xrx
commited on
Commit
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5192214
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Parent(s):
6e106ce
Add initial model parameters and code
Browse files- LICENSE +84 -0
- README.md +75 -3
- config.json +47 -0
- demo.py +48 -0
- example.jpg +0 -0
- generation_config.json +6 -0
- llama3mixsense.py +50 -0
- model-00001-of-00004.safetensors +3 -0
- model-00002-of-00004.safetensors +3 -0
- model-00003-of-00004.safetensors +3 -0
- model-00004-of-00004.safetensors +3 -0
- model.safetensors.index.json +750 -0
- modeling_mixsense_llama.py +687 -0
- special_tokens_map.json +23 -0
- tokenizer.json +0 -0
- tokenizer_config.json +2079 -0
LICENSE
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| 1 |
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META LLAMA 3 COMMUNITY LICENSE AGREEMENT
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Meta Llama 3 Version Release Date: April 18, 2024
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“Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.
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“Documentation” means the specifications, manuals and documentation accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/.
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“Meta Llama 3” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads.
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“Llama Materials” means, collectively, Meta’s proprietary Meta Llama 3 and Documentation (and any portion thereof) made available under this Agreement.
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a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials.
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iv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3/use-policy), which is hereby incorporated by reference into this Agreement.
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v. You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Meta Llama 3 or derivative works thereof).
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3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.
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5. Intellectual Property.
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a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use “Llama 3” (the “Mark”) solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta’s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/ ). All goodwill arising out of your use of the Mark will inure to the benefit of Meta.
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b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications.
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c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Meta Llama 3 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials.
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6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement.
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7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement.
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Meta Llama 3 Acceptable Use Policy
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Meta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at https://llama.meta.com/llama3/use-policy
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Prohibited Uses
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We want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow others to use, Meta Llama 3 to:
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1. Violate the law or others’ rights, including to:
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a. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
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iv. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
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vi. Any other criminal activity
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b. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
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g. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
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2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Meta Llama 3 related to the following:
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a. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
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e. Self-harm or harm to others, including suicide, cutting, and eating disorders
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f. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
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3. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following:
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a. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
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b. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
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c. Generating, promoting, or further distributing spam
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d. Impersonating another individual without consent, authorization, or legal right
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e. Representing that the use of Meta Llama 3 or outputs are human-generated
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f. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
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g. Fail to appropriately disclose to end users any known dangers of your AI system
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Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means:
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* Reporting issues with the model: https://github.com/meta-llama/llama3
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* Reporting risky content generated by the model: developers.facebook.com/llama_output_feedback
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* Reporting bugs and security concerns: facebook.com/whitehat/info
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* Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: LlamaUseReport@meta.com
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README.md
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# Introduction
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MixSense is a series of models based on the widely adopted vision encoder-projector-LLM architecture. In this resource, we release Llama-3-MixSense checkpoint,which is Built with [Meta Llama 3](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) as the text encoder,and [SigLIP 400M](https://huggingface.co/google/siglip-so400m-patch14-384) as the vision encoder .
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We have developed an innovative data processing method that complements the training process, reducing training costs while improving training effectiveness.,The models are trained on our restructured dataset. Details of the data organization and related research papers will be available soon.
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# QuickStart
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## Requirements
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```
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conda create -n mixsense python==3.10 -y
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conda activate mixsense
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pip install torch transformers==4.37.2 accelerate pillow
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```
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## Usage
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Llama-3-Mixsense/demo.py
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``` python
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import torch
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import transformers
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from PIL import Image
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import warnings
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import os
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# disable some warnings
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transformers.logging.set_verbosity_error()
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transformers.logging.disable_progress_bar()
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warnings.filterwarnings("ignore")
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# set device
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device = "cuda" # or cpu
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# create model
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model = AutoModelForCausalLM.from_pretrained(
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"Zero-Vision/Llama-3-MixSense",
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torch_dtype=torch.float16, # float32 for cpu
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device_map="auto",
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trust_remote_code=True,
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)
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tokenizer = AutoTokenizer.from_pretrained(
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"Zero-Vision/Llama-3-MixSense",
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trust_remote_code=True,
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)
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qs = "describe the image detailly."
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input_ids = model.text_process(qs, tokenizer).to(device)
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image = Image.open("example.jpg")
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image_tensor = model.image_process([image]).to(dtype=model.dtype, device=device)
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# generate
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with torch.inference_mode():
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output_ids = model.generate(
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input_ids,
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images=image_tensor,
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max_new_tokens=2048,
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use_cache=True,
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eos_token_id=[
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tokenizer.eos_token_id,
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tokenizer.convert_tokens_to_ids(["<|eot_id|>"])[0],
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],
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)
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print(tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip())
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```
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## Eval
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We offer Llama-3-Mixsense/llama3mixsense.py for [VLMEvalKit](https://github.com/open-compass/VLMEvalKit).
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# License
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This project utilizes certain datasets and checkpoints that are subject to their respective original licenses. Users must comply with all terms and conditions of these original licenses.including but not limited to Llama3 and SigLIP. Meta Llama 3 is licensed under the [Meta Llama 3 Community License](https://llama.meta.com/llama3/license/), Copyright © Meta Platforms, Inc. All Rights Reserved. And [Apache LICENSE 2.0](https://www.apache.org/licenses/LICENSE-2.0) for SigLIP model. The project itself is licensed under the [Apache LICENSE 2.0](https://www.apache.org/licenses/LICENSE-2.0) .
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# Acknowledgement
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Our code is largely borrowed from [LLaVA](https://github.com/haotian-liu/LLaVA)
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We bulid this demo according to [bunny](https://huggingface.co/BAAI/Bunny-Llama-3-8B-V)
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config.json
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{
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"_name_or_path": "ZeroVision/Llama-3-Mixsense",
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"architectures": [
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"MixsenseLlamaForCausalLM"
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],
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"auto_map": {
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"AutoConfig": "modeling_mixsense_llama.MixsenseConfig",
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"AutoModelForCausalLM": "modeling_mixsense_llama.MixsenseLlamaForCausalLM"
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},
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"eos_token_id": 128001,
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"freeze_mm_mlp_adapter": false,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"image_aspect_ratio": "pad",
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 8192,
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"mm_hidden_size": 1152,
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"mm_patch_merge_type": "flat",
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| 23 |
+
"mm_projector_lr": null,
|
| 24 |
+
"mm_projector_type": "mlp2x_gelu",
|
| 25 |
+
"mm_use_im_patch_token": false,
|
| 26 |
+
"mm_use_im_start_end": false,
|
| 27 |
+
"mm_vision_select_feature": "patch",
|
| 28 |
+
"mm_vision_select_layer": -2,
|
| 29 |
+
"mm_vision_tower": "google/siglip-so400m-patch14-384",
|
| 30 |
+
"model_type": "mixsense_llama",
|
| 31 |
+
"num_attention_heads": 32,
|
| 32 |
+
"num_hidden_layers": 32,
|
| 33 |
+
"num_key_value_heads": 8,
|
| 34 |
+
"pretraining_tp": 1,
|
| 35 |
+
"rms_norm_eps": 1e-05,
|
| 36 |
+
"rope_scaling": null,
|
| 37 |
+
"rope_theta": 500000.0,
|
| 38 |
+
"tie_word_embeddings": false,
|
| 39 |
+
"tokenizer_model_max_length": 2048,
|
| 40 |
+
"tokenizer_padding_side": "right",
|
| 41 |
+
"torch_dtype": "bfloat16",
|
| 42 |
+
"transformers_version": "4.37.2",
|
| 43 |
+
"tune_mm_mlp_adapter": false,
|
| 44 |
+
"use_cache": true,
|
| 45 |
+
"use_mm_proj": true,
|
| 46 |
+
"vocab_size": 128257
|
| 47 |
+
}
|
demo.py
ADDED
|
@@ -0,0 +1,48 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import torch
|
| 2 |
+
import transformers
|
| 3 |
+
from transformers import AutoModelForCausalLM, AutoTokenizer
|
| 4 |
+
from PIL import Image
|
| 5 |
+
import warnings
|
| 6 |
+
import os
|
| 7 |
+
|
| 8 |
+
|
| 9 |
+
# disable some warnings
|
| 10 |
+
transformers.logging.set_verbosity_error()
|
| 11 |
+
transformers.logging.disable_progress_bar()
|
| 12 |
+
warnings.filterwarnings("ignore")
|
| 13 |
+
|
| 14 |
+
# set device
|
| 15 |
+
device = "cuda" # or cpu
|
| 16 |
+
|
| 17 |
+
# create model
|
| 18 |
+
model = AutoModelForCausalLM.from_pretrained(
|
| 19 |
+
"Zero-Vision/Llama-3-MixSense",
|
| 20 |
+
torch_dtype=torch.float16, # float32 for cpu
|
| 21 |
+
device_map="auto",
|
| 22 |
+
trust_remote_code=True,
|
| 23 |
+
)
|
| 24 |
+
tokenizer = AutoTokenizer.from_pretrained(
|
| 25 |
+
"Zero-Vision/Llama-3-MixSense",
|
| 26 |
+
trust_remote_code=True,
|
| 27 |
+
)
|
| 28 |
+
|
| 29 |
+
qs = "describe the image detailly."
