Refine stack op to improve xlnet performance, test=develop (#22142)
stack's wait cost a lot of cpu time, use cuda kernel to do memory copy will reduce cpu time. Signed-off-by: zhaoyuchen <zhaoyuchen01@baidu.com>release/1.7
zhaoyuchen2018
5 years ago
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GitHub
parent
5e07db15e6
commit
3d4f2aa689
@ -0,0 +1,103 @@
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// Copyright (c) 2019 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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// Used for compute gpu launch parameter
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#pragma once
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#ifdef PADDLE_WITH_CUDA
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#include <cuda_runtime.h>
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#include <stddef.h>
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#include <algorithm>
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#include <string>
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#include <vector>
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namespace paddle {
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namespace platform {
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inline int DivUp(int a, int b) { return (a + b - 1) / b; }
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struct GpuLaunchParamConfig {
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dim3 theory_thread_count = dim3(0, 0, 0);
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dim3 thread_per_block = dim3(0, 0, 0);
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dim3 block_per_grid = dim3(0, 0, 0);
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};
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inline GpuLaunchParamConfig GetGpuLaunchConfig1D(
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const platform::CUDADeviceContext& context, int element_count) {
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PADDLE_ENFORCE_GT(element_count, 0, platform::errors::InvalidArgument(
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"element count should greater than 0,"
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" but received value is:%d",
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element_count));
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const int theory_thread_count = element_count;
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// Get Max threads in all SM
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int max_pyhsical_threads = context.GetMaxPhysicalThreadCount();
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int sm = context.GetSMCount();
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// Compute pyhsical threads we need, should small than max sm threads
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const int physical_thread_count =
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std::min(max_pyhsical_threads, theory_thread_count);
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// Need get from device
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const int thread_per_block = std::min(1024, context.GetMaxThreadsPerBlock());
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// Suppose block count small than factor * sm, factor is a experiments value.
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int factor = 4;
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const int block_count =
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std::min(DivUp(physical_thread_count, thread_per_block), factor * sm);
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GpuLaunchParamConfig config;
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config.theory_thread_count.x = theory_thread_count;
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config.thread_per_block.x = thread_per_block;
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config.block_per_grid.x = block_count;
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return config;
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}
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inline GpuLaunchParamConfig GetGpuLaunchConfig2D(
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const platform::CUDADeviceContext& context, int xdim, int ydim) {
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PADDLE_ENFORCE_GT(xdim, 0, platform::errors::InvalidArgument(
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"x dim number should greater than 0,"
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" but received value is:%d",
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xdim));
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PADDLE_ENFORCE_GT(ydim, 0, platform::errors::InvalidArgument(
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"y dim number should greater than 0,"
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" but received value is:%d",
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ydim));
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const int kThreadsPerBlock = 256;
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int block_cols = std::min(xdim, kThreadsPerBlock);
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int block_rows = std::max(kThreadsPerBlock / block_cols, 1);
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int max_physical_threads = context.GetMaxPhysicalThreadCount();
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const int max_blocks = std::max(max_physical_threads / kThreadsPerBlock, 1);
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GpuLaunchParamConfig config;
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// Noticed, block size is not align to 32, if needed do it yourself.
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config.theory_thread_count = dim3(xdim, ydim, 1);
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config.thread_per_block = dim3(block_cols, block_rows, 1);
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int grid_x = std::min(DivUp(xdim, block_cols), max_blocks);
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int grid_y = std::min(max_blocks / grid_x, std::max(ydim / block_rows, 1));
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config.block_per_grid = dim3(grid_x, grid_y, 1);
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return config;
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}
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// 3D will add later
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} // namespace platform
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} // namespace paddle
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#endif
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