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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-2-9b-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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README.md
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# Ovis1.6-Gemma2-9B_a14066570331942912351439
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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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studios:
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- AIDC-AI/Ovis1.6-Gemma2-9B
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---
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Ovis1.6-Gemma2-9B
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# Ovis1.6-Gemma2-9B
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<div align="center">
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<img src=https://modelscope.oss-cn-beijing.aliyuncs.com/resource/ovis_logo.png width="30%"/>
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</div>
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## Introduction
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[GitHub](https://github.com/AIDC-AI/Ovis) | [Demo](https://modelscope.cn/studios/AIDC-AI/Ovis1.6-Gemma2-9B) | [Paper](https://arxiv.org/abs/2405.20797)
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We are excited to announce the open-sourcing of **Ovis-1.6**, our latest multi-modal large language model. Ovis is a novel Multimodal Large Language Model (MLLM) architecture, designed to structurally align visual and textual embeddings.
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<div align="center">
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<img src="https://modelscope.oss-cn-beijing.aliyuncs.com/resource/Ovisarchitecture.png" width="100%" />
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</div>
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## Model
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Built upon 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-Gemma2-9B | Siglip-400M | Gemma2-9B-It | [ModelScope](https://modelscope.cn/models/AIDC-AI/Ovis1.6-Gemma2-9B) | [Studio](https://modelscope.cn/studios/AIDC-AI/Ovis1.6-Gemma2-9B) |
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## Performance
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With just **10B** parameters, **Ovis1.6-Gemma2-9B** leads the [OpenCompass](https://github.com/open-compass/VLMEvalKit) benchmark among open-source MLLMs within **30B** parameters.
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<div align="center">
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<img src="https://modelscope.oss-cn-beijing.aliyuncs.com/resource/Ovis_benchmark.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==2.2.0 transformers==4.44.2 numpy==1.24.3 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 modelscope import AutoModelForCausalLM
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# load model
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model = AutoModelForCausalLM.from_pretrained("AIDC-AI/Ovis1.6-Gemma2-9B",
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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>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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The project is licensed under the Apache 2.0 License and is restricted to uses that comply with the license agreements of Gemma2 and Siglip.
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@ -0,0 +1,248 @@
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{
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"architectures": [
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"Ovis"
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],
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"auto_map": {
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"AutoConfig": "configuration_ovis.OvisConfig",
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"AutoModelForCausalLM": "modeling_ovis.Ovis"
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},
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"conversation_formatter_class": "GemmaConversationFormatter",
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"disable_tie_weight": false,
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"hidden_size": 3584,
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"llm_attn_implementation": "eager",
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"llm_config": {
|
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"_name_or_path": "google/gemma-2-9b-it",
|
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"add_cross_attention": false,
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"architectures": [
|
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"Gemma2ForCausalLM"
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],
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"attention_bias": false,
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"attn_logit_softcapping": 50.0,
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"head_dim": 256,
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_activation": "gelu_pytorch_tanh",
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"id2label": {
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"0": "LABEL_0",
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"1": "LABEL_1"
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},
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"min_length": 0,
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"return_dict": true,
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"return_dict_in_generate": false,
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"rms_norm_eps": 1e-06,
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"rope_theta": 10000.0,
|
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"sep_token_id": null,
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"sliding_window": 4096,
|
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"sliding_window_size": 4096,
|
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"top_k": 50,
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"top_p": 1.0,
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"torch_dtype": "bfloat16",
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"torchscript": false,
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"typical_p": 1.0,
|
