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c19cda5782
Summary: Action following https://github.com/pytorch/pytorch/issues/66232 This change does require some context: there were several suggestions regarding what to do about this group of tests: tests that are core and crucial to all of PyTorch and are too broad to be owned by one team. 1. Let's add a "module: core" and put people behind it! This idea sounds appealing unless you are one of the people backing the label. From talking to albanD among others, this idea of putting all these core tests on the shoulder of a few people or one team isn't super fair and I have not yet found anyone willing to take on this job. 2. Taking advantage of the fact that we already have a triaging oncall that takes turns triaging issues, we can leave these tests essentially unlabeled and allow the oncall to triage these tests. Since these tests are crucial to PyTorch, we'll add the "high priority" label to mark them different from other unowned tests (see https://github.com/pytorch/pytorch/issues/67552). 3. I _could_ still create an unbacked label "module: core" and attribute these tests there, but I don't like the idea of creating a facade that the tests are "triaged" to a label when no one is actually taking a look. Now we could potentially break these tests down into smaller files so that each piece _could_ be owned by a team, but 1. I don't know if this is currently feasible and 2. This approach does not prevent that from happening in the future. Pull Request resolved: https://github.com/pytorch/pytorch/pull/67553 Reviewed By: albanD Differential Revision: D32025004 Pulled By: janeyx99 fbshipit-source-id: 1fb1aa4c27e305695ab6e80ae3d02f90519939c0
1135 lines
56 KiB
Python
1135 lines
56 KiB
Python
# Owner(s): ["high priority"]
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from collections.abc import Sequence
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from functools import partial, wraps
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import warnings
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import unittest
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import torch
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from torch.testing import FileCheck, make_tensor
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from torch.testing._internal.common_dtype import floating_and_complex_types_and, get_all_dtypes
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from torch.testing._internal.common_utils import \
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(TestCase, is_iterable_of_tensors, run_tests, IS_SANDCASTLE, clone_input_helper,
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gradcheck, gradgradcheck, IS_IN_CI, suppress_warnings, noncontiguous_like,
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TEST_WITH_ASAN, IS_WINDOWS)
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from torch.testing._internal.common_methods_invocations import \
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(op_db, _NOTHING, UnaryUfuncInfo, ReductionOpInfo, SpectralFuncInfo)
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from torch.testing._internal.common_device_type import \
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(deviceCountAtLeast, instantiate_device_type_tests, ops, onlyCUDA, onlyOnCPUAndCUDA, skipCUDAIfRocm, OpDTypes)
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from torch.testing._internal.common_jit import JitCommonTestCase, check_against_reference
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from torch.testing._internal.jit_metaprogramming_utils import create_script_fn, create_traced_fn, \
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check_alias_annotation
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from torch.testing._internal.jit_utils import disable_autodiff_subgraph_inlining
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import torch.testing._internal.opinfo_helper as opinfo_helper
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# variant testing is only done with torch.float and torch.cfloat to avoid
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# excessive test times and maximize signal to noise ratio
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_variant_ops = partial(ops, dtypes=OpDTypes.supported,
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allowed_dtypes=(torch.float, torch.cfloat))
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# Get names of all the operators which have ref in their entry in OpInfo (testing infra)
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# except for Unary Ufuncs (separately implemented in test/test_unary_ufuncs.py)
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# and Spectral Functions (separately implemented for only 1D as of now, in test/test_spectral_ops.py)
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_ref_test_ops = list(filter(lambda op: not isinstance(op, (UnaryUfuncInfo, ReductionOpInfo,
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SpectralFuncInfo)) and op.ref is not None and op.ref is not _NOTHING, op_db))
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# Tests that apply to all operators and aren't related to any particular
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# system
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class TestCommon(TestCase):
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exact_dtype = True
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# Verifies, on teardown, that no OpInfo is still using dynamic dtypes in CI
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@classmethod
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def tearDownClass(cls):
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super().tearDownClass()
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if IS_IN_CI:
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err_msg = ("The operator(s) below is(are) using dynamic_dtypes in the OpInfo entries."
