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

…46706) Summary: References #42515 cc: mruberry Pull Request resolved: #46706 Reviewed By: izdeby Differential Revision: D24537262 Pulled By: mruberry fbshipit-source-id: e57377a625814a3f34a765ce6bfd63a33c02a5d9
pytorch · Oct 29, 2020 · 5c8aad1 · 5c8aad1
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Showing 5 changed files with 17 additions and 10 deletions.
diff --git a/aten/src/ATen/native/UnaryOps.cpp b/aten/src/ATen/native/UnaryOps.cpp
@@ -339,8 +339,8 @@ 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); }
@@ -452,8 +452,8 @@ 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) {

diff --git a/aten/src/ATen/native/cuda/UnaryGeometricKernels.cu b/aten/src/ATen/native/cuda/UnaryGeometricKernels.cu
@@ -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);
     });
@@ -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);
     });

diff --git a/test/test_type_promotion.py b/test/test_type_promotion.py
@@ -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)
-        float_only_ops = {torch.floor, torch.ceil, torch.cos, torch.erf}
+        ops = (torch.neg, torch.floor, torch.ceil, torch.erf)
+        float_only_ops = {torch.floor, torch.ceil, torch.erf}
         real_only_ops = {torch.floor, torch.ceil, torch.erf}
         for op in ops:
             if dtypes[0] is not dtypes[1]:

diff --git a/test/test_unary_ufuncs.py b/test/test_unary_ufuncs.py
@@ -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)

diff --git a/torch/testing/_internal/common_methods_invocations.py b/torch/testing/_internal/common_methods_invocations.py
@@ -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',
@@ -380,6 +383,10 @@ 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]),