import torch import unittest from copy import deepcopy from functools import wraps, partial from itertools import chain, product import itertools import torch.nn.functional as F from torch.testing import make_tensor from torch.testing._internal.common_cuda import TEST_CUDNN from torch.testing._internal.common_dtype import floating_types from torch.testing._internal.common_device_type import ( _TestParametrizer, _update_param_kwargs, skipIf, toleranceOverride, tol, skipCUDAIfCudnnVersionLessThan, skipCUDAIfRocm, precisionOverride, skipMeta) from torch.testing._internal.common_methods_invocations import DecorateInfo from torch.testing._internal.common_nn import nllloss_reference, get_reduction from torch.testing._internal.common_utils import ( freeze_rng_state, set_single_threaded_if_parallel_tbb, skipIfMps, GRADCHECK_NONDET_TOL, TEST_WITH_ROCM) from types import ModuleType from typing import List, Tuple, Type, Set, Dict # List of all namespaces containing modules to test. MODULE_NAMESPACES: List[ModuleType] = [ torch.nn.modules, torch.nn.qat.modules, torch.nn.quantizable.modules, torch.nn.quantized.modules, ] # Modules that shouldn't be tested for one reason or another. MODULES_TO_SKIP: Set[Type] = { torch.nn.Module, # abstract base class torch.nn.Container, # deprecated torch.nn.NLLLoss2d, # deprecated torch.nn.quantized.modules._ConvNd, # abstract base class torch.nn.quantized.MaxPool2d, # aliases to nn.MaxPool2d } # List of all module classes to test. MODULE_CLASSES: List[Type] = list(chain(*[ [getattr(namespace, module_name) for module_name in namespace.__all__] # type: ignore[attr-defined] for namespace in MODULE_NAMESPACES])) MODULE_CLASSES = [cls for cls in MODULE_CLASSES if cls not in MODULES_TO_SKIP] # Dict of module class -> common name. Useful for making test names more intuitive. # Example: torch.nn.modules.linear.Linear -> "nn.Linear" MODULE_CLASS_NAMES: Dict[Type, str] = {} for namespace in MODULE_NAMESPACES: for module_name in namespace.__all__: # type: ignore[attr-defined] module_cls = getattr(namespace, module_name) namespace_name = namespace.__name__.replace('torch.', '').replace('.modules', '') MODULE_CLASS_NAMES[module_cls] = f'{namespace_name}.{module_name}' class modules(_TestParametrizer): """ PROTOTYPE: Decorator for specifying a list of modules over which to run a test. """ def __init__(self, module_info_list, allowed_dtypes=None): self.module_info_list = module_info_list self.allowed_dtypes = set(allowed_dtypes) if allowed_dtypes is not None else None def _parametrize_test(self, test, generic_cls, device_cls): if device_cls is None: raise RuntimeError('The @modules decorator is only intended to be used in a device-specific ' 'context; use it with instantiate_device_type_tests() instead of ' 'instantiate_parametrized_tests()') for module_info in self.module_info_list: # Construct the test name; device / dtype parts are handled outside. # See [Note: device and dtype suffix placement] test_name = module_info.name.replace('.', '_') dtypes = set(module_info.dtypes) if self.allowed_dtypes is not None: dtypes = dtypes.intersection(self.allowed_dtypes) for dtype in dtypes: # Construct parameter kwargs to pass to the test. param_kwargs = {'module_info': module_info} _update_param_kwargs(param_kwargs, 'dtype', dtype) try: active_decorators = [set_single_threaded_if_parallel_tbb] if module_info.should_skip(generic_cls.__name__, test.__name__, device_cls.device_type, dtype): active_decorators.append(skipIf(True, "Skipped!")) if module_info.decorators is not None: for decorator in module_info.decorators: # Can't use isinstance as it would cause a circular import if decorator.__class__.__name__ == 'DecorateInfo': if decorator.is_active(generic_cls.__name__, test.__name__, device_cls.device_type, dtype): active_decorators += decorator.decorators else: active_decorators.append(decorator) @wraps(test) def test_wrapper(*args, **kwargs): return test(*args, **kwargs) for decorator in active_decorators: test_wrapper = decorator(test_wrapper) yield (test_wrapper, test_name, param_kwargs) except Exception as ex: # Provides an error message for debugging before rethrowing the exception print("Failed to instantiate {0} for module {1}!".format(test_name, module_info.name)) raise ex def formatted_module_name(module_cls): """ Returns the common name of the module class formatted for use in test names. """ return MODULE_CLASS_NAMES[module_cls].replace('.', '_') class FunctionInput(object): """ Contains args and kwargs to pass as input to a function. """ __slots__ = ['args', 'kwargs'] def __init__(self, *args, **kwargs): self.args = args self.kwargs = kwargs class ModuleInput(object): """ Contains args / kwargs for module instantiation + forward pass. """ __slots__ = ['constructor_input', 'forward_input', 'desc', 'reference_fn'] def __init__(self, constructor_input, forward_input=None, desc='', reference_fn=None): self.constructor_input = constructor_input # Inputs to pass during construction self.forward_input = forward_input # Inputs to pass to forward() self.desc = desc # Description for this set of inputs self.reference_fn = reference_fn # Reference with signature: reference_fn(module, parameters, *args, **kwargs) if reference_fn is not None: @wraps(reference_fn) def copy_reference_fn(m, *args, **kwargs): # Copy inputs to avoid undesired side effects from calling the reference. args, kwargs = deepcopy(args), deepcopy(kwargs) # Note that module parameters are passed in for convenience. return reference_fn(m, list(m.parameters()), *args, **kwargs) self.reference_fn = copy_reference_fn class ModuleInfo(object): """ Module information to be used in testing. """ def __init__(self, module_cls, # Class object for the module under test *, module_inputs_func, # Function to generate module inputs skips=(), # Indicates which tests to skip decorators=None, # Additional decorators to apply to generated tests dtypes=floating_types(), # dtypes this function is expected to work with supports_gradgrad=True, # whether the op supports second order gradients gradcheck_nondet_tol=0.0, # tolerance for nondeterminism while performing gradcheck module_memformat_affects_out=False, # whether converting module to channels last will generate # channels last output ): self.module_cls = module_cls self.module_inputs_func = module_inputs_func self.skips = skips self.decorators = decorators self.dtypes = dtypes self.supports_gradgrad = supports_gradgrad self.gradcheck_nondet_tol = gradcheck_nondet_tol self.module_memformat_affects_out = module_memformat_affects_out def should_skip(self, cls_name, test_name, device_type, dtype): return any(si.is_active(cls_name, test_name, device_type, dtype) for si in self.skips) @property def name(self): return formatted_module_name(self.module_cls) @property def formatted_name(self): return self.name.replace('.', '_') def module_inputs_torch_nn_Linear(module_info, device, dtype, requires_grad, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) module_inputs = [ ModuleInput(constructor_input=FunctionInput(10, 8), forward_input=FunctionInput(input=make_input((4, 10))), reference_fn=lambda m, p, input: torch.mm(input, p[0].t()) + p[1].view(1, -1).expand(4, 8)), ModuleInput(constructor_input=FunctionInput(10, 8, bias=False), forward_input=FunctionInput(make_input((4, 10))), desc='no_bias', reference_fn=lambda m, p, i: torch.mm(i, p[0].t())), ModuleInput(constructor_input=FunctionInput(3, 5), forward_input=FunctionInput(make_input(3)), desc='no_batch_dim', reference_fn=lambda m, p, i: torch.mm(i.view(1, -1), p[0].t()).view(-1) + p[1]) ] return module_inputs def module_inputs_torch_nn_Bilinear(module_info, device, dtype, requires_grad, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) def bilinear_reference_fn(m, p, x1, x2, bias=True): result = torch.einsum('bn,anm,bm->ba', x1, p[0], x2) if bias: if x1.shape[0] == 1: result = result.view(-1) + p[1] else: result = result + p[1].view(1, -1).expand(x1.shape[0], p[0].shape[0]) return result module_inputs = [ ModuleInput(constructor_input=FunctionInput(2, 3, 4), forward_input=FunctionInput(make_input((8, 2)), make_input((8, 3))), reference_fn=lambda m, p, x1, x2: bilinear_reference_fn(m, p, x1, x2)), ModuleInput(constructor_input=FunctionInput(2, 3, 4, bias=False), forward_input=FunctionInput(make_input((8, 2)), make_input((8, 3))), desc='no_bias', reference_fn=lambda m, p, x1, x2: bilinear_reference_fn(m, p, x1, x2, bias=False)), ModuleInput(constructor_input=FunctionInput(2, 3, 4), forward_input=FunctionInput(make_input((2)), make_input((3))), desc='no_batch_dim', reference_fn=lambda m, p, x1, x2: bilinear_reference_fn(m, p, x1.view(1, -1), x2.view(1, -1))), ] return module_inputs def module_inputs_torch_nn_NLLLoss(module_info, device, dtype, requires_grad, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) make_weight = partial(make_tensor, device=device, dtype=dtype, requires_grad=False) cases: List[Tuple[str, dict]] = [ ('', {}), ('reduction_sum', {'reduction': 'sum'}), ('reduction_none', {'reduction': 'none'}), ('ignore_index', {'ignore_index': 2}), ('weights', {'weight': make_weight(10).abs()}), ('weights_ignore_index', {'weight': make_weight(10).abs(), 'ignore_index': 2}), ('weights_ignore_index_neg', {'weight': make_weight(10).abs(), 'ignore_index': -1}) ] # TODO: Uncomment when negative weights is supported. # negative_weight = make_weight(10) # negative_weight[0] = -1 # cases.append(('weights_negative', {'weight': negative_weight})) module_inputs = [] for desc, constructor_kwargs in cases: def reference_fn(m, p, i, t, constructor_kwargs=constructor_kwargs): return nllloss_reference(i, t, **constructor_kwargs) module_inputs.append( ModuleInput(constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput(make_input((15, 10)).log_softmax(dim=1), torch.empty(15, device=device).uniform_().mul(10).floor().long()), desc=desc, reference_fn=reference_fn) ) return module_inputs def module_inputs_torch_nn_GaussianNLLLoss(module_info, device, dtype, requires_grad, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) make_target = partial(make_tensor, device=device, dtype=dtype, requires_grad=False) cases: List[Tuple[str, dict]] = [ ('', {}), ('reduction_sum', {'reduction': 'sum'}), ('reduction_mean', {'reduction': 'mean'}), ('reduction_none', {'reduction': 'none'}), ] module_inputs = [] for desc, constructor_kwargs in cases: module_inputs.append( ModuleInput(constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput(make_input((3)), make_target((3)), make_input((1)).abs()), desc=desc, reference_fn=no_batch_dim_reference_fn) ) return module_inputs def no_batch_dim_reference_fn(m, p, *args, **kwargs): """Reference function for modules supporting no batch dimensions. Unbatched inputs are unsqueezed to form a single batch input before passing them to the module. The output is squeezed to compare with the output of unbatched input to the module. Currently it only supports modules which return a single Tensor as output. You can bind the following kwargs. Kwargs: batch_first[bool] : If True, all the Tensors in `args` while be unsqueezed at dim `0` . and output will be squeezed at dim `0` else dim `1` for both. kwargs_to_batchify[dict] : Dictionary specifying the name of the argument and dimension to unsqueeze. Useful if there are few arguments whose batch dimension are different from the ones selected by `batch_first`. is_criterion[bool] : Specify if the module is a criterion and handle the reduction for output accordingly. """ def get_and_pop(key, default): v = kwargs.get(key, default) if key in kwargs: kwargs.pop(key) return v batch_dim = 0 if get_and_pop('batch_first', True) else 1 kwargs_to_batchify = get_and_pop('kwargs_to_batchify', None) is_criterion = get_and_pop('is_criterion', False) if kwargs_to_batchify is not None: assert isinstance(kwargs_to_batchify, dict) for k, v in kwargs.items(): if k in kwargs_to_batchify and v is not None: bdim = kwargs_to_batchify[k] kwargs[k] = v.unsqueeze(bdim) single_batch_input_args = [input.unsqueeze(batch_dim) for input in args] with freeze_rng_state(): output = m(*single_batch_input_args, **kwargs).squeeze(batch_dim) if is_criterion: reduction = get_reduction(m) if reduction == 'none': return output.squeeze(0) return output def no_batch_dim_reference_mha(m, p, *args, **kwargs): """Reference function for MultiheadAttention supporting no batch dimensions. Unbatched inputs are unsqueezed to form a single batch input before passing them to the module. The output is squeezed to compare with the output of unbatched input to the module. """ batch_dim = 0 if kwargs.get('batch_first', True) else 