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4048d4cdd2
pytorch/test/test_ops.py
Mike Ruberry 4048d4cdd2 [primTorch] Prototype tracer and elementwise unary reference opinfo class 
Adds a prototype tracer with no caching support and the `ElementwiseUnaryPythonRefInfo` class. A reference for `floor` is added to test the latter, and the elementwise binary reference inputs are extended to also return noncontiguous inputs. The SampleInput transform operation has been updated to return an actual SampleInput instead of a tuple to facilitate uniform handling of (transformed) SampleInputs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76388
Approved by: https://github.com/ngimel
2022-04-27 14:40:21 +00:00

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# Owner(s): ["module: unknown"]
from collections.abc import Sequence
from functools import partial
import warnings
import unittest
import itertools
import torch
from torch.testing import make_tensor
from torch.testing._internal.common_dtype import floating_and_complex_types_and, all_types_and_complex_and
from torch.testing._internal.common_utils import \
(TestCase, is_iterable_of_tensors, run_tests, IS_SANDCASTLE, clone_input_helper,
IS_IN_CI, suppress_warnings, noncontiguous_like,
TEST_WITH_ASAN, IS_WINDOWS, IS_FBCODE, first_sample)
from torch.testing._internal.common_methods_invocations import \
(op_db, _NOTHING, UnaryUfuncInfo, ReductionOpInfo, SpectralFuncInfo, ops_and_refs,
python_ref_db)
from torch.testing._internal.common_device_type import \
(deviceCountAtLeast, instantiate_device_type_tests, ops,
onlyCUDA, onlyNativeDeviceTypes, OpDTypes, skipMeta)
import torch._prims as prims
import torch.testing._internal.opinfo_helper as opinfo_helper
from torch.testing._internal import composite_compliance
# TODO: fixme https://github.com/pytorch/pytorch/issues/68972
torch.set_default_dtype(torch.float32)
# variant testing is only done with torch.float and torch.cfloat to avoid
# excessive test times and maximize signal to noise ratio
_variant_ops = partial(ops, dtypes=OpDTypes.supported,
allowed_dtypes=(torch.float, torch.cfloat))
# Get names of all the operators which have ref in their entry in OpInfo (testing infra)
# except for Unary Ufuncs (separately implemented in test/test_unary_ufuncs.py)
# and Spectral Functions (separately implemented for only 1D as of now, in test/test_spectral_ops.py)
_ref_test_ops = list(filter(lambda op: not isinstance(op, (UnaryUfuncInfo, ReductionOpInfo,
SpectralFuncInfo)) and op.ref is not None and op.ref is not _NOTHING, op_db))
# Tests that apply to all operators and aren't related to any particular
# system
class TestCommon(TestCase):
exact_dtype = True
# Verifies, on teardown, that no OpInfo is still using dynamic dtypes in CI
@classmethod
def tearDownClass(cls):
super().tearDownClass()
if IS_IN_CI:
err_msg = ("The operator(s) below is(are) using dynamic_dtypes in the OpInfo entries."
"This is OK for testing, but be sure to set the dtypes manually before landing your PR!")
# Assure no opinfo entry has dynamic_dtypes
filtered_ops = list(filter(opinfo_helper.is_dynamic_dtype_set, op_db))
for op in filtered_ops:
fmt_str = opinfo_helper.str_format_dynamic_dtype(op)
err_msg += "\n" + fmt_str
assert len(filtered_ops) == 0, err_msg
# Validates that each OpInfo specifies its forward and backward dtypes
# correctly for CPU and CUDA devices
@skipMeta
@onlyNativeDeviceTypes
@ops(ops_and_refs, dtypes=OpDTypes.none)
def test_dtypes(self, device, op):
