Skip to content

GitLab

Unverified Commit 566b6480 authored by Illia Silin's avatar Illia Silin Committed by GitHub
Browse files

Code clean-up (#1285) 

* code clean-up

* remove the profiling output samples
parent fcba889e
Hide whitespace changes
Inline Side-by-side
Showing with 23 additions and 106 deletions
include/ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_dl.hpp
View file @ 566b6480
......@@ -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_);
......
include/ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_wmma_cshuffle.hpp
View file @ 566b6480
......@@ -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>))
{
......
include/ck/tensor_operation/gpu/device/impl/device_gemm_wmma.hpp
View file @ 566b6480
......@@ -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>))
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_data_multiple_d_wmma_cshuffle.hpp
View file @ 566b6480
......@@ -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>))
{
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_wmma_cshuffle.hpp
View file @ 566b6480
......@@ -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>))
{
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_dl_multiple_d_nhwc_kyxc_nhwk.hpp
View file @ 566b6480
......@@ -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;
}
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_dl_nhwc_kyxc_nhwk.hpp
View file @ 566b6480
......@@ -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;
}
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_d_wmma_cshuffle.hpp
View file @ 566b6480
......@@ -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>))
{
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_multiple_d_dl.hpp
View file @ 566b6480
......@@ -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++)
{
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_query_attention_forward_wmma.hpp
View file @ 566b6480
......@@ -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>))
{
......
include/ck/tensor_operation/gpu/device/impl/device_multi_query_attention_forward_wmma.hpp
View file @ 566b6480
......@@ -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>))
{
......
include/ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp
View file @ 566b6480
......@@ -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
......
include/ck/tensor_operation/gpu/warp/wmma_gemm.hpp
View file @ 566b6480
......@@ -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
......
include/ck/utility/amd_xdlops.hpp
View file @ 566b6480
......@@ -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
......
include/ck/utility/type_convert.hpp
View file @ 566b6480
......@@ -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
......
profiler/README.md
View file @ 566b6480
......@@ -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>
```
script/test_convnd_fwd.sh
View file @ 566b6480
......@@ -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
......
test/grouped_convnd_bwd_weight/test_grouped_convnd_bwd_weight.cpp
View file @ 566b6480
......@@ -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>)
{
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment