use fixture

src/llamafactory/hparams/model_args.py

@@ -163,7 +163,7 @@ class ModelArguments:
     )
     infer_dtype: Literal["auto", "float16", "bfloat16", "float32"] = field(
         default="auto",
-        metadata={"help": "Data type for model weights and activations at inference."}
+        metadata={"help": "Data type for model weights and activations at inference."},
     )
     hf_hub_token: Optional[str] = field(
         default=None,

tests/model/test_base.py

@@ -15,6 +15,7 @@
 import os
 from typing import Dict
 
+import pytest
 import torch
 from transformers import AutoModelForCausalLM
 from trl import AutoModelForCausalLMWithValueHead
@@ -43,10 +44,14 @@ def compare_model(model_a: "torch.nn.Module", model_b: "torch.nn.Module"):
         assert torch.allclose(state_dict_a[name], state_dict_b[name]) is True
 
 
-def post_init(self: "AutoModelForCausalLMWithValueHead", state_dict: Dict[str, "torch.Tensor"]):
-    state_dict = {k[7:]: state_dict[k] for k in state_dict.keys() if k.startswith("v_head.")}
-    self.v_head.load_state_dict(state_dict, strict=False)
-    del state_dict
+@pytest.fixture
+def fix_valuehead_cpu_loading():
+    def post_init(self: "AutoModelForCausalLMWithValueHead", state_dict: Dict[str, "torch.Tensor"]):
+        state_dict = {k[7:]: state_dict[k] for k in state_dict.keys() if k.startswith("v_head.")}
+        self.v_head.load_state_dict(state_dict, strict=False)
+        del state_dict
+
+    AutoModelForCausalLMWithValueHead.post_init = post_init
 
 
 def test_base():
@@ -60,8 +65,8 @@ def test_base():
     compare_model(model, ref_model)
 
 
+@pytest.mark.usefixtures("fix_valuehead_cpu_loading")
 def test_valuehead():
-    AutoModelForCausalLMWithValueHead.post_init = post_init  # patch for CPU test
     model_args, _, finetuning_args, _ = get_infer_args(INFER_ARGS)
     tokenizer_module = load_tokenizer(model_args)
     model = load_model(

tests/model/test_lora.py

@@ -13,8 +13,9 @@
 # limitations under the License.
 
 import os
-from typing import Sequence
+from typing import Dict, Sequence
 
+import pytest
 import torch
 from peft import LoraModel, PeftModel
 from transformers import AutoModelForCausalLM
@@ -71,6 +72,16 @@ def compare_model(model_a: "torch.nn.Module", model_b: "torch.nn.Module", diff_k
             assert torch.allclose(state_dict_a[name], state_dict_b[name]) is True
 
 
+@pytest.fixture
+def fix_valuehead_cpu_loading():
+    def post_init(self: "AutoModelForCausalLMWithValueHead", state_dict: Dict[str, "torch.Tensor"]):
+        state_dict = {k[7:]: state_dict[k] for k in state_dict.keys() if k.startswith("v_head.")}
+        self.v_head.load_state_dict(state_dict, strict=False)
+        del state_dict
+
+    AutoModelForCausalLMWithValueHead.post_init = post_init
+
+
 def test_lora_train_qv_modules():
     model_args, _, _, finetuning_args, _ = get_train_args({"lora_target": "q_proj,v_proj", **TRAIN_ARGS})
     tokenizer_module = load_tokenizer(model_args)
@@ -166,6 +177,7 @@ def test_lora_train_new_adapters():
     )
 
 
+@pytest.mark.usefixtures("fix_valuehead_cpu_loading")
 def test_lora_train_valuehead():
     model_args, _, finetuning_args, _ = get_infer_args(INFER_ARGS)
     tokenizer_module = load_tokenizer(model_args)