clone https://huggingface.co./visheratin/MC-LLaVA-3b
Browse files- README.md +87 -0
- added_tokens.json +44 -0
- config.json +113 -0
- configuration_llava.py +131 -0
- configuration_phi.py +62 -0
- convert_model.py +102 -0
- generation_config.json +4 -0
- merges.txt +0 -0
- model-00001-of-00002.safetensors +3 -0
- model-00002-of-00002.safetensors +3 -0
- model.safetensors.index.json +916 -0
- modeling_llava.py +1789 -0
- modeling_phi.py +988 -0
- preprocessor_config.json +25 -0
- processing_llava.py +182 -0
- special_tokens_map.json +30 -0
- tokenizer.json +0 -0
- tokenizer_config.json +356 -0
- vocab.json +0 -0
README.md
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| 1 |
+
---
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| 2 |
+
datasets:
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| 3 |
+
- liuhaotian/LLaVA-Pretrain
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| 4 |
+
- liuhaotian/LLaVA-Instruct-150K
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language:
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- en
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tags:
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| 8 |
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- llava
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- phi
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license: mit
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library_name: transformers
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+
widget:
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+
- text: "What animal is it?"
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src: "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg"
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- text: "Where is it?"
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src: "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg"
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+
---
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| 18 |
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# Multi-crop LLaVA-3b
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<a target="_blank" href="https://colab.research.google.com/drive/1W7JQrFXwFunAY1XvS31mwC7mrXBgGD_M">
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
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</a>
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## Model details
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Usually, in LLaVA models, we generate N embeddings for the image, which we then combine with text embeddings and send to the LLM. But what if instead of creating N tokens
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for one image, we create K<<N tokens for M<N parts of the image (crops)? It would allow us to get visual information from small parts of the image and not inflate the
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number of image "tokens" too much. I called this method multi-crop LLaVA (MC-LLaVA).
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You can read more about the model in the [blog post](https://huggingface.co/blog/visheratin/vlm-resolution-curse).
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MC-LLaVA-3b was fine-tuned from [Phi-2 merge](vince62s/phi-2-psy) using vision tower from
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[SigLIP 400M](https://huggingface.co/google/siglip-so400m-patch14-384).
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As Dolphin 2.6 Phi, LLaVA-3b uses ChatML prompt format:
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```
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<|im_start|>user
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{prompt}<|im_end|>
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<|im_start|>assistant
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```
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## How to use
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```python
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from transformers import AutoModel, AutoProcessor
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import torch
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model = AutoModel.from_pretrained("visheratin/MC-LLaVA-3b", torch_dtype=torch.float16, trust_remote_code=True).to("cuda")
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processor = AutoProcessor.from_pretrained("visheratin/MC-LLaVA-3b", trust_remote_code=True)
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with torch.inference_mode():
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inputs = processor(prompt, [raw_image], model, max_crops=100, num_tokens=728)
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output = model.generate(**inputs, max_new_tokens=200, use_cache=True, do_sample=False,
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eos_token_id=processor.tokenizer.eos_token_id, pad_token_id=processor.tokenizer.eos_token_id)
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| 59 |
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result = processor.tokenizer.decode(output[0]).replace(prompt, "").replace("<|im_end|>", "")
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print(result)
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```
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## Benchmarks
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| 65 |
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- TextVQA - 50.9%
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| 67 |
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- GQA - 59.5%
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- VQAv2 - 76.72%
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- VizWiz - 32.68%
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- V*-bench - OCR - 56.66%, GPT4V-hard - 52.94%, direct attributes - 40.86%, relative position - 56.57%
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| 71 |
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## Examples
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| 73 |
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| 74 |
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<a target="_blank" href="https://colab.research.google.com/drive/1sXDvVl5s9fTcE0N2bQGOlXhnNlKEdeun">
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| 75 |
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
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| 76 |
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</a>
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## License
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The model is licensed under MIT license, but since the data used for model training is largely synthetic, you should also follow OpenAI and Google Gemini terms of service.
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Which means don't create competitor models for them.
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## Acknowledgments
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Thanks to [Lambda](https://lambdalabs.com/) for providing a machine to train the model.
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Thanks to [ML Collective](https://mlcollective.org/) for continuous support and providing compute resources for testing the model.
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added_tokens.json
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{
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"\t\t": 50294,
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"\t\t\t": 50293,
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"\t\t\t\t": 50292,
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"\t\t\t\t\t": 50291,
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"\t\t\t\t\t\t": 50290,
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"\t\t\t\t\t\t\t": 50289,
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"\t\t\t\t\t\t\t\t": 50288,
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"\t\t\t\t\t\t\t\t\t": 50287,
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" ": 50286,
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" ": 50259,
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" ": 50258,
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" ": 50257,
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"<image>": 50297,
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"<pad>": 50298,
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"<|im_end|>": 50295,
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"<|im_start|>": 50296
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}
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config.json
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{
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| 2 |
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"auto_map": {
|
| 3 |
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"AutoConfig": "modeling_llava.LlavaConfig",
|
| 4 |
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"AutoModel": "modeling_llava.LlavaForCausalLM",
|
| 5 |
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"AutoModelForCausalLM": "modeling_llava.LlavaForCausalLM"
|
| 6 |
+
},
|
| 7 |
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"model_type": "mc-llava",
|
| 8 |
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"ignore_index": -100,
|
| 9 |
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"image_token_index": 50297,
|
| 10 |
+
"projector_hidden_act": "gelu",
|
| 11 |
+
"projector_tokens_num": 1,
|
| 12 |
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"text_config": {
|
| 13 |
+
"_name_or_path": "vince62s/phi-2-psy",
|
| 14 |
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"add_cross_attention": false,
|
| 15 |
+
"architectures": [
|
| 16 |
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"PhiForCausalLM"
|
| 17 |
+
],
|
| 18 |
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"attention_dropout": 0.0,
|
| 19 |
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"auto_map": {
|
| 20 |
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"AutoConfig": "vince62s/phi-2-psy--configuration_phi.PhiConfig",
|
| 21 |
+
"AutoModelForCausalLM": "vince62s/phi-2-psy--modeling_phi.PhiForCausalLM"
|
| 22 |
+
},
|
| 23 |
+
"bad_words_ids": null,
|
| 24 |
+
"begin_suppress_tokens": null,
|
| 25 |
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"bos_token_id": null,
|
| 26 |
+
"chunk_size_feed_forward": 0,
|
| 27 |
+
"cross_attention_hidden_size": null,
|
| 28 |
+
"decoder_start_token_id": null,
|
| 29 |
+
"diversity_penalty": 0.0,
|
| 30 |
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"do_sample": false,
|
| 31 |
+
"early_stopping": false,
|
| 32 |
+
"embd_pdrop": 0.0,
|
| 33 |
+
"encoder_no_repeat_ngram_size": 0,
|
| 34 |
+
"eos_token_id": null,
|
| 35 |
+
"exponential_decay_length_penalty": null,
|
| 36 |
+
"finetuning_task": null,
|
| 37 |
+
"forced_bos_token_id": null,
|
| 38 |
+
"forced_eos_token_id": null,
|
| 39 |
+
"hidden_act": "gelu_new",
|
| 40 |
+
"hidden_size": 2560,
|
| 41 |
+
"id2label": {
|
| 42 |
+
"0": "LABEL_0",
|
| 43 |
+
"1": "LABEL_1"
|
| 44 |
+
},
|
| 45 |
+
"initializer_range": 0.02,
|
| 46 |
+
"intermediate_size": 10240,
|
| 47 |
+
"is_decoder": false,
|
| 48 |
+
"is_encoder_decoder": false,
|
| 49 |
+
"label2id": {
|
| 50 |
+
"LABEL_0": 0,
|
| 51 |
+
"LABEL_1": 1
|
| 52 |
+
},
|
| 53 |
+
"layer_norm_eps": 1e-05,
|
| 54 |
+
"length_penalty": 1.0,
|
| 55 |
+
"max_length": 20,
|
| 56 |
+
"max_position_embeddings": 2048,
|
| 57 |
+
"min_length": 0,
|
| 58 |
+
"model_type": "phi",
|
| 59 |
+
"no_repeat_ngram_size": 0,
|
| 60 |
+
"num_attention_heads": 32,
|
| 61 |
+
"num_beam_groups": 1,
|
| 62 |
+
"num_beams": 1,
|
| 63 |
+
"num_hidden_layers": 32,
|
| 64 |
+
"num_key_value_heads": 32,
|
| 65 |
+
"num_return_sequences": 1,
|
| 66 |
+
"output_attentions": false,
|
| 67 |
+
"output_hidden_states": false,
|
| 68 |
+
"output_scores": false,
|
| 69 |
+
"pad_token_id": null,
|
| 70 |
+
"partial_rotary_factor": 0.4,
|
| 71 |
+
"prefix": null,
|
| 72 |
+
"problem_type": null,
|
| 73 |
+
"pruned_heads": {},
|
| 74 |
+
"qk_layernorm": false,
|
| 75 |
+
"remove_invalid_values": false,
|
| 76 |
+
"repetition_penalty": 1.0,
|
| 77 |
+
"resid_pdrop": 0.1,
|
| 78 |
+
"return_dict": true,
|
| 79 |
+
"return_dict_in_generate": false,
|
| 80 |
+
"rope_scaling": null,
|
| 81 |
+
"rope_theta": 10000.0,
|
| 82 |
+
"sep_token_id": null,
|
| 83 |
+
"suppress_tokens": null,
|
| 84 |
+
"task_specific_params": null,
|
| 85 |
+
"temperature": 1.0,
|
| 86 |
+
"tf_legacy_loss": false,
|
| 87 |
+
"tie_encoder_decoder": false,
|
| 88 |
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"tie_word_embeddings": false,
|
| 89 |
+
"tokenizer_class": null,
|
| 90 |
+
"top_k": 50,
|
| 91 |
+
"top_p": 1.0,
|
| 92 |
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"torch_dtype": "bfloat16",
|
| 93 |
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"torchscript": false,
|
| 94 |
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"typical_p": 1.0,
|
| 95 |
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"use_bfloat16": false,
|
| 96 |
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"use_cache": true,
|
| 97 |
+
"vocab_size": 51200
|
| 98 |
+
},
|
| 99 |
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"torch_dtype": "bfloat16",
|
| 100 |
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"transformers_version": "4.37.2",
|
| 101 |
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"vision_config": {
|
| 102 |
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"hidden_size": 1152,
|
| 103 |
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"image_size": 384,
|
| 104 |
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"intermediate_size": 4304,
|
| 105 |
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"model_type": "siglip_vision_model",
|
| 106 |
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"num_attention_heads": 16,
|
| 107 |
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"num_hidden_layers": 27,
|
| 108 |
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"patch_size": 14
|
| 109 |
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},
|
| 110 |
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"vision_embed_dim": 1152,
|
| 111 |
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"vision_tower_name": "google/siglip-so400m-patch14-384",
|
| 112 |
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"vocab_size": 51200
|
| 113 |
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}
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configuration_llava.py
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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 transformers.configuration_utils import PretrainedConfig
|
| 2 |
+
from transformers.utils import logging
|
| 3 |
+
from transformers import SiglipVisionConfig
|
| 4 |
+
|
| 5 |
+
|
| 6 |
+
logger = logging.get_logger(__name__)
|
| 7 |
+
|
| 8 |
+
|
| 9 |
+
class PhiConfig(PretrainedConfig):
|
| 10 |
+
model_type = "phi"
|
| 11 |
+
keys_to_ignore_at_inference = ["past_key_values"]
|
| 12 |
+
|
| 13 |
+
def __init__(
|
| 14 |
+
self,
|
| 15 |
+
vocab_size=51200,
|
| 16 |
+
hidden_size=2048,
|
| 17 |
+
intermediate_size=8192,
|
| 18 |
+
num_hidden_layers=24,
|
| 19 |
+
num_attention_heads=32,
|
| 20 |
+
num_key_value_heads=None,
|
| 21 |
+
resid_pdrop=0.0,
|
| 22 |
+
embd_pdrop=0.0,
|
| 23 |
+
attention_dropout=0.0,
|
| 24 |
+
hidden_act="gelu_new",
|
| 25 |
+
max_position_embeddings=2048,
|
| 26 |
+
initializer_range=0.02,
|
| 27 |
+
layer_norm_eps=1e-5,
|
| 28 |
+
use_cache=True,
|
| 29 |
+
tie_word_embeddings=False,
|
| 30 |
+
rope_theta=10000.0,
|
| 31 |
+
rope_scaling=None,
|
| 32 |
+
partial_rotary_factor=0.5,
|
| 33 |
+
qk_layernorm=False,
|
| 34 |
+
bos_token_id=1,
|
| 35 |
+
eos_token_id=2,
|
| 36 |
+
**kwargs,
|
| 37 |
+
):
|
| 38 |
+
self.vocab_size = vocab_size
|
| 39 |
+
self.hidden_size = hidden_size
|
| 40 |
+
self.intermediate_size = intermediate_size
|
| 41 |
+
self.num_hidden_layers = num_hidden_layers
|
| 42 |
+
self.num_attention_heads = num_attention_heads
|
| 43 |
+
|
| 44 |
+
if num_key_value_heads is None:
|
| 45 |
+
num_key_value_heads = num_attention_heads
|
| 46 |
+
|
| 47 |
+
self.num_key_value_heads = num_key_value_heads
|
| 48 |
+
self.resid_pdrop = resid_pdrop
|
| 49 |
+
self.embd_pdrop = embd_pdrop
|
| 50 |
+
self.attention_dropout = attention_dropout
|
| 51 |
+
self.hidden_act = hidden_act
|
| 52 |
+
self.max_position_embeddings = max_position_embeddings
|
| 53 |
+
self.initializer_range = initializer_range
|
| 54 |
+
self.layer_norm_eps = layer_norm_eps
|
| 55 |
+
self.use_cache = use_cache
|
| 56 |
+
self.rope_theta = rope_theta
|
| 57 |
+
self.rope_scaling = rope_scaling
|
| 58 |
+
self.partial_rotary_factor = partial_rotary_factor
|
| 59 |
+
self.qk_layernorm = qk_layernorm
|
| 60 |
+
self._rope_scaling_validation()
|
| 61 |
+
|
| 62 |
+
super().__init__(
|
| 63 |
+
bos_token_id=bos_token_id,
|
| 64 |
+
eos_token_id=eos_token_id,
|
| 65 |
+
tie_word_embeddings=tie_word_embeddings,
|
| 66 |
+
**kwargs,
|
| 67 |
+
)
|
| 68 |
+
|
| 69 |
+
def _rope_scaling_validation(self):
|
| 70 |
+
"""
|
| 71 |
+
Validate the `rope_scaling` configuration.
|
| 72 |
+
"""
|
| 73 |
+
if self.rope_scaling is None:
|
| 74 |
+
return
|
| 75 |
+
|
| 76 |
+
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
|
| 77 |
+
raise ValueError(
|
| 78 |
+
"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
|
| 79 |
+
f"got {self.rope_scaling}"
|
| 80 |
+
)
|
| 81 |
+
rope_scaling_type = self.rope_scaling.get("type", None)
|
| 82 |
+
rope_scaling_factor = self.rope_scaling.get("factor", None)
|
| 83 |
+
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
|
| 84 |
+
raise ValueError(
|
| 85 |
+
f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
|
| 86 |
+
)
|
| 87 |
+
if (
|
| 88 |
+
rope_scaling_factor is None
|
| 89 |
+
or not isinstance(rope_scaling_factor, float)
|
| 90 |
+
or rope_scaling_factor <= 1.0
|
| 91 |
+
):
|
| 92 |
+
raise ValueError(
|
| 93 |
+
f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}"
|
| 94 |
+
)
|
| 95 |
+
|
| 96 |
+
|
| 97 |
+
class LlavaConfig(PretrainedConfig):
|
| 98 |
+
model_type = "mc-llava"
|
| 99 |
+
is_composition = False
|
| 100 |
+
|
| 101 |
+
def __init__(
|
| 102 |
+
self,
|
| 103 |
+
text_config=None,
|
| 104 |
+
vision_config=None,
|
| 105 |
+
ignore_index=-100,
|
| 106 |
+
image_token_index=50297,
|
| 107 |
+
projector_hidden_act="gelu",
|
| 108 |
+
projector_tokens_num=1,
|
| 109 |
+
vocab_size=51200,
|
| 110 |
+
**kwargs,
|
| 111 |
+
):
|
| 112 |
+
self.ignore_index = ignore_index
|
| 113 |
+
self.image_token_index = image_token_index
|
| 114 |
+
self.projector_hidden_act = projector_hidden_act
|
| 115 |
+
self.projector_tokens_num = projector_tokens_num
|
| 116 |
+
self.vocab_size = vocab_size
|
| 117 |
+
|
| 118 |
+
self.text_config = text_config
|
| 119 |
+
if isinstance(self.text_config, dict):
|
| 120 |
+
text_config["model_type"] = (
|
| 121 |
+
text_config["model_type"] if "model_type" in text_config else "phi"
|
| 122 |
+
)
|
| 123 |
+
self.text_config = PhiConfig(**text_config)
|
| 124 |
+
self.vocab_size = self.text_config.vocab_size
|
| 125 |
+
|
| 126 |
+
self.vision_config = vision_config
|
| 127 |
+
if isinstance(self.vision_config, dict):
|
| 128 |
+
self.vision_config = SiglipVisionConfig(**vision_config)
|
| 129 |
+
self.vision_embed_dim = self.vision_config.hidden_size
|
| 130 |
+
|
| 131 |
+
super().__init__(**kwargs)
|
configuration_phi.py
ADDED
|
@@ -0,0 +1,62 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Copyright (c) Microsoft Corporation.
|
| 2 |
+
# Licensed under the MIT license.
|
| 3 |
+
|
| 4 |
+
import math
|
| 5 |
+
from typing import Optional
|
| 6 |
+
|
| 7 |
+
from transformers import PretrainedConfig
|
| 8 |
+
|
| 9 |
+
|
| 10 |
+
class PhiConfig(PretrainedConfig):
|
| 11 |
+
"""Phi configuration."""
|
| 12 |
+
|
| 13 |
+
model_type = "phi-msft"
|
| 14 |
+
attribute_map = {
|
| 15 |
+
"max_position_embeddings": "n_positions",
|
| 16 |
+
"hidden_size": "n_embd",
|
| 17 |
+
"num_attention_heads": "n_head",
|
| 18 |
+
"num_hidden_layers": "n_layer",
|
| 19 |
+
}
|
| 20 |
+
|
| 21 |
+
def __init__(
|
| 22 |
+
self,
|
| 23 |
+
vocab_size: int = 51200,
|
| 24 |
+
n_positions: int = 2048,
|
| 25 |
+
n_embd: int = 1024,
|
| 26 |
+
n_layer: int = 20,
|
| 27 |
+
n_inner: Optional[int] = None,
|
| 28 |
+
n_head: int = 16,
|
| 29 |
+
n_head_kv: Optional[int] = None,
|
| 30 |
+
rotary_dim: Optional[int] = 32,
|
| 31 |
+
activation_function: Optional[str] = "gelu_new",
|
| 32 |
+
flash_attn: bool = False,
|
| 33 |
+
flash_rotary: bool = False,
|
| 34 |
+
fused_dense: bool = False,
|
| 35 |
+
attn_pdrop: float = 0.0,
|
| 36 |
+
embd_pdrop: float = 0.0,
|
| 37 |
+
resid_pdrop: float = 0.0,
|
| 38 |
+
layer_norm_epsilon: float = 1e-5,
|
| 39 |
+
initializer_range: float = 0.02,
|
| 40 |
+
tie_word_embeddings: bool = False,
|
| 41 |
+
pad_vocab_size_multiple: int = 64,
|
| 42 |
+
**kwargs
|
| 43 |
+
) -> None:
|
| 44 |
+
self.vocab_size = int(math.ceil(vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple)
|
| 45 |
+
self.n_positions = n_positions
|
| 46 |
+
self.n_embd = n_embd
|
| 47 |
+
self.n_layer = n_layer
|
| 48 |
+
self.n_inner = n_inner
|
| 49 |
+
self.n_head = n_head
|
| 50 |
+
self.n_head_kv = n_head_kv
|
| 51 |
+
self.rotary_dim = min(rotary_dim, n_embd // n_head)
|
| 52 |
+
self.activation_function = activation_function
|
| 53 |
+
self.flash_attn = flash_attn
|
| 54 |
+
self.flash_rotary = flash_rotary
|
| 55 |
+
self.fused_dense = fused_dense
|
| 56 |
+
self.attn_pdrop = attn_pdrop
|
| 57 |
+
self.embd_pdrop = embd_pdrop
|
| 58 |
+
self.resid_pdrop = resid_pdrop
|
| 59 |
+
self.layer_norm_epsilon = layer_norm_epsilon
|
| 60 |
+
self.initializer_range = initializer_range
|
| 61 |
+
|
| 62 |
+
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
|
convert_model.py
ADDED
|
@@ -0,0 +1,102 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
|
| 2 |
+
#
|
| 3 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 4 |
+
# you may not use this file except in compliance with the License.
|
| 5 |
+
# You may obtain a copy of the License at
|
| 6 |
+
#
|
| 7 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
| 8 |
+
#
|
| 9 |
+
# Unless required by applicable law or agreed to in writing, software
|
| 10 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 11 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 12 |
