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android / platform / external / pytorch / 55844dfdbc26383cc03ac212a515257c6a99594c / . / test / test_ops.py
blob: ecf638c9587d208e253e26f814c0d7e27c074978 [file] [log] [blame]
# Owner(s): ["module: unknown"]
from collections.abc import Sequence
from functools import partial
import warnings
import unittest
import itertools
import torch
import contextlib
import re
import os
from collections import defaultdict
from importlib import import_module
from torch.utils._pytree import tree_map
from typing import Dict
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_CI,
set_default_dtype,
suppress_warnings,
noncontiguous_like,
TEST_WITH_ASAN,
TEST_WITH_UBSAN,
IS_WINDOWS,
IS_FBCODE,
first_sample,
parametrize,
skipIfTorchInductor,
slowTest,
)
from torch.testing._internal.common_methods_invocations import (
op_db,
UnaryUfuncInfo,
ReductionOpInfo,
ReductionPythonRefInfo,
SpectralFuncInfo,
ops_and_refs,
python_ref_db,
BinaryUfuncInfo,
xfail,
skip,
skipOps
)
from torch.testing._internal.common_device_type import (
deviceCountAtLeast,
instantiate_device_type_tests,
ops,
onlyCUDA,
onlyCPU,
onlyNativeDeviceTypes,
OpDTypes,
skipMeta,
)
from torch._subclasses.fake_tensor import (
FakeTensor,
FakeTensorMode,
)
from torch._subclasses.fake_utils import outputs_alias_inputs
import torch._prims as prims
from torch._prims.context import TorchRefsMode
from torch.testing._internal import opinfo
from torch.testing._internal import composite_compliance
from torch.utils._pytree import tree_flatten
from torch.utils._python_dispatch import 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,
op_db,
)
)
_ops_and_refs = op_db + python_ref_db
# Create a list of operators that are a subset of _ref_test_ops but don't have a
# numpy ref to compare them too, If both CPU and CUDA are compared to numpy
# then they do not need to be compared to each other
_ops_and_refs_with_no_numpy_ref = [op for op in _ops_and_refs if op.ref is None]
aten = torch.ops.aten
# 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_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.utils.is_dynamic_dtype_set, op_db))
for op in filtered_ops:
fmt_str = opinfo.utils.str_format_dynamic_dtype(op)
err_msg += "\n" + fmt_str
assert len(filtered_ops) == 0, err_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((t.device == cuda_device for t in result)))
else:
self.skipTest(
"Skipped! Only supports single tensor or iterable of tensor outputs."
)
def test_pointwise_tag_coverage(self):
pytorch_dir = os.path.abspath(__file__ + "/../../")
files = [
"aten/src/ATen/native/UnaryOps.cpp",
"aten/src/ATen/native/BinaryOps.cpp",
"aten/src/ATen/native/PointwiseOps.cpp",
"aten/src/ATen/native/TensorCompare.cpp",
]
allowed_functions = (
# reduction version of these operators
"aten.max.default",
"aten.max.dim",
"aten.max.dim_max",
"aten.max.names_dim",
"aten.max.names_dim_max",
"aten.max.unary_out",
"aten.min.default",
"aten.min.dim",
"aten.min.dim_min",
"aten.min.names_dim",
"aten.min.names_dim_min",
"aten.min.unary_out",
# not pointwise
"aten.isin.Tensor_Tensor",
"aten.isin.Tensor_Tensor_out",
"aten.isin.Tensor_Scalar",
"aten.isin.Tensor_Scalar_out",
"aten.isin.Scalar_Tensor",
"aten.isin.Scalar_Tensor_out",
"aten.mode.default",
"aten.mode.dimname",
"aten.mode.dimname_out",
"aten.mode.values",
)
regex = re.compile(r"DEFINE_DISPATCH\(.*_stub")
def get_opoverloadpacket_from_dispatch(kernel):
if hasattr(torch.ops.aten, kernel):
return kernel
if hasattr(torch.ops.aten, f"__{kernel}__"):
return f"__{kernel}__"
if hasattr(torch.ops.aten, f"special_{kernel}"):
return f"special_{kernel}"
if "_" in kernel:
kernel_split = kernel.split("_")
new_kernel = "_".join(kernel_split[:-1])
if hasattr(torch.ops.aten, new_kernel):
return new_kernel
# could not find op from kernel dispatch string
self.assertTrue(False)
for file_name in files:
with open(os.path.join(pytorch_dir, file_name), "r") as f:
lines = f.read()
matches = regex.findall(lines)
for match in matches:
kernel = match[len("DEFINE_DISPATCH("):-len("_stub")]
