mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-20 21:14:14 +08:00
38350acf8f
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79322 Approved by: https://github.com/albanD
1633 lines
70 KiB
Python
1633 lines
70 KiB
Python
# Owner(s): ["module: unknown"]
|
|
|
|
from collections.abc import Sequence
|
|
from functools import partial
|
|
import warnings
|
|
import unittest
|
|
import itertools
|
|
import torch
|
|
import contextlib
|
|
from importlib import import_module
|
|
from torch.utils._pytree import tree_map
|
|
|
|
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,
|
|
TEST_WITH_UBSAN,
|
|
IS_WINDOWS,
|
|
IS_FBCODE,
|
|
first_sample,
|
|
parametrize,
|
|
)
|
|
from torch.testing._internal.common_methods_invocations import (
|
|
op_db,
|
|
_NOTHING,
|
|
UnaryUfuncInfo,
|
|
ReductionOpInfo,
|
|
SpectralFuncInfo,
|
|
ops_and_refs,
|
|
python_ref_db,
|
|
BinaryUfuncInfo,
|
|
)
|
|
from torch.testing._internal.common_device_type import (
|
|
deviceCountAtLeast,
|
|
instantiate_device_type_tests,
|
|
ops,
|
|
onlyCUDA,
|
|
onlyCPU,
|
|
onlyNativeDeviceTypes,
|
|
OpDTypes,
|
|
skipCUDAIfRocm,
|
|
skipMeta,
|
|
)
|
|
from torch._subclasses.fake_tensor import (
|
|
FakeTensor,
|
|
FakeTensorMode,
|
|
)
|
|
from torch.utils._python_dispatch import enable_torch_dispatch_mode
|
|
import torch._prims as prims
|
|
from torch._prims.context import TorchRefsMode
|
|
|
|
import torch.testing._internal.opinfo_helper as opinfo_helper
|
|
from torch.testing._internal import composite_compliance
|
|
|
|
from torch.utils._pytree import tree_flatten
|
|
from torch.utils._python_dispatch import push_torch_dispatch_mode, TorchDispatchMode
|
|
|
|
# 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 elementwise unary operators (separately implemented in test/test_unary_ufuncs.py),
|
|
# elementwise binary operators (separately implemented in test_binary_ufuncs.py),
|
|
# reduction operations (separately impelemented in test_reductions.py),
|
|
# and Spectral Functions (separately implemented for only 1D as of now, in test/test_spectral_ops.py)
|
|
_ref_test_ops = tuple(
|
|
filter(
|
|
lambda op: not isinstance(
|
|
op, (UnaryUfuncInfo, ReductionOpInfo, SpectralFuncInfo, BinaryUfuncInfo)
|
|
)
|
|
and op.ref is not None
|
|
and op.ref is not _NOTHING,
|
|
op_db,
|
|
)
|
|
)
|
|
_ops_and_refs = op_db + python_ref_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
|
|
@unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN")
|
|
@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, if the input has
|
|
# one or more tensors requiring grad
|
|
def _tensor_requires_grad(x):
|
|
if isinstance(x, dict):
|
|
for k, v in x.items():
|
|
if _tensor_requires_grad(v):
|
|
return True
|
|
if isinstance(x, (list, tuple)):
|
|
for a in x:
|
|
if _tensor_requires_grad(a):
|
|
return True
|
|
if isinstance(x, torch.Tensor) and x.requires_grad:
|
|
return True
|
|
|
|
return False
|
|
|
|
requires_grad = _tensor_requires_grad(sample.input) \
|
|
or _tensor_requires_grad(sample.args) or _tensor_requires_grad(sample.kwargs)
|
|
if not requires_grad:
|
|
continue
|
|
|
|
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
|
|
|
|
# Reference operators often support additional dtypes, and that's OK
|
|
if op in python_ref_db:
|
|
if (
|
|
len(claimed_but_unsupported_forward)
|
|
+ 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_numpy_ref(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.