|
| 30 |
+
input_ids = model.text_process(qs, tokenizer).to(device)
|
| 31 |
+
|
| 32 |
+
image = Image.open("example.jpg")
|
| 33 |
+
image_tensor = model.image_process([image]).to(dtype=model.dtype, device=device)
|
| 34 |
+
|
| 35 |
+
# generate
|
| 36 |
+
with torch.inference_mode():
|
| 37 |
+
output_ids = model.generate(
|
| 38 |
+
input_ids,
|
| 39 |
+
images=image_tensor,
|
| 40 |
+
max_new_tokens=2048,
|
| 41 |
+
use_cache=True,
|
| 42 |
+
eos_token_id=[
|
| 43 |
+
tokenizer.eos_token_id,
|
| 44 |
+
tokenizer.convert_tokens_to_ids(["<|eot_id|>"])[0],
|
| 45 |
+
],
|
| 46 |
+
)
|
| 47 |
+
|
| 48 |
+
print(tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip())
|
example.jpg
ADDED
|
generation_config.json
ADDED
|
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_from_model_config": true,
|
| 3 |
+
"bos_token_id": 128000,
|
| 4 |
+
"eos_token_id": 128001,
|
| 5 |
+
"transformers_version": "4.37.2"
|
| 6 |
+
}
|
llama3mixsense.py
ADDED
|
@@ -0,0 +1,50 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
'''
|
| 2 |
+
This file if for VLMEvalKit.
|
| 3 |
+
'''
|
| 4 |
+
import torch
|
| 5 |
+
import transformers
|
| 6 |
+
from transformers import AutoModelForCausalLM, AutoTokenizer
|
| 7 |
+
from PIL import Image
|
| 8 |
+
import warnings
|
| 9 |
+
|
| 10 |
+
from .base import BaseModel
|
| 11 |
+
from ..smp import *
|
| 12 |
+
from ..utils import DATASET_TYPE
|
| 13 |
+
|
| 14 |
+
|
| 15 |
+
class LLama3Mixsense(BaseModel):
|
| 16 |
+
|
| 17 |
+
INSTALL_REQ = False
|
| 18 |
+
INTERLEAVE = False
|
| 19 |
+
|
| 20 |
+
def __init__(self, model_path="ZeroVision/Llama-3-Mixsense", **kwargs):
|
| 21 |
+
assert model_path is not None
|
| 22 |
+
transformers.logging.set_verbosity_error()
|
| 23 |
+
transformers.logging.disable_progress_bar()
|
| 24 |
+
warnings.filterwarnings("ignore")
|
| 25 |
+
self.tokenizer = AutoTokenizer.from_pretrained(
|
| 26 |
+
model_path, trust_remote_code=True
|
| 27 |
+
)
|
| 28 |
+
self.model = AutoModelForCausalLM.from_pretrained(
|
| 29 |
+
model_path, device_map="auto", trust_remote_code=True
|
| 30 |
+
)
|
| 31 |
+
self.kwargs = kwargs
|
| 32 |
+
|
| 33 |
+
def generate_inner(self, message, dataset=None):
|
| 34 |
+
prompt, image_path = self.message_to_promptimg(message)
|
| 35 |
+
input_ids=self.model.text_process(prompt, self.tokenizer)
|
| 36 |
+
image = Image.open(image_path).convert("RGB")
|
| 37 |
+
image_tensor = self.model.image_process([image]).to(dtype=self.model.dtype, device=device)
|
| 38 |
+
# generate
|
| 39 |
+
with torch.inference_mode():
|
| 40 |
+
output_ids = self.model.generate(
|
| 41 |
+
input_ids,
|
| 42 |
+
images=image_tensor,
|
| 43 |
+
max_new_tokens=2048,
|
| 44 |
+
use_cache=True,
|
| 45 |
+
eos_token_id=[
|
| 46 |
+
self.tokenizer.eos_token_id,
|
| 47 |
+
self.tokenizer.convert_tokens_to_ids(["<|eot_id|>"])[0],
|
| 48 |
+
],
|
| 49 |
+
)
|
| 50 |
+
return self.tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
|
model-00001-of-00004.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:ad7392198150b9c3d0851747545602e0eeb8ebe19e4af115d0c564036806bb4d
|
| 3 |
+
size 4976706864
|
model-00002-of-00004.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:e5d93d35cc21942101c184904dbbea8de291954a198d4f192d2a17a7d1b57cd5
|
| 3 |
+
size 4999802720
|
model-00003-of-00004.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:97efeec9598e781e7a0158154779e0e1d058cfb11be22997c5aaf9e8e0fd48e4
|
| 3 |
+
size 4915916176
|
model-00004-of-00004.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3e121982484aa45e2b7161da02e036956cc0759402e06466fc1724990820e12b
|
| 3 |
+
size 2067676864
|
model.safetensors.index.json
ADDED
|
@@ -0,0 +1,750 @@
|
|
|
|
|
|
|
|
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|
|
|
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|
| 749 |
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|
| 750 |
+
}
|
modeling_mixsense_llama.py
ADDED
|
@@ -0,0 +1,687 @@
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|
| 1 |
+
import torch
|
| 2 |
+
import torch.nn as nn
|
| 3 |
+
from transformers import SiglipVisionModel, SiglipImageProcessor, SiglipVisionConfig
|
| 4 |
+
class SiglipVisionTower(nn.Module):
|
| 5 |
+
|
| 6 |
+
def __init__(self, vision_tower, args, delay_load=False):
|
| 7 |
+
super().__init__()
|
| 8 |
+
|
| 9 |
+
self.is_loaded = False
|
| 10 |
+
|
| 11 |
+
self.vision_tower_name = vision_tower
|
| 12 |
+
self.select_layer = args.mm_vision_select_layer
|
| 13 |
+
self.select_feature = getattr(args, "mm_vision_select_feature", "patch")
|
| 14 |
+
|
| 15 |
+
if not delay_load:
|
| 16 |
+
self.load_model()
|
| 17 |
+
else:
|
| 18 |
+
self.cfg_only = SiglipVisionConfig.from_pretrained(self.vision_tower_name)
|
| 19 |
+
|
| 20 |
+
def load_model(self, device_map=None):
|
| 21 |
+
if self.is_loaded:
|
| 22 |
+
print(
|
| 23 |
+
"{} is already loaded, `load_model` called again, skipping.".format(
|
| 24 |
+
self.vision_tower_name
|
| 25 |
+
)
|
| 26 |
+
)
|
| 27 |
+
return
|
| 28 |
+
|
| 29 |
+
self.image_processor = SiglipImageProcessor.from_pretrained(
|
| 30 |
+
self.vision_tower_name
|
| 31 |
+
)
|
| 32 |
+
self.vision_tower = SiglipVisionModel.from_pretrained(
|
| 33 |
+
self.vision_tower_name, device_map=device_map
|
| 34 |
+
)
|
| 35 |
+
self.vision_tower.requires_grad_(False)
|
| 36 |
+
|
| 37 |
+
self.is_loaded = True
|
| 38 |
+
|
| 39 |
+
def feature_select(self, image_forward_outs):
|
| 40 |
+
image_features = image_forward_outs.hidden_states[self.select_layer]
|
| 41 |
+
if self.select_feature == "patch":
|
| 42 |
+
image_features = image_features[:, 1:]
|
| 43 |
+
elif self.select_feature == "cls_patch":
|
| 44 |
+
image_features = image_features
|
| 45 |
+
else:
|
| 46 |
+
raise ValueError(f"Unexpected select feature: {self.select_feature}")
|
| 47 |
+
return image_features
|
| 48 |
+
|
| 49 |
+
@torch.no_grad()
|
| 50 |
+
def forward(self, images):
|
| 51 |
+
if type(images) is list:
|
| 52 |
+
image_features = []
|
| 53 |
+
for image in images:
|
| 54 |
+
image_forward_out = self.vision_tower(
|
| 55 |
+
image.to(device=self.device, dtype=self.dtype).unsqueeze(0),
|
| 56 |
+
output_hidden_states=True,
|
| 57 |
+
)
|
| 58 |
+
image_feature = self.feature_select(image_forward_out).to(image.dtype)
|
| 59 |
+
image_features.append(image_feature)
|
| 60 |
+
else:
|
| 61 |
+
image_forward_outs = self.vision_tower(
|
| 62 |
+
images.to(device=self.device, dtype=self.dtype),
|
| 63 |
+
output_hidden_states=True,
|
| 64 |
+
)
|
| 65 |
+
image_features = self.feature_select(image_forward_outs).to(images.dtype)
|
| 66 |
+
|
| 67 |
+
return image_features
|
| 68 |
+
|
| 69 |
+
@property
|
| 70 |
+
def dummy_feature(self):
|
| 71 |
+
return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
|
| 72 |
+
|
| 73 |
+
@property
|
| 74 |
+
def dtype(self):
|
| 75 |
+
return self.vision_tower.dtype
|
| 76 |
+
|
| 77 |
+
@property
|
| 78 |
+
def device(self):
|
| 79 |
+
return self.vision_tower.device
|
| 80 |
+
|
| 81 |
+
@property
|
| 82 |
+
def config(self):
|
| 83 |
+
if self.is_loaded:
|
| 84 |
+
