tests/model/model_utils/test_visual.py

# Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# See the License for the specific language governing permissions and
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import os

import pytest
import torch
from transformers import AutoConfig, AutoModelForVision2Seq

from llamafactory.extras.packages import is_transformers_version_greater_than
from llamafactory.hparams import FinetuningArguments, ModelArguments
from llamafactory.model.adapter import init_adapter


@pytest.mark.parametrize("freeze_vision_tower", (False, True))
@pytest.mark.parametrize("freeze_multi_modal_projector", (False, True))
@pytest.mark.parametrize("freeze_language_model", (False, True))
def test_visual_full(freeze_vision_tower: bool, freeze_multi_modal_projector: bool, freeze_language_model: bool):
    model_args = ModelArguments(model_name_or_path="Qwen/Qwen2-VL-2B-Instruct")
    finetuning_args = FinetuningArguments(
        finetuning_type="full",
        freeze_vision_tower=freeze_vision_tower,
        freeze_multi_modal_projector=freeze_multi_modal_projector,
        freeze_language_model=freeze_language_model,
    )
    config = AutoConfig.from_pretrained(model_args.model_name_or_path)
    with torch.device("meta"):
        model = AutoModelForVision2Seq.from_config(config)

    model = init_adapter(config, model, model_args, finetuning_args, is_trainable=True)
    for name, param in model.named_parameters():
        if any(key in name for key in ["visual.patch_embed", "visual.blocks"]):
            assert param.requires_grad != freeze_vision_tower
        elif "visual.merger" in name:
            assert param.requires_grad != freeze_multi_modal_projector
        else:
            assert param.requires_grad != freeze_language_model


@pytest.mark.parametrize("freeze_vision_tower,freeze_language_model", ((False, False), (False, True), (True, False)))
def test_visual_lora(freeze_vision_tower: bool, freeze_language_model: bool):
    model_args = ModelArguments(model_name_or_path="Qwen/Qwen2-VL-2B-Instruct")
    finetuning_args = FinetuningArguments(
        finetuning_type="lora", freeze_vision_tower=freeze_vision_tower, freeze_language_model=freeze_language_model
    )
    config = AutoConfig.from_pretrained(model_args.model_name_or_path)
    with torch.device("meta"):
        model = AutoModelForVision2Seq.from_config(config)

    model = init_adapter(config, model, model_args, finetuning_args, is_trainable=True)
    trainable_params, frozen_params = set(), set()
    for name, param in model.named_parameters():
        if param.requires_grad:
            trainable_params.add(name)
        else:
            frozen_params.add(name)

    if is_transformers_version_greater_than("4.52.0"):
        visual_param_name = "base_model.model.model.visual.blocks.0.attn.qkv.lora_A.default.weight"
        language_param_name = "base_model.model.model.language_model.layers.0.self_attn.q_proj.lora_A.default.weight"
        merger_param_name = "base_model.model.model.visual.merger.lora_A.default.weight"
    else:
        visual_param_name = "base_model.model.visual.blocks.0.attn.qkv.lora_A.default.weight"
        language_param_name = "base_model.model.model.layers.0.self_attn.q_proj.lora_A.default.weight"
        merger_param_name = "base_model.model.visual.merger.lora_A.default.weight"

    assert (visual_param_name in trainable_params) != freeze_vision_tower
    assert (language_param_name in trainable_params) != freeze_language_model
    assert (merger_param_name in trainable_params) is False


def test_visual_model_save_load():
    # check VLM's state dict: https://github.com/huggingface/transformers/pull/38385
    model_args = ModelArguments(model_name_or_path="Qwen/Qwen2-VL-2B-Instruct")
    finetuning_args = FinetuningArguments(finetuning_type="full")
    config = AutoConfig.from_pretrained(model_args.model_name_or_path)
    with torch.device("meta"):
        model = AutoModelForVision2Seq.from_config(config)

    model = init_adapter(config, model, model_args, finetuning_args, is_trainable=False)
    loaded_model_weight = dict(model.named_parameters())

    model.save_pretrained(os.path.join("output", "qwen2_vl"), max_shard_size="10GB", safe_serialization=False)
    saved_model_weight = torch.load(os.path.join("output", "qwen2_vl", "pytorch_model.bin"), weights_only=False)

    if is_transformers_version_greater_than("4.52.0"):
        assert "model.language_model.layers.0.self_attn.q_proj.weight" in loaded_model_weight
    else:
        assert "model.layers.0.self_attn.q_proj.weight" in loaded_model_weight

    assert "model.layers.0.self_attn.q_proj.weight" in saved_model_weight