, e.g. instead of `en` it expects `en_XX`, for `ro` it expects `ro_RO`. The full MBart specification for language codes can be found [here](https://huggingface.co/facebook/mbart-large-cc25). For example: ```bash python examples/pytorch/translation/run_translation.py \ --model_name_or_path facebook/mbart-large-en-ro \ --do_train \ --do_eval \ --dataset_name wmt16 \ --dataset_config_name ro-en \ --source_lang en_XX \ --target_lang ro_RO \ --output_dir /tmp/tst-translation \ --per_device_train_batch_size=4 \ --per_device_eval_batch_size=4 \ --overwrite_output_dir \ --predict_with_generate ``` And here is how you would use the translation finetuning on your own files, after adjusting the values for the arguments `--train_file`, `--validation_file` to match your setup: ```bash python examples/pytorch/translation/run_translation.py \ --model_name_or_path google-t5/t5-small \ --do_train \ --do_eval \ --source_lang en \ --target_lang ro \ --source_prefix "translate English to Romanian: " \ --dataset_name wmt16 \ --dataset_config_name ro-en \ --train_file path_to_jsonlines_file \ --validation_file path_to_jsonlines_file \ --output_dir /tmp/tst-translation \ --per_device_train_batch_size=4 \ --per_device_eval_batch_size=4 \ --overwrite_output_dir \ --predict_with_generate ``` The task of translation supports only custom JSONLINES files, with each line being a dictionary with a key `"translation"` and its value another dictionary whose keys is the language pair. For example: ```json { "translation": { "en": "Others have dismissed him as a joke.", "ro": "Alții l-au numit o glumă." } } { "translation": { "en": "And some are holding out for an implosion.", "ro": "Iar alții așteaptă implozia." } } ``` Here the languages are Romanian (`ro`) and English (`en`). If you want to use a pre-processed dataset that leads to high BLEU scores, but for the `en-de` language pair, you can use `--dataset_name stas/wmt14-en-de-pre-processed`, as following: ```bash python examples/pytorch/translation/run_translation.py \ --model_name_or_path google-t5/t5-small \ --do_train \ --do_eval \ --source_lang en \ --target_lang de \ --source_prefix "translate English to German: " \ --dataset_name stas/wmt14-en-de-pre-processed \ --output_dir /tmp/tst-translation \ --per_device_train_batch_size=4 \ --per_device_eval_batch_size=4 \ --overwrite_output_dir \ --predict_with_generate ``` ## With Accelerate Based on the script [`run_translation_no_trainer.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/translation/run_translation_no_trainer.py). Like `run_translation.py`, this script allows you to fine-tune any of the models supported on a translation task, the main difference is that this script exposes the bare training loop, to allow you to quickly experiment and add any customization you would like. It offers less options than the script with `Trainer` (for instance you can easily change the options for the optimizer or the dataloaders directly in the script) but still run in a distributed setup, on TPU and supports mixed precision by the mean of the [🤗 `Accelerate`](https://github.com/huggingface/accelerate) library. You can use the script normally after installing it: ```bash pip install git+https://github.com/huggingface/accelerate ``` then ```bash python run_translation_no_trainer.py \ --model_name_or_path Helsinki-NLP/opus-mt-en-ro \ --source_lang en \ --target_lang ro \ --dataset_name wmt16 \ --dataset_config_name ro-en \ --output_dir ~/tmp/tst-translation ``` You can then use your usual launchers to run in it in a distributed environment, but the easiest way is to run ```bash accelerate config ``` and reply to the questions asked. Then ```bash accelerate test ``` that will check everything is ready for training. Finally, you can launch training with ```bash accelerate launch run_translation_no_trainer.py \ --model_name_or_path Helsinki-NLP/opus-mt-en-ro \ --source_lang en \ --target_lang ro \ --dataset_name wmt16 \ --dataset_config_name ro-en \ --output_dir ~/tmp/tst-translation ``` This command is the same and will work for: - a CPU-only setup - a setup with one GPU - a distributed training with several GPUs (single or multi node) - a training on TPUs Note that this library is in alpha release so your feedback is more than welcome if you encounter any problem using it.

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import argparse import logging import sys from unittest.mock import patch import run_glue_with_pabee from transformers.testing_utils import TestCasePlus logging.basicConfig(level=logging.DEBUG) logger = logging.getLogger() def get_setup_file(): parser = argparse.ArgumentParser() parser.add_argument("-f") args = parser.parse_args() return args.f class PabeeTests(TestCasePlus): def test_run_glue(self): stream_handler = logging.StreamHandler(sys.stdout) logger.addHandler(stream_handler) tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_glue_with_pabee.py --model_type albert --model_name_or_path albert/albert-base-v2 --data_dir ./tests/fixtures/tests_samples/MRPC/ --output_dir {tmp_dir} --overwrite_output_dir --task_name mrpc --do_train --do_eval --per_gpu_train_batch_size=2 --per_gpu_eval_batch_size=1 --learning_rate=2e-5 --max_steps=50 --warmup_steps=2 --seed=42 --max_seq_length=128 """.split() with patch.object(sys, "argv", testargs): result = run_glue_with_pabee.main() for value in result.values(): self.assertGreaterEqual(value, 0.75)

transformers/examples/research_projects/bert-loses-patience/test_run_glue_with_pabee.py/0

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import argparse from copy import deepcopy import numpy as np from datasets import ClassLabel, DatasetDict, load_dataset from evaluate import load from transformers import ( AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, Trainer, TrainerCallback, TrainingArguments, set_seed, ) def get_args(): parser = argparse.ArgumentParser() parser.add_argument("--model_ckpt", type=str, default="microsoft/unixcoder-base-nine") parser.add_argument("--num_epochs", type=int, default=5) parser.add_argument("--batch_size", type=int, default=6) parser.add_argument("--gradient_accumulation_steps", type=int, default=1) parser.add_argument("--freeze", type=bool, default=True) parser.add_argument("--learning_rate", type=float, default=5e-4) parser.add_argument("--seed", type=int, default=0) parser.add_argument("--lr_scheduler_type", type=str, default="cosine") parser.add_argument("--num_warmup_steps", type=int, default=10) parser.add_argument("--weight_decay", type=float, default=0.01) parser.add_argument("--output_dir", type=str, default="./results") return parser.parse_args() metric = load("accuracy") def compute_metrics(eval_pred): predictions, labels = eval_pred predictions = np.argmax(predictions, axis=1) return metric.compute(predictions=predictions, references=labels) class CustomCallback(TrainerCallback): def __init__(self, trainer) -> None: super().__init__() self._trainer = trainer def on_epoch_end(self, args, state, control, **kwargs): if control.should_evaluate: control_copy = deepcopy(control) self._trainer.evaluate(eval_dataset=self._trainer.train_dataset, metric_key_prefix="train") return control_copy def main(): args = get_args() set_seed(args.seed) dataset = load_dataset("codeparrot/codecomplex", split="train") train_test = dataset.train_test_split(test_size=0.2) test_validation = train_test["test"].train_test_split(test_size=0.5) train_test_validation = DatasetDict( { "train": train_test["train"], "test": test_validation["train"], "valid": test_validation["test"], } ) print("Loading tokenizer and model") tokenizer = AutoTokenizer.from_pretrained(args.model_ckpt) tokenizer.pad_token = tokenizer.eos_token model = AutoModelForSequenceClassification.from_pretrained(args.model_ckpt, num_labels=7) model.config.pad_token_id = model.config.eos_token_id if args.freeze: for param in model.roberta.parameters(): param.requires_grad = False labels = ClassLabel(num_classes=7, names=list(set(train_test_validation["train"]["complexity"]))) def tokenize(example): inputs = tokenizer(example["src"], truncation=True, max_length=1024) label = labels.str2int(example["complexity"]) return { "input_ids": inputs["input_ids"], "attention_mask": inputs["attention_mask"], "label": label, } tokenized_datasets = train_test_validation.map( tokenize, batched=True, remove_columns=train_test_validation["train"].column_names, ) data_collator = DataCollatorWithPadding(tokenizer=tokenizer) training_args = TrainingArguments( output_dir=args.output_dir, learning_rate=args.learning_rate, lr_scheduler_type=args.lr_scheduler_type, evaluation_strategy="epoch", save_strategy="epoch", logging_strategy="epoch", per_device_train_batch_size=args.batch_size, per_device_eval_batch_size=args.batch_size, num_train_epochs=args.num_epochs, gradient_accumulation_steps=args.gradient_accumulation_steps, weight_decay=0.01, metric_for_best_model="accuracy", run_name="complexity-java", report_to="wandb", ) trainer = Trainer( model=model, args=training_args, train_dataset=tokenized_datasets["train"], eval_dataset=tokenized_datasets["valid"], tokenizer=tokenizer, data_collator=data_collator, compute_metrics=compute_metrics, ) print("Training...") trainer.add_callback(CustomCallback(trainer)) trainer.train() if __name__ == "__main__": main()

transformers/examples/research_projects/codeparrot/examples/train_complexity_predictor.py/0

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#!/bin/bash export CUDA_VISIBLE_DEVICES=0 PATH_TO_DATA=/h/xinji/projects/GLUE MODEL_TYPE=bert # bert or roberta MODEL_SIZE=base # base or large DATASET=MRPC # SST-2, MRPC, RTE, QNLI, QQP, or MNLI MODEL_NAME=${MODEL_TYPE}-${MODEL_SIZE} if [ $MODEL_TYPE = 'bert' ] then MODEL_NAME=${MODEL_NAME}-uncased fi ENTROPIES="0 0.1 0.2 0.3 0.4 0.5 0.6 0.7" for ENTROPY in $ENTROPIES; do python -u run_glue_deebert.py \ --model_type $MODEL_TYPE \ --model_name_or_path ./saved_models/${MODEL_TYPE}-${MODEL_SIZE}/$DATASET/two_stage \ --task_name $DATASET \ --do_eval \ --do_lower_case \ --data_dir $PATH_TO_DATA/$DATASET \ --output_dir ./saved_models/${MODEL_TYPE}-${MODEL_SIZE}/$DATASET/two_stage \ --plot_data_dir ./results/ \ --max_seq_length 128 \ --early_exit_entropy $ENTROPY \ --eval_highway \ --overwrite_cache \ --per_gpu_eval_batch_size=1 done

transformers/examples/research_projects/deebert/entropy_eval.sh/0

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# coding=utf-8 # Copyright 2019-present, the HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Preprocessing script before training the distilled model. Specific to RoBERTa -> DistilRoBERTa and GPT2 -> DistilGPT2. """ import argparse import torch from transformers import GPT2LMHeadModel, RobertaForMaskedLM if __name__ == "__main__": parser = argparse.ArgumentParser( description=( "Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned" " Distillation" ) ) parser.add_argument("--model_type", default="roberta", choices=["roberta", "gpt2"]) parser.add_argument("--model_name", default="roberta-large", type=str) parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_roberta_048131723.pth", type=str) parser.add_argument("--vocab_transform", action="store_true") args = parser.parse_args() if args.model_type == "roberta": model = RobertaForMaskedLM.from_pretrained(args.model_name) prefix = "roberta" elif args.model_type == "gpt2": model = GPT2LMHeadModel.from_pretrained(args.model_name) prefix = "transformer" state_dict = model.state_dict() compressed_sd = {} # Embeddings # if args.model_type == "gpt2": for param_name in ["wte.weight", "wpe.weight"]: compressed_sd[f"{prefix}.{param_name}"] = state_dict[f"{prefix}.{param_name}"] else: for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]: param_name = f"{prefix}.embeddings.{w}.weight" compressed_sd[param_name] = state_dict[param_name] for w in ["weight", "bias"]: param_name = f"{prefix}.embeddings.LayerNorm.{w}" compressed_sd[param_name] = state_dict[param_name] # Transformer Blocks # std_idx = 0 for teacher_idx in [0, 2, 4, 7, 9, 11]: if args.model_type == "gpt2": for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]: for w in ["weight", "bias"]: compressed_sd[f"{prefix}.h.{std_idx}.{layer}.{w}"] = state_dict[ f"{prefix}.h.{teacher_idx}.{layer}.{w}" ] compressed_sd[f"{prefix}.h.{std_idx}.attn.bias"] = state_dict[f"{prefix}.h.{teacher_idx}.attn.bias"] else: for layer in [ "attention.self.query", "attention.self.key", "attention.self.value", "attention.output.dense", "attention.output.LayerNorm", "intermediate.dense", "output.dense", "output.LayerNorm", ]: for w in ["weight", "bias"]: compressed_sd[f"{prefix}.encoder.layer.{std_idx}.{layer}.{w}"] = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}" ] std_idx += 1 # Language Modeling Head ###s if args.model_type == "roberta": for layer in ["lm_head.decoder.weight", "lm_head.bias"]: compressed_sd[f"{layer}"] = state_dict[f"{layer}"] if args.vocab_transform: for w in ["weight", "bias"]: compressed_sd[f"lm_head.dense.{w}"] = state_dict[f"lm_head.dense.{w}"] compressed_sd[f"lm_head.layer_norm.{w}"] = state_dict[f"lm_head.layer_norm.{w}"] elif args.model_type == "gpt2": for w in ["weight", "bias"]: compressed_sd[f"{prefix}.ln_f.{w}"] = state_dict[f"{prefix}.ln_f.{w}"] compressed_sd["lm_head.weight"] = state_dict["lm_head.weight"] print(f"N layers selected for distillation: {std_idx}") print(f"Number of params transferred for distillation: {len(compressed_sd.keys())}") print(f"Save transferred checkpoint to {args.dump_checkpoint}.") torch.save(compressed_sd, args.dump_checkpoint)

transformers/examples/research_projects/distillation/scripts/extract.py/0

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import torch from transformers import AutoTokenizer class FSNERTokenizerUtils(object): def __init__(self, pretrained_model_name_or_path): self.tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path) def tokenize(self, x): """ Wrapper function for tokenizing query and supports Args: x (`List[str] or List[List[str]]`): List of strings for query or list of lists of strings for supports. Returns: `transformers.tokenization_utils_base.BatchEncoding` dict with additional keys and values for start_token_id, end_token_id and sizes of example lists for each entity type """ if isinstance(x, list) and all(isinstance(_x, list) for _x in x): d = None for l in x: t = self.tokenizer( l, padding="max_length", max_length=384, truncation=True, return_tensors="pt", ) t["sizes"] = torch.tensor([len(l)]) if d is not None: for k in d.keys(): d[k] = torch.cat((d[k], t[k]), 0) else: d = t d["start_token_id"] = torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]")) d["end_token_id"] = torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]")) elif isinstance(x, list) and all(isinstance(_x, str) for _x in x): d = self.tokenizer( x, padding="max_length", max_length=384, truncation=True, return_tensors="pt", ) else: raise Exception( "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of" " strings` for supports are supported." ) return d def extract_entity_from_scores(self, query, W_query, p_start, p_end, thresh=0.70): """ Extracts entities from query and scores given a threshold. Args: query (`List[str]`): List of query strings. W_query (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of query sequence tokens in the vocabulary. p_start (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): Scores of each token as being start token of an entity p_end (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): Scores of each token as being end token of an entity thresh (`float`): Score threshold value Returns: A list of lists of tuples(decoded entity, score) """ final_outputs = [] for idx in range(len(W_query["input_ids"])): start_indexes = end_indexes = range(p_start.shape[1]) output = [] for start_id in start_indexes: for end_id in end_indexes: if start_id < end_id: output.append( ( start_id, end_id, p_start[idx][start_id].item(), p_end[idx][end_id].item(), ) ) output.sort(key=lambda tup: (tup[2] * tup[3]), reverse=True) temp = [] for k in range(len(output)): if output[k][2] * output[k][3] >= thresh: c_start_pos, c_end_pos = output[k][0], output[k][1] decoded = self.tokenizer.decode(W_query["input_ids"][idx][c_start_pos:c_end_pos]) temp.append((decoded, output[k][2] * output[k][3])) final_outputs.append(temp) return final_outputs

transformers/examples/research_projects/fsner/src/fsner/tokenizer_utils.py/0

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# Language model training examples in streaming mode The following examples showcase how to train a language model from scratch using the JAX/Flax backend. JAX/Flax allows you to trace pure functions and compile them into efficient, fused accelerator code on both GPU and TPU. Models written in JAX/Flax are **immutable** and updated in a purely functional way which enables simple and efficient model parallelism. All of the following examples make use of [dataset streaming](https://huggingface.co/docs/datasets/master/dataset_streaming), therefore allowing to train models on massive datasets\ without ever having to download the full dataset. ## Masked language modeling In the following, we demonstrate how to train a bi-directional transformer model using masked language modeling objective as introduced in [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805). More specifically, we demonstrate how JAX/Flax and dataset streaming can be leveraged to pre-train [**`FacebookAI/roberta-base`**](https://huggingface.co/FacebookAI/roberta-base) in English on a single TPUv3-8 pod for 10000 update steps. The example script uses the 🤗 Datasets library. You can easily customize them to your needs if you need extra processing on your datasets. Let's start by creating a model repository to save the trained model and logs. Here we call the model `"english-roberta-base-dummy"`, but you can change the model name as you like. You can do this either directly on [huggingface.co](https://huggingface.co/new) (assuming that you are logged in) or via the command line: ```bash huggingface-cli repo create english-roberta-base-dummy ``` Next we clone the model repository to add the tokenizer and model files. ```bash git clone https://huggingface.co/<your-username>/english-roberta-base-dummy ``` To ensure that all tensorboard traces will be uploaded correctly, we need to track them. You can run the following command inside your model repo to do so. ```bash cd english-roberta-base-dummy git lfs track "*tfevents*" ``` Great, we have set up our model repository. During training, we will automatically push the training logs and model weights to the repo. Next, let's add a symbolic link to the `run_mlm_flax.py`. ```bash export MODEL_DIR="./english-roberta-base-dummy" ln -s ~/transformers/examples/research_projects/jax-projects/dataset-streaming/run_mlm_flax_stream.py ./ ``` ### Copy config and tokenizer of existing model In this example, we will simply copy an existing config and tokenizer in English. You can run the following code in a Python shell to do so. ```python from transformers import RobertaTokenizerFast, RobertaConfig model_dir = "./english-roberta-base-dummy" tokenizer = RobertaTokenizerFast.from_pretrained("FacebookAI/roberta-base") config = RobertaConfig.from_pretrained("FacebookAI/roberta-base") tokenizer.save_pretrained(model_dir) config.save_pretrained(model_dir) ``` ### Train model Next we can run the example script to pretrain the model. Compared to the default [`run_mlm_flax`](https://github.com/huggingface/transformers/blob/main/examples/flax/language-modeling/run_mlm_flax.py), we introduced 4 new training settings: - `num_train_steps` - how many update steps should be run. - `num_eval_samples` - how many training samples should be taken for evaluation. - `logging_steps` - at what rate should the training loss be logged. - `eval_steps` - at what rate should evaluation be run. 10K update steps ```bash ./run_mlm_flax_stream.py \ --output_dir="${MODEL_DIR}" \ --model_type="roberta" \ --config_name="${MODEL_DIR}" \ --tokenizer_name="${MODEL_DIR}" \ --dataset_name="oscar" \ --dataset_config_name="unshuffled_deduplicated_en" \ --max_seq_length="128" \ --per_device_train_batch_size="128" \ --per_device_eval_batch_size="128" \ --learning_rate="3e-4" \ --warmup_steps="1000" \ --overwrite_output_dir \ --adam_beta1="0.9" \ --adam_beta2="0.98" \ --num_train_steps="10000" \ --num_eval_samples="5000" \ --logging_steps="250" \ --eval_steps="1000" \ --push_to_hub ```

transformers/examples/research_projects/jax-projects/dataset-streaming/README.md/0

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import datasets import faiss import numpy as np import streamlit as st import torch from elasticsearch import Elasticsearch from eli5_utils import ( embed_questions_for_retrieval, make_qa_s2s_model, qa_s2s_generate, query_es_index, query_qa_dense_index, ) import transformers from transformers import AutoModel, AutoModelForSeq2SeqLM, AutoTokenizer MODEL_TYPE = "bart" LOAD_DENSE_INDEX = True @st.cache(allow_output_mutation=True) def load_models(): if LOAD_DENSE_INDEX: qar_tokenizer = AutoTokenizer.from_pretrained("yjernite/retribert-base-uncased") qar_model = AutoModel.from_pretrained("yjernite/retribert-base-uncased").to("cuda:0") _ = qar_model.eval() else: qar_tokenizer, qar_model = (None, None) if MODEL_TYPE == "bart": s2s_tokenizer = AutoTokenizer.from_pretrained("yjernite/bart_eli5") s2s_model = AutoModelForSeq2SeqLM.from_pretrained("yjernite/bart_eli5").to("cuda:0") save_dict = torch.load("seq2seq_models/eli5_bart_model_blm_2.pth") s2s_model.load_state_dict(save_dict["model"]) _ = s2s_model.eval() else: s2s_tokenizer, s2s_model = make_qa_s2s_model( model_name="google-t5/t5-small", from_file="seq2seq_models/eli5_t5_model_1024_4.pth", device="cuda:0" ) return (qar_tokenizer, qar_model, s2s_tokenizer, s2s_model) @st.cache(allow_output_mutation=True) def load_indexes(): if LOAD_DENSE_INDEX: faiss_res = faiss.StandardGpuResources() wiki40b_passages = datasets.load_dataset(path="wiki_snippets", name="wiki40b_en_100_0")["train"] wiki40b_passage_reps = np.memmap( "wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat", dtype="float32", mode="r", shape=(wiki40b_passages.num_rows, 128), ) wiki40b_index_flat = faiss.IndexFlatIP(128) wiki40b_gpu_index_flat = faiss.index_cpu_to_gpu(faiss_res, 1, wiki40b_index_flat) wiki40b_gpu_index_flat.add(wiki40b_passage_reps) # TODO fix for larger GPU else: wiki40b_passages, wiki40b_gpu_index_flat = (None, None) es_client = Elasticsearch([{"host": "localhost", "port": "9200"}]) return (wiki40b_passages, wiki40b_gpu_index_flat, es_client) @st.cache(allow_output_mutation=True) def load_train_data(): eli5 = datasets.load_dataset("eli5", name="LFQA_reddit") eli5_train = eli5["train_eli5"] eli5_train_q_reps = np.memmap( "eli5_questions_reps.dat", dtype="float32", mode="r", shape=(eli5_train.num_rows, 128) ) eli5_train_q_index = faiss.IndexFlatIP(128) eli5_train_q_index.add(eli5_train_q_reps) return (eli5_train, eli5_train_q_index) passages, gpu_dense_index, es_client = load_indexes() qar_tokenizer, qar_model, s2s_tokenizer, s2s_model = load_models() eli5_train, eli5_train_q_index = load_train_data() def find_nearest_training(question, n_results=10): q_rep = embed_questions_for_retrieval([question], qar_tokenizer, qar_model) D, I = eli5_train_q_index.search(q_rep, n_results) nn_examples = [eli5_train[int(i)] for i in I[0]] return nn_examples def make_support(question, source="wiki40b", method="dense", n_results=10): if source == "none": support_doc, hit_lst = (" <P> ".join(["" for _ in range(11)]).strip(), []) else: if method == "dense": support_doc, hit_lst = query_qa_dense_index( question, qar_model, qar_tokenizer, passages, gpu_dense_index, n_results ) else: support_doc, hit_lst = query_es_index( question, es_client, index_name="english_wiki40b_snippets_100w", n_results=n_results, ) support_list = [ (res["article_title"], res["section_title"].strip(), res["score"], res["passage_text"]) for res in hit_lst ] question_doc = "question: {} context: {}".format(question, support_doc) return question_doc, support_list @st.cache( hash_funcs={ torch.Tensor: (lambda _: None), transformers.models.bart.tokenization_bart.BartTokenizer: (lambda _: None), } ) def answer_question( question_doc, s2s_model, s2s_tokenizer, min_len=64, max_len=256, sampling=False, n_beams=2, top_p=0.95, temp=0.8 ): with torch.no_grad(): answer = qa_s2s_generate( question_doc, s2s_model, s2s_tokenizer, num_answers=1, num_beams=n_beams, min_len=min_len, max_len=max_len, do_sample=sampling, temp=temp, top_p=top_p, top_k=None, max_input_length=1024, device="cuda:0", )[0] return (answer, support_list) st.title("Long Form Question Answering with ELI5") # Start sidebar header_html = "<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>" header_full = """ <html> <head> <style> .img-container { padding-left: 90px; padding-right: 90px; padding-top: 50px; padding-bottom: 50px; background-color: #f0f3f9; } </style> </head> <body> <span class="img-container">  %s </span> </body> </html> """ % (header_html,) st.sidebar.markdown( header_full, unsafe_allow_html=True, ) # Long Form QA with ELI5 and Wikipedia description = """ This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html). First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset, a pre-processed fixed snapshot of Wikipedia. """ st.sidebar.markdown(description, unsafe_allow_html=True) action_list = [ "Answer the question", "View the retrieved document only", "View the most similar ELI5 question and answer", "Show me everything, please!", ] demo_options = st.sidebar.checkbox("Demo options") if demo_options: action_st = st.sidebar.selectbox( "", action_list, index=3, ) action = action_list.index(action_st) show_type = st.sidebar.selectbox( "", ["Show full text of passages", "Show passage section titles"], index=0, ) show_passages = show_type == "Show full text of passages" else: action = 3 show_passages = True retrieval_options = st.sidebar.checkbox("Retrieval options") if retrieval_options: retriever_info = """ ### Information retriever options The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs. The answer is then generated by sequence to sequence model which takes the question and retrieved document as input. """ st.sidebar.markdown(retriever_info) wiki_source = st.sidebar.selectbox("Which Wikipedia format should the model use?", ["wiki40b", "none"]) index_type = st.sidebar.selectbox("Which Wikipedia indexer should the model use?", ["dense", "sparse", "mixed"]) else: wiki_source = "wiki40b" index_type = "dense" sampled = "beam" n_beams = 2 min_len = 64 max_len = 256 top_p = None temp = None generate_options = st.sidebar.checkbox("Generation options") if generate_options: generate_info = """ ### Answer generation options The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large) weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with **beam** search, or **sample** from the decoder's output probabilities. """ st.sidebar.markdown(generate_info) sampled = st.sidebar.selectbox("Would you like to use beam search or sample an answer?", ["beam", "sampled"]) min_len = st.sidebar.slider( "Minimum generation length", min_value=8, max_value=256, value=64, step=8, format=None, key=None ) max_len = st.sidebar.slider( "Maximum generation length", min_value=64, max_value=512, value=256, step=16, format=None, key=None ) if sampled == "beam": n_beams = st.sidebar.slider("Beam size", min_value=1, max_value=8, value=2, step=None, format=None, key=None) else: top_p = st.sidebar.slider( "Nucleus sampling p", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None ) temp = st.sidebar.slider( "Temperature", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None ) n_beams = None # start main text questions_list = [ "<MY QUESTION>", "How do people make chocolate?", "Why do we get a fever when we are sick?", "How can different animals perceive different colors?", "What is natural language processing?", "What's the best way to treat a sunburn?", "What exactly are vitamins ?", "How does nuclear energy provide electricity?", "What's the difference between viruses and bacteria?", "Why are flutes classified as woodwinds when most of them are made out of metal ?", "Why do people like drinking coffee even though it tastes so bad?", "What happens when wine ages? How does it make the wine taste better?", "If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?", "How can we set a date to the beginning or end of an artistic period? Doesn't the change happen gradually?", "How does New Zealand have so many large bird predators?", ] question_s = st.selectbox( "What would you like to ask? ---- select <MY QUESTION> to enter a new query", questions_list, index=1, ) if question_s == "<MY QUESTION>": question = st.text_input("Enter your question here:", "") else: question = question_s if st.button("Show me!"): if action in [0, 1, 3]: if index_type == "mixed": _, support_list_dense = make_support(question, source=wiki_source, method="dense", n_results=10) _, support_list_sparse = make_support(question, source=wiki_source, method="sparse", n_results=10) support_list = [] for res_d, res_s in zip(support_list_dense, support_list_sparse): if tuple(res_d) not in support_list: support_list += [tuple(res_d)] if tuple(res_s) not in support_list: support_list += [tuple(res_s)] support_list = support_list[:10] question_doc = "<P> " + " <P> ".join([res[-1] for res in support_list]) else: question_doc, support_list = make_support(question, source=wiki_source, method=index_type, n_results=10) if action in [0, 3]: answer, support_list = answer_question( question_doc, s2s_model, s2s_tokenizer, min_len=min_len, max_len=int(max_len), sampling=(sampled == "sampled"), n_beams=n_beams, top_p=top_p, temp=temp, ) st.markdown("### The model generated answer is:") st.write(answer) if action in [0, 1, 3] and wiki_source != "none": st.markdown("--- \n ### The model is drawing information from the following Wikipedia passages:") for i, res in enumerate(support_list): wiki_url = "https://en.wikipedia.org/wiki/{}".format(res[0].replace(" ", "_")) sec_titles = res[1].strip() if sec_titles == "": sections = "[{}]({})".format(res[0], wiki_url) else: sec_list = sec_titles.split(" & ") sections = " & ".join( ["[{}]({}#{})".format(sec.strip(), wiki_url, sec.strip().replace(" ", "_")) for sec in sec_list] ) st.markdown( "{0:02d} - **Article**: {1:<18} <br> _Section_: {2}".format(i + 1, res[0], sections), unsafe_allow_html=True, ) if show_passages: st.write( '> <span style="font-family:arial; font-size:10pt;">' + res[-1] + "</span>", unsafe_allow_html=True ) if action in [2, 3]: nn_train_list = find_nearest_training(question) train_exple = nn_train_list[0] st.markdown( "--- \n ### The most similar question in the ELI5 training set was: \n\n {}".format(train_exple["title"]) ) answers_st = [ "{}. {}".format(i + 1, " \n".join([line.strip() for line in ans.split("\n") if line.strip() != ""])) for i, (ans, sc) in enumerate(zip(train_exple["answers"]["text"], train_exple["answers"]["score"])) if i == 0 or sc > 2 ] st.markdown("##### Its answers were: \n\n {}".format("\n".join(answers_st))) disclaimer = """ --- **Disclaimer** *The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system. Evaluating biases of such a model and ensuring factual generations are still very much open research problems. Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.* """ st.sidebar.markdown(disclaimer, unsafe_allow_html=True)

transformers/examples/research_projects/longform-qa/eli5_app.py/0

{ "file_path": "transformers/examples/research_projects/longform-qa/eli5_app.py", "repo_id": "transformers", "token_count": 5823 }

57

import argparse import json from typing import List from ltp import LTP from transformers.models.bert.tokenization_bert import BertTokenizer def _is_chinese_char(cp): """Checks whether CP is the codepoint of a CJK character.""" # This defines a "chinese character" as anything in the CJK Unicode block: # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) # # Note that the CJK Unicode block is NOT all Japanese and Korean characters, # despite its name. The modern Korean Hangul alphabet is a different block, # as is Japanese Hiragana and Katakana. Those alphabets are used to write # space-separated words, so they are not treated specially and handled # like the all of the other languages. if ( (cp >= 0x4E00 and cp <= 0x9FFF) or (cp >= 0x3400 and cp <= 0x4DBF) # or (cp >= 0x20000 and cp <= 0x2A6DF) # or (cp >= 0x2A700 and cp <= 0x2B73F) # or (cp >= 0x2B740 and cp <= 0x2B81F) # or (cp >= 0x2B820 and cp <= 0x2CEAF) # or (cp >= 0xF900 and cp <= 0xFAFF) or (cp >= 0x2F800 and cp <= 0x2FA1F) # ): # return True return False def is_chinese(word: str): # word like '180' or '身高' or '神' for char in word: char = ord(char) if not _is_chinese_char(char): return 0 return 1 def get_chinese_word(tokens: List[str]): word_set = set() for token in tokens: chinese_word = len(token) > 1 and is_chinese(token) if chinese_word: word_set.add(token) word_list = list(word_set) return word_list def add_sub_symbol(bert_tokens: List[str], chinese_word_set: set()): if not chinese_word_set: return bert_tokens max_word_len = max([len(w) for w in chinese_word_set]) bert_word = bert_tokens start, end = 0, len(bert_word) while start < end: single_word = True if is_chinese(bert_word[start]): l = min(end - start, max_word_len) for i in range(l, 1, -1): whole_word = "".join(bert_word[start : start + i]) if whole_word in chinese_word_set: for j in range(start + 1, start + i): bert_word[j] = "##" + bert_word[j] start = start + i single_word = False break if single_word: start += 1 return bert_word def prepare_ref(lines: List[str], ltp_tokenizer: LTP, bert_tokenizer: BertTokenizer): ltp_res = [] for i in range(0, len(lines), 100): res = ltp_tokenizer.pipeline(lines[i : i + 100], tasks=["cws"]).cws res = [get_chinese_word(r) for r in res] ltp_res.extend(res) assert len(ltp_res) == len(lines) bert_res = [] for i in range(0, len(lines), 100): res = bert_tokenizer(lines[i : i + 100], add_special_tokens=True, truncation=True, max_length=512) bert_res.extend(res["input_ids"]) assert len(bert_res) == len(lines) ref_ids = [] for input_ids, chinese_word in zip(bert_res, ltp_res): input_tokens = [] for id in input_ids: token = bert_tokenizer._convert_id_to_token(id) input_tokens.append(token) input_tokens = add_sub_symbol(input_tokens, chinese_word) ref_id = [] # We only save pos of chinese subwords start with ##, which mean is part of a whole word. for i, token in enumerate(input_tokens): if token[:2] == "##": clean_token = token[2:] # save chinese tokens' pos if len(clean_token) == 1 and _is_chinese_char(ord(clean_token)): ref_id.append(i) ref_ids.append(ref_id) assert len(ref_ids) == len(bert_res) return ref_ids def main(args): # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm) # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp) with open(args.file_name, "r", encoding="utf-8") as f: data = f.readlines() data = [line.strip() for line in data if len(line) > 0 and not line.isspace()] # avoid delimiter like '\u2029' ltp_tokenizer = LTP(args.ltp) # faster in GPU device bert_tokenizer = BertTokenizer.from_pretrained(args.bert) ref_ids = prepare_ref(data, ltp_tokenizer, bert_tokenizer) with open(args.save_path, "w", encoding="utf-8") as f: data = [json.dumps(ref) + "\n" for ref in ref_ids] f.writelines(data) if __name__ == "__main__": parser = argparse.ArgumentParser(description="prepare_chinese_ref") parser.add_argument( "--file_name", required=False, type=str, default="./resources/chinese-demo.txt", help="file need process, same as training data in lm", ) parser.add_argument( "--ltp", required=False, type=str, default="./resources/ltp", help="resources for LTP tokenizer, usually a path", ) parser.add_argument( "--bert", required=False, type=str, default="./resources/robert", help="resources for Bert tokenizer", ) parser.add_argument( "--save_path", required=False, type=str, default="./resources/ref.txt", help="path to save res", ) args = parser.parse_args() main(args)

transformers/examples/research_projects/mlm_wwm/run_chinese_ref.py/0

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58

# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-pruning Masked BERT for question-answering on SQuAD.""" import argparse import glob import logging import os import random import timeit import numpy as np import torch from emmental import MaskedBertConfig, MaskedBertForQuestionAnswering from torch import nn from torch.utils.data import DataLoader, RandomSampler, SequentialSampler from torch.utils.data.distributed import DistributedSampler from tqdm import tqdm, trange from transformers import ( WEIGHTS_NAME, AdamW, BertConfig, BertForQuestionAnswering, BertTokenizer, get_linear_schedule_with_warmup, squad_convert_examples_to_features, ) from transformers.data.metrics.squad_metrics import ( compute_predictions_log_probs, compute_predictions_logits, squad_evaluate, ) from transformers.data.processors.squad import SquadResult, SquadV1Processor, SquadV2Processor try: from torch.utils.tensorboard import SummaryWriter except ImportError: from tensorboardX import SummaryWriter logger = logging.getLogger(__name__) MODEL_CLASSES = { "bert": (BertConfig, BertForQuestionAnswering, BertTokenizer), "masked_bert": (MaskedBertConfig, MaskedBertForQuestionAnswering, BertTokenizer), } def set_seed(args): random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if args.n_gpu > 0: torch.cuda.manual_seed_all(args.seed) def schedule_threshold( step: int, total_step: int, warmup_steps: int, initial_threshold: float, final_threshold: float, initial_warmup: int, final_warmup: int, final_lambda: float, ): if step <= initial_warmup * warmup_steps: threshold = initial_threshold elif step > (total_step - final_warmup * warmup_steps): threshold = final_threshold else: spars_warmup_steps = initial_warmup * warmup_steps spars_schedu_steps = (final_warmup + initial_warmup) * warmup_steps mul_coeff = 1 - (step - spars_warmup_steps) / (total_step - spars_schedu_steps) threshold = final_threshold + (initial_threshold - final_threshold) * (mul_coeff**3) regu_lambda = final_lambda * threshold / final_threshold return threshold, regu_lambda def regularization(model: nn.Module, mode: str): regu, counter = 0, 0 for name, param in model.named_parameters(): if "mask_scores" in name: if mode == "l1": regu += torch.norm(torch.sigmoid(param), p=1) / param.numel() elif mode == "l0": regu += torch.sigmoid(param - 2 / 3 * np.log(0.1 / 1.1)).sum() / param.numel() else: ValueError("Don't know this mode.") counter += 1 return regu / counter def to_list(tensor): return tensor.detach().cpu().tolist() def train(args, train_dataset, model, tokenizer, teacher=None): """Train the model""" if args.local_rank in [-1, 0]: tb_writer = SummaryWriter(log_dir=args.output_dir) args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu) train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset) train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size) if args.max_steps > 0: t_total = args.max_steps args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1 else: t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs # Prepare optimizer and schedule (linear warmup and decay) no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if "mask_score" in n and p.requires_grad], "lr": args.mask_scores_learning_rate, }, { "params": [ p for n, p in model.named_parameters() if "mask_score" not in n and p.requires_grad and not any(nd in n for nd in no_decay) ], "lr": args.learning_rate, "weight_decay": args.weight_decay, }, { "params": [ p for n, p in model.named_parameters() if "mask_score" not in n and p.requires_grad and any(nd in n for nd in no_decay) ], "lr": args.learning_rate, "weight_decay": 0.0, }, ] optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon) scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total ) # Check if saved optimizer or scheduler states exist if os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt")) and os.path.isfile( os.path.join(args.model_name_or_path, "scheduler.pt") ): # Load in optimizer and scheduler states optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt"))) scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt"))) if args.fp16: try: from apex import amp except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level) # multi-gpu training (should be after apex fp16 initialization) if args.n_gpu > 1: model = nn.DataParallel(model) # Distributed training (should be after apex fp16 initialization) if args.local_rank != -1: model = nn.parallel.DistributedDataParallel( model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True, ) # Train! logger.info("***** Running training *****") logger.info(" Num examples = %d", len(train_dataset)) logger.info(" Num Epochs = %d", args.num_train_epochs) logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size) logger.info( " Total train batch size (w. parallel, distributed & accumulation) = %d", args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1), ) logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps) logger.info(" Total optimization steps = %d", t_total) # Distillation if teacher is not None: logger.info(" Training with distillation") global_step = 1 # Global TopK if args.global_topk: threshold_mem = None epochs_trained = 0 steps_trained_in_current_epoch = 0 # Check if continuing training from a checkpoint if os.path.exists(args.model_name_or_path): # set global_step to global_step of last saved checkpoint from model path try: checkpoint_suffix = args.model_name_or_path.split("-")[-1].split("/")[0] global_step = int(checkpoint_suffix) epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps) steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps) logger.info(" Continuing training from checkpoint, will skip to saved global_step") logger.info(" Continuing training from epoch %d", epochs_trained) logger.info(" Continuing training from global step %d", global_step) logger.info(" Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch) except ValueError: logger.info(" Starting fine-tuning.") tr_loss, logging_loss = 0.0, 0.0 model.zero_grad() train_iterator = trange( epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0] ) # Added here for reproducibility set_seed(args) for _ in train_iterator: epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0]) for step, batch in enumerate(epoch_iterator): # Skip past any already trained steps if resuming training if steps_trained_in_current_epoch > 0: steps_trained_in_current_epoch -= 1 continue model.train() batch = tuple(t.to(args.device) for t in batch) threshold, regu_lambda = schedule_threshold( step=global_step, total_step=t_total, warmup_steps=args.warmup_steps, final_threshold=args.final_threshold, initial_threshold=args.initial_threshold, final_warmup=args.final_warmup, initial_warmup=args.initial_warmup, final_lambda=args.final_lambda, ) # Global TopK if args.global_topk: if threshold == 1.0: threshold = -1e2 # Or an indefinitely low quantity else: if (threshold_mem is None) or (global_step % args.global_topk_frequency_compute == 0): # Sort all the values to get the global topK concat = torch.cat( [param.view(-1) for name, param in model.named_parameters() if "mask_scores" in name] ) n = concat.numel() kth = max(n - (int(n * threshold) + 1), 1) threshold_mem = concat.kthvalue(kth).values.item() threshold = threshold_mem else: threshold = threshold_mem inputs = { "input_ids": batch[0], "attention_mask": batch[1], "token_type_ids": batch[2], "start_positions": batch[3], "end_positions": batch[4], } if args.model_type in ["xlm", "roberta", "distilbert", "camembert"]: del inputs["token_type_ids"] if args.model_type in ["xlnet", "xlm"]: inputs.update({"cls_index": batch[5], "p_mask": batch[6]}) if args.version_2_with_negative: inputs.update({"is_impossible": batch[7]}) if hasattr(model, "config") and hasattr(model.config, "lang2id"): inputs.update( {"langs": (torch.ones(batch[0].shape, dtype=torch.int64) * args.lang_id).to(args.device)} ) if "masked" in args.model_type: inputs["threshold"] = threshold outputs = model(**inputs) # model outputs are always tuple in transformers (see doc) loss, start_logits_stu, end_logits_stu = outputs # Distillation loss if teacher is not None: with torch.no_grad(): start_logits_tea, end_logits_tea = teacher( input_ids=inputs["input_ids"], token_type_ids=inputs["token_type_ids"], attention_mask=inputs["attention_mask"], ) loss_start = nn.functional.kl_div( input=nn.functional.log_softmax(start_logits_stu / args.temperature, dim=-1), target=nn.functional.softmax(start_logits_tea / args.temperature, dim=-1), reduction="batchmean", ) * (args.temperature**2) loss_end = nn.functional.kl_div( input=nn.functional.log_softmax(end_logits_stu / args.temperature, dim=-1), target=nn.functional.softmax(end_logits_tea / args.temperature, dim=-1), reduction="batchmean", ) * (args.temperature**2) loss_logits = (loss_start + loss_end) / 2.0 loss = args.alpha_distil * loss_logits + args.alpha_ce * loss # Regularization if args.regularization is not None: regu_ = regularization(model=model, mode=args.regularization) loss = loss + regu_lambda * regu_ if args.n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu parallel training if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps if args.fp16: with amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward() else: loss.backward() tr_loss += loss.item() if (step + 1) % args.gradient_accumulation_steps == 0: if args.fp16: nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm) else: nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm) if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: tb_writer.add_scalar("threshold", threshold, global_step) for name, param in model.named_parameters(): if not param.requires_grad: continue tb_writer.add_scalar("parameter_mean/" + name, param.data.mean(), global_step) tb_writer.add_scalar("parameter_std/" + name, param.data.std(), global_step) tb_writer.add_scalar("parameter_min/" + name, param.data.min(), global_step) tb_writer.add_scalar("parameter_max/" + name, param.data.max(), global_step) if "pooler" in name: continue tb_writer.add_scalar("grad_mean/" + name, param.grad.data.mean(), global_step) tb_writer.add_scalar("grad_std/" + name, param.grad.data.std(), global_step) if args.regularization is not None and "mask_scores" in name: if args.regularization == "l1": perc = (torch.sigmoid(param) > threshold).sum().item() / param.numel() elif args.regularization == "l0": perc = (torch.sigmoid(param - 2 / 3 * np.log(0.1 / 1.1))).sum().item() / param.numel() tb_writer.add_scalar("retained_weights_perc/" + name, perc, global_step) optimizer.step() scheduler.step() # Update learning rate schedule model.zero_grad() global_step += 1 # Log metrics if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: # Only evaluate when single GPU otherwise metrics may not average well if args.local_rank == -1 and args.evaluate_during_training: results = evaluate(args, model, tokenizer) for key, value in results.items(): tb_writer.add_scalar("eval_{}".format(key), value, global_step) learning_rate_scalar = scheduler.get_lr() tb_writer.add_scalar("lr", learning_rate_scalar[0], global_step) if len(learning_rate_scalar) > 1: for idx, lr in enumerate(learning_rate_scalar[1:]): tb_writer.add_scalar(f"lr/{idx+1}", lr, global_step) tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args.logging_steps, global_step) if teacher is not None: tb_writer.add_scalar("loss/distil", loss_logits.item(), global_step) if args.regularization is not None: tb_writer.add_scalar("loss/regularization", regu_.item(), global_step) if (teacher is not None) or (args.regularization is not None): if (teacher is not None) and (args.regularization is not None): tb_writer.add_scalar( "loss/instant_ce", (loss.item() - regu_lambda * regu_.item() - args.alpha_distil * loss_logits.item()) / args.alpha_ce, global_step, ) elif teacher is not None: tb_writer.add_scalar( "loss/instant_ce", (loss.item() - args.alpha_distil * loss_logits.item()) / args.alpha_ce, global_step, ) else: tb_writer.add_scalar( "loss/instant_ce", loss.item() - regu_lambda * regu_.item(), global_step ) logging_loss = tr_loss # Save model checkpoint if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0: output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step)) if not os.path.exists(output_dir): os.makedirs(output_dir) # Take care of distributed/parallel training model_to_save = model.module if hasattr(model, "module") else model model_to_save.save_pretrained(output_dir) tokenizer.save_pretrained(output_dir) torch.save(args, os.path.join(output_dir, "training_args.bin")) logger.info("Saving model checkpoint to %s", output_dir) torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt")) torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt")) logger.info("Saving optimizer and scheduler states to %s", output_dir) if args.max_steps > 0 and global_step > args.max_steps: epoch_iterator.close() break if args.max_steps > 0 and global_step > args.max_steps: train_iterator.close() break if args.local_rank in [-1, 0]: tb_writer.close() return global_step, tr_loss / global_step def evaluate(args, model, tokenizer, prefix=""): dataset, examples, features = load_and_cache_examples(args, tokenizer, evaluate=True, output_examples=True) if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]: os.makedirs(args.output_dir) args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) # Note that DistributedSampler samples randomly eval_sampler = SequentialSampler(dataset) eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) # multi-gpu eval if args.n_gpu > 1 and not isinstance(model, nn.DataParallel): model = nn.DataParallel(model) # Eval! logger.info("***** Running evaluation {} *****".format(prefix)) logger.info(" Num examples = %d", len(dataset)) logger.info(" Batch size = %d", args.eval_batch_size) all_results = [] start_time = timeit.default_timer() # Global TopK if args.global_topk: threshold_mem = None for batch in tqdm(eval_dataloader, desc="Evaluating"): model.eval() batch = tuple(t.to(args.device) for t in batch) with torch.no_grad(): inputs = { "input_ids": batch[0], "attention_mask": batch[1], "token_type_ids": batch[2], } if args.model_type in ["xlm", "roberta", "distilbert", "camembert"]: del inputs["token_type_ids"] example_indices = batch[3] # XLNet and XLM use more arguments for their predictions if args.model_type in ["xlnet", "xlm"]: inputs.update({"cls_index": batch[4], "p_mask": batch[5]}) # for lang_id-sensitive xlm models if hasattr(model, "config") and hasattr(model.config, "lang2id"): inputs.update( {"langs": (torch.ones(batch[0].shape, dtype=torch.int64) * args.lang_id).to(args.device)} ) if "masked" in args.model_type: inputs["threshold"] = args.final_threshold if args.global_topk: if threshold_mem is None: concat = torch.cat( [param.view(-1) for name, param in model.named_parameters() if "mask_scores" in name] ) n = concat.numel() kth = max(n - (int(n * args.final_threshold) + 1), 1) threshold_mem = concat.kthvalue(kth).values.item() inputs["threshold"] = threshold_mem outputs = model(**inputs) for i, example_index in enumerate(example_indices): eval_feature = features[example_index.item()] unique_id = int(eval_feature.unique_id) output = [to_list(output[i]) for output in outputs] # Some models (XLNet, XLM) use 5 arguments for their predictions, while the other "simpler" # models only use two. if len(output) >= 5: start_logits = output[0] start_top_index = output[1] end_logits = output[2] end_top_index = output[3] cls_logits = output[4] result = SquadResult( unique_id, start_logits, end_logits, start_top_index=start_top_index, end_top_index=end_top_index, cls_logits=cls_logits, ) else: start_logits, end_logits = output result = SquadResult(unique_id, start_logits, end_logits) all_results.append(result) evalTime = timeit.default_timer() - start_time logger.info(" Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(dataset)) # Compute predictions output_prediction_file = os.path.join(args.output_dir, "predictions_{}.json".format(prefix)) output_nbest_file = os.path.join(args.output_dir, "nbest_predictions_{}.json".format(prefix)) if args.version_2_with_negative: output_null_log_odds_file = os.path.join(args.output_dir, "null_odds_{}.json".format(prefix)) else: output_null_log_odds_file = None # XLNet and XLM use a more complex post-processing procedure if args.model_type in ["xlnet", "xlm"]: start_n_top = model.config.start_n_top if hasattr(model, "config") else model.module.config.start_n_top end_n_top = model.config.end_n_top if hasattr(model, "config") else model.module.config.end_n_top predictions = compute_predictions_log_probs( examples, features, all_results, args.n_best_size, args.max_answer_length, output_prediction_file, output_nbest_file, output_null_log_odds_file, start_n_top, end_n_top, args.version_2_with_negative, tokenizer, args.verbose_logging, ) else: predictions = compute_predictions_logits( examples, features, all_results, args.n_best_size, args.max_answer_length, args.do_lower_case, output_prediction_file, output_nbest_file, output_null_log_odds_file, args.verbose_logging, args.version_2_with_negative, args.null_score_diff_threshold, tokenizer, ) # Compute the F1 and exact scores. results = squad_evaluate(examples, predictions) return results def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False): if args.local_rank not in [-1, 0] and not evaluate: # Make sure only the first process in distributed training process the dataset, and the others will use the cache torch.distributed.barrier() # Load data features from cache or dataset file input_dir = args.data_dir if args.data_dir else "." cached_features_file = os.path.join( input_dir, "cached_{}_{}_{}_{}".format( "dev" if evaluate else "train", args.tokenizer_name if args.tokenizer_name else list(filter(None, args.model_name_or_path.split("/"))).pop(), str(args.max_seq_length), list(filter(None, args.predict_file.split("/"))).pop() if evaluate else list(filter(None, args.train_file.split("/"))).pop(), ), ) # Init features and dataset from cache if it exists if os.path.exists(cached_features_file) and not args.overwrite_cache: logger.info("Loading features from cached file %s", cached_features_file) features_and_dataset = torch.load(cached_features_file) features, dataset, examples = ( features_and_dataset["features"], features_and_dataset["dataset"], features_and_dataset["examples"], ) else: logger.info("Creating features from dataset file at %s", input_dir) if not args.data_dir and ((evaluate and not args.predict_file) or (not evaluate and not args.train_file)): try: import tensorflow_datasets as tfds except ImportError: raise ImportError("If not data_dir is specified, tensorflow_datasets needs to be installed.") if args.version_2_with_negative: logger.warning("tensorflow_datasets does not handle version 2 of SQuAD.") tfds_examples = tfds.load("squad") examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate) else: processor = SquadV2Processor() if args.version_2_with_negative else SquadV1Processor() if evaluate: examples = processor.get_dev_examples(args.data_dir, filename=args.predict_file) else: examples = processor.get_train_examples(args.data_dir, filename=args.train_file) features, dataset = squad_convert_examples_to_features( examples=examples, tokenizer=tokenizer, max_seq_length=args.max_seq_length, doc_stride=args.doc_stride, max_query_length=args.max_query_length, is_training=not evaluate, return_dataset="pt", threads=args.threads, ) if args.local_rank in [-1, 0]: logger.info("Saving features into cached file %s", cached_features_file) torch.save({"features": features, "dataset": dataset, "examples": examples}, cached_features_file) if args.local_rank == 0 and not evaluate: # Make sure only the first process in distributed training process the dataset, and the others will use the cache torch.distributed.barrier() if output_examples: return dataset, examples, features return dataset def main(): parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_type", default=None, type=str, required=True, help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()), ) parser.add_argument( "--model_name_or_path", default=None, type=str, required=True, help="Path to pretrained model or model identifier from huggingface.co/models", ) parser.add_argument( "--output_dir", default=None, type=str, required=True, help="The output directory where the model checkpoints and predictions will be written.", ) # Other parameters parser.add_argument( "--data_dir", default=None, type=str, help="The input data dir. Should contain the .json files for the task." + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.", ) parser.add_argument( "--train_file", default=None, type=str, help="The input training file. If a data dir is specified, will look for the file there" + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.", ) parser.add_argument( "--predict_file", default=None, type=str, help="The input evaluation file. If a data dir is specified, will look for the file there" + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.", ) parser.add_argument( "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name" ) parser.add_argument( "--tokenizer_name", default="", type=str, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--cache_dir", default="", type=str, help="Where do you want to store the pre-trained models downloaded from huggingface.co", ) parser.add_argument( "--version_2_with_negative", action="store_true", help="If true, the SQuAD examples contain some that do not have an answer.", ) parser.add_argument( "--null_score_diff_threshold", type=float, default=0.0, help="If null_score - best_non_null is greater than the threshold predict null.", ) parser.add_argument( "--max_seq_length", default=384, type=int, help=( "The maximum total input sequence length after WordPiece tokenization. Sequences " "longer than this will be truncated, and sequences shorter than this will be padded." ), ) parser.add_argument( "--doc_stride", default=128, type=int, help="When splitting up a long document into chunks, how much stride to take between chunks.", ) parser.add_argument( "--max_query_length", default=64, type=int, help=( "The maximum number of tokens for the question. Questions longer than this will " "be truncated to this length." ), ) parser.add_argument("--do_train", action="store_true", help="Whether to run training.") parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.") parser.add_argument( "--evaluate_during_training", action="store_true", help="Run evaluation during training at each logging step." ) parser.add_argument( "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model." ) parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.") parser.add_argument( "--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation." ) parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") # Pruning parameters parser.add_argument( "--mask_scores_learning_rate", default=1e-2, type=float, help="The Adam initial learning rate of the mask scores.", ) parser.add_argument( "--initial_threshold", default=1.0, type=float, help="Initial value of the threshold (for scheduling)." ) parser.add_argument( "--final_threshold", default=0.7, type=float, help="Final value of the threshold (for scheduling)." ) parser.add_argument( "--initial_warmup", default=1, type=int, help=( "Run `initial_warmup` * `warmup_steps` steps of threshold warmup during which threshold stays " "at its `initial_threshold` value (sparsity schedule)." ), ) parser.add_argument( "--final_warmup", default=2, type=int, help=( "Run `final_warmup` * `warmup_steps` steps of threshold cool-down during which threshold stays " "at its final_threshold value (sparsity schedule)." ), ) parser.add_argument( "--pruning_method", default="topK", type=str, help=( "Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning," " sigmoied_threshold = Soft movement pruning)." ), ) parser.add_argument( "--mask_init", default="constant", type=str, help="Initialization method for the mask scores. Choices: constant, uniform, kaiming.", ) parser.add_argument( "--mask_scale", default=0.0, type=float, help="Initialization parameter for the chosen initialization method." ) parser.add_argument("--regularization", default=None, help="Add L0 or L1 regularization to the mask scores.") parser.add_argument( "--final_lambda", default=0.0, type=float, help="Regularization intensity (used in conjunction with `regularization`.", ) parser.add_argument("--global_topk", action="store_true", help="Global TopK on the Scores.") parser.add_argument( "--global_topk_frequency_compute", default=25, type=int, help="Frequency at which we compute the TopK global threshold.", ) # Distillation parameters (optional) parser.add_argument( "--teacher_type", default=None, type=str, help=( "Teacher type. Teacher tokenizer and student (model) tokenizer must output the same tokenization. Only for" " distillation." ), ) parser.add_argument( "--teacher_name_or_path", default=None, type=str, help="Path to the already SQuAD fine-tuned teacher model. Only for distillation.", ) parser.add_argument( "--alpha_ce", default=0.5, type=float, help="Cross entropy loss linear weight. Only for distillation." ) parser.add_argument( "--alpha_distil", default=0.5, type=float, help="Distillation loss linear weight. Only for distillation." ) parser.add_argument( "--temperature", default=2.0, type=float, help="Distillation temperature. Only for distillation." ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.") parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.") parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument( "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform.", ) parser.add_argument( "--max_steps", default=-1, type=int, help="If > 0: set total number of training steps to perform. Override num_train_epochs.", ) parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.") parser.add_argument( "--n_best_size", default=20, type=int, help="The total number of n-best predictions to generate in the nbest_predictions.json output file.", ) parser.add_argument( "--max_answer_length", default=30, type=int, help=( "The maximum length of an answer that can be generated. This is needed because the start " "and end predictions are not conditioned on one another." ), ) parser.add_argument( "--verbose_logging", action="store_true", help=( "If true, all of the warnings related to data processing will be printed. " "A number of warnings are expected for a normal SQuAD evaluation." ), ) parser.add_argument( "--lang_id", default=0, type=int, help=( "language id of input for language-specific xlm models (see" " tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)" ), ) parser.add_argument("--logging_steps", type=int, default=500, help="Log every X updates steps.") parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.") parser.add_argument( "--eval_all_checkpoints", action="store_true", help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number", ) parser.add_argument("--no_cuda", action="store_true", help="Whether not to use CUDA when available") parser.add_argument( "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory" ) parser.add_argument( "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets" ) parser.add_argument("--seed", type=int, default=42, help="random seed for initialization") parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus") parser.add_argument( "--fp16", action="store_true", help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit", ) parser.add_argument( "--fp16_opt_level", type=str, default="O1", help=( "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. " "See details at https://nvidia.github.io/apex/amp.html" ), ) parser.add_argument("--server_ip", type=str, default="", help="Can be used for distant debugging.") parser.add_argument("--server_port", type=str, default="", help="Can be used for distant debugging.") parser.add_argument("--threads", type=int, default=1, help="multiple threads for converting example to features") args = parser.parse_args() # Regularization if args.regularization == "null": args.regularization = None if args.doc_stride >= args.max_seq_length - args.max_query_length: logger.warning( "WARNING - You've set a doc stride which may be superior to the document length in some " "examples. This could result in errors when building features from the examples. Please reduce the doc " "stride or increase the maximum length to ensure the features are correctly built." ) if ( os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir ): raise ValueError( "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format( args.output_dir ) ) # Setup distant debugging if needed if args.server_ip and args.server_port: # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script import ptvsd print("Waiting for debugger attach") ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) ptvsd.wait_for_attach() # Setup CUDA, GPU & distributed training if args.local_rank == -1 or args.no_cuda: device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count() else: # Initializes the distributed backend which will take care of synchronizing nodes/GPUs torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) torch.distributed.init_process_group(backend="nccl") args.n_gpu = 1 args.device = device # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN, ) logger.warning( "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16, ) # Set seed set_seed(args) # Load pretrained model and tokenizer if args.local_rank not in [-1, 0]: # Make sure only the first process in distributed training will download model & vocab torch.distributed.barrier() args.model_type = args.model_type.lower() config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type] config = config_class.from_pretrained( args.config_name if args.config_name else args.model_name_or_path, cache_dir=args.cache_dir if args.cache_dir else None, pruning_method=args.pruning_method, mask_init=args.mask_init, mask_scale=args.mask_scale, ) tokenizer = tokenizer_class.from_pretrained( args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case, cache_dir=args.cache_dir if args.cache_dir else None, ) model = model_class.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, cache_dir=args.cache_dir if args.cache_dir else None, ) if args.teacher_type is not None: assert args.teacher_name_or_path is not None assert args.alpha_distil > 0.0 assert args.alpha_distil + args.alpha_ce > 0.0 teacher_config_class, teacher_model_class, _ = MODEL_CLASSES[args.teacher_type] teacher_config = teacher_config_class.from_pretrained(args.teacher_name_or_path) teacher = teacher_model_class.from_pretrained( args.teacher_name_or_path, from_tf=False, config=teacher_config, cache_dir=args.cache_dir if args.cache_dir else None, ) teacher.to(args.device) else: teacher = None if args.local_rank == 0: # Make sure only the first process in distributed training will download model & vocab torch.distributed.barrier() model.to(args.device) logger.info("Training/evaluation parameters %s", args) # Before we do anything with models, we want to ensure that we get fp16 execution of torch.einsum if args.fp16 is set. # Otherwise it'll default to "promote" mode, and we'll get fp32 operations. Note that running `--fp16_opt_level="O2"` will # remove the need for this code, but it is still valid. if args.fp16: try: import apex apex.amp.register_half_function(torch, "einsum") except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") # Training if args.do_train: train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False) global_step, tr_loss = train(args, train_dataset, model, tokenizer, teacher=teacher) logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) # Save the trained model and the tokenizer if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0): logger.info("Saving model checkpoint to %s", args.output_dir) # Save a trained model, configuration and tokenizer using `save_pretrained()`. # They can then be reloaded using `from_pretrained()` # Take care of distributed/parallel training model_to_save = model.module if hasattr(model, "module") else model model_to_save.save_pretrained(args.output_dir) tokenizer.save_pretrained(args.output_dir) # Good practice: save your training arguments together with the trained model torch.save(args, os.path.join(args.output_dir, "training_args.bin")) # Load a trained model and vocabulary that you have fine-tuned model = model_class.from_pretrained(args.output_dir) # , force_download=True) tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) model.to(args.device) # Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory results = {} if args.do_eval and args.local_rank in [-1, 0]: if args.do_train: logger.info("Loading checkpoints saved during training for evaluation") checkpoints = [args.output_dir] if args.eval_all_checkpoints: checkpoints = [ os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True)) ] else: logger.info("Loading checkpoint %s for evaluation", args.model_name_or_path) checkpoints = [args.model_name_or_path] logger.info("Evaluate the following checkpoints: %s", checkpoints) for checkpoint in checkpoints: # Reload the model global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else "" model = model_class.from_pretrained(checkpoint) # , force_download=True) model.to(args.device) # Evaluate result = evaluate(args, model, tokenizer, prefix=global_step) result = {k + ("_{}".format(global_step) if global_step else ""): v for k, v in result.items()} results.update(result) logger.info("Results: {}".format(results)) predict_file = list(filter(None, args.predict_file.split("/"))).pop() if not os.path.exists(os.path.join(args.output_dir, predict_file)): os.makedirs(os.path.join(args.output_dir, predict_file)) output_eval_file = os.path.join(args.output_dir, predict_file, "eval_results.txt") with open(output_eval_file, "w") as writer: for key in sorted(results.keys()): writer.write("%s = %s\n" % (key, str(results[key]))) return results if __name__ == "__main__": main()

transformers/examples/research_projects/movement-pruning/masked_run_squad.py/0

{ "file_path": "transformers/examples/research_projects/movement-pruning/masked_run_squad.py", "repo_id": "transformers", "token_count": 21547 }

