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Commit 5b7c2432 authored by Adam Osewski's avatar Adam Osewski
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Merge remote-tracking branch 'rosenrodt/gemm-standalone-bench' into wavelet_model

parents 7e493730 5a995b14
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example/42_groupnorm/groupnorm_sigmoid_fp16.cpp
View file @ 5b7c2432
...@@ -9,7 +9,7 @@ ...@@ -9,7 +9,7 @@
#include "ck/ck.hpp" #include "ck/ck.hpp"
#include "ck/utility/reduction_enums.hpp" #include "ck/utility/reduction_enums.hpp"
#include "ck/tensor_operation/gpu/device/device_layernorm_impl.hpp" #include "ck/tensor_operation/gpu/device/impl/device_normalization_impl.hpp"
#include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp" #include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"
#include "ck/library/utility/fill.hpp" #include "ck/library/utility/fill.hpp"
...@@ -47,26 +47,26 @@ struct YElementOp ...@@ -47,26 +47,26 @@ struct YElementOp
}; };
using DeviceInstance = using DeviceInstance =
ck::tensor_operation::device::DeviceLayernormImpl<XDataType, ck::tensor_operation::device::DeviceNormalizationImpl<XDataType,
GammaDataType, GammaDataType,
BetaDataType, BetaDataType,
AccDataType, AccDataType,
YDataType, YDataType,
YElementOp, YElementOp,
Rank, Rank,
NumReduceDim, NumReduceDim,
1024, // BlockSize 1024, // BlockSize
1, // ClusterM 1, // ClusterM
1024, // ClusterK 1024, // ClusterK
1, // SliceM 1, // SliceM
32, // SliceK 32, // SliceK
1, // SrcVecDim (0=M, 1=K) 1, // SrcVecDim (0=M, 1=K)
2, // SrcScalarPerVector 2, // SrcScalarPerVector
1, // GammaVecDim (0=M, 1=K) 1, // GammaVecDim (0=M, 1=K)
2, // GammaScalarPerVector 2, // GammaScalarPerVector
1, // BetaVecDim (0=M, 1=K) 1, // BetaVecDim (0=M, 1=K)
2, // BetaScalarPerVector 2, // BetaScalarPerVector
2>; // OutScalarPerVector 2>; // OutScalarPerVector
int main(int argc, char* argv[]) int main(int argc, char* argv[])
{ {
......
example/43_splitk_gemm_bias_e_permute/CMakeLists.txt 0 → 100644
View file @ 5b7c2432
add_example_executable(example_splitk_gemm_bias_e_permute_xdl_fp16 splitk_gemm_bias_e_permute_xdl_fp16.cpp)
add_example_executable(example_splitk_gemm_bias_e_permute_xdl_fp32 splitk_gemm_bias_e_permute_xdl_fp32.cpp)
example/43_splitk_gemm_bias_e_permute/splitk_gemm_bias_e_permute_xdl_fp16.cpp 0 → 100644
View file @ 5b7c2432
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/device_splitk_contraction_multiple_d_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using F16 = ck::half_t;
using F32 = float;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using Add = ck::tensor_operation::element_wise::Add;
using ADataType = F16;
using BDataType = F16;
using AccDataType = F32;
using CShuffleDataType = F16;
using DDataType = F16;
using DsDataType = ck::Tuple<DDataType>;
using EDataType = F16;
static constexpr ck::index_t NumDimG = 2;
static constexpr ck::index_t NumDimM = 2;
static constexpr ck::index_t NumDimN = 2;
static constexpr ck::index_t NumDimK = 1;
using AElementOp = ck::tensor_operation::element_wise::PassThrough;
using BElementOp = ck::tensor_operation::element_wise::PassThrough;
using CDEElementOp = ck::tensor_operation::element_wise::Add;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::Default;
static constexpr auto ABSpec = ck::tensor_operation::device::TensorSpecialization::Packed;
static constexpr auto DESpec = ck::tensor_operation::device::TensorSpecialization::Default;
// clang-format off
using DeviceOpInstanceKKNN = ck::tensor_operation::device::
