Files
composable_kernel/example/10_conv2d_bwd_data
ltqin b51808d7a5 Fix conv2d bwd data bug when filter is 1x1 and stride = 2 (#132)
* fix bwd data filter1strid2 bug

* fichangeshort to ck::bhalf_t

* reset input to zero

Co-authored-by: ltqin <letaoqin@amd.com>
2022-03-21 10:53:23 -05:00
..
2022-03-08 21:46:36 -06:00
2022-03-08 21:46:36 -06:00

Instructions for conv2d_bwd_data_xdl Example

Docker script

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_bwd_data_xdl

mkdir build && cd build
# 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                                         \
..
 make -j conv2d_bwd_data_xdl

Run conv2d_bwd_data_xdl

#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
./bin/conv2d_bwd_data_xdl 0 1 5

Result

in_n_c_hi_wi: dim 4, lengths {128, 256, 71, 71}, strides {1290496, 1, 18176, 256}
wei_k_c_y_x: dim 4, lengths {256, 256, 3, 3}, strides {2304, 1, 768, 256}
out_n_k_ho_wo: dim 4, lengths {128, 256, 36, 36}, strides {331776, 1, 9216, 256}
arg.a_grid_desc_k0_m_k1_container_{128, 175232, 8}
arg.b_grid_desc_k0_n_k1_container_{128, 256, 8}
arg.c_grid_desc_m_n_container_{ 175232, 256}
arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_( 2738, 4, 2, 2, 4, 2 ) 
launch_and_time_kernel: grid_dim {2738, 1, 1}, block_dim {256, 1, 1} 
Warm up
Start running 1 times...
arg.a_grid_desc_k0_m_k1_container_{64, 175232, 8}
arg.b_grid_desc_k0_n_k1_container_{64, 256, 8}
arg.c_grid_desc_m_n_container_{ 175232, 256}
arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_( 2738, 4, 2, 2, 4, 2 ) 
launch_and_time_kernel: grid_dim {2738, 1, 1}, block_dim {256, 1, 1} 
Warm up
Start running 1 times...
arg.a_grid_desc_k0_m_k1_container_{64, 175232, 8}
arg.b_grid_desc_k0_n_k1_container_{64, 256, 8}
arg.c_grid_desc_m_n_container_{ 175232, 256}
arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_( 2738, 4, 2, 2, 4, 2 ) 
launch_and_time_kernel: grid_dim {2738, 1, 1}, block_dim {256, 1, 1} 
Warm up
Start running 1 times...
arg.a_grid_desc_k0_m_k1_container_{32, 175232, 8}
arg.b_grid_desc_k0_n_k1_container_{32, 256, 8}
arg.c_grid_desc_m_n_container_{ 175232, 256}
arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_( 2738, 4, 2, 2, 4, 2 ) 
launch_and_time_kernel: grid_dim {2738, 1, 1}, block_dim {256, 1, 1} 
Warm up
Start running 1 times...
Perf: 2.45966 ms, 79.5597 TFlops, 169.325 GB/s