# no op definition for it, but defined with DEFINE_DISPATCH ?
if kernel == "trigamma":
continue
kernel = get_opoverloadpacket_from_dispatch(kernel)
overloadpacket = getattr(torch.ops.aten, kernel)
for overload_name in overloadpacket.overloads():
overload = getattr(overloadpacket, overload_name)
if not torch._C._dispatch_has_kernel(overload.name()):
continue
# TODO: tags are not propagated to generated overload,
# and there's no way of specifying them
if torch.Tag.generated in overload.tags:
continue
if str(overload) in allowed_functions:
continue
self.assertTrue(torch.Tag.pointwise in overload.tags)
# 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):
# Sets the default dtype to NumPy's default dtype of double
with 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)
)
# Tests that the cpu and gpu results are consistent
@onlyCUDA
@suppress_warnings
@slowTest
@ops(_ops_and_refs_with_no_numpy_ref, dtypes=OpDTypes.any_common_cpu_cuda_one)
def test_compare_cpu(self, device, dtype, op):
def to_cpu(arg):
if isinstance(arg, torch.Tensor):
return arg.to(device='cpu')
return arg
samples = op.reference_inputs(device, dtype)
for sample in samples:
cpu_sample = sample.transform(to_cpu)
cuda_results = op(sample.input, *sample.args, **sample.kwargs)
cpu_results = op(cpu_sample.input, *cpu_sample.args, **cpu_sample.kwargs)
# output_process_fn_grad has a very unfortunate name
# We use this function in linalg extensively to postprocess the inputs of functions
# that are not completely well-defined. Think svd and muliplying the singular vectors by -1.
# CPU and CUDA implementations of the SVD can return valid SVDs that are different.
# We use this function to compare them.
cuda_results = sample.output_process_fn_grad(cuda_results)
cpu_results = cpu_sample.output_process_fn_grad(cpu_results)
# Lower tolerance because we are running this as a `@slowTest`
# Don't want the periodic tests to fail frequently
self.assertEqual(cuda_results, cpu_results, atol=1e-3, rtol=1e-3)
# 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)
@skipIfTorchInductor("Takes too long for inductor")
def test_python_ref_meta(self, device, dtype, op):
with FakeTensorMode() as mode:
pass
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
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 mode:
meta_result = op(meta_sample.input, *meta_sample.args, **meta_sample.kwargs)
except torch._subclasses.fake_tensor.UnsupportedFakeTensorException:
continue
except torch._subclasses.fake_tensor.DataDependentOutputException:
continue
except torch._subclasses.fake_tensor.UnsupportedOperatorException:
continue
if isinstance(result, torch.Tensor):
self.assertTrue(isinstance(meta_result, FakeTensor))
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):
self.assertTrue(isinstance(b, FakeTensor))
prims.utils.compare_tensor_meta(a, b)
def _ref_test_helper(
self,
ctx,
device,
dtype,
op,
skip_zero_numel=False,
skip_zero_dim=False,
skip_bfloat=False,
skip_view_consistency=False,
):
# 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
if isinstance(sample.input, torch.Tensor) and sample.input.ndim == 0 and skip_zero_dim:
continue
if (
skip_bfloat
and (
(
isinstance(sample.input, torch.Tensor)
and sample.input.dtype == torch.bfloat16
)
or any(
isinstance(arg, torch.Tensor) and arg.dtype == torch.bfloat16
for arg in sample.args
)
)
):
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) and not skip_view_consistency:
msg = (f"The torch implementation {'returns' if b._is_view() else 'does not return'} "
f"a view, while the reference {'does' if a._is_view() else 'does not'}")
self.assertEqual(a._is_view(), b._is_view(), msg)
# 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.dtype):
return precise_dtype
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)
@skipIfTorchInductor("Takes too long for inductor")
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(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)
@skipIfTorchInductor("Takes too long for inductor")
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
@ops(python_ref_db)
@parametrize('executor', ['aten', 'nvfuser'])
@skipIfTorchInductor("Takes too long for inductor")
def test_python_ref_executor(self, device, dtype, op, executor):
# TODO: Not all dtypes are supported with nvfuser
from torch._prims_common 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")
# nvFuser doesn't support reduction operations on 0-dim tensors yet
skip_zero_dim = False
if executor == "nvfuser" and isinstance(op, ReductionPythonRefInfo):
skip_zero_dim = True
# skip zero-dim tensors for some composites of reduction operations and view
skip_zero_dim_ops = [
"_refs.logsumexp",
"_refs.log_softmax",
"_refs.native_group_norm",
"_refs.softmax",
"_refs.sum_to_size",
"ops.nvprims.view",
]
if executor == "nvfuser" and op.name in skip_zero_dim_ops:
skip_zero_dim = True
from torch._prims.executor import make_traced
from copy import copy
op = copy(op)
executor = "strictly_nvfuser" if executor == "nvfuser" else executor
op.op = partial(make_traced(op.op), executor=executor)
self._ref_test_helper(
contextlib.nullcontext,
device,
dtype,
op,
skip_zero_numel=("nvfuser" in executor), # nvfuser doesn't support zero-sized tensors
skip_zero_dim=skip_zero_dim,
skip_bfloat=("nvfuser" in executor), # nvfuser doesn't support bfloat tensors for pre-11 cuda TK
# # nvfuser doesn't support view consistency
# https://github.com/pytorch/pytorch/issues/84863
skip_view_consistency=("nvfuser" in executor),
)
@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):
out = op(si.input, *si.args, **si.kwargs)
self.assertFalse(isinstance(out, type(NotImplemented)))
@skipMeta
@onlyNativeDeviceTypes
@ops([op for op in op_db if op.error_inputs_sparse_func is not None], dtypes=OpDTypes.none)
@parametrize("layout", (torch.sparse_csr, torch.sparse_csc, torch.sparse_bsr, torch.sparse_bsc, torch.sparse_coo))
def test_errors_sparse(self, device, op, layout):
for ei in op.error_inputs_sparse(device, layout):
si = ei.sample_input
with self.assertRaisesRegex(ei.error_type, ei.error_regex):
out = op(si.input, *si.args, **si.kwargs)
self.assertFalse(isinstance(out, type(NotImplemented)))
@skipMeta
@onlyNativeDeviceTypes
@ops([op for op in python_ref_db if op.error_inputs_func is not None], dtypes=OpDTypes.none)
@skipIfTorchInductor("Takes too long for inductor")
def test_python_ref_errors(self, device, op):
mode = FakeTensorMode()
with mode:
pass
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)
with self.assertRaisesRegex(ei.error_type, ei.error_regex):
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,
)
# 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)
@skipIfTorchInductor("Inductor does not support complex dtype yet")
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((t.stride() for t in 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((t.data_ptr() for t in 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 correct shape and device, but a dtype that cannot
# "safely" cast to
#
# Case 3 and 4 are slightly different when the op is a factory function:
# - if device, dtype are NOT passed, any combination of dtype/device should be OK for out
# - if device, dtype are passed, device and dtype should match
@ops(_ops_and_refs, dtypes=OpDTypes.any_one)
@skipIfTorchInductor("Inductor does not support complex dtype yet")
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((t.stride() for t in 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((t.data_ptr() for t in 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"
factory_fn_msg = (
"\n\nNOTE: If your op is a factory function (i.e., it accepts TensorOptions) you should mark its "
"OpInfo with `is_factory_function=True`."
