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https://github.com/ROCm/composable_kernel.git
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Pool2d max/avg kernel in the BWD version (#1494)
* Add pool2d instance BWD AVG * Add pool2d instance BWD MAX * Fix: avg review * Fix review: part2 * Fix - enable test when type is compiled * Fix review part3
This commit is contained in:
Mateusz Ozga
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <iostream>
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#include <sstream>
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#include "ck/tensor_description/tensor_descriptor.hpp"
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#include "ck/tensor_description/tensor_descriptor_helper.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"
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#include "ck/tensor_operation/gpu/device/device_avgpool_bwd.hpp"
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#include "ck/tensor_operation/gpu/grid/gridwise_2d_reduction_threadwise.hpp"
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#include "ck/host_utility/device_prop.hpp"
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#include "ck/host_utility/kernel_launch.hpp"
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namespace ck {
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namespace tensor_operation {
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namespace device {
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// In and Din = [N, C, Hi, Wi]
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// Out and Dout = [N, C, Ho, Wo]
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// Out = AvgPool2dFwd(In)
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// Din = AvgPool2dBwd(Dout)
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// Pooling dimension = H, W
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template <typename DOutDataType,
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typename DInDataType,
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typename ComputeDataType,
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ck::index_t BlockSize,
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ck::index_t MThreadClusterSize,
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ck::index_t KThreadClusterSize,
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ck::index_t MThreadSliceSize,
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ck::index_t KThreadSliceSize,
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ck::index_t InSrcOutDstVectorSize>
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struct DeviceAvgPool2dBwd_NHWC_NHWC : public DeviceAvgPoolBwd<2,
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DOutDataType,
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DInDataType,
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tensor_layout::convolution::NHWC,
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tensor_layout::convolution::NHWC>
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{
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static constexpr ck::index_t NDimSpatial = 2;
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static constexpr auto I0 = Number<0>{};
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static constexpr auto I1 = Number<1>{};
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static constexpr ck::index_t M_BlockTileSize = MThreadClusterSize * MThreadSliceSize;
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static constexpr ck::index_t K_BlockTileSize = KThreadClusterSize * KThreadSliceSize;
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static auto
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Make2DGridDescriptor_Out_M_K_In_M(const std::vector<ck::index_t>& dout_n_c_wos_lengths,
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const std::vector<ck::index_t>& din_n_c_wos_length,
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const std::vector<ck::index_t>& dout_n_c_wos_strides,
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const std::vector<ck::index_t>& din_n_c_wos_strides,
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const std::vector<ck::index_t>& window_lengths,
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const std::vector<ck::index_t>& window_strides,
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const std::vector<ck::index_t>& window_dilations,
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const std::vector<ck::index_t>& input_left_pads,
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const std::vector<ck::index_t>& input_right_pads,
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const std::vector<ck::index_t>& tildes)
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{
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index_t i_ytilde = tildes[0];
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index_t i_xtilde = tildes[1];
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const index_t N = dout_n_c_wos_lengths[0];
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const index_t C = dout_n_c_wos_lengths[1];
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const index_t Ho = dout_n_c_wos_lengths[2];
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const index_t Wo = dout_n_c_wos_lengths[3];
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const index_t Hi = din_n_c_wos_length[2];
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const index_t Wi = din_n_c_wos_length[3];
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const index_t Y = window_lengths[0];
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const index_t X = window_lengths[1];
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const index_t InLeftPadH = input_left_pads[0];
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const index_t InLeftPadW = input_left_pads[1];
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const index_t InRightPadH = input_right_pads[0];
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const index_t InRightPadW = input_right_pads[1];
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const index_t ConvStrideH = window_strides[0];
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const index_t ConvStrideW = window_strides[1];
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const index_t ConvDilationH = window_dilations[0];
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const index_t ConvDilationW = window_dilations[1];
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const index_t Ni_stride = dout_n_c_wos_strides[0];
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const index_t Ci_stride = dout_n_c_wos_strides[1];
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const index_t Ho_stride = dout_n_c_wos_strides[2];
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const index_t Wo_stride = dout_n_c_wos_strides[3];
