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test_ops.py
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test_ops.py
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from collections.abc import Sequence
from functools import partial, wraps
import warnings
import torch
from torch.testing import \
(FileCheck, floating_and_complex_types_and, get_all_dtypes)
from torch.testing._internal.common_utils import \
(TestCase, is_iterable_of_tensors, run_tests, IS_SANDCASTLE, clone_input_helper, make_tensor,
gradcheck, gradgradcheck, IS_IN_CI, suppress_warnings)
from torch.testing._internal.common_methods_invocations import \
(op_db, _NOTHING, UnaryUfuncInfo, SpectralFuncInfo)
from torch.testing._internal.common_device_type import \
(instantiate_device_type_tests, ops, onlyOnCPUAndCUDA, skipCUDAIfRocm, OpDTypes)
from torch.testing._internal.common_jit import JitCommonTestCase, check_against_reference
from torch.testing._internal.jit_metaprogramming_utils import create_script_fn, create_traced_fn, \
check_alias_annotation
from torch.testing._internal.jit_utils import disable_autodiff_subgraph_inlining
import torch.testing._internal.opinfo_helper as opinfo_helper
# variant testing is only done with torch.float and torch.cfloat to avoid
# excessive test times and maximize signal to noise ratio
_variant_ops = partial(ops, dtypes=OpDTypes.supported,
allowed_dtypes=(torch.float, torch.cfloat))
# Get names of all the operators which have ref in their entry in OpInfo (testing infra)
# except for Unary Ufuncs (separately implemented in test/test_unary_ufuncs.py)
# and Spectral Functions (separately implemented for only 1D as of now, in test/test_spectral_ops.py)
_ref_test_ops = list(filter(lambda op: not isinstance(op, (UnaryUfuncInfo, SpectralFuncInfo)) and
op.ref is not None and op.ref is not _NOTHING, op_db))
# Tests that apply to all operators and aren't related to any particular
# system
class TestCommon(TestCase):
exact_dtype = True
# Verifies, on teardown, that no OpInfo is still using dynamic dtypes in CI
@classmethod
def tearDownClass(cls):
super().tearDownClass()
if IS_IN_CI:
err_msg = ("The operator(s) below is(are) using dynamic_dtypes in the OpInfo entries."
"This is OK for testing, but be sure to set the dtypes manually before landing your PR!")
# Assure no opinfo entry has dynamic_dtypes
filtered_ops = list(filter(opinfo_helper.is_dynamic_dtype_set, op_db))
for op in filtered_ops:
fmt_str = opinfo_helper.str_format_dynamic_dtype(op)
err_msg += "\n" + fmt_str
assert len(filtered_ops) == 0, err_msg
# Validates that each OpInfo specifies its forward and backward dtypes
# correctly for CPU and CUDA devices
@skipCUDAIfRocm
@onlyOnCPUAndCUDA
@ops(op_db, dtypes=OpDTypes.none)
def test_dtypes(self, device, dtype, op):
# dtypes to try to backward in
allowed_backward_dtypes = floating_and_complex_types_and(torch.bfloat16, torch.float16)
# lists for (un)supported dtypes
supported_dtypes = []
unsupported_dtypes = []
supported_backward_dtypes = []
unsupported_backward_dtypes = []
def unsupported(dtype):
unsupported_dtypes.append(dtype)
if dtype in allowed_backward_dtypes:
unsupported_backward_dtypes.append(dtype)
for dtype in get_all_dtypes():
# tries to acquire samples - failure indicates lack of support
requires_grad = (dtype in allowed_backward_dtypes and op.supports_autograd)
try:
samples = op.sample_inputs(device, dtype, requires_grad=requires_grad)
except Exception as e:
unsupported(dtype)
continue
# Counts number of successful backward attempts
# NOTE: This exists as a kludge because this only understands how to
# request a gradient if the output is a tensor or a sequence with
# a tensor as its first element.
num_backward_successes = 0
for sample in samples:
# tries to call operator with the sample - failure indicates
# lack of support
try:
result = op(sample.input, *sample.args, **sample.kwargs)
except Exception as e:
# NOTE: some ops will fail in forward if their inputs
# require grad but they don't support computing the gradient
# in that type! This is a bug in the op!
unsupported(dtype)
# Short-circuits testing this dtype -- it doesn't work
if dtype in unsupported_dtypes:
break
# Short-circuits if the dtype isn't a backward dtype or
# it's already identified as not supported
if dtype not in allowed_backward_dtypes or dtype in unsupported_backward_dtypes:
continue
# Checks for backward support in the same dtype
try:
result = sample.output_process_fn_grad(result)
if isinstance(result, torch.Tensor):
backward_tensor = result
elif isinstance(result, Sequence) and isinstance(result[0], torch.Tensor):
backward_tensor = result[0]
else:
continue
# Note: this grad may not have the same dtype as dtype
# For functions like complex (float -> complex) or abs
# (complex -> float) the grad tensor will have a
# different dtype than the input.
# For simplicity, this is still modeled as these ops
# supporting grad in the input dtype.
grad = torch.randn_like(backward_tensor)
backward_tensor.backward(grad)
num_backward_successes += 1
except Exception as e:
unsupported_backward_dtypes.append(dtype)
if dtype not in unsupported_dtypes:
supported_dtypes.append(dtype)
if num_backward_successes > 0 and dtype not in unsupported_backward_dtypes:
supported_backward_dtypes.append(dtype)
# Checks that dtypes are listed correctly and generates an informative
# error message
device_type = torch.device(device).type
claimed_supported = set(op.supported_dtypes(device_type))
supported_dtypes = set(supported_dtypes)
supported_but_unclaimed = supported_dtypes - claimed_supported
claimed_but_unsupported = claimed_supported - supported_dtypes
msg = """The supported dtypes for {0} on {1} according to its OpInfo are
{2}, but the detected supported dtypes are {3}.
""".format(op.name, device_type, claimed_supported, supported_dtypes)
if len(supported_but_unclaimed) > 0:
msg += "The following dtypes should be added to the OpInfo: {0}. ".format(supported_but_unclaimed)
if len(claimed_but_unsupported) > 0:
msg += "The following dtypes should be removed from the OpInfo: {0}.".format(claimed_but_unsupported)
self.assertEqual(supported_dtypes, claimed_supported, msg=msg)
# Checks that backward dtypes are listed correctly and generates an
# informative error message
# NOTE: this code is nearly identical to the check + msg generation
claimed_backward_supported = set(op.supported_backward_dtypes(device_type))
supported_backward_dtypes = set(supported_backward_dtypes)
supported_but_unclaimed = supported_backward_dtypes - claimed_backward_supported
claimed_but_unsupported = claimed_backward_supported - supported_backward_dtypes
msg = """The supported backward dtypes for {0} on {1} according to its OpInfo are
{2}, but the detected supported backward dtypes are {3}.
""".format(op.name, device_type, claimed_backward_supported, supported_backward_dtypes)
if len(supported_but_unclaimed) > 0:
msg += "The following backward dtypes should be added to the OpInfo: {0}. ".format(supported_but_unclaimed)
if len(claimed_but_unsupported) > 0:
msg += "The following backward dtypes should be removed from the OpInfo: {0}.".format(claimed_but_unsupported)
self.assertEqual(supported_backward_dtypes, claimed_backward_supported, msg=msg)
# Tests that the function and its (ndarray-accepting) reference produce the same
# values on the tensors from sample_inputs func for the corresponding op.
@onlyOnCPUAndCUDA
@suppress_warnings
@ops(_ref_test_ops, allowed_dtypes=(torch.float32, torch.long))
def test_reference_testing(self, device, dtype, op):
sample_inputs = op.sample_inputs(device, dtype)
for sample_input in sample_inputs:
self.compare_with_reference(op, op.ref, sample_input)
# Validates ops implement the correct out= behavior
# See https://github.com/pytorch/pytorch/wiki/Developer-FAQ#how-does-out-work-in-pytorch
# for a description of the correct behavior
# TODO: operations that support out= but don't support float
# are not covered by this test.
@ops(op_db, allowed_dtypes=(torch.float,))
def test_out(self, device, dtype, op):
# TODO: verify the op doesn't support the out= kwarg
if not op.supports_out:
self.skipTest("Skipped! Op doesn't support out= kwarg.")
# NOTE: only tests on first sample
samples = op.sample_inputs(device, dtype)
sample = samples[0]
# calls it normally to get the expected result
expected = op(sample.input, *sample.args, **sample.kwargs)
op_out = partial(op, sample.input, *sample.args, **sample.kwargs)
# Short-circuits if output is not a single tensor or an
# iterable of tensors
if not isinstance(expected, torch.Tensor) and not is_iterable_of_tensors(expected, include_empty=True):
self.skipTest("Skipped! Only supports single tensor or iterable of tensor outputs.")
