from functools import wraps, partial from itertools import product, chain, islice import itertools import collections import copy from enum import Enum import operator import random import unittest import math import torch import numpy as np from torch._six import inf import collections.abc from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union, Iterable from dataclasses import dataclass, asdict from torchgen.utils import dataclass_repr from torch.testing import make_tensor from torch.testing._internal.common_dtype import ( _dispatch_dtypes, floating_types, floating_types_and, complex_types, floating_and_complex_types, floating_and_complex_types_and, all_types_and_complex_and, all_types_and, all_types_and_complex, integral_types_and, all_types, double_types, empty_types, complex_types_and, integral_types ) from torch.testing._internal.common_device_type import \ (onlyCUDA, onlyNativeDeviceTypes, disablecuDNN, skipCUDAIfNoMagma, skipCUDAIfNoMagmaAndNoCusolver, skipCUDAIfNoCusolver, skipCPUIfNoLapack, skipCPUIfNoFFT, skipCUDAIfRocm, skipCUDAIf, precisionOverride, skipCPUIfNoMklSparse, toleranceOverride, tol, has_cusolver) from torch.testing._internal.common_cuda import ( CUDA11OrLater, SM53OrLater, SM60OrLater, with_tf32_off, TEST_CUDNN, _get_torch_cuda_version, _get_magma_version) from torch.testing._internal.common_utils import \ (is_iterable_of_tensors, random_symmetric_matrix, random_symmetric_psd_matrix, make_fullrank_matrices_with_distinct_singular_values, random_symmetric_pd_matrix, make_symmetric_matrices, make_symmetric_pd_matrices, random_square_matrix_of_rank, TEST_WITH_ROCM, IS_WINDOWS, IS_MACOS, TEST_SCIPY, torch_to_numpy_dtype_dict, TEST_WITH_ASAN, GRADCHECK_NONDET_TOL, slowTest, noncontiguous_like, freeze_rng_state) import torch.testing._internal.opinfo_helper as opinfo_helper import torch._refs as refs # noqa: F401 import torch._refs.nn.functional import torch._refs.special from distutils.version import LooseVersion has_scipy_fft = False if TEST_SCIPY: from scipy import stats import scipy.spatial import scipy.special try: import scipy.fft has_scipy_fft = True except ModuleNotFoundError: pass # Reasonable testing sizes for dimensions L = 20 M = 10 S = 5 # Unique value to distinguish default from anything else _NOTHING = object() class DecorateInfo(object): """Describes which test, or type of tests, should be wrapped in the given decorators when testing an operator. Any test that matches all provided arguments will be decorated. The decorators will only be applied if the active_if argument is True.""" __slots__ = ['decorators', 'cls_name', 'test_name', 'device_type', 'dtypes', 'active_if'] def __init__(self, decorators, cls_name=None, test_name=None, *, device_type=None, dtypes=None, active_if=True): self.decorators = list(decorators) if isinstance(decorators, collections.abc.Sequence) else [decorators] self.cls_name = cls_name self.test_name = test_name self.device_type = device_type self.dtypes = dtypes self.active_if = active_if # Validate dtypes if self.dtypes is not None: for dtype in self.dtypes: assert isinstance(dtype, torch.dtype) def is_active(self, cls_name, test_name, device_type, dtype): return ( self.active_if and (self.cls_name is None or self.cls_name == cls_name) and (self.test_name is None or self.test_name == test_name) and (self.device_type is None or self.device_type == device_type) and (self.dtypes is None or dtype in self.dtypes) ) # FIXME # Note: historically the 'input' kwarg had to be a Tensor or TensorList, but we are trying # to support scalar inputs, too. Some tests still depend on 'input' being a Tensor # or TensorList, however. class SampleInput(object): """Represents sample inputs to a function.""" __slots__ = ['input', 'args', 'kwargs', 'output_process_fn_grad', 'broadcasts_input', 'name'] def __init__(self, input, *, args=tuple(), kwargs=None, output_process_fn_grad=lambda x: x, broadcasts_input=False, name=""): # input is the first input to the op and is typically either a Tensor or TensorList (Sequence[Tensor]). # This follows the typical pattern where for Tensor inputs op(t, ...) = t.op(...). self.input = input self.args = args self.kwargs = kwargs if kwargs is not None else {} self.output_process_fn_grad = output_process_fn_grad self.name = name # Specifies if `self.input` is broadcasted or not, # given that the operator supports broadcasting. # This field is used to verify the behavior for inplace variant. # # If a SampleInput is marked with `broadcasts_input=True`, # it is verified that we get a `RuntimerError` with this sample, # and inplace variant. Also inplace grad{grad} tests are skipped, # for such inputs (as they will error out otherwise). self.broadcasts_input = broadcasts_input def _repr_helper(self, formatter): # Helper function to return the details of the SampleInput as `str` # It consolidates all the fields of SampleInput and allows, # formatting the fields like `input`, `args`, etc with `formatter` # callable to customize the representation. # Look at `summary` method for example. arguments = [ f'input={formatter(self.input)}', f'args={formatter(self.args)}', f'kwargs={formatter(self.kwargs)}', f'output_process_fn_grad={self.output_process_fn_grad}', f'broadcasts_input={self.broadcasts_input}', f'name={repr(self.name)}'] return f'SampleInput({", ".join(a for a in arguments if a is not None)})' def __repr__(self): return self._repr_helper(lambda x: x) def summary(self): # Returns the SampleInput details in a more # friendly format. # It formats `Tensor` and `TensorList` # in a more condensed representation. def formatter(arg): # Format any instance of `Tensor` (standalone, in list, or in dict) # by Tensor[TensorShape] # Eg. Tensor with shape (3, 4) is formatted as Tensor[3, 4] if isinstance(arg, torch.Tensor): shape = str(tuple(arg.shape)).replace('(', '').replace(')', '') return f"Tensor[{shape}]" elif isinstance(arg, dict): return {k: formatter(v) for k, v in arg.items()} elif is_iterable_of_tensors(arg): return "TensorList[" + ", ".join(map(formatter, arg)) + "]" elif isinstance(arg, (list, tuple)): # Handle list, tuple return "(" + ",".join(map(formatter, arg)) + ")" return repr(arg) return self._repr_helper(formatter) # Applies the transform f(t) -> t to each tensor and dtype in the SampleInput def transform(self, f): def tt(t): def _tt(t): with torch.no_grad(): return f(t) if isinstance(t, torch.Tensor): return _tt(t) elif isinstance(t, torch.dtype): return _tt(t) elif isinstance(t, list): return list(map(tt, t)) elif isinstance(t, tuple): return tuple(map(tt, t)) elif isinstance(t, dict): return {k: tt(v) for k, v in t.items()} else: return t sample_tt_input, tt_args, tt_kwargs = tt(self.input), tt(self.args), tt(self.kwargs) # Note the transformed SampleInput assumes metadata like output_process_fn_grad is still valid! return SampleInput( sample_tt_input, args=tt_args, kwargs=tt_kwargs, output_process_fn_grad=self.output_process_fn_grad, broadcasts_input=self.broadcasts_input, name=self.name + "_transformed") # Returns the NumPy version of the sample input object in the form of a tuple: (input, args, kwargs) # Converts tensors to ndarrays by calling .detach().cpu().numpy() on them # Converts dtypes by remapping them using torch_to_numpy_dtype_dict def numpy(self): def to_numpy(t): if isinstance(t, torch.Tensor): if t.dtype is torch.bfloat16: return t.detach().cpu().to(torch.float32).numpy() if t.dtype is torch.chalf: return t.detach().cpu().to(torch.cfloat).numpy() return t.detach().cpu().numpy() elif isinstance(t, torch.dtype): return torch_to_numpy_dtype_dict[t] return t return self.transform(to_numpy) def noncontiguous(self): def to_noncontiguous(t): if isinstance(t, torch.Tensor): return noncontiguous_like(t) elif isinstance(t, torch.dtype): return t return t return self.transform(to_noncontiguous) class ErrorInput(object): """ A SampleInput that will cause the operation to throw an error plus information about the resulting error. """ __slots__ = ['sample_input', 'error_type', 'error_regex'] def __init__(self, sample_input, *, error_type=RuntimeError, error_regex): self.sample_input = sample_input self.error_type = error_type self.error_regex = error_regex class AliasInfo(object): """Class holds alias information. For example, torch.abs -> torch.absolute, torch.Tensor.absolute, torch.Tensor.absolute_ """ def __init__(self, alias_name): self.name = alias_name self.op = _getattr_qual(torch, alias_name) self.method_variant = getattr(torch.Tensor, alias_name, None) self.inplace_variant = getattr(torch.Tensor, alias_name + "_", None) def __call__(self, *args, **kwargs): return self.op(*args, **kwargs) # Extension of getattr to support qualified names # e.g. _getattr_qual(torch, 'linalg.norm') -> torch.linalg.norm def _getattr_qual(obj, name, default=_NOTHING): try: for path in name.split('.'): obj = getattr(obj, path) return obj except AttributeError: if default is not _NOTHING: return default else: raise # test if a tensor is close to an integer def close_to_int(x, eps=0.1): if x.is_complex(): y = torch.abs(torch.view_as_complex(torch.frac(torch.view_as_real(x)))) else: y = torch.abs(torch.frac(x)) return (y < eps) | (y > (1 - eps)) NumericsFilter = collections.namedtuple('NumericsFilter', ['condition', 'safe_val']) # Note [OpInfos] # ~~~~~~~~~~~~~~ # # The majority of this note was written shortly after the PyTorch 1.9 release. # If you notice it's out-of-date or think it could be improved then please # file an issue. # # See also: the OpInfo tracker (https://github.com/pytorch/pytorch/issues/54261) # See also: "Writing Test Templates" in common_device_type.py to learn how to # parametrize a test template using OpInfos. # See also: PyTorch's GitHub wiki on running and writing tests # https://github.com/pytorch/pytorch/wiki/Running-and-writing-tests # See also: ModuleInfos, OpInfo's sister class, defined in common_modules.py # # An OpInfo is a collection of metadata related to a PyTorch operator. This # metadata is used to generate tests that validate properties of the operator, # like if it implements the correct gradient formula. # # WHY OPINFOS? # ~~~~~~~~~~~~ # # OpInfos are principally intended to do three things: # # 1) to allow systematic testing over all PyTorch's operators # 2) to simplify operating testing by autogenerating many tests # 3) to allow systems (like autograd, torchscript, fx, nnc...) to test # against every PyTorch operator # # All these goals are still a work in progress. Not every operator has an # OpInfo, and some operator tests that could be automatically generated # still have to be written manually. # # It's helpful to understand that OpInfos are both about test simplification and # modularity. PyTorch is a complicated framework with many interrelated systems, # too many for any one person to keep track of. An OpInfo can be thought of as the # interface between an operator implementer and those other systems. Instead of # requiring the implementer of torch.foo understand how to test its forward # mode AD or NNC support that's typically handled automatically just by # defining an OpInfo. # # It's often surprising to OpInfo writers that just implementing an OpInfo # typically can't verify an operator is actually implemented correctly: # # "If an OpInfo doesn't validate my op works as expected, what's the point # of it?" # # But the point of is the above. OpInfos are intended to let you focus on testing # the operator logic you're familiar with instead of having to write tests for # how the operator interacts with each of PyTorch's many systems. # # And, OK, it turns out that SOMETIMES just writing an OpInfo DOES # validate your op works as expected, but that's only in special # cases. See below for details. # # WHAT'S AN OPINFO? # ~~~~~~~~~~~~~~~~~ # # So what is an OpInfo? It's a Python class that describes an operator's properties, # like which dtypes it supports on the CPU and whether it has any aliases. # These properties can be divided into three categories: # # 1) Metadata describing the operator, like the operator's name and if it # "supports" the out kwarg. # 2) Test directives, like "skips" that tell the test suite to skip some # tests. # 3) A "sample inputs" function that generates valid inputs for the operator. # # OpInfo attributes are described in more detail below. # # THE SAMPLE INPUTS FUNCTION # ~~~~~~~~~~~~~~~~~~~~~~~~~~ # # The "sample inputs" function merits special elaboration. This function is # crucial to testing with OpInfos. A typical OpInfo test has to treat the operator # as a black box. There's no structure for the test to understand or exploit. # Without "sample inputs" it wouldn't even know how to call the OpInfo's # operator. The sample input function saves the day by providing different # "SampleInputs" that can be used to call the operator. A sample input # function should have the following signature: # # def sample_inputs_foo(op_info, device, dtype, requires_grad, **kwargs): # # And should return an iterable of SampleInputs (see the class description # above). Each SampleInput defines an "input", "args", "kwargs", an # "output_process_fn_grad" function, the "broadcasts_input" bool and a # "name". # # All the "sample_inputs" functions are invoked within a `torch.no_grad()` # environment for efficiency and correctness. As such remember to set the # "requires_grad" flag on the inputs **after** performing any transformations # on them. # # The "input" is the first argument to the operator, or the tensor that # the method or inplace variants of the operator should be called on, and # should be on the requested device, of the requested dtype, and its # requires_grad attribute should be set to the requires_grad argument. # # "args" should contain positional arguments, and "kwargs" keyword arguments. # # "output_process_fn_grad" has an interesting name. It's a function that maps # the operator's output (when given the input, args, and kwargs) to the # portion of the output to gradcheck. For example, consider an operator # like torch.linalg.slogdet # (https://pytorch.org/docs/master/generated/torch.linalg.slogdet.html). # This operator returns a tuple of two tensors, but the first tensor # cannot be backwarded through. Its "output_process_fn_grad" filters # this output tuple to just the second argument, which we can call backward # on. Functions that produce a single tensor can ignore this argument. # # "broadcasts_input" is a bool indicated if the SampleInput causes the operator # to broadcast the "input" argument. This is important for tests to understand # because inplace variants of operations throw a runtime error if they # would broadcast their input arguments, so tests that work with inplace # variants filter SampleInputs that broadcast their input. # # "name" is a string that's just used for debugging. It appears when printing # the SampleInput. # # Sample inputs are designed to be used with many tests, some # that are very time consuming, so they should be a small # set with small tensors. An elaborated set of sample inputs # can be specified using the "reference_inputs_func" attribute. # The "reference inputs" for an operation are an extended # set of sample inputs that can more exhausively test an # operator. They are used by only a few tests that are careful # not to take too long to run. Adding reference inputs # is highly encouraged! # # THE (OPTIONAL) ERROR INPUTS FUNCTION # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # OpInfos may optionally specify "error inputs" through an error function. If # specified test_errors in test_ops.py will call the op with these inputs # and validate that the desired error is thrown. # # Error inputs automate a common testing pattern where multiple inputs are # passed to an operation and the errors they thrown are reviewed. Tests # written in this style should be ported to the new OpInfo pattern. # # Error inputs are specified using the ErrorInputs class, which contains # a SampleInput (see above) and data about the expected error. # # OPINFO FILE ORGANIZATION # ~~~~~~~~~~~~~~~~~~~~~~~~ # # All OpInfos are currently defined in this file. Most OpInfo tests are defined # in test_ops.py, but some system-specific tests are defined in those # systems' test files, and subclass-specific tests are defined in the test # file that corresponds to that subclass (see the below). # Expect a reorganization in the future. # # WHAT'S TESTED? # ~~~~~~~~~~~~~~ # # Every OpInfo in the op_db sequence has the following properties validated in # test_ops.py: # # - that its supported dtypes are specified correctly # - that the operation produces the same results when called with noncontiguous inputs # - that it supports the out= argument properly (if it allows out=), # see https://github.com/pytorch/pytorch/wiki/Developer-FAQ#how-does-out-work-in-pytorch # - that it works with the conjugate view bit properly # - that its function, method, and inplace variants perform the same operation # (that is, that torch.add, torch.Tensor.add, and torch.Tensor.add_ all # do the same thing). # - that its inplace variant preserves the input's storage # - that its gradient formula is implemented correctly, and that it supports # gradgrad and complex grad and gradgrad and forward mode AD properly for # the op's function and inplace variants (method variants are skipped # to reduce test time). # - that the operation performs the same operation when traced or scripted # using the jit # - that the operation is autodifferentiated by the jit as expected # - that the operator's aliases, if any, perform the same operation and that # the jit understands the alias # - that the operator throws the correct errors (if error_inputs is defined) # - that the operator produces the same results as a NumPy reference (if ref is defined) # - that the operator produces the same results as a NumPy reference on an extended # set of "reference inputs" (if both ref and reference_inputs_func are defined) # (NOTE: elementwise unary and elementwise binary OpInfos do this even if only # ref is defined, because they effectively autogenerate reference inputs) # - that the operator works on different CUDA devices # # Additional OpInfo tests are in test_jit_fuser_te.py, test_fx_experimental.py, # and test_fx.py. These tests validate that operators work with NNC and FX # as expected. # # For performance, some of the above tests may only run on the first # SampleInput returned by an OpInfo's sample input function. # # In addition to these tests, some subclasses (discussed in the next section) # define additional tests. # # Critically, as mentioned above, what's not necessarily tested is that the operator # works as expected. When implementing an OpInfo an engineer must still # typically write one or more tests validating the operator's behavior. # The exception to this is if reference testing is sufficient, or if # the operation belongs to an OpInfo subclass that has more exhaustive # operator testing. Elementwise unary and elementwise binary operators, # in particular, usually don't require additional testing beyond # writing an Opinfo. # # # OPINFO (SUB)CLASSES # ~~~~~~~~~~~~~~~~~~~ # # In addition to the OpInfo base class there are several specialized OpInfo # subclasses. For example, the UnaryUfuncInfo subclass is used for # unary elementwise operations. These operations have a common structure # that test_unary_ufuncs.py exploits with additional automated testing. # The automated testing in test_unary_ufuncs.py is so thorough, comparing # the operator to a NumPy reference function on a plethora of values, that # just implementing an OpInfo for a unary elementwise operation is often # sufficient testing. # # The ForeachFuncInfo is another OpInfo subclass that is hyper-specialized to a # very unique class of operations. These OpInfos aren't included in the # op_db sequence and have their own tests. # # Other OpInfo subclasses, like SpectralFuncInfo, are just for convenience # when writing OpInfos. # # TESTING A NEW OPERATOR # ~~~~~~~~~~~~~~~~~~~~~~ # # If you're adding a new operator to any of the following namespaces: # - torch # - torch.fft # - torch.linalg, # - torch.special # - torch.nn.functional # then you should typically add an OpInfo for it. # # As mentioned a couple times above, implementing an OpInfo is not # usually sufficient testing (unless the operator is a unary or binary elementwise # operator). The OpInfo will only test the properties described in the # "WHAT'S TESTED" section. It DOES NOT necessarily verify that the operator is # implemented correctly. # # TIPS FOR WRITING AN OPINFO AND OPINFO TESTS # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # Writing an OpInfo can be a little daunting. Since the point of an OpInfo is to # be consumed by a variety of systems it can be hard to understand how to # deal with test failures or how to set the OpInfo metadata properly. # # Before adding an OpInfo it helps to look at other OpInfos. A sample inputs # function must be defined, and the operator's dtypes must be specified. # Once that's done you should run the operator's tests in test_ops.py # (these can be filtered using the "-k" argument in pytest). Tests that # fail should provide an error message that describes what to change about # your OpInfo. You don't need to worry about changing an OpInfo's default # values unless a test yells at you. # # Similarly, if you're writing a test that consumes OpInfos then it's critical # your test provides a clear error message describing what to do when it # fails. You should not assume the OpInfo implementer is familiar with your # system. # # If you see a confusing error message while developing an OpInfo then please # file an issue describing what happened. # # This trial-and-error approach to writing an OpInfo can be frustrating, # but it's probably necessary as long as OpInfos don't require # learning about all the systems that consume them. One thing that can help # is the get_supported_dtypes() function defined in opinfo_helper.py. This # function can be used to programmatically specify the dtypes an operator # supports, and is especially useful if writing an OpInfo on a machine # without a CUDA device. See its documentation for more details. # # THE FUTURE OF OPINFOS AND OPINFO TESTING # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # In the future we expect OpInfo coverage to improve and cover # the great majority of PyTorch's (public) operators. # # Classes and methods for the operator database @dataclass class OpInfo(object): """Operator information and helper functions for acquiring it.""" # the string name of the function name: str # An optional reference function that accepts ndarrays (AKA "NumPy arrays"). # If given, the op will be compared with its reference on each of its sample inputs. ref: Callable = None # the following metadata describes the operator, its variants, and its aliases, if any # iterable of aliases, e.g. ("absolute",) for torch.abs aliases: Iterable = None # additional string to include in the test name # this is useful when an op needs multiple OpInfos, # like divide does, often because it's really several # different ops behind the scenes variant_test_name: str = '' # the function variant of the operation, populated as torch. if None op: Callable = None # allows the method variant of this operation to be specified as follows: # - if _NOTHING (default), then the OpInfo attempts to discover the variant using its name # - if None, then the OpInfo explicitly specifies is has no associated method # - if a Callable, then that callable should be the method associated with this operation method_variant: Callable = _NOTHING # allows the inplace variant of this operation to be specified as follows: # - if _NOTHING (default), then the OpInfo attempts to discover the variant using its name # - if None, then the OpInfo explicitly specifies is has no associated inplace variant # - if a Callable, then that callable should be the inplace variant associated with this operation inplace_variant: Callable = _NOTHING # allows the operator variant of this operation to be specified as follows: # - if _NOTHING (default), then the OpInfo attempts to discover the variant using its name # - if None, then the OpInfo explicitly specifies is has no associated operator # - if a Callable, then that callable should be the operator associated with this operation operator_variant: Callable = _NOTHING # allows the inplace operator variant of this operation to be specified as follows: # - if _NOTHING (default), then the OpInfo attempts to discover the variant using its name # - if None, then the OpInfo explicitly specifies is has no associated inplace operator # - if a Callable, then that callable should be the inplace operator associated with this operation inplace_operator_variant: Callable = _NOTHING # the following metadata are test directives for skipping or modifying tests # information about which tests to skip skips: Tuple = tuple() # decorators to apply to generated tests decorators: Tuple = tuple() # the following are pointers to functions to generate certain classes of inputs # function to generate sample inputs with strided layouts sample_inputs_func: Callable = None # function to generate a more thorough set of samples inputs with strided layouts reference_inputs_func: Callable = None # function to generate inputs that will throw errors error_inputs_func: Callable = None # function to generate sample inputs with sparse coo layouts sample_inputs_sparse_coo_func: Callable = None # function to generate sample inputs with sparse csr layouts sample_inputs_sparse_csr_func: Callable = None # the following metadata relates to dtype support and is tested for correctness in test_ops.py # dtypes this function works with on the CPU, # inherited by other device types that don't specify their own dtypes dtypes: _dispatch_dtypes = None # the following dtypesIf... options override the dtypes value on their respective device types # dtypes this function is expected to work with on CUDA dtypesIfCUDA: _dispatch_dtypes = None # dtypes this function is expected to work with on ROCM dtypesIfROCM: _dispatch_dtypes = None # backward dtypes this function is expected to work with backward_dtypes: _dispatch_dtypes = None # backward dtypes this function is expected to work with on CUDA backward_dtypesIfCUDA: _dispatch_dtypes = None # backward dtypes this function is expected to work with on ROCM backward_dtypesIfROCM: _dispatch_dtypes = None # the following metadata describes the operators out= support # whether the op supports the out kwarg # defaults to True, if the op does not allow the out kwarg or # supports it incorrectly then test_out in test_ops.py should fail supports_out: bool = True # the following metadata relates to autograd support # whether the operation supports backward mode AD # if true, gradient correctness is tested in test_ops.py # using the op's sample inputs supports_autograd: bool = True # whether the op supports second order gradients # if true, gradgrad correctness is tested in test_ops.py # defaults to support_autograd's value # TODO: rename this to supports_bwgrad_bwgrad to be consistent with below supports_gradgrad: bool = None # whether the ops supports second order gradients via # forward-over-reverse. If True, forward-over-reverse gradgrad correctness # is tested. If False, test that forward grad is not implemented. # Defaults to False. supports_fwgrad_bwgrad: bool = False # whether the operation supports inplace autograd # if true, tested in test_ops.py # defaults to supports_autograd's value supports_inplace_autograd: bool = None # Whether the operation support forward mode AD # If the value is True, we check that the gradients are correct # If the value is False, we test that forward grad is not implemented supports_forward_ad: bool = False # wrapper function for gradcheck gradcheck_wrapper: Callable = lambda op, *args, **kwargs: op(*args, **kwargs) # whether to check batched grad when doing gradcheck # defaults to support_autograd's value check_batched_grad: bool = None # whether to check batched grad grad when doing gradgradcheck # default's to support_gradgrad's value check_batched_gradgrad: bool = None # whether to check batched forward grad when doing gradcheck # defaults to the value of `supports_forward_ad` check_batched_forward_grad: bool = None # whether to check batched forward grad when doing gradcheck # defaults to the value of `check_batched_forward_grad` check_inplace_batched_forward_grad: bool = None # tolerance for nondeterminism while performing gradcheck gradcheck_nondet_tol: float = 0.0 # Whether to use the fast implmentation for gradcheck/gradgradcheck. # When set to None, defers to the default value provided by the wrapper # function around gradcheck (testing._internal.common_utils.gradcheck) gradcheck_fast_mode: bool = None # the following metadata relates to JIT support and is tested for correctness in test_ops.py # name of the corresponding aten:: operator aten_name: str = None # if this is a composite implicit autograd op, the decomposed op decomp_aten_name: Optional[str] = None # name of the corresponding aten:: operator for backwards aten_backward_name: Optional[str] = None # if a op's aten::node is expected to be symbolically autodiffed assert_autodiffed: bool = False # a list of strings with node names that are expected to be in a # DifferentiableGraph when autodiffed. Ex: ['aten::add', 'aten::mm'], # default is populated to be ['aten::(name of Python operator)'] autodiff_nonfusible_nodes: List[str] = None # a list of strings with node names that are expected to be in FusionGroups # inside of DifferentiableGraphs when this operation is autodiffed. # Ex: ['aten::add', 'aten::mm'], defaults to an empty list # Note: currently no ops use fusible nodes autodiff_fusible_nodes: List[str] = None # the following metadata relates to sparse support and is used in test_sparse.py # whether the op supports sparse inputs supports_sparse: bool = False # only run tracing tests supports_scripting: bool = True # the following metadata relates to sparse csr support and is used in test_sparse_csr.py # whether the op supports sparse csr inputs supports_sparse_csr: bool = False # the following metadata relates to complex support and is checked in test_ops.py test_conjugated_samples: bool = True test_neg_view: bool = True # assert that jit shape analysis fully propagates shape assert_jit_shape_analysis: bool = False # the following metadata relates to ExpandedWeights support and is checked in test_expanded_weights.py supports_expanded_weight: bool = False def __post_init__(self): self._original_opinfo_args = asdict(self).copy() assert self.dtypes is not None, "OpInfo for {0} has no dtypes!".format(self.name) dtypes_args = (self.dtypes, self.dtypesIfCUDA, self.dtypesIfROCM) # Validates the dtypes are generated from the dispatch-related functions for dtype_list in dtypes_args: assert isinstance(dtype_list, (_dispatch_dtypes, type(None))) if self.aten_name is None: self.aten_name = self.name # Attribute to verify dynamic_dtypes are used. self.dynamic_dtypes = any(map(lambda dtypes: isinstance( dtypes, opinfo_helper._dynamic_dispatch_dtypes), dtypes_args)) if self.dynamic_dtypes: # Make sure `dtyesIfCUDA` is dynamic, if dynamic dispatch is used for CPU # This is because, below we set dtypesIfCUDA to dtypes if they are None. assert isinstance(self.dtypesIfCUDA, opinfo_helper._dynamic_dispatch_dtypes), \ (f"To use dynamic dypes for operator {self.name}, " "acquire the dtypes dynamically for argument `dtypesIfCUDA`." "This is to ensure that CUDA dtypes are acquired correctly as they" "differ from CPU dtypes occasionally") self.dtypes = set(self.dtypes) # NOTE: backward dtypes must be acquired before forward dtypes # since they fallback to explicit (not implicit!) specifications of # forward dtypes self.backward_dtypesIfROCM = set(self.backward_dtypesIfROCM) if self.backward_dtypesIfROCM is not None else ( self.backward_dtypesIfCUDA if self.backward_dtypesIfCUDA is not None else self.backward_dtypes if self.backward_dtypes is not None else self.dtypesIfROCM if self.dtypesIfROCM is not None else self.dtypesIfCUDA if self.dtypesIfCUDA is not None else self.dtypes) self.backward_dtypesIfCUDA = set(self.backward_dtypesIfCUDA) if self.backward_dtypesIfCUDA is not None else ( self.backward_dtypes if self.backward_dtypes is not None else self.dtypesIfCUDA if self.dtypesIfCUDA is not None else self.dtypes) self.backward_dtypes = set(self.backward_dtypes) if self.backward_dtypes is not None else self.dtypes self.dtypesIfCUDA = set(self.dtypesIfCUDA) if self.dtypesIfCUDA is not None else self.dtypes self.dtypesIfROCM = set(self.dtypesIfROCM) if self.dtypesIfROCM is not None else self.dtypesIfCUDA # NOTE: if the op is unspecified it is assumed to be under the torch namespace if not self.op: self.op = _getattr_qual(torch, self.name) if self.method_variant is _NOTHING: self.method_variant = getattr(torch.Tensor, self.name, None) # attributes like real, imag are not callable if not callable(self.method_variant): self.method_variant = None if self.inplace_variant is _NOTHING: inplace_name = self.name + "_" self.inplace_variant = getattr(torch.Tensor, inplace_name, None) if self.operator_variant is _NOTHING: self.operator_variant = getattr(operator, self.name, None) if self.inplace_operator_variant is _NOTHING: # Note: operator.i will use operator. and assign the result to the lhs when no # __i__ method is found. This results in the appearance of an inplace operator variant which # does not have the correct inplace behavior. To avoid this, we guard automatic detection of the inplace # operator with a check that an inplace variant exists. if self.inplace_variant is not None: inplace_operator_name = "i" + self.name self.inplace_operator_variant = getattr(operator, inplace_operator_name, None) else: self.inplace_operator_variant = None self.decorators = (*self.decorators, *self.skips) # We run the sampling functions without tracking the gradiends of the creation of inputs self.sample_inputs_func = torch.no_grad()(self.sample_inputs_func) self.sample_inputs_sparse_coo_func = torch.no_grad()(self.sample_inputs_sparse_coo_func) self.sample_inputs_sparse_csr_func = torch.no_grad()(self.sample_inputs_sparse_csr_func) if self.reference_inputs_func is not None: self.reference_inputs_func = torch.no_grad()(self.reference_inputs_func) if not self.autodiff_fusible_nodes: self.autodiff_fusible_nodes = [] if self.autodiff_nonfusible_nodes is None: self.autodiff_nonfusible_nodes = ['aten::' + self.name] # Autograd support # Autograd flags that depend on backward AD only # - If setting has been explicitly set, raise error if inconsistent if self.supports_gradgrad is None: self.supports_gradgrad = self.supports_autograd else: assert not (self.supports_gradgrad and not self.supports_autograd), ( "supports_gradgrad refines the part of autograd is supported, so it should " "not be set if supports_autograd is False") if self.check_batched_grad is None: self.check_batched_grad = self.supports_autograd or self.supports_forward_ad else: assert not (self.check_batched_grad and not (self.supports_autograd or self.supports_forward_ad)), ( "check_batched_grad refines the part of autograd that will be checked (by gradcheck), so " "it should not be set if supports_autograd is False") if self.check_batched_gradgrad is None: self.check_batched_gradgrad = self.supports_gradgrad else: assert not (self.check_batched_gradgrad and not self.supports_gradgrad), ( "check_batched_gradgrad refines the part of autograd that will be checked (by " "gradgradcheck), so it should not be set if either supports_gradgrad or supports_autograd " "is False.") if self.check_batched_forward_grad is None: self.check_batched_forward_grad = self.supports_forward_ad else: assert not (self.check_batched_forward_grad and not self.supports_forward_ad), ( "check_batched_forward_grad should only be used when supports_forward_ad " "is True. It is used to disable the test in the specific cases " "where the op supports forward ad but fails to compute " "batched forward grad.") if self.check_inplace_batched_forward_grad is None: self.check_inplace_batched_forward_grad = self.check_batched_forward_grad else: assert not (self.check_inplace_batched_forward_grad and not self.check_batched_forward_grad), ( "check_batched_forward_grad should only be used when check_batched_forward_grad " "is True. It is used to disable the test in the specific cases " "where the op supports batched forward grad but fails to compute batched forward " "grad for the inplace variant of the op.") assert not (self.supports_fwgrad_bwgrad and not self.supports_autograd), ( "supports_fwgrad_bwgrad enables forward-over-backward gradgrad checks and should only be " "True if backward ad is also checked, i.e., supports_forward_ad should be True.", self.name) # Autograd flags that depend on both forward AD and backward AD if self.supports_inplace_autograd is None: self.supports_inplace_autograd = self.supports_autograd or self.supports_forward_ad else: assert not (self.supports_inplace_autograd and not self.supports_autograd and not self.supports_forward_ad), ( "supports_inplace_autograd refines the part of autograd that is supported, so " "it should not be set if both supports_autograd and supports_forward_ad are False") if self.aliases is not None: self.aliases = tuple(AliasInfo(a) for a in self.aliases) # type: ignore[assignment] else: self.aliases = () def __call__(self, *args, **kwargs): """Calls the function variant of the operator.""" return self.op(*args, **kwargs) def __str__(self): return dataclass_repr(self) def get_op(self): """Returns the function variant of the operator, torch..""" return self.op def get_method(self): """Returns the method variant of the operator, torch.Tensor.. Returns None if the operator has no method variant. """ return self.method_variant def get_inplace(self): """Returns the inplace variant of the operator, torch.Tensor._. Returns None if the operator has no inplace variant. """ return self.inplace_variant def get_operator(self): """Returns operator variant of the operator, e.g. operator.neg Returns None if the operator has no operator variant. """ return self.operator_variant def get_inplace_operator(self): """Returns the inplace operator variant of the operator, e.g operator.iadd Returns None if the operator has no inplace operator variant""" return self.inplace_operator_variant def conjugate_sample_inputs(self, device, dtype, requires_grad=False, **kwargs): """Returns an iterable of SampleInputs but with the tensor input or first tensor in a sequence input conjugated. """ samples = self.sample_inputs_func(self, device, dtype, requires_grad, **kwargs) conj_samples = list(samples) def conjugate(tensor): _requires_grad = tensor.requires_grad tensor = tensor.conj() return tensor.requires_grad_(_requires_grad) for i, sample in enumerate(samples): sample = conj_samples[i] # Note: it is assumed that the input here is either a tensor or tensorlist if isinstance(sample.input, torch.Tensor): sample.input = conjugate(sample.input) else: sample.input[0] = conjugate(sample.input[0]) return tuple(conj_samples) def sample_inputs(self, device, dtype, requires_grad=False, **kwargs): """ Returns an iterable of SampleInputs. These samples should be sufficient to test the function works correctly with autograd, TorchScript, etc. """ samples = self.sample_inputs_func(self, device, dtype, requires_grad, **kwargs) if kwargs.get('include_conjugated_inputs', False): conj_samples = self.conjugate_sample_inputs(device, dtype, requires_grad, **kwargs) samples_list = list(samples) samples_list.extend(conj_samples) samples = tuple(samples_list) return samples def reference_inputs(self, device, dtype, requires_grad=False, **kwargs): """ Returns an iterable of SampleInputs. Distinct from sample_inputs() above because this returns an expanded set of inputs when reference_inputs_func is defined. If undefined this returns the sample inputs. """ if self.reference_inputs_func is None: return self.sample_inputs_func(self, device, dtype, requires_grad, **kwargs) if kwargs.get('include_conjugated_inputs', False): raise NotImplementedError return self.reference_inputs_func(self, device, dtype, requires_grad, **kwargs) def error_inputs(self, device, **kwargs): """ Returns an iterable of ErrorInputs. """ return self.error_inputs_func(self, device, **kwargs) def sample_inputs_sparse_coo(self, device, dtype, requires_grad=False, **kwargs): """Returns an iterable of SampleInputs that contain inputs with sparse coo layout. """ return self.sample_inputs_sparse_coo_func(self, device, dtype, requires_grad, **kwargs) def sample_inputs_sparse_csr(self, device, dtype, requires_grad=False, **kwargs): """Returns an iterable of SampleInputs that contain inputs with sparse csr layout. """ return self.sample_inputs_sparse_csr_func(self, device, dtype, requires_grad, **kwargs) def get_decorators(self, test_class, test_name, device, dtype): '''Returns the decorators targeting the given test.''' result = [] for decorator in self.decorators: if isinstance(decorator, DecorateInfo): if decorator.is_active(test_class, test_name, device, dtype): result.extend(decorator.decorators) else: result.append(decorator) return result def supported_dtypes(self, device_type): if device_type == 'cpu': return self.dtypes if device_type == 'cuda': return self.dtypesIfROCM if TEST_WITH_ROCM else self.dtypesIfCUDA else: return self.dtypes def supported_backward_dtypes(self, device_type): if not self.supports_autograd: return set() backward_dtypes = None if device_type == 'cpu': backward_dtypes = self.backward_dtypes elif device_type == 'cuda': backward_dtypes = self.backward_dtypesIfROCM if TEST_WITH_ROCM else self.backward_dtypesIfCUDA else: backward_dtypes = self.backward_dtypes allowed_backward_dtypes = floating_and_complex_types_and(torch.bfloat16, torch.float16, torch.complex32) return set(allowed_backward_dtypes).intersection(backward_dtypes) def supports_dtype(self, dtype, device_type): return dtype in self.supported_dtypes(device_type) @property def formatted_name(self): """Returns a formatted full name for this OpInfo that can be used in test names.""" variant = '_' + self.variant_test_name.replace('.', '_') if self.variant_test_name else '' return '{}{}'.format(self.name.replace('.', '_'), variant) def _generate_reduction_inputs(device, dtype, requires_grad, **kwargs): """Generates input tensors for testing reduction operators""" yield make_tensor([], dtype=dtype, device=device, requires_grad=requires_grad) yield make_tensor([2], dtype=dtype, device=device, requires_grad=requires_grad) yield make_tensor([3, 5], dtype=dtype, device=device, requires_grad=requires_grad) yield make_tensor([3, 2, 1, 2], dtype=dtype, device=device, requires_grad=requires_grad) def _generate_reduction_kwargs(ndim, supports_multiple_dims=True): """Generates a subset of all valid dim and keepdim kwargs given ndim that is appropriate for testing reduction operators. """ # Test default dim and keepdim yield {} # Test reducing inner and outer most dimensions yield {'dim': 0, 'keepdim': True} yield {'dim': -1, 'keepdim': False} # Test reducing middle dimension if ndim > 2: yield {'dim': ndim // 2, 'keepdim': True} if supports_multiple_dims: # Test reducing all dimensions yield {'dim': tuple(range(ndim)), 'keepdim': False} # Test reducing both first and last dimensions if ndim > 1: yield {'dim': (0, -1), 'keepdim': True} # Test reducing every other dimension starting with the second if ndim > 3: yield {'dim': tuple(range(1, ndim, 2)), 'keepdim': False} def sample_inputs_reduction(op_info, device, dtype, requires_grad, **kwargs): """Sample inputs for reduction operators.""" # TODO(@heitorschueroff) Once all reduction operators are using # ReductionOpInfo use op_info.supports_multiple_dims directly. supports_multiple_dims: bool = kwargs.get('supports_multiple_dims', True) # TODO(@heitorschueroff) Once all reduction operators are using ReductionOpInfo # use op_info.generate_args_kwargs directly. generate_args_kwargs = kwargs.get('generate_args_kwargs', lambda *args, **kwargs: (yield tuple(), {})) inputs: List[SampleInput] = [] for t in _generate_reduction_inputs(device, dtype, requires_grad): for reduction_kwargs in _generate_reduction_kwargs(t.ndim, supports_multiple_dims): for args, kwargs in generate_args_kwargs(t, **reduction_kwargs): kwargs.update(reduction_kwargs) inputs.append(SampleInput( t.clone().requires_grad_(requires_grad), args=args, kwargs=kwargs)) return inputs def _generate_masked_op_mask(input_shape, device, **kwargs): yield None yield make_tensor(input_shape, dtype=torch.bool, device=device, requires_grad=False) if len(input_shape) > 2: # broadcast last mask dimension: yield make_tensor(input_shape[:-1] + (1,), dtype=torch.bool, device=device, requires_grad=False) # broadcast middle mask dimension: yield make_tensor(input_shape[:1] + (1,) + input_shape[2:], dtype=torch.bool, device=device, requires_grad=False) # broadcast first mask dimension: yield make_tensor((1,) + input_shape[1:], dtype=torch.bool, device=device, requires_grad=False) # mask.ndim < input.ndim yield make_tensor(input_shape[1:], dtype=torch.bool, device=device, requires_grad=False) # mask.ndim == 1 yield make_tensor(input_shape[-1:], dtype=torch.bool, device=device, requires_grad=False) # masks that require broadcasting of inputs (mask.ndim > # input.ndim) will not be supported, however, we may # reconsider this if there will be demand on this kind of # degenerate cases. def sample_inputs_masked_reduction(op_info, device, dtype, requires_grad, **kwargs): """Sample inputs for masked reduction operators. Masked reduction operator is a reduction operator with trailing mask optional argument. A mask is a bool tensor with the same shape as input or a shape that is broadcastable to input shape. """ inputs: List[SampleInput] = [] kwargs['supports_multiple_dims'] = op_info.supports_multiple_dims for sample_input in sample_inputs_reduction(op_info, device, dtype, requires_grad, **kwargs): for mask in _generate_masked_op_mask(sample_input.input.shape, device, **kwargs): sample_input_args, sample_input_kwargs = sample_input.args, dict(mask=mask, **sample_input.kwargs) inputs.append(SampleInput(sample_input.input.clone().requires_grad_(requires_grad), args=sample_input_args, kwargs=sample_input_kwargs)) if(not requires_grad and dtype.is_floating_point and sample_input.input.ndim == 2 and mask is not None and mask.shape == sample_input.input.shape): for v in [torch.inf, -torch.inf, torch.nan]: t = sample_input.input.clone() t.diagonal()[:] = v inputs.append(SampleInput(t.detach().requires_grad_(requires_grad), args=sample_input_args, kwargs=sample_input_kwargs)) return inputs def sample_inputs_sparse_coo_masked_reduction(op_info, device, dtype, requires_grad, **kwargs): """Sample inputs for masked reduction operators that support inputs with sparse coo layouts. """ inputs: List[SampleInput] = [] if op_info.supports_sparse: op_name = op_info.name.replace('_masked.', '') for sample_input in sample_inputs_masked_reduction(op_info, device, dtype, requires_grad, **kwargs): mask = sample_input.kwargs.get('mask') if mask is not None: sample_input_kwargs = sample_input.kwargs.copy() sample_input_kwargs.update(mask=mask.to_sparse()) inputs.append(SampleInput(sample_input.input.to_sparse(), args=sample_input.args, kwargs=sample_input_kwargs)) else: if op_name in {'prod', 'amax', 'amin'}: # FIXME: for now reductions with non-zero reduction identity and # unspecified mask are not supported for sparse COO # tensors, see torch._masked.prod implementation # for details. continue inputs.append(SampleInput(sample_input.input.to_sparse(), args=sample_input.args, kwargs=sample_input.kwargs)) return inputs def sample_inputs_sparse_csr_masked_reduction(op_info, device, dtype, requires_grad, **kwargs): """Sample inputs for masked reduction operators that support inputs with sparse csr layouts. """ inputs: List[SampleInput] = [] if op_info.supports_sparse_csr: for sample_input in sample_inputs_masked_reduction(op_info, device, dtype, requires_grad, **kwargs): if not (sample_input.input.ndim == 2 and sample_input.kwargs.get('keepdim')): # - sparse CSR tensors are always 2-D tensors # - masked reduction on CSR tensors are defined only if keepdim is True. continue mask = sample_input.kwargs.get('mask') if mask is not None: sample_input_kwargs = sample_input.kwargs.copy() sample_input_kwargs.update(mask=mask.to_sparse_csr()) inputs.append(SampleInput(sample_input.input.to_sparse_csr(), args=sample_input.args, kwargs=sample_input_kwargs)) else: if op_info.name.lstrip('_masked.') in ['prod']: # reductions with non-zero reduction identity and # unspecified mask is not supported for sparse CSR # tensors, see torch._masked.prod implementation # for details. continue inputs.append(SampleInput(sample_input.input.to_sparse_csr(), args=sample_input.args, kwargs=sample_input.kwargs)) if sample_input.kwargs['dim'] == 0: # Reductions of CSR tensors use different implementations for # inner and/or outer dimensions. So, as a minimum of testing CSR # implementations the following kwargs must be generated: # dict(dim=0, keepdim=True) # dict(dim=1, keepdim=True) # dict(dim=(0, 1), keepdim=True) # Here we generate the dim=1 case from the dim=0 case. sample_input = inputs[-1] sample_input_kwargs = sample_input.kwargs.copy() sample_input_kwargs.update(dim=1) inputs.append(SampleInput(sample_input.input.clone(), args=sample_input.args, kwargs=sample_input_kwargs)) return inputs def sample_inputs_masked_norm(op_info, device, dtype, requires_grad, **kwargs): """Sample inputs for masked norm. """ inputs: List[SampleInput] = [] for ord in [2.0, 1, float('inf'), float('-inf'), 0]: for sample_input in sample_inputs_masked_reduction(op_info, device, dtype, requires_grad, **kwargs): sample_input_args, sample_input_kwargs = (ord,) + sample_input.args, sample_input.kwargs.copy() inputs.append(SampleInput(sample_input.input.clone().requires_grad_(requires_grad), args=sample_input_args, kwargs=sample_input_kwargs)) return inputs def sample_inputs_masked_std_var(op_info, device, dtype, requires_grad, **kwargs): """Sample inputs for masked std/var. """ inputs: List[SampleInput] = [] for unbiased in [False, True]: for sample_input in sample_inputs_masked_reduction(op_info, device, dtype, requires_grad, **kwargs): if sample_input.args: dim = sample_input.args[0] sample_input_args = sample_input.args[:1] + (unbiased,) + sample_input.args[1:] sample_input_kwargs = sample_input.kwargs.copy() else: dim = sample_input.kwargs.get('dim') sample_input_args = sample_input.args sample_input_kwargs = dict(sample_input.kwargs, unbiased=unbiased) if requires_grad: if sample_input_kwargs.get('mask') is None: orig_count = torch._masked.sum(torch.ones(sample_input.input.shape, dtype=torch.int64), dim, keepdim=True) else: inmask = torch._masked._input_mask(sample_input.input, *sample_input_args, **sample_input_kwargs) orig_count = torch._masked.sum(inmask.new_ones(sample_input.input.shape, dtype=torch.int64), dim, keepdim=True, mask=inmask) if orig_count.min() <= int(unbiased) + 1: # Skip samples that lead to singularities in var # computation resulting nan values both in var and # autograd output that test_grad_fn cannot handle # correctly. Also, skip samples when the autograd output # for std could not be handled correctly due to torch.sqrt continue inputs.append(SampleInput(sample_input.input.clone().requires_grad_(requires_grad), args=sample_input_args, kwargs=sample_input_kwargs)) return inputs # NOTE [Reductions]: # # For testing purposes, we relax the definition of a reduction operator # as defined in the docstring below. We do this to capture operators with # a similar API so they can be tested automatically. However... # # Strictly speaking a reduction operator is an operator that can reduce an # array to a single scalar value and that can be computed from the partial # result of reducing subarrays. This usually means that the reduction operation # should be commutative and associative. This definition is important when it # comes to implementation as it determines how a reduction can be parallelized. # # For example, many summary statistics such as median, mode and quantile cannot # be computed from partial results because these are sorting and counting based # algorithms that need information that would be lost in the reduced value. class ReductionOpInfo(OpInfo): """Reduction operator information. An operator is a reduction operator if it reduces one or more dimensions of the input tensor to a single value. Reduction operators must implement the following signature: - `op(input, *args, *, dim=None, keepdim=False, **kwargs) -> Tensor` ReductionOpInfo tests that reduction operators implement a consistent API. Optional features such as reducing over multiple dimensions are captured in the optional keyword parameters of the ReductionOpInfo constructor. If a reduction operator does not yet implement the full required API of reduction operators, this should be documented by skipping the failing tests rather than adding optional parameters to ReductionOpInfo. NOTE The API for reduction operators has not yet been finalized and some requirements may change. See tests in test/test_reductions.py """ def __init__( self, name, *, # The identity value for the operator if it has one. identity: Optional[Any] = None, # The nan policy for the operator if it implements one. # - propagate: NaN values are propagated to the output # - omit: NaN values are discarded during the reduction nan_policy: Optional[str] = None, # Whether the operator supports reducing multiple dimensions. supports_multiple_dims: bool = True, # Whether the operator promotes integral to floating point dtypes. promotes_int_to_float: bool = False, # Whether the operator promotes all integral dtypes to int64. promotes_int_to_int64: bool = False, # If a specific dtype is given, then the operator always returns that # dtype irrespective of the input dtype. If None, the operator returns # the dtype according to the type promotion rules above. result_dtype: Optional[torch.dtype] = None, # ReductionOpInfo tests generate their own input, dim and keepdim # arguments and call this function to generate tuples of extra args and # kwargs to use when calling the op. This is required for operators that # have other required parameters besides the input tensor. generate_args_kwargs: Callable = lambda t, dim=None, keepdim=False: (yield tuple(), {}), # Options from the OpInfo base class **kwargs, ): self._original_reduction_args = locals().copy() assert nan_policy in (None, 'propagate', 'omit') # These are mutually exclusive options assert not (result_dtype and promotes_int_to_float) assert not (result_dtype and promotes_int_to_int64) assert not (promotes_int_to_float and promotes_int_to_int64) # Default sample_inputs_func for ReductionOpInfo which augments sample # inputs from sample_inputs_reduction with the args and kwargs from # generate_args_kwargs. This is only used if sample_inputs_func is None. def sample_inputs_func(*args, **kwargs): kwargs['supports_multiple_dims'] = supports_multiple_dims kwargs['generate_args_kwargs'] = generate_args_kwargs return sample_inputs_reduction(*args, **kwargs) # Override OpInfo defaults and call base class __init__ kwargs.setdefault('inplace_variant', None) kwargs.setdefault('sample_inputs_func', sample_inputs_func) super().__init__(name, **kwargs) self.identity = identity self.nan_policy = nan_policy self.supports_multiple_dims = supports_multiple_dims self.promotes_int_to_float = promotes_int_to_float self.promotes_int_to_int64 = promotes_int_to_int64 self.result_dtype = result_dtype self.generate_args_kwargs = generate_args_kwargs def sample_inputs_tensor_split(op_info, device, dtype, requires_grad, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, low=None, high=None, requires_grad=requires_grad) args_cases = ( # Cases with tensor indices. (torch.tensor([1, 2, 3]),), (torch.tensor(1),), (torch.tensor([1, 2, 3]), 1), (torch.tensor([1, 4, 2, 5, 3, 6])[::2], 1), # Cases with list of indices. ((2, 4),), ((2, 4), 1), ((2, 4), -1), # Cases with integer section. (3,), (3, 1), (3, -1), ) for args in args_cases: yield SampleInput(make_input((S, S, S)), args=args) def sample_inputs_linalg_det(op_info, device, dtype, requires_grad, **kwargs): kw = dict(device=device, dtype=dtype) inputs = [ make_tensor((S, S), **kw), make_tensor((1, 1), **kw), # 1x1 random_symmetric_matrix(S, **kw), # symmetric random_symmetric_psd_matrix(S, **kw), # symmetric_psd random_symmetric_pd_matrix(S, **kw), # symmetric_pd random_square_matrix_of_rank(S, S - 2, **kw), # dim2_null random_square_matrix_of_rank(S, 1, **kw), # rank1 random_square_matrix_of_rank(S, 2, **kw), # rank2 make_fullrank_matrices_with_distinct_singular_values(S, S, **kw), # full rank make_tensor((3, 3, S, S), **kw), # batched make_tensor((3, 3, 1, 1), **kw), # batched_1x1 random_symmetric_matrix(S, 3, **kw), # batched_symmetric random_symmetric_psd_matrix(S, 3, **kw), # batched_symmetric_psd random_symmetric_pd_matrix(S, 3, **kw), # batched_symmetric_pd make_fullrank_matrices_with_distinct_singular_values(S, 3, 3, **kw), # batched fullrank make_tensor((0, 0), **kw), make_tensor((0, S, S), **kw), ] for t in inputs: t.requires_grad = requires_grad return [SampleInput(t) for t in inputs] def sample_inputs_linalg_det_singular(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype) def make_singular_matrix_batch_base(size, rank): assert size[-1] == size[-2] assert rank > 0 and rank < size[-1] n = size[-1] a = make_arg(size[:-2] + (n, rank)) / 10 b = make_arg(size[:-2] + (rank, n)) / 10 x = a @ b lu, pivs, _ = torch.linalg.lu_factor_ex(x) p, l, u = torch.lu_unpack(lu, pivs) u_diag_abs = u.diagonal(0, -2, -1).abs() u_diag_abs_largest = u_diag_abs.max(dim=-1, keepdim=True).values u_diag_abs_smallest_idxs = torch.topk(u_diag_abs, k=(n - rank), largest=False).indices u.diagonal(0, -2, -1).div_(u_diag_abs_largest) u.diagonal(0, -2, -1)[..., u_diag_abs_smallest_idxs] = torch.finfo(dtype).eps matrix = p @ l @ u matrix.requires_grad_(requires_grad) return matrix def sample_generator(): for batch, size in product(((), (2,), (2, 2)), range(6)): shape = batch + (size, size) for rank in range(1, size): yield make_singular_matrix_batch_base(shape, rank) return [SampleInput(t) for t in sample_generator()] def sample_inputs_linalg_matrix_power(op_info, device, dtype, requires_grad, **kwargs): make_fullrank = make_fullrank_matrices_with_distinct_singular_values make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) make_arg_fullrank = partial(make_fullrank, dtype=dtype, device=device, requires_grad=requires_grad) # (, ()) test_sizes = [ (1, ()), (2, (0,)), (2, (2,)), ] for matrix_size, batch_sizes in test_sizes: size = batch_sizes + (matrix_size, matrix_size) for n in (0, 3, 5): yield SampleInput(make_arg(size), args=(n,)) for n in [-4, -2, -1]: yield SampleInput(make_arg_fullrank(*size), args=(n,)) def sample_inputs_hsplit(op_info, device, dtype, requires_grad, **kwargs): return (SampleInput(make_tensor((6,), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=(2,),), SampleInput(make_tensor((S, S, S), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=([1, 2, 3],),),) def sample_inputs_vsplit(op_info, device, dtype, requires_grad, **kwargs): return (SampleInput(make_tensor((6, S), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=(2,),), SampleInput(make_tensor((S, S, S), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=([1, 2, 3],),),) def sample_inputs_dsplit(op_info, device, dtype, requires_grad, **kwargs): return (SampleInput(make_tensor((S, S, S), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=([1, 2, 3],),), SampleInput(make_tensor((S, S, 6), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=(2,),),) def error_inputs_hsplit(op_info, device, **kwargs): err_msg1 = ("torch.hsplit requires a tensor with at least 1 dimension, " "but got a tensor with 0 dimensions!") si1 = SampleInput(make_tensor((), dtype=torch.float32, device=device), args=(0,),) err_msg2 = (f"torch.hsplit attempted to split along dimension 1, " f"but the size of the dimension {S} " f"is not divisible by the split_size 0!") si2 = SampleInput(make_tensor((S, S, S), dtype=torch.float32, device=device), args=(0,),) return (ErrorInput(si1, error_regex=err_msg1), ErrorInput(si2, error_regex=err_msg2),) def error_inputs_vsplit(op_info, device, **kwargs): err_msg1 = ("torch.vsplit requires a tensor with at least 2 dimension, " "but got a tensor with 1 dimensions!") si1 = SampleInput(make_tensor((S,), dtype=torch.float32, device=device), args=(0,),) err_msg2 = (f"torch.vsplit attempted to split along dimension 0, " f"but the size of the dimension {S} " f"is not divisible by the split_size 0!") si2 = SampleInput(make_tensor((S, S, S), dtype=torch.float32, device=device), args=(0,),) return (ErrorInput(si1, error_regex=err_msg1), ErrorInput(si2, error_regex=err_msg2),) def error_inputs_dsplit(op_info, device, **kwargs): err_msg1 = ("torch.dsplit requires a tensor with at least 3 dimension, " "but got a tensor with 1 dimensions!") si1 = SampleInput(make_tensor((S,), dtype=torch.float32, device=device), args=(0,),) err_msg2 = (f"torch.dsplit attempted to split along dimension 2, " f"but the size of the dimension {S} " f"is not divisible by the split_size 0!") si2 = SampleInput(make_tensor((S, S, S), dtype=torch.float32, device=device), args=(0,),) return (ErrorInput(si1, error_regex=err_msg1), ErrorInput(si2, error_regex=err_msg2),) def sample_inputs_linalg_multi_dot(op_info, device, dtype, requires_grad, **kwargs): # Each test case consists of the sizes in the chain of multiplications # e.g. [2, 3, 4, 5] generates matrices (2, 3) @ (3, 4) @ (4, 5) test_cases = [ [1, 2, 1], [2, 0, 2], [0, 2, 2], [2, 2, 2, 2], [2, 3, 4, 5], [5, 4, 0, 2], [2, 4, 3, 5, 3, 2] ] result = [] for sizes in test_cases: tensors = [] for size in zip(sizes[:-1], sizes[1:]): t = make_tensor(size, dtype=dtype, device=device, requires_grad=requires_grad) tensors.append(t) result.append(SampleInput(tensors)) return result def sample_inputs_linalg_matrix_norm(op_info, device, dtype, requires_grad, **kwargs): sizes = ((2, 2), (2, 3, 2)) ords = ('fro', 'nuc', inf, -inf, 1, -1, 2, -2) dims = ((-2, -1), (-1, 0)) inputs: List[SampleInput] = [] for size, ord, dim, keepdim in product(sizes, ords, dims, [True, False]): t = make_tensor(size, dtype=dtype, device=device, requires_grad=requires_grad) inputs.append(SampleInput(t, args=(ord, dim, keepdim))) return inputs def sample_inputs_linalg_norm(op_info, device, dtype, requires_grad, *, variant=None, **kwargs): if variant is not None and variant not in ('subgradient_at_zero',): raise ValueError(f"Unsupported variant, expected variant to be 'subgradient_at_zero' but got: {variant}") test_sizes = [ (S,), (0,), (S, S), (0, 0), (S, 0), (0, S), (S, S, S), (0, S, S), (S, 0, S), (0, 0, 0), ] vector_ords = (None, 0, 0.5, 1, 2, 3.5, inf, -0.5, -1, -2, -3.5, -inf) matrix_ords = (None, 'fro', 'nuc', 1, 2, inf, -1, -2, -inf) inputs = [] for test_size in test_sizes: is_vector_norm = len(test_size) == 1 is_matrix_norm = len(test_size) == 2 for keepdim in [False, True]: if not variant == 'subgradient_at_zero': inputs.append(SampleInput( make_tensor( test_size, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), kwargs=dict( keepdim=keepdim))) if not (is_vector_norm or is_matrix_norm): continue ords = vector_ords if is_vector_norm else matrix_ords for ord in ords: if variant == 'subgradient_at_zero': inputs.append(SampleInput( torch.zeros( test_size, dtype=dtype, device=device, requires_grad=requires_grad), args=(ord,), kwargs=dict(keepdim=keepdim))) else: inputs.append(SampleInput( make_tensor( test_size, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=(ord,), kwargs=dict( keepdim=keepdim))) if ord in ['nuc', 'fro']: inputs.append(SampleInput( make_tensor( test_size, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), kwargs=dict( ord=ord, keepdim=keepdim, dim=(0, 1)))) return inputs def sample_inputs_as_strided(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # input shape, output shape, output stride, output storage offset test_cases = ( ((1,), (1,), (1,), 0), ((3, 3), (2, 2), (1, 2), 0), ((3, 3), (2, 2), (1, 2), 1), ((16,), (2, 2, 2, 2), (1, 1, 1, 1), 0), ((16,), (2, 1, 1, 2), (1, 7, 7, 1), 0), ) for input_shape, output_shape, stride, storage_offset in test_cases: input_t = make_arg(input_shape) kwargs = dict(storage_offset=storage_offset) yield SampleInput(input_t, args=(output_shape, stride), kwargs=kwargs) # as_strided on offset, partial views # yield SampleInput(make_arg((20,))[5:15], args=((2, 2), (1, 2))) # yield SampleInput(make_arg((20,))[5:15], args=((2, 2), (1, 2)), kwargs={'storage_offset': 0}) def sample_inputs_combinations(op_info, device, dtype, requires_grad, **kwargs): inputs = ( (0,), (0, 1), (0, 1, 2, 3), ) rvals = [1, 2, 4] products = product(inputs, rvals, [False, True]) samples = [] for input_data, r, with_replacement in products: input_t = torch.tensor(input_data, device=device, dtype=dtype, requires_grad=requires_grad) kwargs = dict(r=r, with_replacement=with_replacement) samples.append(SampleInput(input_t, kwargs=kwargs)) return tuple(samples) def sample_inputs_cartesian_prod(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(torch.tensor, device=device, dtype=dtype, requires_grad=requires_grad) # constructs 1-D tensors with varying number of elements a = make_arg((0,)) b = make_arg((0, 1)) c = make_arg((0, 1, 2, 3)) samples = [] # sample with only 1 tensor samples.append(SampleInput( a )) # sample with 2 tensors samples.append(SampleInput( a, args=(b,) )) # sample with 3 tensors samples.append(SampleInput( a, args=(b, c) )) return tuple(samples) def sample_inputs_cosine_similarity(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # Ordered as input_shape, dict of dim and eps cases: Tuple[tuple, dict] = ( # type: ignore[assignment] ((S, S), {'dim': 1}), ((S, 2), {'dim': -1}), ((S,), {'dim': 0, 'eps': 0.5}), ((), {'dim': 0}), ((S, S, M), {'dim': 2}), ((S, S), {}) ) for input_shape, kwargs in cases: yield SampleInput(make_arg(input_shape), args=(make_arg(input_shape),), kwargs=kwargs) # Test for Broadcasting yield SampleInput(make_arg((1, 2, 3)), args=(make_arg((2, 1, 3)),), kwargs={'dim': -1}) yield SampleInput(make_arg((1, 2, 3)), args=(make_arg((2, 1, 3)),), kwargs={'dim': -2}) yield SampleInput(make_arg((2, 3)), args=(make_arg((2, 1, 3)),), kwargs={'dim': -1}) def sample_inputs_batch_norm(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) make_arg_without_requires_grad = partial(make_tensor, device=device, dtype=dtype, requires_grad=False) # Ordered as: input shape, kwargs for training, momentum, eps cases: Tuple[Tuple[int], dict] = ( # type: ignore[assignment] ((S, S, S), {'training': True, 'momentum': 0.5, 'eps': 0.6}), ((3, 2, 4), {'training': False, 'momentum': -1.2}), ((3, 1), {'training': True, 'momentum': 0.0}), ((0,), {'training': True}), ((0,), {'training': False}), ((3, 2, 3, 4), {'training': True, 'momentum': -1.0, 'eps': 0.5}), ((3, 2, 3, 4), {'training': False, 'momentum': -1.0, 'eps': 0.5}), ((2, 1), {}), ) for input_shape, kwargs in cases: # args: running mean, running var, weight and bias should necessarily be of shape: (channels,) channels = input_shape[1] if len(input_shape) > 1 else 0 weight = make_arg(channels) if channels > 0 else None bias = make_arg(channels) if channels > 0 else None running_mean = make_arg_without_requires_grad(channels, low=0) running_var = make_arg_without_requires_grad(channels, low=0) yield SampleInput( make_arg(input_shape), args=( running_mean, running_var, weight, bias ), kwargs=kwargs ) # Checking for permutations of weights and biases as `None` weights = [channels, None, None] biases = [None, channels, None] is_training = [True, False, False] for weight, bias, training in zip(weights, biases, is_training): yield SampleInput( make_arg(input_shape), args=( running_mean, running_var, make_arg(channels), make_arg(channels) ), kwargs={'training': training} ) # Test case for no optional kwargs # running_mean and running_var are required in evaluation mode (training: False) but not in training mode yield SampleInput(make_arg((1, 2, 3)), args=(None, None), kwargs={'training': True}) def sample_inputs_nn_activation_relu(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) cases = ( (()), ((S, )), ((S, S)), ((S, M, S)) ) for shape in cases: yield SampleInput(make_arg(shape)) def sample_inputs_nn_functional_prelu(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) cases = ( (()), ((S, )), ((S, S)), ((S, M, S)) ) for shape in cases: for weight in [-1., 0., 0.8, 1.]: weight_tensor = torch.tensor(weight, device=device, dtype=dtype, requires_grad=requires_grad) yield SampleInput(make_arg(shape), args=(weight_tensor,)) if len(shape) >= 2: channel_size = shape[1] yield SampleInput(make_arg(shape), args=(make_arg((channel_size,)),)) weight_tensor = torch.tensor(1., device=device, dtype=dtype, requires_grad=requires_grad) yield SampleInput(make_arg((S, S)), kwargs=dict(weight=weight_tensor,)) yield SampleInput(make_arg((S, S)), kwargs=dict(weight=make_arg((S,)),)) def sample_inputs_norm(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) cases = [ ((S, S), (2,), '2'), ((S, S), (0,), '0'), ((S, S), (0.5,), '0_5'), ((S, S), (1,), '1'), ((S, S), (3,), '3'), ((S, S), (-1,), 'neg_1'), ((S, S), (-2,), 'neg_2'), ((S, S), (-0.5,), 'neg_0_5'), ((S, S), (-1.5,), 'neg_1_5'), ] # FIXME gradgrad and noncotiguous_samples fail on inf and -inf norms on CPU because of vectorization # For how to fix this, see the implementation of `linalg_vector_norm`. if torch.device(device).type == "cuda": cases += [((S, S), (inf,), 'inf'), ((S, S), (-inf,), 'neg_inf')] cases_nonzero_input = ( ((S, S, S), (1.5,), '1_5_default'), ((S, S, S), (1.5, 1), '1_5_dim'), ((S, S, S), (1.5, -1), '1_5_neg_dim'), ((S, S, S), (1.5, 1, True), 'keepdim_1_5_dim'), ((S, S, S), (1.5, -1, True), 'keepdim_1_5_neg_dim'), ) cases_posdim = ( ((S, S), (-2, 1,), 'neg_2_dim'), ((S, S), (-1, 1,), 'neg_1_dim'), ((S, S), (0, 1,), '0_dim'), ((S, S), (1, 1,), '1_dim'), ((S, S), (2, 1,), '2_dim'), ((S, S), (3, 1,), '3_dim'), ((S, S, S), (2, 1), '2_dim'), ((S, S, S), (3, 1), '3_dim'), ((S, S, S), (2, 1, True), 'keepdim_2_dim'), ((S, S, S), (3, 1, True), 'keepdim_3_dim'), ((), (2, 0), '2_dim_scalar'), ((), (3, 0), '3_dim_scalar'), ((), (2, 0, True), 'keepdim_2_dim_scalar'), ((), (3, 0, True), 'keepdim_3_dim_scalar'), ) cases_negdim = ((shape, args[:1] + (-args[1],) + args[2:], name.replace("_dim", "_neg_dim")) for shape, args, name in cases_posdim) for shape, args, name in itertools.chain(cases, cases_posdim, cases_negdim): yield SampleInput(make_arg(shape), args=args, name=name) for shape, args, name in cases_nonzero_input: yield SampleInput(make_arg(shape, exclude_zero=True), args=args, name=name) def sample_inputs_norm_fro(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) cases = ( ((S, S), (), 'default'), ((S, S), ('fro',), 'fro_default'), ((S, S), ('fro', [0, 1],), 'fro'), ) for shape, args, name in cases: yield SampleInput(make_arg(shape), args=args, name=name) def sample_inputs_norm_nuc(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) cases = ( ((S, S), ('nuc',), 'nuc'), ((S, S, S), ('nuc', [1, 2]), 'nuc_batched'), ) for shape, args, name in cases: yield SampleInput(make_arg(shape), args=args, name=name) def sample_inputs_norm_inf(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) cases = ( ((S, S), (-inf,), '-inf'), ((S, S), (inf,), 'inf'), ((S, S), (inf, 1,), 'inf_2_dim'), ((S, S), (inf, -1,), 'inf_2_neg_dim'), ) for shape, args, name in cases: yield SampleInput(make_arg(shape), args=args, name=name) def sample_inputs_linalg_vector_norm(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) sizes = ((S,), (2, 2)) dims = (None, 0, -1) ords = (inf, 2, 1, 0, 0.9, -2.1, -inf) for size, ord_, keepdim in product(sizes, ords, (True, False)): for dim in dims: yield SampleInput(make_arg(size), args=(ord_,), kwargs=dict(keepdim=keepdim, dim=dim)) if dtype == torch.float32: yield SampleInput(make_arg(size), args=(ord_,), kwargs=dict(keepdim=keepdim, dim=dim, dtype=torch.float64)) if dtype == torch.complex64: yield SampleInput(make_arg(size), args=(ord_,), kwargs=dict(keepdim=keepdim, dim=dim, dtype=torch.complex128)) # Test several dims if len(size) == 2: yield SampleInput(make_arg(size), args=(ord_,), kwargs=dict(keepdim=keepdim, dim=(-1, 0))) # The following functions and classes are for testing elementwise binary operators. # Returns a generator of pairs of contiguous tensors on the requested device # and with the requested dtype. # # This function is intended to test the non-vectorized and vectorized code # paths of elementwise binary functions, as well as their handling of odd tensor # sizes (like zero-dim tensors and tensors with zero elements). # # Each iterable will include an a tensor with no elements, # zero dim (scalar) tensors, small 1D tensors, a medium 1D tensor, and # a large 2D tensor. def generate_elementwise_binary_tensors(op, *, device, dtype, requires_grad=False): shapes = ( # tensors with no elements (0,), (1, 0, 3), # zero dim (scalar) tensor (), # small 1D tensor (20,), # medium 1D tensor (812,), # large 2D tensor (1029, 917), ) make_arg = partial( make_tensor, device=device, dtype=dtype, requires_grad=requires_grad ) for shape in shapes: lhs = make_arg(shape, **op.lhs_make_tensor_kwargs) rhs = make_arg(shape, **op.rhs_make_tensor_kwargs) yield SampleInput(lhs, args=(rhs,)) def generate_elementwise_binary_arbitrarily_strided_tensors(op, *, device, dtype, requires_grad=False): # shape, strides, offset strided_cases = ( ((5, 6, 2), (1, 1, 7), 2), ((5, 5, 4), (1, 1, 7), 2), ((5, 5, 2), (4, 5, 7), 3), ((5, 5, 2), (5, 5, 7), 3), ((5, 5, 2), (5, 5, 5), 3), ((9, 5, 2), (0, 1, 7), 3), ) make_arg = partial( make_tensor, device=device, dtype=dtype, requires_grad=requires_grad ) for shape, strides, offset in strided_cases: a = make_arg(500,).as_strided(shape, strides, offset) b = make_arg(shape) yield SampleInput(a, args=(b,)) # Returns a generator of pairs of contiguous tensors on the requested device and with # the requested dtype. # # Unlike the previous function, the values in these tensors are specified manually. def generate_elementwise_binary_small_value_tensors( op, *, device, dtype, requires_grad=False, exclude_zero=None ): if exclude_zero is None: if hasattr(op, "rhs_make_tensor_kwargs"): exclude_zero = op.rhs_make_tensor_kwargs.get("exclude_zero", False) # defines interesting values _unsigned_int_vals = (0, 1, 55, 127, 128, 190, 210, 220, 254) _int_vals = (0, -1, 1, -55, 55, -127, 127, -128) _float_vals = ( 0.0, -0.001, 0.001, -0.25, 0.25, -1.0, 1.0, -math.pi / 2, math.pi / 2, -math.pi + 0.00001, math.pi - 0.00001, -math.pi, math.pi, -math.pi - 0.00001, math.pi + 0.00001, ) l_vals = [] r_vals = [] if dtype.is_floating_point: prod = product(_float_vals, _float_vals) elif dtype.is_complex: complex_vals = product(_float_vals, _float_vals) # Note the use of list is required here or the map generator will be # emptied by the following product and it won't produce the desired cross-product complex_vals = list(map(lambda x: complex(*x), complex_vals)) prod = product(complex_vals, complex_vals) elif dtype in (torch.int8, torch.int16, torch.int32, torch.int64): prod = product(_int_vals, _int_vals) elif dtype is torch.uint8: prod = product(_unsigned_int_vals, _unsigned_int_vals) else: raise ValueError("Unsupported dtype!") for l, r in prod: l_vals.append(l) if r == 0 and exclude_zero: r_vals.append(1) else: r_vals.append(r) lhs = torch.tensor(l_vals, device=device, dtype=dtype, requires_grad=requires_grad) rhs = torch.tensor(r_vals, device=device, dtype=dtype, requires_grad=requires_grad) yield SampleInput(lhs, args=(rhs,)) def generate_elementwise_binary_large_value_tensors( op, *, device, dtype, requires_grad=False ): _large_int_vals = (-1113, 1113, -10701, 10701) _large_float16_vals = (-501, 501, -1001.2, 1001.2, -13437.7, 13437.7) _large_float_vals = _large_float16_vals + (-4988429.2, 4988429.2, -1e20, 1e20) l_vals = [] r_vals = [] if dtype == torch.float16: prod = product(_large_float16_vals, _large_float16_vals) elif dtype.is_floating_point: prod = product(_large_float_vals, _large_float_vals) elif dtype.is_complex: complex_vals = product(_large_float_vals, _large_float_vals) # Note the use of list is required here or the map generator will be # emptied by the following product and it won't produce the desired cross-product complex_vals = list(map(lambda x: complex(*x), complex_vals)) prod = product(complex_vals, complex_vals) elif dtype in (torch.int16, torch.int32, torch.int64): prod = product(_large_int_vals, _large_int_vals) else: raise ValueError("Unsupported dtype!") for l, r in prod: l_vals.append(l) r_vals.append(r) lhs = torch.tensor(l_vals, device=device, dtype=dtype, requires_grad=requires_grad) rhs = torch.tensor(r_vals, device=device, dtype=dtype, requires_grad=requires_grad) yield SampleInput(lhs, args=(rhs,)) def generate_elementwise_binary_extremal_value_tensors( op, *, device, dtype, requires_grad=False ): _float_extremals = (float("inf"), float("-inf"), float("nan")) l_vals = [] r_vals = [] if dtype.is_floating_point: prod = product(_float_extremals, _float_extremals) elif dtype.is_complex: complex_vals = product(_float_extremals, _float_extremals) # Note the use of list is required here or the map generator will be # emptied by the following product and it won't produce the desired cross-product complex_vals = list(map(lambda x: complex(*x), complex_vals)) prod = product(complex_vals, complex_vals) else: raise ValueError("Unsupported dtype!") for l, r in prod: l_vals.append(l) r_vals.append(r) lhs = torch.tensor(l_vals, device=device, dtype=dtype, requires_grad=requires_grad) rhs = torch.tensor(r_vals, device=device, dtype=dtype, requires_grad=requires_grad) yield SampleInput(lhs, args=(rhs,)) # Test case for NaN propagation nan = float('nan') if dtype.is_floating_point else complex(float('nan'), float('nan')) lhs = make_tensor((128, 128), device=device, dtype=dtype, requires_grad=requires_grad) lhs.flatten()[::3] = nan rhs = make_tensor((128, 128), device=device, dtype=dtype, requires_grad=requires_grad) rhs.flatten()[::3] = nan yield SampleInput(lhs, args=(rhs,)) # Returns a generator of pairs of contiguous and noncontiguous tensors that # require broadcasting def generate_elementwise_binary_broadcasting_tensors( op, *, device, dtype, requires_grad=False ): shapes = ( ((1,), ()), ((2,), ()), ((1,), (2,)), ((2, 1), (2,)), ((1, 2), (2,)), ((3, 2), (2,)), ((1, 3, 2), (2,)), ((1, 3, 2), (3, 2)), ((3, 1, 2), (3, 2)), ((2, 3, 2), ()), ((3, 1, 2), (1, 3, 2)), ) make_arg = partial( make_tensor, device=device, dtype=dtype, requires_grad=requires_grad ) for shape, noncontiguous in product(shapes, [True, False]): shape_lhs, shape_rhs = shape lhs = make_arg( shape_lhs, noncontiguous=noncontiguous, **op.lhs_make_tensor_kwargs ) rhs = make_arg( shape_rhs, noncontiguous=noncontiguous, **op.rhs_make_tensor_kwargs ) yield SampleInput(lhs, args=(rhs,), broadcasts_input=True) # Returns a generator of pairs of contiguous tensors and scalars def generate_elementwise_binary_with_scalar_samples( op, *, device, dtype, requires_grad=False ): make_arg = partial( make_tensor, device=device, dtype=dtype, requires_grad=requires_grad ) scalar_shapes = ((), (3,), (5, 3), (0, 1, 3), (1, 5)) if op.supports_rhs_python_scalar: for scalar_shape in scalar_shapes: lhs = make_arg(scalar_shape, **op.lhs_make_tensor_kwargs) rhs = make_arg(scalar_shape, **op.rhs_make_tensor_kwargs) lhs_scalar = make_arg((), **op.lhs_make_tensor_kwargs).item() rhs_scalar = make_arg((), **op.rhs_make_tensor_kwargs).item() yield SampleInput(lhs, args=(rhs_scalar,)) # Extends with scalar lhs if op.supports_one_python_scalar: yield SampleInput(lhs_scalar, args=(rhs,)) if op.supports_two_python_scalars: lhs_scalar = make_arg((), **op.lhs_make_tensor_kwargs).item() rhs_scalar = make_arg((), **op.rhs_make_tensor_kwargs).item() yield SampleInput(lhs_scalar, args=(rhs_scalar,)) # Returns a generator of pairs of noncontiguous tensors def generate_elementwise_binary_noncontiguous_tensors( op, *, device, dtype, requires_grad=False ): make_arg = partial( make_tensor, device=device, dtype=dtype, requires_grad=requires_grad ) # Generic noncontiguity lhs = make_arg((1026,), noncontiguous=True, **op.lhs_make_tensor_kwargs) rhs = make_arg((1026,), noncontiguous=True, **op.rhs_make_tensor_kwargs) yield SampleInput(lhs.clone(), args=(rhs.clone(),)) yield SampleInput(lhs.contiguous(), args=(rhs,)) # Transposed lhs = make_arg((789, 357), **op.lhs_make_tensor_kwargs) rhs = make_arg((789, 357), **op.rhs_make_tensor_kwargs) yield SampleInput(lhs.T, args=(rhs.T,)) # More noncontiguity shapes = ((5, 7), (1024,)) for shape in shapes: lhs = make_arg(shape, **op.lhs_make_tensor_kwargs) rhs = make_arg(shape, **op.rhs_make_tensor_kwargs) lhs_non_contig = torch.empty(shape + (2,), device=device, dtype=dtype)[..., 0] lhs_non_contig.copy_(lhs) rhs_non_contig = torch.empty(shape + (2,), device=device, dtype=dtype)[..., 0] rhs_non_contig.copy_(rhs) yield SampleInput(lhs_non_contig.clone(), args=(rhs_non_contig.clone(),)) yield SampleInput(lhs_non_contig.contiguous(), args=(rhs_non_contig,)) # Noncontiguous indices shape = (2, 2, 1, 2) lhs = make_arg(shape, **op.lhs_make_tensor_kwargs) rhs = make_arg(shape, **op.rhs_make_tensor_kwargs) lhs_non_contig = lhs[:, 1, ...] rhs_non_contig = rhs[:, 1, ...] yield SampleInput(lhs_non_contig.clone(), args=(rhs_non_contig.clone(),)) yield SampleInput(lhs_non_contig.contiguous(), args=(rhs_non_contig,)) # Expanded tensors shapes = ((1, 3), (1, 7), (5, 7)) for shape in shapes: lhs = make_arg(shape, **op.lhs_make_tensor_kwargs) rhs = make_arg(shape, **op.rhs_make_tensor_kwargs) lhs_non_contig = lhs.expand(3, -1, -1) rhs_non_contig = rhs.expand(3, -1, -1) yield SampleInput(lhs_non_contig, args=(rhs_non_contig,)) # Sample inputs for elementwise binary operators, like add def sample_inputs_elementwise_binary(op, device, dtype, requires_grad, **kwargs): make_arg = partial( make_tensor, device=device, dtype=dtype, requires_grad=requires_grad ) shapes = ( ((), ()), ((S,), ()), ((S, 1), (S,)), ((M, S), ()), ((S, M, S), (M, S)), ((S, M, S), (S, M, S)), ((M, 1, S), (M, S)), ((M, 1, S), (1, M, S)), ((0, 1, 3), (0, 10, 3)), ) sample_kwargs = kwargs.get("sample_kwargs", {}) for shape_lhs, shape_rhs in shapes: lhs = make_arg(shape_lhs, **op.lhs_make_tensor_kwargs) rhs = make_arg(shape_rhs, **op.rhs_make_tensor_kwargs) broadcasts_input = shape_lhs != torch.broadcast_shapes(shape_lhs, shape_rhs) yield SampleInput( lhs, args=(rhs,), kwargs=sample_kwargs, broadcasts_input=broadcasts_input ) def sample_inputs_jiterator(op, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) shapes = ( ((), ()), ((S,), ()), ((S, 1), (S,)), ((M, S), ()), ((S, M, S), (M, S)), ((S, M, S), (S, M, S)), ((M, 1, S), (M, S)), ((M, 1, S), (1, M, S)), ((0, 1, 3), (0, 10, 3)) ) num_inputs = kwargs.get('num_inputs') sample_kwargs = kwargs.get('sample_kwargs', {}) for shape_lhs, shape_rhs in shapes: lhs = make_arg(shape_lhs) args = [] for i in range(num_inputs - 1): args.append(make_arg(shape_rhs)) broadcasts_input = (shape_lhs != torch.broadcast_shapes(shape_lhs, shape_rhs)) yield SampleInput(lhs, args=tuple(args), kwargs=sample_kwargs, broadcasts_input=broadcasts_input) # The base reference input generation for elementwise binary operations def _reference_inputs_elementwise_binary(op, device, dtype, requires_grad, **kwargs): yield from op.sample_inputs_func(op, device, dtype, requires_grad, **kwargs) yield from generate_elementwise_binary_tensors( op, device=device, dtype=dtype, requires_grad=requires_grad ) if dtype is not torch.bool: yield from generate_elementwise_binary_small_value_tensors( op, device=device, dtype=dtype, requires_grad=requires_grad ) if dtype not in (torch.bool, torch.uint8, torch.int8): yield from generate_elementwise_binary_large_value_tensors( op, device=device, dtype=dtype, requires_grad=requires_grad ) # TODO: FIXME: RuntimeError: "index_select" not implemented for 'ComplexHalf' if dtype not in (torch.chalf,): yield from generate_elementwise_binary_broadcasting_tensors( op, device=device, dtype=dtype, requires_grad=requires_grad ) yield from generate_elementwise_binary_with_scalar_samples( op, device=device, dtype=dtype, requires_grad=requires_grad ) if dtype.is_floating_point or dtype.is_complex: yield from generate_elementwise_binary_extremal_value_tensors( op, device=device, dtype=dtype, requires_grad=requires_grad ) # Note that these references inputs use scalars for the SampleInput.input value, # and many tests require SampleInput.input be a tensor or a list of tensors def reference_inputs_elementwise_binary(op, device, dtype, requires_grad, **kwargs): gen = partial( _reference_inputs_elementwise_binary, op, device, dtype, requires_grad, **kwargs ) # yields "normal" samples yield from gen() # TODO: RuntimeError: "index_select" not implemented for 'ComplexHalf' if dtype is torch.chalf: return # yields noncontiguous samples for sample in gen(): yield sample.noncontiguous() yield from generate_elementwise_binary_noncontiguous_tensors( op, device=device, dtype=dtype, requires_grad=requires_grad ) yield from generate_elementwise_binary_arbitrarily_strided_tensors( op, device=device, dtype=dtype, requires_grad=requires_grad ) # A functional that extends an elementwise binary operator's bespoke error inputs # with generic error inputs for the class of elementwise binary operations def make_error_inputs_elementwise_binary(error_inputs_func): def error_inputs_func_wrapper(op, device, **kwargs): if error_inputs_func is not None: yield from error_inputs_func(op, device, **kwargs) if not op.supports_rhs_python_scalar: si = SampleInput(torch.tensor((1, 2, 3), device=device), args=(2,)) yield ErrorInput(si, error_type=Exception, error_regex="") if not op.supports_one_python_scalar: si = SampleInput(2, args=(torch.tensor((1, 2, 3), device=device),)) yield ErrorInput(si, error_type=Exception, error_regex="") if ( not kwargs.get("skip_two_python_scalars", False) and not op.supports_two_python_scalars ): si = SampleInput(2, args=(3,)) yield ErrorInput(si, error_type=Exception, error_regex="") return error_inputs_func_wrapper # Metadata class for binary "universal functions (ufuncs)" that accept two # tensor and have common properties class BinaryUfuncInfo(OpInfo): """Operator information for 'universal binary functions (binary ufuncs).' These are functions of two tensors with common properties like: - they are elementwise functions - the output shape is determined by the input shape - they typically have method and inplace variants - they typically support the out kwarg - they typically have NumPy or SciPy references See NumPy's universal function documentation (https://numpy.org/doc/stable/reference/ufuncs.html) for more details about the concept of ufuncs. """ def __init__( self, name, *, sample_inputs_func=sample_inputs_elementwise_binary, reference_inputs_func=reference_inputs_elementwise_binary, error_inputs_func=None, lhs_make_tensor_kwargs=None, rhs_make_tensor_kwargs=None, promotes_int_to_float=False, # Set to true if the op promotes integer inputs to float always_returns_bool=False, # Set to true if the op always returns bool tensors supports_rhs_python_scalar=True, # Whether the operator allows Tensor x scalar inputs supports_one_python_scalar=False, # Whether the operator allows scalar x tensor and tensor x scalar inputs supports_two_python_scalars=False, # Whether the operator allows scalar x scalar inputs **kwargs, ): self._original_binary_ufunc_args = locals().copy() # Elementwise binary operations perform the equivalent of test_reference_testing # in test_binary_ufuncs, but with additional test granularity. So the # generic test_ops.py test is skipped because it's redundant. common_skips = ( DecorateInfo( unittest.skip("Skipping redundant test."), "TestCommon", "test_reference_testing", ), ) kwargs["skips"] = kwargs.get("skips", tuple()) + common_skips super(BinaryUfuncInfo, self).