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Browse files- .mdl +0 -0
- .msc +0 -0
- .mv +1 -0
- README.md +121 -0
- config.json +293 -0
- configuration.json +1 -0
- configuration_apollo.py +47 -0
- llm/added_tokens.json +24 -0
- llm/config.json +31 -0
- llm/generation_config.json +14 -0
- llm/merges.txt +0 -0
- llm/model-00001-of-00004.safetensors +3 -0
- llm/model-00002-of-00004.safetensors +3 -0
- llm/model-00003-of-00004.safetensors +3 -0
- llm/model-00004-of-00004.safetensors +3 -0
- llm/model.safetensors.index.json +346 -0
- llm/special_tokens_map.json +31 -0
- llm/tokenizer.json +0 -0
- llm/tokenizer_config.json +209 -0
- llm/vocab.json +0 -0
- mm_connector.py +306 -0
- mm_connector/config.json +30 -0
- mm_connector/configuration_connector.py +38 -0
- mm_connector/model.safetensors +3 -0
- modeling_apollo.py +492 -0
- vision_tower.py +556 -0
- vision_tower/config.json +18 -0
- vision_tower/configuration_hybrid.py +48 -0
- vision_tower/internvideo2/config.json +54 -0
- vision_tower/internvideo2/configuration_internvideo2.py +91 -0
- vision_tower/internvideo2/model.safetensors +3 -0
- vision_tower/internvideo2/modeling_internvideo2.py +934 -0
- vision_tower/internvideo2/preprocessor_config.json +30 -0
- vision_tower/siglip-so400m-patch14-384/config.json +19 -0
- vision_tower/siglip-so400m-patch14-384/model.safetensors +3 -0
- vision_tower/siglip-so400m-patch14-384/preprocessor_config.json +24 -0
.mdl
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.msc
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.mv
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Revision:master,CreatedAt:1734398315
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README.md
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---
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license: apache-2.0
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language:
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- en
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pipeline_tag: video-text-to-text
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tags:
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- video
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- video-understanding
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- vision
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- multimodal
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- conversational
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- custom_code
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- instruction-tuning
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library_name: transformers
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---
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# Apollo: An Exploration of Video Understanding in Large Multimodal Models
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Apollo is a family of Large Multimodal Models (LMMs) that push the state-of-the-art in video understanding. It supports tasks including:
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- Long-form video comprehension
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- Temporal reasoning
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- Complex video question-answering
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- Multi-turn conversations grounded in video content
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Apollo models excel at handling hour-long videos, balancing speed and accuracy through strategic design decisions. Our models outperform most 7B competitors at just 3B parameters and even rival 30B-scale models.
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**Key Highlights:**
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- **7B model varient**
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- **32 tokens/frame**
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## Quick Start
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**Installation:**
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```bash
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pip install -e .
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pip install flash-attn --no-build-isolation
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```
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**Inference Example:**
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```python
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import torch
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from transformers import AutoModelForCausalLM
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from apollo.mm_utils import (
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KeywordsStoppingCriteria,
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tokenizer_mm_token,
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ApolloMMLoader
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)
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from apollo.conversations import conv_templates, SeparatorStyle
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from huggingface_hub import snapshot_download
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model_url = "Apollo-LMMs/Apollo-3B-t32"
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model_path = snapshot_download(model_url, repo_type="model")
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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trust_remote_code=True,
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low_cpu_mem_usage=True
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).to(device=device, dtype=torch.bfloat16)
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tokenizer = model.tokenizer
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vision_processors = model.vision_tower.vision_processor
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config = model.config
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num_repeat_token = config.mm_connector_cfg['num_output_tokens']
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mm_processor = ApolloMMLoader(
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vision_processors,
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config.clip_duration,
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frames_per_clip=4,
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clip_sampling_ratio=0.65,
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model_max_length=config.model_max_length,
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device=device,
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num_repeat_token=num_repeat_token
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)
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video_path = "path/to/video.mp4"
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question = "Describe this video in detail"
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mm_data, replace_string = mm_processor.load_video(video_path)
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conv = conv_templates["qwen_2"].copy()
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conv.append_message(conv.roles[0], replace_string + "\n\n" + question)
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conv.append_message(conv.roles[1], None)
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prompt = conv.get_prompt()
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input_ids = tokenizer_mm_token(prompt, tokenizer, return_tensors="pt").unsqueeze(0).to(device)
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+
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stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
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stopping_criteria = KeywordsStoppingCriteria([stop_str], tokenizer, input_ids)
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with torch.inference_mode():
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output_ids = model.generate(
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input_ids,
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vision_input=[mm_data],
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data_types=['video'],
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do_sample=True,
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temperature=0.4,
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max_new_tokens=256,
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top_p=0.7,
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use_cache=True,
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num_beams=1,
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stopping_criteria=[stopping_criteria]
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)
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pred = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
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print(pred)
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```
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## Citation
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If you find this project useful, please consider citing:
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```BibTeX
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@article{zohar2024apollo,
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title={Apollo: An Exploration of Video Understanding in Large Multimodal Models},
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author={Zohar, Orr and Wang, Xiaohan and Dubois, Yann and Mehta, Nikhil and Xiao, Tong and Hansen-Estruch, Philippe and Yu, Licheng and Wang, Xiaofang and Juefei-Xu, Felix and Zhang, Ning and Yeung-Levy, Serena and Xia, Xide},
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journal={arXiv preprint arXiv:2412.10360},
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year={2024}
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}
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```
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For more details, visit the [project website](https://apollo-lmms.github.io) or check out the [paper](https://arxiv.org/abs/2412.10360).
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config.json
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{
|
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+
"architectures": [
|
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+
"ApolloForCausalLM"
|
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+
],
|
5 |
+
"attention_dropout": 0.0,
|
6 |
+
"attn_implementation": "flash_attention_2",
|
7 |
+
"clip_duration": 2,
|
8 |
+
"drop_path_rate": 0.0,
|
9 |
+
"encode_batch_size": 15,
|
10 |
+
"hidden_act": "silu",
|
11 |
+
"hidden_size": 3584,
|
12 |
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"image_aspect_ratio": "square",
|
13 |
+
"initializer_range": 0.02,
|
14 |
+
"intermediate_size": 18944,
|
15 |
+
"interpolate_mode": "linear",
|
16 |
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"llm_cfg": {
|
17 |
+
"add_cross_attention": false,
|
18 |
+
"architectures": [
|
19 |
+
"Qwen2ForCausalLM"
|
20 |
+
],
|
21 |
+
"attention_dropout": 0.0,
|
22 |
+
"bad_words_ids": null,
|
23 |
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"begin_suppress_tokens": null,
|
24 |
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"bos_token_id": 151643,
|
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"chunk_size_feed_forward": 0,
|
26 |
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"cross_attention_hidden_size": null,
|
27 |
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"decoder_start_token_id": null,
|
28 |
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"diversity_penalty": 0.0,
|
29 |
+
"do_sample": false,
|
30 |
+
"early_stopping": false,
|
31 |
+
"encoder_no_repeat_ngram_size": 0,
|
32 |
+
"eos_token_id": 151645,
|
33 |
+
"exponential_decay_length_penalty": null,
|
34 |
+
"finetuning_task": null,
|
35 |
+
"forced_bos_token_id": null,
|
36 |
+
"forced_eos_token_id": null,
|
37 |
+
"hidden_act": "silu",
|
38 |
+
"hidden_size": 3584,
|
39 |
+
"id2label": {
|
40 |
+
"0": "LABEL_0",
|
41 |
+
"1": "LABEL_1"
|
42 |
+
},
|
43 |
+
"initializer_range": 0.02,
|
44 |
+
"intermediate_size": 18944,
|
45 |
+
"is_decoder": false,
|
46 |
+
"is_encoder_decoder": false,
|
47 |
+
"label2id": {
|
48 |
+
"LABEL_0": 0,
|
49 |
+
"LABEL_1": 1
|
50 |
+
},
|
51 |
+
"length_penalty": 1.0,
|
52 |
+
"max_length": 20,
|
53 |
+
"max_position_embeddings": 32768,
|
54 |
+
"max_window_layers": 28,
|
55 |
+
"min_length": 0,
|
56 |
+
"model_max_length": 16384,
|
57 |
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"model_type": "qwen2",
|
58 |
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"no_repeat_ngram_size": 0,
|
59 |
+
"num_attention_heads": 28,
|
60 |
+
"num_beam_groups": 1,
|
61 |
+
"num_beams": 1,
|
62 |
+
"num_hidden_layers": 28,
|
63 |
+
"num_key_value_heads": 4,
|
64 |
+
"num_return_sequences": 1,
|
65 |
+
"output_attentions": false,
|
66 |
+
"output_hidden_states": false,
|
67 |
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"output_scores": false,
|
68 |
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"pad_token_id": null,
|
69 |
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"prefix": null,
|
70 |
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"problem_type": null,
|
71 |
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"pruned_heads": {},
|
72 |
+
"remove_invalid_values": false,
|
73 |
+
"repetition_penalty": 1.0,
|
74 |
+
"return_dict": true,
|
75 |
+
"return_dict_in_generate": false,
|
76 |
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"rms_norm_eps": 1e-06,
|
77 |
+
"rope_theta": 1000000.0,
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78 |
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"sep_token_id": null,
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"suppress_tokens": null,
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81 |
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"task_specific_params": null,
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82 |
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"temperature": 1.0,
|
83 |
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"tf_legacy_loss": false,
|
84 |
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"tie_encoder_decoder": false,
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85 |
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"tie_word_embeddings": false,
|
86 |
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"tokenizer_class": null,
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87 |
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"tokenizer_model_max_length": 16384,
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88 |
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"tokenizer_padding_side": "right",
|
89 |
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"top_k": 50,
|
90 |
+
"top_p": 1.0,
|
91 |
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"torch_dtype": "bfloat16",
|
92 |
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"torchscript": false,
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93 |
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"typical_p": 1.0,
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94 |
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"use_bfloat16": false,
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"use_cache": true,
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"use_sliding_window": false,
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97 |
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"vocab_size": 152064
|
98 |
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},
|
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"max_position_embeddings": 32768,
|
100 |
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"max_window_layers": 28,
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101 |
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"mm_connector_cfg": {
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102 |
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"add_cross_attention": false,
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103 |
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"architectures": [
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104 |
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"Connector"
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105 |
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],
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106 |
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"attention_dropout": 0.0,
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"bad_words_ids": null,
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"begin_suppress_tokens": null,
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"bos_token_id": null,
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"chunk_size_feed_forward": 0,
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"cross_attention_hidden_size": null,
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"decoder_start_token_id": null,
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"diversity_penalty": 0.0,
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114 |
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"do_sample": false,
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115 |
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"early_stopping": false,
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"encoder_no_repeat_ngram_size": 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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|
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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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|
270 |
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|
271 |
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4,
|
272 |
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27,
|
273 |
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27
|
274 |
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],
|
275 |
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|
276 |
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|
277 |
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|
278 |
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|
279 |
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|
280 |
+
"typical_p": 1.0,
|
281 |
+
"use_bfloat16": false,
|
282 |
+
"vision_towers": [
|
283 |
+
"siglip-so400m-patch14-384",
|
284 |
+
"internvideo2"
|
285 |
+
]
|
286 |
+
},
|
287 |
+
"vocab_size": 152064,
|
288 |
+
"auto_map": {
|
289 |
+
"AutoConfig": "configuration_apollo.ApolloConfig",
|
290 |
+
"AutoModelForCausalLM": "modeling_apollo.ApolloForCausalLM"
|
291 |
+
},
|
292 |
+
"model_max_length": 16384
|
293 |
+
}
|
configuration.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"framework": "pytorch", "task": "video-text-to-text", "allow_remote": true}
|
configuration_apollo.py
ADDED
@@ -0,0 +1,47 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#from transformers import PretrainedConfig
|
2 |
+
from transformers import PretrainedConfig
|
3 |
+
|
4 |
+
|
5 |
+
class ApolloConfig(PretrainedConfig):
|
6 |
+
model_type = "apollo"
|
7 |
+
def __init__(
|
8 |
+
self,
|
9 |
+
llm_cfg=None,
|
10 |
+
vision_tower_cfg=None,
|
11 |
+
mm_connector_cfg=None,
|
12 |
+
architectures=None,
|
13 |
+
resume_path=None,
|
14 |
+
image_aspect_ratio=None,
|
15 |
+
num_video_frames=None,
|
16 |
+
mm_vision_select_layer=None,
|
17 |
+
mm_vision_select_feature=None,
|
18 |
+
use_mm_start_end=False,
|
19 |
+
use_mm_patch_token=True,
|
20 |
+
mm_connector_lr=None,
|
21 |
+
vision_resolution=None,
|
22 |
+
interpolate_mode=None,
|
23 |
+
clip_duration=None,
|
24 |
+
vocab_size=None,
|
25 |
+
auto_map=None,
|
26 |
+
**kwargs
|
27 |
+
):
|
28 |
+
super().__init__(**kwargs)
|
29 |
+
|
30 |
+
self.architectures = architectures
|
31 |
+
self.llm_cfg = llm_cfg
|
32 |
+
self.vision_tower_cfg = vision_tower_cfg
|
33 |
+
self.mm_connector_cfg = mm_connector_cfg
|
34 |
+
self.resume_path = resume_path
|
35 |
+
self.image_aspect_ratio = image_aspect_ratio
|
36 |
+
self.num_video_frames = num_video_frames
|
37 |
+
self.mm_vision_select_layer = mm_vision_select_layer
|
38 |
+
self.mm_vision_select_feature = mm_vision_select_feature
|
39 |
+
self.use_mm_start_end = use_mm_start_end
|
40 |
+
self.use_mm_patch_token = use_mm_patch_token
|
41 |
+
self.mm_connector_lr = mm_connector_lr
|
42 |
+
self.vision_resolution = vision_resolution
|
43 |
+
self.interpolate_mode = interpolate_mode
|
44 |
+
self.clip_duration = clip_duration
|
45 |
+
self.vocab_size=vocab_size
|
46 |
+
self.auto_map=auto_map
|
47 |
+
|
llm/added_tokens.json
ADDED
@@ -0,0 +1,24 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
|
1 |
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{
|
2 |
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"</tool_call>": 151658,
|
3 |
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|
4 |
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|
5 |
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6 |
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"<|endoftext|>": 151643,
|
7 |
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|
8 |
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|
9 |
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|
10 |
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"<|fim_prefix|>": 151659,
|
11 |
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|
12 |
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"<|im_end|>": 151645,
|
13 |
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"<|im_start|>": 151644,
|
14 |
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"<|image_pad|>": 151655,
|
15 |
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"<|object_ref_end|>": 151647,
|
16 |
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"<|object_ref_start|>": 151646,
|
17 |
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"<|quad_end|>": 151651,
|
18 |
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"<|quad_start|>": 151650,
|
19 |
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"<|repo_name|>": 151663,
|
20 |
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"<|video_pad|>": 151656,
|
21 |
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"<|vision_end|>": 151653,
|
22 |
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"<|vision_pad|>": 151654,
|
23 |
+
"<|vision_start|>": 151652
|
24 |
+
}
|
llm/config.json
ADDED
@@ -0,0 +1,31 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_name_or_path": "./work_dirs/final_run/apollo-Qwen2.5-7B-Instruct-internvideo2-siglip-so400m-patch14-384-freeze-perciver_128_2-newprompt-ft/llm",
|
3 |
+
"architectures": [
|
4 |
+
"Qwen2ForCausalLM"
|
5 |
+
],
|
6 |
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"attention_dropout": 0.0,
|
7 |
+
"bos_token_id": 151643,
|
8 |
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"eos_token_id": 151645,
|
9 |
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"hidden_act": "silu",
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10 |
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|
11 |
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|
12 |
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"intermediate_size": 18944,
|
13 |
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"max_position_embeddings": 32768,
|
14 |
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"max_window_layers": 28,
|
15 |
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"model_max_length": 16384,
|
16 |
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"model_type": "qwen2",
|
17 |
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"num_attention_heads": 28,
|
18 |
+
"num_hidden_layers": 28,
|
19 |
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"num_key_value_heads": 4,
|
20 |
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"rms_norm_eps": 1e-06,
|
21 |
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"rope_theta": 1000000.0,
|
22 |
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"sliding_window": null,
|
23 |
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"tie_word_embeddings": false,
|
24 |
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"tokenizer_model_max_length": 16384,
|
25 |
+
"tokenizer_padding_side": "right",
|
26 |
+
"torch_dtype": "bfloat16",
|
27 |
+
"transformers_version": "4.44.0",
|
28 |
+
"use_cache": true,
|
29 |
+
"use_sliding_window": false,
|
30 |
+
"vocab_size": 152064
|
31 |
+
}
|
llm/generation_config.json
ADDED
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
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|
3 |
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"do_sample": true,
|
4 |
+
"eos_token_id": [
|
5 |
+
151645,
|
6 |
+
151643
|
7 |
+
],
|
8 |
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"pad_token_id": 151643,
|
9 |
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"repetition_penalty": 1.05,
|
10 |
+
"temperature": 0.7,
|
11 |
+
"top_k": 20,
|
12 |
+
"top_p": 0.8,
|
13 |
+
"transformers_version": "4.44.0"
|
14 |
+
}
|
llm/merges.txt
ADDED
The diff for this file is too large to render.
See raw diff
|
|
llm/model-00001-of-00004.safetensors
ADDED
@@ -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:ad28602d062e7ce6f51c4343652cef63168989c08ad1a47c11e64033c6c441ef
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size 4877660776
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llm/model-00002-of-00004.safetensors
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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size 4932751008
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llm/model-00003-of-00004.safetensors
ADDED
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version https://git-lfs.github.com/spec/v1
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llm/model-00004-of-00004.safetensors
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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size 1089994880
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llm/model.safetensors.index.json
ADDED
@@ -0,0 +1,346 @@
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332 |
+
"model.layers.9.input_layernorm.weight": "model-00002-of-00004.safetensors",
|
333 |
+
"model.layers.9.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
|
334 |
+
"model.layers.9.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
|
335 |
+
"model.layers.9.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
|
336 |
+
"model.layers.9.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
|
337 |
+
"model.layers.9.self_attn.k_proj.bias": "model-00002-of-00004.safetensors",
|
338 |
+
"model.layers.9.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
|
339 |
+
"model.layers.9.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
|
340 |
+
"model.layers.9.self_attn.q_proj.bias": "model-00002-of-00004.safetensors",
|
341 |
+
"model.layers.9.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
|
342 |
+
"model.layers.9.self_attn.v_proj.bias": "model-00002-of-00004.safetensors",
|
343 |
+
"model.layers.9.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
|
344 |
+
"model.norm.weight": "model-00003-of-00004.safetensors"
|
345 |
+
}
|
346 |
+
}
|
llm/special_tokens_map.json
ADDED
@@ -0,0 +1,31 @@
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1 |
+
{
|
2 |
+
"additional_special_tokens": [
|
3 |
+
"<|im_start|>",
|
4 |
+
"<|im_end|>",
|
5 |
+
"<|object_ref_start|>",
|
6 |
+
"<|object_ref_end|>",
|
7 |
+
"<|box_start|>",
|
8 |
+
"<|box_end|>",
|
9 |
+
"<|quad_start|>",
|
10 |
+
"<|quad_end|>",
|
11 |
+
"<|vision_start|>",
|
12 |
+
"<|vision_end|>",
|
13 |
+
"<|vision_pad|>",
|
14 |
+
"<|image_pad|>",
|
15 |
+
"<|video_pad|>"
|
16 |
+
],
|
17 |
+
"eos_token": {
|
18 |
+
"content": "<|im_end|>",
|
19 |
+
"lstrip": false,
|
20 |
+
"normalized": false,
|
21 |
+
"rstrip": false,
|
22 |
+
"single_word": false
|
23 |
+
},
|
24 |
+
"pad_token": {
|
25 |
+
"content": "<|endoftext|>",
|
26 |
+
"lstrip": false,
|
27 |
+
"normalized": false,
|
28 |
+
"rstrip": false,
|
29 |
+
"single_word": false
|
30 |
+
}
|
31 |
+
}
|
llm/tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
llm/tokenizer_config.json
ADDED
@@ -0,0 +1,209 @@
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|
1 |
+
{
|
2 |
+
"add_bos_token": false,
|
3 |
+
"add_prefix_space": false,
|
4 |
+
"added_tokens_decoder": {
|
5 |
+
"151643": {
|
6 |
+
"content": "<|endoftext|>",
|
7 |
+
"lstrip": false,
|
8 |
+
"normalized": false,
|
9 |
+
"rstrip": false,
|
10 |
+
"single_word": false,
|
11 |
+
"special": true
|
12 |
+
},
|
13 |
+
"151644": {
|
14 |
+
"content": "<|im_start|>",
|
15 |
+
"lstrip": false,
|
16 |
+
"normalized": false,
|
17 |
+
"rstrip": false,
|
18 |
+
"single_word": false,
|
19 |
+
"special": true
|
20 |
+
},
|
21 |
+
"151645": {
|
22 |
+
"content": "<|im_end|>",
|
23 |
+
"lstrip": false,
|
24 |
+
"normalized": false,
|
25 |
+
"rstrip": false,
|
26 |
+
"single_word": false,
|
27 |
+
"special": true
|
28 |
+
},
|
29 |
+
"151646": {
|
30 |
+
"content": "<|object_ref_start|>",
|
31 |
+
"lstrip": false,
|
32 |
+
"normalized": false,
|
33 |
+
"rstrip": false,
|
34 |
+
"single_word": false,
|
35 |
+
"special": true
|
36 |
+
},
|
37 |
+
"151647": {
|
38 |
+
"content": "<|object_ref_end|>",
|
39 |
+
"lstrip": false,
|
40 |
+
"normalized": false,
|
41 |
+
"rstrip": false,
|
42 |
+
"single_word": false,
|
43 |
+
"special": true
|
44 |
+
},
|
45 |
+
"151648": {
|
46 |
+
"content": "<|box_start|>",
|
47 |
+
"lstrip": false,
|
48 |
+
"normalized": false,
|
49 |
+
"rstrip": false,
|
50 |
+
"single_word": false,
|
51 |
+
"special": true
|
52 |
+
},
|
53 |
+
"151649": {
|
54 |
+
"content": "<|box_end|>",
|
55 |
+
"lstrip": false,
|
56 |
+
"normalized": false,
|
57 |
+
"rstrip": false,
|
58 |
+
"single_word": false,
|
59 |
+
"special": true
|
60 |
+
},
|
61 |
+
"151650": {
|
62 |
+
"content": "<|quad_start|>",
|
63 |
+
"lstrip": false,
|
64 |
+
"normalized": false,
|
65 |
+
"rstrip": false,
|
66 |
+
"single_word": false,
|
67 |
+
"special": true
|
68 |
+
},
|
69 |
+
"151651": {
|
70 |
+
"content": "<|quad_end|>",
|
71 |
+
"lstrip": false,
|
72 |
+
"normalized": false,
|
73 |
+
"rstrip": false,
|
74 |
+
"single_word": false,
|
75 |
+
"special": true
|
76 |
+
},
|
77 |
+
"151652": {
|
78 |
+
"content": "<|vision_start|>",
|
79 |
+
"lstrip": false,
|
80 |
+
"normalized": false,
|
81 |
+
"rstrip": false,
|
82 |
+
"single_word": false,
|
83 |
+
"special": true
|
84 |
+
},
|
85 |
+
"151653": {
|
86 |
+
"content": "<|vision_end|>",
|
87 |
+
"lstrip": false,
|
88 |
+
"normalized": false,
|
89 |
+
"rstrip": false,
|
90 |
+
"single_word": false,
|
91 |
+
"special": true
|
92 |
+
},
|
93 |
+
"151654": {
|
94 |
+
"content": "<|vision_pad|>",
|
95 |
+
"lstrip": false,
|
96 |
+
"normalized": false,
|
97 |
+
"rstrip": false,
|
98 |
+
"single_word": false,
|
99 |
+
"special": true
|
100 |
+
},
|
101 |
+
"151655": {
|
102 |
+
"content": "<|image_pad|>",
|
103 |
+
"lstrip": false,
|
104 |
+
"normalized": false,
|
105 |
+
"rstrip": false,
|
106 |
+
"single_word": false,
|
107 |
+
"special": true
|
108 |
+
},
|
109 |
+
"151656": {
|
110 |
+
"content": "<|video_pad|>",
|
111 |
+
"lstrip": false,
|
112 |
+
"normalized": false,
|
113 |
+
"rstrip": false,
|
114 |
+
"single_word": false,
|
115 |
+
"special": true
|
116 |
+
},
|
117 |
+
"151657": {
|
118 |
+
"content": "<tool_call>",
|
119 |
+
"lstrip": false,
|
120 |
+
"normalized": false,
|
121 |
+
"rstrip": false,
|
122 |
+
"single_word": false,
|
123 |
+
"special": false
|
124 |
+
},
|
125 |
+
"151658": {
|
126 |
+
"content": "</tool_call>",
|
127 |
+
"lstrip": false,
|
128 |
+
"normalized": false,
|
129 |
+
"rstrip": false,
|
130 |
+
"single_word": false,
|
131 |
+
"special": false
|
132 |
+
},
|
133 |
+
"151659": {
|
134 |
+
"content": "<|fim_prefix|>",
|
135 |
+
"lstrip": false,
|
136 |
+
"normalized": false,
|
137 |
+
"rstrip": false,
|
138 |
+
"single_word": false,
|
139 |
+
"special": false
|
140 |
+
},
|
141 |
+
"151660": {
|
142 |
+
"content": "<|fim_middle|>",
|
143 |
+
"lstrip": false,
|
144 |
+
"normalized": false,
|
145 |
+
"rstrip": false,
|
146 |
+
"single_word": false,
|
147 |
+
"special": false
|
148 |
+
},
|
149 |
+
"151661": {
|
150 |
+
"content": "<|fim_suffix|>",
|
151 |
+
"lstrip": false,
|
152 |
+
"normalized": false,
|
153 |
+
"rstrip": false,
|
154 |
+
"single_word": false,
|
155 |
+
"special": false
|
156 |
+
},
|
157 |
+
"151662": {
|
158 |
+
"content": "<|fim_pad|>",
|
159 |
+
"lstrip": false,
|
160 |
+
"normalized": false,
|
161 |
+
"rstrip": false,
|
162 |
+
"single_word": false,
|
163 |
+
"special": false
|
164 |
+
},
|
165 |
+
"151663": {
|
166 |
+
"content": "<|repo_name|>",
|
167 |
+
"lstrip": false,
|
168 |
+
"normalized": false,
|
169 |
+
"rstrip": false,
|
170 |
+
"single_word": false,
|
171 |
+
"special": false
|
172 |
+
},
|
173 |
+
"151664": {
|
174 |
+
"content": "<|file_sep|>",
|
175 |
+
"lstrip": false,
|
176 |
+
"normalized": false,
|
177 |
+
"rstrip": false,
|
178 |
+
"single_word": false,
|
179 |
+
"special": false
|
180 |
+
}
|
181 |
+
},
|
182 |
+
"additional_special_tokens": [
|
183 |
+
"<|im_start|>",
|
184 |
+
"<|im_end|>",
|
185 |
+
"<|object_ref_start|>",
|
186 |
+
"<|object_ref_end|>",
|
187 |
+
"<|box_start|>",
|
188 |
+
"<|box_end|>",
|
189 |
+
"<|quad_start|>",
|
190 |
+
"<|quad_end|>",
|
191 |
+
"<|vision_start|>",
|
192 |
+
"<|vision_end|>",
|
193 |
+
"<|vision_pad|>",
|
194 |
+
"<|image_pad|>",
|
195 |
+
"<|video_pad|>"
|
196 |
+
],
|
197 |
+
"bos_token": null,
|
198 |
+
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
199 |
+
"clean_up_tokenization_spaces": false,
|
200 |
+
"eos_token": "<|im_end|>",
|
201 |
+
"errors": "replace",
|
202 |
+
"legacy": false,
|
203 |
+
"model_max_length": 16384,
|
204 |
+
"pad_token": "<|endoftext|>",
|
205 |
+
"padding_side": "right",
|
206 |
+
"split_special_tokens": false,
|
207 |
+
"tokenizer_class": "Qwen2Tokenizer",
|
208 |
+
"unk_token": null
|
209 |
+
}
|
llm/vocab.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
mm_connector.py
ADDED
@@ -0,0 +1,306 @@
|
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|
|
|
1 |
+
import re, math, torch
|
2 |
+
from collections import OrderedDict
|
3 |
+
from typing import Optional, Tuple
|
4 |
+
|
5 |
+
from torch import nn
|
6 |
+
from torch.nn.init import trunc_normal_, normal_
|
7 |
+
import torch.utils.checkpoint
|
8 |
+
|
9 |
+
from transformers import PreTrainedModel, PretrainedConfig, AutoConfig, AutoModel
|
10 |
+
|
11 |
+
|
12 |
+
class ClassInstantier(OrderedDict):
|
13 |
+
def __getitem__(self, key):
|
14 |
+
content = super().__getitem__(key)
|
15 |
+
cls, kwargs = content if isinstance(content, tuple) else (content, {})
|
16 |
+
return cls(**kwargs)
|
17 |
+
|
18 |
+
|
19 |
+
ACT2CLS = {"silu": nn.SiLU}
|
20 |
+
|
21 |
+
ACT2FN = ClassInstantier(ACT2CLS)
|
22 |
+
|
23 |
+
|
24 |
+
class WeightedNorm(nn.Module):
|
25 |
+
def __init__(self, hidden_size):
|
26 |
+
"""
|
27 |
+
WeightedNorm
|
28 |
+
"""
|
29 |
+
super().__init__()
|
30 |
+
self.hidden_size = hidden_size
|
31 |
+
self.norm = nn.LayerNorm(self.hidden_size)
|
32 |
+
self.wheight = nn.Parameter(torch.ones(self.hidden_size))
|
33 |
+
normal_(self.wheight, mean=1, std=.02)
|
34 |
+
|
35 |
+
def forward(self, x):
|
36 |
+
x = self.norm(x)
|
37 |
+
return x * self.wheight
|
38 |
+
|
39 |
+
|
40 |
+
class PerceiverMLP(nn.Module):
|
41 |
+
def __init__(
|
42 |
+
self,
|
43 |
+
hidden_size: int,
|
44 |
+
intermediate_size: int,
|
45 |
+
output_size: int,
|
46 |
+
hidden_act: str,
|
47 |
+
):
|
48 |
+
super().__init__()
|
49 |
+
self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
|
50 |
+
self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
|
51 |
+
self.down_proj = nn.Linear(intermediate_size, output_size, bias=False)
|
52 |
+
self.act_fn = ACT2FN[hidden_act]
|
53 |
+
|
54 |
+
def forward(self, x):
|
55 |
+
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
56 |
+
|
57 |
+
|
58 |
+
# Copied from transformers.models.llama.modeling_llama.repeat_kv
|
59 |
+
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
60 |
+
"""
|
61 |
+
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
62 |
+
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
63 |
+
"""
|
64 |
+
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
65 |
+
if n_rep == 1:
|
66 |
+
return hidden_states
|
67 |
+
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
|
68 |
+
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
69 |
+
|
70 |
+
|
71 |
+
class PerceiverAttention(nn.Module):
|
72 |
+
def __init__(self, connector_config, layer_idx: Optional[int] = None) -> None:
|
73 |
+
"""Perceiver Cross-Attention Module --> let long-form inputs be `context`, resampled embeddings be `latents`"""
|
74 |
+
super().__init__()
|
75 |
+
|
76 |
+
self.layer_idx = None
|
77 |
+
self.hidden_size = connector_config.text_hidden_size
|
78 |
+
self.num_heads = connector_config.resampler_n_heads
|
79 |
+
self.head_dim = connector_config.resampler_head_dim
|
80 |
+
self.num_key_value_heads = connector_config.num_key_value_heads
|
81 |
+
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
|
82 |
+
|
83 |
+
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
|
84 |
+
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
|
85 |
+
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
|
86 |
+
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
|
87 |
+
|
88 |
+
self.is_causal = False
|
89 |
+
|
90 |
+
def forward(
|
91 |
+
self,
|
92 |
+
latents: torch.Tensor,
|
93 |
+
context: torch.Tensor,
|
94 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
95 |
+
output_attentions: bool = False,
|
96 |
+
use_cache: bool = False,
|
97 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
98 |
+
"""
|
99 |
+
Runs Perceiver Self-Attention, with special (context, latents) appended along the `seq` dimension!
|
100 |
+
|
101 |
+
Args:
|
102 |
+
latents (`torch.Tensor`): Tensor of shape [bsz, n_latents, embed_dim] representing fixed length latents to compress to.
|
103 |
+
context (`torch.Tensor`): Tensor of shape [bsz, seq, embed_dim] representing long-form context to resample.
|
104 |
+
output_attentions (`bool`, *optional*, defaults to `False`): Whether to return attention weights.
|
105 |
+
use_cache (`bool`, *optional*, defaults to `False`): Whether to use past_key_value for caching.
|
106 |
+
"""
|
107 |
+
bsz, q_len, _ = latents.size()
|
108 |
+
kv_seq_len = q_len + context.size()[1]
|
109 |
+
|
110 |
+
hidden_states = torch.concat([context, latents], dim=-2)
|
111 |
+
|
112 |
+
query_states = self.q_proj(latents)
|
113 |
+
key_states = self.k_proj(hidden_states)
|
114 |
+
value_states = self.v_proj(hidden_states)
|
115 |
+
|
116 |
+
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
117 |
+
key_states = key_states.view(bsz, kv_seq_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
118 |
+
value_states = value_states.view(bsz, kv_seq_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
119 |
+
|
120 |
+
past_key_value = getattr(self, "past_key_value", past_key_value)
|
121 |
+
|
122 |
+
if past_key_value is not None:
|
123 |
+
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx)
|
124 |
+
|
125 |
+
# repeat k/v heads if n_kv_heads < n_heads
|
126 |
+
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
127 |
+
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
128 |
+
|
129 |
+
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
130 |
+
|
131 |
+
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
132 |
+
raise ValueError(
|
133 |
+
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
|
134 |
+
f" {attn_weights.size()}"
|
135 |
+
)
|
136 |
+
|
137 |
+
# upcast attention to fp32
|
138 |
+
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
139 |
+
attn_output = torch.matmul(attn_weights, value_states)
|
140 |
+
|
141 |
+
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
142 |
+
raise ValueError(
|
143 |
+
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
144 |
+
f" {attn_output.size()}"
|
145 |
+
)
|
146 |
+
|
147 |
+
attn_output = attn_output.transpose(1, 2).contiguous()
|
148 |
+
attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.head_dim)
|
149 |
+
|
150 |
+
attn_output = self.o_proj(attn_output)
|
151 |
+
|
152 |
+
if not output_attentions:
|
153 |
+
attn_weights = None
|
154 |
+
|
155 |
+
return attn_output, attn_weights, past_key_value
|
156 |
+
|
157 |
+
|
158 |
+
PERCEIVER_ATTENTION_CLASSES = {
|
159 |
+
"eager": PerceiverAttention,
|
160 |
+
}
|
161 |
+
|
162 |
+
|
163 |
+
class PerceiverLayer(nn.Module):
|
164 |
+
def __init__(self, connector_config, layer_idx: int):
|
165 |
+
super().__init__()
|
166 |
+
self.hidden_size = connector_config.text_hidden_size
|
167 |
+
self.n_latents = connector_config.num_output_tokens
|
168 |
+
self.depth = connector_config.resampler_depth
|
169 |
+
self.ff_multi = connector_config.ff_multi
|
170 |
+
|
171 |
+
self.input_latents_norm = WeightedNorm(self.hidden_size)
|
172 |
+
self.input_context_norm = WeightedNorm(self.hidden_size)
|
173 |
+
self.self_attn = PERCEIVER_ATTENTION_CLASSES[connector_config._attn_implementation](connector_config,
|
174 |
+
layer_idx=layer_idx)
|
175 |
+
self.post_attention_layernorm = WeightedNorm(self.hidden_size)
|
176 |
+
self.mlp = PerceiverMLP(
|
177 |
+
hidden_size=connector_config.text_hidden_size,
|
178 |
+
intermediate_size=connector_config.text_hidden_size * self.ff_multi,
|
179 |
+
output_size=connector_config.text_hidden_size,
|
180 |
+
hidden_act=connector_config.hidden_act,
|
181 |
+
)
|
182 |
+
|
183 |
+
def forward(
|
184 |
+
self,
|
185 |
+
latents: torch.Tensor,
|
186 |
+
context: torch.Tensor,
|
187 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
188 |
+
output_attentions: Optional[bool] = False,
|
189 |
+
use_cache: Optional[bool] = False,
|
190 |
+
**kwargs,
|
191 |
+
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
192 |
+
"""
|
193 |
+
Args:
|
194 |
+
latents (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
195 |
+
context (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
196 |
+
output_attentions (`bool`, *optional*):
|
197 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
198 |
+
returned tensors for more detail.
|
199 |
+
use_cache (`bool`, *optional*):
|
200 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
201 |
+
(see `past_key_values`).
|
202 |
+
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
203 |
+
"""
|
204 |
+
residual = latents
|
205 |
+
|
206 |
+
latents = self.input_latents_norm(latents)
|
207 |
+
context = self.input_context_norm(context)
|
208 |
+
|
209 |
+
latents, self_attn_weights, present_key_value = self.self_attn(
|
210 |
+
latents=latents,
|
211 |
+
context=context,
|
212 |
+
)
|
213 |
+
|
214 |
+
latents = residual + latents
|
215 |
+
residual = latents
|
216 |
+
|
217 |
+
latents = self.post_attention_layernorm(latents)
|
218 |
+
latents = self.mlp(latents)
|
219 |
+
latents = residual + latents
|
220 |
+
|
221 |
+
outputs = (latents,)
|
222 |
+
|
223 |
+
if output_attentions:
|
224 |
+
outputs += (self_attn_weights,)
|
225 |
+
|
226 |
+
if use_cache:
|
227 |
+
outputs += (present_key_value,)
|
228 |
+
|
229 |
+
return outputs
|
230 |
+
|
231 |
+
|
232 |
+
class PerceiverResampler(nn.Module):
|
233 |
+
"""Perceiver Resampler that compresses input embeddings into a fixed number of latents."""
|
234 |
+
|
235 |
+
def __init__(self, connector_config) -> None:
|
236 |
+
super().__init__()
|
237 |
+
self.hidden_size = connector_config.text_hidden_size
|
238 |
+
self.hidden_act = connector_config.hidden_act
|
239 |
+
self.n_latents = connector_config.num_output_tokens
|
240 |
+
self.depth = connector_config.resampler_depth
|
241 |
+
|
242 |
+
# Create Latents for Perceiver
|
243 |
+
self.latents = nn.Parameter(torch.zeros(self.n_latents, self.hidden_size))
|
244 |
+
|
245 |
+
# Create Transformer Blocks
|
246 |
+
self.layers = nn.ModuleList([PerceiverLayer(connector_config, idx) for idx in range(self.depth)])
|
247 |
+
self.norm = WeightedNorm(self.hidden_size)
|
248 |
+
self._use_flash_attention_2 = connector_config._attn_implementation == "flash_attention_2"
|
249 |
+
|
250 |
+
def forward(
|
251 |
+
self,
|
252 |
+
context: torch.Tensor,
|
253 |
+
attention_mask: torch.Tensor = None,
|
254 |
+
) -> torch.Tensor:
|
255 |
+
# seq embed -> bsz seq embed
|
256 |
+
latents = self.latents.unsqueeze(0).expand((context.shape[0], *self.latents.size()))
|
257 |
+
|
258 |
+
compressed_context = latents
|
259 |
+
for i, perceiver_layer in enumerate(self.layers):
|
260 |
+
layer_outputs = perceiver_layer(
|
261 |
+
compressed_context,
|
262 |
+
context,
|
263 |
+
past_key_value=None,
|
264 |
+
output_attentions=False,
|
265 |
+
use_cache=False,
|
266 |
+
)
|
267 |
+
compressed_context = layer_outputs[0]
|
268 |
+
|
269 |
+
compressed_context = self.norm(compressed_context)
|
270 |
+
return compressed_context
|
271 |
+
|
272 |
+
|
273 |
+
def build_mm_projector(
|
274 |
+
input_dim,
|
275 |
+
output_dim,
|
276 |
+
projector_type,
|
277 |
+
hidden_act='silu',
|
278 |
+
delay_load=False,
|
279 |
+
token_input_shape=0,
|
280 |
+
**kwargs
|
281 |
+
) -> nn.Sequential:
|
282 |
+
|
283 |
+
modules = [nn.Linear(input_dim, output_dim)]
|
284 |
+
mlp_gelu_match = re.match(r'.*mlp(\d+)x_gelu$', projector_type)
|
285 |
+
if mlp_gelu_match is not None:
|
286 |
+
mlp_depth = int(mlp_gelu_match.group(1))
|
287 |
+
for _ in range(mlp_depth - 1):
|
288 |
+
modules.append(nn.GELU())
|
289 |
+
modules.append(nn.Linear(output_dim, output_dim))
|
290 |
+
|
291 |
+
return nn.Sequential(*modules)
|
292 |
+
|
293 |
+
|
294 |
+
class MMConnector(PreTrainedModel):
|
295 |
+
config_class = PretrainedConfig
|
296 |
+
|
297 |
+
def __init__(self, config: PretrainedConfig) -> None:
|
298 |
+
super().__init__(config)
|
299 |
+
self.proj = build_mm_projector(config.vision_hidden_size, config.text_hidden_size,
|
300 |
+
config.projector_type, token_input_shape=config.token_input_shape)
|
301 |
+
self.resampler = PerceiverResampler(config)
|
302 |
+
|
303 |
+
def forward(self, x):
|
304 |
+
x = self.proj(x)
|
305 |
+
x = self.resampler(x)
|
306 |
+
return x
|
mm_connector/config.json
ADDED
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"architectures": [
|
3 |
+
"Connector"
|
4 |
+
],
|
5 |
+
"attention_dropout": 0.0,
|
6 |
+
"ff_multi": 4,
|
7 |
+
"hidden_act": "silu",
|
8 |
+
"model_type": "mm_connector",
|
9 |
+
"num_key_value_heads": 4,
|
10 |
+
"num_output_tokens": 128,
|
11 |
+
"num_patches": 24,
|
12 |
+
"projector_type": "mlp1x_gelu",
|
13 |
+
"resampler_depth": 1,
|
14 |
+
"resampler_head_dim": 96,
|
15 |
+
"resampler_n_heads": 16,
|
16 |
+
"resampler_type": "perciver",
|
17 |
+
"rms_norm_eps": 1e-06,
|
18 |
+
"text_hidden_size": 3584,
|
19 |
+
"token_input_shape": [
|
20 |
+
4,
|
21 |
+
27,
|
22 |
+
27
|
23 |
+
],
|
24 |
+
"torch_dtype": "bfloat16",
|
25 |
+
"transformers_version": "4.44.0",
|
26 |
+
"vision_hidden_size": 2560,
|
27 |
+
"auto_map": {
|
28 |
+
"AutoConfig": "configuration_connector.ConnectorConfig"
|
29 |
+
}
|
30 |
+
}
|
mm_connector/configuration_connector.py
ADDED
@@ -0,0 +1,38 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import torch.nn as nn
|
3 |
+
from typing import Dict, List, Union
|
4 |
+
from transformers import PreTrainedModel, PretrainedConfig, AutoConfig, AutoModel
|
5 |
+
import torch.nn.functional as F
|
6 |
+
import json, os
|
7 |
+
|
8 |
+
|
9 |
+
class ConnectorConfig(PretrainedConfig):
|
10 |
+
model_type = "mm_connector"
|
11 |
+
def __init__(
|
12 |
+
self,
|
13 |
+
vision_hidden_size: List[int] = [],
|
14 |
+
text_hidden_size: int = 0,
|
15 |
+
num_patches: int = 24,
|
16 |
+
rms_norm_eps: float = 1e-4,
|
17 |
+
token_input_shape: List[int] = [],
|
18 |
+
**kwargs,
|
19 |
+
):
|
20 |
+
super().__init__(**kwargs)
|
21 |
+
self.vision_hidden_size = vision_hidden_size
|
22 |
+
self.text_hidden_size = text_hidden_size
|
23 |
+
self.num_patches = num_patches
|
24 |
+
self.rms_norm_eps=rms_norm_eps
|
25 |
+
self.token_input_shape = token_input_shape
|
26 |
+
|
27 |
+
@classmethod
|
28 |
+
def load_config(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "ConnectorConfig":
|
29 |
+
cls._set_token_in_kwargs(kwargs)
|
30 |
+
config_dict, kwargs = cls.get_config_from_json(pretrained_model_name_or_path, **kwargs)
|
31 |
+
return cls.from_dict(config_dict, **kwargs)
|
32 |
+
|
33 |
+
@classmethod
|
34 |
+
def get_config_from_json(cls, config_file, **kwargs):
|
35 |
+
with open(config_file, 'r') as file:
|
36 |
+
config_data = json.load(file)
|
37 |
+
return config_data, kwargs
|
38 |
+
|
mm_connector/model.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:2973ab0aaf61364d182eca589bdc80e28a815eb94112a83cb28d42d24da6156e
|
3 |
+
size 355169704
|
modeling_apollo.py
ADDED
@@ -0,0 +1,492 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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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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|
|
|
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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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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from typing import List, Optional, Tuple, Union
|
2 |
+
import warnings, os, torch
|
3 |
+
import torch.nn as nn
|
4 |
+
|
5 |
+
from transformers import AutoConfig, PretrainedConfig, PreTrainedModel, AutoModelForCausalLM, AutoTokenizer
|
6 |
+
from transformers.modeling_utils import ContextManagers, no_init_weights
|
7 |
+
from transformers.modeling_outputs import CausalLMOutputWithPast
|
8 |
+
from transformers.generation.utils import GenerateOutput
|
9 |
+
from .configuration_apollo import ApolloConfig
|
10 |
+
|
11 |
+
from .vision_tower import ApolloVisionTower
|
12 |
+
from .mm_connector import MMConnector
|
13 |
+
|
14 |
+
IGNORE_INDEX = -100
|
15 |
+
X_TOKEN_INDEX = -200
|
16 |
+
|
17 |
+
|
18 |
+
def get_model_config(config):
|
19 |
+
default_keys = ["llm_cfg", "vision_tower_cfg", "mm_connector_cfg"]
|
20 |
+
if hasattr(config, "_name_or_path") and len(config._name_or_path) >= 2:
|
21 |
+
root_path = config._name_or_path
|
22 |
+
else:
|
23 |
+
root_path = config.resume_path
|
24 |
+
|
25 |
+
return_pths = []
|
26 |
+
for key in default_keys:
|
27 |
+
cfg = getattr(config, key, None)
|
28 |
+
if isinstance(cfg, dict):
|
29 |
+
try:
|
30 |
+
return_pths.append(os.path.join(root_path, key[:-4]))
|
31 |
+
except:
|
32 |
+
raise ValueError(f"Cannot find resume path in config for {key}!")
|
33 |
+
elif isinstance(cfg, PretrainedConfig):
|
34 |
+
return_pths.append(os.path.join(root_path, key[:-4]))
|
35 |
+
elif isinstance(cfg, str):
|
36 |
+
return_pths.append(cfg)
|
37 |
+
|
38 |
+
return_list = []
|
39 |
+
for pth in return_pths:
|
40 |
+
return_list.append(AutoConfig.from_pretrained(pth, trust_remote_code=True))
|
41 |
+
|
42 |
+
return return_list
|
43 |
+
|
44 |
+
|
45 |
+
def build_llm_and_tokenizer(
|
46 |
+
llm_cfg: str,
|
47 |
+
config: PretrainedConfig,
|
48 |
+
attn_implementation=None,
|
49 |
+
model_max_length=None,
|
50 |
+
*args,
|
51 |
+
**kwargs,
|
52 |
+
) -> PreTrainedModel:
|
53 |
+
llm_arch = getattr(llm_cfg, "architectures")[0].lower()
|
54 |
+
|
55 |
+
llm_path = llm_cfg._name_or_path
|
56 |
+
llm = AutoModelForCausalLM.from_pretrained(
|
57 |
+
llm_path, config=llm_cfg, torch_dtype=eval(config.model_dtype), *args, **kwargs
|
58 |
+
)
|
59 |
+
|
60 |
+
tokenizer = AutoTokenizer.from_pretrained(
|
61 |
+
llm_path,
|
62 |
+
model_max_length=llm_cfg.model_max_length,
|
63 |
+
padding_side="right",
|
64 |
+
use_fast=False,
|
65 |
+
legacy=False,
|
66 |
+
**kwargs
|
67 |
+
)
|
68 |
+
|
69 |
+
#config.hidden_size = llm.config.hidden_size
|
70 |
+
return llm, tokenizer
|
71 |
+
|
72 |
+
|
73 |
+
class ApolloForCausalLM(PreTrainedModel):
|
74 |
+
def __init__(self, config: ApolloConfig, *args, **kwargs):
|
75 |
+
super().__init__(config)
|
76 |
+
llm_cfg, vision_tower_cfg, mm_connector_cfg = get_model_config(config)
|
77 |
+
model_dtype = getattr(config, "model_dtype", "torch.float16")
|
78 |
+
if not hasattr(config, "model_dtype"):
|
79 |
+
warnings.warn("model_dtype not found in config, defaulting to torch.float16.")
|
80 |
+
config.model_dtype = model_dtype
|
81 |
+
# Initialize weights and apply final processing
|
82 |
+
|
83 |
+
self.lm_head = nn.Linear(llm_cfg.hidden_size, config.vocab_size, bias=False)
|
84 |
+
self.vision_tower = ApolloVisionTower(config, vision_tower_cfg)
|
85 |
+
self.mm_connector = MMConnector.from_pretrained(mm_connector_cfg._name_or_path)
|
86 |
+
self.llm, self.tokenizer = build_llm_and_tokenizer(llm_cfg, config, *args, **kwargs)
|
87 |
+
self.post_init()
|
88 |
+
self.is_loaded = True
|
89 |
+
|
90 |
+
def forward(
|
91 |
+
self,
|
92 |
+
input_ids: torch.LongTensor = None,
|
93 |
+
attention_mask: Optional[torch.Tensor] = None,
|
94 |
+
position_ids: Optional[torch.LongTensor] = None,
|
95 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
96 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
97 |
+
labels: Optional[torch.LongTensor] = None,
|
98 |
+
use_cache: Optional[bool] = None,
|
99 |
+
output_attentions: Optional[bool] = None,
|
100 |
+
output_hidden_states: Optional[bool] = None,
|
101 |
+
vision_input: Optional[List[torch.FloatTensor]] = None,
|
102 |
+
data_types: Optional[List[str]] = None,
|
103 |
+
return_dict: Optional[bool] = None,
|
104 |
+
cache_position=None,
|
105 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
106 |
+
|
107 |
+
if inputs_embeds is None:
|
108 |
+
(
|
109 |
+
input_ids,
|
110 |
+
position_ids,
|
111 |
+
attention_mask,
|
112 |
+
past_key_values,
|
113 |
+
inputs_embeds,
|
114 |
+
labels
|
115 |
+
) = self.prepare_inputs_labels_for_multimodal(
|
116 |
+
input_ids,
|
117 |
+
position_ids,
|
118 |
+
attention_mask,
|
119 |
+
past_key_values,
|
120 |
+
labels,
|
121 |
+
vision_input,
|
122 |
+
data_types
|
123 |
+
)
|
124 |
+
|
125 |
+
return self.get_llm().forward(
|
126 |
+
input_ids=input_ids,
|
127 |
+
attention_mask=attention_mask,
|
128 |
+
position_ids=position_ids,
|
129 |
+
past_key_values=past_key_values,
|
130 |
+
inputs_embeds=inputs_embeds,
|
131 |
+
labels=labels,
|
132 |
+
use_cache=use_cache,
|
133 |
+
output_attentions=output_attentions,
|
134 |
+
output_hidden_states=output_hidden_states,
|
135 |
+
return_dict=return_dict,
|
136 |
+
)
|
137 |
+
|
138 |
+
@torch.no_grad()
|
139 |
+
def generate(
|
140 |
+
self,
|
141 |
+
inputs: Optional[torch.Tensor] = None,
|
142 |
+
vision_input: Optional[List[torch.Tensor]] = None,
|
143 |
+
data_types: Optional[List[str]] = None,
|
144 |
+
**kwargs,
|
145 |
+
) -> Union[GenerateOutput, torch.LongTensor]:
|
146 |
+
position_ids = kwargs.pop("position_ids", None)
|
147 |
+
attention_mask = kwargs.pop("attention_mask", None)
|
148 |
+
if "inputs_embeds" in kwargs:
|
149 |
+
raise NotImplementedError("`inputs_embeds` is not supported")
|
150 |
+
|
151 |
+
if vision_input is not None:
|
152 |
+
(inputs, position_ids, attention_mask, _, inputs_embeds, _) = self.prepare_inputs_labels_for_multimodal(
|
153 |
+
inputs, position_ids, attention_mask, None, None, vision_input, data_types=data_types)
|
154 |
+
else:
|
155 |
+
inputs_embeds = self.embed_tokens(inputs)
|
156 |
+
|
157 |
+
return self.get_llm().generate(position_ids=position_ids, attention_mask=attention_mask,
|
158 |
+
inputs_embeds=inputs_embeds, **kwargs)
|
159 |
+
|
160 |
+
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
|
161 |
+
vision_input = kwargs.pop("vision_input", None)
|
162 |
+
data_types = kwargs.pop("data_types", None)
|
163 |
+
inputs = self.get_llm().prepare_inputs_for_generation(input_ids, past_key_values=past_key_values,
|
164 |
+
inputs_embeds=inputs_embeds, **kwargs)
|
165 |
+
if vision_input is not None:
|
166 |
+
inputs["vision_input"] = vision_input
|
167 |
+
if data_types is not None:
|
168 |
+
inputs["data_types"] = data_types
|
169 |
+
return inputs
|
170 |
+
|
171 |
+
@classmethod
|
172 |
+
def from_pretrained(
|
173 |
+
cls,
|
174 |
+
pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
|
175 |
+
*model_args,
|
176 |
+
config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None,
|
177 |
+
cache_dir: Optional[Union[str, os.PathLike]] = None,
|
178 |
+
ignore_mismatched_sizes: bool = False,
|
179 |
+
force_download: bool = False,
|
180 |
+
local_files_only: bool = False,
|
181 |
+
token: Optional[Union[str, bool]] = None,
|
182 |
+
revision: str = "main",
|
183 |
+
use_safetensors: bool = None,
|
184 |
+
**kwargs,
|
185 |
+
):
|
186 |
+
|
187 |
+
return cls.load_pretrained(
|
188 |
+
pretrained_model_name_or_path,
|
189 |
+
*model_args,
|
190 |
+
config=config,
|
191 |
+
cache_dir=cache_dir,
|
192 |
+
ignore_mismatched_sizes=ignore_mismatched_sizes,
|
193 |
+
force_download=force_download,
|
194 |
+
local_files_only=local_files_only,
|
195 |
+
token=token,
|
196 |
+
revision=revision,
|
197 |
+
use_safetensors=use_safetensors,
|
198 |
+
**kwargs,
|
199 |
+
)
|
200 |
+
|
201 |
+
def get_llm(self):
|
202 |
+
return self.llm
|
203 |
+
|
204 |
+
def get_vision_tower(self):
|
205 |
+
return self.vision_tower
|
206 |
+
|
207 |
+
def get_mm_connector(self):
|
208 |
+
return self.mm_connector
|
209 |
+
|
210 |
+
@classmethod
|
211 |
+
def load_pretrained(cls, model_path_or_config, *args, **kwargs):
|
212 |
+
kwargs.pop("config", None)
|
213 |
+
|
214 |
+
if isinstance(model_path_or_config, str):
|
215 |
+
config = AutoConfig.from_pretrained(model_path_or_config, trust_remote_code=True, **kwargs)
|
216 |
+
elif isinstance(model_path_or_config, ApolloConfig):
|
217 |
+
config = model_path_or_config
|
218 |
+
else:
|
219 |
+
raise NotImplementedError(f"wrong type, {type(model_path_or_config)} \
|
220 |
+
{isinstance(model_path_or_config, ApolloConfig)}")
|
221 |
+
|
222 |
+
model_dtype = getattr(config, "model_dtype", "torch.float16")
|
223 |
+
if not hasattr(config, "model_dtype"):
|
224 |
+
warnings.warn("model_dtype not found in config, defaulting to torch.float16.")
|
225 |
+
config.model_dtype = model_dtype
|
226 |
+
|
227 |
+
with ContextManagers([no_init_weights(_enable=True), ]):
|
228 |
+
vlm = cls(config, *args, **kwargs)
|
229 |
+
|
230 |
+
if hasattr(vlm, "llm") and hasattr(vlm, "vision_tower") and hasattr(vlm, "mm_connector"):
|
231 |
+
if vlm.is_loaded:
|
232 |
+
return vlm
|
233 |
+
else:
|
234 |
+
print('loading model failed!')
|
235 |
+
else:
|
236 |
+
print('loading model failed!')
|
237 |
+
|
238 |
+
def _encode_mm(self, x):
|
239 |
+
x = self.get_vision_tower()(x)
|
240 |
+
x = self.mm_connector(x)
|
241 |
+
return x
|
242 |
+
|
243 |
+
def encode_mm_minibatch(self, x):
|
244 |
+
split_sizes = [x_s[0].shape[0] for x_s in x]
|
245 |
+
x = [torch.split(torch.cat([x_s[i] for x_s in x], dim=0), self.config.encode_batch_size) for i in
|
246 |
+
range(self.get_vision_tower().num_vision_encoders)]
|
247 |
+
swapped_x = []
|
248 |
+
for i in range(len(x[0])):
|
249 |
+
swapped_x.append([x_s[i] for x_s in x])
|
250 |
+
|
251 |
+
features = []
|
252 |
+
for xx in swapped_x:
|
253 |
+
xx = self._encode_mm(xx)
|
254 |
+
features.append(xx)
|
255 |
+
x = torch.cat(features, dim=0)
|
256 |
+
x = torch.split(x, split_sizes, dim=0)
|
257 |
+
return [xx.contiguous().view(-1, xx.shape[2]) for xx in x]
|
258 |
+
|
259 |
+
def prepare_inputs_labels_for_multimodal(
|
260 |
+
self, input_ids, position_ids, attention_mask, past_key_values, labels, vision_input, data_types
|
261 |
+
):
|
262 |
+
vision_tower = self.get_vision_tower()
|
263 |
+
if vision_tower is None or vision_input is None or input_ids.shape[1] == 1:
|
264 |
+
if (
|
265 |
+
past_key_values is not None
|
266 |
+
and vision_tower is not None
|
267 |
+
and vision_input is not None
|
268 |
+
and input_ids.shape[1] == 1
|
269 |
+
):
|
270 |
+
target_shape = past_key_values[-1][-1].shape[-2] + 1
|
271 |
+
attention_mask = torch.cat(
|
272 |
+
(
|
273 |
+
attention_mask,
|
274 |
+
torch.ones(
|
275 |
+
(
|
276 |
+
attention_mask.shape[0],
|
277 |
+
target_shape - attention_mask.shape[1],
|
278 |
+
),
|
279 |
+
dtype=attention_mask.dtype,
|
280 |
+
device=attention_mask.device,
|
281 |
+
),
|
282 |
+
),
|
283 |
+
dim=1,
|
284 |
+
)
|
285 |
+
position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
|
286 |
+
return (
|
287 |
+
input_ids,
|
288 |
+
position_ids,
|
289 |
+
attention_mask,
|
290 |
+
past_key_values,
|
291 |
+
None,
|
292 |
+
labels,
|
293 |
+
)
|
294 |
+
|
295 |
+
'''
|
296 |
+
vision_input is a list of tuples, and data_type is a list of strings:
|
297 |
+
data_type = ['image', 'video', 'video'..., 'text']
|
298 |
+
(for one video and two image encoders)
|
299 |
+
vision_input =
|
300 |
+
[
|
301 |
+
[image(1, T, C, H, W), image(1, T, C, H, W), image(1, T, C, H, W)],
|
302 |
+
[video(Nc1, C, T, H, W), video(Nc1, T, C, H, W), video(Nc1, T, C, H, W)],
|
303 |
+
[video(Nc2, C, T, H, W), video(Nc2, T, C, H, W), video(Nc2, T, C, H, W)],
|
304 |
+
]
|
305 |
+
-> video encoders typlically expect (C,T,H,W), images expect (C,H,W).
