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Commit f26fb605 authored by wangshaojie6's avatar wangshaojie6
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Merge branch 'develop' into bwd_weight_bf16_splitk

parents 32d06c66 1677cf70
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Showing with 2240 additions and 68 deletions
example/18_batched_gemm_reduce/batched_gemm_reduce_xdl_fp16.cpp
View file @ f26fb605
......@@ -59,7 +59,7 @@ static constexpr auto GemmSpecialization =
// clang-format off
using DeviceBatchedGemmReduceInstance = ck::tensor_operation::device::DeviceBatchedGemmReduce_Xdl_CShuffle
//######| ALayout| BLayout| CLayout|AData| BData| CData| GemmAcc| CShuffle| ReduceAcc| DData| A| B| C| Dxs| DxsInEleOp| DxsOutEleOp| D| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| CReduce| CReduceThreadLds2VGprCopy| CReduceThreadVgpr2GlobalCopy|
//######| ALayout| BLayout| CLayout|AData| BData| CData| GemmAcc| CShuffle| ReduceAcc| DData| A| B| C| Dxs| DxsInEleOp| DxsAccEleOp| D| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| CReduce| CReduceThreadLds2VGprCopy| CReduceThreadVgpr2GlobalCopy|
//######| | | | Type| Type| Type| DataType| DataType| DataType| Type Tuple| Elementwise| Elementwise| Elementwise| Reduce| | | MemoryData| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MPerBlock| ScalarPerVector| ThreadClusterLengths| SrcDstScalarPerVector| SrcDstScalarPerVector|
//######| | | | | | | | | | | Operation| Operation| Operation| Operation| | | Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock| _MPerBlock_NPerBlock| _NPerBlock| _MPerBlock|
//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
......@@ -259,8 +259,8 @@ int main(int argc, char* argv[])
{
for(int m = 0; m < M; ++m)
{
float d0_acc = d0_reduce_op.GetReductionZeroVal();
float d1_acc = d1_reduce_op.GetReductionZeroVal();
float d0_acc = d0_reduce_op.GetIdentityValue();
float d1_acc = d1_reduce_op.GetIdentityValue();
for(int n = 0; n < N; ++n)
{
......
example/19_binary_elementwise/broadcast_add_2d_amn_bn.cpp
View file @ f26fb605
/*******************************************************************************
*
* MIT License
*
* Copyright (c) 2022 Advanced Micro Devices, Inc.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*
*******************************************************************************/
#include <iostream>
#include <cstdlib>
#include "check_err.hpp"
......@@ -17,7 +42,8 @@ using ABDataType = F16;
using CDataType = F16;
using EltwiseComputeDataType = F32;
using Add = ck::tensor_operation::binary_element_wise::Add;
using Add = ck::tensor_operation::binary_element_wise::
Add<EltwiseComputeDataType, EltwiseComputeDataType, EltwiseComputeDataType>;
using DeviceElementwiseAddInstance =
ck::tensor_operation::device::DeviceBinaryElementwise<ABDataType,
......@@ -46,19 +72,19 @@ void host_broadcast2D(
{
for(int n = 0; n < N; ++n)
{
ComputeDataType Amn = static_cast<ComputeDataType>(A(m, n));
ComputeDataType Amn = ck::type_convert<ComputeDataType>(A(m, n));
ComputeDataType Cmn = 0;
if constexpr(broadcastDim == 0)
{
ComputeDataType Bn = static_cast<ComputeDataType>(B(n));
ComputeDataType Bn = ck::type_convert<ComputeDataType>(B(n));
functor(Cmn, Amn, Bn);
}
else
{
ComputeDataType Bm = static_cast<ComputeDataType>(B(m));
ComputeDataType Bm = ck::type_convert<ComputeDataType>(B(m));
functor(Cmn, Amn, Bm);
}
C(m, n) = static_cast<ctype>(Cmn);
C(m, n) = ck::type_convert<ctype>(Cmn);
}
}
}
......
example/19_binary_elementwise/broadcast_add_3d_am_bmnk.cpp
View file @ f26fb605
......@@ -17,7 +17,8 @@ using ABDataType = F16;
using CDataType = F16;
using EltwiseComputeDataType = F32;
using Add = ck::tensor_operation::binary_element_wise::Add;
using Add = ck::tensor_operation::binary_element_wise::
Add<EltwiseComputeDataType, EltwiseComputeDataType, EltwiseComputeDataType>;
using DeviceElementwiseAddInstance =
ck::tensor_operation::device::DeviceBinaryElementwise<ABDataType,
......@@ -48,11 +49,11 @@ void host_broadcast3D_am_bmnk(HostTensorC& C,
for(std::size_t n = 0; n < shape[1]; ++n)
for(std::size_t k = 0; k < shape[2]; ++k)
{
ComputeDataType a_val = static_cast<ComputeDataType>(A(m));
ComputeDataType b_val = static_cast<ComputeDataType>(B(m, n, k));
ComputeDataType a_val = ck::type_convert<ComputeDataType>(A(m));
ComputeDataType b_val = ck::type_convert<ComputeDataType>(B(m, n, k));
ComputeDataType c_val = 0;
functor(c_val, a_val, b_val);
C(m, n, k) = static_cast<ctype>(c_val);
C(m, n, k) = ck::type_convert<ctype>(c_val);
}
}
......
example/19_binary_elementwise/elementwise_add_1d.cpp
View file @ f26fb605
/*******************************************************************************
*
* MIT License
*
* Copyright (c) 2022 Advanced Micro Devices, Inc.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*
*******************************************************************************/
#include <iostream>
#include <cstdlib>
#include "check_err.hpp"
......@@ -17,7 +42,8 @@ using ABDataType = F16;
using CDataType = F16;
using EltwiseComputeDataType = F32;
using Add = ck::tensor_operation::binary_element_wise::Add;
using Add = ck::tensor_operation::binary_element_wise::
Add<EltwiseComputeDataType, EltwiseComputeDataType, EltwiseComputeDataType>;
using DeviceElementwiseAddInstance =
ck::tensor_operation::device::DeviceBinaryElementwise<ABDataType,
......@@ -43,11 +69,11 @@ void host_elementwise1D(
for(int m = 0; m < M; ++m)
{
ComputeDataType Am = static_cast<ComputeDataType>(A(m));
ComputeDataType Bm = static_cast<ComputeDataType>(B(m));
ComputeDataType Am = ck::type_convert<ComputeDataType>(A(m));
ComputeDataType Bm = ck::type_convert<ComputeDataType>(B(m));
ComputeDataType Cm = 0;
functor(Cm, Am, Bm);
C(m) = static_cast<ctype>(Cm);
C(m) = ck::type_convert<ctype>(Cm);
}
}
......
example/19_binary_elementwise/elementwise_add_4d.cpp
View file @ f26fb605
/*******************************************************************************
*
* MIT License
*
* Copyright (c) 2020 Advanced Micro Devices, Inc.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*
*******************************************************************************/
#include <iostream>
#include <cstdlib>
#include "check_err.hpp"
......@@ -17,7 +42,8 @@ using ABDataType = F16;
using CDataType = F16;
using EltwiseComputeDataType = F32;
using Add = ck::tensor_operation::binary_element_wise::Add;
using Add = ck::tensor_operation::binary_element_wise::
Add<EltwiseComputeDataType, EltwiseComputeDataType, EltwiseComputeDataType>;
using DeviceElementwiseAddInstance =
ck::tensor_operation::device::DeviceBinaryElementwise<ABDataType,
......@@ -49,11 +75,11 @@ void host_elementwise4D(HostTensorC& C,
for(std::size_t h = 0; h < shape[2]; ++h)
for(std::size_t w = 0; w < shape[3]; ++w)
{
ComputeDataType a_val = static_cast<ComputeDataType>(A(n, c, h, w));
ComputeDataType b_val = static_cast<ComputeDataType>(B(n, c, h, w));
ComputeDataType a_val = ck::type_convert<ComputeDataType>(A(n, c, h, w));
ComputeDataType b_val = ck::type_convert<ComputeDataType>(B(n, c, h, w));
ComputeDataType c_val = 0;
functor(c_val, a_val, b_val);
C(n, c, h, w) = static_cast<ctype>(c_val);
C(n, c, h, w) = ck::type_convert<ctype>(c_val);
}
}
......
