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upload model
Browse files- .gitattributes +1 -0
- NOTICE +14 -0
- README.md +187 -3
- config.json +252 -0
- configuration_ovis.py +201 -0
- generation_config.json +11 -0
- model-00001-of-00012.safetensors +3 -0
- model-00002-of-00012.safetensors +3 -0
- model-00003-of-00012.safetensors +3 -0
- model-00004-of-00012.safetensors +3 -0
- model-00005-of-00012.safetensors +3 -0
- model-00006-of-00012.safetensors +3 -0
- model-00007-of-00012.safetensors +3 -0
- model-00008-of-00012.safetensors +3 -0
- model-00009-of-00012.safetensors +3 -0
- model-00010-of-00012.safetensors +3 -0
- model-00011-of-00012.safetensors +3 -0
- model-00012-of-00012.safetensors +3 -0
- model.safetensors.index.json +967 -0
- modeling_ovis.py +625 -0
- preprocessor_config.json +24 -0
- special_tokens_map.json +34 -0
- tokenizer.json +3 -0
- tokenizer.model +3 -0
- tokenizer_config.json +1757 -0
.gitattributes
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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NOTICE
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Copyright (C) 2024 AIDC-AI
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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This model was trained based on the following models:
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1. Gemma (https://huggingface.co/google/gemma-2-27b-it), license: (https://ai.google.dev/gemma/terms). Gemma is provided under and subject to the Gemma Terms of Use found at https://ai.google.dev/gemma/terms.
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2. Siglip (https://huggingface.co/google/siglip-so400m-patch14-384), license: (https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md, SPDX-License-Identifier: Apache-2.0).
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README.md
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license: apache-2.0
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---
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license: apache-2.0
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datasets:
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- AIDC-AI/Ovis-dataset
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library_name: transformers
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tags:
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- MLLM
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pipeline_tag: image-text-to-text
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language:
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- en
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---
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# Ovis1.6-Gemma2-27B
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<div align="center">
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<img src=https://cdn-uploads.huggingface.co/production/uploads/637aebed7ce76c3b834cea37/3IK823BZ8w-mz_QfeYkDn.png width="30%"/>
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</div>
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## Introduction
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[GitHub](https://github.com/AIDC-AI/Ovis) | [Paper](https://arxiv.org/abs/2405.20797)
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We are pleased to announce the release of Ovis1.6-Gemma2-27B, our latest advancement in multi-modal large language models (MLLMs). Building upon the foundation of Ovis1.6, this model marks a substantial progression in multimodal AI capabilities, considerably expanding both capacity and performance while preserving the core architectural innovation of structurally aligning visual and textual embeddings.
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Key Features:
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- **Enhanced Model Performance**: Ovis1.6-Gemma2-27B excels in handling complex image-text instruction tasks, demonstrating enhanced understanding and reasoning across diverse modalities.
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- **Advanced Image Processing**: Demonstrates exceptional proficiency in analyzing complex visual inputs with high accuracy and granularity.
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- **Refined Chain-of-Thought Reasoning**: Exhibits markedly improved CoT capabilities, enabling sophisticated problem-solving across visual and textual domains.
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- **Enhanced Document Understanding**: Enhances comprehension of various document types (documents, charts, tables) and improves image recognition for Chinese and English text.
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<div align="center">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/658a8a837959448ef5500ce5/TIlymOb86R6_Mez3bpmcB.png" width="100%" />
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</div>
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## Model Zoo
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Succeeding Ovis1.5, **Ovis1.6** further enhances high-resolution image processing, is trained on a larger, more diverse, and higher-quality dataset, and refines the training process with DPO training following instruction-tuning.
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| Ovis MLLMs | ViT | LLM | Model Weights | Demo |
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|:------------------|:-----------:|:------------------:|:---------------------------------------------------------------:|:----------------------------------------------------------------:|
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| Ovis1.6-Gemma2-27B | Siglip-400M | Gemma2-27B-It | [Huggingface](https://huggingface.co/AIDC-AI/Ovis1.6-Gemma2-27B) | - |
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| Ovis1.6-Gemma2-9B | Siglip-400M | Gemma2-9B-It | [Huggingface](https://huggingface.co/AIDC-AI/Ovis1.6-Gemma2-9B) | [Space](https://huggingface.co/spaces/AIDC-AI/Ovis1.6-Gemma2-9B) |
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| Ovis1.6-Llama3.2-3B | Siglip-400M | Llama-3.2-3B-Instruct | [Huggingface](https://huggingface.co/AIDC-AI/Ovis1.6-Llama3.2-3B) | [Space](https://huggingface.co/spaces/AIDC-AI/Ovis1.6-Llama3.2-3B) |
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## Performance
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With **29B** parameters, **Ovis1.6-Gemma2-27B** achieves exceptional performance in the [OpenCompass](https://github.com/open-compass/VLMEvalKit) benchmark, ranking among the top-tier open-source MLLMs.
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<div align="center">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/658a8a837959448ef5500ce5/Jg89qOB1Qtai7W_6WF-vG.png" width="100%" />
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</div>
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## Usage
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Below is a code snippet to run Ovis with multimodal inputs. For additional usage instructions, including inference wrapper and Gradio UI, please refer to [Ovis GitHub](https://github.com/AIDC-AI/Ovis?tab=readme-ov-file#inference).
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```bash
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pip install torch==2.4.0 transformers==4.46.2 numpy==1.25.0 pillow==10.3.0
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```
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```python
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import torch
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from PIL import Image
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from transformers import AutoModelForCausalLM
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# load model
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model = AutoModelForCausalLM.from_pretrained("AIDC-AI/Ovis1.6-Gemma2-27B",
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torch_dtype=torch.bfloat16,
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multimodal_max_length=8192,
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trust_remote_code=True).cuda()
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text_tokenizer = model.get_text_tokenizer()
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visual_tokenizer = model.get_visual_tokenizer()
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# enter image path and prompt
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image_path = input("Enter image path: ")
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image = Image.open(image_path)
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text = input("Enter prompt: ")
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query = f'<image>\n{text}'
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# format conversation
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prompt, input_ids, pixel_values = model.preprocess_inputs(query, [image])
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attention_mask = torch.ne(input_ids, text_tokenizer.pad_token_id)
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input_ids = input_ids.unsqueeze(0).to(device=model.device)
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attention_mask = attention_mask.unsqueeze(0).to(device=model.device)
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pixel_values = [pixel_values.to(dtype=visual_tokenizer.dtype, device=visual_tokenizer.device)]
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# generate output
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with torch.inference_mode():
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gen_kwargs = dict(
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max_new_tokens=1024,
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do_sample=False,
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top_p=None,
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top_k=None,
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temperature=None,
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repetition_penalty=None,
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eos_token_id=model.generation_config.eos_token_id,
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pad_token_id=text_tokenizer.pad_token_id,
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use_cache=True
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)
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output_ids = model.generate(input_ids, pixel_values=pixel_values, attention_mask=attention_mask, **gen_kwargs)[0]
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output = text_tokenizer.decode(output_ids, skip_special_tokens=True)
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print(f'Output:\n{output}')
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```
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<details>
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<summary>FlashAttention Support</summary>
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To use FlashAttention, first install `flash-attn>=2.6.3`:
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```bash
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pip install flash-attn --no-build-isolation
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```
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Then, load model with `llm_attn_implementation='flash_attention_2'`:
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```python
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model = AutoModelForCausalLM.from_pretrained("AIDC-AI/Ovis1.6-Gemma2-27B",
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torch_dtype=torch.bfloat16,
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multimodal_max_length=8192,
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llm_attn_implementation='flash_attention_2',
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trust_remote_code=True).cuda()
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```
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</details>
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<details>
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<summary>Batch Inference</summary>
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```python
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batch_inputs = [
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('example_image1.jpeg', 'Describe the content of this image.'),
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('example_image2.jpeg', 'What is the equation in the image?')
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]
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batch_input_ids = []
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batch_attention_mask = []
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batch_pixel_values = []
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for image_path, text in batch_inputs:
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image = Image.open(image_path)
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query = f'<image>\n{text}'
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prompt, input_ids, pixel_values = model.preprocess_inputs(query, [image])
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attention_mask = torch.ne(input_ids, text_tokenizer.pad_token_id)
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input_ids = input_ids.unsqueeze(0).to(device=model.device)
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attention_mask = attention_mask.unsqueeze(0).to(device=model.device)
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pixel_values = [pixel_values.to(dtype=visual_tokenizer.dtype, device=visual_tokenizer.device)]
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batch_input_ids.append(input_ids.squeeze())
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batch_attention_mask.append(attention_mask.squeeze())
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batch_pixel_values.append(pixel_values)
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pad_batch_input_ids = torch.nn.utils.rnn.pad_sequence([i.flip(dims=[0]) for i in batch_input_ids],batch_first=True, padding_value=0.0).flip(dims=[1])
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pad_batch_input_ids = pad_batch_input_ids[:,-model.config.multimodal_max_length:]
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pad_batch_attention_mask = torch.nn.utils.rnn.pad_sequence([i.flip(dims=[0]) for i in batch_attention_mask],batch_first=True, padding_value=False).flip(dims=[1])
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pad_batch_attention_mask = pad_batch_attention_mask[:,-model.config.multimodal_max_length:]
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pad_batch_pixel_values = [item for sublist in batch_pixel_values for item in sublist]
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# generate output
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with torch.inference_mode():
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gen_kwargs = dict(
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max_new_tokens=1024,
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do_sample=False,
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top_p=None,
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top_k=None,
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temperature=None,
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repetition_penalty=None,
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eos_token_id=model.generation_config.eos_token_id,
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pad_token_id=text_tokenizer.pad_token_id,
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use_cache=True
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)
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output_ids = model.generate(pad_batch_input_ids, pixel_values=pad_batch_pixel_values, attention_mask=pad_batch_attention_mask, **gen_kwargs)
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for i in range(len(batch_input_ids)):
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output = text_tokenizer.decode(output_ids[i], skip_special_tokens=True)
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print(f'Output_{i}:\n{output}')
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```
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</details>
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## Citation
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If you find Ovis useful, please cite the paper
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```
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@article{lu2024ovis,
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title={Ovis: Structural Embedding Alignment for Multimodal Large Language Model},
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author={Shiyin Lu and Yang Li and Qing-Guo Chen and Zhao Xu and Weihua Luo and Kaifu Zhang and Han-Jia Ye},
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year={2024},
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journal={arXiv:2405.20797}
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}
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```
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## License
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This project is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0.txt) (SPDX-License-Identifier: Apache-2.0). Any use of the model shall be subject to Gemma's use restrictions. You must not use the model for the restricted uses set forth in the [Gemma Prohibited Use Policy](ai.google.dev/gemma/prohibited_use_policy), which is incorporated by reference into Gemma Terms of Use, or in violation of applicable laws and regulations. To the maximum extent permitted by law, Google reserves the right to restrict (remotely or otherwise) usage of any of the Gemma Services that Google reasonably believes are in violation of Gemma Terms of Use.
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## Disclaimer
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We used compliance-checking algorithms during the training process, to ensure the compliance of the trained model to the best of our ability. Due to the complexity of the data and the diversity of language model usage scenarios, we cannot guarantee that the model is completely free of copyright issues or improper content. If you believe anything infringes on your rights or generates improper content, please contact us, and we will promptly address the matter.
