mirror of
https://github.com/ROCm/composable_kernel.git
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Reorganize files, Part 1 (#119)
* delete obselete files
* move files
* build
* update cmake
* update cmake
* fix build
* reorg examples
* update cmake for example and test
[ROCm/composable_kernel commit: 5d37d7bff4]
This commit is contained in:
Chao Liu
3
example/01_gemm/CMakeLists.txt
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3
example/01_gemm/CMakeLists.txt
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@@ -0,0 +1,3 @@
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add_example_executable(example_gemm_xdl_fp16 gemm_xdl_fp16.cpp)
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add_example_executable(example_gemm_xdl_bf16 gemm_xdl_bf16.cpp)
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add_example_executable(example_gemm_xdl_int8 gemm_xdl_int8.cpp)
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1
example/02_gemm_alpha_beta/CMakeLists.txt
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1
example/02_gemm_alpha_beta/CMakeLists.txt
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@@ -0,0 +1 @@
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add_example_executable(example_gemm_xdl_alpha_beta gemm_xdl_alpha_beta.cpp)
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1
example/03_gemm_bias_relu/CMakeLists.txt
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1
example/03_gemm_bias_relu/CMakeLists.txt
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@@ -0,0 +1 @@
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add_example_executable(example_gemm_xdl_bias_relu gemm_xdl_bias_relu.cpp)
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1
example/04_gemm_bias_relu_add/CMakeLists.txt
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1
example/04_gemm_bias_relu_add/CMakeLists.txt
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@@ -0,0 +1 @@
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add_example_executable(example_gemm_xdl_bias_relu_add gemm_xdl_bias_relu_add.cpp)
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2
example/05_conv2d_fwd/CMakeLists.txt
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2
example/05_conv2d_fwd/CMakeLists.txt
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@@ -0,0 +1,2 @@
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add_example_executable(example_conv2d_fwd_xdl_fp16 conv2d_fwd_xdl_fp16.cpp)
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add_example_executable(example_conv2d_fwd_xdl_int8 conv2d_fwd_xdl_int8.cpp)
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1
example/06_conv2d_fwd_bias_relu/CMakeLists.txt
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1
example/06_conv2d_fwd_bias_relu/CMakeLists.txt
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@@ -0,0 +1 @@
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add_example_executable(example_conv2d_fwd_xdl_bias_relu conv2d_fwd_xdl_bias_relu.cpp)
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1
example/07_conv2d_fwd_bias_relu_add/CMakeLists.txt
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1
example/07_conv2d_fwd_bias_relu_add/CMakeLists.txt
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@@ -0,0 +1 @@
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add_example_executable(example_conv2d_fwd_xdl_bias_relu_add conv2d_fwd_xdl_bias_relu_add.cpp)
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1
example/08_conv3d_fwd/CMakeLists.txt
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1
example/08_conv3d_fwd/CMakeLists.txt
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@@ -0,0 +1 @@
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add_example_executable(example_conv3d_fwd_xdl conv3d_fwd_xdl.cpp)
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1
example/09_convnd_fwd/CMakeLists.txt
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1
example/09_convnd_fwd/CMakeLists.txt
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@@ -0,0 +1 @@
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add_example_executable(example_convnd_fwd_xdl convnd_fwd_xdl.cpp)
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@@ -2,7 +2,6 @@
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#include <iostream>
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#include <numeric>
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#include <type_traits>
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#include "config.hpp"
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#include "conv_utils.hpp"
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#include "device.hpp"
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1
example/10_conv2d_bwd_data/CMakeLists.txt
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1
example/10_conv2d_bwd_data/CMakeLists.txt
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@@ -0,0 +1 @@
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add_example_executable(example_conv2d_bwd_data_xdl conv2d_bwd_data_xdl.cpp)
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1
example/11_conv2d_bwd_wgt/CMakeLists.txt
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1
example/11_conv2d_bwd_wgt/CMakeLists.txt
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@@ -0,0 +1 @@
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add_example_executable(example_conv2d_bwd_wgt_xdl conv2d_bwd_wgt_xdl.cpp)
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1
example/12_reduce/CMakeLists.txt
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1
example/12_reduce/CMakeLists.txt
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@@ -0,0 +1 @@
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add_example_executable(example_reduce_blockwise reduce_blockwise.cpp)
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@@ -14,7 +14,6 @@
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#include "device_reduce_blockwise.hpp"
