from functools import reduce from operator import mul, itemgetter import collections import torch import numpy as np from torch._six import inf, istuple from torch.autograd import Variable from typing import List, Tuple, Dict, Any from torch.testing import \ (make_non_contiguous, _dispatch_dtypes, floating_types, floating_types_and, floating_and_complex_types, floating_and_complex_types_and, all_types_and_complex_and) from torch.testing._internal.common_device_type import \ (skipCUDAIfNoMagma, skipCPUIfNoLapack, expectedFailureCUDA, expectedAlertNondeterministic, precisionOverride) from torch.testing._internal.common_utils import \ (prod_single_zero, random_square_matrix_of_rank, random_symmetric_matrix, random_symmetric_psd_matrix, random_symmetric_pd_matrix, make_nonzero_det, random_fullrank_matrix_distinct_singular_value, set_rng_seed, TEST_WITH_ROCM, IS_WINDOWS, IS_MACOS, make_tensor) class SkipInfo(object): """Describes which test, or type of tests, should be skipped when testing an operator. Any test that matches all provided arguments will be skipped. The skip will only be checked if the active_if argument is True.""" __slots__ = ['cls_name', 'test_name', 'device_type', 'dtypes', 'active_if'] def __init__(self, cls_name=None, test_name=None, *, device_type=None, dtypes=None, active_if=True): self.cls_name = cls_name self.test_name = test_name self.device_type = device_type self.dtypes = dtypes self.active_if = active_if class SampleInput(object): """Represents sample inputs to a function.""" __slots__ = ['input', 'args', 'kwargs'] def __init__(self, input, *, args=tuple(), kwargs=None): self.input = input self.args = args self.kwargs = kwargs if kwargs is not None else {} # Classes and methods for the operator database class OpInfo(object): """Operator information and helper functions for acquiring it.""" def __init__(self, name, # the string name of the function *, op=None, # the function variant of the operation, populated as torch. if None dtypes=floating_types(), # dtypes this function is expected to work with dtypesIfCPU=None, # dtypes this function is expected to work with on CPU dtypesIfCUDA=None, # dtypes this function is expected to work with on CUDA dtypesIfROCM=None, # dtypes this function is expected to work with on ROCM test_inplace_grad=True, # whether to gradcheck and gradgradcheck the inplace variant supports_tensor_out=True, # whether the op supports the out kwarg, returning a Tensor skips=tuple(), # information about which tests to skip decorators=None): # decorators to apply to generated tests # Validates the dtypes are generated from the dispatch-related functions for dtype_list in (dtypes, dtypesIfCPU, dtypesIfCUDA, dtypesIfROCM): assert isinstance(dtype_list, (_dispatch_dtypes, type(None))) self.name = name self.dtypes = dtypes self.dtypesIfCPU = dtypesIfCPU if dtypesIfCPU is not None else dtypes self.dtypesIfCUDA = dtypesIfCUDA if dtypesIfCUDA is not None else dtypes self.dtypesIfROCM = dtypesIfROCM if dtypesIfROCM is not None else dtypes # NOTE: if the op is unspecified it is assumed to be under the torch namespace if op is None: assert hasattr(torch, self.name) self.op = op if op else getattr(torch, self.name) self.method_variant = getattr(torch.Tensor, name) if hasattr(torch.Tensor, name) else None inplace_name = name + "_" self.inplace_variant = getattr(torch.Tensor, inplace_name) if hasattr(torch.Tensor, name) else None self.test_inplace_grad = test_inplace_grad self.supports_tensor_out = supports_tensor_out self.skips = skips self.decorators = decorators def __call__(self, *args, **kwargs): """Calls the function variant of the operator.""" return self.op(*args, **kwargs) 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 sample_inputs(self, device, dtype, requires_grad=False): """Returns an iterable of SampleInputs.""" return tuple() # Returns True if the test should be skipped and False otherwise def should_skip(self, cls_name, test_name, device_type, dtype): for si in self.skips: if not si.active_if: continue cls_name_match = si.cls_name is None or cls_name == si.cls_name name_match = si.test_name is None or test_name == si.test_name device_type_match = si.device_type is None or device_type == si.device_type dtype_match = si.dtypes is None or dtype in si.dtypes if cls_name_match and name_match and device_type_match and dtype_match: return True return False def supports_dtype(self, dtype, device_type): if device_type == 'cpu': return dtype in self.dtypesIfCPU if device_type == 'cuda': if TEST_WITH_ROCM: return dtype in self.dtypesIfROCM return dtype in self.dtypesIfCUDA return dtype in self.dtypes L = 20 M = 10 S = 5 # 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(), dtypesIfCPU=floating_and_complex_types_and(torch.bfloat16), dtypesIfCUDA=floating_and_complex_types_and(torch.half), dtypesIfROCM=floating_types_and(torch.half), domain=(None, None), # the [low, high) domain of the function handles_large_floats=True, # whether the op correctly handles large float values (like 1e20) handles_extremals=True, # whether the op correctly handles extremal values (like inf) handles_complex_extremals=True, # whether the op correct handles complex extremals (like inf -infj) **kwargs): super(UnaryUfuncInfo, self).__init__(name, dtypes=dtypes, dtypesIfCPU=dtypesIfCPU, dtypesIfCUDA=dtypesIfCUDA, dtypesIfROCM=dtypesIfROCM, **kwargs) self.ref = ref self.domain = domain self.handles_large_floats = handles_large_floats self.handles_extremals = handles_extremals self.handles_complex_extremals = handles_complex_extremals # Epsilon to ensure grad and gradgrad checks don't test values # outside a function's domain. self._domain_eps = 1e-5 def sample_inputs(self, device, dtype, requires_grad=False): low, high = self.domain low = low if low is None else low + self._domain_eps high = high if high is None else high - self._domain_eps return (SampleInput(make_tensor((L,), device, dtype, low=low, high=high, requires_grad=requires_grad)),) # Operator database (sorted alphabetically) op_db = [ # NOTE: CPU complex acos produces incorrect outputs (https://github.com/pytorch/pytorch/issues/42952) UnaryUfuncInfo('acos', ref=np.arccos, domain=(-1, 1), handles_complex_extremals=False, decorators=(precisionOverride({torch.float16: 1e-2, torch.bfloat16: 1e-1, torch.complex64: 1e-2}),), skips=( SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble]), SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', dtypes=[torch.cfloat, torch.cdouble], active_if=IS_WINDOWS), SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', device_type='cuda', dtypes=[torch.float16], active_if=TEST_WITH_ROCM), SkipInfo('TestGradients', 'test_fn_grad', dtypes=[torch.cdouble], active_if=IS_WINDOWS), SkipInfo('TestGradients', 'test_method_grad', dtypes=[torch.cdouble], active_if=IS_WINDOWS), SkipInfo('TestGradients', 'test_inplace_grad', dtypes=[torch.cdouble], active_if=IS_WINDOWS), )), # NOTE: the derivative for inplace acosh is not implemented