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add vectorization path on maxpool backward channel last #1907
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ac60deb
Update DilatedMaxPool2d.cpp
chunhuanMeng 76a5583
Update DilatedMaxPool2d.cpp
chunhuanMeng 18eb934
Update src/ATen/native/xpu/sycl/DilatedMaxPool2d.cpp
chunhuanMeng cefb88a
remove unnecessary var
chunhuanMeng 25d2766
fix
chunhuanMeng 357beb4
fix
chunhuanMeng ead7971
Merge branch 'main' into meng_opt_max_pool_backward
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| Original file line number | Diff line number | Diff line change | ||||||
|---|---|---|---|---|---|---|---|---|
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@@ -11,6 +11,7 @@ | |||||||
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||||||||
| #include <ATen/native/xpu/sycl/Atomics.h> | ||||||||
| #include <ATen/native/xpu/sycl/BatchKernel.h> | ||||||||
| #include <ATen/native/xpu/sycl/MemoryAccess.h> | ||||||||
| #include <ATen/native/xpu/sycl/NumericLimits.h> | ||||||||
| #include <comm/Runtime.h> | ||||||||
| #include <comm/SYCLHelpers.h> | ||||||||
|
|
@@ -251,30 +252,33 @@ struct MaxPool2dBackwardDeterministicKernelFunctor { | |||||||
| int pwstart = | ||||||||
| p_start(inputW, pad_w_, kernel_w_, dilation_w_, stride_w_); | ||||||||
| int pwend = p_end(inputW, pad_w_, gradOutputSizeW_, stride_w_); | ||||||||
| scalar_t grad = 0; | ||||||||
| if constexpr (is_channels_last) { | ||||||||
| int offset = batch * out_n_stride_ + plane; | ||||||||
| for (int ph = phstart; ph < phend; ++ph) { | ||||||||
| for (int pw = pwstart; pw < pwend; ++pw) { | ||||||||
| if (indices_[offset + (ph * gradOutputSizeW_ + pw) * numPlane_] == | ||||||||
| input_hw_index) { | ||||||||
| gradInput_[inputIndex] += static_cast<scalar_t>( | ||||||||
| grad += static_cast<scalar_t>( | ||||||||
| gradOutput_ | ||||||||
| [offset + (ph * gradOutputSizeW_ + pw) * numPlane_]); | ||||||||
| } | ||||||||
| } | ||||||||
| } | ||||||||
| } else { | ||||||||
| } | ||||||||
| else { | ||||||||
| int offset = batch * out_n_stride_ + plane * out_cf_c_stride_; | ||||||||
| for (int ph = phstart; ph < phend; ++ph) { | ||||||||
| for (int pw = pwstart; pw < pwend; ++pw) { | ||||||||
| if (indices_[offset + ph * gradOutputSizeW_ + pw] == | ||||||||
| input_hw_index) { | ||||||||
| gradInput_[inputIndex] += static_cast<scalar_t>( | ||||||||
| grad += static_cast<scalar_t>( | ||||||||
| gradOutput_[offset + ph * gradOutputSizeW_ + pw]); | ||||||||
| } | ||||||||
| } | ||||||||
| } | ||||||||
| } | ||||||||
| gradInput_[inputIndex] = grad; | ||||||||
| } | ||||||||
| } while (cfg_.next(item, desc)); | ||||||||
| } | ||||||||
|
|
@@ -349,6 +353,122 @@ struct MaxPool2dBackwardDeterministicKernelFunctor { | |||||||
| BatchKernelConfig cfg_; | ||||||||
| }; | ||||||||
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||||||||
| template <typename scalar_t, typename vec_t, int vec_size> | ||||||||
| struct MaxPool2dBackwardChannelLastVec { | ||||||||
| void operator()(sycl::nd_item<1> item) const { | ||||||||
| for (auto inputIndex = item.get_global_linear_id(); | ||||||||
| inputIndex < gradInputSize_ / vec_size; | ||||||||
| inputIndex += item.get_local_range(0) * item.get_group_range(0)) { | ||||||||
| int batch = inputIndex / (in_n_stride_ / vec_size); | ||||||||
| int plane; | ||||||||
| int64_t input_hw_index; | ||||||||
|
|
||||||||
| plane = inputIndex % (numPlane_ / vec_size); | ||||||||
| input_hw_index = | ||||||||
| ((inputIndex % in_n_stride_) - plane) / (numPlane_ / vec_size); | ||||||||
|
|
||||||||