)
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)
if op.is_factory_function and sample.kwargs.get("device", None) is None:
op_out(out=out)
else:
msg_fail = (
f"Expected RuntimeError when calling with input.device={device} and out.device={wrong_device}."
) + factory_fn_msg
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"
)
) + factory_fn_msg
if op.is_factory_function and sample.kwargs.get("dtype", None) is None:
op_out(out=out)
else:
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)
@skipIfTorchInductor("Inductor does not support complex dtype yet")
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):
out = output_process_fn_grad(expected_forward).sum()
if out.dtype.is_complex:
out = out.abs()
out.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
):
out = output_process_fn_grad(variant_forward).sum()
if out.dtype.is_complex:
out = out.abs()
out.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,))
@skipIfTorchInductor("Inductor does not support complex dtype yet")
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")
@skipIfTorchInductor("Inductor does not support view with dtype yet")
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)
# 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()
dtype_error: Dict[torch.dtype, Exception] = dict()
def unsupported(dtype, e):
dtype_error[dtype] = e
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, e)
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, e)
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:
dtype_error[dtype] = 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
)
)
all_claimed_but_unsupported = set.union(claimed_but_unsupported_backward, claimed_but_unsupported_forward)
if all_claimed_but_unsupported:
msg += "Unexpected failures raised the following errors:\n"
for dtype in all_claimed_but_unsupported:
msg += f"{dtype} - {dtype_error[dtype]}\n"
self.fail(msg)
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, self.assertEqual)
composite_compliance.check_all_permutations(op, args, kwargs, self.assertEqual)
@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
# We pass assertEqual so that decorators like `toleranceOverride`
# actually work (otherwise they silently do nothing!)
composite_compliance.check_backward_formula(
op.get_op(), args, kwargs,
sample.output_process_fn_grad,
op.gradcheck_wrapper, self.assertEqual)
@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
# We pass assertEqual so that decorators like `toleranceOverride`
# actually work (otherwise they silently do nothing!)
composite_compliance.check_forward_ad_formula(
op.get_op(), args, kwargs, op.gradcheck_wrapper, self.assertEqual)
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().abs().backward(retain_graph=True)
forward_with_mathview.sum().abs().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,))
@skipIfTorchInductor("Inductor does not support complex dtype yet")
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,))
@skipIfTorchInductor("Inductor does not support complex dtype yet")
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,))
@skipIfTorchInductor("Inductor does not support complex dtype yet")
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 check_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(_legit).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)
check_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 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)
check_inplace_view(opoverloadpacket, input, rs, old_size, old_stride)
class TestRefsOpsInfo(TestCase):
import_paths = ["_refs", "_refs.special", "_refs.nn.functional", "_refs.fft", "_refs._conversions"]
module_alls = [(path, import_module(f"torch.{path}").__all__) for path in import_paths]
ref_ops_names = tuple(itertools.chain.from_iterable(
[f"{path}.{op}" for op in module_all] for path, module_all in module_alls))
