| import os |
| import tempfile |
| from string import Template |
| import copy |
| import unittest |
| import warnings |
| import inspect |
| import re |
| |
| import torch |
| from torch._six import PY2 |
| import common_utils as common |
| import common_nn |
| from common_cuda import TEST_CUDA |
| import torch.utils.cpp_extension |
| from cpp_api_parity import sample_module, torch_nn_modules, TorchNNTestParams, CppArg, parse_parity_tracker_table |
| |
| |
| parity_table_path = os.path.join(os.path.dirname(__file__), 'cpp_api_parity/parity-tracker.md') |
| |
| parity_table = parse_parity_tracker_table(parity_table_path) |
| |
| TORCH_NN_MODULE_COMMON_TEST_HARNESS = """\n |
| #include <torch/script.h> |
| |
| const char * const parity_test_error_msg_prefix = "Parity test failed: "; |
| |
| #define GENERATE_PARITY_TEST_ERROR_MSG(name, cpp_value, python_value) \ |
| parity_test_error_msg_prefix, \ |
| name, " in C++ has value: ", cpp_value, ", which does not match the corresponding value in Python: ", python_value \ |
| |
| bool check_tensor_equality(const torch::Tensor& tensor1, const torch::Tensor& tensor2) { |
| return tensor1.sizes().vec() == tensor2.sizes().vec() && \ |
| tensor1.device() == tensor2.device() && \ |
| tensor1.dtype() == tensor2.dtype() && \ |
| tensor1.allclose(tensor2); |
| } |
| |
| bool check_ivalue_equality(const c10::IValue& ivalue_python, const c10::IValue& ivalue_cpp) { |
| // For Python modules, we allow the use of `int` to represent attributes that |
| // are multidimensional but have the same value in all dimensions. The corresponding |
| // data type for C++ modules is `ExpandingArray` (which is converted to `IntList` by the |
| // `IValue` constructor), and here we check that all elements in the `ExpandingArray` |
| // are equal to the Python `int` attribute. |
| if (ivalue_python.isInt() && ivalue_cpp.isIntList()) { |
| auto ivalue_cpp_list = ivalue_cpp.toIntListRef(); |
| std::vector<int64_t> ivalue_python_vec(ivalue_cpp_list.size()); |
| std::fill(ivalue_python_vec.begin(), ivalue_python_vec.end(), ivalue_python.toInt()); |
| return ivalue_python_vec == ivalue_cpp_list; |
| } |
| |
| // For Python modules, we allow the use of "none" / "mean" / "sum" to represent the reduction type. |
| // The corresponding data type for C++ modules is `torch::Reduction::Reduction` enum, and here we map the |
| // reduction types between Python version and C++ version. |
| if (ivalue_python.isString() && ivalue_cpp.isInt()) { |
| auto& ivalue_python_str = ivalue_python.toStringRef(); |
| auto ivalue_cpp_int = ivalue_cpp.toInt(); |
| if (ivalue_python_str == "none") { |
| return ivalue_cpp_int == torch::Reduction::None; |
| } else if (ivalue_python_str == "mean") { |
| return ivalue_cpp_int == torch::Reduction::Mean; |
| } else if (ivalue_python_str == "sum") { |
| return ivalue_cpp_int == torch::Reduction::Sum; |
| } |
| } |
| |
| if (ivalue_python.tagKind() != ivalue_cpp.tagKind()) { |
| AT_ERROR("Value type mismatch: ", "from Python: ", ivalue_python.tagKind(), ", from C++: ", ivalue_cpp.tagKind()); |
| } |
| |
| if (ivalue_python.isInt()) { |
| return ivalue_python.toInt() == ivalue_cpp.toInt(); |
| } else if (ivalue_python.isDouble()) { |
| return ivalue_python.toDouble() == ivalue_cpp.toDouble(); |
| } else if (ivalue_python.isBool()) { |
| return ivalue_python.toBool() == ivalue_cpp.toBool(); |
| } else if (ivalue_python.isString()) { |
| return ivalue_python.toStringRef() == ivalue_cpp.toStringRef(); |
| } else if (ivalue_python.isTensor()) { |
| return check_tensor_equality(ivalue_python.toTensor(), ivalue_cpp.toTensor()); |
| } else if (ivalue_python.isIntList()) { |
| return ivalue_python.toIntListRef() == ivalue_cpp.toIntListRef(); |
