support PReLU for GPU platform

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

@@ -95,3 +95,19 @@ template void ReluGradV2(const size_t num, const int64_t *dy, const uint32_t *ma
         cudaStream_t cuda_stream);
 template void ReluGradV2(const size_t num, const uint8_t *dy, const uint32_t *mask, uint8_t *dx,
         cudaStream_t cuda_stream);
+
+template <typename T>
+__global__ void CalPReLUKernel(int size, T *input_addr, T *weight_addr, T *output_addr) {
+  for (int pos = blockIdx.x * blockDim.x + threadIdx.x; pos < size; pos += blockDim.x * gridDim.x) {
+    output_addr[pos] = input_addr[pos] > static_cast<T>(0) ? input_addr[pos] : *weight_addr * input_addr[pos];
+  }
+}
+
+template <typename T>
+void CalPReLU(int size, T *input_addr, T *weight_addr, T *output_addr, cudaStream_t cuda_stream) {
+  CalPReLUKernel<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, input_addr, weight_addr, output_addr);
+  return;
+}
+
+template void CalPReLU(int size, float *input_addr, float *weight_addr, float *output_addr, cudaStream_t cuda_stream);
+template void CalPReLU(int size, half *input_addr, half *weight_addr, half *output_addr, cudaStream_t cuda_stream);

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

@@ -25,4 +25,7 @@ template <typename T>
 void ReluV2(const size_t num, const T *x, T *y, uint32_t *mask, cudaStream_t cuda_stream);
 template <typename T>
 void ReluGradV2(const size_t num, const T *dy, const uint32_t *mask, T *dx, cudaStream_t cuda_stream);
+
+template <typename T>
+void CalPReLU(int input_size, T *input_addr, T *weight_addr, T *output_addr, cudaStream_t cuda_stream);
 #endif  // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_RELU_H_

mindspore/ccsrc/backend/kernel_compiler/gpu/nn/prelu_gpu_kernel.cc

@@ -0,0 +1,31 @@
+/**
+ * Copyright 2021 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/nn/prelu_gpu_kernel.h"
+
+namespace mindspore {
+namespace kernel {
+MS_REG_GPU_KERNEL_ONE(
+  PReLU,
+  KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
+  PReLUGpuKernel, half)
+
+MS_REG_GPU_KERNEL_ONE(
+  PReLU,
+  KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+  PReLUGpuKernel, float)
+}  // namespace kernel
+}  // namespace mindspore

mindspore/ccsrc/backend/kernel_compiler/gpu/nn/prelu_gpu_kernel.h

@@ -0,0 +1,111 @@
+/**
+ * Copyright 2021 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_NN_PRELU_GPU_KERNEL_H_
+#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_PRELU_GPU_KERNEL_H_
+
+#include <vector>
+#include <map>
+#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/relu_impl.cuh"
+
+namespace mindspore {
+namespace kernel {
+template <typename T>
+class PReLUGpuKernel : public GpuKernel {
+ public:
+  PReLUGpuKernel() { ResetResource(); }
+  ~PReLUGpuKernel() override {}
+  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> &,
+              const std::vector<AddressPtr> &outputs, void *stream_ptr) override {
+    if (is_null_input_) {
+      return true;
+    }
+    T *input = GetDeviceAddress<T>(inputs, 0);
+    T *weight = GetDeviceAddress<T>(inputs, 1);
+    T *output = GetDeviceAddress<T>(outputs, 0);
+
+    const int size = input_size_ / sizeof(T);
+    CalPReLU(size, input, weight, output, 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 != 2) {
+      MS_LOG(ERROR) << "Argument number is " << input_num << ", but ReLUGpuFwdKernel needs 2.";
+      return false;
+    }
+    auto input_shape = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0);
+    is_null_input_ = CHECK_NULL_INPUT(input_shape);
+    if (is_null_input_) {
+      MS_LOG(WARNING) << "PReLUGpuFwdKernel input is null.";
+    }
+    size_t size = 1;
+    for (size_t i = 0; i < input_shape.size(); i++) {
+      size *= input_shape[i];
+    }
+    input_size_ = size * sizeof(T);
+
+    auto weight_shape = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 1);
+    is_null_input_ = CHECK_NULL_INPUT(weight_shape);
+    if (is_null_input_) {
+      MS_LOG(WARNING) << "PReLUGpuFwdKernel weight is null.";
+    }
+    size = 1;
+    for (size_t i = 0; i < weight_shape.size(); i++) {
+      size *= weight_shape[i];
+    }
+    weight_size_ = size * sizeof(T);
+
+    InitSizeLists();
+    return true;
+  }
+
+  void ResetResource() noexcept override {
+    is_null_input_ = false;
+    input_size_list_.clear();
+    output_size_list_.clear();
+    workspace_size_list_.clear();
+    input_size_ = 0;
+    workspace_size_ = 0;
+  }
+
+ protected:
+  void InitSizeLists() override {
+    input_size_list_.push_back(input_size_);
+    output_size_list_.push_back(input_size_);
+    workspace_size_list_.push_back(workspace_size_);
+  }
+
+ private:
+  bool is_null_input_;
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+  size_t input_size_;
+  size_t weight_size_;
+  size_t workspace_size_;
+};
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_PRELU_GPU_KERNEL_H_

