| # Owner(s): ["module: nn"] |
| |
| import inspect |
| import torch |
| from unittest import mock |
| from unittest.mock import MagicMock, patch |
| from torch.testing._internal.common_dtype import floating_types |
| from torch.testing._internal.common_device_type import instantiate_device_type_tests, dtypes |
| from torch.testing._internal.common_quantization import skipIfNoFBGEMM |
| from torch.testing._internal.common_utils import TestCase, run_tests |
| |
| # Returns a database of args & kwargs that can be used to construct each module. |
| # Each entry is in class -> (args, kwargs) format. |
| # Example: torch.nn.Linear -> ([10, 5], {}) |
| # TODO: Merge this in with the initial ModuleInfo implementation. |
| def build_constructor_arg_db(): |
| return { |
| torch.nn.AdaptiveAvgPool1d: ((5,), {}), |
| torch.nn.AdaptiveAvgPool2d: ((5,), {}), |
| torch.nn.AdaptiveAvgPool3d: ((5,), {}), |
| torch.nn.AdaptiveLogSoftmaxWithLoss: ((100, 20, [5, 10, 15]), {}), |
| torch.nn.AdaptiveMaxPool1d: ((5,), {}), |
| torch.nn.AdaptiveMaxPool2d: ((5,), {}), |
| torch.nn.AdaptiveMaxPool3d: ((5,), {}), |
| torch.nn.AlphaDropout: ((), {}), |
| torch.nn.AvgPool1d: ((3,), {}), |
| torch.nn.AvgPool2d: ((3,), {}), |
| torch.nn.AvgPool3d: ((3,), {}), |
| torch.nn.BCELoss: ((), {}), |
| torch.nn.BCEWithLogitsLoss: ((), {}), |
| torch.nn.BatchNorm1d: ((5,), {}), |
| torch.nn.BatchNorm2d: ((5,), {}), |
| torch.nn.BatchNorm3d: ((5,), {}), |
| torch.nn.Bilinear: ((2, 3, 4), {}), |
| torch.nn.CELU: ((), {}), |
| torch.nn.CTCLoss: ((), {}), |
| torch.nn.ChannelShuffle: ((4,), {}), |
| torch.nn.ConstantPad1d: ((2, 3.5), {}), |
| torch.nn.ConstantPad2d: ((2, 3.5), {}), |
| torch.nn.ConstantPad3d: ((2, 3.5), {}), |
| torch.nn.Conv1d: ((3, 3, 3), {}), |
| torch.nn.Conv2d: ((3, 3, 3), {}), |
| torch.nn.Conv3d: ((3, 3, 3), {}), |
| torch.nn.ConvTranspose1d: ((3, 3, 3), {}), |
| torch.nn.ConvTranspose2d: ((3, 3, 3), {}), |
| torch.nn.ConvTranspose3d: ((3, 3, 3), {}), |
| torch.nn.CosineEmbeddingLoss: ((), {}), |
| torch.nn.CosineSimilarity: ((), {}), |
| torch.nn.CrossEntropyLoss: ((), {}), |
| torch.nn.CrossMapLRN2d: ((5,), {}), |
| torch.nn.Dropout1d: ((), {}), |
| torch.nn.Dropout2d: ((), {}), |
| torch.nn.Dropout3d: ((), {}), |
| torch.nn.Dropout: ((), {}), |
| torch.nn.ELU: ((), {}), |
| torch.nn.Embedding: ((10, 5), {}), |
| torch.nn.EmbeddingBag: ((10, 5), {}), |
| torch.nn.FeatureAlphaDropout: ((), {}), |
| torch.nn.Flatten: ((), {}), |
| torch.nn.Fold: ((5, 2), {}), |
| torch.nn.FractionalMaxPool2d: ((5, 2), {}), |
| torch.nn.FractionalMaxPool3d: ((5, 2), {}), |
| torch.nn.GELU: ((), {}), |
| torch.nn.GLU: ((), {}), |
| torch.nn.GRU: ((5, 10), {}), |
| torch.nn.GRUCell: ((5, 10), {}), |
| torch.nn.GaussianNLLLoss: ((), {}), |