return self.vision_tower.config
|
| 85 |
+
else:
|
| 86 |
+
return self.cfg_only
|
| 87 |
+
|
| 88 |
+
@property
|
| 89 |
+
def hidden_size(self):
|
| 90 |
+
return self.config.hidden_size
|
| 91 |
+
|
| 92 |
+
@property
|
| 93 |
+
def num_patches_per_side(self):
|
| 94 |
+
return self.config.image_size // self.config.patch_size
|
| 95 |
+
|
| 96 |
+
@property
|
| 97 |
+
def num_patches(self):
|
| 98 |
+
return (self.config.image_size // self.config.patch_size) ** 2
|
| 99 |
+
|
| 100 |
+
from abc import ABC, abstractmethod
|
| 101 |
+
|
| 102 |
+
|
| 103 |
+
IGNORE_INDEX = -100
|
| 104 |
+
IMAGE_TOKEN_INDEX = -200
|
| 105 |
+
DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
|
| 106 |
+
DEFAULT_IM_START_TOKEN = "<im_start>"
|
| 107 |
+
DEFAULT_IM_END_TOKEN = "<im_end>"
|
| 108 |
+
|
| 109 |
+
|
| 110 |
+
def build_vision_tower(vision_tower_cfg, **kwargs):
|
| 111 |
+
vision_tower = getattr(
|
| 112 |
+
vision_tower_cfg,
|
| 113 |
+
"mm_vision_tower",
|
| 114 |
+
getattr(vision_tower_cfg, "vision_tower", None),
|
| 115 |
+
)
|
| 116 |
+
return SiglipVisionTower(vision_tower, args=vision_tower_cfg, **kwargs)
|
| 117 |
+
|
| 118 |
+
|
| 119 |
+
import re
|
| 120 |
+
def build_vision_projector(config, delay_load=False, **kwargs):
|
| 121 |
+
projector_type = getattr(config, "mm_projector_type", "linear")
|
| 122 |
+
|
| 123 |
+
mlp_gelu_match = re.match(r"^mlp(\d+)x_gelu$", projector_type)
|
| 124 |
+
if mlp_gelu_match:
|
| 125 |
+
mlp_depth = int(mlp_gelu_match.group(1))
|
| 126 |
+
modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
|
| 127 |
+
for _ in range(1, mlp_depth):
|
| 128 |
+
modules.append(nn.GELU())
|
| 129 |
+
modules.append(nn.Linear(config.hidden_size, config.hidden_size))
|
| 130 |
+
return nn.Sequential(*modules)
|
| 131 |
+
|
| 132 |
+
|
| 133 |
+
class MixsenseMetaModel:
|
| 134 |
+
|
| 135 |
+
def __init__(self, config):
|
| 136 |
+
super(MixsenseMetaModel, self).__init__(config)
|
| 137 |
+
|
| 138 |
+
if hasattr(config, "mm_vision_tower"):
|
| 139 |
+
self.vision_tower = build_vision_tower(config, delay_load=True)
|
| 140 |
+
self.mm_projector = build_vision_projector(config)
|
| 141 |
+
|
| 142 |
+
if "unpad" in getattr(config, "mm_patch_merge_type", ""):
|
| 143 |
+
self.image_newline = nn.Parameter(
|
| 144 |
+
torch.empty(config.hidden_size, dtype=self.dtype)
|
| 145 |
+
)
|
| 146 |
+
|
| 147 |
+
def get_vision_tower(self):
|
| 148 |
+
vision_tower = getattr(self, "vision_tower", None)
|
| 149 |
+
if type(vision_tower) is list:
|
| 150 |
+
vision_tower = vision_tower[0]
|
| 151 |
+
return vision_tower
|
| 152 |
+
|
| 153 |
+
def initialize_vision_modules(self, model_args, fsdp=None):
|
| 154 |
+
vision_tower = model_args.vision_tower
|
| 155 |
+
mm_vision_select_layer = model_args.mm_vision_select_layer
|
| 156 |
+
mm_vision_select_feature = model_args.mm_vision_select_feature
|
| 157 |
+
pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter
|
| 158 |
+
mm_patch_merge_type = model_args.mm_patch_merge_type
|
| 159 |
+
|
| 160 |
+
self.config.mm_vision_tower = vision_tower
|
| 161 |
+
|
| 162 |
+
if self.get_vision_tower() is None:
|
| 163 |
+
vision_tower = build_vision_tower(model_args)
|
| 164 |
+
|
| 165 |
+
if fsdp is not None and len(fsdp) > 0:
|
| 166 |
+
self.vision_tower = [vision_tower]
|
| 167 |
+
else:
|
| 168 |
+
self.vision_tower = vision_tower
|
| 169 |
+
else:
|
| 170 |
+
if fsdp is not None and len(fsdp) > 0:
|
| 171 |
+
vision_tower = self.vision_tower[0]
|
| 172 |
+
else:
|
| 173 |
+
vision_tower = self.vision_tower
|
| 174 |
+
vision_tower.load_model()
|
| 175 |
+
|
| 176 |
+
self.config.use_mm_proj = True
|
| 177 |
+
self.config.mm_projector_type = getattr(
|
| 178 |
+
model_args, "mm_projector_type", "linear"
|
| 179 |
+
)
|
| 180 |
+
self.config.mm_hidden_size = vision_tower.hidden_size
|
| 181 |
+
self.config.mm_vision_select_layer = mm_vision_select_layer
|
| 182 |
+
self.config.mm_vision_select_feature = mm_vision_select_feature
|
| 183 |
+
self.config.mm_patch_merge_type = mm_patch_merge_type
|
| 184 |
+
|
| 185 |
+
if getattr(self, "mm_projector", None) is None:
|
| 186 |
+
self.mm_projector = build_vision_projector(self.config)
|
| 187 |
+
|
| 188 |
+
if "unpad" in mm_patch_merge_type:
|
| 189 |
+
embed_std = 1 / torch.sqrt(
|
| 190 |
+
torch.tensor(self.config.hidden_size, dtype=self.dtype)
|
| 191 |
+
)
|
| 192 |
+
self.image_newline = nn.Parameter(
|
| 193 |
+
torch.randn(self.config.hidden_size, dtype=self.dtype) * embed_std
|
| 194 |
+
)
|
| 195 |
+
else:
|
| 196 |
+
# In case it is frozen by LoRA
|
| 197 |
+
for p in self.mm_projector.parameters():
|
| 198 |
+
p.requires_grad = True
|
| 199 |
+
|
| 200 |
+
if pretrain_mm_mlp_adapter is not None:
|
| 201 |
+
mm_projector_weights = torch.load(
|
| 202 |
+
pretrain_mm_mlp_adapter, map_location="cpu"
|
| 203 |
+
)
|
| 204 |
+
|
| 205 |
+
def get_w(weights, keyword):
|
| 206 |
+
return {
|
| 207 |
+
k.split(keyword + ".")[1]: v
|
| 208 |
+
for k, v in weights.items()
|
| 209 |
+
if keyword in k
|
| 210 |
+
}
|
| 211 |
+
|
| 212 |
+
self.mm_projector.load_state_dict(
|
| 213 |
+
get_w(mm_projector_weights, "mm_projector")
|
| 214 |
+
)
|
| 215 |
+
|
| 216 |
+
|
| 217 |
+
class MixsenseMetaForCausalLM(ABC):
|
| 218 |
+
|
| 219 |
+
@abstractmethod
|
| 220 |
+
def get_model(self):
|
| 221 |
+
pass
|
| 222 |
+
|
| 223 |
+
def get_vision_tower(self):
|
| 224 |
+
return self.get_model().get_vision_tower()
|
| 225 |
+
|
| 226 |
+
def encode_images(self, images):
|
| 227 |
+
image_features = self.get_model().get_vision_tower()(images)
|
| 228 |
+
image_features = self.get_model().mm_projector(image_features)
|
| 229 |
+
return image_features
|
| 230 |
+
|
| 231 |
+
def prepare_inputs_labels_for_multimodal(
|
| 232 |
+
self,
|
| 233 |
+
input_ids,
|
| 234 |
+
position_ids,
|
| 235 |
+
attention_mask,
|
| 236 |
+
past_key_values,
|
| 237 |
+
labels,
|
| 238 |
+
images,
|
| 239 |
+
image_sizes=None,
|
| 240 |
+
):
|
| 241 |
+
vision_tower = self.get_vision_tower()
|
| 242 |
+
if vision_tower is None or images is None or input_ids.shape[1] == 1:
|
| 243 |
+
return (
|
| 244 |
+
input_ids,
|
| 245 |
+
position_ids,
|
| 246 |
+
attention_mask,
|
| 247 |
+
past_key_values,
|
| 248 |
+
None,
|
| 249 |
+
labels,
|
| 250 |
+
)
|
| 251 |
+
elif type(images) is list or images.ndim == 5:
|
| 252 |
+
if type(images) is list:
|
| 253 |
+
images = [x.unsqueeze(0) if x.ndim == 3 else x for x in images]
|
| 254 |
+
concat_images = torch.cat([image for image in images], dim=0)
|
| 255 |
+
image_features = self.encode_images(concat_images)
|
| 256 |
+
split_sizes = [image.shape[0] for image in images]
|
| 257 |
+
image_features = torch.split(image_features, split_sizes, dim=0)
|
| 258 |
+
mm_patch_merge_type = getattr(self.config, "mm_patch_merge_type", "flat")
|
| 259 |
+
image_aspect_ratio = getattr(self.config, "image_aspect_ratio", "square")
|
| 260 |
+
if mm_patch_merge_type == "flat":
|
| 261 |
+
image_features = [x.flatten(0, 1) for x in image_features]
|
| 262 |
+
else:
|
| 263 |
+
image_features = self.encode_images(images)