| int inputW = input_hw_index % gradInputSizeW_; | ||||||||
| int inputH = input_hw_index / gradInputSizeW_; | ||||||||
| int phstart = p_start(inputH, pad_h_, kernel_h_, dilation_h_, stride_h_); | ||||||||
| int phend = p_end(inputH, pad_h_, gradOutputSizeH_, stride_h_); | ||||||||
| int pwstart = p_start(inputW, pad_w_, kernel_w_, dilation_w_, stride_w_); | ||||||||
| int pwend = p_end(inputW, pad_w_, gradOutputSizeW_, stride_w_); | ||||||||
| scalar_t grad = 0; | ||||||||
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|
||||||||
| int64_t load_offset, store_offset; | ||||||||
| store_offset = inputIndex; | ||||||||
| vec_t grad_vec; | ||||||||
| #pragma unroll | ||||||||
| for (int i = 0; i < vec_size; i++) { | ||||||||
| grad_vec[i] = 0; | ||||||||
| } | ||||||||
|
|
||||||||
| int offset = batch * (out_n_stride_ / vec_size) + plane; | ||||||||
| for (int ph = phstart; ph < phend; ++ph) { | ||||||||
| for (int pw = pwstart; pw < pwend; ++pw) { | ||||||||
| load_offset = | ||||||||
| offset + (ph * gradOutputSizeW_ + pw) * (numPlane_ / vec_size); | ||||||||
| vec_t gout_val_vec = gradOutput_[load_offset]; | ||||||||
| #pragma unroll | ||||||||
| for (int i = 0; i < vec_size; i++) { | ||||||||
| if (indices_[load_offset * vec_size + i] == input_hw_index) { | ||||||||
| grad_vec[i] = static_cast<scalar_t>(grad_vec[i]) + | ||||||||
| static_cast<scalar_t>(gout_val_vec[i]); | ||||||||
|
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There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. The cast
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Copilot uses AI. Check for mistakes. Positive FeedbackNegative Feedback |
||||||||
| } | ||||||||
| } | ||||||||
| } | ||||||||
| } | ||||||||
|
|
||||||||
| gradInput_[store_offset] = grad_vec; | ||||||||
| } | ||||||||
| } | ||||||||
| MaxPool2dBackwardChannelLastVec( | ||||||||
| vec_t* gradInput, | ||||||||
| const vec_t* gradOutput, | ||||||||
| const int64_t* indices, | ||||||||
| int numPlane, | ||||||||
| int gradInputSizeH, | ||||||||
| int gradInputSizeW, | ||||||||
| int gradOutputSizeH, | ||||||||
| int gradOutputSizeW, | ||||||||
| int64_t gradInputSize, | ||||||||
| int out_cf_c_stride, | ||||||||
| int in_cf_c_stride, | ||||||||
| int out_n_stride, | ||||||||
| int in_n_stride, | ||||||||
| int kernel_h, | ||||||||
| int kernel_w, | ||||||||
| int stride_h, | ||||||||
| int stride_w, | ||||||||
| int pad_h, | ||||||||
| int pad_w, | ||||||||
| int dilation_h, | ||||||||
| int dilation_w) | ||||||||
| : gradInput_(gradInput), | ||||||||
| gradOutput_(gradOutput), | ||||||||
| indices_(indices), | ||||||||
| numPlane_(numPlane), | ||||||||
| gradInputSizeH_(gradInputSizeH), | ||||||||
| gradInputSizeW_(gradInputSizeW), | ||||||||
| gradOutputSizeH_(gradOutputSizeH), | ||||||||
| gradOutputSizeW_(gradOutputSizeW), | ||||||||
| gradInputSize_(gradInputSize), | ||||||||
| out_cf_c_stride_(out_cf_c_stride), | ||||||||
| in_cf_c_stride_(in_cf_c_stride), | ||||||||
| out_n_stride_(out_n_stride), | ||||||||
| in_n_stride_(in_n_stride), | ||||||||
| kernel_h_(kernel_h), | ||||||||
| kernel_w_(kernel_w), | ||||||||
| stride_h_(stride_h), | ||||||||
| stride_w_(stride_w), | ||||||||
| pad_h_(pad_h), | ||||||||
| pad_w_(pad_w), | ||||||||
| dilation_h_(dilation_h), | ||||||||
| dilation_w_(dilation_w) {} | ||||||||
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||||||||
| private: | ||||||||
| vec_t* gradInput_; | ||||||||
| const vec_t* gradOutput_; | ||||||||
| const int64_t* indices_; | ||||||||
| int numPlane_; | ||||||||
| int gradInputSizeH_; | ||||||||
| int gradInputSizeW_; | ||||||||
