Support erf and erfc ant GPU backend

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

@@ -0,0 +1,32 @@
+/**
+ * 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.
+ */ 
+
+#include "erf_impl.cuh"
+template <typename T>
+__global__ void ErfKernel(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] = (T)erf(input[i]);
+  }
+  return;
+}
+
+template <typename T>
+void Erf(T *input, T *output, size_t count, cudaStream_t cuda_stream) {
+  ErfKernel<<<GET_BLOCKS(count), GET_THREADS, 0, cuda_stream>>>(input, output, count);
+  return;
+}
+
+template void Erf<float>(float *input, float *output, size_t count, cudaStream_t cuda_stream);

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

@@ -0,0 +1,25 @@
+/**
+ * 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.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_ERFIMPL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_ERFIMPL_H_
+
+#include <curand_kernel.h>
+#include "runtime/device/gpu/cuda_common.h"
+
+template <typename T>
+void Erf(T *input, T *output, size_t count, cudaStream_t cuda_stream);
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_ERFIMPL_H_

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

@@ -0,0 +1,32 @@
+/**
+ * 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.
+ */ 
+
+#include "erfc_impl.cuh"
+template <typename T>
+__global__ void ErfcKernel(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] = (T)erfc(input[i]);
+  }
+  return;
+}
+
+template <typename T>
+void Erfc(T *input, T *output, size_t count, cudaStream_t cuda_stream) {
+  ErfcKernel<<<GET_BLOCKS(count), GET_THREADS, 0, cuda_stream>>>(input, output, count);
+  return;
+}
+
+template void Erfc<float>(float *input, float *output, size_t count, cudaStream_t cuda_stream);

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

@@ -0,0 +1,25 @@
+/**
+ * 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.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_ERFIMPL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_ERFIMPL_H_
+
+#include <curand_kernel.h>
+#include "runtime/device/gpu/cuda_common.h"
+
+template <typename T>
+void Erfc(T *input, T *output, size_t count, cudaStream_t cuda_stream);
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_ERFIMPL_H_

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

@@ -0,0 +1,24 @@
+/**
+ * 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.
+ */
+
+#include "backend/kernel_compiler/gpu/math/erf_gpu_kernel.h"
+
+namespace mindspore {
+namespace kernel {
+MS_REG_GPU_KERNEL_ONE(Erf, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+                      ErfGpuKernel, float)
+}  // namespace kernel
+}  // namespace mindspore

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

@@ -0,0 +1,92 @@
+/**
+ * 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.
+ */
+
+#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_ERF_GPU_KERNEL_H_
+#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_ERF_GPU_KERNEL_H_
+
+#include <cuda_runtime_api.h>
+#include <vector>
+#include <string>
+#include "backend/kernel_compiler/gpu/gpu_kernel.h"
+#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
+#include "backend/kernel_compiler/gpu/cuda_impl/erf_impl.cuh"
+
+namespace mindspore {
+namespace kernel {
+template <typename T>
+class ErfGpuKernel : public GpuKernel {
+ public:
+  ErfGpuKernel() : input_size_(sizeof(T)), output_size_(sizeof(T)) {}
+  ~ErfGpuKernel() override = default;
+
+  const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
+  const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
+  const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
+
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs, void *stream_ptr) override {
+    VARIABLE_NOT_USED(workspace);
+    T *input_addr = GetDeviceAddress<T>(inputs, 0);
+    T *output_addr = GetDeviceAddress<T>(outputs, 0);
+
+    Erf(input_addr, output_addr, outputs[0]->size / sizeof(T), reinterpret_cast<cudaStream_t>(stream_ptr));
+    return true;
+  }
+
+  bool Init(const CNodePtr &kernel_node) override {
+    size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+    if (input_num != 1) {
+      MS_LOG(ERROR) << "Input number is " << input_num << ", but erf needs 3 inputs.";
+      return false;
+    }
+    size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
+    if (output_num != 1) {
+      MS_LOG(ERROR) << "Output number is " << output_num << ", but erf needs 1 output.";
+      return false;
+    }
+    auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+    for (size_t i = 0; i < input_shape.size(); i++) {
+      input_size_ *= input_shape[i];
+    }
+    auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0);
+    for (size_t i = 0; i < output_shape.size(); i++) {
+      output_size_ *= output_shape[i];
+    }
+    if (input_size_ != output_size_) {
+      MS_LOG(ERROR) << "Input size and output should be equal for Erf.";
+      return false;
+    }
+    InitSizeLists();
+    return true;
+  }
+
+ protected:
+  void InitSizeLists() override {
+    input_size_list_.push_back(input_size_);
+    output_size_list_.push_back(output_size_);
+  }
+
+ private:
+  size_t input_size_;
+  size_t output_size_;
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+};
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_ERF_GPU_KERNEL_H_

