ROCm/composable_kernel
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Code clean-up (#1285)

Browse Source 
* code clean-up

* remove the profiling output samples

[ROCm/composable_kernel commit: 566b6480a2]
This commit is contained in:
Illia Silin
2024-05-10 09:41:39 -07:00
committed by GitHub
parent 9ca5aca74d
commit 254758813f
38 changed files with 57 additions and 259 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
CMakeLists.txt
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@@ -202,7 +202,7 @@ endif()
option(USE_BITINT_EXTENSION_INT4 "Whether to enable clang's BitInt extension to provide int4 data type." OFF)
option(USE_OPT_NAVI3X "Whether to enable LDS cumode and Wavefront32 mode for NAVI3X silicons." OFF)
option(USE_OPT_GFX11 "Whether to enable LDS cumode and Wavefront32 mode for GFX11 silicons." OFF)
if(USE_BITINT_EXTENSION_INT4)
add_compile_definitions(CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4)
@@ -210,10 +210,10 @@ if(USE_BITINT_EXTENSION_INT4)
message("CK compiled with USE_BITINT_EXTENSION_INT4 set to ${USE_BITINT_EXTENSION_INT4}")
endif()
if(USE_OPT_NAVI3X)
if(USE_OPT_GFX11)
add_compile_options(-mcumode)
add_compile_options(-mno-wavefrontsize64)
message("CK compiled with USE_OPT_NAVI3X set to ${USE_OPT_NAVI3X}")
message("CK compiled with USE_OPT_GFX11 set to ${USE_OPT_GFX11}")
endif()
## Threads

39
Jenkinsfile vendored
View File

@@ -515,30 +515,25 @@ def Build_CK(Map conf=[:]){
withDockerContainer(image: image, args: dockerOpts + ' -v=/var/jenkins/:/var/jenkins') {
timeout(time: 24, unit: 'HOURS')
{
//check whether running on Navi or MI300 node
def navi_node = 0
def mi300_node = 0
//check whether to run performance tests on this node
def do_perf_tests = 0
sh 'rocminfo | tee rocminfo.log'
if ( runShell('grep -n "gfx1030" rocminfo.log') || runShell('grep -n "gfx1101" rocminfo.log') ){
navi_node = 1
echo "This is a Navi node"
}
if ( runShell('grep -n "gfx942" rocminfo.log') ){
mi300_node = 1
echo "This is MI300 node"
if ( runShell('grep -n "gfx1030" rocminfo.log') || runShell('grep -n "gfx1101" rocminfo.log') || runShell('grep -n "gfx942" rocminfo.log') ){
do_perf_tests = 1
echo "Stash profiler and run performance tests"
}
cmake_build(conf)
dir("build"){
//run tests and examples
sh 'make -j check'
if (params.RUN_PERFORMANCE_TESTS && navi_node == 0 && mi300_node == 0 ){
if (params.RUN_PERFORMANCE_TESTS && do_perf_tests == 0 ){
//we only need the ckProfiler to run the performance tests, so we pack and stash it
//do not stash profiler on Navi or MI300 nodes
//do not stash profiler on nodes where we don't need to run performance tests
sh 'tar -zcvf ckProfiler.tar.gz bin/ckProfiler'
stash name: "ckProfiler.tar.gz"
}
if (params.RUN_FULL_QA && mi300_node == 0 ){
// build deb packages for all MI100/200/300 targets and prepare to export
if (params.RUN_FULL_QA && do_perf_tests == 0 ){
// build deb packages for all gfx9 targets and prepare to export
sh 'make -j package'
archiveArtifacts artifacts: 'composablekernel-ckprofiler_*.deb'
archiveArtifacts artifacts: 'composablekernel-tests_*.deb'
@@ -546,7 +541,7 @@ def Build_CK(Map conf=[:]){
stash name: "ckprofiler_0.2.0_amd64.deb"
}
}
if (params.hipTensor_test && navi_node == 0 ){
if (params.hipTensor_test && do_perf_tests == 0 ){
//build and test hipTensor
sh """#!/bin/bash
rm -rf "${params.hipTensor_branch}".zip
@@ -814,7 +809,7 @@ pipeline {
{
parallel
{
stage("Run Codegen Tests on MI200")
stage("Run Codegen Tests on gfx90a")
{
when {
beforeAgent true
@@ -865,7 +860,7 @@ pipeline {
cleanWs()
}
}
stage("Build CK and run Tests on MI300")
stage("Build CK and run Tests on gfx942")
{
when {
beforeAgent true
@@ -885,7 +880,7 @@ pipeline {
cleanWs()
}
}
stage("Build CK and run Tests on MI200")
stage("Build CK and run Tests on gfx90a")
{
when {
beforeAgent true
@@ -925,13 +920,13 @@ pipeline {
cleanWs()
}
}
stage("Build CK and run Tests on Navi21")
stage("Build CK and run Tests on gfx1030")
{
when {
beforeAgent true
expression { !params.RUN_FULL_QA.toBoolean() && !params.BUILD_INSTANCES_ONLY.toBoolean() }
}
agent{ label rocmnode("navi21") }
agent{ label rocmnode("gfx1030") }
environment{
setup_args = """ -DCMAKE_INSTALL_PREFIX=../install -DGPU_TARGETS="gfx1030" -DDL_KERNELS=ON -DCMAKE_CXX_FLAGS=" -O3 " """
execute_args = """ cd ../client_example && rm -rf build && mkdir build && cd build && \
@@ -945,13 +940,13 @@ pipeline {
cleanWs()
}
}
stage("Build CK and run Tests on Navi32")
stage("Build CK and run Tests on gfx1101")
{
when {
beforeAgent true
expression { !params.RUN_FULL_QA.toBoolean() && !params.BUILD_INSTANCES_ONLY.toBoolean() }
}
agent{ label rocmnode("navi32") }
agent{ label rocmnode("gfx1101") }
environment{
setup_args = """ -DCMAKE_INSTALL_PREFIX=../install -DGPU_TARGETS="gfx1101" -DDL_KERNELS=ON -DCMAKE_CXX_FLAGS=" -O3 " """
execute_args = """ cd ../client_example && rm -rf build && mkdir build && cd build && \

