| # 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, skipCUDAIfRocm, 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 |
| 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 |
| @skipCUDAIfRocm |
| @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 |
| @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 = 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) |
| |
| # 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) |
| |
| 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() |