1 if 'batch_first' in kwargs: kwargs.pop('batch_first') if 'key_padding_mask' in kwargs and kwargs['key_padding_mask'] is not None: kwargs['key_padding_mask'] = kwargs['key_padding_mask'].unsqueeze(0) single_batch_input_args = [input.unsqueeze(batch_dim) for input in args] with freeze_rng_state(): output = m(*single_batch_input_args, **kwargs) return (output[0].squeeze(batch_dim), output[1].squeeze(0)) def no_batch_dim_reference_rnn_gru(m, p, *args, **kwargs): """Reference function for RNN and GRU supporting no batch dimensions. Unbatched inputs are unsqueezed to form a single batch input before passing them to the module. The output is squeezed to compare with the output of unbatched input to the module. """ if len(args) == 1: inp, = args h = None elif len(args) == 2: inp, h = args h = h.unsqueeze(1) batch_dim = 0 if kwargs['batch_first'] else 1 kwargs.pop('batch_first') inp = inp.unsqueeze(batch_dim) single_batch_input_args = (inp, h) with freeze_rng_state(): output = m(*single_batch_input_args, **kwargs) return (output[0].squeeze(batch_dim), output[1].squeeze(1)) def no_batch_dim_reference_lstm(m, p, *args, **kwargs): """Reference function for LSTM supporting no batch dimensions. Unbatched inputs are unsqueezed to form a single batch input before passing them to the module. The output is squeezed to compare with the output of unbatched input to the module. """ if len(args) == 1: inp, = args h = None elif len(args) == 2: inp, h = args h = (h[0].unsqueeze(1), h[1].unsqueeze(1)) batch_dim = 0 if kwargs['batch_first'] else 1 kwargs.pop('batch_first') inp = inp.unsqueeze(batch_dim) single_batch_input_args = (inp, h) with freeze_rng_state(): output = m(*single_batch_input_args, **kwargs) return (output[0].squeeze(batch_dim), (output[1][0].squeeze(1), output[1][1].squeeze(1))) def no_batch_dim_reference_lstmcell(m, p, *args, **kwargs): """Reference function for LSTMCell supporting no batch dimensions. The module is passed the input and target in batched form with a single item. The output is squeezed to compare with the no-batch input. """ inp, (h, c) = args single_batch_input_args = (inp.unsqueeze(0), (h.unsqueeze(0), c.unsqueeze(0))) with freeze_rng_state(): output = m(*single_batch_input_args, **kwargs) return (output[0].squeeze(0), output[1].squeeze(0)) def generate_regression_criterion_inputs(make_input): return [ ModuleInput( constructor_input=FunctionInput(reduction=reduction), forward_input=FunctionInput(make_input((4, )), make_input(4,)), reference_fn=partial(no_batch_dim_reference_fn, is_criterion=True), desc='no_batch_dim_{}'.format(reduction) ) for reduction in ['none', 'mean', 'sum']] def module_inputs_torch_nn_AvgPool1d(module_info, device, dtype, requires_grad, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput(kernel_size=2), forward_input=FunctionInput(make_input((3, 6))), desc='no_batch_dim', reference_fn=no_batch_dim_reference_fn)] def module_inputs_torch_nn_AdaptiveAvgPool2d(module_info, device, dtype, requires_grad, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput(3,), forward_input=FunctionInput(make_input((1, 3, 5, 6))), desc='single')] def module_inputs_torch_nn_BatchNorm2d(module_info, device, dtype, requires_grad, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput(3,), forward_input=FunctionInput(make_input((2, 3, 6, 6))))] def module_inputs_torch_nn_BatchNorm3d(module_info, device, dtype, requires_grad, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput(3,), forward_input=FunctionInput(make_input((2, 3, 4, 4, 4))))] def module_inputs_torch_nn_ConvNd(module_info, device, dtype, requires_grad, **kwargs): N = kwargs['N'] lazy = kwargs.get('lazy', False) transposed = kwargs.get('transposed', False) make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) conv_kwargs_list = [{}] if transposed else [{}, {'padding': 'same'}] kernel_size, C_in, C_out = 3, 4, 5 input_no_batch_shape = (C_in,) + tuple((i + 3 for i in range(N))) input_batch_shape = (2,) + input_no_batch_shape return [ ModuleInput(constructor_input=(FunctionInput(C_out, kernel_size, **conv_kwargs) if lazy else FunctionInput(C_in, C_out, kernel_size, **conv_kwargs)), forward_input=FunctionInput(make_input( input_batch_shape if with_batch else input_no_batch_shape)), desc=('' if with_batch else 'no_batch_dim'), reference_fn=(None if with_batch else no_batch_dim_reference_fn)) for with_batch, conv_kwargs in itertools.product([True, False], conv_kwargs_list) ] def module_inputs_torch_nn_ELU(module_info, device, dtype, requires_grad, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput(alpha=2.), forward_input=FunctionInput(make_input((3, 2, 5))), reference_fn=lambda m, p, i: torch.where(i >= 0, i, 2 * (i.exp() - 1))), ModuleInput(constructor_input=FunctionInput(alpha=2.), forward_input=FunctionInput(make_input(())), desc='scalar'), ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input((3,))), desc='no_batch_dim', reference_fn=no_batch_dim_reference_fn), ModuleInput(constructor_input=FunctionInput(alpha=2.), forward_input=FunctionInput(make_input((2, 3, 2, 5))), desc='4d_input')] def module_inputs_torch_nn_CELU(module_info, device, dtype, requires_grad, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput(alpha=2.), forward_input=FunctionInput(make_input((3, 2, 5))), reference_fn=lambda m, p, i: torch.where(i >= 0, i, 2. * ((.5 * i).exp() - 1))), ModuleInput(constructor_input=FunctionInput(alpha=2.), forward_input=FunctionInput(make_input(())), reference_fn=lambda m, p, i: torch.where(i >= 0, i, 2 * (i.exp() - 1)), desc='scalar'), ModuleInput(constructor_input=FunctionInput(alpha=2.), forward_input=FunctionInput(make_input((3,))), desc='no_batch_dim', reference_fn=no_batch_dim_reference_fn)] def module_inputs_torch_nn_ReLU(module_info, device, dtype, requires_grad): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input(4)), desc='no_batch_dim'), ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input((2, 3, 4, 5))), desc='channels_last_mem_format'), ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input((2, 3, 3, 4, 5))), desc='channels_last_3d_mem_format')] def module_inputs_torch_nn_L1Loss(module_info, device, dtype, requires_grad, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input((2, 3, 4)), make_input((2, 3, 4))), reference_fn=lambda m, p, i, t: 1. / i.numel() * sum((a - b).abs().sum() for a, b in zip(i, t))), ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input(()), make_input(())), reference_fn=lambda m, p, i, t: 1. / i.numel() * (i - t).abs().sum(), desc='scalar')] + generate_regression_criterion_inputs(make_input) def module_inputs_torch_nn_CrossEntropyLoss(module_info, device, dtype, requires_grad, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) make_target = partial(make_tensor, device=device, dtype=torch.long, requires_grad=False) make_weight = partial(make_tensor, device=device, dtype=dtype, requires_grad=False) reductions = ['sum', 'mean', 'none'] samples = [] # Samples below are for validating the no-batch-dim support. for reduction in reductions: samples.append( ModuleInput(constructor_input=FunctionInput(reduction=reduction), forward_input=FunctionInput(make_input((9,)), make_target((), low=0, high=9)), reference_fn=partial(no_batch_dim_reference_fn, is_criterion=True)) ) samples.append( ModuleInput(constructor_input=FunctionInput(reduction=reduction, weight=make_weight((9,))), forward_input=FunctionInput(make_input((9,)), make_target((), low=0, high=9)), reference_fn=partial(no_batch_dim_reference_fn, is_criterion=True)) ) samples.append( ModuleInput(constructor_input=FunctionInput(reduction=reduction, label_smoothing=0.5), forward_input=FunctionInput(make_input((9,)), make_target((), low=0, high=9)), reference_fn=partial(no_batch_dim_reference_fn, is_criterion=True)) ) samples.append( ModuleInput(constructor_input=FunctionInput(reduction=reduction, label_smoothing=0.5, weight=make_weight((9,))), forward_input=FunctionInput(make_input((9,)), make_target((), low=0, high=9)), reference_fn=partial(no_batch_dim_reference_fn, is_criterion=True)) ) return samples def module_inputs_torch_nn_Hardswish(module_info, device, dtype, requires_grad, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput( constructor_input=FunctionInput(), forward_input=FunctionInput(make_input(4)), reference_fn=no_batch_dim_reference_fn, desc='no_batch_dim', ), ModuleInput( constructor_input=FunctionInput(), forward_input=FunctionInput(make_input((2, 3, 2, 5))), desc='4d_input') ] def module_inputs_torch_nn_MaxPool2d(module_info, device, dtype, requires_grad, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput( constructor_input=FunctionInput((3, 3), (2, 2), (1, 1)), forward_input=FunctionInput(make_input(((3, 7, 7)))), desc='3d_input'), ModuleInput( constructor_input=FunctionInput((3, 3), (2, 2), (1, 1)), forward_input=FunctionInput(make_input((1, 3, 7, 7))), desc='4d_input'), ModuleInput( constructor_input=FunctionInput((3, 3), (2, 2), (1, 1), return_indices=True), forward_input=FunctionInput(make_input((1, 3, 7, 7))), desc='return_indices'), ] def module_inputs_torch_nn_Sigmoid(module_info, device, dtype, requires_grad, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput( constructor_input=FunctionInput(), forward_input=FunctionInput(make_input((2, 3, 4, 5))), desc='channels_last_mem_format' ), ModuleInput( constructor_input=FunctionInput(), forward_input=FunctionInput(make_input((2, 3, 3, 4, 5))), desc='channels_last_3d_mem_format' ) ] def module_inputs_torch_nn_TransformerEncoderLayer(module_info, device, dtype, requires_grad, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) samples = [ ModuleInput( constructor_input=FunctionInput(4, 2, 16, 0.0), forward_input=FunctionInput( make_input((2, 3, 4)) ), desc='relu_activation' ), ModuleInput( constructor_input=FunctionInput(4, 2, 8, 0.0, F.gelu), forward_input=FunctionInput( make_input((2, 3, 4)) ), desc='gelu_activation' ), ] # Samples below are for validating the no-batch-dim support. key_padding_masks = (None, torch.tensor([False, False, True], device=device, dtype=torch.bool)) attn_masks = (None, torch.tensor([False, False, True], device=device, dtype=torch.bool).expand((3, 3))) for src_mask, src_key_padding_mask, norm_first in itertools.product(attn_masks, key_padding_masks, (True, False)): samples.append( ModuleInput( constructor_input=FunctionInput(d_model=4, nhead=2, dim_feedforward=8, dropout=0.0, batch_first=True, norm_first=norm_first), forward_input=FunctionInput( make_input((3, 4)), src_mask=src_mask, src_key_padding_mask=src_key_padding_mask ), reference_fn=partial(no_batch_dim_reference_fn, batch_first=True, kwargs_to_batchify={'src_key_padding_mask': 0}), desc='no_batch_dim_batch_first' )) samples.append( ModuleInput( constructor_input=FunctionInput(4, 2, 8, dropout=0.0, batch_first=False, norm_first=norm_first), forward_input=FunctionInput( make_input((3, 4)), src_mask=src_mask, src_key_padding_mask=src_key_padding_mask ), reference_fn=partial(no_batch_dim_reference_fn, batch_first=False, kwargs_to_batchify={'src_key_padding_mask': 0}), desc='no_batch_dim' )) return samples def module_inputs_torch_nn_TransformerDecoderLayer(module_info, device, dtype, requires_grad, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) samples = [ ModuleInput( constructor_input=FunctionInput(4, 2, 16, 0.0), forward_input=FunctionInput( make_input((2, 3, 4)), make_input((2, 3, 4)) ), desc='relu_activation' ), ModuleInput( constructor_input=FunctionInput(4, 2, 8, 0.0, F.gelu), forward_input=FunctionInput( make_input((2, 3, 4)), make_input((2, 3, 4)) ), desc='gelu_activation' ), ] # Samples below are for validating the no-batch-dim support. key_padding_masks = (None, torch.tensor([False, False, True], device=device, dtype=torch.bool)) attn_masks = (None, torch.tensor([False, False, True], device=device, dtype=torch.bool).expand((3, 3))) for tgt_mask, tgt_key_padding_mask, norm_first in itertools.product(attn_masks, key_padding_masks, (True, False)): # Using same mask for tgt and memory memory_mask = tgt_mask memory_key_padding_mask = tgt_key_padding_mask samples.append( ModuleInput( constructor_input=FunctionInput(d_model=4, nhead=2, dim_feedforward=8, dropout=0.0, batch_first=True, norm_first=norm_first), forward_input=FunctionInput( make_input((3, 4)), make_input((3, 4)), tgt_mask=tgt_mask, memory_mask=memory_mask, tgt_key_padding_mask=tgt_key_padding_mask, memory_key_padding_mask=memory_key_padding_mask ), reference_fn=partial(no_batch_dim_reference_fn, batch_first=True, kwargs_to_batchify={'tgt_key_padding_mask': 0, 'memory_key_padding_mask': 