| torch.nn.GroupNorm: ((3, 6, 1e-5, True), {}), |
| torch.nn.Hardshrink: ((), {}), |
| torch.nn.Hardsigmoid: ((), {}), |
| torch.nn.Hardswish: ((), {}), |
| torch.nn.Hardtanh: ((), {}), |
| torch.nn.HingeEmbeddingLoss: ((), {}), |
| torch.nn.HuberLoss: ((), {}), |
| torch.nn.Identity: ((), {}), |
| torch.nn.InstanceNorm1d: ((5, 1e-5, 0.1, True), {}), |
| torch.nn.InstanceNorm2d: ((5, 1e-5, 0.1, True), {}), |
| torch.nn.InstanceNorm3d: ((5, 1e-5, 0.1, True), {}), |
| torch.nn.KLDivLoss: ((), {}), |
| torch.nn.L1Loss: ((), {}), |
| torch.nn.LPPool1d: ((2, 3), {}), |
| torch.nn.LPPool2d: ((2, 3), {}), |
| torch.nn.LSTM: ((5, 10), {}), |
| torch.nn.LSTMCell: ((5, 10), {}), |
| torch.nn.LayerNorm: ((2,), {}), |
| torch.nn.LazyBatchNorm1d: ((), {}), |
| torch.nn.LazyBatchNorm2d: ((), {}), |
| torch.nn.LazyBatchNorm3d: ((), {}), |
| torch.nn.LazyConv1d: ((5, 2), {}), |
| torch.nn.LazyConv2d: ((5, 2), {}), |
| torch.nn.LazyConv3d: ((5, 2), {}), |
| torch.nn.LazyConvTranspose1d: ((5, 2), {}), |
| torch.nn.LazyConvTranspose2d: ((5, 2), {}), |
| torch.nn.LazyConvTranspose3d: ((5, 2), {}), |
| torch.nn.LazyInstanceNorm1d: ((), {}), |
| torch.nn.LazyInstanceNorm2d: ((), {}), |
| torch.nn.LazyInstanceNorm3d: ((), {}), |
| torch.nn.LazyLinear: ((5,), {}), |
| torch.nn.LeakyReLU: ((), {}), |
| torch.nn.Linear: ((10, 5), {}), |
| torch.nn.LocalResponseNorm: ((2,), {}), |
| torch.nn.LogSigmoid: ((), {}), |
| torch.nn.LogSoftmax: ((), {}), |
| torch.nn.MSELoss: ((), {}), |
| torch.nn.MarginRankingLoss: ((), {}), |
| torch.nn.MaxPool1d: ((3,), {}), |
| torch.nn.MaxPool2d: ((3,), {}), |
| torch.nn.MaxPool3d: ((3,), {}), |
| torch.nn.MaxUnpool1d: ((5,), {}), |
| torch.nn.MaxUnpool2d: ((5,), {}), |
| torch.nn.MaxUnpool3d: ((5,), {}), |
| torch.nn.Mish: ((), {}), |
| torch.nn.ModuleDict: ((), {}), |
| torch.nn.ModuleList: ((), {}), |
| torch.nn.MultiLabelMarginLoss: ((), {}), |
| torch.nn.MultiLabelSoftMarginLoss: ((), {}), |
| torch.nn.MultiMarginLoss: ((), {}), |
| torch.nn.MultiheadAttention: ((100, 2), {}), |
| torch.nn.NLLLoss2d: ((), {}), |
| torch.nn.NLLLoss: ((), {}), |
| torch.nn.PReLU: ((), {}), |
| torch.nn.PairwiseDistance: ((), {}), |
| torch.nn.ParameterDict: ((), {}), |
| torch.nn.ParameterList: ((), {}), |
| torch.nn.PixelShuffle: ((2,), {}), |
| torch.nn.PixelUnshuffle: ((2,), {}), |
| torch.nn.PoissonNLLLoss: ((), {}), |
| torch.nn.RNN: ((5, 10), {}), |
| torch.nn.RNNBase: (('LSTM', 5, 10), {}), |
| torch.nn.RNNCell: ((5, 10), {}), |
| torch.nn.RNNCellBase: ((5, 10, True, 2), {}), |
| torch.nn.RReLU: ((), {}), |
| torch.nn.ReLU6: ((), {}), |
| torch.nn.ReLU: ((), {}), |
| torch.nn.ReflectionPad1d: ((2,), {}), |
| torch.nn.ReflectionPad2d: ((2,), {}), |
| torch.nn.ReflectionPad3d: ((2,), {}), |
| torch.nn.ReplicationPad1d: ((2,), {}), |
