# Owner(s): ["high priority"] from collections.abc import Sequence from functools import partial, wraps import warnings import unittest import itertools import torch from torch.testing import FileCheck, make_tensor from torch.testing._internal.common_dtype import floating_and_complex_types_and, get_all_dtypes from torch.testing._internal.common_utils import \ (TestCase, is_iterable_of_tensors, run_tests, IS_SANDCASTLE, clone_input_helper, gradcheck, gradgradcheck, IS_IN_CI, suppress_warnings, noncontiguous_like, TEST_WITH_ASAN, IS_WINDOWS, IS_FBCODE, first_sample) from torch.testing._internal.common_methods_invocations import \ (op_db, _NOTHING, UnaryUfuncInfo, ReductionOpInfo, SpectralFuncInfo) from torch.testing._internal.common_device_type import \ (deviceCountAtLeast, instantiate_device_type_tests, ops, onlyCPU, onlyCUDA, onlyNativeDeviceTypes, OpDTypes, skipMeta) from torch.testing._internal.common_jit import JitCommonTestCase, check_against_reference from torch.testing._internal.jit_metaprogramming_utils import create_script_fn, create_traced_fn, \ check_alias_annotation from torch.testing._internal.jit_utils import disable_autodiff_subgraph_inlining, is_lambda import torch.testing._internal.opinfo_helper as opinfo_helper from torch.testing._internal.composite_compliance import _check_composite_compliance # TODO: fixme https://github.com/pytorch/pytorch/issues/68972 torch.set_default_dtype(torch.float32) # variant testing is only done with torch.float and torch.cfloat to avoid # excessive test times and maximize signal to noise ratio _variant_ops = partial(ops, dtypes=OpDTypes.supported, allowed_dtypes=(torch.float, torch.cfloat)) # Get names of all the operators which have ref in their entry in OpInfo (testing infra) # except for Unary Ufuncs (separately implemented in test/test_unary_ufuncs.py) # and Spectral Functions (separately implemented for only 1D as of now, in test/test_spectral_ops.py) _ref_test_ops = list(filter(lambda op: not isinstance(op, (UnaryUfuncInfo, ReductionOpInfo, SpectralFuncInfo)) and op.ref is not None and op.ref is not _NOTHING, op_db)) # Tests that apply to all operators and aren't related to any particular # system class TestCommon(TestCase): exact_dtype = True # Verifies, on teardown, that no OpInfo is still using dynamic dtypes in CI @classmethod def tearDownClass(cls): super().tearDownClass() if IS_IN_CI: err_msg = ("The operator(s) below is(are) using dynamic_dtypes in the OpInfo entries." "This is OK for testing, but be sure to set the dtypes manually before landing your PR!") # Assure no opinfo entry has dynamic_dtypes filtered_ops = list(filter(opinfo_helper.is_dynamic_dtype_set, op_db)) for op in filtered_ops: fmt_str = opinfo_helper.str_format_dynamic_dtype(op) err_msg += "\n" + fmt_str assert len(filtered_ops) == 0, err_msg # Validates that each OpInfo specifies its forward and backward dtypes # correctly for CPU and CUDA devices @skipMeta @onlyNativeDeviceTypes @ops(op_db, dtypes=OpDTypes.none) def test_dtypes(self, device, op): # dtypes to try to backward in allowed_backward_dtypes = floating_and_complex_types_and(torch.bfloat16, torch.float16) # lists for (un)supported dtypes supported_dtypes = [] unsupported_dtypes = [] supported_backward_dtypes = [] unsupported_backward_dtypes = [] def unsupported(dtype): unsupported_dtypes.append(dtype) if dtype in allowed_backward_dtypes: unsupported_backward_dtypes.append(dtype) for dtype in get_all_dtypes(): # tries to acquire samples - failure indicates lack of support requires_grad = (dtype in allowed_backward_dtypes and op.supports_autograd) try: samples = list(op.sample_inputs(device, dtype, requires_grad=requires_grad)) except Exception as e: unsupported(dtype) continue # Counts number of successful backward attempts # NOTE: This exists as a kludge because this only understands how to # request a gradient if the output is a tensor or a sequence with # a tensor as its first element. num_backward_successes = 0 for sample in samples: # tries to call operator with the sample - failure indicates # lack of support try: result = op(sample.input, *sample.args, **sample.kwargs) except Exception as e: # NOTE: some ops will fail in forward if their inputs # require grad but they don't support computing the gradient # in that type! This is a bug in the op! unsupported(dtype) # Short-circuits testing this dtype -- it doesn't work if dtype in unsupported_dtypes: break # Short-circuits if the dtype isn't a backward dtype or # it's already identified as not supported if dtype not in allowed_backward_dtypes or dtype in unsupported_backward_dtypes: continue # Checks for backward support in the same dtype try: result = sample.output_process_fn_grad(result) if isinstance(result, torch.Tensor): backward_tensor = result elif isinstance(result, Sequence) and isinstance(result[0], torch.Tensor): backward_tensor = result[0] else: continue # Note: this grad may not have the same dtype as dtype # For functions like complex (float -> complex) or abs # (complex -> float) the grad tensor will have a # different dtype than the input. # For simplicity, this is still modeled as these ops # supporting grad in the input dtype. grad = torch.randn_like(backward_tensor) backward_tensor.backward(grad) num_backward_successes += 1 except Exception as e: unsupported_backward_dtypes.append(dtype) if dtype not in unsupported_dtypes: supported_dtypes.append(dtype) if num_backward_successes > 0 and dtype not in unsupported_backward_dtypes: supported_backward_dtypes.append(dtype) # Checks that dtypes are listed correctly and generates an informative # error message device_type = torch.device(device).type claimed_supported = set(op.supported_dtypes(device_type)) supported_dtypes = set(supported_dtypes) supported_but_unclaimed = supported_dtypes - claimed_supported claimed_but_unsupported = claimed_supported - supported_dtypes msg = """The supported dtypes for {0} on {1} according to its OpInfo are {2}, but the detected supported dtypes are {3}. """.format(op.name, device_type, claimed_supported, supported_dtypes) if len(supported_but_unclaimed) > 0: msg += "The following dtypes should be added to the OpInfo: {0}. ".format(supported_but_unclaimed) if len(claimed_but_unsupported) > 0: msg += "The following dtypes should be removed from the OpInfo: {0}.".format(claimed_but_unsupported) self.assertEqual(supported_dtypes, claimed_supported, msg=msg) # Checks that backward dtypes are listed correctly and generates an # informative error message # NOTE: this code is nearly identical