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const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
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const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
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const auto YTilde = ConvStrideH / GcdStrideDilationH;
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const auto XTilde = ConvStrideW / GcdStrideDilationW;
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const auto YDot = math::integer_divide_ceil(Y, YTilde);
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const auto XDot = math::integer_divide_ceil(X, XTilde);
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const auto HTilde = Ho + math::integer_divide_ceil(ConvDilationH * (Y - I1), ConvStrideH);
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const auto WTilde = Wo + math::integer_divide_ceil(ConvDilationW * (X - I1), ConvStrideW);
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// only work on Tildes that contribute to non-padding area of input tensor
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const auto IHTildeSliceBegin = math::integer_divide_floor(
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math::max(I0, InLeftPadH - ConvDilationH * (YTilde - I1)), ConvStrideH);
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const auto IWTildeSliceBegin = math::integer_divide_floor(
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math::max(I0, InLeftPadW - ConvDilationW * (XTilde - I1)), ConvStrideW);
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const auto IHTildeSliceEnd =
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math::min(HTilde, math::integer_divide_ceil(InLeftPadH + Hi - I1, ConvStrideH) + I1);
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const auto IWTildeSliceEnd =
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math::min(WTilde, math::integer_divide_ceil(InLeftPadW + Wi - I1, ConvStrideW) + I1);
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const auto HTildeSlice = IHTildeSliceEnd - IHTildeSliceBegin;
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const auto WTildeSlice = IWTildeSliceEnd - IWTildeSliceBegin;
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// ReduceK is different for each Reduce
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const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilde, YTilde);
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const auto XDotSlice = math::integer_divide_ceil(X - i_xtilde, XTilde);
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// Problem size of reduction kernel
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const index_t MRaw = N * HTildeSlice * WTildeSlice * C;
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const index_t MPad = math::integer_least_multiple(MRaw, M_BlockTileSize) - MRaw;
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const index_t KRaw = YDotSlice * XDotSlice;
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const index_t KPad = math::integer_least_multiple(KRaw, K_BlockTileSize) - KRaw;
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const auto out_n_ho_wo_c_grid_desc = make_naive_tensor_descriptor(
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make_tuple(N, Ho, Wo, C), make_tuple(Ni_stride, Ho_stride, Wo_stride, Ci_stride));
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// Out[ReduceM, ReduceK]
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const auto out_n_hop_wop_c_grid_desc = transform_tensor_descriptor(
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out_n_ho_wo_c_grid_desc,
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make_tuple(make_pass_through_transform(N),
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make_pad_transform(Ho, I0, I0),
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make_pad_transform(Wo, I0, I0),
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make_pass_through_transform(C)),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
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const auto out_n_ydot_htilde_xdot_wtilde_c_grid_desc = transform_tensor_descriptor(
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out_n_hop_wop_c_grid_desc,
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make_tuple(make_pass_through_transform(N),
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make_embed_transform(make_tuple(YDot, HTilde),
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make_tuple(-ConvDilationH / GcdStrideDilationH, I1)),
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make_embed_transform(make_tuple(XDot, WTilde),
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make_tuple(-ConvDilationW / GcdStrideDilationW, I1)),
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make_pass_through_transform(C)),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
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make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
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const auto out_n_ydotslice_htildeslice_xdotslice_wtildeslice_c_grid_desc =
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transform_tensor_descriptor(
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out_n_ydot_htilde_xdot_wtilde_c_grid_desc,
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make_tuple(make_pass_through_transform(N),
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make_slice_transform(YDot, I0, YDotSlice),
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make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
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make_slice_transform(XDot, I0, XDotSlice),
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make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
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make_pass_through_transform(C)),
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make_tuple(Sequence<0>{},
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Sequence<1>{},
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Sequence<2>{},
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Sequence<3>{},
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Sequence<4>{},
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Sequence<5>{}),
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make_tuple(Sequence<0>{},
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Sequence<1>{},
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Sequence<2>{},
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Sequence<3>{},
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Sequence<4>{},
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Sequence<5>{}));
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const auto out_grid_desc_reducemraw_reducekraw = transform_tensor_descriptor(
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out_n_ydotslice_htildeslice_xdotslice_wtildeslice_c_grid_desc,
