yhavinga commited on
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16e93f8
1 Parent(s): fd44300

Saving scripts, logs and weights after 5 epochs

Browse files
README.md ADDED
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+ ---
2
+ tags:
3
+ - summarization
4
+ language:
5
+ - dutch
6
+ datasets:
7
+ - cnn_dm_nl
8
+ widget:
9
+ - text: "(CNN) Skywatchers in West-Noord-Amerika zijn in voor een traktatie: een bijna vijf minuten totale maansverduistering vanmorgen. Hier is hoe het zich ontvouwt:. Het begon om 3:16 a.m. Pacific Daylight Tijd, toen de maan begon te bewegen in de schaduw van de Aarde. Voor het volgende uur en 45 minuten, die schaduw zal bewegen over de maan en verzwolgen het om 4:58 a.m. Pacific Time. De totale verduistering zal slechts vier minuten en 43 seconden duren, en NASA zegt dat maakt het de kortste van de eeuw. Kijken live op NASA TV. Terwijl mensen ten westen van de Mississippi River zal het beste uitzicht hebben, ten minste een gedeeltelijke verduistering zal zichtbaar zijn over de hele natie. Maar zonsopgang zal de show te onderbreken op de Oostkust. Delen van Zuid-Amerika, India, China en China Een maansverduistering gebeurt wanneer de zon, de aarde en de maan een rechte lijn vormen in de ruimte, met de aarde in het midden. De zon schijnt op de Aarde en creëert een schaduw. Als de maan dieper in die schaduw beweegt, lijkt het donker te worden en lijkt zelfs een roodachtige kleur te zijn. Waarom rood? Omdat de atmosfeer van de Aarde het grootste deel van het blauwe licht filtert. Sommige mensen hebben het effect van de \"bloedmaan\" bijgenaamd. NASA zegt dat maansverduisteringen meestal ten minste twee keer per jaar plaatsvinden, maar deze verduistering is de derde in een reeks van vier op een rij, bekend als een \"tetrad.\" De eerste was op 15 april 2014. De tweede was in september 2014, de volgende is zaterdag en er zal er een meer zijn, op 28 september. Als je meer wilt weten over de verduistering, NASA astronoom Mitzi Adam. Deel uw foto's met CNN iReport."
10
+ - text: "(CNN) Filipino's worden gewaarschuwd om op wacht te staan voor flash overstromingen en aardverschuivingen als tropische storm Maysak benaderde de Aziatische eiland natie zaterdag. Slechts een paar dagen geleden, Maysak kreeg super tyfoon status dankzij zijn aanhoudende 150 km/h winden. Het heeft sindsdien verloren veel stoom als het naar het westen in de Stille Oceaan heeft gedraaid. Het is nu geclassificeerd als een tropische storm, volgens de Filipijnse nationale weerdienst, die noemt het een andere naam, Chedeng. Het heeft stabiele winden van meer dan 70 km/h (115 km/h) en gusts tot 90 km/h vanaf 17.00 uur (5 uur ET) Zaterdag. Toch, dat betekent niet dat Maysak zal geen pak een wallop. Autoriteiten nam preventieve stappen om mensen veilig te houden zoals barring outdoor activiteiten zoals zwemmen, surfen, di. Gabriel Llave, een ramp ambtenaar, vertelde PNA dat toeristen die aankomen zaterdag in en rond de kustplaats van Aurora \"zal niet worden geaccepteerd door de eigenaren van hotels, resorts, herbergen en dergelijke... en zal worden geadviseerd om terug te keren naar hun respectievelijke plaatsen.\" Aldczar Aurelio, een meteoroloog met de Filippijnse Atmosferische, Geofysische en Astronomische Diensten Administratie (PAGASA), zei dat de storm was gecentreerd 200 mijl ten zuidwesten van de provincie Aurora vanaf 5 uur (5 uur ET) en richting het westen op een 12.5 mph clip. Het is verwacht dat landval zondagochtend maken op de zuidoostelijke kust van de provincie Isabela en zijn uit de Filippijnen tegen maandag. Ahead van de storm. Isabela Gov. Faustino Dry III waarschuwde zaterdag dat bewoners moet handelen als deze zal maken landfall zondagochtend op de zuidoostelijke kust van de provincie Isabela en zijn uit de Filippijnen voor maandag."
11
+ ---
config.json ADDED
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1
+ {
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+ "_name_or_path": "yhavinga/t5-v1.1-large-dutch-cased-2",
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+ "architectures": [
4
+ "T5ForConditionalGeneration"
5
+ ],
6
+ "d_ff": 2816,
7
+ "d_kv": 64,
8
+ "d_model": 1024,
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+ "decoder_start_token_id": 0,
10
+ "dropout_rate": 0.1,
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+ "eos_token_id": 1,
12
+ "feed_forward_proj": "gated-gelu",
13
+ "gradient_checkpointing": false,
14
+ "initializer_factor": 1.0,
15
+ "is_encoder_decoder": true,
16
+ "layer_norm_epsilon": 1e-06,
17
+ "model_type": "t5",
18
+ "num_decoder_layers": 24,
19
+ "num_heads": 16,
20
+ "num_layers": 24,
21
+ "output_past": true,
22
+ "pad_token_id": 0,
23
+ "relative_attention_num_buckets": 32,
24
+ "tie_word_embeddings": false,
25
+ "torch_dtype": "float32",
26
+ "transformers_version": "4.13.0",
27
+ "use_cache": true,
28
+ "vocab_size": 32103
29
+ }
events.out.tfevents.1641239709.t1v-n-aa1c2160-w-0.407004.0.v2 ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:36f2bc11a0e9a631926af0d351d748a1a872d7313635802ac05d4d14cdf3c521
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+ size 13588956
flax_model.msgpack ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:b1b7bd8457d90f1af45c642184666338d163025488df7ed0a26e4846c0b21fc5
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+ size 3132419607
flax_to_pytorch.py ADDED
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1
+ from transformers import T5ForConditionalGeneration, TFT5ForConditionalGeneration
2
+
3
+ pt_model = T5ForConditionalGeneration.from_pretrained(".", from_flax=True)
4
+ pt_model.save_pretrained(".")
5
+
6
+ # tf_model = TFT5ForConditionalGeneration.from_pretrained(".", from_pt=True)
7
+ # tf_model.save_pretrained(".")
8
+
9
+
10
+ exit()
11
+
12
+
13
+
14
+ # from transformers import T5ForConditionalGeneration, TFT5ForConditionalGeneration, FlaxT5ForConditionalGeneration
15
+ # import numpy as np
16
+ # import torch
17
+ #
18
+ # fx_model = FlaxT5ForConditionalGeneration.from_pretrained(".")