# Check complex32 support only if the op claims.
# TODO: Once the complex32 support is better, we should add check for complex32 unconditionally.
device_type = torch.device(device).type
include_complex32 = ((torch.complex32,) if op.supports_dtype(torch.complex32, device_type) else ())
# dtypes to try to backward in
allowed_backward_dtypes = floating_and_complex_types_and(
*((torch.half, torch.bfloat16) + include_complex32))
# lists for (un)supported dtypes
supported_dtypes = set()
unsupported_dtypes = set()
supported_backward_dtypes = set()
unsupported_backward_dtypes = set()
def unsupported(dtype):
unsupported_dtypes.add(dtype)
if dtype in allowed_backward_dtypes:
unsupported_backward_dtypes.add(dtype)
for dtype in all_types_and_complex_and(
*((torch.half, torch.bfloat16, torch.bool) + include_complex32)):
# tries to acquire samples - failure indicates lack of support
requires_grad = (dtype in allowed_backward_dtypes)
try:
samples = tuple(op.sample_inputs(device, dtype, requires_grad=requires_grad))
except Exception as e:
unsupported(dtype)
continue
for sample in samples:
# tries to call operator with the sample - failure indicates
# lack of support
try:
result = op(sample.input, *sample.args, **sample.kwargs)
supported_dtypes.add(dtype)
except Exception as e:
# NOTE: some ops will fail in forward if their inputs
# require grad but they don't support computing the gradient
# in that type! This is a bug in the op!
unsupported(dtype)
continue
# Checks for backward support in the same dtype
try:
result = sample.output_process_fn_grad(result)
if isinstance(result, torch.Tensor):
backward_tensor = result
elif isinstance(result, Sequence) and isinstance(result[0], torch.Tensor):
backward_tensor = result[0]
else:
continue
# Note: this grad may not have the same dtype as dtype
# For functions like complex (float -> complex) or abs
# (complex -> float) the grad tensor will have a
# different dtype than the input.
# For simplicity, this is still modeled as these ops
# supporting grad in the input dtype.
grad = torch.randn_like(backward_tensor)
backward_tensor.backward(grad)
supported_backward_dtypes.add(dtype)
except Exception as e:
unsupported_backward_dtypes.add(dtype)
# Checks that dtypes are listed correctly and generates an informative
# error message
supported_forward = supported_dtypes - unsupported_dtypes
partially_supported_forward = supported_dtypes & unsupported_dtypes
unsupported_forward = unsupported_dtypes - supported_dtypes
supported_backward = supported_backward_dtypes - unsupported_backward_dtypes
partially_supported_backward = supported_backward_dtypes & unsupported_backward_dtypes
unsupported_backward = unsupported_backward_dtypes - supported_backward_dtypes
device_type = torch.device(device).type
claimed_forward = set(op.supported_dtypes(device_type))
supported_but_unclaimed_forward = supported_forward - claimed_forward
claimed_but_unsupported_forward = claimed_forward & unsupported_forward
claimed_backward = set(op.supported_backward_dtypes(device_type))
supported_but_unclaimed_backward = supported_backward - claimed_backward
claimed_but_unsupported_backward = claimed_backward & unsupported_backward
# Partially supporting a dtype is not an error, but we print a warning
if (len(partially_supported_forward) + len(partially_supported_backward)) > 0:
msg = "Some dtypes for {0} on device type {1} are only partially supported!\n".format(
op.name, device_type
)
if len(partially_supported_forward) > 0:
msg = msg + "The following dtypes only worked on some samples during forward: {0}.\n".format(
partially_supported_forward
)
if len(partially_supported_backward) > 0:
msg = msg + "The following dtypes only worked on some samples during backward: {0}.\n".format(
partially_supported_backward
)
print(msg)
if (len(supported_but_unclaimed_forward) + len(claimed_but_unsupported_forward) +
len(supported_but_unclaimed_backward) + len(claimed_but_unsupported_backward)) == 0:
return
# Generates error msg
msg = "The supported dtypes for {0} on device type {1} are incorrect!\n".format(
op.name, device_type
)
if len(supported_but_unclaimed_forward) > 0:
msg = msg + "The following dtypes worked in forward but are not listed by the OpInfo: {0}.\n".format(
supported_but_unclaimed_forward
)
if len(supported_but_unclaimed_backward) > 0:
msg = msg + "The following dtypes worked in backward but are not listed by the OpInfo: {0}.\n".format(
supported_but_unclaimed_backward
)
if len(claimed_but_unsupported_forward) > 0:
msg = msg + "The following dtypes did not work in forward but are listed by the OpInfo: {0}.\n".format(
claimed_but_unsupported_forward
)
if len(claimed_but_unsupported_backward) > 0:
msg = msg + "The following dtypes did not work in backward but are listed by the OpInfo: {0}.\n".format(
claimed_but_unsupported_backward
)
self.fail(msg)
# Validates that each OpInfo works correctly on different CUDA devices
@onlyCUDA
@deviceCountAtLeast(2)
@ops(op_db, allowed_dtypes=(torch.float32, torch.long))
def test_multiple_devices(self, devices, dtype, op):
for cuda_device_str in devices:
cuda_device = torch.device(cuda_device_str)
# NOTE: only tests on first sample
samples = op.sample_inputs(cuda_device, dtype)
sample = first_sample(self, samples)
result = op(sample.input, *sample.args, **sample.kwargs)
if isinstance(result, torch.Tensor):
self.assertTrue(result.device == cuda_device)
elif is_iterable_of_tensors(result):
self.assertTrue(all(map(lambda t: t.device == cuda_device, result)))
else:
self.skipTest("Skipped! Only supports single tensor or iterable of tensor outputs.")