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"use_bfloat16": false,
|
||||
"use_cache": true,
|
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"vocab_size": 256000
|
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},
|
||||
"model_type": "ovis",
|
||||
"multimodal_max_length": 8192,
|
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"torch_dtype": "bfloat16",
|
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"transformers_version": "4.44.2",
|
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"use_cache": true,
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"visual_tokenizer_config": {
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"_name_or_path": "",
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"add_cross_attention": false,
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"architectures": null,
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"backbone_config": {
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"_name_or_path": "google/siglip-so400m-patch14-384",
|
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"0": "LABEL_0",
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},
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},
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},
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|
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|
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|
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|
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|
||||
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|
||||
"tokenizer_class": null,
|
||||
"top_k": 50,
|
||||
"top_p": 1.0,
|
||||
"torch_dtype": null,
|
||||
"torchscript": false,
|
||||
"typical_p": 1.0,
|
||||
"use_bfloat16": false,
|
||||
"vocab_size": 65536
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1 @@
|
|||
{"framework":"Pytorch","task":"visual-question-answering", "pipeline":{"type":"ovis-vl"},"allow_remote":true}
|
||||
|
|
@ -0,0 +1,201 @@
|
|||
from abc import ABC, abstractmethod
|
||||
from typing import List, Dict, Union, Optional
|
||||
|
||||
from transformers import PretrainedConfig, AutoConfig
|
||||
|
||||
IGNORE_ID = -100
|
||||
IMAGE_TOKEN_ID = -200
|
||||
IMAGE_TOKEN = "<image>"
|
||||
IMAGE_ATOM_ID = -300
|
||||
IMAGE_INDICATOR_IDS = [-301, -302, -303, -304, -305]
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------
|
||||
# Visual Tokenizer Configuration
|
||||
# ----------------------------------------------------------------------
|
||||
class BaseVisualTokenizerConfig(PretrainedConfig):
|
||||
def __init__(
|
||||
self,
|
||||
vocab_size=16384,
|
||||
tokenize_function="softmax",
|
||||
tau=1.0,
|
||||
depths=None,
|
||||
drop_cls_token=False,
|
||||
backbone_config: Optional[Union[PretrainedConfig, dict]] = None,
|
||||
hidden_stride: int = 1,
|
||||
**kwargs
|
||||
):
|
||||
super().__init__(**kwargs)
|
||||
self.vocab_size = vocab_size
|
||||
self.tokenize_function = tokenize_function
|
||||
self.tau = tau
|
||||
if isinstance(depths, str):
|
||||
depths = [int(x) for x in depths.split('|')]
|
||||
self.depths = depths
|
||||
self.backbone_kwargs = {}
|
||||
self.drop_cls_token = drop_cls_token
|
||||
if backbone_config is not None:
|
||||
assert isinstance(backbone_config, (PretrainedConfig, dict)), \
|
||||
f"expect `backbone_config` to be instance of PretrainedConfig or dict, but got {type(backbone_config)} type"
|
||||
if not isinstance(backbone_config, PretrainedConfig):
|
||||
model_type = backbone_config['model_type']
|
||||
backbone_config.pop('model_type')
|
||||
backbone_config = AutoConfig.for_model(model_type, **backbone_config)
|
||||
self.backbone_config = backbone_config
|
||||
self.hidden_stride = hidden_stride
|
||||
|
||||
|
||||
class SiglipVisualTokenizerConfig(BaseVisualTokenizerConfig):
|
||||
model_type = "siglip_visual_tokenizer"
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
if self.drop_cls_token:
|
||||
self.drop_cls_token = False
|
||||
if self.depths:
|
||||
assert len(self.depths) == 1
|
||||
self.backbone_kwargs['num_hidden_layers'] = self.depths[0]
|
||||
|
||||
|
||||
AutoConfig.register("siglip_visual_tokenizer", SiglipVisualTokenizerConfig)
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------
|
||||
# Ovis Configuration
|
||||
# ----------------------------------------------------------------------
|
||||
class OvisConfig(PretrainedConfig):
|
||||
model_type = "ovis"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
llm_config: Optional[Union[PretrainedConfig, dict]] = None,
|
||||
visual_tokenizer_config: Optional[Union[PretrainedConfig, dict]] = None,
|
||||
multimodal_max_length=8192,
|
||||
hidden_size=None,
|
||||
conversation_formatter_class=None,
|
||||
llm_attn_implementation=None,
|
||||
disable_tie_weight=False,
|
||||
**kwargs
|
||||
):
|
||||
super().__init__(**kwargs)
|
||||
if llm_config is not None:
|
||||
assert isinstance(llm_config, (PretrainedConfig, dict)), \
|
||||
f"expect `llm_config` to be instance of PretrainedConfig or dict, but got {type(llm_config)} type"
|
||||
if not isinstance(llm_config, PretrainedConfig):
|
||||
model_type = llm_config['model_type']
|
||||
llm_config.pop('model_type')
|
||||
llm_config = AutoConfig.for_model(model_type, **llm_config)
|
||||
self.llm_config = llm_config
|
||||
if visual_tokenizer_config is not None:
|
||||
assert isinstance(visual_tokenizer_config, (PretrainedConfig, dict)), \
|
||||
f"expect `visual_tokenizer_config` to be instance of PretrainedConfig or dict, but got {type(visual_tokenizer_config)} type"
|
||||
if not isinstance(visual_tokenizer_config, PretrainedConfig):
|
||||
model_type = visual_tokenizer_config['model_type']
|
||||
visual_tokenizer_config.pop('model_type')
|
||||
visual_tokenizer_config = AutoConfig.for_model(model_type, **visual_tokenizer_config)
|
||||
self.visual_tokenizer_config = visual_tokenizer_config
|
||||
self.multimodal_max_length = multimodal_max_length
|
||||
self.hidden_size = hidden_size
|
||||
self.conversation_formatter_class = conversation_formatter_class
|
||||
self.llm_attn_implementation = llm_attn_implementation
|
||||
self.disable_tie_weight = disable_tie_weight
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------
|
||||
# Conversation Formatter
|
||||
# ----------------------------------------------------------------------
|
||||
class ConversationFormatter(ABC):
|
||||
support_tokenizer_types = None
|
||||
|
||||
def __init__(self, tokenizer):
|
||||
tokenizer_type = type(tokenizer).__name__
|
||||