| torch.nn.ReplicationPad2d: ((2,), {}), |
| torch.nn.ReplicationPad3d: ((2,), {}), |
| torch.nn.SELU: ((), {}), |
| torch.nn.Sequential: ((), {}), |
| torch.nn.SiLU: ((), {}), |
| torch.nn.Sigmoid: ((), {}), |
| torch.nn.SmoothL1Loss: ((), {}), |
| torch.nn.SoftMarginLoss: ((), {}), |
| torch.nn.Softmax2d: ((), {}), |
| torch.nn.Softmax: ((), {}), |
| torch.nn.Softmin: ((), {}), |
| torch.nn.Softplus: ((), {}), |
| torch.nn.Softshrink: ((), {}), |
| torch.nn.Softsign: ((), {}), |
| torch.nn.SyncBatchNorm: ((5,), {}), |
| torch.nn.Tanh: ((), {}), |
| torch.nn.Tanhshrink: ((), {}), |
| torch.nn.Threshold: ((0.1, 20), {}), |
| torch.nn.Transformer: ((), {}), |
| torch.nn.TransformerDecoder: ((torch.nn.TransformerDecoderLayer, 3), {}), |
| torch.nn.TransformerDecoderLayer: ((10, 2), {}), |
| torch.nn.TransformerEncoder: ((torch.nn.TransformerEncoderLayer, 3), {}), |
| torch.nn.TransformerEncoderLayer: ((10, 2), {}), |
| torch.nn.TripletMarginLoss: ((), {}), |
| torch.nn.TripletMarginWithDistanceLoss: ((), {}), |
| torch.nn.Unflatten: ((1, (2, 5, 5)), {}), |
| torch.nn.Unfold: ((3,), {}), |
| torch.nn.Upsample: ((), {}), |
| torch.nn.UpsamplingBilinear2d: ((), {}), |
| torch.nn.UpsamplingNearest2d: ((), {}), |
| torch.nn.ZeroPad1d: ((0,), {}), |
| torch.nn.ZeroPad2d: ((0,), {}), |
| torch.nn.ZeroPad3d: ((0,), {}), |
| torch.ao.nn.qat.Conv1d: ((3, 3, 3), { |
| 'qconfig': torch.ao.quantization.default_qconfig, |
| }), |
| torch.ao.nn.qat.Conv2d: ((3, 3, 3), { |
| 'qconfig': torch.ao.quantization.default_qconfig, |
| }), |
| torch.ao.nn.qat.Conv3d: ((3, 3, 3), { |
| 'qconfig': torch.ao.quantization.default_qconfig, |
| }), |
| torch.ao.nn.qat.Linear: ((5, 2), { |
| 'qconfig': torch.ao.quantization.default_qconfig, |
| }), |
| torch.ao.nn.qat.Embedding: ((10, 12), { |
| 'qconfig': torch.ao.quantization.float_qparams_weight_only_qconfig, |
| }), |
| torch.ao.nn.qat.EmbeddingBag: ((10, 12), { |
| 'qconfig': torch.ao.quantization.float_qparams_weight_only_qconfig, |
| }), |
| torch.ao.nn.quantizable.LSTM: ((5, 6), {}), |
| torch.ao.nn.quantizable.LSTMCell: ((5, 6), {}), |
| torch.ao.nn.quantizable.MultiheadAttention: ((10, 2), {}), |
| torch.ao.nn.quantized.BatchNorm2d: ((2,), {}), |
| torch.ao.nn.quantized.BatchNorm3d: ((2,), {}), |
| torch.ao.nn.quantized.Dropout: ((), {}), |
| torch.ao.nn.quantized.Conv1d: ((3, 3, 3), {}), |
| torch.ao.nn.quantized.Conv2d: ((3, 3, 3), {}), |
| torch.ao.nn.quantized.Conv3d: ((3, 3, 3), {}), |
| torch.ao.nn.quantized.ConvTranspose1d: ((3, 3, 3), {}), |
| torch.ao.nn.quantized.ConvTranspose2d: ((3, 3, 3), {}), |
| torch.ao.nn.quantized.ConvTranspose3d: ((16, 33, (3, 3, 5)), { |
| 'stride': (2, 1, 1), |
| 'padding': (4, 2, 2), |
| 'output_padding': (2, 2, 2), |
| 'dilation': (1, 1, 1), |