1
client_example/25_wrapper/wrapper_img2col.cpp
View File

@@ -181,4 +181,3 @@ int main(int argc, char* argv[])
{1, 1, 1} /*filter_dilations*/);
return 0;
}
// MI100 Perf: 0.255178 ms, 1698.9 GB/s,

14
example/01_gemm/README.md
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@@ -7,17 +7,3 @@
#arg3: run kernel # of times (>1)
./bin/example_gemm_xdl 0 1 5
```
Result (MI100 @ 1087Mhz, 133.5TFlops peak FP16)
```
a_m_k: dim 2, lengths {3840, 4096}, strides {4096, 1}
b_k_n: dim 2, lengths {4096, 4096}, strides {1, 4096}
c_m_n: dim 2, lengths {3840, 4096}, strides {4096, 1}
arg.a_grid_desc_k0_m_k1_{512, 3840, 8}
arg.b_grid_desc_k0_n_k1_{512, 4096, 8}
arg.c_grid_desc_m_n_{ 3840, 4096}
launch_and_time_kernel: grid_dim {480, 1, 1}, block_dim {256, 1, 1}
Warm up
Start running 5 times...
Perf: 1.19685 ms, 107.657 TFlops, 78.8501 GB/s
```

17
example/02_gemm_bilinear/README.md
View File

@@ -9,20 +9,3 @@
#arg11 to 12: alpha, beta
./bin/example_gemm_bilinear_xdl_fp16 1 1 1 3840 4096 4096 4096 4096 4096 4096 0.5 0.5
```
Result (MI100 @ 1502Mhz, 184.6TFlops peak FP16)
```
a_m_k: dim 2, lengths {3840, 4096}, strides {4096, 1}
b_k_n: dim 2, lengths {4096, 4096}, strides {1, 4096}
c0_m_n: dim 2, lengths {3840, 4096}, strides {4096, 1}
c_m_n: dim 2, lengths {3840, 4096}, strides {4096, 1}
arg.a_grid_desc_k0_m_k1_{512, 3840, 8}
arg.b_grid_desc_k0_n_k1_{512, 4096, 8}
arg.c0_grid_desc_m_n_{ 3840, 4096}
arg.c_grid_desc_m_n_{ 3840, 4096}
launch_and_time_kernel: grid_dim {480, 1, 1}, block_dim {256, 1, 1}
Warm up
Start running 1 times...
Perf: 0.936965 ms, 137.517 TFlops, 102.959 GB/s
error: 0
max_diff: 0, 558.5, 558.5
```

