mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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71 lines
3.2 KiB
Python
71 lines
3.2 KiB
Python
# Copyright 2025 the LlamaFactory team.
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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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import pytest
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import torch
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from transformers import AutoConfig, AutoModelForVision2Seq
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from llamafactory.hparams import FinetuningArguments, ModelArguments
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from llamafactory.model.adapter import init_adapter
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@pytest.mark.parametrize("freeze_vision_tower", (False, True))
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@pytest.mark.parametrize("freeze_multi_modal_projector", (False, True))
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@pytest.mark.parametrize("freeze_language_model", (False, True))
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def test_visual_full(freeze_vision_tower: bool, freeze_multi_modal_projector: bool, freeze_language_model: bool):
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model_args = ModelArguments(model_name_or_path="Qwen/Qwen2-VL-2B-Instruct")
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finetuning_args = FinetuningArguments(
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finetuning_type="full",
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freeze_vision_tower=freeze_vision_tower,
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freeze_multi_modal_projector=freeze_multi_modal_projector,
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freeze_language_model=freeze_language_model,
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)
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config = AutoConfig.from_pretrained(model_args.model_name_or_path)
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with torch.device("meta"):
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model = AutoModelForVision2Seq.from_config(config)
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model = init_adapter(config, model, model_args, finetuning_args, is_trainable=True)
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for name, param in model.named_parameters():
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if any(key in name for key in ["visual.patch_embed", "visual.blocks"]):
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assert param.requires_grad != freeze_vision_tower
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elif "visual.merger" in name:
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assert param.requires_grad != freeze_multi_modal_projector
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else:
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assert param.requires_grad != freeze_language_model
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@pytest.mark.parametrize("freeze_vision_tower", (False, True))
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def test_visual_lora(freeze_vision_tower: bool):
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model_args = ModelArguments(model_name_or_path="Qwen/Qwen2-VL-2B-Instruct")
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finetuning_args = FinetuningArguments(finetuning_type="lora", freeze_vision_tower=freeze_vision_tower)
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config = AutoConfig.from_pretrained(model_args.model_name_or_path)
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with torch.device("meta"):
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model = AutoModelForVision2Seq.from_config(config)
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model = init_adapter(config, model, model_args, finetuning_args, is_trainable=True)
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trainable_params, frozen_params = set(), set()
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for name, param in model.named_parameters():
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if param.requires_grad:
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trainable_params.add(name)
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else:
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frozen_params.add(name)
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if freeze_vision_tower:
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assert "base_model.model.visual.blocks.0.attn.qkv.lora_A.default.weight" not in trainable_params
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else:
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assert "base_model.model.visual.blocks.0.attn.qkv.lora_A.default.weight" in trainable_params
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assert "merger" not in trainable_params
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assert "base_model.model.model.layers.0.self_attn.q_proj.lora_A.default.weight" in trainable_params
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