to the check + msg generation claimed_backward_supported = set(op.supported_backward_dtypes(device_type)) supported_backward_dtypes = set(supported_backward_dtypes) supported_but_unclaimed = supported_backward_dtypes - claimed_backward_supported claimed_but_unsupported = claimed_backward_supported - supported_backward_dtypes msg = """The supported backward dtypes for {0} on {1} according to its OpInfo are {2}, but the detected supported backward dtypes are {3}. """.format(op.name, device_type, claimed_backward_supported, supported_backward_dtypes) if len(supported_but_unclaimed) > 0: msg += "The following backward dtypes should be added to the OpInfo: {0}. ".format(supported_but_unclaimed) if len(claimed_but_unsupported) > 0: msg += "The following backward dtypes should be removed from the OpInfo: {0}.".format(claimed_but_unsupported) self.assertEqual(supported_backward_dtypes, claimed_backward_supported, msg=msg) # Validates that each OpInfo works correctly on different CUDA devices @onlyCUDA @deviceCountAtLeast(2) @ops(op_db, allowed_dtypes=(torch.float32, torch.long)) def test_multiple_devices(self, devices, dtype, op): for cuda_device_str in devices: cuda_device = torch.device(cuda_device_str) # NOTE: only tests on first sample samples = op.sample_inputs(cuda_device, dtype) sample = first_sample(self, samples) result = op(sample.input, *sample.args, **sample.kwargs) if isinstance(result, torch.Tensor): self.assertTrue(result.device == cuda_device) elif is_iterable_of_tensors(result): self.assertTrue(all(map(lambda t: t.device == cuda_device, result))) else: self.skipTest("Skipped! Only supports single tensor or iterable of tensor outputs.") # Tests that the function and its (ndarray-accepting) reference produce the same # values on the tensors from sample_inputs func for the corresponding op. # This test runs in double and complex double precision because # NumPy does computation internally using double precision for many functions # resulting in possible equality check failures. @onlyNativeDeviceTypes @suppress_warnings @ops(_ref_test_ops, allowed_dtypes=(torch.float64, torch.long, torch.complex128)) def test_reference_testing(self, device, dtype, op): try: # Sets the default dtype to NumPy's default dtype of double cur_default = torch.get_default_dtype() torch.set_default_dtype(torch.double) sample_inputs = op.sample_inputs(device, dtype) for sample_input in sample_inputs: self.compare_with_reference(op, op.ref, sample_input, exact_dtype=(dtype is not torch.long)) finally: torch.set_default_dtype(cur_default) @skipMeta @onlyNativeDeviceTypes @ops([op for op in op_db if op.error_inputs_func is not None], dtypes=OpDTypes.none) def test_errors(self, device, op): error_inputs = op.error_inputs(device) for ei in error_inputs: si = ei.sample_input with self.assertRaisesRegex(ei.error_type, ei.error_regex): op(si.input, *si.args, **si.kwargs) # Tests that the function produces the same result when called with # noncontiguous tensors. # TODO: get working with Windows by addressing failing operators # TODO: get working with ASAN by addressing failing operators @unittest.skipIf(IS_WINDOWS, "Skipped under Windows") @unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN") @onlyNativeDeviceTypes @suppress_warnings @ops(op_db, allowed_dtypes=(torch.float32, torch.long, torch.complex64)) def test_noncontiguous_samples(self, device, dtype, op): test_grad = dtype in op.supported_backward_dtypes(torch.device(device).type) sample_inputs = op.sample_inputs(device, dtype, requires_grad=test_grad) for sample_input in sample_inputs: t_inp, t_args, t_kwargs = sample_input.input, sample_input.args, sample_input.kwargs n_inp, n_args, n_kwargs = sample_input.noncontiguous() # Verifies sample input tensors should have no grad or history sample_tensor = t_inp if isinstance(t_inp, torch.Tensor) else t_inp[0] assert sample_tensor.grad is None assert sample_tensor.grad_fn is None # validates forward expected = op(t_inp, *t_args, **t_kwargs) actual = op(n_inp, *n_args, **n_kwargs) self.assertEqual(actual, expected) # Validate backward # Short-circuits if the op doesn't support grad in this device x dtype if not test_grad: continue expected = sample_input.output_process_fn_grad(expected) actual = sample_input.output_process_fn_grad(actual) if isinstance(expected, torch.Tensor): grad_for_expected = torch.randn_like(expected) grad_for_actual = noncontiguous_like(grad_for_expected) elif isinstance(expected, Sequence): # Filter output elements that do not require grad expected = [t for t in expected if isinstance(t, torch.Tensor) and t.requires_grad] actual = [n for n in actual if isinstance(n, torch.Tensor) and n.requires_grad] grad_for_expected = [torch.randn_like(t) for t in expected] grad_for_actual = [noncontiguous_like(n) for n in grad_for_expected] else: # Nothing to do if it returns a scalar or things like that continue # Concatenate inputs into a tuple t_inputs = (t_inp,) + t_args if isinstance(t_inp, torch.Tensor) else tuple(t_inp) + t_args n_inputs = (n_inp,) + n_args if isinstance(n_inp, torch.Tensor) else tuple(n_inp) + n_args # Filter the elemnts that are tensors that require grad t_input_tensors = [t for t in t_inputs if isinstance(t, torch.Tensor) and t.requires_grad] n_input_tensors = [n for n in n_inputs if isinstance(n, torch.Tensor) and n.requires_grad] self.assertEqual(len(t_input_tensors), len(n_input_tensors)) # Some functions may not use all the inputs to generate gradients. One of the # few examples of this "odd" behaviour is F.hinge_embedding_loss t_grads = torch.autograd.grad(expected, t_input_tensors, grad_for_expected, allow_unused=True) n_grads = torch.autograd.grad(actual, n_input_tensors, grad_for_actual, allow_unused=True) msg = "Got different gradients for contiguous / non-contiguous inputs wrt input {}." for i, (t, n) in enumerate(zip(t_grads, n_grads)): self.assertEqual(t, n, msg=msg.format(i)) # Separates one case from the following test_out because many ops don't properly implement the # incorrectly sized out parameter warning properly yet # Cases test here: # - out= with the correct dtype and device, but the wrong shape @ops(op_db, dtypes=OpDTypes.none) def test_out_warning(self, device, op): # TODO: verify the op doesn't support the out= kwarg if not op.supports_out: self.skipTest("Skipped! Op doesn't support out= kwarg.") # Prefers running in float32 but has a fallback for the first listed supported dtype supported_dtypes = op.supported_dtypes(self.device_type) if len(supported_dtypes) == 0: self.skipTest("Skipped! Op has not supported dtypes on this device.") dtype = torch.float32 if torch.float32 in supported_dtypes else list(supported_dtypes)[0] # NOTE: only tests on first sample samples = op.sample_inputs(device, dtype) sample = first_sample(self, samples) # calls it normally to get the expected result expected = op(sample.input, *sample.args, **sample.kwargs) op_out = partial(op, sample.input, *sample.args, **sample.kwargs) # Short-circuits if output is not a single tensor or an # iterable of tensors if not isinstance(expected, torch.Tensor) and not is_iterable_of_tensors(expected, include_empty=True): self.skipTest("Skipped! Only supports single tensor or iterable of tensor outputs.") # A wrapper around map that works with single tensors and always # instantiates the map. Used below to apply transforms to # single tensor and iterable tensor outputs. def _apply_out_transform(fn, out): if isinstance(out, torch.Tensor): return fn(out) # assumes (see above) that out is an iterable of tensors return tuple(map(fn, out)) # Extracts strides from a tensor or iterable of tensors into a tuple def _extract_strides(out): if isinstance(out, torch.Tensor): return (out.stride(),) # assumes (see above) that out is an iterable of tensors return tuple(map(lambda t: t.stride(), out)) # Extracts data pointers from a tensor or iterable of tensors into a tuple # NOTE: only extracts on the CPU and CUDA device types since some # device types don't have storage def _extract_data_ptrs(out): if self.device_type != 'cpu' and self.device_type != 'cuda': return () if isinstance(out, torch.Tensor): return (out.data_ptr(),) # assumes (see above) that out is an iterable of tensors return tuple(map(lambda t: t.data_ptr(), out)) def _compare_out(transform, *, compare_strides_and_data_ptrs=True): out = _apply_out_transform(transform, expected) original_strides = _extract_strides(out) original_ptrs = _extract_data_ptrs(out) op_out(out=out) final_strides = _extract_strides(out) final_ptrs = _extract_data_ptrs(out) self.assertEqual(expected, out) if compare_strides_and_data_ptrs: self.assertEqual(original_strides, final_strides) self.assertEqual(original_ptrs, final_ptrs) # Case: out= with the correct dtype and device, but the wrong shape # Expected behavior: resize with a warning. def _case_two_transform(t): wrong_shape = list(t.shape) if len(wrong_shape) == 0: # Handles scalar tensor case (empty list) wrong_shape = [2] else: wrong_shape[-1] = wrong_shape[-1] + 1 return make_tensor(wrong_shape, dtype=t.dtype, device=t.device) _compare_out(_case_two_transform, compare_strides_and_data_ptrs=False) # Additional validates that the appropriate warning is thrown out = _apply_out_transform(_case_two_transform, expected) msg_fail = "Resized a non-empty tensor but did not warn about it." with self.assertWarnsRegex(UserWarning, "An output with one or more elements", msg=msg_fail): op_out(out=out) # Validates ops implement the correct out= behavior # See https://github.com/pytorch/pytorch/wiki/Developer-FAQ#how-does-out-work-in-pytorch # for a description of the correct behavior # Validates the following cases: # - Case 0: out has the correct shape, dtype, and device but is full of extremal values # - Case 1: out has the correct shape, dtype, and device but is noncontiguous # - Case 2: out has the correct dtype and device, but is zero elements # - Case 3: out has the correct shape and dtype, but is on a different device type # - Case 4: out has the with correct shape and device, but a dtype that cannot # "safely" cast to @ops(op_db, dtypes=OpDTypes.none) def test_out(self, device, op): # TODO: verify the op doesn't support the out= kwarg if not op.supports_out: self.skipTest("Skipped! Op doesn't support out= kwarg.") # Prefers running in float32 but has a fallback for the first listed supported dtype supported_dtypes = op.supported_dtypes(self.device_type) if len(supported_dtypes) == 0: self.skipTest("Skipped! Op has not supported dtypes on this device.") dtype = torch.float32 if torch.float32 in supported_dtypes else list(supported_dtypes)[0] # NOTE: only tests on first sample samples = op.sample_inputs(device, dtype) sample = first_sample(self, samples) # calls it normally to get the expected result expected = op(sample.input, *sample.args, **sample.kwargs) op_out = partial(op, sample.input, *sample.args, **sample.kwargs) # Short-circuits if output is not a single tensor or an # iterable of tensors if not isinstance(expected, torch.Tensor) and not is_iterable_of_tensors(expected, include_empty=True): self.skipTest("Skipped! Only supports single tensor or iterable of tensor outputs.") # A wrapper around map that works with single tensors and always # instantiates the map. Used below to apply transforms to # single tensor and iterable tensor outputs. def _apply_out_transform(fn, out): if isinstance(out, torch.Tensor): return fn(out) # assumes (see above) that out is an iterable of tensors return tuple(map(fn, out)) # Extracts strides from a tensor or iterable of tensors into a tuple def _extract_strides(out): if isinstance(out, torch.Tensor): return (out.stride(),) # assumes (see above) that out is an iterable of tensors return tuple(map(lambda t: t.stride(), out)) # Extracts data pointers from a tensor or iterable of tensors into a tuple # NOTE: only extracts on the CPU and CUDA device types since some # device types don't have storage def _extract_data_ptrs(out): if self.device_type != 'cpu' and self.device_type != 'cuda': return () if isinstance(out, torch.Tensor): return (out.data_ptr(),) # assumes (see above) that out is an iterable of tensors return tuple(map(lambda t: t.data_ptr(), out)) def _compare_out(transform, *, compare_strides_and_data_ptrs=True): out = _apply_out_transform(transform, expected) original_strides = _extract_strides(out) original_ptrs = _extract_data_ptrs(out) op_out(out=out) final_strides = _extract_strides(out) final_ptrs = _extract_data_ptrs(out) self.assertEqual(expected, out) if compare_strides_and_data_ptrs: self.assertEqual(original_strides, final_strides) self.assertEqual(original_ptrs, final_ptrs) # Case 0: out= with the correct shape, dtype, and device # but NaN values for floating point and complex tensors, and # maximum values for integer tensors. # Expected behavior: out= values have no effect on the computation. def _case_zero_transform(t): try: info = torch.iinfo(t.dtype) return torch.full_like(t, info.max) except TypeError as te: # for non-integer types fills with NaN return torch.full_like(t, float('nan')) _compare_out(_case_zero_transform) # Case 