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config.json
ADDED
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|
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|
|
|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"Ovis"
|
| 4 |
+
],
|
| 5 |
+
"auto_map": {
|
| 6 |
+
"AutoConfig": "configuration_ovis.OvisConfig",
|
| 7 |
+
"AutoModelForCausalLM": "modeling_ovis.Ovis"
|
| 8 |
+
},
|
| 9 |
+
"conversation_formatter_class": "GemmaConversationFormatter",
|
| 10 |
+
"disable_tie_weight": false,
|
| 11 |
+
"hidden_size": 4608,
|
| 12 |
+
"llm_attn_implementation": "eager",
|
| 13 |
+
"llm_config": {
|
| 14 |
+
"_attn_implementation_autoset": false,
|
| 15 |
+
"_name_or_path": "google/gemma-2-27b-it",
|
| 16 |
+
"add_cross_attention": false,
|
| 17 |
+
"architectures": [
|
| 18 |
+
"Gemma2ForCausalLM"
|
| 19 |
+
],
|
| 20 |
+
"attention_bias": false,
|
| 21 |
+
"attention_dropout": 0.0,
|
| 22 |
+
"attn_logit_softcapping": 50.0,
|
| 23 |
+
"bad_words_ids": null,
|
| 24 |
+
"begin_suppress_tokens": null,
|
| 25 |
+
"bos_token_id": 2,
|
| 26 |
+
"cache_implementation": "hybrid",
|
| 27 |
+
"chunk_size_feed_forward": 0,
|
| 28 |
+
"cross_attention_hidden_size": null,
|
| 29 |
+
"decoder_start_token_id": null,
|
| 30 |
+
"diversity_penalty": 0.0,
|
| 31 |
+
"do_sample": false,
|
| 32 |
+
"early_stopping": false,
|
| 33 |
+
"encoder_no_repeat_ngram_size": 0,
|
| 34 |
+
"eos_token_id": 1,
|
| 35 |
+
"exponential_decay_length_penalty": null,
|
| 36 |
+
"final_logit_softcapping": 30.0,
|
| 37 |
+
"finetuning_task": null,
|
| 38 |
+
"forced_bos_token_id": null,
|
| 39 |
+
"forced_eos_token_id": null,
|
| 40 |
+
"head_dim": 128,
|
| 41 |
+
"hidden_act": "gelu_pytorch_tanh",
|
| 42 |
+
"hidden_activation": "gelu_pytorch_tanh",
|
| 43 |
+
"hidden_size": 4608,
|
| 44 |
+
"id2label": {
|
| 45 |
+
"0": "LABEL_0",
|
| 46 |
+
"1": "LABEL_1"
|
| 47 |
+
},
|
| 48 |
+
"initializer_range": 0.02,
|
| 49 |
+
"intermediate_size": 36864,
|
| 50 |
+
"is_decoder": false,
|
| 51 |
+
"is_encoder_decoder": false,
|
| 52 |
+
"label2id": {
|
| 53 |
+
"LABEL_0": 0,
|
| 54 |
+
"LABEL_1": 1
|
| 55 |
+
},
|
| 56 |
+
"length_penalty": 1.0,
|
| 57 |
+
"max_length": 20,
|
| 58 |
+
"max_position_embeddings": 8192,
|
| 59 |
+
"min_length": 0,
|
| 60 |
+
"model_type": "gemma2",
|
| 61 |
+
"no_repeat_ngram_size": 0,
|
| 62 |
+
"num_attention_heads": 32,
|
| 63 |
+
"num_beam_groups": 1,
|
| 64 |
+
"num_beams": 1,
|
| 65 |
+
"num_hidden_layers": 46,
|
| 66 |
+
"num_key_value_heads": 16,
|
| 67 |
+
"num_return_sequences": 1,
|
| 68 |
+
"output_attentions": false,
|
| 69 |
+
"output_hidden_states": false,
|
| 70 |
+
"output_scores": false,
|
| 71 |
+
"pad_token_id": 0,
|
| 72 |
+
"prefix": null,
|
| 73 |
+
"problem_type": null,
|
| 74 |
+
"pruned_heads": {},
|
| 75 |
+
"query_pre_attn_scalar": 144,
|
| 76 |
+
"remove_invalid_values": false,
|
| 77 |
+
"repetition_penalty": 1.0,
|
| 78 |
+
"return_dict": true,
|
| 79 |
+
"return_dict_in_generate": false,
|
| 80 |
+
"rms_norm_eps": 1e-06,
|
| 81 |
+
"rope_theta": 10000.0,
|
| 82 |
+
"sep_token_id": null,
|
| 83 |
+
"sliding_window": 4096,
|
| 84 |
+
"sliding_window_size": 4096,
|
| 85 |
+
"suppress_tokens": null,
|
| 86 |
+
"task_specific_params": null,
|
| 87 |
+
"temperature": 1.0,
|
| 88 |
+
"tf_legacy_loss": false,
|
| 89 |
+
"tie_encoder_decoder": false,
|
| 90 |
+
"tie_word_embeddings": true,
|
| 91 |
+
"tokenizer_class": null,
|
| 92 |
+
"top_k": 50,
|
| 93 |
+
"top_p": 1.0,
|
| 94 |
+
"torch_dtype": "bfloat16",
|
| 95 |
+
"torchscript": false,
|
| 96 |
+
"typical_p": 1.0,
|
| 97 |
+
"use_bfloat16": false,
|
| 98 |
+
"use_cache": true,
|
| 99 |
+
"vocab_size": 256000
|
| 100 |
+
},
|
| 101 |
+
"model_type": "ovis",
|
| 102 |
+
"multimodal_max_length": 8192,
|
| 103 |
+
"torch_dtype": "bfloat16",
|
| 104 |
+
"transformers_version": "4.46.2",
|
| 105 |
+
"use_cache": true,
|
| 106 |
+
"visual_tokenizer_config": {
|
| 107 |
+
"_attn_implementation_autoset": false,
|
| 108 |
+
"_name_or_path": "",
|
| 109 |
+
"add_cross_attention": false,
|
| 110 |
+
"architectures": null,
|
| 111 |
+
"backbone_config": {
|
| 112 |
+
"_attn_implementation_autoset": false,
|
| 113 |
+
"_name_or_path": "google/siglip-so400m-patch14-384",
|
| 114 |
+
"add_cross_attention": false,
|
| 115 |
+
"architectures": null,
|
| 116 |
+
"attention_dropout": 0.0,
|
| 117 |
+
"bad_words_ids": null,
|
| 118 |
+
"begin_suppress_tokens": null,
|
| 119 |
+
"bos_token_id": null,
|
| 120 |
+
"chunk_size_feed_forward": 0,
|
| 121 |
+
"cross_attention_hidden_size": null,
|
| 122 |
+
"decoder_start_token_id": null,
|
| 123 |
+
"diversity_penalty": 0.0,
|
| 124 |
+
"do_sample": false,
|
| 125 |
+
"early_stopping": false,
|
| 126 |
+
"encoder_no_repeat_ngram_size": 0,
|
| 127 |
+
"eos_token_id": null,
|
| 128 |
+
"exponential_decay_length_penalty": null,
|
| 129 |
+
"finetuning_task": null,
|
| 130 |
+
"forced_bos_token_id": null,
|
| 131 |
+
"forced_eos_token_id": null,
|
| 132 |
+
"hidden_act": "gelu_pytorch_tanh",
|
| 133 |
+
"hidden_size": 1152,
|
| 134 |
+
"id2label": {
|
| 135 |
+
"0": "LABEL_0",
|
| 136 |
+
"1": "LABEL_1"
|
| 137 |
+
},
|
| 138 |
+
"image_size": 384,
|
| 139 |
+
"intermediate_size": 4304,
|
| 140 |
+
"is_decoder": false,
|
| 141 |
+
"is_encoder_decoder": false,
|
| 142 |
+
"label2id": {
|
| 143 |
+
"LABEL_0": 0,
|
| 144 |
+
"LABEL_1": 1
|
| 145 |
+
},
|
| 146 |
+
"layer_norm_eps": 1e-06,
|
| 147 |
+
"length_penalty": 1.0,
|
| 148 |
+
"max_length": 20,
|
| 149 |
+
"min_length": 0,
|
| 150 |
+
"model_type": "siglip_vision_model",
|
| 151 |
+
"no_repeat_ngram_size": 0,
|
| 152 |
+
"num_attention_heads": 16,
|
| 153 |
+
"num_beam_groups": 1,
|
| 154 |
+
"num_beams": 1,
|
| 155 |
+
"num_channels": 3,
|
| 156 |
+
"num_hidden_layers": 27,
|
| 157 |
+
"num_return_sequences": 1,
|
| 158 |
+
"output_attentions": false,
|
| 159 |
+
"output_hidden_states": false,
|
| 160 |
+
"output_scores": false,
|
| 161 |
+
"pad_token_id": null,
|
| 162 |
+
"patch_size": 14,
|
| 163 |
+
"prefix": null,
|
| 164 |
+
"problem_type": null,
|
| 165 |
+
"pruned_heads": {},
|
| 166 |
+
"remove_invalid_values": false,
|
| 167 |
+
"repetition_penalty": 1.0,
|
| 168 |
+
"return_dict": true,
|
| 169 |
+
"return_dict_in_generate": false,
|
| 170 |
+
"sep_token_id": null,
|
| 171 |
+
"suppress_tokens": null,
|
| 172 |
+
"task_specific_params": null,
|
| 173 |
+
"temperature": 1.0,
|
| 174 |
+
"tf_legacy_loss": false,
|
| 175 |
+
"tie_encoder_decoder": false,
|
| 176 |
+
"tie_word_embeddings": true,
|
| 177 |
+
"tokenizer_class": null,
|
| 178 |
+
"top_k": 50,
|
| 179 |
+
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|
| 180 |
+
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|
| 181 |
+
"torchscript": false,
|
| 182 |
+
"typical_p": 1.0,
|
| 183 |
+
"use_bfloat16": false
|
| 184 |
+
},
|
| 185 |
+
"backbone_kwargs": {},
|
| 186 |
+
"bad_words_ids": null,
|
| 187 |
+
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|
| 188 |
+
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|
| 189 |
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|
| 190 |
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|
| 191 |
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|
| 192 |
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|
| 193 |
+
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|
| 194 |
+
"do_sample": false,
|
| 195 |
+
"drop_cls_token": false,
|
| 196 |
+
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|
| 197 |
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|
| 198 |
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|
| 199 |
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|
| 200 |
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|
| 201 |
+
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|
| 202 |
+
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|
| 203 |
+
"hidden_stride": 2,
|
| 204 |
+
"id2label": {
|
| 205 |
+
"0": "LABEL_0",
|
| 206 |
+
"1": "LABEL_1"
|
| 207 |
+
},
|
| 208 |
+
"is_decoder": false,
|
| 209 |
+
"is_encoder_decoder": false,
|
| 210 |
+
"label2id": {
|
| 211 |
+
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|
| 212 |
+
"LABEL_1": 1
|
| 213 |
+
},
|
| 214 |
+
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|
| 215 |
+
"max_length": 20,
|
| 216 |
+
"min_length": 0,
|
| 217 |
+
"model_type": "siglip_visual_tokenizer",
|
| 218 |
+
"no_repeat_ngram_size": 0,
|
| 219 |
+
"num_beam_groups": 1,
|
| 220 |
+
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|
| 221 |
+
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|
| 222 |
+
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|
| 223 |
+
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|
| 224 |
+
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|
| 225 |
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|
| 226 |
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|
| 227 |
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|
| 228 |
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|
| 229 |
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|
| 230 |
+
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|
| 231 |
+
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|
| 232 |
+
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|
| 233 |
+
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|
| 234 |
+
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|
| 235 |
+
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|
| 236 |
+
"tau": 1.0,
|
| 237 |
+
"temperature": 1.0,
|
| 238 |
+
"tf_legacy_loss": false,
|
| 239 |
+
"tie_encoder_decoder": false,
|
| 240 |
+
"tie_word_embeddings": true,
|
| 241 |
+
"tokenize_function": "softmax",
|
| 242 |
+
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|
| 243 |
+
"top_k": 50,
|
| 244 |
+
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|
| 245 |
+
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|
| 246 |
+
"torchscript": false,
|
| 247 |
+
"typical_p": 1.0,
|
| 248 |
+
"use_bfloat16": false,
|
| 249 |
+
"use_indicators": false,
|
| 250 |
+
"vocab_size": 131072
|
| 251 |
+
}
|
| 252 |
+
}
|
configuration_ovis.py
ADDED
|
@@ -0,0 +1,201 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from abc import ABC, abstractmethod
|
| 2 |
+
from typing import List, Dict, Union, Optional
|
| 3 |
+
|
| 4 |
+
from transformers import PretrainedConfig, AutoConfig
|
| 5 |
+
|
| 6 |
+
IGNORE_ID = -100
|
| 7 |
+
IMAGE_TOKEN_ID = -200
|
| 8 |
+
IMAGE_TOKEN = "<image>"
|
| 9 |
+
IMAGE_ATOM_ID = -300
|
| 10 |
+
IMAGE_INDICATOR_IDS = [-301, -302, -303, -304, -305]
|
| 11 |
+
|
| 12 |
+
|
| 13 |
+
# ----------------------------------------------------------------------
|
| 14 |
+
# Visual Tokenizer Configuration
|
| 15 |
+
# ----------------------------------------------------------------------
|
| 16 |
+
class BaseVisualTokenizerConfig(PretrainedConfig):
|
| 17 |
+
def __init__(
|
| 18 |
+
self,
|
| 19 |
+
vocab_size=16384,
|
| 20 |
+
tokenize_function="softmax",
|
| 21 |
+
tau=1.0,
|
| 22 |
+
depths=None,
|
| 23 |
+
drop_cls_token=False,
|
| 24 |
+
backbone_config: Optional[Union[PretrainedConfig, dict]] = None,
|
| 25 |
+
hidden_stride: int = 1,
|
| 26 |
+
**kwargs
|
| 27 |
+
):
|
| 28 |
+
super().__init__(**kwargs)
|
| 29 |
+
self.vocab_size = vocab_size
|
| 30 |
+
self.tokenize_function = tokenize_function
|
| 31 |
+
self.tau = tau
|
| 32 |
+
if isinstance(depths, str):
|
| 33 |
+
depths = [int(x) for x in depths.split('|')]
|
| 34 |
+
self.depths = depths
|
| 35 |
+
self.backbone_kwargs = {}
|
| 36 |
+
self.drop_cls_token = drop_cls_token
|
| 37 |
+
if backbone_config is not None:
|
| 38 |
+
assert isinstance(backbone_config, (PretrainedConfig, dict)), \
|
| 39 |
+
f"expect `backbone_config` to be instance of PretrainedConfig or dict, but got {type(backbone_config)} type"
|
| 40 |
+
if not isinstance(backbone_config, PretrainedConfig):
|
| 41 |
+
model_type = backbone_config['model_type']
|
| 42 |
+
backbone_config.pop('model_type')
|
| 43 |
+
backbone_config = AutoConfig.for_model(model_type, **backbone_config)
|
| 44 |
+
self.backbone_config = backbone_config
|
| 45 |
+
self.hidden_stride = hidden_stride
|
| 46 |
+
|
| 47 |
+
|
| 48 |
+
class SiglipVisualTokenizerConfig(BaseVisualTokenizerConfig):
|
| 49 |
+
model_type = "siglip_visual_tokenizer"
|
| 50 |
+
|
| 51 |
+
def __init__(self, **kwargs):
|
| 52 |
+
super().__init__(**kwargs)
|
| 53 |
+
if self.drop_cls_token:
|
| 54 |
+
self.drop_cls_token = False
|
| 55 |
+
if self.depths:
|
| 56 |
+
assert len(self.depths) == 1
|
| 57 |
+
self.backbone_kwargs['num_hidden_layers'] = self.depths[0]
|
| 58 |
+
|
| 59 |
+
|
| 60 |
+
AutoConfig.register("siglip_visual_tokenizer", SiglipVisualTokenizerConfig)
|
| 61 |
+
|
| 62 |
+
|
| 63 |
+
# ----------------------------------------------------------------------
|
| 64 |
+
# Ovis Configuration
|
| 65 |
+
# ----------------------------------------------------------------------
|
| 66 |
+
class OvisConfig(PretrainedConfig):
|
| 67 |
+
model_type = "ovis"
|
| 68 |
+
|
| 69 |
+
def __init__(
|
| 70 |
+
self,
|
| 71 |
+
llm_config: Optional[Union[PretrainedConfig, dict]] = None,
|
| 72 |
+
visual_tokenizer_config: Optional[Union[PretrainedConfig, dict]] = None,
|
| 73 |
+
multimodal_max_length=8192,
|
| 74 |
+
hidden_size=None,
|
| 75 |
+
conversation_formatter_class=None,
|
| 76 |
+
llm_attn_implementation=None,
|
| 77 |
+
disable_tie_weight=False,
|
| 78 |
+
**kwargs
|
| 79 |
+
):
|
| 80 |
+
super().__init__(**kwargs)
|
| 81 |
+
if llm_config is not None:
|
| 82 |
+
assert isinstance(llm_config, (PretrainedConfig, dict)), \
|
| 83 |
+
f"expect `llm_config` to be instance of PretrainedConfig or dict, but got {type(llm_config)} type"
|
| 84 |
+
if not isinstance(llm_config, PretrainedConfig):
|
| 85 |
+
model_type = llm_config['model_type']
|
| 86 |
+
llm_config.pop('model_type')
|
| 87 |
+
llm_config = AutoConfig.for_model(model_type, **llm_config)
|
| 88 |
+
self.llm_config = llm_config
|
| 89 |
+
if visual_tokenizer_config is not None:
|
| 90 |
+
assert isinstance(visual_tokenizer_config, (PretrainedConfig, dict)), \
|
| 91 |
+
f"expect `visual_tokenizer_config` to be instance of PretrainedConfig or dict, but got {type(visual_tokenizer_config)} type"
|
| 92 |
+
if not isinstance(visual_tokenizer_config, PretrainedConfig):
|
| 93 |
+
model_type = visual_tokenizer_config['model_type']
|
| 94 |
+
visual_tokenizer_config.pop('model_type')
|
| 95 |
+
visual_tokenizer_config = AutoConfig.for_model(model_type, **visual_tokenizer_config)
|
| 96 |
+
self.visual_tokenizer_config = visual_tokenizer_config
|
| 97 |
+
self.multimodal_max_length = multimodal_max_length
|
| 98 |
+
self.hidden_size = hidden_size
|
| 99 |
+
self.conversation_formatter_class = conversation_formatter_class
|
| 100 |
+
self.llm_attn_implementation = llm_attn_implementation
|
| 101 |
+
self.disable_tie_weight = disable_tie_weight
|
| 102 |
+
|
| 103 |
+
|
| 104 |
+
# ----------------------------------------------------------------------
|
| 105 |
+
# Conversation Formatter
|
| 106 |
+
# ----------------------------------------------------------------------
|
| 107 |
+
class ConversationFormatter(ABC):
|
| 108 |
+
support_tokenizer_types = None
|
| 109 |
+
|
| 110 |
+
def __init__(self, tokenizer):
|
| 111 |
+
tokenizer_type = type(tokenizer).__name__
|
| 112 |
+
assert tokenizer_type in self.support_tokenizer_types, \
|
| 113 |
+
f'Invalid tokenizer type, expected one from `{self.support_tokenizer_types}`, but got `{tokenizer_type}`'
|
| 114 |
+
self.tokenizer = tokenizer
|
| 115 |
+
self.image_token = IMAGE_TOKEN
|
| 116 |
+
self.image_token_id = IMAGE_TOKEN_ID
|
| 117 |
+
self.ignore_id = IGNORE_ID
|
| 118 |
+
|
| 119 |
+
def _tokenize_with_image_symbol(self, text):
|
| 120 |
+
text_chunks = [self.tokenizer(chunk, add_special_tokens=False).input_ids for chunk in
|
| 121 |
+
text.split(self.image_token)]
|
| 122 |
+
token_ids = []
|
| 123 |
+
num_chuck = len(text_chunks)
|
| 124 |
+
for i, chunk in enumerate(text_chunks):
|
| 125 |
+
token_ids.extend(chunk)
|
| 126 |
+
if i < num_chuck - 1:
|
| 127 |
+
token_ids.append(self.image_token_id)
|
| 128 |
+
return token_ids
|
| 129 |
+
|
| 130 |
+
@abstractmethod
|
| 131 |
+
def format(self, conversations: List[Dict], generation_preface=None):
|
| 132 |
+
pass
|
| 133 |
+
|
| 134 |
+
@abstractmethod
|
| 135 |
+
def format_query(self, query, generation_preface=""):
|
| 136 |
+
pass
|
| 137 |
+
|
| 138 |
+
|
| 139 |
+
class GemmaConversationFormatter(ConversationFormatter):
|
| 140 |
+
support_tokenizer_types = ['GemmaTokenizer', 'GemmaTokenizerFast']
|
| 141 |
+
|
| 142 |
+
def __init__(self, tokenizer):
|
| 143 |
+
super().__init__(tokenizer)
|
| 144 |
+
# Gemma does not support system prompt
|
| 145 |
+
self.from2role = {
|
| 146 |
+
"human": "<start_of_turn>user\n",
|
| 147 |
+
"gpt": "<start_of_turn>model\n",
|
| 148 |
+
}
|
| 149 |
+
self.gpt_token_num = None
|
| 150 |
+
self.im_end = "<end_of_turn>\n"
|
| 151 |
+
self.bos_token = "<bos>"
|
| 152 |
+
self.bos_token_ids = None
|
| 153 |
+
|
| 154 |
+
def format(self, conversations: List[Dict], generation_preface=None):
|
| 155 |
+
if self.gpt_token_num is None:
|
| 156 |
+
self.gpt_token_num = len(self.tokenizer(self.from2role["gpt"], add_special_tokens=False).input_ids)
|
| 157 |
+
|
| 158 |
+
if self.bos_token_ids is None:
|
| 159 |
+
self.bos_token_ids = self.tokenizer(self.bos_token, add_special_tokens=False).input_ids
|
| 160 |
+
|
| 161 |
+
if conversations[0]["from"] == "system":
|
| 162 |
+
raise ValueError("Gemma does not support system prompt")
|
| 163 |
+
|
| 164 |
+
if generation_preface is not None:
|
| 165 |
+
conversations.append({
|
| 166 |
+
"from": "gpt",
|
| 167 |
+
"value": generation_preface
|
| 168 |
+
})
|
| 169 |
+
|
| 170 |
+
prompt = "" + self.bos_token
|
| 171 |
+
input_ids = [] + self.bos_token_ids
|
| 172 |
+
labels = [] + [IGNORE_ID] * len(input_ids)
|
| 173 |
+
num_conversation = len(conversations)
|
| 174 |
+
for i, conversation in enumerate(conversations):
|
| 175 |
+
frm = conversation["from"]
|
| 176 |
+
role = self.from2role[frm]
|
| 177 |
+
message = conversation["value"].strip()
|
| 178 |
+
text = role + message
|
| 179 |
+
if i < num_conversation - 1 or generation_preface is None:
|
| 180 |
+
text += self.im_end
|
| 181 |
+
prompt += text
|
| 182 |
+
token_ids = self._tokenize_with_image_symbol(text)
|
| 183 |
+
input_ids.extend(token_ids)
|
| 184 |
+
label_ids = [self.ignore_id] * len(token_ids)
|
| 185 |
+
if frm == "gpt":
|
| 186 |
+
# learning `\n` following `im_end` is meaningless, so the last `\n` token is ignored in label
|
| 187 |
+
label_ids[self.gpt_token_num:-1] = token_ids[self.gpt_token_num:-1]
|
| 188 |
+
labels.extend(label_ids)
|
| 189 |
+
|
| 190 |
+
assert self._tokenize_with_image_symbol(prompt) == input_ids
|
| 191 |
+
assert len(input_ids) == len(labels)
|
| 192 |
+
|
| 193 |
+
return prompt, input_ids, labels
|
| 194 |
+
|
| 195 |
+
def format_query(self, query, generation_preface=""):
|
| 196 |
+
prompt, input_ids, _ = self.format([{
|
| 197 |
+
"from": "human",
|
| 198 |
+
"value": query
|
| 199 |
+
}], generation_preface=generation_preface)
|
| 200 |
+
|
| 201 |
+
return prompt, input_ids
|
generation_config.json
ADDED
|
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_from_model_config": true,
|
| 3 |
+
"bos_token_id": 2,
|
| 4 |
+
"cache_implementation": "hybrid",
|
| 5 |
+
"eos_token_id": [
|
| 6 |
+
1,
|
| 7 |
+
107
|
| 8 |
+
],
|
| 9 |
+
"pad_token_id": 0,
|
| 10 |
+
"transformers_version": "4.46.2"
|
| 11 |
+
}
|
model-00001-of-00012.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:5727effe3cbe41ad62aad7c7c6a93657a51c581a89a73b42db257eda8956244c
|
| 3 |
+
size 4737543384
|
model-00002-of-00012.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:39ce693abaa0a5517434753f9679c3aaa513fbeee6db72ff22c65a101d3e658e
|
| 3 |
+
size 4869739856
|
model-00003-of-00012.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:5d8d45c4917807955c3be755c836cdc52afb8f70d786d35754680fb0a8eaa817
|
| 3 |
+
size 4869739856
|
model-00004-of-00012.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:15d6dbfff6b3297dc6999463f428fa6f94b4e5b13b2050dbdde6f6b0998e1315
|
| 3 |
+
size 4983023944
|
model-00005-of-00012.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:89f66aaf37d81f55fcfae359ad4fdc5916b83765f43c29af00015b134769db6c
|
| 3 |
+
size 4869739896
|
model-00006-of-00012.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:463593e36433bc3115ac05ff1a0efb7fbf3fecc6b26624f1bb16d15ba9fc2672
|
| 3 |
+
size 4869739888
|
model-00007-of-00012.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:6dd4c75b7d0567cfc67353aee3609a2988020888a2480b2e766d18e13562a637
|
| 3 |
+
size 4983023944
|
model-00008-of-00012.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:bb631d2c3e0e6aa5d3e77178f1798548c71b53210cb812635b6bb87dcddd3f82
|
| 3 |
+
size 4869739896
|
model-00009-of-00012.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:37ab5db6f117bc5009d22e3bc95732bf71e1e9472e03d940d0688052c8d28e42
|
| 3 |
+
size 4869739888
|
model-00010-of-00012.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:48b6b016ba6efc7033c8ae1272ff73b328990087ad40e86d51905e18be6cc552
|
| 3 |
+
size 4983023944
|
model-00011-of-00012.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:6dffe5f5952088c1d272284d775744d79d929d4f9cc6db06d99d2f9dce0361ab
|
| 3 |
+
size 4869739896
|
model-00012-of-00012.safetensors
ADDED
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@@ -0,0 +1,3 @@
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| 1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:3f151f31be6c127e1a65c301cf4544a42a3ce16d1a59dd2b86a5dcf07e07bc94
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| 3 |
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size 3952440292
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model.safetensors.index.json
ADDED
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@@ -0,0 +1,967 @@
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|
| 915 |
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|
| 916 |
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|
| 917 |
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|
| 918 |
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|
| 919 |
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|
| 920 |
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| 921 |
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|
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| 923 |
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|
| 925 |
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|
| 926 |
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| 927 |
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| 928 |
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| 929 |
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| 930 |
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|
| 931 |
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| 932 |
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| 933 |
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| 934 |
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| 939 |
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| 942 |
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| 944 |
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| 946 |
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| 948 |
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| 949 |
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| 950 |
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| 951 |
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| 952 |
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| 953 |
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| 954 |
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| 955 |
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| 956 |
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| 957 |
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| 958 |
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|
| 959 |
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| 960 |
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| 961 |
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| 962 |
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|
| 963 |
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| 964 |
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|
| 965 |
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|
| 966 |
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}
|
| 967 |
+
}
|
modeling_ovis.py
ADDED
|
@@ -0,0 +1,625 @@
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| 1 |
+
# Copyright (C) 2024 AIDC-AI
|
| 2 |
+
#
|
| 3 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 4 |
+
# you may not use this file except in compliance with the License.
|
| 5 |
+
# You may obtain a copy of the License at
|
| 6 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
| 7 |
+
#
|
| 8 |
+
# Unless required by applicable law or agreed to in writing, software
|
| 9 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 10 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 11 |
+
#
|
| 12 |
+
# See the License for the specific language governing permissions and
|
| 13 |
+
# limitations under the License.
|
| 14 |
+
|
| 15 |
+
import logging
|
| 16 |
+
import os
|
| 17 |
+
import importlib.metadata
|
| 18 |
+
|
| 19 |
+
from packaging import version
|
| 20 |
+
from importlib import import_module
|
| 21 |
+
from typing import List, Callable, Union, Optional, Dict
|
| 22 |
+
|
| 23 |
+
import PIL.Image
|
| 24 |
+
import torch
|
| 25 |
+
import transformers
|
| 26 |
+
from torch import Tensor
|
| 27 |
+
from torch.nn import init
|
| 28 |
+
from torch.nn.functional import softmax, gumbel_softmax, pad
|
| 29 |
+
from transformers.utils import is_flash_attn_2_available
|
| 30 |
+
from transformers import PreTrainedModel, AutoModel, AutoTokenizer, AutoModelForCausalLM, AutoImageProcessor
|
| 31 |
+
from transformers import SiglipImageProcessor, SiglipVisionModel
|
| 32 |
+
from transformers.cache_utils import HybridCache
|
| 33 |
+
from transformers.generation.utils import GenerateOutput
|
| 34 |
+
|
| 35 |
+
from .configuration_ovis import BaseVisualTokenizerConfig, SiglipVisualTokenizerConfig
|
| 36 |
+
from .configuration_ovis import OvisConfig, ConversationFormatter
|
| 37 |
+
from .configuration_ovis import IGNORE_ID, IMAGE_ATOM_ID, IMAGE_INDICATOR_IDS, IMAGE_TOKEN_ID
|
| 38 |
+
|
| 39 |
+
|
| 40 |
+
# ----------------------------------------------------------------------
|
| 41 |
+
# Visual Tokenizer
|
| 42 |
+
# ----------------------------------------------------------------------
|
| 43 |
+
class BaseVisualTokenizer(PreTrainedModel):
|
| 44 |
+
base_model_prefix = "backbone"
|
| 45 |
+
main_input_name = None
|
| 46 |
+
_image_processor_class = None
|
| 47 |
+
_image_processor_kwargs = {}
|
| 48 |
+
_backbone_class = None
|
| 49 |
+
_backbone_name_or_path = None
|
| 50 |
+
|
| 51 |
+
def __init__(self, config: BaseVisualTokenizerConfig, *inputs, **kwargs):
|
| 52 |
+
super().__init__(config, *inputs, **kwargs)
|
| 53 |
+
self.image_processor = AutoImageProcessor.from_pretrained(kwargs['image_processor_name_or_path'])
|
| 54 |
+
self.backbone = AutoModel.from_config(self.config.backbone_config)
|
| 55 |
+
head_dim = self.config.vocab_size - len(IMAGE_INDICATOR_IDS) # reserved tokens for IMAGE_INDICATORS
|
| 56 |
+
self.head = torch.nn.Sequential(
|
| 57 |
+
torch.nn.Linear(
|
| 58 |
+
self.backbone.config.hidden_size * self.config.hidden_stride * self.config.hidden_stride, head_dim,
|
| 59 |
+
bias=False
|
| 60 |
+
),
|
| 61 |
+
torch.nn.LayerNorm(head_dim)
|
| 62 |
+
)
|
| 63 |
+
|
| 64 |
+
assert all((self.image_processor.do_resize,
|
| 65 |
+
not getattr(self.image_processor, 'do_center_crop', False),
|
| 66 |
+
self.image_processor.do_rescale,
|
| 67 |
+
self.image_processor.do_normalize
|
| 68 |
+
)), f"image_processor `{self.image_processor}` is not supported currently"
|
| 69 |
+
|
| 70 |
+
def get_backbone(self):
|
| 71 |
+
return self.backbone
|
| 72 |
+
|
| 73 |
+
def get_image_processor(self):
|
| 74 |
+
return self.image_processor
|
| 75 |
+
|
| 76 |
+
def mock_input(self):
|
| 77 |
+
height, width = self.get_image_size()
|
| 78 |
+
return torch.zeros(1, 3, height, width), self.construct_image_placeholders((1, 1))
|
| 79 |
+
|
| 80 |
+
def get_head(self):
|
| 81 |
+
return self.head
|
| 82 |
+
|
| 83 |
+
def get_image_size(self):
|
| 84 |
+
raise NotImplementedError
|
| 85 |
+
|
| 86 |
+
@staticmethod
|
| 87 |
+
def construct_image_placeholders(grid):
|
| 88 |
+
image_placeholders = [IMAGE_INDICATOR_IDS[0], IMAGE_ATOM_ID, IMAGE_INDICATOR_IDS[1]]
|
| 89 |
+
if grid[0] * grid[1] > 1:
|
| 90 |
+
for r in range(grid[0]):
|
| 91 |
+
for c in range(grid[1]):
|
| 92 |
+
image_placeholders.append(IMAGE_ATOM_ID)
|
| 93 |
+
if c < grid[1] - 1:
|
| 94 |
+
image_placeholders.append(IMAGE_INDICATOR_IDS[2])
|
| 95 |
+
if r < grid[0] - 1:
|
| 96 |
+
image_placeholders.append(IMAGE_INDICATOR_IDS[3])
|
| 97 |
+
image_placeholders.append(IMAGE_INDICATOR_IDS[4])
|
| 98 |
+
return image_placeholders
|
| 99 |
+
|
| 100 |
+
def preprocess_image(self, image: PIL.Image.Image, max_partition=9, covering_threshold=0.9, convert_to_rgb=True):
|
| 101 |
+
def _preprocess(img: PIL.Image.Image, side):
|
| 102 |
+
# first resize and preprocess
|
| 103 |
+
w, h = img.size
|
| 104 |
+
if w == h:
|
| 105 |
+
new_width = new_height = side
|
| 106 |
+
elif w > h:
|
| 107 |
+
new_width = side
|
| 108 |
+
new_height = int(h / w * new_width)
|
| 109 |
+
else:
|
| 110 |
+
new_height = side
|
| 111 |
+
new_width = int(w / h * new_height)
|
| 112 |
+
new_size = dict(height=new_height, width=new_width)
|
| 113 |
+
pixel_values = self.image_processor.preprocess(img, size=new_size, return_tensors='pt')['pixel_values']
|
| 114 |
+
|
| 115 |
+
# then pad to square
|
| 116 |
+
square_values = torch.zeros([1, 3, side, side], dtype=pixel_values.dtype, device=pixel_values.device)
|
| 117 |
+
new_height, new_width = pixel_values.shape[2:]
|
| 118 |
+
if new_height == new_width:
|
| 119 |
+
square_values[:, :, :, :] = pixel_values
|
| 120 |
+
elif new_height > new_width:
|
| 121 |
+
from_index = (side - new_width) // 2
|
| 122 |
+
square_values[:, :, :, from_index:from_index + new_width] = pixel_values
|
| 123 |
+
else:
|
| 124 |
+
from_index = (side - new_height) // 2
|
| 125 |
+
square_values[:, :, from_index:from_index + new_height, :] = pixel_values
|
| 126 |
+
|
| 127 |
+
return square_values
|
| 128 |
+
|
| 129 |
+
def _partition(img, grid):
|
| 130 |
+
w, h = img.size
|
| 131 |
+
row_height = h // grid[0]
|
| 132 |
+
col_width = w // grid[1]
|
| 133 |
+
|
| 134 |
+
partition = []
|
| 135 |
+
for row in range(grid[0]):
|
| 136 |
+
for col in range(grid[1]):
|
| 137 |
+
left = col * col_width
|
| 138 |
+
upper = row * row_height
|
| 139 |
+
right = w if col == grid[1] - 1 else (col + 1) * col_width
|
| 140 |
+
lower = h if row == grid[0] - 1 else (row + 1) * row_height
|
| 141 |
+
partition.append((left, upper, right, lower))
|
| 142 |
+
|
| 143 |
+
return partition
|
| 144 |
+
|
| 145 |
+
def _covering_area(left, upper, right, lower, side):
|
| 146 |
+
w = right - left
|
| 147 |
+
h = lower - upper
|
| 148 |
+
w, h = max(w, h), min(w, h)
|
| 149 |
+
if w > side:
|
| 150 |
+
h = h / w * side
|
| 151 |
+
w = side
|
| 152 |
+
return w * h
|
| 153 |
+
|
| 154 |
+
def _get_best_grid(img, side):
|
| 155 |
+
img_area = img.size[0] * img.size[1]
|
| 156 |
+
|
| 157 |
+
candidate_grids = []
|
| 158 |
+
for i in range(1, max_partition + 1):
|
| 159 |
+
for j in range(1, max_partition + 1):
|
| 160 |
+
if i * j <= max_partition:
|
| 161 |
+
candidate_grids.append((i, j))
|
| 162 |
+
|
| 163 |
+
all_grids = []
|
| 164 |
+
good_grids = []
|
| 165 |
+
for grid in candidate_grids:
|
| 166 |
+
partition = _partition(img, grid)
|
| 167 |
+
covering_ratio = sum([_covering_area(*p, side) for p in partition]) / img_area
|
| 168 |
+
assert covering_ratio <= 1.0
|
| 169 |
+
all_grids.append((grid, covering_ratio))
|
| 170 |
+
if covering_ratio > covering_threshold:
|
| 171 |
+
good_grids.append((grid, covering_ratio))
|
| 172 |
+
|
| 173 |
+
if len(good_grids) > 0:
|
| 174 |
+
# pick the good partition with minimum #sub_images and break the tie using covering_ratio
|
| 175 |
+
return sorted(good_grids, key=lambda x: (x[0][0] * x[0][1], -x[1]))[0][0]
|
| 176 |
+
else:
|
| 177 |
+
# pick the partition with maximum covering_ratio and break the tie using #sub_images
|
| 178 |
+
return sorted(all_grids, key=lambda x: (-x[1], x[0][0] * x[0][1]))[0][0]
|
| 179 |
+
|
| 180 |
+
if convert_to_rgb and image.mode != 'RGB':
|
| 181 |
+
image = image.convert('RGB')
|
| 182 |
+
|
| 183 |
+
sides = self.get_image_size()
|
| 184 |
+
if sides[0] != sides[1]:
|
| 185 |
+
raise ValueError('get_image_size() returns non-square size')
|
| 186 |
+
side = sides[0]
|
| 187 |
+
grid = _get_best_grid(image, side)
|
| 188 |
+
partition = _partition(image, grid)
|
| 189 |
+
crops = [image.crop(p) for p in partition]
|
| 190 |
+
if len(crops) > 1:
|
| 191 |
+
crops.insert(0, image)
|
| 192 |
+
pixel_values = torch.cat([_preprocess(crop, side) for crop in crops], dim=0)
|
| 193 |
+
image_placeholders = self.construct_image_placeholders(grid)
|
| 194 |
+
return pixel_values, image_placeholders
|
| 195 |
+
|
| 196 |
+
def tokenize(self, logits):
|
| 197 |
+
def st_argmax(y_soft, dim): # straight-through softmax
|
| 198 |
+
index = y_soft.max(dim, keepdim=True)[1]
|
| 199 |
+
y_hard = torch.zeros_like(y_soft, memory_format=torch.legacy_contiguous_format).scatter_(dim, index, 1.0)
|
| 200 |
+
ret = y_hard - y_soft.detach() + y_soft
|
| 201 |
+
return ret
|
| 202 |
+
|
| 203 |
+
if self.config.tokenize_function == 'softmax':
|
| 204 |
+
tokens = softmax(logits, dim=-1)
|
| 205 |
+
elif self.config.tokenize_function == 'gumbel_argmax':
|
| 206 |
+
tokens = gumbel_softmax(logits, tau=self.config.tau, hard=True)
|
| 207 |
+
elif self.config.tokenize_function == 'st_argmax':
|
| 208 |
+
tokens = st_argmax(logits, dim=-1)
|
| 209 |
+
else:
|
| 210 |
+
raise ValueError(
|
| 211 |
+
f'Invalid `max_type`, expected softmax or gumbel_argmax or st_argmax, but got {self.config.tokenize_function}')
|
| 212 |
+
return tokens
|
| 213 |
+
|
| 214 |
+
def encode(self, pixel_values):
|
| 215 |
+
output = self.backbone(pixel_values, output_hidden_states=True, return_dict=True)
|
| 216 |
+
features = output.hidden_states[-1]
|
| 217 |
+
if self.config.drop_cls_token:
|
| 218 |
+
features = features[:, 1:, :]
|
| 219 |
+
|
| 220 |
+
# merge number of `hidden_stride * hidden_stride` hidden states together to reduce token sequence length
|
| 221 |
+
# e.g., for hidden_stride=3, this leads to a token length reduction: 729 -> 81 for siglip
|
| 222 |
+
if self.config.hidden_stride > 1:
|
| 223 |
+
n, l, d = features.shape # this `d` maybe different from the above `d
|
| 224 |
+
sqrt_l = int(l ** 0.5)
|
| 225 |
+
assert sqrt_l ** 2 == l, "The token sequence length should be a perfect square."
|
| 226 |
+
features = features.reshape(n, sqrt_l, sqrt_l, d)
|
| 227 |
+
pl = (self.config.hidden_stride - (sqrt_l % self.config.hidden_stride)) % self.config.hidden_stride
|
| 228 |
+
features = pad(features, (0, 0, 0, pl, 0, pl), "constant", 0)
|
| 229 |
+
sqrt_l += pl
|
| 230 |
+
features = features.reshape(n, sqrt_l // self.config.hidden_stride, self.config.hidden_stride,
|
| 231 |
+
sqrt_l // self.config.hidden_stride, self.config.hidden_stride, d)
|
| 232 |
+
features = features.permute(0, 1, 3, 2, 4, 5) # [n, sqrt_l/hs, sqrt_l/hs, hs, hs, d]
|
| 233 |
+
features = features.flatten(3) # [n, sqrt_l/hs, sqrt_l/hs, hs*hs*d]
|
| 234 |
+
features = features.reshape(
|
| 235 |
+
n, -1, self.config.hidden_stride * self.config.hidden_stride * d)
|
| 236 |
+
|
| 237 |
+
return features
|
| 238 |
+
|
| 239 |
+
def forward(self, pixel_values) -> torch.Tensor: # [BatchSize, ImageShape] -> [BatchSize, #Token, VocabSize]
|
| 240 |
+
features = self.encode(pixel_values)
|
| 241 |
+
logits = self.head(features)
|
| 242 |
+
tokens = self.tokenize(logits)
|
| 243 |
+
# tokens' shape is [BatchSize, #Token, VocabSize-5], so padding with [BatchSize, #Token, 5], after
|
| 244 |
+
# which, tokens' shape should become [BatchSize, #Token, VocabSize]
|
| 245 |
+
batch_size, token_len, _ = tokens.shape
|
| 246 |
+
padding_tensor = torch.zeros(size=(batch_size, token_len, len(IMAGE_INDICATOR_IDS)),
|
| 247 |
+
dtype=tokens.dtype,
|
| 248 |
+
device=tokens.device,
|
| 249 |
+
layout=tokens.layout,
|
| 250 |
+
requires_grad=False)
|
| 251 |
+
tokens = torch.cat((tokens, padding_tensor), dim=2)
|
| 252 |
+
return tokens
|
| 253 |
+
|
| 254 |
+
|
| 255 |
+
class SiglipVisualTokenizer(BaseVisualTokenizer):
|
| 256 |
+
config_class = SiglipVisualTokenizerConfig
|
| 257 |
+
supports_gradient_checkpointing = True
|
| 258 |
+
_no_split_modules = ["SiglipVisionTransformer"]
|
| 259 |
+
_image_processor_class = SiglipImageProcessor
|
| 260 |
+
_image_processor_kwargs = {}
|
| 261 |
+
_backbone_class = SiglipVisionModel
|
| 262 |
+
_backbone_name_or_path = "google/siglip-so400m-patch14-384"
|
| 263 |
+
|
| 264 |
+
def get_image_size(self):
|
| 265 |
+
height = self.image_processor.size["height"]
|
| 266 |
+
width = self.image_processor.size["width"]
|
| 267 |
+
return height, width
|
| 268 |
+
|
| 269 |
+
|
| 270 |
+
AutoModel.register(SiglipVisualTokenizerConfig, SiglipVisualTokenizer)
|
| 271 |
+
|
| 272 |
+
|
| 273 |
+
# ----------------------------------------------------------------------
|
| 274 |
+
# Ovis
|
| 275 |
+
# ----------------------------------------------------------------------
|
| 276 |
+
class VisualEmbedding(torch.nn.Embedding):
|
| 277 |
+
def forward(self, visual_tokens: Tensor) -> Tensor:
|
| 278 |
+
if visual_tokens.dtype in [torch.int8, torch.int16, torch.int32, torch.int64, torch.long]:
|
| 279 |
+
return super().forward(visual_tokens)
|
| 280 |
+
return torch.matmul(visual_tokens, self.weight)
|
| 281 |
+
|
| 282 |
+
def reset_parameters(self, mean=0., std=1.) -> None:
|
| 283 |
+
init.normal_(self.weight, mean=mean, std=std)
|
| 284 |
+
self._fill_padding_idx_with_zero()
|
| 285 |
+
|
| 286 |
+
|
| 287 |
+
class OvisPreTrainedModel(PreTrainedModel):
|
| 288 |
+
config_class = OvisConfig
|
| 289 |
+
base_model_prefix = "ovis"
|
| 290 |
+
|
| 291 |
+
|
| 292 |
+
class Ovis(OvisPreTrainedModel):
|
| 293 |
+
|
| 294 |
+
def __init__(self, config: OvisConfig, *inputs, **kwargs):
|
| 295 |
+
super().__init__(config, *inputs, **kwargs)
|
| 296 |
+
attn_kwargs = dict()
|
| 297 |
+
if self.config.llm_attn_implementation:
|
| 298 |
+
if self.config.llm_attn_implementation == "sdpa":
|
| 299 |
+
raise ValueError("`sdpa` is currently not supported")
|
| 300 |
+
elif self.config.llm_attn_implementation == "flash_attention_2":
|
| 301 |
+
assert (is_flash_attn_2_available() and
|
| 302 |
+
version.parse(importlib.metadata.version("flash_attn")) >= version.parse("2.6.3")), \
|
| 303 |
+
"Using `flash_attention_2` requires having `flash_attn>=2.6.3` installed."
|
| 304 |
+
attn_kwargs["attn_implementation"] = self.config.llm_attn_implementation
|
| 305 |
+
self.llm = AutoModelForCausalLM.from_config(self.config.llm_config, **attn_kwargs)
|
| 306 |
+
assert self.config.hidden_size == self.llm.config.hidden_size, "hidden size mismatch"
|
| 307 |
+
self.text_tokenizer = AutoTokenizer.from_pretrained(self.config.name_or_path)
|
| 308 |
+
self.visual_tokenizer = AutoModel.from_config(self.config.visual_tokenizer_config,
|
| 309 |
+
image_processor_name_or_path=self.config.name_or_path)
|
| 310 |
+
self.vte = VisualEmbedding(
|
| 311 |
+
self.config.visual_tokenizer_config.vocab_size,
|
| 312 |
+
self.config.hidden_size,
|
| 313 |
+
device=self.visual_tokenizer.device,
|
| 314 |
+
dtype=self.visual_tokenizer.dtype
|
| 315 |
+
)
|
| 316 |
+
|
| 317 |
+
def _merge_modules(modules_list: tuple):
|
| 318 |
+
merged_modules = []
|
| 319 |
+
for modules in modules_list:
|
| 320 |
+
merged_modules.extend(modules if modules else [])
|
| 321 |
+
return merged_modules
|
| 322 |
+
|
| 323 |
+
self._no_split_modules = _merge_modules((self.llm._no_split_modules, self.visual_tokenizer._no_split_modules))
|
| 324 |
+
self._skip_keys_device_placement = self.llm._skip_keys_device_placement
|
| 325 |
+
self._keep_in_fp32_modules = _merge_modules(
|
| 326 |
+
(self.llm._keep_in_fp32_modules, self.visual_tokenizer._keep_in_fp32_modules))
|
| 327 |
+
self.is_parallelizable = all((self.llm.is_parallelizable, self.visual_tokenizer.is_parallelizable))
|
| 328 |
+
self.supports_gradient_checkpointing = all(
|
| 329 |
+
(self.llm.supports_gradient_checkpointing, self.visual_tokenizer.supports_gradient_checkpointing))
|
| 330 |
+
self._supports_flash_attn_2 = True
|
| 331 |
+
self._supports_sdpa = False
|
| 332 |
+
|
| 333 |
+
def get_text_tokenizer(self):
|
| 334 |
+
return self.text_tokenizer
|
| 335 |
+
|
| 336 |
+
def get_visual_tokenizer(self):
|
| 337 |
+
return self.visual_tokenizer
|
| 338 |
+
|
| 339 |
+
def tie_weights(self):
|
| 340 |
+
if not self.config.disable_tie_weight:
|
| 341 |
+
self.get_llm().tie_weights()
|
| 342 |
+
|
| 343 |
+
def get_llm(self):
|
| 344 |
+
return self.llm
|
| 345 |
+
|
| 346 |
+
def get_vte(self):
|
| 347 |
+
return self.vte
|
| 348 |
+
|
| 349 |
+
def get_wte(self):
|
| 350 |
+
return self.llm.get_input_embeddings()
|
| 351 |
+
|
| 352 |
+
def get_conversation_formatter(self) -> ConversationFormatter:
|
| 353 |
+
if getattr(self, 'conversation_formatter', None) is None:
|
| 354 |
+
self.conversation_formatter = getattr(import_module(".configuration_ovis", __package__),
|
| 355 |
+
self.config.conversation_formatter_class)(self.text_tokenizer)
|
| 356 |
+
return self.conversation_formatter
|
| 357 |
+
|
| 358 |
+
def forward(
|
| 359 |
+
self,
|
| 360 |
+
input_ids: torch.Tensor,
|
| 361 |
+
attention_mask: torch.Tensor,
|
| 362 |
+
labels: Optional[torch.Tensor],
|
| 363 |
+
pixel_values: List[Optional[torch.Tensor]],
|
| 364 |
+
**kwargs
|
| 365 |
+
):
|
| 366 |
+
assert self.training, "`forward` can only be used in training. For inference, use `generate`."
|
| 367 |
+
_, inputs_embeds, labels, attention_mask = self.merge_multimodal(
|
| 368 |
+
text_input_ids=input_ids,
|
| 369 |
+
text_attention_masks=attention_mask,
|
| 370 |
+
text_labels=labels,
|
| 371 |
+
pixel_values=pixel_values
|
| 372 |
+
)
|
| 373 |
+
return self.llm(inputs_embeds=inputs_embeds, labels=labels, attention_mask=attention_mask, **kwargs)
|
| 374 |
+
|
| 375 |
+
def merge_multimodal(
|
| 376 |
+
self,
|
| 377 |
+
text_input_ids: torch.Tensor,
|
| 378 |
+
text_attention_masks: torch.Tensor,
|
| 379 |
+
text_labels: Optional[torch.Tensor],
|
| 380 |
+
pixel_values: List[Optional[torch.Tensor]],
|
| 381 |
+
left_padding: bool = False
|
| 382 |
+
):
|
| 383 |
+
input_device = text_input_ids.device
|
| 384 |
+
visual_vocab_szie = self.get_visual_tokenizer().config.vocab_size
|
| 385 |
+
visual_indicator_embeds = self.get_vte()(
|
| 386 |
+
torch.tensor(
|
| 387 |
+
list(range(visual_vocab_szie - 5, visual_vocab_szie)),
|
| 388 |
+
dtype=torch.long,
|
| 389 |
+
device=self.get_visual_tokenizer().device
|
| 390 |
+
)
|
| 391 |
+
).to(device=input_device)
|
| 392 |
+
|
| 393 |
+
if self.training:
|
| 394 |
+
# When training, to be compatible with deepspeed zero, each sample has to include pixel_value tensor.
|
| 395 |
+
# For text-only sample, one can simply use a full zero tensor as pixel_value, which will be ignored
|
| 396 |
+
# (see below in this function); so, the gradient will not be affected.
|
| 397 |
+
num_images = [x.shape[0] for x in pixel_values]
|
| 398 |
+
visual_tokens = self.visual_tokenizer(torch.cat([x for x in pixel_values], dim=0))
|
| 399 |
+
visual_embeds = torch.split(self.get_vte()(visual_tokens).to(dtype=self.dtype, device=input_device),
|
| 400 |
+
split_size_or_sections=num_images, dim=0)
|
| 401 |
+
visual_input_ids = torch.split(torch.argmax(visual_tokens, dim=-1).to(device=input_device),
|
| 402 |
+
split_size_or_sections=num_images, dim=0)
|
| 403 |
+
visual_labels = [torch.full(x.shape, IGNORE_ID, dtype=torch.long, device=input_device) for x in
|
| 404 |
+
visual_input_ids]
|
| 405 |
+
else:
|
| 406 |
+
# When inference, sample can include only text with `None` pixel_value
|
| 407 |
+
num_images = [x.shape[0] if x is not None else 0 for x in pixel_values]
|
| 408 |
+
if sum(num_images) > 0:
|
| 409 |
+
visual_tokens = self.visual_tokenizer(torch.cat([x for x in pixel_values if x is not None], dim=0))
|
| 410 |
+
visual_embeds = torch.split(self.get_vte()(visual_tokens).to(dtype=self.dtype, device=input_device),
|
| 411 |
+
split_size_or_sections=num_images, dim=0)
|
| 412 |
+
visual_input_ids = torch.split(torch.argmax(visual_tokens, dim=-1).to(device=input_device),
|
| 413 |
+
split_size_or_sections=num_images, dim=0)
|
| 414 |
+
visual_labels = [torch.full(x.shape, IGNORE_ID, dtype=torch.long, device=input_device) for x in
|
| 415 |
+
visual_input_ids]
|
| 416 |
+
else:
|
| 417 |
+
# just placeholders
|
| 418 |
+
visual_embeds = [None] * len(num_images)
|
| 419 |
+
visual_input_ids = [None] * len(num_images)
|
| 420 |
+
visual_labels = [None] * len(num_images)
|
| 421 |
+
if text_labels is None:
|
| 422 |
+
text_labels = torch.full(text_input_ids.shape, IGNORE_ID, dtype=torch.long, device=input_device)
|
| 423 |
+
|
| 424 |
+
input_embeds = []
|
| 425 |
+
attention_masks = []
|
| 426 |
+
labels = []
|
| 427 |
+
for text_input_id, text_label, text_attention_mask, visual_embed, visual_input_id, visual_label in zip(
|
| 428 |
+
text_input_ids, text_labels, text_attention_masks, visual_embeds, visual_input_ids, visual_labels
|
| 429 |
+
):
|
| 430 |
+
placeholder_token_mask = torch.lt(text_input_id, 0)
|
| 431 |
+
text_embed = self.get_wte()(torch.masked_fill(text_input_id, placeholder_token_mask, 0))
|
| 432 |
+
for i, indicator_id in enumerate(IMAGE_INDICATOR_IDS):
|
| 433 |
+
text_embed[text_input_id == indicator_id] = visual_indicator_embeds[i]
|
| 434 |
+
image_atom_positions = torch.where(torch.eq(text_input_id, IMAGE_ATOM_ID))[0].tolist()
|
| 435 |
+
if len(image_atom_positions) > 0:
|
| 436 |
+
input_embed_parts = []
|
| 437 |
+
attention_mask_parts = []
|
| 438 |
+
label_parts = []
|
| 439 |
+
prev_image_atom_position = -1
|
| 440 |
+
for index, image_atom_position in enumerate(image_atom_positions):
|
| 441 |
+
input_embed_parts.append(
|
| 442 |
+
text_embed[prev_image_atom_position + 1:image_atom_position, :])
|
| 443 |
+
label_parts.append(
|
| 444 |
+
text_label[prev_image_atom_position + 1:image_atom_position])
|
| 445 |
+
attention_mask_parts.append(
|
| 446 |
+
text_attention_mask[prev_image_atom_position + 1:image_atom_position])
|
| 447 |
+
input_embed_parts.append(visual_embed[index])
|
| 448 |
+
attention_mask_parts.append(
|
| 449 |
+
torch.ones_like(visual_label[index], dtype=torch.bool))
|
| 450 |
+
label_parts.append(visual_label[index])
|
| 451 |
+
prev_image_atom_position = image_atom_position
|
| 452 |
+
if prev_image_atom_position + 1 < text_input_id.shape[0]:
|
| 453 |
+
input_embed_parts.append(
|
| 454 |
+
text_embed[prev_image_atom_position + 1:, :])
|
| 455 |
+
attention_mask_parts.append(
|
| 456 |
+
text_attention_mask[prev_image_atom_position + 1:])
|
| 457 |
+
label_parts.append(
|
| 458 |
+
text_label[prev_image_atom_position + 1:])
|
| 459 |
+
input_embed = torch.cat(input_embed_parts, dim=0)
|
| 460 |
+
attention_mask = torch.cat(attention_mask_parts, dim=0)
|
| 461 |
+
label = torch.cat(label_parts, dim=0)
|
| 462 |
+
else:
|
| 463 |
+
input_embed = text_embed
|
| 464 |
+
attention_mask = text_attention_mask
|
| 465 |
+
label = text_label
|
| 466 |
+
if self.training:
|
| 467 |
+
# Make visual_embed & visual_indicator_embeds involved in the backward graph,
|
| 468 |
+
# to be compatible with deepspeed zero and ddp.
|
| 469 |
+
input_embed += torch.sum(visual_embed * 0.0) + torch.sum(visual_indicator_embeds * 0.0)
|
| 470 |
+
input_embeds.append(input_embed)
|
| 471 |
+
attention_masks.append(attention_mask)
|
| 472 |
+
labels.append(label)
|
| 473 |
+
|
| 474 |
+
if self.training: # padding to self.config.multimodal_max_length for increased training speed
|
| 475 |
+
padding_size = max(0, self.config.multimodal_max_length - len(input_embeds[0]))
|
| 476 |
+
input_embeds[0] = torch.nn.ConstantPad2d((0, 0, 0, padding_size), 0.0)(input_embeds[0])
|
| 477 |
+
attention_masks[0] = torch.nn.ConstantPad1d((0, padding_size), False)(attention_masks[0])
|
| 478 |
+
labels[0] = torch.nn.ConstantPad1d((0, padding_size), IGNORE_ID)(labels[0])
|
| 479 |
+
batch_input_embeds = self.pad_truncate_sequence(input_embeds, batch_first=True, padding_value=0.0, left_padding=left_padding)
|
| 480 |
+
batch_attention_mask = self.pad_truncate_sequence(attention_masks, batch_first=True, padding_value=False, left_padding=left_padding)
|
| 481 |
+
batch_labels = self.pad_truncate_sequence(labels, batch_first=True, padding_value=IGNORE_ID, left_padding=left_padding)
|
| 482 |
+
|
| 483 |
+
return visual_input_ids, batch_input_embeds, batch_labels, batch_attention_mask
|
| 484 |
+
|
| 485 |
+
def pad_truncate_sequence(self, sequences: List[torch.Tensor], batch_first: bool = True, padding_value: float = 0.0, left_padding: bool = False) -> torch.Tensor:
|
| 486 |
+
if left_padding == False:
|
| 487 |
+
pad_sequence = torch.nn.utils.rnn.pad_sequence(sequences, batch_first=batch_first, padding_value=padding_value)
|
| 488 |
+
return pad_sequence[:,:self.config.multimodal_max_length]
|
| 489 |
+
else:
|
| 490 |
+
pad_sequence = torch.nn.utils.rnn.pad_sequence([i.flip(dims=[0]) for i in sequences],batch_first=True, padding_value=padding_value).flip(dims=[1])
|
| 491 |
+
return pad_sequence[:,-self.config.multimodal_max_length:]
|
| 492 |
+
|
| 493 |
+
def preprocess_inputs(
|
| 494 |
+
self,
|
| 495 |
+
text_or_conversations: Union[List[Dict], str],
|
| 496 |
+
images: Optional[List[PIL.Image.Image]],
|
| 497 |
+
max_partition=9,
|
| 498 |
+
generation_preface='',
|
| 499 |
+
return_labels=False,
|
| 500 |
+
propagate_exception=True
|
| 501 |
+
):
|
| 502 |
+
# convert text to conversations
|
| 503 |
+
if isinstance(text_or_conversations, str):
|
| 504 |
+
conversations = [{
|
| 505 |
+
"from": "human",
|
| 506 |
+
"value": text_or_conversations
|
| 507 |
+
}]
|
| 508 |
+
elif isinstance(text_or_conversations, list):
|
| 509 |
+
conversations = text_or_conversations
|
| 510 |
+
else:
|
| 511 |
+
raise ValueError(f'Invalid type of `text_or_conversations`, expected `List[Dict]` or `str`,'
|
| 512 |
+
f' but got {type(text_or_conversations)}')
|
| 513 |
+
|
| 514 |
+
# format conversations
|
| 515 |
+
prompt, raw_input_ids, raw_labels = self.get_conversation_formatter().format(
|
| 516 |
+
conversations, generation_preface=generation_preface)
|
| 517 |
+
|
| 518 |
+
# place image placeholders
|
| 519 |
+
input_ids = []
|
| 520 |
+
labels = []
|
| 521 |
+
pixel_values = []
|
| 522 |
+
invalidate_label = False
|
| 523 |
+
image_token_indices = [i for i, v in enumerate(raw_input_ids) if v == IMAGE_TOKEN_ID]
|
| 524 |
+
last_image_token_index = -1
|
| 525 |
+
for i in range(len(image_token_indices)):
|
| 526 |
+
head = 0 if i == 0 else image_token_indices[i - 1] + 1
|
| 527 |
+
tail = image_token_indices[i]
|
| 528 |
+
last_image_token_index = tail
|
| 529 |
+
input_ids.extend(raw_input_ids[head:tail])
|
| 530 |
+
labels.extend(raw_labels[head:tail])
|
| 531 |
+
try:
|
| 532 |
+
image = images[i]
|
| 533 |
+
raw_pixel_values, image_placeholders = self.visual_tokenizer.preprocess_image(
|
| 534 |
+
image, max_partition=max_partition)
|
| 535 |
+
except Exception as e:
|
| 536 |
+
if propagate_exception:
|
| 537 |
+
raise e
|
| 538 |
+
logging.exception(e)
|
| 539 |
+
invalidate_label = True
|
| 540 |
+
raw_pixel_values, image_placeholders = self.visual_tokenizer.mock_input()
|
| 541 |
+
input_ids.extend(image_placeholders)
|
| 542 |
+
labels.extend([IGNORE_ID] * len(image_placeholders))
|
| 543 |
+
pixel_values.append(raw_pixel_values)
|
| 544 |
+
input_ids.extend(raw_input_ids[last_image_token_index + 1:])
|
| 545 |
+
labels.extend(raw_labels[last_image_token_index + 1:])
|
| 546 |
+
|
| 547 |
+
# return tensors
|
| 548 |
+
input_ids = torch.tensor(input_ids, dtype=torch.long)
|
| 549 |
+
labels = torch.tensor([IGNORE_ID] * len(labels) if invalidate_label else labels, dtype=torch.long)
|
| 550 |
+
pixel_values = torch.cat(pixel_values, dim=0) if len(pixel_values) > 0 else None
|
| 551 |
+
|
| 552 |
+
if return_labels:
|
| 553 |
+
return prompt, input_ids, pixel_values, labels
|
| 554 |
+
else:
|
| 555 |
+
return prompt, input_ids, pixel_values
|
| 556 |
+
|
| 557 |
+
def save_pretrained(
|
| 558 |
+
self,
|
| 559 |
+
save_directory: Union[str, os.PathLike],
|
| 560 |
+
is_main_process: bool = True,
|
| 561 |
+
state_dict: Optional[dict] = None,
|
| 562 |
+
save_function: Callable = torch.save,
|
| 563 |
+
push_to_hub: bool = False,
|
| 564 |
+
max_shard_size: Union[int, str] = "5GB",
|
| 565 |
+
safe_serialization: bool = True,
|
| 566 |
+
variant: Optional[str] = None,
|
| 567 |
+
token: Optional[Union[str, bool]] = None,
|
| 568 |
+
save_peft_format: bool = True,
|
| 569 |
+
**kwargs
|
| 570 |
+
):
|
| 571 |
+
super().save_pretrained(save_directory,
|
| 572 |
+
is_main_process=is_main_process,
|
| 573 |
+
state_dict=state_dict,
|
| 574 |
+
save_function=save_function,
|
| 575 |
+
safe_serialization=safe_serialization)
|
| 576 |
+
self.get_text_tokenizer().save_pretrained(save_directory)
|
| 577 |
+
self.get_visual_tokenizer().get_image_processor().save_pretrained(save_directory)
|
| 578 |
+
|
| 579 |
+
def _get_hybrid_cache_for_llm(self, batch_size: int, max_cache_len: int):
|
| 580 |
+
cache_cls = HybridCache
|
| 581 |
+
llm = self.get_llm()
|
| 582 |
+
|
| 583 |
+
need_new_cache = (
|
| 584 |
+
not hasattr(llm, "_cache")
|
| 585 |
+
or (not isinstance(llm._cache, cache_cls))
|
| 586 |
+
or llm._cache.batch_size != batch_size
|
| 587 |
+
or llm._cache.max_cache_len < max_cache_len
|
| 588 |
+
)
|
| 589 |
+
|
| 590 |
+
if need_new_cache:
|
| 591 |
+
if hasattr(llm.config, "_pre_quantization_dtype"):
|
| 592 |
+
cache_dtype = llm.config._pre_quantization_dtype
|
| 593 |
+
else:
|
| 594 |
+
cache_dtype = llm.dtype
|
| 595 |
+
llm._cache = cache_cls(
|
| 596 |
+
config=llm.config,
|
| 597 |
+
batch_size=batch_size,
|
| 598 |
+
max_cache_len=max_cache_len,
|
| 599 |
+
device=llm.device,
|
| 600 |
+
dtype=cache_dtype,
|
| 601 |
+
)
|
| 602 |
+
else:
|
| 603 |
+
llm._cache.reset()
|
| 604 |
+
return llm._cache
|
| 605 |
+
|
| 606 |
+
# TODO: support batch generation
|
| 607 |
+
def generate(
|
| 608 |
+
self,
|
| 609 |
+
inputs: Optional[torch.Tensor] = None,
|
| 610 |
+
**kwargs
|
| 611 |
+
) -> Union[GenerateOutput, torch.LongTensor]:
|
| 612 |
+
_, inputs_embeds, labels, attention_mask = self.merge_multimodal(
|
| 613 |
+
text_input_ids=inputs,
|
| 614 |
+
text_attention_masks=kwargs.pop('attention_mask'),
|
| 615 |
+
text_labels=None,
|
| 616 |
+
pixel_values=kwargs.pop('pixel_values'),
|
| 617 |
+
left_padding=True
|
| 618 |
+
)
|
| 619 |
+
if getattr(self.generation_config, 'cache_implementation') == 'hybrid': # mainly for Gemma2
|
| 620 |
+
kwargs['past_key_values'] = self._get_hybrid_cache_for_llm(
|
| 621 |
+
getattr(kwargs, "num_beams", inputs_embeds.shape[0]), kwargs['max_new_tokens'] + inputs_embeds.shape[-2])
|
| 622 |
+
self.get_llm()._supports_cache_class = True
|
| 623 |
+
kwargs['cache_implementation'] = None
|
| 624 |
+
|
| 625 |
+
return self.llm.generate(inputs=None, inputs_embeds=inputs_embeds, attention_mask=attention_mask, **kwargs)
|
preprocessor_config.json
ADDED
|
@@ -0,0 +1,24 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"do_convert_rgb": null,
|
| 3 |
+
"do_normalize": true,
|
| 4 |
+
"do_rescale": true,
|
| 5 |
+
"do_resize": true,
|
| 6 |
+
"image_mean": [
|
| 7 |
+
0.5,
|
| 8 |
+
0.5,
|
| 9 |
+
0.5
|
| 10 |
+
],
|
| 11 |
+
"image_processor_type": "SiglipImageProcessor",
|
| 12 |
+
"image_std": [
|
| 13 |
+
0.5,
|
| 14 |
+
0.5,
|
| 15 |
+
0.5
|
| 16 |
+
],
|
| 17 |
+
"processor_class": "SiglipProcessor",
|
| 18 |
+
"resample": 3,
|
| 19 |
+
"rescale_factor": 0.00392156862745098,
|
| 20 |
+
"size": {
|
| 21 |
+
"height": 384,
|
| 22 |
+
"width": 384
|
| 23 |
+
}
|
| 24 |
+
}
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,34 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"additional_special_tokens": [
|
| 3 |
+
"<start_of_turn>",
|
| 4 |
+
"<end_of_turn>"
|
| 5 |
+
],
|
| 6 |
+
"bos_token": {
|
| 7 |
+
"content": "<bos>",
|
| 8 |
+
"lstrip": false,
|
| 9 |
+
"normalized": false,
|
| 10 |
+
"rstrip": false,
|
| 11 |
+
"single_word": false
|
| 12 |
+
},
|
| 13 |
+
"eos_token": {
|
| 14 |
+
"content": "<eos>",
|
| 15 |
+
"lstrip": false,
|
| 16 |
+
"normalized": false,
|
| 17 |
+
"rstrip": false,
|
| 18 |
+
"single_word": false
|
| 19 |
+
},
|
| 20 |
+
"pad_token": {
|
| 21 |
+
"content": "<pad>",
|
| 22 |
+
"lstrip": false,
|
| 23 |
+
"normalized": false,
|
| 24 |
+
"rstrip": false,
|
| 25 |
+
"single_word": false
|
| 26 |
+
},
|
| 27 |
+
"unk_token": {
|
| 28 |
+
"content": "<unk>",
|
| 29 |
+
"lstrip": false,
|
| 30 |
+
"normalized": false,
|
| 31 |
+
"rstrip": false,
|
| 32 |
+
"single_word": false
|
| 33 |
+
}
|
| 34 |
+
}
|
tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f559f2189f392b4555613965f089e7c4d300b41fbe080bf79da0d676e33ee7f0
|
| 3 |
+
size 34356041
|
tokenizer.model
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:61a7b147390c64585d6c3543dd6fc636906c9af3865a5548f27f31aee1d4c8e2
|
| 3 |
+
size 4241003
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,1757 @@
|
|
|
|
|
|
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|
| 1 |
+
{
|
| 2 |
+
"add_bos_token": true,
|
| 3 |
+
"add_eos_token": false,
|
| 4 |
+
"added_tokens_decoder": {
|
| 5 |
+
"0": {
|
| 6 |
+
"content": "<pad>",
|
| 7 |
+
"lstrip": false,
|
| 8 |
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"normalized": false,
|
| 9 |
+
"rstrip": false,
|
| 10 |
+
"single_word": false,
|
| 11 |
+
"special": true
|
| 12 |
+
},
|
| 13 |
+
"1": {
|
| 14 |
+
"content": "<eos>",
|
| 15 |
+
"lstrip": false,
|
| 16 |
+
"normalized": false,
|
| 17 |
+
"rstrip": false,
|
| 18 |
+
"single_word": false,
|
| 19 |
+
"special": true
|
| 20 |
+
},
|
| 21 |
+
"2": {
|
| 22 |
+
"content": "<bos>",
|
| 23 |
+
"lstrip": false,
|
| 24 |
+
"normalized": false,
|
| 25 |
+
"rstrip": false,
|
| 26 |
+
"single_word": false,
|
| 27 |
+
"special": true
|
| 28 |
+
},
|
| 29 |
+
"3": {
|
| 30 |
+
"content": "<unk>",
|
| 31 |
+
"lstrip": false,
|
| 32 |
+
"normalized": false,
|
| 33 |
+
"rstrip": false,
|
| 34 |
+
"single_word": false,
|
| 35 |
+
"special": true
|
| 36 |
+
},
|
| 37 |
+
"4": {
|
| 38 |
+
"content": "<mask>",
|
| 39 |
+
"lstrip": false,
|
| 40 |
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"normalized": false,
|
| 41 |
+
"rstrip": false,
|
| 42 |
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|
| 43 |
+
"special": false
|
| 44 |
+
},
|
| 45 |
+
"5": {
|
| 46 |
+
"content": "<2mass>",
|
| 47 |
+
"lstrip": false,
|
| 48 |
+
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|
| 49 |
+
"rstrip": false,
|
| 50 |
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|
| 51 |
+
"special": false
|
| 52 |
+
},
|
| 53 |
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"6": {
|
| 54 |
+
"content": "[@BOS@]",
|
| 55 |
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|
| 56 |
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|
| 57 |
+
"rstrip": false,
|
| 58 |
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|
| 59 |
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|
| 60 |
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},
|
| 61 |
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|
| 62 |
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|
| 63 |
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|
| 64 |
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|
| 65 |
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|
| 66 |
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|
| 67 |
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"special": false
|
| 68 |
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},
|
| 69 |
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"8": {
|
| 70 |
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|
| 71 |
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|
| 72 |
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|
| 73 |
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|
| 74 |
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|
| 75 |
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"special": false
|
| 76 |
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},
|
| 77 |
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"9": {
|
| 78 |
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|
| 79 |
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|
| 80 |
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|
| 81 |
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|
| 82 |
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|
| 83 |
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|
| 84 |
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|
| 85 |
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|
| 86 |
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|
| 87 |
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|
| 88 |
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|
| 89 |
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|
| 90 |
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|
| 91 |
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|
| 92 |
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|
| 93 |
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|
| 94 |
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|
| 95 |
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|
| 96 |
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|
| 97 |
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|
| 98 |
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|
| 99 |
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|
| 100 |
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|
| 101 |
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|
| 102 |
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|
| 103 |
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|
| 104 |
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|
| 105 |
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|
| 106 |
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|
| 107 |
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|
| 108 |
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|
| 109 |
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|
| 110 |
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|
| 111 |
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| 112 |
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| 113 |
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|
| 114 |
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|
| 115 |