assert tokenizer_type in self.support_tokenizer_types, \
|
||||
f'Invalid tokenizer type, expected one from `{self.support_tokenizer_types}`, but got `{tokenizer_type}`'
|
||||
self.tokenizer = tokenizer
|
||||
self.image_token = IMAGE_TOKEN
|
||||
self.image_token_id = IMAGE_TOKEN_ID
|
||||
self.ignore_id = IGNORE_ID
|
||||
|
||||
def _tokenize_with_image_symbol(self, text):
|
||||
text_chunks = [self.tokenizer(chunk, add_special_tokens=False).input_ids for chunk in
|
||||
text.split(self.image_token)]
|
||||
token_ids = []
|
||||
num_chuck = len(text_chunks)
|
||||
for i, chunk in enumerate(text_chunks):
|
||||
token_ids.extend(chunk)
|
||||
if i < num_chuck - 1:
|
||||
token_ids.append(self.image_token_id)
|
||||
return token_ids
|
||||
|
||||
@abstractmethod
|
||||
def format(self, conversations: List[Dict], generation_preface=None):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def format_query(self, query, generation_preface=""):
|
||||
pass
|
||||
|
||||
|
||||
class GemmaConversationFormatter(ConversationFormatter):
|
||||
support_tokenizer_types = ['GemmaTokenizer', 'GemmaTokenizerFast']
|
||||
|
||||
def __init__(self, tokenizer):
|
||||
super().__init__(tokenizer)
|
||||
# Gemma does not support system prompt
|
||||
self.from2role = {
|
||||
"human": "<start_of_turn>user\n",
|
||||
"gpt": "<start_of_turn>model\n",
|
||||
}
|
||||
self.gpt_token_num = None
|
||||
self.im_end = "<end_of_turn>\n"
|
||||
self.bos_token = "<bos>"
|
||||
self.bos_token_ids = None
|
||||
|
||||
def format(self, conversations: List[Dict], generation_preface=None):
|
||||
if self.gpt_token_num is None:
|
||||
self.gpt_token_num = len(self.tokenizer(self.from2role["gpt"], add_special_tokens=False).input_ids)
|
||||
|
||||
if self.bos_token_ids is None:
|
||||
self.bos_token_ids = self.tokenizer(self.bos_token, add_special_tokens=False).input_ids
|
||||
|
||||
if conversations[0]["from"] == "system":
|
||||
raise ValueError("Gemma does not support system prompt")
|
||||
|
||||
if generation_preface is not None:
|
||||
conversations.append({
|
||||
"from": "gpt",
|
||||
"value": generation_preface
|
||||
})
|
||||
|
||||
prompt = "" + self.bos_token
|
||||
input_ids = [] + self.bos_token_ids
|
||||
labels = [] + [IGNORE_ID] * len(input_ids)
|
||||
num_conversation = len(conversations)
|
||||
for i, conversation in enumerate(conversations):
|
||||
frm = conversation["from"]
|
||||
role = self.from2role[frm]
|
||||
message = conversation["value"].strip()
|
||||
text = role + message
|
||||
if i < num_conversation - 1 or generation_preface is None:
|
||||
text += self.im_end
|
||||
prompt += text
|
||||
token_ids = self._tokenize_with_image_symbol(text)
|
||||
input_ids.extend(token_ids)
|
||||
label_ids = [self.ignore_id] * len(token_ids)
|
||||
if frm == "gpt":
|
||||
# learning `\n` following `im_end` is meaningless, so the last `\n` token is ignored in label
|
||||
label_ids[self.gpt_token_num:-1] = token_ids[self.gpt_token_num:-1]
|
||||
labels.extend(label_ids)
|
||||
|
||||
assert self._tokenize_with_image_symbol(prompt) == input_ids
|
||||
assert len(input_ids) == len(labels)
|
||||
|
||||
return prompt, input_ids, labels
|
||||
|
||||
def format_query(self, query, generation_preface=""):
|
||||
prompt, input_ids, _ = self.format([{
|
||||
"from": "human",
|
||||
"value": query
|
||||
}], generation_preface=generation_preface)
|
||||
|
||||
return prompt, input_ids
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 2,
|
||||
"cache_implementation": "hybrid",
|
||||
"eos_token_id": [
|
||||
1,
|
||||
107
|
||||
],
|
||||
"pad_token_id": 0,
|
||||
"transformers_version": "4.44.2"
|
||||
}
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
|
|
@ -0,0 +1,923 @@
|
|||
{
|
||||
"metadata": {
|
||||
"total_size": 20413821036
|
||||
},
|
||||
"weight_map": {
|
||||
"llm.model.embed_tokens.weight": "model-00001-of-00005.safetensors",
|
||||
"llm.model.layers.0.input_layernorm.weight": "model-00001-of-00005.safetensors",
|
||||
"llm.model.layers.0.mlp.down_proj.weight": "model-00001-of-00005.safetensors",
|
||||
"llm.model.layers.0.mlp.gate_proj.weight": "model-00001-of-00005.safetensors",
|
||||
"llm.model.layers.0.mlp.up_proj.weight": "model-00001-of-00005.safetensors",
|
||||
"llm.model.layers.0.post_attention_layernorm.weight": "model-00001-of-00005.safetensors",
|
||||
"llm.model.layers.0.post_feedforward_layernorm.weight": "model-00001-of-00005.safetensors",
|
||||
"llm.model.layers.0.pre_feedforward_layernorm.weight": "model-00001-of-00005.safetensors",
|
||||
"llm.model.layers.0.self_attn.k_proj.weight": "model-00001-of-00005.safetensors",
|
||||
"llm.model.layers.0.self_attn.o_proj.weight": "model-00001-of-00005.safetensors",
|
||||
"llm.model.layers.0.self_attn.q_proj.weight": "model-00001-of-00005.safetensors",
|
||||
"llm.model.layers.0.self_attn.v_proj.weight": "model-00001-of-00005.safetensors",
|
||||
"llm.model.layers.1.input_layernorm.weight": "model-00001-of-00005.safetensors",
|
||||
"llm.model.layers.1.mlp.down_proj.weight": "model-00001-of-00005.safetensors",
|
||||
"llm.model.layers.1.mlp.gate_proj.weight": "model-00001-of-00005.safetensors",
|
||||
"llm.model.layers.1.mlp.up_proj.weight": "model-00001-of-00005.safetensors",
|
||||
"llm.model.layers.1.post_attention_layernorm.weight": "model-00001-of-00005.safetensors",
|
||||
"llm.model.layers.1.post_feedforward_layernorm.weight": "model-00001-of-00005.safetensors",
|
||||
"llm.model.layers.1.pre_feedforward_layernorm.weight": "model-00001-of-00005.safetensors",
|
||||
"llm.model.layers.1.self_attn.k_proj.weight": "model-00001-of-00005.safetensors",
|
||||
"llm.model.layers.1.self_attn.o_proj.weight": "model-00001-of-00005.safetensors",
|
||||
"llm.model.layers.1.self_attn.q_proj.weight": "model-00001-of-00005.safetensors",
|
||||
"llm.model.layers.1.self_attn.v_proj.weight": "model-00001-of-00005.safetensors",
|
||||