UnaryUfuncInfo('acosh', ref=np.arccosh, domain=(1, float('inf')), dtypesIfCPU=floating_types(), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), decorators=(precisionOverride({torch.bfloat16: 5e-2}),), test_inplace_grad=False), UnaryUfuncInfo('asin', ref=np.arcsin, domain=(-1, 1), decorators=(precisionOverride({torch.bfloat16: 1e-2}),), skips=( SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble]), SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', device_type='cuda', dtypes=[torch.cfloat, torch.cdouble], active_if=IS_WINDOWS), )), # NOTE: derivative for inplace asinh is not implemented UnaryUfuncInfo('asinh', ref=np.arcsinh, dtypesIfCPU=floating_types(), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), decorators=(precisionOverride({torch.bfloat16: 5e-2}),), test_inplace_grad=False), UnaryUfuncInfo('atan', ref=np.arctan, decorators=(precisionOverride({torch.bfloat16: 1e-2}),), skips=( SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble]), SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', device_type='cuda', dtypes=[torch.cfloat, torch.cdouble], active_if=IS_WINDOWS), )), UnaryUfuncInfo('atanh', ref=np.arctanh, domain=(-1, 1), dtypesIfCPU=floating_types(), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), decorators=(precisionOverride({torch.bfloat16: 1e-2}),), test_inplace_grad=False), UnaryUfuncInfo('cos', ref=np.cos, dtypesIfCUDA=floating_and_complex_types_and(torch.half, torch.bfloat16), handles_large_floats=False, decorators=(precisionOverride({torch.bfloat16: 1e-2}),), skips=( SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', dtypes=[torch.cfloat, torch.cdouble], active_if=IS_WINDOWS), SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble], active_if=IS_MACOS), SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', dtypes=[torch.float], active_if=TEST_WITH_ROCM), )), UnaryUfuncInfo('cosh', ref=np.cosh, dtypesIfCPU=floating_and_complex_types(), skips=( SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', dtypes=[torch.cfloat, torch.cdouble], active_if=IS_WINDOWS), SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble], active_if=IS_MACOS), )), UnaryUfuncInfo('log', ref=np.log, domain=(0, float('inf')), dtypesIfCUDA=floating_and_complex_types_and(torch.half, torch.bfloat16), decorators=(precisionOverride({torch.bfloat16: 5e-2}),), skips=( SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', device_type='cpu', dtypes=[torch.bfloat16]), SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', device_type='cuda', dtypes=[torch.cfloat, torch.cdouble]), SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble], active_if=IS_WINDOWS), )), UnaryUfuncInfo('log10', ref=np.log10, domain=(0, float('inf')), decorators=(precisionOverride({torch.bfloat16: 5e-2}),), dtypesIfCUDA=floating_and_complex_types_and(torch.half, torch.bfloat16), skips=( SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', device_type='cuda', dtypes=[torch.cfloat, torch.cdouble]), SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble], active_if=IS_WINDOWS), )), UnaryUfuncInfo('log1p', ref=np.log1p, domain=(-1, float('inf')), dtypesIfCPU=floating_types_and(torch.bfloat16), dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16), decorators=(precisionOverride({torch.bfloat16: 1e-1}),)), UnaryUfuncInfo('log2', ref=np.log2, domain=(0, float('inf')), dtypesIfCUDA=floating_and_complex_types_and(torch.half, torch.bfloat16), decorators=(precisionOverride({torch.bfloat16: 1e-1}),), skips=( SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', device_type='cpu', dtypes=[torch.bfloat16]), SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', dtypes=[torch.cfloat, torch.cdouble]), )), UnaryUfuncInfo('neg', ref=np.negative, dtypes=all_types_and_complex_and(torch.half, torch.bfloat16), dtypesIfCPU=all_types_and_complex_and(torch.half, torch.bfloat16), dtypesIfCUDA=all_types_and_complex_and(torch.half, torch.bfloat16)), UnaryUfuncInfo('sin', ref=np.sin, handles_large_floats=False, handles_complex_extremals=False, decorators=(precisionOverride({torch.bfloat16: 1e-2}),), skips=( SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', dtypes=[torch.cfloat, torch.cdouble], active_if=IS_WINDOWS), SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', dtypes=[torch.float], active_if=TEST_WITH_ROCM), )), UnaryUfuncInfo('sinh', ref=np.sinh, dtypesIfCPU=floating_and_complex_types(), decorators=(precisionOverride({torch.float16: 1e-2}),), skips=( SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble], active_if=(IS_MACOS or IS_WINDOWS)), SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', device_type='cuda', dtypes=[torch.cfloat, torch.cdouble], active_if=IS_WINDOWS), )), UnaryUfuncInfo('tan', ref=np.tan, skips=( SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', device_type='cuda', dtypes=[torch.cfloat, torch.cdouble]), SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', device_type='cpu', dtypes=[torch.bfloat16]), SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble], active_if=(IS_MACOS or IS_WINDOWS)), SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', device_type='cuda', dtypes=[torch.float64], active_if=TEST_WITH_ROCM), )), UnaryUfuncInfo('tanh', ref=np.tanh, decorators=(precisionOverride({torch.bfloat16: 1e-2}),), dtypesIfCUDA=floating_and_complex_types_and(torch.half, torch.bfloat16), skips=( SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', device_type='cuda', dtypes=[torch.cfloat, torch.cdouble]), SkipInfo('TestUnaryUfuncs', 'test_reference_numerics', device_type='cpu', dtypes=[torch.cfloat, torch.cdouble], active_if=(IS_MACOS or IS_WINDOWS)), )), UnaryUfuncInfo('exp2', ref=np.exp2, dtypes=floating_types_and(torch.half), dtypesIfCPU=None, dtypesIfCUDA=None) ] # Common operator groupings unary_ufuncs = [op for op in op_db if isinstance(op, UnaryUfuncInfo)] def index_variable(shape, max_indices): if not isinstance(shape, tuple): shape = (shape,) index = torch.rand(*shape).mul_(max_indices).floor_().long() return index def index_perm_variable(shape, max_indices): if not isinstance(shape, tuple): shape = (shape,) index = torch.randperm(max_indices).narrow(0, 0, reduce(mul, shape)).view(shape) return index def gather_variable(shape, index_dim, max_indices, duplicate=False): assert len(shape) == 2 assert index_dim < 2 batch_dim = 1 - index_dim index = torch.LongTensor(*shape) for i in range(shape[index_dim]): index.select(index_dim, i).copy_( torch.randperm(max_indices)[: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 def uniform_scalar(offset=0, requires_grad=False): v = torch.rand(()) + offset v.requires_grad = requires_grad return v def normal_scalar_clamp(amin, amax, requires_grad=False): v = torch.randn(()).clamp(amin, amax) v.requires_grad = requires_grad return v def prod_zeros(dim_size, dim_select): assert len(dim_select) == 2 result = torch.randn(dim_size, dim_size, dim_size) 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 non_differentiable = collections.namedtuple('non_differentiable', ['tensor']) class