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https://github.com/ROCm/composable_kernel.git
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conv+conv (1x1 only) example using gemm+gemm (#393)
* refactor conv
* add conv+conv example, 1x1 only
[ROCm/composable_kernel commit: 4df6d93f60]
This commit is contained in:
Chao Liu
@@ -76,8 +76,6 @@ using DeviceGroupedConvNDFwdInstance =
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int main(int argc, char* argv[])
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{
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namespace ctc = ck::tensor_layout::convolution;
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print_helper_msg();
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bool do_verification = true;
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@@ -111,11 +109,12 @@ int main(int argc, char* argv[])
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const auto wei_element_op = WeiElementOp{};
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const auto out_element_op = OutElementOp{};
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if(conv_param.num_dim_spatial_ == 1)
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{
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using InLayout = ctc::GNWC;
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using WeiLayout = ctc::GKXC;
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using OutLayout = ctc::GNWK;
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const auto run = [&](auto ndim_spatial, auto in_layout, auto wei_layout, auto out_layout) {
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constexpr ck::index_t ndim_spatial_value = ndim_spatial.value;
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using InLayout = decltype(in_layout);
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using WeiLayout = decltype(wei_layout);
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using OutLayout = decltype(out_layout);
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const auto in_g_n_c_wis_desc =
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ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(
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@@ -130,97 +129,39 @@ int main(int argc, char* argv[])
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conv_param);
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return run_grouped_conv_fwd<
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1,
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ndim_spatial_value,
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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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DeviceGroupedConvNDFwdInstance<1, InLayout, WeiLayout, OutLayout>>(do_verification,
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init_method,
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time_kernel,
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conv_param,
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in_g_n_c_wis_desc,
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wei_g_k_c_xs_desc,
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out_g_n_k_wos_desc,
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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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DeviceGroupedConvNDFwdInstance<ndim_spatial_value, InLayout, WeiLayout, OutLayout>>(
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do_verification,
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init_method,
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time_kernel,
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conv_param,
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in_g_n_c_wis_desc,
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wei_g_k_c_xs_desc,
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out_g_n_k_wos_desc,
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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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namespace ctc = ck::tensor_layout::convolution;
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if(conv_param.num_dim_spatial_ == 1)
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{
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run(ck::Number<1>{}, ctc::GNWC{}, ctc::GKXC{}, ctc::GNWK{});
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}
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else if(conv_param.num_dim_spatial_ == 2)
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{
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using InLayout = ctc::GNHWC;
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using WeiLayout = ctc::GKYXC;
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using OutLayout = ctc::GNHWK;
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const auto in_g_n_c_wis_desc =
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ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(
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conv_param);
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const auto wei_g_k_c_xs_desc =
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ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(
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conv_param);
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const auto out_g_n_k_wos_desc =
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ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(