"llm.model.layers.10.input_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.10.mlp.down_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.10.mlp.gate_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.10.mlp.up_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.10.post_attention_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.10.post_feedforward_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.10.pre_feedforward_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.10.self_attn.k_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.10.self_attn.o_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.10.self_attn.q_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.10.self_attn.v_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.11.input_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.11.mlp.down_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.11.mlp.gate_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.11.mlp.up_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.11.post_attention_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.11.post_feedforward_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.11.pre_feedforward_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.11.self_attn.k_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.11.self_attn.o_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.11.self_attn.q_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.11.self_attn.v_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.12.input_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.12.mlp.down_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.12.mlp.gate_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.12.mlp.up_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.12.post_attention_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.12.post_feedforward_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.12.pre_feedforward_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.12.self_attn.k_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.12.self_attn.o_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.12.self_attn.q_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.12.self_attn.v_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.13.input_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.13.mlp.down_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.13.mlp.gate_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.13.mlp.up_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.13.post_attention_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.13.post_feedforward_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.13.pre_feedforward_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.13.self_attn.k_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.13.self_attn.o_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.13.self_attn.q_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.13.self_attn.v_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.14.input_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.14.mlp.down_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.14.mlp.gate_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.14.mlp.up_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.14.post_attention_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.14.post_feedforward_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.14.pre_feedforward_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.14.self_attn.k_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.14.self_attn.o_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.14.self_attn.q_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.14.self_attn.v_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.15.input_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.15.mlp.down_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.15.mlp.gate_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.15.mlp.up_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.15.post_attention_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.15.post_feedforward_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.15.pre_feedforward_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.15.self_attn.k_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.15.self_attn.o_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"llm.model.layers.15.self_attn.q_proj.weight": "model-00002-of-00005.safetensors",
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}
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||||
}
|
||||
|
|
@ -0,0 +1,620 @@
|
|||
import logging
|
||||
import os
|
||||
from packaging import version
|
||||
from importlib import import_module
|
||||
from typing import List, Callable, Union, Optional, Dict
|
||||
|
||||
import PIL.Image
|
||||
import torch
|
||||
import transformers
|
||||
from torch import Tensor
|
||||
from torch.nn import init
|
||||
from torch.nn.functional import softmax, gumbel_softmax, pad
|
||||
from transformers import PreTrainedModel, AutoModel, AutoTokenizer, AutoModelForCausalLM, AutoImageProcessor
|
||||
from transformers import SiglipImageProcessor, SiglipVisionModel
|
||||
from transformers.cache_utils import HybridCache
|
||||
from transformers.generation.utils import GenerateOutput
|
||||
|
||||
from .configuration_ovis import BaseVisualTokenizerConfig, SiglipVisualTokenizerConfig
|
||||
from .configuration_ovis import OvisConfig, ConversationFormatter
|
||||
from .configuration_ovis import IGNORE_ID, IMAGE_ATOM_ID, IMAGE_INDICATOR_IDS, IMAGE_TOKEN_ID
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------
|
||||
# Visual Tokenizer
|
||||
# ----------------------------------------------------------------------
|
||||
class BaseVisualTokenizer(PreTrainedModel):
|
||||
base_model_prefix = "backbone"
|
||||
main_input_name = None
|
||||
_image_processor_class = None
|
||||
_image_processor_kwargs = {}
|
||||
_backbone_class = None
|
||||
_backbone_name_or_path = None
|
||||
|
||||
def __init__(self, config: BaseVisualTokenizerConfig, *inputs, **kwargs):
|
||||
super().__init__(config, *inputs, **kwargs)
|
||||
self.image_processor = AutoImageProcessor.from_pretrained(kwargs['image_processor_name_or_path'])
|
||||
self.backbone = AutoModel.from_config(self.config.backbone_config)
|
||||
head_dim = self.config.vocab_size - len(IMAGE_INDICATOR_IDS) # reserved tokens for IMAGE_INDICATORS
|
||||
self.head = torch.nn.Sequential(
|
||||
torch.nn.Linear(
|
||||
self.backbone.config.hidden_size * self.config.hidden_stride * self.config.hidden_stride, head_dim,
|
||||
bias=False
|
||||
),
|
||||
torch.nn.LayerNorm(head_dim)
|
||||
)
|
||||
|
||||
assert all((self.image_processor.do_resize,
|
||||
not getattr(self.image_processor, 'do_center_crop', False),
|
||||
self.image_processor.do_rescale,
|
||||
self.image_processor.do_normalize
|
||||
)), f"image_processor `{self.image_processor}` is not supported currently"
|
||||
|
||||
def get_backbone(self):
|
||||
return self.backbone
|
||||
|
||||
def get_image_processor(self):
|
||||
return self.image_processor
|
||||
|
||||
def mock_input(self):
|
||||
height, width = self.get_image_size()
