# mypy: ignore-errors import torch import unittest from copy import deepcopy from enum import Enum from functools import wraps, partial from itertools import chain, product import itertools import math import torch.nn.functional as F from torch.nn.utils.rnn import pack_padded_sequence from torch.testing import make_tensor from torch.testing._internal.common_cuda import TEST_CUDNN from torch.testing._internal.common_dtype import ( floating_types, floating_and_complex_types_and, get_all_fp_dtypes) from torch.testing._internal.common_device_type import ( _TestParametrizer, _update_param_kwargs, expectedFailureMPS, toleranceOverride, tol, skipCUDAIfCudnnVersionLessThan, skipCUDAIfRocm, precisionOverride, skipMeta, skipMPS, skipCUDAVersionIn) from torch.testing._internal.common_methods_invocations import DecorateInfo from torch.testing._internal.common_nn import ( cosineembeddingloss_reference, cross_entropy_loss_reference, ctcloss_reference, hingeembeddingloss_reference, huberloss_reference, kldivloss_reference, marginrankingloss_reference, multimarginloss_reference, multilabelmarginloss_reference, nllloss_reference, nlllossNd_reference, smoothl1loss_reference, softmarginloss_reference, get_reduction) from torch.testing._internal.common_utils import ( freeze_rng_state, skipIfMPS, skipIfMPSOnMacOS13, GRADCHECK_NONDET_TOL, TEST_WITH_ROCM, IS_WINDOWS, skipIfTorchDynamo) from types import ModuleType import operator # List of all namespaces containing modules to test. MODULE_NAMESPACES: list[ModuleType] = [ torch.nn.modules, torch.ao.nn.qat.modules, torch.ao.nn.quantizable.modules, torch.ao.nn.quantized.modules, torch.ao.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.ao.nn.quantized.MaxPool2d, # aliases to nn.MaxPool2d torch.ao.nn.quantized.MaxPool2d, # aliases to nn.MaxPool2d } # List of all module classes to test. MODULE_CLASSES: list[type] = [*chain.from_iterable([ [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', '') # Deal with any aliases by preferring earlier names. if module_cls not in MODULE_CLASS_NAMES: MODULE_CLASS_NAMES[module_cls] = f'{namespace_name}.{module_name}' # Specifies the modes (i.e. train, eval) to test over. TrainEvalMode = Enum('TrainEvalMode', ('train_only', 'eval_only', 'train_and_eval')) class modules(_TestParametrizer): """ PROTOTYPE: Decorator for specifying a list of modules over which to run a test. """ def __init__(self, module_info_iterable, allowed_dtypes=None, train_eval_mode=TrainEvalMode.train_and_eval, skip_if_dynamo=True): self.module_info_list = list(module_info_iterable) self.allowed_dtypes = set(allowed_dtypes) if allowed_dtypes is not None else None self.train_eval_mode = train_eval_mode self.skip_if_dynamo = skip_if_dynamo def _get_training_flags(self, module_info): training_flags = [] if (self.train_eval_mode == TrainEvalMode.train_only or self.train_eval_mode == TrainEvalMode.train_and_eval): training_flags.append(True) if (self.train_eval_mode == TrainEvalMode.eval_only or self.train_eval_mode == TrainEvalMode.train_and_eval): training_flags.append(False) # If train and eval modes don't differ for the module, don't bother using more than one. if not module_info.train_and_eval_differ: training_flags = training_flags[:1] return training_flags 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: dtypes = set(module_info.supported_dtypes(device_cls.device_type)) if self.allowed_dtypes is not None: dtypes = dtypes.intersection(self.allowed_dtypes) training_flags = self._get_training_flags(module_info) for (training, dtype) in product(training_flags, dtypes): # Construct the test name; device / dtype parts are handled outside. # See [Note: device and dtype suffix placement] test_name = module_info.formatted_name if len(training_flags) > 1: test_name += f"_{'train_mode' if training else 'eval_mode'}" # Construct parameter kwargs to pass to the test. param_kwargs = {'module_info': module_info} _update_param_kwargs(param_kwargs, 'dtype', dtype) _update_param_kwargs(param_kwargs, 'training', training) try: @wraps(test) def test_wrapper(*args, **kwargs): return test(*args, **kwargs) if self.skip_if_dynamo and not torch.testing._internal.common_utils.TEST_WITH_TORCHINDUCTOR: test_wrapper = skipIfTorchDynamo("Policy: we don't run ModuleInfo tests w/ Dynamo")(test_wrapper) decorator_fn = partial(module_info.get_decorators, generic_cls.__name__, test.__name__, device_cls.device_type, dtype) yield (test_wrapper, test_name, param_kwargs, decorator_fn) except Exception as ex: # Provides an error message for debugging before rethrowing the exception print(f"Failed to instantiate {test_name} for module {module_info.name}!") raise ex def get_module_common_name(module_cls): if module_cls in MODULE_CLASS_NAMES: # Example: "nn.Linear" return MODULE_CLASS_NAMES[module_cls] else: return module_cls.__name__ class FunctionInput: """ 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: """ 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 ModuleErrorEnum(Enum): """ Enumerates when error is raised when testing modules. """ CONSTRUCTION_ERROR = 0 FORWARD_ERROR = 1 class ErrorModuleInput: """ A ModuleInput that will cause the operation to throw an error plus information about the resulting error. """ __slots__ = ["module_error_input", "error_on", "error_type", "error_regex"] def __init__(self, module_error_input, *, error_on=ModuleErrorEnum.CONSTRUCTION_ERROR, error_type=RuntimeError, error_regex): self.module_error_input = module_error_input self.error_on = error_on self.error_type = error_type self.error_regex = error_regex class ModuleInfo: """ 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 dtypesIfMPS=(torch.float16, torch.float32,), # dtypes this function is expected to work with on MPS dtypesIfHpu=(torch.bfloat16, torch.float32,), 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 train_and_eval_differ=False, # whether the module has differing behavior between train and eval module_error_inputs_func=None, # Function to generate module inputs that error gradcheck_fast_mode=None, # Whether to use the fast implementation for gradcheck/gradgradcheck. # When set to None, defers to the default value provided by the wrapper # function around gradcheck (testing._internal.common_utils.gradcheck) ): self.module_cls = module_cls self.module_inputs_func = module_inputs_func self.decorators = (*(decorators if decorators else []), *(skips if skips else [])) self.dtypes = dtypes self.dtypesIfMPS = dtypesIfMPS self.dtypesIfHpu = dtypesIfHpu self.supports_gradgrad = supports_gradgrad self.gradcheck_nondet_tol = gradcheck_nondet_tol self.module_memformat_affects_out = module_memformat_affects_out self.train_and_eval_differ = train_and_eval_differ self.module_error_inputs_func = module_error_inputs_func self.gradcheck_fast_mode = gradcheck_fast_mode self.is_lazy = issubclass(module_cls, torch.nn.modules.lazy.LazyModuleMixin) def get_decorators(self, test_class, test_name, device, dtype, param_kwargs): result = [] for decorator in self.decorators: if isinstance(decorator, DecorateInfo): if decorator.is_active(test_class, test_name, device, dtype, param_kwargs): result.extend(decorator.decorators) else: result.append(decorator) return result def supported_dtypes(self, device_type): if device_type == 'mps': return self.dtypesIfMPS elif device_type == 'hpu': return self.dtypesIfHpu else: return self.dtypes @property def name(self): return get_module_common_name(self.module_cls) @property def formatted_name(self): return self.name.replace('.', '_') # Start of module inputs functions. def module_inputs_torch_nn_Linear(module_info, device, dtype, requires_grad, training, **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, training, **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=bilinear_reference_fn), 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_KLDivLoss(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) cases: list[tuple[str, dict]] = [ ('', {}), ('reduction_sum', {'reduction': 'sum'}), ('reduction_batchmean', {'reduction': 'batchmean'}), ('reduction_none', {'reduction': 'none'}), ('log_target', {'log_target': True}) ] module_inputs = [] for desc, constructor_kwargs in cases: def reference_fn(m, p, i, t, constructor_kwargs=constructor_kwargs): return kldivloss_reference(i, t, **constructor_kwargs) input = make_input((10, 10)).log() target = make_input((10, 10)) if kwargs.get('log_target', False) else make_input((10, 10)).log() module_inputs.append( ModuleInput(constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput(input, target), desc=desc, reference_fn=reference_fn) ) scalar_input = make_input(()).log() # FIXME(rec): scalar_target is unused, perhaps should be argument to FunctionInput? scalar_target = ( # noqa: F841 make_input(()) if kwargs.get('log_target', False) else make_input(()).log() ) module_inputs.append( ModuleInput(constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput(scalar_input, scalar_input), desc='scalar_' + desc, reference_fn=reference_fn) ) return module_inputs def module_inputs_torch_nn_NLLLoss(module_info, device, dtype, requires_grad, training, **kwargs): def make_input(shape, device=device, dtype=dtype, requires_grad=requires_grad): return make_tensor(shape, device=device, dtype=dtype, requires_grad=False).log_softmax(dim=1).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(4).abs()}), ('weights_ignore_index', {'weight': make_weight(4).abs(), 'ignore_index': 2}), ('weights_ignore_index_neg', {'weight': make_weight(4).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, 4)), torch.empty(15, device=device).uniform_().mul(4).floor().long()), desc=desc, reference_fn=reference_fn) ) def nd_reference_fn(m, p, i, t, constructor_kwargs=constructor_kwargs): return nlllossNd_reference(i, t, **constructor_kwargs) module_inputs.append( ModuleInput(constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput( make_input((2, 4, 5, 5)), torch.empty(2, 5, 5, device=device).uniform_().mul(4).floor().long()), desc=f"nd_{desc}", reference_fn=nd_reference_fn) ) module_inputs.append( ModuleInput(constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput( make_input((2, 4, 5, 5, 2, 2)), torch.empty(2, 5, 5, 2, 2, device=device).uniform_().mul(4).floor().long()), desc=f"higher_dim_{desc}", reference_fn=nd_reference_fn) ) module_inputs.append( ModuleInput(constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput( make_input((2, 4, 5)), torch.empty(2, 5, device=device).uniform_().mul(4).floor().long()), desc=f"3d_{desc}", reference_fn=nd_reference_fn) ) return module_inputs def module_inputs_torch_nn_GaussianNLLLoss(module_info, device, dtype, requires_grad, training, **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 module_inputs_torch_nn_PoissonNLLLoss(module_info, device, dtype, requires_grad, training, **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'}), ('full', {'full': True}), ('no_log_input', {'log_input': False}), ('full_no_log_input', {'full': True, 'log_input': False}), ] def poissonnllloss_reference_fn(i, t, log_input=True, full=False, reduction='mean', eps=1e-8): if log_input: result = i.exp() - t.mul(i) else: result = i - t.mul((i + eps).log()) if full: result += (t.mul(t.log()) - t + 0.5 * (2. * math.pi * t).log()).masked_fill(t <= 1, 0) if reduction == 'none': return result elif reduction == 'mean': return result.sum() / i.numel() else: return result.sum() module_inputs = [] for desc, constructor_kwargs in cases: def reference_fn(m, p, i, t, constructor_kwargs=constructor_kwargs): return poissonnllloss_reference_fn(i, t, **constructor_kwargs) log_input = constructor_kwargs.get('log_input', True) input = make_input((2, 3, 4, 5)) if log_input else make_input((2, 3, 4, 5)).abs().add(0.001) module_inputs.append( ModuleInput(constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput(input, make_target((2, 3, 4, 5)).floor_().abs_()), desc=desc, reference_fn=reference_fn) ) return module_inputs def module_inputs_torch_nn_MSELoss(module_info, device, dtype, requires_grad, training, **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'}), ] def mse_loss_reference_fn(m, p, i, t, reduction='mean'): if reduction == 'none': return (i - t).pow(2) elif reduction == 'mean': return (i - t).pow(2).sum() / i.numel() else: return (i - t).pow(2).sum() module_inputs = [] for desc, constructor_kwargs in cases: module_inputs.append( ModuleInput(constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput(make_input((2, 3, 4, 5)), make_target((2, 3, 4, 5))), desc=desc, reference_fn=partial(mse_loss_reference_fn, **constructor_kwargs)) ) module_inputs.append( ModuleInput(constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput(make_input(()), make_target(())), desc=f'{desc}_scalar', reference_fn=partial(mse_loss_reference_fn, **constructor_kwargs)) ) 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=f'no_batch_dim_{reduction}' ) for reduction in ['none', 'mean', 'sum']] def module_inputs_torch_nn_AvgPool1d(module_info, device, dtype, requires_grad, training, **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), ModuleInput(constructor_input=FunctionInput(2), forward_input=FunctionInput(make_input((2, 3, 6)))), ModuleInput(constructor_input=FunctionInput((2,), (2,)), forward_input=FunctionInput(make_input((2, 3, 6))), desc='stride'), ModuleInput(constructor_input=FunctionInput(2, 2, 1), forward_input=FunctionInput(make_input((2, 3, 6))), desc='stride_pad')] def module_inputs_torch_nn_AvgPool2d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput((2, 2)), forward_input=FunctionInput(make_input((3, 6, 6))), desc='no_batch_dim', reference_fn=no_batch_dim_reference_fn), ModuleInput(constructor_input=FunctionInput((2, 2)), forward_input=FunctionInput(make_input((2, 3, 6, 6)))), ModuleInput(constructor_input=FunctionInput((2, 2), (2, 2)), forward_input=FunctionInput(make_input((2, 3, 6, 6))), desc='stride'), ModuleInput(constructor_input=FunctionInput((2, 2), (2, 2), (1, 1)), forward_input=FunctionInput(make_input((2, 3, 6, 6))), desc='stride_pad'), ModuleInput(constructor_input=FunctionInput((2, 2), divisor_override=1), forward_input=FunctionInput(make_input((2, 3, 6, 6))), desc='divisor'), ModuleInput(constructor_input=FunctionInput((2, 2), (2, 2), divisor_override=1), forward_input=FunctionInput(make_input((2, 3, 6, 6))), desc='divisor_stride'), ModuleInput(constructor_input=FunctionInput((2, 2), (2, 2), (1, 1), divisor_override=1), forward_input=FunctionInput(make_input((2, 3, 6, 6))), desc='divisor_stride_pad')] def module_inputs_torch_nn_AvgPool3d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput((2, 2, 2)), forward_input=FunctionInput(make_input((3, 4, 4, 4))), desc='no_batch_dim', reference_fn=no_batch_dim_reference_fn), ModuleInput(constructor_input=FunctionInput((2, 2, 2)), forward_input=FunctionInput(make_input((2, 3, 4, 4, 4)))), ModuleInput(constructor_input=FunctionInput(2, (2, 2, 2)), forward_input=FunctionInput(make_input((2, 3, 5, 5, 