dont_convert(tuple): pass class NoArgsClass(object): def __iter__(self): return self def __next__(self): raise StopIteration() next = __next__ # Python 2 compatibility def __len__(self): return 0 NO_ARGS = NoArgsClass() def ident(x): return x # ( # method name, # input size/constructing fn, # args (tuple represents shape of a tensor arg), # test variant name (will be used at test name suffix), // optional # (should_check_autodiff[bool], nonfusible_nodes, fusible_nodes) for autodiff, // optional # indices for possible dim arg, // optional # fn mapping output to part that should be gradcheck'ed, // optional # kwargs // optional # ) # Note: some functions have separate schema for (Tensor other) and (Scalar other), # and it's possible that we only support AD for Scalar version but not Tensor # version, and vice versa. # When writing tests, only scalar(float/int) input triggers the Scalar schema. # uniform_scalar produces a scalar **Tensor** which won't match Scalar input. def method_tests(): set_rng_seed(0) return [ ('acosh', torch.rand(S, S, S).add(1), NO_ARGS, ''), ('acosh', torch.rand(tuple()).add(1), NO_ARGS, 'scalar'), ('add', (S, S, S), ((S, S, S),), '', (True,)), ('add', (S, S, S), ((S, S),), 'broadcast_rhs', (True,)), ('add', (S, S), ((S, S, S),), 'broadcast_lhs', (True,)), ('add', (S, 1, S), ((M, S),), 'broadcast_all', (True,)), ('add', (), ((),), 'scalar', (True,)), ('add', (S, S, S), ((),), 'scalar_broadcast_rhs', (True,)), ('add', (), ((S, S, S),), 'scalar_broadcast_lhs', (True,)), ('add', (S, S, S), (3.14,), 'constant', (True,)), ('add', (), (3.14,), 'scalar_constant', (True,)), ('asinh', (S, S, S), NO_ARGS, ''), ('asinh', (), NO_ARGS, 'scalar'), ('atanh', torch.rand(S, S, S), NO_ARGS, ''), ('atanh', torch.rand(tuple()), NO_ARGS, 'scalar'), ('__radd__', (S, S, S), (3.14,), 'constant', (True, 'aten::add')), ('__radd__', (), (3.14,), 'scalar_constant', (True, 'aten::add')), ('sub', (S, S, S), ((S, S, S),), '', (True,)), ('sub', (S, S, S), ((S, S),), 'broadcast_rhs', (True,)), ('sub', (S, S), ((S, S, S),), 'broadcast_lhs', (True,)), ('sub', (S, 1, S), ((M, S),), 'broadcast_all', (True,)), ('sub', (S, S, S), ((),), 'scalar_broadcast_rhs', (True,)), ('sub', (), ((S, S, S),), 'scalar_broadcast_lhs', (True,)), ('sub', (S, S, S), (3.14,), 'constant', (True,)), ('sub', (), (3.14,), 'scalar_constant', (True,)), ('__rsub__', (S, S, S), (3.14,), 'constant', (True, 'aten::rsub')), ('__rsub__', (), (3.14,), 'scalar_constant', (True, 'aten::rsub')), ('mul', (S, S, S), ((S, S, S),), '', (True,)), ('mul', (), ((),), 'scalar', (True,)), ('mul', (S, S, S), ((S, S),), 'broadcast_rhs', (True,)), ('mul', (S, S), ((S, S, S),), 'broadcast_lhs', (True,)), ('mul', (S, 1, S), ((M, S),), 'broadcast_all', (True,)), ('mul', (S, S, S), ((),), 'scalar_broadcast_rhs', (True,)), ('mul', (), ((S, S, S),), 'scalar_broadcast_lhs', (True,)), ('mul', (S, S, S), (3.14,), 'constant', (True,)), ('mul', (), (3.14,), 'scalar_constant', (True,)), # TODO(@anjali411): enable these tests # ('mul', (S, S, S), (3.14j,), 'imaginary_constant', (True,)), # ('mul', (), (3.14j,), 'imaginary_scalar_constant', (True,)), ('__rmul__', (S, S, S), (3.14,), 'constant', (True, 'aten::mul')), ('__rmul__', (), (3.14,), 'scalar_constant', (True, 'aten::mul')), ('div', (S, S, S), (torch.rand(S, S, S) + 0.1,), '', (True,)), ('div', (S, S, S), (torch.rand(S, S) + 0.1,), 'broadcast_rhs', (True,)), ('div', (S, S), (torch.rand(S, S, S) + 0.1,), 'broadcast_lhs', (True,)), ('div', (S, 1, S), (torch.rand(M, S) + 0.1,), 'broadcast_all', (True,)), ('div', (), (uniform_scalar(0.1),), 'scalar', (True,)), ('div', (S, S, S), (uniform_scalar(0.1),), 'scalar_broadcast_rhs', (True,)), ('div', (), (uniform_scalar(0.1),), 'scalar_broadcast_lhs', (True,)), ('div', torch.rand(S, S, S) + 1e-1, (3.14,), 'constant', (True,)), ('div', uniform_scalar(1e-1, requires_grad=True), (3.14,), 'scalar_constant', (True,)), ('true_divide', (S, S, S), (torch.rand(S, S, S) + 0.1,), '', (True,)), ('true_divide', (S, S, S), (torch.rand(S, S) + 0.1,), 'broadcast_rhs', (True,)), ('true_divide', (S, S), (torch.rand(S, S, S) + 0.1,), 'broadcast_lhs', (True,)), ('true_divide', (S, 1, S), (torch.rand(M, S) + 0.1,), 'broadcast_all', (True,)), ('true_divide', (), (uniform_scalar(0.1),), 'scalar', (True,)), ('true_divide', (S, S, S), (uniform_scalar(0.1),), 'scalar_broadcast_rhs', (True,)), ('true_divide', (), (uniform_scalar(0.1),), 'scalar_broadcast_lhs', (True,)), ('true_divide', torch.rand(S, S, S) + 1e-1, (3.14,), 'constant', (True,)), ('true_divide', uniform_scalar(1e-1, requires_grad=True), (3.14,), 'scalar_constant', (True,)), ('__rdiv__', torch.rand(S, S, S) + 1e-1, (3.14,), 'constant', (True, [], ['aten::mul', 'aten::reciprocal'])), ('__rdiv__', uniform_scalar(1e-1, requires_grad=True), (3.14,), 'scalar_constant', (True, [], ['aten::mul', 'aten::reciprocal'])), ('pow', torch.rand(S, S, S) + 1e-3, (torch.rand(S, S, S) + 0.1,), '', (True,)), ('pow', torch.rand(S, S, S) + 1e-3, (torch.rand(1,) + 0.1,), 'broadcast_rhs', (True,)), ('pow', torch.rand(1,) + 1e-3, (torch.rand(S, S, S) + 0.1,), 'broadcast_lhs', (True,)), ('pow', torch.rand(S, 1, S) + 1e-3, (torch.rand(1, S, 1) + 0.1,), 'broadcast_all', (True,)), ('pow', uniform_scalar(1e-3, requires_grad=True), (uniform_scalar(0.1),), 'scalar', (True,)), ('pow', torch.rand(S, S, S) + 1e-3, (uniform_scalar(0.1),), 'scalar_broadcast_rhs', (True,)), ('pow', uniform_scalar(1e-3, requires_grad=True), (torch.rand(S, S, S) + 0.1,), 'scalar_broadcast_lhs', (True,)), ('pow', torch.rand(S, S, S) + 1e-3, (3.14,), 'constant', (True,)), ('__rpow__', torch.rand(S, S, S) + 1e-3, (3.14,), 'constant', (True, 'aten::pow')), ('pow', uniform_scalar(1e-3, requires_grad=True), (3.14,), 'scalar_constant', (True,)), ('__rpow__', uniform_scalar(1e-3, requires_grad=True), (3.14,), 'scalar_constant', (True, 'aten::pow')), ('transpose', (1, 2, 3), (1, 2), 'dim', (False,), [0, 1]), ('transpose', (), (0, 0), 'scalar', (False,)), ('transpose', (1,), (0, 0), '1d', (False,)), ('transpose', (L, L), (0, 1), '2d', (False,)), ('transpose', (S, S, S), (2, 0), '3d', (False,)), ('t', (1, 2), NO_ARGS, '', (False,)), ('view', (S, S, S), (S * S, S), '', (False,)), ('view', (S, S, S), (torch.Size([S * S, S]),), 'size', (False,)), ('view', (S,), (S,), '1d', (False,)), ('view', (), (dont_convert(()),), 'scalar_to_scalar', (False,)), ('view', (), (1,), 'scalar_to_1d', (False,)), ('reshape', (S, S, S), (S * S, S), '', (False,)), ('reshape', (S, S, S), (torch.Size([S * S, S]),), 'size', (False,)), ('reshape', (S,), (S,), '1d', (False,)), ('reshape', (), (dont_convert(()),), 'scalar_to_scalar', (False,)), ('reshape', (), (1,), 'scalar_to_1d', (False,)), ('reshape_as', (S, S, S), (non_differentiable(torch.rand(S * S, S)),)), ('reshape_as', (), (non_differentiable(torch.tensor(42.)),), 'scalar'), ('reshape_as', (), (non_differentiable(torch.rand(1, 1)),), 'scalar_to_dims'), ('flip', (S, S, S), ([0],), 'd0'), ('flip', (S, S, S), ([0, 