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make_tuple(make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice, C)),
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make_merge_transform(make_tuple(YDotSlice, XDotSlice))),
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make_tuple(Sequence<0, 2, 4, 5>{}, Sequence<1, 3>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}));
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const auto out_grid_desc_reducem_reducek = transform_tensor_descriptor(
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out_grid_desc_reducemraw_reducekraw,
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make_tuple(make_right_pad_transform(MRaw, MPad), make_right_pad_transform(KRaw, KPad)),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}));
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// In[ReduceM]
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const auto in_n_hi_wi_c_grid_desc =
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make_naive_tensor_descriptor(make_tuple(N, Hi, Wi, C),
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make_tuple(din_n_c_wos_strides[0],
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din_n_c_wos_strides[2],
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din_n_c_wos_strides[3],
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din_n_c_wos_strides[1]));
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const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
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in_n_hi_wi_c_grid_desc,
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make_tuple(make_pass_through_transform(N),
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make_pad_transform(Hi, InLeftPadH, InRightPadH),
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make_pad_transform(Wi, InLeftPadW, InRightPadW),
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make_pass_through_transform(C)),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
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const auto in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc = transform_tensor_descriptor(
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in_n_hip_wip_c_grid_desc,
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make_tuple(make_pass_through_transform(N),
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make_embed_transform(make_tuple(YTilde, HTilde),
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make_tuple(ConvDilationH, ConvStrideH)),
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make_embed_transform(make_tuple(XTilde, WTilde),
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make_tuple(ConvDilationW, ConvStrideW)),
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make_pass_through_transform(C)),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
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make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
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const auto in_n_htildeslice_wtildeslice_c_grid_desc = transform_tensor_descriptor(
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in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc,
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make_tuple(make_pass_through_transform(N),
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make_freeze_transform(i_ytilde),
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make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
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make_freeze_transform(i_xtilde),
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make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
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make_pass_through_transform(C)),
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make_tuple(Sequence<0>{},
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Sequence<1>{},
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Sequence<2>{},
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Sequence<3>{},
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Sequence<4>{},
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Sequence<5>{}),
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make_tuple(Sequence<0>{},
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Sequence<>{},
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Sequence<1>{},
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Sequence<>{},
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Sequence<2>{},
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Sequence<3>{}));
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const auto in_grid_desc_reducemraw = transform_tensor_descriptor(
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in_n_htildeslice_wtildeslice_c_grid_desc,
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make_tuple(make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice, C))),
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make_tuple(Sequence<0, 1, 2, 3>{}),
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make_tuple(Sequence<0>{}));
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const auto in_grid_desc_reducem =
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transform_tensor_descriptor(in_grid_desc_reducemraw,
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make_tuple(make_right_pad_transform(MRaw, MPad)),
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make_tuple(Sequence<0>{}),
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make_tuple(Sequence<0>{}));
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return make_tuple(out_grid_desc_reducem_reducek, in_grid_desc_reducem);
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}
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using DoutDinGridDesc = decltype(Make2DGridDescriptor_Out_M_K_In_M({0, 0, 0, 0},
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{0, 0, 0, 0},
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{0, 0, 0, 0},
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{0, 0, 0, 0},
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{0, 0},
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{0, 0},
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{0, 0},
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{0, 0},
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{0, 0},
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{0, 0}));
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using DoutGridDesc_M_K = remove_cvref_t<tuple_element_t<0, DoutDinGridDesc>>;
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using DinGridDesc_M = remove_cvref_t<tuple_element_t<1, DoutDinGridDesc>>;
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// FIXME
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// for NHWC, the dim C is the fastest dimension, and is not reduced.
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// Hence, it is in M dimension for reduction kernel.