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conv_param);
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return run_grouped_conv_fwd<
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2,
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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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DeviceGroupedConvNDFwdInstance<2, InLayout, WeiLayout, OutLayout>>(do_verification,
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init_method,
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time_kernel,
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conv_param,
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in_g_n_c_wis_desc,
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wei_g_k_c_xs_desc,
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out_g_n_k_wos_desc,
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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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run(ck::Number<2>{}, ctc::GNHWC{}, ctc::GKYXC{}, ctc::GNHWK{});
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}
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else if(conv_param.num_dim_spatial_ == 3)
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{
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using InLayout = ctc::GNDHWC;
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using WeiLayout = ctc::GKZYXC;
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using OutLayout = ctc::GNDHWK;
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const auto in_g_n_c_wis_desc =
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ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(
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conv_param);
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const auto wei_g_k_c_xs_desc =
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ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(
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conv_param);
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const auto out_g_n_k_wos_desc =
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ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(
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conv_param);
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return run_grouped_conv_fwd<
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3,
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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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DeviceGroupedConvNDFwdInstance<3, InLayout, WeiLayout, OutLayout>>(do_verification,
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init_method,
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time_kernel,
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conv_param,
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in_g_n_c_wis_desc,
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wei_g_k_c_xs_desc,
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out_g_n_k_wos_desc,
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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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run(ck::Number<3>{}, ctc::GNDHWC{}, ctc::GKZYXC{}, ctc::GNDHWK{});
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}
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return 0;
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@@ -163,9 +163,9 @@ int main(int argc, char* argv[])
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{conv_param.G_, conv_param.N_, conv_param.K_, conv_param.output_spatial_lengths_[0]},
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{
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conv_param.K_, // g
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0, // k
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1, // c
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0 // x
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0, // n
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1, // k
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0 // wo
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});
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const auto residual_g_n_k_wos_desc = HostTensorDescriptor(
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1
example/41_grouped_conv_conv_fwd/CMakeLists.txt
Normal file
1
example/41_grouped_conv_conv_fwd/CMakeLists.txt
Normal file
@@ -0,0 +1 @@
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add_example_executable(example_grouped_conv_conv_fwd_xdl_fp16 grouped_conv_conv_fwd_xdl_fp16.cpp)
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@@ -0,0 +1,257 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, 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/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_fwd.hpp"
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template <ck::index_t NDimSpatial,
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typename In0DataType,
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typename Wei0DataType,
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typename Acc0DataType,
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typename Wei1DataType,
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typename Out1DataType,
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typename In0ElementOp,
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typename Wei0ElementOp,
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typename Out0ElementOp,