|
| 264 |
+
|
| 265 |
+
# TODO: image start / end is not implemented here to support pretraining.
|
| 266 |
+
if getattr(self.config, "tune_mm_mlp_adapter", False) and getattr(
|
| 267 |
+
self.config, "mm_use_im_start_end", False
|
| 268 |
+
):
|
| 269 |
+
raise NotImplementedError
|
| 270 |
+
|
| 271 |
+
# Let's just add dummy tensors if they do not exist,
|
| 272 |
+
# it is a headache to deal with None all the time.
|
| 273 |
+
# But it is not ideal, and if you have a better idea,
|
| 274 |
+
# please open an issue / submit a PR, thanks.
|
| 275 |
+
_labels = labels
|
| 276 |
+
_position_ids = position_ids
|
| 277 |
+
_attention_mask = attention_mask
|
| 278 |
+
if attention_mask is None:
|
| 279 |
+
attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
|
| 280 |
+
else:
|
| 281 |
+
attention_mask = attention_mask.bool()
|
| 282 |
+
if position_ids is None:
|
| 283 |
+
position_ids = torch.arange(
|
| 284 |
+
0, input_ids.shape[1], dtype=torch.long, device=input_ids.device
|
| 285 |
+
)
|
| 286 |
+
if labels is None:
|
| 287 |
+
labels = torch.full_like(input_ids, IGNORE_INDEX)
|
| 288 |
+
|
| 289 |
+
# remove the padding using attention_mask -- FIXME
|
| 290 |
+
_input_ids = input_ids
|
| 291 |
+
input_ids = [
|
| 292 |
+
cur_input_ids[cur_attention_mask]
|
| 293 |
+
for cur_input_ids, cur_attention_mask in zip(input_ids, attention_mask)
|
| 294 |
+
]
|
| 295 |
+
labels = [
|
| 296 |
+
cur_labels[cur_attention_mask]
|
| 297 |
+
for cur_labels, cur_attention_mask in zip(labels, attention_mask)
|
| 298 |
+
]
|
| 299 |
+
|
| 300 |
+
new_input_embeds = []
|
| 301 |
+
new_labels = []
|
| 302 |
+
cur_image_idx = 0
|
| 303 |
+
for batch_idx, cur_input_ids in enumerate(input_ids):
|
| 304 |
+
num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum()
|
| 305 |
+
if num_images == 0:
|
| 306 |
+
cur_image_features = image_features[cur_image_idx]
|
| 307 |
+
cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids)
|
| 308 |
+
cur_input_embeds = torch.cat(
|
| 309 |
+
[cur_input_embeds_1, cur_image_features[0:0]], dim=0
|
| 310 |
+
)
|
| 311 |
+
new_input_embeds.append(cur_input_embeds)
|
| 312 |
+
new_labels.append(labels[batch_idx])
|
| 313 |
+
cur_image_idx += 1
|
| 314 |
+
continue
|
| 315 |
+
|
| 316 |
+
image_token_indices = (
|
| 317 |
+
[-1]
|
| 318 |
+
+ torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0].tolist()
|
| 319 |
+
+ [cur_input_ids.shape[0]]
|
| 320 |
+
)
|
| 321 |
+
cur_input_ids_noim = []
|
| 322 |
+
cur_labels = labels[batch_idx]
|
| 323 |
+
cur_labels_noim = []
|
| 324 |
+
for i in range(len(image_token_indices) - 1):
|
| 325 |
+
cur_input_ids_noim.append(
|
| 326 |
+
cur_input_ids[
|
| 327 |
+
image_token_indices[i] + 1 : image_token_indices[i + 1]
|
| 328 |
+
]
|
| 329 |
+
)
|
| 330 |
+
cur_labels_noim.append(
|
| 331 |
+
cur_labels[image_token_indices[i] + 1 : image_token_indices[i + 1]]
|
| 332 |
+
)
|
| 333 |
+
split_sizes = [x.shape[0] for x in cur_labels_noim]
|
| 334 |
+
cur_input_embeds = self.get_model().embed_tokens(
|
| 335 |
+
torch.cat(cur_input_ids_noim)
|
| 336 |
+
)
|
| 337 |
+
|
| 338 |
+
cur_input_embeds_no_im = torch.split(cur_input_embeds, split_sizes, dim=0)
|
| 339 |
+
cur_new_input_embeds = []
|
| 340 |
+
cur_new_labels = []
|
| 341 |
+
|
| 342 |
+
for i in range(num_images + 1):
|
| 343 |
+
cur_new_input_embeds.append(cur_input_embeds_no_im[i])
|
| 344 |
+
cur_new_labels.append(cur_labels_noim[i])
|
| 345 |
+
if i < num_images:
|
| 346 |
+
cur_image_features = image_features[cur_image_idx]
|
| 347 |
+
cur_image_idx += 1
|
| 348 |
+
cur_new_input_embeds.append(cur_image_features)
|
| 349 |
+
cur_new_labels.append(
|
| 350 |
+
torch.full(
|
| 351 |
+
(cur_image_features.shape[0],),
|
| 352 |
+
IGNORE_INDEX,
|
| 353 |
+
device=cur_labels.device,
|
| 354 |
+
dtype=cur_labels.dtype,
|
| 355 |
+
)
|
| 356 |
+
)
|
| 357 |
+
|
| 358 |
+
cur_new_input_embeds = [x.to(self.device) for x in cur_new_input_embeds]
|
| 359 |
+
|
| 360 |
+
cur_new_input_embeds = torch.cat(cur_new_input_embeds)
|
| 361 |
+
cur_new_labels = torch.cat(cur_new_labels)
|
| 362 |
+
|
| 363 |
+
new_input_embeds.append(cur_new_input_embeds)
|
| 364 |
+
new_labels.append(cur_new_labels)
|
| 365 |
+
|
| 366 |
+
# Truncate sequences to max length as image embeddings can make the sequence longer
|
| 367 |
+
tokenizer_model_max_length = getattr(
|
| 368 |
+
self.config, "tokenizer_model_max_length", None
|
| 369 |
+
)
|
| 370 |
+
if tokenizer_model_max_length is not None:
|
| 371 |
+
new_input_embeds = [
|
| 372 |
+
x[:tokenizer_model_max_length] for x in new_input_embeds
|
| 373 |
+
]
|
| 374 |
+
new_labels = [x[:tokenizer_model_max_length] for x in new_labels]
|
| 375 |
+
|
| 376 |
+
# Combine them
|
| 377 |
+
max_len = max(x.shape[0] for x in new_input_embeds)
|
| 378 |
+
batch_size = len(new_input_embeds)
|
| 379 |
+
|
| 380 |
+
new_input_embeds_padded = []
|
| 381 |
+
new_labels_padded = torch.full(
|
| 382 |
+
(batch_size, max_len),
|
| 383 |
+
IGNORE_INDEX,
|
| 384 |
+
dtype=new_labels[0].dtype,
|
| 385 |
+
device=new_labels[0].device,
|
| 386 |
+
)
|
| 387 |
+
attention_mask = torch.zeros(
|
| 388 |
+
(batch_size, max_len),
|
| 389 |
+
dtype=attention_mask.dtype,
|
| 390 |
+
device=attention_mask.device,
|
| 391 |
+
)
|
| 392 |
+
position_ids = torch.zeros(
|
| 393 |
+
(batch_size, max_len), dtype=position_ids.dtype, device=position_ids.device
|
| 394 |
+
)
|
| 395 |
+
|
| 396 |
+
for i, (cur_new_embed, cur_new_labels) in enumerate(
|
| 397 |
+
zip(new_input_embeds, new_labels)
|
| 398 |
+
):
|
| 399 |
+
cur_len = cur_new_embed.shape[0]
|
| 400 |
+
if getattr(self.config, "tokenizer_padding_side", "right") == "left":
|
| 401 |
+
new_input_embeds_padded.append(
|
| 402 |
+
torch.cat(
|
| 403 |
+
(
|
| 404 |
+
torch.zeros(
|
| 405 |
+
(max_len - cur_len, cur_new_embed.shape[1]),
|
| 406 |
+
dtype=cur_new_embed.dtype,
|
| 407 |
+
device=cur_new_embed.device,
|
| 408 |
+
),
|
| 409 |
+
cur_new_embed,
|
| 410 |
+
),
|
| 411 |
+
dim=0,
|
| 412 |
+
)
|
| 413 |
+
)
|
| 414 |
+
if cur_len > 0:
|
| 415 |
+
new_labels_padded[i, -cur_len:] = cur_new_labels
|
| 416 |
+
attention_mask[i, -cur_len:] = True
|
| 417 |
+
position_ids[i, -cur_len:] = torch.arange(
|
| 418 |
+
0, cur_len, dtype=position_ids.dtype, device=position_ids.device
|
| 419 |
+
)
|
| 420 |
+
else:
|
| 421 |
+
new_input_embeds_padded.append(
|
| 422 |
+
torch.cat(
|
| 423 |
+
(
|
| 424 |
+
cur_new_embed,
|
| 425 |
+
torch.zeros(
|
| 426 |
+
(max_len - cur_len, cur_new_embed.shape[1]),
|
| 427 |
+
dtype=cur_new_embed.dtype,
|
| 428 |
+
device=cur_new_embed.device,
|
| 429 |
+
),
|
| 430 |
+
),
|
| 431 |
+
dim=0,
|
| 432 |
+
)
|
| 433 |
+
)
|
| 434 |
+
if cur_len > 0:
|
| 435 |
+
new_labels_padded[i, :cur_len] = cur_new_labels
|
| 436 |
+
attention_mask[i, :cur_len] = True
|
| 437 |
+
position_ids[i, :cur_len] = torch.arange(
|
| 438 |
+
0, cur_len, dtype=position_ids.dtype, device=position_ids.device
|
| 439 |
+
)
|
| 440 |
+
|
| 441 |
+
new_input_embeds = torch.stack(new_input_embeds_padded, dim=0)
|
| 442 |
+
|