|
|
# See https://github.com/pytorch/pytorch/issues/78050 for a discussion of stride propagation.
|
|
@unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN")
|
|
@onlyNativeDeviceTypes
|
|
@ops(python_ref_db)
|
|
def test_python_ref_meta(self, device, dtype, op):
|
|
if dtype is torch.chalf:
|
|
self.skipTest("Skipping chalf until it has more operator support")
|
|
|
|
mode = torch._prims.utils.get_prim_fake_mode()
|
|
|
|
def _to_tensormeta(x):
|
|
if isinstance(x, torch.Tensor):
|
|
out = FakeTensor.from_tensor(x, mode)
|
|
return out
|
|
return x
|
|
|
|
# TODO: iterate over requires_grad true/false
|
|
inps = tuple(op.reference_inputs(device, dtype, requires_grad=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)
|
|
try:
|
|
with enable_torch_dispatch_mode(mode):
|
|
meta_result = op(meta_sample.input, *meta_sample.args, **meta_sample.kwargs)
|
|
except torch._subclasses.fake_tensor.ComplexInputException:
|
|
continue
|
|
except torch._subclasses.fake_tensor.SparseInputException:
|
|
continue
|
|
|
|
if isinstance(result, torch.Tensor):
|
|
prims.utils.compare_tensor_meta(result, meta_result)
|
|
elif isinstance(result, Sequence):
|
|
for a, b in zip(result, meta_result):
|
|
if isinstance(a, torch.Tensor) or isinstance(b, torch.Tensor):
|
|
prims.utils.compare_tensor_meta(a, b)
|
|
|
|
def _ref_test_helper(self, ctx, device, dtype, op, skip_zero_numel=False):
|
|
if dtype is torch.chalf:
|
|
self.skipTest("Skipping chalf until it has more operator support")
|
|
|
|
# NOTE: this test works by comparing the reference
|
|
ex = None
|
|
for sample in op.reference_inputs(device, dtype, requires_grad=False):
|
|
if isinstance(sample.input, torch.Tensor) and sample.input.numel() == 0 and skip_zero_numel:
|
|
continue
|
|
with ctx():
|
|
ref_result = op(sample.input, *sample.args, **sample.kwargs)
|
|
torch_result = op.torch_opinfo(sample.input, *sample.args, **sample.kwargs)
|
|
|
|
for a, b in zip(tree_flatten(ref_result)[0], tree_flatten(torch_result)[0]):
|
|
if isinstance(a, torch.Tensor) or isinstance(b, torch.Tensor):
|
|
prims.utils.compare_tensor_meta(a, b)
|
|
if getattr(op, 'validate_view_consistency', True):
|
|
self.assertEqual(a._is_view(), b._is_view())
|
|
|
|
# Computes the dtype the more precise computatino would occur in
|
|
precise_dtype = torch.bool
|
|
if prims.utils.is_integer_dtype(dtype):
|
|
# Note: bool and integer dtypes do not have more
|
|
# precise dtypes -- they simply must be close
|
|
precise_dtype = dtype
|
|
if prims.utils.is_float_dtype(dtype):
|
|
precise_dtype = torch.double
|
|
if prims.utils.is_complex_dtype(dtype):
|
|
precise_dtype = torch.cdouble
|
|
|
|
# Checks if the results are close
|
|
try:
|
|
self.assertEqual(
|
|
ref_result,
|
|
torch_result,
|
|
exact_stride=False,
|
|
exact_device=True,
|
|
exact_layout=True,
|
|
exact_is_coalesced=True,
|
|
)
|
|
except AssertionError as e:
|
|
# Raises the error if the precise dtype comparison wouldn't be
|
|
# different
|
|
if dtype is precise_dtype:
|
|
raise e
|
|
|
|
ex = e
|
|
|
|
|
|
# Goes to next sample if these results are close
|
|
if not ex:
|
|
continue
|
|
|
|
# If the results are not close, checks that the
|
|
# reference is more accurate than the torch op
|
|
def _make_precise(x):
|
|