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typename Wei1ElementOp,
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typename Out1ElementOp,
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typename DeviceOpInstance>
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int run_grouped_conv_conv_fwd(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& conv0_param,
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const ck::utils::conv::ConvParam& conv1_param,
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const HostTensorDescriptor& in0_g_n_c_wis_desc,
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const HostTensorDescriptor& wei0_g_k_c_xs_desc,
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const HostTensorDescriptor& out0_g_n_k_wos_desc,
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const HostTensorDescriptor& wei1_g_k_c_xs_desc,
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const HostTensorDescriptor& out1_g_n_k_wos_desc,
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const In0ElementOp& in0_element_op,
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const Wei0ElementOp& wei0_element_op,
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const Wei1ElementOp& wei1_element_op,
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const Out0ElementOp& out0_element_op,
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const Out1ElementOp& out1_element_op)
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{
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Tensor<In0DataType> in0(in0_g_n_c_wis_desc);
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Tensor<Wei0DataType> wei0(wei0_g_k_c_xs_desc);
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Tensor<Wei1DataType> wei1(wei1_g_k_c_xs_desc);
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Tensor<Out1DataType> out1_host(out1_g_n_k_wos_desc);
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Tensor<Out1DataType> out1_device(out1_g_n_k_wos_desc);
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std::cout << "in0: " << in0.mDesc << std::endl;
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std::cout << "wei0: " << wei0.mDesc << std::endl;
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std::cout << "wei1: " << wei1.mDesc << std::endl;
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std::cout << "out1: " << out1_host.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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in0.GenerateTensorValue(GeneratorTensor_2<In0DataType>{-5, 5});
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wei0.GenerateTensorValue(GeneratorTensor_2<Wei0DataType>{-5, 5});
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wei1.GenerateTensorValue(GeneratorTensor_2<Wei1DataType>{-5, 5});
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break;
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default:
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in0.GenerateTensorValue(GeneratorTensor_3<In0DataType>{0.0, 1.0});
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wei0.GenerateTensorValue(GeneratorTensor_3<Wei0DataType>{-0.5, 0.5});
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wei1.GenerateTensorValue(GeneratorTensor_3<Wei1DataType>{-0.5, 0.5});
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}
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DeviceMem in0_device_buf(sizeof(In0DataType) * in0.mDesc.GetElementSpaceSize());
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DeviceMem wei0_device_buf(sizeof(Wei0DataType) * wei0.mDesc.GetElementSpaceSize());
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DeviceMem wei1_device_buf(sizeof(Wei1DataType) * wei1.mDesc.GetElementSpaceSize());
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DeviceMem out1_device_buf(sizeof(Out1DataType) * out1_device.mDesc.GetElementSpaceSize());
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in0_device_buf.ToDevice(in0.mData.data());
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wei0_device_buf.ToDevice(wei0.mData.data());
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wei1_device_buf.ToDevice(wei1.mData.data());
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std::array<ck::index_t, NDimSpatial + 3> a0_g_n_c_wis_lengths{};
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std::array<ck::index_t, NDimSpatial + 3> a0_g_n_c_wis_strides{};
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std::array<ck::index_t, NDimSpatial + 3> b0_g_k_c_xs_lengths{};
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std::array<ck::index_t, NDimSpatial + 3> b0_g_k_c_xs_strides{};
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std::array<ck::index_t, NDimSpatial + 3> b1_g_k_c_xs_lengths{};
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std::array<ck::index_t, NDimSpatial + 3> b1_g_k_c_xs_strides{};
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std::array<ck::index_t, NDimSpatial + 3> e1_g_n_k_wos_lengths{};
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std::array<ck::index_t, NDimSpatial + 3> e1_g_n_k_wos_strides{};
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std::array<ck::index_t, NDimSpatial> conv0_filter_strides{};
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std::array<ck::index_t, NDimSpatial> conv0_filter_dilations{};
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std::array<ck::index_t, NDimSpatial> input0_left_pads{};
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std::array<ck::index_t, NDimSpatial> input0_right_pads{};