ref_db_names = {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_permuted',
'_refs.empty_strided',
'_refs.equal',
'_refs.full',
'_refs.full_like',
'_refs.item',
'_refs.to',
'_refs.ones',
'_refs.ones_like',
'_refs.special.expit',
'_refs.std_var',
'_refs.swap_axes',
'_refs.uniform',
'_refs.scalar_tensor',
'_refs.trunc_divide',
'_refs.zeros',
'_refs.zeros_like',
'_refs.rfloordiv',
'_refs.rtruediv',
'_refs.rpow',
# These should be tested with their out-of-place counterparts
'_refs.index_add_',
'_refs.index_copy_',
'_refs.index_fill_',
'_refs.native_group_norm',
}
not_in_decomp_table = {
# duplicated in _decomp and _refs
'_refs.nn.functional.group_norm',
'_refs.nn.functional.mse_loss',
'_refs.rsub',
# duplicated as refs do not have decent support for advanced indexing
'_refs.index_copy',
'_refs.index_copy_',
'_refs.index_add',
'_refs.index_add_',
# these are not aten ops?
'_refs._conversions.bfloat16',
'_refs._conversions.bool',
'_refs._conversions.byte',
'_refs._conversions.char',
'_refs._conversions.double',
'_refs._conversions.float',
'_refs._conversions.half',
'_refs._conversions.int',
'_refs._conversions.long',
'_refs._conversions.short',
'_refs._conversions.chalf',
'_refs._conversions.cfloat',
'_refs._conversions.cdouble',
'_refs.broadcast_shapes',
'_refs.broadcast_tensors',
'_refs.nn.functional.tanhshrink',
'_refs.nn.functional.triplet_margin_loss',
'_refs.rfloordiv',
'_refs.rtruediv',
'_refs.rpow',
# CompositeImplicitAutograd
'_refs.allclose',
'_refs.atleast_1d',
'_refs.atleast_2d',
'_refs.atleast_3d',
'_refs.broadcast_to',
'_refs.chunk',
'_refs.column_stack',
'_refs.contiguous',
'_refs.dsplit',
'_refs.dstack',
'_refs.fill',
'_refs.flatten',
'_refs.fliplr',
'_refs.flipud',
'_refs.float_power',
'_refs.hsplit',
'_refs.hstack',
'_refs.isclose',
'_refs.isfinite',
'_refs.isreal',
'_refs.log_softmax',
'_refs.movedim',
'_refs.narrow',
'_refs.nn.functional.l1_loss',
'_refs.nn.functional.log_softmax',
'_refs.nn.functional.poisson_nll_loss',
'_refs.nn.functional.softmax',
'_refs.nn.functional.softmin',
'_refs.positive',
'_refs.ravel',
'_refs.reshape',
'_refs.softmax',
'_refs.special.expit',
'_refs.special.log_softmax',
'_refs.special.softmax',
'_refs.square',
'_refs.T',
'_refs.tensor_split',
'_refs.to',
'_refs.true_divide',
'_refs.trunc_divide',
'_refs.vsplit',
'_refs.vstack',
'_refs.linalg.matrix_norm',
'_refs.linalg.norm',
'_refs.linalg.svd',
'_refs.linalg.svdvals',
'_refs.unflatten',
'_refs.sum_to_size',
# ref implementation missing kwargs
'_refs.full_like', # missing "layout"
'_refs.round', # missing "decimals"
'_refs.scalar_tensor', # missing "layout"
# other
'_refs.empty', # intentional; direct empty is faster and has less guards
'_refs.empty_permuted', # intentional; direct empty is faster and has less guards
'_refs.expand_as',
'_refs.as_strided', # _prims._as_strided_meta: "reduce() of empty sequence with no initial value"
'_refs.copy_to', # torch._C._jit_get_operation: No such operator aten::copy_to
'_refs.equal', # 'bool' object has no attribute 'dtype'
'_refs.conj', # Calls _prims.conj
'_refs.real',
'_refs.imag',
}
@parametrize("op", ref_ops_names)
def test_refs_are_in_python_ref_db(self, op):
inplace = op[-1] == "_"
if op in self.skip_ref_ops:
raise unittest.SkipTest(f"{op} does not have an entry in python_ref_db")
elif inplace:
self.assertNotIn(op, self.ref_db_names, msg=f"{op} is an in-place operation and should not have an OpInfo")
else:
# Intentionally don't use assertIn to avoid printing the
# (very large) container
self.assertTrue(op in self.ref_db_names, msg="{op} not in ref_db_names")
@parametrize("op", ref_ops_names)
def test_refs_are_in_decomp_table(self, op):
path = op.split('.')