# A wrapper around map that works with single tensors and always
# instantiates the map. Used below to apply transforms to
# single tensor and iterable tensor outputs.
def _apply_out_transform(fn, out):
if isinstance(out, torch.Tensor):
return fn(out)
# assumes (see above) that out is an iterable of tensors
return tuple(map(fn, out))
# Case 0: out= with the correct shape, dtype, and device
# but NaN values for floating point and complex tensors, and
# maximum values for integer tensors.
# Expected behavior: out= values have no effect on the computation.
def _case_zero_transform(t):
try:
info = torch.iinfo(t.dtype)
return torch.full_like(t, info.max)
except TypeError as te:
# for non-integer types fills with NaN
return torch.full_like(t, float('nan'))
out = _apply_out_transform(_case_zero_transform, expected)
result = op_out(out=out)
self.assertEqual(expected, out)
# Checks that the returned value shares storage with out
# NOTE: only checks on the CPU and CUDA device types since some
# device types don't have storage
if self.device_type == 'cpu' or self.device_type == 'cuda':
if isinstance(out, torch.Tensor):
self.assertEqual(out.storage().data_ptr(), result.storage().data_ptr())
else:
for out_t, result_t in zip(out, result):
self.assertEqual(out_t.storage().data_ptr(), result_t.storage().data_ptr())
# Case 1: out= with the correct shape, dtype, and device,
# but noncontiguous.
# Expected behavior: strides are respected and `out` storage is not changed.
def _case_one_transform(t):
return make_tensor(t.shape,
dtype=t.dtype,
device=t.device,
noncontiguous=True)
# Extracts strides from a tensor or iterable of tensors into a tuple
def _extract_strides(out):
if isinstance(out, torch.Tensor):
return (out.stride(),)
# assumes (see above) that out is an iterable of tensors
return tuple(map(lambda t: t.stride(), out))
def _extract_data_ptrs(out):
if isinstance(out, torch.Tensor):
return (out.data_ptr(),)
# assumes (see above) that out is an iterable of tensors
return tuple(map(lambda t: t.data_ptr(), out))
out = _apply_out_transform(_case_one_transform, expected)
original_strides = _extract_strides(out)
original_ptrs = _extract_data_ptrs(out)
op_out(out=out)
final_strides = _extract_strides(out)
final_ptrs = _extract_data_ptrs(out)
self.assertEqual(expected, out)
self.assertEqual(original_strides, final_strides)
self.assertEqual(original_ptrs, final_ptrs)
# Case 2: out= with the correct dtype and device, but the wrong shape
# Expected behavior: resize with a warning.
def _case_two_transform(t):
wrong_shape = list(t.shape)
if len(wrong_shape) == 0:
# Handles scalar tensor case (empty list)
wrong_shape = [2]
else:
wrong_shape[-1] = wrong_shape[-1] + 1
return make_tensor(wrong_shape, dtype=t.dtype, device=t.device)
out = _apply_out_transform(_case_two_transform, expected)
msg_fail = "Resized a non-empty tensor but did not warn about it."
with self.assertWarnsRegex(UserWarning, "An output with one or more elements", msg=msg_fail):
op_out(out=out)
self.assertEqual(expected, out)
# Case 3: out= with the correct dtype and device, but an empty
# tensor.
# Expected behavior: resize without warning.
def _case_three_transform(t):
return make_tensor((0,),
dtype=t.dtype,
device=t.device)
out = _apply_out_transform(_case_three_transform, expected)
with warnings.catch_warnings(record=True) as caught:
warnings.simplefilter("always")
op_out(out=out)
# Verifies no warning is a resize warning
for w in caught:
if "An output with one or more elements" in str(w.message):
self.fail("Resizing an out= argument with no elements threw a resize warning!")