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std::array<ck::index_t, NDimSpatial> conv1_filter_strides{};
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std::array<ck::index_t, NDimSpatial> conv1_filter_dilations{};
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std::array<ck::index_t, NDimSpatial> input1_left_pads{};
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std::array<ck::index_t, NDimSpatial> input1_right_pads{};
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auto copy = [](auto& x, auto& y) { std::copy(x.begin(), x.end(), y.begin()); };
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copy(in0_g_n_c_wis_desc.GetLengths(), a0_g_n_c_wis_lengths);
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copy(in0_g_n_c_wis_desc.GetStrides(), a0_g_n_c_wis_strides);
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copy(wei0_g_k_c_xs_desc.GetLengths(), b0_g_k_c_xs_lengths);
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copy(wei0_g_k_c_xs_desc.GetStrides(), b0_g_k_c_xs_strides);
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copy(wei1_g_k_c_xs_desc.GetLengths(), b1_g_k_c_xs_lengths);
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copy(wei1_g_k_c_xs_desc.GetStrides(), b1_g_k_c_xs_strides);
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copy(out1_g_n_k_wos_desc.GetLengths(), e1_g_n_k_wos_lengths);
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copy(out1_g_n_k_wos_desc.GetStrides(), e1_g_n_k_wos_strides);
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copy(conv0_param.conv_filter_strides_, conv0_filter_strides);
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copy(conv0_param.conv_filter_dilations_, conv0_filter_dilations);
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copy(conv0_param.input_left_pads_, input0_left_pads);
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copy(conv0_param.input_right_pads_, input0_right_pads);
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copy(conv1_param.conv_filter_strides_, conv1_filter_strides);
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copy(conv1_param.conv_filter_dilations_, conv1_filter_dilations);
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copy(conv1_param.input_left_pads_, input1_left_pads);
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copy(conv1_param.input_right_pads_, input1_right_pads);
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#if 1
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// do Conv using GEMM, only works for 1x1 conv for now
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const ck::index_t gemm_batch = a0_g_n_c_wis_lengths[0];
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const ck::index_t gemm0_m_length =
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e1_g_n_k_wos_lengths[1] * std::accumulate(e1_g_n_k_wos_lengths.begin() + 3,
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e1_g_n_k_wos_lengths.begin() + 3 + NDimSpatial,
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ck::index_t{1},
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std::multiplies<ck::index_t>{});
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const ck::index_t gemm0_n_length = b0_g_k_c_xs_lengths[1];
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const ck::index_t gemm0_k_length =
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std::accumulate(b0_g_k_c_xs_lengths.begin() + 2,
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b0_g_k_c_xs_lengths.begin() + 2 + NDimSpatial + 1,
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ck::index_t{1},
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std::multiplies<ck::index_t>{});
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const ck::index_t gemm1_n_length = b1_g_k_c_xs_lengths[1];
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//
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const ck::index_t a0_stride = a0_g_n_c_wis_strides[2 + NDimSpatial];
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const ck::index_t b0_stride = b0_g_k_c_xs_strides[2 + NDimSpatial];
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const ck::index_t b1_stride = b1_g_k_c_xs_strides[2 + NDimSpatial];
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const ck::index_t e1_stride = e1_g_n_k_wos_strides[2 + NDimSpatial];
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//
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const ck::index_t a0_batch_stride = a0_g_n_c_wis_strides[0];
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const ck::index_t b0_batch_stride = b0_g_k_c_xs_strides[0];
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const ck::index_t b1_batch_stride = b1_g_k_c_xs_strides[0];
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const ck::index_t e1_batch_stride = e1_g_n_k_wos_strides[0];
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auto device_op = DeviceOpInstance{};
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auto invoker = device_op.MakeInvoker();
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auto argument =
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device_op.MakeArgument(static_cast<In0DataType*>(in0_device_buf.GetDeviceBuffer()),
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static_cast<Wei0DataType*>(wei0_device_buf.GetDeviceBuffer()),
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static_cast<Wei1DataType*>(wei1_device_buf.GetDeviceBuffer()),
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static_cast<Out1DataType*>(out1_device_buf.GetDeviceBuffer()),
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gemm0_m_length,