example/21_gemm_layernorm/CMakeLists.txt 0 → 100644
View file @ f26fb605
add_example_executable(example_gemm_layernorm_xdl_fp16 gemm_layernorm_xdl_fp16.cpp)
example/21_gemm_layernorm/gemm_layernorm_xdl_fp16.cpp 0 → 100644
View file @ f26fb605
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include <stdlib.h>
#include "check_err.hpp"
#include "config.hpp"
#include "device.hpp"
#include "host_tensor.hpp"
#include "host_tensor_generator.hpp"
#include "device_tensor.hpp"
#include "device_5ary_elementwise.hpp"
#include "device_gemm_reduce_xdl_cshuffle.hpp"
#include "element_wise_operation.hpp"
#include "reference_gemm.hpp"
#include "gemm_specialization.hpp"
#include "element_wise_reduce_operation.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using ADataType = F16;
using BDataType = F16;
using CDataType = F16;
using GemmAccDataType = F32;
using ReduceAccDataType = F32;
using DDataType = F32;
using DPtrsGlobal = ck::Tuple<DDataType*, DDataType*>;
using GammaDataType = F16;
using BetaDataType = F16;
using LayerNormOutDataType = F16;
using NormalizeComputeDataType = F32;
using ALayout = ck::tensor_layout::gemm::RowMajor;
using BLayout = ck::tensor_layout::gemm::ColumnMajor;
using CLayout = ck::tensor_layout::gemm::RowMajor;
using AElementOp = ck::tensor_operation::element_wise::PassThrough;
using BElementOp = ck::tensor_operation::element_wise::PassThrough;
using CElementOp = ck::tensor_operation::element_wise::PassThrough;
using ReduceSumOp = ck::reduce::Add<ReduceAccDataType>;
using DxsReduceOp = ck::Tuple<ReduceSumOp, ReduceSumOp>;
using UnaryIdenticElementOp =
ck::tensor_operation::element_wise::UnaryIdentic<ReduceAccDataType, ReduceAccDataType, false>;
using UnaryDivElementOp =
ck::tensor_operation::element_wise::UnaryIdentic<ReduceAccDataType, ReduceAccDataType, true>;
using UnarySquareElementOp =
ck::tensor_operation::element_wise::UnarySquare<ReduceAccDataType, ReduceAccDataType, false>;
using DxsInElementOp = ck::Tuple<UnaryIdenticElementOp, UnarySquareElementOp>;
using DxsOutElementOp = ck::Tuple<UnaryDivElementOp, UnaryDivElementOp>;
using DxsGlobalMemOp =
ck::InMemoryDataOperationEnumSequence<ck::InMemoryDataOperationEnum::AtomicAdd,
ck::InMemoryDataOperationEnum::AtomicAdd>;
static constexpr auto GemmSpecialization =
ck::tensor_operation::device::GemmSpecialization::Default;
// clang-format off
using DeviceGemmReduceInstance = ck::tensor_operation::device::DeviceGemmReduce_Xdl_CShuffle
//######| ALayout| BLayout| CLayout|AData| BData| CData| GemmAcc| CShuffle| ReduceAcc| DData| A| B| C| Dxs| DxsInEleOp| DxsAccEleOp| D| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| CReduce| CReduceThreadLds2VGprCopy| CReduceThreadVgpr2GlobalCopy|
//######| | | | Type| Type| Type| DataType| DataType| DataType| Type Tuple| Elementwise| Elementwise| Elementwise| Reduce| | | MemoryData| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MPerBlock| ScalarPerVector| ThreadClusterLengths| SrcDstScalarPerVector| SrcDstScalarPerVector|
//######| | | | | | | | | | | Operation| Operation| Operation| Operation| | | Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock| _MPerBlock_NPerBlock| _NPerBlock| _MPerBlock|
//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< Row, Col, Row, F16, F16, F16, F32, F32, F32, DPtrsGlobal, AElementOp, BElementOp, CElementOp, DxsReduceOp, DxsInElementOp, DxsOutElementOp, DxsGlobalMemOp, GemmSpecialization, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>;
// clang-format on
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType,
CDataType,
GemmAccDataType,
AElementOp,
BElementOp,
CElementOp>;
using NormalizeFunctor = ck::tensor_operation::element_wise::Normalize;
// A:x, B:E[x], C:E[x^2], D:Gamma, E:Beta , F:y
using DeviceNormalizeInstance =
ck::tensor_operation::device::Device5AryElementwise<CDataType,
DDataType,
DDataType,
GammaDataType,
BetaDataType,
LayerNormOutDataType,
NormalizeComputeDataType,
NormalizeFunctor,
2,
8,
8, // scalarPerVector: gemm_out
1, // scalarPerVector: reduce_mean
1, // scalarPerVector: reduce_mean_square
8, // scalarPerVector: Gamma
8, // scalarPerVector: Beta
8>; // scalarPerVector: LayerNorm_out
auto f_host_tensor_descriptor1d = [](std::size_t len, std::size_t stride) {
return HostTensorDescriptor(std::vector<std::size_t>({len}),
std::vector<std::size_t>({stride}));
};
auto f_host_tensor_descriptor2d =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
std::vector<std::size_t>({stride, 1}));
}
else
{
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
std::vector<std::size_t>({1, stride}));
}
};
template <typename CDataType,
typename DDataType,
typename A_functor,
typename B_functor,
typename C_functor>
void host_gemm_layernorm(Tensor<LayerNormOutDataType>& out_m_n,
const Tensor<ADataType>& a_m_k,
const Tensor<ADataType>& b_k_n,
const Tensor<GammaDataType>& gamma_n,
const Tensor<GammaDataType>& beta_n,
A_functor a_element_op,
B_functor b_element_op,
C_functor c_element_op,
int M,
int N)
{
using out_type = ck::remove_reference_t<decltype(out_m_n(0, 0))>;
int StrideC = N;
Tensor<CDataType> c_m_n(f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
Tensor<DDataType> mean_m(f_host_tensor_descriptor1d(M, 1));
Tensor<DDataType> meanSquare_m(f_host_tensor_descriptor1d(M, 1));
auto averageOpInst = UnaryDivElementOp{M};
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument =
ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, a_element_op, b_element_op, c_element_op);
ref_invoker.Run(ref_argument);
// reduce_mean and reduce_square_mean
auto reduceSumOpInst = ReduceSumOp{};
for(int m = 0; m < M; ++m)
{
float mean_acc = reduceSumOpInst.GetIdentityValue();
float square_mean_acc = reduceSumOpInst.GetIdentityValue();
for(int n = 0; n < N; ++n)
{
ReduceAccDataType c_val = ck::type_convert<float>(c_m_n(m, n));
ReduceAccDataType square_c_val = 0;
UnarySquareElementOp{}(square_c_val, c_val);
reduceSumOpInst(mean_acc, c_val);
reduceSumOpInst(square_mean_acc, square_c_val);
}
averageOpInst(mean_acc, mean_acc);
averageOpInst(square_mean_acc, square_mean_acc);
mean_m(m) = ck::type_convert<DDataType>(mean_acc);
meanSquare_m(m) = ck::type_convert<DDataType>(square_mean_acc);
}
// LayerNorm
auto layerNormInst = NormalizeFunctor{};
for(int m = 0; m < M; ++m)
{
for(int n = 0; n < N; ++n)
{
float out_f32 = 0;
layerNormInst(out_f32, c_m_n(m, n), mean_m(m), meanSquare_m(m), gamma_n(n), beta_n(n));
out_m_n(m, n) = static_cast<out_type>(out_f32);
}
}
}
template <typename ADataType,
typename BDataType,
typename CDataType,
typename DDataType,
typename GammaDataType,
typename BetaDataType,
typename NormalizeDataType>
void DumpGemmLayerNormPerf(float gemm_reduce_time, float normalize_time, int M, int N, int K)
{
std::size_t gemm_flop = std::size_t(2) * M * N * K;
