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Add grouped conv bwd data wmma (#950)
* Add grouped conv bwd data wmma * Fix copyrights * Add instances with smaller NPerBlock * Update interface test * Minor stylistic fixes * Minor stylistic fixes
This commit is contained in:
Bartłomiej Kocot
@@ -0,0 +1,879 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2023, 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/utility/common_header.hpp"
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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/device_grouped_conv_bwd_data_multiple_d.hpp"
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#include "ck/tensor_operation/gpu/device/convolution_backward_data_specialization.hpp"
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#include "ck/tensor_operation/operator_transform/transform_conv_bwd_data_to_gemm_v1.hpp"
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#include "ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_d_wmma_cshuffle.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_utils.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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#include "ck/host_utility/io.hpp"
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namespace ck {
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namespace tensor_operation {
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namespace device {
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// Conv backward data multiple D:
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// input : output image A: [G, N, K, Ho, Wo]
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// input : weight B: [G, K, C, Y, X],
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// input : D0, D1, ... : [G, N, K, Ho, Wo]
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// output : input image E: [G, N, C, Hi, Wi]
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// C = a_op(A) * b_op(B)
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// E = cde_op(C, D0, D1, ...)
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template <index_t NDimSpatial,
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typename ALayout, // output image
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typename BLayout, // weight
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typename DsLayout, // bias
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typename ELayout, // input image
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typename ADataType, // output image
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typename BDataType, // weight
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typename AccDataType,
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typename CShuffleDataType,
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typename DsDataType, // bias
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typename EDataType, // input image
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typename AElementwiseOp, // output image
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typename BElementwiseOp, // weight
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typename CDEElementwiseOp, // C, bias, and input image
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ConvolutionBackwardDataSpecialization ConvBackwardDataSpecialization,
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ck::index_t BlockSize,
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ck::index_t MPerBlock,
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ck::index_t NPerBlock,
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ck::index_t K0PerBlock,
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ck::index_t K1,
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ck::index_t MPerWMMA,
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ck::index_t NPerWMMA,
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ck::index_t MRepeat,
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ck::index_t NRepeat,
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typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
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typename ABlockTransferThreadClusterArrangeOrder,
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typename ABlockTransferSrcAccessOrder,
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index_t ABlockTransferSrcVectorDim,
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index_t ABlockTransferSrcScalarPerVector,
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index_t ABlockTransferDstScalarPerVector_AK1,
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bool ABlockLdsExtraM,
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typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
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typename BBlockTransferThreadClusterArrangeOrder,
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typename BBlockTransferSrcAccessOrder,
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index_t BBlockTransferSrcVectorDim,
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index_t BBlockTransferSrcScalarPerVector,
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index_t BBlockTransferDstScalarPerVector_BK1,
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bool BBlockLdsExtraN,
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index_t CShuffleMRepeatPerShuffle,
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index_t CShuffleNRepeatPerShuffle,
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typename CDEShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
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index_t CDEShuffleBlockTransferScalarPerVector_NPerBlock,
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index_t NumGemmKPrefetchStage = 1,
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LoopScheduler LoopSched = make_default_loop_scheduler(),
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ck::PipelineVersion PipelineVer = ck::PipelineVersion::v1>
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struct DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle
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: public DeviceGroupedConvBwdDataMultipleD<NDimSpatial,
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ALayout, // output image
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BLayout, // weight
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DsLayout, // bias
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ELayout, // input image
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ADataType, // output image
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BDataType, // weight
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DsDataType, // bias
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EDataType, // input image
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AElementwiseOp,
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BElementwiseOp,
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CDEElementwiseOp>
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{
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// TODO: Extend support for more spatial dimensions.
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static_assert(NDimSpatial == 2 || NDimSpatial == 3,
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"wrong! only implemented for 2D and 3D now");
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using DeviceOp = DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle;
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static constexpr index_t NumDTensor = DsDataType::Size();
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// TODO: Add support for different A and B data types.
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using ABDataType = ADataType;
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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 auto I2 = Number<2>{};