|
306 |
+
'''
|
307 |
+
# ====================================================================================================
|
308 |
+
merged_mm_features = self.encode_mm_minibatch(vision_input)
|
309 |
+
|
310 |
+
if not getattr(self.config, "tune_language_model", True) and getattr(self.config, "use_mm_start_end", False):
|
311 |
+
raise NotImplementedError
|
312 |
+
# ====================================================================================================
|
313 |
+
# Let's just add dummy tensors if they do not exist,
|
314 |
+
# it is a headache to deal with None all the time.
|
315 |
+
# But it is not ideal, and if you have a better idea,
|
316 |
+
# please open an issue / submit a PR, thanks.
|
317 |
+
_labels = labels
|
318 |
+
_position_ids = position_ids
|
319 |
+
_attention_mask = attention_mask
|
320 |
+
if attention_mask is None:
|
321 |
+
attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
|
322 |
+
else:
|
323 |
+
attention_mask = attention_mask.bool()
|
324 |
+
if position_ids is None:
|
325 |
+
position_ids = torch.arange(0, input_ids.shape[1], dtype=torch.long, device=input_ids.device)
|
326 |
+
if labels is None:
|
327 |
+
labels = torch.full_like(input_ids, IGNORE_INDEX)
|
328 |
+
|
329 |
+
# remove the padding using attention_mask
|
330 |
+
input_ids_copy = input_ids.clone()
|
331 |
+
# kentang-mit@: Otherwise tokenizer out of bounds. Embeddings of image tokens will not be used.
|
332 |
+
input_ids_copy[input_ids_copy == X_TOKEN_INDEX] = 0
|
333 |
+
input_embeds = self.get_llm().model.embed_tokens(input_ids_copy)
|
334 |
+
|
335 |
+
input_ids = [
|
336 |
+
cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in zip(input_ids, attention_mask)
|
337 |
+
]
|
338 |
+
input_embeds_1 = [
|
339 |
+
cur_input_embeds[cur_attention_mask]
|
340 |
+
for cur_input_embeds, cur_attention_mask in zip(input_embeds, attention_mask)
|
341 |
+
]
|
342 |
+
labels = [cur_labels[cur_attention_mask] for cur_labels, cur_attention_mask in zip(labels, attention_mask)]
|
343 |
+
# input_ids, new_input_embeds = self.inputs_merger(input_ids, input_embeds_1, merged_mm_features)
|
344 |
+
new_labels = []
|
345 |
+
new_input_embeds = []
|
346 |
+
# print("BEFORE BATCH LOOP:", len(input_ids), input_ids[0].shape, input_ids[0].device, [(x == X_TOKEN_INDEX).sum() for x in input_ids])
|
347 |
+
# kentang-mit@: If some part of the model is executed in the loop, the the loop length needs to be a constant.
|
348 |
+
for batch_idx, (cur_labels, cur_input_ids, mm_features) in enumerate(
|
349 |
+
zip(labels, input_ids, merged_mm_features)):
|
350 |
+
cur_input_ids = input_ids[batch_idx]
|
351 |
+
num_mm = (cur_input_ids == X_TOKEN_INDEX).sum()
|
352 |
+
if num_mm == 0:
|
353 |
+
cur_input_embeds_1 = input_embeds_1[batch_idx]
|
354 |
+
cur_input_embeds = torch.cat([cur_input_embeds_1, mm_features[0:0]], dim=0)
|
355 |
+
new_input_embeds.append(cur_input_embeds)
|
356 |
+
new_labels.append(cur_labels)
|
357 |
+
# kenang-mit@: we do not have placeholdr image for text-only data now.
|
358 |
+
continue
|
359 |
+
|
360 |
+
if mm_features.shape[0] != num_mm:
|
361 |
+
print(data_types[batch_idx])
|
362 |
+
assert num_mm == len(
|
363 |
+
mm_features), f'Error in {data_types[batch_idx]}{num_mm}=/={len(mm_features)} not the same number of vision tokens in and vision embeddings!'
|
364 |
+
|
365 |
+
cur_input_embeds = input_embeds_1[batch_idx]
|
366 |
+
image_token_indices = (
|
367 |
+
[-1] + torch.where(cur_input_ids == X_TOKEN_INDEX)[0].tolist() + [cur_input_ids.shape[0]]
|
368 |
+
)
|
369 |
+
cur_input_ids_noim = []
|
370 |
+
cur_labels = labels[batch_idx]
|
371 |
+
cur_labels_noim = []
|
372 |
+
cur_input_embeds_no_im = []
|
373 |
+
for i in range(len(image_token_indices) - 1):
|
374 |
+
cur_input_ids_noim.append(cur_input_ids[image_token_indices[i] + 1: image_token_indices[i + 1]])
|
375 |
+
cur_labels_noim.append(cur_labels[image_token_indices[i] + 1: image_token_indices[i + 1]])
|
376 |
+
cur_input_embeds_no_im.append(cur_input_embeds[image_token_indices[i] + 1: image_token_indices[i + 1]])
|
377 |
+
|
378 |
+
cur_new_input_embeds = []
|
379 |
+
cur_new_labels = []
|
380 |
+
for i in range(num_mm + 1):
|
381 |
+
cur_new_input_embeds.append(cur_input_embeds_no_im[i])
|
382 |
+
# print("cur_new_input_embeds1", cur_new_input_embeds.shape[-1])
|
383 |
+
cur_new_labels.append(cur_labels_noim[i])
|
384 |
+
if i < num_mm:
|
385 |
+
cur_image_features = mm_features[i:i + 1]
|
386 |
+
cur_new_input_embeds.append(cur_image_features)
|
387 |
+
# print("cur_new_input_embeds2", cur_new_input_embeds.shape[-1])
|
388 |
+
cur_new_labels.append(
|
389 |
+
torch.full(
|
390 |
+
(cur_image_features.shape[0],),
|
391 |
+
IGNORE_INDEX,
|
392 |
+
device=cur_labels.device,
|
393 |
+
dtype=cur_labels.dtype,
|
394 |
+
)
|
395 |
+
)
|
396 |
+
|
397 |
+
cur_new_input_embeds = torch.cat(cur_new_input_embeds)
|
398 |
+
cur_new_labels = torch.cat(cur_new_labels)
|
399 |
+
|
400 |
+
new_input_embeds.append(cur_new_input_embeds)
|
401 |
+
new_labels.append(cur_new_labels)
|
402 |
+
|
403 |
+
# Truncate sequences to max length as image embeddings can make the sequence longer
|
404 |
+
tokenizer_model_max_length = getattr(self.get_llm().config, "tokenizer_model_max_length", None)
|
405 |
+
if tokenizer_model_max_length is not None:
|
406 |
+
if any(len(x) > tokenizer_model_max_length for x in new_input_embeds):
|
407 |
+
priny("Inputs truncated!")
|
408 |
+
new_input_embeds = [x[:tokenizer_model_max_length] for x in new_input_embeds]
|
409 |
+
new_labels = [x[:tokenizer_model_max_length] for x in new_labels]
|
410 |
+
# Combine them
|
411 |
+
max_len = max(x.shape[0] for x in new_input_embeds)
|
412 |
+
batch_size = len(new_input_embeds)
|
413 |
+
|
414 |
+
new_input_embeds_padded = []
|
415 |
+
new_labels_padded = torch.full(
|
416 |
+
(batch_size, max_len),
|
417 |
+
IGNORE_INDEX,
|
418 |
+
dtype=new_labels[0].dtype,
|
419 |
+
device=new_labels[0].device,
|
420 |
+
)
|
421 |
+
attention_mask = torch.zeros(
|
422 |
+
(batch_size, max_len),
|
423 |
+
dtype=attention_mask.dtype,
|
424 |
+
device=attention_mask.device,
|
425 |
+
)
|
426 |
+
position_ids = torch.zeros((batch_size, max_len), dtype=position_ids.dtype, device=position_ids.device)
|
427 |
+
for i, (cur_new_embed, cur_new_labels) in enumerate(zip(new_input_embeds, new_labels)):
|
428 |
+
cur_len = cur_new_embed.shape[0]
|
429 |
+
if getattr(self.get_llm().config, "tokenizer_padding_side", "right") == "left":
|
430 |
+
new_input_embeds_padded.append(
|
431 |
+
torch.cat(
|
432 |
+
(
|
433 |
+
torch.zeros(
|
434 |
+
(max_len - cur_len, cur_new_embed.shape[1]),
|
435 |
+
dtype=cur_new_embed.dtype,
|
436 |
+
device=cur_new_embed.device,
|
437 |
+
),
|
438 |
+
cur_new_embed,
|
439 |
+
),
|
440 |
+
dim=0,
|
441 |
+
)
|
442 |
+
)
|
443 |
+
if cur_len > 0:
|
444 |
+
new_labels_padded[i, -cur_len:] = cur_new_labels
|
445 |
+
attention_mask[i, -cur_len:] = True
|
446 |
+
position_ids[i, -cur_len:] = torch.arange(
|
447 |
+
0, cur_len, dtype=position_ids.dtype, device=position_ids.device
|
448 |
+
)
|
449 |
+
else:
|
450 |
+
new_input_embeds_padded.append(
|
451 |
+
torch.cat(
|
452 |
+
(
|
453 |
+
cur_new_embed,
|
454 |
+
torch.zeros(
|
455 |
+
(max_len - cur_len, cur_new_embed.shape[1]),
|
456 |
+
dtype=cur_new_embed.dtype,
|
457 |
+
device=cur_new_embed.device,
|
458 |
+
),
|
459 |
+
),
|
460 |
+
dim=0,
|
461 |
+
)
|
462 |
+
)
|
463 |
+
if cur_len > 0:
|
464 |
+
new_labels_padded[i, :cur_len] = cur_new_labels
|
465 |
+
attention_mask[i, :cur_len] = True
|
466 |
+
position_ids[i, :cur_len] = torch.arange(
|
467 |
+
0, cur_len, dtype=position_ids.dtype, device=position_ids.device
|
468 |
+
)
|
469 |
+
|
470 |
+
new_input_embeds = torch.stack(new_input_embeds_padded, dim=0)
|
471 |
+
|
472 |
+
if _labels is None:
|
473 |
+
new_labels = None
|
474 |
+
else:
|
475 |
+
new_labels = new_labels_padded
|
476 |
+
|
477 |
+
if _attention_mask is None:
|
478 |
+
attention_mask = None
|
479 |
+
else:
|
480 |
+
attention_mask = attention_mask.to(dtype=_attention_mask.dtype)
|
481 |
+
|
482 |
+
if _position_ids is None:
|
483 |
+
position_ids = None
|
484 |
+
|
485 |
+
return (
|
486 |
+
None,
|
487 |
+
position_ids,
|
488 |
+
attention_mask,
|
489 |
+
past_key_values,
|
490 |
+
new_input_embeds,
|
491 |
+
new_labels,
|
492 |
+
)
|
vision_tower.py
ADDED
@@ -0,0 +1,556 @@
|
|
|
|
|
|
|
|
|
|
|
|
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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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|
|
|
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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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|
1 |
+
import torch, os, PIL, numbers
|
2 |
+
from PIL import Image
|
3 |
+
import cv2
|
4 |
+
|
5 |
+
from transformers.modeling_utils import PreTrainedModel
|
6 |
+
from transformers.models.siglip.modeling_siglip import SiglipVisionModel
|
7 |
+
from transformers import AutoConfig, AutoModel, SiglipImageProcessor, SiglipVisionConfig, PretrainedConfig
|
8 |
+
from typing import Union
|
9 |
+
import torch.nn.functional as F
|
10 |
+
import numpy as np
|
11 |
+
|
12 |
+
|
13 |
+
def crop_clip(clip, min_h, min_w, h, w):
|
14 |
+
if isinstance(clip[0], np.ndarray):
|
15 |
+
cropped = [img[min_h:min_h + h, min_w:min_w + w, :] for img in clip]
|
16 |
+
|
17 |
+
elif isinstance(clip[0], PIL.Image.Image):
|
18 |
+
cropped = [
|
19 |
+
img.crop((min_w, min_h, min_w + w, min_h + h)) for img in clip
|
20 |
+
]
|
21 |
+
else:
|
22 |
+
raise TypeError('Expected numpy.ndarray or PIL.Image' +
|
23 |
+
'but got list of {0}'.format(type(clip[0])))
|
24 |
+
return cropped
|
25 |
+
|
26 |
+
|
27 |
+
class Normalize(object):
|
28 |
+
"""Normalize a clip with mean and standard deviation.
|
29 |
+
Given mean: ``(M1,...,Mn)`` and std: ``(S1,..,Sn)`` for ``n`` channels, this transform
|
30 |
+
will normalize each channel of the input ``torch.*Tensor`` i.e.
|
31 |
+
``input[channel] = (input[channel] - mean[channel]) / std[channel]``
|
32 |
+
.. note::
|
33 |
+
This transform acts out of place, i.e., it does not mutates the input tensor.
|
34 |
+
Args:
|
35 |
+
mean (sequence): Sequence of means for each channel.
|
36 |
+
std (sequence): Sequence of standard deviations for each channel.
|
37 |
+
"""
|
38 |
+
|
39 |
+
def __init__(self, mean, std):
|
40 |
+
self.mean = mean
|
41 |
+
self.std = std
|
42 |
+
|
43 |
+
def __call__(self, clip):
|
44 |
+
"""
|
45 |
+
Args:
|
46 |
+
clip (Tensor): Tensor clip of size (T, C, H, W) to be normalized.
|
47 |
+
Returns:
|
48 |
+
Tensor: Normalized Tensor clip.
|
49 |
+
"""
|
50 |
+
return normalize(clip, self.mean, self.std)
|
51 |
+
|
52 |
+
def __repr__(self):
|
53 |
+
return self.__class__.__name__ + '(mean={0}, std={1})'.format(self.mean, self.std)
|
54 |
+
|
55 |
+
|
56 |
+
class CenterCrop(object):
|
57 |
+
"""Extract center crop at the same location for a list of images
|
58 |
+
Args:
|
59 |
+
size (sequence or int): Desired output size for the
|
60 |
+
crop in format (h, w)
|
61 |
+
"""
|
62 |
+
|
63 |
+
def __init__(self, size):
|
64 |
+
if isinstance(size, numbers.Number):
|
65 |
+
size = (size, size)
|
66 |
+
|
67 |
+
self.size = size
|
68 |
+
|
69 |
+
def __call__(self, clip):
|
70 |
+
"""
|
71 |
+
Args:
|
72 |
+
img (PIL.Image or numpy.ndarray): List of images to be cropped
|
73 |
+
in format (h, w, c) in numpy.ndarray
|
74 |
+
Returns:
|
75 |
+
PIL.Image or numpy.ndarray: Cropped list of images
|
76 |
+
"""
|
77 |
+
h, w = self.size
|
78 |
+
if isinstance(clip[0], np.ndarray):
|
79 |
+
im_h, im_w, im_c = clip[0].shape
|
80 |
+
elif isinstance(clip[0], PIL.Image.Image):
|
81 |
+
im_w, im_h = clip[0].size
|
82 |
+
else:
|
83 |
+
raise TypeError('Expected numpy.ndarray or PIL.Image' +
|
84 |
+
'but got list of {0}'.format(type(clip[0])))
|
85 |
+
if w > im_w or h > im_h:
|
86 |
+
error_msg = (
|
87 |
+
'Initial image size should be larger then '
|
88 |
+
'cropped size but got cropped sizes : ({w}, {h}) while '
|
89 |
+
'initial image is ({im_w}, {im_h})'.format(
|
90 |
+
im_w=im_w, im_h=im_h, w=w, h=h))
|
91 |
+
raise ValueError(error_msg)
|
92 |
+
|
93 |
+
x1 = int(round((im_w - w) / 2.))
|
94 |
+
y1 = int(round((im_h - h) / 2.))
|
95 |
+
cropped = crop_clip(clip, y1, x1, h, w)
|
96 |
+
|
97 |
+
return cropped
|
98 |
+
|
99 |
+
|
100 |
+
def resize_clip(clip, size, interpolation='bilinear'):
|
101 |
+
if isinstance(clip[0], np.ndarray):
|
102 |
+
if isinstance(size, numbers.Number):
|
103 |
+
im_h, im_w, im_c = clip[0].shape
|
104 |
+
# Min spatial dim already matches minimal size
|
105 |
+
if (im_w <= im_h and im_w == size) or (im_h <= im_w
|
106 |
+
and im_h == size):
|
107 |
+
return clip
|
108 |
+
new_h, new_w = get_resize_sizes(im_h, im_w, size)
|
109 |
+
size = (new_w, new_h)
|
110 |
+
else:
|
111 |
+
size = size[0], size[1]
|
112 |
+
if interpolation == 'bilinear':
|
113 |
+
np_inter = cv2.INTER_LINEAR
|
114 |
+
else:
|
115 |
+
np_inter = cv2.INTER_NEAREST
|
116 |
+
scaled = [
|
117 |
+
cv2.resize(img, size, interpolation=np_inter) for img in clip
|
118 |
+
]
|
119 |
+
elif isinstance(clip[0], PIL.Image.Image):
|
120 |
+
if isinstance(size, numbers.Number):
|
121 |
+
im_w, im_h = clip[0].size
|
122 |
+
# Min spatial dim already matches minimal size
|
123 |
+
if (im_w <= im_h and im_w == size) or (im_h <= im_w
|
124 |
+
and im_h == size):
|
125 |
+
return clip
|
126 |
+
new_h, new_w = get_resize_sizes(im_h, im_w, size)
|
127 |
+
size = (new_w, new_h)
|
128 |
+
else:
|
129 |
+
size = size[1], size[0]
|
130 |
+
if interpolation == 'bilinear':
|
131 |
+
pil_inter = PIL.Image.BILINEAR
|
132 |
+
else:
|
133 |
+
pil_inter = PIL.Image.NEAREST
|
134 |
+
scaled = [img.resize(size, pil_inter) for img in clip]
|
135 |
+
else:
|
136 |
+
raise TypeError('Expected numpy.ndarray or PIL.Image' +
|
137 |
+
'but got list of {0}'.format(type(clip[0])))
|
138 |
+
return scaled
|
139 |
+
|
140 |
+
|
141 |
+
def _is_tensor_clip(clip):
|
142 |
+
return torch.is_tensor(clip) and clip.ndimension() == 4
|
143 |
+
|
144 |
+
|
145 |
+
def get_resize_sizes(im_h, im_w, size):
|
146 |
+
if im_w < im_h:
|
147 |
+
ow = size
|
148 |
+
oh = int(size * im_h / im_w)
|
149 |
+
else:
|
150 |
+
oh = size
|
151 |
+
ow = int(size * im_w / im_h)
|
152 |
+
return oh, ow
|
153 |
+
|
154 |
+
|
155 |
+
def normalize(clip, mean, std, inplace=False):
|
156 |
+
if not _is_tensor_clip(clip):
|
157 |
+
raise TypeError('tensor is not a torch clip.')