self.assertEqual(expected, out)
# Case 4: out= with correct shape and dtype, but wrong device.
wrong_device = None
if torch.device(device).type != 'cpu':
wrong_device = 'cpu'
elif torch.cuda.is_available():
wrong_device = 'cuda'
if wrong_device is not None:
def _case_four_transform(t):
return make_tensor(t.shape, dtype=t.dtype, device=wrong_device)
out = _apply_out_transform(_case_four_transform, expected)
msg_fail = f"Expected RuntimeError when calling with input.device={device} and out.device={wrong_device}"
with self.assertRaises(RuntimeError, msg=msg_fail):
op_out(out=out)
# Case 5: out= with correct shape and device, but a dtype
# that output cannot be "safely" cast to (long).
# Expected behavior: error.
# NOTE: this case is filtered by dtype since some ops produce
# bool tensors, for example, which can be safely cast to any
# dtype. It is applied when single tensors are floating point or complex
# dtypes, or if an op returns multiple tensors when at least one such
# tensor is a floating point or complex dtype.
_dtypes = floating_and_complex_types_and(torch.float16, torch.bfloat16)
if (isinstance(expected, torch.Tensor) and expected.dtype in _dtypes or
(not isinstance(expected, torch.Tensor) and any(t.dtype in _dtypes for t in expected))):
def _case_five_transform(t):
return make_tensor(t.shape, dtype=torch.long, device=t.device)
out = _apply_out_transform(_case_five_transform, expected)
msg_fail = "" if not isinstance(expected, torch.Tensor) else \
("Expected RuntimeError when doing an unsafe cast from a result of dtype "
f"{expected.dtype} into an out= with dtype torch.long")
with self.assertRaises(RuntimeError, msg=msg_fail):
op_out(out=out)
# Tests that
# 1. The operator's output for physically conjugated tensors and conjugate 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 view tensor and that the output
# has its conj 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.
@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.")
_requires_grad = (op.supports_autograd and op.supports_complex_autograd(torch.device(device).type))
samples = op.sample_inputs(device, dtype, requires_grad=_requires_grad)
inplace_variant = op.inplace_variant
# helper function to physically conjugate the tensor
def conjugate_physical(input):
if isinstance(input, torch.Tensor):
tensor_requires_grad = input.requires_grad
with torch.no_grad():
input = input.conj_physical()
return input.requires_grad_(tensor_requires_grad)
if isinstance(input, Sequence):
out = list(map(clone_input_helper, input))
out[0] = conjugate_physical(out[0])
return tuple(out)
# helper function to clone and conjugate the input if its a tensor
# else clone the sequence and conjugate the first element in the sequence
# If a requires_grad argument is provided the tensor being conjugated will
# have its requires_grad set to that value.
def clone_conj_input_helper(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 = input.conj()
assert input.is_leaf
return input.requires_grad_(requires_grad)
if isinstance(input, Sequence):
out = list(map(clone_input_helper, input))
out[0] = clone_conj_input_helper(out[0])
return tuple(out)
for sample in samples:
tensor = sample.input if isinstance(sample.input, torch.Tensor) else sample.input[0]
cloned1 = clone_conj_input_helper(sample.input)
sample.input = conjugate_physical(sample.input)
# Computes function forward value with a physically conjugated tensor and
# a conj view tensor and verifies that the output in both case are equal.
expected_forward = op(sample.input, *sample.args, **sample.kwargs)
forward_with_conjview = op(cloned1, *sample.args, **sample.kwargs)
self.assertEqual(expected_forward, forward_with_conjview)
# 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
# input produces correct output, and the output tensor has the conj bit set to True
if inplace_variant is not None and not sample.broadcasts_input:
cloned2 = clone_conj_input_helper(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(inplace_forward.is_conj())
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_conjview.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 (expected_forward.is_complex()):
grad = torch.randn_like(expected_forward)
expected_forward.backward(grad.conj_physical())
forward_with_conjview.backward(grad.conj())
self.assertEqual(tensor.grad, cloned1_tensor.grad)
# Tests that the forward and backward passes of operations produce the
# same values for the cross-product of op variants (method, inplace)
# against eager's gold standard op function variant
@_variant_ops(op_db)
def test_variant_consistency_eager(self, device, dtype, op):
# Acquires variants (method variant, inplace variant, aliases)
method = op.method_variant
inplace = op.inplace_variant