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static constexpr auto I3 = Number<3>{};
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static constexpr index_t KPerBlock = K0PerBlock * K1;
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static constexpr auto transform_conv_to_gemm =
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TransformConvBwdDataToGemm_v1<NDimSpatial,
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ConvBackwardDataSpecialization,
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K1,
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K1,
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MPerBlock,
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NPerBlock,
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KPerBlock,
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true /* DoPadGemmM */,
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true /* DoPadGemmN */>{};
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static auto GetDummyABDsEGridDescriptor()
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{
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const std::array<index_t, NDimSpatial + 3> dummy_tensor_lengths = {1};
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const std::array<index_t, NDimSpatial + 3> dummy_tensor_strides = {1};
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const std::array<index_t, NDimSpatial> dummy_spatial_lengths = {1};
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const auto a_grid_desc_ak0_m_ak1 =
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transform_conv_to_gemm.template MakeADescriptor_AK0_M_AK1<ALayout>(
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dummy_tensor_lengths,
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dummy_tensor_strides,
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dummy_tensor_lengths,
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dummy_tensor_strides,
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dummy_tensor_lengths,
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dummy_tensor_strides,
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dummy_spatial_lengths,
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dummy_spatial_lengths,
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dummy_spatial_lengths,
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dummy_spatial_lengths,
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dummy_spatial_lengths);
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const auto b_grid_desc_bk0_n_bk1 =
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transform_conv_to_gemm.template MakeBDescriptor_BK0_N_BK1<BLayout>(
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dummy_tensor_lengths,
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dummy_tensor_strides,
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dummy_tensor_lengths,
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dummy_tensor_strides,
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dummy_tensor_lengths,
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dummy_tensor_strides,
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dummy_spatial_lengths,
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dummy_spatial_lengths,
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dummy_spatial_lengths,
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dummy_spatial_lengths,
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dummy_spatial_lengths);
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const auto ds_grid_desc_m_n = generate_tuple(
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[&](auto i) {
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using DLayout = remove_cvref_t<tuple_element_t<i.value, DsLayout>>;
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return transform_conv_to_gemm.template MakeCDescriptor_M_N<DLayout>(
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dummy_tensor_lengths,
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dummy_tensor_strides,
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dummy_tensor_lengths,
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dummy_tensor_strides,
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dummy_tensor_lengths,
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dummy_tensor_strides,
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dummy_spatial_lengths,
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dummy_spatial_lengths,
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dummy_spatial_lengths,
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dummy_spatial_lengths,
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dummy_spatial_lengths);
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},
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Number<NumDTensor>{});
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const auto e_grid_desc_m_n =
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transform_conv_to_gemm.template MakeCDescriptor_M_N<ELayout>(dummy_tensor_lengths,
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dummy_tensor_strides,
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dummy_tensor_lengths,
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dummy_tensor_strides,
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dummy_tensor_lengths,
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dummy_tensor_strides,
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dummy_spatial_lengths,
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dummy_spatial_lengths,
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dummy_spatial_lengths,
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dummy_spatial_lengths,
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dummy_spatial_lengths);
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return make_tuple(
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a_grid_desc_ak0_m_ak1, b_grid_desc_bk0_n_bk1, ds_grid_desc_m_n, e_grid_desc_m_n);
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}
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// desc
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using ABDsEGridDesc = decltype(GetDummyABDsEGridDescriptor());
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using AGridDesc_AK0_M_AK1 = remove_cvref_t<tuple_element_t<0, ABDsEGridDesc>>;
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using BGridDesc_BK0_N_BK1 = remove_cvref_t<tuple_element_t<1, ABDsEGridDesc>>;
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using DsGridDesc_M_N = remove_cvref_t<tuple_element_t<2, ABDsEGridDesc>>;
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using EGridDesc_M_N = remove_cvref_t<tuple_element_t<3, ABDsEGridDesc>>;
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// GridwiseGemm
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using GridwiseGemm = GridwiseGemmMultipleD_k0mk1_k0nk1_mn_wmma_cshuffle<
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// DataType Family
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ADataType,
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BDataType,
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AccDataType,
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CShuffleDataType,
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DsDataType,
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EDataType,
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// InMemory Data Descriptor
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AGridDesc_AK0_M_AK1,
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BGridDesc_BK0_N_BK1,
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DsGridDesc_M_N,
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EGridDesc_M_N,
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// ElementwiseOp Family
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AElementwiseOp,
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BElementwiseOp,
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CDEElementwiseOp,