# Tests that the function and its (ndarray-accepting) reference produce the same
# values on the tensors from sample_inputs func for the corresponding op.
# This test runs in double and complex double precision because
# NumPy does computation internally using double precision for many functions
# resulting in possible equality check failures.
@unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN")
@onlyNativeDeviceTypes
@suppress_warnings
@ops(_ref_test_ops, allowed_dtypes=(torch.float64, torch.long, torch.complex128))
def test_reference_testing(self, device, dtype, op):
try:
# Sets the default dtype to NumPy's default dtype of double
cur_default = torch.get_default_dtype()
torch.set_default_dtype(torch.double)
for sample_input in op.reference_inputs(device, dtype):
self.compare_with_reference(op, op.ref, sample_input, exact_dtype=(dtype is not torch.long))
finally:
torch.set_default_dtype(cur_default)
# Tests that experimental Python References' can propagate shape, dtype,
# and device metadata properly.
# TODO: include stride propagation.
@onlyNativeDeviceTypes
@ops(python_ref_db)
def test_python_reference_meta_functions(self, device, dtype, op):
def _to_tensormeta(x):
if isinstance(x, torch.Tensor):
return prims.utils.TensorMeta(x)
# TODO: iterate over requires_grad true/false
for sample in op.reference_inputs(device, dtype, requires_grad=False):
result = op(sample.input, *sample.args, **sample.kwargs)
meta_sample = sample.transform(_to_tensormeta)
meta_result = op(meta_sample.input, *meta_sample.args, **meta_sample.kwargs)
prims.utils.compare_tensor_meta(result, meta_result)
@skipMeta
@onlyNativeDeviceTypes
@ops([op for op in op_db if op.error_inputs_func is not None], dtypes=OpDTypes.none)
def test_errors(self, device, op):
error_inputs = op.error_inputs(device)
for ei in error_inputs:
si = ei.sample_input
with self.assertRaisesRegex(ei.error_type, ei.error_regex):
op(si.input, *si.args, **si.kwargs)
# Tests that the function produces the same result when called with
# noncontiguous tensors.
# TODO: get working with Windows by addressing failing operators
# TODO: get working with ASAN by addressing failing operators
@unittest.skipIf(IS_WINDOWS, "Skipped under Windows")
@unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN")
@onlyNativeDeviceTypes
@suppress_warnings
@ops(op_db, allowed_dtypes=(torch.float32, torch.long, torch.complex64))
def test_noncontiguous_samples(self, device, dtype, op):
test_grad = dtype in op.supported_backward_dtypes(torch.device(device).type)
sample_inputs = op.sample_inputs(device, dtype, requires_grad=test_grad)
for sample_input in sample_inputs:
t_inp, t_args, t_kwargs = sample_input.input, sample_input.args, sample_input.kwargs
noncontig_sample = sample_input.noncontiguous()
n_inp, n_args, n_kwargs = noncontig_sample.input, noncontig_sample.args, noncontig_sample.kwargs
# Verifies sample input tensors should have no grad or history
sample_tensor = t_inp if isinstance(t_inp, torch.Tensor) else t_inp[0]
assert sample_tensor.grad is None
assert sample_tensor.grad_fn is None
# validates forward
expected = op(t_inp, *t_args, **t_kwargs)
actual = op(n_inp, *n_args, **n_kwargs)
self.assertEqual(actual, expected)
# Validate backward
# Short-circuits if the op doesn't support grad in this device x dtype
if not test_grad:
continue
expected = sample_input.output_process_fn_grad(expected)
actual = sample_input.output_process_fn_grad(actual)
if isinstance(expected, torch.Tensor):
grad_for_expected = torch.randn_like(expected)
grad_for_actual = noncontiguous_like(grad_for_expected)
elif isinstance(expected, Sequence):
# Filter output elements that do not require grad
expected = [t for t in expected
if isinstance(t, torch.Tensor) and t.requires_grad]
actual = [n for n in actual
if isinstance(n, torch.Tensor) and n.requires_grad]
grad_for_expected = [torch.randn_like(t) for t in expected]
grad_for_actual = [noncontiguous_like(n) for n in grad_for_expected]
else:
# Nothing to do if it returns a scalar or things like that
continue
