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https://github.com/ROCm/composable_kernel.git
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Add grouped conv bwd weight multi d kernel (#1237)
* Add grouped conv bwd weight multi d kernel * Reference fix * Fix cmake files * bwd weight scale only xdl * Fixes * Fix client conv fwd example
This commit is contained in:
Bartłomiej Kocot
@@ -8,6 +8,8 @@ foreach(gpu IN LISTS GPU_TARGETS)
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add_example_dependencies(example_convnd_activ_binary_xdl example_convnd_fwd_xdl_bilinear_residual_fp16)
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add_example_executable(example_convnd_bwd_data_xdl_bilinear_residual_fp16 convnd_bwd_data_xdl_bilinear_residual_fp16.cpp)
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add_example_dependencies(example_convnd_activ_binary_xdl example_convnd_bwd_data_xdl_bilinear_residual_fp16)
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add_example_executable(example_convnd_bwd_weight_xdl_bilinear_residual_fp16 convnd_bwd_weight_xdl_bilinear_residual_fp16.cpp)
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add_example_dependencies(example_convnd_activ_binary_xdl example_convnd_bwd_weight_xdl_bilinear_residual_fp16)
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set(target 1)
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endif()
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endforeach()
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@@ -0,0 +1,260 @@
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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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#include <cstdlib>
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#include <iostream>
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#include <numeric>
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#include <type_traits>
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_multiple_d_xdl_cshuffle.hpp"
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#include "ck/library/utility/algorithm.hpp"
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#include "ck/library/utility/check_err.hpp"
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#include "ck/library/utility/device_memory.hpp"
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/host_tensor_generator.hpp"
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#include "ck/library/utility/convolution_parameter.hpp"
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#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_conv_bwd_weight.hpp"
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#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
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constexpr ck::index_t NDimSpatial = 3;
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using InDataType = ck::half_t;
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using WeiDataType = ck::half_t;
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using AccDataType = float;
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using OutDataType = ck::half_t;
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template <ck::index_t... Is>
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using S = ck::Sequence<Is...>;
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using InLayout = ck::tensor_layout::convolution::GNDHWC;
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using WeiLayout = ck::tensor_layout::convolution::GKZYXC;
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using OutLayout = ck::tensor_layout::convolution::GNDHWK;
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using InElementOp = ck::tensor_operation::element_wise::PassThrough;
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using WeiElementOp = ck::tensor_operation::element_wise::Bilinear;
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using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
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static constexpr auto ConvBwdWeightDefault =
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ck::tensor_operation::device::ConvolutionBackwardWeightSpecialization::Default;
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template <typename WeiElementOp>
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using DeviceGroupedConvNDBwdWeightInstance =
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ck::tensor_operation::device::DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle<
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NDimSpatial,
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InLayout, // InLayout
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WeiLayout, // WeiLayout
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OutLayout, // OutLayout
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ck::Tuple<WeiLayout>, // DsLayout
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InDataType, // InDataType
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WeiDataType, // WeiDataType
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OutDataType, // OutDataType
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AccDataType, // AccDataType
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ck::Tuple<WeiDataType>, // DsLayout
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InElementOp, // InElementwiseOperation
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WeiElementOp, // WeiElementwiseOperation
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OutElementOp, // OutElementwiseOperation
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ConvBwdWeightDefault, // ConvolutionBackwardWeightSpecialization
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256, // BlockSize
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128, // MPerBlock
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128, // NPerBlock
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4, // K0PerBlock
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8, // K1
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32, // MPerXdl
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32, // NPerXdl
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2, // MXdlPerWave
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2, // NXdlPerWave
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S<1, 4, 16, 4>, // ABlockTransferThreadClusterLengths_K0_M_K1
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S<0, 3, 1, 2>, // ABlockTransferThreadClusterArrangeOrder
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S<0, 2, 1, 3>, // ABlockTransferSrcAccessOrder
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2, // ABlockTransferSrcVectorDim
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8, // ABlockTransferSrcScalarPerVector
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2, // ABlockTransferDstScalarPerVector_K1
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true, // ABlockLdsAddExtraM
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S<1, 4, 16, 4>, // BBlockTransferThreadClusterLengths_K0_N_K1
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S<0, 3, 1, 2>, // BBlockTransferThreadClusterArrangeOrder
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S<0, 2, 1, 3>, // BBlockTransferSrcAccessOrder