59

from torch import nn class ClassificationHead(nn.Module): """Classification Head for transformer encoders""" def __init__(self, class_size, embed_size): super().__init__() self.class_size = class_size self.embed_size = embed_size # self.mlp1 = nn.Linear(embed_size, embed_size) # self.mlp2 = (nn.Linear(embed_size, class_size)) self.mlp = nn.Linear(embed_size, class_size) def forward(self, hidden_state): # hidden_state = nn.functional.relu(self.mlp1(hidden_state)) # hidden_state = self.mlp2(hidden_state) logits = self.mlp(hidden_state) return logits

transformers/examples/research_projects/pplm/pplm_classification_head.py/0

{ "file_path": "transformers/examples/research_projects/pplm/pplm_classification_head.py", "repo_id": "transformers", "token_count": 281 }

60

"""Finetuning script for RAG models. Adapted from examples.seq2seq.finetune.py""" import argparse import copy import json import logging import multiprocessing import os import random import shutil import sys import time from collections import defaultdict from pathlib import Path from typing import Any, Dict, List, Tuple import numpy as np import pytorch_lightning as pl import torch import torch.distributed as dist from datasets import concatenate_datasets, load_from_disk from torch.utils.data import DataLoader from transformers import ( AutoConfig, AutoTokenizer, BartForConditionalGeneration, BatchEncoding, DPRConfig, DPRContextEncoder, DPRContextEncoderTokenizerFast, RagConfig, RagSequenceForGeneration, RagTokenForGeneration, RagTokenizer, T5ForConditionalGeneration, ) from transformers import logging as transformers_logging from transformers.integrations import is_ray_available if is_ray_available(): import ray from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever from glob import glob from callbacks_rag import Seq2SeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback from kb_encode_utils import add_index, embed_update from lightning_base import BaseTransformer, add_generic_args, generic_train from pynvml import nvmlDeviceGetCount, nvmlDeviceGetHandleByIndex, nvmlDeviceGetMemoryInfo, nvmlInit from utils_rag import ( Seq2SeqDataset, calculate_exact_match, get_git_info, is_rag_model, lmap, pickle_save, save_git_info, save_json, set_extra_model_params, ) logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) transformers_logging.set_verbosity_info() sys.path.insert(2, str(Path(__file__).resolve().parents[1])) isEmUpdateBusy = False isAddIndexBusy = False processes = [] threadHandle_index = None class AttrDict(dict): def __init__(self, *args, **kwargs): super(AttrDict, self).__init__(*args, **kwargs) self.__dict__ = self class GenerativeQAModule(BaseTransformer): mode = "generative_qa" loss_names = ["loss"] metric_names = ["em"] val_metric = "em" def __init__(self, hparams, **kwargs): # when loading from a pytorch lightning checkpoint, hparams are passed as dict if isinstance(hparams, dict): hparams = AttrDict(hparams) if hparams.model_type == "rag_sequence": self.model_class = RagSequenceForGeneration elif hparams.model_type == "rag_token": self.model_class = RagTokenForGeneration elif hparams.model_type == "bart": self.model_class = BartForConditionalGeneration else: self.model_class = T5ForConditionalGeneration self.is_rag_model = is_rag_model(hparams.model_type) config_class = RagConfig if self.is_rag_model else AutoConfig config = config_class.from_pretrained(hparams.model_name_or_path) # set retriever parameters config.index_name = hparams.index_name or config.index_name config.passages_path = hparams.passages_path or config.passages_path config.index_path = hparams.index_path or config.index_path config.use_dummy_dataset = hparams.use_dummy_dataset # set extra_model_params for generator configs and load_model extra_model_params = ("encoder_layerdrop", "decoder_layerdrop", "attention_dropout", "dropout") if self.is_rag_model: if hparams.prefix is not None: config.generator.prefix = hparams.prefix config.label_smoothing = hparams.label_smoothing hparams, config.generator = set_extra_model_params(extra_model_params, hparams, config.generator) if hparams.distributed_retriever == "ray": # The Ray retriever needs the handles to the retriever actors. retriever = RagRayDistributedRetriever.from_pretrained( hparams.model_name_or_path, hparams.actor_handles, config=config ) if hparams.end2end: ctx_encoder_tokenizer = DPRContextEncoderTokenizerFast.from_pretrained( "facebook/dpr-ctx_encoder-multiset-base" ) retriever.set_ctx_encoder_tokenizer(ctx_encoder_tokenizer) else: logger.info("please use RAY as the distributed retrieval method") model = self.model_class.from_pretrained(hparams.model_name_or_path, config=config, retriever=retriever) if hparams.end2end: ctx_encoder = DPRContextEncoder.from_pretrained(hparams.context_encoder_name) model.set_context_encoder_for_training(ctx_encoder) prefix = config.question_encoder.prefix else: if hparams.prefix is not None: config.prefix = hparams.prefix hparams, config = set_extra_model_params(extra_model_params, hparams, config) model = self.model_class.from_pretrained(hparams.model_name_or_path, config=config) prefix = config.prefix tokenizer = ( RagTokenizer.from_pretrained(hparams.model_name_or_path) if self.is_rag_model else AutoTokenizer.from_pretrained(hparams.model_name_or_path) ) self.config_dpr = DPRConfig.from_pretrained(hparams.context_encoder_name) self.custom_config = hparams self.context_tokenizer = DPRContextEncoderTokenizerFast.from_pretrained(hparams.context_encoder_name) super().__init__(hparams, config=config, tokenizer=tokenizer, model=model) save_git_info(self.hparams.output_dir) self.output_dir = Path(self.hparams.output_dir) self.dpr_ctx_check_dir = str(Path(self.hparams.output_dir)) + "/dpr_ctx_checkpoint" self.metrics_save_path = Path(self.output_dir) / "metrics.json" self.hparams_save_path = Path(self.output_dir) / "hparams.pkl" pickle_save(self.hparams, self.hparams_save_path) self.step_count = 0 self.metrics = defaultdict(list) self.dataset_kwargs: dict = { "data_dir": self.hparams.data_dir, "max_source_length": self.hparams.max_source_length, "prefix": prefix or "", } n_observations_per_split = { "train": self.hparams.n_train, "val": self.hparams.n_val, "test": self.hparams.n_test, } self.n_obs = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()} self.target_lens = { "train": self.hparams.max_target_length, "val": self.hparams.val_max_target_length, "test": self.hparams.test_max_target_length, } assert self.target_lens["train"] <= self.target_lens["val"], f"target_lens: {self.target_lens}" assert self.target_lens["train"] <= self.target_lens["test"], f"target_lens: {self.target_lens}" self.hparams.git_sha = get_git_info()["repo_sha"] self.num_workers = hparams.num_workers self.distributed_port = self.hparams.distributed_port # For single GPU training, init_ddp_connection is not called. # So we need to initialize the retrievers here. if hparams.gpus <= 1: if hparams.distributed_retriever == "ray": self.model.retriever.init_retrieval() else: logger.info("please use RAY as the distributed retrieval method") self.distributed_retriever = hparams.distributed_retriever def forward(self, input_ids, **kwargs): return self.model(input_ids, **kwargs) def ids_to_clean_text(self, generated_ids: List[int]): gen_text = self.tokenizer.batch_decode( generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True ) return lmap(str.strip, gen_text) def _step(self, batch: dict) -> Tuple: source_ids, source_mask, target_ids = batch["input_ids"], batch["attention_mask"], batch["decoder_input_ids"] rag_kwargs = {} if isinstance(self.model, T5ForConditionalGeneration): decoder_input_ids = self.model._shift_right(target_ids) lm_labels = target_ids elif isinstance(self.model, BartForConditionalGeneration): decoder_input_ids = target_ids[:, :-1].contiguous() lm_labels = target_ids[:, 1:].clone() else: assert self.is_rag_model generator = self.model.rag.generator if isinstance(generator, T5ForConditionalGeneration): decoder_start_token_id = generator.config.decoder_start_token_id decoder_input_ids = ( torch.cat( [torch.tensor([[decoder_start_token_id]] * target_ids.shape[0]).to(target_ids), target_ids], dim=1, ) if target_ids.shape[0] < self.target_lens["train"] else generator._shift_right(target_ids) ) elif isinstance(generator, BartForConditionalGeneration): decoder_input_ids = target_ids lm_labels = decoder_input_ids rag_kwargs["reduce_loss"] = True assert decoder_input_ids is not None outputs = self( source_ids, attention_mask=source_mask, decoder_input_ids=decoder_input_ids, use_cache=False, labels=lm_labels, **rag_kwargs, ) loss = outputs["loss"] return (loss,) @property def pad(self) -> int: raise NotImplementedError("pad not implemented") def training_step(self, batch, batch_idx) -> Dict: global isEmUpdateBusy # use to check whether the entire embedding update process is finished or not global isAddIndexBusy # use to check whether the entire indexing process is finished or not global processes # use to keep threads embedding update processes global threadHandle_index # use to keep thread in embedding indexing processes if (self.trainer.global_rank == 0) and (self.custom_config.end2end): if (not batch_idx == 0) and (batch_idx % self.custom_config.indexing_freq == 0): free_gpu_list = [] nvmlInit() deviceCount = nvmlDeviceGetCount() my_list = json.loads(self.custom_config.gpu_order) for i in range(deviceCount): handle = nvmlDeviceGetHandleByIndex(i) info = nvmlDeviceGetMemoryInfo(handle) if info.used / 1e6 < 15: position = my_list.index(i) free_gpu_list.append("cuda:" + str(position)) if len(free_gpu_list) >= self.custom_config.index_gpus: has_free_gpus = True else: has_free_gpus = False if (not isEmUpdateBusy) and has_free_gpus: model_copy = type(self.model.rag.ctx_encoder)( self.config_dpr ) # get a new instance #this will be load in the CPU model_copy.load_state_dict(self.model.rag.ctx_encoder.state_dict()) # copy weights processes = [] if len(free_gpu_list) > self.custom_config.index_gpus: cuda_devices = random.sample(free_gpu_list, self.custom_config.index_gpus) else: cuda_devices = free_gpu_list num_processes = len(cuda_devices) for rank in range(num_processes): logger.info("Iniitializing embedding calculation process rank{}".format(rank)) device = cuda_devices[rank] p = multiprocessing.Process( target=embed_update, args=( copy.deepcopy(model_copy), num_processes, device, rank, self.custom_config.shard_dir, self.custom_config.csv_path, ), ) processes.append(p) for p in processes: p.start() isEmUpdateBusy = True if isEmUpdateBusy and (not isAddIndexBusy): index_process_list = [processes[k].is_alive() for k in range(self.custom_config.index_gpus)] if ( sum(index_process_list) == 0 ): # If entire list is false, we can say all embedding calculation process has finished logger.info("Start adding the index") threadHandle_index = multiprocessing.Process( target=add_index, args=( self.custom_config.shard_dir, self.config.index_path, ), ) threadHandle_index.start() isAddIndexBusy = True # check when index building has started if isAddIndexBusy: # check still the index_building process is happening if not threadHandle_index.is_alive(): logger.info("Merging the dataset shards") saved_dataset_shards = [] for address in glob(str(self.custom_config.shard_dir) + "/*/"): saved_dataset_shards.append(load_from_disk(address)) concat = concatenate_datasets(saved_dataset_shards) concat.save_to_disk(self.config.passages_path) # here we update the main passage file on the disk logger.info("done updating the dataset") # To Do (@Aaron) : Useful in the future dynamic memory implementation. # if you load the index from the disk make sure to update the index file here, otherwise it is ok to update the index file from the worker. # logger.info("then updating the index") # shutil.copy(self.custom_config.temp_index, self.config.idex_path) logger.info("Loading new passages and iniitalzing new index") self.trainer.model.module.module.model.rag.retriever.re_load() self.trainer.model.module.module.model.rag.retriever.init_retrieval() isEmUpdateBusy = False isAddIndexBusy = False self.trainer.strategy.barrier("barrier") loss_tensors = self._step(batch) logs = dict(zip(self.loss_names, loss_tensors)) # tokens per batch tgt_pad_token_id = ( self.tokenizer.generator.pad_token_id if isinstance(self.tokenizer, RagTokenizer) else self.tokenizer.pad_token_id ) src_pad_token_id = ( self.tokenizer.question_encoder.pad_token_id if isinstance(self.tokenizer, RagTokenizer) else self.tokenizer.pad_token_id ) logs["tpb"] = ( batch["input_ids"].ne(src_pad_token_id).sum() + batch["decoder_input_ids"].ne(tgt_pad_token_id).sum() ) self.log("loss", loss_tensors[0]) return loss_tensors[0] def validation_step(self, batch, batch_idx) -> Dict: return self._generative_step(batch) def validation_epoch_end(self, outputs, prefix="val") -> Dict: self.step_count += 1 losses = {k: torch.stack([x[k] for x in outputs]).mean() for k in self.loss_names} loss = losses["loss"] gen_metrics = { k: np.array([x[k] for x in outputs]).mean() for k in self.metric_names + ["gen_time", "gen_len"] } metrics_tensor: torch.FloatTensor = torch.tensor(gen_metrics[self.val_metric]).type_as(loss) gen_metrics.update({k: v.item() for k, v in losses.items()}) # fix for https://github.com/PyTorchLightning/pytorch-lightning/issues/2424 if dist.is_initialized(): dist.all_reduce(metrics_tensor, op=dist.ReduceOp.SUM) metrics_tensor = metrics_tensor / dist.get_world_size() gen_metrics.update({self.val_metric: metrics_tensor.item()}) losses.update(gen_metrics) metrics = {f"{prefix}_avg_{k}": x for k, x in losses.items()} metrics["step_count"] = self.step_count self.save_metrics(metrics, prefix) # writes to self.metrics_save_path log_dict = { f"{prefix}_avg_em": metrics[f"{prefix}_avg_em"], "step_count": metrics["step_count"], f"{prefix}_avg_loss": metrics[f"{prefix}_avg_loss"], f"{prefix}_loss": loss, f"{prefix}_em": metrics_tensor, } self.log_dict(log_dict) def save_metrics(self, latest_metrics, type_path) -> None: self.metrics[type_path].append(latest_metrics) save_json(self.metrics, self.metrics_save_path) def calc_generative_metrics(self, preds, target) -> Dict: return calculate_exact_match(preds, target) def _generative_step(self, batch: dict) -> dict: start_time = time.time() batch = BatchEncoding(batch).to(device=self.model.device) generated_ids = self.model.generate( batch["input_ids"], attention_mask=batch["attention_mask"], do_deduplication=False, # rag specific parameter use_cache=True, min_length=1, max_length=self.target_lens["val"], ) gen_time = (time.time() - start_time) / batch["input_ids"].shape[0] preds: List[str] = self.ids_to_clean_text(generated_ids) target: List[str] = self.ids_to_clean_text(batch["decoder_input_ids"]) # print(preds,target) loss_tensors = self._step(batch) base_metrics = dict(zip(self.loss_names, loss_tensors)) gen_metrics: Dict = self.calc_generative_metrics(preds, target) summ_len = np.mean(lmap(len, generated_ids)) base_metrics.update(gen_time=gen_time, gen_len=summ_len, preds=preds, target=target, **gen_metrics) return base_metrics def test_step(self, batch, batch_idx): return self._generative_step(batch) def test_epoch_end(self, outputs): return self.validation_epoch_end(outputs, prefix="test") def get_dataset(self, type_path) -> Seq2SeqDataset: n_obs = self.n_obs[type_path] max_target_length = self.target_lens[type_path] dataset = Seq2SeqDataset( self.tokenizer, type_path=type_path, n_obs=n_obs, max_target_length=max_target_length, **self.dataset_kwargs, ) return dataset def get_dataloader(self, type_path: str, batch_size: int, shuffle: bool = False) -> DataLoader: dataset = self.get_dataset(type_path) dataloader = DataLoader( dataset, batch_size=batch_size, collate_fn=dataset.collate_fn, shuffle=shuffle, num_workers=self.num_workers, ) return dataloader def train_dataloader(self) -> DataLoader: dataloader = self.get_dataloader("train", batch_size=self.hparams.train_batch_size, shuffle=True) return dataloader def val_dataloader(self) -> DataLoader: return self.get_dataloader("val", batch_size=self.hparams.eval_batch_size) def test_dataloader(self) -> DataLoader: return self.get_dataloader("test", batch_size=self.hparams.eval_batch_size) @pl.utilities.rank_zero_only def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None: save_path = self.output_dir.joinpath("checkpoint{}".format(self.step_count)) self.model.config.save_step = self.step_count # self.model.save_pretrained(save_path) self.tokenizer.save_pretrained(save_path) if self.custom_config.end2end: modified_state_dict = self.model.state_dict() for key in self.model.state_dict().keys(): if key.split(".")[1] == "ctx_encoder": del modified_state_dict[key] self.model.save_pretrained(save_directory=save_path, state_dict=modified_state_dict) save_path_dpr = os.path.join(self.dpr_ctx_check_dir, "checkpoint{}".format(self.step_count)) self.model.rag.ctx_encoder.save_pretrained(save_path_dpr) self.context_tokenizer.save_pretrained(save_path_dpr) @staticmethod def add_model_specific_args(parser, root_dir): BaseTransformer.add_model_specific_args(parser, root_dir) add_generic_args(parser, root_dir) parser.add_argument( "--max_source_length", default=128, type=int, help=( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded." ), ) parser.add_argument( "--max_target_length", default=25, type=int, help=( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded." ), ) parser.add_argument( "--val_max_target_length", default=25, type=int, help=( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded." ), ) parser.add_argument( "--test_max_target_length", default=25, type=int, help=( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded." ), ) parser.add_argument("--logger_name", type=str, choices=["default", "wandb", "wandb_shared"], default="default") parser.add_argument("--n_train", type=int, default=-1, required=False, help="# examples. -1 means use all.") parser.add_argument("--n_val", type=int, default=-1, required=False, help="# examples. -1 means use all.") parser.add_argument("--n_test", type=int, default=-1, required=False, help="# examples. -1 means use all.") parser.add_argument("--label_smoothing", type=float, default=0.0, required=False) parser.add_argument( "--prefix", type=str, default=None, help="Prefix added at the beginning of each text, typically used with T5-based models.", ) parser.add_argument( "--early_stopping_patience", type=int, default=-1, required=False, help=( "-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So" " val_check_interval will effect it." ), ) parser.add_argument( "--distributed-port", type=int, default=-1, required=False, help="Port number for distributed training." ) parser.add_argument( "--model_type", choices=["rag_sequence", "rag_token", "bart", "t5"], type=str, help=( "RAG model type: sequence or token, if none specified, the type is inferred from the" " model_name_or_path" ), ) parser.add_argument( "--context_encoder_name", default="facebook/dpr-ctx_encoder-multiset-base", type=str, help="Name of the pre-trained context encoder checkpoint from the DPR", ) parser.add_argument( "--csv_path", default=str(Path(__file__).parent / "test_run" / "dummy-kb" / "my_knowledge_dataset.csv"), type=str, help="path of the raw KB csv", ) parser.add_argument("--end2end", action="store_true", help="whether to train the system end2end or not") parser.add_argument("--index_gpus", type=int, help="how many GPUs used in re-encoding process") parser.add_argument( "--shard_dir", type=str, default=str(Path(__file__).parent / "test_run" / "kb-shards"), help="directory used to keep temporary shards during the re-encode process", ) parser.add_argument( "--gpu_order", type=str, help=( "order of the GPU used during the fine-tuning. Used to finding free GPUs during the re-encode" " process. I do not have many GPUs :)" ), ) parser.add_argument("--indexing_freq", type=int, help="frequency of re-encode process") return parser @staticmethod def add_retriever_specific_args(parser): parser.add_argument( "--index_name", type=str, default=None, help=( "Name of the index to use: 'hf' for a canonical dataset from the datasets library (default), 'custom'" " for a local index, or 'legacy' for the orignal one)" ), ) parser.add_argument( "--passages_path", type=str, default=str(Path(__file__).parent / "test_run" / "dummy-kb" / "my_knowledge_dataset"), help=( "Path to the dataset of passages for custom index. More info about custom indexes in the RagRetriever" " documentation as well as in `examples/rag/use_own_knowledge_dataset.py`" ), ) parser.add_argument( "--index_path", type=str, default=str(Path(__file__).parent / "test_run" / "dummy-kb" / "my_knowledge_dataset_hnsw_index.faiss"), help=( "Path to the faiss index for custom index. More info about custom indexes in the RagRetriever" " documentation as well as in `examples/rag/use_own_knowledge_dataset.py`" ), ) parser.add_argument( "--distributed_retriever", choices=["ray", "pytorch"], type=str, default="ray", help=( "What implementation to use for distributed retriever? If " "pytorch is selected, the index is loaded on training " "worker 0, and torch.distributed is used to handle " "communication between training worker 0, and the other " "training workers. If ray is selected, the Ray library is " "used to create load the index on separate processes, " "and Ray handles the communication between the training " "workers and the retrieval actors." ), ) parser.add_argument( "--use_dummy_dataset", type=bool, default=False, help=( "Whether to use the dummy version of the dataset index. More info about custom indexes in the" " RagRetriever documentation as well as in `examples/rag/use_own_knowledge_dataset.py`" ), ) return parser @staticmethod def add_ray_specific_args(parser): # Ray cluster address. parser.add_argument( "--ray-address", default="auto", type=str, help=( "The address of the Ray cluster to connect to. If not " "specified, Ray will attempt to automatically detect the " "cluster. Has no effect if pytorch is used as the distributed " "retriever." ), ) parser.add_argument( "--num_retrieval_workers", type=int, default=1, help=( "The number of retrieval actors to use when Ray is selected " "for the distributed retriever. Has no effect when " "distributed_retriever is set to pytorch." ), ) return parser def main(args=None, model=None) -> GenerativeQAModule: parser = argparse.ArgumentParser() parser = pl.Trainer.add_argparse_args(parser) parser = GenerativeQAModule.add_model_specific_args(parser, os.getcwd()) parser = GenerativeQAModule.add_retriever_specific_args(parser) args = args or parser.parse_args() Path(args.output_dir).mkdir(exist_ok=True) Path(args.output_dir + "/dpr_ctx_checkpoint").mkdir( exist_ok=True ) # save dpr_context encoder seprately for the future use print(args.shard_dir) if os.path.exists(args.shard_dir): # we do not need previous kb shards used in dataset re-conding and re-indexing shutil.rmtree(args.shard_dir) Path(args.shard_dir).mkdir(exist_ok=True) if os.path.exists( args.cache_dir ): # we do not need previous cache files used in dataset re-conding and re-indexing shutil.rmtree(args.cache_dir) Path(args.cache_dir).mkdir(exist_ok=True) named_actors = [] if args.distributed_retriever == "ray" and args.gpus > 1: if not is_ray_available(): raise RuntimeError("Please install Ray to use the Ray distributed retriever.") # Connect to an existing Ray cluster. try: ray.init(address=args.ray_address, namespace="rag") except (ConnectionError, ValueError): logger.warning( "Connection to Ray cluster failed. Make sure a Ray " "cluster is running by either using Ray's cluster " "launcher (`ray up`) or by manually starting Ray on " "each node via `ray start --head` for the head node " "and `ray start --address='<ip address>:6379'` for " "additional nodes. See " "https://docs.ray.io/en/master/cluster/index.html " "for more info." ) raise # Create Ray actors only for rank 0. if ("LOCAL_RANK" not in os.environ or os.environ["LOCAL_RANK"] == 0) and ( "NODE_RANK" not in os.environ or os.environ["NODE_RANK"] == 0 ): remote_cls = ray.remote(RayRetriever) named_actors = [ remote_cls.options(name="retrieval_worker_{}".format(i)).remote() for i in range(args.num_retrieval_workers) ] else: logger.info( "Getting named actors for NODE_RANK {}, LOCAL_RANK {}".format( os.environ["NODE_RANK"], os.environ["LOCAL_RANK"] ) ) named_actors = [ray.get_actor("retrieval_worker_{}".format(i)) for i in range(args.num_retrieval_workers)] args.actor_handles = named_actors assert args.actor_handles == named_actors if model is None: model: GenerativeQAModule = GenerativeQAModule(args) dataset = Path(args.data_dir).name if ( args.logger_name == "default" or args.fast_dev_run or str(args.output_dir).startswith("/tmp") or str(args.output_dir).startswith("/var") ): training_logger = True # don't pollute wandb logs unnecessarily elif args.logger_name == "wandb": from pytorch_lightning.loggers import WandbLogger project = os.environ.get("WANDB_PROJECT", dataset) training_logger = WandbLogger(name=model.output_dir.name, project=project) elif args.logger_name == "wandb_shared": from pytorch_lightning.loggers import WandbLogger training_logger = WandbLogger(name=model.output_dir.name, project=f"hf_{dataset}") es_callback = ( get_early_stopping_callback(model.val_metric, args.early_stopping_patience) if args.early_stopping_patience >= 0 else False ) trainer: pl.Trainer = generic_train( model, args, logging_callback=Seq2SeqLoggingCallback(), checkpoint_callback=get_checkpoint_callback(args.output_dir, model.val_metric), early_stopping_callback=es_callback, logger=training_logger, profiler=pl.profiler.AdvancedProfiler() if args.profile else None, ) pickle_save(model.hparams, model.output_dir / "hparams.pkl") if not args.do_predict: return model # test() without a model tests using the best checkpoint automatically trainer.test() return model if __name__ == "__main__": multiprocessing.set_start_method("spawn") parser = argparse.ArgumentParser() parser = pl.Trainer.add_argparse_args(parser) parser = GenerativeQAModule.add_model_specific_args(parser, os.getcwd()) parser = GenerativeQAModule.add_retriever_specific_args(parser) parser = GenerativeQAModule.add_ray_specific_args(parser) # Pytorch Lightning Profiler parser.add_argument( "--profile", action="store_true", help="If True, use pytorch_lightning.profiler.AdvancedProfiler to profile the Trainer.", ) args = parser.parse_args() main(args)

transformers/examples/research_projects/rag-end2end-retriever/finetune_rag.py/0

{ "file_path": "transformers/examples/research_projects/rag-end2end-retriever/finetune_rag.py", "repo_id": "transformers", "token_count": 15752 }

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# Intro Authors: @patrickvonplaten and @lhoestq Aimed at tackling the knowledge-intensive NLP tasks (think tasks a human wouldn't be expected to solve without access to external knowledge sources), RAG models are seq2seq models with access to a retrieval mechanism providing relevant context documents at training and evaluation time. A RAG model encapsulates two core components: a question encoder and a generator. During a forward pass, we encode the input with the question encoder and pass it to the retriever to extract relevant context documents. The documents are then prepended to the input. Such contextualized inputs are passed to the generator. Read more about RAG at https://arxiv.org/abs/2005.11401. # Note ⚠️ This project should be run with pytorch-lightning==1.3.1 which has a potential security vulnerability # Finetuning Our finetuning logic is based on scripts from [`examples/legacy/seq2seq`](https://github.com/huggingface/transformers/tree/main/examples/legacy/seq2seq). We accept training data in the same format as specified there - we expect a directory consisting of 6 text files: ```bash train.source train.target val.source val.target test.source test.target ``` A sample finetuning command (run ` ./examples/research_projects/rag/finetune_rag.py --help` to list all available options): ```bash python examples/research_projects/rag/finetune_rag.py \ --data_dir $DATA_DIR \ --output_dir $OUTPUT_DIR \ --model_name_or_path $MODEL_NAME_OR_PATH \ --model_type rag_sequence \ --fp16 \ --gpus 8 ``` We publish two `base` models which can serve as a starting point for finetuning on downstream tasks (use them as `model_name_or_path`): - [`facebook/rag-sequence-base`](https://huggingface.co/facebook/rag-sequence-base) - a base for finetuning `RagSequenceForGeneration` models, - [`facebook/rag-token-base`](https://huggingface.co/facebook/rag-token-base) - a base for finetuning `RagTokenForGeneration` models. The `base` models initialize the question encoder with [`facebook/dpr-question_encoder-single-nq-base`](https://huggingface.co/facebook/dpr-question_encoder-single-nq-base) and the generator with [`facebook/bart-large`](https://huggingface.co/facebook/bart-large). If you would like to initialize finetuning with a base model using different question encoder and generator architectures, you can build it with a consolidation script, e.g.: ```bash python examples/research_projects/rag/consolidate_rag_checkpoint.py \ --model_type rag_sequence \ --generator_name_or_path facebook/bart-large-cnn \ --question_encoder_name_or_path facebook/dpr-question_encoder-single-nq-base \ --dest path/to/checkpoint ``` You will then be able to pass `path/to/checkpoint` as `model_name_or_path` to the `finetune_rag.py` script. ## Document Retrieval When running distributed fine-tuning, each training worker needs to retrieve contextual documents for its input by querying a index loaded into memory. RAG provides two implementations for document retrieval, one with [`torch.distributed`](https://pytorch.org/docs/stable/distributed.html) communication package and the other with [`Ray`](https://docs.ray.io/en/master/). This option can be configured with the `--distributed_retriever` flag which can either be set to `pytorch` or `ray`. By default this flag is set to `pytorch`. For the Pytorch implementation, only training worker 0 loads the index into CPU memory, and a gather/scatter pattern is used to collect the inputs from the other training workers and send back the corresponding document embeddings. For the Ray implementation, the index is loaded in *separate* process(es). The training workers randomly select which retriever worker to query. To use Ray for distributed retrieval, you have to set the `--distributed_retriever` arg to `ray`. To configure the number of retrieval workers (the number of processes that load the index), you can set the `num_retrieval_workers` flag. Also make sure to start the Ray cluster before running fine-tuning. ```bash # Start a single-node Ray cluster. ray start --head python examples/research_projects/rag/finetune_rag.py \ --data_dir $DATA_DIR \ --output_dir $OUTPUT_DIR \ --model_name_or_path $MODEL_NAME_OR_PATH \ --model_type rag_sequence \ --fp16 \ --gpus 8 --distributed_retriever ray \ --num_retrieval_workers 4 # Stop the ray cluster once fine-tuning has finished. ray stop ``` Using Ray can lead to retrieval speedups on multi-GPU settings since multiple processes load the index rather than just the rank 0 training worker. Using Ray also allows you to load the index on GPU since the index is loaded on a separate processes than the model, while with pytorch distributed retrieval, both are loaded in the same process potentially leading to GPU OOM. # Evaluation Our evaluation script enables two modes of evaluation (controlled by the `eval_mode` argument): `e2e` - end2end evaluation, returns EM (exact match) and F1 scores calculated for the downstream task and `retrieval` - which returns precision@k of the documents retrieved for provided inputs. The evaluation script expects paths to two files: - `evaluation_set` - a path to a file specifying the evaluation dataset, a single input per line. - `gold_data_path` - a path to a file contaning ground truth answers for datapoints from the `evaluation_set`, a single output per line. Check below for expected formats of the gold data files. ## Retrieval evaluation For `retrieval` evaluation, we expect a gold data file where each line will consist of a tab-separated list of document titles constituting positive contexts for respective datapoints from the `evaluation_set`. E.g. given a question `who sings does he love me with reba` in the `evaluation_set`, a respective ground truth line could look as follows: ``` Does He Love You Does He Love You Red Sandy Spika dress of Reba McEntire Greatest Hits Volume Two (Reba McEntire album) Shoot for the Moon (album) ``` We demonstrate how to evaluate retrieval against DPR evaluation data. You can download respective files from links listed [here](https://github.com/facebookresearch/DPR/blob/master/data/download_data.py#L39-L45). 1. Download and unzip the gold data file. We use the `biencoder-nq-dev` from https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-dev.json.gz. ```bash wget https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-dev.json.gz && gzip -d biencoder-nq-dev.json.gz ``` 2. Parse the unziped file using the `parse_dpr_relevance_data.py` ```bash mkdir output # or wherever you want to save this python examples/research_projects/rag/parse_dpr_relevance_data.py \ --src_path biencoder-nq-dev.json \ --evaluation_set output/biencoder-nq-dev.questions \ --gold_data_path output/biencoder-nq-dev.pages ``` 3. Run evaluation: ```bash python examples/research_projects/rag/eval_rag.py \ --model_name_or_path facebook/rag-sequence-nq \ --model_type rag_sequence \ --evaluation_set output/biencoder-nq-dev.questions \ --gold_data_path output/biencoder-nq-dev.pages \ --predictions_path output/retrieval_preds.tsv \ --eval_mode retrieval \ --k 1 ``` ```bash # EXPLANATION python examples/research_projects/rag/eval_rag.py \ --model_name_or_path facebook/rag-sequence-nq \ # model name or path of the model we're evaluating --model_type rag_sequence \ # RAG model type (rag_token or rag_sequence) --evaluation_set output/biencoder-nq-dev.questions \ # an input dataset for evaluation --gold_data_path poutput/biencoder-nq-dev.pages \ # a dataset containing ground truth answers for samples from the evaluation_set --predictions_path output/retrieval_preds.tsv \ # name of file where predictions will be stored --eval_mode retrieval \ # indicates whether we're performing retrieval evaluation or e2e evaluation --k 1 # parameter k for the precision@k metric ``` ## End-to-end evaluation We support two formats of the gold data file (controlled by the `gold_data_mode` parameter): - `qa` - where a single line has the following format: `input [tab] output_list`, e.g.: ``` who is the owner of reading football club ['Xiu Li Dai', 'Dai Yongge', 'Dai Xiuli', 'Yongge Dai'] ``` - `ans` - where a single line contains a single expected answer, e.g.: ``` Xiu Li Dai ``` Predictions of the model for the samples from the `evaluation_set` will be saved under the path specified by the `predictions_path` parameter. If this path already exists, the script will use saved predictions to calculate metrics. Add `--recalculate` parameter to force the script to perform inference from scratch. An example e2e evaluation run could look as follows: ```bash python examples/research_projects/rag/eval_rag.py \ --model_name_or_path facebook/rag-sequence-nq \ --model_type rag_sequence \ --evaluation_set path/to/test.source \ --gold_data_path path/to/gold_data \ --predictions_path path/to/e2e_preds.txt \ --eval_mode e2e \ --gold_data_mode qa \ --n_docs 5 \ # You can experiment with retrieving different number of documents at evaluation time --print_predictions \ --recalculate \ # adding this parameter will force recalculating predictions even if predictions_path already exists ``` # Use your own knowledge source By default, RAG uses the English Wikipedia as a knowledge source, known as the 'wiki_dpr' dataset. With `use_custom_knowledge_dataset.py` you can build your own knowledge source, *e.g.* for RAG. For instance, if documents are serialized as tab-separated csv files with the columns "title" and "text", one can use `use_own_knowledge_dataset.py` as follows: ```bash python examples/research_projects/rag/use_own_knowledge_dataset.py \ --csv_path path/to/my_csv \ --output_dir path/to/my_knowledge_dataset \ ``` The created outputs in `path/to/my_knowledge_dataset` can then be used to finetune RAG as follows: ```bash python examples/research_projects/rag/finetune_rag.py \ --data_dir $DATA_DIR \ --output_dir $OUTPUT_DIR \ --model_name_or_path $MODEL_NAME_OR_PATH \ --model_type rag_sequence \ --fp16 \ --gpus 8 --index_name custom --passages_path path/to/data/my_knowledge_dataset --index_path path/to/my_knowledge_dataset_hnsw_index.faiss ```

transformers/examples/research_projects/rag/README.md/0

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import logging import os from dataclasses import dataclass, field from functools import partial from pathlib import Path from tempfile import TemporaryDirectory from typing import List, Optional import faiss import torch from datasets import Features, Sequence, Value, load_dataset from transformers import ( DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser, RagRetriever, RagSequenceForGeneration, RagTokenizer, ) logger = logging.getLogger(__name__) torch.set_grad_enabled(False) device = "cuda" if torch.cuda.is_available() else "cpu" def split_text(text: str, n=100, character=" ") -> List[str]: """Split the text every ``n``-th occurrence of ``character``""" text = text.split(character) return [character.join(text[i : i + n]).strip() for i in range(0, len(text), n)] def split_documents(documents: dict) -> dict: """Split documents into passages""" titles, texts = [], [] for title, text in zip(documents["title"], documents["text"]): if text is not None: for passage in split_text(text): titles.append(title if title is not None else "") texts.append(passage) return {"title": titles, "text": texts} def embed(documents: dict, ctx_encoder: DPRContextEncoder, ctx_tokenizer: DPRContextEncoderTokenizerFast) -> dict: """Compute the DPR embeddings of document passages""" input_ids = ctx_tokenizer( documents["title"], documents["text"], truncation=True, padding="longest", return_tensors="pt" )["input_ids"] embeddings = ctx_encoder(input_ids.to(device=device), return_dict=True).pooler_output return {"embeddings": embeddings.detach().cpu().numpy()} def main( rag_example_args: "RagExampleArguments", processing_args: "ProcessingArguments", index_hnsw_args: "IndexHnswArguments", ): ###################################### logger.info("Step 1 - Create the dataset") ###################################### # The dataset needed for RAG must have three columns: # - title (string): title of the document # - text (string): text of a passage of the document # - embeddings (array of dimension d): DPR representation of the passage # Let's say you have documents in tab-separated csv files with columns "title" and "text" assert os.path.isfile(rag_example_args.csv_path), "Please provide a valid path to a csv file" # You can load a Dataset object this way dataset = load_dataset( "csv", data_files=[rag_example_args.csv_path], split="train", delimiter="\t", column_names=["title", "text"] ) # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets?highlight=csv#csv-files # Then split the documents into passages of 100 words dataset = dataset.map(split_documents, batched=True, num_proc=processing_args.num_proc) # And compute the embeddings ctx_encoder = DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name).to(device=device) ctx_tokenizer = DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name) new_features = Features( {"text": Value("string"), "title": Value("string"), "embeddings": Sequence(Value("float32"))} ) # optional, save as float32 instead of float64 to save space dataset = dataset.map( partial(embed, ctx_encoder=ctx_encoder, ctx_tokenizer=ctx_tokenizer), batched=True, batch_size=processing_args.batch_size, features=new_features, ) # And finally save your dataset passages_path = os.path.join(rag_example_args.output_dir, "my_knowledge_dataset") dataset.save_to_disk(passages_path) # from datasets import load_from_disk # dataset = load_from_disk(passages_path) # to reload the dataset ###################################### logger.info("Step 2 - Index the dataset") ###################################### # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search index = faiss.IndexHNSWFlat(index_hnsw_args.d, index_hnsw_args.m, faiss.METRIC_INNER_PRODUCT) dataset.add_faiss_index("embeddings", custom_index=index) # And save the index index_path = os.path.join(rag_example_args.output_dir, "my_knowledge_dataset_hnsw_index.faiss") dataset.get_index("embeddings").save(index_path) # dataset.load_faiss_index("embeddings", index_path) # to reload the index ###################################### logger.info("Step 3 - Load RAG") ###################################### # Easy way to load the model retriever = RagRetriever.from_pretrained( rag_example_args.rag_model_name, index_name="custom", indexed_dataset=dataset ) model = RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name, retriever=retriever) tokenizer = RagTokenizer.from_pretrained(rag_example_args.rag_model_name) # For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately. # retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path) ###################################### logger.info("Step 4 - Have fun") ###################################### question = rag_example_args.question or "What does Moses' rod turn into ?" input_ids = tokenizer.question_encoder(question, return_tensors="pt")["input_ids"] generated = model.generate(input_ids) generated_string = tokenizer.batch_decode(generated, skip_special_tokens=True)[0] logger.info("Q: " + question) logger.info("A: " + generated_string) @dataclass class RagExampleArguments: csv_path: str = field( default=str(Path(__file__).parent / "test_data" / "my_knowledge_dataset.csv"), metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"}, ) question: Optional[str] = field( default=None, metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."}, ) rag_model_name: str = field( default="facebook/rag-sequence-nq", metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"}, ) dpr_ctx_encoder_model_name: str = field( default="facebook/dpr-ctx_encoder-multiset-base", metadata={ "help": ( "The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or" " 'facebook/dpr-ctx_encoder-multiset-base'" ) }, ) output_dir: Optional[str] = field( default=None, metadata={"help": "Path to a directory where the dataset passages and the index will be saved"}, ) @dataclass class ProcessingArguments: num_proc: Optional[int] = field( default=None, metadata={ "help": "The number of processes to use to split the documents into passages. Default is single process." }, ) batch_size: int = field( default=16, metadata={ "help": "The batch size to use when computing the passages embeddings using the DPR context encoder." }, ) @dataclass class IndexHnswArguments: d: int = field( default=768, metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}, ) m: int = field( default=128, metadata={ "help": ( "The number of bi-directional links created for every new element during the HNSW index construction." ) }, ) if __name__ == "__main__": logging.basicConfig(level=logging.WARNING) logger.setLevel(logging.INFO) parser = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments)) rag_example_args, processing_args, index_hnsw_args = parser.parse_args_into_dataclasses() with TemporaryDirectory() as tmp_dir: rag_example_args.output_dir = rag_example_args.output_dir or tmp_dir main(rag_example_args, processing_args, index_hnsw_args)

transformers/examples/research_projects/rag/use_own_knowledge_dataset.py/0

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import logging from pathlib import Path import numpy as np import pytorch_lightning as pl import torch from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint from pytorch_lightning.utilities import rank_zero_only from utils import save_json def count_trainable_parameters(model): model_parameters = filter(lambda p: p.requires_grad, model.parameters()) params = sum([np.prod(p.size()) for p in model_parameters]) return params logger = logging.getLogger(__name__) class Seq2SeqLoggingCallback(pl.Callback): def on_batch_end(self, trainer, pl_module): lrs = {f"lr_group_{i}": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups)} pl_module.logger.log_metrics(lrs) @rank_zero_only def _write_logs( self, trainer: pl.Trainer, pl_module: pl.LightningModule, type_path: str, save_generations=True ) -> None: logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****") metrics = trainer.callback_metrics trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]}) # Log results od = Path(pl_module.hparams.output_dir) if type_path == "test": results_file = od / "test_results.txt" generations_file = od / "test_generations.txt" else: # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json # If people want this it will be easy enough to add back. results_file = od / f"{type_path}_results/{trainer.global_step:05d}.txt" generations_file = od / f"{type_path}_generations/{trainer.global_step:05d}.txt" results_file.parent.mkdir(exist_ok=True) generations_file.parent.mkdir(exist_ok=True) with open(results_file, "a+") as writer: for key in sorted(metrics): if key in ["log", "progress_bar", "preds"]: continue val = metrics[key] if isinstance(val, torch.Tensor): val = val.item() msg = f"{key}: {val:.6f}\n" writer.write(msg) if not save_generations: return if "preds" in metrics: content = "\n".join(metrics["preds"]) generations_file.open("w+").write(content) @rank_zero_only def on_train_start(self, trainer, pl_module): try: npars = pl_module.model.model.num_parameters() except AttributeError: npars = pl_module.model.num_parameters() n_trainable_pars = count_trainable_parameters(pl_module) # mp stands for million parameters trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6}) @rank_zero_only def on_test_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule): save_json(pl_module.metrics, pl_module.metrics_save_path) return self._write_logs(trainer, pl_module, "test") @rank_zero_only def on_validation_end(self, trainer: pl.Trainer, pl_module): save_json(pl_module.metrics, pl_module.metrics_save_path) # Uncommenting this will save val generations # return self._write_logs(trainer, pl_module, "valid") def get_checkpoint_callback(output_dir, metric, save_top_k=1, lower_is_better=False): """Saves the best model by validation ROUGE2 score.""" if metric == "rouge2": exp = "{val_avg_rouge2:.4f}-{step_count}" elif metric == "bleu": exp = "{val_avg_bleu:.4f}-{step_count}" elif metric == "loss": exp = "{val_avg_loss:.4f}-{step_count}" else: raise NotImplementedError( f"seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to" " this function." ) checkpoint_callback = ModelCheckpoint( dirpath=output_dir, filename=exp, monitor=f"val_{metric}", mode="min" if "loss" in metric else "max", save_top_k=save_top_k, ) return checkpoint_callback def get_early_stopping_callback(metric, patience): return EarlyStopping( monitor=f"val_{metric}", # does this need avg? mode="min" if "loss" in metric else "max", patience=patience, verbose=True, )

transformers/examples/research_projects/seq2seq-distillation/callbacks.py/0

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import re from filelock import FileLock try: import nltk NLTK_AVAILABLE = True except (ImportError, ModuleNotFoundError): NLTK_AVAILABLE = False if NLTK_AVAILABLE: with FileLock(".lock") as lock: nltk.download("punkt", quiet=True) def add_newline_to_end_of_each_sentence(x: str) -> str: """This was added to get rougeLsum scores matching published rougeL scores for BART and PEGASUS.""" re.sub("<n>", "", x) # remove pegasus newline char assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)" return "\n".join(nltk.sent_tokenize(x))

transformers/examples/research_projects/seq2seq-distillation/sentence_splitter.py/0

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#!/usr/bin/env bash python run_asr.py \ --output_dir="./wav2vec2-large-lv60-100h" \ --num_train_epochs="30" \ --per_device_train_batch_size="16" \ --per_device_eval_batch_size="16" \ --evaluation_strategy="steps" \ --save_total_limit="3" \ --save_steps="500" \ --eval_steps="100" \ --logging_steps="50" \ --learning_rate="5e-4" \ --warmup_steps="3000" \ --model_name_or_path="facebook/wav2vec2-large-lv60" \ --fp16 \ --dataset_name="librispeech_asr" \ --dataset_config_name="clean" \ --train_split_name="train.100" \ --preprocessing_num_workers="32" \ --group_by_length \ --freeze_feature_extractor

transformers/examples/research_projects/wav2vec2/finetune_large_lv60_100.sh/0

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#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import shlex import runhouse as rh if __name__ == "__main__": # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access # setup instructions, if using on-demand hardware # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster # Throw an error if user passes both BYO and on-demand cluster args # Otherwise, use default values parser = argparse.ArgumentParser() parser.add_argument("--user", type=str, default="ubuntu") parser.add_argument("--host", type=str, default="localhost") parser.add_argument("--key_path", type=str, default=None) parser.add_argument("--instance", type=str, default="V100:1") parser.add_argument("--provider", type=str, default="cheapest") parser.add_argument("--use_spot", type=bool, default=False) parser.add_argument("--example", type=str, default="pytorch/text-generation/run_generation.py") args, unknown = parser.parse_known_args() if args.host != "localhost": if args.instance != "V100:1" or args.provider != "cheapest": raise ValueError("Cannot specify both BYO and on-demand cluster args") cluster = rh.cluster( name="rh-cluster", ips=[args.host], ssh_creds={"ssh_user": args.user, "ssh_private_key": args.key_path} ) else: cluster = rh.cluster( name="rh-cluster", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot ) example_dir = args.example.rsplit("/", 1)[0] # Set up remote environment cluster.install_packages(["pip:./"]) # Installs transformers from local source # Note transformers is copied into the home directory on the remote machine, so we can install from there cluster.run([f"pip install -r transformers/examples/{example_dir}/requirements.txt"]) cluster.run(["pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117"]) # Run example. You can bypass the CLI wrapper and paste your own code here. cluster.run([f'python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}']) # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI): # from my_script... import train # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard'] # launch_train_gpu = rh.function(fn=train, # system=gpu, # reqs=reqs, # name='train_bert_glue') # # We can pass in arguments just like we would to a function: # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16 # stream_logs=True)

transformers/examples/run_on_remote.py/0

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#!/usr/bin/env python # coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Script for training a masked language model on TPU.""" import argparse import logging import os import re import tensorflow as tf from packaging.version import parse from transformers import ( AutoConfig, AutoTokenizer, DataCollatorForLanguageModeling, PushToHubCallback, TFAutoModelForMaskedLM, create_optimizer, ) try: import tf_keras as keras except (ModuleNotFoundError, ImportError): import keras if parse(keras.__version__).major > 2: raise ValueError( "Your currently installed version of Keras is Keras 3, but this is not yet supported in " "Transformers. Please install the backwards-compatible tf-keras package with " "`pip install tf-keras`." ) logger = logging.getLogger(__name__) AUTO = tf.data.AUTOTUNE def parse_args(): parser = argparse.ArgumentParser(description="Train a masked language model on TPU.") parser.add_argument( "--pretrained_model_config", type=str, default="FacebookAI/roberta-base", help="The model config to use. Note that we don't copy the model's weights, only the config!", ) parser.add_argument( "--tokenizer", type=str, default="unigram-tokenizer-wikitext", help="The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model's vocab size.", ) parser.add_argument( "--per_replica_batch_size", type=int, default=8, help="Batch size per TPU core.", ) parser.add_argument( "--no_tpu", action="store_true", help="If set, run on CPU and don't try to initialize a TPU. Useful for debugging on non-TPU instances.", ) parser.add_argument( "--tpu_name", type=str, help="Name of TPU resource to initialize. Should be blank on Colab, and 'local' on TPU VMs.", default="local", ) parser.add_argument( "--tpu_zone", type=str, help="Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.", ) parser.add_argument( "--gcp_project", type=str, help="Google cloud project name. Only used for non-Colab TPU nodes." ) parser.add_argument( "--bfloat16", action="store_true", help="Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.", ) parser.add_argument( "--train_dataset", type=str, help="Path to training dataset to load. If the path begins with `gs://`" " then the dataset will be loaded from a Google Cloud Storage bucket.", ) parser.add_argument( "--shuffle_buffer_size", type=int, default=2**18, # Default corresponds to a 1GB buffer for seq_len 512 help="Size of the shuffle buffer (in samples)", ) parser.add_argument( "--eval_dataset", type=str, help="Path to evaluation dataset to load. If the path begins with `gs://`" " then the dataset will be loaded from a Google Cloud Storage bucket.", ) parser.add_argument( "--num_epochs", type=int, default=1, help="Number of epochs to train for.", ) parser.add_argument( "--learning_rate", type=float, default=1e-4, help="Learning rate to use for training.", ) parser.add_argument( "--weight_decay_rate", type=float, default=1e-3, help="Weight decay rate to use for training.", ) parser.add_argument( "--max_length", type=int, default=512, help="Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py", ) parser.add_argument( "--mlm_probability", type=float, default=0.15, help="Fraction of tokens to mask during training.", ) parser.add_argument("--output_dir", type=str, required=True, help="Path to save model checkpoints to.") parser.add_argument("--hub_model_id", type=str, help="Model ID to upload to on the Hugging Face Hub.") args = parser.parse_args() return args def initialize_tpu(args): try: if args.tpu_name: tpu = tf.distribute.cluster_resolver.TPUClusterResolver( args.tpu_name, zone=args.tpu_zone, project=args.gcp_project ) else: tpu = tf.distribute.cluster_resolver.TPUClusterResolver() except ValueError: raise RuntimeError( "Couldn't connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or " "--gcp_project. When running on a TPU VM, use --tpu_name local." ) tf.config.experimental_connect_to_cluster(tpu) tf.tpu.experimental.initialize_tpu_system(tpu) return tpu def count_samples(file_list): num_samples = 0 for file in file_list: filename = file.split("/")[-1] sample_count = re.search(r"-\d+-(\d+)\.tfrecord", filename).group(1) sample_count = int(sample_count) num_samples += sample_count return num_samples def prepare_dataset(records, decode_fn, mask_fn, batch_size, shuffle, shuffle_buffer_size=None): num_samples = count_samples(records) dataset = tf.data.Dataset.from_tensor_slices(records) if shuffle: dataset = dataset.shuffle(len(dataset)) dataset = tf.data.TFRecordDataset(dataset, num_parallel_reads=AUTO) # TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here dataset = dataset.apply(tf.data.experimental.assert_cardinality(num_samples)) dataset = dataset.map(decode_fn, num_parallel_calls=AUTO) if shuffle: assert shuffle_buffer_size is not None dataset = dataset.shuffle(args.shuffle_buffer_size) dataset = dataset.batch(batch_size, drop_remainder=True) dataset = dataset.map(mask_fn, num_parallel_calls=AUTO) dataset = dataset.prefetch(AUTO) return dataset def main(args): if not args.no_tpu: tpu = initialize_tpu(args) strategy = tf.distribute.TPUStrategy(tpu) else: strategy = tf.distribute.OneDeviceStrategy(device="/gpu:0") if args.bfloat16: keras.mixed_precision.set_global_policy("mixed_bfloat16") tokenizer = AutoTokenizer.from_pretrained(args.tokenizer) config = AutoConfig.from_pretrained(args.pretrained_model_config) config.vocab_size = tokenizer.vocab_size training_records = tf.io.gfile.glob(os.path.join(args.train_dataset, "*.tfrecord")) if not training_records: raise ValueError(f"No .tfrecord files found in {args.train_dataset}.") eval_records = tf.io.gfile.glob(os.path.join(args.eval_dataset, "*.tfrecord")) if not eval_records: raise ValueError(f"No .tfrecord files found in {args.eval_dataset}.") num_train_samples = count_samples(training_records) steps_per_epoch = num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync) total_train_steps = steps_per_epoch * args.num_epochs with strategy.scope(): model = TFAutoModelForMaskedLM.from_config(config) model(model.dummy_inputs) # Pass some dummy inputs through the model to ensure all the weights are built optimizer, schedule = create_optimizer( num_train_steps=total_train_steps, num_warmup_steps=total_train_steps // 20, init_lr=args.learning_rate, weight_decay_rate=args.weight_decay_rate, ) # Transformers models compute the right loss for their task by default when labels are passed, and will # use this for training unless you specify your own loss function in compile(). model.compile(optimizer=optimizer, metrics=["accuracy"]) def decode_fn(example): features = { "input_ids": tf.io.FixedLenFeature(dtype=tf.int64, shape=(args.max_length,)), "attention_mask": tf.io.FixedLenFeature(dtype=tf.int64, shape=(args.max_length,)), } return tf.io.parse_single_example(example, features) # Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can # use their methods in our data pipeline. data_collator = DataCollatorForLanguageModeling( tokenizer=tokenizer, mlm_probability=args.mlm_probability, mlm=True, return_tensors="tf" ) def mask_with_collator(batch): # TF really needs an isin() function special_tokens_mask = ( ~tf.cast(batch["attention_mask"], tf.bool) | (batch["input_ids"] == tokenizer.cls_token_id) | (batch["input_ids"] == tokenizer.sep_token_id) ) batch["input_ids"], batch["labels"] = data_collator.tf_mask_tokens( batch["input_ids"], vocab_size=len(tokenizer), mask_token_id=tokenizer.mask_token_id, special_tokens_mask=special_tokens_mask, ) return batch batch_size = args.per_replica_batch_size * strategy.num_replicas_in_sync train_dataset = prepare_dataset( training_records, decode_fn=decode_fn, mask_fn=mask_with_collator, batch_size=batch_size, shuffle=True, shuffle_buffer_size=args.shuffle_buffer_size, ) eval_dataset = prepare_dataset( eval_records, decode_fn=decode_fn, mask_fn=mask_with_collator, batch_size=batch_size, shuffle=False, ) callbacks = [] if args.hub_model_id: callbacks.append( PushToHubCallback(output_dir=args.output_dir, hub_model_id=args.hub_model_id, tokenizer=tokenizer) ) model.fit( train_dataset, validation_data=eval_dataset, epochs=args.num_epochs, callbacks=callbacks, ) model.save_pretrained(args.output_dir) if __name__ == "__main__": args = parse_args() main(args)

transformers/examples/tensorflow/language-modeling-tpu/run_mlm.py/0

{ "file_path": "transformers/examples/tensorflow/language-modeling-tpu/run_mlm.py", "repo_id": "transformers", "token_count": 4387 }