| }), |
| torch.ao.nn.quantized.DeQuantize: ((), {}), |
| torch.ao.nn.quantized.ELU: ((0.01, 0), {}), |
| torch.ao.nn.quantized.Embedding: ((10, 3), { |
| 'factory_kwargs': {}, |
| }), |
| torch.ao.nn.quantized.EmbeddingBag: ((10, 3), { |
| 'factory_kwargs': {}, |
| }), |
| torch.ao.nn.quantized.GroupNorm: ((2, 4, torch.nn.Parameter(torch.tensor(2.)), |
| torch.nn.Parameter(torch.tensor(2.)), 0.1, 0), {}), |
| torch.ao.nn.quantized.Hardswish: ((0.1, 0,), {}), |
| torch.ao.nn.quantized.InstanceNorm1d: ((2, torch.nn.Parameter(torch.tensor(2.)), |
| torch.nn.Parameter(torch.tensor(2.)), 0.1, 0), {}), |
| torch.ao.nn.quantized.InstanceNorm2d: ((2, torch.nn.Parameter(torch.tensor(2.)), |
| torch.nn.Parameter(torch.tensor(2.)), 0.1, 0), {}), |
| torch.ao.nn.quantized.InstanceNorm3d: ((2, torch.nn.Parameter(torch.tensor(2.)), |
| torch.nn.Parameter(torch.tensor(2.)), 0.1, 0), {}), |
| torch.ao.nn.quantized.LayerNorm: ((2, torch.nn.Parameter(torch.tensor(2.)), |
| torch.nn.Parameter(torch.tensor(2.)), 0.1, 0), {}), |
| torch.ao.nn.quantized.LeakyReLU: ((0.01, 0), {}), |
| torch.ao.nn.quantized.Linear: ((5, 2), { |
| 'factory_kwargs': {}, |
| }), |
| torch.ao.nn.quantized.MaxPool2d: ((3,), {}), |
| torch.ao.nn.quantized.Quantize: ((0.1, 0), { |
| 'dtype': torch.int16, |
| 'factory_kwargs': {}, |
| }), |
| torch.ao.nn.quantized.ReLU6: ((), {}), |
| torch.ao.nn.quantized.Sigmoid: ((0.1, 0), {}), |
| torch.ao.nn.quantized.Softmax: ((), {}), |
| torch.ao.nn.quantized.FloatFunctional: ((), {}), |
| torch.ao.nn.quantized.FXFloatFunctional: ((), {}), |
| torch.ao.nn.quantized.QFunctional: ((), {}), |
| # Remove torch.ao.nn.quantized after the migration completes: |
| torch.ao.nn.qat.Conv1d: ((3, 3, 3), { |
| 'qconfig': torch.ao.quantization.default_qconfig, |
| }), |
| torch.ao.nn.qat.Conv2d: ((3, 3, 3), { |
| 'qconfig': torch.ao.quantization.default_qconfig, |
| }), |
| torch.ao.nn.qat.Conv3d: ((3, 3, 3), { |
| 'qconfig': torch.ao.quantization.default_qconfig, |
| }), |
| torch.ao.nn.qat.Linear: ((5, 2), { |
| 'qconfig': torch.ao.quantization.default_qconfig, |
| }), |
| torch.ao.nn.qat.Embedding: ((10, 12), { |
| 'qconfig': torch.ao.quantization.float_qparams_weight_only_qconfig, |
| }), |
| torch.ao.nn.qat.EmbeddingBag: ((10, 12), { |
| 'qconfig': torch.ao.quantization.float_qparams_weight_only_qconfig, |
| }), |
| torch.ao.nn.quantized.BatchNorm2d: ((2,), {}), |
| torch.ao.nn.quantized.BatchNorm3d: ((2,), {}), |
| torch.ao.nn.quantized.Dropout: ((), {}), |
| torch.ao.nn.quantized.Conv1d: ((3, 3, 3), {}), |
| torch.ao.nn.quantized.Conv2d: ((3, 3, 3), {}), |
| torch.ao.nn.quantized.Conv3d: ((3, 3, 3), {}), |
| torch.ao.nn.quantized.ConvTranspose1d: ((3, 3, 3), {}), |
| torch.ao.nn.quantized.ConvTranspose2d: ((3, 3, 3), {}), |