|
||||
return torch.zeros(1, 3, height, width), self.construct_image_placeholders((1, 1))
|
||||
|
||||
def get_head(self):
|
||||
return self.head
|
||||
|
||||
def get_image_size(self):
|
||||
raise NotImplementedError
|
||||
|
||||
@staticmethod
|
||||
def construct_image_placeholders(grid):
|
||||
image_placeholders = [IMAGE_INDICATOR_IDS[0], IMAGE_ATOM_ID, IMAGE_INDICATOR_IDS[1]]
|
||||
if grid[0] * grid[1] > 1:
|
||||
for r in range(grid[0]):
|
||||
for c in range(grid[1]):
|
||||
image_placeholders.append(IMAGE_ATOM_ID)
|
||||
if c < grid[1] - 1:
|
||||
image_placeholders.append(IMAGE_INDICATOR_IDS[2])
|
||||
if r < grid[0] - 1:
|
||||
image_placeholders.append(IMAGE_INDICATOR_IDS[3])
|
||||
image_placeholders.append(IMAGE_INDICATOR_IDS[4])
|
||||
return image_placeholders
|
||||
|
||||
def preprocess_image(self, image: PIL.Image.Image, max_partition=9, covering_threshold=0.9, convert_to_rgb=True):
|
||||
def _preprocess(img: PIL.Image.Image, side):
|
||||
# first resize and preprocess
|
||||
w, h = img.size
|
||||
if w == h:
|
||||
new_width = new_height = side
|
||||
elif w > h:
|
||||
new_width = side
|
||||
new_height = int(h / w * new_width)
|
||||
else:
|
||||
new_height = side
|
||||
new_width = int(w / h * new_height)
|
||||
new_size = dict(height=new_height, width=new_width)
|
||||
pixel_values = self.image_processor.preprocess(img, size=new_size, return_tensors='pt')['pixel_values']
|
||||
|
||||
# then pad to square
|
||||
square_values = torch.zeros([1, 3, side, side], dtype=pixel_values.dtype, device=pixel_values.device)
|
||||
new_height, new_width = pixel_values.shape[2:]
|
||||
if new_height == new_width:
|
||||
square_values[:, :, :, :] = pixel_values
|
||||
elif new_height > new_width:
|
||||
from_index = (side - new_width) // 2
|
||||
square_values[:, :, :, from_index:from_index + new_width] = pixel_values
|
||||
else:
|
||||
from_index = (side - new_height) // 2
|
||||
square_values[:, :, from_index:from_index + new_height, :] = pixel_values
|
||||
|
||||
return square_values
|
||||
|
||||
def _partition(img, grid):
|
||||
w, h = img.size
|
||||
row_height = h // grid[0]
|
||||
col_width = w // grid[1]
|
||||
|
||||
partition = []
|
||||
for row in range(grid[0]):
|
||||
for col in range(grid[1]):
|
||||
left = col * col_width
|
||||
upper = row * row_height
|
||||
right = w if col == grid[1] - 1 else (col + 1) * col_width
|
||||
lower = h if row == grid[0] - 1 else (row + 1) * row_height
|
||||
partition.append((left, upper, right, lower))
|
||||
|
||||
return partition
|
||||
|
||||
def _covering_area(left, upper, right, lower, side):
|
||||
w = right - left
|
||||
h = lower - upper
|
||||
w, h = max(w, h), min(w, h)
|
||||
if w > side:
|
||||
h = h / w * side
|
||||
w = side
|
||||
return w * h
|
||||
|
||||
def _get_best_grid(img, side):
|
||||
img_area = img.size[0] * img.size[1]
|
||||
|
||||
candidate_grids = []
|
||||
for i in range(1, max_partition + 1):
|
||||
for j in range(1, max_partition + 1):
|
||||
if i * j <= max_partition:
|
||||
candidate_grids.append((i, j))
|
||||
|
||||
all_grids = []
|
||||
good_grids = []
|
||||
for grid in candidate_grids:
|
||||
partition = _partition(img, grid)
|
||||
covering_ratio = sum([_covering_area(*p, side) for p in partition]) / img_area
|
||||
assert covering_ratio <= 1.0
|
||||
all_grids.append((grid, covering_ratio))
|
||||
if covering_ratio > covering_threshold:
|
||||
good_grids.append((grid, covering_ratio))
|
||||
|
||||
if len(good_grids) > 0:
|
||||
# pick the good partition with minimum #sub_images and break the tie using covering_ratio
|
||||
return sorted(good_grids, key=lambda x: (x[0][0] * x[0][1], -x[1]))[0][0]
|
||||
else:
|
||||
# pick the partition with maximum covering_ratio and break the tie using #sub_images
|
||||
return sorted(all_grids, key=lambda x: (-x[1], x[0][0] * x[0][1]))[0][0]
|
||||
|
||||
if convert_to_rgb and image.mode != 'RGB':
|
||||
image = image.convert('RGB')
|
||||
|
||||
sides = self.get_image_size()
|
||||
if sides[0] != sides[1]:
|
||||
raise ValueError('get_image_size() returns non-square size')
|
||||
side = sides[0]
|
||||
grid = _get_best_grid(image, side)
|
||||
partition = _partition(image, grid)
|
||||
crops = [image.crop(p) for p in partition]
|
||||
if len(crops) > 1:
|
||||
crops.insert(0, image)
|
||||
pixel_values = torch.cat([_preprocess(crop, side) for crop in crops], dim=0)
|
||||
image_placeholders = self.construct_image_placeholders(grid)
|
||||
return pixel_values, image_placeholders
|
||||
|
||||
def tokenize(self, logits):
|
||||
def st_argmax(y_soft, dim): # straight-through softmax
|
||||
index = y_soft.max(dim, keepdim=True)[1]
|
||||
y_hard = torch.zeros_like(y_soft, memory_format=torch.legacy_contiguous_format).scatter_(dim, index, 1.0)
|
||||
ret = y_hard - y_soft.detach() + y_soft
|
||||
return ret
|
||||
|
||||
if self.config.tokenize_function == 'softmax':
|
||||
tokens = softmax(logits, dim=-1)
|
||||
elif self.config.tokenize_function == 'gumbel_argmax':
|
||||
tokens = gumbel_softmax(logits, tau=self.config.tau, hard=True)
|
||||
elif self.config.tokenize_function == 'st_argmax':
|
||||
tokens = st_argmax(logits, dim=-1)
|
||||
else:
|
||||
raise ValueError(
|
||||
f'Invalid `max_type`, expected softmax or gumbel_argmax or st_argmax, but got {self.config.tokenize_function}')
|
||||
return tokens
|
||||
|
||||
def encode(self, pixel_values):
|
||||
output = self.backbone(pixel_values, output_hidden_states=True, return_dict=True)
|
||||
features = output.hidden_states[-1]
|
||||
if self.config.drop_cls_token:
|
||||
features = features[:, 1:, :]
|
||||
|
||||
# merge number of `hidden_stride * hidden_stride` hidden states together to reduce token sequence length
|
||||
# e.g., for hidden_stride=3, this leads to a token length reduction: 729 -> 81 for siglip
|
||||
if self.config.hidden_stride > 1:
|
||||
n, l, d = features.shape # this `d` maybe different from the above `d
|
||||
sqrt_l = int(l ** 0.5)
|
||||
assert sqrt_l ** 2 == l, "The token sequence length should be a perfect square."
|
||||
features = features.reshape(n, sqrt_l, sqrt_l, d)
|
||||
pl = (self.config.hidden_stride - (sqrt_l % self.config.hidden_stride)) % self.config.hidden_stride
|
||||
features = pad(features, (0, 0, 0, pl, 0, pl), "constant", 0)
|
||||
sqrt_l += pl
|
||||
features = features.reshape(n, sqrt_l // self.config.hidden_stride, self.config.hidden_stride,
|
||||
sqrt_l // self.config.hidden_stride, self.config.hidden_stride, d)
|
||||
features = features.permute(0, 1, 3, 2, 4, 5) # [n, sqrt_l/hs, sqrt_l/hs, hs, hs, d]