mindspore/nn/layer/activation.py

@@ -548,7 +548,7 @@ class PReLU(Cell):
         ValueError: If length of shape of `input_data` is equal to 1.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``GPU``
 
     Examples:
         >>> input_x = Tensor(np.array([[[[0.1, 0.6], [0.9, 0.9]]]]), mindspore.float32)

mindspore/ops/operations/nn_ops.py

@@ -1145,7 +1145,7 @@ class BatchNorm(PrimitiveWithInfer):
         TypeError: If dtype of `input_x`, `scale` or `mean` is neither float16 nor float32.
 
     Supported Platforms:
-        ``Ascend`` ``CPU``
+        ``Ascend`` ``CPU`` ``GPU``
 
     Examples:
         >>> input_x = Tensor(np.ones([2, 2]), mindspore.float32)
@@ -3533,7 +3533,7 @@ class PReLU(PrimitiveWithInfer):
         ValueError: If length of shape of `weight` is not equal to 1.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``GPU``
 
     Examples:
         >>> import mindspore

tests/st/ops/gpu/test_prelu_op.py

@@ -0,0 +1,74 @@
+# Copyright 2021 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
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import operations as P
+
+class NetPReLU(nn.Cell):
+    def __init__(self):
+        super(NetPReLU, self).__init__()
+        self.prelu = P.PReLU()
+
+    def construct(self, x, weight):
+        return self.prelu(x, weight)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_prelu_float16():
+    weight = Tensor(np.array([0.25]).astype(np.float16))
+    x = Tensor(np.array([[[[-1, 1, 10],
+                           [1, -1, 1],
+                           [10, 1, -1]]]]).astype(np.float16))
+    expect = np.array([[[[-0.25, 1, 10,],
+                         [1, -0.25, 1,],
+                         [10, 1, -0.25]]]]).astype(np.float16)
+
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
+    prelu = NetPReLU()
+    output = prelu(x, weight)
+    assert (output.asnumpy() == expect).all()
+
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+    prelu = NetPReLU()
+    output = prelu(x, weight)
+    assert (output.asnumpy() == expect).all()
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_prelu_float32():
+    weight = Tensor(np.array([0.25]).astype(np.float32))
+    x = Tensor(np.array([[[[-1, 1, 10],
+                           [1, -1, 1],
+                           [10, 1, -1]]]]).astype(np.float32))
+    expect = np.array([[[[-0.25, 1, 10,],
+                         [1, -0.25, 1,],
+                         [10, 1, -0.25]]]]).astype(np.float32)
+
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
+    prelu = NetPReLU()
+    output = prelu(x, weight)
+    assert (output.asnumpy() == expect).all()
+
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+    prelu = NetPReLU()
+    output = prelu(x, weight)
+    assert (output.asnumpy() == expect).all()