//############################################| NumDimG| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| Gemm| A| B| DE| 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|
//############################################| | | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Spacialization| Spacialization| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//############################################| | | | | | | | | | | 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_NWaveNPerXdl| _NWaveNPerXdl|
//############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceSplitKContractionMultipleD_Xdl_CShuffle< NumDimG, NumDimM, NumDimN, NumDimK, F16, F16, F32, F16, DsDataType, F16, AElementOp, BElementOp, CDEElementOp, GemmSpec, ABSpec, ABSpec, DESpec, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>;
// clang-format on
using DeviceOpInstance = DeviceOpInstanceKKNN;
// hardcoded for NumDimM == NumDimN == NumDimK == 2
template <ck::index_t NumDimG,
ck::index_t NumDimM,
ck::index_t NumDimN,
ck::index_t NumDimK,
typename ADataType,
typename BDataType,
typename EDataType,
typename AccDataType,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CDEElementwiseOperation,
ck::enable_if_t<NumDimG == 2 && NumDimM == 2 && NumDimN == 2 && NumDimK == 1, bool> =
false>
struct ReferenceContraction_G2_M2_N2_K1 : public ck::tensor_operation::device::BaseOperator
{
// Argument
struct Argument : public ck::tensor_operation::device::BaseArgument
{
Argument(const Tensor<ADataType>& a_gs_ms_ks,
const Tensor<BDataType>& b_gs_ns_ks,
Tensor<EDataType>& e_gs_ms_ns,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation cde_element_op)
: a_gs_ms_ks_{a_gs_ms_ks},
b_gs_ns_ks_{b_gs_ns_ks},
e_gs_ms_ns_{e_gs_ms_ns},
a_element_op_{a_element_op},
b_element_op_{b_element_op},
cde_element_op_{cde_element_op}
{
}
const Tensor<ADataType>& a_gs_ms_ks_;
const Tensor<BDataType>& b_gs_ns_ks_;
Tensor<EDataType>& e_gs_ms_ns_;
AElementwiseOperation a_element_op_;
BElementwiseOperation b_element_op_;
CDEElementwiseOperation cde_element_op_;
};
// Invoker
struct Invoker : public ck::tensor_operation::device::BaseInvoker
{
using Argument = ReferenceContraction_G2_M2_N2_K1::Argument;
float Run(const Argument& arg)
{
auto f_ms_ns = [&](auto g0, auto g1, auto m0, auto m1, auto n0, auto n1) {
const int K0 = arg.a_gs_ms_ks_.mDesc.GetLengths()[4];
AccDataType v_acc = 0;
for(int k0 = 0; k0 < K0; ++k0)
{
AccDataType v_a;
AccDataType v_b;
arg.a_element_op_(
v_a,
ck::type_convert<const AccDataType>(arg.a_gs_ms_ks_(g0, g1, m0, m1, k0)));
arg.b_element_op_(
v_b,
ck::type_convert<const AccDataType>(arg.b_gs_ns_ks_(g0, g1, n0, n1, k0)));
v_acc += v_a * v_b;
}
AccDataType v_c;
arg.cde_element_op_(v_c, v_acc);
arg.e_gs_ms_ns_(g0, g1, m0, m1, n0, n1) = v_c;
};
make_ParallelTensorFunctor(f_ms_ns,
arg.e_gs_ms_ns_.mDesc.GetLengths()[0],
arg.e_gs_ms_ns_.mDesc.GetLengths()[1],
arg.e_gs_ms_ns_.mDesc.GetLengths()[2],
arg.e_gs_ms_ns_.mDesc.GetLengths()[3],
arg.e_gs_ms_ns_.mDesc.GetLengths()[4],
arg.e_gs_ms_ns_.mDesc.GetLengths()[5])(
std::thread::hardware_concurrency());
return 0;
}
float Run(const ck::tensor_operation::device::BaseArgument* p_arg,
const StreamConfig& /* stream_config */ = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg));
}
};
static constexpr bool IsValidCompilationParameter()
{
// TODO: properly implement this check
return true;
}
bool IsSupportedArgument(const ck::tensor_operation::device::BaseArgument*) override
{
return true;
}
static auto MakeArgument(const Tensor<ADataType>& a_gs_ms_ks,
const Tensor<BDataType>& b_gs_ns_ks,