+
# See the License for the specific language governing permissions and
|
| 13 |
+
# limitations under the License.
|
| 14 |
+
import argparse
|
| 15 |
+
|
| 16 |
+
import torch
|
| 17 |
+
|
| 18 |
+
from transformers import (
|
| 19 |
+
AddedToken,
|
| 20 |
+
AutoConfig,
|
| 21 |
+
AutoTokenizer,
|
| 22 |
+
)
|
| 23 |
+
from configuration_llava import LlavaConfig
|
| 24 |
+
from modeling_llava import LlavaForConditionalGeneration
|
| 25 |
+
|
| 26 |
+
|
| 27 |
+
KEYS_TO_MODIFY_MAPPING = {
|
| 28 |
+
"transformer.vision_tower.vision_tower": "vision_model",
|
| 29 |
+
"transformer.mm_projector": "multi_modal_projector",
|
| 30 |
+
"transformer": "language_model.transformer",
|
| 31 |
+
"lm_head": "language_model.lm_head",
|
| 32 |
+
"model.model": "language_model.transformer",
|
| 33 |
+
"multi_modal_projector.0": "multi_modal_projector.linear_1",
|
| 34 |
+
"multi_modal_projector.2": "multi_modal_projector.linear_2",
|
| 35 |
+
}
|
| 36 |
+
|
| 37 |
+
|
| 38 |
+
def convert_state_dict_to_hf(state_dict):
|
| 39 |
+
new_state_dict = {}
|
| 40 |
+
for key, value in state_dict.items():
|
| 41 |
+
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
|
| 42 |
+
if key_to_modify in key:
|
| 43 |
+
key = key.replace(key_to_modify, new_key)
|
| 44 |
+
|
| 45 |
+
new_state_dict[key] = value
|
| 46 |
+
return new_state_dict
|
| 47 |
+
|
| 48 |
+
|
| 49 |
+
def convert_llava_llama_to_hf(text_model_id, vision_model_id, projector_tokens_num, output_path, old_state_dict_path):
|
| 50 |
+
torch.set_default_dtype(torch.float16)
|
| 51 |
+
text_config = AutoConfig.from_pretrained(text_model_id, trust_remote_code=True)
|
| 52 |
+
|
| 53 |
+
tokenizer = AutoTokenizer.from_pretrained(text_model_id)
|
| 54 |
+
tokenizer.add_tokens(AddedToken("<image>", special=True, normalized=False), special_tokens=True)
|
| 55 |
+
tokenizer.add_special_tokens({"pad_token": "<pad>"})
|
| 56 |
+
|
| 57 |
+
config = LlavaConfig(text_config=text_config, vocab_size=51200, vision_tower_name=vision_model_id, projector_tokens_num=projector_tokens_num)
|
| 58 |
+
config.text_config.vocab_size = config.vocab_size
|
| 59 |
+
|
| 60 |
+
with torch.device("cuda"):
|
| 61 |
+
model = LlavaForConditionalGeneration(config)
|
| 62 |
+
|
| 63 |
+
state_dict = torch.load(old_state_dict_path, map_location="cpu")
|
| 64 |
+
state_dict = convert_state_dict_to_hf(state_dict)
|
| 65 |
+
model.load_state_dict(state_dict, strict=True, assign=True)
|
| 66 |
+
|
| 67 |
+
model.config.vocab_size = model.config.vocab_size
|
| 68 |
+
model.config.text_config.vocab_size = model.config.text_config.vocab_size
|
| 69 |
+
|
| 70 |
+
model.save_pretrained(output_path)
|
| 71 |
+
tokenizer.save_pretrained(output_path)
|
| 72 |
+
|
| 73 |
+
|
| 74 |
+
def main():
|
| 75 |
+
parser = argparse.ArgumentParser()
|
| 76 |
+
parser.add_argument(
|
| 77 |
+
"--text_model_id",
|
| 78 |
+
help="Hub location of the text model",
|
| 79 |
+
)
|
| 80 |
+
parser.add_argument(
|
| 81 |
+
"--vision_model_id",
|
| 82 |
+
help="Hub location of the vision model",
|
| 83 |
+
)
|
| 84 |
+
parser.add_argument(
|
| 85 |
+
"--output_path",
|
| 86 |
+
help="Location of the converted model",
|
| 87 |
+
)
|
| 88 |
+
parser.add_argument(
|
| 89 |
+
"--old_state_dict_path",
|
| 90 |
+
help="Location on the hub of the raw state dict of the original model. The filename needs to be `model_state_dict.bin`",
|
| 91 |
+
)
|
| 92 |
+
parser.add_argument(
|
| 93 |
+
"--tokens_num",
|
| 94 |
+
type=int,
|
| 95 |
+
default=1
|
| 96 |
+
)
|
| 97 |
+
args = parser.parse_args()
|
| 98 |
+
convert_llava_llama_to_hf(args.text_model_id, args.vision_model_id, args.tokens_num, args.output_path, args.old_state_dict_path)
|
| 99 |
+
|
| 100 |
+
|
| 101 |
+
if __name__ == "__main__":
|
| 102 |
+
main()
|
generation_config.json
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_from_model_config": true,
|
| 3 |
+
"transformers_version": "4.37.2"
|
| 4 |
+
}
|
merges.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
model-00001-of-00002.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:0fa70d660b4f81b87cc3a93e89e8da38f6abf08dae6f51b2bef772f420799cec
|
| 3 |
+
size 4969060728
|
model-00002-of-00002.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:2e45b58c3bebb723eb7493f75356e75e090f0c62865e49a19e19ba73b80241a2
|
| 3 |
+
size 1468562584
|
model.safetensors.index.json
ADDED
|
@@ -0,0 +1,916 @@
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|
| 899 |
+
"vision_model.vision_tower.vision_model.encoder.layers.9.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
| 900 |
+
"vision_model.vision_tower.vision_model.encoder.layers.9.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
| 901 |
+
"vision_model.vision_tower.vision_model.encoder.layers.9.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
| 902 |
+
"vision_model.vision_tower.vision_model.head.attention.in_proj_bias": "model-00001-of-00002.safetensors",
|
| 903 |
+
"vision_model.vision_tower.vision_model.head.attention.in_proj_weight": "model-00001-of-00002.safetensors",
|
| 904 |
+
"vision_model.vision_tower.vision_model.head.attention.out_proj.bias": "model-00001-of-00002.safetensors",
|
| 905 |
+
"vision_model.vision_tower.vision_model.head.attention.out_proj.weight": "model-00001-of-00002.safetensors",
|
| 906 |
+
"vision_model.vision_tower.vision_model.head.layernorm.bias": "model-00001-of-00002.safetensors",
|
| 907 |
+
"vision_model.vision_tower.vision_model.head.layernorm.weight": "model-00001-of-00002.safetensors",
|
| 908 |
+
"vision_model.vision_tower.vision_model.head.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
| 909 |
+
"vision_model.vision_tower.vision_model.head.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
| 910 |
+
"vision_model.vision_tower.vision_model.head.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
| 911 |
+
"vision_model.vision_tower.vision_model.head.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
| 912 |
+
"vision_model.vision_tower.vision_model.head.probe": "model-00001-of-00002.safetensors",
|
| 913 |
+
"vision_model.vision_tower.vision_model.post_layernorm.bias": "model-00001-of-00002.safetensors",
|
| 914 |
+
"vision_model.vision_tower.vision_model.post_layernorm.weight": "model-00001-of-00002.safetensors"
|
| 915 |
+
}
|
| 916 |
+
}
|
modeling_llava.py
ADDED
|
@@ -0,0 +1,1789 @@
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|
| 1 |
+
# coding=utf-8
|
| 2 |
+
import math
|
| 3 |
+
from dataclasses import dataclass
|
| 4 |
+
from typing import List, Optional, Tuple, Union
|
| 5 |
+
|
| 6 |
+
import torch
|
| 7 |
+
import torch.nn.functional as F
|
| 8 |
+
import torch.utils.checkpoint
|
| 9 |
+
from torch import nn
|
| 10 |
+
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
| 11 |
+
from transformers import PreTrainedModel, SiglipVisionModel
|
| 12 |
+
from transformers.activations import ACT2FN
|
| 13 |
+
from transformers.cache_utils import Cache, DynamicCache
|
| 14 |
+
from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
|
| 15 |
+
from transformers.modeling_outputs import (
|
| 16 |
+
BaseModelOutputWithPast,
|
| 17 |
+
CausalLMOutputWithPast,
|
| 18 |
+
ModelOutput,
|
| 19 |
+
SequenceClassifierOutputWithPast,
|
| 20 |
+
TokenClassifierOutput,
|
| 21 |
+
)
|
| 22 |
+
from transformers.utils import (
|
| 23 |
+
is_flash_attn_2_available,
|
| 24 |
+
is_flash_attn_greater_or_equal_2_10,
|
| 25 |
+
logging,
|
| 26 |
+
)
|
| 27 |
+
|
| 28 |
+
try:
|
| 29 |
+
from flash_attn import flash_attn_func, flash_attn_varlen_func
|
| 30 |
+
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
|
| 31 |
+
except Exception as exp:
|
| 32 |
+
print(exp)
|
| 33 |
+
|
| 34 |
+
|
| 35 |
+
from transformers.configuration_utils import PretrainedConfig
|
| 36 |
+
from transformers import SiglipVisionConfig
|
| 37 |
+
|
| 38 |
+
|
| 39 |
+
logger = logging.get_logger(__name__)
|
| 40 |
+
|
| 41 |
+
|
| 42 |
+
class PhiConfig(PretrainedConfig):
|
| 43 |
+
model_type = "phi"
|
| 44 |
+
keys_to_ignore_at_inference = ["past_key_values"]
|
| 45 |
+
|
| 46 |
+
def __init__(
|
| 47 |
+
self,
|
| 48 |
+
vocab_size=51200,
|
| 49 |
+
hidden_size=2048,
|
| 50 |
+
intermediate_size=8192,
|
| 51 |
+
num_hidden_layers=24,
|
| 52 |
+
num_attention_heads=32,
|
| 53 |
+
num_key_value_heads=None,
|
| 54 |
+
resid_pdrop=0.0,
|
| 55 |
+
embd_pdrop=0.0,
|
| 56 |
+
attention_dropout=0.0,
|
| 57 |
+
hidden_act="gelu_new",
|
| 58 |
+
max_position_embeddings=2048,
|
| 59 |
+
initializer_range=0.02,
|
| 60 |
+
layer_norm_eps=1e-5,
|
| 61 |
+
use_cache=True,
|
| 62 |
+
tie_word_embeddings=False,
|
| 63 |
+
rope_theta=10000.0,
|
| 64 |
+
rope_scaling=None,
|
| 65 |
+
partial_rotary_factor=0.5,
|
| 66 |
+
qk_layernorm=False,
|
| 67 |
+
bos_token_id=1,
|
| 68 |
+
eos_token_id=2,
|
| 69 |
+
**kwargs,
|
| 70 |
+
):
|
| 71 |
+
self.vocab_size = vocab_size
|
| 72 |
+
self.hidden_size = hidden_size
|
| 73 |
+
self.intermediate_size = intermediate_size
|
| 74 |
+
self.num_hidden_layers = num_hidden_layers
|
| 75 |
+
self.num_attention_heads = num_attention_heads
|
| 76 |
+
|
| 77 |
+
if num_key_value_heads is None:
|
| 78 |
+
num_key_value_heads = num_attention_heads
|
| 79 |
+
|
| 80 |
+
self.num_key_value_heads = num_key_value_heads
|
| 81 |
+
self.resid_pdrop = resid_pdrop
|
| 82 |
+
self.embd_pdrop = embd_pdrop
|
| 83 |
+
self.attention_dropout = attention_dropout
|
| 84 |
+
self.hidden_act = hidden_act
|
| 85 |
+
self.max_position_embeddings = max_position_embeddings
|
| 86 |
+
self.initializer_range = initializer_range
|
| 87 |
+
self.layer_norm_eps = layer_norm_eps
|
| 88 |
+
self.use_cache = use_cache
|
| 89 |
+
self.rope_theta = rope_theta
|
| 90 |
+
self.rope_scaling = rope_scaling
|
| 91 |
+
self.partial_rotary_factor = partial_rotary_factor
|
| 92 |
+
self.qk_layernorm = qk_layernorm
|
| 93 |
+
self._rope_scaling_validation()
|
| 94 |
+
|
| 95 |
+
super().__init__(
|
| 96 |
+
bos_token_id=bos_token_id,
|
| 97 |
+
eos_token_id=eos_token_id,
|
| 98 |
+
tie_word_embeddings=tie_word_embeddings,
|
| 99 |
+
**kwargs,
|
| 100 |
+
)
|
| 101 |
+
|
| 102 |
+
def _rope_scaling_validation(self):
|
| 103 |
+
"""
|
| 104 |
+
Validate the `rope_scaling` configuration.
|
| 105 |
+
"""
|
| 106 |
+
if self.rope_scaling is None:
|
| 107 |
+
return
|
| 108 |
+
|
| 109 |
+
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
|
| 110 |
+
raise ValueError(
|
| 111 |
+
"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
|
| 112 |
+
f"got {self.rope_scaling}"
|
| 113 |
+
)
|
| 114 |
+
rope_scaling_type = self.rope_scaling.get("type", None)
|
| 115 |
+
rope_scaling_factor = self.rope_scaling.get("factor", None)
|
| 116 |
+
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
|
| 117 |
+
raise ValueError(
|
| 118 |
+
f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
|
| 119 |
+
)
|
| 120 |
+
if (
|
| 121 |
+
rope_scaling_factor is None
|
| 122 |
+
or not isinstance(rope_scaling_factor, float)
|
| 123 |
+
or rope_scaling_factor <= 1.0
|
| 124 |
+
):
|
| 125 |
+
raise ValueError(
|
| 126 |
+
f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}"
|
| 127 |
+
)
|
| 128 |
+
|
| 129 |
+
|
| 130 |
+
class LlavaConfig(PretrainedConfig):
|
| 131 |
+
model_type = "mc-llava"
|
| 132 |
+
is_composition = False
|
| 133 |
+
|
| 134 |
+
def __init__(
|
| 135 |
+
self,
|
| 136 |
+
text_config=None,
|
| 137 |
+
vision_config=None,
|
| 138 |
+
ignore_index=-100,
|
| 139 |
+
image_token_index=50297,
|
| 140 |
+
projector_hidden_act="gelu",
|
| 141 |
+
projector_tokens_num=1,
|
| 142 |
+
vocab_size=51200,
|
| 143 |
+
**kwargs,
|
| 144 |
+
):
|
| 145 |
+
self.ignore_index = ignore_index
|
| 146 |
+
self.image_token_index = image_token_index
|
| 147 |
+
self.projector_hidden_act = projector_hidden_act
|
| 148 |
+
self.projector_tokens_num = projector_tokens_num
|
| 149 |
+
self.vocab_size = vocab_size
|
| 150 |
+
|
| 151 |
+
self.text_config = text_config
|
| 152 |
+
if isinstance(self.text_config, dict):
|
| 153 |
+
text_config["model_type"] = (
|
| 154 |
+
text_config["model_type"] if "model_type" in text_config else "phi"
|
| 155 |
+
)
|
| 156 |
+
self.text_config = PhiConfig(**text_config)
|
| 157 |
+
self.vocab_size = self.text_config.vocab_size
|
| 158 |
+
|
| 159 |
+
self.vision_config = vision_config
|
| 160 |
+
if isinstance(self.vision_config, dict):
|
| 161 |
+
self.vision_config = SiglipVisionConfig(**vision_config)
|
| 162 |
+
self.vision_embed_dim = self.vision_config.hidden_size
|
| 163 |
+
|
| 164 |
+
super().__init__(**kwargs)
|
| 165 |
+
|
| 166 |
+
|
| 167 |
+
# Copied from transformers.models.llama.modeling_llama._get_unpad_data
|
| 168 |
+
def _get_unpad_data(attention_mask):
|
| 169 |
+
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
|
| 170 |
+
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
|
| 171 |
+
max_seqlen_in_batch = seqlens_in_batch.max().item()
|
| 172 |
+
cu_seqlens = F.pad(
|
| 173 |
+
torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)
|
| 174 |
+
)
|
| 175 |
+
return (
|
| 176 |
+
indices,
|
| 177 |
+
cu_seqlens,
|
| 178 |
+
max_seqlen_in_batch,
|
| 179 |
+
)
|
| 180 |
+
|
| 181 |
+
|
| 182 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Phi
|
| 183 |
+
class PhiRotaryEmbedding(nn.Module):
|
| 184 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
|
| 185 |
+
super().__init__()
|
| 186 |
+
|
| 187 |
+
self.dim = dim
|
| 188 |
+
self.max_position_embeddings = max_position_embeddings
|
| 189 |
+
self.base = base
|
| 190 |
+
inv_freq = 1.0 / (
|
| 191 |
+
self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
|
| 192 |
+
)
|
| 193 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
| 194 |
+
|
| 195 |
+
# Build here to make `torch.jit.trace` work.
|
| 196 |
+
self._set_cos_sin_cache(
|
| 197 |
+
seq_len=max_position_embeddings,
|
| 198 |
+
device=self.inv_freq.device,
|
| 199 |
+
dtype=torch.get_default_dtype(),
|
| 200 |
+
)
|
| 201 |
+
|
| 202 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
| 203 |
+
self.max_seq_len_cached = seq_len
|
| 204 |
+
t = torch.arange(
|
| 205 |
+
self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
|
| 206 |
+
)
|
| 207 |
+
|
| 208 |
+
freqs = torch.outer(t, self.inv_freq)
|
| 209 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
| 210 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
| 211 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
| 212 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
| 213 |
+
|
| 214 |
+
def forward(self, x, seq_len=None):
|
| 215 |
+
# x: [bs, num_attention_heads, seq_len, head_size]
|
| 216 |
+
if seq_len > self.max_seq_len_cached:
|
| 217 |
+
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
|
| 218 |
+
|
| 219 |
+
return (
|
| 220 |
+
self.cos_cached[:seq_len].to(dtype=x.dtype),
|
| 221 |
+
self.sin_cached[:seq_len].to(dtype=x.dtype),
|
| 222 |
+
)
|
| 223 |
+
|
| 224 |
+
|
| 225 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->Phi
|
| 226 |
+
class PhiLinearScalingRotaryEmbedding(PhiRotaryEmbedding):
|
| 227 |
+
"""PhiRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
|
| 228 |
+
|
| 229 |
+
def __init__(
|
| 230 |
+
self,
|
| 231 |
+
dim,
|
| 232 |
+
max_position_embeddings=2048,
|
| 233 |
+
base=10000,
|
| 234 |
+
device=None,
|
| 235 |
+
scaling_factor=1.0,
|
| 236 |
+
):
|
| 237 |
+
self.scaling_factor = scaling_factor
|
| 238 |
+
super().__init__(dim, max_position_embeddings, base, device)
|
| 239 |
+
|
| 240 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
| 241 |
+
self.max_seq_len_cached = seq_len
|
| 242 |
+
t = torch.arange(
|
| 243 |
+
self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
|
| 244 |
+
)
|
| 245 |
+
t = t / self.scaling_factor
|
| 246 |
+
|
| 247 |
+
freqs = torch.outer(t, self.inv_freq)
|
| 248 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
| 249 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
| 250 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
| 251 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
| 252 |
+
|
| 253 |
+
|
| 254 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->Phi
|
| 255 |
+
class PhiDynamicNTKScalingRotaryEmbedding(PhiRotaryEmbedding):
|
| 256 |
+
"""PhiRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
|
| 257 |
+
|
| 258 |
+
def __init__(
|
| 259 |
+
self,
|
| 260 |
+
dim,
|
| 261 |
+
max_position_embeddings=2048,
|
| 262 |
+
base=10000,
|
| 263 |
+
device=None,
|
| 264 |
+
scaling_factor=1.0,
|
| 265 |
+
):
|
| 266 |
+
self.scaling_factor = scaling_factor
|
| 267 |
+
super().__init__(dim, max_position_embeddings, base, device)
|
| 268 |
+
|
| 269 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
| 270 |
+
self.max_seq_len_cached = seq_len
|
| 271 |
+
|
| 272 |
+
if seq_len > self.max_position_embeddings:
|
| 273 |
+
base = self.base * (
|
| 274 |
+
(self.scaling_factor * seq_len / self.max_position_embeddings)
|
| 275 |
+
- (self.scaling_factor - 1)
|
| 276 |
+
) ** (self.dim / (self.dim - 2))
|
| 277 |
+
inv_freq = 1.0 / (
|
| 278 |
+
base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
|
| 279 |
+
)
|
| 280 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
| 281 |
+
|
| 282 |
+
t = torch.arange(
|
| 283 |
+
self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
|
| 284 |
+
)
|
| 285 |
+
|
| 286 |
+
freqs = torch.outer(t, self.inv_freq)
|
| 287 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
| 288 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
| 289 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
| 290 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
| 291 |
+
|
| 292 |
+
|
| 293 |
+
# Copied from transformers.models.llama.modeling_llama.rotate_half
|
| 294 |
+
def rotate_half(x):
|
| 295 |
+
"""Rotates half the hidden dims of the input."""
|
| 296 |
+
x1 = x[..., : x.shape[-1] // 2]
|
| 297 |
+
x2 = x[..., x.shape[-1] // 2 :]
|
| 298 |
+
return torch.cat((-x2, x1), dim=-1)
|
| 299 |
+
|
| 300 |
+
|
| 301 |
+
# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
|
| 302 |
+
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
|
| 303 |
+
cos = cos[position_ids].unsqueeze(unsqueeze_dim)
|
| 304 |
+
sin = sin[position_ids].unsqueeze(unsqueeze_dim)
|
| 305 |
+
q_embed = (q * cos) + (rotate_half(q) * sin)
|
| 306 |
+
k_embed = (k * cos) + (rotate_half(k) * sin)
|
| 307 |
+
return q_embed, k_embed
|
| 308 |
+
|
| 309 |
+
|
| 310 |
+
# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Phi
|
| 311 |
+
class PhiMLP(nn.Module):
|
| 312 |
+
def __init__(self, config):
|
| 313 |
+
super().__init__()
|
| 314 |
+
self.config = config
|
| 315 |
+
self.activation_fn = ACT2FN[config.hidden_act]
|
| 316 |
+
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
|
| 317 |
+
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
|
| 318 |
+
|
| 319 |
+
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
| 320 |
+
hidden_states = self.fc1(hidden_states)
|
| 321 |
+
hidden_states = self.activation_fn(hidden_states)
|
| 322 |
+
hidden_states = self.fc2(hidden_states)
|
| 323 |
+
return hidden_states
|
| 324 |
+
|
| 325 |
+
|
| 326 |
+
# Copied from transformers.models.llama.modeling_llama.repeat_kv with llama->phi
|
| 327 |
+
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
| 328 |
+
"""
|
| 329 |
+
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
| 330 |
+
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
| 331 |
+
"""
|
| 332 |
+
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
| 333 |
+
if n_rep == 1:
|
| 334 |
+
return hidden_states
|
| 335 |
+
hidden_states = hidden_states[:, :, None, :, :].expand(
|
| 336 |
+
batch, num_key_value_heads, n_rep, slen, head_dim
|
| 337 |
+
)
|
| 338 |
+
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
| 339 |
+
|
| 340 |
+
|
| 341 |
+
class PhiAttention(nn.Module):
|
| 342 |
+
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
| 343 |
+
|
| 344 |
+
def __init__(self, config: PhiConfig, layer_idx: Optional[int] = None):
|
| 345 |
+
super().__init__()
|
| 346 |
+
self.config = config
|
| 347 |
+
self.layer_idx = layer_idx
|
| 348 |
+
if layer_idx is None:
|
| 349 |
+
logger.warning_once(
|
| 350 |
+
f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
|
| 351 |
+
"to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
|
| 352 |
+
"when creating this class."
|
| 353 |
+
)
|
| 354 |
+
|
| 355 |
+
self.attention_dropout = config.attention_dropout
|
| 356 |
+
self.hidden_size = config.hidden_size
|
| 357 |
+
self.num_heads = config.num_attention_heads
|
| 358 |
+
self.head_dim = self.hidden_size // self.num_heads
|
| 359 |
+
self.num_key_value_heads = config.num_key_value_heads
|
| 360 |
+
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
|
| 361 |
+
self.max_position_embeddings = config.max_position_embeddings
|
| 362 |
+
self.rope_theta = config.rope_theta
|
| 363 |
+
self.partial_rotary_factor = config.partial_rotary_factor
|
| 364 |
+
self.is_causal = True
|
| 365 |
+
|
| 366 |
+
if (self.head_dim * self.num_heads) != self.hidden_size:
|
| 367 |
+
raise ValueError(
|
| 368 |
+
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
|
| 369 |
+
f" and `num_heads`: {self.num_heads})."