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gemm0_n_length,
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gemm0_k_length,
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gemm1_n_length,
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gemm_batch,
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a0_stride,
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b0_stride,
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b1_stride,
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e1_stride,
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a0_batch_stride,
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b0_batch_stride,
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b1_batch_stride,
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e1_batch_stride,
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in0_element_op,
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wei0_element_op,
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out0_element_op,
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wei1_element_op,
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out1_element_op);
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if(!device_op.IsSupportedArgument(argument))
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{
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throw std::runtime_error(
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"wrong! device_conv with the specified compilation parameters does "
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"not support this Conv 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 = conv0_param.GetFlops() + conv1_param.GetFlops();
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std::size_t num_btype = conv0_param.template GetInputByte<In0DataType>() +
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conv0_param.template GetWeightByte<Wei0DataType>() +
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conv1_param.template GetWeightByte<Wei1DataType>() +
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conv1_param.template GetOutputByte<Out1DataType>();
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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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<< device_op.GetTypeString() << std::endl;
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#endif
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if(do_verification)
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{
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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Tensor<Acc0DataType> out0_host(out0_g_n_k_wos_desc);
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auto ref_conv0 = ck::tensor_operation::host::ReferenceConvFwd<NDimSpatial,
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In0DataType,
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Wei0DataType,
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Acc0DataType,
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In0ElementOp,
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Wei0ElementOp,
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Out0ElementOp>();
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|
||||
auto ref_conv1 = ck::tensor_operation::host::ReferenceConvFwd<NDimSpatial,
|
||||
Acc0DataType,
|
||||
Wei1DataType,
|
||||
Out1DataType,
|
||||
PassThrough,
|
||||
Wei1ElementOp,
|
||||
Out1ElementOp>();
|
||||
|
||||
auto ref_conv0_invoker = ref_conv0.MakeInvoker();
|
||||
auto ref_conv1_invoker = ref_conv1.MakeInvoker();
|
||||
|
||||
auto ref_conv0_argument = ref_conv0.MakeArgument(in0,
|
||||
wei0,
|
||||
out0_host,
|
||||
conv0_param.conv_filter_strides_,
|
||||
conv0_param.conv_filter_dilations_,
|
||||
conv0_param.input_left_pads_,
|
||||
conv0_param.input_right_pads_,
|
||||
in0_element_op,
|
||||
wei0_element_op,
|
||||
out0_element_op);
|
||||
|
||||
auto ref_conv1_argument = ref_conv1.MakeArgument(out0_host,
|
||||
wei1,
|
||||
out1_host,
|
||||
conv1_param.conv_filter_strides_,
|
||||
conv1_param.conv_filter_dilations_,
|
||||
conv1_param.input_left_pads_,
|
||||
conv1_param.input_right_pads_,
|
||||
out0_element_op,
|
||||
wei1_element_op,
|
||||
out1_element_op);
|
||||
|
||||
ref_conv0_invoker.Run(ref_conv0_argument);
|
||||
ref_conv1_invoker.Run(ref_conv1_argument);
|
||||
|
||||
out1_device_buf.FromDevice(out1_device.mData.data());
|
||||
|
||||
return ck::utils::check_err(
|
||||
out1_device.mData, out1_host.mData, "Error: incorrect results!", 1e-5f, 1e-4f)
|
||||
? 0
|
||||
: 1;
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
@@ -0,0 +1,204 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#include "grouped_conv_conv_fwd_common.hpp"
|
||||
|
||||
#include "ck/tensor_operation/gpu/device/device_batched_gemm_gemm_xdl_cshuffle.hpp"
|
||||
|
||||
#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
|
||||
|
||||
using In0DataType = ck::half_t;
|
||||
using Wei0DataType = ck::half_t;
|
||||
using Acc0DataType = float;
|
||||
using Wei1DataType = ck::half_t;
|
||||
using Acc1DataType = float;
|
||||
using C1ShuffleDataType = float;
|
||||
using Out1DataType = ck::half_t;
|
||||
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using In0ElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using Wei0ElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using Wei1ElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using Out0ElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using Out1ElementOp = ck::tensor_operation::element_wise::UnaryConvert;
|
||||
|