13
example/04_gemm_add_add_fastgelu/README.md
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@@ -8,16 +8,3 @@
#arg4 to 11: M (256x), N(128x), K(32x), StrideA, StrideB, StrideD0, StrideD1, StrideE"
./bin/example_gemm_add_add_fastgelu_xdl_fp16 1 1 1
```
Result (MI100 @ 1087Mhz, 133.5TFlops peak FP16)
```
a_m_k: dim 2, lengths {3840, 4096}, strides {4096, 1}
b_k_n: dim 2, lengths {4096, 4096}, strides {1, 4096}
d0_m_n: dim 2, lengths {3840, 4096}, strides {0, 1}
d1_m_n: dim 2, lengths {3840, 4096}, strides {4096, 1}
e_m_n: dim 2, lengths {3840, 4096}, strides {4096, 1}
launch_and_time_kernel: grid_dim {480, 1, 1}, block_dim {256, 1, 1}
Warm up 1 time
Start running 10 times...
Perf: 1.26914 ms, 101.525 TFlops, 100.804 GB/s, DeviceGemmMultipleD_Xdl_CShuffle<256, 256, 128, 32, 8, 8>
```

14
example/09_convnd_fwd/README.md
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@@ -16,17 +16,3 @@
# <right padding>, (ie RightPy, RightPx for 2D)
./bin/example_convnd_fwd_xdl 0 1 100
```
Result (MI100 @ 1087Mhz, 33.4TFlops peak FP32)
```
input: dim 4, lengths {128, 192, 71, 71}, strides {967872, 1, 13632, 192}
weights: dim 4, lengths {256, 192, 3, 3}, strides {1728, 1, 576, 192}
output: dim 4, lengths {128, 256, 36, 36}, strides {331776, 1, 9216, 256}
arg.a_grid_desc_k0_m_k1_{432, 165888, 4}
arg.b_grid_desc_k0_n_k1_{432, 256, 4}
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 100 times...
Perf: 4.43736 ms, 33.0753 TFlops, 150.357 GB/s
```

16
example/15_grouped_gemm/README.md
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@@ -7,19 +7,3 @@
#arg3: run kernel # of times (>1)
./bin/example_grouped_gemm_xdl_fp16 0 1 5
```
Result (MI100 @ 1087Mhz, 133.5TFlops peak FP16)
```
gemm[0] a_m_k: dim 2, lengths {256, 64}, strides {64, 1} b_k_n: dim 2, lengths {64, 128}, strides {1, 64} c_m_n: dim 2, lengths {256, 128}, strides {128, 1}
gemm[1] a_m_k: dim 2, lengths {512, 128}, strides {128, 1} b_k_n: dim 2, lengths {128, 256}, strides {1, 128} c_m_n: dim 2, lengths {512, 256}, strides {256, 1}
gemm[2] a_m_k: dim 2, lengths {768, 192}, strides {192, 1} b_k_n: dim 2, lengths {192, 384}, strides {1, 192} c_m_n: dim 2, lengths {768, 384}, strides {384, 1}
gemm[3] a_m_k: dim 2, lengths {1024, 256}, strides {256, 1} b_k_n: dim 2, lengths {256, 512}, strides {1, 256} c_m_n: dim 2, lengths {1024, 512}, strides {512, 1}
group: 0 arg.a_grid_desc_k0_m_k1_{8, 256, 8}, arg.b_grid_desc_k0_n_k1_{8, 128, 8}, arg.c_grid_desc_m_n_{ 256, 128}
group: 1 arg.a_grid_desc_k0_m_k1_{16, 512, 8}, arg.b_grid_desc_k0_n_k1_{16, 256, 8}, arg.c_grid_desc_m_n_{ 512, 256}
group: 2 arg.a_grid_desc_k0_m_k1_{24, 768, 8}, arg.b_grid_desc_k0_n_k1_{24, 384, 8}, arg.c_grid_desc_m_n_{ 768, 384}
group: 3 arg.a_grid_desc_k0_m_k1_{32, 1024, 8}, arg.b_grid_desc_k0_n_k1_{32, 512, 8}, arg.c_grid_desc_m_n_{ 1024, 512}
launch_and_time_kernel: grid_dim {30, 1, 1}, block_dim {256, 1, 1}
Warm up
Start running 5 times...
Perf: 0.037887 ms, 11.0706 TFlops, 90.8132 GB/s, DeviceGroupedGemmXdl<256, 256, 128, 4, 8, 32, 32, 4, 2>
```

11
example/26_contraction/README.md
View File

@@ -7,14 +7,3 @@
#arg3: time kernel (0=no, 1=yes)
./bin/example_contraction_bilinear_xdl_fp32 1 1 1
```
Result (MI100 @ dynammic freq, 46TFlops peak FP32)
```
a_ms_ks: dim 4, lengths {30, 128, 32, 64}, strides {524288, 4096, 128, 1}
b_ks_ns: dim 4, lengths {32, 64, 32, 64}, strides {128, 1, 524288, 4096}
c_ms_ns: dim 4, lengths {30, 128, 32, 64}, strides {524288, 4096, 128, 1}
launch_and_time_kernel: grid_dim {240, 1, 1}, block_dim {256, 1, 1}
Warm up 1 time
Start running 10 times...
Perf: 0.843286 ms, 38.1985 TFlops, 94.5014 GB/s, DeviceContractionMultipleD_Xdl_CShuffle<256, 256, 128, 16, 4, 4>
```