|
| 370 |
+
)
|
| 371 |
+
|
| 372 |
+
self.q_proj = nn.Linear(
|
| 373 |
+
self.hidden_size, self.num_heads * self.head_dim, bias=True
|
| 374 |
+
)
|
| 375 |
+
self.k_proj = nn.Linear(
|
| 376 |
+
self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True
|
| 377 |
+
)
|
| 378 |
+
self.v_proj = nn.Linear(
|
| 379 |
+
self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True
|
| 380 |
+
)
|
| 381 |
+
self.dense = nn.Linear(
|
| 382 |
+
self.num_heads * self.head_dim, self.hidden_size, bias=True
|
| 383 |
+
)
|
| 384 |
+
|
| 385 |
+
self.qk_layernorm = config.qk_layernorm
|
| 386 |
+
if self.qk_layernorm:
|
| 387 |
+
self.q_layernorm = nn.LayerNorm(
|
| 388 |
+
config.hidden_size // self.num_heads,
|
| 389 |
+
eps=config.layer_norm_eps,
|
| 390 |
+
elementwise_affine=True,
|
| 391 |
+
)
|
| 392 |
+
self.k_layernorm = nn.LayerNorm(
|
| 393 |
+
config.hidden_size // self.num_heads,
|
| 394 |
+
eps=config.layer_norm_eps,
|
| 395 |
+
elementwise_affine=True,
|
| 396 |
+
)
|
| 397 |
+
|
| 398 |
+
self._init_rope()
|
| 399 |
+
|
| 400 |
+
def _init_rope(self):
|
| 401 |
+
if self.config.rope_scaling is None:
|
| 402 |
+
self.rotary_emb = PhiRotaryEmbedding(
|
| 403 |
+
int(self.partial_rotary_factor * self.head_dim),
|
| 404 |
+
max_position_embeddings=self.max_position_embeddings,
|
| 405 |
+
base=self.rope_theta,
|
| 406 |
+
)
|
| 407 |
+
else:
|
| 408 |
+
scaling_type = self.config.rope_scaling["type"]
|
| 409 |
+
scaling_factor = self.config.rope_scaling["factor"]
|
| 410 |
+
if scaling_type == "linear":
|
| 411 |
+
self.rotary_emb = PhiLinearScalingRotaryEmbedding(
|
| 412 |
+
int(self.partial_rotary_factor * self.head_dim),
|
| 413 |
+
max_position_embeddings=self.max_position_embeddings,
|
| 414 |
+
scaling_factor=scaling_factor,
|
| 415 |
+
base=self.rope_theta,
|
| 416 |
+
)
|
| 417 |
+
elif scaling_type == "dynamic":
|
| 418 |
+
self.rotary_emb = PhiDynamicNTKScalingRotaryEmbedding(
|
| 419 |
+
int(self.partial_rotary_factor * self.head_dim),
|
| 420 |
+
max_position_embeddings=self.max_position_embeddings,
|
| 421 |
+
scaling_factor=scaling_factor,
|
| 422 |
+
base=self.rope_theta,
|
| 423 |
+
)
|
| 424 |
+
else:
|
| 425 |
+
raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
|
| 426 |
+
|
| 427 |
+
# Phi-2 has an attention overflow issue (with FP16) and requires autocast to be disabled
|
| 428 |
+
@torch.autocast("cpu", enabled=False)
|
| 429 |
+
@torch.autocast("cuda", enabled=False)
|
| 430 |
+
def forward(
|
| 431 |
+
self,
|
| 432 |
+
hidden_states: torch.Tensor,
|
| 433 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 434 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 435 |
+
past_key_value: Optional[Cache] = None,
|
| 436 |
+
output_attentions: bool = False,
|
| 437 |
+
use_cache: bool = False,
|
| 438 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
| 439 |
+
bsz, q_len, _ = hidden_states.size()
|
| 440 |
+
|
| 441 |
+
query_states = self.q_proj(hidden_states)
|
| 442 |
+
key_states = self.k_proj(hidden_states)
|
| 443 |
+
value_states = self.v_proj(hidden_states)
|
| 444 |
+
|
| 445 |
+
if self.qk_layernorm:
|
| 446 |
+
query_states = self.q_layernorm(query_states)
|
| 447 |
+
key_states = self.k_layernorm(key_states)
|
| 448 |
+
|
| 449 |
+
query_states = query_states.view(
|
| 450 |
+
bsz, q_len, self.num_heads, self.head_dim
|
| 451 |
+
).transpose(1, 2)
|
| 452 |
+
key_states = key_states.view(
|
| 453 |
+
bsz, q_len, self.num_key_value_heads, self.head_dim
|
| 454 |
+
).transpose(1, 2)
|
| 455 |
+
value_states = value_states.view(
|
| 456 |
+
bsz, q_len, self.num_key_value_heads, self.head_dim
|
| 457 |
+
).transpose(1, 2)
|
| 458 |
+
|
| 459 |
+
kv_seq_len = key_states.shape[-2]
|
| 460 |
+
if past_key_value is not None:
|
| 461 |
+
if self.layer_idx is None:
|
| 462 |
+
raise ValueError(
|
| 463 |
+
f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
|
| 464 |
+
"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
|
| 465 |
+
"with a layer index."
|
| 466 |
+
)
|
| 467 |
+
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
| 468 |
+
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
| 469 |
+
|
| 470 |
+
# Partial rotary embedding
|
| 471 |
+
query_rot, query_pass = (
|
| 472 |
+
query_states[..., : self.rotary_emb.dim],
|
| 473 |
+
query_states[..., self.rotary_emb.dim :],
|
| 474 |
+
)
|
| 475 |
+
key_rot, key_pass = (
|
| 476 |
+
key_states[..., : self.rotary_emb.dim],
|
| 477 |
+
key_states[..., self.rotary_emb.dim :],
|
| 478 |
+
)
|
| 479 |
+
# [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
|
| 480 |
+
query_rot, key_rot = apply_rotary_pos_emb(
|
| 481 |
+
query_rot, key_rot, cos, sin, position_ids
|
| 482 |
+
)
|
| 483 |
+
|
| 484 |
+
# [batch_size, seq_length, num_heads, head_dim]
|
| 485 |
+
query_states = torch.cat((query_rot, query_pass), dim=-1)
|
| 486 |
+
key_states = torch.cat((key_rot, key_pass), dim=-1)
|
| 487 |
+
|
| 488 |
+
if past_key_value is not None:
|
| 489 |
+
cache_kwargs = {
|
| 490 |
+
"sin": sin,
|
| 491 |
+
"cos": cos,
|
| 492 |
+
"partial_rotation_size": self.rotary_emb.dim,
|
| 493 |
+
}
|
| 494 |
+
key_states, value_states = past_key_value.update(
|
| 495 |
+
key_states, value_states, self.layer_idx, cache_kwargs
|
| 496 |
+
)
|
| 497 |
+
|
| 498 |
+
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
| 499 |
+
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
| 500 |
+
|
| 501 |
+
# Queries and keys upcast to fp32 is required by Phi-2 to avoid overflow
|
| 502 |
+
attn_weights = torch.matmul(
|
| 503 |
+
query_states.to(torch.float32), key_states.to(torch.float32).transpose(2, 3)
|
| 504 |
+
) / math.sqrt(self.head_dim)
|
| 505 |
+
|
| 506 |
+
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
| 507 |
+
raise ValueError(
|
| 508 |
+
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
|
| 509 |
+
f" {attn_weights.size()}"
|
| 510 |
+
)
|
| 511 |
+
|
| 512 |
+
if attention_mask is not None:
|
| 513 |
+
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
| 514 |
+
raise ValueError(
|
| 515 |
+
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
|
| 516 |
+
)
|
| 517 |
+
attn_weights = attn_weights + attention_mask
|
| 518 |
+
|
| 519 |
+
# upcast attention to fp32
|
| 520 |
+
attn_weights = nn.functional.softmax(
|
| 521 |
+
attn_weights, dim=-1, dtype=torch.float32
|
| 522 |
+
).to(value_states.dtype)
|
| 523 |
+
attn_weights = nn.functional.dropout(
|
| 524 |
+
attn_weights, p=self.attention_dropout, training=self.training
|
| 525 |
+
)
|
| 526 |
+
|
| 527 |
+
attn_output = torch.matmul(attn_weights, value_states)
|
| 528 |
+
|
| 529 |
+
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
| 530 |
+
raise ValueError(
|
| 531 |
+
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
| 532 |
+
f" {attn_output.size()}"
|
| 533 |
+
)
|
| 534 |
+
|
| 535 |
+
attn_output = attn_output.transpose(1, 2).contiguous()
|
| 536 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
| 537 |
+
|
| 538 |
+
attn_output = self.dense(attn_output)
|
| 539 |
+
|
| 540 |
+
if not output_attentions:
|
| 541 |
+
attn_weights = None
|
| 542 |
+
|
| 543 |
+
return attn_output, attn_weights, past_key_value
|
| 544 |
+
|
| 545 |
+
|
| 546 |
+
class PhiFlashAttention2(PhiAttention):
|
| 547 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
|
| 548 |
+
def __init__(self, *args, **kwargs):
|
| 549 |
+
super().__init__(*args, **kwargs)
|
| 550 |
+
|
| 551 |
+
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
|
| 552 |
+
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
|
| 553 |
+
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
|
| 554 |
+
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
|
| 555 |
+
|
| 556 |
+
def forward(
|
| 557 |
+
self,
|
| 558 |
+
hidden_states: torch.Tensor,
|
| 559 |
+
attention_mask: Optional[torch.LongTensor] = None,
|
| 560 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 561 |
+
past_key_value: Optional[Cache] = None,
|
| 562 |
+
output_attentions: bool = False,
|
| 563 |
+
use_cache: bool = False,
|
| 564 |
+
**kwargs,
|
| 565 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
| 566 |
+
# PhiFlashAttention2 attention does not support output_attentions
|
| 567 |
+
|
| 568 |
+
output_attentions = False
|
| 569 |
+
|
| 570 |
+
bsz, q_len, _ = hidden_states.size()
|
| 571 |
+
|
| 572 |
+
query_states = self.q_proj(hidden_states)
|
| 573 |
+
key_states = self.k_proj(hidden_states)
|
| 574 |
+
value_states = self.v_proj(hidden_states)
|
| 575 |
+
|
| 576 |
+
if self.qk_layernorm:
|
| 577 |
+
query_states = self.q_layernorm(query_states)
|
| 578 |
+
key_states = self.k_layernorm(key_states)
|
| 579 |
+
|
| 580 |
+
# Flash attention requires the input to have the shape
|
| 581 |
+
# batch_size x seq_length x head_dim x hidden_dim
|
| 582 |
+
# therefore we just need to keep the original shape
|
| 583 |
+
query_states = query_states.view(
|
| 584 |
+
bsz, q_len, self.num_heads, self.head_dim
|
| 585 |
+
).transpose(1, 2)
|
| 586 |
+
key_states = key_states.view(
|
| 587 |
+
bsz, q_len, self.num_key_value_heads, self.head_dim
|
| 588 |
+
).transpose(1, 2)
|
| 589 |
+
value_states = value_states.view(
|
| 590 |
+
bsz, q_len, self.num_key_value_heads, self.head_dim
|
| 591 |
+
).transpose(1, 2)
|
| 592 |
+
|
| 593 |
+
kv_seq_len = key_states.shape[-2]
|
| 594 |
+
if past_key_value is not None:
|
| 595 |
+
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
| 596 |
+
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
| 597 |
+
|
| 598 |
+
# Partial rotary embedding
|
| 599 |
+
query_rot, query_pass = (
|
| 600 |
+
query_states[..., : self.rotary_emb.dim],
|
| 601 |
+
query_states[..., self.rotary_emb.dim :],
|
| 602 |
+
)
|
| 603 |
+
key_rot, key_pass = (
|
| 604 |
+
key_states[..., : self.rotary_emb.dim],
|
| 605 |
+
key_states[..., self.rotary_emb.dim :],
|
| 606 |
+
)
|
| 607 |
+
# [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
|
| 608 |
+
query_rot, key_rot = apply_rotary_pos_emb(
|
| 609 |
+
query_rot, key_rot, cos, sin, position_ids
|
| 610 |
+
)
|
| 611 |
+
|
| 612 |
+
# [batch_size, seq_length, num_heads, head_dim]
|
| 613 |
+
query_states = torch.cat((query_rot, query_pass), dim=-1)
|
| 614 |
+
key_states = torch.cat((key_rot, key_pass), dim=-1)
|
| 615 |
+
|
| 616 |
+
if past_key_value is not None:
|
| 617 |
+
cache_kwargs = {
|
| 618 |
+
"sin": sin,
|
| 619 |
+
"cos": cos,
|
| 620 |
+
"partial_rotation_size": self.rotary_emb.dim,
|
| 621 |
+
}
|
| 622 |
+
key_states, value_states = past_key_value.update(
|
| 623 |
+
key_states, value_states, self.layer_idx, cache_kwargs
|
| 624 |
+
)
|
| 625 |
+
|
| 626 |
+
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
|
| 627 |
+
# to be able to avoid many of these transpose/reshape/view.
|
| 628 |
+
query_states = query_states.transpose(1, 2)
|
| 629 |
+
key_states = key_states.transpose(1, 2)
|
| 630 |
+
value_states = value_states.transpose(1, 2)
|
| 631 |
+
|
| 632 |
+
attn_dropout = self.attention_dropout if self.training else 0.0
|
| 633 |
+
|
| 634 |
+
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
|
| 635 |
+
# therefore the input hidden states gets silently casted in float32. Hence, we need
|
| 636 |
+
# cast them back in the correct dtype just to be sure everything works as expected.
|
| 637 |
+
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
|
| 638 |
+
# in fp32.
|
| 639 |
+
|
| 640 |
+
if query_states.dtype == torch.float32:
|
| 641 |
+
if torch.is_autocast_enabled():
|
| 642 |
+
target_dtype = torch.get_autocast_gpu_dtype()
|
| 643 |
+
# Handle the case where the model is quantized
|
| 644 |
+
elif hasattr(self.config, "_pre_quantization_dtype"):
|
| 645 |
+
target_dtype = self.config._pre_quantization_dtype
|
| 646 |
+
else:
|
| 647 |
+
target_dtype = self.q_proj.weight.dtype
|
| 648 |
+
|
| 649 |
+
logger.warning_once(
|
| 650 |
+
f"The input hidden states seems to be silently casted in float32, this might be related to"
|
| 651 |
+
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
|
| 652 |
+
f" {target_dtype}."
|
| 653 |
+
)
|
| 654 |
+
|
| 655 |
+
query_states = query_states.to(target_dtype)
|
| 656 |
+
key_states = key_states.to(target_dtype)
|
| 657 |
+
value_states = value_states.to(target_dtype)
|
| 658 |
+
|
| 659 |
+
attn_output = self._flash_attention_forward(
|
| 660 |
+
query_states,
|
| 661 |
+
key_states,
|
| 662 |
+
value_states,
|
| 663 |
+
attention_mask,
|
| 664 |
+
q_len,
|
| 665 |
+
dropout=attn_dropout,
|
| 666 |
+
softmax_scale=None,
|
| 667 |
+
)
|
| 668 |
+
|
| 669 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
|
| 670 |
+
attn_output = self.dense(attn_output)
|
| 671 |
+
|
| 672 |
+
if not output_attentions:
|
| 673 |
+
attn_weights = None
|
| 674 |
+
|
| 675 |
+
return attn_output, attn_weights, past_key_value
|
| 676 |
+
|
| 677 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward
|
| 678 |
+
def _flash_attention_forward(
|
| 679 |
+
self,
|
| 680 |
+
query_states,
|
| 681 |
+
key_states,
|
| 682 |
+
value_states,
|
| 683 |
+
attention_mask,
|
| 684 |
+
query_length,
|
| 685 |
+
dropout=0.0,
|
| 686 |
+
softmax_scale=None,
|
| 687 |
+
):
|
| 688 |
+
if not self._flash_attn_uses_top_left_mask:
|
| 689 |
+
causal = self.is_causal
|
| 690 |
+
else:
|
| 691 |
+
# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
|
| 692 |
+
causal = self.is_causal and query_length != 1
|
| 693 |
+
|
| 694 |
+
# Contains at least one padding token in the sequence
|
| 695 |
+
if attention_mask is not None:
|
| 696 |
+
batch_size = query_states.shape[0]
|
| 697 |
+
(
|
| 698 |
+
query_states,
|
| 699 |
+
key_states,
|
| 700 |
+
value_states,
|
| 701 |
+
indices_q,
|
| 702 |
+
cu_seq_lens,
|
| 703 |
+
max_seq_lens,
|
| 704 |
+
) = self._upad_input(
|
| 705 |
+
query_states, key_states, value_states, attention_mask, query_length
|
| 706 |
+
)
|
| 707 |
+
|
| 708 |
+
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
|
| 709 |
+
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
|
| 710 |
+
|
| 711 |
+
attn_output_unpad = flash_attn_varlen_func(
|
| 712 |
+
query_states,
|
| 713 |
+
key_states,
|
| 714 |
+
value_states,
|
| 715 |
+
cu_seqlens_q=cu_seqlens_q,
|
| 716 |
+
cu_seqlens_k=cu_seqlens_k,
|
| 717 |
+
max_seqlen_q=max_seqlen_in_batch_q,
|
| 718 |
+
max_seqlen_k=max_seqlen_in_batch_k,
|
| 719 |
+
dropout_p=dropout,
|
| 720 |
+
softmax_scale=softmax_scale,
|
| 721 |
+
causal=causal,
|
| 722 |
+
)
|
| 723 |
+
|
| 724 |
+
attn_output = pad_input(
|
| 725 |
+
attn_output_unpad, indices_q, batch_size, query_length
|
| 726 |
+
)
|
| 727 |
+
else:
|
| 728 |
+
attn_output = flash_attn_func(
|
| 729 |
+
query_states,
|
| 730 |
+
key_states,
|
| 731 |
+
value_states,
|
| 732 |
+
dropout,
|
| 733 |
+
softmax_scale=softmax_scale,
|
| 734 |
+
causal=causal,
|
| 735 |
+
)
|
| 736 |
+
|
| 737 |
+
return attn_output
|
| 738 |
+
|
| 739 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input
|
| 740 |
+
def _upad_input(
|
| 741 |
+
self, query_layer, key_layer, value_layer, attention_mask, query_length
|
| 742 |
+
):
|
| 743 |
+
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
|
| 744 |
+
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
|
| 745 |
+
|
| 746 |
+
key_layer = index_first_axis(
|
| 747 |
+
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
|
| 748 |
+
indices_k,
|
| 749 |
+
)
|
| 750 |
+
value_layer = index_first_axis(
|
| 751 |
+
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
|
| 752 |
+
indices_k,
|
| 753 |
+
)
|
| 754 |
+
if query_length == kv_seq_len:
|
| 755 |
+
query_layer = index_first_axis(
|
| 756 |
+
query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim),
|
| 757 |
+
indices_k,
|
| 758 |
+
)
|
| 759 |
+
cu_seqlens_q = cu_seqlens_k
|
| 760 |
+
max_seqlen_in_batch_q = max_seqlen_in_batch_k
|
| 761 |
+
indices_q = indices_k
|
| 762 |
+
elif query_length == 1:
|
| 763 |
+
max_seqlen_in_batch_q = 1
|
| 764 |
+
cu_seqlens_q = torch.arange(
|
| 765 |
+
batch_size + 1, dtype=torch.int32, device=query_layer.device
|
| 766 |
+
) # There is a memcpy here, that is very bad.
|
| 767 |
+
indices_q = cu_seqlens_q[:-1]
|
| 768 |
+
query_layer = query_layer.squeeze(1)
|
| 769 |
+
else:
|
| 770 |
+
# The -q_len: slice assumes left padding.
|
| 771 |
+
attention_mask = attention_mask[:, -query_length:]
|
| 772 |
+
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(
|
| 773 |
+
query_layer, attention_mask
|
| 774 |
+
)
|
| 775 |
+
|
| 776 |
+
return (
|
| 777 |
+
query_layer,
|
| 778 |
+
key_layer,
|
| 779 |
+
value_layer,
|
| 780 |
+
indices_q,
|
| 781 |
+
(cu_seqlens_q, cu_seqlens_k),
|
| 782 |
+
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
|
| 783 |
+
)
|
| 784 |
+
|
| 785 |
+
|
| 786 |
+
PHI_ATTENTION_CLASSES = {
|
| 787 |
+
"flash_attention_2": PhiFlashAttention2,
|
| 788 |
+
"eager": PhiAttention,
|
| 789 |
+
}
|
| 790 |
+
|
| 791 |
+
|
| 792 |
+
class PhiDecoderLayer(nn.Module):
|
| 793 |
+
def __init__(self, config: PhiConfig, layer_idx: int):
|
| 794 |
+
super().__init__()
|
| 795 |
+
if is_flash_attn_2_available():
|
| 796 |
+
config._attn_implementation = "flash_attention_2"
|
| 797 |
+
self.self_attn = PHI_ATTENTION_CLASSES[config._attn_implementation](
|
| 798 |
+
config, layer_idx=layer_idx
|
| 799 |
+
)
|
| 800 |
+
self.mlp = PhiMLP(config)
|
| 801 |
+
self.input_layernorm = nn.LayerNorm(
|
| 802 |
+
config.hidden_size, eps=config.layer_norm_eps
|
| 803 |
+
)
|
| 804 |
+
self.resid_dropout = nn.Dropout(config.resid_pdrop)
|
| 805 |
+
|
| 806 |
+
def forward(
|
| 807 |
+
self,
|
| 808 |
+
hidden_states: torch.Tensor,
|
| 809 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 810 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 811 |
+
output_attentions: Optional[bool] = False,
|
| 812 |
+
use_cache: Optional[bool] = False,
|
| 813 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
| 814 |
+
) -> Tuple[
|
| 815 |
+
torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
|
| 816 |
+
]:
|
| 817 |
+
residual = hidden_states
|
| 818 |
+
|
| 819 |
+
hidden_states = self.input_layernorm(hidden_states)
|
| 820 |
+
|
| 821 |
+
# Self Attention
|
| 822 |
+
attn_outputs, self_attn_weights, present_key_value = self.self_attn(
|
| 823 |
+
hidden_states=hidden_states,
|
| 824 |
+
attention_mask=attention_mask,
|
| 825 |
+
position_ids=position_ids,
|
| 826 |
+
past_key_value=past_key_value,
|
| 827 |
+
output_attentions=output_attentions,
|
| 828 |
+
use_cache=use_cache,
|
| 829 |
+
)
|
| 830 |
+
attn_outputs = self.resid_dropout(attn_outputs)
|
| 831 |
+
|
| 832 |
+
feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states))
|
| 833 |
+
hidden_states = attn_outputs + feed_forward_hidden_states + residual
|
| 834 |
+
outputs = (hidden_states,)
|
| 835 |
+
|
| 836 |
+
if output_attentions:
|
| 837 |
+
outputs += (self_attn_weights,)
|
| 838 |
+
|
| 839 |
+
if use_cache:
|
| 840 |
+
outputs += (present_key_value,)
|
| 841 |
+
|
| 842 |
+
return outputs
|
| 843 |
+
|
| 844 |
+
|
| 845 |
+
class PhiPreTrainedModel(PreTrainedModel):
|
| 846 |
+
config_class = PhiConfig
|
| 847 |
+
base_model_prefix = "model"
|
| 848 |
+
supports_gradient_checkpointing = True
|
| 849 |
+
_no_split_modules = ["PhiDecoderLayer"]
|
| 850 |
+
_skip_keys_device_placement = "past_key_values"
|
| 851 |
+
_supports_flash_attn_2 = True
|
| 852 |
+
_supports_cache_class = True
|
| 853 |
+
|
| 854 |
+
|
| 855 |
+
class PhiModel(PhiPreTrainedModel):
|
| 856 |
+
"""
|
| 857 |
+
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`PhiDecoderLayer`]
|
| 858 |
+
|
| 859 |
+
Args:
|
| 860 |
+
config: PhiConfig
|
| 861 |
+
"""
|
| 862 |
+
|
| 863 |
+
def __init__(self, config: PhiConfig):
|
| 864 |
+
super().__init__(config)
|
| 865 |
+
self.padding_idx = config.pad_token_id
|
| 866 |
+
self.vocab_size = config.vocab_size
|
| 867 |
+
|
| 868 |
+
self.embed_tokens = nn.Embedding(
|
| 869 |
+
config.vocab_size, config.hidden_size, self.padding_idx
|
| 870 |
+
)
|
| 871 |
+
self.embed_dropout = nn.Dropout(config.embd_pdrop)
|
| 872 |
+
self.layers = nn.ModuleList(
|
| 873 |
+
[
|
| 874 |
+
PhiDecoderLayer(config, layer_idx)
|
| 875 |
+
for layer_idx in range(config.num_hidden_layers)
|
| 876 |
+
]
|
| 877 |
+
)
|
| 878 |
+
self.final_layernorm = nn.LayerNorm(
|
| 879 |
+
config.hidden_size, eps=config.layer_norm_eps
|
| 880 |
+
)
|
| 881 |
+
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
|
| 882 |
+
|
| 883 |
+
self.gradient_checkpointing = False
|
| 884 |
+
# Initialize weights and apply final processing
|
| 885 |
+
self.post_init()
|
| 886 |
+
|
| 887 |
+
def get_input_embeddings(self):
|
| 888 |
+
return self.embed_tokens
|
| 889 |
+
|
| 890 |
+
def set_input_embeddings(self, value):
|
| 891 |
+
self.embed_tokens = value
|
| 892 |
+
|
| 893 |
+
def forward(
|
| 894 |
+
self,
|
| 895 |
+
input_ids: torch.LongTensor = None,
|
| 896 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 897 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 898 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
| 899 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 900 |
+
use_cache: Optional[bool] = None,
|
| 901 |
+
output_attentions: Optional[bool] = None,
|
| 902 |
+
output_hidden_states: Optional[bool] = None,
|
| 903 |
+
return_dict: Optional[bool] = None,
|
| 904 |
+
) -> Union[Tuple, BaseModelOutputWithPast]:
|
| 905 |
+
output_attentions = (
|
| 906 |
+
output_attentions
|
| 907 |
+
if output_attentions is not None
|
| 908 |
+
else self.config.output_attentions
|
| 909 |
+
)
|
| 910 |
+
output_hidden_states = (
|
| 911 |
+
output_hidden_states
|
| 912 |
+
if output_hidden_states is not None
|
| 913 |
+
else self.config.output_hidden_states
|
| 914 |
+
)
|
| 915 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
| 916 |
+
|
| 917 |
+
return_dict = (
|
| 918 |
+
return_dict if return_dict is not None else self.config.use_return_dict
|
| 919 |
+
)
|
| 920 |
+
|
| 921 |
+
# retrieve input_ids and inputs_embeds
|
| 922 |
+
if input_ids is not None and inputs_embeds is not None:
|
| 923 |
+
raise ValueError(
|
| 924 |
+
"You cannot specify both input_ids and inputs_embeds at the same time"
|
| 925 |
+
)
|
| 926 |
+
elif input_ids is not None:
|
| 927 |
+
batch_size, seq_length = input_ids.shape[:2]
|
| 928 |
+
elif inputs_embeds is not None:
|
| 929 |
+
batch_size, seq_length = inputs_embeds.shape[:2]
|
| 930 |
+
else:
|
| 931 |
+
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
| 932 |
+
|
| 933 |
+
past_key_values_length = 0
|
| 934 |
+
|
| 935 |
+
if self.gradient_checkpointing and self.training:
|
| 936 |
+
if use_cache:
|
| 937 |
+
logger.warning_once(
|
| 938 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
| 939 |
+
)
|
| 940 |
+
use_cache = False
|
| 941 |
+
|
| 942 |
+
if use_cache:
|
| 943 |
+
use_legacy_cache = not isinstance(past_key_values, Cache)
|
| 944 |
+
if use_legacy_cache:
|
| 945 |
+
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
|
| 946 |
+
past_key_values_length = past_key_values.get_usable_length(seq_length)
|
| 947 |
+
|
| 948 |
+
if position_ids is None:
|
| 949 |
+
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
| 950 |
+
position_ids = torch.arange(
|
| 951 |
+
past_key_values_length,
|
| 952 |
+
seq_length + past_key_values_length,
|
| 953 |
+
dtype=torch.long,
|
| 954 |
+
device=device,
|
| 955 |
+
)
|
| 956 |
+
position_ids = position_ids.unsqueeze(0)
|
| 957 |
+
|
| 958 |
+
if inputs_embeds is None:
|
| 959 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
| 960 |
+
|
| 961 |
+
inputs_embeds = self.embed_dropout(inputs_embeds)
