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Unverified Commit 31b40352 authored by Chao Liu's avatar Chao Liu Committed by GitHub
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Merge pull request #16 from ROCmSoftwarePlatform/develop 

Merge develop into master
parents 5781adf5 b62bf8c3
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Showing with 1252 additions and 2281 deletions
host/driver_offline/include/driver_dynamic_gemm_dlops_v1r2.hpp → host/driver_offline/include/driver_gemm_dlops_v1r2.hpp
View file @ 31b40352
#ifndef DRIVER_DYNAMIC_GEMM_DLOPS_V1R2
#define DRIVER_DYNAMIC_GEMM_DLOPS_V1R2
#ifndef DRIVER_GEMM_DLOPS_V1R2
#define DRIVER_GEMM_DLOPS_V1R2
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "gridwise_dynamic_gemm_dlops_v1r2.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_gemm_dlops_v1r2.hpp"
template <ck::index_t BlockSize,
typename FloatAB,
......@@ -43,22 +43,22 @@ template <ck::index_t BlockSize,
typename CThreadTransferSrcDstAccessOrder,
ck::index_t CThreadTransferSrcDstVectorDim,
ck::index_t CThreadTransferDstScalarPerVector,
typename AGridIteratorHacks,
typename BGridIteratorHacks,
typename CGridIteratorHacks,
typename AGridMoveSliceWindowIteratorHacks,
typename BGridMoveSliceWindowIteratorHacks>
__host__ float driver_dynamic_gemm_dlops_v1r2(const FloatAB* p_a_grid,
typename AGridStepHacks,
typename BGridStepHacks,
typename CGridStepHacks,
typename AGridMoveSliceWindowStepHacks,
typename BGridMoveSliceWindowStepHacks>
__host__ float driver_gemm_dlops_v1r2(const FloatAB* p_a_grid,
const FloatAB* p_b_grid,
FloatC* p_c_grid,
const AKMGridDesc& a_k_m_grid_desc,
const BKNGridDesc& b_k_n_grid_desc,
const CMNGridDesc& c_m_n_grid_desc,
AGridIteratorHacks,
BGridIteratorHacks,
CGridIteratorHacks,
AGridMoveSliceWindowIteratorHacks,
BGridMoveSliceWindowIteratorHacks,
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks,
ck::index_t nrepeat)
{
......@@ -72,8 +72,7 @@ __host__ float driver_dynamic_gemm_dlops_v1r2(const FloatAB* p_a_grid,
constexpr auto I5 = Number<5>{};
// GEMM
using GridwiseGemm =
GridwiseDynamicGemmDlops_km_kn_mn_v1r2<BlockSize,
using GridwiseGemm = GridwiseGemmDlops_km_kn_mn_v1r2<BlockSize,
FloatAB,
FloatAcc,
FloatC,
......@@ -110,11 +109,11 @@ __host__ float driver_dynamic_gemm_dlops_v1r2(const FloatAB* p_a_grid,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridIteratorHacks,
BGridIteratorHacks,
CGridIteratorHacks,
AGridMoveSliceWindowIteratorHacks,
BGridMoveSliceWindowIteratorHacks>;
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks>;
const auto M = a_k_m_grid_desc.GetLength(I1);
const auto N = b_k_n_grid_desc.GetLength(I1);
......@@ -122,8 +121,7 @@ __host__ float driver_dynamic_gemm_dlops_v1r2(const FloatAB* p_a_grid,
if(!GridwiseGemm::CheckValidity(a_k_m_grid_desc, b_k_n_grid_desc, c_m_n_grid_desc))
{
throw std::runtime_error(
"wrong! GridwiseDynamicGemmDlops_km_kn_mn_v1r2 has invalid setting");
throw std::runtime_error("wrong! GridwiseGemmDlops_km_kn_mn_v1r2 has invalid setting");
}
const auto a_k_m0_m1_grid_desc = GridwiseGemm::MakeAKM0M1GridDescriptor(a_k_m_grid_desc);
......@@ -174,7 +172,7 @@ __host__ float driver_dynamic_gemm_dlops_v1r2(const FloatAB* p_a_grid,
if(has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r2<GridwiseGemm,
kernel_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
......@@ -189,7 +187,6 @@ __host__ float driver_dynamic_gemm_dlops_v1r2(const FloatAB* p_a_grid,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
......@@ -201,7 +198,7 @@ __host__ float driver_dynamic_gemm_dlops_v1r2(const FloatAB* p_a_grid,
else if(has_main_k_block_loop && !has_double_tail_k_block_loop)
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r2<GridwiseGemm,
kernel_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
......@@ -216,7 +213,6 @@ __host__ float driver_dynamic_gemm_dlops_v1r2(const FloatAB* p_a_grid,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
......@@ -228,7 +224,7 @@ __host__ float driver_dynamic_gemm_dlops_v1r2(const FloatAB* p_a_grid,
else if(!has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r2<GridwiseGemm,
kernel_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
......@@ -243,7 +239,6 @@ __host__ float driver_dynamic_gemm_dlops_v1r2(const FloatAB* p_a_grid,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
......@@ -255,7 +250,7 @@ __host__ float driver_dynamic_gemm_dlops_v1r2(const FloatAB* p_a_grid,
else
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r2<GridwiseGemm,
kernel_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
......@@ -270,7 +265,6 @@ __host__ float driver_dynamic_gemm_dlops_v1r2(const FloatAB* p_a_grid,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
......@@ -299,7 +293,7 @@ __host__ float driver_dynamic_gemm_dlops_v1r2(const FloatAB* p_a_grid,
if(has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r2<GridwiseGemm,
kernel_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
......@@ -315,19 +309,20 @@ __host__ float driver_dynamic_gemm_dlops_v1r2(const FloatAB* p_a_grid,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
(void CONSTANT*)a_k_m0_m1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)b_k_n0_n1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer());
cast_pointer_to_constant_address_space(a_k_m0_m1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(b_k_n0_n1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer()));
}
else if(has_main_k_block_loop && !has_double_tail_k_block_loop)
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r2<GridwiseGemm,
kernel_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
......@@ -343,19 +338,20 @@ __host__ float driver_dynamic_gemm_dlops_v1r2(const FloatAB* p_a_grid,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
(void CONSTANT*)a_k_m0_m1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)b_k_n0_n1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer());
cast_pointer_to_constant_address_space(a_k_m0_m1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(b_k_n0_n1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer()));
}
else if(!has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r2<GridwiseGemm,
kernel_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
......@@ -371,19 +367,20 @@ __host__ float driver_dynamic_gemm_dlops_v1r2(const FloatAB* p_a_grid,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
(void CONSTANT*)a_k_m0_m1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)b_k_n0_n1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer());
cast_pointer_to_constant_address_space(a_k_m0_m1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(b_k_n0_n1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer()));
}
else
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r2<GridwiseGemm,
kernel_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
......@@ -399,14 +396,15 @@ __host__ float driver_dynamic_gemm_dlops_v1r2(const FloatAB* p_a_grid,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
(void CONSTANT*)a_k_m0_m1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)b_k_n0_n1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer());
cast_pointer_to_constant_address_space(a_k_m0_m1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(b_k_n0_n1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer()));
}
return ave_time;
......
host/driver_offline/include/driver_dynamic_gemm_dlops_v1r3.hpp → host/driver_offline/include/driver_gemm_dlops_v1r3.hpp
View file @ 31b40352
#ifndef DRIVER_DYNAMIC_GEMM_DLOPS_V1R3
#define DRIVER_DYNAMIC_GEMM_DLOPS_V1R3
#ifndef DRIVER_GEMM_DLOPS_V1R3
#define DRIVER_GEMM_DLOPS_V1R3
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "gridwise_dynamic_gemm_dlops_v1r3.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_gemm_dlops_v1r3.hpp"
template <ck::index_t BlockSize,
typename FloatAB,
......@@ -39,22 +39,22 @@ template <ck::index_t BlockSize,
typename CThreadTransferSrcDstAccessOrder,
ck::index_t CThreadTransferSrcDstVectorDim,
ck::index_t CThreadTransferDstScalarPerVector,
typename AGridIteratorHacks,
typename BGridIteratorHacks,
typename CGridIteratorHacks,
typename AGridMoveSliceWindowIteratorHacks,
typename BGridMoveSliceWindowIteratorHacks>
__host__ float driver_dynamic_gemm_dlops_v1r3(const FloatAB* p_a_grid,
typename AGridStepHacks,
typename BGridStepHacks,
typename CGridStepHacks,
typename AGridMoveSliceWindowStepHacks,
typename BGridMoveSliceWindowStepHacks>
__host__ float driver_gemm_dlops_v1r3(const FloatAB* p_a_grid,
const FloatAB* p_b_grid,
FloatC* p_c_grid,
const AK0MK1GridDesc& a_k0_m_k1_grid_desc,
const BK0NK1GridDesc& b_k0_n_k1_grid_desc,
const CMNGridDesc& c_m_n_grid_desc,
AGridIteratorHacks,
BGridIteratorHacks,
CGridIteratorHacks,
AGridMoveSliceWindowIteratorHacks,
BGridMoveSliceWindowIteratorHacks,
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks,
ck::index_t nrepeat)
{
......@@ -69,7 +69,7 @@ __host__ float driver_dynamic_gemm_dlops_v1r3(const FloatAB* p_a_grid,
// GEMM
using GridwiseGemm =
GridwiseDynamicGemmDlops_km_kn_mn_v1r3<BlockSize,
GridwiseGemmDlops_km_kn_mn_v1r3<BlockSize,
FloatAB,
FloatAcc,
FloatC,
......@@ -102,11 +102,11 @@ __host__ float driver_dynamic_gemm_dlops_v1r3(const FloatAB* p_a_grid,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridIteratorHacks,
BGridIteratorHacks,
CGridIteratorHacks,
AGridMoveSliceWindowIteratorHacks,
BGridMoveSliceWindowIteratorHacks>;
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks>;
const auto M = a_k0_m_k1_grid_desc.GetLength(I1);
const auto N = b_k0_n_k1_grid_desc.GetLength(I1);
......@@ -114,8 +114,7 @@ __host__ float driver_dynamic_gemm_dlops_v1r3(const FloatAB* p_a_grid,
if(!GridwiseGemm::CheckValidity(a_k0_m_k1_grid_desc, b_k0_n_k1_grid_desc, c_m_n_grid_desc))
{
throw std::runtime_error(
"wrong! GridwiseDynamicGemmDlops_km_kn_mn_v1r3 has invalid setting");
throw std::runtime_error("wrong! GridwiseGemmDlops_km_kn_mn_v1r3 has invalid setting");
}
const auto a_k0_m0_m1_k1_grid_desc =
......@@ -170,7 +169,7 @@ __host__ float driver_dynamic_gemm_dlops_v1r3(const FloatAB* p_a_grid,
if(has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r3<GridwiseGemm,
kernel_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
......@@ -185,7 +184,6 @@ __host__ float driver_dynamic_gemm_dlops_v1r3(const FloatAB* p_a_grid,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
......@@ -197,7 +195,7 @@ __host__ float driver_dynamic_gemm_dlops_v1r3(const FloatAB* p_a_grid,
else if(has_main_k_block_loop && !has_double_tail_k_block_loop)
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r3<GridwiseGemm,
kernel_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
......@@ -212,7 +210,6 @@ __host__ float driver_dynamic_gemm_dlops_v1r3(const FloatAB* p_a_grid,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
......@@ -224,7 +221,7 @@ __host__ float driver_dynamic_gemm_dlops_v1r3(const FloatAB* p_a_grid,
else if(!has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r3<GridwiseGemm,
kernel_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
......@@ -239,7 +236,6 @@ __host__ float driver_dynamic_gemm_dlops_v1r3(const FloatAB* p_a_grid,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
......@@ -251,7 +247,7 @@ __host__ float driver_dynamic_gemm_dlops_v1r3(const FloatAB* p_a_grid,
else
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r3<GridwiseGemm,
kernel_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
......@@ -266,7 +262,6 @@ __host__ float driver_dynamic_gemm_dlops_v1r3(const FloatAB* p_a_grid,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
......@@ -295,7 +290,7 @@ __host__ float driver_dynamic_gemm_dlops_v1r3(const FloatAB* p_a_grid,
if(has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r3<GridwiseGemm,
kernel_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
......@@ -311,19 +306,22 @@ __host__ float driver_dynamic_gemm_dlops_v1r3(const FloatAB* p_a_grid,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
(void CONSTANT*)a_k0_m0_m1_k1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)b_k0_n0_n1_k1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer());
cast_pointer_to_constant_address_space(
a_k0_m0_m1_k1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
b_k0_n0_n1_k1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer()));
}
else if(has_main_k_block_loop && !has_double_tail_k_block_loop)
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r3<GridwiseGemm,
kernel_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
......@@ -339,19 +337,22 @@ __host__ float driver_dynamic_gemm_dlops_v1r3(const FloatAB* p_a_grid,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
(void CONSTANT*)a_k0_m0_m1_k1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)b_k0_n0_n1_k1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer());
cast_pointer_to_constant_address_space(
a_k0_m0_m1_k1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
b_k0_n0_n1_k1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer()));
}
else if(!has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r3<GridwiseGemm,
kernel_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
......@@ -367,19 +368,22 @@ __host__ float driver_dynamic_gemm_dlops_v1r3(const FloatAB* p_a_grid,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
(void CONSTANT*)a_k0_m0_m1_k1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)b_k0_n0_n1_k1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer());
cast_pointer_to_constant_address_space(
a_k0_m0_m1_k1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
b_k0_n0_n1_k1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer()));
}
else
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r3<GridwiseGemm,
kernel_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
......@@ -395,14 +399,17 @@ __host__ float driver_dynamic_gemm_dlops_v1r3(const FloatAB* p_a_grid,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
(void CONSTANT*)a_k0_m0_m1_k1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)b_k0_n0_n1_k1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer());
cast_pointer_to_constant_address_space(
a_k0_m0_m1_k1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
b_k0_n0_n1_k1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer()));
}
return ave_time;
......
host/driver_offline/include/driver_dynamic_gemm_xdlops_v2r3.hpp → host/driver_offline/include/driver_gemm_xdlops_v2r3.hpp
View file @ 31b40352
#ifndef DRIVER_DYNAMIC_GEMM_XDLOPS_V2R3
#define DRIVER_DYNAMIC_GEMM_XDLOPS_V2R3
#ifndef DRIVER_GEMM_XDLOPS_V2R3
#define DRIVER_GEMM_XDLOPS_V2R3
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "gridwise_dynamic_gemm_xdlops_v2r3.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_gemm_xdlops_v2r3.hpp"
template <ck::index_t BlockSize,
typename FloatAB,
......@@ -41,23 +41,23 @@ template <ck::index_t BlockSize,
typename CThreadTransferSrcDstAccessOrder,
ck::index_t CThreadTransferSrcDstVectorDim,
ck::index_t CThreadTransferDstScalarPerVector,
typename AGridIteratorHacks,
typename BGridIteratorHacks,
typename CGridIteratorHacks,
typename AGridMoveSliceWindowIteratorHacks,
typename BGridMoveSliceWindowIteratorHacks,
typename AGridStepHacks,
typename BGridStepHacks,
typename CGridStepHacks,
typename AGridMoveSliceWindowStepHacks,
typename BGridMoveSliceWindowStepHacks,
bool CAccessOrderMRepeatNRepeat>
__host__ float driver_dynamic_gemm_xdlops_v2r3(const FloatAB* p_a_grid,
__host__ float driver_gemm_xdlops_v2r3(const FloatAB* p_a_grid,
const FloatAB* p_b_grid,
FloatC* p_c_grid,
const AK0MK1GridDesc& a_k0_m_k1_grid_desc,
const BK0NK1GridDesc& b_k0_n_k1_grid_desc,
const CMNGridDesc& c_m_n_grid_desc,
AGridIteratorHacks,
BGridIteratorHacks,
CGridIteratorHacks,
AGridMoveSliceWindowIteratorHacks,
BGridMoveSliceWindowIteratorHacks,
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks,
ck::index_t nrepeat)
{
......@@ -66,12 +66,9 @@ __host__ float driver_dynamic_gemm_xdlops_v2r3(const FloatAB* p_a_grid,
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
constexpr auto I5 = Number<5>{};
using GridwiseGemm =
GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3<BlockSize,
GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3<BlockSize,
FloatAB,
FloatAcc,
FloatC,
......@@ -106,11 +103,11 @@ __host__ float driver_dynamic_gemm_xdlops_v2r3(const FloatAB* p_a_grid,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridIteratorHacks,
BGridIteratorHacks,
CGridIteratorHacks,
AGridMoveSliceWindowIteratorHacks,
BGridMoveSliceWindowIteratorHacks,
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks,
CAccessOrderMRepeatNRepeat>;
{
......@@ -129,7 +126,7 @@ __host__ float driver_dynamic_gemm_xdlops_v2r3(const FloatAB* p_a_grid,
if(!GridwiseGemm::CheckValidity(a_k0_m_k1_grid_desc, b_k0_n_k1_grid_desc, c_m_n_grid_desc))
{
throw std::runtime_error(
"wrong! GridwiseDynamicGemm_km_kn_m0m1n0n1_xdlops_v2r3 has invalid setting");
"wrong! GridwiseGemm_km_kn_m0m1n0n1_xdlops_v2r3 has invalid setting");
}
const auto c_m0_m1_m2_n_grid_desc = GridwiseGemm::MakeCM0M1M2NGridDescriptor(c_m_n_grid_desc);
......@@ -142,7 +139,7 @@ __host__ float driver_dynamic_gemm_xdlops_v2r3(const FloatAB* p_a_grid,
const index_t grid_size = GridwiseGemm::CalculateGridSize(c_m_n_grid_desc);
const auto kernel = kernel_dynamic_gemm_xdlops_v2r3<GridwiseGemm,
const auto kernel = kernel_gemm_xdlops_v2r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0MK1GridDesc>,
......@@ -156,7 +153,6 @@ __host__ float driver_dynamic_gemm_xdlops_v2r3(const FloatAB* p_a_grid,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
......@@ -176,20 +172,19 @@ __host__ float driver_dynamic_gemm_xdlops_v2r3(const FloatAB* p_a_grid,
c_m0_m1_m2_n_grid_desc_dev_buf.ToDevice(&c_m0_m1_m2_n_grid_desc);
c_block_cluster_adaptor_dev_buf.ToDevice(&c_block_cluster_adaptor);
float ave_time =
launch_and_time_kernel(kernel,
float ave_time = launch_and_time_kernel(
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
(void CONSTANT*)a_k0_m_k1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)b_k0_n_k1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_m0_m1_m2_n_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_block_cluster_adaptor_dev_buf.GetDeviceBuffer());
cast_pointer_to_constant_address_space(a_k0_m_k1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(b_k0_n_k1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(c_m0_m1_m2_n_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(c_block_cluster_adaptor_dev_buf.GetDeviceBuffer()));
#endif
return ave_time;
}
......