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InMemoryDataOperationEnum::Set,
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// Tiling Family
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MPerBlock,
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NPerBlock,
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K0PerBlock,
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MPerWMMA,
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NPerWMMA,
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K1,
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MRepeat,
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NRepeat,
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// ThreadCluster Family
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BlockSize,
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ABlockTransferThreadClusterLengths_AK0_M_AK1,
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ABlockTransferThreadClusterArrangeOrder,
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ABlockTransferSrcAccessOrder,
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ABlockTransferSrcVectorDim,
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ABlockTransferSrcScalarPerVector,
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ABlockTransferDstScalarPerVector_AK1,
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false,
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ABlockLdsExtraM,
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BBlockTransferThreadClusterLengths_BK0_N_BK1,
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BBlockTransferThreadClusterArrangeOrder,
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BBlockTransferSrcAccessOrder,
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BBlockTransferSrcVectorDim,
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BBlockTransferSrcScalarPerVector,
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BBlockTransferDstScalarPerVector_BK1,
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false,
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BBlockLdsExtraN,
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CShuffleMRepeatPerShuffle,
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CShuffleNRepeatPerShuffle,
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CDEShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
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CDEShuffleBlockTransferScalarPerVector_NPerBlock,
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NumGemmKPrefetchStage,
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LoopSched,
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PipelineVer>;
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using DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock =
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decltype(GridwiseGemm::MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
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DsGridDesc_M_N{}));
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using EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock =
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decltype(GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
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EGridDesc_M_N{}));
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// Argument
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struct Argument : public BaseArgument
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{
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Argument(const void* p_a, // output image
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const void* p_b, // weight
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const std::array<const void*, NumDTensor>& p_ds, // bias
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void* p_e, // input image
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const std::array<index_t, NDimSpatial + 3>& a_g_n_k_wos_lengths,
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const std::array<index_t, NDimSpatial + 3>& a_g_n_k_wos_strides,
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const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_lengths,
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const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_strides,
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const std::array<std::array<index_t, NDimSpatial + 3>, NumDTensor>&
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ds_g_n_c_wis_lengths,
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const std::array<std::array<index_t, NDimSpatial + 3>, NumDTensor>&
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ds_g_n_c_wis_strides,
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const std::array<index_t, NDimSpatial + 3>& e_g_n_c_wis_lengths,
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const std::array<index_t, NDimSpatial + 3>& e_g_n_c_wis_strides,
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const std::array<index_t, NDimSpatial>& conv_filter_strides,
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const std::array<index_t, NDimSpatial>& conv_filter_dilations,
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const std::array<index_t, NDimSpatial>& input_left_pads,
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const std::array<index_t, NDimSpatial>& input_right_pads,
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const AElementwiseOp& a_element_op,
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const BElementwiseOp& b_element_op,
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const CDEElementwiseOp& cde_element_op)
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: p_a_grid_{static_cast<const ADataType*>(p_a)},
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p_b_grid_{static_cast<const BDataType*>(p_b)},
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p_ds_grid_{},
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p_e_grid_{static_cast<EDataType*>(p_e)},
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num_group_{a_g_n_k_wos_lengths[0]},
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a_element_op_{a_element_op},
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b_element_op_{b_element_op},
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cde_element_op_{cde_element_op},
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a_g_n_k_wos_lengths_{a_g_n_k_wos_lengths},
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a_g_n_k_wos_strides_{a_g_n_k_wos_strides},
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b_g_k_c_xs_lengths_{b_g_k_c_xs_lengths},
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b_g_k_c_xs_strides_{b_g_k_c_xs_strides},
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ds_g_n_c_wis_lengths_{ds_g_n_c_wis_lengths},
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ds_g_n_c_wis_strides_{ds_g_n_c_wis_strides},
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e_g_n_c_wis_lengths_{e_g_n_c_wis_lengths},
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e_g_n_c_wis_strides_{e_g_n_c_wis_strides},
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conv_filter_strides_{conv_filter_strides},
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conv_filter_dilations_{conv_filter_dilations},
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input_left_pads_{input_left_pads},
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input_right_pads_{input_right_pads}
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{
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// populate Ds pointer
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static_for<0, NumDTensor, 1>{}([&](auto i) {
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using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
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p_ds_grid_(i) = static_cast<const DDataType*>(p_ds[i]);
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});