module_path = '.'.join(path[:-1])
op_name = path[-1]
op_impl = getattr(import_module(f"torch.{module_path}"), op_name)
if op in self.not_in_decomp_table:
self.assertNotIn(op_impl, torch._decomp.decomposition_table.values(),
f"Unexpectedly found {op} in torch._decomp.decomposition_table.values()")
else:
self.assertIn(op_impl, torch._decomp.decomposition_table.values(),
f"Did not find {op} in torch._decomp.decomposition_table.values()")
fake_skips = (
"aminmax", # failing input
"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.lengths", # 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",
"narrow", # Fails only for one overload with DataDependentOutputException (hence skip).
)
fake_autocast_device_skips = defaultdict(dict)
# TODO: investigate/fix
fake_autocast_device_skips["cpu"] = {"linalg.pinv"}
dynamic_output_op_tests = (
"argwhere",
"bincount",
"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__",
"index_select",
)
data_dependent_op_tests = (
"equal",
"corrcoef",
"nn.functional.gaussian_nll_loss",
"allclose",
)
aliasing_failures = (
"histogramdd",
)
# tests which have inconsistent fake tensor stride propagation
# XXX: no new tests should be added to this list as a result of a
# decomp or prim, see https://github.com/pytorch/pytorch/issues/78050#issuecomment-1253950325
fake_tensor_stride_failing_ops = {
"fft.fft2",
"fft.fft",
"fft.fftn",
"fft.hfft2",
"fft.hfft",
"fft.hfftn",
"fft.ifft2",
"fft.ifft",
"fft.ifftn",
"fft.ihfft2",
"fft.ihfft",
"fft.ihfftn",
"fft.irfft2",
"fft.irfft",
"fft.irfftn",
"fft.rfft2",
"fft.rfft",
"fft.rfftn",
"svd",
"linalg.svd",
}
fake_backward_skips = {
"linalg.cond",
"linalg.matrix_norm",
"linalg.norm",
"linalg.svd",
"linalg.svdvals",
"pca_lowrank",
"roll",
"svd_lowrank",
"sgn",
"cholesky",
}
fake_backward_xfails = {skip(s) for s in fake_backward_skips} | {
xfail("_segment_reduce", "lengths"),
skip('nn.functional.ctc_loss'),
} | {skip(stride_skip) for stride_skip in fake_tensor_stride_failing_ops}
fake_autocast_backward_xfails = {
skip("nn.functional.binary_cross_entropy"),
skip("sparse.sampled_addmm"),
skip("linalg.pinv"),
skip("linalg.pinv", "hermitian"),
skip("linalg.pinv", "singular"),
skip('pinverse'),
}
class TestFakeTensor(TestCase):
def _test_fake_helper(self, device, dtype, op, context):
name = op.name
if op.variant_test_name:
name += "." + op.variant_test_name
if name in fake_skips or "sparse" in name or "jiterator" in name:
self.skipTest("Skip failing test")
samples = op.sample_inputs(device, dtype, requires_grad=False)
for sample in samples:
try:
mode = FakeTensorMode()
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)
try:
with context():
res = op(sample.input, *sample.args, **sample.kwargs)
except Exception as e:
continue
with context():
with mode:
res_fake = op(input, *args, **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
check_strides = name not in fake_tensor_stride_failing_ops
# prims/decomps must correctly model strides,
# see https://github.com/pytorch/pytorch/issues/78050#issuecomment-1253950325
prims.utils.compare_tensor_meta(fake_out, real_out, check_strides)
if name not in aliasing_failures:
fake_aliasing = outputs_alias_inputs((input, args, kwargs), res_fake)
real_aliasing = outputs_alias_inputs((sample.input, sample, args, sample.kwargs), res)
self.assertEqual(fake_aliasing, real_aliasing)
self.assertTrue(name not in dynamic_output_op_tests and name not in data_dependent_op_tests)
except torch._subclasses.fake_tensor.UnsupportedFakeTensorException:
pass
except torch._subclasses.fake_tensor.UnsupportedOperatorException:
pass
except torch._subclasses.fake_tensor.DynamicOutputShapeException:
self.assertTrue(name in dynamic_output_op_tests or name in sometimes_dynamic_output_op_test)
except torch._subclasses.fake_tensor.DataDependentOutputException:
self.assertTrue(name in data_dependent_op_tests)
@ops(op_db, dtypes=OpDTypes.any_one)
def test_pointwise_ops(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 or "jiterator" in name:
self.skipTest("Skip failing test")
test_self = self
class TestPointwiseMode(TorchDispatchMode):
def __torch_dispatch__(self, func, types, args=(), kwargs=None):
kwargs = kwargs or {}
out = func(*args, **kwargs)
if torch.Tag.pointwise in func.tags:
shapes = []
for inp in tree_flatten((args, kwargs)):
if isinstance(inp, torch.Tensor):
shapes.append(inp.shape)
out_shape = torch._refs._broadcast_shapes(*shapes)
for out_elem in tree_flatten(out):
if isinstance(out_elem, torch.Tensor):
test_self.assertEqual(out_elem.shape, out_shape)
return out
samples = op.sample_inputs(device, dtype, requires_grad=False)
for sample in samples:
mode = FakeTensorMode()
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)
try:
op(input, *args, **kwargs)
except Exception as e:
continue
with TestPointwiseMode():
with mode:
op(input, *args, **kwargs)
@ops(op_db, dtypes=OpDTypes.any_one)
def test_fake(self, device, dtype, op):
self._test_fake_helper(device, dtype, op, contextlib.nullcontext)
@ops(op_db, dtypes=OpDTypes.any_one)
def test_fake_autocast(self, device, dtype, op):
if op.name in fake_autocast_device_skips[device]:
self.skipTest("Skip failing test")
context = torch.cuda.amp.autocast if device == "cuda" else torch.cpu.amp.autocast
self._test_fake_helper(device, dtype, op, context)
def _test_fake_crossref_helper(self, device, dtype, op, context):
samples = op.sample_inputs(device, dtype, requires_grad=True)
for iter, sample in enumerate(samples):
args = [sample.input] + list(sample.args)
kwargs = sample.kwargs
# skip these to speed up tests
common_skip_ops = (
aten.detach.default,
aten.empty_strided.default,
aten.copy_.default,
aten.is_same_size.default,
)
# TODO: enable check_aliasing, batch norm fails
try:
with torch._subclasses.CrossRefFakeMode(ignore_op_fn=lambda fn: fn in common_skip_ops, check_aliasing=True):
with warnings.catch_warnings(), context(), torch.autograd.set_multithreading_enabled(False):
composite_compliance.compute_expected_grads(
op.get_op(), args, kwargs,
sample.output_process_fn_grad,
op.gradcheck_wrapper)
except torch._subclasses.fake_tensor.UnsupportedOperatorException:
pass
@onlyCUDA
@ops([op for op in op_db if op.supports_autograd], allowed_dtypes=(torch.float,))
@skipOps('TestFakeTensor', 'test_fake_crossref_backward_no_amp', fake_backward_xfails)
def test_fake_crossref_backward_no_amp(self, device, dtype, op):
self._test_fake_crossref_helper(device, dtype, op, contextlib.nullcontext)
@onlyCUDA
@ops([op for op in op_db if op.supports_autograd], allowed_dtypes=(torch.float,))
@skipOps('TestFakeTensor', 'test_fake_crossref_backward_amp', fake_backward_xfails | fake_autocast_backward_xfails)
def test_fake_crossref_backward_amp(self, device, dtype, op):
self._test_fake_crossref_helper(device, dtype, op, torch.cuda.amp.autocast)
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(TestFakeTensor, globals())
instantiate_device_type_tests(TestTags, globals())
if __name__ == "__main__":
run_tests()