19
+ #
20
+ # pt_model = T5ForConditionalGeneration.from_pretrained(".", from_flax=True)
21
+ # pt_model.save_pretrained(".")
22
+ #
23
+ #
24
+ # # tf_model = TFT5ForConditionalGeneration.from_pretrained(".", from_pt=True)
25
+ # # tf_model.save_pretrained(".")
26
+ #
27
+
28
+ #!/usr/bin/env python
29
+ import tempfile
30
+ import jax
31
+ import numpy as np
32
+ import torch
33
+ from jax import numpy as jnp
34
+ from transformers import AutoTokenizer, FlaxT5ForConditionalGeneration, T5ForConditionalGeneration
35
+
36
+ def to_f32(t):
37
+ return jax.tree_map(lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x, t)
38
+
39
+ def main():
40
+ # Saving extra files from config.json and tokenizer.json files
41
+ tokenizer = AutoTokenizer.from_pretrained("./")
42
+ tokenizer.save_pretrained("./")
43
+ # Temporary saving bfloat16 Flax model into float32
44
+ tmp = tempfile.mkdtemp()
45
+ flax_model = FlaxT5ForConditionalGeneration.from_pretrained("./")
46
+ flax_model.params = to_f32(flax_model.params)
47
+ flax_model.save_pretrained(tmp)
48
+ # Converting float32 Flax to PyTorch
49
+ pt_model = T5ForConditionalGeneration.from_pretrained(tmp, from_flax=True)
50
+ pt_model.save_pretrained("./", save_config=False)
51
+
52
+ input_ids = np.asarray(2 * [128 * [0]], dtype=np.int32)
53
+ input_ids_pt = torch.tensor(input_ids)
54
+ logits_pt = pt_model(input_ids_pt).logits
55
+ print(logits_pt)
56
+ logits_fx = flax_model(input_ids).logits
57
+ print(logits_fx)
58
+
59
+ if __name__ == "__main__":
60
+ main()
run_sum.sh ADDED
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+ python run_summarization_flax.py \
2
+ --output_dir . \
3
+ --model_name_or_path yhavinga/t5-v1.1-large-dutch-cased-2 \
4
+ --tokenizer_name yhavinga/t5-v1.1-large-dutch-cased-2 \
5
+ --dataset_name="ml6team/cnn_dailymail_nl" \
6
+ --text_column article \
7
+ --summary_column highlights \
8
+ --do_train --do_eval --do_predict --predict_with_generate \
9
+ --num_train_epochs 5 \
10
+ --learning_rate 5e-5 --warmup_steps 0 \
11
+ --per_device_train_batch_size 2 \
12
+ --per_device_eval_batch_size 2 \
13
+ --overwrite_output_dir \
14
+ --adafactor \
15
+ --max_source_length 1024 --max_target_length 96 \
16
+ --save_steps="20000" \
17
+ --eval_steps="5000" \
18
+
19
+
20
+ # --push_to_hub
run_summarization_flax.py ADDED
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1
+ #!/usr/bin/env python
2
+ # coding=utf-8
3
+ # Copyright 2021 The HuggingFace Team All rights reserved.
4
+ #
5
+ # Licensed under the Apache License, Version 2.0 (the "License");
6
+ # you may not use this file except in compliance with the License.
7
+ # You may obtain a copy of the License at
8
+ #
9
+ # http://www.apache.org/licenses/LICENSE-2.0
10
+ #
11
+ # Unless required by applicable law or agreed to in writing, software
12
+ # distributed under the License is distributed on an "AS IS" BASIS,
13
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14
+ # See the License for the specific language governing permissions and
15
+ # limitations under the License.
16
+ """
17
+ Fine-tuning the library models for summarization.
18
+ """
19
+ # You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.
20
+
21
+ import json
22
+ import logging
23
+ import os
24
+ import sys
25
+ import time
26
+ from dataclasses import asdict, dataclass, field
27
+ from enum import Enum
28
+ from functools import partial
29
+ from pathlib import Path
30
+ from typing import Callable, Optional
31
+
32
+ import datasets
33
+ import nltk # Here to have a nice missing dependency error message early on
34
+ import numpy as np
35
+ from datasets import Dataset, load_dataset, load_metric
36
+ from tqdm import tqdm
37
+
38
+ import jax
39
+ import jax.numpy as jnp
40
+ import optax
41
+ import transformers
42
+ from filelock import FileLock
43
+ from flax import jax_utils, traverse_util
44
+ from flax.jax_utils import unreplicate
45
+ from flax.training import train_state
46
+ from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
47
+ from huggingface_hub import Repository
48
+ from transformers import (
49
+ CONFIG_MAPPING,
50
+ FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
51
+ AutoConfig,
52
+ AutoTokenizer,
53
+ FlaxAutoModelForSeq2SeqLM,
54
+ HfArgumentParser,
55
+ is_tensorboard_available,
56
+ )
57
+ from transformers.file_utils import get_full_repo_name, is_offline_mode
58
+
59
+
60
+ logger = logging.getLogger(__name__)
61
+
62
+ try:
63
+ nltk.data.find("tokenizers/punkt")
64
+ except (LookupError, OSError):
65
+ if is_offline_mode():
66
+ raise LookupError(
67
+ "Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files"
68
+ )
69
+ with FileLock(".lock") as lock:
70
+ nltk.download("punkt", quiet=True)
71
+
72
+
73
+ MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.keys())
74
+ MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
75
+
76
+
77
+ @dataclass
78
+ class TrainingArguments:
79
+ output_dir: str = field(
80
+ metadata={"help": "The output directory where the model predictions and checkpoints will be written."},
81
+ )
82
+ overwrite_output_dir: bool = field(
83
+ default=False,
84
+ metadata={
85
+ "help": (
86
+ "Overwrite the content of the output directory. "
87
+ "Use this to continue training if output_dir points to a checkpoint directory."