|
| 962 |
+
|
| 963 |
+
# Attention mask.
|
| 964 |
+
if self._use_flash_attention_2:
|
| 965 |
+
# 2d mask is passed through the layers
|
| 966 |
+
attention_mask = (
|
| 967 |
+
attention_mask
|
| 968 |
+
if (attention_mask is not None and 0 in attention_mask)
|
| 969 |
+
else None
|
| 970 |
+
)
|
| 971 |
+
else:
|
| 972 |
+
# 4d mask is passed through the layers
|
| 973 |
+
attention_mask = _prepare_4d_causal_attention_mask(
|
| 974 |
+
attention_mask,
|
| 975 |
+
(batch_size, seq_length),
|
| 976 |
+
inputs_embeds,
|
| 977 |
+
past_key_values_length,
|
| 978 |
+
)
|
| 979 |
+
|
| 980 |
+
hidden_states = inputs_embeds
|
| 981 |
+
|
| 982 |
+
# decoder layers
|
| 983 |
+
all_hidden_states = () if output_hidden_states else None
|
| 984 |
+
all_self_attns = () if output_attentions else None
|
| 985 |
+
next_decoder_cache = None
|
| 986 |
+
|
| 987 |
+
for decoder_layer in self.layers:
|
| 988 |
+
if output_hidden_states:
|
| 989 |
+
all_hidden_states += (hidden_states,)
|
| 990 |
+
|
| 991 |
+
if self.gradient_checkpointing and self.training:
|
| 992 |
+
layer_outputs = self._gradient_checkpointing_func(
|
| 993 |
+
decoder_layer.__call__,
|
| 994 |
+
hidden_states,
|
| 995 |
+
attention_mask,
|
| 996 |
+
position_ids,
|
| 997 |
+
past_key_values,
|
| 998 |
+
output_attentions,
|
| 999 |
+
)
|
| 1000 |
+
else:
|
| 1001 |
+
layer_outputs = decoder_layer(
|
| 1002 |
+
hidden_states,
|
| 1003 |
+
attention_mask=attention_mask,
|
| 1004 |
+
position_ids=position_ids,
|
| 1005 |
+
past_key_value=past_key_values,
|
| 1006 |
+
output_attentions=output_attentions,
|
| 1007 |
+
use_cache=use_cache,
|
| 1008 |
+
)
|
| 1009 |
+
|
| 1010 |
+
hidden_states = layer_outputs[0]
|
| 1011 |
+
|
| 1012 |
+
if use_cache:
|
| 1013 |
+
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
|
| 1014 |
+
|
| 1015 |
+
if output_attentions:
|
| 1016 |
+
all_self_attns += (layer_outputs[1],)
|
| 1017 |
+
|
| 1018 |
+
hidden_states = self.final_layernorm(hidden_states)
|
| 1019 |
+
|
| 1020 |
+
# add hidden states from the last decoder layer
|
| 1021 |
+
if output_hidden_states:
|
| 1022 |
+
all_hidden_states += (hidden_states,)
|
| 1023 |
+
|
| 1024 |
+
next_cache = None
|
| 1025 |
+
if use_cache:
|
| 1026 |
+
next_cache = (
|
| 1027 |
+
next_decoder_cache.to_legacy_cache()
|
| 1028 |
+
if use_legacy_cache
|
| 1029 |
+
else next_decoder_cache
|
| 1030 |
+
)
|
| 1031 |
+
if not return_dict:
|
| 1032 |
+
return tuple(
|
| 1033 |
+
v
|
| 1034 |
+
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
|
| 1035 |
+
if v is not None
|
| 1036 |
+
)
|
| 1037 |
+
return BaseModelOutputWithPast(
|
| 1038 |
+
last_hidden_state=hidden_states,
|
| 1039 |
+
past_key_values=next_cache,
|
| 1040 |
+
hidden_states=all_hidden_states,
|
| 1041 |
+
attentions=all_self_attns,
|
| 1042 |
+
)
|
| 1043 |
+
|
| 1044 |
+
|
| 1045 |
+
class PhiForCausalLM(PhiPreTrainedModel):
|
| 1046 |
+
_tied_weights_keys = ["lm_head.weight"]
|
| 1047 |
+
|
| 1048 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Phi,bias=False->bias=True
|
| 1049 |
+
def __init__(self, config):
|
| 1050 |
+
super().__init__(config)
|
| 1051 |
+
self.model = PhiModel(config)
|
| 1052 |
+
self.vocab_size = config.vocab_size
|
| 1053 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=True)
|
| 1054 |
+
|
| 1055 |
+
# Initialize weights and apply final processing
|
| 1056 |
+
self.post_init()
|
| 1057 |
+
|
| 1058 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_input_embeddings
|
| 1059 |
+
def get_input_embeddings(self):
|
| 1060 |
+
return self.model.embed_tokens
|
| 1061 |
+
|
| 1062 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings
|
| 1063 |
+
def set_input_embeddings(self, value):
|
| 1064 |
+
self.model.embed_tokens = value
|
| 1065 |
+
|
| 1066 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings
|
| 1067 |
+
def get_output_embeddings(self):
|
| 1068 |
+
return self.lm_head
|
| 1069 |
+
|
| 1070 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_output_embeddings
|
| 1071 |
+
def set_output_embeddings(self, new_embeddings):
|
| 1072 |
+
self.lm_head = new_embeddings
|
| 1073 |
+
|
| 1074 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_decoder
|
| 1075 |
+
def set_decoder(self, decoder):
|
| 1076 |
+
self.model = decoder
|
| 1077 |
+
|
| 1078 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_decoder
|
| 1079 |
+
def get_decoder(self):
|
| 1080 |
+
return self.model
|
| 1081 |
+
|
| 1082 |
+
def forward(
|
| 1083 |
+
self,
|
| 1084 |
+
input_ids: torch.LongTensor = None,
|
| 1085 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 1086 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 1087 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
| 1088 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 1089 |
+
labels: Optional[torch.LongTensor] = None,
|
| 1090 |
+
use_cache: Optional[bool] = None,
|
| 1091 |
+
output_attentions: Optional[bool] = None,
|
| 1092 |
+
output_hidden_states: Optional[bool] = None,
|
| 1093 |
+
return_dict: Optional[bool] = None,
|
| 1094 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
| 1095 |
+
output_attentions = (
|
| 1096 |
+
output_attentions
|
| 1097 |
+
if output_attentions is not None
|
| 1098 |
+
else self.config.output_attentions
|
| 1099 |
+
)
|
| 1100 |
+
output_hidden_states = (
|
| 1101 |
+
output_hidden_states
|
| 1102 |
+
if output_hidden_states is not None
|
| 1103 |
+
else self.config.output_hidden_states
|
| 1104 |
+
)
|
| 1105 |
+
return_dict = (
|
| 1106 |
+
return_dict if return_dict is not None else self.config.use_return_dict
|
| 1107 |
+
)
|
| 1108 |
+
|
| 1109 |
+
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
| 1110 |
+
outputs = self.model(
|
| 1111 |
+
input_ids=input_ids,
|
| 1112 |
+
attention_mask=attention_mask,
|
| 1113 |
+
position_ids=position_ids,
|
| 1114 |
+
past_key_values=past_key_values,
|
| 1115 |
+
inputs_embeds=inputs_embeds,
|
| 1116 |
+
use_cache=use_cache,
|
| 1117 |
+
output_attentions=output_attentions,
|
| 1118 |
+
output_hidden_states=output_hidden_states,
|
| 1119 |
+
return_dict=return_dict,
|
| 1120 |
+
)
|
| 1121 |
+
|
| 1122 |
+
hidden_states = outputs[0]
|
| 1123 |
+
logits = self.lm_head(hidden_states)
|
| 1124 |
+
logits = logits.float()
|
| 1125 |
+
|
| 1126 |
+
loss = None
|
| 1127 |
+
if labels is not None:
|
| 1128 |
+
# Shift so that tokens < n predict n
|
| 1129 |
+
shift_logits = logits[..., :-1, :].contiguous()
|
| 1130 |
+
shift_labels = labels[..., 1:].contiguous()
|
| 1131 |
+
# Flatten the tokens
|
| 1132 |
+
loss_fct = CrossEntropyLoss()
|
| 1133 |
+
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
| 1134 |
+
shift_labels = shift_labels.view(-1)
|
| 1135 |
+
# Enable model parallelism
|
| 1136 |
+
shift_labels = shift_labels.to(shift_logits.device)
|
| 1137 |
+
loss = loss_fct(shift_logits, shift_labels)
|
| 1138 |
+
|
| 1139 |
+
if not return_dict:
|
| 1140 |
+
output = (logits,) + outputs[1:]
|
| 1141 |
+
return (loss,) + output if loss is not None else output
|
| 1142 |
+
|
| 1143 |
+
return CausalLMOutputWithPast(
|
| 1144 |
+
loss=loss,
|
| 1145 |
+
logits=logits,
|
| 1146 |
+
past_key_values=outputs.past_key_values,
|
| 1147 |
+
hidden_states=outputs.hidden_states,
|
| 1148 |
+
attentions=outputs.attentions,
|
| 1149 |
+
)
|
| 1150 |
+
|
| 1151 |
+
@staticmethod
|
| 1152 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM._reorder_cache
|
| 1153 |
+
def _reorder_cache(past_key_values, beam_idx):
|
| 1154 |
+
reordered_past = ()
|
| 1155 |
+
for layer_past in past_key_values:
|
| 1156 |
+
reordered_past += (
|
| 1157 |
+
tuple(
|
| 1158 |
+
past_state.index_select(0, beam_idx.to(past_state.device))
|
| 1159 |
+
for past_state in layer_past
|
| 1160 |
+
),
|
| 1161 |
+
)
|
| 1162 |
+
return reordered_past
|
| 1163 |
+
|
| 1164 |
+
|
| 1165 |
+
class PhiForSequenceClassification(PhiPreTrainedModel):
|
| 1166 |
+
def __init__(self, config):
|
| 1167 |
+
super().__init__(config)
|
| 1168 |
+
self.num_labels = config.num_labels
|
| 1169 |
+
self.model = PhiModel(config)
|
| 1170 |
+
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
|
| 1171 |
+
|
| 1172 |
+
# Initialize weights and apply final processing
|
| 1173 |
+
self.post_init()
|
| 1174 |
+
|
| 1175 |
+
def get_input_embeddings(self):
|
| 1176 |
+
return self.model.embed_tokens
|
| 1177 |
+
|
| 1178 |
+
def set_input_embeddings(self, value):
|
| 1179 |
+
self.model.embed_tokens = value
|
| 1180 |
+
|
| 1181 |
+
def forward(
|
| 1182 |
+
self,
|
| 1183 |
+
input_ids: torch.LongTensor = None,
|
| 1184 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 1185 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 1186 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
| 1187 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 1188 |
+
labels: Optional[torch.LongTensor] = None,
|
| 1189 |
+
use_cache: Optional[bool] = None,
|
| 1190 |
+
output_attentions: Optional[bool] = None,
|
| 1191 |
+
output_hidden_states: Optional[bool] = None,
|
| 1192 |
+
return_dict: Optional[bool] = None,
|
| 1193 |
+
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
|
| 1194 |
+
r"""
|
| 1195 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
| 1196 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
| 1197 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
| 1198 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
| 1199 |
+
"""
|
| 1200 |
+
return_dict = (
|
| 1201 |
+
return_dict if return_dict is not None else self.config.use_return_dict
|
| 1202 |
+
)
|
| 1203 |
+
|
| 1204 |
+
model_outputs = self.model(
|
| 1205 |
+
input_ids,
|
| 1206 |
+
attention_mask=attention_mask,
|
| 1207 |
+
position_ids=position_ids,
|
| 1208 |
+
past_key_values=past_key_values,
|
| 1209 |
+
inputs_embeds=inputs_embeds,
|
| 1210 |
+
use_cache=use_cache,
|
| 1211 |
+
output_attentions=output_attentions,
|
| 1212 |
+
output_hidden_states=output_hidden_states,
|
| 1213 |
+
return_dict=return_dict,
|
| 1214 |
+
)
|
| 1215 |
+
hidden_states = model_outputs[0]
|
| 1216 |
+
logits = self.score(hidden_states)
|
| 1217 |
+
|
| 1218 |
+
if input_ids is not None:
|
| 1219 |
+
batch_size = input_ids.shape[0]
|
| 1220 |
+
else:
|
| 1221 |
+
batch_size = inputs_embeds.shape[0]
|
| 1222 |
+
|
| 1223 |
+
if self.config.pad_token_id is None and batch_size != 1:
|
| 1224 |
+
raise ValueError(
|
| 1225 |
+
"Cannot handle batch sizes > 1 if no padding token is defined."
