def generate_pt_tests_from_op_list(ops_list, configs, pt_bench_op): """ This function creates pt op tests one by one from a list of dictionaries. ops_list is a list of dictionary. Each dictionary includes the name of the operator and the math operation. Here is an example of using this API: unary_ops_configs = op_bench.config_list( attrs=[...], attr_names=["M", "N"], ) unary_ops_list = op_bench.op_list( attr_names=["op_name", "op_func"], attrs=[ ["abs", torch.abs], ], ) class UnaryOpBenchmark(op_bench.TorchBenchmarkBase): def init(self, M, N, op_name, op_func): ... def forward(self): ... op_bench.generate_pt_tests_from_op_list(unary_ops_list, unary_ops_configs, UnaryOpBenchmark) """ for op in ops_list: _register_test(configs, pt_bench_op, create_pytorch_op_test_case, False, op)
def generate_pt_test(configs, pt_bench_op): """ This function creates PyTorch op test based on the given operator """ _register_test(configs, pt_bench_op, create_pytorch_op_test_case, False)
def register_caffe2_op_test_case(op_bench, test_config): test_case = Caffe2OperatorTestCase(op_bench, test_config) benchmark_core._register_test(test_case)
def register_pytorch_op_test_case(op_bench, test_config): test_case = PyTorchOperatorTestCase(op_bench, test_config) benchmark_core._register_test(test_case)
def generate_pt_gradient_tests_from_op_list(ops_list, configs, pt_bench_op): for op in ops_list: _register_test(configs, pt_bench_op, create_pytorch_op_test_case, True, op)