88
+ )
89
+ },
90
+ )
91
+ do_train: bool = field(default=False, metadata={"help": "Whether to run training."})
92
+ do_eval: bool = field(default=False, metadata={"help": "Whether to run eval on the dev set."})
93
+ do_predict: bool = field(default=False, metadata={"help": "Whether to run predictions on the test set."})
94
+ per_device_train_batch_size: int = field(
95
+ default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for training."}
96
+ )
97
+ per_device_eval_batch_size: int = field(
98
+ default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."}
99
+ )
100
+ learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for AdamW."})
101
+ weight_decay: float = field(default=0.0, metadata={"help": "Weight decay for AdamW if we apply some."})
102
+ adam_beta1: float = field(default=0.9, metadata={"help": "Beta1 for AdamW optimizer"})
103
+ adam_beta2: float = field(default=0.999, metadata={"help": "Beta2 for AdamW optimizer"})
104
+ adam_epsilon: float = field(default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."})
105
+ label_smoothing_factor: float = field(
106
+ default=0.0, metadata={"help": "The label smoothing epsilon to apply (zero means no label smoothing)."}
107
+ )
108
+ adafactor: bool = field(default=False, metadata={"help": "Whether or not to replace AdamW by Adafactor."})
109
+ num_train_epochs: float = field(default=3.0, metadata={"help": "Total number of training epochs to perform."})
110
+ warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."})
111
+ logging_steps: int = field(default=500, metadata={"help": "Log every X updates steps."})
112
+ save_steps: int = field(default=500, metadata={"help": "Save checkpoint every X updates steps."})
113
+ eval_steps: int = field(default=None, metadata={"help": "Run an evaluation every X steps."})
114
+ seed: int = field(default=42, metadata={"help": "Random seed that will be set at the beginning of training."})
115
+ push_to_hub: bool = field(
116
+ default=False, metadata={"help": "Whether or not to upload the trained model to the model hub after training."}
117
+ )
118
+ hub_model_id: str = field(
119
+ default=None, metadata={"help": "The name of the repository to keep in sync with the local `output_dir`."}
120
+ )
121
+ hub_token: str = field(default=None, metadata={"help": "The token to use to push to the Model Hub."})
122
+
123
+ def __post_init__(self):
124
+ if self.output_dir is not None:
125
+ self.output_dir = os.path.expanduser(self.output_dir)
126
+
127
+ def to_dict(self):
128
+ """
129
+ Serializes this instance while replace `Enum` by their values (for JSON serialization support). It obfuscates
130
+ the token values by removing their value.
131
+ """
132
+ d = asdict(self)
133
+ for k, v in d.items():
134
+ if isinstance(v, Enum):
135
+ d[k] = v.value
136
+ if isinstance(v, list) and len(v) > 0 and isinstance(v[0], Enum):
137
+ d[k] = [x.value for x in v]
138
+ if k.endswith("_token"):
139
+ d[k] = f"<{k.upper()}>"
140
+ return d
141
+
142
+
143
+ @dataclass
144
+ class ModelArguments:
145
+ """
146
+ Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
147
+ """
148
+
149
+ model_name_or_path: Optional[str] = field(
150
+ default=None,
151
+ metadata={
152
+ "help": "The model checkpoint for weights initialization."
153
+ "Don't set if you want to train a model from scratch."
154
+ },
155
+ )
156
+ model_type: Optional[str] = field(
157
+ default=None,
158
+ metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
159
+ )
160
+ config_name: Optional[str] = field(
161
+ default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
162
+ )
163
+ tokenizer_name: Optional[str] = field(
164
+ default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
165
+ )
166
+ cache_dir: Optional[str] = field(
167
+ default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
168
+ )
169
+ use_fast_tokenizer: bool = field(
170
+ default=True,
171
+ metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
172
+ )
173
+ dtype: Optional[str] = field(
174
+ default="float32",
175
+ metadata={
176
+ "help": "Floating-point format in which the model weights should be initialized and trained. Choose one of `[float32, float16, bfloat16]`."
177
+ },
178
+ )
179
+
180
+
181
+ @dataclass
182
+ class DataTrainingArguments:
183
+ """
184
+ Arguments pertaining to what data we are going to input our model for training and eval.
185
+ """
186
+
187
+ dataset_name: Optional[str] = field(
188
+ default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
189
+ )
190
+ dataset_config_name: Optional[str] = field(
191
+ default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
192
+ )
193
+ text_column: Optional[str] = field(
194
+ default=None,
195
+ metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."},
196
+ )
197
+ summary_column: Optional[str] = field(
198
+ default=None,
199
+ metadata={"help": "The name of the column in the datasets containing the summaries (for summarization)."},
200
+ )
201
+ train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
202
+ validation_file: Optional[str] = field(
203
+ default=None,
204
+ metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
205
+ )
206
+ test_file: Optional[str] = field(
207
+ default=None,
208
+ metadata={"help": "An optional input predict data file to do prediction on (a text file)."},
209
+ )
210
+ max_source_length: Optional[int] = field(
211
+ default=1024,
212
+ metadata={
213
+ "help": "The maximum total input sequence length after tokenization. Sequences longer "
214
+ "than this will be truncated, sequences shorter will be padded."
215
+ },
216
+ )
217
+ max_target_length: Optional[int] = field(
218
+ default=128,
219
+ metadata={
220
+ "help": "The maximum total sequence length for target text after tokenization. Sequences longer "
221
+ "than this will be truncated, sequences shorter will be padded."
222
+ },
223
+ )
224
+ val_max_target_length: Optional[int] = field(
225
+ default=None,
226
+ metadata={
227
+ "help": "The maximum total sequence length for validation target text after tokenization. Sequences longer "
228
+ "than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`."
229
+ "This argument is also used to override the `max_length` param of `model.generate`, which is used "
230
+ "during evaluation."
231
+ },
232
+ )
233
+ max_train_samples: Optional[int] = field(
234
+ default=None,
235
+ metadata={
236
+ "help": "For debugging purposes or quicker training, truncate the number of training examples to this "
237
+ "value if set."
238
+ },
239
+ )
240
+ max_eval_samples: Optional[int] = field(
241
+ default=None,
242
+ metadata={
243
+ "help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
244
+ "value if set."
245
+ },
246
+ )
247
+ max_predict_samples: Optional[int] = field(
248
+ default=None,
249
+ metadata={
250
+ "help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
251
+ "value if set."
252
+ },
253
+ )
254
+ preprocessing_num_workers: Optional[int] = field(
255
+ default=None,
256
+ metadata={"help": "The number of processes to use for the preprocessing."},
257
+ )
258
+ source_prefix: Optional[str] = field(
259
+ default=None, metadata={"help": "A prefix to add before every source text (useful for T5 models)."}
260
+ )
261
+ predict_with_generate: bool = field(
262
+ default=False, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}
263
+ )
264
+ num_beams: Optional[int] = field(
265
+ default=None,
266
+ metadata={
267
+ "help": "Number of beams to use for evaluation. This argument will be passed to `model.generate`, "
268
+ "which is used during evaluation."