__init__( name, sample_inputs_func=sample_inputs_func, reference_inputs_func=reference_inputs_func, error_inputs_func=make_error_inputs_elementwise_binary(error_inputs_func), **kwargs, ) # [lr]hs_make_tensor_kwargs are part of the OpInfo to be able to dynamically generate valid samples later on. if lhs_make_tensor_kwargs is None: lhs_make_tensor_kwargs = {} self.lhs_make_tensor_kwargs = lhs_make_tensor_kwargs if rhs_make_tensor_kwargs is None: rhs_make_tensor_kwargs = {} self.rhs_make_tensor_kwargs = rhs_make_tensor_kwargs self.promotes_int_to_float = promotes_int_to_float self.always_returns_bool = always_returns_bool self.supports_rhs_python_scalar = supports_rhs_python_scalar self.supports_one_python_scalar = supports_one_python_scalar self.supports_two_python_scalars = supports_two_python_scalars if self.supports_two_python_scalars: self.supports_one_python_scalar = True if self.supports_one_python_scalar: assert ( supports_rhs_python_scalar ), "Can't support lhs and rhs Python scalars but not rhs scalars!" # The following functions and classes are for testing elementwise unary operators. def sample_inputs_elementwise_unary( op_info, device, dtype, requires_grad, op_kwargs=None, **kwargs ): if not op_kwargs: op_kwargs = {} low, high = op_info.domain low = low if low is None else low + op_info._domain_eps high = high if high is None else high - op_info._domain_eps if op_info.supports_sparse_csr: # Tensors with dim=2 for sparse CSR testing yield SampleInput( make_tensor( (L, L), device=device, dtype=dtype, low=low, high=high, requires_grad=requires_grad, ), kwargs=op_kwargs, ) else: # Creates a 1D, empty, and scalar tensor for shape in ((L,), (1, 0, 3), ()): yield SampleInput( make_tensor( shape, device=device, dtype=dtype, low=low, high=high, requires_grad=requires_grad, ), kwargs=op_kwargs, ) # Replace values satisfying condition with a safe value. This is used to block # out values the could cause singularity like tan(pi/2) def _replace_values_in_tensor(tensor, condition, safe_value): mask = condition(tensor) tensor.masked_fill_(mask, safe_value) # Helper to create a unary elementwise tensor with valid inputs def _make_unary_elementwise_tensor(shape, *, op, dtype, **kwargs): low, high = op.domain low = low if low is None else low + op._domain_eps high = high if high is None else high - op._domain_eps a = make_tensor(shape, low=low, high=high, dtype=dtype, **kwargs) if op.reference_numerics_filter is not None and dtype is not torch.bool: condition, safe_value = op.reference_numerics_filter _replace_values_in_tensor(a, condition, safe_value) return a # Restricts the values in the tensor to the domain of the # given elementwise unary operator def _filter_unary_elementwise_tensor(a, *, op): # short-circuits for boolean tensors if a.dtype is torch.bool: return a low, high = op.domain low = low if low is None else low + op._domain_eps high = high if high is None else high - op._domain_eps if a.dtype is torch.uint8 and low is not None: low = max(low, 0) if not a.dtype.is_floating_point and not a.dtype.is_complex: low = math.ceil(low) if low is not None else None high = math.floor(high) if high is not None else None if op.reference_numerics_filter is not None: condition, safe_value = op.reference_numerics_filter _replace_values_in_tensor(a, condition, safe_value) if low is not None or high is not None: if a.dtype.is_complex: a.real.clamp_(low, high) a.imag.clamp_(low, high) else: a.clamp_(min=low, max=high) return a def generate_elementwise_unary_tensors(op, *, device, dtype, requires_grad, **kwargs): # Special-cases bool if dtype is torch.bool: tensors = ( torch.empty(0, device=device, dtype=torch.bool), torch.tensor(True, device=device), torch.tensor(False, device=device), torch.tensor((True, False), device=device), make_tensor((812,), device=device, dtype=dtype), make_tensor((1029, 917), device=device, dtype=dtype), ) for a in tensors: yield SampleInput(a, kwargs=op.sample_kwargs(device, dtype, a)[0]) shapes = ( (1029, 917), (812,), # Empty sizes (0,), (0, 3, 3), (1, 0, 5), (6, 0, 0, 0), (3, 0, 1, 0), ) make_arg = partial( _make_unary_elementwise_tensor, op=op, device=device, dtype=dtype, requires_grad=requires_grad, ) for shape in shapes: a = make_arg(shape) yield SampleInput(a, kwargs=op.sample_kwargs(device, dtype, a)[0]) def generate_elementwise_unary_small_value_tensors( op, *, device, dtype, requires_grad=False ): for sample in generate_elementwise_binary_small_value_tensors( op, device=device, dtype=dtype, requires_grad=requires_grad ): a = _filter_unary_elementwise_tensor(sample.input, op=op) yield SampleInput(a, kwargs=op.sample_kwargs(device, dtype, a)[0]) def generate_elementwise_unary_large_value_tensors( op, *, device, dtype, requires_grad=False ): for sample in generate_elementwise_binary_large_value_tensors( op, device=device, dtype=dtype, requires_grad=requires_grad ): a = _filter_unary_elementwise_tensor(sample.input, op=op) yield SampleInput(sample.input, kwargs=op.sample_kwargs(device, dtype, a)[0]) def generate_elementwise_unary_extremal_value_tensors( op, *, device, dtype, requires_grad=False ): for sample in generate_elementwise_binary_extremal_value_tensors( op, device=device, dtype=dtype, requires_grad=requires_grad ): yield SampleInput( sample.input, kwargs=op.sample_kwargs(device, dtype, sample.input)[0] ) def generate_elementwise_unary_noncontiguous_tensors( op, *, device, dtype, requires_grad=False ): low, high = op.domain low = low if low is None else low + op._domain_eps high = high if high is None else high - op._domain_eps make_arg = partial( _make_unary_elementwise_tensor, op=op, device=device, dtype=dtype, requires_grad=requires_grad, ) # Generic noncontiguity t = make_arg((1026,), noncontiguous=True) yield SampleInput(t, kwargs=op.sample_kwargs(device, dtype, t)[0]) # Transposed t = make_arg((1024, 1024)).T yield SampleInput(t, kwargs=op.sample_kwargs(device, dtype, t)[0]) # Expanded tensors shapes = ((1, 3), (1, 7), (5, 7)) for shape in shapes: t = make_arg(shape) t_non_contig = t.expand(3, -1, -1) yield SampleInput( t_non_contig, kwargs=op.sample_kwargs(device, dtype, t_non_contig)[0] ) def generate_elementwise_unary_arbitrarily_strided_tensors(op, *, device, dtype, requires_grad=False): # shape, strides, offset strided_cases = ( ((5, 6, 2), (1, 1, 7), 2), ((5, 5, 4), (1, 1, 7), 2), ((5, 5, 2), (4, 5, 7), 3), ((5, 5, 2), (5, 5, 7), 3), ((5, 5, 2), (5, 5, 5), 3), ((9, 5, 2), (0, 1, 7), 3), ) make_arg = partial( make_tensor, device=device, dtype=dtype, requires_grad=requires_grad ) for shape, strides, offset in strided_cases: a = make_arg(500,).as_strided(shape, strides, offset) yield SampleInput(a) # Reuses the elementwise binary generators for consistency # TODO: in the future generalize the reference generators to handle n-ary elementwise operations def _reference_inputs_elementwise_unary(op, device, dtype, requires_grad, **kwargs): yield from op.sample_inputs_func(op, device, dtype, requires_grad, **kwargs) yield from generate_elementwise_unary_tensors( op, device=device, dtype=dtype, requires_grad=requires_grad, **kwargs ) if dtype is not torch.bool: yield from generate_elementwise_unary_small_value_tensors( op, device=device, dtype=dtype, requires_grad=requires_grad, **kwargs ) if dtype not in (torch.bool, torch.uint8, torch.int8) and ( op.handles_large_floats or (not dtype.is_floating_point and not dtype.is_complex) ): yield from generate_elementwise_unary_large_value_tensors( op, device=device, dtype=dtype, requires_grad=requires_grad, **kwargs ) if dtype.is_floating_point or dtype.is_complex: yield from generate_elementwise_unary_extremal_value_tensors( op, device=device, dtype=dtype, requires_grad=requires_grad, **kwargs ) def reference_inputs_elementwise_unary(op, device, dtype, requires_grad, **kwargs): gen = partial( _reference_inputs_elementwise_unary, op, device, dtype, requires_grad, **kwargs ) # yields "normal" samples yield from gen() # yields noncontiguous samples for sample in gen(): yield sample.noncontiguous() yield from generate_elementwise_unary_noncontiguous_tensors( op, device=device, dtype=dtype, requires_grad=requires_grad, **kwargs ) yield from generate_elementwise_unary_arbitrarily_strided_tensors( op, device=device, dtype=dtype, requires_grad=requires_grad, **kwargs ) # Metadata class for unary "universal functions (ufuncs)" that accept a single # tensor and have common properties like: class UnaryUfuncInfo(OpInfo): """Operator information for 'universal unary functions (unary ufuncs).' These are functions of a single tensor with common properties like: - they are elementwise functions - the input shape is the output shape - they typically have method and inplace variants - they typically support the out kwarg - they typically have NumPy or SciPy references See NumPy's universal function documentation (https://numpy.org/doc/1.18/reference/ufuncs.html) for more details about the concept of ufuncs. """ def __init__( self, name, # the string name of the function *, ref, # a reference function dtypes=floating_types(), dtypesIfCUDA=None, dtypesIfROCM=None, domain=(None, None), # the [low, high) domain of the function handles_large_floats=True, # whether the op correctly handles large float values (like 1e20) supports_complex_to_float=False, # op supports casting from complex input to real output safely eg. angle sample_inputs_func=sample_inputs_elementwise_unary, reference_inputs_func=reference_inputs_elementwise_unary, sample_kwargs=lambda device, dtype, input: ({}, {}), supports_sparse=False, reference_numerics_filter=None, # Filters values in the range of the domain specified above but that should not be tested **kwargs, ): self._original_unary_ufunc_args = locals().copy() super(UnaryUfuncInfo, self).__init__( name, dtypes=dtypes, dtypesIfCUDA=dtypesIfCUDA, dtypesIfROCM=dtypesIfROCM, sample_inputs_func=sample_inputs_func, reference_inputs_func=reference_inputs_func, supports_sparse=supports_sparse, **kwargs, ) self.ref = ref self.domain = domain self.handles_large_floats = handles_large_floats self.supports_complex_to_float = supports_complex_to_float self.reference_numerics_filter = reference_numerics_filter # test_unary_ufuncs.py generates its own inputs to test the consistency # of the operator on sliced tensors, non-contig tensors, etc. # `sample_kwargs` is a utility function to provide kwargs # along with those inputs if required (eg. clamp). # It should return two dictionaries, first holding kwarg for # torch operator and second one for reference NumPy operator. self.sample_kwargs = sample_kwargs # Epsilon to ensure grad and gradgrad checks don't test values # outside a function's domain. self._domain_eps = 1e-5 def sample_inputs_add_sub(op, device, dtype, requires_grad, **kwargs): yield from sample_inputs_elementwise_binary(op, device, dtype, requires_grad, **kwargs) # Adds alpha kwarg cases make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) lhs = make_arg((S, S), **op.lhs_make_tensor_kwargs) rhs = make_arg((S, S), **op.rhs_make_tensor_kwargs) if dtype is not torch.bool: yield SampleInput(lhs, args=(rhs,), kwargs={'alpha': 2}) else: yield SampleInput(lhs, args=(rhs,), kwargs={'alpha': True}) neg_alpha = -3.14 if (dtype.is_floating_point or dtype.is_complex) else -3 lhs = make_arg((S, S), **op.lhs_make_tensor_kwargs) rhs = make_arg((S, S), **op.rhs_make_tensor_kwargs) if dtype is not torch.bool: yield SampleInput(lhs, args=(rhs,), kwargs={'alpha': neg_alpha}) else: yield SampleInput(lhs, args=(rhs,), kwargs={'alpha': False}) def sample_inputs_isclose(op, device, dtype, requires_grad, **kwargs): yield from sample_inputs_elementwise_binary(op, device, dtype, requires_grad, **kwargs) # Creates additional inputs to test the rtol, atol, and equal_nan params rtols = [0., 1e-7] atols = [0., 1e-7] equal_nans = [False, True] products = product(rtols, atols, equal_nans) make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) for rtol, atol, equal_nan in products: lhs = make_arg((S, S), **op.lhs_make_tensor_kwargs) rhs = make_arg((S, S), **op.rhs_make_tensor_kwargs) yield SampleInput(lhs, args=(rhs,), kwargs=dict(rtol=rtol, atol=atol, equal_nan=equal_nan)) def sample_inputs_t(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return (SampleInput(make_arg((1, 2))), SampleInput(make_arg((2,))), SampleInput(make_arg(()))) def sample_inputs_mm(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) def make_arg_conj(size): return make_arg(size).conj().requires_grad_(requires_grad) first_shape, second_shape = (S, M), (M, S) yield SampleInput(make_arg(first_shape), args=(make_arg(second_shape),)) if dtype.is_complex: yield SampleInput(make_arg(first_shape), args=(make_arg_conj(second_shape),)) def sample_inputs_addmm(op_info, device, dtype, requires_grad, **kwargs): alpha_val = kwargs.get('alpha', 2 + 3j if dtype.is_complex else 0.6) beta_val = kwargs.get('beta', 1 + 2j if dtype.is_complex else 0.2) tests_list = [ ((2, 3), (2, 2), (2, 3), False) ] tests_with_lhs_broadcasting = [ ((1,), (2, 2), (2, 3), True), ((), (2, 2), (2, 3), True) ] test_cases = tests_list + tests_with_lhs_broadcasting # type: ignore[operator] sample_inputs = [] for shape_a, shape_b, shape_c, broadcasts_input in test_cases: sample_inputs.append( SampleInput( make_tensor(shape_a, dtype=dtype, device=device, requires_grad=requires_grad), args=( make_tensor(shape_b, dtype=dtype, device=device, requires_grad=requires_grad), make_tensor(shape_c, dtype=dtype, device=device, requires_grad=requires_grad)), kwargs={'alpha': alpha_val, 'beta': beta_val}, broadcasts_input=broadcasts_input)) if dtype.is_complex: shape = (3, 3) sample_inputs.append( SampleInput(make_tensor(shape, dtype=dtype, device=device, requires_grad=requires_grad), args=( make_tensor(shape, dtype=dtype, device=device).mH.requires_grad_(requires_grad), make_tensor(shape, dtype=dtype, device=device, requires_grad=requires_grad)), kwargs={'alpha': alpha_val, 'beta': beta_val},)) sample_inputs.append( SampleInput(make_tensor(shape, dtype=dtype, device=device, requires_grad=requires_grad), args=( make_tensor(shape, dtype=dtype, device=device, requires_grad=requires_grad), make_tensor(shape, dtype=dtype, device=device).mH.requires_grad_(requires_grad)), kwargs={'alpha': alpha_val, 'beta': beta_val},)) return sample_inputs def sample_inputs_sparse_sampled_addmm(op_info, device, dtype, requires_grad, **kwargs): alpha = 2 + 3j if dtype.is_complex else 0.6 beta = 1 + 2j if dtype.is_complex else 0.2 def generator(): # sparse.sampled_addmm performs: alpha * (A @ B) * sparse_ones_like(C) + beta * C for m, n, k in itertools.product([0, 5], repeat=3): yield SampleInput( torch.eye(m, n, device=device, dtype=dtype) .to_sparse_csr() .requires_grad_(requires_grad), args=( make_tensor( (m, k), device=device, dtype=dtype, requires_grad=requires_grad, ), make_tensor( (k, n), device=device, dtype=dtype, requires_grad=requires_grad, ), ), kwargs={"alpha": alpha, "beta": beta}, ) return list(generator()) def sample_inputs_mv(self, device, dtype, requires_grad, **kwargs): return ( SampleInput( make_tensor((S, M, ), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=( make_tensor((M, ), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), ) ), ) def sample_inputs_bmm(self, device, dtype, requires_grad, **kwargs): return ( SampleInput( make_tensor((M, S, M, ), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=( make_tensor((M, M, S, ), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), ) ), ) def sample_inputs_dot_vdot(self, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) def make_arg_conj(size): return make_arg(size).conj().requires_grad_(requires_grad) sample_inputs = [] sample_inputs.append(SampleInput(make_arg((S, )), args=(make_arg((S, )),))) if dtype.is_complex: # dot/vdot for (conj(input), conj(arg_tensor)) and (conj(input), arg_tensor) # is tested in test_conj_view (which tests operations with only conjugated input tensor # -- not conjugated arg tensors) sample_inputs.append(SampleInput(make_arg((S, )), args=(make_arg_conj((S, )),))) return sample_inputs def sample_inputs_addmv(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) test_cases = (((S,), (S, M), (M,), 1, 1, False), ((S,), (S, M), (M,), 0.2, 0.6, False), ) test_cases_with_broadcast = (((1,), (S, M), (M,), 1, 1, True), ((1,), (S, M), (M,), 0.2, 0.6, True), ((), (S, M), (M,), 1, 1, True), ((), (S, M), (M,), 0.2, 0.6, True), ) cases = test_cases + test_cases_with_broadcast # addmv performs: beta * M + alpha * (mat @ vec) for size, mat, vec, beta, alpha, broadcasts_input in cases: yield SampleInput(make_arg(size), args=(make_arg(mat), make_arg(vec)), kwargs=dict(beta=beta, alpha=alpha), broadcasts_input=broadcasts_input) def sample_inputs_addbmm(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # input_shape, batch1_shape, batch2_shape, beta_val, alpha_val, is_broadcasting test_cases = [((S, M), (S, S, S), (S, S, M), 1, 1, False), ((1,), (S, S, S), (S, S, M), 1, 1, True), ((S, M), (S, S, S), (S, S, M), 0.6, 0.2, False), ((1,), (S, S, S), (S, S, M), 0.6, 0.2, True), ((), (S, S, S), (S, S, M), 1, 1, True), ((), (S, S, S), (S, S, M), 0.6, 0.2, True), ] for input_shape, batch1_shape, batch2_shape, beta, alpha, is_broadcasting in test_cases: if dtype.is_complex: beta_complex, alpha_complex = beta * (1 + 2j), alpha * (2 + 3j) yield SampleInput(make_arg(input_shape), args=(make_arg(batch1_shape), make_arg(batch2_shape)), kwargs=dict(beta=beta_complex, alpha=alpha_complex), broadcasts_input=is_broadcasting) yield SampleInput(make_arg(input_shape), args=(make_arg(batch1_shape), make_arg(batch2_shape)), kwargs=dict(beta=beta, alpha=alpha), broadcasts_input=is_broadcasting) def sample_inputs_addcmul_addcdiv(op_info, device, dtype, requires_grad, **kwargs): test_cases = [(((S, S), (S, S), (S, S)), False), (((S, S), (S, 1), (1, S)), False), (((1,), (S, S, 1), (1, S)), True), (((), (), ()), False), (((S, S), (), ()), True), (((), (S, S, 1), (1, S)), True) ] sample_inputs = [] for input_args, broadcasts_input in test_cases: # addcdiv should accept inputs with zero value # Currently, it throws ZeroDivisionError when the denominator is zero # TODO: exclude_zeros can be removed after https://github.com/pytorch/pytorch/issues/73638 is fixed args = tuple(make_tensor(arg, dtype=dtype, device=device, requires_grad=requires_grad, exclude_zero=True) if isinstance(arg, tuple) else arg for arg in input_args) sample_inputs.append(SampleInput( args[0], args=args[1:], broadcasts_input=broadcasts_input)) # addcdiv should accept inputs with zero value # Currently, it throws ZeroDivisionError when the denominator is zero # TODO: exclude_zeros can be removed after https://github.com/pytorch/pytorch/issues/73638 is fixed args = tuple(make_tensor(arg, dtype=dtype, device=device, requires_grad=requires_grad, exclude_zero=True) if isinstance(arg, tuple) else arg for arg in input_args) sample_inputs.append(SampleInput( args[0], args=args[1:], kwargs=dict(value=3.14), broadcasts_input=broadcasts_input)) return tuple(sample_inputs) def sample_inputs_baddbmm(op_info, device, dtype, requires_grad, **kwargs): test_cases = [((S, S, M), (S, S, S), (S, S, M), 1, 1, False), ((1,), (S, S, S), (S, S, M), 1, 1, True), ((S, S, M), (S, S, S), (S, S, M), 0.6, 0.2, False), ((1,), (S, S, S), (S, S, M), 0.6, 0.2, True), ((), (S, S, S), (S, S, M), 1, 1, True), ((), (S, S, S), (S, S, M), 0.6, 0.2, True), ] sample_inputs = [] for (input_shape, batch1_shape, batch2_shape, alpha, beta, broadcasts_input) in test_cases: args = (make_tensor(input_shape, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), make_tensor(batch1_shape, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), make_tensor(batch2_shape, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad)) sample_inputs.append(SampleInput(args[0], args=(args[1], args[2]), kwargs=dict(beta=beta, alpha=alpha), broadcasts_input=broadcasts_input)) if dtype.is_complex: sample_inputs.append(SampleInput( args[0].clone().requires_grad_(requires_grad), args=(args[1].clone().requires_grad_(requires_grad), args[2].clone().requires_grad_(requires_grad)), kwargs=dict(beta=beta * (1 + 2j), alpha=alpha * (2 + 3j)), broadcasts_input=broadcasts_input)) if dtype.is_complex: shapes = [(S, S, S), (S, M, S), (S, S, M)] args = (make_tensor(shapes[0], dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), make_tensor(shapes[1], dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), make_tensor(shapes[2], dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad)) sample_inputs.append( SampleInput( args[0].transpose_(-1, 1), args=(args[1].transpose(-1, 1).conj().requires_grad_(requires_grad), args[2].transpose(-1, 1).conj().requires_grad_(requires_grad)), kwargs=dict(beta=beta * (1 + 2j), alpha=alpha * (2 + 3j)),)) return tuple(sample_inputs) # TODO: add reduction kwargs def sample_inputs_multilabel_soft_margin_loss(op_info, device, dtype, requires_grad, **kwargs): _make_tensor = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) shapes = ( (S,), (S, S), ) for shape in shapes: # Produce one with weight and one without. yield SampleInput(_make_tensor(shape), args=(_make_tensor(shape, requires_grad=False),), kwargs={}) yield SampleInput(_make_tensor(shape), args=(_make_tensor(shape, requires_grad=False),), kwargs={'weight': _make_tensor(shape, requires_grad=False)}) def sample_inputs_addr(op_info, device, dtype, requires_grad, **kwargs): input1 = SampleInput( make_tensor((S, M), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=( make_tensor((S, ), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), make_tensor((M, ), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad))) input2 = SampleInput( make_tensor((), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=( make_tensor((S, ), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), make_tensor((M, ), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad)), broadcasts_input=True) if dtype.is_complex: alpha, beta = 0.1 + 0.3j, 0.4 + 0.6j elif dtype.is_floating_point: alpha, beta = 0.2, 0.6 else: alpha, beta = 2, 3 input3 = SampleInput( make_tensor((S, M), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=( make_tensor((S, ), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), make_tensor((M, ), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad)), kwargs=dict(beta=beta, alpha=alpha)) input4 = SampleInput( make_tensor((), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=( make_tensor((S, ), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), make_tensor((M, ), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad)), kwargs=dict(beta=beta, alpha=alpha), broadcasts_input=True) return (input1, input2, input3, input4) def sample_inputs_zero_(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) cases = ((), (S, S, S), (S,)) for shape in cases: yield(SampleInput(make_arg(shape))) # TODO: add reduction kwargs def sample_inputs_multi_margin_loss(op_info, device, dtype, requires_grad, **kwargs): _make_tensor = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) make_target = partial(_make_tensor, dtype=torch.long, requires_grad=False) inputs = ( ((), make_target([], low=0, high=1), {}), ((S,), make_target([], low=0, high=S), {"p": 1}), ((S,), make_target([1], low=0, high=S), {"p": 2}), ((S, M), make_target([S], low=0, high=M), {"margin": 1.0}), ((M, S), make_target([M], low=0, high=S), {"weight": None}), ) for input_shape, target, kwargs in inputs: yield SampleInput(_make_tensor(input_shape), args=(target,), kwargs=kwargs) def sample_inputs_logsumexp(self, device, dtype, requires_grad, **kwargs): inputs = ( ((), (0,), True), ((S, S), (1,), True), ((S, S), (1,), False), ((S, S), (-2,), False), ) samples = [] # Test large inputs to check numerical stability lows = (None, 1e3, 1e6) if dtype in (torch.float32, torch.float64) else (None,) for low in lows: high = low * 2 if low is not None else None for shape, dim, keepdim in inputs: t = make_tensor(shape, dtype=dtype, device=device, low=low, high=high, requires_grad=requires_grad) samples.append(SampleInput(t, args=(dim, keepdim))) return tuple(samples) def sample_inputs_like_fns(self, device, dtype, requires_grad, **kwargs): inputs = [ ((), {}), ((S, S), {}), ((0, S, 0), {}), ((S,), {'dtype': dtype, 'device': device}), # Hard-code some dtypes/devices. We want to test cases where the # (dtype, device) is different from the input's (dtype, device) ((S,), {'dtype': torch.double}), ((S,), {'device': 'cpu'}), ((S,), {'dtype': torch.double, 'device': 'cpu'}), ] if torch.cuda.is_available(): inputs.append(((S,), {'device': 'cuda'})) samples = [] for shape, kwargs in inputs: t = make_tensor(shape, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad) samples.append(SampleInput(t, kwargs=kwargs)) return tuple(samples) def reference_inputs_like_fns(op, device, dtype, requires_grad, **kwargs): yield from sample_inputs_like_fns(op, device, dtype, requires_grad, **kwargs) # shape cases = ( (), (0,), (1, 0), (1, 1, 4, 5), (5, 3, 0, 1), (1, 4, 3, 1, 1) ) make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) for shape in cases: yield SampleInput(make_arg(shape)) yield SampleInput(make_arg(shape).transpose(0, -1)) yield SampleInput(make_arg(shape, noncontiguous=True)) yield SampleInput(make_arg(shape, noncontiguous=True).transpose(0, -1)) # TODO: add reduction kwargs def sample_inputs_multilabel_margin_loss(op_info, device, dtype, requires_grad, **kwargs): _make_tensor = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) make_target = partial(_make_tensor, dtype=torch.long, requires_grad=False) inputs = ( ([], make_target([], low=0, high=1)), ([S], make_target([S], low=0, high=S)), ([M, S], make_target([M, S], low=0, high=S)), ) for shape, target in inputs: yield SampleInput(_make_tensor(shape), args=(target,)) def get_independent_tensor(tensor): return tensor.clone().requires_grad_(tensor.requires_grad) def sample_inputs_randint_like(self, device, dtype, requires_grad, **kwargs): samples = [] low = 2 high = 10 for sample in sample_inputs_like_fns(self, device, dtype, requires_grad, **kwargs): # With high samples.append(SampleInput( sample.input, args=(high,) + sample.args, kwargs=sample.kwargs)) # With low and high samples.append(SampleInput( get_independent_tensor(sample.input), args=(low, high,) + sample.args, kwargs=sample.kwargs)) return tuple(samples) # TODO: add reduction kwargs def sample_inputs_margin_ranking_loss(op_info, device, dtype, requires_grad, **kwargs): _make_tensor = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) shapes = ( (), (S,), (S, S), (S, S, S), ) for shape in shapes: for kwargs in [{}, {'margin': 1.0}]: yield SampleInput(_make_tensor(shape), args=(_make_tensor(shape, requires_grad=False), _make_tensor(shape, requires_grad=False)), kwargs=kwargs) def sample_inputs_new_fns(self, device, dtype, requires_grad, **kwargs): inputs = [ ((), (), {}), ((S, S), (2, 0), {}), ((0, S, 0), (3, 2, 2), {}), ((S,), (2, 3), {'dtype': dtype, 'device': device}), # Hard-code some dtypes/devices. We want to test cases where the # (dtype, device) is different from the input's (dtype, device) ((S,), (10,), {'dtype': torch.double}), ((S,), (1, 1, 12), {'device': 'cpu'}), ((S,), (2, 2, 2), {'dtype': torch.double, 'device': 'cpu'}), ] if torch.cuda.is_available(): inputs.append(((S,), (7, 2), {'device': 'cuda'})) samples = [] for input_shape, output_shape, kwargs in inputs: t = make_tensor(input_shape, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad) samples.append(SampleInput(t, args=(output_shape,), kwargs=kwargs)) return tuple(samples) def sample_inputs_empty(op, device, dtype, requires_grad, **kwargs): # shape cases = ( (), (0,), (1,), (1, 3, 5), (5, 3, 1), (1, 0, 5, 1), ) for case in cases: _kwargs = {'device': device, 'dtype': dtype, 'requires_grad': requires_grad} yield SampleInput(case, args=(), kwargs=_kwargs) def sample_inputs_new_full(self, device, dtype, requires_grad, **kwargs): def get_val(dtype): return make_tensor([], dtype=dtype, device="cpu").item() samples = [] for sample in sample_inputs_new_fns(self, device, dtype, requires_grad, **kwargs): # The scalar we are passing to new_full must be the same dtype # as the one of the resulting tensor use_dtype = sample.kwargs['dtype'] if 'dtype' in sample.kwargs else dtype samples.append(SampleInput( sample.input, args=sample.args + (get_val(use_dtype),), kwargs=sample.kwargs)) return tuple(samples) def sample_inputs_full_like(self, device, dtype, requires_grad, **kwargs): def get_val(dtype): return make_tensor([], dtype=dtype, device="cpu").item() inputs = [ ((), get_val(dtype), {}), ((S, S), get_val(dtype), {}), ((0, S, 0), get_val(dtype), {}), ((S,), get_val(dtype), {'dtype': dtype, 'device': device}), # Hard-code some dtypes/devices. We want to test cases where the # (dtype, device) is different from the input's (dtype, device) ((S,), get_val(torch.double), {'dtype': torch.double}), ((S,), get_val(dtype), {'device': 'cpu'}), ((S,), get_val(torch.double), {'dtype': torch.double, 'device': 'cpu'}), ] if torch.cuda.is_available(): inputs.append(((S,), get_val(dtype), {'device': 'cuda'})) samples = [] for shape, fill_value, kwargs in inputs: t = make_tensor(shape, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad) samples.append(SampleInput(t, args=(fill_value,), kwargs=kwargs)) return tuple(samples) def sample_inputs_multinomial(self, device, dtype, requires_grad, **kwargs): cases = [ ([3], 3, dict()), ([10], 3, dict()), ([3, 10], 3, dict()), ([3], 3, dict(replacement=False)), ([3], 3, dict(replacement=True)), ([3, 4], 4, dict(replacement=True)), ([3, 4], 4, dict(replacement=False)), ] samples = [] for shape, num_samples, kwargs in cases: t = make_tensor(shape, dtype=dtype, device=device, low=0, high=None, requires_grad=requires_grad) samples.append(SampleInput(t, args=(num_samples,), kwargs=kwargs)) return tuple(samples) def sample_inputs_normal_common(self, device, dtype, requires_grad, cases, **kwargs): def get_value_or_make_tensor(value_or_shape): if isinstance(value_or_shape, list): return make_tensor(value_or_shape, dtype=dtype, device=device, low=0, high=None, requires_grad=requires_grad) return value_or_shape samples = [] for value_or_mean_shape, value_or_std_shape, kwargs in cases: mean = get_value_or_make_tensor(value_or_mean_shape) std = get_value_or_make_tensor(value_or_std_shape) samples.append(SampleInput(mean, args=(std,), kwargs=kwargs)) return tuple(samples) def sample_inputs_normal_tensor_first(self, device, dtype, requires_grad, **kwargs): # value_or_size, value_or_size, kwargs cases = [ ([], [], {}), ([3], [3], {}), ([3, 4, 2], [3, 4, 2], {}), ([2, 3], 1.1, {}), ([1, 2, 3], [5, 2, 3], {}), # broadcasting ] return sample_inputs_normal_common(self, device, dtype, requires_grad, cases, **kwargs) def sample_inputs_normal_tensor_second(self, device, dtype, requires_grad, **kwargs): cases = [ ([3, 4], 0.3, {}), ] return sample_inputs_normal_common(self, device, dtype, requires_grad, cases, **kwargs) def sample_inputs_bernoulli(self, device, dtype, requires_grad, **kwargs): shapes = [ [3], [], [0, 3], [2, 3, 4], ] samples = [] for shape in shapes: t = make_tensor(shape, dtype=dtype, device=device, low=0, high=1, requires_grad=requires_grad) samples.append(SampleInput(t)) return tuple(samples) def sample_inputs_logcumsumexp(self, device, dtype, requires_grad, **kwargs): inputs = ( ((S, S, S), 0), ((S, S, S), 1), ((), 0), ) samples = [] for large_number in (True, False): for shape, dim in inputs: t = make_tensor(shape, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad) if large_number and t.dim() > 0: t[0] = 10000 samples.append(SampleInput(t, args=(dim,))) return tuple(samples) def sample_inputs_trace(self, device, dtype, requires_grad, **kwargs): return (SampleInput((make_tensor((S, S), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad))),) def sample_inputs_renorm(self, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) cases = (((S, S, S), (2, 1, 0.5)), ((S, S, S), (2, -1, 0.5)), ((S, S, S), (1, 2, 3)), ((S, S, S), (float('inf'), 2, 0.5)), ) for shape, args in cases: yield SampleInput(make_arg(shape), args=args) def sample_inputs_transpose_swapdims(self, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) cases = (((1, 2, 3), (-1, -2)), ((1, 2, 3), (-1, 2)), ((1, 2, 3), (1, -2)), ((1, 2, 3), (1, 2)), ((), (0, 0)), ((1, ), (0, 0)), ((M, M), (0, 1)), ((S, S, S), (2, 0)), ) for shape, args in cases: yield SampleInput(make_arg(shape), args=args) def _numpy_ref_transpose(a, dim0, dim1): if a.ndim <= 1: return a return np.swapaxes(a, dim0, dim1) def sample_inputs_adjoint(self, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) shapes = ((1, 2, 3), (), (M, M), (S, S, S), (S, M, S), (M, S, M, S)) return (SampleInput(make_arg(shape)) for shape in shapes) def sample_inputs_T(self, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) shapes = ((), (M, M)) return (SampleInput(make_arg(shape)) for shape in shapes) def sample_inputs_linalg_invertible(op_info, device, dtype, requires_grad=False, **kwargs): """ This function generates invertible inputs for linear algebra ops The input is generated as the itertools.product of 'batches' and 'ns'. In total this function generates 8 SampleInputs 'batches' cases include: () - single input, (0,) - zero batched dimension, (2,) - batch of two matrices, (1, 1) - 1x1 batch of matrices 'ns' gives 0x0 and 5x5 matrices. Zeros in dimensions are edge cases in the implementation and important to test for in order to avoid unexpected crashes. """ make_fn = make_fullrank_matrices_with_distinct_singular_values make_arg = partial(make_fn, dtype=dtype, device=device, requires_grad=requires_grad) batches = [(), (0, ), (2, ), (1, 1)] ns = [5, 0] for batch, n in product(batches, ns): yield SampleInput(make_arg(*batch, n, n)) def sample_inputs_linalg_pinv_singular(op_info, device, dtype, requires_grad=False, **kwargs): """ This function produces factors `a` and `b` to generate inputs of the form `a @ b.t()` to test the backward method of `linalg_pinv`. That way we always preserve the rank of the input no matter the perturbations applied to it by the gradcheck. Note that `pinv` is Frechet-differentiable in a rank-preserving neighborhood. """ batches = [(), (0, ), (2, ), (1, 1)] # the size of at least 30 is required to cause failures for the previous implicit implementation # of the pinv's backward method, albeit it is slow. size = [0, 3, 50] for batch, m, n in product(batches, size, size): for k in range(min(3, min(m, n))): # Note that by making the columns of `a` and `b` orthonormal we make sure that # the product matrix `a @ b.t()` has condition number 1 when restricted to its image a = torch.rand(*batch, m, k, device=device, dtype=dtype).qr().Q.requires_grad_(requires_grad) b = torch.rand(*batch, n, k, device=device, dtype=dtype).qr().Q.requires_grad_(requires_grad) yield SampleInput(a, args=(b,)) def sample_inputs_singular_matrix_factors(op_info, device, dtype, requires_grad=False, **kwargs): """ This function produces two tensors of shape (*, m, k) and (*, n, k) with k <= min(m, n). Their matrix product could be used to generate tensor of shape (*, m, n) of rank k. """ batches = [(), (0, ), (2, ), (1, 1)] size = [1, 5, 10] for batch, m, n in product(batches, size, size): for k in range(min(3, min(m, n))): a = make_tensor((*batch, m, k), dtype=dtype, device=device, requires_grad=requires_grad) b = make_tensor((*batch, n, k), dtype=dtype, device=device, requires_grad=requires_grad) yield SampleInput(a, args=(b,), kwargs=kwargs) def clone_sample(sample, **kwargs): """ Given a SampleInput, this function analyzes its input, args and kwargs, and produces a copy with each non-Tensor entry being copied by reference, and with each Tensor entry cloned with `t.clone().requires_grad_(t.requires_grad)` """ def clone_tensor(t): if isinstance(t, torch.Tensor): return t.detach().clone().requires_grad_(t.requires_grad) else: return t sample_kwargs = kwargs if kwargs else sample.kwargs return SampleInput( clone_tensor(sample.input), args=tuple(map(clone_tensor, sample.args)), kwargs=dict(((k, clone_tensor(v)) for k, v in sample_kwargs.items())) ) def sample_inputs_svd_lowrank(op_info, device, dtype, requires_grad=False, **kwargs): for sample in sample_inputs_singular_matrix_factors(op_info, device, dtype, requires_grad, **kwargs): *batch, m, k = sample.input.shape *_, n, _ = sample.args[0].shape # NOTE: since svd_lowrank relies on non rank-revealing SVD, # it inherits the problem of unstable behavior with repeated # singular values including zeros. # Since we want to avoid (repeated) zeros as singular values, # we can only use k for q. # This issues could be resolved with using a rank-revealing SVD # which does not include "zero" singular values. op_kwargs = { 'q': k, 'M': None } # without M specified yield clone_sample(sample, **op_kwargs) # now with M # TODO: fix bug in the documentation for svd_lowrank: # M has to be (*, m, n), and not (*, 1, n) as written # in the documentation op_kwargs['M'] = make_tensor((*batch, m, n), dtype=dtype, device=device, requires_grad=requires_grad) yield clone_sample(sample, **op_kwargs) def chunk_iter(iterable, size): it = iter(iterable) while True: chunk = tuple(islice(it, size)) if not chunk: break yield chunk def sample_inputs_pca_lowrank(op_info, device, dtype, requires_grad=False, **kwargs): # we reuse samples from svd_lowrank which come in group of two with # kwarg['M'] = None and with kwarg['M'] = samples = sample_inputs_svd_lowrank(op_info, device, dtype, requires_grad, **kwargs) for s1, s2 in chunk_iter(samples, 2): del s1.kwargs['M'] del s2.kwargs['M'] s1.kwargs['center'] = False s2.kwargs['center'] = True yield s1 yield s2 def sample_inputs_linalg_cond(op_info, device, dtype, requires_grad=False, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) # autograd is not supported for inputs with zero number of elements shapes = ((S, S), (2, S, S), (2, 1, S, S), ) for shape in shapes: yield SampleInput(make_arg(shape)) def sample_inputs_linalg_vander(op_info, device, dtype, requires_grad=False, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) shapes = ((), (1,), (S,), (2, S),) for shape in shapes: if len(shape) > 0 and shape[-1] > 1: yield SampleInput(make_arg(shape)) n = shape[-1] if len(shape) > 0 else 1 for i in range(3): # n-1, n, n+1 N = n + i - 1 if N < 2: continue yield SampleInput(make_arg(shape), kwargs=dict(N=N)) def np_vander_batched(x, N=None): # Wrapper around np.vander that supports batches of 1 dimension (enough for the tests) if x.ndim == 0: x = x[np.newaxis] if x.ndim == 1: y = np.vander(x, N=N, increasing=True) return y else: if N is None: N = x.shape[-1] y = np.vander(x.ravel(), N=N, increasing=True).reshape((*x.shape, N)) return y def np_sinc_with_fp16_as_fp32(x): # Wraps numpy's sinc function so that fp16 values are promoted to fp32 # before sinc is invoked. Context: numpy's sinc returns NaN when evaluated # at 0 for fp16. if x.dtype == np.float16: return np.sinc(x.astype(np.float32)) else: return np.sinc(x) def sample_inputs_broadcast_to(op_info, device, dtype, requires_grad, **kwargs): test_cases = ( ((S, 1, 1), (S, S, S)), ((S, 1, S), (S, S, S)), ((S, 1), (S, S, S)), ((1,), (S, S, S)), ((1, S), (1, 1, S)), ((), ()), ((), (1, 3, 2)), ) return tuple( SampleInput( make_tensor(size, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=(shape,)) for size, shape in test_cases) def sample_inputs_broadcast_tensors(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) test_cases: Tuple[tuple] = (((3,), (1, 2, 1), (1, 1), (5, 1, 1),),) samples: List[SampleInput] = [] for shape, *other_shapes in test_cases: samples.append(SampleInput(make_arg(shape), args=tuple(make_arg(s) for s in other_shapes))) return samples def sample_inputs_block_diag(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) test_cases: Tuple[tuple] = (((1, S), (2, S), (3, S),),) samples: List[SampleInput] = [] for shape, *other_shapes in test_cases: samples.append(SampleInput(make_arg(shape), args=tuple(make_arg(s) for s in other_shapes))) return samples def sample_inputs_cdist(op_info, device, dtype, requires_grad, **kwargs): small_S = 2 test_cases = ( ((S, S, 2), (S, S + 1, 2)), ((S, S), (S, S)), ((S, S, S), (S, S, S)), ((3, 5), (3, 5)), ((2, 3, 5), (2, 3, 5)), ((1, 2, 3), (1, 2, 3)), ((1, 1), (S, 1)), ((0, 5), (4, 5)), ((4, 5), (0, 5)), ((0, 4, 5), (3, 5)), ((4, 5), (0, 3, 5)), ((0, 4, 5), (1, 3, 5)), ((1, 4, 5), (0, 3, 5)), # Using S here would make this one test take 9s ((small_S, small_S, small_S + 1, 2), (small_S, small_S, small_S + 2, 2)), ((small_S, 1, 1, small_S), (1, small_S, small_S)), ((1, 1, small_S), (small_S, 1, small_S, small_S)), ) samples = [] for cm in ['use_mm_for_euclid_dist', 'donot_use_mm_for_euclid_dist']: # FIXME add an override for JIT and revert 0. back to 0 # since it's accepted by eager for p in [0., 1., 2., 3., 0.5, 1.5, 2.5, float("inf")]: for t1_size, t2_size in test_cases: # The args should never be non-contiguous as this is not supported in the backward samples.append(SampleInput( make_tensor(t1_size, dtype=dtype, device=device, requires_grad=requires_grad), args=(make_tensor(t2_size, dtype=dtype, device=device, requires_grad=requires_grad), p, cm))) return samples def sample_inputs_fill_(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, low=None, high=None, requires_grad=requires_grad) cases = (((S, S, S), (1,)), ((), (1,)), ((S, S, S), (make_arg(()),))) for shape, args in cases: yield SampleInput(make_arg(shape), args=args) def sample_inputs_comparison_ops(op, device, dtype, requires_grad, **kwargs): yield from sample_inputs_elementwise_binary(op, device, dtype, requires_grad, **kwargs) # Adds a sample input where both tensors have the same values make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) lhs = make_arg((S, S)) yield SampleInput(lhs, args=(lhs.clone(),)) def sample_inputs_stack(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # shape x number of tensors cases = ( ((3, 4), 1), ((1, 2, 1, 4), 3), ((0, 1, 0), 2),) for shape, num_tensors in cases: tensors = [] for _ in range(num_tensors): tensors.append(make_arg(shape)) for dim in range(-1, len(shape) - 1): yield SampleInput(tensors, args=(dim,)) def sample_inputs_cat_concat(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) cases: Tuple[tuple, tuple, dict] = ( # type: ignore[assignment] ((S, S), (S, S), {'dim': -1}), ((S, S), (S, S), {'dim': 1}), ((M, S), (S, S), {'dim': 0}), # different shapes ((1, 2, 3), (1, 2, 3), {'dim': -2}), ((0,), (0,), {'dim': 0}), # empty tensor ((0, S), (S, S), {'dim': 0}), ((1,), (1,), {}) # dim not passed, fallback to default ) for input_shape1, input_shape2, kwargs in cases: yield SampleInput([make_arg(input_shape1), make_arg(input_shape2)], kwargs=kwargs) def reference_inputs_cat(op, device, dtype, requires_grad, **kwargs): yield from sample_inputs_cat_concat(op, device, dtype, requires_grad, **kwargs) make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # Noncontiguous type promoting tensors a = make_arg((3, 4, 2)) b = make_arg((3, 2, 2), noncontiguous=True, dtype=torch.double) c = make_arg((3, 3, 2), dtype=torch.float16).permute(1, 0, 2) yield SampleInput((a, b, c), kwargs={'dim': 1}) def sample_inputs_hstack_dstack_vstack(op_info, device, dtype, requires_grad, **kwargs): tensors = [ make_tensor((S, S), dtype=dtype, device=device, requires_grad=requires_grad), make_tensor((S, S), dtype=dtype, device=device, requires_grad=requires_grad), make_tensor((S, S), dtype=dtype, device=device, requires_grad=requires_grad), ] return (SampleInput(tensors),) def sample_inputs_gather(op_info, device, dtype, requires_grad, **kwargs): return ( SampleInput( make_tensor((M, S), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=(0, gather_variable((S, S), 1, M, True, device=device))), SampleInput( make_tensor((M, S), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=(1, gather_variable((M, S // 2), 0, S, True, device=device))), SampleInput( make_tensor((), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=(0, torch.tensor([0], dtype=torch.int64, device=device))), # Empty index tensor case, see: https://github.com/pytorch/pytorch/pull/65006 SampleInput( make_tensor((S,), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=(0, torch.tensor([], dtype=torch.uint8, device=device))), SampleInput( make_tensor((), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=(0, torch.tensor(0, dtype=torch.int64, device=device))), ) def _fill_indices(idx, dim, dim_size, elems_per_row, m, n, o): for i in range(1 if dim == 0 else m): for j in range(1 if dim == 1 else n): for k in range(1 if dim == 2 else o): ii = [i, j, k] ii[dim] = slice(0, idx.size(dim) + 1) idx[tuple(ii)] = torch.randperm(dim_size)[0:elems_per_row] def error_inputs_gather(op_info, device, **kwargs): # src is [1, 2] # [3, 4] src = torch.tensor(((1, 2), (3, 4)), device=device, dtype=torch.float32) # idx is [0, 0] # [1, 0] idx = torch.tensor(((0, 0), (1, 0)), device=device, dtype=torch.long) # Index should be smaller than self except on dimesion 1 bad_src = make_tensor((1, 1), device=device, dtype=torch.float32) yield ErrorInput(SampleInput(bad_src, args=(1, idx,)), error_regex="Size does not match at dimension 0") # Index must have long dtype bad_idx = idx.to(torch.int32) yield ErrorInput(SampleInput(src, args=(1, bad_idx)), error_regex="Expected dtype int64 for index") # TODO: FIXME # out.dtype must match src.dtype # Creates new src & idx since SampleInputs can't share tensors src = torch.tensor(((1, 2), (3, 4)), device=device, dtype=torch.float32) idx = torch.tensor(((0, 0), (1, 0)), device=device, dtype=torch.long) out = torch.empty((2, 2), device=device, dtype=torch.float64) yield ErrorInput(SampleInput(src, args=(1, idx), kwargs={'out': out}), error_regex="Expected out tensor to have dtype") # src and index tensors must have the same # of dimensions # idx too few dimensions src = torch.tensor(((1, 2), (3, 4)), device=device, dtype=torch.float32) idx = torch.tensor((0, 0), device=device, dtype=torch.long) yield ErrorInput(SampleInput(src, args=(1, idx)), error_regex="Index tensor must have the same number of dimensions") # src too few dimensions src = torch.tensor((1, 2), device=device, dtype=torch.float32) idx = torch.tensor(((0, 0), (1, 0)), device=device, dtype=torch.long) yield ErrorInput(SampleInput(src, args=(0, idx)), error_regex="Index tensor must have the same number of dimensions") # index out of bounds # NOTE: this ErrorInput is guarded because bounds checking does not occur on CUDA devices if torch.device(device).type == 'cpu': src = torch.tensor(((1, 2), (3, 4)), device=device, dtype=torch.float32) idx = torch.tensor(((0, 23), (1, 0)), device=device, dtype=torch.long) yield ErrorInput(SampleInput(src, args=(1, idx,)), error_regex="index 23 is out of bounds for dimension") x = torch.rand((1,), device=device).expand((3,)) src = torch.rand((6,), device=device) ind = torch.tensor([2, 1, 0], device=device, dtype=torch.int64) yield ErrorInput(SampleInput(src, args=(0, ind,), kwargs=dict(out=x)), error_type=RuntimeError, error_regex='unsupported operation') yield ErrorInput(SampleInput(src, args=(0, ind,), kwargs=dict(out=src)), error_type=RuntimeError, error_regex='unsupported operation') yield ErrorInput(SampleInput(ind.clone(), args=(0, ind[1:],), kwargs=dict(out=ind[:1])), error_type=RuntimeError, error_regex='unsupported operation') def error_inputs_take(op_info, device, **kwargs): x = torch.rand((1,), device=device).expand((3,)) src = torch.rand((6,), device=device) ind = torch.tensor([2, 1, 0], device=device, dtype=torch.int64) yield ErrorInput(SampleInput(src, args=(ind,), kwargs=dict(out=x)), error_type=RuntimeError, error_regex='unsupported operation') yield ErrorInput(SampleInput(src, args=(ind,), kwargs=dict(out=src)), error_type=RuntimeError, error_regex='unsupported operation') yield ErrorInput(SampleInput(ind.clone(), args=(ind[1:],), kwargs=dict(out=ind[:-1])), error_type=RuntimeError, error_regex='unsupported operation') # Error inputs for scatter def error_inputs_scatter_and_scatter_add(op_info, device, **kwargs): # Error when self.dtype != src.dtype (and src is not a scalar) src = make_tensor((2, 5), device=device, dtype=torch.float32) idx = torch.tensor(((0, 1), (1, 2)), device=device, dtype=torch.long) dst = torch.zeros((3, 5), device=device, dtype=torch.double) yield ErrorInput(SampleInput(dst, args=(0, idx, src)), error_regex="Expected self.dtype to be equal to src.dtype") # Index dtype must be long src = make_tensor((2, 5), device=device, dtype=torch.float32) idx = torch.tensor(((0, 1), (1, 2)), device=device, dtype=torch.int32) dst = torch.zeros((3, 5), device=device, dtype=torch.float32) yield ErrorInput(SampleInput(dst, args=(0, idx, src)), error_regex="Expected dtype int64 for index") # Index and destination must have the same number of dimensions src = make_tensor((2, 5), device=device, dtype=torch.float32) idx = torch.tensor(((0, 1), (1, 2)), device=device, dtype=torch.long) dst = torch.zeros((3, 5, 3), device=device, dtype=torch.float32) yield ErrorInput(SampleInput(dst, args=(0, idx, src)), error_regex="Index tensor must have the same number of dimensions as self tensor") # Index and src must have the same number of dimensions when src is not a scalar src = make_tensor((2, 5, 2), device=device, dtype=torch.float32) idx = torch.tensor(((34, 1), (1, 2)), device=device, dtype=torch.long) dst = torch.zeros((3, 5), device=device, dtype=torch.float32) yield ErrorInput(SampleInput(dst, args=(0, idx, src)), error_regex="Index tensor must have the same number of dimensions as src tensor") # Index out of bounds # NOTE: this ErrorInput is guarded because bounds checking does not occur on CUDA devices if torch.device(device).type == 'cpu': src = make_tensor((2, 5), device=device, dtype=torch.float32) idx = torch.tensor(((34, 1), (1, 2)), device=device, dtype=torch.long) dst = torch.zeros((3, 5), device=device, dtype=torch.float32) yield ErrorInput(SampleInput(dst, args=(0, idx, src)), error_regex="index 34 is out of bounds for dimension 0 with size 3") def error_inputs_renorm(op_info, device, **kwargs): zero_d = torch.randn((), device=device) yield ErrorInput(SampleInput(zero_d, args=(0.5, 0, 1.0)), error_type=RuntimeError, error_regex="needs at least 2 dimensions, got 0 dimensions") def error_inputs_lstsq(op_info, device, **kwargs): zero_d = torch.randn((), device=device) yield ErrorInput(SampleInput(zero_d, args=(zero_d)), error_type=TypeError, error_regex="iteration over a 0-d tensor") def error_inputs_eig(op_info, device, **kwargs): zero_d = torch.randn((), device=device) yield ErrorInput(SampleInput(zero_d, args=(False,)), error_type=RuntimeError, error_regex="input should be 2 dimensional") yield ErrorInput(SampleInput(zero_d, args=(True,)), error_type=RuntimeError, error_regex="input should be 2 dimensional") def error_inputs_ormqr(op_info, device, **kwargs): # this is only implemented on cpu if (torch.device(device).type == 'cpu'): zero_d = torch.randn((), device=device) yield ErrorInput(SampleInput(zero_d, args=(zero_d, zero_d)), error_type=RuntimeError, error_regex="input must have at least 2 dimensions") def error_inputs_diag(op_info, device, **kwargs): zero_d = torch.randn((), device=device) yield ErrorInput(SampleInput(zero_d, args=(zero_d)), error_type=TypeError, error_regex="iteration over a 0-d tensor") def error_inputs_embedding(op_info, device, **kwargs): indices = torch.rand(2, 2, device=device).long() weights = [ torch.tensor(1.0, device=device), torch.tensor(1.0, device=device).reshape(1, 1, 1), ] for weight in weights: yield ErrorInput(SampleInput(weight, args=(indices,)), error_type=RuntimeError, error_regex="'weight' must be 2-D") def error_inputs_multinomial(op_info, device, **kwargs): x = torch.empty(1, 2, 3, dtype=torch.double, device=device) yield ErrorInput(SampleInput(x, args=(2,)), error_type=RuntimeError, error_regex="prob_dist must be 1 or 2 dim") x = torch.empty(1, 2, dtype=torch.long, device=device) yield ErrorInput(SampleInput(x, args=(2,)), error_type=RuntimeError, error_regex="multinomial only supports floating-point dtypes for input") x = torch.empty(1, 2, dtype=torch.double, device=device) y = torch.empty(1, 2, dtype=torch.double, device=device) yield ErrorInput(SampleInput(x, args=(2,), kwargs=dict(out=y)), error_type=RuntimeError, error_regex="multinomial expects Long tensor out") x = torch.empty(2, dtype=torch.double, device=device) yield ErrorInput(SampleInput(x, args=(0,)), error_type=RuntimeError, error_regex="cannot sample n_sample <= 0 samples") x = torch.empty(2, dtype=torch.double, device=device) yield ErrorInput(SampleInput(x, args=(-1,)), error_type=RuntimeError, error_regex="cannot sample n_sample <= 0 samples") x = torch.empty(2, dtype=torch.double, device=device) yield ErrorInput(SampleInput(x, args=(3, False,)), error_type=RuntimeError, error_regex="cannot sample n_sample > prob_dist") x = torch.empty(16777217, dtype=torch.double, device=device) yield ErrorInput(SampleInput(x, args=(3,)), error_type=RuntimeError, error_regex="number of categories cannot exceed") def error_inputs_gradient(op_info, device, **kwargs): for dtype in [torch.long, torch.float32, torch.complex64]: t = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], device=device, dtype=dtype) dim = (1, 0) spacing = [0.1] yield ErrorInput(SampleInput(t, kwargs=dict(spacing=spacing, dim=dim, edge_order=1)), error_type=RuntimeError, error_regex='torch.gradient expected spacing to be unspecified, a scalar ') yield ErrorInput(SampleInput(t, kwargs=dict(edge_order=3)), error_type=RuntimeError, error_regex='torch.gradient only supports edge_order=1 and edge_order=2.') dim = (1, 1) spacing = 0.1 yield ErrorInput(SampleInput(t, kwargs=dict(spacing=spacing, dim=dim, edge_order=1)), error_type=RuntimeError, error_regex='dim 1 appears multiple times in the list of dims') dim = (0, 1) coordinates = [torch.tensor([1, 2, 4], device='cpu'), torch.tensor([1, 2, 4], device='meta')] yield ErrorInput(SampleInput(t, kwargs=dict(spacing=coordinates, dim=dim, edge_order=1)), error_type=RuntimeError, error_regex='torch.gradient expected each tensor to be on the same device,') yield ErrorInput(SampleInput(t, kwargs=dict(dim=3)), error_type=IndexError, error_regex='') t = torch.tensor([[1], [2], [3]]) yield ErrorInput(SampleInput(t, kwargs=dict(edge_order=1)), error_type=RuntimeError, error_regex='torch.gradient expected each dimension size to be at least') t = torch.tensor([[1, 2], [3, 4]]) yield ErrorInput(SampleInput(t, kwargs=dict(edge_order=2)), error_type=RuntimeError, error_regex='torch.gradient expected each dimension size to be at least') def error_inputs_masked_select(op_info, device, **kwargs): x = torch.rand((1,), device=device).expand((3,)) y = torch.rand((6,), device=device) mask = torch.tensor([True, False, True, True, False, False], device=device) yield ErrorInput(SampleInput(y, args=(mask,), kwargs=dict(out=x)), error_type=RuntimeError, error_regex='unsupported operation') yield ErrorInput(SampleInput(y, args=(mask,), kwargs=dict(out=y)), error_type=RuntimeError, error_regex='unsupported operation') yield ErrorInput(SampleInput(mask.clone(), args=(mask,), kwargs=dict(out=mask)), error_type=RuntimeError, error_regex='unsupported operation') def error_inputs_index_select(op_info, device, **kwargs): x = torch.rand((1, 6), device=device).expand((2, 6)) y = torch.rand((3, 6), device=device) ind = torch.tensor([0, 1], dtype=torch.int64, device=device) yield ErrorInput(SampleInput(y, args=(1, ind,), kwargs=dict(out=x)), error_type=RuntimeError, error_regex='unsupported operation') def sample_inputs_take_along_dim(op_info, device, dtype, requires_grad, **kwargs): return (SampleInput(make_tensor((S, S), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=(gather_variable((S, S), 1, S, True, device=device), 0)), # `indices` broadcast SampleInput(make_tensor((S, S), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=(gather_variable((1, S // 2), 0, S, True, device=device), 1)), # `self` broadcast SampleInput(make_tensor((1, S), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=(gather_variable((S, S // 2), 0, S, True, device=device), 1)), # without `dim` arg SampleInput(make_tensor((S, S), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=(gather_variable((S, S // 2), 0, S, True, device=device), )), SampleInput(make_tensor((S, S), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=(gather_variable((S, S // 2), 0, S, True, device=device),)), ) def error_inputs_aminmax_amax_amin(op_info, device, **kwargs): # Error Inputs for zero-dim tensors, when 'dim' arg is not provided. shape = (S, 0, S) err_msg_amax_amin = "reduction" err_msg_aminmax = "cannot compute aminmax over an empty dimension as the operation has no identity" if op_info.name in ['amax', 'amin', '_refs.amax', '_refs.amin']: yield ErrorInput(SampleInput(torch.rand(shape, device=device)), error_regex=err_msg_amax_amin) elif op_info.name in ['aminmax']: yield ErrorInput(SampleInput(torch.rand(shape, device=device)), error_regex=err_msg_aminmax) # Error Inputs for tensors with more than 64 dimension sizes = [1] * 65 err_msg1 = "only tensors with up to 64 dims are supported" yield ErrorInput(SampleInput(torch.randn(sizes, device=device), kwargs={'dim': -1}), error_regex=err_msg1) yield ErrorInput(SampleInput(torch.randn(sizes, device=device), kwargs={'dim': 64}), error_regex=err_msg1) # Error Inputs for repeated 'dim' if op_info.name in ['amax', 'amin', '_refs.amax', '_refs.amin']: dims = [(0, 0), (0, -4)] err_msg2 = "in the list of dims" x = torch.randn(S, S, S, S, device=device) for dim in dims: yield ErrorInput(SampleInput(x, kwargs={'dim': dim}), error_regex=err_msg2) # Error Input for illegal dtype input5 = torch.randn(L, L, dtype=torch.float32, device=device) max_values = torch.empty(L, dtype=torch.float32, device=device) min_values = torch.empty(L, dtype=torch.double, device=device) illegal_values = torch.empty(L, dtype=torch.int, device=device) err_msg_amax_amin2 = "Expected the dtype for input and out to match" err_msg_aminmax2 = "Expected out tensor to have dtype float, but got double instead" if op_info.name in ['amax', 'amin', '_refs.amax', '_refs.amin']: yield ErrorInput(SampleInput(input5, kwargs={'dim': 0, 'out': illegal_values}), error_regex=err_msg_amax_amin2) elif op_info.name in ['aminmax']: yield ErrorInput(SampleInput(input5, kwargs={'dim': 0, 'out': (max_values, min_values)}), error_regex=err_msg_aminmax2) # Error Inputs for functions to raise an error on specified zero'd dimension as reduction dim err_msg3 = "reduction" # FIXME: eager and ref impl throw different types of errors error_type = IndexError if 'refs' not in op_info.name else RuntimeError yield ErrorInput(SampleInput(torch.rand(shape, device=device), kwargs={'dim': 1}), error_type=error_type, error_regex=err_msg3) def sample_inputs_aminmax(op_info, device, dtype, requires_grad, **kwargs): test_cases: Tuple[tuple, dict] = ( # type: ignore[assignment] ((S, S, S), {}), ((S, S, S), {'dim': 1}), ((S, S, S), {'dim': 1, 'keepdim': True}), ((), {'dim': 0}), ((), {}), ((), {'dim': 0, 'keepdim': True}), ) samples: List[SampleInput] = [] for shape, kwargs in test_cases: samples.append(SampleInput( make_tensor(shape, dtype=dtype, device=device, requires_grad=requires_grad), kwargs=kwargs)) return samples def sample_inputs_diff(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) test_cases = ( ((1,), 0, None, None), ((S,), 0, None, None), ((S, 1), 0, None, None), ((S, 1), 1, None, None), ((S, S), 0, None, None), ((S, S), 1, None, None), ((S, S), 0, (1, S), (2, S)), ((S, S), 0, None, (2, S)), ((S, S, S), 1, None, None), ((S, S, S), 2, None, None), ((S, S, S), 1, (S, 1, S), (S, 1, S)), ((S, S, S), 2, (S, S, 1), (S, S, 1)), ((S, S, S), 2, (S, S, S), (S, S, S)),) sample_inputs = [] for size, dim, size_prepend, size_append in test_cases: prepend_size = 0 if (size_prepend is None) else size_prepend[dim] append_size = 0 if (size_append is None) else size_append[dim] dim_size = size[dim] + prepend_size + append_size for n in range(dim_size): input_tensor = make_arg(size) prepend = make_arg(size_prepend) if size_prepend else None append = make_arg(size_append) if size_append else None sample_inputs.append(SampleInput(input_tensor, args=(n, dim, prepend, append,))) # add some samples with n > dim_size sample_inputs.append(SampleInput(make_arg((S, S, S)), args=(S + 1, 1,))) sample_inputs.append(SampleInput(make_arg((S, S, S)), args=(S * 3 + 2, 2, make_arg((S, S, S)), make_arg((S, S, S)),))) return sample_inputs def sample_inputs_histogram(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) sizes = ((), (S,), (S, S), (S, S, S), (S, 1, S), (S, 0, S)) sample_inputs = [] for size, bin_ct, weighted, density in product(sizes, range(1, 5), [False, True], [False, True]): input_tensor = make_arg(size) weight_tensor = make_arg(size) if weighted else None sample_inputs.append(SampleInput(input_tensor, args=(bin_ct,), kwargs=dict(weight=weight_tensor, density=density))) bins_tensor = make_arg((bin_ct + 1,)) sample_inputs.append(SampleInput(input_tensor, args=(bins_tensor,), kwargs=dict(weight=weight_tensor, density=density))) return sample_inputs def sample_inputs_histogramdd(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) sizes = ((S, S), (S, S, S), (S, 1, S), (S, 0, S)) bin_ct_patterns = ((1, 1, 1, 1, 1), (2, 3, 2, 3, 2), (3, 2, 3, 2, 3)) sample_inputs = [] for size, bin_ct_pattern, weighted, density in product(sizes, bin_ct_patterns, [False, True], [False, True]): input_tensor = make_arg(size) bin_ct = bin_ct_pattern[:size[-1]] weight_tensor = make_arg(size[:-1]) if weighted else None sample_inputs.append(SampleInput(input_tensor, args=(bin_ct,), kwargs=dict(weight=weight_tensor, density=density))) bins_tensor = [make_arg(ct + 1) for ct in bin_ct] sample_inputs.append(SampleInput(input_tensor, args=(bins_tensor,), kwargs=dict(weight=weight_tensor, density=density))) return sample_inputs def sample_inputs_histc(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) sizes = ((), (S,), (S, S), (S, S, S), (S, 1, S), (S, 0, S)) sample_inputs = [] for size, min, max in product(sizes, [0, -10], [0, 10]): # construct sample input omitting bins arg sample_inputs.append(SampleInput(make_arg(size), kwargs=dict(min=min, max=max))) # construct sample inputs with a few different bins values for bins in [1, 3, 10]: sample_inputs.append(SampleInput(make_arg(size), kwargs=dict(bins=bins, min=min, max=max))) return sample_inputs def sample_inputs_bincount(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) sample_inputs = [] for size, weighted in product((S, M), [False, True]): input_tensor = torch.randint(0, size, (size,), dtype=dtype, device=device) weight_tensor = make_arg((size,)) if weighted else None max_val = int(input_tensor.max().item()) for minlength in [0, max_val // 2, max_val, 2 * max_val]: sample_inputs.append(SampleInput(input_tensor, kwargs=dict(weights=weight_tensor, minlength=minlength))) return sample_inputs def sample_inputs_bucketize(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) sizes = ((), (S,), (S, S), (S, S, S), (S, 1, S), (S, 0, S)) sample_inputs = [] for size, out_int32, right in product(sizes, [False, True], [False, True]): input_tensor = make_arg(size) boundaries = make_arg((S,)).msort() sample_inputs.append(SampleInput(input_tensor, args=(boundaries, ), kwargs=dict(out_int32=out_int32, right=right))) return sample_inputs def sample_inputs_searchsorted(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) sizes = ((0,), (M,), (0, 0), (M, M), (0, 0, 0), (M, M, M)) inputs = [] for size, noncontiguous, out_int32, right in product(sizes, [False, True], [False, True], [False, True]): unsorted_tensor = make_arg(size, noncontiguous=noncontiguous) input_tensor = make_arg(size, noncontiguous=noncontiguous) if np.product(size) == 0: boundary_tensor = unsorted_tensor sorter = make_tensor(size, dtype=torch.int64, device=device, noncontiguous=noncontiguous) else: boundary_tensor, sorter = torch.sort(unsorted_tensor) side = "right" if right else "left" inputs.append(SampleInput(boundary_tensor, args=(input_tensor,), kwargs=dict(out_int32=out_int32, right=right))) inputs.append(SampleInput(boundary_tensor, args=(input_tensor,), kwargs=dict(out_int32=out_int32, side=side))) inputs.append( SampleInput(unsorted_tensor, args=(input_tensor,), kwargs=dict(out_int32=out_int32, right=right, sorter=sorter))) inputs.append( SampleInput(unsorted_tensor, args=(input_tensor,), kwargs=dict(out_int32=out_int32, side=side, sorter=sorter))) return inputs def sample_inputs_gradient(op_info, device, dtype, requires_grad, **kwargs): sample_inputs = [] test_cases_float = ( ((S,), None, None, 1), ((S,), 2., None, 1), ((S, S), None, None, 2), ((S, S), [2.0, 2.1], None, 1), ((S, S), [2.0, 2.1], (0, 1), 1), ((4, 4, 4), [2., 1.], (0, 1), 2), ) for size, spacing, dim, edge_order in test_cases_float: t = make_tensor(size, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad) sample_inputs.append(SampleInput(t, kwargs=dict(dim=dim, spacing=spacing, edge_order=edge_order))) test_cases_tensor = ( ((3, 3, 3), ((1.1, 2.0, 3.5), (4.0, 2, 6.0)), (0, -1), 1), ((3, 3, 3), ((1.0, 3.0, 2.0), (8.0, 6.0, 1.0)), (0, 1), 2), ) for size, coordinates, dim, edge_order in test_cases_tensor: t = make_tensor(size, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad) coordinates_tensor_list = [] for coords in coordinates: # `coords` will always contain floating point values and Python 3.10 does not support this # implicit conversion to an integer using `__int__` # TODO: this can be simplified after https://github.com/pytorch/pytorch/issues/69316 is fixed a = torch.tensor(coords, device=device) coordinates_tensor_list.append(a.to(dtype)) sample_inputs.append(SampleInput(t, kwargs=dict(dim=dim, spacing=coordinates_tensor_list, edge_order=edge_order))) return tuple(sample_inputs) def sample_inputs_getitem(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) test_args = [ ([1, 2],), (slice(0, 3),), ([slice(0, 3), 1],), ([[0, 2, 3], [1, 3, 3], [0, 0, 2]],), ([[0, 0, 3], [1, 1, 3], [0, 0, 2]],), ([slice(None), slice(None), [0, 3]],), ([slice(None), [0, 3], slice(None)],), ([[0, 3], slice(None), slice(None)],), ([[0, 3], [1, 2], slice(None)],), ([[0, 3], ],), ([[0, 3], slice(None)],), ([[0, 3], Ellipsis],), ([[0, 2, 3], [1, 3, 3], torch.LongTensor([0, 0, 2])],), (index_variable(2, S, device=device),), (mask_not_all_zeros((S,)),), ] for args in test_args: yield SampleInput(make_arg((S, S, S)), args=args) def sample_inputs_index_put(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) inputs = [] for accumulate in [False, True]: # Test with indices arg inputs.append(SampleInput( make_arg((S, S,)), args=((index_variable(2, S, device=device),), make_arg((2, S))), kwargs=dict(accumulate=accumulate))) # Test with mask arg mask = torch.zeros(S, dtype=torch.bool) if accumulate else mask_not_all_zeros((S,)) inputs.append(SampleInput( make_arg((S, S)), args=((mask, ), make_arg((S,))), kwargs=dict(accumulate=accumulate))) return inputs def sample_inputs_sort(op_info, device, dtype, requires_grad, **kwargs): def small_3d_unique(): res = torch.randperm(S * S * S, dtype=torch.int64, device=device).view(S, S, S) res = res.to(dtype).requires_grad_(requires_grad) return res def large_1d_unique(): res = torch.randperm(L * L * L, dtype=torch.int64, device=device) res = res.to(dtype).requires_grad_(requires_grad) return res samples = [] # Test case for large tensor. samples.append(SampleInput(large_1d_unique())) # Test cases for small 3d tensors. # Imitates legacy tests from test/test_torch.py dims = range(-3, 3) flag = [True, False] for dim, descending, stable in product(dims, flag, flag): # default schema without stable sort samples.append(SampleInput(small_3d_unique(), args=(dim, descending))) # schema with stable sort, no CUDA support yet if torch.device(device).type == 'cpu': samples.append( SampleInput(small_3d_unique(), kwargs=dict(dim=dim, descending=descending, stable=stable)) ) # Test cases for scalar tensor samples.append(SampleInput(torch.tensor(1, dtype=dtype, device=device, requires_grad=requires_grad))) samples.append(SampleInput(torch.tensor(1, dtype=dtype, device=device, requires_grad=requires_grad), args=(0,))) samples.append(SampleInput(torch.tensor(1, dtype=dtype, device=device, requires_grad=requires_grad), args=(0, True))) # Test cases for stable sort samples.append(SampleInput(small_3d_unique(), kwargs=dict(stable=True))) samples.append(SampleInput(small_3d_unique(), kwargs=dict(dim=0, stable=True))) samples.append(SampleInput(small_3d_unique(), kwargs=dict(dim=0, descending=True, stable=True))) return samples def sample_inputs_threshold(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) sizes = ((), (S,), (S, S), (S, S, S)) samples = [] for x_size in sizes: # threshold and values args must be numbers samples.append(SampleInput(make_arg(x_size), args=(make_arg(()).item(), make_arg(()).item()))) return samples def sample_inputs_argsort(*args, **kwargs): return [sample_input for sample_input in sample_inputs_sort(*args, **kwargs) if "stable" not in sample_input.kwargs] def sample_inputs_unique(op_info, device, dtype, requires_grad, **kwargs): sizes = ((), (S,), (S, S), (S, S, S), (S, 1, S), (S, 0, S)) sample_inputs = [] for shape, sorted, return_inverse, return_counts, dim in \ product(sizes, [False, True], [False, True], [False, True], [None, -2, -1, 0, 1, 2]): # torch.unique cannot be called if the input tensor has a zero dimension which isn't the selected dim if 0 in shape and shape.index(0) is not dim: continue # skip invalid dim args if dim is not None and (dim < -len(shape) or dim >= len(shape)): continue kwargs = dict(sorted=sorted, return_inverse=return_inverse, return_counts=return_counts, dim=dim) # construct a test case with only one distinct value input_t = torch.zeros(shape, dtype=dtype, device=device, requires_grad=requires_grad) sample_inputs.append(SampleInput(input_t, kwargs=kwargs.copy())) # construct a test case with mixed 0s and 1s input_t = make_tensor(shape, dtype=torch.bool, device=device, requires_grad=False)\ .to(dtype).requires_grad_(requires_grad) sample_inputs.append(SampleInput(input_t, kwargs=kwargs.copy())) # construct a test case with many different values input_t = make_tensor(shape, dtype=dtype, device=device, requires_grad=requires_grad) sample_inputs.append(SampleInput(input_t, kwargs=kwargs.copy())) return sample_inputs def sample_inputs_unique_consecutive(*args, **kwargs): for sample_input in sample_inputs_unique(*args, **kwargs): if not sample_input.kwargs["sorted"]: sample_input.kwargs.pop("sorted") yield sample_input def sample_inputs_adaptive_avg_pool1d(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # Ordered as (input shape, output size) cases = ( ((0, 8, 8), (5,)), ((3, 8, 8), 5), ((3, 8, 8), 1) ) for input_shape, output_size in cases: yield SampleInput(make_arg(input_shape), args=(output_size,)) def sample_inputs_adaptive_avg_pool2d(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # Ordered as (input shape, output size) cases = ( ((1, 8, 8, 8), (5, 7)), ((2, 8, 8, 8), (None, 7)), ((1, 8, 4, 3), (5, None)), ((1, 8, 4, 3), (None, None)), ((1, 8, 4, 3), (5)), ) for input_shape, output_size in cases: yield SampleInput(make_arg(input_shape), args=(output_size,)) def sample_inputs_adaptive_avg_pool3d(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # Ordered as (input shape, output size) cases = ( ((0, 8, 8, 8, 8), (5, 7, 4)), ((1, 8, 4, 3, 7), (None, None, None)), ((1, 8, 4, 3, 7), (1, 1, 1)), ((3, 3, 8, 8, 6), (5, 7, None)), ((1, 3, 8, 8, 6), (5, None, 2)), ((3, 3, 8, 8, 6), (None, 3, 2)), ) for input_shape, output_size in cases: yield SampleInput(make_arg(input_shape), args=(output_size,)) def sample_inputs_adaptive_max_pool1d(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # Ordered as (input shape, output size) cases = ( # ((0, 8, 8), (5,)), # 0 batch size doesn't work, cannot reshape tensor of 0 elements into shape [0, 8, -1] ((3, 4, 4), 3), ((3, 4, 4), 1) ) for shapes, return_idx in product(cases, (True, False)): yield SampleInput(make_arg(shapes[0]), args=(shapes[1], return_idx)) def sample_inputs_adaptive_max_pool2d(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # Ordered as (input shape, output size) cases = ( # ((0, 8, 8, 8), (5, 7)), # 0 batch size doesn't work, cannot reshape tensor of 0 elements into shape [0, 8, -1] ((1, 4, 4, 4), (2, 3)), ((2, 4, 4, 4), (None, 3)), ((2, 4, 4, 4), (1, 1)), ((1, 4, 4, 3), (3, None)), ((1, 4, 4, 3), (None, None)), ((1, 4, 4, 3), (3)), ) for shapes, return_idx in product(cases, (True, False)): yield SampleInput(make_arg(shapes[0]), args=(shapes[1], return_idx)) def sample_inputs_adaptive_max_pool3d(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # Ordered as (input shape, output size) cases = ( # ((0, 8, 8, 8, 8), (5, 7, 4)), # 0 batch size doesn't work, cannot reshape tensor of 0 elements into shape [0, 8, -1] ((1, 4, 4, 3, 5), (None, None, None)), ((1, 4, 4, 3, 5), (1, 1, 1)), ((3, 3, 4, 4, 6), (2, 3, None)), ((1, 3, 4, 4, 6), (3, None, 2)), ((3, 3, 4, 4, 6), (None, 3, 2)), ) for shapes, return_idx in product(cases, (True, False)): yield SampleInput(make_arg(shapes[0]), args=(shapes[1], return_idx)) class _TestParamsMaxPoolBase(object): def __init__(self): self.kwargs = { 'kernel_size': [3], 'stride': [2, None], 'ceil_mode': [True, False], 'padding': [0, 1], 'dilation': [1], 'return_indices': [True, False] } self.shapes = [ [1, 2, None], # batch [2], # channels [3, 6] # signal ] def _gen_shape(self): for shape in product(*self.shapes): # shape[0] is None indicates missing batch dimension if shape[0] is None: shape = shape[1:] yield shape, torch.contiguous_format # only 2d (N, C, H, W) rank 4 tensors support channels_last memory format if len(self.shapes) == 4 and len(shape) == 4: yield shape, torch.channels_last def _gen_kwargs(self): keys = self.kwargs.keys() for values in product(*self.kwargs.values()): yield dict(zip(keys, values)) def gen_input_params(self): yield from product(self._gen_shape(), self._gen_kwargs()) class _TestParamsMaxPool1d(_TestParamsMaxPoolBase): def __init__(self): super().__init__() self.kwargs['kernel_size'] += [(3,)] self.kwargs['stride'] += [(2,)] self.kwargs['padding'] += [(1,)] self.kwargs['dilation'] += [(1,)] class _TestParamsMaxPool2d(_TestParamsMaxPoolBase): def __init__(self): super().__init__() self.kwargs['kernel_size'] += [(3, 2)] self.kwargs['stride'] += [(2, 1)] self.kwargs['padding'] += [(1, 1)] self.kwargs['dilation'] += [(1, 2)] self.shapes.append([6]) class _TestParamsMaxPool3d(_TestParamsMaxPoolBase): def __init__(self): super().