host/driver_offline/conv_bwd_driver_offline.cpp → host/driver_offline/src/conv_bwd_driver_offline.cpp
View file @ 31b40352
......@@ -12,10 +12,10 @@
#include "conv_common.hpp"
#include "host_conv_bwd_data.hpp"
#include "device_tensor.hpp"
#include "device_dynamic_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyxc_nhwk.hpp"
#include "device_dynamic_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_kyxc_nhwk.hpp"
#include "device_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyxc_nhwk.hpp"
#include "device_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_kyxc_nhwk.hpp"
#define USE_DYNAMIC_MODE 1
#define USE_MODE 1
#define USE_CONV_BWD_V4R1_XDL_NHWC 1
#define USE_CONV_BWD_V4R1R2_XDL_NHWC 1
......@@ -37,7 +37,7 @@ int main(int argc, char* argv[])
constexpr auto I5 = Number<5>{};
constexpr auto I6 = Number<6>{};
#if USE_DYNAMIC_MODE
#if USE_MODE
// dynamic mode
if(argc != 22)
{
......@@ -46,29 +46,29 @@ int main(int argc, char* argv[])
exit(1);
}
const ConvTensorLayout layout = static_cast<ConvTensorLayout>(atoi(argv[1]));
const ConvBackwardDataAlgo algo = static_cast<ConvBackwardDataAlgo>(atoi(argv[2]));
const bool do_verification = atoi(argv[3]);
const int init_method = atoi(argv[4]);
const bool do_log = atoi(argv[5]);
const int nrepeat = atoi(argv[6]);
const index_t N = atoi(argv[7]);
const index_t K = atoi(argv[8]);
const index_t C = atoi(argv[9]);
const index_t Y = atoi(argv[10]);
const index_t X = atoi(argv[11]);
const index_t Hi = atoi(argv[12]);
const index_t Wi = atoi(argv[13]);
const index_t conv_stride_h = atoi(argv[14]);
const index_t conv_stride_w = atoi(argv[15]);
const index_t conv_dilation_h = atoi(argv[16]);
const index_t conv_dilation_w = atoi(argv[17]);
const index_t in_left_pad_h = atoi(argv[18]);
const index_t in_left_pad_w = atoi(argv[19]);
const index_t in_right_pad_h = atoi(argv[20]);
const index_t in_right_pad_w = atoi(argv[21]);
const ConvTensorLayout layout = static_cast<ConvTensorLayout>(std::stoi(argv[1]));
const ConvBackwardDataAlgo algo = static_cast<ConvBackwardDataAlgo>(std::stoi(argv[2]));
const bool do_verification = std::stoi(argv[3]);
const int init_method = std::stoi(argv[4]);
const bool do_log = std::stoi(argv[5]);
const int nrepeat = std::stoi(argv[6]);
const index_t N = std::stoi(argv[7]);
const index_t K = std::stoi(argv[8]);
const index_t C = std::stoi(argv[9]);
const index_t Y = std::stoi(argv[10]);
const index_t X = std::stoi(argv[11]);
const index_t Hi = std::stoi(argv[12]);
const index_t Wi = std::stoi(argv[13]);
const index_t conv_stride_h = std::stoi(argv[14]);
const index_t conv_stride_w = std::stoi(argv[15]);
const index_t conv_dilation_h = std::stoi(argv[16]);
const index_t conv_dilation_w = std::stoi(argv[17]);
const index_t in_left_pad_h = std::stoi(argv[18]);
const index_t in_left_pad_w = std::stoi(argv[19]);
const index_t in_right_pad_h = std::stoi(argv[20]);
const index_t in_right_pad_w = std::stoi(argv[21]);
const index_t YEff = (Y - 1) * conv_dilation_h + 1;
const index_t XEff = (X - 1) * conv_dilation_w + 1;
......@@ -83,12 +83,12 @@ int main(int argc, char* argv[])
exit(1);
}
const ConvTensorLayout layout = static_cast<ConvTensorLayout>(atoi(argv[1]));
const ConvBackwardDataAlgo algo = static_cast<ConvBackwardDataAlgo>(atoi(argv[2]));
const bool do_verification = atoi(argv[3]);
const int init_method = atoi(argv[4]);
const bool do_log = atoi(argv[5]);
const int nrepeat = atoi(argv[6]);
const ConvTensorLayout layout = static_cast<ConvTensorLayout>(std::stoi(argv[1]));
const ConvBackwardDataAlgo algo = static_cast<ConvBackwardDataAlgo>(std::stoi(argv[2]));
const bool do_verification = std::stoi(argv[3]);
const int init_method = std::stoi(argv[4]);
const bool do_log = std::stoi(argv[5]);
const int nrepeat = std::stoi(argv[6]);
constexpr index_t N = 128;
constexpr index_t C = 192;
......@@ -115,12 +115,10 @@ int main(int argc, char* argv[])
#endif
#if 0
constexpr index_t in_vector_size = 1;
using in_data_t = float;
using acc_data_t = float;
using out_data_t = float;
#elif 1
constexpr index_t in_vector_size = 1;
using in_data_t = half_t;
using acc_data_t = float;
using out_data_t = half_t;
......@@ -128,10 +126,8 @@ int main(int argc, char* argv[])
std::vector<std::size_t> in_lengths_host(4), wei_lengths_host(4), out_lengths_host(4);
switch(layout)
if(layout == ConvTensorLayout::NCHW)
{
case ConvTensorLayout::NCHW:
// NCHW
in_lengths_host[0] = static_cast<std::size_t>(N);
in_lengths_host[1] = static_cast<std::size_t>(C);
in_lengths_host[2] = static_cast<std::size_t>(Hi);
......@@ -144,9 +140,9 @@ int main(int argc, char* argv[])
out_lengths_host[1] = static_cast<std::size_t>(K);
out_lengths_host[2] = static_cast<std::size_t>(Ho);
out_lengths_host[3] = static_cast<std::size_t>(Wo);
break;
case ConvTensorLayout::NHWC:
// NHWC
}
else if(layout == ConvTensorLayout::NHWC)
{
in_lengths_host[0] = static_cast<std::size_t>(N);
in_lengths_host[1] = static_cast<std::size_t>(Hi);
in_lengths_host[2] = static_cast<std::size_t>(Wi);
......@@ -159,8 +155,10 @@ int main(int argc, char* argv[])
out_lengths_host[1] = static_cast<std::size_t>(Ho);
out_lengths_host[2] = static_cast<std::size_t>(Wo);
out_lengths_host[3] = static_cast<std::size_t>(K);
break;
default: throw std::runtime_error("wrong! not implemented");
}
else
{
throw std::runtime_error("wrong! not implemented");
}
Tensor<in_data_t> in_host(in_lengths_host);
......@@ -213,40 +211,8 @@ int main(int argc, char* argv[])
wei.GenerateTensorValue(gen_wei, num_thread);
}
auto f_make_for_device_nchw = [&]() {
#if USE_DYNAMIC_MODE
const auto in_lengths_dev = make_tuple(N, C, Hi, Wi);
const auto wei_lengths_dev = make_tuple(K, C, Y, X);
const auto out_lengths_dev = make_tuple(N, K, Ho, Wo);
const auto conv_strides_dev = make_tuple(conv_stride_h, conv_stride_w);
const auto conv_dilations_dev = make_tuple(conv_dilation_h, conv_dilation_w);
const auto in_left_pads_dev = make_tuple(in_left_pad_h, in_left_pad_w);
const auto in_right_pads_dev = make_tuple(in_right_pad_h, in_right_pad_w);
#else
const auto in_lengths_dev =
make_tuple(Number<N>{}, Number<C>{}, Number<Hi>{}, Number<Wi>{});
const auto wei_lengths_dev = make_tuple(Number<K>{}, Number<C>{}, Number<Y>{}, Number<X>{});
const auto out_lengths_dev =
make_tuple(Number<N>{}, Number<K>{}, Number<Ho>{}, Number<Wo>{});
const auto conv_strides_dev = make_tuple(Number<conv_stride_h>{}, Number<conv_stride_w>{});
const auto conv_dilations_dev =
make_tuple(Number<conv_dilation_h>{}, Number<conv_dilation_w>{});
const auto in_left_pads_dev = make_tuple(Number<in_left_pad_h>{}, Number<in_left_pad_w>{});
const auto in_right_pads_dev =
make_tuple(Number<in_right_pad_h>{}, Number<in_right_pad_w>{});
#endif
return make_tuple(in_lengths_dev,
wei_lengths_dev,
out_lengths_dev,
conv_strides_dev,
conv_dilations_dev,
in_left_pads_dev,
in_right_pads_dev);
};
auto f_make_for_device_nhwc = [&]() {
#if USE_DYNAMIC_MODE
#if USE_MODE
const auto in_lengths_dev = make_tuple(N, Hi, Wi, C);
const auto wei_lengths_dev = make_tuple(K, Y, X, C);
const auto out_lengths_dev = make_tuple(N, Ho, Wo, K);
......@@ -277,8 +243,6 @@ int main(int argc, char* argv[])
in_right_pads_dev);
};
const auto nhwc_desc = f_make_for_device_nhwc();
#if USE_CONV_BWD_V4R1_XDL_NHWC
if(algo == ConvBackwardDataAlgo::V4R1XDLNHWC)
{
......@@ -289,10 +253,10 @@ int main(int argc, char* argv[])
const auto tmp = f_make_for_device_nhwc();
device_dynamic_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyxc_nhwk<
in_data_t,
device_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyxc_nhwk<in_data_t,
acc_data_t,
out_data_t>(tmp[I0],
out_data_t>(
tmp[I0],
tmp[I1],
tmp[I2],
tmp[I3],
......@@ -316,10 +280,10 @@ int main(int argc, char* argv[])
const auto tmp = f_make_for_device_nhwc();
device_dynamic_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_kyxc_nhwk<
in_data_t,
device_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_kyxc_nhwk<in_data_t,
acc_data_t,
out_data_t>(tmp[I0],
out_data_t>(
tmp[I0],
tmp[I1],
tmp[I2],
tmp[I3],
......
host/driver_offline/conv_fwd_driver_offline.cpp → host/driver_offline/src/conv_fwd_driver_offline.cpp
View file @ 31b40352
......@@ -12,17 +12,17 @@
#include "conv_common.hpp"
#include "host_conv.hpp"
#include "device_tensor.hpp"
#include "device_dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw.hpp"
#include "device_dynamic_convolution_forward_implicit_gemm_v4r4r2_dlops_nhwc_kyxc_nhwk.hpp"
#include "device_dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw.hpp"
#include "device_dynamic_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw.hpp"
#include "device_dynamic_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw.hpp"
#include "device_dynamic_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk.hpp"
#define USE_DYNAMIC_MODE 1
#include "device_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw.hpp"
#include "device_convolution_forward_implicit_gemm_v4r4r2_dlops_nhwc_kyxc_nhwk.hpp"
#include "device_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw.hpp"
#include "device_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw.hpp"
#include "device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw.hpp"
#include "device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk.hpp"
#define USE_MODE 1
#define USE_CONV_FWD_V4R4_NCHW 1
#define USE_CONV_FWD_V4R4R2_NHWC 1
#define USE_CONV_FWD_V6R1_NCHW 1
#define USE_CONV_FWD_V6R1_NCHW 0
#define USE_CONV_FWD_V5R1_NCHW 0
#define USE_CONV_FWD_V4R4R2_XDL_NCHW 0
#define USE_CONV_FWD_V4R4R4_XDL_NHWC 0
......@@ -49,7 +49,7 @@ int main(int argc, char* argv[])
constexpr auto I5 = Number<5>{};
constexpr auto I6 = Number<6>{};
#if USE_DYNAMIC_MODE
#if USE_MODE
// dynamic mode
if(argc != 22)
{
......@@ -58,29 +58,29 @@ int main(int argc, char* argv[])
exit(1);
}
const ConvTensorLayout layout = static_cast<ConvTensorLayout>(atoi(argv[1]));
const ConvForwardAlgo algo = static_cast<ConvForwardAlgo>(atoi(argv[2]));
const bool do_verification = atoi(argv[3]);
const int init_method = atoi(argv[4]);
const bool do_log = atoi(argv[5]);
const int nrepeat = atoi(argv[6]);
const index_t N = atoi(argv[7]);
const index_t K = atoi(argv[8]);
const index_t C = atoi(argv[9]);
const index_t Y = atoi(argv[10]);
const index_t X = atoi(argv[11]);
const index_t Hi = atoi(argv[12]);
const index_t Wi = atoi(argv[13]);
const index_t conv_stride_h = atoi(argv[14]);
const index_t conv_stride_w = atoi(argv[15]);
const index_t conv_dilation_h = atoi(argv[16]);
const index_t conv_dilation_w = atoi(argv[17]);
const index_t in_left_pad_h = atoi(argv[18]);
const index_t in_left_pad_w = atoi(argv[19]);
const index_t in_right_pad_h = atoi(argv[20]);
const index_t in_right_pad_w = atoi(argv[21]);
const ConvTensorLayout layout = static_cast<ConvTensorLayout>(std::stoi(argv[1]));
const ConvForwardAlgo algo = static_cast<ConvForwardAlgo>(std::stoi(argv[2]));
const bool do_verification = std::stoi(argv[3]);
const int init_method = std::stoi(argv[4]);
const bool do_log = std::stoi(argv[5]);
const int nrepeat = std::stoi(argv[6]);
const index_t N = std::stoi(argv[7]);
const index_t K = std::stoi(argv[8]);
const index_t C = std::stoi(argv[9]);
const index_t Y = std::stoi(argv[10]);
const index_t X = std::stoi(argv[11]);
const index_t Hi = std::stoi(argv[12]);
const index_t Wi = std::stoi(argv[13]);
const index_t conv_stride_h = std::stoi(argv[14]);
const index_t conv_stride_w = std::stoi(argv[15]);
const index_t conv_dilation_h = std::stoi(argv[16]);
const index_t conv_dilation_w = std::stoi(argv[17]);
const index_t in_left_pad_h = std::stoi(argv[18]);
const index_t in_left_pad_w = std::stoi(argv[19]);
const index_t in_right_pad_h = std::stoi(argv[20]);
const index_t in_right_pad_w = std::stoi(argv[21]);
const index_t YEff = (Y - 1) * conv_dilation_h + 1;
const index_t XEff = (X - 1) * conv_dilation_w + 1;
......@@ -95,12 +95,12 @@ int main(int argc, char* argv[])
exit(1);
}
const ConvTensorLayout layout = static_cast<ConvTensorLayout>(atoi(argv[1]));
const ConvForwardAlgo algo = static_cast<ConvForwardAlgo>(atoi(argv[2]));
const bool do_verification = atoi(argv[3]);
const int init_method = atoi(argv[4]);
const bool do_log = atoi(argv[5]);
const int nrepeat = atoi(argv[6]);
const ConvTensorLayout layout = static_cast<ConvTensorLayout>(std::stoi(argv[1]));
const ConvForwardAlgo algo = static_cast<ConvForwardAlgo>(std::stoi(argv[2]));
const bool do_verification = std::stoi(argv[3]);
const int init_method = std::stoi(argv[4]);
const bool do_log = std::stoi(argv[5]);
const int nrepeat = std::stoi(argv[6]);
constexpr index_t N = 128;
constexpr index_t C = 192;
......@@ -142,10 +142,8 @@ int main(int argc, char* argv[])
std::vector<std::size_t> in_lengths_host(4), wei_lengths_host(4), out_lengths_host(4);
switch(layout)
if(layout == ConvTensorLayout::NCHW)
{
case ConvTensorLayout::NCHW:
// NCHW
in_lengths_host[0] = static_cast<std::size_t>(N);
in_lengths_host[1] = static_cast<std::size_t>(C);
in_lengths_host[2] = static_cast<std::size_t>(Hi);
......@@ -158,9 +156,9 @@ int main(int argc, char* argv[])
out_lengths_host[1] = static_cast<std::size_t>(K);
out_lengths_host[2] = static_cast<std::size_t>(Ho);
out_lengths_host[3] = static_cast<std::size_t>(Wo);
break;
case ConvTensorLayout::NHWC:
// NHWC
}
else if(layout == ConvTensorLayout::NHWC)
{
in_lengths_host[0] = static_cast<std::size_t>(N);
in_lengths_host[1] = static_cast<std::size_t>(Hi);
in_lengths_host[2] = static_cast<std::size_t>(Wi);
......@@ -173,8 +171,10 @@ int main(int argc, char* argv[])
out_lengths_host[1] = static_cast<std::size_t>(Ho);
out_lengths_host[2] = static_cast<std::size_t>(Wo);
out_lengths_host[3] = static_cast<std::size_t>(K);
break;
default: throw std::runtime_error("wrong! not implemented");
}
else
{
std::runtime_error("wrong! not implemented");
}
Tensor<in_data_t> in(in_lengths_host);
......@@ -228,7 +228,7 @@ int main(int argc, char* argv[])
}
auto f_make_for_device_nchw = [&]() {
#if USE_DYNAMIC_MODE
#if USE_MODE
const auto in_lengths_dev = make_tuple(N, C, Hi, Wi);