12
example/30_grouped_conv_fwd_multiple_d/README.md
View File

@@ -16,15 +16,3 @@ Following arguments (depending on number of spatial dims):
./bin/example_grouped_conv_fwd_bias_relu_add_xdl_fp16 1 1 1
```
Result (MI100)
```
in: dim 5, lengths {1, 128, 192, 71, 71}, strides {192, 967872, 1, 13632, 192}
wei: dim 5, lengths {1, 256, 192, 3, 3}, strides {442368, 1728, 1, 576, 192}
bias: dim 5, lengths {1, 128, 256, 36, 36}, strides {256, 0, 1, 0, 0}
residual: dim 5, lengths {1, 128, 256, 36, 36}, strides {256, 0, 1, 0, 0}
out: dim 5, lengths {1, 128, 256, 36, 36}, strides {256, 331776, 1, 9216, 256}
launch_and_time_kernel: grid_dim {1296, 1, 1}, block_dim {256, 1, 1}
Warm up 1 time
Start running 10 times...
Perf: 1.55981 ms, 94.0927 TFlops, 213.868 GB/s, DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<256, 128, 256, 16, Default>
```

16
example/46_gemm_add_multiply/README.md
View File

@@ -8,19 +8,3 @@
#arg4 to 11: M (256x), N(128x), K(32x), StrideA, StrideB, StrideD0, StrideD1, StrideE"
./bin/example_gemm_add_multiply_dl_fp16 1 1 1
```
Result (MI100 @ 1087Mhz, 133.5TFlops peak FP16)
```
a_m_k: dim 2, lengths {3840, 4096}, strides {4096, 1}
b_k_n: dim 2, lengths {4096, 4096}, strides {4096, 1}
d0_m_n: dim 2, lengths {3840, 4096}, strides {0, 1}
d1_m_n: dim 2, lengths {3840, 4096}, strides {4096, 1}
e_m_n: dim 2, lengths {3840, 4096}, strides {4096, 1}
arg.a_grid_desc_k0_m0_m1_k1_{2048, 3840, 2}
arg.b_grid_desc_k0_n0_n1_k1_{2048, 4096, 2}
arg.e_grid_desc_m_n_{ 3840, 4096}
launch_and_time_kernel: grid_dim {960, 1, 1}, block_dim {256, 1, 1}
Warm up 1 time
Start running 10 times...
Perf: 3.99904 ms, 32.22 TFlops, 31.9913 GB/s, DeviceGemmMultipleD_Dl<256, 128, 128, 16, 2, 4, 4, 1>
```

2
include/ck/ck.hpp
View File

@@ -236,7 +236,7 @@ CK_DECLARE_ENV_VAR_BOOL(CK_LOGGING)
#ifndef CK_WORKAROUND_DENORM_FIX
#define CK_WORKAROUND_DENORM_FIX 0
#else
// enable only on MI200
// enable only for gfx90a
#define CK_WORKAROUND_DENORM_FIX = CK_WORKAROUND_DENORM_FIX && defined(__gfx90a__)
#endif // CK_WORKAROUND_DENORM_FIX

6
include/ck/host_utility/device_prop.hpp
View File

@@ -65,20 +65,20 @@ inline bool is_lds_direct_load_supported()
ck::get_device_name() == "gfx941" || ck::get_device_name() == "gfx942";
}
inline bool is_navi1_supported()
inline bool is_gfx101_supported()
{
return ck::get_device_name() == "gfx1010" || ck::get_device_name() == "gfx1011" ||
ck::get_device_name() == "gfx1012";
}
inline bool is_navi2_supported()
inline bool is_gfx103_supported()
{
return ck::get_device_name() == "gfx1030" || ck::get_device_name() == "gfx1031" ||
ck::get_device_name() == "gfx1032" || ck::get_device_name() == "gfx1034" ||
ck::get_device_name() == "gfx1035" || ck::get_device_name() == "gfx1036";
}
inline bool is_navi3_supported()
inline bool is_gfx11_supported()
{
return ck::get_device_name() == "gfx1100" || ck::get_device_name() == "gfx1101" ||
ck::get_device_name() == "gfx1102" || ck::get_device_name() == "gfx1103";

2
include/ck/tensor_operation/gpu/device/impl/device_batched_contraction_multiple_d_wmma_cshuffle.hpp
View File

@@ -829,7 +829,7 @@ struct DeviceBatchedContractionMultipleD_Wmma_CShuffle
static bool IsSupportedArgument(const Argument& arg)
{
if(ck::is_navi3_supported())
if(ck::is_gfx11_supported())
{
if constexpr(!(is_same_v<AccDataType, float> || is_same_v<AccDataType, int32_t>))
{

2
include/ck/tensor_operation/gpu/device/impl/device_batched_gemm_multiple_d_dl.hpp
View File

@@ -648,7 +648,7 @@ struct DeviceBatchedGemmMultipleD_Dl : public DeviceBatchedGemmMultiD<ALayout,
static bool IsSupportedArgument(const Argument& arg)
{
if(ck::get_device_name() == "gfx906" || ck::is_xdl_supported() ||
ck::is_navi2_supported() || ck::is_navi3_supported())
ck::is_gfx103_supported() || ck::is_gfx11_supported())
{
bool pass = true;
pass = pass && arg.K_ % K1 == 0;