269
+ },
270
+ )
271
+ overwrite_cache: bool = field(
272
+ default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
273
+ )
274
+
275
+ def __post_init__(self):
276
+ if self.dataset_name is None and self.train_file is None and self.validation_file is None:
277
+ raise ValueError("Need either a dataset name or a training/validation file.")
278
+ else:
279
+ if self.train_file is not None:
280
+ extension = self.train_file.split(".")[-1]
281
+ assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
282
+ if self.validation_file is not None:
283
+ extension = self.validation_file.split(".")[-1]
284
+ assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
285
+ if self.val_max_target_length is None:
286
+ self.val_max_target_length = self.max_target_length
287
+
288
+
289
+ summarization_name_mapping = {
290
+ "amazon_reviews_multi": ("review_body", "review_title"),
291
+ "big_patent": ("description", "abstract"),
292
+ "cnn_dailymail": ("article", "highlights"),
293
+ "orange_sum": ("text", "summary"),
294
+ "pn_summary": ("article", "summary"),
295
+ "psc": ("extract_text", "summary_text"),
296
+ "samsum": ("dialogue", "summary"),
297
+ "thaisum": ("body", "summary"),
298
+ "xglue": ("news_body", "news_title"),
299
+ "xsum": ("document", "summary"),
300
+ "wiki_summary": ("article", "highlights"),
301
+ }
302
+
303
+
304
+ class TrainState(train_state.TrainState):
305
+ dropout_rng: jnp.ndarray
306
+
307
+ def replicate(self):
308
+ return jax_utils.replicate(self).replace(dropout_rng=shard_prng_key(self.dropout_rng))
309
+
310
+
311
+ def data_loader(rng: jax.random.PRNGKey, dataset: Dataset, batch_size: int, shuffle: bool = False):
312
+ """
313
+ Returns batches of size `batch_size` from truncated `dataset`, sharded over all local devices.
314
+ Shuffle batches if `shuffle` is `True`.
315
+ """
316
+ steps_per_epoch = len(dataset) // batch_size
317
+
318
+ if shuffle:
319
+ batch_idx = jax.random.permutation(rng, len(dataset))
320
+ else:
321
+ batch_idx = jnp.arange(len(dataset))
322
+
323
+ batch_idx = batch_idx[: steps_per_epoch * batch_size] # Skip incomplete batch.
324
+ batch_idx = batch_idx.reshape((steps_per_epoch, batch_size))
325
+
326
+ for idx in batch_idx:
327
+ batch = dataset[idx]
328
+ batch = {k: jnp.array(v) for k, v in batch.items()}
329
+
330
+ batch = shard(batch)
331
+
332
+ yield batch
333
+
334
+
335
+ def write_metric(summary_writer, train_metrics, eval_metrics, train_time, step):
336
+ summary_writer.scalar("train_time", train_time, step)
337
+
338
+ train_metrics = get_metrics(train_metrics)
339
+ for key, vals in train_metrics.items():
340
+ tag = f"train_{key}"
341
+ for i, val in enumerate(vals):
342
+ summary_writer.scalar(tag, val, step - len(vals) + i + 1)
343
+
344
+ for metric_name, value in eval_metrics.items():
345
+ summary_writer.scalar(f"eval_{metric_name}", value, step)
346
+
347
+
348
+ def create_learning_rate_fn(
349
+ train_ds_size: int, train_batch_size: int, num_train_epochs: int, num_warmup_steps: int, learning_rate: float
350
+ ) -> Callable[[int], jnp.array]:
351
+ """Returns a linear warmup, linear_decay learning rate function."""
352
+ steps_per_epoch = train_ds_size // train_batch_size
353
+ num_train_steps = steps_per_epoch * num_train_epochs
354
+ warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
355
+ decay_fn = optax.linear_schedule(
356
+ init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps
357
+ )
358
+ schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
359
+ return schedule_fn
360
+
361
+
362
+ def main():
363
+ # See all possible arguments in src/transformers/training_args.py
364
+ # or by passing the --help flag to this script.
365
+ # We now keep distinct sets of args, for a cleaner separation of concerns.
366
+
367
+ parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
368
+ if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
369
+ # If we pass only one argument to the script and it's the path to a json file,
370
+ # let's parse it to get our arguments.
371
+ model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
372
+ else:
373
+ model_args, data_args, training_args = parser.parse_args_into_dataclasses()
374
+
375
+ if (
376
+ os.path.exists(training_args.output_dir)
377
+ and os.listdir(training_args.output_dir)
378
+ and training_args.do_train
379
+ and not training_args.overwrite_output_dir
380
+ ):
381
+ raise ValueError(
382
+ f"Output directory ({training_args.output_dir}) already exists and is not empty."
383
+ "Use --overwrite_output_dir to overcome."
384
+ )
385
+
386
+ # Make one log on every process with the configuration for debugging.
387
+ logging.basicConfig(
388
+ format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
389
+ datefmt="%m/%d/%Y %H:%M:%S",
390
+ level=logging.INFO,
391
+ )
392
+ # Setup logging, we only want one process per machine to log things on the screen.
393
+ logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
394
+ if jax.process_index() == 0:
395
+ datasets.utils.logging.set_verbosity_warning()
396
+ transformers.utils.logging.set_verbosity_info()
397
+ else:
398
+ datasets.utils.logging.set_verbosity_error()
399
+ transformers.utils.logging.set_verbosity_error()
400
+
401
+ # Set the verbosity to info of the Transformers logger (on main process only):
402
+ logger.info(f"Training/evaluation parameters {training_args}")
403
+
404
+ # Handle the repository creation
405
+ if training_args.push_to_hub:
406
+ if training_args.hub_model_id is None:
407
+ repo_name = get_full_repo_name(
408
+ Path(training_args.output_dir).absolute().name, token=training_args.hub_token
409
+ )
410
+ else:
411
+ repo_name = training_args.hub_model_id
412
+ repo = Repository(training_args.output_dir, clone_from=repo_name)
413
+
414
+ # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
415
+ # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
416
+ # (the dataset will be downloaded automatically from the datasets Hub).
417
+ #
418
+ # For CSV/JSON files this script will use the first column for the full texts and the second column for the
419
+ # summaries (unless you specify column names for this with the `text_column` and `summary_column` arguments).
420
+ #
421
+ if data_args.dataset_name is not None:
422
+ # Downloading and loading a dataset from the hub.