Tensor<EDataType>& e_gs_ms_ns,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation cde_element_op)
{
return Argument{
a_gs_ms_ks, b_gs_ns_ks, e_gs_ms_ns, a_element_op, b_element_op, cde_element_op};
}
static auto MakeInvoker() { return Invoker{}; }
virtual std::unique_ptr<ck::tensor_operation::device::BaseInvoker> MakeInvokerPointer()
{
return std::make_unique<Invoker>(Invoker{});
}
std::string GetTypeString() const override
{
auto str = std::stringstream();
// clang-format off
str << "ReferenceContraction_G2_M2_N2_K1"
<< std::endl;
// clang-format on
return str.str();
}
};
int main(int argc, char* argv[])
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
int split_k = 1;
ck::index_t G0 = 1;
ck::index_t G1 = 2;
ck::index_t M0 = 4;
ck::index_t M1 = 256;
ck::index_t N0 = 16;
ck::index_t N1 = 128;
ck::index_t K0 = 64 * 2;
// A[G0, G1, M0, M1, K0]
std::vector<ck::index_t> a_gs_ms_ks_lengths{G0, G1, M0, M1, K0};
std::vector<ck::index_t> a_gs_ms_ks_strides{G1 * M0 * M1 * K0, M0 * M1 * K0, M1 * K0, K0, 1};
// B[G0, G1, N0, N1, K0]
std::vector<ck::index_t> b_gs_ns_ks_lengths{G0, G1, N0, N1, K0};
std::vector<ck::index_t> b_gs_ns_ks_strides{G1 * N0 * N1 * K0, N0 * N1 * K0, N1 * K0, K0, 1};
// D[G0, G1, M0, N0, M1, N1]
std::vector<ck::index_t> d_gs_ms_ns_lengths{G0, G1, M0, M1, N0, N1};
std::vector<ck::index_t> d_gs_ms_ns_strides{G1 * N0 * N1, N0 * N1, 0, 0, N1, 1};
// E[G0, G1, M0, N0, M1, N1]
std::vector<ck::index_t> e_gs_ms_ns_lengths{G0, G1, M0, M1, N0, N1};
std::vector<ck::index_t> e_gs_ms_ns_strides{
G1 * M0 * N0 * M1 * N1, M0 * N0 * M1 * N1, N0 * M1 * N1, N1, M1 * N1, 1};
if(argc == 1)
{
// use default case
}
else if(argc == 5)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
split_k = std::stoi(argv[4]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=no, 1=yes)\n");
exit(0);
}
Tensor<ADataType> a_gs_ms_ks(
std::vector<std::size_t>(a_gs_ms_ks_lengths.begin(), a_gs_ms_ks_lengths.end()),
std::vector<std::size_t>(a_gs_ms_ks_strides.begin(), a_gs_ms_ks_strides.end()));
Tensor<BDataType> b_gs_ns_ks(
std::vector<std::size_t>(b_gs_ns_ks_lengths.begin(), b_gs_ns_ks_lengths.end()),
std::vector<std::size_t>(b_gs_ns_ks_strides.begin(), b_gs_ns_ks_strides.end()));
Tensor<DDataType> d_gs_ms_ns(
std::vector<std::size_t>(d_gs_ms_ns_lengths.begin(), d_gs_ms_ns_lengths.end()),
std::vector<std::size_t>(d_gs_ms_ns_strides.begin(), d_gs_ms_ns_strides.end()));
Tensor<EDataType> e_gs_ms_ns_host_result(
std::vector<std::size_t>(e_gs_ms_ns_lengths.begin(), e_gs_ms_ns_lengths.end()),
std::vector<std::size_t>(e_gs_ms_ns_strides.begin(), e_gs_ms_ns_strides.end()));
Tensor<EDataType> e_gs_ms_ns_device_result(
std::vector<std::size_t>(e_gs_ms_ns_lengths.begin(), e_gs_ms_ns_lengths.end()),
std::vector<std::size_t>(e_gs_ms_ns_strides.begin(), e_gs_ms_ns_strides.end()));
std::cout << "a_gs_ms_ks: " << a_gs_ms_ks.mDesc << std::endl;
std::cout << "b_gs_ns_ks: " << b_gs_ns_ks.mDesc << std::endl;
std::cout << "d_gs_ms_ns: " << d_gs_ms_ns.mDesc << std::endl;
std::cout << "e_gs_ms_ns: " << e_gs_ms_ns_host_result.mDesc << std::endl;
switch(init_method)
{
case 0: break;
case 1:
a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
b_gs_ns_ks.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
d_gs_ms_ns.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
break;
case 2:
a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
b_gs_ns_ks.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
d_gs_ms_ns.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
break;
default:
a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
b_gs_ns_ks.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