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// A/B/Ds/E Batch Stride
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compute_ptr_offset_of_batch_.BatchStrideA_ = a_g_n_k_wos_strides[0];
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compute_ptr_offset_of_batch_.BatchStrideB_ = b_g_k_c_xs_strides[0];
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compute_ptr_offset_of_batch_.BatchStrideE_ = e_g_n_c_wis_strides[0];
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static_for<0, NumDTensor, 1>{}([&](auto i) {
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compute_ptr_offset_of_batch_.BatchStrideDs_(i) = ds_g_n_c_wis_strides[i][0];
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});
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static constexpr auto NonSpatialDimsNum = Number<3>{};
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static constexpr auto DIdx = Number<NonSpatialDimsNum>{};
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static constexpr auto HIdx =
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NDimSpatial == 2 ? Number<NonSpatialDimsNum>{} : Number<NonSpatialDimsNum + 1>{};
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static constexpr auto WIdx = NDimSpatial == 2 ? Number<NonSpatialDimsNum + 1>{}
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: Number<NonSpatialDimsNum + 2>{};
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static constexpr auto ZIdx = Number<NonSpatialDimsNum>{};
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static constexpr auto YIdx =
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NDimSpatial == 2 ? Number<NonSpatialDimsNum>{} : Number<NonSpatialDimsNum + 1>{};
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static constexpr auto XIdx = NDimSpatial == 2 ? Number<NonSpatialDimsNum + 1>{}
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: Number<NonSpatialDimsNum + 2>{};
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// problem definition
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const index_t Z = b_g_k_c_xs_lengths[ZIdx];
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const index_t Y = b_g_k_c_xs_lengths[YIdx];
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const index_t X = b_g_k_c_xs_lengths[XIdx];
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const index_t ConvStrideD = conv_filter_strides[DIdx - NonSpatialDimsNum];
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const index_t ConvStrideH = conv_filter_strides[HIdx - NonSpatialDimsNum];
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const index_t ConvStrideW = conv_filter_strides[WIdx - NonSpatialDimsNum];
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const index_t ConvDilationD = conv_filter_dilations[DIdx - NonSpatialDimsNum];
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const index_t ConvDilationH = conv_filter_dilations[HIdx - NonSpatialDimsNum];
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const index_t ConvDilationW = conv_filter_dilations[WIdx - NonSpatialDimsNum];
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const auto GcdStrideDilationD = math::gcd(ConvStrideD, ConvDilationD);
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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 ZTilde = NDimSpatial == 3 ? ConvStrideD / GcdStrideDilationD : 1;
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const auto YTilde = ConvStrideH / GcdStrideDilationH;
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const auto XTilde = ConvStrideW / GcdStrideDilationW;
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for(index_t i_ztilde = 0; i_ztilde < ZTilde; ++i_ztilde)
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{
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for(index_t i_ytilde = 0; i_ytilde < YTilde; ++i_ytilde)
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{
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for(index_t i_xtilde = 0; i_xtilde < XTilde; ++i_xtilde)
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{
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// check slice is valid
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const auto ZDotSlice =
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NDimSpatial == 3 ? math::integer_divide_ceil(Z - i_ztilde, ZTilde) : 1;
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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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if(YDotSlice * XDotSlice * ZDotSlice <= 0)
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{
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continue;
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}
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std::array<index_t, NDimSpatial> tildes;
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if constexpr(NDimSpatial == 2)
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{
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tildes = {i_ytilde, i_xtilde};
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}
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else if constexpr(NDimSpatial == 3)
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{
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tildes = {i_ztilde, i_ytilde, i_xtilde};
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}
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else
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{
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throw std::runtime_error("wrong! only implemented for 2D and 3D now");
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}
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const auto a_grid_desc_ak0_m_ak1 =
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transform_conv_to_gemm.template MakeADescriptor_AK0_M_AK1<ALayout>(
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a_g_n_k_wos_lengths,
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a_g_n_k_wos_strides,
|
||||
b_g_k_c_xs_lengths,
|
||||
b_g_k_c_xs_strides,
|
||||
e_g_n_c_wis_lengths,
|
||||
e_g_n_c_wis_strides,
|
||||
conv_filter_strides,
|
||||
conv_filter_dilations,
|
||||
input_left_pads,
|
||||
input_right_pads,
|
||||
tildes);
|
||||
|
||||
const auto b_grid_desc_bk0_n_bk1 =
|
||||
transform_conv_to_gemm.template MakeBDescriptor_BK0_N_BK1<BLayout>(
|
||||
a_g_n_k_wos_lengths,
|
||||
a_g_n_k_wos_strides,
|
||||
b_g_k_c_xs_lengths,
|
||||
b_g_k_c_xs_strides,
|
||||
e_g_n_c_wis_lengths,
|
||||
e_g_n_c_wis_strides,
|
||||
conv_filter_strides,
|
||||
conv_filter_dilations,
|
||||
input_left_pads,
|
||||
input_right_pads,
|
||||
tildes);
|
||||
|
||||
DsGridDesc_M_N ds_grid_desc_m_n;
|
||||
|
||||
// populate Ds desc
|
||||
static_for<0, NumDTensor, 1>{}([&](auto i) {
|
||||
using DLayout = remove_cvref_t<tuple_element_t<i.value, DsLayout>>;
|
||||
|
||||
ds_grid_desc_m_n(i) =
|
||||
transform_conv_to_gemm.template MakeCDescriptor_M_N<DLayout>(
|
||||
a_g_n_k_wos_lengths,
|
||||
a_g_n_k_wos_strides,
|
||||
b_g_k_c_xs_lengths,
|
||||
b_g_k_c_xs_strides,
|
||||
ds_g_n_c_wis_lengths[i],
|
||||
ds_g_n_c_wis_strides[i],
|
||||
conv_filter_strides,
|
||||
conv_filter_dilations,
|
||||
input_left_pads,
|
||||
input_right_pads,
|
||||
tildes);
|
||||
});
|
||||
|
||||
const auto e_grid_desc_m_n =
|
||||
transform_conv_to_gemm.template MakeCDescriptor_M_N<ELayout>(
|
||||
a_g_n_k_wos_lengths,
|
||||
a_g_n_k_wos_strides,
|
||||
b_g_k_c_xs_lengths,
|
||||
b_g_k_c_xs_strides,
|
||||
e_g_n_c_wis_lengths,
|
||||
e_g_n_c_wis_strides,
|
||||
conv_filter_strides,
|
||||
conv_filter_dilations,
|
||||
input_left_pads,
|
||||
input_right_pads,
|
||||
tildes);
|
||||
|
||||
// for check validity
|
||||
ds_grid_desc_m_n_container_.push_back(ds_grid_desc_m_n);
|
||||
e_grid_desc_m_n_container_.push_back(e_grid_desc_m_n);
|
||||
|
||||
// desc for blockwise copy
|
||||
a_grid_desc_ak0_m_ak1_container_.push_back(a_grid_desc_ak0_m_ak1);
|
||||
b_grid_desc_bk0_n_bk1_container_.push_back(b_grid_desc_bk0_n_bk1);
|
||||
|