| } else if (ivalue_python.isNone()) { |
| return ivalue_cpp.isNone(); |
| } else { |
| AT_ERROR("Unsupported value type: ", ivalue_python.tagKind()); |
| } |
| } |
| """ |
| |
| CHECK_MODULE_PARAM_EQUALITY = Template("""\ |
| TORCH_CHECK( |
| check_tensor_equality(${script_module_prefix}.get_parameter("${param_name}"), ${cpp_module_prefix}->${param_name}), |
| GENERATE_PARITY_TEST_ERROR_MSG( |
| "`${cpp_module_prefix}->${param_name}`", |
| ${cpp_module_prefix}->${param_name}, |
| ${script_module_prefix}.get_parameter("${param_name}"))); |
| TORCH_CHECK( |
| ${script_module_prefix}.get_parameter("${param_name}").requires_grad() == ${cpp_module_prefix}->${param_name}.requires_grad(), |
| GENERATE_PARITY_TEST_ERROR_MSG( |
| "`${cpp_module_prefix}->${param_name}.requires_grad()`", |
| ${cpp_module_prefix}->${param_name}.requires_grad(), |
| ${script_module_prefix}.get_parameter("${param_name}").requires_grad())); |
| """) |
| |
| CHECK_MODULE_BUFFER_EQUALITY = Template("""\ |
| TORCH_CHECK( |
| check_tensor_equality(${script_module_prefix}.get_buffer("${buffer_name}"), ${cpp_module_prefix}->${buffer_name}), |
| GENERATE_PARITY_TEST_ERROR_MSG( |
| "`${cpp_module_prefix}->${buffer_name}`", |
| ${cpp_module_prefix}->${buffer_name}, |
| ${script_module_prefix}.get_buffer("${buffer_name}"))); |
| """) |
| |
| CHECK_MODULE_ATTR_EQUALITY = Template("""\ |
| TORCH_CHECK( |
| check_ivalue_equality( |
| ${script_module_prefix}.get_attribute("${python_attr_name}"), c10::IValue(${cpp_module_prefix}->${cpp_attr_name})), |
| GENERATE_PARITY_TEST_ERROR_MSG( |
| "`${cpp_module_prefix}->${cpp_attr_name}`", |
| c10::IValue(${cpp_module_prefix}->${cpp_attr_name}), |
| ${script_module_prefix}.get_attribute("${python_attr_name}"))); |
| """) |
| |
| TORCH_NN_MODULE_TEST_CTOR_ARGS = Template("""\n |
| void ${module_name}_test_ctor_args() { |
| ${module_qualified_name} m_init_by_cpp(${module_option}); |
| |
| ${extra_stmts} |
| } |
| """) |
| |
| TORCH_NN_MODULE_TEST_OPTIONS_ARG = Template("""\ |
| m_init_by_cpp->options.${options_arg_name}(); |
| """) |
| |
| TORCH_NN_MODULE_TEST_INIT = Template("""\n |
| void ${module_variant_name}_test_init( |
| const std::string& saved_module_path, |
| const std::string& device) { |
| torch::jit::script::Module m_init_by_python = torch::jit::load(saved_module_path); |
| |
| torch::manual_seed(2); |
| ${module_qualified_name} m_init_by_cpp${cpp_constructor_args}; |
| m_init_by_cpp->to(device); |
| |
| ${extra_stmts} |
| } |
| """) |
| |
| TORCH_NN_MODULE_TEST_FORWARD = Template("""\n |
| void ${module_variant_name}_test_forward( |
| const std::string& saved_module_path, |
| const std::string& device, |
| torch::Tensor python_output, |
| ${input_arg_declarations}) { |
| torch::manual_seed(2); |
| ${module_qualified_name} module${cpp_constructor_args}; |
| torch::load(module, saved_module_path); |
| module->to(device); |
| |
| auto cpp_output = module(${input_args}); |
| |
| TORCH_CHECK( |
| check_tensor_equality(cpp_output, python_output), |
| GENERATE_PARITY_TEST_ERROR_MSG( |
| "forward output", |
| cpp_output, |
| python_output)); |
| |
| ${extra_stmts} |
| } |
| """) |
| |
| TORCH_NN_MODULE_TEST_BACKWARD = Template("""\n |
| void ${module_variant_name}_test_backward( |
| const std::string& saved_module_path, |
| const std::string& saved_grad_module_path, |
| const std::string& device, |
| ${input_arg_declarations}) { |
| ${module_qualified_name} python_grad_module${cpp_constructor_args}; |
| torch::load(python_grad_module, saved_grad_module_path); |
| |
| torch::manual_seed(2); |
| ${module_qualified_name} module${cpp_constructor_args}; |
| torch::load(module, saved_module_path); |
| module->to(device); |
| |
| auto cpp_output = module(${input_args}); |