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"This is OK for testing, but be sure to set the dtypes manually before landing your PR!")
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# Assure no opinfo entry has dynamic_dtypes
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filtered_ops = list(filter(opinfo_helper.is_dynamic_dtype_set, op_db))
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for op in filtered_ops:
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fmt_str = opinfo_helper.str_format_dynamic_dtype(op)
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err_msg += "\n" + fmt_str
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assert len(filtered_ops) == 0, err_msg
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# Validates that each OpInfo specifies its forward and backward dtypes
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# correctly for CPU and CUDA devices
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@skipCUDAIfRocm
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@onlyOnCPUAndCUDA
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@ops(op_db, dtypes=OpDTypes.none)
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def test_dtypes(self, device, op):
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# dtypes to try to backward in
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allowed_backward_dtypes = floating_and_complex_types_and(torch.bfloat16, torch.float16)
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# lists for (un)supported dtypes
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supported_dtypes = []
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unsupported_dtypes = []
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supported_backward_dtypes = []
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unsupported_backward_dtypes = []
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def unsupported(dtype):
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unsupported_dtypes.append(dtype)
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if dtype in allowed_backward_dtypes:
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unsupported_backward_dtypes.append(dtype)
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for dtype in get_all_dtypes():
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# tries to acquire samples - failure indicates lack of support
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requires_grad = (dtype in allowed_backward_dtypes and op.supports_autograd)
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try:
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samples = op.sample_inputs(device, dtype, requires_grad=requires_grad)
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except Exception as e:
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unsupported(dtype)
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continue
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# Counts number of successful backward attempts
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# NOTE: This exists as a kludge because this only understands how to
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# request a gradient if the output is a tensor or a sequence with
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# a tensor as its first element.
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num_backward_successes = 0
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for sample in samples:
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# tries to call operator with the sample - failure indicates
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# lack of support
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try:
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result = op(sample.input, *sample.args, **sample.kwargs)
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except Exception as e:
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# NOTE: some ops will fail in forward if their inputs
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# require grad but they don't support computing the gradient
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# in that type! This is a bug in the op!
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unsupported(dtype)
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# Short-circuits testing this dtype -- it doesn't work
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if dtype in unsupported_dtypes:
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break
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# Short-circuits if the dtype isn't a backward dtype or
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# it's already identified as not supported
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if dtype not in allowed_backward_dtypes or dtype in unsupported_backward_dtypes:
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continue
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# Checks for backward support in the same dtype
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try:
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result = sample.output_process_fn_grad(result)
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if isinstance(result, torch.Tensor):
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backward_tensor = result
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elif isinstance(result, Sequence) and isinstance(result[0], torch.Tensor):
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backward_tensor = result[0]
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else:
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continue
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# Note: this grad may not have the same dtype as dtype
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# For functions like complex (float -> complex) or abs
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# (complex -> float) the grad tensor will have a
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# different dtype than the input.
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# For simplicity, this is still modeled as these ops
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# supporting grad in the input dtype.
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grad = torch.randn_like(backward_tensor)
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backward_tensor.backward(grad)
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num_backward_successes += 1
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except Exception as e:
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unsupported_backward_dtypes.append(dtype)
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if dtype not in unsupported_dtypes:
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supported_dtypes.append(dtype)
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if num_backward_successes > 0 and dtype not in unsupported_backward_dtypes:
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supported_backward_dtypes.append(dtype)
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# Checks that dtypes are listed correctly and generates an informative
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# error message
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device_type = torch.device(device).type
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claimed_supported = set(op.supported_dtypes(device_type))
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supported_dtypes = set(supported_dtypes)
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supported_but_unclaimed = supported_dtypes - claimed_supported
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claimed_but_unsupported = claimed_supported - supported_dtypes
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msg = """The supported dtypes for {0} on {1} according to its OpInfo are
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{2}, but the detected supported dtypes are {3}.