||||
// block-to-e-tile-map
|
||||
auto block_2_ctile_map = GridwiseGemm::MakeDefaultBlock2CTileMap(
|
||||
e_grid_desc_m_n, 1 /* M01 */, 1 /* N01 */);
|
||||
|
||||
block_2_ctile_map_container_.push_back(block_2_ctile_map);
|
||||
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock_container_.push_back(
|
||||
GridwiseGemm::MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
|
||||
ds_grid_desc_m_n));
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock_container_.push_back(
|
||||
GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
|
||||
e_grid_desc_m_n));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void Print() const
|
||||
{
|
||||
for(std::size_t i = 0; i < a_grid_desc_ak0_m_ak1_container_.size(); i++)
|
||||
{
|
||||
std::cout << "a_grid_desc_ak0_m_ak1_container_"
|
||||
<< a_grid_desc_ak0_m_ak1_container_[i] << std::endl;
|
||||
|
||||
std::cout << "b_grid_desc_bk0_n_bk1_container_"
|
||||
<< b_grid_desc_bk0_n_bk1_container_[i] << std::endl;
|
||||
|
||||
static_for<0, NumDTensor, 1>{}([&](auto j) {
|
||||
std::cout << "ds_grid_desc_mblock_mperblock_nblock_nperblock_container_"
|
||||
<< ds_grid_desc_mblock_mperblock_nblock_nperblock_container_[i][j]
|
||||
<< std::endl;
|
||||
});
|
||||
|
||||
std::cout << "e_grid_desc_mblock_mperblock_nblock_nperblock_container_"
|
||||
<< e_grid_desc_mblock_mperblock_nblock_nperblock_container_[i]
|
||||
<< std::endl;
|
||||
}
|
||||
}
|
||||
|
||||
// pointers
|
||||
const ADataType* p_a_grid_;
|
||||
const BDataType* p_b_grid_;
|
||||
typename GridwiseGemm::DsGridPointer p_ds_grid_;
|
||||
EDataType* p_e_grid_;
|
||||
|
||||
// tensor descriptor for problem definition
|
||||
index_t num_group_;
|
||||
std::vector<DsGridDesc_M_N> ds_grid_desc_m_n_container_;
|
||||
std::vector<EGridDesc_M_N> e_grid_desc_m_n_container_;
|
||||
|
||||
// tensor descriptor for block-wise copy
|
||||
std::vector<AGridDesc_AK0_M_AK1> a_grid_desc_ak0_m_ak1_container_;
|
||||
std::vector<BGridDesc_BK0_N_BK1> b_grid_desc_bk0_n_bk1_container_;
|
||||
std::vector<DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock_container_;
|
||||
std::vector<EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock_container_;
|
||||
|
||||
// block-to-e-tile map
|
||||
std::vector<typename GridwiseGemm::DefaultBlock2CTileMap> block_2_ctile_map_container_;
|
||||
|
||||
// for computing batch offset
|
||||
ComputePtrOffsetOfStridedBatch<NumDTensor> compute_ptr_offset_of_batch_;
|
||||
|
||||
// element-wise op
|
||||
AElementwiseOp a_element_op_;
|
||||
BElementwiseOp b_element_op_;
|
||||
CDEElementwiseOp cde_element_op_;
|
||||
|
||||
// for checking IsSupportedArgument()
|
||||
std::array<index_t, NDimSpatial + 3> a_g_n_k_wos_lengths_;
|
||||
std::array<index_t, NDimSpatial + 3> a_g_n_k_wos_strides_;
|
||||
std::array<index_t, NDimSpatial + 3> b_g_k_c_xs_lengths_;
|
||||
std::array<index_t, NDimSpatial + 3> b_g_k_c_xs_strides_;
|
||||
std::array<std::array<index_t, NDimSpatial + 3>, NumDTensor> ds_g_n_c_wis_lengths_;
|
||||
std::array<std::array<index_t, NDimSpatial + 3>, NumDTensor> ds_g_n_c_wis_strides_;
|
||||
std::array<index_t, NDimSpatial + 3> e_g_n_c_wis_lengths_;
|
||||
std::array<index_t, NDimSpatial + 3> e_g_n_c_wis_strides_;
|
||||
std::array<index_t, NDimSpatial> conv_filter_strides_;
|
||||
std::array<index_t, NDimSpatial> conv_filter_dilations_;
|
||||
std::array<index_t, NDimSpatial> input_left_pads_;
|
||||
std::array<index_t, NDimSpatial> input_right_pads_;
|
||||
};
|
||||
|
||||
// Invoker
|
||||
struct Invoker : public BaseInvoker
|
||||
{
|
||||
using Argument = DeviceOp::Argument;
|
||||
|
||||
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
|
||||
{
|
||||
if(stream_config.log_level_ > 0)
|
||||
{
|
||||
arg.Print();
|
||||
}
|
||||
|
||||
float ave_time = 0;
|
||||
|
||||
for(std::size_t i = 0; i < arg.a_grid_desc_ak0_m_ak1_container_.size(); i++)
|
||||
{
|
||||
const index_t grid_size = arg.block_2_ctile_map_container_[i].CalculateGridSize(
|
||||
arg.e_grid_desc_m_n_container_[i]) *
|
||||
arg.num_group_;
|
||||
|
||||
const auto GemmK = arg.a_grid_desc_ak0_m_ak1_container_[i].GetLength(I0) *
|
||||
arg.a_grid_desc_ak0_m_ak1_container_[i].GetLength(I2);
|
||||
|
||||
auto launch_kernel = [&](auto has_main_k_block_loop) {
|
||||
constexpr bool has_main_loop = has_main_k_block_loop.value;
|
||||
|
||||
const auto kernel = kernel_grouped_conv_fwd_multiple_d_wmma_cshuffle<
|
||||
GridwiseGemm,
|
||||
ADataType,
|
||||
BDataType,
|
||||
typename GridwiseGemm::DsGridPointer,
|
||||
EDataType,
|
||||
AElementwiseOp,
|
||||
BElementwiseOp,
|
||||
CDEElementwiseOp,
|
||||
DeviceOp::AGridDesc_AK0_M_AK1,
|
||||
DeviceOp::BGridDesc_BK0_N_BK1,
|
||||
typename GridwiseGemm::DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
remove_reference_t<typename GridwiseGemm::DefaultBlock2CTileMap>,
|
||||
ComputePtrOffsetOfStridedBatch<NumDTensor>,
|
||||
has_main_loop>;
|
||||
|
||||
return launch_and_time_kernel(
|
||||
stream_config,
|
||||
kernel,
|
||||
dim3(grid_size),
|
||||
dim3(BlockSize),
|
||||
0,
|
||||
arg.p_a_grid_,
|
||||
arg.p_b_grid_,
|
||||
arg.p_ds_grid_,
|
||||
arg.p_e_grid_,
|
||||
arg.a_element_op_,
|
||||
arg.b_element_op_,
|
||||
arg.cde_element_op_,
|
||||
arg.a_g_n_k_wos_lengths_[0], // Group count
|
||||
arg.a_grid_desc_ak0_m_ak1_container_[i],
|
||||
arg.b_grid_desc_bk0_n_bk1_container_[i],
|
||||
arg.ds_grid_desc_mblock_mperblock_nblock_nperblock_container_[i],
|
||||
arg.e_grid_desc_mblock_mperblock_nblock_nperblock_container_[i],
|
||||
arg.block_2_ctile_map_container_[i],
|
||||
arg.compute_ptr_offset_of_batch_);
|
||||
};
|
||||
|
||||
if(GridwiseGemm::CalculateHasMainKBlockLoop(GemmK))
|
||||
{
|
||||
ave_time += launch_kernel(integral_constant<bool, true>{});
|
||||
}
|
||||
else
|
||||
{
|
||||
ave_time += launch_kernel(integral_constant<bool, false>{});
|
||||
}
|
||||
}
|
||||
|
||||
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)
|
||||
{
|
||||
// check device
|
||||
if(get_device_name() == "gfx1100" || get_device_name() == "gfx1101" ||
|
||||
ck::get_device_name() == "gfx1102")
|
||||
{
|
||||
if constexpr(!(is_same_v<AccDataType, float> || is_same_v<AccDataType, int32_t>))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
const index_t ConvK = arg.b_g_k_c_xs_lengths_[1];
|
||||
const index_t ConvC = arg.b_g_k_c_xs_lengths_[2];
|
||||
|
||||
// Specialization
|
||||
if constexpr(ConvBackwardDataSpecialization ==
|
||||
ConvolutionBackwardDataSpecialization::Filter1x1Stride1Pad0)
|
||||
{
|
||||
// check if it's a 1x1 convolution with stride=1 and no padding
|
||||
for(int i = 0; i < NDimSpatial; i++)
|
||||
{
|
||||
if(!(arg.b_g_k_c_xs_lengths_[3 + i] == 1 && arg.conv_filter_strides_[i] == 1 &&
|
||||
arg.input_left_pads_[i] == 0 && arg.input_right_pads_[i] == 0))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// vector load for A matrix from global memory to LDS
|
||||
if constexpr(is_same_v<ALayout, tensor_layout::convolution::GNHWK> ||
|
||||
is_same_v<ALayout, tensor_layout::convolution::GNDHWK> ||
|
||||
is_same_v<ALayout, tensor_layout::convolution::NHWGK> ||
|
||||
is_same_v<ALayout, tensor_layout::convolution::NDHWGK>)
|
||||
{
|
||||
if(!(ABlockTransferSrcVectorDim == 2 && ConvK % ABlockTransferSrcScalarPerVector == 0))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
// vector load for B matrix from global memory to LDS
|
||||
if constexpr(is_same_v<BLayout, tensor_layout::convolution::GKYXC> ||
|
||||
is_same_v<BLayout, tensor_layout::convolution::GKZYXC>)
|
||||
{
|
||||
if(!(BBlockTransferSrcVectorDim == 1 && ConvC % BBlockTransferSrcScalarPerVector == 0))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
// vector store for Ds
|
||||
bool ds_valid = true;
|
||||
|
||||
static_for<0, NumDTensor, 1>{}([&](auto i) {
|
||||
using DLayout = remove_cvref_t<tuple_element_t<i.value, DsLayout>>;
|
||||
|
||||
if constexpr(is_same_v<DLayout, tensor_layout::convolution::GNHWC> ||
|
||||
is_same_v<DLayout, tensor_layout::convolution::GNDHWC> ||
|
||||
is_same_v<DLayout, tensor_layout::convolution::NHWGC> ||
|
||||