423
+ dataset = load_dataset(
424
+ data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, keep_in_memory=False
425
+ )
426
+ else:
427
+ data_files = {}
428
+ if data_args.train_file is not None:
429
+ data_files["train"] = data_args.train_file
430
+ extension = data_args.train_file.split(".")[-1]
431
+ if data_args.validation_file is not None:
432
+ data_files["validation"] = data_args.validation_file
433
+ extension = data_args.validation_file.split(".")[-1]
434
+ if data_args.test_file is not None:
435
+ data_files["test"] = data_args.test_file
436
+ extension = data_args.test_file.split(".")[-1]
437
+ dataset = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
438
+ # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
439
+ # https://huggingface.co/docs/datasets/loading_datasets.html.
440
+
441
+ # Load pretrained model and tokenizer
442
+
443
+ if model_args.config_name:
444
+ config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
445
+ elif model_args.model_name_or_path:
446
+ config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
447
+ else:
448
+ config = CONFIG_MAPPING[model_args.model_type]()
449
+ logger.warning("You are instantiating a new config instance from scratch.")
450
+
451
+ if model_args.tokenizer_name:
452
+ tokenizer = AutoTokenizer.from_pretrained(
453
+ model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
454
+ )
455
+ elif model_args.model_name_or_path:
456
+ tokenizer = AutoTokenizer.from_pretrained(
457
+ model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
458
+ )
459
+ else:
460
+ raise ValueError(
461
+ "You are instantiating a new tokenizer from scratch. This is not supported by this script."
462
+ "You can do it from another script, save it, and load it from here, using --tokenizer_name."
463
+ )
464
+
465
+ if model_args.model_name_or_path:
466
+ model = FlaxAutoModelForSeq2SeqLM.from_pretrained(
467
+ model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
468
+ )
469
+ else:
470
+ model = FlaxAutoModelForSeq2SeqLM.from_config(
471
+ config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
472
+ )
473
+
474
+ if model.config.decoder_start_token_id is None:
475
+ raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
476
+
477
+ prefix = data_args.source_prefix if data_args.source_prefix is not None else ""
478
+
479
+ # Preprocessing the datasets.
480
+ # We need to tokenize inputs and targets.
481
+ if training_args.do_train:
482
+ column_names = dataset["train"].column_names
483
+ elif training_args.do_eval:
484
+ column_names = dataset["validation"].column_names
485
+ elif training_args.do_predict:
486
+ column_names = dataset["test"].column_names
487
+ else:
488
+ logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
489
+ return
490
+
491
+ # Get the column names for input/target.
492
+ dataset_columns = summarization_name_mapping.get(data_args.dataset_name, None)
493
+ if data_args.text_column is None:
494
+ text_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
495
+ else:
496
+ text_column = data_args.text_column
497
+ if text_column not in column_names:
498
+ raise ValueError(
499
+ f"--text_column' value '{data_args.text_column}' needs to be one of: {', '.join(column_names)}"
500
+ )
501
+ if data_args.summary_column is None:
502
+ summary_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
503
+ else:
504
+ summary_column = data_args.summary_column
505
+ if summary_column not in column_names:
506
+ raise ValueError(
507
+ f"--summary_column' value '{data_args.summary_column}' needs to be one of: {', '.join(column_names)}"
508
+ )
509
+
510
+ # Temporarily set max_target_length for training.
511
+ max_target_length = data_args.max_target_length
512
+
513
+ # In Flax, for seq2seq models we need to pass `decoder_input_ids`
514
+ # as the Flax models don't accept `labels`, we need to prepare the decoder_input_ids here
515
+ # for that dynamically import the `shift_tokens_right` function from the model file
516
+ model_module = __import__(model.__module__, fromlist=["shift_tokens_tight"])
517
+ shift_tokens_right_fn = getattr(model_module, "shift_tokens_right")
518
+
519
+ # Setting padding="max_length" as we need fixed length inputs for jitted functions
520
+ def preprocess_function(examples):
521
+ inputs = examples[text_column]
522
+ targets = examples[summary_column]
523
+ inputs = [prefix + inp for inp in inputs]
524
+ model_inputs = tokenizer(
525
+ inputs, max_length=data_args.max_source_length, padding="max_length", truncation=True, return_tensors="np"
526
+ )
527
+
528
+ # Setup the tokenizer for targets
529
+ with tokenizer.as_target_tokenizer():
530
+ labels = tokenizer(
531
+ targets, max_length=max_target_length, padding="max_length", truncation=True, return_tensors="np"
532
+ )
533
+
534
+ model_inputs["labels"] = labels["input_ids"]
535
+ decoder_input_ids = shift_tokens_right_fn(
536
+ labels["input_ids"], config.pad_token_id, config.decoder_start_token_id
537
+ )
538
+ model_inputs["decoder_input_ids"] = np.asarray(decoder_input_ids)
539
+
540
+ # We need decoder_attention_mask so we can ignore pad tokens from loss
541
+ model_inputs["decoder_attention_mask"] = labels["attention_mask"]
542
+
543
+ return model_inputs
544
+
545
+ if training_args.do_train:
546
+ if "train" not in dataset:
547
+ raise ValueError("--do_train requires a train dataset")
548
+ train_dataset = dataset["train"]
549
+ if data_args.max_train_samples is not None:
550
+ train_dataset = train_dataset.select(range(data_args.max_train_samples))
551
+ train_dataset = train_dataset.map(
552
+ preprocess_function,
553
+ batched=True,
554
+ num_proc=data_args.preprocessing_num_workers,
555
+ remove_columns=column_names,
556
+ load_from_cache_file=not data_args.overwrite_cache,
557
+ desc="Running tokenizer on train dataset",
558
+ )
559
+
560
+ if training_args.do_eval:
561
+ max_target_length = data_args.val_max_target_length
562
+ if "validation" not in dataset:
563
+ raise ValueError("--do_eval requires a validation dataset")
564
+ eval_dataset = dataset["validation"]
565
+ if data_args.max_eval_samples is not None:
566
+ eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
567
+ eval_dataset = eval_dataset.map(
568
+ preprocess_function,
569
+ batched=True,
570
+ num_proc=data_args.preprocessing_num_workers,
571
+ remove_columns=column_names,
572
+ load_from_cache_file=not data_args.overwrite_cache,
573
+ desc="Running tokenizer on validation dataset",
574
+ )
575
+
576
+ if training_args.do_predict:
577
+ max_target_length = data_args.val_max_target_length
578
+ if "test" not in dataset:
579
+ raise ValueError("--do_predict requires a test dataset")
580
+ predict_dataset = dataset["test"]
581
+ if data_args.max_predict_samples is not None:
582
+ predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))
583
+ predict_dataset = predict_dataset.map(
584
+ preprocess_function,
585
+ batched=True,
586
+ num_proc=data_args.preprocessing_num_workers,
587
+ remove_columns=column_names,
588
+ load_from_cache_file=not data_args.overwrite_cache,
589
+ desc="Running tokenizer on prediction dataset",
590
+ )
591
+
592
+ # Metric
593
+ metric = load_metric("rouge")
594
+
595
+ def postprocess_text(preds, labels):
596
+ preds = [pred.strip() for pred in preds]
597
+ labels = [label.strip() for label in labels]
598
+
599
+ # rougeLSum expects newline after each sentence
600
+ preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
601
+ labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels]
602
+
603
+ return preds, labels
604
+
605
+ def compute_metrics(preds, labels):
606
+ decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
607
+ decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
608
+
609
+ # Some simple post-processing
610
+ decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
611
+
612
+ result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
613
+ # Extract a few results from ROUGE
614
+ result = {key: value.mid.fmeasure * 100 for key, value in result.items()}
615
+
616
+ prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
617
+ result["gen_len"] = np.mean(prediction_lens)
618
+ result = {k: round(v, 4) for k, v in result.items()}
619
+ return result
620
+
621
+ # Enable tensorboard only on the master node
622
+ has_tensorboard = is_tensorboard_available()
623
+ if has_tensorboard and jax.process_index() == 0:
624
+ try:
625
+ from flax.metrics.tensorboard import SummaryWriter
626
+
627
+ summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))
628
+ except ImportError as ie:
629
+ has_tensorboard = False
630
+ logger.warning(
631
+ f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
632
+ )
633
+ else:
634
+ logger.warning(
635
+ "Unable to display metrics through TensorBoard because the package is not installed: "
636
+ "Please run pip install tensorboard to enable."
637
+ )
638
+
639
+ # Initialize our training
640
+ rng = jax.random.PRNGKey(training_args.seed)
641
+ rng, dropout_rng = jax.random.split(rng)
642
+
643
+ # Store some constant
644
+ num_epochs = int(training_args.num_train_epochs)
645
+ train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count()
646
+ eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count()
647
+ steps_per_epoch = len(train_dataset) // train_batch_size
648
+ total_train_steps = steps_per_epoch * num_epochs
649
+
650
+ # Create learning rate schedule
651
+ linear_decay_lr_schedule_fn = create_learning_rate_fn(
652
+ len(train_dataset),
653
+ train_batch_size,
654
+ training_args.num_train_epochs,
655
+ training_args.warmup_steps,
656
+ training_args.learning_rate,
657
+ )
658
+
659
+ # We use Optax's "masking" functionality to not apply weight decay
660
+ # to bias and LayerNorm scale parameters. decay_mask_fn returns a
661
+ # mask boolean with the same structure as the parameters.
662
+ # The mask is True for parameters that should be decayed.
663
+ # Note that this mask is specifically adapted for FlaxBart.
664
+ # For FlaxT5, one should correct the layer norm parameter naming
665
+ # accordingly - see `run_t5_mlm_flax.py` e.g.
666
+ def decay_mask_fn(params):
667
+ flat_params = traverse_util.flatten_dict(params)
668
+ layer_norm_params = [
669
+ (name, "scale") for name in ["self_attn_layer_norm", "layernorm_embedding", "final_layer_norm"]
670
+ ]
671
+ flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_params) for path in flat_params}
672
+ return traverse_util.unflatten_dict(flat_mask)
673
+
674
+ # create adam optimizer
675
+ adamw = optax.adamw(
676
+ learning_rate=linear_decay_lr_schedule_fn,
677
+ b1=training_args.adam_beta1,
678
+ b2=training_args.adam_beta2,
679
+ eps=training_args.adam_epsilon,
680
+ weight_decay=training_args.weight_decay,
681
+ mask=decay_mask_fn,
682
+ )
683
+
684
+ # Setup train state
685
+ state = TrainState.create(apply_fn=model.__call__, params=model.params, tx=adamw, dropout_rng=dropout_rng)
686
+
687
+ # label smoothed cross entropy
688
+ def loss_fn(logits, labels, padding_mask, label_smoothing_factor=0.0):
689
+ """
690
+ The label smoothing implementation is adapted from Flax's official example:
691
+ https://github.com/google/flax/blob/87a211135c6a377c8f29048a1cac3840e38b9da4/examples/wmt/train.py#L104
692
+ """
693
+ vocab_size = logits.shape[-1]
694
+ confidence = 1.0 - label_smoothing_factor
695
+ low_confidence = (1.0 - confidence) / (vocab_size - 1)
696
+ normalizing_constant = -(
697
+ confidence * jnp.log(confidence) + (vocab_size - 1) * low_confidence * jnp.log(low_confidence + 1e-20)
698
+ )
699
+ soft_labels = onehot(labels, vocab_size, on_value=confidence, off_value=low_confidence)
700
+
701
+ loss = optax.softmax_cross_entropy(logits, soft_labels)
702
+ loss = loss - normalizing_constant
703
+
704
+ # ignore padded tokens from loss
705
+ loss = loss * padding_mask
706
+ loss = loss.sum() / padding_mask.sum()
707
+ return loss
708
+
709
+ # Define gradient update step fn
710
+ def train_step(state, batch, label_smoothing_factor=0.0):
711
+ dropout_rng, new_dropout_rng = jax.random.split(state.dropout_rng)
712
+
713
+ def compute_loss(params):
714
+ labels = batch.pop("labels")
715
+ logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
716
+ loss = loss_fn(logits, labels, batch["decoder_attention_mask"], label_smoothing_factor)
717
+ return loss
718
+
719
+ grad_fn = jax.value_and_grad(compute_loss)
720
+ loss, grad = grad_fn(state.params)
721
+ grad = jax.lax.pmean(grad, "batch")
722
+
723
+ new_state = state.apply_gradients(grads=grad, dropout_rng=new_dropout_rng)
724
+
725
+ metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}
726
+ metrics = jax.lax.pmean(metrics, axis_name="batch")
727
+
728
+ return new_state, metrics
729
+
730
+ # Define eval fn
731
+ def eval_step(params, batch, label_smoothing_factor=0.0):
732
+ labels = batch.pop("labels")
733
+ logits = model(**batch, params=params, train=False)[0]
734
+ loss = loss_fn(logits, labels, batch["decoder_attention_mask"], label_smoothing_factor)
735
+
736
+ # summarize metrics
737
+ metrics = {"loss": loss}
738
+ metrics = jax.lax.pmean(metrics, axis_name="batch")
739
+ return metrics
740
+
741
+ # Define generation function
742
+ max_length = (
743
+ data_args.val_max_target_length if data_args.val_max_target_length is not None else model.config.max_length
744
+ )
745
+ num_beams = data_args.num_beams if data_args.num_beams is not None else model.config.num_beams
746
+ gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
747
+
748
+ def generate_step(params, batch):
749
+ model.params = params
750
+ output_ids = model.generate(batch["input_ids"], attention_mask=batch["attention_mask"], **gen_kwargs)
751
+ return output_ids.sequences
752
+
753
+ # Create parallel version of the train and eval step
754
+ p_train_step = jax.pmap(
755
+ partial(train_step, label_smoothing_factor=training_args.label_smoothing_factor), "batch", donate_argnums=(0,)
756
+ )
757
+ p_eval_step = jax.pmap(partial(eval_step, label_smoothing_factor=training_args.label_smoothing_factor), "batch")
758
+ p_generate_step = jax.pmap(generate_step, "batch")
759
+
760
+ # Replicate the train state on each device
761
+ state = state.replicate()
762
+
763
+ logger.info("***** Running training *****")
764
+ logger.info(f" Num examples = {len(train_dataset)}")
765
+ logger.info(f" Num Epochs = {num_epochs}")
766
+ logger.info(f" Instantaneous batch size per device = {training_args.per_device_train_batch_size}")
767
+ logger.info(f" Total train batch size (w. parallel & distributed) = {train_batch_size}")
768
+ logger.info(f" Total optimization steps = {total_train_steps}")
769
+
770
+ train_time = 0
771
+ epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0)
772
+ for epoch in epochs:
773
+ # ======================== Training ================================
774
+ train_start = time.time()
775
+
776
+ # Create sampling rng
777
+ rng, input_rng = jax.random.split(rng)
778
+ train_metrics = []
779
+
780
+ # Generate an epoch by shuffling sampling indices from the train dataset
781
+ train_loader = data_loader(input_rng, train_dataset, train_batch_size, shuffle=True)
782
+ steps_per_epoch = len(train_dataset) // train_batch_size
783
+ # train
784
+ for _ in tqdm(range(steps_per_epoch), desc="Training...", position=1, leave=False):
785
+ batch = next(train_loader)
786
+ state, train_metric = p_train_step(state, batch)
787
+ train_metrics.append(train_metric)
788
+
789
+ train_time += time.time() - train_start
790
+
791
+ train_metric = unreplicate(train_metric)
792
+
793
+ epochs.write(
794
+ f"Epoch... ({epoch + 1}/{num_epochs} | Loss: {train_metric['loss']}, Learning Rate: {train_metric['learning_rate']})"
795
+ )
796
+
797
+ # ======================== Evaluating ==============================
798
+ eval_metrics = []
799
+ eval_preds = []
800
+ eval_labels = []
801
+
802
+ eval_loader = data_loader(input_rng, eval_dataset, eval_batch_size)
803
+ eval_steps = len(eval_dataset) // eval_batch_size
804
+ for _ in tqdm(range(eval_steps), desc="Evaluating...", position=2, leave=False):
805
+ # Model forward
806
+ batch = next(eval_loader)
807
+ labels = batch["labels"]
808
+
809
+ metrics = p_eval_step(state.params, batch)
810
+ eval_metrics.append(metrics)
811
+
812
+ # generation
813
+ if data_args.predict_with_generate:
814
+ generated_ids = p_generate_step(state.params, batch)
815
+ eval_preds.extend(jax.device_get(generated_ids.reshape(-1, gen_kwargs["max_length"])))
816
+ eval_labels.extend(jax.device_get(labels.reshape(-1, labels.shape[-1])))
817
+
818
+ # normalize eval metrics
819
+ eval_metrics = get_metrics(eval_metrics)
820
+ eval_metrics = jax.tree_map(jnp.mean, eval_metrics)
821
+
822
+ # compute ROUGE metrics
823
+ rouge_desc = ""
824
+ if data_args.predict_with_generate:
825
+ rouge_metrics = compute_metrics(eval_preds, eval_labels)
826
+ eval_metrics.update(rouge_metrics)
827
+ rouge_desc = " ".join([f"Eval {key}: {value} |" for key, value in rouge_metrics.items()])
828
+
829
+ # Print metrics and update progress bar
830
+ desc = f"Epoch... ({epoch + 1}/{num_epochs} | Eval Loss: {eval_metrics['loss']} | {rouge_desc})"
831
+ epochs.write(desc)
832
+ epochs.desc = desc
833
+
834
+ # Save metrics
835
+ if has_tensorboard and jax.process_index() == 0:
836
+ cur_step = epoch * (len(train_dataset) // train_batch_size)
837
+ write_metric(summary_writer, train_metrics, eval_metrics, train_time, cur_step)
838
+
839
+ # save checkpoint after each epoch and push checkpoint to the hub
840
+ if jax.process_index() == 0:
841
+ params = jax.device_get(jax.tree_map(lambda x: x[0], state.params))
842
+ model.save_pretrained(training_args.output_dir, params=params)
843
+ tokenizer.save_pretrained(training_args.output_dir)
844
+ if training_args.push_to_hub:
845
+ repo.push_to_hub(commit_message=f"Saving weights and logs of epoch {epoch}", blocking=False)
846
+
847
+ # ======================== Prediction loop ==============================
848
+ if training_args.do_predict:
849
+ logger.info("*** Predict ***")
850
+
851
+ pred_metrics = []
852
+ pred_generations = []
853
+ pred_labels = []
854
+
855
+ pred_loader = data_loader(input_rng, predict_dataset, eval_batch_size)
856
+ pred_steps = len(predict_dataset) // eval_batch_size
857
+ for _ in tqdm(range(pred_steps), desc="Predicting...", position=2, leave=False):
858
+ # Model forward
859
+ batch = next(pred_loader)
860
+ labels = batch["labels"]
861
+
862
+ metrics = p_eval_step(state.params, batch)
863
+ pred_metrics.append(metrics)
864
+
865
+ # generation
866
+ if data_args.predict_with_generate:
867
+ generated_ids = p_generate_step(state.params, batch)
868
+ pred_generations.extend(jax.device_get(generated_ids.reshape(-1, gen_kwargs["max_length"])))
869
+ pred_labels.extend(jax.device_get(labels.reshape(-1, labels.shape[-1])))
