From 63ad533ef699cece0f9343388e77e59de24fda01 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9Csunxiyin=E2=80=9D?= <“sunxiyin@huawei.com”> Date: Mon, 16 Jan 2023 11:42:30 +0800 Subject: [PATCH] fix CrossEntropyLoss tensor args --- solution_test/README.md | 7 ++++ solution_test/common/base.py | 32 ++++++++----------- .../test_msadapter_ops_crossentropyloss.yaml | 10 +++--- 3 files changed, 26 insertions(+), 23 deletions(-) diff --git a/solution_test/README.md b/solution_test/README.md index 2a5b15a..9756331 100644 --- a/solution_test/README.md +++ b/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, diff --git a/solution_test/common/base.py b/solution_test/common/base.py index d0f1878..6d5f533 100644 --- a/solution_test/common/base.py +++ b/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 diff --git a/solution_test/pytorch_cases/demo/CrossEntropyLoss/test_msadapter_ops_crossentropyloss.yaml b/solution_test/pytorch_cases/demo/CrossEntropyLoss/test_msadapter_ops_crossentropyloss.yaml index d5abd8d..f439f06 100644 --- a/solution_test/pytorch_cases/demo/CrossEntropyLoss/test_msadapter_ops_crossentropyloss.yaml +++ b/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"] \ No newline at end of file + variation2: params["weight"]["numpy_shape"]=params["input"]["input_shape"][1] \ No newline at end of file -- 2.34.1