1, 2],), 'd012'), ('flip', (S, S, S), ([0, 2],), 'd02'), ('flip', (S, S, S), ([2, 0],), 'd20'), ('flip', (S, S, S), ([-1],), 'neg_d'), ('fliplr', (S, S, S), ()), ('flipud', (S, S, S), ()), ('roll', (S, S, S), (0, 0), 'd0'), ('roll', (S, S, S), (1, 2), 'd12'), ('roll', (S, S, S), (0, 2,), 'd02'), ('roll', (S, S, S), (2, 0,), 'd20'), ('roll', (S, S, S), (-1, 0), 'neg_shift'), ('roll', (S, S, S), (10000, 1), 'loop_shift'), ('roll', (S, S, S), (2,), 'flattened'), ('roll', (S, S, S), ([1, 2, -1], [0, 1, 2]), 'three_dims'), ('rot90', (S, S, S), (1, [0, 1],), 'k1_d01'), ('rot90', (S, S, S), (1, [1, 2],), 'k1_d12'), ('rot90', (S, S, S), (1, [1, -1],), 'k1_neg_d'), ('rot90', (S, S, S), (), 'default'), ('view_as', (S, S, S), (non_differentiable(torch.rand(S * S, S)),)), ('view_as', (), (non_differentiable(torch.tensor(5.5)),), 'scalar'), ('view_as', (), (non_differentiable(torch.rand(1, 1)),), 'scalar_to_dims'), ('expand', (S, 1, 1), (S, S, S), '', (False,)), ('expand', (torch.Size([S, 1, S]),), (S, S, S), 'size', (False,)), ('expand', (S, 1), (S, S, S), 'new_dim', (False,)), ('expand', (1,), (S, S, S), '1_element', (False,)), ('expand', (1, S), (1, 1, S), 'new_dim_front_old_front_1', (False,)), ('expand', (), (dont_convert(()),), 'scalar_to_scalar'), ('expand', (), (1, 3, 2), 'scalar_to_dims', (False,)), ('expand_as', (S, 1, 1), (torch.rand(S, S, S),), '', (False,)), ('exp', (S, S, S), NO_ARGS, '', (True,)), ('exp', (), NO_ARGS, 'scalar', (True,)), ('exp2', (S, S, S), NO_ARGS, '', (False,)), ('exp2', (), NO_ARGS, 'scalar', (False,)), ('expm1', (S, S, S), NO_ARGS, '', (True,)), ('expm1', (), NO_ARGS, 'scalar', (True,)), ('erf', torch.rand(S, S, S), NO_ARGS, '', (True,)), ('erf', uniform_scalar(requires_grad=True), NO_ARGS, 'scalar', (True,)), ('erfc', torch.rand(S, S, S), NO_ARGS, '', (True,)), ('erfc', uniform_scalar(requires_grad=True), NO_ARGS, 'scalar', (True,)), ('erfinv', torch.rand(S, S, S).clamp(-0.9, 0.9), NO_ARGS), ('erfinv', normal_scalar_clamp(-0.9, 0.9, requires_grad=True), NO_ARGS, 'scalar'), ('log', torch.rand(S, S, S) + 1e-2, NO_ARGS, '', (True,)), ('log', uniform_scalar(1e-2, requires_grad=True), NO_ARGS, 'scalar', (True,)), ('log10', torch.rand(S, S, S) + 1e-2, NO_ARGS, '', (True,)), ('log10', uniform_scalar(1e-2, requires_grad=True), NO_ARGS, 'scalar', (True,)), ('log1p', torch.rand(S, S, S), NO_ARGS, '', (True,)), ('log1p', uniform_scalar(requires_grad=True), NO_ARGS, 'scalar', (True,)), ('log2', torch.rand(S, S, S) + 1e-2, NO_ARGS, '', (True,)), ('log2', uniform_scalar(1e-2, requires_grad=True), NO_ARGS, 'scalar', (True,)), # TODO(@anjali411): add the commented tests back after updating the formula based on tensorflow definition. # ('log', torch.randn(S, S, S, dtype=torch.cfloat) + 1e-2, NO_ARGS, 'complex', (True,)), # ('log', uniform_scalar(1e-2j, requires_grad=True), NO_ARGS, 'complex_scalar', (True,)), # ('log10', torch.randn(S, S, S, dtype=torch.cfloat) + 1e-2, NO_ARGS, 'complex', (True,)), # ('log10', uniform_scalar(1e-2j, requires_grad=True), NO_ARGS, 'complex_scalar', (True,)), # ('log2', torch.randn(S, S, S, dtype=torch.cfloat) + 1e-2, NO_ARGS, 'complex', (True,)), # ('log2', uniform_scalar(1e-2j, requires_grad=True), NO_ARGS, 'complex_scalar', (True,)), ('tanh', (S, S, S), NO_ARGS, '', (True,)), ('tanh', (), NO_ARGS, 'scalar', (True,)), ('sigmoid', (S, S, S), NO_ARGS, '', (True,)), ('sigmoid', (), NO_ARGS, 'scalar', (True,)), ('logit', torch.randn(S, S, S).clamp(0.1, 0.9).requires_grad_(True), NO_ARGS, ''), ('logit', torch.randn(S, S, S).clamp(0.1, 0.9).requires_grad_(True), (0.2,), 'eps'), ('logit', uniform_scalar().clamp(0.1, 0.9).requires_grad_(True), NO_ARGS, 'scalar'), ('logit', uniform_scalar().clamp(0.1, 0.9).requires_grad_(True), (0.2,), 'scalar_eps'), ('sinh', (S, S, S), NO_ARGS, '', (True,)), ('sinh', (), NO_ARGS, 'scalar', (True,)), ('cosh', (S, S, S), NO_ARGS, '', (True,)), ('cosh', (), NO_ARGS, 'scalar', (True,)), ('conj', (S, S, S), NO_ARGS), ('real', (S, S, S), NO_ARGS, 'complex'), ('imag', (S, S, S), NO_ARGS, 'complex'), ('view_as_real', (S, S, S), NO_ARGS, 'complex'), ('view_as_complex', (S, S, 2), NO_ARGS), ('complex', (S, S, S), ((S, S, S),), ''), ('abs', (S, S, S), NO_ARGS, '', (True,)), ('abs', (), NO_ARGS, 'scalar', (True,)), ('clamp', (S, S, S), (0, 1), '', (True,)), ('clamp', (S, S, S), (None, 0.5), 'min', (True,)), ('clamp', (S, S, S), (0.5, None), 'max', (True,)), ('clamp', (), (0, 1), 'scalar', (True,)), ('clamp', (), (None, 0.5), 'min_scalar', (True,)), ('clamp', (), (0.5, None), 'max_scalar', (True,)), ('clamp', (S, S), (), 'max_scalar_kwarg', (True,), (), (), ident, {'max': 1}), ('sqrt', torch.rand(S, S, S) + 5e-4, NO_ARGS, '', (True,)), ('sqrt', uniform_scalar(5e-4, requires_grad=True), NO_ARGS, 'scalar', (True,)), ('sin', (S, S, S), NO_ARGS, '', (True,)), ('sin', (), NO_ARGS, 'scalar', (True,)), ('cos', (S, S, S), NO_ARGS, '', (True,)), ('cos', (), NO_ARGS, 'scalar', (True,)), ('tan', torch.randn(S, S, S).clamp(-1, 1), NO_ARGS, '', (True,)), # TODO(@anjali411): add the commented test back after updating the formula based on tensorflow definition. # ('tan', (S, S, S), NO_ARGS, 'complex', (True,)), ('asin', torch.randn(S, S, S).clamp(-0.9, 0.9), NO_ARGS, '', (True,)), ('acos', torch.randn(S, S, S).clamp(-0.9, 0.9), NO_ARGS, '', (True,)), ('atan', (S, S, S), NO_ARGS, '', (True,)), ('atan', (), NO_ARGS, 'scalar', (True,)), ('atan2', (S, S, S), ((S, S, S),)), ('atan2', (), ((),), 'scalar'), ('atan2', (S, S, S), ((S,),), 'broadcast_rhs'), ('atan2', (S,), ((S, S, S),), 'broadcast_lhs'), ('atan2', (S, 1, S), ((S, S),), 'broadcast_all'), ('reciprocal', torch.rand(S, S, S) + 0.1, NO_ARGS, '', (True,)), ('reciprocal', uniform_scalar(0.1, requires_grad=True), NO_ARGS, 'scalar', (True,)), # TODO(@anjali411): add the commented tests back after updating the formula based on tensorflow definition. # ('reciprocal', torch.randn(S, S, S, dtype=torch.cdouble) + 0.1, NO_ARGS, 'complex', (True,)), # ('reciprocal', uniform_scalar(0.1j), NO_ARGS, 'complex_scalar', (True,)), ('round', (S, S, S), NO_ARGS, '', (True,)), ('round', (), NO_ARGS, 'scalar', (True,)), ('sign', (S, S, S), NO_ARGS), ('sign', (), NO_ARGS, 'scalar'), ('sgn', (S, S, S), NO_ARGS), ('sgn', (), NO_ARGS, 'scalar'), ('trunc', (S, S, S), NO_ARGS, '', (True,)), ('trunc', (), NO_ARGS, 'scalar', (True,)), ('floor', (S, S, S), NO_ARGS, '', (True,)), ('floor', (), NO_ARGS, 'scalar', (True,)), ('ceil', (S, S, S), NO_ARGS, '', (True,)), ('ceil', (), NO_ARGS, 'scalar', (True,)), ('rad2deg', (S, S, S), NO_ARGS), ('deg2rad', (S, S, S), NO_ARGS), ('rsqrt', torch.rand(S, S, S) + 1e-2, NO_ARGS, '', (True,)), ('rsqrt', uniform_scalar(1e-2, requires_grad=True), NO_ARGS, 'scalar', (True,)), ('frac', (S, S, S), NO_ARGS, '', (True,)), ('frac', (), NO_ARGS, 'scalar', (True,)), ('fmod', (S, S, S), (1.5,), '', (True,)), ('fmod', (), (1.5,), 'scalar', (True,)), ('fmod', (S, S, S), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'tensor'), ('fmod', (S,), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'tensor_broadcast_lhs'), ('fmod', (S, S, S), (non_differentiable(torch.rand(S) + 