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static constexpr index_t OutSrcInDstVectorDim = 0; // 0: M, 1: K
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using PassThrough = tensor_operation::element_wise::PassThrough;
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using Div = tensor_operation::element_wise::UnaryDivide;
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using gridwise_reduce = GridwiseReduction_mk_to_m_threadwise<DOutDataType,
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DInDataType,
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ComputeDataType,
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int,
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DoutGridDesc_M_K,
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DinGridDesc_M,
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reduce::Add,
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PassThrough,
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Div,
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InMemoryDataOperationEnum::Set,
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false, // propagate_nan
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BlockSize,
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MThreadSliceSize,
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KThreadSliceSize,
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OutSrcInDstVectorDim,
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InSrcOutDstVectorSize,
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InSrcOutDstVectorSize>;
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struct Argument : public BaseArgument
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{
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Argument(const DOutDataType* p_dout,
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DInDataType* p_din,
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std::vector<ck::index_t> dout_n_c_wos_lengths,
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std::vector<ck::index_t> din_n_c_wos_length,
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std::vector<ck::index_t> dout_n_c_wos_strides,
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std::vector<ck::index_t> din_n_c_wos_strides,
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std::vector<ck::index_t> window_lengths,
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std::vector<ck::index_t> window_strides,
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std::vector<ck::index_t> window_dilations,
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std::vector<ck::index_t> input_left_pads,
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std::vector<ck::index_t> input_right_pads)
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: p_dout_grid_{p_dout},
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p_din_grid_{p_din},
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dout_n_c_wos_lengths_{dout_n_c_wos_lengths},
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din_n_c_wos_length_{din_n_c_wos_length},
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dout_n_c_wos_strides_{dout_n_c_wos_strides},
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din_n_c_wos_strides_{din_n_c_wos_strides},
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num_reduce_{1},
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div_element_op_{window_lengths[0] * window_lengths[1]}
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{
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std::vector<ck::index_t> Tildes(NDimSpatial);
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for(int i = 0; i < NDimSpatial; ++i)
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{
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int GcdStrideDilation = math::gcd(window_strides[i], window_dilations[i]);
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Tildes[i] = window_strides[i] / GcdStrideDilation;
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num_reduce_ *= Tildes[i];
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}
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for(index_t i_ytilde = 0; i_ytilde < Tildes[0]; ++i_ytilde)
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{
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for(index_t i_xtilde = 0; i_xtilde < Tildes[1]; ++i_xtilde)
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{
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const auto YDotSlice =
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math::integer_divide_ceil(window_lengths[0] - i_ytilde, Tildes[0]);
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const auto XDotSlice =
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math::integer_divide_ceil(window_lengths[1] - i_xtilde, Tildes[1]);
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if(YDotSlice * XDotSlice <= 0)
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{
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continue;
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}
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const auto dout_din_grid_desc =
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Make2DGridDescriptor_Out_M_K_In_M(dout_n_c_wos_lengths,
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din_n_c_wos_length,
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dout_n_c_wos_strides,
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din_n_c_wos_strides,
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window_lengths,
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window_strides,
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window_dilations,
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input_left_pads,
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input_right_pads,
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{i_ytilde, i_xtilde});
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dout_grid_desc_m_k_container_.push_back(dout_din_grid_desc[I0]);
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din_grid_desc_m_container_.push_back(dout_din_grid_desc[I1]);
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}
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}