4
include/ck/tensor_operation/gpu/device/impl/device_batched_gemm_softmax_gemm_permute_wmma_cshuffle.hpp
View File

@@ -858,7 +858,7 @@ struct DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle
static bool IsSupportedArgument(const RawArg& arg)
{
if(ck::is_navi3_supported())
if(ck::is_gfx11_supported())
{
if constexpr(!(is_same_v<Acc0DataType, float> || is_same_v<Acc0DataType, int32_t>))
{
@@ -1435,7 +1435,7 @@ struct DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle
#if 0
static bool IsSupportedArgument(const Argument& arg)
{
if(ck::is_navi3_supported())
if(ck::is_gfx11_supported())
{
if constexpr(!(is_same_v<Acc0DataType, float> || is_same_v<Acc0DataType, int32_t>))
{

4
include/ck/tensor_operation/gpu/device/impl/device_convnd_bwd_data_nwc_kxc_nwk_dl.hpp
View File

@@ -1392,8 +1392,8 @@ struct DeviceConvNdBwdDataNwcKxcNwk_Dl
static bool IsSupportedArgument(const Argument& arg)
{
// check device
if(!(ck::get_device_name() == "gfx906" || ck::is_navi2_supported() ||
ck::is_navi3_supported()))
if(!(ck::get_device_name() == "gfx906" || ck::is_gfx103_supported() ||
ck::is_gfx11_supported()))
{
return false;
}

2
include/ck/tensor_operation/gpu/device/impl/device_fpAintB_gemm_wmma.hpp
View File

@@ -509,7 +509,7 @@ struct DeviceFpAintBGemm_Wmma_CShuffle : public DeviceGemm_dequantB<ALayout,
static bool IsSupportedArgument(const Argument& arg)
{
if(ck::is_navi3_supported())
if(ck::is_gfx11_supported())
{
if constexpr(!(is_same_v<AccDataType, float> || is_same_v<AccDataType, ck::half_t> ||
is_same_v<AccDataType, int32_t>))

4
include/ck/tensor_operation/gpu/device/impl/device_gemm_dl.hpp
View File

@@ -535,8 +535,8 @@ struct DeviceGemmDl : public DeviceGemm<ALayout,
}
}
if(ck::get_device_name() == "gfx906" || ck::is_navi2_supported() ||
ck::is_navi3_supported())
if(ck::get_device_name() == "gfx906" || ck::is_gfx103_supported() ||
ck::is_gfx11_supported())
{
return GridwiseGemm::CheckValidity(
arg.a_grid_desc_k0_m_k1_, arg.b_grid_desc_k0_n_k1_, arg.c_grid_desc_m_n_);

2
include/ck/tensor_operation/gpu/device/impl/device_gemm_dpp.hpp
View File

@@ -168,7 +168,7 @@ struct DeviceGemmDpp : public DeviceGemm<ALayout,
static bool IsSupportedArgument(const Argument& karg)
{
if(ck::is_navi2_supported() || ck::is_navi3_supported())
if(ck::is_gfx103_supported() || ck::is_gfx11_supported())
{
return GridwiseGemm::CheckValidity(karg);
}

2
include/ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_dl.hpp
View File

@@ -552,7 +552,7 @@ struct DeviceGemmMultipleD_Dl : public DeviceGemmMultipleD<ALayout,
static bool IsSupportedArgument(const Argument& arg)
{
if(ck::get_device_name() == "gfx906" || ck::is_xdl_supported() ||
ck::is_navi2_supported() || ck::is_navi3_supported())
ck::is_gfx103_supported() || ck::is_gfx11_supported())
{
return GridwiseGemm::CheckValidity(
arg.a_grid_desc_k0_m_k1_, arg.b_grid_desc_k0_n_k1_, arg.e_grid_desc_m_n_);

2
include/ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_wmma_cshuffle.hpp
View File

@@ -515,7 +515,7 @@ struct DeviceGemmMultipleD_Wmma_CShuffle : public DeviceGemmMultipleD<ALayout,
static bool IsSupportedArgument(const Argument& arg)
{
if(ck::is_navi3_supported())
if(ck::is_gfx11_supported())
{
if constexpr(!(is_same_v<AccDataType, float> || is_same_v<AccDataType, int32_t>))
{

2
include/ck/tensor_operation/gpu/device/impl/device_gemm_wmma.hpp
View File

@@ -443,7 +443,7 @@ struct DeviceGemmWmma_CShuffle : public DeviceGemm<ALayout,
static bool IsSupportedArgument(const Argument& arg)
{
if(ck::is_navi3_supported())
if(ck::is_gfx11_supported())
{
if constexpr(!(is_same_v<AccDataType, float> || is_same_v<AccDataType, ck::half_t> ||
is_same_v<AccDataType, int32_t>))

2
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_data_multiple_d_wmma_cshuffle.hpp
View File