68

# coding=utf-8 # Copyright 2022 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import json import logging import os import sys from unittest import skip from unittest.mock import patch import tensorflow as tf from packaging.version import parse try: import tf_keras as keras except (ModuleNotFoundError, ImportError): import keras if parse(keras.__version__).major > 2: raise ValueError( "Your currently installed version of Keras is Keras 3, but this is not yet supported in " "Transformers. Please install the backwards-compatible tf-keras package with " "`pip install tf-keras`." ) from transformers.testing_utils import TestCasePlus, get_gpu_count, slow SRC_DIRS = [ os.path.join(os.path.dirname(__file__), dirname) for dirname in [ "text-generation", "text-classification", "token-classification", "language-modeling", "multiple-choice", "question-answering", "summarization", "translation", "image-classification", ] ] sys.path.extend(SRC_DIRS) if SRC_DIRS is not None: import run_clm import run_image_classification import run_mlm import run_ner import run_qa as run_squad import run_summarization import run_swag import run_text_classification import run_translation logging.basicConfig(level=logging.DEBUG) logger = logging.getLogger() def get_setup_file(): parser = argparse.ArgumentParser() parser.add_argument("-f") args = parser.parse_args() return args.f def get_results(output_dir): results = {} path = os.path.join(output_dir, "all_results.json") if os.path.exists(path): with open(path, "r") as f: results = json.load(f) else: raise ValueError(f"can't find {path}") return results def is_cuda_available(): return bool(tf.config.list_physical_devices("GPU")) stream_handler = logging.StreamHandler(sys.stdout) logger.addHandler(stream_handler) class ExamplesTests(TestCasePlus): @skip("Skipping until shape inference for to_tf_dataset PR is merged.") def test_run_text_classification(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_text_classification.py --model_name_or_path distilbert/distilbert-base-uncased --output_dir {tmp_dir} --overwrite_output_dir --train_file ./tests/fixtures/tests_samples/MRPC/train.csv --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv --do_train --do_eval --per_device_train_batch_size=2 --per_device_eval_batch_size=1 --learning_rate=1e-4 --max_steps=10 --warmup_steps=2 --seed=42 --max_seq_length=128 """.split() if is_cuda_available(): testargs.append("--fp16") with patch.object(sys, "argv", testargs): run_text_classification.main() # Reset the mixed precision policy so we don't break other tests keras.mixed_precision.set_global_policy("float32") result = get_results(tmp_dir) self.assertGreaterEqual(result["eval_accuracy"], 0.75) def test_run_clm(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_clm.py --model_name_or_path distilbert/distilgpt2 --train_file ./tests/fixtures/sample_text.txt --validation_file ./tests/fixtures/sample_text.txt --do_train --do_eval --block_size 128 --per_device_train_batch_size 2 --per_device_eval_batch_size 1 --num_train_epochs 2 --output_dir {tmp_dir} --overwrite_output_dir """.split() if len(tf.config.list_physical_devices("GPU")) > 1: # Skipping because there are not enough batches to train the model + would need a drop_last to work. return with patch.object(sys, "argv", testargs): run_clm.main() result = get_results(tmp_dir) self.assertLess(result["eval_perplexity"], 100) def test_run_mlm(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_mlm.py --model_name_or_path distilbert/distilroberta-base --train_file ./tests/fixtures/sample_text.txt --validation_file ./tests/fixtures/sample_text.txt --max_seq_length 64 --output_dir {tmp_dir} --overwrite_output_dir --do_train --do_eval --prediction_loss_only --num_train_epochs=1 --learning_rate=1e-4 """.split() with patch.object(sys, "argv", testargs): run_mlm.main() result = get_results(tmp_dir) self.assertLess(result["eval_perplexity"], 42) def test_run_ner(self): # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu epochs = 7 if get_gpu_count() > 1 else 2 tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_ner.py --model_name_or_path google-bert/bert-base-uncased --train_file tests/fixtures/tests_samples/conll/sample.json --validation_file tests/fixtures/tests_samples/conll/sample.json --output_dir {tmp_dir} --overwrite_output_dir --do_train --do_eval --warmup_steps=2 --learning_rate=2e-4 --per_device_train_batch_size=2 --per_device_eval_batch_size=2 --num_train_epochs={epochs} --seed 7 """.split() with patch.object(sys, "argv", testargs): run_ner.main() result = get_results(tmp_dir) self.assertGreaterEqual(result["accuracy"], 0.75) def test_run_squad(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_qa.py --model_name_or_path google-bert/bert-base-uncased --version_2_with_negative --train_file tests/fixtures/tests_samples/SQUAD/sample.json --validation_file tests/fixtures/tests_samples/SQUAD/sample.json --output_dir {tmp_dir} --overwrite_output_dir --max_steps=10 --warmup_steps=2 --do_train --do_eval --learning_rate=2e-4 --per_device_train_batch_size=2 --per_device_eval_batch_size=1 """.split() with patch.object(sys, "argv", testargs): run_squad.main() result = get_results(tmp_dir) self.assertGreaterEqual(result["f1"], 30) self.assertGreaterEqual(result["exact"], 30) def test_run_swag(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_swag.py --model_name_or_path google-bert/bert-base-uncased --train_file tests/fixtures/tests_samples/swag/sample.json --validation_file tests/fixtures/tests_samples/swag/sample.json --output_dir {tmp_dir} --overwrite_output_dir --max_steps=20 --warmup_steps=2 --do_train --do_eval --learning_rate=2e-4 --per_device_train_batch_size=2 --per_device_eval_batch_size=1 """.split() with patch.object(sys, "argv", testargs): run_swag.main() result = get_results(tmp_dir) self.assertGreaterEqual(result["val_accuracy"], 0.8) @slow def test_run_summarization(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_summarization.py --model_name_or_path google-t5/t5-small --train_file tests/fixtures/tests_samples/xsum/sample.json --validation_file tests/fixtures/tests_samples/xsum/sample.json --output_dir {tmp_dir} --overwrite_output_dir --max_steps=50 --warmup_steps=8 --do_train --do_eval --learning_rate=2e-4 --per_device_train_batch_size=2 --per_device_eval_batch_size=1 """.split() with patch.object(sys, "argv", testargs): run_summarization.main() result = get_results(tmp_dir) self.assertGreaterEqual(result["rouge1"], 10) self.assertGreaterEqual(result["rouge2"], 2) self.assertGreaterEqual(result["rougeL"], 7) self.assertGreaterEqual(result["rougeLsum"], 7) @slow def test_run_translation(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_translation.py --model_name_or_path Rocketknight1/student_marian_en_ro_6_1 --source_lang en --target_lang ro --train_file tests/fixtures/tests_samples/wmt16/sample.json --validation_file tests/fixtures/tests_samples/wmt16/sample.json --output_dir {tmp_dir} --overwrite_output_dir --warmup_steps=8 --do_train --do_eval --learning_rate=3e-3 --num_train_epochs 12 --per_device_train_batch_size=2 --per_device_eval_batch_size=1 --source_lang en_XX --target_lang ro_RO """.split() with patch.object(sys, "argv", testargs): run_translation.main() result = get_results(tmp_dir) self.assertGreaterEqual(result["bleu"], 30) def test_run_image_classification(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_image_classification.py --dataset_name hf-internal-testing/cats_vs_dogs_sample --model_name_or_path microsoft/resnet-18 --do_train --do_eval --learning_rate 1e-4 --per_device_train_batch_size 2 --per_device_eval_batch_size 1 --output_dir {tmp_dir} --overwrite_output_dir --dataloader_num_workers 16 --num_train_epochs 2 --train_val_split 0.1 --seed 42 --ignore_mismatched_sizes True """.split() with patch.object(sys, "argv", testargs): run_image_classification.main() result = get_results(tmp_dir) self.assertGreaterEqual(result["accuracy"], 0.7)

transformers/examples/tensorflow/test_tensorflow_examples.py/0

{ "file_path": "transformers/examples/tensorflow/test_tensorflow_examples.py", "repo_id": "transformers", "token_count": 5468 }

69

from collections import Counter import datasets import transformers from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS from transformers.utils import logging logging.set_verbosity_info() TOKENIZER_CLASSES = { name: (getattr(transformers, name), getattr(transformers, name + "Fast")) for name in SLOW_TO_FAST_CONVERTERS } dataset = datasets.load_dataset("xnli", split="test+validation") total = 0 perfect = 0 imperfect = 0 wrong = 0 def check_diff(spm_diff, tok_diff, slow, fast): if spm_diff == list(reversed(tok_diff)): # AAA -> AA+A vs A+AA case. return True elif len(spm_diff) == len(tok_diff) and fast.decode(spm_diff) == fast.decode(tok_diff): # Second order OK # Barrich -> Barr + ich vs Bar + rich return True spm_reencoded = slow.encode(slow.decode(spm_diff)) tok_reencoded = fast.encode(fast.decode(spm_diff)) if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded: # Type 3 error. # Snehagatha -> # Sne, h, aga, th, a # Sne, ha, gat, ha # Encoding the wrong with sp does not even recover what spm gave us # It fits tokenizer however... return True return False def check_LTR_mark(line, idx, fast): enc = fast.encode_plus(line)[0] offsets = enc.offsets curr, prev = offsets[idx], offsets[idx - 1] if curr is not None and line[curr[0] : curr[1]] == "\u200f": return True if prev is not None and line[prev[0] : prev[1]] == "\u200f": return True def check_details(line, spm_ids, tok_ids, slow, fast): # Encoding can be the same with same result AAA -> A + AA vs AA + A # We can check that we use at least exactly the same number of tokens. for i, (spm_id, tok_id) in enumerate(zip(spm_ids, tok_ids)): if spm_id != tok_id: break first = i for i, (spm_id, tok_id) in enumerate(zip(reversed(spm_ids), reversed(tok_ids))): if spm_id != tok_id: break last = len(spm_ids) - i spm_diff = spm_ids[first:last] tok_diff = tok_ids[first:last] if check_diff(spm_diff, tok_diff, slow, fast): return True if check_LTR_mark(line, first, fast): return True if last - first > 5: # We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems spms = Counter(spm_ids[first:last]) toks = Counter(tok_ids[first:last]) removable_tokens = {spm_ for (spm_, si) in spms.items() if toks.get(spm_, 0) == si} min_width = 3 for i in range(last - first - min_width): if all(spm_ids[first + i + j] in removable_tokens for j in range(min_width)): possible_matches = [ k for k in range(last - first - min_width) if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width] ] for j in possible_matches: if check_diff(spm_ids[first : first + i], tok_ids[first : first + j], sp, tok) and check_details( line, spm_ids[first + i : last], tok_ids[first + j : last], slow, fast, ): return True print(f"Spm: {[fast.decode([spm_ids[i]]) for i in range(first, last)]}") try: print(f"Tok: {[fast.decode([tok_ids[i]]) for i in range(first, last)]}") except Exception: pass fast.decode(spm_ids[:first]) fast.decode(spm_ids[last:]) wrong = fast.decode(spm_ids[first:last]) print() print(wrong) return False def test_string(slow, fast, text): global perfect global imperfect global wrong global total slow_ids = slow.encode(text) fast_ids = fast.encode(text) skip_assert = False total += 1 if slow_ids != fast_ids: if check_details(text, slow_ids, fast_ids, slow, fast): skip_assert = True imperfect += 1 else: wrong += 1 else: perfect += 1 if total % 10000 == 0: print(f"({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})") if skip_assert: return assert ( slow_ids == fast_ids ), f"line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(text)}\n{fast.tokenize(text)}" def test_tokenizer(slow, fast): global batch_total for i in range(len(dataset)): # premise, all languages for text in dataset[i]["premise"].values(): test_string(slow, fast, text) # hypothesis, all languages for text in dataset[i]["hypothesis"]["translation"]: test_string(slow, fast, text) if __name__ == "__main__": for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items(): checkpoint_names = list(slow_class.max_model_input_sizes.keys()) for checkpoint in checkpoint_names: imperfect = 0 perfect = 0 wrong = 0 total = 0 print(f"========================== Checking {name}: {checkpoint} ==========================") slow = slow_class.from_pretrained(checkpoint, force_download=True) fast = fast_class.from_pretrained(checkpoint, force_download=True) test_tokenizer(slow, fast) print(f"Accuracy {perfect * 100 / total:.2f}")

transformers/scripts/check_tokenizers.py/0

{ "file_path": "transformers/scripts/check_tokenizers.py", "repo_id": "transformers", "token_count": 2563 }

70

# Copyright 2021 The HuggingFace Team, the AllenNLP library authors. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Script to close stale issue. Taken in part from the AllenNLP repository. https://github.com/allenai/allennlp. """ import os from datetime import datetime as dt import github.GithubException from github import Github LABELS_TO_EXEMPT = [ "good first issue", "good second issue", "good difficult issue", "feature request", "new model", "wip", ] def main(): g = Github(os.environ["GITHUB_TOKEN"]) repo = g.get_repo("huggingface/transformers") open_issues = repo.get_issues(state="open") for i, issue in enumerate(open_issues): print(i, issue) comments = sorted(list(issue.get_comments()), key=lambda i: i.created_at, reverse=True) last_comment = comments[0] if len(comments) > 0 else None if ( last_comment is not None and last_comment.user.login == "github-actions[bot]" and (dt.utcnow() - issue.updated_at.replace(tzinfo=None)).days > 7 and (dt.utcnow() - issue.created_at.replace(tzinfo=None)).days >= 30 and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()) ): # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.") try: issue.edit(state="closed") except github.GithubException as e: print("Couldn't close the issue:", repr(e)) elif ( (dt.utcnow() - issue.updated_at.replace(tzinfo=None)).days > 23 and (dt.utcnow() - issue.created_at.replace(tzinfo=None)).days >= 30 and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()) ): # print(f"Would add stale comment to {issue.number}") try: issue.create_comment( "This issue has been automatically marked as stale because it has not had " "recent activity. If you think this still needs to be addressed " "please comment on this thread.\n\nPlease note that issues that do not follow the " "[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) " "are likely to be ignored." ) except github.GithubException as e: print("Couldn't create comment:", repr(e)) if __name__ == "__main__": main()

transformers/scripts/stale.py/0

{ "file_path": "transformers/scripts/stale.py", "repo_id": "transformers", "token_count": 1217 }

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# coding=utf-8 # Copyright 2018 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Utilities to convert slow tokenizers in their fast tokenizers counterparts. All the conversions are grouped here to gather SentencePiece dependencies outside of the fast tokenizers files and allow to make our dependency on SentencePiece optional. """ import warnings from typing import Dict, List, Tuple from packaging import version from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors from tokenizers.models import BPE, Unigram, WordPiece from .utils import is_protobuf_available, requires_backends from .utils.import_utils import PROTOBUF_IMPORT_ERROR def import_protobuf(error_message=""): if is_protobuf_available(): import google.protobuf if version.parse(google.protobuf.__version__) < version.parse("4.0.0"): from transformers.utils import sentencepiece_model_pb2 else: from transformers.utils import sentencepiece_model_pb2_new as sentencepiece_model_pb2 return sentencepiece_model_pb2 else: raise ImportError(PROTOBUF_IMPORT_ERROR.format(error_message)) class SentencePieceExtractor: """ Extractor implementation for SentencePiece trained models. https://github.com/google/sentencepiece """ def __init__(self, model: str): requires_backends(self, "sentencepiece") from sentencepiece import SentencePieceProcessor self.sp = SentencePieceProcessor() self.sp.Load(model) def extract(self, vocab_scores=None) -> Tuple[Dict[str, int], List[Tuple]]: """ By default will return vocab and merges with respect to their order, by sending `vocab_scores` we're going to order the merges with respect to the piece scores instead. """ sp = self.sp vocab = {sp.id_to_piece(index): index for index in range(sp.GetPieceSize())} if vocab_scores is not None: vocab_scores, reverse = dict(vocab_scores), True else: vocab_scores, reverse = vocab, False # Merges merges = [] for merge, piece_score in vocab_scores.items(): local = [] for index in range(1, len(merge)): piece_l, piece_r = merge[:index], merge[index:] if piece_l in vocab and piece_r in vocab: local.append((piece_l, piece_r, piece_score)) local = sorted(local, key=lambda x: (vocab[x[0]], vocab[x[1]])) merges.extend(local) merges = sorted(merges, key=lambda val: val[2], reverse=reverse) merges = [(val[0], val[1]) for val in merges] return vocab, merges class GemmaSentencePieceExtractor(SentencePieceExtractor): def extract(self, vocab_scores=None) -> Tuple[Dict[str, int], List[Tuple]]: """ By default will return vocab and merges with respect to their order, by sending `vocab_scores` we're going to order the merges with respect to the piece scores instead. """ sp = self.sp vocab = {sp.id_to_piece(index): index for index in range(sp.GetPieceSize())} # there is a missing token in the vocab. We have to do this to support merges # "<0x09>" is the bytefallback for `\t` vocab["\t"] = vocab.pop("<0x09>") if vocab_scores is not None: vocab_scores, reverse = dict(vocab_scores), True else: vocab_scores, reverse = vocab, False # Merges merges = [] for merge, piece_score in vocab_scores.items(): local = [] for index in range(1, len(merge)): piece_l, piece_r = merge[:index], merge[index:] if piece_l in vocab and piece_r in vocab: local.append((piece_l, piece_r, piece_score)) local = sorted(local, key=lambda x: (vocab[x[0]], vocab[x[1]])) merges.extend(local) merges = sorted(merges, key=lambda val: val[2], reverse=reverse) merges = [(val[0], val[1]) for val in merges] return vocab, merges def check_number_comma(piece: str) -> bool: return len(piece) < 2 or piece[-1] != "," or not piece[-2].isdigit() class Converter: def __init__(self, original_tokenizer): self.original_tokenizer = original_tokenizer def converted(self) -> Tokenizer: raise NotImplementedError() class BertConverter(Converter): def converted(self) -> Tokenizer: vocab = self.original_tokenizer.vocab tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token))) tokenize_chinese_chars = False strip_accents = False do_lower_case = False if hasattr(self.original_tokenizer, "basic_tokenizer"): tokenize_chinese_chars = self.original_tokenizer.basic_tokenizer.tokenize_chinese_chars strip_accents = self.original_tokenizer.basic_tokenizer.strip_accents do_lower_case = self.original_tokenizer.basic_tokenizer.do_lower_case tokenizer.normalizer = normalizers.BertNormalizer( clean_text=True, handle_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, lowercase=do_lower_case, ) tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer() cls = str(self.original_tokenizer.cls_token) sep = str(self.original_tokenizer.sep_token) cls_token_id = self.original_tokenizer.cls_token_id sep_token_id = self.original_tokenizer.sep_token_id tokenizer.post_processor = processors.TemplateProcessing( single=f"{cls}:0 $A:0 {sep}:0", pair=f"{cls}:0 $A:0 {sep}:0 $B:1 {sep}:1", special_tokens=[ (cls, cls_token_id), (sep, sep_token_id), ], ) tokenizer.decoder = decoders.WordPiece(prefix="##") return tokenizer class SplinterConverter(Converter): def converted(self) -> Tokenizer: vocab = self.original_tokenizer.vocab tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token))) tokenize_chinese_chars = False strip_accents = False do_lower_case = False if hasattr(self.original_tokenizer, "basic_tokenizer"): tokenize_chinese_chars = self.original_tokenizer.basic_tokenizer.tokenize_chinese_chars strip_accents = self.original_tokenizer.basic_tokenizer.strip_accents do_lower_case = self.original_tokenizer.basic_tokenizer.do_lower_case tokenizer.normalizer = normalizers.BertNormalizer( clean_text=True, handle_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, lowercase=do_lower_case, ) tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer() cls = str(self.original_tokenizer.cls_token) sep = str(self.original_tokenizer.sep_token) question = str(self.original_tokenizer.question_token) dot = "." cls_token_id = self.original_tokenizer.cls_token_id sep_token_id = self.original_tokenizer.sep_token_id question_token_id = self.original_tokenizer.question_token_id dot_token_id = self.original_tokenizer.convert_tokens_to_ids(".") if self.original_tokenizer.padding_side == "right": pair = f"{cls}:0 $A:0 {question} {dot} {sep}:0 $B:1 {sep}:1" else: pair = f"{cls}:0 $A:0 {sep}:0 $B:1 {question} {dot} {sep}:1" tokenizer.post_processor = processors.TemplateProcessing( single=f"{cls}:0 $A:0 {sep}:0", pair=pair, special_tokens=[ (cls, cls_token_id), (sep, sep_token_id), (question, question_token_id), (dot, dot_token_id), ], ) tokenizer.decoder = decoders.WordPiece(prefix="##") return tokenizer class FunnelConverter(Converter): def converted(self) -> Tokenizer: vocab = self.original_tokenizer.vocab tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token))) tokenize_chinese_chars = False strip_accents = False do_lower_case = False if hasattr(self.original_tokenizer, "basic_tokenizer"): tokenize_chinese_chars = self.original_tokenizer.basic_tokenizer.tokenize_chinese_chars strip_accents = self.original_tokenizer.basic_tokenizer.strip_accents do_lower_case = self.original_tokenizer.basic_tokenizer.do_lower_case tokenizer.normalizer = normalizers.BertNormalizer( clean_text=True, handle_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, lowercase=do_lower_case, ) tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer() cls = str(self.original_tokenizer.cls_token) sep = str(self.original_tokenizer.sep_token) cls_token_id = self.original_tokenizer.cls_token_id sep_token_id = self.original_tokenizer.sep_token_id tokenizer.post_processor = processors.TemplateProcessing( single=f"{cls}:2 $A:0 {sep}:0", # token_type_id is 2 for Funnel transformer pair=f"{cls}:2 $A:0 {sep}:0 $B:1 {sep}:1", special_tokens=[ (cls, cls_token_id), (sep, sep_token_id), ], ) tokenizer.decoder = decoders.WordPiece(prefix="##") return tokenizer class MPNetConverter(Converter): def converted(self) -> Tokenizer: vocab = self.original_tokenizer.vocab tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token))) tokenize_chinese_chars = False strip_accents = False do_lower_case = False if hasattr(self.original_tokenizer, "basic_tokenizer"): tokenize_chinese_chars = self.original_tokenizer.basic_tokenizer.tokenize_chinese_chars strip_accents = self.original_tokenizer.basic_tokenizer.strip_accents do_lower_case = self.original_tokenizer.basic_tokenizer.do_lower_case tokenizer.normalizer = normalizers.BertNormalizer( clean_text=True, handle_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, lowercase=do_lower_case, ) tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer() cls = str(self.original_tokenizer.cls_token) sep = str(self.original_tokenizer.sep_token) cls_token_id = self.original_tokenizer.cls_token_id sep_token_id = self.original_tokenizer.sep_token_id tokenizer.post_processor = processors.TemplateProcessing( single=f"{cls}:0 $A:0 {sep}:0", pair=f"{cls}:0 $A:0 {sep}:0 {sep}:0 $B:1 {sep}:1", # MPNet uses two [SEP] tokens special_tokens=[ (cls, cls_token_id), (sep, sep_token_id), ], ) tokenizer.decoder = decoders.WordPiece(prefix="##") return tokenizer class OpenAIGPTConverter(Converter): def converted(self) -> Tokenizer: vocab = self.original_tokenizer.encoder merges = list(self.original_tokenizer.bpe_ranks.keys()) unk_token = self.original_tokenizer.unk_token tokenizer = Tokenizer( BPE( vocab=vocab, merges=merges, dropout=None, unk_token=str(unk_token), end_of_word_suffix="</w>", fuse_unk=False, ) ) if tokenizer.token_to_id(str(unk_token)) is not None: tokenizer.add_special_tokens([str(unk_token)]) tokenizer.normalizer = normalizers.BertNormalizer(lowercase=True) tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer() tokenizer.decoder = decoders.BPEDecoder(suffix="</w>") return tokenizer class GPT2Converter(Converter): def converted(self) -> Tokenizer: vocab = self.original_tokenizer.encoder merges = list(self.original_tokenizer.bpe_ranks.keys()) tokenizer = Tokenizer( BPE( vocab=vocab, merges=merges, dropout=None, continuing_subword_prefix="", end_of_word_suffix="", fuse_unk=False, ) ) tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=self.original_tokenizer.add_prefix_space) tokenizer.decoder = decoders.ByteLevel() if self.original_tokenizer.add_bos_token: bos = self.original_tokenizer.bos_token bos_token_id = self.original_tokenizer.bos_token_id tokenizer.post_processor = processors.TemplateProcessing( single=f"{bos}:0 $A:0", pair=f"{bos}:0 $A:0 $B:1", special_tokens=[ (bos, bos_token_id), ], ) else: # XXX trim_offsets=False actually means this post_processor doesn't # really do anything. tokenizer.post_processor = processors.ByteLevel(trim_offsets=False) return tokenizer class HerbertConverter(Converter): def converted(self) -> Tokenizer: tokenizer_info_str = "#version:" token_suffix = "</w>" vocab = self.original_tokenizer.encoder merges = list(self.original_tokenizer.bpe_ranks.keys()) if tokenizer_info_str in merges[0][0]: merges = merges[1:] tokenizer = Tokenizer( BPE( vocab, merges, dropout=None, unk_token=self.original_tokenizer.unk_token, end_of_word_suffix=token_suffix, ) ) tokenizer.normalizer = normalizers.BertNormalizer(lowercase=False, strip_accents=False) tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer() tokenizer.decoder = decoders.BPEDecoder(suffix=token_suffix) tokenizer.post_processor = processors.BertProcessing( sep=(self.original_tokenizer.sep_token, self.original_tokenizer.sep_token_id), cls=(self.original_tokenizer.cls_token, self.original_tokenizer.cls_token_id), ) return tokenizer class Qwen2Converter(Converter): def converted(self) -> Tokenizer: vocab = self.original_tokenizer.encoder merges = list(self.original_tokenizer.bpe_ranks.keys()) tokenizer = Tokenizer( BPE( vocab=vocab, merges=merges, dropout=None, unk_token=None, continuing_subword_prefix="", end_of_word_suffix="", fuse_unk=False, byte_fallback=False, ) ) tokenizer.normalizer = normalizers.NFC() tokenizer.pre_tokenizer = pre_tokenizers.Sequence( [ pre_tokenizers.Split( Regex( r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+""" ), behavior="isolated", invert=False, ), pre_tokenizers.ByteLevel( add_prefix_space=getattr(self.original_tokenizer, "add_prefix_space", False), use_regex=False, ), ] ) tokenizer.decoder = decoders.ByteLevel() tokenizer.post_processor = processors.ByteLevel(trim_offsets=False) return tokenizer class RobertaConverter(Converter): def converted(self) -> Tokenizer: ot = self.original_tokenizer vocab = ot.encoder merges = list(ot.bpe_ranks.keys()) tokenizer = Tokenizer( BPE( vocab=vocab, merges=merges, dropout=None, continuing_subword_prefix="", end_of_word_suffix="", fuse_unk=False, ) ) tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=ot.add_prefix_space) tokenizer.decoder = decoders.ByteLevel() tokenizer.post_processor = processors.RobertaProcessing( sep=(ot.sep_token, ot.sep_token_id), cls=(ot.cls_token, ot.cls_token_id), add_prefix_space=ot.add_prefix_space, trim_offsets=True, # True by default on Roberta (historical) ) return tokenizer class RoFormerConverter(Converter): def converted(self) -> Tokenizer: from .models.roformer.tokenization_utils import JiebaPreTokenizer vocab = self.original_tokenizer.vocab tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token))) strip_accents = False do_lower_case = False if hasattr(self.original_tokenizer, "basic_tokenizer"): strip_accents = self.original_tokenizer.basic_tokenizer.strip_accents do_lower_case = self.original_tokenizer.basic_tokenizer.do_lower_case tokenizer.normalizer = normalizers.BertNormalizer( clean_text=True, handle_chinese_chars=False, strip_accents=strip_accents, lowercase=do_lower_case, ) tokenizer.pre_tokenizer = pre_tokenizers.PreTokenizer.custom(JiebaPreTokenizer(vocab)) cls = str(self.original_tokenizer.cls_token) sep = str(self.original_tokenizer.sep_token) cls_token_id = self.original_tokenizer.cls_token_id sep_token_id = self.original_tokenizer.sep_token_id tokenizer.post_processor = processors.TemplateProcessing( single=f"{cls}:0 $A:0 {sep}:0", pair=f"{cls}:0 $A:0 {sep}:0 $B:1 {sep}:1", special_tokens=[ (cls, cls_token_id), (sep, sep_token_id), ], ) tokenizer.decoder = decoders.WordPiece(prefix="##") return tokenizer class DebertaConverter(Converter): def converted(self) -> Tokenizer: ot = self.original_tokenizer vocab = ot.encoder merges = list(ot.bpe_ranks.keys()) tokenizer = Tokenizer( BPE( vocab=vocab, merges=merges, dropout=None, continuing_subword_prefix="", end_of_word_suffix="", fuse_unk=False, ) ) tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=ot.add_prefix_space) tokenizer.decoder = decoders.ByteLevel() tokenizer.post_processor = processors.TemplateProcessing( single="[CLS]:0 $A:0 [SEP]:0", pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1", special_tokens=[ ("[CLS]", self.original_tokenizer.convert_tokens_to_ids("[CLS]")), ("[SEP]", self.original_tokenizer.convert_tokens_to_ids("[SEP]")), ], ) return tokenizer class SpmConverter(Converter): def __init__(self, *args): requires_backends(self, "protobuf") super().__init__(*args) # from .utils import sentencepiece_model_pb2 as model_pb2 model_pb2 = import_protobuf() m = model_pb2.ModelProto() with open(self.original_tokenizer.vocab_file, "rb") as f: m.ParseFromString(f.read()) self.proto = m if self.proto.trainer_spec.byte_fallback: if not getattr(self, "handle_byte_fallback", None): warnings.warn( "The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option" " which is not implemented in the fast tokenizers. In practice this means that the fast version of the" " tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these " "unknown tokens into a sequence of byte tokens matching the original piece of text." ) def vocab(self, proto): return [(piece.piece, piece.score) for piece in proto.pieces] def unk_id(self, proto): return proto.trainer_spec.unk_id def tokenizer(self, proto): model_type = proto.trainer_spec.model_type vocab_scores = self.vocab(proto) unk_id = self.unk_id(proto) if model_type == 1: tokenizer = Tokenizer(Unigram(vocab_scores, unk_id)) elif model_type == 2: _, merges = SentencePieceExtractor(self.original_tokenizer.vocab_file).extract() bpe_vocab = {word: i for i, (word, score) in enumerate(vocab_scores)} tokenizer = Tokenizer( BPE( bpe_vocab, merges, unk_token=proto.trainer_spec.unk_piece, fuse_unk=True, ) ) else: raise Exception( "You're trying to run a `Unigram` model but you're file was trained with a different algorithm" ) return tokenizer def normalizer(self, proto): precompiled_charsmap = proto.normalizer_spec.precompiled_charsmap _normalizers = [ normalizers.Strip(left=False, right=True), # stripping is important normalizers.Replace(Regex(" {2,}"), "▁"), ] if not precompiled_charsmap: return normalizers.Sequence(_normalizers) else: return normalizers.Sequence([normalizers.Precompiled(precompiled_charsmap)] + _normalizers) def pre_tokenizer(self, replacement, add_prefix_space): prepend_scheme = "always" if hasattr(self.original_tokenizer, "legacy") and not self.original_tokenizer.legacy: prepend_scheme = "first" return pre_tokenizers.Metaspace( replacement=replacement, add_prefix_space=add_prefix_space, prepend_scheme=prepend_scheme ) def post_processor(self): return None def decoder(self, replacement, add_prefix_space): return decoders.Metaspace(replacement=replacement, add_prefix_space=add_prefix_space) def converted(self) -> Tokenizer: tokenizer = self.tokenizer(self.proto) # Tokenizer assemble normalizer = self.normalizer(self.proto) if normalizer is not None: tokenizer.normalizer = normalizer replacement = "▁" add_prefix_space = True if hasattr(self.original_tokenizer, "add_prefix_space"): add_prefix_space = self.original_tokenizer.add_prefix_space pre_tokenizer = self.pre_tokenizer(replacement, add_prefix_space) if pre_tokenizer is not None: tokenizer.pre_tokenizer = pre_tokenizer tokenizer.decoder = self.decoder(replacement, add_prefix_space) post_processor = self.post_processor() if post_processor: tokenizer.post_processor = post_processor return tokenizer class AlbertConverter(SpmConverter): def vocab(self, proto): return [ (piece.piece, piece.score) if check_number_comma(piece.piece) else (piece.piece, piece.score - 100) for piece in proto.pieces ] def normalizer(self, proto): list_normalizers = [ normalizers.Replace("``", '"'), normalizers.Replace("''", '"'), ] if not self.original_tokenizer.keep_accents: list_normalizers.append(normalizers.NFKD()) list_normalizers.append(normalizers.StripAccents()) if self.original_tokenizer.do_lower_case: list_normalizers.append(normalizers.Lowercase()) precompiled_charsmap = proto.normalizer_spec.precompiled_charsmap if precompiled_charsmap: list_normalizers.append(normalizers.Precompiled(precompiled_charsmap)) list_normalizers.append(normalizers.Replace(Regex(" {2,}"), " ")) return normalizers.Sequence(list_normalizers) def post_processor(self): return processors.TemplateProcessing( single="[CLS]:0 $A:0 [SEP]:0", pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1", special_tokens=[ ("[CLS]", self.original_tokenizer.convert_tokens_to_ids("[CLS]")), ("[SEP]", self.original_tokenizer.convert_tokens_to_ids("[SEP]")), ], ) class BarthezConverter(SpmConverter): def unk_id(self, proto): unk_id = 3 return unk_id def post_processor(self): return processors.TemplateProcessing( single="<s> $A </s>", pair="<s> $A </s> </s> $B </s>", special_tokens=[ ("<s>", self.original_tokenizer.convert_tokens_to_ids("<s>")), ("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")), ], ) class CamembertConverter(SpmConverter): def vocab(self, proto): vocab = [ ("<s>NOTUSED", 0.0), ("<pad>", 0.0), ("</s>NOTUSED", 0.0), ("<unk>", 0.0), ("<unk>NOTUSED", -100), ] # We down-grade the original SentencePiece by -100 to avoid using it and use our added token instead vocab += [(piece.piece, piece.score) for piece in proto.pieces[1:]] vocab += [("<mask>", 0.0)] return vocab def unk_id(self, proto): # See vocab unk position return 3 def post_processor(self): return processors.TemplateProcessing( single="<s> $A </s>", pair="<s> $A </s> </s> $B </s>", special_tokens=[ ("<s>", self.original_tokenizer.convert_tokens_to_ids("<s>")), ("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")), ], ) class DebertaV2Converter(SpmConverter): def pre_tokenizer(self, replacement, add_prefix_space): list_pretokenizers = [] if self.original_tokenizer.split_by_punct: list_pretokenizers.append(pre_tokenizers.Punctuation(behavior="isolated")) list_pretokenizers.append(pre_tokenizers.Metaspace(replacement=replacement, add_prefix_space=add_prefix_space)) return pre_tokenizers.Sequence(list_pretokenizers) def normalizer(self, proto): list_normalizers = [] if self.original_tokenizer.do_lower_case: list_normalizers.append(normalizers.Lowercase()) list_normalizers.append(normalizers.Strip()) precompiled_charsmap = proto.normalizer_spec.precompiled_charsmap if precompiled_charsmap: list_normalizers.append(normalizers.Precompiled(precompiled_charsmap)) list_normalizers.append(normalizers.Replace(Regex(" {2,}"), " ")) return normalizers.Sequence(list_normalizers) def post_processor(self): return processors.TemplateProcessing( single="[CLS]:0 $A:0 [SEP]:0", pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1", special_tokens=[ ("[CLS]", self.original_tokenizer.convert_tokens_to_ids("[CLS]")), ("[SEP]", self.original_tokenizer.convert_tokens_to_ids("[SEP]")), ], ) class MBartConverter(SpmConverter): def vocab(self, proto): vocab = [ ("<s>", 0.0), ("<pad>", 0.0), ("</s>", 0.0), ("<unk>", 0.0), ] vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]] vocab += [ ("ar_AR", 0.0), ("cs_CZ", 0.0), ("de_DE", 0.0), ("en_XX", 0.0), ("es_XX", 0.0), ("et_EE", 0.0), ("fi_FI", 0.0), ("fr_XX", 0.0), ("gu_IN", 0.0), ("hi_IN", 0.0), ("it_IT", 0.0), ("ja_XX", 0.0), ("kk_KZ", 0.0), ("ko_KR", 0.0), ("lt_LT", 0.0), ("lv_LV", 0.0), ("my_MM", 0.0), ("ne_NP", 0.0), ("nl_XX", 0.0), ("ro_RO", 0.0), ("ru_RU", 0.0), ("si_LK", 0.0), ("tr_TR", 0.0), ("vi_VN", 0.0), ("zh_CN", 0.0), ] vocab += [("<mask>", 0.0)] return vocab def unk_id(self, proto): return 3 def post_processor(self): return processors.TemplateProcessing( single="$A </s> en_XX", pair="$A $B </s> en_XX", special_tokens=[ ("en_XX", self.original_tokenizer.convert_tokens_to_ids("en_XX")), ("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")), ], ) class MBart50Converter(SpmConverter): def vocab(self, proto): vocab = [ ("<s>", 0.0), ("<pad>", 0.0), ("</s>", 0.0), ("<unk>", 0.0), ] vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]] vocab += [("ar_AR", 0.0), ("cs_CZ", 0.0), ("de_DE", 0.0), ("en_XX", 0.0), ("es_XX", 0.0), ("et_EE", 0.0), ("fi_FI", 0.0), ("fr_XX", 0.0), ("gu_IN", 0.0), ("hi_IN", 0.0), ("it_IT", 0.0), ("ja_XX", 0.0), ("kk_KZ", 0.0), ("ko_KR", 0.0), ("lt_LT", 0.0), ("lv_LV", 0.0), ("my_MM", 0.0), ("ne_NP", 0.0), ("nl_XX", 0.0), ("ro_RO", 0.0), ("ru_RU", 0.0), ("si_LK", 0.0), ("tr_TR", 0.0), ("vi_VN", 0.0), ("zh_CN", 0.0), ("af_ZA", 0.0), ("az_AZ", 0.0), ("bn_IN", 0.0), ("fa_IR", 0.0), ("he_IL", 0.0), ("hr_HR", 0.0), ("id_ID", 0.0), ("ka_GE", 0.0), ("km_KH", 0.0), ("mk_MK", 0.0), ("ml_IN", 0.0), ("mn_MN", 0.0), ("mr_IN", 0.0), ("pl_PL", 0.0), ("ps_AF", 0.0), ("pt_XX", 0.0), ("sv_SE", 0.0), ("sw_KE", 0.0), ("ta_IN", 0.0), ("te_IN", 0.0), ("th_TH", 0.0), ("tl_XX", 0.0), ("uk_UA", 0.0), ("ur_PK", 0.0), ("xh_ZA", 0.0), ("gl_ES", 0.0), ("sl_SI", 0.0)] # fmt: skip vocab += [("<mask>", 0.0)] return vocab def unk_id(self, proto): return 3 def post_processor(self): return processors.TemplateProcessing( single="en_XX $A </s>", pair="en_XX $A $B </s>", special_tokens=[ ("en_XX", self.original_tokenizer.convert_tokens_to_ids("en_XX")), ("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")), ], ) class NllbConverter(SpmConverter): def vocab(self, proto): vocab = [ ("<s>", 0.0), ("<pad>", 0.0), ("</s>", 0.0), ("<unk>", 0.0), ] vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]] return vocab def unk_id(self, proto): return 3 def post_processor(self): return processors.TemplateProcessing( single="eng_Latn $A </s>", pair="eng_Latn $A $B </s>", special_tokens=[ ("eng_Latn", self.original_tokenizer.convert_tokens_to_ids("eng_Latn")), ("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")), ], ) class SeamlessM4TConverter(SpmConverter): def vocab(self, proto): vocab = [ ("<pad>", 0.0), ("<unk>", 0.0), ("<s>", 0.0), ("</s>", 0.0), ] vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]] return vocab def unk_id(self, proto): return self.original_tokenizer.unk_token_id def post_processor(self): return processors.TemplateProcessing( single="__eng__ $A </s>", pair="__eng__ $A $B </s>", special_tokens=[ ("__eng__", self.original_tokenizer.convert_tokens_to_ids("__eng__")), ("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")), ], ) class XLMRobertaConverter(SpmConverter): def vocab(self, proto): vocab = [ ("<s>", 0.0), ("<pad>", 0.0), ("</s>", 0.0), ("<unk>", 0.0), ] vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]] vocab += [("<mask>", 0.0)] return vocab def unk_id(self, proto): unk_id = 3 return unk_id def post_processor(self): return processors.TemplateProcessing( single="<s> $A </s>", pair="<s> $A </s> </s> $B </s>", special_tokens=[ ("<s>", self.original_tokenizer.convert_tokens_to_ids("<s>")), ("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")), ], ) class XLNetConverter(SpmConverter): def vocab(self, proto): return [ (piece.piece, piece.score) if check_number_comma(piece.piece) else (piece.piece, piece.score - 100) for piece in proto.pieces ] def normalizer(self, proto): list_normalizers = [ normalizers.Replace("``", '"'), normalizers.Replace("''", '"'), ] if not self.original_tokenizer.keep_accents: list_normalizers.append(normalizers.NFKD()) list_normalizers.append(normalizers.StripAccents()) if self.original_tokenizer.do_lower_case: list_normalizers.append(normalizers.Lowercase()) precompiled_charsmap = proto.normalizer_spec.precompiled_charsmap if precompiled_charsmap: list_normalizers.append(normalizers.Precompiled(precompiled_charsmap)) list_normalizers.append(normalizers.Replace(Regex(" {2,}"), " ")) return normalizers.Sequence(list_normalizers) def post_processor(self): return processors.TemplateProcessing( single="$A:0 <sep>:0 <cls>:2", pair="$A:0 <sep>:0 $B:1 <sep>:1 <cls>:2", special_tokens=[ ("<sep>", self.original_tokenizer.convert_tokens_to_ids("<sep>")), ("<cls>", self.original_tokenizer.convert_tokens_to_ids("<cls>")), ], ) class ReformerConverter(SpmConverter): pass class RemBertConverter(SpmConverter): # Inspired from AlbertConverter def normalizer(self, proto): list_normalizers = [ normalizers.Replace("``", '"'), normalizers.Replace("''", '"'), normalizers.Replace(Regex(" {2,}"), " "), ] if not self.original_tokenizer.keep_accents: list_normalizers.append(normalizers.NFKD()) list_normalizers.append(normalizers.StripAccents()) if self.original_tokenizer.do_lower_case: list_normalizers.append(normalizers.Lowercase()) precompiled_charsmap = proto.normalizer_spec.precompiled_charsmap if precompiled_charsmap: list_normalizers.append(normalizers.Precompiled(precompiled_charsmap)) return normalizers.Sequence(list_normalizers) def post_processor(self): return processors.TemplateProcessing( single="[CLS]:0 $A:0 [SEP]:0", pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1", special_tokens=[ ("[CLS]", self.original_tokenizer.convert_tokens_to_ids("[CLS]")), ("[SEP]", self.original_tokenizer.convert_tokens_to_ids("[SEP]")), ], ) class BertGenerationConverter(SpmConverter): pass class PegasusConverter(SpmConverter): def vocab(self, proto): vocab = [ (self.original_tokenizer.pad_token, 0.0), (self.original_tokenizer.eos_token, 0.0), ] if self.original_tokenizer.mask_token_sent is not None: vocab += [(self.original_tokenizer.mask_token_sent, 0.0)] if ( self.original_tokenizer.mask_token is not None and self.original_tokenizer.mask_token_id < self.original_tokenizer.offset ): vocab += [(self.original_tokenizer.mask_token, 0.0)] vocab += [(f"<unk_{i}>", -100.0) for i in range(2, self.original_tokenizer.offset)] vocab += [(piece.piece, piece.score) for piece in proto.pieces[2:]] return vocab def unk_id(self, proto): return proto.trainer_spec.unk_id + self.original_tokenizer.offset def pre_tokenizer(self, replacement, add_prefix_space): return pre_tokenizers.Sequence( [ pre_tokenizers.WhitespaceSplit(), pre_tokenizers.Metaspace(replacement=replacement, add_prefix_space=add_prefix_space), ] ) def post_processor(self): eos = self.original_tokenizer.eos_token special_tokens = [ (eos, self.original_tokenizer.eos_token_id), ] return processors.TemplateProcessing(single=["$A", eos], pair=["$A", "$B", eos], special_tokens=special_tokens) class T5Converter(SpmConverter): def vocab(self, proto): num_extra_ids = self.original_tokenizer._extra_ids vocab = [(piece.piece, piece.score) for piece in proto.pieces] vocab += [(f"<extra_id_{i}>", 0.0) for i in range(num_extra_ids - 1, -1, -1)] return vocab def post_processor(self): return processors.TemplateProcessing( single=["$A", "</s>"], pair=["$A", "</s>", "$B", "</s>"], special_tokens=[ ("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")), ], ) class WhisperConverter(Converter): def converted(self) -> Tokenizer: vocab = self.original_tokenizer.encoder merges = list(self.original_tokenizer.bpe_ranks.keys()) tokenizer = Tokenizer( BPE( vocab=vocab, merges=merges, dropout=None, continuing_subword_prefix="", end_of_word_suffix="", fuse_unk=False, ) ) tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=self.original_tokenizer.add_prefix_space) tokenizer.decoder = decoders.ByteLevel() prefix_token_ids = self.original_tokenizer.prefix_tokens prefixes = self.original_tokenizer.convert_ids_to_tokens(prefix_token_ids) eos = self.original_tokenizer.eos_token eos_token_id = self.original_tokenizer.eos_token_id prefix_template = " ".join([f"{token}:0" for token in prefixes]) tokenizer.post_processor = processors.TemplateProcessing( single=f"{prefix_template} $A:0 {eos}:0", pair=f"{prefix_template} $A:0 $B:1 {eos}:1", special_tokens=[ (eos, eos_token_id), *zip(prefixes, prefix_token_ids), ], ) return tokenizer class BigBirdConverter(SpmConverter): def post_processor(self): return processors.TemplateProcessing( single="[CLS]:0 $A:0 [SEP]:0", pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1", special_tokens=[ ("[CLS]", self.original_tokenizer.convert_tokens_to_ids("[CLS]")), ("[SEP]", self.original_tokenizer.convert_tokens_to_ids("[SEP]")), ], ) class CLIPConverter(Converter): def converted(self) -> Tokenizer: vocab = self.original_tokenizer.encoder merges = list(self.original_tokenizer.bpe_ranks.keys()) unk_token = self.original_tokenizer.unk_token tokenizer = Tokenizer( BPE( vocab=vocab, merges=merges, dropout=None, continuing_subword_prefix="", end_of_word_suffix="</w>", fuse_unk=False, unk_token=str(unk_token), ) ) tokenizer.normalizer = normalizers.Sequence( [normalizers.NFC(), normalizers.Replace(Regex(r"\s+"), " "), normalizers.Lowercase()] ) tokenizer.pre_tokenizer = pre_tokenizers.Sequence( [ pre_tokenizers.Split( Regex(r"""'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+"""), behavior="removed", invert=True, ), pre_tokenizers.ByteLevel(add_prefix_space=False), ] ) tokenizer.decoder = decoders.ByteLevel() # Hack to have a ByteLevel and TemplaceProcessor tokenizer.post_processor = processors.RobertaProcessing( sep=(self.original_tokenizer.eos_token, self.original_tokenizer.eos_token_id), cls=(self.original_tokenizer.bos_token, self.original_tokenizer.bos_token_id), add_prefix_space=False, trim_offsets=False, ) return tokenizer class LayoutLMv2Converter(Converter): def converted(self) -> Tokenizer: vocab = self.original_tokenizer.vocab tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token))) tokenize_chinese_chars = False strip_accents = False do_lower_case = True if hasattr(self.original_tokenizer, "basic_tokenizer"): tokenize_chinese_chars = self.original_tokenizer.basic_tokenizer.tokenize_chinese_chars strip_accents = self.original_tokenizer.basic_tokenizer.strip_accents do_lower_case = self.original_tokenizer.basic_tokenizer.do_lower_case tokenizer.normalizer = normalizers.BertNormalizer( clean_text=True, handle_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, lowercase=do_lower_case, ) tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer() cls = str(self.original_tokenizer.cls_token) sep = str(self.original_tokenizer.sep_token) cls_token_id = self.original_tokenizer.cls_token_id sep_token_id = self.original_tokenizer.sep_token_id tokenizer.post_processor = processors.TemplateProcessing( single=f"{cls}:0 $A:0 {sep}:0", pair=f"{cls}:0 $A:0 {sep}:0 $B:1 {sep}:1", special_tokens=[ (cls, cls_token_id), (sep, sep_token_id), ], ) tokenizer.decoder = decoders.WordPiece(prefix="##") return tokenizer class BlenderbotConverter(Converter): def converted(self) -> Tokenizer: ot = self.original_tokenizer vocab = ot.encoder merges = list(ot.bpe_ranks.keys()) tokenizer = Tokenizer( BPE( vocab=vocab, merges=merges, dropout=None, continuing_subword_prefix="", end_of_word_suffix="", fuse_unk=False, ) ) tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=ot.add_prefix_space) tokenizer.decoder = decoders.ByteLevel() tokenizer.post_processor = processors.TemplateProcessing( single=f"$A:0 {ot.eos_token}:0", special_tokens=[ (ot.eos_token, ot.eos_token_id), ], ) return tokenizer class XGLMConverter(SpmConverter): def vocab(self, proto): vocab = [ ("<s>", 0.0), ("<pad>", 0.0), ("</s>", 0.0), ("<unk>", 0.0), ] vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]] vocab += [("<madeupword0>", 0.0), ("<madeupword1>", 0.0), ("<madeupword2>", 0.0), ("<madeupword3>", 0.0), ("<madeupword4>", 0.0), ("<madeupword5>", 0.0), ("<madeupword6>", 0.0)] # fmt: skip return vocab def unk_id(self, proto): unk_id = 3 return unk_id def post_processor(self): return processors.TemplateProcessing( single="</s> $A", pair="</s> $A </s> </s> $B", special_tokens=[ ("<s>", self.original_tokenizer.convert_tokens_to_ids("<s>")), ("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")), ], ) class GemmaConvert(SpmConverter): handle_byte_fallback = True """" split_by_unicode_script: true split_by_number: true split_by_whitespace: true treat_whitespace_as_suffix: false allow_whitespace_only_pieces: true split_digits: true byte_fallback: true """ def normalizer(self, proto): return normalizers.Replace(" ", "▁") def vocab(self, proto): vocab = [ (self.original_tokenizer.pad_token, 0.0), (self.original_tokenizer.eos_token, 0.0), (self.original_tokenizer.bos_token, 0.0), ] for piece in proto.pieces[3:]: if piece.piece == "<0x09>": vocab += [("\t", piece.score)] else: vocab += [(piece.piece, piece.score)] # vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]] return vocab def pre_tokenizer(self, replacement, add_prefix_space): return None def unk_id(self, proto): unk_id = 3 return unk_id def decoder(self, replacement, add_prefix_space): return decoders.Sequence( [ decoders.Replace("▁", " "), decoders.ByteFallback(), decoders.Fuse(), ] ) def tokenizer(self, proto): model_type = proto.trainer_spec.model_type vocab_scores = self.vocab(proto) if model_type == 1: import tokenizers if version.parse(tokenizers.__version__) < version.parse("0.14.0"): tokenizer = Tokenizer(Unigram(vocab_scores, 0)) else: tokenizer = Tokenizer(Unigram(vocab_scores, 0, byte_fallback=True)) elif model_type == 2: _, merges = GemmaSentencePieceExtractor(self.original_tokenizer.vocab_file).extract(vocab_scores) bpe_vocab = {word: i for i, (word, _score) in enumerate(vocab_scores)} tokenizer = Tokenizer( BPE( bpe_vocab, merges, unk_token=proto.trainer_spec.unk_piece, fuse_unk=True, byte_fallback=True, dropout=None, ) ) tokenizer.add_special_tokens( [ AddedToken("<pad>", normalized=False, special=True), AddedToken("<eos>", normalized=False, special=True), AddedToken("<bos>", normalized=False, special=True), AddedToken("<unk>", normalized=False, special=True), ] ) else: raise Exception( "You're trying to run a `Unigram` model but you're file was trained with a different algorithm" ) return tokenizer class LlamaConverter(SpmConverter): handle_byte_fallback = True def vocab(self, proto): vocab = [ ("<unk>", 0.0), ("<s>", 0.0), ("</s>", 0.0), ] vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]] return vocab def unk_id(self, proto): unk_id = 0 return unk_id def decoder(self, replacement, add_prefix_space): sequence = [ decoders.Replace("▁", " "), decoders.ByteFallback(), decoders.Fuse(), ] if add_prefix_space: sequence += [decoders.Strip(content=" ", left=1)] return decoders.Sequence(sequence) def tokenizer(self, proto): model_type = proto.trainer_spec.model_type vocab_scores = self.vocab(proto) if model_type == 1: import tokenizers if version.parse(tokenizers.__version__) < version.parse("0.14.0"): tokenizer = Tokenizer(Unigram(vocab_scores, 0)) else: tokenizer = Tokenizer(Unigram(vocab_scores, 0, byte_fallback=True)) elif model_type == 2: _, merges = SentencePieceExtractor(self.original_tokenizer.vocab_file).extract(vocab_scores) bpe_vocab = {word: i for i, (word, _score) in enumerate(vocab_scores)} tokenizer = Tokenizer( BPE(bpe_vocab, merges, unk_token=proto.trainer_spec.unk_piece, fuse_unk=True, byte_fallback=True) ) tokenizer.add_special_tokens( [ AddedToken("<unk>", normalized=False, special=True), AddedToken("<s>", normalized=False, special=True), AddedToken("</s>", normalized=False, special=True), ] ) else: raise Exception( "You're trying to run a `Unigram` model but you're file was trained with a different algorithm" ) return tokenizer def normalizer(self, proto): sequence = [] if hasattr(self.original_tokenizer, "add_prefix_space"): if self.original_tokenizer.add_prefix_space: sequence += [normalizers.Prepend(prepend="▁")] sequence += [normalizers.Replace(pattern=" ", content="▁")] return normalizers.Sequence(sequence) def pre_tokenizer(self, replacement, add_prefix_space): return None def post_processor(self): # the processor is defined in the LlamaTokenizerFast class. return None class MarkupLMConverter(Converter): def converted(self) -> Tokenizer: ot = self.original_tokenizer vocab = ot.encoder merges = list(ot.bpe_ranks.keys()) tokenizer = Tokenizer( BPE( vocab=vocab, merges=merges, dropout=None, continuing_subword_prefix="", end_of_word_suffix="", fuse_unk=False, unk_token=self.original_tokenizer.unk_token, ) ) tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=ot.add_prefix_space) tokenizer.decoder = decoders.ByteLevel() cls = str(self.original_tokenizer.cls_token) sep = str(self.original_tokenizer.sep_token) cls_token_id = self.original_tokenizer.cls_token_id sep_token_id = self.original_tokenizer.sep_token_id tokenizer.post_processor = processors.TemplateProcessing( single=f"{cls} $A {sep}", pair=f"{cls} $A {sep} $B {sep}", special_tokens=[ (cls, cls_token_id), (sep, sep_token_id), ], ) return tokenizer SLOW_TO_FAST_CONVERTERS = { "AlbertTokenizer": AlbertConverter, "BartTokenizer": RobertaConverter, "BarthezTokenizer": BarthezConverter, "BertTokenizer": BertConverter, "BigBirdTokenizer": BigBirdConverter, "BlenderbotTokenizer": BlenderbotConverter, "CamembertTokenizer": CamembertConverter, "CLIPTokenizer": CLIPConverter, "CodeGenTokenizer": GPT2Converter, "ConvBertTokenizer": BertConverter, "DebertaTokenizer": DebertaConverter, "DebertaV2Tokenizer": DebertaV2Converter, "DistilBertTokenizer": BertConverter, "DPRReaderTokenizer": BertConverter, "DPRQuestionEncoderTokenizer": BertConverter, "DPRContextEncoderTokenizer": BertConverter, "ElectraTokenizer": BertConverter, "FNetTokenizer": AlbertConverter, "FunnelTokenizer": FunnelConverter, "GPT2Tokenizer": GPT2Converter, "HerbertTokenizer": HerbertConverter, "LayoutLMTokenizer": BertConverter, "LayoutLMv2Tokenizer": BertConverter, "LayoutLMv3Tokenizer": RobertaConverter, "LayoutXLMTokenizer": XLMRobertaConverter, "LongformerTokenizer": RobertaConverter, "LEDTokenizer": RobertaConverter, "LxmertTokenizer": BertConverter, "MarkupLMTokenizer": MarkupLMConverter, "MBartTokenizer": MBartConverter, "MBart50Tokenizer": MBart50Converter, "MPNetTokenizer": MPNetConverter, "MobileBertTokenizer": BertConverter, "MvpTokenizer": RobertaConverter, "NllbTokenizer": NllbConverter, "OpenAIGPTTokenizer": OpenAIGPTConverter, "PegasusTokenizer": PegasusConverter, "Qwen2Tokenizer": Qwen2Converter, "RealmTokenizer": BertConverter, "ReformerTokenizer": ReformerConverter, "RemBertTokenizer": RemBertConverter, "RetriBertTokenizer": BertConverter, "RobertaTokenizer": RobertaConverter, "RoFormerTokenizer": RoFormerConverter, "SeamlessM4TTokenizer": SeamlessM4TConverter, "SqueezeBertTokenizer": BertConverter, "T5Tokenizer": T5Converter, "WhisperTokenizer": WhisperConverter, "XLMRobertaTokenizer": XLMRobertaConverter, "XLNetTokenizer": XLNetConverter, "SplinterTokenizer": SplinterConverter, "XGLMTokenizer": XGLMConverter, "LlamaTokenizer": LlamaConverter, "CodeLlamaTokenizer": LlamaConverter, "GemmaTokenizer": GemmaConvert, } def convert_slow_tokenizer(transformer_tokenizer) -> Tokenizer: """ Utilities to convert a slow tokenizer instance in a fast tokenizer instance. Args: transformer_tokenizer ([`~tokenization_utils_base.PreTrainedTokenizer`]): Instance of a slow tokenizer to convert in the backend tokenizer for [`~tokenization_utils_base.PreTrainedTokenizerFast`]. Return: A instance of [`~tokenizers.Tokenizer`] to be used as the backend tokenizer of a [`~tokenization_utils_base.PreTrainedTokenizerFast`] """ tokenizer_class_name = transformer_tokenizer.__class__.__name__ if tokenizer_class_name not in SLOW_TO_FAST_CONVERTERS: raise ValueError( f"An instance of tokenizer class {tokenizer_class_name} cannot be converted in a Fast tokenizer instance." " No converter was found. Currently available slow->fast convertors:" f" {list(SLOW_TO_FAST_CONVERTERS.keys())}" ) converter_class = SLOW_TO_FAST_CONVERTERS[tokenizer_class_name] return converter_class(transformer_tokenizer).converted()

transformers/src/transformers/convert_slow_tokenizer.py/0

{ "file_path": "transformers/src/transformers/convert_slow_tokenizer.py", "repo_id": "transformers", "token_count": 26949 }

