add GPU operator

mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/unary_op_impl.cu

@@ -95,6 +95,34 @@ __global__ void RsqrtKernel(half *input, half *output, size_t count) {
   return;
 }
 template <typename T>
+__global__ void SinKernel(T *input, T *output, size_t count) {
+  for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < (count); i += blockDim.x * gridDim.x) {
+    output[i] = sin(input[i]);
+  }
+  return;
+}
+template <>
+__global__ void SinKernel(half *input, half *output, size_t count) {
+  for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < (count); i += blockDim.x * gridDim.x) {
+    output[i] = hsin(input[i]);
+  }
+  return;
+}
+template <typename T>
+__global__ void CosKernel(T *input, T *output, size_t count) {
+  for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < (count); i += blockDim.x * gridDim.x) {
+    output[i] = cos(input[i]);
+  }
+  return;
+}
+template <>
+__global__ void CosKernel(half *input, half *output, size_t count) {
+  for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < (count); i += blockDim.x * gridDim.x) {
+    output[i] = hcos(input[i]);
+  }
+  return;
+}
+template <typename T>
 __global__ void ZeroslikeKernel(T *output, size_t count) {
   T zero = 0.0;
   for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < (count); i += blockDim.x * gridDim.x) {
@@ -167,6 +195,16 @@ void Sqrt(T *input, T *output, size_t count, cudaStream_t cuda_stream) {
   return;
 }
 template <typename T>
+void Sin(T *input, T *output, size_t count, cudaStream_t cuda_stream) {
+  SinKernel<<<GET_BLOCKS(count), GET_THREADS, 0, cuda_stream>>>(input, output, count);
+  return;
+}
+template <typename T>
+void Cos(T *input, T *output, size_t count, cudaStream_t cuda_stream) {
+  CosKernel<<<GET_BLOCKS(count), GET_THREADS, 0, cuda_stream>>>(input, output, count);
+  return;
+}
+template <typename T>
 void Rsqrt(T *input, T *output, size_t count, cudaStream_t cuda_stream) {
   RsqrtKernel<<<GET_BLOCKS(count), GET_THREADS, 0, cuda_stream>>>(input, output, count);
   return;
@@ -193,6 +231,8 @@ template void Negative<float>(float *input, float *output, size_t count, cudaStr
 template void Reciprocal<float>(float *input, float *output, size_t count, cudaStream_t cuda_stream);
 template void Square<float>(float *input, float *output, size_t count, cudaStream_t cuda_stream);
 template void Sqrt<float>(float *input, float *output, size_t count, cudaStream_t cuda_stream);
+template void Sin<float>(float *input, float *output, size_t count, cudaStream_t cuda_stream);
+template void Cos<float>(float *input, float *output, size_t count, cudaStream_t cuda_stream);
 template void Rsqrt<float>(float *input, float *output, size_t count, cudaStream_t cuda_stream);
 template void Zeroslike<float>(float *output, size_t count, cudaStream_t cuda_stream);
 template void Abs<float>(float *input, float *output, size_t count, cudaStream_t cuda_stream);
@@ -203,6 +243,8 @@ template void Negative<half>(half *input, half *output, size_t count, cudaStream
 template void Reciprocal<half>(half *input, half *output, size_t count, cudaStream_t cuda_stream);
 template void Square<half>(half *input, half *output, size_t count, cudaStream_t cuda_stream);
 template void Sqrt<half>(half *input, half *output, size_t count, cudaStream_t cuda_stream);
+template void Sin<half>(half *input, half *output, size_t count, cudaStream_t cuda_stream);
+template void Cos<half>(half *input, half *output, size_t count, cudaStream_t cuda_stream);
 template void Rsqrt<half>(half *input, half *output, size_t count, cudaStream_t cuda_stream);
 template void Zeroslike<half>(half *output, size_t count, cudaStream_t cuda_stream);
 template void Abs<half>(half *input, half *output, size_t count, cudaStream_t cuda_stream);

mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/unary_op_impl.cuh

@@ -33,6 +33,10 @@ void Sqrt(T *input, T *output, size_t count, cudaStream_t cuda_stream);
 template <typename T>
 void Rsqrt(T *input, T *output, size_t count, cudaStream_t cuda_stream);
 template <typename T>
+void Sin(T *input, T *output, size_t count, cudaStream_t cuda_stream);
+template <typename T>
+void Cos(T *input, T *output, size_t count, cudaStream_t cuda_stream);
+template <typename T>
 void Zeroslike(T *output, size_t count, cudaStream_t cuda_stream);
 template <typename T>
 void Abs(T *input, T *output, size_t count, cudaStream_t cuda_stream);