1.5),), 'tensor_broadcast_rhs'), ('fmod', (S, 1, S), (non_differentiable(torch.rand(S, S) + 1.5),), 'tensor_broadcast_all'), ('fmod', (), (non_differentiable(uniform_scalar(1.5)),), 'scalar_tensor'), ('fmod', (), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'scalar_tensor_broadcast_lhs'), ('fmod', (S, S, S), (non_differentiable(uniform_scalar(1.5)),), 'scalar_tensor_broadcast_rhs'), ('hypot', (S, S), ((S, S),)), ('remainder', (S, S, S), (1.5,), '', (True,)), ('remainder', (), (1.5,), 'scalar', (True,)), ('remainder', (S, S, S), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'tensor'), ('remainder', (S,), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'tensor_broadcast_lhs'), ('remainder', (S, 1, S), (non_differentiable(torch.rand(S, S) + 1.5),), 'tensor_broadcast_all'), ('remainder', (), (non_differentiable(uniform_scalar(1.5)),), 'scalar_tensor'), ('remainder', (), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'scalar_tensor_broadcast_lhs'), ('lerp', (S, S, S), ((S, S, S), 0.4), 'scalar_no_broadcast', (True,)), ('lerp', (S, S, S), ((S,), 0.4), 'broadcast_rhs', (True,)), ('lerp', (S,), ((S, S, S), 0.4), 'broadcast_lhs', (True,)), ('lerp', (S, 1, S), ((S, S), 0.4), 'broadcast_all', (True,)), ('lerp', (), ((), 0.4), 'scalar', (True,)), ('lerp', (S, S, S), ((), 0.4), 'scalar_broadcast_rhs', (True,)), ('lerp', (), ((S, S, S), 0.4), 'scalar_broadcast_lhs', (True,)), ('max', (S, S, S), NO_ARGS), ('max', (S, S, S), (1,), 'dim', (), [0]), ('max', (S, S, S), (1, True,), 'keepdim_dim', (), [0]), ('max', (), NO_ARGS, 'scalar'), ('max', (), (0,), 'scalar_dim', (), [0]), ('max', (), (0, True,), 'scalar_keepdim_dim', (), [0]), ('max', (S, S, S), ((S, S, S),), 'elementwise', (True,)), ('max', (S, S, S), ((S,),), 'elementwise_broadcast_rhs', (True,)), ('max', (S,), ((S, S, S),), 'elementwise_broadcast_lhs', (True,)), ('max', (S, 1, S), ((S, S),), 'elementwise_broadcast_all', (True,)), ('max', (), ((),), 'scalar_elementwise', (True,)), ('max', (S, S, S), ((),), 'scalar_elementwise_broadcast_rhs', (True,)), ('max', (), ((S, S, S),), 'scalar_elementwise_broadcast_lhs', (True,)), ('min', (S, S, S), NO_ARGS, ), ('min', (S, S, S), (1,), 'dim', (), [0]), ('min', (S, S, S), (1, True,), 'keepdim_dim', (), [0]), ('min', (), NO_ARGS, 'scalar'), ('min', (), (0,), 'scalar_dim', (), [0]), ('min', (), (0, True,), 'scalar_keepdim_dim', (), [0]), ('min', (S, S, S), ((S, S, S),), 'elementwise', (True,)), ('min', (S, S, S), ((S,),), 'elementwise_broadcast_rhs', (True,)), ('min', (S,), ((S, S, S),), 'elementwise_broadcast_lhs', (True,)), ('min', (S, 1, S), ((S, S),), 'elementwise_broadcast_all', (True,)), ('min', (), ((),), 'scalar_elementwise', (True,)), ('min', (S, S, S), ((),), 'scalar_elementwise_broadcast_rhs', (True,)), ('min', (), ((S, S, S),), 'scalar_elementwise_broadcast_lhs', (True,)), ('amax', (S, S, S), NO_ARGS), ('amax', (S, S, S), (1,), 'dim'), ('amax', (S, S, S), ([1, 2],), 'multiple_dim'), ('amax', (S, S, S), (1, True,), 'keepdim_dim'), ('amax', (), NO_ARGS, 'scalar'), ('amax', (), (0,), 'scalar_dim'), ('amax', (), (0, True,), 'scalar_keepdim_dim'), ('amin', (S, S, S), NO_ARGS, ), ('amin', (S, S, S), (1,), 'dim',), ('amin', (S, S, S), ([1, 2],), 'multiple_dim'), ('amin', (S, S, S), (1, True,), 'keepdim_dim'), ('amin', (), NO_ARGS, 'scalar'), ('amin', (), (0,), 'scalar_dim'), ('amin', (), (0, True,), 'scalar_keepdim_dim'), ('mean', (S, S, S), NO_ARGS, '', (True,)), ('mean', (S, S, S), (1,), 'dim', (True,), [0]), ('mean', (S, S, S), (1, True,), 'keepdim_dim', (True,), [0]), ('mean', (), NO_ARGS, 'scalar', (True,)), ('mean', (), (0,), 'scalar_dim', (True,), [0]), ('mean', (), (0, True,), 'scalar_keepdim_dim', (True,), [0]), ('mean', (S, S, S), (), 'dtype', (True,), (), (), ident, {'dtype': torch.float64}), ('kthvalue', (S, S, S), (2,)), ('kthvalue', (S, S, S), (2, 1,), 'dim', (), [1]), ('kthvalue', (S, S, S), (2, 1, True,), 'keepdim_dim', (), [1]), ('kthvalue', (S,), (2, 0,), 'dim_1d', (), [1]), ('kthvalue', (S,), (2, 0, True,), 'keepdim_dim_1d', (), [1]), # TODO: https://github.com/pytorch/pytorch/issues/30818 ('kthvalue', (), (1,), 'scalar', (), (), [expectedFailureCUDA]), ('kthvalue', (), (1, 0,), 'scalar_dim', (), [1], [expectedFailureCUDA]), ('kthvalue', (), (1, 0, True), 'scalar_keepdim_dim', (), [1], [expectedFailureCUDA]), # END TODO ('quantile', (S, S, S), (0.5,)), ('quantile', (S, S, S), (0.5, 0), 'dim', (), [1]), ('quantile', (S, S, S), (0.5, None, True), 'keepdim'), ('quantile', (S, S, S), (0.5, 0, True), 'keepdim_dim', (), [1]), ('quantile', (), (0.5,), 'scalar'), ('nanquantile', (S, S, S), (0.5,)), ('nanquantile', (S, S, S), (0.5, 0), 'dim', (), [1]), ('nanquantile', (S, S, S), (0.5, None, True), 'keepdim'), ('nanquantile', (S, S, S), (0.5, 0, True), 'keepdim_dim', (), [1]), ('nanquantile', (), (0.5,), 'scalar'), ('median', (S, S, S), NO_ARGS), ('median', (S, S, S), (1,), 'dim', (), [0]), ('median', (S, S, S), (1, True,), 'keepdim_dim', (), [0]), ('median', (), NO_ARGS, 'scalar'), # TODO: https://github.com/pytorch/pytorch/issues/30818 ('median', (), (0,), 'scalar_dim', (), [0], [expectedFailureCUDA]), ('median', (), (0, True,), 'scalar_keepdim_dim', (), [0], [expectedFailureCUDA]), # END TODO ('mode', (S, S, S), NO_ARGS), ('mode', (S, S, S), (1,), 'dim', (), [0]), ('mode', (S, S, S), (1, True,), 'keepdim_dim', (), [0]), ('mode', (), NO_ARGS, 'scalar'), ('mode', (), (0,), 'scalar_dim', (), [0]), ('mode', (), (0, True,), 'scalar_keepdim_dim', (), [0]), ('sum', (S, S, S), NO_ARGS), ('sum', (S, S, S), (1,), 'dim', (), [0]), ('sum', (S, S, S), (1, True,), 'keepdim_dim', (), [0]), ('sum', (), NO_ARGS, 'scalar'), ('sum', (), (0,), 'scalar_dim', (), [0]), ('sum', (), (0, True,), 'scalar_keepdim_dim', (), [0]), ('sum', (S, S, S), ([1, 2],), 'multi_dim'), ('sum', (S, S, S), ([1, 2], True,), 'multi_dim_keepdim'), ('nansum', (S, S, S), NO_ARGS), ('nansum', (S, S, S), (1,), 'dim', (), [0]), ('nansum', (S, S, S), (1, True,), 'keepdim_dim', (), [0]), ('nansum', (), NO_ARGS, 'scalar'), ('nansum', (), (0,), 'scalar_dim', (), [0]), ('nansum', (), (0, True,), 'scalar_keepdim_dim', (), [0]), ('nansum', (S, S, S), ([1, 2],), 'multi_dim'), ('nansum', (S, S, S), ([1, 2], True,), 'multi_dim_keepdim'), ('prod', (S, S, S), NO_ARGS), ('prod', (S, S, S), (1,), 'dim', (), [0]), ('prod', (S, S, S), (1, True,), 'keepdim_dim', (), [0]), ('prod', (), NO_ARGS, 'scalar'), ('prod', (), (0,), 'scalar_dim', (), [0]), ('prod', (), (0, True,), 'scalar_keepdim_dim', 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[skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('det', lambda: random_symmetric_pd_matrix(S, 3), NO_ARGS, 'batched_symmetric_pd', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('det', lambda: random_fullrank_matrix_distinct_singular_value(S, 3, 3), NO_ARGS, 'batched_distinct_singular_values', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), # For `logdet` and `slogdet`, the function at det=0 is not smooth. # We need to exclude tests with det=0 (e.g. dim2_null, rank1, rank2) and use # `make_nonzero_det` to make the random matrices have nonzero det. For # `logdet`, we also set `make_nonzero_det(matrix, sign=1)` to make the # matrix have positive det. ('logdet', lambda: make_nonzero_det(torch.randn(S, S), 1), NO_ARGS, '', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('logdet', lambda: make_nonzero_det(torch.randn(1, 1), 1), NO_ARGS, '1x1', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('logdet', lambda: make_nonzero_det(random_symmetric_matrix(S), 1), NO_ARGS, 'symmetric', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('logdet', lambda: make_nonzero_det(random_symmetric_pd_matrix(S), 1), NO_ARGS, 'symmetric_pd', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('logdet', lambda: make_nonzero_det(random_fullrank_matrix_distinct_singular_value(S), 1, 0), NO_ARGS, 'distinct_singular_values', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('logdet', lambda: make_nonzero_det(torch.randn(3, 3, S, S), 1), NO_ARGS, 'batched', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('logdet', lambda: make_nonzero_det(torch.randn(3, 3, 1, 1), 1), NO_ARGS, 'batched_1x1', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('logdet', lambda: make_nonzero_det(random_symmetric_matrix(S, 3), 1), NO_ARGS, 'batched_symmetric', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('logdet', lambda: make_nonzero_det(random_symmetric_pd_matrix(S, 3), 1), NO_ARGS, 'batched_symmetric_pd', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('logdet', lambda: make_nonzero_det(random_fullrank_matrix_distinct_singular_value(S, 3), 1, 0), NO_ARGS, 'batched_distinct_singular_values', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('slogdet', lambda: make_nonzero_det(torch.randn(1, 1), 1), NO_ARGS, '1x1_pos_det', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma], itemgetter(1)), ('slogdet', lambda: make_nonzero_det(torch.randn(1, 1), -1), NO_ARGS, '1x1_neg_det', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma], itemgetter(1)), ('slogdet', lambda: make_nonzero_det(torch.randn(S, S), 1), NO_ARGS, 'pos_det', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma], itemgetter(1)), ('slogdet', lambda: make_nonzero_det(torch.randn(S, S), -1), NO_ARGS, 'neg_det', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma], itemgetter(1)), ('slogdet', lambda: make_nonzero_det(random_symmetric_matrix(S)), NO_ARGS, 'symmetric', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma], itemgetter(1)), ('slogdet', lambda: random_symmetric_pd_matrix(S), NO_ARGS, 'symmetric_pd', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma], itemgetter(1)), ('slogdet', lambda: random_fullrank_matrix_distinct_singular_value(S), NO_ARGS, 'distinct_singular_values', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma], itemgetter(1)), ('slogdet', lambda: make_nonzero_det(torch.randn(3, 3, 1, 1), -1), NO_ARGS, 'batched_1x1_neg_det', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma], itemgetter(1)), ('slogdet', lambda: make_nonzero_det(torch.randn(3, 3, S, S), 1), NO_ARGS, 'batched_pos_det', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma], itemgetter(1)), ('slogdet', lambda: make_nonzero_det(random_symmetric_matrix(S, 3)), NO_ARGS, 'batched_symmetric', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma], itemgetter(1)), ('slogdet', lambda: random_symmetric_pd_matrix(S, 3), NO_ARGS, 'batched_symmetric_pd', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma], itemgetter(1)), ('slogdet', lambda: random_fullrank_matrix_distinct_singular_value(S, 3), NO_ARGS, 'batched_distinct_singular_values', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma], itemgetter(1)), ('svd', lambda: random_fullrank_matrix_distinct_singular_value(S), NO_ARGS, '', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('svd', lambda: random_fullrank_matrix_distinct_singular_value(S)[:(S - 2)], NO_ARGS, 'wide', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('svd', lambda: random_fullrank_matrix_distinct_singular_value(S)[:, :(S - 2)], NO_ARGS, 'tall', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('svd', lambda: random_fullrank_matrix_distinct_singular_value(S)[:(S - 2)], (False,), 'wide_all', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma], lambda usv: (usv[0], usv[1], usv[2][:, :(S - 2)])), ('svd', lambda: random_fullrank_matrix_distinct_singular_value(S)[:, :(S - 2)], (False,), 'tall_all', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma], lambda usv: (usv[0][:, :(S - 2)], usv[1], usv[2])), ('svd', lambda: random_fullrank_matrix_distinct_singular_value(M), NO_ARGS, 'large', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('svd', lambda: random_fullrank_matrix_distinct_singular_value(S, 3), NO_ARGS, 'batched', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('svd', lambda: random_fullrank_matrix_distinct_singular_value(S, 3)[..., :(S - 2), :], NO_ARGS, 'wide_batched', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('svd', lambda: random_fullrank_matrix_distinct_singular_value(S, 3)[..., :, :(S - 2)], NO_ARGS, 'tall_batched', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('svd', lambda: random_fullrank_matrix_distinct_singular_value(S, 3, 3)[..., :(S - 2), :], (False,), 'wide_all_batched', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma], lambda usv: (usv[0], usv[1], usv[2][..., :, :(S - 2)])), ('svd', lambda: random_fullrank_matrix_distinct_singular_value(S, 3, 3)[..., :, :(S - 2)], (False,), 'tall_all_batched', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma], lambda usv: (usv[0][..., :, :(S - 2)], usv[1], usv[2])), ('qr', (S, S), (False,), 'square_single', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('qr', (S, S - 2), (True,), 'tall_single' , (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('qr', (S - 2, S), (False,), 'wide_single' , (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('qr', (3, S, S), (False,), 'square_batched', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('qr', (3, S, S - 2), (True,), 'tall_batched', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('qr', (3, S - 2, S), (True,), 'wide_batched' , (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('qr', (3, 2, S, S), (False,), 'square_many_batched', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('qr', (3, 2, S, S - 2), (True,), 'tall_many_batched', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('qr', (3, 2, S - 2, S), (True,), 'wide_many_batched', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('solve', (S, S), (random_fullrank_matrix_distinct_singular_value( S, silent=True),), '', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('solve', (S, S, S), (random_fullrank_matrix_distinct_singular_value(S, S, silent=True),), 'batched', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('solve', (2, 3, S, S), (random_fullrank_matrix_distinct_singular_value(S, 