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#include "host_reduce_util.hpp"
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#include "host_generic_reduction.hpp"
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#include "reduction_enums.hpp"
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#include "reduction_operator_mapping.hpp"
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1
example/13_pool2d_fwd/CMakeLists.txt
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1
example/13_pool2d_fwd/CMakeLists.txt
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@@ -0,0 +1 @@
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add_example_executable(example_pool2d_fwd pool2d_fwd.cpp)
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@@ -12,7 +12,7 @@
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#include "device_tensor.hpp"
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#include "tensor_layout.hpp"
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#include "reduction_operator.hpp"
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#include "device_operation/include/device_pool2d_fwd_nhwc_nhwc.hpp"
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#include "device_pool2d_fwd_nhwc_nhwc.hpp"
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using InDataType = ck::half_t;
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using OutDataType = ck::half_t;
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@@ -1,61 +0,0 @@
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# Instructions for ```conv_xdl_bias_relu_add``` Example
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## Docker script
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```bash
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docker run \
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-it \
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--rm \
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--privileged \
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--group-add sudo \
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-w /root/workspace \
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-v ${PATH_TO_LOCAL_WORKSPACE}:/root/workspace \
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rocm/tensorflow:rocm4.3.1-tf2.6-dev \
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/bin/bash
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```
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## Build ```conv_xdl_bias_relu_add```
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```bash
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mkdir build && cd build
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```
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```bash
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# Need to specify target ID, example below is gfx908
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cmake \
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-D BUILD_DEV=OFF \
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-D CMAKE_BUILD_TYPE=Release \
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-D CMAKE_CXX_FLAGS="-DCK_AMD_GPU_GFX908 --amdgpu-target=gfx908 -O3 " \
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-D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc \
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-D CMAKE_PREFIX_PATH=/opt/rocm \
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..
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```
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```bash
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make -j conv_xdl_bias_relu_add
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```
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## Run ```conv_xdl_bias_relu_add```
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```bash
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#arg1: verification (0=no, 1=yes)
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#arg2: initialization (0=no init, 1=integer value, 2=decimal value)
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#arg3: run kernel # of times (>1)
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#arg4 to 18: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, RightPx
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./example/conv_xdl_bias_relu_add 0 1 5
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```
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Result (MI100 @ 1087Mhz, 133.5TFlops peak FP16)
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```
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in_n_c_hi_wi: dim 4, lengths {128, 192, 71, 71}, strides {967872, 1, 13632, 192}
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wei_k_c_y_x: dim 4, lengths {256, 192, 3, 3}, strides {1728, 1, 576, 192}
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out_n_k_ho_wo: dim 4, lengths {128, 256, 36, 36}, strides {331776, 1, 9216, 256}
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bias_k: dim 1, lengths {256}, strides {1}
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resi_n_k_ho_wo: dim 4, lengths {128, 256, 36, 36}, strides {331776, 1, 9216, 256}
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arg.a_grid_desc_k0_m_k1_{216, 165888, 8}
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arg.b_grid_desc_k0_n_k1_{216, 256, 8}
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arg.c_grid_desc_m_n_{ 165888, 256}
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arg.c0_grid_desc_m_n_{ 165888, 256}
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arg.c1_grid_desc_m_n_{ 165888, 256}
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launch_and_time_kernel: grid_dim {1296, 1, 1}, block_dim {256, 1, 1}
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Warm up
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Start running 5 times...