mindspore/ccsrc/backend/kernel_compiler/gpu/math/unary_op_gpu_kernel.cc

@@ -46,6 +46,14 @@ MS_REG_GPU_KERNEL_ONE(Sqrt, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOut
                       UnaryOpGpuKernel, float)
 MS_REG_GPU_KERNEL_ONE(Rsqrt, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
                       UnaryOpGpuKernel, float)
+MS_REG_GPU_KERNEL_ONE(Sin, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+                      UnaryOpGpuKernel, float)
+MS_REG_GPU_KERNEL_ONE(Sin, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
+                      UnaryOpGpuKernel, half)
+MS_REG_GPU_KERNEL_ONE(Cos, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+                      UnaryOpGpuKernel, float)
+MS_REG_GPU_KERNEL_ONE(Cos, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
+                      UnaryOpGpuKernel, half)
 MS_REG_GPU_KERNEL_ONE(Abs, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
                       UnaryOpGpuKernel, float)
 MS_REG_GPU_KERNEL_ONE(Abs, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),

mindspore/ccsrc/backend/kernel_compiler/gpu/math/unary_op_gpu_kernel.h

@@ -36,6 +36,8 @@ enum UnaryOptype {
   UNARY_OP_SQUARE,
   UNARY_OP_SQRT,
   UNARY_OP_RSQRT,
+  UNARY_OP_SIN,
+  UNARY_OP_COS,
   UNARY_OP_ABS,
   UNARY_OP_FLOOR,
   UNARY_OP_INVALID_TYPE = 255
@@ -48,6 +50,8 @@ static const std::map<std::string, UnaryOptype> kUnaryOpTypeMap = {{"Exp", UNARY
                                                                    {"Square", UNARY_OP_SQUARE},
                                                                    {"Sqrt", UNARY_OP_SQRT},
                                                                    {"Rsqrt", UNARY_OP_RSQRT},
+                                                                   {"Sin", UNARY_OP_SIN},
+                                                                   {"Cos", UNARY_OP_COS},
                                                                    {"Abs", UNARY_OP_ABS},
                                                                    {"Floor", UNARY_OP_FLOOR}};
 template <typename T>
@@ -100,6 +104,14 @@ class UnaryOpGpuKernel : public GpuKernel {
         Rsqrt(input_addr, output_addr, inputs[0]->size / sizeof(T), reinterpret_cast<cudaStream_t>(stream_ptr));
         break;
       }
+      case UNARY_OP_SIN: {
+        Sin(input_addr, output_addr, inputs[0]->size / sizeof(T), reinterpret_cast<cudaStream_t>(stream_ptr));
+        break;
+      }
+      case UNARY_OP_COS: {
+        Cos(input_addr, output_addr, inputs[0]->size / sizeof(T), reinterpret_cast<cudaStream_t>(stream_ptr));
+        break;
+      }
       case UNARY_OP_ZEROSLIKE: {
         Zeroslike(output_addr, output_size_ / sizeof(T), reinterpret_cast<cudaStream_t>(stream_ptr));
         return true;

tests/st/ops/gpu/test_cos_op.py

@@ -0,0 +1,33 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import numpy as np
+import pytest
+
+import mindspore.context as context
+from mindspore import Tensor
+from mindspore.ops import operations as P
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_cos():
+    x_np = np.random.rand(2, 3, 4, 4).astype(np.float32)
+
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
+    output_ms = P.Cos()(Tensor(x_np))
+    output_np = np.cos(x_np)
+    assert np.allclose(output_ms.asnumpy(), output_np)

tests/st/ops/gpu/test_sin_op.py

@@ -0,0 +1,33 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import numpy as np
+import pytest
+
+import mindspore.context as context
+from mindspore import Tensor
+from mindspore.ops import operations as P
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_sin():
+    x_np = np.random.rand(2, 3, 4, 4).astype(np.float32)
+
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
+    output_ms = P.Sin()(Tensor(x_np))
+    output_np = np.sin(x_np)
+    assert np.allclose(output_ms.asnumpy(), output_np)