81 lines
3.3 KiB
Python
81 lines
3.3 KiB
Python
# Copyright (c) ModelScope Contributors. All rights reserved.
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import sys
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from functools import wraps
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from transformers import PreTrainedModel
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from typing import Any, Dict
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from swift.template import TemplateType
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from swift.utils import git_clone_github, safe_snapshot_download
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from ..constant import MLLMModelType
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from ..model_arch import ModelArch
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from ..model_meta import Model, ModelGroup, ModelMeta
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from ..register import ModelLoader, register_model
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class ValleyLoader(ModelLoader):
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def get_config(self, model_dir: str):
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local_repo_path = self.local_repo_path
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if not local_repo_path:
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repo_path = 'https://github.com/bytedance/Valley.git'
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local_repo_path = git_clone_github(repo_path)
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sys.path.append(local_repo_path)
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from valley_eagle.model.language_model.valley_qwen2 import ValleyConfig
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self.auto_config_cls = ValleyConfig
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return super().get_config(model_dir)
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def get_model(self, model_dir: str, config, processor, model_kwargs) -> PreTrainedModel:
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from transformers.modeling_outputs import CausalLMOutputWithPast
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from valley_eagle.model.language_model.valley_qwen2 import ValleyQwen2ForCausalLM
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config.mm_vision_tower = safe_snapshot_download('AI-ModelScope/siglip-so400m-patch14-384', check_local=True)
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config.eagle_vision_tower = safe_snapshot_download('Qwen/Qwen2-VL-7B-Instruct', check_local=True)
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auto_model_cls = ValleyQwen2ForCausalLM
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if not hasattr(ValleyQwen2ForCausalLM, '_origin_forward'):
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forward = ValleyQwen2ForCausalLM.forward
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ValleyQwen2ForCausalLM._origin_forward = forward
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@wraps(forward)
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def new_forward(*args, **kwargs):
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import torch
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outputs = forward(*args, **kwargs)
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loss = outputs.loss
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if loss is not None and loss.shape[-1] > 0:
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loss = torch.mean(loss, dim=-1)
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return CausalLMOutputWithPast(
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loss=loss,
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logits=outputs.logits,
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past_key_values=outputs.past_key_values,
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hidden_states=outputs.hidden_states,
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attentions=outputs.attentions,
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)
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ValleyQwen2ForCausalLM.forward = new_forward
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self.auto_model_cls = auto_model_cls
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model = super().get_model(model_dir, config, processor, model_kwargs)
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model.generation_config.repetition_penalty = 1.0 # Otherwise, Error. Same for original code.
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from transformers import AutoProcessor, SiglipImageProcessor
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processor.image_processor = SiglipImageProcessor.from_pretrained(model.config.mm_vision_tower)
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processor.qwen2vl_processor = AutoProcessor.from_pretrained(
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model.config.eagle_vision_tower, max_pixels=1280 * 28 * 28)
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processor.image_processor.crop_size = processor.image_processor.size['height']
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return model
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register_model(
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ModelMeta(
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MLLMModelType.valley,
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[
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ModelGroup([
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Model('bytedance-research/Valley-Eagle-7B'),
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], ),
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],
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ValleyLoader,
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template=TemplateType.valley,
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architectures=['ValleyQwen2ForCausalLM'],
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model_arch=ModelArch.valley,
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requires=['transformers>=4.42', 'av'],
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tags=['vision'],
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))
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