class TestForeach(TestCase):
@property
def is_cuda(self):
return self.device_type == 'cuda'
# note(mkozuki): It might be the case that the expected number of `cudaLaunchKernel`s
# is greater than 1 once foreach functions internally separate their input `TensorList`s by
# devices & dtypes into vectors of tensors.
def _get_funcs(self, op, n_expected_cudaLaunchKernels):
return (
ForeachFuncWrapper(op.method_variant,
n_expected_cudaLaunchKernels),
RegularFuncWrapper(op.ref),
ForeachFuncWrapper(op.inplace_variant,
n_expected_cudaLaunchKernels),
RegularFuncWrapper(op.ref_inplace),
)
def _binary_test(self,
dtype,
op,
ref,
inputs,
is_fastpath,
is_inplace,
*,
alpha=None):
ref_inputs = [[t.clone().detach() for t in inputs[0]], inputs[1]
] if is_inplace else inputs
try:
actual = op(inputs, self.is_cuda, is_fastpath)
except RuntimeError as e:
with self.assertRaisesRegex(type(e), re.escape(str(e))):
ref(ref_inputs)
else:
expected = ref(ref_inputs)
self.assertEqual(actual, expected)
if alpha is not None:
kwargs = {'alpha': alpha}
ref_inputs = inputs
try:
actual = op(inputs, self.is_cuda, is_fastpath, **kwargs)
except RuntimeError as e:
with self.assertRaisesRegex(type(e), re.escape(str(e))):
ref(ref_inputs, **kwargs)
else:
expected = ref(ref_inputs, **kwargs)
if dtype in (torch.float16, torch.bfloat16) and TEST_WITH_ROCM:
self.assertEqual(expected,
actual,
atol=1.e-3,
rtol=self.dtype_precisions[dtype][0])
else:
self.assertEqual(expected, actual)
def _test_binary_op_tensorlists(self, device, dtype, opinfo, N,
is_fastpath, disable_fastpath):
n_expected_cudaLaunchKernels = N if disable_fastpath else 1
op, ref, inplace_op, inplace_ref = self._get_funcs(
opinfo, n_expected_cudaLaunchKernels)
inputs = [
opinfo.sample_inputs(device,
dtype,
N,
noncontiguous=not is_fastpath),
opinfo.sample_inputs(device,
dtype,
N,
noncontiguous=not is_fastpath),
]
self._binary_test(dtype,
op,
ref,
inputs,
is_fastpath,
is_inplace=False)
self._binary_test(dtype,
inplace_op,
inplace_ref,
inputs,
is_fastpath,
is_inplace=True)
if opinfo.supports_alpha_param:
alpha = None
if dtype in get_all_int_dtypes():
alpha = 3
elif dtype.is_complex:
alpha = complex(3, 3)
else:
alpha = 3.14
self._binary_test(dtype,
op,
ref,
inputs,
is_fastpath,
is_inplace=False,
alpha=alpha)
self._binary_test(dtype,
inplace_op,
inplace_ref,
inputs,
is_fastpath,
is_inplace=True,
alpha=alpha)
# Tests of implicit broadcasting
# When sizes of tensors don't match, foreach functions are supposed to choose slow path
# even if this methods's argument `is_fastpath` is True.
# `cudaLaunchKernel` will be equal to `N`. For assert in `ForeachFuncWrapper` to pass,
# we pass `is_fastpath and disable_fastpath` to `_binary_test`'s argument of is_fastpath.
# as n_expected_cudaLaunchKernels is N if disable_fastpath.
inputs = [
opinfo.sample_inputs(device,
dtype,
N,
noncontiguous=not is_fastpath),
[
make_tensor((N - i, 1),
device=device,
dtype=dtype,
noncontiguous=not is_fastpath) for i in range(N)
],
]
self._binary_test(dtype,
op,
ref,
inputs,
is_fastpath and disable_fastpath,
is_inplace=False)
self._binary_test(dtype,
inplace_op,
inplace_ref,
inputs,
is_fastpath and disable_fastpath,
is_inplace=True)
# note(mkozuki): Why ROCm?
# ROCm is supposed to compile slow path as in
# https://github.com/pytorch/pytorch/blob/7e032f18cf1405804c4f787b05ea2de5e08a091e/aten/src/ATen/native/ForeachUtils.h#L148-L164, # noqa: E501
# Therefore `[torch.add(*args, alpha=alpha) for args in zip(tensors1, tensors2)]` and
# `torch._foreach_add(tensors1, tensors2, alpha=alpha)`
# are expected to return the same outputs, however, the outputs look unstable for torch.bfloat16 and torch.half.
# log: https://ci.pytorch.org/jenkins/job/pytorch-builds/job/pytorch-linux-bionic-rocm4.2-py3.6-test1/2741/console
@skipCUDAIfRocm
@skipMeta
@ops(foreach_binary_op_db)
def test_binary_op_tensorlists_fastpath(self, device, dtype, op):
for N in N_values:
disable_fastpath = op.ref == torch.div and dtype in get_all_int_dtypes(
) + [torch.bool]
if op.ref == torch.add and dtype == torch.bool:
disable_fastpath = True
self._test_binary_op_tensorlists(device, dtype, op, N, True,
disable_fastpath)
@ops(foreach_binary_op_db)
def test_binary_op_tensorlists_slowpath(self, device, dtype, op):
for N in N_values:
self._test_binary_op_tensorlists(device, dtype, op, N, False,
False)
def _test_binary_op_scalar(self, device, dtype, opinfo, N, scalar,
is_fastpath, disable_fastpath):
n_expected_cudaLaunchKernels = N if disable_fastpath else 1
op, ref, inplace_op, inplace_ref = self._get_funcs(
opinfo, n_expected_cudaLaunchKernels)
inputs = [
opinfo.sample_inputs(device,
dtype,
N,
noncontiguous=not is_fastpath), scalar
]
self._binary_test(dtype,
op,
ref,
inputs,
is_fastpath,
is_inplace=False)
self._binary_test(dtype,
inplace_op,
inplace_ref,
inputs,
is_fastpath,
is_inplace=True)
@skipCUDAIfRocm
@skipMeta
@ops(foreach_binary_op_db)
def test_binary_op_scalar_fastpath(self, device, dtype, op):
for N, scalar in itertools.product(N_values, Scalars):
disable_fastpath = op.ref == torch.div and dtype in get_all_int_dtypes(
) + [torch.bool]
if isinstance(scalar, int):
disable_fastpath |= dtype == torch.bool
if isinstance(scalar, float):
disable_fastpath |= dtype in get_all_int_dtypes() + [
torch.bool
]
if isinstance(scalar, bool):
disable_fastpath |= dtype == torch.bool
if op.ref in (torch.add, torch.mul):
disable_fastpath = False
if isinstance(scalar, complex):
disable_fastpath |= dtype not in get_all_complex_dtypes()
self._test_binary_op_scalar(device, dtype, op, N, scalar, True,
disable_fastpath)
@ops(foreach_binary_op_db)
def test_binary_op_scalar_slowpath(self, device, dtype, op):
for N, scalar in itertools.product(N_values, Scalars):
self._test_binary_op_scalar(device, dtype, op, N, scalar, False,
False)
def _test_binary_op_scalarlist(self, device, dtype, opinfo, N, scalarlist,
is_fastpath, disable_fastpath):
n_expected_cudaLaunchKernels = N if disable_fastpath else 1
op, ref, inplace_op, inplace_ref = self._get_funcs(
opinfo, n_expected_cudaLaunchKernels)
inputs = [
opinfo.sample_inputs(device,
dtype,
N,
noncontiguous=not is_fastpath), scalarlist
]
self._binary_test(dtype,
op,
ref,
inputs,
is_fastpath,
is_inplace=False)
self._binary_test(dtype,
inplace_op,
inplace_ref,
inputs,
is_fastpath,
is_inplace=True)