# Concatenate inputs into a tuple
t_inputs = (t_inp,) + t_args if isinstance(t_inp, torch.Tensor) else tuple(t_inp) + t_args
n_inputs = (n_inp,) + n_args if isinstance(n_inp, torch.Tensor) else tuple(n_inp) + n_args
# Filter the elemnts that are tensors that require grad
t_input_tensors = [t for t in t_inputs if isinstance(t, torch.Tensor) and t.requires_grad]
n_input_tensors = [n for n in n_inputs if isinstance(n, torch.Tensor) and n.requires_grad]
self.assertEqual(len(t_input_tensors), len(n_input_tensors))
# Some functions may not use all the inputs to generate gradients. One of the
# few examples of this "odd" behaviour is F.hinge_embedding_loss
t_grads = torch.autograd.grad(expected, t_input_tensors, grad_for_expected, allow_unused=True)
n_grads = torch.autograd.grad(actual, n_input_tensors, grad_for_actual, allow_unused=True)
msg = "Got different gradients for contiguous / non-contiguous inputs wrt input {}."
for i, (t, n) in enumerate(zip(t_grads, n_grads)):
self.assertEqual(t, n, msg=msg.format(i))
# Separates one case from the following test_out because many ops don't properly implement the
# incorrectly sized out parameter warning properly yet
# Cases test here:
# - out= with the correct dtype and device, but the wrong shape
@ops(op_db, dtypes=OpDTypes.none)
def test_out_warning(self, device, op):
# Prefers running in float32 but has a fallback for the first listed supported dtype
supported_dtypes = op.supported_dtypes(self.device_type)
if len(supported_dtypes) == 0:
self.skipTest("Skipped! Op has not supported dtypes on this device.")
dtype = torch.float32 if torch.float32 in supported_dtypes else list(supported_dtypes)[0]
samples = op.sample_inputs(device, dtype)
for sample in samples:
# calls it normally to get the expected result
expected = op(sample.input, *sample.args, **sample.kwargs)
op_out = partial(op, sample.input, *sample.args, **sample.kwargs)
# Short-circuits if output is not a single tensor or an
# iterable of tensors
if not isinstance(expected, torch.Tensor) and not is_iterable_of_tensors(expected, include_empty=True):
self.skipTest("Skipped! Only supports single tensor or iterable of tensor outputs.")
# Validates the op doesn't support out if it claims not to
if not op.supports_out:
with self.assertRaises(Exception):
assert op_out(out=expected) != NotImplemented
return
# A wrapper around map that works with single tensors and always
# instantiates the map. Used below to apply transforms to
# single tensor and iterable tensor outputs.
def _apply_out_transform(fn, out):
if isinstance(out, torch.Tensor):
return fn(out)
# assumes (see above) that out is an iterable of tensors
return tuple(map(fn, out))
# Extracts strides from a tensor or iterable of tensors into a tuple
def _extract_strides(out):
if isinstance(out, torch.Tensor):
return (out.stride(),)
# assumes (see above) that out is an iterable of tensors
return tuple(map(lambda t: t.stride(), out))
# Extracts data pointers from a tensor or iterable of tensors into a tuple
# NOTE: only extracts on the CPU and CUDA device types since some
# device types don't have storage
def _extract_data_ptrs(out):
if self.device_type != 'cpu' and self.device_type != 'cuda':
return ()
if isinstance(out, torch.Tensor):
return (out.data_ptr(),)
# assumes (see above) that out is an iterable of tensors
return tuple(map(lambda t: t.data_ptr(), out))
@suppress_warnings
def _compare_out(transform, *, compare_strides_and_data_ptrs=True):
out = _apply_out_transform(transform, expected)
original_strides = _extract_strides(out)
original_ptrs = _extract_data_ptrs(out)
op_out(out=out)
final_strides = _extract_strides(out)
final_ptrs = _extract_data_ptrs(out)
self.assertEqual(expected, out)
if compare_strides_and_data_ptrs:
stride_msg = "Strides are not the same! Original strides were {0} and strides are now {1}".format(
original_strides, final_strides)
self.assertEqual(original_strides, final_strides, msg=stride_msg)
self.assertEqual(original_ptrs, final_ptrs)