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}
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const DOutDataType* p_dout_grid_;
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DInDataType* p_din_grid_;
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std::vector<ck::index_t> dout_n_c_wos_lengths_;
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std::vector<ck::index_t> din_n_c_wos_length_;
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std::vector<ck::index_t> dout_n_c_wos_strides_;
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std::vector<ck::index_t> din_n_c_wos_strides_;
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int num_reduce_;
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std::vector<DoutGridDesc_M_K> dout_grid_desc_m_k_container_;
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std::vector<DinGridDesc_M> din_grid_desc_m_container_;
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Div div_element_op_;
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};
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struct Invoker : public BaseInvoker
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{
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float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
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{
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float ave_time = 0;
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for(index_t i = 0; i < arg.num_reduce_; i++)
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{
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const auto kernel = kernel_reduce_threadwise<gridwise_reduce,
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false,
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||||
false,
|
||||
false, // don't have index input
|
||||
DOutDataType,
|
||||
DInDataType,
|
||||
ComputeDataType,
|
||||
int,
|
||||
DoutGridDesc_M_K,
|
||||
DinGridDesc_M,
|
||||
PassThrough,
|
||||
Div>;
|
||||
|
||||
ck::index_t M = arg.dout_grid_desc_m_k_container_[i].GetLength(I0);
|
||||
const index_t grid_size = (M / M_BlockTileSize);
|
||||
|
||||
ave_time += launch_and_time_kernel(stream_config,
|
||||
kernel,
|
||||
dim3(grid_size),
|
||||
dim3(BlockSize),
|
||||
0,
|
||||
arg.dout_grid_desc_m_k_container_[i],
|
||||
arg.din_grid_desc_m_container_[i],
|
||||
PassThrough{},
|
||||
arg.div_element_op_,
|
||||
float(1),
|
||||
arg.p_dout_grid_,
|
||||
nullptr,
|
||||
float(0),
|
||||
arg.p_din_grid_,
|
||||
nullptr);
|
||||
}
|
||||
|
||||
return ave_time;
|
||||
}
|
||||
|
||||
float Run(const BaseArgument* p_arg,
|
||||
const StreamConfig& stream_config = StreamConfig{}) override
|
||||
{
|
||||
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
|
||||
}
|
||||
};
|
||||
|
||||
static bool IsSupportedArgument(const Argument& arg)
|
||||
{
|
||||
constexpr index_t Rank = NDimSpatial + 2;
|
||||
int doutFastestDim = -1;
|
||||
int dinFastestDim = -1;
|
||||
|
||||
for(int i = 0; i < Rank; ++i)
|
||||
{
|
||||
if(arg.dout_n_c_wos_strides_[i] == 1)
|
||||
doutFastestDim = i;
|
||||
if(arg.din_n_c_wos_strides_[i] == 1)
|
||||
dinFastestDim = i;
|
||||
}
|
||||
if(InSrcOutDstVectorSize != 1 && (dinFastestDim != 1 || doutFastestDim != 1))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
if(doutFastestDim == -1 || dinFastestDim == -1)
|
||||
{
|
||||
if constexpr(InSrcOutDstVectorSize != 1)
|
||||
return false;
|
||||
}
|
||||
else
|
||||
{
|
||||
if(arg.dout_n_c_wos_lengths_[doutFastestDim] % InSrcOutDstVectorSize != 0)
|
||||
return false;
|
||||
if(arg.din_n_c_wos_length_[dinFastestDim] % InSrcOutDstVectorSize != 0)
|
||||
return false;
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
bool IsSupportedArgument(const BaseArgument* p_arg) override
|
||||
{
|
||||
return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
|
||||
}
|
||||
|
||||
std::unique_ptr<BaseArgument>
|
||||
MakeArgumentPointer(const void* p_dout,
|
||||
void* p_din,
|
||||
std::vector<ck::index_t> dout_n_c_wos_lengths,
|
||||
std::vector<ck::index_t> din_n_c_wos_length,
|
||||
std::vector<ck::index_t> dout_n_c_wos_strides,
|
||||
std::vector<ck::index_t> din_n_c_wos_strides,
|
||||
std::vector<ck::index_t> window_lengths,
|
||||
std::vector<ck::index_t> window_strides,
|
||||
std::vector<ck::index_t> window_dilations,
|
||||
std::vector<ck::index_t> input_left_pads,
|
||||
std::vector<ck::index_t> input_right_pads) override
|
||||
{
|
||||
constexpr index_t Rank = NDimSpatial + 2;
|
||||
|
||||
if(dout_n_c_wos_strides.size() != Rank || din_n_c_wos_strides.size() != Rank ||
|
||||
dout_n_c_wos_lengths.size() != Rank || din_n_c_wos_length.size() != Rank)
|
||||
{
|
||||
throw std::runtime_error("dimension of [dout|din]_n_c_wos_strides or "
|
||||
"[dout|din]_n_c_wos_lengths is not equal to Rank");
|
||||
}
|
||||
|
||||
if(window_lengths.size() != NDimSpatial || window_strides.size() != NDimSpatial ||
|
||||
window_dilations.size() != NDimSpatial || input_left_pads.size() != NDimSpatial ||
|
||||
input_right_pads.size() != NDimSpatial)
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"dimension of [window_lengths, window_strides, window_dilations, input_left_pads, "
|
||||
"input_right_pads] is not equal to Rank");
|
||||
}
|
||||
return std::make_unique<Argument>(static_cast<const DOutDataType*>(p_dout),
|
||||
static_cast<DInDataType*>(p_din),
|
||||
dout_n_c_wos_lengths,
|
||||
din_n_c_wos_length,
|
||||
dout_n_c_wos_strides,
|
||||
din_n_c_wos_strides,
|
||||
window_lengths,
|
||||
window_strides,
|
||||
window_dilations,
|
||||
input_left_pads,
|
||||
input_right_pads);
|
||||
}
|
||||
|
||||
std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
|
||||
{
|
||||
return std::make_unique<Invoker>(Invoker{});
|
||||
}
|
||||
|
||||
std::string GetTypeString() const override
|
||||
{
|
||||
auto str = std::stringstream();
|
||||
|
||||
// clang-format off
|
||||
str << "DeviceAvgPool2dBwd<" << BlockSize << ",";
|
||||
str << "M_C" << MThreadClusterSize << "_S" << MThreadSliceSize << ",";
|
||||
str << "K_C" << KThreadClusterSize << "_S" << KThreadSliceSize << ",";
|
||||
str <<"InSrcOutDstVectorSize_" << InSrcOutDstVectorSize << ">";
|
||||
// clang-format on
|
||||
|
||||
return str.str();
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
Reference in New Issue
Block a user