const auto wei_lengths_dev = make_tuple(K, C, Y, X);
const auto out_lengths_dev = make_tuple(N, K, Ho, Wo);
......@@ -260,7 +260,7 @@ int main(int argc, char* argv[])
};
auto f_make_for_device_nhwc = [&]() {
#if USE_DYNAMIC_MODE
#if USE_MODE
const auto in_lengths_dev = make_tuple(N, Hi, Wi, C);
const auto wei_lengths_dev = make_tuple(K, Y, X, C);
const auto out_lengths_dev = make_tuple(N, Ho, Wo, K);
......@@ -301,10 +301,9 @@ int main(int argc, char* argv[])
const auto tmp = f_make_for_device_nchw();
device_dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw<in_data_t,
device_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw<in_data_t,
acc_data_t,
out_data_t>(
tmp[I0],
out_data_t>(tmp[I0],
tmp[I1],
tmp[I2],
tmp[I3],
......@@ -328,10 +327,9 @@ int main(int argc, char* argv[])
const auto tmp = f_make_for_device_nhwc();
device_dynamic_convolution_forward_implicit_gemm_v4r4r2_dlops_nhwc_kyxc_nhwk<in_data_t,
device_convolution_forward_implicit_gemm_v4r4r2_dlops_nhwc_kyxc_nhwk<in_data_t,
acc_data_t,
out_data_t>(
tmp[I0],
out_data_t>(tmp[I0],
tmp[I1],
tmp[I2],
tmp[I3],
......@@ -355,10 +353,9 @@ int main(int argc, char* argv[])
const auto tmp = f_make_for_device_nchw();
device_dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw<in_data_t,
device_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw<in_data_t,
acc_data_t,
out_data_t>(
tmp[I0],
out_data_t>(tmp[I0],
tmp[I1],
tmp[I2],
tmp[I3],
......@@ -382,11 +379,10 @@ int main(int argc, char* argv[])
const auto tmp = f_make_for_device_nchw();
device_dynamic_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw<in_data_t,
device_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw<in_data_t,
16,
acc_data_t,
out_data_t>(
tmp[I0],
out_data_t>(tmp[I0],
tmp[I1],
tmp[I2],
tmp[I3],
......@@ -410,7 +406,7 @@ int main(int argc, char* argv[])
const auto tmp = f_make_for_device_nchw();
device_dynamic_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw<in_data_t,
device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw<in_data_t,
acc_data_t,
out_data_t>(
tmp[I0],
......@@ -437,7 +433,7 @@ int main(int argc, char* argv[])
const auto tmp = f_make_for_device_nhwc();
device_dynamic_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk<in_data_t,
device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk<in_data_t,
acc_data_t,
out_data_t>(
tmp[I0],
......@@ -467,7 +463,6 @@ int main(int argc, char* argv[])
check_error(out_host, out_device);
#if 0
if(do_log)
{
LogRangeAsType<float>(std::cout << "in : ", in.mData, ",") << std::endl;
......@@ -475,6 +470,5 @@ int main(int argc, char* argv[])
LogRangeAsType<float>(std::cout << "out_host : ", out_host.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "out_device: ", out_device.mData, ",") << std::endl;
}
#endif
}
}
host/driver_online/CMakeLists.txt deleted 100644 → 0
View file @ 5781adf5
include_directories(BEFORE
include
${PROJECT_BINARY_DIR}/host/online_compile/include
${PROJECT_SOURCE_DIR}/host/online_compile/include
${PROJECT_SOURCE_DIR}/host/host_tensor/include
${PROJECT_SOURCE_DIR}/host/solver/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}/composable_kernel/include/driver
${PROJECT_SOURCE_DIR}/external/rocm/include
${PROJECT_SOURCE_DIR}/external/half/include
)
set(CONV_FWD_DRIVER_ONLINE_SOURCE conv_fwd_driver_online.cpp)
add_executable(conv_fwd_driver_online ${CONV_FWD_DRIVER_ONLINE_SOURCE})
target_link_libraries(conv_fwd_driver_online PRIVATE host_tensor)
target_link_libraries(conv_fwd_driver_online PRIVATE online_compile)
host/driver_online/conv_fwd_driver_online.cpp deleted 100644 → 0
View file @ 5781adf5
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include <stdlib.h>
#include <half.hpp>
#include "config.hpp"
#include "print.hpp"
#include "device.hpp"
#include "host_tensor.hpp"
#include "host_tensor_generator.hpp"
#include "conv_common.hpp"
#include "host_conv.hpp"
#include "device_tensor.hpp"
#include "handle.hpp"
#include "hipCheck.hpp"
#include "online_device_dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw.hpp"
#include "online_device_dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw.hpp"
#include "online_device_dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw.hpp"
#include "online_device_dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nhwc_kyxc_nhwk.hpp"
#define USE_CONV_FWD_V4R4_NCHW 1
#define USE_CONV_FWD_V6R1_NCHW 1
#define USE_CONV_FWD_V4R4_XDLOPS_NCHW 1
#define USE_CONV_FWD_V4R4_XDLOPS_NHWC 1
enum ConvForwardAlgo
{
V4R4NCHW, // 0
V6R1NCHW, // 1
V4R4XDLNCHW, // 2
V4R4XDLNHWC // 3
};
int main(int argc, char* argv[])
{
using namespace ck;
using namespace ck_driver;
using size_t = std::size_t;
hipStream_t stream;
online_compile::Handle* handle;
MY_HIP_CHECK(hipStreamCreate(&stream));
handle = new online_compile::Handle(stream);
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
constexpr auto I5 = Number<5>{};
constexpr auto I6 = Number<6>{};
if(argc != 22)
{
printf("arg1 to 5: layout, algo, do_verification, init_method, do_log, nrepeat\n");
printf("rest: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, RightPx\n");
exit(1);
}
const ConvTensorLayout layout = static_cast<ConvTensorLayout>(atoi(argv[1]));
const ConvForwardAlgo algo = static_cast<ConvForwardAlgo>(atoi(argv[2]));
const bool do_verification = atoi(argv[3]);
const int init_method = atoi(argv[4]);
const bool do_log = atoi(argv[5]);
const int nrepeat = atoi(argv[6]);
const index_t N = atoi(argv[7]);
const index_t K = atoi(argv[8]);
const index_t C = atoi(argv[9]);
const index_t Y = atoi(argv[10]);
const index_t X = atoi(argv[11]);
const index_t Hi = atoi(argv[12]);
const index_t Wi = atoi(argv[13]);
const index_t conv_stride_h = atoi(argv[14]);
const index_t conv_stride_w = atoi(argv[15]);
const index_t conv_dilation_h = atoi(argv[16]);
const index_t conv_dilation_w = atoi(argv[17]);
const index_t in_left_pad_h = atoi(argv[18]);
const index_t in_left_pad_w = atoi(argv[19]);
const index_t in_right_pad_h = atoi(argv[20]);
const index_t in_right_pad_w = atoi(argv[21]);
const index_t YEff = (Y - 1) * conv_dilation_h + 1;
const index_t XEff = (X - 1) * conv_dilation_w + 1;
const index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - YEff) / conv_stride_h + 1;
const index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1;
#if 1
using in_data_t = float;
using acc_data_t = float;
using out_data_t = float;
#elif 0
using in_data_t = half_t;
using acc_data_t = float;
using out_data_t = half_t;
#elif 1
using in_data_t = int8_t;
using acc_data_t = int32_t;
using out_data_t = int8_t;
#endif
std::vector<std::size_t> in_lengths_host(4), wei_lengths_host(4), out_lengths_host(4);
switch(layout)
{
case ConvTensorLayout::NCHW:
// NCHW
in_lengths_host[0] = static_cast<std::size_t>(N);
in_lengths_host[1] = static_cast<std::size_t>(C);
in_lengths_host[2] = static_cast<std::size_t>(Hi);
in_lengths_host[3] = static_cast<std::size_t>(Wi);
wei_lengths_host[0] = static_cast<std::size_t>(K);
wei_lengths_host[1] = static_cast<std::size_t>(C);
wei_lengths_host[2] = static_cast<std::size_t>(Y);
wei_lengths_host[3] = static_cast<std::size_t>(X);
out_lengths_host[0] = static_cast<std::size_t>(N);
out_lengths_host[1] = static_cast<std::size_t>(K);
out_lengths_host[2] = static_cast<std::size_t>(Ho);
out_lengths_host[3] = static_cast<std::size_t>(Wo);
break;
case ConvTensorLayout::NHWC:
// NHWC
in_lengths_host[0] = static_cast<std::size_t>(N);
in_lengths_host[1] = static_cast<std::size_t>(Hi);
in_lengths_host[2] = static_cast<std::size_t>(Wi);
in_lengths_host[3] = static_cast<std::size_t>(C);
wei_lengths_host[0] = static_cast<std::size_t>(K);
wei_lengths_host[1] = static_cast<std::size_t>(Y);
wei_lengths_host[2] = static_cast<std::size_t>(X);
wei_lengths_host[3] = static_cast<std::size_t>(C);
out_lengths_host[0] = static_cast<std::size_t>(N);
out_lengths_host[1] = static_cast<std::size_t>(Ho);
out_lengths_host[2] = static_cast<std::size_t>(Wo);
out_lengths_host[3] = static_cast<std::size_t>(K);
break;
default: throw std::runtime_error("wrong! not implemented");
}
Tensor<in_data_t> in(in_lengths_host);
Tensor<in_data_t> wei(wei_lengths_host);
Tensor<out_data_t> out_host(out_lengths_host);
Tensor<out_data_t> out_device(out_lengths_host);
std::cout << "layout: " << layout << std::endl;
ostream_HostTensorDescriptor(in.mDesc, std::cout << "in: ");
ostream_HostTensorDescriptor(wei.mDesc, std::cout << "wei: ");
ostream_HostTensorDescriptor(out_host.mDesc, std::cout << "out: ");
print_array("InLeftPads", make_tuple(in_left_pad_h, in_left_pad_w));
print_array("InRightPads", make_tuple(in_right_pad_h, in_right_pad_w));
print_array("ConvStrides", make_tuple(conv_stride_h, conv_stride_w));
print_array("ConvDilations", make_tuple(conv_dilation_h, conv_dilation_w));
std::size_t num_thread = std::thread::hardware_concurrency();
switch(init_method)
{
case 0:
// no initialization
break;
case 1:
in.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
wei.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
break;
case 2:
in.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
wei.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
break;
case 3:
in.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
wei.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
break;
case 4:
in.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
wei.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
break;
case 5:
in.GenerateTensorValue(GeneratorTensor_3<float>{0.0, 1.0}, num_thread);
wei.GenerateTensorValue(GeneratorTensor_3<float>{-0.5, 0.5}, num_thread);
break;
default:
in.GenerateTensorValue(GeneratorTensor_2{1, 5}, num_thread);
auto gen_wei = [](auto... is) {
return GeneratorTensor_2{1, 5}(is...) * GeneratorTensor_Checkboard{}(is...);
};
wei.GenerateTensorValue(gen_wei, num_thread);
}
auto f_make_for_device_nchw = [&]() {
const auto in_lengths_dev = make_tuple(N, C, Hi, Wi);
const auto wei_lengths_dev = make_tuple(K, C, Y, X);
const auto out_lengths_dev = make_tuple(N, K, Ho, Wo);
return make_tuple(in_lengths_dev, wei_lengths_dev, out_lengths_dev);
};
auto f_make_for_device_nhwc = [&]() {
const auto in_lengths_dev = make_tuple(N, Hi, Wi, C);
const auto wei_lengths_dev = make_tuple(K, Y, X, C);
const auto out_lengths_dev = make_tuple(N, Ho, Wo, K);
return make_tuple(in_lengths_dev, wei_lengths_dev, out_lengths_dev);
};
const auto conv_strides = make_tuple(conv_stride_h, conv_stride_w);
const auto conv_dilations = make_tuple(conv_dilation_h, conv_dilation_w);
const auto in_left_pads = make_tuple(in_left_pad_h, in_left_pad_w);
const auto in_right_pads = make_tuple(in_right_pad_h, in_right_pad_w);
#if USE_CONV_FWD_V4R4_NCHW
if(algo == ConvForwardAlgo::V4R4NCHW)
{
if(layout != ConvTensorLayout::NCHW)
{
throw std::runtime_error("wrong! layout");
}
const auto tmp = f_make_for_device_nchw();
tunable_dyn_conv_fwd_v4r4_dlops_nchw_kcyx_nkhw* tunable =
&default_tunable_dyn_conv_fwd_v4r4_dlops_nchw_kcyx_nkhw;
online_device_dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw<
in_data_t,
acc_data_t,
out_data_t>(handle,
tmp[I0],
tmp[I1],
tmp[I2],
conv_strides,
conv_dilations,
in_left_pads,
in_right_pads,
in,
wei,
out_device,
tunable,
nrepeat);
}
#endif
#if USE_CONV_FWD_V6R1_NCHW
if(algo == ConvForwardAlgo::V6R1NCHW)
{
if(layout != ConvTensorLayout::NCHW)
{
throw std::runtime_error("wrong! layout");
}
const auto tmp = f_make_for_device_nchw();
#if 1
const CompileParameterConvIgemmFwdV6r1DlopsNchwKcyxNkhw compile_param = {
get_datatype_enum_from_type<in_data_t>::value,
get_datatype_enum_from_type<acc_data_t>::value,
get_datatype_enum_from_type<out_data_t>::value,
256,
4,
1,
128,
32,
8,
4,
4,
1,
{8, 2},
{8, 2},
{4, 1, 1, 1, 1},
{2, 1, 1, 128, 1},
{4, 1, 1, 1, 1},
{1, 1, 1, 1, 1},
{1, 4, 1, 1, 1},
{8, 1, 1, 32, 1},
{1, 1, 1, 1, 1},
{1, 1, 1, 1, 1},
4,
true,
true};
#elif 0
const CompileParameterConvIgemmFwdV6r1DlopsNchwKcyxNkhw compile_param = {
get_datatype_enum_from_type<in_data_t>::value,
get_datatype_enum_from_type<acc_data_t>::value,
get_datatype_enum_from_type<out_data_t>::value,
256,
4,
2,
128,
32,
8,
4,
4,
1,
{8, 2},
{8, 2},
{4, 1, 1, 1, 2},
{2, 1, 1, 128, 1},
{4, 1, 1, 1, 1},
{1, 1, 1, 1, 1},
{1, 4, 1, 1, 2},
{8, 1, 1, 32, 1},
{1, 1, 1, 1, 1},
{1, 1, 1, 1, 1},
4,
true,
true};
#elif 1
const CompileParameterConvIgemmFwdV6r1DlopsNchwKcyxNkhw compile_param = {
get_datatype_enum_from_type<in_data_t>::value,
get_datatype_enum_from_type<acc_data_t>::value,
get_datatype_enum_from_type<out_data_t>::value,
256,
4,
4,
128,
32,
8,
4,
4,
1,
{8, 2},
{8, 2},
{4, 1, 1, 1, 4},
{2, 1, 1, 128, 1},
{4, 1, 1, 1, 1},
{1, 1, 1, 1, 1},
{1, 4, 1, 1, 4},
{8, 1, 1, 32, 1},
{1, 1, 1, 1, 1},
{1, 1, 1, 1, 1},
4,
true,
true};
#endif
online_device_dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw<
in_data_t,
acc_data_t,
out_data_t>(handle,
tmp[I0],
tmp[I1],
tmp[I2],
conv_strides,
conv_dilations,
in_left_pads,
in_right_pads,
in,
wei,
out_device,
compile_param,
nrepeat);
}
#endif
#if USE_CONV_FWD_V4R4_XDLOPS_NCHW
if(algo == ConvForwardAlgo::V4R4XDLNCHW)
{
if(layout != ConvTensorLayout::NCHW)
{
throw std::runtime_error("wrong! layout");
}
const auto tmp = f_make_for_device_nchw();
tunable_dyn_conv_fwd_v4r4_xdlops_nchw_kcyx_nkhw* tunable =
&default_tunable_dyn_conv_fwd_v4r4_xdlops_nchw_kcyx_nkhw;
online_device_dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw<
in_data_t,
acc_data_t,
out_data_t>(handle,
tmp[I0],
tmp[I1],
tmp[I2],
conv_strides,
conv_dilations,
in_left_pads,
in_right_pads,
in,
wei,
out_device,
tunable,
nrepeat);
}
#endif
#if USE_CONV_FWD_V4R4_XDLOPS_NHWC
if(algo == ConvForwardAlgo::V4R4XDLNHWC)
{
if(layout != ConvTensorLayout::NHWC)
{
throw std::runtime_error("wrong! layout");
}
const auto tmp = f_make_for_device_nhwc();
tunable_dyn_conv_fwd_v4r4_xdlops_nhwc_kyxc_nhwk* tunable =
&default_tunable_dyn_conv_fwd_v4r4_xdlops_nhwc_kyxc_nhwk;
online_device_dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nhwc_kyxc_nhwk<
in_data_t,
acc_data_t,
out_data_t>(handle,
tmp[I0],
tmp[I1],
tmp[I2],
