lint and ruff fix

Signed-off-by: cliu-us <[email protected]>

Author: chichun-charlie-liu

fms_mo/utils/qconfig_utils.py

@@ -342,7 +342,7 @@ def set_mx_specs(
 
     # Check args for any mx_specs vars
     use_mx_args = args is not None and any(
-        hasattr(args, key) for key,_ in fms_defaults.items()
+        hasattr(args, key) for key, _ in fms_defaults.items()
     )
 
     # Lastly, check for BMM consistency to enable QBmmMX
@@ -403,7 +403,7 @@ def set_mx_specs(
                 if config["qa_mode"].startswith(mx_prefix):
                     mx_specs["a_elem_format"] = config["qa_mode"].replace(mx_prefix, "")
 
-                for mx_var,_ in fms_defaults.items():
+                for mx_var, _ in fms_defaults.items():
                     fms_var = "mx_" + mx_var
                     # Only update if its in config; default values already set
                     if fms_var in config:
@@ -1194,8 +1194,6 @@ def check_config(config, model_dtype=None):
                 raise ValueError("MX mapping for nn.Linear is not QLinearMX")
 
             if mapping["matmul_or_bmm"].func is QBmmMX:
-                raise ValueError(
-                    "MX mapping for matmul_or_bmm is not QBmmMX"
-                )
+                raise ValueError("MX mapping for matmul_or_bmm is not QBmmMX")
 
     # End mx_specs checks

tests/models/conftest.py

@@ -312,6 +312,7 @@ def not_which2patch_contextmanager_settings():
     """
     return ["torch.vmm", "torch.natnul", "None"]
 
+
 @pytest.fixture(scope="session")
 def bad_mx_specs_settings():
     """
@@ -330,6 +331,7 @@ def bad_mx_specs_settings():
         ("custom_cuda", "yes"),
     ]
 
+
 @pytest.fixture(scope="session")
 def bad_mx_config_settings():
     """
@@ -348,6 +350,7 @@ def bad_mx_config_settings():
         ("mx_custom_cuda", "custom_cuda", "yes", "yes"),
     ]
 
+
 ################################
 # Toy Model Classes + Fixtures #
 ################################
@@ -464,6 +467,7 @@ def forward(self, input_tensor):
         out = self.fourth_layer(out)
         return out
 
+
 model_fp32_params = [
     ToyModel1(),
     ToyModel2(),
@@ -819,11 +823,12 @@ def config_fp32(request):
     qconfig = request.param
     return deepcopy(qconfig)
 
+
 @pytest.fixture(scope="function", params=default_config_params)
 def config_fp32_mx(request):
     """
     Create fp32 qconfig w/ mx_specs vars set in qconfig.
-    
+
     Args:
         request (dict): qconfig_init
 
@@ -856,11 +861,12 @@ def config_fp32_mx(request):
 
     return qconfig
 
+
 @pytest.fixture(scope="function", params=mx_config_params)
 def config_fp32_mx_specs(request):
     """
     Create fp32 qconfig w/ mx_specs.
-    
+
 
     Args:
         request (dict): qconfig_init
@@ -1176,7 +1182,7 @@ def model_bert():
     """
     return BertModel.from_pretrained("google-bert/bert-base-uncased", torchscript=True)
 
-  
+
 @pytest.fixture(scope="function")
 def model_bert_eager():
     """
@@ -1192,10 +1198,12 @@ def model_bert_eager():
 
 # MX reference class for quantization
 if torch.cuda.is_available():
+
     class ResidualMLP(torch.nn.Module):
         """
         Test Linear model for MX library
         """
+
         def __init__(self, hidden_size, device="cuda"):
             super().__init__()
 
@@ -1230,8 +1238,10 @@ def forward(self, inputs):
 
             return outputs
 
+
 mx_format_params = ["int8", "int4", "fp8_e4m3", "fp8_e5m2", "fp4_e2m1"]
 
+
 @pytest.fixture(scope="session", params=mx_format_params)
 def mx_format(request):
     """
@@ -1254,6 +1264,7 @@ def input_residualMLP():
     x = np.random.randn(16, 128)
     return torch.tensor(x, dtype=torch.float32, device="cuda")
 
+
 @pytest.fixture(scope="function")
 def model_residualMLP():
     """