# note(mkozuki): Why two functions depending on with/without bool?
# `foreach_sub` & `foreach_sub_` do `sub_check(tensors[i], scalars[i])` from i=1...N.
# So, if scalarlist has one or more bool values, `foreach_sub` and `foreach_sub_`
# raise bool subtraction error before doing any math.
# While regular `sub` and `sub_` do some math until they encounter bool.
# So, foreach sub's throw bool sub error first. However, regular sub's throw different
# errors depending on the order of scalarlist. To keep actual unit test impl simple,
# separating mixed scalarlist tests. By setting the first element of scalarlist to bool,
# they are expected to throw bool sub error even in inplace test.
@skipCUDAIfRocm
@skipMeta
@ops(foreach_binary_op_db)
def test_binary_op_scalarlist_fastpath(self, device, dtype, op):
for N in N_values:
for type_str, scalarlist in getScalarLists(N):
bool_int_div = op.ref == torch.div and dtype in get_all_int_dtypes(
) + [torch.bool]
disable_fastpath = bool_int_div
if type_str == "int":
disable_fastpath |= dtype == torch.bool
if type_str == "float":
disable_fastpath |= dtype in get_all_int_dtypes() + [
torch.bool
]
if type_str == "complex":
disable_fastpath |= dtype not in get_all_complex_dtypes()
if type_str == "mixed":
disable_fastpath |= True and dtype not in get_all_complex_dtypes(
)
self._test_binary_op_scalarlist(device, dtype, op, N,
scalarlist, True,
disable_fastpath)
@ops(foreach_binary_op_db)
def test_binary_op_scalarlist_slowpath(self, device, dtype, op):
for N in N_values:
for _, scalarlist in getScalarLists(N):
self._test_binary_op_scalarlist(device, dtype, op, N,
scalarlist, False, False)
def _pointwise_test(self,
dtype,
op,
ref,
inputs,
is_fastpath,
is_inplace,
*,
values=None):
ref_inputs = [[t.clone().detach() for t in inputs[0]], inputs[1],
inputs[2]] if is_inplace else inputs
try:
actual = op(inputs, self.is_cuda, is_fastpath)
except RuntimeError as e:
with self.assertRaisesRegex(type(e), re.escape(str(e))):
ref(ref_inputs)
else:
expected = ref(ref_inputs)
self.assertEqual(expected, actual)
if values is not None:
try:
actual = op(inputs + [values], self.is_cuda, is_fastpath)
except RuntimeError as e:
with self.assertRaisesRegex(type(e), re.escape(str(e))):
ref(ref_inputs, values=values)
else:
expected = ref(ref_inputs, values=values)
self.assertEqual(expected, actual)
def _test_pointwise_op(self,
device,
dtype,
opinfo,
N,
is_fastpath,
disable_fastpath,
*,
values=None):
n_expected_cudaLaunchKernels = N if disable_fastpath else 1
op, ref, inplace_op, inplace_ref = self._get_funcs(
opinfo, n_expected_cudaLaunchKernels)
inputs = [
opinfo.sample_inputs(device,
dtype,
N,
noncontiguous=not is_fastpath),
opinfo.sample_inputs(device,
dtype,
N,
noncontiguous=not is_fastpath),
opinfo.sample_inputs(device,
dtype,
N,
noncontiguous=not is_fastpath),
]
self._pointwise_test(dtype,
op,
ref,
inputs,
is_fastpath,
is_inplace=False,
values=values)
self._pointwise_test(dtype,
inplace_op,
inplace_ref,
inputs,
is_fastpath,
is_inplace=True,
values=values)
# Tests of implicit broadcasting
inputs = [
opinfo.sample_inputs(device,
dtype,
N,
noncontiguous=not is_fastpath,
same_size=True),
[
make_tensor((N - i, 1),
device=device,
dtype=dtype,
noncontiguous=not is_fastpath) for i in range(N)
],
[
make_tensor((1, N - i),
device=device,
dtype=dtype,
noncontiguous=not is_fastpath) for i in range(N)
],
]
self._pointwise_test(dtype,
op,
ref,
inputs,
is_fastpath and disable_fastpath,
is_inplace=False,
values=values)
self._pointwise_test(dtype,
inplace_op,
inplace_ref,
inputs,
is_fastpath and disable_fastpath,
is_inplace=True,
values=values)
@skipMeta
@ops(foreach_pointwise_op_db)
def test_pointwise_op_fastpath(self, device, dtype, op):
disable_fastpath = dtype in get_all_int_dtypes() + [torch.bool]
# for N, scalar in itertools.product(N_values, Scalars):
for N in N_values:
self._test_pointwise_op(device, dtype, op, N, True,
disable_fastpath)
for scalar in Scalars:
self._test_pointwise_op(device,
dtype,
op,
N,
True,
disable_fastpath,
values=scalar)
for _, scalarlist in getScalarLists(N):
self._test_pointwise_op(device,
dtype,
op,
N,
True,
disable_fastpath,
values=scalarlist)
@ops(foreach_pointwise_op_db)
def test_pointwise_op_slowpath(self, device, dtype, op):
# for N, scalar in itertools.product(N_values, Scalars):
for N in N_values:
self._test_pointwise_op(device, dtype, op, N, False, False)
for scalar in Scalars:
self._test_pointwise_op(device,
dtype,
op,
N,
False,
False,
values=scalar)
for _, scalarlist in getScalarLists(N):
self._test_pointwise_op(device,
dtype,
op,
N,
False,
False,
values=scalarlist)
# note(mkozuki): fastpath test uses dtypes which fastpath implementation supports.
# To confirm the dtypes of `OpInfo` cover the dtypes that the function support,
# this test does not use `try-except` for fastpath.
def _regular_unary_test(self, dtype, op, ref, inputs, is_fastpath):
if is_fastpath:
self.assertEqual(ref(inputs), op(inputs, self.is_cuda,
is_fastpath))
return
try:
actual = op(inputs, self.is_cuda, is_fastpath)
except RuntimeError as e:
with self.assertRaisesRegex(type(e), re.escape(str(e))):
ref(inputs)
else:
expected = ref(inputs)
self.assertEqual(actual, expected)
# note(mkozuki): why `try-except` for both fastpath?
# - inputs for fastpath can be integer tensors.
# - this is becase opinfo dtypes are configured for outpulace implementation
# - for integer inputs, trigonometric functions and exponential function returns float outputs,
# which causes "result type Float can't be case to the desired type" error.
# Thus, `try-except` is used even if `is_fastpath` is `True`.
def _inplace_unary_test(self, dtype, inplace, inplace_ref, inputs,
is_fastpath):
copied_inputs = [[t.clone().detach() for t in tensors]
for tensors in inputs]
try:
inplace(inputs, self.is_cuda, is_fastpath)
except RuntimeError as e:
with self.assertRaisesRegex(type(e), re.escape(str(e))):
inplace_ref(copied_inputs)
else:
inplace_ref(copied_inputs),
self.assertEqual(copied_inputs, inputs)
def _test_unary(self, device, dtype, opinfo, N, is_fastpath):
op, ref, inplace_op, inplace_ref = self._get_funcs(opinfo, 1)
inputs = opinfo.sample_inputs(device,
dtype,
N,
noncontiguous=not is_fastpath),
# note(mkozuki): Complex inputs for `_foreach_abs` go through slowpath.
if opinfo.name == "_foreach_abs" and dtype in get_all_complex_dtypes():
is_fastpath = False
self._regular_unary_test(dtype, op, ref, inputs, is_fastpath)
self._inplace_unary_test(dtype, inplace_op, inplace_ref, inputs,
is_fastpath)
@skipMeta
@ops(foreach_unary_op_db)
def test_unary_fastpath(self, device, dtype, op):
for N in N_values:
self._test_unary(device, dtype, op, N, is_fastpath=True)
@ops(foreach_unary_op_db, dtypes=get_all_dtypes())
def test_unary_slowpath(self, device, dtype, op):
for N in N_values:
self._test_unary(device, dtype, op, N, is_fastpath=False)
def _minmax_test(self, opinfo, inputs, is_fastpath,
n_expected_cudaLaunchKernels):
op, ref, _, _ = self._get_funcs(opinfo, n_expected_cudaLaunchKernels)
self.assertEqual(ref(inputs), op(inputs, self.is_cuda, is_fastpath))
# note(mkozuki): in-place of foreach_minimum and foreach_maximum aren't implemented.
@ops(foreach_minmax_op_db)
def test_minmax_fastpath(self, device, dtype, op):
for N in N_values:
inputs = tuple(
op.sample_inputs(device, dtype, N) for _ in range(2))
self._minmax_test(op, inputs, True,
N if dtype == torch.bool else 1)
@ops(foreach_minmax_op_db,
dtypes=get_all_dtypes(include_half=True,
include_bfloat16=True,
include_complex=False))
def test_minmax_slowpath(self, device, dtype, op):
for N in N_values:
inputs = tuple(
op.sample_inputs(device, dtype, N, noncontiguous=True)
for _ in range(2))
self._minmax_test(op, inputs, False, 1)
# note(mkozuki): ForeachFuncInfo's of both `_foreach_maximum` and `_foreach_minimum` include integer types.
# so, manually limit dtypes to fp types for inf&nan tests.
@ops(foreach_minmax_op_db,
dtypes=get_all_fp_dtypes(include_bfloat16=True, include_half=True))
def test_minmax_float_inf_nan(self, device, dtype, op):
inputs = (
[
torch.tensor([float('inf')], device=device, dtype=dtype),
torch.tensor([-float('inf')], device=device, dtype=dtype),
torch.tensor([float('nan')], device=device, dtype=dtype),
torch.tensor([float('nan')], device=device, dtype=dtype)
],
[
torch.tensor([-float('inf')], device=device, dtype=dtype),
torch.tensor([float('inf')], device=device, dtype=dtype),
torch.tensor([float('inf')], device=device, dtype=dtype),
torch.tensor([float('nan')], device=device, dtype=dtype)
],
)
self._minmax_test(op, inputs, True, 1)
def _reduce_test(self, opinfo, inputs, ord, is_fastpath,
n_expected_cudaLaunchKernels):
op, ref, _, _ = self._get_funcs(opinfo, n_expected_cudaLaunchKernels)
self.assertEqual(ref(inputs, ord=ord),
op(inputs, self.is_cuda, is_fastpath, ord=ord))
@ops(foreach_reduce_op_db)
def test_reduce_fastpath(self, device, dtype, op):
for N, ord in itertools.product(N_values, (0, 1, 2, -1, -2)):
if ord in (1, 2) and dtype in torch.testing.get_all_fp_dtypes():
n_expected_cudaLaunchKernels = 3
else:
n_expected_cudaLaunchKernels = N
inputs = op.sample_inputs(device, dtype, N, noncontiguous=False),
self._reduce_test(op, inputs, ord, True,
n_expected_cudaLaunchKernels)
@ops(foreach_reduce_op_db)
def test_reduce_slowpath(self, device, dtype, op):
for N, ord in itertools.product(N_values, (0, 1, 2, -1, -2)):
inputs = op.sample_inputs(device, dtype, N, noncontiguous=True),
self._reduce_test(op, inputs, ord, False, 1)
@dtypes(*get_all_dtypes())
def test_add_scalar_with_empty_list_and_empty_tensor(self, device, dtype):
# TODO: enable empty list case
for tensors in [[torch.randn([0])]]:
res = torch._foreach_add(tensors, 1)
self.assertEqual(res, tensors)
torch._foreach_add_(tensors, 1)
self.assertEqual(res, tensors)
@ops(foreach_binary_op_db, dtypes=get_all_dtypes())
def test_binary_op_scalar_with_overlapping_tensors(self, device, dtype,
op):
foreach_op, ref = op.method_variant, op.ref
tensors = [
torch.ones(1, 1, device=device, dtype=dtype).expand(2, 1, 3)
]
if ref == torch.sub and dtype == torch.bool:
with self.assertRaisesRegex(RuntimeError,
re.escape(_BOOL_SUB_ERR_MSG)):
[ref(t, 1) for t in tensors]
with self.assertRaisesRegex(RuntimeError,
re.escape(_BOOL_SUB_ERR_MSG)):
foreach_op(tensors, 1)
return
expected = [ref(t, 1) for t in tensors]
res = foreach_op(tensors, 1)
self.assertEqual(res, expected)
# note(mkozuki): this test case fails with Meta at least in my local environment.
# The message was
# `AssertionError: NotImplementedError("Could not run 'aten::_foreach_add.Scalar' with arguments from the 'Meta' backend.`
@skipMeta
@ops(foreach_binary_op_db, allowed_dtypes=[torch.float])
def test_binary_op_scalar_with_different_tensor_dtypes(
self, device, dtype, op):
foreach_op = op.method_variant
tensors = [
torch.tensor([1.1], dtype=torch.float, device=device),
torch.tensor([1], dtype=torch.long, device=device)
]
runtime_error = None
try:
foreach_op(tensors, 1)
except RuntimeError as e:
runtime_error = e
self.assertIsNone(runtime_error)
@ops(foreach_binary_op_db, dtypes=get_all_dtypes())
def test_binary_op_list_error_cases(self, device, dtype, op):
foreach_op, foreach_op_, ref, ref_ = op.method_variant, op.inplace_variant, op.ref, op.ref_inplace
tensors1 = []
tensors2 = []
# Empty lists
with self.assertRaisesRegex(
RuntimeError,
"There were no tensor arguments to this function"):
foreach_op(tensors1, tensors2)
with self.assertRaisesRegex(
RuntimeError,
"There were no tensor arguments to this function"):
foreach_op_(tensors1, tensors2)
# One empty list
tensors1.append(torch.tensor([1], device=device, dtype=dtype))
with self.assertRaisesRegex(
RuntimeError,
"Tensor list must have same number of elements as scalar list."