|
158 |
+
|
159 |
+
if not inplace:
|
160 |
+
clip = clip.clone()
|
161 |
+
|
162 |
+
dtype = clip.dtype
|
163 |
+
mean = torch.as_tensor(mean, dtype=dtype, device=clip.device)
|
164 |
+
std = torch.as_tensor(std, dtype=dtype, device=clip.device)
|
165 |
+
clip.sub_(mean[:, None, None, None]).div_(std[:, None, None, None])
|
166 |
+
|
167 |
+
return clip
|
168 |
+
|
169 |
+
|
170 |
+
class Resize(object):
|
171 |
+
"""Resizes a list of (H x W x C) numpy.ndarray to the final size
|
172 |
+
The larger the original image is, the more times it takes to
|
173 |
+
interpolate
|
174 |
+
Args:
|
175 |
+
interpolation (str): Can be one of 'nearest', 'bilinear'
|
176 |
+
defaults to nearest
|
177 |
+
size (tuple): (widht, height)
|
178 |
+
"""
|
179 |
+
|
180 |
+
def __init__(self, size, interpolation='nearest'):
|
181 |
+
self.size = size
|
182 |
+
self.interpolation = interpolation
|
183 |
+
|
184 |
+
def __call__(self, clip):
|
185 |
+
resized = resize_clip(
|
186 |
+
clip, self.size, interpolation=self.interpolation)
|
187 |
+
return resized
|
188 |
+
|
189 |
+
|
190 |
+
class Compose(object):
|
191 |
+
"""Composes several transforms
|
192 |
+
Args:
|
193 |
+
transforms (list of ``Transform`` objects): list of transforms
|
194 |
+
to compose
|
195 |
+
"""
|
196 |
+
|
197 |
+
def __init__(self, transforms):
|
198 |
+
self.transforms = transforms
|
199 |
+
|
200 |
+
def __call__(self, clip):
|
201 |
+
for t in self.transforms:
|
202 |
+
clip = t(clip)
|
203 |
+
return clip
|
204 |
+
|
205 |
+
|
206 |
+
def convert_img(img):
|
207 |
+
"""Converts (H, W, C) numpy.ndarray to (C, W, H) format"""
|
208 |
+
if len(img.shape) == 3:
|
209 |
+
img = img.transpose(2, 0, 1)
|
210 |
+
if len(img.shape) == 2:
|
211 |
+
img = np.expand_dims(img, 0)
|
212 |
+
return img
|
213 |
+
|
214 |
+
|
215 |
+
class ClipToTensor(object):
|
216 |
+
"""Convert a list of m (H x W x C) numpy.ndarrays in the range [0, 255]
|
217 |
+
to a torch.FloatTensor of shape (C x m x H x W) in the range [0, 1.0]
|
218 |
+
"""
|
219 |
+
|
220 |
+
def __init__(self, channel_nb=3, div_255=True, numpy=False):
|
221 |
+
self.channel_nb = channel_nb
|
222 |
+
self.div_255 = div_255
|
223 |
+
self.numpy = numpy
|
224 |
+
|
225 |
+
def __call__(self, clip):
|
226 |
+
"""
|
227 |
+
Args: clip (list of numpy.ndarray): clip (list of images)
|
228 |
+
to be converted to tensor.
|
229 |
+
"""
|
230 |
+
# Retrieve shape
|
231 |
+
if isinstance(clip[0], np.ndarray):
|
232 |
+
h, w, ch = clip[0].shape
|
233 |
+
assert ch == self.channel_nb, "Got {0} instead of 3 channels".format(ch)
|
234 |
+
elif isinstance(clip[0], Image.Image):
|
235 |
+
w, h = clip[0].size
|
236 |
+
else:
|
237 |
+
raise TypeError(
|
238 |
+
"Expected numpy.ndarray or PIL.Image\
|
239 |
+
but got list of {0}".format(
|
240 |
+
type(clip[0])
|
241 |
+
)
|
242 |
+
)
|
243 |
+
|
244 |
+
np_clip = np.zeros([self.channel_nb, len(clip), int(h), int(w)])
|
245 |
+
|
246 |
+
# Convert
|
247 |
+
for img_idx, img in enumerate(clip):
|
248 |
+
if isinstance(img, np.ndarray):
|
249 |
+
pass
|
250 |
+
elif isinstance(img, Image.Image):
|
251 |
+
img = np.array(img, copy=False)
|
252 |
+
else:
|
253 |
+
raise TypeError(
|
254 |
+
"Expected numpy.ndarray or PIL.Image\
|
255 |
+
but got list of {0}".format(
|
256 |
+
type(clip[0])
|
257 |
+
)
|
258 |
+
)
|
259 |
+
img = convert_img(img)
|
260 |
+
np_clip[:, img_idx, :, :] = img
|
261 |
+
if self.numpy:
|
262 |
+
if self.div_255:
|
263 |
+
np_clip = np_clip / 255.0
|
264 |
+
return np_clip
|
265 |
+
|
266 |
+
else:
|
267 |
+
tensor_clip = torch.from_numpy(np_clip)
|
268 |
+
|
269 |
+
if not isinstance(tensor_clip, torch.FloatTensor):
|
270 |
+
tensor_clip = tensor_clip.float()
|
271 |
+
if self.div_255:
|
272 |
+
tensor_clip = torch.div(tensor_clip, 255)
|
273 |
+
return tensor_clip
|
274 |
+
|
275 |
+
|
276 |
+
class VisionTowerConfig(PretrainedConfig):
|
277 |
+
model_type = "vision_tower"
|
278 |
+
|
279 |
+
def __init__(self, vision_tower_name: str = None, **kwargs):
|
280 |
+
super().__init__()
|
281 |
+
self.vision_tower_name = vision_tower_name
|
282 |
+
|
283 |
+
|
284 |
+
class ProcessorWrapper:
|
285 |
+
def __init__(self, transform=None, processor=None, height=378, width=378, frames_per_clip=1,
|
286 |
+
image_mean=[0.48145466, 0.4578275, 0.40821073]):
|
287 |
+
assert transform is not None or processor is not None, "ERROR: you did not define both `transform` and `processor`! You must define either transform or processor"
|
288 |
+
assert transform is None or processor is None, "ERROR: you did defined both `transform` and `processor`! You must define only one of: transform or processor"
|
289 |
+
self._size = {
|
290 |
+
"height": height,
|
291 |
+
"width": width,
|
292 |
+
"frames_per_clip": frames_per_clip
|
293 |
+
}
|
294 |
+
self._transforms = transform
|
295 |
+
self._processor = processor
|
296 |
+
self.image_mean = image_mean
|
297 |
+
|
298 |
+
@property
|
299 |
+
def size(self):
|
300 |
+
return self._size
|
301 |
+
|
302 |
+
def preprocess(self, image, return_tensors='pt'):
|
303 |
+
# Ensure image is a PIL Image
|
304 |
+
output = {}
|
305 |
+
if self._transforms is not None:
|
306 |
+
output['pixel_values'] = [self._transforms(image)]
|
307 |
+
|
308 |
+
else:
|
309 |
+
output = self._processor(image, return_tensors='pt')
|
310 |
+
return output
|
311 |
+
|
312 |
+
def save_pretrained(self, save_path):
|
313 |
+
if self._transforms is not None:
|
314 |
+
transform_dict = transform_to_dict(self._transforms)
|
315 |
+
transform_dict["image_processor_type"] = "transforms"
|
316 |
+
with open(os.path.join(save_path, 'preprocessor_config.json'), 'w') as f:
|
317 |
+
json.dump(transform_dict, f, indent=4)
|
318 |
+
else:
|
319 |
+
self._processor.save_pretrained(save_path)
|
320 |
+
return
|
321 |
+
|
322 |
+
|
323 |
+
class VisionTower(PreTrainedModel):
|
324 |
+
config_class = VisionTowerConfig
|
325 |
+
|
326 |
+
def __init__(self, model_name_or_path: str, config: PretrainedConfig, vision_config: VisionTowerConfig = None):
|
327 |
+
super().__init__(vision_config)
|
328 |
+
self.vision_tower_name = model_name_or_path
|
329 |
+
self.vision_config = vision_config
|
330 |
+
self.select_layer = getattr(config, "mm_vision_select_layer", -2)
|
331 |
+
self.select_feature = getattr(config, "mm_vision_select_feature", "patch")
|
332 |
+
self.encode_batch_size = getattr(config, "encode_batch_size", 0) // 2
|
333 |
+
self.num_encode_batch = getattr(config, "num_encode_batch", 0) // 2
|
334 |
+
self.temporal_tubelet_size = getattr(vision_config, "tubelet_size", 1)
|
335 |
+
|
336 |
+
def feature_select(self, image_features):
|
337 |
+
if self.select_layer is not None:
|
338 |
+
image_features = image_features.hidden_states[self.select_layer]
|
339 |
+
|
340 |
+
if self.select_feature == "patch":
|
341 |
+
image_features = image_features[:, 1:]
|
342 |
+
elif self.select_feature == "cls_patch":
|
343 |
+
image_features = image_features
|
344 |
+
else:
|
345 |
+
raise ValueError(f"Unexpected select feature: {self.select_feature}")
|
346 |
+
|
347 |
+
return image_features
|
348 |
+
|
349 |
+
def vision_tower_forward(self, image):
|
350 |
+
image_feature = self.vision_tower(image, output_hidden_states=True)
|
351 |
+
return image_feature
|
352 |
+
|
353 |
+
def _forward(self, images, out_T=1):
|
354 |
+
if type(images) is list:
|
355 |
+
image_features = []
|
356 |
+
for image in images:
|
357 |
+
image_feature = self.vision_tower_forward(image.to(device=self.device, dtype=self.dtype).unsqueeze(0))
|
358 |
+
image_feature = self.feature_select(image_feature).to(image.dtype)
|
359 |
+
image_feature = image_features.reshape(image_feature.shape[0], self.W, self.H, self.D)
|
360 |
+
image_features.append(image_feature)
|
361 |
+
else:
|
362 |
+
original_shape = images.shape
|
363 |
+
if len(original_shape) == 5 and self.T == 1:
|
364 |
+
# downsample temporally if needed, and reshape from (B, T, C, W, H) to (B*T, C, W, H).
|
365 |
+
images = images[:, ::original_shape[1] // out_T, ...]
|
366 |
+
original_shape = images.shape
|
367 |
+
images = images.view(-1, *original_shape[2:])
|
368 |
+
|
369 |
+
image_features = self.vision_tower_forward(images.to(device=self.device, dtype=self.dtype))
|
370 |
+
image_features = self.feature_select(image_features).to(images.dtype)
|
371 |
+
# Reshape back to (B, T, ...) if necessary
|
372 |
+
if len(original_shape) == 5 and self.T == 1:
|
373 |
+
# Assuming the feature dimension does not change, adapt the following line if it does
|
374 |
+
new_shape = list(image_features.shape[:-2]) + [self.W, self.H, self.hidden_size]
|
375 |
+
image_features = image_features.reshape(new_shape)
|
376 |
+
feature_size = image_features.shape[1:]
|
377 |
+
image_features = image_features.view(original_shape[0], original_shape[1], *feature_size)
|
378 |
+
|
379 |
+
else:
|
380 |
+
image_features = image_features.reshape(image_features.shape[0], self.T, self.W, self.H, self.hidden_size)
|
381 |
+
|
382 |
+
return image_features
|
383 |
+
|
384 |
+
def forward(self, images):
|
385 |
+
return self._forward(images)
|
386 |
+
|
387 |
+
@property
|
388 |
+
def dummy_feature(self):
|
389 |
+
return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
|
390 |
+
|
391 |
+
@property
|
392 |
+
def dtype(self):
|
393 |
+
return self.vision_tower.dtype
|
394 |
+
|
395 |
+
@property
|
396 |
+
def device(self):
|
397 |
+
return self.vision_tower.device
|
398 |
+
|
399 |
+
@property
|
400 |
+
def num_patches(self):
|
401 |
+
return (self.config.image_size // self.config.patch_size) ** 2
|
402 |
+
|
403 |
+
|
404 |
+
class InternVideoTower(VisionTower):
|
405 |
+
def __init__(self, model_name_or_path: str, config: PretrainedConfig, vision_config: PretrainedConfig = None):
|
406 |
+
if vision_config is None:
|
407 |
+
vision_config = AutoConfig.from_pretrained(model_name_or_path, trust_remote_code=True)
|
408 |
+
|
409 |
+
super().__init__(model_name_or_path, config, vision_config)
|
410 |
+
self.vision_config = vision_config
|
411 |
+
normalize = ((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
|
412 |
+
|
413 |
+
print('loading: ', model_name_or_path)
|
414 |
+
model = AutoModel.from_pretrained(model_name_or_path, trust_remote_code=True)
|
415 |
+
self.vision_tower = model.to(dtype=eval(config.model_dtype))
|
416 |
+
|
417 |
+
transform = Compose([
|
418 |
+
Resize(self.vision_config.img_size, interpolation='bilinear'),
|
419 |
+
CenterCrop(size=(self.vision_config.img_size, self.vision_config.img_size)),
|
420 |
+
ClipToTensor(),
|
421 |
+
Normalize(mean=normalize[0], std=normalize[1])
|
422 |
+
])
|
423 |
+
|
424 |
+
self.vision_processor = ProcessorWrapper(transform=transform,
|
425 |
+
height=self.vision_config.img_size,
|
426 |
+
width=self.vision_config.img_size,
|
427 |
+
frames_per_clip=self.vision_config.num_frames,
|
428 |
+
image_mean=normalize[0])
|
429 |
+
|
430 |
+
self.W = self.H = vision_config.img_size // vision_config.patch_size
|
431 |
+
self.T = self.vision_config.num_frames // self.vision_config.tubelet_size
|
432 |
+
self.num_frames = self.vision_config.num_frames
|
433 |
+
self.hidden_size = vision_config.d_model
|
434 |
+
self.vision_select_layer=self.select_layer
|
435 |
+
self.select_layer=None
|
436 |
+
|
437 |
+
def vision_tower_forward(self, video):
|
438 |
+
if video.shape[-3] < self.num_frames:
|
439 |
+
video = video.repeat_interleave(self.num_frames, dim=-3)
|
440 |
+
elif video.shape[-3] > self.num_frames:
|
441 |
+
video = video[:, :, ::video.shape[-3] // self.num_frames, ...]
|
442 |
+
|
443 |
+
video_feature = self.vision_tower(video.to(device=self.device, dtype=self.dtype),
|
444 |
+
x_vis_return_idx=self.vision_select_layer, x_vis_only=True)
|
445 |
+
|
446 |
+
return video_feature
|
447 |
+
|
448 |
+
@property
|
449 |
+
def device(self):
|
450 |
+
return self.vision_tower.pos_embed.device
|
451 |
+
|
452 |
+
|
453 |
+
class SiglipVisionTower(VisionTower):
|
454 |
+
def __init__(self, model_name_or_path: str, config: PretrainedConfig, vision_config: PretrainedConfig = None):
|
455 |
+
if vision_config is None:
|
456 |
+
vision_config = SiglipVisionConfig.from_pretrained(model_name_or_path)
|
457 |
+
|
458 |
+
super().__init__(model_name_or_path, config, vision_config)
|
459 |
+
self.vision_config = vision_config
|
460 |
+
self.vision_tower_name = model_name_or_path
|
461 |
+
self.vision_processor = SiglipImageProcessor.from_pretrained(self.vision_tower_name)
|
462 |
+
|
463 |
+
print('loading: ', model_name_or_path)
|
464 |
+
self.vision_tower = SiglipVisionModel.from_pretrained(self.vision_tower_name)
|
465 |
+
|
466 |
+
self.hidden_size = self.vision_config.hidden_size
|
467 |
+
self.W = self.H = self.vision_config.image_size // self.vision_config.patch_size
|
468 |
+
self.T = 1
|
469 |
+
self.select_feature = "cls_patch"
|
470 |
+
|
471 |
+
|
472 |
+
class ApolloVisionTower(PreTrainedModel):
|
473 |
+
def __init__(self, config, vision_tower_cfg):
|
474 |
+
super(ApolloVisionTower, self).__init__(config, vision_tower_cfg)
|
475 |
+
self.model_name_or_path = vision_tower_cfg._name_or_path
|
476 |
+
self.vision_towers = vision_tower_cfg.vision_towers
|
477 |
+
self._config = vision_tower_cfg
|
478 |
+
|
479 |
+
for vision_tower_name in self.vision_towers:
|
480 |
+
if 'internvideo' in vision_tower_name.lower():
|
481 |
+
vision_tower = InternVideoTower(os.path.join(vision_tower_cfg._name_or_path, vision_tower_name), config)
|
482 |
+
elif 'siglip' in vision_tower_name.lower():
|
483 |
+
vision_tower = SiglipVisionTower(os.path.join(vision_tower_cfg._name_or_path, vision_tower_name),
|
484 |
+
config)
|
485 |
+
|
486 |
+
setattr(self, vision_tower_name, vision_tower)
|
487 |
+
|
488 |
+
self.vision_processor = [getattr(self, vt).vision_processor for vt in self.vision_towers]
|
489 |
+
self.num_vision_encoders = len(self.vision_towers)
|
490 |
+
self.W = self.H = max([getattr(self, vt).W for vt in self.vision_towers])
|
491 |
+
self.T = max([getattr(self, vt).T for vt in self.vision_towers])
|
492 |
+
self.max_tubelet_size = max(
|
493 |
+
[getattr(getattr(self, vt).vision_config, 'tubelet_size', 1) for vt in self.vision_towers])
|
494 |
+
|
495 |
+
self._hidden_size = sum([getattr(self, vt).hidden_size for vt in self.vision_towers])
|
496 |
+
self.token_output_shape = (self.T, self.W, self.H)
|
497 |
+
self.config.num_vision_encoders = self.num_vision_encoders
|
498 |
+
self.config.vision_towers = self.vision_towers
|
499 |
+
self.config.token_output_shape = self.token_output_shape
|
500 |
+
|
501 |
+
def forward(self, x):
|
502 |
+
output_features = []
|
503 |
+
for x_s, vision_tower_name in zip(x, self.vision_towers):
|
504 |
+
vision_tower = getattr(self, vision_tower_name)
|
505 |
+
features = vision_tower._forward(x_s, out_T=self.T)
|
506 |
+
|
507 |
+
if len(features.shape) != len(self.token_output_shape) + 2:
|
508 |
+
features = features.unsqueeze(1)
|
509 |
+
|
510 |
+
if features.shape[-len(self.token_output_shape) - 1:-1] != self.token_output_shape:
|
511 |
+
features = features.permute(0, 4, 1, 2, 3).contiguous() # shape [B, D, T, W, H]
|
512 |
+
features = F.interpolate(features.to(torch.float32), size=self.token_output_shape, mode='trilinear',
|
513 |
+
align_corners=False).to(features.dtype)
|
514 |
+
features = features.permute(0, 2, 3, 4, 1).contiguous()
|
515 |
+
|
516 |
+
output_features.append(features)
|
517 |
+
|
518 |
+
output_features = torch.cat(output_features, dim=-1)
|
519 |
+
output_features = torch.flatten(output_features, start_dim=1, end_dim=-2)
|
520 |
+
return output_features
|
521 |
+
|
522 |
+
def save_pretrained(
|
523 |
+
self,
|
524 |
+
save_directory: Union[str, os.PathLike],
|
525 |
+
state_dict=None,
|
526 |
+
**kwargs,
|
527 |
+
):
|
528 |
+
if state_dict is None:
|
529 |
+
state_dict = self.state_dict()
|
530 |
+
|
531 |
+
for vision_tower_name in self.vision_towers:
|
532 |
+
vision_tower = getattr(self, vision_tower_name)
|
533 |
+
vision_tower_state_dict = OrderedDict(
|
534 |
+
{k.split(f"vision_tower.{vision_tower_name}.vision_tower.")[-1]: v for k, v in state_dict.items() if
|
535 |
+
vision_tower_name in k}
|
536 |
+
)
|
537 |
+
vision_tower.vision_tower.save_pretrained(os.path.join(save_directory, vision_tower_name),
|
538 |
+
state_dict=vision_tower_state_dict, **kwargs)
|
539 |
+
vision_tower.vision_processor.save_pretrained(os.path.join(save_directory, vision_tower_name))
|
540 |
+
|
541 |
+
config = self.config
|
542 |
+
config.configs = {}
|
543 |
+
config.save_pretrained(save_directory)
|
544 |
+
|
545 |
+
@property
|
546 |
+
def patch_size(self):
|
547 |
+
return self._patch_size
|
548 |
+
|
549 |
+
@property
|
550 |
+
def image_size(self):
|
551 |
+
return self._image_size
|
552 |
+
|
553 |
+
@property
|
554 |
+
def hidden_size(self):
|
555 |
+
return self._hidden_size
|
556 |
+
|
vision_tower/config.json
ADDED
@@ -0,0 +1,18 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"configs": {},
|
3 |
+
"model_type": "hybrid_vision_tower",
|
4 |
+
"num_vision_encoders": 2,
|
5 |
+
"token_output_shape": [
|
6 |
+
4,
|
7 |
+
27,
|
8 |
+
27
|
9 |
+
],
|
10 |
+
"transformers_version": "4.44.0",
|
11 |
+
"vision_towers": [
|
12 |
+
"siglip-so400m-patch14-384",
|
13 |
+
"internvideo2"
|
14 |
+
],
|
15 |
+
"auto_map": {
|
16 |
+
"AutoConfig": "configuration_hybrid.HybridTowerConfig"
|
17 |
+
}
|
18 |
+
}
|
vision_tower/configuration_hybrid.py
ADDED
@@ -0,0 +1,48 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import numpy as np
|
2 |
+
import torch
|
3 |
+
import torch.nn as nn
|
4 |
+
from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
|
5 |
+
import os
|
6 |
+
import torch.nn.functional as F
|
7 |
+
from transformers.modeling_utils import PreTrainedModel
|
8 |
+
from transformers.configuration_utils import PretrainedConfig
|
9 |
+
from transformers import AutoConfig
|
10 |
+
from collections import OrderedDict
|
11 |
+
|
12 |
+
|
13 |
+
class HybridTowerConfig(PretrainedConfig):
|
14 |
+
model_type = "hybrid_vision_tower"
|
15 |
+
|
16 |
+
def __init__(self, configs=None, **kwargs):
|
17 |
+
"""
|
18 |
+
Initializes the HybridTowerConfig.