72

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import collections from .utils import ExplicitEnum, is_torch_available, logging if is_torch_available(): import torch logger = logging.get_logger(__name__) class DebugUnderflowOverflow: """ This debug class helps detect and understand where the model starts getting very large or very small, and more importantly `nan` or `inf` weight and activation elements. There are 2 working modes: 1. Underflow/overflow detection (default) 2. Specific batch absolute min/max tracing without detection Mode 1: Underflow/overflow detection To activate the underflow/overflow detection, initialize the object with the model : ```python debug_overflow = DebugUnderflowOverflow(model) ``` then run the training as normal and if `nan` or `inf` gets detected in at least one of the weight, input or output elements this module will throw an exception and will print `max_frames_to_save` frames that lead to this event, each frame reporting 1. the fully qualified module name plus the class name whose `forward` was run 2. the absolute min and max value of all elements for each module weights, and the inputs and output For example, here is the header and the last few frames in detection report for `google/mt5-small` run in fp16 mixed precision : ``` Detected inf/nan during batch_number=0 Last 21 forward frames: abs min abs max metadata [...] encoder.block.2.layer.1.DenseReluDense.wi_0 Linear 2.17e-07 4.50e+00 weight 1.79e-06 4.65e+00 input[0] 2.68e-06 3.70e+01 output encoder.block.2.layer.1.DenseReluDense.wi_1 Linear 8.08e-07 2.66e+01 weight 1.79e-06 4.65e+00 input[0] 1.27e-04 2.37e+02 output encoder.block.2.layer.1.DenseReluDense.wo Linear 1.01e-06 6.44e+00 weight 0.00e+00 9.74e+03 input[0] 3.18e-04 6.27e+04 output encoder.block.2.layer.1.DenseReluDense T5DenseGatedGeluDense 1.79e-06 4.65e+00 input[0] 3.18e-04 6.27e+04 output encoder.block.2.layer.1.dropout Dropout 3.18e-04 6.27e+04 input[0] 0.00e+00 inf output ``` You can see here, that `T5DenseGatedGeluDense.forward` resulted in output activations, whose absolute max value was around 62.7K, which is very close to fp16's top limit of 64K. In the next frame we have `Dropout` which renormalizes the weights, after it zeroed some of the elements, which pushes the absolute max value to more than 64K, and we get an overlow. As you can see it's the previous frames that we need to look into when the numbers start going into very large for fp16 numbers. The tracking is done in a forward hook, which gets invoked immediately after `forward` has completed. By default the last 21 frames are printed. You can change the default to adjust for your needs. For example : ```python debug_overflow = DebugUnderflowOverflow(model, max_frames_to_save=100) ``` To validate that you have set up this debugging feature correctly, and you intend to use it in a training that may take hours to complete, first run it with normal tracing enabled for one of a few batches as explained in the next section. Mode 2. Specific batch absolute min/max tracing without detection The second work mode is per-batch tracing with the underflow/overflow detection feature turned off. Let's say you want to watch the absolute min and max values for all the ingredients of each `forward` call of a given batch, and only do that for batches 1 and 3. Then you instantiate this class as : ```python debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[1, 3]) ``` And now full batches 1 and 3 will be traced using the same format as explained above. Batches are 0-indexed. This is helpful if you know that the program starts misbehaving after a certain batch number, so you can fast-forward right to that area. Early stopping: You can also specify the batch number after which to stop the training, with : ```python debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[1, 3], abort_after_batch_num=3) ``` This feature is mainly useful in the tracing mode, but you can use it for any mode. **Performance**: As this module measures absolute `min`/``max` of each weight of the model on every forward it'll slow the training down. Therefore remember to turn it off once the debugging needs have been met. Args: model (`nn.Module`): The model to debug. max_frames_to_save (`int`, *optional*, defaults to 21): How many frames back to record trace_batch_nums(`List[int]`, *optional*, defaults to `[]`): Which batch numbers to trace (turns detection off) abort_after_batch_num (`int``, *optional*): Whether to abort after a certain batch number has finished """ def __init__(self, model, max_frames_to_save=21, trace_batch_nums=[], abort_after_batch_num=None): self.model = model self.trace_batch_nums = trace_batch_nums self.abort_after_batch_num = abort_after_batch_num # keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence self.frames = collections.deque([], max_frames_to_save) self.frame = [] self.batch_number = 0 self.total_calls = 0 self.detected_overflow = False self.prefix = " " self.analyse_model() self.register_forward_hook() def save_frame(self, frame=None): if frame is not None: self.expand_frame(frame) self.frames.append("\n".join(self.frame)) self.frame = [] # start a new frame def expand_frame(self, line): self.frame.append(line) def trace_frames(self): print("\n".join(self.frames)) self.frames = [] def reset_saved_frames(self): self.frames = [] def dump_saved_frames(self): print(f"\nDetected inf/nan during batch_number={self.batch_number}") print(f"Last {len(self.frames)} forward frames:") print(f"{'abs min':8} {'abs max':8} metadata") print("\n".join(self.frames)) print("\n\n") self.frames = [] def analyse_model(self): # extract the fully qualified module names, to be able to report at run time. e.g.: # encoder.block.2.layer.0.SelfAttention.o # # for shared weights only the first shared module name will be registered self.module_names = {m: name for name, m in self.model.named_modules()} # self.longest_module_name = max(len(v) for v in self.module_names.values()) def analyse_variable(self, var, ctx): if torch.is_tensor(var): self.expand_frame(get_abs_min_max(var, ctx)) if detect_overflow(var, ctx): self.detected_overflow = True elif var is None: self.expand_frame(f"{'None':>17} {ctx}") else: self.expand_frame(f"{'not a tensor':>17} {ctx}") def batch_start_frame(self): self.expand_frame(f"\n\n{self.prefix} *** Starting batch number={self.batch_number} ***") self.expand_frame(f"{'abs min':8} {'abs max':8} metadata") def batch_end_frame(self): self.expand_frame(f"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n") def create_frame(self, module, input, output): self.expand_frame(f"{self.prefix} {self.module_names[module]} {module.__class__.__name__}") # params for name, p in module.named_parameters(recurse=False): self.analyse_variable(p, name) # inputs if isinstance(input, tuple): for i, x in enumerate(input): self.analyse_variable(x, f"input[{i}]") else: self.analyse_variable(input, "input") # outputs if isinstance(output, tuple): for i, x in enumerate(output): # possibly a tuple of tuples if isinstance(x, tuple): for j, y in enumerate(x): self.analyse_variable(y, f"output[{i}][{j}]") else: self.analyse_variable(x, f"output[{i}]") else: self.analyse_variable(output, "output") self.save_frame() def register_forward_hook(self): self.model.apply(self._register_forward_hook) def _register_forward_hook(self, module): module.register_forward_hook(self.forward_hook) def forward_hook(self, module, input, output): # - input is a tuple of packed inputs (could be non-Tensors) # - output could be a Tensor or a tuple of Tensors and non-Tensors last_frame_of_batch = False trace_mode = True if self.batch_number in self.trace_batch_nums else False if trace_mode: self.reset_saved_frames() if self.total_calls == 0: self.batch_start_frame() self.total_calls += 1 # count batch numbers - the very first forward hook of the batch will be called when the # batch completes - i.e. it gets called very last - we know this batch has finished if module == self.model: self.batch_number += 1 last_frame_of_batch = True self.create_frame(module, input, output) # if last_frame_of_batch: # self.batch_end_frame() if trace_mode: self.trace_frames() if last_frame_of_batch: self.batch_start_frame() if self.detected_overflow and not trace_mode: self.dump_saved_frames() # now we can abort, as it's pointless to continue running raise ValueError( "DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. " "Please scroll up above this traceback to see the activation values prior to this event." ) # abort after certain batch if requested to do so if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num: raise ValueError( f"DebugUnderflowOverflow: aborting after {self.batch_number} batches due to" f" `abort_after_batch_num={self.abort_after_batch_num}` arg" ) def get_abs_min_max(var, ctx): abs_var = var.abs() return f"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}" def detect_overflow(var, ctx): """ Report whether the tensor contains any `nan` or `inf` entries. This is useful for detecting overflows/underflows and best to call right after the function that did some math that modified the tensor in question. This function contains a few other helper features that you can enable and tweak directly if you want to track various other things. Args: var: the tensor variable to check ctx: the message to print as a context Return: `True` if `inf` or `nan` was detected, `False` otherwise """ detected = False if torch.isnan(var).any().item(): detected = True print(f"{ctx} has nans") if torch.isinf(var).any().item(): detected = True print(f"{ctx} has infs") # if needed to monitor large elements can enable the following if 0: # and detected: n100 = var[torch.ge(var.abs(), 100)] if n100.numel() > 0: print(f"{ctx}: n100={n100.numel()}") n1000 = var[torch.ge(var.abs(), 1000)] if n1000.numel() > 0: print(f"{ctx}: n1000={n1000.numel()}") n10000 = var[torch.ge(var.abs(), 10000)] if n10000.numel() > 0: print(f"{ctx}: n10000={n10000.numel()}") if 0: print(f"min={var.min():9.2e} max={var.max():9.2e}") if 0: print(f"min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})") return detected class DebugOption(ExplicitEnum): UNDERFLOW_OVERFLOW = "underflow_overflow" TPU_METRICS_DEBUG = "tpu_metrics_debug"

transformers/src/transformers/debug_utils.py/0

{ "file_path": "transformers/src/transformers/debug_utils.py", "repo_id": "transformers", "token_count": 5154 }

73

import time import warnings from abc import ABC from copy import deepcopy from typing import Optional import torch from ..utils import add_start_docstrings, logging logger = logging.get_logger(__name__) STOPPING_CRITERIA_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`): Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax or scores for each vocabulary token after SoftMax. If this stopping criteria depends on the `scores` input, make sure you pass `return_dict_in_generate=True, output_scores=True` to `generate`. kwargs (`Dict[str, Any]`, *optional*): Additional stopping criteria specific kwargs. Return: `bool`. `False` indicates we should continue, `True` indicates we should stop. """ class StoppingCriteria(ABC): """Abstract base class for all stopping criteria that can be applied during generation. If your stopping criteria depends on the `scores` input, make sure you pass `return_dict_in_generate=True, output_scores=True` to `generate`. """ @add_start_docstrings(STOPPING_CRITERIA_INPUTS_DOCSTRING) def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: raise NotImplementedError("StoppingCriteria needs to be subclassed") class MaxLengthCriteria(StoppingCriteria): """ This class can be used to stop generation whenever the full generated number of tokens exceeds `max_length`. Keep in mind for decoder-only type of transformers, this will include the initial prompted tokens. Args: max_length (`int`): The maximum length that the output sequence can have in number of tokens. max_position_embeddings (`int`, *optional*): The maximum model length, as defined by the model's `config.max_position_embeddings` attribute. """ def __init__(self, max_length: int, max_position_embeddings: Optional[int] = None): self.max_length = max_length self.max_position_embeddings = max_position_embeddings @add_start_docstrings(STOPPING_CRITERIA_INPUTS_DOCSTRING) def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: cur_len = input_ids.shape[-1] is_done = cur_len >= self.max_length if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings: logger.warning_once( "This is a friendly reminder - the current text generation call will exceed the model's predefined " f"maximum length ({self.max_position_embeddings}). Depending on the model, you may observe " "exceptions, performance degradation, or nothing at all." ) return is_done class MaxNewTokensCriteria(StoppingCriteria): """ This class can be used to stop generation whenever the generated number of tokens exceeds `max_new_tokens`. Keep in mind for decoder-only type of transformers, this will **not** include the initial prompted tokens. This is very close to `MaxLengthCriteria` but ignores the number of initial tokens. Args: start_length (`int`): The number of initial tokens. max_new_tokens (`int`): The maximum number of tokens to generate. """ def __init__(self, start_length: int, max_new_tokens: int): warnings.warn( "The class `MaxNewTokensCriteria` is deprecated. " f"Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` " "with `max_length = start_length + max_new_tokens` instead.", FutureWarning, ) self.start_length = start_length self.max_new_tokens = max_new_tokens self.max_length = start_length + max_new_tokens @add_start_docstrings(STOPPING_CRITERIA_INPUTS_DOCSTRING) def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: return input_ids.shape[-1] >= self.max_length class MaxTimeCriteria(StoppingCriteria): """ This class can be used to stop generation whenever the full generation exceeds some amount of time. By default, the time will start being counted when you initialize this function. You can override this by passing an `initial_time`. Args: max_time (`float`): The maximum allowed time in seconds for the generation. initial_time (`float`, *optional*, defaults to `time.time()`): The start of the generation allowed time. """ def __init__(self, max_time: float, initial_timestamp: Optional[float] = None): self.max_time = max_time self.initial_timestamp = time.time() if initial_timestamp is None else initial_timestamp @add_start_docstrings(STOPPING_CRITERIA_INPUTS_DOCSTRING) def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: return time.time() - self.initial_timestamp > self.max_time class StoppingCriteriaList(list): @add_start_docstrings(STOPPING_CRITERIA_INPUTS_DOCSTRING) def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: return any(criteria(input_ids, scores, **kwargs) for criteria in self) @property def max_length(self) -> Optional[int]: for stopping_criterium in self: if isinstance(stopping_criterium, MaxLengthCriteria): return stopping_criterium.max_length elif isinstance(stopping_criterium, MaxNewTokensCriteria): return stopping_criterium.max_length return None def validate_stopping_criteria(stopping_criteria: StoppingCriteriaList, max_length: int) -> StoppingCriteriaList: stopping_max_length = stopping_criteria.max_length new_stopping_criteria = deepcopy(stopping_criteria) if stopping_max_length is not None and stopping_max_length != max_length: warnings.warn("You set different `max_length` for stopping criteria and `max_length` parameter", UserWarning) elif stopping_max_length is None: new_stopping_criteria.append(MaxLengthCriteria(max_length=max_length)) return new_stopping_criteria

transformers/src/transformers/generation/stopping_criteria.py/0

{ "file_path": "transformers/src/transformers/generation/stopping_criteria.py", "repo_id": "transformers", "token_count": 2484 }

74

import importlib.metadata import warnings from copy import deepcopy from packaging import version from ..utils import is_accelerate_available, is_bitsandbytes_available, logging if is_bitsandbytes_available(): import bitsandbytes as bnb import torch import torch.nn as nn from ..pytorch_utils import Conv1D if is_accelerate_available(): from accelerate import init_empty_weights from accelerate.utils import find_tied_parameters logger = logging.get_logger(__name__) def set_module_quantized_tensor_to_device(module, tensor_name, device, value=None, quantized_stats=None): """ A helper function to set a given tensor (parameter of buffer) of a module on a specific device (note that doing `param.to(device)` creates a new tensor not linked to the parameter, which is why we need this function). The function is adapted from `set_module_tensor_to_device` function from accelerate that is adapted to support the class `Int8Params` from `bitsandbytes`. Args: module (`torch.nn.Module`): The module in which the tensor we want to move lives. tensor_name (`str`): The full name of the parameter/buffer. device (`int`, `str` or `torch.device`): The device on which to set the tensor. value (`torch.Tensor`, *optional*): The value of the tensor (useful when going from the meta device to any other device). quantized_stats (`dict[str, Any]`, *optional*): Dict with items for either 4-bit or 8-bit serialization """ # Recurse if needed if "." in tensor_name: splits = tensor_name.split(".") for split in splits[:-1]: new_module = getattr(module, split) if new_module is None: raise ValueError(f"{module} has no attribute {split}.") module = new_module tensor_name = splits[-1] if tensor_name not in module._parameters and tensor_name not in module._buffers: raise ValueError(f"{module} does not have a parameter or a buffer named {tensor_name}.") is_buffer = tensor_name in module._buffers old_value = getattr(module, tensor_name) if old_value.device == torch.device("meta") and device not in ["meta", torch.device("meta")] and value is None: raise ValueError(f"{tensor_name} is on the meta device, we need a `value` to put in on {device}.") prequantized_loading = quantized_stats is not None if is_buffer or not is_bitsandbytes_available(): is_8bit = False is_4bit = False else: is_4bit = hasattr(bnb.nn, "Params4bit") and isinstance(module._parameters[tensor_name], bnb.nn.Params4bit) is_8bit = isinstance(module._parameters[tensor_name], bnb.nn.Int8Params) if is_8bit or is_4bit: param = module._parameters[tensor_name] if param.device.type != "cuda": if value is None: new_value = old_value.to(device) elif isinstance(value, torch.Tensor): new_value = value.to("cpu") else: new_value = torch.tensor(value, device="cpu") # Support models using `Conv1D` in place of `nn.Linear` (e.g. openai-community/gpt2) by transposing the weight matrix prior to quantization. # Since weights are saved in the correct "orientation", we skip transposing when loading. if issubclass(module.source_cls, Conv1D) and not prequantized_loading: new_value = new_value.T kwargs = old_value.__dict__ if prequantized_loading != (new_value.dtype in (torch.int8, torch.uint8)): raise ValueError( f"Value dtype `{new_value.dtype}` is not compatible with parameter quantization status." ) if is_8bit: is_8bit_serializable = version.parse(importlib.metadata.version("bitsandbytes")) > version.parse( "0.37.2" ) if new_value.dtype in (torch.int8, torch.uint8) and not is_8bit_serializable: raise ValueError( "Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. " "Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`." ) new_value = bnb.nn.Int8Params(new_value, requires_grad=False, **kwargs).to(device) if prequantized_loading: setattr(new_value, "SCB", quantized_stats["SCB"].to(device)) elif is_4bit: if prequantized_loading: is_4bit_serializable = version.parse(importlib.metadata.version("bitsandbytes")) >= version.parse( "0.41.3" ) if new_value.dtype in (torch.int8, torch.uint8) and not is_4bit_serializable: raise ValueError( "Detected 4-bit weights but the version of bitsandbytes is not compatible with 4-bit serialization. " "Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`." ) new_value = bnb.nn.Params4bit.from_prequantized( data=new_value, quantized_stats=quantized_stats, requires_grad=False, device=device, **kwargs, ) else: new_value = bnb.nn.Params4bit(new_value, requires_grad=False, **kwargs).to(device) module._parameters[tensor_name] = new_value else: if value is None: new_value = old_value.to(device) elif isinstance(value, torch.Tensor): new_value = value.to(device) else: new_value = torch.tensor(value, device=device) if is_buffer: module._buffers[tensor_name] = new_value else: new_value = nn.Parameter(new_value, requires_grad=old_value.requires_grad) module._parameters[tensor_name] = new_value def _replace_with_bnb_linear( model, modules_to_not_convert=None, current_key_name=None, quantization_config=None, has_been_replaced=False, ): """ Private method that wraps the recursion for module replacement. Returns the converted model and a boolean that indicates if the conversion has been successfull or not. """ for name, module in model.named_children(): if current_key_name is None: current_key_name = [] current_key_name.append(name) if (isinstance(module, nn.Linear) or isinstance(module, Conv1D)) and name not in modules_to_not_convert: # Check if the current key is not in the `modules_to_not_convert` if not any(key in ".".join(current_key_name) for key in modules_to_not_convert): with init_empty_weights(): if isinstance(module, Conv1D): in_features, out_features = module.weight.shape else: in_features = module.in_features out_features = module.out_features if quantization_config.quantization_method() == "llm_int8": model._modules[name] = bnb.nn.Linear8bitLt( in_features, out_features, module.bias is not None, has_fp16_weights=quantization_config.llm_int8_has_fp16_weight, threshold=quantization_config.llm_int8_threshold, ) has_been_replaced = True else: if ( quantization_config.llm_int8_skip_modules is not None and name in quantization_config.llm_int8_skip_modules ): pass else: model._modules[name] = bnb.nn.Linear4bit( in_features, out_features, module.bias is not None, quantization_config.bnb_4bit_compute_dtype, compress_statistics=quantization_config.bnb_4bit_use_double_quant, quant_type=quantization_config.bnb_4bit_quant_type, ) has_been_replaced = True # Store the module class in case we need to transpose the weight later model._modules[name].source_cls = type(module) # Force requires grad to False to avoid unexpected errors model._modules[name].requires_grad_(False) if len(list(module.children())) > 0: _, has_been_replaced = _replace_with_bnb_linear( module, modules_to_not_convert, current_key_name, quantization_config, has_been_replaced=has_been_replaced, ) # Remove the last key for recursion current_key_name.pop(-1) return model, has_been_replaced def replace_with_bnb_linear(model, modules_to_not_convert=None, current_key_name=None, quantization_config=None): """ A helper function to replace all `torch.nn.Linear` modules by `bnb.nn.Linear8bit` modules from the `bitsandbytes` library. This will enable running your models using mixed int8 precision as described by the paper `LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale`. Make sure `bitsandbytes` compiled with the correct CUDA version of your hardware is installed before running this function. `pip install -i https://test.pypi.org/simple/ bitsandbytes` The function will be run recursively and replace all `torch.nn.Linear` modules except for the `lm_head` that should be kept as a `torch.nn.Linear` module. The replacement is done under `init_empty_weights` context manager so no CPU/GPU memory is required to run this function. Int8 mixed-precision matrix decomposition works by separating a matrix multiplication into two streams: (1) and systematic feature outlier stream matrix multiplied in fp16 (0.01%), (2) a regular stream of int8 matrix multiplication (99.9%). With this method, int8 inference with no predictive degradation is possible for very large models (>=176B parameters). Parameters: model (`torch.nn.Module`): Input model or `torch.nn.Module` as the function is run recursively. modules_to_not_convert (`List[`str`]`, *optional*, defaults to `["lm_head"]`): Names of the modules to not convert in `Linear8bitLt`. In practice we keep the `lm_head` in full precision for numerical stability reasons. current_key_name (`List[`str`]`, *optional*): An array to track the current key of the recursion. This is used to check whether the current key (part of it) is not in the list of modules to not convert (for instances modules that are offloaded to `cpu` or `disk`). """ modules_to_not_convert = ["lm_head"] if modules_to_not_convert is None else modules_to_not_convert model, has_been_replaced = _replace_with_bnb_linear( model, modules_to_not_convert, current_key_name, quantization_config ) if not has_been_replaced: logger.warning( "You are loading your model in 8bit or 4bit but no linear modules were found in your model." " Please double check your model architecture, or submit an issue on github if you think this is" " a bug." ) return model # For backward compatibility def replace_8bit_linear(*args, **kwargs): warnings.warn( "`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead", FutureWarning, ) return replace_with_bnb_linear(*args, **kwargs) # For backward compatiblity def set_module_8bit_tensor_to_device(*args, **kwargs): warnings.warn( "`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead", FutureWarning, ) return set_module_quantized_tensor_to_device(*args, **kwargs) def get_keys_to_not_convert(model): r""" An utility function to get the key of the module to keep in full precision if any For example for CausalLM modules we may want to keep the lm_head in full precision for numerical stability reasons. For other architectures, we want to keep the tied weights of the model. The function will return a list of the keys of the modules to not convert in int8. Parameters: model (`torch.nn.Module`): Input model """ # Create a copy of the model and tie the weights, then # check if it contains tied weights tied_model = deepcopy(model) # this has 0 cost since it is done inside `init_empty_weights` context manager` tied_model.tie_weights() tied_params = find_tied_parameters(tied_model) # For compatibility with Accelerate < 0.18 if isinstance(tied_params, dict): tied_keys = sum(list(tied_params.values()), []) + list(tied_params.keys()) else: tied_keys = sum(tied_params, []) has_tied_params = len(tied_keys) > 0 # If there is not tied weights, we want to keep the lm_head（output_embedding) in full precision if not has_tied_params: output_emb = model.get_output_embeddings() if output_emb is not None: list_last_module = [name for name, module in model.named_modules() if id(module) == id(output_emb)] return list_last_module # otherwise, no tied weights, no output embedding defined, simply keep the last module in full precision list_modules = list(model.named_parameters()) list_last_module = [list_modules[-1][0]] # add last module together with tied weights intersection = set(list_last_module) - set(tied_keys) list_untouched = list(set(tied_keys)) + list(intersection) # remove ".weight" from the keys names_to_remove = [".weight", ".bias"] filtered_module_names = [] for name in list_untouched: for name_to_remove in names_to_remove: if name_to_remove in name: name = name.replace(name_to_remove, "") filtered_module_names.append(name) return filtered_module_names

transformers/src/transformers/integrations/bitsandbytes.py/0

{ "file_path": "transformers/src/transformers/integrations/bitsandbytes.py", "repo_id": "transformers", "token_count": 6450 }

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/*! ************************************************************************************************** * Deformable DETR * Copyright (c) 2020 SenseTime. All Rights Reserved. * Licensed under the Apache License, Version 2.0 [see LICENSE for details] ************************************************************************************************** * Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 ************************************************************************************************** */ #include <vector> #include "cuda/ms_deform_im2col_cuda.cuh" #include <ATen/ATen.h> #include <ATen/cuda/CUDAContext.h> #include <cuda.h> #include <cuda_runtime.h> #pragma once #include <torch/extension.h> at::Tensor ms_deform_attn_cuda_forward( const at::Tensor &value, const at::Tensor &spatial_shapes, const at::Tensor &level_start_index, const at::Tensor &sampling_loc, const at::Tensor &attn_weight, const int im2col_step) { AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous"); AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous"); AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous"); AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous"); AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous"); AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor"); AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor"); AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor"); AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor"); AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor"); const int batch = value.size(0); const int spatial_size = value.size(1); const int num_heads = value.size(2); const int channels = value.size(3); const int num_levels = spatial_shapes.size(0); const int num_query = sampling_loc.size(1); const int num_point = sampling_loc.size(4); const int im2col_step_ = std::min(batch, im2col_step); AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_); auto output = at::zeros({batch, num_query, num_heads, channels}, value.options()); const int batch_n = im2col_step_; auto output_n = output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels}); auto per_value_size = spatial_size * num_heads * channels; auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2; auto per_attn_weight_size = num_query * num_heads * num_levels * num_point; for (int n = 0; n < batch/im2col_step_; ++n) { auto columns = output_n.select(0, n); AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_forward_cuda", ([&] { ms_deformable_im2col_cuda(at::cuda::getCurrentCUDAStream(), value.data<scalar_t>() + n * im2col_step_ * per_value_size, spatial_shapes.data<int64_t>(), level_start_index.data<int64_t>(), sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size, attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size, batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point, columns.data<scalar_t>()); })); } output = output.view({batch, num_query, num_heads*channels}); return output; } std::vector<at::Tensor> ms_deform_attn_cuda_backward( const at::Tensor &value, const at::Tensor &spatial_shapes, const at::Tensor &level_start_index, const at::Tensor &sampling_loc, const at::Tensor &attn_weight, const at::Tensor &grad_output, const int im2col_step) { AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous"); AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous"); AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous"); AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous"); AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous"); AT_ASSERTM(grad_output.is_contiguous(), "grad_output tensor has to be contiguous"); AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor"); AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor"); AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor"); AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor"); AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor"); AT_ASSERTM(grad_output.type().is_cuda(), "grad_output must be a CUDA tensor"); const int batch = value.size(0); const int spatial_size = value.size(1); const int num_heads = value.size(2); const int channels = value.size(3); const int num_levels = spatial_shapes.size(0); const int num_query = sampling_loc.size(1); const int num_point = sampling_loc.size(4); const int im2col_step_ = std::min(batch, im2col_step); AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_); auto grad_value = at::zeros_like(value); auto grad_sampling_loc = at::zeros_like(sampling_loc); auto grad_attn_weight = at::zeros_like(attn_weight); const int batch_n = im2col_step_; auto per_value_size = spatial_size * num_heads * channels; auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2; auto per_attn_weight_size = num_query * num_heads * num_levels * num_point; auto grad_output_n = grad_output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels}); for (int n = 0; n < batch/im2col_step_; ++n) { auto grad_output_g = grad_output_n.select(0, n); AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_backward_cuda", ([&] { ms_deformable_col2im_cuda(at::cuda::getCurrentCUDAStream(), grad_output_g.data<scalar_t>(), value.data<scalar_t>() + n * im2col_step_ * per_value_size, spatial_shapes.data<int64_t>(), level_start_index.data<int64_t>(), sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size, attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size, batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point, grad_value.data<scalar_t>() + n * im2col_step_ * per_value_size, grad_sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size, grad_attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size); })); } return { grad_value, grad_sampling_loc, grad_attn_weight }; }

transformers/src/transformers/kernels/deta/cuda/ms_deform_attn_cuda.cu/0

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#include "common.h" template<typename T> __device__ int set_insert(T *set, int set_size, T value) { int slot = value % set_size; int start_slot = slot; while (true) { T prev = atomicCAS(&set[slot], EMPTY_VALUE, value); if (prev == EMPTY_VALUE || prev == value) { return slot; } slot = (slot + 1) % set_size; if (slot == start_slot) { return -1; } } return -1; } template<typename T> __device__ int set_lookup(T *set, int set_size, T value) { int slot = value % set_size; int start_slot = slot; while (true) { if (set[slot] == value) { return slot; } slot = (slot + 1) % set_size; if (slot == start_slot) { return -1; } } return -1; } template<typename T> __device__ void init_buffer(T init_value, T *buffer, int buffer_size, int num_threads, int thread_id) { __syncthreads(); for (int i = 0; i < buffer_size; i = i + num_threads) { int offset_idx = i + thread_id; if (offset_idx < buffer_size) { buffer[offset_idx] = init_value; } } __syncthreads(); } template<typename T> __device__ void copy_data(T *src_pt, T *dist_pt, int data_length, int num_threads, int thread_id) { __syncthreads(); for (int i = 0; i < data_length; i = i + num_threads) { int offset_idx = i + thread_id; if (offset_idx < data_length) { dist_pt[offset_idx] = src_pt[offset_idx]; } } __syncthreads(); } template<typename T> __device__ void init_buffer_nonblocking(T init_value, T *buffer, int buffer_size, int num_threads, int thread_id) { for (int i = 0; i < buffer_size; i = i + num_threads) { int offset_idx = i + thread_id; if (offset_idx < buffer_size) { buffer[offset_idx] = init_value; } } } template<typename T> __device__ void copy_data_nonblocking(T *src_pt, T *dist_pt, int data_length, int num_threads, int thread_id) { for (int i = 0; i < data_length; i = i + num_threads) { int offset_idx = i + thread_id; if (offset_idx < data_length) { dist_pt[offset_idx] = src_pt[offset_idx]; } } }

transformers/src/transformers/kernels/yoso/common_cuda_device.h/0

{ "file_path": "transformers/src/transformers/kernels/yoso/common_cuda_device.h", "repo_id": "transformers", "token_count": 892 }

77

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from . import ( albert, align, altclip, audio_spectrogram_transformer, auto, autoformer, bark, bart, barthez, bartpho, beit, bert, bert_generation, bert_japanese, bertweet, big_bird, bigbird_pegasus, biogpt, bit, blenderbot, blenderbot_small, blip, blip_2, bloom, bridgetower, bros, byt5, camembert, canine, chinese_clip, clap, clip, clipseg, clvp, code_llama, codegen, conditional_detr, convbert, convnext, convnextv2, cpm, cpmant, ctrl, cvt, data2vec, deberta, deberta_v2, decision_transformer, deformable_detr, deit, deprecated, depth_anything, deta, detr, dialogpt, dinat, dinov2, distilbert, dit, donut, dpr, dpt, efficientformer, efficientnet, electra, encodec, encoder_decoder, ernie, ernie_m, esm, falcon, fastspeech2_conformer, flaubert, flava, fnet, focalnet, fsmt, funnel, fuyu, gemma, git, glpn, gpt2, gpt_bigcode, gpt_neo, gpt_neox, gpt_neox_japanese, gpt_sw3, gptj, gptsan_japanese, graphormer, groupvit, herbert, hubert, ibert, idefics, imagegpt, informer, instructblip, jukebox, kosmos2, layoutlm, layoutlmv2, layoutlmv3, layoutxlm, led, levit, lilt, llama, llava, longformer, longt5, luke, lxmert, m2m_100, marian, markuplm, mask2former, maskformer, mbart, mbart50, mega, megatron_bert, megatron_gpt2, mgp_str, mistral, mixtral, mluke, mobilebert, mobilenet_v1, mobilenet_v2, mobilevit, mobilevitv2, mpnet, mpt, mra, mt5, musicgen, mvp, nat, nezha, nllb, nllb_moe, nougat, nystromformer, oneformer, openai, opt, owlv2, owlvit, patchtsmixer, patchtst, pegasus, pegasus_x, perceiver, persimmon, phi, phobert, pix2struct, plbart, poolformer, pop2piano, prophetnet, pvt, qdqbert, qwen2, rag, realm, reformer, regnet, rembert, resnet, roberta, roberta_prelayernorm, roc_bert, roformer, rwkv, sam, seamless_m4t, seamless_m4t_v2, segformer, sew, sew_d, siglip, speech_encoder_decoder, speech_to_text, speech_to_text_2, speecht5, splinter, squeezebert, stablelm, swiftformer, swin, swin2sr, swinv2, switch_transformers, t5, table_transformer, tapas, time_series_transformer, timesformer, timm_backbone, trocr, tvlt, tvp, umt5, unispeech, unispeech_sat, univnet, upernet, videomae, vilt, vipllava, vision_encoder_decoder, vision_text_dual_encoder, visual_bert, vit, vit_hybrid, vit_mae, vit_msn, vitdet, vitmatte, vits, vivit, wav2vec2, wav2vec2_bert, wav2vec2_conformer, wav2vec2_phoneme, wav2vec2_with_lm, wavlm, whisper, x_clip, xglm, xlm, xlm_prophetnet, xlm_roberta, xlm_roberta_xl, xlnet, xmod, yolos, yoso, )

transformers/src/transformers/models/__init__.py/0

{ "file_path": "transformers/src/transformers/models/__init__.py", "repo_id": "transformers", "token_count": 2136 }

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# coding=utf-8 # Copyright 2022 The BAAI Teams Authors and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch AltCLIP model.""" import math from dataclasses import dataclass from typing import Any, List, Optional, Tuple, Union import torch import torch.nn as nn import torch.utils.checkpoint from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPooling, BaseModelOutputWithPoolingAndCrossAttentions, BaseModelOutputWithPoolingAndProjection, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ModelOutput, add_start_docstrings_to_model_forward, logging, replace_return_docstrings from .configuration_altclip import AltCLIPConfig, AltCLIPTextConfig, AltCLIPVisionConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "BAAI/AltCLIP" _CONFIG_FOR_DOC = "AltCLIPConfig" ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST = [ "BAAI/AltCLIP", # See all AltCLIP models at https://huggingface.co/models?filter=altclip ] ALTCLIP_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`CLIPConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ ALTCLIP_TEXT_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ ALTCLIP_VISION_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ ALTCLIP_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details. return_loss (`bool`, *optional*): Whether or not to return the contrastive loss. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ # contrastive loss function, adapted from # https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html def contrastive_loss(logits: torch.Tensor) -> torch.Tensor: return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device)) def clip_loss(similarity: torch.Tensor) -> torch.Tensor: caption_loss = contrastive_loss(similarity) image_loss = contrastive_loss(similarity.t()) return (caption_loss + image_loss) / 2.0 @dataclass # Copied from transformers.models.clip.modeling_clip.CLIPOutput with CLIP->AltCLIP class AltCLIPOutput(ModelOutput): """ Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`): Contrastive loss for image-text similarity. logits_per_image:(`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`): The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text similarity scores. logits_per_text:(`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`): The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image similarity scores. text_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of [`AltCLIPTextModel`]. image_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of [`AltCLIPVisionModel`]. text_model_output(`BaseModelOutputWithPooling`): The output of the [`AltCLIPTextModel`]. vision_model_output(`BaseModelOutputWithPooling`): The output of the [`AltCLIPVisionModel`]. """ loss: Optional[torch.FloatTensor] = None logits_per_image: torch.FloatTensor = None logits_per_text: torch.FloatTensor = None text_embeds: torch.FloatTensor = None image_embeds: torch.FloatTensor = None text_model_output: BaseModelOutputWithPooling = None vision_model_output: BaseModelOutputWithPooling = None def to_tuple(self) -> Tuple[Any]: return tuple( self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple() for k in self.keys() ) # Copied from transformers.models.roberta.modeling_roberta.RobertaEmbeddings with Roberta->AltRoberta class AltRobertaEmbeddings(nn.Module): """ Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. """ # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") self.register_buffer( "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False ) self.register_buffer( "token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False ) # End copy self.padding_idx = config.pad_token_id self.position_embeddings = nn.Embedding( config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx ) def forward( self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 ): if position_ids is None: if input_ids is not None: # Create the position ids from the input token ids. Any padded tokens remain padded. position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length) else: position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds) if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves # issue #5664 if token_type_ids is None: if hasattr(self, "token_type_ids"): buffered_token_type_ids = self.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings if self.position_embedding_type == "absolute": position_embeddings = self.position_embeddings(position_ids) embeddings += position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings def create_position_ids_from_inputs_embeds(self, inputs_embeds): """ We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. Args: inputs_embeds: torch.Tensor Returns: torch.Tensor """ input_shape = inputs_embeds.size()[:-1] sequence_length = input_shape[1] position_ids = torch.arange( self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device ) return position_ids.unsqueeze(0).expand(input_shape) # Copied from transformers.models.roberta.modeling_roberta.RobertaSelfAttention with Roberta->AltRoberta class AltRobertaSelfAttention(nn.Module): def __init__(self, config, position_embedding_type=None): super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size) self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.position_embedding_type = position_embedding_type or getattr( config, "position_embedding_type", "absolute" ) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": self.max_position_embeddings = config.max_position_embeddings self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) self.is_decoder = config.is_decoder def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: mixed_query_layer = self.query(hidden_states) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. is_cross_attention = encoder_hidden_states is not None if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_layer = past_key_value[0] value_layer = past_key_value[1] attention_mask = encoder_attention_mask elif is_cross_attention: key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) attention_mask = encoder_attention_mask elif past_key_value is not None: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) key_layer = torch.cat([past_key_value[0], key_layer], dim=2) value_layer = torch.cat([past_key_value[1], value_layer], dim=2) else: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) use_cache = past_key_value is not None if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_layer, value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": query_length, key_length = query_layer.shape[2], key_layer.shape[2] if use_cache: position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view( -1, 1 ) else: position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1) distance = position_ids_l - position_ids_r positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility if self.position_embedding_type == "relative_key": relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores elif self.position_embedding_type == "relative_key_query": relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in AltRobertaModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) if self.is_decoder: outputs = outputs + (past_key_value,) return outputs # Copied from transformers.models.roberta.modeling_roberta.RobertaSelfOutput class AltRobertaSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states # Copied from transformers.models.roberta.modeling_roberta.RobertaAttention with Roberta->AltRoberta class AltRobertaAttention(nn.Module): def __init__(self, config, position_embedding_type=None): super().__init__() self.self = AltRobertaSelfAttention(config, position_embedding_type=position_embedding_type) self.output = AltRobertaSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: self_outputs = self.self( hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs # Copied from transformers.models.roberta.modeling_roberta.RobertaIntermediate with Roberta->AltRoberta class AltRobertaIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states # Copied from transformers.models.roberta.modeling_roberta.RobertaOutput class AltRobertaOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states # Copied from transformers.models.roberta.modeling_roberta.RobertaLayer with Roberta->AltRoberta class AltRobertaLayer(nn.Module): def __init__(self, config): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = AltRobertaAttention(config) self.is_decoder = config.is_decoder self.add_cross_attention = config.add_cross_attention if self.add_cross_attention: if not self.is_decoder: raise ValueError(f"{self} should be used as a decoder model if cross attention is added") self.crossattention = AltRobertaAttention(config, position_embedding_type="absolute") self.intermediate = AltRobertaIntermediate(config) self.output = AltRobertaOutput(config) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None self_attention_outputs = self.attention( hidden_states, attention_mask, head_mask, output_attentions=output_attentions, past_key_value=self_attn_past_key_value, ) attention_output = self_attention_outputs[0] # if decoder, the last output is tuple of self-attn cache if self.is_decoder: outputs = self_attention_outputs[1:-1] present_key_value = self_attention_outputs[-1] else: outputs = self_attention_outputs[1:] # add self attentions if we output attention weights cross_attn_present_key_value = None if self.is_decoder and encoder_hidden_states is not None: if not hasattr(self, "crossattention"): raise ValueError( f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers" " by setting `config.add_cross_attention=True`" ) # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None cross_attention_outputs = self.crossattention( attention_output, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, cross_attn_past_key_value, output_attentions, ) attention_output = cross_attention_outputs[0] outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights # add cross-attn cache to positions 3,4 of present_key_value tuple cross_attn_present_key_value = cross_attention_outputs[-1] present_key_value = present_key_value + cross_attn_present_key_value layer_output = apply_chunking_to_forward( self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output ) outputs = (layer_output,) + outputs # if decoder, return the attn key/values as the last output if self.is_decoder: outputs = outputs + (present_key_value,) return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output # Copied from transformers.models.roberta.modeling_roberta.RobertaEncoder with Roberta->AltRoberta class AltRobertaEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.layer = nn.ModuleList([AltRobertaLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False next_decoder_cache = () if use_cache else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None past_key_value = past_key_values[i] if past_key_values is not None else None if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( layer_module.__call__, hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) else: layer_outputs = layer_module( hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[-1],) if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if self.config.add_cross_attention: all_cross_attentions = all_cross_attentions + (layer_outputs[2],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, next_decoder_cache, all_hidden_states, all_self_attentions, all_cross_attentions, ] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions, ) # Copied from transformers.models.roberta.modeling_roberta.RobertaPooler class AltRobertaPooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output # Copied from transformers.models.clip.modeling_clip.CLIPAttention with CLIP->AltCLIP class AltCLIPAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config): super().__init__() self.config = config self.embed_dim = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.embed_dim // self.num_heads if self.head_dim * self.num_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" f" {self.num_heads})." ) self.scale = self.head_dim**-0.5 self.dropout = config.attention_dropout self.k_proj = nn.Linear(self.embed_dim, self.embed_dim) self.v_proj = nn.Linear(self.embed_dim, self.embed_dim) self.q_proj = nn.Linear(self.embed_dim, self.embed_dim) self.out_proj = nn.Linear(self.embed_dim, self.embed_dim) def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, causal_attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" bsz, tgt_len, embed_dim = hidden_states.size() # get query proj query_states = self.q_proj(hidden_states) * self.scale key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) proj_shape = (bsz * self.num_heads, -1, self.head_dim) query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) key_states = key_states.view(*proj_shape) value_states = value_states.view(*proj_shape) src_len = key_states.size(1) attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): raise ValueError( f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" f" {attn_weights.size()}" ) # apply the causal_attention_mask first if causal_attention_mask is not None: if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is" f" {causal_attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + causal_attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) if attention_mask is not None: if attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) attn_weights = nn.functional.softmax(attn_weights, dim=-1) if output_attentions: # this operation is a bit akward, but it's required to # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to reshaped # twice and have to be reused in the following attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) else: attn_weights_reshaped = None attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.bmm(attn_probs, value_states) if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) attn_output = attn_output.transpose(1, 2) attn_output = attn_output.reshape(bsz, tgt_len, embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped # Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->AltCLIP class AltCLIPMLP(nn.Module): def __init__(self, config): super().__init__() self.config = config self.activation_fn = ACT2FN[config.hidden_act] self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.fc1(hidden_states) hidden_states = self.activation_fn(hidden_states) hidden_states = self.fc2(hidden_states) return hidden_states # Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->AltCLIP class AltCLIPEncoderLayer(nn.Module): def __init__(self, config: AltCLIPConfig): super().__init__() self.embed_dim = config.hidden_size self.self_attn = AltCLIPAttention(config) self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) self.mlp = AltCLIPMLP(config) self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, causal_attention_mask: torch.Tensor, output_attentions: Optional[bool] = False, ) -> Tuple[torch.FloatTensor]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. `(config.encoder_attention_heads,)`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states hidden_states = self.layer_norm1(hidden_states) hidden_states, attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, causal_attention_mask=causal_attention_mask, output_attentions=output_attentions, ) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.layer_norm2(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs # Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->AltCLIP class AltCLIPEncoder(nn.Module): """ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a [`AltCLIPEncoderLayer`]. Args: config: AltCLIPConfig """ def __init__(self, config: AltCLIPConfig): super().__init__() self.config = config self.layers = nn.ModuleList([AltCLIPEncoderLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, inputs_embeds, attention_mask: Optional[torch.Tensor] = None, causal_attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutput]: r""" Args: inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Causal mask for the text model. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None hidden_states = inputs_embeds for idx, encoder_layer in enumerate(self.layers): if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( encoder_layer.__call__, hidden_states, attention_mask, causal_attention_mask, output_attentions, ) else: layer_outputs = encoder_layer( hidden_states, attention_mask, causal_attention_mask, output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions ) # Copied from transformers.models.clip.modeling_clip.CLIPVisionEmbeddings with CLIP->AltCLIP class AltCLIPVisionEmbeddings(nn.Module): def __init__(self, config: AltCLIPVisionConfig): super().__init__() self.config = config self.embed_dim = config.hidden_size self.image_size = config.image_size self.patch_size = config.patch_size self.class_embedding = nn.Parameter(torch.randn(self.embed_dim)) self.patch_embedding = nn.Conv2d( in_channels=config.num_channels, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size, bias=False, ) self.num_patches = (self.image_size // self.patch_size) ** 2 self.num_positions = self.num_patches + 1 self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim) self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False) def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor: batch_size = pixel_values.shape[0] target_dtype = self.patch_embedding.weight.dtype patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, width, grid, grid] patch_embeds = patch_embeds.flatten(2).transpose(1, 2) class_embeds = self.class_embedding.expand(batch_size, 1, -1) embeddings = torch.cat([class_embeds, patch_embeds], dim=1) embeddings = embeddings + self.position_embedding(self.position_ids) return embeddings class AltCLIPPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = AltCLIPConfig base_model_prefix = "altclip" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" factor = self.config.initializer_factor if isinstance(module, AltCLIPVisionEmbeddings): factor = self.config.initializer_factor nn.init.normal_(module.class_embedding, mean=0.0, std=module.embed_dim**-0.5 * factor) nn.init.normal_(module.patch_embedding.weight, std=module.config.initializer_range * factor) nn.init.normal_(module.position_embedding.weight, std=module.config.initializer_range * factor) elif isinstance(module, AltCLIPAttention): factor = self.config.initializer_factor in_proj_std = (module.embed_dim**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor out_proj_std = (module.embed_dim**-0.5) * factor nn.init.normal_(module.q_proj.weight, std=in_proj_std) nn.init.normal_(module.k_proj.weight, std=in_proj_std) nn.init.normal_(module.v_proj.weight, std=in_proj_std) nn.init.normal_(module.out_proj.weight, std=out_proj_std) elif isinstance(module, AltCLIPMLP): factor = self.config.initializer_factor in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor fc_std = (2 * module.config.hidden_size) ** -0.5 * factor nn.init.normal_(module.fc1.weight, std=fc_std) nn.init.normal_(module.fc2.weight, std=in_proj_std) elif isinstance(module, AltCLIPModel): nn.init.normal_( module.text_projection.weight, std=module.text_embed_dim**-0.5 * self.config.initializer_factor, ) module.text_projection._is_hf_initialized = True nn.init.normal_( module.visual_projection.weight, std=module.vision_embed_dim**-0.5 * self.config.initializer_factor, ) module.visual_projection._is_hf_initialized = True elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) elif isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=self.config.initializer_factor) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_factor) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() # Copied from transformers.models.clip.modeling_clip.CLIPVisionTransformer with CLIPVisionTransformer->AltCLIPVisionTransformer,CLIPVisionConfig->AltCLIPVisionConfig,CLIPVisionEmbeddings->AltCLIPVisionEmbeddings,CLIPEncoder->AltCLIPEncoder,CLIP_VISION_INPUTS_DOCSTRING->ALTCLIP_VISION_INPUTS_DOCSTRING class AltCLIPVisionTransformer(nn.Module): def __init__(self, config: AltCLIPVisionConfig): super().__init__() self.config = config embed_dim = config.hidden_size self.embeddings = AltCLIPVisionEmbeddings(config) self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) self.encoder = AltCLIPEncoder(config) self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) @add_start_docstrings_to_model_forward(ALTCLIP_VISION_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=AltCLIPVisionConfig) def forward( self, pixel_values: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: r""" Returns: """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("You have to specify pixel_values") hidden_states = self.embeddings(pixel_values) hidden_states = self.pre_layrnorm(hidden_states) encoder_outputs = self.encoder( inputs_embeds=hidden_states, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_state = encoder_outputs[0] pooled_output = last_hidden_state[:, 0, :] pooled_output = self.post_layernorm(pooled_output) if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) class AltCLIPVisionModel(AltCLIPPreTrainedModel): config_class = AltCLIPVisionConfig main_input_name = "pixel_values" def __init__(self, config: AltCLIPVisionConfig): super().__init__(config) self.vision_model = AltCLIPVisionTransformer(config) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self) -> nn.Module: return self.vision_model.embeddings.patch_embedding @add_start_docstrings_to_model_forward(ALTCLIP_VISION_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=AltCLIPVisionConfig) def forward( self, pixel_values: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, AltCLIPVisionModel >>> model = AltCLIPVisionModel.from_pretrained("BAAI/AltCLIP") >>> processor = AutoProcessor.from_pretrained("BAAI/AltCLIP") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, return_tensors="pt") >>> outputs = model(**inputs) >>> last_hidden_state = outputs.last_hidden_state >>> pooled_output = outputs.pooler_output # pooled CLS states ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict return self.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) class AltRobertaModel(AltCLIPPreTrainedModel): """ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in *Attention is all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 """ config_class = AltCLIPTextConfig # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->AltRoberta def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config self.embeddings = AltRobertaEmbeddings(config) self.encoder = AltRobertaEncoder(config) self.pooler = AltRobertaPooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) # Copied from transformers.models.bert.modeling_bert.BertModel.forward def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if self.config.is_decoder: use_cache = use_cache if use_cache is not None else self.config.use_cache else: use_cache = False if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape device = input_ids.device if input_ids is not None else inputs_embeds.device # past_key_values_length past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) if token_type_ids is None: if hasattr(self.embeddings, "token_type_ids"): buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if self.config.is_decoder and encoder_hidden_states is not None: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_extended_attention_mask = None # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, past_key_values_length=past_key_values_length, ) encoder_outputs = self.encoder( embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=sequence_output, pooler_output=pooled_output, past_key_values=encoder_outputs.past_key_values, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions, ) class AltCLIPTextModel(AltCLIPPreTrainedModel): config_class = AltCLIPTextConfig def __init__(self, config): super().__init__(config) self.roberta = AltRobertaModel(config, add_pooling_layer=False) self.transformation = nn.Linear(config.hidden_size, config.project_dim) self.pre_LN = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.post_init() def get_input_embeddings(self) -> nn.Module: return self.roberta.embeddings.word_embeddings def set_input_embeddings(self, value: nn.Embedding) -> None: self.roberta.embeddings.word_embeddings = value def resize_token_embeddings(self, new_num_tokens: Optional[int] = None) -> nn.Embedding: return super().resize_token_embeddings(new_num_tokens) @add_start_docstrings_to_model_forward(ALTCLIP_TEXT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPoolingAndProjection, config_class=AltCLIPTextConfig) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None, output_hidden_states: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPoolingAndProjection]: r""" Returns: Examples: ```python >>> from transformers import AutoProcessor, AltCLIPTextModel >>> model = AltCLIPTextModel.from_pretrained("BAAI/AltCLIP") >>> processor = AutoProcessor.from_pretrained("BAAI/AltCLIP") >>> texts = ["it's a cat", "it's a dog"] >>> inputs = processor(text=texts, padding=True, return_tensors="pt") >>> outputs = model(**inputs) >>> last_hidden_state = outputs.last_hidden_state >>> pooled_output = outputs.pooler_output # pooled CLS states ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roberta( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # last module outputs sequence_output = outputs[0] # project every module sequence_output = self.pre_LN(sequence_output) # pooler projection_state = self.transformation(sequence_output) pooler_output = projection_state[:, 0] if not return_dict: return (projection_state, pooler_output) + outputs[2:4] return BaseModelOutputWithPoolingAndProjection( last_hidden_state=projection_state, pooler_output=pooler_output, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class AltCLIPModel(AltCLIPPreTrainedModel): config_class = AltCLIPConfig def __init__(self, config: AltCLIPConfig): super().__init__(config) if not isinstance(config.vision_config, AltCLIPVisionConfig): raise ValueError( "config.vision_config is expected to be of type AltCLIPVisionConfig but is of type" f" {type(config.vision_config)}." ) if not isinstance(config.text_config, AltCLIPTextConfig): raise ValueError( "config.text_config is expected to be of type AltCLIPTextConfig but is of type" f" {type(config.text_config)}." ) text_config = config.text_config vision_config = config.vision_config self.projection_dim = config.projection_dim self.text_embed_dim = text_config.project_dim self.vision_embed_dim = vision_config.hidden_size self.text_model = AltCLIPTextModel(text_config) self.vision_model = AltCLIPVisionTransformer(vision_config) self.visual_projection = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False) self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim, bias=False) self.logit_scale = nn.Parameter(torch.tensor(self.config.logit_scale_init_value)) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(ALTCLIP_TEXT_INPUTS_DOCSTRING) def get_text_features( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, token_type_ids=None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> torch.FloatTensor: r""" Returns: text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of [`AltCLIPTextModel`]. Examples: ```python >>> from transformers import AutoProcessor, AltCLIPModel >>> model = AltCLIPModel.from_pretrained("BAAI/AltCLIP") >>> processor = AutoProcessor.from_pretrained("BAAI/AltCLIP") >>> inputs = processor(text=["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt") >>> text_features = model.get_text_features(**inputs) ```""" # Use AltCLIP model's config for some fields (if specified) instead of those of vision & text components. output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict text_outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, token_type_ids=token_type_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = text_outputs[1] text_features = self.text_projection(pooled_output) return text_features @add_start_docstrings_to_model_forward(ALTCLIP_VISION_INPUTS_DOCSTRING) def get_image_features( self, pixel_values: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> torch.FloatTensor: r""" Returns: image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of [`AltCLIPVisionModel`]. Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, AltCLIPModel >>> model = AltCLIPModel.from_pretrained("BAAI/AltCLIP") >>> processor = AutoProcessor.from_pretrained("BAAI/AltCLIP") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, return_tensors="pt") >>> image_features = model.get_image_features(**inputs) ```""" # Use AltCLIP model's config for some fields (if specified) instead of those of vision & text components. output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict vision_outputs = self.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = vision_outputs[1] # pooled_output image_features = self.visual_projection(pooled_output) return image_features @add_start_docstrings_to_model_forward(ALTCLIP_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=AltCLIPOutput, config_class=AltCLIPConfig) def forward( self, input_ids: Optional[torch.LongTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.Tensor] = None, return_loss: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, AltCLIPOutput]: r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, AltCLIPModel >>> model = AltCLIPModel.from_pretrained("BAAI/AltCLIP") >>> processor = AutoProcessor.from_pretrained("BAAI/AltCLIP") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor( ... text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True ... ) >>> outputs = model(**inputs) >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score >>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities ```""" # Use AltCLIP model's config for some fields (if specified) instead of those of vision & text components. output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict text_outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) vision_outputs = self.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) image_embeds = vision_outputs[1] image_embeds = self.visual_projection(image_embeds) text_embeds = text_outputs[1] text_embeds = self.text_projection(text_embeds) # normalized features image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True) text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True) # cosine similarity as logits logit_scale = self.logit_scale.exp() logits_per_text = torch.matmul(text_embeds, image_embeds.t()) * logit_scale logits_per_image = logits_per_text.T loss = None if return_loss: loss = clip_loss(logits_per_text) if not return_dict: output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs) return ((loss,) + output) if loss is not None else output return AltCLIPOutput( loss=loss, logits_per_image=logits_per_image, logits_per_text=logits_per_text, text_embeds=text_embeds, image_embeds=image_embeds, text_model_output=text_outputs, vision_model_output=vision_outputs, ) # Copied from transformers.models.roberta.modeling_roberta.create_position_ids_from_input_ids def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0): """ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's `utils.make_positions`. Args: x: torch.Tensor x: Returns: torch.Tensor """ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. mask = input_ids.ne(padding_idx).int() incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask return incremental_indices.long() + padding_idx

transformers/src/transformers/models/altclip/modeling_altclip.py/0

{ "file_path": "transformers/src/transformers/models/altclip/modeling_altclip.py", "repo_id": "transformers", "token_count": 33182 }