5))), desc='stride'), ModuleInput(constructor_input=FunctionInput(2, 2, (1, 1, 1)), forward_input=FunctionInput(make_input((2, 3, 5, 5, 5))), desc='stride_pad'), ModuleInput(constructor_input=FunctionInput(4, 2, (1, 2, 1)), forward_input=FunctionInput(make_input((2, 3, 5, 5, 5))), desc='stride_pad_gpu_fixedkw_output'), ModuleInput(constructor_input=FunctionInput((2, 4, 8), 1, (1, 1, 2)), forward_input=FunctionInput(make_input((2, 3, 2, 4, 8))), desc='stride_pad_gpu_general_output'), ModuleInput(constructor_input=FunctionInput(3, 1, 0), forward_input=FunctionInput(make_input((2, 3, 4, 4, 4))), desc='stride1_pad0_gpu_input'), ModuleInput(constructor_input=FunctionInput(2, 2, (1, 1, 1)), forward_input=FunctionInput(make_input((2, 3, 4, 4, 4))), desc='stride_pad_gpu_input_nooverlap'), ModuleInput(constructor_input=FunctionInput((2, 2, 2), divisor_override=1), forward_input=FunctionInput(make_input((2, 3, 4, 4, 4))), desc='divisor'), ModuleInput(constructor_input=FunctionInput(2, (2, 2, 2), divisor_override=1), forward_input=FunctionInput(make_input((2, 3, 5, 5, 5))), desc='divisor_stride'), ModuleInput(constructor_input=FunctionInput(2, 2, (1, 1, 1), divisor_override=1), forward_input=FunctionInput(make_input((2, 3, 5, 5, 5))), desc='divisor_stride_pad'), ModuleInput(constructor_input=FunctionInput(4, 2, (1, 2, 1), divisor_override=1), forward_input=FunctionInput(make_input((2, 3, 5, 5, 5))), desc='divisor_stride_pad_gpu_fixedkw_output'), ModuleInput(constructor_input=FunctionInput((2, 4, 8), 1, (1, 1, 2), divisor_override=1), forward_input=FunctionInput(make_input((2, 3, 2, 4, 8))), desc='divisor_stride_pad_gpu_general_output'), ModuleInput(constructor_input=FunctionInput(3, 1, 0, divisor_override=1), forward_input=FunctionInput(make_input((2, 3, 4, 4, 4))), desc='divisor_stride1_pad0_gpu_input'), ModuleInput(constructor_input=FunctionInput(2, 2, (1, 1, 1), divisor_override=1), forward_input=FunctionInput(make_input((2, 3, 4, 4, 4))), desc='divisor_stride_pad_gpu_input_nooverlap')] def module_inputs_torch_nn_AdaptiveAvgPool1d(module_info, device, dtype, requires_grad, training, **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))), desc='single'), ModuleInput(constructor_input=FunctionInput(3,), forward_input=FunctionInput(make_input((3, 5))), reference_fn=no_batch_dim_reference_fn, desc='no_batch_dim'), ModuleInput(constructor_input=FunctionInput(1,), forward_input=FunctionInput(make_input((1, 3, 5))), desc='one_output')] def module_inputs_torch_nn_AdaptiveAvgPool2d(module_info, device, dtype, requires_grad, training, **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'), ModuleInput(constructor_input=FunctionInput(3,), forward_input=FunctionInput(make_input((3, 5, 6))), reference_fn=no_batch_dim_reference_fn, desc='no_batch_dim'), ModuleInput(constructor_input=FunctionInput(1,), forward_input=FunctionInput(make_input((1, 3, 5, 6))), desc='single_1x1output'), ModuleInput(constructor_input=FunctionInput((3, 4)), forward_input=FunctionInput(make_input((1, 3, 5, 6))), desc='tuple'), ModuleInput(constructor_input=FunctionInput((3, None)), forward_input=FunctionInput(make_input((1, 3, 5, 6))), desc='tuple_none')] def module_inputs_torch_nn_AdaptiveAvgPool3d(module_info, device, dtype, requires_grad, training, **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, 5, 2, 7))), desc='single'), ModuleInput(constructor_input=FunctionInput(3,), forward_input=FunctionInput(make_input((3, 5, 2, 7))), reference_fn=no_batch_dim_reference_fn, desc='no_batch_dim'), ModuleInput(constructor_input=FunctionInput((3, 4, 5)), forward_input=FunctionInput(make_input((2, 3, 5, 3, 7))), desc='tuple'), ModuleInput(constructor_input=FunctionInput((None, 4, 5)), forward_input=FunctionInput(make_input((2, 3, 5, 3, 7))), desc='tuple_none'), ModuleInput(constructor_input=FunctionInput((3, 2, 2)), forward_input=FunctionInput(make_input((1, 1, 3, 2, 6))), desc='last_dim')] def module_inputs_torch_nn_AdaptiveMaxPool1d(module_info, device, dtype, requires_grad, training, **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))), desc='single'), ModuleInput(constructor_input=FunctionInput(3,), forward_input=FunctionInput(make_input((3, 5))), reference_fn=no_batch_dim_reference_fn, desc='no_batch_dim')] def module_inputs_torch_nn_AdaptiveMaxPool2d(module_info, device, dtype, requires_grad, training, **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'), ModuleInput(constructor_input=FunctionInput(3,), forward_input=FunctionInput(make_input((3, 5, 6))), reference_fn=no_batch_dim_reference_fn, desc='no_batch_dim'), ModuleInput(constructor_input=FunctionInput((3, 4)), forward_input=FunctionInput(make_input((1, 3, 5, 6))), desc='tuple'), ModuleInput(constructor_input=FunctionInput((3, None)), forward_input=FunctionInput(make_input((1, 3, 5, 6))), desc='tuple_none')] def module_inputs_torch_nn_AdaptiveMaxPool3d(module_info, device, dtype, requires_grad, training, **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, 5, 6, 7))), desc='single'), ModuleInput(constructor_input=FunctionInput(3,), forward_input=FunctionInput(make_input((3, 5, 6, 7))), reference_fn=no_batch_dim_reference_fn, desc='no_batch_dim'), ModuleInput(constructor_input=FunctionInput((3, 4, 5)), forward_input=FunctionInput(make_input((2, 3, 5, 6, 7))), desc='tuple'), ModuleInput(constructor_input=FunctionInput((3, None, 5)), forward_input=FunctionInput(make_input((2, 3, 5, 6, 7))), desc='tuple_none'), ModuleInput(constructor_input=FunctionInput(3), forward_input=FunctionInput(make_input((2, 3, 12, 9, 3))), desc='single_nonatomic'), ModuleInput(constructor_input=FunctionInput((3, 4, 5)), forward_input=FunctionInput(make_input((2, 3, 6, 4, 10))), desc='tuple_nonatomic')] def module_inputs_torch_nn_BatchNorm1d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput(10,), forward_input=FunctionInput(make_input((4, 10))), desc='affine'), ModuleInput(constructor_input=FunctionInput(5,), forward_input=FunctionInput(make_input((4, 5, 3))), desc='3d_input'), ModuleInput(constructor_input=FunctionInput(10, 1e-3, None), forward_input=FunctionInput(make_input((4, 10))), desc='affine_simple_average'), ModuleInput(constructor_input=FunctionInput(10, 1e-3, 0.3, False), forward_input=FunctionInput(make_input((4, 10))), desc='not_affine'), ModuleInput(constructor_input=FunctionInput(10, 1e-3, 0.3, True, False), forward_input=FunctionInput(make_input((4, 10))), desc='not_tracking_stats'), ModuleInput(constructor_input=FunctionInput(5, 1e-3, 0.3, False), forward_input=FunctionInput(make_input((4, 5, 3))), desc='3d_input_not_affine'), ModuleInput(constructor_input=FunctionInput(5, 1e-3, 0.3, False), forward_input=FunctionInput(make_input((0, 5, 9))), desc='zero_batch')] def module_inputs_torch_nn_BatchNorm2d(module_info, device, dtype, requires_grad, training, **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)))), ModuleInput(constructor_input=FunctionInput(3, 1e-3, None), forward_input=FunctionInput(make_input((2, 3, 6, 6))), desc='2d_simple_average'), ModuleInput(constructor_input=FunctionInput(3, 1e-3, 0.8), forward_input=FunctionInput(make_input((2, 3, 6, 6))), desc='momentum'), ModuleInput(constructor_input=FunctionInput(3, 1e-3, 0.8, False), forward_input=FunctionInput(make_input((2, 3, 6, 6))), desc='not_affine'), ModuleInput(constructor_input=FunctionInput(3, 1e-3, 0.8, True, False), forward_input=FunctionInput(make_input((2, 3, 6, 6))), desc='not_tracking_stats'), ModuleInput(constructor_input=FunctionInput(5, 1e-3, 0.3, False), forward_input=FunctionInput(make_input((0, 5, 2, 2))), desc='zero_batch')] def module_inputs_torch_nn_BatchNorm3d(module_info, device, dtype, requires_grad, training, **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)))), ModuleInput(constructor_input=FunctionInput(3, 1e-3, None), forward_input=FunctionInput(make_input((2, 3, 4, 4, 4))), desc='3d_simple_average'), ModuleInput(constructor_input=FunctionInput(3, 1e-3, 0.7), forward_input=FunctionInput(make_input((2, 3, 4, 4, 4))), desc='momentum'), ModuleInput(constructor_input=FunctionInput(3, 1e-3, 0.7, False), forward_input=FunctionInput(make_input((2, 3, 4, 4, 4))), desc='not_affine'), ModuleInput(constructor_input=FunctionInput(3, 1e-3, 0.7, True, False), forward_input=FunctionInput(make_input((2, 3, 4, 4, 4))), desc='not_tracking_stats'), ModuleInput(constructor_input=FunctionInput(5, 1e-3, 0.3, False), forward_input=FunctionInput(make_input((0, 5, 2, 2, 2))), desc='zero_batch')] def module_inputs_torch_nn_ConvNd(module_info, device, dtype, requires_grad, training, **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_CosineEmbeddingLoss(module_info, device, dtype, requires_grad, training, **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'}), ('margin', {'margin': 0.7}) ] module_inputs = [] for desc, constructor_kwargs in cases: def reference_fn(m, p, i1, i2, t, constructor_kwargs=constructor_kwargs): return cosineembeddingloss_reference(i1, i2, t, **constructor_kwargs) module_inputs.append( ModuleInput(constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput(make_input((15, 10)), make_input((15, 10)), make_target((15,)).sign()), desc=desc, reference_fn=reference_fn) ) return module_inputs def module_inputs_torch_nn_ELU(module_info, device, dtype, requires_grad, training, **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, training, **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. * ((.5 * 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_GLU(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input((5, 6)))), ModuleInput(constructor_input=FunctionInput(1), forward_input=FunctionInput(make_input((5, 6, 7))), desc='dim'), ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input((4,))), desc='no_batch_dim', reference_fn=no_batch_dim_reference_fn)] def module_inputs_torch_nn_GELU(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput('none'), forward_input=FunctionInput(make_input(())), reference_fn=lambda m, p, x, *_: x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0))), desc='scalar'), ModuleInput(constructor_input=FunctionInput('none'), forward_input=FunctionInput(make_input((3, 2, 5))), reference_fn=lambda m, p, x, *_: x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))), ModuleInput(constructor_input=FunctionInput(), 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, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input(())), desc='scalar'), 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, 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_ReLU6(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input(())), desc='scalar'), 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, 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_LeakyReLU(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input((3, 2, 5)))), 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(0.5), forward_input=FunctionInput(make_input((3, 2, 5))), desc='with_negval'), ModuleInput(constructor_input=FunctionInput(0.0), forward_input=FunctionInput(make_input((10, 10))), desc='with_zero_negval'), ModuleInput(constructor_input=FunctionInput(0.5), forward_input=FunctionInput(make_input(())), desc='with_negval_scalar')] def module_inputs_torch_nn_PReLU(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input(())), desc='scalar'), 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, 4))), reference_fn=lambda m, p, i: torch.clamp(i, min=0) + torch.clamp(i, max=0) * p[0][0], desc='1d'), ModuleInput(constructor_input=FunctionInput(3), forward_input=FunctionInput(make_input((2, 3, 4))), reference_fn=lambda m, p, i: torch.clamp(i, min=0) + torch.clamp(i, max=0) * p[0][0], desc='1d_multiparam'), ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input((2, 3, 4, 5))), reference_fn=lambda m, p, i: torch.clamp(i, min=0) + torch.clamp(i, max=0) * p[0][0], desc='2d'), ModuleInput(constructor_input=FunctionInput(3), forward_input=FunctionInput(make_input((2, 3, 4, 5))), reference_fn=lambda m, p, i: torch.clamp(i, min=0) + torch.clamp(i, max=0) * p[0][0], desc='2d_multiparam'), ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input((2, 3, 4, 5, 6))), reference_fn=lambda m, p, i: torch.clamp(i, min=0) + torch.clamp(i, max=0) * p[0][0], desc='3d'), ModuleInput(constructor_input=FunctionInput(3), forward_input=FunctionInput(make_input((2, 3, 4, 5, 6))), reference_fn=lambda m, p, i: torch.clamp(i, min=0) + torch.clamp(i, max=0) * p[0][0], desc='3d_multiparam')] def module_inputs_torch_nn_SELU(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input((3, 2, 5)))), 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(())), desc='scalar')] def module_inputs_torch_nn_SiLU(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input(())), reference_fn=lambda m, p, x, *_: x * torch.sigmoid(x), desc='scalar'), 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((5, 6, 7))), reference_fn=lambda m, p, x, *_: x * torch.sigmoid(x))] def module_inputs_torch_nn_Softmax(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput(1), forward_input=FunctionInput(make_input((10, 20))), reference_fn=lambda m, p, i: torch.exp(i).div(torch.exp(i).sum(1, True).expand(10, 20))), ModuleInput(constructor_input=FunctionInput(0), forward_input=FunctionInput(make_input(())), reference_fn=lambda m, p, i: torch.exp(i).div(torch.exp(i).sum(0, True)), desc='scalar'), ModuleInput(constructor_input=FunctionInput(-1), forward_input=FunctionInput(make_input((4, 5))), reference_fn=no_batch_dim_reference_fn, desc='no_batch_dim')] def module_inputs_torch_nn_Softmax2d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input((1, 3, 10, 20))), reference_fn=lambda m, p, i: torch.exp(i).div(torch.exp(i).sum(1, False))), ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input((3, 4, 5))), reference_fn=no_batch_dim_reference_fn, desc='no_batch_dim')] def module_inputs_torch_nn_LogSoftmax(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput(1), forward_input=FunctionInput(make_input((10, 20))), reference_fn=lambda m, p, i: torch.exp(i).div_(torch.exp(i).sum(1, True).expand(10, 20)).log_()), ModuleInput(constructor_input=FunctionInput(1), forward_input=FunctionInput(make_input((1, 3, 10, 20))), reference_fn=lambda m, p, i: torch.exp(i).div_(torch.exp(i).sum(1, False)).log_(), desc='multiparam'), ModuleInput(constructor_input=FunctionInput(0), forward_input=FunctionInput(make_input(())), reference_fn=lambda m, p, i: torch.exp(i).div_(torch.exp(i).sum(0, False)).log_(), desc='multiparam_scalar'), ModuleInput(constructor_input=FunctionInput(-1), forward_input=FunctionInput(make_input((4, 5))), reference_fn=no_batch_dim_reference_fn, desc='no_batch_dim')] def module_inputs_torch_nn_Softmin(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput(1), forward_input=FunctionInput(make_input((10, 20)))), ModuleInput(constructor_input=FunctionInput(1), forward_input=FunctionInput(make_input((2, 3, 5, 