# Case Zero: out= with the correct dtype and device, but the wrong shape
# Expected behavior: if nonempty, resize with a warning.
def _case_zero_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)
# Verifies the out values are correct
_compare_out(_case_zero_transform, compare_strides_and_data_ptrs=False)
# Additionally validates that the appropriate warning is thrown if a nonempty
# tensor is resized.
def _any_nonempty(out):
if isinstance(out, torch.Tensor):
return out.numel() > 0
return any(x.numel() > 0 for x in out)
out = _apply_out_transform(_case_zero_transform, expected)
msg_fail = "Resized a non-empty tensor but did not warn about it."
if _any_nonempty(out):
with self.assertWarnsRegex(UserWarning, "An output with one or more elements", msg=msg_fail):
op_out(out=out)
# Validates ops implement the correct out= behavior
# See https://github.com/pytorch/pytorch/wiki/Developer-FAQ#how-does-out-work-in-pytorch
# for a description of the correct behavior
# Validates the following cases:
# - Case 0: out has the correct shape, dtype, and device but is full of extremal values
# - Case 1: out has the correct shape, dtype, and device but is noncontiguous
# - Case 2: out has the correct dtype and device, but is zero elements
# - Case 3: out has the correct shape and dtype, but is on a different device type
# - Case 4: out has the with correct shape and device, but a dtype that cannot
# "safely" cast to
@ops(op_db, dtypes=OpDTypes.none)
def test_out(self, device, op):
# Prefers running in float32 but has a fallback for the first listed supported dtype
supported_dtypes = op.supported_dtypes(self.device_type)
if len(supported_dtypes) == 0:
self.skipTest("Skipped! Op has not supported dtypes on this device.")
dtype = torch.float32 if torch.float32 in supported_dtypes else list(supported_dtypes)[0]
samples = op.sample_inputs(device, dtype)
for sample in samples:
# calls it normally to get the expected result
expected = op(sample.input, *sample.args, **sample.kwargs)
op_out = partial(op, sample.input, *sample.args, **sample.kwargs)
# Short-circuits if output is not a single tensor or an
# iterable of tensors
if not isinstance(expected, torch.Tensor) and not is_iterable_of_tensors(expected, include_empty=True):
self.skipTest("Skipped! Only supports single tensor or iterable of tensor outputs.")
# Validates the op doesn't support out if it claims not to
if not op.supports_out:
with self.assertRaises(Exception):
assert op_out(out=expected) != NotImplemented
return
# A wrapper around map that works with single tensors and always
# instantiates the map. Used below to apply transforms to
# single tensor and iterable tensor outputs.
def _apply_out_transform(fn, out):
if isinstance(out, torch.Tensor):
return fn(out)
# assumes (see above) that out is an iterable of tensors
return tuple(map(fn, out))
# Extracts strides from a tensor or iterable of tensors into a tuple
def _extract_strides(out):
if isinstance(out, torch.Tensor):
return (out.stride(),)
# assumes (see above) that out is an iterable of tensors
return tuple(map(lambda t: t.stride(), out))
# Extracts data pointers from a tensor or iterable of tensors into a tuple
# NOTE: only extracts on the CPU and CUDA device types since some
# device types don't have storage
def _extract_data_ptrs(out):
if self.device_type != 'cpu' and self.device_type != 'cuda':
return ()
if isinstance(out, torch.Tensor):
return (out.data_ptr(),)
# assumes (see above) that out is an iterable of tensors
return tuple(map(lambda t: t.data_ptr(), out))
def _compare_out(transform, *, compare_strides_and_data_ptrs=True):
out = _apply_out_transform(transform, expected)
original_strides = _extract_strides(out)
original_ptrs = _extract_data_ptrs(out)
op_out(out=out)
final_strides = _extract_strides(out)
final_ptrs = _extract_data_ptrs(out)
self.assertEqual(expected, out)
if compare_strides_and_data_ptrs:
stride_msg = "Strides are not the same! Original strides were {0} and strides are now {1}".format(
original_strides, final_strides)
self.assertEqual(original_strides, final_strides, msg=stride_msg)
self.assertEqual(original_ptrs, final_ptrs)