0}), desc='no_batch_dim_batch_first' )) samples.append( ModuleInput( constructor_input=FunctionInput(4, 2, 8, dropout=0.0, batch_first=False, norm_first=norm_first), forward_input=FunctionInput( make_input((3, 4)), make_input((3, 4)), tgt_mask=tgt_mask, memory_mask=memory_mask, tgt_key_padding_mask=tgt_key_padding_mask, memory_key_padding_mask=memory_key_padding_mask ), reference_fn=partial(no_batch_dim_reference_fn, batch_first=False, kwargs_to_batchify={'tgt_key_padding_mask': 0, 'memory_key_padding_mask': 0}), desc='no_batch_dim' )) return samples def module_inputs_torch_nn_Transformer(module_info, device, dtype, requires_grad, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) samples = [] # Samples below are for validating the no-batch-dim support. key_padding_masks = (None, torch.tensor([False, False, True], device=device, dtype=torch.bool)) attn_masks = (None, torch.tensor([False, False, True], device=device, dtype=torch.bool).expand((3, 3))) for mask, key_padding_mask, norm_first in itertools.product(attn_masks, key_padding_masks, (True, False)): # Using same mask for tgt and memory src_mask , tgt_mask = (mask,) * 2 src_key_padding_mask, tgt_key_padding_mask = (key_padding_mask,) * 2 samples.append( ModuleInput( constructor_input=FunctionInput(d_model=4, nhead=2, dim_feedforward=8, num_encoder_layers=1, num_decoder_layers=1, dropout=0.0, batch_first=True, norm_first=norm_first), forward_input=FunctionInput( make_input((3, 4)), make_input((3, 4)), tgt_mask=tgt_mask, src_mask=src_mask, tgt_key_padding_mask=tgt_key_padding_mask, src_key_padding_mask=src_key_padding_mask ), reference_fn=partial(no_batch_dim_reference_fn, batch_first=True, kwargs_to_batchify={'tgt_key_padding_mask': 0, 'src_key_padding_mask': 0}), desc='no_batch_dim_batch_first' )) samples.append( ModuleInput( constructor_input=FunctionInput(d_model=4, nhead=2, dim_feedforward=8, num_encoder_layers=1, num_decoder_layers=1, dropout=0.0, batch_first=False, norm_first=norm_first), forward_input=FunctionInput( make_input((3, 4)), make_input((3, 4)), tgt_mask=tgt_mask, src_mask=src_mask, tgt_key_padding_mask=tgt_key_padding_mask, src_key_padding_mask=src_key_padding_mask ), reference_fn=partial(no_batch_dim_reference_fn, batch_first=False, kwargs_to_batchify={'tgt_key_padding_mask': 0, 'src_key_padding_mask': 0}), desc='no_batch_dim' )) return samples def module_inputs_torch_nn_Embedding(module_info, device, dtype, requires_grad, **kwargs): make_empty = partial(torch.empty, device=device, dtype=torch.long, requires_grad=False) return [ ModuleInput( constructor_input=FunctionInput(num_embeddings=4, embedding_dim=3), forward_input=FunctionInput(make_empty(2, 3).random_(4)) ), ModuleInput( constructor_input=FunctionInput(num_embeddings=4, embedding_dim=3), forward_input=FunctionInput(make_empty(1, 512).random_(4).expand(7, 512)), desc='discontiguous' ), ] def module_inputs_torch_nn_MultiheadAttention(module_info, device, dtype, requires_grad, **kwargs): # Currently all samples below are for validating the no-batch-dim support. make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) samples = [] bool_vals = (True, False) key_padding_masks = (None, torch.tensor([False, False, True], device=device, dtype=torch.bool)) attn_masks = (None, torch.tensor([False, False, True], device=device, dtype=torch.bool).expand((3, 3, 3))) products = itertools.product(bool_vals, bool_vals, bool_vals, key_padding_masks, attn_masks) for bias, add_bias_kv, add_zero_attn, key_padding_mask, attn_mask in products: samples.append( ModuleInput( constructor_input=FunctionInput(embed_dim=3, num_heads=3, batch_first=True, bias=bias, add_bias_kv=add_bias_kv, add_zero_attn=add_zero_attn), forward_input=FunctionInput(make_input((3, 3)), make_input((3, 3)), make_input((3, 3)), key_padding_mask=key_padding_mask, attn_mask=attn_mask), reference_fn=no_batch_dim_reference_mha, ) ) samples.append( ModuleInput( constructor_input=FunctionInput(embed_dim=3, num_heads=3, batch_first=False, bias=bias, add_bias_kv=add_bias_kv, add_zero_attn=add_zero_attn), forward_input=FunctionInput(make_input((3, 3)), make_input((3, 3)), make_input((3, 3)), key_padding_mask=key_padding_mask, attn_mask=attn_mask), reference_fn=partial(no_batch_dim_reference_mha, batch_first=False), ) ) return samples def module_inputs_torch_nn_RNN_GRU_Cell(module_info, device, dtype, requires_grad, **kwargs): # Currently all samples below are for validating the no-batch-dim support. make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) samples = [ ModuleInput( constructor_input=FunctionInput(5, 10), forward_input=FunctionInput(make_input(5), make_input(10)), reference_fn=no_batch_dim_reference_fn, ), ModuleInput( constructor_input=FunctionInput(5, 10, bias=True), forward_input=FunctionInput(make_input(5), make_input(10)), reference_fn=no_batch_dim_reference_fn, ) ] is_rnn = kwargs.get('is_rnn', False) if is_rnn: # RNN also supports `nonlinearity` argument. # `tanh` is the default, so we check with `relu` samples.append( ModuleInput( constructor_input=FunctionInput(5, 10, bias=True, nonlinearity='relu'), forward_input=FunctionInput(make_input(5), make_input(10)), reference_fn=no_batch_dim_reference_fn, ) ) return samples def module_inputs_torch_nn_LSTMCell(module_info, device, dtype, requires_grad, **kwargs): # Currently all samples below are for validating the no-batch-dim support. make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) samples = ( ModuleInput( constructor_input=FunctionInput(5, 10), forward_input=FunctionInput(make_input(5), (make_input(10), make_input(10))), reference_fn=no_batch_dim_reference_lstmcell, ), ModuleInput( constructor_input=FunctionInput(5, 10, bias=True), forward_input=FunctionInput(make_input(5), (make_input(10), make_input(10))), reference_fn=no_batch_dim_reference_lstmcell, ), ) return samples def module_inputs_torch_nn_RNN_GRU(module_info, device, dtype, requires_grad, **kwargs): # Currently all samples below are for validating the no-batch-dim support. make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) is_rnn = kwargs['is_rnn'] nonlinearity = ('relu', 'tanh') bias = (False, True) batch_first = (False, True) bidirectional = (False, True) samples = [] if is_rnn: prod_gen = product(nonlinearity, bias, batch_first, bidirectional) else: prod_gen = product(bias, batch_first, bidirectional) for args in