10))), desc='multidim'), ModuleInput(constructor_input=FunctionInput(0), forward_input=FunctionInput(make_input(())), desc='scalar'), ModuleInput(constructor_input=FunctionInput(-1), forward_input=FunctionInput(make_input((3, 4, 10))), reference_fn=no_batch_dim_reference_fn, desc='no_batch_dim')] def module_inputs_torch_nn_Softplus(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input((10, 20))), reference_fn=lambda m, p, i: torch.log1p(torch.exp(i))), ModuleInput(constructor_input=FunctionInput(2), forward_input=FunctionInput(make_input((10, 20))), reference_fn=lambda m, p, i: 1. / 2. * torch.log1p(torch.exp(2 * i)), desc='beta'), ModuleInput(constructor_input=FunctionInput(2, -100), forward_input=FunctionInput(make_input((10, 20))), reference_fn=( lambda m, p, i: ((i * 2) > -100).type_as(i) * i + ((i * 2) <= -100).type_as(i) * 1. / 2. * torch.log1p(torch.exp(2 * i))), desc='beta_threshold'), ModuleInput(constructor_input=FunctionInput(2, -100), forward_input=FunctionInput(make_input(())), reference_fn=( lambda m, p, i: ((i * 2) > -100).type_as(i) * i + ((i * 2) <= -100).type_as(i) * 1. / 2. * torch.log1p(torch.exp(2 * i))), desc='beta_threshold_scalar'), ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input(4)), reference_fn=no_batch_dim_reference_fn, desc='no_batch_dim')] def module_inputs_torch_nn_Softshrink(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input((3, 2, 5)))), ModuleInput(constructor_input=FunctionInput(1,), forward_input=FunctionInput(make_input((3, 2, 5))), desc='lambda'), ModuleInput(constructor_input=FunctionInput(1,), forward_input=FunctionInput(make_input(())), desc='lambda_scalar'), ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input(4)), reference_fn=no_batch_dim_reference_fn, desc='no_batch_dim')] def module_inputs_torch_nn_Softsign(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input((3, 2, 5))), reference_fn=lambda m, p, i: i.div(1 + torch.abs(i))), ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input(())), reference_fn=lambda m, p, i: i.div(1 + torch.abs(i)), desc='scalar'), ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input(4)), reference_fn=no_batch_dim_reference_fn, desc='no_batch_dim')] def module_inputs_torch_nn_Tanh(module_info, device, dtype, requires_grad, training, **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)))), ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input(())), desc='scalar'), ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input(4)), reference_fn=no_batch_dim_reference_fn, desc='no_batch_dim')] def module_inputs_torch_nn_Tanhshrink(module_info, device, dtype, requires_grad, training, **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)))), ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input(())), desc='scalar'), ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input(4)), reference_fn=no_batch_dim_reference_fn, desc='no_batch_dim')] def module_inputs_torch_nn_Threshold(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput(2., 1.), forward_input=FunctionInput(make_input((2, 3, 4, 5))), desc='threshold_value'), ModuleInput(constructor_input=FunctionInput(2., 10.), forward_input=FunctionInput(make_input((2, 3, 4, 5))), desc='large_value'), ModuleInput(constructor_input=FunctionInput(2., 1.), forward_input=FunctionInput(make_input(())), desc='threshold_value_scalar'), ModuleInput(constructor_input=FunctionInput(2., 1.), forward_input=FunctionInput(make_input(4)), reference_fn=no_batch_dim_reference_fn, desc='no_batch_dim')] def module_inputs_torch_nn_Mish(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input((5, 6, 7))), reference_fn=lambda m, p, i: i * torch.tanh(F.softplus(i))), ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input(())), reference_fn=lambda m, p, i: i * torch.tanh(F.softplus(i)), desc='scalar'), ModuleInput(constructor_input=FunctionInput(), forward_input=FunctionInput(make_input(4)), reference_fn=no_batch_dim_reference_fn, desc='no_batch_dim')] def module_inputs_torch_nn_L1Loss(module_info, device, dtype, requires_grad, training, **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_SmoothL1Loss(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) 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: def reference_fn(m, p, i, t, constructor_kwargs=constructor_kwargs): return smoothl1loss_reference(i, t, **constructor_kwargs) module_inputs.append( ModuleInput(constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput(make_input((5, 10)), make_input((5, 10))), desc=desc, reference_fn=reference_fn) ) module_inputs.append( ModuleInput(constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput(make_input(()), make_input(())), desc=f'scalar_{desc}', reference_fn=reference_fn) ) return module_inputs def module_inputs_torch_nn_BCELoss(module_info, device, dtype, requires_grad, training, **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) make_weight = 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'}), ('weights', {'weight': make_weight((10,))}), ] def bce_loss_reference_fn(m, p, i, t, reduction='mean', weight=None): result = -(t * i.log() + (1 - t) * (1 - i).log()) if weight is not None: result = result * weight if reduction == 'none': return result elif reduction == 'mean': return result.sum() / i.numel() else: return result.sum() module_inputs = [] for desc, constructor_kwargs in cases: module_inputs.append( ModuleInput(constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput(make_input((15, 10), low=1e-2, high=1 - 1e-2), make_target((15, 10)).gt(0).to(dtype)), desc=desc, reference_fn=partial(bce_loss_reference_fn, **constructor_kwargs)) ) scalar_weight = make_weight(()) module_inputs.append( ModuleInput(constructor_input=FunctionInput(weight=scalar_weight), forward_input=FunctionInput(make_input((), low=1e-2, high=1 - 1e-2), make_target(()).gt(0).to(dtype)), desc='scalar_weight', reference_fn=partial(bce_loss_reference_fn, weight=scalar_weight)) ) return module_inputs def module_inputs_torch_nn_BCEWithLogitsLoss(module_info, device, dtype, requires_grad, training, **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) make_weight = 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'}), ('weights', {'weight': make_weight((10,))}), ('scalar_weights', {'weight': make_weight(())}) ] def bce_withlogitsloss_reference_fn(m, p, i, t, reduction='mean', weight=None): # TODO: add pos_weight to the definition here and corresponding SampleInputs max_val = (-i).clamp(min=0) result = (1 - t).mul_(i).add_(max_val).add_((-max_val).exp_().add_((-i - max_val).exp_()).log_()) if weight is not None: result = result * weight if reduction == 'none': return result elif reduction == 'mean': return result.sum() / i.numel() else: return result.sum() module_inputs = [] for desc, constructor_kwargs in cases: module_inputs.append( ModuleInput(constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput(make_input((15, 10), low=1e-2, high=1 - 1e-2), make_target((15, 10)).gt(0).to(dtype)), desc=desc, reference_fn=partial(bce_withlogitsloss_reference_fn, **constructor_kwargs)) ) return module_inputs def module_inputs_torch_nn_CrossEntropyLoss(module_info, device, dtype, requires_grad, training, **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: list[str] = ['mean', 'sum', 'none'] cases: list[tuple[str, dict]] = [ ('', {}), ('weights', {'weight': make_weight((3,))}), ('ignore_index', {'ignore_index': 1}), ('label_smoothing', {'label_smoothing': 0.15}), ('ignore_index_label_smoothing', {'ignore_index': 1, 'label_smoothing': 0.15}) ] module_inputs = [] for reduction, (desc, constructor_kwargs) in product(reductions, cases): def reference_fn(m, p, i, t, reduction=reduction, constructor_kwargs=constructor_kwargs): return cross_entropy_loss_reference(i, t, reduction=reduction, **constructor_kwargs) module_inputs.append( ModuleInput(constructor_input=FunctionInput(reduction=reduction, **constructor_kwargs), forward_input=FunctionInput(make_input((2, 3, 5, 5)), make_target((2, 5, 5), low=0, high=3)), desc=f"4d_{desc}_{reduction}", reference_fn=reference_fn) ) module_inputs.append( ModuleInput(constructor_input=FunctionInput(reduction=reduction, **constructor_kwargs), forward_input=FunctionInput(make_input((2, 3, 5)), make_target((2, 5), low=0, high=3)), desc=f"3d_{desc}_{reduction}", reference_fn=reference_fn) ) module_inputs.append( ModuleInput(constructor_input=FunctionInput(reduction=reduction, **constructor_kwargs), forward_input=FunctionInput(make_input((2, 3)), make_target((2), low=0, high=3)), desc=f"2d_{desc}_{reduction}", reference_fn=reference_fn) ) module_inputs.append( ModuleInput(constructor_input=FunctionInput(reduction=reduction, **constructor_kwargs), forward_input=FunctionInput(make_input((2, 3, 5, 5, 2, 2)), make_target((2, 5, 5, 2, 2), low=0, high=3)), desc=f"higher_dim_{desc}_{reduction}", reference_fn=reference_fn) ) if constructor_kwargs.get('ignore_index', None) is None: module_inputs.append( ModuleInput(constructor_input=FunctionInput(reduction=reduction, **constructor_kwargs), forward_input=FunctionInput(make_input((5, 3, 4, 2)), make_input((5, 3, 4, 2)).softmax(dim=1)), desc=f"4d_prob_target_{desc}_{reduction}", reference_fn=reference_fn) ) module_inputs.append( ModuleInput(constructor_input=FunctionInput(reduction=reduction, **constructor_kwargs), forward_input=FunctionInput(make_input((5, 3, 4)), make_input((5, 3, 4)).softmax(dim=1)), desc=f"3d_prob_target_{desc}_{reduction}", reference_fn=reference_fn) ) module_inputs.append( ModuleInput(constructor_input=FunctionInput(reduction=reduction, **constructor_kwargs), forward_input=FunctionInput(make_input((5, 3)), make_input((5, 3)).softmax(dim=1)), desc=f"2d_prob_target_{desc}_{reduction}", reference_fn=reference_fn) ) module_inputs.append( ModuleInput(constructor_input=FunctionInput(reduction=reduction, **constructor_kwargs), forward_input=FunctionInput(make_input((2, 3, 5, 5, 2, 2)), make_input((2, 3, 5, 5, 2, 2)).softmax(dim=1)), desc=f"higher_dim_prob_target_{desc}_{reduction}", reference_fn=reference_fn) ) module_inputs.append( ModuleInput(constructor_input=FunctionInput(reduction=reduction, **constructor_kwargs), forward_input=FunctionInput(make_input((3,)), make_target((), low=0, high=3)), desc=f"no_batch_dim_{desc}_{reduction}", reference_fn=partial(no_batch_dim_reference_fn, is_criterion=True)) ) return module_inputs def module_inputs_torch_nn_CTCLoss(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) make_target = partial(make_tensor, device=device, requires_grad=False) cases: list[tuple[str, dict]] = [ ('', {}), ('reduction_sum', {'reduction': 'sum'}), ('reduction_mean', {'reduction': 'mean'}), ('reduction_none', {'reduction': 'none'}), ('blank', {'blank': 14}) ] target_dtypes = [torch.int, torch.long] module_inputs = [] for target_dtype, (desc, constructor_kwargs) in product(target_dtypes, cases): def reference_fn(m, p, i, t, il, tl, constructor_kwargs=constructor_kwargs): return ctcloss_reference(i, t, il, tl, **constructor_kwargs) blank = constructor_kwargs.get('blank', 0) low = 0 if blank == 14 else 1 high = 14 if blank == 14 else 15 module_inputs.append( ModuleInput( constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput(make_input((50, 3, 15)).log_softmax(2), make_target((3, 30), dtype=target_dtype, low=low, high=high), (50, 50, 50), (30, 25, 20)), desc=f'{desc}_lengths_intlists', reference_fn=reference_fn) ) module_inputs.append( ModuleInput( constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput(make_input((50, 3, 15)).log_softmax(2), make_target((3, 30), dtype=target_dtype, low=low, high=high), torch.tensor((50, 50, 50), device=device), torch.tensor((30, 25, 20), device=device)), desc=f'{desc}_lengths_tensors', reference_fn=reference_fn) ) module_inputs.append( ModuleInput( constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput(make_input((50, 3, 15)).log_softmax(2), make_target((30 + 25 + 20,), dtype=target_dtype, low=low, high=high), (50, 50, 50), (30, 25, 20)), desc=f'{desc}_1d_target_lengths_intlists', reference_fn=reference_fn) ) module_inputs.append( ModuleInput( constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput(make_input((50, 3, 15)).log_softmax(2), make_target((30 + 25 + 20,), dtype=target_dtype, low=low, high=high), torch.tensor((50, 50, 50), device=device), torch.tensor((30, 25, 20), device=device)), desc=f'{desc}_1d_target_lengths_tensors', reference_fn=reference_fn) ) return module_inputs def module_inputs_torch_nn_GroupNorm(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput( constructor_input=FunctionInput(3, 6, 1e-3), forward_input=FunctionInput(make_input((4, 6, 5))), desc='1d_affine'), ModuleInput( constructor_input=FunctionInput(3, 12, 1e-3), forward_input=FunctionInput(make_input((4, 12))), desc='1d_affine_GN'), ModuleInput( constructor_input=FunctionInput(1, 6, 1e-3), forward_input=FunctionInput(make_input((150, 6))), desc='1d_affine_large_batch'), ModuleInput( constructor_input=FunctionInput(5, 5, 1e-3, False), forward_input=FunctionInput(make_input((4, 5, 5))), desc='1d_no_affine_IN'), ModuleInput( constructor_input=FunctionInput(1, 10, 1e-3, False), forward_input=FunctionInput(make_input((4, 10))), desc='1d_no_affine_LN'), ModuleInput( constructor_input=FunctionInput(3, 6, 1e-3), forward_input=FunctionInput(make_input((4, 6, 2, 3))), desc='2d_affine'), ModuleInput( constructor_input=FunctionInput(3, 3, 1e-3, False), forward_input=FunctionInput(make_input((4, 3, 2, 3))), desc='2d_no_affine_IN'), ModuleInput( constructor_input=FunctionInput(1, 3, 1e-3, False), forward_input=FunctionInput(make_input((4, 3, 2, 3))), desc='2d_no_affine_LN'), ] def module_inputs_torch_nn_Hardshrink(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput( constructor_input=FunctionInput(2.), forward_input=FunctionInput(make_input((4, 3, 2, 4))), ), ModuleInput( constructor_input=FunctionInput(2.), forward_input=FunctionInput(make_input(())), desc='scalar', ), ModuleInput( constructor_input=FunctionInput(), forward_input=FunctionInput(make_input(4)), reference_fn=no_batch_dim_reference_fn, desc='no_batch_dim', ) ] def module_inputs_torch_nn_Hardswish(module_info, device, dtype, requires_grad, training, **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_Hardtanh(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput( constructor_input=FunctionInput(), forward_input=FunctionInput(make_input((3, 2, 5))), reference_fn=lambda m, p, i: i.clamp(-1, 1), ), ModuleInput( constructor_input=FunctionInput(), forward_input=FunctionInput(make_input(())), reference_fn=lambda m, p, i: i.clamp(-1, 1), desc='scalar', ), ModuleInput( constructor_input=FunctionInput(), forward_input=FunctionInput(make_input(4)), reference_fn=no_batch_dim_reference_fn, desc='no_batch_dim', ) ] def module_inputs_torch_nn_HingeEmbeddingLoss(module_info, device, dtype, requires_grad, training, **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'}), ('margin', {'margin': 0.5}) ] module_inputs = [] for desc, constructor_kwargs in cases: def reference_fn(m, p, i, t, constructor_kwargs=constructor_kwargs): return