|
| 1226 |
+
)
|
| 1227 |
+
if self.config.pad_token_id is None:
|
| 1228 |
+
sequence_lengths = -1
|
| 1229 |
+
else:
|
| 1230 |
+
if input_ids is not None:
|
| 1231 |
+
# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
|
| 1232 |
+
sequence_lengths = (
|
| 1233 |
+
torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
|
| 1234 |
+
)
|
| 1235 |
+
sequence_lengths = sequence_lengths % input_ids.shape[-1]
|
| 1236 |
+
sequence_lengths = sequence_lengths.to(logits.device)
|
| 1237 |
+
else:
|
| 1238 |
+
sequence_lengths = -1
|
| 1239 |
+
|
| 1240 |
+
pooled_logits = logits[
|
| 1241 |
+
torch.arange(batch_size, device=logits.device), sequence_lengths
|
| 1242 |
+
]
|
| 1243 |
+
|
| 1244 |
+
loss = None
|
| 1245 |
+
if labels is not None:
|
| 1246 |
+
labels = labels.to(logits.device)
|
| 1247 |
+
if self.config.problem_type is None:
|
| 1248 |
+
if self.num_labels == 1:
|
| 1249 |
+
self.config.problem_type = "regression"
|
| 1250 |
+
elif self.num_labels > 1 and (
|
| 1251 |
+
labels.dtype == torch.long or labels.dtype == torch.int
|
| 1252 |
+
):
|
| 1253 |
+
self.config.problem_type = "single_label_classification"
|
| 1254 |
+
else:
|
| 1255 |
+
self.config.problem_type = "multi_label_classification"
|
| 1256 |
+
|
| 1257 |
+
if self.config.problem_type == "regression":
|
| 1258 |
+
loss_fct = MSELoss()
|
| 1259 |
+
if self.num_labels == 1:
|
| 1260 |
+
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
| 1261 |
+
else:
|
| 1262 |
+
loss = loss_fct(pooled_logits, labels)
|
| 1263 |
+
elif self.config.problem_type == "single_label_classification":
|
| 1264 |
+
loss_fct = CrossEntropyLoss()
|
| 1265 |
+
loss = loss_fct(
|
| 1266 |
+
pooled_logits.view(-1, self.num_labels), labels.view(-1)
|
| 1267 |
+
)
|
| 1268 |
+
elif self.config.problem_type == "multi_label_classification":
|
| 1269 |
+
loss_fct = BCEWithLogitsLoss()
|
| 1270 |
+
loss = loss_fct(pooled_logits, labels)
|
| 1271 |
+
if not return_dict:
|
| 1272 |
+
output = (pooled_logits,) + model_outputs[1:]
|
| 1273 |
+
return ((loss,) + output) if loss is not None else output
|
| 1274 |
+
|
| 1275 |
+
return SequenceClassifierOutputWithPast(
|
| 1276 |
+
loss=loss,
|
| 1277 |
+
logits=pooled_logits,
|
| 1278 |
+
past_key_values=model_outputs.past_key_values,
|
| 1279 |
+
hidden_states=model_outputs.hidden_states,
|
| 1280 |
+
attentions=model_outputs.attentions,
|
| 1281 |
+
)
|
| 1282 |
+
|
| 1283 |
+
|
| 1284 |
+
class PhiForTokenClassification(PhiPreTrainedModel):
|
| 1285 |
+
def __init__(self, config: PhiConfig):
|
| 1286 |
+
super().__init__(config)
|
| 1287 |
+
self.num_labels = config.num_labels
|
| 1288 |
+
|
| 1289 |
+
self.model = PhiModel(config)
|
| 1290 |
+
if (
|
| 1291 |
+
hasattr(config, "classifier_dropout")
|
| 1292 |
+
and config.classifier_dropout is not None
|
| 1293 |
+
):
|
| 1294 |
+
classifier_dropout = config.classifier_dropout
|
| 1295 |
+
elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
|
| 1296 |
+
classifier_dropout = config.hidden_dropout
|
| 1297 |
+
else:
|
| 1298 |
+
classifier_dropout = 0.1
|
| 1299 |
+
self.dropout = nn.Dropout(classifier_dropout)
|
| 1300 |
+
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
| 1301 |
+
|
| 1302 |
+
# Initialize weights and apply final processing
|
| 1303 |
+
self.post_init()
|
| 1304 |
+
|
| 1305 |
+
def forward(
|
| 1306 |
+
self,
|
| 1307 |
+
input_ids: Optional[torch.LongTensor] = None,
|
| 1308 |
+
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
| 1309 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 1310 |
+
inputs_embeds: Optional[torch.Tensor] = None,
|
| 1311 |
+
labels: Optional[torch.Tensor] = None,
|
| 1312 |
+
use_cache: Optional[bool] = None,
|
| 1313 |
+
output_attentions: Optional[bool] = None,
|
| 1314 |
+
output_hidden_states: Optional[bool] = None,
|
| 1315 |
+
return_dict: Optional[bool] = None,
|
| 1316 |
+
**deprecated_arguments,
|
| 1317 |
+
) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
|
| 1318 |
+
r"""
|
| 1319 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
| 1320 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
| 1321 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
| 1322 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
| 1323 |
+
"""
|
| 1324 |
+
return_dict = (
|
| 1325 |
+
return_dict if return_dict is not None else self.config.use_return_dict
|
| 1326 |
+
)
|
| 1327 |
+
|
| 1328 |
+
model_outputs = self.model(
|
| 1329 |
+
input_ids,
|
| 1330 |
+
past_key_values=past_key_values,
|
| 1331 |
+
attention_mask=attention_mask,
|
| 1332 |
+
inputs_embeds=inputs_embeds,
|
| 1333 |
+
use_cache=use_cache,
|
| 1334 |
+
output_attentions=output_attentions,
|
| 1335 |
+
output_hidden_states=output_hidden_states,
|
| 1336 |
+
return_dict=return_dict,
|
| 1337 |
+
)
|
| 1338 |
+
|
| 1339 |
+
hidden_states = model_outputs[0]
|
| 1340 |
+
hidden_states = self.dropout(hidden_states)
|
| 1341 |
+
logits = self.classifier(hidden_states)
|
| 1342 |
+
|
| 1343 |
+
loss = None
|
| 1344 |
+
if labels is not None:
|
| 1345 |
+
# move labels to correct device to enable model parallelism
|
| 1346 |
+
labels = labels.to(logits.device)
|
| 1347 |
+
batch_size, seq_length = labels.shape
|
| 1348 |
+
loss_fct = CrossEntropyLoss()
|
| 1349 |
+
loss = loss_fct(
|
| 1350 |
+
logits.view(batch_size * seq_length, self.num_labels),
|
| 1351 |
+
labels.view(batch_size * seq_length),
|
| 1352 |
+
)
|
| 1353 |
+
|
| 1354 |
+
if not return_dict:
|
| 1355 |
+
output = (logits,) + model_outputs[2:]
|
| 1356 |
+
return ((loss,) + output) if loss is not None else output
|
| 1357 |
+
|
| 1358 |
+
return TokenClassifierOutput(
|
| 1359 |
+
loss=loss,
|
| 1360 |
+
logits=logits,
|
| 1361 |
+
hidden_states=model_outputs.hidden_states,
|
| 1362 |
+
attentions=model_outputs.attentions,
|
| 1363 |
+
)
|
| 1364 |
+
|
| 1365 |
+
|
| 1366 |
+
@dataclass
|
| 1367 |
+
class LlavaCausalLMOutputWithPast(ModelOutput):
|
| 1368 |
+
loss: Optional[torch.FloatTensor] = None
|
| 1369 |
+
logits: torch.FloatTensor = None
|
| 1370 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None
|
| 1371 |
+
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
|
| 1372 |
+
attentions: Optional[Tuple[torch.FloatTensor]] = None
|
| 1373 |
+
image_features: Optional[torch.FloatTensor] = None
|
| 1374 |
+
|
| 1375 |
+
|
| 1376 |
+
class SiglipVisionEncoder(nn.Module):
|
| 1377 |
+
def __init__(self, config: LlavaConfig):
|
| 1378 |
+
super().__init__()
|
| 1379 |
+
self.vision_tower = SiglipVisionModel(config.vision_config)
|
| 1380 |
+
|
| 1381 |
+
self.coord_embed = nn.Sequential(
|
| 1382 |
+
nn.Linear(2, config.vision_embed_dim),
|
| 1383 |
+
nn.GELU(),
|
| 1384 |
+
nn.Linear(config.vision_embed_dim, config.vision_embed_dim),
|
| 1385 |
+
)
|
| 1386 |
+
|
| 1387 |
+
self.num_tokens = 728
|
| 1388 |
+
|
| 1389 |
+
def feature_select(self, image_forward_outs, coord_feature, num_tokens=None):
|
| 1390 |
+
image_features = image_forward_outs
|
| 1391 |
+
image_features = image_features[:, 1:]
|
| 1392 |
+
if num_tokens is None:
|
| 1393 |
+
num_tokens = self.num_tokens
|
| 1394 |
+
split_size = int(num_tokens / image_features.shape[0])
|
| 1395 |
+
sum = 0
|
| 1396 |
+
output_list = []
|
| 1397 |
+
for i in range(image_features.shape[0]):
|
| 1398 |
+
if i == image_features.shape[0] - 1:
|
| 1399 |
+
size = num_tokens - sum
|
| 1400 |
+
else:
|
| 1401 |
+
size = split_size
|
| 1402 |
+
sum += size
|
| 1403 |
+
chunk_output = image_features[i, -size:, :]
|
| 1404 |
+
chunk_output = chunk_output + coord_feature[i]
|
| 1405 |
+
output_list.append(chunk_output)
|
| 1406 |
+
image_features = torch.cat(output_list)
|
| 1407 |
+
return image_features
|
| 1408 |
+
|
| 1409 |
+
def process_image_chunks(self, image_tensor, coord_tensor, num_tokens=None):
|
| 1410 |
+
if image_tensor.shape[0] > 50:
|
| 1411 |
+
image_forward_out = []
|
| 1412 |
+
for i in range(0, image_tensor.shape[0], 50):
|
| 1413 |
+
part_forward_out = self.vision_tower(
|
| 1414 |
+
image_tensor[i : i + 50], output_hidden_states=True
|
| 1415 |
+
).hidden_states[-1]
|
| 1416 |
+
image_forward_out.append(part_forward_out)
|
| 1417 |
+
image_forward_out = torch.cat(image_forward_out, dim=0)
|
| 1418 |
+
else:
|
| 1419 |
+
image_forward_out = self.vision_tower(
|
| 1420 |
+
image_tensor, output_hidden_states=True
|
| 1421 |
+
).hidden_states[-1]
|
| 1422 |
+
coord_feature = self.coord_embed(coord_tensor)
|
| 1423 |
+
if len(coord_feature.shape) == 1:
|
| 1424 |
+
coord_feature = coord_feature.unsqueeze(0)
|
| 1425 |
+
image_feature = self.feature_select(
|
| 1426 |
+
image_forward_out, coord_feature, num_tokens
|
| 1427 |
+
).to(image_tensor.dtype)
|
| 1428 |
+
return image_feature
|
| 1429 |
+
|
| 1430 |
+
def forward(
|
| 1431 |
+
self, images: List[torch.Tensor], coords: List[torch.Tensor], num_tokens=None
|
| 1432 |
+
):
|
| 1433 |
+
image_features = []
|
| 1434 |
+
for i, image in enumerate(images):
|
| 1435 |
+
image_feature = self.process_image_chunks(image, coords[i], num_tokens)
|
| 1436 |
+
image_features.append(image_feature)
|
| 1437 |
+
image_features = torch.stack(image_features)
|
| 1438 |
+
return image_features
|
| 1439 |
+
|
| 1440 |
+
|
| 1441 |
+
class LlavaMultiModalProjector(nn.Module):
|
| 1442 |
+
def __init__(self, config: LlavaConfig):
|
| 1443 |
+
super().__init__()
|
| 1444 |
+
|
| 1445 |
+
self.linear_1 = nn.Linear(
|
| 1446 |
+
config.vision_embed_dim,
|
| 1447 |
+
config.text_config.hidden_size,
|
| 1448 |
+
bias=True,
|
| 1449 |
+
)
|
| 1450 |
+
self.act = nn.GELU()
|
| 1451 |
+
self.linear_2 = nn.Linear(
|
| 1452 |
+
config.text_config.hidden_size,
|
| 1453 |
+
config.text_config.hidden_size,
|
| 1454 |
+
bias=True,
|
| 1455 |
+
)
|
| 1456 |
+
|
| 1457 |
+
def forward(self, image_features):
|
| 1458 |
+
hidden_states = self.linear_1(image_features)
|
| 1459 |
+
hidden_states = self.act(hidden_states)
|
| 1460 |
+
hidden_states = self.linear_2(hidden_states)
|
| 1461 |
+
return hidden_states
|
| 1462 |
+
|
| 1463 |
+
|
| 1464 |
+
class LlavaPreTrainedModel(PreTrainedModel):
|
| 1465 |
+
config_class = LlavaConfig
|
| 1466 |
+
base_model_prefix = "model"
|
| 1467 |
+
supports_gradient_checkpointing = True
|
| 1468 |
+
_no_split_modules = ["LlavaVisionAttention"]
|
| 1469 |
+
_skip_keys_device_placement = "past_key_values"
|
| 1470 |
+
_supports_flash_attn_2 = True
|
| 1471 |
+
|
| 1472 |
+
def __init__(self, config):
|
| 1473 |
+
super().__init__(config)
|
| 1474 |
+
|
| 1475 |
+
def _init_weights(self, module):
|
| 1476 |
+
return
|
| 1477 |
+
|
| 1478 |
+
@property
|
| 1479 |
+
def _supports_sdpa(self):
|
| 1480 |
+
"""
|
| 1481 |
+
Retrieve language_model's attribute to check whether the model supports
|
| 1482 |
+
SDPA or not.
|
| 1483 |
+
"""
|
| 1484 |
+
return self.language_model._supports_sdpa
|
| 1485 |
+
|
| 1486 |
+
|
| 1487 |
+
class LlavaForCausalLM(LlavaPreTrainedModel):
|
| 1488 |
+
def __init__(self, config: LlavaConfig):
|
| 1489 |
+
super().__init__(config)
|
| 1490 |
+
self.vision_model = SiglipVisionEncoder(config)
|
| 1491 |
+
|
| 1492 |
+
self.multi_modal_projector = LlavaMultiModalProjector(config)
|
| 1493 |
+
self.vocab_size = config.vocab_size
|
| 1494 |
+
self.language_model = PhiForCausalLM(config.text_config)
|
| 1495 |
+
self.pad_token_id = (
|
| 1496 |
+
self.config.pad_token_id if self.config.pad_token_id is not None else -1
|
| 1497 |
+
)
|
| 1498 |
+
self.post_init()
|
| 1499 |
+
|
| 1500 |
+
def get_input_embeddings(self):
|
| 1501 |
+
return self.language_model.get_input_embeddings()
|
| 1502 |
+
|
| 1503 |
+
def set_input_embeddings(self, value):
|
| 1504 |
+
self.language_model.set_input_embeddings(value)
|
| 1505 |
+
|
| 1506 |
+
def get_output_embeddings(self):
|
| 1507 |
+
return self.language_model.get_output_embeddings()
|
| 1508 |
+
|
| 1509 |
+
def set_output_embeddings(self, new_embeddings):
|
| 1510 |
+
self.language_model.set_output_embeddings(new_embeddings)
|
| 1511 |
+
|
| 1512 |
+
def set_decoder(self, decoder):
|
| 1513 |
+
self.language_model.transformer = decoder
|
| 1514 |
+
|
| 1515 |
+
def get_decoder(self):
|
| 1516 |
+
return self.language_model.transformer
|
| 1517 |
+
|
| 1518 |
+
def tie_weights(self):
|
| 1519 |
+
return self.language_model.tie_weights()
|
| 1520 |
+
|
| 1521 |
+
def resize_token_embeddings(
|
| 1522 |
+
self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None
|
| 1523 |
+
) -> nn.Embedding:
|
| 1524 |
+
model_embeds = self.language_model.resize_token_embeddings(
|
| 1525 |
+
new_num_tokens, pad_to_multiple_of
|
| 1526 |
+
)
|
| 1527 |
+
# update vocab size
|
| 1528 |
+
self.config.text_config.vocab_size = model_embeds.num_embeddings
|
| 1529 |
+
self.config.vocab_size = model_embeds.num_embeddings
|
| 1530 |
+
self.vocab_size = model_embeds.num_embeddings
|
| 1531 |
+
return model_embeds
|
| 1532 |
+
|
| 1533 |
+
def _merge_input_ids_with_image_features(
|
| 1534 |
+
self, image_features, inputs_embeds, input_ids, attention_mask, position_ids
|
| 1535 |
+
):
|
| 1536 |
+
num_images, num_image_patches, embed_dim = image_features.shape
|
| 1537 |
+
batch_size, sequence_length = input_ids.shape
|
| 1538 |
+
left_padding = not torch.sum(
|
| 1539 |
+
input_ids[:, -1] == torch.tensor(self.pad_token_id)
|
| 1540 |
+
)
|
| 1541 |
+
# 1. Create a mask to know where special image tokens are
|
| 1542 |
+
special_image_token_mask = input_ids == self.config.image_token_index
|
| 1543 |
+
num_special_image_tokens = torch.sum(special_image_token_mask, dim=-1)
|
| 1544 |
+
# Compute the maximum embed dimension
|
| 1545 |
+
max_embed_dim = (
|
| 1546 |
+
num_special_image_tokens.max() * (num_image_patches - 1)
|
| 1547 |
+
) + sequence_length
|
| 1548 |
+
batch_indices, non_image_indices = torch.where(
|
| 1549 |
+
input_ids != self.config.image_token_index
|
| 1550 |
+
)
|
| 1551 |
+
|
| 1552 |
+
# 2. Compute the positions where text should be written
|
| 1553 |
+
# Calculate new positions for text tokens in merged image-text sequence.
|
| 1554 |
+
# `special_image_token_mask` identifies image tokens. Each image token will be replaced by `nb_text_tokens_per_images - 1` text tokens.
|
| 1555 |
+
# `torch.cumsum` computes how each image token shifts subsequent text token positions.
|
| 1556 |
+
# - 1 to adjust for zero-based indexing, as `cumsum` inherently increases indices by one.
|
| 1557 |
+
new_token_positions = (
|
| 1558 |
+
torch.cumsum((special_image_token_mask * (num_image_patches - 1) + 1), -1)
|
| 1559 |
+
- 1
|
| 1560 |
+
)
|
| 1561 |
+
nb_image_pad = max_embed_dim - 1 - new_token_positions[:, -1]
|
| 1562 |
+
if left_padding:
|
| 1563 |
+
new_token_positions += nb_image_pad[:, None] # offset for left padding
|
| 1564 |
+
text_to_overwrite = new_token_positions[batch_indices, non_image_indices]
|
| 1565 |
+
|
| 1566 |
+
# 3. Create the full embedding, already padded to the maximum position
|
| 1567 |
+
final_embedding = torch.zeros(
|
| 1568 |
+
batch_size,
|
| 1569 |
+
max_embed_dim,
|
| 1570 |
+
embed_dim,
|
| 1571 |
+
dtype=inputs_embeds.dtype,
|
| 1572 |
+
device=inputs_embeds.device,
|
| 1573 |
+
)
|
| 1574 |
+
final_attention_mask = torch.zeros(
|
| 1575 |
+
batch_size,
|
| 1576 |
+
max_embed_dim,
|
| 1577 |
+
dtype=attention_mask.dtype,
|
| 1578 |
+
device=inputs_embeds.device,
|
| 1579 |
+
)
|
| 1580 |
+
# In case the Vision model or the Language model has been offloaded to CPU, we need to manually
|
| 1581 |
+
# set the corresponding tensors into their correct target device.
|
| 1582 |
+
target_device = inputs_embeds.device
|
| 1583 |
+
batch_indices, non_image_indices, text_to_overwrite = (
|
| 1584 |
+
batch_indices.to(target_device),
|
| 1585 |
+
non_image_indices.to(target_device),
|
| 1586 |
+
text_to_overwrite.to(target_device),
|
| 1587 |
+
)
|
| 1588 |
+
attention_mask = attention_mask.to(target_device)
|
| 1589 |
+
|
| 1590 |
+
# 4. Fill the embeddings based on the mask. If we have ["hey" "<image>", "how", "are"]
|
| 1591 |
+
# we need to index copy on [0, 577, 578, 579] for the text and [1:576] for the image features
|
| 1592 |
+
final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[
|
| 1593 |
+
batch_indices, non_image_indices
|
| 1594 |
+
]
|
| 1595 |
+
final_attention_mask[batch_indices, text_to_overwrite] = attention_mask[
|
| 1596 |
+
batch_indices, non_image_indices
|
| 1597 |
+
]
|
| 1598 |
+
|
| 1599 |
+
# 5. Fill the embeddings corresponding to the images. Anything that is still zeros needs filling
|
| 1600 |
+
image_to_overwrite = torch.all(final_embedding == 0, dim=-1)
|
| 1601 |
+
image_to_overwrite &= image_to_overwrite.cumsum(-1) - 1 >= nb_image_pad[
|
| 1602 |
+
:, None
|
| 1603 |
+
].to(target_device)
|
| 1604 |
+
|
| 1605 |
+
if image_to_overwrite.sum() != image_features.shape[:-1].numel():
|
| 1606 |
+
raise ValueError(
|
| 1607 |
+
f"The input provided to the model are wrong. The number of image tokens is {torch.sum(special_image_token_mask)} while"
|
| 1608 |
+
f" the number of image given to the model is {num_images}. This prevents correct indexing and breaks batch generation."
|
| 1609 |
+
)
|
| 1610 |
+
|
| 1611 |
+
final_embedding[image_to_overwrite] = (
|
| 1612 |
+
image_features.contiguous().reshape(-1, embed_dim).to(target_device)
|
| 1613 |
+
)
|
| 1614 |
+
final_attention_mask |= image_to_overwrite
|
| 1615 |
+
position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_(
|
| 1616 |
+
(final_attention_mask == 0), 1
|
| 1617 |
+
)
|
| 1618 |
+
return final_embedding, final_attention_mask, position_ids
|
| 1619 |
+
|
| 1620 |
+
def forward(
|
| 1621 |
+
self,
|
| 1622 |
+
input_ids: torch.LongTensor = None,
|
| 1623 |
+
image_features: torch.FloatTensor = None,
|
| 1624 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 1625 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 1626 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
| 1627 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 1628 |
+
use_cache: Optional[bool] = None,
|
| 1629 |
+
output_attentions: Optional[bool] = None,
|
| 1630 |
+
output_hidden_states: Optional[bool] = None,
|
| 1631 |
+
return_dict: Optional[bool] = None,
|
| 1632 |
+
) -> Union[Tuple, LlavaCausalLMOutputWithPast]:
|
| 1633 |
+
output_attentions = (
|
| 1634 |
+
output_attentions
|
| 1635 |
+
if output_attentions is not None
|
| 1636 |
+
else self.config.output_attentions
|
| 1637 |
+
)
|
| 1638 |
+
output_hidden_states = (
|
| 1639 |
+
output_hidden_states
|
| 1640 |
+
if output_hidden_states is not None
|
| 1641 |
+
else self.config.output_hidden_states
|
| 1642 |
+
)
|
| 1643 |
+
return_dict = (
|
| 1644 |
+
return_dict if return_dict is not None else self.config.use_return_dict
|
| 1645 |
+
)
|
| 1646 |
+
|
| 1647 |
+
if inputs_embeds is None:
|
| 1648 |
+
inputs_embeds = self.get_input_embeddings()(input_ids)
|
| 1649 |
+
if image_features is not None and input_ids.shape[1] != 1:
|
| 1650 |
+
(
|
| 1651 |
+
inputs_embeds,
|
| 1652 |
+
attention_mask,
|
| 1653 |
+
position_ids,
|
| 1654 |
+
) = self._merge_input_ids_with_image_features(
|
| 1655 |
+
image_features,
|
| 1656 |
+
inputs_embeds,
|
| 1657 |
+
input_ids,
|
| 1658 |
+
attention_mask,
|
| 1659 |
+
position_ids,
|
| 1660 |
+
)
|
| 1661 |
+
else:
|
| 1662 |
+
# In case input_ids.shape[1] == 1 & pixel_values==None & past_key_values != None, we are in the case of
|
| 1663 |
+
# generation with cache
|
| 1664 |
+
if past_key_values is not None and image_features is not None and input_ids.shape[1] == 1:
|
| 1665 |
+
# Retrieve the first layer to inspect the logits and mask out the hidden states
|
| 1666 |
+
# that are set to 0
|
| 1667 |
+
first_layer_past_key_value = past_key_values[0][0][:, :, :, 0]
|
| 1668 |
+
|
| 1669 |
+
# Sum all dimensions of head_dim (-2) to avoid random errors such as: https://github.com/huggingface/transformers/pull/28032#issuecomment-1863691941
|
| 1670 |
+
batch_index, non_attended_tokens = torch.where(first_layer_past_key_value.float().sum(-2) == 0)
|
| 1671 |
+
|
| 1672 |
+
# Get the target length
|
| 1673 |
+
target_seqlen = first_layer_past_key_value.shape[-1] + 1
|
| 1674 |
+
|
| 1675 |
+
extended_attention_mask = torch.ones(
|
| 1676 |
+
(attention_mask.shape[0], target_seqlen - attention_mask.shape[1]),
|
| 1677 |
+
dtype=attention_mask.dtype,
|
| 1678 |
+
device=attention_mask.device,
|
| 1679 |
+
)
|
| 1680 |
+
|
| 1681 |
+
# Filter out only the tokens that can be un-attended, this can happen
|
| 1682 |
+
# if one uses Llava + Fused modules where the cache on the
|
| 1683 |
+
# first iteration is already big enough, or if one passes custom cache
|
| 1684 |
+
valid_indices = non_attended_tokens < extended_attention_mask.size(-1)
|
| 1685 |
+
new_batch_index = batch_index[valid_indices]
|
| 1686 |
+
new_non_attended_tokens = non_attended_tokens[valid_indices]
|
| 1687 |
+
|
| 1688 |
+
# Zero-out the places where we don't need to attend
|
| 1689 |
+
extended_attention_mask[new_batch_index, new_non_attended_tokens] = 0
|
| 1690 |
+
|
| 1691 |
+
attention_mask = torch.cat((attention_mask, extended_attention_mask), dim=1)
|
| 1692 |
+
position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
|
| 1693 |
+
|
| 1694 |
+
outputs = self.language_model(
|
| 1695 |
+
input_ids=None,
|
| 1696 |
+
attention_mask=attention_mask,
|
| 1697 |
+
position_ids=position_ids,
|
| 1698 |
+
past_key_values=past_key_values,
|
| 1699 |
+
inputs_embeds=inputs_embeds,
|
| 1700 |
+
use_cache=use_cache,
|
| 1701 |
+
output_attentions=output_attentions,
|
| 1702 |
+
output_hidden_states=output_hidden_states,
|
| 1703 |
+
return_dict=return_dict,
|
| 1704 |
+
)
|
| 1705 |
+
|
| 1706 |
+
logits = outputs[0]
|
| 1707 |
+
|
| 1708 |
+
if not return_dict:
|
| 1709 |
+
output = (logits,) + outputs[1:]
|
| 1710 |
+
return output
|
| 1711 |
+
|
| 1712 |
+
return LlavaCausalLMOutputWithPast(
|
| 1713 |
+
logits=logits,
|
| 1714 |
+
past_key_values=outputs.past_key_values,
|
| 1715 |
+
hidden_states=outputs.hidden_states,
|
| 1716 |
+
attentions=outputs.attentions,
|
| 1717 |
+
image_features=image_features,
|
| 1718 |
+
)
|
| 1719 |
+
|
| 1720 |
+
def prepare_inputs_for_generation(
|
| 1721 |
+
self,
|
| 1722 |
+
input_ids,
|
| 1723 |
+
past_key_values=None,
|
| 1724 |
+
inputs_embeds=None,
|
| 1725 |
+
attention_mask=None,
|
| 1726 |
+
image_features=None,
|
| 1727 |
+
**kwargs,
|
| 1728 |
+
):
|
| 1729 |
+
if past_key_values is not None:
|
| 1730 |
+
if isinstance(past_key_values, Cache):
|
| 1731 |
+
cache_length = past_key_values.get_seq_length()
|
| 1732 |
+
past_length = past_key_values.seen_tokens
|
| 1733 |
+
max_cache_length = past_key_values.get_max_length()
|
| 1734 |
+
else:
|
| 1735 |
+
cache_length = past_length = past_key_values[0][0].shape[2]
|
| 1736 |
+
max_cache_length = None
|
| 1737 |
+
|
| 1738 |
+
# Keep only the unprocessed tokens:
|
| 1739 |
+
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
|
| 1740 |
+
# some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
|
| 1741 |
+
# input)
|
| 1742 |
+
if (
|
| 1743 |
+
attention_mask is not None
|
| 1744 |
+
and attention_mask.shape[1] > input_ids.shape[1]
|
| 1745 |
+
):
|
| 1746 |
+
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
|
| 1747 |
+
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
|
| 1748 |
+
# input_ids based on the past_length.
|
| 1749 |
+
elif past_length < input_ids.shape[1]+image_features.shape[1]-1:
|
| 1750 |
+
past_length -= image_features.shape[1]-1
|
| 1751 |
+
input_ids = input_ids[:, past_length:]
|
| 1752 |
+
attention_mask = attention_mask[:, past_length:]
|
| 1753 |
+
# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
|
| 1754 |
+
|
| 1755 |
+
# If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
|
| 1756 |
+
if (
|
| 1757 |
+
max_cache_length is not None
|
| 1758 |
+
and attention_mask is not None
|
| 1759 |
+
and cache_length + input_ids.shape[1] > max_cache_length
|
| 1760 |
+
):
|
| 1761 |
+
attention_mask = attention_mask[:, -max_cache_length:]
|
| 1762 |
+
|
| 1763 |
+
position_ids = kwargs.get("position_ids", None)
|
| 1764 |
+
if attention_mask is not None and position_ids is None:
|
| 1765 |
+
# create position_ids on the fly for batch generation
|
| 1766 |
+
position_ids = attention_mask.long().cumsum(-1) - 1
|
| 1767 |
+
position_ids.masked_fill_(attention_mask == 0, 1)
|
| 1768 |
+
if past_key_values:
|
| 1769 |
+
position_ids = position_ids[:, -input_ids.shape[1] :]
|
| 1770 |
+
|
| 1771 |
+
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
| 1772 |
+
if inputs_embeds is not None and past_key_values is None:
|
| 1773 |
+
model_inputs = {"inputs_embeds": inputs_embeds}
|
| 1774 |
+
else:
|
| 1775 |
+
model_inputs = {"input_ids": input_ids}
|
| 1776 |
+
|
| 1777 |
+
model_inputs.update(
|
| 1778 |
+
{
|
| 1779 |
+
"position_ids": position_ids,
|
| 1780 |
+
"past_key_values": past_key_values,
|
| 1781 |
+
"use_cache": kwargs.get("use_cache"),
|
| 1782 |
+
"attention_mask": attention_mask,
|
| 1783 |
+
"image_features": image_features,
|
| 1784 |
+
}
|
| 1785 |
+
)
|
| 1786 |
+
return model_inputs
|
| 1787 |
+
|
| 1788 |
+
def _reorder_cache(self, *args, **kwargs):
|
| 1789 |
+
return self.language_model._reorder_cache(*args, **kwargs)
|
modeling_phi.py
ADDED
|
@@ -0,0 +1,988 @@
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|
| 1 |
+
# Copyright (c) Microsoft Corporation.
|
| 2 |
+
# Licensed under the MIT license.
|
| 3 |
+
#
|
| 4 |
+
# Copyright (c) 2022, Tri Dao, [email protected].
|
| 5 |
+
# Licensed under the BSD 3-Clause License.
|
| 6 |
+
|
| 7 |
+
from __future__ import annotations
|
| 8 |
+
|
| 9 |
+
import math
|
| 10 |
+
from dataclasses import dataclass, field
|
| 11 |
+
from typing import Any, Dict, Optional, Tuple, Union
|
| 12 |
+
|
| 13 |
+
import torch
|
| 14 |
+
import torch.nn as nn
|
| 15 |
+
from einops import rearrange, repeat
|
| 16 |
+
from transformers import PretrainedConfig, PreTrainedModel
|
| 17 |
+
from transformers.activations import ACT2FN
|
| 18 |
+
from transformers.modeling_outputs import CausalLMOutputWithPast
|
| 19 |
+
|
| 20 |
+
from configuration_phi import PhiConfig
|
| 21 |
+
|
| 22 |
+
try:
|
| 23 |
+
from flash_attn.bert_padding import pad_input, unpad_input
|
| 24 |
+
from flash_attn.layers.rotary import RotaryEmbedding as FlashRotaryEmbedding
|
| 25 |
+
from flash_attn.modules.mha import FlashCrossAttention, FlashSelfAttention
|
| 26 |
+
from flash_attn.ops.fused_dense import FusedDense
|
| 27 |
+
print("Using Flash Attention!")