| torch.ao.nn.quantized.ConvTranspose3d: ((16, 33, (3, 3, 5)), { |
| 'stride': (2, 1, 1), |
| 'padding': (4, 2, 2), |
| 'output_padding': (2, 2, 2), |
| 'dilation': (1, 1, 1), |
| }), |
| torch.ao.nn.quantized.DeQuantize: ((), {}), |
| torch.ao.nn.quantized.ELU: ((0.01, 0), {}), |
| torch.ao.nn.quantized.Embedding: ((10, 3), { |
| 'factory_kwargs': {}, |
| }), |
| torch.ao.nn.quantized.EmbeddingBag: ((10, 3), { |
| 'factory_kwargs': {}, |
| }), |
| torch.ao.nn.quantized.GroupNorm: ((2, 4, torch.nn.Parameter(torch.tensor(2.)), |
| torch.nn.Parameter(torch.tensor(2.)), 0.1, 0), {}), |
| torch.ao.nn.quantized.Hardswish: ((0.1, 0,), {}), |
| torch.ao.nn.quantized.InstanceNorm1d: ((2, torch.nn.Parameter(torch.tensor(2.)), |
| torch.nn.Parameter(torch.tensor(2.)), 0.1, 0), {}), |
| torch.ao.nn.quantized.InstanceNorm2d: ((2, torch.nn.Parameter(torch.tensor(2.)), |
| torch.nn.Parameter(torch.tensor(2.)), 0.1, 0), {}), |
| torch.ao.nn.quantized.InstanceNorm3d: ((2, torch.nn.Parameter(torch.tensor(2.)), |
| torch.nn.Parameter(torch.tensor(2.)), 0.1, 0), {}), |
| torch.ao.nn.quantized.LayerNorm: ((2, torch.nn.Parameter(torch.tensor(2.)), |
| torch.nn.Parameter(torch.tensor(2.)), 0.1, 0), {}), |
| torch.ao.nn.quantized.LeakyReLU: ((0.01, 0), {}), |
| torch.ao.nn.quantized.Linear: ((5, 2), { |
| 'factory_kwargs': {}, |
| }), |
| torch.ao.nn.quantized.MaxPool2d: ((3,), {}), |
| torch.ao.nn.quantized.PReLU: ((0.01, 0), {}), |
| torch.ao.nn.quantized.Quantize: ((0.1, 0), { |
| 'dtype': torch.int16, |
| 'factory_kwargs': {}, |
| }), |
| torch.ao.nn.quantized.ReLU6: ((), {}), |
| torch.ao.nn.quantized.Sigmoid: ((0.1, 0), {}), |
| torch.ao.nn.quantized.Softmax: ((), {}), |
| torch.ao.nn.quantized.FloatFunctional: ((), {}), |
| torch.ao.nn.quantized.FXFloatFunctional: ((), {}), |
| torch.ao.nn.quantized.QFunctional: ((), {}), |
| } |
| |
| |
| # Instantiates the given class with the given args, kwargs, optionally on a given device. |
| def instantiate_class(cls, args, kwargs, extra_kwargs): |
| return cls(*args, **kwargs) if extra_kwargs is None else cls(*args, **kwargs, **extra_kwargs) |
| |
| |
| # Returns a function that calls the real implementation of a method |
| # in addition to passing args to a mock object. |
| def mock_wrapper(method): |
| mock = MagicMock() |
| |
| def wrapper(self, *args, **kwargs): |
| mock(*args, **kwargs) |
| return method(self, *args, **kwargs) |
| wrapper.mock = mock |
| return wrapper |
| |
| |
| # Returns a set of args / kwargs that can be used to construct the module. |
| def get_example_args(module_cls, constructor_arg_db, extra_kwargs=None): |
| assert module_cls in constructor_arg_db, \ |
| f"No entry for {module_cls} in the constructor arg DB. Please add it to pass these tests." |
