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[numpy] torch.erfinv: promote integer inputs to float #49155

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[numpy] torch.erfinv: promote integer inputs to float #49155

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03f7ac1
erfinv: support int -> float promotion
kshitij12345 Dec 10, 2020
0759a06
update relevant test
kshitij12345 Dec 10, 2020
cea5d4f
skip test for scipy version less than 1.4.0
kshitij12345 Dec 10, 2020
0a671dd
remove redundant tests
kshitij12345 Dec 10, 2020
86b2ef5
Merge branch 'master' into develop/numpy/unary-float-op/erfinv
kshitij12345 Dec 10, 2020
327c5cc
move helper to common_utils
kshitij12345 Dec 11, 2020
c8c5794
pass required_version as named arg
kshitij12345 Dec 11, 2020
431e9b0
fix typo
kshitij12345 Dec 11, 2020
bfce787
fix condition for skip
kshitij12345 Dec 11, 2020
3617c29
use default value for handles_extremals and handles_large_floats
kshitij12345 Dec 15, 2020
6304dec
Merge branch 'master' into develop/numpy/unary-float-op/erfinv
kshitij12345 Dec 15, 2020
a17a769
remove bfloat16 skips
kshitij12345 Dec 21, 2020
40f5148
use LooseVersion
kshitij12345 Dec 21, 2020
72c0f9b
Merge branch 'master' into develop/numpy/unary-float-op/erfinv
kshitij12345 Dec 21, 2020
894a1ad
Merge branch 'master' into develop/numpy/unary-float-op/erfinv
kshitij12345 Dec 22, 2020
08a59d7
add bfloat16 skip
kshitij12345 Dec 22, 2020
c3166a1
Merge branch 'master' into develop/numpy/unary-float-op/erfinv
kshitij12345 Dec 23, 2020
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5 changes: 4 additions & 1 deletion aten/src/ATen/native/UnaryOps.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -266,6 +266,10 @@ Tensor& erfc_out(Tensor& result, const Tensor& self) { return unary_op_impl_floa
Tensor erfc(const Tensor& self) { return unary_op_impl_float(self, erfc_stub); }
Tensor& erfc_(Tensor& self) { return unary_op_impl_(self, at::erfc_out); }

Tensor& erfinv_out(Tensor& result, const Tensor& self) { return unary_op_impl_float_out(result, self, erfinv_stub); }
Tensor erfinv(const Tensor& self) { return unary_op_impl_float(self, erfinv_stub); }
Tensor& erfinv_(Tensor& self) { return unary_op_impl_(self, at::erfinv_out); }

Tensor& frac_out(Tensor& result, const Tensor& self) { return unary_op_impl_out(result, self, frac_stub); }
Tensor frac(const Tensor& self) { return unary_op_impl(self, at::frac_out); }
Tensor& frac_(Tensor& self) { return unary_op_impl_(self, at::frac_out); }
Expand Down Expand Up @@ -669,7 +673,6 @@ Tensor& mvlgamma_(Tensor& self, int64_t p) {
IMPLEMENT_UNARY_OP_OUT_INPLACE(op, cpu, CPU) \
IMPLEMENT_UNARY_OP_OUT_INPLACE(op, cuda, CUDA)

IMPLEMENT_UNARY_OP_VEC_CUDA(erfinv)
IMPLEMENT_UNARY_OP_VEC_CUDA(lgamma)

DEFINE_DISPATCH(abs_stub);
Expand Down
2 changes: 1 addition & 1 deletion aten/src/ATen/native/cuda/UnaryOpsKernel.cu
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Expand Up @@ -147,7 +147,7 @@ void erfc_kernel_cuda(TensorIterator& iter) {
}

void erfinv_kernel_cuda(TensorIterator& iter) {
AT_DISPATCH_FLOATING_TYPES_AND_HALF(iter.dtype(), "erfinv_cuda", [&]() {
AT_DISPATCH_FLOATING_TYPES_AND_HALF(iter.common_dtype(), "erfinv_cuda", [&]() {
gpu_kernel(iter, []GPU_LAMBDA(scalar_t a) -> scalar_t {
return ::erfinv(a);
});
Expand Down
6 changes: 2 additions & 4 deletions aten/src/ATen/native/native_functions.yaml
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Expand Up @@ -6500,13 +6500,11 @@
use_c10_dispatcher: full
variants: method
dispatch:
CPU: _erfinv__cpu
CUDA: _erfinv__cuda
CPU, CUDA: erfinv_

- func: erfinv.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
dispatch:
CPU: _erfinv_out_cpu
CUDA: _erfinv_out_cuda
CPU, CUDA: erfinv_out