is_same_v<DLayout, tensor_layout::convolution::NDHWGC> ||
|
||||
is_same_v<DLayout, tensor_layout::convolution::G_NHW_C> ||
|
||||
is_same_v<DLayout, tensor_layout::convolution::GC> ||
|
||||
is_same_v<DLayout, tensor_layout::convolution::G_C>)
|
||||
{
|
||||
// vector load D matrix from global memory
|
||||
if(!(ConvC % CDEShuffleBlockTransferScalarPerVector_NPerBlock == 0))
|
||||
{
|
||||
ds_valid = false;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
ds_valid = false;
|
||||
}
|
||||
});
|
||||
|
||||
if(!ds_valid)
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
// vector store for E
|
||||
if constexpr(is_same_v<ELayout, tensor_layout::convolution::GNHWC> ||
|
||||
is_same_v<ELayout, tensor_layout::convolution::GNDHWC> ||
|
||||
is_same_v<ELayout, tensor_layout::convolution::NHWGC> ||
|
||||
is_same_v<ELayout, tensor_layout::convolution::NDHWGC>)
|
||||
{
|
||||
// vector store C matrix into global memory
|
||||
if(!(ConvC % CDEShuffleBlockTransferScalarPerVector_NPerBlock == 0))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
// Gridwise GEMM size
|
||||
for(std::size_t i = 0; i < arg.a_grid_desc_ak0_m_ak1_container_.size(); i++)
|
||||
{
|
||||
if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_ak0_m_ak1_container_[i],
|
||||
arg.b_grid_desc_bk0_n_bk1_container_[i],
|
||||
arg.ds_grid_desc_m_n_container_[i],
|
||||
arg.e_grid_desc_m_n_container_[i],
|
||||
arg.block_2_ctile_map_container_[i]))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool IsSupportedArgument(const BaseArgument* p_arg) override
|
||||
{
|
||||
return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
|
||||
}
|
||||
|
||||
static auto
|
||||
MakeArgument(const void* p_a, // output image
|
||||
const void* p_b, // weight
|
||||
const std::array<const void*, NumDTensor>& p_ds, // bias
|
||||
void* p_e, // input image
|
||||
const std::array<index_t, NDimSpatial + 3>& a_g_n_k_wos_lengths, // output image
|
||||
const std::array<index_t, NDimSpatial + 3>& a_g_n_k_wos_strides, // output image
|
||||
const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_lengths, // weight
|
||||
const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_strides, // weight
|
||||
const std::array<std::array<index_t, NDimSpatial + 3>, NumDTensor>&
|
||||
ds_g_n_c_wis_lengths, // bias
|
||||
const std::array<std::array<index_t, NDimSpatial + 3>, NumDTensor>&
|
||||
ds_g_n_c_wis_strides, // bias
|
||||
const std::array<index_t, NDimSpatial + 3>& e_g_n_c_wis_lengths, // input image
|
||||
const std::array<index_t, NDimSpatial + 3>& e_g_n_c_wis_strides, // input image
|
||||
const std::array<index_t, NDimSpatial>& conv_filter_strides,
|
||||
const std::array<index_t, NDimSpatial>& conv_filter_dilations,
|
||||
const std::array<index_t, NDimSpatial>& input_left_pads,
|
||||
const std::array<index_t, NDimSpatial>& input_right_pads,
|
||||
const AElementwiseOp& a_element_op,
|
||||
const BElementwiseOp& b_element_op,
|
||||
const CDEElementwiseOp& cde_element_op)
|
||||
{
|
||||
return Argument{p_a,
|
||||
p_b,
|
||||
p_ds,
|
||||
p_e,
|
||||
a_g_n_k_wos_lengths,
|
||||
a_g_n_k_wos_strides,
|
||||
b_g_k_c_xs_lengths,
|
||||
b_g_k_c_xs_strides,
|
||||
ds_g_n_c_wis_lengths,
|
||||
ds_g_n_c_wis_strides,
|
||||
e_g_n_c_wis_lengths,
|
||||
e_g_n_c_wis_strides,
|
||||
conv_filter_strides,
|
||||
conv_filter_dilations,
|
||||
input_left_pads,
|
||||
input_right_pads,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op};
|
||||
}
|
||||
|
||||
static auto MakeInvoker() { return Invoker{}; }
|
||||
|
||||
std::unique_ptr<BaseArgument> MakeArgumentPointer(
|
||||
const void* p_a, // output image
|
||||
const void* p_b, // weight
|
||||
const std::array<const void*, NumDTensor>& p_ds, // bias
|
||||
void* p_e, // input image
|
||||
const std::array<index_t, NDimSpatial + 3>& a_g_n_k_wos_lengths, // output image
|
||||
const std::array<index_t, NDimSpatial + 3>& a_g_n_k_wos_strides, // output image
|
||||
const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_lengths, // weight
|
||||
const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_strides, // weight
|
||||
const std::array<std::array<index_t, NDimSpatial + 3>, NumDTensor>&
|
||||
ds_g_n_c_wis_lengths, // bias
|
||||
const std::array<std::array<index_t, NDimSpatial + 3>, NumDTensor>&
|
||||
ds_g_n_c_wis_strides, // bias
|
||||
const std::array<index_t, NDimSpatial + 3>& e_g_n_c_wis_lengths, // input image
|
||||
const std::array<index_t, NDimSpatial + 3>& e_g_n_c_wis_strides, // input image
|
||||
const std::array<index_t, NDimSpatial>& conv_filter_strides,
|
||||
const std::array<index_t, NDimSpatial>& conv_filter_dilations,
|
||||
const std::array<index_t, NDimSpatial>& input_left_pads,
|
||||
const std::array<index_t, NDimSpatial>& input_right_pads,
|
||||
const AElementwiseOp& a_element_op,
|
||||
const BElementwiseOp& b_element_op,
|
||||
const CDEElementwiseOp& cde_element_op) override
|
||||
{
|
||||
return std::make_unique<Argument>(p_a,
|
||||
p_b,
|
||||
p_ds,
|
||||
p_e,
|
||||
a_g_n_k_wos_lengths,
|
||||
a_g_n_k_wos_strides,
|
||||
b_g_k_c_xs_lengths,
|
||||
b_g_k_c_xs_strides,
|
||||
ds_g_n_c_wis_lengths,
|
||||
ds_g_n_c_wis_strides,
|
||||
e_g_n_c_wis_lengths,
|
||||
e_g_n_c_wis_strides,
|
||||
conv_filter_strides,
|
||||
conv_filter_dilations,
|
||||
input_left_pads,
|
||||
input_right_pads,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op);
|
||||
}
|
||||
|
||||
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 << "DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle"
|
||||
<< "<"
|
||||
<< BlockSize << ", "
|
||||
<< MPerBlock << ", "
|
||||
<< NPerBlock << ", "
|
||||
<< KPerBlock << ", "
|
||||
<< getConvBackwardDataSpecializationString(ConvBackwardDataSpecialization) << ", "
|
||||
<< K1 << ", "
|
||||
<< ABlockTransferSrcScalarPerVector << ", "
|
||||
<< BBlockTransferSrcScalarPerVector
|
||||
<< ">";
|
||||
// clang-format on
|
||||
|
||||
return str.str();
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -14,6 +14,7 @@
|
||||
#include "ck/tensor_operation/gpu/device/convolution_backward_data_specialization.hpp"
|
||||
#include "ck/tensor_operation/operator_transform/transform_conv_bwd_data_to_gemm_v1.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_d_xdl_cshuffle.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_utils.hpp"
|
||||
#include "ck/host_utility/device_prop.hpp"
|
||||
#include "ck/host_utility/kernel_launch.hpp"
|
||||
#include "ck/host_utility/io.hpp"
|
||||
@@ -24,51 +25,6 @@ namespace device {
|
||||
|
||||
namespace {
|
||||
|
||||
template <index_t NumDTensor>
|
||||
struct ComputePtrOffsetOfStridedBatch
|
||||
{
|
||||
ComputePtrOffsetOfStridedBatch() = default;
|
||||
|
||||
ComputePtrOffsetOfStridedBatch(index_t BatchStrideA,
|
||||
index_t BatchStrideB,
|
||||
Array<ck::index_t, NumDTensor> BatchStrideDs,
|
||||
index_t BatchStrideE)
|
||||
: BatchStrideA_(BatchStrideA),
|
||||
BatchStrideB_(BatchStrideB),
|
||||
BatchStrideDs_(BatchStrideDs),
|
||||
BatchStrideE_(BatchStrideE)
|
||||
{
|
||||
}
|
||||
|
||||
__host__ __device__ constexpr long_index_t GetAPtrOffset(index_t g_idx) const
|
||||
{
|
||||
return g_idx * static_cast<long_index_t>(BatchStrideA_);
|
||||
}
|
||||
|
||||
__host__ __device__ constexpr long_index_t GetBPtrOffset(index_t g_idx) const
|
||||
{
|
||||
return g_idx * static_cast<long_index_t>(BatchStrideB_);
|
||||
}
|
||||
|
||||
__host__ __device__ constexpr auto GetDsPtrOffset(index_t g_idx) const
|
||||
{
|
||||
Array<long_index_t, NumDTensor> ds_offset;
|
||||
static_for<0, NumDTensor, 1>{}(
|
||||
[&](auto i) { ds_offset(i) = g_idx * static_cast<long_index_t>(BatchStrideDs_[i]); });
|
||||
return ds_offset;
|
||||
}
|
||||
|
||||
__host__ __device__ constexpr long_index_t GetEPtrOffset(index_t g_idx) const
|
||||
{
|
||||
return g_idx * static_cast<long_index_t>(BatchStrideE_);
|
||||
}
|
||||
|
||||
index_t BatchStrideA_;
|
||||
index_t BatchStrideB_;
|
||||
Array<ck::index_t, NumDTensor> BatchStrideDs_;
|
||||
index_t BatchStrideE_;
|
||||
};
|
||||
|
||||
/*
|
||||
* \brief Wrapper function of GridwiseGemm::Run to realize BatchedGEMM.