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Perf: 1.71779 ms, 85.4396 TFlops, 194.2 GB/s
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```
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@@ -1,314 +0,0 @@
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#include <iostream>
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#include <numeric>
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#include <initializer_list>
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#include <cstdlib>
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#include <stdlib.h>
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#include <half.hpp>
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#include "config.hpp"
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#include "print.hpp"
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#include "device.hpp"
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#include "host_tensor.hpp"
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#include "host_tensor_generator.hpp"
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#include "device_tensor.hpp"
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#include "tensor_layout.hpp"
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#include "device_conv2d_fwd_xdl_c_shuffle_bias_activation_nhwc_kyxc_nhwk.hpp"
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#include "element_wise_operation.hpp"
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#include "convolution_utility.hpp"
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using InDataType = ck::half_t;
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using WeiDataType = ck::half_t;
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using OutDataType = ck::half_t;
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using AccDataType = float;
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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::NHWC;
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using WeiLayout = ck::tensor_layout::convolution::KYXC;
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using OutLayout = ck::tensor_layout::convolution::NHWK;
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using InElementOp = ck::tensor_operation::element_wise::PassThrough;
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using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
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using OutElementOp = ck::tensor_operation::element_wise::AddRelu;
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static constexpr auto MemoryAtomicAdd = ck::InMemoryDataOperationEnum_t::AtomicAdd;
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static constexpr auto ConvFwdDefault =
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ck::tensor_operation::device::ConvolutionForwardSpecialization_t::Default;
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// clang-format off
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using DeviceConvFwdInstance = ck::tensor_operation::device::
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DeviceConv2dFwdXdl_C_Shuffle_Bias_Activation_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
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// clang-format off
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// | InData| WeiData| OutData| AccData| In| Wei| Out| Out| ConvForward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
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// | Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| GlobalMemory| Specialization| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MXdlPerWave_MWaveMPerXdl| ScalarPerVector|
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// | | | | | Operation| Operation| Operation| DataOperation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NXdlPerWave_NWaveNPerXdl| _NWaveNPerXdl|
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// | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
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<InDataType, WeiDataType, OutDataType, AccDataType, InElementOp, WeiElementOp, OutElementOp, MemoryAtomicAdd, ConvFwdDefault, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 1, 8, 1, 1,32>, 2>;
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// clang-format on
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template <typename TIn,
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typename TWei,
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typename TOut,
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typename InElementOp,
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typename WeiElementOp,
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typename OutElementOp>
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void host_reference_calculation(const Tensor<TIn>& in_n_c_hi_wi,
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const Tensor<TWei>& wei_k_c_y_x,
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Tensor<TOut>& out_n_k_ho_wo,
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const Tensor<TOut>& bias_k,
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const std::vector<ck::index_t>& conv_strides,
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const std::vector<ck::index_t>& conv_dilations,
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const std::vector<ck::index_t>& in_left_pads,
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const std::vector<ck::index_t>& /* in_right_pads */,
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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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auto f_nchw = [&](auto n, auto k, auto ho, auto wo) {
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float v_acc = 0;
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for(int c = 0; c < wei_k_c_y_x.mDesc.GetLengths()[1]; ++c)
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{
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for(int y = 0; y < wei_k_c_y_x.mDesc.GetLengths()[2]; ++y)
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{
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int hi = ho * conv_strides[0] + y * conv_dilations[0] - in_left_pads[0];
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for(int x = 0; x < wei_k_c_y_x.mDesc.GetLengths()[3]; ++x)
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{
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int wi = wo * conv_strides[1] + x * conv_dilations[1] - in_left_pads[1];
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if(hi >= 0 && hi < in_n_c_hi_wi.mDesc.GetLengths()[2] && wi >= 0 &&
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wi < in_n_c_hi_wi.mDesc.GetLengths()[3])
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{
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float v_in;
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float v_wei;
