| from functools import partial, wraps |
| import warnings |
| |
| import torch |
| |
| from torch.testing import \ |
| (FileCheck, floating_and_complex_types_and) |
| from torch.testing._internal.common_utils import \ |
| (TestCase, is_iterable_of_tensors, run_tests, IS_SANDCASTLE, clone_input_helper, make_tensor, |
| gradcheck, gradgradcheck) |
| from torch.testing._internal.common_methods_invocations import \ |
| (op_db, method_tests) |
| from torch.testing._internal.common_device_type import \ |
| (instantiate_device_type_tests, ops, onlyCPU, 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 |
| |
| |
| # Get names of all the operators which have entry in `method_tests` (legacy testing infra) |
| method_tested_operators = set(map(lambda test_details: test_details[0], method_tests())) |
| |
| # Tests that apply to all operators |
| |
| class TestOpInfo(TestCase): |
| exact_dtype = True |
| |
| # Verifies that ops have their unsupported dtypes |
| # registered correctly by testing that each claimed unsupported dtype |
| # throws a runtime error |
| @skipCUDAIfRocm |
| @onlyOnCPUAndCUDA |
| @ops(op_db, dtypes=OpDTypes.unsupported) |
| def test_unsupported_dtypes(self, device, dtype, op): |
| # sample_inputs can have a function for generating the input that doesn't work for specified dtype |
| # https://github.com/pytorch/pytorch/issues/49024 |
| with self.assertRaises(RuntimeError): |
| samples = op.sample_inputs(device, dtype) |
| if len(samples) == 0: |
| self.skipTest("Skipped! No sample inputs!") |
| |
| # NOTE: only tests on first sample |
| sample = samples[0] |
| op(sample.input, *sample.args, **sample.kwargs) |
| |
| # Verifies that ops have their supported dtypes |
| # registered correctly by testing that each claimed supported dtype |
| # does NOT throw a runtime error |
| # In addition verifies that the generated sample_inputs have the requested device and dtype |
| @onlyOnCPUAndCUDA |
| @ops(op_db, dtypes=OpDTypes.supported) |
| def test_supported_dtypes(self, device, dtype, op): |
| for sample in op.sample_inputs(device, dtype): |
| op(sample.input, *sample.args, **sample.kwargs) |
| # NOTE: only check the first tensor in the iterable of tensors |
| sample_input = sample.input[0] if is_iterable_of_tensors(sample.input) else sample.input |
| self.assertTrue(sample_input.dtype == dtype) |
| self.assertTrue(sample_input.device.type == self.device_type) |
| |
| # Verifies that backward for each supported floating or complex dtype |
| # does NOT throw a runtime error. |
| # TODO: support multi-tensor outputs |
| @onlyOnCPUAndCUDA |
| @ops(op_db, dtypes=OpDTypes.supported_backward, |
| allowed_dtypes=floating_and_complex_types_and(torch.float16, torch.bfloat16)) |
| def test_supported_backward(self, device, dtype, op): |
| if not op.supports_autograd: |
| self.skipTest("Skipped! Autograd not supported.") |
| |
| for sample in op.sample_inputs(device, dtype, requires_grad=True): |
| result = op(sample.input, *sample.args, **sample.kwargs) |
| if not isinstance(result, torch.Tensor): |
| continue |
| result.sum().backward() |
| |
| # Verifies that ops do not have an entry in |
| # `method_tests` (legacy testing infra). |
| @onlyCPU |
| @ops(op_db, allowed_dtypes=[torch.float32]) |
| def test_duplicate_method_tests(self, device, dtype, op): |
| self.assertFalse(op.name in method_tested_operators) |
| |
| # 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): |
| 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() |
| |
| samples = op.sample_inputs(device, dtype, requires_grad=True) |
| 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)) |
| elif check == '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): |
| return self._check_helper(device, dtype, op, variant, 'gradcheck') |
| |
| 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())) |
| |
| |
| # 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 TestCommon(JitCommonTestCase): |
| exact_dtype = True |
| |
| # 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)) |
| |
| # alias testing is only done with troch.float for the same reason |
| _alias_ops = partial(ops, dtypes=OpDTypes.supported, |