|
| 28 |
+
except Exception as exc:
|
| 29 |
+
print(exc)
|
| 30 |
+
pad_input, unpad_input = None, None
|
| 31 |
+
FlashRotaryEmbedding = None
|
| 32 |
+
FlashSelfAttention, FlashCrossAttention = None, None
|
| 33 |
+
FusedDense = None
|
| 34 |
+
print("Not using Flash Attention!")
|
| 35 |
+
|
| 36 |
+
|
| 37 |
+
@dataclass
|
| 38 |
+
class InferenceParams:
|
| 39 |
+
"""Inference parameters passed to model to efficiently calculate
|
| 40 |
+
and store context during inference.
|
| 41 |
+
|
| 42 |
+
Reference:
|
| 43 |
+
https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/utils/generation.py.
|
| 44 |
+
|
| 45 |
+
Args:
|
| 46 |
+
max_seqlen: Maximum sequence length.
|
| 47 |
+
max_batch_size: Maximum batch size.
|
| 48 |
+
seqlen_offset: Sequence length offset.
|
| 49 |
+
batch_size_offset: Batch size offset.
|
| 50 |
+
key_value_memory_dict: Key value memory dictionary.
|
| 51 |
+
lengths_per_sample: Lengths per sample.
|
| 52 |
+
|
| 53 |
+
"""
|
| 54 |
+
|
| 55 |
+
max_seqlen: int = field(metadata={"help": "Maximum sequence length."})
|
| 56 |
+
|
| 57 |
+
max_batch_size: int = field(metadata={"help": "Maximum batch size."})
|
| 58 |
+
|
| 59 |
+
seqlen_offset: int = field(default=0, metadata={"help": "Sequence length offset."})
|
| 60 |
+
|
| 61 |
+
batch_size_offset: int = field(default=0, metadata={"help": "Batch size offset."})
|
| 62 |
+
|
| 63 |
+
key_value_memory_dict: Dict[str, Any] = field(
|
| 64 |
+
default_factory=dict, metadata={"help": "Key value memory dictionary."}
|
| 65 |
+
)
|
| 66 |
+
|
| 67 |
+
lengths_per_sample: torch.Tensor = field(default=None, metadata={"help": "Lengths per sample."})
|
| 68 |
+
|
| 69 |
+
|
| 70 |
+
class Embedding(nn.Module):
|
| 71 |
+
"""Token embedding with dropout."""
|
| 72 |
+
|
| 73 |
+
def __init__(self, config: PretrainedConfig) -> None:
|
| 74 |
+
super().__init__()
|
| 75 |
+
|
| 76 |
+
self.wte = nn.Embedding(config.vocab_size, config.n_embd)
|
| 77 |
+
self.drop = nn.Dropout(config.embd_pdrop)
|
| 78 |
+
|
| 79 |
+
def forward(self, input_ids: torch.LongTensor) -> torch.FloatTensor:
|
| 80 |
+
input_shape = input_ids.size()
|
| 81 |
+
input_ids = input_ids.view(-1, input_shape[-1])
|
| 82 |
+
|
| 83 |
+
hidden_states = self.wte(input_ids)
|
| 84 |
+
hidden_states = self.drop(hidden_states)
|
| 85 |
+
|
| 86 |
+
return hidden_states
|
| 87 |
+
|
| 88 |
+
|
| 89 |
+
def _apply_rotary_emb(
|
| 90 |
+
x: torch.FloatTensor,
|
| 91 |
+
cos: torch.FloatTensor,
|
| 92 |
+
sin: torch.FloatTensor,
|
| 93 |
+
) -> torch.FloatTensor:
|
| 94 |
+
_, seqlen, _, _ = x.shape
|
| 95 |
+
_, rotary_dim = cos.shape
|
| 96 |
+
rotary_dim *= 2
|
| 97 |
+
|
| 98 |
+
x_rot = x[:, :, :, :rotary_dim]
|
| 99 |
+
x_pass = x[:, :, :, rotary_dim:]
|
| 100 |
+
|
| 101 |
+
x1, x2 = x_rot.chunk(2, dim=-1)
|
| 102 |
+
c, s = rearrange(cos[:seqlen], "s d -> s 1 d"), rearrange(sin[:seqlen], "s d -> s 1 d")
|
| 103 |
+
x1, x2, c, s = [t.to(dtype=torch.float32) for t in [x1, x2, c, s]]
|
| 104 |
+
|
| 105 |
+
x_rot = torch.cat([x1 * c - x2 * s, x1 * s + x2 * c], axis=-1).to(x.dtype)
|
| 106 |
+
|
| 107 |
+
return torch.cat([x_rot, x_pass], axis=-1)
|
| 108 |
+
|
| 109 |
+
|
| 110 |
+
def _apply_rotary_emb_kv(
|
| 111 |
+
kv: torch.FloatTensor,
|
| 112 |
+
cos: torch.FloatTensor,
|
| 113 |
+
sin: torch.FloatTensor,
|
| 114 |
+
cos_k: Optional[torch.FloatTensor] = None,
|
| 115 |
+
sin_k: Optional[torch.FloatTensor] = None,
|
| 116 |
+
) -> torch.FloatTensor:
|
| 117 |
+
_, seqlen, _, _, _ = kv.shape
|
| 118 |
+
_, rotary_dim = cos.shape
|
| 119 |
+
rotary_dim *= 2
|
| 120 |
+
|
| 121 |
+
k_rot = kv[:, :, 0, :, :rotary_dim]
|
| 122 |
+
k_pass = kv[:, :, 0, :, rotary_dim:]
|
| 123 |
+
|
| 124 |
+
k1, k2 = k_rot.chunk(2, dim=-1)
|
| 125 |
+
c, s = rearrange(cos[:seqlen], "s d -> s 1 d"), rearrange(sin[:seqlen], "s d -> s 1 d")
|
| 126 |
+
k1, k2, c, s = [t.to(dtype=torch.float32) for t in [k1, k2, c, s]]
|
| 127 |
+
|
| 128 |
+
k_rot = torch.cat([k1 * c - k2 * s, k1 * s + k2 * c], axis=-1).to(kv.dtype)
|
| 129 |
+
|
| 130 |
+
return torch.cat(
|
| 131 |
+
[
|
| 132 |
+
torch.cat([k_rot, k_pass], axis=-1).unsqueeze(2),
|
| 133 |
+
kv[:, :, 1:2, :, :],
|
| 134 |
+
],
|
| 135 |
+
axis=2,
|
| 136 |
+
)
|
| 137 |
+
|
| 138 |
+
|
| 139 |
+
def _apply_rotary_emb_qkv(
|
| 140 |
+
qkv: torch.FloatTensor,
|
| 141 |
+
cos: torch.FloatTensor,
|
| 142 |
+
sin: torch.FloatTensor,
|
| 143 |
+
cos_k: Optional[torch.FloatTensor] = None,
|
| 144 |
+
sin_k: Optional[torch.FloatTensor] = None,
|
| 145 |
+
) -> torch.FloatTensor:
|
| 146 |
+
_, seqlen, _, _, _ = qkv.shape
|
| 147 |
+
_, rotary_dim = cos.shape
|
| 148 |
+
rotary_dim *= 2
|
| 149 |
+
|
| 150 |
+
q_rot = qkv[:, :, 0, :, :rotary_dim]
|
| 151 |
+
q_pass = qkv[:, :, 0, :, rotary_dim:]
|
| 152 |
+
|
| 153 |
+
k_rot = qkv[:, :, 1, :, :rotary_dim]
|
| 154 |
+
k_pass = qkv[:, :, 1, :, rotary_dim:]
|
| 155 |
+
|
| 156 |
+
q1, q2 = q_rot.chunk(2, dim=-1)
|
| 157 |
+
k1, k2 = k_rot.chunk(2, dim=-1)
|
| 158 |
+
c, s = rearrange(cos[:seqlen], "s d -> s 1 d"), rearrange(sin[:seqlen], "s d -> s 1 d")
|
| 159 |
+
q1, q2, k1, k2, c, s = [t.to(dtype=torch.float32) for t in [q1, q2, k1, k2, c, s]]
|
| 160 |
+
|
| 161 |
+
q_rot = torch.cat([q1 * c - q2 * s, q1 * s + q2 * c], axis=-1).to(qkv.dtype)
|
| 162 |
+
k_rot = torch.cat([k1 * c - k2 * s, k1 * s + k2 * c], axis=-1).to(qkv.dtype)
|
| 163 |
+
|
| 164 |
+
return torch.cat(
|
| 165 |
+
[
|
| 166 |
+
torch.cat([q_rot, q_pass], axis=-1).unsqueeze(2),
|
| 167 |
+
torch.cat([k_rot, k_pass], axis=-1).unsqueeze(2),
|
| 168 |
+
qkv[:, :, 2:3, :, :],
|
| 169 |
+
],
|
| 170 |
+
axis=2,
|
| 171 |
+
)
|
| 172 |
+
|
| 173 |
+
|
| 174 |
+
class RotaryEmbedding(nn.Module):
|
| 175 |
+
"""Rotary positional embedding (RoPE).
|
| 176 |
+
|
| 177 |
+
Reference:
|
| 178 |
+
RoFormer: Enhanced Transformer with Rotary Position Embedding.
|
| 179 |
+
https://arxiv.org/pdf/2104.09864.pdf.
|
| 180 |
+
|
| 181 |
+
"""
|
| 182 |
+
|
| 183 |
+
def __init__(
|
| 184 |
+
self,
|
| 185 |
+
dim: int,
|
| 186 |
+
base: int = 10000,
|
| 187 |
+
scale_base: Optional[float] = None,
|
| 188 |
+
pos_idx_in_fp32: bool = True,
|
| 189 |
+
max_position_embeddings: int = 2048,
|
| 190 |
+
device: Optional[str] = None,
|
| 191 |
+
**kwargs,
|
| 192 |
+
) -> None:
|
| 193 |
+
super().__init__()
|
| 194 |
+
|
| 195 |
+
if scale_base is not None:
|
| 196 |
+
raise NotImplementedError
|
| 197 |
+
|
| 198 |
+
self.dim = dim
|
| 199 |
+
self.base = float(base)
|
| 200 |
+
self.scale_base = scale_base
|
| 201 |
+
self.pos_idx_in_fp32 = pos_idx_in_fp32
|
| 202 |
+
self.max_position_embeddings = max_position_embeddings
|
| 203 |
+
self.device = device
|
| 204 |
+
|
| 205 |
+
# Generate and save the inverse frequency buffer (non-trainable)
|
| 206 |
+
inv_freq = self._compute_inv_freq(device)
|
| 207 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
| 208 |
+
|
| 209 |
+
# Generate and save the scale buffer (non-trainable)
|
| 210 |
+
scale = (
|
| 211 |
+
(torch.arange(0, dim, 2, device=device, dtype=torch.float32) + 0.4 * dim) / (1.4 * dim)
|
| 212 |
+
if scale_base is not None
|
| 213 |
+
else None
|
| 214 |
+
)
|
| 215 |
+
self.register_buffer("scale", scale, persistent=False)
|
| 216 |
+
|
| 217 |
+
# Initialize cached attributes since ONNX can't rely on dynamic initialization
|
| 218 |
+
self._update_cos_sin_cache(max_position_embeddings, device=device, dtype=torch.float32)
|
| 219 |
+
|
| 220 |
+
def _compute_inv_freq(self, device: Optional[str] = None) -> torch.FloatTensor:
|
| 221 |
+
return 1.0 / (self.base ** (torch.arange(0, self.dim, 2, device=device, dtype=torch.float32) / self.dim))
|
| 222 |
+
|
| 223 |
+
def _update_cos_sin_cache(
|
| 224 |
+
self,
|
| 225 |
+
seqlen: int,
|
| 226 |
+
device: Optional[str] = None,
|
| 227 |
+
dtype: Optional[torch.dtype] = None,
|
| 228 |
+
) -> None:
|
| 229 |
+
self._seq_len_cached = seqlen
|
| 230 |
+
|
| 231 |
+
# fp32 is preferred since the output of `torch.arange` can be quite large
|
| 232 |
+
# and bf16 would lose a lot of precision
|
| 233 |
+
if self.pos_idx_in_fp32:
|
| 234 |
+
t = torch.arange(seqlen, device=device, dtype=torch.float32)
|
| 235 |
+
if self.inv_freq.dtype != torch.float32:
|
| 236 |
+
inv_freq = self._compute_inv_freq(device=device)
|
| 237 |
+
else:
|
| 238 |
+
inv_freq = self.inv_freq
|
| 239 |
+
else:
|
| 240 |
+
t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
|
| 241 |
+
inv_freq = self.inv_freq
|
| 242 |
+
|
| 243 |
+
# `torch.outer` is preferred since `torch.einsum` converts from fp32 to fp16 if used with AMP
|
| 244 |
+
freqs = torch.outer(t, inv_freq)
|
| 245 |
+
if self.scale is None:
|
| 246 |
+
self._cos_cached = torch.cos(freqs).to(dtype)
|
| 247 |
+
self._sin_cached = torch.sin(freqs).to(dtype)
|
| 248 |
+
else:
|
| 249 |
+
power = (
|
| 250 |
+
torch.arange(seqlen, dtype=self.scale.dtype, device=self.scale.device) - seqlen // 2
|
| 251 |
+
) / self.scale_base
|
| 252 |
+
scale = self.scale.to(device=power.device) ** rearrange(power, "s -> s 1")
|
| 253 |
+
|
| 254 |
+
# Force the scale multiplication to happen in fp32
|
| 255 |
+
self._cos_cached = (torch.cos(freqs) * scale).to(dtype)
|
| 256 |
+
self._sin_cached = (torch.sin(freqs) * scale).to(dtype)
|
| 257 |
+
self._cos_k_cached = (torch.cos(freqs) / scale).to(dtype)
|
| 258 |
+
self._sin_k_cached = (torch.sin(freqs) / scale).to(dtype)
|
| 259 |
+
|
| 260 |
+
def forward(
|
| 261 |
+
self,
|
| 262 |
+
qkv: torch.Tensor,
|
| 263 |
+
kv: Optional[torch.Tensor] = None,
|
| 264 |
+
seqlen_offset: int = 0,
|
| 265 |
+
**kwargs,
|
| 266 |
+
) -> Tuple[torch.Tensor, torch.Tensor]:
|
| 267 |
+
if (
|
| 268 |
+
self._seq_len_cached < qkv.shape[1] + seqlen_offset
|
| 269 |
+
or self._cos_cached.device != qkv.device
|
| 270 |
+
or self._cos_cached.dtype != qkv.dtype
|
| 271 |
+
or (self.training and self._cos_cached.is_inference())
|
| 272 |
+
):
|
| 273 |
+
self._update_cos_sin_cache(qkv.shape[1] + seqlen_offset, device=qkv.device, dtype=qkv.dtype)
|
| 274 |
+
|
| 275 |
+
if kv is None:
|
| 276 |
+
return _apply_rotary_emb_qkv(
|
| 277 |
+
qkv,
|
| 278 |
+
self._cos_cached[seqlen_offset:],
|
| 279 |
+
self._sin_cached[seqlen_offset:],
|
| 280 |
+
)
|
| 281 |
+
else:
|
| 282 |
+
q = _apply_rotary_emb(
|
| 283 |
+
qkv,
|
| 284 |
+
self._cos_cached[seqlen_offset:],
|
| 285 |
+
self._sin_cached[seqlen_offset:],
|
| 286 |
+
)
|
| 287 |
+
kv = _apply_rotary_emb_kv(
|
| 288 |
+
kv,
|
| 289 |
+
self._cos_cached[seqlen_offset:],
|
| 290 |
+
self._sin_cached[seqlen_offset:],
|
| 291 |
+
)
|
| 292 |
+
|
| 293 |
+
return q, kv
|
| 294 |
+
|
| 295 |
+
|
| 296 |
+
class MLP(nn.Module):
|
| 297 |
+
"""Multi-Layer Perceptron.
|
| 298 |
+
|
| 299 |
+
Reference:
|
| 300 |
+
Attention Is All You Need.
|
| 301 |
+
https://arxiv.org/pdf/1706.03762.pdf.
|
| 302 |
+
|
| 303 |
+
"""
|
| 304 |
+
|
| 305 |
+
def __init__(
|
| 306 |
+
self,
|
| 307 |
+
config: PretrainedConfig,
|
| 308 |
+
n_inner: Optional[int] = None,
|
| 309 |
+
act_fn: Optional[str] = None,
|
| 310 |
+
) -> None:
|
| 311 |
+
super().__init__()
|
| 312 |
+
|
| 313 |
+
act_fn = config.activation_function if act_fn is None else act_fn
|
| 314 |
+
|
| 315 |
+
n_inner = getattr(config, "n_inner", None) if n_inner is None else n_inner
|
| 316 |
+
n_inner = n_inner if n_inner is not None else 4 * config.n_embd
|
| 317 |
+
|
| 318 |
+
self.fc1 = nn.Linear(config.n_embd, n_inner)
|
| 319 |
+
self.fc2 = nn.Linear(n_inner, config.n_embd)
|
| 320 |
+
self.act = ACT2FN[act_fn]
|
| 321 |
+
|
| 322 |
+
def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
|
| 323 |
+
hidden_states = self.fc1(hidden_states)
|
| 324 |
+
hidden_states = self.act(hidden_states)
|
| 325 |
+
hidden_states = self.fc2(hidden_states)
|
| 326 |
+
|
| 327 |
+
return hidden_states
|
| 328 |
+
|
| 329 |
+
|
| 330 |
+
class SelfAttention(nn.Module):
|
| 331 |
+
"""Self-attention layer (compatible with PyTorch).
|
| 332 |
+
|
| 333 |
+
Reference:
|
| 334 |
+
https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py.
|
| 335 |
+
|
| 336 |
+
"""
|
| 337 |
+
|
| 338 |
+
def __init__(
|
| 339 |
+
self,
|
| 340 |
+
causal: bool = True,
|
| 341 |
+
softmax_scale: Optional[float] = None,
|
| 342 |
+
attention_dropout: float = 0.0,
|
| 343 |
+
) -> None:
|
| 344 |
+
super().__init__()
|
| 345 |
+
|
| 346 |
+
self.causal = causal
|
| 347 |
+
self.softmax_scale = softmax_scale
|
| 348 |
+
self.drop = nn.Dropout(attention_dropout)
|
| 349 |
+
|
| 350 |
+
@torch.autocast("cpu", enabled=False)
|
| 351 |
+
@torch.autocast("cuda", enabled=False)
|
| 352 |
+
def forward(
|
| 353 |
+
self,
|
| 354 |
+
qkv: torch.FloatTensor,
|
| 355 |
+
causal: bool = None,
|
| 356 |
+
key_padding_mask: Optional[torch.BoolTensor] = None,
|
| 357 |
+
**kwargs,
|
| 358 |
+
) -> torch.FloatTensor:
|
| 359 |
+
batch_size, seqlen = qkv.shape[0], qkv.shape[1]
|
| 360 |
+
q, k, v = qkv.unbind(dim=2)
|
| 361 |
+
|
| 362 |
+
q = q.to(torch.float32)
|
| 363 |
+
k = k.to(torch.float32)
|
| 364 |
+
|
| 365 |
+
causal = self.causal if causal is None else causal
|
| 366 |
+
softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
|
| 367 |
+
|
| 368 |
+
# Autocast is manually disabled to avoid `torch.einsum` performing the operation
|
| 369 |
+
# using float16, which might lead to overflow
|
| 370 |
+
scores = torch.einsum("bthd,bshd->bhts", q, k * softmax_scale)
|
| 371 |
+
|
| 372 |
+
if key_padding_mask is not None:
|
| 373 |
+
padding_mask = torch.full((batch_size, seqlen), -10000.0, dtype=scores.dtype, device=scores.device)
|
| 374 |
+
padding_mask.masked_fill_(key_padding_mask, 0.0)
|
| 375 |
+
|
| 376 |
+
scores = scores + rearrange(padding_mask, "b s -> b 1 1 s")
|
| 377 |
+
|
| 378 |
+
if causal:
|
| 379 |
+
causal_mask = torch.triu(torch.full((seqlen, seqlen), -10000.0, device=scores.device), 1)
|
| 380 |
+
scores = scores + causal_mask.to(dtype=scores.dtype)
|
| 381 |
+
|
| 382 |
+
attention = torch.softmax(scores, dim=-1).to(v.dtype)
|
| 383 |
+
attention = self.drop(attention)
|
| 384 |
+
|
| 385 |
+
output = torch.einsum("bhts,bshd->bthd", attention, v)
|
| 386 |
+
|
| 387 |
+
return output
|
| 388 |
+
|
| 389 |
+
|
| 390 |
+
class CrossAttention(nn.Module):
|
| 391 |
+
"""Cross-attention layer (compatible with PyTorch).