| args, kwargs = constructor_arg_db[module_cls] |
| extra_kwargs = {} if extra_kwargs is None else extra_kwargs |
| |
| # Recursively instantiate args / kwargs that are class objects. |
| args = [instantiate_class(arg, *get_example_args(arg, constructor_arg_db), extra_kwargs=extra_kwargs) |
| if inspect.isclass(arg) else torch.nn.Parameter(arg.to(**extra_kwargs)) |
| if isinstance(arg, torch.nn.Parameter) else arg for arg in args] |
| kwargs = {k: instantiate_class(v, *get_example_args(v, constructor_arg_db), extra_kwargs=extra_kwargs) |
| if inspect.isclass(v) else torch.nn.Parameter(v.to(*extra_kwargs)) |
| if isinstance(v, torch.nn.Parameter) else v for k, v in kwargs.items()} |
| kwargs.update(extra_kwargs) |
| return args, kwargs |
| |
| |
| def generate_test_func(test_cls, module_cls, constructor_arg_db, |
| verify_kwargs=True, module_is_lazy=False, check_nonexistent_arg=True): |
| # Generate a function for testing the given module. |
| @dtypes(*floating_types()) |
| def run_test(test_cls, device, dtype, module_cls=module_cls): |
| # Check if this module creates parameters or registers buffers. |
| # The mock magic here passes through to the real Parameter / register_buffer |
| # logic and is only used to check for calls. |
| args, kwargs = get_example_args(module_cls, constructor_arg_db) |
| |
| # Some modules need to pass factory_kwargs so as not to conflict with existing args such as dtype. |
| module_needs_factory_kwargs = 'factory_kwargs' in kwargs |
| if module_needs_factory_kwargs: |
| del kwargs['factory_kwargs'] |
| extra_kwargs = { |
| 'factory_kwargs': { |
| 'device': device, |
| 'dtype': dtype, |
| } |
| } |
| else: |
| extra_kwargs = { |
| 'device': device, |
| 'dtype': dtype, |
| } |
| |
| parameter_new = mock_wrapper(torch.nn.Parameter.__new__) |
| with patch.object(torch.nn.Parameter, '__new__', parameter_new): |
| register_buffer = mock_wrapper(torch.nn.Module.register_buffer) |
| with patch.object(torch.nn.Module, 'register_buffer', register_buffer): |
| m = module_cls(*args, **kwargs) |
| module_creates_params_or_buffers = parameter_new.mock.called or register_buffer.mock.called |
| |
| # == Verify factory kwargs are supported. == |
| if verify_kwargs and module_creates_params_or_buffers: |
| args, kwargs = get_example_args(module_cls, constructor_arg_db, |
| extra_kwargs=extra_kwargs) |
| |
| if module_is_lazy: |
| # Ensure device and dtype are passed to all UninitializedParameters and UninitializedBuffers. |
| uninit_param_new = mock_wrapper(torch.nn.UninitializedParameter.__new__) |
| with patch.object(torch.nn.UninitializedParameter, '__new__', uninit_param_new): |
| uninit_buffer_new = mock_wrapper(torch.nn.UninitializedBuffer.__new__) |
| with patch.object(torch.nn.UninitializedBuffer, '__new__', uninit_buffer_new): |