| allowed_dtypes=(torch.float,)) |
| |
| # 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))) |
| |
| samples = op.sample_inputs(device, dtype, requires_grad=_requires_grad) |
| |
| 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 |
| |
| # 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))): |
| 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): |
| 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): |
| 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) |
| |
| # 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)) |
| # TODO: fix this |
| if _requires_grad and not op.supports_gradgrad: |
| self.skipTest("skipped! This test does not handle ops that don't support gragrad properly") |
| |
| samples = op.sample_inputs(device, dtype, requires_grad=_requires_grad) |
| |
| for sample in samples: |
| # 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, |
| } |
| |
| # Test traced and scripted consistency |
| for func_type, variant in variants.items(): |
| if variant is None: |
| continue |
| |
| # 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 |
| |
| check_against_reference(self, |
| script_fn, |
| func, |
| out_fn, |
| (sample.input,) + sample.args, |
| sample.kwargs, |
| no_grad=not _requires_grad) |
| |
| # Check traced forward, grad, and grad grad |
| traced_fn = create_traced_fn(self, variant) |
| check_against_reference(self, |
| traced_fn, |
| func, |
| out_fn, |
| (sample.input,) + sample.args, |
| sample.kwargs, |
| no_grad=not _requires_grad) |
| |
| # Check alias annotation schema for correctness (make |
| # sure inputs that aren't supposed to be modified aren't) |
| # Note: only runs in float32 and int64 because schema isn't affected by dtype, |
| # so running it on all dtypes is would be excessive |
| if dtype in [torch.float32, torch.int32]: |
| check_alias_annotation(name, (sample.input,) + sample.args, sample.kwargs, |
| func_type=func_type, aten_name=op.aten_name) |
| |
| # 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 |
| |
| 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) |
| |
| @_alias_ops((op for op in op_db if op.aliases)) |
| def test_jit_alias_remapping(self, device, dtype, op): |
| 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_annot_kw is the string for the template function signature, for example, |
| # ["t0", "s0: float", "s1: bool", "max: float = 1.0", "min: float = 0.0"] -> |
| # def fn(t0, s0: float, s1: bool, max: float = 1.0, min: float = 0.0) |
| # - args_kw is the string of args/kwargs used to call the op, same as args_annot_kw but |
| # without type annotations |
| args = ["t0"] |
| |
| def quote_strs(v): |
| if isinstance(v, str): |
| return f"'{v}'" |
| |
| return str(v) |
| |
| args_annot_kw = args + \ |
| [f"s{i}: {type(v).__name__}" for i, v in enumerate(sample.args)] + \ |
| [f"{k}: {type(v).__name__} = {quote_strs(v)}" for k, v in sample.kwargs.items()] |
| args_kw = args + \ |
| [f"s{i}" for i in range(len(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}{args_annot_kw}): |
| return t0.{alias_name}({args_kw}) |
| ''' |
| # remove the first input tensor |
| script = fn_template.format( |
| c=", " if len(args_kw[1:]) > 1 or len(args_annot_kw[1:]) >= 1 else "", |
| args_annot_kw=", ".join(args_annot_kw[1:]), |
| args_kw=", ".join(args_kw[1:]), |
| alias_name=variant_name, |
| ) |
| else: |
| fn_template = ''' |
| def _fn({args_annot_kw}): |
| return variant({args_kw}) |
| ''' |
| script = fn_template.format( |
| args_annot_kw=", ".join(args_annot_kw), |
| 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, *sample.args, **sample.kwargs) |
| 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, *sample.args, **sample.kwargs) |
| inp = clone_input_helper(sample.input) |
| graph = scripted.graph_for(inp, *sample.args, **sample.kwargs) |
| 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) |
| |
| # 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) |
| |
| |
| instantiate_device_type_tests(TestOpInfo, globals()) |
| instantiate_device_type_tests(TestGradients, globals()) |
| instantiate_device_type_tests(TestCommon, globals()) |
| |
| if __name__ == '__main__': |
| run_tests() |