@@ -629,7 +629,7 @@ struct DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle
static bool IsSupportedArgument(const Argument& arg)
{
// check device
if(ck::is_navi3_supported())
if(ck::is_gfx11_supported())
{
if constexpr(!(is_same_v<AccDataType, float> || is_same_v<AccDataType, int32_t>))
{

2
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_wmma_cshuffle.hpp
View File

@@ -692,7 +692,7 @@ struct DeviceGroupedConvBwdWeight_Wmma_CShuffle
static bool IsSupportedArgument(const Argument& arg)
{
// check device
if(ck::is_navi3_supported())
if(ck::is_gfx11_supported())
{
if constexpr(!(is_same_v<AccDataType, float> || is_same_v<AccDataType, int32_t>))
{

2
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_dl_multiple_d_nhwc_kyxc_nhwk.hpp
View File

@@ -666,7 +666,7 @@ struct DeviceGroupedConvFwdDlMultipleD_NHWC_KYXC_NHWK
// check device
if(!(ck::get_device_name() == "gfx906" || ck::is_xdl_supported() ||
ck::is_navi2_supported() || ck::is_navi3_supported()))
ck::is_gfx103_supported() || ck::is_gfx11_supported()))
{
return false;
}

4
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_dl_nhwc_kyxc_nhwk.hpp
View File

@@ -601,8 +601,8 @@ struct DeviceGroupedConvFwdDl_NHWC_KYXC_NHWK : public DeviceGroupedConvFwd<NDimS
namespace ctc = tensor_layout::convolution;
// check device
if(!(ck::get_device_name() == "gfx906" || ck::is_navi2_supported() ||
ck::is_navi3_supported()))
if(!(ck::get_device_name() == "gfx906" || ck::is_gfx103_supported() ||
ck::is_gfx11_supported()))
{
return false;
}

2
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_d_wmma_cshuffle.hpp
View File

@@ -581,7 +581,7 @@ struct DeviceGroupedConvFwdMultipleD_Wmma_CShuffle
namespace ctc = tensor_layout::convolution;
// check device
if(ck::is_navi3_supported())
if(ck::is_gfx11_supported())
{
if constexpr(!(is_same_v<AccDataType, float> || is_same_v<AccDataType, int32_t>))
{

2
include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_multiple_d_dl.hpp
View File

@@ -673,7 +673,7 @@ struct DeviceGroupedGemmMultipleD_Dl : public DeviceGroupedGemm<ALayout,
}
if(ck::get_device_name() == "gfx906" || ck::is_xdl_supported() ||
ck::is_navi2_supported() || ck::is_navi3_supported())
ck::is_gfx103_supported() || ck::is_gfx11_supported())
{
for(std::size_t i = 0; i < arg.gemm_desc_kernel_arg_.size(); i++)
{

4
include/ck/tensor_operation/gpu/device/impl/device_grouped_query_attention_forward_wmma.hpp
View File

@@ -596,7 +596,7 @@ struct DeviceGroupedQueryAttentionForward_Wmma
static bool IsSupportedArgument(const RawArg& arg)
{
if(ck::is_navi3_supported())
if(ck::is_gfx11_supported())
{
if constexpr(!(is_same_v<Acc0DataType, float> || is_same_v<Acc0DataType, int32_t>))
{
@@ -958,7 +958,7 @@ struct DeviceGroupedQueryAttentionForward_Wmma
#if 0
static bool IsSupportedArgument(const Argument& arg)
{
if(ck::is_navi3_supported())
if(ck::is_gfx11_supported())
{
if constexpr(!(is_same_v<Acc0DataType, float> || is_same_v<Acc0DataType, int32_t>))
{

4
include/ck/tensor_operation/gpu/device/impl/device_multi_query_attention_forward_wmma.hpp
View File

@@ -594,7 +594,7 @@ struct DeviceMultiQueryAttentionForward_Wmma
static bool IsSupportedArgument(const RawArg& arg)
{
if(ck::is_navi3_supported())
if(ck::is_gfx11_supported())
{
if constexpr(!(is_same_v<Acc0DataType, float> || is_same_v<Acc0DataType, int32_t>))
{
@@ -950,7 +950,7 @@ struct DeviceMultiQueryAttentionForward_Wmma
#if 0
static bool IsSupportedArgument(const Argument& arg)
{
if(ck::is_navi3_supported())
if(ck::is_gfx11_supported())
{
if constexpr(!(is_same_v<Acc0DataType, float> || is_same_v<Acc0DataType, int32_t>))
{

2
include/ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp
View File

@@ -260,7 +260,7 @@ struct BlockToCTileMap_M00_N0_M01Adapt : BlockToCTileMap_M00_N0_M01Adapt<MPerBlo
};
// Grouped Rows of column-vectors WGP mapping
// Optimized for MI300-like multipe-die chip
// Optimized for gfx94x-like multipe-die chip
template <index_t GroupNum, index_t MPerBlock, index_t NPerBlock>
struct BlockToCTileMap_Grouped_M00_N0_M01Adapt