| cpp_output.sum().backward(); |
| |
| for (size_t i = 0; i < module->parameters().size(); i++) { |
| auto named_param = module->named_parameters()[i]; |
| auto grad = python_grad_module->parameters()[i]; |
| TORCH_CHECK( |
| check_tensor_equality(named_param->grad(), grad), |
| GENERATE_PARITY_TEST_ERROR_MSG( |
| "gradient of `" + named_param.key() + "`", |
| named_param->grad(), |
| grad)); |
| } |
| |
| ${extra_stmts} |
| } |
| """) |
| |
| TORCH_NN_MODULE_IGNORED_ATTRS = { |
| '_backend', '_parameters', '_buffers', '_backward_hooks', '_forward_hooks', '_forward_pre_hooks', |
| '_state_dict_hooks', '_load_state_dict_pre_hooks', '_modules', 'training', |
| } |
| |
| class TestCppApiParity(common.TestCase): |
| def _python_arg_to_cpp_arg(self, python_arg): |
| if type(python_arg) == int: |
| return CppArg(type='int64_t', value=str(python_arg)) |
| elif type(python_arg) == float: |
| return CppArg(type='double', value=str(python_arg)) |
| elif type(python_arg) == bool: |
| return CppArg(type='bool', value=str(python_arg).lower()) |
| elif type(python_arg) == str: |
| # if `python_arg` is one of the reduction types, we use the corresponding `torch::Reduction::Reduction` enum. |
| if python_arg in ['none', 'mean', 'sum']: |
| if python_arg == 'none': |
| cpp_arg = 'torch::Reduction::None' |
| elif python_arg == 'mean': |
| cpp_arg = 'torch::Reduction::Mean' |
| elif python_arg == 'sum': |
| cpp_arg = 'torch::Reduction::Sum' |
| return CppArg(type='torch::Reduction::Reduction', value='{}'.format(cpp_arg)) |
| else: |
| return CppArg(type='std::string', value='"{}"'.format(python_arg)) |
| elif type(python_arg) == torch.Tensor: |
| return CppArg( |
| type='torch::Tensor', |
| value='torch::empty({})'.format(str(list(python_arg.shape)).replace('[', '{').replace(']', '}'))) |
| else: |
| raise RuntimeError( |
| "{} is not a supported arg type for C++ module methods".format(type(python_arg))) |
| |
| def _compile_cpp_code_inline(self, name, cpp_sources, functions): |
| # Just-in-time compile the C++ test code |
| cpp_module = torch.utils.cpp_extension.load_inline( |
| name=name, |
| cpp_sources=cpp_sources, |
| functions=functions, |
| verbose=False, |
| ) |
| return cpp_module |
| |
| def _get_python_module_init_arg_spec(self, module_name): |
| python_module_class = getattr(torch.nn, module_name) |
| if PY2: |
| init_arg_spec = inspect.getargspec(python_module_class.__init__) |
| else: |
| init_arg_spec = inspect.getfullargspec(python_module_class.__init__) |
| return init_arg_spec |
| |
| def _prepare_tensors_for_module_input_or_target(self, test_params, tensors): |
| if type(tensors) == tuple: |
| tensors = list(tensors) |
| elif type(tensors) == torch.Tensor: |
| tensors = [tensors] |
| else: |
| raise RuntimeError("Unexpected input type: {}".format(type(tensors))) |
| |
| if test_params.device != 'cuda' or TEST_CUDA: |
| tensors = [x.to(test_params.device) for x in tensors] |
| |
| return tensors |
| |
| def _get_example_inputs(self, test_params): |
| example_inputs = test_params.test_instance._get_input() |
| example_inputs = self._prepare_tensors_for_module_input_or_target(test_params, example_inputs) |
| |
| # We set all inputs to torch.nn module to requires grad, so that the backward test can always be run. |
| # However, we skip embedding layers for now, because they only accept LongTensor as inputs, |
| # And LongTensor cannot require grad. |
| if test_params.module_name not in ["Embedding", "Embedding_sparse", "EmbeddingBag", "EmbeddingBag_sparse"]: |
| example_inputs = [x.requires_grad_() for x in example_inputs] |
| |
| return example_inputs |
| |
| def _get_example_targets(self, test_params): |
| example_targets = test_params.test_instance._get_target() |