|
||||
features = features.flatten(3) # [n, sqrt_l/hs, sqrt_l/hs, hs*hs*d]
|
||||
features = features.reshape(
|
||||
n, -1, self.config.hidden_stride * self.config.hidden_stride * d)
|
||||
|
||||
return features
|
||||
|
||||
def forward(self, pixel_values) -> torch.Tensor: # [BatchSize, ImageShape] -> [BatchSize, #Token, VocabSize]
|
||||
features = self.encode(pixel_values)
|
||||
logits = self.head(features)
|
||||
tokens = self.tokenize(logits)
|
||||
# tokens' shape is [BatchSize, #Token, VocabSize-5], so padding with [BatchSize, #Token, 5], after
|
||||
# which, tokens' shape should become [BatchSize, #Token, VocabSize]
|
||||
batch_size, token_len, _ = tokens.shape
|
||||
padding_tensor = torch.zeros(size=(batch_size, token_len, len(IMAGE_INDICATOR_IDS)),
|
||||
dtype=tokens.dtype,
|
||||
device=tokens.device,
|
||||
layout=tokens.layout,
|
||||
requires_grad=False)
|
||||
tokens = torch.cat((tokens, padding_tensor), dim=2)
|
||||
return tokens
|
||||
|
||||
|
||||
class SiglipVisualTokenizer(BaseVisualTokenizer):
|
||||
config_class = SiglipVisualTokenizerConfig
|
||||
supports_gradient_checkpointing = True
|
||||
_no_split_modules = ["SiglipVisionTransformer"]
|
||||
_image_processor_class = SiglipImageProcessor
|
||||
_image_processor_kwargs = {}
|
||||
_backbone_class = SiglipVisionModel
|
||||
_backbone_name_or_path = "google/siglip-so400m-patch14-384"
|
||||
|
||||
def get_image_size(self):
|
||||
height = self.image_processor.size["height"]
|
||||
width = self.image_processor.size["width"]
|
||||
return height, width
|
||||
|
||||
|
||||
AutoModel.register(SiglipVisualTokenizerConfig, SiglipVisualTokenizer)
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------
|
||||
# Ovis
|
||||
# ----------------------------------------------------------------------
|
||||
class VisualEmbedding(torch.nn.Embedding):
|
||||
def forward(self, visual_tokens: Tensor) -> Tensor:
|
||||
if visual_tokens.dtype in [torch.int8, torch.int16, torch.int32, torch.int64, torch.long]:
|
||||
return super().forward(visual_tokens)
|
||||
return torch.matmul(visual_tokens, self.weight)
|
||||
|
||||
def reset_parameters(self, mean=0., std=1.) -> None:
|
||||
init.normal_(self.weight, mean=mean, std=std)
|
||||
self._fill_padding_idx_with_zero()
|
||||
|
||||
|
||||
class OvisPreTrainedModel(PreTrainedModel):
|
||||
config_class = OvisConfig
|
||||
base_model_prefix = "ovis"
|
||||
|
||||
|
||||
class Ovis(OvisPreTrainedModel):
|
||||
|
||||
def __init__(self, config: OvisConfig, *inputs, **kwargs):
|
||||
super().__init__(config, *inputs, **kwargs)
|
||||
attn_kwargs = dict()
|
||||
if self.config.llm_attn_implementation:
|
||||
attn_kwargs['attn_implementation'] = self.config.llm_attn_implementation
|
||||
self.llm = AutoModelForCausalLM.from_config(self.config.llm_config, **attn_kwargs)
|
||||
assert self.config.hidden_size == self.llm.config.hidden_size, "hidden size mismatch"
|
||||
self.text_tokenizer = AutoTokenizer.from_pretrained(self.config.name_or_path)
|
||||
self.visual_tokenizer = AutoModel.from_config(self.config.visual_tokenizer_config,
|
||||
image_processor_name_or_path=self.config.name_or_path)
|
||||
self.vte = VisualEmbedding(
|
||||
self.config.visual_tokenizer_config.vocab_size,
|
||||
self.config.hidden_size,
|
||||
device=self.visual_tokenizer.device,
|
||||
dtype=self.visual_tokenizer.dtype
|
||||
)
|
||||
|
||||
def _merge_modules(modules_list: tuple):
|
||||
merged_modules = []
|
||||
for modules in modules_list:
|
||||
merged_modules.extend(modules if modules else [])
|
||||
return merged_modules
|
||||
|
||||
self._no_split_modules = _merge_modules((self.llm._no_split_modules, self.visual_tokenizer._no_split_modules))
|
||||
self._skip_keys_device_placement = self.llm._skip_keys_device_placement
|
||||
self._keep_in_fp32_modules = _merge_modules(
|
||||
(self.llm._keep_in_fp32_modules, self.visual_tokenizer._keep_in_fp32_modules))
|
||||
self.is_parallelizable = all((self.llm.is_parallelizable, self.visual_tokenizer.is_parallelizable))
|
||||
self.supports_gradient_checkpointing = all(
|
||||
(self.llm.supports_gradient_checkpointing, self.visual_tokenizer.supports_gradient_checkpointing))
|
||||
self._supports_flash_attn_2 = all(
|
||||
(self.llm._supports_flash_attn_2, self.visual_tokenizer._supports_flash_attn_2))
|
||||
self._supports_sdpa = all((self.llm._supports_sdpa, self.visual_tokenizer._supports_sdpa))
|
||||
|
||||
def get_text_tokenizer(self):
|
||||
return self.text_tokenizer
|
||||
|
||||
def get_visual_tokenizer(self):
|
||||
return self.visual_tokenizer
|
||||
|
||||
def tie_weights(self):
|
||||
if not self.config.disable_tie_weight:
|
||||
self.get_llm().tie_weights()
|
||||
|
||||
def get_llm(self):
|
||||
return self.llm
|
||||
|
||||
def get_vte(self):
|
||||
return self.vte
|
||||
|
||||
def get_wte(self):
|
||||
return self.llm.get_input_embeddings()
|
||||
|
||||
def get_conversation_formatter(self) -> ConversationFormatter:
|
||||
if getattr(self, 'conversation_formatter', None) is None:
|
||||
self.conversation_formatter = getattr(import_module(".configuration_ovis", __package__),
|
||||
self.config.conversation_formatter_class)(self.text_tokenizer)
|
||||
return self.conversation_formatter
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
attention_mask: torch.Tensor,
|
||||
labels: Optional[torch.Tensor],
|
||||
pixel_values: List[Optional[torch.Tensor]],
|
||||
**kwargs
|
||||
):
|
||||
assert self.training, "`forward` can only be used in training. For inference, use `generate`."
|
||||
_, inputs_embeds, labels, attention_mask = self.merge_multimodal(
|
||||
text_input_ids=input_ids,
|
||||
text_attention_masks=attention_mask,
|
||||
text_labels=labels,
|
||||
pixel_values=pixel_values
|
||||
)
|
||||
return self.llm(inputs_embeds=inputs_embeds, labels=labels, attention_mask=attention_mask, **kwargs)
|
||||
|
||||
def merge_multimodal(
|
||||
self,
|
||||
text_input_ids: torch.Tensor,
|
||||
text_attention_masks: torch.Tensor,
|
||||
text_labels: Optional[torch.Tensor],
|
||||
pixel_values: List[Optional[torch.Tensor]],
|
||||
left_padding: bool = False
|
||||
):
|
||||
input_device = text_input_ids.device
|
||||
visual_vocab_szie = self.get_visual_tokenizer().config.vocab_size
|
||||
visual_indicator_embeds = self.get_vte()(
|
||||
torch.tensor(
|
||||
list(range(visual_vocab_szie - 5, visual_vocab_szie)),
|
||||
dtype=torch.long,
|
||||
device=self.get_visual_tokenizer().device
|
||||
)
|
||||
).to(device=input_device)
|
||||
|
||||
if self.training:
|
||||
# When training, to be compatible with deepspeed zero, each sample has to include pixel_value tensor.
|
||||
# For text-only sample, one can simply use a full zero tensor as pixel_value, which will be ignored
|
||||
# (see below in this function); so, the gradient will not be affected.
|
||||
num_images = [x.shape[0] for x in pixel_values]
|
||||
visual_tokens = self.visual_tokenizer(torch.cat([x for x in pixel_values], dim=0))
|
||||
visual_embeds = torch.split(self.get_vte()(visual_tokens).to(dtype=self.dtype, device=input_device),
|
||||
split_size_or_sections=num_images, dim=0)
|
||||
visual_input_ids = torch.split(torch.argmax(visual_tokens, dim=-1).to(device=input_device),
|
||||
split_size_or_sections=num_images, dim=0)
|
||||
visual_labels = [torch.full(x.shape, IGNORE_ID, dtype=torch.long, device=input_device) for x in
|
||||
visual_input_ids]
|
||||
else:
|
||||
# When inference, sample can include only text with `None` pixel_value
|
||||
num_images = [x.shape[0] if x is not None else 0 for x in pixel_values]
|
||||
if sum(num_images) > 0:
|
||||
visual_tokens = self.visual_tokenizer(torch.cat([x for x in pixel_values if x is not None], dim=0))
|
||||
visual_embeds = torch.split(self.get_vte()(visual_tokens).to(dtype=self.dtype, device=input_device),
|
||||
split_size_or_sections=num_images, dim=0)
|
||||
visual_input_ids = torch.split(torch.argmax(visual_tokens, dim=-1).to(device=input_device),
|
||||
split_size_or_sections=num_images, dim=0)
|
||||
visual_labels = [torch.full(x.shape, IGNORE_ID, dtype=torch.long, device=input_device) for x in
|
||||
visual_input_ids]
|
||||
else:
|
||||
# just placeholders
|
||||
visual_embeds = [None] * len(num_images)
|
||||
visual_input_ids = [None] * len(num_images)
|
||||
visual_labels = [None] * len(num_images)
|
||||
if text_labels is None:
|
||||
text_labels = torch.full(text_input_ids.shape, IGNORE_ID, dtype=torch.long, device=input_device)
|
||||
|
||||
input_embeds = []
|
||||
attention_masks = []
|
||||
labels = []
|
||||
for text_input_id, text_label, text_attention_mask, visual_embed, visual_input_id, visual_label in zip(
|
||||
text_input_ids, text_labels, text_attention_masks, visual_embeds, visual_input_ids, visual_labels
|
||||
):
|
||||
placeholder_token_mask = torch.lt(text_input_id, 0)
|
||||
text_embed = self.get_wte()(torch.masked_fill(text_input_id, placeholder_token_mask, 0))
|
||||
for i, indicator_id in enumerate(IMAGE_INDICATOR_IDS):
|
||||
text_embed[text_input_id == indicator_id] = visual_indicator_embeds[i]
|
||||
image_atom_positions = torch.where(torch.eq(text_input_id, IMAGE_ATOM_ID))[0].tolist()
|
||||
if len(image_atom_positions) > 0:
|
||||
input_embed_parts = []
|
||||
attention_mask_parts = []
|
||||
label_parts = []
|
||||
prev_image_atom_position = -1
|
||||
for index, image_atom_position in enumerate(image_atom_positions):
|
||||
input_embed_parts.append(
|
||||
text_embed[prev_image_atom_position + 1:image_atom_position, :])
|
||||
label_parts.append(
|
||||
text_label[prev_image_atom_position + 1:image_atom_position])
|
||||
attention_mask_parts.append(
|
||||
text_attention_mask[prev_image_atom_position + 1:image_atom_position])
|
||||
input_embed_parts.append(visual_embed[index])
|
||||
attention_mask_parts.append(
|
||||
torch.ones_like(visual_label[index], dtype=torch.bool))
|
||||
label_parts.append(visual_label[index])
|
||||
prev_image_atom_position = image_atom_position
|
||||
if prev_image_atom_position + 1 < text_input_id.shape[0]:
|
||||
input_embed_parts.append(
|
||||
text_embed[prev_image_atom_position + 1:, :])
|
||||
attention_mask_parts.append(
|
||||
text_attention_mask[prev_image_atom_position + 1:])
|
||||
label_parts.append(
|
||||
text_label[prev_image_atom_position + 1:])
|
||||
input_embed = torch.cat(input_embed_parts, dim=0)
|
||||
attention_mask = torch.cat(attention_mask_parts, dim=0)
|
||||
label = torch.cat(label_parts, dim=0)
|
||||
else:
|
||||
input_embed = text_embed
|
||||
attention_mask = text_attention_mask
|
||||
label = text_label
|
||||
if self.training:
|
||||
# Make visual_embed & visual_indicator_embeds involved in the backward graph,
|
||||
# to be compatible with deepspeed zero and ddp.
|
||||
input_embed += torch.sum(visual_embed * 0.0) + torch.sum(visual_indicator_embeds * 0.0)
|
||||
input_embeds.append(input_embed)
|
||||
attention_masks.append(attention_mask)
|
||||
labels.append(label)
|
||||
|
||||
if self.training: # padding to self.config.multimodal_max_length for increased training speed
|
||||
padding_size = max(0, self.config.multimodal_max_length - len(input_embeds[0]))
|
||||
input_embeds[0] = torch.nn.ConstantPad2d((0, 0, 0, padding_size), 0.0)(input_embeds[0])
|
||||
attention_masks[0] = torch.nn.ConstantPad1d((0, padding_size), False)(attention_masks[0])
|
||||
labels[0] = torch.nn.ConstantPad1d((0, padding_size), IGNORE_ID)(labels[0])
|
||||
batch_input_embeds = self.pad_truncate_sequence(input_embeds, batch_first=True, padding_value=0.0, left_padding=left_padding)
|
||||
batch_attention_mask = self.pad_truncate_sequence(attention_masks, batch_first=True, padding_value=False, left_padding=left_padding)
|
||||
batch_labels = self.pad_truncate_sequence(labels, batch_first=True, padding_value=IGNORE_ID, left_padding=left_padding)
|
||||
|
||||
return visual_input_ids, batch_input_embeds, batch_labels, batch_attention_mask
|
||||
|
||||
def pad_truncate_sequence(self, sequences: List[torch.Tensor], batch_first: bool = True, padding_value: float = 0.0, left_padding: bool = False) -> torch.Tensor:
|
||||
if left_padding == False:
|
||||
pad_sequence = torch.nn.utils.rnn.pad_sequence(sequences, batch_first=batch_first, padding_value=padding_value)
|
||||
return pad_sequence[:,:self.config.multimodal_max_length]
|
||||
else:
|
||||
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])
|
||||
return pad_sequence[:,-self.config.multimodal_max_length:]
|
||||
|
||||
def preprocess_inputs(
|
||||
self,
|
||||
text_or_conversations: Union[List[Dict], str],
|
||||
images: Optional[List[PIL.Image.Image]],
|
||||
max_partition=9,
|
||||
generation_preface='',
|
||||
return_labels=False,
|
||||
propagate_exception=True
|
||||
):
|
||||
# convert text to conversations
|
||||
if isinstance(text_or_conversations, str):
|
||||
conversations = [{
|
||||