|
19 |
+
|
20 |
+
Args:
|
21 |
+
configs (dict, optional): A dictionary where keys are component names and values are
|
22 |
+
instances of configurations that have a `to_dict()` method.
|
23 |
+
**kwargs: Additional keyword arguments that are passed to the superclass.
|
24 |
+
"""
|
25 |
+
super().__init__(**kwargs)
|
26 |
+
self.configs = {}
|
27 |
+
|
28 |
+
if configs is not None:
|
29 |
+
if not isinstance(configs, dict):
|
30 |
+
raise TypeError("configs must be a dictionary where keys are component names and values are configuration objects.")
|
31 |
+
|
32 |
+
for component_name, config in configs.items():
|
33 |
+
if hasattr(config, 'to_dict'):
|
34 |
+
self.configs[component_name] = config.to_dict()
|
35 |
+
else:
|
36 |
+
raise TypeError(f"The configuration for '{component_name}' does not have a to_dict() method and cannot be serialized.")
|
37 |
+
|
38 |
+
def to_dict(self):
|
39 |
+
"""
|
40 |
+
Serializes this instance to a Python dictionary.
|
41 |
+
|
42 |
+
Returns:
|
43 |
+
dict: A dictionary containing all the keys and values of this configuration instance.
|
44 |
+
"""
|
45 |
+
config_dict = super().to_dict()
|
46 |
+
config_dict['configs'] = self.configs
|
47 |
+
return config_dict
|
48 |
+
|
vision_tower/internvideo2/config.json
ADDED
@@ -0,0 +1,54 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"architectures": [
|
3 |
+
"PretrainInternVideo2"
|
4 |
+
],
|
5 |
+
"attention_probs_dropout_prob": 0.0,
|
6 |
+
"attn_pool_num_heads": 16,
|
7 |
+
"checkpoint_num": 40,
|
8 |
+
"clip_embed_dim": 768,
|
9 |
+
"clip_input_resolution": 224,
|
10 |
+
"clip_norm_type": "l2",
|
11 |
+
"clip_return_layer": 6,
|
12 |
+
"clip_student_return_interval": 1,
|
13 |
+
"clip_teacher": null,
|
14 |
+
"clip_teacher_embed_dim": 3200,
|
15 |
+
"clip_teacher_final_dim": 768,
|
16 |
+
"clip_teacher_return_interval": 1,
|
17 |
+
"d_model": 1408,
|
18 |
+
"encoder_stride": 16,
|
19 |
+
"hidden_act": "gelu",
|
20 |
+
"hidden_dropout_prob": 0.0,
|
21 |
+
"hidden_size": 768,
|
22 |
+
"image_mask_ratio": 0.5,
|
23 |
+
"image_mask_type": "random",
|
24 |
+
"image_size": 224,
|
25 |
+
"img_size": 224,
|
26 |
+
"initializer_range": 0.02,
|
27 |
+
"intermediate_size": 3072,
|
28 |
+
"keep_temporal": false,
|
29 |
+
"layer_norm_eps": 1e-12,
|
30 |
+
"model_type": "internvideo2",
|
31 |
+
"name": "pretrain_internvideo2_1b_patch14_224",
|
32 |
+
"num_attention_heads": 12,
|
33 |
+
"num_channels": 3,
|
34 |
+
"num_frames": 4,
|
35 |
+
"num_heads": 16,
|
36 |
+
"num_hidden_layers": 12,
|
37 |
+
"only_mask": true,
|
38 |
+
"patch_size": 14,
|
39 |
+
"qkv_bias": false,
|
40 |
+
"sep_image_video_pos_embed": true,
|
41 |
+
"torch_dtype": "bfloat16",
|
42 |
+
"transformers_version": "4.44.0",
|
43 |
+
"tubelet_size": 1,
|
44 |
+
"use_checkpoint": true,
|
45 |
+
"use_flash_attn": false,
|
46 |
+
"use_fused_mlp": false,
|
47 |
+
"use_fused_rmsnorm": false,
|
48 |
+
"video_mask_ratio": 0.8,
|
49 |
+
"video_mask_type": "random",
|
50 |
+
"auto_map": {
|
51 |
+
"AutoConfig": "configuration_internvideo2.InternVideo2Config",
|
52 |
+
"AutoModel": "modeling_internvideo2.InternVideo2Model"
|
53 |
+
}
|
54 |
+
}
|
vision_tower/internvideo2/configuration_internvideo2.py
ADDED
@@ -0,0 +1,91 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from transformers import PretrainedConfig
|
2 |
+
|
3 |
+
|
4 |
+
class InternVideo2Config(PretrainedConfig):
|
5 |
+
model_type = "internvideo2"
|
6 |
+
|
7 |
+
def __init__(
|
8 |
+
self,
|
9 |
+
img_size=224,
|
10 |
+
patch_size=14,
|
11 |
+
tubelet_size=1,
|
12 |
+
num_frames=8,
|
13 |
+
d_model=1408,
|
14 |
+
num_heads=16,
|
15 |
+
depth=40,
|
16 |
+
mlp_ratio=48 / 11,
|
17 |
+
qkv_bias=False,
|
18 |
+
init_values=1e-5,
|
19 |
+
use_checkpoint=False,
|
20 |
+
checkpoint_num=0,
|
21 |
+
use_flash_attn=False,
|
22 |
+
use_fused_mlp=False,
|
23 |
+
use_fused_rmsnorm=False,
|
24 |
+
qk_normalization=True,
|
25 |
+
clip_embed_dim=1408,
|
26 |
+
attn_pool_num_heads=16,
|
27 |
+
clip_teacher_embed_dim=512,
|
28 |
+
clip_teacher_final_dim=512,
|
29 |
+
clip_student_return_interval=4,
|
30 |
+
clip_return_layer=3,
|
31 |
+
clip_norm_type="l2",
|
32 |
+
sep_image_video_pos_embed=False,
|
33 |
+
**kwargs,
|
34 |
+
):
|
35 |
+
"""
|
36 |
+
This is the configuration class to store the configuration of a `InternVideo2Model`.
|
37 |
+
It is used to instantiate a InternVideo2 model according to the specified arguments,
|
38 |
+
defining the model architecture.
|
39 |
+
|
40 |
+
Args:
|
41 |
+
img_size (int, optional): Input image size. Defaults to 224.
|
42 |
+
patch_size (int, optional): Size of each patch. Defaults to 14.
|
43 |
+
tubelet_size (int, optional): Temporal tubelet size. Defaults to 1.
|
44 |
+
num_frames (int, optional): Number of frames in the video input. Defaults to 8.
|
45 |
+
d_model (int, optional): Dimension of the model embeddings. Defaults to 1408.
|
46 |
+
num_heads (int, optional): Number of attention heads. Defaults to 16.
|
47 |
+
depth (int, optional): Number of transformer encoder layers. Defaults to 40.
|
48 |
+
mlp_ratio (float, optional): Ratio of MLP hidden dim to embedding dim. Defaults to 48/11.
|
49 |
+
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Defaults to False.
|
50 |
+
init_values (float, optional): Initial values for layer scale. Defaults to 1e-5.
|
51 |
+
use_checkpoint (bool, optional): Whether to use gradient checkpointing. Defaults to False.
|
52 |
+
checkpoint_num (int, optional): Number of layers to apply checkpointing. Defaults to 0.
|
53 |
+
use_flash_attn (bool, optional): Whether to use FlashAttention. Defaults to False.
|
54 |
+
use_fused_mlp (bool, optional): Whether to use fused MLP. Defaults to False.
|
55 |
+
use_fused_rmsnorm (bool, optional): Whether to use fused RMSNorm. Defaults to False.
|
56 |
+
qk_normalization (bool, optional): Whether to apply QK normalization. Defaults to True.
|
57 |
+
clip_embed_dim (int, optional): Embedding dimension for CLIP. Defaults to 1408.
|
58 |
+
attn_pool_num_heads (int, optional): Number of heads for attention pooling. Defaults to 16.
|
59 |
+
clip_teacher_embed_dim (int, optional): Embedding dimension for CLIP teacher model. Defaults to 512.
|
60 |
+
clip_teacher_final_dim (int, optional): Final embedding dimension for CLIP teacher model. Defaults to 512.
|
61 |
+
clip_student_return_interval (int, optional): Interval for returning student layers. Defaults to 4.
|
62 |
+
clip_return_layer (int, optional): Number of layers to return for alignment. Defaults to 3.
|
63 |
+
clip_norm_type (str, optional): Normalization type for CLIP ('l2' or 'none'). Defaults to 'l2'.
|
64 |
+
sep_image_video_pos_embed (bool, optional): Whether to use separate position embeddings for image and video. Defaults to False.
|
65 |
+
**kwargs: Additional keyword arguments.
|
66 |
+
"""
|
67 |
+
super().__init__(**kwargs)
|
68 |
+
self.img_size = img_size
|
69 |
+
self.patch_size = patch_size
|
70 |
+
self.tubelet_size = tubelet_size
|
71 |
+
self.num_frames = num_frames
|
72 |
+
self.d_model = d_model
|
73 |
+
self.num_heads = num_heads
|
74 |
+
self.depth = depth
|
75 |
+
self.mlp_ratio = mlp_ratio
|
76 |
+
self.qkv_bias = qkv_bias
|
77 |
+
self.init_values = init_values
|
78 |
+
self.use_checkpoint = use_checkpoint
|
79 |
+
self.checkpoint_num = checkpoint_num
|
80 |
+
self.use_flash_attn = use_flash_attn
|
81 |
+
self.use_fused_mlp = use_fused_mlp
|
82 |
+
self.use_fused_rmsnorm = use_fused_rmsnorm
|
83 |
+
self.qk_normalization = qk_normalization
|
84 |
+
self.clip_embed_dim = clip_embed_dim
|
85 |
+
self.attn_pool_num_heads = attn_pool_num_heads
|
86 |
+
self.clip_teacher_embed_dim = clip_teacher_embed_dim
|
87 |
+
self.clip_teacher_final_dim = clip_teacher_final_dim
|
88 |
+
self.clip_student_return_interval = clip_student_return_interval
|
89 |
+
self.clip_return_layer = clip_return_layer
|
90 |
+
self.clip_norm_type = clip_norm_type
|
91 |
+
self.sep_image_video_pos_embed = sep_image_video_pos_embed
|
vision_tower/internvideo2/model.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:fc98ff193aca2992ed2f56fea01c4d7be2e2c737cf9fcc5a73ef31663e728624
|
3 |
+
size 2098289968
|
vision_tower/internvideo2/modeling_internvideo2.py
ADDED
@@ -0,0 +1,934 @@
|
|
|
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1 |
+
# modeling_internvideo2.py
|
2 |
+
|
3 |
+
import logging
|
4 |
+
import math
|
5 |
+
import numpy as np
|
6 |
+
import torch
|
7 |
+
import torch.nn as nn
|
8 |
+
import torch.nn.functional as F
|
9 |
+
|
10 |
+
from transformers import PreTrainedModel
|
11 |
+
from transformers.utils import logging as hf_logging
|
12 |
+
|
13 |
+
from torch.utils.checkpoint import checkpoint # Correct
|
14 |
+
|
15 |
+
from functools import partial
|
16 |
+
|
17 |
+
from .configuration_internvideo2 import InternVideo2Config # Import the configuration
|
18 |
+
|
19 |
+
try:
|
20 |
+
from einops import rearrange
|
21 |
+
except ImportError:
|
22 |
+
raise ImportError("Please install einops to use this model.")
|
23 |
+
|
24 |
+
try:
|
25 |
+
from timm.models.layers import DropPath, to_2tuple
|
26 |
+
except ImportError:
|
27 |
+
raise ImportError("Please install timm to use this model.")
|
28 |
+
|
29 |
+
logger = hf_logging.get_logger(__name__)
|
30 |
+
|
31 |
+
# Position embedding functions
|
32 |
+
def get_3d_sincos_pos_embed(embed_dim, grid_size, t_size, cls_token=False):
|
33 |
+
assert embed_dim % 4 == 0
|
34 |
+
embed_dim_spatial = embed_dim // 4 * 3
|
35 |
+
embed_dim_temporal = embed_dim // 4
|
36 |
+
|
37 |
+
# Spatial
|
38 |
+
grid_h = np.arange(grid_size, dtype=np.float32)
|
39 |
+
grid_w = np.arange(grid_size, dtype=np.float32)
|
40 |
+
grid = np.meshgrid(grid_w, grid_h) # W first
|
41 |
+
grid = np.stack(grid, axis=0)
|
42 |
+
|
43 |
+
grid = grid.reshape([2, 1, grid_size, grid_size])
|
44 |
+
pos_embed_spatial = get_2d_sincos_pos_embed_from_grid(embed_dim_spatial, grid)
|
45 |
+
|
46 |
+
# Temporal
|
47 |
+
grid_t = np.arange(t_size, dtype=np.float32)
|
48 |
+
pos_embed_temporal = get_1d_sincos_pos_embed_from_grid(embed_dim_temporal, grid_t)
|
49 |
+
|
50 |
+
# Combine spatial and temporal embeddings
|
51 |
+
pos_embed_temporal = pos_embed_temporal[:, np.newaxis, :]
|
52 |
+
pos_embed_temporal = np.repeat(pos_embed_temporal, grid_size**2, axis=1)
|
53 |
+
pos_embed_spatial = pos_embed_spatial[np.newaxis, :, :]
|
54 |
+
pos_embed_spatial = np.repeat(pos_embed_spatial, t_size, axis=0)
|
55 |
+
|
56 |
+
pos_embed = np.concatenate([pos_embed_temporal, pos_embed_spatial], axis=-1)
|
57 |
+
pos_embed = pos_embed.reshape([-1, embed_dim])
|
58 |
+
|
59 |
+
if cls_token:
|
60 |
+
pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
|
61 |
+
return pos_embed
|
62 |
+
|
63 |
+
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
|
64 |
+
assert embed_dim % 2 == 0
|
65 |
+
|
66 |
+
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])
|
67 |
+
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])
|
68 |
+
|
69 |
+
emb = np.concatenate([emb_h, emb_w], axis=1)
|
70 |
+
return emb
|
71 |
+
|
72 |
+
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
|
73 |
+
assert embed_dim % 2 == 0
|
74 |
+
omega = np.arange(embed_dim // 2, dtype=np.float32)
|
75 |
+
omega /= embed_dim / 2.0
|
76 |
+
omega = 1.0 / (10000 ** omega)
|
77 |
+
|
78 |
+
pos = pos.reshape(-1)
|
79 |
+
out = np.einsum('m,d->md', pos, omega)
|
80 |
+
|
81 |
+
emb_sin = np.sin(out)
|
82 |
+
emb_cos = np.cos(out)
|
83 |
+
|
84 |
+
emb = np.concatenate([emb_sin, emb_cos], axis=1)
|
85 |
+
return emb
|
86 |
+
|
87 |
+
# Define necessary classes: CrossAttention, AttentiveBlock, AttentionPoolingBlock, RMSNorm, LayerScale, Attention, Mlp, Block, PatchEmbed, Linear_Decoder
|
88 |
+
|
89 |
+
|
90 |
+
class CrossAttention(nn.Module):
|
91 |
+
def __init__(
|
92 |
+
self,
|
93 |
+
dim,
|
94 |
+
num_heads=8,
|
95 |
+
qkv_bias=False,
|
96 |
+
qk_scale=None,
|
97 |
+
attn_drop=0.0,
|
98 |
+
proj_drop=0.0,
|
99 |
+
attn_head_dim=None,
|
100 |
+
out_dim=None,
|
101 |
+
):
|
102 |
+
super().__init__()
|
103 |
+
if out_dim is None:
|
104 |
+
out_dim = dim
|
105 |
+
self.num_heads = num_heads
|
106 |
+
head_dim = dim // num_heads
|
107 |
+
if attn_head_dim is not None:
|
108 |
+
head_dim = attn_head_dim
|
109 |
+
all_head_dim = head_dim * self.num_heads
|
110 |
+
self.scale = qk_scale or head_dim ** -0.5
|
111 |
+
assert all_head_dim == dim
|
112 |
+
|
113 |
+
self.q = nn.Linear(dim, all_head_dim, bias=False)
|
114 |
+
self.k = nn.Linear(dim, all_head_dim, bias=False)
|
115 |
+
self.v = nn.Linear(dim, all_head_dim, bias=False)
|
116 |
+
|
117 |
+
if qkv_bias:
|
118 |
+
self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
|
119 |
+
self.k_bias = nn.Parameter(torch.zeros(all_head_dim))
|
120 |
+
self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
|
121 |
+
else:
|
122 |
+
self.q_bias = None
|
123 |
+
self.k_bias = None
|
124 |
+
self.v_bias = None
|
125 |
+
|
126 |
+
self.attn_drop = nn.Dropout(attn_drop)
|
127 |
+
self.proj = nn.Linear(all_head_dim, out_dim)
|
128 |
+
self.proj_drop = nn.Dropout(proj_drop)
|
129 |
+
|
130 |
+
def forward(self, x, k=None, v=None):
|
131 |
+
B, N, C = x.shape
|
132 |
+
N_k = k.shape[1]
|
133 |
+
N_v = v.shape[1]
|
134 |
+
|
135 |
+
q_bias, k_bias, v_bias = None, None, None
|
136 |
+
if self.q_bias is not None:
|
137 |
+
q_bias = self.q_bias
|
138 |
+
k_bias = self.k_bias
|
139 |
+
v_bias = self.v_bias
|
140 |
+
|
141 |
+
q = F.linear(input=x, weight=self.q.weight, bias=q_bias)
|
142 |
+
q = (
|
143 |
+
q.reshape(B, N, 1, self.num_heads, -1)
|
144 |
+
.permute(2, 0, 3, 1, 4)
|
145 |
+
.squeeze(0)
|
146 |
+
) # (B, N_head, N_q, dim)
|
147 |
+
|
148 |
+
k = F.linear(input=k, weight=self.k.weight, bias=k_bias)
|
149 |
+
k = (
|
150 |
+
k.reshape(B, N_k, 1, self.num_heads, -1)
|
151 |
+
.permute(2, 0, 3, 1, 4)
|
152 |
+
.squeeze(0)
|
153 |
+
)
|
154 |
+
|
155 |
+
v = F.linear(input=v, weight=self.v.weight, bias=v_bias)
|
156 |
+
v = (
|
157 |
+
v.reshape(B, N_v, 1, self.num_heads, -1)
|
158 |
+
.permute(2, 0, 3, 1, 4)
|
159 |
+
.squeeze(0)
|
160 |
+
)
|
161 |
+
|
162 |
+
q = q * self.scale
|
163 |
+
attn = q @ k.transpose(-2, -1) # (B, N_head, N_q, N_k)
|
164 |
+
|
165 |
+
attn = attn.softmax(dim=-1)
|
166 |
+
attn = self.attn_drop(attn)
|
167 |
+
|
168 |
+
x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
|
169 |
+
x = self.proj(x)
|
170 |
+
x = self.proj_drop(x)
|
171 |
+
|
172 |
+
return x
|
173 |
+
|
174 |
+
|
175 |
+
class AttentiveBlock(nn.Module):
|
176 |
+
def __init__(
|
177 |
+
self,
|
178 |
+
dim,
|
179 |
+
num_heads,
|
180 |
+
qkv_bias=False,
|
181 |
+
qk_scale=None,
|
182 |
+
drop=0.0,
|
183 |
+
attn_drop=0.0,
|
184 |
+
drop_path=0.0,
|
185 |
+
norm_layer=nn.LayerNorm,
|
186 |
+
attn_head_dim=None,
|
187 |
+
out_dim=None,
|
188 |
+
):
|
189 |
+
super().__init__()
|
190 |
+
|
191 |
+
self.norm1_q = norm_layer(dim)
|
192 |
+
self.norm1_k = norm_layer(dim)
|
193 |
+
self.norm1_v = norm_layer(dim)