# list of all inplace ops: inplace variant + alias inplace variants if exist
inplace_ops = [inplace, ]
variants = [method, inplace]
for a_op in op.aliases:
variants.append(a_op.op)
variants.append(a_op.method_variant)
variants.append(a_op.inplace_variant)
inplace_ops.append(a_op.inplace_variant)
inplace_variants = tuple(filter(None, inplace_ops))
variants = tuple(filter(None, variants))
_requires_grad = (op.supports_autograd and
(dtype.is_floating_point or op.supports_complex_autograd(torch.device(device).type)))
include_conjugated_inputs = op.test_conjugated_samples and dtype.is_complex
samples = op.sample_inputs(device, dtype, requires_grad=_requires_grad, include_conjugated_inputs=include_conjugated_inputs)
def _test_consistency_helper(samples, variants):
for sample in samples:
# TODO: Check grad for all Tensors requiring grad if sample.input is TensorList
tensor = sample.input if isinstance(sample.input, torch.Tensor) else sample.input[0]
# Computes function forward and backward values
tensor.grad = None
expected_forward = op(sample.input, *sample.args, **sample.kwargs)
expected_grad = None
output_process_fn_grad = sample.output_process_fn_grad if sample.output_process_fn_grad \
else lambda x: x
# Skips inplace variants if the output dtype is not the same as
# the input dtype
skip_inplace = False
if (isinstance(expected_forward, torch.Tensor) and
expected_forward.dtype is not tensor.dtype):
skip_inplace = True
# TODO: backward consistency only supported for single tensor outputs
# TODO: backward consistency only checked on sample.input, not all
# tensor inputs
# TODO: update to handle checking grads of all tensor inputs as
# derived from each tensor output
if (op.supports_autograd and isinstance(expected_forward, torch.Tensor)
and (dtype.is_floating_point or op.supports_complex_autograd(torch.device(device).type))):
output_process_fn_grad(expected_forward).sum().backward()
expected_grad = tensor.grad
# Test eager consistency
for variant in variants:
# Skips inplace ops
if variant in inplace_ops and skip_inplace:
continue
# Compares variant's forward
# Note: copies the to-be-modified input when testing the inplace variant
tensor.grad = None
cloned = clone_input_helper(sample.input) if variant in inplace_ops else sample.input
if variant in inplace_ops and sample.broadcasts_input:
with self.assertRaises(RuntimeError,
msg=('inplace variant either incorrectly allowed '
'resizing or you have marked the sample {}'
' incorrectly with `broadcasts_self=True'.format(sample.summary()))):
variant_forward = variant(cloned,
*sample.args,
**sample.kwargs)
continue
variant_forward = variant(cloned,
*sample.args,
**sample.kwargs)
self.assertEqual(expected_forward, variant_forward)
# Compares variant's backward
if expected_grad is not None and \
(variant not in inplace_ops or op.supports_inplace_autograd):
output_process_fn_grad(variant_forward).sum().backward()
self.assertEqual(expected_grad, tensor.grad)
_test_consistency_helper(samples, variants)
def _test_inplace_preserve_storage(samples, variants):
for sample in samples:
# Skips inplace variants if the output dtype is not the same as
# the input dtype
expected_forward = op(sample.input, *sample.args, **sample.kwargs)
tensor = sample.input if isinstance(sample.input, torch.Tensor) else sample.input[0]
skip_inplace = False
if (isinstance(expected_forward, torch.Tensor) and
expected_forward.dtype is not tensor.dtype):
skip_inplace = True
if skip_inplace:
return
for variant in variants:
cloned = clone_input_helper(sample.input) if variant in inplace_ops else sample.input
inp_tensor = cloned if isinstance(cloned, torch.Tensor) else cloned[0]
data_ptr = inp_tensor.data_ptr()
variant_forward = variant(cloned,
*sample.args,
**sample.kwargs)
# TODO Support non-tensor outputs if they exist for inplace ops
if (isinstance(variant_forward, torch.Tensor)):
self.assertEqual(data_ptr, variant_forward.data_ptr(), atol=0, rtol=0)
else:
self.assertTrue(False, "Non-tensor outputs for inplace ops are not supported")
if len(inplace_ops) > 0:
inplace_samples = list(filter(lambda sample: not sample.broadcasts_input, samples))
_test_inplace_preserve_storage(inplace_samples, inplace_variants)
# gradcheck requires double precision
_gradcheck_ops = partial(ops, dtypes=OpDTypes.supported,
allowed_dtypes=[torch.double, torch.cdouble])
class TestGradients(TestCase):
exact_dtype = True
# Copies inputs to inplace operations to avoid inplace modifications
# to leaves requiring gradient
def _get_safe_inplace(self, inplace_variant):
@wraps(inplace_variant)
def _fn(t, *args, **kwargs):
return inplace_variant(t.clone(), *args, **kwargs)
return _fn
def _check_helper(self, device, dtype, op, variant, check, *, check_forward_ad=False):
if variant is None:
self.skipTest("Skipped! Variant not implemented.")