1: out= with the correct shape, dtype, and device, # but noncontiguous. # Expected behavior: strides are respected and `out` storage is not changed. def _case_one_transform(t): return make_tensor(t.shape, dtype=t.dtype, device=t.device, noncontiguous=True) _compare_out(_case_one_transform) # Case 2: out= with the correct dtype and device, but has no elements. # Expected behavior: resize without warning. def _case_two_transform(t): return make_tensor((0,), dtype=t.dtype, device=t.device) _compare_out(_case_two_transform, compare_strides_and_data_ptrs=False) # Also validates that no warning is thrown when this out is resized out = _apply_out_transform(_case_two_transform, expected) with warnings.catch_warnings(record=True) as caught: warnings.simplefilter("always") op_out(out=out) # Verifies no warning is a resize warning for w in caught: if "An output with one or more elements" in str(w.message): self.fail("Resizing an out= argument with no elements threw a resize warning!") # Case 3: out= with correct shape and dtype, but wrong device. wrong_device = None if torch.device(device).type != 'cpu': wrong_device = 'cpu' elif torch.cuda.is_available(): wrong_device = 'cuda' if wrong_device is not None: def _case_three_transform(t): return make_tensor(t.shape, dtype=t.dtype, device=wrong_device) out = _apply_out_transform(_case_three_transform, expected) msg_fail = f"Expected RuntimeError when calling with input.device={device} and out.device={wrong_device}" with self.assertRaises(RuntimeError, msg=msg_fail): op_out(out=out) # Case 4: out= with correct shape and device, but a dtype # that output cannot be "safely" cast to (long). # Expected behavior: error. # NOTE: this case is filtered by dtype since some ops produce # bool tensors, for example, which can be safely cast to any # dtype. It is applied when single tensors are floating point or complex # dtypes, or if an op returns multiple tensors when at least one such # tensor is a floating point or complex dtype. _dtypes = floating_and_complex_types_and(torch.float16, torch.bfloat16) if (isinstance(expected, torch.Tensor) and expected.dtype in _dtypes or (not isinstance(expected, torch.Tensor) and any(t.dtype in _dtypes for t in expected))): def _case_four_transform(t): return make_tensor(t.shape, dtype=torch.long, device=t.device) out = _apply_out_transform(_case_four_transform, expected) msg_fail = "" if not isinstance(expected, torch.Tensor) else \ ("Expected RuntimeError when doing an unsafe cast from a result of dtype " f"{expected.dtype} into an out= with dtype torch.long") with self.assertRaises(RuntimeError, msg=msg_fail): op_out(out=out) # Tests that the forward and backward passes of operations produce the # same values for the cross-product of op variants (method, inplace) # against eager's gold standard op function variant @_variant_ops(op_db) def test_variant_consistency_eager(self, device, dtype, op): # Acquires variants (method variant, inplace variant, aliases) method = op.method_variant inplace = op.inplace_variant # list of all inplace ops: inplace variant + alias inplace variants if exist inplace_ops = [inplace, ] variants = [method, inplace] for a_op in op.aliases: variants.append(a_op.op) variants.append(a_op.method_variant) variants.append(a_op.inplace_variant) inplace_ops.append(a_op.inplace_variant) inplace_variants = tuple(filter(None, inplace_ops)) variants = tuple(filter(None, variants)) _requires_grad = (op.supports_autograd and (dtype.is_floating_point or op.supports_complex_autograd(torch.device(device).type))) include_conjugated_inputs = op.test_conjugated_samples and dtype.is_complex samples = op.sample_inputs(device, dtype, requires_grad=_requires_grad, include_conjugated_inputs=include_conjugated_inputs) samples = list(samples) def _test_consistency_helper(samples, variants): for sample in samples: # TODO: Check grad for all Tensors requiring grad if sample.input is TensorList tensor = sample.input if isinstance(sample.input, torch.Tensor) else sample.input[0] # Computes function forward and backward values tensor.grad = None expected_forward = op(sample.input, *sample.args, **sample.kwargs) expected_grad = None output_process_fn_grad = sample.output_process_fn_grad if sample.output_process_fn_grad \ else lambda x: x # Skips inplace variants if the output dtype is not the same as # the input dtype skip_inplace = False if (isinstance(expected_forward, torch.Tensor) and expected_forward.dtype is not tensor.dtype): skip_inplace = True # TODO: backward consistency only supported for single tensor outputs # TODO: backward consistency only checked on sample.input, not all # tensor inputs # TODO: update to handle checking grads of all tensor inputs as # derived from each tensor output if (op.supports_autograd and isinstance(expected_forward, torch.Tensor) and (dtype.is_floating_point or op.supports_complex_autograd(torch.device(device).type))): output_process_fn_grad(expected_forward).sum().backward() expected_grad = tensor.grad # Test eager consistency for variant in variants: # Skips inplace ops if variant in inplace_ops and skip_inplace: continue # Compares variant's forward # Note: copies the to-be-modified input when testing the inplace variant tensor.grad = None cloned = clone_input_helper(sample.input) if variant in inplace_ops else sample.input if variant in inplace_ops and sample.broadcasts_input: with self.assertRaises(RuntimeError, msg=('inplace variant either incorrectly allowed ' 'resizing or you have marked the sample {}' ' incorrectly with `broadcasts_self=True'.format(sample.summary()))): variant_forward = variant(cloned, *sample.args, **sample.kwargs) continue variant_forward = variant(cloned, *sample.args, **sample.kwargs) self.assertEqual(expected_forward, variant_forward) # Compares variant's backward if expected_grad is not None and \ (variant not in inplace_ops or op.supports_inplace_autograd): output_process_fn_grad(variant_forward).sum().backward() self.assertEqual(expected_grad, tensor.grad) _test_consistency_helper(samples, variants) def _test_inplace_preserve_storage(samples, variants): for sample in samples: # Skips inplace variants if the output dtype is not the same as # the input dtype expected_forward = op(sample.input, *sample.args, **sample.kwargs) tensor = sample.input if isinstance(sample.input, torch.Tensor) else sample.input[0] skip_inplace = False if (isinstance(expected_forward, torch.Tensor) and expected_forward.dtype is not tensor.dtype): skip_inplace = True if skip_inplace: return for variant in variants: cloned = clone_input_helper(sample.input) if variant in inplace_ops else sample.input inp_tensor = cloned if isinstance(cloned, torch.Tensor) else cloned[0] data_ptr = inp_tensor.data_ptr() variant_forward = variant(cloned, *sample.args, **sample.kwargs) # TODO Support non-tensor outputs if they exist for inplace ops if (isinstance(variant_forward, torch.Tensor)): self.assertEqual(data_ptr, variant_forward.data_ptr(), atol=0, rtol=0) else: self.assertTrue(False, "Non-tensor outputs for inplace ops are not supported") if len(inplace_ops) > 0: inplace_samples = list(filter(lambda sample: not sample.broadcasts_input, samples)) _test_inplace_preserve_storage(inplace_samples, inplace_variants) # Checks if the operator (if it is composite) is written to support most # backends and Tensor subclasses. See "CompositeImplicitAutograd Compliance" # in aten/src/ATen/native/README.md for more details # # NB: onlyCPU because CompositeImplicitAutograd ops go through the same # codepath on all devices. Ideally we'd use a meta device here but coverage # for that is not good yet. @unittest.skipIf(IS_FBCODE or IS_SANDCASTLE, '__torch_dispatch__ does not work in fbcode') @onlyCPU @ops(op_db, allowed_dtypes=(torch.float,)) def test_composite_compliance(self, device, dtype, op): samples = op.sample_inputs(device, dtype, requires_grad=False) for sample in samples: args = [sample.input] + list(sample.args) kwargs = sample.kwargs _check_composite_compliance(op, args, kwargs) @onlyCPU @ops(op_db, allowed_dtypes=(torch.float,)) def test_floating_inputs_are_differentiable(self, device, dtype, op): # Nothing to check if the operation it's not differentiable if not op.supports_autograd: return floating_dtypes = list(floating_and_complex_types_and(torch.bfloat16, torch.float16)) def check_tensor_floating_is_differentiable(t): if isinstance(t, torch.Tensor) and t.dtype in floating_dtypes: msg = (f"Found a sampled tensor of floating-point dtype {t.dtype} sampled with " "requires_grad=False. If this is intended, please skip/xfail this test. " "Remember that sampling operations are executed under a torch.no_grad contextmanager.") self.assertTrue(t.requires_grad, msg) samples = op.sample_inputs(device, dtype, requires_grad=True) for sample in samples: check_tensor_floating_is_differentiable(sample.input) for arg in sample.args: check_tensor_floating_is_differentiable(arg) for arg in sample.kwargs.values(): check_tensor_floating_is_differentiable(arg) # gradcheck requires double precision _gradcheck_ops = partial(ops, dtypes=OpDTypes.supported, allowed_dtypes=[torch.double, torch.cdouble]) class TestGradients(TestCase): exact_dtype = True # Copies inputs to inplace operations to avoid inplace modifications # to leaves requiring gradient def _get_safe_inplace(self, inplace_variant): @wraps(inplace_variant) def _fn(t, *args, **kwargs): return inplace_variant(t.clone(), *args, **kwargs) return _fn def _check_helper(self, device, dtype, op, variant, check, *, check_forward_ad=False, check_backward_ad=True, check_batched_grad=None, check_batched_forward_grad=False): assert check in ('gradcheck', 'bwgrad_bwgrad', 'fwgrad_bwgrad') # NB: check_backward_ad does not affect gradgradcheck (always True) if variant is None: self.skipTest("Skipped! Variant not implemented.") if not op.supports_dtype(dtype, torch.device(device).type): self.skipTest(f"Skipped! {op.name} does not support dtype {str(dtype)}") def is_inplace(variant): if hasattr(variant, "__wrapped__"): return variant.__wrapped__ is op.get_inplace() return variant is op.get_inplace() include_conjugated_inputs = op.test_conjugated_samples and dtype.is_complex samples = op.sample_inputs(device, dtype, requires_grad=True, include_conjugated_inputs=include_conjugated_inputs) for sample in samples: if sample.broadcasts_input and is_inplace(variant): continue # Note on TensorList inputs # # gradcheck does not support TensorList inputs so here we pass TensorList # inputs of size n as n single Tensor inputs to gradcheck and wrap the op # in a function that puts the n Tensor inputs back into a TensorList def fn(*inputs): # Put tensors back into TensorList since we splat them when passing to gradcheck if is_iterable_of_tensors(sample.input): n = len(sample.input) inputs = (inputs[:n], *inputs[n:]) output = op.gradcheck_wrapper(variant, *inputs, **sample.kwargs) if sample.output_process_fn_grad is not None: return sample.output_process_fn_grad(output) return output # Splat TensorList inputs into single Tensor inputs gradcheck_args = (sample.input,) if isinstance(sample.input, torch.Tensor) else tuple(sample.input) gradcheck_args += sample.args if check == 'gradcheck': if check_batched_grad is None: check_batched_grad = op.check_batched_grad self.assertTrue(gradcheck(fn, gradcheck_args, check_batched_grad=check_batched_grad, check_grad_dtypes=True, nondet_tol=op.gradcheck_nondet_tol, fast_mode=op.gradcheck_fast_mode, check_forward_ad=check_forward_ad, check_backward_ad=check_backward_ad, check_undefined_grad=True, check_batched_forward_grad=check_batched_forward_grad)) elif check in ('bwgrad_bwgrad', 'fwgrad_bwgrad'): # gradgrad check self.assertFalse(check_forward_ad, msg="Cannot run forward AD check for gradgradcheck") for gen_non_contig_grad_outputs in (False, True): kwargs = { "gen_non_contig_grad_outputs": gen_non_contig_grad_outputs, "check_batched_grad": op.check_batched_gradgrad, "check_grad_dtypes": True, "nondet_tol": op.gradcheck_nondet_tol, "fast_mode": op.gradcheck_fast_mode } if check == "fwgrad_bwgrad": kwargs["check_fwd_over_rev"] = True kwargs["check_rev_over_rev"] = False kwargs["check_batched_grad"] = False kwargs["check_undefined_grad"] = False self.assertTrue(gradgradcheck(fn, gradcheck_args, **kwargs)) else: self.assertTrue(False, msg="Unknown check requested!") def _grad_test_helper(self, device, dtype, op, variant, *, check_forward_ad=False, check_backward_ad=True, check_batched_grad=None, check_batched_forward_grad=False): return self._check_helper(device, dtype, op, variant, 'gradcheck', check_forward_ad=check_forward_ad, check_backward_ad=check_backward_ad, check_batched_grad=check_batched_grad, check_batched_forward_grad=check_batched_forward_grad) def _skip_helper(self, op, device, dtype): if not op.supports_autograd and not op.supports_forward_ad: self.skipTest("Skipped! autograd not supported.") if not op.supports_complex_autograd(torch.device(device).type) and dtype.is_complex: self.skipTest("Skipped! Complex autograd not supported.") # Tests that