|
| 392 |
+
|
| 393 |
+
Reference:
|
| 394 |
+
https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py.
|
| 395 |
+
|
| 396 |
+
"""
|
| 397 |
+
|
| 398 |
+
def __init__(
|
| 399 |
+
self,
|
| 400 |
+
causal: bool = True,
|
| 401 |
+
softmax_scale: Optional[float] = None,
|
| 402 |
+
attention_dropout: float = 0.0,
|
| 403 |
+
) -> None:
|
| 404 |
+
super().__init__()
|
| 405 |
+
|
| 406 |
+
self.causal = causal
|
| 407 |
+
self.softmax_scale = softmax_scale
|
| 408 |
+
self.drop = nn.Dropout(attention_dropout)
|
| 409 |
+
|
| 410 |
+
@torch.autocast("cpu", enabled=False)
|
| 411 |
+
@torch.autocast("cuda", enabled=False)
|
| 412 |
+
def forward(
|
| 413 |
+
self,
|
| 414 |
+
q: torch.FloatTensor,
|
| 415 |
+
kv: torch.FloatTensor,
|
| 416 |
+
causal: bool = None,
|
| 417 |
+
key_padding_mask: Optional[torch.BoolTensor] = None,
|
| 418 |
+
**kwargs,
|
| 419 |
+
) -> torch.FloatTensor:
|
| 420 |
+
batch_size, seqlen_q = q.shape[0], q.shape[1]
|
| 421 |
+
seqlen_k = kv.shape[1]
|
| 422 |
+
|
| 423 |
+
if kv.shape[3] != q.shape[2]:
|
| 424 |
+
kv = repeat(kv, "... hkv d -> ... (hkv g) d", g=q.shape[2] // kv.shape[3])
|
| 425 |
+
k, v = kv.unbind(dim=2)
|
| 426 |
+
|
| 427 |
+
q = q.to(torch.float32)
|
| 428 |
+
k = k.to(torch.float32)
|
| 429 |
+
|
| 430 |
+
causal = self.causal if causal is None else causal
|
| 431 |
+
softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
|
| 432 |
+
|
| 433 |
+
# Autocast is manually disabled to avoid `torch.einsum` performing the operation
|
| 434 |
+
# using float16, which might lead to overflow
|
| 435 |
+
scores = torch.einsum("bthd,bshd->bhts", q, k * softmax_scale)
|
| 436 |
+
|
| 437 |
+
if key_padding_mask is not None:
|
| 438 |
+
padding_mask = torch.full(
|
| 439 |
+
(batch_size, seqlen_k),
|
| 440 |
+
-10000.0,
|
| 441 |
+
dtype=scores.dtype,
|
| 442 |
+
device=scores.device,
|
| 443 |
+
)
|
| 444 |
+
padding_mask.masked_fill_(key_padding_mask, 0.0)
|
| 445 |
+
|
| 446 |
+
scores = scores + rearrange(padding_mask, "b s -> b 1 1 s")
|
| 447 |
+
|
| 448 |
+
if causal:
|
| 449 |
+
rows = rearrange(torch.arange(seqlen_q, device=q.device, dtype=torch.long), "s -> s 1")
|
| 450 |
+
cols = torch.arange(seqlen_k, device=k.device, dtype=torch.long)
|
| 451 |
+
causal_mask = cols > rows + seqlen_k - seqlen_q
|
| 452 |
+
|
| 453 |
+
scores = scores.masked_fill(causal_mask, -10000.0)
|
| 454 |
+
|
| 455 |
+
attention = torch.softmax(scores, dim=-1).to(v.dtype)
|
| 456 |
+
attention = self.drop(attention)
|
| 457 |
+
|
| 458 |
+
output = torch.einsum("bhts,bshd->bthd", attention, v)
|
| 459 |
+
|
| 460 |
+
return output
|
| 461 |
+
|
| 462 |
+
|
| 463 |
+
def _find_mha_dims(
|
| 464 |
+
config: PretrainedConfig,
|
| 465 |
+
n_head: Optional[int] = None,
|
| 466 |
+
n_head_kv: Optional[int] = None,
|
| 467 |
+
head_dim: Optional[int] = None,
|
| 468 |
+
) -> Tuple[int, int]:
|
| 469 |
+
if n_head is None and head_dim is None:
|
| 470 |
+
head_dim = config.n_embd // config.n_head
|
| 471 |
+
n_head = config.n_head
|
| 472 |
+
elif n_head is None or head_dim is None:
|
| 473 |
+
raise ValueError("`n_head` and `head_dim` must be both specified or `None`.")
|
| 474 |
+
|
| 475 |
+
if n_head_kv is None:
|
| 476 |
+
n_head_kv = getattr(config, "n_head_kv", None) or n_head
|
| 477 |
+
|
| 478 |
+
return n_head, n_head_kv, head_dim
|
| 479 |
+
|
| 480 |
+
|
| 481 |
+
def _update_kv_cache(kv: torch.FloatTensor, inference_params: InferenceParams, layer_idx: int) -> torch.FloatTensor:
|
| 482 |
+
num_heads, head_dim = kv.shape[-2:]
|
| 483 |
+
|
| 484 |
+
if layer_idx not in inference_params.key_value_memory_dict:
|
| 485 |
+
inference_params.key_value_memory_dict[layer_idx] = torch.empty(
|
| 486 |
+
inference_params.max_batch_size,
|
| 487 |
+
inference_params.max_seqlen,
|
| 488 |
+
2,
|
| 489 |
+
num_heads,
|
| 490 |
+
head_dim,
|
| 491 |
+
dtype=kv.dtype,
|
| 492 |
+
device=kv.device,
|
| 493 |
+
)
|
| 494 |
+
|
| 495 |
+
batch_start = inference_params.batch_size_offset
|
| 496 |
+
batch_end = batch_start + kv.shape[0]
|
| 497 |
+
|
| 498 |
+
sequence_start = inference_params.seqlen_offset
|
| 499 |
+
sequence_end = sequence_start + kv.shape[1]
|
| 500 |
+
|
| 501 |
+
# When the current sequence length is equal to or larger than the maximum sequence length,
|
| 502 |
+
# we need to concatenate the current `kv` with the cached `kv` to expand its length
|
| 503 |
+
if sequence_end >= inference_params.max_seqlen:
|
| 504 |
+
inference_params.key_value_memory_dict[layer_idx] = torch.concatenate((inference_params.key_value_memory_dict[layer_idx], kv), dim=1)
|
| 505 |
+
|
| 506 |
+
inference_params.key_value_memory_dict[layer_idx][batch_start:batch_end, sequence_start:sequence_end, ...] = kv
|
| 507 |
+
kv = inference_params.key_value_memory_dict[layer_idx][batch_start:batch_end, :sequence_end, ...]
|
| 508 |
+
|
| 509 |
+
return kv
|
| 510 |
+
|
| 511 |
+
|
| 512 |
+
class MHA(nn.Module):
|
| 513 |
+
"""Multi-head attention layer."""
|
| 514 |
+
|
| 515 |
+
def __init__(
|
| 516 |
+
self,
|
| 517 |
+
config: PretrainedConfig,
|
| 518 |
+
dtype: Optional[torch.dtype] = None,
|
| 519 |
+
device: Optional[str] = None,
|
| 520 |
+
rotary_dim: Optional[int] = None,
|
| 521 |
+
rotary_base: float = 10000.0,
|
| 522 |
+
rotary_scale_base: Optional[float] = None,
|
| 523 |
+
n_head: Optional[int] = None,
|
| 524 |
+
n_head_kv: Optional[int] = None,
|
| 525 |
+
head_dim: Optional[int] = None,
|
| 526 |
+
bias: bool = True,
|
| 527 |
+
causal: bool = True,
|
| 528 |
+
softmax_scale: Optional[float] = None,
|
| 529 |
+
layer_idx: Optional[int] = None,
|
| 530 |
+
return_residual: bool = False,
|
| 531 |
+
checkpointing: bool = True,
|
| 532 |
+
) -> None:
|
| 533 |
+
super().__init__()
|
| 534 |
+
|
| 535 |
+
# Rotary embedding
|
| 536 |
+
self.rotary_dim = rotary_dim if rotary_dim is not None else getattr(config, "rotary_dim", 0)
|
| 537 |
+
if self.rotary_dim > 0:
|
| 538 |
+
rotary_cls = FlashRotaryEmbedding if config.flash_rotary else RotaryEmbedding
|
| 539 |
+
if rotary_cls is None:
|
| 540 |
+
rotary_cls = RotaryEmbedding
|
| 541 |
+
|
| 542 |
+
rotary_kwargs = {}
|
| 543 |
+
if rotary_cls is RotaryEmbedding:
|
| 544 |
+
rotary_kwargs["max_position_embeddings"] = config.n_positions
|
| 545 |
+
|
| 546 |
+
self.rotary_emb = rotary_cls(
|
| 547 |
+
self.rotary_dim,
|
| 548 |
+
base=rotary_base,
|
| 549 |
+
scale_base=rotary_scale_base,
|
| 550 |
+
device=device,
|
| 551 |
+
**rotary_kwargs,
|
| 552 |
+
)
|
| 553 |
+
|
| 554 |
+
# MLP
|
| 555 |
+
self.n_head, self.n_head_kv, self.head_dim = _find_mha_dims(
|
| 556 |
+
config, n_head=n_head, n_head_kv=n_head_kv, head_dim=head_dim
|
| 557 |
+
)
|
| 558 |
+
op_size = self.head_dim * (self.n_head + 2 * self.n_head_kv)
|
| 559 |
+
hidden_size = config.n_embd
|
| 560 |
+
|
| 561 |
+
linear_cls = FusedDense if config.fused_dense else nn.Linear
|
| 562 |
+
if linear_cls is None:
|
| 563 |
+
linear_cls = nn.Linear
|
| 564 |
+
|
| 565 |
+
self.Wqkv = linear_cls(hidden_size, op_size, bias=bias, device=device, dtype=dtype)
|
| 566 |
+
self.out_proj = linear_cls(hidden_size, hidden_size, bias=bias, device=device, dtype=dtype)
|
| 567 |
+
|
| 568 |
+
# Attention
|
| 569 |
+
attn_cls = FlashSelfAttention if config.flash_attn else SelfAttention
|
| 570 |
+
if attn_cls is None:
|
| 571 |
+
attn_cls = SelfAttention
|
| 572 |
+
|
| 573 |
+
cross_attn_cls = FlashCrossAttention if config.flash_attn else CrossAttention
|
| 574 |
+
if cross_attn_cls is None:
|
| 575 |
+
cross_attn_cls = CrossAttention
|
| 576 |
+
|
| 577 |
+
self.inner_attn = attn_cls(
|
| 578 |
+
causal=causal,
|
| 579 |
+
softmax_scale=softmax_scale,
|
| 580 |
+
attention_dropout=config.attn_pdrop,
|
| 581 |
+
)
|
| 582 |
+
self.inner_cross_attn = cross_attn_cls(
|
| 583 |
+
causal=causal,
|
| 584 |
+
softmax_scale=softmax_scale,
|
| 585 |
+
attention_dropout=config.attn_pdrop,
|
| 586 |
+
)
|
| 587 |
+
|
| 588 |
+
self.flash_attn = config.flash_attn and attn_cls is FlashSelfAttention
|
| 589 |
+
self.layer_idx = layer_idx
|
| 590 |
+
self.return_residual = return_residual
|
| 591 |
+
self.checkpointing = checkpointing
|
| 592 |
+
|
| 593 |
+
def _forward_self_attn(
|
| 594 |
+
self, x: torch.FloatTensor, key_padding_mask: Optional[torch.BoolTensor]
|
| 595 |
+
) -> torch.FloatTensor:
|
| 596 |
+
qkv = self.Wqkv(x)
|
| 597 |
+
qkv = rearrange(qkv, "... (three h d) -> ... three h d", three=3, d=self.head_dim)
|
| 598 |
+
|
| 599 |
+
if self.rotary_dim > 0:
|
| 600 |
+
qkv = self.rotary_emb(qkv)
|
| 601 |
+
|
| 602 |
+
if self.flash_attn:
|
| 603 |
+
batch_size, seqlen = qkv.shape[0], qkv.shape[1]
|
| 604 |
+
|
| 605 |
+
cu_seqlens, max_seqlen = None, None
|
| 606 |
+
if key_padding_mask is not None:
|
| 607 |
+
# If `key_padding_mask` is supplied, we need to unpad the input and retrieve
|
| 608 |
+
# the `cu_seqlens` and `max_seqlen` to be used by `flash-attn`
|
| 609 |
+
qkv, indices, cu_seqlens, max_seqlen = unpad_input(qkv, key_padding_mask)
|
| 610 |
+
|
| 611 |
+
if self.checkpointing:
|
| 612 |
+
attn_output = torch.utils.checkpoint.checkpoint(
|
| 613 |
+
self.inner_attn, qkv, None, cu_seqlens, max_seqlen, use_reentrant=False
|
| 614 |
+
)
|
| 615 |
+
else:
|
| 616 |
+
attn_output = self.inner_attn(qkv, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen).to(qkv.device)
|
| 617 |
+
|
| 618 |
+
# If `key_padding_mask` is supplied, we need to pad the output back to the original shape
|
| 619 |
+
return pad_input(attn_output, indices, batch_size, seqlen) if key_padding_mask is not None else attn_output
|
| 620 |
+
|
| 621 |
+
if self.checkpointing:
|
| 622 |
+
return torch.utils.checkpoint.checkpoint(self.inner_attn, qkv, None, key_padding_mask, use_reentrant=False)
|
| 623 |
+
|
| 624 |
+
return self.inner_attn(qkv, key_padding_mask=key_padding_mask)
|
| 625 |
+
|
| 626 |
+
def _forward_cross_attn(
|
| 627 |
+
self,
|
| 628 |
+
x: torch.FloatTensor,
|
| 629 |
+
past_key_values: Optional[InferenceParams],
|
| 630 |
+
key_padding_mask: Optional[torch.BoolTensor],
|
| 631 |
+
) -> torch.FloatTensor:
|
| 632 |
+
batch_size = x.shape[0]
|
| 633 |
+
|
| 634 |
+
qkv = self.Wqkv(x)
|
| 635 |
+
|
| 636 |
+
q = qkv[..., : self.n_head * self.head_dim]
|
| 637 |
+
q = rearrange(q, "... (h d) -> ... h d", d=self.head_dim)
|
| 638 |
+
|
| 639 |
+
kv = qkv[..., self.n_head * self.head_dim :]
|
| 640 |
+
kv = rearrange(kv, "... (two hkv d) -> ... two hkv d", two=2, d=self.head_dim)
|
| 641 |
+
|
| 642 |
+
seqlen_offset = past_key_values.seqlen_offset if past_key_values is not None else 0
|
| 643 |
+
causal = None if seqlen_offset == 0 else False
|
| 644 |
+
if self.rotary_dim > 0:
|
| 645 |
+
q, kv = self.rotary_emb(q, kv=kv, seqlen_offset=seqlen_offset)
|
| 646 |
+
|
| 647 |
+
if past_key_values is not None:
|
| 648 |
+
kv = _update_kv_cache(kv, past_key_values, self.layer_idx)
|
| 649 |
+
|
| 650 |
+
if self.flash_attn:
|
| 651 |
+
batch_size, seqlen_q = q.shape[0], q.shape[1]
|
| 652 |
+
seqlen_k = kv.shape[1]
|
| 653 |
+
|
| 654 |
+
cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k = (
|
| 655 |
+
None,
|
| 656 |
+
None,
|
| 657 |
+
None,
|
| 658 |
+
None,
|
| 659 |
+
)
|
| 660 |
+
if key_padding_mask is not None:
|
| 661 |
+
kv, _, cu_seqlens_k, max_seqlen_k = unpad_input(kv, key_padding_mask)
|
| 662 |
+
|
| 663 |
+
if seqlen_q == 1:
|
| 664 |
+
key_padding_mask = torch.ones(batch_size, 1, device=q.device)
|
| 665 |
+
elif seqlen_q != seqlen_k:
|
| 666 |
+
key_padding_mask = key_padding_mask[:, -seqlen_q:]
|
| 667 |
+
|
| 668 |
+
q, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(q, key_padding_mask)
|
| 669 |
+
|
| 670 |
+
if self.checkpointing:
|
| 671 |
+
attn_output = torch.utils.checkpoint.checkpoint(
|
| 672 |
+
self.inner_cross_attn,
|
| 673 |
+
q,
|
| 674 |
+
kv,
|
| 675 |
+
causal,
|
| 676 |
+
cu_seqlens_q,
|
| 677 |
+
max_seqlen_q,
|
| 678 |
+
cu_seqlens_k,
|
| 679 |
+
max_seqlen_k,
|
| 680 |
+
use_reentrant=False,
|
| 681 |
+
)
|
| 682 |
+
else:
|
| 683 |
+
attn_output = self.inner_cross_attn(
|
| 684 |
+
q,
|
| 685 |
+
kv,
|
| 686 |
+
causal=causal,
|
| 687 |
+
cu_seqlens=cu_seqlens_q,
|
| 688 |
+
max_seqlen=max_seqlen_q,
|
| 689 |
+
cu_seqlens_k=cu_seqlens_k,
|
| 690 |
+
max_seqlen_k=max_seqlen_k,
|
| 691 |
+
)
|
| 692 |
+
|
| 693 |
+
return (
|
| 694 |
+
pad_input(attn_output, indices_q, batch_size, max_seqlen_q)
|
| 695 |
+
if key_padding_mask is not None
|
| 696 |
+
else attn_output
|
| 697 |
+
)
|
| 698 |
+
|
| 699 |
+
if self.checkpointing:
|
| 700 |
+
return torch.utils.checkpoint.checkpoint(
|
| 701 |
+
self.inner_cross_attn,
|
| 702 |
+
q,
|
| 703 |
+
kv,
|
| 704 |
+
causal,
|
| 705 |
+
key_padding_mask,
|
| 706 |
+
use_reentrant=False,
|
| 707 |
+
)
|
| 708 |
+
|
| 709 |
+
return self.inner_cross_attn(q, kv, key_padding_mask=key_padding_mask, causal=causal)
|
| 710 |
+
|
| 711 |
+
def forward(
|
| 712 |
+
self,
|
| 713 |
+
x: torch.FloatTensor,
|
| 714 |
+
past_key_values: Optional[InferenceParams] = None,
|
| 715 |
+
attention_mask: Optional[Union[torch.LongTensor, torch.BoolTensor]] = None,
|
| 716 |
+
**kwargs,
|
| 717 |
+
) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
|
| 718 |
+
if attention_mask is not None:
|
| 719 |
+
attention_mask = attention_mask.bool()
|
| 720 |
+
else:
|
| 721 |
+
attention_mask = None
|
| 722 |
+
|
| 723 |
+
# MHA
|
| 724 |
+
if self.n_head == self.n_head_kv:
|
| 725 |
+
if past_key_values is None:
|
| 726 |
+
# If `past_key_values` are not supplied, we run self-attention
|
| 727 |
+
attn_output = self._forward_self_attn(x, attention_mask)
|
| 728 |
+
else:
|
| 729 |
+
# If `past_key_values` are supplied, it means that we might have cached values and
|
| 730 |
+
# could take advantage of cross-attention
|
| 731 |
+
attn_output = self._forward_cross_attn(x, past_key_values, attention_mask)
|
| 732 |
+
# MQA / GQA
|
| 733 |
+
else:
|
| 734 |
+
# Regardless of `past_key_values` being supplied or not, it always use cross-attention
|
| 735 |
+
# because `q` and `kv` lengths might be different
|
| 736 |
+
attn_output = self._forward_cross_attn(x, past_key_values, attention_mask)
|
| 737 |
+
|
| 738 |
+
output = rearrange(attn_output, "... h d -> ... (h d)")
|
| 739 |
+
output = self.out_proj(output)
|
| 740 |
+
|
| 741 |
+
return output if not self.return_residual else (output, x)
|
| 742 |
+
|
| 743 |
+
|
| 744 |
+
class ParallelBlock(nn.Module):
|
| 745 |
+
"""Parallel block.
|
| 746 |
+
|
| 747 |
+
This block applies parallel mixer and MLP layers to the input (used in GPT-J and CodeGen).
|
| 748 |
+
|
| 749 |
+
"""
|
| 750 |
+
|
| 751 |
+
def __init__(
|
| 752 |
+
self,
|
| 753 |
+
config: PretrainedConfig,
|
| 754 |
+
block_idx: Optional[int] = None,
|
| 755 |
+
) -> None:
|
| 756 |
+
super().__init__()
|
| 757 |
+
|
| 758 |
+
self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
|
| 759 |
+
self.resid_dropout = nn.Dropout(config.resid_pdrop)
|
| 760 |
+
self.block_idx = block_idx
|
| 761 |
+
|
| 762 |
+
self.mixer = MHA(config, layer_idx=block_idx)
|
| 763 |
+
self.mlp = MLP(config)
|
| 764 |
+
|
| 765 |
+
def forward(
|
| 766 |
+
self,
|
| 767 |
+
hidden_states: torch.FloatTensor,
|
| 768 |
+
past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
|
| 769 |
+
attention_mask: Optional[torch.BoolTensor] = None,
|
| 770 |
+
**kwargs,
|
| 771 |
+
) -> torch.FloatTensor:
|
| 772 |
+
residual = hidden_states
|
| 773 |
+
hidden_states = self.ln(hidden_states)
|
| 774 |
+
|
| 775 |
+
attn_outputs = self.mixer(
|
| 776 |
+
hidden_states,
|
| 777 |
+
past_key_values=past_key_values,
|
| 778 |
+
attention_mask=attention_mask,
|
| 779 |
+
)
|
| 780 |
+
if isinstance(attn_outputs, tuple):
|
| 781 |
+
attn_outputs = attn_outputs[0]
|
| 782 |
+
|
| 783 |
+
attn_outputs = self.resid_dropout(attn_outputs)
|
| 784 |
+
feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states))
|
| 785 |
+
|
| 786 |
+
hidden_states = attn_outputs + feed_forward_hidden_states + residual
|
| 787 |
+
|
| 788 |
+
return hidden_states
|
| 789 |
+
|
| 790 |
+
|
| 791 |
+
class CausalLMHead(nn.Module):
|
| 792 |
+
"""Causal Language Modeling head.
|
| 793 |
+
|
| 794 |
+
Reference:
|
| 795 |
+
Improving Language Understanding by Generative Pre-Training.
|
| 796 |
+
https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf.