if not op.supports_dtype(dtype, torch.device(device).type):
self.skipTest(f"Skipped! {op.name} does not support dtype {str(dtype)}")
def is_inplace(variant):
if hasattr(variant, "__wrapped__"):
return variant.__wrapped__ is op.get_inplace()
return variant is op.get_inplace()
include_conjugated_inputs = op.test_conjugated_samples and dtype.is_complex
samples = op.sample_inputs(device, dtype, requires_grad=True, include_conjugated_inputs=include_conjugated_inputs)
for sample in samples:
if sample.broadcasts_input and is_inplace(variant):
continue
# Note on TensorList inputs
#
# gradcheck does not support TensorList inputs so here we pass TensorList
# inputs of size n as n single Tensor inputs to gradcheck and wrap the op
# in a function that puts the n Tensor inputs back into a TensorList
def fn(*inputs):
# Put tensors back into TensorList since we splat them when passing to gradcheck
if is_iterable_of_tensors(sample.input):
n = len(sample.input)
inputs = (inputs[:n], *inputs[n:])
output = op.gradcheck_wrapper(variant, *inputs, **sample.kwargs)
if sample.output_process_fn_grad is not None:
return sample.output_process_fn_grad(output)
return output
# Splat TensorList inputs into single Tensor inputs
gradcheck_args = (sample.input,) if isinstance(sample.input, torch.Tensor) else tuple(sample.input)
gradcheck_args += sample.args
if check == 'gradcheck':
self.assertTrue(gradcheck(fn, gradcheck_args,
check_batched_grad=op.check_batched_grad,
check_grad_dtypes=True,
nondet_tol=op.gradcheck_nondet_tol,
fast_mode=op.gradcheck_fast_mode,
check_forward_ad=check_forward_ad))
elif check == 'gradgradcheck':
self.assertFalse(check_forward_ad, msg="Cannot run forward AD check for gradgradcheck")
self.assertTrue(gradgradcheck(fn, gradcheck_args,
gen_non_contig_grad_outputs=False,
check_batched_grad=op.check_batched_gradgrad,
check_grad_dtypes=True,
nondet_tol=op.gradcheck_nondet_tol,
fast_mode=op.gradcheck_fast_mode))
self.assertTrue(gradgradcheck(fn, gradcheck_args,
gen_non_contig_grad_outputs=True,
check_batched_grad=op.check_batched_gradgrad,
check_grad_dtypes=True,
nondet_tol=op.gradcheck_nondet_tol,
fast_mode=op.gradcheck_fast_mode))
else:
self.assertTrue(False, msg="Unknown check requested!")
def _grad_test_helper(self, device, dtype, op, variant, *, check_forward_ad=False):
return self._check_helper(device, dtype, op, variant, 'gradcheck', check_forward_ad=check_forward_ad)
def _gradgrad_test_helper(self, device, dtype, op, variant):
return self._check_helper(device, dtype, op, variant, 'gradgradcheck')
def _skip_helper(self, op, device, dtype):
if not op.supports_autograd:
self.skipTest("Skipped! autograd not supported.")
if not op.supports_complex_autograd(torch.device(device).type) and dtype.is_complex:
self.skipTest("Skipped! Complex autograd not supported.")
# Tests that gradients are computed correctly
@_gradcheck_ops(op_db)
def test_fn_grad(self, device, dtype, op):
self._skip_helper(op, device, dtype)
self._grad_test_helper(device, dtype, op, op.get_op())
# Method grad (and gradgrad, see below) tests are disabled since they're
# costly and redundant with function grad (and gradgad) tests
# @_gradcheck_ops(op_db)
# def test_method_grad(self, device, dtype, op):
# self._skip_helper(op, device, dtype)
# self._grad_test_helper(device, dtype, op, op.get_method())
@_gradcheck_ops(op_db)
def test_inplace_grad(self, device, dtype, op):
self._skip_helper(op, device, dtype)
if not op.inplace_variant or not op.supports_inplace_autograd:
self.skipTest("Skipped! Operation does not support inplace autograd.")