79

# coding=utf-8 # Copyright 2018 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Auto Tokenizer class.""" import importlib import json import os import warnings from collections import OrderedDict from typing import TYPE_CHECKING, Dict, Optional, Tuple, Union from ...configuration_utils import PretrainedConfig from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code from ...tokenization_utils import PreTrainedTokenizer from ...tokenization_utils_base import TOKENIZER_CONFIG_FILE from ...utils import ( cached_file, extract_commit_hash, is_g2p_en_available, is_sentencepiece_available, is_tokenizers_available, logging, ) from ..encoder_decoder import EncoderDecoderConfig from .auto_factory import _LazyAutoMapping from .configuration_auto import ( CONFIG_MAPPING_NAMES, AutoConfig, config_class_to_model_type, model_type_to_module_name, replace_list_option_in_docstrings, ) if is_tokenizers_available(): from ...tokenization_utils_fast import PreTrainedTokenizerFast else: PreTrainedTokenizerFast = None logger = logging.get_logger(__name__) if TYPE_CHECKING: # This significantly improves completion suggestion performance when # the transformers package is used with Microsoft's Pylance language server. TOKENIZER_MAPPING_NAMES: OrderedDict[str, Tuple[Optional[str], Optional[str]]] = OrderedDict() else: TOKENIZER_MAPPING_NAMES = OrderedDict( [ ( "albert", ( "AlbertTokenizer" if is_sentencepiece_available() else None, "AlbertTokenizerFast" if is_tokenizers_available() else None, ), ), ("align", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ("bark", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ("bart", ("BartTokenizer", "BartTokenizerFast")), ( "barthez", ( "BarthezTokenizer" if is_sentencepiece_available() else None, "BarthezTokenizerFast" if is_tokenizers_available() else None, ), ), ("bartpho", ("BartphoTokenizer", None)), ("bert", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ("bert-generation", ("BertGenerationTokenizer" if is_sentencepiece_available() else None, None)), ("bert-japanese", ("BertJapaneseTokenizer", None)), ("bertweet", ("BertweetTokenizer", None)), ( "big_bird", ( "BigBirdTokenizer" if is_sentencepiece_available() else None, "BigBirdTokenizerFast" if is_tokenizers_available() else None, ), ), ("bigbird_pegasus", ("PegasusTokenizer", "PegasusTokenizerFast" if is_tokenizers_available() else None)), ("biogpt", ("BioGptTokenizer", None)), ("blenderbot", ("BlenderbotTokenizer", "BlenderbotTokenizerFast")), ("blenderbot-small", ("BlenderbotSmallTokenizer", None)), ("blip", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ("blip-2", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)), ("bloom", (None, "BloomTokenizerFast" if is_tokenizers_available() else None)), ("bridgetower", ("RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None)), ("bros", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ("byt5", ("ByT5Tokenizer", None)), ( "camembert", ( "CamembertTokenizer" if is_sentencepiece_available() else None, "CamembertTokenizerFast" if is_tokenizers_available() else None, ), ), ("canine", ("CanineTokenizer", None)), ("chinese_clip", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ( "clap", ( "RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None, ), ), ( "clip", ( "CLIPTokenizer", "CLIPTokenizerFast" if is_tokenizers_available() else None, ), ), ( "clipseg", ( "CLIPTokenizer", "CLIPTokenizerFast" if is_tokenizers_available() else None, ), ), ("clvp", ("ClvpTokenizer", None)), ( "code_llama", ( "CodeLlamaTokenizer" if is_sentencepiece_available() else None, "CodeLlamaTokenizerFast" if is_tokenizers_available() else None, ), ), ("codegen", ("CodeGenTokenizer", "CodeGenTokenizerFast" if is_tokenizers_available() else None)), ("convbert", ("ConvBertTokenizer", "ConvBertTokenizerFast" if is_tokenizers_available() else None)), ( "cpm", ( "CpmTokenizer" if is_sentencepiece_available() else None, "CpmTokenizerFast" if is_tokenizers_available() else None, ), ), ("cpmant", ("CpmAntTokenizer", None)), ("ctrl", ("CTRLTokenizer", None)), ("data2vec-audio", ("Wav2Vec2CTCTokenizer", None)), ("data2vec-text", ("RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None)), ("deberta", ("DebertaTokenizer", "DebertaTokenizerFast" if is_tokenizers_available() else None)), ( "deberta-v2", ( "DebertaV2Tokenizer" if is_sentencepiece_available() else None, "DebertaV2TokenizerFast" if is_tokenizers_available() else None, ), ), ("distilbert", ("DistilBertTokenizer", "DistilBertTokenizerFast" if is_tokenizers_available() else None)), ( "dpr", ( "DPRQuestionEncoderTokenizer", "DPRQuestionEncoderTokenizerFast" if is_tokenizers_available() else None, ), ), ("electra", ("ElectraTokenizer", "ElectraTokenizerFast" if is_tokenizers_available() else None)), ("ernie", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ("ernie_m", ("ErnieMTokenizer" if is_sentencepiece_available() else None, None)), ("esm", ("EsmTokenizer", None)), ("falcon", (None, "PreTrainedTokenizerFast" if is_tokenizers_available() else None)), ( "fastspeech2_conformer", ("FastSpeech2ConformerTokenizer" if is_g2p_en_available() else None, None), ), ("flaubert", ("FlaubertTokenizer", None)), ("fnet", ("FNetTokenizer", "FNetTokenizerFast" if is_tokenizers_available() else None)), ("fsmt", ("FSMTTokenizer", None)), ("funnel", ("FunnelTokenizer", "FunnelTokenizerFast" if is_tokenizers_available() else None)), ( "gemma", ( "GemmaTokenizer" if is_sentencepiece_available() else None, "GemmaTokenizerFast" if is_tokenizers_available() else None, ), ), ("git", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ("gpt-sw3", ("GPTSw3Tokenizer" if is_sentencepiece_available() else None, None)), ("gpt2", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)), ("gpt_bigcode", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)), ("gpt_neo", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)), ("gpt_neox", (None, "GPTNeoXTokenizerFast" if is_tokenizers_available() else None)), ("gpt_neox_japanese", ("GPTNeoXJapaneseTokenizer", None)), ("gptj", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)), ("gptsan-japanese", ("GPTSanJapaneseTokenizer", None)), ("groupvit", ("CLIPTokenizer", "CLIPTokenizerFast" if is_tokenizers_available() else None)), ("herbert", ("HerbertTokenizer", "HerbertTokenizerFast" if is_tokenizers_available() else None)), ("hubert", ("Wav2Vec2CTCTokenizer", None)), ("ibert", ("RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None)), ("idefics", (None, "LlamaTokenizerFast" if is_tokenizers_available() else None)), ("instructblip", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)), ("jukebox", ("JukeboxTokenizer", None)), ( "kosmos-2", ( "XLMRobertaTokenizer" if is_sentencepiece_available() else None, "XLMRobertaTokenizerFast" if is_tokenizers_available() else None, ), ), ("layoutlm", ("LayoutLMTokenizer", "LayoutLMTokenizerFast" if is_tokenizers_available() else None)), ("layoutlmv2", ("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast" if is_tokenizers_available() else None)), ("layoutlmv3", ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast" if is_tokenizers_available() else None)), ("layoutxlm", ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast" if is_tokenizers_available() else None)), ("led", ("LEDTokenizer", "LEDTokenizerFast" if is_tokenizers_available() else None)), ("lilt", ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast" if is_tokenizers_available() else None)), ( "llama", ( "LlamaTokenizer" if is_sentencepiece_available() else None, "LlamaTokenizerFast" if is_tokenizers_available() else None, ), ), ("llava", ("LlamaTokenizer", "LlamaTokenizerFast" if is_tokenizers_available() else None)), ("longformer", ("LongformerTokenizer", "LongformerTokenizerFast" if is_tokenizers_available() else None)), ( "longt5", ( "T5Tokenizer" if is_sentencepiece_available() else None, "T5TokenizerFast" if is_tokenizers_available() else None, ), ), ("luke", ("LukeTokenizer", None)), ("lxmert", ("LxmertTokenizer", "LxmertTokenizerFast" if is_tokenizers_available() else None)), ("m2m_100", ("M2M100Tokenizer" if is_sentencepiece_available() else None, None)), ("marian", ("MarianTokenizer" if is_sentencepiece_available() else None, None)), ( "mbart", ( "MBartTokenizer" if is_sentencepiece_available() else None, "MBartTokenizerFast" if is_tokenizers_available() else None, ), ), ( "mbart50", ( "MBart50Tokenizer" if is_sentencepiece_available() else None, "MBart50TokenizerFast" if is_tokenizers_available() else None, ), ), ("mega", ("RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None)), ("megatron-bert", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ("mgp-str", ("MgpstrTokenizer", None)), ( "mistral", ( "LlamaTokenizer" if is_sentencepiece_available() else None, "LlamaTokenizerFast" if is_tokenizers_available() else None, ), ), ( "mixtral", ( "LlamaTokenizer" if is_sentencepiece_available() else None, "LlamaTokenizerFast" if is_tokenizers_available() else None, ), ), ("mluke", ("MLukeTokenizer" if is_sentencepiece_available() else None, None)), ("mobilebert", ("MobileBertTokenizer", "MobileBertTokenizerFast" if is_tokenizers_available() else None)), ("mpnet", ("MPNetTokenizer", "MPNetTokenizerFast" if is_tokenizers_available() else None)), ("mpt", (None, "GPTNeoXTokenizerFast" if is_tokenizers_available() else None)), ("mra", ("RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None)), ( "mt5", ( "MT5Tokenizer" if is_sentencepiece_available() else None, "MT5TokenizerFast" if is_tokenizers_available() else None, ), ), ("musicgen", ("T5Tokenizer", "T5TokenizerFast" if is_tokenizers_available() else None)), ("mvp", ("MvpTokenizer", "MvpTokenizerFast" if is_tokenizers_available() else None)), ("nezha", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ( "nllb", ( "NllbTokenizer" if is_sentencepiece_available() else None, "NllbTokenizerFast" if is_tokenizers_available() else None, ), ), ( "nllb-moe", ( "NllbTokenizer" if is_sentencepiece_available() else None, "NllbTokenizerFast" if is_tokenizers_available() else None, ), ), ( "nystromformer", ( "AlbertTokenizer" if is_sentencepiece_available() else None, "AlbertTokenizerFast" if is_tokenizers_available() else None, ), ), ("oneformer", ("CLIPTokenizer", "CLIPTokenizerFast" if is_tokenizers_available() else None)), ( "openai-gpt", ("OpenAIGPTTokenizer", "OpenAIGPTTokenizerFast" if is_tokenizers_available() else None), ), ("opt", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)), ("owlv2", ("CLIPTokenizer", "CLIPTokenizerFast" if is_tokenizers_available() else None)), ("owlvit", ("CLIPTokenizer", "CLIPTokenizerFast" if is_tokenizers_available() else None)), ( "pegasus", ( "PegasusTokenizer" if is_sentencepiece_available() else None, "PegasusTokenizerFast" if is_tokenizers_available() else None, ), ), ( "pegasus_x", ( "PegasusTokenizer" if is_sentencepiece_available() else None, "PegasusTokenizerFast" if is_tokenizers_available() else None, ), ), ( "perceiver", ( "PerceiverTokenizer", None, ), ), ( "persimmon", ( "LlamaTokenizer" if is_sentencepiece_available() else None, "LlamaTokenizerFast" if is_tokenizers_available() else None, ), ), ("phi", ("CodeGenTokenizer", "CodeGenTokenizerFast" if is_tokenizers_available() else None)), ("phobert", ("PhobertTokenizer", None)), ("pix2struct", ("T5Tokenizer", "T5TokenizerFast" if is_tokenizers_available() else None)), ("plbart", ("PLBartTokenizer" if is_sentencepiece_available() else None, None)), ("prophetnet", ("ProphetNetTokenizer", None)), ("qdqbert", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ( "qwen2", ( "Qwen2Tokenizer", "Qwen2TokenizerFast" if is_tokenizers_available() else None, ), ), ("rag", ("RagTokenizer", None)), ("realm", ("RealmTokenizer", "RealmTokenizerFast" if is_tokenizers_available() else None)), ( "reformer", ( "ReformerTokenizer" if is_sentencepiece_available() else None, "ReformerTokenizerFast" if is_tokenizers_available() else None, ), ), ( "rembert", ( "RemBertTokenizer" if is_sentencepiece_available() else None, "RemBertTokenizerFast" if is_tokenizers_available() else None, ), ), ("retribert", ("RetriBertTokenizer", "RetriBertTokenizerFast" if is_tokenizers_available() else None)), ("roberta", ("RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None)), ( "roberta-prelayernorm", ("RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None), ), ("roc_bert", ("RoCBertTokenizer", None)), ("roformer", ("RoFormerTokenizer", "RoFormerTokenizerFast" if is_tokenizers_available() else None)), ("rwkv", (None, "GPTNeoXTokenizerFast" if is_tokenizers_available() else None)), ( "seamless_m4t", ( "SeamlessM4TTokenizer" if is_sentencepiece_available() else None, "SeamlessM4TTokenizerFast" if is_tokenizers_available() else None, ), ), ( "seamless_m4t_v2", ( "SeamlessM4TTokenizer" if is_sentencepiece_available() else None, "SeamlessM4TTokenizerFast" if is_tokenizers_available() else None, ), ), ("siglip", ("SiglipTokenizer" if is_sentencepiece_available() else None, None)), ("speech_to_text", ("Speech2TextTokenizer" if is_sentencepiece_available() else None, None)), ("speech_to_text_2", ("Speech2Text2Tokenizer", None)), ("speecht5", ("SpeechT5Tokenizer" if is_sentencepiece_available() else None, None)), ("splinter", ("SplinterTokenizer", "SplinterTokenizerFast")), ( "squeezebert", ("SqueezeBertTokenizer", "SqueezeBertTokenizerFast" if is_tokenizers_available() else None), ), ("stablelm", (None, "GPTNeoXTokenizerFast" if is_tokenizers_available() else None)), ( "switch_transformers", ( "T5Tokenizer" if is_sentencepiece_available() else None, "T5TokenizerFast" if is_tokenizers_available() else None, ), ), ( "t5", ( "T5Tokenizer" if is_sentencepiece_available() else None, "T5TokenizerFast" if is_tokenizers_available() else None, ), ), ("tapas", ("TapasTokenizer", None)), ("tapex", ("TapexTokenizer", None)), ("transfo-xl", ("TransfoXLTokenizer", None)), ("tvp", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ( "umt5", ( "T5Tokenizer" if is_sentencepiece_available() else None, "T5TokenizerFast" if is_tokenizers_available() else None, ), ), ("vilt", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ("vipllava", ("LlamaTokenizer", "LlamaTokenizerFast" if is_tokenizers_available() else None)), ("visual_bert", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ("vits", ("VitsTokenizer", None)), ("wav2vec2", ("Wav2Vec2CTCTokenizer", None)), ("wav2vec2-bert", ("Wav2Vec2CTCTokenizer", None)), ("wav2vec2-conformer", ("Wav2Vec2CTCTokenizer", None)), ("wav2vec2_phoneme", ("Wav2Vec2PhonemeCTCTokenizer", None)), ("whisper", ("WhisperTokenizer", "WhisperTokenizerFast" if is_tokenizers_available() else None)), ("xclip", ("CLIPTokenizer", "CLIPTokenizerFast" if is_tokenizers_available() else None)), ( "xglm", ( "XGLMTokenizer" if is_sentencepiece_available() else None, "XGLMTokenizerFast" if is_tokenizers_available() else None, ), ), ("xlm", ("XLMTokenizer", None)), ("xlm-prophetnet", ("XLMProphetNetTokenizer" if is_sentencepiece_available() else None, None)), ( "xlm-roberta", ( "XLMRobertaTokenizer" if is_sentencepiece_available() else None, "XLMRobertaTokenizerFast" if is_tokenizers_available() else None, ), ), ( "xlm-roberta-xl", ( "XLMRobertaTokenizer" if is_sentencepiece_available() else None, "XLMRobertaTokenizerFast" if is_tokenizers_available() else None, ), ), ( "xlnet", ( "XLNetTokenizer" if is_sentencepiece_available() else None, "XLNetTokenizerFast" if is_tokenizers_available() else None, ), ), ( "xmod", ( "XLMRobertaTokenizer" if is_sentencepiece_available() else None, "XLMRobertaTokenizerFast" if is_tokenizers_available() else None, ), ), ( "yoso", ( "AlbertTokenizer" if is_sentencepiece_available() else None, "AlbertTokenizerFast" if is_tokenizers_available() else None, ), ), ] ) TOKENIZER_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, TOKENIZER_MAPPING_NAMES) CONFIG_TO_TYPE = {v: k for k, v in CONFIG_MAPPING_NAMES.items()} def tokenizer_class_from_name(class_name: str): if class_name == "PreTrainedTokenizerFast": return PreTrainedTokenizerFast for module_name, tokenizers in TOKENIZER_MAPPING_NAMES.items(): if class_name in tokenizers: module_name = model_type_to_module_name(module_name) module = importlib.import_module(f".{module_name}", "transformers.models") try: return getattr(module, class_name) except AttributeError: continue for config, tokenizers in TOKENIZER_MAPPING._extra_content.items(): for tokenizer in tokenizers: if getattr(tokenizer, "__name__", None) == class_name: return tokenizer # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main # init and we return the proper dummy to get an appropriate error message. main_module = importlib.import_module("transformers") if hasattr(main_module, class_name): return getattr(main_module, class_name) return None def get_tokenizer_config( pretrained_model_name_or_path: Union[str, os.PathLike], cache_dir: Optional[Union[str, os.PathLike]] = None, force_download: bool = False, resume_download: bool = False, proxies: Optional[Dict[str, str]] = None, token: Optional[Union[bool, str]] = None, revision: Optional[str] = None, local_files_only: bool = False, subfolder: str = "", **kwargs, ): """ Loads the tokenizer configuration from a pretrained model tokenizer configuration. Args: pretrained_model_name_or_path (`str` or `os.PathLike`): This can be either: - a string, the *model id* of a pretrained model configuration hosted inside a model repo on huggingface.co. - a path to a *directory* containing a configuration file saved using the [`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`. cache_dir (`str` or `os.PathLike`, *optional*): Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force to (re-)download the configuration files and override the cached versions if they exist. resume_download (`bool`, *optional*, defaults to `False`): Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request. token (`str` or *bool*, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`). revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. local_files_only (`bool`, *optional*, defaults to `False`): If `True`, will only try to load the tokenizer configuration from local files. subfolder (`str`, *optional*, defaults to `""`): In case the tokenizer config is located inside a subfolder of the model repo on huggingface.co, you can specify the folder name here. <Tip> Passing `token=True` is required when you want to use a private model. </Tip> Returns: `Dict`: The configuration of the tokenizer. Examples: ```python # Download configuration from huggingface.co and cache. tokenizer_config = get_tokenizer_config("google-bert/bert-base-uncased") # This model does not have a tokenizer config so the result will be an empty dict. tokenizer_config = get_tokenizer_config("FacebookAI/xlm-roberta-base") # Save a pretrained tokenizer locally and you can reload its config from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased") tokenizer.save_pretrained("tokenizer-test") tokenizer_config = get_tokenizer_config("tokenizer-test") ```""" use_auth_token = kwargs.pop("use_auth_token", None) if use_auth_token is not None: warnings.warn( "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.", FutureWarning, ) if token is not None: raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.") token = use_auth_token commit_hash = kwargs.get("_commit_hash", None) resolved_config_file = cached_file( pretrained_model_name_or_path, TOKENIZER_CONFIG_FILE, cache_dir=cache_dir, force_download=force_download, resume_download=resume_download, proxies=proxies, token=token, revision=revision, local_files_only=local_files_only, subfolder=subfolder, _raise_exceptions_for_gated_repo=False, _raise_exceptions_for_missing_entries=False, _raise_exceptions_for_connection_errors=False, _commit_hash=commit_hash, ) if resolved_config_file is None: logger.info("Could not locate the tokenizer configuration file, will try to use the model config instead.") return {} commit_hash = extract_commit_hash(resolved_config_file, commit_hash) with open(resolved_config_file, encoding="utf-8") as reader: result = json.load(reader) result["_commit_hash"] = commit_hash return result class AutoTokenizer: r""" This is a generic tokenizer class that will be instantiated as one of the tokenizer classes of the library when created with the [`AutoTokenizer.from_pretrained`] class method. This class cannot be instantiated directly using `__init__()` (throws an error). """ def __init__(self): raise EnvironmentError( "AutoTokenizer is designed to be instantiated " "using the `AutoTokenizer.from_pretrained(pretrained_model_name_or_path)` method." ) @classmethod @replace_list_option_in_docstrings(TOKENIZER_MAPPING_NAMES) def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs): r""" Instantiate one of the tokenizer classes of the library from a pretrained model vocabulary. The tokenizer class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it's missing, by falling back to using pattern matching on `pretrained_model_name_or_path`: List options Params: pretrained_model_name_or_path (`str` or `os.PathLike`): Can be either: - A string, the *model id* of a predefined tokenizer hosted inside a model repo on huggingface.co. - A path to a *directory* containing vocabulary files required by the tokenizer, for instance saved using the [`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`. - A path or url to a single saved vocabulary file if and only if the tokenizer only requires a single vocabulary file (like Bert or XLNet), e.g.: `./my_model_directory/vocab.txt`. (Not applicable to all derived classes) inputs (additional positional arguments, *optional*): Will be passed along to the Tokenizer `__init__()` method. config ([`PretrainedConfig`], *optional*) The configuration object used to determine the tokenizer class to instantiate. cache_dir (`str` or `os.PathLike`, *optional*): Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force the (re-)download the model weights and configuration files and override the cached versions if they exist. resume_download (`bool`, *optional*, defaults to `False`): Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. subfolder (`str`, *optional*): In case the relevant files are located inside a subfolder of the model repo on huggingface.co (e.g. for facebook/rag-token-base), specify it here. use_fast (`bool`, *optional*, defaults to `True`): Use a [fast Rust-based tokenizer](https://huggingface.co/docs/tokenizers/index) if it is supported for a given model. If a fast tokenizer is not available for a given model, a normal Python-based tokenizer is returned instead. tokenizer_type (`str`, *optional*): Tokenizer type to be loaded. trust_remote_code (`bool`, *optional*, defaults to `False`): Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. kwargs (additional keyword arguments, *optional*): Will be passed to the Tokenizer `__init__()` method. Can be used to set special tokens like `bos_token`, `eos_token`, `unk_token`, `sep_token`, `pad_token`, `cls_token`, `mask_token`, `additional_special_tokens`. See parameters in the `__init__()` for more details. Examples: ```python >>> from transformers import AutoTokenizer >>> # Download vocabulary from huggingface.co and cache. >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> # Download vocabulary from huggingface.co (user-uploaded) and cache. >>> tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-german-cased") >>> # If vocabulary files are in a directory (e.g. tokenizer was saved using *save_pretrained('./test/saved_model/')*) >>> # tokenizer = AutoTokenizer.from_pretrained("./test/bert_saved_model/") >>> # Download vocabulary from huggingface.co and define model-specific arguments >>> tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-base", add_prefix_space=True) ```""" use_auth_token = kwargs.pop("use_auth_token", None) if use_auth_token is not None: warnings.warn( "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.", FutureWarning, ) if kwargs.get("token", None) is not None: raise ValueError( "`token` and `use_auth_token` are both specified. Please set only the argument `token`." ) kwargs["token"] = use_auth_token config = kwargs.pop("config", None) kwargs["_from_auto"] = True use_fast = kwargs.pop("use_fast", True) tokenizer_type = kwargs.pop("tokenizer_type", None) trust_remote_code = kwargs.pop("trust_remote_code", None) # First, let's see whether the tokenizer_type is passed so that we can leverage it if tokenizer_type is not None: tokenizer_class = None tokenizer_class_tuple = TOKENIZER_MAPPING_NAMES.get(tokenizer_type, None) if tokenizer_class_tuple is None: raise ValueError( f"Passed `tokenizer_type` {tokenizer_type} does not exist. `tokenizer_type` should be one of " f"{', '.join(c for c in TOKENIZER_MAPPING_NAMES.keys())}." ) tokenizer_class_name, tokenizer_fast_class_name = tokenizer_class_tuple if use_fast: if tokenizer_fast_class_name is not None: tokenizer_class = tokenizer_class_from_name(tokenizer_fast_class_name) else: logger.warning( "`use_fast` is set to `True` but the tokenizer class does not have a fast version. " " Falling back to the slow version." ) if tokenizer_class is None: tokenizer_class = tokenizer_class_from_name(tokenizer_class_name) if tokenizer_class is None: raise ValueError(f"Tokenizer class {tokenizer_class_name} is not currently imported.") return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) # Next, let's try to use the tokenizer_config file to get the tokenizer class. tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs) if "_commit_hash" in tokenizer_config: kwargs["_commit_hash"] = tokenizer_config["_commit_hash"] config_tokenizer_class = tokenizer_config.get("tokenizer_class") tokenizer_auto_map = None if "auto_map" in tokenizer_config: if isinstance(tokenizer_config["auto_map"], (tuple, list)): # Legacy format for dynamic tokenizers tokenizer_auto_map = tokenizer_config["auto_map"] else: tokenizer_auto_map = tokenizer_config["auto_map"].get("AutoTokenizer", None) # If that did not work, let's try to use the config. if config_tokenizer_class is None: if not isinstance(config, PretrainedConfig): config = AutoConfig.from_pretrained( pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs ) config_tokenizer_class = config.tokenizer_class if hasattr(config, "auto_map") and "AutoTokenizer" in config.auto_map: tokenizer_auto_map = config.auto_map["AutoTokenizer"] has_remote_code = tokenizer_auto_map is not None has_local_code = type(config) in TOKENIZER_MAPPING or ( config_tokenizer_class is not None and ( tokenizer_class_from_name(config_tokenizer_class) is not None or tokenizer_class_from_name(config_tokenizer_class + "Fast") is not None ) ) trust_remote_code = resolve_trust_remote_code( trust_remote_code, pretrained_model_name_or_path, has_local_code, has_remote_code ) if has_remote_code and trust_remote_code: if use_fast and tokenizer_auto_map[1] is not None: class_ref = tokenizer_auto_map[1] else: class_ref = tokenizer_auto_map[0] tokenizer_class = get_class_from_dynamic_module(class_ref, pretrained_model_name_or_path, **kwargs) _ = kwargs.pop("code_revision", None) if os.path.isdir(pretrained_model_name_or_path): tokenizer_class.register_for_auto_class() return tokenizer_class.from_pretrained( pretrained_model_name_or_path, *inputs, trust_remote_code=trust_remote_code, **kwargs ) elif config_tokenizer_class is not None: tokenizer_class = None if use_fast and not config_tokenizer_class.endswith("Fast"): tokenizer_class_candidate = f"{config_tokenizer_class}Fast" tokenizer_class = tokenizer_class_from_name(tokenizer_class_candidate) if tokenizer_class is None: tokenizer_class_candidate = config_tokenizer_class tokenizer_class = tokenizer_class_from_name(tokenizer_class_candidate) if tokenizer_class is None: raise ValueError( f"Tokenizer class {tokenizer_class_candidate} does not exist or is not currently imported." ) return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) # Otherwise we have to be creative. # if model is an encoder decoder, the encoder tokenizer class is used by default if isinstance(config, EncoderDecoderConfig): if type(config.decoder) is not type(config.encoder): # noqa: E721 logger.warning( f"The encoder model config class: {config.encoder.__class__} is different from the decoder model " f"config class: {config.decoder.__class__}. It is not recommended to use the " "`AutoTokenizer.from_pretrained()` method in this case. Please use the encoder and decoder " "specific tokenizer classes." ) config = config.encoder model_type = config_class_to_model_type(type(config).__name__) if model_type is not None: tokenizer_class_py, tokenizer_class_fast = TOKENIZER_MAPPING[type(config)] if tokenizer_class_fast and (use_fast or tokenizer_class_py is None): return tokenizer_class_fast.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) else: if tokenizer_class_py is not None: return tokenizer_class_py.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) else: raise ValueError( "This tokenizer cannot be instantiated. Please make sure you have `sentencepiece` installed " "in order to use this tokenizer." ) raise ValueError( f"Unrecognized configuration class {config.__class__} to build an AutoTokenizer.\n" f"Model type should be one of {', '.join(c.__name__ for c in TOKENIZER_MAPPING.keys())}." ) def register(config_class, slow_tokenizer_class=None, fast_tokenizer_class=None, exist_ok=False): """ Register a new tokenizer in this mapping. Args: config_class ([`PretrainedConfig`]): The configuration corresponding to the model to register. slow_tokenizer_class ([`PretrainedTokenizer`], *optional*): The slow tokenizer to register. fast_tokenizer_class ([`PretrainedTokenizerFast`], *optional*): The fast tokenizer to register. """ if slow_tokenizer_class is None and fast_tokenizer_class is None: raise ValueError("You need to pass either a `slow_tokenizer_class` or a `fast_tokenizer_class") if slow_tokenizer_class is not None and issubclass(slow_tokenizer_class, PreTrainedTokenizerFast): raise ValueError("You passed a fast tokenizer in the `slow_tokenizer_class`.") if fast_tokenizer_class is not None and issubclass(fast_tokenizer_class, PreTrainedTokenizer): raise ValueError("You passed a slow tokenizer in the `fast_tokenizer_class`.") if ( slow_tokenizer_class is not None and fast_tokenizer_class is not None and issubclass(fast_tokenizer_class, PreTrainedTokenizerFast) and fast_tokenizer_class.slow_tokenizer_class != slow_tokenizer_class ): raise ValueError( "The fast tokenizer class you are passing has a `slow_tokenizer_class` attribute that is not " "consistent with the slow tokenizer class you passed (fast tokenizer has " f"{fast_tokenizer_class.slow_tokenizer_class} and you passed {slow_tokenizer_class}. Fix one of those " "so they match!" ) # Avoid resetting a set slow/fast tokenizer if we are passing just the other ones. if config_class in TOKENIZER_MAPPING._extra_content: existing_slow, existing_fast = TOKENIZER_MAPPING[config_class] if slow_tokenizer_class is None: slow_tokenizer_class = existing_slow if fast_tokenizer_class is None: fast_tokenizer_class = existing_fast TOKENIZER_MAPPING.register(config_class, (slow_tokenizer_class, fast_tokenizer_class), exist_ok=exist_ok)

transformers/src/transformers/models/auto/tokenization_auto.py/0

{ "file_path": "transformers/src/transformers/models/auto/tokenization_auto.py", "repo_id": "transformers", "token_count": 20707 }

80

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This script can be used to convert a head-less TF2.x Bert model to PyTorch, as published on the official (now deprecated) GitHub: https://github.com/tensorflow/models/tree/v2.3.0/official/nlp/bert TF2.x uses different variable names from the original BERT (TF 1.4) implementation. The script re-maps the TF2.x Bert weight names to the original names, so the model can be imported with Huggingface/transformer. You may adapt this script to include classification/MLM/NSP/etc. heads. Note: This script is only working with an older version of the TensorFlow models repository (<= v2.3.0). Models trained with never versions are not compatible with this script. """ import argparse import os import re import tensorflow as tf import torch from transformers import BertConfig, BertModel from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) def load_tf2_weights_in_bert(model, tf_checkpoint_path, config): tf_path = os.path.abspath(tf_checkpoint_path) logger.info(f"Converting TensorFlow checkpoint from {tf_path}") # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] arrays = [] layer_depth = [] for full_name, shape in init_vars: # logger.info(f"Loading TF weight {name} with shape {shape}") name = full_name.split("/") if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]: logger.info(f"Skipping non-model layer {full_name}") continue if "optimizer" in full_name: logger.info(f"Skipping optimization layer {full_name}") continue if name[0] == "model": # ignore initial 'model' name = name[1:] # figure out how many levels deep the name is depth = 0 for _name in name: if _name.startswith("layer_with_weights"): depth += 1 else: break layer_depth.append(depth) # read data array = tf.train.load_variable(tf_path, full_name) names.append("/".join(name)) arrays.append(array) logger.info(f"Read a total of {len(arrays):,} layers") # Sanity check if len(set(layer_depth)) != 1: raise ValueError(f"Found layer names with different depths (layer depth {list(set(layer_depth))})") layer_depth = list(set(layer_depth))[0] if layer_depth != 1: raise ValueError( "The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP" " heads." ) # convert layers logger.info("Converting weights...") for full_name, array in zip(names, arrays): name = full_name.split("/") pointer = model trace = [] for i, m_name in enumerate(name): if m_name == ".ATTRIBUTES": # variable names end with .ATTRIBUTES/VARIABLE_VALUE break if m_name.startswith("layer_with_weights"): layer_num = int(m_name.split("-")[-1]) if layer_num <= 2: # embedding layers # layer_num 0: word_embeddings # layer_num 1: position_embeddings # layer_num 2: token_type_embeddings continue elif layer_num == 3: # embedding LayerNorm trace.extend(["embeddings", "LayerNorm"]) pointer = getattr(pointer, "embeddings") pointer = getattr(pointer, "LayerNorm") elif layer_num > 3 and layer_num < config.num_hidden_layers + 4: # encoder layers trace.extend(["encoder", "layer", str(layer_num - 4)]) pointer = getattr(pointer, "encoder") pointer = getattr(pointer, "layer") pointer = pointer[layer_num - 4] elif layer_num == config.num_hidden_layers + 4: # pooler layer trace.extend(["pooler", "dense"]) pointer = getattr(pointer, "pooler") pointer = getattr(pointer, "dense") elif m_name == "embeddings": trace.append("embeddings") pointer = getattr(pointer, "embeddings") if layer_num == 0: trace.append("word_embeddings") pointer = getattr(pointer, "word_embeddings") elif layer_num == 1: trace.append("position_embeddings") pointer = getattr(pointer, "position_embeddings") elif layer_num == 2: trace.append("token_type_embeddings") pointer = getattr(pointer, "token_type_embeddings") else: raise ValueError(f"Unknown embedding layer with name {full_name}") trace.append("weight") pointer = getattr(pointer, "weight") elif m_name == "_attention_layer": # self-attention layer trace.extend(["attention", "self"]) pointer = getattr(pointer, "attention") pointer = getattr(pointer, "self") elif m_name == "_attention_layer_norm": # output attention norm trace.extend(["attention", "output", "LayerNorm"]) pointer = getattr(pointer, "attention") pointer = getattr(pointer, "output") pointer = getattr(pointer, "LayerNorm") elif m_name == "_attention_output_dense": # output attention dense trace.extend(["attention", "output", "dense"]) pointer = getattr(pointer, "attention") pointer = getattr(pointer, "output") pointer = getattr(pointer, "dense") elif m_name == "_output_dense": # output dense trace.extend(["output", "dense"]) pointer = getattr(pointer, "output") pointer = getattr(pointer, "dense") elif m_name == "_output_layer_norm": # output dense trace.extend(["output", "LayerNorm"]) pointer = getattr(pointer, "output") pointer = getattr(pointer, "LayerNorm") elif m_name == "_key_dense": # attention key trace.append("key") pointer = getattr(pointer, "key") elif m_name == "_query_dense": # attention query trace.append("query") pointer = getattr(pointer, "query") elif m_name == "_value_dense": # attention value trace.append("value") pointer = getattr(pointer, "value") elif m_name == "_intermediate_dense": # attention intermediate dense trace.extend(["intermediate", "dense"]) pointer = getattr(pointer, "intermediate") pointer = getattr(pointer, "dense") elif m_name == "_output_layer_norm": # output layer norm trace.append("output") pointer = getattr(pointer, "output") # weights & biases elif m_name in ["bias", "beta"]: trace.append("bias") pointer = getattr(pointer, "bias") elif m_name in ["kernel", "gamma"]: trace.append("weight") pointer = getattr(pointer, "weight") else: logger.warning(f"Ignored {m_name}") # for certain layers reshape is necessary trace = ".".join(trace) if re.match(r"(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)", trace) or re.match( r"(\S+)\.attention\.output\.dense\.weight", trace ): array = array.reshape(pointer.data.shape) if "kernel" in full_name: array = array.transpose() if pointer.shape == array.shape: pointer.data = torch.from_numpy(array) else: raise ValueError( f"Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:" f" {array.shape}" ) logger.info(f"Successfully set variable {full_name} to PyTorch layer {trace}") return model def convert_tf2_checkpoint_to_pytorch(tf_checkpoint_path, config_path, pytorch_dump_path): # Instantiate model logger.info(f"Loading model based on config from {config_path}...") config = BertConfig.from_json_file(config_path) model = BertModel(config) # Load weights from checkpoint logger.info(f"Loading weights from checkpoint {tf_checkpoint_path}...") load_tf2_weights_in_bert(model, tf_checkpoint_path, config) # Save pytorch-model logger.info(f"Saving PyTorch model to {pytorch_dump_path}...") torch.save(model.state_dict(), pytorch_dump_path) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow 2.x checkpoint path." ) parser.add_argument( "--bert_config_file", type=str, required=True, help="The config json file corresponding to the BERT model. This specifies the model architecture.", ) parser.add_argument( "--pytorch_dump_path", type=str, required=True, help="Path to the output PyTorch model (must include filename).", ) args = parser.parse_args() convert_tf2_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)

transformers/src/transformers/models/bert/convert_bert_original_tf2_checkpoint_to_pytorch.py/0

{ "file_path": "transformers/src/transformers/models/bert/convert_bert_original_tf2_checkpoint_to_pytorch.py", "repo_id": "transformers", "token_count": 4807 }

81

# coding=utf-8 # Copyright 2022 The HuggingFace Team and Microsoft Research AI4Science All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch BioGPT model.""" import math from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask from ...modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, SequenceClassifierOutputWithPast, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, ) from .configuration_biogpt import BioGptConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "microsoft/biogpt" _CONFIG_FOR_DOC = "BioGptConfig" BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "microsoft/biogpt", "microsoft/BioGPT-Large", # See all BioGPT models at https://huggingface.co/models?filter=biogpt ] # Copied from transformers.models.opt.modeling_opt.OPTLearnedPositionalEmbedding with OPT->BioGpt class BioGptLearnedPositionalEmbedding(nn.Embedding): """ This module learns positional embeddings up to a fixed maximum size. """ def __init__(self, num_embeddings: int, embedding_dim: int): # BioGpt is set up so that if padding_idx is specified then offset the embedding ids by 2 # and adjust num_embeddings appropriately. Other models don't have this hack self.offset = 2 super().__init__(num_embeddings + self.offset, embedding_dim) def forward(self, attention_mask: torch.LongTensor, past_key_values_length: int = 0): """`input_ids_shape` is expected to be [bsz x seqlen].""" attention_mask = attention_mask.long() # create positions depending on attention_mask positions = (torch.cumsum(attention_mask, dim=1).type_as(attention_mask) * attention_mask).long() - 1 # cut positions if `past_key_values_length` is > 0 positions = positions[:, past_key_values_length:] return super().forward(positions + self.offset) # Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->BioGpt class BioGptAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__( self, embed_dim: int, num_heads: int, dropout: float = 0.0, is_decoder: bool = False, bias: bool = True, is_causal: bool = False, config: Optional[BioGptConfig] = None, ): super().__init__() self.embed_dim = embed_dim self.num_heads = num_heads self.dropout = dropout self.head_dim = embed_dim // num_heads self.config = config if (self.head_dim * num_heads) != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" f" and `num_heads`: {num_heads})." ) self.scaling = self.head_dim**-0.5 self.is_decoder = is_decoder self.is_causal = is_causal self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def forward( self, hidden_states: torch.Tensor, key_value_states: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None bsz, tgt_len, _ = hidden_states.size() # get query proj query_states = self.q_proj(hidden_states) * self.scaling # get key, value proj # `past_key_value[0].shape[2] == key_value_states.shape[1]` # is checking that the `sequence_length` of the `past_key_value` is the same as # the provided `key_value_states` to support prefix tuning if ( is_cross_attention and past_key_value is not None and past_key_value[0].shape[2] == key_value_states.shape[1] ): # reuse k,v, cross_attentions key_states = past_key_value[0] value_states = past_key_value[1] elif is_cross_attention: # cross_attentions key_states = self._shape(self.k_proj(key_value_states), -1, bsz) value_states = self._shape(self.v_proj(key_value_states), -1, bsz) elif past_key_value is not None: # reuse k, v, self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) key_states = torch.cat([past_key_value[0], key_states], dim=2) value_states = torch.cat([past_key_value[1], value_states], dim=2) else: # self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_states, value_states) proj_shape = (bsz * self.num_heads, -1, self.head_dim) query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) key_states = key_states.reshape(*proj_shape) value_states = value_states.reshape(*proj_shape) src_len = key_states.size(1) attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): raise ValueError( f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" f" {attn_weights.size()}" ) if attention_mask is not None: if attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) attn_weights = nn.functional.softmax(attn_weights, dim=-1) if layer_head_mask is not None: if layer_head_mask.size() != (self.num_heads,): raise ValueError( f"Head mask for a single layer should be of size {(self.num_heads,)}, but is" f" {layer_head_mask.size()}" ) attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) if output_attentions: # this operation is a bit awkward, but it's required to # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to be reshaped # twice and have to be reused in the following attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) else: attn_weights_reshaped = None attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.bmm(attn_probs, value_states) if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) attn_output = attn_output.transpose(1, 2) # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be # partitioned across GPUs when using tensor-parallelism. attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped, past_key_value class BioGptDecoderLayer(nn.Module): def __init__(self, config: BioGptConfig): super().__init__() self.embed_dim = config.hidden_size self.self_attn = BioGptAttention( embed_dim=self.embed_dim, num_heads=config.num_attention_heads, dropout=config.attention_probs_dropout_prob, is_decoder=True, ) self.dropout = config.hidden_dropout_prob self.activation_fn = ACT2FN[config.hidden_act] self.activation_dropout = config.activation_dropout self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.fc1 = nn.Linear(self.embed_dim, config.intermediate_size) self.fc2 = nn.Linear(config.intermediate_size, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = True, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size `(encoder_attention_heads,)`. past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). """ residual = hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) # Self Attention # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None # add present self-attn cache to positions 1,2 of present_key_value tuple hidden_states, self_attn_weights, present_key_value = self.self_attn( hidden_states=hidden_states, past_key_value=self_attn_past_key_value, attention_mask=attention_mask, layer_head_mask=layer_head_mask, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states # Fully Connected residual = hidden_states hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.fc1(hidden_states) hidden_states = self.activation_fn(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if use_cache: outputs += (present_key_value,) return outputs class BioGptPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = BioGptConfig base_model_prefix = "biogpt" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, nn.Linear): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) BIOGPT_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`~BioGptConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ BIOGPT_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert *input_ids* indices into associated vectors than the model's internal embedding lookup matrix. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare BioGPT Model transformer outputting raw hidden-states without any specific head on top.", BIOGPT_START_DOCSTRING, ) class BioGptModel(BioGptPreTrainedModel): def __init__(self, config: BioGptConfig): super().__init__(config) self.config = config self.layerdrop = config.layerdrop self.dropout = config.hidden_dropout_prob self.embed_dim = config.hidden_size self.padding_idx = config.pad_token_id self.embed_scale = math.sqrt(config.hidden_size) if config.scale_embedding else 1.0 self.embed_tokens = nn.Embedding(config.vocab_size, self.embed_dim, self.padding_idx) self.embed_positions = BioGptLearnedPositionalEmbedding(config.max_position_embeddings, self.embed_dim) self.layers = nn.ModuleList([BioGptDecoderLayer(config) for _ in range(config.num_hidden_layers)]) self.layer_norm = nn.LayerNorm(self.embed_dim) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value @add_start_docstrings_to_model_forward(BIOGPT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPastAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict # retrieve input_ids and inputs_embeds if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input = input_ids input_shape = input.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] input = inputs_embeds[:, :, -1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") # past_key_values_length past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 if inputs_embeds is None: inputs_embeds = self.embed_tokens(input) * self.embed_scale if attention_mask is None: attention_mask = torch.ones( (inputs_embeds.shape[0], inputs_embeds.shape[1] + past_key_values_length), dtype=torch.bool, device=inputs_embeds.device, ) elif attention_mask.shape[1] != past_key_values_length + input_shape[1]: raise ValueError( f"The provided attention mask has length {attention_mask.shape[1]}, but its length should be " f"{past_key_values_length + input_shape[1]} (sum of the lengths of current and past inputs)" ) # embed positions positions = self.embed_positions(attention_mask, past_key_values_length) attention_mask = _prepare_4d_causal_attention_mask( attention_mask, input_shape, inputs_embeds, past_key_values_length ) hidden_states = inputs_embeds + positions hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_cross_attentions = None next_decoder_cache = () if use_cache else None for idx, decoder_layer in enumerate(self.layers): # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) if output_hidden_states: all_hidden_states += (hidden_states,) if self.training: dropout_probability = torch.rand([]) if dropout_probability < self.layerdrop: continue past_key_value = past_key_values[idx] if past_key_values is not None else None if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( decoder_layer.__call__, hidden_states, attention_mask, head_mask[idx] if head_mask is not None else None, None, output_attentions, use_cache, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, layer_head_mask=(head_mask[idx] if head_mask is not None else None), past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) if output_attentions: all_self_attns += (layer_outputs[1],) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) hidden_states = self.layer_norm(hidden_states) next_cache = next_decoder_cache if use_cache else None if not return_dict: return tuple( v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions, ) @add_start_docstrings( """BioGPT Model with a `language modeling` head on top for CLM fine-tuning.""", BIOGPT_START_DOCSTRING ) class BioGptForCausalLM(BioGptPreTrainedModel): _tied_weights_keys = ["output_projection.weight"] def __init__(self, config): super().__init__(config) self.biogpt = BioGptModel(config) self.output_projection = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.output_projection def set_output_embeddings(self, new_embeddings): self.output_projection = new_embeddings @add_start_docstrings_to_model_forward(BIOGPT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.biogpt( input_ids, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.output_projection(sequence_output) lm_loss = None if labels is not None: # we are doing next-token prediction; shift prediction scores and input ids by one shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() labels = labels[:, 1:].contiguous() loss_fct = CrossEntropyLoss() lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[1:] return ((lm_loss,) + output) if lm_loss is not None else output return CausalLMOutputWithCrossAttentions( loss=lm_loss, logits=prediction_scores, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) def prepare_inputs_for_generation( self, input_ids, attention_mask, inputs_embeds=None, past_key_values=None, **kwargs ): # only last tokens for inputs_ids if past is defined in kwargs if past_key_values is not None: past_length = past_key_values[0][0].shape[2] # Some generation methods already pass only the last input ID if input_ids.shape[1] > past_length: remove_prefix_length = past_length else: # Default to old behavior: keep only final ID remove_prefix_length = input_ids.shape[1] - 1 input_ids = input_ids[:, remove_prefix_length:] if inputs_embeds is not None and past_key_values is None: model_inputs = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids} model_inputs.update( { "attention_mask": attention_mask, "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), } ) return model_inputs @staticmethod def _reorder_cache(past_key_values, beam_idx): reordered_past = () for layer_past in past_key_values: reordered_past += ( tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), ) return reordered_past @add_start_docstrings( """ BioGPT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, BIOGPT_START_DOCSTRING, ) class BioGptForTokenClassification(BioGptPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.biogpt = BioGptModel(config) if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None: classifier_dropout = config.classifier_dropout else: classifier_dropout = config.hidden_dropout_prob self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) self.post_init() @add_start_docstrings_to_model_forward(BIOGPT_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict transformer_outputs = self.biogpt( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = transformer_outputs[0] hidden_states = self.dropout(hidden_states) logits = self.classifier(hidden_states) loss = None if labels is not None: loss_fct = CrossEntropyLoss() # Only keep active parts of the loss if attention_mask is not None: active_loss = attention_mask.view(-1) == 1 active_logits = logits.view(-1, self.num_labels) active_labels = torch.where( active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels) ) loss = loss_fct(active_logits, active_labels) else: loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + transformer_outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) @add_start_docstrings( """ The BioGpt Model transformer with a sequence classification head on top (linear layer). [`BioGptForSequenceClassification`] uses the last token in order to do the classification, as other causal models (e.g. GPT-2) do. Since it does classification on the last token, it is required to know the position of the last token. If a `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in each row of the batch). """, BIOGPT_START_DOCSTRING, ) class BioGptForSequenceClassification(BioGptPreTrainedModel): def __init__(self, config: BioGptConfig): super().__init__(config) self.num_labels = config.num_labels self.biogpt = BioGptModel(config) self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(BIOGPT_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=SequenceClassifierOutputWithPast, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, SequenceClassifierOutputWithPast]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict transformer_outputs = self.biogpt( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = transformer_outputs[0] logits = self.score(hidden_states) if input_ids is not None: batch_size, sequence_length = input_ids.shape[:2] else: batch_size, sequence_length = inputs_embeds.shape[:2] if self.config.pad_token_id is None: sequence_length = -1 else: if input_ids is not None: sequence_length = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device) else: sequence_length = -1 logger.warning( f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " "unexpected if using padding tokens in conjunction with `inputs_embeds.`" ) pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_length] loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) else: loss = loss_fct(pooled_logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(pooled_logits, labels) if not return_dict: output = (pooled_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutputWithPast( loss=loss, logits=pooled_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) def get_input_embeddings(self): return self.biogpt.embed_tokens def set_input_embeddings(self, value): self.biogpt.embed_tokens = value

transformers/src/transformers/models/biogpt/modeling_biogpt.py/0

{ "file_path": "transformers/src/transformers/models/biogpt/modeling_biogpt.py", "repo_id": "transformers", "token_count": 17918 }

82

# coding=utf-8 # Copyright 2021 The Facebook, Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ BlenderbotSmall model configuration""" from collections import OrderedDict from typing import Any, Mapping, Optional from ... import PreTrainedTokenizer from ...configuration_utils import PretrainedConfig from ...file_utils import TensorType, is_torch_available from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeq2SeqConfigWithPast from ...onnx.utils import compute_effective_axis_dimension from ...utils import logging logger = logging.get_logger(__name__) BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP = { "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json", # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small } class BlenderbotSmallConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`BlenderbotSmallModel`]. It is used to instantiate an BlenderbotSmall model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the BlenderbotSmall [facebook/blenderbot_small-90M](https://huggingface.co/facebook/blenderbot_small-90M) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 50265): Vocabulary size of the BlenderbotSmall model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`BlenderbotSmallModel`] or [`TFBlenderbotSmallModel`]. d_model (`int`, *optional*, defaults to 512): Dimensionality of the layers and the pooler layer. encoder_layers (`int`, *optional*, defaults to 8): Number of encoder layers. decoder_layers (`int`, *optional*, defaults to 8): Number of decoder layers. encoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. decoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. decoder_ffn_dim (`int`, *optional*, defaults to 2048): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. encoder_ffn_dim (`int`, *optional*, defaults to 2048): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. activation_function (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. max_position_embeddings (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. encoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. decoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. scale_embedding (`bool`, *optional*, defaults to `False`): Scale embeddings by diving by sqrt(d_model). use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models) forced_eos_token_id (`int`, *optional*, defaults to 2): The id of the token to force as the last generated token when `max_length` is reached. Usually set to `eos_token_id`. Example: ```python >>> from transformers import BlenderbotSmallConfig, BlenderbotSmallModel >>> # Initializing a BlenderbotSmall facebook/blenderbot_small-90M style configuration >>> configuration = BlenderbotSmallConfig() >>> # Initializing a model (with random weights) from the facebook/blenderbot_small-90M style configuration >>> model = BlenderbotSmallModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "blenderbot-small" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} def __init__( self, vocab_size=50265, max_position_embeddings=512, encoder_layers=8, encoder_ffn_dim=2048, encoder_attention_heads=16, decoder_layers=8, decoder_ffn_dim=2048, decoder_attention_heads=16, encoder_layerdrop=0.0, decoder_layerdrop=0.0, use_cache=True, is_encoder_decoder=True, activation_function="gelu", d_model=512, dropout=0.1, attention_dropout=0.0, activation_dropout=0.0, init_std=0.02, decoder_start_token_id=1, scale_embedding=False, pad_token_id=0, bos_token_id=1, eos_token_id=2, forced_eos_token_id=2, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.d_model = d_model self.encoder_ffn_dim = encoder_ffn_dim self.encoder_layers = encoder_layers self.encoder_attention_heads = encoder_attention_heads self.decoder_ffn_dim = decoder_ffn_dim self.decoder_layers = decoder_layers self.decoder_attention_heads = decoder_attention_heads self.dropout = dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.activation_function = activation_function self.init_std = init_std self.encoder_layerdrop = encoder_layerdrop self.decoder_layerdrop = decoder_layerdrop self.use_cache = use_cache self.num_hidden_layers = encoder_layers self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, is_encoder_decoder=is_encoder_decoder, decoder_start_token_id=decoder_start_token_id, forced_eos_token_id=forced_eos_token_id, **kwargs, ) # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig class BlenderbotSmallOnnxConfig(OnnxSeq2SeqConfigWithPast): @property def inputs(self) -> Mapping[str, Mapping[int, str]]: if self.task in ["default", "seq2seq-lm"]: common_inputs = OrderedDict( [ ("input_ids", {0: "batch", 1: "encoder_sequence"}), ("attention_mask", {0: "batch", 1: "encoder_sequence"}), ] ) if self.use_past: common_inputs["decoder_input_ids"] = {0: "batch"} common_inputs["decoder_attention_mask"] = {0: "batch", 1: "past_decoder_sequence + sequence"} else: common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"} common_inputs["decoder_attention_mask"] = {0: "batch", 1: "decoder_sequence"} if self.use_past: self.fill_with_past_key_values_(common_inputs, direction="inputs") elif self.task == "causal-lm": # TODO: figure this case out. common_inputs = OrderedDict( [ ("input_ids", {0: "batch", 1: "encoder_sequence"}), ("attention_mask", {0: "batch", 1: "encoder_sequence"}), ] ) if self.use_past: num_encoder_layers, _ = self.num_layers for i in range(num_encoder_layers): common_inputs[f"past_key_values.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"} common_inputs[f"past_key_values.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"} else: common_inputs = OrderedDict( [ ("input_ids", {0: "batch", 1: "encoder_sequence"}), ("attention_mask", {0: "batch", 1: "encoder_sequence"}), ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}), ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}), ] ) return common_inputs @property def outputs(self) -> Mapping[str, Mapping[int, str]]: if self.task in ["default", "seq2seq-lm"]: common_outputs = super().outputs else: common_outputs = super(OnnxConfigWithPast, self).outputs if self.use_past: num_encoder_layers, _ = self.num_layers for i in range(num_encoder_layers): common_outputs[f"present.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"} common_outputs[f"present.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"} return common_outputs def _generate_dummy_inputs_for_default_and_seq2seq_lm( self, tokenizer: PreTrainedTokenizer, batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional[TensorType] = None, ) -> Mapping[str, Any]: encoder_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering( tokenizer, batch_size, seq_length, is_pair, framework ) # Generate decoder inputs decoder_seq_length = seq_length if not self.use_past else 1 decoder_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering( tokenizer, batch_size, decoder_seq_length, is_pair, framework ) decoder_inputs = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()} common_inputs = dict(**encoder_inputs, **decoder_inputs) if self.use_past: if not is_torch_available(): raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.") else: import torch batch, encoder_seq_length = common_inputs["input_ids"].shape decoder_seq_length = common_inputs["decoder_input_ids"].shape[1] num_encoder_attention_heads, num_decoder_attention_heads = self.num_attention_heads encoder_shape = ( batch, num_encoder_attention_heads, encoder_seq_length, self._config.hidden_size // num_encoder_attention_heads, ) decoder_past_length = decoder_seq_length + 3 decoder_shape = ( batch, num_decoder_attention_heads, decoder_past_length, self._config.hidden_size // num_decoder_attention_heads, ) common_inputs["decoder_attention_mask"] = torch.cat( [common_inputs["decoder_attention_mask"], torch.ones(batch, decoder_past_length)], dim=1 ) common_inputs["past_key_values"] = [] # If the number of encoder and decoder layers are present in the model configuration, both are considered num_encoder_layers, num_decoder_layers = self.num_layers min_num_layers = min(num_encoder_layers, num_decoder_layers) max_num_layers = max(num_encoder_layers, num_decoder_layers) - min_num_layers remaining_side_name = "encoder" if num_encoder_layers > num_decoder_layers else "decoder" for _ in range(min_num_layers): common_inputs["past_key_values"].append( ( torch.zeros(decoder_shape), torch.zeros(decoder_shape), torch.zeros(encoder_shape), torch.zeros(encoder_shape), ) ) # TODO: test this. shape = encoder_shape if remaining_side_name == "encoder" else decoder_shape for _ in range(min_num_layers, max_num_layers): common_inputs["past_key_values"].append((torch.zeros(shape), torch.zeros(shape))) return common_inputs def _generate_dummy_inputs_for_causal_lm( self, tokenizer: PreTrainedTokenizer, batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional[TensorType] = None, ) -> Mapping[str, Any]: common_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering( tokenizer, batch_size, seq_length, is_pair, framework ) if self.use_past: if not is_torch_available(): raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.") else: import torch batch, seqlen = common_inputs["input_ids"].shape # Not using the same length for past_key_values past_key_values_length = seqlen + 2 num_encoder_layers, _ = self.num_layers num_encoder_attention_heads, _ = self.num_attention_heads past_shape = ( batch, num_encoder_attention_heads, past_key_values_length, self._config.hidden_size // num_encoder_attention_heads, ) mask_dtype = common_inputs["attention_mask"].dtype common_inputs["attention_mask"] = torch.cat( [common_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1 ) common_inputs["past_key_values"] = [ (torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(num_encoder_layers) ] return common_inputs def _generate_dummy_inputs_for_sequence_classification_and_question_answering( self, tokenizer: PreTrainedTokenizer, batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional[TensorType] = None, ) -> Mapping[str, Any]: # Copied from OnnxConfig.generate_dummy_inputs # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity. # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX batch_size = compute_effective_axis_dimension( batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0 ) # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX token_to_add = tokenizer.num_special_tokens_to_add(is_pair) seq_length = compute_effective_axis_dimension( seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add ) # Generate dummy inputs according to compute batch and sequence dummy_input = [" ".join([tokenizer.unk_token]) * seq_length] * batch_size common_inputs = dict(tokenizer(dummy_input, return_tensors=framework)) return common_inputs def generate_dummy_inputs( self, tokenizer: PreTrainedTokenizer, batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional[TensorType] = None, ) -> Mapping[str, Any]: if self.task in ["default", "seq2seq-lm"]: common_inputs = self._generate_dummy_inputs_for_default_and_seq2seq_lm( tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework ) elif self.task == "causal-lm": common_inputs = self._generate_dummy_inputs_for_causal_lm( tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework ) else: common_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering( tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework ) return common_inputs def _flatten_past_key_values_(self, flattened_output, name, idx, t): if self.task in ["default", "seq2seq-lm"]: flattened_output = super()._flatten_past_key_values_(flattened_output, name, idx, t) else: flattened_output = super(OnnxSeq2SeqConfigWithPast, self)._flatten_past_key_values_( flattened_output, name, idx, t )

transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py/0

{ "file_path": "transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py", "repo_id": "transformers", "token_count": 8145 }

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# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ BLIP-2 model configuration""" import os from typing import Union from ...configuration_utils import PretrainedConfig from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES from ...utils import logging from ..auto import CONFIG_MAPPING logger = logging.get_logger(__name__) BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP = { "salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json", } class Blip2VisionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Blip2VisionModel`]. It is used to instantiate a BLIP-2 vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration defaults will yield a similar configuration to that of the BLIP-2 [Salesforce/blip2-opt-2.7b](https://huggingface.co/Salesforce/blip2-opt-2.7b) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 1408): Dimensionality of the encoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 6144): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. num_hidden_layers (`int`, *optional*, defaults to 39): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. image_size (`int`, *optional*, defaults to 224): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 14): The size (resolution) of each patch. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported. layer_norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon used by the layer normalization layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. qkv_bias (`bool`, *optional*, defaults to `True`): Whether to add a bias to the queries and values in the self-attention layers. Example: ```python >>> from transformers import Blip2VisionConfig, Blip2VisionModel >>> # Initializing a Blip2VisionConfig with Salesforce/blip2-opt-2.7b style configuration >>> configuration = Blip2VisionConfig() >>> # Initializing a Blip2VisionModel (with random weights) from the Salesforce/blip2-opt-2.7b style configuration >>> model = Blip2VisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "blip_2_vision_model" def __init__( self, hidden_size=1408, intermediate_size=6144, num_hidden_layers=39, num_attention_heads=16, image_size=224, patch_size=14, hidden_act="gelu", layer_norm_eps=1e-6, attention_dropout=0.0, initializer_range=1e-10, qkv_bias=True, **kwargs, ): super().__init__(**kwargs) self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.patch_size = patch_size self.image_size = image_size self.initializer_range = initializer_range self.attention_dropout = attention_dropout self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act self.qkv_bias = qkv_bias @classmethod def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": cls._set_token_in_kwargs(kwargs) config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) # get the vision config dict if we are loading from Blip2Config if config_dict.get("model_type") == "blip-2": config_dict = config_dict["vision_config"] if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: logger.warning( f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." ) return cls.from_dict(config_dict, **kwargs) class Blip2QFormerConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Blip2QFormerModel`]. It is used to instantiate a BLIP-2 Querying Transformer (Q-Former) model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the BLIP-2 [Salesforce/blip2-opt-2.7b](https://huggingface.co/Salesforce/blip2-opt-2.7b) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Note that [`Blip2QFormerModel`] is very similar to [`BertLMHeadModel`] with interleaved cross-attention. Args: vocab_size (`int`, *optional*, defaults to 30522): Vocabulary size of the Q-Former model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling the model. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. position_embedding_type (`str`, *optional*, defaults to `"absolute"`): Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155). For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658). cross_attention_frequency (`int`, *optional*, defaults to 2): The frequency of adding cross-attention to the Transformer layers. encoder_hidden_size (`int`, *optional*, defaults to 1408): The hidden size of the hidden states for cross-attention. Examples: ```python >>> from transformers import Blip2QFormerConfig, Blip2QFormerModel >>> # Initializing a BLIP-2 Salesforce/blip2-opt-2.7b style configuration >>> configuration = Blip2QFormerConfig() >>> # Initializing a model (with random weights) from the Salesforce/blip2-opt-2.7b style configuration >>> model = Blip2QFormerModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "blip_2_qformer" def __init__( self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, position_embedding_type="absolute", cross_attention_frequency=2, encoder_hidden_size=1408, **kwargs, ): super().__init__(pad_token_id=pad_token_id, **kwargs) self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.position_embedding_type = position_embedding_type self.cross_attention_frequency = cross_attention_frequency self.encoder_hidden_size = encoder_hidden_size @classmethod def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": cls._set_token_in_kwargs(kwargs) config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) # get the qformer config dict if we are loading from Blip2Config if config_dict.get("model_type") == "blip-2": config_dict = config_dict["qformer_config"] if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: logger.warning( f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." ) return cls.from_dict(config_dict, **kwargs) class Blip2Config(PretrainedConfig): r""" [`Blip2Config`] is the configuration class to store the configuration of a [`Blip2ForConditionalGeneration`]. It is used to instantiate a BLIP-2 model according to the specified arguments, defining the vision model, Q-Former model and language model configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the BLIP-2 [Salesforce/blip2-opt-2.7b](https://huggingface.co/Salesforce/blip2-opt-2.7b) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vision_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`Blip2VisionConfig`]. qformer_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`Blip2QFormerConfig`]. text_config (`dict`, *optional*): Dictionary of configuration options used to initialize any [`PretrainedConfig`]. num_query_tokens (`int`, *optional*, defaults to 32): The number of query tokens passed through the Transformer. kwargs (*optional*): Dictionary of keyword arguments. Example: ```python >>> from transformers import ( ... Blip2VisionConfig, ... Blip2QFormerConfig, ... OPTConfig, ... Blip2Config, ... Blip2ForConditionalGeneration, ... ) >>> # Initializing a Blip2Config with Salesforce/blip2-opt-2.7b style configuration >>> configuration = Blip2Config() >>> # Initializing a Blip2ForConditionalGeneration (with random weights) from the Salesforce/blip2-opt-2.7b style configuration >>> model = Blip2ForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config >>> # We can also initialize a Blip2Config from a Blip2VisionConfig, Blip2QFormerConfig and any PretrainedConfig >>> # Initializing BLIP-2 vision, BLIP-2 Q-Former and language model configurations >>> vision_config = Blip2VisionConfig() >>> qformer_config = Blip2QFormerConfig() >>> text_config = OPTConfig() >>> config = Blip2Config.from_text_vision_configs(vision_config, qformer_config, text_config) ```""" model_type = "blip-2" def __init__(self, vision_config=None, qformer_config=None, text_config=None, num_query_tokens=32, **kwargs): super().__init__(**kwargs) if vision_config is None: vision_config = {} logger.info("vision_config is None. initializing the Blip2VisionConfig with default values.") if qformer_config is None: qformer_config = {} logger.info("qformer_config is None. Initializing the Blip2QFormerConfig with default values.") if text_config is None: text_config = {} logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`).") self.vision_config = Blip2VisionConfig(**vision_config) self.qformer_config = Blip2QFormerConfig(**qformer_config) text_model_type = text_config["model_type"] if "model_type" in text_config else "opt" self.text_config = CONFIG_MAPPING[text_model_type](**text_config) self.tie_word_embeddings = self.text_config.tie_word_embeddings self.is_encoder_decoder = self.text_config.is_encoder_decoder self.num_query_tokens = num_query_tokens self.qformer_config.encoder_hidden_size = self.vision_config.hidden_size self.use_decoder_only_language_model = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES self.initializer_factor = 1.0 self.initializer_range = 0.02 @classmethod def from_vision_qformer_text_configs( cls, vision_config: Blip2VisionConfig, qformer_config: Blip2QFormerConfig, text_config: PretrainedConfig, **kwargs, ): r""" Instantiate a [`Blip2Config`] (or a derived class) from a BLIP-2 vision model, Q-Former and language model configurations. Returns: [`Blip2Config`]: An instance of a configuration object """ return cls( vision_config=vision_config.to_dict(), qformer_config=qformer_config.to_dict(), text_config=text_config.to_dict(), **kwargs, )

transformers/src/transformers/models/blip_2/configuration_blip_2.py/0

{ "file_path": "transformers/src/transformers/models/blip_2/configuration_blip_2.py", "repo_id": "transformers", "token_count": 6313 }