):
foreach_op(tensors1, tensors2)
with self.assertRaisesRegex(
RuntimeError,
"Tensor list must have same number of elements as scalar list."
):
foreach_op_(tensors1, tensors2)
# Lists have different amount of tensors
tensors2.append(torch.tensor([1], device=device))
tensors2.append(torch.tensor([1], device=device))
with self.assertRaisesRegex(
RuntimeError,
"Tensor lists must have the same number of tensors, got 1 and 2"
):
foreach_op(tensors1, tensors2)
with self.assertRaisesRegex(
RuntimeError,
"Tensor lists must have the same number of tensors, got 1 and 2"
):
foreach_op_(tensors1, tensors2)
# Corresponding tensors with different sizes that aren't compatible with broadcast
# If sizes are different then foreach chooses slow path, thus error messages are expected
# to be the same as torch regular function.
tensors1 = [
torch.zeros(10, 10, device=device, dtype=dtype) for _ in range(10)
]
tensors2 = [
torch.ones(11, 11, device=device, dtype=dtype) for _ in range(10)
]
try:
foreach_op(tensors1, tensors2)
except RuntimeError as e:
with self.assertRaisesRegex(type(e), re.escape(str(e))):
[ref(t1, t2) for t1, t2 in zip(tensors1, tensors2)]
try:
foreach_op_(tensors1, tensors2)
except RuntimeError as e:
with self.assertRaisesRegex(type(e), re.escape(str(e))):
[ref_(t1, t2) for t1, t2 in zip(tensors1, tensors2)]
# different devices
if self.device_type == "cuda" and torch.cuda.device_count() > 1:
tensor1 = torch.zeros(10, 10, device="cuda:0", dtype=dtype)
tensor2 = torch.ones(10, 10, device="cuda:1", dtype=dtype)
if dtype == torch.bool and foreach_op == torch._foreach_sub:
with self.assertRaisesRegex(RuntimeError,
re.escape(_BOOL_SUB_ERR_MSG)):
foreach_op([tensor1], [tensor2])
with self.assertRaisesRegex(RuntimeError,
re.escape(_BOOL_SUB_ERR_MSG)):
foreach_op_([tensor1], [tensor2])
return
with self.assertRaisesRegex(
RuntimeError,
"Expected all tensors to be on the same device"):
foreach_op([tensor1], [tensor2])
if dtype in get_all_int_dtypes() + [
torch.bool
] and foreach_op == torch._foreach_div:
with self.assertRaisesRegex(RuntimeError, "result type"):
foreach_op_([tensor1], [tensor2])
else:
with self.assertRaisesRegex(
RuntimeError,
"Expected all tensors to be on the same device"):
foreach_op_([tensor1], [tensor2])
@skipMeta
@unittest.skipIf(not torch.cuda.is_available(), "CUDA not found")
@ops(foreach_binary_op_db, dtypes=get_all_dtypes())
def test_binary_op_list_slow_path(self, device, dtype, op):
# note(mkozuki): why `n_expected_cudaLaunchKernels=0`?
# In this test, foreach functions don't go through fast path,
# but as there is only one tensor in each list of tensors,
# `cudaLaunchKernel` is 1 so ForeachFuncWrapper internal assert fails.
foreach_op, native_op, foreach_op_, native_op_ = self._get_funcs(
op, n_expected_cudaLaunchKernels=0)
# 0-strides
tensor1 = make_tensor((10, 10), dtype=dtype, device=device)
tensor2 = make_tensor((1, ), device=device,
dtype=dtype).expand_as(tensor1)
inputs = ([tensor1], [tensor2])
self._binary_test(dtype,
foreach_op,
native_op,
inputs,
is_fastpath=False,
is_inplace=False)
self._binary_test(dtype,
foreach_op_,
native_op_,
inputs,
is_fastpath=False,
is_inplace=True)
# different strides
tensor1 = torch.zeros(10, 10, device=device, dtype=dtype)
tensor2 = torch.ones(10, 10, device=device, dtype=dtype)
inputs = ([tensor1], [tensor2.t()])
self._binary_test(dtype,
foreach_op,
native_op,
inputs,
is_fastpath=False,
is_inplace=False)
self._binary_test(dtype,
foreach_op_,
native_op_,
inputs,
is_fastpath=False,
is_inplace=True)
# non contiguous
tensor1 = make_tensor((5, 2, 1, 3),
device=device,
dtype=dtype,
noncontiguous=True)
tensor2 = make_tensor((5, 2, 1, 3),
device=device,
dtype=dtype,
noncontiguous=True)
self.assertFalse(tensor1.is_contiguous())
self.assertFalse(tensor2.is_contiguous())
inputs = ([tensor1], [tensor2])
self._binary_test(dtype,
foreach_op,
native_op,
inputs,
is_fastpath=False,
is_inplace=False)
self._binary_test(dtype,
foreach_op_,
native_op_,
inputs,
is_fastpath=False,
is_inplace=True)
# sliced tensor
tensor1 = make_tensor((5, 2, 1, 3), device=device, dtype=dtype)
tensor2 = make_tensor((5, 2, 1, 3 * 7), device=device,
dtype=dtype)[:, :, :, ::7]
inputs = ([tensor1], [tensor2])
self._binary_test(dtype,
foreach_op,
native_op,
inputs,
is_fastpath=False,
is_inplace=False)
self._binary_test(dtype,
foreach_op_,
native_op_,
inputs,
is_fastpath=False,
is_inplace=True)
# note: Below three tests (postfixed with `_tensors_on_different_devices`)
# checks whether foreach works with lists of tensors on different devices
# but tensors of the same index are on the same device, e.g., ['cuda', 'cpu].
@onlyCUDA
@ops(foreach_unary_op_db)
def test_unary_op_tensors_on_different_devices(self, device, dtype, op):
method, ref, inplace_method, ref_inplace = self._get_funcs(op, 1)
# tensors: ['cuda', 'cpu]
tensors = op.sample_inputs(device, dtype, 2)
tensors[1] = tensors[1].to('cpu')
try:
actual = method((tensors, ), False, False)
except RuntimeError as e:
with self.assertRaisesRegex(type(e), str(e)):
ref((tensors, ))
else:
expected = ref((tensors, ))
self.assertEqual(expected, actual)
try:
inplace_method((tensors, ), False, False)
except RuntimeError as e:
with self.assertRaisesRegex(type(e), str(e)):
ref_inplace((tensors, ))
else:
self.assertEqual(expected, tensors)
@onlyCUDA
@ops(foreach_binary_op_db)
def test_binary_op_tensors_on_different_devices(self, device, dtype, op):
# `tensors1`: ['cuda', 'cpu']
# `tensors2`: ['cuda', 'cpu']
_cuda_tensors = op.sample_inputs(device, dtype, 2, same_size=True)
_cpu_tensors = op.sample_inputs('cpu', dtype, 2, same_size=True)
tensors1, tensors2 = list(
tensors for tensors in zip(_cuda_tensors, _cpu_tensors))
foreach_op, foreach_op_ = op.method_variant, op.inplace_variant
native_op, native_op_ = op.ref, op.ref_inplace
try:
actual = foreach_op(tensors1, tensors2)
except RuntimeError as e:
with self.assertRaisesRegex(type(e), re.escape(str(e))):
[native_op(t1, t2) for t1, t2 in zip(tensors1, tensors2)]
else:
expected = [
native_op(t1, t2) for t1, t2 in zip(tensors1, tensors2)
]
self.assertEqual(expected, actual)
try:
foreach_op_(tensors1, tensors2)
except RuntimeError as e:
with self.assertRaisesRegex(type(e), re.escape(str(e))):
[native_op_(t1, t2) for t1, t2 in zip(tensors1, tensors2)]
else:
self.assertEqual(actual, tensors1)
@onlyCUDA
@ops(foreach_pointwise_op_db,
allowed_dtypes=get_all_fp_dtypes(include_half=False,
include_bfloat16=False))
def test_pointwise_op_tensors_on_different_devices(self, device, dtype,
op):
# tensors1: ['cuda', 'cpu]
# tensors2: ['cuda', 'cpu]
# tensors3: ['cuda', 'cpu]
_cuda_tensors = op.sample_inputs(device, dtype, 3, same_size=True)
_cpu_tensors = op.sample_inputs('cpu', dtype, 3, same_size=True)
tensors1, tensors2, tensors3 = list(
tensors for tensors in zip(_cuda_tensors, _cpu_tensors))
foreach_op, foreach_op_, native_op = op.method_variant, op.inplace_variant, op.ref
actual = foreach_op(tensors1, tensors2, tensors3)
expected = [native_op(*_cuda_tensors), native_op(*_cpu_tensors)]
self.assertEqual(expected, actual)
# note(mkozuki): Limiting dtypes to FP32&FP64, we can safely run inplace ops.
foreach_op_(tensors1, tensors2, tensors3)
self.assertEqual(expected, tensors1)
class TestSortAndSelect(TestCase):
def assertIsOrdered(self, order, x, mxx, ixx, task):
SIZE = x.size(1)
if order == 'descending':
def check_order(a, b):
