parallel_tool

tests/e2e/test_sglang.py

@@ -20,7 +20,7 @@ from llamafactory.chat import ChatModel
 from llamafactory.extras.packages import is_sglang_available
 
 
-MODEL_NAME = "meta-llama/Llama-3.2-1B-Instruct"
+MODEL_NAME = "Qwen/Qwen2.5-0.5B"
 
 
 INFER_ARGS = {

tests/model/model_utils/test_visual.py

@@ -16,6 +16,7 @@ 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
 
@@ -45,10 +46,12 @@ def test_visual_full(freeze_vision_tower: bool, freeze_multi_modal_projector: bo
             assert param.requires_grad != freeze_language_model
 
 
-@pytest.mark.parametrize("freeze_vision_tower", (False, True))
-def test_visual_lora(freeze_vision_tower: bool):
+@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)
+    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)
@@ -61,10 +64,15 @@ def test_visual_lora(freeze_vision_tower: bool):
         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
+    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:
-        assert "base_model.model.visual.blocks.0.attn.qkv.lora_A.default.weight" in trainable_params
+        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 "merger" not in trainable_params
-    assert "base_model.model.model.layers.0.self_attn.q_proj.lora_A.default.weight" in trainable_params
+    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

tests/version.txt

 # change if test fails or cache is outdated
-0.9.3.106
+0.9.3.107