self._grad_test_helper(device, dtype, op, self._get_safe_inplace(op.get_inplace()))
# Test that gradients of gradients are computed correctly
@_gradcheck_ops(op_db)
def test_fn_gradgrad(self, device, dtype, op):
self._skip_helper(op, device, dtype)
if not op.supports_gradgrad:
self.skipTest("Skipped! Operation does not support gradgrad")
self._gradgrad_test_helper(device, dtype, op, op.get_op())
# Test that gradients of gradients are properly raising
@_gradcheck_ops(op_db)
def test_fn_fail_gradgrad(self, device, dtype, op):
self._skip_helper(op, device, dtype)
if op.supports_gradgrad:
self.skipTest("Skipped! Operation does support gradgrad")
err_msg = r"derivative for .* is not implemented"
with self.assertRaisesRegex(RuntimeError, err_msg):
self._gradgrad_test_helper(device, dtype, op, op.get_op())
# Method gradgrad (and grad, see above) tests are disabled since they're
# costly and redundant with function gradgrad (and grad) tests
# @_gradcheck_ops(op_db)
# def test_method_gradgrad(self, device, dtype, op):
# self._skip_helper(op, device, dtype)
# self._gradgrad_test_helper(device, dtype, op, op.get_method())
@_gradcheck_ops(op_db)
def test_inplace_gradgrad(self, device, dtype, op):
self._skip_helper(op, device, dtype)
if not op.inplace_variant or not op.supports_inplace_autograd:
self.skipTest("Skipped! Operation does not support inplace autograd.")
self._gradgrad_test_helper(device, dtype, op, self._get_safe_inplace(op.get_inplace()))
@_gradcheck_ops(op_db)
def test_forward_mode_AD(self, device, dtype, op):
self._skip_helper(op, device, dtype)
if op.supports_forward_ad:
self._grad_test_helper(device, dtype, op, op.get_op(), check_forward_ad=True)
else:
err_msg = r"Trying to use forward AD with .* that does not support it\."
hint_msg = ("Running forward AD for an OP that has does not support it did not "
"raise any error. If your op supports forward AD, you should set supports_forward_ad=True")
with self.assertRaisesRegex(NotImplementedError, err_msg, msg=hint_msg):
self._grad_test_helper(device, dtype, op, op.get_op(), check_forward_ad=True)
# 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)
for sample in samples:
# Acquires variants to test
func = op.get_op()
method = op.get_method()
variants = {
# TODO: inplace tests currently fail, fix and add inplace variant
'function': func, 'method': method,
}
# Test traced and scripted consistency
for func_type, variant in variants.items():
if variant is None:
continue
# Create accessor for script function variant
name = op.name + '_' if func_type == 'inplace' else op.name
# run with disable_autodiff_subgraph_inlining(True) to test
# autodiff support. Context manager forces the graph to contain
# DifferentiableGraph nodes if they are present
with disable_autodiff_subgraph_inlining():
# Check scripted forward, grad, and grad grad
script_fn = create_script_fn(self, name, func_type)
def out_fn(output):
# Processes the output for autograd
if sample.output_process_fn_grad is not None:
return sample.output_process_fn_grad(output)
return output
check_against_reference(self,
script_fn,
func,
out_fn,
(sample.input,) + sample.args,
sample.kwargs,
no_grad=not _requires_grad, no_gradgrad=not op.supports_gradgrad)
# Check traced forward, grad, and grad grad
traced_fn = create_traced_fn(self, variant)
check_against_reference(self,
traced_fn,
func,
out_fn,
(sample.input,) + sample.args,
sample.kwargs,
no_grad=not _requires_grad, 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 and int64 because schema isn't affected by dtype,
# so running it on all dtypes is would be excessive
if dtype in [torch.float32, torch.int32]:
check_alias_annotation(name, (sample.input,) + sample.args, sample.kwargs,
func_type=func_type, aten_name=op.aten_name)
# Check autodifferentiation of nodes for traced and scripted graphs, only need to check once per sample
if dtype is torch.float32:
# Sandcastle doesn't fuse nodes
if IS_SANDCASTLE:
# fusible nodes are expected to be found in FusionGroups in the DifferentiableGraphs
nonfusible_nodes = op.autodiff_nonfusible_nodes + op.autodiff_fusible_nodes
fusible_nodes = []
else:
nonfusible_nodes = op.autodiff_nonfusible_nodes
fusible_nodes = op.autodiff_fusible_nodes
self.assertAutodiffNode(traced_fn.last_graph, op.assert_autodiffed, nonfusible_nodes, fusible_nodes)
self.assertAutodiffNode(script_fn.last_graph, op.assert_autodiffed, nonfusible_nodes, fusible_nodes)
# alias 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):
samples = op.sample_inputs(device, dtype, requires_grad=True)
if len(samples) == 0:
self.skipTest("Skipped! No sample inputs!")