conv_strides,
conv_dilations,
in_left_pads,
in_right_pads,
in,
wei,
out_device,
tunable,
nrepeat);
}
#endif
if(do_verification)
{
host_direct_convolution(in,
wei,
out_host,
make_tuple(conv_stride_h, conv_stride_w),
make_tuple(conv_dilation_h, conv_dilation_w),
make_tuple(in_left_pad_h, in_left_pad_w),
make_tuple(in_right_pad_h, in_right_pad_w),
layout);
check_error(out_host, out_device);
#if 0
if(do_log)
{
LogRangeAsType<float>(std::cout << "in : ", in.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "wei: ", wei.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "out_host : ", out_host.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "out_device: ", out_device.mData, ",") << std::endl;
}
#endif
}
delete handle;
MY_HIP_CHECK(hipStreamDestroy(stream));
}
host/driver_online/include/online_device_dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw.hpp deleted 100644 → 0
View file @ 5781adf5
#pragma once
#include "device.hpp"
#include "host_tensor.hpp"
#include "handle.hpp"
#include "online_driver_common.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "transform_forward_convolution_into_gemm_v4r4_nchw_kcyx_nkhw.hpp"
#include "conv_tunable_fwd_v4r4_dlops_nchw_kcyx_nkhw.hpp"
namespace detail_dyn_conv_fwd_v4r4_nchw_kcyx_nkhw {
template <typename TInWei, typename TAcc, typename TOut>
static std::string get_network_config_string_from_types()
{
using namespace ck;
std::string out;
out += std::to_string(get_datatype_enum_from_type<TInWei>::value) + "_" +
std::to_string(get_datatype_enum_from_type<TAcc>::value) + "_" +
std::to_string(get_datatype_enum_from_type<TOut>::value);
return (out);
};
static std::string
get_network_config_string_from_tunable(const tunable_dyn_conv_fwd_v4r4_dlops_nchw_kcyx_nkhw* pt)
{
std::string out("TUN_");
out += std::to_string(pt->BlockSize) + "_";
out += std::to_string(pt->MPerBlock) + "x" + std::to_string(pt->NPerBlock) + "x" +
std::to_string(pt->KPerBlock) + "_";
out += std::to_string(pt->M1PerThread) + "x" + std::to_string(pt->N1PerThread) + "x" +
std::to_string(pt->KPerThread) + "_";
out += std::to_string(pt->M1N1ThreadClusterM10) + "x" +
std::to_string(pt->M1N1ThreadClusterN10) + "x" +
std::to_string(pt->M1N1ThreadClusterM11) + "x" +
std::to_string(pt->M1N1ThreadClusterN11) + "_";
out += std::to_string(pt->ABlockTransferThreadSliceLengths_K_M0_M1[0]) + "x" +
std::to_string(pt->ABlockTransferThreadSliceLengths_K_M0_M1[1]) + "x" +
std::to_string(pt->ABlockTransferThreadSliceLengths_K_M0_M1[2]) + "_";
out += std::to_string(pt->ABlockTransferThreadClusterLengths_K_M0_M1[0]) + "x" +
std::to_string(pt->ABlockTransferThreadClusterLengths_K_M0_M1[1]) + "x" +
std::to_string(pt->ABlockTransferThreadClusterLengths_K_M0_M1[2]) + "_";
out += std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[0]) + "x" +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[1]) + "x" +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[2]) + "_";
out += std::to_string(pt->ABlockTransferSrcAccessOrder[0]) + "x" +
std::to_string(pt->ABlockTransferSrcAccessOrder[1]) + "x" +
std::to_string(pt->ABlockTransferSrcAccessOrder[2]) + "_";
out += std::to_string(pt->ABlockTransferSrcVectorDim) + "_";
out += std::to_string(pt->ABlockTransferSrcScalarPerVector) + "_";
out += std::to_string(pt->ABlockTransferDstScalarPerVector_M1) + "_";
out += std::to_string(pt->AThreadTransferSrcResetCoordinateAfterRun) + "_";
out += std::to_string(pt->BBlockTransferThreadSliceLengths_K_N0_N1[0]) + "x" +
std::to_string(pt->BBlockTransferThreadSliceLengths_K_N0_N1[1]) + "x" +
std::to_string(pt->BBlockTransferThreadSliceLengths_K_N0_N1[2]) + "_";
out += std::to_string(pt->BBlockTransferThreadClusterLengths_K_N0_N1[0]) + "x" +
std::to_string(pt->BBlockTransferThreadClusterLengths_K_N0_N1[1]) + "x" +
std::to_string(pt->BBlockTransferThreadClusterLengths_K_N0_N1[2]) + "_";
out += std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[0]) + "x" +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[1]) + "x" +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[2]) + "_";
out += std::to_string(pt->BBlockTransferSrcAccessOrder[0]) + "x" +
std::to_string(pt->BBlockTransferSrcAccessOrder[1]) + "x" +
std::to_string(pt->BBlockTransferSrcAccessOrder[2]) + "_";
out += std::to_string(pt->BBlockTransferSrcVectorDim) + "_";
out += std::to_string(pt->BBlockTransferSrcScalarPerVector) + "_";
out += std::to_string(pt->BBlockTransferDstScalarPerVector_N1) + "_";
out += std::to_string(pt->BThreadTransferSrcResetCoordinateAfterRun) + "_";
out += std::to_string(pt->CThreadTransferSrcDstAccessOrder[0]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[1]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[2]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[3]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[4]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[5]) + "_";
out += std::to_string(pt->CThreadTransferSrcDstVectorDim) + "_";
out += std::to_string(pt->CThreadTransferDstScalarPerVector);
return (out);
};
template <typename TInWei, typename TAcc, typename TOut>
static std::string get_definition_string_from_types()
{
using namespace ck;
std::string out;
out +=
" -DCK_PARAM_ABDataTypeEnum=" + std::to_string(get_datatype_enum_from_type<TInWei>::value) +
" -DCK_PARAM_AccDataTypeEnum=" + std::to_string(get_datatype_enum_from_type<TAcc>::value) +
" -DCK_PARAM_CDataTypeEnum=" + std::to_string(get_datatype_enum_from_type<TOut>::value);
return (out);
};
static std::string
get_definition_string_from_tunable(const tunable_dyn_conv_fwd_v4r4_dlops_nchw_kcyx_nkhw* pt)
{
std::string out;
out += " -DCK_PARAM_BlockSize=" + std::to_string(pt->BlockSize);
out += " -DCK_PARAM_MPerBlock=" + std::to_string(pt->MPerBlock) +
" -DCK_PARAM_NPerBlock=" + std::to_string(pt->NPerBlock) +
" -DCK_PARAM_KPerBlock=" + std::to_string(pt->KPerBlock);
out += " -DCK_PARAM_M1PerThread=" + std::to_string(pt->M1PerThread) +
" -DCK_PARAM_N1PerThread=" + std::to_string(pt->N1PerThread) +
" -DCK_PARAM_KPerThread=" + std::to_string(pt->KPerThread);
out += " -DCK_PARAM_M1N1ThreadClusterM10=" + std::to_string(pt->M1N1ThreadClusterM10) +
" -DCK_PARAM_M1N1ThreadClusterN10=" + std::to_string(pt->M1N1ThreadClusterN10) +
" -DCK_PARAM_M1N1ThreadClusterM11=" + std::to_string(pt->M1N1ThreadClusterM11) +
" -DCK_PARAM_M1N1ThreadClusterN11=" + std::to_string(pt->M1N1ThreadClusterN11);
out += " -DCK_PARAM_ABlockTransferThreadSliceLengths_K_M0_M1=" +
std::to_string(pt->ABlockTransferThreadSliceLengths_K_M0_M1[0]) + "," +
std::to_string(pt->ABlockTransferThreadSliceLengths_K_M0_M1[1]) + "," +
std::to_string(pt->ABlockTransferThreadSliceLengths_K_M0_M1[2]);
out += " -DCK_PARAM_ABlockTransferThreadClusterLengths_K_M0_M1=" +
std::to_string(pt->ABlockTransferThreadClusterLengths_K_M0_M1[0]) + "," +
std::to_string(pt->ABlockTransferThreadClusterLengths_K_M0_M1[1]) + "," +
std::to_string(pt->ABlockTransferThreadClusterLengths_K_M0_M1[2]);
out += " -DCK_PARAM_ABlockTransferThreadClusterArrangeOrder=" +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[0]) + "," +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[1]) + "," +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[2]);
out += " -DCK_PARAM_ABlockTransferSrcAccessOrder=" +
std::to_string(pt->ABlockTransferSrcAccessOrder[0]) + "," +
std::to_string(pt->ABlockTransferSrcAccessOrder[1]) + "," +
std::to_string(pt->ABlockTransferSrcAccessOrder[2]);
out +=
" -DCK_PARAM_ABlockTransferSrcVectorDim=" + std::to_string(pt->ABlockTransferSrcVectorDim);
out += " -DCK_PARAM_ABlockTransferSrcScalarPerVector=" +
std::to_string(pt->ABlockTransferSrcScalarPerVector);
out += " -DCK_PARAM_ABlockTransferDstScalarPerVector_M1=" +
std::to_string(pt->ABlockTransferDstScalarPerVector_M1);
out += " -DCK_PARAM_AThreadTransferSrcResetCoordinateAfterRun=" +
std::to_string(pt->AThreadTransferSrcResetCoordinateAfterRun);
out += " -DCK_PARAM_BBlockTransferThreadSliceLengths_K_N0_N1=" +
std::to_string(pt->BBlockTransferThreadSliceLengths_K_N0_N1[0]) + "," +
std::to_string(pt->BBlockTransferThreadSliceLengths_K_N0_N1[1]) + "," +
std::to_string(pt->BBlockTransferThreadSliceLengths_K_N0_N1[2]);
out += " -DCK_PARAM_BBlockTransferThreadClusterLengths_K_N0_N1=" +
std::to_string(pt->BBlockTransferThreadClusterLengths_K_N0_N1[0]) + "," +
std::to_string(pt->BBlockTransferThreadClusterLengths_K_N0_N1[1]) + "," +
std::to_string(pt->BBlockTransferThreadClusterLengths_K_N0_N1[2]);
out += " -DCK_PARAM_BBlockTransferThreadClusterArrangeOrder=" +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[0]) + "," +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[1]) + "," +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[2]);
out += " -DCK_PARAM_BBlockTransferSrcAccessOrder=" +
std::to_string(pt->BBlockTransferSrcAccessOrder[0]) + "," +
std::to_string(pt->BBlockTransferSrcAccessOrder[1]) + "," +
std::to_string(pt->BBlockTransferSrcAccessOrder[2]);
out +=
" -DCK_PARAM_BBlockTransferSrcVectorDim=" + std::to_string(pt->BBlockTransferSrcVectorDim);
out += " -DCK_PARAM_BBlockTransferSrcScalarPerVector=" +
std::to_string(pt->BBlockTransferSrcScalarPerVector);
out += " -DCK_PARAM_BBlockTransferDstScalarPerVector_N1=" +
std::to_string(pt->BBlockTransferDstScalarPerVector_N1);
out += " -DCK_PARAM_BThreadTransferSrcResetCoordinateAfterRun=" +
std::to_string(pt->BThreadTransferSrcResetCoordinateAfterRun);
out += " -DCK_PARAM_CThreadTransferSrcDstAccessOrder=" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[0]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[1]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[2]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[3]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[4]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[5]);
out += " -DCK_PARAM_CThreadTransferSrcDstVectorDim=" +
std::to_string(pt->CThreadTransferSrcDstVectorDim);
out += " -DCK_PARAM_CThreadTransferDstScalarPerVector=" +
std::to_string(pt->CThreadTransferDstScalarPerVector);
return (out);
};
} // namespace detail_dyn_conv_fwd_v4r4_nchw_kcyx_nkhw
template <typename TInWei,
typename TAcc,
typename TOut,
typename InLengths,
typename WeiLengths,
typename OutLengths,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void online_device_dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw(
online_compile::Handle* handle,
const InLengths& in_n_c_hi_wi_lengths,
const WeiLengths& wei_k_c_y_x_lengths,
const OutLengths& out_n_k_ho_wo_lengths,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
const Tensor<TInWei>& in_n_c_hi_wi,
const Tensor<TInWei>& wei_k_c_y_x,
Tensor<TOut>& out_n_k_ho_wo,
const tunable_dyn_conv_fwd_v4r4_dlops_nchw_kcyx_nkhw* tunable,
ck::index_t nrepeat)
{
using namespace ck;
using namespace ck_driver;
using namespace detail_dyn_conv_fwd_v4r4_nchw_kcyx_nkhw;
using size_t = std::size_t;
/////////////////////////////////////////////////////////////////////////////////////////////////////////////
// The follow codes are only used for computing the grid_size, hasMainKBlockLoop,
// hasDoubleTailKBlockLoop
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
const auto in_n_c_hi_wi_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(in_n_c_hi_wi_lengths);
const auto wei_k_c_y_x_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(wei_k_c_y_x_lengths);
const auto out_n_k_ho_wo_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(out_n_k_ho_wo_lengths);
const auto descs =
transform_forward_convolution_into_gemm_v4r4_nchw_kcyx_nkhw_pad(wei_k_c_y_x_desc,
in_n_c_hi_wi_desc,
out_n_k_ho_wo_desc,
conv_strides,
conv_dilations,
in_left_pads,
in_right_pads);
const auto a_k_m_grid_desc = descs[I0];
const auto c_m_n_grid_desc = descs[I2];
const auto M = c_m_n_grid_desc.GetLength(I0);
const auto N = c_m_n_grid_desc.GetLength(I1);
const auto K = a_k_m_grid_desc.GetLength(I0);
const index_t grid_size = (M / tunable->MPerBlock) * (N / tunable->NPerBlock);
const bool hasMainKBlockLoop = ((K + tunable->KPerBlock) / (2 * tunable->KPerBlock) > 1);
const bool hasDoubleTailKBlockLoop = ((K / tunable->KPerBlock) % 2 == 0);
/////////////////////////////////////////////////////////////////////////////////////////////////////////////
// these buffers are usually provided by the user application
DeviceMem in_n_c_hi_wi_dev_buf(sizeof(TInWei) * in_n_c_hi_wi.mDesc.GetElementSpace());
DeviceMem wei_k_c_y_x_dev_buf(sizeof(TInWei) * wei_k_c_y_x.mDesc.GetElementSpace());
DeviceMem out_n_k_ho_wo_dev_buf(sizeof(TOut) * out_n_k_ho_wo.mDesc.GetElementSpace());
in_n_c_hi_wi_dev_buf.ToDevice(in_n_c_hi_wi.mData.data());
wei_k_c_y_x_dev_buf.ToDevice(wei_k_c_y_x.mData.data());
out_n_k_ho_wo_dev_buf.ToDevice(out_n_k_ho_wo.mData.data());
// these are workspace buffers that should be expressed to the user by the corresponding
// workspace API
DeviceMem workspace_buf(4096);
void* a_k_m0_m1_grid_desc_dev_buf = workspace_buf.GetDeviceBuffer();
void* b_k_n0_n1_grid_desc_dev_buf =
static_cast<void*>(static_cast<unsigned char*>(workspace_buf.GetDeviceBuffer()) + 1024);
void* c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf =
static_cast<void*>(static_cast<unsigned char*>(workspace_buf.GetDeviceBuffer()) + 2048);
void* c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf =
static_cast<void*>(static_cast<unsigned char*>(workspace_buf.GetDeviceBuffer()) + 3072);
const std::vector<size_t> vld = {static_cast<size_t>(tunable->BlockSize), 1, 1};
const std::vector<size_t> vgd1 = {static_cast<size_t>(tunable->BlockSize), 1, 1};
const std::vector<size_t> vgd2 = {static_cast<size_t>(grid_size * tunable->BlockSize), 1, 1};
std::string program_name =
"dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw.cpp";
std::string algo_name = "implicit_gemm_conv_fwd_v4r4_dlops_nchw";
std::string param = " -std=c++17 ";
std::string network_config;
param += get_definition_string_from_types<TInWei, TAcc, TOut>() + " " +
get_definition_string_from_tunable(tunable) +
" -DCK_PARAM_HAS_MAIN_KBLOCK_LOOP=" + std::to_string(hasMainKBlockLoop) +