# Case 0: out= with the correct shape, dtype, and device
# but NaN values for floating point and complex tensors, and
# maximum values for integer tensors.
# Expected behavior: out= values have no effect on the computation.
def _case_zero_transform(t):
try:
info = torch.iinfo(t.dtype)
return torch.full_like(t, info.max)
except TypeError as te:
# for non-integer types fills with NaN
return torch.full_like(t, float('nan'))
_compare_out(_case_zero_transform)
# Case 1: out= with the correct shape, dtype, and device,
# but noncontiguous.
# Expected behavior: strides are respected and `out` storage is not changed.
def _case_one_transform(t):
return make_tensor(t.shape,
dtype=t.dtype,
device=t.device,
noncontiguous=True)
_compare_out(_case_one_transform)
# Case 2: out= with the correct dtype and device, but has no elements.
# Expected behavior: resize without warning.
def _case_two_transform(t):
return make_tensor((0,), dtype=t.dtype, device=t.device)
_compare_out(_case_two_transform, compare_strides_and_data_ptrs=False)
# Also validates that no warning is thrown when this out is resized
out = _apply_out_transform(_case_two_transform, expected)
with warnings.catch_warnings(record=True) as caught:
warnings.simplefilter("always")
op_out(out=out)
# Verifies no warning is a resize warning
for w in caught:
if "An output with one or more elements" in str(w.message):
self.fail("Resizing an out= argument with no elements threw a resize warning!")
# Case 3: out= with correct shape and dtype, but wrong device.
wrong_device = None
if torch.device(device).type != 'cpu':
wrong_device = 'cpu'
elif torch.cuda.is_available():
wrong_device = 'cuda'
if wrong_device is not None:
def _case_three_transform(t):
return make_tensor(t.shape, dtype=t.dtype, device=wrong_device)
out = _apply_out_transform(_case_three_transform, expected)
msg_fail = f"Expected RuntimeError when calling with input.device={device} and out.device={wrong_device}"
with self.assertRaises(RuntimeError, msg=msg_fail):
op_out(out=out)
# Case 4: out= with correct shape and device, but a dtype
# that output cannot be "safely" cast to (long).
# Expected behavior: error.
# NOTE: this case is filtered by dtype since some ops produce
# bool tensors, for example, which can be safely cast to any
# dtype. It is applied when single tensors are floating point or complex
# dtypes, or if an op returns multiple tensors when at least one such
# tensor is a floating point or complex dtype.
_dtypes = floating_and_complex_types_and(torch.float16, torch.bfloat16)
if (isinstance(expected, torch.Tensor) and expected.dtype in _dtypes or
(not isinstance(expected, torch.Tensor) and any(t.dtype in _dtypes for t in expected))):
def _case_four_transform(t):
return make_tensor(t.shape, dtype=torch.long, device=t.device)
out = _apply_out_transform(_case_four_transform, expected)
msg_fail = "Expected RuntimeError when doing an unsafe cast!"
msg_fail = 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 = (dtype in op.supported_backward_dtypes(torch.device(device).type))
include_conjugated_inputs = op.test_conjugated_samples and dtype.is_complex
samples = op.sample_inputs(device, dtype, requires_grad=_requires_grad, include_conjugated_inputs=include_conjugated_inputs)
samples = list(samples)
def _test_consistency_helper(samples, variants):
for sample in samples:
# TODO: Check grad for all Tensors requiring grad if sample.input is TensorList
tensor = sample.input if isinstance(sample.input, torch.Tensor) else sample.input[0]
# Computes function forward and backward values
tensor.grad = None
expected_forward = op(sample.input, *sample.args, **sample.kwargs)
expected_grad = None
output_process_fn_grad = sample.output_process_fn_grad if sample.output_process_fn_grad \
else lambda x: x
# Skips inplace variants if the output dtype is not the same as
# the input dtype
skip_inplace = False
if (isinstance(expected_forward, torch.Tensor) and
expected_forward.dtype is not tensor.dtype):
skip_inplace = True
# TODO: backward consistency only supported for single tensor outputs
# TODO: backward consistency only checked on sample.input, not all
# tensor inputs
# TODO: update to handle checking grads of all tensor inputs as
# derived from each tensor output
if (isinstance(expected_forward, torch.Tensor)
and dtype in op.supported_backward_dtypes(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)