if isinstance(x, torch.Tensor) and x.dtype is dtype:
|
|
return x.to(precise_dtype)
|
|
return x
|
|
|
|
precise_sample = sample.transform(_make_precise)
|
|
precise_result = op.torch_opinfo(precise_sample.input, *precise_sample.args, **precise_sample.kwargs)
|
|
|
|
def _distance(a, b):
|
|
# Special-cases boolean comparisons
|
|
if prims.utils.is_boolean_dtype(a.dtype):
|
|
assert b.dtype is torch.bool
|
|
return (a ^ b).sum()
|
|
|
|
same = (a == b)
|
|
if prims.utils.is_float_dtype(a.dtype) or prims.utils.is_complex_dtype(a.dtype):
|
|
same = torch.logical_or(same, torch.logical_and(torch.isnan(a), torch.isnan(b)))
|
|
|
|
actual_error = torch.where(same, 0, torch.abs(a - b)).sum()
|
|
return actual_error
|
|
|
|
ref_distance = 0
|
|
for a, b in zip(tree_flatten(ref_result)[0], tree_flatten(precise_result)[0]):
|
|
ref_distance = ref_distance + _distance(a, b)
|
|
|
|
torch_distance = 0
|
|
for a, b in zip(tree_flatten(torch_result)[0], tree_flatten(precise_result)[0]):
|
|
torch_distance = torch_distance + _distance(a, b)
|
|
|
|
# TODO: consider adding some tolerance to this comparison
|
|
msg = f"Reference result was farther ({ref_distance}) from the precise " \
|
|
f"computation than the torch result was ({torch_distance})!"
|
|
self.assertTrue(ref_distance <= torch_distance, msg=msg)
|
|
|
|
# Reports numerical accuracy discrepancies
|
|
if ex is not None:
|
|
msg = "Test passed because the reference was more accurate than the torch operator."
|
|
warnings.warn(msg)
|
|
|
|
# Tests that experimental Python References perform the same computation
|
|
# as the operators they reference, when operator calls in the torch
|
|
# namesapce are remapped to the refs namespace (torch.foo becomes refs.foo).
|
|
@unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN")
|
|
@onlyNativeDeviceTypes
|
|
@ops(python_ref_db)
|
|
def test_python_ref(self, device, dtype, op):
|
|
# In this test, primTorch refs call into the refs namespace
|
|
# For example, a ref with torch.foo in it will calls refs.foo instead
|
|
# Direct calls to refs and prims are not affected
|
|
self._ref_test_helper(lambda: TorchRefsMode.push(strict=True), device, dtype, op)
|
|
|
|
# Tests that experimental Python References perform the same computation
|
|
# as the operators they reference, when operator calls in the torch
|
|
# namespace are preserved (torch.foo remains torch.foo).
|
|
@unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN")
|
|
@onlyNativeDeviceTypes
|
|
@ops(python_ref_db)
|
|
def test_python_ref_torch_fallback(self, device, dtype, op):
|
|
# In this test, refs call into the torch namespace (after the initial invocation)
|
|
# For example, a ref with torch.foo in it will call torch.foo instead of refs.foo
|
|
# Direct calls to refs and prims are not translated
|
|
self._ref_test_helper(contextlib.nullcontext, device, dtype, op)
|
|
|
|
@unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN")
|
|
@onlyCUDA
|
|
@skipCUDAIfRocm
|
|
@ops(python_ref_db)
|
|
@parametrize('executor', ['aten', 'nvfuser'])
|
|
def test_python_ref_executor(self, device, dtype, op, executor):
|
|
# TODO: Not all dtypes are supported with nvfuser
|
|
from torch._prims.utils import _torch_dtype_to_nvfuser_dtype_map
|
|
