from collections.abc import Sequence from functools import partial, wraps import warnings import torch from torch.testing import \ (FileCheck, floating_and_complex_types_and, get_all_dtypes, make_tensor) 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) 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, onlyCUDA, onlyOnCPUAndCUDA, skipCUDAIfRocm, OpDTypes) 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 import torch.testing._internal.opinfo_helper as opinfo_helper # 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 @skipCUDAIfRocm @onlyOnCPUAndCUDA @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 = 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 @skipCUDAIfRocm @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 = samples[0] 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. @onlyOnCPUAndCUDA @suppress_warnings @ops(_ref_test_ops, allowed_dtypes=(torch.float32, torch.long, torch.complex64)) def test_reference_testing(self, device, dtype, op): sample_inputs = op.sample_inputs(device, dtype) for sample_input in sample_inputs: self.compare_with_reference(op, op.ref, sample_input) # 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 # TODO: operations that support out= but don't support float # are not covered by this test. @ops(op_db, allowed_dtypes=(torch.float,)) def test_out(self, device, dtype, 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.") # NOTE: only tests on first sample samples = op.sample_inputs(device, dtype) sample = samples[0] # 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)) # 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')) out = _apply_out_transform(_case_zero_transform, expected) result = op_out(out=out) self.assertEqual(expected, out) # Checks that the returned value shares storage with out # NOTE: only checks on the CPU and CUDA device types since some # device types don't have storage if self.device_type == 'cpu' or self.device_type == 'cuda': if isinstance(out, torch.Tensor): self.assertEqual(out.storage().data_ptr(), result.storage().data_ptr()) else: for out_t, result_t in zip(out, result): self.assertEqual(out_t.storage().data_ptr(), result_t.storage().data_ptr()) # 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) # 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)) def _extract_data_ptrs(out): 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)) out = _apply_out_transform(_case_one_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) self.assertEqual(original_strides, final_strides) self.assertEqual(original_ptrs, final_ptrs) # Case 2: 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) 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) self.assertEqual(expected, out) # Case 3: out= with the correct dtype and device, but an empty # tensor. # Expected behavior: resize without warning. def _case_three_transform(t): return make_tensor((0,), dtype=t.dtype, device=t.device) out = _apply_out_transform(_case_three_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!") self.assertEqual(expected, out) # Case 4: 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_four_transform(t): return make_tensor(t.shape, dtype=t.dtype, device=wrong_device) out = _apply_out_transform(_case_four_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 5: 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_five_transform(t): return make_tensor(t.shape, dtype=torch.long, device=t.device) out = _apply_out_transform(_case_five_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) 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) # 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): 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': self.assertTrue(gradcheck(fn, gradcheck_args, check_batched_grad=op.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)) elif check == 'gradgradcheck': self.assertFalse(check_forward_ad, msg="Cannot run forward AD check for gradgradcheck") self.assertTrue(gradgradcheck(fn, gradcheck_args, gen_non_contig_grad_outputs=False, check_batched_grad=op.check_batched_gradgrad, check_grad_dtypes=True, nondet_tol=op.gradcheck_nondet_tol, fast_mode=op.gradcheck_fast_mode)) self.assertTrue(gradgradcheck(fn, gradcheck_args, gen_non_contig_grad_outputs=True, check_batched_grad=op.check_batched_gradgrad, check_grad_dtypes=True, nondet_tol=op.gradcheck_nondet_tol, fast_mode=op.gradcheck_fast_mode)) else: self.assertTrue(False, msg="Unknown check requested!") def _grad_test_helper(self, device, dtype, op, variant, *, check_forward_ad=False): return self._check_helper(device, dtype, op, variant, 'gradcheck', check_forward_ad=check_forward_ad) def _gradgrad_test_helper(self, device, dtype, op, variant): return self._check_helper(device, dtype, op, variant, 'gradgradcheck') def _skip_helper(self, op, device, dtype): if not op.supports_autograd: 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._gradgrad_test_helper(device, dtype, op, op.get_op()) # 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._gradgrad_test_helper(device, dtype, op, op.get_op()) # 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._gradgrad_test_helper(device, dtype, op, self._get_safe_inplace(op.get_inplace())) def _forward_grad_helper(self, device, dtype, op, variant): if op.supports_forward_ad: self._grad_test_helper(device, dtype, op, variant, check_forward_ad=True) 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): self._grad_test_helper(device, dtype, op, variant, check_forward_ad=True) @_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()) @_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())) # types.LambdaType gave false positives def is_lambda(lamb): LAMBDA = lambda: 0 # noqa: E731 return isinstance(lamb, type(LAMBDA)) and lamb.__name__ == LAMBDA.__name__ # 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) 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 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 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 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: 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) # TODO: handle multiple outputs if isinstance(out, torch.Tensor): self.checkShapeAnalysis(out.size(), 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) 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 samples = op.sample_inputs(device, dtype, requires_grad=True) if len(samples) == 0: self.skipTest("Skipped! No sample inputs!") # NOTE: only tests on first sample sample = samples[0] # [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, _requires_grad, math_op_physical, math_op_view, is_bit_set, out_type): samples = op.sample_inputs(device, dtype, requires_grad=_requires_grad) inplace_variant = op.inplace_variant # helper function to physically conjugate/negate the tensor def math_physical(input): if isinstance(input, torch.Tensor): tensor_requires_grad = input.requires_grad with torch.no_grad(): input = math_op_physical(input) return input.requires_grad_(tensor_requires_grad) if isinstance(input, Sequence): out = list(map(clone_input_helper, input)) out[0] = math_physical(out[0]) return tuple(out) # 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(): input = input.clone() # 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) sample.input = math_physical(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: 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(math_op_physical(grad)) forward_with_mathview.backward(math_op_view(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 self._test_math_view(device, dtype, op, _requires_grad, 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 def math_op_view(x): return torch.conj(x * 1j).imag _requires_grad = (op.supports_autograd and op.supports_complex_autograd(torch.device(device).type)) is_bit_set = torch.is_neg self._test_math_view(device, dtype, op, _requires_grad, math_op_physical, math_op_view, is_bit_set, lambda x: not torch.is_complex(x)) 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()