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# coding=utf-8 # Copyright 2023-present NAVER Corp, The Microsoft Research Asia LayoutLM Team Authors and the HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Bros model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) BROS_PRETRAINED_CONFIG_ARCHIVE_MAP = { "jinho8345/bros-base-uncased": "https://huggingface.co/jinho8345/bros-base-uncased/blob/main/config.json", "jinho8345/bros-large-uncased": "https://huggingface.co/jinho8345/bros-large-uncased/blob/main/config.json", } class BrosConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`BrosModel`] or a [`TFBrosModel`]. It is used to instantiate a Bros model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Bros [jinho8345/bros-base-uncased](https://huggingface.co/jinho8345/bros-base-uncased) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 30522): Vocabulary size of the Bros model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`BrosModel`] or [`TFBrosModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). type_vocab_size (`int`, *optional*, defaults to 2): The vocabulary size of the `token_type_ids` passed when calling [`BrosModel`] or [`TFBrosModel`]. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. pad_token_id (`int`, *optional*, defaults to 0): The index of the padding token in the token vocabulary. dim_bbox (`int`, *optional*, defaults to 8): The dimension of the bounding box coordinates. (x0, y1, x1, y0, x1, y1, x0, y1) bbox_scale (`float`, *optional*, defaults to 100.0): The scale factor of the bounding box coordinates. n_relations (`int`, *optional*, defaults to 1): The number of relations for SpadeEE(entity extraction), SpadeEL(entity linking) head. classifier_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the classifier head. Examples: ```python >>> from transformers import BrosConfig, BrosModel >>> # Initializing a BROS jinho8345/bros-base-uncased style configuration >>> configuration = BrosConfig() >>> # Initializing a model from the jinho8345/bros-base-uncased style configuration >>> model = BrosModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "bros" def __init__( self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, dim_bbox=8, bbox_scale=100.0, n_relations=1, classifier_dropout_prob=0.1, **kwargs, ): super().__init__( vocab_size=vocab_size, hidden_size=hidden_size, num_hidden_layers=num_hidden_layers, num_attention_heads=num_attention_heads, intermediate_size=intermediate_size, hidden_act=hidden_act, hidden_dropout_prob=hidden_dropout_prob, attention_probs_dropout_prob=attention_probs_dropout_prob, max_position_embeddings=max_position_embeddings, type_vocab_size=type_vocab_size, initializer_range=initializer_range, layer_norm_eps=layer_norm_eps, pad_token_id=pad_token_id, **kwargs, ) self.dim_bbox = dim_bbox self.bbox_scale = bbox_scale self.n_relations = n_relations self.dim_bbox_sinusoid_emb_2d = self.hidden_size // 4 self.dim_bbox_sinusoid_emb_1d = self.dim_bbox_sinusoid_emb_2d // self.dim_bbox self.dim_bbox_projection = self.hidden_size // self.num_attention_heads self.classifier_dropout_prob = classifier_dropout_prob

transformers/src/transformers/models/bros/configuration_bros.py/0

{ "file_path": "transformers/src/transformers/models/bros/configuration_bros.py", "repo_id": "transformers", "token_count": 2599 }

85

# coding=utf-8 # Copyright 2021 Google AI The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch CANINE model.""" import copy import math import os from dataclasses import dataclass from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutput, ModelOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_canine import CanineConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "google/canine-s" _CONFIG_FOR_DOC = "CanineConfig" CANINE_PRETRAINED_MODEL_ARCHIVE_LIST = [ "google/canine-s", "google/canine-r", # See all CANINE models at https://huggingface.co/models?filter=canine ] # Support up to 16 hash functions. _PRIMES = [31, 43, 59, 61, 73, 97, 103, 113, 137, 149, 157, 173, 181, 193, 211, 223] @dataclass class CanineModelOutputWithPooling(ModelOutput): """ Output type of [`CanineModel`]. Based on [`~modeling_outputs.BaseModelOutputWithPooling`], but with slightly different `hidden_states` and `attentions`, as these also include the hidden states and attentions of the shallow Transformer encoders. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model (i.e. the output of the final shallow Transformer encoder). pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`): Hidden-state of the first token of the sequence (classification token) at the last layer of the deep Transformer encoder, further processed by a Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence prediction (classification) objective during pretraining. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the input to each encoder + one for the output of each layer of each encoder) of shape `(batch_size, sequence_length, hidden_size)` and `(batch_size, sequence_length // config.downsampling_rate, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial input to each Transformer encoder. The hidden states of the shallow encoders have length `sequence_length`, but the hidden states of the deep encoder have length `sequence_length` // `config.downsampling_rate`. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of the 3 Transformer encoders of shape `(batch_size, num_heads, sequence_length, sequence_length)` and `(batch_size, num_heads, sequence_length // config.downsampling_rate, sequence_length // config.downsampling_rate)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: torch.FloatTensor = None pooler_output: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None def load_tf_weights_in_canine(model, config, tf_checkpoint_path): """Load tf checkpoints in a pytorch model.""" try: import re import numpy as np import tensorflow as tf except ImportError: logger.error( "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " "https://www.tensorflow.org/install/ for installation instructions." ) raise tf_path = os.path.abspath(tf_checkpoint_path) logger.info(f"Converting TensorFlow checkpoint from {tf_path}") # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] arrays = [] for name, shape in init_vars: logger.info(f"Loading TF weight {name} with shape {shape}") array = tf.train.load_variable(tf_path, name) names.append(name) arrays.append(array) for name, array in zip(names, arrays): name = name.split("/") # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v # which are not required for using pretrained model # also discard the cls weights (which were used for the next sentence prediction pre-training task) if any( n in [ "adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step", "cls", "autoregressive_decoder", "char_output_weights", ] for n in name ): logger.info(f"Skipping {'/'.join(name)}") continue # if first scope name starts with "bert", change it to "encoder" if name[0] == "bert": name[0] = "encoder" # remove "embeddings" middle name of HashBucketCodepointEmbedders elif name[1] == "embeddings": name.remove(name[1]) # rename segment_embeddings to token_type_embeddings elif name[1] == "segment_embeddings": name[1] = "token_type_embeddings" # rename initial convolutional projection layer elif name[1] == "initial_char_encoder": name = ["chars_to_molecules"] + name[-2:] # rename final convolutional projection layer elif name[0] == "final_char_encoder" and name[1] in ["LayerNorm", "conv"]: name = ["projection"] + name[1:] pointer = model for m_name in name: if (re.fullmatch(r"[A-Za-z]+_\d+", m_name)) and "Embedder" not in m_name: scope_names = re.split(r"_(\d+)", m_name) else: scope_names = [m_name] if scope_names[0] == "kernel" or scope_names[0] == "gamma": pointer = getattr(pointer, "weight") elif scope_names[0] == "output_bias" or scope_names[0] == "beta": pointer = getattr(pointer, "bias") elif scope_names[0] == "output_weights": pointer = getattr(pointer, "weight") else: try: pointer = getattr(pointer, scope_names[0]) except AttributeError: logger.info(f"Skipping {'/'.join(name)}") continue if len(scope_names) >= 2: num = int(scope_names[1]) pointer = pointer[num] if m_name[-11:] == "_embeddings": pointer = getattr(pointer, "weight") elif m_name[-10:] in [f"Embedder_{i}" for i in range(8)]: pointer = getattr(pointer, "weight") elif m_name == "kernel": array = np.transpose(array) if pointer.shape != array.shape: raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched") logger.info(f"Initialize PyTorch weight {name}") pointer.data = torch.from_numpy(array) return model class CanineEmbeddings(nn.Module): """Construct the character, position and token_type embeddings.""" def __init__(self, config): super().__init__() self.config = config # character embeddings shard_embedding_size = config.hidden_size // config.num_hash_functions for i in range(config.num_hash_functions): name = f"HashBucketCodepointEmbedder_{i}" setattr(self, name, nn.Embedding(config.num_hash_buckets, shard_embedding_size)) self.char_position_embeddings = nn.Embedding(config.num_hash_buckets, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.register_buffer( "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False ) self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") def _hash_bucket_tensors(self, input_ids, num_hashes: int, num_buckets: int): """ Converts ids to hash bucket ids via multiple hashing. Args: input_ids: The codepoints or other IDs to be hashed. num_hashes: The number of hash functions to use. num_buckets: The number of hash buckets (i.e. embeddings in each table). Returns: A list of tensors, each of which is the hash bucket IDs from one hash function. """ if num_hashes > len(_PRIMES): raise ValueError(f"`num_hashes` must be <= {len(_PRIMES)}") primes = _PRIMES[:num_hashes] result_tensors = [] for prime in primes: hashed = ((input_ids + 1) * prime) % num_buckets result_tensors.append(hashed) return result_tensors def _embed_hash_buckets(self, input_ids, embedding_size: int, num_hashes: int, num_buckets: int): """Converts IDs (e.g. codepoints) into embeddings via multiple hashing.""" if embedding_size % num_hashes != 0: raise ValueError(f"Expected `embedding_size` ({embedding_size}) % `num_hashes` ({num_hashes}) == 0") hash_bucket_tensors = self._hash_bucket_tensors(input_ids, num_hashes=num_hashes, num_buckets=num_buckets) embedding_shards = [] for i, hash_bucket_ids in enumerate(hash_bucket_tensors): name = f"HashBucketCodepointEmbedder_{i}" shard_embeddings = getattr(self, name)(hash_bucket_ids) embedding_shards.append(shard_embeddings) return torch.cat(embedding_shards, dim=-1) def forward( self, input_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, ) -> torch.FloatTensor: if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] if position_ids is None: position_ids = self.position_ids[:, :seq_length] if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds = self._embed_hash_buckets( input_ids, self.config.hidden_size, self.config.num_hash_functions, self.config.num_hash_buckets ) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings if self.position_embedding_type == "absolute": position_embeddings = self.char_position_embeddings(position_ids) embeddings += position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class CharactersToMolecules(nn.Module): """Convert character sequence to initial molecule sequence (i.e. downsample) using strided convolutions.""" def __init__(self, config): super().__init__() self.conv = nn.Conv1d( in_channels=config.hidden_size, out_channels=config.hidden_size, kernel_size=config.downsampling_rate, stride=config.downsampling_rate, ) self.activation = ACT2FN[config.hidden_act] # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, char_encoding: torch.Tensor) -> torch.Tensor: # `cls_encoding`: [batch, 1, hidden_size] cls_encoding = char_encoding[:, 0:1, :] # char_encoding has shape [batch, char_seq, hidden_size] # We transpose it to be [batch, hidden_size, char_seq] char_encoding = torch.transpose(char_encoding, 1, 2) downsampled = self.conv(char_encoding) downsampled = torch.transpose(downsampled, 1, 2) downsampled = self.activation(downsampled) # Truncate the last molecule in order to reserve a position for [CLS]. # Often, the last position is never used (unless we completely fill the # text buffer). This is important in order to maintain alignment on TPUs # (i.e. a multiple of 128). downsampled_truncated = downsampled[:, 0:-1, :] # We also keep [CLS] as a separate sequence position since we always # want to reserve a position (and the model capacity that goes along # with that) in the deep BERT stack. # `result`: [batch, molecule_seq, molecule_dim] result = torch.cat([cls_encoding, downsampled_truncated], dim=1) result = self.LayerNorm(result) return result class ConvProjection(nn.Module): """ Project representations from hidden_size*2 back to hidden_size across a window of w = config.upsampling_kernel_size characters. """ def __init__(self, config): super().__init__() self.config = config self.conv = nn.Conv1d( in_channels=config.hidden_size * 2, out_channels=config.hidden_size, kernel_size=config.upsampling_kernel_size, stride=1, ) self.activation = ACT2FN[config.hidden_act] # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward( self, inputs: torch.Tensor, final_seq_char_positions: Optional[torch.Tensor] = None, ) -> torch.Tensor: # inputs has shape [batch, mol_seq, molecule_hidden_size+char_hidden_final] # we transpose it to be [batch, molecule_hidden_size+char_hidden_final, mol_seq] inputs = torch.transpose(inputs, 1, 2) # PyTorch < 1.9 does not support padding="same" (which is used in the original implementation), # so we pad the tensor manually before passing it to the conv layer # based on https://github.com/google-research/big_transfer/blob/49afe42338b62af9fbe18f0258197a33ee578a6b/bit_tf2/models.py#L36-L38 pad_total = self.config.upsampling_kernel_size - 1 pad_beg = pad_total // 2 pad_end = pad_total - pad_beg pad = nn.ConstantPad1d((pad_beg, pad_end), 0) # `result`: shape (batch_size, char_seq_len, hidden_size) result = self.conv(pad(inputs)) result = torch.transpose(result, 1, 2) result = self.activation(result) result = self.LayerNorm(result) result = self.dropout(result) final_char_seq = result if final_seq_char_positions is not None: # Limit transformer query seq and attention mask to these character # positions to greatly reduce the compute cost. Typically, this is just # done for the MLM training task. # TODO add support for MLM raise NotImplementedError("CanineForMaskedLM is currently not supported") else: query_seq = final_char_seq return query_seq class CanineSelfAttention(nn.Module): def __init__(self, config): super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size) self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": self.max_position_embeddings = config.max_position_embeddings self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, from_tensor: torch.Tensor, to_tensor: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: mixed_query_layer = self.query(from_tensor) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. key_layer = self.transpose_for_scores(self.key(to_tensor)) value_layer = self.transpose_for_scores(self.value(to_tensor)) query_layer = self.transpose_for_scores(mixed_query_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": seq_length = from_tensor.size()[1] position_ids_l = torch.arange(seq_length, dtype=torch.long, device=from_tensor.device).view(-1, 1) position_ids_r = torch.arange(seq_length, dtype=torch.long, device=from_tensor.device).view(1, -1) distance = position_ids_l - position_ids_r positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility if self.position_embedding_type == "relative_key": relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores elif self.position_embedding_type == "relative_key_query": relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: if attention_mask.ndim == 3: # if attention_mask is 3D, do the following: attention_mask = torch.unsqueeze(attention_mask, dim=1) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and the dtype's smallest value for masked positions. attention_mask = (1.0 - attention_mask.float()) * torch.finfo(attention_scores.dtype).min # Apply the attention mask (precomputed for all layers in CanineModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs class CanineSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward( self, hidden_states: Tuple[torch.FloatTensor], input_tensor: torch.FloatTensor ) -> Tuple[torch.FloatTensor, torch.FloatTensor]: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class CanineAttention(nn.Module): """ Additional arguments related to local attention: - **local** (`bool`, *optional*, defaults to `False`) -- Whether to apply local attention. - **always_attend_to_first_position** (`bool`, *optional*, defaults to `False`) -- Should all blocks be able to attend to the `to_tensor`'s first position (e.g. a [CLS] position)? - **first_position_attends_to_all** (`bool`, *optional*, defaults to `False`) -- Should the *from_tensor*'s first position be able to attend to all positions within the *from_tensor*? - **attend_from_chunk_width** (`int`, *optional*, defaults to 128) -- The width of each block-wise chunk in `from_tensor`. - **attend_from_chunk_stride** (`int`, *optional*, defaults to 128) -- The number of elements to skip when moving to the next block in `from_tensor`. - **attend_to_chunk_width** (`int`, *optional*, defaults to 128) -- The width of each block-wise chunk in *to_tensor*. - **attend_to_chunk_stride** (`int`, *optional*, defaults to 128) -- The number of elements to skip when moving to the next block in `to_tensor`. """ def __init__( self, config, local=False, always_attend_to_first_position: bool = False, first_position_attends_to_all: bool = False, attend_from_chunk_width: int = 128, attend_from_chunk_stride: int = 128, attend_to_chunk_width: int = 128, attend_to_chunk_stride: int = 128, ): super().__init__() self.self = CanineSelfAttention(config) self.output = CanineSelfOutput(config) self.pruned_heads = set() # additional arguments related to local attention self.local = local if attend_from_chunk_width < attend_from_chunk_stride: raise ValueError( "`attend_from_chunk_width` < `attend_from_chunk_stride` would cause sequence positions to get skipped." ) if attend_to_chunk_width < attend_to_chunk_stride: raise ValueError( "`attend_to_chunk_width` < `attend_to_chunk_stride`would cause sequence positions to get skipped." ) self.always_attend_to_first_position = always_attend_to_first_position self.first_position_attends_to_all = first_position_attends_to_all self.attend_from_chunk_width = attend_from_chunk_width self.attend_from_chunk_stride = attend_from_chunk_stride self.attend_to_chunk_width = attend_to_chunk_width self.attend_to_chunk_stride = attend_to_chunk_stride def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: Tuple[torch.FloatTensor], attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]: if not self.local: self_outputs = self.self(hidden_states, hidden_states, attention_mask, head_mask, output_attentions) attention_output = self_outputs[0] else: from_seq_length = to_seq_length = hidden_states.shape[1] from_tensor = to_tensor = hidden_states # Create chunks (windows) that we will attend *from* and then concatenate them. from_chunks = [] if self.first_position_attends_to_all: from_chunks.append((0, 1)) # We must skip this first position so that our output sequence is the # correct length (this matters in the *from* sequence only). from_start = 1 else: from_start = 0 for chunk_start in range(from_start, from_seq_length, self.attend_from_chunk_stride): chunk_end = min(from_seq_length, chunk_start + self.attend_from_chunk_width) from_chunks.append((chunk_start, chunk_end)) # Determine the chunks (windows) that will will attend *to*. to_chunks = [] if self.first_position_attends_to_all: to_chunks.append((0, to_seq_length)) for chunk_start in range(0, to_seq_length, self.attend_to_chunk_stride): chunk_end = min(to_seq_length, chunk_start + self.attend_to_chunk_width) to_chunks.append((chunk_start, chunk_end)) if len(from_chunks) != len(to_chunks): raise ValueError( f"Expected to have same number of `from_chunks` ({from_chunks}) and " f"`to_chunks` ({from_chunks}). Check strides." ) # next, compute attention scores for each pair of windows and concatenate attention_output_chunks = [] attention_probs_chunks = [] for (from_start, from_end), (to_start, to_end) in zip(from_chunks, to_chunks): from_tensor_chunk = from_tensor[:, from_start:from_end, :] to_tensor_chunk = to_tensor[:, to_start:to_end, :] # `attention_mask`: <float>[batch_size, from_seq, to_seq] # `attention_mask_chunk`: <float>[batch_size, from_seq_chunk, to_seq_chunk] attention_mask_chunk = attention_mask[:, from_start:from_end, to_start:to_end] if self.always_attend_to_first_position: cls_attention_mask = attention_mask[:, from_start:from_end, 0:1] attention_mask_chunk = torch.cat([cls_attention_mask, attention_mask_chunk], dim=2) cls_position = to_tensor[:, 0:1, :] to_tensor_chunk = torch.cat([cls_position, to_tensor_chunk], dim=1) attention_outputs_chunk = self.self( from_tensor_chunk, to_tensor_chunk, attention_mask_chunk, head_mask, output_attentions ) attention_output_chunks.append(attention_outputs_chunk[0]) if output_attentions: attention_probs_chunks.append(attention_outputs_chunk[1]) attention_output = torch.cat(attention_output_chunks, dim=1) attention_output = self.output(attention_output, hidden_states) outputs = (attention_output,) if not self.local: outputs = outputs + self_outputs[1:] # add attentions if we output them else: outputs = outputs + tuple(attention_probs_chunks) # add attentions if we output them return outputs class CanineIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class CanineOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: Tuple[torch.FloatTensor], input_tensor: torch.FloatTensor) -> torch.FloatTensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class CanineLayer(nn.Module): def __init__( self, config, local, always_attend_to_first_position, first_position_attends_to_all, attend_from_chunk_width, attend_from_chunk_stride, attend_to_chunk_width, attend_to_chunk_stride, ): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = CanineAttention( config, local, always_attend_to_first_position, first_position_attends_to_all, attend_from_chunk_width, attend_from_chunk_stride, attend_to_chunk_width, attend_to_chunk_stride, ) self.intermediate = CanineIntermediate(config) self.output = CanineOutput(config) def forward( self, hidden_states: Tuple[torch.FloatTensor], attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]: self_attention_outputs = self.attention( hidden_states, attention_mask, head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights layer_output = apply_chunking_to_forward( self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output ) outputs = (layer_output,) + outputs return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output class CanineEncoder(nn.Module): def __init__( self, config, local=False, always_attend_to_first_position=False, first_position_attends_to_all=False, attend_from_chunk_width=128, attend_from_chunk_stride=128, attend_to_chunk_width=128, attend_to_chunk_stride=128, ): super().__init__() self.config = config self.layer = nn.ModuleList( [ CanineLayer( config, local, always_attend_to_first_position, first_position_attends_to_all, attend_from_chunk_width, attend_from_chunk_stride, attend_to_chunk_width, attend_to_chunk_stride, ) for _ in range(config.num_hidden_layers) ] ) self.gradient_checkpointing = False def forward( self, hidden_states: Tuple[torch.FloatTensor], attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True, ) -> Union[Tuple, BaseModelOutput]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( layer_module.__call__, hidden_states, attention_mask, layer_head_mask, output_attentions, ) else: layer_outputs = layer_module(hidden_states, attention_mask, layer_head_mask, output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) class CaninePooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states: Tuple[torch.FloatTensor]) -> torch.FloatTensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class CaninePredictionHeadTransform(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act] else: self.transform_act_fn = config.hidden_act self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states: Tuple[torch.FloatTensor]) -> torch.FloatTensor: hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states class CanineLMPredictionHead(nn.Module): def __init__(self, config): super().__init__() self.transform = CaninePredictionHeadTransform(config) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` self.decoder.bias = self.bias def forward(self, hidden_states: Tuple[torch.FloatTensor]) -> torch.FloatTensor: hidden_states = self.transform(hidden_states) hidden_states = self.decoder(hidden_states) return hidden_states class CanineOnlyMLMHead(nn.Module): def __init__(self, config): super().__init__() self.predictions = CanineLMPredictionHead(config) def forward( self, sequence_output: Tuple[torch.Tensor], ) -> Tuple[torch.Tensor]: prediction_scores = self.predictions(sequence_output) return prediction_scores class CaninePreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = CanineConfig load_tf_weights = load_tf_weights_in_canine base_model_prefix = "canine" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv1d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) CANINE_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`CanineConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ CANINE_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert *input_ids* indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare CANINE Model transformer outputting raw hidden-states without any specific head on top.", CANINE_START_DOCSTRING, ) class CanineModel(CaninePreTrainedModel): def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config shallow_config = copy.deepcopy(config) shallow_config.num_hidden_layers = 1 self.char_embeddings = CanineEmbeddings(config) # shallow/low-dim transformer encoder to get a initial character encoding self.initial_char_encoder = CanineEncoder( shallow_config, local=True, always_attend_to_first_position=False, first_position_attends_to_all=False, attend_from_chunk_width=config.local_transformer_stride, attend_from_chunk_stride=config.local_transformer_stride, attend_to_chunk_width=config.local_transformer_stride, attend_to_chunk_stride=config.local_transformer_stride, ) self.chars_to_molecules = CharactersToMolecules(config) # deep transformer encoder self.encoder = CanineEncoder(config) self.projection = ConvProjection(config) # shallow/low-dim transformer encoder to get a final character encoding self.final_char_encoder = CanineEncoder(shallow_config) self.pooler = CaninePooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) def _create_3d_attention_mask_from_input_mask(self, from_tensor, to_mask): """ Create 3D attention mask from a 2D tensor mask. Args: from_tensor: 2D or 3D Tensor of shape [batch_size, from_seq_length, ...]. to_mask: int32 Tensor of shape [batch_size, to_seq_length]. Returns: float Tensor of shape [batch_size, from_seq_length, to_seq_length]. """ batch_size, from_seq_length = from_tensor.shape[0], from_tensor.shape[1] to_seq_length = to_mask.shape[1] to_mask = torch.reshape(to_mask, (batch_size, 1, to_seq_length)).float() # We don't assume that `from_tensor` is a mask (although it could be). We # don't actually care if we attend *from* padding tokens (only *to* padding) # tokens so we create a tensor of all ones. broadcast_ones = torch.ones(size=(batch_size, from_seq_length, 1), dtype=torch.float32, device=to_mask.device) # Here we broadcast along two dimensions to create the mask. mask = broadcast_ones * to_mask return mask def _downsample_attention_mask(self, char_attention_mask: torch.Tensor, downsampling_rate: int): """Downsample 2D character attention mask to 2D molecule attention mask using MaxPool1d layer.""" # first, make char_attention_mask 3D by adding a channel dim batch_size, char_seq_len = char_attention_mask.shape poolable_char_mask = torch.reshape(char_attention_mask, (batch_size, 1, char_seq_len)) # next, apply MaxPool1d to get pooled_molecule_mask of shape (batch_size, 1, mol_seq_len) pooled_molecule_mask = torch.nn.MaxPool1d(kernel_size=downsampling_rate, stride=downsampling_rate)( poolable_char_mask.float() ) # finally, squeeze to get tensor of shape (batch_size, mol_seq_len) molecule_attention_mask = torch.squeeze(pooled_molecule_mask, dim=-1) return molecule_attention_mask def _repeat_molecules(self, molecules: torch.Tensor, char_seq_length: torch.Tensor) -> torch.Tensor: """Repeats molecules to make them the same length as the char sequence.""" rate = self.config.downsampling_rate molecules_without_extra_cls = molecules[:, 1:, :] # `repeated`: [batch_size, almost_char_seq_len, molecule_hidden_size] repeated = torch.repeat_interleave(molecules_without_extra_cls, repeats=rate, dim=-2) # So far, we've repeated the elements sufficient for any `char_seq_length` # that's a multiple of `downsampling_rate`. Now we account for the last # n elements (n < `downsampling_rate`), i.e. the remainder of floor # division. We do this by repeating the last molecule a few extra times. last_molecule = molecules[:, -1:, :] remainder_length = torch.fmod(torch.tensor(char_seq_length), torch.tensor(rate)).item() remainder_repeated = torch.repeat_interleave( last_molecule, # +1 molecule to compensate for truncation. repeats=remainder_length + rate, dim=-2, ) # `repeated`: [batch_size, char_seq_len, molecule_hidden_size] return torch.cat([repeated, remainder_repeated], dim=-2) @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=CanineModelOutputWithPooling, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CanineModelOutputWithPooling]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape device = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length)), device=device) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) molecule_attention_mask = self._downsample_attention_mask( attention_mask, downsampling_rate=self.config.downsampling_rate ) extended_molecule_attention_mask: torch.Tensor = self.get_extended_attention_mask( molecule_attention_mask, (batch_size, molecule_attention_mask.shape[-1]) ) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) # `input_char_embeddings`: shape (batch_size, char_seq, char_dim) input_char_embeddings = self.char_embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, ) # Contextualize character embeddings using shallow Transformer. # We use a 3D attention mask for the local attention. # `input_char_encoding`: shape (batch_size, char_seq_len, char_dim) char_attention_mask = self._create_3d_attention_mask_from_input_mask( input_ids if input_ids is not None else inputs_embeds, attention_mask ) init_chars_encoder_outputs = self.initial_char_encoder( input_char_embeddings, attention_mask=char_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) input_char_encoding = init_chars_encoder_outputs.last_hidden_state # Downsample chars to molecules. # The following lines have dimensions: [batch, molecule_seq, molecule_dim]. # In this transformation, we change the dimensionality from `char_dim` to # `molecule_dim`, but do *NOT* add a resnet connection. Instead, we rely on # the resnet connections (a) from the final char transformer stack back into # the original char transformer stack and (b) the resnet connections from # the final char transformer stack back into the deep BERT stack of # molecules. # # Empirically, it is critical to use a powerful enough transformation here: # mean pooling causes training to diverge with huge gradient norms in this # region of the model; using a convolution here resolves this issue. From # this, it seems that molecules and characters require a very different # feature space; intuitively, this makes sense. init_molecule_encoding = self.chars_to_molecules(input_char_encoding) # Deep BERT encoder # `molecule_sequence_output`: shape (batch_size, mol_seq_len, mol_dim) encoder_outputs = self.encoder( init_molecule_encoding, attention_mask=extended_molecule_attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) molecule_sequence_output = encoder_outputs[0] pooled_output = self.pooler(molecule_sequence_output) if self.pooler is not None else None # Upsample molecules back to characters. # `repeated_molecules`: shape (batch_size, char_seq_len, mol_hidden_size) repeated_molecules = self._repeat_molecules(molecule_sequence_output, char_seq_length=input_shape[-1]) # Concatenate representations (contextualized char embeddings and repeated molecules): # `concat`: shape [batch_size, char_seq_len, molecule_hidden_size+char_hidden_final] concat = torch.cat([input_char_encoding, repeated_molecules], dim=-1) # Project representation dimension back to hidden_size # `sequence_output`: shape (batch_size, char_seq_len, hidden_size]) sequence_output = self.projection(concat) # Apply final shallow Transformer # `sequence_output`: shape (batch_size, char_seq_len, hidden_size]) final_chars_encoder_outputs = self.final_char_encoder( sequence_output, attention_mask=extended_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) sequence_output = final_chars_encoder_outputs.last_hidden_state if output_hidden_states: deep_encoder_hidden_states = encoder_outputs.hidden_states if return_dict else encoder_outputs[1] all_hidden_states = ( all_hidden_states + init_chars_encoder_outputs.hidden_states + deep_encoder_hidden_states + final_chars_encoder_outputs.hidden_states ) if output_attentions: deep_encoder_self_attentions = encoder_outputs.attentions if return_dict else encoder_outputs[-1] all_self_attentions = ( all_self_attentions + init_chars_encoder_outputs.attentions + deep_encoder_self_attentions + final_chars_encoder_outputs.attentions ) if not return_dict: output = (sequence_output, pooled_output) output += tuple(v for v in [all_hidden_states, all_self_attentions] if v is not None) return output return CanineModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=all_hidden_states, attentions=all_self_attentions, ) @add_start_docstrings( """ CANINE Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, CANINE_START_DOCSTRING, ) class CanineForSequenceClassification(CaninePreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.canine = CanineModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, SequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.canine( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CANINE Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, CANINE_START_DOCSTRING, ) class CanineForMultipleChoice(CaninePreTrainedModel): def __init__(self, config): super().__init__(config) self.canine = CanineModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, 1) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, MultipleChoiceModelOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.canine( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CANINE Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, CANINE_START_DOCSTRING, ) class CanineForTokenClassification(CaninePreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.canine = CanineModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. Returns: Example: ```python >>> from transformers import AutoTokenizer, CanineForTokenClassification >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("google/canine-s") >>> model = CanineForTokenClassification.from_pretrained("google/canine-s") >>> inputs = tokenizer( ... "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="pt" ... ) >>> with torch.no_grad(): ... logits = model(**inputs).logits >>> predicted_token_class_ids = logits.argmax(-1) >>> # Note that tokens are classified rather then input words which means that >>> # there might be more predicted token classes than words. >>> # Multiple token classes might account for the same word >>> predicted_tokens_classes = [model.config.id2label[t.item()] for t in predicted_token_class_ids[0]] >>> predicted_tokens_classes # doctest: +SKIP ``` ```python >>> labels = predicted_token_class_ids >>> loss = model(**inputs, labels=labels).loss >>> round(loss.item(), 2) # doctest: +SKIP ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.canine( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CANINE Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, CANINE_START_DOCSTRING, ) class CanineForQuestionAnswering(CaninePreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.canine = CanineModel(config) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint="Splend1dchan/canine-c-squad", output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, expected_output="'nice puppet'", expected_loss=8.81, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.canine( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1) end_logits = end_logits.squeeze(-1) total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions.clamp_(0, ignored_index) end_positions.clamp_(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

transformers/src/transformers/models/canine/modeling_canine.py/0

{ "file_path": "transformers/src/transformers/models/canine/modeling_canine.py", "repo_id": "transformers", "token_count": 31277 }

86

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ CLIP model configuration""" import os from collections import OrderedDict from typing import TYPE_CHECKING, Any, Mapping, Optional, Union if TYPE_CHECKING: from ...processing_utils import ProcessorMixin from ...utils import TensorType from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging logger = logging.get_logger(__name__) CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP = { "openai/clip-vit-base-patch32": "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/config.json", # See all CLIP models at https://huggingface.co/models?filter=clip } class CLIPTextConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`CLIPTextModel`]. It is used to instantiate a CLIP text encoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the text encoder of the CLIP [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 49408): Vocabulary size of the CLIP text model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`CLIPModel`]. hidden_size (`int`, *optional*, defaults to 512): Dimensionality of the encoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 2048): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. projection_dim (`int`, *optional*, defaults to 512): Dimentionality of text and vision projection layers. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 8): Number of attention heads for each attention layer in the Transformer encoder. max_position_embeddings (`int`, *optional*, defaults to 77): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported. layer_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the layer normalization layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. initializer_factor (`float`, *optional*, defaults to 1.0): A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). pad_token_id (`int`, *optional*, defaults to 1): Padding token id. bos_token_id (`int`, *optional*, defaults to 49406): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 49407): End of stream token id. Example: ```python >>> from transformers import CLIPTextConfig, CLIPTextModel >>> # Initializing a CLIPTextConfig with openai/clip-vit-base-patch32 style configuration >>> configuration = CLIPTextConfig() >>> # Initializing a CLIPTextModel (with random weights) from the openai/clip-vit-base-patch32 style configuration >>> model = CLIPTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "clip_text_model" def __init__( self, vocab_size=49408, hidden_size=512, intermediate_size=2048, projection_dim=512, num_hidden_layers=12, num_attention_heads=8, max_position_embeddings=77, hidden_act="quick_gelu", layer_norm_eps=1e-5, attention_dropout=0.0, initializer_range=0.02, initializer_factor=1.0, # This differs from `CLIPTokenizer`'s default and from openai/clip # See https://github.com/huggingface/transformers/pull/24773#issuecomment-1632287538 pad_token_id=1, bos_token_id=49406, eos_token_id=49407, **kwargs, ): super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) self.vocab_size = vocab_size self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.projection_dim = projection_dim self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.max_position_embeddings = max_position_embeddings self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act self.initializer_range = initializer_range self.initializer_factor = initializer_factor self.attention_dropout = attention_dropout @classmethod def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": cls._set_token_in_kwargs(kwargs) config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) # get the text config dict if we are loading from CLIPConfig if config_dict.get("model_type") == "clip": config_dict = config_dict["text_config"] if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: logger.warning( f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." ) return cls.from_dict(config_dict, **kwargs) class CLIPVisionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`CLIPVisionModel`]. It is used to instantiate a CLIP vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the vision encoder of the CLIP [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. projection_dim (`int`, *optional*, defaults to 512): Dimentionality of text and vision projection layers. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. num_channels (`int`, *optional*, defaults to 3): The number of input channels. image_size (`int`, *optional*, defaults to 224): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 32): The size (resolution) of each patch. hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported. layer_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the layer normalization layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. initializer_factor (`float`, *optional*, defaults to 1.0): A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). Example: ```python >>> from transformers import CLIPVisionConfig, CLIPVisionModel >>> # Initializing a CLIPVisionConfig with openai/clip-vit-base-patch32 style configuration >>> configuration = CLIPVisionConfig() >>> # Initializing a CLIPVisionModel (with random weights) from the openai/clip-vit-base-patch32 style configuration >>> model = CLIPVisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "clip_vision_model" def __init__( self, hidden_size=768, intermediate_size=3072, projection_dim=512, num_hidden_layers=12, num_attention_heads=12, num_channels=3, image_size=224, patch_size=32, hidden_act="quick_gelu", layer_norm_eps=1e-5, attention_dropout=0.0, initializer_range=0.02, initializer_factor=1.0, **kwargs, ): super().__init__(**kwargs) self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.projection_dim = projection_dim self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.num_channels = num_channels self.patch_size = patch_size self.image_size = image_size self.initializer_range = initializer_range self.initializer_factor = initializer_factor self.attention_dropout = attention_dropout self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act @classmethod def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": cls._set_token_in_kwargs(kwargs) config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) # get the vision config dict if we are loading from CLIPConfig if config_dict.get("model_type") == "clip": config_dict = config_dict["vision_config"] if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: logger.warning( f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." ) return cls.from_dict(config_dict, **kwargs) class CLIPConfig(PretrainedConfig): r""" [`CLIPConfig`] is the configuration class to store the configuration of a [`CLIPModel`]. It is used to instantiate a CLIP model according to the specified arguments, defining the text model and vision model configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the CLIP [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: text_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`CLIPTextConfig`]. vision_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`CLIPVisionConfig`]. projection_dim (`int`, *optional*, defaults to 512): Dimentionality of text and vision projection layers. logit_scale_init_value (`float`, *optional*, defaults to 2.6592): The inital value of the *logit_scale* paramter. Default is used as per the original CLIP implementation. kwargs (*optional*): Dictionary of keyword arguments. Example: ```python >>> from transformers import CLIPConfig, CLIPModel >>> # Initializing a CLIPConfig with openai/clip-vit-base-patch32 style configuration >>> configuration = CLIPConfig() >>> # Initializing a CLIPModel (with random weights) from the openai/clip-vit-base-patch32 style configuration >>> model = CLIPModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config >>> # We can also initialize a CLIPConfig from a CLIPTextConfig and a CLIPVisionConfig >>> from transformers import CLIPTextConfig, CLIPVisionConfig >>> # Initializing a CLIPText and CLIPVision configuration >>> config_text = CLIPTextConfig() >>> config_vision = CLIPVisionConfig() >>> config = CLIPConfig.from_text_vision_configs(config_text, config_vision) ```""" model_type = "clip" def __init__( self, text_config=None, vision_config=None, projection_dim=512, logit_scale_init_value=2.6592, **kwargs ): # If `_config_dict` exist, we use them for the backward compatibility. # We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot # of confusion!). text_config_dict = kwargs.pop("text_config_dict", None) vision_config_dict = kwargs.pop("vision_config_dict", None) super().__init__(**kwargs) # Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in # `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most # cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`. if text_config_dict is not None: if text_config is None: text_config = {} # This is the complete result when using `text_config_dict`. _text_config_dict = CLIPTextConfig(**text_config_dict).to_dict() # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different. for key, value in _text_config_dict.items(): if key in text_config and value != text_config[key] and key not in ["transformers_version"]: # If specified in `text_config_dict` if key in text_config_dict: message = ( f"`{key}` is found in both `text_config_dict` and `text_config` but with different values. " f'The value `text_config_dict["{key}"]` will be used instead.' ) # If inferred from default argument values (just to be super careful) else: message = ( f"`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The " f'value `text_config["{key}"]` will be overriden.' ) logger.info(message) # Update all values in `text_config` with the ones in `_text_config_dict`. text_config.update(_text_config_dict) if vision_config_dict is not None: if vision_config is None: vision_config = {} # This is the complete result when using `vision_config_dict`. _vision_config_dict = CLIPVisionConfig(**vision_config_dict).to_dict() # convert keys to string instead of integer if "id2label" in _vision_config_dict: _vision_config_dict["id2label"] = { str(key): value for key, value in _vision_config_dict["id2label"].items() } # Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different. for key, value in _vision_config_dict.items(): if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]: # If specified in `vision_config_dict` if key in vision_config_dict: message = ( f"`{key}` is found in both `vision_config_dict` and `vision_config` but with different " f'values. The value `vision_config_dict["{key}"]` will be used instead.' ) # If inferred from default argument values (just to be super careful) else: message = ( f"`vision_config_dict` is provided which will be used to initialize `CLIPVisionConfig`. " f'The value `vision_config["{key}"]` will be overriden.' ) logger.info(message) # Update all values in `vision_config` with the ones in `_vision_config_dict`. vision_config.update(_vision_config_dict) if text_config is None: text_config = {} logger.info("`text_config` is `None`. Initializing the `CLIPTextConfig` with default values.") if vision_config is None: vision_config = {} logger.info("`vision_config` is `None`. initializing the `CLIPVisionConfig` with default values.") self.text_config = CLIPTextConfig(**text_config) self.vision_config = CLIPVisionConfig(**vision_config) self.projection_dim = projection_dim self.logit_scale_init_value = logit_scale_init_value self.initializer_factor = 1.0 @classmethod def from_text_vision_configs(cls, text_config: CLIPTextConfig, vision_config: CLIPVisionConfig, **kwargs): r""" Instantiate a [`CLIPConfig`] (or a derived class) from clip text model configuration and clip vision model configuration. Returns: [`CLIPConfig`]: An instance of a configuration object """ return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs) class CLIPOnnxConfig(OnnxConfig): @property def inputs(self) -> Mapping[str, Mapping[int, str]]: return OrderedDict( [ ("input_ids", {0: "batch", 1: "sequence"}), ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}), ("attention_mask", {0: "batch", 1: "sequence"}), ] ) @property def outputs(self) -> Mapping[str, Mapping[int, str]]: return OrderedDict( [ ("logits_per_image", {0: "batch"}), ("logits_per_text", {0: "batch"}), ("text_embeds", {0: "batch"}), ("image_embeds", {0: "batch"}), ] ) @property def atol_for_validation(self) -> float: return 1e-4 def generate_dummy_inputs( self, processor: "ProcessorMixin", batch_size: int = -1, seq_length: int = -1, framework: Optional["TensorType"] = None, ) -> Mapping[str, Any]: text_input_dict = super().generate_dummy_inputs( processor.tokenizer, batch_size=batch_size, seq_length=seq_length, framework=framework ) image_input_dict = super().generate_dummy_inputs( processor.image_processor, batch_size=batch_size, framework=framework ) return {**text_input_dict, **image_input_dict} @property def default_onnx_opset(self) -> int: return 14

transformers/src/transformers/models/clip/configuration_clip.py/0

{ "file_path": "transformers/src/transformers/models/clip/configuration_clip.py", "repo_id": "transformers", "token_count": 8388 }

87

# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ CLVP model configuration""" import os from typing import TYPE_CHECKING, Union if TYPE_CHECKING: pass from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) CLVP_PRETRAINED_CONFIG_ARCHIVE_MAP = { "susnato/clvp_dev": "https://huggingface.co/susnato/clvp_dev/resolve/main/config.json", } class ClvpEncoderConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`ClvpEncoder`]. It is used to instantiate a CLVP text or CLVP speech encoder according to the specified arguments. Instantiating a configuration with the defaults will yield a similar configuration to that of the encoder of the CLVP [susnato/clvp_dev](https://huggingface.co/susnato/clvp_dev) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 256): Vocabulary size of the CLVP Encoder model. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 1536): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. projection_dim (`int`, *optional*, defaults to 768): Dimensionality of the projection vector. num_hidden_layers (`int`, *optional*, defaults to 20): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported. layer_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the layer normalization layers. attention_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for the feed-forward layers in [`ClvpEncoderMLP`]. use_rotary_embedding (`bool`, *optional*, defaults to `True`): Whether to use rotary_embedding or not. use_attention_bias (`bool`, *optional*, defaults to `False`): Whether to use bias in Query, Key and Value layers during self attention. summary_type (`str`, *optional*, defaults to `"mean"`): What strategy to use to get pooler_output from the last_hidden_state. `"last"`, `"first"`, `"mean"` and `"cls_index"` are supported. initializer_factor (`float`, *optional*, defaults to 1.0): A factor for initializing all weight matrices (should be kept to 1.0, used internally for initialization testing). bos_token_id (`int`, *optional*, defaults to 255): Beginning of sequence token id. eos_token_id (`int`, *optional*, defaults to 0): End of sequence token id. Example: ```python >>> from transformers import ClvpEncoderConfig, ClvpEncoder >>> # Initializing a ClvpEncoderConfig with susnato/clvp_dev style configuration >>> encoder_configuration = ClvpEncoderConfig() >>> # Initializing a ClvpEncoder (with random weights) from the susnato/clvp_dev style configuration >>> model = ClvpEncoder(encoder_configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "clvp_encoder" def __init__( self, vocab_size=256, hidden_size=768, intermediate_size=1536, projection_dim=768, num_hidden_layers=20, num_attention_heads=12, hidden_act="gelu", layer_norm_eps=1e-5, attention_dropout=0.1, dropout=0.1, use_rotary_embedding=True, use_attention_bias=False, summary_type="mean", initializer_factor=1.0, bos_token_id=255, eos_token_id=0, **kwargs, ): self.vocab_size = vocab_size self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.projection_dim = projection_dim self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act self.initializer_factor = initializer_factor self.attention_dropout = attention_dropout self.dropout = dropout self.use_rotary_embedding = use_rotary_embedding self.use_attention_bias = use_attention_bias self.summary_type = summary_type self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) @classmethod def from_pretrained( cls, pretrained_model_name_or_path: Union[str, os.PathLike], config_type: str = "text_config", **kwargs ) -> "PretrainedConfig": cls._set_token_in_kwargs(kwargs) config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) # make sure to have the config_type be either "text_config" or "speech_config" # this is to make sure that we can load only text or speech configs from the nested ClvpConfig. if config_type not in ["text_config", "speech_config"]: raise ValueError( f"We can only load either 'text_config' or 'speech_config' but you are trying to load" f"{config_type}" ) # get the text config dict if we are loading from ClvpConfig if config_dict.get("model_type") == "clvp": config_dict = config_dict[config_type] if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: logger.warning( f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." ) return cls.from_dict(config_dict, **kwargs) class ClvpDecoderConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`ClvpDecoder`]. It is used to instantiate a CLVP Decoder Model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Decoder part of the CLVP [susnato/clvp_dev](https://huggingface.co/susnato/clvp_dev) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. The architecture is similar to GPT2. Args: vocab_size (`int`, *optional*, defaults to 8194): Vocabulary size of the model. max_position_embeddings (`int`, *optional*, defaults to 608): The maximum sequence length of mel tokens that this model might ever be used with. Similar to `n_positions` in `GPT2Config`. max_text_tokens (`int`, *optional*, defaults to 404): The maximum sequence length of text tokens that this model might ever be used with. Similar to `n_positions` in `GPT2Config`. hidden_size (`int`, *optional*, defaults to 1024): Dimensionality of the embeddings and hidden states. num_hidden_layers (`int`, *optional*, defaults to 30): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. n_inner (`int`, *optional*): Dimensionality of the inner feed-forward layers. `None` will set it to 4 times `hidden_size`. num_mel_attn_blocks (`int`, *optional*, defaults to 6): Denotes the number of self attention layers in [`ClvpConditioningEncoder`]. activation_function (`str`, *optional*, defaults to `"gelu_new"`): Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`. resid_pdrop (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. embd_pdrop (`float`, *optional*, defaults to 0.1): The dropout ratio for the embeddings. attention_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention. layer_norm_epsilon (`float`, *optional*, defaults to 1e-05): The epsilon to use in the layer normalization layers. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. summary_type (`string`, *optional*, defaults to `"cls_index"`): Argument used when doing sequence summary. Has to be one of the following options: - `"last"`: Take the last token hidden state (like XLNet). - `"first"`: Take the first token hidden state (like BERT). - `"mean"`: Take the mean of all tokens hidden states. - `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2). - `"attn"`: Not implemented now, use multi-head attention. summary_use_proj (`bool`, *optional*, defaults to `True`): Whether or not to add a projection after the vector extraction. summary_activation (`str`, *optional*): Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation. summary_proj_to_labels (`bool`, *optional*, defaults to `True`): Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes. summary_first_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio to be used after the projection and activation. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). bos_token_id (`int`, *optional*, defaults to 8192): Beginning of sequence token id, used at the start of the generation. eos_token_id (`int`, *optional*, defaults to 8193): End of sequence token id, used in the method [`ClvpModelForConditionalGeneration.fix_speech_decoder_output()`] to correct decoder outputs. feature_size (`int`, *optional*, defaults to 80): The feature dimension of the extracted mel features. This value is used in [`ClvpConditioningEncoder`]. use_attention_bias (`bool`, *optional*, defaults to `True`): Whether to use bias in Query, Key and Value layers during self attention. initializer_factor (`float`, *optional*, defaults to 1.0): A factor for initializing all weight matrices (should be kept to 1.0, used internally for initialization testing). decoder_fixing_codes (`list`, *optional*, defaults to `[83, 45, 45, 248]`): These values are used in the method `fix_speech_decoder_output` to fix decoder generated outputs. Example: ```python >>> from transformers import ClvpDecoderConfig, ClvpDecoder >>> # Initializing a ClvpDecoderConfig with susnato/clvp_dev style configuration >>> decoder_configuration = ClvpDecoderConfig() >>> # Initializing a ClvpDecoder (with random weights) from the susnato/clvp_dev style configuration >>> model = ClvpDecoder(decoder_configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "clvp_decoder" def __init__( self, vocab_size=8194, max_position_embeddings=608, max_text_tokens=404, hidden_size=1024, num_hidden_layers=30, num_attention_heads=16, n_inner=None, num_mel_attn_blocks=6, activation_function="gelu_new", resid_pdrop=0.1, embd_pdrop=0.1, attention_dropout=0.1, layer_norm_epsilon=1e-5, initializer_range=0.02, summary_type="cls_index", summary_use_proj=True, summary_activation=None, summary_proj_to_labels=True, summary_first_dropout=0.1, use_cache=True, bos_token_id=8192, eos_token_id=8193, feature_size=80, use_attention_bias=True, initializer_factor=1.0, decoder_fixing_codes=[83, 45, 45, 248], **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.max_text_tokens = max_text_tokens self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.n_inner = n_inner self.num_mel_attn_blocks = num_mel_attn_blocks self.activation_function = activation_function self.resid_pdrop = resid_pdrop self.embd_pdrop = embd_pdrop self.attention_dropout = attention_dropout self.layer_norm_epsilon = layer_norm_epsilon self.initializer_range = initializer_range self.summary_type = summary_type self.summary_use_proj = summary_use_proj self.summary_activation = summary_activation self.summary_first_dropout = summary_first_dropout self.summary_proj_to_labels = summary_proj_to_labels self.use_cache = use_cache self.feature_size = feature_size self.use_attention_bias = use_attention_bias self.initializer_factor = initializer_factor self.decoder_fixing_codes = decoder_fixing_codes self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) @classmethod def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": cls._set_token_in_kwargs(kwargs) config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) # get the speech config dict if we are loading from ClvpConfig if config_dict.get("model_type") == "clvp": config_dict = config_dict["decoder_config"] if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: logger.warning( f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." ) return cls.from_dict(config_dict, **kwargs) class ClvpConfig(PretrainedConfig): r""" [`ClvpConfig`] is the configuration class to store the configuration of a [`ClvpModelForConditionalGeneration`]. It is used to instantiate a CLVP model according to the specified arguments, defining the text model, speech model and decoder model configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the CLVP [susnato/clvp_dev](https://huggingface.co/susnato/clvp_dev) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: text_config (`dict`, *optional*): Dictionary of configuration options used to initialize the CLVP text encoder. speech_config (`dict`, *optional*): Dictionary of configuration options used to initialize CLVP speech encoder. decoder_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`ClvpDecoderConfig`]. projection_dim (`int`, *optional*, defaults to 768): Dimentionality of text and speech projection layers. logit_scale_init_value (`float`, *optional*, defaults to 2.6592): The inital value of the *logit_scale* paramter. Default is used as per the original CLVP implementation. initializer_factor (`float`, *optional*, defaults to 1.0): A factor for initializing all weight matrices (should be kept to 1.0, used internally for initialization testing). kwargs (*optional*): Dictionary of keyword arguments. Example: ```python >>> from transformers import ClvpConfig, ClvpModelForConditionalGeneration >>> # Initializing a ClvpConfig with susnato/clvp_dev style configuration >>> configuration = ClvpConfig() >>> # Initializing a ClvpModelForConditionalGeneration (with random weights) from the susnato/clvp_dev style configuration >>> model = ClvpModelForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config >>> # We can also initialize a CLVPConfig from a CLVPTextConfig, CLVPSpeechConfig and a CLVPAutoRegressiveConfig >>> from transformers import ClvpEncoderConfig, ClvpDecoderConfig >>> # Initializing a CLVP text, CLVP speech and CLVP decoder configuration >>> config_text = ClvpEncoderConfig() >>> config_speech = ClvpEncoderConfig() >>> decoder_config = ClvpDecoderConfig() >>> config = ClvpConfig.from_sub_model_configs(config_text, config_speech, decoder_config) ```""" model_type = "clvp" is_composition = True def __init__( self, text_config=None, speech_config=None, decoder_config=None, projection_dim=768, logit_scale_init_value=2.6592, initializer_factor=1.0, **kwargs, ): super().__init__(**kwargs) if text_config is None: text_config = {} logger.info("`text_config` is `None`. Initializing the `ClvpEncoderConfig` with default values.") if speech_config is None: speech_config = {} logger.info("`speech_config` is `None`. initializing the `ClvpEncoderConfig` with default values.") if decoder_config is None: decoder_config = {} logger.info("`decoder_config` is `None`. initializing the `ClvpDecoderConfig` with default values.") self.text_config = ClvpEncoderConfig(**text_config) self.speech_config = ClvpEncoderConfig(**speech_config) self.decoder_config = ClvpDecoderConfig(**decoder_config) self.projection_dim = projection_dim self.logit_scale_init_value = logit_scale_init_value self.initializer_factor = initializer_factor @classmethod def from_sub_model_configs( cls, text_config: ClvpEncoderConfig, speech_config: ClvpEncoderConfig, decoder_config: ClvpDecoderConfig, **kwargs, ): r""" Instantiate a [`ClvpConfig`] (or a derived class) from CLVP text model configuration, CLVP speech model configuration and CLVP decoder model configuration. Args: text_config (`ClvpEncoderConfig`): Text model configuration of type [`ClvpEncoderConfig`]. speech_config (`ClvpEncoderConfig`): Speech model configuration of type [`ClvpEncoderConfig`]. decoder_config (`ClvpDecoderConfig`): Decoder model configuration of type [`ClvpDecoderConfig`]. Returns: [`ClvpConfig`]: An instance of a configuration object """ return cls( text_config=text_config.to_dict(), speech_config=speech_config.to_dict(), decoder_config=decoder_config.to_dict(), **kwargs, )

transformers/src/transformers/models/clvp/configuration_clvp.py/0

{ "file_path": "transformers/src/transformers/models/clvp/configuration_clvp.py", "repo_id": "transformers", "token_count": 8205 }

88

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Conditional DETR model configuration""" from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging from ..auto import CONFIG_MAPPING logger = logging.get_logger(__name__) CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP = { "microsoft/conditional-detr-resnet-50": ( "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json" ), } class ConditionalDetrConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`ConditionalDetrModel`]. It is used to instantiate a Conditional DETR model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Conditional DETR [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: use_timm_backbone (`bool`, *optional*, defaults to `True`): Whether or not to use the `timm` library for the backbone. If set to `False`, will use the [`AutoBackbone`] API. backbone_config (`PretrainedConfig` or `dict`, *optional*): The configuration of the backbone model. Only used in case `use_timm_backbone` is set to `False` in which case it will default to `ResNetConfig()`. num_channels (`int`, *optional*, defaults to 3): The number of input channels. num_queries (`int`, *optional*, defaults to 100): Number of object queries, i.e. detection slots. This is the maximal number of objects [`ConditionalDetrModel`] can detect in a single image. For COCO, we recommend 100 queries. d_model (`int`, *optional*, defaults to 256): Dimension of the layers. encoder_layers (`int`, *optional*, defaults to 6): Number of encoder layers. decoder_layers (`int`, *optional*, defaults to 6): Number of decoder layers. encoder_attention_heads (`int`, *optional*, defaults to 8): Number of attention heads for each attention layer in the Transformer encoder. decoder_attention_heads (`int`, *optional*, defaults to 8): Number of attention heads for each attention layer in the Transformer decoder. decoder_ffn_dim (`int`, *optional*, defaults to 2048): Dimension of the "intermediate" (often named feed-forward) layer in decoder. encoder_ffn_dim (`int`, *optional*, defaults to 2048): Dimension of the "intermediate" (often named feed-forward) layer in decoder. activation_function (`str` or `function`, *optional*, defaults to `"relu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. init_xavier_std (`float`, *optional*, defaults to 1): The scaling factor used for the Xavier initialization gain in the HM Attention map module. encoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. decoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. auxiliary_loss (`bool`, *optional*, defaults to `False`): Whether auxiliary decoding losses (loss at each decoder layer) are to be used. position_embedding_type (`str`, *optional*, defaults to `"sine"`): Type of position embeddings to be used on top of the image features. One of `"sine"` or `"learned"`. backbone (`str`, *optional*, defaults to `"resnet50"`): Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone` is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights. use_pretrained_backbone (`bool`, *optional*, defaults to `True`): Whether to use pretrained weights for the backbone. backbone_kwargs (`dict`, *optional*): Keyword arguments to be passed to AutoBackbone when loading from a checkpoint e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set. dilation (`bool`, *optional*, defaults to `False`): Whether to replace stride with dilation in the last convolutional block (DC5). Only supported when `use_timm_backbone` = `True`. class_cost (`float`, *optional*, defaults to 1): Relative weight of the classification error in the Hungarian matching cost. bbox_cost (`float`, *optional*, defaults to 5): Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost. giou_cost (`float`, *optional*, defaults to 2): Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost. mask_loss_coefficient (`float`, *optional*, defaults to 1): Relative weight of the Focal loss in the panoptic segmentation loss. dice_loss_coefficient (`float`, *optional*, defaults to 1): Relative weight of the DICE/F-1 loss in the panoptic segmentation loss. bbox_loss_coefficient (`float`, *optional*, defaults to 5): Relative weight of the L1 bounding box loss in the object detection loss. giou_loss_coefficient (`float`, *optional*, defaults to 2): Relative weight of the generalized IoU loss in the object detection loss. eos_coefficient (`float`, *optional*, defaults to 0.1): Relative classification weight of the 'no-object' class in the object detection loss. focal_alpha (`float`, *optional*, defaults to 0.25): Alpha parameter in the focal loss. Examples: ```python >>> from transformers import ConditionalDetrConfig, ConditionalDetrModel >>> # Initializing a Conditional DETR microsoft/conditional-detr-resnet-50 style configuration >>> configuration = ConditionalDetrConfig() >>> # Initializing a model (with random weights) from the microsoft/conditional-detr-resnet-50 style configuration >>> model = ConditionalDetrModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "conditional_detr" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { "hidden_size": "d_model", "num_attention_heads": "encoder_attention_heads", } def __init__( self, use_timm_backbone=True, backbone_config=None, num_channels=3, num_queries=300, encoder_layers=6, encoder_ffn_dim=2048, encoder_attention_heads=8, decoder_layers=6, decoder_ffn_dim=2048, decoder_attention_heads=8, encoder_layerdrop=0.0, decoder_layerdrop=0.0, is_encoder_decoder=True, activation_function="relu", d_model=256, dropout=0.1, attention_dropout=0.0, activation_dropout=0.0, init_std=0.02, init_xavier_std=1.0, auxiliary_loss=False, position_embedding_type="sine", backbone="resnet50", use_pretrained_backbone=True, backbone_kwargs=None, dilation=False, class_cost=2, bbox_cost=5, giou_cost=2, mask_loss_coefficient=1, dice_loss_coefficient=1, cls_loss_coefficient=2, bbox_loss_coefficient=5, giou_loss_coefficient=2, focal_alpha=0.25, **kwargs, ): if not use_timm_backbone and use_pretrained_backbone: raise ValueError( "Loading pretrained backbone weights from the transformers library is not supported yet. `use_timm_backbone` must be set to `True` when `use_pretrained_backbone=True`" ) if backbone_config is not None and backbone is not None: raise ValueError("You can't specify both `backbone` and `backbone_config`.") if backbone_config is not None and use_timm_backbone: raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`.") if backbone_kwargs is not None and backbone_kwargs and backbone_config is not None: raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.") if not use_timm_backbone: if backbone_config is None: logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.") backbone_config = CONFIG_MAPPING["resnet"](out_features=["stage4"]) elif isinstance(backbone_config, dict): backbone_model_type = backbone_config.get("model_type") config_class = CONFIG_MAPPING[backbone_model_type] backbone_config = config_class.from_dict(backbone_config) self.use_timm_backbone = use_timm_backbone self.backbone_config = backbone_config self.num_channels = num_channels self.num_queries = num_queries self.d_model = d_model self.encoder_ffn_dim = encoder_ffn_dim self.encoder_layers = encoder_layers self.encoder_attention_heads = encoder_attention_heads self.decoder_ffn_dim = decoder_ffn_dim self.decoder_layers = decoder_layers self.decoder_attention_heads = decoder_attention_heads self.dropout = dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.activation_function = activation_function self.init_std = init_std self.init_xavier_std = init_xavier_std self.encoder_layerdrop = encoder_layerdrop self.decoder_layerdrop = decoder_layerdrop self.num_hidden_layers = encoder_layers self.auxiliary_loss = auxiliary_loss self.position_embedding_type = position_embedding_type self.backbone = backbone self.use_pretrained_backbone = use_pretrained_backbone self.backbone_kwargs = backbone_kwargs self.dilation = dilation # Hungarian matcher self.class_cost = class_cost self.bbox_cost = bbox_cost self.giou_cost = giou_cost # Loss coefficients self.mask_loss_coefficient = mask_loss_coefficient self.dice_loss_coefficient = dice_loss_coefficient self.cls_loss_coefficient = cls_loss_coefficient self.bbox_loss_coefficient = bbox_loss_coefficient self.giou_loss_coefficient = giou_loss_coefficient self.focal_alpha = focal_alpha super().__init__(is_encoder_decoder=is_encoder_decoder, **kwargs) @property def num_attention_heads(self) -> int: return self.encoder_attention_heads @property def hidden_size(self) -> int: return self.d_model class ConditionalDetrOnnxConfig(OnnxConfig): torch_onnx_minimum_version = version.parse("1.11") @property def inputs(self) -> Mapping[str, Mapping[int, str]]: return OrderedDict( [ ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}), ("pixel_mask", {0: "batch"}), ] ) @property def atol_for_validation(self) -> float: return 1e-5 @property def default_onnx_opset(self) -> int: return 12

transformers/src/transformers/models/conditional_detr/configuration_conditional_detr.py/0