if executor == "nvfuser" and dtype not in _torch_dtype_to_nvfuser_dtype_map:
|
|
raise unittest.SkipTest(f"nvfuser doesn't support dtype {dtype}")
|
|
|
|
# nvFuser tests are rather slow so we only run int32 and float32 types
|
|
if executor == "nvfuser" and dtype not in [torch.int32, torch.float32]:
|
|
raise unittest.SkipTest("skipped for speed")
|
|
|
|
if executor == "nvfuser" and not op.supports_nvfuser:
|
|
raise unittest.SkipTest(f"{op.name} doesn't support nvfuser")
|
|
|
|
from torch._prims.executor import make_traced
|
|
from copy import copy
|
|
op = copy(op)
|
|
op.op = partial(make_traced(op.op), executor=executor)
|
|
self._ref_test_helper(
|
|
contextlib.nullcontext,
|
|
device,
|
|
dtype,
|
|
op,
|
|
skip_zero_numel=(executor == "nvfuser"), # nvfuser doesn't support zero-sized tensors
|
|
)
|
|
|
|
@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)
|
|
|
|
@skipMeta
|
|
@onlyNativeDeviceTypes
|
|
@ops([op for op in python_ref_db if op.error_inputs_func is not None], dtypes=OpDTypes.none)
|
|
def test_python_ref_errors(self, device, op):
|
|
mode = torch._prims.utils.get_prim_fake_mode()
|
|
|
|
def _to_tensormeta(x):
|
|
if isinstance(x, torch.Tensor):
|
|
return FakeTensor.from_tensor(x, mode)
|
|
return x
|
|
|
|
error_inputs = op.error_inputs(device)
|
|
for ei in error_inputs:
|
|
si = ei.sample_input
|
|
meta_sample = si.transform(_to_tensormeta)
|
|
# TODO: match strings
|
|
with self.assertRaisesRegex(ei.error_type, ""):
|
|
op(meta_sample.input, *meta_sample.args, **meta_sample.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(_ops_and_refs, 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(_ops_and_refs, dtypes=OpDTypes.any_one)
|
|
def test_out(self, device, dtype, op):
|
|
# Prefers running in float32 but has a fallback for the first listed supported dtype
|
|
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, operator variant, inplace_operator variant, aliases)
|
|
|
|
method = op.method_variant
|
|
inplace = op.inplace_variant
|
|
operator = op.operator_variant
|
|
inplace_operator = op.inplace_operator_variant
|
|
|
|
|
|
# list of all inplace ops: inplace variant + alias inplace variants if exist
|
|
inplace_ops = [inplace, inplace_operator]
|
|
variants = [method, inplace, operator, inplace_operator]
|
|
operators = [operator, inplace_operator]
|
|
|
|
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))
|
|
operators = tuple(filter(None, operators))
|
|
|
|
_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
|
|
|
|
if variant in operators and sample.kwargs:
|
|
# skip samples with kwargs for operator variants
|
|
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()
|
|
if variant in operators and sample.kwargs:
|
|
# skip samples with kwargs for operator variants
|
|
continue
|
|
|
|
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)
|
|
# sample.transform applies the lambda to torch.Tensor and torch.dtype.
|
|
# However, we only want to apply it to Tensors with dtype `torch.complex32`..
|
|
transformed_sample = sample.transform(lambda x: x.to(torch.complex64) if isinstance(
|
|
x, torch.Tensor) and x.dtype is torch.complex32 else x)
|
|
expected = op(
|
|
transformed_sample.input,
|
|
*transformed_sample.args,
|
|
**transformed_sample.kwargs,
|
|
)