" -DCK_PARAM_HAS_DOUBLE_TAIL_KBLOCK_LOOP=" + std::to_string(hasDoubleTailKBlockLoop);
network_config = get_network_config_string_from_types<TInWei, TAcc, TOut>() + "_" +
get_network_config_string_from_tunable(tunable) + "_" +
std::to_string(hasMainKBlockLoop) + "_" +
std::to_string(hasDoubleTailKBlockLoop);
std::vector<float> kernel1_times;
std::vector<float> kernel2_times;
for(index_t i = 0; i < nrepeat; ++i)
{
KernelTimer timer1, timer2;
std::string kernel_name;
kernel_name = "dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw_prepare";
auto network_config_1 = network_config + "_1";
timer1.Start();
handle->AddKernel(algo_name, network_config_1, program_name, kernel_name, vld, vgd1, param)(
static_cast<index_t>(in_n_c_hi_wi_lengths[I0]),
static_cast<index_t>(in_n_c_hi_wi_lengths[I1]),
static_cast<index_t>(in_n_c_hi_wi_lengths[I2]),
static_cast<index_t>(in_n_c_hi_wi_lengths[I3]),
static_cast<index_t>(wei_k_c_y_x_lengths[I0]),
static_cast<index_t>(wei_k_c_y_x_lengths[I2]),
static_cast<index_t>(wei_k_c_y_x_lengths[I3]),
conv_strides[I0],
conv_strides[I1],
conv_dilations[I0],
conv_dilations[I1],
in_left_pads[I0],
in_left_pads[I1],
in_right_pads[I0],
in_right_pads[I1],
a_k_m0_m1_grid_desc_dev_buf,
b_k_n0_n1_grid_desc_dev_buf,
c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf,
c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf);
timer1.End();
kernel_name = "dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw";
auto network_config_2 = network_config + "_2";
timer2.Start();
handle->AddKernel(algo_name, network_config_2, program_name, kernel_name, vld, vgd2, param)(
reinterpret_cast<const TInWei*>(wei_k_c_y_x_dev_buf.GetDeviceBuffer()),
reinterpret_cast<const TInWei*>(in_n_c_hi_wi_dev_buf.GetDeviceBuffer()),
reinterpret_cast<TOut*>(out_n_k_ho_wo_dev_buf.GetDeviceBuffer()),
(const void*)(a_k_m0_m1_grid_desc_dev_buf),
(const void*)(b_k_n0_n1_grid_desc_dev_buf),
(const void*)(c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf),
(const void*)(c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf));
timer2.End();
kernel1_times.push_back(timer1.GetElapsedTime());
kernel2_times.push_back(timer2.GetElapsedTime());
}
{
auto ave_time1 =
std::accumulate(
std::next(kernel1_times.begin()), kernel1_times.end(), 0., std::plus<float>{}) /
(nrepeat - 1);
auto ave_time2 =
std::accumulate(
std::next(kernel2_times.begin()), kernel2_times.end(), 0., std::plus<float>{}) /
(nrepeat - 1);
const auto N = in_n_c_hi_wi_lengths[I0];
const auto C = in_n_c_hi_wi_lengths[I1];
const auto K = out_n_k_ho_wo_lengths[I1];
const auto Ho = out_n_k_ho_wo_lengths[I2];
const auto Wo = out_n_k_ho_wo_lengths[I3];
const auto Y = wei_k_c_y_x_lengths[I2];
const auto X = wei_k_c_y_x_lengths[I3];
float perf = (float)(std::size_t(2) * N * K * Ho * Wo * C * Y * X) /
(std::size_t(1000) * 1000 * 1000) / (ave_time1 + ave_time2);
std::cout << "Average time : " << ave_time1 + ave_time2 << " ms(" << ave_time1 << ", "
<< ave_time2 << "), " << perf << " TFlop/s" << std::endl;
};
// copy result back to host
out_n_k_ho_wo_dev_buf.FromDevice(out_n_k_ho_wo.mData.data());
}
host/driver_online/include/online_device_dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw.hpp deleted 100644 → 0
View file @ 5781adf5
#include "device.hpp"
#include "host_tensor.hpp"
#include "handle.hpp"
#include "online_driver_common.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "conv_tunable_fwd_v4r4_xdlops_nchw_kcyx_nkhw.hpp"
namespace detail_dyn_conv_fwd_v4r4_xdlops_nchw_kcyx_nkhw {
template <typename TInWei, typename TAcc, typename TOut>
static std::string get_network_config_string_from_types()
{
using namespace ck;
std::string out;
out += std::to_string(get_datatype_enum_from_type<TInWei>::value) + "_" +
std::to_string(get_datatype_enum_from_type<TAcc>::value) + "_" +
std::to_string(get_datatype_enum_from_type<TOut>::value);
return (out);
};
static std::string
get_network_config_string_from_tunable(const tunable_dyn_conv_fwd_v4r4_xdlops_nchw_kcyx_nkhw* pt)
{
std::string out("TUN_");
out += std::to_string(pt->BlockSize) + "_";
out += std::to_string(pt->MPerBlock) + "x" + std::to_string(pt->NPerBlock) + "x" +
std::to_string(pt->KPerBlock) + "_";
out += std::to_string(pt->MPerWave) + "x" + std::to_string(pt->NPerWave) + "x" +
std::to_string(pt->MRepeat) + "x" + std::to_string(pt->NRepeat) + "x" +
std::to_string(pt->K1) + "_";
out += std::to_string(pt->ABlockTransferThreadSliceLengths_K0_M_K1[0]) + "x" +
std::to_string(pt->ABlockTransferThreadSliceLengths_K0_M_K1[1]) + "x" +
std::to_string(pt->ABlockTransferThreadSliceLengths_K0_M_K1[2]) + "_";
out += std::to_string(pt->ABlockTransferThreadClusterLengths_K0_M_K1[0]) + "x" +
std::to_string(pt->ABlockTransferThreadClusterLengths_K0_M_K1[1]) + "x" +
std::to_string(pt->ABlockTransferThreadClusterLengths_K0_M_K1[2]) + "_";
out += std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[0]) + "x" +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[1]) + "x" +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[2]) + "_";
out += std::to_string(pt->ABlockTransferSrcAccessOrder[0]) + "x" +
std::to_string(pt->ABlockTransferSrcAccessOrder[1]) + "x" +
std::to_string(pt->ABlockTransferSrcAccessOrder[2]) + "_";
out += std::to_string(pt->ABlockTransferSrcVectorDim) + "_";
out += std::to_string(pt->ABlockTransferSrcScalarPerVector) + "_";
out += std::to_string(pt->ABlockTransferDstScalarPerVector_K1) + "_";
out += std::to_string(pt->AThreadTransferSrcResetCoordinateAfterRun) + "_";
out += std::to_string(pt->BBlockTransferThreadSliceLengths_K0_N_K1[0]) + "x" +
std::to_string(pt->BBlockTransferThreadSliceLengths_K0_N_K1[1]) + "x" +
std::to_string(pt->BBlockTransferThreadSliceLengths_K0_N_K1[2]) + "_";
out += std::to_string(pt->BBlockTransferThreadClusterLengths_K0_N_K1[0]) + "x" +
std::to_string(pt->BBlockTransferThreadClusterLengths_K0_N_K1[1]) + "x" +
std::to_string(pt->BBlockTransferThreadClusterLengths_K0_N_K1[2]) + "_";
out += std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[0]) + "x" +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[1]) + "x" +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[2]) + "_";
out += std::to_string(pt->BBlockTransferSrcAccessOrder[0]) + "x" +
std::to_string(pt->BBlockTransferSrcAccessOrder[1]) + "x" +
std::to_string(pt->BBlockTransferSrcAccessOrder[2]) + "_";
out += std::to_string(pt->BBlockTransferSrcVectorDim) + "_";
out += std::to_string(pt->BBlockTransferSrcScalarPerVector) + "_";
out += std::to_string(pt->BBlockTransferDstScalarPerVector_K1) + "_";
out += std::to_string(pt->BThreadTransferSrcResetCoordinateAfterRun) + "_";
out += std::to_string(pt->CThreadTransferSrcDstAccessOrder[0]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[1]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[2]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[3]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[4]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[5]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[6]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[7]) + "_";
out += std::to_string(pt->CThreadTransferSrcDstVectorDim) + "_";
out += std::to_string(pt->CThreadTransferDstScalarPerVector);
return (out);
};
template <typename TInWei, typename TAcc, typename TOut>
static std::string get_definition_string_from_types()
{
using namespace ck;
std::string out;
out +=
" -DCK_PARAM_ABDataTypeEnum=" + std::to_string(get_datatype_enum_from_type<TInWei>::value) +
" -DCK_PARAM_AccDataTypeEnum=" + std::to_string(get_datatype_enum_from_type<TAcc>::value) +
" -DCK_PARAM_CDataTypeEnum=" + std::to_string(get_datatype_enum_from_type<TOut>::value);
return (out);
};
static std::string
get_definition_string_from_tunable(const tunable_dyn_conv_fwd_v4r4_xdlops_nchw_kcyx_nkhw* pt)
{
std::string out;
out += " -DCK_PARAM_BlockSize=" + std::to_string(pt->BlockSize);
out += " -DCK_PARAM_MPerBlock=" + std::to_string(pt->MPerBlock) +
" -DCK_PARAM_NPerBlock=" + std::to_string(pt->NPerBlock) +
" -DCK_PARAM_KPerBlock=" + std::to_string(pt->KPerBlock);
out += " -DCK_PARAM_MPerWave=" + std::to_string(pt->MPerWave) +
" -DCK_PARAM_NPerWave=" + std::to_string(pt->NPerWave) +
" -DCK_PARAM_K1=" + std::to_string(pt->K1) +
" -DCK_PARAM_MRepeat=" + std::to_string(pt->MRepeat) +
" -DCK_PARAM_NRepeat=" + std::to_string(pt->NRepeat);
out += " -DCK_PARAM_ABlockTransferThreadSliceLengths_K0_M_K1=" +
std::to_string(pt->ABlockTransferThreadSliceLengths_K0_M_K1[0]) + "," +
std::to_string(pt->ABlockTransferThreadSliceLengths_K0_M_K1[1]) + "," +
std::to_string(pt->ABlockTransferThreadSliceLengths_K0_M_K1[2]);
out += " -DCK_PARAM_ABlockTransferThreadClusterLengths_K0_M_K1=" +
std::to_string(pt->ABlockTransferThreadClusterLengths_K0_M_K1[0]) + "," +
std::to_string(pt->ABlockTransferThreadClusterLengths_K0_M_K1[1]) + "," +
std::to_string(pt->ABlockTransferThreadClusterLengths_K0_M_K1[2]);
out += " -DCK_PARAM_ABlockTransferThreadClusterArrangeOrder=" +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[0]) + "," +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[1]) + "," +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[2]);
out += " -DCK_PARAM_ABlockTransferSrcAccessOrder=" +
std::to_string(pt->ABlockTransferSrcAccessOrder[0]) + "," +
std::to_string(pt->ABlockTransferSrcAccessOrder[1]) + "," +
std::to_string(pt->ABlockTransferSrcAccessOrder[2]);
out +=
" -DCK_PARAM_ABlockTransferSrcVectorDim=" + std::to_string(pt->ABlockTransferSrcVectorDim);
out += " -DCK_PARAM_ABlockTransferSrcScalarPerVector=" +
std::to_string(pt->ABlockTransferSrcScalarPerVector);
out += " -DCK_PARAM_ABlockTransferDstScalarPerVector_K1=" +
std::to_string(pt->ABlockTransferDstScalarPerVector_K1);
out += " -DCK_PARAM_AThreadTransferSrcResetCoordinateAfterRun=" +
std::to_string(pt->AThreadTransferSrcResetCoordinateAfterRun);
out += " -DCK_PARAM_BBlockTransferThreadSliceLengths_K0_N_K1=" +
std::to_string(pt->BBlockTransferThreadSliceLengths_K0_N_K1[0]) + "," +
std::to_string(pt->BBlockTransferThreadSliceLengths_K0_N_K1[1]) + "," +
std::to_string(pt->BBlockTransferThreadSliceLengths_K0_N_K1[2]);
out += " -DCK_PARAM_BBlockTransferThreadClusterLengths_K0_N_K1=" +
std::to_string(pt->BBlockTransferThreadClusterLengths_K0_N_K1[0]) + "," +
std::to_string(pt->BBlockTransferThreadClusterLengths_K0_N_K1[1]) + "," +
std::to_string(pt->BBlockTransferThreadClusterLengths_K0_N_K1[2]);
out += " -DCK_PARAM_BBlockTransferThreadClusterArrangeOrder=" +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[0]) + "," +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[1]) + "," +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[2]);
out += " -DCK_PARAM_BBlockTransferSrcAccessOrder=" +
std::to_string(pt->BBlockTransferSrcAccessOrder[0]) + "," +
std::to_string(pt->BBlockTransferSrcAccessOrder[1]) + "," +
std::to_string(pt->BBlockTransferSrcAccessOrder[2]);
out +=
" -DCK_PARAM_BBlockTransferSrcVectorDim=" + std::to_string(pt->BBlockTransferSrcVectorDim);
out += " -DCK_PARAM_BBlockTransferSrcScalarPerVector=" +
std::to_string(pt->BBlockTransferSrcScalarPerVector);
out += " -DCK_PARAM_BBlockTransferDstScalarPerVector_K1=" +
std::to_string(pt->BBlockTransferDstScalarPerVector_K1);
out += " -DCK_PARAM_BThreadTransferSrcResetCoordinateAfterRun=" +
std::to_string(pt->BThreadTransferSrcResetCoordinateAfterRun);
out += " -DCK_PARAM_CThreadTransferSrcDstAccessOrder=" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[0]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[1]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[2]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[3]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[4]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[5]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[6]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[7]);
out += " -DCK_PARAM_CThreadTransferSrcDstVectorDim=" +
std::to_string(pt->CThreadTransferSrcDstVectorDim);
out += " -DCK_PARAM_CThreadTransferDstScalarPerVector=" +
std::to_string(pt->CThreadTransferDstScalarPerVector);
return (out);
};
} // namespace detail_dyn_conv_fwd_v4r4_xdlops_nchw_kcyx_nkhw
template <typename TInWei,
typename TAcc,
typename TOut,
typename InLengths,
typename WeiLengths,
typename OutLengths,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void online_device_dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw(
online_compile::Handle* handle,
const InLengths& in_n_c_hi_wi_lengths,
const WeiLengths& wei_k_c_y_x_lengths,
const OutLengths& out_n_k_ho_wo_lengths,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
const Tensor<TInWei>& in_n_c_hi_wi,
const Tensor<TInWei>& wei_k_c_y_x,
Tensor<TOut>& out_n_k_ho_wo,
const tunable_dyn_conv_fwd_v4r4_xdlops_nchw_kcyx_nkhw* tunable,
ck::index_t nrepeat)
{
using namespace ck;
using namespace ck_driver;
using namespace detail_dyn_conv_fwd_v4r4_xdlops_nchw_kcyx_nkhw;
using size_t = std::size_t;
/////////////////////////////////////////////////////////////////////////////////////////////////////////////
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
const auto in_n_c_hi_wi_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(in_n_c_hi_wi_lengths);
const auto wei_k_c_y_x_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(wei_k_c_y_x_lengths);
const auto out_n_k_ho_wo_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(out_n_k_ho_wo_lengths);
const auto n = in_n_c_hi_wi_desc.GetLength(I0);
const auto c = in_n_c_hi_wi_desc.GetLength(I1);
const auto hi = in_n_c_hi_wi_desc.GetLength(I2);
const auto wi = in_n_c_hi_wi_desc.GetLength(I3);
const auto k = wei_k_c_y_x_desc.GetLength(I0);
const auto y = wei_k_c_y_x_desc.GetLength(I2);
const auto x = wei_k_c_y_x_desc.GetLength(I3);
const auto ho = out_n_k_ho_wo_desc.GetLength(I2);
const auto wo = out_n_k_ho_wo_desc.GetLength(I3);
const auto M = k;
const auto N = n * ho * wo;
const auto K = c * y * x;
const auto K0 = K / tunable->K1;
const index_t grid_size = (M / tunable->MPerBlock) * (N / tunable->NPerBlock);