hingeembeddingloss_reference(i, t, **constructor_kwargs) module_inputs.append( ModuleInput(constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput(make_input((10,)), make_target((10,)).gt(0).to(dtype).mul_(2).sub_(1)), desc=desc, reference_fn=reference_fn) ) module_inputs.append( ModuleInput(constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput(make_input(()), make_target(()).gt(0).to(dtype).mul_(2).sub_(1)), desc=f'scalar_{desc}', reference_fn=reference_fn) ) return module_inputs def module_inputs_torch_nn_HuberLoss(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) 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: def reference_fn(m, p, i, t, constructor_kwargs=constructor_kwargs): return huberloss_reference(i, t, **constructor_kwargs) module_inputs.append( ModuleInput(constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput(make_input((5, 10)), make_input((5, 10))), desc=desc, reference_fn=reference_fn) ) return module_inputs def module_inputs_torch_nn_InstanceNormNd(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) lazy = kwargs.get('lazy', False) N = kwargs['N'] num_features, eps, momentum, affine, track_running_stats = 3, 1e-3, 0.3, False, True input_no_batch_shape_dict = {1: (3, 15), 2: (3, 6, 6), 3: (3, 4, 4, 4)} input_no_batch_shape = input_no_batch_shape_dict[N] input_batch_shape = (4,) + input_no_batch_shape return [ ModuleInput( constructor_input=( FunctionInput(eps, momentum) if lazy else FunctionInput(num_features, eps, momentum) ), forward_input=FunctionInput(make_input(input_batch_shape))), ModuleInput( constructor_input=( FunctionInput(eps, momentum, affine, track_running_stats) if lazy else FunctionInput(num_features, eps, momentum, affine, track_running_stats) ), forward_input=FunctionInput(make_input(input_batch_shape)), desc='tracking_stats'), ModuleInput( constructor_input=( FunctionInput(eps, momentum) if lazy else FunctionInput(num_features, eps, momentum) ), forward_input=FunctionInput(make_input(input_no_batch_shape)), reference_fn=no_batch_dim_reference_fn, desc='tracking_stats_no_batch_dim'), ModuleInput( constructor_input=( FunctionInput(eps, momentum, affine, track_running_stats) if lazy else FunctionInput(num_features, eps, momentum, affine, track_running_stats) ), forward_input=FunctionInput(make_input(input_no_batch_shape)), reference_fn=no_batch_dim_reference_fn, desc='no_batch_dim') ] def module_inputs_torch_nn_LayerNorm(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput( constructor_input=FunctionInput([5], 1e-3), forward_input=FunctionInput(make_input((4, 5, 5))), desc='1d_elementwise_affine'), ModuleInput( constructor_input=FunctionInput([5], 1e-3), forward_input=FunctionInput(make_input((128, 5, 5))), desc='1d_elementwise_affine_large_batch'), ModuleInput( constructor_input=FunctionInput([5], 1e-3, False), forward_input=FunctionInput(make_input((4, 5, 5))), desc='1d_no_elementwise_affine'), ModuleInput( constructor_input=FunctionInput([2, 2, 5], 1e-3), forward_input=FunctionInput(make_input((4, 2, 2, 5))), desc='3d_elementwise_affine'), ModuleInput( constructor_input=FunctionInput([2, 2, 5], 1e-3, False), forward_input=FunctionInput(make_input((4, 2, 2, 5))), desc='3d_no_elementwise_affine'), ModuleInput( constructor_input=FunctionInput([5], 1e-3), forward_input=FunctionInput(make_input((0, 5))), desc='1d_empty_elementwise_affine'), ModuleInput( constructor_input=FunctionInput([2, 2, 5], 1e-3, elementwise_affine=True, bias=False), forward_input=FunctionInput(make_input((4, 2, 2, 5))), desc='3d_elementwise_affine_no_bias'), ] def module_inputs_torch_nn_RMSNorm(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) def rms_norm_reference_fn(m, p, i): eps = m.eps if eps is None: eps = torch.finfo(i.dtype).eps ndim = i.ndim normalized_shape = m.normalized_shape weight = m.weight dims = [ndim - i - 1 for i in range(len(normalized_shape))] upcasted_i = i.float() result = upcasted_i * torch.rsqrt(upcasted_i.pow(2).mean(dim=dims, keepdim=True) + m.eps) if weight is not None: result *= weight return result.type_as(i) return [ ModuleInput( constructor_input=FunctionInput([5], 1e-3), forward_input=FunctionInput(make_input((4, 5, 5))), desc='1d_elementwise_affine', reference_fn=rms_norm_reference_fn), ModuleInput( constructor_input=FunctionInput([5], 1e-3), forward_input=FunctionInput(make_input((128, 5, 5))), desc='1d_elementwise_affine_large_batch', reference_fn=rms_norm_reference_fn), ModuleInput( constructor_input=FunctionInput([5], 1e-3, False), forward_input=FunctionInput(make_input((4, 5, 5))), desc='1d_no_elementwise_affine', reference_fn=rms_norm_reference_fn), ModuleInput( constructor_input=FunctionInput([2, 2, 5], 1e-3), forward_input=FunctionInput(make_input((4, 2, 2, 5))), desc='3d_elementwise_affine', reference_fn=rms_norm_reference_fn), ModuleInput( constructor_input=FunctionInput([2, 2, 5], 1e-3, False), forward_input=FunctionInput(make_input((4, 2, 2, 5))), desc='3d_no_elementwise_affine', reference_fn=rms_norm_reference_fn), ModuleInput( constructor_input=FunctionInput([5], 1e-3), forward_input=FunctionInput(make_input((0, 5))), desc='1d_empty_elementwise_affine', reference_fn=rms_norm_reference_fn), ] def module_inputs_torch_nn_LocalResponseNorm(module_info, device, dtype, requires_grad, training, **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, 5, 7))), desc='1d'), ModuleInput( constructor_input=FunctionInput(2,), forward_input=FunctionInput(make_input((1, 5, 7, 7))), desc='2d_uneven_pad'), ModuleInput( constructor_input=FunctionInput(1, 1., 0.5, 2.), forward_input=FunctionInput(make_input((1, 5, 7, 7, 7))), desc='3d_custom_params'), ] def module_inputs_torch_nn_LPPool1d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput( constructor_input=FunctionInput(1.5, 2), forward_input=FunctionInput(make_input((1, 3, 7))), desc='norm'), ModuleInput( constructor_input=FunctionInput(2, 2, 3), forward_input=FunctionInput(make_input((1, 3, 7)))), ModuleInput( constructor_input=FunctionInput(2, 2, 3), forward_input=FunctionInput(make_input((3, 7))), reference_fn=no_batch_dim_reference_fn, desc='no_batch_dim'), ] def module_inputs_torch_nn_LPPool2d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput( constructor_input=FunctionInput(2, 2, 2), forward_input=FunctionInput(make_input((1, 3, 7, 7)))), ModuleInput( constructor_input=FunctionInput(2, 2, 2), forward_input=FunctionInput(make_input((3, 7, 7))), reference_fn=no_batch_dim_reference_fn, desc='no_batch_dim'), ModuleInput( constructor_input=FunctionInput(1.5, 2), forward_input=FunctionInput(make_input((1, 3, 7, 7))), desc='norm'), ] def module_inputs_torch_nn_LPPool3d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput( constructor_input=FunctionInput(2, 2, 2), forward_input=FunctionInput(make_input((1, 3, 7, 7, 7)))), ModuleInput( constructor_input=FunctionInput(2, 2, 2), forward_input=FunctionInput(make_input((3, 7, 7, 7))), reference_fn=no_batch_dim_reference_fn, desc='no_batch_dim'), ModuleInput( constructor_input=FunctionInput(1.5, 2), forward_input=FunctionInput(make_input((1, 3, 7, 7, 7))), desc='norm'), ] def module_inputs_torch_nn_MaxPool1d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput( constructor_input=FunctionInput(4), forward_input=FunctionInput(make_input((2, 10, 4))), desc='3d_input'), ModuleInput( constructor_input=FunctionInput(4, 4), forward_input=FunctionInput(make_input((2, 10, 4))), desc='stride'), ModuleInput( constructor_input=FunctionInput(4, return_indices=True), forward_input=FunctionInput(make_input((2, 10, 4))), desc='return_indices'), ] def module_inputs_torch_nn_MaxPool2d(module_info, device, dtype, requires_grad, training, **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_MaxPool3d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput( constructor_input=FunctionInput((2, 2, 2)), forward_input=FunctionInput(make_input((2, 3, 5, 5, 5)))), ModuleInput( constructor_input=FunctionInput(2, (2, 2, 2)), forward_input=FunctionInput(make_input((2, 3, 5, 5, 5))), desc='stride'), ModuleInput( constructor_input=FunctionInput(2, 2, (1, 1, 1)), forward_input=FunctionInput(make_input((2, 3, 5, 5, 5))), desc='stride_padding'), ModuleInput( constructor_input=FunctionInput(2, 2, (1, 1, 1), return_indices=True), forward_input=FunctionInput(make_input((2, 3, 5, 5, 5))), desc='return_indices'), ] def module_inputs_torch_nn_FractionalMaxPool2d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) def make_random_samples(): return torch.empty((1, 3, 2), dtype=torch.double, device=device).uniform_() return [ ModuleInput( constructor_input=FunctionInput(2, output_ratio=0.5, _random_samples=make_random_samples()), forward_input=FunctionInput(make_input((1, 3, 5, 7))), desc='ratio'), ModuleInput( constructor_input=FunctionInput((2, 3), output_size=(4, 3), _random_samples=make_random_samples()), forward_input=FunctionInput(make_input((1, 3, 7, 6))), desc='size'), ModuleInput( constructor_input=FunctionInput( 2, output_ratio=0.5, _random_samples=make_random_samples(), return_indices=True ), forward_input=FunctionInput(make_input((1, 3, 5, 7))), desc='ratio_return_indices'), ModuleInput( constructor_input=FunctionInput(2, output_ratio=0.5, _random_samples=make_random_samples()), forward_input=FunctionInput(make_input((3, 5, 7))), reference_fn=no_batch_dim_reference_fn, desc='ratio_no_batch_dim'), ModuleInput( constructor_input=FunctionInput((2, 3), output_size=(4, 3), _random_samples=make_random_samples()), forward_input=FunctionInput(make_input((3, 7, 6))), reference_fn=no_batch_dim_reference_fn, desc='size_no_batch_dim'), ] def module_inputs_torch_nn_FractionalMaxPool3d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) def make_random_samples(): return torch.empty((2, 4, 3), dtype=torch.double, device=device).uniform_() return [ ModuleInput( constructor_input=FunctionInput(2, output_ratio=0.5, _random_samples=make_random_samples()), forward_input=FunctionInput(make_input((2, 4, 5, 5, 5))), desc='ratio'), ModuleInput( constructor_input=FunctionInput((2, 2, 2), output_size=(4, 4, 4), _random_samples=make_random_samples()), forward_input=FunctionInput(make_input((2, 4, 7, 7, 7))), desc='size'), ModuleInput( constructor_input=FunctionInput((4, 2, 3), output_size=(10, 3, 2), _random_samples=make_random_samples()), forward_input=FunctionInput(make_input((2, 4, 16, 7, 5))), desc='asymsize'), ModuleInput( constructor_input=FunctionInput( 2, output_ratio=0.5, _random_samples=make_random_samples(), return_indices=True ), forward_input=FunctionInput(make_input((2, 4, 5, 5, 5))), desc='ratio_return_indices'), ModuleInput( constructor_input=FunctionInput(2, output_ratio=0.5, _random_samples=make_random_samples()), forward_input=FunctionInput(make_input((4, 5, 5, 5))), reference_fn=no_batch_dim_reference_fn, desc='ratio_no_batch_dim'), ModuleInput( constructor_input=FunctionInput((2, 2, 2), output_size=(4, 4, 4), _random_samples=make_random_samples()), forward_input=FunctionInput(make_input((4, 7, 7, 7))), reference_fn=no_batch_dim_reference_fn, desc='size_no_batch_dim'), ] def module_inputs_torch_nn_Sigmoid(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput( constructor_input=FunctionInput(), forward_input=FunctionInput(make_input(())), desc='scalar' ), 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, 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_LogSigmoid(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput( constructor_input=FunctionInput(), forward_input=FunctionInput(make_input(())), reference_fn=lambda m, p, i: i.sigmoid().log(), desc='scalar' ), ModuleInput( constructor_input=FunctionInput(), forward_input=FunctionInput(make_input((2, 3, 4))), reference_fn=lambda m, p, i: i.sigmoid().log(), ), ModuleInput( constructor_input=FunctionInput(), forward_input=FunctionInput(make_input(4)), reference_fn=no_batch_dim_reference_fn, desc='no_batch_dim', ), ] def module_inputs_torch_nn_MarginRankingLoss(module_info, device, dtype, requires_grad, training, **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) cases: list[tuple[str, dict]] = [ ('', {}), ('reduction_sum', {'reduction': 'sum'}), ('reduction_mean', {'reduction': 'mean'}), ('reduction_none', {'reduction': 'none'}), ('margin', {'margin': 0.5}) ] module_inputs = [] for desc, constructor_kwargs in cases: def reference_fn(m, p, i1, i2, t, constructor_kwargs=constructor_kwargs): return marginrankingloss_reference(i1, i2, t, **constructor_kwargs) module_inputs.append( ModuleInput(constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput(make_input((50,)), make_input((50,)), make_target((50,)).sign()), desc=desc, reference_fn=reference_fn) ) return module_inputs def module_inputs_torch_nn_MultiLabelMarginLoss(module_info, device, dtype, requires_grad, training, **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) 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: def reference_fn(m, p, i, t, constructor_kwargs=constructor_kwargs): return multilabelmarginloss_reference(i, t, **constructor_kwargs) module_inputs.append( ModuleInput(constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput(make_input((10,)), make_target((10), low=0, high=10)), desc=f'1d_{desc}', reference_fn=reference_fn) ) module_inputs.append( ModuleInput(constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput(make_input((5, 10)), make_target((5, 10), low=0, high=10)), desc=desc, reference_fn=reference_fn) ) return module_inputs def module_inputs_torch_nn_MultiMarginLoss(module_info, device, dtype, requires_grad, training, **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) cases: list[tuple[str, dict]] = [ ('', {}), ('reduction_sum', {'reduction': 'sum'}), ('reduction_mean', {'reduction': 'mean'}), ('reduction_none', {'reduction': 'none'}), ('p', {'p': 2}), ('margin', {'margin': 0.5}), ('weights', {'weight': make_weight(10)}) ] module_inputs = [] for desc, constructor_kwargs in cases: def reference_fn(m, p, i, t, constructor_kwargs=constructor_kwargs): return multimarginloss_reference(i, t, **constructor_kwargs) module_inputs.append( ModuleInput(constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput(make_input((5, 10)), make_target((5), low=0, high=10)), desc=desc, reference_fn=reference_fn) ) return module_inputs def module_inputs_torch_nn_MultiLabelSoftMarginLoss(module_info, device, dtype, requires_grad, training, **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) cases: list[tuple[str, dict]] = [ ('', {}), ('reduction_sum', {'reduction': 'sum'}), ('reduction_mean', {'reduction': 'mean'}), ('reduction_none', {'reduction': 'none'}), ('weight', {'weight': make_weight(10)}), ] def multilabelsoftmargin_loss_reference_fn(m, p, i, t, reduction='mean', weight=None): result = t * i.sigmoid().log() + (1 - t) * (-i).sigmoid().log() if weight is not None: result *= weight result = (-result).sum(i.dim() - 1) / i.size(-1) if reduction == 'none': return result elif reduction == 'mean': return result.mean() else: return