# `a != a` because we put NaNs
# at the end of ascending sorted lists,
# and the beginning of descending ones.
return ((a != a) | (a >= b)).all().item()
elif order == 'ascending':
def check_order(a, b):
# see above
return ((b != b) | (a <= b)).all().item()
else:
error('unknown order "{}", must be "ascending" or "descending"'.format(order))
are_ordered = True
for k in range(1, SIZE):
self.assertTrue(check_order(mxx[:, k - 1], mxx[:, k]),
'torch.sort ({}) values unordered for {}'.format(order, task))
seen = set()
indicesCorrect = True
size0 = x.size(0)
size = x.size(x.dim() - 1)
x = x.tolist()
mxx = mxx.tolist()
ixx = ixx.tolist()
for k in range(size0):
seen.clear()
for j in range(size):
self.assertEqual(x[k][ixx[k][j]], mxx[k][j],
msg='torch.sort ({}) indices wrong for {}'.format(order, task))
seen.add(ixx[k][j])
self.assertEqual(len(seen), size)
def test_sort(self, device):
# on CUDA 2048 vs >2048 have different code path for the dim being sorted
for SIZE in (4, 2049):
x = torch.rand(4, SIZE, device=device)
res1val, res1ind = torch.sort(x)
# Test inplace
y = x.clone()
y_inds = torch.tensor((), dtype=torch.int64, device=device)
torch.sort(y, out=(y, y_inds))
x_vals, x_inds = torch.sort(x)
self.assertEqual(x_vals, y)
self.assertEqual(x_inds, y_inds)
# Test use of result tensor
res2val = torch.tensor((), device=device)
res2ind = torch.tensor((), device=device, dtype=torch.long)
torch.sort(x, out=(res2val, res2ind))
self.assertEqual(res1val, res2val, atol=0, rtol=0)
self.assertEqual(res1ind, res2ind, atol=0, rtol=0)
self.assertEqual(torch.argsort(x), res1ind)
self.assertEqual(x.argsort(), res1ind)
# Test sorting of random numbers
self.assertIsOrdered('ascending', x, res2val, res2ind, 'random')
# Test simple sort
self.assertEqual(
torch.sort(torch.tensor((50, 40, 30, 20, 10), device=device))[0],
torch.tensor((10, 20, 30, 40, 50), device=device),
atol=0, rtol=0
)
# Test that we still have proper sorting with duplicate keys
x = torch.floor(torch.rand(4, SIZE, device=device) * 10)
torch.sort(x, out=(res2val, res2ind))
self.assertIsOrdered('ascending', x, res2val, res2ind, 'random with duplicate keys')
# DESCENDING SORT
x = torch.rand(4, SIZE, device=device)
res1val, res1ind = torch.sort(x, x.dim() - 1, True)
# Test use of result tensor
res2val = torch.tensor((), device=device)
res2ind = torch.tensor((), device=device, dtype=torch.long)
torch.sort(x, x.dim() - 1, True, out=(res2val, res2ind))
self.assertEqual(res1val, res2val, atol=0, rtol=0)
self.assertEqual(res1ind, res2ind, atol=0, rtol=0)
self.assertEqual(torch.argsort(x, x.dim() - 1, True), res1ind)
self.assertEqual(x.argsort(x.dim() - 1, True), res1ind)
# Test sorting of random numbers
self.assertIsOrdered('descending', x, res2val, res2ind, 'random')
# Test simple sort task
self.assertEqual(
torch.sort(torch.tensor((10, 20, 30, 40, 50), device=device), 0, True)[0],
torch.tensor((50, 40, 30, 20, 10), device=device),
atol=0, rtol=0
)
# Test that we still have proper sorting with duplicate keys
self.assertIsOrdered('descending', x, res2val, res2ind, 'random with duplicate keys')
# Test sorting with NaNs
x = torch.rand(4, SIZE, device=device)
x[1][2] = float('NaN')
x[3][0] = float('NaN')
torch.sort(x, out=(res2val, res2ind))
self.assertIsOrdered('ascending', x, res2val, res2ind,
'random with NaNs')
torch.sort(x, out=(res2val, res2ind), descending=True)
self.assertIsOrdered('descending', x, res2val, res2ind,
'random with NaNs')
# FIXME: remove torch.bool from unsupported types once support is added for cub sort
@dtypes(*set(get_all_dtypes()) - {torch.bool, torch.complex64, torch.complex128})
def test_stable_sort(self, device, dtype):
if TEST_WITH_ROCM and dtype == torch.bfloat16:
return
sizes = (100, 1000, 10000)
for ncopies in sizes:
x = torch.tensor([0, 1] * ncopies, dtype=dtype, device=device)
_, idx = x.sort(stable=True)
self.assertEqual(
idx[:ncopies],
torch.arange(start=0, end=2 * ncopies, step=2, device=device)
)
self.assertEqual(
idx[ncopies:],
torch.arange(start=1, end=2 * ncopies, step=2, device=device)
)
@onlyCUDA
@dtypes(torch.uint8)
@largeTensorTest('200GB') # Unfortunately 80GB A100 is not large enough
def test_sort_large(self, device, dtype):
t0 = torch.randperm(8192, device=device).to(dtype)
t = t0.view(1, 8192).expand(2 ** 18 + 1, -1).contiguous()
v, i = t.sort()
del t
iv, im = i.var_mean(dim=0)
del i
vv, vm = v.var_mean(dim=0)
del v
self.assertEqual(vv, torch.zeros_like(vv))
self.assertEqual(iv, torch.zeros_like(iv))
self.assertEqual(vm, torch.arange(255, dtype=dtype, device=device))
self.assertEqual(im, t0.sort().indices)
def _test_sort_discontiguous(self, device, dtype):
# on CUDA 2048 vs >2048 have different code path for the dim being sorted
sizes = (5, 7, 2049)
for shape in permutations(sizes):
for perm in permutations((0, 1, 2)):
for dim in range(3):
t = torch.randn(shape, device=device, dtype=dtype).permute(perm)
r1 = t.sort(dim=dim)
r2 = t.contiguous().sort(dim=dim)
self.assertEqual(r1, r2)
n = t.size(dim)
# assert ordered
self.assertTrue((r1.values.narrow(dim, 1, n - 1) >= r1.values.narrow(dim, 0, n - 1)).all())
# assert that different segments does not mix, which can easily happen
# if the stride is not handled correctly
self.assertTrue((t.unsqueeze(-1).transpose(dim, -1) == r1.values.unsqueeze(-1)).any(dim=dim).any(dim=-1).all())
# assert stride is preserved
if self.device_type == 'cuda':
# FIXME: this behavior should be true for all cases, not
# just the one specified in if condition
self.assertEqual(r1.values.stride(), t.stride())
self.assertEqual(r1.indices.stride(), t.stride())
@onlyCUDA
@dtypes(torch.float32)
def test_sort_discontiguous(self, device, dtype):
self._test_sort_discontiguous(device, dtype)
@slowTest # this test is slow on CPU, but not on CUDA
@onlyCPU
@dtypes(torch.float32)
def test_sort_discontiguous_slow(self, device, dtype):
self._test_sort_discontiguous(device, dtype)
@dtypes(torch.float32)
def test_sort_1d_output_discontiguous(self, device, dtype):
tensor = torch.randn(12, device=device, dtype=dtype)[:6]
values = torch.empty_like(tensor)[::2]
indices = torch.empty(18, device=device, dtype=torch.long)[::3]
torch.sort(tensor, out=(values, indices))
values_cont, indices_cont = tensor.sort()
self.assertEqual(indices, indices_cont)
self.assertEqual(values, values_cont)
@dtypes(torch.float32)
def test_topk_1d_output_discontiguous(self, device, dtype):
tensor = torch.randn(12, device=device, dtype=dtype)
values = torch.empty_like(tensor)[::2]
indices = torch.empty(18, device=device, dtype=torch.long)[::3]
for sorted in (True, False):
# outputs of `sorted=False` test are not guaranteed to be the same,
# but with current implementation they are
torch.topk(tensor, 6, sorted=sorted, out=(values, indices))
values_cont, indices_cont = tensor.topk(6, sorted=sorted)
self.assertEqual(indices, indices_cont)
self.assertEqual(values, values_cont)
# FIXME: remove torch.bool from unsupported types once support is added for cub sort
@dtypes(*set(get_all_dtypes()) - {torch.bool, torch.complex64, torch.complex128})
def test_stable_sort_against_numpy(self, device, dtype):
if TEST_WITH_ROCM and dtype == torch.bfloat16:
return
if dtype in floating_types_and(torch.float16, torch.bfloat16):
inf = float('inf')
neg_inf = -float('inf')
nan = float('nan')
else:
if dtype != torch.bool:
# no torch.iinfo support for torch.bool
inf = torch.iinfo(dtype).max
neg_inf = torch.iinfo(dtype).min
else:
inf = True
neg_inf = ~inf
# no nan for integral types, we use inf instead for simplicity
nan = inf
def generate_samples():
from itertools import chain, combinations
for sizes in [(1025,), (10000,)]:
size = sizes[0]
# binary strings
yield (torch.tensor([0, 1] * size, dtype=dtype, device=device), 0)
if self.device_type == 'cuda':
return
yield (torch.tensor([0, 1] * 100, dtype=dtype, device=device), 0)
def repeated_index_fill(t, dim, idxs, vals):
res = t
for idx, val in zip(idxs, vals):
res = res.index_fill(dim, idx, val)
return res
for sizes in [(1, 10), (10, 1), (10, 10), (10, 10, 10)]:
size = min(*sizes)
x = (torch.randn(*sizes, device=device) * size).to(dtype)
yield (x, 0)
# Generate tensors which are being filled at random locations
# with values from the non-empty subsets of the set (inf, neg_inf, nan)
# for each dimension.
n_fill_vals = 3 # cardinality of (inf, neg_inf, nan)
for dim in range(len(sizes)):
idxs = (torch.randint(high=size, size=(size // 10,)) for i in range(n_fill_vals))
vals = (inf, neg_inf, nan)
subsets = chain.from_iterable(combinations(list(zip(idxs, vals)), r)
for r in range(1, n_fill_vals + 1))
for subset in subsets:
idxs_subset, vals_subset = zip(*subset)
yield (repeated_index_fill(x, dim, idxs_subset, vals_subset), dim)
for sample, dim in generate_samples():
_, idx_torch = sample.sort(dim=dim, stable=True)
if dtype is torch.bfloat16:
sample_numpy = sample.float().cpu().numpy()
else:
sample_numpy = sample.cpu().numpy()
idx_numpy = np.argsort(sample_numpy, axis=dim, kind='stable')
self.assertEqual(idx_torch, idx_numpy)
@dtypes(*(get_all_int_dtypes() + get_all_fp_dtypes()))
def test_msort(self, device, dtype):
if TEST_WITH_ROCM and dtype == torch.bfloat16:
return
def test(shape):
tensor = make_tensor(shape, device, dtype, low=-9, high=9)
if tensor.size() != torch.Size([]):
if dtype is torch.bfloat16:
expected = torch.from_numpy(np.msort(tensor.float().cpu().numpy())).bfloat16()
else:
expected = torch.from_numpy(np.msort(tensor.cpu().numpy()))
else:
expected = tensor # numpy.msort() does not support empty shapes tensor
result = torch.msort(tensor)
self.assertEqual(result, expected)
out = torch.empty_like(result)
torch.msort(tensor, out=out)
self.assertEqual(out, expected)
shapes = (
[],
[0, ],
[20, ],
[1, 20],
[30, 30],
[10, 20, 30]
)
for shape in shapes:
test(shape)
def test_topk(self, device):
def topKViaSort(t, k, dim, dir):
sorted, indices = t.sort(dim, dir)
return sorted.narrow(dim, 0, k), indices.narrow(dim, 0, k)
def compareTensors(t, res1, ind1, res2, ind2, dim):
# Values should be exactly equivalent
self.assertEqual(res1, res2, atol=0, rtol=0)
# Indices might differ based on the implementation, since there is
# no guarantee of the relative order of selection
if not ind1.eq(ind2).all():
# To verify that the indices represent equivalent elements,
# gather from the input using the topk indices and compare against
# the sort indices
vals = t.gather(dim, ind2)
self.assertEqual(res1, vals, atol=0, rtol=0)
def compare(t, k, dim, dir):
topKVal, topKInd = t.topk(k, dim, dir, True)
sortKVal, sortKInd = topKViaSort(t, k, dim, dir)
compareTensors(t, sortKVal, sortKInd, topKVal, topKInd, dim)
t = torch.rand(random.randint(1, SIZE),
random.randint(1, SIZE),
random.randint(1, SIZE), device=device)
for _kTries in range(3):
for _dimTries in range(3):
for transpose in (True, False):
for dir in (True, False):
testTensor = t
if transpose:
dim1 = random.randrange(t.ndimension())
dim2 = dim1
while dim1 == dim2:
dim2 = random.randrange(t.ndimension())
testTensor = t.transpose(dim1, dim2)
dim = random.randrange(testTensor.ndimension())
k = random.randint(1, testTensor.size(dim))
compare(testTensor, k, dim, dir)