| 443 |
+
if _labels is None:
|
| 444 |
+
new_labels = None
|
| 445 |
+
else:
|
| 446 |
+
new_labels = new_labels_padded
|
| 447 |
+
|
| 448 |
+
if _attention_mask is None:
|
| 449 |
+
attention_mask = None
|
| 450 |
+
else:
|
| 451 |
+
attention_mask = attention_mask.to(dtype=_attention_mask.dtype)
|
| 452 |
+
|
| 453 |
+
if _position_ids is None:
|
| 454 |
+
position_ids = None
|
| 455 |
+
|
| 456 |
+
return (
|
| 457 |
+
None,
|
| 458 |
+
position_ids,
|
| 459 |
+
attention_mask,
|
| 460 |
+
past_key_values,
|
| 461 |
+
new_input_embeds,
|
| 462 |
+
new_labels,
|
| 463 |
+
)
|
| 464 |
+
|
| 465 |
+
def initialize_vision_tokenizer(self, model_args, tokenizer):
|
| 466 |
+
if model_args.mm_use_im_patch_token:
|
| 467 |
+
tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
|
| 468 |
+
self.resize_token_embeddings(len(tokenizer))
|
| 469 |
+
|
| 470 |
+
if model_args.mm_use_im_start_end:
|
| 471 |
+
num_new_tokens = tokenizer.add_tokens(
|
| 472 |
+
[DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True
|
| 473 |
+
)
|
| 474 |
+
self.resize_token_embeddings(len(tokenizer))
|
| 475 |
+
|
| 476 |
+
if num_new_tokens > 0:
|
| 477 |
+
input_embeddings = self.get_input_embeddings().weight.data
|
| 478 |
+
output_embeddings = self.get_output_embeddings().weight.data
|
| 479 |
+
|
| 480 |
+
input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
|
| 481 |
+
dim=0, keepdim=True
|
| 482 |
+
)
|
| 483 |
+
output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
|
| 484 |
+
dim=0, keepdim=True
|
| 485 |
+
)
|
| 486 |
+
|
| 487 |
+
input_embeddings[-num_new_tokens:] = input_embeddings_avg
|
| 488 |
+
output_embeddings[-num_new_tokens:] = output_embeddings_avg
|
| 489 |
+
|
| 490 |
+
if model_args.tune_mm_mlp_adapter:
|
| 491 |
+
for p in self.get_input_embeddings().parameters():
|
| 492 |
+
p.requires_grad = True
|
| 493 |
+
for p in self.get_output_embeddings().parameters():
|
| 494 |
+
p.requires_grad = False
|
| 495 |
+
|
| 496 |
+
if model_args.pretrain_mm_mlp_adapter:
|
| 497 |
+
mm_projector_weights = torch.load(
|
| 498 |
+
model_args.pretrain_mm_mlp_adapter, map_location="cpu"
|
| 499 |
+
)
|
| 500 |
+
embed_tokens_weight = mm_projector_weights["model.embed_tokens.weight"]
|
| 501 |
+
assert num_new_tokens == 2
|
| 502 |
+
if input_embeddings.shape == embed_tokens_weight.shape:
|
| 503 |
+
input_embeddings[-num_new_tokens:] = embed_tokens_weight[
|
| 504 |
+
-num_new_tokens:
|
| 505 |
+
]
|
| 506 |
+
elif embed_tokens_weight.shape[0] == num_new_tokens:
|
| 507 |
+
input_embeddings[-num_new_tokens:] = embed_tokens_weight
|
| 508 |
+
else:
|
| 509 |
+
raise ValueError(
|
| 510 |
+
f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}."
|
| 511 |
+
)
|
| 512 |
+
elif model_args.mm_use_im_patch_token:
|
| 513 |
+
if model_args.tune_mm_mlp_adapter:
|
| 514 |
+
for p in self.get_input_embeddings().parameters():
|
| 515 |
+
p.requires_grad = False
|
| 516 |
+
for p in self.get_output_embeddings().parameters():
|
| 517 |
+
p.requires_grad = False
|
| 518 |
+
|
| 519 |
+
from typing import List, Optional, Tuple, Union
|
| 520 |
+
from transformers import (
|
| 521 |
+
AutoConfig,
|
| 522 |
+
AutoModelForCausalLM,
|
| 523 |
+
LlamaConfig,
|
| 524 |
+
LlamaModel,
|
| 525 |
+
LlamaForCausalLM,
|
| 526 |
+
)
|
| 527 |
+
from transformers.modeling_outputs import CausalLMOutputWithPast
|
| 528 |
+
from transformers.generation.utils import GenerateOutput
|
| 529 |
+
|
| 530 |
+
|
| 531 |
+
class MixsenseConfig(LlamaConfig):
|
| 532 |
+
model_type = "mixsense_llama"
|
| 533 |
+
|
| 534 |
+
|
| 535 |
+
class MixsenseLlamaModel(MixsenseMetaModel, LlamaModel):
|
| 536 |
+
config_class = MixsenseConfig
|
| 537 |
+
|
| 538 |
+
def __init__(self, config: LlamaConfig):
|
| 539 |
+
super(MixsenseLlamaModel, self).__init__(config)
|
| 540 |
+
|
| 541 |
+
|
| 542 |
+
class MixsenseLlamaForCausalLM(LlamaForCausalLM, MixsenseMetaForCausalLM):
|
| 543 |
+
config_class = MixsenseConfig
|
| 544 |
+
|
| 545 |
+
def __init__(self, config):
|
| 546 |
+
super(LlamaForCausalLM, self).__init__(config)
|
| 547 |
+
self.model = MixsenseLlamaModel(config)
|
| 548 |
+
self.pretraining_tp = config.pretraining_tp
|
| 549 |
+
self.vocab_size = config.vocab_size
|
| 550 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
| 551 |
+
|
| 552 |
+
# Initialize weights and apply final processing
|
| 553 |
+
self.post_init()
|
| 554 |
+
|
| 555 |
+
def get_model(self):
|
| 556 |
+
return self.model
|
| 557 |
+
|
| 558 |
+
def forward(
|
| 559 |
+
self,
|
| 560 |
+
input_ids: torch.LongTensor = None,
|
| 561 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 562 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 563 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
| 564 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 565 |
+
labels: Optional[torch.LongTensor] = None,
|
| 566 |
+
use_cache: Optional[bool] = None,
|
| 567 |
+
output_attentions: Optional[bool] = None,
|
| 568 |
+
output_hidden_states: Optional[bool] = None,
|
| 569 |
+
images: Optional[torch.FloatTensor] = None,
|
| 570 |
+
image_sizes: Optional[List[List[int]]] = None,
|
| 571 |
+
return_dict: Optional[bool] = None,
|
| 572 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
| 573 |
+
if inputs_embeds is None:
|
| 574 |
+
(
|
| 575 |
+
input_ids,
|
| 576 |
+
position_ids,
|
| 577 |
+
attention_mask,
|
| 578 |
+
past_key_values,
|
| 579 |
+
inputs_embeds,
|
| 580 |
+
labels,
|
| 581 |
+
) = self.prepare_inputs_labels_for_multimodal(
|
| 582 |
+
input_ids,
|
| 583 |
+
position_ids,
|
| 584 |
+
attention_mask,
|
| 585 |
+
past_key_values,
|
| 586 |
+
labels,
|
| 587 |
+
images,
|
| 588 |
+
image_sizes,
|
| 589 |
+
)
|
| 590 |
+
return super().forward(
|
| 591 |
+
input_ids=input_ids,
|
| 592 |
+
attention_mask=attention_mask,
|
| 593 |
+
position_ids=position_ids,
|
| 594 |
+
past_key_values=past_key_values,
|
| 595 |
+
inputs_embeds=inputs_embeds,
|
| 596 |
+
labels=labels,
|
| 597 |
+
use_cache=use_cache,
|
| 598 |
+
output_attentions=output_attentions,
|
| 599 |
+
output_hidden_states=output_hidden_states,
|
| 600 |
+
return_dict=return_dict,
|
| 601 |
+
)
|
| 602 |
+
|
| 603 |
+
@torch.no_grad()
|
| 604 |
+
def generate(
|
| 605 |
+
self,
|
| 606 |
+
inputs: Optional[torch.Tensor] = None,
|
| 607 |
+
images: Optional[torch.Tensor] = None,
|
| 608 |
+
image_sizes: Optional[torch.Tensor] = None,
|
| 609 |
+
**kwargs,
|
| 610 |
+
) -> Union[GenerateOutput, torch.LongTensor]:
|
| 611 |
+
position_ids = kwargs.pop("position_ids", None)
|
| 612 |
+
attention_mask = kwargs.pop("attention_mask", None)
|
| 613 |
+
if "inputs_embeds" in kwargs:
|
| 614 |
+
raise NotImplementedError("`inputs_embeds` is not supported")
|
| 615 |
+
if images is not None:
|
| 616 |
+
(inputs, position_ids, attention_mask, _, inputs_embeds, _) = (
|
| 617 |
+
self.prepare_inputs_labels_for_multimodal(
|
| 618 |
+
inputs,
|
| 619 |
+
position_ids,
|
| 620 |
+
attention_mask,
|
| 621 |
+
None,
|
| 622 |
+
None,
|
| 623 |
+
images,
|
| 624 |
+
image_sizes=image_sizes,
|
| 625 |
+