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2, // BBlockTransferSrcVectorDim
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8, // BBlockTransferSrcScalarPerVector
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2, // BBlockTransferDstScalarPerVector_K1
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true, // BBlockLdsAddExtraN
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1, // CShuffleMXdlPerWavePerShuffle
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1, // CShuffleNXdlPerWavePerShuffle
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S<1, 32, 1, 4>, // CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
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128 / (sizeof(WeiDataType) * CHAR_BIT)>; // CBlockTransferScalarPerVector_NWaveNPerXdl
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using DeviceGroupedConvNDActivInstance = DeviceGroupedConvNDBwdWeightInstance<WeiElementOp>;
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namespace {
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// Use custom implementation to pass two more tensors for post op
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template <ck::index_t NDimSpatial,
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typename InDataType,
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typename WeiDataType,
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typename OutDataType,
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typename InElementOp,
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typename WeiElementOp,
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typename OutElementOp,
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typename DeviceConvNDFwdInstance>
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bool run_grouped_conv(bool do_verification,
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int init_method,
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bool time_kernel,
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const ck::utils::conv::ConvParam& conv_param,
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const HostTensorDescriptor& in_g_n_c_wis_desc,
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const HostTensorDescriptor& wei_g_k_c_xs_desc,
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const HostTensorDescriptor& out_g_n_k_wos_desc,
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const InElementOp& in_element_op,
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const WeiElementOp& wei_element_op,
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const OutElementOp& out_element_op)
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{
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constexpr ck::index_t split_k = 1;
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constexpr ck::index_t NumDs = 1;
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Tensor<InDataType> in(in_g_n_c_wis_desc);
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Tensor<WeiDataType> wei_host(wei_g_k_c_xs_desc);
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Tensor<OutDataType> out(out_g_n_k_wos_desc);
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std::cout << "in: " << in.mDesc << std::endl;
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std::cout << "wei: " << wei_host.mDesc << std::endl;
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std::cout << "out: " << out.mDesc << std::endl;
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switch(init_method)
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{
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case 0: break;
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case 1:
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in.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5});
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out.GenerateTensorValue(GeneratorTensor_2<OutDataType>{-5, 5});
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wei_host.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5});
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break;
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default:
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in.GenerateTensorValue(GeneratorTensor_3<InDataType>{0.0, 1.0});
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out.GenerateTensorValue(GeneratorTensor_3<OutDataType>{0.0, 1.0});
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wei_host.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.5, 0.5});
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}
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// Initialize based on wei_host
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Tensor<WeiDataType> wei_device(wei_host);
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DeviceMem in_device_buf(sizeof(InDataType) * in.mDesc.GetElementSpaceSize());
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DeviceMem wei_device_buf(sizeof(WeiDataType) * wei_device.mDesc.GetElementSpaceSize());
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DeviceMem out_device_buf(sizeof(OutDataType) * out.mDesc.GetElementSpaceSize());
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in_device_buf.ToDevice(in.mData.data());
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wei_device_buf.ToDevice(wei_device.mData.data());
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out_device_buf.ToDevice(out.mData.data());
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std::array<ck::index_t, NDimSpatial + 3> b_g_n_c_wis_lengths{};
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std::array<ck::index_t, NDimSpatial + 3> b_g_n_c_wis_strides{};
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std::array<ck::index_t, NDimSpatial + 3> e_g_k_c_xs_lengths{};
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std::array<ck::index_t, NDimSpatial + 3> e_g_k_c_xs_strides{};
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std::array<ck::index_t, NDimSpatial + 3> a_g_n_k_wos_lengths{};
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std::array<ck::index_t, NDimSpatial + 3> a_g_n_k_wos_strides{};
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std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
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std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
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std::array<ck::index_t, NDimSpatial> input_left_pads{};
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std::array<ck::index_t, NDimSpatial> input_right_pads{};
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auto copy = [](const auto& x, auto& y) { ck::ranges::copy(x, y.begin()); };
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copy(in_g_n_c_wis_desc.GetLengths(), b_g_n_c_wis_lengths);
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copy(in_g_n_c_wis_desc.GetStrides(), b_g_n_c_wis_strides);
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copy(wei_g_k_c_xs_desc.GetLengths(), e_g_k_c_xs_lengths);
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copy(wei_g_k_c_xs_desc.GetStrides(), e_g_k_c_xs_strides);
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copy(out_g_n_k_wos_desc.GetLengths(), a_g_n_k_wos_lengths);
