def run_cpp_test_fn_and_check_output():
forward_output_file_path = compute_temp_file_path(cpp_tmp_folder, module_variant_name, 'forward_output')
backward_grad_dict_file_path = compute_temp_file_path(cpp_tmp_folder, module_variant_name, 'backward_grad_dict')
cpp_test_fn(arg_dict_file_path, module_file_path, forward_output_file_path, backward_grad_dict_file_path)
cpp_output = torch.load(forward_output_file_path)
cpp_grad_dict = torch.load(backward_grad_dict_file_path)
# Check that forward outputs are equal
unit_test_class.assertEqual(python_output, cpp_output,
msg=generate_error_msg("forward output", cpp_output, python_output))
# Check that module parameter gradients are equal after backward pass
unit_test_class.assertEqual(
len(python_grad_dict), len(cpp_grad_dict),
msg=generate_error_msg("# of parameters", len(cpp_grad_dict), len(python_grad_dict)))
for key in python_grad_dict:
param_name = None
for suffix in ['_grad', '_grad_indices', '_grad_values']:
if key.endswith(suffix):
param_name = key[:-len(suffix)]
break
assert param_name is not None
sparsity_str = 'sparse' if key.endswith('_grad_indices') or key.endswith('_grad_values') else 'dense'
unit_test_class.assertTrue(
key in cpp_grad_dict,
msg=generate_error_msg(
"\"Does module have a parameter named `{}` with {} gradient?\"".format(param_name, sparsity_str),
False, True))
unit_test_class.assertEqual(
python_grad_dict[key], cpp_grad_dict[key],
msg=generate_error_msg(
"`{}`'s {} gradient (`{}`)".format(param_name, sparsity_str, key),
cpp_grad_dict[key], python_grad_dict[key]))
def test_forward_backward(unit_test_class, test_params):
module_variant_name = test_params.module_variant_name
cpp_tmp_folder = test_params.cpp_tmp_folder
# Remove the temporary folder if it exists already
try_remove_folder(cpp_tmp_folder)
os.mkdir(cpp_tmp_folder)
# Run forward and backward on Python module
script_module, python_output, python_grad_dict = run_python_forward_backward(unit_test_class, test_params)
# Save Python module and arguments to be used from C++ function
module_file_path = compute_temp_file_path(cpp_tmp_folder, module_variant_name, 'module')
arg_dict_file_path = compute_temp_file_path(cpp_tmp_folder, module_variant_name, 'arg_dict')
script_module.save(module_file_path)
serialize_arg_dict_as_script_module(test_params.arg_dict).save(arg_dict_file_path)
cpp_test_name = '{}_test_forward_backward'.format(test_params.module_variant_name)
cpp_test_fn = getattr(unit_test_class.module_impl_check_cpp_module, cpp_test_name)
def run_cpp_test_fn_and_check_output():
forward_output_file_path = compute_temp_file_path(cpp_tmp_folder, module_variant_name, 'forward_output')
backward_grad_dict_file_path = compute_temp_file_path(cpp_tmp_folder, module_variant_name, 'backward_grad_dict')
cpp_test_fn(arg_dict_file_path, module_file_path, forward_output_file_path, backward_grad_dict_file_path)
cpp_output = torch.load(forward_output_file_path)
cpp_grad_dict = torch.load(backward_grad_dict_file_path)
# Check that forward outputs are equal
unit_test_class.assertEqual(python_output, cpp_output,
msg=generate_error_msg("forward output", cpp_output, python_output))
# Check that module parameter gradients are equal after backward pass
unit_test_class.assertEqual(
len(python_grad_dict), len(cpp_grad_dict),
msg=generate_error_msg("# of parameters", len(cpp_grad_dict), len(python_grad_dict)))
for key in python_grad_dict:
param_name = None
for suffix in ['_grad', '_grad_indices', '_grad_values']:
if key.endswith(suffix):
param_name = key[:-len(suffix)]
break
assert param_name is not None
sparsity_str = 'sparse' if key.endswith('_grad_indices') or key.endswith('_grad_values') else 'dense'
unit_test_class.assertTrue(
key in cpp_grad_dict,
msg=generate_error_msg(
"\"Does module have a parameter named `{}` with {} gradient?\"".format(param_name, sparsity_str),
False, True))
unit_test_class.assertEqual(
python_grad_dict[key], cpp_grad_dict[key],
msg=generate_error_msg(
"`{}`'s {} gradient (`{}`)".format(param_name, sparsity_str, key),
cpp_grad_dict[key], python_grad_dict[key]))
run_cpp_test_fn_and_check_output()
# Remove temporary folder that stores C++ outputs
try_remove_folder(cpp_tmp_folder)
def test_forward(unit_test_class, test_params):
functional_variant_name = test_params.functional_variant_name
cpp_tmp_folder = test_params.cpp_tmp_folder
# Remove the temporary folder if it exists already
try_remove_folder(cpp_tmp_folder)
os.mkdir(cpp_tmp_folder)
# Run forward on Python functional
python_output = run_forward(unit_test_class, test_params)
# Save Python arguments to be used from C++ function
arg_dict_file_path = compute_temp_file_path(cpp_tmp_folder, functional_variant_name, 'arg_dict')
serialize_arg_dict_as_script_module(test_params.arg_dict).save(arg_dict_file_path)
cpp_test_name = '{}_test_forward'.format(test_params.functional_variant_name)
cpp_test_fn = getattr(unit_test_class.functional_impl_check_cpp_module, cpp_test_name)
def run_cpp_test_fn_and_check_output():
forward_output_file_path = compute_temp_file_path(cpp_tmp_folder, functional_variant_name, 'forward_output')
cpp_test_fn(arg_dict_file_path, forward_output_file_path)
cpp_output = torch.load(forward_output_file_path)
# Check that forward outputs are equal
unit_test_class.assertEqual(
python_output, cpp_output,
msg=generate_error_msg("forward output", cpp_output, python_output))
run_cpp_test_fn_and_check_output()
# Remove temporary folder that stores C++ outputs
try_remove_folder(cpp_tmp_folder)
def run_cpp_test_fn_and_check_output():
forward_output_file_path = compute_temp_file_path(cpp_tmp_folder, functional_variant_name, 'forward_output')
cpp_test_fn(arg_dict_file_path, forward_output_file_path)
cpp_output = torch.load(forward_output_file_path)
# Check that forward outputs are equal
unit_test_class.assertEqual(
python_output, cpp_output,
msg=generate_error_msg("forward output", cpp_output, python_output))