)
|
| 626 |
+
)
|
| 627 |
+
else:
|
| 628 |
+
inputs_embeds = self.get_model().embed_tokens(inputs)
|
| 629 |
+
output = super().generate(
|
| 630 |
+
position_ids=position_ids,
|
| 631 |
+
attention_mask=attention_mask,
|
| 632 |
+
inputs_embeds=inputs_embeds,
|
| 633 |
+
**kwargs,
|
| 634 |
+
)
|
| 635 |
+
return output
|
| 636 |
+
|
| 637 |
+
def prepare_inputs_for_generation(
|
| 638 |
+
self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs
|
| 639 |
+
):
|
| 640 |
+
images = kwargs.pop("images", None)
|
| 641 |
+
image_sizes = kwargs.pop("image_sizes", None)
|
| 642 |
+
inputs = super().prepare_inputs_for_generation(
|
| 643 |
+
input_ids,
|
| 644 |
+
past_key_values=past_key_values,
|
| 645 |
+
inputs_embeds=inputs_embeds,
|
| 646 |
+
**kwargs,
|
| 647 |
+
)
|
| 648 |
+
if images is not None:
|
| 649 |
+
inputs["images"] = images
|
| 650 |
+
if image_sizes is not None:
|
| 651 |
+
inputs["image_sizes"] = image_sizes
|
| 652 |
+
return inputs
|
| 653 |
+
def image_process(self,images):
|
| 654 |
+
vision_tower = self.get_vision_tower()
|
| 655 |
+
if not vision_tower.is_loaded:
|
| 656 |
+
vision_tower.load_model()
|
| 657 |
+
processor = vision_tower.image_processor
|
| 658 |
+
def expand2square(pil_img, background_color):
|
| 659 |
+
from PIL import Image
|
| 660 |
+
width, height = pil_img.size
|
| 661 |
+
if width == height:
|
| 662 |
+
return pil_img
|
| 663 |
+
elif width > height:
|
| 664 |
+
result = Image.new(pil_img.mode, (width, width), background_color)
|
| 665 |
+
result.paste(pil_img, (0, (width - height) // 2))
|
| 666 |
+
return result
|
| 667 |
+
else:
|
| 668 |
+
result = Image.new(pil_img.mode, (height, height), background_color)
|
| 669 |
+
result.paste(pil_img, ((height - width) // 2, 0))
|
| 670 |
+
return result
|
| 671 |
+
processed_images=[]
|
| 672 |
+
for image in images:
|
| 673 |
+
image = expand2square(image, tuple(int(x*255) for x in processor.image_mean))
|
| 674 |
+
image = processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
|
| 675 |
+
processed_images.append(image)
|
| 676 |
+
if all(x.shape == processed_images[0].shape for x in processed_images):
|
| 677 |
+
processed_images = torch.stack(processed_images, dim=0)
|
| 678 |
+
return processed_images
|
| 679 |
+
def text_process(self,text,tokenizer):
|
| 680 |
+
prompt=f"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful language and vision assistant. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n<image>\n{text}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
|
| 681 |
+
text_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split('<image>')]
|
| 682 |
+
input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1][1:], dtype=torch.long).unsqueeze(0)
|
| 683 |
+
return input_ids
|
| 684 |
+
|
| 685 |
+
|
| 686 |
+
AutoConfig.register("mixsense_llama", MixsenseConfig)
|
| 687 |
+
AutoModelForCausalLM.register(MixsenseConfig, MixsenseLlamaForCausalLM)
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,23 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token": {
|
| 3 |
+
"content": "<|begin_of_text|>",
|
| 4 |
+
"lstrip": false,
|
| 5 |
+
"normalized": false,
|
| 6 |
+
"rstrip": false,
|
| 7 |
+
"single_word": false
|
| 8 |
+
},
|
| 9 |
+
"eos_token": {
|
| 10 |
+
"content": "<|end_of_text|>",
|
| 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 |
+
}
|
tokenizer.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,2079 @@
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|
| 1 |
+
{
|
| 2 |
+
"added_tokens_decoder": {
|
| 3 |
+
"0": {
|
| 4 |
+
"content": "!",
|
| 5 |
+
"lstrip": false,
|
| 6 |
+
"normalized": false,
|
| 7 |
+
"rstrip": false,
|
| 8 |
+
"single_word": false,
|
| 9 |
+
"special": true
|
| 10 |
+
},
|
| 11 |
+
"128000": {
|
| 12 |
+
"content": "<|begin_of_text|>",
|
| 13 |
+
"lstrip": false,
|
| 14 |
+
"normalized": false,
|
| 15 |
+
"rstrip": false,
|
| 16 |
+
"single_word": false,
|
| 17 |
+
"special": true
|
| 18 |
+
},
|
| 19 |
+
"128001": {
|
| 20 |
+
"content": "<|end_of_text|>",
|
| 21 |
+
"lstrip": false,
|
| 22 |
+
"normalized": false,
|
| 23 |
+
"rstrip": false,
|
| 24 |
+
"single_word": false,
|
| 25 |
+
"special": true
|
| 26 |
+
},
|
| 27 |
+
"128002": {
|
| 28 |
+
"content": "<|reserved_special_token_0|>",
|
| 29 |
+
"lstrip": false,
|
| 30 |
+
"normalized": false,
|
| 31 |
+
"rstrip": false,
|
| 32 |
+
"single_word": false,
|
| 33 |
+
"special": true
|
| 34 |
+
},
|
| 35 |
+
"128003": {
|
| 36 |
+
"content": "<|reserved_special_token_1|>",
|
| 37 |
+
"lstrip": false,
|
| 38 |
+
"normalized": false,
|
| 39 |
+
"rstrip": false,
|
| 40 |
+
"single_word": false,
|
| 41 |
+
"special": true
|
| 42 |
+
},
|
| 43 |
+
"128004": {
|
| 44 |
+
"content": "<|reserved_special_token_2|>",
|
| 45 |
+
"lstrip": false,
|
| 46 |
+
"normalized": false,
|
| 47 |
+
"rstrip": false,
|
| 48 |
+
"single_word": false,
|
| 49 |
+
"special": true
|
| 50 |
+
},
|
| 51 |
+
"128005": {
|
| 52 |
+
"content": "<|reserved_special_token_3|>",
|
| 53 |
+
"lstrip": false,
|
| 54 |
+
"normalized": false,
|
| 55 |
+
"rstrip": false,
|
| 56 |
+
"single_word": false,
|
| 57 |
+
"special": true
|
| 58 |
+
},
|
| 59 |
+
"128006": {
|
| 60 |
+
"content": "<|start_header_id|>",
|
| 61 |
+
"lstrip": false,
|
| 62 |
+
"normalized": false,
|
| 63 |
+
"rstrip": false,
|
| 64 |
+
"single_word": false,
|
| 65 |
+
"special": true
|
| 66 |
+
},
|
| 67 |
+
"128007": {
|
| 68 |
+
"content": "<|end_header_id|>",
|
| 69 |
+
"lstrip": false,
|
| 70 |
+
"normalized": false,
|
| 71 |
+
"rstrip": false,
|
| 72 |
+
"single_word": false,
|
| 73 |
+
"special": true
|
| 74 |
+
},
|
| 75 |
+
"128008": {
|
| 76 |
+
"content": "<|reserved_special_token_4|>",
|
| 77 |
+
"lstrip": false,
|
| 78 |
+
"normalized": false,
|
| 79 |
+
"rstrip": false,
|
| 80 |
+
"single_word": false,
|
| 81 |
+
"special": true
|
| 82 |
+
},
|
| 83 |
+
"128009": {
|
| 84 |
+
"content": "<|eot_id|>",
|
| 85 |
+
"lstrip": false,
|
| 86 |
+
"normalized": false,
|
| 87 |
+
"rstrip": false,
|
| 88 |
+
"single_word": false,
|
| 89 |
+
"special": true
|
| 90 |
+
},
|
| 91 |
+
"128010": {
|
| 92 |
+
"content": "<|reserved_special_token_5|>",
|
| 93 |
+
"lstrip": false,
|
| 94 |
+
"normalized": false,
|
| 95 |
+
"rstrip": false,
|
| 96 |
+
"single_word": false,
|
| 97 |
+
"special": true
|
| 98 |
+
},
|
| 99 |
+
"128011": {
|
| 100 |
+
"content": "<|reserved_special_token_6|>",
|
| 101 |
+
"lstrip": false,
|
| 102 |
+
"normalized": false,
|
| 103 |
+
"rstrip": false,
|
| 104 |
+
"single_word": false,
|
| 105 |
+
"special": true
|
| 106 |
+
},
|
| 107 |
+
"128012": {
|
| 108 |
+
"content": "<|reserved_special_token_7|>",
|
| 109 |
+
"lstrip": false,
|
| 110 |
+
"normalized": false,
|
| 111 |
+
"rstrip": false,
|
| 112 |
+
"single_word": false,
|
| 113 |
+
"special": true
|
| 114 |
+
},
|
| 115 |
+
"128013": {
|
| 116 |
+
"content": "<|reserved_special_token_8|>",
|
| 117 |
+
"lstrip": false,
|
| 118 |
+
"normalized": false,
|
| 119 |
+
"rstrip": false,
|
| 120 |
+
"single_word": false,
|
| 121 |
+
"special": true
|
| 122 |