2, 3, silent=True),), 'batched_dims', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('solve', (2, 2, S, S), (random_fullrank_matrix_distinct_singular_value(S, 1, silent=True),), 'batched_broadcast_A', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('solve', (1, S, S), (random_fullrank_matrix_distinct_singular_value(S, 2, 2, silent=True),), 'batched_broadcast_b', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), ('fill_', (S, S, S), (1,), 'number'), ('fill_', (), (1,), 'number_scalar'), ('fill_', (S, S, S), ((),), 'variable'), ('eq_', (S, S, S), ((S, S, S),)), ('eq_', (S, S, S), ((1,),), 'broadcast_rhs'), ('eq_', (), ((),), 'scalar'), ('eq_', (S, S, S), ((),), 'scalar_broadcast_rhs'), ('ne_', (S, S, S), ((S, S, S),)), ('ne_', (S, S, S), ((1,),), 'broadcast_rhs'), ('ne_', (), ((),), 'scalar'), ('ne_', (S, S, S), ((),), 'scalar_broadcast_rhs'), ('gt_', (S, S, S), ((S, S, S),)), ('gt_', (S, S, S), ((1,),), 'broadcast_rhs'), ('gt_', (), ((),), 'scalar'), ('gt_', (S, S, S), ((),), 'scalar_broadcast_rhs'), ('ge_', (S, S, S), ((S, S, S),)), ('ge_', (S, S, S), ((1,),), 'broadcast_rhs'), ('ge_', (), ((),), 'scalar'), ('ge_', (S, S, S), ((),), 'scalar_broadcast_rhs'), ('lt_', (S, S, S), ((S, S, S),)), ('lt_', (S, S, S), ((1,),), 'broadcast_rhs'), ('lt_', (), ((),), 'scalar'), ('lt_', (S, S, S), ((),), 'scalar_broadcast_rhs'), ('le_', (S, S, S), ((S, S, S),)), ('le_', (S, S, S), ((1,),), 'broadcast_rhs'), ('le_', (), ((),), 'scalar'), ('le_', (S, S, S), ((),), 'scalar_broadcast_rhs'), ('eq_', (S, S, S), (0,), 'pyscalar'), ('ne_', (S, S, S), (0,), 'pyscalar'), ('gt_', (S, S, S), (0,), 'pyscalar'), ('ge_', (S, S, S), (0,), 'pyscalar'), ('le_', (S, S, S), (0,), 'pyscalar'), ('lt_', (), (0,), 'pyscalar'), ('eq_', (), (0,), 'pyscalar_scalar'), ('ne_', (), (0,), 'pyscalar_scalar'), ('gt_', (), (0,), 'pyscalar_scalar'), ('ge_', (), (0,), 'pyscalar_scalar'), ('lt_', (), (0,), 'pyscalar_scalar'), ('le_', (), (0,), 'pyscalar_scalar'), ('permute', (1, 2, 3, 4), (0, 2, 3, 1), '', (True,)), ('permute', (1, 2, 3, 4), (0, -2, -1, 1), 'neg_dim', (True,)), ('permute', (), (dont_convert(()),), 'scalar', (True,)), ('select', (S, S, S), (1, 2), 'dim', (), [0]), ('select', (S, S, S), (1, -1), 'wrap_dim', (), [0]), ('select', (S,), (0, 2), '1d'), ('narrow', (S, S, S), (1, 2, 2), 'dim', (), [0]), ('narrow', (S, S, S), (1, 0, 0), 'empty_dim', (), [0]), ('squeeze', (S, 1, S, 1), NO_ARGS, '', (True,)), ('squeeze', (1, 1, 1, 1), NO_ARGS, 'input_sizes_are_ones', (True,)), ('squeeze', (S, 1, S, 1), (1,), '1_dim', (True,), [0]), ('squeeze', (S, 1, S, 1), (2,), 'not_1_dim', (True,), [0]), ('squeeze', (), (0,), 'scalar', (True,), [0]), ('unsqueeze', (S, S, S), (0,), 'first', (True,), [0]), ('unsqueeze', (S, S, S), (1,), 'middle', (True,), [0]), ('unsqueeze', (S, S, S), (3,), 'last', (True,), [0]), ('unsqueeze', (), (0,), 'scalar', (True,), [0]), ('chunk', (S, S, S), (2,), '', (True, 'prim::ConstantChunk')), ('chunk', (S, S, S), (S, 1), 'dim', (True, 'prim::ConstantChunk'), [1]), ('split', (S, S, S), (2,), '', (True,)), ('split', (S, S, S), (S, 1), 'dim', (True,), [1]), ('split', (S, S, S), ([int(S / 3), S - int(S / 3) * 2, int(S / 3)],), 'size_list', (True, 'aten::split_with_sizes')), ('split', (S, S, S), ([int(S / 2), S - int(S / 2) * 2, int(S / 2)], 2), 'size_list_dim', (True, 'aten::split_with_sizes'), [1]), ('split_with_sizes', (S, S, S), ([int(S / 3), S - int(S / 3) * 2, int(S / 3)],), '', (True,)), ('split_with_sizes', (S, S, S), ([int(S / 3), S - int(S / 3), 0],), 'size_0', (True, )), ('split_with_sizes', (S, S, S), ([int(S / 3), S - int(S / 3) * 2, int(S / 3)],), 'dim', (True, ), [1]), ('gather', (M, S), (0, gather_variable((S, S), 1, M, True)), 'dim0', (), [0]), ('gather', (M, S), (1, gather_variable((M, S // 2), 0, S, True)), 'dim1', (), [0]), ('gather', (), (0, torch.tensor([0], dtype=torch.int64)), 'scalar_input', (), [0]), ('gather', (S,), (0, torch.tensor(0, dtype=torch.int64)), 'scalar_index', (), [0]), ('gather', (), (0, torch.tensor(0, dtype=torch.int64)), 'scalar_both', (), [0]), ('scatter', (M, S), (0, gather_variable((S, S), 1, M), (S, S)), 'dim0', (), [0]), ('scatter', (M, S), (1, gather_variable((M, S // 2), 0, S), (M, S // 2)), 'dim1', (), [0]), ('scatter', (), (0, torch.tensor(0, dtype=torch.int64), ()), 'scalartensor_all_dim0', (), [0]), ('scatter', (), (0, torch.tensor(0, dtype=torch.int64), 2.5), 'scalar_all_dim0', (), [0]), ('scatter_add', (M, S), (0, gather_variable((S, S), 1, M), (S, S)), 'dim0', (), [0]), ('scatter_add', (M, S), (1, gather_variable((M, S // 2), 0, S), (M, S // 2)), 'dim1', (), [0]), ('scatter_add', (), (0, torch.tensor(0, dtype=torch.int64), ()), 'scalar_all_dim0', (), [0]), ('scatter_add', (M, S), (0, gather_variable((S, S), 1, M), (S, S)), 'alert_nondeterministic', (), [0], [expectedAlertNondeterministic('scatter_add_cuda_kernel', 'cuda')]), ('masked_select', (M, M), (mask_not_all_zeros((M, M)),)), ('masked_select', (M, M), (mask_not_all_zeros((M,)),), 'broadcast_rhs'), ('masked_select', (M,), (mask_not_all_zeros((M, M)),), 'broadcast_lhs'), ('masked_select', (M, 1, M), (mask_not_all_zeros((M, M)),), 'broadcast_all'), ('masked_select', (), (torch.tensor(1, dtype=torch.bool),), 'scalar'), ('masked_select', (M, M), (torch.tensor(1, dtype=torch.bool),), 'scalar_broadcast_rhs'), ('masked_select', (), (mask_not_all_zeros((M, M)),), 'scalar_broadcast_lhs'), ('masked_fill', (M, M), (torch.BoolTensor(M, M).bernoulli_(), 10)), ('masked_fill', (M, M), (torch.BoolTensor(M, M).bernoulli_(), ()), 'tensor'), ('masked_fill', (M,), (torch.BoolTensor(M, M).bernoulli_(), 10), 'broadcast_lhs'), ('masked_fill', (M, M), (torch.BoolTensor(M,).bernoulli_(), 10), 'broadcast_rhs'), ('masked_fill', (), (torch.tensor(0, dtype=torch.bool).bernoulli_(), 10), 'scalar'), ('masked_fill', (), (torch.tensor(0, dtype=torch.bool).bernoulli_(), ()), 'scalar_variable'), ('masked_fill', (M, M), (torch.tensor(0, dtype=torch.bool).bernoulli_(), 10), 'scalar_broadcast_rhs'), ('masked_scatter', (M, M), (torch.BoolTensor(M, M).bernoulli_(), (M, M))), ('masked_scatter', (M,), (torch.BoolTensor(M, M).bernoulli_(), (M, M)), 'broadcast_lhs'), ('masked_scatter', (M, M), (torch.BoolTensor(M,).bernoulli_(), (M, M)), 'broadcast_rhs'), ('masked_scatter', (M, M), (bernoulli_scalar(), (M, M)), 'scalar'), ('masked_scatter', (M, M), (bernoulli_scalar(), (M, M)), 'scalar_broadcast_rhs'), ('maximum', (S, S), ((S, S),)), ('minimum', (S, S), ((S, S),)), ('resize_', (S, S, S), (torch.Size([S * S, S])), 'fewer_dims'), ('resize_', (), (dont_convert(()),), 'scalar'), ('resize_', (), (torch.Size([1, 1, 1])), 'scalar_to_dims'), ('resize_as_', (), (non_differentiable(torch.tensor(5.)),), 'scalar'), ('resize_as_', (), (non_differentiable(torch.randn((1, 1, 1))),), 'scalar_to_dims'), ('resize_as_', (S, S, S), (non_differentiable(torch.randn(S * S, S)),)), ('sort', (S, M, S), NO_ARGS), ('sort', (S, M, S), (1,), 'dim'), ('sort', (S, M, S), (1, True), 'dim_desc'), ('sort', (), NO_ARGS, 'scalar'), ('sort', (), (0,), 'dim_scalar'), ('sort', (), (0, True), 'dim_desc_scalar'), ('topk', (S, M, S), (3,)), ('topk', (S, M, S), (3, 1), 'dim', (), [1]), ('topk', (S, M, S), (3, 1, True), 'dim_desc', (), [1]), ('topk', (S, M, S), (3, 1, True, True), 'dim_desc_sort', (), [1]), ('topk', (), (1,), 'scalar'), ('topk', (), (1, 0), 'dim_scalar', (), [1]), ('topk', (), (1, 0, True), 'dim_desc_scalar', (), [1]), ('topk', (), (1, 0, True, True), 'dim_desc_sort_scalar', (), [1]), ('take', (S, S, S), (torch.LongTensor([[-3, 2], [20, 2]]),)), ('take', (S, S, S), (torch.tensor(0, dtype=torch.int64),), 'scalar_index'), ('take', (), (torch.LongTensor([0]),), 'scalar_data'), ('take', (), (torch.tensor(0, dtype=torch.int64),), 'scalar_both'), ('where', (M, M), (mask_not_all_zeros((M, M)), (M, M)), '', (True,)), ('where', (M, 1, M), (mask_not_all_zeros((M, M)), (M, M, 1)), 'broadcast_all', (True,)), ('where', (), (bernoulli_scalar(), ()), 'scalar', (True,)), ('where', (M, 1, M), (bernoulli_scalar(), (M, M, 1)), 'scalar_broadcast_mask', (True,)), ('where', (), (mask_not_all_zeros((M, M)), ()), 'scalar_broadcast_non_mask', (True,)), ('__getitem__', torch.randn(S, S, S), (dont_convert([1, 2]),)), ('__getitem__', torch.randn(S, S, S), (slice(0, 3),), 'slice'), ('__getitem__', torch.randn(S, S, S), (dont_convert([slice(0, 3), 1]),), 'slice_index'), ('__getitem__', torch.randn(S, S, S), (dont_convert([[0, 2, 3], [1, 3, 3], [0, 0, 2]]),), 'adv_index'), ('__getitem__', torch.randn(S, S, S), (dont_convert([[0, 0, 3], [1, 1, 3], [0, 0, 2]]),), 'adv_index_dup'), ('__getitem__', torch.randn(S, S, S), (dont_convert([slice(None), slice(None), [0, 3]]),), 'adv_index_end'), ('__getitem__', torch.randn(S, S, S), (dont_convert([slice(None), [0, 3], slice(None)]),), 'adv_index_mid'), ('__getitem__', torch.randn(S, S, S), (dont_convert([[0, 3], slice(None), slice(None)]),), 'adv_index_beg'), ('__getitem__', torch.randn(S, S, S), (dont_convert([[0, 3], [1, 2], slice(None)]),), 'adv_index_comb'), ('__getitem__', torch.randn(S, S, S), (dont_convert([[0, 3], ]),), 'adv_index_sub'), ('__getitem__', torch.randn(S, S, S), (dont_convert([[0, 3], slice(None)]),), 'adv_index_sub_2'), ('__getitem__', torch.randn(S, S, S), (dont_convert([[0, 3], Ellipsis]),), 'adv_index_sub_3'), ('__getitem__', torch.randn(S, S, S), (dont_convert([[0, 2, 3], [1, 3, 3], torch.LongTensor([0, 0, 2])]),), 'adv_index_var'), ('to_sparse', (S, S), (), '', (), (), [], lambda x: x.to_dense()), ] def create_input(call_args, requires_grad=True, non_contiguous=False, call_kwargs=None, dtype=torch.double, device=None): if not isinstance(call_args, tuple): call_args = (call_args,) def map_arg(arg): def maybe_non_contig(tensor): return tensor if not non_contiguous else make_non_contiguous(tensor) if isinstance(arg, torch.Size) or isinstance(arg, dont_convert): return arg elif isinstance(arg, tuple) and len(arg) == 0: var = torch.randn((), dtype=dtype, device=device) var.requires_grad = requires_grad return var elif isinstance(arg, tuple) and not isinstance(arg[0], torch.Tensor): return Variable(maybe_non_contig(torch.randn(*arg, dtype=dtype, device=device)), requires_grad=requires_grad) # double check casting elif isinstance(arg, non_differentiable): if isinstance(arg.tensor, torch.Tensor): return maybe_non_contig(arg.tensor.to(device=device)) return maybe_non_contig(arg.tensor.to(device=device)) elif isinstance(arg, torch.Tensor): if arg.dtype == torch.float: arg = arg.double() if arg.dtype == torch.cfloat: arg = arg.to(torch.cdouble) if arg.is_complex() != dtype.is_complex: raise RuntimeError("User provided tensor is real for a test that runs with complex dtype, ", "which is not supported for now") # NOTE: We do clone() after detach() here because we need to be able to change size/storage of v afterwards v = maybe_non_contig(arg).detach().to(device=device).clone() v.requires_grad = requires_grad and (v.is_floating_point() or v.is_complex()) return v elif callable(arg): return map_arg(arg()) else: return arg args_out = tuple(map_arg(arg) for arg in call_args) kwargs_out = {k: map_arg(v) for k, v in call_kwargs.items()} if call_kwargs else {} return args_out, kwargs_out 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') .tril(offset).nonzero().to(dtype).transpose(0, 1), torch.tril_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), (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)) def unpack_variables(args): if istuple(args): return tuple(unpack_variables(elem) for elem in args) else: return args EXCLUDE_FUNCTIONAL = { 'addmm', 'addmm_', 'addbmm', 'baddbmm', 'addmv', 'addmv_', 'addr', 'addr_', 'reshape', 'where' # argument order } EXCLUDE_GRADCHECK: Dict[str, Any] = { } EXCLUDE_GRADGRADCHECK: Dict[str, Any] = { } EXCLUDE_GRADGRADCHECK_BY_TEST_NAME = { # *det methods uses svd in backward when matrix is not invertible. However, # svd backward is unstable unless the matrix has positive distinct singular # values. Generated random matrices satisfy this with high probability, but # we can't rely on it. So only test gradgrad on invertible test cases and # _distinct_singular_values. 'test_det', 'test_det_1x1', 'test_det_symmetric', 'test_det_symmetric_psd', 'test_det_dim2_null', 'test_det_rank1', 'test_det_rank2', 'test_det_batched', 'test_det_batched_1x1', 'test_det_batched_symmetric', 'test_det_batched_symmetric_psd', # `other` expand_as(self, other) is not used in autograd. 'test_expand_as', 'test_logdet', 'test_logdet_1x1', 'test_logdet_symmetric', 'test_logdet_batched', 'test_logdet_batched_1x1', 'test_logdet_batched_symmetric', 'test_slogdet_1x1_neg_det', 'test_slogdet_neg_det', 'test_slogdet_symmetric', 'test_slogdet_batched_1x1_neg_det', 'test_slogdet_batched_symmetric', 'test_cdist', } def exclude_tensor_method(name, test_name): # there are no tensor equivalents for these (inplace or out) exclude_all_tensor_method_by_test_name = { 'test_clamp_min', 'test_clamp_max', 'test_clamp_min_scalar', 'test_clamp_max_scalar', 'test_slice', 'test_where', 'test_where_broadcast_all', 'test_where_scalar', 'test_where_scalar_broadcast_mask', 'test_where_scalar_broadcast_non_mask', 'test_var_mean_keepdim_dim_1d', 'test_var_mean_keepdim_dim', 'test_var_mean_dim_1d', 'test_var_mean_dim', 'test_var_mean', 'test_std_mean_keepdim_dim_1d', 'test_std_mean_keepdim_dim', 'test_std_mean_dim_1d', 'test_std_mean_dim', 'test_std_mean', 'test_view_as_complex', 'test_view_as_real_complex', 'test_real_complex', 'test_imag_complex', 'test_complex' } # there are no out-of-place tensor equivalents for these exclude_outplace_tensor_method = { 'index_add', 'index_copy', 'index_fill', 'masked_fill', 'masked_scatter', 'scatter', 'scatter_add', 'det', } if test_name in exclude_all_tensor_method_by_test_name: return True is_magic_method = name[:2] == '__' and name[-2:] == '__' is_inplace = name[-1] == "_" and not is_magic_method if not is_inplace and name in exclude_outplace_tensor_method: return True if 'fft.' in name: return True return False