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""".format(op.name, device_type, claimed_supported, supported_dtypes)
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if len(supported_but_unclaimed) > 0:
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msg += "The following dtypes should be added to the OpInfo: {0}. ".format(supported_but_unclaimed)
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if len(claimed_but_unsupported) > 0:
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msg += "The following dtypes should be removed from the OpInfo: {0}.".format(claimed_but_unsupported)
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self.assertEqual(supported_dtypes, claimed_supported, msg=msg)
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# Checks that backward dtypes are listed correctly and generates an
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# informative error message
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# NOTE: this code is nearly identical to the check + msg generation
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claimed_backward_supported = set(op.supported_backward_dtypes(device_type))
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supported_backward_dtypes = set(supported_backward_dtypes)
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supported_but_unclaimed = supported_backward_dtypes - claimed_backward_supported
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claimed_but_unsupported = claimed_backward_supported - supported_backward_dtypes
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msg = """The supported backward dtypes for {0} on {1} according to its OpInfo are
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{2}, but the detected supported backward dtypes are {3}.
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""".format(op.name, device_type, claimed_backward_supported, supported_backward_dtypes)
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if len(supported_but_unclaimed) > 0:
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msg += "The following backward dtypes should be added to the OpInfo: {0}. ".format(supported_but_unclaimed)
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if len(claimed_but_unsupported) > 0:
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msg += "The following backward dtypes should be removed from the OpInfo: {0}.".format(claimed_but_unsupported)
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self.assertEqual(supported_backward_dtypes, claimed_backward_supported, msg=msg)
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# Validates that each OpInfo works correctly on different CUDA devices
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@skipCUDAIfRocm
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@onlyCUDA
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@deviceCountAtLeast(2)
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@ops(op_db, allowed_dtypes=(torch.float32, torch.long))
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def test_multiple_devices(self, devices, dtype, op):
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for cuda_device_str in devices:
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cuda_device = torch.device(cuda_device_str)
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# NOTE: only tests on first sample
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samples = op.sample_inputs(cuda_device, dtype)
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sample = samples[0]
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result = op(sample.input, *sample.args, **sample.kwargs)
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if isinstance(result, torch.Tensor):
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self.assertTrue(result.device == cuda_device)
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elif is_iterable_of_tensors(result):
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self.assertTrue(all(map(lambda t: t.device == cuda_device, result)))
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else:
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self.skipTest("Skipped! Only supports single tensor or iterable of tensor outputs.")
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# Tests that the function and its (ndarray-accepting) reference produce the same
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# values on the tensors from sample_inputs func for the corresponding op.
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@onlyOnCPUAndCUDA
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@suppress_warnings
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@ops(_ref_test_ops, allowed_dtypes=(torch.float32, torch.long, torch.complex64))
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def test_reference_testing(self, device, dtype, op):
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sample_inputs = op.sample_inputs(device, dtype)
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for sample_input in sample_inputs:
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self.compare_with_reference(op, op.ref, sample_input)
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# Tests that the function produces the same result when called with
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# noncontiguous tensors.