| m = module_cls(*args, **kwargs) |
| uninit_param_new.mock.assert_has_calls( |
| [mock.call(device=device, dtype=dtype) for _ in uninit_param_new.mock.mock_calls]) |
| uninit_buffer_new.mock.assert_has_calls( |
| [mock.call(device=device, dtype=dtype) for _ in uninit_buffer_new.mock.mock_calls]) |
| else: |
| # Check device placement and dtype for parameters and buffers. |
| # Only verify floating point dtypes since that's what the kwarg applies to. |
| # Note that dtype verification is also skipped if the module requires factory_kwargs. |
| m = module_cls(*args, **kwargs) |
| for name, param in m.named_parameters(): |
| test_cls.assertEqual( |
| str(param.device), device, |
| f'Parameter {name} is on {param.device.type} instead of the expected device {device}') |
| if param.dtype.is_floating_point and not module_needs_factory_kwargs: |
| test_cls.assertEqual( |
| param.dtype, dtype, |
| f'Parameter {name} is of dtype {param.dtype} instead of the expected dtype {dtype}') |
| for name, buffer in m.named_buffers(): |
| test_cls.assertEqual( |
| str(buffer.device), device, |
| f'Buffer {name} is on {buffer.device.type} instead of the expected device {device}') |
| if buffer.dtype.is_floating_point and not module_needs_factory_kwargs: |
| test_cls.assertEqual( |
| buffer.dtype, dtype, |
| f'Buffer {name} is of dtype {buffer.dtype} instead of the expected dtype {dtype}') |
| |
| # == Verify passing a nonexistent arg errors out. == |
| if check_nonexistent_arg: |
| with test_cls.assertRaises(TypeError): |
| m = module_cls(*args, **kwargs, nonexistent_arg='foo') |
| |
| return run_test |
| |
| |
| def generate_tests(test_cls, constructor_arg_db): |
| # test all modules underneath these namespaces... |
| NAMESPACES = [ |
| torch.nn, |
| torch.ao.nn.qat, |
| torch.ao.nn.quantized, |
| torch.ao.nn.qat, |
| torch.ao.nn.quantizable, |
| torch.ao.nn.quantized, |
| ] |
| # ...except these |
| MODULES_TO_SKIP = { |
| torch.nn.Module, |
| torch.nn.Container, # deprecated |
| torch.nn.NLLLoss2d, # deprecated |
| # TODO: Remove these 4 from this list once the ASan issue is fixed. |
| # See https://github.com/pytorch/pytorch/issues/55396 |
| torch.ao.nn.quantized.Embedding, |
| torch.ao.nn.quantized.EmbeddingBag, |
| torch.ao.nn.quantized.Embedding, |
| torch.ao.nn.quantized.EmbeddingBag, |
| torch.ao.nn.quantized.LSTM, |
| torch.ao.nn.quantized.MultiheadAttention, |
| } |
| # no need to support kwargs for these modules even though |
| # they have parameters / buffers because they are passed in |
| # already instantiated s |
| MODULES_WITHOUT_KWARGS_SUPPORT = { |
| torch.nn.BCELoss, |
| torch.nn.BCEWithLogitsLoss, |
| torch.nn.CrossEntropyLoss, |
| torch.nn.FractionalMaxPool2d, |
| torch.nn.FractionalMaxPool3d, |