+
},
|
| 123 |
+
"128014": {
|
| 124 |
+
"content": "<|reserved_special_token_9|>",
|
| 125 |
+
"lstrip": false,
|
| 126 |
+
"normalized": false,
|
| 127 |
+
"rstrip": false,
|
| 128 |
+
"single_word": false,
|
| 129 |
+
"special": true
|
| 130 |
+
},
|
| 131 |
+
"128015": {
|
| 132 |
+
"content": "<|reserved_special_token_10|>",
|
| 133 |
+
"lstrip": false,
|
| 134 |
+
"normalized": false,
|
| 135 |
+
"rstrip": false,
|
| 136 |
+
"single_word": false,
|
| 137 |
+
"special": true
|
| 138 |
+
},
|
| 139 |
+
"128016": {
|
| 140 |
+
"content": "<|reserved_special_token_11|>",
|
| 141 |
+
"lstrip": false,
|
| 142 |
+
"normalized": false,
|
| 143 |
+
"rstrip": false,
|
| 144 |
+
"single_word": false,
|
| 145 |
+
"special": true
|
| 146 |
+
},
|
| 147 |
+
"128017": {
|
| 148 |
+
"content": "<|reserved_special_token_12|>",
|
| 149 |
+
"lstrip": false,
|
| 150 |
+
"normalized": false,
|
| 151 |
+
"rstrip": false,
|
| 152 |
+
"single_word": false,
|
| 153 |
+
"special": true
|
| 154 |
+
},
|
| 155 |
+
"128018": {
|
| 156 |
+
"content": "<|reserved_special_token_13|>",
|
| 157 |
+
"lstrip": false,
|
| 158 |
+
"normalized": false,
|
| 159 |
+
"rstrip": false,
|
| 160 |
+
"single_word": false,
|
| 161 |
+
"special": true
|
| 162 |
+
},
|
| 163 |
+
"128019": {
|
| 164 |
+
"content": "<|reserved_special_token_14|>",
|
| 165 |
+
"lstrip": false,
|
| 166 |
+
"normalized": false,
|
| 167 |
+
"rstrip": false,
|
| 168 |
+
"single_word": false,
|
| 169 |
+
"special": true
|
| 170 |
+
},
|
| 171 |
+
"128020": {
|
| 172 |
+
"content": "<|reserved_special_token_15|>",
|
| 173 |
+
"lstrip": false,
|
| 174 |
+
"normalized": false,
|
| 175 |
+
"rstrip": false,
|
| 176 |
+
"single_word": false,
|
| 177 |
+
"special": true
|
| 178 |
+
},
|
| 179 |
+
"128021": {
|
| 180 |
+
"content": "<|reserved_special_token_16|>",
|
| 181 |
+
"lstrip": false,
|
| 182 |
+
"normalized": false,
|
| 183 |
+
"rstrip": false,
|
| 184 |
+
"single_word": false,
|
| 185 |
+
"special": true
|
| 186 |
+
},
|
| 187 |
+
"128022": {
|
| 188 |
+
"content": "<|reserved_special_token_17|>",
|
| 189 |
+
"lstrip": false,
|
| 190 |
+
"normalized": false,
|
| 191 |
+
"rstrip": false,
|
| 192 |
+
"single_word": false,
|
| 193 |
+
"special": true
|
| 194 |
+
},
|
| 195 |
+
"128023": {
|
| 196 |
+
"content": "<|reserved_special_token_18|>",
|
| 197 |
+
"lstrip": false,
|
| 198 |
+
"normalized": false,
|
| 199 |
+
"rstrip": false,
|
| 200 |
+
"single_word": false,
|
| 201 |
+
"special": true
|
| 202 |
+
},
|
| 203 |
+
"128024": {
|
| 204 |
+
"content": "<|reserved_special_token_19|>",
|
| 205 |
+
"lstrip": false,
|
| 206 |
+
"normalized": false,
|
| 207 |
+
"rstrip": false,
|
| 208 |
+
"single_word": false,
|
| 209 |
+
"special": true
|
| 210 |
+
},
|
| 211 |
+
"128025": {
|
| 212 |
+
"content": "<|reserved_special_token_20|>",
|
| 213 |
+
"lstrip": false,
|
| 214 |
+
"normalized": false,
|
| 215 |
+
"rstrip": false,
|
| 216 |
+
"single_word": false,
|
| 217 |
+
"special": true
|
| 218 |
+
},
|
| 219 |
+
"128026": {
|
| 220 |
+
"content": "<|reserved_special_token_21|>",
|
| 221 |
+
"lstrip": false,
|
| 222 |
+
"normalized": false,
|
| 223 |
+
"rstrip": false,
|
| 224 |
+
"single_word": false,
|
| 225 |
+
"special": true
|
| 226 |
+
},
|
| 227 |
+
"128027": {
|
| 228 |
+
"content": "<|reserved_special_token_22|>",
|
| 229 |
+
"lstrip": false,
|
| 230 |
+
"normalized": false,
|
| 231 |
+
"rstrip": false,
|
| 232 |
+
"single_word": false,
|
| 233 |
+
"special": true
|
| 234 |
+
},
|
| 235 |
+
"128028": {
|
| 236 |
+
"content": "<|reserved_special_token_23|>",
|
| 237 |
+
"lstrip": false,
|
| 238 |
+
"normalized": false,
|
| 239 |
+
"rstrip": false,
|
| 240 |
+
"single_word": false,
|
| 241 |
+
"special": true
|
| 242 |
+
},
|
| 243 |
+
"128029": {
|
| 244 |
+
"content": "<|reserved_special_token_24|>",
|
| 245 |
+
"lstrip": false,
|
| 246 |
+
"normalized": false,
|
| 247 |
+
"rstrip": false,
|
| 248 |
+
"single_word": false,
|
| 249 |
+
"special": true
|
| 250 |
+
},
|
| 251 |
+
"128030": {
|
| 252 |
+
"content": "<|reserved_special_token_25|>",
|
| 253 |
+
"lstrip": false,
|
| 254 |
+
"normalized": false,
|
| 255 |
+
"rstrip": false,
|
| 256 |
+
"single_word": false,
|
| 257 |
+
"special": true
|
| 258 |
+
},
|
| 259 |
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| 1500 |
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| 1850 |
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| 1860 |
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| 1861 |
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| 1866 |
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| 1867 |
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| 1868 |
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| 1876 |
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| 1882 |
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| 1884 |
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| 1885 |
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| 1886 |
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| 1887 |
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| 1890 |
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| 1892 |
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| 1893 |
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| 1898 |
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| 1900 |
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| 1901 |
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"lstrip": false,
|
| 1902 |
+
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|
| 1903 |
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|
| 1904 |
+
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|
| 1905 |
+
"special": true
|
| 1906 |
+
},
|
| 1907 |
+
"128237": {
|
| 1908 |
+
"content": "<|reserved_special_token_232|>",
|
| 1909 |
+
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|
| 1910 |
+
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|
| 1911 |
+
"rstrip": false,
|
| 1912 |
+
"single_word": false,
|
| 1913 |
+
"special": true
|
| 1914 |
+
},
|
| 1915 |
+
"128238": {
|
| 1916 |
+
"content": "<|reserved_special_token_233|>",
|
| 1917 |
+
"lstrip": false,
|
| 1918 |
+
"normalized": false,
|
| 1919 |
+
"rstrip": false,
|
| 1920 |
+
"single_word": false,
|
| 1921 |
+
"special": true
|
| 1922 |
+
},
|
| 1923 |
+
"128239": {
|
| 1924 |
+
"content": "<|reserved_special_token_234|>",
|
| 1925 |
+
"lstrip": false,
|
| 1926 |
+
"normalized": false,
|
| 1927 |
+
"rstrip": false,
|
| 1928 |
+
"single_word": false,
|
| 1929 |
+
"special": true
|
| 1930 |
+
},
|
| 1931 |
+
"128240": {
|
| 1932 |
+
"content": "<|reserved_special_token_235|>",
|
| 1933 |
+
"lstrip": false,
|
| 1934 |
+
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|
| 1935 |
+
"rstrip": false,
|
| 1936 |
+
"single_word": false,
|
| 1937 |
+
"special": true
|
| 1938 |
+
},
|
| 1939 |
+
"128241": {
|
| 1940 |
+
"content": "<|reserved_special_token_236|>",
|
| 1941 |
+
"lstrip": false,
|
| 1942 |
+
"normalized": false,
|
| 1943 |
+