__init__() self.kwargs['kernel_size'] += [(3, 2, 3)] self.kwargs['stride'] += [(2, 1, 2)] self.kwargs['dilation'] += [(1, 2, 1)] self.shapes.append([6]) self.shapes.append([5]) def sample_inputs_max_pool(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=False) params_generator_type_dict = { 'nn.functional.max_pool1d': _TestParamsMaxPool1d, 'nn.functional.max_pool2d': _TestParamsMaxPool2d, 'nn.functional.max_pool3d': _TestParamsMaxPool3d, } params_generator = params_generator_type_dict[op_info.name]() for (shape, memory_format), kwargs in params_generator.gen_input_params(): arg = make_arg(shape).to(memory_format=memory_format).requires_grad_(requires_grad) yield SampleInput(arg, kwargs=kwargs) def sample_inputs_normalize(self, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, low=-1, high=1, device=device, dtype=dtype, requires_grad=requires_grad) cases: Tuple[Tuple[int], dict] = ( # type: ignore[assignment] ((2, 1, 4, 5), {'p': 1., 'dim': 2}), ((2, 3, 4, 5), {'p': 2., 'dim': 1}), ((1, 2, 4, 5), {'p': 0.5, 'dim': 0}), ((1, 3, 4, 5), {'p': -1., 'dim': 1}), ((1, 3, 4, 5), {'p': 0., 'dim': -1}), ((), {'p': 1.2, 'dim': 0}), ((2, 3, 4, 5), {}), ((2, 3, 4, 5), {'eps': 1e-4})) for input_shape, kwargs in cases: yield SampleInput(make_arg(input_shape), kwargs=kwargs) def sample_inputs_conv_transpose1d(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # Ordered as shapes for input, weight, bias # and a dict of values of (stride, padding, output_padding, groups, dilation) cases: Tuple[Tuple[int], Tuple[int], Tuple[int], dict] = ( # type: ignore[assignment] ((1, 3, 4), (3, 3, 3), (3,), {'stride': (2,), 'padding': 2, 'output_padding': (1,), 'groups': 1}), ((2, 2, 4), (2, 2, 4), (4,), {'stride': (3,), 'padding': (1,), 'output_padding': (2,), 'groups': 2, 'dilation': (4,)}), ((1, 1, 4), (1, 1, 4), (1,), {'stride': 2, 'padding': 1, 'output_padding': 1, 'groups': 1, 'dilation': (2,)}), ((1, 1, 4), (1, 2, 3), None, {'stride': 2, 'padding': 1, 'output_padding': 1, 'groups': 1}), ((1, 4, 5), (4, 8, 3), None, {}) ) for input_shape, weight, bias, kwargs in cases: yield SampleInput(make_arg(input_shape), args=( make_arg(weight), make_arg(bias) if bias is not None else bias ), kwargs=kwargs) def sample_inputs_conv_transpose2d(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # Ordered as shapes for input, weight, bias # and a dict of values of (stride, padding, output_padding, groups, dilation) cases: Tuple[Tuple[int], Tuple[int], Tuple[int], dict] = ( # type: ignore[assignment] ((1, 3, 4, 4), (3, 3, 3, 3), (3,), {'stride': (2, 2), 'padding': 2, 'output_padding': (1, 1), 'groups': 1}), ((2, 2, 4, 4), (2, 2, 4, 5), (4,), {'stride': (3, 2), 'padding': (1, 2), 'output_padding': (2, 3), 'groups': 2, 'dilation': (4, 4)}), ((1, 1, 4, 5), (1, 1, 4, 3), (1,), {'stride': 2, 'padding': 1, 'output_padding': 1, 'groups': 1, 'dilation': (2, 3)}), ((1, 1, 4, 3), (1, 2, 3, 4), None, {'stride': 2, 'padding': 1, 'output_padding': 1, 'groups': 1}), ((1, 4, 5, 5), (4, 8, 3, 3), None, {}) ) for input_shape, weight, bias, kwargs in cases: yield SampleInput(make_arg(input_shape), args=( make_arg(weight), make_arg(bias) if bias is not None else bias ), kwargs=kwargs) def sample_inputs_conv_transpose3d(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # Ordered as shapes for input, weight, bias # and a dict of values of (stride, padding, output_padding, groups, dilation) cases: Tuple[Tuple[int], Tuple[int], Tuple[int], dict] = ( # type: ignore[assignment] ((1, 3, 4, 4, 4), (3, 3, 3, 3, 3), (3,), {'stride': (2, 2, 2), 'padding': 2, 'output_padding': (1, 1, 1), 'groups': 1}), ((2, 2, 4, 4, 4), (2, 2, 4, 5, 6), (4,), {'stride': (3, 2, 1), 'padding': (1, 2, 3), 'output_padding': (2, 3, 1), 'groups': 2, 'dilation': (4, 4, 4)}), ((1, 1, 4, 5, 2), (1, 1, 4, 3, 1), (1,), {'stride': 2, 'padding': 1, 'output_padding': 1, 'groups': 1, 'dilation': (2, 3, 2)}), ((1, 1, 4, 3, 4), (1, 2, 3, 4, 5), None, {'stride': 2, 'padding': 1, 'output_padding': 1, 'groups': 1}), ((1, 4, 5, 5, 5), (4, 8, 3, 3, 3), None, {}) ) for input_shape, weight, bias, kwargs in cases: yield SampleInput(make_arg(input_shape), args=( make_arg(weight), make_arg(bias) if bias is not None else bias ), kwargs=kwargs) def sample_inputs_conv1d(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # Ordered as shapes for input, weight, bias, # and a dict of values of (stride, padding, dilation, groups) cases: Tuple = ( ((1, 3, 4), (3, 3, 3), (3,), {'stride': (2,), 'padding': 2, 'groups': 1}), ((2, 4, 8), (2, 2, 3), (2,), {'stride': 3, 'padding': 1, 'groups': 2, 'dilation': 2}), ((1, 4, 5), (1, 4, 3), None, {'stride': (2,), 'padding': 'valid'}), ((2, 2, 4), (2, 1, 4), (2,), {'stride': (1,), 'padding': 'same', 'groups': 2, 'dilation': (2,)}), # With defaults ((1, 4, 5), (3, 4, 3), None, {}), ) # TODO: (@krshrimali), add error_inputs_func once https://github.com/pytorch/pytorch/pull/67354 is merged # Should replace test_conv_modules_raise_error_on_incorrect_input_size and test_conv_shapecheck # in test/test_nn.py for input_shape, weight, bias, kwargs in cases: yield SampleInput(make_arg(input_shape), args=( make_arg(weight), make_arg(bias) if bias is not None else bias ), kwargs=kwargs) def sample_inputs_conv2d(op_info, device, dtype, requires_grad, jit_fail_sample=False, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # Ordered as shapes for input, weight, bias # and a dict of values of (stride, padding, groups, dilation) cases: Tuple = ( ((1, 3, 4, 4), (3, 3, 3, 3), (3,), {'stride': (2, 2), 'padding': 2, 'groups': 1}), ((2, 4, 8, 8), (2, 2, 3, 3), (2,), {'stride': (3, 2), 'padding': (2, 1), 'groups': 2, 'dilation': (4, 4)}), ((1, 4, 5, 5), (1, 4, 2, 3), (1,), {'stride': 2, 'padding': 1, 'groups': 1, 'dilation': (2, 3)}), ((1, 4, 5, 5), (1, 4, 2, 3), (1,), {'stride': 2, 'padding': 1, 'groups': 1, 'dilation': (2, 3)}), ((1, 2, 4, 3), (4, 2, 3, 4), None, {'stride': 2, 'padding': 1, 'groups': 1}), ((1, 4, 5, 5), (1, 4, 2, 3), (1,), {'stride': 2, 'padding': "valid"}), ((1, 4, 5, 5), (1, 4, 2, 3), (1,), {'stride': 1, 'padding': "same", 'dilation': 3}), # Below are the group related samples from common_nn.py ((2, 4, 6, 6), (4, 1, 3, 3), (4,), {'groups': 4}), ((2, 4, 6, 6), (8, 1, 3, 3), (8,), {'groups': 4}), ((2, 4, 6, 6), (8, 1, 3, 3), None, {'groups': 4}), ((2, 4, 6, 6), (4, 1, 3, 3), (4,), {'groups': 4, 'stride': (3, 2)}), ((2, 4, 6, 6), (4, 1, 3, 3), (4,), {'groups': 4, 'padding': (1, 1)}), ((2, 4, 5, 5), (4, 1, 2, 2), (4,), {'groups': 4, 'dilation': (2, 2)}), ((2, 4, 6, 5), (6, 2, 3, 2), (6,), {'groups': 2}), # With defaults ((1, 4, 5, 5), (3, 4, 3, 3), None, {}), ) for input_shape, weight, bias, kwargs in cases: yield SampleInput(make_arg(input_shape), args=( make_arg(weight), make_arg(bias) if bias is not None else bias ), kwargs=kwargs) def sample_inputs_group_norm(opinfo, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # Ordered as input shape, num groups, and eps cases: Tuple[Tuple[int], int, float] = ( # type: ignore[assignment] ((1, 6, 3), 2, 0.5), ((2, 6, 3), 2, -0.5), ((1, 2), 1, None), ((0, 2), 1, None), ) for input_shape, num_groups, eps in cases: # Shape of weight and bias should be the same as num_channels weight = make_arg(input_shape[1]) bias = make_arg(input_shape[1]) kwargs = {'weight': weight, 'bias': bias} if eps is None else {'weight': weight, 'bias': bias, 'eps': eps} yield SampleInput( make_arg(input_shape), args=(num_groups,), kwargs=kwargs ) # Without any optional args yield SampleInput(make_arg((1, 2)), args=(1,)) def sample_inputs_instance_norm(opinfo, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) make_arg_without_requires_grad = partial(make_tensor, device=device, dtype=dtype, requires_grad=False) # Ordered as: input shape, kwargs for momentum, eps cases: Tuple[Tuple[int], dict] = ( # type: ignore[assignment] ((S, S, S), {'momentum': 0.5, 'eps': 0.6}), ((S, S, S), {'momentum': 0.5, 'eps': 0.6, 'use_input_stats': True}), ((3, 2, 4), {'momentum': -1.2}), ((3, 2, 4), {'momentum': 0.0}), ((3, 2, 3, 4), {'momentum': -1.0, 'eps': 0.5}), ((3, 2, 3, 4), {'momentum': -1.0, 'eps': 0.5}), ) for input_shape, kwargs in cases: # args: running mean, running var, weight and bias should necessarily be of shape: (channels,) channels = input_shape[1] weight = make_arg(channels) bias = make_arg(channels) running_mean = make_arg_without_requires_grad(channels, low=0) running_var = make_arg_without_requires_grad(channels, low=0) new_kwargs = { 'running_mean': running_mean, 'running_var': running_var, 'weight': weight, 'bias': bias, **kwargs } yield SampleInput( make_arg(input_shape), args=(), kwargs=new_kwargs ) # Checking for permutations of weights and biases as `None` # instance_norm assumes that if there's a bias, there's a weight weights = [channels, None] biases = [None, None] for weight_channels, bias_channels in zip(weights, biases): running_mean = make_arg_without_requires_grad(channels, low=0) running_var = make_arg_without_requires_grad(channels, low=0) yield SampleInput( make_arg(input_shape), args=(), kwargs={ 'running_mean': running_mean, 'running_var': running_var, 'weight': make_arg(weight_channels) if weight_channels is not None else None, 'bias': make_arg(bias_channels) if bias_channels is not None else None } ) # Test case for no optional kwargs yield SampleInput(make_arg((1, 2, 3)), kwargs={}) def sample_inputs_layer_norm(opinfo, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # Ordered as input shape, normalized_shape and a kwarg dict for eps cases: Tuple[Tuple[int], Tuple[int], dict] = ( # type: ignore[assignment] ((1, 2, 3), (1, 2, 3), {'eps': 0.5}), ((2, 2, 3), (2, 3), {'eps': -0.5}), ((1,), (1,), {}), ((1, 2), (2,), {}), ((0, 1), (1,), {}), ) for input_shape, normalized_shape, kwargs in cases: # Shape of weight and bias should be the same as normalized_shape weight = make_arg(normalized_shape) bias = make_arg(normalized_shape) yield SampleInput( make_arg(input_shape), args=(normalized_shape, weight, bias), kwargs=kwargs ) # Without any optional args yield SampleInput(make_arg((1, 2)), args=((2,),)) # TODO: @krshrimali, once to_numpy method in SampleInput class is modified to take None inputs, # enable these inputs; see https://github.com/pytorch/pytorch/pull/63276#discussion_r691950400 # With weight and a `None` bias # yield SampleInput(make_arg((1, 2)), args=((2,), make_arg((2,)), None)) # With `None` weight and bias (tests failing for this, see the link above) # yield SampleInput(make_arg((1, 2)), args=((2,), None, make_arg((2,)))) def sample_inputs_local_response_norm(opinfo, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # Ordered as input shape, size and a kwarg dict for alpha, beta, and k cases: Tuple[Tuple[int], Tuple[int], dict] = ( # type: ignore[assignment] ((1, 6, 3), 2, {'alpha': 3e-05, 'beta': 0.5, 'k': 1.25}), ((1, 6, 3), 2, {'beta': 0.5, 'k': 1.25}), ((1, 6, 3), 2, {'alpha': 3e-05, 'k': 1.25}), ((1, 6, 3), 2, {'alpha': 3e-05, 'beta': 0.5}), ((1, 6, 3), 2, {'alpha': 3e-05}), ((1, 6, 3), 2, {'beta': 0.5}), ((1, 6, 3), 2, {'k': 1.25}), ((1, 6, 3), 2, {}), ((2, 6, 3), 2, {'alpha': 3e-05, 'beta': 0.5, 'k': 1.25}), ((1, 1, 2), 1, {'alpha': 3e-05, 'beta': 0.5, 'k': 1.25}), ((0, 1, 2), 1, {'alpha': 3e-05, 'beta': 0.5, 'k': 1.25}), ) for input_shape, size, kwargs in cases: yield SampleInput(make_arg(input_shape), args=(size,), kwargs=kwargs) def sample_inputs_hardswish(self, device, dtype, requires_grad, **kwargs): N = 5 # make sure we are testing -3 -> 3 range. default is -10 -> 10 so maybe unnecessary ? tensors = [SampleInput(make_tensor((N * 2, N * 2), device=device, dtype=dtype, requires_grad=requires_grad, low=-5, high=5)) for _ in range(1, N)] return tensors def sample_inputs_linear(self, device, dtype, requires_grad, **kwargs): features_options = [[3, 4], [8, 8]] batch_options: List[List[int]] = [ [], # no batch [0], [8], [2, 3], ] create_tensor = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad, low=-2, high=2) sample_inputs = [] for has_bias, (in_feat, out_feat), batch_shape in \ itertools.product([True, False], features_options, batch_options): input_tensor = create_tensor(batch_shape + [in_feat]) weight = create_tensor([out_feat, in_feat]) if not has_bias: sample_inputs.append(SampleInput(input_tensor, args=(weight,))) continue bias = create_tensor([out_feat]) sample_inputs.append(SampleInput(input_tensor, args=(weight, bias))) return sample_inputs def sample_inputs_bilinear(self, device, dtype, requires_grad, **kwargs): features_options = [[3, 4, 5], [8, 8, 8]] batch_options: List[List[int]] = [ [], # no batch [0], [8], [2, 3], ] create_tensor = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad, low=-2, high=2) sample_inputs = [] for has_bias, (in_feat1, in_feat2, out_feat), batch_shape in \ itertools.product([True, False], features_options, batch_options): input_tensor1 = create_tensor(batch_shape + [in_feat1]) input_tensor2 = create_tensor(batch_shape + [in_feat2]) weight = create_tensor([out_feat, in_feat1, in_feat2]) if not has_bias: sample_inputs.append(SampleInput(input_tensor1, args=(input_tensor2, weight,))) continue bias = create_tensor([out_feat]) sample_inputs.append(SampleInput(input_tensor1, args=(input_tensor2, weight, bias))) return sample_inputs def sample_inputs_glu(self, device, dtype, requires_grad, **kwargs): features_options = [[2], [2, 4], [8, 8], [3, 6, 8], [1, 4, 6, 7]] batch_options: List[List[int]] = [ [], # no batch [0], [8], [2, 3], ] create_tensor = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad, low=-2, high=2) sample_inputs = [] for features, batch_shape in itertools.product(features_options, batch_options): ndim = len(features) + len(batch_shape) for dim in range(ndim): input_tensor = create_tensor(batch_shape + features) dim_size = input_tensor.size(dim) if dim_size > 0 and dim_size % 2 == 0: sample_inputs.append(SampleInput(input_tensor, args=(dim,))) return sample_inputs def sample_inputs_interpolate(mode, self, device, dtype, requires_grad, **kwargs): N, C = 2, 3 D = 4 S = 3 L = 5 align_corners_options: Tuple[Any, ...] = (None,) if mode in ('linear', 'bilinear', 'bicubic', 'trilinear'): align_corners_options = (True, False, None) ranks_for_mode = { 'nearest': [1, 2, 3], 'linear': [1], 'bilinear': [2], 'bicubic': [2], 'trilinear': [3], 'area': [1, 2, 3] } def shape(size, rank, with_batch_channel=True): if with_batch_channel: return tuple([N, C] + ([size] * rank)) return tuple([size] * rank) make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad, low=-1, high=1) sample_inputs = [] for align_corners in align_corners_options: for rank in ranks_for_mode[mode]: sample_inputs.extend([ SampleInput(make_arg(shape(D, rank)), args=(shape(S, rank, False), None, mode, align_corners)), SampleInput(make_arg(shape(D, rank)), args=(shape(L, rank, False), None, mode, align_corners)), SampleInput(make_arg(shape(D, rank)), args=(None, 1.7, mode, align_corners)), SampleInput(make_arg(shape(D, rank)), args=(None, 0.6, mode, align_corners)), ]) return sample_inputs def sample_inputs_upsample(mode, self, device, dtype, requires_grad, **kwargs): N, C = 2, 3 D = 4 S = 3 L = 5 ranks_for_mode = { 'nearest': [1, 2, 3], 'bilinear': [2], } def shape(size, rank, with_batch_channel=True): if with_batch_channel: return tuple([N, C] + ([size] * rank)) return tuple([size] * rank) make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad, low=-1, high=1) sample_inputs = [] for rank in ranks_for_mode[mode]: sample_inputs.extend([ SampleInput(make_arg(shape(D, rank)), kwargs=dict(size=shape(S, rank, False))), SampleInput(make_arg(shape(D, rank)), kwargs=dict(size=shape(L, rank, False))), SampleInput(make_arg(shape(D, rank)), kwargs=dict(scale_factor=1.7)), SampleInput(make_arg(shape(D, rank)), kwargs=dict(scale_factor=0.6)), ]) return sample_inputs def sample_inputs_gelu(self, device, dtype, requires_grad, **kwargs): N = 5 tensors = [] for _ in range(1, N): for approximate in ['none', 'tanh']: tensors.append(SampleInput( make_tensor((N * 2, N * 2), device=device, dtype=dtype, requires_grad=requires_grad, low=-3, high=3), kwargs=dict(approximate=approximate))) return tensors def sample_inputs_max_min_reduction_with_dim(op_info, device, dtype, requires_grad, **kwargs): inputs = [] args_for_reduction_with_dim = ( ((S, S, S), (1,),), ((S, S, S), (1, True, ),), ((), (0,),), ((), (0, True,),), ) inputs = list((SampleInput(make_tensor(input_tensor, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=args,)) for input_tensor, args in args_for_reduction_with_dim) return inputs def sample_inputs_max_min_reduction_no_dim(op_info, device, dtype, requires_grad, **kwargs): inputs = [] inputs.append(SampleInput(make_tensor((S, S, S), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad),)) inputs.append(SampleInput(make_tensor((), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad),)) return inputs def _generate_nan_reduction_inputs(device, dtype, requires_grad, **kwargs): yield from _generate_reduction_inputs(device, dtype, requires_grad) yield torch.tensor([2, torch.nan, -1], device=device, dtype=dtype, requires_grad=requires_grad) yield torch.tensor([[torch.nan, 2], [0, 1]], device=device, dtype=dtype, requires_grad=requires_grad) def sample_inputs_nan_reduction(supports_multiple_dims): # Generates sample inputs for reduction ops that contain the input tensor # and dim and keepdim kwargs. If a reduction op needs to test additional # args/kwargs then create a separate sample_inputs function def fn(op_info, device, dtype, requires_grad, **kwargs): inputs = [] for t in _generate_nan_reduction_inputs(device, dtype, requires_grad): # Add case without dim and keepdim kwargs inputs.append(SampleInput(t.clone().requires_grad_(requires_grad))) for kwargs in _generate_reduction_kwargs(t.ndim, supports_multiple_dims): inputs.append(SampleInput(t.clone().requires_grad_(requires_grad), kwargs=kwargs)) return inputs return fn def sample_inputs_reduction_quantile(op_info, device, dtype, requires_grad, **kwargs): test_quantiles = (0.5, make_tensor((2,), dtype=dtype, device=device, low=0, high=1, requires_grad=requires_grad)) test_interpolations = ['linear', 'midpoint'] inputs = [] for quantiles in test_quantiles: for t in _generate_reduction_inputs(device, dtype, requires_grad): # Add case without dim and keepdim kwargs inputs.append(SampleInput(t.clone().requires_grad_(requires_grad), args=(quantiles,))) for kwargs in _generate_reduction_kwargs(t.ndim, supports_multiple_dims=False): # Interpolation kwarg for now is only supported when providing both dim and keepdim kwargs.setdefault('dim', 0) kwargs.setdefault('keepdim', False) for interpolation in test_interpolations: kwargs['interpolation'] = interpolation inputs.append(SampleInput(t.clone().requires_grad_(requires_grad), args=(quantiles,), kwargs=kwargs)) return inputs def sample_inputs_reduction_count_nonzero(*args, **kwargs): """Sample inputs for count_nonzero""" samples: List[SampleInput] = sample_inputs_reduction(*args, **kwargs) # count_nonzero does not support keepdim yet for sample in samples: sample.kwargs.pop('keepdim', None) return samples def sample_inputs_leaky_relu(op_info, device, dtype, requires_grad, **kwargs): N = 10 tensors = [SampleInput(make_tensor((N, N), device=device, dtype=dtype, requires_grad=requires_grad)) for _ in range(1, N)] return tensors def sample_inputs_fractional_max_pool2d(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # Order: input_shape, kernel_size cases = (((1, 3, 9, 9), 3), ((1, 3, 9, 9), (4, 4)), ((1, 3, 9, 9), (6, 6)), ((2, 3, 9, 9), (3, 3)), ((1, 1, 4, 4), (2, 2)), ((1, 2, 6, 6), (4, 4))) samples = [] for input_shape, kernel_size in cases: for return_indices in [False, True]: # test case passing a single output size samples.append(SampleInput( make_arg(input_shape), args=(kernel_size,), kwargs=dict(output_size=(2), return_indices=return_indices) )) # test case passing a tuple output size samples.append(SampleInput( make_arg(input_shape), args=(kernel_size,), kwargs=dict(output_size=(2, 3), return_indices=return_indices) )) # test case passing an output ratio samples.append(SampleInput( make_arg(input_shape), args=(kernel_size,), kwargs=dict(output_ratio=(0.5, 0.5), return_indices=return_indices) )) return samples def sample_inputs_fractional_max_pool3d(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # Order: input_shape, kernel_size cases = (((2, 3, 5, 5, 5), (2, 2, 2)), ((1, 2, 6, 5, 4), 2), ((1, 2, 5, 6, 5), (2, 3, 2)), ((1, 2, 6, 6, 6), (2, 3, 2)), ((1, 1, 7, 6, 7), (2, 3, 4)), ((1, 1, 4, 5, 4), (2, 2, 1)), ((1, 1, 8, 7, 6), (4, 3, 2)), ((0, 1, 4, 5, 4), (2, 2, 1))) samples = [] for input_shape, kernel_size in cases: for return_indices in [False, True]: # test case passing a single output size samples.append(SampleInput( make_arg(input_shape), args=(kernel_size,), kwargs=dict(output_size=(2), return_indices=return_indices) )) # test case passing a tuple output size samples.append(SampleInput( make_arg(input_shape), args=(kernel_size,), kwargs=dict(output_size=(2, 3, 2), return_indices=return_indices) )) # test case passing an output ratio samples.append(SampleInput( make_arg(input_shape), args=(kernel_size,), kwargs=dict(output_ratio=(0.5, 0.5, 0.5), return_indices=return_indices) )) return samples def sample_inputs_avgpool2d(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # Order: input_shape, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override cases = (((1, 3, 9, 9), 3, 1, 1, True, False, 2), ((1, 3, 9, 9), (4, 4), (2, 3), 1, True, False, 2), ((1, 3, 9, 9), (6, 6), (3, 3), (2, 3), True, True, 2), ((2, 3, 9, 9), (3, 3), (1, 1), (1, ), True, False, 2), ((1, 1, 4, 4), (2, 2), (), (0, ), False, True, -2), ((1, 2, 6, 6), (4, 4), (2, 2), (2, ), True, True, None)) for input_shape, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override in cases: yield SampleInput(make_arg(input_shape), args=(kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override)) # Case with just input_shape and kernel_size yield SampleInput(make_arg((1, 3, 9, 9)), args=((3, 3))) def sample_inputs_avgpool1d(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # Order: input_shape, kernel_size, kwargs cases: List[Tuple[Tuple[int, ...], Union[int, Tuple[int, ...]], Dict]] = [ ((2, 3, 9), (3,), dict()), ((1, 3, 9), 3, dict(stride=1, padding=1, ceil_mode=True, count_include_pad=False)), ((1, 3, 9), (6,), dict(stride=(3,), padding=(2,), ceil_mode=True, count_include_pad=True)), ((2, 3, 9), (3,), dict(stride=(1,), padding=(1,), ceil_mode=False, count_include_pad=True)), ((0, 3, 9), (6,), dict(stride=(3,), padding=(2,), ceil_mode=False, count_include_pad=True)), ((1, 2, 9), (7,), dict(stride=(3,), padding=(2,), ceil_mode=False)), ((1, 2, 9), (7,), dict(stride=(3,), padding=(3,), ceil_mode=True)), ((1, 2, 9), (7,), dict(stride=(3,), ceil_mode=False)), ((1, 2, 9), (7,), dict(stride=(3,), ceil_mode=True)), ] for input_shape, kernel_size, kwargs in cases: yield SampleInput(make_arg(input_shape), args=(kernel_size,), kwargs=kwargs) def sample_inputs_avgpool3d(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # Order: input_shape, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override cases: List[Tuple[Tuple[int, ...], Union[int, Tuple[int, ...]], Dict]] = [ ((2, 3, 3, 4, 4), (2, 2, 2), dict()), ((1, 2, 4, 4, 4), 2, dict(stride=1, padding=1, ceil_mode=True, count_include_pad=False, divisor_override=2)), ((1, 2, 5, 5, 5), (2, 3, 4), dict(stride=(1, 2, 2), padding=(0, 1, 2), ceil_mode=True, count_include_pad=True, divisor_override=2)), ((1, 2, 5, 5, 5), (2, 3, 4), dict(stride=(1, 2, 2), padding=(0, 1, 2), ceil_mode=False)), ((1, 1, 7, 5, 7), (6, 3, 4), dict(stride=(2, 3, 2), padding=(3, 1, 0), ceil_mode=False, count_include_pad=False, divisor_override=2)), ((1, 1, 4, 5, 4), (2, 2, 3), dict(stride=(2, 2, 1), padding=0, ceil_mode=False, count_include_pad=True, divisor_override=-2)), ((1, 1, 6, 5, 6), (4, 5, 6), dict(stride=(2, 3, 2), padding=2, ceil_mode=True, count_include_pad=True, divisor_override=None)), ((0, 1, 4, 5, 4), (2, 3, 1), dict(stride=(2, 1, 2), padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None)), ] for input_shape, kernel_size, kwargs in cases: yield SampleInput(make_arg(input_shape), args=(kernel_size,), kwargs=kwargs) def sample_inputs_topk(op_info, device, dtype, requires_grad, **kwargs): def get_tensor_input(size): return make_tensor(size, dtype=dtype, device=device, requires_grad=requires_grad) inputs = [] inputs.append(SampleInput(get_tensor_input((S, M, S)), args=(3,))) inputs.append(SampleInput(get_tensor_input((S, M, S)), args=(3, 1))) inputs.append(SampleInput(get_tensor_input((S, M, S)), args=(3, -2))) inputs.append(SampleInput(get_tensor_input((S, M, S)), args=(3, 1, True))) inputs.append(SampleInput(get_tensor_input((S, M, S)), args=(3, -2, True))) inputs.append(SampleInput(get_tensor_input((S, M, S)), args=(3, 1, True, True))) inputs.append(SampleInput(get_tensor_input((S, M, S)), args=(3, -2, True, True))) inputs.append(SampleInput(get_tensor_input(()), args=(1,))) inputs.append(SampleInput(get_tensor_input(()), args=(1, 0))) inputs.append(SampleInput(get_tensor_input(()), args=(1, -1))) inputs.append(SampleInput(get_tensor_input(()), args=(1, 0, True))) inputs.append(SampleInput(get_tensor_input(()), args=(1, -1, True))) inputs.append(SampleInput(get_tensor_input(()), args=(1, 0, True, True))) inputs.append(SampleInput(get_tensor_input(()), args=(1, -1, True, True))) return inputs def sample_inputs_outer(op_info, device, dtype, requires_grad, **kwargs): inputs = [] arg_a = make_tensor((S,), dtype=dtype, device=device, requires_grad=requires_grad) arg_b = make_tensor((M,), dtype=dtype, device=device, requires_grad=requires_grad) inputs.append(SampleInput(arg_a, args=(arg_b,))) return inputs def sample_inputs_dist(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) sizes = ((S, S, S), (S,), (S, 1, S), (), (S, S)) ps = (2, 4) for size_x, size_y, p in product(sizes, sizes, ps): yield SampleInput(make_arg(size_x), args=(make_arg(size_y), p)) # Missing to test the nondeterminism of the operation # https://github.com/pytorch/pytorch/issues/53352 def sample_inputs_index(op_info, device, dtype, requires_grad, **kwargs): # target.index_select(dim, idx) select = op_info.name == "index_select" # target.index_add(dim, idx, source, *, alpha=1) add = op_info.name == "index_add" # target.index_copy(dim, idx, source) copy = op_info.name == "index_copy" # target.index_fill(dim, idx, value) fill = op_info.name == "index_fill" make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) make_permutation = partial(torch.randperm, device=device, dtype=torch.int64) def make_idx(n): return make_tensor((n,), device=device, dtype=torch.int64, low=0, high=n) shapes = [(), (1,), (S, S)] # extra parameter for add alphas = (-1, 0, 2) if add else (None,) for shape, alpha in product(shapes, alphas): t = make_arg(shape) args = [] # dim. We handle the scalar case dim = 1 if t.ndim == 2 else 0 args.append(dim) # idx They need to be different for copy and add to be deterministic make_idx_fn = make_permutation if copy or add else make_idx idx = make_idx_fn(t.shape[dim] if t.ndim != 0 else 1) args.append(idx) # source if copy or add: args.append(make_arg(shape)) elif fill: # A weird number to catch errors args.append(make_arg((1,)).item()) args = tuple(args) kwargs = {} if alpha is None else {"alpha": alpha} yield SampleInput(t, args=args, kwargs=kwargs) def sample_inputs_index_reduce(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) def make_idx(n, m): return make_tensor((n,), device=device, dtype=torch.int64, low=0, high=m) shapes = [((), ()), ((1,), (1,)), ((S, S), (S, M)), ((S, S, S), (S, M, S))] include_selfs = (True, False) reduces = ('prod', 'mean', 'amin', 'amax') for shape, include_self, reduce in product(shapes, include_selfs, reduces): self_shape, src_shape = shape # dim. We handle the scalar case dim = 1 if len(self_shape) >= 2 else 0 idx = make_idx(src_shape[dim] if len(src_shape) != 0 else 1, self_shape[dim] if len(self_shape) != 0 else 1) args = (dim, idx, make_arg(src_shape), reduce) yield SampleInput(make_arg(self_shape), args=args, kwargs={'include_self' : include_self}) # Sample inputs to test edge cases for backward if requires_grad: # Check that gradients are propagated correctly for prod when zeros in self/src are reduced # This sample tests gradients for the following cases # (a) 1 zero reduced (from source (self[0, 1]), from self (self[0, 0])) # (b) 2 zeros reduced (1 from src and 1 from self (self[1, 0], self[1, 1]) # (c) no zeros reduced (self[2, 1], self[2, 2]) # (d) 2 zeros reduced (both from src) is tested in test/test_autograd.py # test_scatter_index_reduce_prod_gradgrad_error as this case is not supported for gradgrad input = torch.tensor([[0, 13], [0, 0], [15, 19]], dtype=dtype, device=device, requires_grad=requires_grad) src = torch.tensor([[2, 0], [0, 0], [2, 3], [2, 2]], dtype=dtype, device=device, requires_grad=requires_grad) idx = torch.tensor([0, 1, 2, 0], dtype=torch.long, device=device) yield SampleInput(input, args=(0, idx, src, 'prod'), kwargs={'include_self': True}) def sample_inputs_mode(op_info, device, dtype, requires_grad, **kwargs): inputs = [] args = ( ((S, S, S), (),), ((S, S, S), (1, ),), ((S, S, S), (1, True, ),), ((), (),), ((), (0,),), ((), (0, True,),), ) inputs = list((SampleInput(make_tensor(input_tensor, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=args,)) for input_tensor, args in args) return inputs # Missing to test the nondeterminism of the operation # https://github.com/pytorch/pytorch/issues/53352 def sample_inputs_put(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) make_idx = partial(make_tensor, low=0, dtype=torch.int64, device=device, requires_grad=False) S = 3 # Generic inputs idx = torch.randperm(S * S, device=device, dtype=torch.int64)[:S] idx_list = [idx, -idx - 1] for idx, acc in product(idx_list, (True, False)): yield SampleInput(input=make_arg((S, S)), args=(idx.clone(), make_arg((S,)), acc)) # Scalar cases scalar_sizes = [(), (1,)] tgt_gen = (make_arg(size) for size in scalar_sizes) idx_gen = (make_idx(size, high=1) for size in scalar_sizes) src_gen = (make_arg(size) for size in scalar_sizes) for tgt, idx, src, acc in product(tgt_gen, idx_gen, src_gen, (True, False)): yield SampleInput(input=tgt.clone().requires_grad_(requires_grad), args=(idx.clone(), src.clone().requires_grad_(requires_grad), acc)) # Empty cases tgt_sizes = [(0,), (), (1,), (3, 2)] tgt_gen = (make_arg(size) for size in tgt_sizes) idx = make_idx((0,), high=1) src = make_arg((0,)) for tgt, acc in product(tgt, (True, False)): yield SampleInput(input=tgt.clone().requires_grad_(requires_grad), args=(idx.clone(), src.clone().requires_grad_(requires_grad), acc)) def sample_inputs_take(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) make_idx = partial(make_tensor, low=0, dtype=torch.int64, device=device, requires_grad=False) S = 3 # Generic inputs: take S elements out of S * S index = make_idx((S,), high=(S * S)) for idx in (index, -index - 1): yield SampleInput(input=make_arg((S, S)), args=(idx,)) # Scalar cases scalar_sizes = [(), (1,)] src_gen = (make_arg(size) for size in scalar_sizes) idx_gen = (make_idx(size, high=1) for size in scalar_sizes) for src, idx in product(src_gen, idx_gen): yield SampleInput(input=src.clone().requires_grad_(requires_grad), args=(idx.clone(),)) # Empty cases src_sizes = [(0,), (), (1,), (3, 2)] src_gen = (make_arg(size) for size in src_sizes) idx = make_idx((0,), high=1) for src in src_gen: yield SampleInput(input=src.clone().requires_grad_(requires_grad), args=(idx.clone(),)) def sample_movedim_moveaxis(op_info, device, dtype, requires_grad, **kwargs): return ( SampleInput( make_tensor((4, 3, 2, 1), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=([0, 1, 2, 3], [3, 2, 1, 0])), SampleInput( make_tensor((4, 3, 2, 1), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=([0, -1, -2, -3], [-3, -2, -1, -0])) ) def sample_repeat_tile(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) rep_dims = ((), (0, ), (1, ), (0, 2), (1, 1), (2, 3), (2, 3, 2), (0, 2, 3), (2, 1, 1, 1),) shapes = ((), (0,), (2,), (3, 0), (3, 2), (3, 0, 1)) if requires_grad: # Tests for variant_consistency_jit, grad, gradgrad # are slower. Use smaller bags of `rep_dims` and `shapes` # in this case. rep_dims = ((), (0, ), (0, 2), (1, 1), (2, 3), (1, 3, 2), (3, 1, 1)) # type: ignore[assignment] shapes = ((), (0,), (2,), (3, 2)) # type: ignore[assignment] samples = [] for rep_dim, shape in product(rep_dims, shapes): # `torch.repeat` errors for `len(rep_dims) < t.dim()`, # so we filter such combinations. if op_info.name == 'repeat' and len(rep_dim) < len(shape): continue samples.append(SampleInput(make_arg(shape), args=(rep_dim,),)) return samples def sample_inputs_narrow(op_info, device, dtype, requires_grad, **kwargs): shapes_and_args = ( ((S, S, S), (1, 2, 2)), ((S, S, S), (-1, 2, 2)), ((S, S, S), (1, 0, 0)), ((S, S, S), (-1, 0, 0)), ((S, S, S), (2, 1, 2)), ) for shape, args in shapes_and_args: tensor = make_tensor(shape, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad) yield SampleInput(tensor, args=args) def sample_trapezoid(op_info, device, dtype, requires_grad, **kwargs): y_shape_x_shape_and_kwargs = [ ((2, 3), (2, 3), {}), ((2, 3), (2, 3), {'dim': 1}), ((6,), (6,), {}), ((6,), None, {}), # When 'trapezoid' is called with an empty input, it does not produce an output with requires_grad # See Issue #{61619} # ((6,0), (6,0), {}), ((2, 3), (1, 3), {}), ((3, 3), (3, 3), {}), ((3, 3), (3, 3), {'dim': -2}), ((5,), None, {'dx': 2.0}), ((2, 2), None, {'dx': 3.0}) ] samples = [] for y_shape, x_shape, kwarg in y_shape_x_shape_and_kwargs: y_tensor = make_tensor(y_shape, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad) if x_shape is not None: x_tensor = make_tensor(x_shape, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad) samples.append(SampleInput(y_tensor, args=(x_tensor,), kwargs=kwarg)) else: samples.append(SampleInput(y_tensor, kwargs=kwarg)) return samples def sample_cumulative_trapezoid(op_info, device, dtype, requires_grad, **kwargs): y_shape_x_shape_and_kwargs = [ ((2, 3), (2, 3), {}), ((2, 3), (2, 3), {'dim': 1}), ((6,), (6,), {}), ((6,), None, {}), # When 'cumulative_trapezoid' is called with an empty input, it does not produce an output with requires_grad # See Issue #{61619} # ((6,0), (6,0), {}), ((2, 3), (1, 3), {}), ((3, 3), (3, 3), {}), ((3, 3), (3, 3), {'dim': -2}), ((5,), None, {'dx': 2.0}), ((2, 2), None, {'dx': 3.0}) ] samples = [] for y_shape, x_shape, kwarg in y_shape_x_shape_and_kwargs: y_tensor = make_tensor(y_shape, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad) if x_shape is not None: x_tensor = make_tensor(x_shape, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad) samples.append(SampleInput(y_tensor, args=(x_tensor,), kwargs=kwarg)) else: samples.append(SampleInput(y_tensor, kwargs=kwarg)) return samples def sample_unsqueeze(op_info, device, dtype, requires_grad, **kwargs): shapes_and_axes = [ ((3, 4, 5), 0), ((3, 4, 5), 1), ((3, 4, 5), 3), ((3, 4, 5), -1), ((3, 4, 5), -3), ((), 0), ((), -1), ((1,), 0), ((1,), -1), ] samples = [] for shape, axis in shapes_and_axes: tensor = make_tensor(shape, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad) samples.append(SampleInput(tensor, args=(axis,),)) return samples def sample_inputs_nn_unfold(op_info, device, dtype, requires_grad, **kwargs): shapes = ((0, 1, 5, 5), (1, 1, 5, 5), (2, 3, 5, 5)) kernel_sizes = (2, (2, 2), (3, 3)) dilations = (1, 2, (1, 2)) paddings = (0, 1, (1, 1)) strides = (1, 2, (1, 2)) cases = product(shapes, kernel_sizes, dilations, paddings, strides) for shape, kernel_size, dilation, padding, stride in cases: tensor = make_tensor(shape, dtype=dtype, device=device, requires_grad=requires_grad) yield SampleInput(tensor, args=(kernel_size, dilation, padding, stride)) # With default args yield SampleInput(make_tensor((1, 1, 5, 5), dtype=dtype, device=device, requires_grad=requires_grad), args=((3, 3),)) def sample_inputs_squeeze(op_info, device, dtype, requires_grad, **kwargs): shapes_and_args = ( ((S, 1, S, 1), ()), ((1, 1, 1, 1), ()), ((S, 1, S, 1), (1,)), ((S, 1, S, 1), (-1,)), ((S, 1, S, 1), (2,)), ((S, 1, S, 1), (-2,)), ((), (0, )), ) for shape, args in shapes_and_args: tensor = make_tensor(shape, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad) yield SampleInput(tensor, args=args) def sample_inputs_nn_pad(op_info, device, dtype, requires_grad, mode, **kwargs): assert mode in ('constant', 'reflect', 'replicate', 'circular') if mode in ['reflect', 'replicate']: cases: tuple = ( # ignore ((1, 3), (1, 2)), ((1, 3), (0, 1)), ((0, 3, 3), (1, 2)), ((0, 3, 3), (0, 1)), ((1, 3, 3), (1, 2)), ((1, 3, 3), (0, 1)), ((1, 3, 3), (0, 2, 0, 1)), ((0, 3, 3, 3), (0, 2, 0, 1)), ((3, 3, 5, 5), (0, 2, 0, 1)), ((3, 3, 5, 5), (1, 1, 1, 1, 1, 1)), ((1, 3, 3, 3, 3), (1, 1, 1, 1, 1, 1)), ((1, 3, 4, 4), (-1, 1, -2, 1)), ) elif mode == 'constant': cases = ( ((1, 3), (1, 2)), ((1, 3), (0, 1)), ((1, 3), (0, 2, 0, 1)), ((0, 3, 3), (1, 2)), ((0, 3, 3), (0, 1)), ((0, 3, 3), (0, 2, 0, 1)), ((0, 3, 3), (1, 1, 1, 1, 1, 1)), ((1, 3, 3), (1, 2)), ((1, 3, 3), (0, 1)), ((1, 3, 3), (0, 2, 0, 1)), ((1, 3, 3), (1, 1, 1, 1, 1, 1)), ((0, 3, 3, 3), (1, 2)), ((0, 3, 3, 3), (0, 1)), ((0, 3, 3, 3), (0, 2, 0, 1)), ((0, 3, 3, 3), (1, 1, 1, 1, 1, 1)), ((3, 3, 5, 5), (1, 2)), ((3, 3, 5, 5), (0, 1)), ((3, 3, 5, 5), (0, 2, 0, 1)), ((3, 3, 5, 5), (1, 1, 1, 1, 1, 1)), ((1, 3, 3, 3, 3), (1, 2)), ((1, 3, 3, 3, 3), (0, 1)), ((1, 3, 3, 3, 3), (0, 2, 0, 1)), ((1, 3, 3, 3, 3), (1, 1, 1, 1, 1, 1)), ((1, 3, 4, 4), (-1, 1, -2, 1)), ) else: # mode == 'circular' if dtype == torch.bool: # test_dtypes fails on ASAN with for the case ab # runtime error: load of value 190, which is not a valid value for type 'bool' # Reference: https://github.com/pytorch/pytorch/pull/62814#issuecomment-894156562 # Reference Issue: https://github.com/pytorch/pytorch/issues/63034 cases = ( ((2, 3, 3), (1, 2)), ((1, 3, 3), (1, 2)), ) else: cases = ( ((0, 3, 3), (1, 2)), ((0, 3, 3), (0, 1)), ((1, 3, 3), (1, 2)), ((1, 3, 3), (0, 1)), ((0, 3, 3, 3), (0, 2, 0, 1)), ((3, 3, 5, 5), (0, 2, 0, 1)), ((1, 3, 3, 3, 3), (1, 1, 1, 1, 1, 1)), ((1, 3, 4, 4), (-1, 1, -2, 1)), ) make_inp = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) if mode == 'constant': # Default args yield SampleInput(make_inp((1, 3, 3)), args=((2, 2),)) if mode in ['reflect', 'replicate', 'circular']: for shape, pad in cases: yield SampleInput(make_inp(shape), args=(pad, mode)) else: # mode == 'constant' for pad_value in (1., 2.): for shape, pad in cases: yield SampleInput(make_inp(shape), args=(pad, mode, pad_value)) # TODO: reconcile with torch.linalg.det and torch.linalg.slogdet # Creates matrices with a positive nonzero determinant def sample_inputs_logdet(op_info, device, dtype, requires_grad, **kwargs): def make_nonzero_det(A, *, sign=1, min_singular_value=0.1, **kwargs): u, s, vh = torch.linalg.svd(A, full_matrices=False) s.clamp_(min=min_singular_value) A = (u * s.unsqueeze(-2)) @ vh det = A.det() if sign is not None: if A.dim() == 2: if (det < 0) ^ (sign < 0): A[0, :].neg_() else: cond = ((det < 0) ^ (sign < 0)).nonzero() if cond.size(0) > 0: for i in range(cond.size(0)): A[list(cond[i])][0, :].neg_() return A # cases constructed using make_tensor() tensor_shapes = ( (S, S), (1, 1), (3, 3, S, S), (3, 3, 1, 1) ) for shape in tensor_shapes: t = make_tensor(shape, device=device, dtype=dtype) d = make_nonzero_det(t).requires_grad_(requires_grad) yield SampleInput(d) # cases constructed using: # 1) make_symmetric_matrices # 2) make_symmetric_pd_matrices # 3) make_fullrank_matrices_with_distinct_singular_values symmetric_shapes = ( (S, S), (3, S, S), ) def _helper(constructor, *shape, **kwargs): t = constructor(*shape, device=device, dtype=dtype) d = make_nonzero_det(t, **kwargs).requires_grad_(requires_grad) yield SampleInput(d) for shape in symmetric_shapes: _helper(make_symmetric_matrices, *shape) _helper(make_symmetric_pd_matrices, *shape) _helper(make_fullrank_matrices_with_distinct_singular_values, *shape, min_singular_value=0) def np_unary_ufunc_integer_promotion_wrapper(fn): # Wrapper that passes PyTorch's default scalar # type as an argument to the wrapped NumPy # unary ufunc when given an integer input. # This mimicks PyTorch's integer->floating point # type promotion. # # This is necessary when NumPy promotes # integer types to double, since PyTorch promotes # integer types to the default scalar type. # Helper to determine if promotion is needed def is_integral(dtype): return dtype in [np.bool_, bool, np.uint8, np.int8, np.int16, np.int32, np.int64] @wraps(fn) def wrapped_fn(x): # As the default dtype can change, acquire it when function is called. # NOTE: Promotion in PyTorch is from integer types to the default dtype np_dtype = torch_to_numpy_dtype_dict[torch.get_default_dtype()] if is_integral(x.dtype): return fn(x.astype(np_dtype)) return fn(x) return wrapped_fn def sample_inputs_spectral_ops(self, device, dtype, requires_grad=False, **kwargs): is_fp16_or_chalf = dtype == torch.complex32 or dtype == torch.half if not is_fp16_or_chalf: nd_tensor = partial(make_tensor, (S, S + 1, S + 2), device=device, dtype=dtype, requires_grad=requires_grad) oned_tensor = partial(make_tensor, (31,), device=device, dtype=dtype, requires_grad=requires_grad) else: # cuFFT supports powers of 2 for half and complex half precision # NOTE: For hfft, hfft2, hfftn, irfft, irfft2, irfftn with default args # where output_size n=2*(input_size - 1), we make sure that logical fft size is a power of two if self.name in ['fft.hfft', 'fft.irfft']: shapes = ((2, 9, 9), (33,)) elif self.name in ['fft.hfft2', 'fft.irfft2']: shapes = ((2, 8, 9), (33,)) elif self.name in ['fft.hfftn', 'fft.irfftn']: shapes = ((2, 2, 33), (33,)) else: shapes = ((2, 8, 16), (32,)) nd_tensor = partial(make_tensor, shapes[0], device=device, dtype=dtype, requires_grad=requires_grad) oned_tensor = partial(make_tensor, shapes[1], device=device, dtype=dtype, requires_grad=requires_grad) if self.ndimensional == SpectralFuncType.ND: return [ SampleInput(nd_tensor(), kwargs=dict(s=(3, 10) if not is_fp16_or_chalf else (4, 8), dim=(1, 2), norm='ortho')), SampleInput(nd_tensor(), kwargs=dict(norm='ortho')), SampleInput(nd_tensor(), kwargs=dict(s=(8,))), SampleInput(oned_tensor()), *(SampleInput(nd_tensor(), kwargs=dict(dim=dim)) for dim in [-1, -2, -3, (0, -1)]), ] elif self.ndimensional == SpectralFuncType.TwoD: return [ SampleInput(nd_tensor(), kwargs=dict(s=(3, 10) if not is_fp16_or_chalf else (4, 8), dim=(1, 2), norm='ortho')), SampleInput(nd_tensor(), kwargs=dict(norm='ortho')), SampleInput(nd_tensor(), kwargs=dict(s=(6, 8) if not is_fp16_or_chalf else (4, 8))), SampleInput(nd_tensor(), kwargs=dict(dim=0)), SampleInput(nd_tensor(), kwargs=dict(dim=(0, -1))), SampleInput(nd_tensor(), kwargs=dict(dim=(-3, -2, -1))), ] else: return [ SampleInput(nd_tensor(), kwargs=dict(n=10 if not is_fp16_or_chalf else 8, dim=1, norm='ortho')), SampleInput(nd_tensor(), kwargs=dict(norm='ortho')), SampleInput(nd_tensor(), kwargs=dict(n=7 if not is_fp16_or_chalf else 8) ), SampleInput(oned_tensor()), *(SampleInput(nd_tensor(), kwargs=dict(dim=dim)) for dim in [-1, -2, -3]), ] def sample_inputs_repeat_interleave(op_info, device, dtype, requires_grad, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ SampleInput(make_input(()), kwargs=dict(repeats=2)), SampleInput(make_input((2, 3, 4)), kwargs=dict(repeats=2)), SampleInput(make_input((2, 3, 4)), kwargs=dict(repeats=2, dim=1)), SampleInput(make_input((2, 3, 4)), kwargs=dict(repeats=torch.arange(3, device=device), dim=1)) ] SpectralFuncType = Enum('SpectralFuncType', ('OneD', 'TwoD', 'ND')) # Metadata class for Fast Fourier Transforms in torch.fft. class SpectralFuncInfo(OpInfo): """Operator information for torch.fft transforms. """ def __init__(self, name, # the string name of the function *, ref=None, # Reference implementation (probably in np.fft namespace) dtypes=floating_and_complex_types(), ndimensional: SpectralFuncType, sample_inputs_func=sample_inputs_spectral_ops, decorators=None, **kwargs): decorators = list(decorators) if decorators is not None else [] decorators += [ skipCPUIfNoFFT, DecorateInfo(toleranceOverride({torch.chalf: tol(4e-2, 4e-2)}), "TestCommon", "test_complex_half_reference_testing") ] super().__init__(name=name, dtypes=dtypes, decorators=decorators, sample_inputs_func=sample_inputs_func, **kwargs) self.ref = ref self.ndimensional = ndimensional def sample_inputs_stft(op_info, device, dtype, requires_grad, **kwargs): def mt(shape, **kwargs): return make_tensor(shape, device=device, dtype=dtype, requires_grad=requires_grad, **kwargs) yield SampleInput(mt(100), kwargs=dict(n_fft=10)) for center in [False, True]: yield SampleInput(mt(10), kwargs=dict(n_fft=7, center=center)) yield SampleInput(mt((10, 100)), kwargs=dict(n_fft=16, hop_length=4, center=center)) window = make_tensor(16, low=.5, high=2.0, dtype=dtype, device=device, requires_grad=requires_grad) yield SampleInput( mt((2, 100)), kwargs=dict(n_fft=16, window=window, return_complex=True, center=center)) yield SampleInput( mt((3, 100)), kwargs=dict(n_fft=16, window=window, return_complex=True, center=center)) if not dtype.is_complex: yield SampleInput( mt((10, 100)), kwargs=dict(n_fft=16, window=window, onesided=False)) def sample_inputs_istft(op_info, device, dtype, requires_grad, **kwargs): def mt(shape, **kwargs): real_shape = shape if dtype.is_complex else shape + (2,) return make_tensor(real_shape, device=device, dtype=dtype, requires_grad=requires_grad, **kwargs) yield SampleInput(mt((10, 2)), kwargs=dict(n_fft=10)) yield SampleInput(mt((6, 3)), kwargs=dict(n_fft=6, onesided=False)) yield SampleInput(mt((6, 4)), kwargs=dict(n_fft=10, onesided=True)) for center in [False, True]: yield SampleInput(mt((10, 10, 6)), kwargs=dict(n_fft=10, center=center)) yield SampleInput(mt((1, 9, 10)), kwargs=dict(n_fft=16, hop_length=4, center=center)) window = make_tensor(10, low=.5, high=2.0, dtype=dtype, device=device, requires_grad=requires_grad) yield SampleInput(mt((10, 10, 6)), kwargs=dict( n_fft=10, window=window, center=center, return_complex=dtype.is_complex)) yield SampleInput(mt((10, 10, 10)), kwargs=dict( n_fft=10, window=window[:8], win_length=8, center=center, return_complex=True)) real_window = window if not dtype.is_complex else window.real yield SampleInput(mt((10, 5, 6)), kwargs=dict(n_fft=8, window=real_window[:8], center=center)) def sample_inputs_fftshift(op_info, device, dtype, requires_grad, **kwargs): def mt(shape, **kwargs): return make_tensor(shape, device=device, dtype=dtype, requires_grad=requires_grad, **kwargs) yield SampleInput(mt((9, 10))) yield SampleInput(mt((50,)), kwargs=dict(dim=0)) yield SampleInput(mt((5, 11)), kwargs=dict(dim=(1,))) yield SampleInput(mt((5, 6)), kwargs=dict(dim=(0, 1))) yield SampleInput(mt((5, 6, 2)), kwargs=dict(dim=(0, 2))) class ShapeFuncInfo(OpInfo): """Early version of a specialized OpInfo for Shape manipulating operations like tile and roll""" def __init__(self, name, # the string name of the function *, ref, # a reference function dtypes=floating_types(), dtypesIfCUDA=None, dtypesIfROCM=None, sample_inputs_func=None, **kwargs): super(ShapeFuncInfo, self).__init__(name, dtypes=dtypes, dtypesIfCUDA=dtypesIfCUDA, dtypesIfROCM=dtypesIfROCM, sample_inputs_func=sample_inputs_func, **kwargs) self.ref = ref def sample_inputs_foreach(self, device, dtype, N, *, noncontiguous=False, same_size=False, low=None, high=None): if same_size: return [make_tensor((N, N), dtype=dtype, device=device, noncontiguous=noncontiguous) for _ in range(N)] else: return [make_tensor((N - i, N - i), dtype=dtype, device=device, noncontiguous=noncontiguous) for i in range(N)] def get_foreach_method_names(name): # get torch inplace reference function op_name = "_foreach_" + name inplace_op_name = "_foreach_" + name + "_" op = getattr(torch, op_name, None) inplace_op = getattr(torch, inplace_op_name, None) ref = getattr(torch, name, None) ref_inplace = getattr(torch.Tensor, name + "_", None) return op, inplace_op, ref, ref_inplace class ForeachFuncInfo(OpInfo): """Early version of a specialized OpInfo for foreach functions""" def __init__(self, name, dtypes=floating_and_complex_types(), dtypesIfCUDA=floating_and_complex_types_and(torch.half), dtypesIfROCM=None, supports_alpha_param=False, sample_inputs_func=sample_inputs_foreach, **kwargs): super().__init__( "_foreach_" + name, dtypes=dtypes, dtypesIfCUDA=dtypesIfCUDA, dtypesIfROCM=dtypesIfROCM, sample_inputs_func=sample_inputs_func, **kwargs ) foreach_method, foreach_method_inplace, torch_ref_method, torch_ref_inplace = get_foreach_method_names(name) self.method_variant = foreach_method self.inplace_variant = foreach_method_inplace self.ref = torch_ref_method self.ref_inplace = torch_ref_inplace self.supports_alpha_param = supports_alpha_param if name == "norm": self.ref = torch.linalg.vector_norm def sample_inputs_linalg_cholesky_inverse(op_info, device, dtype, requires_grad=False, **kwargs): from torch.testing._internal.common_utils import random_well_conditioned_matrix # Cholesky factorization is for positive-definite matrices single_well_conditioned_matrix = random_well_conditioned_matrix(S, S, dtype=dtype, device=device) batch_well_conditioned_matrices = random_well_conditioned_matrix(2, S, S, dtype=dtype, device=device) single_pd = single_well_conditioned_matrix @ single_well_conditioned_matrix.mH batch_pd = batch_well_conditioned_matrices @ batch_well_conditioned_matrices.mH inputs = ( torch.zeros(0, 0, dtype=dtype, device=device), # 0x0 matrix torch.zeros(0, 2, 2, dtype=dtype, device=device), # zero batch of matrices single_pd, batch_pd ) test_cases = (torch.linalg.cholesky(a, upper=False) for a in inputs) for l in test_cases: # generated lower-triangular samples l.requires_grad = requires_grad yield SampleInput(l) # upper=False by default yield SampleInput(l.detach().clone().requires_grad_(requires_grad), kwargs=dict(upper=False)) # generate upper-triangular inputs u = l.detach().clone().mT.contiguous().requires_grad_(requires_grad) yield SampleInput(u, kwargs=dict(upper=True)) def sample_inputs_linalg_ldl_factor(op_info, device, dtype, requires_grad=False, **kwargs): from torch.testing._internal.common_utils import ( random_hermitian_pd_matrix, random_symmetric_pd_matrix, ) device = torch.device(device) # Symmetric inputs yield SampleInput( random_symmetric_pd_matrix(S, dtype=dtype, device=device), kwargs=dict(hermitian=False), ) # single matrix yield SampleInput( random_symmetric_pd_matrix(S, 2, dtype=dtype, device=device), kwargs=dict(hermitian=False), ) # batch of matrices yield SampleInput( torch.zeros(0, 0, dtype=dtype, device=device), kwargs=dict(hermitian=False) ) # 0x0 matrix yield SampleInput( torch.zeros(0, 2, 2, dtype=dtype, device=device), kwargs=dict(hermitian=False) ) # zero batch of matrices # Hermitian inputs # hermitian=True for complex inputs on CUDA is supported only with MAGMA 2.5.4+ magma_254_available = device.type == 'cuda' and _get_magma_version() >= (2, 5, 4) if dtype.is_complex and (device.type == 'cpu' or magma_254_available): yield SampleInput( random_hermitian_pd_matrix(S, dtype=dtype, device=device), kwargs=dict(hermitian=True), ) # single matrix yield SampleInput( random_hermitian_pd_matrix(S, 2, dtype=dtype, device=device), kwargs=dict(hermitian=True), ) # batch of matrices def sample_inputs_linalg_ldl_solve(op_info, device, dtype, requires_grad=False, **kwargs): # Generate LDL factors of symmetric (and Hermitian on CPU) matrices from torch.testing._internal.common_utils import random_hermitian_pd_matrix, random_symmetric_pd_matrix device = torch.device(device) symmetric_inputs = ( random_symmetric_pd_matrix(S, dtype=dtype, device=device), # single matrix random_symmetric_pd_matrix(S, 2, dtype=dtype, device=device), # batch of matrices torch.zeros(0, 0, dtype=dtype, device=device), # 0x0 matrix torch.zeros(0, 2, 2, dtype=dtype, device=device), # zero batch of matrices ) hermitian_inputs = ( random_hermitian_pd_matrix(S, dtype=dtype, device=device), random_hermitian_pd_matrix(S, 2, dtype=dtype, device=device), ) if device.type == 'cpu' and dtype.is_complex else () test_cases1 = (torch.linalg.ldl_factor_ex(a, hermitian=False) for a in symmetric_inputs) test_cases2 = (torch.linalg.ldl_factor_ex(a, hermitian=True) for a in hermitian_inputs) # Symmetric case for test_case in test_cases1: factors, pivots, _ = test_case factors.requires_grad = requires_grad for B_batch_shape in ((), factors.shape[:-2]): B = make_tensor((*B_batch_shape, factors.shape[-1], S), device=device, dtype=dtype, requires_grad=requires_grad) yield SampleInput(factors, args=(pivots, B), kwargs=dict(hermitian=False)) clone_factors = factors.detach().clone().requires_grad_(requires_grad) yield SampleInput(clone_factors, args=(pivots, B), kwargs=dict(hermitian=False)) # Hermitian case for test_case in test_cases2: factors, pivots, _ = test_case factors.requires_grad = requires_grad for B_batch_shape in ((), factors.shape[:-2]): B = make_tensor((*B_batch_shape, factors.shape[-1], S), device=device, dtype=dtype, requires_grad=requires_grad) yield SampleInput(factors, args=(pivots, B), kwargs=dict(hermitian=True)) clone_factors = factors.detach().clone().requires_grad_(requires_grad) yield SampleInput(clone_factors, args=(pivots, B), kwargs=dict(hermitian=True)) def sample_inputs_linalg_lstsq(op_info, device, dtype, requires_grad=False, **kwargs): from torch.testing._internal.common_utils import random_well_conditioned_matrix device = torch.device(device) drivers: Tuple[str, ...] if device.type == 'cuda': drivers = ('gels',) else: drivers = ('gels', 'gelsy', 'gelss', 'gelsd') # we generate matrices of shape (..., n + delta, n) deltas: Tuple[int, ...] if device.type == 'cpu' or has_cusolver(): deltas = (-1, 0, +1) # only square systems if Cusolver is not available # becase we solve a lstsq problem with a transposed matrix in the backward else: deltas = (0,) out = [] for batch, driver, delta in product(((), (3,), (3, 3)), drivers, deltas): shape = batch + (3 + delta, 3) a = random_well_conditioned_matrix(*shape, dtype=dtype, device=device) a.requires_grad_(requires_grad) b = make_tensor(shape, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad) out.append(SampleInput(a, args=(b,), kwargs=dict(driver=driver))) return out def sample_inputs_householder_product(op_info, device, dtype, requires_grad, **kwargs): """ This function generates input for torch.linalg.householder_product (torch.orgqr). The first argument should be a square matrix or batch of square matrices, the second argument is a vector or batch of vectors. Empty, square, rectangular, batched square and batched rectangular input is generated. """ # Each column of the matrix is getting multiplied many times leading to very large values for # the Jacobian matrix entries and making the finite-difference result of grad check less accurate. # That's why gradcheck with the default range [-9, 9] fails and [-2, 2] is used here. samples = ( SampleInput(make_tensor((S, S), dtype=dtype, device=device, low=-2, high=2, requires_grad=requires_grad), args=(make_tensor((S,), dtype=dtype, device=device, low=-2, high=2, requires_grad=requires_grad),)), SampleInput(make_tensor((S + 1, S), dtype=dtype, device=device, low=-2, high=2, requires_grad=requires_grad), args=(make_tensor((S,), dtype=dtype, device=device, low=-2, high=2, requires_grad=requires_grad),)), SampleInput(make_tensor((2, 1, S, S), dtype=dtype, device=device, low=-2, high=2, requires_grad=requires_grad), args=(make_tensor((2, 1, S,), dtype=dtype, device=device, low=-2, high=2, requires_grad=requires_grad),)), SampleInput(make_tensor((2, 1, S + 1, S), dtype=dtype, device=device, low=-2, high=2, requires_grad=requires_grad), args=(make_tensor((2, 1, S,), dtype=dtype, device=device, low=-2, high=2, requires_grad=requires_grad),)), SampleInput(make_tensor((0, 0), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=(make_tensor((0,), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad),)), SampleInput(make_tensor((S, S), dtype=dtype, device=device, low=-2, high=2, requires_grad=requires_grad), args=(make_tensor((0,), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad),)), # m = n = S, k = S - 2 SampleInput(make_tensor((S, S), dtype=dtype, device=device, low=-2, high=2, requires_grad=requires_grad), args=(make_tensor((S - 2,), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad),)), # m = S, n = S -1, k = S - 2 SampleInput(make_tensor((S, S - 1), dtype=dtype, device=device, low=-2, high=2, requires_grad=requires_grad), args=(make_tensor((S - 2,), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad),)), ) return samples def sample_inputs_ormqr(op_info, device, dtype, requires_grad, **kwargs): # create a helper function wrapping `make_tensor` make_input = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) def gen_inputs(): batches = [(), (0, ), (2, ), (2, 1)] ns = [5, 2, 0] tf = [True, False] for batch, (m, n), left, transpose in product(batches, product(ns, ns), tf, tf): reflectors = make_input((*batch, m, n)) tau = make_input((*batch, min(m, n))) other_matrix_shape = (m, n) if left else (n, m) other = make_input((*batch, *other_matrix_shape)) kwargs = {"left": left, "transpose": transpose} yield SampleInput(reflectors, args=(tau, other,), kwargs=kwargs) return tuple(gen_inputs()) def sample_inputs_linalg_cholesky(op_info, device, dtype, requires_grad=False, **kwargs): """ This function generates always positive-definite input for torch.linalg.cholesky using random_hermitian_pd_matrix. The input is generated as the itertools.product of 'batches' and 'ns'. In total this function generates 8 SampleInputs 'batches' cases include: () - single input, (0,) - zero batched dimension, (2,) - batch of two matrices, (1, 1) - 1x1 batch of matrices 'ns' gives 0x0 and 5x5 matrices. Zeros in dimensions are edge cases in the implementation and important to test for in order to avoid unexpected crashes. """ from torch.testing._internal.common_utils import random_hermitian_pd_matrix batches = [(), (0, ), (2, ), (1, 1)] ns = [5, 0] out = [] for batch, n, upper in product(batches, ns, [True, False]): a = random_hermitian_pd_matrix(n, *batch, dtype=dtype, device=device) a.requires_grad = requires_grad out.append(SampleInput(a, kwargs={"upper": upper})) return out def sample_inputs_symeig(op_info, device, dtype, requires_grad=False, **kwargs): out = sample_inputs_linalg_invertible(op_info, device, dtype, requires_grad) for o in out: o.kwargs = {"upper": bool(np.random.choice([True, False])), "eigenvectors": True} # A gauge-invariant function o.output_process_fn_grad = lambda output: (output[0], abs(output[1])) yield o def sample_inputs_linalg_eig(op_info, device, dtype, requires_grad=False, **kwargs): """ This function generates input for torch.linalg.eig """ def out_fn(output): return output[0], abs(output[1]) samples = sample_inputs_linalg_invertible(op_info, device, dtype, requires_grad) for sample in samples: sample.output_process_fn_grad = out_fn yield sample def sample_inputs_linalg_eigh(op_info, device, dtype, requires_grad=False, **kwargs): """ This function generates input for torch.linalg.eigh/eigvalsh with UPLO="U" or "L" keyword argument. """ def out_fn(output): if isinstance(output, tuple): # eigh function return output[0], abs(output[1]) else: # eigvalsh function return output # Samples do not need to be Hermitian, as we're using gradcheck_wrapper_hermitian_input samples = sample_inputs_linalg_invertible(op_info, device, dtype, requires_grad) for sample in samples: sample.kwargs = {"UPLO": np.random.choice(["L", "U"])} sample.output_process_fn_grad = out_fn yield sample def sample_inputs_linalg_slogdet(op_info, device, dtype, requires_grad=False, **kwargs): def out_fn(output): return output[1] samples = sample_inputs_linalg_invertible(op_info, device, dtype, requires_grad) for sample in samples: sample.output_process_fn_grad = out_fn yield sample def sample_inputs_linalg_pinv(op_info, device, dtype, requires_grad=False, **kwargs): """ This function generates input for torch.linalg.pinv with hermitian=False keyword argument. """ for o in sample_inputs_linalg_invertible(op_info, device, dtype, requires_grad, **kwargs): real_dtype = o.input.real.dtype if dtype.is_complex else dtype # requires_grad path for rtol tensor is not implemented for rtol in (None, 1.0, torch.tensor(1.0, dtype=real_dtype, device=device)): o = clone_sample(o) o.kwargs = {"rtol": rtol} yield o def sample_inputs_linalg_pinv_hermitian(op_info, device, dtype, requires_grad=False, **kwargs): """ This function generates input for torch.linalg.pinv with hermitian=True keyword argument. """ for o in sample_inputs_linalg_invertible(op_info, device, dtype, requires_grad, **kwargs): o.kwargs = {"hermitian": True} yield o def sample_inputs_linalg_solve(op_info, device, dtype, requires_grad=False, vector_rhs_allowed=True, **kwargs): """ This function generates always solvable input for torch.linalg.solve We sample a fullrank square matrix (i.e. invertible) A The first input to torch.linalg.solve is generated as the itertools.product of 'batches' and 'ns'. The second input is generated as the product of 'batches', 'ns' and 'nrhs'. In total this function generates 18 SampleInputs 'batches' cases include: () - single input, (0,) - zero batched dimension, (2,) - batch of two matrices. 'ns' gives 0x0 and 5x5 matrices. and 'nrhs' controls the number of vectors to solve for: () - using 1 as the number of vectors implicitly (1,) - same as () but explicit (3,) - solve for 3 vectors. Zeros in dimensions are edge cases in the implementation and important to test for in order to avoid unexpected crashes. 'vector_rhs_allowed' controls whether to include nrhs = () to the list of SampleInputs. torch.solve / triangular_solve / cholesky_solve (opposed to torch.linalg.solve) do not allow 1D tensors (vectors) as the right-hand-side. Once torch.solve / triangular_solve / cholesky_solve and its testing are removed, 'vector_rhs_allowed' may be removed here as well. """ make_fullrank = make_fullrank_matrices_with_distinct_singular_values make_a = partial(make_fullrank, dtype=dtype, device=device, requires_grad=requires_grad) make_b = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) batches = [(), (0, ), (2, )] ns = [5, 0] if vector_rhs_allowed: nrhs = [(), (1,), (3,)] else: nrhs = [(1,), (3,)] for n, batch, rhs in product(ns, batches, nrhs): yield SampleInput(make_a(*batch, n, n), args=(make_b((batch + (n,) + rhs)),)) def sample_inputs_linalg_solve_triangular(op_info, device, dtype, requires_grad=False, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device) bs = (1, 2, 0) ns = (3, 0) ks = (1, 3, 0) for b, n, k, (left, upper, uni) in product(bs, ns, ks, product((True, False), repeat=3)): with torch.no_grad(): if b == 1: A = make_arg((n, n)) if left else make_arg((k, k)) B = make_arg((n, k)) else: A = make_arg((b, n, n)) if left else make_arg((b, k, k)) B = make_arg((b, n, k)) if uni: # Not really necessary, but writing it for consistency A.diagonal(0, -2, -1).fill_(1.) else: d = A.diagonal(0, -2, -1) d[d.abs() < 1e-6] = 1. if upper: A.triu_() else: A.tril_() kwargs = {"upper": upper, "left": left, "unitriangular": uni} if requires_grad: for grad_A, grad_B in product((True, False), repeat=2): # Either A or B needs to have a gradient if not grad_A and not grad_B: continue yield SampleInput( A.clone().requires_grad_(grad_A), args=(B.clone().requires_grad_(grad_B),), kwargs=kwargs) else: yield SampleInput(A, args=(B,), kwargs=kwargs) def sample_inputs_legacy_solve(op_info, device, dtype, requires_grad=False, **kwargs): """ This function generates always solvable input for legacy solve functions (the ones that are not in torch.linalg module). The difference from sample_inputs_linalg_solve is that here the right-hand-side of A x = b equation should have b.ndim >= 2, vectors are not allowed. Also the arguments order is swapped. """ out = sample_inputs_linalg_solve( op_info, device, dtype, requires_grad=requires_grad, vector_rhs_allowed=False ) # Reverses tensor order for sample in out: sample.input, sample.args = sample.args[0], (sample.input,) yield sample def sample_inputs_cholesky_solve(op_info, device, dtype, requires_grad=False, **kwargs): cholesky_inverse_samples = sample_inputs_linalg_cholesky_inverse( op_info, device, dtype, requires_grad=False ) for sample in cholesky_inverse_samples: psd_matrix = sample.input sample.input = make_tensor(psd_matrix.shape, dtype=dtype, device=device, requires_grad=requires_grad, low=None, high=None) sample.args = (psd_matrix.requires_grad_(requires_grad),) yield sample def sample_inputs_lu(op_info, device, dtype, requires_grad=False, **kwargs): make_arg = partial(make_fullrank_matrices_with_distinct_singular_values, dtype=dtype, device=device, requires_grad=requires_grad) # not needed once OpInfo tests support Iterables batch_shapes = ((), (3,), (3, 3)) for batch_shape, get_infos, size_delta in product(batch_shapes, (True, False), (-2, -1, 0, +1, +2)): shape = batch_shape + (S + size_delta, S) input = make_arg(*shape) yield SampleInput(input, args=(True, get_infos)) def sample_inputs_linalg_lu(op_info, device, dtype, requires_grad=False, **kwargs): full_rank = (op_info.name == "linalg.lu_factor") make_fn = make_tensor if not full_rank else make_fullrank_matrices_with_distinct_singular_values make_arg = partial(make_fn, dtype=dtype, device=device, requires_grad=requires_grad) def out_fn(output): if op_info.name in ("linalg.lu"): return output[1], output[2] else: return output batch_shapes = ((), (3,), (3, 3)) # pivot=False only supported in CUDA pivots = (True, False) if torch.device(device).type == "cuda" else (True,) deltas = (-2, -1, 0, +1, +2) for batch_shape, pivot, delta in product(batch_shapes, pivots, deltas): shape = batch_shape + (S + delta, S) # Insanely annoying that make_fullrank_blablabla accepts a *shape and not a tuple! A = make_arg(shape) if not full_rank else make_arg(*shape) yield SampleInput(A, kwargs={"pivot": pivot}, output_process_fn_grad=out_fn) def sample_inputs_lu_solve(op_info, device, dtype, requires_grad=False, **kwargs): make_fn = make_fullrank_matrices_with_distinct_singular_values make_a = partial(make_fn, dtype=dtype, device=device) make_b = partial(make_tensor, dtype=dtype, device=device) batches = ((), (0, ), (2, )) ns = (5, 3, 0) nrhs = (0, 1, 6) for n, batch, rhs in product(ns, batches, nrhs): shape_a = batch + (n, n) a = make_a(*shape_a) lu, pivs = a.lu() lu = lu.contiguous() shape_b = batch + (n, rhs) b = make_b(shape_b) grads = (False,) if not requires_grad else (True, False) # we try all possible combinations of requires_grad for each input for lu_grad, b_grad in product(grads, grads): # when requires_grad == True, at least one input has to have requires_grad enabled if requires_grad and not lu_grad and not b_grad: continue lu_ = lu.clone() lu_.requires_grad_(lu_grad) b_ = b.clone() b_.requires_grad_(b_grad) yield SampleInput(b_, args=(lu_, pivs)) def sample_inputs_lu_unpack(op_info, device, dtype, requires_grad=False, **kwargs): def out_fn(output): return output[1], output[2] for lu_sample in sample_inputs_linalg_lu(op_info, device, dtype, requires_grad, **kwargs): lu_data, pivots = torch.linalg.lu_factor(lu_sample.input) lu_data.requires_grad_(requires_grad) yield SampleInput(lu_data, args=(pivots,), output_process_fn_grad=out_fn) def sample_inputs_roll(op_info, device, dtype, requires_grad=False, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) args = ((0, 0), (1, 2), (0, 2), (2, 0), (-1, 0), (10000, 1), (2,), ((1, 2, -1), (0, 1, 2))) for arg in args: yield SampleInput(make_arg((S, S, S)), args=arg) def sample_inputs_rot90(op_info, device, dtype, requires_grad=False, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) args = ((1, (0, 1),), (1, (1, 2),), (1, (1, -1),), ()) for arg in args: yield SampleInput(make_arg((S, S, S)), args=arg) def sample_inputs_std_var(op_info, device, dtype, requires_grad, **kwargs): tensor_nd = partial(make_tensor, (S, S, S), device=device, dtype=dtype, requires_grad=requires_grad) tensor_1d = partial(make_tensor, (S,), device=device, dtype=dtype, requires_grad=requires_grad) return [ SampleInput(tensor_nd()), SampleInput(tensor_nd(), kwargs=dict(dim=1)), SampleInput(tensor_nd(), kwargs=dict(dim=1, unbiased=True, keepdim=True)), SampleInput(tensor_1d(), kwargs=dict(dim=0, unbiased=True, keepdim=True)), SampleInput(tensor_1d(), kwargs=dict(dim=0, unbiased=False, keepdim=False)), SampleInput(tensor_nd(), kwargs=dict(dim=(1,), correction=S // 2)), SampleInput(tensor_nd(), kwargs=dict(dim=None, correction=0, keepdim=True)), ] def _generate_correlation_inputs(device, dtype, requires_grad, **kwargs): shapes = [(2,), (1, 2), (3, 2), (2, 3)] for shape in shapes: yield make_tensor(shape, dtype=dtype, device=device, requires_grad=requires_grad) def sample_inputs_corrcoef(op_info, device, dtype, requires_grad, **kwargs): return [SampleInput(t) for t in _generate_correlation_inputs(device, dtype, requires_grad)] def sample_inputs_cov(op_info, device, dtype, requires_grad, **kwargs): inputs = [] for t in _generate_correlation_inputs(device, dtype, requires_grad): inputs.append(SampleInput(t)) num_observations = t.numel() if t.ndimension() < 2 else t.size(1) fweights = make_tensor((num_observations,), dtype=torch.int, device=device, low=1, high=10) aweights = make_tensor((num_observations,), dtype=torch.float, device=device, low=0, high=1, requires_grad=requires_grad) for correction, fw, aw in product(range(num_observations), [None, fweights], [None, aweights]): inputs.append(SampleInput(t.clone().requires_grad_(requires_grad), kwargs={'correction': correction, 'fweights': fw, 'aweights': aw})) return inputs def error_inputs_cov(op_info, device, **kwargs): a = torch.rand(S, device=device) error_inputs = [] error_inputs.append(ErrorInput( SampleInput(torch.rand(S, S, S, device=device)), error_regex="expected input to have two or fewer dimensions")) error_inputs.append(ErrorInput( SampleInput(a, kwargs={'fweights': torch.rand(S, S, device=device)}), error_regex="expected fweights to have one or fewer dimensions")) error_inputs.append(ErrorInput( SampleInput(a, kwargs={'aweights': torch.rand(S, S, device=device)}), error_regex="expected aweights to have one or fewer dimensions")) error_inputs.append(ErrorInput( SampleInput(a, kwargs={'fweights': torch.rand(S, device=device)}), error_regex="expected fweights to have integral dtype")) error_inputs.append(ErrorInput( SampleInput(a, kwargs={'aweights': torch.tensor([1, 1], device=device)}), error_regex="expected aweights to have floating point dtype")) error_inputs.append(ErrorInput( SampleInput(a, kwargs={'fweights': torch.tensor([1], device=device)}), error_regex="expected fweights to have the same numel")) error_inputs.append(ErrorInput( SampleInput(a, kwargs={'aweights': torch.rand(1, device=device)}), error_regex="expected aweights to have the same numel")) error_inputs.append(ErrorInput( SampleInput(a, kwargs={'fweights': torch.tensor([-1, -2, -3, -4 , -5], device=device)}), error_regex="fweights cannot be negative")) error_inputs.append(ErrorInput( SampleInput(a, kwargs={'aweights': torch.tensor([-1., -2., -3., -4., -5.], device=device)}), error_regex="aweights cannot be negative")) return error_inputs def sample_inputs_svd(op_info, device, dtype, requires_grad=False, **kwargs): make_fullrank = make_fullrank_matrices_with_distinct_singular_values make_arg = partial(make_fullrank, dtype=dtype, device=device, requires_grad=requires_grad) is_linalg_svd = (op_info.name == "linalg.svd") batches = [(), (0, ), (3, )] ns = [0, 3, 5] def uniformize(usv): S = usv[1] k = S.shape[-1] U = usv[0][..., :k] Vh = usv[2] if is_linalg_svd else usv[2].mH Vh = Vh[..., :k, :] return U, S, Vh def fn_U(usv): U, _, _ = uniformize(usv) return U.abs() def fn_S(usv): return uniformize(usv)[1] def fn_Vh(usv): # We also return S to test _, S, Vh = uniformize(usv) return S, Vh.abs() def fn_UVh(usv): U, S, Vh = uniformize(usv) return U @ Vh, S fns = (fn_U, fn_S, fn_Vh, fn_UVh) fullmat = 'full_matrices' if is_linalg_svd else 'some' for batch, n, k, fullmat_val, fn in product(batches, ns, ns, (True, False), fns): shape = batch + (n, k) yield SampleInput(make_arg(*shape), kwargs={fullmat: fullmat_val}, output_process_fn_grad=fn) def sample_inputs_permute(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) cases = [((1, 2, 3, 4), (0, 2, 3, 1)), ((1, 2, 3, 4), (0, -2, -1, 1)), ((), ()), ((1, 2, 3, 4), (2, 1, 3, 0))] for shape, args in cases: yield SampleInput(make_arg(shape), args=(args,)) def reference_inputs_permute(op, device, dtype, requires_grad, **kwargs): yield from sample_inputs_permute(op, device, dtype, requires_grad, **kwargs) make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) cases = ( ((), ()), ((1,), (0,)), ((2, 2), (1, 0)), ((2, 2), (0, 1)), ((2, 0, 1), (0, 2, 1)), ((3, 4, 2), (2, 1, 0)), ((3, 4, 2), (1, 0, 2)), ((3, 4, 2), (0, 1, 2)), ) # Adds tricky permutations and permutations with noncontiguity for shape, permutation in cases: for p in itertools.permutations(permutation): a = make_arg(shape).permute(p) yield SampleInput(a, args=(permutation,)) a = make_arg(shape, noncontiguous=True).permute(p) yield SampleInput(a, args=(permutation,)) def sample_inputs_linalg_svdvals(op_info, device, dtype, requires_grad=False, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) batches = [(), (0, ), (2, ), (1, 1)] ns = [5, 2, 0] for batch, m, n in product(batches, ns, ns): yield SampleInput(make_arg(batch + (m, n))) def sample_inputs_softshrink_hardshrink_hardtanh(op_info, device, dtype, requires_grad=False, **kwargs): N = 10 tensors = [SampleInput(make_tensor((N, N), device=device, dtype=dtype, requires_grad=requires_grad)) for _ in range(1, N)] return tensors def sample_inputs_eig(op_info, device, dtype, requires_grad=False, **kwargs): eigvecs = make_tensor((S, S), device=device, dtype=dtype, low=None, high=None) eigvals = make_tensor((S,), device=device, dtype=dtype, low=None, high=None) # we produce only diagonazible inputs which do not have # complex eigenvalues for real inputs, as there is no # backward implementation for real inputs with complex # eigenvalues yet. input = (eigvecs * eigvals.unsqueeze(-2)) @ eigvecs.inverse() input.requires_grad_(requires_grad) def process_output(eigpair): eigvals, eigvecs = eigpair if dtype.is_complex: # eig produces eigenvectors which are normalized to 1 norm. # Note that if v is an eigenvector, so is v * e^{i \phi}, # and |v| = |v * e^{i \phi}| = 1. # This, however, makes the eigenvector backward computation process # rather unstable unless the objective function is gauge-invariant, # that is if f(z) == f(|z|), for example. # Hence for complex inputs we ignore the phases and return only # the absolute values. return eigvals, eigvecs.abs() else: return eigvals, eigvecs return [ SampleInput( input, kwargs=dict(eigenvectors=True), output_process_fn_grad=process_output ), ] def sample_inputs_einsum(op_info, device, dtype, requires_grad=False, **kwargs): def c(t): return t.clone().requires_grad_(requires_grad) x = make_tensor((3,), dtype=dtype, device=device, requires_grad=requires_grad) y = make_tensor((4,), dtype=dtype, device=device, requires_grad=requires_grad) A = make_tensor((2, 3,), dtype=dtype, device=device, requires_grad=requires_grad) B = make_tensor((1, 3,), dtype=dtype, device=device, requires_grad=requires_grad) C = make_tensor((1, 2, 3,), dtype=dtype, device=device, requires_grad=requires_grad) D = make_tensor((1, 3, 4,), dtype=dtype, device=device, requires_grad=requires_grad) E = make_tensor((4, 4,), dtype=dtype, device=device, requires_grad=requires_grad) H = make_tensor((3, 3,), dtype=dtype, device=device, requires_grad=requires_grad) I = make_tensor((1, 3, 1,), dtype=dtype, device=device, requires_grad=requires_grad) inputs = [] # Vector operations inputs.append(SampleInput([c(x)], args=('i->',))) # sum inputs.append(SampleInput([c(x), c(y)], args=('i,j->ij',))) # outer # Matrix operations inputs.append(SampleInput([c(A)], args=("ij->i",))) # col sum inputs.append(SampleInput([c(A), c(B)], args=("ij,kj->ik",))) # matmul inputs.append(SampleInput([c(A), c(E)], args=("ij,Ab->ijAb",))) # matrix outer product # Tensor operations inputs.append(SampleInput([c(C), c(D)], args=("aij,ajk->aik",))) # batch matmul inputs.append(SampleInput([c(D), c(E)], args=("aij,jk->aik",))) # tensor matrix contraction inputs.append(SampleInput([c(C), c(B)], args=("ijk,ik->j",))) # non contiguous # Test diagonals inputs.append(SampleInput([c(I)], args=('iji->j',))) # non-contiguous trace # Test ellipsis inputs.append(SampleInput([c(H)], args=("i...->...",))) inputs.append(SampleInput([c(C), c(x)], args=('...ik, ...j -> ij',))) return inputs def sample_inputs_linalg_qr_geqrf(op_info, device, dtype, requires_grad=False, **kwargs): # QR is just well defined when the matrix is full rank make_fullrank = make_fullrank_matrices_with_distinct_singular_values make_arg = partial(make_fullrank, dtype=dtype, device=device, requires_grad=requires_grad) batches = [(), (0,), (2, ), (1, 1)] ns = [5, 2, 0] for batch, (m, n) in product(batches, product(ns, ns)): shape = batch + (m, n) yield SampleInput(make_arg(*shape)) def sample_inputs_flip(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) sizes = ((S, M, S), (S, 0, M)) all_dims = ((0, 1, 2), (0,), (0, 2), (-1,), ()) for size, dims in product(sizes, all_dims): yield SampleInput(make_arg(size), kwargs={"dims": dims}) def sample_inputs_fliplr_flipud(op_info, device, dtype, requires_grad, **kwargs): tensors = ( make_tensor((S, M, S), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), make_tensor((S, 0, M), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad) ) return [SampleInput(tensor) for tensor in tensors] # TODO: clamp shares tensors among its sample inputs --- we should prohibit this! def sample_inputs_clamp(op_info, device, dtype, requires_grad, **kwargs): x = make_tensor((S, M, S), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad) lb = make_tensor((S, M, S), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad) ub = make_tensor((S, M, S), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad) def detach(tensor): return tensor.clone().detach_().requires_grad_(requires_grad) return [ SampleInput(detach(x), args=(lb, ub)), SampleInput(detach(x), args=(detach(lb[0]), detach(ub[0]))), SampleInput(detach(x), args=(detach(lb[:, :1]),)), ] def sample_inputs_clamp_scalar(op_info, device, dtype, requires_grad, **kwargs): tensors = ( make_tensor((2, 3, 2), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), make_tensor((2, 0, 3), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), ) if dtype is torch.uint8: min_max_vals = ((2, 5), (3, 7)) else: min_max_vals = ((0, 1), (-1, 1)) output = [SampleInput( tensor.clone().requires_grad_(requires_grad), args=vals) for tensor, vals in product(tensors, min_max_vals)] output += [ SampleInput(tensors[0].clone().requires_grad_(requires_grad), args=(0.5, None)), SampleInput(tensors[0].clone().requires_grad_(requires_grad), args=(None, 0.5))] empty_tensor = make_tensor((), device=device, dtype=dtype, low=None, high=None, requires_grad=requires_grad) output.append(SampleInput(empty_tensor, args=(0.0, 1.0))) return output def sample_kwargs_clamp_scalar(device, dtype, input): if dtype is torch.uint8: min_val, max_val = (random.randint(1, 3), random.randint(4, 8)) elif dtype.is_floating_point: min_val, max_val = (random.uniform(-8, 0), random.uniform(1, 8)) # type: ignore[assignment] else: min_val, max_val = (random.randint(-8, 0), random.randint(1, 8)) return {'min': min_val, 'max': max_val}, {'a_min': min_val, 'a_max': max_val} def sample_inputs_cross(op_info, device, dtype, requires_grad, **kwargs): sample0 = SampleInput(make_tensor((S, 3), device=device, dtype=dtype, requires_grad=requires_grad), args=(make_tensor((S, 3), device=device, dtype=dtype, requires_grad=requires_grad),)) sample1 = SampleInput(make_tensor((S, 3, S), device=device, dtype=dtype, requires_grad=requires_grad), args=(make_tensor((S, 3, S), device=device, dtype=dtype, requires_grad=requires_grad),), kwargs={'dim': 1}) sample2 = SampleInput(make_tensor((S, 3), device=device, dtype=dtype, requires_grad=requires_grad), args=(make_tensor((S, 3), device=device, dtype=dtype, requires_grad=requires_grad),), kwargs={'dim': -1}) return (sample0, sample1, sample2) def sample_inputs_cumprod(op_info, device, dtype, requires_grad, **kwargs): def make_arg(shape): # shrink values to be in the interval [-1, +1] for better precision in gradgradcheck return make_tensor(shape, dtype=dtype, device=device, low=-1, high=+1, requires_grad=requires_grad) def prod_zeros(dim_select): assert len(dim_select) == 2 result = make_arg(3 * (S,)) result.narrow(dim_select[0], 0, 1).narrow(dim_select[1], 1, 1).zero_() result.narrow(dim_select[0], 2, 1).narrow(dim_select[1], 3, 1).zero_() result.narrow(dim_select[0], 4, 1).narrow(dim_select[1], 3, 1).zero_() return result for dim in range(3): yield SampleInput(make_arg((S, S, S)), args=(dim,)) # Scalar tensors and empty tensor for size in [(), (1,), (0,)]: yield SampleInput(make_arg(size), args=(0,)) yield SampleInput(prod_zeros([0, 1]), args=(1,)) yield SampleInput(prod_zeros([0, 2]), args=(1,)) yield SampleInput(prod_zeros([1, 2]), args=(1,)) # test dtype kwarg yield SampleInput(prod_zeros([1, 2]), args=(1,), kwargs={'dtype': dtype}) def sample_inputs_view_as_complex(op_info, device, dtype, requires_grad, **kwargs): return [SampleInput(make_tensor((S, 2), dtype=dtype, device=device, requires_grad=requires_grad),)] def sample_inputs_view_as_real(op_info, device, dtype, requires_grad, **kwargs): tensors = ( make_tensor((S, S), dtype=dtype, device=device, requires_grad=requires_grad), make_tensor((), dtype=dtype, device=device, requires_grad=requires_grad) ) return [SampleInput(tensor) for tensor in tensors] def sample_inputs_prod(op_info, device, dtype, requires_grad, **kwargs): def make_arg(shape): # shrink values to be in the interval [-1, +1] for better precision in gradgradcheck return make_tensor(shape, dtype=dtype, device=device, low=-1, high=+1, requires_grad=requires_grad) def prod_single_zero(): result = make_arg(2 * (S,)) result[0, 1] = 0 return result for sample in sample_inputs_cumprod(op_info, device, dtype, requires_grad): # only Tensor, ignore other inputs yield SampleInput(sample.input.clone().requires_grad_(requires_grad)) yield sample # Generates samples with keepdim = True for sample in sample_inputs_cumprod(op_info, device, dtype, requires_grad): sample.kwargs['keepdim'] = True yield sample yield SampleInput(prod_single_zero()) yield SampleInput(make_arg((3, 3, 3)), args=(1,)) yield SampleInput(make_arg((3, 3, 3)), args=(1,), kwargs={'keepdim': True}) # test zero scalar tensor zero = make_arg(()) zero.zero_() yield SampleInput(zero.clone().requires_grad_(requires_grad)) yield SampleInput(zero.clone().requires_grad_(requires_grad), args=(0,)) yield SampleInput(zero.clone().requires_grad_(requires_grad), args=(0,), kwargs={'keepdim': True}) def error_inputs_neg(op_info, device, **kwargs): si = SampleInput(torch.tensor((False, True), device=device)) msg = ("Negation, the `\\-` operator, on a bool tensor is not supported." " If you are trying to invert a mask, use the `\\~` or" " `logical_not\\(\\)` operator instead.") return (ErrorInput(si, error_regex=msg),) def sample_inputs_diag(op_info, device, dtype, requires_grad, **kwargs): vec_sample = SampleInput(make_tensor((M, ), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad)) tensors = ( make_tensor((M, M), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), make_tensor((3, 5), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), make_tensor((5, 3), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), ) args = ((), (2,), (-2,), (1,), (2,)) samples = [] for tensor, arg in product(tensors, args): samples.append(SampleInput(tensor.clone().requires_grad_(requires_grad), args=arg)) return samples + [vec_sample] def sample_inputs_diagonal_diag_embed(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) # Shapes for 2D Tensors shapes_2d = ((M, M), (3, 5), (5, 3)) # Shapes for 3D Tensors shapes_3d = ((M, M, M),) kwargs_2d = (dict(), dict(offset=2), dict(offset=2), dict(offset=1)) kwargs_3d = (dict(offset=1, dim1=1, dim2=2), dict(offset=2, dim1=0, dim2=1), dict(offset=-2, dim1=0, dim2=1)) for shape, kwarg in chain(product(shapes_2d, kwargs_2d), product(shapes_3d, kwargs_3d)): yield SampleInput(make_arg(shape), kwargs=kwarg) def sample_inputs_diagonal_scatter(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) # Shapes for 2D Tensors shapes_2d = ((M, M), (3, 5), (5, 3)) # Shapes for 3D Tensors shapes_3d = ((M, M, M),) args_2d = ((), (2,), (-2,), (1,)) args_3d = ((1, 1, 2), (2, 0, 1), (-2, 0, 1)) for input_shape, arg in chain(product(shapes_2d, args_2d), product(shapes_3d, args_3d)): input_ = make_arg(input_shape) # We can programatically figure out the right shape for src: # It should be the same size as input.diagonal(other_args...) if not isinstance(arg, tuple): arg_tuple = (arg,) else: arg_tuple = arg src_shape = input_.diagonal(*arg_tuple).size() src = make_arg(src_shape) yield SampleInput(input_, args=(src, *arg_tuple)) def sample_inputs_to_sparse(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return (SampleInput(make_arg((S, S)), args=(), output_process_fn_grad=lambda x: x.to_dense()), SampleInput(make_arg((S, S)), args=(1,), output_process_fn_grad=lambda x: x.to_dense()),) def sample_inputs_cross_entropy(op_info, device, dtype, requires_grad, **kwargs): batch_size, num_classes = shape = (2, 3) reductions = ("mean", "sum", "none") input_shape_and_kwargs: List[Tuple[Tuple[int, ...], Dict[str, Any]]] = [ (shape, dict()), ((*shape, 1), dict()), ((*shape, 1, 2), dict()), ((*shape, 1, 2, 3), dict()), *[(shape, dict(reduction=reduction)) for reduction in reductions], *[ ( shape, dict( weight=make_tensor((num_classes,), device=device, dtype=dtype), reduction=reduction, ), ) for reduction in reductions ], (shape, dict(ignore_index=1)), ] sample_inputs = [] for (input_shape, kwargs), probabilities_target in itertools.product(input_shape_and_kwargs, (False, True)): input = make_tensor(input_shape, device=device, dtype=dtype, requires_grad=requires_grad) if probabilities_target: # ignore_index is not supported for probabilities target if "ignore_index" in kwargs: continue target = make_tensor( input_shape, low=0, high=1, device=device, dtype=dtype, requires_grad=requires_grad, ) else: target = make_tensor( (batch_size, *input_shape[2:]), low=0, high=num_classes, device=device, dtype=torch.long, ) if "ignore_index" in kwargs and torch.all(target == kwargs["ignore_index"]): # make sure at least one item in target is not ignored target[0] = random.sample(set(range(num_classes)) - {kwargs["ignore_index"]}, 1)[0] sample_inputs.append(SampleInput(input, args=(target,), kwargs=kwargs)) return sample_inputs # Used for log_softmax, softmax, softmin def sample_inputs_softmax_variant(op_info, device, dtype, requires_grad, with_dtype=False, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) cases = [ ((S, ), (0, )), ((S, S), (0, )), ((S, S), (1, )), ((S, S), (-1, )), ((S, M, S), (2, )), ] # PyTorch on XLA throws an error when passed with dim argument for 0d tensor. # See https://github.com/pytorch/xla/issues/3061 for more details. if torch.device(device).type != 'xla': cases.append(((), (0, ))) return [ SampleInput(make_arg(shape), args=dim, kwargs=dict(dtype=torch.float64) if with_dtype else None) for shape, dim in cases ] def sample_inputs_masked_softmax(op_info, device, dtype, requires_grad, with_dtype=False, **kwargs): """Sample inputs for masked softmax, log_softmax, and softmin. Masked normalization operator is a reduction operator with trailing mask optional argument. A mask is a bool tensor with the same shape as input or a shape that is broadcastable to input shape. """ inputs: List[SampleInput] = [] for sample_input in sample_inputs_softmax_variant(op_info, device, dtype, requires_grad, with_dtype=with_dtype, **kwargs): for mask in _generate_masked_op_mask(sample_input.input.shape, device, **kwargs): sample_input_args, sample_input_kwargs = sample_input.args, dict(mask=mask, **sample_input.kwargs) inputs.append(SampleInput(sample_input.input.clone().requires_grad_(requires_grad), args=sample_input_args, kwargs=sample_input_kwargs)) return inputs def sample_inputs_masked_cumops(op_info, device, dtype, requires_grad, **kwargs): """Sample inputs for masked cumsum and cumprod. """ inputs: List[SampleInput] = [] for sample_input in sample_inputs_softmax_variant(op_info, device, dtype, requires_grad, **kwargs): for mask in _generate_masked_op_mask(sample_input.input.shape, device, **kwargs): if type(mask) != torch.Tensor: continue sample_input_args, sample_input_kwargs = sample_input.args, dict(mask=mask, **sample_input.kwargs) if 'keepdim' in sample_input_kwargs: sample_input_kwargs.pop('keepdim') # dimension is required if sample_input_args: dim = sample_input.args[0] else: if 'dim' not in sample_input_kwargs: continue dim = sample_input_kwargs.pop('dim') sample_input_args = (dim,) inputs.append(SampleInput(sample_input.input.clone().requires_grad_(requires_grad), args=sample_input_args, kwargs=sample_input_kwargs)) return inputs def sample_inputs_masked_normalize(op_info, device, dtype, requires_grad, **kwargs): """Sample inputs for masked normalize. """ inputs: List[SampleInput] = [] for ord in [2.0, 1, float('inf'), float('-inf'), 0]: for sample_input in sample_inputs_softmax_variant(op_info, device, dtype, requires_grad, **kwargs): sample_input_args, sample_input_kwargs = (ord,) + sample_input.args, sample_input.kwargs.copy() inputs.append(SampleInput(sample_input.input.clone().requires_grad_(requires_grad), args=sample_input_args, kwargs=sample_input_kwargs)) return inputs def sample_inputs_logit(op_info, device, dtype, requires_grad, **kwargs): low, high = op_info.domain # Note: Operator is very sensitive at points near the # start and end of domain and leads to NaN for float16 # if domain_eps is 1e-5. domain_eps = op_info._domain_eps if dtype != torch.float16 else 3e-2 low = low + domain_eps high = high - domain_eps samples = ( SampleInput(make_tensor((S, S, S), dtype=dtype, device=device, low=low, high=high, requires_grad=requires_grad)), SampleInput(make_tensor((S, S, S), dtype=dtype, device=device, low=low, high=high, requires_grad=requires_grad), args=(0.2,)), SampleInput(make_tensor((), dtype=dtype, device=device, low=low, high=high, requires_grad=requires_grad)), SampleInput(make_tensor((), dtype=dtype, device=device, low=low, high=high, requires_grad=requires_grad), args=(0.2,)), ) return samples def sample_inputs_isin(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # isin has two paths based on the size of elements and test_elements. # if elements.numel() < 10 * pow(test_elements.numel(), 0.145): yield SampleInput(make_arg((L,)), args=(make_arg((S,)),)) # else: yield SampleInput(make_arg((S,)), args=(make_arg((L,)),)) def sample_inputs_masked_scatter(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) yield SampleInput(make_arg((S, S)), args=(torch.randn(S, S, device=device) > 0, make_arg((S, S)))) yield SampleInput(make_arg((S, S)), args=(torch.randn((S,), device=device) > 0, make_arg((S, S)))) yield SampleInput(make_arg((S, S)), args=(bernoulli_scalar().to(device), make_arg((S, S)))) yield SampleInput(make_arg((S,)), args=(torch.randn(S, S, device=device) > 0, make_arg((S, S))), broadcasts_input=True) def sample_inputs_masked_fill(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) yield SampleInput(make_arg((S, S)), args=(torch.randn(S, S, device=device) > 0, 10)) yield SampleInput(make_arg((S, S)), args=(torch.randn(S, S, device=device) > 0, make_arg(()))) yield SampleInput(make_arg((S, S)), args=(torch.randn(S, device=device) > 0, 10)) yield SampleInput(make_arg(()), args=(torch.randn((), device=device) > 0, 10)) yield SampleInput(make_arg(()), args=(torch.randn((), device=device) > 0, make_arg(()))) yield SampleInput(make_arg((S, S)), args=(torch.randn((), device=device) > 0, 10)) yield SampleInput(make_arg((S,)), args=(torch.randn(S, S, device=device) > 0, make_arg(())), broadcasts_input=True) yield SampleInput(make_arg((S,)), args=(torch.randn(S, S, device=device) > 0, 10), broadcasts_input=True) def sample_inputs_masked_select(op_info, device, dtype, requires_grad, **kwargs): samples = ( SampleInput(make_tensor((M, M), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=(torch.randn(M, M, device=device) > 0,)), SampleInput(make_tensor((M, M), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=(torch.randn((M,), device=device) > 0,)), SampleInput(make_tensor((M,), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=(torch.randn((M, M), device=device) > 0,)), SampleInput(make_tensor((M, 1, M), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=(torch.randn((M, M), device=device) > 0,)), SampleInput(make_tensor((), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=(torch.tensor(1, device=device, dtype=torch.bool),)), SampleInput(make_tensor((M, M), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=(torch.tensor(1, device=device, dtype=torch.bool),)), SampleInput(make_tensor((), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=(torch.randn((M, M), device=device) > 0,)), ) return samples def sample_inputs_matrix_exp(op_info, device, dtype, requires_grad, **kwargs): samples = ( SampleInput(make_tensor((S, S), dtype=dtype, device=device, requires_grad=requires_grad)), SampleInput(make_tensor((S, S, S), dtype=dtype, device=device, requires_grad=requires_grad)), ) return samples def sample_inputs_matmul(op_info, device, dtype, requires_grad, **kwargs): test_cases = (((L,), (L,)), ((S, M), (M,)), ((M,), (M, S)), ((S, M), (M, S)), ((S, 0), (0, M)), ((S, S, M), (M,)), ((S, S, M), (M, S)), ((S, S, 0), (0, S)), ((M,), (S, M, S)), ((S, M), (S, M, S)), ((0, 0), (S, 0, 0)), ((S, S, M, M), (S, S, M, S)), ((S, S, M, M), (M,)), ((M,), (S, S, M, S))) sample_inputs = [] for lhs_shape, rhs_shape in test_cases: lhs = make_tensor(lhs_shape, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad) rhs = make_tensor(rhs_shape, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad) if op_info.name == 'matmul': sample_inputs.append(SampleInput(lhs, args=(rhs,))) elif op_info.name == '__rmatmul__': sample_inputs.append(SampleInput(rhs, args=(lhs,))) else: raise RuntimeError("`op_info.name` must be 'matmul' or '__rmatmul__'") return tuple(sample_inputs) def sample_inputs_meshgrid(op_info: OpInfo, device: torch.device, dtype: torch.dtype, requires_grad: bool, *, variant: str, **kwargs) -> List[SampleInput]: if variant == 'variadic': def make_inputs( tensors: List[torch.Tensor]) -> Tuple[Union[torch.Tensor, List[torch.Tensor]], Tuple[torch.Tensor, ...]]: return tensors[0], tuple(tensors[1:]) elif variant == 'list': def make_inputs( tensors: List[torch.Tensor]) -> Tuple[Union[torch.Tensor, List[torch.Tensor]], Tuple[torch.Tensor, ...]]: return tensors, () else: raise ValueError( 'Unsupported variant, must be one of {"variadic", "list"}. ' f'Got "{variant}".') SCALAR = torch.Size([]) VECTOR = torch.Size([3]) test_cases: List[List[torch.Size]] = [ [SCALAR], [VECTOR], [VECTOR, SCALAR], [VECTOR, SCALAR, VECTOR], [VECTOR, SCALAR, VECTOR, SCALAR], ] sample_inputs = [] for shapes, indexing in itertools.product(test_cases, {'xy', 'ij'}): input, args = make_inputs( [make_tensor(shape, dtype=dtype, device=device, requires_grad=requires_grad) for shape in shapes]) sample_inputs.append(SampleInput(input=input, args=args, kwargs=dict(indexing=indexing))) return sample_inputs def sample_inputs_polygamma(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) tensor_shapes = ((S, S), ()) ns = (1, 2, 3, 4, 5) for shape, n in product(tensor_shapes, ns): yield SampleInput(make_arg(shape), args=(n,)) def sample_inputs_mvlgamma(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) tensor_shapes = ((S, S), ()) ns = (1, 2, 3, 4, 5) # Since the accepted lower bound for input # to mvlgamma depends on `p` argument, # the following function computes the lower bound # which we pass to `make_tensor`. def compute_min_val(p): return (p - 1.) / 2 for shape, n in product(tensor_shapes, ns): min_val = compute_min_val(n) if not dtype.is_floating_point: # Round-up minimum value for integral dtypes min_val += 1 else: min_val += 2 * torch.finfo(dtype).eps yield SampleInput(make_arg(shape, low=min_val), args=(n,)) # Since `mvlgamma` has multiple entries, # there are multiple common skips for the additional # entries. Following function is a helper to that end. def skips_mvlgamma(skip_redundant=False): skips = ( # outside domain values are hard error for mvlgamma op. DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_float_domains'), ) if skip_redundant: # Redundant tests skips = skips + ( # type: ignore[assignment] DecorateInfo(unittest.skip("Skipped!"), 'TestGradients'), DecorateInfo(unittest.skip("Skipped!"), 'TestJit'), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon'), ) return skips # To test reference numerics against multiple values of argument `p`, # we make multiple OpInfo entries with each entry corresponding to different value of p. # We run the op tests from test_ops.py only for `p=1` to avoid redundancy in testing. # Class `MvlGammaInfo` already contains the basic information related to the operator, # it only takes arguments like `domain`, `skips` and `sample_kwargs`, which # differ between the entries. class MvlGammaInfo(UnaryUfuncInfo): def __init__(self, variant_test_name, domain, skips, sample_kwargs): super(MvlGammaInfo, self).