| torch.nn.MultiLabelSoftMarginLoss, |
| torch.nn.MultiMarginLoss, |
| torch.nn.NLLLoss, |
| torch.nn.TransformerDecoder, |
| torch.nn.TransformerEncoder, |
| } |
| # modules that supported kwargs before |
| MODULES_WITH_PREVIOUS_KWARGS = { |
| torch.nn.Identity, |
| } |
| # lazy modules don't instantiate parameters right away |
| LAZY_MODULES = { |
| torch.nn.LazyBatchNorm1d, |
| torch.nn.LazyBatchNorm2d, |
| torch.nn.LazyBatchNorm3d, |
| torch.nn.LazyConv1d, |
| torch.nn.LazyConv2d, |
| torch.nn.LazyConv3d, |
| torch.nn.LazyConvTranspose1d, |
| torch.nn.LazyConvTranspose2d, |
| torch.nn.LazyConvTranspose3d, |
| torch.nn.LazyConvTranspose3d, |
| torch.nn.LazyInstanceNorm1d, |
| torch.nn.LazyInstanceNorm2d, |
| torch.nn.LazyInstanceNorm3d, |
| torch.nn.LazyLinear, |
| } |
| # these modules requires FBGEMM backend to instantiate |
| MODULES_THAT_REQUIRE_FBGEMM = { |
| torch.ao.nn.quantized.Conv1d, |
| torch.ao.nn.quantized.Conv2d, |
| torch.ao.nn.quantized.Conv3d, |
| torch.ao.nn.quantized.ConvTranspose1d, |
| torch.ao.nn.quantized.ConvTranspose2d, |
| torch.ao.nn.quantized.ConvTranspose3d, |
| torch.ao.nn.quantized.Linear, |
| # Remove the lines below after AO migration is complete |
| torch.ao.nn.quantized.Conv1d, |
| torch.ao.nn.quantized.Conv2d, |
| torch.ao.nn.quantized.Conv3d, |
| torch.ao.nn.quantized.ConvTranspose1d, |
| torch.ao.nn.quantized.ConvTranspose2d, |
| torch.ao.nn.quantized.ConvTranspose3d, |
| torch.ao.nn.quantized.Linear, |
| } |
| |
| for namespace in NAMESPACES: |
| # the "nn" in "torch.nn" |
| namespace_basename = namespace.__name__.split('.')[-1] |
| for module_name in namespace.modules.__all__: |
| # class object for this module (e.g. torch.nn.Linear) |
| module_cls = getattr(namespace.modules, module_name) |
| if module_cls in MODULES_TO_SKIP: |
| continue |
| verify_kwargs = module_cls not in MODULES_WITHOUT_KWARGS_SUPPORT |
| module_is_lazy = module_cls in LAZY_MODULES |
| check_nonexistent_arg = module_cls not in MODULES_WITH_PREVIOUS_KWARGS |
| # Generate a function for testing this module and setattr it onto the test class. |
| run_test = generate_test_func(test_cls, module_cls, constructor_arg_db, |
| verify_kwargs=verify_kwargs, |
| module_is_lazy=module_is_lazy, |
| check_nonexistent_arg=check_nonexistent_arg) |
| test_name = f'test_{namespace_basename}_{module_name}' |
| if module_cls in MODULES_THAT_REQUIRE_FBGEMM: |
| run_test = skipIfNoFBGEMM(run_test) |
| setattr(TestModuleInit, test_name, run_test) |
| |
| |
| class TestModuleInit(TestCase): |
| _ignore_not_implemented_error = False |
| |
| |
| generate_tests(TestModuleInit, build_constructor_arg_db()) |
| instantiate_device_type_tests(TestModuleInit, globals()) |
| |
| |
| if __name__ == '__main__': |
| run_tests() |