| int gradOutputSizeH_; | ||||||||
| int gradOutputSizeW_; | ||||||||
| int64_t gradInputSize_; | ||||||||
| int out_cf_c_stride_; | ||||||||
| int in_cf_c_stride_; | ||||||||
| int out_n_stride_; | ||||||||
| int in_n_stride_; | ||||||||
| int kernel_h_; | ||||||||
| int kernel_w_; | ||||||||
| int stride_h_; | ||||||||
| int stride_w_; | ||||||||
| int pad_h_; | ||||||||
| int pad_w_; | ||||||||
| int dilation_h_; | ||||||||
| int dilation_w_; | ||||||||
| }; | ||||||||
|
|
||||||||
| template <typename scalar_t, bool is_channels_last> | ||||||||
| void launch_max_pool2d_kernel( | ||||||||
| scalar_t* output, | ||||||||
|
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@@ -397,6 +517,62 @@ void launch_max_pool2d_kernel( | |||||||
| sycl_kernel_submit(cfg.global_size(), cfg.group_size(), queue, kfn); | ||||||||
| } | ||||||||
|
|
||||||||
| #define LAUNCH_MAXPOOL_BACKWARD_CHANNEL_LAST_VEC( \ | ||||||||
| scalar_t, \ | ||||||||
| vec_size, \ | ||||||||
| num_wg, \ | ||||||||
| wg_size, \ | ||||||||
| queue, \ | ||||||||
| gradInput, \ | ||||||||
| gradOutput, \ | ||||||||
| indices, \ | ||||||||
| numPlane, \ | ||||||||
| gradInputSizeH, \ | ||||||||
| gradInputSizeW, \ | ||||||||
| gradOutputSizeH, \ | ||||||||
| gradOutputSizeW, \ | ||||||||
| gradInputSize, \ | ||||||||
| out_cf_c_stride, \ | ||||||||
| in_cf_c_stride, \ | ||||||||
| out_n_stride, \ | ||||||||
| in_n_stride, \ | ||||||||
| kernel_h, \ | ||||||||
| kernel_w, \ | ||||||||
| stride_h, \ | ||||||||
| stride_w, \ | ||||||||
| pad_h, \ | ||||||||
| pad_w, \ | ||||||||
| dilation_h, \ | ||||||||
| dilation_w) \ | ||||||||
| { \ | ||||||||
| using vec_t = memory::aligned_vector<scalar_t, vec_size>; \ | ||||||||
| const vec_t* grad_output_vec = reinterpret_cast<const vec_t*>(gradOutput); \ | ||||||||
| vec_t* grad_input_vec = reinterpret_cast<vec_t*>(gradInput); \ | ||||||||
| auto kfn = MaxPool2dBackwardChannelLastVec<scalar_t, vec_t, vec_size>( \ | ||||||||
| grad_input_vec, \ | ||||||||
| grad_output_vec, \ | ||||||||
| indices, \ | ||||||||
| numPlane, \ | ||||||||
| gradInputSizeH, \ | ||||||||
| gradInputSizeW, \ | ||||||||
| gradOutputSizeH, \ | ||||||||
| gradOutputSizeW, \ | ||||||||
| gradInputSize, \ | ||||||||
| out_cf_c_stride, \ | ||||||||
| in_cf_c_stride, \ | ||||||||
| out_n_stride, \ | ||||||||
| in_n_stride, \ | ||||||||
| kernel_h, \ | ||||||||
| kernel_w, \ | ||||||||
| stride_h, \ | ||||||||
| stride_w, \ | ||||||||
| pad_h, \ | ||||||||
| pad_w, \ | ||||||||
| dilation_h, \ | ||||||||
| dilation_w); \ | ||||||||
| sycl_kernel_submit(num_wg* wg_size, wg_size, queue, kfn); \ | ||||||||
| } | ||||||||
|
|
||||||||
| template <typename scalar_t, bool is_channels_last> | ||||||||
| void launch_max_pool2d_backward_kernel( | ||||||||
| scalar_t* gradInput, | ||||||||
|
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@@ -435,6 +611,119 @@ void launch_max_pool2d_backward_kernel( | |||||||
| // with CUDA in alexnet To avoid future problem, we decided to always use | ||||||||
| // deterministic path. | ||||||||
|
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||||||||
|
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||||||||
| // int vec_size = 1; | ||||||||
| // int thread_slots = syclGpuEuCount() * syclGpuHWThreadsPerEU(); | ||||||||
| // int num_sub_wg; | ||||||||
| // auto wg_size = syclDeviceMaxWorkGroupSize(); | ||||||||
| // int64_t num_wg; | ||||||||
| // if constexpr (is_channels_last) { | ||||||||
| // for (vec_size = std::min( | ||||||||
| // 8, memory::can_vectorize_up_to<scalar_t>((char*)gradOutput)); | ||||||||
| // vec_size >= 1; | ||||||||