|
|
# Since range of chalf is much less compared to cfloat,
|
|
# we get `inf`s easily (eg. with `pow`, `exp`),
|
|
# so we cast `cfloat` back to `chalf`.
|
|
expected = tree_map(lambda x: x.to(torch.complex32) if isinstance(
|
|
x, torch.Tensor) and x.dtype is torch.complex64 else x, expected)
|
|
|
|
# `exact_dtype` is False because for ops like real, imag
|
|
# we get different dtypes for `actual` and `expected`
|
|
# `chalf` input -> `half` output
|
|
# `cfloat` input -> `float` output
|
|
self.assertEqual(actual, expected, exact_dtype=False)
|
|
|
|
@ops(op_db, allowed_dtypes=(torch.bool,))
|
|
@unittest.skipIf(TEST_WITH_UBSAN, "Test uses undefined behavior")
|
|
def test_non_standard_bool_values(self, device, dtype, op):
|
|
# Test boolean values other than 0x00 and 0x01 (gh-54789)
|
|
def convert_boolean_tensors(x):
|
|
if not isinstance(x, torch.Tensor) or x.dtype != torch.bool:
|
|
return x
|
|
|
|
# Map False -> 0 and True -> Random value in [2, 255]
|
|
true_vals = torch.randint(2, 255, x.shape, dtype=torch.uint8, device=x.device)
|
|
false_vals = torch.zeros((), dtype=torch.uint8, device=x.device)
|
|
x_int = torch.where(x, true_vals, false_vals)
|
|
|
|
ret = x_int.view(torch.bool)
|
|
self.assertEqual(ret, x)
|
|
return ret
|
|
|
|
for sample in op.sample_inputs(device, dtype):
|
|
expect = op(sample.input, *sample.args, **sample.kwargs)
|
|
|
|
transformed = sample.transform(convert_boolean_tensors)
|
|
actual = op(transformed.input, *transformed.args, **transformed.kwargs)
|
|
|
|
self.assertEqual(expect, actual)
|
|
|
|
|
|
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(ops_and_refs, 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(ops_and_refs, 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(ops_and_refs, 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,
|
|
)
|
|
|
|
# input strides and size may have been altered due to the result of an inplace op
|
|
def test_inplace_view(func, input, rs, input_size, input_strides):
|
|
if func is None:
|
|
return
|
|
# TODO: extend this test to test ops with multiple outputs and ops like native_batch_norm.out
|
|
# which mutate not necessarily the first input.
|
|
if isinstance(rs, torch.Tensor) and rs is input:
|
|
unequal_size = rs.size() != input_size
|
|
unequal_strides = rs.stride() != input_strides
|
|
# resize_ should probably have inplace_view tag. Not adding the tag since it
|
|
# breaks some codegen logic
|
|
if (unequal_size or unequal_strides):
|
|
if isinstance(func, torch._ops.OpOverloadPacket):
|
|
func = func.default
|
|
# Reference: https://github.com/pytorch/pytorch/issues/78759
|
|
if func is not torch.ops.aten.resize_.default:
|
|
# TODO: use self.assertIn when we have separate tests for each tag
|
|
assert torch.Tag.inplace_view in func.tags
|
|
|
|
# A mode that when enabled runs correctness checks to ensure
|
|
# that operators have expected tags based on their input and
|
|
# ouput tensor properties
|
|
class TestTagsMode(TorchDispatchMode):
|
|
def __torch_dispatch__(self, func, types, args=(), kwargs=None):
|
|
if isinstance(args[0], torch.Tensor):
|
|
old_size = args[0].size()
|
|
old_stride = args[0].stride()
|
|
rs = func(*args, **kwargs)
|
|
test_inplace_view(func, args[0], rs, old_size, old_stride)
|
|
else:
|
|
rs = func(*args, **kwargs)
|
|
return rs
|
|
|
|
# Test to verify the correctness for tags in `tags.yaml`, also available for access through `torch.Tags`
|
|
class TestTags(TestCase):
|
|
@onlyCPU
|
|
@ops(ops_and_refs, dtypes=OpDTypes.any_one)
|
|
def test_tags(self, device, dtype, op):
|
|
samples = op.sample_inputs(device, dtype, requires_grad=False)
|
|
for sample in samples:
|
|
# TODO: Test tags for ops that return a list of tensors
|
|
input = sample.input
|
|
if isinstance(input, torch.Tensor):
|
|
old_size = input.size()
|
|
old_stride = input.stride()
|
|