| example_targets = self._prepare_tensors_for_module_input_or_target(test_params, example_targets) |
| return example_targets |
| |
| def _get_forward_input_args(self, test_params): |
| example_inputs = self._get_example_inputs(test_params) |
| if isinstance(test_params.test_instance, common_nn.CriterionTest): |
| example_targets = self._get_example_targets(test_params) |
| else: |
| example_targets = [] |
| |
| input_args = () |
| for example_input in example_inputs: |
| input_args += (example_input, ) |
| for example_target in example_targets: |
| input_args += (example_target, ) |
| |
| return input_args |
| |
| # This tests that Python and C++ torch.nn modules have matching constructor arg names and types. |
| def _test_torch_nn_module_ctor_args(self, module_name): |
| module_metadata = torch_nn_modules.module_metadata_map[module_name] |
| cpp_default_constructor_args_str = module_metadata.cpp_default_constructor_args |
| init_arg_spec = self._get_python_module_init_arg_spec(module_name) |
| init_kwargs_defaults = init_arg_spec.defaults |
| python_default_constructor_arg_names = [ |
| x for x in init_arg_spec.args[1:-len(init_kwargs_defaults)] |
| if x not in module_metadata.python_ignored_constructor_args] |
| # NOTE: the regex is used here to split up e.g. `(1, {2, 3}, 4)` into `['1', '{2, 3}', '4']` |
| cpp_default_constructor_arg_values = re.findall(r'{[^}]*}|[^,\s()]+', cpp_default_constructor_args_str) |
| |
| # Step 1: Check that the # of non-keyword args in C++ module constructor is equal to that in Python module constructor. |
| self.assertEqual( |
| len(cpp_default_constructor_arg_values), |
| len(python_default_constructor_arg_names), |
| "The constructor of `torch::nn::{}` in C++ ".format(module_name) + |
| "must take the exact same number of non-keyword arguments " + |
| "as the constructor of `torch.nn.{}` in Python. ".format(module_name) + |
| "However, currently the C++ constructor expects {} non-keyword argument(s) ".format( |
| len(cpp_default_constructor_arg_values)) + |
| "while the Python constructor expects {} non-keyword argument(s): {}".format( |
| len(python_default_constructor_arg_names), |
| python_default_constructor_arg_names)) |
| |
| # Step 2: Generate code to construct C++ module options using values from `cpp_default_constructor_args`. |
| cpp_module_option = 'torch::nn::{}Options{}'.format(module_name, cpp_default_constructor_args_str) |
| init_kwargs = init_arg_spec.args[-len(init_kwargs_defaults):] |
| for arg_name, python_default_value in zip(init_kwargs, init_kwargs_defaults): |
| # NOTE: If a Python module constructor arg's default value is None, we don't test its corresponding |
| # options arg in C++ module (because the way to set the C++ options arg to an empty value is to not |
| # specify it, which means we can't test that the options arg exists). |
| # Instead, we test that all options args exist by calling their accessors after constructing the |
| # C++ module with the options. |
| if arg_name not in module_metadata.python_ignored_constructor_args and python_default_value is not None: |
| cpp_module_option += '.{}({})'.format(arg_name, self._python_arg_to_cpp_arg(python_default_value).value) |
| |
| # Step 3: Generate code to check existence of all Python module constructor args in the C++ module options. |
| extra_stmts = [TORCH_NN_MODULE_TEST_OPTIONS_ARG.substitute(options_arg_name=arg_name) |
| for arg_name in python_default_constructor_arg_names + init_kwargs |
| if arg_name not in module_metadata.python_ignored_constructor_args] |
| |
| # Step 4: Compile the test code and run the tests. |
| cpp_sources = TORCH_NN_MODULE_COMMON_TEST_HARNESS + module_metadata.cpp_sources |
| cpp_sources += TORCH_NN_MODULE_TEST_CTOR_ARGS.substitute( |