# Reference testing for operations in complex32 against complex64.
# NOTE: We test against complex64 as NumPy doesn't have a complex32 equivalent dtype.
@ops(op_db, allowed_dtypes=(torch.complex32,))
def test_complex_half_reference_testing(self, device, dtype, op):
if not op.supports_dtype(torch.complex32, device):
unittest.skip("Does not support complex32")
for sample in op.sample_inputs(device, dtype):
actual = op(sample.input, *sample.args, **sample.kwargs)
transformed_sample = sample.transform(lambda x: x.to(torch.complex64))
expected = op(transformed_sample.input, *transformed_sample.args, **transformed_sample.kwargs)
self.assertEqual(actual, expected, exact_dtype=False)
class TestCompositeCompliance(TestCase):
# Checks if the operator (if it is composite) is written to support most
# backends and Tensor subclasses. See "CompositeImplicitAutograd Compliance"
# in aten/src/ATen/native/README.md for more details
@unittest.skipIf(IS_FBCODE or IS_SANDCASTLE, '__torch_dispatch__ does not work in fbcode')
@ops(op_db, allowed_dtypes=(torch.float,))
def test_operator(self, device, dtype, op):
samples = op.sample_inputs(device, dtype, requires_grad=False)
for sample in samples:
args = [sample.input] + list(sample.args)
kwargs = sample.kwargs
composite_compliance.check_with_mode(op, args, kwargs)
composite_compliance.check_all_permutations(op, args, kwargs)
@unittest.skipIf(IS_FBCODE or IS_SANDCASTLE, '__torch_dispatch__ does not work in fbcode')
@ops([op for op in op_db if op.supports_autograd], allowed_dtypes=(torch.float,))
def test_backward(self, device, dtype, op):
samples = op.sample_inputs(device, dtype, requires_grad=True)
for sample in samples:
args = [sample.input] + list(sample.args)
kwargs = sample.kwargs
composite_compliance.check_backward_formula(op, args, kwargs)
@unittest.skipIf(IS_FBCODE or IS_SANDCASTLE, '__torch_dispatch__ does not work in fbcode')
@ops(op_db, allowed_dtypes=(torch.float,))
def test_forward_ad(self, device, dtype, op):
if torch.float not in op.supported_backward_dtypes(device):
raise unittest.SkipTest("Does not support autograd")
if not op.supports_forward_ad:
raise unittest.SkipTest("Does not support forward_ad")
samples = op.sample_inputs(device, dtype, requires_grad=True)
for sample in samples:
args = [sample.input] + list(sample.args)
kwargs = sample.kwargs
composite_compliance.check_forward_ad_formula(op, args, kwargs)
class TestMathBits(TestCase):
# Tests that
# 1. The operator's output for physically conjugated/negated tensors and conjugate/negative view tensors
# produces the same value
# 2. The gradients are same in both cases mentioned in (1)
# 3. If the operator's inplace variant is supported, tests that the inplace operation
# produces the correct value when called on a conjugate/negative view tensor and that the output
# has its conj/neg bit set to true
# This test only runs for C -> R and C -> C functions
# TODO: add tests for `R->C` functions
# Note: This test runs for functions that take both tensors and tensorlists as input.
def _test_math_view(self, device, dtype, op, samples, math_op_physical, math_op_view, is_bit_set, out_type):
inplace_variant = op.inplace_variant
# helper function to clone and conjugate/negate the input if its a tensor
# else clone the sequence and conjugate/negate the first element in the sequence
# If a requires_grad argument is provided the tensor being conjugated/negated will
# have its requires_grad set to that value.
def clone_and_perform_view(input, **kwargs):
if isinstance(input, torch.Tensor):
requires_grad = kwargs.get('requires_grad', input.requires_grad)
with torch.no_grad():
# Ensure view represents the original sample input
input = math_op_physical(input)
# Note: .conj() is not called under no_grad mode since it's not allowed to modify a
# view created in no_grad mode. Here it's ok to do so, so as a workaround we call conj
# before resetting the requires_grad field for input
input = math_op_view(input)
assert input.is_leaf
return input.requires_grad_(requires_grad)
if isinstance(input, Sequence):
out = list(map(clone_input_helper, input))
out[0] = clone_and_perform_view(out[0])
return tuple(out)
for sample in samples:
tensor = sample.input if isinstance(sample.input, torch.Tensor) else sample.input[0]
cloned1 = clone_and_perform_view(sample.input)