# NOTE: only tests on first sample
sample = samples[0]
# [Scripting Data Preparation]
# Prepare data for test scripting
# Below we prepare strings of args/kwargs with and without type annotations.
# These strings are inserted into function template strings which is then torch scripted.
# - args string is ["t0"] corresponding to the "input" tensor required by the op
# - args_annot_kw is the string for the template function signature, for example,
# ["t0", "s0: float", "s1: bool", "max: float = 1.0", "min: float = 0.0"] ->
# def fn(t0, s0: float, s1: bool, max: float = 1.0, min: float = 0.0)
# - args_kw is the string of args/kwargs used to call the op, same as args_annot_kw but
# without type annotations
args = ["t0"]
def quote_strs(v):
if isinstance(v, str):
return f"'{v}'"
return str(v)
args_annot_kw = args + \
[f"s{i}: {type(v).__name__}" for i, v in enumerate(sample.args)] + \
[f"{k}: {type(v).__name__} = {quote_strs(v)}" for k, v in sample.kwargs.items()]
args_kw = args + \
[f"s{i}" for i in range(len(sample.args))] + \
[f"{k}={quote_strs(v)}" for k, v in sample.kwargs.items()]
# Prepare data for test tracing
sample_args_kwargs = ()
if len(sample.args) > 0:
sample_args_kwargs += (sample.args, )
if len(sample.kwargs) > 0:
sample_args_kwargs += (sample.kwargs, )
original_name = op.aten_name
original_name_inplace = original_name + "_"
expected_dtype = op(sample.input, *sample.args, **sample.kwargs).dtype
for a_op in op.aliases:
inplace = a_op.inplace_variant
method_or_inplace = [a_op.inplace_variant, a_op.method_variant]
variants = (v for v in (a_op.op, a_op.method_variant, a_op.inplace_variant) if v is not None)
# Test scripting:
for variant in variants:
variant_name = variant.__name__
op_name = original_name_inplace if variant is inplace else original_name
if variant in method_or_inplace:
fn_template = '''
def _fn(t0{c}{args_annot_kw}):
return t0.{alias_name}({args_kw})
'''
# remove the first input tensor
script = fn_template.format(
c=", " if len(args_kw[1:]) > 1 or len(args_annot_kw[1:]) >= 1 else "",
args_annot_kw=", ".join(args_annot_kw[1:]),
args_kw=", ".join(args_kw[1:]),
alias_name=variant_name,
)
else:
fn_template = '''
def _fn({args_annot_kw}):
return variant({args_kw})
'''
script = fn_template.format(
args_annot_kw=", ".join(args_annot_kw),
args_kw=", ".join(args_kw),
)
scripted = torch.jit.CompilationUnit(script)._fn
if (variant is inplace and not torch.can_cast(expected_dtype, dtype)):
try:
inp = clone_input_helper(sample.input)
scripted(inp, *sample.args, **sample.kwargs)
except Exception as e:
continue
self.fail("Inplace operation on integer tensor that should be promoted to float didn't fail!")
inp = clone_input_helper(sample.input)
scripted(inp, *sample.args, **sample.kwargs)
inp = clone_input_helper(sample.input)
graph = scripted.graph_for(inp, *sample.args, **sample.kwargs)
FileCheck().check(op.aten_name).check_not(variant_name).run(graph)
# Test tracing:
for variant in variants:
variant_name = variant.__name__
op_name = original_name_inplace if variant is inplace else original_name
def _fn(*sample_args, **sample_kwargs):
return variant(*sample_args, **sample_kwargs)
inp = (clone_input_helper(sample.input),) + sample_args_kwargs
traced = torch.jit.trace(_fn, *inp)
inp = (clone_input_helper(sample.input),) + sample_args_kwargs
traced(*inp)
inp = (clone_input_helper(sample.input),) + sample_args_kwargs
graph = traced.graph_for(*inp)
FileCheck().check(op_name).check_not(variant_name).run(graph)
instantiate_device_type_tests(TestCommon, globals())
instantiate_device_type_tests(TestGradients, globals())
instantiate_device_type_tests(TestJit, globals())
if __name__ == '__main__':
run_tests()