| module_name=module_name, |
| module_qualified_name='torch::nn::{}'.format(module_name), |
| module_option=cpp_module_option, |
| extra_stmts=''.join(extra_stmts)) |
| cpp_test_name = module_name + '_test_ctor_args' |
| cpp_module = self._compile_cpp_code_inline( |
| name=cpp_test_name, cpp_sources=cpp_sources, functions=cpp_test_name) |
| |
| getattr(cpp_module, cpp_test_name)() |
| |
| def _test_torch_nn_module_variant(self, test_params): |
| def get_python_ignored_attrs(module_metadata): |
| return list(TORCH_NN_MODULE_IGNORED_ATTRS) + module_metadata.python_ignored_attrs |
| |
| def generate_test_cpp_sources(test_params, template, extra_stmts): |
| input_args = self._get_forward_input_args(test_params) |
| input_arg_types = [self._python_arg_to_cpp_arg(arg).type for arg in list(input_args)] |
| input_args = ['arg{}'.format(str(i)) for i in range(len(input_arg_types))] |
| input_arg_declarations = ['{} {}'.format(arg_type, arg_name) for arg_type, arg_name in zip(input_arg_types, input_args)] |
| test_cpp_sources = template.substitute( |
| module_variant_name=test_params.module_variant_name, |
| module_qualified_name='torch::nn::{}'.format(test_params.module_name), |
| cpp_constructor_args=test_params.cpp_constructor_args, |
| input_arg_declarations=',\n'.join(input_arg_declarations), |
| input_args=',\n'.join(input_args), |
| extra_stmts=extra_stmts) |
| return test_cpp_sources |
| |
| def setup_init_test(test_params): |
| module_metadata = torch_nn_modules.module_metadata_map[test_params.module_name] |
| |
| # We are generating the attribute equality checks manually here, |
| # because it is not possible to have a `.attributes()` API that returns |
| # non-parameter / non-buffer attributes in a C++ torch::nn module. |
| def generate_attr_equality_checks(module, |
| script_module_prefix='m_init_by_python', |
| cpp_module_prefix='m_init_by_cpp'): |
| stmts = [] |
| for name, sub_module in module.named_children(): |
| sub_script_module_prefix = '{}.get_module("{}")'.format(script_module_prefix, name) |
| sub_cpp_module_prefix = '{}->{}'.format(cpp_module_prefix, name) |
| stmts = generate_attr_equality_checks(sub_module, sub_script_module_prefix, sub_cpp_module_prefix) |
| for name, param in module._parameters.items(): |
| stmts.append(CHECK_MODULE_PARAM_EQUALITY.substitute( |
| script_module_prefix=script_module_prefix, |
| cpp_module_prefix=cpp_module_prefix, |
| param_name=name)) |
| for name, buffer in module._buffers.items(): |
| stmts.append(CHECK_MODULE_BUFFER_EQUALITY.substitute( |
| script_module_prefix=script_module_prefix, |
| cpp_module_prefix=cpp_module_prefix, |
| buffer_name=name)) |
| |
| init_arg_spec = self._get_python_module_init_arg_spec(module.__class__.__name__) |
| # NOTE: `init_arg_spec.args[0]` is `self`, which is not counted as a constructor arg in the API parity test. |
| python_constructor_arg_names = [ |
| x for x in init_arg_spec.args[1:] if x not in module_metadata.python_ignored_constructor_args] |
| for name, attr in module.__dict__.items(): |
| if name not in get_python_ignored_attrs(module_metadata): |
| # Every constructor arg of the Python module must have |
| # a corresponding C++ module options arg. |
| if name in python_constructor_arg_names: |
| cpp_attr_name = 'options.{}()'.format(name) |
| else: |
| cpp_attr_name = name |
| stmts.append(CHECK_MODULE_ATTR_EQUALITY.substitute( |
| script_module_prefix=script_module_prefix, |
| cpp_module_prefix=cpp_module_prefix, |
| python_attr_name=name, |
| cpp_attr_name=cpp_attr_name)) |
| return stmts |
| |
| device = test_params.device |
| python_constructor = test_params.test_instance.constructor |
| python_constructor_args = test_params.test_instance.constructor_args |