gradients are computed correctly @_gradcheck_ops(op_db) def test_fn_grad(self, device, dtype, op): self._skip_helper(op, device, dtype) self._grad_test_helper(device, dtype, op, op.get_op()) # Method grad (and gradgrad, see below) tests are disabled since they're # costly and redundant with function grad (and gradgad) tests # @_gradcheck_ops(op_db) # def test_method_grad(self, device, dtype, op): # self._skip_helper(op, device, dtype) # self._grad_test_helper(device, dtype, op, op.get_method()) @_gradcheck_ops(op_db) def test_inplace_grad(self, device, dtype, op): self._skip_helper(op, device, dtype) if not op.inplace_variant or not op.supports_inplace_autograd: self.skipTest("Skipped! Operation does not support inplace autograd.") self._grad_test_helper(device, dtype, op, self._get_safe_inplace(op.get_inplace())) # Test that gradients of gradients are computed correctly @_gradcheck_ops(op_db) def test_fn_gradgrad(self, device, dtype, op): self._skip_helper(op, device, dtype) if not op.supports_gradgrad: self.skipTest("Skipped! Operation does not support gradgrad") self._check_helper(device, dtype, op, op.get_op(), 'bwgrad_bwgrad') # Test that forward-over-reverse gradgrad is computed correctly @_gradcheck_ops(op_db) def test_fn_fwgrad_bwgrad(self, device, dtype, op): self._skip_helper(op, device, dtype) if op.supports_fwgrad_bwgrad: self._check_helper(device, dtype, op, op.get_op(), "fwgrad_bwgrad") else: err_msg = r"Trying to use forward AD with .* that does not support it\." hint_msg = ("Running forward-over-backward gradgrad for an OP that has does not support it did not " "raise any error. If your op supports forward AD, you should set supports_fwgrad_bwgrad=True.") with self.assertRaisesRegex(NotImplementedError, err_msg, msg=hint_msg): self._check_helper(device, dtype, op, op.get_op(), "fwgrad_bwgrad") # Test that gradients of gradients are properly raising @_gradcheck_ops(op_db) def test_fn_fail_gradgrad(self, device, dtype, op): self._skip_helper(op, device, dtype) if op.supports_gradgrad: self.skipTest("Skipped! Operation does support gradgrad") err_msg = r"derivative for .* is not implemented" with self.assertRaisesRegex(RuntimeError, err_msg): self._check_helper(device, dtype, op, op.get_op(), 'bwgrad_bwgrad') # Method gradgrad (and grad, see above) tests are disabled since they're # costly and redundant with function gradgrad (and grad) tests # @_gradcheck_ops(op_db) # def test_method_gradgrad(self, device, dtype, op): # self._skip_helper(op, device, dtype) # self._gradgrad_test_helper(device, dtype, op, op.get_method()) @_gradcheck_ops(op_db) def test_inplace_gradgrad(self, device, dtype, op): self._skip_helper(op, device, dtype) if not op.inplace_variant or not op.supports_inplace_autograd: self.skipTest("Skipped! Operation does not support inplace autograd.") self._check_helper(device, dtype, op, self._get_safe_inplace(op.get_inplace()), "bwgrad_bwgrad") def _forward_grad_helper(self, device, dtype, op, variant, is_inplace): # TODO: clean up how attributes are passed to gradcheck from OpInfos def call_grad_test_helper(): check_batched_forward_grad = ((op.check_batched_forward_grad and not is_inplace) or (op.check_inplace_batched_forward_grad and is_inplace)) self._grad_test_helper(device, dtype, op, variant, check_forward_ad=True, check_backward_ad=False, check_batched_grad=False, check_batched_forward_grad=check_batched_forward_grad) if op.supports_forward_ad: call_grad_test_helper() else: err_msg = r"Trying to use forward AD with .* that does not support it\." hint_msg = ("Running forward AD for an OP that has does not support it did not " "raise any error. If your op supports forward AD, you should set supports_forward_ad=True") with self.assertRaisesRegex(NotImplementedError, err_msg, msg=hint_msg): call_grad_test_helper() @_gradcheck_ops(op_db) def test_forward_mode_AD(self, device, dtype, op): self._skip_helper(op, device, dtype) self._forward_grad_helper(device, dtype, op, op.get_op(), is_inplace=False) @_gradcheck_ops(op_db) def test_inplace_forward_mode_AD(self, device, dtype, op): self._skip_helper(op, device, dtype) if not op.inplace_variant or not op.supports_inplace_autograd: self.skipTest("Skipped! Operation does not support inplace autograd.") self._forward_grad_helper(device, dtype, op, self._get_safe_inplace(op.get_inplace()), is_inplace=True) # Functions that do not support autograd should not fail in forward mode # Inplace functions (such as "resize_") are expected to fail in forward mode and should be skipped # Test only when supports_autograd=False and for double dtype @ops(filter(lambda op: not op.supports_autograd, op_db), dtypes=OpDTypes.supported, allowed_dtypes=(torch.double,)) def test_nondifferentiable(self, device, dtype, op): # Expecting no errors samples = op.sample_inputs(device, dtype, requires_grad=True) sample = first_sample(self, samples) result = op(sample.input, *sample.args, **sample.kwargs) # Tests operators for consistency between JIT and eager, also checks # correctness of JIT specific alias schemas and intended # autodifferentiation behavior. # Inherits from JitCommonTestCase instead of TestCase directly to share # functionality with original test_jit.py method operator tests class TestJit(JitCommonTestCase): exact_dtype = True # Tests that the forward and backward passes of operations produce the # same values for the cross-product of op variants (function, method, inplace) # and runtimes (eager, traced, scripted). # TODO WARNING: inplace x {traced, scripted} not currently tested @_variant_ops(op_db) def test_variant_consistency_jit(self, device, dtype, op): _requires_grad = op.supports_autograd and (dtype.is_floating_point or op.supports_complex_autograd(torch.device(device).type)) include_conjugated_inputs = op.test_conjugated_samples and dtype.is_complex samples = op.sample_inputs(device, dtype, requires_grad=_requires_grad, include_conjugated_inputs=include_conjugated_inputs) # Acquires variants to test func = op.get_op() method = op.get_method() variants = { # TODO: inplace tests currently fail, fix and add inplace variant 'function': func, 'method': method, } # TODO: find better way to standardize on op registration itself.. has_fake_function = op.name in ["resize_", 'resize_as_'] if has_fake_function: variants = {'method': getattr(torch.Tensor, op.name)} samples = op.sample_inputs(device, dtype, requires_grad=False) support_script = op.supports_scripting tested = False for sample in samples: # Test traced and scripted consistency for func_type, variant