__init__( 'mvlgamma', ref=reference_mvlgamma if TEST_SCIPY else _NOTHING, aliases=('special.multigammaln',), variant_test_name=variant_test_name, domain=domain, decorators=(precisionOverride({torch.float16: 5e-2}),), dtypes=all_types_and(torch.bfloat16), dtypesIfCUDA=all_types_and(torch.half), sample_inputs_func=sample_inputs_mvlgamma, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=skips, sample_kwargs=sample_kwargs) def sample_inputs_entr(op_info, device, dtype, requires_grad, **kwargs): low, _ = op_info.domain if requires_grad: low = 0 + op_info._domain_eps return (SampleInput(make_tensor((L,), dtype=dtype, device=device, low=low, requires_grad=requires_grad)), SampleInput(make_tensor((), dtype=dtype, device=device, low=low, requires_grad=requires_grad))) # TODO: Consolidate `i0e` with sample_inputs_unary when `make_tensor`, # supports `exclude` argument. # For more context: https://github.com/pytorch/pytorch/pull/56352#discussion_r633277617 def sample_inputs_i0_i1(op_info, device, dtype, requires_grad, **kwargs): samples = (SampleInput(make_tensor((S,), dtype=dtype, device=device, requires_grad=requires_grad)), SampleInput(make_tensor((), dtype=dtype, device=device, requires_grad=requires_grad))) if requires_grad and op_info.op == torch.special.i0e: # NOTE: `i0e`'s first-order gradient is not continous # at `0`, hence we don't test `i0e` with any input being `0`. # TODO: Remove this when `make_tensor` supports excluding `0`. for sample in samples: t = sample.input t[t == 0] = torch.finfo(dtype).eps # type: ignore[index] elif requires_grad and op_info.op != torch.special.i0e: # Special Case for gradient # Sample with `0` in the input t = make_tensor((S,), dtype=dtype, device=device, requires_grad=requires_grad) t[0] = 0 samples += (SampleInput(t),) # type: ignore[assignment] return samples def sample_inputs_cumulative_ops(op_info, device, dtype, requires_grad, supports_dtype_kwargs=True, **kwargs): def _make_tensor_helper(shape, low=None, high=None): return make_tensor(shape, dtype=dtype, device=device, low=low, high=high, requires_grad=requires_grad) samples = [ SampleInput(_make_tensor_helper((S, S, S)), args=(0,)), SampleInput(_make_tensor_helper((S, S, S)), args=(1,)), SampleInput(_make_tensor_helper(()), args=(0,)), ] if supports_dtype_kwargs: # NOTE: if `dtype` is not same as input, then inplace variants fail with # `provided dtype must match the dtype of self tensor in cumsum` samples.append(SampleInput(_make_tensor_helper((S, S, S)), args=(1,), kwargs={'dtype': dtype})) return samples def sample_inputs_unfold(op_info, device, dtype, requires_grad, **kwargs): test_cases = ( ((), (0, 1, 1)), ((S, S, S, S), (0, 3, 1)), ((S, S, S, S), (1, 3, 1)), ((S, S, S, S), (2, 3, 1)), ((S, S, S, S), (3, 3, 1)), ((S, S, S, S), (0, 3, 2)), ((S, S, S, S), (1, 3, 2)), ((S, S, S, S), (2, 3, 2)), ((S, S, S, S), (3, 3, 2)), ((S, S, S, S), (0, 4, 1)), ((S, S, S, S), (1, 4, 1)), ((S, S, S, S), (2, 4, 1)), ((S, S, S, S), (3, 4, 1)), ((M,), (0, 3, 1)), ((M,), (0, 3, 2)), ((M,), (0, 3, 3)), ((1000,), (0, 3, 11)), ((1000,), (0, 2, 27)), ((10, 10), (0, 1, 2)), ((10, 10), (1, 2, 3)), ((10, 10), (1, 2, 2)), ((S, S, S), (2, 3, 2)), ) sample_inputs = [] for shape, arguments in test_cases: sample_inputs += [SampleInput(make_tensor(shape, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad), args=arguments)] return sample_inputs def sample_inputs_split(op_info, device, dtype, requires_grad, *, list_args=False, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) if list_args: cases = ( ((S, S, S), ([int(S / 3), S - int(S / 3) * 2, int(S / 3)],)), ((S, S, S), ([int(S / 2), S - int(S / 2) * 2, int(S / 2)], 2),), ((S, S, S), ([int(S / 2), S - int(S / 2) * 2, int(S / 2)], -2),) ) else: cases = ( # type: ignore[assignment] ((S, S, S), (2,)), ((S, S, S), (S, 1)), ) for shape, args in cases: yield SampleInput(make_arg(shape), args=args) def sample_inputs_split_with_sizes(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) cases = (((S, S, S), ([int(S / 3), S - int(S / 3) * 2, int(S / 3)],)), ((S, S, S), ([int(S / 3), S - int(S / 3), 0],)), ((S, S, S), ([int(S / 3), S - int(S / 3) * 2, int(S / 3)], 2)), ((S, S, S), ([int(S / 3), S - int(S / 3) * 2, int(S / 3)], -2)), ) for shape, args in cases: yield SampleInput(make_arg(shape), args=args) def sample_inputs_msort(op_info, device, dtype, requires_grad, **kwargs): def apply_grad(t): if dtype in floating_types_and(torch.float16, torch.bfloat16): t.requires_grad_(requires_grad) def large_1d_unique(dtype, device): res = torch.randperm(L * L * L, dtype=torch.int64, device=device) res = res.to(dtype) apply_grad(res) return res samples = [] # Test case for large tensor. largesample = SampleInput(large_1d_unique(dtype, device)) sample = SampleInput(make_tensor((S, M, S), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad)) return [largesample, sample] def sample_inputs_lerp(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) samples = ( # no broadcast SampleInput(make_arg((S, S)), args=(make_arg((S, S)), 0.4)), # broadcast rhs SampleInput(make_arg((S, S)), args=(make_arg((S,)), 0.4)), # scalar tensor SampleInput(make_arg(()), args=(make_arg(()), 0.4)), # broadcast rhs scalar-tensor SampleInput(make_arg((S, S)), args=(make_arg(()), 0.4)), # broadcast rhs with weight tensor SampleInput(make_arg((S, S)), args=(make_arg((S,)), make_arg((S, S)))), # broadcast rhs and weight tensor SampleInput(make_arg((S, S)), args=(make_arg((S, 1)), make_arg((S,)))), # broadcast lhs SampleInput(make_arg((S,)), args=(make_arg((S, S)), 0.4), broadcasts_input=True), # scalar broadcast_lhs SampleInput(make_arg(()), args=(make_arg((S, S)), 0.4), broadcasts_input=True), # broadcast all SampleInput(make_arg((S, 1)), args=(make_arg((S, S)), 0.4), broadcasts_input=True), # tensor broadcast all SampleInput(make_arg((S, 1)), args=(make_arg((S, S)), make_arg((S, 1))), broadcasts_input=True), # no broadcast with weight tensor SampleInput(make_arg((S, S)), args=(make_arg((S, S)), make_arg((S, S)))), # broadcast lhs with weight tensor SampleInput(make_arg((S,)), args=(make_arg((S, S)), make_arg((S, S))), broadcasts_input=True), # broadcast lhs and weight tensor SampleInput(make_arg((S,)), args=(make_arg((S, S, S)), make_arg((S, S))), broadcasts_input=True), # broadcast lhs and weight tensor variant SampleInput(make_arg((S, S)), args=(make_arg((S, S, S)), make_arg((S,))), broadcasts_input=True), ) if dtype.is_complex: samples = samples + ( # type: ignore[assignment] # no broadcast SampleInput(make_arg((S, S)), args=(make_arg((S, S)), 0.4j)), SampleInput(make_arg((S, S)), args=(make_arg((S, S)), 1.2 + 0.1j)), # broadcast rhs SampleInput(make_arg((S, S)), args=(make_arg((S,)), 0.4j)), SampleInput(make_arg((S, S)), args=(make_arg((S, S)), 5.4 + 9j)), # scalar tensor SampleInput(make_arg(()), args=(make_arg(()), 0.4j)), SampleInput(make_arg(()), args=(make_arg(()), 6.1 + 0.004j)), # broadcast rhs scalar-tensor SampleInput(make_arg((S, S)), args=(make_arg(()), 0.4j)), SampleInput(make_arg((S, S)), args=(make_arg(()), 1 + 2j)), ) return samples def sample_inputs_tensordot(self, device, dtype, requires_grad, **kwargs): cases = ( ((2, 2, 2), (2, 2, 2), (2)), ((2, 2, 1), (2, 1, 2), ([0, 1], [2, 0])), ) samples = [] for first_shape, second_shape, dims in cases: samples.append(SampleInput(make_tensor(first_shape, dtype=dtype, device=device, requires_grad=requires_grad), args=(make_tensor(second_shape, dtype=dtype, device=device, requires_grad=requires_grad),), kwargs=dict(dims=dims,))) return tuple(samples) def sample_inputs_kron(op_info, device, dtype, requires_grad, **kwargs): test_cases = ( ((S, S), (M, L)), ) sample_inputs = [] for input_shape, other_shape in test_cases: input = make_tensor(input_shape, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad) other = make_tensor(other_shape, dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad) sample = SampleInput(input, args=(other,)) sample_inputs.append(sample) return tuple(sample_inputs) def sample_inputs_inner(self, device, dtype, requires_grad, **kwargs): return ( SampleInput( make_tensor((S, ), dtype=dtype, device=device, requires_grad=requires_grad), args=( make_tensor((S, ), dtype=dtype, device=device, requires_grad=requires_grad), ) ), SampleInput( make_tensor((), dtype=dtype, device=device, requires_grad=requires_grad), args=( make_tensor((S, S), dtype=dtype, device=device, requires_grad=requires_grad), ) ), ) def sample_inputs_scatter(op_info, device, dtype, requires_grad, **kwargs): def _tensor(shape, dtype=dtype, low=None, high=None): return make_tensor(shape, dtype=dtype, device=device, low=low, high=high, requires_grad=requires_grad) def _gather(shape, index_dim, max_indices): return gather_variable(shape, index_dim, max_indices, device=device) zero = torch.tensor(0, dtype=torch.long, device=device) test_cases = ( (_tensor((M, S)), (0, _gather((S, S), 1, M), _tensor((S, S)))), (_tensor((M, S)), (1, _gather((S, S), 0, S), _tensor((S, S)))), (_tensor((M, S)), (-1, _gather((S, S), 0, S), _tensor((S, S)))), (_tensor((M, S)), (0, _gather((M, S // 2), 1, M), _tensor((M, S // 2)))), (_tensor((M, S)), (1, _gather((M, S // 2), 0, S), _tensor((M, S // 2)))), (_tensor((M, S)), (-1, _gather((M, S // 2), 0, S), _tensor((M, S // 2)))), (_tensor(()), (0, zero.clone().detach(), _tensor(()))), (_tensor(()), (0, zero.clone().detach(), 2.5)), ) samples = [] for tensor, args in test_cases: samples.append(SampleInput(tensor, args=args)) if not requires_grad: samples.append(SampleInput( tensor.clone().detach(), args=args, kwargs={'reduce': 'add'} )) if dtype.is_floating_point: samples.append(SampleInput( tensor.clone().detach(), args=args, kwargs={'reduce': 'multiply'} )) return samples def sample_inputs_scatter_add(op_info, device, dtype, requires_grad, **kwargs): def _tensor(shape, dtype=dtype, low=None, high=None): return make_tensor(shape, dtype=dtype, device=device, low=low, high=high, requires_grad=requires_grad) def _gather(shape, index_dim, max_indices): return gather_variable(shape, index_dim, max_indices, device=device) zero = torch.tensor(0, dtype=torch.long, device=device) test_cases = ( (_tensor((M, S)), (0, _gather((S, S), 1, M), _tensor((S, S)))), (_tensor((M, S)), (1, _gather((S, S), 0, S), _tensor((S, S)))), (_tensor((M, S)), (-1, _gather((S, S), 0, S), _tensor((S, S)))), (_tensor((M, S)), (0, _gather((M, S // 2), 1, M), _tensor((M, S // 2)))), (_tensor((M, S)), (1, _gather((M, S // 2), 0, S), _tensor((M, S // 2)))), (_tensor((M, S)), (-1, _gather((M, S // 2), 0, S), _tensor((M, S // 2)))), (_tensor(()), (0, zero.clone().detach(), _tensor(()))), ) return [SampleInput(tensor, args=args) for tensor, args in test_cases] def sample_inputs_scatter_reduce(op_info, device, dtype, requires_grad, **kwargs): def _tensor(shape, dtype=dtype, low=None, high=None): return make_tensor(shape, dtype=dtype, device=device, low=low, high=high, requires_grad=requires_grad) def _gather(shape, index_dim, max_indices): return gather_variable(shape, index_dim, max_indices, device=device) zero = torch.tensor(0, dtype=torch.long, device=device) test_cases = ( ((M, S), 0, _gather((S, S), 1, M), (S, S)), ((M, S), 1, _gather((S, S), 0, S), (S, S)), ((M, S), -1, _gather((S, S), 0, S), (S, S)), ((M, S), 0, _gather((M, S // 2), 1, M), (M, S // 2)), ((M, S), 1, _gather((M, S // 2), 0, S), (M, S // 2)), ((M, S), -1, _gather((M, S // 2), 0, S), (M, S // 2)), ((), 0, zero.clone().detach(), ()), ) reduce = op_info.variant_test_name for args, include_self in product(test_cases, [True, False]): inp_shape, dim, index, src_shape = args yield SampleInput(_tensor(inp_shape), args=(dim, index, _tensor(src_shape), reduce), kwargs={'include_self': include_self}) # Sample inputs to test edge cases for backward # Check that gradients are propagated correctly for prod when zeros in self/src are reduced if requires_grad and reduce == 'prod': # This sample tests gradients for the following cases # (a) 1 zero reduced (from src (self[0, 1], self[1, 1]), from self (self[0, 0], self[2, 0])) # (b) 2 zeros reduced (1 from src and 1 from self (self[1, 0]) # (c) no zeros reduced (self([2, 1])) # (d) 2 zeros reduced (both from src) is tested in test/test_autograd.py # test_scatter_index_reduce_prod_gradgrad_error as this case is not supported for gradgrad input = torch.tensor([[0, 13], [0, 17], [0, 19]], dtype=dtype, device=device, requires_grad=requires_grad) src = torch.tensor([[0, 1, 2, 3], [0, 4, 0, 1], [2, 3, 5, 6]], dtype=dtype, device=device, requires_grad=requires_grad) idx = torch.tensor([[1, 1, 0, 0], [0, 0, 1, 1], [0, 0, 0, 1]], dtype=torch.long, device=device) yield SampleInput(input, args=(1, idx, src, reduce), kwargs={'include_self': True}) def sample_inputs_ravel(op_info, device, dtype, requires_grad, **kwargs): samples = (SampleInput(make_tensor((S, S, S), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad)), SampleInput(make_tensor((), dtype=dtype, device=device, low=None, high=None, requires_grad=requires_grad)),) return samples def sample_inputs_tril_triu(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) cases = (((M, M), ()), ((M, M), (2,),), ((S, M, M), ()), ((S, M, M), (2,)), ((3, 3, S, S), ()),) for shape, args in cases: yield SampleInput(make_arg(shape), args=args) def sample_inputs_clone(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) yield SampleInput(make_arg((S, M, S))) yield SampleInput(make_arg(())) def reference_inputs_clone(op, device, dtype, requires_grad, **kwargs): yield from sample_inputs_clone(op, device, dtype, requires_grad, **kwargs) shapes = ( (3, 5, 6), (1, 1, 3, 5, 6), (1, 1, 3, 5, 6, 1, 1), (1, 0, 3, 5, 0, 2), (1, 0, 3, 5, 0, 0, 1, 1, 2), (), ) make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) for shape in shapes: yield SampleInput(make_arg(shape)) yield SampleInput(make_arg(shape).transpose(0, -1)) yield SampleInput(make_arg(shape, noncontiguous=True)) yield SampleInput(make_arg(shape, noncontiguous=True).transpose(0, -1)) # shape, strides, offset strided_cases = ( ((5, 6, 2), (1, 1, 7), 2), ((5, 5, 4), (1, 1, 7), 2), ((5, 5, 2), (4, 5, 7), 3), ((5, 5, 2), (5, 5, 7), 3), ((5, 5, 2), (5, 5, 5), 3), ((9, 5, 2), (0, 1, 7), 3), ) for shape, strides, offset in strided_cases: yield SampleInput(make_arg(500,).as_strided(shape, strides, offset)) def sample_inputs_contiguous(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) yield SampleInput(make_arg((S, S))) def sample_inputs_sum_to_size(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) # list of tuples (shape, shape) defining the shapes of the input and output tensors sample_shapes = [ ((), ()), ((S), (1)), ((S, S), (1, 1)), ((S, S), (1, S)), ((S, S), (S, S)), ((S, S, S), (S, 1, S)), ] samples = [] for input_shape, output_shape in sample_shapes: input_t = make_arg(input_shape) samples.append(SampleInput(input_t, args=(output_shape,))) return samples def sample_inputs_resize_ops(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device) cases = (((S, S, S), (S * S, S)), ((), ()), ((), (1, 1, 1)), ) for shape, args_or_shape in cases: # Update `args` based on operator if op_info.name == 'resize_': # resize_ takes shape/tuple of ints, args = (args_or_shape, ) elif op_info.name == 'resize_as_': # resize_as_ takes another tensor args = (make_arg(shape, requires_grad=False), ) # type:ignore[assignment] else: raise ValueError("sample_inputs_resize_ops is being used with incorrect operator") yield(SampleInput(make_arg(shape, requires_grad=requires_grad), args=args)) def sample_inputs_view_reshape(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) cases = ( ((S, S, S), (S * S, S)), ((S * S, S), (S, S, S)), ((S * S, S), (S, -1, S)), ((S * S * 2, S), (S, -1)), ((S,), (S,)), ((), ()), ((), (1,)), ) for shape, args in cases: yield SampleInput(make_arg(shape), args=(args,)) if kwargs.get("transpose_samples", False) and len(shape) >= 2: transposed = make_arg(shape).transpose(0, 1).detach().requires_grad_(requires_grad) yield SampleInput(transposed, args=(args,)) def reference_inputs_view_reshape(op, device, dtype, requires_grad, **kwargs): yield from sample_inputs_view_reshape(op, device, dtype, requires_grad, **kwargs) cases = ( ((125,), (25, 5)), ((25, 25), (1, 5, 5, 1, 5, 1, 5, 1)), ((16, 32), (2, 4, 1, 4, 4, 1, 4)), ((16, 12), (12, 16)), ((1, 16, 12), (12, 16)), ((1, 5, 1, 5), (25, 1)), ((2, 4, 2), (4, 4)), ((1, 4), (1, 1, 2, 1, 2)), ((3, 5, 7), (7, 5, 3)), ((1,), ()), ((5, 0, 2, 3), (5, 0, 2, 3)), ((2, 1, 0, 3, 1), (5, 0)), ((1,), ()), ((4, 5, 6), (4, 5, 6, 1, 1, 1)), ((), (1, 1, 1, 1)), ) irreversible_cases = ( ((), (-1,)), ((4, 7, 9, 1, 1), (1, 4, 3, -1, 1)), ) make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) for a, b in cases: yield SampleInput(make_arg(a), args=(b,)) yield SampleInput(make_arg(b), args=(a,)) if kwargs.get("transpose_samples", False): yield SampleInput(make_arg(a, noncontiguous=True).transpose(0, -1), args=(b,)) else: yield SampleInput(make_arg(a, noncontiguous=True), args=(b,)) for a, b in irreversible_cases: yield SampleInput(make_arg(a), args=(b,)) def error_inputs_reshape(op, device, **kwargs): cases = ( # Reshape to different numel ((2,), ()), ((1, 3, 0), ()), ((4, 3), (4, 2)), ((1, 3, 5), (5, 2, 2)), # No valid inference ((1, 3, 5), (5, -1, 2)), # Two inferred shapes ((1, 3, 5), (5, -1, -1)), ((1), (0, -1)), ((0, 5), (0, -1)), ) make_arg = partial(make_tensor, dtype=torch.float32, device=device, requires_grad=False) for a, b in cases: yield ErrorInput(SampleInput(make_arg(a), args=(b,)), error_type=Exception, error_regex="") def sample_inputs_view_as_reshape_as(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device) cases = (((S, S, S), (S * S, S)), ((), ()), ((), (1, 1)), ) for case in cases: shape, shape_other = case inp = make_arg(shape, requires_grad=requires_grad) yield(SampleInput(inp, args=(make_arg(shape_other, requires_grad=False),))) if op_info.name != "view_as" and len(shape) >= 2: yield(SampleInput( inp.clone().transpose(0, 1).requires_grad_(requires_grad), args=(make_arg(shape_other, requires_grad=False),))) def sample_inputs_atleast1d2d3d(op_info, device, dtype, requires_grad, **kwargs): input_list = [] shapes = ((S, S, S, S), (S, S, S), (S, S), (S, ), (),) make_tensor_partial = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) samples = [] for shape in shapes: input_list.append(make_tensor_partial(shape)) samples.append(SampleInput(make_tensor_partial(shape))) samples.append(SampleInput(input_list, )) return samples def sample_inputs_column_stack(op_info, device, dtype, requires_grad, **kwargs): input_list = [] cases: Tuple[tuple, tuple] = ( # type: ignore[assignment] ((S, 2, 1), (S, 3, 1)), ((S), (S, 5)), ((), (1, S)) ) make_tensor_partial = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) for shape1, shape2 in cases: input_list.append(SampleInput([make_tensor_partial(shape1), make_tensor_partial(shape2)])) return input_list def sample_inputs_flatten(op_info, device, dtype, requires_grad, **kwargs): samples = [] shapes = ((S, S, S), (S, S), (S, ), (),) make_tensor_partial = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) for shape in shapes: samples.append(SampleInput(make_tensor_partial(shape))) if len(shape) > 1: samples.append(SampleInput(make_tensor_partial(shape), kwargs=dict(start_dim=1, end_dim=-1))) return samples def reference_inputs_flatten(op, device, dtype, requires_grad, **kwargs): yield from sample_inputs_flatten(op, device, dtype, requires_grad, **kwargs) # shape x start_dim x end_dim cases = ( ((5, 4, 0, 1, 3, 7), 1, 3), ((5, 4, 0, 1, 3, 7), 4, 5), ((5, 4, 1, 1, 3, 7), 2, 3), ((), 0, -1), ((1,), 0, -1), ((3, 7, 5), 1, 2), ((4, 5), 1, 1), ((1, 5, 5, 1, 5, 1, 5, 1), 0, 2), ((1, 5, 5, 1, 5, 1, 5, 1), 3, -1), ((1, 5, 5, 1, 5, 7, 5, 1), -2, -1), ((2, 4, 2), 0, 1), ((4, 2, 2), 1, 2), ((0, 3, 4, 5), 1, 3), ) make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) for shape, start, end in cases: yield SampleInput(make_arg(shape), args=(start, end,)) yield SampleInput(make_arg(shape, noncontiguous=True).transpose(0, -1), args=(start, end,)) yield SampleInput(make_arg(shape).transpose(0, -1), args=(start, end,)) def sample_inputs_select(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) cases = (((S, S, S), (1, 2)), ((S, S, S), (-1, 2)), ((S, S, S), (-1, -1)), ((S, S, S), (1, -1)), ((S,), (0, 2)) ) for shape, args in cases: yield SampleInput(make_arg(shape), args=args) def sample_inputs_select_scatter(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) cases = (((S, S, S), (S, S), (1, 2)), ((S, S, S), (S, S), (-1, 2)), ((S, S, S), (S, S), (-1, -1)), ((S, S, S), (S, S), (1, -1)), ((S,), (), (0, 2)) ) for input_shape, src_shape, args in cases: input_ = make_arg(input_shape) src = make_arg(src_shape) yield SampleInput(input_, args=(src, *args)) def sample_inputs_slice_scatter(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) cases = (((L, L, L), (L, L, L,), (0, 0, L, 1)), ((L, L, L), (L // 2, L, L,), (0, L // 2, L, 1)), ((L, L, L), (L // 4, L, L,), (0, L // 2, L, 2)), ((L, L, L), (L, L, L,), (1, 0, L, 1)), ((L, L, L), (L, L // 2, L,), (1, L // 2, L, 1)), ((L, L, L), (L, L // 4, L,), (1, L // 2, L, 2)), ((L, L, L), (L, L, L,), (2, 0, L, 1)), ((L, L, L), (L, L, L // 2,), (2, L // 2, L, 1)), ((L, L, L), (L, L, L // 4,), (2, L // 2, L, 2)), ) for input_shape, src_shape, args in cases: input_ = make_arg(input_shape) src = make_arg(src_shape) yield SampleInput(input_, args=(src, *args)) def sample_inputs_expand(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) cases = (((S, 1, 1), (S, S, S)), ((S, 1, S), (S, S, S)), ((S, 1, S), (-1, S, -1)), ((S, 1, S), (-1, S, S)), ((S, 1), (S, S, S)), ((1,), (S, S, S)), ((1, S), (1, 1, S)), ((), ()), ((), (1, 3, 2)), ) for case in cases: shape, args = case yield(SampleInput(make_arg(shape), args=(args, ))) def sample_inputs_conversion(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) shapes = ((), (2, 3)) memory_format_options = [None, torch.contiguous_format] for shape, memory_format in itertools.product(shapes, memory_format_options): yield SampleInput(make_arg(shape), kwargs={'memory_format': memory_format} if memory_format else {}) yield SampleInput(make_arg((2, 3, 2, 3)), kwargs={'memory_format': torch.channels_last}) def sample_inputs_expand_as(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device) cases = (((S, 1, 1), (S, S, S)), ((), ()), ((), (1, 1)), ) for shape, shape_other in cases: yield(SampleInput(make_arg(shape, requires_grad=requires_grad), args=(make_arg(shape_other, requires_grad=False), ))) def sample_inputs_where(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) def make_bool_mask(shape): # Make sure atleast one element is nonzero, # except for empty tensor mask_t = make_tensor(shape, dtype=torch.bool, device=device, requires_grad=False) if mask_t.numel() == 0: return mask_t elif mask_t.numel() == 1: mask_t.fill_(True) return mask_t if mask_t.sum() == 0: def random_index(shape): return tuple(map(lambda max_idx: random.randint(0, max_idx), shape)) mask_t[random_index(mask_t.shape)] = True return mask_t return mask_t cases = (((M, M), (M, M), (M, M), False), ((M, 1, M), (M, M), (M, M, 1), True), ((), (), (), False), ((M, 1, M), (), (M, M, 1), True), ((), (M, M), (), True),) for shape, mask_shape, other_shape, broadcasts_input in cases: yield SampleInput(make_arg(shape), args=(make_bool_mask(mask_shape), make_arg(other_shape)), broadcasts_input=broadcasts_input) def error_inputs_where(op_info, device, **kwargs): shape = (S,) err_msg = "Expected all tensors to be on the same device" for devices in product(('cpu', device), repeat=3): if len(set(devices)) == 2: si = SampleInput(make_tensor(shape, device=devices[0], dtype=torch.float32), args=(make_tensor(shape, dtype=torch.bool, device=devices[1]), make_tensor(shape, device=devices[2], dtype=torch.float32))) yield ErrorInput(si, error_regex=err_msg) def sample_inputs_nonzero(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) sizes = ((), (S,), (S, S), (S, S, S), (S, 1, S), (S, 0, S)) inputs = [] for shape in sizes: # construct input without any non-zero elements zeros = torch.zeros(shape, dtype=dtype, device=device, requires_grad=requires_grad) inputs.append(zeros) # construct input with mixed zero and non-zero elements mixed = make_arg(shape).requires_grad_(False) mask_t = make_tensor(shape, dtype=torch.bool, device=device, requires_grad=False) mixed[mask_t] = 0 inputs.append(mixed) for input_t, as_tuple in product(inputs, [False, True]): yield(SampleInput(input_t.clone().requires_grad_(requires_grad), kwargs=dict(as_tuple=as_tuple))) def sample_inputs_chunk(op_info, device, dtype, requires_grad, **kwargs): make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) cases = (((S, S, S), (2,)), ((S, S, S), (S, 1)), ((S, S, S), (S, -1))) for case in cases: shape, args = case yield(SampleInput(make_arg(shape), args=args)) def reference_inputs_chunk(op, device, dtype, requires_grad, **kwargs): yield from sample_inputs_chunk(op, device, dtype, requires_grad, **kwargs) make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad) # shape x chunks x dim cases = ( ((13, 9, 11), 17, -1), ((13, 9, 11), 11, -1), ((13,), 12, -1), ((15,), 12, -1), ((15,), 7, 0), ((15,), 9, 0), ((3, 7), 9, 1), ((3, 7), 9, 0), ((3, 7), 2, 0), ((3, 7), 3, 0), ((3, 7), 1, 0), ((3, 7), 1, 1), ((4, 4), 2, 0), ) for shape, chunks, dim in cases: yield SampleInput(make_arg(shape), args=(chunks, dim)) def sample_inputs_kthvalue(op_info, device, dtype, requires_grad, **kwargs): def _tensor(shape, dtype=dtype, low=None, high=None): return make_tensor(shape, dtype=dtype, device=device, low=low, high=high, requires_grad=requires_grad) test_cases = [ (_tensor((S, S, S)), (2,)), (_tensor((S, S, S)), (2, 1,)), (_tensor((S, S, S)), (2, -1,)), (_tensor((S, S, S)), (2, 1, True,)), (_tensor((S, S, S)), (2, -1, True,)), (_tensor((S,)), (2, 0,)), (_tensor((S,)), (2, 0, True,)), (_tensor(()), (1,)), (_tensor(()), (1, 0,)), (_tensor(()), (1, 0, True)) ] return [SampleInput(tensor, args=args) for tensor, args in test_cases] def error_inputs_kthvalue(op_info, device, **kwargs): # tests overlapping output fails t = make_tensor(10, dtype=torch.float32, device=device) indices = torch.empty((), device=device, dtype=torch.long) si = SampleInput(t, args=(5,), kwargs={'out': (t, indices)}) k_out_of_range_err = "selected number k out of range for dimension" return (ErrorInput(si, error_regex="unsupported operation"), ErrorInput(SampleInput(torch.randn(2, 2, device=device), args=(3, 0)), error_regex=k_out_of_range_err), ErrorInput(SampleInput(torch.randn(2, 2, device=device), args=(3,)), error_regex=k_out_of_range_err), ErrorInput(SampleInput(torch.tensor(2, device=device), args=(3,)), error_regex=k_out_of_range_err),) def sample_inputs_dropout(op_info, device, dtype, requires_grad, *, train=None, valid_input_dim=None, **kwargs): make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) if valid_input_dim: cases = ((S,) * i for i in valid_input_dim) else: cases = ((S, S), (S,), ()) p_vals = [0.0, 0.5, 1.0] # This is to handle special case for feature_alpha_dropout which has different # supported dtypes depending on `train` parameter training_vals = [train] if train is not None else [True, False] for case, p, training in product(cases, p_vals, training_vals): yield SampleInput(make_arg(case), kwargs=dict(p=p, training=training)) yield SampleInput(make_arg(case), kwargs=dict()) def sample_inputs_embedding_bag(op_info, device, dtype, requires_grad, **kwargs): def make_input(shape): return make_tensor(shape, device=device, dtype=dtype, requires_grad=requires_grad) def make_long_input(shape, *, low, high, noncontiguous=False): return make_tensor(shape, device=device, dtype=torch.long, low=low, high=high, noncontiguous=noncontiguous) def make_per_sample_weight(flag, idx): # a tensor of float / double weights, or None # to indicate all weights should be taken to be 1 if flag: return make_input(idx.shape) return None offsets = torch.tensor([0, 3], device=device, dtype=torch.long) for generate_per_sample_weight in (True, False): for mode in ('sum', 'mean', 'max'): # per_sample_weights is only supported for mode='sum' (got mode='****') if generate_per_sample_weight and mode in ('mean', 'max'): continue # 1-D index tensor idx = make_long_input((S,), low=0, high=M) per_sample_weights = make_per_sample_weight(generate_per_sample_weight, idx) yield SampleInput(make_input((M, S)), args=(idx,), kwargs={'offsets': offsets, 'mode': mode, 'per_sample_weights': per_sample_weights}) idx = make_long_input((S,), low=0, high=M, noncontiguous=True) per_sample_weights = make_per_sample_weight(generate_per_sample_weight, idx) yield SampleInput(make_input((M, S)), args=(idx,), kwargs={'offsets': offsets, 'mode': mode, 'per_sample_weights': per_sample_weights}) # bag with zero length idx = make_long_input((S,), low=0, high=M, noncontiguous=True) per_sample_weights = make_per_sample_weight(generate_per_sample_weight, idx) yield SampleInput(make_input((M, S)), args=(idx,), kwargs={'offsets': torch.tensor([0, 0, 3], device=device, dtype=torch.long), 'mode': mode, 'per_sample_weights': per_sample_weights}) # 2-D index tensor idx = make_long_input((S, S), low=0, high=M) per_sample_weights = make_per_sample_weight(generate_per_sample_weight, idx) yield SampleInput(make_input((M, S)), args=(idx,), kwargs={'mode': mode, 'per_sample_weights': per_sample_weights}) idx = make_long_input((S, S), low=0, high=M, noncontiguous=True) per_sample_weights = make_per_sample_weight(generate_per_sample_weight, idx) yield SampleInput(make_input((M, S)), args=(idx,), kwargs={'mode': mode, 'per_sample_weights': per_sample_weights}) # The gradient vector at `padding_idx` is not updated. # Negative padding_idx idx = make_long_input((6,), low=0, high=S) idx[0] = 4 idx[4] = 4 per_sample_weights = make_per_sample_weight(generate_per_sample_weight, idx) yield SampleInput(make_input((S, S)), args=(idx,), kwargs={'padding_idx': -1, 'offsets': offsets, 'mode': mode, 'per_sample_weights': per_sample_weights},) idx = make_long_input((3, 3), low=0, high=S) # Positive padding_idx idx[0, 0] = 2 idx[1, 1] = 2 per_sample_weights = make_per_sample_weight(generate_per_sample_weight, idx) yield SampleInput(make_input((S, S)), args=(idx,), kwargs={'padding_idx': 2, 'mode': mode, 'per_sample_weights': per_sample_weights},) idx = make_long_input((6, ), low=0, high=S) weights = make_input((S, S)) offsets_ = torch.tensor([0, 3, 6], device=device, dtype=torch.long) per_sample_weights = make_per_sample_weight(generate_per_sample_weight, idx) yield SampleInput(weights, args=(idx,), kwargs={'mode': mode, 'offsets': offsets_, 'include_last_offset': True},) if not requires_grad: # Following inputs return different gradient from the numerical gradient. # This is expected and relevant tests are present in `test_nn.py`. # Due to inplace renorming of weight, the numerical gradient doesn't match the # analytical gradient. idx = make_long_input((2, 2), low=0, high=S) weights = make_input((S, S)) * 2 per_sample_weights = make_per_sample_weight(generate_per_sample_weight, idx) yield SampleInput(weights, args=(idx,), kwargs={'max_norm': 1., 'mode': mode, 'per_sample_weights': per_sample_weights},) idx = make_long_input((6, ), low=0, high=S) weights = make_input((S, S)) * 2 per_sample_weights = make_per_sample_weight(generate_per_sample_weight, idx) yield SampleInput(weights, args=(idx,), kwargs={'max_norm': 1., 'norm_type': 1.0, 'mode': mode, 'offsets': offsets, 'per_sample_weights': per_sample_weights},) if mode != 'max': # Scale the gradient based on the inverse frequency of a particular index. # Note : smax mode does not support sparse weights idx = make_long_input((2, 2), low=0, high=S) idx[0, 0] = 1 idx[0, 1] = 1 weights = make_input((S, S)) per_sample_weights = make_per_sample_weight(generate_per_sample_weight, idx) yield SampleInput(weights, args=(idx,), kwargs={'scale_grad_by_freq': True, 'mode': mode, 'per_sample_weights': per_sample_weights},) # gradcheck not implemented for sparse tensors. # Note : max mode does not support sparse weights idx = make_long_input((6, ), low=0, high=S) weights = make_input((S, S)) per_sample_weights = make_per_sample_weight(generate_per_sample_weight, idx) yield SampleInput(weights, args=(idx,), kwargs={'sparse': True, 'offsets': offsets, 'mode': mode, 'per_sample_weights': per_sample_weights}) idx = make_long_input((6, ), low=0, high=S) idx[0] = 1 # freq more than 1 idx[1] = 1 # freq more than 1 idx[3] = 0 # padding_idx weights = make_input((S, S)) * 2 per_sample_weights = make_per_sample_weight(generate_per_sample_weight, idx) yield SampleInput(weights, args=(idx,), kwargs={'sparse': True, 'scale_grad_by_freq': True, 'padding_idx': 0, 'max_norm': 1., 'offsets': offsets, 'mode': mode, 'per_sample_weights': per_sample_weights}) def sample_inputs_embedding(op_info, device, dtype, requires_grad, **kwargs): def make_input(shape): return make_tensor(shape, device=device, dtype=dtype, requires_grad=requires_grad) def make_long_input(shape, *, low, high): return make_tensor(shape, device=device, dtype=torch.long, low=low, high=high) # 0-D index tensor idx = make_long_input((), low=0, high=M) yield SampleInput(make_input((M, S)), args=(idx,),) # 1-D index tensor idx = make_long_input((S,), low=0, high=M) yield SampleInput(make_input((M, S)), args=(idx,),) # 2-D index tensor idx = make_long_input((S, S), low=0, high=M) yield SampleInput(make_input((M, S)), args=(idx,),) if not requires_grad: # Following inputs return different gradient from the numerical gradient. # This is expected and relevant tests are present in `test_nn.py`. # The gradient vector at `padding_idx` is not updated. idx = make_long_input((2, 2), low=0, high=S) idx[0, 0] = 2 idx[1, 1] = 2 yield SampleInput(make_input((S, S)), args=(idx,), kwargs={'padding_idx': 2},) idx = make_long_input((2, 2), low=0, high=S) idx[0, 0] = 4 idx[1, 1] = 4 yield SampleInput(make_input((S, S)), args=(idx,), kwargs={'padding_idx': -1},) # Due to inplace renorming of weight, the numerical gradient doesn't match the # analytical gradient. idx = make_long_input((2, 2), low=0, high=S) weights = make_input((S, S)) * 2 yield SampleInput(weights, args=(idx,), kwargs={'max_norm': 1.},) idx = make_long_input((2, 2), low=0, high=S) weights = make_input((S, S)) * 2 yield SampleInput(weights, args=(idx,), kwargs={'max_norm': 1., 'norm_type': 1.0},) # Scale the gradient based on the inverse frequency of a particular index. idx = make_long_input((2, 2), low=0, high=S) idx[0, 0] = 1 idx[0, 1] = 1 weights = make_input((S, S)) yield SampleInput(weights, args=(idx,), kwargs={'scale_grad_by_freq': True},) # gradcheck not implemented for sparse tensors. idx = make_long_input((2, 2), low=0, high=S) weights = make_input((S, S)) yield SampleInput(weights, args=(idx,), kwargs={'sparse': True}) idx = make_long_input((3, 3), low=0, high=S) idx[0, 0] = 1 # freq more than 1 idx[0, 1] = 1 # freq more than 1 idx[1, 0] = 0 # padding_idx weights = make_input((S, S)) * 2 yield SampleInput(weights, args=(idx,), kwargs={'sparse': True, 'scale_grad_by_freq': True, 'padding_idx': 0, 'max_norm': 1.}) def sample_inputs_one_hot(op_info, device, dtype, requires_grad, **kwargs): def make_input(shape, *, low, high): return make_tensor(shape, device=device, dtype=dtype, low=low, high=high, requires_grad=requires_grad) shapes = ((), (S,), (L, M, S)) num_classess = (-1, 10) return [ SampleInput( make_input( shape, low=0, high=10 if num_classes == -1 else num_classes // 2, ), kwargs=dict(num_classes=num_classes), ) for shape, num_classes in itertools.product(shapes, num_classess) ] def sample_inputs_softplus(op_info, device, dtype, requires_grad, **kwargs): make_input = partial(make_tensor, (S,), device=device, dtype=dtype, requires_grad=requires_grad) return [ SampleInput(make_input()), SampleInput(make_input(), kwargs=dict(beta=3)), SampleInput(make_input(low=1), kwargs=dict(threshold=1)), ] def sample_inputs_tensorinv(op_info, device, dtype, requires_grad, **kwargs): make_arg = make_fullrank_matrices_with_distinct_singular_values def make_input(): return make_arg(12, 12, device=device, dtype=dtype, requires_grad=requires_grad) # lhs / rhs shape can have any number of dimensions as long as their product equals 12 shapes = [ ((2, 2, 3), (12, 1)), ((4, 3), (6, 1, 2)), ] samples = [] for shape_lhs, shape_rhs in shapes: inp = make_input().reshape(*shape_lhs, *shape_rhs).detach() inp.requires_grad_(requires_grad) samples.append(SampleInput(inp, kwargs=dict(ind=len(shape_lhs)))) return samples def sample_inputs_tensorsolve(op_info, device, dtype, requires_grad, **kwargs): a_shapes = [(2, 3, 6), (3, 4, 4, 3)] # Zero-dim tensors are not supported in NumPy, so we skip them for now. # NumPy is used in reference check tests. # See https://github.com/numpy/numpy/pull/20482 for tracking NumPy bugfix. # a_shapes += [(0, 0, 1, 2, 3, 0)] dimss = [None, (0, 2)] for a_shape, dims in itertools.product(a_shapes, dimss): a = make_tensor(a_shape, dtype=dtype, device=device, requires_grad=requires_grad) b = make_tensor(a_shape[:2], dtype=dtype, device=device, requires_grad=requires_grad) yield SampleInput(a, args=(b,), kwargs=dict(dims=dims)) def sample_inputs_loss(op_info, device, dtype, requires_grad, **kwargs): rhs_requires_grad = kwargs.get('rhs_requires_grad', requires_grad) _make_tensor = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # Although most losses also support the reduce and size_average combination instead of reduce, the former is # deprecated since 0.4.1 and thus is not tested shapes_and_kwargs = ( ((), None), ((S,), dict(reduction="mean")), ((S,), dict(reduction="sum")), ((S,), dict(reduction="none")), ((S, S), None), ((S, S, S), None), ) for shape, kwargs in shapes_and_kwargs: yield SampleInput(_make_tensor(shape), args=(_make_tensor(shape, requires_grad=rhs_requires_grad),), kwargs=kwargs) def sample_inputs_grid_sample(op_info, device, dtype, requires_grad, **kwargs): _make_tensor = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) batch_size = 2 num_channels = 3 modes = ("bilinear", "nearest") align_cornerss = (False, True) padding_modes = ("zeros", "border", "reflection") sample_inputs = [] for dim in (2, 3): modes_ = (*modes, "bicubic") if dim == 2 else modes for mode, padding_mode, align_corners in itertools.product(modes_, padding_modes, align_cornerss): sample_inputs.append( SampleInput( _make_tensor((batch_size, num_channels, *[S] * dim)), args=(_make_tensor((batch_size, *[S] * dim, dim)),), kwargs=dict( mode=mode, padding_mode=padding_mode, align_corners=align_corners, ) ) ) return sample_inputs def sample_inputs_cosine_embedding_loss(op_info, device, dtype, requires_grad, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) def make_target(shape): shape = () if len(shape) == 1 else (shape[0], ) t = torch.randint(0, 2, shape, device=device, dtype=torch.long) # Label with -1 or 1 t = t * 2 - 1 target = t.to(dtype=dtype).detach_().requires_grad_(requires_grad) return target shapes = ((S, S), (S,)) reductions = ('none', 'mean', 'sum') for s, r in product(shapes, reductions): yield SampleInput( make_input(s), args=(make_input(s), make_target(s)), kwargs=dict(reduction=r, margin=random.uniform(-1, 1)) ) def sample_inputs_ctc_loss(op_info, device, dtype, requires_grad, **kwargs): input_length = 50 batch = 16 num_char = 20 target_length = 30 def make_log_probs(s): t = make_tensor(s, device=device, dtype=dtype) log_probs = t.log_softmax(2).to(device=device, dtype=dtype).detach().requires_grad_(requires_grad=requires_grad) return log_probs reductions = ('none', 'mean', 'sum') zero_inf = (True, False) for r, z in product(reductions, zero_inf): log_probs = make_log_probs((input_length, batch, num_char)) targets = torch.randint(1, num_char, (batch, target_length), dtype=torch.long, device=device) input_lengths = torch.full((batch, ), input_length, dtype=torch.long, device=device) target_lengths = torch.randint(10, target_length, (batch, ), dtype=torch.long, device=device) yield SampleInput(log_probs, args=(targets, input_lengths, target_lengths,), kwargs=dict(reduction=r, zero_infinity=z)) def sample_inputs_nll_loss(op_info, device, dtype, requires_grad, **kwargs): shape = (2, 3) num_classes = shape[1] make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # FIXME: Derivative wrt. weight not implemented make_weight = partial(make_tensor, num_classes, device=device, dtype=dtype, requires_grad=False) def make_target(shape, zeros=False): s = (shape[0], *shape[2:]) if len(shape) > 1 else () if zeros: return torch.zeros(s, device=device, dtype=torch.long) else: return make_tensor(s, low=0, high=shape[1] if len(shape) > 1 else shape[0], device=device, dtype=torch.long) def gen_shape_kwargs(): # Batched, non-batched and 2d shapes = (shape, (num_classes,), shape + (2, 2)) reductions = ('none', 'mean', 'sum') for reduction, s in product(reductions, shapes): yield make_input(s), make_target(s), dict(reduction=reduction) yield make_input(s), make_target(s), dict(weight=make_weight(), reduction=reduction) yield make_input(s), make_target(s), dict(weight=make_weight(low=0), reduction=reduction) yield make_input(s), make_target(s), dict(weight=make_weight(high=0), reduction=reduction) t = make_target(s) ignore = num_classes // 2 # If "mean", nll returns NaN, so it's not differentiable at those points if t.eq(ignore).all() and reduction == "mean": t.fill_(0) yield make_input(s), t, dict(ignore_index=num_classes // 2, reduction=reduction) yield make_input(s), t, dict(ignore_index=num_classes // 2, reduction=reduction, weight=make_weight()) # Test ignoring all the targets # If "mean", nll returns NaN, so it's not differentiable at those points if reduction != "mean": yield make_input(s), make_target(s, zeros=True), dict(ignore_index=0, reduction=reduction) for input, target, kwargs in gen_shape_kwargs(): yield SampleInput(input, args=(target,), kwargs=kwargs) def sample_inputs_binary_cross_entropy_with_logits( op_info, device, dtype, requires_grad, **kwargs ): make = partial(make_tensor, device=device, dtype=dtype) make_prob = partial(make, low=0, high=1) reductions = ("mean", "sum", "none") def make_weight_shape_kwargs(): kwargs = [] for shape in ((1,), (1, S), (S), (S, S)): kwargs.extend([((S, S), dict(reduction=reduction, weight=make(shape))) for reduction in reductions]) return kwargs shapes_and_kwargs = [ *[(shape, None) for shape in ((), (1,), (S,), (S, S), (S, S, S))], *[((S, S), dict(reduction=reduction)) for reduction in reductions], *make_weight_shape_kwargs(), *[((S, S), dict(reduction=reduction, pos_weight=make((S,), low=0))) for reduction in reductions], *[((S, S), dict(reduction=reduction, weight=make((S, S)), pos_weight=make((S,), low=0))) for reduction in reductions], ] for shape, kwargs in shapes_and_kwargs: yield SampleInput( make(shape, requires_grad=requires_grad), args=(make_prob(shape, requires_grad=requires_grad),), kwargs=kwargs, ) def sample_inputs_argwhere(op_info, device, dtype, requires_grad, **kwargs): yield SampleInput(torch.tensor([1, 0, 2, 0], dtype=dtype, device=device, requires_grad=requires_grad)) mask = torch.tensor([[0, 1, 0, 1, 0], [1, 1, 1, 1, 0], [0, 0, 0, 1, 0], [1, 0, 1, 1, 0], [1, 0, 0, 1, 0]], dtype=torch.bool, device=device) t = make_tensor((S, S), dtype=dtype, device=device, requires_grad=requires_grad) t[mask] = 0 yield SampleInput(t) t = make_tensor((S, S), dtype=dtype, device=device, requires_grad=requires_grad, noncontiguous=True) t[mask] = 0 yield SampleInput(t) t = make_tensor((S, 0), dtype=dtype, device=device, requires_grad=requires_grad) yield SampleInput(t) yield SampleInput(torch.zeros((S,), dtype=dtype, device=device, requires_grad=requires_grad)) yield SampleInput(make_tensor((), dtype=dtype, device=device, requires_grad=requires_grad)) def _generate_sample_shape_reduction(): shapes = ((S,), (S, S), (S, S, S)) reductions = ('none', 'mean', 'sum') for s, r in product(shapes, reductions): yield s, r def sample_inputs_gaussian_nll_loss(op_info, device, dtype, requires_grad, **kwargs): _make_tensor = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) # Set low slightly above 0 so gradcheck doesn't accidentally dip below 0 make_var = partial(make_tensor, low=0.1, device=device, dtype=dtype, requires_grad=requires_grad) def gen_shape(shape): yield shape # Broadcast yield (*shape[:-1], 1) yield shape[:-1] def gen_shape_kwargs(): for s, r in _generate_sample_shape_reduction(): for t_s, v_s in product(gen_shape(s), gen_shape(s)): yield _make_tensor(s), _make_tensor(t_s), make_var(v_s), dict(reduction=r) yield ( _make_tensor(s), _make_tensor(t_s), make_var(v_s), dict(full=True, reduction=r) ) yield ( _make_tensor(s), _make_tensor(t_s), make_var(v_s), dict(eps=random.uniform(1e-6, 1e-3), reduction=r) ) yield ( _make_tensor(s), _make_tensor(t_s), make_var(v_s), dict(full=True, eps=random.uniform(1e-6, 1e-3), reduction=r) ) for input, target, var, kwargs in gen_shape_kwargs(): yield SampleInput(input, args=(target, var, ), kwargs=kwargs) def _generate_sample_inputs_nn_loss(op_info, device, dtype, requires_grad, **kwargs): _make_tensor = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) for s, r in _generate_sample_shape_reduction(): yield _make_tensor(s), _make_tensor(s), dict(reduction=r) def sample_inputs_hinge_embedding_loss(op_info, device, dtype, requires_grad, **kwargs): for input, target, d in _generate_sample_inputs_nn_loss(op_info, device, dtype, requires_grad, **kwargs): d['margin'] = random.uniform(-9, 9) yield SampleInput(input, args=(target, ), kwargs=d) def sample_inputs_huber_loss(op_info, device, dtype, requires_grad, **kwargs): for input, target, d in _generate_sample_inputs_nn_loss(op_info, device, dtype, requires_grad, **kwargs): d['delta'] = random.uniform(1e-3, 9) yield SampleInput(input, args=(target, ), kwargs=d) def sample_inputs_poisson_nll_loss(op_info, device, dtype, requires_grad, **kwargs): _make_tensor = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) def gen_shape_kwargs(): for s, r in _generate_sample_shape_reduction(): for li in (True, False): for f in (True, False): i1 = _make_tensor(s) i2 = _make_tensor(s) # For Poisson NLL Loss, # target is assumed to be from # Poisson Distribution which # always has positive samples t1 = _make_tensor(s, low=0) t2 = _make_tensor(s, low=0) with torch.no_grad(): if not li: i1.abs_() i2.abs_() t1.abs_() t2.abs_() yield ( i1, t1, dict(log_input=li, full=f, reduction=r) ) yield ( i2, t2, dict(log_input=li, full=f, eps=random.uniform(1e-8, 1e-3), reduction=r) ) for input, target, kwargs in gen_shape_kwargs(): yield SampleInput(input, args=(target, ), kwargs=kwargs) def sample_inputs_triplet_margin_loss(op_info, device, dtype, requires_grad, with_distance=False, **kwargs): make = partial(make_tensor, (S, M), device=device, dtype=dtype, requires_grad=requires_grad) kwargss = ( *[dict(margin=margin) for margin in (1e-6, 1.0, 10.0)], dict(swap=True), *[dict(reduction=reduction) for reduction in ("mean", "sum", "none")], ) for kwargs in kwargss: input = make() args = (make(), make()) if with_distance: kwargs["distance_function"] = torch.nn.PairwiseDistance() yield SampleInput(input, args=args, kwargs=kwargs) def sample_inputs_pairwise_distance(op_info, device, dtype, requires_grad, **kwargs): make = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) shape = (3,) batched_shape = (2, *shape) shapes_and_kwargs = [ (shape, None), (batched_shape, None), (shape, dict(keepdim=True)), (batched_shape, dict(keepdim=True)), (shape, dict(p=5.0)), (shape, dict(p=-1.0)), (shape, dict(eps=1.0)), ] return [ SampleInput(make(shape), args=(make(shape),), kwargs=kwargs) for shape, kwargs in shapes_and_kwargs ] def sample_inputs_pixel_shuffle(op_info, device, dtype, requires_grad, **kwargs): return [ SampleInput( make_tensor((1, 9, 2, 2), device=device, dtype=dtype, requires_grad=requires_grad), kwargs=dict(upscale_factor=upscale_factor), ) for upscale_factor in (1, 3) ] def sample_inputs_pixel_unshuffle(op_info, device, dtype, requires_grad, **kwargs): return [ SampleInput( make_tensor((1, 1, 6, 6), device=device, dtype=dtype, requires_grad=requires_grad), kwargs=dict(downscale_factor=downscale_factor), ) for downscale_factor in (1, 3) ] def sample_inputs_binary_cross_entropy(op_info, device, dtype, requires_grad, logits=False, **kwargs): make = partial(make_tensor, device=device, dtype=dtype) make_prob = partial(make, low=0, high=1) reductions = ("mean", "sum", "none") shapes_and_kwargs = [ *[(shape, None) for shape in ((), (1,), (S,), (S, S), (S, S, S))], *[((S, S), dict(reduction=reduction)) for reduction in reductions], *[((S, S), dict(reduction=reduction, weight=make((S, S)))) for reduction in reductions], ] if logits: shapes_and_kwargs.extend( [((S, S), dict(reduction=reduction, pos_weight=make((S,), low=0))) for reduction in reductions] ) for shape, kwargs in shapes_and_kwargs: yield SampleInput( (make if logits else make_prob)(shape, requires_grad=requires_grad), args=(make_prob(shape, requires_grad=requires_grad),), kwargs=kwargs, ) def sample_inputs_allclose(op_info, device, dtype, requires_grad, **kwargs): samples = [] sample_shapes = [(), (S), (S, S, S)] atols = [1e-2, 1e-16] rtols = [1e-1, 0.5] eps = 1e-8 for s, rtol, atol in product(sample_shapes, rtols, atols): # close sample t = make_tensor(s, device=device, dtype=dtype, requires_grad=requires_grad) close = (t + atol).detach().requires_grad_(requires_grad) close_sample = SampleInput(t, args=(close,), kwargs=dict(rtol=rtol, atol=atol)) samples.append(close_sample) # random sample a = make_tensor(s, device=device, dtype=dtype, requires_grad=requires_grad) b = make_tensor(s, device=device, dtype=dtype, requires_grad=requires_grad) r_sample = SampleInput(a, args=(b,), kwargs=dict(rtol=rtol, atol=atol)) samples.append(r_sample) return samples def sample_inputs_l1_loss(op_info, device, dtype, requires_grad, **kwargs): yield from sample_inputs_loss(op_info, device, dtype, requires_grad, **kwargs) # In addition to the regular test cases, we add two for mixed floating point and complex inputs if dtype.is_complex: make = partial(make_tensor, (), device=device, requires_grad=requires_grad) yield SampleInput(make(dtype=dtype), args=(make(dtype=torch.double),)) yield SampleInput(make(dtype=torch.double), args=(make(dtype=dtype),)) def sample_inputs_smooth_l1_loss(op_info, device, dtype, requires_grad, **kwargs): yield from sample_inputs_loss(op_info, device, dtype, requires_grad, **kwargs) make = partial(make_tensor, (S, S), device=device, dtype=dtype, requires_grad=requires_grad) # This test case always triggers the smooth condition, since absolute difference of input and target # is smaller than beta yield SampleInput(make(low=0, high=2), args=(make(low=-2, high=0),), kwargs=dict(beta=5)) yield SampleInput(make(), args=(make(),), kwargs=dict(beta=0)) def sample_inputs_kl_div(op_info, device, dtype, requires_grad, **kwargs): make = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) shapes_and_reduction = [ ((2,), "mean"), ((2, 3), "mean"), ((2, 3, 4), "mean"), ((2,), "none"), ((2,), "batchmean"), ((2,), "sum"), ] sample_inputs = [] for (shape, reduction), log_target in itertools.product(shapes_and_reduction, (True, False)): # input should be log-probability, i.e. lie in (-inf, 0] input = make(shape, low=None, high=0) # target should be a probability by default, i.e. lie in [0, 1], and a log-probability if log_target is set, # i.e. lie in (-inf, 0] target = make(shape, low=None, high=0) if log_target else make(shape, low=0, high=1) sample_inputs.append( SampleInput(input, args=(target,), kwargs=dict(reduction=reduction, log_target=log_target)) ) return sample_inputs def sample_inputs_pdist(op_info, device, dtype, requires_grad, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) yield from (SampleInput(make_input((n, m))) for n, m in itertools.product((1, S), repeat=2)) yield from (SampleInput(make_input((S, S)), kwargs=dict(p=p)) for p in (0.0, 1.0, 2.0, 10.0, float("inf"))) def reference_pdist(input, p=2): pdist = scipy.spatial.distance.pdist if p == 0: output = pdist(input, "hamming") * input.shape[1] elif p == float("inf"): output = pdist(input, lambda x, y: np.abs(x - y).max()) else: output = pdist(input, "minkowski", p=p) return output.astype(input.dtype) def sample_inputs_diagflat(op_info, device, dtype, requires_grad, **kwargs): make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad) return [ SampleInput(make_input(())), SampleInput(make_input((2,))), SampleInput(make_input((2, 2))), SampleInput(make_input((2,)), kwargs=dict(offset=1)), SampleInput(make_input((2,)), kwargs=dict(offset=-1)), ] def sample_inputs_max_unpool(op_info, device, dtype, requires_grad, **kwargs): unpool_name_to_pool_method_dict = { 'nn.functional.max_unpool1d': torch.nn.functional.max_pool1d, 'nn.functional.max_unpool2d': torch.nn.functional.max_pool2d, 'nn.functional.max_unpool3d': torch.nn.functional.max_pool3d } unpool_name_to_dim = { 'nn.functional.max_unpool1d': 1, 'nn.functional.max_unpool2d': 2, 'nn.functional.max_unpool3d': 3 } unpool_to_pool_name_dict = dict(( (k, f'nn.functional.{v.__name__}') for k, v in unpool_name_to_pool_method_dict.items() )) pool_dim = unpool_name_to_dim[op_info.name] pool_method = unpool_name_to_pool_method_dict[op_info.name] pool_op_info = copy.copy(op_info) pool_op_info.name = unpool_to_pool_name_dict[op_info.name] for sample in sample_inputs_max_pool(pool_op_info, device, dtype, requires_grad, **kwargs): # shapes (C, ...) do not work as of now, # see https://github.com/pytorch/pytorch/issues/68337 # TODO: remove once the issue is resolved if sample.input.dim() != pool_dim + 2: continue # No dilation > 1 for max_unpool, # see https://github.com/pytorch/pytorch/issues/68420 if sample.kwargs['dilation'] != 1: continue # Can't unpool without indices if sample.kwargs['return_indices']: pool, indices = pool_method(sample.input, **sample.kwargs) # arg has to be a leaf arg = pool.detach().requires_grad_(requires_grad) sample_kwargs = { 'kernel_size': sample.kwargs['kernel_size'], 'stride': sample.kwargs['stride'], 'padding': sample.kwargs['padding'], # output_size could be None but we specify it explicitly # to compensate for the information lose in pool due # to the floor/ceil operation used to compute the shapes 'output_size': sample.input.size() } yield SampleInput(arg, args=(indices,), kwargs=sample_kwargs) def sample_inputs_max_unpool_grad(op_info, device, dtype, requires_grad, **kwargs): for sample in sample_inputs_max_unpool(op_info, device, dtype, requires_grad, **kwargs): indices = sample.args[0] # The samples for max_unpool are generated with max_pool. # It could be that a single element from the max_pool's # input is mapped to several locations in its output. # This situation leads to failed gradchecks because # the finite difference algorithm perturbes the elements # of the output one by one, and not in classes of # equivalences determined by whether two elements # in the output are coming from the same location in the # input (simply put, they have the same corresponding index). # So, there are two ways to resolve this issue: # 1. Extract a pertubation for one element and apply it all # the elements from the same equivalence class, or # 2. Make sure that the equivalence classes are all singletons, # i.e. the index tensor has to be comprised of only unique # indices. # Here we go with the solution 2, the easiest of all. if indices.unique().numel() == indices.numel(): yield sample foreach_unary_op_db: List[OpInfo] = [ ForeachFuncInfo('exp'), ForeachFuncInfo('acos'), ForeachFuncInfo('asin'), ForeachFuncInfo('atan'), ForeachFuncInfo('cos'), ForeachFuncInfo('cosh'), ForeachFuncInfo('log'), ForeachFuncInfo('log10'), ForeachFuncInfo('log2'), ForeachFuncInfo('tan'), ForeachFuncInfo('tanh'), ForeachFuncInfo('sin'), ForeachFuncInfo('sinh'), ForeachFuncInfo( 'neg', dtypes=all_types_and_complex(), dtypesIfCUDA=all_types_and_complex(), sample_inputs_func=sample_inputs_foreach, ), ForeachFuncInfo( 'sqrt', dtypes=floating_and_complex_types_and(torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.half), ), ForeachFuncInfo( 'ceil', dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), ), ForeachFuncInfo( 'erf', dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), ), ForeachFuncInfo( 'erfc', dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), ), ForeachFuncInfo( 'expm1', dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), ), ForeachFuncInfo( 'floor', dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), ), ForeachFuncInfo( 'log1p', dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half), ), ForeachFuncInfo( 'round', dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), ), ForeachFuncInfo( 'frac', dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), ), ForeachFuncInfo( 'reciprocal', dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half), ), ForeachFuncInfo( 'sigmoid', dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half), ), ForeachFuncInfo( 'trunc', dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), ), ForeachFuncInfo( 'abs', dtypes=all_types_and_complex_and(torch.bfloat16, torch.half), dtypesIfCUDA=all_types_and_complex_and(torch.bfloat16, torch.half, torch.bool), supports_forward_ad=True, supports_fwgrad_bwgrad=True, ), ] foreach_binary_op_db: List[OpInfo] = [ ForeachFuncInfo( "add", dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16), supports_alpha_param=True, ), ForeachFuncInfo( "sub", dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16), supports_alpha_param=True, ), ForeachFuncInfo( "mul", dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16), skips=( # Ref: https://github.com/pytorch/pytorch/issues/77946 DecorateInfo(unittest.skip("Unable to reproduce failure locally"), "TestForeach", "test_binary_op_scalarlist_fastpath", device_type='cuda', dtypes=(torch.float16,)), ) ), ForeachFuncInfo( "div", dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16), skips=( # Ref: https://github.com/pytorch/pytorch/issues/77946 DecorateInfo(unittest.skip("Unable to reproduce failure locally"), "TestForeach", "test_binary_op_scalarlist_fastpath", device_type='cuda', dtypes=(torch.float16,)), ) ), ] foreach_pointwise_op_db: List[ForeachFuncInfo] = [ ForeachFuncInfo( "addcmul", dtypes=all_types_and_complex(), dtypesIfCUDA=all_types_and_complex_and(torch.half, torch.bfloat16), ), ForeachFuncInfo( "addcdiv", dtypes=all_types_and_complex(), dtypesIfCUDA=all_types_and_complex_and(torch.half, torch.bfloat16), ), ] foreach_minmax_op_db: List[ForeachFuncInfo] = [ ForeachFuncInfo( "maximum", dtypes=all_types_and(torch.float16, torch.bfloat16, torch.bool), dtypesIfCUDA=all_types_and(torch.float16, torch.bool), ), ForeachFuncInfo( "minimum", dtypes=all_types_and(torch.float16, torch.bfloat16, torch.bool), dtypesIfCUDA=all_types_and(torch.float16, torch.bool), ), ] foreach_reduce_op_db: List[ForeachFuncInfo] = [ ForeachFuncInfo( "norm", dtypes=floating_and_complex_types_and(torch.float16, torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.float16, torch.bfloat16), ), ] def reference_sign(x): if x.dtype == np.bool_: # `np.sign` doesn't support `bool`. # >>> np.sign(True) # ufunc 'sign' did not contain a loop # with signature matching types dtype('bool') -> dtype('bool') return np.sign(x, dtype=np.uint8).astype(np.bool_) return np.sign(x) def reference_sgn(x): # NumPy doesn't have an equivalent to `torch.sgn` when the dtype is complex. # For complex inputs, `np.sign` returns sign(x.real) + 0j if x.real != 0 else sign(x.imag) + 0j. # while `torch.sgn` returns, 0 if abs(input) == 0 else input/abs(input) if x.dtype not in [np.complex64, np.complex128]: return reference_sign(x) out = (x / np.abs(x)) if out.ndim == 0: # Handle x == 0 case if (x == 0): # Can't assign to np.complex object # So make a new one. return np.array(complex(0, 0), dtype=x.dtype) return out # Handle x == 0 case mask = (x == 0) out[mask] = complex(0, 0) return out def reference_sigmoid(x): # 'scipy.special.expit' not supported for the input types if x.dtype in [np.complex64, np.complex128]: return (1 / (1 + np.exp(-x))) return scipy.special.expit(x) def reference_logsigmoid(x): return np.where( x < 0, x - np.log1p(np.exp(x)), -np.log1p(np.exp(-x))) def reference_hardsigmoid(x): intermediate = x / 6 + 0.5 y = np.clip(intermediate, 0, None) return np.where(y > 1, 1, y).astype(x.dtype) def reference_lgamma(x): # scipy.special.gammaln returns `-inf` when input is `-inf`. # While Pytorch, C and C++, all return `inf` when input is `-inf`. # Reference: # https://en.cppreference.com/w/cpp/numeric/math/lgamma # https://en.cppreference.com/w/c/numeric/math/lgamma # To handle the above discrepancy, # we replace -inf with inf so values # that were originally -inf map to inf as expected if x.dtype.kind == 'f': x = np.where(x == float('-inf'), np.array(float('inf'), dtype=x.dtype), x) out = scipy.special.gammaln(x) if x.dtype == np.float16: # `scipy.special.gammaln` returns output of float32 when input is float16, # while `torch.lgamma` preserves `float16`. But due to smaller range of float16, # Pytorch version outputs `inf` while SciPy returns finite values. out = out.astype(np.float16) return out def reference_polygamma(x, n): # WEIRD `scipy.special.polygamma` behavior # >>> scipy.special.polygamma(0, np.array(501, dtype=np.float32)).dtype # dtype('float64') # >>> scipy.special.polygamma(0, np.array([501], dtype=np.float32)).dtype # dtype('float32') # # Thus we cast output to the default torch dtype or preserve double result_dtype = torch_to_numpy_dtype_dict[torch.get_default_dtype()] if x.dtype == np.double: result_dtype = np.double return scipy.special.polygamma(n, x).astype(result_dtype) def reference_mvlgamma(x, d): if x.dtype == np.float16: return scipy.special.multigammaln(x, d).astype(np.float16) return scipy.special.multigammaln(x, d) def reference_softplus(input, beta=1, threshold=20): non_linear = input * beta <= threshold output = input.copy() output[non_linear] = np.log(1 + np.exp(beta * input[non_linear])) / beta return output def reference_gelu(X, *, approximate='none'): def _gelu_ref(X): return X * stats.norm.cdf(X) def _tanh_gelu_ref(X): M_SQRT_2_PI = math.sqrt(2 / math.pi) Z = M_SQRT_2_PI * (X + 0.044715 * np.power(X, 3.0)) return 0.5 * X * (1.0 + np.tanh(Z)) if approximate == 'tanh': return _tanh_gelu_ref(X) else: return _gelu_ref(X) def reference_one_hot(a: np.ndarray, num_classes: int = -1) -> np.ndarray: if num_classes == -1: num_classes = int(np.amax(a) + 1) idcs = a.reshape(-1) + np.arange(0, a.size, dtype=np.int64) * num_classes one_hot = np.zeros((a.size, num_classes), dtype=a.dtype) np.put(one_hot, idcs, 1) return one_hot.reshape(*a.shape, -1) def reference_mse_loss(input, target, reduction="mean"): se = (input - target) ** 2 if reduction == "mean": return np.mean(se) elif reduction == "sum": return np.sum(se) else: # reduction == "none" return se def wrapper_set_seed(op, *args, **kwargs): """Wrapper to set seed manually for some functions like dropout See: https://github.com/pytorch/pytorch/pull/62315#issuecomment-896143189 for more details. """ with freeze_rng_state(): torch.manual_seed(42) return op(*args, **kwargs) def reference_layer_norm(inp: np.ndarray, normalized_shape: Tuple[int], weight=None, bias=None, eps=1e-5): feature_size = np.prod(normalized_shape) inp_view = inp.reshape(-1, feature_size) # type: ignore[call-overload] mean = inp_view.mean(axis=-1, keepdims=True) var = inp_view.var(axis=-1, ddof=0, keepdims=True) Y = (inp_view - mean) / np.sqrt(var + eps) if weight is None and bias is not None: Y = Y + bias.reshape(-1) elif weight is not None and bias is None: Y = Y * weight.reshape(-1) elif weight is not None and bias is not None: Y = Y * weight.reshape(-1) + bias.reshape(-1) return Y.reshape(*inp.shape) def reference_group_norm(inp: np.ndarray, num_groups: int, weight=None, bias=None, eps=1e-5): inp_view = inp if np.prod(inp.shape) != 0: inp_view = inp.reshape((inp.shape[0], num_groups, -1)) mean = inp_view.mean(axis=-1, keepdims=True) var = inp_view.var(axis=-1, ddof=0, keepdims=True) Y = (inp_view - mean) / np.sqrt(var + eps) Y = Y.reshape(inp.shape) if weight is not None: # weight is a vector of length equal to the channel if len(Y.shape) > 2: weight = np.tile(np.expand_dims(weight, 1), [1] + list(inp.shape[2:])) Y = Y * weight if bias is not None: # bias is a vector of length equal to the channel if len(Y.shape) > 2: bias = np.tile(np.expand_dims(bias, 1), [1] + list(inp.shape[2:])) Y = Y + bias return Y # using a custom reference function since numpy only has a string side arg (instead of right and side) and doesn't # have an out_int32 arg. Additionally, numpy doesn't support searchsorted with ND arrays, so this splits those into # stacked 1D cases def reference_searchsorted(sorted_sequence, boundary, out_int32=False, right=False, side='left', sorter=None): side = 'right' if (right or side == 'right') else 'left' if len(sorted_sequence.shape) == 1 : ret = np.searchsorted(sorted_sequence, boundary, side=side, sorter=sorter) return ret.astype(np.int32) if out_int32 else ret elif sorted_sequence.shape[0] == 0: if sorter is not None: sorter = sorter.flatten() ret = np.searchsorted(sorted_sequence.flatten(), boundary.flatten(), side=side, sorter=sorter) ret = ret.astype(np.int32) if out_int32 else ret return ret.reshape(boundary.shape) else: # numpy searchsorted only supports 1D inputs so we split up ND inputs orig_shape = boundary.shape num_splits = np.prod(sorted_sequence.shape[:-1]) splits = range(0, num_splits) sorted_sequence, boundary = sorted_sequence.reshape(num_splits, -1), boundary.reshape(num_splits, -1) if sorter is not None: sorter = sorter.reshape(num_splits, -1) split_sequence = [sorted_sequence[i] for i in splits] split_boundary = [boundary[i] for i in splits] split_sorter = [sorter[i] if (sorter is not None) else None for i in splits] split_ret = [np.searchsorted(s_seq, b, side=side, sorter=s_sort) for (s_seq, b, s_sort) in zip(split_sequence, split_boundary, split_sorter)] split_ret = [i.astype(np.int32) for i in split_ret] if out_int32 else split_ret return np.stack(split_ret).reshape(orig_shape) def gradcheck_wrapper_hermitian_input(op, input, *args, **kwargs): """Gradcheck wrapper for functions that take Hermitian matrices as input. They require a modified function because the finite-difference algorithm for calculating derivatives does not preserve the Hermitian property of the input. """ return op(input + input.mH, *args, **kwargs) def gradcheck_wrapper_triangular_input(op, *args, upper=False, idx=0, **kwargs): """Gradcheck wrapper for functions that take lower or upper triangular matrices as input. They require a modified function because the finite-difference algorithm for calculating derivatives does not preserve the triangular property of the input. `idx` is used to specific which `args[idx]` is to be triangularized. """ triangular_arg = args[idx].triu() if upper else args[idx].tril() return op(*args[:idx], triangular_arg, *args[idx + 1:], upper, **kwargs) def gradcheck_wrapper_triangular_input_real_positive_diagonal(op, *args, upper=False, idx=0, **kwargs): """Gradcheck wrapper for functions that take lower/upper triangular matrices with real and positive diagonals, for example, cholesky-like operations. """ arg = args[idx] arg_diag = arg.diagonal(0, -2, -1) arg_diag_embed = torch.diag_embed(arg_diag) id_diag_tensor = torch.ones_like(arg_diag) id_tensor = torch.diag_embed(id_diag_tensor) # new_arg = arg - diag(arg) + I new_arg = arg - arg_diag_embed + id_tensor return gradcheck_wrapper_triangular_input( op, *args[:idx], new_arg, *args[idx + 1:], upper=upper, idx=idx, **kwargs ) def gradcheck_wrapper_masked_operation(op, input, *args, **kwargs): """Gradcheck wrapper for masked operations. When mask is specified, replaces masked-out elements with zeros. Use for operations that produce non-finite masked-out elements, for instance, for minimum and maximum reductions. """ output = op(input, *args, **kwargs) mask = kwargs.get('mask') if mask is not None: output_mask = torch._masked._output_mask(op, input, *args, **kwargs) output = torch.where(output_mask, output, output.new_zeros([])) return output def reference_reduction_numpy(f, supports_keepdims=True): """Wraps a NumPy reduction operator. The wrapper function will forward dim, keepdim, mask, and identity kwargs to the wrapped function as the NumPy equivalent axis, keepdims, where, and initiak kwargs, respectively. Args: f: NumPy reduction operator to wrap supports_keepdims (bool, optional): Whether the NumPy operator accepts keepdims parameter. If it does not, the wrapper will manually unsqueeze the reduced dimensions if it was called with keepdim=True. Defaults to True. Returns: Wrapped function """ @wraps(f) def wrapper(x: np.ndarray, *args, **kwargs): # Copy keys into a set keys = set(kwargs.keys()) dim = kwargs.pop('dim', None) keepdim = kwargs.pop('keepdim', False) if 'dim' in keys: dim = tuple(dim) if isinstance(dim, Sequence) else dim # NumPy reductions don't accept dim=0 for scalar inputs # so we convert it to None if and only if dim is equivalent if x.ndim == 0 and dim in {0, -1, (0,), (-1,)}: kwargs['axis'] = None else: kwargs['axis'] = dim if 'keepdim' in keys and supports_keepdims: kwargs['keepdims'] = keepdim if 'mask' in keys: mask = kwargs.pop('mask') if mask is not None: assert mask.layout == torch.strided kwargs['where'] = mask.cpu().numpy() if 'identity' in keys: identity = kwargs.pop('identity') if identity is not None: if identity.dtype is torch.bfloat16: identity = identity.cpu().to(torch.float32) else: identity = identity.cpu() kwargs['initial'] = identity.numpy() if 'unbiased' in keys: unbiased = kwargs.pop('unbiased') if unbiased is not None: kwargs['ddof'] = int(unbiased) result = f(x, *args, **kwargs) # Unsqueeze reduced dimensions if NumPy does not support keepdims if keepdim and not supports_keepdims and x.ndim > 0: dim = list(range(x.ndim)) if dim is None else dim result = np.expand_dims(result, dim) return result return wrapper def loss_reference_reduction_wrapper(fn): def wrapper(input, target, *, size_average=None, reduce=None, reduction="mean", **other_kwargs): if size_average is not None or reduce is not None: raise RuntimeError( "The keyword arguments 'size_average' and 'reduce' are deprecated and not supported by this wrapper" ) output = fn(input, target, **other_kwargs) if reduction == "mean": return np.mean(output) elif reduction == "sum": return np.sum(output) else: # reduction == "none" return output return wrapper @loss_reference_reduction_wrapper def reference_smooth_l1_loss(input, target, beta=1.0): diff = input - target abs_diff = np.abs(diff) above_threshold = abs_diff >= beta loss = np.empty_like(input) loss[above_threshold] = abs_diff[above_threshold] - 0.5 * beta loss[~above_threshold] = diff[~above_threshold] ** 2 / (2 * beta) return loss def reference_std_var(f): """Forwards unbiased/correction kwargs as NumPy's equivalent ddof""" g = reference_reduction_numpy(f) @wraps(g) def wrapper(x: np.ndarray, *args, **kwargs): assert not ('unbiased' in kwargs and 'correction' in kwargs) if 'unbiased' in kwargs: kwargs['ddof'] = int(kwargs.pop('unbiased')) elif 'correction' in kwargs: kwargs['ddof'] = kwargs.pop('correction') return g(x, *args, **kwargs) return wrapper def generate_std_var_kwargs(t: torch.Tensor, **kwargs): """Generates unbiased/correction kwargs for std/var operators""" yield ((), {'unbiased': True}) yield ((), {'unbiased': False}) # Currently, calling std with correction is only enabled when # both dim and keepdim are provided. if 'dim' in kwargs and 'keepdim' in kwargs: yield ((), {'correction': 0}) yield ((), {'correction': 1}) numel = torch.tensor(t.shape)[kwargs.get('dim')].prod() yield ((), {'correction': numel // 2}) def error_inputs_mean(op_info, device, **kwargs): err_msg1 = (r"mean\(\): could not infer output dtype. " r"Input dtype must be either a floating point or complex dtype. " r"Got: Long") si1 = SampleInput( make_tensor((3, 4, 5), dtype=torch.int64, device=device), args=([],)) err_msg2 = (r"mean\(\): could not infer output dtype. " r"Optional dtype must be either a floating point or complex dtype. " r"Got: Long") si2 = SampleInput( make_tensor((3, 4, 5), dtype=torch.float32, device=device), args=([],), kwargs={"dtype": torch.int64}) err_msg3 = "Expected out tensor to have dtype double, but got float instead" si3 = SampleInput( make_tensor((3, 4, 5), dtype=torch.int64, device=device), args=([],), kwargs={ "dtype": torch.float64, "out": make_tensor([], dtype=torch.float32, device=device), }) return (ErrorInput(si1, error_regex=err_msg1), ErrorInput(si2, error_regex=err_msg2), ErrorInput(si3, error_regex=err_msg3)) # Operator database (sorted alphabetically) op_db: List[OpInfo] = [ UnaryUfuncInfo('abs', aliases=('absolute', ), ref=np.abs, dtypes=all_types_and_complex_and(torch.half, torch.bfloat16, torch.chalf), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16, torch.chalf), skips=( # Inplace abs doesn't support complex inputs DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_inplace_grad', dtypes=(torch.cdouble,)), DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_inplace_gradgrad', dtypes=(torch.cdouble,)), DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_inplace_forward_mode_AD', dtypes=(torch.cdouble,)), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', device_type='cpu', dtypes=[torch.cfloat]), # Reference: https://github.com/pytorch/pytorch/issues/49224 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_small', dtypes=[torch.int8], active_if=TEST_WITH_ASAN), # TODO: Fix test_out_arg_all_dtypes as torch.empty_like(expected_output) where expected_output=op(input) # We can break the logic of the loop over all possible types but it is OK. # https://github.com/pytorch/pytorch/blob/master/test/test_unary_ufuncs.py#L440-L449 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_out_arg_all_dtypes', dtypes=[torch.cfloat, torch.cdouble]), # The complex formula might be wrong DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_forward_mode_AD', dtypes=complex_types()), # Forward-over-reverse gradgrad might be wrong for complex (see above): DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_fn_fwgrad_bwgrad', dtypes=complex_types()), # nonzero_count not implemented DecorateInfo(unittest.expectedFailure, 'TestSparseCSR', 'test_sparse_csr_consistency', dtypes=(torch.chalf,)), # nonzero_count not implemented DecorateInfo(unittest.expectedFailure, 'TestSparseCSR', 'test_sparse_csr_unary_inplace', dtypes=(torch.chalf,)), # nonzero_count not implemented DecorateInfo(unittest.expectedFailure, 'TestSparseCSR', 'test_sparse_csr_unary_out', dtypes=(torch.chalf,)), # add_out_op2_sparse_csr DecorateInfo(unittest.expectedFailure, 'TestSparseCSR', 'test_zero_to_zero_correspondence_unary', dtypes=(torch.chalf,)), ), supports_fwgrad_bwgrad=True, assert_autodiffed=True, supports_sparse_csr=True, supports_forward_ad=True), # NOTE: CPU complex acos produces incorrect outputs (https://github.com/pytorch/pytorch/issues/42952) UnaryUfuncInfo('acos', aliases=('arccos', ), ref=np.arccos, domain=(-1, 1), dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), assert_autodiffed=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, decorators=(precisionOverride({torch.float16: 1e-2, torch.bfloat16: 1e-1, torch.complex64: 1e-2}),), skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_normal', device_type='cuda', dtypes=[torch.cdouble], active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', device_type='cuda', dtypes=[torch.cdouble], active_if=IS_WINDOWS), # Failing with wrong imaginary sign on at least some Windows jobs DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_small', device_type='cuda', dtypes=[torch.cdouble], active_if=IS_WINDOWS), # Failing with wrong imaginary sign on at least some Windows jobs DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', device_type='cuda', dtypes=[torch.cdouble], active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble]), DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_fn_grad', dtypes=[torch.cdouble], active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_method_grad', dtypes=[torch.cdouble], active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_inplace_grad', dtypes=[torch.cdouble], active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_forward_mode_AD', dtypes=[torch.cdouble], active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_inplace_forward_mode_AD', dtypes=[torch.cdouble], active_if=IS_WINDOWS), )), # NOTE: the derivative for inplace acosh is not implemented UnaryUfuncInfo('acosh', aliases=('arccosh', ), ref=np.arccosh, domain=(1, None), dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), # "rsqrt_cuda" not implemented for 'BFloat16' backward_dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), decorators=(precisionOverride({torch.bfloat16: 5e-2}),), supports_inplace_autograd=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_normal', device_type='cuda', dtypes=[torch.cdouble], active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', device_type='cuda', dtypes=[torch.cdouble], active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', device_type='cuda', dtypes=[torch.cdouble], active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', device_type='cuda', dtypes=[torch.cdouble], active_if=IS_WINDOWS), # Failing with wrong imaginary sign on at least some Windows jobs DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_small', device_type='cuda', dtypes=[torch.cdouble], active_if=IS_WINDOWS), # Reference: https://github.com/pytorch/pytorch/issues/50692 DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_fn_grad', device_type='cuda', dtypes=[torch.cdouble], active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_method_grad', device_type='cuda', dtypes=[torch.cdouble], active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_forward_mode_AD', device_type='cuda', dtypes=[torch.cdouble], active_if=IS_WINDOWS), ), # acosh is not defined at x < 1 (real) or |z| < 1 (complex) reference_numerics_filter=NumericsFilter( condition=lambda x: (torch.abs(x) < 1 if x.is_complex() else x < 1), safe_val=2)), BinaryUfuncInfo('add', # NumPy has no builtin reference for the alpha kwarg, but it is easy enough to emulate ref=lambda input, other, *, alpha=1: np.add(input, other) if alpha == 1 \ else np.add(input, np.multiply(alpha, other)), dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16, torch.chalf), assert_autodiffed=True, sample_inputs_func=sample_inputs_add_sub, supports_fwgrad_bwgrad=True, supports_forward_ad=True, supports_two_python_scalars=True, decorators=( DecorateInfo( toleranceOverride({torch.chalf: tol(atol=1e-2, rtol=0)}), 'TestBinaryUfuncs', 'test_reference_numerics'), ), skips=( # boolean alpha not handled properly DecorateInfo(unittest.expectedFailure, 'TestCudaFuserOpInfo', 'test_nvfuser_correctness', dtypes=(torch.bool,)), # boolean alpha not handled properly DecorateInfo(unittest.expectedFailure, 'TestNNCOpInfo', 'test_nnc_correctness', dtypes=(torch.bool,)), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_reference_testing', dtypes=(torch.complex128,)), DecorateInfo(unittest.skip("Skipped!"), 'TestBinaryUfuncs', 'test_reference_numerics_extremal_values', dtypes=(torch.complex64, torch.complex128)), )), BinaryUfuncInfo('mul', aliases=('multiply',), dtypes=all_types_and_complex_and(torch.chalf, torch.float16, torch.bfloat16, torch.bool), assert_autodiffed=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_two_python_scalars=True), BinaryUfuncInfo('sub', # NumPy has no builtin reference for the alpha kwarg, but it is easy enough to emulate ref=lambda input, other, *, alpha=1: np.subtract(input, np.multiply(alpha, other)), aliases=('subtract',), dtypes=all_types_and_complex_and(torch.bfloat16, torch.float16, torch.chalf), assert_autodiffed=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_add_sub, supports_two_python_scalars=True, decorators=( DecorateInfo( toleranceOverride({torch.float16: tol(atol=1e-2, rtol=0)}), 'TestBinaryUfuncs', 'test_reference_numerics'), DecorateInfo( toleranceOverride({torch.chalf: tol(atol=1e-2, rtol=0)}), 'TestCommon', 'test_complex_half_reference_testing', device_type='cpu'), DecorateInfo( toleranceOverride({torch.chalf: tol(atol=5e-3, rtol=0)}), 'TestDecomp', 'test_comprehensive', device_type='cpu'), DecorateInfo( toleranceOverride({torch.chalf: tol(atol=5e-3, rtol=0)}), 'TestDecomp', 