d_gs_ms_ns.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
break;
}
DeviceMem a_device_buf(sizeof(ADataType) * a_gs_ms_ks.mDesc.GetElementSpaceSize());
DeviceMem b_device_buf(sizeof(BDataType) * b_gs_ns_ks.mDesc.GetElementSpaceSize());
DeviceMem d_device_buf(sizeof(DDataType) * d_gs_ms_ns.mDesc.GetElementSpaceSize());
DeviceMem e_device_buf(sizeof(EDataType) *
e_gs_ms_ns_device_result.mDesc.GetElementSpaceSize());
a_device_buf.ToDevice(a_gs_ms_ks.mData.data());
b_device_buf.ToDevice(b_gs_ns_ks.mData.data());
d_device_buf.ToDevice(d_gs_ms_ns.mData.data());
// set zero
e_device_buf.SetZero();
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto cde_element_op = CDEElementOp{};
// device operation
auto op = DeviceOpInstance{};
auto invoker = op.MakeInvoker();
auto argument = op.MakeArgument(a_device_buf.GetDeviceBuffer(),
b_device_buf.GetDeviceBuffer(),
std::array<const void*, 1>{d_device_buf.GetDeviceBuffer()},
e_device_buf.GetDeviceBuffer(),
a_gs_ms_ks_lengths,
a_gs_ms_ks_strides,
b_gs_ns_ks_lengths,
b_gs_ns_ks_strides,
std::array<std::vector<ck::index_t>, 1>{d_gs_ms_ns_lengths},
std::array<std::vector<ck::index_t>, 1>{d_gs_ms_ns_strides},
e_gs_ms_ns_lengths,
e_gs_ms_ns_strides,
a_element_op,
b_element_op,
cde_element_op,
split_k);
if(!op.IsSupportedArgument(argument))
{
std::cout << op.GetTypeString() << " does not support this problem" << std::endl;
return 0;
}
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
ck::index_t G = std::accumulate(e_gs_ms_ns_lengths.begin(),
e_gs_ms_ns_lengths.begin() + NumDimG,
ck::index_t{1},
std::multiplies<ck::index_t>{});
ck::index_t M = std::accumulate(e_gs_ms_ns_lengths.begin() + NumDimG,
e_gs_ms_ns_lengths.begin() + NumDimG + NumDimM,
ck::index_t{1},
std::multiplies<ck::index_t>{});
ck::index_t N = std::accumulate(e_gs_ms_ns_lengths.begin() + NumDimG + NumDimM,
e_gs_ms_ns_lengths.begin() + NumDimG + NumDimM + NumDimN,
ck::index_t{1},
std::multiplies<ck::index_t>{});
ck::index_t K = std::accumulate(a_gs_ms_ks_lengths.begin() + NumDimG + NumDimM,
a_gs_ms_ks_lengths.begin() + NumDimG + NumDimM + NumDimK,
ck::index_t{1},
std::multiplies<ck::index_t>{});
std::size_t flop = std::size_t(2) * G * M * N * K;
std::size_t num_btype = sizeof(ADataType) * G * M * K + sizeof(BDataType) * G * K * N +
sizeof(DDataType) * G * M * N + sizeof(EDataType) * G * 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, "
<< op.GetTypeString() << std::endl;
e_device_buf.FromDevice(e_gs_ms_ns_device_result.mData.data());
if(do_verification)
{
Tensor<CShuffleDataType> c_ms_ns_host_result(
std::vector<std::size_t>(e_gs_ms_ns_lengths.begin(), e_gs_ms_ns_lengths.end()),
std::vector<std::size_t>(e_gs_ms_ns_strides.begin(), e_gs_ms_ns_strides.end()));
using ReferenceOpInstance = ReferenceContraction_G2_M2_N2_K1<NumDimG,
NumDimM,
NumDimN,
NumDimK,
ADataType,
BDataType,
CShuffleDataType,
AccDataType,
AElementOp,
BElementOp,
PassThrough>;
auto ref_gemm = ReferenceOpInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(
a_gs_ms_ks, b_gs_ns_ks, c_ms_ns_host_result, a_element_op, b_element_op, PassThrough{});
ref_invoker.Run(ref_argument);
e_gs_ms_ns_host_result.ForEach([&](auto&, auto idx) {
cde_element_op(e_gs_ms_ns_host_result(idx), c_ms_ns_host_result(idx), d_gs_ms_ns(idx));
});
return ck::utils::check_err(e_gs_ms_ns_device_result.mData, e_gs_ms_ns_host_result.mData)
? 0
: 1;
}
return 0;
}
example/43_splitk_gemm_bias_e_permute/splitk_gemm_bias_e_permute_xdl_fp32.cpp 0 → 100644
View file @ 5b7c2432