result.sum() module_inputs = [] for desc, constructor_kwargs in cases: module_inputs.append( ModuleInput(constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput(make_input((5, 10)), make_target((5, 10), low=0, high=2)), desc=desc, reference_fn=partial(multilabelsoftmargin_loss_reference_fn, **constructor_kwargs)) ) return module_inputs def module_inputs_torch_nn_SoftMarginLoss(module_info, device, dtype, requires_grad, training, **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: def reference_fn(m, p, i, t, constructor_kwargs=constructor_kwargs): return softmarginloss_reference(i, t, **constructor_kwargs) module_inputs.append( ModuleInput(constructor_input=FunctionInput(**constructor_kwargs), forward_input=FunctionInput(make_input((5, 5)), make_target((5, 5)).sign()), desc=desc, reference_fn=reference_fn) ) return module_inputs def module_inputs_torch_nn_TransformerEncoder(module_info, device, dtype, requires_grad, training, **kwargs): # Reuse the TransformerEncoderLayer samples since the forward args are nearly the same. samples = [] for layer_module_input in module_inputs_torch_nn_TransformerEncoderLayer( None, device, dtype, requires_grad, training): # Construct a TransformerEncoderLayer object to pass to TransformerEncoder. l_args, l_kwargs = (layer_module_input.constructor_input.args, layer_module_input.constructor_input.kwargs) l_kwargs['device'] = device l_kwargs['dtype'] = dtype encoder_layer = torch.nn.TransformerEncoderLayer(*l_args, **l_kwargs) num_layers = 2 # Note: TransformerEncoderLayer takes a "src_mask" while # TransformerEncoder takes a "mask"; rename kwarg appropriately. forward_input = layer_module_input.forward_input if 'src_mask' in forward_input.kwargs: forward_input.kwargs['mask'] = forward_input.kwargs['src_mask'] del forward_input.kwargs['src_mask'] samples.append(ModuleInput( constructor_input=FunctionInput(encoder_layer, num_layers), forward_input=forward_input, desc=layer_module_input.desc )) return samples def module_inputs_torch_nn_TransformerEncoderLayer(module_info, device, dtype, requires_grad, training, **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' ), ModuleInput( constructor_input=FunctionInput(4, 2, 8, 0.0, bias=False), forward_input=FunctionInput( make_input((2, 3, 4)) ), desc='no_bias' ), ] # 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, batch_first, bias in \ itertools.product(attn_masks, key_padding_masks, (True, False), (True, False), (True, False)): samples.append( ModuleInput( constructor_input=FunctionInput(d_model=4, nhead=2, dim_feedforward=8, dropout=0.0, batch_first=batch_first, norm_first=norm_first, bias=bias), 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=batch_first, kwargs_to_batchify={'src_key_padding_mask': 0}), desc=f'no_batch_dim_batch_first_{batch_first}' )) # Samples below where we pass reference_fn are for validating the fast path, # since the fast path requires no_grad mode, we run the fast path in .eval() # and no_grad() in the reference_fn and verify that against the results in train mode. def fast_path_reference_fn(module, parameters, *args, **kwargs): assert module.training module.train(False) with torch.no_grad(): output = module(*args, **kwargs) module.train(True) return output if training: for norm_first, bias in itertools.product((True, False), (True, False)): samples.append( ModuleInput( constructor_input=FunctionInput( 4, 2, 8, dropout=0.0, batch_first=True, norm_first=norm_first, bias=bias ), forward_input=FunctionInput( make_input((2, 3, 4)), ), # fastpath doesn't run when bias=False reference_fn=fast_path_reference_fn if bias else None, desc=f'fastpath_{bias}_norm_first_{norm_first}' ) ) return samples def module_inputs_torch_nn_TransformerDecoderLayer(module_info, device, dtype, requires_grad, training, **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' ), ModuleInput( constructor_input=FunctionInput(4, 2, 8, 0.0, bias=False), forward_input=FunctionInput( make_input((2, 3, 4)), make_input((2, 3, 4)) ), desc='no_bias' ), ] 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, bias, batch_first in \ itertools.product(attn_masks, key_padding_masks, (True, False), (True, False), (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=batch_first, norm_first=norm_first, bias=bias), 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=batch_first, kwargs_to_batchify={'tgt_key_padding_mask': 0, 'memory_key_padding_mask': 0}), desc=f'no_batch_dim_batch_first_{batch_first}' )) src, tgt = make_input((2, 3, 4)), make_input((2, 3, 4)) if not batch_first: src, tgt = src.transpose(0, 1), tgt.transpose(0, 1) if tgt_key_padding_mask is not None: memory_key_padding_mask, tgt_key_padding_mask = (tgt_key_padding_mask.expand(2, 3),) * 2 samples.append( ModuleInput( constructor_input=FunctionInput(d_model=4, nhead=2, dim_feedforward=8, dropout=0.0, batch_first=batch_first, norm_first=norm_first, bias=bias), forward_input=FunctionInput( src, tgt, tgt_mask=tgt_mask, memory_mask=memory_mask, tgt_key_padding_mask=tgt_key_padding_mask, memory_key_padding_mask=memory_key_padding_mask ), desc=f'norm_first_{norm_first}_batch_first_{batch_first}_bias_{bias}' )) return samples def module_inputs_torch_nn_Transformer(module_info, device, dtype, requires_grad, training, **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, bias, batch_first in \ itertools.product(attn_masks, key_padding_masks, (True, False), (True, False), (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=batch_first, norm_first=norm_first, bias=bias), 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=batch_first, kwargs_to_batchify={'tgt_key_padding_mask': 0, 'src_key_padding_mask': 0}), desc=f'no_batch_dim_batch_first_{batch_first}' )) src, tgt = make_input((2, 3, 4)), make_input((2, 3, 4)) if not batch_first: src = src.transpose(0, 1) tgt = tgt.transpose(0, 1) if key_padding_mask is not None: src_key_padding_mask, tgt_key_padding_mask = (key_padding_mask.expand(2, 3),) * 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=batch_first, norm_first=norm_first, bias=bias), forward_input=FunctionInput( src, tgt, tgt_mask=tgt_mask, src_mask=src_mask, tgt_key_padding_mask=tgt_key_padding_mask, src_key_padding_mask=src_key_padding_mask ), )) return samples def module_inputs_torch_nn_Embedding(module_info, device, dtype, requires_grad, training, **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, training, **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, training, **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, training, **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 make_packed_sequence(inp, batch_sizes): required_grad = inp.requires_grad inp.requires_grad_(False) # user won't have access to inp so won't be able to get its grads seq = pack_padded_sequence(inp, batch_sizes) seq.data.requires_grad_(required_grad) return seq def module_inputs_torch_nn_RNN_GRU(module_info, device, dtype, requires_grad, training, with_packed_sequence=False, **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((3, 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), ) ) if with_packed_sequence: samples.append( ModuleInput( constructor_input=FunctionInput(**cons_args), forward_input=FunctionInput(make_packed_sequence(make_input((5, 2, 2)), torch.tensor([5, 3]))), reference_fn=partial(no_batch_dim_reference_rnn_gru, batch_first=b_f), ) ) samples.append( ModuleInput( constructor_input=FunctionInput(**cons_args), forward_input=FunctionInput(make_packed_sequence(make_input((5, 5, 2)), torch.tensor([5, 3, 3, 2, 2]))), 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, training, **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 def module_inputs_torch_nn_ReflectionPad1d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput( constructor_input=FunctionInput(1), forward_input=FunctionInput(make_input((2, 3))), reference_fn=no_batch_dim_reference_fn, ), ModuleInput( constructor_input=FunctionInput((1, 2)), forward_input=FunctionInput(make_input((2, 3, 4))), ), ] def module_inputs_torch_nn_ReflectionPad2d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput( constructor_input=FunctionInput(1), forward_input=FunctionInput(make_input((3, 4, 5))), reference_fn=no_batch_dim_reference_fn, ), ModuleInput( constructor_input=FunctionInput((1, 2, 3, 4)), forward_input=FunctionInput(make_input((3, 4, 5, 6))), ), ] def module_inputs_torch_nn_ReflectionPad3d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput( constructor_input=FunctionInput(1), forward_input=FunctionInput(make_input((2, 3, 4, 5))), reference_fn=no_batch_dim_reference_fn ), ModuleInput( constructor_input=FunctionInput((1, 2, 1, 2, 1, 2)), forward_input=FunctionInput(make_input((3, 3, 3, 3, 3))), ), ] def module_inputs_torch_nn_ReplicationPad1d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput( constructor_input=FunctionInput(1), forward_input=FunctionInput(make_input((3, 4))), reference_fn=no_batch_dim_reference_fn ), ModuleInput( constructor_input=FunctionInput((1, 2)), forward_input=FunctionInput(make_input((3, 4, 5))), ), ] def module_inputs_torch_nn_ReplicationPad2d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput( constructor_input=FunctionInput(1), forward_input=FunctionInput(make_input((3, 4, 5))), reference_fn=no_batch_dim_reference_fn, ), ModuleInput( constructor_input=FunctionInput((1, 2, 3, 4)), forward_input=FunctionInput(make_input((3, 4, 5, 6))), ), ] def module_inputs_torch_nn_ReplicationPad3d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput( constructor_input=FunctionInput(1), forward_input=FunctionInput(make_input((3, 4, 5, 6))), reference_fn=no_batch_dim_reference_fn, ), ModuleInput( constructor_input=FunctionInput((1, 2, 3, 4, 5, 6)), forward_input=FunctionInput(make_input((3, 4, 5, 6, 7))), ), ] def module_inputs_torch_nn_ZeroPad1d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput( constructor_input=FunctionInput(1), forward_input=FunctionInput(make_input((3, 4))), reference_fn=no_batch_dim_reference_fn, ), ModuleInput( constructor_input=FunctionInput((1, 2)), forward_input=FunctionInput(make_input((3, 4, 5))), ), ] def module_inputs_torch_nn_ZeroPad2d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput( constructor_input=FunctionInput(1), forward_input=FunctionInput(make_input((1, 2, 3))), reference_fn=no_batch_dim_reference_fn ), ModuleInput( constructor_input=FunctionInput((1, 2, 3, 4)), forward_input=FunctionInput(make_input((1, 2, 3, 4))), ), ] def module_inputs_torch_nn_ZeroPad3d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput( constructor_input=FunctionInput(1), forward_input=FunctionInput(make_input((3, 4, 5, 6))), reference_fn=no_batch_dim_reference_fn, ), ModuleInput( constructor_input=FunctionInput((1, 2, 3, 4, 5, 6)), forward_input=FunctionInput(make_input((1, 2, 3, 4, 5))), ), ] def module_inputs_torch_nn_ConstantPad1d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput( constructor_input=FunctionInput(1, 2), forward_input=FunctionInput(make_input((3, 4))), reference_fn=no_batch_dim_reference_fn, ), ModuleInput( constructor_input=FunctionInput((1, 2), 3), forward_input=FunctionInput(make_input((3, 4, 5))), ), ] def module_inputs_torch_nn_ConstantPad2d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput( constructor_input=FunctionInput(1, 3), forward_input=FunctionInput(make_input((3, 4, 5))), reference_fn=no_batch_dim_reference_fn ), ModuleInput( constructor_input=FunctionInput((1, 2, 3, 4), 5), forward_input=FunctionInput(make_input((1, 2, 3, 4))), ), ] def module_inputs_torch_nn_ConstantPad3d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ ModuleInput( constructor_input=FunctionInput(1, 3), forward_input=FunctionInput(make_input((3, 4, 5, 6))), reference_fn=no_batch_dim_reference_fn, ), ModuleInput( constructor_input=FunctionInput((1, 2, 3, 4, 5, 6), 7), forward_input=FunctionInput(make_input((1, 2, 1, 2, 1))), ), ] def module_inputs_torch_nn_CircularPad1d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) def padding1d_circular_ref(inp, pad): r""" input: [[[0., 1., 2.], [3., 4., 5.]]] pad: (1, 2) output: [[[2., 0., 1., 2., 0., 1.], [5., 3., 4., 5., 3., 4.]]] """ return torch.cat([inp[:, :, -pad[0]:], inp, inp[:, :, :pad[1]]], dim=2) return [ ModuleInput( constructor_input=FunctionInput(1), forward_input=FunctionInput(make_input((3, 4))), reference_fn=no_batch_dim_reference_fn ), ModuleInput( constructor_input=FunctionInput((1, 2)), forward_input=FunctionInput(make_input((1, 2, 3))), reference_fn=lambda m, p, i: padding1d_circular_ref(i, m.padding), ), ModuleInput( constructor_input=FunctionInput((3, 1)), forward_input=FunctionInput(make_input((1, 2, 3))), reference_fn=lambda m, p, i: padding1d_circular_ref(i, m.padding), ), ModuleInput( constructor_input=FunctionInput((3, 3)), forward_input=FunctionInput(make_input((1, 2, 3))), reference_fn=lambda m, p, i: padding1d_circular_ref(i, m.padding), ), ] def module_inputs_torch_nn_CircularPad2d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) def padding2d_circular_ref(inp, pad): r"""input: [[[[0., 1., 2], [3., 4., 5.]]]] pad: (1, 2, 2, 1) output: [[[[2., 0., 1., 2., 0., 1.], [5., 3., 4., 5., 3., 4.], [2., 0., 1., 2., 0., 1.], [5., 3., 4., 5., 3., 4.], [2., 0., 1., 2., 0., 1.]]]] """ inp = torch.cat([inp[:, :, -pad[2]:], inp, inp[:, :, :pad[3]]], dim=2) return torch.cat([inp[:, :, :, -pad[0]:], inp, inp[:, :, :, :pad[1]]], dim=3) return [ ModuleInput( constructor_input=FunctionInput(1), forward_input=FunctionInput(make_input((3, 4, 5))), reference_fn=no_batch_dim_reference_fn, ), ModuleInput( constructor_input=FunctionInput((1, 2, 2, 1)), forward_input=FunctionInput(make_input((1, 1, 2, 3))), reference_fn=lambda m, p, i: padding2d_circular_ref(i, m.padding), ), ModuleInput( constructor_input=FunctionInput((2, 3, 2, 2)), forward_input=FunctionInput(make_input((1, 1, 2, 3))), reference_fn=lambda m, p, i: padding2d_circular_ref(i, m.padding), ), ModuleInput( constructor_input=FunctionInput((3, 3, 3, 1)), forward_input=FunctionInput(make_input((1, 1, 3, 3))), reference_fn=lambda m, p, i: padding2d_circular_ref(i, m.padding), ), ] def module_inputs_torch_nn_CircularPad3d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) def padding3d_circular_ref(inp, pad): r"""input: [[[[[ 0., 1., 2.], [ 3., 4., 5.]], [[ 6., 7., 8.], [ 9., 10., 11.]]]]] pad: (1, 2, 2, 1, 1, 2) output: [[[[[ 8., 6., 7., 8., 6., 7.], [11., 9., 10., 11., 9., 10.], [ 8., 6., 7., 8., 6., 7.], [11., 9., 10., 11., 9., 10.], [ 8., 6., 7., 8., 6., 7.]], [[ 2., 0., 1., 2., 0., 1.], [ 5., 3., 4., 5., 3., 4.], [ 2., 0., 1., 2., 0., 1.], [ 5., 3., 4., 5., 3., 4.], [ 2., 0., 1., 2., 0., 1.]], [[ 8., 6., 7., 8., 6., 7.], [11., 9., 10., 11., 9., 10.], [ 8., 6., 7., 8., 6., 7.], [11., 9., 10., 11., 9., 10.], [ 8., 6., 7., 8., 6., 7.]], [[ 2., 0., 1., 2., 0., 1.], [ 5., 3., 4., 5., 3., 4.], [ 2., 0., 1., 2., 0., 1.], [ 5., 3., 4., 5., 3., 4.], [ 2., 0., 1., 2., 0., 1.]], [[ 8., 6., 7., 8., 6., 7.], [11., 9., 10., 11., 9., 10.], [ 8., 6., 7., 8., 