# This tests the code path where on CUDA, topk is implemented with sort.
t = torch.randn((2, 100000), device=device)
compare(t, 2000, 1, True)
compare(t, 2000, 1, False)
def test_topk_arguments(self, device):
q = torch.randn(10, 2, 10, device=device)
# Make sure True isn't mistakenly taken as the 2nd dimension (interpreted as 1)
self.assertRaises(TypeError, lambda: q.topk(4, True))
@skipCUDAIfRocm
def test_unique_dim(self, device):
self.assertFalse(hasattr(torch, 'unique_dim'))
def run_test(device, dtype):
x = torch.tensor([[[1., 1.],
[0., 1.],
[2., 1.],
[0., 1.]],
[[1., 1.],
[0., 1.],
[2., 1.],
[0., 1.]]],
dtype=dtype,
device=device)
x_empty = torch.empty(5, 0, dtype=dtype, device=device)
x_ill_formed_empty = torch.empty(5, 0, 0, dtype=dtype, device=device)
x_ill_formed_empty_another = torch.empty(5, 0, 5, dtype=dtype, device=device)
if dtype in floating_types_and(torch.float16, torch.bfloat16):
x_nan = torch.tensor([float("nan"), 0, 0, float("nan"), float("nan"), 1], dtype=dtype, device=device)
expected_unique_dim0 = torch.tensor([[[1., 1.],
[0., 1.],
[2., 1.],
[0., 1.]]],
dtype=dtype,
device=device)
expected_inverse_dim0 = torch.tensor([0, 0])
expected_counts_dim0 = torch.tensor([2])
expected_unique_dim1 = torch.tensor([[[0., 1.],
[1., 1.],
[2., 1.]],
[[0., 1.],
[1., 1.],
[2., 1.]]],
dtype=dtype,
device=device)
expected_unique_dim1_bool = torch.tensor([[[False, True], [True, True]],
[[False, True], [True, True]]],
dtype=torch.bool,
device=device)
expected_inverse_dim1 = torch.tensor([1, 0, 2, 0])
expected_inverse_dim1_bool = torch.tensor([1, 0, 1, 0])
expected_counts_dim1 = torch.tensor([2, 1, 1])
expected_counts_dim1_bool = torch.tensor([2, 2])
expected_unique_dim2 = torch.tensor([[[1., 1.],
[0., 1.],
[2., 1.],
[0., 1.]],
[[1., 1.],
[0., 1.],
[2., 1.],
[0., 1.]]],
dtype=dtype,
device=device)
expected_inverse_dim2 = torch.tensor([0, 1])
expected_counts_dim2 = torch.tensor([1, 1])
expected_unique_empty = torch.tensor([], dtype=dtype, device=device)
expected_inverse_empty = torch.tensor([], dtype=torch.long, device=device)
expected_counts_empty = torch.tensor([], dtype=torch.long, device=device)
if dtype in floating_types_and(torch.float16, torch.bfloat16):
expected_unique_nan = torch.tensor([float("nan"), 0, float("nan"), float("nan"), 1], dtype=dtype, device=device)
expected_inverse_nan = torch.tensor([0, 1, 1, 2, 3, 4], dtype=torch.long, device=device)
expected_counts_nan = torch.tensor([1, 2, 1, 1, 1], dtype=torch.long, device=device)
# dim0
x_unique = torch.unique(x, dim=0)
self.assertEqual(expected_unique_dim0, x_unique)
x_unique, x_inverse = torch.unique(
x,
return_inverse=True,
dim=0)
self.assertEqual(expected_unique_dim0, x_unique)
self.assertEqual(expected_inverse_dim0, x_inverse)
x_unique, x_counts = torch.unique(
x,
return_inverse=False,
return_counts=True,
dim=0)
self.assertEqual(expected_unique_dim0, x_unique)
self.assertEqual(expected_counts_dim0, x_counts)
x_unique, x_inverse, x_counts = torch.unique(
x,
return_inverse=True,
return_counts=True,
dim=0)
self.assertEqual(expected_unique_dim0, x_unique)
self.assertEqual(expected_inverse_dim0, x_inverse)
self.assertEqual(expected_counts_dim0, x_counts)
# dim1
x_unique = torch.unique(x, dim=1)
if x.dtype == torch.bool:
self.assertEqual(expected_unique_dim1_bool, x_unique)
else:
self.assertEqual(expected_unique_dim1, x_unique)
x_unique, x_inverse = torch.unique(
x,
return_inverse=True,
dim=1)
if x.dtype == torch.bool:
self.assertEqual(expected_unique_dim1_bool, x_unique)
self.assertEqual(expected_inverse_dim1_bool, x_inverse)
else:
self.assertEqual(expected_unique_dim1, x_unique)
self.assertEqual(expected_inverse_dim1, x_inverse)
x_unique, x_counts = torch.unique(
x,
return_inverse=False,
return_counts=True,
dim=1)
if x.dtype == torch.bool:
self.assertEqual(expected_unique_dim1_bool, x_unique)
self.assertEqual(expected_counts_dim1_bool, x_counts)
else:
self.assertEqual(expected_unique_dim1, x_unique)
self.assertEqual(expected_counts_dim1, x_counts)
x_unique, x_inverse, x_counts = torch.unique(
x,
return_inverse=True,
return_counts=True,
dim=1)
if x.dtype == torch.bool:
self.assertEqual(expected_unique_dim1_bool, x_unique)
self.assertEqual(expected_inverse_dim1_bool, x_inverse)
self.assertEqual(expected_counts_dim1_bool, x_counts)
else:
self.assertEqual(expected_unique_dim1, x_unique)
self.assertEqual(expected_inverse_dim1, x_inverse)
self.assertEqual(expected_counts_dim1, x_counts)
# dim2
x_unique = torch.unique(x, dim=2)
self.assertEqual(expected_unique_dim2, x_unique)
x_unique, x_inverse = torch.unique(
x,
return_inverse=True,
dim=2)
self.assertEqual(expected_unique_dim2, x_unique)
self.assertEqual(expected_inverse_dim2, x_inverse)
x_unique, x_counts = torch.unique(
x,
return_inverse=False,
return_counts=True,
dim=2)
self.assertEqual(expected_unique_dim2, x_unique)
self.assertEqual(expected_counts_dim2, x_counts)
x_unique, x_inverse, x_counts = torch.unique(
x,
return_inverse=True,
return_counts=True,
dim=2)
self.assertEqual(expected_unique_dim2, x_unique)
self.assertEqual(expected_inverse_dim2, x_inverse)
self.assertEqual(expected_counts_dim2, x_counts)
# test empty tensor
x_unique, x_inverse, x_counts = torch.unique(
x_empty,
return_inverse=True,
return_counts=True,
dim=1)
self.assertEqual(expected_unique_empty, x_unique)
self.assertEqual(expected_inverse_empty, x_inverse)
self.assertEqual(expected_counts_empty, x_counts)
# test tensor with nan
if dtype in floating_types_and(torch.float16, torch.bfloat16):
x_unique, x_inverse, x_counts = torch.unique(
x_nan,
return_inverse=True,
return_counts=True,
dim=0)
self.assertEqual(expected_unique_nan, x_unique)
self.assertEqual(expected_inverse_nan, x_inverse)
self.assertEqual(expected_counts_nan, x_counts)
# test not a well formed tensor
# Checking for runtime error, as this is the expected behaviour
with self.assertRaises(RuntimeError):
torch.unique(
x_ill_formed_empty,
return_inverse=True,
return_counts=True,
dim=1)
# test along dim2
with self.assertRaises(RuntimeError):
torch.unique(
x_ill_formed_empty_another,
return_inverse=True,
return_counts=True,
dim=2)
# test consecutive version
y = torch.tensor(
[[0, 1],
[0, 1],
[0, 1],
[1, 2],
[1, 2],
[3, 4],
[0, 1],
[0, 1],
[3, 4],
[1, 2]],
dtype=dtype,
device=device
)
# test tensor with nan
if dtype in floating_types_and(torch.float16, torch.bfloat16):
y_nan = torch.tensor([float("nan"), 0, 0, float("nan"), float("nan"), 1], dtype=dtype, device=device)
expected_y_unique = torch.tensor(
[[0, 1],
[1, 2],
[3, 4],
[0, 1],
[3, 4],
[1, 2]],
dtype=dtype,
device=device
)
expected_y_inverse = torch.tensor([0, 0, 0, 1, 1, 2, 3, 3, 4, 5], dtype=torch.int64, device=device)
expected_y_counts = torch.tensor([3, 2, 1, 2, 1, 1], dtype=torch.int64, device=device)
expected_y_inverse_bool = torch.tensor([0, 0, 0, 1, 1, 1, 2, 2, 3, 3], dtype=torch.int64, device=device)
expected_y_counts_bool = torch.tensor([3, 3, 2, 2], dtype=torch.int64, device=device)
if dtype in floating_types_and(torch.float16, torch.bfloat16):
expected_y_unique_nan = torch.tensor([float("nan"), 0, float("nan"), float("nan"), 1], dtype=dtype, device=device)
expected_y_inverse_nan = torch.tensor([0, 1, 1, 2, 3, 4], dtype=torch.long, device=device)
expected_y_counts_nan = torch.tensor([1, 2, 1, 1, 1], dtype=torch.long, device=device)
y_unique, y_inverse, y_counts = torch.unique_consecutive(y, return_inverse=True, return_counts=True, dim=0)
if x.dtype == torch.bool:
self.assertEqual(expected_y_inverse_bool, y_inverse)
self.assertEqual(expected_y_counts_bool, y_counts)
else:
self.assertEqual(expected_y_inverse, y_inverse)
self.assertEqual(expected_y_counts, y_counts)
# test tensor with nan
if dtype in floating_types_and(torch.float16, torch.bfloat16):
y_unique, y_inverse, y_counts = torch.unique_consecutive(
y_nan,
return_inverse=True,
return_counts=True,
dim=0)
self.assertEqual(expected_y_unique_nan, y_unique)
self.assertEqual(expected_y_inverse_nan, y_inverse)
self.assertEqual(expected_y_counts_nan, y_counts)
run_test(device, torch.float)
run_test(device, torch.double)
run_test(device, torch.long)
run_test(device, torch.uint8)
run_test(device, torch.bool)
@onlyCUDA
def test_topk_noncontiguous_gpu(self, device):
t = torch.randn(20, device=device)[::2]
top1, idx1 = t.topk(5)
top2, idx2 = t.contiguous().topk(5)
self.assertEqual(top1, top2)
self.assertEqual(idx1, idx2)
def _test_topk_dtype(self, device, dtype, integral, size):