with push_torch_dispatch_mode(TestTagsMode):
|
|
rs = op(input, *sample.args, **sample.kwargs)
|
|
# TODO: add test for aliases: https://github.com/pytorch/pytorch/issues/78761
|
|
aten_name = op.aten_name if op.aten_name is not None else op.name
|
|
opoverloadpacket = getattr(torch.ops.aten, aten_name, None)
|
|
test_inplace_view(opoverloadpacket, input, rs, old_size, old_stride)
|
|
|
|
|
|
class TestRefsOpsInfo(TestCase):
|
|
|
|
import_paths = ["_refs", "_refs.special", "_refs.nn.functional"]
|
|
module_alls = [(path, import_module(f"torch.{path}").__all__) for path in import_paths]
|
|
ref_ops_names = itertools.chain.from_iterable(
|
|
[f"{path}.{op}" for op in module_all] for path, module_all in module_alls)
|
|
ref_db_names = set(ref_op.name for ref_op in python_ref_db)
|
|
|
|
# TODO: References that do not have an entry in python_ref_db
|
|
skip_ref_ops = {
|
|
'_refs.bitwise_right_shift',
|
|
'_refs.copy_to',
|
|
'_refs.empty_strided',
|
|
'_refs.equal',
|
|
'_refs.full',
|
|
'_refs.full_like',
|
|
'_refs.item',
|
|
'_refs.ones',
|
|
'_refs.ones_like',
|
|
'_refs.std_var',
|
|
'_refs.swap_axes',
|
|
'_refs.uniform',
|
|
'_refs.zeros',
|
|
'_refs.zeros_like'
|
|
}
|
|
|
|
@parametrize("op", ref_ops_names)
|
|
def test_refs_are_in_python_ref_db(self, op):
|
|
if op in self.skip_ref_ops:
|
|
raise unittest.SkipTest(f"{op} does not have an entry in python_ref_db")
|
|
self.assertIn(op, self.ref_db_names)
|
|
|
|
|
|
fake_skips = (
|
|
"cholesky", # Could not run 'aten::cholesky' with arguments from the 'Meta' backend
|
|
"cholesky_inverse", # Could not run 'aten::cholesky' with arguments from the 'Meta' backend
|
|
"cov", # aweights cannot be negtaive
|
|
"istft", # window overlap add min: 0
|
|
"linalg.eigvals", # The tensor has a non-zero number of elements, but its data is not allocated yet
|
|
"linalg.eigvalsh", # aten::linalg_eigvalsh.out' with arguments from the 'Meta' backend
|
|
"linalg.matrix_power", # Could not run 'aten::eye.m_out' with arguments from the 'Meta' backend
|
|
# "linalg.pinv", # Could not run 'aten::pinv.out' with arguments from the 'Meta' backen
|
|
"linalg.matrix_rank.hermitian", # Could not run 'aten::linalg_eigvalsh.out' with arguments from the 'Meta' backend
|
|
"linalg.pinv.hermitian", # tensor.mH is only supported on matrices or batches of matrices. Got 1-D tensor
|
|
"linalg.solve", # Could not run 'aten::linalg_solve' with arguments from the 'Meta' backend
|
|
"linalg.tensorsolve", # Could not run 'aten::linalg_solve' with arguments from the 'Meta'
|
|
"lu_solve", # MALLOC ERROR: debug
|
|
"multinomial", # Could not run 'aten::multinomial' with arguments from the 'Meta' backend
|
|
"mvlgamma.mvlgamma_p_1", # Could not run 'aten::_local_scalar_dense' with arguments from the 'Meta' backend
|
|
"mvlgamma.mvlgamma_p_3", # Could not run 'aten::_local_scalar_dense' with arguments from the 'Meta' backend
|
|
"mvlgamma.mvlgamma_p_5", # Could not run 'aten::_local_scalar_dense' with arguments from the 'Meta' backend
|
|
"nanmean", # logical_not() got an unexpected keyword argument 'out'
|
|
"quantile", # quantile() q values must be in the range [0, 1]
|
|
"nanquantile", # quantile() q values must be in the range [0, 1]
|
|
"nn.functional.ctc_loss", # The tensor has a non-zero number of elements, but its data is not allocated yet
|
|
"nn.functional.embedding_bag", # sometimes errors
|
|
"nn.functional.nll_loss", # sometimes errors
|
|
"nn.functional.max_pool1d", # The tensor has a non-zero number of elements
|
|
"to_sparse", # Could not run 'aten::to_sparse' with arguments from the 'Meta' backend
|
|
"tensor_split", # The tensor has a non-zero number of elements, but its data is not allocated yet
|
|
"repeat_interleave", # cannot repeat_interleave a meta tensor without output_size
|
|
"segment_reduce", # Could not run 'aten::segment_reduce' with arguments from the 'Meta' backend.