|
||||
*
|
||||
@@ -257,7 +213,7 @@ struct DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1
|
||||
BElementwiseOp,
|
||||
CDEElementwiseOp>
|
||||
{
|
||||
// FIXME
|
||||
// TODO: Extend support for more spatial dimensions.
|
||||
static_assert(NDimSpatial == 2 || NDimSpatial == 3,
|
||||
"wrong! only implemented for 2D and 3D now");
|
||||
|
||||
@@ -265,7 +221,7 @@ struct DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1
|
||||
|
||||
static constexpr index_t NumDTensor = DsDataType::Size();
|
||||
|
||||
// TODO make A/B datatype different
|
||||
// TODO: Add support for different A and B data types.
|
||||
using ABDataType = ADataType;
|
||||
|
||||
static constexpr auto I0 = Number<0>{};
|
||||
|
||||
@@ -19,6 +19,7 @@
|
||||
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_dl_multiple_d.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_utils.hpp"
|
||||
#include "ck/host_utility/device_prop.hpp"
|
||||
#include "ck/host_utility/kernel_launch.hpp"
|
||||
#include "ck/host_utility/io.hpp"
|
||||
@@ -29,51 +30,6 @@ namespace device {
|
||||
|
||||
namespace {
|
||||
|
||||
template <index_t NumDTensor>
|
||||
struct ComputePtrOffsetOfStridedBatch
|
||||
{
|
||||
ComputePtrOffsetOfStridedBatch() = default;
|
||||
|
||||
ComputePtrOffsetOfStridedBatch(index_t BatchStrideA,
|
||||
index_t BatchStrideB,
|
||||
Array<ck::index_t, NumDTensor> BatchStrideDs,
|
||||
index_t BatchStrideE)
|
||||
: BatchStrideA_(BatchStrideA),
|
||||
BatchStrideB_(BatchStrideB),
|
||||
BatchStrideDs_(BatchStrideDs),
|
||||
BatchStrideE_(BatchStrideE)
|
||||
{
|
||||
}
|
||||
|
||||
__host__ __device__ constexpr long_index_t GetAPtrOffset(index_t g_idx) const
|
||||
{
|
||||
return g_idx * static_cast<long_index_t>(BatchStrideA_);
|
||||
}
|
||||
|
||||
__host__ __device__ constexpr long_index_t GetBPtrOffset(index_t g_idx) const
|
||||
{
|
||||
return g_idx * static_cast<long_index_t>(BatchStrideB_);
|
||||
}
|
||||
|
||||
__host__ __device__ constexpr auto GetDsPtrOffset(index_t g_idx) const
|
||||
{
|
||||
Array<long_index_t, NumDTensor> ds_offset;
|
||||
static_for<0, NumDTensor, 1>{}(
|
||||
[&](auto i) { ds_offset(i) = g_idx * static_cast<long_index_t>(BatchStrideDs_[i]); });
|
||||
return ds_offset;
|
||||
}
|
||||
|
||||
__host__ __device__ constexpr long_index_t GetEPtrOffset(index_t g_idx) const
|
||||
{
|
||||
return g_idx * static_cast<long_index_t>(BatchStrideE_);
|
||||
}
|
||||
|
||||
index_t BatchStrideA_;
|
||||
index_t BatchStrideB_;
|
||||
Array<ck::index_t, NumDTensor> BatchStrideDs_;
|
||||
index_t BatchStrideE_;
|
||||
};
|
||||
|
||||
/*
|
||||
* \brief Wrapper function of GridwiseGemm::Run to realize BatchedGEMM.
