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#
# 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,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from types import SimpleNamespace
import pytest
import torch
from safetensors.torch import load_file
from transformers import AutoConfig, AutoModelForImageTextToText
from llamafactory.extras.packages import is_transformers_version_greater_than
from llamafactory.hparams import FinetuningArguments, ModelArguments
from llamafactory.model.adapter import _setup_freeze_tuning, _setup_full_tuning, init_adapter
from llamafactory.model.model_utils.misc import find_all_linear_modules
from llamafactory.model.model_utils.visual import COMPOSITE_MODELS, autocast_projector_dtype, patch_target_modules
class _MossVLFixture(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.config = SimpleNamespace(
model_type="moss_vl",
text_config=SimpleNamespace(num_hidden_layers=2),
)
self.model = torch.nn.Module()
self.model.separator_token = torch.nn.Parameter(torch.empty(4))
self.model.visual = torch.nn.Module()
self.model.visual.pos_embed = torch.nn.Embedding(4, 4)
self.model.visual.patch_embed = torch.nn.Module()
self.model.visual.patch_embed.proj = torch.nn.Linear(4, 4)
self.model.visual.blocks = torch.nn.ModuleList([self._make_block(), self._make_block()])
self.model.visual.merger = torch.nn.Module()
self.model.visual.merger.linear_fc1 = torch.nn.Linear(4, 4)
self.model.language_model = torch.nn.Module()
self.model.language_model.layers = torch.nn.ModuleList([self._make_layer(), self._make_layer()])
self.lm_head = torch.nn.Linear(4, 4)
@staticmethod
def _make_block() -> torch.nn.Module:
block = torch.nn.Module()
block.attn = torch.nn.Module()
block.attn.qkv = torch.nn.Linear(4, 4)
return block
@staticmethod
def _make_layer() -> torch.nn.Module:
layer = torch.nn.Module()
layer.self_attn = torch.nn.Module()
layer.self_attn.q_proj = torch.nn.Linear(4, 4)
return layer
@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_moss_vl_full(
freeze_vision_tower: bool,
freeze_multi_modal_projector: bool,
freeze_language_model: bool,
):
model = _MossVLFixture()
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,
)
_setup_full_tuning(model, finetuning_args, is_trainable=True, cast_trainable_params_to_fp32=False)
for name, param in model.named_parameters():
if name.startswith("model.visual.merger") or name == "model.separator_token":
assert param.requires_grad != freeze_multi_modal_projector
elif name.startswith("model.visual"):
assert param.requires_grad != freeze_vision_tower
else:
assert param.requires_grad != freeze_language_model
@pytest.mark.parametrize("freeze_multi_modal_projector", (False, True))
def test_moss_vl_freeze(freeze_multi_modal_projector: bool):
model = _MossVLFixture()
finetuning_args = FinetuningArguments(
finetuning_type="freeze",
freeze_trainable_layers=1,
freeze_vision_tower=True,
freeze_multi_modal_projector=freeze_multi_modal_projector,
freeze_language_model=False,
)
_setup_freeze_tuning(model, finetuning_args, is_trainable=True, cast_trainable_params_to_fp32=False)
assert model.model.separator_token.requires_grad != freeze_multi_modal_projector
assert model.model.visual.merger.linear_fc1.weight.requires_grad != freeze_multi_modal_projector
assert model.model.visual.patch_embed.proj.weight.requires_grad is False
assert model.model.language_model.layers[0].self_attn.q_proj.weight.requires_grad is False
assert model.model.language_model.layers[1].self_attn.q_proj.weight.requires_grad is True
@pytest.mark.parametrize("freeze_vision_tower", (False, True))
def test_moss_vl_lora_target_all(freeze_vision_tower: bool):
model = _MossVLFixture()
finetuning_args = FinetuningArguments(
finetuning_type="lora",
lora_target="all",
freeze_vision_tower=freeze_vision_tower,
freeze_multi_modal_projector=True,
freeze_language_model=False,
)
target_modules = find_all_linear_modules(model, freeze_vision_tower)
target_modules = patch_target_modules(model, finetuning_args, target_modules)
assert any(name.startswith("model.language_model") and name.endswith("q_proj") for name in target_modules)
assert any(name.startswith("model.visual.blocks") and name.endswith("qkv") for name in target_modules) != (
freeze_vision_tower
)
assert all("patch_embed" not in name for name in target_modules)
assert all("merger" not in name for name in target_modules)
assert all("lm_head" not in name for name in target_modules)
def test_moss_vl_projector_modules():
model = _MossVLFixture()
composite_model = COMPOSITE_MODELS["moss_vl"]
assert composite_model.projector_keys == ["model.visual.merger", "model.separator_token"]
assert composite_model.get_projectors(model) == [model.model.visual.merger]
def test_moss_vl_quantized_projector_hook_skips_parameter():
model = _MossVLFixture()
model.quantization_method = "bitsandbytes"
autocast_projector_dtype(model, SimpleNamespace(compute_dtype=torch.float16))
assert len(model.model.visual.merger._forward_hooks) == 1
@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 = AutoModelForImageTextToText.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 = AutoModelForImageTextToText.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 = AutoModelForImageTextToText.from_config(config)
model = init_adapter(config, model, model_args, finetuning_args, is_trainable=False)
model.to_empty(device="cpu")
loaded_model_weight = dict(model.named_parameters())
model.save_pretrained(os.path.join("output", "qwen2_vl"), max_shard_size="10GB", safe_serialization=True)
saved_model_weight = load_file(os.path.join("output", "qwen2_vl", "model.safetensors"))
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
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