2
include/ck/tensor_operation/gpu/warp/wmma_gemm.hpp
View File

@@ -95,7 +95,7 @@ struct wmma_type<WmmaInstr::wmma_f32_16x16x16_f16,
// Wave mode dependent propety
static constexpr index_t wave_size = Number<WaveSize>{};
// * Fixed in Navi3x, Will be wave mode dependent on Navi4x
// * Fixed on gfx11, Will be wave mode dependent for future architectures
static constexpr index_t num_src_a_vgprs_per_wave = m_per_wmma * src_a_data_size / 4;
static constexpr index_t num_src_b_vgprs_per_wave = n_per_wmma * src_b_data_size / 4;
// * num_acc_vgprs_per_wave alone M direction

2
include/ck/utility/amd_xdlops.hpp
View File

@@ -4,7 +4,7 @@
#pragma once
namespace ck {
// Define the common macro for MI300 models
// Define the common macro for gfx94x models
#if defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__)
#define __gfx94__
#endif

2
include/ck/utility/type_convert.hpp
View File

@@ -8,7 +8,7 @@
#include "ck/utility/random_gen.hpp"
namespace ck {
// Define the common macro for MI300 models
// Define the common macro for gfx94x models
#if defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__)
#define __gfx94__
#endif

83
profiler/README.md
View File

@@ -13,15 +13,6 @@
./bin/ckProfiler gemm 1 1 1 1 0 5 3840 4096 4096 4096 4096 4096
```
Result (MI100 @ 1087Mhz, 133.5TFlops peak FP16)
```bash
a_m_k: dim 2, lengths {3840, 4096}, strides {4096, 1}
b_k_n: dim 2, lengths {4096, 4096}, strides {1, 4096}
c_m_n: dim 2, lengths {3840, 4096}, strides {4096, 1}
....
Best Perf: 1.1933 ms, 107.977 TFlops, 79.0848 GB/s
```
## Profile 2D forward convolution kernels
```bash
#arg1: tensor operation (conv=Convolution)
@@ -37,15 +28,6 @@ Best Perf: 1.1933 ms, 107.977 TFlops, 79.0848 GB/s
################ op datatype in_layout wei_layout out_layout verify init log repeat N__ K___ C___ Y X Hi__ Wi__ Strides Dilations LeftPads RightPads
./bin/ckProfiler conv2d_fwd 1 1 1 1 1 1 0 5 128 256 192 3 3 71 71 2 2 1 1 1 1 1 1
```
Result (MI100 @ 1087Mhz, 133.5TFlops peak FP16)
```bash
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}
....
Best Perf: 1.42509 ms, 102.988 TFlops, 234.086 GB/s
```
## Profile contraction kernels
```bash
@@ -71,16 +53,6 @@ Best Perf: 1.42509 ms, 102.988 TFlops, 234.086 GB/s
./bin/ckProfiler contraction_bilinear 0 0 2 1 0 0 0 1 1.0 1.0 128 128 128 128 128 128
```
Result (MI100)
```bash
a_m_k: dim 4, lengths {128, 128, 128, 128}, strides {2097152, 16384, 128, 1}
b_k_n: dim 4, lengths {128, 128, 128, 128}, strides {128, 1, 2097152, 16384}
d_m_n: dim 4, lengths {128, 128, 128, 128}, strides {2097152, 16384, 128, 1}
e_m_n: dim 4, lengths {128, 128, 128, 128}, strides {2097152, 16384, 128, 1}
....
Best Perf: 211.405 ms, 41.6077 TFlops, 15.2372 GB/s
```
## Profile batched gemm multiple D kernels
```bash
#arg1: tensor operation (batched_gemm_multi_d=Batched GEMM multi D);
@@ -99,14 +71,6 @@ Best Perf: 211.405 ms, 41.6077 TFlops, 15.2372 GB/s
./bin/ckProfiler batched_gemm_multi_d 0 1 0 0 0 1 4096 4096 4096 4096 4096 4096 16777216 16777216 16777216 16
```
Result (Radeon RX 6800 XT)
```bash
arg.a_grid_desc_k0_m0_m1_k1_{2048, 4096, 2}
arg.b_grid_desc_k0_n0_n1_k1_{2048, 4096, 2}
arg.e_grid_desc_m_n_{ 4096, 4096}
....
Best Perf: 58.0306 ms, 37.8942 TFlops, 27.7545 GB/s
```
## Profile grouped convolution backward data kernels
```bash
# arg1: tensor operation (grouped_conv_bwd_data: Grouped Convolution Backward Data)
@@ -134,20 +98,6 @@ Best Perf: 58.0306 ms, 37.8942 TFlops, 27.7545 GB/s
```
Result (MI100, FP16, GNHWC_GKYXC_GNHWK)
```bash
out: dim 5, lengths {32, 4, 192, 28, 28}, strides {602112, 150528, 1, 5376, 192}
wei: dim 5, lengths {32, 192, 192, 3, 3}, strides {331776, 1728, 1, 576, 192}