{ "file_path": "transformers/src/transformers/models/conditional_detr/configuration_conditional_detr.py", "repo_id": "transformers", "token_count": 5203 }

89

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Image processor class for ConvNeXT.""" from typing import Dict, List, Optional, Union import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import ( center_crop, get_resize_output_image_size, resize, to_channel_dimension_format, ) from ...image_utils import ( IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD, ChannelDimension, ImageInput, PILImageResampling, infer_channel_dimension_format, is_scaled_image, make_list_of_images, to_numpy_array, valid_images, validate_kwargs, validate_preprocess_arguments, ) from ...utils import TensorType, is_vision_available, logging if is_vision_available(): import PIL logger = logging.get_logger(__name__) class ConvNextImageProcessor(BaseImageProcessor): r""" Constructs a ConvNeXT image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Controls whether to resize the image's (height, width) dimensions to the specified `size`. Can be overriden by `do_resize` in the `preprocess` method. size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 384}`): Resolution of the output image after `resize` is applied. If `size["shortest_edge"]` >= 384, the image is resized to `(size["shortest_edge"], size["shortest_edge"])`. Otherwise, the smaller edge of the image will be matched to `int(size["shortest_edge"]/crop_pct)`, after which the image is cropped to `(size["shortest_edge"], size["shortest_edge"])`. Only has an effect if `do_resize` is set to `True`. Can be overriden by `size` in the `preprocess` method. crop_pct (`float` *optional*, defaults to 224 / 256): Percentage of the image to crop. Only has an effect if `do_resize` is `True` and size < 384. Can be overriden by `crop_pct` in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`): Resampling filter to use if resizing the image. Can be overriden by `resample` in the `preprocess` method. do_rescale (`bool`, *optional*, defaults to `True`): Whether to rescale the image by the specified scale `rescale_factor`. Can be overriden by `do_rescale` in the `preprocess` method. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Scale factor to use if rescaling the image. Can be overriden by `rescale_factor` in the `preprocess` method. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` method. image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`): Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`): Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. """ model_input_names = ["pixel_values"] def __init__( self, do_resize: bool = True, size: Dict[str, int] = None, crop_pct: float = None, resample: PILImageResampling = PILImageResampling.BILINEAR, do_rescale: bool = True, rescale_factor: Union[int, float] = 1 / 255, do_normalize: bool = True, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, **kwargs, ) -> None: super().__init__(**kwargs) size = size if size is not None else {"shortest_edge": 384} size = get_size_dict(size, default_to_square=False) self.do_resize = do_resize self.size = size # Default value set here for backwards compatibility where the value in config is None self.crop_pct = crop_pct if crop_pct is not None else 224 / 256 self.resample = resample self.do_rescale = do_rescale self.rescale_factor = rescale_factor self.do_normalize = do_normalize self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD self._valid_processor_keys = [ "images", "do_resize", "size", "crop_pct", "resample", "do_rescale", "rescale_factor", "do_normalize", "image_mean", "image_std", "return_tensors", "data_format", "input_data_format", ] def resize( self, image: np.ndarray, size: Dict[str, int], crop_pct: float, resample: PILImageResampling = PILImageResampling.BICUBIC, data_format: Optional[Union[str, ChannelDimension]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs, ) -> np.ndarray: """ Resize an image. Args: image (`np.ndarray`): Image to resize. size (`Dict[str, int]`): Dictionary of the form `{"shortest_edge": int}`, specifying the size of the output image. If `size["shortest_edge"]` >= 384 image is resized to `(size["shortest_edge"], size["shortest_edge"])`. Otherwise, the smaller edge of the image will be matched to `int(size["shortest_edge"] / crop_pct)`, after which the image is cropped to `(size["shortest_edge"], size["shortest_edge"])`. crop_pct (`float`): Percentage of the image to crop. Only has an effect if size < 384. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): Resampling filter to use when resizing the image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred from the input image. """ size = get_size_dict(size, default_to_square=False) if "shortest_edge" not in size: raise ValueError(f"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}") shortest_edge = size["shortest_edge"] if shortest_edge < 384: # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct resize_shortest_edge = int(shortest_edge / crop_pct) resize_size = get_resize_output_image_size( image, size=resize_shortest_edge, default_to_square=False, input_data_format=input_data_format ) image = resize( image=image, size=resize_size, resample=resample, data_format=data_format, input_data_format=input_data_format, **kwargs, ) # then crop to (shortest_edge, shortest_edge) return center_crop( image=image, size=(shortest_edge, shortest_edge), data_format=data_format, input_data_format=input_data_format, **kwargs, ) else: # warping (no cropping) when evaluated at 384 or larger return resize( image, size=(shortest_edge, shortest_edge), resample=resample, data_format=data_format, input_data_format=input_data_format, **kwargs, ) def preprocess( self, images: ImageInput, do_resize: bool = None, size: Dict[str, int] = None, crop_pct: float = None, resample: PILImageResampling = None, do_rescale: bool = None, rescale_factor: float = None, do_normalize: bool = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, return_tensors: Optional[Union[str, TensorType]] = None, data_format: ChannelDimension = ChannelDimension.FIRST, input_data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs, ) -> PIL.Image.Image: """ Preprocess an image or batch of images. Args: images (`ImageInput`): Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`. do_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the image. size (`Dict[str, int]`, *optional*, defaults to `self.size`): Size of the output image after `resize` has been applied. If `size["shortest_edge"]` >= 384, the image is resized to `(size["shortest_edge"], size["shortest_edge"])`. Otherwise, the smaller edge of the image will be matched to `int(size["shortest_edge"]/ crop_pct)`, after which the image is cropped to `(size["shortest_edge"], size["shortest_edge"])`. Only has an effect if `do_resize` is set to `True`. crop_pct (`float`, *optional*, defaults to `self.crop_pct`): Percentage of the image to crop if size < 384. resample (`int`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the image. This can be one of `PILImageResampling`, filters. Only has an effect if `do_resize` is set to `True`. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image values between [0 - 1]. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Rescale factor to rescale the image by if `do_rescale` is set to `True`. do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): Whether to normalize the image. image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`): Image mean. image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): Image standard deviation. return_tensors (`str` or `TensorType`, *optional*): The type of tensors to return. Can be one of: - Unset: Return a list of `np.ndarray`. - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): The channel dimension format for the output image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - Unset: Use the channel dimension format of the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. """ do_resize = do_resize if do_resize is not None else self.do_resize crop_pct = crop_pct if crop_pct is not None else self.crop_pct resample = resample if resample is not None else self.resample do_rescale = do_rescale if do_rescale is not None else self.do_rescale rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor do_normalize = do_normalize if do_normalize is not None else self.do_normalize image_mean = image_mean if image_mean is not None else self.image_mean image_std = image_std if image_std is not None else self.image_std size = size if size is not None else self.size size = get_size_dict(size, default_to_square=False) validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys) images = make_list_of_images(images) if not valid_images(images): raise ValueError( "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " "torch.Tensor, tf.Tensor or jax.ndarray." ) validate_preprocess_arguments( do_rescale=do_rescale, rescale_factor=rescale_factor, do_normalize=do_normalize, image_mean=image_mean, image_std=image_std, do_resize=do_resize, size=size, resample=resample, ) # All transformations expect numpy arrays. images = [to_numpy_array(image) for image in images] if is_scaled_image(images[0]) and do_rescale: logger.warning_once( "It looks like you are trying to rescale already rescaled images. If the input" " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again." ) if input_data_format is None: # We assume that all images have the same channel dimension format. input_data_format = infer_channel_dimension_format(images[0]) if do_resize: images = [ self.resize( image=image, size=size, crop_pct=crop_pct, resample=resample, input_data_format=input_data_format ) for image in images ] if do_rescale: images = [ self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format) for image in images ] if do_normalize: images = [ self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format) for image in images ] images = [ to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images ] data = {"pixel_values": images} return BatchFeature(data=data, tensor_type=return_tensors)

transformers/src/transformers/models/convnext/image_processing_convnext.py/0

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90

# coding=utf-8 # Copyright 2018 Salesforce and HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Salesforce CTRL configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP = { "Salesforce/ctrl": "https://huggingface.co/Salesforce/ctrl/resolve/main/config.json" } class CTRLConfig(PretrainedConfig): """ This is the configuration class to store the configuration of a [`CTRLModel`] or a [`TFCTRLModel`]. It is used to instantiate a CTRL model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the [Salesforce/ctrl](https://huggingface.co/Salesforce/ctrl) architecture from SalesForce. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 246534): Vocabulary size of the CTRL model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`CTRLModel`] or [`TFCTRLModel`]. n_positions (`int`, *optional*, defaults to 256): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). n_embd (`int`, *optional*, defaults to 1280): Dimensionality of the embeddings and hidden states. dff (`int`, *optional*, defaults to 8192): Dimensionality of the inner dimension of the feed forward networks (FFN). n_layer (`int`, *optional*, defaults to 48): Number of hidden layers in the Transformer encoder. n_head (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. resid_pdrop (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. embd_pdrop (`int`, *optional*, defaults to 0.1): The dropout ratio for the embeddings. layer_norm_epsilon (`float`, *optional*, defaults to 1e-06): The epsilon to use in the layer normalization layers initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Examples: ```python >>> from transformers import CTRLConfig, CTRLModel >>> # Initializing a CTRL configuration >>> configuration = CTRLConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = CTRLModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "ctrl" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { "max_position_embeddings": "n_positions", "hidden_size": "n_embd", "num_attention_heads": "n_head", "num_hidden_layers": "n_layer", } def __init__( self, vocab_size=246534, n_positions=256, n_embd=1280, dff=8192, n_layer=48, n_head=16, resid_pdrop=0.1, embd_pdrop=0.1, layer_norm_epsilon=1e-6, initializer_range=0.02, use_cache=True, **kwargs, ): self.vocab_size = vocab_size self.n_positions = n_positions self.n_embd = n_embd self.n_layer = n_layer self.n_head = n_head self.dff = dff self.resid_pdrop = resid_pdrop self.embd_pdrop = embd_pdrop self.layer_norm_epsilon = layer_norm_epsilon self.initializer_range = initializer_range self.use_cache = use_cache super().__init__(**kwargs)

transformers/src/transformers/models/ctrl/configuration_ctrl.py/0

{ "file_path": "transformers/src/transformers/models/ctrl/configuration_ctrl.py", "repo_id": "transformers", "token_count": 1767 }

91

# coding=utf-8 # Copyright 2021 The Fairseq Authors and the HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch Data2VecAudio model.""" import math import warnings from typing import Optional, Tuple, Union import numpy as np import torch import torch.utils.checkpoint from torch import nn from torch.nn import CrossEntropyLoss from ...activations import ACT2FN from ...integrations.deepspeed import is_deepspeed_zero3_enabled from ...modeling_outputs import ( BaseModelOutput, CausalLMOutput, SequenceClassifierOutput, TokenClassifierOutput, Wav2Vec2BaseModelOutput, XVectorOutput, ) from ...modeling_utils import PreTrainedModel from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, is_peft_available, logging, ) from .configuration_data2vec_audio import Data2VecAudioConfig logger = logging.get_logger(__name__) _HIDDEN_STATES_START_POSITION = 2 # General docstring _CONFIG_FOR_DOC = "Data2VecAudioConfig" # Base docstring _CHECKPOINT_FOR_DOC = "facebook/data2vec-audio-base-960h" _EXPECTED_OUTPUT_SHAPE = [1, 292, 768] # CTC docstring _CTC_EXPECTED_OUTPUT = "'MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL'" _CTC_EXPECTED_LOSS = 66.95 DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST = [ "facebook/data2vec-audio-base", "facebook/data2vec-audio-base-10m", "facebook/data2vec-audio-base-100h", "facebook/data2vec-audio-base-960h", # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio ] # Copied from transformers.models.wav2vec2.modeling_wav2vec2._compute_mask_indices def _compute_mask_indices( shape: Tuple[int, int], mask_prob: float, mask_length: int, attention_mask: Optional[torch.LongTensor] = None, min_masks: int = 0, ) -> np.ndarray: """ Computes random mask spans for a given shape. Used to implement [SpecAugment: A Simple Data Augmentation Method for ASR](https://arxiv.org/abs/1904.08779). Note that this method is not optimized to run on TPU and should be run on CPU as part of the preprocessing during training. Args: shape: The shape for which to compute masks. This should be of a tuple of size 2 where the first element is the batch size and the second element is the length of the axis to span. mask_prob: The percentage of the whole axis (between 0 and 1) which will be masked. The number of independently generated mask spans of length `mask_length` is computed by `mask_prob*shape[1]/mask_length`. Note that due to overlaps, `mask_prob` is an upper bound and the actual percentage will be smaller. mask_length: size of the mask min_masks: minimum number of masked spans attention_mask: A (right-padded) attention mask which independently shortens the feature axis of each batch dimension. """ batch_size, sequence_length = shape if mask_length < 1: raise ValueError("`mask_length` has to be bigger than 0.") if mask_length > sequence_length: raise ValueError( f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length}" f" and `sequence_length`: {sequence_length}`" ) # epsilon is used for probabilistic rounding epsilon = np.random.rand(1).item() def compute_num_masked_span(input_length): """Given input length, compute how many spans should be masked""" num_masked_span = int(mask_prob * input_length / mask_length + epsilon) num_masked_span = max(num_masked_span, min_masks) # make sure num masked span <= sequence_length if num_masked_span * mask_length > sequence_length: num_masked_span = sequence_length // mask_length # make sure num_masked span is also <= input_length - (mask_length - 1) if input_length - (mask_length - 1) < num_masked_span: num_masked_span = max(input_length - (mask_length - 1), 0) return num_masked_span # compute number of masked spans in batch input_lengths = ( attention_mask.sum(-1).detach().tolist() if attention_mask is not None else [sequence_length for _ in range(batch_size)] ) # SpecAugment mask to fill spec_aug_mask = np.zeros((batch_size, sequence_length), dtype=bool) spec_aug_mask_idxs = [] max_num_masked_span = compute_num_masked_span(sequence_length) if max_num_masked_span == 0: return spec_aug_mask for input_length in input_lengths: # compute num of masked spans for this input num_masked_span = compute_num_masked_span(input_length) # get random indices to mask spec_aug_mask_idx = np.random.choice( np.arange(input_length - (mask_length - 1)), num_masked_span, replace=False ) # pick first sampled index that will serve as a dummy index to pad vector # to ensure same dimension for all batches due to probabilistic rounding # Picking first sample just pads those vectors twice. if len(spec_aug_mask_idx) == 0: # this case can only happen if `input_length` is strictly smaller then # `sequence_length` in which case the last token has to be a padding # token which we can use as a dummy mask id dummy_mask_idx = sequence_length - 1 else: dummy_mask_idx = spec_aug_mask_idx[0] spec_aug_mask_idx = np.concatenate( [spec_aug_mask_idx, np.ones(max_num_masked_span - num_masked_span, dtype=np.int32) * dummy_mask_idx] ) spec_aug_mask_idxs.append(spec_aug_mask_idx) spec_aug_mask_idxs = np.array(spec_aug_mask_idxs) # expand masked indices to masked spans spec_aug_mask_idxs = np.broadcast_to( spec_aug_mask_idxs[:, :, None], (batch_size, max_num_masked_span, mask_length) ) spec_aug_mask_idxs = spec_aug_mask_idxs.reshape(batch_size, max_num_masked_span * mask_length) # add offset to the starting indexes so that indexes now create a span offsets = np.arange(mask_length)[None, None, :] offsets = np.broadcast_to(offsets, (batch_size, max_num_masked_span, mask_length)).reshape( batch_size, max_num_masked_span * mask_length ) spec_aug_mask_idxs = spec_aug_mask_idxs + offsets # ensure that we cannot have indices larger than sequence_length if spec_aug_mask_idxs.max() > sequence_length - 1: spec_aug_mask_idxs[spec_aug_mask_idxs > sequence_length - 1] = sequence_length - 1 # scatter indices to mask np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1) return spec_aug_mask class Data2VecAudioConvLayer(nn.Module): def __init__(self, config, layer_id=0): super().__init__() self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1 self.out_conv_dim = config.conv_dim[layer_id] self.conv = nn.Conv1d( self.in_conv_dim, self.out_conv_dim, kernel_size=config.conv_kernel[layer_id], stride=config.conv_stride[layer_id], bias=config.conv_bias, ) self.layer_norm = nn.LayerNorm(self.out_conv_dim, elementwise_affine=True) self.activation = ACT2FN[config.feat_extract_activation] def forward(self, hidden_states): hidden_states = self.conv(hidden_states) hidden_states = hidden_states.transpose(-2, -1) hidden_states = self.layer_norm(hidden_states) hidden_states = hidden_states.transpose(-2, -1) hidden_states = self.activation(hidden_states) return hidden_states # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2SamePadLayer with Wav2Vec2->Data2VecAudio class Data2VecAudioPadLayer(nn.Module): def __init__(self, num_conv_pos_embeddings): super().__init__() self.num_pad_remove = 1 if num_conv_pos_embeddings % 2 == 0 else 0 def forward(self, hidden_states): if self.num_pad_remove > 0: hidden_states = hidden_states[:, :, : -self.num_pad_remove] return hidden_states class Data2VecAudioPositionalConvLayer(nn.Module): def __init__(self, config): super().__init__() self.conv = nn.Conv1d( config.hidden_size, config.hidden_size, kernel_size=config.conv_pos_kernel_size, padding=config.conv_pos_kernel_size // 2, groups=config.num_conv_pos_embedding_groups, ) self.padding = Data2VecAudioPadLayer(config.conv_pos_kernel_size) self.activation = ACT2FN[config.feat_extract_activation] # no learnable parameters self.layer_norm = nn.LayerNorm(config.hidden_size, elementwise_affine=False) def forward(self, hidden_states): hidden_states = self.conv(hidden_states) hidden_states = self.padding(hidden_states) hidden_states = hidden_states.transpose(1, 2) hidden_states = self.layer_norm(hidden_states) hidden_states = hidden_states.transpose(1, 2) hidden_states = self.activation(hidden_states) return hidden_states class Data2VecAudioPositionalConvEmbedding(nn.Module): def __init__(self, config): super().__init__() self.layers = nn.ModuleList( [Data2VecAudioPositionalConvLayer(config) for _ in range(config.num_conv_pos_embeddings)] ) def forward(self, hidden_states): hidden_states = hidden_states.transpose(1, 2) for layer in self.layers: hidden_states = layer(hidden_states) hidden_states = hidden_states.transpose(1, 2) return hidden_states class Data2VecAudioFeatureEncoder(nn.Module): """Construct the features from raw audio waveform""" def __init__(self, config): super().__init__() self.conv_layers = nn.ModuleList( [Data2VecAudioConvLayer(config, layer_id=i) for i in range(config.num_feat_extract_layers)] ) self.gradient_checkpointing = False self._requires_grad = True # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2FeatureEncoder._freeze_parameters def _freeze_parameters(self): for param in self.parameters(): param.requires_grad = False self._requires_grad = False # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2FeatureEncoder.forward def forward(self, input_values): hidden_states = input_values[:, None] # make sure hidden_states require grad for gradient_checkpointing if self._requires_grad and self.training: hidden_states.requires_grad = True for conv_layer in self.conv_layers: if self._requires_grad and self.gradient_checkpointing and self.training: hidden_states = self._gradient_checkpointing_func( conv_layer.__call__, hidden_states, ) else: hidden_states = conv_layer(hidden_states) return hidden_states # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2FeatureProjection with Wav2Vec2->Data2VecAudio class Data2VecAudioFeatureProjection(nn.Module): def __init__(self, config): super().__init__() self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.layer_norm_eps) self.projection = nn.Linear(config.conv_dim[-1], config.hidden_size) self.dropout = nn.Dropout(config.feat_proj_dropout) def forward(self, hidden_states): # non-projected hidden states are needed for quantization norm_hidden_states = self.layer_norm(hidden_states) hidden_states = self.projection(norm_hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states, norm_hidden_states # Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->Data2VecAudio class Data2VecAudioAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__( self, embed_dim: int, num_heads: int, dropout: float = 0.0, is_decoder: bool = False, bias: bool = True, is_causal: bool = False, config: Optional[Data2VecAudioConfig] = None, ): super().__init__() self.embed_dim = embed_dim self.num_heads = num_heads self.dropout = dropout self.head_dim = embed_dim // num_heads self.config = config if (self.head_dim * num_heads) != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" f" and `num_heads`: {num_heads})." ) self.scaling = self.head_dim**-0.5 self.is_decoder = is_decoder self.is_causal = is_causal self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def forward( self, hidden_states: torch.Tensor, key_value_states: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None bsz, tgt_len, _ = hidden_states.size() # get query proj query_states = self.q_proj(hidden_states) * self.scaling # get key, value proj # `past_key_value[0].shape[2] == key_value_states.shape[1]` # is checking that the `sequence_length` of the `past_key_value` is the same as # the provided `key_value_states` to support prefix tuning if ( is_cross_attention and past_key_value is not None and past_key_value[0].shape[2] == key_value_states.shape[1] ): # reuse k,v, cross_attentions key_states = past_key_value[0] value_states = past_key_value[1] elif is_cross_attention: # cross_attentions key_states = self._shape(self.k_proj(key_value_states), -1, bsz) value_states = self._shape(self.v_proj(key_value_states), -1, bsz) elif past_key_value is not None: # reuse k, v, self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) key_states = torch.cat([past_key_value[0], key_states], dim=2) value_states = torch.cat([past_key_value[1], value_states], dim=2) else: # self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_states, value_states) proj_shape = (bsz * self.num_heads, -1, self.head_dim) query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) key_states = key_states.reshape(*proj_shape) value_states = value_states.reshape(*proj_shape) src_len = key_states.size(1) attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): raise ValueError( f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" f" {attn_weights.size()}" ) if attention_mask is not None: if attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) attn_weights = nn.functional.softmax(attn_weights, dim=-1) if layer_head_mask is not None: if layer_head_mask.size() != (self.num_heads,): raise ValueError( f"Head mask for a single layer should be of size {(self.num_heads,)}, but is" f" {layer_head_mask.size()}" ) attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) if output_attentions: # this operation is a bit awkward, but it's required to # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to be reshaped # twice and have to be reused in the following attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) else: attn_weights_reshaped = None attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.bmm(attn_probs, value_states) if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) attn_output = attn_output.transpose(1, 2) # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be # partitioned across GPUs when using tensor-parallelism. attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped, past_key_value # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2FeedForward with Wav2Vec2->Data2VecAudio class Data2VecAudioFeedForward(nn.Module): def __init__(self, config): super().__init__() self.intermediate_dropout = nn.Dropout(config.activation_dropout) self.intermediate_dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act self.output_dense = nn.Linear(config.intermediate_size, config.hidden_size) self.output_dropout = nn.Dropout(config.hidden_dropout) def forward(self, hidden_states): hidden_states = self.intermediate_dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) hidden_states = self.intermediate_dropout(hidden_states) hidden_states = self.output_dense(hidden_states) hidden_states = self.output_dropout(hidden_states) return hidden_states # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2EncoderLayer with Wav2Vec2->Data2VecAudio class Data2VecAudioEncoderLayer(nn.Module): def __init__(self, config): super().__init__() self.attention = Data2VecAudioAttention( embed_dim=config.hidden_size, num_heads=config.num_attention_heads, dropout=config.attention_dropout, is_decoder=False, ) self.dropout = nn.Dropout(config.hidden_dropout) self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.feed_forward = Data2VecAudioFeedForward(config) self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states, attention_mask=None, output_attentions=False): attn_residual = hidden_states hidden_states, attn_weights, _ = self.attention( hidden_states, attention_mask=attention_mask, output_attentions=output_attentions ) hidden_states = self.dropout(hidden_states) hidden_states = attn_residual + hidden_states hidden_states = self.layer_norm(hidden_states) hidden_states = hidden_states + self.feed_forward(hidden_states) hidden_states = self.final_layer_norm(hidden_states) outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Encoder with Wav2Vec2->Data2VecAudio class Data2VecAudioEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.pos_conv_embed = Data2VecAudioPositionalConvEmbedding(config) self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout) self.layers = nn.ModuleList([Data2VecAudioEncoderLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states: torch.tensor, attention_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None if attention_mask is not None: # make sure padded tokens output 0 expand_attention_mask = attention_mask.unsqueeze(-1).repeat(1, 1, hidden_states.shape[2]) hidden_states[~expand_attention_mask] = 0 # extend attention_mask attention_mask = 1.0 - attention_mask[:, None, None, :].to(dtype=hidden_states.dtype) attention_mask = attention_mask * torch.finfo(hidden_states.dtype).min attention_mask = attention_mask.expand( attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1] ) position_embeddings = self.pos_conv_embed(hidden_states) hidden_states = hidden_states + position_embeddings hidden_states = self.layer_norm(hidden_states) hidden_states = self.dropout(hidden_states) deepspeed_zero3_is_enabled = is_deepspeed_zero3_enabled() for layer in self.layers: if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) dropout_probability = torch.rand([]) skip_the_layer = True if self.training and (dropout_probability < self.config.layerdrop) else False if not skip_the_layer or deepspeed_zero3_is_enabled: # under deepspeed zero3 all gpus must run in sync if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( layer.__call__, hidden_states, attention_mask, output_attentions, ) else: layer_outputs = layer( hidden_states, attention_mask=attention_mask, output_attentions=output_attentions ) hidden_states = layer_outputs[0] if skip_the_layer: layer_outputs = (None, None) if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Adapter with Wav2Vec2->Data2VecAudio class Data2VecAudioAdapter(nn.Module): def __init__(self, config): super().__init__() # feature dim might need to be down-projected if config.output_hidden_size != config.hidden_size: self.proj = nn.Linear(config.hidden_size, config.output_hidden_size) self.proj_layer_norm = nn.LayerNorm(config.output_hidden_size) else: self.proj = self.proj_layer_norm = None self.layers = nn.ModuleList(Data2VecAudioAdapterLayer(config) for _ in range(config.num_adapter_layers)) self.layerdrop = config.layerdrop def forward(self, hidden_states): # down project hidden_states if necessary if self.proj is not None and self.proj_layer_norm is not None: hidden_states = self.proj(hidden_states) hidden_states = self.proj_layer_norm(hidden_states) hidden_states = hidden_states.transpose(1, 2) for layer in self.layers: layerdrop_prob = np.random.random() if not self.training or (layerdrop_prob > self.layerdrop): hidden_states = layer(hidden_states) hidden_states = hidden_states.transpose(1, 2) return hidden_states # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2AdapterLayer with Wav2Vec2->Data2VecAudio class Data2VecAudioAdapterLayer(nn.Module): def __init__(self, config): super().__init__() self.conv = nn.Conv1d( config.output_hidden_size, 2 * config.output_hidden_size, config.adapter_kernel_size, stride=config.adapter_stride, padding=1, ) def forward(self, hidden_states): hidden_states = self.conv(hidden_states) hidden_states = nn.functional.glu(hidden_states, dim=1) return hidden_states class Data2VecAudioPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = Data2VecAudioConfig base_model_prefix = "data2vec_audio" main_input_name = "input_values" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, Data2VecAudioFeatureProjection): k = math.sqrt(1 / module.projection.in_features) nn.init.uniform_(module.projection.weight, a=-k, b=k) nn.init.uniform_(module.projection.bias, a=-k, b=k) elif isinstance(module, Data2VecAudioPositionalConvLayer): nn.init.constant_(module.conv.bias, 0) elif isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, (nn.LayerNorm, nn.GroupNorm)): if module.bias is not None: module.bias.data.zero_() if module.weight is not None: module.weight.data.fill_(1.0) elif isinstance(module, nn.Conv1d): nn.init.kaiming_normal_(module.weight) if module.bias is not None: k = math.sqrt(module.groups / (module.in_channels * module.kernel_size[0])) nn.init.uniform_(module.bias, a=-k, b=k) # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2PreTrainedModel._get_feat_extract_output_lengths with def _get_feat_extract_output_lengths( self, input_lengths: Union[torch.LongTensor, int], add_adapter: Optional[bool] = None ): """ Computes the output length of the convolutional layers """ add_adapter = self.config.add_adapter if add_adapter is None else add_adapter def _conv_out_length(input_length, kernel_size, stride): # 1D convolutional layer output length formula taken # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html return torch.div(input_length - kernel_size, stride, rounding_mode="floor") + 1 for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride): input_lengths = _conv_out_length(input_lengths, kernel_size, stride) if add_adapter: for _ in range(self.config.num_adapter_layers): input_lengths = _conv_out_length(input_lengths, 1, self.config.adapter_stride) return input_lengths # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2PreTrainedModel._get_feature_vector_attention_mask def _get_feature_vector_attention_mask( self, feature_vector_length: int, attention_mask: torch.LongTensor, add_adapter=None ): # Effectively attention_mask.sum(-1), but not inplace to be able to run # on inference mode. non_padded_lengths = attention_mask.cumsum(dim=-1)[:, -1] output_lengths = self._get_feat_extract_output_lengths(non_padded_lengths, add_adapter=add_adapter) output_lengths = output_lengths.to(torch.long) batch_size = attention_mask.shape[0] attention_mask = torch.zeros( (batch_size, feature_vector_length), dtype=attention_mask.dtype, device=attention_mask.device ) # these two operations makes sure that all values before the output lengths idxs are attended to attention_mask[(torch.arange(attention_mask.shape[0], device=attention_mask.device), output_lengths - 1)] = 1 attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool() return attention_mask DATA2VEC_AUDIO_START_DOCSTRING = r""" Data2VecAudio was proposed in [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://arxiv.org/pdf/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu and Michael Auli. This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving etc.). This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`Data2VecAudioConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ DATA2VEC_AUDIO_INPUTS_DOCSTRING = r""" Args: input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): Float values of input raw speech waveform. Values can be obtained by loading a *.flac* or *.wav* audio file into an array of type *List[float]* or a *numpy.ndarray*, *e.g.* via the soundfile library (*pip install soundfile*). To prepare the array into *input_values*, the [`AutoProcessor`] should be used for padding and conversion into a tensor of type *torch.FloatTensor*. See [`Wav2Vec2Processor.__call__`] for details. attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing convolution and attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) <Tip warning={true}> `attention_mask` should be passed if the corresponding processor has `config.return_attention_mask == True`, which is the case for all pre-trained Data2Vec Audio models. Be aware that that even with `attention_mask`, zero-padded inputs will have slightly different outputs compared to non-padded inputs because there are more than one convolutional layer in the positional encodings. For a more detailed explanation, see [here](https://github.com/huggingface/transformers/issues/25621#issuecomment-1713759349). </Tip> output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare Data2VecAudio Model transformer outputting raw hidden-states without any specific head on top.", DATA2VEC_AUDIO_START_DOCSTRING, ) class Data2VecAudioModel(Data2VecAudioPreTrainedModel): def __init__(self, config: Data2VecAudioConfig): super().__init__(config) self.config = config self.feature_extractor = Data2VecAudioFeatureEncoder(config) self.feature_projection = Data2VecAudioFeatureProjection(config) # model only needs masking vector if mask prob is > 0.0 if config.mask_time_prob > 0.0 or config.mask_feature_prob > 0.0: self.masked_spec_embed = nn.Parameter(torch.FloatTensor(config.hidden_size).uniform_()) self.encoder = Data2VecAudioEncoder(config) self.adapter = Data2VecAudioAdapter(config) if config.add_adapter else None # Initialize weights and apply final processing self.post_init() def freeze_feature_encoder(self): """ Calling this function will disable the gradient computation for the feature encoder so that its parameter will not be updated during training. """ self.feature_extractor._freeze_parameters() def _mask_hidden_states( self, hidden_states: torch.FloatTensor, mask_time_indices: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.LongTensor] = None, ): """ Masks extracted features along time axis and/or along feature axis according to [SpecAugment](https://arxiv.org/abs/1904.08779). """ # `config.apply_spec_augment` can set masking to False if not getattr(self.config, "apply_spec_augment", True): return hidden_states # generate indices & apply SpecAugment along time axis batch_size, sequence_length, hidden_size = hidden_states.size() if mask_time_indices is not None: # apply SpecAugment along time axis with given mask_time_indices hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype) elif self.config.mask_time_prob > 0 and self.training: mask_time_indices = _compute_mask_indices( (batch_size, sequence_length), mask_prob=self.config.mask_time_prob, mask_length=self.config.mask_time_length, attention_mask=attention_mask, min_masks=self.config.mask_time_min_masks, ) mask_time_indices = torch.tensor(mask_time_indices, device=hidden_states.device, dtype=torch.bool) hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype) if self.config.mask_feature_prob > 0 and self.training: # generate indices & apply SpecAugment along feature axis mask_feature_indices = _compute_mask_indices( (batch_size, hidden_size), mask_prob=self.config.mask_feature_prob, mask_length=self.config.mask_feature_length, min_masks=self.config.mask_feature_min_masks, ) mask_feature_indices = torch.tensor(mask_feature_indices, device=hidden_states.device, dtype=torch.bool) mask_feature_indices = mask_feature_indices[:, None].expand(-1, sequence_length, -1) hidden_states[mask_feature_indices] = 0 return hidden_states @add_start_docstrings_to_model_forward(DATA2VEC_AUDIO_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=Wav2Vec2BaseModelOutput, config_class=_CONFIG_FOR_DOC, modality="audio", expected_output=_EXPECTED_OUTPUT_SHAPE, ) def forward( self, input_values: Optional[torch.Tensor], attention_mask: Optional[torch.Tensor] = None, mask_time_indices: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, Wav2Vec2BaseModelOutput]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict extract_features = self.feature_extractor(input_values) extract_features = extract_features.transpose(1, 2) if attention_mask is not None: # compute reduced attention_mask corresponding to feature vectors attention_mask = self._get_feature_vector_attention_mask( extract_features.shape[1], attention_mask, add_adapter=False ) hidden_states, extract_features = self.feature_projection(extract_features) hidden_states = self._mask_hidden_states( hidden_states, mask_time_indices=mask_time_indices, attention_mask=attention_mask ) encoder_outputs = self.encoder( hidden_states, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = encoder_outputs[0] if self.adapter is not None: hidden_states = self.adapter(hidden_states) if not return_dict: return (hidden_states, extract_features) + encoder_outputs[1:] return Wav2Vec2BaseModelOutput( last_hidden_state=hidden_states, extract_features=extract_features, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) @add_start_docstrings( """Data2VecAudio Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).""", DATA2VEC_AUDIO_START_DOCSTRING, ) class Data2VecAudioForCTC(Data2VecAudioPreTrainedModel): def __init__(self, config): super().__init__(config) self.data2vec_audio = Data2VecAudioModel(config) self.dropout = nn.Dropout(config.final_dropout) if config.vocab_size is None: raise ValueError( f"You are trying to instantiate {self.__class__} with a configuration that " "does not define the vocabulary size of the language model head. Please " "instantiate the model as follows: `Data2VecAudioForCTC.from_pretrained(..., vocab_size=vocab_size)`. " "or define `vocab_size` of your model's configuration." ) output_hidden_size = ( config.output_hidden_size if hasattr(config, "add_adapter") and config.add_adapter else config.hidden_size ) self.lm_head = nn.Linear(output_hidden_size, config.vocab_size) # Initialize weights and apply final processing self.post_init() def freeze_feature_extractor(self): """ Calling this function will disable the gradient computation for the feature encoder so that its parameter will not be updated during training. """ warnings.warn( "The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5. " "Please use the equivalent `freeze_feature_encoder` method instead.", FutureWarning, ) self.freeze_feature_encoder() def freeze_feature_encoder(self): """ Calling this function will disable the gradient computation for the feature encoder so that its parameter will not be updated during training. """ self.data2vec_audio.feature_extractor._freeze_parameters() @add_start_docstrings_to_model_forward(DATA2VEC_AUDIO_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=CausalLMOutput, config_class=_CONFIG_FOR_DOC, expected_output=_CTC_EXPECTED_OUTPUT, expected_loss=_CTC_EXPECTED_LOSS, ) # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC.forward with wav2vec2->data2vec_audio def forward( self, input_values: Optional[torch.Tensor], attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[torch.Tensor] = None, ) -> Union[Tuple, CausalLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*): Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to the sequence length of the output logits. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size - 1]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.data2vec_audio( input_values, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] hidden_states = self.dropout(hidden_states) logits = self.lm_head(hidden_states) loss = None if labels is not None: if labels.max() >= self.config.vocab_size: raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}") # retrieve loss input_lengths from attention_mask attention_mask = ( attention_mask if attention_mask is not None else torch.ones_like(input_values, dtype=torch.long) ) input_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long) # assuming that padded tokens are filled with -100 # when not being attended to labels_mask = labels >= 0 target_lengths = labels_mask.sum(-1) flattened_targets = labels.masked_select(labels_mask) # ctc_loss doesn't support fp16 log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1) with torch.backends.cudnn.flags(enabled=False): loss = nn.functional.ctc_loss( log_probs, flattened_targets, input_lengths, target_lengths, blank=self.config.pad_token_id, reduction=self.config.ctc_loss_reduction, zero_infinity=self.config.ctc_zero_infinity, ) if not return_dict: output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:] return ((loss,) + output) if loss is not None else output return CausalLMOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions ) @add_start_docstrings( """ Data2VecAudio Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like SUPERB Keyword Spotting. """, DATA2VEC_AUDIO_START_DOCSTRING, ) class Data2VecAudioForSequenceClassification(Data2VecAudioPreTrainedModel): def __init__(self, config): super().__init__(config) if hasattr(config, "add_adapter") and config.add_adapter: raise ValueError( "Sequence classification does not support the use of Data2VecAudio adapters (config.add_adapter=True)" ) self.data2vec_audio = Data2VecAudioModel(config) num_layers = config.num_hidden_layers + 1 # transformer layers + input embeddings if config.use_weighted_layer_sum: self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers) self.projector = nn.Linear(config.hidden_size, config.classifier_proj_size) self.classifier = nn.Linear(config.classifier_proj_size, config.num_labels) # Initialize weights and apply final processing self.post_init() def freeze_feature_extractor(self): """ Calling this function will disable the gradient computation for the feature encoder so that its parameters will not be updated during training. """ warnings.warn( "The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5. " "Please use the equivalent `freeze_feature_encoder` method instead.", FutureWarning, ) self.freeze_feature_encoder() def freeze_feature_encoder(self): """ Calling this function will disable the gradient computation for the feature encoder so that its parameter will not be updated during training. """ self.data2vec_audio.feature_extractor._freeze_parameters() def freeze_base_model(self): """ Calling this function will disable the gradient computation for the base model so that its parameters will not be updated during training. Only the classification head will be updated. """ for param in self.data2vec_audio.parameters(): param.requires_grad = False @add_start_docstrings_to_model_forward(DATA2VEC_AUDIO_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, modality="audio", ) # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.forward with wav2vec2->data2vec_audio def forward( self, input_values: Optional[torch.Tensor], attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[torch.Tensor] = None, ) -> Union[Tuple, SequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states outputs = self.data2vec_audio( input_values, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if self.config.use_weighted_layer_sum: hidden_states = outputs[_HIDDEN_STATES_START_POSITION] hidden_states = torch.stack(hidden_states, dim=1) norm_weights = nn.functional.softmax(self.layer_weights, dim=-1) hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1) else: hidden_states = outputs[0] hidden_states = self.projector(hidden_states) if attention_mask is None: pooled_output = hidden_states.mean(dim=1) else: padding_mask = self._get_feature_vector_attention_mask(hidden_states.shape[1], attention_mask) hidden_states[~padding_mask] = 0.0 pooled_output = hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1) logits = self.classifier(pooled_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ Data2VecAudio Model with a frame classification head on top for tasks like Speaker Diarization. """, DATA2VEC_AUDIO_START_DOCSTRING, ) class Data2VecAudioForAudioFrameClassification(Data2VecAudioPreTrainedModel): def __init__(self, config): super().__init__(config) if hasattr(config, "add_adapter") and config.add_adapter: raise ValueError( "Audio frame classification does not support the use of Data2VecAudio adapters" " (config.add_adapter=True)" ) self.data2vec_audio = Data2VecAudioModel(config) num_layers = config.num_hidden_layers + 1 # transformer layers + input embeddings if config.use_weighted_layer_sum: self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers) self.classifier = nn.Linear(config.hidden_size, config.num_labels) self.num_labels = config.num_labels self.init_weights() def freeze_feature_extractor(self): """ Calling this function will disable the gradient computation for the feature encoder so that its parameter will not be updated during training. """ warnings.warn( "The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5. " "Please use the equivalent `freeze_feature_encoder` method instead.", FutureWarning, ) self.freeze_feature_encoder() def freeze_feature_encoder(self): """ Calling this function will disable the gradient computation for the feature encoder so that its parameter will not be updated during training. """ self.data2vec_audio.feature_extractor._freeze_parameters() def freeze_base_model(self): """ Calling this function will disable the gradient computation for the base model so that its parameters will not be updated during training. Only the classification head will be updated. """ for param in self.data2vec_audio.parameters(): param.requires_grad = False @add_start_docstrings_to_model_forward(DATA2VEC_AUDIO_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, modality="audio", ) # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForAudioFrameClassification.forward with wav2vec2->data2vec_audio def forward( self, input_values: Optional[torch.Tensor], attention_mask: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states outputs = self.data2vec_audio( input_values, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if self.config.use_weighted_layer_sum: hidden_states = outputs[_HIDDEN_STATES_START_POSITION] hidden_states = torch.stack(hidden_states, dim=1) norm_weights = nn.functional.softmax(self.layer_weights, dim=-1) hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1) else: hidden_states = outputs[0] logits = self.classifier(hidden_states) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), torch.argmax(labels.view(-1, self.num_labels), axis=1)) if not return_dict: output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:] return output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) # Copied from transformers.models.wav2vec2.modeling_wav2vec2.AMSoftmaxLoss class AMSoftmaxLoss(nn.Module): def __init__(self, input_dim, num_labels, scale=30.0, margin=0.4): super(AMSoftmaxLoss, self).__init__() self.scale = scale self.margin = margin self.num_labels = num_labels self.weight = nn.Parameter(torch.randn(input_dim, num_labels), requires_grad=True) self.loss = nn.CrossEntropyLoss() def forward(self, hidden_states, labels): labels = labels.flatten() weight = nn.functional.normalize(self.weight, dim=0) hidden_states = nn.functional.normalize(hidden_states, dim=1) cos_theta = torch.mm(hidden_states, weight) psi = cos_theta - self.margin onehot = nn.functional.one_hot(labels, self.num_labels) logits = self.scale * torch.where(onehot.bool(), psi, cos_theta) loss = self.loss(logits, labels) return loss # Copied from transformers.models.wav2vec2.modeling_wav2vec2.TDNNLayer class TDNNLayer(nn.Module): def __init__(self, config, layer_id=0): super().__init__() self.in_conv_dim = config.tdnn_dim[layer_id - 1] if layer_id > 0 else config.tdnn_dim[layer_id] self.out_conv_dim = config.tdnn_dim[layer_id] self.kernel_size = config.tdnn_kernel[layer_id] self.dilation = config.tdnn_dilation[layer_id] self.kernel = nn.Linear(self.in_conv_dim * self.kernel_size, self.out_conv_dim) self.activation = nn.ReLU() def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: if is_peft_available(): from peft.tuners.lora import LoraLayer if isinstance(self.kernel, LoraLayer): warnings.warn( "Detected LoRA on TDNNLayer. LoRA weights won't be applied due to optimization. " "You should exclude TDNNLayer from LoRA's target modules.", ) # for backward compatibility, we keep nn.Linear but call F.conv1d for speed up hidden_states = hidden_states.transpose(1, 2) weight = self.kernel.weight.view(self.out_conv_dim, self.kernel_size, self.in_conv_dim).transpose(1, 2) hidden_states = nn.functional.conv1d(hidden_states, weight, self.kernel.bias, dilation=self.dilation) hidden_states = hidden_states.transpose(1, 2) hidden_states = self.activation(hidden_states) return hidden_states @add_start_docstrings( """ Data2VecAudio Model with an XVector feature extraction head on top for tasks like Speaker Verification. """, DATA2VEC_AUDIO_START_DOCSTRING, ) class Data2VecAudioForXVector(Data2VecAudioPreTrainedModel): def __init__(self, config): super().__init__(config) self.data2vec_audio = Data2VecAudioModel(config) num_layers = config.num_hidden_layers + 1 # transformer layers + input embeddings if config.use_weighted_layer_sum: self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers) self.projector = nn.Linear(config.hidden_size, config.tdnn_dim[0]) tdnn_layers = [TDNNLayer(config, i) for i in range(len(config.tdnn_dim))] self.tdnn = nn.ModuleList(tdnn_layers) self.feature_extractor = nn.Linear(config.tdnn_dim[-1] * 2, config.xvector_output_dim) self.classifier = nn.Linear(config.xvector_output_dim, config.xvector_output_dim) self.objective = AMSoftmaxLoss(config.xvector_output_dim, config.num_labels) self.init_weights() def freeze_feature_extractor(self): """ Calling this function will disable the gradient computation for the feature encoder so that its parameter will not be updated during training. """ warnings.warn( "The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5. " "Please use the equivalent `freeze_feature_encoder` method instead.", FutureWarning, ) self.freeze_feature_encoder() def freeze_feature_encoder(self): """ Calling this function will disable the gradient computation for the feature encoder so that its parameter will not be updated during training. """ self.data2vec_audio.feature_extractor._freeze_parameters() def freeze_base_model(self): """ Calling this function will disable the gradient computation for the base model so that its parameters will not be updated during training. Only the classification head will be updated. """ for param in self.data2vec_audio.parameters(): param.requires_grad = False def _get_tdnn_output_lengths(self, input_lengths: Union[torch.LongTensor, int]): """ Computes the output length of the TDNN layers """ def _conv_out_length(input_length, kernel_size, stride): # 1D convolutional layer output length formula taken # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html return (input_length - kernel_size) // stride + 1 for kernel_size in self.config.tdnn_kernel: input_lengths = _conv_out_length(input_lengths, kernel_size, 1) return input_lengths @add_start_docstrings_to_model_forward(DATA2VEC_AUDIO_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=XVectorOutput, config_class=_CONFIG_FOR_DOC, modality="audio", ) # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForXVector.forward with wav2vec2->data2vec_audio def forward( self, input_values: Optional[torch.Tensor], attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[torch.Tensor] = None, ) -> Union[Tuple, XVectorOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states outputs = self.data2vec_audio( input_values, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if self.config.use_weighted_layer_sum: hidden_states = outputs[_HIDDEN_STATES_START_POSITION] hidden_states = torch.stack(hidden_states, dim=1) norm_weights = nn.functional.softmax(self.layer_weights, dim=-1) hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1) else: hidden_states = outputs[0] hidden_states = self.projector(hidden_states) for tdnn_layer in self.tdnn: hidden_states = tdnn_layer(hidden_states) # Statistic Pooling if attention_mask is None: mean_features = hidden_states.mean(dim=1) std_features = hidden_states.std(dim=1) else: feat_extract_output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(dim=1)) tdnn_output_lengths = self._get_tdnn_output_lengths(feat_extract_output_lengths) mean_features = [] std_features = [] for i, length in enumerate(tdnn_output_lengths): mean_features.append(hidden_states[i, :length].mean(dim=0)) std_features.append(hidden_states[i, :length].std(dim=0)) mean_features = torch.stack(mean_features) std_features = torch.stack(std_features) statistic_pooling = torch.cat([mean_features, std_features], dim=-1) output_embeddings = self.feature_extractor(statistic_pooling) logits = self.classifier(output_embeddings) loss = None if labels is not None: loss = self.objective(logits, labels) if not return_dict: output = (logits, output_embeddings) + outputs[_HIDDEN_STATES_START_POSITION:] return ((loss,) + output) if loss is not None else output return XVectorOutput( loss=loss, logits=logits, embeddings=output_embeddings, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py/0

{ "file_path": "transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py", "repo_id": "transformers", "token_count": 27881 }