6., 7.], [11., 9., 10., 11., 9., 10.], [ 8., 6., 7., 8., 6., 7.]]]]] """ inp = torch.cat([inp[:, :, -pad[4]:], inp, inp[:, :, :pad[5]]], dim=2) inp = torch.cat([inp[:, :, :, -pad[2]:], inp, inp[:, :, :, :pad[3]]], dim=3) return torch.cat([inp[:, :, :, :, -pad[0]:], inp, inp[:, :, :, :, :pad[1]]], dim=4) return [ ModuleInput( constructor_input=FunctionInput(1), forward_input=FunctionInput(make_input((3, 4, 5, 6))), reference_fn=no_batch_dim_reference_fn, ), ModuleInput( constructor_input=FunctionInput((1, 2, 1, 2, 1, 2)), forward_input=FunctionInput(make_input((1, 1, 2, 2, 3))), reference_fn=lambda m, p, i: padding3d_circular_ref(i, m.padding) ), ModuleInput( constructor_input=FunctionInput((3, 2, 2, 1, 1, 2)), forward_input=FunctionInput(make_input((1, 1, 2, 2, 3))), reference_fn=lambda m, p, i: padding3d_circular_ref(i, m.padding) ), ModuleInput( constructor_input=FunctionInput((3, 3, 2, 1, 2, 2)), forward_input=FunctionInput(make_input((1, 1, 2, 2, 3))), reference_fn=lambda m, p, i: padding3d_circular_ref(i, m.padding) ), ] # 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' ), DecorateInfo( skipCUDAVersionIn([(11, 7)]), "TestExpandedWeightModule", "test_module", device_type='cuda' ), DecorateInfo( skipCUDAVersionIn([(11, 7)]), "TestDecomp", "test_rnn_decomp_module", device_type='cuda' ) ) # Start of module error inputs functions. def module_error_inputs_torch_nn_RNN_GRU_Cell(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) samples = [ ErrorModuleInput( ModuleInput( constructor_input=FunctionInput(10, 20), forward_input=FunctionInput(make_input(3, 11), make_input(3, 20)), ), error_on=ModuleErrorEnum.FORWARD_ERROR, error_type=RuntimeError, error_regex="input has inconsistent input_size: got 11 expected 10" ), ErrorModuleInput( ModuleInput( constructor_input=FunctionInput(10, 20), forward_input=FunctionInput(make_input(3, 10), make_input(3, 21)), ), error_on=ModuleErrorEnum.FORWARD_ERROR, error_type=RuntimeError, error_regex="hidden0 has inconsistent hidden_size: got 21, expected 20" ), ErrorModuleInput( ModuleInput( constructor_input=FunctionInput(10, 20), forward_input=FunctionInput(make_input(3, 10), make_input(5, 20)), ), error_on=ModuleErrorEnum.FORWARD_ERROR, error_type=RuntimeError, error_regex="Input batch size 3 doesn't match hidden0 batch size 5" ), ErrorModuleInput( ModuleInput( constructor_input=FunctionInput(10, 20), forward_input=FunctionInput(make_input(3, 10), make_input(3, 1, 1, 20)), ), error_on=ModuleErrorEnum.FORWARD_ERROR, error_type=ValueError, error_regex="Expected hidden to be 1D or 2D, got 4D instead" ), ErrorModuleInput( ModuleInput( constructor_input=FunctionInput(10, 20, 'relu'), forward_input=FunctionInput(make_input(3, 10), make_input(3, 21)), ), error_on=ModuleErrorEnum.FORWARD_ERROR, error_type=RuntimeError, error_regex="hidden0 has inconsistent hidden_size: got 21, expected 20" ), ErrorModuleInput( ModuleInput( constructor_input=FunctionInput(10, 20, 'tanh'), forward_input=FunctionInput(make_input(3, 10), make_input(3, 21)), ), error_on=ModuleErrorEnum.FORWARD_ERROR, error_type=RuntimeError, error_regex="hidden0 has inconsistent hidden_size: got 21, expected 20" ), ] return samples def module_error_inputs_torch_nn_LSTMCell(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) samples = [ ErrorModuleInput( ModuleInput( constructor_input=FunctionInput(10, 20), forward_input=FunctionInput(make_input(3, 11), (make_input(3, 20), make_input(3, 20))), ), error_on=ModuleErrorEnum.FORWARD_ERROR, error_type=RuntimeError, error_regex="input has inconsistent input_size: got 11 expected 10" ), ErrorModuleInput( ModuleInput( constructor_input=FunctionInput(10, 20), forward_input=FunctionInput(make_input(3, 10), (make_input(3, 21), make_input(3, 21))), ), error_on=ModuleErrorEnum.FORWARD_ERROR, error_type=RuntimeError, error_regex="hidden0 has inconsistent hidden_size: got 21, expected 20" ), ErrorModuleInput( ModuleInput( constructor_input=FunctionInput(10, 20), forward_input=FunctionInput(make_input(3, 10), (make_input(5, 20), make_input(5, 20))), ), error_on=ModuleErrorEnum.FORWARD_ERROR, error_type=RuntimeError, error_regex="Input batch size 3 doesn't match hidden0 batch size 5" ), ErrorModuleInput( ModuleInput( constructor_input=FunctionInput(10, 20), forward_input=FunctionInput(make_input(3, 10), (make_input(3, 1, 1, 20), make_input(3, 1, 1, 20))), ), error_on=ModuleErrorEnum.FORWARD_ERROR, error_type=ValueError, error_regex="Expected hx\\[0\\] to be 1D or 2D, got 4D instead" ), ] return samples def module_error_inputs_torch_nn_RNN_GRU(module_info, device, dtype, requires_grad, training, **kwargs): samples = [ ErrorModuleInput( ModuleInput(constructor_input=FunctionInput(10, 0, 1)), error_on=ModuleErrorEnum.CONSTRUCTION_ERROR, error_type=ValueError, error_regex="hidden_size must be greater than zero" ), ErrorModuleInput( ModuleInput(constructor_input=FunctionInput(10, 10, 0)), error_on=ModuleErrorEnum.CONSTRUCTION_ERROR, error_type=ValueError, error_regex="num_layers must be greater than zero" ), ] return samples def module_error_inputs_torch_nn_Pad1d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) is_constant = kwargs.get('is_constant', False) return [ ErrorModuleInput( ModuleInput( constructor_input=FunctionInput(1, 3) if is_constant else FunctionInput(3), forward_input=FunctionInput(make_input((2, 3, 4, 5))), ), error_on=ModuleErrorEnum.FORWARD_ERROR, error_type=ValueError, error_regex=r"expected 2D or 3D input \(got 4D input\)", ), ] def module_error_inputs_torch_nn_Pad2d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) is_constant = kwargs.get('is_constant', False) return [ ErrorModuleInput( ModuleInput( constructor_input=FunctionInput(1, 3) if is_constant else FunctionInput(3), forward_input=FunctionInput(make_input((2, 3))), ), error_on=ModuleErrorEnum.FORWARD_ERROR, error_type=ValueError, error_regex=r"expected 3D or 4D input \(got 2D input\)", ), ] def module_error_inputs_torch_nn_Pad3d(module_info, device, dtype, requires_grad, training, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) is_constant = kwargs.get('is_constant', False) return [ ErrorModuleInput( ModuleInput( constructor_input=FunctionInput(1, 3) if is_constant else FunctionInput(3), forward_input=FunctionInput(make_input((2, 3))), ), error_on=ModuleErrorEnum.FORWARD_ERROR, error_type=ValueError, error_regex=r"expected 4D or 5D input \(got 2D input\)", ), ] _macos15_or_newer = torch.backends.mps.is_available() and torch.backends.mps.is_macos_or_newer(15, 0) # Database of ModuleInfo entries in alphabetical order. module_db: list[ModuleInfo] = [ ModuleInfo(torch.nn.AdaptiveAvgPool1d, module_inputs_func=module_inputs_torch_nn_AdaptiveAvgPool1d, skips=( # Fails on MPS backend if input/output sizes are not divisible DecorateInfo(skipMPS),) ), ModuleInfo(torch.nn.AdaptiveAvgPool2d, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, module_inputs_func=module_inputs_torch_nn_AdaptiveAvgPool2d, skips=( # Fails on MPS backend if input/output sizes are not divisible DecorateInfo(skipMPS), # Fails on backward check if output size is 1x1 DecorateInfo( unittest.expectedFailure, 'TestModule', 'test_memory_format', active_if=operator.itemgetter('training'), ),) ), ModuleInfo(torch.nn.AdaptiveAvgPool3d, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, module_inputs_func=module_inputs_torch_nn_AdaptiveAvgPool3d, skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'), # not supported on MPS backend DecorateInfo(skipMPS),) ), ModuleInfo(torch.nn.AdaptiveMaxPool1d, module_inputs_func=module_inputs_torch_nn_AdaptiveMaxPool1d, ), ModuleInfo(torch.nn.AdaptiveMaxPool2d, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, module_inputs_func=module_inputs_torch_nn_AdaptiveMaxPool2d, ), ModuleInfo(torch.nn.AdaptiveMaxPool3d, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, module_inputs_func=module_inputs_torch_nn_AdaptiveMaxPool3d, skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'), # not supported on MPS backend DecorateInfo(skipMPS),) ), ModuleInfo(torch.nn.AvgPool1d, module_inputs_func=module_inputs_torch_nn_AvgPool1d, ), ModuleInfo(torch.nn.AvgPool2d, module_inputs_func=module_inputs_torch_nn_AvgPool2d, skips=( # The difference between channels last backward and # channels first backward of AvgPool2d on CUDA is too large # See https://github.com/pytorch/pytorch/issues/107201 DecorateInfo( unittest.expectedFailure, 'TestModule', 'test_memory_format', active_if=operator.itemgetter('training'), device_type='cuda', ), # error: input types 'tensor' and 'tensor<15x10xf16>' are not broadcast compatible DecorateInfo(skipIfMPSOnMacOS13, 'TestModule', dtypes=[torch.float16], device_type='mps',),), ), ModuleInfo(torch.nn.AvgPool3d, module_inputs_func=module_inputs_torch_nn_AvgPool3d, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, skips=( # No channels_last support for AvgPool1d as it does not take 4D inputs DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'), # not supported on MPS backend DecorateInfo(skipMPS),) ), ModuleInfo(torch.nn.BatchNorm1d, train_and_eval_differ=True, module_inputs_func=module_inputs_torch_nn_BatchNorm1d, skips=( # tracking here rather than in the list in test_aotdispatch.py as eval mode passes # RuntimeError: tried to get Double out of SymInt DecorateInfo( unittest.expectedFailure, 'TestEagerFusionModuleInfo', 'test_aot_autograd_symbolic_module_exhaustive', active_if=operator.itemgetter('training') ), # torch._subclasses.fake_tensor.DataDependentOutputException: aten._local_scalar_dense.default DecorateInfo( unittest.expectedFailure, 'TestEagerFusionModuleInfo', 'test_aot_autograd_module_exhaustive', active_if=operator.itemgetter('training') )) ), ModuleInfo(torch.nn.BatchNorm2d, train_and_eval_differ=True, module_inputs_func=module_inputs_torch_nn_BatchNorm2d, skips=( # See https://github.com/pytorch/pytorch/issues/134580 DecorateInfo(expectedFailureMPS, 'TestModule', 'test_memory_format', active_if=operator.itemgetter('training')), # tracking here rather than in the list in test_aotdispatch.py as eval mode passes # RuntimeError: tried to get Double out of SymInt DecorateInfo( unittest.expectedFailure, 'TestEagerFusionModuleInfo', 'test_aot_autograd_symbolic_module_exhaustive', active_if=operator.itemgetter('training') ), # torch._subclasses.fake_tensor.DataDependentOutputException: aten._local_scalar_dense.default DecorateInfo( unittest.expectedFailure, 'TestEagerFusionModuleInfo', 'test_aot_autograd_module_exhaustive', active_if=operator.itemgetter('training') ),) ), ModuleInfo(torch.nn.BatchNorm3d, train_and_eval_differ=True, module_inputs_func=module_inputs_torch_nn_BatchNorm3d, skips=( # not supported on MPS backend DecorateInfo(skipMPS), # tracking here rather than in the list in test_aotdispatch.py as eval mode passes # RuntimeError: tried to get Double out of SymInt DecorateInfo( unittest.expectedFailure, 'TestEagerFusionModuleInfo', 'test_aot_autograd_symbolic_module_exhaustive', active_if=operator.itemgetter('training') ), # torch._subclasses.fake_tensor.DataDependentOutputException: aten._local_scalar_dense.default DecorateInfo( unittest.expectedFailure, 'TestEagerFusionModuleInfo', 'test_aot_autograd_module_exhaustive', active_if=operator.itemgetter('training') ),) ), ModuleInfo(torch.nn.CELU, module_inputs_func=module_inputs_torch_nn_CELU, # not MPS specific, will be xfailed for all devices in next PR skips=( DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_check_inplace', device_type='mps', dtypes=[torch.float16]),) ), 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]), # See #119108: MPSNDArrayConvolutionA14.mm:3976: failed assertion `destination datatype must be fp32' # xfail does not work due to Fatal Python error: Aborted DecorateInfo(skipIfMPSOnMacOS13, "TestModule", "test_memory_format", device_type='mps', dtypes=[torch.float16]), DecorateInfo(skipIfMPSOnMacOS13, "TestModule", "test_non_contiguous_tensors", device_type='mps', dtypes=[torch.float16]), ), 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]), # This was wrongly being skipped before and needs investigation. # See https://github.com/pytorch/pytorch/issues/80247 DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format", device_type='cuda', dtypes=[torch.float64]), # Fails with channels last test on MPS backend DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format", device_type='mps', dtypes=[torch.float32, torch.float16]), # See #119108: MPSNDArrayConvolutionA14.mm:3976: failed assertion `destination datatype must be fp32' # xfail does not work due to Fatal Python error: Aborted DecorateInfo(skipIfMPSOnMacOS13, "TestModule", "test_memory_format", device_type='mps', dtypes=[torch.float16]), DecorateInfo(skipIfMPSOnMacOS13, "TestModule", "test_non_contiguous_tensors", device_type='mps', dtypes=[torch.float16]), ), 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]), # Conv3d is not supported on MPS backend DecorateInfo(skipMPS, device_type="mps"), # This was wrongly being skipped before and needs investigation. # See https://github.com/pytorch/pytorch/issues/80247 DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format"), ), 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, dtypes=floating_and_complex_types_and(torch.chalf), 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]), # Not implemented for chalf on CPU DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_cpu_gpu_parity', dtypes=(torch.chalf,), device_type='cuda'), # See #119108: MPSNDArrayConvolutionA14.mm:3976: failed assertion `destination datatype must be fp32' # xfail does not work due to Fatal Python error: Aborted DecorateInfo(skipIfMPSOnMacOS13, "TestModule", "test_memory_format", device_type='mps', dtypes=[torch.float16]), DecorateInfo(skipIfMPSOnMacOS13, "TestModule", "test_non_contiguous_tensors", device_type='mps', dtypes=[torch.float16]),), decorators=( DecorateInfo(precisionOverride({torch.float32: 1e-04}), 'TestModule', 'test_memory_format'), DecorateInfo(precisionOverride({torch.chalf: 5e-03}), '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, dtypes=floating_and_complex_types_and(torch.chalf), 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]), # Fails on backward check because ViewAsRealBackward apply contiguous for grad DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_memory_format', dtypes=(torch.complex32, torch.complex64, torch.complex128)), # This was wrongly being skipped before and needs investigation. # See https://github.com/pytorch/pytorch/issues/80247 DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format", device_type='cuda', dtypes=[torch.float64, torch.complex128]), # Fails with channels last test on MPS backend DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format", device_type='mps', dtypes=[torch.float16, torch.float32]), # Not implemented for chalf on CPU DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_cpu_gpu_parity', dtypes=(torch.chalf,), device_type='cuda'), # See #119108: MPSNDArrayConvolutionA14.mm:3976: failed assertion `destination datatype must be fp32' # xfail does not work due to Fatal Python error: Aborted DecorateInfo(skipIfMPSOnMacOS13, "TestModule", "test_memory_format", device_type='mps', dtypes=[torch.float16]), DecorateInfo(skipIfMPSOnMacOS13, "TestModule", "test_non_contiguous_tensors", device_type='mps', dtypes=[torch.float16]), ), decorators=( DecorateInfo(precisionOverride({torch.float32: 1e-04}), 'TestModule', 'test_memory_format'), DecorateInfo(precisionOverride({torch.chalf: 5e-03}), 'TestModule', 'test_memory_format'), )), ModuleInfo(torch.nn.ConvTranspose3d, module_inputs_func=partial(module_inputs_torch_nn_ConvNd, N=3, lazy=False, transposed=True), dtypes=floating_and_complex_types_and(torch.chalf), 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]), # ConvTranspose3d is not supported on MPS backend DecorateInfo(skipMPS), # This was wrongly being skipped before and needs investigation. # See https://github.com/pytorch/pytorch/issues/80247 DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format"), # These fail only on ROCm DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format", device_type='cuda', dtypes=[torch.complex32, torch.complex64], active_if=TEST_WITH_ROCM), # Not implemented for chalf on CPU DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_cpu_gpu_parity', dtypes=(torch.chalf,), device_type='cuda'), ), decorators=( DecorateInfo(precisionOverride({torch.float32: 1e-04}), 'TestModule', 'test_memory_format'), DecorateInfo(precisionOverride({torch.complex64: 1e-04}), 'TestModule', 'test_cpu_gpu_parity'), DecorateInfo(precisionOverride({torch.chalf: 5e-03}), 'TestModule', 'test_memory_format'), )), ModuleInfo(torch.nn.CosineEmbeddingLoss, module_inputs_func=module_inputs_torch_nn_CosineEmbeddingLoss, skips=( # No channels_last support for loss functions. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'),) ), ModuleInfo(torch.nn.ELU, module_inputs_func=module_inputs_torch_nn_ELU, # not MPS specific, will be xfailed for all devices in next PR skips=( DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_check_inplace', device_type='mps', dtypes=[torch.float16]),) ), ModuleInfo(torch.nn.FractionalMaxPool2d, module_inputs_func=module_inputs_torch_nn_FractionalMaxPool2d, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, skips=( # not supported on MPS backend DecorateInfo(skipMPS), DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'),) ), ModuleInfo(torch.nn.FractionalMaxPool3d, module_inputs_func=module_inputs_torch_nn_FractionalMaxPool3d, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, skips=( # not supported on MPS backend DecorateInfo(skipMPS), DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'),) ), 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'),) ), ModuleInfo(torch.nn.SmoothL1Loss, module_inputs_func=module_inputs_torch_nn_SmoothL1Loss, skips=( # No channels_last support for loss functions. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'), # See #119108: input types 'tensor' and 'tensor<15x10xf16>' are not broadcast compatible # NS: Still fails on MacOS15.1 DecorateInfo(skipIfMPS, 'TestModule', 'test_non_contiguous_tensors', dtypes=[torch.float16], device_type='mps'),), ), 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), # See #119108: MPSNDArrayConvolutionA14.mm:3976: failed assertion `destination datatype must be fp32' # xfail does not work due to Fatal Python error: Aborted DecorateInfo(skipIfMPSOnMacOS13, "TestModule", "test_memory_format", device_type='mps', dtypes=[torch.float16]), DecorateInfo(skipIfMPSOnMacOS13, "TestModule", "test_non_contiguous_tensors", device_type='mps', dtypes=[torch.float16]), ), 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=( # 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), # This was wrongly being skipped before and needs investigation. # See https://github.com/pytorch/pytorch/issues/80247 DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format", device_type='cuda', dtypes=[torch.float64]), # Fails with channels last test on MPS backend DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format", device_type='mps', dtypes=[torch.float32, torch.float16]), # See #119108: MPSNDArrayConvolutionA14.mm:3976: failed assertion `destination datatype must be fp32' # xfail does not work due to Fatal Python error: Aborted DecorateInfo(skipIfMPSOnMacOS13, "TestModule", "test_memory_format", device_type='mps', dtypes=[torch.float16]), DecorateInfo(skipIfMPSOnMacOS13, "TestModule", "test_non_contiguous_tensors", device_type='mps', dtypes=[torch.float16]), ), 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), # LazyConv3d is not supported on MPS backend DecorateInfo(skipMPS), # This was wrongly being skipped before and needs investigation. # See https://github.com/pytorch/pytorch/issues/80247 DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format"), ), 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), # See #119108: MPSNDArrayConvolutionA14.mm:3976: failed assertion `destination datatype must be fp32' # xfail does not work due to Fatal Python error: Aborted DecorateInfo(skipIfMPSOnMacOS13, "TestModule", "test_memory_format", device_type='mps', dtypes=[torch.float16]), DecorateInfo(skipIfMPSOnMacOS13, "TestModule", "test_non_contiguous_tensors", device_type='mps', dtypes=[torch.float16]), ), 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), # This was wrongly being skipped before and needs investigation. # See https://github.com/pytorch/pytorch/issues/80247 DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format", device_type='cuda', dtypes=[torch.float64]), # Fails with channels last test on MPS backend DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format", device_type='mps', dtypes=[torch.float32, torch.float16]), # See #119108: MPSNDArrayConvolutionA14.mm:3976: failed assertion `destination datatype must be fp32' # xfail does not work due to Fatal Python error: Aborted DecorateInfo(skipIfMPSOnMacOS13, "TestModule", "test_memory_format", device_type='mps', dtypes=[torch.float16]), DecorateInfo(skipIfMPSOnMacOS13, "TestModule", "test_non_contiguous_tensors", device_type='mps', dtypes=[torch.float16]), ), 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), # LazyConvTranspose3d is not supported on MPS backend DecorateInfo(skipMPS), # This was wrongly being skipped before and needs investigation. # See https://github.com/pytorch/pytorch/issues/80247 DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format"), ), decorators=( DecorateInfo(precisionOverride({torch.float32: 1e-04}), 'TestModule', 'test_memory_format'), )), ModuleInfo(torch.nn.Linear, module_inputs_func=module_inputs_torch_nn_Linear, skips=( # 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=( # No channels_last support for Bilinear currently. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'), # See #119108: tolerance issue DecorateInfo(unittest.expectedFailure, "TestModule", "test_forward", device_type='mps', dtypes=[torch.float16]),) ), ModuleInfo(torch.nn.LPPool1d, module_inputs_func=module_inputs_torch_nn_LPPool1d, skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_grad'), DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_gradgrad'),) ), ModuleInfo(torch.nn.LPPool2d, module_inputs_func=module_inputs_torch_nn_LPPool2d, skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_grad'), DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_gradgrad'), # Fails on backward check on MPS # See https://github.com/pytorch/pytorch/issues/107214 DecorateInfo( unittest.expectedFailure, 'TestModule', 'test_memory_format', active_if=operator.itemgetter('training'), device_type='mps', ),) ), ModuleInfo(torch.nn.LPPool3d, module_inputs_func=module_inputs_torch_nn_LPPool3d, skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_grad'), DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_gradgrad'), DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'), DecorateInfo(skipIfMPS, device_type='mps'),) ), ModuleInfo(torch.nn.MaxPool1d, module_inputs_func=module_inputs_torch_nn_MaxPool1d, ), ModuleInfo(torch.nn.MaxPool2d, module_inputs_func=module_inputs_torch_nn_MaxPool2d, ), ModuleInfo(torch.nn.MaxPool3d, module_inputs_func=module_inputs_torch_nn_MaxPool3d, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, skips=( # not supported on MPS backend DecorateInfo(skipIfMPS, device_type='mps'),) ), ModuleInfo(torch.nn.KLDivLoss, module_inputs_func=module_inputs_torch_nn_KLDivLoss, skips=( # No channels_last support for loss functions. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'), # https://github.com/pytorch/pytorch/issues/115588 DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_cpu_gpu_parity'), DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_grad'), DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_gradgrad'),) ), ModuleInfo(torch.nn.MSELoss, module_inputs_func=module_inputs_torch_nn_MSELoss, skips=( # No channels_last support for loss functions. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'), # See #119108: input types 'tensor' and 'tensor<15x10xf16>' are not broadcast compatible DecorateInfo(skipIfMPSOnMacOS13, 'TestModule', 'test_non_contiguous_tensors', device_type='mps', dtypes=[torch.float16],), # See #119108: tolerance issue DecorateInfo(unittest.expectedFailure, "TestModule", "test_forward", device_type='mps', dtypes=[torch.float16]),) ), ModuleInfo(torch.nn.MarginRankingLoss, module_inputs_func=module_inputs_torch_nn_MarginRankingLoss, skips=( # No channels_last support for loss functions. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'),) ), ModuleInfo(torch.nn.MultiLabelMarginLoss, module_inputs_func=module_inputs_torch_nn_MultiLabelMarginLoss, skips=( # No channels_last support for loss functions. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'), # 'aten::multilabel_margin_loss_forward' is not currently implemented for the MPS device. DecorateInfo(skipIfMPS, 'TestModule', device_type='mps'), # derivative for aten::multilabel_margin_loss_backward is not implemented DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_gradgrad'),) ), ModuleInfo(torch.nn.MultiMarginLoss, module_inputs_func=module_inputs_torch_nn_MultiMarginLoss, skips=( # No channels_last support for loss functions. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'), # 'aten::multi_margin_loss' is not currently implemented for the MPS device. DecorateInfo(skipIfMPS, 'TestModule', device_type='mps'), # RuntimeError: derivative for aten::multi_margin_loss_backward is not implemented DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_gradgrad'),) ), ModuleInfo(torch.nn.SoftMarginLoss, module_inputs_func=module_inputs_torch_nn_SoftMarginLoss, skips=( # No channels_last support for loss functions. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'), # See #119108: tolerance issue DecorateInfo(unittest.expectedFailure, "TestModule", "test_forward", device_type='mps', dtypes=[torch.float16]),) ), ModuleInfo(torch.nn.MultiLabelSoftMarginLoss, module_inputs_func=module_inputs_torch_nn_MultiLabelSoftMarginLoss, skips=( # No channels_last support for loss functions. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'),) ), 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'), # See #119108: tolerance issue DecorateInfo(unittest.expectedFailure, "TestModule", "test_forward", device_type='mps', dtypes=[torch.float16]),) ), ModuleInfo(torch.nn.GaussianNLLLoss, module_inputs_func=module_inputs_torch_nn_GaussianNLLLoss, skips=( # No channels_last support for loss functions. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'),)), ModuleInfo(torch.nn.PoissonNLLLoss, module_inputs_func=module_inputs_torch_nn_PoissonNLLLoss, skips=( # No channels_last support for loss functions. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'),)), ModuleInfo(torch.nn.HingeEmbeddingLoss, module_inputs_func=module_inputs_torch_nn_HingeEmbeddingLoss, skips=( # No channels_last support for loss functions. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'),) ), ModuleInfo(torch.nn.HuberLoss, module_inputs_func=module_inputs_torch_nn_HuberLoss, skips=( # No channels_last support for loss functions. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'), # See #119108: seemingly incorrect output dtype DecorateInfo(unittest.expectedFailure, "TestModule", "test_forward", device_type='mps', dtypes=[torch.float16]),) ), ModuleInfo(torch.nn.BCELoss, module_inputs_func=module_inputs_torch_nn_BCELoss, skips=( # No channels_last support for loss functions. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'), # error: input types 'tensor' and 'tensor<15x10xf16>' are not broadcast compatible DecorateInfo(skipIfMPS, 'TestModule', dtypes=[torch.float16], device_type='mps'),) ), ModuleInfo(torch.nn.BCEWithLogitsLoss, module_inputs_func=module_inputs_torch_nn_BCEWithLogitsLoss, skips=( # No channels_last support for loss functions. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'), # see #119108: tolerance issue DecorateInfo(skipIfMPS, 'TestModule', dtypes=[torch.float16], device_type='mps'),) ), ModuleInfo(torch.nn.CrossEntropyLoss, module_inputs_func=module_inputs_torch_nn_CrossEntropyLoss, dtypes=get_all_fp_dtypes(include_half=True, include_bfloat16=False), decorators=( # No channels_last support for loss functions. DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_memory_format'), DecorateInfo(toleranceOverride({torch.float16: tol(atol=3e-2, rtol=1e-3)}), "TestModule", "test_forward", dtypes=[torch.float16], device_type='cpu'), DecorateInfo(unittest.expectedFailure, "TestModule", "test_cpu_gpu_parity", dtypes=[torch.float16], device_type='cuda'),), ), ModuleInfo(torch.nn.CTCLoss, module_inputs_func=module_inputs_torch_nn_CTCLoss, skips=( # No channels_last support for loss functions. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'), # The operator aten::_ctc_loss is not currently implemented for the MPS device. DecorateInfo(skipIfMPS, 'TestModule', device_type='mps',), # derivative for aten::_ctc_loss_backward is not implemented DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_grad'), DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_gradgrad'), # https://github.com/pytorch/pytorch/issues/115585 DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_non_contiguous_tensors'),) ), ModuleInfo(torch.nn.GELU, module_inputs_func=module_inputs_torch_nn_GELU, skips=( # See #119108: tolerance issue DecorateInfo(unittest.expectedFailure, "TestModule", "test_forward", device_type='mps', dtypes=[torch.float16]),) ), ModuleInfo(torch.nn.GLU, module_inputs_func=module_inputs_torch_nn_GLU, ), ModuleInfo(torch.nn.GroupNorm, module_inputs_func=module_inputs_torch_nn_GroupNorm, dtypes=get_all_fp_dtypes(include_bfloat16=True, include_half=True), skips=( # Tracking at https://github.com/pytorch/pytorch/issues/98089 DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_cpu_gpu_parity'), DecorateInfo(toleranceOverride({torch.float32: tol(atol=1e-4, rtol=1e-4)}), 'TestModule', 'test_memory_format', device_type='cpu'), # No channels_last support for GroupNorm currently. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format', device_type='cuda'), DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format', device_type='mps'), DecorateInfo(unittest.skip("Skipped!"), "TestModule", "test_grad", active_if=TEST_WITH_ROCM, device_type='cuda'),) ), ModuleInfo(torch.nn.Hardshrink, module_inputs_func=module_inputs_torch_nn_Hardshrink, skips=( # not supported on MPS backend DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_forward', device_type='mps'), DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_if_train_and_eval_modes_differ', device_type='mps'), DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_memory_format', device_type='mps'), DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_non_contiguous_tensors', device_type='mps'), DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_save_load', device_type='mps'),), ), ModuleInfo(torch.nn.Hardswish, module_inputs_func=module_inputs_torch_nn_Hardswish, skips=None if _macos15_or_newer else ( # Fails on backward check on MPS # See https://github.com/pytorch/pytorch/issues/107214 DecorateInfo( unittest.expectedFailure, 'TestModule', 'test_memory_format', active_if=operator.itemgetter('training'), device_type='mps', ),), supports_gradgrad=False), ModuleInfo(torch.nn.Hardtanh, module_inputs_func=module_inputs_torch_nn_Hardtanh, ), ModuleInfo(torch.nn.InstanceNorm1d, module_inputs_func=partial(module_inputs_torch_nn_InstanceNormNd, N=1), train_and_eval_differ=True, skips=( # No channels_last support for InstanceNorm1d currently. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'),) ), ModuleInfo(torch.nn.InstanceNorm2d, module_inputs_func=partial(module_inputs_torch_nn_InstanceNormNd, N=2), train_and_eval_differ=True, skips=( # No channels_last support for InstanceNorm2d currently. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'),) ), ModuleInfo(torch.nn.InstanceNorm3d, module_inputs_func=partial(module_inputs_torch_nn_InstanceNormNd, N=3), train_and_eval_differ=True, skips=( # not supported on MPS backend DecorateInfo(expectedFailureMPS, 'TestModuleMPS', 'test_memory_format'), DecorateInfo(expectedFailureMPS, 'TestModuleMPS', 'test_non_contiguous_tensors'), DecorateInfo(expectedFailureMPS, 'TestModuleMPS', 'test_forward'), DecorateInfo(expectedFailureMPS, 'TestModuleMPS', 'test_non_contiguous'), DecorateInfo(expectedFailureMPS, 'TestModuleMPS', 'test_save_load'), # No channels_last support for InstanceNorm3d currently. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'),) ), ModuleInfo(torch.nn.LocalResponseNorm, module_inputs_func=module_inputs_torch_nn_LocalResponseNorm, skips=( # uses avg_pool3d which is not supported on MPS backend DecorateInfo(expectedFailureMPS, 'TestModule', 'test_memory_format'), DecorateInfo(expectedFailureMPS, 'TestModule', 'test_non_contiguous_tensors'), DecorateInfo(expectedFailureMPS, 'TestModule', 'test_forward'), DecorateInfo(expectedFailureMPS, 'TestModule', 'test_if_train_and_eval_modes_differ'), DecorateInfo(expectedFailureMPS, 'TestModule', 'test_non_contiguous'), DecorateInfo(expectedFailureMPS, 'TestModule', 'test_save_load'),) ), ModuleInfo(torch.nn.LayerNorm, module_inputs_func=module_inputs_torch_nn_LayerNorm, skips=( # No channels_last support for LayerNorm currently. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'),) ), ModuleInfo(torch.nn.RMSNorm, module_inputs_func=module_inputs_torch_nn_RMSNorm, ), # TransformerEncoder takes the same inputs as TransformerEncoderLayer ModuleInfo(torch.nn.TransformerEncoder, train_and_eval_differ=True, module_inputs_func=module_inputs_torch_nn_TransformerEncoder, decorators=[ # Not implemented for SDPA backward derivative DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_gradgrad', device_type='cpu'), ], skips=( # No channels_last support for TransformerEncoderLayer currently. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'), # Doesn't support device / dtype kwargs directly because it is just a # container of TransformerEncoderLayers. DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_factory_kwargs'),) ), ModuleInfo(torch.nn.TransformerEncoderLayer, train_and_eval_differ=True, module_inputs_func=module_inputs_torch_nn_TransformerEncoderLayer, decorators=[ DecorateInfo(toleranceOverride({torch.float32: tol(atol=1e-4, rtol=1e-4)}), 'TestModule', 'test_non_contiguous_tensors', device_type='cpu', active_if=IS_WINDOWS), DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-4, rtol=2e-3)}), 'TestModule', 'test_forward', device_type='mps'), # Not implemented for SDPA backward derivative DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_gradgrad', device_type='cpu'), ], skips=( # No channels_last support for TransformerEncoderLayer currently. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'),) ), ModuleInfo(torch.nn.TransformerDecoderLayer, module_inputs_func=module_inputs_torch_nn_TransformerDecoderLayer, decorators=[ # Not implemented for SDPA backward derivative DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_gradgrad', device_type='cpu'), ], skips=( # No channels_last support for TransformerDecoderLayer currently. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'),) ), ModuleInfo(torch.nn.Transformer, module_inputs_func=module_inputs_torch_nn_Transformer, # Inputs are too large to run with slow gradcheck # https://github.com/pytorch/pytorch/issues/117140 gradcheck_fast_mode=True, decorators=[ # Not implemented for SDPA backward derivative DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_gradgrad', device_type='cpu'), ], skips=( # No channels_last support for Transformer currently. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'),) ), ModuleInfo(torch.nn.MultiheadAttention, train_and_eval_differ=True, module_inputs_func=module_inputs_torch_nn_MultiheadAttention, skips=( # No channels_last support for MultiheadAttention currently. DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'),) ), ModuleInfo(torch.nn.Embedding, module_inputs_func=module_inputs_torch_nn_Embedding, decorators=[ DecorateInfo(toleranceOverride({torch.float32: tol(atol=1e-4, rtol=1e-4)}), 'TestModule', 'test_non_contiguous_tensors', device_type='mps')], skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'),) ), ModuleInfo(torch.nn.ReLU, module_inputs_func=module_inputs_torch_nn_ReLU, skips=None if _macos15_or_newer else ( # Fails on backward check on MPS # See https://github.com/pytorch/pytorch/issues/107214 DecorateInfo( unittest.expectedFailure, 'TestModule', 'test_memory_format', active_if=operator.itemgetter('training'), device_type='mps', ),) ), ModuleInfo(torch.nn.LeakyReLU, module_inputs_func=module_inputs_torch_nn_LeakyReLU, ), ModuleInfo(torch.nn.ReLU6, module_inputs_func=module_inputs_torch_nn_ReLU6, skips=( # test fails on MPS backend and is being investigated. # See https://github.com/pytorch/pytorch/issues/100914 DecorateInfo(skipMPS),) ), ModuleInfo(torch.nn.PReLU, module_inputs_func=module_inputs_torch_nn_PReLU, skips=( # test fails on MPS backend and is being investigated. # See https://github.com/pytorch/pytorch/issues/100914 DecorateInfo(skipMPS),) ), ModuleInfo(torch.nn.RNNCell, module_inputs_func=partial(module_inputs_torch_nn_RNN_GRU_Cell, is_rnn=True), module_error_inputs_func=module_error_inputs_torch_nn_RNN_GRU_Cell, ), ModuleInfo(torch.nn.GRUCell, module_inputs_func=module_inputs_torch_nn_RNN_GRU_Cell, module_error_inputs_func=module_error_inputs_torch_nn_RNN_GRU_Cell, ), ModuleInfo(torch.nn.LSTMCell, module_inputs_func=module_inputs_torch_nn_LSTMCell, module_error_inputs_func=module_error_inputs_torch_nn_LSTMCell, ), ModuleInfo(torch.nn.Sigmoid, module_inputs_func=module_inputs_torch_nn_Sigmoid, skips=None if _macos15_or_newer else ( # Fails on backward check on MPS # See https://github.com/pytorch/pytorch/issues/107214 DecorateInfo( unittest.expectedFailure, 'TestModule', 'test_memory_format', active_if=operator.itemgetter('training'), device_type='mps', ),) ), ModuleInfo(torch.nn.LogSigmoid, module_inputs_func=module_inputs_torch_nn_LogSigmoid, skips=( # See #119108: tolerance issue DecorateInfo(unittest.expectedFailure, "TestModule", "test_forward", device_type='mps', dtypes=[torch.float16]),) ), ModuleInfo(torch.nn.SiLU, module_inputs_func=module_inputs_torch_nn_SiLU, ), ModuleInfo(torch.nn.Softmax, module_inputs_func=module_inputs_torch_nn_Softmax, ), ModuleInfo(torch.nn.Softmax2d, module_inputs_func=module_inputs_torch_nn_Softmax2d, skips=( # no channels last support for Softmax2d currently DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'), # See #119108: tolerance issue DecorateInfo(unittest.expectedFailure, "TestModule", "test_forward", device_type='mps', dtypes=[torch.float16]),) ), ModuleInfo(torch.nn.LogSoftmax, module_inputs_func=module_inputs_torch_nn_LogSoftmax, skips=( # no channels last support for LogSoftmax currently DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'), # See #119108: inf nan error DecorateInfo(unittest.expectedFailure, "TestModule", "test_forward", device_type='mps', dtypes=[torch.float16]),) ), ModuleInfo(torch.nn.Softmin, module_inputs_func=module_inputs_torch_nn_Softmin, skips=( # no channels last support for Softmin currently DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'),) ), ModuleInfo(torch.nn.Softplus, module_inputs_func=module_inputs_torch_nn_Softplus, skips=( # test fails on MPS backend and is being investigated. # See https://github.com/pytorch/pytorch/issues/100914 DecorateInfo(skipMPS),) ), ModuleInfo(torch.nn.Softshrink, module_inputs_func=module_inputs_torch_nn_Softshrink, skips=( # not supported on MPS backend DecorateInfo(skipMPS),) ), ModuleInfo(torch.nn.Softsign, module_inputs_func=module_inputs_torch_nn_Softsign, ), ModuleInfo(torch.nn.Tanh, module_inputs_func=module_inputs_torch_nn_Tanh, skips=None if _macos15_or_newer else ( # Fails on backward check on MPS # See https://github.com/pytorch/pytorch/issues/107214 DecorateInfo( unittest.expectedFailure, 'TestModule', 'test_memory_format', active_if=operator.itemgetter('training'), device_type='mps', ),) ), ModuleInfo(torch.nn.Tanhshrink, module_inputs_func=module_inputs_torch_nn_Tanhshrink, skips=None if _macos15_or_newer else ( # Fails on backward check on MPS # See https://github.com/pytorch/pytorch/issues/107214 DecorateInfo( unittest.expectedFailure, 'TestModule', 'test_memory_format', active_if=operator.itemgetter('training'), device_type='mps', ),) ), ModuleInfo(torch.nn.Threshold, module_inputs_func=module_inputs_torch_nn_Threshold, skips=( # test fails on MPS backend and is being investigated. # See https://github.com/pytorch/pytorch/issues/100914 DecorateInfo(skipMPS),) ), ModuleInfo(torch.nn.Mish, module_inputs_func=module_inputs_torch_nn_Mish, skips=( # not supported on MPS backend DecorateInfo(skipMPS),) ), ModuleInfo(torch.nn.RNN, train_and_eval_differ=True, module_inputs_func=partial(module_inputs_torch_nn_RNN_GRU, is_rnn=True), module_error_inputs_func=module_error_inputs_torch_nn_RNN_GRU, decorators=rnn_gru_lstm_module_info_decorators ), ModuleInfo(torch.nn.GRU, train_and_eval_differ=True, module_inputs_func=partial(module_inputs_torch_nn_RNN_GRU, is_rnn=False), module_error_inputs_func=module_error_inputs_torch_nn_RNN_GRU, decorators=rnn_gru_lstm_module_info_decorators), ModuleInfo(torch.nn.LSTM, train_and_eval_differ=True, module_inputs_func=module_inputs_torch_nn_LSTM, module_error_inputs_func=module_error_inputs_torch_nn_RNN_GRU, skips=( # LSTM with projections is not currently supported with MPS DecorateInfo(skipMPS),), decorators=rnn_gru_lstm_module_info_decorators), ModuleInfo(torch.nn.ReflectionPad1d, module_inputs_func=module_inputs_torch_nn_ReflectionPad1d, ), ModuleInfo(torch.nn.ReflectionPad2d, module_inputs_func=module_inputs_torch_nn_ReflectionPad2d, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format', device_type='cuda'), DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format', device_type='mps'),) ), ModuleInfo(torch.nn.ReflectionPad3d, module_inputs_func=module_inputs_torch_nn_ReflectionPad3d, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format', device_type='cuda'), DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format', device_type='mps'),) ), ModuleInfo(torch.nn.ReplicationPad1d, module_inputs_func=module_inputs_torch_nn_ReplicationPad1d, ), ModuleInfo(torch.nn.ReplicationPad2d, module_inputs_func=module_inputs_torch_nn_ReplicationPad2d, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format', device_type='cuda'), DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format', device_type='mps'),) ), ModuleInfo(torch.nn.ReplicationPad3d, module_inputs_func=module_inputs_torch_nn_ReplicationPad3d, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format', device_type='cuda'), DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format', device_type='mps'),) ), ModuleInfo(torch.nn.SELU, module_inputs_func=module_inputs_torch_nn_SELU, skips=( # test fails on MPS backend and is being investigated. # See https://github.com/pytorch/pytorch/issues/100914 DecorateInfo(skipMPS),) ), ModuleInfo(torch.nn.ZeroPad1d, module_inputs_func=module_inputs_torch_nn_ZeroPad1d, ), ModuleInfo(torch.nn.ZeroPad2d, module_inputs_func=module_inputs_torch_nn_ZeroPad2d, skips=( # Fails with channels last test on MPS backend DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format", device_type='mps'),) ), ModuleInfo(torch.nn.ZeroPad3d, module_inputs_func=module_inputs_torch_nn_ZeroPad3d, skips=( # Fails with channels last test on MPS backend DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format", device_type='mps'),) ), ModuleInfo(torch.nn.CircularPad1d, module_inputs_func=module_inputs_torch_nn_CircularPad1d, module_error_inputs_func=module_error_inputs_torch_nn_Pad1d, ), ModuleInfo(torch.nn.CircularPad2d, module_inputs_func=module_inputs_torch_nn_CircularPad2d, module_error_inputs_func=module_error_inputs_torch_nn_Pad2d, ), ModuleInfo(torch.nn.CircularPad3d, module_inputs_func=module_inputs_torch_nn_CircularPad3d, module_error_inputs_func=module_error_inputs_torch_nn_Pad3d, skips=( # Fails with channels last test on MPS backend DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format"),) ), ModuleInfo(torch.nn.ConstantPad1d, module_inputs_func=module_inputs_torch_nn_ConstantPad1d, ), ModuleInfo(torch.nn.ConstantPad2d, module_inputs_func=module_inputs_torch_nn_ConstantPad2d, skips=( # Fails with channels last test on MPS backend DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format", device_type='mps'),) ), ModuleInfo(torch.nn.ConstantPad3d, module_inputs_func=module_inputs_torch_nn_ConstantPad3d, skips=( # Fails with channels last test on MPS backend DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format", device_type='mps'),) ) ]