|
194 |
+
self.cross_attn = CrossAttention(
|
195 |
+
dim,
|
196 |
+
num_heads=num_heads,
|
197 |
+
qkv_bias=qkv_bias,
|
198 |
+
qk_scale=qk_scale,
|
199 |
+
attn_drop=attn_drop,
|
200 |
+
proj_drop=drop,
|
201 |
+
attn_head_dim=attn_head_dim,
|
202 |
+
out_dim=out_dim,
|
203 |
+
)
|
204 |
+
|
205 |
+
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
|
206 |
+
|
207 |
+
def forward(
|
208 |
+
self, x_q, x_kv, pos_q, pos_k, bool_masked_pos, rel_pos_bias=None
|
209 |
+
):
|
210 |
+
x_q = self.norm1_q(x_q + pos_q)
|
211 |
+
x_k = self.norm1_k(x_kv + pos_k)
|
212 |
+
x_v = self.norm1_v(x_kv)
|
213 |
+
x = self.cross_attn(x_q, k=x_k, v=x_v)
|
214 |
+
|
215 |
+
return x
|
216 |
+
|
217 |
+
|
218 |
+
class AttentionPoolingBlock(AttentiveBlock):
|
219 |
+
def forward(self, x):
|
220 |
+
x_q = x.mean(1, keepdim=True)
|
221 |
+
x_kv, pos_q, pos_k = x, 0, 0
|
222 |
+
x = super().forward(
|
223 |
+
x_q, x_kv, pos_q, pos_k, bool_masked_pos=None, rel_pos_bias=None
|
224 |
+
)
|
225 |
+
x = x.squeeze(1)
|
226 |
+
return x
|
227 |
+
|
228 |
+
|
229 |
+
class RMSNorm(nn.Module):
|
230 |
+
def __init__(self, hidden_size, eps=1e-6):
|
231 |
+
super().__init__()
|
232 |
+
self.weight = nn.Parameter(torch.ones(hidden_size))
|
233 |
+
self.variance_epsilon = eps
|
234 |
+
|
235 |
+
def forward(self, hidden_states):
|
236 |
+
input_dtype = hidden_states.dtype
|
237 |
+
hidden_states = hidden_states.to(torch.float32)
|
238 |
+
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
239 |
+
hidden_states = hidden_states * torch.rsqrt(
|
240 |
+
variance + self.variance_epsilon
|
241 |
+
)
|
242 |
+
return self.weight * hidden_states.to(input_dtype)
|
243 |
+
|
244 |
+
|
245 |
+
class LayerScale(nn.Module):
|
246 |
+
def __init__(
|
247 |
+
self, dim, init_values=1e-5, inplace=False, force_fp32=False
|
248 |
+
):
|
249 |
+
super().__init__()
|
250 |
+
self.inplace = inplace
|
251 |
+
self.weight = nn.Parameter(init_values * torch.ones(dim))
|
252 |
+
self.force_fp32 = force_fp32
|
253 |
+
|
254 |
+
@torch.cuda.amp.autocast(enabled=False)
|
255 |
+
def forward(self, x):
|
256 |
+
if self.force_fp32:
|
257 |
+
output_type = x.dtype
|
258 |
+
out = (
|
259 |
+
x.float().mul_(self.weight.float())
|
260 |
+
if self.inplace
|
261 |
+
else x.float() * self.weight.float()
|
262 |
+
)
|
263 |
+
return out.to(dtype=output_type)
|
264 |
+
else:
|
265 |
+
out = x.mul_(self.weight) if self.inplace else x * self.weight
|
266 |
+
return out
|
267 |
+
|
268 |
+
|
269 |
+
class Attention(nn.Module):
|
270 |
+
def __init__(
|
271 |
+
self,
|
272 |
+
dim,
|
273 |
+
num_heads=8,
|
274 |
+
qkv_bias=False,
|
275 |
+
attn_drop=0.0,
|
276 |
+
proj_drop=0.0,
|
277 |
+
use_flash_attn=False,
|
278 |
+
causal=False,
|
279 |
+
norm_layer=nn.LayerNorm,
|
280 |
+
qk_normalization=False,
|
281 |
+
use_fused_rmsnorm=False,
|
282 |
+
):
|
283 |
+
super().__init__()
|
284 |
+
assert (
|
285 |
+
dim % num_heads == 0
|
286 |
+
), "dim should be divisible by num_heads"
|
287 |
+
self.num_heads = num_heads
|
288 |
+
head_dim = dim // num_heads
|
289 |
+
self.scale = head_dim ** -0.5
|
290 |
+
|
291 |
+
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
292 |
+
self.attn_drop = nn.Dropout(attn_drop)
|
293 |
+
self.proj = nn.Linear(dim, dim)
|
294 |
+
self.proj_drop = nn.Dropout(proj_drop)
|
295 |
+
|
296 |
+
self.use_flash_attn = use_flash_attn
|
297 |
+
if use_flash_attn:
|
298 |
+
self.causal = causal
|
299 |
+
try:
|
300 |
+
from flash_attn.flash_attention import FlashAttention
|
301 |
+
|
302 |
+
self.inner_attn = FlashAttention(
|
303 |
+
attention_dropout=attn_drop
|
304 |
+
)
|
305 |
+
except ImportError:
|
306 |
+
raise ImportError(
|
307 |
+
"Please install flash_attn to use flash attention."
|
308 |
+
)
|
309 |
+
|
310 |
+
self.qk_normalization = qk_normalization
|
311 |
+
self.q_norm = norm_layer(dim) if qk_normalization else nn.Identity()
|
312 |
+
self.k_norm = norm_layer(dim) if qk_normalization else nn.Identity()
|
313 |
+
self.use_fused_rmsnorm = use_fused_rmsnorm
|
314 |
+
|
315 |
+
def _naive_attn(self, x):
|
316 |
+
B, N, C = x.shape
|
317 |
+
# print(x.shape, torch.cuda.memory_allocated(), torch.cuda.memory_allocated())
|
318 |
+
qkv = (
|
319 |
+
self.qkv(x)
|
320 |
+
.reshape(B, N, 3, self.num_heads, C // self.num_heads)
|
321 |
+
.permute(2, 0, 3, 1, 4)
|
322 |
+
)
|
323 |
+
q, k, v = qkv.unbind(
|
324 |
+
0
|
325 |
+
) # make torchscript happy (cannot use tensor as tuple)
|
326 |
+
|
327 |
+
if self.qk_normalization:
|
328 |
+
B_, H_, N_, D_ = q.shape
|
329 |
+
q = (
|
330 |
+
self.q_norm(q.transpose(1, 2).flatten(-2, -1))
|
331 |
+
.view(B_, N_, H_, D_)
|
332 |
+
.transpose(1, 2)
|
333 |
+
)
|
334 |
+
k = (
|
335 |
+
self.k_norm(k.transpose(1, 2).flatten(-2, -1))
|
336 |
+
.view(B_, N_, H_, D_)
|
337 |
+
.transpose(1, 2)
|
338 |
+
)
|
339 |
+
|
340 |
+
attn = (q * self.scale) @ k.transpose(-2, -1)
|
341 |
+
# attn = attn - attn.max(-1)[0].unsqueeze(-1) # in case of overflow for fp16
|
342 |
+
attn = attn.softmax(dim=-1)
|
343 |
+
attn = self.attn_drop(attn)
|
344 |
+
# print(torch.cuda.memory_allocated(), torch.cuda.memory_allocated())
|
345 |
+
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
|
346 |
+
x = self.proj(x)
|
347 |
+
x = self.proj_drop(x)
|
348 |
+
return x
|
349 |
+
|
350 |
+
def _flash_attn(
|
351 |
+
self, x, key_padding_mask=None, need_weights=False
|
352 |
+
):
|
353 |
+
qkv = self.qkv(x)
|
354 |
+
qkv = rearrange(
|
355 |
+
qkv, "b s (three h d) -> b s three h d", three=3, h=self.num_heads
|
356 |
+
)
|
357 |
+
|
358 |
+
if self.qk_normalization:
|
359 |
+
q, k, v = qkv.unbind(2)
|
360 |
+
if self.use_fused_rmsnorm:
|
361 |
+
q = self.q_norm(q.flatten(-2, -1))[0].view(q.shape)
|
362 |
+
k = self.k_norm(k.flatten(-2, -1))[0].view(k.shape)
|
363 |
+
else:
|
364 |
+
q = self.q_norm(q.flatten(-2, -1)).view(q.shape)
|
365 |
+
k = self.k_norm(k.flatten(-2, -1)).view(k.shape)
|
366 |
+
qkv = torch.stack([q, k, v], dim=2)
|
367 |
+
|
368 |
+
context, _ = self.inner_attn(
|
369 |
+
qkv,
|
370 |
+
key_padding_mask=key_padding_mask,
|
371 |
+
need_weights=need_weights,
|
372 |
+
causal=self.causal,
|
373 |
+
)
|
374 |
+
outs = self.proj(rearrange(context, "b s h d -> b s (h d)"))
|
375 |
+
outs = self.proj_drop(outs)
|
376 |
+
return outs
|
377 |
+
|
378 |
+
def forward(self, x):
|
379 |
+
x = (
|
380 |
+
self._naive_attn(x)
|
381 |
+
if not self.use_flash_attn
|
382 |
+
else self._flash_attn(x)
|
383 |
+
)
|
384 |
+
return x
|
385 |
+
|
386 |
+
|
387 |
+
class Mlp(nn.Module):
|
388 |
+
"""MLP as used in Vision Transformer, MLP-Mixer and related networks"""
|
389 |
+
|
390 |
+
def __init__(
|
391 |
+
self,
|
392 |
+
in_features,
|
393 |
+
hidden_features=None,
|
394 |
+
out_features=None,
|
395 |
+
act_layer=nn.GELU,
|
396 |
+
bias=True,
|
397 |
+
drop=0.0,
|
398 |
+
):
|
399 |
+
super().__init__()
|
400 |
+
out_features = out_features or in_features
|
401 |
+
hidden_features = hidden_features or in_features
|
402 |
+
bias = to_2tuple(bias)
|
403 |
+
drop_probs = to_2tuple(drop)
|
404 |
+
|
405 |
+
self.fc1 = nn.Linear(in_features, hidden_features, bias=bias[0])
|
406 |
+
self.act = act_layer()
|
407 |
+
self.drop1 = nn.Dropout(drop_probs[0])
|
408 |
+
self.fc2 = nn.Linear(hidden_features, out_features, bias=bias[1])
|
409 |
+
self.drop2 = nn.Dropout(drop_probs[1])
|
410 |
+
|
411 |
+
def forward(self, x):
|
412 |
+
x = self.fc1(x)
|
413 |
+
x = self.act(x)
|
414 |
+
x = self.drop1(x)
|
415 |
+
x = self.fc2(x)
|
416 |
+
x = self.drop2(x)
|
417 |
+
return x
|
418 |
+
|
419 |
+
|
420 |
+
class Block(nn.Module):
|
421 |
+
def __init__(
|
422 |
+
self,
|
423 |
+
dim,
|
424 |
+
num_heads,
|
425 |
+
mlp_ratio=4.0,
|
426 |
+
qkv_bias=False,
|
427 |
+
drop=0.0,
|
428 |
+
attn_drop=0.0,
|
429 |
+
init_values=None,
|
430 |
+
drop_path=0.0,
|
431 |
+
act_layer=nn.GELU,
|
432 |
+
norm_layer=nn.LayerNorm,
|
433 |
+
use_flash_attn=False,
|
434 |
+
use_fused_mlp=False,
|
435 |
+
fused_mlp_heuristic=1,
|
436 |
+
with_cp=False,
|
437 |
+
qk_normalization=False,
|
438 |
+
layerscale_no_force_fp32=False,
|
439 |
+
use_fused_rmsnorm=False,
|
440 |
+
):
|
441 |
+
super().__init__()
|
442 |
+
|
443 |
+
self.norm1 = norm_layer(dim)
|
444 |
+
self.attn = Attention(
|
445 |
+
dim,
|
446 |
+
num_heads=num_heads,
|
447 |
+
qkv_bias=qkv_bias,
|
448 |
+
attn_drop=attn_drop,
|
449 |
+
proj_drop=drop,
|
450 |
+
use_flash_attn=use_flash_attn,
|
451 |
+
causal=False,
|
452 |
+
norm_layer=norm_layer,
|
453 |
+
qk_normalization=qk_normalization,
|
454 |
+
use_fused_rmsnorm=use_fused_rmsnorm,
|
455 |
+
)
|
456 |
+
self.ls1 = (
|
457 |
+
LayerScale(
|
458 |
+
dim,
|
459 |
+
init_values=init_values,
|
460 |
+
force_fp32=(not layerscale_no_force_fp32),
|
461 |
+
)
|
462 |
+
if init_values
|
463 |
+
else nn.Identity()
|
464 |
+
)
|
465 |
+
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
|
466 |
+
self.drop_path1 = (
|
467 |
+
DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
|
468 |
+
)
|
469 |
+
|
470 |
+
self.norm2 = norm_layer(dim)
|
471 |
+
mlp_hidden_dim = int(dim * mlp_ratio)
|
472 |
+
if use_fused_mlp:
|
473 |
+
try:
|
474 |
+
from flash_attn.modules.mlp import FusedMLP
|
475 |
+
except ImportError:
|
476 |
+
raise ImportError(
|
477 |
+
"Please install flash_attn to use fused MLP."
|
478 |
+
)
|
479 |
+
self.mlp = FusedMLP(
|
480 |
+
in_features=dim,
|
481 |
+
hidden_features=mlp_hidden_dim,
|
482 |
+
heuristic=fused_mlp_heuristic,
|
483 |
+
)
|
484 |
+
else:
|
485 |
+
self.mlp = Mlp(
|
486 |
+
in_features=dim,
|
487 |
+
hidden_features=mlp_hidden_dim,
|
488 |
+
act_layer=act_layer,
|
489 |
+
drop=drop,
|
490 |
+
)
|
491 |
+
self.ls2 = (
|
492 |
+
LayerScale(
|
493 |
+
dim,
|
494 |
+
init_values=init_values,
|
495 |
+
force_fp32=(not layerscale_no_force_fp32),
|
496 |
+
)
|
497 |
+
if init_values
|
498 |
+
else nn.Identity()
|
499 |
+
)
|
500 |
+
self.drop_path2 = (
|
501 |
+
DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
|
502 |
+
)
|
503 |
+
|
504 |
+
self.with_cp = with_cp
|
505 |
+
self.use_fused_rmsnorm = use_fused_rmsnorm
|
506 |
+
|
507 |
+
def forward(self, x, residual=None):
|
508 |
+
def _inner_forward(x, residual=None):
|
509 |
+
if self.use_fused_rmsnorm:
|
510 |
+
x, residual = self.norm1(x, residual)
|
511 |
+
x = self.drop_path1(self.ls1(self.attn(x)))
|
512 |
+
x, residual = self.norm2(x, residual)
|
513 |
+
x = self.drop_path2(self.ls2(self.mlp(x)))
|
514 |
+
return x, residual
|
515 |
+
else:
|
516 |
+
assert residual is None
|
517 |
+
x = x + self.drop_path1(
|
518 |
+
self.ls1(self.attn(self.norm1(x)))
|
519 |
+
)
|
520 |
+
x = x + self.drop_path2(
|
521 |
+
self.ls2(self.mlp(self.norm2(x)))
|
522 |
+
)
|
523 |
+
return x
|
524 |
+
|
525 |
+
if self.with_cp:
|
526 |
+
return checkpoint(_inner_forward, x, residual)
|
527 |
+
else:
|
528 |
+
return _inner_forward(x, residual=residual)
|
529 |
+
|
530 |
+
|
531 |
+
class PatchEmbed(nn.Module):
|
532 |
+
"""3D Image to Patch Embedding"""
|
533 |
+
|
534 |
+
def __init__(
|
535 |
+
self,
|
536 |
+
img_size=224,
|
537 |
+
patch_size=16,
|
538 |
+
in_chans=3,
|
539 |
+
embed_dim=768,
|
540 |
+
num_frames=8,
|
541 |
+
tubelet_size=1,
|
542 |
+
norm_layer=None,
|
543 |
+
):
|
544 |
+
super().__init__()
|
545 |
+
img_size = to_2tuple(img_size)
|
546 |
+
patch_size = to_2tuple(patch_size)
|
547 |
+
self.img_size = img_size
|
548 |
+
self.patch_size = patch_size
|
549 |
+
self.grid_size = (
|
550 |
+
num_frames // tubelet_size,
|
551 |
+
img_size[0] // patch_size[0],
|
552 |
+
img_size[1] // patch_size[1],
|
553 |
+
) # (T, H, W)
|
554 |
+
self.num_patches = (
|
555 |
+
self.grid_size[0] * self.grid_size[1] * self.grid_size[2]
|
556 |
+
)
|
557 |
+
self.num_img_patches = self.grid_size[1] * self.grid_size[2]
|
558 |
+
|
559 |
+
self.proj = nn.Conv3d(
|
560 |
+
in_channels=in_chans,
|
561 |
+
out_channels=embed_dim,
|
562 |
+
kernel_size=(tubelet_size, patch_size[0], patch_size[1]),
|
563 |
+
stride=(tubelet_size, patch_size[0], patch_size[1]),
|
564 |
+
)
|
565 |
+
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
|
566 |
+
|
567 |
+
def forward(self, x):
|
568 |
+
x = self.proj(x)
|
569 |
+
x = (
|
570 |
+
x.flatten(3)
|
571 |
+
.permute(0, 2, 3, 1)
|
572 |
+
) # B x C x T x HW => B x T x HW x C
|
573 |
+
x = self.norm(x)
|
574 |
+
return x
|
575 |
+
|
576 |
+
|
577 |
+
|
578 |
+
class Linear_Decoder(nn.Module):
|
579 |
+
def __init__(self, in_channels=1408, out_channels=3200, norm_layer=nn.LayerNorm, clip_norm_type='l2'):
|
580 |
+
super().__init__()
|
581 |
+
self.clip_norm_type = clip_norm_type
|
582 |
+
logger.info(f'Normalization Type: {clip_norm_type}')
|
583 |
+
|
584 |
+
self.head = nn.Linear(in_channels, out_channels)
|
585 |
+
self.norm = norm_layer(out_channels)
|
586 |
+
|
587 |
+
def forward(self, x):
|
588 |
+
x = self.norm(self.head(x))
|
589 |
+
|
590 |
+
if self.clip_norm_type == 'l2':
|
591 |
+
x = x / x.norm(dim=-1, keepdim=True)
|
592 |
+
elif self.clip_norm_type == 'none':
|
593 |
+
pass
|
594 |
+
else:
|
595 |
+
raise NotImplementedError
|
596 |
+
|
597 |
+
return x
|
598 |
+
|
599 |
+
class InternVideo2Model(PreTrainedModel):
|
600 |
+
config_class = InternVideo2Config
|
601 |
+
base_model_prefix = "internvideo2"
|
602 |
+
|
603 |
+
def __init__(self, config: InternVideo2Config):
|
604 |
+
super().__init__(config)
|
605 |
+
|
606 |
+
in_chans = 3
|
607 |
+
drop_path_rate = 0.25
|
608 |
+
qk_normalization = config.qk_normalization
|
609 |
+
clip_embed_dim = config.clip_embed_dim
|
610 |
+
num_heads = config.num_heads
|
611 |
+
qkv_bias = config.qkv_bias
|
612 |
+
init_values = config.init_values
|
613 |
+
mlp_ratio = config.mlp_ratio
|
614 |
+
depth = config.depth
|
615 |
+
num_frames = config.num_frames
|
616 |
+
self.num_frames = num_frames
|
617 |
+
self.tubelet_size = config.tubelet_size
|
618 |
+
use_fused_mlp = config.use_fused_mlp
|
619 |
+
use_fused_rmsnorm = config.use_fused_rmsnorm
|
620 |
+
use_flash_attn = config.use_flash_attn
|
621 |
+
assert (
|
622 |
+
use_flash_attn
|
623 |
+
== use_fused_rmsnorm
|
624 |
+
== use_fused_mlp
|
625 |
+
), "use_flash_attn, use_fused_rmsnorm and use_fused_mlp should be consistent"
|
626 |
+
|
627 |
+
self.use_flash_attn = use_flash_attn
|
628 |
+
embed_dim = config.d_model
|
629 |
+
self.embed_dim = embed_dim
|
630 |
+
|
631 |
+
self.depth = depth
|
632 |
+
self.clip_norm_type = config.clip_norm_type
|
633 |
+
self.return_index = []
|
634 |
+
for i in range(config.clip_return_layer):
|
635 |
+
self.return_index.append(
|
636 |
+
depth - int(i * config.clip_student_return_interval) - 1
|
637 |
+
)
|
638 |
+
logger.info(f"Normalization Type: {config.clip_norm_type}")
|
639 |
+
logger.info(f"Student Return Index: {self.return_index}")
|
640 |
+
|
641 |
+
if use_fused_rmsnorm:
|
642 |
+
try:
|
643 |
+
from flash_attn.ops.rms_norm import DropoutAddRMSNorm
|
644 |
+
except ImportError:
|
645 |
+
raise ImportError(
|
646 |
+
"Please install flash_attn to use fused RMSNorm."