std::size_t gemm_num_byte = sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
sizeof(CDataType) * M * N + sizeof(DDataType) * M +
sizeof(DDataType) * M;
std::size_t normalize_num_btye = sizeof(CDataType) * M * N + sizeof(DDataType) * M +
sizeof(DDataType) * M + sizeof(GammaDataType) * N +
sizeof(BetaDataType) * N + sizeof(NormalizeDataType) * M * N;
float tflops = static_cast<float>(gemm_flop) / 1.E9 / gemm_reduce_time;
float gemm_gb_per_sec = gemm_num_byte / 1.E6 / gemm_reduce_time;
float normalize_gb_per_sec = normalize_num_btye / 1.E6 / normalize_time;
std::cout << "gemm + reduce_mean + reduce_square_mean Perf: " << gemm_reduce_time << " ms, "
<< tflops << " TFlops, " << gemm_gb_per_sec << " GB/s, " << std::endl;
std::cout << "5-ary elementwise Perf: " << normalize_time << " ms, " << normalize_gb_per_sec
<< " GB/s, " << std::endl;
}
int main()
{
// GEMM shape
ck::index_t M = 1024;
ck::index_t N = 1024;
ck::index_t K = 1024;
ck::index_t StrideA = 1024;
ck::index_t StrideB = 1024;
ck::index_t StrideC = 1024;
Tensor<ADataType> a_m_k(f_host_tensor_descriptor2d(M, K, StrideA, ALayout{}));
Tensor<BDataType> b_k_n(f_host_tensor_descriptor2d(K, N, StrideB, BLayout{}));
Tensor<CDataType> c_m_n(f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
Tensor<DDataType> reduceMean_m(f_host_tensor_descriptor1d(M, 1));
Tensor<DDataType> reduceMeanSquare_m(f_host_tensor_descriptor1d(M, 1));
Tensor<GammaDataType> gamma_n(f_host_tensor_descriptor1d(N, 1));
Tensor<BetaDataType> beta_n(f_host_tensor_descriptor1d(N, 1));
Tensor<LayerNormOutDataType> layerNorm_m_n(
f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{-1, 1});
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-1, 1});
gamma_n.GenerateTensorValue(GeneratorTensor_3<GammaDataType>{-1, 1});
beta_n.GenerateTensorValue(GeneratorTensor_3<BetaDataType>{-1, 1});
DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace());
DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace());
DeviceMem c_device_buf(sizeof(CDataType) * c_m_n.mDesc.GetElementSpace());
DeviceMem reduceMean_device_buf(sizeof(DDataType) * reduceMean_m.mDesc.GetElementSpace());
DeviceMem reduceMeanSquare_device_buf(sizeof(DDataType) *
reduceMeanSquare_m.mDesc.GetElementSpace());
DeviceMem gamma_device_buf(sizeof(GammaDataType) * gamma_n.mDesc.GetElementSpace());
DeviceMem beta_device_buf(sizeof(BetaDataType) * beta_n.mDesc.GetElementSpace());
DeviceMem layerNorm_device_buf(sizeof(LayerNormOutDataType) *
layerNorm_m_n.mDesc.GetElementSpace());
a_device_buf.ToDevice(a_m_k.mData.data());
b_device_buf.ToDevice(b_k_n.mData.data());
gamma_device_buf.ToDevice(gamma_n.mData.data());
beta_device_buf.ToDevice(beta_n.mData.data());
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto c_element_op = CElementOp{};
auto dxs_global =
ck::make_tuple(static_cast<DDataType*>(reduceMean_device_buf.GetDeviceBuffer()),
static_cast<DDataType*>(reduceMeanSquare_device_buf.GetDeviceBuffer()));
auto dxs_in_element_op = DxsInElementOp{};
auto dxs_out_element_op = DxsOutElementOp{M, M};
// Prepare GEMM, reduce_mean, reduce_mean_square
auto gemmReduce = DeviceGemmReduceInstance{};
auto gemmReduce_invoker = gemmReduce.MakeInvoker();
auto gemmReduce_argument =
gemmReduce.MakeArgument(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
dxs_global,
M,
N,
K,
StrideA,
StrideB,
StrideC,
a_element_op,
b_element_op,
c_element_op,
dxs_in_element_op,
dxs_out_element_op);
if(!gemmReduce.IsSupportedArgument(gemmReduce_argument))
{
throw std::runtime_error(
"wrong! device_gemm with the specified compilation parameters does "
"not support this GEMM problem");
}
reduceMean_device_buf.SetZero();
reduceMeanSquare_device_buf.SetZero();
// Prepare LayerNorm
auto normalize = DeviceNormalizeInstance{};
auto normalize_invoker = normalize.MakeInvoker();
auto normalize_argument = normalize.MakeArgument(
static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
static_cast<DDataType*>(reduceMean_device_buf.GetDeviceBuffer()),
static_cast<DDataType*>(reduceMeanSquare_device_buf.GetDeviceBuffer()),
static_cast<GammaDataType*>(gamma_device_buf.GetDeviceBuffer()),
static_cast<BetaDataType*>(beta_device_buf.GetDeviceBuffer()),
static_cast<LayerNormOutDataType*>(layerNorm_device_buf.GetDeviceBuffer()),
{M, N},
{StrideC, 1},
{1, 0},
{1, 0},
{0, 1},
{0, 1},
{StrideC, 1},
NormalizeFunctor{});
if(!normalize.IsSupportedArgument(normalize_argument))
{
throw std::runtime_error("The runtime parameters seems not supported by the "
"Device5AryElementwise instance, exiting!");
}
// run kernel
gemmReduce_invoker.Run(gemmReduce_argument, StreamConfig{nullptr, false});
normalize_invoker.Run(normalize_argument, StreamConfig{nullptr, false});
bool pass = true;
{
// verification
Tensor<LayerNormOutDataType> host_layerNorm_m_n(
f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
host_gemm_layernorm<CDataType, DDataType>(host_layerNorm_m_n,
a_m_k,
b_k_n,
gamma_n,
beta_n,
a_element_op,
b_element_op,
c_element_op,
M,
N);
layerNorm_device_buf.FromDevice(layerNorm_m_n.mData.data());
pass &= ck::utils::check_err(layerNorm_m_n.mData,
host_layerNorm_m_n.mData,
"Error: Incorrect results d1",
1e-3,
1e-3);
}
{
// evaluate kernel perf
bool time_kernel = true;
float gemm_reduce_mean_reduce_square_mean_ave_time =
gemmReduce_invoker.Run(gemmReduce_argument, StreamConfig{nullptr, time_kernel});
float normalize_ave_time =
normalize_invoker.Run(normalize_argument, StreamConfig{nullptr, time_kernel});
if(time_kernel)
DumpGemmLayerNormPerf<ADataType,
BDataType,
CDataType,
DDataType,
GammaDataType,
BetaDataType,
LayerNormOutDataType>(
gemm_reduce_mean_reduce_square_mean_ave_time, normalize_ave_time, M, N, K);
}
return pass ? 0 : 1;
}
example/22_cgemm/CMakeLists.txt 0 → 100644
View file @ f26fb605
add_example_executable(example_cgemm_xdl_fp16 cgemm_xdl_fp16.cpp)
example/22_cgemm/cgemm_xdl_fp16.cpp 0 → 100644
View file @ f26fb605
/*******************************************************************************
*
* MIT License
*
* Copyright (c) 2022 Advanced Micro Devices, Inc.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*
*******************************************************************************/
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include <stdlib.h>
#include <half.hpp>
#include "check_err.hpp"
#include "config.hpp"
#include "device.hpp"
#include "host_tensor.hpp"
#include "host_tensor_generator.hpp"
#include "device_tensor.hpp"
#include "device_cgemm_4gemm_xdl_cshuffle.hpp"
#include "element_wise_operation.hpp"
#include "reference_cgemm.hpp"
#include "gemm_specialization.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ADataType = F16;
using BDataType = F16;
using CDataType = F16;
using AccDataType = F32;
using ALayout = ck::tensor_layout::gemm::RowMajor;