prod_gen: if is_rnn: nl, b, b_f, bidir = args else: b, b_f, bidir = args cons_args = {'input_size': 2, 'hidden_size': 2, 'num_layers': 2, 'batch_first': b_f, 'bias': b, 'bidirectional': bidir} cons_args_hidden = {'input_size': 2, 'hidden_size': 3, 'num_layers': 2, 'batch_first': b_f, 'bias': b, 'bidirectional': bidir} if is_rnn: cons_args['nonlinearity'] = nl cons_args_hidden['nonlinearity'] = nl samples.append( ModuleInput( constructor_input=FunctionInput(**cons_args), forward_input=FunctionInput(make_input((2, 2))), reference_fn=partial(no_batch_dim_reference_rnn_gru, batch_first=b_f), ) ) samples.append( ModuleInput( constructor_input=FunctionInput(**cons_args_hidden), forward_input=FunctionInput(make_input((3, 2)), make_input((4 if bidir else 2, 3))), reference_fn=partial(no_batch_dim_reference_rnn_gru, batch_first=b_f), ) ) return samples def module_inputs_torch_nn_LSTM(module_info, device, dtype, requires_grad, **kwargs): # Currently all samples below are for validating the no-batch-dim support. make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) bias = (False, True) batch_first = (False, True) bidirectional = (False, True) proj_sizes = (0, 2) samples = [] prod_gen = product(bias, batch_first, bidirectional, proj_sizes) for args in prod_gen: b, b_f, bidir, proj_size = args hidden_size = 3 cons_args = {'input_size': 2, 'hidden_size': hidden_size, 'num_layers': 2, 'proj_size': proj_size, 'batch_first': b_f, 'bias': b, 'bidirectional': bidir} cons_args_hidden = {'input_size': 2, 'hidden_size': hidden_size, 'num_layers': 2, 'proj_size': proj_size, 'batch_first': b_f, 'bias': b, 'bidirectional': bidir} samples.append( ModuleInput( constructor_input=FunctionInput(**cons_args), forward_input=FunctionInput(make_input((2, 2))), reference_fn=partial(no_batch_dim_reference_lstm, batch_first=b_f), ) ) h_out = proj_size if proj_size > 0 else hidden_size hx = (make_input((4 if bidir else 2, h_out)), make_input((4 if bidir else 2, hidden_size))) samples.append( ModuleInput( constructor_input=FunctionInput(**cons_args_hidden), forward_input=FunctionInput(make_input((3, 2)), hx), reference_fn=partial(no_batch_dim_reference_lstm, batch_first=b_f), ) ) return samples # All these operators share similar issues on cuDNN and MIOpen rnn_gru_lstm_module_info_decorators = ( # RuntimeError: Batching rule not implemented for aten::_cudnn_rnn_backward. # We could not generate a fallback DecorateInfo( unittest.expectedFailure, "TestModule", "test_grad", active_if=(TEST_CUDNN and not TEST_WITH_ROCM), device_type='cuda' ), # NotImplementedError: the derivative for '_cudnn_rnn_backward' is not implemented. # Double backwards is not supported for CuDNN RNNs due to limitations in the CuDNN API DecorateInfo( unittest.expectedFailure, "TestModule", "test_gradgrad", active_if=(TEST_CUDNN and not TEST_WITH_ROCM), device_type='cuda' ), # CUDNN GRU doesn't accept non-contiguous hx DecorateInfo( unittest.expectedFailure, "TestModule", "test_non_contiguous_tensors", active_if=(TEST_CUDNN and not TEST_WITH_ROCM), device_type='cuda' ), # MIOPEN GRU doesn't accept non-contiguous hx (this is dispatched to miopen only for float). DecorateInfo( unittest.expectedFailure, "TestModule", "test_non_contiguous_tensors", active_if=(TEST_CUDNN and TEST_WITH_ROCM), dtypes=(torch.float,), device_type='cuda' ), ) # Database of ModuleInfo entries in alphabetical order. module_db: List[ModuleInfo] = [ ModuleInfo(torch.nn.AdaptiveAvgPool2d, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, module_inputs_func=module_inputs_torch_nn_AdaptiveAvgPool2d, skips=( DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),) ), ModuleInfo(torch.nn.AvgPool1d, module_inputs_func=module_inputs_torch_nn_AvgPool1d, skips=( # No channels_last support for AvgPool1d as it does not take 4D inputs DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'), DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),) ), ModuleInfo(torch.nn.BatchNorm2d, module_inputs_func=module_inputs_torch_nn_BatchNorm2d, skips=( DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),), decorators=( # Failure on ROCM for BatchNorm2d float32 issue #70125 DecorateInfo(skipCUDAIfRocm, 'TestModule', 'test_memory_format', dtypes=[torch.float32]),) ), ModuleInfo(torch.nn.BatchNorm3d, module_inputs_func=module_inputs_torch_nn_BatchNorm3d, skips=( DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),), decorators=( # Failure on ROCM for BatchNorm3d float32 issue #70125 DecorateInfo(skipCUDAIfRocm, 'TestModule', 'test_memory_format', dtypes=[torch.float32]),) ), ModuleInfo(torch.nn.Conv1d, module_inputs_func=partial(module_inputs_torch_nn_ConvNd, N=1, lazy=False), gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, module_memformat_affects_out=True, skips=( # channels_last support on cuda requires cudnn >= 7603 DecorateInfo(skipCUDAIfCudnnVersionLessThan(version=7603), 'TestModule', 'test_memory_format'), # Failure on ROCM for float32 issue #70125 DecorateInfo(skipCUDAIfRocm, 'TestModule', 'test_memory_format', dtypes=[torch.float32]), DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]) ), decorators=( DecorateInfo(precisionOverride({torch.float32: 1e-04}), 'TestModule', 'test_memory_format'), )), ModuleInfo(torch.nn.Conv2d, module_inputs_func=partial(module_inputs_torch_nn_ConvNd, N=2, lazy=False), gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, module_memformat_affects_out=True, skips=( # channels_last support on cuda requires cudnn >= 7603 DecorateInfo(skipCUDAIfCudnnVersionLessThan(version=7603), 'TestModule', 'test_memory_format'), # Failure on ROCM for float32 issue #70125 DecorateInfo(skipCUDAIfRocm, 'TestModule', 'test_memory_format', dtypes=[torch.float32]), DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]), ), decorators=( DecorateInfo(precisionOverride({torch.float32: 1e-04}), 'TestModule', 'test_memory_format'), )), ModuleInfo(torch.nn.Conv3d, module_inputs_func=partial(module_inputs_torch_nn_ConvNd, N=3, lazy=False), gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, module_memformat_affects_out=True, skips=( # channels_last support on cuda requires cudnn >= 8005 DecorateInfo(skipCUDAIfCudnnVersionLessThan(version=8005), 'TestModule', 'test_memory_format'), # Failure on ROCM for float32 issue #70125 DecorateInfo(skipCUDAIfRocm, 'TestModule', 