|
647 |
+
)
|
648 |
+
norm_layer_for_blocks = partial(
|
649 |
+
DropoutAddRMSNorm, eps=1e-6, prenorm=True
|
650 |
+
)
|
651 |
+
else:
|
652 |
+
norm_layer_for_blocks = partial(RMSNorm, eps=1e-6)
|
653 |
+
self.norm_layer_for_blocks = norm_layer_for_blocks
|
654 |
+
self.patch_embed = PatchEmbed(
|
655 |
+
config.img_size,
|
656 |
+
config.patch_size,
|
657 |
+
in_chans,
|
658 |
+
embed_dim,
|
659 |
+
num_frames=num_frames,
|
660 |
+
tubelet_size=self.tubelet_size,
|
661 |
+
)
|
662 |
+
num_patches = self.patch_embed.num_patches
|
663 |
+
num_img_patches = self.patch_embed.num_img_patches
|
664 |
+
|
665 |
+
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
|
666 |
+
|
667 |
+
self.sep_pos_embed = False
|
668 |
+
self.sep_image_video_pos_embed = config.sep_image_video_pos_embed
|
669 |
+
if self.sep_pos_embed:
|
670 |
+
raise NotImplementedError
|
671 |
+
else:
|
672 |
+
if self.sep_image_video_pos_embed:
|
673 |
+
logger.info(
|
674 |
+
"Use joint position embedding, for image and video we use different pos_embed."
|
675 |
+
)
|
676 |
+
self.pos_embed = nn.Parameter(
|
677 |
+
torch.zeros(1, num_patches + 1, embed_dim)
|
678 |
+
)
|
679 |
+
self.img_pos_embed = nn.Parameter(
|
680 |
+
torch.zeros(1, num_img_patches + 1, embed_dim)
|
681 |
+
)
|
682 |
+
# for CLIP decoder
|
683 |
+
self.clip_pos_embed = nn.Parameter(
|
684 |
+
torch.zeros(1, num_patches + 1, embed_dim)
|
685 |
+
)
|
686 |
+
self.clip_img_pos_embed = nn.Parameter(
|
687 |
+
torch.zeros(1, num_img_patches + 1, embed_dim)
|
688 |
+
)
|
689 |
+
else:
|
690 |
+
logger.info(
|
691 |
+
"Use joint position embedding, for image and video we use same pos_embed."
|
692 |
+
)
|
693 |
+
self.pos_embed = nn.Parameter(
|
694 |
+
torch.zeros(1, num_patches + 1, embed_dim)
|
695 |
+
)
|
696 |
+
self.clip_pos_embed = nn.Parameter(
|
697 |
+
torch.zeros(1, num_patches + 1, embed_dim)
|
698 |
+
)
|
699 |
+
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
|
700 |
+
# choose which layer to use checkpoint
|
701 |
+
with_cp_list = [False] * depth
|
702 |
+
if config.use_checkpoint:
|
703 |
+
for idx in range(depth):
|
704 |
+
if idx < config.checkpoint_num:
|
705 |
+
with_cp_list[idx] = True
|
706 |
+
logger.info(f"Droppath rate: {dpr}")
|
707 |
+
logger.info(f"Checkpoint list: {with_cp_list}")
|
708 |
+
|
709 |
+
self.blocks = nn.ModuleList(
|
710 |
+
[
|
711 |
+
Block(
|
712 |
+
embed_dim,
|
713 |
+
num_heads,
|
714 |
+
mlp_ratio,
|
715 |
+
qkv_bias=qkv_bias,
|
716 |
+
norm_layer=norm_layer_for_blocks,
|
717 |
+
drop_path=dpr[i],
|
718 |
+
init_values=init_values,
|
719 |
+
attn_drop=0.0,
|
720 |
+
use_flash_attn=use_flash_attn,
|
721 |
+
use_fused_mlp=use_fused_mlp,
|
722 |
+
fused_mlp_heuristic=1,
|
723 |
+
with_cp=with_cp_list[i],
|
724 |
+
qk_normalization=qk_normalization,
|
725 |
+
layerscale_no_force_fp32=False,
|
726 |
+
use_fused_rmsnorm=use_fused_rmsnorm,
|
727 |
+
)
|
728 |
+
for i in range(depth)
|
729 |
+
]
|
730 |
+
)
|
731 |
+
self.clip_projector = AttentionPoolingBlock(
|
732 |
+
dim=embed_dim,
|
733 |
+
num_heads=config.attn_pool_num_heads,
|
734 |
+
qkv_bias=True,
|
735 |
+
qk_scale=None,
|
736 |
+
drop=0.0,
|
737 |
+
attn_drop=0.0,
|
738 |
+
norm_layer=partial(nn.LayerNorm, eps=1e-5),
|
739 |
+
out_dim=clip_embed_dim,
|
740 |
+
)
|
741 |
+
|
742 |
+
# CLIP decoder
|
743 |
+
self.clip_decoder = nn.ModuleList(
|
744 |
+
[
|
745 |
+
Linear_Decoder(
|
746 |
+
in_channels=embed_dim,
|
747 |
+
out_channels=config.clip_teacher_embed_dim,
|
748 |
+
norm_layer=partial(nn.LayerNorm, eps=1e-5),
|
749 |
+
clip_norm_type=config.clip_norm_type,
|
750 |
+
)
|
751 |
+
for _ in range(config.clip_return_layer)
|
752 |
+
]
|
753 |
+
)
|
754 |
+
self.final_clip_decoder = nn.Identity()
|
755 |
+
if config.clip_teacher_final_dim > 0:
|
756 |
+
self.final_clip_decoder = Linear_Decoder(
|
757 |
+
in_channels=clip_embed_dim,
|
758 |
+
out_channels=config.clip_teacher_final_dim,
|
759 |
+
norm_layer=partial(nn.LayerNorm, eps=1e-5),
|
760 |
+
clip_norm_type=config.clip_norm_type,
|
761 |
+
)
|
762 |
+
|
763 |
+
# Removed initialization methods and code
|
764 |
+
|
765 |
+
@property
|
766 |
+
def dtype(self):
|
767 |
+
return self.patch_embed.proj.weight.dtype
|
768 |
+
|
769 |
+
def get_num_layers(self):
|
770 |
+
return len(self.blocks)
|
771 |
+
|
772 |
+
@torch.jit.ignore
|
773 |
+
def no_weight_decay(self):
|
774 |
+
return {
|
775 |
+
"pos_embed",
|
776 |
+
"pos_embed_spatial",
|
777 |
+
"pos_embed_temporal",
|
778 |
+
"pos_embed_cls",
|
779 |
+
"img_pos_embed",
|
780 |
+
"cls_token",
|
781 |
+
"clip_pos_embed",
|
782 |
+
"clip_pos_embed_spatial",
|
783 |
+
"clip_pos_embed_temporal",
|
784 |
+
"clip_pos_embed_cls",
|
785 |
+
"clip_img_pos_embed",
|
786 |
+
}
|
787 |
+
|
788 |
+
def forward(
|
789 |
+
self,
|
790 |
+
x,
|
791 |
+
mask=None,
|
792 |
+
use_image=False,
|
793 |
+
x_vis_return_idx=-1,
|
794 |
+
x_vis_only=False,
|
795 |
+
):
|
796 |
+
x = self.patch_embed(x.type(self.dtype))
|
797 |
+
B, T, L, C = x.shape
|
798 |
+
x = x.view([B, T * L, C])
|
799 |
+
|
800 |
+
# Append cls token
|
801 |
+
cls_tokens = self.cls_token.expand(B, -1, -1)
|
802 |
+
x = torch.cat((cls_tokens, x), dim=1)
|
803 |
+
|
804 |
+
# Add positional embeddings
|
805 |
+
if self.sep_pos_embed:
|
806 |
+
raise NotImplementedError
|
807 |
+
else:
|
808 |
+
if use_image:
|
809 |
+
if self.sep_image_video_pos_embed:
|
810 |
+
pos_embed = self.img_pos_embed
|
811 |
+
else:
|
812 |
+
cls_pos_embed = self.pos_embed[:, 0:1, :]
|
813 |
+
img_pos_embed = (
|
814 |
+
self.pos_embed[:, 1:, :]
|
815 |
+
.view(
|
816 |
+
1,
|
817 |
+
self.num_frames,
|
818 |
+
self.patch_embed.num_patches // self.num_frames,
|
819 |
+
self.embed_dim,
|
820 |
+
)
|
821 |
+
.mean(dim=1)
|
822 |
+
)
|
823 |
+
pos_embed = torch.cat(
|
824 |
+
[cls_pos_embed, img_pos_embed], dim=1
|
825 |
+
)
|
826 |
+
else:
|
827 |
+
pos_embed = self.pos_embed
|
828 |
+
x = x + pos_embed
|
829 |
+
|
830 |
+
# Mask tokens
|
831 |
+
if mask is not None:
|
832 |
+
x = x[~mask].reshape(B, -1, C)
|
833 |
+
else:
|
834 |
+
x = x.reshape(B, -1, C)
|
835 |
+
|
836 |
+
residual = None
|
837 |
+
x_clip = []
|
838 |
+
for idx, blk in enumerate(self.blocks):
|
839 |
+
if isinstance(x, tuple) and len(x) == 2:
|
840 |
+
x, residual = x
|
841 |
+
x = blk(x, residual=residual)
|
842 |
+
# Return intermediate features
|
843 |
+
if idx in self.return_index:
|
844 |
+
if isinstance(x, tuple) and len(x) == 2:
|
845 |
+
tmp_x, tmp_residual = x
|
846 |
+
if residual is not None:
|
847 |
+
x_clip.append(tmp_x + tmp_residual)
|
848 |
+
else:
|
849 |
+
x_clip.append(x)
|
850 |
+
if idx == (self.depth + x_vis_return_idx):
|
851 |
+
break
|
852 |
+
|
853 |
+
if isinstance(x, tuple) and len(x) == 2:
|
854 |
+
x, residual = x
|
855 |
+
if residual is not None:
|
856 |
+
x = x + residual
|
857 |
+
|
858 |
+
x_vis = x
|
859 |
+
if x_vis_only:
|
860 |
+
return x_vis
|
861 |
+
|
862 |
+
x_pool_vis = self.clip_projector(x_vis)
|
863 |
+
x_align = self.final_clip_decoder(x_pool_vis)
|
864 |
+
|
865 |
+
# Align CLIP
|
866 |
+
x_clip = torch.stack(x_clip)
|
867 |
+
K, B, _, C_CLIP = x_clip.shape
|
868 |
+
# Add positional embeddings
|
869 |
+
if self.sep_pos_embed:
|
870 |
+
raise NotImplementedError
|
871 |
+
else:
|
872 |
+
if use_image:
|
873 |
+
if self.sep_image_video_pos_embed:
|
874 |
+
clip_pos_embed = self.clip_img_pos_embed
|
875 |
+
else:
|
876 |
+
clip_cls_pos_embed = self.clip_pos_embed[:, 0:1, :]
|
877 |
+
clip_img_pos_embed = (
|
878 |
+
self.clip_pos_embed[:, 1:, :]
|
879 |
+
.view(
|
880 |
+
1,
|
881 |
+
self.num_frames,
|
882 |
+
self.patch_embed.num_patches // self.num_frames,
|
883 |
+
self.embed_dim,
|
884 |
+
)
|
885 |
+
.mean(dim=1)
|
886 |
+
)
|
887 |
+
clip_pos_embed = torch.cat(
|
888 |
+
[clip_cls_pos_embed, clip_img_pos_embed], dim=1
|
889 |
+
)
|
890 |
+
|
891 |
+
else:
|
892 |
+
clip_pos_embed = self.clip_pos_embed
|
893 |
+
|
894 |
+
clip_pos_embed = clip_pos_embed.repeat(B, 1, 1)
|
895 |
+
if mask is not None:
|
896 |
+
x_clip = x_clip + clip_pos_embed[~mask].view(
|
897 |
+
B, -1, C_CLIP
|
898 |
+
).unsqueeze(0).repeat(K, 1, 1, 1)
|
899 |
+
else:
|
900 |
+
x_clip = x_clip + clip_pos_embed.view(B, -1, C_CLIP).unsqueeze(
|
901 |
+
0
|
902 |
+
).repeat(K, 1, 1, 1)
|
903 |
+
|
904 |
+
# CLIP decoder
|
905 |
+
x_clip_align = []
|
906 |
+
for idx, clip_decoder in enumerate(self.clip_decoder):
|
907 |
+
x_clip_align.append(clip_decoder(x_clip[idx]))
|
908 |
+
x_clip_align = torch.stack(x_clip_align)
|
909 |
+
|
910 |
+
return x_vis, x_pool_vis, x_clip_align, x_align
|
911 |
+
|
912 |
+
|
913 |
+
def load_pretrained_weights(self):
|
914 |
+
if self.config.pretrained is not None:
|
915 |
+
logger.info(f"Loading pretrained weights from {self.config.pretrained}")
|
916 |
+
state_dict = torch.load(self.config.pretrained, map_location='cpu')
|
917 |
+
|
918 |
+
# Rename 'ls1.weight' to 'ls1.weight' and 'ls2.weight' to 'ls2.weight'
|
919 |
+
new_state_dict = {}
|
920 |
+
for key, value in state_dict.items():
|
921 |
+
if key.endswith('.ls1.weight'):
|
922 |
+
new_key = key.replace('.ls1.weight', '.ls1.weight')
|
923 |
+
new_state_dict[new_key] = value
|
924 |
+
elif key.endswith('.ls2.weight'):
|
925 |
+
new_key = key.replace('.ls2.weight', '.ls2.weight')
|
926 |
+
new_state_dict[new_key] = value
|
927 |
+
else:
|
928 |
+
new_state_dict[key] = value
|
929 |
+
|
930 |
+
# Load the adjusted state_dict
|
931 |
+
message = self.load_state_dict(new_state_dict, strict=False)
|
932 |
+
logger.info(message)
|
933 |
+
else:
|
934 |
+
logger.info("No pretrained weights provided.")
|
vision_tower/internvideo2/preprocessor_config.json
ADDED
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"Resize": {
|
3 |
+
"size": 224,
|
4 |
+
"interpolation": "bilinear"
|
5 |
+
},
|
6 |
+
"CenterCrop": {
|
7 |
+
"size": [
|
8 |
+
224,
|
9 |
+
224
|
10 |
+
]
|
11 |
+
},
|
12 |
+
"ClipToTensor": {
|
13 |
+
"channel_nb": 3,
|
14 |
+
"div_255": true,
|
15 |
+
"numpy": false
|
16 |
+
},
|
17 |
+
"Normalize": {
|
18 |
+
"mean": [
|
19 |
+
0.485,
|
20 |
+
0.456,
|
21 |
+
0.406
|
22 |
+
],
|
23 |
+
"std": [
|
24 |
+
0.229,
|
25 |
+
0.224,
|
26 |
+
0.225
|
27 |
+
]
|
28 |
+
},
|
29 |
+
"image_processor_type": "transforms"
|
30 |
+
}
|
vision_tower/siglip-so400m-patch14-384/config.json
ADDED
@@ -0,0 +1,19 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_name_or_path": "/opt/hpcaas/.mounts/fs-0663e2d3c38211883/home/orrzohar/Artemis/work_dirs/final_run/apollo-Qwen2.5-7B-Instruct-internvideo2-siglip-so400m-patch14-384-freeze-perciver_128_2-newprompt-ft/checkpoint-13300/vision_tower/siglip-so400m-patch14-384",
|
3 |
+
"architectures": [
|
4 |
+
"SiglipVisionModel"
|
5 |
+
],
|
6 |
+
"attention_dropout": 0.0,
|
7 |
+
"hidden_act": "gelu_pytorch_tanh",
|
8 |
+
"hidden_size": 1152,
|
9 |
+
"image_size": 384,
|
10 |
+
"intermediate_size": 4304,
|
11 |
+
"layer_norm_eps": 1e-06,
|
12 |
+
"model_type": "siglip_vision_model",
|
13 |
+
"num_attention_heads": 16,
|
14 |
+
"num_channels": 3,
|
15 |
+
"num_hidden_layers": 27,
|
16 |
+
"patch_size": 14,
|
17 |
+
"torch_dtype": "bfloat16",
|
18 |
+
"transformers_version": "4.44.0"
|
19 |
+
}
|
vision_tower/siglip-so400m-patch14-384/model.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:0e9299e05ae6cc8366374648c1a8d202d57baa136e117c2203a23d135dbb0707
|
3 |
+
size 856506120
|
vision_tower/siglip-so400m-patch14-384/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 |
+
}
|