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【Hackathon No.38】为 Paddle 优化 deformable_conv op 在 GPU 上的计算性能 #218

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merged 6 commits into from Sep 7, 2022
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【Hackathon No.38】为 Paddle 优化 deformable_conv op 在 GPU 上的计算性能 #218

merged 6 commits into from Sep 7, 2022

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Rayman96
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@Rayman96 Rayman96 commented Aug 23, 2022 •
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提案内容有修改 辛苦审核

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@Rayman96 Rayman96 mentioned this pull request Aug 23, 2022
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ZzSean
ZzSean reviewed Aug 24, 2022
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rfcs/OPs-Perf/20220823_deformable_op_optimization.md Outdated
@@ -0,0 +1,91 @@
Poisson OP性能优化设计文档
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题目修改

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已修改

rfcs/OPs-Perf/20220823_deformable_op_optimization.md Outdated

# 1 背景与意义

目前Paddle中的Deformable_conv的GPU版是使用cuBlas+CUDA kernel实现的，kernel实现方式与论文原作者的实现类似是讲CPU的kernel迁移到了GPU上，对于CUDA代码未进行针对性优化。
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讲->将

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已修改

rfcs/OPs-Perf/20220823_deformable_op_optimization.md Outdated
对于此OP在目前飞桨框架（Develop分支）中的性能现状调研，表格形式列出[OP Benchmark](https://github.com/PaddlePaddle/benchmark/tree/master/api/tests_v2)中各种case场景下的OP性能数据（Tesla P4）。

### 时间分析
通过benchmark运行deformable_conv的测试，其中执行了前向和后向的过程，需要对二者使用时间进行拆分, 下表列出核心的几部分。
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其实运行op benchmark时将 --backward设置为False就可以，这样数据仅包含前向看起来更直观

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好的 学会了🙏 已更新数据

rfcs/OPs-Perf/20220823_deformable_op_optimization.md Outdated

根据kernels运行时间分析：65%时间消耗在No.5上，而cuBlas本身的实现难以有较大优化空间，所以单独优化两个kernel较难实现目标。

根据CUDA API时间分析：63%时间消耗在同步，26%时间消耗在内存分配。通过减少需要线程同步的次数，降低数据在内存和CUDA间迁移应该可以较大程度优化。故优化点1和优化点2是重点考虑的对象。总体来看运行过程是串行的，每个im2col结束后执行gemm，然后再进行下一个im2col，gemm的过程。
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降低数据在内存和CUDA间迁移，这里指的是Host与Device之间的数据拷贝吗？
这里能否将API的测试时间也补充一下

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指的是Host与Device之间的数据拷贝的。
这里API数据只计算前向的话分布有更新，添加到时间描述一节中，此处也已修改描述，通过API的情况无法看不出什么明显的结论

rfcs/OPs-Perf/20220823_deformable_op_optimization.md Outdated
+ 优化点1: 通过优化grid， block数量寻找更优配置。
+ 优化点2: 将deformable_conv_kernel_impl中计算像素和权重乘积的循环迁移到ModulatedDeformableIm2colGpuKernel中，将col_buffer的并行计算和output_3d的计算整合，减少部分搬运开销
+ 优化点3: 将deformable_conv_kernel_impl中的（batch_size / im2col_step）次循环并行化，目前用循环的方式im2col_step完成后才能进行下一个step，等待时间是无必要的。
+ 优化点4: 单独优化deformable_conv_kernel_impl中计算像素和权重乘积的循环；
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句尾标点符号还是统一一下

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已修改

rfcs/OPs-Perf/20220823_deformable_op_optimization.md
## 2.2 Host / Device 端计算流程
1. 针对优化点1: 考虑通过paddle已实现的gpu_launch_config.h中GetGpuLaunchConfig1D方法获得较优的参数配置，或手动对BlockSize的不同大小进行性能测试验证（可能有一定优化空间）

2. 针对优化点2: ModulatedDeformableIm2colGpuKernel的Host端多接入两个参数，Device端计算完成col_buffer后继续计算output（可能有较大优化空间）
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这里能否描述的更详细些？如使用配图或者伪代码，因为这个优化在上述描述中应该是重点

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已添加伪代码描述

rfcs/OPs-Perf/20220823_deformable_op_optimization.md

2. 针对优化点2: ModulatedDeformableIm2colGpuKernel的Host端多接入两个参数，Device端计算完成col_buffer后继续计算output（可能有较大优化空间）

3. 针对优化点3: 将整个im2col_step的过程并行化形成新的kernel，包含im2col和gemm两个步骤(可能有较大优化空间)
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同上

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已添加伪代码描述

rfcs/OPs-Perf/20220823_deformable_op_optimization.md Outdated

## 3 测试和验收的考量

实现前向速度提升超过25%
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能否给出提升超过25%的评估逻辑

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已修改

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请问下现在Paddle有办法使用动态并行吗？想实现在gpu kernel中调用blas计算矩阵乘法。
测试在kernel直接使用blas = phi::funcs::GetBlas会报错说calling a host function from a global function is not allowed

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ZzSean commented Sep 1, 2022

请问下现在Paddle有办法使用动态并行吗？想实现在gpu kernel中调用blas计算矩阵乘法。 测试在kernel直接使用blas = phi::funcs::GetBlas会报错说calling a host function from a global function is not allowed

cublas函数都是直接在host端调用的，内部可以理解为就是调用了cuda kernel，所以无法在global函数中再调用global函数

ZzSean
ZzSean approved these changes Sep 7, 2022
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LGTM

@ZzSean ZzSean merged commit cc3d3fc into PaddlePaddle:master Sep 7, 2022
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