if integral:
a = torch.randint(torch.iinfo(dtype).min, torch.iinfo(dtype).max,
size=(size,), dtype=dtype, device=device)
else:
a = torch.randn(size=(size,), dtype=dtype, device=device)
sort_topk = a.sort()[0][-(size // 2):].flip(0)
topk = a.topk(size // 2)
self.assertEqual(sort_topk, topk[0]) # check values
self.assertEqual(sort_topk, a[topk[1]]) # check indices
@dtypes(torch.int8, torch.uint8, torch.int16, torch.int32, torch.int64)
def test_topk_integral(self, device, dtype):
small = 10
large = 4096
for curr_size in (small, large):
self._test_topk_dtype(device, dtype, True, curr_size)
@onlyCUDA
@dtypes(torch.bfloat16)
@skipCUDAIfRocm
def test_topk_bfloat16(self, device, dtype):
small = 10
large = 8192
for curr_size in (small, large):
self._test_topk_dtype(device, dtype, False, curr_size)
@dtypesIfCUDA(*get_all_fp_dtypes())
@dtypes(torch.float, torch.double, torch.bfloat16)
def test_topk_nonfinite(self, device, dtype):
if TEST_WITH_ROCM and dtype == torch.bfloat16:
return
x = torch.tensor([float('nan'), float('inf'), 1e4, 0, -1e4, -float('inf')], device=device, dtype=dtype)
val, idx = x.topk(4)
expect = torch.tensor([float('nan'), float('inf'), 1e4, 0], device=device, dtype=dtype)
self.assertEqual(val, expect)
self.assertEqual(idx, [0, 1, 2, 3])
val, idx = x.topk(4, largest=False)
expect = torch.tensor([-float('inf'), -1e4, 0, 1e4], device=device, dtype=dtype)
self.assertEqual(val, expect)
self.assertEqual(idx, [5, 4, 3, 2])
def test_topk_4d(self, device):
x = torch.ones(2, 3072, 2, 2, device=device)
x[:, 1, :, :] *= 2.
x[:, 10, :, :] *= 1.5
val, ind = torch.topk(x, k=2, dim=1)
expected_ind = torch.ones(2, 2, 2, 2, dtype=torch.long, device=device)
expected_ind[:, 1, :, :] = 10
expected_val = torch.ones(2, 2, 2, 2, device=device)
expected_val[:, 0, :, :] *= 2.
expected_val[:, 1, :, :] *= 1.5
self.assertEqual(val, expected_val, atol=0, rtol=0)
self.assertEqual(ind, expected_ind, atol=0, rtol=0)
@onlyNativeDeviceTypes
@dtypesIfCUDA(*(get_all_dtypes(include_complex=False,
include_bool=False,
include_half=False,
include_bfloat16=True)))
@dtypes(*(get_all_dtypes(include_complex=False, include_bool=False, include_half=False, include_bfloat16=False)))
def test_topk_zero(self, device, dtype):
if TEST_WITH_ROCM and dtype == torch.bfloat16:
return
# https://github.com/pytorch/pytorch/issues/49205
t = torch.rand(2, 2, device=device).to(dtype=dtype)
val, idx = torch.topk(t, k=0, largest=False)
self.assertEqual(val.size(), torch.Size([2, 0]))
self.assertEqual(idx.size(), torch.Size([2, 0]))
def _test_unique_scalar_empty(self, dtype, device, f):
# test scalar
x = torch.tensor(0, dtype=dtype, device=device)
unique, inverse, counts = f(x, return_inverse=True, return_counts=True)
expected_unique = torch.tensor([0], dtype=dtype, device=device)
expected_inverse = torch.tensor(0, device=device)
expected_counts = torch.tensor([1], device=device)
self.assertEqual(unique, expected_unique)
self.assertEqual(inverse, expected_inverse)
self.assertEqual(counts, expected_counts)
# test zero sized tensor
x = torch.zeros((0, 0, 3), dtype=dtype, device=device)
unique, inverse, counts = f(x, return_inverse=True, return_counts=True)
expected_unique = torch.tensor([], dtype=dtype, device=device)
expected_inverse = torch.empty((0, 0, 3), dtype=torch.long, device=device)
expected_counts = torch.tensor([], dtype=torch.long, device=device)
self.assertEqual(unique, expected_unique)
self.assertEqual(inverse, expected_inverse)
self.assertEqual(counts, expected_counts)
def _test_unique_with_expects(self, device, dtype, f, x, expected_unique, expected_inverse, expected_counts, additional_shape):
def ensure_tuple(x):
if isinstance(x, torch.Tensor):
return (x,)
return x
for return_inverse in [True, False]:
for return_counts in [True, False]:
# test with expected
ret = ensure_tuple(f(x, return_inverse=return_inverse, return_counts=return_counts))
self.assertEqual(len(ret), 1 + int(return_inverse) + int(return_counts))
self.assertEqual(expected_unique, ret[0])
if return_inverse:
self.assertEqual(expected_inverse, ret[1])
if return_counts:
count_index = 1 + int(return_inverse)
self.assertEqual(expected_counts, ret[count_index])
# tests per-element unique on a higher rank tensor.
y = x.view(additional_shape)
y_unique, y_inverse, y_counts = f(y, return_inverse=True, return_counts=True)
self.assertEqual(expected_unique, y_unique)
self.assertEqual(expected_inverse.view(additional_shape), y_inverse)
self.assertEqual(expected_counts, y_counts)
@dtypesIfCPU(*set(get_all_dtypes()) - {torch.complex64, torch.complex128})
@dtypes(*set(get_all_dtypes()) - {torch.bfloat16, torch.complex64, torch.complex128})
def test_unique(self, device, dtype):
if dtype is torch.half and self.device_type == 'cpu':
return # CPU does not have half support
def ensure_tuple(x):
if isinstance(x, torch.Tensor):
return (x,)
return x
if dtype is torch.bool:
x = torch.tensor([True, False, False, False, True, False, True, False], dtype=torch.bool, device=device)
expected_unique = torch.tensor([False, True], dtype=torch.bool, device=device)
expected_inverse = torch.tensor([1, 0, 0, 0, 1, 0, 1, 0], dtype=torch.long, device=device)
expected_counts = torch.tensor([5, 3], dtype=torch.long, device=device)
else:
x = torch.tensor([1, 2, 3, 2, 8, 5, 2, 3], dtype=dtype, device=device)
expected_unique = torch.tensor([1, 2, 3, 5, 8], dtype=dtype, device=device)
expected_inverse = torch.tensor([0, 1, 2, 1, 4, 3, 1, 2], device=device)
expected_counts = torch.tensor([1, 3, 2, 1, 1], device=device)
# test sorted unique
fs = (
lambda x, **kwargs: torch.unique(x, sorted=True, **kwargs),
lambda x, **kwargs: x.unique(sorted=True, **kwargs),
)
x_sliced = torch.empty(x.size(0) * 2, dtype=dtype, device=device)[::2].copy_(x)
xs = (x, x_sliced)
for f, x in product(fs, xs):
self._test_unique_with_expects(device, dtype, f, x, expected_unique, expected_inverse, expected_counts, (2, 2, 2))
self._test_unique_scalar_empty(dtype, device, f)
# test unsorted unique
fs = (
lambda x, **kwargs: torch.unique(x, sorted=False, **kwargs),
lambda x, **kwargs: x.unique(sorted=False, **kwargs)
)
for f, x in product(fs, xs):
self._test_unique_scalar_empty(dtype, device, f)
for return_inverse, return_counts in product((True, False), repeat=2):
ret = ensure_tuple(f(x, return_inverse=return_inverse, return_counts=return_counts))
self.assertEqual(len(ret), 1 + int(return_inverse) + int(return_counts))
x_list = x.tolist()
x_unique_list = ret[0].tolist()
self.assertEqual(expected_unique.tolist(), sorted(x_unique_list))
if return_inverse:
x_inverse_list = ret[1].tolist()
for i, j in enumerate(x_inverse_list):
self.assertEqual(x_list[i], x_unique_list[j])
if return_counts:
count_index = 1 + int(return_inverse)
x_counts_list = ret[count_index].tolist()
for i, j in zip(x_unique_list, x_counts_list):
count = 0
for k in x_list:
if k == i:
count += 1
self.assertEqual(j, count)
@dtypesIfCPU(*set(get_all_dtypes()) - {torch.complex64, torch.complex128})
@dtypes(*set(get_all_dtypes()) - {torch.bfloat16, torch.complex64, torch.complex128})
def test_unique_consecutive(self, device, dtype):
if dtype is torch.half and self.device_type == 'cpu':
return # CPU does not have half support
if dtype is torch.bool:
x = torch.tensor([True, False, False, False, True, True, False, False, False], dtype=torch.bool, device=device)
expected_unique = torch.tensor([True, False, True, False], dtype=torch.bool, device=device)
expected_inverse = torch.tensor([0, 1, 1, 1, 2, 2, 3, 3, 3], dtype=torch.long, device=device)
expected_counts = torch.tensor([1, 3, 2, 3], dtype=torch.long, device=device)
else:
x = torch.tensor([1, 2, 2, 2, 5, 5, 2, 2, 3], dtype=dtype, device=device)
expected_unique = torch.tensor([1, 2, 5, 2, 3], dtype=dtype, device=device)
expected_inverse = torch.tensor([0, 1, 1, 1, 2, 2, 3, 3, 4], device=device)
expected_counts = torch.tensor([1, 3, 2, 2, 1], device=device)
for f in [torch.unique_consecutive, lambda x, **kwargs: x.unique_consecutive(**kwargs)]:
self._test_unique_with_expects(device, dtype, f, x, expected_unique, expected_inverse, expected_counts, (3, 3))
self._test_unique_scalar_empty(dtype, device, f)
@dtypes(torch.double)
def test_kthvalue(self, device, dtype):
SIZE = 50
x = torch.rand(SIZE, SIZE, SIZE, dtype=dtype, device=device)
x0 = x.clone()
k = random.randint(1, SIZE)
res1val, res1ind = torch.kthvalue(x, k, keepdim=False)