'test_quick', device_type='cpu'), ), skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestBinaryUfuncs', 'test_reference_numerics', dtypes=(torch.uint8,)), DecorateInfo(unittest.skip("Skipped!"), 'TestBinaryUfuncs', 'test_reference_numerics_small_values', dtypes=(torch.uint8,)), )), OpInfo('addmm', # This addmm OpInfo is for when alpha and beta are not both equal to 1. # alpha=beta=1 is tested in the following opinfo, because that special case will # trigger addmm being decomposed by a jit pass. dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfROCM=floating_and_complex_types_and(torch.float16, torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.float16, *[torch.bfloat16] if CUDA11OrLater else []), assert_autodiffed=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, sample_inputs_func=sample_inputs_addmm), OpInfo('addmm', # When alpha=beta=1 as compile-time constants, JIT will decompose addmm into mm and add. variant_test_name='decomposed', dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.float16, *[torch.bfloat16] if(CUDA11OrLater or TEST_WITH_ROCM) else []), assert_autodiffed=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, autodiff_nonfusible_nodes=['aten::add', 'aten::mm'], sample_inputs_func=partial(sample_inputs_addmm, alpha=1, beta=1), skips=( # https://github.com/pytorch/pytorch/issues/71784 DecorateInfo(unittest.skip('Skipped!'), 'TestNNCOpInfo', 'test_nnc_correctness', device_type='cpu', dtypes=(torch.float16,)), DecorateInfo(unittest.skip('Skipped!'), 'TestCudaFuserOpInfo', 'test_nvfuser_correctness', dtypes=(torch.float16,)), )), OpInfo('addmv', dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.complex64, torch.complex128, *[torch.bfloat16] if (CUDA11OrLater or TEST_WITH_ROCM) else []), supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_addmv), OpInfo('addbmm', ref=lambda M, batch1, batch2, beta=1, alpha=1: np.add(np.multiply(np.asarray(beta, dtype=M.dtype), M), np.multiply(np.asarray(alpha, dtype=batch1.dtype), np.sum(np.matmul(batch1, batch2), axis=0))), dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.float16, *[torch.bfloat16] if (SM53OrLater and CUDA11OrLater) or TEST_WITH_ROCM else []), supports_forward_ad=True, supports_fwgrad_bwgrad=True, decorators=[ DecorateInfo( toleranceOverride({torch.float32: tol(atol=1.3e-05, rtol=1.3e-05), torch.complex64: tol(atol=1e-05, rtol=1.2e-03)}), 'TestCommon', 'test_reference_testing')], skips=( # NVIDIA only assures that bfloat16 is supported by bmm if SM >= 5.3 DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_dtypes', device_type='cuda', active_if=not SM53OrLater), # addbmm does not correctly warn when resizing out= inputs DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out_warning'), # https://github.com/pytorch/pytorch/issues/55907 DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_variant_consistency_eager'), ), sample_inputs_func=sample_inputs_addbmm), OpInfo('baddbmm', dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.complex64, torch.complex128, *[torch.bfloat16] if CUDA11OrLater or TEST_WITH_ROCM else []), backward_dtypesIfCUDA=floating_types_and(torch.float16, *[torch.bfloat16] if SM53OrLater or TEST_WITH_ROCM else [], torch.complex64, torch.complex128), supports_forward_ad=True, supports_fwgrad_bwgrad=True, decorators=[ DecorateInfo( toleranceOverride({torch.complex64: tol(atol=1e-05, rtol=1.2e-03)}), 'TestCommon', 'test_variant_consistency_eager', device_type='cuda'), DecorateInfo( toleranceOverride({torch.complex64: tol(atol=1e-05, rtol=1.2e-03)}), 'TestMathBits', 'test_conj_view', device_type='cuda')], sample_inputs_func=sample_inputs_baddbmm), OpInfo('dot', dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.float16, *[torch.bfloat16] if (CUDA11OrLater or TEST_WITH_ROCM) else []), assert_autodiffed=True, sample_inputs_func=sample_inputs_dot_vdot, supports_forward_ad=True, supports_fwgrad_bwgrad=True, ), OpInfo('vdot', dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.float16, *[torch.bfloat16] if (CUDA11OrLater or TEST_WITH_ROCM) else []), sample_inputs_func=sample_inputs_dot_vdot, supports_forward_ad=True, supports_fwgrad_bwgrad=True, ), OpInfo('bmm', dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.float16, *[torch.bfloat16] if (SM53OrLater and CUDA11OrLater) or TEST_WITH_ROCM else []), assert_autodiffed=True, assert_jit_shape_analysis=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # NVIDIA only assures that bfloat16 is supported by bmm if SM >= 5.3 DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_dtypes', device_type='cuda', active_if=not SM53OrLater), ), sample_inputs_func=sample_inputs_bmm), OpInfo('mv', dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.float16, *[torch.bfloat16] if (CUDA11OrLater or TEST_WITH_ROCM) else []), assert_autodiffed=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_mv), OpInfo('addr', dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16), backward_dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16), backward_dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.float16, *[torch.bfloat16] if (CUDA11OrLater or TEST_WITH_ROCM) else []), # Reference: https://github.com/pytorch/pytorch/issues/50747 supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # Reference: https://github.com/pytorch/pytorch/issues/50747 DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_variant_consistency_eager', dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16)), ), sample_inputs_func=sample_inputs_addr, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL), OpInfo('addcmul', dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.float16, torch.bfloat16), assert_autodiffed=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # TODO: update sample inputs with for_inplace_variant kwarg to support this test DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_variant_consistency_eager'), # 76047 DecorateInfo(unittest.expectedFailure, 'TestNNCOpInfo', 'test_nnc_correctness', dtypes=(torch.int8, torch.int16, torch.int32, torch.int64)), ), sample_inputs_func=sample_inputs_addcmul_addcdiv), OpInfo('addcdiv', dtypes=floating_and_complex_types_and(torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.float16, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # TODO: update sample inputs with for_inplace_variant kwarg to support this test DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_variant_consistency_eager'), ), sample_inputs_func=sample_inputs_addcmul_addcdiv), UnaryUfuncInfo('asin', aliases=('arcsin', ), ref=np.arcsin, domain=(-1, 1), supports_sparse=True, supports_sparse_csr=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), assert_autodiffed=True, decorators=[ DecorateInfo( toleranceOverride({torch.float16: tol(atol=1e-05, rtol=1e-03)}), 'TestUnaryUfuncs', device_type='cuda'), precisionOverride({torch.bfloat16: 1e-2}), ], skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', device_type='cuda', dtypes=[torch.cdouble], active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', device_type='cuda', dtypes=[torch.cdouble], active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped! sparse backward not supported"), 'TestSparseUnaryUfuncs', 'test_sparse_fn_grad'), )), # NOTE: derivative for inplace asinh is not implemented UnaryUfuncInfo('asinh', aliases=('arcsinh', ), ref=np.arcsinh, dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), decorators=(precisionOverride({torch.bfloat16: 5e-2}),), supports_inplace_autograd=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_sparse=True, supports_sparse_csr=True, skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_small', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_normal', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', device_type='cuda', dtypes=[torch.cdouble], active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', device_type='cuda', dtypes=[torch.cdouble], active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped! sparse backward not supported"), 'TestSparseUnaryUfuncs', 'test_sparse_fn_grad'), )), UnaryUfuncInfo('atan', aliases=('arctan', ), ref=np.arctan, dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), assert_autodiffed=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_sparse=True, supports_sparse_csr=True, decorators=(precisionOverride({torch.bfloat16: 1e-2}),), skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_small', active_if=TEST_WITH_ROCM, device_type='cuda'), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', active_if=TEST_WITH_ROCM, device_type='cuda', dtypes=[torch.complex128]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_small', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', device_type='cuda', dtypes=[torch.cfloat, torch.cdouble], active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', device_type='cuda', dtypes=[torch.cfloat, torch.cdouble], active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped! sparse backward not supported"), 'TestSparseUnaryUfuncs', 'test_sparse_fn_grad'), )), BinaryUfuncInfo('atan2', aliases=('arctan2',), dtypes=all_types_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bool, torch.half, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, promotes_int_to_float=True, supports_rhs_python_scalar=False, skips=( # Incorrectly attempts to use a scalar for the second argument DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_jit_alias_remapping'), )), UnaryUfuncInfo('atanh', aliases=('arctanh', ), ref=np.arctanh, domain=(-1, 1), dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), decorators=(precisionOverride({torch.bfloat16: 1e-2}),), supports_inplace_autograd=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_sparse=True, supports_sparse_csr=True, skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_small', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', device_type='cuda', dtypes=[torch.cfloat, torch.cdouble], active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', device_type='cuda', dtypes=[torch.cfloat], active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped! sparse backward not supported"), 'TestSparseUnaryUfuncs', 'test_sparse_fn_grad'), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', active_if=TEST_WITH_ROCM, device_type='cuda', dtypes=[torch.complex128]), )), OpInfo('allclose', dtypes=floating_and_complex_types_and(torch.float16, torch.bfloat16), ref=np.allclose, supports_autograd=False, supports_forward_ad=False, sample_inputs_func=sample_inputs_allclose, skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'), DecorateInfo(unittest.skip("Skipped!"), 'TestNNCOpInfo', 'test_nnc_correctness'), DecorateInfo(unittest.skip("Skipped!"), 'TestCudaFuserOpInfo'), ), supports_out=False), OpInfo('broadcast_to', ref=np.broadcast_to, dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_broadcast_to), OpInfo('broadcast_tensors', ref=np.broadcast_arrays, dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # https://github.com/pytorch/pytorch/issues/64997 DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # JIT does not support variadic tensors. # RuntimeError: input->type()->kind() == TypeKind::OptionalType # INTERNAL ASSERT FAILED at "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":252, # please report a bug to PyTorch. DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit', dtypes=[torch.float32]), ), sample_inputs_func=sample_inputs_broadcast_tensors), OpInfo('block_diag', dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16, torch.chalf), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # https://github.com/pytorch/pytorch/issues/64997 DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # JIT does not support variadic tensors. # RuntimeError: input->type()->kind() == TypeKind::OptionalType # INTERNAL ASSERT FAILED at "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":252, # please report a bug to PyTorch. DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit', dtypes=[torch.float32]), # Problem; should be fixed DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_operator'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_backward'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), ), sample_inputs_func=sample_inputs_block_diag), UnaryUfuncInfo('bitwise_not', ref=np.bitwise_not, dtypes=integral_types_and(torch.bool), operator_variant=operator.invert, supports_autograd=False), BinaryUfuncInfo('bitwise_left_shift', op=torch.bitwise_left_shift, dtypes=integral_types(), dtypesIfCUDA=integral_types(), operator_variant=operator.lshift, inplace_operator_variant=operator.ilshift, supports_autograd=False, supports_one_python_scalar=True, rhs_make_tensor_kwargs=dict(low=0), skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestBinaryUfuncs', 'test_type_promotion'), )), BinaryUfuncInfo('bitwise_right_shift', op=torch.bitwise_right_shift, dtypes=integral_types(), dtypesIfCUDA=integral_types(), operator_variant=operator.rshift, inplace_operator_variant=operator.irshift, supports_autograd=False, supports_one_python_scalar=True, rhs_make_tensor_kwargs=dict(low=0), skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestBinaryUfuncs', 'test_type_promotion'), )), OpInfo('combinations', op=torch.combinations, dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False, sample_inputs_func=sample_inputs_combinations), OpInfo('cartesian_prod', op=torch.cartesian_prod, dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_cartesian_prod, skips=( DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_neg_view'), DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_conj_view'), DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # RuntimeError: input->type()->kind() == TypeKind::OptionalType # INTERNAL ASSERT FAILED at "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":270 DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit', dtypes=(torch.float32,)), )), OpInfo('cdist', dtypes=floating_types(), supports_out=False, supports_gradgrad=False, assert_autodiffed=False, sample_inputs_func=sample_inputs_cdist), UnaryUfuncInfo('ceil', ref=np.ceil, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_sparse=True, supports_sparse_csr=True, assert_autodiffed=True), OpInfo('cholesky', dtypes=floating_and_complex_types(), sample_inputs_func=sample_inputs_linalg_cholesky, gradcheck_wrapper=gradcheck_wrapper_hermitian_input, decorators=[skipCUDAIfNoMagma, skipCPUIfNoLapack],), OpInfo('cholesky_inverse', dtypes=floating_and_complex_types(), backward_dtypes=floating_and_complex_types(), supports_fwgrad_bwgrad=True, supports_forward_ad=True, check_batched_gradgrad=True, sample_inputs_func=sample_inputs_linalg_cholesky_inverse, gradcheck_wrapper=gradcheck_wrapper_triangular_input_real_positive_diagonal, decorators=[skipCUDAIfNoMagma, skipCPUIfNoLapack], skips=( DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), # Strides are not the same! Original strides were ((4, 2, 1),) and strides are now ((4, 1, 2),) DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out'),)), OpInfo('cholesky_solve', op=torch.cholesky_solve, dtypes=floating_and_complex_types(), sample_inputs_func=sample_inputs_cholesky_solve, check_batched_gradgrad=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, gradcheck_wrapper=lambda *args, **kwargs: gradcheck_wrapper_triangular_input(*args, idx=1, **kwargs), decorators=[skipCUDAIfNoMagma, skipCPUIfNoLapack]), OpInfo('chunk', dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16, torch.chalf), sample_inputs_func=sample_inputs_chunk, reference_inputs_func=reference_inputs_chunk, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False), OpInfo('clone', ref=np.copy, dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16, torch.chalf), sample_inputs_func=sample_inputs_clone, reference_inputs_func=reference_inputs_clone, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False), OpInfo('contiguous', op=lambda x, *args, **kwargs: x.contiguous(*args, **kwargs), dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16, torch.chalf), sample_inputs_func=sample_inputs_contiguous, supports_forward_ad=True, supports_fwgrad_bwgrad=True, autodiff_fusible_nodes=['aten::contiguous'], assert_jit_shape_analysis=True, supports_out=False, skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), )), OpInfo('sum_to_size', op=lambda x, *args, **kwargs: x.sum_to_size(*args, **kwargs), dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), sample_inputs_func=sample_inputs_sum_to_size, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False, skips=( # lambda impl DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit', dtypes=(torch.float,)),),), OpInfo('symeig', dtypes=floating_and_complex_types(), check_batched_grad=False, check_batched_gradgrad=False, sample_inputs_func=sample_inputs_symeig, gradcheck_wrapper=gradcheck_wrapper_hermitian_input, skips=( DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_backward'), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_out', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_variant_consistency_eager', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit', device_type='mps', dtypes=[torch.float32]), ), decorators=[skipCUDAIfNoMagma, skipCPUIfNoLapack, with_tf32_off]), # NOTE: clamp has separate opinfos for scalar min/max (unary op) vs. tensors OpInfo('clamp', aliases=('clip',), dtypes=all_types_and(torch.bfloat16), dtypesIfCUDA=all_types_and(torch.half, torch.bfloat16), assert_autodiffed=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_clamp, skips=( # nvFuser and NNC appear to not handle boolean clamp DecorateInfo(unittest.expectedFailure, 'TestCudaFuserOpInfo', 'test_nvfuser_correctness', dtypes=(torch.bool,)), DecorateInfo(unittest.expectedFailure, 'TestNNCOpInfo', 'test_nnc_correctness', dtypes=(torch.bool,)), )), UnaryUfuncInfo('clamp', variant_test_name='scalar', aliases=('clip', ), decorators=(precisionOverride({torch.bfloat16: 7e-2, torch.float16: 1e-2}),), ref=np.clip, dtypes=all_types_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bool, torch.half, torch.bfloat16), assert_autodiffed=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # Reference: https://github.com/pytorch/pytorch/issues/54841 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', device_type='cpu', dtypes=[torch.bfloat16]), # nvFuser and NNC appear to not handle clamp type promotion DecorateInfo(unittest.expectedFailure, 'TestCudaFuserOpInfo', 'test_nvfuser_correctness', dtypes=(torch.bool, torch.int32, torch.int64)), DecorateInfo(unittest.skip("Failing on some jobs!"), 'TestNNCOpInfo', 'test_nnc_correctness', dtypes=(torch.bool, torch.int8, torch.int16, torch.int32, torch.int64)), ), sample_kwargs=sample_kwargs_clamp_scalar, sample_inputs_func=sample_inputs_clamp_scalar), UnaryUfuncInfo('positive', ref=np.positive, dtypes=all_types_and_complex_and(torch.half, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, ), UnaryUfuncInfo('conj', ref=np.conj, dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.half, torch.chalf), supports_sparse=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False), UnaryUfuncInfo('conj_physical', ref=np.conj, dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.half, torch.chalf), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_sparse=True, supports_sparse_csr=True, skips=( # RuntimeError: inputSet && outputSet # INTERNAL ASSERT FAILED at "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":118, # please report a bug to PyTorch. DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit', dtypes=(torch.float32, )), DecorateInfo(unittest.skip("Skipped! conj_physical_ not implemented for sparse"), 'TestSparseUnaryUfuncs', 'test_inplace'), # RuntimeError: "nonzero_count_cpu" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, "TestSparseCSR", "test_sparse_csr_consistency", dtypes=(torch.complex32,)), # RuntimeError: "nonzero_count_cpu" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, "TestSparseCSR", "test_sparse_csr_unary_inplace", dtypes=(torch.complex32,)), # RuntimeError: "nonzero_count_cpu" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, "TestSparseCSR", "test_sparse_csr_unary_out", dtypes=(torch.complex32,)), # RuntimeError: "add_out_op2_sparse_csr" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, "TestSparseCSR", "test_zero_to_zero_correspondence_unary", dtypes=(torch.complex32,)), )), OpInfo('resolve_conj', dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), sample_inputs_func=sample_inputs_view_as_real, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False, ), OpInfo('resolve_neg', dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16, torch.chalf), sample_inputs_func=sample_inputs_view_as_real, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False, ), OpInfo('view_as_real', dtypes=complex_types(), supports_forward_ad=True, supports_out=False, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_view_as_real, test_conjugated_samples=False, ), OpInfo('view_as_complex', dtypes=floating_types_and(torch.half), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, test_neg_view=False, sample_inputs_func=sample_inputs_view_as_complex, skips=( # RuntimeError: Tensor must have a last dimension with stride 1 DecorateInfo(unittest.expectedFailure, "TestCommon", "test_noncontiguous_samples"), # RuntimeError: "eq_cpu" not implemented for 'ComplexHalf' DecorateInfo(unittest.skip("Skipped!"), 'TestNNCOpInfo', 'test_nnc_correctness', dtypes=(torch.half,)), # RuntimeError: "eq_cpu" not implemented for 'ComplexHalf' DecorateInfo(unittest.skip("Skipped!"), 'TestCudaFuserOpInfo', 'test_nvfuser_correctness', dtypes=(torch.half,)), )), BinaryUfuncInfo('complex', dtypes=floating_types_and(torch.half), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_rhs_python_scalar=False, skips=( # Test doesn't account for complex's type promotion semantics DecorateInfo(unittest.expectedFailure, 'TestBinaryUfuncs', 'test_type_promotion'), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_out', device_type='mps'), )), BinaryUfuncInfo('copysign', dtypes=all_types_and(torch.bool, torch.half, torch.bfloat16), promotes_int_to_float=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True), OpInfo('corrcoef', dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.half, *[torch.bfloat16] if (CUDA11OrLater or TEST_WITH_ROCM) else []), sample_inputs_func=sample_inputs_corrcoef, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False), UnaryUfuncInfo('cos', ref=np.cos, dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), assert_autodiffed=True, handles_large_floats=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, decorators=(precisionOverride({torch.bfloat16: 1e-2}),), skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', dtypes=(torch.cfloat, torch.cdouble,), device_type='cpu', active_if=IS_WINDOWS), # This fails on CUDA but passes on ROCm DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', dtypes=(torch.cdouble,), device_type='cuda'), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', dtypes=[torch.cfloat, torch.cdouble], active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble], active_if=IS_MACOS), )), UnaryUfuncInfo('cosh', ref=np_unary_ufunc_integer_promotion_wrapper(np.cosh), dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), assert_autodiffed=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # Reference: https://github.com/pytorch/pytorch/issues/48641 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', device_type='cpu', dtypes=[torch.int8]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', dtypes=[torch.cdouble]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', dtypes=[torch.cfloat, torch.cdouble], active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', dtypes=[torch.cfloat, torch.cdouble], active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble], active_if=IS_MACOS), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble], active_if=IS_MACOS), )), OpInfo('cov', dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.half, *[torch.bfloat16] if (CUDA11OrLater or TEST_WITH_ROCM) else []), backward_dtypesIfCUDA=all_types_and_complex_and(torch.half, *[torch.bfloat16] if (CUDA11OrLater or TEST_WITH_ROCM) else []), sample_inputs_func=sample_inputs_cov, error_inputs_func=error_inputs_cov, supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # Float did not match double DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_fn_grad'), # Jacobian mismatch DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_fn_gradgrad'), DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_forward_mode_AD'), DecorateInfo(unittest.skip("Barely fails"), 'TestGradients', 'test_fn_fwgrad_bwgrad'), # JIT test not working for tensor kwargs (https://github.com/pytorch/pytorch/issues/58507) # RuntimeError: # undefined value tensor: # File "", line 3 # def the_method(i0): # return torch.cov(i0, correction=0, fweights=None, aweights=tensor([0.0518, 0.4681], dtype=torch.float32, requires_grad=True)) # noqa: B950 # ~~~~~~ <--- HERE DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), DecorateInfo(unittest.skip('Skipped!'), 'TestCudaFuserOpInfo', 'test_nvfuser_extremal_values'), )), OpInfo('cross', dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.half), sample_inputs_func=sample_inputs_cross, supports_fwgrad_bwgrad=True, supports_out=True, supports_forward_ad=True), OpInfo('linalg.cross', ref=lambda x, y, dim=-1: np.cross(x, y, axis=dim), op=torch.linalg.cross, dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.half), aten_name='linalg_cross', sample_inputs_func=sample_inputs_cross, supports_out=True, supports_fwgrad_bwgrad=True, supports_forward_ad=True), OpInfo('cumsum', dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.half, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # cumsum does not handle correctly out= dtypes DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out'), ), sample_inputs_func=sample_inputs_cumulative_ops), OpInfo('cumprod', dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.float16, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # cumprod does not handle correctly out= dtypes DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_backward'), ), # gradgradcheck fails in fast_mode=True: #56275 sample_inputs_func=sample_inputs_cumprod, gradcheck_fast_mode=False), OpInfo('cummax', dtypes=all_types_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bool, torch.half, torch.bfloat16), sample_inputs_func=partial(sample_inputs_cumulative_ops, supports_dtype_kwargs=False), supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_backward'), ), gradcheck_nondet_tol=GRADCHECK_NONDET_TOL), OpInfo('cummin', dtypes=all_types_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bool, torch.half, torch.bfloat16), sample_inputs_func=partial(sample_inputs_cumulative_ops, supports_dtype_kwargs=False), supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_backward'), ), gradcheck_nondet_tol=GRADCHECK_NONDET_TOL), UnaryUfuncInfo('deg2rad', ref=np.radians, decorators=(precisionOverride({torch.bfloat16: 7e-1, torch.float16: 7e-1}),), dtypes=all_types_and(torch.bool, torch.half, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # Reference: https://github.com/pytorch/pytorch/pull/51283#issuecomment-770614273 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', dtypes=[torch.bfloat16]), )), OpInfo('diff', op=torch.diff, # np.diff has np._NoValue as default values for prepend and append, compare_with_reference breaks if prepend/append # are set as None when converting to numpy ref=lambda input, n=1, dim=-1, prepend=np._NoValue, append=np._NoValue: ( np.diff(input, n, dim, np._NoValue if prepend is None else prepend, np._NoValue if append is None else append) ), dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_diff), BinaryUfuncInfo('div', aliases=('divide',), variant_test_name='no_rounding_mode', dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16, torch.chalf), supports_forward_ad=True, promotes_int_to_float=True, supports_fwgrad_bwgrad=True, supports_two_python_scalars=True, assert_autodiffed=True, rhs_make_tensor_kwargs=dict(exclude_zero=True),), BinaryUfuncInfo('div', aliases=('divide',), variant_test_name='trunc_rounding', dtypes=all_types_and(torch.half, torch.bfloat16), sample_inputs_func=partial(sample_inputs_elementwise_binary, sample_kwargs=dict(rounding_mode="trunc")), supports_forward_ad=True, promotes_int_to_float=True, supports_fwgrad_bwgrad=True, supports_two_python_scalars=True, assert_autodiffed=True, rhs_make_tensor_kwargs=dict(exclude_zero=True), skips=( # RuntimeError: MALFORMED INPUT: Unhandled node kind (in computeValue): aten::div DecorateInfo(unittest.expectedFailure, 'TestNNCOpInfo', 'test_working'), )), BinaryUfuncInfo('div', aliases=('divide',), variant_test_name='floor_rounding', dtypes=all_types_and(torch.half, torch.bfloat16), sample_inputs_func=partial(sample_inputs_elementwise_binary, sample_kwargs=dict(rounding_mode="floor")), supports_forward_ad=True, promotes_int_to_float=True, supports_fwgrad_bwgrad=True, supports_two_python_scalars=True, assert_autodiffed=True, rhs_make_tensor_kwargs=dict(exclude_zero=True), skips=( # RuntimeError: MALFORMED INPUT: Unhandled node kind (in computeValue): aten::div DecorateInfo(unittest.expectedFailure, 'TestNNCOpInfo', 'test_working'), )), BinaryUfuncInfo('true_divide', dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16, torch.chalf), supports_forward_ad=True, promotes_int_to_float=True, supports_fwgrad_bwgrad=True, supports_two_python_scalars=True, rhs_make_tensor_kwargs=dict(exclude_zero=True)), UnaryUfuncInfo('exp', ref=np_unary_ufunc_integer_promotion_wrapper(np.exp), dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16, torch.chalf), skips=( # Reference: https://github.com/pytorch/pytorch/pull/50093#pullrequestreview-561791547 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', dtypes=[torch.bfloat16]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', dtypes=[torch.bfloat16, torch.cdouble]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_small', dtypes=[torch.bfloat16]), # Reference: https://github.com/pytorch/pytorch/issues/48010 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble]), ), assert_autodiffed=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True), OpInfo('expand', op=lambda self, shape: self.expand(shape), dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), sample_inputs_func=sample_inputs_expand, supports_forward_ad=True, supports_fwgrad_bwgrad=True, assert_jit_shape_analysis=True, supports_out=False, skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), )), OpInfo('expand_as', op=lambda self, other: self.expand_as(other), dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_expand_as, supports_out=False, skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'),), ), OpInfo('diag', dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_diag, error_inputs_func=error_inputs_diag), OpInfo('diag_embed', dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16, torch.chalf), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_diagonal_diag_embed), OpInfo('diagonal', # They are not strictly aliases as they have diverging defaults, but we can see them as aliases for testing purposes # If we add tests that test the function against the alias, make linalg.diagonal into its own OpInfo aliases=('linalg.diagonal',), aten_backward_name='diagonal_backward', dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16, torch.chalf), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_diagonal_diag_embed), OpInfo('diagonal_scatter', dtypes=all_types_and(torch.bool, torch.bfloat16, torch.float16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_diagonal_scatter), BinaryUfuncInfo('eq', dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16, torch.chalf), always_returns_bool=True, supports_autograd=False, sample_inputs_func=sample_inputs_comparison_ops, skips=( # https://github.com/pytorch/pytorch/issues/76805 DecorateInfo(unittest.expectedFailure, 'TestBinaryUfuncs', 'test_type_promotion'), )), BinaryUfuncInfo('fmax', op=torch.fmax, dtypes=all_types_and(torch.float16, torch.bfloat16, torch.bool), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_rhs_python_scalar=False, skips=( # RuntimeError: "max_elementwise_cuda" not implemented for 'ComplexFloat' DecorateInfo(unittest.skip("Skipped!"), 'TestBinaryUfuncs', 'test_type_promotion'), )), BinaryUfuncInfo('fmin', op=torch.fmin, dtypes=all_types_and(torch.float16, torch.bfloat16, torch.bool), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_rhs_python_scalar=False, skips=( # RuntimeError: "min_elementwise_cuda" not implemented for 'ComplexFloat' DecorateInfo(unittest.skip("Skipped!"), 'TestBinaryUfuncs', 'test_type_promotion'), )), BinaryUfuncInfo('fmod', ref=np.fmod, dtypes=all_types_and(torch.float16, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.float16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, assert_autodiffed=None, rhs_make_tensor_kwargs={'exclude_zero': True}, decorators=( DecorateInfo(unittest.skip("Skipped!"), 'TestBinaryUfuncs', 'test_contig_vs_every_other', dtypes=(torch.bfloat16,)), DecorateInfo(unittest.skip("Skipped!"), 'TestBinaryUfuncs', 'test_non_contig', dtypes=(torch.bfloat16,)), DecorateInfo(unittest.skip("Skipped!"), 'TestBinaryUfuncs', 'test_reference_numerics', dtypes=(torch.bfloat16,)), DecorateInfo(unittest.skip("Skipped!"), 'TestBinaryUfuncs', 'test_reference_numerics_small_values', dtypes=(torch.uint8,)), )), BinaryUfuncInfo('remainder', ref=np.remainder, dtypes=all_types_and(torch.float16, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.float16, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, assert_autodiffed=None, operator_variant=operator.mod, inplace_operator_variant=operator.imod, supports_one_python_scalar=True, rhs_make_tensor_kwargs={'exclude_zero': True}, decorators=( DecorateInfo(unittest.skip("Skipped!"), 'TestBinaryUfuncs', 'test_contig_vs_every_other', dtypes=(torch.bfloat16,)), DecorateInfo(unittest.skip("Skipped!"), 'TestBinaryUfuncs', 'test_non_contig', dtypes=(torch.bfloat16,)), DecorateInfo(unittest.skip("Skipped!"), 'TestBinaryUfuncs', 'test_reference_numerics', dtypes=(torch.bfloat16,)), DecorateInfo(unittest.skip("Skipped!"), 'TestBinaryUfuncs', 'test_reference_numerics_small_values', dtypes=(torch.uint8,)), # Fails on XLA # False is not true : Tensors failed to compare as equal! # Attempted to compare equality of tensors with different dtypes DecorateInfo(unittest.skip("Skipped!"), 'TestOpInfo', device_type='xla', dtypes=(torch.long,)), )), UnaryUfuncInfo('frac', ref=lambda x: np.modf(x)[0], dtypes=floating_types_and(torch.bfloat16, torch.float16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), assert_autodiffed=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', dtypes=(torch.bfloat16, torch.float16, torch.float32, torch.float64)), # 76047 DecorateInfo(unittest.expectedFailure, 'TestNNCOpInfo', 'test_nnc_correctness', dtypes=(torch.float32, torch.float64)), )), SpectralFuncInfo('fft.fft', aten_name='fft_fft', ref=np.fft.fft, ndimensional=SpectralFuncType.OneD, dtypes=all_types_and_complex_and(torch.bool), # rocFFT doesn't support Half/Complex Half Precision FFT # CUDA supports Half/ComplexHalf Precision FFT only on SM53 or later archs dtypesIfCUDA=all_types_and_complex_and( torch.bool, *() if (TEST_WITH_ROCM or not SM53OrLater) else (torch.half, torch.complex32)), supports_forward_ad=True, supports_fwgrad_bwgrad=True, ), SpectralFuncInfo('fft.fft2', aten_name='fft_fft2', ref=np.fft.fft2, ndimensional=SpectralFuncType.TwoD, dtypes=all_types_and_complex_and(torch.bool), # rocFFT doesn't support Half/Complex Half Precision FFT # CUDA supports Half/ComplexHalf Precision FFT only on SM53 or later archs dtypesIfCUDA=all_types_and_complex_and( torch.bool, *() if (TEST_WITH_ROCM or not SM53OrLater) else (torch.half, torch.complex32)), supports_forward_ad=True, supports_fwgrad_bwgrad=True, decorators=[precisionOverride( {torch.float: 1e-4, torch.cfloat: 1e-4})], ), SpectralFuncInfo('fft.fftn', aten_name='fft_fftn', ref=np.fft.fftn, ndimensional=SpectralFuncType.ND, dtypes=all_types_and_complex_and(torch.bool), # rocFFT doesn't support Half/Complex Half Precision FFT # CUDA supports Half/ComplexHalf Precision FFT only on SM53 or later archs dtypesIfCUDA=all_types_and_complex_and( torch.bool, *() if (TEST_WITH_ROCM or not SM53OrLater) else (torch.half, torch.complex32)), supports_forward_ad=True, supports_fwgrad_bwgrad=True, decorators=[precisionOverride( {torch.float: 1e-4, torch.cfloat: 1e-4})], ), SpectralFuncInfo('fft.hfft', aten_name='fft_hfft', ref=np.fft.hfft, ndimensional=SpectralFuncType.OneD, dtypes=all_types_and_complex_and(torch.bool), # rocFFT doesn't support Half/Complex Half Precision FFT # CUDA supports Half/ComplexHalf Precision FFT only on SM53 or later archs dtypesIfCUDA=all_types_and_complex_and( torch.bool, *() if (TEST_WITH_ROCM or not SM53OrLater) else (torch.half, torch.complex32)), supports_forward_ad=True, supports_fwgrad_bwgrad=True, check_batched_gradgrad=False), SpectralFuncInfo('fft.hfft2', aten_name='fft_hfft2', ref=scipy.fft.hfft2 if has_scipy_fft else None, ndimensional=SpectralFuncType.TwoD, dtypes=all_types_and_complex_and(torch.bool), # rocFFT doesn't support Half/Complex Half Precision FFT # CUDA supports Half/ComplexHalf Precision FFT only on SM53 or later archs dtypesIfCUDA=all_types_and_complex_and( torch.bool, *() if (TEST_WITH_ROCM or not SM53OrLater) else (torch.half, torch.complex32)), supports_forward_ad=True, supports_fwgrad_bwgrad=True, check_batched_gradgrad=False, decorators=[ DecorateInfo( precisionOverride({torch.float: 2e-4, torch.cfloat: 2e-4}), 'TestFFT', 'test_reference_nd')], ), SpectralFuncInfo('fft.hfftn', aten_name='fft_hfftn', ref=scipy.fft.hfftn if has_scipy_fft else None, ndimensional=SpectralFuncType.ND, dtypes=all_types_and_complex_and(torch.bool), # rocFFT doesn't support Half/Complex Half Precision FFT # CUDA supports Half/ComplexHalf Precision FFT only on SM53 or later archs dtypesIfCUDA=all_types_and_complex_and( torch.bool, *() if (TEST_WITH_ROCM or not SM53OrLater) else (torch.half, torch.complex32)), supports_forward_ad=True, supports_fwgrad_bwgrad=True, check_batched_gradgrad=False, decorators=[ DecorateInfo( precisionOverride({torch.float: 2e-4, torch.cfloat: 2e-4}), 'TestFFT', 'test_reference_nd')], ), SpectralFuncInfo('fft.rfft', aten_name='fft_rfft', ref=np.fft.rfft, ndimensional=SpectralFuncType.OneD, dtypes=all_types_and(torch.bool), # rocFFT doesn't support Half/Complex Half Precision FFT # CUDA supports Half/ComplexHalf Precision FFT only on SM53 or later archs dtypesIfCUDA=all_types_and(torch.bool, *() if (TEST_WITH_ROCM or not SM53OrLater) else (torch.half,)), supports_forward_ad=True, supports_fwgrad_bwgrad=True, check_batched_grad=False, skips=( ), check_batched_gradgrad=False), SpectralFuncInfo('fft.rfft2', aten_name='fft_rfft2', ref=np.fft.rfft2, ndimensional=SpectralFuncType.TwoD, dtypes=all_types_and(torch.bool), # rocFFT doesn't support Half/Complex Half Precision FFT # CUDA supports Half/ComplexHalf Precision FFT only on SM53 or later archs dtypesIfCUDA=all_types_and(torch.bool, *() if (TEST_WITH_ROCM or not SM53OrLater) else (torch.half,)), supports_forward_ad=True, supports_fwgrad_bwgrad=True, check_batched_grad=False, check_batched_gradgrad=False, decorators=[ precisionOverride({torch.float: 1e-4}), ],), SpectralFuncInfo('fft.rfftn', aten_name='fft_rfftn', ref=np.fft.rfftn, ndimensional=SpectralFuncType.ND, dtypes=all_types_and(torch.bool), # rocFFT doesn't support Half/Complex Half Precision FFT # CUDA supports Half/ComplexHalf Precision FFT only on SM53 or later archs dtypesIfCUDA=all_types_and(torch.bool, *() if (TEST_WITH_ROCM or not SM53OrLater) else (torch.half,)), supports_forward_ad=True, supports_fwgrad_bwgrad=True, check_batched_grad=False, check_batched_gradgrad=False, decorators=[ precisionOverride({torch.float: 1e-4}), ],), SpectralFuncInfo('fft.ifft', aten_name='fft_ifft', ref=np.fft.ifft, ndimensional=SpectralFuncType.OneD, supports_forward_ad=True, supports_fwgrad_bwgrad=True, dtypes=all_types_and_complex_and(torch.bool), # rocFFT doesn't support Half/Complex Half Precision FFT # CUDA supports Half/ComplexHalf Precision FFT only on SM53 or later archs dtypesIfCUDA=all_types_and_complex_and( torch.bool, *() if (TEST_WITH_ROCM or not SM53OrLater) else (torch.half, torch.complex32)),), SpectralFuncInfo('fft.ifft2', aten_name='fft_ifft2', ref=np.fft.ifft2, ndimensional=SpectralFuncType.TwoD, supports_forward_ad=True, supports_fwgrad_bwgrad=True, dtypes=all_types_and_complex_and(torch.bool), # rocFFT doesn't support Half/Complex Half Precision FFT # CUDA supports Half/ComplexHalf Precision FFT only on SM53 or later archs dtypesIfCUDA=all_types_and_complex_and( torch.bool, *() if (TEST_WITH_ROCM or not SM53OrLater) else (torch.half, torch.complex32)), decorators=[ DecorateInfo( precisionOverride({torch.float: 1e-4, torch.cfloat: 1e-4}), 'TestFFT', 'test_reference_nd')], ), SpectralFuncInfo('fft.ifftn', aten_name='fft_ifftn', ref=np.fft.ifftn, ndimensional=SpectralFuncType.ND, supports_forward_ad=True, supports_fwgrad_bwgrad=True, dtypes=all_types_and_complex_and(torch.bool), # rocFFT doesn't support Half/Complex Half Precision FFT # CUDA supports Half/ComplexHalf Precision FFT only on SM53 or later archs dtypesIfCUDA=all_types_and_complex_and( torch.bool, *() if (TEST_WITH_ROCM or not SM53OrLater) else (torch.half, torch.complex32)), decorators=[ DecorateInfo( precisionOverride({torch.float: 1e-4, torch.cfloat: 1e-4}), 'TestFFT', 'test_reference_nd')], ), SpectralFuncInfo('fft.ihfft', aten_name='fft_ihfft', ref=np.fft.ihfft, ndimensional=SpectralFuncType.OneD, supports_forward_ad=True, supports_fwgrad_bwgrad=True, dtypes=all_types_and(torch.bool), # rocFFT doesn't support Half/Complex Half Precision FFT # CUDA supports Half/ComplexHalf Precision FFT only on SM53 or later archs dtypesIfCUDA=all_types_and(torch.bool, *() if (TEST_WITH_ROCM or not SM53OrLater) else (torch.half,)), skips=( ), check_batched_grad=False), SpectralFuncInfo('fft.ihfft2', aten_name='fft_ihfft2', ref=scipy.fft.ihfftn if has_scipy_fft else None, ndimensional=SpectralFuncType.TwoD, supports_forward_ad=True, supports_fwgrad_bwgrad=True, dtypes=all_types_and(torch.bool), # rocFFT doesn't support Half/Complex Half Precision FFT # CUDA supports Half/ComplexHalf Precision FFT only on SM53 or later archs dtypesIfCUDA=all_types_and(torch.bool, *() if (TEST_WITH_ROCM or not SM53OrLater) else (torch.half,)), check_batched_grad=False, check_batched_gradgrad=False, decorators=( # The values for attribute 'shape' do not match: torch.Size([5, 6, 5]) != torch.Size([5, 6, 6]). DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out_warning'), DecorateInfo(precisionOverride({torch.float: 2e-4}), 'TestFFT', 'test_reference_nd'), # Mismatched elements! DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out'), DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out_warnings'))), SpectralFuncInfo('fft.ihfftn', aten_name='fft_ihfftn', ref=scipy.fft.ihfftn if has_scipy_fft else None, ndimensional=SpectralFuncType.ND, supports_forward_ad=True, supports_fwgrad_bwgrad=True, dtypes=all_types_and(torch.bool), # rocFFT doesn't support Half/Complex Half Precision FFT # CUDA supports Half/ComplexHalf Precision FFT only on SM53 or later archss dtypesIfCUDA=all_types_and(torch.bool, *() if (TEST_WITH_ROCM or not SM53OrLater) else (torch.half,)), check_batched_grad=False, check_batched_gradgrad=False, decorators=[ # The values for attribute 'shape' do not match: torch.Size([5, 6, 5]) != torch.Size([5, 6, 6]). DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out_warning'), # Mismatched elements! DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out'), DecorateInfo( precisionOverride({torch.float: 2e-4}), 'TestFFT', 'test_reference_nd')], ), SpectralFuncInfo('fft.irfft', aten_name='fft_irfft', ref=np.fft.irfft, ndimensional=SpectralFuncType.OneD, supports_forward_ad=True, supports_fwgrad_bwgrad=True, dtypes=all_types_and_complex_and(torch.bool), # rocFFT doesn't support Half/Complex Half Precision FFT # CUDA supports Half/ComplexHalf Precision FFT only on SM53 or later archs dtypesIfCUDA=all_types_and_complex_and( torch.bool, *() if (TEST_WITH_ROCM or not SM53OrLater) else (torch.half, torch.complex32)), check_batched_gradgrad=False), SpectralFuncInfo('fft.irfft2', aten_name='fft_irfft2', ref=np.fft.irfft2, ndimensional=SpectralFuncType.TwoD, supports_forward_ad=True, supports_fwgrad_bwgrad=True, dtypes=all_types_and_complex_and(torch.bool), # rocFFT doesn't support Half/Complex Half Precision FFT # CUDA supports Half/ComplexHalf Precision FFT only on SM53 or later archs dtypesIfCUDA=all_types_and_complex_and( torch.bool, *() if (TEST_WITH_ROCM or not SM53OrLater) else (torch.half, torch.complex32)), check_batched_gradgrad=False, decorators=[ DecorateInfo( precisionOverride({torch.float: 1e-4, torch.cfloat: 1e-4}), 'TestFFT', 'test_reference_nd')], ), SpectralFuncInfo('fft.irfftn', aten_name='fft_irfftn', ref=np.fft.irfftn, ndimensional=SpectralFuncType.ND, supports_forward_ad=True, supports_fwgrad_bwgrad=True, dtypes=all_types_and_complex_and(torch.bool), # rocFFT doesn't support Half/Complex Half Precision FFT # CUDA supports Half/ComplexHalf Precision FFT only on SM53 or later archs dtypesIfCUDA=all_types_and_complex_and( torch.bool, *() if (TEST_WITH_ROCM or not SM53OrLater) else (torch.half, torch.complex32)), check_batched_gradgrad=False, decorators=[ DecorateInfo( precisionOverride({torch.float: 1e-4, torch.cfloat: 1e-4}), 'TestFFT', 'test_reference_nd')], ), OpInfo('fft.fftshift', dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.half), sample_inputs_func=sample_inputs_fftshift, supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, ), OpInfo('fft.ifftshift', dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.half), sample_inputs_func=sample_inputs_fftshift, supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, ), OpInfo('stft', decorators=[ skipCPUIfNoFFT, DecorateInfo(unittest.skip("Skipped! stft does not match the native function"), 'TestJit', 'test_variant_consistency_jit'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), ], dtypes=floating_and_complex_types(), sample_inputs_func=sample_inputs_stft, supports_forward_ad=True, supports_fwgrad_bwgrad=True, check_batched_forward_grad=False, check_batched_grad=False, check_batched_gradgrad=False, supports_out=False, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, ), OpInfo('istft', dtypes=floating_and_complex_types(), sample_inputs_func=sample_inputs_istft, supports_forward_ad=True, supports_fwgrad_bwgrad=True, check_batched_forward_grad=False, check_batched_grad=False, check_batched_gradgrad=False, supports_out=False, decorators=( DecorateInfo(unittest.skip("Skipped! istft does not match the native function"), 'TestJit', 'test_variant_consistency_jit'), ), skips=( skipCPUIfNoFFT, # gradcheck fails on ROCm (gh-68429) # grad is computed improperly (probably for weights tensor) DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_fn_grad'), )), UnaryUfuncInfo('floor', ref=np.floor, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_sparse=True, supports_sparse_csr=True, assert_autodiffed=True), OpInfo('flip', op=torch.flip, dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), sample_inputs_func=sample_inputs_flip, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False), OpInfo('fliplr', op=torch.fliplr, dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), sample_inputs_func=sample_inputs_fliplr_flipud, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False), OpInfo('flipud', op=torch.flipud, dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), sample_inputs_func=sample_inputs_fliplr_flipud, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False), OpInfo('sparse.sampled_addmm', dtypes=floating_and_complex_types(), supports_autograd=True, sample_inputs_func=sample_inputs_sparse_sampled_addmm, decorators=[ skipCUDAIf(_get_torch_cuda_version() < (11, 3), "cusparseSDDMM was added in 11.2.1"), skipCPUIfNoMklSparse, ], skips=( # NotImplementedError: Tensors of type SparseCsrTensorImpl do not have is_contiguous DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_noncontiguous_samples'), # RuntimeError: Sparse CSR tensors do not have strides. DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_out'), # RuntimeError: sampled_addmm: Expected result to have sparse csr layout, but got Strided DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_out_warning'), # RuntimeError: Sparse CSR tensors do not have strides DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_variant_consistency_eager'), # RuntimeError: Sparse CSR tensors do not have strides DecorateInfo(unittest.skip("Skipped!"), 'TestCompositeCompliance', 'test_operator'), # RuntimeError: Sparse CSR tensors do not have strides DecorateInfo(unittest.skip("Skipped!"), 'TestCompositeCompliance', 'test_backward'), # RuntimeError: Sparse CSR tensors do not have strides DecorateInfo(unittest.skip("Skipped!"), 'TestMathBits', 'test_conj_view'), # RuntimeError: Sparse CSR tensors do not have strides DecorateInfo(unittest.skip("Skipped!"), 'TestMathBits', 'test_neg_conj_view'), # RuntimeError: Sparse CSR tensors do not have strides DecorateInfo(unittest.skip("Skipped!"), 'TestMathBits', 'test_neg_view'), # RuntimeError: Sparse CSR tensors do not have strides DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'), # RuntimeError: unsupported memory format option Preserve DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'), # GradcheckError: gradcheck expects all tensor inputs are dense when check_sparse_nnz is set to False DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_fn_fwgrad_bwgrad'), # GradcheckError: gradcheck expects all tensor inputs are dense when check_sparse_nnz is set to False DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_fn_grad'), # GradcheckError: gradcheck expects all tensor inputs are dense when check_sparse_nnz is set to False DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_fn_gradgrad'), # GradcheckError: gradcheck expects all tensor inputs are dense when check_sparse_nnz is set to False DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_forward_mode_AD'), )), UnaryUfuncInfo('i0', ref=np_unary_ufunc_integer_promotion_wrapper( scipy.special.i0) if TEST_SCIPY else _NOTHING, aliases=('special.i0',), decorators=(precisionOverride({torch.bfloat16: 3e-1, torch.float16: 5e-1}),), dtypes=all_types_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bool, torch.half, torch.bfloat16), backward_dtypes=floating_types(), supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_i0_i1, skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', dtypes=(torch.int8,)), )), UnaryUfuncInfo('special.i0e', aten_name='special_i0e', ref=scipy.special.i0e if TEST_SCIPY else _NOTHING, decorators=(precisionOverride({torch.bfloat16: 3e-1, torch.float16: 3e-1}),), dtypes=all_types_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bool, torch.half, torch.bfloat16), backward_dtypes=floating_types(), sample_inputs_func=sample_inputs_i0_i1, supports_forward_ad=True, supports_fwgrad_bwgrad=True), UnaryUfuncInfo('special.i1', aten_name='special_i1', ref=np_unary_ufunc_integer_promotion_wrapper(scipy.special.i1) if TEST_SCIPY else _NOTHING, dtypes=all_types_and(torch.bool), dtypesIfCUDA=all_types_and(torch.bool), sample_inputs_func=sample_inputs_i0_i1, decorators=( DecorateInfo(toleranceOverride({ torch.float32: tol(atol=1e-4, rtol=0), torch.bool: tol(atol=1e-4, rtol=0)})), ), skips=( DecorateInfo(unittest.skip("Incorrect result!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', dtypes=(torch.int8,)), ), supports_fwgrad_bwgrad=True, supports_forward_ad=True), UnaryUfuncInfo('special.i1e', aten_name='special_i1e', ref=scipy.special.i1e if TEST_SCIPY else _NOTHING, dtypes=all_types_and(torch.bool), dtypesIfCUDA=all_types_and(torch.bool), sample_inputs_func=sample_inputs_i0_i1, supports_forward_ad=True, supports_fwgrad_bwgrad=True), UnaryUfuncInfo('special.ndtr', aten_name='special_ndtr', decorators=(precisionOverride({torch.bfloat16: 5e-3, torch.float16: 5e-4}),), ref=scipy.special.ndtr if TEST_SCIPY else _NOTHING, dtypes=all_types_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bool, torch.bfloat16, torch.float16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # Dispatch stub: unsupported device typemeta DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_fn_fwgrad_bwgrad', device_type='meta'), )), BinaryUfuncInfo('floor_divide', dtypes=all_types_and(torch.half, torch.bfloat16), supports_autograd=False, rhs_make_tensor_kwargs=dict(exclude_zero=True), supports_two_python_scalars=True, skips=( # AssertionError: Results of original model and exported/imported version of model differed DecorateInfo(unittest.skip('Skipped!'), 'TestJit', 'test_variant_consistency_jit'), )), UnaryUfuncInfo('frexp', op=torch.frexp, ref=np.frexp, dtypes=floating_types_and(torch.half, torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half), # skip testing torch.frexp as it is not supported by ROCm platform yet decorators=[], supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # skips below tests as torch.frexp returns tuple-like (mantissa, exponent) as outputs, # while theses tests currently requires output to a single tensor. DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_batch_vs_slicing'), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_contig_vs_every_other'), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_contig_vs_transposed'), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_non_contig_expand'), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_variant_consistency'), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_out_arg_all_dtypes'), # skips test_reference_numerics due to error in Windows CI. # The np.frexp returns exponent as np.intc dtype on Windows platform, # and np.intc does not have the correspond torch dtype DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_small', active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', active_if=IS_WINDOWS), )), BinaryUfuncInfo('ge', aliases=('greater_equal',), dtypes=all_types_and(torch.bool, torch.bfloat16, torch.float16), always_returns_bool=True, supports_autograd=False,), OpInfo('geqrf', dtypes=floating_and_complex_types(), sample_inputs_func=sample_inputs_linalg_qr_geqrf, decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack], supports_autograd=False, skips=( # FIXME: geqrf can't forward with complex inputs that require grad DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_dtypes'), # Strides are not the same! DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out'), )), BinaryUfuncInfo('gt', aliases=('greater',), dtypes=all_types_and(torch.bool, torch.bfloat16, torch.float16), always_returns_bool=True, supports_autograd=False,), UnaryUfuncInfo('imag', ref=np.imag, dtypes=complex_types_and(torch.chalf), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, # See https://github.com/pytorch/pytorch/issues/66357 # RuntimeError: view_as_real doesn't work on unresolved conjugated tensors. check_batched_forward_grad=False, skips=( # Skip since real and imag don't have out variants. DecorateInfo(unittest.expectedFailure, 'TestUnaryUfuncs', 'test_out_arg_all_dtypes'), )), OpInfo('gradient', dtypes=floating_and_complex_types_and(torch.int8, torch.int16, torch.int32, torch.int64, torch.bfloat16, torch.half), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # following tests give a runtime error with undefined value tensor # see discussion : https://github.com/pytorch/pytorch/issues/56660 # RuntimeError: # Arguments for call are not valid. DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit', dtypes=(torch.float32, torch.complex64)), # noqa: B950 DecorateInfo(unittest.skip("Skipped!"), 'TestNNCOpInfo', 'test_nnc_correctness'), DecorateInfo(unittest.skip("Skipped!"), 'TestCudaFuserOpInfo'), ), supports_inplace_autograd=False, sample_inputs_func=sample_inputs_gradient, error_inputs_func=error_inputs_gradient), OpInfo('inverse', op=torch.inverse, dtypes=floating_and_complex_types(), check_batched_gradgrad=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, sample_inputs_func=sample_inputs_linalg_invertible, decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack], skips=( # Strides are not the same! DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out'), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_out', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', '.test_variant_consistency_eager', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestJit', '.test_variant_consistency_jit', device_type='mps', dtypes=[torch.float32]), )), OpInfo('isin', dtypes=all_types(), dtypesIfCUDA=all_types_and(torch.half), supports_autograd=False, sample_inputs_func=sample_inputs_isin), OpInfo('kthvalue', dtypes=all_types_and(torch.bfloat16), dtypesIfCUDA=all_types_and(torch.float16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_kthvalue, error_inputs_func=error_inputs_kthvalue), BinaryUfuncInfo('le', aliases=('less_equal',), dtypes=all_types_and(torch.bool, torch.bfloat16, torch.float16), always_returns_bool=True, supports_autograd=False,), OpInfo('linalg.det', op=torch.linalg.det, aliases=('det',), dtypes=floating_and_complex_types(), backward_dtypes=floating_and_complex_types(), aten_name='linalg_det', sample_inputs_func=sample_inputs_linalg_det, decorators=[skipCUDAIfNoMagma, skipCPUIfNoLapack, DecorateInfo(toleranceOverride({torch.complex64: tol(atol=1e-3, rtol=1e-3)}))], check_batched_gradgrad=False, supports_inplace_autograd=False), OpInfo('linalg.det', op=torch.linalg.det, variant_test_name='singular', aliases=('det',), dtypes=double_types(), backward_dtypes=double_types(), aten_name='linalg_det', sample_inputs_func=sample_inputs_linalg_det_singular, decorators=[skipCUDAIfNoMagma, skipCPUIfNoLapack, DecorateInfo(toleranceOverride({torch.complex64: tol(atol=1e-3, rtol=1e-3)}))], check_batched_gradgrad=False, supports_inplace_autograd=False, skips=( # These tests started breaking after touching the SVD. DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_fn_grad', device_type='cpu', dtypes=(torch.complex128,), active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_fn_gradgrad'), # dtypes are tested in the suite above, no need to repeat it for singular DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_dtypes'), )), OpInfo('linalg.cholesky', aten_name='linalg_cholesky', dtypes=floating_and_complex_types(), supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_linalg_cholesky, gradcheck_wrapper=gradcheck_wrapper_hermitian_input, skips=( DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), # Strides are not the same! DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out'), ), decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack],), OpInfo('linalg.cholesky_ex', aten_name='linalg_cholesky_ex', dtypes=floating_and_complex_types(), supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_linalg_cholesky, gradcheck_wrapper=gradcheck_wrapper_hermitian_input, skips=( # AssertionError: Scalars are not equal! DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), ), decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack], ), OpInfo('linalg.cond', aten_name='linalg_cond', dtypes=floating_and_complex_types(), sample_inputs_func=sample_inputs_linalg_cond, check_batched_gradgrad=False, check_batched_forward_grad=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack, with_tf32_off],), OpInfo('linalg.eig', aten_name='linalg_eig', op=torch.linalg.eig, dtypes=floating_and_complex_types(), sample_inputs_func=sample_inputs_linalg_eig, check_batched_forward_grad=False, check_batched_grad=False, check_batched_gradgrad=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # AssertionError: Scalars are not equal! DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out', device_type='cpu'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_backward'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), # Forward-over-reverse gradgrad might be incorrect DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_fn_fwgrad_bwgrad'), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_out', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_variant_consistency_eager', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit', device_type='mps', dtypes=[torch.float32]), ), decorators=[skipCUDAIfNoMagma, skipCPUIfNoLapack, with_tf32_off], ), OpInfo('linalg.eigvals', aten_name='linalg_eigvals', op=torch.linalg.eigvals, dtypes=floating_and_complex_types(), sample_inputs_func=sample_inputs_linalg_invertible, check_batched_forward_grad=False, check_batched_grad=False, check_batched_gradgrad=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, decorators=[skipCUDAIfNoMagma, skipCPUIfNoLapack], skips=( # Pre-existing condition; Needs to be fixed DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_operator'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), # exits early on eager extremal value test DecorateInfo(unittest.skip('Skipped!'), 'TestCudaFuserOpInfo', 'test_nvfuser_extremal_values'), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_out', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_variant_consistency_eager', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit', device_type='mps', dtypes=[torch.float32]), )), OpInfo('linalg.eigh', aten_name='linalg_eigh', dtypes=floating_and_complex_types(), sample_inputs_func=sample_inputs_linalg_eigh, gradcheck_wrapper=gradcheck_wrapper_hermitian_input, check_batched_forward_grad=False, check_batched_grad=False, check_batched_gradgrad=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, decorators=[skipCUDAIfNoMagma, skipCPUIfNoLapack, with_tf32_off], skips=( # Forward-over-reverse gradgrad might be incorrect DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_fn_fwgrad_bwgrad', dtypes=complex_types()), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_backward'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_out', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_variant_consistency_eager', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit', device_type='mps', dtypes=[torch.float32]), )), OpInfo('linalg.eigvalsh', aten_name='linalg_eigvalsh', dtypes=floating_and_complex_types(), sample_inputs_func=sample_inputs_linalg_eigh, gradcheck_wrapper=gradcheck_wrapper_hermitian_input, check_batched_forward_grad=False, check_batched_grad=False, check_batched_gradgrad=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, decorators=[skipCUDAIfNoMagma, skipCPUIfNoLapack], skips=( # Pre-existing condition; Needs to be fixed DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_operator'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_out', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_variant_consistency_eager', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit', device_type='mps', dtypes=[torch.float32]), )), OpInfo('linalg.householder_product', aten_name='linalg_householder_product', op=torch.linalg.householder_product, aliases=('orgqr', ), dtypes=floating_and_complex_types(), # TODO: backward uses in-place operations that vmap doesn't like check_batched_grad=False, check_batched_gradgrad=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, check_batched_forward_grad=False, sample_inputs_func=sample_inputs_householder_product, decorators=[ skipCUDAIfNoCusolver, skipCPUIfNoLapack, DecorateInfo(toleranceOverride({torch.complex64: tol(atol=1e-3, rtol=1e-3)})), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_backward'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), ]), OpInfo('linalg.ldl_factor', aten_name='linalg_ldl_factor', dtypes=floating_and_complex_types(), supports_autograd=False, sample_inputs_func=sample_inputs_linalg_ldl_factor, decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack, skipCUDAIfRocm], ), OpInfo('linalg.ldl_factor_ex', aten_name='linalg_ldl_factor_ex', dtypes=floating_and_complex_types(), supports_autograd=False, sample_inputs_func=sample_inputs_linalg_ldl_factor, decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack, skipCUDAIfRocm], ), OpInfo('linalg.ldl_solve', aten_name='linalg_ldl_solve', dtypes=floating_and_complex_types(), supports_autograd=False, sample_inputs_func=sample_inputs_linalg_ldl_solve, decorators=[ skipCUDAIf(_get_torch_cuda_version() < (11, 4), "not available before CUDA 11.3.1"), skipCUDAIfNoCusolver, skipCUDAIfRocm, skipCPUIfNoLapack], ), OpInfo('linalg.lstsq', aten_name='linalg_lstsq', dtypes=floating_and_complex_types(), supports_out=True, sample_inputs_func=sample_inputs_linalg_lstsq, error_inputs_func=error_inputs_lstsq, decorators=[skipCUDAIfNoMagma, skipCPUIfNoLapack], skips=( # we skip gradient checks for this suite as they are tested in # variant_test_name='grad_oriented' DecorateInfo(unittest.skip("Skipped!"), 'TestGradients'), # At this time ROCm uses magma instead of rocSolver, and the test passes DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_backward', active_if=(not TEST_WITH_ROCM)), # The values for attribute 'shape' do not match DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_out'), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_out', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_variant_consistency_eager', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit', device_type='mps', dtypes=[torch.float32]), )), OpInfo('linalg.lstsq', aten_name='linalg_lstsq', variant_test_name='grad_oriented', # gradchecks for forward AD fails with multi-Tensor outputs op=lambda a, b, driver: torch.linalg.lstsq(a, b, driver=driver)[0], supports_out=False, dtypes=floating_and_complex_types(), sample_inputs_func=sample_inputs_linalg_lstsq, error_inputs_func=error_inputs_lstsq, supports_autograd=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, decorators=[skipCUDAIfNoMagma, skipCPUIfNoLapack], skips=( # tests do not work with passing lambda for op DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), DecorateInfo(unittest.expectedFailure, 'TestOperatorSignatures', 'test_get_torch_func_signature_exhaustive'), # At this time ROCm uses magma instead of rocSolver, and the test passes DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_backward', active_if=(not TEST_WITH_ROCM)), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad', active_if=(not TEST_WITH_ROCM)), )), OpInfo('linalg.matrix_power', aliases=('matrix_power',), aten_name='linalg_matrix_power', dtypes=floating_and_complex_types(), supports_inplace_autograd=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, check_batched_grad=False, decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack, with_tf32_off], sample_inputs_func=sample_inputs_linalg_matrix_power, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, skips=( # Strides are not the same! DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out'), )), OpInfo('linalg.multi_dot', # Need this lambda because gradcheck does not work with TensorList inputs aten_name='linalg_multi_dot', dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.half, *[torch.bfloat16] if (CUDA11OrLater or TEST_WITH_ROCM) else []), supports_inplace_autograd=False, # Batched grad checks fail for empty input tensors (see https://github.com/pytorch/pytorch/issues/53407) check_batched_grad=False, check_batched_gradgrad=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, # https://github.com/pytorch/pytorch/issues/66357 check_batched_forward_grad=False, sample_inputs_func=sample_inputs_linalg_multi_dot, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, skips=( # https://github.com/pytorch/pytorch/issues/67470 DecorateInfo(unittest.skip("67470!"), 'TestCommon', 'test_noncontiguous_samples'), # Fails on XLA. # AssertionError: False is not true : Tensors failed to compare as equal! DecorateInfo(unittest.skip("Skipped!"), 'TestOpInfo', device_type='xla', dtypes=(torch.long,)), # https://github.com/pytorch/pytorch/issues/71774 DecorateInfo(unittest.skip('Skipped!'), 'TestNNCOpInfo', 'test_nnc_correctness', device_type='cpu', dtypes=(torch.long,)), )), # NB: linalg.norm has two variants so that different skips can be used for different sample inputs OpInfo('linalg.norm', op=torch.linalg.norm, dtypes=floating_and_complex_types_and(torch.float16, torch.bfloat16), decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack, with_tf32_off], sample_inputs_func=sample_inputs_linalg_norm, supports_forward_ad=True, # torch.autograd.gradcheck.GradcheckError: While computing batched gradients, got: # Could not allocate memory to change Tensor SizesAndStrides! check_batched_forward_grad=False, supports_fwgrad_bwgrad=True, aten_name='linalg_norm', skips=( DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_fn_fwgrad_bwgrad', dtypes=[torch.complex128]), )), OpInfo('linalg.norm', op=torch.linalg.norm, variant_test_name='subgradients_at_zero', dtypes=floating_and_complex_types_and(torch.float16, torch.bfloat16), decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack, with_tf32_off], sample_inputs_func=partial(sample_inputs_linalg_norm, variant='subgradient_at_zero'), aten_name='linalg_norm', supports_forward_ad=True, # torch.autograd.gradcheck.GradcheckError: While computing batched gradients, got: # Could not allocate memory to change Tensor SizesAndStrides! check_batched_forward_grad=False, supports_fwgrad_bwgrad=True, skips=( # [NEW] Skips specifically for sample inputs at zero # norm's vjp/jvp are not well-conditioned near zero DecorateInfo(unittest.expectedFailure, "TestGradients", 'test_fn_gradgrad'), DecorateInfo(unittest.expectedFailure, "TestGradients", 'test_fn_fwgrad_bwgrad') )), OpInfo('linalg.matrix_norm', aten_name='linalg_matrix_norm', dtypes=floating_and_complex_types(), check_batched_gradgrad=False, decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack, with_tf32_off], sample_inputs_func=sample_inputs_linalg_matrix_norm), OpInfo('linalg.qr', aten_name='linalg_qr', op=torch.linalg.qr, dtypes=floating_and_complex_types(), supports_forward_ad=True, supports_fwgrad_bwgrad=True, # In-place ops check_batched_gradgrad=False, sample_inputs_func=sample_inputs_linalg_qr_geqrf, skips=( # The test is wrong # https://github.com/pytorch/pytorch/pull/76115#discussion_r854328384 DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_backward'),), decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack]), OpInfo('linalg.slogdet', aten_name='linalg_slogdet', op=torch.linalg.slogdet, dtypes=floating_and_complex_types(), sample_inputs_func=sample_inputs_linalg_slogdet, decorators=[skipCUDAIfNoMagma, skipCPUIfNoLapack]), OpInfo('linalg.vander', aten_name='linalg_vander', ref=np_vander_batched, op=torch.linalg.vander, dtypes=all_types_and_complex(), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False, sample_inputs_func=sample_inputs_linalg_vander), OpInfo('linalg.vector_norm', op=torch.linalg.vector_norm, dtypes=floating_and_complex_types_and(torch.float16, torch.bfloat16), sample_inputs_func=sample_inputs_linalg_vector_norm, aten_name='linalg_vector_norm', supports_forward_ad=True, # torch.autograd.gradcheck.GradcheckError: While computing batched gradients # got: Could not allocate memory to change Tensor SizesAndStrides! check_batched_forward_grad=False, supports_fwgrad_bwgrad=True, skips=( DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_fn_fwgrad_bwgrad', dtypes=[torch.complex128]), )), UnaryUfuncInfo('log', ref=np.log, domain=(0, None), dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16, torch.chalf), backward_dtypesIfCUDA=floating_and_complex_types_and(torch.half, torch.bfloat16, torch.chalf), assert_autodiffed=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, decorators=(precisionOverride({torch.bfloat16: 5e-2}),), skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble], active_if=IS_WINDOWS), ), # log(z)->-inf for |z|->0 reference_numerics_filter=NumericsFilter(condition=lambda x: torch.abs(x) < 0.1, safe_val=1)), UnaryUfuncInfo('log10', ref=np.log10, domain=(0, None), decorators=(precisionOverride({torch.bfloat16: 5e-2}),), dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16), assert_autodiffed=True, dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble], active_if=IS_WINDOWS), ), # log10(z)->-inf for |z|->0 reference_numerics_filter=NumericsFilter(condition=lambda x: torch.abs(x) < 0.1, safe_val=1)), UnaryUfuncInfo('log1p', ref=np.log1p, aliases=('special.log1p',), domain=(-1, None), dtypes=all_types_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bool, torch.half, torch.bfloat16), decorators=(precisionOverride({torch.bfloat16: 1e-1}),), skips=( DecorateInfo(unittest.skip("Skipped! sparse backward not supported"), 'TestSparseUnaryUfuncs', 'test_sparse_fn_grad'), ), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_sparse=True, supports_sparse_csr=True, assert_autodiffed=True), UnaryUfuncInfo('log2', ref=np.log2, domain=(0, None), dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), assert_autodiffed=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, decorators=(precisionOverride({torch.bfloat16: 1e-1}),), skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', dtypes=[torch.cfloat, torch.cdouble]), ), # log2(z)->-inf for |z|->0 reference_numerics_filter=NumericsFilter(condition=lambda x: torch.abs(x) < 0.1, safe_val=1)), BinaryUfuncInfo('ldexp', dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_inplace_autograd=False, promotes_int_to_float=True, supports_out=True, supports_rhs_python_scalar=False, skips=( # RuntimeError: mul(): functions with out=... arguments don't support # automatic differentiation, but one of the arguments requires grad # https://github.com/pytorch/pytorch/issues/68966 DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_variant_consistency_eager'), DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_conj_view'), DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_neg_view'), DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_neg_conj_view'), ), decorators=[ DecorateInfo( toleranceOverride({ torch.complex64: tol(atol=1e-05, rtol=1e-05) }), 'TestCommon', device_type='cpu', ), ], ), OpInfo('logaddexp', dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.bfloat16), dtypesIfROCM=floating_types_and(torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=lambda op_info, device, dtype, requires_grad=False, **kwargs: (SampleInput(make_tensor((S, S), dtype=dtype, device=device, requires_grad=requires_grad), args=(make_tensor((S, S), dtype=dtype, device=device, requires_grad=requires_grad),)),)), OpInfo('logaddexp2', dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.bfloat16), dtypesIfROCM=floating_types_and(torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=lambda op_info, device, dtype, requires_grad=False, **kwargs: (SampleInput(make_tensor((S, S), dtype=dtype, device=device, requires_grad=requires_grad), args=(make_tensor((S, S), dtype=dtype, device=device, requires_grad=requires_grad),)),)), UnaryUfuncInfo('logical_not', ref=np.logical_not, decorators=(precisionOverride({torch.bfloat16: 7e-1, torch.float16: 5e-1}),), dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), supports_autograd=False, skips=( # The function variant always returns BoolTensor # while the inplace variant preserves the input dtype. # >>> t = torch.randn(3) # >>> torch.logical_not(t) # tensor([False, False, False]) # >>> torch.logical_not(t).dtype # torch.bool # >>> t.logical_not_().dtype # torch.float32 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_variant_consistency', dtypes=all_types_and_complex_and(torch.half, torch.bfloat16)), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_variant_consistency_eager', dtypes=all_types_and_complex_and(torch.half, torch.bfloat16)), )), BinaryUfuncInfo('lt', aliases=('less',), dtypes=all_types_and(torch.bool, torch.bfloat16, torch.float16), always_returns_bool=True, supports_autograd=False,), OpInfo('linalg.lu_factor', aten_name='linalg_lu_factor', op=torch.linalg.lu_factor, dtypes=floating_and_complex_types(), supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_linalg_lu, decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack]), OpInfo('linalg.lu_factor_ex', aten_name='linalg_lu_factor_ex', op=torch.linalg.lu_factor_ex, dtypes=floating_and_complex_types(), supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_linalg_lu, decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack]), OpInfo('linalg.lu', aten_name='linalg_lu', op=torch.linalg.lu, dtypes=floating_and_complex_types(), supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_linalg_lu, decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack]), OpInfo('lu_unpack', op=torch.lu_unpack, dtypes=floating_and_complex_types(), supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=(skipCPUIfNoLapack,), sample_inputs_func=sample_inputs_lu_unpack), OpInfo('lu', op=torch.lu, dtypes=floating_and_complex_types(), supports_forward_ad=True, supports_fwgrad_bwgrad=True, # https://github.com/pytorch/pytorch/issues/66357 check_batched_forward_grad=False, sample_inputs_func=sample_inputs_lu, decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack], skips=( # we skip jit tests because `lu` is a torch function # RuntimeError: # 'Tensor (inferred)' object has no attribute or method 'lu'.: # File "", line 3 # def the_method(i0): # return i0.lu(True, True) # ~~~~~ <--- HERE DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # RuntimeError not raised: Expected RuntimeError when calling with input.device=cpu and out.device=cuda DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out'), # UserWarning not triggered : Resized a non-empty tensor but did not warn about it. DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out_warning'), )), OpInfo('lu_solve', op=torch.lu_solve, dtypes=floating_and_complex_types(), check_batched_gradgrad=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, # See https://github.com/pytorch/pytorch/issues/66357 check_batched_forward_grad=False, sample_inputs_func=sample_inputs_lu_solve, decorators=[skipCUDAIfNoMagma, skipCPUIfNoLapack], skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_out', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_variant_consistency_eager', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Tests different backward implementations"), "TestCommon", "test_floating_inputs_are_differentiable"),), ), OpInfo('masked_fill', dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16, torch.chalf), sample_inputs_func=sample_inputs_masked_fill, supports_forward_ad=True, supports_fwgrad_bwgrad=True, check_batched_forward_grad=False, supports_out=False, skips=( # RuntimeError: "where_cpu" not implemented for 'ComplexHalf' # RuntimeError: "where_cuda" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, 'TestDecomp', 'test_comprehensive', dtypes=(torch.chalf,)), # RuntimeError: "where_cpu" not implemented for 'ComplexHalf' # RuntimeError: "where_cuda" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, 'TestDecomp', 'test_quick', dtypes=(torch.chalf,)), )), OpInfo('masked_scatter', dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), sample_inputs_func=sample_inputs_masked_scatter, supports_forward_ad=True, supports_fwgrad_bwgrad=True, # https://github.com/pytorch/pytorch/issues/66357 check_batched_forward_grad=False, supports_out=False), OpInfo('masked_select', dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_masked_select, error_inputs_func=error_inputs_masked_select), OpInfo('matrix_exp', dtypes=floating_and_complex_types_and(torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.float16, *[torch.bfloat16] if (CUDA11OrLater or TEST_WITH_ROCM) else []), aliases=('linalg.matrix_exp',), sample_inputs_func=sample_inputs_matrix_exp, # Needs to construct a 2nx2n matrix by copy_ ing into it check_batched_grad=False, check_batched_gradgrad=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, # https://github.com/pytorch/pytorch/issues/66357 check_batched_forward_grad=False, skips=( DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_backward'), # times out DecorateInfo(unittest.skip('Skipped!'), 'TestCudaFuserOpInfo', 'test_nvfuser_extremal_values'), ), supports_out=False, ), OpInfo('matmul', aliases=('linalg.matmul',), dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.float16, *[torch.bfloat16] if (SM53OrLater and CUDA11OrLater) or TEST_WITH_ROCM else []), assert_autodiffed=True, assert_jit_shape_analysis=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, check_batched_forward_grad=False, sample_inputs_func=sample_inputs_matmul, decorators=[ # NVIDIA only assures that bfloat16 is supported by bmm if SM >= 5.3 DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_dtypes', device_type='cuda', active_if=not SM53OrLater), # ROCm intermittently fails the test with standard atol/rtol DecorateInfo(toleranceOverride({torch.float32: tol(atol=1e-4, rtol=0)}), 'TestCommon', 'test_noncontiguous_samples', device_type='cuda', active_if=TEST_WITH_ROCM), DecorateInfo(toleranceOverride({torch.float32: tol(atol=1e-4, rtol=0)}), 'TestCommon', 'test_out', device_type='cuda', active_if=TEST_WITH_ROCM), # mv for the sample with shapes (S, S, M, M), (M,) has some variance in the # backward on CPU DecorateInfo(toleranceOverride({torch.float32: tol(atol=0, rtol=1e-5)}), 'TestCommon', 'test_noncontiguous_samples', device_type='cpu'), ], skips=( # Strides are not the same! DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out'), # https://github.com/pytorch/pytorch/issues/67470 DecorateInfo(unittest.skip("67470!"), 'TestCommon', 'test_noncontiguous_samples', device_type='cpu', dtypes=(torch.long,)), # AssertionError: False is not true : Tensors failed to compare as equal! DecorateInfo(unittest.skip("Skipped!"), 'TestOpInfo', device_type='xla', dtypes=(torch.long,)), # https://github.com/pytorch/pytorch/issues/71774 DecorateInfo(unittest.skip('Skipped!'), 'TestNNCOpInfo', 'test_nnc_correctness', device_type='cpu', dtypes=(torch.long,)), )), OpInfo('max', variant_test_name='reduction_with_dim', dtypes=all_types_and(torch.float16, torch.bfloat16, torch.bool), sample_inputs_func=sample_inputs_max_min_reduction_with_dim, supports_fwgrad_bwgrad=True, supports_forward_ad=True), OpInfo('max', variant_test_name='reduction_no_dim', dtypes=all_types_and(torch.float16, torch.bfloat16, torch.bool), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_max_min_reduction_no_dim), OpInfo('median', dtypes=all_types_and(torch.bfloat16), dtypesIfCUDA=all_types_and(torch.float16), # TODO: some signatures of median do support out supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=partial(sample_inputs_reduction, supports_multiple_dims=False)), OpInfo('nanmedian', dtypes=all_types_and(torch.bfloat16), dtypesIfCUDA=all_types_and(torch.float16), # TODO: some signatures of nanmedian do support out supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=partial(sample_inputs_reduction, supports_multiple_dims=False)), OpInfo('var_mean', dtypes=floating_and_complex_types_and(torch.half, torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.half, torch.bfloat16), sample_inputs_func=partial(sample_inputs_reduction, supports_multiple_dims=False), backward_dtypes=floating_types_and(torch.half, torch.bfloat16), backward_dtypesIfCUDA=floating_types_and(torch.half), # TODO: some signatures of var_mean do support out supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=False, # Need: var_mean skips=( # var_mean does not support automatic differentiation for outputs with complex dtype DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_dtypes'), # https://github.com/pytorch/pytorch/issues/67539 DecorateInfo(unittest.skip("67539"), 'TestCommon', 'test_noncontiguous_samples', active_if=TEST_WITH_ASAN, device_type='cpu'), # TODO: FIXME: complex inputs requiring grad error in forward DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_dtypes'), # TODO: review with var_mean tests in test_autograd.py DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'), DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_fn_grad'), DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_fn_gradgrad'), DecorateInfo(unittest.skip("Fails on ASAN!"), 'TestCompositeCompliance', 'test_backward'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_forward_mode_AD'), # Division by zero, may be related to above? DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_fn_fwgrad_bwgrad'))), OpInfo('std_mean', dtypes=floating_and_complex_types_and(torch.half, torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.half, torch.bfloat16), sample_inputs_func=partial(sample_inputs_reduction, supports_multiple_dims=False), backward_dtypes=floating_types_and(torch.half, torch.bfloat16), backward_dtypesIfCUDA=floating_types_and(torch.half), # TODO: some signatures of std_mean do support out supports_out=False, supports_forward_ad=True, # Supports only certain variants? supports_fwgrad_bwgrad=False, # Need: std_mean skips=( DecorateInfo(unittest.skip("ASAN: division by zero!"), active_if=TEST_WITH_ASAN), # std_mean does not support forward when complex inputs require grad DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_dtypes'), # https://github.com/pytorch/pytorch/issues/67539 DecorateInfo(unittest.skip("67539"), 'TestCommon', 'test_noncontiguous_samples', active_if=TEST_WITH_ASAN, device_type='cpu'), # TODO: fix along with var_mean autograd tests DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'), DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_fn_grad'), DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_fn_gradgrad'), DecorateInfo(unittest.skip("Fails on ASAN!"), 'TestCompositeCompliance', 'test_backward'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_forward_mode_AD'), # Division by zero, may be related to above? DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_fn_fwgrad_bwgrad'))), OpInfo('meshgrid', variant_test_name='variadic_tensors', ref=np.meshgrid, dtypes=all_types_and_complex_and(torch.bfloat16, torch.bool, torch.float16), sample_inputs_func=partial(sample_inputs_meshgrid, variant='variadic'), skips=[ # JIT does not support variadic tensors. # RuntimeError: input->type()->kind() == TypeKind::OptionalType # INTERNAL ASSERT FAILED at "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":252, # please report a bug to PyTorch. DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'), # meshgrid is defined in torch.functional to take a # variadic list of tensors. Variadic parameters are not # compatible with the normalize operator tests. DecorateInfo(unittest.skip("Skipped!"), 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # Skip operator schema test because this is a functional and not an operator DecorateInfo(unittest.skip("Skipped!"), 'TestOperatorSignatures', 'test_get_torch_func_signature_exhaustive'), ], supports_out=False, supports_fwgrad_bwgrad=True, supports_forward_ad=True), OpInfo('meshgrid', variant_test_name='list_of_tensors', # Unlike the variant above, we do not use np.meshgrid as a # ref since it does not officially support list of numpy # arrays. dtypes=all_types_and_complex_and(torch.bfloat16, torch.bool, torch.float16), sample_inputs_func=partial(sample_inputs_meshgrid, variant='list'), skips=[ # meshgrid is defined in torch.functional to take a # variadic list of tensors. Variadic parameters are not # compatible with the normalize operator tests. DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), ], assert_autodiffed=True, supports_out=False, autodiff_nonfusible_nodes=[], supports_fwgrad_bwgrad=True, supports_forward_ad=True), OpInfo('min', variant_test_name='reduction_with_dim', dtypes=all_types_and(torch.float16, torch.bfloat16, torch.bool), sample_inputs_func=sample_inputs_max_min_reduction_with_dim, supports_fwgrad_bwgrad=True, supports_forward_ad=True), OpInfo('min', variant_test_name='reduction_no_dim', dtypes=all_types_and(torch.float16, torch.bfloat16, torch.bool), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_max_min_reduction_no_dim), OpInfo('quantile', dtypes=floating_types(), sample_inputs_func=sample_inputs_reduction_quantile, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), DecorateInfo(unittest.skip('Skipped!'), 'TestCudaFuserOpInfo', 'test_nvfuser_extremal_values'), ), # See https://github.com/pytorch/pytorch/issues/66357 # Relies on copy_ to broadcast, but the forward AD path calls broadcast_to which # does not have a batching rule in core check_batched_forward_grad=False), OpInfo('nanquantile', dtypes=floating_types(), sample_inputs_func=sample_inputs_reduction_quantile, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), DecorateInfo(unittest.skip('Skipped!'), 'TestCudaFuserOpInfo', 'test_nvfuser_extremal_values'), ), # See https://github.com/pytorch/pytorch/issues/66357 # Relies on copy_ to broadcast, but the forward AD path calls broadcast_to which # does not have a batching rule in core check_batched_forward_grad=False), BinaryUfuncInfo( 'max', aliases=('maximum',), variant_test_name='binary', dtypes=all_types_and(torch.float16, torch.bfloat16, torch.bool), supports_forward_ad=True, supports_fwgrad_bwgrad=True, assert_autodiffed=True, ref=np.maximum, supports_rhs_python_scalar=False, skips=( # Incorrectly attempts to use a scalar for the second argument DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_jit_alias_remapping'), # TODO: FIXME: RuntimeError: "max_elementwise_cuda" not implemented for 'ComplexFloat' DecorateInfo(unittest.expectedFailure, 'TestBinaryUfuncs', 'test_type_promotion', device_type='cuda'), )), BinaryUfuncInfo( 'maximum', dtypes=all_types_and(torch.float16, torch.bfloat16, torch.bool), supports_forward_ad=True, supports_fwgrad_bwgrad=True, ref=np.maximum, supports_rhs_python_scalar=False, skips=( # TODO: FIXME: RuntimeError: "max_elementwise_cuda" not implemented for 'ComplexFloat' DecorateInfo(unittest.expectedFailure, 'TestBinaryUfuncs', 'test_type_promotion', device_type='cuda'), )), BinaryUfuncInfo( 'min', aliases=('minimum',), variant_test_name='binary', dtypes=all_types_and(torch.float16, torch.bfloat16, torch.bool), supports_forward_ad=True, supports_fwgrad_bwgrad=True, assert_autodiffed=True, ref=np.minimum, supports_rhs_python_scalar=False, skips=( # Incorrectly attempts to use a scalar for the second argument DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_jit_alias_remapping'), # TODO: FIXME: RuntimeError: "min_elementwise_cuda" not implemented for 'ComplexFloat' DecorateInfo(unittest.expectedFailure, 'TestBinaryUfuncs', 'test_type_promotion', device_type='cuda'), )), BinaryUfuncInfo( 'minimum', dtypes=all_types_and(torch.float16, torch.bfloat16, torch.bool), supports_forward_ad=True, supports_fwgrad_bwgrad=True, ref=np.minimum, supports_rhs_python_scalar=False, skips=( # TODO: FIXME: RuntimeError: "min_elementwise_cuda" not implemented for 'ComplexFloat' DecorateInfo(unittest.expectedFailure, 'TestBinaryUfuncs', 'test_type_promotion', device_type='cuda'), ), ), BinaryUfuncInfo('logical_and', ref=np.logical_and, dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), supports_autograd=False, always_returns_bool=True, supports_rhs_python_scalar=False), BinaryUfuncInfo('logical_or', ref=np.logical_or, dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), supports_autograd=False, always_returns_bool=True, supports_rhs_python_scalar=False), BinaryUfuncInfo('logical_xor', ref=np.logical_xor, dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), supports_autograd=False, always_returns_bool=True, supports_rhs_python_scalar=False), BinaryUfuncInfo('bitwise_and', ref=np.bitwise_and, dtypes=integral_types_and(torch.bool), operator_variant=operator.and_, inplace_operator_variant=operator.iand, supports_autograd=False, supports_one_python_scalar=True, skips=( # RuntimeError: "bitwise_and_cuda" not implemented for 'Half' DecorateInfo(unittest.expectedFailure, 'TestBinaryUfuncs', 'test_type_promotion', device_type='cuda'), )), BinaryUfuncInfo('bitwise_or', ref=np.bitwise_or, dtypes=integral_types_and(torch.bool), operator_variant=operator.or_, inplace_operator_variant=operator.ior, supports_autograd=False, supports_one_python_scalar=True, skips=( # TODO: FIXME: RuntimeError: "bitwise_or_cuda" not implemented for 'Half' DecorateInfo(unittest.expectedFailure, 'TestBinaryUfuncs', 'test_type_promotion', device_type='cuda'), )), BinaryUfuncInfo('bitwise_xor', ref=np.bitwise_xor, dtypes=integral_types_and(torch.bool), operator_variant=operator.xor, inplace_operator_variant=operator.ixor, supports_autograd=False, supports_one_python_scalar=True, skips=( # TODO: FIXME: RuntimeError: "bitwise_xor_cuda" not implemented for 'Half' DecorateInfo(unittest.expectedFailure, 'TestBinaryUfuncs', 'test_type_promotion', device_type='cuda'), )), BinaryUfuncInfo('heaviside', ref=lambda a, b: ( # necessary because np.heaviside incorrectly returns float64 when passed args of dtype int64 np.int64(np.heaviside(a, b)) if a.dtype == np.int64 and b.dtype == np.int64 else np.heaviside(a, b) ), dtypes=all_types_and(torch.bool, torch.float16, torch.bfloat16), supports_autograd=False, supports_rhs_python_scalar=False, skips=( # RuntimeError: heaviside is not yet implemented for tensors with different dtypes. DecorateInfo(unittest.expectedFailure, 'TestBinaryUfuncs', 'test_type_promotion'), # PyTorch's heaviside does not appear to propagate NaNs DecorateInfo(unittest.skip("Skipped!"), 'TestBinaryUfuncs', 'test_reference_numerics_extremal_values'), )), BinaryUfuncInfo('lcm', ref=np.lcm, dtypes=integral_types_and(), supports_autograd=False, supports_rhs_python_scalar=False), BinaryUfuncInfo('gcd', ref=np.gcd, dtypes=integral_types_and(), supports_autograd=False, supports_rhs_python_scalar=False, skips=( DecorateInfo(unittest.expectedFailure, 'TestBinaryUfuncs', 'test_reference_numerics_small_values', dtypes=(torch.int8,)),)), BinaryUfuncInfo('isclose', ref=np.isclose, dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), sample_inputs_func=sample_inputs_isclose, supports_autograd=False, supports_out=False, supports_rhs_python_scalar=False, skips=( DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_reference_testing', dtypes=(torch.complex128,)), # RuntimeError: Short did not match Int DecorateInfo(unittest.expectedFailure, 'TestBinaryUfuncs', 'test_type_promotion'), DecorateInfo(unittest.skip("Skipped!"), 'TestBinaryUfuncs', 'test_reference_numerics_extremal_values'), # Problem due to internal inplace operations DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_operator'), )), # `softmax` supports different dtypes based on whether `dtype` argument, # is passed or not. Hence two OpInfo entries, one with dtype and other without. # https://github.com/pytorch/pytorch/issues/68752 OpInfo('softmax', aliases=('special.softmax', 'nn.functional.softmax',), aten_name='softmax', aten_backward_name='_softmax_backward_data', dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), sample_inputs_func=sample_inputs_softmax_variant, assert_jit_shape_analysis=True, assert_autodiffed=True, supports_forward_ad=True, supports_out=True), OpInfo('softmax', aliases=('special.softmax', 'nn.functional.softmax',), variant_test_name="with_dtype", aten_name='softmax', dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), sample_inputs_func=partial(sample_inputs_softmax_variant, with_dtype=True), assert_autodiffed=True, supports_forward_ad=True, supports_out=True), # `softmin` supports different dtypes based on whether `dtype` argument, # is passed or not. Hence two OpInfo entries, one with dtype and other without. # https://github.com/pytorch/pytorch/issues/68752 OpInfo('nn.functional.softmin', aten_name='softmin', dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), sample_inputs_func=sample_inputs_softmax_variant, assert_jit_shape_analysis=False, assert_autodiffed=False, supports_forward_ad=True, supports_out=False), OpInfo('nn.functional.softmin', variant_test_name="with_dtype", aten_name='softmin', dtypes=all_types_and_complex_and(torch.float16, torch.bfloat16), sample_inputs_func=partial(sample_inputs_softmax_variant, with_dtype=True), assert_autodiffed=False, supports_forward_ad=True, supports_out=False), OpInfo( "nn.functional.cross_entropy", dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), sample_inputs_func=sample_inputs_cross_entropy, supports_out=False, supports_forward_ad=True, decorators=( DecorateInfo( toleranceOverride({torch.float32: tol(atol=1e-5, rtol=1e-3)}), "TestJit", "test_variant_consistency_jit", device_type="cpu", ), ), skips=( # AssertionError: False is not true : Scalars failed to compare as equal! 