| |
| torch.manual_seed(2) |
| module = python_constructor(*python_constructor_args).to(device) |
| |
| extra_stmts = generate_attr_equality_checks(module) |
| assert len(extra_stmts) == module_metadata.num_attrs_recursive |
| extra_stmts_str = ''.join(extra_stmts) |
| return (([module], device), |
| generate_test_cpp_sources( |
| test_params=test_params, template=TORCH_NN_MODULE_TEST_INIT, extra_stmts=extra_stmts_str)) |
| |
| def setup_forward_test(test_params): |
| device = test_params.device |
| python_constructor = test_params.test_instance.constructor |
| python_constructor_args = test_params.test_instance.constructor_args |
| input_args = self._get_forward_input_args(test_params) |
| |
| torch.manual_seed(2) |
| module = python_constructor(*python_constructor_args).to(device) |
| python_output = module(*input_args) |
| |
| return (([module], device, python_output, input_args), |
| generate_test_cpp_sources( |
| test_params=test_params, template=TORCH_NN_MODULE_TEST_FORWARD, extra_stmts='')) |
| |
| def setup_backward_test(test_params): |
| device = test_params.device |
| python_constructor = test_params.test_instance.constructor |
| python_constructor_args = test_params.test_instance.constructor_args |
| input_args = self._get_forward_input_args(test_params) |
| |
| torch.manual_seed(2) |
| module = python_constructor(*python_constructor_args).to(device) |
| python_output = module(*input_args) |
| python_output.sum().backward() |
| # JIT tracing does not save a module's parameters' gradients into ScriptModule. |
| # Instead, we create another module `grad_module` with the same structure as `module`, |
| # and use `grad_module`'s parameters to save `module`'s corresponding parameters' |
| # gradients. Then, we trace both `module` and `grad_module`, serialize them and |
| # pass them into C++ for parity testing. |
| grad_module = copy.deepcopy(module) |
| for param, grad_param in zip(module.parameters(), grad_module.parameters()): |
| if param.grad is not None: |
| grad_param.data = param.grad |
| |
| return (([module, grad_module], device, input_args), |
| generate_test_cpp_sources( |
| test_params=test_params, template=TORCH_NN_MODULE_TEST_BACKWARD, extra_stmts='')) |
| |
| def trace_module(module, input_args): |
| module_metadata = torch_nn_modules.module_metadata_map[module.__class__.__name__] |
| |
| # JIT tracing does not automatically save a module's non-parameter / non-buffer attributes |
| # into a ScriptModule's slots, which means we can't access them via `get_attributes()` in C++. |
| # Here, we manually register these attributes into the ScriptModule so that we can access them |
| # via `get_attributes()` in C++. |
| def register_attrs(module, script_module): |
| for sub_module, sub_script_module in zip(module.children(), script_module.children()): |
| register_attrs(sub_module, sub_script_module) |
| for key, value in module.__dict__.items(): |
| if key not in get_python_ignored_attrs(module_metadata): |
| if value is None: |
| value_type = module_metadata.python_optional_attribute_to_jit_type[key] |
| elif type(value) == tuple: |
| assert all(isinstance(x, type(value[0])) for x in value), \ |
| "All elements in a tuple attribute of a Python torch.nn module must have the same type." |
| # Here, we set the Python tuple attribute's type to `ListType` in the ScriptModule, |
| # which will automatically be converted to `IntList` later and match the type |
| # of the corresponding attribute in C++ module (which is initially an `ExpandingArray` |
| # and is converted to `IntList` by the `IValue` constructor). |
| value_type = torch._C.ListType(torch.jit.annotations.ann_to_type(type(value[0]))) |
| else: |
| value_type = torch.jit.annotations.ann_to_type(type(value)) |