in: dim 5, lengths {32, 4, 192, 28, 28}, strides {602112, 150528, 1, 5376, 192}
....
Best configuration parameters:
name: DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<256, 128, 256, 32, 8, 2, Default, 32, 32, 2, 4, 8, 4, 1, 1>
avg_time: 0.768321
tflops: 86.6679
GB/s: 127.947
```
## Profile grouped convolution backward weight kernels
```bash
# arg1: tensor operation (grouped_conv_bwd_weight: Grouped Convolution Backward Weight)
@@ -179,19 +129,6 @@ GB/s: 127.947
```
Result (MI100, FP16, GNHWC_GKYXC_GNHWK)
```bash
input: dim 5, lengths {32, 512, 1024, 28, 28}, strides {411041792, 802816, 1, 28672, 1024}
weight: dim 5, lengths {32, 512, 1024, 3, 3}, strides {4718592, 9216, 1, 3072, 1024}
output: dim 5, lengths {32, 512, 512, 26, 26}, strides {177209344, 346112, 1, 13312, 512}
....
Best configuration parameters:
name: DeviceGroupedConvBwdWeight_Xdl_CShuffle<256, 256, 128, 4, Default, 8, 4, 2, 8, 4, 8, 2, 1, 1, 8>
avg_time: 68.5216
tflops: 95.337
GB/s: 69.2301
```
Note: This kernel use atomic add, this will cause output buffer to be accumulated multiple times, causing verification failure. To work around it, do not use CK's own timer and do verification at the same time.
## Profile image to column/column to image kernels
@@ -224,17 +161,6 @@ Note: This kernel use atomic add, this will cause output buffer to be accumulate
```
Result (MI210, FP32, NHWC)
```bash
input: dim 5, lengths {1, 256, 512, 28, 28}, strides {102760448, 401408, 1, 14336, 512}
output: dim 2, lengths {173056, 4608}, strides {4608, 1}
....
Best configuration parameters:
name: DeviceImageToColumn<128, 32, 64, 4>
avg_time: 3.12326
GB/s: 2042.59
```
Note: Column to image kernel adds to the output memory, this will cause output buffer to be accumulated multiple times, causing verification failure. To work around it, do not use CK's own timer and do verification at the same time.
## Profile Permute scale kernels
@@ -254,12 +180,3 @@ Note: Column to image kernel adds to the output memory, this will cause output b
################ op datatype verify init log time dim0 dim1 dim2 in_stride0 in_stride1 in_stride2 out_stride0 out_stride1 out_stride2
./bin/ckProfiler permute_scale 0 1 1 0 1 64 64 64 4096 64 1 1 64 4096
```
Result (MI100, FP32)
```bash
A: dim 3, lengths {64, 64, 64}, strides {4096, 64, 1}
B: dim 3, lengths {64, 64, 64}, strides {1, 64, 4096}
....
Best perf = 0.0146878 ms, 142.782 GB/s, DeviceElementwiseNormalizationImpl<3, 2>
```

2
script/test_convnd_fwd.sh
View File

@@ -65,7 +65,7 @@ set -- "${POSITIONAL[@]}" # restore positional parameters
# NUMACTL="numactl --cpunodebind=1 --membind=1"
NUMACTL=
# ENV_CONF=
GPU=mi100
GPU=gfx908
PROF_ITER_COUNT=10000
LOG_DIR_PATH=../log/${LOG_DIR}
set -x

8
test/grouped_convnd_bwd_weight/test_grouped_convnd_bwd_weight.cpp
View File

@@ -55,14 +55,14 @@ class TestGroupedConvndBwdWeight : public ::testing::Test
}
}
if(ck::is_navi3_supported())
if(ck::is_gfx11_supported())
{
// on navi3x only support for 3d is implemented
// on gfx11 only support for 3d is implemented
if constexpr(NDimSpatial{} != 3)
{
return true;
}
// on navi3x only support for i8 and fp16 is implemented
// on gfx11 only support for i8 and fp16 is implemented
if constexpr(!((std::is_same_v<InDataType, int8_t> &&
std::is_same_v<WeiDataType, int8_t> &&
std::is_same_v<OutDataType, int8_t>) ||
@@ -80,7 +80,7 @@ class TestGroupedConvndBwdWeight : public ::testing::Test
}
else
{
// support for i8 is only implemented on navi3x
// support for i8 is only implemented on gfx11
if constexpr(std::is_same_v<InDataType, int8_t> &&
std::is_same_v<WeiDataType, int8_t> && std::is_same_v<OutDataType, int8_t>)
{