0 != 1536 # test_ops.TestJitCUDA.test_variant_consistency_jit_nn_functional_cross_entropy_cuda_float32 leaked # 1536 bytes CUDA memory on device 0 DecorateInfo( unittest.expectedFailure, "TestJit", "test_variant_consistency_jit", device_type="cuda", ), ) ), OpInfo('nn.functional.normalize', dtypes=floating_and_complex_types_and(torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.half, torch.bfloat16), sample_inputs_func=sample_inputs_normalize, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_fn_fwgrad_bwgrad', dtypes=[torch.complex128]), )), OpInfo('aminmax', ref=lambda x, dim=None, keepdim=False: (np.amin(x, axis=dim, keepdims=keepdim), np.amax(x, axis=dim, keepdims=keepdim)), dtypes=all_types_and(torch.bool), dtypesIfCUDA=all_types_and(torch.bool, torch.float16, torch.bfloat16), decorators=(onlyNativeDeviceTypes,), supports_autograd=False, sample_inputs_func=sample_inputs_aminmax, error_inputs_func=error_inputs_aminmax_amax_amin, skips=( # AssertionError: Resizing an out= argument with no elements threw a resize warning! DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out', device_type='cpu'), )), OpInfo('as_strided', op=lambda x, size, stride, storage_offset=0: torch.as_strided(x, size, stride, storage_offset=storage_offset), dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16, torch.chalf), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, # vmap does not support inplace views check_inplace_batched_forward_grad=False, sample_inputs_func=sample_inputs_as_strided, skips=( # Note: This xfail is fine -- it's inherent to how as_strided works DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_noncontiguous_samples'), # AssertionError: False is not true : Scalars failed to compare as equal! DecorateInfo(unittest.skip("Errors when storage_offset is included"), 'TestCommon', 'test_variant_consistency_eager'), # RuntimeError: This operator is not Composite Compliant DecorateInfo(unittest.skip("Errors when storage_offset is included"), 'TestCompositeCompliance', 'test_backward'), DecorateInfo(unittest.skip("Errors when storage_offset is included"), 'TestCompositeCompliance', 'test_forward_ad'), # Not close DecorateInfo(unittest.skip("Errors when storage_offset is included"), 'TestCommon', 'test_complex_half_reference_testing'), # Not close DecorateInfo(unittest.skip("Errors when storage_offset is included"), 'TestMathBits', 'test_conj_view'), DecorateInfo(unittest.skip("Errors when storage_offset is included"), 'TestMathBits', 'test_neg_view'), DecorateInfo(unittest.skip("Numerous errors"), 'TestGradients'))), OpInfo('nn.functional.cosine_similarity', aten_name="cosine_similarity", dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_cosine_similarity), OpInfo('nn.functional.adaptive_avg_pool1d', dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, sample_inputs_func=sample_inputs_adaptive_avg_pool1d), OpInfo('nn.functional.adaptive_avg_pool2d', dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), decorators=( # RuntimeError: # adaptive_avg_pool2d(Tensor input, int[2] output_size) -> (Tensor): # Expected a value of type 'List[int]' for argument 'output_size' but # instead found type 'Tuple[NoneType, int]'. : # File "", line 3 # def the_method(i0): # return torch.nn.functional.adaptive_avg_pool2d(i0, (None, 7)) # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), ), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, sample_inputs_func=sample_inputs_adaptive_avg_pool2d), OpInfo('nn.functional.adaptive_avg_pool3d', dtypes=floating_types_and(torch.half), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), decorators=( # RuntimeError: # adaptive_avg_pool3d(Tensor input, int[3] output_size) -> (Tensor): # Expected a value of type 'List[int]' for argument 'output_size' but # instead found type 'Tuple[NoneType, NoneType, NoneType]'. : # File "", line 3 # # def the_method(i0): # return torch.nn.functional.adaptive_avg_pool3d(i0, (None, None, None)) # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE # DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), ), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, sample_inputs_func=sample_inputs_adaptive_avg_pool3d), OpInfo('nn.functional.adaptive_max_pool1d', dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, # got: Batching rule not implemented for aten::flatten.using_ints check_batched_forward_grad=False, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, sample_inputs_func=sample_inputs_adaptive_max_pool1d), OpInfo('nn.functional.adaptive_max_pool2d', dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), decorators=( # RuntimeError: # adaptive_max_pool2d(Tensor input, int[2] output_size) -> (Tensor): # Expected a value of type 'List[int]' for argument 'output_size' but # instead found type 'Tuple[NoneType, int]'. : # File "", line 3 # def the_method(i0): # return torch.nn.functional.adaptive_max_pool2d(i0, (None, 7)) # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), ), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, # got: Batching rule not implemented for aten::flatten.using_ints check_batched_forward_grad=False, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, sample_inputs_func=sample_inputs_adaptive_max_pool2d), OpInfo('nn.functional.adaptive_max_pool3d', dtypes=floating_types(), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), decorators=( # RuntimeError: # adaptive_max_pool3d(Tensor input, int[3] output_size) -> (Tensor): # Expected a value of type 'List[int]' for argument 'output_size' but # instead found type 'Tuple[NoneType, NoneType, NoneType]'. : # File "", line 3 # # def the_method(i0): # return torch.nn.functional.adaptive_max_pool3d(i0, (None, None, None)) # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE # DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), ), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, # got: Batching rule not implemented for aten::flatten.using_ints check_batched_forward_grad=False, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, sample_inputs_func=sample_inputs_adaptive_max_pool3d), OpInfo('nn.functional.avg_pool1d', aten_name='avg_pool1d', supports_autograd=True, supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, dtypes=floating_types_and(torch.int64, torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, sample_inputs_func=sample_inputs_avgpool1d), OpInfo('nn.functional.avg_pool3d', aten_name='avg_pool3d', supports_autograd=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, dtypes=floating_types_and(torch.int64), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, sample_inputs_func=sample_inputs_avgpool3d, skips=( # AssertionError: Tensor-likes are not close! DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out', device_type='cpu'), )), OpInfo( "nn.functional.binary_cross_entropy_with_logits", aten_name="binary_cross_entropy_with_logits", supports_autograd=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, sample_inputs_func=sample_inputs_binary_cross_entropy_with_logits, skips=( DecorateInfo( unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit', dtypes=(torch.float32,) ), DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', "test_fn_gradgrad", dtypes=(torch.float64,)), DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', "test_fn_fwgrad_bwgrad", dtypes=(torch.float64,)), ), ), OpInfo('nn.functional.relu', aten_name="relu", supports_autograd=True, dtypes=all_types_and(torch.bfloat16), dtypesIfCUDA=all_types_and(torch.half, torch.bfloat16), sample_inputs_func=sample_inputs_nn_activation_relu, supports_out=False, supports_fwgrad_bwgrad=True, supports_forward_ad=True), OpInfo('nn.functional.conv_transpose1d', aten_name='conv_transpose1d', aliases=('conv_transpose1d',), dtypes=floating_types_and(torch.int64), dtypesIfCUDA=floating_types_and(torch.float16, *[torch.bfloat16] if (CUDA11OrLater or TEST_WITH_ROCM) else []), sample_inputs_func=sample_inputs_conv_transpose1d, supports_forward_ad=True, supports_fwgrad_bwgrad=True, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, decorators=[ DecorateInfo( toleranceOverride({torch.float32: tol(atol=1e-04, rtol=1.3e-06), }), 'TestCommon', 'test_variant_consistency_eager', device_type='cuda')], skips=( # RuntimeError: !lhs.isAliasOf(rhs)INTERNAL ASSERT FAILED at # "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":104, please report a bug to PyTorch. DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), ), supports_out=False,), OpInfo('nn.functional.conv_transpose2d', aten_name='conv_transpose2d', aliases=('conv_transpose2d',), dtypes=floating_types_and(torch.int64), dtypesIfCUDA=floating_types_and(torch.float16, *[torch.bfloat16] if (CUDA11OrLater or TEST_WITH_ROCM) else []), sample_inputs_func=sample_inputs_conv_transpose2d, supports_forward_ad=True, supports_fwgrad_bwgrad=True, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, decorators=[ DecorateInfo( toleranceOverride({torch.float32: tol(atol=1e-04, rtol=1.3e-06), }), 'TestCommon', 'test_variant_consistency_eager', device_type='cuda')], skips=( # RuntimeError: !lhs.isAliasOf(rhs)INTERNAL ASSERT FAILED at # "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":104, please report a bug to PyTorch. DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'), ), supports_out=False,), OpInfo('nn.functional.conv_transpose3d', aten_name='conv_transpose3d', aliases=('conv_transpose3d',), dtypes=floating_types_and(torch.int64), dtypesIfCUDA=floating_types_and(torch.float16, *[torch.bfloat16] if (CUDA11OrLater or TEST_WITH_ROCM) else []), sample_inputs_func=sample_inputs_conv_transpose3d, supports_forward_ad=True, supports_fwgrad_bwgrad=True, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, decorators=[ DecorateInfo( toleranceOverride({torch.float32: tol(atol=1e-04, rtol=1.3e-06), }), 'TestCommon', 'test_variant_consistency_eager', device_type='cuda'), DecorateInfo( toleranceOverride({torch.float32: tol(atol=1e-04, rtol=1.3e-06), }), 'TestCommon', 'test_noncontiguous_samples', device_type='cuda')], skips=( # RuntimeError: !lhs.isAliasOf(rhs)INTERNAL ASSERT FAILED at # "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":104, please report a bug to PyTorch. DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'), DecorateInfo(unittest.skip("Skipped! 75029"), 'TestCudaFuserOpInfo', 'test_nvfuser_correctness'), DecorateInfo(unittest.skip("Skipped! 75363"), 'TestCudaFuserOpInfo', 'test_nvfuser_extremal_values'), DecorateInfo(unittest.skip("Skipped! RuntimeError: bias tensor has to be contiguous"), 'TestGradients', 'test_forward_mode_AD', device_type='cuda', active_if=TEST_WITH_ROCM), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad', device_type='cuda', active_if=(not TEST_CUDNN)), ), supports_out=False,), OpInfo('nn.functional.conv1d', aliases=('conv1d',), aten_name='conv1d', dtypes=floating_and_complex_types_and(torch.int64, torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.float16, *[torch.bfloat16] if (CUDA11OrLater or TEST_WITH_ROCM) else []), sample_inputs_func=sample_inputs_conv1d, supports_forward_ad=True, supports_fwgrad_bwgrad=True, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, skips=( # RuntimeError: !lhs.isAliasOf(rhs)INTERNAL ASSERT FAILED at # "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":103, please report a bug to PyTorch. DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'), # Ref: https://github.com/pytorch/pytorch/issues/75309 # AssertionError: None mismatch: torch.complex128 is not None DecorateInfo(unittest.expectedFailure, 'TestDtypeCustomRules', 'test_custom_rules', dtypes=(torch.complex64, torch.complex128)), # Ref: https://github.com/pytorch/pytorch/issues/75309 # RuntimeError: UNSUPPORTED DTYPE: complex DecorateInfo(unittest.expectedFailure, 'TestNNCOpInfo', 'test_nnc_correctness', dtypes=(torch.complex64, torch.complex128)), ), supports_expanded_weight=True, supports_out=False,), OpInfo('nn.functional.conv2d', aliases=('conv2d',), aten_name='conv2d', dtypes=floating_and_complex_types_and(torch.int64, torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.float16, *[torch.bfloat16] if (CUDA11OrLater or TEST_WITH_ROCM) else []), sample_inputs_func=partial(sample_inputs_conv2d), gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # RuntimeError: !lhs.isAliasOf(rhs)INTERNAL ASSERT FAILED at # "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":103, please report a bug to PyTorch. DecorateInfo(unittest.skip("Works on some configs!"), 'TestJit', 'test_variant_consistency_jit'), # Ref: https://github.com/pytorch/pytorch/issues/75309 # AssertionError: None mismatch: torch.complex128 is not None DecorateInfo(unittest.expectedFailure, 'TestDtypeCustomRules', 'test_custom_rules', dtypes=(torch.complex64, torch.complex128)), # RuntimeError: UNSUPPORTED DTYPE: complex DecorateInfo(unittest.expectedFailure, 'TestNNCOpInfo', 'test_nnc_correctness', dtypes=(torch.complex64, torch.complex128)), ), supports_expanded_weight=True, supports_out=False,), OpInfo('nn.functional.group_norm', aten_name='group_norm', aliases=('group_norm',), ref=reference_group_norm, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, decorators=[ # RuntimeError: Cannot insert a Tensor that requires grad as a constant. # Consider making it a parameter or input, or detaching the gradient DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit', dtypes=(torch.float32,)) ], sample_inputs_func=sample_inputs_group_norm, supports_expanded_weight=True,), OpInfo('nn.functional.instance_norm', # no ref because instance_norm will often have numerical instability (large numbers or nan) dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, decorators=[ # RuntimeError: Cannot insert a Tensor that requires grad as a constant. # Consider making it a parameter or input, or detaching the gradient DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit', dtypes=(torch.float32,)), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad', active_if=TEST_WITH_ROCM) ], sample_inputs_func=sample_inputs_instance_norm, supports_expanded_weight=True,), OpInfo('nn.functional.layer_norm', aten_name='layer_norm', aten_backward_name='layer_norm_backward', aliases=('layer_norm',), ref=reference_layer_norm, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, assert_jit_shape_analysis=True, decorators=[ DecorateInfo( toleranceOverride({torch.float32: tol(atol=1e-05, rtol=1e-03)}), 'TestCommon', 'test_reference_testing' ) ], sample_inputs_func=sample_inputs_layer_norm, supports_expanded_weight=True,), OpInfo('nn.functional.local_response_norm', dtypes=floating_types_and(torch.int64, torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, decorators=[ # RuntimeError: falseINTERNAL ASSERT FAILED at # "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":185, please report a bug to PyTorch. DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit', dtypes=(torch.float32,)), DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-02, rtol=1e-02)}), 'TestCudaFuserOpInfo', 'test_nvfuser_correctness'), ], sample_inputs_func=sample_inputs_local_response_norm,), OpInfo('nn.functional.pad', variant_test_name='constant', aten_name='constant_pad_nd', supports_forward_ad=True, supports_fwgrad_bwgrad=True, dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.half), sample_inputs_func=partial(sample_inputs_nn_pad, mode='constant'), supports_out=False), OpInfo('nn.functional.pad', variant_test_name='reflect', supports_forward_ad=True, supports_fwgrad_bwgrad=True, dtypes=floating_and_complex_types(), dtypesIfCUDA=floating_and_complex_types_and(torch.half), sample_inputs_func=partial(sample_inputs_nn_pad, mode='reflect'), skips=( # Doesn't have a corresponding aten operator. # RuntimeError: falseINTERNAL ASSERT FAILED at # "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":185, please report a bug to PyTorch. DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit', dtypes=(torch.float32,)), ), gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, supports_out=False), OpInfo('nn.functional.pad', variant_test_name='replicate', supports_forward_ad=True, supports_fwgrad_bwgrad=True, dtypes=floating_and_complex_types(), dtypesIfCUDA=floating_and_complex_types_and(torch.half), sample_inputs_func=partial(sample_inputs_nn_pad, mode='replicate'), skips=( # Doesn't have a corresponding aten operator. # RuntimeError: falseINTERNAL ASSERT FAILED at # "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":185, please report a bug to PyTorch. DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit', dtypes=(torch.float32,)), ), gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, supports_out=False), OpInfo('nn.functional.pad', variant_test_name='circular', dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.half), sample_inputs_func=partial(sample_inputs_nn_pad, mode='circular'), supports_forward_ad=True, supports_fwgrad_bwgrad=True, check_batched_grad=False, # https://github.com/pytorch/pytorch/issues/66357 check_batched_forward_grad=False, skips=( # Doesn't have a corresponding aten operator. # RuntimeError: falseINTERNAL ASSERT FAILED at # "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":185, please report a bug to PyTorch. DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit', dtypes=(torch.float32,)), ), supports_out=False), OpInfo('nn.functional.hardswish', aten_name="hardswish", aten_backward_name='hardswish_backward', supports_autograd=True, assert_autodiffed=True, sample_inputs_func=sample_inputs_hardswish, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), supports_gradgrad=False, supports_forward_ad=True, supports_fwgrad_bwgrad=False, # Need: hardswish_backward supports_out=False, autodiff_nonfusible_nodes=["aten::hardswish"]), OpInfo('nn.functional.unfold', aten_name='im2col', aten_backward_name='im2col_backward', dtypes=floating_and_complex_types_and(torch.half, torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.half), sample_inputs_func=sample_inputs_nn_unfold, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False, skips=( # NOTE: this failure may not reproduce consistently on different systems # false INTERNAL ASSERT FAILED at "...torch/csrc/jit/passes/utils/check_alias_annotation.cpp":185 DecorateInfo(unittest.skip("Internal assert failed!"), 'TestJit', 'test_variant_consistency_jit'), )), OpInfo('nn.functional.interpolate', aten_name="interpolate", variant_test_name='nearest', supports_autograd=True, supports_fwgrad_bwgrad=True, supports_forward_ad=True, dtypes=floating_types_and(torch.uint8, torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.uint8), sample_inputs_func=partial(sample_inputs_interpolate, 'nearest'), skips=( # RuntimeError: false # INTERNAL ASSERT FAILED at "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":185, # please report a bug to PyTorch. DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), ), supports_out=False), OpInfo('nn.functional.interpolate', aten_name="interpolate", variant_test_name='linear', supports_autograd=True, supports_fwgrad_bwgrad=True, supports_forward_ad=True, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half), sample_inputs_func=partial(sample_inputs_interpolate, 'linear'), skips=( # RuntimeError: false # INTERNAL ASSERT FAILED at "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":185, # please report a bug to PyTorch. DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), ), supports_out=False), OpInfo('nn.functional.interpolate', aten_name="interpolate", variant_test_name='bilinear', supports_fwgrad_bwgrad=True, supports_autograd=True, supports_forward_ad=True, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half), gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, sample_inputs_func=partial(sample_inputs_interpolate, 'bilinear'), skips=( # RuntimeError: false # INTERNAL ASSERT FAILED at "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":185, # please report a bug to PyTorch. DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), ), supports_out=False), OpInfo('nn.functional.interpolate', aten_name="interpolate", variant_test_name='bicubic', supports_autograd=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half), sample_inputs_func=partial(sample_inputs_interpolate, 'bicubic'), gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, skips=( # RuntimeError: false # INTERNAL ASSERT FAILED at "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":185, # please report a bug to PyTorch. DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), ), supports_out=False), OpInfo('nn.functional.interpolate', aten_name="interpolate", variant_test_name='trilinear', supports_autograd=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half), gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, sample_inputs_func=partial(sample_inputs_interpolate, 'trilinear'), skips=( # RuntimeError: false # INTERNAL ASSERT FAILED at "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":185, # please report a bug to PyTorch. DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), ), supports_out=False), OpInfo('nn.functional.interpolate', aten_name="interpolate", variant_test_name='area', supports_autograd=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, dtypes=floating_types(), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), sample_inputs_func=partial(sample_inputs_interpolate, 'area'), gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, skips=( # RuntimeError: false # INTERNAL ASSERT FAILED at "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":185, # please report a bug to PyTorch. DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), ), supports_out=False), OpInfo('nn.functional.upsample_bilinear', supports_autograd=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half), gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, sample_inputs_func=partial(sample_inputs_upsample, 'bilinear'), skips=( # RuntimeError: false # INTERNAL ASSERT FAILED at "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":185, # please report a bug to PyTorch. DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), ), supports_out=False), OpInfo( "nn.functional.soft_margin_loss", ref=_NOTHING, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.bfloat16, torch.float16), supports_out=False, supports_forward_ad=True, # doesn't support grad on target sample_inputs_func=partial(sample_inputs_loss, rhs_requires_grad=False), ), OpInfo('nn.functional.upsample_nearest', supports_autograd=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, dtypes=floating_types_and(torch.uint8, torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.uint8), gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, sample_inputs_func=partial(sample_inputs_upsample, 'nearest'), skips=( # RuntimeError: false # INTERNAL ASSERT FAILED at "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":185, # please report a bug to PyTorch. DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), ), supports_out=False), OpInfo( "nn.functional.margin_ranking_loss", ref=_NOTHING, dtypes=all_types_and(torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bfloat16, torch.float16), supports_out=False, sample_inputs_func=sample_inputs_margin_ranking_loss, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), )), OpInfo( "nn.functional.multi_margin_loss", ref=_NOTHING, dtypes=floating_types(), dtypesIfCUDA=floating_types_and(torch.bfloat16, torch.float16), supports_out=False, supports_gradgrad=False, sample_inputs_func=sample_inputs_multi_margin_loss, ), OpInfo( "nn.functional.multilabel_margin_loss", ref=_NOTHING, dtypes=floating_types(), dtypesIfCUDA=floating_types_and(torch.bfloat16, torch.float16), supports_out=False, supports_gradgrad=False, sample_inputs_func=sample_inputs_multilabel_margin_loss ), OpInfo('nn.functional.leaky_relu', aliases=None, aten_name="leaky_relu", aten_backward_name='leaky_relu_backward', sample_inputs_func=sample_inputs_leaky_relu, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), supports_autograd=True, assert_autodiffed=True, supports_gradgrad=True, supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, autodiff_nonfusible_nodes=["aten::leaky_relu"]), OpInfo( "nn.functional.multilabel_soft_margin_loss", ref=_NOTHING, supports_out=False, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16), sample_inputs_func=sample_inputs_multilabel_soft_margin_loss, supports_forward_ad=True, decorators=( DecorateInfo( toleranceOverride({torch.float32: tol(atol=1e-4, rtol=1e-4)}), "TestJit", "test_variant_consistency_jit", ), DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-02, rtol=1e-02)}), 'TestCudaFuserOpInfo', 'test_nvfuser_correctness'), ), skips=( # AssertionError: False is not true : Scalars failed to compare as equal! 0 != 4096 # __main__.TestJitCUDA.test_variant_consistency_jit_nn_functional_multilabel_soft_margin_loss_cuda_float32 # leaked 4096 bytes CUDA memory on device 0 DecorateInfo( # Skip instead of expectedFailure because this fails # locally for me but passes in CI. unittest.skip("Skipped!"), "TestJit", "test_variant_consistency_jit", device_type="cuda", ), ), ), OpInfo('nn.functional.avg_pool2d', aten_name='avg_pool2d', supports_autograd=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, dtypes=floating_types_and(torch.int64, torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), sample_inputs_func=sample_inputs_avgpool2d, skips=( DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out', device_type='cuda'), )), OpInfo('nn.functional.fractional_max_pool2d', supports_autograd=True, supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, op=lambda input, *args, **kwargs: wrapper_set_seed(torch.nn.functional.fractional_max_pool2d, input, *args, **kwargs), # vmap does not support random operations check_batched_forward_grad=False, dtypes=floating_types(), dtypesIfCUDA=floating_types_and(torch.float16), test_neg_view=False, sample_inputs_func=sample_inputs_fractional_max_pool2d, decorators=( # FIXME: AssertionError: False is not true : Tensors failed to compare as equal! DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # RuntimeError: input->type()->kind() == TypeKind::OptionalType # INTERNAL ASSERT FAILED at "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":270 DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'))), OpInfo('nn.functional.fractional_max_pool3d', supports_autograd=True, supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, op=lambda input, *args, **kwargs: wrapper_set_seed(torch.nn.functional.fractional_max_pool3d, input, *args, **kwargs), # vmap does not support random operations check_batched_forward_grad=False, dtypes=floating_types(), dtypesIfCUDA=floating_types_and(torch.float16), test_neg_view=False, sample_inputs_func=sample_inputs_fractional_max_pool3d, decorators=( # FIXME: both derivatives are implemented incorrectly # https://github.com/pytorch/pytorch/issues/69322 # FIXME: AssertionError: False is not true : Tensors failed to compare as equal! DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # RuntimeError: input->type()->kind() == TypeKind::OptionalType # INTERNAL ASSERT FAILED at "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":270 DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'),)), OpInfo('nn.functional.max_pool1d', aten_name='max_pool1d', supports_autograd=True, supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, # got: Batching rule not implemented for aten::flatten.using_ints check_batched_forward_grad=False, # TODO: add shape checks assert_jit_shape_analysis=False, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), skips=( # Pre-existing condition; Needs to be fixed DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_operator', device_type='cpu'), DecorateInfo(unittest.skip("Works on some configs"), 'TestNNCOpInfo', 'test_nnc_correctness', dtypes=(torch.bfloat16,)), DecorateInfo(unittest.skip("Works on some conifgs"), 'TestCudaFuserOpInfo', 'test_nvfuser_correctness', dtypes=(torch.bfloat16,)), ), sample_inputs_func=sample_inputs_max_pool), OpInfo('nn.functional.max_pool2d', aten_name='max_pool2d', supports_autograd=True, # Vmap is not happy with non-contiguous (channels_last) inputs check_batched_gradgrad=False, supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, # got: Batching rule not implemented for aten::flatten.using_ints check_batched_forward_grad=False, assert_jit_shape_analysis=True, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), sample_inputs_func=sample_inputs_max_pool), OpInfo('nn.functional.max_pool3d', aten_name='max_pool3d', supports_autograd=True, supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, # got: Batching rule not implemented for aten::flatten.using_ints check_batched_forward_grad=False, # TODO: add shape checks assert_jit_shape_analysis=False, dtypes=floating_types(), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), # TODO: investigate nondeterminism gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, sample_inputs_func=sample_inputs_max_pool), OpInfo('nn.functional.max_unpool1d', aten_name='max_unpool1d', supports_autograd=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False, assert_jit_shape_analysis=False, dtypes=floating_types(), dtypesIfCUDA=floating_types_and(torch.float16), sample_inputs_func=sample_inputs_max_unpool, skips=( # Gradients are tested in `variant_test_name=grad` below. # We skip tests here because there is non-determinism in backward # with gather, when there are writes into the same memory location, # and if there are several indices pointing to the same memory, # gradcheck is oblivious about that and cannot perturb them all at once # (see sample_inputs_max_unpool_grad to find out more). DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_fn_grad'), DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_fn_gradgrad'), DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_forward_mode_AD'), )), OpInfo('nn.functional.max_unpool1d', variant_test_name='grad', aten_name='max_unpool1d', supports_autograd=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False, assert_jit_shape_analysis=False, dtypes=floating_types(), dtypesIfCUDA=floating_types_and(torch.float16), sample_inputs_func=sample_inputs_max_unpool_grad), OpInfo('nn.functional.max_unpool2d', aten_name='max_unpool2d', supports_autograd=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False, assert_jit_shape_analysis=False, dtypes=floating_types(), dtypesIfCUDA=floating_types_and(torch.float16), sample_inputs_func=sample_inputs_max_unpool, skips=( # Gradients are tested in `variant_test_name=grad` below. # We skip tests here because there is non-determinism in backward # with gather, when there are writes into the same memory location, # and if there are several indices pointing to the same memory, # gradcheck is oblivious about that and cannot perturb them all at once # (see sample_inputs_max_unpool_grad to find out more). DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_forward_mode_AD'), DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_fn_gradgrad'), DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_fn_grad'), )), OpInfo('nn.functional.max_unpool2d', variant_test_name='grad', aten_name='max_unpool2d', supports_autograd=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, # Vmap is not happy with non-contiguous (channels_last) inputs check_batched_grad=False, supports_out=False, assert_jit_shape_analysis=False, dtypes=floating_types(), dtypesIfCUDA=floating_types_and(torch.float16), sample_inputs_func=sample_inputs_max_unpool_grad), OpInfo('nn.functional.max_unpool3d', aten_name='max_unpool3d', supports_autograd=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False, assert_jit_shape_analysis=False, dtypes=floating_types(), dtypesIfCUDA=floating_types_and(torch.float16), sample_inputs_func=sample_inputs_max_unpool, skips=( # Gradients are tested in `variant_test_name=grad` below. # We skip tests here because there is non-determinism in backward # with gather, when there are writes into the same memory location, # and if there are several indices pointing to the same memory, # gradcheck is oblivious about that and cannot perturb them all at once # (see sample_inputs_max_unpool_grad to find out more). DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_forward_mode_AD'), DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_fn_gradgrad'), DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_fn_grad'), )), OpInfo('nn.functional.max_unpool3d', variant_test_name='grad', aten_name='max_unpool3d', supports_autograd=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False, assert_jit_shape_analysis=False, dtypes=floating_types(), dtypesIfCUDA=floating_types_and(torch.float16), sample_inputs_func=sample_inputs_max_unpool_grad), OpInfo('nn.functional.linear', aten_name='linear', supports_autograd=True, sample_inputs_func=sample_inputs_linear, dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfROCM=floating_and_complex_types_and(torch.float16, torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.float16, *[torch.bfloat16] if (CUDA11OrLater or TEST_WITH_ROCM) else []), backward_dtypesIfCUDA=floating_and_complex_types_and(torch.float16, *[torch.bfloat16] if (CUDA11OrLater or TEST_WITH_ROCM) else []), # linear calls mm under the hood which is nondeterministic on CUDA # https://pytorch.org/docs/stable/generated/torch.use_deterministic_algorithms.html#torch.use_deterministic_algorithms gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, supports_forward_ad=True, supports_fwgrad_bwgrad=True, # See https://github.com/pytorch/pytorch/issues/66357 check_batched_forward_grad=False, supports_expanded_weight=True, skips=( # Problem, needs to be fixed DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), ), decorators=( # Strides are not the same! DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out'), DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-02, rtol=1e-02)}), 'TestCudaFuserOpInfo', 'test_nvfuser_correctness'), )), OpInfo('nn.functional.bilinear', aten_name='bilinear', supports_autograd=True, sample_inputs_func=sample_inputs_bilinear, dtypes=all_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, *[torch.bfloat16] if (SM53OrLater and CUDA11OrLater) or TEST_WITH_ROCM else []), skips=( # NVIDIA only assures that bfloat16 is supported by bmm if SM >= 5.3 DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_dtypes', device_type='cuda', active_if=not SM53OrLater), DecorateInfo(unittest.skip("Skipped!"), 'TestNNCOpInfo', 'test_nnc_correctness', dtypes=(torch.bfloat16,)), ), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False), OpInfo('nn.functional.glu', aten_name='glu', supports_autograd=True, sample_inputs_func=sample_inputs_glu, dtypes=floating_types_and(torch.bfloat16), dtypesIfROCM=floating_types_and(torch.float16, torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), backward_dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False), UnaryUfuncInfo( 'nn.functional.elu', aten_backward_name='elu_backward', ref=lambda x, alpha=1.0, inplace=False: np.maximum(0., x) + np.minimum(0., alpha * (np.exp(x) - 1)), dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_autograd=True, assert_autodiffed=False, supports_gradgrad=True, supports_out=False, sample_kwargs=lambda device, dtype, input: ({'alpha': 0.8}, {'alpha': 0.8}), inplace_variant=lambda x, alpha=1.0: torch.nn.functional.elu(x, alpha, inplace=True), decorators=[ # Not implemented yet DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_inplace_forward_mode_AD'), DecorateInfo( toleranceOverride({ torch.float16: tol(atol=1e-03, rtol=1.2e-03), torch.bfloat16: tol(atol=1e-03, rtol=1.2e-03) }), 'TestUnaryUfuncs', device_type='cuda', ), ], ), OpInfo( 'nn.functional.prelu', aten_backward_name='prelu_backward', ref=lambda x, weight: np.maximum(0., x) + np.minimum(0., x) * (weight if x.ndim == 1 else weight.reshape([weight.size if i == 1 else 1 for i in range(0, x.ndim)])), dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16), supports_forward_ad=True, supports_autograd=True, assert_autodiffed=False, supports_gradgrad=True, supports_out=False, sample_inputs_func=sample_inputs_nn_functional_prelu, decorators=[ # FIXME: second derivative is implemented but seems to be incorrect # https://github.com/pytorch/pytorch/issues/68760 DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_fn_gradgrad'), # RuntimeError: Cannot insert a Tensor that requires grad as a constant. # Consider making it a parameter or input, or detaching the gradient # https://github.com/pytorch/pytorch/issues/68752 DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), ], ), UnaryUfuncInfo( 'nn.functional.celu', ref=lambda x, alpha=1.0, inplace=False: np.maximum(0., x) + np.minimum(0., alpha * (np.exp(x / alpha) - 1)), dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_autograd=True, assert_autodiffed=False, supports_gradgrad=True, supports_out=False, sample_kwargs=lambda device, dtype, input: ({'alpha': 0.8}, {'alpha': 0.8}), inplace_variant=lambda x, alpha=1.0: torch.nn.functional.celu(x, alpha, inplace=True), decorators=[ # Not implemented yet DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_inplace_forward_mode_AD'), DecorateInfo( toleranceOverride({ torch.float16: tol(atol=1e-03, rtol=1.2e-03), torch.bfloat16: tol(atol=1e-03, rtol=1.2e-03) }), 'TestUnaryUfuncs', device_type='cuda', ), ], ), UnaryUfuncInfo( 'nn.functional.rrelu', aten_backward_name='rrelu_with_noise_backward', op=lambda input, *args, **kwargs: wrapper_set_seed(torch.nn.functional.rrelu, input, *args, **kwargs), ref=_NOTHING, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), gradcheck_wrapper=wrapper_set_seed, supports_forward_ad=True, supports_autograd=True, assert_autodiffed=False, supports_gradgrad=True, supports_out=False, sample_kwargs=lambda device, dtype, input: ({'lower': 0., 'upper': 1.}, {'lower': 0., 'upper': 1.}), inplace_variant=lambda input, *args, **kwargs: wrapper_set_seed(partial(torch.nn.functional.rrelu, inplace=True), input, *args, **kwargs), decorators=( DecorateInfo( toleranceOverride({ torch.float16: tol(atol=1e-03, rtol=1.2e-03), torch.bfloat16: tol(atol=1e-03, rtol=1.2e-03) }), 'TestUnaryUfuncs', device_type='cuda', ),), skips=( # lambda impl DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # In-place operations do not play well with forward AD # https://github.com/pytorch/pytorch/issues/77447 DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_inplace_forward_mode_AD'), DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'),)), UnaryUfuncInfo( 'nn.functional.selu', ref=lambda x, inplace=False: 1.0507009873554804934193349852946 * ( np.maximum(0., x) + np.minimum(0., 1.6732632423543772848170429916717 * (np.exp(x) - 1)) ), dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), supports_forward_ad=True, # depends on 'elu' supports_fwgrad_bwgrad=True, supports_autograd=True, assert_autodiffed=False, supports_gradgrad=True, supports_out=False, inplace_variant=lambda x: torch.nn.functional.selu(x, inplace=True), decorators=[ # Not implemented yet (depends on 'elu_') DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_inplace_forward_mode_AD'), DecorateInfo( toleranceOverride({ torch.float16: tol(atol=1e-2, rtol=1.8e-2), torch.bfloat16: tol(atol=1e-2, rtol=1.8e-2) }), 'TestUnaryUfuncs', device_type='cuda', ), ], ), UnaryUfuncInfo( 'nn.functional.silu', aten_backward_name='silu_backward', ref=lambda x, inplace=False: x / (1 + np.exp(-x)), dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), supports_forward_ad=True, supports_autograd=True, supports_fwgrad_bwgrad=True, assert_autodiffed=False, supports_out=False, inplace_variant=lambda x: torch.nn.functional.silu(x, inplace=True), decorators=[ DecorateInfo( toleranceOverride({ torch.float16: tol(atol=1e-3, rtol=1e-3), torch.bfloat16: tol(atol=1e-4, rtol=1e-4) }), 'TestUnaryUfuncs', device_type='cuda', ), ], skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_normal', dtypes=(torch.cfloat,), device_type='cpu'), ) ), # TODO: combine this with the nn.functional.silu OpInfo when # complex autodiff for silu is supported or when # the forward bug is fixed # Note: silu errors when given inputs that require grad # but it doesn't support grad in their dtype # This is why the dtypes list above passes test_dtypes, # because it's getting lucky and failing in forward # because test_dtypes sets requires_grad to True # THIS IS A BUG UnaryUfuncInfo( 'nn.functional.silu', variant_test_name='complex', ref=lambda x, inplace=False: x / (1 + np.exp(-x)), dtypes=complex_types(), dtypesIfCUDA=empty_types(), supports_forward_ad=False, supports_autograd=False, assert_autodiffed=False, supports_out=False, inplace_variant=lambda x: torch.nn.functional.silu(x, inplace=True), decorators=[ DecorateInfo( toleranceOverride({ torch.float16: tol(atol=1e-3, rtol=1e-3), torch.bfloat16: tol(atol=1e-4, rtol=1e-4) }), 'TestUnaryUfuncs', device_type='cuda', ), ], skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_normal', dtypes=(torch.cfloat,), device_type='cpu'), # FIXME: intentionally misreports dtypes DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_dtypes'), # FIXME: numpy reference diverges: Comparing (nan+nanj) and (-0+0j) DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', dtypes=(torch.complex64, torch.cdouble)), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_small', dtypes=(torch.complex64,)), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', dtypes=(torch.complex64,)))), UnaryUfuncInfo( 'nn.functional.hardsigmoid', aten_backward_name='hardsigmoid_backward', ref=reference_hardsigmoid, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), supports_autograd=True, assert_autodiffed=False, supports_gradgrad=False, supports_forward_ad=True, supports_out=False, inplace_variant=partial(torch.nn.functional.hardsigmoid, inplace=True), decorators=[ DecorateInfo( toleranceOverride({torch.float16: tol(atol=1e-04, rtol=0.001)}), 'TestUnaryUfuncs', device_type='cuda',), ], skips=[ # still want to test that first derivative works though second derivative isn't supported DecorateInfo(unittest.expectedFailure, 'TestGradients', "test_inplace_gradgrad"), # produces 0 instead of nan on ROCM DecorateInfo(unittest.expectedFailure, 'TestUnaryUfuncs', "test_reference_numerics_extremal", device_type='cuda', active_if=(TEST_WITH_ROCM)), ] ), UnaryUfuncInfo( 'nn.functional.logsigmoid', aten_name="log_sigmoid", aten_backward_name='log_sigmoid_backward', ref=reference_logsigmoid, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16), supports_autograd=True, assert_autodiffed=False, supports_forward_ad=True, supports_gradgrad=True, # autodiff_nonfusible_nodes=["aten::log_sigmoid"], decorators=[ DecorateInfo( precisionOverride({torch.float16: 1e-2, torch.bfloat16: 5e-3}), 'TestUnaryUfuncs', 'test_reference_numerics_small'), DecorateInfo( precisionOverride({torch.float16: 1e-2, torch.bfloat16: 5e-3}), 'TestUnaryUfuncs', 'test_reference_numerics_large'), DecorateInfo( precisionOverride({torch.float16: 1e-2, torch.bfloat16: 5e-3}), 'TestUnaryUfuncs', 'test_reference_numerics_extremal'), ], skips=( # Resized a non-empty tensor but did not warn about it. DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out_warning', device_type='cpu'), ), ), UnaryUfuncInfo( 'nn.functional.mish', aten_backward_name='mish_backward', ref=lambda x: x * np.tanh(reference_softplus(x)), dtypes=floating_types(), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_autograd=True, assert_autodiffed=False, supports_gradgrad=True, supports_out=False, inplace_variant=partial(torch.nn.functional.mish, inplace=True), decorators=[ DecorateInfo( toleranceOverride({torch.float16: tol(atol=1e-02, rtol=1e-03)}), 'TestUnaryUfuncs', device_type='cuda',), ], ), UnaryUfuncInfo( 'nn.functional.softsign', ref=lambda x: x / (np.abs(x) + 1), dtypes=all_types_and_complex_and(torch.float16, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.float16, torch.bfloat16, torch.bool), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_autograd=True, assert_autodiffed=False, supports_gradgrad=True, supports_out=False, decorators=[ DecorateInfo( toleranceOverride({torch.float16: tol(atol=1e-03, rtol=1.3e-04)}), 'TestUnaryUfuncs',), ], skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_small', dtypes=(torch.int, torch.int8)), DecorateInfo(unittest.expectedFailure, 'TestGradients', "test_fn_fwgrad_bwgrad", dtypes=(torch.complex128,)), # pytorch computes (0+nanj), numpy computes (-5e-18-1j) for input (-501.-1.0000e+20j) DecorateInfo(unittest.expectedFailure, 'TestUnaryUfuncs', "test_reference_numerics_large", dtypes=(torch.complex64,)),), ), UnaryUfuncInfo( 'nn.functional.tanhshrink', ref=lambda x: x - np.tanh(x), dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.float16, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_autograd=True, assert_autodiffed=False, supports_gradgrad=True, supports_out=False, decorators=[ DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_normal', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble]), DecorateInfo( toleranceOverride({torch.bfloat16: tol(atol=1e-02, rtol=1.6e-02)}), 'TestUnaryUfuncs',), DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-02, rtol=1e-02)}), 'TestCudaFuserOpInfo', 'test_nvfuser_correctness'), ], skips=( # in each case, pytorch will produce a nan while numpy will not DecorateInfo(unittest.expectedFailure, 'TestUnaryUfuncs', "test_reference_numerics_small", dtypes=(torch.complex64, torch.complex128), active_if=(IS_MACOS)), DecorateInfo(unittest.skip("Fails on some jobs works on others!"), 'TestUnaryUfuncs', "test_reference_numerics_large", dtypes=(torch.complex64, torch.complex128), active_if=(IS_MACOS)), DecorateInfo(unittest.skip("Fails on some jobs works on others!"), 'TestUnaryUfuncs', "test_reference_numerics_extremal", dtypes=(torch.complex64, torch.complex128), device_type='cpu', active_if=(IS_MACOS or IS_WINDOWS)), ), ), OpInfo( 'nn.functional.threshold', aten_backward_name='threshold_backward', ref=lambda x, threshold, value: np.where(x > threshold, x, value).astype(x.dtype), dtypes=all_types_and(torch.bfloat16), dtypesIfCUDA=all_types_and(torch.float16, torch.bfloat16), supports_autograd=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, assert_autodiffed=False, supports_gradgrad=True, supports_out=False, sample_inputs_func=sample_inputs_threshold, ), OpInfo( "nn.functional.triplet_margin_loss", sample_inputs_func=sample_inputs_triplet_margin_loss, dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, ), OpInfo( "nn.functional.triplet_margin_with_distance_loss", sample_inputs_func=partial(sample_inputs_triplet_margin_loss, with_distance=True), dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # This test cannot handle a callable passed to `distance_function`. If we would use # `distance_function=None`, the test would pass fine. DecorateInfo( unittest.expectedFailure, "TestJit", "test_variant_consistency_jit", ), DecorateInfo( unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive", ), ), ), BinaryUfuncInfo('nextafter', dtypes=floating_types_and(torch.bfloat16), supports_autograd=False, supports_rhs_python_scalar=False), OpInfo('topk', dtypes=all_types_and(torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bfloat16, torch.float16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, assert_jit_shape_analysis=True, sample_inputs_func=sample_inputs_topk), # Multiple variants for batch_norm to test with and without cuDNN disabled # See https://github.com/pytorch/pytorch/pull/63218#discussion_r688549391 for more details OpInfo('nn.functional.batch_norm', aten_name='batch_norm', dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, assert_jit_shape_analysis=True, sample_inputs_func=sample_inputs_batch_norm, skips=( # see https://github.com/pytorch/pytorch/issues/71286 DecorateInfo(unittest.expectedFailure, 'TestNNCOpInfo', 'test_nnc_correctness'), DecorateInfo(unittest.skip('Skipped!'), 'TestNNCOpInfo', 'test_nnc_correctness', device_type='cpu', dtypes=(torch.bfloat16,)), # see https://github.com/pytorch/pytorch/issues/76283 DecorateInfo(unittest.skip("Fails on UBSAN!"), 'TestCompositeCompliance', 'test_forward_ad', device_type="cpu"), # Trying to use forward AD with miopen_batch_norm that does not support it # because it has not been implemented yet. DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad', device_type="cuda", active_if=TEST_WITH_ROCM), DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-02, rtol=1e-02)}), 'TestCudaFuserOpInfo', 'test_nvfuser_correctness'), )), # This variant tests batch_norm with cuDNN disabled only on CUDA devices OpInfo('nn.functional.batch_norm', variant_test_name='without_cudnn', aten_name='batch_norm', dtypes=empty_types(), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, decorators=[onlyCUDA, disablecuDNN], skips=( DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad', device_type='cpu'), DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-02, rtol=1e-02)}), 'TestCudaFuserOpInfo', 'test_nvfuser_correctness'), ), sample_inputs_func=sample_inputs_batch_norm), OpInfo( "nn.functional.binary_cross_entropy", aten_backward_name='binary_cross_entropy_backward', sample_inputs_func=sample_inputs_binary_cross_entropy, dtypes=floating_types(), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), supports_out=False, gradcheck_fast_mode=False, supports_autograd=True, supports_forward_ad=True, supports_fwgrad_bwgrad=False, decorators=( # RuntimeError: expected int at position 0, but got: Tensor DecorateInfo( unittest.skip("Skipped!"), "TestCudaFuserOpInfo", ), # RuntimeError: expected int at position 0, but got: Tensor DecorateInfo( unittest.skip("Skipped!"), "TestNNCOpInfo", "test_nnc_correctness", ), DecorateInfo( toleranceOverride({torch.float32: tol(atol=1e-3, rtol=1e-3)}), "TestJit", "test_variant_consistency_jit", ), ), skips=( # RuntimeError: expected int at position 0, but got: Tensor DecorateInfo( unittest.expectedFailure, "TestJit", "test_variant_consistency_jit", ), ), ), # We have to add 2 OpInfo entry for `igamma` and `igammac`.First is the # standard entry, second is to run gradcheck tests on the second argument. BinaryUfuncInfo('igamma', dtypes=floating_types_and(torch.bfloat16, torch.float16), aliases=('torch.special.gammainc',), dtypesIfCUDA=floating_types(), # TODO: FIXME supports_rhs_python_scalar=False, supports_autograd=False, skips=( # FIXME: incorrectly tries to pass a rhs scalar DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_jit_alias_remapping'), )), # TODO: FIXME, ideally by implemented grad for both inputs # BinaryUfuncInfo('igamma', # variant_test_name='grad_other', # # Since autograd formula is implemented only for other and # # gradcheck test verifies the formula for input in SampleInput, # # we permute the arguments. # op=lambda self, other, **kwargs: torch.igamma(other, self, **kwargs), # inplace_variant=None, # method_variant=None, # supports_rhs_python_scalar=False, # rhs_make_tensor_kwargs=dict(requires_grad=False), # dtypes=floating_types_and(torch.bfloat16, torch.float16), # backward_dtypesIfCPU=floating_types_and(torch.bfloat16), # dtypesIfCUDA=floating_types(), # backward_dtypesIfCUDA=floating_types(), # supports_inplace_autograd=False, # skips=( # # Derivative wrt first tensor not implemented # DecorateInfo(unittest.expectedFailure, "TestCommon", # "test_floating_inputs_are_differentiable"),"), # # test does not work with passing lambda for op # # AssertionError: False is not true : Tensors failed to compare as equal! # DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'), # # test fails are we permute the arguments function variant # # but not for inplace or method. # DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_variant_consistency_eager'), # # TypeError: igamma(): argument 'input' (position 1) must be Tensor, not float # DecorateInfo(unittest.skip('Skipped!'), 'TestBinaryUfuncs'), # )), BinaryUfuncInfo('igammac', dtypes=floating_types_and(torch.bfloat16, torch.float16), aliases=('torch.special.gammaincc',), dtypesIfCUDA=floating_types(), supports_autograd=False, supports_rhs_python_scalar=False, skips=( # FIXME: incorrectly tries to pass a rhs scalar DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_jit_alias_remapping'), )), # TODO: FIXME, ideally by implementing grad for both inputs # BinaryUfuncInfo('igammac', # variant_test_name='grad_other', # # Since autograd formula is implemented only for other and # # gradcheck test verifies the formula for input in SampleInput, # # we permute the arguments # op=lambda self, other, **kwargs: torch.igammac(other, self, **kwargs), # inplace_variant=None, # method_variant=None, # supports_rhs_python_scalar=False, # rhs_make_tensor_kwargs=dict(requires_grad=False), # dtypes=floating_types_and(torch.bfloat16, torch.float16), # backward_dtypesIfCPU=floating_types_and(torch.bfloat16), # dtypesIfCUDA=floating_types(), # backward_dtypesIfCUDA=floating_types(), # supports_inplace_autograd=False, # decorators=[ # # Derivative wrt first tensor not implemented # DecorateInfo(unittest.expectedFailure, "TestCommon", # "test_floating_inputs_are_differentiable"), # ], # skips=( # # test does not work with passing lambda for op # # AssertionError: False is not true : Tensors failed to compare as equal! # DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'), # # test fails are we permute the arguments function variant # # but not for inplace or method. # DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_variant_consistency_eager'), # # TypeError: igammac(): argument 'input' (position 1) must be Tensor, not float # DecorateInfo(unittest.skip('Skipped!'), 'TestBinaryUfuncs'), # )), OpInfo('nn.functional.softshrink', aten_name="softshrink", aten_backward_name='softshrink_backward', dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), supports_autograd=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, assert_autodiffed=False, sample_inputs_func=sample_inputs_softshrink_hardshrink_hardtanh, supports_gradgrad=True, ), OpInfo('nn.functional.hardshrink', aten_name="hardshrink", aten_backward_name='hardshrink_backward', dtypes=floating_types_and(torch.bfloat16,), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), supports_autograd=True, assert_autodiffed=True, sample_inputs_func=sample_inputs_softshrink_hardshrink_hardtanh, supports_gradgrad=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, autodiff_nonfusible_nodes=["aten::hardshrink"]), OpInfo('nn.functional.hardtanh', aten_name="hardtanh", aten_backward_name='hardtanh_backward', dtypes=floating_types_and(torch.int8, torch.int16, torch.int32, torch.int64, torch.bfloat16), backward_dtypes=all_types(), dtypesIfCUDA=floating_types_and(torch.int8, torch.int16, torch.int32, torch.int64, torch.float16, torch.bfloat16), backward_dtypesIfCUDA=floating_types_and(torch.float16), supports_autograd=True, assert_autodiffed=True, sample_inputs_func=sample_inputs_softshrink_hardshrink_hardtanh, supports_gradgrad=True, supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, autodiff_nonfusible_nodes=["aten::hardtanh"], ), OpInfo('nn.functional.gelu', aten_name="gelu", aten_backward_name='gelu_backward', ref=reference_gelu if TEST_SCIPY else _NOTHING, supports_autograd=True, assert_autodiffed=True, sample_inputs_func=sample_inputs_gelu, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), supports_gradgrad=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, autodiff_nonfusible_nodes=["aten::gelu"], skips=( # AssertionError: Tensor-likes are not close! # May not replicate in CI DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_out'),)), OpInfo('nn.functional.relu6', aten_name="relu6", dtypes=all_types_and(torch.bfloat16), backward_dtypes=floating_types(), dtypesIfCUDA=all_types_and(torch.float16, torch.bfloat16), backward_dtypesIfCUDA=floating_types_and(torch.float16), supports_autograd=True, assert_autodiffed=True, sample_inputs_func=sample_inputs_softshrink_hardshrink_hardtanh, supports_gradgrad=True, supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, autodiff_nonfusible_nodes=["aten::relu6"]), OpInfo('mm', dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.float16, *[torch.bfloat16] if (CUDA11OrLater or TEST_WITH_ROCM) else []), assert_autodiffed=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_mm), OpInfo('mode', op=torch.mode, dtypes=all_types_and(torch.float16, torch.bfloat16, torch.bool), supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # Resized a non-empty tensor but did not warn about it DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out_warning'), ), sample_inputs_func=sample_inputs_mode,), MvlGammaInfo(variant_test_name='mvlgamma_p_1', domain=(1, None), skips=skips_mvlgamma() + \ (DecorateInfo(unittest.expectedFailure, 'TestUnaryUfuncs', 'test_reference_numerics_extremal'), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', dtypes=(torch.float16, torch.int8)), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_small', dtypes=(torch.int8,)),), sample_kwargs=lambda device, dtype, input: ({'p': 1}, {'d': 1})), MvlGammaInfo(variant_test_name='mvlgamma_p_3', domain=(2, None), skips=skips_mvlgamma(skip_redundant=True) + ( DecorateInfo(unittest.expectedFailure, 'TestUnaryUfuncs', 'test_reference_numerics_extremal'), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', dtypes=(torch.float16, torch.int8)), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_small', dtypes=(torch.int8,)), ), sample_kwargs=lambda device, dtype, input: ({'p': 3}, {'d': 3})), MvlGammaInfo(variant_test_name='mvlgamma_p_5', domain=(3, None), skips=skips_mvlgamma(skip_redundant=True) + ( DecorateInfo(unittest.expectedFailure, 'TestUnaryUfuncs', 'test_reference_numerics_extremal'), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', dtypes=(torch.float16, torch.int8)), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_small', dtypes=(torch.int8,)), ), sample_kwargs=lambda device, dtype, input: ({'p': 5}, {'d': 5})), BinaryUfuncInfo('ne', aliases=('not_equal',), dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16), always_returns_bool=True, supports_autograd=False, skips=( # https://github.com/pytorch/pytorch/issues/76805 DecorateInfo(unittest.expectedFailure, 'TestBinaryUfuncs', 'test_type_promotion'), )), OpInfo('narrow', dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16, torch.chalf), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_narrow), UnaryUfuncInfo('neg', aliases=('negative', ), ref=np.negative, dtypes=all_types_and_complex_and(torch.half, torch.bfloat16, torch.chalf), error_inputs_func=error_inputs_neg, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_sparse=True, supports_sparse_csr=True, assert_autodiffed=True, skips=( # RuntimeError: "nonzero_count_cpu" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, 'TestSparseCSR', 'test_sparse_csr_consistency', dtypes=(torch.chalf,),), # RuntimeError: "nonzero_count_cpu" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, 'TestSparseCSR', 'test_sparse_csr_unary_inplace', dtypes=(torch.chalf,),), # RuntimeError: "nonzero_count_cpu" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, 'TestSparseCSR', 'test_sparse_csr_unary_out', dtypes=(torch.chalf,),), # RuntimeError: "add_out_op2_sparse_csr" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, 'TestSparseCSR', 'test_zero_to_zero_correspondence_unary', dtypes=(torch.chalf,),) )), OpInfo('dist', op=torch.dist, dtypes=floating_and_complex_types_and(torch.half, torch.bfloat16), supports_out=False, supports_forward_ad=True, # torch.autograd.gradcheck.GradcheckError: While computing batched gradients, got: # Could not allocate memory to change Tensor SizesAndStrides! check_batched_forward_grad=False, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_dist), OpInfo('outer', op=torch.outer, aliases=('ger', ), dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_outer,), OpInfo('ormqr', op=torch.ormqr, dtypes=floating_and_complex_types(), supports_autograd=False, sample_inputs_func=sample_inputs_ormqr, error_inputs_func=error_inputs_ormqr, decorators=[skipCUDAIfNoCusolver, skipCPUIfNoLapack], skips=( # ormqr does not support forward when complex inputs require grad DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_dtypes'), # Strides are not the same! DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out'), )), OpInfo('permute', ref=np.transpose, dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16, torch.chalf), supports_out=False, assert_autodiffed=True, autodiff_fusible_nodes=[], # aliases inputs, shouldn't be fused autodiff_nonfusible_nodes=[], # aliases inputs, shouldn't be fused assert_jit_shape_analysis=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_permute, reference_inputs_func=reference_inputs_permute), BinaryUfuncInfo('pow', dtypes=all_types_and_complex_and(torch.half, torch.bfloat16), ref=np.power, # Due to AVX2 curently not being fully supported for Float16, log_vml_cpu can't be enabled # for Float16, causing this test to fail. pow's autograd for Float16 is thus currently # unsupported on CPU. backward_dtypes=floating_and_complex_types_and(torch.bfloat16), backward_dtypesIfCUDA=floating_and_complex_types_and(torch.bfloat16, torch.half), supports_inplace_autograd=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, assert_autodiffed=True, supports_one_python_scalar=True, # Integer types do not support negative exponentes rhs_make_tensor_kwargs=dict(low=0), # Raising negative real numbers to fractional powers is not supported lhs_make_tensor_kwargs=dict(low=0), decorators=( DecorateInfo(toleranceOverride({torch.complex64: tol(atol=1e-4, rtol=1.3e-05)}), 'TestBinaryUfuncs', 'test_reference_numerics'), DecorateInfo(toleranceOverride({torch.complex64: tol(atol=1e-4, rtol=1.3e-05), torch.complex128: tol(atol=1e-4, rtol=1.3e-05)}), 'TestBinaryUfuncs', 'test_scalar_support'), ), skips=( # Skipping integers because they are being raised to negative powers causing an error DecorateInfo(unittest.expectedFailure, 'TestBinaryUfuncs', 'test_reference_numerics_small_values', dtypes=[torch.int8, torch.int16, torch.int32, torch.int64]), DecorateInfo(unittest.expectedFailure, 'TestBinaryUfuncs', 'test_reference_numerics_large_values', dtypes=[torch.int16, torch.int32, torch.int64]), # FIXME Complex values error with: Greatest absolute difference: nan at index DecorateInfo(unittest.skip("Skipped!"), 'TestBinaryUfuncs', 'test_reference_numerics_small_values', dtypes=[torch.complex64, torch.complex128]), DecorateInfo(unittest.skip("Skipped!"), 'TestBinaryUfuncs', 'test_reference_numerics_large_values', dtypes=[torch.complex64, torch.complex128]), DecorateInfo(unittest.skip("Skipped!"), 'TestBinaryUfuncs', 'test_reference_numerics_extremal_values', dtypes=[torch.complex64, torch.complex128]), )), BinaryUfuncInfo('float_power', ref=np.float_power, dtypes=all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool), promotes_int_to_float=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_one_python_scalar=True, # Integer types do not support negative exponentes rhs_make_tensor_kwargs=dict(low=0), # Raising negative real numbers to fractional powers is not supported lhs_make_tensor_kwargs=dict(low=0), decorators=( DecorateInfo(toleranceOverride({torch.complex64: tol(atol=1e-4, rtol=1.3e-05), torch.complex128: tol(atol=1e-4, rtol=1.3e-05)}), 'TestBinaryUfuncs', 'test_scalar_support'), ), skips=( # FIXME # AssertionError: Object comparison failed: torch.float64 != torch.float32 DecorateInfo(unittest.skip("Skipped!"), 'TestBinaryUfuncs', 'test_type_promotion'), # -3.43399e+38 is outside the range of representable values of type 'float' DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'), # Complex values error with: Greatest absolute difference: nan at index DecorateInfo(unittest.skip("Skipped!"), 'TestBinaryUfuncs', 'test_reference_numerics_small_values', dtypes=[torch.complex64, torch.complex128]), DecorateInfo(unittest.skip("Skipped!"), 'TestBinaryUfuncs', 'test_reference_numerics_large_values', dtypes=[torch.complex64, torch.complex128]), DecorateInfo(unittest.skip("Skipped!"), 'TestBinaryUfuncs', 'test_reference_numerics_extremal_values', dtypes=[torch.complex64, torch.complex128]), )), OpInfo('qr', op=torch.qr, dtypes=floating_and_complex_types(), sample_inputs_func=sample_inputs_linalg_qr_geqrf, supports_forward_ad=True, supports_fwgrad_bwgrad=True, # In-place ops check_batched_gradgrad=False, skips=( # The test is wrong # https://github.com/pytorch/pytorch/pull/76115#discussion_r854328384 DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_backward'),), decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack]), UnaryUfuncInfo('rad2deg', ref=np.degrees, decorators=(precisionOverride({torch.bfloat16: 7e-1, torch.float16: 7e-1}),), dtypes=all_types_and(torch.bool, torch.half, torch.bfloat16), skips=( # Reference: https://github.com/pytorch/pytorch/pull/51283#issuecomment-770614273 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_small', dtypes=[torch.bfloat16]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', dtypes=[torch.bfloat16]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', dtypes=[torch.bfloat16]), ), supports_forward_ad=True, supports_fwgrad_bwgrad=True), UnaryUfuncInfo('real', ref=np.real, dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.half, torch.chalf), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, # See https://github.com/pytorch/pytorch/issues/66357 check_batched_forward_grad=False, skips=( # Skip since real and imag don't have out variants. DecorateInfo(unittest.expectedFailure, 'TestUnaryUfuncs', 'test_out_arg_all_dtypes'), )), OpInfo('roll', ref=np.roll, dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.half), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_roll), OpInfo('rot90', dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.half), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_rot90), # To test reference numerics against multiple values of argument `decimals`, # we make multiple OpInfo entries with each entry corresponding to different value of decimals. UnaryUfuncInfo('round', ref=np.round, aliases=('special.round',), dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_sparse=True, supports_sparse_csr=True, assert_autodiffed=True,), UnaryUfuncInfo('round', ref=np.round, variant_test_name='decimals_0', aliases=('special.round',), dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), sample_kwargs=lambda device, dtype, input: ({'decimals': 0}, {'decimals': 0}), sample_inputs_func=partial(sample_inputs_elementwise_unary, op_kwargs={'decimals': 0}), supports_forward_ad=True, supports_fwgrad_bwgrad=True, assert_autodiffed=False, supports_sparse_csr=False), UnaryUfuncInfo('round', ref=np.round, variant_test_name='decimals_3', aliases=('special.round',), dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), sample_kwargs=lambda device, dtype, input: ({'decimals': 3}, {'decimals': 3}), sample_inputs_func=partial(sample_inputs_elementwise_unary, op_kwargs={'decimals': 3}), skips=( # test_ops already tested for this overload with `decimals_0` opinfo entry DecorateInfo(unittest.skip("Skipped!"), 'TestCommon'), DecorateInfo(unittest.skip("Skipped!"), 'TestGradients'), DecorateInfo(unittest.skip("Skipped!"), 'TestJit'), DecorateInfo(unittest.skip("Skipped!"), 'TestMathBits'), ), supports_forward_ad=True, supports_fwgrad_bwgrad=True, assert_autodiffed=False, supports_sparse_csr=False), UnaryUfuncInfo('round', ref=np.round, variant_test_name='decimals_neg_3', aliases=('special.round',), dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), sample_kwargs=lambda device, dtype, input: ({'decimals': -3}, {'decimals': -3}), sample_inputs_func=partial(sample_inputs_elementwise_unary, op_kwargs={'decimals': -3}), skips=( # test_ops already tested for this overload with `decimals_0` opinfo entry DecorateInfo(unittest.skip("Skipped!"), 'TestCommon'), DecorateInfo(unittest.skip("Skipped!"), 'TestGradients'), DecorateInfo(unittest.skip("Skipped!"), 'TestJit'), DecorateInfo(unittest.skip("Skipped!"), 'TestMathBits'), ), supports_forward_ad=True, supports_fwgrad_bwgrad=True, assert_autodiffed=False, supports_sparse_csr=False), UnaryUfuncInfo('sin', ref=np.sin, dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), assert_autodiffed=True, handles_large_floats=False, supports_sparse=True, supports_sparse_csr=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # Fails on CUDA but passes on ROCm DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', dtypes=(torch.cdouble,), device_type='cuda'), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', dtypes=(torch.cfloat, torch.cdouble,), device_type='cpu', active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', dtypes=(torch.cfloat, torch.cdouble,), device_type='cpu', active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped! sparse backward not supported"), 'TestSparseUnaryUfuncs', 'test_sparse_fn_grad'), ), decorators=(precisionOverride({torch.bfloat16: 1e-2}),)), UnaryUfuncInfo('sinc', ref=np_sinc_with_fp16_as_fp32, aliases=('special.sinc',), dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), handles_large_floats=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, decorators=(precisionOverride({torch.bfloat16: 1e-2, torch.float16: 1e-2}),), skips=( # Reference: https://github.com/pytorch/pytorch/issues/49133 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_small', dtypes=[torch.cfloat]), )), UnaryUfuncInfo('sinh', ref=np_unary_ufunc_integer_promotion_wrapper(np.sinh), dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), assert_autodiffed=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_sparse=True, supports_sparse_csr=True, decorators=(precisionOverride({torch.float16: 1e-2}),), skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble], active_if=(IS_MACOS or IS_WINDOWS)), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble], active_if=(IS_MACOS or IS_WINDOWS)), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', dtypes=(torch.cdouble,)), # Reference: https://github.com/pytorch/pytorch/issues/48641 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', device_type='cpu', dtypes=[torch.int8]), DecorateInfo(unittest.skip("Skipped! sparse backward not supported"), 'TestSparseUnaryUfuncs', 'test_sparse_fn_grad'), )), UnaryUfuncInfo('sign', ref=reference_sign, dtypes=all_types_and(torch.bool, torch.bfloat16, torch.half), dtypesIfCUDA=all_types_and(torch.bool, torch.bfloat16, torch.half), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_sparse=True, supports_sparse_csr=True, skips=( # Reference: https://github.com/pytorch/pytorch/issues/41245 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', dtypes=[torch.bfloat16, torch.float16, torch.float32, torch.float64]), )), UnaryUfuncInfo('sgn', ref=reference_sgn, dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.half, torch.chalf), backward_dtypes=floating_and_complex_types_and(torch.half, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_sparse=True, supports_sparse_csr=True, skips=( # Reference: https://github.com/pytorch/pytorch/issues/41245 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', dtypes=[torch.bfloat16, torch.float16, torch.float32, torch.float64]), # Reference: https://github.com/pytorch/pytorch/issues/53958 # Test fails in comparison on Nan as the `equal_nan` is True for # comparing the CPU tensors. DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', device_type='cpu', dtypes=[torch.complex64, torch.complex128]), # Reference: https://github.com/pytorch/pytorch/issues/48486 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', device_type='cpu', dtypes=[torch.complex64]), # The complex formula might be wrong DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_forward_mode_AD', dtypes=complex_types()), # Passes for float, but for complex - Need: _s_where DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_fn_fwgrad_bwgrad', dtypes=complex_types()), DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_inplace_forward_mode_AD', dtypes=complex_types()), DecorateInfo(unittest.skip("Skipped! sparse backward not supported"), 'TestSparseUnaryUfuncs', 'test_sparse_fn_grad'), # nonzero_count not implemented DecorateInfo(unittest.expectedFailure, 'TestSparseCSR', 'test_sparse_csr_consistency', dtypes=(torch.chalf,)), # nonzero_count not implemented DecorateInfo(unittest.expectedFailure, 'TestSparseCSR', 'test_sparse_csr_unary_inplace', dtypes=(torch.chalf,)), # nonzero_count not implemented DecorateInfo(unittest.expectedFailure, 'TestSparseCSR', 'test_sparse_csr_unary_out', dtypes=(torch.chalf,)), # add_out_op2_sparse_csr DecorateInfo(unittest.expectedFailure, 'TestSparseCSR', 'test_zero_to_zero_correspondence_unary', dtypes=(torch.chalf,)), )), OpInfo('split', dtypes=all_types_and_complex_and(torch.bfloat16, torch.half, torch.bool, torch.chalf), sample_inputs_func=partial(sample_inputs_split, list_args=False), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False, autodiff_fusible_nodes=[], # aliases inputs, shouldn't be fused autodiff_nonfusible_nodes=[], # aliases inputs, shouldn't be fused assert_autodiffed=True), OpInfo('split', # Cannot declare this aten_name because of # test_variant_consistency_jit_split_list_args_cpu_float32 decomp_aten_name='split_with_sizes', variant_test_name='list_args', dtypes=all_types_and_complex_and(torch.bfloat16, torch.half, torch.bool), sample_inputs_func=partial(sample_inputs_split, list_args=True), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False), OpInfo('split_with_sizes', dtypes=all_types_and_complex_and(torch.bfloat16, torch.half, torch.bool, torch.chalf), sample_inputs_func=sample_inputs_split_with_sizes, autodiff_fusible_nodes=[], # aliases inputs, shouldn't be fused autodiff_nonfusible_nodes=[], # aliases inputs, shouldn't be fused supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, assert_autodiffed=True), BinaryUfuncInfo('__radd__', op=torch.Tensor.