| script_module._c._register_attribute(key, value_type, value) |
| |
| # We use JIT tracing to serialize Python module state, so that we can load it into C++ |
| traced_script_module = torch.jit.trace(module, input_args) |
| register_attrs(module, traced_script_module) |
| return traced_script_module |
| |
| def serialize_module_into_file(script_module): |
| module_file = tempfile.NamedTemporaryFile(delete=False) |
| script_module.save(module_file.name) |
| module_file.close() |
| return module_file.name |
| |
| def test_methods(test_params): |
| module_metadata = torch_nn_modules.module_metadata_map[test_params.module_name] |
| module_variant_name = test_params.module_variant_name |
| input_args = self._get_forward_input_args(test_params) |
| |
| args_map = {} |
| |
| cpp_sources = TORCH_NN_MODULE_COMMON_TEST_HARNESS + module_metadata.cpp_sources |
| |
| torch_nn_test_methods = [ |
| ('init', setup_init_test), |
| ('forward', setup_forward_test), |
| ('backward', setup_backward_test), |
| ] |
| for method_name, setup_test in torch_nn_test_methods: |
| args_map[method_name], test_cpp_sources = setup_test(test_params) |
| cpp_sources += test_cpp_sources |
| |
| cpp_module = self._compile_cpp_code_inline( |
| name=test_params.module_variant_name, |
| cpp_sources=cpp_sources, |
| functions=['{}_test_{}'.format( |
| test_params.module_variant_name, |
| method_name) for method_name, _ in torch_nn_test_methods]) |
| |
| for method_name, _ in torch_nn_test_methods: |
| args = args_map[method_name] |
| modules = args[0] |
| script_modules = [trace_module(module, input_args) for module in modules] |
| module_file_names = [serialize_module_into_file(script_module) for script_module in script_modules] |
| |
| cpp_args = module_file_names[:] |
| for arg in args[1:]: |
| if isinstance(arg, tuple): |
| cpp_args += list(arg) |
| elif isinstance(arg, list): |
| cpp_args += arg |
| else: |
| cpp_args.append(arg) |
| |
| try: |
| cpp_test_name = '{}_test_{}'.format(module_variant_name, method_name) |
| cpp_test_fn = getattr(cpp_module, cpp_test_name) |
| if not test_params.has_parity: |
| with self.assertRaisesRegex(RuntimeError, "Parity test failed"): |
| cpp_test_fn(*cpp_args) |
| else: |
| cpp_test_fn(*cpp_args) |
| finally: |
| # Ideally we would like to not have to manually delete the file, but NamedTemporaryFile |
| # opens the file, and it cannot be opened multiple times in Windows. To support Windows, |
| # we close the file after creation and try to remove it manually. |
| for module_file_name in module_file_names: |
| try: |
| os.remove(module_file_name) |
| except OSError as e: |
| warnings.warn("Unable to remove {}, got error: {}".format(module_file_name, str(e))) |
| |
| test_methods(test_params) |
| |
| |
| def _compute_module_name(test_params_dict): |
| fullname = test_params_dict.get('fullname', None) |
| if fullname: |
| # NOTE: This doesn't work for some of the `wrap_functional` module tests such as "interpolate_nearest_1d", |
| # because in that case the module `interpolate` is not in `torch.nn` but rather in `torch.nn.functional`. |
| # We will fix this when we have parity tests for `torch.nn.functional` modules. |
| module_name = fullname.split('_')[0] |
| else: |
| module_name = test_params_dict.get('module_name') |
| return module_name |
| |
| |
| def _process_test_params(test_params_dict, module_metadata, device, is_criterion): |
| module_name = _compute_module_name(test_params_dict) |
| test_params_dict['constructor'] = test_params_dict.get('constructor', getattr(torch.nn, module_name)) |
| if is_criterion: |
| test = common_nn.CriterionTest(**test_params_dict) |
| else: |
| test = common_nn.ModuleTest(**test_params_dict) |