"from": "human",
|
||||
"value": text_or_conversations
|
||||
}]
|
||||
elif isinstance(text_or_conversations, list):
|
||||
conversations = text_or_conversations
|
||||
else:
|
||||
raise ValueError(f'Invalid type of `text_or_conversations`, expected `List[Dict]` or `str`,'
|
||||
f' but got {type(text_or_conversations)}')
|
||||
|
||||
# format conversations
|
||||
prompt, raw_input_ids, raw_labels = self.get_conversation_formatter().format(
|
||||
conversations, generation_preface=generation_preface)
|
||||
|
||||
# place image placeholders
|
||||
input_ids = []
|
||||
labels = []
|
||||
pixel_values = []
|
||||
invalidate_label = False
|
||||
image_token_indices = [i for i, v in enumerate(raw_input_ids) if v == IMAGE_TOKEN_ID]
|
||||
last_image_token_index = -1
|
||||
for i in range(len(image_token_indices)):
|
||||
head = 0 if i == 0 else image_token_indices[i - 1] + 1
|
||||
tail = image_token_indices[i]
|
||||
last_image_token_index = tail
|
||||
input_ids.extend(raw_input_ids[head:tail])
|
||||
labels.extend(raw_labels[head:tail])
|
||||
try:
|
||||
image = images[i]
|
||||
raw_pixel_values, image_placeholders = self.visual_tokenizer.preprocess_image(
|
||||
image, max_partition=max_partition)
|
||||
except Exception as e:
|
||||
if propagate_exception:
|
||||
raise e
|
||||
logging.exception(e)
|
||||
invalidate_label = True
|
||||
raw_pixel_values, image_placeholders = self.visual_tokenizer.mock_input()
|
||||
input_ids.extend(image_placeholders)
|
||||
labels.extend([IGNORE_ID] * len(image_placeholders))
|
||||
pixel_values.append(raw_pixel_values)
|
||||
input_ids.extend(raw_input_ids[last_image_token_index + 1:])
|
||||
labels.extend(raw_labels[last_image_token_index + 1:])
|
||||
|
||||
# return tensors
|
||||
input_ids = torch.tensor(input_ids, dtype=torch.long)
|
||||
labels = torch.tensor([IGNORE_ID] * len(labels) if invalidate_label else labels, dtype=torch.long)
|
||||
pixel_values = torch.cat(pixel_values, dim=0) if len(pixel_values) > 0 else None
|
||||
|
||||
if return_labels:
|
||||
return prompt, input_ids, pixel_values, labels
|
||||
else:
|
||||
return prompt, input_ids, pixel_values
|
||||
|
||||
def save_pretrained(
|
||||
self,
|
||||
save_directory: Union[str, os.PathLike],
|
||||
is_main_process: bool = True,
|
||||
state_dict: Optional[dict] = None,
|
||||
save_function: Callable = torch.save,
|
||||
push_to_hub: bool = False,
|
||||
max_shard_size: Union[int, str] = "5GB",
|
||||
safe_serialization: bool = True,
|
||||
variant: Optional[str] = None,
|
||||
token: Optional[Union[str, bool]] = None,
|
||||
save_peft_format: bool = True,
|
||||
**kwargs
|
||||
):
|
||||
super().save_pretrained(save_directory,
|
||||
is_main_process=is_main_process,
|
||||
state_dict=state_dict,
|
||||
save_function=save_function,
|
||||
safe_serialization=safe_serialization)
|
||||
self.get_text_tokenizer().save_pretrained(save_directory)
|
||||
self.get_visual_tokenizer().get_image_processor().save_pretrained(save_directory)
|
||||
|
||||
def _get_hybrid_cache_for_llm(self, batch_size: int, max_cache_len: int):
|
||||
cache_cls = HybridCache
|
||||
llm = self.get_llm()
|
||||
|
||||
if version.parse(transformers.__version__) >= version.parse("4.46.0"):
|
||||
need_new_cache = (
|
||||
not hasattr(llm, "_cache")
|
||||
or (not isinstance(llm._cache, cache_cls))
|
||||
or llm._cache.batch_size != batch_size
|
||||
or llm._cache.max_cache_len < max_cache_len
|
||||
)
|
||||
else:
|
||||
need_new_cache = (
|
||||
not hasattr(llm, "_cache")
|
||||
or (not isinstance(llm._cache, cache_cls))
|
||||
or llm._cache.max_batch_size != batch_size
|
||||
or llm._cache.max_cache_len < max_cache_len
|
||||
)
|
||||
|
||||
if need_new_cache:
|
||||
if hasattr(llm.config, "_pre_quantization_dtype"):
|
||||
cache_dtype = llm.config._pre_quantization_dtype
|
||||
else:
|
||||
cache_dtype = llm.dtype
|
||||
if version.parse(transformers.__version__) >= version.parse("4.46.0"):
|
||||
llm._cache = cache_cls(
|
||||
config=llm.config,
|
||||
batch_size=batch_size,
|
||||
max_cache_len=max_cache_len,
|
||||
device=llm.device,
|
||||
dtype=cache_dtype,
|
||||
)
|
||||
else:
|
||||
llm._cache = cache_cls(
|
||||
config=llm.config,
|
||||
max_batch_size=batch_size,
|
||||
max_cache_len=max_cache_len,
|
||||
device=llm.device,
|
||||
dtype=cache_dtype,
|
||||
)
|
||||
else:
|
||||
llm._cache.reset()
|
||||
return llm._cache
|
||||
|
||||
# TODO: support batch generation
|
||||
def generate(
|
||||
self,
|
||||
inputs: Optional[torch.Tensor] = None,
|
||||
**kwargs
|
||||
) -> Union[GenerateOutput, torch.LongTensor]:
|
||||
_, inputs_embeds, labels, attention_mask = self.merge_multimodal(
|
||||
text_input_ids=inputs,
|
||||
text_attention_masks=kwargs.pop('attention_mask'),
|
||||
text_labels=None,
|
||||
pixel_values=kwargs.pop('pixel_values'),
|
||||
left_padding=True
|
||||
)
|
||||
if getattr(self.generation_config, 'cache_implementation') == 'hybrid': # mainly for Gemma2
|
||||
kwargs['past_key_values'] = self._get_hybrid_cache_for_llm(
|
||||
getattr(kwargs, "num_beams", inputs_embeds.shape[0]), kwargs['max_new_tokens'] + inputs_embeds.shape[-2])
|
||||
self.get_llm()._supports_cache_class = True
|
||||
kwargs['cache_implementation'] = None
|
||||
|
||||
return self.llm.generate(inputs=None, inputs_embeds=inputs_embeds, attention_mask=attention_mask, **kwargs)
|
||||
|
|
@ -0,0 +1,24 @@
|
|||
{
|
||||
"do_convert_rgb": null,
|
||||
"do_normalize": true,
|
||||
"do_rescale": true,
|
||||
"do_resize": true,
|
||||
"image_mean": [
|
||||
0.5,
|
||||
0.5,
|
||||
0.5
|
||||
],
|
||||
"image_processor_type": "SiglipImageProcessor",
|
||||
"image_std": [
|
||||
0.5,
|
||||
0.5,
|
||||
0.5
|
||||
],
|
||||
"processor_class": "SiglipProcessor",
|
||||
"resample": 3,
|
||||
"rescale_factor": 0.00392156862745098,
|
||||
"size": {
|
||||
"height": 384,
|
||||
"width": 384
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,34 @@
|
|||
{
|
||||
"additional_special_tokens": [
|
||||
"<start_of_turn>",
|
||||
"<end_of_turn>"
|
||||
],
|
||||
"bos_token": {
|
||||
"content": "<bos>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<eos>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<pad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
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Reference in New Issue