solution_test/README.md

@@ -74,6 +74,13 @@ args: 对应各个算子入参
     support_type: ["int", "tuple"] #表示支持int、tuple类型
     support_value_range: {"int": [1, 10], "tuple": [1, 10]} #表示各个类型取值范围是[1, 10]
     support_value_length: {"tuple": {2}} #表示tuple类型只支持2维
+支持参数配置指定tensor数据和类型：
+args:
+  weight: #参数名weight
+    support_type: ["numpy"] #参数weight支持tensor
+    numpy_shape: [[5, 10], [10, 30, 256], [1, 10, 10, 10, 10], [1, 10, 128, 128]]#参数weight支持tensor的shape
+    numpy_type: ["float32"， "float16"] #表示支持float32、float16类型
+具体使用示例可参考用例demo/CrossEntropyLoss/
 # 对比精度，指定精度配置，可选
 默认精度为：
 "float16": 1e-3,"float32": 1e-4,"float64": 1e-5,

solution_test/common/base.py

@@ -29,6 +29,8 @@ class SolutionTestBase:
         self.init_success_flag = False
         self.base_dir = os.getcwd()
         self.case_name = case_name
+        self.args_dict = dict()
+        self.input_dict = dict()
         self.params_dict = dict()
         # 从环境获取执行模式
         self.execute_mode = os.environ.get("TRAIN_MODE", None)
@@ -80,8 +82,6 @@ class SolutionTestBase:
         return True
 
     def generate_case_params(self):
-        args_dict = dict()
-        input_dict = dict()
         yaml_content = yaml_read(self.yaml_path)
         args = yaml_content.get("args")
         variation = yaml_content.get("variation")
@@ -106,13 +106,13 @@ class SolutionTestBase:
         if args is not None:
             for key in args.keys():
                 if self.params_dict.get(key) is not None:
-                    args_dict[key] = self.params_dict.get(key)
+                    self.args_dict[key] = self.params_dict.get(key)
         if input_shape is not None:
             for key in input_shape.keys():
                 if self.params_dict.get(key) is not None:
-                    input_dict[key] = self.params_dict.get(key)
-        self.ms_log.info("params_dict: %s, args_dict: %s", self.params_dict, args_dict)
-        return self.params_dict, args_dict, input_shape
+                    self.input_dict[key] = self.params_dict.get(key)
+        self.ms_log.info("params_dict: %s, args_dict: %s", self.params_dict, self.args_dict)
+        return self.params_dict, self.args_dict, self.input_dict
 
     def generate_input_shape(self):
         """
@@ -120,33 +120,29 @@ class SolutionTestBase:
         """
         ms_input_shape_list = list()
         torch_input_shape_list = list()
-        _, args_dict, input_dict = self.generate_case_params()
+        _, _, input_dict = self.generate_case_params()
         for key, value in input_dict.items():
             random_shape = np.random.randn(*input_dict.get(key).get("input_shape"))
             torch_input_shape = torch.tensor(random_shape, dtype=input_dict.get("torch_dtype"))
             ms_input_shape = ms_pytorch.tensor(random_shape, dtype=input_dict.get("ms_dtype"))
             ms_input_shape_list.append(ms_input_shape)
             torch_input_shape_list.append(torch_input_shape)
-
         return torch_input_shape_list, ms_input_shape_list
 
     def generate_args_with_tensor(self, args):
         """
         生成参数中的tensor类型数据
         :param args: 设为tesnor数据的参数名
-        :param frame_name: 使用的框架名,取值分别为:"torch"或"ms_pytorch"
         """
-        _, args_dict, _ = self.generate_case_params()
-        numpy_shape = args_dict.get(args).get("numpy_shape")
-        numpy_type = args_dict.get(args).get("numpy_type")
-        random_shape = np.random.randn(*numpy_shape)
-
+        numpy_shape = self.args_dict.get(args).get("numpy_shape")
+        numpy_type = self.args_dict.get(args).get("numpy_type")
+        random_shape = np.random.randn(numpy_shape)
         torch_tensor = torch.tensor(random_shape, dtype=eval(".".join(["torch", numpy_type])))
-        args_dict["weight"] = torch_tensor
-        torch_args = args_dict.copy()
+        self.args_dict["weight"] = torch_tensor
+        torch_args = copy.deepcopy(self.args_dict)
         ms_tensor = ms_pytorch.tensor(random_shape, dtype=eval(".".join(["ms_pytorch", numpy_type])))
-        args_dict["weight"] = ms_tensor
-        ms_args = args_dict
+        self.args_dict["weight"] = ms_tensor
+        ms_args = self.args_dict
         self.ms_log.info("func generate_args_with_tensor get torch_args: %s\n ms_args: %s\n", torch_args, ms_args)
         return torch_args, ms_args
 

solution_test/pytorch_cases/demo/CrossEntropyLoss/test_msadapter_ops_crossentropyloss.yaml

@@ -2,19 +2,19 @@ op_name: CrossEntropyLoss
 # 输入shape
 input_shape:
   input:
-    input_shape: [[256], [5, 10], [10, 30, 256], [1, 10, 10, 10, 10], [1, 10, 128, 128]]
+    input_shape: [[5, 10], [10, 30, 256], [1, 10, 10, 10, 10], [1, 10, 128, 128]]
     dtype: float32
 
   target:
-    input_shape: [[256], [5, 10], [10, 30, 256], [1, 10, 10, 10, 10], [1, 10, 128, 128]]
+    input_shape: [[5, 10], [10, 30, 256], [1, 10, 10, 10, 10], [1, 10, 128, 128]]
     dtype: float32
 
 # 参数
 args:
   weight:
     support_type: ["numpy"]
-    numpy_shape: [[256], [5, 10], [10, 30, 256], [1, 10, 10, 10, 10], [1, 10, 128, 128]]
-    numpy_type: ["float32", "float16"]
+    numpy_shape: [[5, 10], [10, 30, 256], [1, 10, 10, 10, 10], [1, 10, 128, 128]]
+    numpy_type: ["float32"]
   size_average:
     support_type: ["bool"]
     support_value_range: {"bool": [True, False]}
@@ -34,4 +34,4 @@ args:
 # 参数间约束规则，可选
 variation:
   variation1: params["target"]["input_shape"]=params["input"]["input_shape"]
-  variation2: params["weight"]["numpy_shape"]=params["input"]["input_shape"]
+  variation2: params["weight"]["numpy_shape"]=params["input"]["input_shape"][1]