|
||||
*
|
||||
|
||||
@@ -19,6 +19,7 @@
|
||||
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_d_wmma_cshuffle.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_utils.hpp"
|
||||
#include "ck/host_utility/device_prop.hpp"
|
||||
#include "ck/host_utility/kernel_launch.hpp"
|
||||
#include "ck/host_utility/io.hpp"
|
||||
@@ -27,55 +28,6 @@ namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
|
||||
namespace {
|
||||
|
||||
template <index_t NumDTensor>
|
||||
struct ComputePtrOffsetOfStridedBatch
|
||||
{
|
||||
ComputePtrOffsetOfStridedBatch() = default;
|
||||
|
||||
ComputePtrOffsetOfStridedBatch(index_t BatchStrideA,
|
||||
index_t BatchStrideB,
|
||||
Array<ck::index_t, NumDTensor> BatchStrideDs,
|
||||
index_t BatchStrideE)
|
||||
: BatchStrideA_(BatchStrideA),
|
||||
BatchStrideB_(BatchStrideB),
|
||||
BatchStrideDs_(BatchStrideDs),
|
||||
BatchStrideE_(BatchStrideE)
|
||||
{
|
||||
}
|
||||
|
||||
__host__ __device__ constexpr long_index_t GetAPtrOffset(index_t g_idx) const
|
||||
{
|
||||
return g_idx * static_cast<long_index_t>(BatchStrideA_);
|
||||
}
|
||||
|
||||
__host__ __device__ constexpr long_index_t GetBPtrOffset(index_t g_idx) const
|
||||
{
|
||||
return g_idx * static_cast<long_index_t>(BatchStrideB_);
|
||||
}
|
||||
|
||||
__host__ __device__ constexpr auto GetDsPtrOffset(index_t g_idx) const
|
||||
{
|
||||
Array<long_index_t, NumDTensor> ds_offset;
|
||||
static_for<0, NumDTensor, 1>{}(
|
||||
[&](auto i) { ds_offset(i) = g_idx * static_cast<long_index_t>(BatchStrideDs_[i]); });
|
||||
return ds_offset;
|
||||
}
|
||||
|
||||
__host__ __device__ constexpr long_index_t GetEPtrOffset(index_t g_idx) const
|
||||
{
|
||||
return g_idx * static_cast<long_index_t>(BatchStrideE_);
|
||||
}
|
||||
|
||||
index_t BatchStrideA_;
|
||||
index_t BatchStrideB_;
|
||||
Array<ck::index_t, NumDTensor> BatchStrideDs_;
|
||||
index_t BatchStrideE_;
|
||||
};
|
||||
|
||||
} // namespace
|
||||
|
||||
//
|
||||
// @brief Device Convolution operation.
|
||||
//
|
||||
|
||||
@@ -19,6 +19,7 @@
|
||||
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_d_xdl_cshuffle.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_utils.hpp"
|
||||
#include "ck/host_utility/device_prop.hpp"
|
||||
#include "ck/host_utility/kernel_launch.hpp"
|
||||
#include "ck/host_utility/io.hpp"
|
||||
@@ -29,51 +30,6 @@ namespace device {
|
||||
|
||||
namespace {
|
||||
|
||||
template <index_t NumDTensor>
|
||||
struct ComputePtrOffsetOfStridedBatch
|
||||
{
|
||||
ComputePtrOffsetOfStridedBatch() = default;
|
||||
|
||||
ComputePtrOffsetOfStridedBatch(index_t BatchStrideA,
|
||||
index_t BatchStrideB,
|
||||
Array<ck::index_t, NumDTensor> BatchStrideDs,
|
||||
index_t BatchStrideE)
|
||||
: BatchStrideA_(BatchStrideA),
|
||||
BatchStrideB_(BatchStrideB),
|
||||
BatchStrideDs_(BatchStrideDs),
|
||||
BatchStrideE_(BatchStrideE)
|
||||
{
|
||||
}
|
||||
|
||||
__host__ __device__ constexpr long_index_t GetAPtrOffset(index_t g_idx) const
|
||||
{
|
||||
return g_idx * static_cast<long_index_t>(BatchStrideA_);
|
||||
}
|
||||
|
||||
__host__ __device__ constexpr long_index_t GetBPtrOffset(index_t g_idx) const
|
||||
{
|
||||
return g_idx * static_cast<long_index_t>(BatchStrideB_);
|
||||
}
|
||||
|
||||
__host__ __device__ constexpr auto GetDsPtrOffset(index_t g_idx) const
|
||||
{
|
||||
Array<long_index_t, NumDTensor> ds_offset;
|
||||
static_for<0, NumDTensor, 1>{}(
|
||||
[&](auto i) { ds_offset(i) = g_idx * static_cast<long_index_t>(BatchStrideDs_[i]); });
|
||||
return ds_offset;
|
||||
}
|
||||
|
||||
__host__ __device__ constexpr long_index_t GetEPtrOffset(index_t g_idx) const
|
||||
{
|
||||
return g_idx * static_cast<long_index_t>(BatchStrideE_);
|
||||
}
|
||||
|
||||
index_t BatchStrideA_;
|
||||
index_t BatchStrideB_;
|
||||
Array<ck::index_t, NumDTensor> BatchStrideDs_;
|
||||
index_t BatchStrideE_;
|
||||
};
|
||||
|
||||
/*
|
||||
* \brief Wrapper function of GridwiseGemm::Run to realize BatchedGEMM.
|
||||
*
|
||||
|
||||
@@ -0,0 +1,59 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "ck/utility/common_header.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
|
||||
template <index_t NumDTensor>
|
||||
struct ComputePtrOffsetOfStridedBatch
|
||||
{
|
||||
ComputePtrOffsetOfStridedBatch() = default;
|
||||
|
||||
ComputePtrOffsetOfStridedBatch(index_t BatchStrideA,
|
||||
index_t BatchStrideB,
|
||||
Array<ck::index_t, NumDTensor> BatchStrideDs,
|
||||
index_t BatchStrideE)
|
||||
: BatchStrideA_(BatchStrideA),
|
||||
BatchStrideB_(BatchStrideB),
|
||||
BatchStrideDs_(BatchStrideDs),
|
||||
BatchStrideE_(BatchStrideE)
|
||||
{
|
||||
}
|
||||
|
||||
__host__ __device__ constexpr long_index_t GetAPtrOffset(index_t g_idx) const
|
||||
{
|
||||
return g_idx * static_cast<long_index_t>(BatchStrideA_);
|
||||
}
|
||||
|
||||
__host__ __device__ constexpr long_index_t GetBPtrOffset(index_t g_idx) const
|
||||
{
|
||||
return g_idx * static_cast<long_index_t>(BatchStrideB_);
|
||||
}
|
||||
|
||||
__host__ __device__ constexpr auto GetDsPtrOffset(index_t g_idx) const
|
||||
{
|
||||
Array<long_index_t, NumDTensor> ds_offset;
|
||||
static_for<0, NumDTensor, 1>{}(
|
||||
[&](auto i) { ds_offset(i) = g_idx * static_cast<long_index_t>(BatchStrideDs_[i]); });
|
||||
return ds_offset;
|
||||
}
|
||||
|
||||
__host__ __device__ constexpr long_index_t GetEPtrOffset(index_t g_idx) const
|
||||
{
|
||||
return g_idx * static_cast<long_index_t>(BatchStrideE_);
|
||||
}
|
||||
|
||||
index_t BatchStrideA_;
|
||||
index_t BatchStrideB_;
|
||||
Array<ck::index_t, NumDTensor> BatchStrideDs_;
|
||||
index_t BatchStrideE_;
|
||||
};
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
Reference in New Issue
Block a user