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|
| 116 |
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|
| 117 |
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|
| 118 |
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|
| 119 |
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| 120 |
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| 121 |
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|
| 122 |
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|
| 123 |
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|
| 124 |
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|
| 125 |
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|
| 126 |
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|
| 127 |
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|
| 128 |
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|
| 129 |
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|
| 130 |
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|
| 131 |
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|
| 132 |
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|
| 133 |
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|
| 134 |
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|
| 135 |
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|
| 136 |
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|
| 137 |
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|
| 138 |
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|
| 139 |
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|
| 140 |
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| 141 |
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|
| 142 |
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|
| 143 |
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|
| 144 |
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|
| 145 |
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|
| 146 |
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|
| 147 |
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|
| 148 |
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| 149 |
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|
| 150 |
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|
| 151 |
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| 152 |
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| 153 |
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| 154 |
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|
| 155 |
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|
| 156 |
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|
| 157 |
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|
| 158 |
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|
| 159 |
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|
| 160 |
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|
| 161 |
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|
| 162 |
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|
| 163 |
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|
| 164 |
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|
| 165 |
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|
| 166 |
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|
| 167 |
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|
| 168 |
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|
| 169 |
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| 170 |
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|
| 171 |
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|
| 172 |
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| 173 |
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|
| 174 |
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|
| 175 |
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|
| 176 |
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|
| 177 |
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|
| 178 |
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|
| 179 |
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|
| 180 |
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|
| 181 |
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|
| 182 |
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|
| 183 |
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|
| 184 |
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|
| 185 |
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|
| 186 |
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|
| 187 |
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|
| 188 |
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|
| 189 |
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|
| 190 |
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|
| 191 |
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|
| 192 |
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|
| 193 |
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|
| 194 |
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|
| 195 |
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|
| 196 |
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|
| 197 |
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|
| 198 |
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|
| 199 |
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|
| 200 |
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|
| 201 |
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|
| 202 |
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|
| 203 |
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|
| 204 |
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|
| 205 |
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|
| 206 |
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|
| 207 |
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|
| 208 |
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|
| 209 |
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|
| 210 |
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|
| 211 |
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|
| 212 |
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|
| 213 |
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|
| 214 |
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|
| 215 |
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|
| 216 |
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|
| 217 |
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|
| 218 |
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|
| 219 |
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|
| 220 |
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|
| 221 |
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|
| 222 |
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|
| 223 |
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|
| 224 |
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|
| 225 |
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|
| 226 |
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|
| 227 |
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|
| 228 |
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|
| 229 |
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|
| 230 |
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|
| 231 |
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|
| 232 |
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|
| 233 |
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|
| 234 |
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|
| 235 |
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|
| 236 |
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| 237 |
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|
| 238 |
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|
| 239 |
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|
| 240 |
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|
| 241 |
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|
| 243 |
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|
| 244 |
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| 245 |
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|
| 246 |
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|
| 247 |
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|
| 248 |
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|
| 249 |
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| 250 |
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|
| 251 |
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|
| 252 |
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| 254 |
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|
| 255 |
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|
| 256 |
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| 257 |
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| 259 |
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| 263 |
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| 264 |
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| 265 |
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| 267 |
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| 283 |
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| 284 |
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| 287 |
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| 294 |
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| 295 |
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| 296 |
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| 299 |
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|
| 300 |
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| 319 |
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| 323 |
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| 326 |
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| 327 |
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| 336 |
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| 339 |
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| 344 |
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| 346 |
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| 347 |
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| 348 |
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| 350 |
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| 351 |
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| 355 |
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| 356 |
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| 388 |
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|
| 1599 |
+
"lstrip": false,
|
| 1600 |
+
"normalized": false,
|
| 1601 |
+
"rstrip": false,
|
| 1602 |
+
"single_word": false,
|
| 1603 |
+
"special": false
|
| 1604 |
+
},
|
| 1605 |
+
"200": {
|
| 1606 |
+
"content": "<em>",
|
| 1607 |
+
"lstrip": false,
|
| 1608 |
+
"normalized": false,
|
| 1609 |
+
"rstrip": false,
|
| 1610 |
+
"single_word": false,
|
| 1611 |
+
"special": false
|
| 1612 |
+
},
|
| 1613 |
+
"201": {
|
| 1614 |
+
"content": "<b>",
|
| 1615 |
+
"lstrip": false,
|
| 1616 |
+
"normalized": false,
|
| 1617 |
+
"rstrip": false,
|
| 1618 |
+
"single_word": false,
|
| 1619 |
+
"special": false
|
| 1620 |
+
},
|
| 1621 |
+
"202": {
|
| 1622 |
+
"content": "<i>",
|
| 1623 |
+
"lstrip": false,
|
| 1624 |
+
"normalized": false,
|
| 1625 |
+
"rstrip": false,
|
| 1626 |
+
"single_word": false,
|
| 1627 |
+
"special": false
|
| 1628 |
+
},
|
| 1629 |
+
"203": {
|
| 1630 |
+
"content": "<u>",
|
| 1631 |
+
"lstrip": false,
|
| 1632 |
+
"normalized": false,
|
| 1633 |
+
"rstrip": false,
|
| 1634 |
+
"single_word": false,
|
| 1635 |
+
"special": false
|
| 1636 |
+
},
|
| 1637 |
+
"204": {
|
| 1638 |
+
"content": "<s>",
|
| 1639 |
+
"lstrip": false,
|
| 1640 |
+
"normalized": false,
|
| 1641 |
+
"rstrip": false,
|
| 1642 |
+
"single_word": false,
|
| 1643 |
+
"special": false
|
| 1644 |
+
},
|
| 1645 |
+
"205": {
|
| 1646 |
+
"content": "<sub>",
|
| 1647 |
+
"lstrip": false,
|
| 1648 |
+
"normalized": false,
|
| 1649 |
+
"rstrip": false,
|
| 1650 |
+
"single_word": false,
|
| 1651 |
+
"special": false
|
| 1652 |
+
},
|
| 1653 |
+
"206": {
|
| 1654 |
+
"content": "<sup>",
|
| 1655 |
+
"lstrip": false,
|
| 1656 |
+
"normalized": false,
|
| 1657 |
+
"rstrip": false,
|
| 1658 |
+
"single_word": false,
|
| 1659 |
+
"special": false
|
| 1660 |
+
},
|
| 1661 |
+
"207": {
|
| 1662 |
+
"content": "<code>",
|
| 1663 |
+
"lstrip": false,
|
| 1664 |
+
"normalized": false,
|
| 1665 |
+
"rstrip": false,
|
| 1666 |
+
"single_word": false,
|
| 1667 |
+
"special": false
|
| 1668 |
+
},
|
| 1669 |
+
"208": {
|
| 1670 |
+
"content": "</strong>",
|
| 1671 |
+
"lstrip": false,
|
| 1672 |
+
"normalized": false,
|
| 1673 |
+
"rstrip": false,
|
| 1674 |
+
"single_word": false,
|
| 1675 |
+
"special": false
|
| 1676 |
+
},
|
| 1677 |
+
"209": {
|
| 1678 |
+
"content": "</em>",
|
| 1679 |
+
"lstrip": false,
|
| 1680 |
+
"normalized": false,
|
| 1681 |
+
"rstrip": false,
|
| 1682 |
+
"single_word": false,
|
| 1683 |
+
"special": false
|
| 1684 |
+
},
|
| 1685 |
+
"210": {
|
| 1686 |
+
"content": "</b>",
|
| 1687 |
+
"lstrip": false,
|
| 1688 |
+
"normalized": false,
|
| 1689 |
+
"rstrip": false,
|
| 1690 |
+
"single_word": false,
|
| 1691 |
+
"special": false
|
| 1692 |
+
},
|
| 1693 |
+
"211": {
|
| 1694 |
+
"content": "</i>",
|
| 1695 |
+
"lstrip": false,
|
| 1696 |
+
"normalized": false,
|
| 1697 |
+
"rstrip": false,
|
| 1698 |
+
"single_word": false,
|
| 1699 |
+
"special": false
|
| 1700 |
+
},
|
| 1701 |
+
"212": {
|
| 1702 |
+
"content": "</u>",
|
| 1703 |
+
"lstrip": false,
|
| 1704 |
+
"normalized": false,
|
| 1705 |
+
"rstrip": false,
|
| 1706 |
+
"single_word": false,
|
| 1707 |
+
"special": false
|
| 1708 |
+
},
|
| 1709 |
+
"213": {
|
| 1710 |
+
"content": "</s>",
|
| 1711 |
+
"lstrip": false,
|
| 1712 |
+
"normalized": false,
|
| 1713 |
+
"rstrip": false,
|
| 1714 |
+
"single_word": false,
|
| 1715 |
+
"special": false
|
| 1716 |
+
},
|
| 1717 |
+
"214": {
|
| 1718 |
+
"content": "</sub>",
|
| 1719 |
+
"lstrip": false,
|
| 1720 |
+
"normalized": false,
|
| 1721 |
+
"rstrip": false,
|
| 1722 |
+
"single_word": false,
|
| 1723 |
+
"special": false
|
| 1724 |
+
},
|
| 1725 |
+
"215": {
|
| 1726 |
+
"content": "</sup>",
|
| 1727 |
+
"lstrip": false,
|
| 1728 |
+
"normalized": false,
|
| 1729 |
+
"rstrip": false,
|
| 1730 |
+
"single_word": false,
|
| 1731 |
+
"special": false
|
| 1732 |
+
},
|
| 1733 |
+
"216": {
|
| 1734 |
+
"content": "</code>",
|
| 1735 |
+
"lstrip": false,
|
| 1736 |
+
"normalized": false,
|
| 1737 |
+
"rstrip": false,
|
| 1738 |
+
"single_word": false,
|
| 1739 |
+
"special": false
|
| 1740 |
+
}
|
| 1741 |
+
},
|
| 1742 |
+
"additional_special_tokens": [
|
| 1743 |
+
"<start_of_turn>",
|
| 1744 |
+
"<end_of_turn>"
|
| 1745 |
+
],
|
| 1746 |
+
"bos_token": "<bos>",
|
| 1747 |
+
"chat_template": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\n' + message['content'] | trim + '<end_of_turn>\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\n'}}{% endif %}",
|
| 1748 |
+
"clean_up_tokenization_spaces": false,
|
| 1749 |
+
"eos_token": "<eos>",
|
| 1750 |
+
"model_max_length": 1000000000000000019884624838656,
|
| 1751 |
+
"pad_token": "<pad>",
|
| 1752 |
+
"sp_model_kwargs": {},
|
| 1753 |
+
"spaces_between_special_tokens": false,
|
| 1754 |
+
"tokenizer_class": "GemmaTokenizer",
|
| 1755 |
+
"unk_token": "<unk>",
|
| 1756 |
+
"use_default_system_prompt": false
|
| 1757 |
+
}
|