|
|
"sparse.sampled.addmm", # sparsity not supported
|
|
# Can not infer total number of classes from meta. no way at present to throw DynamicOutputShapeException
|
|
"nn.functional.one_hot",
|
|
)
|
|
|
|
dynamic_output_op_tests = (
|
|
"argwhere",
|
|
"bincount",
|
|
"index_select",
|
|
"combinations",
|
|
"linalg.lstsq",
|
|
"masked_select",
|
|
"nonzero",
|
|
"unique_consecutive",
|
|
"unique",
|
|
"linalg.lstsq.grad_oriented",
|
|
)
|
|
|
|
# some inputs invoke dynamic output shape operators, some do not
|
|
sometimes_dynamic_output_op_test = (
|
|
"__getitem__",
|
|
)
|
|
|
|
class TestFakeTensorNonErroring(TestCase):
|
|
@onlyCPU
|
|
@ops(op_db, dtypes=OpDTypes.any_one)
|
|
def test_fake(self, device, dtype, op):
|
|
name = op.name
|
|
if op.variant_test_name:
|
|
name += "." + op.variant_test_name
|
|
if name in fake_skips or "sparse" in name:
|
|
self.skipTest("Skip failing test")
|
|
samples = op.sample_inputs(device, dtype, requires_grad=False)
|
|
for sample in samples:
|
|
try:
|
|
mode = FakeTensorMode(inner=None)
|
|
|
|
def map_to_fake(e):
|
|
if isinstance(e, torch.Tensor):
|
|
return mode.from_tensor(e)
|
|
else:
|
|
return e
|
|
|
|
input = tree_map(map_to_fake, sample.input)
|
|
args = tree_map(map_to_fake, sample.args)
|
|
kwargs = tree_map(map_to_fake, sample.kwargs)
|
|
|
|
with enable_torch_dispatch_mode(mode):
|
|
res_fake = op(input, *args, **kwargs)
|
|
|
|
res = op(sample.input, *sample.args, **sample.kwargs)
|
|
|
|
for fake_out, real_out in zip(
|
|
tree_flatten(res_fake)[0], tree_flatten(res)[0]
|
|
):
|
|
if not isinstance(fake_out, torch.Tensor):
|
|
self.assertTrue(not isinstance(real_out, torch.Tensor))
|
|
continue
|
|
|
|
self.assertTrue(isinstance(fake_out, FakeTensor))
|
|
# if you see a shape exception here, you may need to add
|
|
# a `dynamic_output_shape` tag to an operator
|
|
prims.utils.compare_tensor_meta(fake_out, real_out)
|
|
self.assertTrue(name not in dynamic_output_op_tests)
|
|
|
|
except torch._subclasses.fake_tensor.ComplexInputException:
|
|
pass
|
|
except torch._subclasses.fake_tensor.SparseInputException:
|
|
pass
|
|
except torch._subclasses.fake_tensor.DynamicOutputShapeException:
|
|
self.assertTrue(name in dynamic_output_op_tests or name in sometimes_dynamic_output_op_test)
|
|
|
|
|
|
instantiate_device_type_tests(TestCommon, globals())
|
|
instantiate_device_type_tests(TestCompositeCompliance, globals())
|
|
instantiate_device_type_tests(TestMathBits, globals())
|
|
instantiate_device_type_tests(TestRefsOpsInfo, globals(), only_for="cpu")
|
|
instantiate_device_type_tests(TestFakeTensorNonErroring, globals())
|
|
instantiate_device_type_tests(TestTags, globals())
|
|
|
|
if __name__ == "__main__":
|
|
run_tests()
|