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[numpy] torch.cos, torch.tan : promote integer inputs to float #46706

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[numpy] torch.cos, torch.tan : promote integer inputs to float #46706

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1fdd381
promote integer to float for cos and tan
kshitij12345 Oct 22, 2020
03e19c5
update tests
kshitij12345 Oct 22, 2020
ec4c55d
relax tolerace for tan
kshitij12345 Oct 22, 2020
2fa6235
remove cos from unary_op_out_casting
kshitij12345 Oct 22, 2020
89eaf29
update cos unaryop test to verify bfloat16
kshitij12345 Oct 22, 2020
02c01a6
Merge branch 'master' into develop/numpy/unary-float-ops/cos-tan
kshitij12345 Oct 24, 2020
11309b2
Merge branch 'master' into develop/numpy/unary-float-ops/cos-tan
kshitij12345 Oct 28, 2020
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8 changes: 4 additions & 4 deletions aten/src/ATen/native/UnaryOps.cpp
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Expand Up @@ -335,8 +335,8 @@ Tensor& sin_out(Tensor& result, const Tensor& self) { return unary_op_impl_float
Tensor sin(const Tensor& self) { return unary_op_impl_float(self, sin_stub); }
Tensor& sin_(Tensor& self) { return unary_op_impl_(self, at::sin_out); }

Tensor& cos_out(Tensor& result, const Tensor& self) { return unary_op_impl_out(result, self, cos_stub); }
Tensor cos(const Tensor& self) { return unary_op_impl(self, at::cos_out); }
Tensor& cos_out(Tensor& result, const Tensor& self) { return unary_op_impl_float_out(result, self, cos_stub); }
Tensor cos(const Tensor& self) { return unary_op_impl_float(self, cos_stub); }
Tensor& cos_(Tensor& self) { return unary_op_impl_(self, at::cos_out); }

Tensor& sinh_out(Tensor& result, const Tensor& self) { return unary_op_impl_out(result, self, sinh_stub); }
Expand Down Expand Up @@ -448,8 +448,8 @@ Tensor& tanh_out(Tensor& result, const Tensor& self) { return unary_op_impl_out(
Tensor tanh(const Tensor& self) { return unary_op_impl(self, at::tanh_out); }
Tensor& tanh_(Tensor& self) { return unary_op_impl_(self, at::tanh_out); }

Tensor& tan_out(Tensor& result, const Tensor& self) { return unary_op_impl_out(result, self, tan_stub); }
Tensor tan(const Tensor& self) { return unary_op_impl(self, at::tan_out); }
Tensor& tan_out(Tensor& result, const Tensor& self) { return unary_op_impl_float_out(result, self, tan_stub); }
Tensor tan(const Tensor& self) { return unary_op_impl_float(self, tan_stub); }
Tensor& tan_(Tensor& self) { return unary_op_impl_(self, at::tan_out); }

Tensor& trunc_out(Tensor& result, const Tensor& self) {
Expand Down
4 changes: 2 additions & 2 deletions aten/src/ATen/native/cuda/UnaryGeometricKernels.cu
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Expand Up @@ -43,7 +43,7 @@ void sin_kernel_cuda(TensorIterator& iter) {
}

void cos_kernel_cuda(TensorIterator& iter) {
AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND2(ScalarType::Half, ScalarType::BFloat16, iter.dtype(), "cos_cuda", [&]() {
AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND2(ScalarType::Half, ScalarType::BFloat16, iter.common_dtype(), "cos_cuda", [&]() {
gpu_kernel(iter, []GPU_LAMBDA(scalar_t a) -> scalar_t {
return ::cos(a);
});
Expand Down Expand Up @@ -99,7 +99,7 @@ void atanh_kernel_cuda(TensorIterator& iter) {
}

void tan_kernel_cuda(TensorIterator& iter) {
AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND1(ScalarType::Half, iter.dtype(), "tan_cuda", [&]() {
AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND1(ScalarType::Half, iter.common_dtype(), "tan_cuda", [&]() {
gpu_kernel(iter, []GPU_LAMBDA(scalar_t a) -> scalar_t {
return ::tan(a);
});
Expand Down
4 changes: 2 additions & 2 deletions test/test_type_promotion.py
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Expand Up @@ -919,8 +919,8 @@ def test_unary_op_out_casting(self, device, dtypes):
t = torch.tensor((1), dtype=dtypes[0], device=device)
out = torch.empty(0, dtype=dtypes[1], device=device)

ops = (torch.neg, torch.floor, torch.ceil, torch.cos, torch.erf, torch.log)
float_only_ops = {torch.floor, torch.ceil, torch.cos, torch.erf, torch.log}
ops = (torch.neg, torch.floor, torch.ceil, torch.erf, torch.log)
float_only_ops = {torch.floor, torch.ceil, torch.erf, torch.log}
real_only_ops = {torch.floor, torch.ceil, torch.erf}
for op in ops:
if dtypes[0] is not dtypes[1]:
Expand Down
2 changes: 1 addition & 1 deletion test/test_unary_ufuncs.py
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Expand Up @@ -288,7 +288,7 @@ def test_reference_numerics(self, device, dtype, op):
# NOTE: For these dtypes, PyTorch computes in the default scalar type (float)
# while NumPy computes in float16
self.assertEqualHelper(actual, expected, msg, dtype=dtype,
exact_dtype=exact_dtype, rtol=1e-4, atol=1e-3)
exact_dtype=exact_dtype, rtol=1e-3, atol=1e-2)
continue

self.assertEqualHelper(actual, expected, msg, dtype=dtype, exact_dtype=exact_dtype)
Expand Down
9 changes: 8 additions & 1 deletion torch/testing/_internal/common_methods_invocations.py
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Expand Up @@ -274,8 +274,11 @@ def sample_inputs(self, device, dtype, requires_grad=False):
test_inplace_grad=False),
UnaryUfuncInfo('cos',
ref=np.cos,
dtypesIfCUDA=floating_and_complex_types_and(torch.half, torch.bfloat16),
dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16),
dtypesIfCPU=all_types_and_complex_and(torch.bool, torch.bfloat16),
dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16),
handles_large_floats=False,
promotes_integers_to_float=True,
decorators=(precisionOverride({torch.bfloat16: 1e-2}),),
skips=(
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
Expand Down Expand Up @@ -371,6 +374,10 @@ def sample_inputs(self, device, dtype, requires_grad=False):
)),
UnaryUfuncInfo('tan',
ref=np.tan,
dtypes=all_types_and_complex_and(torch.bool, torch.bfloat16),
dtypesIfCPU=all_types_and_complex_and(torch.bool, torch.bfloat16),
dtypesIfCUDA=all_types_and_complex_and(torch.bool, torch.half),
promotes_integers_to_float=True,
skips=(
SkipInfo('TestUnaryUfuncs', 'test_reference_numerics',
device_type='cuda', dtypes=[torch.cfloat, torch.cdouble]),
Expand Down