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

@@ -0,0 +1,24 @@
+/**
+ * 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.
+ */
+
+#include "backend/kernel_compiler/gpu/math/erfc_gpu_kernel.h"
+
+namespace mindspore {
+namespace kernel {
+MS_REG_GPU_KERNEL_ONE(Erfc, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+                      ErfcGpuKernel, float)
+}  // namespace kernel
+}  // namespace mindspore

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

@@ -0,0 +1,92 @@
+/**
+ * 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.
+ */
+
+#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_ERF_GPU_KERNEL_H_
+#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_ERF_GPU_KERNEL_H_
+
+#include <cuda_runtime_api.h>
+#include <vector>
+#include <string>
+#include "backend/kernel_compiler/gpu/gpu_kernel.h"
+#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
+#include "backend/kernel_compiler/gpu/cuda_impl/erfc_impl.cuh"
+
+namespace mindspore {
+namespace kernel {
+template <typename T>
+class ErfcGpuKernel : public GpuKernel {
+ public:
+  ErfcGpuKernel() : input_size_(sizeof(T)), output_size_(sizeof(T)) {}
+  ~ErfcGpuKernel() override = default;
+
+  const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
+  const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
+  const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
+
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs, void *stream_ptr) override {
+    VARIABLE_NOT_USED(workspace);
+    T *input_addr = GetDeviceAddress<T>(inputs, 0);
+    T *output_addr = GetDeviceAddress<T>(outputs, 0);
+
+    Erfc(input_addr, output_addr, outputs[0]->size / sizeof(T), reinterpret_cast<cudaStream_t>(stream_ptr));
+    return true;
+  }
+
+  bool Init(const CNodePtr &kernel_node) override {
+    size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+    if (input_num != 1) {
+      MS_LOG(ERROR) << "Input number is " << input_num << ", but erfc needs 3 inputs.";
+      return false;
+    }
+    size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
+    if (output_num != 1) {
+      MS_LOG(ERROR) << "Output number is " << output_num << ", but erfc needs 1 output.";
+      return false;
+    }
+    auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+    for (size_t i = 0; i < input_shape.size(); i++) {
+      input_size_ *= input_shape[i];
+    }
+    auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0);
+    for (size_t i = 0; i < output_shape.size(); i++) {
+      output_size_ *= output_shape[i];
+    }
+    if (input_size_ != output_size_) {
+      MS_LOG(ERROR) << "Input size and output should be equal for Erfc.";
+      return false;
+    }
+    InitSizeLists();
+    return true;
+  }
+
+ protected:
+  void InitSizeLists() override {
+    input_size_list_.push_back(input_size_);
+    output_size_list_.push_back(output_size_);
+  }
+
+ private:
+  size_t input_size_;
+  size_t output_size_;
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+};
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_ERF_GPU_KERNEL_H_

tests/st/ops/gpu/test_erf_op.py

@@ -0,0 +1,46 @@
+# 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
+from scipy import special
+
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import operations as P
+from mindspore import dtype
+
+context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+
+class NetErf(nn.Cell):
+    def __init__(self):
+        super(NetErf, self).__init__()
+        self.erf = P.Erf()
+
+    def construct(self, x):
+        return self.erf(x)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_exp():
+    erf = NetErf()
+    x = np.array([2.0, 3.0, 4.0, 5.0]).astype(np.float32)
+    output = erf(Tensor(x, dtype=dtype.float32))
+    expect = special.erf(x)
+    tol = 1e-6
+    assert (np.abs(output.asnumpy() - expect) < tol).all()

tests/st/ops/gpu/test_erfc_op.py

@@ -0,0 +1,46 @@
+# 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
+from scipy import special
+
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import operations as P
+from mindspore import dtype
+
+context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+
+class NetErfc(nn.Cell):
+    def __init__(self):
+        super(NetErfc, self).__init__()
+        self.erfc = P.Erfc()
+
+    def construct(self, x):
+        return self.erfc(x)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_exp():
+    erfc = NetErfc()
+    x = np.array([2.0, 3.0, 4.0, 5.0]).astype(np.float32)
+    output = erfc(Tensor(x, dtype=dtype.float32))
+    expect = special.erfc(x)
+    tol = 1e-6
+    assert (np.abs(output.asnumpy() - expect) < tol).all()