| // vec_size /= 2) { | ||||||||
| // if (numPlane % vec_size != 0) { | ||||||||
| // continue; | ||||||||
| // } | ||||||||
| // num_sub_wg = gradInputSize / vec_size / syclMaxSubGroupSize(); | ||||||||
| // if (2 * num_sub_wg > thread_slots) { | ||||||||
| // int total_thread = gradInputSize / vec_size; | ||||||||
| // num_wg = (total_thread + wg_size - 1) / wg_size; | ||||||||
| // break; | ||||||||
| // } | ||||||||
| // } | ||||||||
| // switch (vec_size) { | ||||||||
| // case 8: | ||||||||
| // LAUNCH_MAXPOOL_BACKWARD_CHANNEL_LAST_VEC( | ||||||||
| // scalar_t, | ||||||||
| // 8, | ||||||||
| // num_wg, | ||||||||
| // wg_size, | ||||||||
| // queue, | ||||||||
| // gradInput, | ||||||||
| // gradOutput, | ||||||||
| // indices, | ||||||||
| // numPlane, | ||||||||
| // gradInputSizeH, | ||||||||
| // gradInputSizeW, | ||||||||
| // gradOutputSizeH, | ||||||||
| // gradOutputSizeW, | ||||||||
| // gradInputSize, | ||||||||
| // out_cf_c_stride, | ||||||||
| // in_cf_c_stride, | ||||||||
| // out_n_stride, | ||||||||
| // in_n_stride, | ||||||||
| // kernel_h, | ||||||||
| // kernel_w, | ||||||||
| // stride_h, | ||||||||
| // stride_w, | ||||||||
| // pad_h, | ||||||||
| // pad_w, | ||||||||
| // dilation_h, | ||||||||
| // dilation_w); | ||||||||
| // return; | ||||||||
| // case 4: | ||||||||
| // LAUNCH_MAXPOOL_BACKWARD_CHANNEL_LAST_VEC( | ||||||||
| // scalar_t, | ||||||||
| // 1, | ||||||||
|
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. The vec_size parameter should be 4, not 1, for the case 4 branch. This appears to be a copy-paste error that would prevent proper vectorization when vec_size is 4.
Suggested change
Copilot uses AI. Check for mistakes. Positive FeedbackNegative Feedback |
||||||||
| // num_wg, | ||||||||
| // wg_size, | ||||||||
| // queue, | ||||||||
| // gradInput, | ||||||||
| // gradOutput, | ||||||||
| // indices, | ||||||||
| // numPlane, | ||||||||
| // gradInputSizeH, | ||||||||
| // gradInputSizeW, | ||||||||
| // gradOutputSizeH, | ||||||||
| // gradOutputSizeW, | ||||||||
| // gradInputSize, | ||||||||
| // out_cf_c_stride, | ||||||||
| // in_cf_c_stride, | ||||||||
| // out_n_stride, | ||||||||
| // in_n_stride, | ||||||||
| // kernel_h, | ||||||||
| // kernel_w, | ||||||||
| // stride_h, | ||||||||
| // stride_w, | ||||||||
| // pad_h, | ||||||||
| // pad_w, | ||||||||
| // dilation_h, | ||||||||
| // dilation_w); | ||||||||
| // return; | ||||||||
| // case 2: | ||||||||
| // LAUNCH_MAXPOOL_BACKWARD_CHANNEL_LAST_VEC( | ||||||||
| // scalar_t, | ||||||||
| // 2, | ||||||||
| // num_wg, | ||||||||
| // wg_size, | ||||||||
| // queue, | ||||||||
| // gradInput, | ||||||||
| // gradOutput, | ||||||||
| // indices, | ||||||||
| // numPlane, | ||||||||
| // gradInputSizeH, | ||||||||
| // gradInputSizeW, | ||||||||
| // gradOutputSizeH, | ||||||||
| // gradOutputSizeW, | ||||||||
| // gradInputSize, | ||||||||
| // out_cf_c_stride, | ||||||||
| // in_cf_c_stride, | ||||||||
| // out_n_stride, | ||||||||
| // in_n_stride, | ||||||||
| // kernel_h, | ||||||||
| // kernel_w, | ||||||||
| // stride_h, | ||||||||
| // stride_w, | ||||||||
| // pad_h, | ||||||||
| // pad_w, | ||||||||
| // dilation_h, | ||||||||
| // dilation_w); | ||||||||
| // return; | ||||||||
| // default: | ||||||||
| // break; | ||||||||
| // }; | ||||||||
| // } | ||||||||
| using KernelClass = | ||||||||
| MaxPool2dBackwardDeterministicKernelFunctor<scalar_t, is_channels_last>; | ||||||||
| BatchKernelConfig cfg = BatchKernelConfig::make_config<KernelClass>( | ||||||||
|
|
||||||||
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