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in_element_op(v_in, static_cast<const float>(in_n_c_hi_wi(n, c, hi, wi)));
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wei_element_op(v_wei, static_cast<const float>(wei_k_c_y_x(k, c, y, x)));
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v_acc += v_in * v_wei;
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}
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}
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}
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}
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float v_out;
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out_element_op(v_out, v_acc, static_cast<float>(bias_k(k)));
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out_n_k_ho_wo(n, k, ho, wo) += v_out;
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};
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make_ParallelTensorFunctor(f_nchw,
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out_n_k_ho_wo.mDesc.GetLengths()[0],
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out_n_k_ho_wo.mDesc.GetLengths()[1],
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out_n_k_ho_wo.mDesc.GetLengths()[2],
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out_n_k_ho_wo.mDesc.GetLengths()[3])(
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std::thread::hardware_concurrency());
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}
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int main(int argc, char* argv[])
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{
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bool do_verification = 0;
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int init_method = 0;
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int nrepeat = 5;
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// Conv shape
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ck::index_t N = 128;
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ck::index_t K = 256;
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ck::index_t C = 192;
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ck::index_t Y = 3;
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ck::index_t X = 3;
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ck::index_t Hi = 71;
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ck::index_t Wi = 71;
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ck::index_t conv_stride_h = 2;
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ck::index_t conv_stride_w = 2;
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ck::index_t conv_dilation_h = 1;
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ck::index_t conv_dilation_w = 1;
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ck::index_t in_left_pad_h = 1;
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ck::index_t in_left_pad_w = 1;
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ck::index_t in_right_pad_h = 1;
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ck::index_t in_right_pad_w = 1;
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if(argc == 4)
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{
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do_verification = std::stoi(argv[1]);
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init_method = std::stoi(argv[2]);
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nrepeat = std::stoi(argv[3]);
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}
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else if(argc == 19)
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{
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do_verification = std::stoi(argv[1]);
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init_method = std::stoi(argv[2]);
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nrepeat = std::stoi(argv[3]);
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N = std::stoi(argv[4]);
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K = std::stoi(argv[5]);
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C = std::stoi(argv[6]);
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Y = std::stoi(argv[7]);
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X = std::stoi(argv[8]);
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Hi = std::stoi(argv[9]);
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Wi = std::stoi(argv[10]);
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conv_stride_h = std::stoi(argv[11]);
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conv_stride_w = std::stoi(argv[12]);
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conv_dilation_h = std::stoi(argv[13]);
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conv_dilation_w = std::stoi(argv[14]);
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in_left_pad_h = std::stoi(argv[15]);
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in_left_pad_w = std::stoi(argv[16]);
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in_right_pad_h = std::stoi(argv[17]);
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in_right_pad_w = std::stoi(argv[18]);
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}
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else
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{
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printf("arg1: verification (0=no, 1=yes)\n");
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printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
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printf("arg3: run kernel # of times (>1)\n");
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printf("arg4 to 18: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, "
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"RightPx\n");
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exit(0);
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}
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const std::vector<ck::index_t> conv_filter_strides{conv_stride_h, conv_stride_w};
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const std::vector<ck::index_t> conv_filter_dilations{conv_dilation_h, conv_dilation_w};
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const std::vector<ck::index_t> input_left_pads{in_left_pad_h, in_left_pad_w};
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const std::vector<ck::index_t> input_right_pads{in_right_pad_h, in_right_pad_w};
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const auto output_spatial_lengths =
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ck::tensor_operation::ConvolutionUtility::ComputeOutputSpatialLengths({Hi, Wi},
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{Y, X},
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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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const ck::index_t Ho = output_spatial_lengths[0];
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const ck::index_t Wo = output_spatial_lengths[1];