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# TODO: get working with Windows by addressing failing operators
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# TODO: get working with ASAN by addressing failing operators
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@unittest.skipIf(IS_WINDOWS, "Skipped under Windows")
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@unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN")
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@onlyOnCPUAndCUDA
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@suppress_warnings
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@ops(op_db, allowed_dtypes=(torch.float32, torch.long, torch.complex64))
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def test_noncontiguous_samples(self, device, dtype, op):
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test_grad = dtype in op.supported_backward_dtypes(torch.device(device).type)
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sample_inputs = op.sample_inputs(device, dtype, requires_grad=test_grad)
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for sample_input in sample_inputs:
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t_inp, t_args, t_kwargs = sample_input.input, sample_input.args, sample_input.kwargs
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n_inp, n_args, n_kwargs = sample_input.noncontiguous()
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# Verifies sample input tensors should have no grad or history
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sample_tensor = t_inp if isinstance(t_inp, torch.Tensor) else t_inp[0]
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assert sample_tensor.grad is None
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assert sample_tensor.grad_fn is None
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# validates forward
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expected = op(t_inp, *t_args, **t_kwargs)
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actual = op(n_inp, *n_args, **n_kwargs)
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self.assertEqual(actual, expected)
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# validates backward
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# NOTE: only handles single tensor outputs and the first tensor
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# of ops that output a sequence
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# Short-circuits if the op doesn't support grad in this device x dtype
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if not test_grad:
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continue
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if isinstance(expected, torch.Tensor):
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expected_backward_tensor = expected
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actual_backward_tensor = actual
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elif isinstance(expected, Sequence) and isinstance(expected[0], torch.Tensor):
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expected_backward_tensor = expected[0]
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actual_backward_tensor = actual[0]
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else:
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continue
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grad_for_expected = torch.randn_like(expected_backward_tensor)
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grad_for_actual = noncontiguous_like(grad_for_expected)
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expected_backward_tensor.backward(grad_for_expected)
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actual_backward_tensor.backward(grad_for_actual)
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# Acquires grad (which may be on the first element in a list)
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expected_grad = t_inp.grad if isinstance(t_inp, torch.Tensor) else t_inp[0].grad
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actual_grad = n_inp.grad if isinstance(n_inp, torch.Tensor) else n_inp[0].grad
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# TODO: FIXME: only validates grad on first tensor input
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self.assertEqual(actual_grad, expected_grad)
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# Validates ops implement the correct out= behavior
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# See https://github.com/pytorch/pytorch/wiki/Developer-FAQ#how-does-out-work-in-pytorch
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# for a description of the correct behavior
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# TODO: operations that support out= but don't support float
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# are not covered by this test.
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@ops(op_db, allowed_dtypes=(torch.float,))
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def test_out(self, device, dtype, op):
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# TODO: verify the op doesn't support the out= kwarg
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if not op.supports_out:
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self.skipTest("Skipped! Op doesn't support out= kwarg.")
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# NOTE: only tests on first sample
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samples = op.sample_inputs(device, dtype)
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sample = samples[0]
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# calls it normally to get the expected result
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expected = op(sample.input, *sample.args, **sample.kwargs)
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op_out = partial(op, sample.input, *sample.args, **sample.kwargs)
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# Short-circuits if output is not a single tensor or an
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# iterable of tensors
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if not isinstance(expected, torch.Tensor) and not is_iterable_of_tensors(expected, include_empty=True):
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self.skipTest("Skipped! Only supports single tensor or iterable of tensor outputs.")
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# A wrapper around map that works with single tensors and always
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# instantiates the map. Used below to apply transforms to
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# single tensor and iterable tensor outputs.
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def _apply_out_transform(fn, out):
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if isinstance(out, torch.Tensor):
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return fn(out)
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# assumes (see above) that out is an iterable of tensors
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return tuple(map(fn, out))
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# Case 0: out= with the correct shape, dtype, and device
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# but NaN values for floating point and complex tensors, and
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# maximum values for integer tensors.
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# Expected behavior: out= values have no effect on the computation.
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def _case_zero_transform(t):
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try:
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info = torch.iinfo(t.dtype)
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return torch.full_like(t, info.max)
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except TypeError as te:
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# for non-integer types fills with NaN
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return torch.full_like(t, float('nan'))
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out = _apply_out_transform(_case_zero_transform, expected)
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result = op_out(out=out)
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self.assertEqual(expected, out)
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# Checks that the returned value shares storage with out
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# NOTE: only checks on the CPU and CUDA device types since some
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# device types don't have storage
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if self.device_type == 'cpu' or self.device_type == 'cuda':
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if isinstance(out, torch.Tensor):
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self.assertEqual(out.storage().data_ptr(), result.storage().data_ptr())
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else:
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for out_t, result_t in zip(out, result):
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self.assertEqual(out_t.storage().data_ptr(), result_t.storage().data_ptr())
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# Case 1: out= with the correct shape, dtype, and device,
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# but noncontiguous.
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# Expected behavior: strides are respected and `out` storage is not changed.
