132 lines
5.0 KiB
132 lines
5.0 KiB
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include <stdio.h>
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#include <cassert>
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#include "glog/logging.h"
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#include "paddle/fluid/inference/tensorrt/plugin/prelu_op_plugin.h"
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namespace paddle {
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namespace inference {
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namespace tensorrt {
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static const int CUDA_NUM_THREADS = 1024;
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static const int CUDA_MAX_NUM_BLOCKS = 65535;
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inline static int GET_NUM_BLOCKS(const int N) {
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return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS;
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}
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__global__ void PReluChannelWiseKernel(const float *input, const float *alpha,
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float *output, int channel,
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size_t spatial_size) {
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size_t offset = blockIdx.x * spatial_size;
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const float *in = input + offset;
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float *out = output + offset;
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float scale = alpha[blockIdx.x % channel];
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for (size_t i = threadIdx.x; i < spatial_size; i += blockDim.x) {
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float x = in[i];
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out[i] = (x > 0) ? x : scale * x;
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}
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}
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__global__ void PReluElementWiseKernel(const float *input, const float *alpha,
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float *output, size_t spatial_size) {
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size_t offset = blockIdx.x * spatial_size;
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const float *in = input + offset;
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const float *scale = alpha + offset;
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float *out = output + offset;
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for (size_t i = threadIdx.x; i < spatial_size; i += blockDim.x) {
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float x = in[i];
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out[i] = (x > 0) ? x : scale[i] * x;
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}
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}
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__global__ void PReluScalarKernel(const float *input, const float *alpha,
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float *output, size_t spatial_size) {
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size_t offset = blockIdx.x * spatial_size;
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const float *in = input + offset;
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float scale = *alpha;
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float *out = output + offset;
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for (size_t i = threadIdx.x; i < spatial_size; i += blockDim.x) {
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float x = in[i];
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out[i] = (x > 0) ? x : scale * x;
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}
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}
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static inline void PReluChannelWise(cudaStream_t stream, const float *input,
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const float *alpha, float *output,
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int batch_size,
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const nvinfer1::Dims &dims) {
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size_t unroll = batch_size * dims.d[0];
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size_t spatial_size = dims.d[1] * dims.d[2];
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CHECK_LT(unroll, CUDA_MAX_NUM_BLOCKS);
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PReluChannelWiseKernel<<<unroll, CUDA_NUM_THREADS, 0, stream>>>(
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input, alpha, output, dims.d[0], spatial_size);
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}
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static inline void PReluElementWise(cudaStream_t stream, const float *input,
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const float *alpha, float *output,
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int batch_size,
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const nvinfer1::Dims &dims) {
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size_t unroll = batch_size * dims.d[0];
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size_t spatial_size = dims.d[1] * dims.d[2];
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CHECK_LT(unroll, CUDA_MAX_NUM_BLOCKS);
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PReluElementWiseKernel<<<unroll, CUDA_NUM_THREADS, 0, stream>>>(
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input, alpha, output, spatial_size);
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}
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static inline void PReluScalar(cudaStream_t stream, const float *input,
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const float *alpha, float *output,
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int batch_size, const nvinfer1::Dims &dims) {
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size_t unroll = batch_size * dims.d[0];
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size_t spatial_size = dims.d[1] * dims.d[2];
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CHECK_LT(unroll, CUDA_MAX_NUM_BLOCKS);
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PReluScalarKernel<<<unroll, CUDA_NUM_THREADS, 0, stream>>>(
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input, alpha, output, spatial_size);
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}
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nvinfer1::Dims PReluPlugin::getOutputDimensions(int index,
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const nvinfer1::Dims *inputDims,
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int nbInputs) {
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assert(nbInputs == 1);
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assert(index < this->getNbOutputs());
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nvinfer1::Dims const &input_dims = inputDims[0];
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nvinfer1::Dims output_dims = input_dims;
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return output_dims;
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}
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int PReluPlugin::enqueue(int batchSize, const void *const *inputs,
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void **outputs, void *workspace, cudaStream_t stream) {
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// input dims is CHW.
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const auto &input_dims = this->getInputDims(0);
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const float *input = reinterpret_cast<const float *>(inputs[0]);
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const float *alpha = reinterpret_cast<const float *>(alpha_.get().values);
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float *output = reinterpret_cast<float **>(outputs)[0];
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if (mode_ == "channel") {
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PReluChannelWise(stream, input, alpha, output, batchSize, input_dims);
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} else if (mode_ == "element") {
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PReluElementWise(stream, input, alpha, output, batchSize, input_dims);
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} else {
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PReluScalar(stream, input, alpha, output, batchSize, input_dims);
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}
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return cudaGetLastError() != cudaSuccess;
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}
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} // namespace tensorrt
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} // namespace inference
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} // namespace paddle
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