92

# coding=utf-8 # Copyright 2022 Facebook AI Research (FAIR) and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ TensorFlow DeiT model.""" from __future__ import annotations import collections.abc import math from dataclasses import dataclass from typing import Optional, Tuple, Union import tensorflow as tf from ...activations_tf import get_tf_activation from ...modeling_tf_outputs import ( TFBaseModelOutput, TFBaseModelOutputWithPooling, TFImageClassifierOutput, TFMaskedImageModelingOutput, ) from ...modeling_tf_utils import ( TFPreTrainedModel, TFSequenceClassificationLoss, get_initializer, keras, keras_serializable, unpack_inputs, ) from ...tf_utils import shape_list, stable_softmax from ...utils import ( ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_deit import DeiTConfig logger = logging.get_logger(__name__) # General docstring _CONFIG_FOR_DOC = "DeiTConfig" # Base docstring _CHECKPOINT_FOR_DOC = "facebook/deit-base-distilled-patch16-224" _EXPECTED_OUTPUT_SHAPE = [1, 198, 768] # Image classification docstring _IMAGE_CLASS_CHECKPOINT = "facebook/deit-base-distilled-patch16-224" _IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat" TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "facebook/deit-base-distilled-patch16-224", # See all DeiT models at https://huggingface.co/models?filter=deit ] @dataclass class TFDeiTForImageClassificationWithTeacherOutput(ModelOutput): """ Output type of [`DeiTForImageClassificationWithTeacher`]. Args: logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`): Prediction scores as the average of the cls_logits and distillation logits. cls_logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`): Prediction scores of the classification head (i.e. the linear layer on top of the final hidden state of the class token). distillation_logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`): Prediction scores of the distillation head (i.e. the linear layer on top of the final hidden state of the distillation token). hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ logits: tf.Tensor = None cls_logits: tf.Tensor = None distillation_logits: tf.Tensor = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None class TFDeiTEmbeddings(keras.layers.Layer): """ Construct the CLS token, distillation token, position and patch embeddings. Optionally, also the mask token. """ def __init__(self, config: DeiTConfig, use_mask_token: bool = False, **kwargs) -> None: super().__init__(**kwargs) self.config = config self.use_mask_token = use_mask_token self.patch_embeddings = TFDeiTPatchEmbeddings(config=config, name="patch_embeddings") self.dropout = keras.layers.Dropout(config.hidden_dropout_prob, name="dropout") def build(self, input_shape=None): self.cls_token = self.add_weight( shape=(1, 1, self.config.hidden_size), initializer=keras.initializers.zeros(), trainable=True, name="cls_token", ) self.distillation_token = self.add_weight( shape=(1, 1, self.config.hidden_size), initializer=keras.initializers.zeros(), trainable=True, name="distillation_token", ) self.mask_token = None if self.use_mask_token: self.mask_token = self.add_weight( shape=(1, 1, self.config.hidden_size), initializer=keras.initializers.zeros(), trainable=True, name="mask_token", ) num_patches = self.patch_embeddings.num_patches self.position_embeddings = self.add_weight( shape=(1, num_patches + 2, self.config.hidden_size), initializer=keras.initializers.zeros(), trainable=True, name="position_embeddings", ) if self.built: return self.built = True if getattr(self, "patch_embeddings", None) is not None: with tf.name_scope(self.patch_embeddings.name): self.patch_embeddings.build(None) if getattr(self, "dropout", None) is not None: with tf.name_scope(self.dropout.name): self.dropout.build(None) def call( self, pixel_values: tf.Tensor, bool_masked_pos: tf.Tensor | None = None, training: bool = False ) -> tf.Tensor: embeddings = self.patch_embeddings(pixel_values) batch_size, seq_length, _ = shape_list(embeddings) if bool_masked_pos is not None: mask_tokens = tf.tile(self.mask_token, [batch_size, seq_length, 1]) # replace the masked visual tokens by mask_tokens mask = tf.expand_dims(bool_masked_pos, axis=-1) mask = tf.cast(mask, dtype=mask_tokens.dtype) embeddings = embeddings * (1.0 - mask) + mask_tokens * mask cls_tokens = tf.repeat(self.cls_token, repeats=batch_size, axis=0) distillation_tokens = tf.repeat(self.distillation_token, repeats=batch_size, axis=0) embeddings = tf.concat((cls_tokens, distillation_tokens, embeddings), axis=1) embeddings = embeddings + self.position_embeddings embeddings = self.dropout(embeddings, training=training) return embeddings class TFDeiTPatchEmbeddings(keras.layers.Layer): """ This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a Transformer. """ def __init__(self, config: DeiTConfig, **kwargs) -> None: super().__init__(**kwargs) image_size, patch_size = config.image_size, config.patch_size num_channels, hidden_size = config.num_channels, config.hidden_size image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.num_patches = num_patches self.projection = keras.layers.Conv2D( hidden_size, kernel_size=patch_size, strides=patch_size, name="projection" ) def call(self, pixel_values: tf.Tensor) -> tf.Tensor: batch_size, height, width, num_channels = shape_list(pixel_values) if tf.executing_eagerly() and num_channels != self.num_channels: raise ValueError( "Make sure that the channel dimension of the pixel values match with the one set in the configuration." ) if tf.executing_eagerly() and (height != self.image_size[0] or width != self.image_size[1]): raise ValueError( f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})." ) x = self.projection(pixel_values) batch_size, height, width, num_channels = shape_list(x) x = tf.reshape(x, (batch_size, height * width, num_channels)) return x def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "projection", None) is not None: with tf.name_scope(self.projection.name): self.projection.build([None, None, None, self.num_channels]) # Copied from transformers.models.vit.modeling_tf_vit.TFViTSelfAttention with ViT->DeiT class TFDeiTSelfAttention(keras.layers.Layer): def __init__(self, config: DeiTConfig, **kwargs): super().__init__(**kwargs) if config.hidden_size % config.num_attention_heads != 0: raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number " f"of attention heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.sqrt_att_head_size = math.sqrt(self.attention_head_size) self.query = keras.layers.Dense( units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query" ) self.key = keras.layers.Dense( units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key" ) self.value = keras.layers.Dense( units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value" ) self.dropout = keras.layers.Dropout(rate=config.attention_probs_dropout_prob) self.config = config def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor: # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size] tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size)) # Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size] return tf.transpose(tensor, perm=[0, 2, 1, 3]) def call( self, hidden_states: tf.Tensor, head_mask: tf.Tensor, output_attentions: bool, training: bool = False, ) -> Tuple[tf.Tensor]: batch_size = shape_list(hidden_states)[0] mixed_query_layer = self.query(inputs=hidden_states) mixed_key_layer = self.key(inputs=hidden_states) mixed_value_layer = self.value(inputs=hidden_states) query_layer = self.transpose_for_scores(mixed_query_layer, batch_size) key_layer = self.transpose_for_scores(mixed_key_layer, batch_size) value_layer = self.transpose_for_scores(mixed_value_layer, batch_size) # Take the dot product between "query" and "key" to get the raw attention scores. # (batch size, num_heads, seq_len_q, seq_len_k) attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True) dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype) attention_scores = tf.divide(attention_scores, dk) # Normalize the attention scores to probabilities. attention_probs = stable_softmax(logits=attention_scores, axis=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(inputs=attention_probs, training=training) # Mask heads if we want to if head_mask is not None: attention_probs = tf.multiply(attention_probs, head_mask) attention_output = tf.matmul(attention_probs, value_layer) attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3]) # (batch_size, seq_len_q, all_head_size) attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size)) outputs = (attention_output, attention_probs) if output_attentions else (attention_output,) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "query", None) is not None: with tf.name_scope(self.query.name): self.query.build([None, None, self.config.hidden_size]) if getattr(self, "key", None) is not None: with tf.name_scope(self.key.name): self.key.build([None, None, self.config.hidden_size]) if getattr(self, "value", None) is not None: with tf.name_scope(self.value.name): self.value.build([None, None, self.config.hidden_size]) # Copied from transformers.models.vit.modeling_tf_vit.TFViTSelfOutput with ViT->DeiT class TFDeiTSelfOutput(keras.layers.Layer): """ The residual connection is defined in TFDeiTLayer instead of here (as is the case with other models), due to the layernorm applied before each block. """ def __init__(self, config: DeiTConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) self.config = config def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.dropout(inputs=hidden_states, training=training) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) # Copied from transformers.models.vit.modeling_tf_vit.TFViTAttention with ViT->DeiT class TFDeiTAttention(keras.layers.Layer): def __init__(self, config: DeiTConfig, **kwargs): super().__init__(**kwargs) self.self_attention = TFDeiTSelfAttention(config, name="attention") self.dense_output = TFDeiTSelfOutput(config, name="output") def prune_heads(self, heads): raise NotImplementedError def call( self, input_tensor: tf.Tensor, head_mask: tf.Tensor, output_attentions: bool, training: bool = False, ) -> Tuple[tf.Tensor]: self_outputs = self.self_attention( hidden_states=input_tensor, head_mask=head_mask, output_attentions=output_attentions, training=training ) attention_output = self.dense_output( hidden_states=self_outputs[0], input_tensor=input_tensor, training=training ) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "self_attention", None) is not None: with tf.name_scope(self.self_attention.name): self.self_attention.build(None) if getattr(self, "dense_output", None) is not None: with tf.name_scope(self.dense_output.name): self.dense_output.build(None) # Copied from transformers.models.vit.modeling_tf_vit.TFViTIntermediate with ViT->DeiT class TFDeiTIntermediate(keras.layers.Layer): def __init__(self, config: DeiTConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) if isinstance(config.hidden_act, str): self.intermediate_act_fn = get_tf_activation(config.hidden_act) else: self.intermediate_act_fn = config.hidden_act self.config = config def call(self, hidden_states: tf.Tensor) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) # Copied from transformers.models.vit.modeling_tf_vit.TFViTOutput with ViT->DeiT class TFDeiTOutput(keras.layers.Layer): def __init__(self, config: DeiTConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) self.config = config def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.dropout(inputs=hidden_states, training=training) hidden_states = hidden_states + input_tensor return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.intermediate_size]) class TFDeiTLayer(keras.layers.Layer): """This corresponds to the Block class in the timm implementation.""" def __init__(self, config: DeiTConfig, **kwargs): super().__init__(**kwargs) self.attention = TFDeiTAttention(config, name="attention") self.intermediate = TFDeiTIntermediate(config, name="intermediate") self.deit_output = TFDeiTOutput(config, name="output") self.layernorm_before = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm_before") self.layernorm_after = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm_after") self.config = config def call( self, hidden_states: tf.Tensor, head_mask: tf.Tensor, output_attentions: bool, training: bool = False, ) -> Tuple[tf.Tensor]: attention_outputs = self.attention( # in DeiT, layernorm is applied before self-attention input_tensor=self.layernorm_before(inputs=hidden_states, training=training), head_mask=head_mask, output_attentions=output_attentions, training=training, ) attention_output = attention_outputs[0] # first residual connection hidden_states = attention_output + hidden_states # in DeiT, layernorm is also applied after self-attention layer_output = self.layernorm_after(inputs=hidden_states, training=training) intermediate_output = self.intermediate(hidden_states=layer_output, training=training) # second residual connection is done here layer_output = self.deit_output( hidden_states=intermediate_output, input_tensor=hidden_states, training=training ) outputs = (layer_output,) + attention_outputs[1:] # add attentions if we output them return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "attention", None) is not None: with tf.name_scope(self.attention.name): self.attention.build(None) if getattr(self, "intermediate", None) is not None: with tf.name_scope(self.intermediate.name): self.intermediate.build(None) if getattr(self, "deit_output", None) is not None: with tf.name_scope(self.deit_output.name): self.deit_output.build(None) if getattr(self, "layernorm_before", None) is not None: with tf.name_scope(self.layernorm_before.name): self.layernorm_before.build([None, None, self.config.hidden_size]) if getattr(self, "layernorm_after", None) is not None: with tf.name_scope(self.layernorm_after.name): self.layernorm_after.build([None, None, self.config.hidden_size]) # Copied from transformers.models.vit.modeling_tf_vit.TFViTEncoder with ViT->DeiT class TFDeiTEncoder(keras.layers.Layer): def __init__(self, config: DeiTConfig, **kwargs): super().__init__(**kwargs) self.layer = [TFDeiTLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)] def call( self, hidden_states: tf.Tensor, head_mask: tf.Tensor, output_attentions: bool, output_hidden_states: bool, return_dict: bool, training: bool = False, ) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]: all_hidden_states = () if output_hidden_states else None all_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_outputs = layer_module( hidden_states=hidden_states, head_mask=head_mask[i], output_attentions=output_attentions, training=training, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) # Add last layer if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None) return TFBaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "layer", None) is not None: for layer in self.layer: with tf.name_scope(layer.name): layer.build(None) @keras_serializable class TFDeiTMainLayer(keras.layers.Layer): config_class = DeiTConfig def __init__( self, config: DeiTConfig, add_pooling_layer: bool = True, use_mask_token: bool = False, **kwargs ) -> None: super().__init__(**kwargs) self.config = config self.embeddings = TFDeiTEmbeddings(config, use_mask_token=use_mask_token, name="embeddings") self.encoder = TFDeiTEncoder(config, name="encoder") self.layernorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm") self.pooler = TFDeiTPooler(config, name="pooler") if add_pooling_layer else None def get_input_embeddings(self) -> TFDeiTPatchEmbeddings: return self.embeddings.patch_embeddings def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ raise NotImplementedError def get_head_mask(self, head_mask): if head_mask is not None: raise NotImplementedError else: head_mask = [None] * self.config.num_hidden_layers return head_mask @unpack_inputs def call( self, pixel_values: tf.Tensor | None = None, bool_masked_pos: tf.Tensor | None = None, head_mask: tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor, ...]]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("You have to specify pixel_values") # TF 2.0 image layers can't use NCHW format when running on CPU. # (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels) pixel_values = tf.transpose(pixel_values, (0, 2, 3, 1)) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask) embedding_output = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos, training=training) encoder_outputs = self.encoder( embedding_output, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = encoder_outputs[0] sequence_output = self.layernorm(sequence_output, training=training) pooled_output = self.pooler(sequence_output, training=training) if self.pooler is not None else None if not return_dict: head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,) return head_outputs + encoder_outputs[1:] return TFBaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "embeddings", None) is not None: with tf.name_scope(self.embeddings.name): self.embeddings.build(None) if getattr(self, "encoder", None) is not None: with tf.name_scope(self.encoder.name): self.encoder.build(None) if getattr(self, "layernorm", None) is not None: with tf.name_scope(self.layernorm.name): self.layernorm.build([None, None, self.config.hidden_size]) if getattr(self, "pooler", None) is not None: with tf.name_scope(self.pooler.name): self.pooler.build(None) # Copied from transformers.models.vit.modeling_tf_vit.TFViTPreTrainedModel with ViT->DeiT all-casing class TFDeiTPreTrainedModel(TFPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = DeiTConfig base_model_prefix = "deit" main_input_name = "pixel_values" DEIT_START_DOCSTRING = r""" This model is a TensorFlow [keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer). Use it as a regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and behavior. Parameters: config ([`DeiTConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ DEIT_INPUTS_DOCSTRING = r""" Args: pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`DeiTImageProcessor.__call__`] for details. head_mask (`tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare DeiT Model transformer outputting raw hidden-states without any specific head on top.", DEIT_START_DOCSTRING, ) class TFDeiTModel(TFDeiTPreTrainedModel): def __init__( self, config: DeiTConfig, add_pooling_layer: bool = True, use_mask_token: bool = False, **kwargs ) -> None: super().__init__(config, **kwargs) self.deit = TFDeiTMainLayer( config, add_pooling_layer=add_pooling_layer, use_mask_token=use_mask_token, name="deit" ) @unpack_inputs @add_start_docstrings_to_model_forward(DEIT_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFBaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC, modality="vision", expected_output=_EXPECTED_OUTPUT_SHAPE, ) def call( self, pixel_values: tf.Tensor | None = None, bool_masked_pos: tf.Tensor | None = None, head_mask: tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[Tuple, TFBaseModelOutputWithPooling]: outputs = self.deit( pixel_values=pixel_values, bool_masked_pos=bool_masked_pos, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "deit", None) is not None: with tf.name_scope(self.deit.name): self.deit.build(None) # Copied from transformers.models.vit.modeling_tf_vit.TFViTPooler with ViT->DeiT class TFDeiTPooler(keras.layers.Layer): def __init__(self, config: DeiTConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), activation="tanh", name="dense", ) self.config = config def call(self, hidden_states: tf.Tensor) -> tf.Tensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(inputs=first_token_tensor) return pooled_output def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) class TFDeitPixelShuffle(keras.layers.Layer): """TF layer implementation of torch.nn.PixelShuffle""" def __init__(self, upscale_factor: int, **kwargs) -> None: super().__init__(**kwargs) if not isinstance(upscale_factor, int) or upscale_factor < 2: raise ValueError(f"upscale_factor must be an integer value >= 2 got {upscale_factor}") self.upscale_factor = upscale_factor def call(self, x: tf.Tensor) -> tf.Tensor: hidden_states = x batch_size, _, _, num_input_channels = shape_list(hidden_states) block_size_squared = self.upscale_factor**2 output_depth = int(num_input_channels / block_size_squared) # When the number of output channels >= 2, PyTorch's PixelShuffle and # TF's depth_to_space differ in their output as the order of channels selected for combining # is a permutation of the other c.f. # https://stackoverflow.com/questions/68272502/tf-depth-to-space-not-same-as-torchs-pixelshuffle-when-output-channels-1 permutation = tf.constant( [[i + j * block_size_squared for i in range(block_size_squared) for j in range(output_depth)]] ) hidden_states = tf.gather(params=hidden_states, indices=tf.tile(permutation, [batch_size, 1]), batch_dims=-1) hidden_states = tf.nn.depth_to_space(hidden_states, block_size=self.upscale_factor, data_format="NHWC") return hidden_states class TFDeitDecoder(keras.layers.Layer): def __init__(self, config: DeiTConfig, **kwargs) -> None: super().__init__(**kwargs) self.conv2d = keras.layers.Conv2D( filters=config.encoder_stride**2 * config.num_channels, kernel_size=1, name="0" ) self.pixel_shuffle = TFDeitPixelShuffle(config.encoder_stride, name="1") self.config = config def call(self, inputs: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_states = inputs hidden_states = self.conv2d(hidden_states) hidden_states = self.pixel_shuffle(hidden_states) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "conv2d", None) is not None: with tf.name_scope(self.conv2d.name): self.conv2d.build([None, None, None, self.config.hidden_size]) if getattr(self, "pixel_shuffle", None) is not None: with tf.name_scope(self.pixel_shuffle.name): self.pixel_shuffle.build(None) @add_start_docstrings( "DeiT Model with a decoder on top for masked image modeling, as proposed in" " [SimMIM](https://arxiv.org/abs/2111.09886).", DEIT_START_DOCSTRING, ) class TFDeiTForMaskedImageModeling(TFDeiTPreTrainedModel): def __init__(self, config: DeiTConfig) -> None: super().__init__(config) self.deit = TFDeiTMainLayer(config, add_pooling_layer=False, use_mask_token=True, name="deit") self.decoder = TFDeitDecoder(config, name="decoder") @unpack_inputs @add_start_docstrings_to_model_forward(DEIT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFMaskedImageModelingOutput, config_class=_CONFIG_FOR_DOC) def call( self, pixel_values: tf.Tensor | None = None, bool_masked_pos: tf.Tensor | None = None, head_mask: tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[tuple, TFMaskedImageModelingOutput]: r""" bool_masked_pos (`tf.Tensor` of type bool and shape `(batch_size, num_patches)`): Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). Returns: Examples: ```python >>> from transformers import AutoImageProcessor, TFDeiTForMaskedImageModeling >>> import tensorflow as tf >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> image_processor = AutoImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224") >>> model = TFDeiTForMaskedImageModeling.from_pretrained("facebook/deit-base-distilled-patch16-224") >>> num_patches = (model.config.image_size // model.config.patch_size) ** 2 >>> pixel_values = image_processor(images=image, return_tensors="tf").pixel_values >>> # create random boolean mask of shape (batch_size, num_patches) >>> bool_masked_pos = tf.cast(tf.random.uniform((1, num_patches), minval=0, maxval=2, dtype=tf.int32), tf.bool) >>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos) >>> loss, reconstructed_pixel_values = outputs.loss, outputs.reconstruction >>> list(reconstructed_pixel_values.shape) [1, 3, 224, 224] ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.deit( pixel_values, bool_masked_pos=bool_masked_pos, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] # Reshape to (batch_size, num_channels, height, width) sequence_output = sequence_output[:, 1:-1] batch_size, sequence_length, num_channels = shape_list(sequence_output) height = width = int(sequence_length**0.5) sequence_output = tf.reshape(sequence_output, (batch_size, height, width, num_channels)) # Reconstruct pixel values reconstructed_pixel_values = self.decoder(sequence_output, training=training) # TF 2.0 image layers can't use NCHW format when running on CPU, so intermediate layers use NHWC, # including the decoder. We transpose to compute the loss against the pixel values # (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width) reconstructed_pixel_values = tf.transpose(reconstructed_pixel_values, (0, 3, 1, 2)) masked_im_loss = None if bool_masked_pos is not None: size = self.config.image_size // self.config.patch_size bool_masked_pos = tf.reshape(bool_masked_pos, (-1, size, size)) mask = tf.repeat(bool_masked_pos, self.config.patch_size, 1) mask = tf.repeat(mask, self.config.patch_size, 2) mask = tf.expand_dims(mask, 1) mask = tf.cast(mask, tf.float32) reconstruction_loss = keras.losses.mean_absolute_error( # Swap axes as metric calculation reduces over the final dimension tf.transpose(pixel_values, (1, 2, 3, 0)), tf.transpose(reconstructed_pixel_values, (1, 2, 3, 0)), ) reconstruction_loss = tf.expand_dims(reconstruction_loss, 0) total_loss = tf.reduce_sum(reconstruction_loss * mask) num_masked_pixels = (tf.reduce_sum(mask) + 1e-5) * self.config.num_channels masked_im_loss = total_loss / num_masked_pixels masked_im_loss = tf.reshape(masked_im_loss, (1,)) if not return_dict: output = (reconstructed_pixel_values,) + outputs[1:] return ((masked_im_loss,) + output) if masked_im_loss is not None else output return TFMaskedImageModelingOutput( loss=masked_im_loss, reconstruction=reconstructed_pixel_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "deit", None) is not None: with tf.name_scope(self.deit.name): self.deit.build(None) if getattr(self, "decoder", None) is not None: with tf.name_scope(self.decoder.name): self.decoder.build(None) @add_start_docstrings( """ DeiT Model transformer with an image classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for ImageNet. """, DEIT_START_DOCSTRING, ) class TFDeiTForImageClassification(TFDeiTPreTrainedModel, TFSequenceClassificationLoss): def __init__(self, config: DeiTConfig): super().__init__(config) self.num_labels = config.num_labels self.deit = TFDeiTMainLayer(config, add_pooling_layer=False, name="deit") # Classifier head self.classifier = ( keras.layers.Dense(config.num_labels, name="classifier") if config.num_labels > 0 else keras.layers.Activation("linear", name="classifier") ) self.config = config @unpack_inputs @add_start_docstrings_to_model_forward(DEIT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFImageClassifierOutput, config_class=_CONFIG_FOR_DOC) def call( self, pixel_values: tf.Tensor | None = None, head_mask: tf.Tensor | None = None, labels: tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[tf.Tensor, TFImageClassifierOutput]: r""" labels (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). Returns: Examples: ```python >>> from transformers import AutoImageProcessor, TFDeiTForImageClassification >>> import tensorflow as tf >>> from PIL import Image >>> import requests >>> keras.utils.set_random_seed(3) # doctest: +IGNORE_RESULT >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> # note: we are loading a TFDeiTForImageClassificationWithTeacher from the hub here, >>> # so the head will be randomly initialized, hence the predictions will be random >>> image_processor = AutoImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224") >>> model = TFDeiTForImageClassification.from_pretrained("facebook/deit-base-distilled-patch16-224") >>> inputs = image_processor(images=image, return_tensors="tf") >>> outputs = model(**inputs) >>> logits = outputs.logits >>> # model predicts one of the 1000 ImageNet classes >>> predicted_class_idx = tf.math.argmax(logits, axis=-1)[0] >>> print("Predicted class:", model.config.id2label[int(predicted_class_idx)]) Predicted class: little blue heron, Egretta caerulea ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.deit( pixel_values, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] logits = self.classifier(sequence_output[:, 0, :]) # we don't use the distillation token loss = None if labels is None else self.hf_compute_loss(labels, logits) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return TFImageClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "deit", None) is not None: with tf.name_scope(self.deit.name): self.deit.build(None) if getattr(self, "classifier", None) is not None: with tf.name_scope(self.classifier.name): self.classifier.build([None, None, self.config.hidden_size]) @add_start_docstrings( """ DeiT Model transformer with image classification heads on top (a linear layer on top of the final hidden state of the [CLS] token and a linear layer on top of the final hidden state of the distillation token) e.g. for ImageNet. .. warning:: This model supports inference-only. Fine-tuning with distillation (i.e. with a teacher) is not yet supported. """, DEIT_START_DOCSTRING, ) class TFDeiTForImageClassificationWithTeacher(TFDeiTPreTrainedModel): def __init__(self, config: DeiTConfig) -> None: super().__init__(config) self.num_labels = config.num_labels self.deit = TFDeiTMainLayer(config, add_pooling_layer=False, name="deit") # Classifier heads self.cls_classifier = ( keras.layers.Dense(config.num_labels, name="cls_classifier") if config.num_labels > 0 else keras.layers.Activation("linear", name="cls_classifier") ) self.distillation_classifier = ( keras.layers.Dense(config.num_labels, name="distillation_classifier") if config.num_labels > 0 else keras.layers.Activation("linear", name="distillation_classifier") ) self.config = config @unpack_inputs @add_start_docstrings_to_model_forward(DEIT_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_IMAGE_CLASS_CHECKPOINT, output_type=TFDeiTForImageClassificationWithTeacherOutput, config_class=_CONFIG_FOR_DOC, expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT, ) def call( self, pixel_values: tf.Tensor | None = None, head_mask: tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[tuple, TFDeiTForImageClassificationWithTeacherOutput]: return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.deit( pixel_values, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] cls_logits = self.cls_classifier(sequence_output[:, 0, :]) distillation_logits = self.distillation_classifier(sequence_output[:, 1, :]) # during inference, return the average of both classifier predictions logits = (cls_logits + distillation_logits) / 2 if not return_dict: output = (logits, cls_logits, distillation_logits) + outputs[1:] return output return TFDeiTForImageClassificationWithTeacherOutput( logits=logits, cls_logits=cls_logits, distillation_logits=distillation_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "deit", None) is not None: with tf.name_scope(self.deit.name): self.deit.build(None) if getattr(self, "cls_classifier", None) is not None: with tf.name_scope(self.cls_classifier.name): self.cls_classifier.build([None, None, self.config.hidden_size]) if getattr(self, "distillation_classifier", None) is not None: with tf.name_scope(self.distillation_classifier.name): self.distillation_classifier.build([None, None, self.config.hidden_size])

transformers/src/transformers/models/deit/modeling_tf_deit.py/0

{ "file_path": "transformers/src/transformers/models/deit/modeling_tf_deit.py", "repo_id": "transformers", "token_count": 21239 }

93

# coding=utf-8 # Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ RetriBERT model configuration""" from ....configuration_utils import PretrainedConfig from ....utils import logging logger = logging.get_logger(__name__) # TODO: upload to AWS RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = { "yjernite/retribert-base-uncased": ( "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json" ), } class RetriBertConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`RetriBertModel`]. It is used to instantiate a RetriBertModel model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the RetriBERT [yjernite/retribert-base-uncased](https://huggingface.co/yjernite/retribert-base-uncased) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 30522): Vocabulary size of the RetriBERT model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`RetriBertModel`] hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). type_vocab_size (`int`, *optional*, defaults to 2): The vocabulary size of the *token_type_ids* passed into [`BertModel`]. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. share_encoders (`bool`, *optional*, defaults to `True`): Whether or not to use the same Bert-type encoder for the queries and document projection_dim (`int`, *optional*, defaults to 128): Final dimension of the query and document representation after projection """ model_type = "retribert" def __init__( self, vocab_size=30522, hidden_size=768, num_hidden_layers=8, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, share_encoders=True, projection_dim=128, pad_token_id=0, **kwargs, ): super().__init__(pad_token_id=pad_token_id, **kwargs) self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.share_encoders = share_encoders self.projection_dim = projection_dim

transformers/src/transformers/models/deprecated/retribert/configuration_retribert.py/0

{ "file_path": "transformers/src/transformers/models/deprecated/retribert/configuration_retribert.py", "repo_id": "transformers", "token_count": 2013 }

94

# coding=utf-8 # Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Utilities for PyTorch Transformer XL model. Directly adapted from https://github.com/kimiyoung/transformer-xl. """ import torch from torch import nn # CUDA_MAJOR = int(torch.version.cuda.split('.')[0]) # CUDA_MINOR = int(torch.version.cuda.split('.')[1]) class ProjectedAdaptiveLogSoftmax(nn.Module): def __init__(self, n_token, d_embed, d_proj, cutoffs, div_val=1, keep_order=False): super().__init__() self.n_token = n_token self.d_embed = d_embed self.d_proj = d_proj self.cutoffs = cutoffs + [n_token] self.cutoff_ends = [0] + self.cutoffs self.div_val = div_val self.shortlist_size = self.cutoffs[0] self.n_clusters = len(self.cutoffs) - 1 self.head_size = self.shortlist_size + self.n_clusters if self.n_clusters > 0: self.cluster_weight = nn.Parameter(torch.zeros(self.n_clusters, self.d_embed)) self.cluster_bias = nn.Parameter(torch.zeros(self.n_clusters)) self.out_layers = nn.ModuleList() self.out_projs = nn.ParameterList() if div_val == 1: for i in range(len(self.cutoffs)): if d_proj != d_embed: self.out_projs.append(nn.Parameter(torch.FloatTensor(d_proj, d_embed))) else: self.out_projs.append(None) self.out_layers.append(nn.Linear(d_embed, n_token)) else: for i in range(len(self.cutoffs)): l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1] d_emb_i = d_embed // (div_val**i) self.out_projs.append(nn.Parameter(torch.FloatTensor(d_proj, d_emb_i))) self.out_layers.append(nn.Linear(d_emb_i, r_idx - l_idx)) self.keep_order = keep_order def _compute_logit(self, hidden, weight, bias, proj): if proj is None: logit = nn.functional.linear(hidden, weight, bias=bias) else: # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1: proj_hid = nn.functional.linear(hidden, proj.t().contiguous()) logit = nn.functional.linear(proj_hid, weight, bias=bias) # else: # logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t())) # if bias is not None: # logit = logit + bias return logit def forward(self, hidden, labels=None, keep_order=False): """ Params: hidden :: [len*bsz x d_proj] labels :: [len*bsz] Return: if labels is None: out :: [len*bsz x n_tokens] log probabilities of tokens over the vocabulary else: out :: [(len-1)*bsz] Negative log likelihood. We could replace this implementation by the native PyTorch one if theirs had an option to set bias on all clusters in the native one. here: https://github.com/pytorch/pytorch/blob/dbe6a7a9ff1a364a8706bf5df58a1ca96d2fd9da/torch/nn/modules/adaptive.py#L138 """ if labels is not None: # Shift so that tokens < n predict n hidden = hidden[..., :-1, :].contiguous() labels = labels[..., 1:].contiguous() hidden = hidden.view(-1, hidden.size(-1)) labels = labels.view(-1) if hidden.size(0) != labels.size(0): raise RuntimeError("Input and labels should have the same size in the batch dimension.") else: hidden = hidden.view(-1, hidden.size(-1)) if self.n_clusters == 0: logit = self._compute_logit(hidden, self.out_layers[0].weight, self.out_layers[0].bias, self.out_projs[0]) if labels is not None: mask = labels != -100 out = torch.zeros_like(labels, dtype=hidden.dtype, device=hidden.device) out[mask] = ( -nn.functional.log_softmax(logit, dim=-1)[mask].gather(1, labels[mask].unsqueeze(1)).squeeze(1) ) else: out = nn.functional.log_softmax(logit, dim=-1) else: # construct weights and biases weights, biases = [], [] for i in range(len(self.cutoffs)): if self.div_val == 1: l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1] weight_i = self.out_layers[0].weight[l_idx:r_idx] bias_i = self.out_layers[0].bias[l_idx:r_idx] else: weight_i = self.out_layers[i].weight bias_i = self.out_layers[i].bias if i == 0: weight_i = torch.cat([weight_i, self.cluster_weight], dim=0) bias_i = torch.cat([bias_i, self.cluster_bias], dim=0) weights.append(weight_i) biases.append(bias_i) head_weight, head_bias, head_proj = weights[0], biases[0], self.out_projs[0] head_logit = self._compute_logit(hidden, head_weight, head_bias, head_proj) head_logprob = nn.functional.log_softmax(head_logit, dim=1) if labels is None: out = hidden.new_empty((head_logit.size(0), self.n_token)) else: out = torch.zeros_like(labels, dtype=hidden.dtype, device=hidden.device) offset = 0 cutoff_values = [0] + self.cutoffs for i in range(len(cutoff_values) - 1): l_idx, r_idx = cutoff_values[i], cutoff_values[i + 1] if labels is not None: mask_i = (labels >= l_idx) & (labels < r_idx) indices_i = mask_i.nonzero().squeeze() if indices_i.numel() == 0: continue target_i = labels.index_select(0, indices_i) - l_idx head_logprob_i = head_logprob.index_select(0, indices_i) hidden_i = hidden.index_select(0, indices_i) else: hidden_i = hidden if i == 0: if labels is not None: logprob_i = head_logprob_i.gather(1, target_i[:, None]).squeeze(1) else: out[:, : self.cutoffs[0]] = head_logprob[:, : self.cutoffs[0]] else: weight_i, bias_i, proj_i = weights[i], biases[i], self.out_projs[i] tail_logit_i = self._compute_logit(hidden_i, weight_i, bias_i, proj_i) tail_logprob_i = nn.functional.log_softmax(tail_logit_i, dim=1) cluster_prob_idx = self.cutoffs[0] + i - 1 # No probability for the head cluster if labels is not None: logprob_i = head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather( 1, target_i[:, None] ).squeeze(1) else: logprob_i = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i out[:, l_idx:r_idx] = logprob_i if labels is not None: if (hasattr(self, "keep_order") and self.keep_order) or keep_order: out.index_copy_(0, indices_i, -logprob_i) else: out[offset : offset + logprob_i.size(0)].copy_(-logprob_i) offset += logprob_i.size(0) return out def log_prob(self, hidden): r""" Computes log probabilities for all \$n\_classes\$ From: https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/adaptive.p Args: hidden (Tensor): a minibatch of example Returns: log-probabilities of for each class \$c\$ in range \$0 <= c <= n\_classes\$, where \$n\_classes\$ is a parameter passed to `AdaptiveLogSoftmaxWithLoss` constructor. Shape: - Input: \$(N, in\_features)\$ - Output: \$(N, n\_classes)\$ """ if self.n_clusters == 0: logit = self._compute_logit(hidden, self.out_layers[0].weight, self.out_layers[0].bias, self.out_projs[0]) return nn.functional.log_softmax(logit, dim=-1) else: # construct weights and biases weights, biases = [], [] for i in range(len(self.cutoffs)): if self.div_val == 1: l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1] weight_i = self.out_layers[0].weight[l_idx:r_idx] bias_i = self.out_layers[0].bias[l_idx:r_idx] else: weight_i = self.out_layers[i].weight bias_i = self.out_layers[i].bias if i == 0: weight_i = torch.cat([weight_i, self.cluster_weight], dim=0) bias_i = torch.cat([bias_i, self.cluster_bias], dim=0) weights.append(weight_i) biases.append(bias_i) head_weight, head_bias, head_proj = weights[0], biases[0], self.out_projs[0] head_logit = self._compute_logit(hidden, head_weight, head_bias, head_proj) out = hidden.new_empty((head_logit.size(0), self.n_token)) head_logprob = nn.functional.log_softmax(head_logit, dim=1) cutoff_values = [0] + self.cutoffs for i in range(len(cutoff_values) - 1): start_idx, stop_idx = cutoff_values[i], cutoff_values[i + 1] if i == 0: out[:, : self.cutoffs[0]] = head_logprob[:, : self.cutoffs[0]] else: weight_i, bias_i, proj_i = weights[i], biases[i], self.out_projs[i] tail_logit_i = self._compute_logit(hidden, weight_i, bias_i, proj_i) tail_logprob_i = nn.functional.log_softmax(tail_logit_i, dim=1) logprob_i = head_logprob[:, -i] + tail_logprob_i out[:, start_idx, stop_idx] = logprob_i return out

transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl_utilities.py/0

{ "file_path": "transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl_utilities.py", "repo_id": "transformers", "token_count": 5685 }

95

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available _import_structure = {"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]} try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["feature_extraction_detr"] = ["DetrFeatureExtractor"] _import_structure["image_processing_detr"] = ["DetrImageProcessor"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_detr"] = [ "DETR_PRETRAINED_MODEL_ARCHIVE_LIST", "DetrForObjectDetection", "DetrForSegmentation", "DetrModel", "DetrPreTrainedModel", ] if TYPE_CHECKING: from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_detr import DetrFeatureExtractor from .image_processing_detr import DetrImageProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_detr import ( DETR_PRETRAINED_MODEL_ARCHIVE_LIST, DetrForObjectDetection, DetrForSegmentation, DetrModel, DetrPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

transformers/src/transformers/models/detr/__init__.py/0

{ "file_path": "transformers/src/transformers/models/detr/__init__.py", "repo_id": "transformers", "token_count": 915 }

96

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available, ) _import_structure = { "configuration_distilbert": [ "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DistilBertConfig", "DistilBertOnnxConfig", ], "tokenization_distilbert": ["DistilBertTokenizer"], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["tokenization_distilbert_fast"] = ["DistilBertTokenizerFast"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_distilbert"] = [ "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "DistilBertForMaskedLM", "DistilBertForMultipleChoice", "DistilBertForQuestionAnswering", "DistilBertForSequenceClassification", "DistilBertForTokenClassification", "DistilBertModel", "DistilBertPreTrainedModel", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_tf_distilbert"] = [ "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFDistilBertForMaskedLM", "TFDistilBertForMultipleChoice", "TFDistilBertForQuestionAnswering", "TFDistilBertForSequenceClassification", "TFDistilBertForTokenClassification", "TFDistilBertMainLayer", "TFDistilBertModel", "TFDistilBertPreTrainedModel", ] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_flax_distilbert"] = [ "FlaxDistilBertForMaskedLM", "FlaxDistilBertForMultipleChoice", "FlaxDistilBertForQuestionAnswering", "FlaxDistilBertForSequenceClassification", "FlaxDistilBertForTokenClassification", "FlaxDistilBertModel", "FlaxDistilBertPreTrainedModel", ] if TYPE_CHECKING: from .configuration_distilbert import ( DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DistilBertConfig, DistilBertOnnxConfig, ) from .tokenization_distilbert import DistilBertTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_distilbert_fast import DistilBertTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_distilbert import ( DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST, DistilBertForMaskedLM, DistilBertForMultipleChoice, DistilBertForQuestionAnswering, DistilBertForSequenceClassification, DistilBertForTokenClassification, DistilBertModel, DistilBertPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_distilbert import ( TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFDistilBertForMaskedLM, TFDistilBertForMultipleChoice, TFDistilBertForQuestionAnswering, TFDistilBertForSequenceClassification, TFDistilBertForTokenClassification, TFDistilBertMainLayer, TFDistilBertModel, TFDistilBertPreTrainedModel, ) try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_distilbert import ( FlaxDistilBertForMaskedLM, FlaxDistilBertForMultipleChoice, FlaxDistilBertForQuestionAnswering, FlaxDistilBertForSequenceClassification, FlaxDistilBertForTokenClassification, FlaxDistilBertModel, FlaxDistilBertPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

transformers/src/transformers/models/distilbert/__init__.py/0

{ "file_path": "transformers/src/transformers/models/distilbert/__init__.py", "repo_id": "transformers", "token_count": 2215 }

97

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available, ) _import_structure = { "configuration_dpr": ["DPR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPRConfig"], "tokenization_dpr": [ "DPRContextEncoderTokenizer", "DPRQuestionEncoderTokenizer", "DPRReaderOutput", "DPRReaderTokenizer", ], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["tokenization_dpr_fast"] = [ "DPRContextEncoderTokenizerFast", "DPRQuestionEncoderTokenizerFast", "DPRReaderTokenizerFast", ] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_dpr"] = [ "DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST", "DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST", "DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST", "DPRContextEncoder", "DPRPretrainedContextEncoder", "DPRPreTrainedModel", "DPRPretrainedQuestionEncoder", "DPRPretrainedReader", "DPRQuestionEncoder", "DPRReader", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_tf_dpr"] = [ "TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST", "TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST", "TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST", "TFDPRContextEncoder", "TFDPRPretrainedContextEncoder", "TFDPRPretrainedQuestionEncoder", "TFDPRPretrainedReader", "TFDPRQuestionEncoder", "TFDPRReader", ] if TYPE_CHECKING: from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig from .tokenization_dpr import ( DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderOutput, DPRReaderTokenizer, ) try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_dpr_fast import ( DPRContextEncoderTokenizerFast, DPRQuestionEncoderTokenizerFast, DPRReaderTokenizerFast, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_dpr import ( DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, DPRContextEncoder, DPRPretrainedContextEncoder, DPRPreTrainedModel, DPRPretrainedQuestionEncoder, DPRPretrainedReader, DPRQuestionEncoder, DPRReader, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_dpr import ( TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, TFDPRContextEncoder, TFDPRPretrainedContextEncoder, TFDPRPretrainedQuestionEncoder, TFDPRPretrainedReader, TFDPRQuestionEncoder, TFDPRReader, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

transformers/src/transformers/models/dpr/__init__.py/0

{ "file_path": "transformers/src/transformers/models/dpr/__init__.py", "repo_id": "transformers", "token_count": 2002 }

98

# coding=utf-8 # Copyright 2022 Intel Labs, OpenMMLab and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch DPT (Dense Prediction Transformers) model. This implementation is heavily inspired by OpenMMLab's implementation, found here: https://github.com/open-mmlab/mmsegmentation/blob/master/mmseg/models/decode_heads/dpt_head.py. """ import collections.abc import math from dataclasses import dataclass from typing import List, Optional, Set, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import CrossEntropyLoss from ...activations import ACT2FN from ...file_utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings, ) from ...modeling_outputs import BaseModelOutput, DepthEstimatorOutput, SemanticSegmenterOutput from ...modeling_utils import PreTrainedModel from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ModelOutput, logging from ...utils.backbone_utils import load_backbone from .configuration_dpt import DPTConfig logger = logging.get_logger(__name__) # General docstring _CONFIG_FOR_DOC = "DPTConfig" # Base docstring _CHECKPOINT_FOR_DOC = "Intel/dpt-large" _EXPECTED_OUTPUT_SHAPE = [1, 577, 1024] DPT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "Intel/dpt-large", "Intel/dpt-hybrid-midas", # See all DPT models at https://huggingface.co/models?filter=dpt ] @dataclass class BaseModelOutputWithIntermediateActivations(ModelOutput): """ Base class for model's outputs that also contains intermediate activations that can be used at later stages. Useful in the context of Vision models.: Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. intermediate_activations (`tuple(torch.FloatTensor)`, *optional*): Intermediate activations that can be used to compute hidden states of the model at various layers. """ last_hidden_states: torch.FloatTensor = None intermediate_activations: Optional[Tuple[torch.FloatTensor, ...]] = None @dataclass class BaseModelOutputWithPoolingAndIntermediateActivations(ModelOutput): """ Base class for model's outputs that also contains a pooling of the last hidden states as well as intermediate activations that can be used by the model at later stages. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`): Last layer hidden-state of the first token of the sequence (classification token) after further processing through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns the classification token after processing through a linear layer and a tanh activation function. The linear layer weights are trained from the next sentence prediction (classification) objective during pretraining. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. intermediate_activations (`tuple(torch.FloatTensor)`, *optional*): Intermediate activations that can be used to compute hidden states of the model at various layers. """ last_hidden_state: torch.FloatTensor = None pooler_output: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None attentions: Optional[Tuple[torch.FloatTensor, ...]] = None intermediate_activations: Optional[Tuple[torch.FloatTensor, ...]] = None class DPTViTHybridEmbeddings(nn.Module): """ This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a Transformer. """ def __init__(self, config, feature_size=None): super().__init__() image_size, patch_size = config.image_size, config.patch_size num_channels, hidden_size = config.num_channels, config.hidden_size image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.backbone = load_backbone(config) feature_dim = self.backbone.channels[-1] if len(self.backbone.channels) != 3: raise ValueError(f"Expected backbone to have 3 output features, got {len(self.backbone.channels)}") self.residual_feature_map_index = [0, 1] # Always take the output of the first and second backbone stage if feature_size is None: feat_map_shape = config.backbone_featmap_shape feature_size = feat_map_shape[-2:] feature_dim = feat_map_shape[1] else: feature_size = ( feature_size if isinstance(feature_size, collections.abc.Iterable) else (feature_size, feature_size) ) feature_dim = self.backbone.channels[-1] self.image_size = image_size self.patch_size = patch_size[0] self.num_channels = num_channels self.projection = nn.Conv2d(feature_dim, hidden_size, kernel_size=1) self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.hidden_size)) def _resize_pos_embed(self, posemb, grid_size_height, grid_size_width, start_index=1): posemb_tok = posemb[:, :start_index] posemb_grid = posemb[0, start_index:] old_grid_size = int(math.sqrt(len(posemb_grid))) posemb_grid = posemb_grid.reshape(1, old_grid_size, old_grid_size, -1).permute(0, 3, 1, 2) posemb_grid = nn.functional.interpolate(posemb_grid, size=(grid_size_height, grid_size_width), mode="bilinear") posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, grid_size_height * grid_size_width, -1) posemb = torch.cat([posemb_tok, posemb_grid], dim=1) return posemb def forward( self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False, return_dict: bool = False ) -> torch.Tensor: batch_size, num_channels, height, width = pixel_values.shape if num_channels != self.num_channels: raise ValueError( "Make sure that the channel dimension of the pixel values match with the one set in the configuration." ) if not interpolate_pos_encoding: if height != self.image_size[0] or width != self.image_size[1]: raise ValueError( f"Input image size ({height}*{width}) doesn't match model" f" ({self.image_size[0]}*{self.image_size[1]})." ) position_embeddings = self._resize_pos_embed( self.position_embeddings, height // self.patch_size, width // self.patch_size ) backbone_output = self.backbone(pixel_values) features = backbone_output.feature_maps[-1] # Retrieve also the intermediate activations to use them at later stages output_hidden_states = [backbone_output.feature_maps[index] for index in self.residual_feature_map_index] embeddings = self.projection(features).flatten(2).transpose(1, 2) cls_tokens = self.cls_token.expand(batch_size, -1, -1) embeddings = torch.cat((cls_tokens, embeddings), dim=1) # add positional encoding to each token embeddings = embeddings + position_embeddings if not return_dict: return (embeddings, output_hidden_states) # Return hidden states and intermediate activations return BaseModelOutputWithIntermediateActivations( last_hidden_states=embeddings, intermediate_activations=output_hidden_states, ) class DPTViTEmbeddings(nn.Module): """ Construct the CLS token, position and patch embeddings. """ def __init__(self, config): super().__init__() self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) self.patch_embeddings = DPTViTPatchEmbeddings(config) num_patches = self.patch_embeddings.num_patches self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.hidden_size)) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.config = config def _resize_pos_embed(self, posemb, grid_size_height, grid_size_width, start_index=1): posemb_tok = posemb[:, :start_index] posemb_grid = posemb[0, start_index:] old_grid_size = int(math.sqrt(len(posemb_grid))) posemb_grid = posemb_grid.reshape(1, old_grid_size, old_grid_size, -1).permute(0, 3, 1, 2) posemb_grid = nn.functional.interpolate(posemb_grid, size=(grid_size_height, grid_size_width), mode="bilinear") posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, grid_size_height * grid_size_width, -1) posemb = torch.cat([posemb_tok, posemb_grid], dim=1) return posemb def forward(self, pixel_values, return_dict=False): batch_size, num_channels, height, width = pixel_values.shape # possibly interpolate position encodings to handle varying image sizes patch_size = self.config.patch_size position_embeddings = self._resize_pos_embed( self.position_embeddings, height // patch_size, width // patch_size ) embeddings = self.patch_embeddings(pixel_values) batch_size, seq_len, _ = embeddings.size() # add the [CLS] token to the embedded patch tokens cls_tokens = self.cls_token.expand(batch_size, -1, -1) embeddings = torch.cat((cls_tokens, embeddings), dim=1) # add positional encoding to each token embeddings = embeddings + position_embeddings embeddings = self.dropout(embeddings) if not return_dict: return (embeddings,) return BaseModelOutputWithIntermediateActivations(last_hidden_states=embeddings) class DPTViTPatchEmbeddings(nn.Module): """ Image to Patch Embedding. """ def __init__(self, config): super().__init__() image_size, patch_size = config.image_size, config.patch_size num_channels, hidden_size = config.num_channels, config.hidden_size image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.num_patches = num_patches self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size) def forward(self, pixel_values): batch_size, num_channels, height, width = pixel_values.shape if num_channels != self.num_channels: raise ValueError( "Make sure that the channel dimension of the pixel values match with the one set in the configuration." ) embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2) return embeddings # Copied from transformers.models.vit.modeling_vit.ViTSelfAttention with ViT->DPT class DPTViTSelfAttention(nn.Module): def __init__(self, config: DPTConfig) -> None: super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size {config.hidden_size,} is not a multiple of the number of attention " f"heads {config.num_attention_heads}." ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: mixed_query_layer = self.query(hidden_states) key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs # Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->DPT class DPTViTSelfOutput(nn.Module): """ The residual connection is defined in DPTLayer instead of here (as is the case with other models), due to the layernorm applied before each block. """ def __init__(self, config: DPTConfig) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states class DPTViTAttention(nn.Module): def __init__(self, config: DPTConfig) -> None: super().__init__() self.attention = DPTViTSelfAttention(config) self.output = DPTViTSelfOutput(config) self.pruned_heads = set() # Copied from transformers.models.vit.modeling_vit.ViTAttention.prune_heads def prune_heads(self, heads: Set[int]) -> None: if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads ) # Prune linear layers self.attention.query = prune_linear_layer(self.attention.query, index) self.attention.key = prune_linear_layer(self.attention.key, index) self.attention.value = prune_linear_layer(self.attention.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) # Copied from transformers.models.vit.modeling_vit.ViTAttention.forward def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_outputs = self.attention(hidden_states, head_mask, output_attentions) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs # Copied from transformers.models.vit.modeling_vit.ViTIntermediate with ViT->DPT class DPTViTIntermediate(nn.Module): def __init__(self, config: DPTConfig) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states # Copied from transformers.models.vit.modeling_vit.ViTOutput with ViT->DPT class DPTViTOutput(nn.Module): def __init__(self, config: DPTConfig) -> None: super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = hidden_states + input_tensor return hidden_states # copied from transformers.models.vit.modeling_vit.ViTLayer with ViTConfig->DPTConfig, ViTAttention->DPTViTAttention, ViTIntermediate->DPTViTIntermediate, ViTOutput->DPTViTOutput class DPTViTLayer(nn.Module): """This corresponds to the Block class in the timm implementation.""" def __init__(self, config: DPTConfig) -> None: super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = DPTViTAttention(config) self.intermediate = DPTViTIntermediate(config) self.output = DPTViTOutput(config) self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_attention_outputs = self.attention( self.layernorm_before(hidden_states), # in ViT, layernorm is applied before self-attention head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights # first residual connection hidden_states = attention_output + hidden_states # in ViT, layernorm is also applied after self-attention layer_output = self.layernorm_after(hidden_states) layer_output = self.intermediate(layer_output) # second residual connection is done here layer_output = self.output(layer_output, hidden_states) outputs = (layer_output,) + outputs return outputs # copied from transformers.models.vit.modeling_vit.ViTEncoder with ViTConfig -> DPTConfig, ViTLayer->DPTViTLayer class DPTViTEncoder(nn.Module): def __init__(self, config: DPTConfig) -> None: super().__init__() self.config = config self.layer = nn.ModuleList([DPTViTLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ) -> Union[tuple, BaseModelOutput]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( layer_module.__call__, hidden_states, layer_head_mask, output_attentions, ) else: layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) class DPTReassembleStage(nn.Module): """ This class reassembles the hidden states of the backbone into image-like feature representations at various resolutions. This happens in 3 stages: 1. Map the N + 1 tokens to a set of N tokens, by taking into account the readout ([CLS]) token according to `config.readout_type`. 2. Project the channel dimension of the hidden states according to `config.neck_hidden_sizes`. 3. Resizing the spatial dimensions (height, width). Args: config (`[DPTConfig]`): Model configuration class defining the model architecture. """ def __init__(self, config): super().__init__() self.config = config self.layers = nn.ModuleList() if config.is_hybrid: self._init_reassemble_dpt_hybrid(config) else: self._init_reassemble_dpt(config) self.neck_ignore_stages = config.neck_ignore_stages def _init_reassemble_dpt_hybrid(self, config): r""" " For DPT-Hybrid the first 2 reassemble layers are set to `nn.Identity()`, please check the official implementation: https://github.com/isl-org/DPT/blob/f43ef9e08d70a752195028a51be5e1aff227b913/dpt/vit.py#L438 for more details. """ for i, factor in zip(range(len(config.neck_hidden_sizes)), config.reassemble_factors): if i <= 1: self.layers.append(nn.Identity()) elif i > 1: self.layers.append(DPTReassembleLayer(config, channels=config.neck_hidden_sizes[i], factor=factor)) if config.readout_type != "project": raise ValueError(f"Readout type {config.readout_type} is not supported for DPT-Hybrid.") # When using DPT-Hybrid the readout type is set to "project". The sanity check is done on the config file self.readout_projects = nn.ModuleList() hidden_size = _get_backbone_hidden_size(config) for i in range(len(config.neck_hidden_sizes)): if i <= 1: self.readout_projects.append(nn.Sequential(nn.Identity())) elif i > 1: self.readout_projects.append( nn.Sequential(nn.Linear(2 * hidden_size, hidden_size), ACT2FN[config.hidden_act]) ) def _init_reassemble_dpt(self, config): for i, factor in zip(range(len(config.neck_hidden_sizes)), config.reassemble_factors): self.layers.append(DPTReassembleLayer(config, channels=config.neck_hidden_sizes[i], factor=factor)) if config.readout_type == "project": self.readout_projects = nn.ModuleList() hidden_size = _get_backbone_hidden_size(config) for _ in range(len(config.neck_hidden_sizes)): self.readout_projects.append( nn.Sequential(nn.Linear(2 * hidden_size, hidden_size), ACT2FN[config.hidden_act]) ) def forward(self, hidden_states: List[torch.Tensor], patch_height=None, patch_width=None) -> List[torch.Tensor]: """ Args: hidden_states (`List[torch.FloatTensor]`, each of shape `(batch_size, sequence_length + 1, hidden_size)`): List of hidden states from the backbone. """ out = [] for i, hidden_state in enumerate(hidden_states): if i not in self.neck_ignore_stages: # reshape to (batch_size, num_channels, height, width) cls_token, hidden_state = hidden_state[:, 0], hidden_state[:, 1:] batch_size, sequence_length, num_channels = hidden_state.shape if patch_height is not None and patch_width is not None: hidden_state = hidden_state.reshape(batch_size, patch_height, patch_width, num_channels) else: size = int(math.sqrt(sequence_length)) hidden_state = hidden_state.reshape(batch_size, size, size, num_channels) hidden_state = hidden_state.permute(0, 3, 1, 2).contiguous() feature_shape = hidden_state.shape if self.config.readout_type == "project": # reshape to (batch_size, height*width, num_channels) hidden_state = hidden_state.flatten(2).permute((0, 2, 1)) readout = cls_token.unsqueeze(1).expand_as(hidden_state) # concatenate the readout token to the hidden states and project hidden_state = self.readout_projects[i](torch.cat((hidden_state, readout), -1)) # reshape back to (batch_size, num_channels, height, width) hidden_state = hidden_state.permute(0, 2, 1).reshape(feature_shape) elif self.config.readout_type == "add": hidden_state = hidden_state.flatten(2) + cls_token.unsqueeze(-1) hidden_state = hidden_state.reshape(feature_shape) hidden_state = self.layers[i](hidden_state) out.append(hidden_state) return out def _get_backbone_hidden_size(config): if config.backbone_config is not None and config.is_hybrid is False: return config.backbone_config.hidden_size else: return config.hidden_size class DPTReassembleLayer(nn.Module): def __init__(self, config, channels, factor): super().__init__() # projection hidden_size = _get_backbone_hidden_size(config) self.projection = nn.Conv2d(in_channels=hidden_size, out_channels=channels, kernel_size=1) # up/down sampling depending on factor if factor > 1: self.resize = nn.ConvTranspose2d(channels, channels, kernel_size=factor, stride=factor, padding=0) elif factor == 1: self.resize = nn.Identity() elif factor < 1: # so should downsample self.resize = nn.Conv2d(channels, channels, kernel_size=3, stride=int(1 / factor), padding=1) def forward(self, hidden_state): hidden_state = self.projection(hidden_state) hidden_state = self.resize(hidden_state) return hidden_state class DPTFeatureFusionStage(nn.Module): def __init__(self, config): super().__init__() self.layers = nn.ModuleList() for _ in range(len(config.neck_hidden_sizes)): self.layers.append(DPTFeatureFusionLayer(config)) def forward(self, hidden_states): # reversing the hidden_states, we start from the last hidden_states = hidden_states[::-1] fused_hidden_states = [] # first layer only uses the last hidden_state fused_hidden_state = self.layers[0](hidden_states[0]) fused_hidden_states.append(fused_hidden_state) # looping from the last layer to the second for hidden_state, layer in zip(hidden_states[1:], self.layers[1:]): fused_hidden_state = layer(fused_hidden_state, hidden_state) fused_hidden_states.append(fused_hidden_state) return fused_hidden_states class DPTPreActResidualLayer(nn.Module): """ ResidualConvUnit, pre-activate residual unit. Args: config (`[DPTConfig]`): Model configuration class defining the model architecture. """ def __init__(self, config): super().__init__() self.use_batch_norm = config.use_batch_norm_in_fusion_residual use_bias_in_fusion_residual = ( config.use_bias_in_fusion_residual if config.use_bias_in_fusion_residual is not None else not self.use_batch_norm ) self.activation1 = nn.ReLU() self.convolution1 = nn.Conv2d( config.fusion_hidden_size, config.fusion_hidden_size, kernel_size=3, stride=1, padding=1, bias=use_bias_in_fusion_residual, ) self.activation2 = nn.ReLU() self.convolution2 = nn.Conv2d( config.fusion_hidden_size, config.fusion_hidden_size, kernel_size=3, stride=1, padding=1, bias=use_bias_in_fusion_residual, ) if self.use_batch_norm: self.batch_norm1 = nn.BatchNorm2d(config.fusion_hidden_size) self.batch_norm2 = nn.BatchNorm2d(config.fusion_hidden_size) def forward(self, hidden_state: torch.Tensor) -> torch.Tensor: residual = hidden_state hidden_state = self.activation1(hidden_state) hidden_state = self.convolution1(hidden_state) if self.use_batch_norm: hidden_state = self.batch_norm1(hidden_state) hidden_state = self.activation2(hidden_state) hidden_state = self.convolution2(hidden_state) if self.use_batch_norm: hidden_state = self.batch_norm2(hidden_state) return hidden_state + residual class DPTFeatureFusionLayer(nn.Module): """Feature fusion layer, merges feature maps from different stages. Args: config (`[DPTConfig]`): Model configuration class defining the model architecture. align_corners (`bool`, *optional*, defaults to `True`): The align_corner setting for bilinear upsample. """ def __init__(self, config, align_corners=True): super().__init__() self.align_corners = align_corners self.projection = nn.Conv2d(config.fusion_hidden_size, config.fusion_hidden_size, kernel_size=1, bias=True) self.residual_layer1 = DPTPreActResidualLayer(config) self.residual_layer2 = DPTPreActResidualLayer(config) def forward(self, hidden_state, residual=None): if residual is not None: if hidden_state.shape != residual.shape: residual = nn.functional.interpolate( residual, size=(hidden_state.shape[2], hidden_state.shape[3]), mode="bilinear", align_corners=False ) hidden_state = hidden_state + self.residual_layer1(residual) hidden_state = self.residual_layer2(hidden_state) hidden_state = nn.functional.interpolate( hidden_state, scale_factor=2, mode="bilinear", align_corners=self.align_corners ) hidden_state = self.projection(hidden_state) return hidden_state class DPTPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = DPTConfig base_model_prefix = "dpt" main_input_name = "pixel_values" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv2d, nn.ConvTranspose2d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) DPT_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`ViTConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ DPT_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`DPTImageProcessor.__call__`] for details. head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare DPT Model transformer outputting raw hidden-states without any specific head on top.", DPT_START_DOCSTRING, ) class DPTModel(DPTPreTrainedModel): def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config # vit encoder if config.is_hybrid: self.embeddings = DPTViTHybridEmbeddings(config) else: self.embeddings = DPTViTEmbeddings(config) self.encoder = DPTViTEncoder(config) self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.pooler = DPTViTPooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): if self.config.is_hybrid: return self.embeddings else: return self.embeddings.patch_embeddings def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(DPT_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPoolingAndIntermediateActivations, config_class=_CONFIG_FOR_DOC, modality="vision", expected_output=_EXPECTED_OUTPUT_SHAPE, ) def forward( self, pixel_values: torch.FloatTensor, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPoolingAndIntermediateActivations]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings(pixel_values, return_dict=return_dict) embedding_last_hidden_states = embedding_output[0] if not return_dict else embedding_output.last_hidden_states encoder_outputs = self.encoder( embedding_last_hidden_states, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] sequence_output = self.layernorm(sequence_output) pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,) return head_outputs + encoder_outputs[1:] + embedding_output[1:] return BaseModelOutputWithPoolingAndIntermediateActivations( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, intermediate_activations=embedding_output.intermediate_activations, ) # Copied from transformers.models.vit.modeling_vit.ViTPooler with ViT->DPT class DPTViTPooler(nn.Module): def __init__(self, config: DPTConfig): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states): # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class DPTNeck(nn.Module): """ DPTNeck. A neck is a module that is normally used between the backbone and the head. It takes a list of tensors as input and produces another list of tensors as output. For DPT, it includes 2 stages: * DPTReassembleStage * DPTFeatureFusionStage. Args: config (dict): config dict. """ def __init__(self, config): super().__init__() self.config = config # postprocessing: only required in case of a non-hierarchical backbone (e.g. ViT, BEiT) if config.backbone_config is not None and config.backbone_config.model_type in ["swinv2"]: self.reassemble_stage = None else: self.reassemble_stage = DPTReassembleStage(config) self.convs = nn.ModuleList() for channel in config.neck_hidden_sizes: self.convs.append(nn.Conv2d(channel, config.fusion_hidden_size, kernel_size=3, padding=1, bias=False)) # fusion self.fusion_stage = DPTFeatureFusionStage(config) def forward(self, hidden_states: List[torch.Tensor], patch_height=None, patch_width=None) -> List[torch.Tensor]: """ Args: hidden_states (`List[torch.FloatTensor]`, each of shape `(batch_size, sequence_length, hidden_size)` or `(batch_size, hidden_size, height, width)`): List of hidden states from the backbone. """ if not isinstance(hidden_states, (tuple, list)): raise ValueError("hidden_states should be a tuple or list of tensors") if len(hidden_states) != len(self.config.neck_hidden_sizes): raise ValueError("The number of hidden states should be equal to the number of neck hidden sizes.") # postprocess hidden states if self.reassemble_stage is not None: hidden_states = self.reassemble_stage(hidden_states, patch_height, patch_width) features = [self.convs[i](feature) for i, feature in enumerate(hidden_states)] # fusion blocks output = self.fusion_stage(features) return output class DPTDepthEstimationHead(nn.Module): """ Output head head consisting of 3 convolutional layers. It progressively halves the feature dimension and upsamples the predictions to the input resolution after the first convolutional layer (details can be found in the paper's supplementary material). """ def __init__(self, config): super().__init__() self.config = config self.projection = None if config.add_projection: self.projection = nn.Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) features = config.fusion_hidden_size self.head = nn.Sequential( nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1), nn.Upsample(scale_factor=2, mode="bilinear", align_corners=True), nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0), nn.ReLU(), ) def forward(self, hidden_states: List[torch.Tensor]) -> torch.Tensor: # use last features hidden_states = hidden_states[self.config.head_in_index] if self.projection is not None: hidden_states = self.projection(hidden_states) hidden_states = nn.ReLU()(hidden_states) predicted_depth = self.head(hidden_states) predicted_depth = predicted_depth.squeeze(dim=1) return predicted_depth @add_start_docstrings( """ DPT Model with a depth estimation head on top (consisting of 3 convolutional layers) e.g. for KITTI, NYUv2. """, DPT_START_DOCSTRING, ) class DPTForDepthEstimation(DPTPreTrainedModel): def __init__(self, config): super().__init__(config) self.backbone = None if config.backbone_config is not None and config.is_hybrid is False: self.backbone = load_backbone(config) else: self.dpt = DPTModel(config, add_pooling_layer=False) # Neck self.neck = DPTNeck(config) # Depth estimation head self.head = DPTDepthEstimationHead(config) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(DPT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=DepthEstimatorOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: torch.FloatTensor, head_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], DepthEstimatorOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*): Ground truth depth estimation maps for computing the loss. Returns: Examples: ```python >>> from transformers import AutoImageProcessor, DPTForDepthEstimation >>> import torch >>> import numpy as np >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> image_processor = AutoImageProcessor.from_pretrained("Intel/dpt-large") >>> model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large") >>> # prepare image for the model >>> inputs = image_processor(images=image, return_tensors="pt") >>> with torch.no_grad(): ... outputs = model(**inputs) ... predicted_depth = outputs.predicted_depth >>> # interpolate to original size >>> prediction = torch.nn.functional.interpolate( ... predicted_depth.unsqueeze(1), ... size=image.size[::-1], ... mode="bicubic", ... align_corners=False, ... ) >>> # visualize the prediction >>> output = prediction.squeeze().cpu().numpy() >>> formatted = (output * 255 / np.max(output)).astype("uint8") >>> depth = Image.fromarray(formatted) ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions if self.backbone is not None: outputs = self.backbone.forward_with_filtered_kwargs( pixel_values, output_hidden_states=output_hidden_states, output_attentions=output_attentions ) hidden_states = outputs.feature_maps else: outputs = self.dpt( pixel_values, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=True, # we need the intermediate hidden states return_dict=return_dict, ) hidden_states = outputs.hidden_states if return_dict else outputs[1] # only keep certain features based on config.backbone_out_indices # note that the hidden_states also include the initial embeddings if not self.config.is_hybrid: hidden_states = [ feature for idx, feature in enumerate(hidden_states[1:]) if idx in self.config.backbone_out_indices ] else: backbone_hidden_states = outputs.intermediate_activations if return_dict else list(outputs[-1]) backbone_hidden_states.extend( feature for idx, feature in enumerate(hidden_states[1:]) if idx in self.config.backbone_out_indices[2:] ) hidden_states = backbone_hidden_states patch_height, patch_width = None, None if self.config.backbone_config is not None and self.config.is_hybrid is False: _, _, height, width = pixel_values.shape patch_size = self.config.backbone_config.patch_size patch_height = height // patch_size patch_width = width // patch_size hidden_states = self.neck(hidden_states, patch_height, patch_width) predicted_depth = self.head(hidden_states) loss = None if labels is not None: raise NotImplementedError("Training is not implemented yet") if not return_dict: if output_hidden_states: output = (predicted_depth,) + outputs[1:] else: output = (predicted_depth,) + outputs[2:] return ((loss,) + output) if loss is not None else output return DepthEstimatorOutput( loss=loss, predicted_depth=predicted_depth, hidden_states=outputs.hidden_states if output_hidden_states else None, attentions=outputs.attentions, ) class DPTSemanticSegmentationHead(nn.Module): def __init__(self, config): super().__init__() self.config = config features = config.fusion_hidden_size self.head = nn.Sequential( nn.Conv2d(features, features, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(features), nn.ReLU(), nn.Dropout(config.semantic_classifier_dropout), nn.Conv2d(features, config.num_labels, kernel_size=1), nn.Upsample(scale_factor=2, mode="bilinear", align_corners=True), ) def forward(self, hidden_states: List[torch.Tensor]) -> torch.Tensor: # use last features hidden_states = hidden_states[self.config.head_in_index] logits = self.head(hidden_states) return logits class DPTAuxiliaryHead(nn.Module): def __init__(self, config): super().__init__() features = config.fusion_hidden_size self.head = nn.Sequential( nn.Conv2d(features, features, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(features), nn.ReLU(), nn.Dropout(0.1, False), nn.Conv2d(features, config.num_labels, kernel_size=1), ) def forward(self, hidden_states): logits = self.head(hidden_states) return logits @add_start_docstrings( """ DPT Model with a semantic segmentation head on top e.g. for ADE20k, CityScapes. """, DPT_START_DOCSTRING, ) class DPTForSemanticSegmentation(DPTPreTrainedModel): def __init__(self, config): super().__init__(config) self.dpt = DPTModel(config, add_pooling_layer=False) # Neck self.neck = DPTNeck(config) # Segmentation head(s) self.head = DPTSemanticSegmentationHead(config) self.auxiliary_head = DPTAuxiliaryHead(config) if config.use_auxiliary_head else None # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(DPT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=SemanticSegmenterOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], SemanticSegmenterOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*): Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy). Returns: Examples: ```python >>> from transformers import AutoImageProcessor, DPTForSemanticSegmentation >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> image_processor = AutoImageProcessor.from_pretrained("Intel/dpt-large-ade") >>> model = DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade") >>> inputs = image_processor(images=image, return_tensors="pt") >>> outputs = model(**inputs) >>> logits = outputs.logits ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) outputs = self.dpt( pixel_values, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=True, # we need the intermediate hidden states return_dict=return_dict, ) hidden_states = outputs.hidden_states if return_dict else outputs[1] # only keep certain features based on config.backbone_out_indices # note that the hidden_states also include the initial embeddings if not self.config.is_hybrid: hidden_states = [ feature for idx, feature in enumerate(hidden_states[1:]) if idx in self.config.backbone_out_indices ] else: backbone_hidden_states = outputs.intermediate_activations if return_dict else list(outputs[-1]) backbone_hidden_states.extend( feature for idx, feature in enumerate(hidden_states[1:]) if idx in self.config.backbone_out_indices[2:] ) hidden_states = backbone_hidden_states hidden_states = self.neck(hidden_states=hidden_states) logits = self.head(hidden_states) auxiliary_logits = None if self.auxiliary_head is not None: auxiliary_logits = self.auxiliary_head(hidden_states[-1]) loss = None if labels is not None: if self.config.num_labels == 1: raise ValueError("The number of labels should be greater than one") else: # upsample logits to the images' original size upsampled_logits = nn.functional.interpolate( logits, size=labels.shape[-2:], mode="bilinear", align_corners=False ) if auxiliary_logits is not None: upsampled_auxiliary_logits = nn.functional.interpolate( auxiliary_logits, size=labels.shape[-2:], mode="bilinear", align_corners=False ) # compute weighted loss loss_fct = CrossEntropyLoss(ignore_index=self.config.semantic_loss_ignore_index) main_loss = loss_fct(upsampled_logits, labels) auxiliary_loss = loss_fct(upsampled_auxiliary_logits, labels) loss = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss if not return_dict: if output_hidden_states: output = (logits,) + outputs[1:] else: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SemanticSegmenterOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states if output_hidden_states else None, attentions=outputs.attentions, )

transformers/src/transformers/models/dpt/modeling_dpt.py/0

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