'test_memory_format', dtypes=[torch.float32]), DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]), ), decorators=( DecorateInfo(precisionOverride({torch.float32: 1e-04}), 'TestModule', 'test_memory_format'), )), ModuleInfo(torch.nn.ConvTranspose1d, module_inputs_func=partial(module_inputs_torch_nn_ConvNd, N=1, lazy=False, transposed=True), gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, module_memformat_affects_out=True, skips=( # channels_last support on cuda requires cudnn >= 7603 DecorateInfo(skipCUDAIfCudnnVersionLessThan(version=7603), 'TestModule', 'test_memory_format'), # Failure on ROCM for float32 issue #70125 DecorateInfo(skipCUDAIfRocm, 'TestModule', 'test_memory_format', dtypes=[torch.float32]), DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]), ), decorators=( DecorateInfo(precisionOverride({torch.float32: 1e-04}), 'TestModule', 'test_memory_format'), )), ModuleInfo(torch.nn.ConvTranspose2d, module_inputs_func=partial(module_inputs_torch_nn_ConvNd, N=2, lazy=False, transposed=True), gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, module_memformat_affects_out=True, skips=( # channels_last support on cuda requires cudnn >= 7603 DecorateInfo(skipCUDAIfCudnnVersionLessThan(version=7603), 'TestModule', 'test_memory_format'), # Failure on ROCM for float32 issue #70125 DecorateInfo(skipCUDAIfRocm, 'TestModule', 'test_memory_format', dtypes=[torch.float32]), DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]), ), decorators=( DecorateInfo(precisionOverride({torch.float32: 1e-04}), 'TestModule', 'test_memory_format'), )), ModuleInfo(torch.nn.ConvTranspose3d, module_inputs_func=partial(module_inputs_torch_nn_ConvNd, N=3, lazy=False, transposed=True), gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, module_memformat_affects_out=True, skips=( # channels_last support on cuda requires cudnn >= 8005 DecorateInfo(skipCUDAIfCudnnVersionLessThan(version=8005), 'TestModule', 'test_memory_format'), # Failure on ROCM for float32 issue #70125 DecorateInfo(skipCUDAIfRocm, 'TestModule', 'test_memory_format', dtypes=[torch.float32]), DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]), ), decorators=( DecorateInfo(precisionOverride({torch.float32: 1e-04}), 'TestModule', 'test_memory_format'), )), ModuleInfo(torch.nn.ELU, module_inputs_func=module_inputs_torch_nn_ELU, skips=( DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),) ), ModuleInfo(torch.nn.L1Loss, module_inputs_func=module_inputs_torch_nn_L1Loss, skips=( # No channels_last support for loss functions. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'), DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),) ), ModuleInfo(torch.nn.LazyConv1d, module_inputs_func=partial(module_inputs_torch_nn_ConvNd, N=1, lazy=True), gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, module_memformat_affects_out=True, skips=( # channels_last support on cuda requires cudnn >= 7603 DecorateInfo(skipCUDAIfCudnnVersionLessThan(version=7603), 'TestModule', 'test_memory_format'), # Failure on ROCM for float32 issue #70125 DecorateInfo(skipCUDAIfRocm, 'TestModule', 'test_memory_format', dtypes=[torch.float32]), # Lazy modules don't currently play well with ModuleInfo tests on the meta device. # See https://github.com/pytorch/pytorch/issues/70505 for more info. DecorateInfo(skipMeta), DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]), ), decorators=( DecorateInfo(precisionOverride({torch.float32: 1e-04}), 'TestModule', 'test_memory_format'), )), ModuleInfo(torch.nn.LazyConv2d, module_inputs_func=partial(module_inputs_torch_nn_ConvNd, N=2, lazy=True), gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, module_memformat_affects_out=True, skips=( DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]), # channels_last support on cuda requires cudnn >= 7603 DecorateInfo(skipCUDAIfCudnnVersionLessThan(version=7603), 'TestModule', 'test_memory_format'), # Failure on ROCM for float32 issue #70125 DecorateInfo(skipCUDAIfRocm, 'TestModule', 'test_memory_format', dtypes=[torch.float32]), # Lazy modules don't currently play well with ModuleInfo tests on the meta device. # See https://github.com/pytorch/pytorch/issues/70505 for more info. DecorateInfo(skipMeta), ), decorators=( DecorateInfo(precisionOverride({torch.float32: 1e-04}), 'TestModule', 'test_memory_format'), )), ModuleInfo(torch.nn.LazyConv3d, module_inputs_func=partial(module_inputs_torch_nn_ConvNd, N=3, lazy=True), gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, module_memformat_affects_out=True, skips=( # channels_last support on cuda requires cudnn >= 8005 DecorateInfo(skipCUDAIfCudnnVersionLessThan(version=8005), 'TestModule', 'test_memory_format'), # Failure on ROCM for float32 issue #70125 DecorateInfo(skipCUDAIfRocm, 'TestModule', 'test_memory_format', dtypes=[torch.float32]), # Lazy modules don't currently play well with ModuleInfo tests on the meta device. # See https://github.com/pytorch/pytorch/issues/70505 for more info. DecorateInfo(skipMeta), DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]), ), decorators=( DecorateInfo(precisionOverride({torch.float32: 1e-04}), 'TestModule', 'test_memory_format'), )), ModuleInfo(torch.nn.LazyConvTranspose1d, module_inputs_func=partial(module_inputs_torch_nn_ConvNd, N=1, lazy=True, transposed=True), gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, module_memformat_affects_out=True, skips=( # channels_last support on cuda requires cudnn >= 7603 DecorateInfo(skipCUDAIfCudnnVersionLessThan(version=7603), 'TestModule', 'test_memory_format'), # Failure on ROCM for float32 issue #70125 DecorateInfo(skipCUDAIfRocm, 'TestModule', 'test_memory_format', dtypes=[torch.float32]), # Lazy modules don't currently play well with ModuleInfo tests on the meta device. # See https://github.com/pytorch/pytorch/issues/70505 for more info. DecorateInfo(skipMeta), DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]), ), decorators=( DecorateInfo(precisionOverride({torch.float32: 1e-04}), 'TestModule', 'test_memory_format'), )), ModuleInfo(torch.nn.LazyConvTranspose2d, module_inputs_func=partial(module_inputs_torch_nn_ConvNd, N=2, lazy=True, transposed=True), gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, module_memformat_affects_out=True, skips=( # channels_last support on cuda requires cudnn >= 7603 DecorateInfo(skipCUDAIfCudnnVersionLessThan(version=7603), 'TestModule', 'test_memory_format'), # Failure on ROCM for float32 issue #70125 DecorateInfo(skipCUDAIfRocm, 'TestModule', 'test_memory_format', dtypes=[torch.float32]), # Lazy modules don't currently play well with ModuleInfo tests on the meta device. # See https://github.com/pytorch/pytorch/issues/70505 for more info. DecorateInfo(skipMeta), DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]), ), decorators=( DecorateInfo(precisionOverride({torch.float32: 1e-04}), 'TestModule', 'test_memory_format'), )), ModuleInfo(torch.nn.LazyConvTranspose3d, module_inputs_func=partial(module_inputs_torch_nn_ConvNd, N=3, lazy=True, transposed=True), gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, module_memformat_affects_out=True, skips=( # channels_last support on cuda requires cudnn >= 8005 DecorateInfo(skipCUDAIfCudnnVersionLessThan(version=8005), 'TestModule', 'test_memory_format'), # Failure on ROCM for float32 issue #70125 DecorateInfo(skipCUDAIfRocm, 'TestModule', 'test_memory_format', dtypes=[torch.float32]), # Lazy modules don't currently play well with ModuleInfo tests on the meta device. # See https://github.com/pytorch/pytorch/issues/70505 for more info. DecorateInfo(skipMeta), DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]), ), decorators=( DecorateInfo(precisionOverride({torch.float32: 1e-04}), 'TestModule', 'test_memory_format'), )), ModuleInfo(torch.nn.Linear, module_inputs_func=module_inputs_torch_nn_Linear, skips=( DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]), # No channels_last support for Linear currently. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'),) ), ModuleInfo(torch.nn.Bilinear, module_inputs_func=module_inputs_torch_nn_Bilinear, decorators=[ DecorateInfo( toleranceOverride({ torch.float32: tol(atol=1e-4, rtol=1e-4), torch.float64: tol(atol=1e-4, rtol=1e-4)}), 'TestModule', 'test_forward', device_type='cpu') ], skips=( DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]), # No channels_last support for Bilinear currently. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'),) ), ModuleInfo(torch.nn.MaxPool2d, module_inputs_func=module_inputs_torch_nn_MaxPool2d, skips=( # TODO: test_non_contiguous_tensors doesn't handle case where output is not a singleton (such as # return_indices=True for MaxPool2D), submit fix DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_non_contiguous_tensors'), # TODO: test_cpu_gpu_parity doesn't handle case where output is not a singleton, submit fix DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_cpu_gpu_parity'), DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),) ), ModuleInfo(torch.nn.NLLLoss, module_inputs_func=module_inputs_torch_nn_NLLLoss, skips=( # No channels_last support for loss functions. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'), DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),) ), ModuleInfo(torch.nn.GaussianNLLLoss, module_inputs_func=module_inputs_torch_nn_GaussianNLLLoss, skips=( # No channels_last support for loss functions. DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]), DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'),)), ModuleInfo(torch.nn.CrossEntropyLoss, module_inputs_func=module_inputs_torch_nn_CrossEntropyLoss, skips=( DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),) ), ModuleInfo(torch.nn.Hardswish, module_inputs_func=module_inputs_torch_nn_Hardswish, skips=( DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),), supports_gradgrad=False), ModuleInfo(torch.nn.TransformerEncoderLayer, module_inputs_func=module_inputs_torch_nn_TransformerEncoderLayer, skips=( # No channels_last support for TransformerEncoderLayer currently. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'), DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),) ), ModuleInfo(torch.nn.TransformerDecoderLayer, module_inputs_func=module_inputs_torch_nn_TransformerDecoderLayer, skips=( # No channels_last support for TransformerDecoderLayer currently. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'), DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),) ), ModuleInfo(torch.nn.Transformer, module_inputs_func=module_inputs_torch_nn_Transformer, skips=( # No channels_last support for Transformer currently. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'), DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),) ), ModuleInfo(torch.nn.MultiheadAttention, module_inputs_func=module_inputs_torch_nn_MultiheadAttention, skips=( # No channels_last support for MultiheadAttention currently. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'), DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),) ), ModuleInfo(torch.nn.Embedding, module_inputs_func=module_inputs_torch_nn_Embedding, skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'), DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),) ), ModuleInfo(torch.nn.ReLU, module_inputs_func=module_inputs_torch_nn_ReLU, skips=( DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),) ), ModuleInfo(torch.nn.RNNCell, module_inputs_func=partial(module_inputs_torch_nn_RNN_GRU_Cell, is_rnn=True), skips=( DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),) ), ModuleInfo(torch.nn.GRUCell, module_inputs_func=module_inputs_torch_nn_RNN_GRU_Cell, skips=( DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),) ), ModuleInfo(torch.nn.LSTMCell, module_inputs_func=module_inputs_torch_nn_LSTMCell, skips=( DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),) ), ModuleInfo(torch.nn.Sigmoid, module_inputs_func=module_inputs_torch_nn_Sigmoid, skips=( DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),) ), ModuleInfo(torch.nn.RNN, module_inputs_func=partial(module_inputs_torch_nn_RNN_GRU, is_rnn=True), skips=( DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),), decorators=rnn_gru_lstm_module_info_decorators ), ModuleInfo(torch.nn.GRU, module_inputs_func=partial(module_inputs_torch_nn_RNN_GRU, is_rnn=False), skips=( DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),), decorators=rnn_gru_lstm_module_info_decorators), ModuleInfo(torch.nn.LSTM, module_inputs_func=module_inputs_torch_nn_LSTM, skips=( DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),), decorators=rnn_gru_lstm_module_info_decorators) ]