using BLayout = ck::tensor_layout::gemm::ColumnMajor;
using CLayout = ck::tensor_layout::gemm::RowMajor;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
// clang-format off
using DeviceCGemmInstance = ck::tensor_operation::device::DeviceCGemm_4Gemm_Xdl_CShuffle
<ALayout, // typename ALayout
BLayout, // typename BLayout
CLayout, // typename CLayout
ADataType, // typename ADataType
BDataType, // typename BDataType
CDataType, // typename CDataType
AccDataType, // typename GemmAccDataType
CDataType, // typename CShuffleDataType
PassThrough, // typename AElementwiseOperation
PassThrough, // typename BElementwiseOperation
PassThrough, // typename CElementwiseOperation
GemmDefault, // GemmSpecialization GemmSpec
1, // index_t NumGemmKPrefetchStage
256, // index_t BlockSize
256, // index_t MPerBlock
128, // index_t NPerBlock
32, // index_t KPerBlock
8, // index_t AK1
8, // index_t BK1
32, // index_t MPerXDL
32, // index_t NPerXDL
4, // index_t MXdlPerWave
2, // index_t NXdlPerWave
S<4, 64, 1>, // typename ABlockTransferThreadClusterLengths_AK0_M_AK1
S<1, 0, 2>, // typename ABlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // typename ABlockTransferSrcAccessOrder
2, // index_t ABlockTransferSrcVectorDim
8, // index_t ABlockTransferSrcScalarPerVector
8, // index_t ABlockTransferDstScalarPerVector_AK1
1, // index_t ABlockLdsExtraM
S<4, 64, 1>, // typename BBlockTransferThreadClusterLengths_BK0_N_BK1
S<1, 0, 2>, // typename BBlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // typename BBlockTransferSrcAccessOrder
2, // index_t BBlockTransferSrcVectorDim
8, // index_t BBlockTransferSrcScalarPerVector
8, // index_t BBlockTransferDstScalarPerVector_BK1
1, // index_t BBlockLdsExtraN
1, // index_t CShuffleMXdlPerWavePerShuffle
1, // index_t CShuffleNXdlPerWavePerShuffle
S<1, 32, 1, 8>, // typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8>; // index_t CShuffleBlockTransferScalarPerVector_NPerBlock
// clang-format on
using ReferenceCGemmInstance = ck::tensor_operation::host::
ReferenceCGemm<ADataType, BDataType, CDataType, PassThrough, PassThrough, PassThrough>;
int main(int argc, char* argv[])
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
// CGEMM shape
ck::index_t M = 3840;
ck::index_t N = 4096;
ck::index_t K = 4096;
ck::index_t StrideA = 4096;
ck::index_t StrideB = 4096;
ck::index_t StrideC = 4096;
if(argc == 4)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 10)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);
StrideA = std::stoi(argv[7]);
StrideB = std::stoi(argv[8]);
StrideC = std::stoi(argv[9]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: run kernel # of times (>1)\n");
printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n");
exit(0);
}
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
std::vector<std::size_t>({stride, 1}));
}
else
{
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
std::vector<std::size_t>({1, stride}));
}
};
Tensor<ADataType> a_m_k_real(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
Tensor<ADataType> a_m_k_imag(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
Tensor<BDataType> b_k_n_real(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
Tensor<BDataType> b_k_n_imag(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
Tensor<CDataType> c_m_n_real_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<CDataType> c_m_n_imag_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
std::cout << "a_m_k_real: " << a_m_k_real.mDesc << std::endl;
std::cout << "a_m_k_imag: " << a_m_k_imag.mDesc << std::endl;
std::cout << "b_k_n_real: " << b_k_n_real.mDesc << std::endl;
std::cout << "b_k_n_imag: " << b_k_n_imag.mDesc << std::endl;
std::cout << "c_m_n_real: " << c_m_n_real_device_result.mDesc << std::endl;
std::cout << "c_m_n_imag: " << c_m_n_imag_device_result.mDesc << std::endl;
switch(init_method)
{
case 0: break;
case 1:
a_m_k_real.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
a_m_k_imag.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
b_k_n_real.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
b_k_n_imag.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
break;
default:
a_m_k_real.GenerateTensorValue(GeneratorTensor_3<ADataType>{-0.5, 0.5});
a_m_k_imag.GenerateTensorValue(GeneratorTensor_3<ADataType>{-0.5, 0.5});
b_k_n_real.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
b_k_n_imag.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
}
auto cgemm = DeviceCGemmInstance{};
DeviceMem a_m_k_real_device_buf(sizeof(ADataType) * a_m_k_real.mDesc.GetElementSpace());
DeviceMem a_m_k_imag_device_buf(sizeof(ADataType) * a_m_k_imag.mDesc.GetElementSpace());
DeviceMem b_k_n_real_device_buf(sizeof(BDataType) * b_k_n_real.mDesc.GetElementSpace());
DeviceMem b_k_n_imag_device_buf(sizeof(BDataType) * b_k_n_imag.mDesc.GetElementSpace());
DeviceMem c_m_n_real_device_buf(sizeof(CDataType) *
c_m_n_real_device_result.mDesc.GetElementSpace());
DeviceMem c_m_n_imag_device_buf(sizeof(CDataType) *
c_m_n_imag_device_result.mDesc.GetElementSpace());
DeviceMem workspace_device_buf(cgemm.GetWorkspaceSize(M, N, K, StrideA, StrideB, StrideC));
a_m_k_real_device_buf.ToDevice(a_m_k_real.mData.data());
a_m_k_imag_device_buf.ToDevice(a_m_k_imag.mData.data());
b_k_n_real_device_buf.ToDevice(b_k_n_real.mData.data());
b_k_n_imag_device_buf.ToDevice(b_k_n_imag.mData.data());
auto a_element_op = PassThrough{};
auto b_element_op = PassThrough{};
auto c_element_op = PassThrough{};
// do GEMM
auto invoker = cgemm.MakeInvoker();
auto argument =
cgemm.MakeArgument(static_cast<ADataType*>(a_m_k_real_device_buf.GetDeviceBuffer()),
static_cast<ADataType*>(a_m_k_imag_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_k_n_real_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_k_n_imag_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(c_m_n_real_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(c_m_n_imag_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(workspace_device_buf.GetDeviceBuffer()),
M,
N,
K,
StrideA,
StrideB,
StrideC,
a_element_op,
b_element_op,
c_element_op);
if(!cgemm.IsSupportedArgument(argument))
{
throw std::runtime_error(
"wrong! device_cgemm with the specified compilation parameters does "
"not support this CGEMM problem");
}
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(8) * M * N * K;
std::size_t num_btype =
std::size_t(2) *
(sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(CDataType) * M * N);
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
<< cgemm.GetTypeString() << std::endl;
c_m_n_real_device_buf.FromDevice(c_m_n_real_device_result.mData.data());
c_m_n_imag_device_buf.FromDevice(c_m_n_imag_device_result.mData.data());
if(do_verification)
{
Tensor<CDataType> c_m_n_real_host_result(
f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<CDataType> c_m_n_imag_host_result(
f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
auto ref_cgemm = ReferenceCGemmInstance{};
auto ref_invoker = ref_cgemm.MakeInvoker();
auto ref_argument = ref_cgemm.MakeArgument(a_m_k_real,
a_m_k_imag,
b_k_n_real,
b_k_n_imag,
c_m_n_real_host_result,
c_m_n_imag_host_result,
a_element_op,
b_element_op,
c_element_op);
ref_invoker.Run(ref_argument);
ck::utils::check_err(c_m_n_real_device_result.mData,
c_m_n_real_host_result.mData,
"Verification error: incorrect results in real part!",
1e-2f,
1e-1f);
ck::utils::check_err(c_m_n_imag_device_result.mData,
c_m_n_imag_host_result.mData,
"Verification error: incorrect results in imaginary part!",
1e-2f,
1e-1f);
}
return 0;
}
example/CMakeLists.txt
View file @ f26fb605
......@@ -48,9 +48,11 @@ add_subdirectory(11_conv2d_bwd_weight)
add_subdirectory(12_reduce)
add_subdirectory(13_pool2d_fwd)
add_subdirectory(14_gemm_xdl_requant_relu_requant)
add_subdirectory(17_convnd_bwd_data_xdl)
add_subdirectory(15_grouped_gemm)
add_subdirectory(16_gemm_reduce)
add_subdirectory(17_convnd_bwd_data_xdl)
add_subdirectory(18_batched_gemm_reduce)
add_subdirectory(19_binary_elementwise)
add_subdirectory(20_convnd_bwd_weight_xdl)
add_subdirectory(21_gemm_layernorm)
add_subdirectory(22_cgemm)
include/ck/tensor_operation/gpu/device/device_5ary_elementwise.hpp 0 → 100644
View file @ f26fb605
#pragma once
#include <iostream>
#include <sstream>
#include "device.hpp"
#include "device_base.hpp"
#include "common_header.hpp"
#include "gridwise_5ary_Elementwise_1d.hpp"
#include "tensor_layout.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
template <typename ADataType,
typename BDataType,
typename CDataType,
typename DDataType,
typename EDataType,
typename FDataType,
typename ComputeDataType,
typename ElementwiseFunctor,
index_t NDim,
index_t MPerThread,
index_t AScalarPerVector,
index_t BScalarPerVector,
index_t CScalarPerVector,
index_t DScalarPerVector,
index_t EScalarPerVector,
index_t FScalarPerVector>
struct Device5AryElementwise : public BaseOperator
{
static constexpr auto I0 = Number<0>{};
template <typename Desc_M>
static auto PadDescriptor_M_1d(Desc_M desc_m, index_t gridSize, index_t blockSize)
{
const auto m = desc_m.GetLength(I0);
const index_t loop_step = gridSize * blockSize * MPerThread;
const auto pad = math::integer_least_multiple(m, loop_step) - m;
const auto desc_m_pad =
transform_tensor_descriptor(desc_m,
make_tuple(make_right_pad_transform(m, pad)),
make_tuple(Sequence<0>{}),
make_tuple(Sequence<0>{}));
return desc_m_pad;
}
static auto MakeDescriptor_M(const std::vector<index_t>& lengths,
const std::vector<index_t>& stride,
index_t gridSize,
index_t blockSize)
{
auto tupleOfShape = generate_tuple([&](auto I) { return lengths[I]; }, Number<NDim>{});
auto tupleOfStride = generate_tuple([&](auto I) { return stride[I]; }, Number<NDim>{});
// nd desc - [s0, s1, s2, ...]
const auto desc = make_naive_tensor_descriptor(tupleOfShape, tupleOfStride);
// merge nd to 1d desc - [s0 * s1 * ...]
if constexpr(NDim > 1)
{
const auto desc_m = transform_tensor_descriptor(
desc,
make_tuple(make_merge_transform(tupleOfShape)),
make_tuple(generate_sequence_v2([&](auto I) { return I; }, Number<NDim>{})),
make_tuple(Sequence<0>{}));
return PadDescriptor_M_1d(desc_m, gridSize, blockSize);
}
else
return PadDescriptor_M_1d(desc, gridSize, blockSize);
}
using AGridDesc_M = decltype(MakeDescriptor_M({1, 1}, {1, 1}, 1, 1));
using BGridDesc_M = decltype(MakeDescriptor_M({1, 1}, {1, 1}, 1, 1));
using CGridDesc_M = decltype(MakeDescriptor_M({1, 1}, {1, 1}, 1, 1));
using DGridDesc_M = decltype(MakeDescriptor_M({1, 1}, {1, 1}, 1, 1));
using EGridDesc_M = decltype(MakeDescriptor_M({1, 1}, {1, 1}, 1, 1));
using FGridDesc_M = decltype(MakeDescriptor_M({1, 1}, {1, 1}, 1, 1));
using Gridwise5AryEltwise = Gridwise5AryElementwise_1D<ADataType,
BDataType,
CDataType,
DDataType,
EDataType,
FDataType,
ComputeDataType,
AGridDesc_M,
BGridDesc_M,
CGridDesc_M,
DGridDesc_M,
EGridDesc_M,
FGridDesc_M,
ElementwiseFunctor,
MPerThread,
AScalarPerVector,
BScalarPerVector,
CScalarPerVector,
DScalarPerVector,
EScalarPerVector,
FScalarPerVector>;
struct Argument : public BaseArgument
{
Argument(const ADataType* p_a,
const BDataType* p_b,
const CDataType* p_c,
const DDataType* p_d,
const EDataType* p_e,
FDataType* p_f,
const std::vector<index_t>& lengths,
const std::vector<index_t>& a_strides,
const std::vector<index_t>& b_strides,
const std::vector<index_t>& c_strides,
const std::vector<index_t>& d_strides,
const std::vector<index_t>& e_strides,
const std::vector<index_t>& f_strides,
ElementwiseFunctor functor)
: p_a_(p_a),
p_b_(p_b),
p_c_(p_c),
p_d_(p_d),
p_e_(p_e),
p_f_(p_f),
lengths_(lengths),
a_strides_(a_strides),
b_strides_(b_strides),
c_strides_(c_strides),
d_strides_(d_strides),
e_strides_(e_strides),
f_strides_(f_strides),
functor_(functor),
blockSize_(256),
gridSize_(120) // FIXME - Calculate the grid size by number of CU in the future
{
a_grid_desc_m_ = MakeDescriptor_M(lengths, a_strides, gridSize_, blockSize_);
b_grid_desc_m_ = MakeDescriptor_M(lengths, b_strides, gridSize_, blockSize_);
c_grid_desc_m_ = MakeDescriptor_M(lengths, c_strides, gridSize_, blockSize_);
d_grid_desc_m_ = MakeDescriptor_M(lengths, d_strides, gridSize_, blockSize_);
e_grid_desc_m_ = MakeDescriptor_M(lengths, e_strides, gridSize_, blockSize_);
f_grid_desc_m_ = MakeDescriptor_M(lengths, f_strides, gridSize_, blockSize_);
}
const ADataType* p_a_;
const BDataType* p_b_;
const CDataType* p_c_;
const DDataType* p_d_;
const EDataType* p_e_;
FDataType* p_f_;
std::vector<index_t> lengths_;
AGridDesc_M a_grid_desc_m_;
BGridDesc_M b_grid_desc_m_;
CGridDesc_M c_grid_desc_m_;
DGridDesc_M d_grid_desc_m_;
EGridDesc_M e_grid_desc_m_;
FGridDesc_M f_grid_desc_m_;
std::vector<index_t> a_strides_;
std::vector<index_t> b_strides_;
std::vector<index_t> c_strides_;
std::vector<index_t> d_strides_;
std::vector<index_t> e_strides_;
std::vector<index_t> f_strides_;
ElementwiseFunctor functor_;
index_t blockSize_;
index_t gridSize_;
};
struct Invoker : public BaseInvoker
{
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
const auto kernel = kernel_5ary_elementwise_1d<Gridwise5AryEltwise,
ADataType,
BDataType,
CDataType,
DDataType,
EDataType,
FDataType,
AGridDesc_M,
BGridDesc_M,
CGridDesc_M,
DGridDesc_M,
EGridDesc_M,
FGridDesc_M,
ElementwiseFunctor>;
float elapsed_time = launch_and_time_kernel(stream_config,
kernel,
dim3(arg.gridSize_),
dim3(arg.blockSize_),
0,
arg.p_a_,
arg.p_b_,
arg.p_c_,
arg.p_d_,
arg.p_e_,
arg.p_f_,
arg.a_grid_desc_m_,
arg.b_grid_desc_m_,
arg.c_grid_desc_m_,
arg.d_grid_desc_m_,
arg.e_grid_desc_m_,
arg.f_grid_desc_m_,
arg.functor_);
return elapsed_time;
}
// polymorphic
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};
bool IsSupportedArgument(const BaseArgument& p_arg) { return IsSupportedArgument(&p_arg); }
bool IsSupportedArgument(const BaseArgument* p_arg) override
{
const Argument* pArg = dynamic_cast<const Argument*>(p_arg);
if(pArg == nullptr)
return false;
if(pArg->lengths_.size() != NDim)
return false;
if(pArg->lengths_.back() % MPerThread != 0)
return false;
auto IsScalarPerVectorValid = [](bool isLastDimensionCoalesced, int scalarPerVector) {
bool ret = true;
if(!isLastDimensionCoalesced)
ret = scalarPerVector == 1;
else
ret = MPerThread % scalarPerVector == 0;
return ret;
};
if(!IsScalarPerVectorValid(pArg->a_strides_.back() == 1, AScalarPerVector))
return false;
if(!IsScalarPerVectorValid(pArg->b_strides_.back() == 1, BScalarPerVector))
return false;
if(!IsScalarPerVectorValid(pArg->c_strides_.back() == 1, CScalarPerVector))
return false;
if(!IsScalarPerVectorValid(pArg->d_strides_.back() == 1, DScalarPerVector))
return false;
if(!IsScalarPerVectorValid(pArg->e_strides_.back() == 1, EScalarPerVector))
return false;
if(!IsScalarPerVectorValid(pArg->f_strides_.back() == 1, FScalarPerVector))
return false;
return true;
};
static auto MakeArgument(const ADataType* p_a,
const BDataType* p_b,
const CDataType* p_c,
const DDataType* p_d,
const EDataType* p_e,
FDataType* p_f,
std::vector<index_t> lengths,
std::vector<index_t> a_strides,
std::vector<index_t> b_strides,
std::vector<index_t> c_strides,
std::vector<index_t> d_strides,
std::vector<index_t> e_strides,
std::vector<index_t> f_strides,
ElementwiseFunctor functor)
{
return Argument{p_a,
p_b,
p_c,
p_d,
p_e,
p_f,
lengths,
a_strides,
b_strides,
c_strides,
d_strides,
e_strides,
f_strides,
functor};
}
std::unique_ptr<BaseArgument> MakeArgumentPointer(const void* p_a,
const void* p_b,
const void* p_c,
const void* p_d,
const void* p_e,
void* p_f,
std::vector<index_t> lengths,
std::vector<index_t> a_strides,
std::vector<index_t> b_strides,
std::vector<index_t> c_strides,
std::vector<index_t> d_strides,
std::vector<index_t> e_strides,
std::vector<index_t> f_strides,
ElementwiseFunctor functor)
{
return std::make_unique<Argument>(static_cast<const ADataType*>(p_a),
static_cast<const BDataType*>(p_b),
static_cast<const CDataType*>(p_c),
static_cast<const DDataType*>(p_d),
static_cast<const EDataType*>(p_e),
static_cast<FDataType*>(p_f),
lengths,
a_strides,
b_strides,
c_strides,
d_strides,
e_strides,
f_strides,
functor);
}
static auto MakeInvoker() { return Invoker{}; }
std::unique_ptr<BaseInvoker> MakeInvokerPointer() { return std::make_unique<Invoker>(); }
}; // namespace device
} // namespace device
} // namespace tensor_operation
} // namespace ck
include/ck/tensor_operation/gpu/device/device_batched_gemm_reduce_xdl_cshuffle.hpp
View file @ f26fb605
......@@ -22,7 +22,7 @@ template <typename GridwiseGemm,
typename BElementwiseOperation,
typename CElementwiseOperation,
typename DxsInElementwiseOperation,
typename DxsOutElementwiseOperation,
typename DxsAccElementwiseOperation,
typename AGridDesc_AK0_M_AK1,
typename BGridDesc_BK0_N_BK1,
typename CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
......@@ -44,7 +44,7 @@ __global__ void
const BElementwiseOperation b_element_op,
const CElementwiseOperation c_element_op,
const DxsInElementwiseOperation dxs_in_element_op,
const DxsOutElementwiseOperation dxs_out_element_op,
const DxsAccElementwiseOperation dxs_out_element_op,
const AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1,
const BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1,
const CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
......@@ -126,7 +126,7 @@ template <typename ALayout,
typename CElementwiseOperation,
typename DxsReduceOperation,
typename DxsInElementwiseOperation,
typename DxsOutElementwiseOperation,
typename DxsAccElementwiseOperation,
typename DGlobalMemoryDataOperation,
GemmSpecialization GemmSpec,
index_t NumGemmKPrefetchStage,
......@@ -167,7 +167,7 @@ struct DeviceBatchedGemmReduce_Xdl_CShuffle : public DeviceGemmReduce<DPtrsGloba
BElementwiseOperation,
CElementwiseOperation,
DxsInElementwiseOperation,
DxsOutElementwiseOperation>
DxsAccElementwiseOperation>
{
using DeviceOp = DeviceBatchedGemmReduce_Xdl_CShuffle;
......@@ -527,7 +527,7 @@ struct DeviceBatchedGemmReduce_Xdl_CShuffle : public DeviceGemmReduce<DPtrsGloba
CElementwiseOperation,
DxsReduceOperation,
DxsInElementwiseOperation,
DxsOutElementwiseOperation,
DxsAccElementwiseOperation,
InMemoryDataOperationEnum::Set,
DGlobalMemoryDataOperation,
AGridDesc_AK0_M_AK1,
......@@ -587,7 +587,7 @@ struct DeviceBatchedGemmReduce_Xdl_CShuffle : public DeviceGemmReduce<DPtrsGloba
BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op,
DxsInElementwiseOperation dxs_in_element_op,
DxsOutElementwiseOperation dxs_out_element_op,
DxsAccElementwiseOperation dxs_out_element_op,
index_t BatchCount)
: p_a_grid_{p_a_grid},
p_b_grid_{p_b_grid},
......@@ -645,7 +645,7 @@ struct DeviceBatchedGemmReduce_Xdl_CShuffle : public DeviceGemmReduce<DPtrsGloba
BElementwiseOperation b_element_op_;
CElementwiseOperation c_element_op_;
DxsInElementwiseOperation dxs_in_element_op_;
DxsOutElementwiseOperation dxs_out_element_op_;
DxsAccElementwiseOperation dxs_out_element_op_;
};
// Invoker
......@@ -703,7 +703,7 @@ struct DeviceBatchedGemmReduce_Xdl_CShuffle : public DeviceGemmReduce<DPtrsGloba
BElementwiseOperation,
CElementwiseOperation,
DxsInElementwiseOperation,
DxsOutElementwiseOperation,
DxsAccElementwiseOperation,
DeviceOp::AGridDesc_AK0_M_AK1,
DeviceOp::BGridDesc_BK0_N_BK1,
typename GridwiseGemm::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
......@@ -746,7 +746,7 @@ struct DeviceBatchedGemmReduce_Xdl_CShuffle : public DeviceGemmReduce<DPtrsGloba
BElementwiseOperation,
CElementwiseOperation,
DxsInElementwiseOperation,
DxsOutElementwiseOperation,
DxsAccElementwiseOperation,
DeviceOp::AGridDesc_AK0_M_AK1,
DeviceOp::BGridDesc_BK0_N_BK1,
typename GridwiseGemm::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
......@@ -832,7 +832,7 @@ struct DeviceBatchedGemmReduce_Xdl_CShuffle : public DeviceGemmReduce<DPtrsGloba
BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op,
DxsInElementwiseOperation dxs_in_element_op,
DxsOutElementwiseOperation dxs_out_element_op,
DxsAccElementwiseOperation dxs_out_element_op,
index_t BatchCount)
{
return Argument{p_a,
......@@ -870,7 +870,7 @@ struct DeviceBatchedGemmReduce_Xdl_CShuffle : public DeviceGemmReduce<DPtrsGloba
BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op,
DxsInElementwiseOperation dxs_in_element_op,
DxsOutElementwiseOperation dxs_out_element_op,
DxsAccElementwiseOperation dxs_out_element_op,
index_t BatchCount) override
{
return std::make_unique<Argument>(static_cast<const ADataType*>(p_a),
......
include/ck/tensor_operation/gpu/device/device_cgemm.hpp 0 → 100644
View file @ f26fb605
/*******************************************************************************
*
* MIT License
*
* Copyright (c) 2022 Advanced Micro Devices, Inc.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*
*******************************************************************************/
#pragma once
#include "device_base.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
template <typename AElementwiseOperation,
typename BElementwiseOperation,
typename CElementwiseOperation>
struct DeviceCGemm : public BaseOperator
{
virtual std::unique_ptr<BaseArgument> MakeArgumentPointer(const void* p_a_real,
const void* p_a_imag,
const void* p_b_real,
const void* p_b_imag,
void* p_c_real,
void* p_c_imag,
void* p_workspace,
ck::index_t M,
ck::index_t N,
ck::index_t K,
ck::index_t StrideA,
ck::index_t StrideB,
ck::index_t StrideC,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op,
ck::index_t KBatch = 1) = 0;
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
virtual std::size_t GetWorkspaceSize(index_t MRaw,
index_t NRaw,
index_t KRaw,
index_t StrideA,
index_t StrideB,
index_t StrideC) = 0;
};
template <typename AElementwiseOperation,
typename BElementwiseOperation,
typename CElementwiseOperation>
using DeviceCGemmPtr = std::unique_ptr<
DeviceCGemm<AElementwiseOperation, BElementwiseOperation, CElementwiseOperation>>;
} // namespace device
} // namespace tensor_operation
} // namespace ck
include/ck/tensor_operation/gpu/device/device_conv2d_fwd_xdl_c_shuffle_bias_activation_add_nhwc_kyxc_nhwk.hpp
View file @ f26fb605
......@@ -460,6 +460,8 @@ struct
using C0GridDesc_M_N = remove_cvref_t<decltype(GridDescs{}[I3])>;
using C1GridDesc_M_N = remove_cvref_t<decltype(GridDescs{}[I4])>;
using Block2CTileMap = BlockToCTileMap_M00_N0_M01<MPerBlock, NPerBlock, CGridDesc_M_N>;
// GridwiseGemm
using GridwiseGemm = GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v3r3<
BlockSize,
......@@ -522,8 +524,6 @@ struct
std::vector<ck::index_t> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads,
std::vector<ck::index_t> input_right_pads,
ck::index_t M01,
ck::index_t N01,
InElementwiseOperation in_element_op,
WeiElementwiseOperation wei_element_op,
OutElementwiseOperation out_element_op)
......@@ -540,10 +540,7 @@ struct
c_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_{},
c0_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_{},
c1_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_{},
block_2_ctile_map_{
GridwiseGemm::MakeDefaultBlock2CTileMap(c_grid_desc_m_n_, M01, N01)},
M01_{M01},
N01_{N01},
block_2_ctile_map_{},
in_element_op_{in_element_op},
wei_element_op_{wei_element_op},
out_element_op_{out_element_op},
......@@ -576,6 +573,8 @@ struct
c0_grid_desc_m_n_ = descs[I3];
c1_grid_desc_m_n_ = descs[I4];
block_2_ctile_map_ = Block2CTileMap{c_grid_desc_m_n_};
if(GridwiseGemm::CheckValidity(a_grid_desc_k0_m_k1_,
b_grid_desc_k0_n_k1_,
c_grid_desc_m_n_,
......@@ -618,9 +617,7 @@ struct
typename GridwiseGemm::
C1GridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl
c1_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_;
typename GridwiseGemm::DefaultBlock2CTileMap block_2_ctile_map_;
index_t M01_;
index_t N01_;
Block2CTileMap block_2_ctile_map_;
InElementwiseOperation in_element_op_;
WeiElementwiseOperation wei_element_op_;
OutElementwiseOperation out_element_op_;
......@@ -723,7 +720,7 @@ struct
InElementwiseOperation,
WeiElementwiseOperation,
OutElementwiseOperation,
remove_reference_t<typename GridwiseGemm::DefaultBlock2CTileMap>,
Block2CTileMap,
true>;
ave_time = launch_and_time_kernel(
......@@ -767,7 +764,7 @@ struct
InElementwiseOperation,
WeiElementwiseOperation,
OutElementwiseOperation,
remove_reference_t<typename GridwiseGemm::DefaultBlock2CTileMap>,
Block2CTileMap,
false>;
ave_time = launch_and_time_kernel(
......@@ -894,8 +891,6 @@ struct
conv_filter_dilations,
input_left_pads,
input_right_pads,
1,
1,
in_element_op,
wei_element_op,
out_element_op};
......@@ -938,8 +933,6 @@ struct
conv_filter_dilations,
input_left_pads,
input_right_pads,
1,
1,
in_element_op,
wei_element_op,
out_element_op);
......
include/ck/tensor_operation/gpu/device/device_convnd_fwd_xdl_nhwc_kyxc_nhwk.hpp
View file @ f26fb605
#ifndef DEVICE_CONVND_FWD_XDL_NHWC_KYXC_NHWK_HPP
#define DEVICE_CONVND_FWD_XDL_NHWC_KYXC_NHWK_HPP
#pragma once
#include <functional>
#include <iostream>
......@@ -8,6 +7,7 @@
#include <sstream>
#include "device.hpp"
#include "device_prop.hpp"
#include "device_base.hpp"
#include "device_conv_fwd.hpp"
#include "convolution_forward_specialization.hpp"
......@@ -858,6 +858,27 @@ struct DeviceConvNDFwdXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
static bool IsSupportedArgument(const Argument& arg)
{
if(ck::get_device_name() == "gfx908")
{
if constexpr(!(is_same_v<AccDataType, float> || is_same_v<AccDataType, float> ||
is_same_v<AccDataType, int32_t>))
{
return false;
}
}
else if(ck::get_device_name() == "gfx90a")
{
if constexpr(!(is_same_v<AccDataType, float> || is_same_v<AccDataType, float> ||
is_same_v<AccDataType, int32_t> || is_same_v<AccDataType, double>))
{
return false;
}
}
else
{
return false;
}
// Input tensors can't be bigger than 2GB each.
constexpr ck::long_index_t GB2 = (ck::long_index_t{1} << 31);
......@@ -1021,4 +1042,3 @@ struct DeviceConvNDFwdXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
} // namespace device
} // namespace tensor_operation
} // namespace ck
#endif
include/ck/tensor_operation/gpu/device/device_gemm_dl.hpp
View file @ f26fb605
......@@ -4,6 +4,7 @@
#include <sstream>
#include "device.hpp"
#include "device_prop.hpp"
#include "device_base.hpp"
#include "device_gemm.hpp"
#include "common_header.hpp"
......@@ -13,7 +14,6 @@
#include "gemm_specialization.hpp"
#include "element_wise_operation.hpp"
#include "gridwise_gemm_dl_v1r3.hpp"
#include "device_prop.hpp"
namespace ck {
namespace tensor_operation {
......@@ -60,8 +60,8 @@ template <
index_t CThreadTransferDstScalarPerVector,
enable_if_t<
is_same_v<AElementwiseOperation, ck::tensor_operation::element_wise::PassThrough> &&
is_same_v<AElementwiseOperation, ck::tensor_operation::element_wise::PassThrough> &&
is_same_v<AElementwiseOperation, ck::tensor_operation::element_wise::PassThrough>,
is_same_v<BElementwiseOperation, ck::tensor_operation::element_wise::PassThrough> &&
is_same_v<CElementwiseOperation, ck::tensor_operation::element_wise::PassThrough>,
bool> = false>
struct DeviceGemmDl
: public DeviceGemm<AElementwiseOperation, BElementwiseOperation, CElementwiseOperation>
......
include/ck/tensor_operation/gpu/device/device_gemm_reduce.hpp
View file @ f26fb605
......@@ -11,7 +11,7 @@ template <typename DPtrsGlobal,
typename BElementwiseOperation,
typename CElementwiseOperation,
typename DxsInElementwiseOperation,
typename DxsOutElementwiseOperation>
typename DxsAccElementwiseOperation>
struct DeviceGemmReduce : public BaseOperator
{
virtual std::unique_ptr<BaseArgument>
......@@ -29,7 +29,7 @@ struct DeviceGemmReduce : public BaseOperator
BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op,
DxsInElementwiseOperation dxs_in_element_op,
DxsOutElementwiseOperation dxs_out_element_op,
DxsAccElementwiseOperation dxs_out_element_op,
ck::index_t BatchCount = 1) = 0;
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
......@@ -40,13 +40,13 @@ template <typename DPtrsGlobal,
typename BElementwiseOperation,
typename CElementwiseOperation,
typename DxsInElementwiseOperation,
typename DxsOutElementwiseOperation>
typename DxsAccElementwiseOperation>
using DeviceGemmReducePtr = std::unique_ptr<DeviceGemmReduce<DPtrsGlobal,
AElementwiseOperation,
BElementwiseOperation,
CElementwiseOperation,
DxsInElementwiseOperation,
DxsOutElementwiseOperation>>;
DxsAccElementwiseOperation>>;
} // namespace device
} // namespace tensor_operation
......
include/ck/tensor_operation/gpu/device/device_gemm_reduce_xdl_cshuffle.hpp
View file @ f26fb605
......@@ -32,7 +32,7 @@ template <typename ALayout,
typename CElementwiseOperation,
typename DxsReduceOperation,
typename DxsInElementwiseOperation,
typename DxsOutElementwiseOperation,
typename DxsAccElementwiseOperation,
typename DGlobalMemoryDataOperation,
GemmSpecialization GemmSpec,
index_t NumGemmKPrefetchStage,
......@@ -73,7 +73,7 @@ struct DeviceGemmReduce_Xdl_CShuffle : public DeviceGemmReduce<DPtrsGlobal,
BElementwiseOperation,
CElementwiseOperation,
DxsInElementwiseOperation,
DxsOutElementwiseOperation>
DxsAccElementwiseOperation>
{
using DeviceOp = DeviceGemmReduce_Xdl_CShuffle;
......@@ -389,7 +389,7 @@ struct DeviceGemmReduce_Xdl_CShuffle : public DeviceGemmReduce<DPtrsGlobal,
CElementwiseOperation,
DxsReduceOperation,
DxsInElementwiseOperation,
DxsOutElementwiseOperation,
DxsAccElementwiseOperation,
InMemoryDataOperationEnum::Set,
DGlobalMemoryDataOperation,
AGridDesc_AK0_M_AK1,
......@@ -449,7 +449,7 @@ struct DeviceGemmReduce_Xdl_CShuffle : public DeviceGemmReduce<DPtrsGlobal,
BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op,
DxsInElementwiseOperation dxs_in_element_op,
DxsOutElementwiseOperation dxs_out_element_op)
DxsAccElementwiseOperation dxs_out_element_op)
: p_a_grid_{p_a_grid},
p_b_grid_{p_b_grid},
p_c_grid_{p_c_grid},
......@@ -498,7 +498,7 @@ struct DeviceGemmReduce_Xdl_CShuffle : public DeviceGemmReduce<DPtrsGlobal,
BElementwiseOperation b_element_op_;
CElementwiseOperation c_element_op_;
DxsInElementwiseOperation dxs_in_element_op_;
DxsOutElementwiseOperation dxs_out_element_op_;
DxsAccElementwiseOperation dxs_out_element_op_;
};
// Invoker
......@@ -554,7 +554,7 @@ struct DeviceGemmReduce_Xdl_CShuffle : public DeviceGemmReduce<DPtrsGlobal,
BElementwiseOperation,
CElementwiseOperation,
DxsInElementwiseOperation,
DxsOutElementwiseOperation,
DxsAccElementwiseOperation,
DeviceOp::AGridDesc_AK0_M_AK1,
DeviceOp::BGridDesc_BK0_N_BK1,
typename GridwiseGemm::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
......@@ -594,7 +594,7 @@ struct DeviceGemmReduce_Xdl_CShuffle : public DeviceGemmReduce<DPtrsGlobal,
BElementwiseOperation,
CElementwiseOperation,
DxsInElementwiseOperation,
DxsOutElementwiseOperation,
DxsAccElementwiseOperation,
DeviceOp::AGridDesc_AK0_M_AK1,
DeviceOp::BGridDesc_BK0_N_BK1,
typename GridwiseGemm::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
......@@ -669,7 +669,7 @@ struct DeviceGemmReduce_Xdl_CShuffle : public DeviceGemmReduce<DPtrsGlobal,
BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op,
DxsInElementwiseOperation dxs_in_element_op,
DxsOutElementwiseOperation dxs_out_element_op)
DxsAccElementwiseOperation dxs_out_element_op)
{
return Argument{p_a,
p_b,
......@@ -705,7 +705,7 @@ struct DeviceGemmReduce_Xdl_CShuffle : public DeviceGemmReduce<DPtrsGlobal,
BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op,
DxsInElementwiseOperation dxs_in_element_op,
DxsOutElementwiseOperation dxs_out_element_op,
DxsAccElementwiseOperation dxs_out_element_op,
index_t /* KBatch */ = 1) override
{
return std::make_unique<Argument>(static_cast<const ADataType*>(p_a),
......
include/ck/tensor_operation/gpu/device/device_gemm_xdl.hpp
View file @ f26fb605
......@@ -3,6 +3,7 @@
#include <iostream>
#include <sstream>
#include "device.hpp"
#include "device_prop.hpp"
#include "device_base.hpp"
#include "device_gemm.hpp"
#include "common_header.hpp"
......@@ -11,7 +12,6 @@
#include "tensor_descriptor_helper.hpp"
#include "gridwise_gemm_xdlops_v2r3.hpp"
#include "gemm_specialization.hpp"
#include "device_prop.hpp"
namespace ck {
namespace tensor_operation {
......@@ -408,7 +408,23 @@ struct DeviceGemmXdl
static bool IsSupportedArgument(const Argument& arg)
{
if(!(ck::get_device_name() == "gfx908" || ck::get_device_name() == "gfx90a"))
if(ck::get_device_name() == "gfx908")
{
if constexpr(!(is_same_v<AccDataType, float> || is_same_v<AccDataType, float> ||
is_same_v<AccDataType, int32_t>))
{
return false;
}
}
else if(ck::get_device_name() == "gfx90a")
{
if constexpr(!(is_same_v<AccDataType, float> || is_same_v<AccDataType, float> ||
is_same_v<AccDataType, int32_t> || is_same_v<AccDataType, double>))
{
return false;
}
}
else
{
return false;
}
......
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