in variants.items(): if variant is None: continue # scripting and check_alias_analysis do not work with lambdas # lambdas are typically used as a way to simulate methods without # functional variants, so rely on the other variant for testing # for now if is_lambda(variant): continue tested = True # Create accessor for script function variant name = op.name + '_' if func_type == 'inplace' else op.name # run with disable_autodiff_subgraph_inlining(True) to test # autodiff support. Context manager forces the graph to contain # DifferentiableGraph nodes if they are present with disable_autodiff_subgraph_inlining(): # Check scripted forward, grad, and grad grad if support_script: script_fn = create_script_fn(self, name, func_type) def out_fn(output): # Processes the output for autograd if sample.output_process_fn_grad is not None: return sample.output_process_fn_grad(output) return output def get_sample(): return clone_input_helper(sample.input) if op.name[-1] == '_' else sample.input if support_script: check_against_reference(self, script_fn, func, out_fn, (get_sample(),) + sample.args, sample.kwargs, no_grad=not _requires_grad, no_gradgrad=not op.supports_gradgrad) # Check traced forward, grad, and grad grad # TODO: fix tracing here supports_tracing = not has_fake_function if op.assert_jit_shape_analysis: self.assertTrue(supports_tracing) if supports_tracing: traced_fn = create_traced_fn(self, variant) check_against_reference(self, traced_fn, func, out_fn, (get_sample(),) + sample.args, sample.kwargs, no_grad=not _requires_grad, no_gradgrad=not op.supports_gradgrad) # Check alias annotation schema for correctness (make # sure inputs that aren't supposed to be modified aren't) # Note: only runs in float32 because schema isn't affected by dtype, # so running it on all dtypes is would be excessive if dtype == torch.float32: # TODO: no reason why we cant run this with tracing graph if support_script and op.name != "rsub": check_alias_annotation(name, (get_sample(),) + sample.args, sample.kwargs, func_type=func_type, aten_name=op.aten_name) # TODO: use script graph as well checked_shape_analysis = False if supports_tracing: out = variant(get_sample(), *sample.args, **sample.kwargs) # right now, tuple of outputs and tensor output supported # TODO: list of tensor outputs tuple_of_tensors = isinstance(out, tuple) and all([isinstance(elem, torch.Tensor) for elem in out]) if isinstance(out, torch.Tensor) or tuple_of_tensors: if tuple_of_tensors: sizes = [elem.size() for elem in out] else: sizes = out.size() self.checkShapeAnalysis(sizes, traced_fn.graph, op.assert_jit_shape_analysis) checked_shape_analysis = True if op.assert_jit_shape_analysis: self.assertTrue(checked_shape_analysis) # Check autodifferentiation of nodes for traced and scripted graphs, only need to check once per sample if dtype is torch.float32: # Sandcastle doesn't fuse nodes if IS_SANDCASTLE: # fusible nodes are expected to be found in FusionGroups in the DifferentiableGraphs nonfusible_nodes = op.autodiff_nonfusible_nodes + op.autodiff_fusible_nodes fusible_nodes = [] else: nonfusible_nodes = op.autodiff_nonfusible_nodes fusible_nodes = op.autodiff_fusible_nodes if supports_tracing: self.assertAutodiffNode(traced_fn.last_graph, op.assert_autodiffed, nonfusible_nodes, fusible_nodes) if support_script: self.assertAutodiffNode(script_fn.last_graph, op.assert_autodiffed, nonfusible_nodes, fusible_nodes) assert tested, "JIT Test does not execute any logic" # alias testing is only done with torch.float for the same reason _alias_ops = partial(ops, dtypes=OpDTypes.supported, allowed_dtypes=(torch.float,)) @_alias_ops((op for op in op_db if op.aliases)) def test_jit_alias_remapping(self, device, dtype, op): # Required to avoid undefined value: tensor error in JIT compilation of the function template tensor = torch.tensor # NOTE: only tests on first sample samples = op.sample_inputs(device, dtype, requires_grad=True) sample = first_sample(self, samples) # [Scripting Data Preparation] # Prepare data for test scripting # Below we prepare strings of args/kwargs with and without type annotations. # These strings are inserted into function template strings which is then torch scripted. # - args string is ["t0"] corresponding to the "input" tensor required by the op # - args_kw is the value of args and strings of kwargs used to call the op (without type annotations), for example, # ["to", "1.0", "(1,)", "True", "tensor(1.0)"] -> def fn(t0): return variant(t0, 1.0, (1,), True, tensor(1.0)) args = ["t0"] def quote_strs(v): if isinstance(v, str): return f"'{v}'" return str(v) args_kw = args + \ [f"{v}" for v in sample.args] + \ [f"{k}={quote_strs(v)}" for k, v in sample.kwargs.items()] # Prepare data for test tracing sample_args_kwargs = () if len(sample.args) > 0: sample_args_kwargs += (sample.args, ) if len(sample.kwargs) > 0: sample_args_kwargs += (sample.kwargs, ) original_name = op.aten_name original_name_inplace = original_name + "_" expected_dtype = op(sample.input, *sample.args, **sample.kwargs).dtype for a_op in op.aliases: inplace = a_op.inplace_variant method_or_inplace = [a_op.inplace_variant, a_op.method_variant] variants = (v for v in (a_op.op, a_op.method_variant, a_op.inplace_variant) if v is not None) # Test scripting: for variant in variants: variant_name = variant.__name__ op_name = original_name_inplace if variant is inplace else original_name if variant in method_or_inplace: fn_template = ''' def _fn(t0{c}): return t0.{alias_name}({args_kw}) ''' # remove the first input tensor script = fn_template.format( c=", " if len(args_kw[1:]) > 1 else "", args_kw=", ".join(args_kw[1:]), alias_name=variant_name, ) else: fn_template = ''' def _fn({args}): return variant({args_kw}) ''' script = fn_template.format( args=", ".join(args), args_kw=", ".join(args_kw), ) scripted = torch.jit.CompilationUnit(script)._fn if (variant is inplace and not torch.can_cast(expected_dtype, dtype)): try: inp = clone_input_helper(sample.input) scripted(inp) except Exception as e: continue self.fail("Inplace operation on integer tensor that should be promoted to float didn't fail!") inp = clone_input_helper(sample.input) scripted(inp) inp = clone_input_helper(sample.input) graph = scripted.graph_for(inp) FileCheck().check(op.aten_name).check_not(variant_name).run(graph) # Test tracing: for variant in variants: variant_name = variant.