# Computes function forward value with a physically conjugated/negated tensor and
# a conj/neg view tensor and verifies that the output in both case are equal.
expected_forward = op(sample.input, *sample.args, **sample.kwargs)
forward_with_mathview = op(cloned1, *sample.args, **sample.kwargs)
self.assertEqual(expected_forward, forward_with_mathview)
# If the op has an inplace variant, and the input doesn't require broadcasting
# and has the same dtype as output, verify that the inplace operation on a conjugated/negated
# input produces correct output, and the output tensor has the conj/neg bit set to True
if inplace_variant is not None and not sample.broadcasts_input:
cloned2 = clone_and_perform_view(tensor, requires_grad=False)
if (isinstance(expected_forward, torch.Tensor) and
expected_forward.dtype is tensor.dtype):
inplace_forward = inplace_variant(cloned2, *sample.args, **sample.kwargs)
self.assertTrue(is_bit_set(inplace_forward))
self.assertEqual(inplace_forward, expected_forward)
# TODO: backward consistency only supported for single tensor outputs
# TODO: backward consistency only checked on sample.input, not all
# tensor inputs
# TODO: update to handle checking grads of all tensor inputs as
# derived from each tensor output
if isinstance(expected_forward, torch.Tensor) and expected_forward.requires_grad:
output_process_fn_grad = sample.output_process_fn_grad or (lambda x: x)
expected_forward = output_process_fn_grad(expected_forward)
forward_with_mathview = output_process_fn_grad(forward_with_mathview)
tensor = sample.input if isinstance(sample.input, torch.Tensor) else sample.input[0]
expected_forward.sum().backward(retain_graph=True)
forward_with_mathview.sum().backward(retain_graph=True)
if tensor.grad is not None:
cloned1_tensor = cloned1 if isinstance(cloned1, torch.Tensor) else cloned1[0]
self.assertEqual(tensor.grad, cloned1_tensor.grad)
tensor.grad, cloned1_tensor.grad = None, None
# a repeat of the above test if output is not complex valued
if (out_type(expected_forward)):
grad = torch.randn_like(expected_forward)
expected_forward.backward(grad)
forward_with_mathview.backward(math_op_view(math_op_physical(grad)))
self.assertEqual(tensor.grad, cloned1_tensor.grad)
@ops(op_db, allowed_dtypes=(torch.cfloat,))
def test_conj_view(self, device, dtype, op):
if not op.test_conjugated_samples:
self.skipTest("Operation doesn't support conjugated inputs.")
math_op_physical = torch.conj_physical
math_op_view = torch.conj
_requires_grad = torch.cfloat in op.supported_backward_dtypes(torch.device(device).type)
is_bit_set = torch.is_conj
samples = op.sample_inputs(device, dtype, requires_grad=_requires_grad)
self._test_math_view(device, dtype, op, samples, math_op_physical, math_op_view, is_bit_set, torch.is_complex)
@ops(op_db, allowed_dtypes=(torch.double,))
def test_neg_view(self, device, dtype, op):
if not op.test_neg_view:
self.skipTest("Operation not tested with tensors with negative bit.")
math_op_physical = torch.neg
math_op_view = torch._neg_view
is_bit_set = torch.is_neg
samples = op.sample_inputs(device, dtype, requires_grad=op.supports_autograd)
self._test_math_view(device, dtype, op, samples, math_op_physical, math_op_view, is_bit_set,
lambda x: True)
@ops(op_db, allowed_dtypes=(torch.cdouble,))
def test_neg_conj_view(self, device, dtype, op):
if not op.test_neg_view:
self.skipTest("Operation not tested with tensors with negative bit.")
if not op.test_conjugated_samples:
self.skipTest("Operation doesn't support conjugated inputs.")
def math_op_physical(x):
return -x.conj_physical()
def math_op_view(x):
return torch._neg_view(x).conj()
def is_bit_set(x):
return torch.is_neg(x) and torch.is_conj(x)
_requires_grad = dtype in op.supported_backward_dtypes(torch.device(device).type)
samples = op.sample_inputs(device, dtype, requires_grad=_requires_grad)
# Only test one sample
samples = itertools.islice(samples, 1)
self._test_math_view(device, dtype, op, samples, math_op_physical, math_op_view, is_bit_set,
torch.is_complex)
instantiate_device_type_tests(TestCommon, globals())
instantiate_device_type_tests(TestCompositeCompliance, globals())
instantiate_device_type_tests(TestMathBits, globals())
if __name__ == '__main__':
run_tests()
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