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// tensor layout
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auto f_host_tensor_descriptor = [](std::size_t N_,
|
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std::size_t C_,
|
||||
std::size_t H,
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||||
std::size_t W,
|
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auto layout) {
|
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if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NCHW>::value ||
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ck::is_same<decltype(layout), ck::tensor_layout::convolution::KCYX>::value ||
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ck::is_same<decltype(layout), ck::tensor_layout::convolution::NKHW>::value)
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{
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return HostTensorDescriptor(std::vector<std::size_t>({N_, C_, H, W}),
|
||||
std::vector<std::size_t>({C_ * H * W, H * W, W, 1}));
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}
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else if constexpr(ck::is_same<decltype(layout),
|
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ck::tensor_layout::convolution::NHWC>::value ||
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||||
ck::is_same<decltype(layout),
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||||
ck::tensor_layout::convolution::KYXC>::value ||
|
||||
ck::is_same<decltype(layout),
|
||||
ck::tensor_layout::convolution::NHWK>::value)
|
||||
{
|
||||
return HostTensorDescriptor(std::vector<std::size_t>({N_, C_, H, W}),
|
||||
std::vector<std::size_t>({C_ * H * W, 1, W * C_, C_}));
|
||||
}
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||||
};
|
||||
|
||||
Tensor<InDataType> in_n_c_hi_wi(f_host_tensor_descriptor(N, C, Hi, Wi, InLayout{}));
|
||||
Tensor<WeiDataType> wei_k_c_y_x(f_host_tensor_descriptor(K, C, Y, X, WeiLayout{}));
|
||||
Tensor<OutDataType> out_n_k_ho_wo_host_result(
|
||||
f_host_tensor_descriptor(N, K, Ho, Wo, OutLayout{}));
|
||||
Tensor<OutDataType> out_n_k_ho_wo_device_result(
|
||||
f_host_tensor_descriptor(N, K, Ho, Wo, OutLayout{}));
|
||||
|
||||
// bias: assume contiguous 1d vector
|
||||
Tensor<OutDataType> bias_k(
|
||||
HostTensorDescriptor(std::vector<std::size_t>({static_cast<std::size_t>(K)})));
|
||||
|
||||
std::cout << "in_n_c_hi_wi: " << in_n_c_hi_wi.mDesc << std::endl;
|
||||
std::cout << "wei_k_c_y_x: " << wei_k_c_y_x.mDesc << std::endl;
|
||||
std::cout << "out_n_k_ho_wo: " << out_n_k_ho_wo_host_result.mDesc << std::endl;
|
||||
std::cout << "bias_k: " << bias_k.mDesc << std::endl;
|
||||
|
||||
switch(init_method)
|
||||
{
|
||||
case 0: break;
|
||||
case 1:
|
||||
in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5});
|
||||
wei_k_c_y_x.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5});
|
||||
out_n_k_ho_wo_host_result.GenerateTensorValue(GeneratorTensor_2<OutDataType>{-5, 5});
|
||||
bias_k.GenerateTensorValue(GeneratorTensor_2<OutDataType>{-5, 5});
|
||||
break;
|
||||
default:
|
||||
in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_3<InDataType>{0.0, 1.0});
|
||||
wei_k_c_y_x.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.5, 0.5});
|
||||
out_n_k_ho_wo_host_result.GenerateTensorValue(GeneratorTensor_3<OutDataType>{-0.5, 0.5});
|
||||
bias_k.GenerateTensorValue(GeneratorTensor_3<OutDataType>{0.0, 1.0});
|
||||
}
|
||||
|
||||
DeviceMem in_device_buf(sizeof(InDataType) * in_n_c_hi_wi.mDesc.GetElementSpace());
|
||||
DeviceMem wei_device_buf(sizeof(WeiDataType) * wei_k_c_y_x.mDesc.GetElementSpace());
|
||||
DeviceMem out_device_buf(sizeof(OutDataType) *
|
||||
out_n_k_ho_wo_device_result.mDesc.GetElementSpace());
|
||||
DeviceMem bias_device_buf(sizeof(OutDataType) * bias_k.mDesc.GetElementSpace());
|
||||
|
||||
in_device_buf.ToDevice(in_n_c_hi_wi.mData.data());
|
||||
wei_device_buf.ToDevice(wei_k_c_y_x.mData.data());
|
||||
out_device_buf.ToDevice(out_n_k_ho_wo_host_result.mData.data());
|
||||
bias_device_buf.ToDevice(bias_k.mData.data());
|
||||
|
||||
auto conv = DeviceConvFwdInstance{};
|
||||
auto invoker = conv.MakeInvoker();
|
||||
auto argument =
|
||||
conv.MakeArgument(static_cast<const InDataType*>(in_device_buf.GetDeviceBuffer()),
|
||||
static_cast<const WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
|
||||
static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
|
||||
static_cast<const OutDataType*>(bias_device_buf.GetDeviceBuffer()),
|
||||
N,
|
||||
K,
|
||||
C,
|
||||
std::vector<ck::index_t>{Hi, Wi},
|
||||
std::vector<ck::index_t>{Y, X},
|
||||
std::vector<ck::index_t>{Ho, Wo},
|
||||
conv_filter_strides,
|
||||
conv_filter_dilations,
|
||||
input_left_pads,
|
||||
input_right_pads,
|
||||
InElementOp{},
|
||||
WeiElementOp{},
|
||||
OutElementOp{});
|
||||
|
||||
if(!conv.IsSupportedArgument(argument))
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"wrong! device operator with the specified compilation parameters does "
|
||||
"not support this problem");
|
||||
}
|
||||
|
||||
float ave_time = invoker.Run(argument, nrepeat);
|
||||
|
||||
std::size_t flop = std::size_t(2) * N * K * Ho * Wo * C * Y * X;
|
||||
|
||||
std::size_t num_btype = sizeof(InDataType) * (N * C * Hi * Wi) +
|
||||
sizeof(WeiDataType) * (K * C * Y * X) +
|
||||
sizeof(OutDataType) * (N * K * Ho * Wo) + sizeof(OutDataType) * (K);
|
||||
|
||||
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
|
||||
|
||||
float gb_per_sec = num_btype / 1.E6 / ave_time;
|
||||
|
||||
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
|
||||
<< std::endl;
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
host_reference_calculation(in_n_c_hi_wi,
|
||||
wei_k_c_y_x,
|
||||
out_n_k_ho_wo_host_result,
|
||||
bias_k,
|
||||
conv_filter_strides,
|
||||
conv_filter_dilations,
|
||||
input_left_pads,
|
||||
input_right_pads,
|
||||
InElementOp{},
|
||||
WeiElementOp{},
|
||||
OutElementOp{});
|
||||
|
||||
out_device_buf.FromDevice(out_n_k_ho_wo_device_result.mData.data());
|
||||
|
||||
check_error(out_n_k_ho_wo_host_result, out_n_k_ho_wo_device_result);
|
||||
}
|
||||
}
|
||||
@@ -1,57 +0,0 @@
|
||||
# Instructions for ```conv2d_fwd_xdl``` Example
|
||||
|
||||
## Docker script
|
||||
```bash
|
||||
docker run \
|
||||
-it \
|
||||
--rm \
|
||||
--privileged \
|
||||
--group-add sudo \
|
||||
-w /root/workspace \
|
||||
-v ${PATH_TO_LOCAL_WORKSPACE}:/root/workspace \
|
||||
rocm/tensorflow:rocm4.3.1-tf2.6-dev \
|
||||
/bin/bash
|
||||
```
|
||||
|
||||
## Build ```conv2d_fwd_xdl```
|
||||
```bash
|
||||
mkdir build && cd build
|
||||
```
|
||||
|
||||
```bash
|
||||
# Need to specify target ID, example below is gfx908
|
||||
cmake \
|
||||
-D BUILD_DEV=OFF \
|
||||
-D CMAKE_BUILD_TYPE=Release \
|
||||
-D CMAKE_CXX_FLAGS="-DCK_AMD_GPU_GFX908 --amdgpu-target=gfx908 -O3 " \
|
||||
-D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc \
|
||||
-D CMAKE_PREFIX_PATH=/opt/rocm \
|
||||
..