res2val, res2ind = torch.sort(x)
self.assertEqual(res1val[:, :], res2val[:, :, k - 1], atol=0, rtol=0)
self.assertEqual(res1ind[:, :], res2ind[:, :, k - 1], atol=0, rtol=0)
# test use of result tensors
k = random.randint(1, SIZE)
res1val = torch.tensor([], dtype=dtype, device=device)
res1ind = torch.tensor([], dtype=torch.long, device=device)
torch.kthvalue(x, k, keepdim=False, out=(res1val, res1ind))
res2val, res2ind = torch.sort(x)
self.assertEqual(res1val[:, :], res2val[:, :, k - 1], atol=0, rtol=0)
self.assertEqual(res1ind[:, :], res2ind[:, :, k - 1], atol=0, rtol=0)
# test non-default dim
k = random.randint(1, SIZE)
res1val, res1ind = torch.kthvalue(x, k, 0, keepdim=False)
res2val, res2ind = torch.sort(x, 0)
self.assertEqual(res1val, res2val[k - 1], atol=0, rtol=0)
self.assertEqual(res1ind, res2ind[k - 1], atol=0, rtol=0)
# non-contiguous
y = x.narrow(1, 0, 1)
y0 = y.contiguous()
k = random.randint(1, SIZE)
res1val, res1ind = torch.kthvalue(y, k)
res2val, res2ind = torch.kthvalue(y0, k)
self.assertEqual(res1val, res2val, atol=0, rtol=0)
self.assertEqual(res1ind, res2ind, atol=0, rtol=0)
# non-contiguous [Reference: https://github.com/pytorch/pytorch/issues/45721]
non_contig_t = torch.tensor([0, -1, 1, -2, 2], dtype=dtype, device=device)[::2]
expected_val, expected_ind = non_contig_t.contiguous().kthvalue(2)
non_contig_cpu_t = non_contig_t.cpu()
expected_val_cpu, expected_ind_cpu = non_contig_cpu_t.kthvalue(2)
out_val, out_ind = non_contig_t.kthvalue(2)
self.assertEqual(expected_val, out_val, atol=0, rtol=0)
self.assertEqual(expected_ind, out_ind, atol=0, rtol=0)
self.assertEqual(expected_val_cpu, out_val, atol=0, rtol=0)
self.assertEqual(expected_ind_cpu, out_ind, atol=0, rtol=0)
# check that the input wasn't modified
self.assertEqual(x, x0, atol=0, rtol=0)
# simple test case (with repetitions)
y = torch.tensor((3., 5, 4, 1, 1, 5), dtype=dtype, device=device)
self.assertEqual(torch.kthvalue(y, 3)[0], 3, atol=0, rtol=0)
self.assertEqual(torch.kthvalue(y, 2)[0], 1, atol=0, rtol=0)
# simple test case (with NaN)
SIZE = 50
x = torch.rand(SIZE, SIZE, SIZE, dtype=dtype, device=device)
x[torch.arange(SIZE), :, torch.randint(50, (50,))] = nan
ks = [random.randint(1, SIZE), 1, SIZE, SIZE - 1]
res2val, res2ind = torch.sort(x)
for k in ks:
res1val, res1ind = torch.kthvalue(x, k, keepdim=False)
self.assertEqual(res1val[:, :], res2val[:, :, k - 1], atol=0, rtol=0)
self.assertEqual(res1ind[:, :], res2ind[:, :, k - 1], atol=0, rtol=0)
@dtypes(torch.float)
@onlyNativeDeviceTypes # Fails on XLA
def test_kthvalue_scalar(self, device, dtype):
# Test scalar input (test case from https://github.com/pytorch/pytorch/issues/30818)
# Tests that passing a scalar tensor or 1D tensor with 1 element work either way
res = torch.tensor(2, device=device, dtype=dtype).kthvalue(1)
ref = torch.tensor([2], device=device, dtype=dtype).kthvalue(1)
self.assertEqual(res[0], ref[0].squeeze())
self.assertEqual(res[1], ref[1].squeeze())
@dtypes(*all_types())
@dtypesIfCUDA(*all_types_and(torch.half))
def test_isin(self, device, dtype):
def assert_isin_equal(a, b):
# Compare to the numpy reference implementation.
x = torch.isin(a, b)
a = a.cpu().numpy() if torch.is_tensor(a) else np.array(a)
b = b.cpu().numpy() if torch.is_tensor(b) else np.array(b)
y = np.isin(a, b)
self.assertEqual(x, y)
# multi-dim tensor, multi-dim tensor
a = torch.arange(24, device=device, dtype=dtype).reshape([2, 3, 4])
b = torch.tensor([[10, 20, 30], [0, 1, 3], [11, 22, 33]], device=device, dtype=dtype)
assert_isin_equal(a, b)
# zero-dim tensor
zero_d = torch.tensor(3, device=device, dtype=dtype)
assert_isin_equal(zero_d, b)
assert_isin_equal(a, zero_d)
assert_isin_equal(zero_d, zero_d)
# empty tensor
empty = torch.tensor([], device=device, dtype=dtype)
assert_isin_equal(empty, b)
assert_isin_equal(a, empty)
assert_isin_equal(empty, empty)
# scalar
assert_isin_equal(a, 6)
assert_isin_equal(5, b)
def define_expected(lst, invert=False):
expected = torch.tensor(lst, device=device)
if invert:
expected = expected.logical_not()
return expected
# Adapted from numpy's in1d tests
for mult in [1, 10]:
for invert in [False, True]:
a = torch.tensor([5, 7, 1, 2], device=device, dtype=dtype)
b = torch.tensor([2, 4, 3, 1, 5] * mult, device=device, dtype=dtype)
ec = define_expected([True, False, True, True], invert=invert)
c = torch.isin(a, b, assume_unique=True, invert=invert)
self.assertEqual(c, ec)
a[0] = 8
ec = define_expected([False, False, True, True], invert=invert)
c = torch.isin(a, b, assume_unique=True, invert=invert)
self.assertEqual(c, ec)
a[0], a[3] = 4, 8
ec = define_expected([True, False, True, False], invert=invert)
c = torch.isin(a, b, assume_unique=True, invert=invert)
self.assertEqual(c, ec)
a = torch.tensor([5, 4, 5, 3, 4, 4, 3, 4, 3, 5, 2, 1, 5, 5], device=device, dtype=dtype)
b = torch.tensor([2, 3, 4] * mult, device=device, dtype=dtype)
ec = define_expected([False, True, False, True, True, True, True, True, True,
False, True, False, False, False], invert=invert)
c = torch.isin(a, b, invert=invert)
self.assertEqual(c, ec)
b = torch.tensor([2, 3, 4] * mult + [5, 5, 4] * mult, device=device, dtype=dtype)
ec = define_expected([True, True, True, True, True, True, True, True, True, True,
True, False, True, True], invert=invert)
c = torch.isin(a, b, invert=invert)
self.assertEqual(c, ec)
a = torch.tensor([5, 7, 1, 2], device=device, dtype=dtype)
b = torch.tensor([2, 4, 3, 1, 5] * mult, device=device, dtype=dtype)
ec = define_expected([True, False, True, True], invert=invert)
c = torch.isin(a, b, invert=invert)
self.assertEqual(c, ec)
a = torch.tensor([5, 7, 1, 1, 2], device=device, dtype=dtype)
b = torch.tensor([2, 4, 3, 3, 1, 5] * mult, device=device, dtype=dtype)
ec = define_expected([True, False, True, True, True], invert=invert)
c = torch.isin(a, b, invert=invert)
self.assertEqual(c, ec)
a = torch.tensor([5, 5], device=device, dtype=dtype)
b = torch.tensor([2, 2] * mult, device=device, dtype=dtype)
ec = define_expected([False, False], invert=invert)
c = torch.isin(a, b, invert=invert)
self.assertEqual(c, ec)
# multi-dimensional input case using sort-based algo
for assume_unique in [False, True]:
a = torch.arange(6, device=device, dtype=dtype).reshape([2, 3])
b = torch.arange(3, 30, device=device, dtype=dtype)
ec = define_expected([[False, False, False], [True, True, True]], invert=invert)
c = torch.isin(a, b, invert=invert, assume_unique=assume_unique)
self.assertEqual(c, ec)
def test_isin_different_dtypes(self, device):
supported_types = all_types() if device == 'cpu' else all_types_and(torch.half)
for mult in [1, 10]:
for assume_unique in [False, True]:
for dtype1, dtype2 in product(supported_types, supported_types):
a = torch.tensor([1, 2, 3], device=device, dtype=dtype1)
b = torch.tensor([3, 4, 5] * mult, device=device, dtype=dtype2)
ec = torch.tensor([False, False, True], device=device)
c = torch.isin(a, b, assume_unique=assume_unique)
self.assertEqual(c, ec)
@onlyCUDA
@dtypes(*all_types())
def test_isin_different_devices(self, device, dtype):
a = torch.arange(6, device=device, dtype=dtype).reshape([2, 3])
b = torch.arange(3, 30, device='cpu', dtype=dtype)
with self.assertRaises(RuntimeError):
torch.isin(a, b)
c = torch.arange(6, device='cpu', dtype=dtype).reshape([2, 3])
d = torch.arange(3, 30, device=device, dtype=dtype)
with self.assertRaises(RuntimeError):
torch.isin(c, d)
class TestScatterGather(TestCase):
# Fills an index tensor with valid indices
def _fill_indices(self, idx, dim, dim_size, elems_per_row, m, n, o, unique_indices=True):
for i in range(1 if dim == 0 else m):
for j in range(1 if dim == 1 else n):
for k in range(1 if dim == 2 else o):
ii = [i, j, k]
ii[dim] = slice(0, idx.size(dim) + 1)
if unique_indices:
idx[tuple(ii)] = torch.randperm(dim_size)[0:elems_per_row]
else:
idx[tuple(ii)] = torch.randint(dim_size, (elems_per_row,))
@dtypes(torch.float32, torch.complex64)
def test_gather(self, device, dtype):
m, n, o = random.randint(10, 20), random.randint(10, 20), random.randint(10, 20)
elems_per_row = random.randint(1, 10)
dim = random.randrange(3)
src = make_tensor((m, n, o), device=device, dtype=dtype)
idx_size = [m, n, o]
idx_size[dim] = elems_per_row
idx = make_tensor(idx_size, device=device, dtype=torch.long)
self._fill_indices(idx, dim, src.size(dim), elems_per_row, m, n, o)
actual = torch.gather(src, dim, idx)