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copy(out_g_n_k_wos_desc.GetStrides(), a_g_n_k_wos_strides);
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copy(conv_param.conv_filter_strides_, conv_filter_strides);
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copy(conv_param.conv_filter_dilations_, conv_filter_dilations);
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copy(conv_param.input_left_pads_, input_left_pads);
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copy(conv_param.input_right_pads_, input_right_pads);
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// Use weight as D
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const std::array<const void*, NumDs> ds = {wei_device_buf.GetDeviceBuffer()};
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auto conv = DeviceConvNDFwdInstance{};
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auto invoker = conv.MakeInvoker();
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auto argument = conv.MakeArgument(
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static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
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static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
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static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
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ds,
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b_g_n_c_wis_lengths,
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b_g_n_c_wis_strides,
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e_g_k_c_xs_lengths,
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e_g_k_c_xs_strides,
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a_g_n_k_wos_lengths,
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a_g_n_k_wos_strides,
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std::array<std::array<ck::index_t, NDimSpatial + 3>, NumDs>{e_g_k_c_xs_lengths},
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std::array<std::array<ck::index_t, NDimSpatial + 3>, NumDs>{e_g_k_c_xs_strides},
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conv_filter_strides,
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conv_filter_dilations,
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input_left_pads,
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input_right_pads,
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in_element_op,
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wei_element_op,
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out_element_op,
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split_k);
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DeviceMem workspace_buf(argument.GetWorkspaceSizeBytes());
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conv.SetWorkSpacePointer(&argument, workspace_buf.GetDeviceBuffer());
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if(!conv.IsSupportedArgument(argument))
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{
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throw std::runtime_error("The device op with the specified compilation parameters does "
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"not support this convolution problem.");
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}
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float avg_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
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std::size_t flop =
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conv_param.GetFlops() + 3 * conv_param.GetOutputByte<WeiDataType>() / sizeof(WeiDataType);
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std::size_t num_btype = conv_param.GetByte<InDataType, WeiDataType, OutDataType>() +
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conv_param.GetOutputByte<WeiDataType>();
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float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
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float gb_per_sec = num_btype / 1.E6 / avg_time;
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std::cout << "Perf: " << avg_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
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<< conv.GetTypeString() << std::endl;
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if(do_verification)
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{
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std::array<Tensor<OutDataType>, NumDs> d_tensors = {wei_host};
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auto ref_conv =
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ck::tensor_operation::host::ReferenceConvBwdWeight<NDimSpatial,
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InDataType,
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WeiDataType,
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OutDataType,
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InElementOp,
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WeiElementOp,
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OutElementOp,
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0, /*Num A Elementwise Tensors*/
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0, /*Num B Elementwise Tensors*/
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NumDs>{};
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auto ref_invoker = ref_conv.MakeInvoker();
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auto ref_argument = ref_conv.MakeArgument(in,
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wei_host,
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out,
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conv_param.conv_filter_strides_,
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conv_param.conv_filter_dilations_,
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conv_param.input_left_pads_,
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conv_param.input_right_pads_,
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in_element_op,
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wei_element_op,
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out_element_op,
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{},
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{},
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d_tensors);
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ref_invoker.Run(ref_argument);
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wei_device_buf.FromDevice(wei_device.mData.data());
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return ck::utils::check_err(wei_device, wei_host, "Error: incorrect results!");
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}
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return true;
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}
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} // namespace
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#include "../run_convnd_activ_example.inc"
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int main(int argc, char* argv[]) { return !run_convnd_example(argc, argv); }
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