refactor mllm param logic

Former-commit-id: b895c190945cf5d991cb4e4dea2ae73cc9c8d246

tests/data/test_template.py

@@ -100,8 +100,7 @@ def test_encode_multiturn(use_fast: bool):
     )
     answer_str_1 = "I am fine!<|eot_id|>"
     prompt_str_2 = (
-        "<|start_header_id|>user<|end_header_id|>\n\n你好<|eot_id|>"
-        "<|start_header_id|>assistant<|end_header_id|>\n\n"
+        "<|start_header_id|>user<|end_header_id|>\n\n你好<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
     )
     answer_str_2 = "很高兴认识你！<|eot_id|>"
     _check_tokenization(

tests/model/model_utils/test_checkpointing.py

@@ -14,6 +14,7 @@
 
 import os
 
+import pytest
 import torch
 
 from llamafactory.extras.misc import get_current_device
@@ -39,16 +40,11 @@ TRAIN_ARGS = {
 }
 
 
-def test_checkpointing_enable():
-    model = load_train_model(disable_gradient_checkpointing=False, **TRAIN_ARGS)
+@pytest.mark.parametrize("disable_gradient_checkpointing", [False, True])
+def test_vanilla_checkpointing(disable_gradient_checkpointing: bool):
+    model = load_train_model(disable_gradient_checkpointing=disable_gradient_checkpointing, **TRAIN_ARGS)
     for module in filter(lambda m: hasattr(m, "gradient_checkpointing"), model.modules()):
-        assert getattr(module, "gradient_checkpointing") is True
-
-
-def test_checkpointing_disable():
-    model = load_train_model(disable_gradient_checkpointing=True, **TRAIN_ARGS)
-    for module in filter(lambda m: hasattr(m, "gradient_checkpointing"), model.modules()):
-        assert getattr(module, "gradient_checkpointing") is False
+        assert getattr(module, "gradient_checkpointing") != disable_gradient_checkpointing
 
 
 def test_unsloth_gradient_checkpointing():

tests/model/model_utils/test_visual.py

@@ -0,0 +1,77 @@
+# Copyright 2024 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,
+# 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 pytest
+import torch
+from transformers import AutoConfig, AutoModelForVision2Seq
+
+from llamafactory.hparams import FinetuningArguments, ModelArguments
+from llamafactory.model.adapter import init_adapter
+
+
+@pytest.mark.parametrize(
+    "freeze_vision_tower,freeze_multi_modal_projector,train_mm_proj_only",
+    [
+        (False, False, False),
+        (False, True, False),
+        (True, False, False),
+        (True, True, False),
+        (True, False, True),
+    ],
+)
+def test_visual_full(freeze_vision_tower: bool, freeze_multi_modal_projector: bool, train_mm_proj_only: 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,
+        train_mm_proj_only=train_mm_proj_only,
+    )
+    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 != train_mm_proj_only
+
+
+@pytest.mark.parametrize("freeze_vision_tower", [False, True])
+def test_visual_lora(freeze_vision_tower: 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)
+    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 freeze_vision_tower:
+        assert "base_model.model.visual.blocks.0.attn.qkv.lora_A.default.weight" not in trainable_params
+    else:
+        assert "base_model.model.visual.blocks.0.attn.qkv.lora_A.default.weight" in trainable_params
+
+    assert "merger" not in trainable_params
+    assert "base_model.model.model.layers.0.self_attn.q_proj.lora_A.default.weight" in trainable_params