- func: i0(Tensor self) -> Tensor
use_c10_dispatcher: full
Expand Down
1 change: 0 additions & 1 deletion test/test_torch.py
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Expand Up @@ -6901,7 +6901,6 @@ def inner(self, device, dtype):
('atanh', '', _small_3d, lambda t, d: [], 1e-3, 1e-2, 1e-5, torch.testing.get_all_fp_dtypes()),
('erf', '', _small_3d, lambda t, d: [], 1e-3, 1e-2, 1e-5, torch.testing.get_all_fp_dtypes(), [torch.bfloat16]),
('erfc', '', _small_3d, lambda t, d: [], 1e-3, 1e-2, 1e-5, _float_types, [torch.bfloat16]),
('erfinv', '', _small_3d, lambda t, d: [], 1e-3, 1e-2, 1e-5, _float_types, [torch.bfloat16]),
('exp', '', _small_3d, lambda t, d: [], 1e-2, 5e-2, 1e-5, torch.testing.get_all_fp_dtypes()),
('exp', 'small', lambda t, d: _small_3d(t, d).clamp(-1, 1),
lambda t, d: [], 1e-2, 5e-2, 1e-5, torch.testing.get_all_fp_dtypes(), [torch.bfloat16]),
Expand Down
20 changes: 0 additions & 20 deletions test/test_unary_ufuncs.py
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Expand Up @@ -764,26 +764,6 @@ def test_ceil_out_mismatch(self, device):
b = torch.randn(1, device=device)
self.assertRaises(RuntimeError, lambda: torch.ceil(a, out=b))

# TODO: review with erfinv opinfo
@dtypesIfCUDA(torch.half, torch.float, torch.double)
@dtypes(torch.float, torch.double)
def test_erfinv(self, device, dtype):
# general testing. Narrow the range to avoid accuracy issues
input_values = torch.randn(4, 4, dtype=dtype, device=device).clamp(-0.3, 0.3)
self.assertEqual(input_values.erf().erfinv(), input_values)
# test inf
self.assertTrue(torch.equal(torch.tensor([-1, 1], dtype=dtype, device=device).erfinv(),
torch.tensor([-inf, inf], dtype=dtype, device=device)))
# test nan
self.assertEqual(torch.tensor([-2, 2], dtype=dtype, device=device).erfinv(),
torch.tensor([nan, nan], dtype=dtype, device=device))

if dtype == torch.double:
# double precision
a = torch.tensor([0.5, 0.8], dtype=torch.double, device=device).erfinv()
self.assertEqual(a[0].item(), 0.47693627620447, atol=1e-13, rtol=0)
self.assertEqual(a[1].item(), 0.90619380243682, atol=1e-13, rtol=0)

# TODO: opinfo hardshrink
@onlyCPU
@dtypes(torch.float, torch.double)
Expand Down
29 changes: 27 additions & 2 deletions torch/testing/_internal/common_methods_invocations.py
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Expand Up @@ -27,6 +27,14 @@
if TEST_SCIPY:
import scipy.special


def versiontuple(v):
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I would revise this to be a helper in torch/testing/_internal/common_utils.py called "version_at_least" and it accepts a version string plus a required version string, and returns true if the given version string is after the required version string. For example:

# True if version is at least required version, False otherwise
def version_at_least(version, required_version):
  ...

The version strings can be compared left to right and test should just be that any of version's ints are greater than the corresponding int in required_version or the version strings are the same.

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Sure. Will move the helper.

Also, comparison of two tuple is left to right and short-circuits at the first difference.
https://stackoverflow.com/a/60303999/5602957

"""
Helper function to compare versions.
Reference: https://stackoverflow.com/a/11887825/5602957
"""
return tuple(map(int, (v.split("."))))

class SkipInfo(object):
"""Describes which test, or type of tests, should be skipped when testing
an operator. Any test that matches all provided arguments will be skipped.
Expand Down Expand Up @@ -863,6 +871,25 @@ def reference_sigmoid(x):
dtypes=[torch.bfloat16]),),
assert_autodiffed=True,
promotes_integers_to_float=True),
UnaryUfuncInfo('erfinv',
ref=scipy.special.erfinv,
decorators=(precisionOverride({torch.float16: 1e-2,
torch.bfloat16: 1e-2,
torch.float32: 1e-4}),),
dtypes=all_types_and(torch.bool),
dtypesIfCPU=all_types_and(torch.bool, torch.bfloat16),
dtypesIfCUDA=all_types_and(torch.bool, torch.half),
promotes_integers_to_float=True,
handles_extremals=False,
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handles_large_floats=False,
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domain=(-1, 1),
skips=(
# Reference: https://github.com/pytorch/pytorch/pull/49155#issuecomment-742664611
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
active_if=versiontuple(scipy.__version__) < versiontuple("1.4.0")),
# RuntimeError: "pow" not implemented for 'BFloat16'
SkipInfo('TestCommon', 'test_variant_consistency_jit',
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dtypes=[torch.bfloat16]),),)
]
op_db = op_db + op_db_scipy_reference

Expand Down Expand Up @@ -1128,8 +1155,6 @@ def method_tests():
('expand_as', (S, 1, 1), (torch.rand(S, S, S),), '', (False,)),
('exp', (S, S, S), NO_ARGS, '', (True,)),
('exp', (), NO_ARGS, 'scalar', (True,)),
('erfinv', torch.rand(S, S, S).clamp(-0.9, 0.9), NO_ARGS),
('erfinv', normal_scalar_clamp(-0.9, 0.9, requires_grad=True), NO_ARGS, 'scalar'),
('logit', torch.randn(S, S, S).clamp(0.1, 0.9).requires_grad_(True), NO_ARGS, ''),
('logit', torch.randn(S, S, S).clamp(0.1, 0.9).requires_grad_(True), (0.2,), 'eps'),
('logit', uniform_scalar().clamp(0.1, 0.9).requires_grad_(True), NO_ARGS, 'scalar'),
Expand Down