|
||||
```
|
||||
|
||||
```bash
|
||||
make -j conv2d_fwd_xdl
|
||||
```
|
||||
|
||||
## Run ```conv2d_fwd_xdl_int8```
|
||||
```bash
|
||||
#arg1: verification (0=no, 1=yes)
|
||||
#arg2: initialization (0=no init, 1=integer value, 2=decimal value)
|
||||
#arg3: run kernel # of times (>1)
|
||||
#arg4 to 18: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, RightPx
|
||||
./example/conv2d_fwd_xdl_int8 0 1 5
|
||||
```
|
||||
|
||||
Result (MI100 @ 1087Mhz, 133.5TFlops peak FP16)
|
||||
```
|
||||
in_n_c_hi_wi: dim 4, lengths {128, 192, 71, 71}, strides {967872, 1, 13632, 192}
|
||||
wei_k_c_y_x: dim 4, lengths {256, 192, 3, 3}, strides {1728, 1, 576, 192}
|
||||
out_n_k_ho_wo: dim 4, lengths {128, 256, 36, 36}, strides {331776, 1, 9216, 256}
|
||||
arg.a_grid_desc_k0_m_k1_{216, 165888, 8}
|
||||
arg.b_grid_desc_k0_n_k1_{216, 256, 8}
|
||||
arg.c_grid_desc_m_n_{ 165888, 256}
|
||||
launch_and_time_kernel: grid_dim {1296, 1, 1}, block_dim {256, 1, 1}
|
||||
Warm up
|
||||
Start running 5 times...
|
||||
Perf: 1.43206 ms, 102.486 TFlops, 232.947 GB/s
|
||||
```
|
||||
@@ -1,69 +1,40 @@
|
||||
include_directories(BEFORE
|
||||
${PROJECT_SOURCE_DIR}
|
||||
${PROJECT_SOURCE_DIR}/host/host_tensor/include
|
||||
${PROJECT_SOURCE_DIR}/host/device/include
|
||||
${PROJECT_SOURCE_DIR}/device_operation/include
|
||||
${PROJECT_SOURCE_DIR}/reference_operation/include
|
||||
${PROJECT_SOURCE_DIR}/composable_kernel/include
|
||||
${PROJECT_SOURCE_DIR}/composable_kernel/include/utility
|
||||
${PROJECT_SOURCE_DIR}/composable_kernel/include/tensor_description
|
||||
${PROJECT_SOURCE_DIR}/composable_kernel/include/tensor_operation
|
||||
${PROJECT_SOURCE_DIR}/composable_kernel/include/problem_transform
|
||||
${PROJECT_SOURCE_DIR}/external/rocm/include
|
||||
${PROJECT_SOURCE_DIR}/device_operation_reference/include
|
||||
${PROJECT_SOURCE_DIR}/include/ck
|
||||
${PROJECT_SOURCE_DIR}/include/ck/utility
|
||||
${PROJECT_SOURCE_DIR}/include/ck/tensor_description
|
||||
${PROJECT_SOURCE_DIR}/include/ck/tensor
|
||||
${PROJECT_SOURCE_DIR}/include/ck/problem_transform
|
||||
${PROJECT_SOURCE_DIR}/include/ck/tensor_operation/gpu/device
|
||||
${PROJECT_SOURCE_DIR}/include/ck/tensor_operation/gpu/grid
|
||||
${PROJECT_SOURCE_DIR}/include/ck/tensor_operation/gpu/block
|
||||
${PROJECT_SOURCE_DIR}/include/ck/tensor_operation/gpu/warp
|
||||
${PROJECT_SOURCE_DIR}/include/ck/tensor_operation/gpu/thread
|
||||
${PROJECT_SOURCE_DIR}/include/ck/tensor_operation/gpu/element
|
||||
