mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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203 lines
7.9 KiB
Python
203 lines
7.9 KiB
Python
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
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#
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# This code is inspired by the HuggingFace's Transformers library.
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# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/models/llava/modeling_llava.py
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import TYPE_CHECKING, List, Sequence, Set, Tuple, Union
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import torch
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import transformers
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import transformers.models
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from transformers.activations import ACT2FN
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from ...extras import logging
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if TYPE_CHECKING:
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from transformers import LlavaConfig, PretrainedConfig, PreTrainedModel
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from ...hparams import FinetuningArguments, ModelArguments
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logger = logging.get_logger(__name__)
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transformers_logger = transformers.utils.logging.get_logger(__name__)
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class LlavaMultiModalProjectorForYiVL(torch.nn.Module):
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def __init__(self, config: "LlavaConfig") -> None:
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super().__init__()
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self.config = config
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if config is None:
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return
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self.linear_1 = torch.nn.Linear(config.vision_config.hidden_size, config.text_config.hidden_size, bias=True)
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self.linear_2 = torch.nn.LayerNorm(config.text_config.hidden_size, bias=True)
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self.linear_3 = torch.nn.Linear(config.text_config.hidden_size, config.text_config.hidden_size, bias=True)
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self.linear_4 = torch.nn.LayerNorm(config.text_config.hidden_size, bias=True)
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self.act = ACT2FN[config.projector_hidden_act]
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def forward(self, image_features: "torch.Tensor") -> "torch.Tensor":
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hidden_states = self.linear_1(image_features)
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hidden_states = self.linear_2(hidden_states)
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hidden_states = self.act(hidden_states)
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hidden_states = self.linear_3(hidden_states)
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hidden_states = self.linear_4(hidden_states)
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if hidden_states.dtype == torch.float32:
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if torch.is_autocast_enabled():
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target_dtype = torch.get_autocast_gpu_dtype()
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elif hasattr(self.config, "_pre_quantization_dtype"):
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target_dtype = self.config._pre_quantization_dtype
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else:
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target_dtype = self.linear_1.weight.dtype
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transformers_logger.warning_once("The hidden states seems to be silently casted in float32.")
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hidden_states = hidden_states.to(target_dtype)
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return hidden_states
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class LlavaMultiModalProjectorForYiVLForVLLM(LlavaMultiModalProjectorForYiVL):
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def __init__(self, vision_hidden_size: int, text_hidden_size: int, projector_hidden_act: str) -> None:
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super().__init__(config=None)
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self.linear_1 = torch.nn.Linear(vision_hidden_size, text_hidden_size, bias=True)
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self.linear_2 = torch.nn.LayerNorm(text_hidden_size, bias=True)
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self.linear_3 = torch.nn.Linear(text_hidden_size, text_hidden_size, bias=True)
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self.linear_4 = torch.nn.LayerNorm(text_hidden_size, bias=True)
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self.act = ACT2FN[projector_hidden_act]
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def autocast_projector_dtype(model: "PreTrainedModel", model_args: "ModelArguments") -> None:
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r"""
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Casts projector output to half precision for fine-tuning quantized VLMs.
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"""
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def _mm_projector_forward_post_hook(
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module: "torch.nn.Module", args: Tuple["torch.Tensor"], output: "torch.Tensor"
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) -> "torch.Tensor":
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return output.to(model_args.compute_dtype)
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if getattr(model, "quantization_method", None):
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model_type = getattr(model.config, "model_type", None)
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if model_type in ["llava", "llava_next", "llava_next_video", "paligemma", "pixtral", "video_llava"]:
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mm_projector: "torch.nn.Module" = getattr(model, "multi_modal_projector")
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elif model_type == "qwen2_vl":
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mm_projector: "torch.nn.Module" = getattr(getattr(model, "visual"), "merger")
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else:
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return
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logger.info_rank0(f"Casting multimodal projector outputs in {model_args.compute_dtype}.")
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mm_projector.register_forward_hook(_mm_projector_forward_post_hook)
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def configure_visual_model(config: "PretrainedConfig") -> None:
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r"""
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Patches VLMs before loading them.
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"""
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model_type = getattr(config, "model_type", None)
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if model_type in [
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"llava",
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"llava_next",
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"llava_next_video",
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"paligemma",
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"pixtral",
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"video_llava",
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]: # required for ds zero3 and valuehead models
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setattr(config, "hidden_size", getattr(config.text_config, "hidden_size", None))
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if getattr(config, "is_yi_vl_derived_model", None):
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logger.info_rank0("Detected Yi-VL model, applying projector patch.")
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transformers.models.llava.modeling_llava.LlavaMultiModalProjector = LlavaMultiModalProjectorForYiVL
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def get_forbidden_modules(config: "PretrainedConfig", finetuning_args: "FinetuningArguments") -> Set[str]:
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r"""
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Freezes vision tower and language model for VLM full/freeze tuning.
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"""
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model_type = getattr(config, "model_type", None)
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forbidden_modules = set()
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if model_type in ["llava", "llava_next", "llava_next_video", "paligemma", "pixtral", "video_llava"]:
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if finetuning_args.freeze_vision_tower:
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forbidden_modules.add("vision_tower")
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if finetuning_args.train_mm_proj_only:
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forbidden_modules.add("language_model")
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elif model_type == "qwen2_vl":
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if finetuning_args.freeze_vision_tower:
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forbidden_modules.add("visual")
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if finetuning_args.train_mm_proj_only:
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raise ValueError("Qwen2-VL models do not support `train_mm_proj_only`.")
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return forbidden_modules
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def get_image_seqlen(config: "PretrainedConfig") -> int:
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r"""
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Computes the number of special tokens per image.
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"""
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model_type = getattr(config, "model_type", None)
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if model_type == "llava":
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image_seqlen = (config.vision_config.image_size // config.vision_config.patch_size) ** 2
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if getattr(config, "vision_feature_select_strategy", "default") == "full": # add [CLS] token
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image_seqlen += 1
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elif model_type == "paligemma":
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image_seqlen = config.vision_config.num_image_tokens
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else:
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image_seqlen = -1
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return image_seqlen
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def get_patch_size(config: "PretrainedConfig") -> int:
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r"""
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Computes the patch size of the vit.
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"""
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patch_size = getattr(config.vision_config, "patch_size", -1)
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return patch_size
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def get_vision_feature_select_strategy(config: "PretrainedConfig") -> int:
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r"""
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Get the vision_feature_select_strategy.
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"""
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vision_feature_select_strategy = getattr(config, "vision_feature_select_strategy", "default")
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return vision_feature_select_strategy
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def patch_target_modules(
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config: "PretrainedConfig", finetuning_args: "FinetuningArguments", target_modules: Sequence[str]
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) -> Union[str, List[str]]:
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r"""
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Freezes vision tower for VLM LoRA tuning.
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"""
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model_type = getattr(config, "model_type", None)
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if finetuning_args.freeze_vision_tower:
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if model_type in ["llava", "llava_next", "llava_next_video", "paligemma", "pixtral", "video_llava"]:
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return "^(?!.*vision_tower).*(?:{}).*".format("|".join(target_modules))
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elif model_type == "qwen2_vl":
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return "^(?!.*visual).*(?:{}).*".format("|".join(target_modules))
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else:
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return target_modules
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else:
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if model_type == "qwen2_vl":
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return "^(?!.*patch_embed).*(?:{}).*".format("|".join(target_modules))
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elif model_type == "pixtral":
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return "^(?!.*patch_conv).*(?:{}).*".format("|".join(target_modules))
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else:
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return target_modules
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