||||
using Row = ck::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck::tensor_layout::gemm::ColumnMajor;
|
||||
|
||||
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
|
||||
|
||||
using DeviceBatchedGemmGemmInstance =
|
||||
ck::tensor_operation::device::DeviceBatchedGemmGemm_Xdl_CShuffle<
|
||||
Row, // ALayout
|
||||
Col, // B0Layout
|
||||
Col, // B1Layout
|
||||
Row, // CLayout
|
||||
In0DataType, // ADataType,
|
||||
Wei0DataType, // B0DataType,
|
||||
Wei1DataType, // B1DataType,
|
||||
Out1DataType, // CDataType,
|
||||
Acc0DataType, // AccDataType,
|
||||
C1ShuffleDataType, // CShuffleDataType,
|
||||
In0ElementOp, // AElementOp,
|
||||
Wei0ElementOp, // B0ElementOp,
|
||||
Out0ElementOp, // Acc0ElementOp,
|
||||
Wei1ElementOp, // B1ElementOp,
|
||||
Out1ElementOp, // CElementOp,
|
||||
GemmDefault,
|
||||
1,
|
||||
256,
|
||||
128, // MPerBlock
|
||||
128, // NPerBlock
|
||||
32, // KPerBlock
|
||||
128, // Gemm1NPerBlock
|
||||
32, // Gemm1KPerBlock
|
||||
8, // AK1
|
||||
8, // BK1
|
||||
4, // B1K1
|
||||
32, // MPerXDL
|
||||
32, // NPerXDL
|
||||
1, // MXdlPerWave
|
||||
4, // NXdlPerWave
|
||||
4, // Gemm1NXdlPerWave
|
||||
S<4, 64, 1>, // ABlockTransfer
|
||||
S<1, 0, 2>,
|
||||
S<1, 0, 2>,
|
||||
2,
|
||||
8,
|
||||
8,
|
||||
true,
|
||||
S<4, 64, 1>, // BBlockTransfer
|
||||
S<1, 0, 2>,
|
||||
S<1, 0, 2>,
|
||||
2,
|
||||
8,
|
||||
8,
|
||||
true,
|
||||
S<4, 64, 1>, // B1BlockTransfer
|
||||
S<1, 0, 2>,
|
||||
S<1, 0, 2>,
|
||||
2,
|
||||
4,
|
||||
4,
|
||||
true,
|
||||
1, // CShuffleMXdlPerWavePerShuffle
|
||||
2, // CShuffleNXdlPerWavePerShuffle
|
||||
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
|
||||
8>; // CShuffleBlockTransferScalarPerVector_NPerBlock
|
||||
|
||||
int main(int argc, char* argv[])
|
||||
{
|
||||
bool do_verification = true;
|
||||
int init_method = 1;
|
||||
bool time_kernel = false;
|
||||
|
||||
ck::utils::conv::ConvParam conv0_param{
|
||||
2, 1, 128, 512, 128, {1, 1}, {28, 28}, {1, 1}, {1, 1}, {0, 0}, {0, 0}};
|
||||
|
||||
ck::utils::conv::ConvParam conv1_param{
|
||||
2, 1, 128, 128, 512, {1, 1}, {28, 28}, {1, 1}, {1, 1}, {0, 0}, {0, 0}};
|
||||
|
||||
if(argc == 1)
|
||||
{
|
||||
// use default case
|
||||
}
|
||||
else if(argc == 4)
|
||||
{
|
||||
do_verification = std::stoi(argv[1]);
|
||||
init_method = std::stoi(argv[2]);
|
||||
time_kernel = std::stoi(argv[3]);
|
||||
}
|
||||
else
|
||||
{
|
||||
printf("arg1: verification (0=no, 1=yes)\n");
|
||||
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
|
||||
printf("arg3: time kernel (0=no, 1=yes)\n");
|
||||
exit(0);
|
||||
}
|
||||
|
||||
const auto in0_element_op = In0ElementOp{};
|
||||
const auto wei0_element_op = Wei0ElementOp{};
|
||||
const auto wei1_element_op = Wei1ElementOp{};
|
||||
const auto out0_element_op = Out0ElementOp{};
|
||||
const auto out1_element_op = Out1ElementOp{};
|
||||
|
||||
const auto run = [&](auto ndim_spatial,
|
||||
auto in0_layout,
|
||||
auto wei0_layout,
|
||||
auto wei1_layout,
|
||||
auto out1_layout) {
|
||||
constexpr ck::index_t ndim_spatial_value = ndim_spatial.value;
|
||||
|
||||
using In0Layout = decltype(in0_layout);
|
||||
using Wei0Layout = decltype(wei0_layout);
|
||||
using Wei1Layout = decltype(wei1_layout);
|
||||
using Out1Layout = decltype(out1_layout);
|
||||
|
||||
const auto in0_g_n_c_wis_desc =
|
||||
ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<In0Layout>(
|
||||
conv0_param);
|
||||
|
||||
const auto wei0_g_k_c_xs_desc =
|
||||
ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<Wei0Layout>(
|
||||
conv0_param);
|
||||
|
||||
// out0 doesn't physical exist, any layout for host verification is OK
|
||||
const auto out0_g_n_k_wos_desc =
|
||||
ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<Out1Layout>(
|
||||
conv0_param);
|
||||
|
||||
const auto wei1_g_k_c_xs_desc =
|
||||
ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<Wei1Layout>(
|
||||
conv1_param);
|
||||
|
||||
const auto out1_g_n_k_wos_desc =
|
||||
ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<Out1Layout>(
|
||||
conv1_param);
|
||||
|
||||
return run_grouped_conv_conv_fwd<ndim_spatial_value,
|
||||
In0DataType,
|
||||
Wei0DataType,
|
||||
Acc0DataType,
|
||||
Wei1DataType,
|
||||
Out1DataType,
|
||||
In0ElementOp,
|
||||
Wei0ElementOp,
|
||||
Out0ElementOp,
|
||||
Wei1ElementOp,
|
||||
Out1ElementOp,
|
||||
DeviceBatchedGemmGemmInstance>(do_verification,
|
||||
init_method,
|
||||
time_kernel,
|
||||
conv0_param,
|
||||
conv1_param,
|
||||
in0_g_n_c_wis_desc,
|
||||
wei0_g_k_c_xs_desc,
|
||||
out0_g_n_k_wos_desc,
|
||||
wei1_g_k_c_xs_desc,
|
||||
out1_g_n_k_wos_desc,
|
||||
in0_element_op,
|
||||
wei0_element_op,
|
||||
wei1_element_op,
|
||||
out0_element_op,
|
||||
out1_element_op);
|
||||
};
|
||||
|
||||
namespace ctc = ck::tensor_layout::convolution;
|
||||
|
||||
if(conv0_param.num_dim_spatial_ == 1)
|
||||
{
|
||||
run(ck::Number<1>{}, ctc::GNWC{}, ctc::GKXC{}, ctc::GKXC{}, ctc::GNWK{});
|
||||
}
|
||||
else if(conv0_param.num_dim_spatial_ == 2)
|
||||
{
|
||||
run(ck::Number<2>{}, ctc::GNHWC{}, ctc::GKYXC{}, ctc::GKYXC{}, ctc::GNHWK{});
|
||||
}
|
||||
else if(conv0_param.num_dim_spatial_ == 3)
|
||||
{
|
||||
run(ck::Number<3>{}, ctc::GNDHWC{}, ctc::GKZYXC{}, ctc::GKZYXC{}, ctc::GNDHWK{});
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
@@ -50,3 +50,4 @@ add_subdirectory(32_batched_gemm_scale_softmax_gemm)
|
||||
add_subdirectory(33_multiple_reduce)
|
||||
add_subdirectory(34_batchnorm)
|
||||
add_subdirectory(35_splitK_gemm)
|
||||
add_subdirectory(41_grouped_conv_conv_fwd)
|
||||
|
||||
Reference in New Issue
Block a user