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)