__radd__, dtypes=all_types_and_complex_and(torch.bfloat16, torch.half, torch.bool), supports_out=False, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit',), ), assert_autodiffed=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, autodiff_nonfusible_nodes=['aten::add'],), BinaryUfuncInfo('__rdiv__', op=torch.Tensor.__rdiv__, dtypes=all_types_and_complex_and(torch.bfloat16, torch.half, torch.bool), promotes_int_to_float=True, lhs_make_tensor_kwargs={'exclude_zero': True}, supports_out=False, skips=( # https://github.com/pytorch/pytorch/issues/76806 DecorateInfo(unittest.expectedFailure, 'TestBinaryUfuncs', 'test_type_promotion'), DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit',), ), supports_forward_ad=True, supports_fwgrad_bwgrad=True, assert_autodiffed=True, autodiff_nonfusible_nodes=['aten::mul', 'aten::reciprocal'],), BinaryUfuncInfo('__rmul__', op=torch.Tensor.__rmul__, dtypes=all_types_and_complex_and(torch.bfloat16, torch.half, torch.bool), supports_out=False, skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit',), ), assert_autodiffed=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, autodiff_nonfusible_nodes=['aten::mul'],), BinaryUfuncInfo('__rand__', op=torch.Tensor.__rand__, dtypes=integral_types_and(torch.bool), supports_out=False, supports_autograd=False, supports_forward_ad=True, skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), )), BinaryUfuncInfo('__ror__', op=torch.Tensor.__ror__, dtypes=integral_types_and(torch.bool), supports_out=False, supports_autograd=False, supports_forward_ad=True, skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), )), BinaryUfuncInfo('__rxor__', op=torch.Tensor.__rxor__, dtypes=integral_types_and(torch.bool), supports_out=False, supports_autograd=False, supports_forward_ad=True, skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), )), OpInfo('__rmatmul__', op=torch.Tensor.__rmatmul__, dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.float16, *[torch.bfloat16] if (SM53OrLater and CUDA11OrLater) or TEST_WITH_ROCM else []), assert_autodiffed=True, sample_inputs_func=sample_inputs_matmul, supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, check_batched_forward_grad=False, decorators=( # NVIDIA only assures that bfloat16 is supported by bmm if SM >= 5.3 DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_dtypes', device_type='cuda', active_if=not SM53OrLater), DecorateInfo(toleranceOverride({torch.complex64: tol(atol=1e-05, rtol=1.2e-03)}), 'TestMathBits', 'test_conj_view'), DecorateInfo(toleranceOverride({torch.float32: tol(atol=1e-05, rtol=1.2e-03)}), 'TestCommon', 'test_noncontiguous_samples'), ), skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit',), # https://github.com/pytorch/pytorch/issues/67470 DecorateInfo(unittest.skip("67470!"), 'TestCommon', 'test_noncontiguous_samples', device_type='cpu', dtypes=(torch.long,)), # Fails on XLA. # AssertionError: False is not true : Tensors failed to compare as equal DecorateInfo(unittest.skip("Skipped!"), 'TestOpInfo', device_type='xla', dtypes=(torch.long,)), # https://github.com/pytorch/pytorch/issues/71774 DecorateInfo(unittest.skip('Skipped!'), 'TestNNCOpInfo', 'test_nnc_correctness', device_type='cpu', dtypes=(torch.long,)), )), BinaryUfuncInfo('__rmod__', op=torch.Tensor.__rmod__, dtypes=floating_types_and(torch.bfloat16, torch.half,), dtypesIfCUDA=all_types_and(torch.bfloat16, torch.half), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_two_python_scalars=True, skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit',), ), # Support autograd after torch.remainder(Tensor, Tensor) supports # autograd of the second argument. # https://github.com/pytorch/pytorch/pull/58476/files#r637167630 # supports_autograd=False, assert_autodiffed=True, autodiff_nonfusible_nodes=['aten::remainder'],), BinaryUfuncInfo('__rpow__', op=torch.Tensor.__rpow__, dtypes=all_types_and_complex_and(torch.bfloat16, torch.half), # Reference: https://github.com/pytorch/pytorch/issues/54774 # "log2" "_vml_cpu" not implemented for Half backward_dtypes=all_types_and_complex_and(torch.bfloat16), backward_dtypesIfCUDA=all_types_and_complex_and(torch.bfloat16, torch.half), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit',), # TODO: FIXME tolerance is too high DecorateInfo(unittest.skip('Skipped!'), 'TestGradients'), ), assert_autodiffed=True, autodiff_nonfusible_nodes=['aten::pow'],), BinaryUfuncInfo('__rsub__', op=torch.Tensor.__rsub__, dtypes=all_types_and_complex_and(torch.bfloat16, torch.half), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False, supports_two_python_scalars=True, skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit',), ), assert_autodiffed=True, autodiff_nonfusible_nodes=['aten::rsub'],), BinaryUfuncInfo('rsub', dtypes=all_types_and_complex_and(torch.bfloat16, torch.half), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False, supports_inplace_autograd=False, assert_autodiffed=None, sample_inputs_func=sample_inputs_add_sub), OpInfo('select', aten_backward_name='select_backward', dtypes=all_types_and_complex_and(torch.bfloat16, torch.half, torch.bool, torch.chalf), sample_inputs_func=sample_inputs_select, assert_jit_shape_analysis=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False), OpInfo('select_scatter', dtypes=all_types_and(torch.bfloat16, torch.half, torch.bool), sample_inputs_func=sample_inputs_select_scatter, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False), OpInfo('slice_scatter', dtypes=all_types_and(torch.bfloat16, torch.half, torch.bool), sample_inputs_func=sample_inputs_slice_scatter, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False), UnaryUfuncInfo('signbit', ref=np.signbit, dtypes=all_types_and(torch.bool, torch.bfloat16, torch.half), supports_sparse=True, supports_sparse_csr=True, supports_autograd=False,), UnaryUfuncInfo('tan', ref=np.tan, dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), assert_autodiffed=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_sparse=True, supports_sparse_csr=True, skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', device_type='cpu', dtypes=[torch.bfloat16]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', device_type='cpu', dtypes=[torch.bfloat16]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_small', device_type='cpu', dtypes=[torch.bfloat16]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble], active_if=(IS_MACOS or IS_WINDOWS)), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble], active_if=(IS_MACOS or IS_WINDOWS)), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', device_type='cuda', dtypes=[torch.float64], active_if=TEST_WITH_ROCM), DecorateInfo(unittest.skip("Skipped! sparse backward not supported"), 'TestSparseUnaryUfuncs', 'test_sparse_fn_grad'), ), # tan(pi/2 * odd_number) is nan reference_numerics_filter=NumericsFilter( condition=lambda x: close_to_int(x / (math.pi * 0.5)), safe_val=math.pi)), UnaryUfuncInfo('tanh', ref=np.tanh, aten_backward_name='tanh_backward', aliases=('nn.functional.tanh',), decorators=(precisionOverride({torch.bfloat16: 1e-2}),), dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), assert_autodiffed=True, assert_jit_shape_analysis=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_sparse=True, supports_sparse_csr=True, skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble], active_if=(IS_MACOS or IS_WINDOWS)), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble], active_if=(IS_MACOS or IS_WINDOWS)), # alias, nn.functional.tanh, will produce (because of warning string saved): # "RuntimeError: Expected to not find "tanh" but found it" DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_jit_alias_remapping'), DecorateInfo(unittest.skip("Skipped! sparse backward not supported"), 'TestSparseUnaryUfuncs', 'test_sparse_fn_grad'), ), # tan(j * pi/2 * odd_number) is nan reference_numerics_filter=NumericsFilter( condition=lambda x: (close_to_int(x / (math.pi * 0.5j)) if x.is_complex() else x.new_tensor(False, dtype=torch.bool)), safe_val=0)), OpInfo('tensor_split', ref=np.array_split, dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # Pre-existing condition; Needs to be fixed DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_operator'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_backward'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), ), sample_inputs_func=sample_inputs_tensor_split,), OpInfo('hsplit', dtypes=all_types_and_complex_and(torch.complex32, torch.bool, torch.bfloat16, torch.float16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_hsplit, error_inputs_func=error_inputs_hsplit,), OpInfo('vsplit', dtypes=all_types_and_complex_and(torch.complex32, torch.bool, torch.bfloat16, torch.float16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_vsplit, error_inputs_func=error_inputs_vsplit,), OpInfo('dsplit', dtypes=all_types_and_complex_and(torch.complex32, torch.bool, torch.bfloat16, torch.float16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_dsplit, error_inputs_func=error_inputs_dsplit,), OpInfo('triangular_solve', op=torch.triangular_solve, dtypes=floating_and_complex_types(), sample_inputs_func=sample_inputs_legacy_solve, check_batched_gradgrad=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, gradcheck_wrapper=lambda *args, **kwargs: gradcheck_wrapper_triangular_input(*args, idx=1, **kwargs), decorators=[skipCUDAIfNoMagma, skipCPUIfNoLapack], skips=( # AssertionError: Scalars are not equal! DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out'), # Gradcheck fails DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_fn_fwgrad_bwgrad', dtypes=floating_and_complex_types()), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_out', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_variant_consistency_eager', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit', device_type='mps', dtypes=[torch.float32]), )), UnaryUfuncInfo('trunc', aliases=('fix', ), ref=np.trunc, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_sparse=True, supports_sparse_csr=True, assert_autodiffed=True), UnaryUfuncInfo('exp2', aliases=('special.exp2', ), ref=np_unary_ufunc_integer_promotion_wrapper(np.exp2), dtypes=all_types_and(torch.bool, torch.half, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bool, torch.half, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True), UnaryUfuncInfo('expm1', aliases=('special.expm1', ), ref=np_unary_ufunc_integer_promotion_wrapper(np.expm1), dtypes=all_types_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bool, torch.half, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_sparse=True, supports_sparse_csr=True, assert_autodiffed=True, skips=( # Reference: https://github.com/pytorch/pytorch/pull/48926#issuecomment-739734774 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', device_type='cpu', dtypes=[torch.bfloat16]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', device_type='cpu', dtypes=[torch.bfloat16]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_small', device_type='cpu', dtypes=[torch.bfloat16]), DecorateInfo(unittest.skip("Skipped! sparse backward not supported"), 'TestSparseUnaryUfuncs', 'test_sparse_fn_grad'), )), UnaryUfuncInfo('nan_to_num', ref=np.nan_to_num, dtypes=all_types_and(torch.half, torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.half, torch.bool, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_sparse=True, skips=( DecorateInfo(unittest.skip("Skipped! sparse backward not supported"), 'TestSparseUnaryUfuncs', 'test_sparse_fn_grad'), ), # Passing numpy_kwargs via sample_kwargs, as numpy does comparison # with BFloat16 in float, since it currently doesn't support BFloat16. # Ref: https://github.com/pytorch/pytorch/issues/57982#issuecomment-839150556 sample_kwargs=lambda device, dtype, input: ({}, {'posinf': torch.finfo(torch.bfloat16).max, 'neginf': torch.finfo(torch.bfloat16).min}) if dtype is torch.bfloat16 else ({}, {})), UnaryUfuncInfo('reciprocal', ref=np_unary_ufunc_integer_promotion_wrapper(np.reciprocal), dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), assert_autodiffed=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # Reference: https://github.com/pytorch/pytorch/issues/45690 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', dtypes=[torch.cfloat, torch.cdouble]), # Reference: https://github.com/pytorch/pytorch/pull/49102#issuecomment-744604601 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', dtypes=[torch.bfloat16]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', dtypes=[torch.bfloat16]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_small', dtypes=[torch.bfloat16]), )), UnaryUfuncInfo('rsqrt', ref=lambda x: np.reciprocal(np.sqrt(x)), domain=(0, None), dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), decorators=(precisionOverride({torch.half: 5e-2}),), assert_autodiffed=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', dtypes=(torch.cfloat, torch.cdouble)), )), UnaryUfuncInfo('sqrt', ref=np.sqrt, supports_sparse=True, domain=(0, None), dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), assert_autodiffed=True, supports_forward_ad=True, supports_sparse_csr=True, supports_fwgrad_bwgrad=True, decorators=(precisionOverride({torch.bfloat16: 7e-2}),), skips=( # Reference: https://github.com/pytorch/pytorch/issues/47358 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble], active_if=IS_MACOS), # Reference: https://github.com/pytorch/pytorch/pull/47293#issuecomment-721774436 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', dtypes=[torch.bfloat16]), DecorateInfo(unittest.skip("Skipped! sparse backward not supported"), 'TestSparseUnaryUfuncs', 'test_sparse_fn_grad'), )), UnaryUfuncInfo('square', ref=np.square, dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), decorators=(precisionOverride({torch.complex64: 3e-4, torch.bfloat16: 3e-1}),), supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # Reference: https://github.com/pytorch/pytorch/issues/52549 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', dtypes=[torch.cfloat, torch.cdouble]), # >>> t = torch.tensor(complex(-0.01, float("inf"))) # >>> np.square(t.numpy()) # (-inf-infj) # >>> t.square() # tensor(-inf-infj) # >>> t.cuda().square() # tensor(inf+nanj, device='cuda:0') DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', device_type='cuda', dtypes=[torch.cfloat, torch.cdouble]), # Reference: https://github.com/pytorch/pytorch/pull/52551#issuecomment-782596181 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', dtypes=[torch.bfloat16]), ),), OpInfo('lerp', dtypes=floating_and_complex_types(), dtypesIfCUDA=floating_and_complex_types_and(torch.half, torch.bfloat16), dtypesIfROCM=floating_and_complex_types_and(torch.half, torch.bfloat16), sample_inputs_func=sample_inputs_lerp, supports_forward_ad=True, supports_fwgrad_bwgrad=True, assert_autodiffed=True), OpInfo('linalg.inv', aten_name='linalg_inv', op=torch.linalg.inv, dtypes=floating_and_complex_types(), sample_inputs_func=sample_inputs_linalg_invertible, check_batched_gradgrad=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack], skips=( # AssertionError: Scalars are not equal! DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out'), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_out', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_variant_consistency_eager', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit', device_type='mps', dtypes=[torch.float32]), )), OpInfo('linalg.inv_ex', aten_name='linalg_inv_ex', dtypes=floating_and_complex_types(), sample_inputs_func=sample_inputs_linalg_invertible, check_batched_gradgrad=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack], skips=( # AssertionError: Scalars are not equal! DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out'), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_out', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_variant_consistency_eager', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit', device_type='mps', dtypes=[torch.float32]), )), UnaryUfuncInfo('angle', ref=np.angle, dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16), dtypesIfCUDA=all_types_and_complex_and(torch.chalf, torch.bool), decorators=(precisionOverride({torch.float16: 1e-2, torch.bfloat16: 1e-2}),), # TODO: add `torch.chalf` backward dtype support. # AssertionError: The supported dtypes for angle on device type cuda are incorrect! # The following dtypes did not work in backward but are listed by the OpInfo: {torch.complex32}. backward_dtypes=floating_and_complex_types_and(torch.bfloat16, torch.float16), backward_dtypesIfCUDA=floating_and_complex_types(), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_sparse_csr=True, supports_complex_to_float=True, skips=( # RuntimeError: "add_out_op2_sparse_csr" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, 'TestSparseCSR', 'test_zero_to_zero_correspondence_unary', dtypes=(torch.chalf,),), # RuntimeError: "nonzero_count_cpu" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, 'TestSparseCSR', 'test_sparse_csr_unary_out', dtypes=(torch.chalf,),), # RuntimeError: "nonzero_count_cpu" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, 'TestSparseCSR', 'test_sparse_csr_consistency', dtypes=(torch.chalf,),), )), UnaryUfuncInfo('isfinite', ref=np.isfinite, dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16, torch.chalf), supports_out=False, supports_autograd=False), UnaryUfuncInfo('isinf', ref=np.isinf, dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16, torch.chalf), supports_out=False, supports_sparse=True, supports_sparse_csr=True, supports_autograd=False, skips=( # "nonzero_count_cpu" not implemented for 'ComplexHalf' # "nonzero_cuda" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, "TestSparseCSR", "test_sparse_csr_consistency", dtypes=(torch.chalf,)), # "add_out_op2_sparse_csr" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, "TestSparseCSR", "test_zero_to_zero_correspondence_unary", dtypes=(torch.chalf,)), )), UnaryUfuncInfo('isposinf', ref=np.isposinf, dtypes=all_types_and(torch.bool, torch.bfloat16, torch.float16), supports_sparse=True, supports_sparse_csr=True, supports_autograd=False), UnaryUfuncInfo('isneginf', ref=np.isneginf, dtypes=all_types_and(torch.bool, torch.bfloat16, torch.float16), supports_sparse=True, supports_sparse_csr=True, supports_autograd=False), UnaryUfuncInfo('isreal', ref=np.isreal, dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16, torch.chalf), supports_out=False, supports_autograd=False), UnaryUfuncInfo('isnan', ref=np.isnan, dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16), supports_out=False, supports_sparse=True, supports_sparse_csr=True, supports_autograd=False), OpInfo('linalg.solve', aten_name='linalg_solve', op=torch.linalg.solve, dtypes=floating_and_complex_types(), sample_inputs_func=sample_inputs_linalg_solve, check_batched_gradgrad=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, decorators=[skipCUDAIfNoMagma, skipCPUIfNoLapack], skips=( # AssertionError: Scalars are not equal! DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out'), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_out', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_variant_consistency_eager', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit', device_type='mps', dtypes=[torch.float32]), )), OpInfo('linalg.solve_triangular', aten_name='linalg_solve_triangular', op=torch.linalg.solve_triangular, dtypes=floating_and_complex_types(), sample_inputs_func=sample_inputs_linalg_solve_triangular, supports_fwgrad_bwgrad=True, skips=(skipCPUIfNoLapack,), # linalg.solve_triangular cannot be batched over because of a call to out.copy_(result); supports_forward_ad=True), OpInfo('linalg.matrix_rank', aten_name='linalg_matrix_rank', dtypes=floating_and_complex_types(), supports_autograd=False, sample_inputs_func=sample_inputs_linalg_invertible, decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack], skips=( # Pre-existing condition; Needs to be fixed DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_operator'), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_out', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_variant_consistency_eager', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit', device_type='mps', dtypes=[torch.float32]), ), ), OpInfo('linalg.matrix_rank', aten_name='linalg_matrix_rank', variant_test_name='hermitian', dtypes=floating_and_complex_types(), supports_autograd=False, sample_inputs_func=sample_inputs_linalg_pinv_hermitian, decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack], skips=( # Pre-existing condition; Needs to be fixed DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_operator'), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_out', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit', device_type='mps', dtypes=[torch.float32]), ), ), OpInfo('linalg.pinv', aten_name='linalg_pinv', op=torch.linalg.pinv, dtypes=floating_and_complex_types(), check_batched_grad=False, check_batched_gradgrad=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_linalg_pinv, decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack], skips=( # errors with "leaked XXXX bytes CUDA memory on device 0" DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit', device_type='cuda'),) ), OpInfo('linalg.pinv', aten_name='linalg_pinv', variant_test_name='singular', # pinv is Frechet-differentiable in a rank-preserving neighborhood, # so we feed inputs that are the products of two full-rank factors, # to avoid any rank changes caused by the perturbations in the gradcheck op=lambda a, b: torch.linalg.pinv(a @ b.mT), dtypes=floating_and_complex_types(), supports_out=False, check_batched_grad=False, check_batched_gradgrad=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_linalg_pinv_singular, # Only large tensors show issues with implicit backward used prior to # explicit backward implementation. decorators=[slowTest, skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack], skips=( DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # CUDA runs out of memory DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_fn_fwgrad_bwgrad', device_type='cuda', dtypes=[torch.cdouble]), )), OpInfo('linalg.pinv', aten_name='linalg_pinv', variant_test_name='hermitian', dtypes=floating_and_complex_types(), check_batched_grad=False, check_batched_gradgrad=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_linalg_pinv_hermitian, gradcheck_wrapper=gradcheck_wrapper_hermitian_input, decorators=[skipCUDAIfNoMagma, skipCPUIfNoLapack], skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_out', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_variant_consistency_eager', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit', device_type='mps', dtypes=[torch.float32]), ) ), OpInfo('eig', op=torch.eig, dtypes=floating_and_complex_types(), sample_inputs_func=sample_inputs_eig, error_inputs_func=error_inputs_eig, decorators=[ skipCUDAIfNoMagma, skipCPUIfNoLapack, ], skips=( # following 2 tests failed intermittenly DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_fn_grad', device_type='cuda', dtypes=[torch.complex128], active_if=TEST_WITH_ROCM), DecorateInfo(unittest.skip("Skipped!"), 'TestGradients', 'test_fn_gradgrad', device_type='cuda', dtypes=[torch.complex128], active_if=TEST_WITH_ROCM)), ), OpInfo('einsum', # we need this lambda because SampleInput expects tensor input as the first argument # TODO(@heitorschueroff) update SampleInput to handle such cases op=lambda tensors, equation: torch.einsum(equation, tensors), dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.half, *[torch.bfloat16] if (CUDA11OrLater or TEST_WITH_ROCM) else []), backward_dtypesIfCUDA=floating_and_complex_types_and(torch.half, *[torch.bfloat16] if ((SM60OrLater and CUDA11OrLater) or TEST_WITH_ROCM) else []), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, check_batched_forward_grad=False, # See https://github.com/pytorch/pytorch/issues/66357 sample_inputs_func=sample_inputs_einsum, skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # test does not work with passing lambda for op # there's a test `test_einsum` in `test_jit.py` to handle this case # AssertionError: JIT Test does not execute any logic DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'), )), OpInfo('svd', op=torch.svd, dtypes=floating_and_complex_types(), sample_inputs_func=sample_inputs_svd, supports_forward_ad=True, supports_fwgrad_bwgrad=True, check_batched_forward_grad=False, # We're using at::allclose, which does not have a batching rule check_batched_grad=False, check_batched_gradgrad=False, decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack, with_tf32_off], skips=( # Fixme, forward over backward gives a numerical error DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_fn_fwgrad_bwgrad', dtypes=(torch.complex128,)), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_backward'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_out', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_variant_consistency_eager', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit', device_type='mps', dtypes=[torch.float32]), )), OpInfo('linalg.svd', op=torch.linalg.svd, aten_name='linalg_svd', dtypes=floating_and_complex_types(), supports_fwgrad_bwgrad=True, supports_forward_ad=True, check_batched_forward_grad=False, # We're using at::allclose, which does not have a batching rule check_batched_grad=False, check_batched_gradgrad=False, sample_inputs_func=sample_inputs_svd, decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack, with_tf32_off], skips=( # FIXME forward over backward gives a numerical error DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_fn_fwgrad_bwgrad', dtypes=(torch.complex128,)), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_backward'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_out', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_variant_consistency_eager', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit', device_type='mps', dtypes=[torch.float32]), )), OpInfo('linalg.svdvals', op=torch.linalg.svdvals, aten_name='linalg_svdvals', dtypes=floating_and_complex_types(), check_batched_forward_grad=False, supports_fwgrad_bwgrad=True, supports_forward_ad=True, # We're using at::allclose, which does not have a batching rule check_batched_gradgrad=False, sample_inputs_func=sample_inputs_linalg_svdvals, skips=( DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), ), decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack, with_tf32_off]), OpInfo('svd_lowrank', op=lambda *args, **kwargs: wrapper_set_seed( lambda a, b, **kwargs: torch.svd_lowrank(a @ b.mT, **kwargs), *args, **kwargs ), dtypes=floating_types(), supports_out=False, check_batched_grad=False, check_batched_gradgrad=False, check_batched_forward_grad=False, supports_fwgrad_bwgrad=True, supports_forward_ad=True, sample_inputs_func=sample_inputs_svd_lowrank, decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack, with_tf32_off, DecorateInfo(toleranceOverride({torch.float32: tol(atol=1e-03, rtol=1e-03)}), 'TestCommon', 'test_noncontiguous_samples', device_type='cuda')], skips=( # test does not work with passing lambda for op DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_backward'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), )), OpInfo('pca_lowrank', op=lambda *args, **kwargs: wrapper_set_seed( lambda a, b, **kwargs: torch.pca_lowrank(a @ b.mT, **kwargs), *args, **kwargs ), dtypes=floating_types(), supports_out=False, check_batched_forward_grad=False, check_batched_grad=False, check_batched_gradgrad=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_pca_lowrank, decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack, with_tf32_off, DecorateInfo(toleranceOverride({torch.float32: tol(atol=1e-03, rtol=1e-03)}), 'TestCommon', 'test_noncontiguous_samples', device_type='cuda')], skips=( # test does not work with passing lambda for op DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_backward'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), )), BinaryUfuncInfo('polar', dtypes=floating_types(), # this function is undefined if 'abs' values are <0 supports_forward_ad=True, lhs_make_tensor_kwargs=dict(low=0), supports_rhs_python_scalar=False, skips=( # RuntimeError: Expected object of scalar type Float but got scalar type Double for second argument DecorateInfo(unittest.skip('Skipped!'), 'TestBinaryUfuncs', 'test_type_promotion'), # GradcheckError: Jacobian computed with forward mode mismatch for output 0 with respect to input 0 # Numerical: # tensor([[0.]], dtype=torch.float64) # Analytical: # tensor([[-0.0047]], dtype=torch.float64, grad_fn=) DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_fn_fwgrad_bwgrad'), )), # TODO(@kshitij12345): Refactor similar to `mvlgamma` entries. # To test reference numerics against multiple values of argument `n`, # we make multiple OpInfo entries with each entry corresponding to different value of n (currently 0 to 4). # We run the op tests from test_ops.py only for `n=0` to avoid redundancy in testing. UnaryUfuncInfo('polygamma', op=lambda x, n, **kwargs: torch.polygamma(n, x, **kwargs), variant_test_name='polygamma_n_0', ref=reference_polygamma if TEST_SCIPY else _NOTHING, dtypes=all_types_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bool, torch.half), supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_polygamma, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), ), sample_kwargs=lambda device, dtype, input: ({'n': 0}, {'n': 0})), # A separate OpInfo entry for special.polygamma is needed to reorder the arguments # for the alias. See the discussion here: https://github.com/pytorch/pytorch/pull/59691#discussion_r650261939 UnaryUfuncInfo('special.polygamma', op=lambda x, n, **kwargs: torch.special.polygamma(n, x, **kwargs), variant_test_name='special_polygamma_n_0', ref=reference_polygamma if TEST_SCIPY else _NOTHING, dtypes=all_types_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bool, torch.half), supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_polygamma, skips=( # lambda impl DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), ), sample_kwargs=lambda device, dtype, input: ({'n': 0}, {'n': 0}), # polygamma functions have multiple singularities at x <= 0 reference_numerics_filter=NumericsFilter(condition=lambda x: x < 0.1, safe_val=1)), UnaryUfuncInfo('polygamma', op=lambda x, n, **kwargs: torch.polygamma(n, x, **kwargs), variant_test_name='polygamma_n_1', ref=reference_polygamma if TEST_SCIPY else _NOTHING, dtypes=all_types_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bool, torch.half), supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_polygamma, skips=( # Redundant tests DecorateInfo(unittest.skip("Skipped!"), 'TestGradients'), DecorateInfo(unittest.skip("Skipped!"), 'TestJit'), DecorateInfo(unittest.skip("Skipped!"), 'TestNormalizeOperators'), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon'), # Mismatch: https://github.com/pytorch/pytorch/issues/55357 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal'), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large'), ), sample_kwargs=lambda device, dtype, input: ({'n': 1}, {'n': 1}), # polygamma functions have multiple singularities at x <= 0 reference_numerics_filter=NumericsFilter(condition=lambda x: x < 0.1, safe_val=1)), UnaryUfuncInfo('polygamma', op=lambda x, n, **kwargs: torch.polygamma(n, x, **kwargs), variant_test_name='polygamma_n_2', ref=reference_polygamma if TEST_SCIPY else _NOTHING, dtypes=all_types_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bool, torch.half), supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_polygamma, skips=( # Redundant tests DecorateInfo(unittest.skip("Skipped!"), 'TestGradients'), DecorateInfo(unittest.skip("Skipped!"), 'TestJit'), DecorateInfo(unittest.skip("Skipped!"), 'TestNormalizeOperators'), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon'), # Mismatch: https://github.com/pytorch/pytorch/issues/55357 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal'), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', active_if=TEST_WITH_ROCM), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_small', active_if=TEST_WITH_ROCM),), sample_kwargs=lambda device, dtype, input: ({'n': 2}, {'n': 2}), # polygamma functions have multiple singularities at x <= 0 reference_numerics_filter=NumericsFilter(condition=lambda x: x < 0.1, safe_val=1)), UnaryUfuncInfo('polygamma', op=lambda x, n, **kwargs: torch.polygamma(n, x, **kwargs), variant_test_name='polygamma_n_3', ref=reference_polygamma if TEST_SCIPY else _NOTHING, dtypes=all_types_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bool, torch.half), supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_polygamma, skips=( # Redundant tests DecorateInfo(unittest.skip("Skipped!"), 'TestGradients'), DecorateInfo(unittest.skip("Skipped!"), 'TestJit'), DecorateInfo(unittest.skip("Skipped!"), 'TestNormalizeOperators'), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon'), # Mismatch: https://github.com/pytorch/pytorch/issues/55357 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal'),), sample_kwargs=lambda device, dtype, input: ({'n': 3}, {'n': 3}), # polygamma functions have multiple singularities at x <= 0 reference_numerics_filter=NumericsFilter(condition=lambda x: x < 0.1, safe_val=1)), UnaryUfuncInfo('polygamma', op=lambda x, n, **kwargs: torch.polygamma(n, x, **kwargs), variant_test_name='polygamma_n_4', ref=reference_polygamma if TEST_SCIPY else _NOTHING, decorators=(precisionOverride({torch.float16: 5e-4, torch.float32: 5e-4}),), dtypes=all_types_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bool, torch.half), supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_polygamma, skips=( # Redundant tests DecorateInfo(unittest.skip("Skipped!"), 'TestGradients'), DecorateInfo(unittest.skip("Skipped!"), 'TestJit'), DecorateInfo(unittest.skip("Skipped!"), 'TestNormalizeOperators'), DecorateInfo(unittest.skip("Skipped!"), 'TestCommon'), # Mismatch: https://github.com/pytorch/pytorch/issues/55357 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal'), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', active_if=TEST_WITH_ROCM), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_small', active_if=TEST_WITH_ROCM),), sample_kwargs=lambda device, dtype, input: ({'n': 4}, {'n': 4}), # polygamma functions have multiple singularities at x <= 0 reference_numerics_filter=NumericsFilter(condition=lambda x: x < 0.1, safe_val=1)), OpInfo('ravel', ref=np.ravel, dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16, torch.chalf), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_ravel, ), OpInfo('reshape', dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16, torch.chalf), sample_inputs_func=partial(sample_inputs_view_reshape, transpose_samples=True), reference_inputs_func=partial(reference_inputs_view_reshape, transpose_samples=True), error_inputs_func=error_inputs_reshape, supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, ), OpInfo('reshape_as', op=lambda x, other: x.reshape_as(other), dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16, torch.chalf), sample_inputs_func=sample_inputs_view_as_reshape_as, supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), )), OpInfo('view', op=lambda x, shape: x.view(shape), dtypes=all_types_and_complex_and(torch.complex32, torch.bool, torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, assert_jit_shape_analysis=True, sample_inputs_func=partial(sample_inputs_view_reshape, transpose_samples=False), reference_inputs_func=partial(reference_inputs_view_reshape, transpose_samples=False), skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), )), OpInfo('view_as', op=lambda x, other: x.view_as(other), dtypes=all_types_and_complex_and(torch.complex32, torch.bool, torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_view_as_reshape_as, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), )), OpInfo('atleast_1d', dtypes=all_types_and_complex_and(torch.complex32, torch.bool, torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_atleast1d2d3d, skips=( # JIT does not support variadic tensors. # RuntimeError: input->type()->kind() == TypeKind::OptionalType # INTERNAL ASSERT FAILED at "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":252, # please report a bug to PyTorch. DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit', dtypes=[torch.float32]), ), ), OpInfo('atleast_2d', dtypes=all_types_and_complex_and(torch.complex32, torch.bool, torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit', dtypes=[torch.float32]), ), sample_inputs_func=sample_inputs_atleast1d2d3d, ), OpInfo('atleast_3d', dtypes=all_types_and_complex_and(torch.complex32, torch.bool, torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit', dtypes=[torch.float32]), ), sample_inputs_func=sample_inputs_atleast1d2d3d, ), OpInfo('flatten', dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16, torch.chalf), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_flatten, reference_inputs_func=reference_inputs_flatten, ), OpInfo('column_stack', dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16, torch.chalf), supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # lambda impl DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"),), sample_inputs_func=sample_inputs_column_stack,), OpInfo('pinverse', op=torch.pinverse, dtypes=floating_and_complex_types(), check_batched_grad=False, check_batched_gradgrad=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, supports_out=False, sample_inputs_func=sample_inputs_linalg_invertible, decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack], skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_variant_consistency_eager', device_type='mps', dtypes=[torch.float32]), DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit', device_type='mps', dtypes=[torch.float32]), )), OpInfo('gather', dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), sample_inputs_func=sample_inputs_gather, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, supports_forward_ad=True, supports_fwgrad_bwgrad=True, error_inputs_func=error_inputs_gather, ), OpInfo('index_fill', dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, # https://github.com/pytorch/pytorch/issues/66357 check_batched_forward_grad=False, sample_inputs_func=sample_inputs_index), OpInfo('index_copy', dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), supports_out=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, # https://github.com/pytorch/pytorch/issues/66357 check_batched_forward_grad=False, skips=( ), sample_inputs_func=sample_inputs_index, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL), OpInfo('index_select', dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), sample_inputs_func=sample_inputs_index, error_inputs_func=error_inputs_index_select, supports_forward_ad=True, supports_fwgrad_bwgrad=True, assert_jit_shape_analysis=True, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL), OpInfo('index_add', dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), supports_out=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, # https://github.com/pytorch/pytorch/issues/66357 check_batched_forward_grad=False, sample_inputs_func=sample_inputs_index, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL), OpInfo('index_reduce', dtypes=floating_types_and(torch.float16, torch.bfloat16), supports_out=True, sample_inputs_func=sample_inputs_index_reduce), OpInfo('__getitem__', dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16, torch.chalf), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_inplace_autograd=False, supports_scripting=False, op=torch.Tensor.__getitem__, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # AssertionError: False is not true : Scalars failed to compare as equal! 0 != 104448 DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit', device_type='cuda'),), sample_inputs_func=sample_inputs_getitem), OpInfo('index_put', dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16, torch.chalf), supports_out=False, supports_inplace_autograd=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, # https://github.com/pytorch/pytorch/issues/66357 check_batched_forward_grad=False, test_neg_view=False, sample_inputs_func=sample_inputs_index_put, skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # RuntimeError: The following operation failed in the TorchScript interpreter. # Traceback of TorchScript (most recent call last): # File "", line 3, in forward # def the_method(i0, i1: List[torch.Tensor], i2): # return torch.index_put(i0, i1, i2, accumulate=False) # ~~~~~~~~~~~~~~~ <--- HERE # RuntimeError: a leaf Variable that requires grad is being used in an in-place operation. DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), )), OpInfo('sort', dtypes=all_types_and(torch.bool, torch.float16, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.float16, torch.bfloat16), sample_inputs_func=sample_inputs_sort, supports_forward_ad=True, supports_fwgrad_bwgrad=True), OpInfo('unique', dtypes=all_types_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bool, torch.float16), sample_inputs_func=sample_inputs_unique, supports_out=False, supports_autograd=False, skips=( # lambda impl DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # 76571 DecorateInfo(unittest.expectedFailure, 'TestCudaFuserOpInfo', 'test_nvfuser_extremal_values', dtypes=(torch.float16, torch.float32, torch.float64)), )), OpInfo('unique_consecutive', dtypes=all_types_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bool, torch.float16), sample_inputs_func=sample_inputs_unique_consecutive, supports_out=False, supports_autograd=False, skips=( # lambda impl DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # 76571 DecorateInfo(unittest.expectedFailure, 'TestCudaFuserOpInfo', 'test_nvfuser_extremal_values', dtypes=(torch.float16, torch.float32, torch.float64)), )), OpInfo('put', dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, check_batched_forward_grad=False, check_batched_gradgrad=False, # vmap complains of the sizes skips=( # Problem, needs to be fixed DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_operator'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_backward'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), ), sample_inputs_func=sample_inputs_put), OpInfo('take', dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), check_batched_grad=False, # vmap complains of the sizes supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_take, skips=( DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_backward'), ), error_inputs_func=error_inputs_take), OpInfo('scatter', dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_scatter, error_inputs_func=error_inputs_scatter_and_scatter_add), OpInfo('bfloat16', op=lambda x, *args, **kwargs: x.bfloat16(*args, **kwargs), dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16, torch.chalf), supports_out=False, sample_inputs_func=sample_inputs_conversion, skips=( # autograd tests don't handle operators that change dtype DecorateInfo(unittest.expectedFailure, 'TestGradients'), DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # RuntimeError: attribute lookup is not defined on builtin DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), DecorateInfo(unittest.skip("Skipped!"), 'TestNNCOpInfo', 'test_nnc_correctness'), DecorateInfo(unittest.skip("Skipped!"), 'TestCudaFuserOpInfo', 'test_nvfuser_correctness'), )), OpInfo('bool', op=lambda x, *args, **kwargs: x.bool(*args, **kwargs), dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16, torch.chalf), supports_out=False, sample_inputs_func=sample_inputs_conversion, supports_autograd=False, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # RuntimeError: attribute lookup is not defined on builtin DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # 76047 DecorateInfo(unittest.expectedFailure, 'TestNNCOpInfo', 'test_nnc_correctness', dtypes=(torch.int8,)), )), OpInfo('byte', op=lambda x, *args, **kwargs: x.byte(*args, **kwargs), dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), supports_out=False, sample_inputs_func=sample_inputs_conversion, # The autograd test runner cannot handle functions that change dtype supports_autograd=False, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # RuntimeError: attribute lookup is not defined on builtin DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), )), OpInfo('char', op=lambda x, *args, **kwargs: x.char(*args, **kwargs), dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16, torch.chalf), supports_out=False, sample_inputs_func=sample_inputs_conversion, # The autograd test runner cannot handle functions that change dtype supports_autograd=False, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # RuntimeError: attribute lookup is not defined on builtin DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), )), OpInfo('double', op=lambda x, *args, **kwargs: x.double(*args, **kwargs), dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16, torch.chalf), supports_out=False, sample_inputs_func=sample_inputs_conversion, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # RuntimeError: attribute lookup is not defined on builtin DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), )), OpInfo('float', op=lambda x, *args, **kwargs: x.float(*args, **kwargs), dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16, torch.chalf), supports_out=False, sample_inputs_func=sample_inputs_conversion, skips=( # autograd tests don't handle operators that change dtype DecorateInfo(unittest.expectedFailure, 'TestGradients'), DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # RuntimeError: attribute lookup is not defined on builtin DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), )), OpInfo('half', op=lambda x, *args, **kwargs: x.half(*args, **kwargs), dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), supports_out=False, sample_inputs_func=sample_inputs_conversion, supports_autograd=True, skips=( # autograd tests don't handle operators that change dtype DecorateInfo(unittest.expectedFailure, 'TestGradients'), DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # RuntimeError: attribute lookup is not defined on builtin DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), )), OpInfo('int', op=lambda x, *args, **kwargs: x.int(*args, **kwargs), dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), supports_out=False, sample_inputs_func=sample_inputs_conversion, supports_autograd=False, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # RuntimeError: attribute lookup is not defined on builtin DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), )), OpInfo('long', op=lambda x, *args, **kwargs: x.long(*args, **kwargs), dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), supports_out=False, sample_inputs_func=sample_inputs_conversion, supports_autograd=False, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # RuntimeError: attribute lookup is not defined on builtin DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), )), OpInfo('short', op=lambda x, *args, **kwargs: x.short(*args, **kwargs), dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), supports_out=False, sample_inputs_func=sample_inputs_conversion, supports_autograd=False, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # RuntimeError: attribute lookup is not defined on builtin DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), )), OpInfo('chalf', op=lambda x, *args, **kwargs: x.chalf(*args, **kwargs), dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16, torch.chalf), supports_out=False, sample_inputs_func=sample_inputs_conversion, skips=( # autograd tests don't handle operators that change dtype DecorateInfo(unittest.expectedFailure, 'TestGradients'), # use of lambda doesn't work with test_normalize_operator_exhaustive DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # RuntimeError: "index_select" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_noncontiguous_samples', dtypes=(torch.float, torch.cfloat)), # RuntimeError: "sum_cpu" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_variant_consistency_eager', device_type='cpu'), # TypeError: 'int' object is not iterable DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # RuntimeError: "sum_cpu" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_conj_view', device_type='cpu'), # RuntimeError: "sum_cpu" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_neg_view', device_type='cpu'), # RuntimeError: "sum_cpu" not implemented for 'ComplexHalf' # RuntimeError: "neg_conj_cuda" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_neg_conj_view'), ) ), OpInfo('empty_like', dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16, torch.chalf), supports_out=False, sample_inputs_func=sample_inputs_like_fns, reference_inputs_func=reference_inputs_like_fns, supports_autograd=False, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # Empty tensor data is garbage so it's hard to make comparisons with it. DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_noncontiguous_samples'), # Empty tensor data is garbage so it's hard to make comparisons with it. DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'), # Empty tensor data is garbage so it's hard to make comparisons with it. DecorateInfo(unittest.skip("Skipped!"), 'TestMathBits', 'test_conj_view'), # Empty tensor data is garbage so it's hard to make comparisons with it. DecorateInfo(unittest.skip("Skipped!"), 'TestMathBits', 'test_neg_view'), # Empty tensor data is garbage so it's hard to make comparisons with it. DecorateInfo(unittest.skip("Skipped!"), 'TestMathBits', 'test_neg_conj_view'), # Empty tensor data is garbage so it's hard to make comparisons with it. DecorateInfo(unittest.skip("Skipped!"), 'TestNNCOpInfo', 'test_nnc_correctness'), # Empty tensor data is garbage so it's hard to make comparisons with it. DecorateInfo(unittest.skip("Skipped!"), 'TestCudaFuserOpInfo'), # Empty tensor data is garbage so it's hard to make comparisons with it. DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_complex_half_reference_testing'), # Can't find schemas for this operator for some reason DecorateInfo(unittest.expectedFailure, 'TestOperatorSignatures', 'test_get_torch_func_signature_exhaustive'), )), OpInfo('zeros_like', dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), supports_out=False, sample_inputs_func=sample_inputs_like_fns, supports_autograd=False, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # Can't find schemas for this operator for some reason DecorateInfo(unittest.expectedFailure, 'TestOperatorSignatures', 'test_get_torch_func_signature_exhaustive'), )), OpInfo('ones_like', dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), supports_out=False, sample_inputs_func=sample_inputs_like_fns, supports_autograd=False, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # Can't find schemas for this operator for some reason DecorateInfo(unittest.expectedFailure, 'TestOperatorSignatures', 'test_get_torch_func_signature_exhaustive'), )), OpInfo('randn_like', dtypes=floating_types_and(torch.half, torch.bfloat16, torch.complex64, torch.complex128), op=lambda inp, *args, **kwargs: wrapper_set_seed(torch.randn_like, inp, *args, **kwargs), supports_out=False, sample_inputs_func=sample_inputs_like_fns, supports_autograd=False, supports_sparse_csr=True, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # AssertionError: JIT Test does not execute any logic DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), )), OpInfo('rand_like', dtypes=floating_types_and(torch.half, torch.bfloat16, torch.complex64, torch.complex128), op=lambda inp, *args, **kwargs: wrapper_set_seed(torch.randn_like, inp, *args, **kwargs), supports_out=False, sample_inputs_func=sample_inputs_like_fns, supports_autograd=False, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # AssertionError: JIT Test does not execute any logic DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # Can't find schemas for this operator for some reason DecorateInfo(unittest.expectedFailure, 'TestOperatorSignatures', 'test_get_torch_func_signature_exhaustive'), )), OpInfo('randint_like', dtypes=all_types_and(torch.half, torch.bfloat16), op=lambda inp, *args, **kwargs: wrapper_set_seed(torch.randint_like, inp, *args, **kwargs), supports_out=False, sample_inputs_func=sample_inputs_randint_like, supports_autograd=False, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # AssertionError: JIT Test does not execute any logic DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # Can't find schemas for this operator for some reason DecorateInfo(unittest.expectedFailure, 'TestOperatorSignatures', 'test_get_torch_func_signature_exhaustive'), )), OpInfo('full_like', dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), supports_out=False, sample_inputs_func=sample_inputs_full_like, supports_autograd=False, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # Can't find schemas for this operator for some reason DecorateInfo(unittest.expectedFailure, 'TestOperatorSignatures', 'test_get_torch_func_signature_exhaustive'), )), OpInfo('new_zeros', op=lambda x, *args, **kwargs: x.new_zeros(*args, **kwargs), dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), supports_out=False, sample_inputs_func=sample_inputs_new_fns, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # Can't find schemas for this operator for some reason DecorateInfo(unittest.expectedFailure, 'TestOperatorSignatures', 'test_get_torch_func_signature_exhaustive'), ), supports_autograd=False), OpInfo('new_ones', op=lambda x, *args, **kwargs: x.new_ones(*args, **kwargs), dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), supports_out=False, sample_inputs_func=sample_inputs_new_fns, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # Can't find schemas for this operator for some reason DecorateInfo(unittest.expectedFailure, 'TestOperatorSignatures', 'test_get_torch_func_signature_exhaustive'), ), supports_autograd=False), OpInfo('new_empty', op=lambda x, *args, **kwargs: x.new_empty(*args, **kwargs), dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), supports_out=False, sample_inputs_func=sample_inputs_new_fns, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # Empty tensor data is garbage so it's hard to make comparisons with it. DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'), # Empty tensor data is garbage so it's hard to make comparisons with it. DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_variant_consistency_eager'), # Empty tensor data is garbage so it's hard to make comparisons with it. DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_noncontiguous_samples'), # Empty tensor data is garbage so it's hard to make comparisons with it. DecorateInfo(unittest.skip("Skipped!"), 'TestMathBits', 'test_conj_view'), # Empty tensor data is garbage so it's hard to make comparisons with it. DecorateInfo(unittest.skip("Skipped!"), 'TestMathBits', 'test_neg_view'), # Empty tensor data is garbage so it's hard to make comparisons with it. DecorateInfo(unittest.skip("Skipped!"), 'TestMathBits', 'test_neg_conj_view'), # Empty tensor data is garbage so it's hard to make comparisons with it. DecorateInfo(unittest.skip("Skipped!"), 'TestNNCOpInfo', 'test_nnc_correctness'), # Empty tensor data is garbage so it's hard to make comparisons with it. DecorateInfo(unittest.skip("Skipped!"), 'TestCudaFuserOpInfo'), # Can't find schemas for this operator for some reason DecorateInfo(unittest.expectedFailure, 'TestOperatorSignatures', 'test_get_torch_func_signature_exhaustive'), ), supports_autograd=False), OpInfo('empty', dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), sample_inputs_func=sample_inputs_empty, supports_autograd=False, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # Empty tensor data is garbage so it's hard to make comparisons with it. DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'), # Empty tensor data is garbage so it's hard to make comparisons with it. DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_variant_consistency_eager'), # Empty tensor data is garbage so it's hard to make comparisons with it. DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_noncontiguous_samples'), # Empty tensor data is garbage so it's hard to make comparisons with it. DecorateInfo(unittest.skip("Skipped!"), 'TestMathBits', 'test_conj_view'), # Empty tensor data is garbage so it's hard to make comparisons with it. DecorateInfo(unittest.skip("Skipped!"), 'TestMathBits', 'test_neg_view'), # Empty tensor data is garbage so it's hard to make comparisons with it. DecorateInfo(unittest.skip("Skipped!"), 'TestMathBits', 'test_neg_conj_view'), # Empty tensor data is garbage so it's hard to make comparisons with it. DecorateInfo(unittest.skip("Skipped!"), 'TestNNCOpInfo', 'test_nnc_correctness'), # Empty tensor data is garbage so it's hard to make comparisons with it. DecorateInfo(unittest.skip("Skipped!"), 'TestCudaFuserOpInfo'), # Can't find schemas for this operator for some reason DecorateInfo(unittest.expectedFailure, 'TestOperatorSignatures', 'test_get_torch_func_signature_exhaustive'), DecorateInfo(unittest.skip("Expected: empty is not comparable"), 'TestCommon', 'test_out'), DecorateInfo(unittest.skip("Expected: empty is not comparable"), 'TestCommon', 'test_out_warning'), DecorateInfo(unittest.skip("Expected: empty is not comparable"), 'TestLazyOpInfo'), )), OpInfo('new_full', op=lambda x, *args, **kwargs: x.new_full(*args, **kwargs), dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), supports_out=False, sample_inputs_func=sample_inputs_new_full, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # Can't find schemas for this operator for some reason DecorateInfo(unittest.expectedFailure, 'TestOperatorSignatures', 'test_get_torch_func_signature_exhaustive'), ), supports_autograd=False), OpInfo('multinomial', op=lambda inp, *args, **kwargs: wrapper_set_seed(torch.multinomial, inp, *args, **kwargs), method_variant=lambda inp, *args, **kwargs: wrapper_set_seed(torch.Tensor.multinomial, inp, *args, **kwargs), dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half), supports_out=True, sample_inputs_func=sample_inputs_multinomial, error_inputs_func=error_inputs_multinomial, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # Strides are not the same! # This may not be reproducible in CI DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_out'), # AssertionError: JIT Test does not execute any logic DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # UserWarning not triggered : Resized a non-empty tensor but did not warn about it. DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out_warning')), supports_autograd=False), OpInfo('normal', op=lambda inp, *args, **kwargs: wrapper_set_seed(torch.normal, inp, *args, **kwargs), # The inplace variant (Tensor.normal_) is different from torch.normal inplace_variant=None, dtypes=floating_types_and(torch.bfloat16, torch.half), dtypesIfCUDA=floating_types_and(torch.bfloat16, torch.half), supports_out=True, sample_inputs_func=sample_inputs_normal_tensor_first, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # Tensor-likes are not close! DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out'), # AssertionError: JIT Test does not execute any logic DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # UserWarning not triggered : Resized a non-empty tensor but did not warn about it. DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out_warning'), # NotImplementedError not raised DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_fn_fwgrad_bwgrad'), # Computed gradient is incorrect -- would be an exfail but gradgrad somehow passes DecorateInfo(unittest.skip("Gradients are incorrect!"), 'TestGradients'),)), OpInfo('normal', # This has its own variant b/c OpInfos assume the first arg is a Tensor but it is not here variant_test_name='number_mean', op=lambda std, mean, *args, **kwargs: wrapper_set_seed(torch.normal, mean, std, *args, **kwargs), # The inplace variant (Tensor.normal_) is different from torch.normal inplace_variant=None, dtypes=floating_types_and(torch.bfloat16, torch.half), dtypesIfCUDA=floating_types_and(torch.bfloat16, torch.half), supports_out=True, sample_inputs_func=sample_inputs_normal_tensor_second, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # AssertionError: JIT Test does not execute any logic DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # NotImplementedError not raised DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_fn_fwgrad_bwgrad'), # Computed gradient is incorrect -- would be an exfail but gradgrad somehow passes DecorateInfo(unittest.skip("Gradients are incorrect!"), 'TestGradients'),)), OpInfo('bernoulli', op=lambda inp, *args, **kwargs: wrapper_set_seed(torch.bernoulli, inp, *args, **kwargs), # The inplace variant (Tensor.bernoulli_) is different from torch.bernoulli inplace_variant=None, method_variant=lambda inp, *args, **kwargs: wrapper_set_seed(torch.Tensor.bernoulli, inp, *args, **kwargs), dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.bfloat16, torch.half), supports_out=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_bernoulli, skips=( # vmap: We do not yet support calling random operations inside of vmap DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_forward_mode_AD'), DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # AssertionError: JIT Test does not execute any logic DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # Expected RuntimeError when doing an unsafe cast from a result of # dtype torch.float32 into an out= with dtype torch.lon DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out'), # UserWarning not triggered : Resized a non-empty tensor but did not warn about it. DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out_warning'), DecorateInfo(unittest.skip('Skipped!'), 'TestCudaFuserOpInfo', 'test_nvfuser_extremal_values'))), OpInfo('scatter_add', dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), sample_inputs_func=sample_inputs_scatter_add, error_inputs_func=error_inputs_scatter_and_scatter_add, supports_forward_ad=True, supports_fwgrad_bwgrad=True, ), OpInfo('stack', dtypes=all_types_and_complex_and(torch.complex32, torch.bool, torch.float16, torch.bfloat16), sample_inputs_func=sample_inputs_stack, assert_autodiffed=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # https://github.com/pytorch/pytorch/issues/77046 DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_conj_view'), DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_neg_view'), ), ), OpInfo('hstack', dtypes=all_types_and_complex_and(torch.complex32, torch.bool, torch.float16, torch.bfloat16), sample_inputs_func=sample_inputs_hstack_dstack_vstack, supports_forward_ad=True, supports_fwgrad_bwgrad=True, ), BinaryUfuncInfo('hypot', dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_rhs_python_scalar=False), OpInfo('histogram', dtypes=floating_types(), dtypesIfCUDA=_dispatch_dtypes(), # histogram is only implemented on CPU sample_inputs_func=sample_inputs_histogram, supports_autograd=False, skips=( # JIT tests don't work with Tensor keyword arguments # https://github.com/pytorch/pytorch/issues/58507 # RuntimeError: # undefined value tensor: # File "", line 3 # def the_method(i0): # return torch.histogram(i0, 1, weight=tensor(-0.5735, dtype=torch.float32), density=False) # ~~~~~~ <--- HERE DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # Not Implemented on XLA. DecorateInfo(unittest.skip("Skipped!"), 'TestOpInfo', device_type='xla'), )), OpInfo('histogramdd', dtypes=floating_types(), dtypesIfCUDA=_dispatch_dtypes(), # histogramdd is only implemented on CPU sample_inputs_func=sample_inputs_histogramdd, supports_autograd=False, skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # JIT tests don't work with Tensor keyword arguments # https://github.com/pytorch/pytorch/issues/58507 DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), )), OpInfo('histc', dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.int8, torch.int16, torch.int32, torch.int64), sample_inputs_func=sample_inputs_histc, supports_out=True, supports_autograd=False, skips=( # CUDA histc returns a float tensor but does not correctly warn when passed an integral out tensor # "AssertionError: RuntimeError not raised : Expected RuntimeError when doing an unsafe cast # from a result of dtype torch.float32 into an out= with dtype torch.long" DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out', device_type='cuda'), DecorateInfo(unittest.skip('Skipped!'), 'TestCudaFuserOpInfo', 'test_nvfuser_extremal_values'), )), OpInfo('bincount', dtypes=integral_types_and(), sample_inputs_func=sample_inputs_bincount, supports_out=False, supports_autograd=False, skips=( # JIT tests don't work with Tensor keyword arguments # https://github.com/pytorch/pytorch/issues/58507 DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'), )), OpInfo('bucketize', dtypes=all_types_and(torch.float16, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.float16), sample_inputs_func=sample_inputs_bucketize, supports_autograd=False, skips=( # JIT tests don't work with Tensor keyword arguments DecorateInfo(unittest.skip("Expected failure!"), 'TestJit', 'test_variant_consistency_jit'), )), OpInfo('searchsorted', dtypes=all_types_and(torch.bfloat16, torch.float16), dtypesIfCUDA=all_types_and(torch.float16), sample_inputs_func=sample_inputs_searchsorted, supports_autograd=False, ref=reference_searchsorted, skips=( # JIT tests don't work with Tensor keyword arguments # https://github.com/pytorch/pytorch/issues/58507 DecorateInfo(unittest.skip("Expected failure!"), 'TestJit', 'test_variant_consistency_jit'), )), OpInfo('cat', ref=lambda input_seq, dim=0, **kwargs: np.concatenate(input_seq, axis=dim, **kwargs), aliases=('concat',), dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16, torch.complex32), sample_inputs_func=sample_inputs_cat_concat, reference_inputs_func=reference_inputs_cat, supports_forward_ad=True, supports_fwgrad_bwgrad=True, assert_autodiffed=True, skips=( # RuntimeError: Arguments for call not valid. # Expected a value of type 'List[Tensor]' for argument # 'tensors' but instead found type 'Tensor (inferred)'. DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_jit_alias_remapping'), # see https://github.com/pytorch/pytorch/issues/71286 DecorateInfo(unittest.expectedFailure, 'TestNNCOpInfo', 'test_nnc_correctness'),)), OpInfo('vstack', aliases=('row_stack',), dtypes=all_types_and_complex_and(torch.complex32, torch.bool, torch.float16, torch.bfloat16), sample_inputs_func=sample_inputs_hstack_dstack_vstack, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # RuntimeError: _fn() Expected a value of type # 'Tensor (inferred)' for argument 't0' but instead found type 'tuple'. DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_jit_alias_remapping'),)), OpInfo('dstack', dtypes=all_types_and_complex_and(torch.complex32, torch.bool, torch.float16, torch.bfloat16), sample_inputs_func=sample_inputs_hstack_dstack_vstack, supports_forward_ad=True, supports_fwgrad_bwgrad=True, ), OpInfo('unfold', op=lambda x, *args: x.unfold(*args), dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16, torch.chalf), backward_dtypes=floating_and_complex_types_and(torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, check_batched_gradgrad=False, # See https://github.com/pytorch/pytorch/issues/66357 check_batched_forward_grad=False, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # Skip operator schema test because this is a functional and not an operator DecorateInfo(unittest.expectedFailure, 'TestOperatorSignatures', 'test_get_torch_func_signature_exhaustive'), ), sample_inputs_func=sample_inputs_unfold), OpInfo('msort', dtypes=all_types_and(torch.bool, torch.float16, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.float16, torch.bfloat16), check_batched_gradgrad=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_msort), OpInfo('movedim', aliases=('moveaxis',), dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16, torch.chalf), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_movedim_moveaxis), OpInfo('renorm', dtypes=floating_and_complex_types_and(torch.float16, torch.bfloat16), sample_inputs_func=sample_inputs_renorm, error_inputs_func=error_inputs_renorm), ShapeFuncInfo('repeat', op=lambda x, dims: x.repeat(dims), ref=np.tile, dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_repeat_tile, skips=( DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), )), OpInfo('squeeze', dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16, torch.chalf), supports_out=False, assert_autodiffed=True, autodiff_fusible_nodes=[], # aliases inputs, shouldn't be fused autodiff_nonfusible_nodes=[], # aliases inputs, shouldn't be fused assert_jit_shape_analysis=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, # vmap does not support inplace views check_inplace_batched_forward_grad=False, # https://github.com/pytorch/pytorch/issues/66357 check_batched_forward_grad=False, sample_inputs_func=sample_inputs_squeeze), OpInfo('fill_', op=lambda x, scalar: torch.fill_(x.clone(), scalar), method_variant=None, inplace_variant=torch.Tensor.fill_, supports_forward_ad=True, supports_fwgrad_bwgrad=True, # https://github.com/pytorch/pytorch/issues/66357 check_batched_forward_grad=False, dtypes=all_types_and_complex_and(torch.complex32, torch.bool, torch.float16, torch.bfloat16), backward_dtypes=floating_and_complex_types_and(torch.float16, torch.bfloat16), backward_dtypesIfCUDA=floating_and_complex_types_and(torch.float16, torch.bfloat16, torch.chalf), supports_out=False, skips=( # JIT has issue when op is passed as lambda # AssertionError: JIT Test does not execute any logic DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # Fails due to a limitation of gradgradcheck # https://github.com/pytorch/pytorch/issues/59137 DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_fn_gradgrad'), DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_inplace_gradgrad'), DecorateInfo(unittest.skip('Allowed exemption'), 'TestCompositeCompliance', 'test_operator'), DecorateInfo(unittest.skip('Allowed exemption'), 'TestCompositeCompliance', 'test_backward'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), ), sample_inputs_func=sample_inputs_fill_), OpInfo('resize_', op=lambda x, shape: x.clone().resize_(shape), method_variant=None, inplace_variant=torch.Tensor.resize_, # the test fails because resize_ doesn't work with imag views as expected by the test # https://github.com/pytorch/pytorch/issues/65945 test_neg_view=False, dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), supports_out=False, supports_autograd=False, skips=( # Cannot resize variables that require grad DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_dtypes'), DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), DecorateInfo(unittest.skip("Allowed exception"), 'TestCompositeCompliance', 'test_operator'), ), sample_inputs_func=sample_inputs_resize_ops), OpInfo('resize_as_', op=lambda x, other: torch.resize_as_(x.clone(), other), method_variant=None, inplace_variant=torch.Tensor.resize_as_, dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), supports_out=False, supports_autograd=False, skips=( # Cannot resize variables that require grad DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_dtypes'), DecorateInfo(unittest.skip('Allowed exemption'), 'TestCompositeCompliance', 'test_operator'), ), sample_inputs_func=sample_inputs_resize_ops), OpInfo('take_along_dim', dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), supports_inplace_autograd=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_take_along_dim, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL), ShapeFuncInfo('tile', ref=np.tile, dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_repeat_tile), OpInfo('trapz', # TODO: in the future, 'trapz' should be made a proper alias of 'trapezoid' dtypes=all_types_and_complex_and(torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_trapezoid), OpInfo('trapezoid', dtypes=all_types_and_complex_and(torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_trapezoid), OpInfo('cumulative_trapezoid', dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bfloat16, torch.float16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False, sample_inputs_func=sample_cumulative_trapezoid,), OpInfo('unsqueeze', dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16, torch.chalf), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, # vmap does not support inplace views check_inplace_batched_forward_grad=False, assert_jit_shape_analysis=True, assert_autodiffed=True, autodiff_fusible_nodes=[], # aliases inputs, shouldn't be fused autodiff_nonfusible_nodes=[], # aliases inputs, shouldn't be fused sample_inputs_func=sample_unsqueeze), BinaryUfuncInfo('xlogy', aliases=('special.xlogy',), dtypes=all_types_and(torch.bool, torch.half, torch.bfloat16), promotes_int_to_float=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_one_python_scalar=True, skips=( # nan vs nan comparisons # https://github.com/pytorch/pytorch/issues/74279 DecorateInfo(unittest.skip("Skipped!"), 'TestGradients'), )), OpInfo('zero_', op=lambda x: torch.zero_(x.clone()), method_variant=None, inplace_variant=torch.Tensor.zero_, dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_gradgrad=True, skips=( DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), ), sample_inputs_func=sample_inputs_zero_), BinaryUfuncInfo('special.xlog1py', aten_name='special_xlog1py', dtypes=all_types_and(torch.bool, torch.half, torch.bfloat16), backward_dtypes=all_types_and(torch.bool, torch.bfloat16), backward_dtypesIfCUDA=all_types_and(torch.bool, torch.half, torch.bfloat16), promotes_int_to_float=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_one_python_scalar=True, skips=( # nan vs 0 comparisons # https://github.com/pytorch/pytorch/issues/74279 DecorateInfo(unittest.skip("Skipped!"), 'TestGradients'), )), BinaryUfuncInfo('special.zeta', aten_name='special_zeta', dtypes=all_types_and(torch.bool), promotes_int_to_float=True, supports_autograd=False, supports_one_python_scalar=True), # TODO: FIXME # OpInfo entry to verify the gradient formula of `other`/`q` # BinaryUfuncInfo('special.zeta', # op=lambda q, x, **kwargs: torch.special.zeta(x, q, **kwargs), # aten_name='special_zeta', # variant_test_name='grad', # dtypes=all_types_and(torch.bool), # promotes_int_to_float=True, # supports_autograd=True, # supports_rhs_python_scalar=False, # decorators=[ # # Derivative wrt first tensor not implemented # DecorateInfo(unittest.expectedFailure, "TestCommon", # "test_floating_inputs_are_differentiable") # ], # skips=( # # Lambda doesn't work in JIT test # # AssertionError: JIT Test does not execute any logic # DecorateInfo(unittest.skip("Skipped!"), "TestJit", "test_variant_consistency_jit"), # )), OpInfo('logsumexp', aliases=('special.logsumexp',), dtypes=all_types_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bool, torch.bfloat16, torch.half), assert_autodiffed=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_logsumexp), OpInfo('trace', dtypes=all_types_and_complex(), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), supports_inplace_autograd=False, supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_trace), OpInfo('transpose', ref=_numpy_ref_transpose, aliases=('swapdims', 'swapaxes'), assert_jit_shape_analysis=True, dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.half, torch.chalf), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, # vmap does not support inplace views check_inplace_batched_forward_grad=False, sample_inputs_func=sample_inputs_transpose_swapdims), OpInfo('T', op=lambda x: x.T, dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.half, torch.chalf), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # lambda impl DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), DecorateInfo(unittest.expectedFailure, "TestJit", "test_variant_consistency_jit"),), sample_inputs_func=sample_inputs_T), OpInfo('H', op=lambda x: x.H, dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.half, torch.chalf), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # lambda impl DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), DecorateInfo(unittest.expectedFailure, "TestJit", "test_variant_consistency_jit"),), sample_inputs_func=sample_inputs_T), OpInfo('mT', op=lambda x: x.mT, dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.half, torch.chalf), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # lambda impl DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), DecorateInfo(unittest.expectedFailure, "TestJit", "test_variant_consistency_jit"),), sample_inputs_func=sample_inputs_adjoint), OpInfo('mH', op=lambda x: x.mH, aliases=('adjoint',), dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.half, torch.chalf), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # lambda impl DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), DecorateInfo(unittest.expectedFailure, "TestJit", "test_variant_consistency_jit"),), sample_inputs_func=sample_inputs_adjoint), OpInfo('tril', dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half), supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_tril_triu), OpInfo('triu', dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half), supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_tril_triu), OpInfo('kron', dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16), supports_inplace_autograd=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_kron), OpInfo('inner', dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.float16, *[torch.bfloat16] if (CUDA11OrLater or TEST_WITH_ROCM) else []), dtypesIfROCM=floating_and_complex_types_and(torch.half, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_inner, ), OpInfo('tensordot', dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.float16, *[torch.bfloat16] if (CUDA11OrLater or TEST_WITH_ROCM) else []), dtypesIfROCM=floating_and_complex_types_and(torch.half, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_tensordot, skips=( # Skip operator schema test because this is a functional and not an operator. # Reference: https://github.com/pytorch/pytorch/issues/54574 DecorateInfo(unittest.skip("Skipped!"), 'TestOperatorSignatures', 'test_get_torch_func_signature_exhaustive'), ) ), OpInfo('to_sparse', op=lambda x, *args: x.to_sparse(*args), sample_inputs_func=sample_inputs_to_sparse, dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), backward_dtypes=floating_types(), backward_dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), supports_out=False, supports_sparse_csr=True, check_batched_grad=False, check_batched_gradgrad=False, skips=( # to_sparse does not support automatic differentiation for outputs with complex dtype DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_nondifferentiable', dtypes=(torch.cdouble,)), # NotImplementedError: Could not run 'aten::normal_' with arguments from the 'SparseCPU' backend DecorateInfo(unittest.skip(""), 'TestCommon', 'test_noncontiguous_samples'), # TODO: FIXME: complex inputs requiring grad error in forward DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_dtypes'), # lambda impl DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), # Allowed exception: sparse tensors don't have strides DecorateInfo(unittest.skip("Allowed exception"), 'TestCompositeCompliance', 'test_operator'), DecorateInfo(unittest.skip("Allowed exception"), 'TestCompositeCompliance', 'test_backward'), # TODO: implement csr.to_sparse(sample_dim) where sampled_dim is 1. DecorateInfo(unittest.skip("csr.to_sparse(1) not implemented. Skipped!"), 'TestSparseCSR', 'test_sparse_csr_consistency'), ) ), OpInfo('logcumsumexp', dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), backward_dtypes=floating_types_and(torch.bfloat16), backward_dtypesIfCUDA=floating_types_and(torch.bfloat16), skips=( # AssertionError: UserWarning not triggered : Resized a non-empty tensor but did not warn about it. DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out_warning', device_type='cuda'), ), sample_inputs_func=sample_inputs_logcumsumexp), UnaryUfuncInfo('sigmoid', aliases=('special.expit', 'nn.functional.sigmoid'), aten_backward_name='sigmoid_backward', ref=reference_sigmoid if TEST_SCIPY else _NOTHING, decorators=(precisionOverride({torch.float16: 1e-2, torch.complex64: 1e-1, torch.bfloat16: 1e-2}),), skips=( # Reference: https://github.com/pytorch/pytorch/issues/56012 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', dtypes=[torch.complex64, torch.cdouble]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', dtypes=[torch.chalf, torch.complex64, torch.cdouble]), # alias, nn.functional.sigmoid, will produce (because of warning string saved): # "RuntimeError: Expected to not find "sigmoid" but found it" DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_jit_alias_remapping')), dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.complex32, torch.bool, torch.half, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, assert_autodiffed=True, # sigmoid(z) = 1 / (1 + exp(-z)), at z = j * pi * odd_number, the denominator is zero reference_numerics_filter=NumericsFilter( condition=lambda x: (close_to_int(x / (math.pi * 1j)) if x.is_complex() else x.new_tensor(False, dtype=torch.bool)), safe_val=0)), UnaryUfuncInfo('digamma', ref=scipy.special.digamma if TEST_SCIPY else _NOTHING, aliases=('special.psi', 'special.digamma',), decorators=(precisionOverride({torch.float16: 5e-1}),), dtypes=all_types_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bool, torch.half), supports_forward_ad=True, supports_fwgrad_bwgrad=True), UnaryUfuncInfo('special.entr', ref=scipy.special.entr if TEST_SCIPY else _NOTHING, aten_name='special_entr', supports_forward_ad=True, supports_fwgrad_bwgrad=True, decorators=(precisionOverride({torch.float16: 1e-1, torch.bfloat16: 1e-1}),), dtypes=all_types_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bool, torch.half, torch.bfloat16), skips=( DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', dtypes=[torch.bfloat16, torch.float16]), ), supports_inplace_autograd=False, sample_inputs_func=sample_inputs_entr), UnaryUfuncInfo('special.ndtri', ref=scipy.special.ndtri if TEST_SCIPY else _NOTHING, domain=(0, 1), aten_name='special_ndtri', dtypes=all_types_and(torch.bool), supports_forward_ad=True, supports_fwgrad_bwgrad=True), UnaryUfuncInfo('special.log_ndtr', aten_name='special_log_ndtr', ref=scipy.special.log_ndtr if TEST_SCIPY else _NOTHING, dtypes=all_types_and(torch.bool), supports_forward_ad=True, supports_fwgrad_bwgrad=True, ), UnaryUfuncInfo('erf', ref=scipy.special.erf if TEST_SCIPY else _NOTHING, aliases=('special.erf', ), decorators=(precisionOverride({torch.float16: 1e-2, torch.bfloat16: 1e-2}),), skips=( DecorateInfo(unittest.skip("Skipped! sparse backward not supported"), 'TestSparseUnaryUfuncs', 'test_sparse_fn_grad'), ), dtypes=all_types_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bool, torch.half, torch.bfloat16), assert_autodiffed=True, assert_jit_shape_analysis=True, supports_sparse=True, supports_sparse_csr=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True), UnaryUfuncInfo('erfc', ref=scipy.special.erfc if TEST_SCIPY else _NOTHING, aliases=('special.erfc', ), decorators=(precisionOverride({torch.float16: 1e-2, torch.bfloat16: 1e-2}),), dtypes=all_types_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bool, torch.half, torch.bfloat16), assert_autodiffed=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True), UnaryUfuncInfo('erfinv', ref=scipy.special.erfinv if TEST_SCIPY else _NOTHING, aliases=('special.erfinv', ), decorators=(precisionOverride({torch.float16: 1e-2, torch.bfloat16: 1e-2, torch.float32: 1e-4}),), dtypes=all_types_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bool, torch.half), supports_sparse_csr=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, domain=(-1, 1), skips=( # Reference: https://github.com/pytorch/pytorch/pull/49155#issuecomment-742664611 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', active_if=TEST_SCIPY and LooseVersion(scipy.__version__) < "1.4.0"), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', active_if=TEST_SCIPY and LooseVersion(scipy.__version__) < "1.4.0"), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_small', active_if=TEST_SCIPY and LooseVersion(scipy.