${PROJECT_SOURCE_DIR}/library/include/ck/library/host_tensor
|
||||
${PROJECT_SOURCE_DIR}/library/include/ck/library/reference_tensor_operation/cpu
|
||||
${PROJECT_SOURCE_DIR}/library/include/ck/library/reference_tensor_operation/gpu
|
||||
${PROJECT_SOURCE_DIR}/external/include/half
|
||||
)
|
||||
|
||||
set(GEMM_XDL_SOURCE 1_gemm_xdl/gemm_xdl.cpp)
|
||||
set(GEMM_XDL_INT8_SOURCE 1_gemm_xdl/gemm_xdl_int8.cpp)
|
||||
set(GEMM_XDL_BF16_SOURCE 1_gemm_xdl/gemm_xdl_bf16.cpp)
|
||||
set(GEMM_XDL_BIAS_RELU_SOURCE 2_gemm_xdl_bias_relu/gemm_xdl_bias_relu.cpp)
|
||||
set(GEMM_XDL_BIAS_RELU_ADD_SOURCE 3_gemm_xdl_bias_relu_add/gemm_xdl_bias_relu_add.cpp)
|
||||
set(CONV2D_FWD_XDL_SOURCE 4_conv2d_fwd_xdl/conv2d_fwd_xdl.cpp)
|
||||
set(CONV2D_FWD_XDL_BIAS_RELU_SOURCE 5_conv2d_fwd_xdl_bias_relu/conv2d_fwd_xdl_bias_relu.cpp)
|
||||
set(CONV2D_FWD_XDL_BIAS_RELU_ADD_SOURCE 6_conv2d_fwd_xdl_bias_relu_add/conv2d_fwd_xdl_bias_relu_add.cpp)
|
||||
set(CONV2D_FWD_XDL_BIAS_RELU_ATOMIC_ADD_SOURCE 7_conv2d_fwd_xdl_bias_relu_atomic_add/conv2d_fwd_xdl_bias_relu_atomic_add.cpp)
|
||||
set(GEMM_XDL_ALPHA_BETA_SOURCE 8_gemm_xdl_alpha_beta/gemm_xdl_alpha_beta.cpp)
|
||||
set(CONV2D_FWD_XDL_INT8_SOURCE 9_conv2d_fwd_xdl_int8/conv2d_fwd_xdl_int8.cpp)
|
||||
set(CONV2D_WRW_XDL_SOURCE 13_conv2d_backward_weight_xdl/main.cpp)
|
||||
set(CONV3D_FWD_XDL_SOURCE 10_conv3d_fwd_xdl/conv3d_fwd_xdl.cpp)
|
||||
set(CONVND_FWD_XDL_SOURCE 11_convnd_fwd_xdl/convnd_fwd_xdl.cpp)
|
||||
set(CONV2D_BWD_DATA_XDL_SOURCE 12_conv2d_bwd_data_xdl/conv2d_bwd_data_xdl.cpp)
|
||||
set(POOL2D_FWD_SOURCE 12_pool2d_fwd/pool2d_fwd.cpp)
|
||||
set(REDUCE_BLOCKWISE_SOURCE 13_reduce_blockwise/reduce_blockwise.cpp)
|
||||
add_custom_target(examples)
|
||||
|
||||
add_executable(gemm_xdl ${GEMM_XDL_SOURCE})
|
||||
add_executable(gemm_xdl_int8 ${GEMM_XDL_INT8_SOURCE})
|
||||
add_executable(gemm_xdl_bf16 ${GEMM_XDL_BF16_SOURCE})
|
||||
add_executable(gemm_xdl_bias_relu ${GEMM_XDL_BIAS_RELU_SOURCE})
|
||||
add_executable(gemm_xdl_bias_relu_add ${GEMM_XDL_BIAS_RELU_ADD_SOURCE})
|
||||
add_executable(conv2d_fwd_xdl ${CONV2D_FWD_XDL_SOURCE})
|
||||
add_executable(conv2d_fwd_xdl_bias_relu ${CONV2D_FWD_XDL_BIAS_RELU_SOURCE})
|
||||
add_executable(conv2d_fwd_xdl_bias_relu_add ${CONV2D_FWD_XDL_BIAS_RELU_ADD_SOURCE})
|
||||
add_executable(conv2d_fwd_xdl_bias_relu_atomic_add ${CONV2D_FWD_XDL_BIAS_RELU_ATOMIC_ADD_SOURCE})
|
||||
add_executable(gemm_xdl_alpha_beta ${GEMM_XDL_ALPHA_BETA_SOURCE})
|
||||
add_executable(conv2d_fwd_xdl_int8 ${CONV2D_FWD_XDL_INT8_SOURCE})