/////////////////////////////////////////////////////////////////////////////////////////////////////////////
// these buffers are usually provided by the user application
DeviceMem in_n_c_hi_wi_dev_buf(sizeof(TInWei) * in_n_c_hi_wi.mDesc.GetElementSpace());
DeviceMem wei_k_c_y_x_dev_buf(sizeof(TInWei) * wei_k_c_y_x.mDesc.GetElementSpace());
DeviceMem out_n_k_ho_wo_dev_buf(sizeof(TOut) * out_n_k_ho_wo.mDesc.GetElementSpace());
in_n_c_hi_wi_dev_buf.ToDevice(in_n_c_hi_wi.mData.data());
wei_k_c_y_x_dev_buf.ToDevice(wei_k_c_y_x.mData.data());
out_n_k_ho_wo_dev_buf.ToDevice(out_n_k_ho_wo.mData.data());
// these are workspace buffers that should be expressed to the user by the corresponding
// workspace API
DeviceMem workspace_buf(4096);
void* a_k_m0_m1_grid_desc_dev_buf = workspace_buf.GetDeviceBuffer();
void* b_k_n0_n1_grid_desc_dev_buf =
static_cast<void*>(static_cast<unsigned char*>(workspace_buf.GetDeviceBuffer()) + 1024);
void* c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf =
static_cast<void*>(static_cast<unsigned char*>(workspace_buf.GetDeviceBuffer()) + 2048);
void* c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf =
static_cast<void*>(static_cast<unsigned char*>(workspace_buf.GetDeviceBuffer()) + 3072);
const std::vector<size_t> vld = {static_cast<size_t>(tunable->BlockSize), 1, 1};
const std::vector<size_t> vgd1 = {static_cast<size_t>(tunable->BlockSize), 1, 1};
const std::vector<size_t> vgd2 = {static_cast<size_t>(grid_size * tunable->BlockSize), 1, 1};
std::string program_name =
"dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw.cpp";
std::string algo_name = "implicit_gemm_conv_fwd_v4r4_xdlops_nchw";
std::string param = " -std=c++17 ";
std::string network_config;
param += get_definition_string_from_types<TInWei, TAcc, TOut>() + " " + " -DCK_USE_AMD_XDLOPS" +
get_definition_string_from_tunable(tunable);
network_config = get_network_config_string_from_types<TInWei, TAcc, TOut>() + "_" +
get_network_config_string_from_tunable(tunable);
std::vector<float> kernel1_times;
std::vector<float> kernel2_times;
for(index_t i = 0; i < nrepeat; ++i)
{
KernelTimer timer1, timer2;
std::string kernel_name;
kernel_name =
"dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw_prepare";
auto network_config_1 = network_config + "_1";
timer1.Start();
handle->AddKernel(algo_name, network_config_1, program_name, kernel_name, vld, vgd1, param)(
static_cast<index_t>(in_n_c_hi_wi_lengths[I0]),
static_cast<index_t>(in_n_c_hi_wi_lengths[I1]),
static_cast<index_t>(in_n_c_hi_wi_lengths[I2]),
static_cast<index_t>(in_n_c_hi_wi_lengths[I3]),
static_cast<index_t>(wei_k_c_y_x_lengths[I0]),
static_cast<index_t>(wei_k_c_y_x_lengths[I2]),
static_cast<index_t>(wei_k_c_y_x_lengths[I3]),
conv_strides[I0],
conv_strides[I1],
conv_dilations[I0],
conv_dilations[I1],
in_left_pads[I0],
in_left_pads[I1],
in_right_pads[I0],
in_right_pads[I1],
a_k_m0_m1_grid_desc_dev_buf,
b_k_n0_n1_grid_desc_dev_buf,
c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf,
c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf);
timer1.End();
kernel_name = "dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw";
auto network_config_2 = network_config + "_2";
timer2.Start();
handle->AddKernel(algo_name, network_config_2, program_name, kernel_name, vld, vgd2, param)(
reinterpret_cast<const TInWei*>(wei_k_c_y_x_dev_buf.GetDeviceBuffer()),
reinterpret_cast<const TInWei*>(in_n_c_hi_wi_dev_buf.GetDeviceBuffer()),
reinterpret_cast<TOut*>(out_n_k_ho_wo_dev_buf.GetDeviceBuffer()),
(const void*)(a_k_m0_m1_grid_desc_dev_buf),
(const void*)(b_k_n0_n1_grid_desc_dev_buf),
(const void*)(c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf),
(const void*)(c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf));
timer2.End();
kernel1_times.push_back(timer1.GetElapsedTime());
kernel2_times.push_back(timer2.GetElapsedTime());
}
{
auto ave_time1 =
std::accumulate(
std::next(kernel1_times.begin()), kernel1_times.end(), 0., std::plus<float>{}) /
(nrepeat - 1);
auto ave_time2 =
std::accumulate(
std::next(kernel2_times.begin()), kernel2_times.end(), 0., std::plus<float>{}) /
(nrepeat - 1);
const auto N = in_n_c_hi_wi_lengths[I0];
const auto C = in_n_c_hi_wi_lengths[I1];
const auto K = out_n_k_ho_wo_lengths[I1];
const auto Ho = out_n_k_ho_wo_lengths[I2];
const auto Wo = out_n_k_ho_wo_lengths[I3];
const auto Y = wei_k_c_y_x_lengths[I2];
const auto X = wei_k_c_y_x_lengths[I3];
float perf = (float)(std::size_t(2) * N * K * Ho * Wo * C * Y * X) /
(std::size_t(1000) * 1000 * 1000) / (ave_time1 + ave_time2);
std::cout << "Average time : " << ave_time1 + ave_time2 << " ms(" << ave_time1 << ", "
<< ave_time2 << "), " << perf << " TFlop/s" << std::endl;
};
// copy result back to host
out_n_k_ho_wo_dev_buf.FromDevice(out_n_k_ho_wo.mData.data());
}
host/driver_online/include/online_device_dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nhwc_kyxc_nhwk.hpp deleted 100644 → 0
View file @ 5781adf5
#include "device.hpp"
#include "host_tensor.hpp"
#include "handle.hpp"
#include "online_driver_common.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "transform_forward_convolution_into_gemm_v4r4r4_nhwc_kyxc_nhwk.hpp"
#include "conv_tunable_fwd_v4r4_xdlops_nhwc_kyxc_nhwk.hpp"
namespace detail_dyn_conv_fwd_v4r4_xdlops_nhwc_kyxc_nhwk {
template <typename TInWei, typename TAcc, typename TOut>
static std::string get_network_config_string_from_types()
{
using namespace ck;
std::string out;
out += std::to_string(get_datatype_enum_from_type<TInWei>::value) + "_" +
std::to_string(get_datatype_enum_from_type<TAcc>::value) + "_" +
std::to_string(get_datatype_enum_from_type<TOut>::value);
return (out);
};
static std::string
get_network_config_string_from_tunable(const tunable_dyn_conv_fwd_v4r4_xdlops_nhwc_kyxc_nhwk* pt)
{
std::string out("TUN_");
out += std::to_string(pt->BlockSize) + "_";
out += std::to_string(pt->MPerBlock) + "x" + std::to_string(pt->NPerBlock) + "x" +
std::to_string(pt->KPerBlock) + "_";
out += std::to_string(pt->MPerWave) + "x" + std::to_string(pt->NPerWave) + "x" +
std::to_string(pt->MRepeat) + "x" + std::to_string(pt->NRepeat) + "x" +
std::to_string(pt->K1) + "_";
out += std::to_string(pt->ABlockTransferThreadSliceLengths_K0_M_K1[0]) + "x" +
std::to_string(pt->ABlockTransferThreadSliceLengths_K0_M_K1[1]) + "x" +
std::to_string(pt->ABlockTransferThreadSliceLengths_K0_M_K1[2]) + "_";
out += std::to_string(pt->ABlockTransferThreadClusterLengths_K0_M_K1[0]) + "x" +
std::to_string(pt->ABlockTransferThreadClusterLengths_K0_M_K1[1]) + "x" +
std::to_string(pt->ABlockTransferThreadClusterLengths_K0_M_K1[2]) + "_";
out += std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[0]) + "x" +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[1]) + "x" +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[2]) + "_";
out += std::to_string(pt->ABlockTransferSrcAccessOrder[0]) + "x" +
std::to_string(pt->ABlockTransferSrcAccessOrder[1]) + "x" +
std::to_string(pt->ABlockTransferSrcAccessOrder[2]) + "_";
out += std::to_string(pt->ABlockTransferSrcVectorDim) + "_";
out += std::to_string(pt->ABlockTransferSrcScalarPerVector) + "_";
out += std::to_string(pt->ABlockTransferDstScalarPerVector_K1) + "_";
out += std::to_string(pt->AThreadTransferSrcResetCoordinateAfterRun) + "_";
out += std::to_string(pt->BBlockTransferThreadSliceLengths_K0_N_K1[0]) + "x" +
std::to_string(pt->BBlockTransferThreadSliceLengths_K0_N_K1[1]) + "x" +
std::to_string(pt->BBlockTransferThreadSliceLengths_K0_N_K1[2]) + "_";
out += std::to_string(pt->BBlockTransferThreadClusterLengths_K0_N_K1[0]) + "x" +
std::to_string(pt->BBlockTransferThreadClusterLengths_K0_N_K1[1]) + "x" +
std::to_string(pt->BBlockTransferThreadClusterLengths_K0_N_K1[2]) + "_";
out += std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[0]) + "x" +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[1]) + "x" +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[2]) + "_";
out += std::to_string(pt->BBlockTransferSrcAccessOrder[0]) + "x" +
std::to_string(pt->BBlockTransferSrcAccessOrder[1]) + "x" +
std::to_string(pt->BBlockTransferSrcAccessOrder[2]) + "_";
out += std::to_string(pt->BBlockTransferSrcVectorDim) + "_";
out += std::to_string(pt->BBlockTransferSrcScalarPerVector) + "_";
out += std::to_string(pt->BBlockTransferDstScalarPerVector_K1) + "_";
out += std::to_string(pt->BThreadTransferSrcResetCoordinateAfterRun) + "_";
out += std::to_string(pt->CThreadTransferSrcDstAccessOrder[0]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[1]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[2]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[3]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[4]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[5]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[6]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[7]) + "_";
out += std::to_string(pt->CThreadTransferSrcDstVectorDim) + "_";
out += std::to_string(pt->CThreadTransferDstScalarPerVector);
return (out);
};
template <typename TInWei, typename TAcc, typename TOut>
static std::string get_definition_string_from_types()
{
using namespace ck;
std::string out;
out +=
" -DCK_PARAM_ABDataTypeEnum=" + std::to_string(get_datatype_enum_from_type<TInWei>::value) +
" -DCK_PARAM_AccDataTypeEnum=" + std::to_string(get_datatype_enum_from_type<TAcc>::value) +
" -DCK_PARAM_CDataTypeEnum=" + std::to_string(get_datatype_enum_from_type<TOut>::value);
return (out);
};
static std::string
get_definition_string_from_tunable(const tunable_dyn_conv_fwd_v4r4_xdlops_nhwc_kyxc_nhwk* pt)
{
std::string out;
out += " -DCK_PARAM_BlockSize=" + std::to_string(pt->BlockSize);
out += " -DCK_PARAM_MPerBlock=" + std::to_string(pt->MPerBlock) +
" -DCK_PARAM_NPerBlock=" + std::to_string(pt->NPerBlock) +
" -DCK_PARAM_KPerBlock=" + std::to_string(pt->KPerBlock);
out += " -DCK_PARAM_MPerWave=" + std::to_string(pt->MPerWave) +
" -DCK_PARAM_NPerWave=" + std::to_string(pt->NPerWave) +
" -DCK_PARAM_K1=" + std::to_string(pt->K1) +
" -DCK_PARAM_MRepeat=" + std::to_string(pt->MRepeat) +
" -DCK_PARAM_NRepeat=" + std::to_string(pt->NRepeat);
out += " -DCK_PARAM_ABlockTransferThreadSliceLengths_K0_M_K1=" +
std::to_string(pt->ABlockTransferThreadSliceLengths_K0_M_K1[0]) + "," +
std::to_string(pt->ABlockTransferThreadSliceLengths_K0_M_K1[1]) + "," +
std::to_string(pt->ABlockTransferThreadSliceLengths_K0_M_K1[2]);
out += " -DCK_PARAM_ABlockTransferThreadClusterLengths_K0_M_K1=" +
std::to_string(pt->ABlockTransferThreadClusterLengths_K0_M_K1[0]) + "," +
std::to_string(pt->ABlockTransferThreadClusterLengths_K0_M_K1[1]) + "," +
std::to_string(pt->ABlockTransferThreadClusterLengths_K0_M_K1[2]);
out += " -DCK_PARAM_ABlockTransferThreadClusterArrangeOrder=" +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[0]) + "," +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[1]) + "," +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[2]);
out += " -DCK_PARAM_ABlockTransferSrcAccessOrder=" +
std::to_string(pt->ABlockTransferSrcAccessOrder[0]) + "," +
std::to_string(pt->ABlockTransferSrcAccessOrder[1]) + "," +
std::to_string(pt->ABlockTransferSrcAccessOrder[2]);
out +=
" -DCK_PARAM_ABlockTransferSrcVectorDim=" + std::to_string(pt->ABlockTransferSrcVectorDim);
out += " -DCK_PARAM_ABlockTransferSrcScalarPerVector=" +
std::to_string(pt->ABlockTransferSrcScalarPerVector);
out += " -DCK_PARAM_ABlockTransferDstScalarPerVector_K1=" +
std::to_string(pt->ABlockTransferDstScalarPerVector_K1);
out += " -DCK_PARAM_AThreadTransferSrcResetCoordinateAfterRun=" +
std::to_string(pt->AThreadTransferSrcResetCoordinateAfterRun);
out += " -DCK_PARAM_BBlockTransferThreadSliceLengths_K0_N_K1=" +
std::to_string(pt->BBlockTransferThreadSliceLengths_K0_N_K1[0]) + "," +
std::to_string(pt->BBlockTransferThreadSliceLengths_K0_N_K1[1]) + "," +
std::to_string(pt->BBlockTransferThreadSliceLengths_K0_N_K1[2]);
out += " -DCK_PARAM_BBlockTransferThreadClusterLengths_K0_N_K1=" +
std::to_string(pt->BBlockTransferThreadClusterLengths_K0_N_K1[0]) + "," +
std::to_string(pt->BBlockTransferThreadClusterLengths_K0_N_K1[1]) + "," +
std::to_string(pt->BBlockTransferThreadClusterLengths_K0_N_K1[2]);
out += " -DCK_PARAM_BBlockTransferThreadClusterArrangeOrder=" +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[0]) + "," +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[1]) + "," +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[2]);
out += " -DCK_PARAM_BBlockTransferSrcAccessOrder=" +
std::to_string(pt->BBlockTransferSrcAccessOrder[0]) + "," +
std::to_string(pt->BBlockTransferSrcAccessOrder[1]) + "," +
std::to_string(pt->BBlockTransferSrcAccessOrder[2]);
out +=
" -DCK_PARAM_BBlockTransferSrcVectorDim=" + std::to_string(pt->BBlockTransferSrcVectorDim);
out += " -DCK_PARAM_BBlockTransferSrcScalarPerVector=" +
std::to_string(pt->BBlockTransferSrcScalarPerVector);
out += " -DCK_PARAM_BBlockTransferDstScalarPerVector_K1=" +
std::to_string(pt->BBlockTransferDstScalarPerVector_K1);
out += " -DCK_PARAM_BThreadTransferSrcResetCoordinateAfterRun=" +
std::to_string(pt->BThreadTransferSrcResetCoordinateAfterRun);
out += " -DCK_PARAM_CThreadTransferSrcDstAccessOrder=" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[0]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[1]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[2]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[3]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[4]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[5]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[6]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[7]);
out += " -DCK_PARAM_CThreadTransferSrcDstVectorDim=" +
std::to_string(pt->CThreadTransferSrcDstVectorDim);
out += " -DCK_PARAM_CThreadTransferDstScalarPerVector=" +
std::to_string(pt->CThreadTransferDstScalarPerVector);
return (out);
};
} // namespace detail_dyn_conv_fwd_v4r4_xdlops_nhwc_kyxc_nhwk
template <typename TInWei,
typename TAcc,
typename TOut,
typename InLengths,
typename WeiLengths,
typename OutLengths,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void online_device_dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nhwc_kyxc_nhwk(
online_compile::Handle* handle,
const InLengths& in_n_hi_wi_c_lengths,
const WeiLengths& wei_k_y_x_c_lengths,
const OutLengths& out_n_ho_wo_k_lengths,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
const Tensor<TInWei>& in_n_hi_wi_c,
const Tensor<TInWei>& wei_k_y_x_c,
Tensor<TOut>& out_n_ho_wo_k,
const tunable_dyn_conv_fwd_v4r4_xdlops_nhwc_kyxc_nhwk* tunable,
ck::index_t nrepeat)
{
using namespace ck;
using namespace detail_dyn_conv_fwd_v4r4_xdlops_nhwc_kyxc_nhwk;
using size_t = std::size_t;
/////////////////////////////////////////////////////////////////////////////////////////////////////////////
// The follow codes are only used for computing the grid_size, hasMainKBlockLoop,
// hasDoubleTailKBlockLoop
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
const auto in_n_hi_wi_c_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(in_n_hi_wi_c_lengths);
const auto wei_k_y_x_c_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(wei_k_y_x_c_lengths);
const auto out_n_ho_wo_k_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(out_n_ho_wo_k_lengths);
const auto n = in_n_hi_wi_c_desc.GetLength(I0);
const auto hi = in_n_hi_wi_c_desc.GetLength(I1);
const auto wi = in_n_hi_wi_c_desc.GetLength(I2);
const auto c = in_n_hi_wi_c_desc.GetLength(I3);
const auto k = wei_k_y_x_c_desc.GetLength(I0);
const auto y = wei_k_y_x_c_desc.GetLength(I1);
const auto x = wei_k_y_x_c_desc.GetLength(I2);
const auto ho = out_n_ho_wo_k_desc.GetLength(I1);
const auto wo = out_n_ho_wo_k_desc.GetLength(I2);
const auto M = k;
const auto N = n * ho * wo;
const auto K = c * y * x;
const auto K0 = K / tunable->K1;
const index_t grid_size = (M / tunable->MPerBlock) * (N / tunable->NPerBlock);
// these buffers are usually provided by the user application
DeviceMem in_n_hi_wi_c_dev_buf(sizeof(TInWei) * in_n_hi_wi_c.mDesc.GetElementSpace());
DeviceMem wei_k_y_x_c_dev_buf(sizeof(TInWei) * wei_k_y_x_c.mDesc.GetElementSpace());
DeviceMem out_n_ho_wo_k_dev_buf(sizeof(TOut) * out_n_ho_wo_k.mDesc.GetElementSpace());
in_n_hi_wi_c_dev_buf.ToDevice(in_n_hi_wi_c.mData.data());
wei_k_y_x_c_dev_buf.ToDevice(wei_k_y_x_c.mData.data());
out_n_ho_wo_k_dev_buf.ToDevice(out_n_ho_wo_k.mData.data());
// these are workspace buffers that should be expressed to the user by the corresponding
// workspace API
DeviceMem workspace_buf(4096);
void* a_k0_m_k1_grid_desc_dev_buf = workspace_buf.GetDeviceBuffer();
void* b_k0_n_k1_grid_desc_dev_buf =
static_cast<void*>(static_cast<unsigned char*>(workspace_buf.GetDeviceBuffer()) + 1024);
void* c_m0_m1_m2_n_grid_desc_dev_buf =
static_cast<void*>(static_cast<unsigned char*>(workspace_buf.GetDeviceBuffer()) + 2048);
void* c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf =
static_cast<void*>(static_cast<unsigned char*>(workspace_buf.GetDeviceBuffer()) + 3072);
const std::vector<size_t> vld = {static_cast<size_t>(tunable->BlockSize), 1, 1};
const std::vector<size_t> vgd1 = {static_cast<size_t>(tunable->BlockSize), 1, 1};
const std::vector<size_t> vgd2 = {static_cast<size_t>(grid_size * tunable->BlockSize), 1, 1};
std::string program_name =
"dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nhwc_kyxc_nhwk.cpp";
std::string algo_name = "implicit_gemm_conv_fwd_v4r4_xdlops_nhwc";
std::string param = " -std=c++17 ";
std::string network_config;
param += get_definition_string_from_types<TInWei, TAcc, TOut>() + " -DCK_USE_AMD_XDLOPS ";
param += get_definition_string_from_tunable(tunable);
network_config = get_network_config_string_from_types<TInWei, TAcc, TOut>() + "_" +
get_network_config_string_from_tunable(tunable);
std::vector<float> kernel1_times;
std::vector<float> kernel2_times;
for(index_t i = 0; i < nrepeat; ++i)
{
KernelTimer timer1, timer2;
std::string kernel_name;
kernel_name =
"dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nhwc_kyxc_nhwk_prepare";
auto network_config_1 = network_config + "_1";
timer1.Start();
handle->AddKernel(algo_name, network_config_1, program_name, kernel_name, vld, vgd1, param)(
static_cast<index_t>(in_n_hi_wi_c_lengths[I0]),
static_cast<index_t>(in_n_hi_wi_c_lengths[I1]),
static_cast<index_t>(in_n_hi_wi_c_lengths[I2]),
static_cast<index_t>(in_n_hi_wi_c_lengths[I3]),
static_cast<index_t>(wei_k_y_x_c_lengths[I0]),
static_cast<index_t>(wei_k_y_x_c_lengths[I1]),
static_cast<index_t>(wei_k_y_x_c_lengths[I2]),
conv_strides[I0],
conv_strides[I1],
conv_dilations[I0],
conv_dilations[I1],
in_left_pads[I0],
in_left_pads[I1],
in_right_pads[I0],
in_right_pads[I1],
a_k0_m_k1_grid_desc_dev_buf,
b_k0_n_k1_grid_desc_dev_buf,
c_m0_m1_m2_n_grid_desc_dev_buf,
c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf);
timer1.End();
kernel_name = "dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nhwc_kyxc_nhwk";
auto network_config_2 = network_config + "_2";
timer2.Start();
handle->AddKernel(algo_name, network_config_2, program_name, kernel_name, vld, vgd2, param)(
reinterpret_cast<const TInWei*>(in_n_hi_wi_c_dev_buf.GetDeviceBuffer()),
reinterpret_cast<const TInWei*>(wei_k_y_x_c_dev_buf.GetDeviceBuffer()),
reinterpret_cast<TOut*>(out_n_ho_wo_k_dev_buf.GetDeviceBuffer()),
(const void*)(a_k0_m_k1_grid_desc_dev_buf),
(const void*)(b_k0_n_k1_grid_desc_dev_buf),
(const void*)(c_m0_m1_m2_n_grid_desc_dev_buf),
(const void*)(c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf));
timer2.End();
kernel1_times.push_back(timer1.GetElapsedTime());
kernel2_times.push_back(timer2.GetElapsedTime());
}
{
auto ave_time1 =
std::accumulate(
std::next(kernel1_times.begin()), kernel1_times.end(), 0., std::plus<float>{}) /
(nrepeat - 1);
auto ave_time2 =
std::accumulate(
std::next(kernel2_times.begin()), kernel2_times.end(), 0., std::plus<float>{}) /
(nrepeat - 1);
const auto N = in_n_hi_wi_c_lengths[I0];
const auto C = in_n_hi_wi_c_lengths[I3];
const auto Ho = out_n_ho_wo_k_lengths[I1];
const auto Wo = out_n_ho_wo_k_lengths[I2];
const auto K = out_n_ho_wo_k_lengths[I3];
const auto Y = wei_k_y_x_c_lengths[I1];
const auto X = wei_k_y_x_c_lengths[I2];
float perf = (float)(std::size_t(2) * N * K * Ho * Wo * C * Y * X) /
(std::size_t(1000) * 1000 * 1000) / ave_time2;
std::cout << "Average time : " << ave_time1 + ave_time2 << " ms(" << ave_time1 << ", "
<< ave_time2 << "), " << perf << " TFlop/s" << std::endl;
};
// copy result back to host
out_n_ho_wo_k_dev_buf.FromDevice(out_n_ho_wo_k.mData.data());
}
host/driver_online/include/online_device_dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw.hpp deleted 100644 → 0
View file @ 5781adf5
#pragma once
#include "device.hpp"
#include "host_tensor.hpp"
#include "handle.hpp"
#include "online_driver_common.hpp"
#include "convolution_problem_descriptor.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "transform_forward_convolution_into_gemm_v6r1_nchw_kcyx_nkhw.hpp"
#include "conv_igemm_fwd_v6r1_dlops_nchw_kcyx_nkhw.hpp"
template <typename TInWei,
typename TAcc,
typename TOut,
typename InLengths,
typename WeiLengths,
typename OutLengths,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void online_device_dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw(
online_compile::Handle* handle,
const InLengths& in_n_c_hi_wi_lengths,
const WeiLengths& wei_k_c_y_x_lengths,
const OutLengths& out_n_k_ho_wo_lengths,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
const Tensor<TInWei>& in_n_c_hi_wi,
const Tensor<TInWei>& wei_k_c_y_x,
Tensor<TOut>& out_n_k_ho_wo,
const ck_driver::CompileParameterConvIgemmFwdV6r1DlopsNchwKcyxNkhw& compile_param,
ck::index_t nrepeat)
{
using namespace ck;
using namespace ck_driver;
using size_t = std::size_t;
std::cout << __func__ << std::endl;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
ConvolutionProblemDescriptor conv_problem_desc{in_n_c_hi_wi_lengths[I0],
out_n_k_ho_wo_lengths[I1],
in_n_c_hi_wi_lengths[I1],
wei_k_c_y_x_lengths[I2],
wei_k_c_y_x_lengths[I3],
in_n_c_hi_wi_lengths[I2],
in_n_c_hi_wi_lengths[I3],
out_n_k_ho_wo_lengths[I2],
out_n_k_ho_wo_lengths[I3],
conv_strides[I0],
conv_strides[I1],
conv_dilations[I0],
conv_dilations[I1],
in_left_pads[I0],
in_left_pads[I1],
in_right_pads[I0],
in_right_pads[I1],
get_datatype_enum_from_type<TInWei>::value,
get_datatype_enum_from_type<TInWei>::value,
get_datatype_enum_from_type<TOut>::value};
if(!ConvIgemmFwdV6r1DlopsNchwKcyxNkhw::IsValidCompileParameter(conv_problem_desc,
compile_param))
{
throw std::runtime_error("wrong! IsValidCompileParameter fail");
}
DeviceMem in_n_c_hi_wi_dev_buf(sizeof(TInWei) * in_n_c_hi_wi.mDesc.GetElementSpace());
DeviceMem wei_k_c_y_x_dev_buf(sizeof(TInWei) * wei_k_c_y_x.mDesc.GetElementSpace());
DeviceMem out_n_k_ho_wo_dev_buf(sizeof(TOut) * out_n_k_ho_wo.mDesc.GetElementSpace());
in_n_c_hi_wi_dev_buf.ToDevice(in_n_c_hi_wi.mData.data());
wei_k_c_y_x_dev_buf.ToDevice(wei_k_c_y_x.mData.data());
out_n_k_ho_wo_dev_buf.ToDevice(out_n_k_ho_wo.mData.data());
// workspace is used for save transformed tensor descritpors created by prepare kernel
DeviceMem workspace_dev_buf(
ConvIgemmFwdV6r1DlopsNchwKcyxNkhw::GetWorkSpaceSize(conv_problem_desc, compile_param));
const auto block_size = std::size_t(
ConvIgemmFwdV6r1DlopsNchwKcyxNkhw::GetBlockSize(conv_problem_desc, compile_param));
const auto grid_size = std::size_t(
ConvIgemmFwdV6r1DlopsNchwKcyxNkhw::GetGridSize(conv_problem_desc, compile_param));
const std::vector<size_t> vld1 = {1, 1, 1};
const std::vector<size_t> vgd1 = {1, 1, 1};
const std::vector<size_t> vld2 = {static_cast<size_t>(block_size), 1, 1};
const std::vector<size_t> vgd2 = {static_cast<size_t>(grid_size * block_size), 1, 1};
std::string program_name =
"dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw.cpp";
std::string algo_name = "implicit_gemm_conv_fwd_v6r1_dlops_nchw";
std::string compile_param_string = get_ck_hip_online_compile_common_flag() + compile_param.GetCompileParameterString();
std::string network_config = compile_param_string;
std::vector<float> kernel1_times;
std::vector<float> kernel2_times;
for(index_t i = 0; i < nrepeat + 1; ++i)
{
KernelTimer timer1, timer2;
std::string kernel_name;
kernel_name = "dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw_prepare";
auto network_config_1 = network_config + "_1";
timer1.Start();
handle->AddKernel(algo_name,
network_config_1,
program_name,
kernel_name,
vld1,
vgd1,
compile_param_string)(static_cast<index_t>(in_n_c_hi_wi_lengths[I0]),
static_cast<index_t>(in_n_c_hi_wi_lengths[I1]),
static_cast<index_t>(in_n_c_hi_wi_lengths[I2]),
static_cast<index_t>(in_n_c_hi_wi_lengths[I3]),
static_cast<index_t>(wei_k_c_y_x_lengths[I0]),
static_cast<index_t>(wei_k_c_y_x_lengths[I2]),
static_cast<index_t>(wei_k_c_y_x_lengths[I3]),
conv_strides[I0],
conv_strides[I1],
conv_dilations[I0],
conv_dilations[I1],
in_left_pads[I0],
in_left_pads[I1],
in_right_pads[I0],
in_right_pads[I1],
(void*)(workspace_dev_buf.GetDeviceBuffer()));
timer1.End();
kernel_name = "dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw";
auto network_config_2 = network_config + "_2";
timer2.Start();
handle->AddKernel(algo_name,
network_config_2,
program_name,
kernel_name,
vld2,
vgd2,
compile_param_string)(
reinterpret_cast<const TInWei*>(wei_k_c_y_x_dev_buf.GetDeviceBuffer()),
reinterpret_cast<const TInWei*>(in_n_c_hi_wi_dev_buf.GetDeviceBuffer()),
reinterpret_cast<TOut*>(out_n_k_ho_wo_dev_buf.GetDeviceBuffer()),
(const void*)(workspace_dev_buf.GetDeviceBuffer()));
timer2.End();
kernel1_times.push_back(timer1.GetElapsedTime());
kernel2_times.push_back(timer2.GetElapsedTime());
}
{
auto ave_time1 =
std::accumulate(
std::next(kernel1_times.begin()), kernel1_times.end(), 0., std::plus<float>{}) /
nrepeat;
auto ave_time2 =
std::accumulate(
std::next(kernel2_times.begin()), kernel2_times.end(), 0., std::plus<float>{}) /
nrepeat;
float perf = (float)(conv_problem_desc.CalculateFlop()) /
(std::size_t(1000) * 1000 * 1000) / (ave_time1 + ave_time2);
std::cout << "Average time : " << ave_time1 + ave_time2 << " ms(" << ave_time1 << ", "
<< ave_time2 << "), " << perf << " TFlop/s" << std::endl;
};
// copy result back to host
out_n_k_ho_wo_dev_buf.FromDevice(out_n_k_ho_wo.mData.data());
}
host/host_tensor/CMakeLists.txt
View file @ 31b40352
......@@ -10,6 +10,8 @@ set(HOST_TENSOR_SOURCE
## the library target
add_library(host_tensor SHARED ${HOST_TENSOR_SOURCE})
target_include_directories(host_tensor SYSTEM PUBLIC $<BUILD_INTERFACE:${HALF_INCLUDE_DIR}>)
target_link_libraries(host_tensor PRIVATE hip::device)
target_link_libraries(host_tensor INTERFACE hip::host)
......
host/host_tensor/include/conv_common.hpp
View file @ 31b40352
#ifndef CONV_COMMON_HPP
#define CONV_COMMON_HPP
#include "dynamic_tensor_descriptor.hpp"
#include "tensor_descriptor.hpp"
enum ConvTensorLayout
{
......@@ -19,8 +19,8 @@ template <typename... InDesc,
typename LeftPads,
typename RightPads>
constexpr auto get_convolution_output_default_4d_tensor_descriptor(
const ck::DynamicTensorDescriptor<InDesc...>& in_desc,
const ck::DynamicTensorDescriptor<WeiDesc...>& wei_desc,
const ck::TensorDescriptor<InDesc...>& in_desc,
const ck::TensorDescriptor<WeiDesc...>& wei_desc,
const ConvStrides& conv_strides,
const ConvDilations conv_dilations,
const LeftPads& left_pads,
......@@ -57,12 +57,12 @@ constexpr auto get_convolution_output_default_4d_tensor_descriptor(
const auto Ho = (Hi + LeftPadH + RightPadH - YEff) / conv_strides[I0] + I1;
const auto Wo = (Wi + LeftPadW + RightPadW - XEff) / conv_strides[I1] + I1;
return make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(N, K, Ho, Wo));
return make_naive_tensor_descriptor_packed(make_tuple(N, K, Ho, Wo));
}
template <class InDesc, class WeiDesc, class OutDesc>
constexpr std::size_t
calculate_convolution_flops(const InDesc& in_desc, const WeiDesc& wei_desc, const OutDesc& out_desc)
calculate_convolution_flops(const InDesc&, const WeiDesc& wei_desc, const OutDesc& out_desc)
{
using namespace ck;
......
host/host_tensor/include/device.hpp
View file @ 31b40352
......@@ -34,24 +34,16 @@ struct KernelTimer
using device_stream_t = hipStream_t;
template <typename... Args, typename F>
void launch_kernel(F kernel,
dim3 grid_dim,
dim3 block_dim,
std::size_t lds_byte,
hipStream_t stream_id,
Args... args)
void launch_kernel(F kernel, dim3 grid_dim, dim3 block_dim, std::size_t lds_byte, Args... args)
{
hipStream_t stream_id = nullptr;
hipLaunchKernelGGL(kernel, grid_dim, block_dim, lds_byte, stream_id, args...);
}
template <typename... Args, typename F>
float launch_and_time_kernel(F kernel,
int nrepeat,
dim3 grid_dim,
dim3 block_dim,
std::size_t lds_byte,
hipStream_t stream_id,
Args... args)
float launch_and_time_kernel(
F kernel, int nrepeat, dim3 grid_dim, dim3 block_dim, std::size_t lds_byte, Args... args)
{
KernelTimer timer;
......@@ -66,6 +58,8 @@ float launch_and_time_kernel(F kernel,
printf("Warm up\n");
hipStream_t stream_id = nullptr;
// warm up
hipLaunchKernelGGL(kernel, grid_dim, block_dim, lds_byte, stream_id, args...);
......
host/host_tensor/include/host_conv.hpp
View file @ 31b40352
......@@ -14,15 +14,13 @@ void host_direct_convolution(const Tensor<TIn>& in,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
const InRightPads&,
const ConvTensorLayout layout = ConvTensorLayout::NCHW)
{
using namespace ck;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
auto f_nchw = [&](auto n, auto k, auto ho, auto wo) {
double v = 0;
......@@ -68,23 +66,25 @@ void host_direct_convolution(const Tensor<TIn>& in,
out(n, ho, wo, k) = v;
};
switch(layout)
if(layout == ConvTensorLayout::NCHW)
{
case ConvTensorLayout::NCHW:
make_ParallelTensorFunctor(f_nchw,
out.mDesc.GetLengths()[0],
out.mDesc.GetLengths()[1],
out.mDesc.GetLengths()[2],
out.mDesc.GetLengths()[3])(std::thread::hardware_concurrency());
break;
case ConvTensorLayout::NHWC:
}
else if(layout == ConvTensorLayout::NHWC)
{
make_ParallelTensorFunctor(f_nhwc,
out.mDesc.GetLengths()[0],
out.mDesc.GetLengths()[1],
out.mDesc.GetLengths()[2],
out.mDesc.GetLengths()[3])(std::thread::hardware_concurrency());
break;
default: throw std::runtime_error("wrong! not supported layout");
}
else
{
throw std::runtime_error("wrong! not supported layout");
}
}
......@@ -102,15 +102,13 @@ void host_winograd_3x3_convolution(const Tensor<TIn>& in_nchw,
std::size_t N = in_nchw.mDesc.GetLengths()[0];
std::size_t C = in_nchw.mDesc.GetLengths()[1];
std::size_t HI = in_nchw.mDesc.GetLengths()[2];
std::size_t WI = in_nchw.mDesc.GetLengths()[3];
std::size_t K = wei_kcyx.mDesc.GetLengths()[0];
std::size_t Y = wei_kcyx.mDesc.GetLengths()[2];
std::size_t X = wei_kcyx.mDesc.GetLengths()[3];
std::size_t HO = out_nkhw.mDesc.GetLengths()[2];
std::size_t WO = out_nkhw.mDesc.GetLengths()[3];
std::size_t Ho = out_nkhw.mDesc.GetLengths()[2];
std::size_t Wo = out_nkhw.mDesc.GetLengths()[3];
index_t h_pad_low = InLeftPads{}.Get(Number<0>{});
index_t w_pad_low = InLeftPads{}.Get(Number<1>{});
......@@ -118,8 +116,8 @@ void host_winograd_3x3_convolution(const Tensor<TIn>& in_nchw,
std::size_t HiPerTile = HoPerTile + Y - 1;
std::size_t WiPerTile = WoPerTile + X - 1;
std::size_t HTile = (HO + HoPerTile - 1) / HoPerTile;
std::size_t WTile = (WO + WoPerTile - 1) / WoPerTile;
std::size_t HTile = (Ho + HoPerTile - 1) / HoPerTile;
std::size_t WTile = (Wo + WoPerTile - 1) / WoPerTile;
Tensor<double> in_hold({N, C, HTile, WTile, HiPerTile, WiPerTile});
Tensor<double> in_transform({N, C, HTile, WTile, HiPerTile, WiPerTile});
......
host/host_tensor/include/host_conv_bwd_data.hpp
View file @ 31b40352
......@@ -14,7 +14,7 @@ void host_direct_convolution_backward_data(Tensor<TIn>& in,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
const InRightPads& /* in_right_pads */,
const ConvTensorLayout layout = ConvTensorLayout::NCHW)
{
using namespace ck;
......@@ -25,11 +25,6 @@ void host_direct_convolution_backward_data(Tensor<TIn>& in,
constexpr auto I3 = Number<3>{};
auto f_nchw = [&](auto n, auto c, auto hi, auto wi) {
std::size_t N = in.mDesc.GetLengths()[I0];
std::size_t C = in.mDesc.GetLengths()[I1];
std::size_t Hi = in.mDesc.GetLengths()[I2];
std::size_t Wi = in.mDesc.GetLengths()[I3];
std::size_t K = wei.mDesc.GetLengths()[I0];
std::size_t Y = wei.mDesc.GetLengths()[I2];
std::size_t X = wei.mDesc.GetLengths()[I3];
......@@ -74,11 +69,6 @@ void host_direct_convolution_backward_data(Tensor<TIn>& in,
};
auto f_nhwc = [&](auto n, auto hi, auto wi, auto c) {
std::size_t N = in.mDesc.GetLengths()[I0];
std::size_t Hi = in.mDesc.GetLengths()[I1];
std::size_t Wi = in.mDesc.GetLengths()[I2];
std::size_t C = in.mDesc.GetLengths()[I3];
std::size_t K = wei.mDesc.GetLengths()[I0];
std::size_t Y = wei.mDesc.GetLengths()[I1];
std::size_t X = wei.mDesc.GetLengths()[I2];
......@@ -122,22 +112,24 @@ void host_direct_convolution_backward_data(Tensor<TIn>& in,
in(n, hi, wi, c) = v;
};
switch(layout)
if(layout == ConvTensorLayout::NCHW)
{
case ConvTensorLayout::NCHW:
make_ParallelTensorFunctor(f_nchw,
in.mDesc.GetLengths()[0],
in.mDesc.GetLengths()[1],
in.mDesc.GetLengths()[2],
in.mDesc.GetLengths()[3])(std::thread::hardware_concurrency());
break;
case ConvTensorLayout::NHWC:
}
else if(layout == ConvTensorLayout::NHWC)
{
make_ParallelTensorFunctor(f_nhwc,
in.mDesc.GetLengths()[0],
in.mDesc.GetLengths()[1],
in.mDesc.GetLengths()[2],
in.mDesc.GetLengths()[3])(std::thread::hardware_concurrency());
break;
default: throw std::runtime_error("wrong! not supported layout");
}
else
{
throw std::runtime_error("wrong! not supported layout");
}
}
host/host_tensor/include/host_tensor.hpp
View file @ 31b40352
......@@ -34,7 +34,7 @@ std::ostream& LogRangeAsType(std::ostream& os, Range&& range, std::string delim)
first = false;
else
os << delim;
os << T{v};
os << static_cast<T>(v);
}
return os;
}
......
host/host_tensor/include/host_tensor_generator.hpp
View file @ 31b40352
......@@ -9,7 +9,7 @@ struct GeneratorTensor_1
int value = 1;
template <typename... Is>
float operator()(Is... is)
float operator()(Is...)
{
return value;
}
......
host/host_tensor/src/device.cpp
View file @ 31b40352
......@@ -24,32 +24,32 @@ struct KernelTimerImpl
{
KernelTimerImpl()
{
hipEventCreate(&mStart);
hipEventCreate(&mEnd);
hipGetErrorString(hipEventCreate(&mStart));
hipGetErrorString(hipEventCreate(&mEnd));
}
~KernelTimerImpl()
{
hipEventDestroy(mStart);
hipEventDestroy(mEnd);
hipGetErrorString(hipEventDestroy(mStart));
hipGetErrorString(hipEventDestroy(mEnd));
}
void Start()
{
hipDeviceSynchronize();
hipEventRecord(mStart, 0);
hipGetErrorString(hipDeviceSynchronize());
hipGetErrorString(hipEventRecord(mStart, nullptr));
}
void End()
{
hipEventRecord(mEnd, 0);
hipEventSynchronize(mEnd);
hipGetErrorString(hipEventRecord(mEnd, nullptr));
hipGetErrorString(hipEventSynchronize(mEnd));
}
float GetElapsedTime() const
{
float time;
hipEventElapsedTime(&time, mStart, mEnd);
hipGetErrorString(hipEventElapsedTime(&time, mStart, mEnd));
return time;
}
......
host/online_compile/CMakeLists.txt deleted 100644 → 0
View file @ 5781adf5
set(CMAKE_CXX_COMPILER /opt/rocm/llvm/bin/clang++)
## for online-compiling of HIP kernels
set(OLC_HIP_COMPILER ${CMAKE_CXX_COMPILER} CACHE PATH "")
## reset to avoid the C++ options from the parent project
set(CMAKE_CXX_FLAGS "")
message("Compiling options for library and kernels: ${CMAKE_CXX_FLAGS}")
# look for and register clang-offload-bundler
if(OLC_HIP_COMPILER MATCHES ".*clang\\+\\+$")
find_program(OLC_OFFLOADBUNDLER_BIN clang-offload-bundler
PATH_SUFFIXES bin
PATHS
/opt/rocm/llvm
${CMAKE_INSTALL_PREFIX}/llvm
)
endif()
if(OLC_OFFLOADBUNDLER_BIN)
message(STATUS "clang-offload-bundler found: ${OLC_OFFLOADBUNDLER_BIN}")
set(OLC_OFFLOADBUNDLER_BIN "${OLC_OFFLOADBUNDLER_BIN}")
else()
# look for and register extractkernel
message(STATUS "clang-offload-bundler not found")
find_program(EXTRACTKERNEL_BIN extractkernel
PATH_SUFFIXES bin
PATHS
/opt/rocm/hip
/opt/rocm/hcc
/opt/rocm
${CMAKE_INSTALL_PREFIX}/hip
${CMAKE_INSTALL_PREFIX}/hcc
${CMAKE_INSTALL_PREFIX}
)
if(EXTRACTKERNEL_BIN)
message(STATUS "extractkernel found: ${EXTRACTKERNEL_BIN}")
set(EXTRACTKERNEL_BIN "${EXTRACTKERNEL_BIN}")
else()
message(FATAL_ERROR "extractkernel not found")
endif()
endif()
option(Boost_USE_STATIC_LIBS "Use boost static libraries" OFF)
set(BOOST_COMPONENTS filesystem)
add_definitions(-DBOOST_ALL_NO_LIB=1)
find_package(Boost REQUIRED COMPONENTS ${BOOST_COMPONENTS})
# HIP is always required
find_package(hip REQUIRED PATHS /opt/rocm)
message(STATUS "Build with HIP ${hip_VERSION}")
target_flags(HIP_COMPILER_FLAGS hip::device)
# Remove cuda arch flags
string(REGEX REPLACE --cuda-gpu-arch=[a-z0-9]+ "" HIP_COMPILER_FLAGS "${HIP_COMPILER_FLAGS}")
string(REGEX REPLACE --offload-arch=[a-z0-9]+ "" HIP_COMPILER_FLAGS "${HIP_COMPILER_FLAGS}")
set(OLC_hip_VERSION_MAJOR "${hip_VERSION_MAJOR}")
set(OLC_hip_VERSION_MINOR "${hip_VERSION_MINOR}")
set(OLC_hip_VERSION_PATCH "${hip_VERSION_PATCH}")
option(ENABLE_DEBUG "Build to enable debugging" ON)
if(ENABLE_DEBUG)
set(OLC_DEBUG 1)
else()
set(OLC_DEBUG 0)
endif()
configure_file("${PROJECT_SOURCE_DIR}/host/online_compile/include/config.h.in" "${PROJECT_BINARY_DIR}/host/online_compile/include/config.h")
include_directories(BEFORE
${PROJECT_BINARY_DIR}/host/online_compile/include
)
message(STATUS "Hip compiler flags: ${HIP_COMPILER_FLAGS}")
## HIP_COMPILER_FLAGS will be used for on-line compiling of the HIP kernels
set(HIP_COMPILER_FLAGS "${HIP_COMPILER_FLAGS} ${HIP_ONLINE_COMPILER_FLAGS}")
add_definitions("-DHIP_COMPILER_FLAGS=${HIP_COMPILER_FLAGS}")
file(GLOB_RECURSE COMPOSABLE_KERNEL_INCLUDE_1 "${PROJECT_SOURCE_DIR}/composable_kernel/include/*/*.hpp")
file(GLOB COMPOSABLE_KERNEL_INCLUDE_2 "${PROJECT_SOURCE_DIR}/external/rocm/include/bfloat16_dev.hpp")
set(MCONV_KERNEL_INCLUDES
${COMPOSABLE_KERNEL_INCLUDE_1}
${COMPOSABLE_KERNEL_INCLUDE_2}
)
file(GLOB_RECURSE MCONV_KERNELS "${PROJECT_SOURCE_DIR}/composable_kernel/src/kernel_wrapper/*.cpp")
add_kernels(${CMAKE_CURRENT_SOURCE_DIR} "${MCONV_KERNELS}")
add_kernel_includes(${CMAKE_CURRENT_SOURCE_DIR} "${MCONV_KERNEL_INCLUDES}")
set(ONLINE_COMPILATION_SOURCE
${PROJECT_BINARY_DIR}/kernel.cpp
${PROJECT_BINARY_DIR}/kernel_includes.cpp
)
include_directories(BEFORE
${PROJECT_BINARY_DIR}/host/online_compile/include
include
)
set(OLC_HIP_UTILITY_CPPS
hip_utility/logger.cpp
hip_utility/tmp_dir.cpp
hip_utility/md5.cpp
hip_utility/exec_utils.cpp
hip_utility/target_properties.cpp
hip_utility/handlehip.cpp
hip_utility/kernel_build_params.cpp
hip_utility/hip_build_utils.cpp
hip_utility/hipoc_program.cpp
hip_utility/hipoc_kernel.cpp
hip_utility/kernel_cache.cpp
hip_utility/binary_cache.cpp
)
list(APPEND OLC_SOURCES ${OLC_HIP_UTILITY_CPPS} ${OLC_HIP_UTILITY_HEADERS})
## addkernels provide the tool to create inlined kernels in one header
add_subdirectory(addkernels)
function(inline_kernels_src KERNELS KERNEL_INCLUDES)
set(KERNEL_SRC_HPP_FILENAME batch_all.cpp.hpp)
set(KERNEL_SRC_HPP_PATH ${PROJECT_BINARY_DIR}/inlined_kernels/${KERNEL_SRC_HPP_FILENAME})
set(KERNEL_SRC_CPP_PATH ${PROJECT_BINARY_DIR}/inlined_kernels/batch_all.cpp)
add_custom_command(
OUTPUT ${KERNEL_SRC_HPP_PATH}
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
DEPENDS addkernels ${KERNELS} ${KERNEL_INCLUDES}
COMMAND $<TARGET_FILE:addkernels> -target ${KERNEL_SRC_HPP_PATH} -extern -source ${KERNELS}
COMMENT "Inlining All kernels"
)
configure_file(kernels_batch.cpp.in ${KERNEL_SRC_CPP_PATH})
list(APPEND OLC_SOURCES ${KERNEL_SRC_CPP_PATH} ${KERNEL_SRC_HPP_PATH})
set(OLC_SOURCES ${OLC_SOURCES} PARENT_SCOPE)
endfunction()
inline_kernels_src("${MCONV_KERNELS}" "${MCONV_KERNEL_INCLUDES}")
list(APPEND ONLINE_COMPILATION_SOURCE ${OLC_SOURCES} ${PROJECT_BINARY_DIR}/olc_kernel_includes.h)
add_custom_command(
OUTPUT ${PROJECT_BINARY_DIR}/olc_kernel_includes.h
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
DEPENDS addkernels ${MCONV_KERNEL_INCLUDES}
COMMAND $<TARGET_FILE:addkernels> -no-recurse -guard GUARD_OLC_KERNEL_INCLUDES_HPP_ -target ${PROJECT_BINARY_DIR}/olc_kernel_includes.h -source ${MCONV_KERNEL_INCLUDES}
COMMENT "Inlining HIP kernel includes"
)
## the library target
add_library(online_compile SHARED ${ONLINE_COMPILATION_SOURCE})
target_include_directories(online_compile PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/online_compile/include/)
target_include_directories(online_compile PRIVATE ${PROJECT_BINARY_DIR})
target_include_directories(online_compile PRIVATE ${PROJECT_SOURCE_DIR}/external/half/include/)
target_link_libraries(online_compile PRIVATE hip::device)
target_link_libraries(online_compile INTERFACE hip::host)
target_link_libraries(online_compile PRIVATE Boost::filesystem)
target_compile_features(online_compile PUBLIC)
set_target_properties(online_compile PROPERTIES POSITION_INDEPENDENT_CODE ON)
install(TARGETS online_compile LIBRARY DESTINATION lib)
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