__name__ op_name = original_name_inplace if variant is inplace else original_name def _fn(*sample_args, **sample_kwargs): return variant(*sample_args, **sample_kwargs) inp = (clone_input_helper(sample.input),) + sample_args_kwargs traced = torch.jit.trace(_fn, *inp) inp = (clone_input_helper(sample.input),) + sample_args_kwargs traced(*inp) inp = (clone_input_helper(sample.input),) + sample_args_kwargs graph = traced.graph_for(*inp) FileCheck().check(op_name).check_not(variant_name).run(graph) class TestMathBits(TestCase): # Tests that # 1. The operator's output for physically conjugated/negated tensors and conjugate/negative view tensors # produces the same value # 2. The gradients are same in both cases mentioned in (1) # 3. If the operator's inplace variant is supported, tests that the inplace operation # produces the correct value when called on a conjugate/negative view tensor and that the output # has its conj/neg bit set to true # This test only runs for C -> R and C -> C functions # TODO: add tests for `R->C` functions # Note: This test runs for functions that take both tensors and tensorlists as input. def _test_math_view(self, device, dtype, op, samples, math_op_physical, math_op_view, is_bit_set, out_type): inplace_variant = op.inplace_variant # helper function to clone and conjugate/negate the input if its a tensor # else clone the sequence and conjugate/negate the first element in the sequence # If a requires_grad argument is provided the tensor being conjugated/negated will # have its requires_grad set to that value. def clone_and_perform_view(input, **kwargs): if isinstance(input, torch.Tensor): requires_grad = kwargs.get('requires_grad', input.requires_grad) with torch.no_grad(): # Ensure view represents the original sample input input = math_op_physical(input) # Note: .conj() is not called under no_grad mode since it's not allowed to modify a # view created in no_grad mode. Here it's ok to do so, so as a workaround we call conj # before resetting the requires_grad field for input input = math_op_view(input) assert input.is_leaf return input.requires_grad_(requires_grad) if isinstance(input, Sequence): out = list(map(clone_input_helper, input)) out[0] = clone_and_perform_view(out[0]) return tuple(out) for sample in samples: tensor = sample.input if isinstance(sample.input, torch.Tensor) else sample.input[0] cloned1 = clone_and_perform_view(sample.input) # Computes function forward value with a physically conjugated/negated tensor and # a conj/neg view tensor and verifies that the output in both case are equal. expected_forward = op(sample.input, *sample.args, **sample.kwargs) forward_with_mathview = op(cloned1, *sample.args, **sample.kwargs) self.assertEqual(expected_forward, forward_with_mathview) # If the op has an inplace variant, and the input doesn't require broadcasting # and has the same dtype as output, verify that the inplace operation on a conjugated/negated # input produces correct output, and the output tensor has the conj/neg bit set to True if inplace_variant is not None and not sample.broadcasts_input: cloned2 = clone_and_perform_view(tensor, requires_grad=False) if (isinstance(expected_forward, torch.Tensor) and expected_forward.dtype is tensor.dtype): inplace_forward = inplace_variant(cloned2, *sample.args, **sample.kwargs) self.assertTrue(is_bit_set(inplace_forward)) self.assertEqual(inplace_forward, expected_forward) # TODO: backward consistency only supported for single tensor outputs # TODO: backward consistency only checked on sample.input, not all # tensor inputs # TODO: update to handle checking grads of all tensor inputs as # derived from each tensor output if isinstance(expected_forward, torch.Tensor) and expected_forward.requires_grad: output_process_fn_grad = sample.output_process_fn_grad or (lambda x: x) expected_forward = output_process_fn_grad(expected_forward) forward_with_mathview = output_process_fn_grad(forward_with_mathview) tensor = sample.input if isinstance(sample.input, torch.Tensor) else sample.input[0] expected_forward.sum().backward(retain_graph=True) forward_with_mathview.sum().backward(retain_graph=True) if tensor.grad is not None: cloned1_tensor = cloned1 if isinstance(cloned1, torch.Tensor) else cloned1[0] self.assertEqual(tensor.grad, cloned1_tensor.grad) tensor.grad, cloned1_tensor.grad = None, None # a repeat of the above test if output is not complex valued if (out_type(expected_forward)): grad = torch.randn_like(expected_forward) expected_forward.backward(grad) forward_with_mathview.backward(math_op_view(math_op_physical(grad))) self.assertEqual(tensor.grad, cloned1_tensor.grad) @ops(op_db, allowed_dtypes=(torch.cfloat,)) def test_conj_view(self, device, dtype, op): if not op.test_conjugated_samples: self.skipTest("Operation doesn't support conjugated inputs.") math_op_physical = torch.conj_physical math_op_view = torch.conj _requires_grad = (op.supports_autograd and op.supports_complex_autograd(torch.device(device).type)) is_bit_set = torch.is_conj samples = op.sample_inputs(device, dtype, requires_grad=_requires_grad) self._test_math_view(device, dtype, op, samples, math_op_physical, math_op_view, is_bit_set, torch.is_complex) @ops(op_db, allowed_dtypes=(torch.double,)) def test_neg_view(self, device, dtype, op): if not op.test_neg_view: self.skipTest("Operation not tested with tensors with negative bit.") math_op_physical = torch.neg math_op_view = torch._neg_view is_bit_set = torch.is_neg samples = op.sample_inputs(device, dtype, requires_grad=op.supports_autograd) self._test_math_view(device, dtype, op, samples, math_op_physical, math_op_view, is_bit_set, lambda x: True) @ops(op_db, allowed_dtypes=(torch.cdouble,)) def test_neg_conj_view(self, device, dtype, op): if not op.test_neg_view: self.skipTest("Operation not tested with tensors with negative bit.") if not op.test_conjugated_samples: self.skipTest("Operation doesn't support conjugated inputs.") def math_op_physical(x): return -x.conj_physical() def math_op_view(x): return torch._neg_view(x).conj() def is_bit_set(x): return torch.is_neg(x) and torch.is_conj(x) _requires_grad = (op.supports_autograd and op.supports_complex_autograd(torch.device(device).type)) samples = op.sample_inputs(device, dtype, requires_grad=_requires_grad) # Only test one sample samples = itertools.islice(samples, 1) self._test_math_view(device, dtype, op, samples, math_op_physical, math_op_view, is_bit_set, torch.is_complex) instantiate_device_type_tests(TestCommon, globals()) instantiate_device_type_tests(TestGradients, globals()) instantiate_device_type_tests(TestJit, globals()) instantiate_device_type_tests(TestMathBits, globals()) if __name__ == '__main__': run_tests()