|
| 797 |
+
|
| 798 |
+
"""
|
| 799 |
+
|
| 800 |
+
def __init__(self, config: PretrainedConfig) -> None:
|
| 801 |
+
super().__init__()
|
| 802 |
+
|
| 803 |
+
self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
|
| 804 |
+
self.linear = nn.Linear(config.n_embd, config.vocab_size)
|
| 805 |
+
|
| 806 |
+
def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
|
| 807 |
+
hidden_states = self.ln(hidden_states)
|
| 808 |
+
logits = self.linear(hidden_states).to(torch.float32)
|
| 809 |
+
|
| 810 |
+
return logits
|
| 811 |
+
|
| 812 |
+
|
| 813 |
+
class CausalLMLoss(nn.Module):
|
| 814 |
+
"""Causal Language Modeling loss.
|
| 815 |
+
|
| 816 |
+
Reference:
|
| 817 |
+
Improving Language Understanding by Generative Pre-Training.
|
| 818 |
+
https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf.
|
| 819 |
+
|
| 820 |
+
"""
|
| 821 |
+
|
| 822 |
+
def __init__(self, shift_labels: bool = True) -> None:
|
| 823 |
+
super().__init__()
|
| 824 |
+
|
| 825 |
+
self.shift_labels = shift_labels
|
| 826 |
+
self.loss_fct = nn.CrossEntropyLoss()
|
| 827 |
+
|
| 828 |
+
def forward(self, logits: torch.FloatTensor, labels: torch.LongTensor) -> torch.FloatTensor:
|
| 829 |
+
if self.shift_labels:
|
| 830 |
+
logits = logits[..., :-1, :].contiguous()
|
| 831 |
+
labels = labels[..., 1:].contiguous()
|
| 832 |
+
|
| 833 |
+
loss = self.loss_fct(logits.view(-1, logits.size(-1)), labels.view(-1))
|
| 834 |
+
|
| 835 |
+
return loss
|
| 836 |
+
|
| 837 |
+
|
| 838 |
+
class PhiPreTrainedModel(PreTrainedModel):
|
| 839 |
+
"""Phi pre-trained model."""
|
| 840 |
+
|
| 841 |
+
config_class = PhiConfig
|
| 842 |
+
base_model_prefix = "transformer"
|
| 843 |
+
supports_gradient_checkpointing = True
|
| 844 |
+
_no_split_modules = ["ParallelBlock"]
|
| 845 |
+
|
| 846 |
+
def __init__(self, *inputs, **kwargs) -> None:
|
| 847 |
+
super().__init__(*inputs, **kwargs)
|
| 848 |
+
|
| 849 |
+
def _init_weights(self, module: nn.Module) -> None:
|
| 850 |
+
if isinstance(module, (nn.Linear,)):
|
| 851 |
+
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
| 852 |
+
if module.bias is not None:
|
| 853 |
+
module.bias.data.zero_()
|
| 854 |
+
elif isinstance(module, nn.Embedding):
|
| 855 |
+
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
| 856 |
+
if module.padding_idx is not None:
|
| 857 |
+
module.weight.data[module.padding_idx].zero_()
|
| 858 |
+
elif isinstance(module, nn.LayerNorm):
|
| 859 |
+
if module.bias is not None:
|
| 860 |
+
module.bias.data.zero_()
|
| 861 |
+
module.weight.data.fill_(1.0)
|
| 862 |
+
|
| 863 |
+
def prepare_inputs_for_generation(
|
| 864 |
+
self,
|
| 865 |
+
input_ids: torch.LongTensor,
|
| 866 |
+
past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
|
| 867 |
+
attention_mask: Optional[Union[torch.LongTensor, torch.BoolTensor]] = None,
|
| 868 |
+
**kwargs,
|
| 869 |
+
) -> Dict[str, Any]:
|
| 870 |
+
# if past_key_values is None or not (isinstance(past_key_values, InferenceParams)):
|
| 871 |
+
# past_key_values = InferenceParams(
|
| 872 |
+
# max_seqlen=self.config.n_positions,
|
| 873 |
+
# max_batch_size=input_ids.shape[0],
|
| 874 |
+
# seqlen_offset=0,
|
| 875 |
+
# batch_size_offset=0,
|
| 876 |
+
# key_value_memory_dict={},
|
| 877 |
+
# lengths_per_sample=None,
|
| 878 |
+
# )
|
| 879 |
+
# else:
|
| 880 |
+
# # Assume that `past_key_values` has cached all tokens up to the last token in `input_ids`
|
| 881 |
+
# past_key_values.seqlen_offset = input_ids.shape[1] - 1
|
| 882 |
+
# input_ids = input_ids[:, -1].unsqueeze(-1)
|
| 883 |
+
# attention_mask = attention_mask[:, -1].unsqueeze(-1)
|
| 884 |
+
|
| 885 |
+
return {
|
| 886 |
+
"input_ids": input_ids,
|
| 887 |
+
"past_key_values": past_key_values,
|
| 888 |
+
"attention_mask": attention_mask,
|
| 889 |
+
}
|
| 890 |
+
|
| 891 |
+
|
| 892 |
+
class PhiModel(PhiPreTrainedModel):
|
| 893 |
+
"""Phi model."""
|
| 894 |
+
|
| 895 |
+
_keys_to_ignore_on_load_missing = [""]
|
| 896 |
+
_keys_to_ignore_on_load_unexpected = [r"h\.\d+\.mlp.(fc_in|fc_out)\.(weight|bias)"]
|
| 897 |
+
|
| 898 |
+
def __init__(self, config: PhiConfig) -> None:
|
| 899 |
+
config.flash_attn = True
|
| 900 |
+
config.flash_rotary = True
|
| 901 |
+
super().__init__(config)
|
| 902 |
+
|
| 903 |
+
self.embd = Embedding(config)
|
| 904 |
+
self.h = nn.ModuleList([ParallelBlock(config, block_idx=i) for i in range(config.n_layer)])
|
| 905 |
+
self.gradient_checkpointing = True
|
| 906 |
+
self.post_init()
|
| 907 |
+
|
| 908 |
+
def get_input_embeddings(self) -> nn.Embedding:
|
| 909 |
+
return self.embd.wte
|
| 910 |
+
|
| 911 |
+
def set_input_embeddings(self, new_embeddings: nn.Embedding) -> None:
|
| 912 |
+
self.embd.wte = new_embeddings
|
| 913 |
+
|
| 914 |
+
def forward(
|
| 915 |
+
self,
|
| 916 |
+
input_ids: torch.LongTensor,
|
| 917 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 918 |
+
past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
|
| 919 |
+
attention_mask: Optional[torch.BoolTensor] = None,
|
| 920 |
+
) -> torch.FloatTensor:
|
| 921 |
+
if input_ids is not None:
|
| 922 |
+
hidden_states = self.embd(input_ids)
|
| 923 |
+
elif inputs_embeds is not None:
|
| 924 |
+
hidden_states = inputs_embeds
|
| 925 |
+
else:
|
| 926 |
+
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
| 927 |
+
|
| 928 |
+
for layer in self.h:
|
| 929 |
+
if self.gradient_checkpointing:
|
| 930 |
+
hidden_states = torch.utils.checkpoint.checkpoint(
|
| 931 |
+
layer.__call__,
|
| 932 |
+
hidden_states,
|
| 933 |
+
past_key_values,
|
| 934 |
+
attention_mask,
|
| 935 |
+
use_reentrant=False,
|
| 936 |
+
)
|
| 937 |
+
else:
|
| 938 |
+
hidden_states = layer(
|
| 939 |
+
hidden_states,
|
| 940 |
+
past_key_values=past_key_values,
|
| 941 |
+
attention_mask=attention_mask,
|
| 942 |
+
)
|
| 943 |
+
|
| 944 |
+
return hidden_states
|
| 945 |
+
|
| 946 |
+
|
| 947 |
+
class PhiForCausalLM(PhiPreTrainedModel):
|
| 948 |
+
"""Phi for Causal Language Modeling."""
|
| 949 |
+
|
| 950 |
+
_keys_to_ignore_on_load_missing = [""]
|
| 951 |
+
_keys_to_ignore_on_load_unexpected = [r"transformer\.h\.\d+\.mlp.(fc_in|fc_out)\.(weight|bias)"]
|
| 952 |
+
|
| 953 |
+
supports_gradient_checkpointing = True
|
| 954 |
+
_no_split_modules = ["ParallelBlock"]
|
| 955 |
+
_skip_keys_device_placement = "past_key_values"
|
| 956 |
+
|
| 957 |
+
def __init__(self, config: PhiConfig) -> None:
|
| 958 |
+
super().__init__(config)
|
| 959 |
+
|
| 960 |
+
self.transformer = PhiModel(config)
|
| 961 |
+
self.lm_head = CausalLMHead(config)
|
| 962 |
+
self.loss = CausalLMLoss()
|
| 963 |
+
|
| 964 |
+
self.post_init()
|
| 965 |
+
|
| 966 |
+
def get_output_embeddings(self) -> nn.Linear:
|
| 967 |
+
return self.lm_head.linear
|
| 968 |
+
|
| 969 |
+
def set_output_embeddings(self, new_embeddings: nn.Linear) -> None:
|
| 970 |
+
self.lm_head.linear = new_embeddings
|
| 971 |
+
|
| 972 |
+
def forward(
|
| 973 |
+
self,
|
| 974 |
+
input_ids: torch.LongTensor,
|
| 975 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 976 |
+
past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
|
| 977 |
+
attention_mask: Optional[torch.BoolTensor] = None,
|
| 978 |
+
labels: Optional[torch.LongTensor] = None,
|
| 979 |
+
**kwargs,
|
| 980 |
+
) -> CausalLMOutputWithPast:
|
| 981 |
+
hidden_states = self.transformer(input_ids, inputs_embeds=inputs_embeds, past_key_values=past_key_values, attention_mask=attention_mask)
|
| 982 |
+
lm_logits = self.lm_head(hidden_states)
|
| 983 |
+
|
| 984 |
+
loss = None
|
| 985 |
+
if labels is not None:
|
| 986 |
+
loss = self.loss(lm_logits, labels)
|
| 987 |
+
|
| 988 |
+
return CausalLMOutputWithPast(loss=loss, logits=lm_logits, past_key_values=past_key_values)
|
preprocessor_config.json
ADDED
|
@@ -0,0 +1,25 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"auto_map": {
|
| 3 |
+
"AutoProcessor": "processing_llava.LlavaProcessor",
|
| 4 |
+
"AutoImageProcessor": "processing_llava.MultiCropImageProcessor"
|
| 5 |
+
},
|
| 6 |
+
"do_normalize": true,
|
| 7 |
+
"do_rescale": true,
|
| 8 |
+
"do_resize": true,
|
| 9 |
+
"image_mean": [
|
| 10 |
+
0.5,
|
| 11 |
+
0.5,
|
| 12 |
+
0.5
|
| 13 |
+
],
|
| 14 |
+
"image_std": [
|
| 15 |
+
0.5,
|
| 16 |
+
0.5,
|
| 17 |
+
0.5
|
| 18 |
+
],
|
| 19 |
+
"resample": 3,
|
| 20 |
+
"rescale_factor": 0.00392156862745098,
|
| 21 |
+
"size": {
|
| 22 |
+
"height": 384,
|
| 23 |
+
"width": 384
|
| 24 |
+
}
|
| 25 |
+
}
|
processing_llava.py
ADDED
|
@@ -0,0 +1,182 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import math
|
| 2 |
+
from typing import List, Optional, Union
|
| 3 |
+
|
| 4 |
+
import torch
|
| 5 |
+
from PIL import Image
|
| 6 |
+
from transformers import ImageProcessingMixin, ProcessorMixin, SiglipImageProcessor, AutoTokenizer, AutoImageProcessor
|
| 7 |
+
from transformers.feature_extraction_utils import BatchFeature
|
| 8 |
+
from transformers.image_utils import ImageInput
|
| 9 |
+
from transformers.tokenization_utils_base import (
|
| 10 |
+
PaddingStrategy,
|
| 11 |
+
PreTokenizedInput,
|
| 12 |
+
TextInput,
|
| 13 |
+
TruncationStrategy,
|
| 14 |
+
)
|
| 15 |
+
from transformers.utils import TensorType
|
| 16 |
+
|
| 17 |
+
|
| 18 |
+
class MultiCropImageProcessor(ImageProcessingMixin):
|
| 19 |
+
def __init__(self, model_name, max_crops=0, **kwargs):
|
| 20 |
+
self.processor = SiglipImageProcessor.from_pretrained(model_name)
|
| 21 |
+
self.crop_size = 384
|
| 22 |
+
self.max_crops = max_crops
|
| 23 |
+
self.stride_ratio = 2
|
| 24 |
+
|
| 25 |
+
def __call__(
|
| 26 |
+
self,
|
| 27 |
+
images: List[Image.Image],
|
| 28 |
+
max_crops: int = -1,
|
| 29 |
+
):
|
| 30 |
+
res = {
|
| 31 |
+
"pixel_values": [],
|
| 32 |
+
"coords": [],
|
| 33 |
+
}
|
| 34 |
+
if max_crops < 0:
|
| 35 |
+
max_crops = self.max_crops
|
| 36 |
+
for image in images:
|
| 37 |
+
outputs, output_coords = self.process_image(image, max_crops)
|
| 38 |
+
res["pixel_values"].append(outputs)
|
| 39 |
+
res["coords"].append(output_coords)
|
| 40 |
+
return res
|
| 41 |
+
|
| 42 |
+
def process_image(
|
| 43 |
+
self,
|
| 44 |
+
image: Image.Image,
|
| 45 |
+
max_crops: int
|
| 46 |
+
):
|
| 47 |
+
outputs = []
|
| 48 |
+
output_coords = []
|
| 49 |
+
outputs.append(self.processor(image, return_tensors="pt").pixel_values)
|
| 50 |
+
output_coords.append(torch.tensor([0.5, 0.5]))
|
| 51 |
+
width, height = image.size
|
| 52 |
+
crop_size = self.crop_size
|
| 53 |
+
stride = crop_size // self.stride_ratio
|
| 54 |
+
if (
|
| 55 |
+
max_crops == 0
|
| 56 |
+
or width <= (crop_size + stride)
|
| 57 |
+
and height <= (crop_size + stride)
|
| 58 |
+
):
|
| 59 |
+
outputs = torch.cat(outputs, dim=0)
|
| 60 |
+
output_coords = torch.cat(output_coords, dim=0)
|
| 61 |
+
return outputs, output_coords
|
| 62 |
+
total_tokens = math.inf
|
| 63 |
+
while total_tokens > max_crops:
|
| 64 |
+
total_tokens = (
|
| 65 |
+
math.floor((width - crop_size) / stride) + 1
|
| 66 |
+
) * (
|
| 67 |
+
math.floor((height - crop_size) / stride) + 1
|
| 68 |
+
)
|
| 69 |
+
if total_tokens > max_crops:
|
| 70 |
+
crop_size += 10
|
| 71 |
+
stride = crop_size // self.stride_ratio
|
| 72 |
+
stride = crop_size // self.stride_ratio
|
| 73 |
+
x_steps = int(math.floor((width - crop_size) / stride) + 1)
|
| 74 |
+
if x_steps < 1:
|
| 75 |
+
x_steps = 1
|
| 76 |
+
y_steps = int(math.floor((height - crop_size) / stride) + 1)
|
| 77 |
+
if y_steps < 1:
|
| 78 |
+
y_steps = 1
|
| 79 |
+
if x_steps == 1 and y_steps == 1:
|
| 80 |
+
outputs = torch.cat(outputs, dim=0)
|
| 81 |
+
output_coords = torch.cat(output_coords, dim=0)
|
| 82 |
+
return outputs, output_coords
|
| 83 |
+
x_coords = []
|
| 84 |
+
y_coords = []
|
| 85 |
+
for i in range(x_steps):
|
| 86 |
+
x_coords.append([i * stride, i * stride + crop_size])
|
| 87 |
+
if x_coords[-1][1] != width:
|
| 88 |
+
x_coords[-1][1] = width
|
| 89 |
+
for i in range(y_steps):
|
| 90 |
+
y_coords.append([i * stride, i * stride + crop_size])
|
| 91 |
+
if y_coords[-1][1] != height:
|
| 92 |
+
y_coords[-1][1] = height
|
| 93 |
+
image_parts = []
|
| 94 |
+
part_coords = []
|
| 95 |
+
for i in range(len(x_coords)):
|
| 96 |
+
for j in range(len(y_coords)):
|
| 97 |
+
image_parts.append(
|
| 98 |
+
image.crop(
|
| 99 |
+
(x_coords[i][0], y_coords[j][0], x_coords[i][1], y_coords[j][1])
|
| 100 |
+
)
|
| 101 |
+
)
|
| 102 |
+
part_coords.append(
|
| 103 |
+
torch.tensor(
|
| 104 |
+
[
|
| 105 |
+
(x_coords[i][0] + x_coords[i][1]) / 2 / width,
|
| 106 |
+
(y_coords[j][0] + y_coords[j][1]) / 2 / height,
|
| 107 |
+
]
|
| 108 |
+
)
|
| 109 |
+
)
|
| 110 |
+
for image_part in image_parts:
|
| 111 |
+
outputs.append(self.processor(image_part, return_tensors="pt").pixel_values)
|
| 112 |
+
for part_coord in part_coords:
|
| 113 |
+
output_coords.append(part_coord)
|
| 114 |
+
outputs = torch.cat(outputs, dim=0)
|
| 115 |
+
output_coords = torch.stack(output_coords, dim=0)
|
| 116 |
+
return outputs, output_coords
|
| 117 |
+
|
| 118 |
+
|
| 119 |
+
class LlavaProcessor(ProcessorMixin):
|
| 120 |
+
attributes = ["image_processor", "tokenizer"]
|
| 121 |
+
image_processor_class = MultiCropImageProcessor
|
| 122 |
+
tokenizer_class = "SiglipTokenizer"
|
| 123 |
+
|
| 124 |
+
def __init__(self, image_processor: MultiCropImageProcessor, tokenizer):
|
| 125 |
+
self.image_processor = image_processor
|
| 126 |
+
self.tokenizer = tokenizer
|
| 127 |
+
self.search_model = None
|
| 128 |
+
|
| 129 |
+
@classmethod
|
| 130 |
+
def from_pretrained(cls, path, trust_remote_code=True, **kwargs):
|
| 131 |
+
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=trust_remote_code)
|
| 132 |
+
image_processor = MultiCropImageProcessor(path, trust_remote_code=trust_remote_code)
|
| 133 |
+
return LlavaProcessor(image_processor, tokenizer)
|
| 134 |
+
|
| 135 |
+
def __call__(
|
| 136 |
+
self,
|
| 137 |
+
text: Union[
|
| 138 |
+
TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]
|
| 139 |
+
] = None,
|
| 140 |
+
images: ImageInput = None,
|
| 141 |
+
model = None,
|
| 142 |
+
max_crops: int = 0,
|
| 143 |
+
num_tokens = None,
|
| 144 |
+
padding: Union[bool, str, PaddingStrategy] = False,
|
| 145 |
+
truncation: Union[bool, str, TruncationStrategy] = None,
|
| 146 |
+
max_length=None,
|
| 147 |
+
return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
|
| 148 |
+
) -> BatchFeature:
|
| 149 |
+
if images is not None:
|
| 150 |
+
processor_outputs = self.image_processor(images, max_crops)
|
| 151 |
+
pixel_values = processor_outputs["pixel_values"]
|
| 152 |
+
pixel_values = [
|
| 153 |
+
value.to(model.device).to(model.dtype) for value in pixel_values
|
| 154 |
+
]
|
| 155 |
+
coords = processor_outputs["coords"]
|
| 156 |
+
coords = [value.to(model.device).to(model.dtype) for value in coords]
|
| 157 |
+
image_outputs = model.vision_model(pixel_values, coords, num_tokens)
|
| 158 |
+
image_features = model.multi_modal_projector(image_outputs)
|
| 159 |
+
else:
|
| 160 |
+
image_features = None
|
| 161 |
+
text_inputs = self.tokenizer(
|
| 162 |
+
text,
|
| 163 |
+
return_tensors=return_tensors,
|
| 164 |
+
padding=padding,
|
| 165 |
+
truncation=truncation,
|
| 166 |
+
max_length=max_length,
|
| 167 |
+
)
|
| 168 |
+
text_inputs['input_ids'] = text_inputs['input_ids'].to(model.device)
|
| 169 |
+
text_inputs['attention_mask'] = text_inputs['attention_mask'].to(model.device)
|
| 170 |
+
return BatchFeature(data={**text_inputs, "image_features": image_features})
|
| 171 |
+
|
| 172 |
+
def batch_decode(self, *args, **kwargs):
|
| 173 |
+
return self.tokenizer.batch_decode(*args, **kwargs)
|
| 174 |
+
|
| 175 |
+
def decode(self, *args, **kwargs):
|
| 176 |
+
return self.tokenizer.decode(*args, **kwargs)
|
| 177 |
+
|
| 178 |
+
@property
|
| 179 |
+
def model_input_names(self):
|
| 180 |
+
tokenizer_input_names = self.tokenizer.model_input_names
|
| 181 |
+
image_processor_input_names = self.image_processor.model_input_names
|
| 182 |
+
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token": {
|
| 3 |
+
"content": "<|endoftext|>",
|
| 4 |
+
"lstrip": false,
|
| 5 |
+
"normalized": false,
|
| 6 |
+
"rstrip": false,
|
| 7 |
+
"single_word": false
|
| 8 |
+
},
|
| 9 |
+
"eos_token": {
|
| 10 |
+
"content": "<|im_end|>",
|
| 11 |
+
"lstrip": false,
|
| 12 |
+
"normalized": false,
|
| 13 |
+
"rstrip": false,
|
| 14 |
+
"single_word": false
|
| 15 |
+
},
|
| 16 |
+
"pad_token": {
|
| 17 |
+
"content": "<pad>",
|
| 18 |
+
"lstrip": false,
|
| 19 |
+
"normalized": false,
|
| 20 |
+
"rstrip": false,
|
| 21 |
+
"single_word": false
|
| 22 |
+
},
|
| 23 |
+
"unk_token": {
|
| 24 |
+
"content": "<|endoftext|>",
|
| 25 |
+
"lstrip": false,
|
| 26 |
+
"normalized": false,
|
| 27 |
+
"rstrip": false,
|
| 28 |
+
"single_word": false
|
| 29 |
+
}
|
| 30 |
+
}
|
tokenizer.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,356 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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| 1 |
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| 2 |
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| 3 |
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| 11 |
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| 16 |
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| 17 |
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| 18 |
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| 19 |
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| 26 |
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| 27 |
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| 28 |
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| 32 |
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| 34 |
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+
"content": "<image>",
|
| 334 |
+
"lstrip": false,
|
| 335 |
+
"normalized": false,
|
| 336 |
+
"rstrip": false,
|
| 337 |
+
"single_word": false,
|
| 338 |
+
"special": true
|
| 339 |
+
},
|
| 340 |
+
"50298": {
|
| 341 |
+
"content": "<pad>",
|
| 342 |
+
"lstrip": false,
|
| 343 |
+
"normalized": false,
|
| 344 |
+
"rstrip": false,
|
| 345 |
+
"single_word": false,
|
| 346 |
+
"special": true
|
| 347 |
+
}
|
| 348 |
+
},
|
| 349 |
+
"bos_token": "<|endoftext|>",
|
| 350 |
+
"clean_up_tokenization_spaces": true,
|
| 351 |
+
"eos_token": "<|im_end|>",
|
| 352 |
+
"model_max_length": 2048,
|
| 353 |
+
"pad_token": "<pad>",
|
| 354 |
+
"tokenizer_class": "CodeGenTokenizer",
|
| 355 |
+
"unk_token": "<|endoftext|>"
|
| 356 |
+
}
|
vocab.json
ADDED
|
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|
|