| module_variant_name = test.get_name()[5:] + (('_' + device) if device != 'cpu' else '') |
| |
| return TorchNNTestParams( |
| module_name=module_name, |
| module_variant_name=module_variant_name, |
| test_instance=test, |
| cpp_constructor_args=test_params_dict.get('cpp_constructor_args'), |
| has_parity=test_params_dict.get('has_parity', True), |
| device=device, |
| ) |
| |
| def has_test(test_name): |
| return hasattr(TestCppApiParity, test_name) |
| |
| def add_test(test_name, test_fn): |
| if has_test(test_name): |
| raise RuntimeError("Found two tests with the same name: " + test_name) |
| setattr(TestCppApiParity, test_name, test_fn) |
| |
| devices = ['cpu', 'cuda'] |
| |
| torch_nn_test_params_map = {} |
| |
| |
| def add_torch_nn_module_tests(module_tests, is_criterion): |
| for test_params_dict in module_tests: |
| # We skip all `torch.nn.functional` tests for now |
| if 'FunctionalModule' in str(test_params_dict.get('constructor', '')): |
| continue |
| |
| module_name = _compute_module_name(test_params_dict) |
| |
| assert hasattr(torch.nn, module_name), \ |
| "`torch.nn` doesn't have module `{}`. ".format(module_name) + \ |
| "If you are adding a new test, please set `fullname` using format `ModuleName_desc`, " + \ |
| "or set `module_name` using format `ModuleName`." |
| |
| module_full_name = 'torch.nn.' + module_name |
| if module_full_name not in parity_table['torch.nn']: |
| raise RuntimeError( |
| 'Module `{}` is not found in Python / C++ API parity table. Please update parity table at {}.'.format( |
| module_full_name, parity_table_path)) |
| |
| has_impl_parity, _ = parity_table['torch.nn'][module_full_name] |
| |
| def add_ctor_args_test_for_module(module_name, has_impl_parity): |
| ctor_args_test_name = 'test_torch_nn_{}_ctor_args'.format(module_name) |
| |
| def ctor_args_test(self): |
| self._test_torch_nn_module_ctor_args( |
| module_name=self._testMethodName.replace('test_torch_nn_', '').replace('_ctor_args', '')) |
| |
| if not has_impl_parity: |
| ctor_args_test = unittest.expectedFailure(ctor_args_test) |
| |
| # We only run one constructor args test per module |
| if not has_test(ctor_args_test_name): |
| add_test(ctor_args_test_name, ctor_args_test) |
| |
| def add_variant_test_for_module(module_name, test_params_dict, has_impl_parity): |
| module_metadata = torch_nn_modules.module_metadata_map[module_name] |
| for device in devices: |
| test_params = _process_test_params( |
| test_params_dict=test_params_dict, |
| module_metadata=module_metadata, |
| device=device, |
| is_criterion=is_criterion) |
| test_name = 'test_torch_nn_{}'.format(test_params.module_variant_name) |
| torch_nn_test_params_map[test_name] = test_params |
| |
| def test_fn(self): |
| self._test_torch_nn_module_variant(test_params=torch_nn_test_params_map[self._testMethodName]) |
| |
| if device == 'cuda': |
| test_fn = unittest.skipIf(not TEST_CUDA, "CUDA unavailable")(test_fn) |
| |
| if not has_impl_parity: |
| test_fn = unittest.expectedFailure(test_fn) |
| |
| add_test(test_name, test_fn) |
| |
| add_ctor_args_test_for_module(module_name, has_impl_parity) |
| add_variant_test_for_module(module_name, test_params_dict, has_impl_parity) |
| |
| add_torch_nn_module_tests( |
| sample_module.module_tests + common_nn.module_tests + common_nn.new_module_tests, |
| is_criterion=False) |
| |
| add_torch_nn_module_tests( |
| common_nn.criterion_tests + common_nn.new_criterion_tests, |
| is_criterion=True) |
| |
| # Assert that there exists auto-generated tests for SampleModule. |
| assert len([name for name in TestCppApiParity.__dict__ if 'SampleModule' in name]) == \ |
| len(sample_module.module_tests) * len(devices) + 1 |
| |
| |
| if __name__ == "__main__": |
| common.run_tests() |