|
||||
add_executable(conv2d_wrw_xdl ${CONV2D_WRW_XDL_SOURCE})
|
||||
add_executable(conv3d_fwd_xdl ${CONV3D_FWD_XDL_SOURCE})
|
||||
add_executable(convnd_fwd_xdl ${CONVND_FWD_XDL_SOURCE})
|
||||
add_executable(conv2d_bwd_data_xdl ${CONV2D_BWD_DATA_XDL_SOURCE})
|
||||
add_executable(pool2d_fwd ${POOL2D_FWD_SOURCE})
|
||||
add_executable(reduce_blockwise ${REDUCE_BLOCKWISE_SOURCE})
|
||||
|
||||
target_link_libraries(gemm_xdl PRIVATE host_tensor)
|
||||
target_link_libraries(gemm_xdl_int8 PRIVATE host_tensor)
|
||||
target_link_libraries(gemm_xdl_bf16 PRIVATE host_tensor)
|
||||
target_link_libraries(gemm_xdl_bias_relu PRIVATE host_tensor)
|
||||
target_link_libraries(gemm_xdl_bias_relu_add PRIVATE host_tensor)
|
||||
target_link_libraries(conv2d_fwd_xdl PRIVATE host_tensor)
|
||||
target_link_libraries(conv2d_fwd_xdl_bias_relu PRIVATE host_tensor)
|
||||
target_link_libraries(conv2d_fwd_xdl_bias_relu_add PRIVATE host_tensor)
|
||||
target_link_libraries(conv2d_fwd_xdl_bias_relu_atomic_add PRIVATE host_tensor)
|
||||
target_link_libraries(gemm_xdl_alpha_beta PRIVATE host_tensor)
|
||||
target_link_libraries(conv2d_fwd_xdl_int8 PRIVATE host_tensor)
|
||||
target_link_libraries(conv2d_wrw_xdl PRIVATE host_tensor)
|
||||
target_link_libraries(conv3d_fwd_xdl PRIVATE host_tensor)
|
||||
target_link_libraries(convnd_fwd_xdl PRIVATE host_tensor)
|
||||
target_link_libraries(conv2d_bwd_data_xdl PRIVATE host_tensor)
|
||||
target_link_libraries(pool2d_fwd PRIVATE host_tensor)
|
||||
target_link_libraries(reduce_blockwise PRIVATE host_tensor)
|
||||
function(add_example_executable EXAMPLE_NAME)
|
||||
message("adding example ${EXAMPLE_NAME}")
|
||||
add_executable(${EXAMPLE_NAME} ${ARGN})
|
||||
target_link_libraries(${EXAMPLE_NAME} PRIVATE host_tensor)
|
||||
add_dependencies(examples ${EXAMPLE_NAME})
|
||||
endfunction(add_example_executable EXAMPLE_NAME)
|
||||
|
||||
add_subdirectory(01_gemm)
|
||||
add_subdirectory(02_gemm_alpha_beta)
|
||||
add_subdirectory(03_gemm_bias_relu)
|
||||
add_subdirectory(04_gemm_bias_relu_add)
|
||||
add_subdirectory(05_conv2d_fwd)
|
||||
add_subdirectory(06_conv2d_fwd_bias_relu)
|
||||
add_subdirectory(07_conv2d_fwd_bias_relu_add)
|
||||
add_subdirectory(08_conv3d_fwd)
|
||||
add_subdirectory(09_convnd_fwd)
|
||||
add_subdirectory(10_conv2d_bwd_data)
|
||||
add_subdirectory(11_conv2d_bwd_wgt)
|
||||
add_subdirectory(12_reduce)
|
||||
add_subdirectory(13_pool2d_fwd)
|
||||
|
||||
Reference in New Issue
Block a user