870
+
871
+ # normalize prediction metrics
872
+ pred_metrics = get_metrics(pred_metrics)
873
+ pred_metrics = jax.tree_map(jnp.mean, pred_metrics)
874
+
875
+ # compute ROUGE metrics
876
+ rouge_desc = ""
877
+ if data_args.predict_with_generate:
878
+ rouge_metrics = compute_metrics(pred_generations, pred_labels)
879
+ pred_metrics.update(rouge_metrics)
880
+ rouge_desc = " ".join([f"Predict {key}: {value} |" for key, value in rouge_metrics.items()])
881
+
882
+ # Print metrics
883
+ desc = f"Predict Loss: {pred_metrics['loss']} | {rouge_desc})"
884
+ logger.info(desc)
885
+
886
+ # save final metrics in json
887
+ if jax.process_index() == 0:
888
+ rouge_metrics = {f"test_{metric_name}": value for metric_name, value in rouge_metrics.items()}
889
+ path = os.path.join(training_args.output_dir, "test_results.json")
890
+ with open(path, "w") as f:
891
+ json.dump(rouge_metrics, f, indent=4, sort_keys=True)
892
+
893
+
894
+ if __name__ == "__main__":
895
+ main()
special_tokens_map.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>", "additional_special_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "<extra_id_4>", "<extra_id_5>", "<extra_id_6>", "<extra_id_7>", "<extra_id_8>", "<extra_id_9>", "<extra_id_10>", "<extra_id_11>", "<extra_id_12>", "<extra_id_13>", "<extra_id_14>", "<extra_id_15>", "<extra_id_16>", "<extra_id_17>", "<extra_id_18>", "<extra_id_19>", "<extra_id_20>", "<extra_id_21>", "<extra_id_22>", "<extra_id_23>", "<extra_id_24>", "<extra_id_25>", "<extra_id_26>", "<extra_id_27>", "<extra_id_28>", "<extra_id_29>", "<extra_id_30>", "<extra_id_31>", "<extra_id_32>", "<extra_id_33>", "<extra_id_34>", "<extra_id_35>", "<extra_id_36>", "<extra_id_37>", "<extra_id_38>", "<extra_id_39>", "<extra_id_40>", "<extra_id_41>", "<extra_id_42>", "<extra_id_43>", "<extra_id_44>", "<extra_id_45>", "<extra_id_46>", "<extra_id_47>", "<extra_id_48>", "<extra_id_49>", "<extra_id_50>", "<extra_id_51>", "<extra_id_52>", "<extra_id_53>", "<extra_id_54>", "<extra_id_55>", "<extra_id_56>", "<extra_id_57>", "<extra_id_58>", "<extra_id_59>", "<extra_id_60>", "<extra_id_61>", "<extra_id_62>", "<extra_id_63>", "<extra_id_64>", "<extra_id_65>", "<extra_id_66>", "<extra_id_67>", "<extra_id_68>", "<extra_id_69>", "<extra_id_70>", "<extra_id_71>", "<extra_id_72>", "<extra_id_73>", "<extra_id_74>", "<extra_id_75>", "<extra_id_76>", "<extra_id_77>", "<extra_id_78>", "<extra_id_79>", "<extra_id_80>", "<extra_id_81>", "<extra_id_82>", "<extra_id_83>", "<extra_id_84>", "<extra_id_85>", "<extra_id_86>", "<extra_id_87>", "<extra_id_88>", "<extra_id_89>", "<extra_id_90>", "<extra_id_91>", "<extra_id_92>", "<extra_id_93>", "<extra_id_94>", "<extra_id_95>", "<extra_id_96>", "<extra_id_97>", "<extra_id_98>", "<extra_id_99>"]}
test_results.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "test_gen_len": 81.2739,
3
+ "test_rouge1": 34.4741,
4
+ "test_rouge2": 13.6346,
5
+ "test_rougeL": 25.2719,
6
+ "test_rougeLsum": 31.7216
7
+ }
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer_config.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>", "extra_ids": 100, "additional_special_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "<extra_id_4>", "<extra_id_5>", "<extra_id_6>", "<extra_id_7>", "<extra_id_8>", "<extra_id_9>", "<extra_id_10>", "<extra_id_11>", "<extra_id_12>", "<extra_id_13>", "<extra_id_14>", "<extra_id_15>", "<extra_id_16>", "<extra_id_17>", "<extra_id_18>", "<extra_id_19>", "<extra_id_20>", "<extra_id_21>", "<extra_id_22>", "<extra_id_23>", "<extra_id_24>", "<extra_id_25>", "<extra_id_26>", "<extra_id_27>", "<extra_id_28>", "<extra_id_29>", "<extra_id_30>", "<extra_id_31>", "<extra_id_32>", "<extra_id_33>", "<extra_id_34>", "<extra_id_35>", "<extra_id_36>", "<extra_id_37>", "<extra_id_38>", "<extra_id_39>", "<extra_id_40>", "<extra_id_41>", "<extra_id_42>", "<extra_id_43>", "<extra_id_44>", "<extra_id_45>", "<extra_id_46>", "<extra_id_47>", "<extra_id_48>", "<extra_id_49>", "<extra_id_50>", "<extra_id_51>", "<extra_id_52>", "<extra_id_53>", "<extra_id_54>", "<extra_id_55>", "<extra_id_56>", "<extra_id_57>", "<extra_id_58>", "<extra_id_59>", "<extra_id_60>", "<extra_id_61>", "<extra_id_62>", "<extra_id_63>", "<extra_id_64>", "<extra_id_65>", "<extra_id_66>", "<extra_id_67>", "<extra_id_68>", "<extra_id_69>", "<extra_id_70>", "<extra_id_71>", "<extra_id_72>", "<extra_id_73>", "<extra_id_74>", "<extra_id_75>", "<extra_id_76>", "<extra_id_77>", "<extra_id_78>", "<extra_id_79>", "<extra_id_80>", "<extra_id_81>", "<extra_id_82>", "<extra_id_83>", "<extra_id_84>", "<extra_id_85>", "<extra_id_86>", "<extra_id_87>", "<extra_id_88>", "<extra_id_89>", "<extra_id_90>", "<extra_id_91>", "<extra_id_92>", "<extra_id_93>", "<extra_id_94>", "<extra_id_95>", "<extra_id_96>", "<extra_id_97>", "<extra_id_98>", "<extra_id_99>"], "special_tokens_map_file": null, "name_or_path": "yhavinga/t5-v1.1-large-dutch-cased-2", "tokenizer_class": "T5Tokenizer"}