__version__) < "1.4.0"), )), OpInfo("nn.functional.smooth_l1_loss", ref=reference_smooth_l1_loss, sample_inputs_func=sample_inputs_smooth_l1_loss, dtypes=floating_types_and(torch.float16, torch.bfloat16), backward_dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16), backward_dtypesIfCUDA=floating_types_and(torch.float16), supports_out=False, supports_forward_ad=True, skips=( # RuntimeError: input->type()->kind() == TypeKind::OptionalTypeINTERNAL ASSERT FAILED # at "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":270, please report a bug to PyTorch. DecorateInfo(unittest.expectedFailure, "TestJit", "test_variant_consistency_jit"),)), OpInfo( "nn.functional.l1_loss", ref=loss_reference_reduction_wrapper(lambda input, target: np.abs(input - target)), aten_backward_name='l1_loss_backward', sample_inputs_func=sample_inputs_l1_loss, dtypes=floating_and_complex_types_and(torch.float16, torch.bfloat16), backward_dtypes=all_types_and(torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, skips=( # RuntimeError: input->type()->kind() == TypeKind::OptionalTypeINTERNAL ASSERT FAILED # at "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":270, please report a bug to PyTorch. DecorateInfo( unittest.expectedFailure, "TestJit", "test_variant_consistency_jit", dtypes=(torch.float32,), ), ), ), UnaryUfuncInfo('lgamma', ref=reference_lgamma if TEST_SCIPY else _NOTHING, aliases=('special.gammaln', ), decorators=(precisionOverride({torch.float16: 7e-1}),), dtypes=all_types_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bool, torch.half), supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # Reference: https://github.com/pytorch/pytorch/pull/50140#discussion_r552615345 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', dtypes=[torch.bfloat16]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', device_type='cpu', dtypes=[torch.bfloat16]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_small', device_type='cpu', dtypes=[torch.bfloat16]), # Reference: https://github.com/pytorch/pytorch/pull/50140#issuecomment-756150214 DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', dtypes=[torch.float32, torch.float64], active_if=IS_WINDOWS), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_large', dtypes=[torch.float32, torch.float64], active_if=IS_WINDOWS), ), # lgamma have multiple singularities at x <= 0 reference_numerics_filter=NumericsFilter(condition=lambda x: x < 0.1, safe_val=1)), OpInfo( 'logdet', dtypes=floating_types(), supports_out=False, sample_inputs_func=sample_inputs_logdet, decorators=(skipCPUIfNoLapack, skipCUDAIfNoMagma)), # `log_softmax` supports different dtypes based on whether `dtype` argument, # is passed or not. Hence two OpInfo entries, one with dtype and other without. OpInfo( 'log_softmax', aliases=('special.log_softmax', 'nn.functional.log_softmax'), supports_out=True, aten_backward_name='_log_softmax_backward_data', dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), sample_inputs_func=sample_inputs_softmax_variant, supports_forward_ad=True, assert_autodiffed=True), OpInfo( 'log_softmax', variant_test_name='dtype', aliases=('special.log_softmax', 'nn.functional.log_softmax'), supports_out=True, dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), sample_inputs_func=partial(sample_inputs_softmax_variant, with_dtype=True), supports_forward_ad=True, assert_autodiffed=True), UnaryUfuncInfo('logit', aten_backward_name='logit_backward', ref=scipy.special.logit if TEST_SCIPY else _NOTHING, domain=(0, 1), aliases=('special.logit', ), supports_forward_ad=True, supports_fwgrad_bwgrad=True, decorators=(precisionOverride({torch.bfloat16: 5e-1, torch.float16: 5e-1}),), dtypes=all_types_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.bool, torch.half, torch.bfloat16), sample_inputs_func=sample_inputs_logit), OpInfo('where', # Currently only the `input` is tested in gradcheck. # If we pass `condition` first, none of the input which supports # autograd will be tested. Hence the following lambda. op=lambda self, condition, other: torch.where(condition, self, other), ref=lambda self, condition, other: np.where(condition, self, other), sample_inputs_func=sample_inputs_where, error_inputs_func=error_inputs_where, supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, decorators=( DecorateInfo(onlyCUDA, "TestCommon", 'test_errors'),), skips=( # lambda impl DecorateInfo(unittest.expectedFailure, "TestNormalizeOperators", "test_normalize_operator_exhaustive"), DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'), ), dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16)), OpInfo('nonzero', dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16, torch.float16), sample_inputs_func=sample_inputs_nonzero, supports_autograd=False, skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # nonzero(): argument 'out' must be Tensor, not tuple DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out'), # https://github.com/pytorch/pytorch/issues/67458 DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # nonzero is not raising a warning when the out is resized DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out_warning'), # Can't find schemas for this operator for some reason DecorateInfo(unittest.expectedFailure, 'TestOperatorSignatures', 'test_get_torch_func_signature_exhaustive'), )), # Following tests are for jiterator's python interface # Jiterator can be used to author elementwise CUDA kernel # jiterator._create_jit_fn returns a callable that behaves like a regular pytorch op # See create_jit_fn in jiterator.py for more information UnaryUfuncInfo( 'jiterator_unary', op=torch.cuda.jiterator._create_jit_fn("template T unary(T x) { return x * x + x; }"), ref=lambda x: x * x + x, dtypes=all_types_and_complex_and(torch.bfloat16, torch.float16, torch.bool), supports_out=False, supports_autograd=False, # jiterator ops doesn't have backward defined decorators=[ onlyCUDA, skipCUDAIfRocm, DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-02, rtol=1e-02)}), 'TestUnaryUfuncs', 'test_reference_numerics_extremal'), DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-02, rtol=1e-02)}), 'TestUnaryUfuncs', 'test_reference_numerics_hard'), DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-02, rtol=1e-02)}), 'TestUnaryUfuncs', 'test_reference_numerics_normal'), DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-02, rtol=1e-02)}), 'TestUnaryUfuncs', 'test_reference_numerics_small'), ], skips=( # Jiterator ops doesn't support neg or conj view DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_neg_view'), DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_conj_view'), DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_neg_conj_view'), # Jiterator ops doesn't suport CompositeCompliantTensor # Following test should expectedFailure, but it's causing cascading failures in CUDA, thus skipped DecorateInfo(unittest.skip("skip"), 'TestCompositeCompliance', 'test_operator'), # Skip reference_numerics tests for bool type, as the defined function doesn't work for bool DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_extremal', dtypes=[torch.bool]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_hard', dtypes=[torch.bool]), DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_normal', dtypes=[torch.bool]), # Expected failure: torch.jiterator_unary is not a valid op DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # Skip Nvfuser DecorateInfo(unittest.skip('Skipped!'), 'TestCudaFuserOpInfo'), ) ), BinaryUfuncInfo( 'jiterator_binary', op=torch.cuda.jiterator._create_jit_fn( "template T binary(T x, T y, T alpha) { return x + alpha * y; }", alpha=1), ref=lambda input, other, *, alpha=1: np.add(input, other) if alpha == 1 \ else np.add(input, np.multiply(alpha, other)), dtypes=all_types_and_complex_and(torch.bfloat16, torch.float16, torch.bool), sample_inputs_func=partial(sample_inputs_jiterator, num_inputs=2, alpha=-3.14), supports_out=False, supports_autograd=False, # jiterator ops doesn't have backward defined supports_rhs_python_scalar=False, decorators=[onlyCUDA, skipCUDAIfRocm], skips=( # Jiterator ops doesn't support neg or conj view DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_neg_view'), DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_conj_view'), DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_neg_conj_view'), # Jiterator ops doesn't suport CompositeCompliantTensor # Following test should expectedFailure, but it's causing cascading failures in CUDA, thus skipped DecorateInfo(unittest.skip("skip"), 'TestCompositeCompliance', 'test_operator'), # Expected failure: torch.jiterator_binary is not a valid op DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # Skip Nvfuser DecorateInfo(unittest.skip('Skipped!'), 'TestCudaFuserOpInfo'), ) ), OpInfo( 'jiterator_4inputs_with_extra_args', op=torch.cuda.jiterator._create_jit_fn( "template T binary(T i0, T i1, T i2, T i3, T alpha, T beta) { return alpha * i0 + beta * i1 + i2 + i3; }", alpha=1, beta=1), ref=lambda i0, i1, i2, i3, *, alpha=1, beta=1: alpha * i0 + beta * i1 + i2 + i3, dtypes=all_types_and_complex_and(torch.bfloat16, torch.float16, torch.bool), sample_inputs_func=partial(sample_inputs_jiterator, num_inputs=4, alpha=3.14, beta=-4.20), supports_out=False, supports_autograd=False, # jiterator ops doesn't have backward defined decorators=[onlyCUDA, skipCUDAIfRocm], skips=( # Jiterator ops doesn't support neg or conj view DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_neg_view'), DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_conj_view'), DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_neg_conj_view'), # Jiterator ops doesn't suport CompositeCompliantTensor # Following test should expectedFailure, but it's causing cascading failures in CUDA, thus skipped DecorateInfo(unittest.skip("skip"), 'TestCompositeCompliance', 'test_operator'), # Expected failure: torch.jiterator_4inputs_with_extra_args is not a valid op DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # Skip Nvfuser DecorateInfo(unittest.skip('Skipped!'), 'TestCudaFuserOpInfo'), ) ), # `torch.norm` has multiple code paths depending on the value of `p`. # These paths have different dtype support. Also JIT supports, # most variants but not all of them. So we split the OpInfo entries, # for `norm` based on the code-paths and JIT support. OpInfo( "norm", sample_inputs_func=sample_inputs_norm, dtypes=floating_and_complex_types_and(torch.float16, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # AssertionError: RuntimeError not raised : Expected RuntimeError when doing an unsafe cast from a result # of dtype torch.float32 into an out= with dtype torch.long DecorateInfo( unittest.expectedFailure, "TestCommon", "test_out", device_type="meta", ), DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_fn_fwgrad_bwgrad', dtypes=[torch.complex128]), ), ), OpInfo('norm', variant_test_name='nuc', sample_inputs_func=sample_inputs_norm_nuc, decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack], check_batched_gradgrad=False, # torch.autograd.gradcheck.GradcheckError: While computing batched gradients # got: Could not allocate memory to change Tensor SizesAndStrides! check_batched_forward_grad=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, dtypes=floating_and_complex_types(), dtypesIfCUDA=floating_and_complex_types(), skips=( # RuntimeError not raised : # Expected RuntimeError when calling with input.device=cpu and out.device=cuda DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out'), # RuntimeError: # Arguments for call are not valid. DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit', dtypes=(torch.complex64, torch.float32,)), # noqa: B950 ) ), OpInfo('norm', variant_test_name='fro', sample_inputs_func=sample_inputs_norm_fro, dtypes=floating_and_complex_types_and(torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.float16, torch.bfloat16), supports_forward_ad=True, # torch.autograd.gradcheck.GradcheckError: While computing batched gradients # got: Could not allocate memory to change Tensor SizesAndStrides! check_batched_forward_grad=False, supports_fwgrad_bwgrad=True, skips=( # Pre-existing condition; Needs to be fixed DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_operator'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_backward'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), # Expected RuntimeError when calling with input.device=cpu and out.device=cuda DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_out'), # Arguments for call are not valid. DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit', dtypes=(torch.complex64, torch.float32,)), # noqa: B950 )), OpInfo( "norm", variant_test_name="inf", sample_inputs_func=sample_inputs_norm_inf, dtypes=floating_and_complex_types_and(torch.float16, torch.bfloat16), supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( # https://github.com/pytorch/pytorch/issues/67517 DecorateInfo(unittest.skip("Skipped!"), "TestCommon", "test_noncontiguous_samples"), # following 2 tests failed intermittenly DecorateInfo( unittest.skip("Skipped!"), "TestGradients", "test_fn_grad", device_type="cpu", dtypes=(torch.complex128,), ), DecorateInfo( unittest.skip("Skipped!"), "TestGradients", "test_fn_gradgrad", device_type="cpu", dtypes=(torch.complex128,), ), DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_fn_fwgrad_bwgrad', dtypes=[torch.complex128]), # AssertionError: RuntimeError not raised : Expected RuntimeError when doing an unsafe cast from a result # of dtype torch.float32 into an out= with dtype torch.long DecorateInfo( unittest.expectedFailure, "TestCommon", "test_out", device_type="meta", ), ), ), OpInfo('t', sample_inputs_func=sample_inputs_t, supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, # vmap does not support inplace views check_inplace_batched_forward_grad=False, autodiff_fusible_nodes=[], # aliases inputs, shouldn't be fused autodiff_nonfusible_nodes=[], # aliases inputs, shouldn't be fused dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), assert_autodiffed=True,), UnaryUfuncInfo('special.erfcx', ref=scipy.special.erfcx if TEST_SCIPY else _NOTHING, aten_name='special_erfcx', decorators=(toleranceOverride({torch.float32: tol(atol=0, rtol=4e-6), }),), dtypes=all_types_and(torch.bool), supports_forward_ad=True, supports_fwgrad_bwgrad=True), OpInfo( "nn.functional.dropout", op=lambda input, *args, **kwargs: wrapper_set_seed(torch.nn.functional.dropout, input, *args, **kwargs), ref=_NOTHING, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # Probably because we have used lambda for the op here # AssertionError: JIT Test does not execute any logic DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # inplace variant dispatches to dropout kernel, while on CUDA # the op dispatches to _fused_dropout (with a few more conditions) # hence, different values and this skip here DecorateInfo(unittest.skip("Skipped!"), 'TestMathBits', 'test_neg_view', device_type='cuda'),), gradcheck_wrapper=wrapper_set_seed, supports_forward_ad=True, supports_fwgrad_bwgrad=True, # https://github.com/pytorch/pytorch/issues/66357 check_batched_forward_grad=False, supports_out=False, sample_inputs_func=sample_inputs_dropout, inplace_variant=lambda input, *args, **kwargs: wrapper_set_seed(torch.nn.functional.dropout, input, *args, **kwargs, inplace=True)), OpInfo( "nn.functional.dropout2d", op=lambda input, *args, **kwargs: wrapper_set_seed(torch.nn.functional.dropout2d, input, *args, **kwargs), ref=_NOTHING, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), skips=( # lambda impl DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # torch.autograd.gradcheck.GradcheckError: While computing batched gradients, got: # vmap: We do not yet support calling random operations inside of vmap. # Please perform random operations outside of vmap as a workaround DecorateInfo(unittest.expectedFailure, 'TestGradients', "test_forward_mode_AD"), DecorateInfo(unittest.expectedFailure, 'TestGradients', "test_inplace_forward_mode_AD"),), gradcheck_wrapper=wrapper_set_seed, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False, # As per the docs, valid input dims are (3, 4) sample_inputs_func=partial(sample_inputs_dropout, valid_input_dim=(3, 4)), inplace_variant=lambda input, *args, **kwargs: wrapper_set_seed(torch.nn.functional.dropout2d, input, *args, **kwargs, inplace=True)), # In training mode, feature_alpha_dropout currently doesn't support inputs of complex dtype # unlike when `train=False`, it supports complex inputs, hence 2 OpInfos to cover all cases OpInfo( "nn.functional.feature_alpha_dropout", op=lambda input, *args, **kwargs: wrapper_set_seed(torch.nn.functional.feature_alpha_dropout, input, *args, **kwargs), variant_test_name="with_train", ref=_NOTHING, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), skips=( # lambda impl DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # torch.autograd.gradcheck.GradcheckError: While computing batched gradients, got: # vmap: We do not yet support calling random operations inside of vmap. # Please perform random operations outside of vmap as a workaround DecorateInfo(unittest.expectedFailure, 'TestGradients', "test_forward_mode_AD"), DecorateInfo(unittest.expectedFailure, 'TestGradients', "test_inplace_forward_mode_AD"),), gradcheck_wrapper=wrapper_set_seed, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False, # As per the docs, valid input dims are (4, 5) sample_inputs_func=partial(sample_inputs_dropout, train=True, valid_input_dim=(4, 5)), inplace_variant=lambda input, *args, **kwargs: wrapper_set_seed(torch.nn.functional.feature_alpha_dropout, input, *args, **kwargs, inplace=True)), OpInfo( "nn.functional.feature_alpha_dropout", op=lambda input, *args, **kwargs: wrapper_set_seed(torch.nn.functional.feature_alpha_dropout, input, *args, **kwargs), variant_test_name="without_train", ref=_NOTHING, dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), skips=( # lambda impl DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'),), gradcheck_wrapper=wrapper_set_seed, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False, sample_inputs_func=partial(sample_inputs_dropout, train=False), inplace_variant=lambda input, *args, **kwargs: wrapper_set_seed(torch.nn.functional.feature_alpha_dropout, input, *args, **kwargs, inplace=True)), OpInfo( "nn.functional.one_hot", ref=reference_one_hot, supports_out=False, dtypes=_dispatch_dtypes((torch.int64,)), sample_inputs_func=sample_inputs_one_hot, ), OpInfo( "nn.functional.embedding", aten_backward_name="embedding_dense_backward", # We use lambda to reshuffle the positional arguments. # This is because currently only the `input` field of SampleInput # is tested in gradient tests. op=lambda weight, idx, **kwargs: torch.nn.functional.embedding(idx, weight, **kwargs), dtypes=floating_types_and(torch.bfloat16, torch.float16), sample_inputs_func=sample_inputs_embedding, error_inputs_func=error_inputs_embedding, supports_forward_ad=True, skips=( # lambda impl DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # Reference: https://github.com/pytorch/pytorch/issues/67084 DecorateInfo(unittest.skip("Skipped!"), 'TestMathBits', 'test_neg_view', device_type='cuda'), # Not a problem: embedding does weird stuff to its input (it renormalizes) DecorateInfo(unittest.skip('Allowed exemption'), 'TestCompositeCompliance', 'test_operator'), ), supports_expanded_weight=True, supports_out=False, ), OpInfo( "nn.functional.embedding_bag", # We use lambda to reshuffle the positional arguments. # This is because currently only the `input` field of SampleInput # is tested in gradient tests. op=lambda weight, idx, **kwargs: torch.nn.functional.embedding_bag(idx, weight, **kwargs), dtypes=floating_types_and(torch.float16), dtypesIfCUDA=floating_types_and(torch.bfloat16, torch.float16), # backward is not supported for mode `max` and dtype `bfloat16` backward_dtypesIfCUDA=floating_types_and(torch.float16), sample_inputs_func=sample_inputs_embedding_bag, skips=( # lambda impl DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # Not a problem: embedding_bag does weird stuff to its input (it renormalizes) DecorateInfo(unittest.skip('Allowed exemption'), 'TestCompositeCompliance', 'test_operator'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_backward', device_type='cpu'), ), gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, supports_out=False, supports_gradgrad=False, ), OpInfo( "nn.functional.softplus", aten_backward_name='softplus_backward', ref=reference_softplus, sample_inputs_func=sample_inputs_softplus, supports_forward_ad=True, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.bfloat16, torch.float16), ), OpInfo( "linalg.tensorinv", ref=np.linalg.tensorinv, dtypes=floating_and_complex_types(), sample_inputs_func=sample_inputs_tensorinv, supports_forward_ad=True, supports_fwgrad_bwgrad=True, decorators=[skipCPUIfNoLapack, skipCUDAIfNoMagmaAndNoCusolver], ), OpInfo( "linalg.tensorsolve", ref=lambda a, b, dims=None: np.linalg.tensorsolve(a, b, axes=dims), dtypes=floating_and_complex_types(), sample_inputs_func=sample_inputs_tensorsolve, supports_forward_ad=True, supports_fwgrad_bwgrad=True, decorators=[skipCPUIfNoLapack, skipCUDAIfNoMagma], ), OpInfo( "nn.functional.mse_loss", aten_backward_name='mse_loss_backward', ref=loss_reference_reduction_wrapper(lambda input, target: (input - target) ** 2), sample_inputs_func=sample_inputs_loss, supports_out=False, supports_forward_ad=True, dtypes=floating_types_and(torch.float16), backward_dtypes=floating_types(), dtypesIfCUDA=floating_types_and(torch.bfloat16, torch.float16), backward_dtypesIfCUDA=floating_types_and(torch.bfloat16, torch.float16), skips=( # RuntimeError: input->type()->kind() == TypeKind::OptionalType # INTERNAL ASSERT FAILED at "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":252, # please report a bug to PyTorch. DecorateInfo(unittest.expectedFailure, "TestJit", "test_variant_consistency_jit", dtypes=(torch.float32,),), ), ), OpInfo( "nn.functional.grid_sample", ref=_NOTHING, dtypes=floating_types(), dtypesIfCUDA=floating_types_and(torch.float16), supports_out=False, sample_inputs_func=sample_inputs_grid_sample, supports_gradgrad=False, gradcheck_nondet_tol=1e-15), OpInfo( "argwhere", ref=np.argwhere, dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), supports_out=False, supports_autograd=False, sample_inputs_func=sample_inputs_argwhere, ), ReductionOpInfo( 'all', identity=True, supports_multiple_dims=False, supports_autograd=False, result_dtype=torch.bool, dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), ref=reference_reduction_numpy(np.all), skips=( # FIXME: does not support passing keepdim without dim DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_default_keepdim'), # FIXME: does not support dim=None DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_none'), DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_none_keepdim'), # FIXME: uint8 input returns uint8 instead of bool DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_result_dtype', dtypes=[torch.uint8]), ), ), ReductionOpInfo( 'any', identity=False, supports_multiple_dims=False, supports_autograd=False, result_dtype=torch.bool, dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), ref=reference_reduction_numpy(np.any), skips=( # FIXME: does not support passing keepdim without dim DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_default_keepdim'), # FIXME: does not support dim=None DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_none'), DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_none_keepdim'), # FIXME: uint8 input returns uint8 instead of bool DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_result_dtype', dtypes=[torch.uint8]), ), ), ReductionOpInfo( 'amax', nan_policy='propagate', dtypes=all_types_and(torch.float16, torch.bfloat16, torch.bool), ref=reference_reduction_numpy(np.amax), skips=( # FIXME: reduces all dimensions when dim=[] DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_empty'), DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_empty_keepdim'), ), error_inputs_func=error_inputs_aminmax_amax_amin, ), ReductionOpInfo( 'amin', nan_policy='propagate', dtypes=all_types_and(torch.float16, torch.bfloat16, torch.bool), ref=reference_reduction_numpy(np.amin), skips=( # FIXME: reduces all dimensions when dim=[] DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_empty'), DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_empty_keepdim'), ), error_inputs_func=error_inputs_aminmax_amax_amin, ), ReductionOpInfo( 'argmax', supports_multiple_dims=False, supports_autograd=False, assert_jit_shape_analysis=True, result_dtype=torch.int64, dtypes=all_types_and(torch.float16, torch.bfloat16), ref=reference_reduction_numpy(np.argmax, supports_keepdims=False), skips=( # FIXME: keepdim parameter is ignored when dim=None DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_default_keepdim'), DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_none_keepdim'), ), ), ReductionOpInfo( 'argmin', supports_multiple_dims=False, supports_autograd=False, result_dtype=torch.int64, dtypes=all_types_and(torch.float16, torch.bfloat16), ref=reference_reduction_numpy(np.argmin, supports_keepdims=False), skips=( # FIXME: keepdim parameter is ignored when dim=None DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_default_keepdim'), DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_none_keepdim'), ), ), ReductionOpInfo( 'count_nonzero', identity=0, supports_out=False, supports_autograd=False, result_dtype=torch.int64, dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), sample_inputs_func=sample_inputs_reduction_count_nonzero, ref=reference_reduction_numpy(np.count_nonzero), skips=( # FIXME: count_nonzero does not accept keepdim kwarg DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_default_keepdim'), DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_none_keepdim'), DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_single_keepdim'), DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_empty_keepdim'), DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_multi_keepdim'), DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_multi_unsorted_keepdim'), DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_offbounds_keepdim'), # FIXME: dim=[] reduces all dimensions DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_empty'), ), ), ReductionOpInfo( 'mean', nan_policy='propagate', supports_forward_ad=True, supports_fwgrad_bwgrad=True, # FIXME: mean needs 'dim' parameter when using the 'out' overload. # Adding it with 'generate_args_kwargs' does not work, since these also get passed # onto the reference implementations. supports_out=False, assert_autodiffed=True, assert_jit_shape_analysis=True, promotes_int_to_float=True, dtypes=floating_and_complex_types_and(torch.float16, torch.bfloat16), ref=reference_reduction_numpy(np.mean), error_inputs_func=error_inputs_mean, skips=( # FIXME: mean does not support passing keepdim without passing dim DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_default_keepdim'), # FIXME: mean reduces all dimensions when dim=[] DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_empty'), DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_empty_keepdim'), # FIXME: mean does not support passing None to dim DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_none'), DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_none_keepdim'), # FIXME: improve precision DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_ref_small_input', dtypes=[torch.float16]), DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_ref_extremal_values', device_type='cuda', dtypes=[torch.complex64]), ), ), ReductionOpInfo( 'nanmean', nan_policy='omit', assert_autodiffed=True, promotes_int_to_float=True, dtypes=floating_types_and(torch.float16, torch.bfloat16), sample_inputs_func=sample_inputs_nan_reduction(supports_multiple_dims=True), ref=reference_reduction_numpy(np.nanmean), skips=( # AssertionError: False is not true : # Failure in testing nodes' autodifferentiation. DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'), # FIXME: prod reduces all dimensions when dim=[] DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_empty'), DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_empty_keepdim'), # FIXME: improve precision DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_ref_small_input', dtypes=[torch.float16]), DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_ref_duplicate_values', device_type='cuda', dtypes=[torch.float16]), DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_ref_extremal_values', device_type='cuda', dtypes=[torch.complex64]), ), ), ReductionOpInfo( 'std', nan_policy='propagate', supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, assert_autodiffed=True, promotes_int_to_float=True, dtypes=floating_and_complex_types_and(torch.half, torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.half, torch.bfloat16), sample_inputs_func=sample_inputs_std_var, ref=reference_std_var(np.std), generate_args_kwargs=generate_std_var_kwargs, skips=( # FIXME: cannot specify keepdim without dim DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_default_keepdim'), # FIXME: dim=None not supported DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_none'), DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_none_keepdim'), # FIXME: dim=[] reduces all dimensions DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_empty'), DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_empty_keepdim'), # TODO(@heitorschueroff) std return float for complex types # need to find a better way to model result dtype DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_result_dtype'), # FIXME: improve precision DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_ref_small_input'), DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_ref_duplicate_values'), # NumPy is giving NaN for this DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_ref_large_input'), ), ), ReductionOpInfo( 'var', nan_policy='propagate', supports_out=False, assert_autodiffed=True, promotes_int_to_float=True, supports_forward_ad=True, supports_fwgrad_bwgrad=True, dtypes=floating_and_complex_types_and(torch.half, torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.half, torch.bfloat16), sample_inputs_func=sample_inputs_std_var, ref=reference_std_var(np.var), generate_args_kwargs=generate_std_var_kwargs, skips=( # FIXME: cannot specify keepdim without dim DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_default_keepdim'), # FIXME: dim=None not supported DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_none'), DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_none_keepdim'), # FIXME: dim=[] reduces all dimensions DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_empty'), DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_empty_keepdim'), # TODO(@heitorschueroff) std return float for complex types # need to find a better way to model result dtype DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_result_dtype'), # FIXME: improve precision DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_ref_small_input'), DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_ref_duplicate_values'), # NumPy is giving NaN for this DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_ref_large_input'), ), ), ReductionOpInfo( 'prod', identity=1, nan_policy='propagate', supports_multiple_dims=False, supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, promotes_int_to_int64=True, gradcheck_nondet_tol=GRADCHECK_NONDET_TOL, dtypes=all_types_and_complex_and(torch.bool), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16, torch.chalf), sample_inputs_func=sample_inputs_prod, ref=reference_reduction_numpy(np.prod), skips=( # FIXME: prod does not support passing keepdim without passing dim DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_default_keepdim'), # FIXME: prod reduces all dimensions when dim=[] DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_empty'), DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_empty_keepdim'), # FIXME: prod does not support passing None to dim DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_none'), DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_none_keepdim'), DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_ref_small_input', dtypes=[torch.float16, torch.complex64]), DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_ref_duplicate_values', dtypes=[torch.uint8, torch.float16, torch.complex64]), ), ), ReductionOpInfo( 'sum', identity=0, nan_policy='propagate', supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, promotes_int_to_int64=True, dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16, torch.chalf), ref=reference_reduction_numpy(np.sum), skips=( # FIXME: sum does not support passing keepdim without passing dim DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_default_keepdim'), # FIXME: sum reduces all dimensions when dim=[] DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_empty'), DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_empty_keepdim'), # FIXME: sum does not support passing None to dim DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_none'), DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_none_keepdim'), # FIXME: improve precision DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_ref_small_input', dtypes=[torch.float16]), DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_ref_duplicate_values', dtypes=[torch.float16]), ), ), ReductionOpInfo( 'nansum', identity=0, nan_policy='omit', supports_out=True, promotes_int_to_int64=True, dtypes=all_types_and(torch.bool, torch.float16, torch.bfloat16), ref=reference_reduction_numpy(np.nansum), skips=( # FIXME: nansum reduces all dimensions when dim=[] DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_empty'), DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_empty_keepdim'), # FIXME: flaky test so skipped instead of xfailed # possibly bad low precision reference in numpy DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_ref_small_input', dtypes=[torch.float16]), ), ), ReductionOpInfo( '_masked.sum', ref=reference_reduction_numpy(np.sum), method_variant=None, identity=0, nan_policy='propagate', supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_sparse=True, supports_sparse_csr=True, promotes_int_to_int64=True, dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), skips=( DecorateInfo(unittest.skip("Failing on some jobs"), 'TestReductions', 'test_reference_masked', dtypes=(torch.bool, torch.int8, torch.int16, torch.int32)), DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # FIXME: sum reduces all dimensions when dim=[] DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_empty'), DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_empty_keepdim'), # RuntimeError: undefined value tensor DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # see https://github.com/pytorch/pytorch/issues/76227 DecorateInfo(unittest.skip("Fails on UBSAN!"), 'TestCompositeCompliance', 'test_forward_ad', device_type='cpu'), ), decorators=[ DecorateInfo(toleranceOverride({torch.bfloat16: tol(atol=1e-03, rtol=1e-03)}), 'TestReductions', 'test_reference_masked'), DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-03, rtol=1e-03)}), 'TestReductions', 'test_reference_masked'), DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-02, rtol=1e-03)}), 'TestReductions', 'test_ref_small_input'), ], sample_inputs_func=sample_inputs_masked_reduction, sample_inputs_sparse_coo_func=sample_inputs_sparse_coo_masked_reduction, sample_inputs_sparse_csr_func=sample_inputs_sparse_csr_masked_reduction ), ReductionOpInfo( '_masked.prod', ref=reference_reduction_numpy(np.prod), method_variant=None, identity=1, nan_policy='propagate', supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_sparse=True, supports_sparse_csr=True, promotes_int_to_int64=True, # FIXME: "prod_cpu" not implemented for 'BFloat16' # FIXME: "prod_cpu" not implemented for 'Half' dtypes=all_types_and_complex_and(torch.bool), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), DecorateInfo(unittest.skip("Failing on some jobs"), 'TestReductions', 'test_reference_masked', dtypes=(torch.bool, torch.int8, torch.int16, torch.int32),), # see https://github.com/pytorch/pytorch/issues/76227 DecorateInfo(unittest.skip("Fails on UBSAN!"), 'TestCompositeCompliance', 'test_forward_ad', device_type='cpu'), # FIXME: "cuda_scatter_gather_base_kernel_func" not implemented for ... (used for sparse_coo inputs) DecorateInfo(unittest.skip("Skipped!"), 'TestMasked', 'test_mask_layout', device_type='cuda', dtypes=(torch.bool, torch.int8, torch.uint8, torch.int16, torch.int32, torch.int64, torch.complex64, torch.complex128)), ), decorators=[ DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-03, rtol=1e-02)}), 'TestReductions', 'test_reference_masked'), DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-03, rtol=1e-03)}), 'TestReductions', 'test_ref_duplicate_values'), ], sample_inputs_func=sample_inputs_masked_reduction, sample_inputs_sparse_coo_func=sample_inputs_sparse_coo_masked_reduction, sample_inputs_sparse_csr_func=sample_inputs_sparse_csr_masked_reduction, ), OpInfo( '_masked.cumsum', dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.float16, torch.bfloat16), method_variant=None, supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # NotSupportedError: Compiled functions can't ... use keyword-only arguments with defaults DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'), ), # Can reuse the same inputs; dim is required in both sample_inputs_func=sample_inputs_masked_cumops, gradcheck_wrapper=gradcheck_wrapper_masked_operation, ), OpInfo( '_masked.cumprod', dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.float16, torch.bfloat16), method_variant=None, supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # NotSupportedError: Compiled functions can't ... use keyword-only arguments with defaults DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'), ), # Can reuse the same inputs; dim is required in both sample_inputs_func=sample_inputs_masked_cumops, gradcheck_wrapper=gradcheck_wrapper_masked_operation, ), ReductionOpInfo( '_masked.amax', nan_policy='propagate', supports_out=False, dtypes=all_types_and(torch.float16, torch.bfloat16), supports_sparse=True, ref=reference_reduction_numpy(np.amax), skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # FIXME: amax reduces all dimensions when dim=[] DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_empty'), DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_empty_keepdim'), # RuntimeError: Unknown builtin op: aten::iinfo DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'), # FIXME: "cuda_scatter_gather_base_kernel_func" not implemented for ... (used for sparse_coo inputs) DecorateInfo(unittest.skip("Skipped!"), 'TestMasked', 'test_mask_layout', device_type='cuda', dtypes=(torch.bool, torch.int8, torch.uint8, torch.int16, torch.int32, torch.int64, torch.complex64, torch.complex128)), ), sample_inputs_func=sample_inputs_masked_reduction, sample_inputs_sparse_coo_func=sample_inputs_sparse_coo_masked_reduction, gradcheck_wrapper=gradcheck_wrapper_masked_operation ), ReductionOpInfo( '_masked.amin', nan_policy='propagate', supports_out=False, dtypes=all_types_and(torch.float16, torch.bfloat16), supports_sparse=True, ref=reference_reduction_numpy(np.amin), skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # FIXME: amax reduces all dimensions when dim=[] DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_empty'), DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_empty_keepdim'), # RuntimeError: Unknown builtin op: aten::iinfo DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # FIXME: "cuda_scatter_gather_base_kernel_func" not implemented for ... (used for sparse_coo inputs) DecorateInfo(unittest.skip("Skipped!"), 'TestMasked', 'test_mask_layout', device_type='cuda', dtypes=(torch.bool, torch.int8, torch.uint8, torch.int16, torch.int32, torch.int64, torch.complex64, torch.complex128)), ), sample_inputs_func=sample_inputs_masked_reduction, sample_inputs_sparse_coo_func=sample_inputs_sparse_coo_masked_reduction, gradcheck_wrapper=gradcheck_wrapper_masked_operation ), ReductionOpInfo( '_masked.argmax', supports_out=False, supports_multiple_dims=False, supports_autograd=False, dtypes=all_types_and(torch.float16, torch.bfloat16), ref=reference_reduction_numpy(np.argmax, supports_keepdims=False), skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # initial is not a keyword for argmax DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_reference_masked'), # NotSupportedError: Compiled functions can't ... use keyword-only arguments with defaults DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), DecorateInfo(unittest.expectedFailure, 'TestNNCOpInfo', 'test_nnc_correctness', dtypes=(torch.bfloat16,)), ), sample_inputs_func=sample_inputs_masked_reduction, gradcheck_wrapper=gradcheck_wrapper_masked_operation ), ReductionOpInfo( '_masked.argmin', supports_out=False, supports_multiple_dims=False, supports_autograd=False, dtypes=all_types_and(torch.float16, torch.bfloat16), ref=reference_reduction_numpy(np.argmin, supports_keepdims=False), skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # initial is not a keyword for argmin DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_reference_masked'), # NotSupportedError: Compiled functions can't ... use keyword-only arguments with defaults DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), DecorateInfo(unittest.expectedFailure, 'TestNNCOpInfo', 'test_nnc_correctness', dtypes=(torch.bfloat16,)), ), sample_inputs_func=sample_inputs_masked_reduction, gradcheck_wrapper=gradcheck_wrapper_masked_operation ), ReductionOpInfo( '_masked.mean', ref=reference_reduction_numpy(np.mean) if np.lib.NumpyVersion(np.__version__) >= '1.20.2' else None, method_variant=None, nan_policy='propagate', supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, promotes_int_to_float=True, dtypes=all_types_and_complex_and(torch.float16, torch.bfloat16, torch.bool), skips=( DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_ref_duplicate_values', dtypes=(torch.bool,)), DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_reference_masked', dtypes=(torch.bool,)), DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_ref_small_input', dtypes=(torch.bool,)), DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # FIXME: sum reduces all dimensions when dim=[] DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_empty'), DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_empty_keepdim'), # RuntimeError: undefined value tensor DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # see https://github.com/pytorch/pytorch/issues/76227 DecorateInfo(unittest.skip("Fails on UBSAN!"), 'TestCompositeCompliance', 'test_forward_ad', device_type='cpu'), ), decorators=[ DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-03, rtol=1e-03)}), 'TestReductions', 'test_reference_masked'), ], sample_inputs_func=sample_inputs_masked_reduction, gradcheck_wrapper=gradcheck_wrapper_masked_operation ), OpInfo( '_masked.median', dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16), method_variant=None, supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # NotSupportedError: Compiled functions can't ... use keyword-only arguments with defaults DecorateInfo(unittest.skip("Skipped!"), 'TestJit', 'test_variant_consistency_jit'), ), sample_inputs_func=sample_inputs_masked_softmax, gradcheck_wrapper=gradcheck_wrapper_masked_operation ), ReductionOpInfo( '_masked.norm', identity=0, method_variant=None, nan_policy='propagate', supports_out=False, promotes_int_to_float=True, dtypes=floating_types_and(torch.float16, torch.bfloat16), skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # FIXME: sum reduces all dimensions when dim=[] DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_empty'), DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_empty_keepdim'), # torch.jit.frontend.NotSupportedError: Compiled functions # can't take variable number of arguments or use # keyword-only arguments with defaults DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # see https://github.com/pytorch/pytorch/issues/76227 DecorateInfo(unittest.skip("Fails on UBSAN!"), 'TestCompositeCompliance', 'test_forward_ad', device_type='cpu'), ), supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_masked_norm, gradcheck_wrapper=gradcheck_wrapper_masked_operation ), ReductionOpInfo( '_masked.var', ref=reference_reduction_numpy(np.var) if np.lib.NumpyVersion(np.__version__) >= '1.20.2' else None, method_variant=None, nan_policy='propagate', supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, promotes_int_to_float=True, dtypes=all_types_and_complex_and(torch.float16, torch.bfloat16), skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # FIXME: sum reduces all dimensions when dim=[] DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_empty'), DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_empty_keepdim'), # RuntimeError: undefined value tensor DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # see https://github.com/pytorch/pytorch/issues/76227 DecorateInfo(unittest.skip("Fails on UBSAN!"), 'TestCompositeCompliance', 'test_forward_ad', device_type='cpu'), ), decorators=[ DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-02, rtol=1e-02), torch.bfloat16: tol(atol=1e-03, rtol=1e-03)}), 'TestReductions', 'test_reference_masked'), DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-02, rtol=1e-02)}), 'TestReductions', 'test_ref_small_input'), DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-02, rtol=1e-02)}), 'TestMasked', 'test_reference_masked'), DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-02, rtol=1e-02)}), 'TestCudaFuserOpInfo', 'test_nvfuser_correctness'), ], sample_inputs_func=sample_inputs_masked_std_var, gradcheck_wrapper=gradcheck_wrapper_masked_operation, check_batched_grad=True, check_batched_forward_grad=True, ), ReductionOpInfo( '_masked.std', ref=reference_reduction_numpy(np.std) if np.lib.NumpyVersion(np.__version__) >= '1.20.2' else None, method_variant=None, nan_policy='propagate', supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, promotes_int_to_float=True, dtypes=all_types_and_complex_and(torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.float16, torch.bfloat16), skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), # FIXME: sum reduces all dimensions when dim=[] DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_empty'), DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_empty_keepdim'), # RuntimeError: undefined value tensor DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # see https://github.com/pytorch/pytorch/issues/76227 DecorateInfo(unittest.skip("Fails on UBSAN!"), 'TestCompositeCompliance', 'test_forward_ad', device_type='cpu'), DecorateInfo(unittest.skip('Skipped!'), 'TestCudaFuserOpInfo', 'test_nvfuser_correctness', dtypes=(torch.float16,)), ), decorators=[ DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-02, rtol=1e-02)}), 'TestReductions', 'test_reference_masked'), DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-02, rtol=1e-02)}), 'TestReductions', 'test_ref_small_input'), DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-02, rtol=1e-02)}), 'TestMasked', 'test_reference_masked'), ], sample_inputs_func=sample_inputs_masked_std_var, gradcheck_wrapper=gradcheck_wrapper_masked_operation, check_batched_grad=True, check_batched_forward_grad=True, ), OpInfo( '_masked.softmax', method_variant=None, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), sample_inputs_func=sample_inputs_masked_softmax, skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # see https://github.com/pytorch/pytorch/issues/76227 DecorateInfo(unittest.skip("Fails on UBSAN!"), 'TestCompositeCompliance', 'test_forward_ad', device_type='cpu'), ), gradcheck_wrapper=gradcheck_wrapper_masked_operation, supports_forward_ad=True, supports_out=False), OpInfo( '_masked.log_softmax', method_variant=None, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), sample_inputs_func=sample_inputs_masked_softmax, skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # see https://github.com/pytorch/pytorch/issues/76227 DecorateInfo(unittest.skip("Fails on UBSAN!"), 'TestCompositeCompliance', 'test_forward_ad', device_type='cpu'), ), decorators=[ DecorateInfo(toleranceOverride({torch.bfloat16: tol(atol=1e-02, rtol=1e-02)}), 'TestMasked', 'test_reference_masked'), ], gradcheck_wrapper=gradcheck_wrapper_masked_operation, supports_forward_ad=True, supports_out=False), OpInfo( '_masked.softmin', method_variant=None, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), sample_inputs_func=sample_inputs_masked_softmax, skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # see https://github.com/pytorch/pytorch/issues/76227 DecorateInfo(unittest.skip("Fails on UBSAN!"), 'TestCompositeCompliance', 'test_forward_ad', device_type='cpu'), ), gradcheck_wrapper=gradcheck_wrapper_masked_operation, supports_forward_ad=True, supports_out=False), OpInfo( '_masked.normalize', method_variant=None, dtypes=floating_and_complex_types_and(torch.half, torch.bfloat16), sample_inputs_func=sample_inputs_masked_normalize, skips=( DecorateInfo(unittest.expectedFailure, 'TestNormalizeOperators', 'test_normalize_operator_exhaustive'), DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'), # Prexisting issue with linalg.vector_norm DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_fn_fwgrad_bwgrad', dtypes=[torch.complex128]), # RuntimeError: "clamp_min_cpu" not implemented for 'Half' DecorateInfo(unittest.expectedFailure, 'TestMasked', 'test_reference_masked', device_type='cpu', dtypes=[torch.half]), ), gradcheck_wrapper=gradcheck_wrapper_masked_operation, supports_forward_ad=True, supports_fwgrad_bwgrad=True, supports_out=False), OpInfo( "nn.functional.ctc_loss", ref=_NOTHING, dtypes=floating_types(), supports_out=False, sample_inputs_func=sample_inputs_ctc_loss, skips=( # https://github.com/pytorch/pytorch/issues/67462 # torch.autograd.gradcheck.GradcheckError: Jacobian mismatch for output 0 with respect to input 0 DecorateInfo( unittest.expectedFailure, "TestGradients", "test_fn_grad", dtypes=(torch.float64,), ), # RuntimeError: derivative for aten::_ctc_loss_backward is not implemented DecorateInfo( unittest.expectedFailure, "TestGradients", "test_fn_gradgrad", dtypes=(torch.float64,), ), # RuntimeError: derivative for aten::_ctc_loss_backward is not implemented DecorateInfo( unittest.skip("Skipped!"), "TestJit", "test_variant_consistency_jit", dtypes=(torch.float32,), ), # Operation calls data_ptr() somewhere; needs to be fixed DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_operator'), DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_backward'), ), ), OpInfo( "nn.functional.cosine_embedding_loss", ref=_NOTHING, dtypes=all_types_and(torch.bfloat16, torch.bool), dtypesIfCUDA=all_types_and(torch.float16, torch.bfloat16, torch.bool), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_cosine_embedding_loss, ), OpInfo( "nn.functional.nll_loss", ref=_NOTHING, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), supports_out=False, sample_inputs_func=sample_inputs_nll_loss, supports_forward_ad=True, assert_jit_shape_analysis=True, skips=( # RuntimeError: # undefined value tensor: # File "", line 3 # def the_method(i0, i1): # return torch.nn.functional.nll_loss(i0, i1, weight=tensor([8.4784, 1.7658, 4.3228], dtype=torch.float32)) # ~~~~~~ <--- HERE DecorateInfo(unittest.skip("Skipped!"), "TestJit", "test_variant_consistency_jit", dtypes=(torch.float32,),), ), ), OpInfo( "nn.functional.gaussian_nll_loss", ref=_NOTHING, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_gaussian_nll_loss, skips=( DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), # JIT does not support variadic tensors. # RuntimeError: input->type()->kind() == TypeKind::OptionalType # INTERNAL ASSERT FAILED at "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":270, # please report a bug to PyTorch. DecorateInfo(unittest.skip("Skipped!"), "TestJit", "test_variant_consistency_jit", dtypes=(torch.float32,),), ), decorators=( DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-02, rtol=1e-02)}), 'TestCudaFuserOpInfo', 'test_nvfuser_correctness'), ) ), OpInfo( "nn.functional.hinge_embedding_loss", ref=_NOTHING, dtypes=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_hinge_embedding_loss, skips=( DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_forward_ad'), ) ), OpInfo( "nn.functional.huber_loss", aten_backward_name='huber_loss_backward', ref=_NOTHING, dtypes=floating_types_and(torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, sample_inputs_func=sample_inputs_huber_loss, skips=( # JIT does not support variadic tensors. # RuntimeError: input->type()->kind() == TypeKind::OptionalType # INTERNAL ASSERT FAILED at "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":270, # please report a bug to PyTorch. DecorateInfo(unittest.skip("Skipped!"), "TestJit", "test_variant_consistency_jit", dtypes=(torch.float32,),), ) ), OpInfo( "nn.functional.pdist", ref=reference_pdist, sample_inputs_func=sample_inputs_pdist, dtypes=floating_types(), supports_out=False, supports_gradgrad=False), OpInfo( "nn.functional.poisson_nll_loss", ref=_NOTHING, dtypes=all_types_and(torch.bfloat16), dtypesIfCUDA=all_types_and(torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, sample_inputs_func=sample_inputs_poisson_nll_loss, ), OpInfo( "argsort", dtypes=all_types_and(torch.bool, torch.float16, torch.bfloat16), dtypesIfCUDA=all_types_and(torch.float16, torch.bfloat16), sample_inputs_func=sample_inputs_argsort, supports_out=False, supports_autograd=False, skips=( DecorateInfo( unittest.skip("Skipped!"), "TestJit", "test_variant_consistency_jit", dtypes=(torch.float32,), ), ), ), OpInfo( "repeat_interleave", dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), sample_inputs_func=sample_inputs_repeat_interleave, supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( DecorateInfo( unittest.skip("Skipped!"), "TestJit", "test_variant_consistency_jit", dtypes=(torch.float32, torch.complex64), ), ), ), OpInfo( "nn.functional.pairwise_distance", ref=lambda a, b, p=2.0, eps=1e-6, keepdim=False: ( np.sum(np.abs(a - b + eps) ** p, axis=-1, keepdims=keepdim) ** (1 / p) ), sample_inputs_func=sample_inputs_pairwise_distance, dtypes=all_types_and_complex_and(torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( DecorateInfo( unittest.skip("Skipped!"), "TestJit", "test_variant_consistency_jit", dtypes=(torch.float32, torch.complex64), ), DecorateInfo(unittest.expectedFailure, 'TestGradients', 'test_fn_fwgrad_bwgrad', dtypes=[torch.complex128]), ), ), OpInfo( "nn.functional.pixel_shuffle", sample_inputs_func=sample_inputs_pixel_shuffle, dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( DecorateInfo( unittest.skip("Skipped!"), "TestJit", "test_variant_consistency_jit", dtypes=(torch.float32, torch.complex64), ), ), ), OpInfo( "nn.functional.pixel_unshuffle", sample_inputs_func=sample_inputs_pixel_unshuffle, dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, skips=( DecorateInfo( unittest.skip("Skipped!"), "TestJit", "test_variant_consistency_jit", dtypes=(torch.float32, torch.complex64), ), ), ), OpInfo( "nn.functional.kl_div", sample_inputs_func=sample_inputs_kl_div, dtypes=floating_types_and(torch.bfloat16, torch.int8, torch.int16, torch.int32, torch.int64), backward_dtypes=floating_types_and(torch.int8, torch.int16, torch.int32, torch.int64), dtypesIfCUDA=floating_types_and( torch.float16, torch.bfloat16, torch.int8, torch.int16, torch.int32, torch.int64 ), backward_dtypesIfCUDA=floating_types_and(torch.float16, torch.int8, torch.int16, torch.int32, torch.int64), supports_out=False, check_batched_grad=False, supports_forward_ad=True, skips=( # See https://github.com/pytorch/pytorch/issues/65466 DecorateInfo( unittest.expectedFailure, "TestGradients", "test_fn_gradgrad", ), ), ), OpInfo( "diagflat", ref=lambda input, offset=0: np.diagflat(input, k=offset), sample_inputs_func=sample_inputs_diagflat, dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), supports_out=False, supports_forward_ad=True, supports_fwgrad_bwgrad=True, ), OpInfo( 'scatter_reduce', variant_test_name='sum', # complex not added to dtypes as complex gradients are not properly handled # and scatter_reduce hasn't been added to the whitelist in gen_variable_type yet dtypes=all_types_and(torch.float16, torch.bfloat16, torch.bool), sample_inputs_func=sample_inputs_scatter_reduce, ), OpInfo( 'scatter_reduce', variant_test_name='prod', # complex not added to dtypes as complex gradients are not properly handled # and scatter_reduce hasn't been added to the whitelist in gen_variable_type yet dtypes=all_types_and(torch.float16, torch.bfloat16, torch.bool), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), sample_inputs_func=sample_inputs_scatter_reduce, ), OpInfo( 'scatter_reduce', variant_test_name='mean', # complex not added to dtypes as complex gradients are not properly handled # and scatter_reduce hasn't been added to the whitelist in gen_variable_type yet dtypes=all_types_and(torch.float16, torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), sample_inputs_func=sample_inputs_scatter_reduce, ), OpInfo( 'scatter_reduce', variant_test_name='amin', dtypes=all_types_and(torch.float16, torch.bfloat16, torch.bool), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), sample_inputs_func=sample_inputs_scatter_reduce, ), OpInfo( 'scatter_reduce', variant_test_name='amax', dtypes=all_types_and(torch.float16, torch.bfloat16, torch.bool), dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16), sample_inputs_func=sample_inputs_scatter_reduce, ), ] # NOTE [Python References] # Python References emulate existing PyTorch operations, but can ultimately # be expressed in terms of "primitive" operations from torch._prims. # # These references are experimental. # See https://dev-discuss.pytorch.org/t/tracing-with-primitives-update-0/577 # for additional context. # # Python Reference OpInfos should be added to the python_ref_db list below. # Tests can opt-into running on these references by including # that list in the Sequence they pass to the @ops decorator. # # When a Python Reference OpInfo is constructed a pointer to an # existing OpInfo must be provided using the torch_opinfo_name kwarg. # The existing OpInfo with that name and no variant will be found # to inherit from. # # Instead of just inheriting the existing OpInfo's metadata, the # Python Reference OpInfos inherit the existing OpInfo's # construction arguments. These arguments can be overridden # by adding kwargs to the constructor. def _find_referenced_opinfo(referenced_name): ''' Finds the OpInfo with the given name that has no variant name. ''' for opinfo in op_db: if opinfo.name == referenced_name and opinfo.variant_test_name == '': return opinfo def _inherit_constructor_args(name, op, inherited, overrides): # inherits metadata common_kwargs = { 'name': name, 'op': op, 'aliases': None, # TODO add a check for alias coverage 'method_variant': None, 'inplace_variant': None, # TODO: add a check for inplace coverage 'supports_scripting': False, } # Acquires inherited kwargs kwargs = inherited.copy() # Fixes metadata if 'kwargs' in kwargs: kwargs.update(kwargs['kwargs']) del kwargs['kwargs'] if 'self' in kwargs: del kwargs['self'] if '__class__' in kwargs: del kwargs['__class__'] if 'skips' in kwargs: del kwargs['skips'] if 'decorators' in kwargs: del kwargs['decorators'] # Overrides metadata kwargs.update(common_kwargs) kwargs.update(overrides) return kwargs class PythonRefInfo(OpInfo): ''' An OpInfo for a Python reference of an OpInfo base class operation. ''' def __init__( self, name, # the stringname of the callable Python reference *, op=None, # the function variant of the operation, populated as torch. if None torch_opinfo_name, # the string name of the corresponding torch opinfo **kwargs): # additional kwargs override kwargs inherited from the torch opinfo self.torch_opinfo_name = torch_opinfo_name self.torch_opinfo = _find_referenced_opinfo(torch_opinfo_name) assert isinstance(self.torch_opinfo, OpInfo) inherited = self.torch_opinfo._original_opinfo_args ukwargs = _inherit_constructor_args(name, op, inherited, kwargs) super(PythonRefInfo, self).__init__(**ukwargs) class ReductionPythonRefInfo(ReductionOpInfo): ''' An OpInfo for a Python reference of an elementwise unary operation. ''' def __init__( self, name, # the stringname of the callable Python reference *, op=None, # the function variant of the operation, populated as torch. if None torch_opinfo_name, # the string name of the corresponding torch opinfo **kwargs): # additional kwargs override kwargs inherited from the torch opinfo self.torch_opinfo_name = torch_opinfo_name self.torch_opinfo = _find_referenced_opinfo(torch_opinfo_name) assert isinstance(self.torch_opinfo, ReductionOpInfo) inherited = self.torch_opinfo._original_reduction_args ukwargs = _inherit_constructor_args(name, op, inherited, kwargs) # See https://github.com/pytorch/pytorch/issues/77216 self.validate_view_consistency = False super().__init__(**ukwargs) class ElementwiseUnaryPythonRefInfo(UnaryUfuncInfo): ''' An OpInfo for a Python reference of an elementwise unary operation. ''' def __init__( self, name, # the stringname of the callable Python reference *, op=None, # the function variant of the operation, populated as torch. if None torch_opinfo_name, # the string name of the corresponding torch opinfo **kwargs): # additional kwargs override kwargs inherited from the torch opinfo self.torch_opinfo_name = torch_opinfo_name self.torch_opinfo = _find_referenced_opinfo(torch_opinfo_name) assert isinstance(self.torch_opinfo, UnaryUfuncInfo) inherited = self.torch_opinfo._original_unary_ufunc_args ukwargs = _inherit_constructor_args(name, op, inherited, kwargs) super(ElementwiseUnaryPythonRefInfo, self).__init__(**ukwargs) class ElementwiseBinaryPythonRefInfo(BinaryUfuncInfo): ''' An OpInfo for a Python reference of an elementwise binary operation. ''' def __init__( self, name, # the stringname of the callable Python reference *, op=None, # the function variant of the operation, populated as torch. if None torch_opinfo_name, # the string name of the corresponding torch opinfo **kwargs): # additional kwargs override kwargs inherited from the torch opinfo self.torch_opinfo_name = torch_opinfo_name self.torch_opinfo = _find_referenced_opinfo(torch_opinfo_name) assert isinstance(self.torch_opinfo, BinaryUfuncInfo) inherited = self.torch_opinfo._original_binary_ufunc_args ukwargs = _inherit_constructor_args(name, op, inherited, kwargs) super(ElementwiseBinaryPythonRefInfo, self).__init__(**ukwargs) # Separate registry for experimental Python Reference OpInfos. python_ref_db = [ # # Elementwise Unary OpInfos # ElementwiseUnaryPythonRefInfo( "_refs.abs", torch_opinfo_name="abs", skips=( # On CPU: Output Mismatch as complexhalf uses non-vectorized path vs ref which seems to use # vectorized path # See also : https://github.com/pytorch/pytorch/issues/48486 # On CUDA: RuntimeError: "index_select_cuda" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_reference_consistency', dtypes=(torch.chalf,)), # On CPU: RuntimeError: unsupported Storage type: torch.complex32 # On CUDA: RuntimeError: "index_select_cuda" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_reference_meta_functions', dtypes=(torch.chalf,)), ) ), ElementwiseUnaryPythonRefInfo( "_refs.acos", torch_opinfo_name="acos", ), ElementwiseUnaryPythonRefInfo( "_refs.acosh", torch_opinfo_name="acosh", ), ElementwiseUnaryPythonRefInfo( "_refs.asin", torch_opinfo_name="asin", ), ElementwiseUnaryPythonRefInfo( "_refs.atan", torch_opinfo_name="atan", ), ElementwiseUnaryPythonRefInfo( "_refs.bitwise_not", torch_opinfo_name="bitwise_not", ), ElementwiseUnaryPythonRefInfo( "_refs.ceil", torch_opinfo_name="ceil", ), ElementwiseUnaryPythonRefInfo( "_refs.cos", torch_opinfo_name="cos", ), ElementwiseUnaryPythonRefInfo( "_refs.cosh", torch_opinfo_name="cosh", ), ElementwiseUnaryPythonRefInfo( "_refs.digamma", torch_opinfo_name="digamma", ), ElementwiseUnaryPythonRefInfo( "_refs.erf", torch_opinfo_name="erf", ), ElementwiseUnaryPythonRefInfo( "_refs.erfinv", torch_opinfo_name="erfinv", ), ElementwiseUnaryPythonRefInfo( "_refs.erfc", torch_opinfo_name="erfc", ), ElementwiseUnaryPythonRefInfo( "_refs.exp", torch_opinfo_name="exp", skips=( # RuntimeError: "index_select" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, "TestCommon", 'test_python_reference_consistency', dtypes=(torch.chalf,)), # RuntimeError: "index_select" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, "TestCommon", 'test_python_reference_meta_functions', dtypes=(torch.chalf,)) ) ), ElementwiseUnaryPythonRefInfo( "_refs.expm1", torch_opinfo_name="expm1", ), ElementwiseUnaryPythonRefInfo( "_refs.floor", torch_opinfo_name="floor", ), ElementwiseUnaryPythonRefInfo( "_refs.isfinite", torch_opinfo_name="isfinite", supports_out=True, skips=( # RuntimeError: "index_select" not implemented for 'ComplexHalf' # RuntimeError: "index_select_cuda" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_reference_consistency', dtypes=(torch.chalf,)), # Same reason as `test_python_reference_consistency` DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_reference_meta_functions', dtypes=(torch.chalf,)), ) ), ElementwiseUnaryPythonRefInfo( "_refs.isinf", torch_opinfo_name="isinf", supports_out=True, skips=( # RuntimeError: "index_select" not implemented for 'ComplexHalf' # RuntimeError: "index_select_cuda" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_reference_consistency', dtypes=(torch.chalf,)), # Same reason as `test_python_reference_consistency` DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_reference_meta_functions', dtypes=(torch.chalf,)), ) ), ElementwiseUnaryPythonRefInfo( "_refs.isnan", torch_opinfo_name="isnan", supports_out=True, ), ElementwiseUnaryPythonRefInfo( "_refs.lgamma", torch_opinfo_name="lgamma", ), ElementwiseUnaryPythonRefInfo( "_refs.log", torch_opinfo_name="log", skips=( # RuntimeError: "masked_fill_" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, "TestCommon", 'test_python_reference_consistency', dtypes=(torch.chalf,)), # RuntimeError: "masked_fill_" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, "TestCommon", 'test_python_reference_meta_functions', dtypes=(torch.chalf,)) ) ), ElementwiseUnaryPythonRefInfo( "_refs.log1p", torch_opinfo_name="log1p", ), ElementwiseUnaryPythonRefInfo( "_refs.log2", torch_opinfo_name="log2", ), ElementwiseUnaryPythonRefInfo( "_refs.neg", torch_opinfo_name="neg", skips=( # On CPU # RuntimeError: unsupported Storage type: torch.complex32 # https://github.com/pytorch/pytorch/issues/73502 # On CUDA # RuntimeError: "index_select_cuda" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_reference_consistency', dtypes=(torch.chalf,)), # Same reason as `test_python_reference_consistency` DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_reference_meta_functions', dtypes=(torch.chalf,)), ) ), ElementwiseUnaryPythonRefInfo( "_refs.reciprocal", torch_opinfo_name="reciprocal", ), ElementwiseUnaryPythonRefInfo( "_refs.round", torch_opinfo_name="round", ), ElementwiseUnaryPythonRefInfo( "_refs.sign", torch_opinfo_name="sign", ), ElementwiseUnaryPythonRefInfo( "_refs.sin", torch_opinfo_name="sin", ), ElementwiseUnaryPythonRefInfo( "_refs.sinh", torch_opinfo_name="sinh", ), ElementwiseUnaryPythonRefInfo( "_refs.sqrt", torch_opinfo_name="sqrt", ), ElementwiseUnaryPythonRefInfo( "_refs.square", torch_opinfo_name="square", ), ElementwiseUnaryPythonRefInfo( "_refs.tan", torch_opinfo_name="tan", ), ElementwiseUnaryPythonRefInfo( "_refs.tanh", torch_opinfo_name="tanh", ), # # Elementwise Unary Special OpInfos # ElementwiseUnaryPythonRefInfo( "_refs.special.i0e", torch_opinfo_name="special.i0e", decorators=( DecorateInfo(toleranceOverride({ torch.bfloat16: tol(atol=1e-2, rtol=0), }), 'TestCommon', 'test_python_reference_consistency', device_type='cpu'), ), ), ElementwiseUnaryPythonRefInfo( "_refs.special.i1e", torch_opinfo_name="special.i1e", ), # # Elementwise Unary nn.functional OpInfos # ElementwiseUnaryPythonRefInfo( "_refs.nn.functional.celu", torch_opinfo_name="nn.functional.celu", decorators=( DecorateInfo(toleranceOverride({ torch.bfloat16: tol(atol=1e-2, rtol=0), torch.float16: tol(atol=1e-3, rtol=0), }), 'TestCommon', 'test_python_reference_consistency', device_type='cpu'), ), ), ElementwiseUnaryPythonRefInfo( "_refs.nn.functional.elu", torch_opinfo_name="nn.functional.elu", decorators=( # https://github.com/pytorch/pytorch/issues/77054 DecorateInfo(toleranceOverride({ torch.bfloat16: tol(atol=1e-2, rtol=0), torch.float16: tol(atol=1e-3, rtol=0), }), 'TestCommon', 'test_python_reference_consistency', device_type='cpu'), ), ), ElementwiseUnaryPythonRefInfo( "_refs.nn.functional.mish", torch_opinfo_name="nn.functional.mish", decorators=( DecorateInfo(toleranceOverride({ torch.bfloat16: tol(atol=1e-2, rtol=0), torch.float16: tol(atol=1e-3, rtol=0), }), 'TestCommon', 'test_python_reference_consistency', device_type='cpu'), ), ), ElementwiseUnaryPythonRefInfo( "_refs.nn.functional.selu", torch_opinfo_name="nn.functional.selu", decorators=( DecorateInfo(toleranceOverride({ torch.bfloat16: tol(atol=1e-2, rtol=0), torch.float16: tol(atol=1e-3, rtol=0), }), 'TestCommon', 'test_python_reference_consistency', device_type='cpu'), ), ), PythonRefInfo( "_refs.nn.functional.softplus", torch_opinfo_name="nn.functional.softplus", decorators=( DecorateInfo(toleranceOverride({ torch.bfloat16: tol(atol=1e-2, rtol=0), torch.float16: tol(atol=1e-3, rtol=0), }), 'TestCommon', 'test_python_reference_consistency', device_type='cpu'), ), ), # # Elementwise Binary OpInfos # ElementwiseBinaryPythonRefInfo( "_refs.add", torch_opinfo_name="add", # https://github.com/pytorch/pytorch/issues/76944 supports_two_python_scalars=False, supports_one_python_scalar=True, decorators=( DecorateInfo( toleranceOverride( { torch.bfloat16: tol(atol=1, rtol=0), torch.float16: tol(atol=1e-2, rtol=0), torch.chalf: tol(atol=1e-2, rtol=0), } ), "TestCommon", "test_python_reference_consistency", device_type='cpu' ), ), ), ElementwiseBinaryPythonRefInfo( "_refs.atan2", torch_opinfo_name="atan2", ), ElementwiseBinaryPythonRefInfo( "_refs.bitwise_and", torch_opinfo_name="bitwise_and", ), ElementwiseBinaryPythonRefInfo( "_refs.bitwise_left_shift", torch_opinfo_name="bitwise_left_shift", ), ElementwiseBinaryPythonRefInfo( "_refs.bitwise_or", torch_opinfo_name="bitwise_or", ), ElementwiseBinaryPythonRefInfo( "_refs.bitwise_xor", torch_opinfo_name="bitwise_xor", ), ElementwiseBinaryPythonRefInfo( "_refs.eq", torch_opinfo_name="eq", ), ElementwiseBinaryPythonRefInfo( "_refs.float_power", torch_opinfo_name="float_power", skips=( # Test doesn't account for float -> double type promotion DecorateInfo(unittest.expectedFailure, 'TestBinaryUfuncs', 'test_type_promotion'), # TODO: FIXME: meta strides for to_dtype are incorrect DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_python_reference_meta_functions', device_type='cuda'), ) ), ElementwiseBinaryPythonRefInfo( "_refs.ge", torch_opinfo_name="ge", ), ElementwiseBinaryPythonRefInfo( "_refs.gt", torch_opinfo_name="gt", ), ElementwiseBinaryPythonRefInfo( "_refs.igamma", torch_opinfo_name="igamma", ), ElementwiseBinaryPythonRefInfo( "_refs.igammac", torch_opinfo_name="igammac", ), ElementwiseBinaryPythonRefInfo( "_refs.isclose", torch_opinfo_name="isclose", skips=( # Intentional xfail -- isclose does not type promote DecorateInfo(unittest.expectedFailure, 'TestBinaryUfuncs', 'test_type_promotion'), ), ), ElementwiseBinaryPythonRefInfo( "_refs.le", torch_opinfo_name="le", ), ElementwiseBinaryPythonRefInfo( "_refs.logical_and", torch_opinfo_name="logical_and", ), ElementwiseBinaryPythonRefInfo( "_refs.logical_or", torch_opinfo_name="logical_or", ), ElementwiseBinaryPythonRefInfo( "_refs.lt", torch_opinfo_name="lt", ), ElementwiseBinaryPythonRefInfo( "_refs.maximum", torch_opinfo_name="maximum", skips=( # refs.maximum supports scalars, unlike torch.maximum DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_reference_errors'), ), ), ElementwiseBinaryPythonRefInfo( "_refs.minimum", torch_opinfo_name="minimum", skips=( # refs.minimum supports scalars, unlike torch.minimum DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_reference_errors'), ), ), ElementwiseBinaryPythonRefInfo( "_refs.mul", torch_opinfo_name="mul", # https://github.com/pytorch/pytorch/issues/76944 supports_two_python_scalars=False, supports_one_python_scalar=True, skips=( DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_reference_consistency', dtypes=(torch.chalf,), device_type='cuda', active_if=(not TEST_WITH_ROCM)), ), ), ElementwiseBinaryPythonRefInfo( "_refs.ne", torch_opinfo_name="ne", ), ElementwiseBinaryPythonRefInfo( "_refs.nextafter", torch_opinfo_name="nextafter", ), ElementwiseBinaryPythonRefInfo( "_refs.pow", torch_opinfo_name="pow", ), ElementwiseBinaryPythonRefInfo( "_refs.sub", torch_opinfo_name="sub", # https://github.com/pytorch/pytorch/issues/76944 supports_two_python_scalars=False, supports_one_python_scalar=True, decorators=( DecorateInfo( toleranceOverride( { torch.bfloat16: tol(atol=1, rtol=0), torch.float16: tol(atol=1e-2, rtol=0), torch.chalf: tol(atol=1e-2, rtol=0), } ), "TestCommon", "test_python_reference_consistency", device_type='cpu' ), ), ), ElementwiseBinaryPythonRefInfo( "_refs.true_divide", torch_opinfo_name="true_divide", # https://github.com/pytorch/pytorch/issues/76944 supports_two_python_scalars=False, supports_one_python_scalar=True, skips=( # complex("-501.-501.j")/complex("-501.-infj") # PyTorch jiterated Path : 0 # Python : 0 # PyTorch non-jiterated path : nan + nanj # See also: https://github.com/pytorch/pytorch/issues/52332 DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_reference_consistency', dtypes=(torch.chalf,), device_type='cuda', active_if=not TEST_WITH_ROCM), ), ), # # Data Conversion & Data Movement Opinfos # PythonRefInfo( "_refs.clone", torch_opinfo_name="clone", skips=( # RuntimeError: "index_select_cuda" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_reference_consistency', dtypes=(torch.chalf,), device_type='cuda'), DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_reference_meta_functions', dtypes=(torch.chalf,), device_type='cuda'), ), ), # # View & Shape OpInfos # PythonRefInfo( "_refs.as_strided", torch_opinfo_name="as_strided", # FIXME: doesn't support chalf dtypes=all_types_and_complex_and(torch.bool, torch.float16, torch.bfloat16), skips=( # TODO: fix and/or update to xfails DecorateInfo(unittest.skip("Errors when storage_offset is included"), 'TestCommon', 'test_python_reference_meta_functions'), # cloned_mutable_input.is_same(returned_output) INTERNAL ASSERT FAILED DecorateInfo(unittest.skip("Errors when storage_offset is included"), 'TestMathBits', 'test_neg_view'), DecorateInfo(unittest.skip("Errors when storage_offset is included"), 'TestMathBits', 'test_conj_view'), DecorateInfo(unittest.skip("Errors when storage_offset is included"), 'TestMathBits', 'test_neg_conj_view'), ), ), PythonRefInfo( "_refs.cat", torch_opinfo_name="cat", ), PythonRefInfo( "_refs.chunk", torch_opinfo_name="chunk", ), PythonRefInfo( "_refs.flatten", torch_opinfo_name="flatten", skips=( # RuntimeError: "index_select_cuda" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_reference_consistency', dtypes=(torch.chalf,), device_type='cuda'), # RuntimeError: "index_select_cuda" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_reference_meta_functions', dtypes=(torch.chalf,), device_type='cuda'), ), ), PythonRefInfo( "_refs.flip", torch_opinfo_name="flip", ), PythonRefInfo( "_refs.narrow", torch_opinfo_name="narrow", ), PythonRefInfo( "_refs.permute", torch_opinfo_name="permute", skips=( # RuntimeError: "index_select_cuda" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_reference_consistency', dtypes=(torch.chalf,), device_type='cuda'), # RuntimeError: "index_select_cuda" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_reference_meta_functions', dtypes=(torch.chalf,), device_type='cuda'), ), ), PythonRefInfo( "_refs.reshape", torch_opinfo_name="reshape", skips=( # RuntimeError: "index_select" not implemented for 'ComplexHalf' # RuntimeError: "index_select_cuda" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, "TestCommon", "test_python_reference_consistency", dtypes=(torch.chalf,)), # RuntimeError: "index_select" not implemented for 'ComplexHalf' # RuntimeError: "index_select_cuda" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, "TestCommon", "test_python_reference_meta_functions", dtypes=(torch.chalf,)), ), ), PythonRefInfo( "_refs.stack", torch_opinfo_name="stack", skips=( # https://github.com/pytorch/pytorch/issues/77046 DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_conj_view'), DecorateInfo(unittest.expectedFailure, 'TestMathBits', 'test_neg_view'), ), ), PythonRefInfo( "_refs.squeeze", torch_opinfo_name="squeeze", ), PythonRefInfo( "_refs.tensor_split", torch_opinfo_name="tensor_split", skips=( # TensorMeta doesn't support tolist DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_reference_meta_functions'), ) ), PythonRefInfo( "_refs.transpose", torch_opinfo_name="transpose", ), PythonRefInfo( "_refs.unsqueeze", torch_opinfo_name="unsqueeze", ), PythonRefInfo( "_refs.view", torch_opinfo_name="view", skips=( # RuntimeError: "index_select" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_reference_consistency', dtypes=(torch.chalf,)), DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_reference_meta_functions', dtypes=(torch.chalf,)), ), ), # # Reduction Reference OpInfos # ReductionPythonRefInfo( "_refs.sum", torch_opinfo_name="sum", supports_out=True, ), ReductionPythonRefInfo( "_refs.amin", torch_opinfo_name="amin", ), ReductionPythonRefInfo( "_refs.amax", torch_opinfo_name="amax", ), # # Tensor Creation Reference OpInfos # PythonRefInfo( "_refs.empty", torch_opinfo_name="empty", skips=( DecorateInfo(unittest.skip("Expected: empty is not comparable"), 'TestCommon', 'test_python_reference_consistency'), DecorateInfo(unittest.skip("Expected: empty is not comparable"), 'TestCommon', 'test_out'), DecorateInfo(unittest.skip("Expected: empty is not comparable"), 'TestCommon', 'test_out_warning'), DecorateInfo(unittest.skip("Expected: empty is not comparable"), 'TestMathBits', 'test_conj_view'), DecorateInfo(unittest.skip("Expected: empty is not comparable"), 'TestMathBits', 'test_neg_conj_view'), DecorateInfo(unittest.skip("Expected: empty is not comparable"), 'TestMathBits', 'test_neg_view'), ), ), PythonRefInfo( "_refs.empty_like", torch_opinfo_name="empty_like", skips=( DecorateInfo(unittest.skip("Expected: empty is not comparable"), 'TestCommon', 'test_python_reference_consistency'), DecorateInfo(unittest.skip("Expected: empty is not comparable"), 'TestCommon', 'test_out'), DecorateInfo(unittest.skip("Expected: empty is not comparable"), 'TestCommon', 'test_out_warning'), DecorateInfo(unittest.skip("Expected: empty is not comparable"), 'TestMathBits', 'test_conj_view'), DecorateInfo(unittest.skip("Expected: empty is not comparable"), 'TestMathBits', 'test_neg_conj_view'), DecorateInfo(unittest.skip("Expected: empty is not comparable"), 'TestMathBits', 'test_neg_view'), # RuntimeError: "index_select_cuda" not implemented for 'ComplexHalf' DecorateInfo(unittest.expectedFailure, dtypes=(torch.chalf,), device_type='cuda'), ), ), # TODO: add full and full_like OpInfos ] # Common operator groupings ops_and_refs = op_db + python_ref_db unary_ufuncs = [op for op in op_db if isinstance(op, UnaryUfuncInfo)] binary_ufuncs = [op for op in op_db if isinstance(op, BinaryUfuncInfo)] binary_ufuncs_and_refs = tuple(op for op in ops_and_refs if isinstance(op, BinaryUfuncInfo)) spectral_funcs = [op for op in op_db if isinstance(op, SpectralFuncInfo)] sparse_unary_ufuncs = [op for op in op_db if isinstance(op, UnaryUfuncInfo) and op.supports_sparse] sparse_csr_unary_ufuncs = [op for op in op_db if isinstance(op, UnaryUfuncInfo) and op.supports_sparse_csr] sparse_reduction_ops = [op for op in op_db if isinstance(op, ReductionOpInfo) and op.supports_sparse] shape_funcs = [op for op in op_db if isinstance(op, ShapeFuncInfo)] reduction_ops = [op for op in op_db if isinstance(op, ReductionOpInfo)] reference_filtered_ops = [op for op in reduction_ops if op.ref not in (_NOTHING, None)] reference_masked_ops = [op for op in reference_filtered_ops if op.name.startswith('_masked.')] sparse_masked_reduction_ops = [op for op in sparse_reduction_ops if op.name.startswith('_masked.')] # TODO: review porting these to make_tensor def index_variable(shape, max_indices, device=torch.device('cpu')): if not isinstance(shape, tuple): shape = (shape,) index = torch.rand(*shape, dtype=torch.double, device=device).mul_(max_indices).floor_().long() return index def gather_variable(shape, index_dim, max_indices, duplicate=False, device=torch.device('cpu')): assert len(shape) == 2 assert index_dim < 2 batch_dim = 1 - index_dim index = torch.zeros(*shape, dtype=torch.long, device=device) for i in range(shape[index_dim]): index.select(index_dim, i).copy_( torch.randperm(max_indices, device=device)[:shape[batch_dim]]) if duplicate: index.select(batch_dim, 0).copy_(index.select(batch_dim, 1)) return index def bernoulli_scalar(): return torch.tensor(0, dtype=torch.bool).bernoulli_() def mask_not_all_zeros(shape): assert len(shape) > 0 while True: result = torch.randn(shape).gt(0) if result.sum() > 0: return result # TODO: move all tri/tril/triu testing to tensor creation op test suite and remove # these from here def _compare_trilu_indices( self, row, col, offset=0, dtype=torch.long, device='cpu'): if row == 0 or col == 0: # have to handle this separately as tril and triu does not take # empty matrix as input self.assertEqual( torch.empty(0, 2, dtype=dtype, device=device).transpose(0, 1), torch.tril_indices(row, col, offset, dtype=dtype, device=device)) self.assertEqual( torch.empty(0, 2, dtype=dtype, device=device).transpose(0, 1), torch.triu_indices(row, col, offset, dtype=dtype, device=device)) else: # TODO(#38095): Replace assertEqualIgnoreType. See issue #38095 self.assertEqualIgnoreType( torch.ones(row, col, device='cpu') .tril(offset).nonzero().to(dtype).transpose(0, 1), torch.tril_indices(row, col, offset, dtype=dtype, device=device)) # TODO(#38095): Replace assertEqualIgnoreType. See issue #38095 self.assertEqualIgnoreType( torch.ones(row, col, device='cpu') .triu(offset).nonzero().to(dtype).transpose(0, 1), torch.triu_indices(row, col, offset, dtype=dtype, device=device)) def _compare_large_trilu_indices( self, row, col, offset=0, dtype=torch.long, device='cpu'): l = torch.ones(row, col, dtype=dtype, device='cpu').tril(offset) \ .nonzero()[-100:-1, :].transpose(0, 1).to(device) torch.cuda.empty_cache() r = torch.tril_indices( row, col, offset, dtype=dtype, device=device)[:, -100:-1] self.assertEqual(l, r) torch.cuda.empty_cache() l = torch.ones(row, col, dtype=dtype, device='cpu').triu(offset) \ .nonzero()[-100:-1, :].transpose(0, 1).to(device) torch.cuda.empty_cache() r = torch.triu_indices( row, col, offset, dtype=dtype, device=device)[:, -100:-1] self.assertEqual(l, r) torch.cuda.empty_cache() # ( # row # col # offset (optional) # dtype (optional) # ) tri_tests_args = [ (1, 1), (3, 3), (3, 3, 1), (3, 3, 2), (3, 3, 200), (3, 3, -1), (3, 3, -2), (3, 3, -200), (0, 3, 0), (0, 3, 1), (0, 3, -1), (0, 1, 2), (1, 0, 2), (3, 0, 0), (3, 0, 1), (3, 0, -1), (0, 0, 0), (0, 0, 1), (0, 0, -1), (3, 6, 0), (3, 6, 1), (3, 6, 3), (3, 6, 9), (3, 6, -1), (3, 6, -3), (3, 6, -9), (6, 3, 0), (6, 3, 1), (6, 3, 3), (6, 3, 9), (6, 3, -1), (6, 3, -3), (6, 3, -9), (258, 253, 1, torch.float32), (257, 258, 1, torch.float64), (258, 258, 1, torch.short), (3, 513, 1, torch.long), (513, 3, 1, torch.int), (513, 0, 1, torch.double), (1024, 1024), (1024, 1024, 500, torch.float32), (1024, 1024, 1023), (1024, 1024, -500), (1023, 1025), (1025, 1023, 1022), (1024, 1024, -500), (3, 2028), (3, 2028, 1), (3, 2028, -1), (2028, 3), (2028, 1), (2028, 1, -1) ] tri_large_tests_args: List[Tuple[int, ...]] = [ # Large test cases below are deliberately commented out to speed up CI # tests and to avoid OOM error. When modifying implementations of # tril_indices and triu_indices, please enable these tests and make sure # they pass. # # (1, 268435455), # (5000, 5000), # (10000, 10000), # (268435455, 1), # (134217727, 2, 1), # (2, 134217727, 1), # (536870901, 1), # (1, 536870901), # (268435455, 2, 1), # (2, 268435455, 1) ] def run_additional_tri_tests(self, device): x = torch.ones( 3, 3, dtype=torch.long, device=device, layout=torch.strided) l = x.tril(0).nonzero().transpose(0, 1) u = x.triu(0).nonzero().transpose(0, 1) self.assertEqual(l, torch.tril_indices(3, 3, device=device)) self.assertEqual( l, torch.tril_indices(3, 3, device=device, layout=torch.strided)) self.assertEqual(u, torch.triu_indices(3, 3, device=device)) self.assertEqual( u, torch.triu_indices(3, 3, device=device, layout=torch.strided)) self.assertRaises( RuntimeError, lambda: torch.triu_indices( 1, 1, device=device, layout=torch.sparse_coo)) self.assertRaises( RuntimeError, lambda: torch.tril_indices( 1, 1, device=device, layout=torch.sparse_coo))