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|
| 1944 |
+
"single_word": false,
|
| 1945 |
+
"special": true
|
| 1946 |
+
},
|
| 1947 |
+
"128242": {
|
| 1948 |
+
"content": "<|reserved_special_token_237|>",
|
| 1949 |
+
"lstrip": false,
|
| 1950 |
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|
| 1951 |
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"rstrip": false,
|
| 1952 |
+
"single_word": false,
|
| 1953 |
+
"special": true
|
| 1954 |
+
},
|
| 1955 |
+
"128243": {
|
| 1956 |
+
"content": "<|reserved_special_token_238|>",
|
| 1957 |
+
"lstrip": false,
|
| 1958 |
+
"normalized": false,
|
| 1959 |
+
"rstrip": false,
|
| 1960 |
+
"single_word": false,
|
| 1961 |
+
"special": true
|
| 1962 |
+
},
|
| 1963 |
+
"128244": {
|
| 1964 |
+
"content": "<|reserved_special_token_239|>",
|
| 1965 |
+
"lstrip": false,
|
| 1966 |
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"normalized": false,
|
| 1967 |
+
"rstrip": false,
|
| 1968 |
+
"single_word": false,
|
| 1969 |
+
"special": true
|
| 1970 |
+
},
|
| 1971 |
+
"128245": {
|
| 1972 |
+
"content": "<|reserved_special_token_240|>",
|
| 1973 |
+
"lstrip": false,
|
| 1974 |
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|
| 1975 |
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|
| 1976 |
+
"single_word": false,
|
| 1977 |
+
"special": true
|
| 1978 |
+
},
|
| 1979 |
+
"128246": {
|
| 1980 |
+
"content": "<|reserved_special_token_241|>",
|
| 1981 |
+
"lstrip": false,
|
| 1982 |
+
"normalized": false,
|
| 1983 |
+
"rstrip": false,
|
| 1984 |
+
"single_word": false,
|
| 1985 |
+
"special": true
|
| 1986 |
+
},
|
| 1987 |
+
"128247": {
|
| 1988 |
+
"content": "<|reserved_special_token_242|>",
|
| 1989 |
+
"lstrip": false,
|
| 1990 |
+
"normalized": false,
|
| 1991 |
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"rstrip": false,
|
| 1992 |
+
"single_word": false,
|
| 1993 |
+
"special": true
|
| 1994 |
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},
|
| 1995 |
+
"128248": {
|
| 1996 |
+
"content": "<|reserved_special_token_243|>",
|
| 1997 |
+
"lstrip": false,
|
| 1998 |
+
"normalized": false,
|
| 1999 |
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"rstrip": false,
|
| 2000 |
+
"single_word": false,
|
| 2001 |
+
"special": true
|
| 2002 |
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},
|
| 2003 |
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"128249": {
|
| 2004 |
+
"content": "<|reserved_special_token_244|>",
|
| 2005 |
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|
| 2006 |
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|
| 2007 |
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|
| 2008 |
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"single_word": false,
|
| 2009 |
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"special": true
|
| 2010 |
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},
|
| 2011 |
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"128250": {
|
| 2012 |
+
"content": "<|reserved_special_token_245|>",
|
| 2013 |
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"lstrip": false,
|
| 2014 |
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|
| 2015 |
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|
| 2016 |
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"single_word": false,
|
| 2017 |
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"special": true
|
| 2018 |
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},
|
| 2019 |
+
"128251": {
|
| 2020 |
+
"content": "<|reserved_special_token_246|>",
|
| 2021 |
+
"lstrip": false,
|
| 2022 |
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|
| 2023 |
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|
| 2024 |
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"single_word": false,
|
| 2025 |
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"special": true
|
| 2026 |
+
},
|
| 2027 |
+
"128252": {
|
| 2028 |
+
"content": "<|reserved_special_token_247|>",
|
| 2029 |
+
"lstrip": false,
|
| 2030 |
+
"normalized": false,
|
| 2031 |
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|
| 2032 |
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"single_word": false,
|
| 2033 |
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|
| 2034 |
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},
|
| 2035 |
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"128253": {
|
| 2036 |
+
"content": "<|reserved_special_token_248|>",
|
| 2037 |
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|
| 2038 |
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|
| 2039 |
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|
| 2040 |
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|
| 2041 |
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"special": true
|
| 2042 |
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},
|
| 2043 |
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"128254": {
|
| 2044 |
+
"content": "<|reserved_special_token_249|>",
|
| 2045 |
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|
| 2046 |
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|
| 2047 |
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|
| 2048 |
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|
| 2049 |
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"special": true
|
| 2050 |
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},
|
| 2051 |
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"128255": {
|
| 2052 |
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"content": "<|reserved_special_token_250|>",
|
| 2053 |
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|
| 2054 |
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|
| 2055 |
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|
| 2056 |
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|
| 2057 |
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|
| 2058 |
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},
|
| 2059 |
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"128256": {
|
| 2060 |
+
"content": "<pad>",
|
| 2061 |
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|
| 2062 |
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|
| 2063 |
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|
| 2064 |
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|
| 2065 |
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"special": true
|
| 2066 |
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}
|
| 2067 |
+
},
|
| 2068 |
+
"bos_token": "<|begin_of_text|>",
|
| 2069 |
+
"clean_up_tokenization_spaces": true,
|
| 2070 |
+
"eos_token": "<|end_of_text|>",
|
| 2071 |
+
"model_input_names": [
|
| 2072 |
+
"input_ids",
|
| 2073 |
+
"attention_mask"
|
| 2074 |
+
],
|
| 2075 |
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"model_max_length": 2048,
|
| 2076 |
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"pad_token": "<pad>",
|
| 2077 |
+
"padding_side": "right",
|
| 2078 |
+
"tokenizer_class": "PreTrainedTokenizerFast"
|
| 2079 |
+
}
|