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def _case_one_transform(t):
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return make_tensor(t.shape,
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dtype=t.dtype,
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device=t.device,
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noncontiguous=True)
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# Extracts strides from a tensor or iterable of tensors into a tuple
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def _extract_strides(out):
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if isinstance(out, torch.Tensor):
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return (out.stride(),)
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# assumes (see above) that out is an iterable of tensors
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return tuple(map(lambda t: t.stride(), out))
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def _extract_data_ptrs(out):
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if isinstance(out, torch.Tensor):
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return (out.data_ptr(),)
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# assumes (see above) that out is an iterable of tensors
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return tuple(map(lambda t: t.data_ptr(), out))
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out = _apply_out_transform(_case_one_transform, expected)
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original_strides = _extract_strides(out)
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original_ptrs = _extract_data_ptrs(out)
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op_out(out=out)
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final_strides = _extract_strides(out)
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final_ptrs = _extract_data_ptrs(out)
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self.assertEqual(expected, out)
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self.assertEqual(original_strides, final_strides)
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self.assertEqual(original_ptrs, final_ptrs)
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# Case 2: out= with the correct dtype and device, but the wrong shape
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# Expected behavior: resize with a warning.
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def _case_two_transform(t):
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wrong_shape = list(t.shape)
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if len(wrong_shape) == 0:
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# Handles scalar tensor case (empty list)
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wrong_shape = [2]
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else:
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wrong_shape[-1] = wrong_shape[-1] + 1
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return make_tensor(wrong_shape, dtype=t.dtype, device=t.device)
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out = _apply_out_transform(_case_two_transform, expected)
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msg_fail = "Resized a non-empty tensor but did not warn about it."
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with self.assertWarnsRegex(UserWarning, "An output with one or more elements", msg=msg_fail):
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op_out(out=out)
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self.assertEqual(expected, out)
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# Case 3: out= with the correct dtype and device, but an empty
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# tensor.
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# Expected behavior: resize without warning.
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def _case_three_transform(t):
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return make_tensor((0,),
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dtype=t.dtype,
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device=t.device)
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out = _apply_out_transform(_case_three_transform, expected)
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with warnings.catch_warnings(record=True) as caught:
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warnings.simplefilter("always")
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op_out(out=out)
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# Verifies no warning is a resize warning
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for w in caught:
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if "An output with one or more elements" in str(w.message):
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self.fail("Resizing an out= argument with no elements threw a resize warning!")
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self.assertEqual(expected, out)
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# Case 4: out= with correct shape and dtype, but wrong device.
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wrong_device = None
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if torch.device(device).type != 'cpu':
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wrong_device = 'cpu'
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elif torch.cuda.is_available():
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wrong_device = 'cuda'
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if wrong_device is not None:
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def _case_four_transform(t):
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return make_tensor(t.shape, dtype=t.dtype, device=wrong_device)
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out = _apply_out_transform(_case_four_transform, expected)
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msg_fail = f"Expected RuntimeError when calling with input.device={device} and out.device={wrong_device}"
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with self.assertRaises(RuntimeError, msg=msg_fail):
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op_out(out=out)
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# 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, check_backward_ad=True,
|
|
check_undefined_grad=True, check_batched_grad=None):
|
|
# 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=check_undefined_grad))
|
|
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, check_backward_ad=True,
|
|
check_undefined_grad=True, check_batched_grad=None):
|
|
return self._check_helper(device, dtype, op, variant, 'gradcheck', check_forward_ad=check_forward_ad,
|
|
check_backward_ad=check_backward_ad, check_undefined_grad=check_undefined_grad,
|
|
check_batched_grad=check_batched_grad)
|
|
|
|
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 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._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, check_backward_ad=False,
|
|
check_undefined_grad=False, check_batched_grad=False)
|
|
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, check_backward_ad=False,
|
|
check_undefined_grad=False, check_batched_grad=False)
|
|
|
|
@_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()))
|
|
|
|
# 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 = samples[0]
|
|
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
|
|
|
|
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.")
|
|
|
|
# The view op here is an identity, but math_op_physical's output is
|
|
# modified inplace, so we must at least clone
|
|
math_op_physical = torch.clone
|
|
|
|
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()
|