expected = torch.zeros(idx_size, device=device, dtype=dtype)
for i in range(idx_size[0]):
for j in range(idx_size[1]):
for k in range(idx_size[2]):
ii = [i, j, k]
ii[dim] = idx[i, j, k]
expected[i, j, k] = src[tuple(ii)]
self.assertEqual(actual, expected, atol=0, rtol=0)
# Guarded because torch.max isn't defined for complex types
if not dtype.is_complex:
src = make_tensor((3, 4, 5), device=device, dtype=dtype)
expected, idx = src.max(2, True)
actual = torch.gather(src, 2, idx)
self.assertEqual(actual, expected, atol=0, rtol=0)
@dtypes(torch.bool)
def test_gather_bool(self, device, dtype):
src = torch.tensor(((False, True), (True, True)), device=device, dtype=dtype)
idx = torch.tensor(((0, 0), (1, 0)), device=device, dtype=torch.long)
actual = torch.gather(src, 1, idx)
expected = torch.tensor(((False, False), (True, True)), device=device, dtype=dtype)
self.assertEqual(actual, expected, atol=0, rtol=0)
def _test_scatter_base(self, fn, *, device, dtype, is_scalar, reduction,
unique_indices=True, include_self=True):
m, n, o = random.randint(10, 20), random.randint(10, 20), random.randint(10, 20)
elems_per_row = random.randint(1, 10)
dim = random.randrange(3)
idx_size = [m, n, o]
idx_size[dim] = elems_per_row
idx = torch.empty(tuple(idx_size), device=device, dtype=torch.long)
self._fill_indices(idx, dim, ([m, n, o])[dim], elems_per_row, m, n, o, unique_indices)
if is_scalar:
src = random.random()
else:
src_size = [random.randint(1, 5) + s for s in idx_size]
src = make_tensor(tuple(src_size), device=device, dtype=dtype)
base = make_tensor((m, n, o), device=device, dtype=dtype)
if reduction is not None:
if fn is torch.Tensor.scatter_reduce_:
actual = fn(base.clone(), dim, idx, src, reduce=reduction, include_self=include_self)
else:
actual = fn(base.clone(), dim, idx, src, reduce=reduction)
else:
actual = fn(base.clone(), dim, idx, src)
expected = base.clone()
counts = torch.zeros(base.shape, dtype=torch.long, device=device) + include_self
for i in range(idx_size[0]):
for j in range(idx_size[1]):
for k in range(idx_size[2]):
ii = [i, j, k]
ii[dim] = idx[i, j, k]
if fn is torch.Tensor.scatter_add_:
expected[tuple(ii)] += src[i, j, k]
else:
# method may be 'scatter_', 'scatter', 'scatter_reduce'
# or 'scatter_reduce_', the former two might have a reduction argument
# while the latter two always do
value = src if is_scalar else src[i, j, k]
if ((not include_self) and counts[tuple(ii)] == 0):
expected[tuple(ii)] = value
else:
if reduction == "add" or reduction == "sum":
expected[tuple(ii)] += value
elif reduction == "multiply" or reduction == "prod":
expected[tuple(ii)] *= value
elif reduction == "amax":
expected[tuple(ii)] = max(expected[tuple(ii)], value)
elif reduction == "amin":
expected[tuple(ii)] = min(expected[tuple(ii)], value)
elif reduction == "mean":
expected[tuple(ii)] += value
else:
expected[tuple(ii)] = value
counts[tuple(ii)] += 1
if (reduction == "mean"):
counts.masked_fill_(counts == 0, 1)
if (dtype.is_floating_point or dtype.is_complex):
expected /= counts
else:
expected.div_(counts, rounding_mode="floor")
self.assertEqual(actual, expected, atol=0, rtol=0)
# Tests empty index
dst = make_tensor((2, 2), device=device, dtype=dtype)
idx = torch.tensor((), device=device, dtype=torch.long)
src = make_tensor((2, 2), device=device, dtype=dtype)
if reduction is not None:
actual = fn(dst, 0, idx, src, reduce=reduction)
else:
actual = fn(dst, 0, idx, src)
self.assertEqual(actual, dst, atol=0, rtol=0)
@dtypes(torch.float16, torch.float32, torch.complex64)
def test_scatter_(self, device, dtype):
self._test_scatter_base(torch.Tensor.scatter_, device=device, dtype=dtype,
is_scalar=False, reduction=None)
@dtypes(torch.float16, torch.float32, torch.complex64)
def test_scatter__scalar(self, device, dtype):
self._test_scatter_base(torch.Tensor.scatter_, device=device, dtype=dtype,
is_scalar=True, reduction=None)
# FIXME: RuntimeError: "cuda_scatter_gather_base_kernel_reduce_multiply" not implemented for 'ComplexFloat'
@toleranceOverride({torch.float16: tol(atol=1e-2, rtol=0)})
@dtypesIfCUDA(torch.float16, torch.float32)
@dtypes(torch.float16, torch.float32, torch.complex64)
def test_scatter__reductions(self, device, dtype):
for reduction in ("add", "multiply"):
self._test_scatter_base(torch.Tensor.scatter_, device=device, dtype=dtype,
is_scalar=False, reduction=reduction)
self._test_scatter_base(torch.Tensor.scatter_, device=device, dtype=dtype,
is_scalar=True, reduction=reduction)
@dtypes(torch.float16, torch.float32, torch.complex64)
def test_scatter_add_(self, device, dtype):
self._test_scatter_base(torch.Tensor.scatter_add_, device=device, dtype=dtype,
is_scalar=False, reduction=None)
@dtypes(torch.float32)
def test_scatter_add_mult_index_base(self, device, dtype):
m, n = 30, 40
idx = torch.zeros(m, n, device=device, dtype=torch.long)
src = torch.ones(m, n, device=device, dtype=dtype)
res0 = torch.zeros(m, n, device=device, dtype=dtype).scatter_add_(0, idx, src)
res1 = torch.zeros(m, n, device=device, dtype=dtype).scatter_add_(1, idx, src)
self.assertEqual(res0[0, :], m * torch.ones(n, device=device, dtype=dtype), atol=0, rtol=0)
self.assertEqual(res1[:, 0], n * torch.ones(m, device=device, dtype=dtype), atol=0, rtol=0)
# FIXME: discrepancy between bool ReduceAdd on CUDA and CPU (a + b on CPU and buggy a && b on CUDA)
@dtypes(*get_all_dtypes(include_half=True, include_bfloat16=True, include_bool=False))
def test_scatter_reduce_sum(self, device, dtype):
for include_self in (True, False):
self._test_scatter_base(torch.Tensor.scatter_reduce_, device=device, dtype=dtype,
is_scalar=False, reduction='sum', unique_indices=False,
include_self=include_self)
@dtypes(*get_all_dtypes(include_half=True, include_bfloat16=True))
@dtypesIfCUDA(*get_all_fp_dtypes(include_half=True, include_bfloat16=True))
def test_scatter_reduce_prod(self, device, dtype):
for include_self in (True, False):
self._test_scatter_base(torch.Tensor.scatter_reduce_, device=device, dtype=dtype,
is_scalar=False, reduction='prod', unique_indices=False,
include_self=include_self)
@dtypes(*get_all_dtypes(include_half=True, include_bfloat16=True, include_bool=False))
@dtypesIfCUDA(*get_all_fp_dtypes(include_half=True, include_bfloat16=True))
def test_scatter_reduce_mean(self, device, dtype):
for include_self in (True, False):
self._test_scatter_base(torch.Tensor.scatter_reduce_, device=device, dtype=dtype,
is_scalar=False, reduction='mean', unique_indices=False,
include_self=include_self)
@dtypes(*get_all_dtypes(include_half=True, include_bfloat16=True, include_complex=False))
@dtypesIfCUDA(*get_all_fp_dtypes(include_half=True, include_bfloat16=True))
def test_scatter_reduce_amax(self, device, dtype):
for include_self in (True, False):
self._test_scatter_base(torch.Tensor.scatter_reduce_, device=device, dtype=dtype,
is_scalar=False, reduction='amax', unique_indices=False,
include_self=include_self)
# simple test for nan/inf propagation
if (dtype.is_floating_point):
input = torch.zeros(3, device=device, dtype=dtype)
src = torch.tensor([1, float('nan'), -float('inf'), -float('inf'), 2, float('inf')], device=device, dtype=dtype)
idx = torch.tensor([0, 0, 1, 1, 2, 2], device=device)
input.scatter_reduce_(0, idx, src, 'amax', include_self=include_self)
expected_result = torch.tensor([float('nan'), -float('inf'), float('inf')], device=device, dtype=dtype)
if (include_self):
expected_result[1] = 0
self.assertEqual(input, expected_result)
@dtypes(*get_all_dtypes(include_half=True, include_bfloat16=True, include_complex=False))
@dtypesIfCUDA(*get_all_fp_dtypes(include_half=True, include_bfloat16=True))
def test_scatter_reduce_amin(self, device, dtype):
for include_self in (True, False):
self._test_scatter_base(torch.Tensor.scatter_reduce_, device=device, dtype=dtype,
is_scalar=False, reduction='amin', unique_indices=False,
include_self=include_self)
# simple test for nan/inf propagation
if (dtype.is_floating_point):
input = torch.zeros(3, device=device, dtype=dtype)
src = torch.tensor([1, float('nan'), -2, -float('inf'), float('inf'), float('inf')], device=device, dtype=dtype)
idx = torch.tensor([0, 0, 1, 1, 2, 2], device=device)
input.scatter_reduce_(0, idx, src, 'amin', include_self=include_self)
expected_result = torch.tensor([float('nan'), -float('inf'), float('inf')], device=device, dtype=dtype)
if (include_self):
expected_result[2] = 0
self.assertEqual(input, expected_result)