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/device_splitk_contraction_multiple_d_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using F16 = ck::half_t;
using F32 = float;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using Add = ck::tensor_operation::element_wise::Add;
using ADataType = F32;
using BDataType = F32;
using AccDataType = F32;
using CShuffleDataType = F32;
using DDataType = F32;
using DsDataType = ck::Tuple<DDataType>;
using EDataType = F32;
static constexpr ck::index_t NumDimG = 2;
static constexpr ck::index_t NumDimM = 2;
static constexpr ck::index_t NumDimN = 2;
static constexpr ck::index_t NumDimK = 1;
using AElementOp = ck::tensor_operation::element_wise::PassThrough;
using BElementOp = ck::tensor_operation::element_wise::PassThrough;
using CDEElementOp = ck::tensor_operation::element_wise::Add;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::Default;
static constexpr auto ABSpec = ck::tensor_operation::device::TensorSpecialization::Packed;
static constexpr auto DESpec = ck::tensor_operation::device::TensorSpecialization::Default;
// clang-format off
using DeviceOpInstanceKKNN = ck::tensor_operation::device::
//############################################| NumDimG| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| Gemm| A| B| DE| 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|
//############################################| | | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Spacialization| Spacialization| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//############################################| | | | | | | | | | | 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_NWaveNPerXdl| _NWaveNPerXdl|
//############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceSplitKContractionMultipleD_Xdl_CShuffle< NumDimG, NumDimM, NumDimN, NumDimK, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, ABSpec, ABSpec, DESpec, 1, 256, 256, 128, 32, 4, 4, 32, 32, 4, 2, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 4, 4, 1, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 4, 4, 1, 1, 1, S<1, 32, 1, 4>, 4>;
// clang-format on
using DeviceOpInstance = DeviceOpInstanceKKNN;
// hardcoded for NumDimM == NumDimN == NumDimK == 2
template <ck::index_t NumDimG,
ck::index_t NumDimM,
ck::index_t NumDimN,
ck::index_t NumDimK,
typename ADataType,
typename BDataType,
typename EDataType,
typename AccDataType,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CDEElementwiseOperation,
ck::enable_if_t<NumDimG == 2 && NumDimM == 2 && NumDimN == 2 && NumDimK == 1, bool> =
false>
struct ReferenceContraction_G2_M2_N2_K1 : public ck::tensor_operation::device::BaseOperator
{
// Argument
struct Argument : public ck::tensor_operation::device::BaseArgument
{
Argument(const Tensor<ADataType>& a_gs_ms_ks,
const Tensor<BDataType>& b_gs_ns_ks,
Tensor<EDataType>& e_gs_ms_ns,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation cde_element_op)
: a_gs_ms_ks_{a_gs_ms_ks},
b_gs_ns_ks_{b_gs_ns_ks},
e_gs_ms_ns_{e_gs_ms_ns},
a_element_op_{a_element_op},
b_element_op_{b_element_op},
cde_element_op_{cde_element_op}
{
}
const Tensor<ADataType>& a_gs_ms_ks_;
const Tensor<BDataType>& b_gs_ns_ks_;
Tensor<EDataType>& e_gs_ms_ns_;
AElementwiseOperation a_element_op_;
BElementwiseOperation b_element_op_;
CDEElementwiseOperation cde_element_op_;
};
// Invoker
struct Invoker : public ck::tensor_operation::device::BaseInvoker
{
using Argument = ReferenceContraction_G2_M2_N2_K1::Argument;
float Run(const Argument& arg)
{
auto f_ms_ns = [&](auto g0, auto g1, auto m0, auto m1, auto n0, auto n1) {
const int K0 = arg.a_gs_ms_ks_.mDesc.GetLengths()[4];
AccDataType v_acc = 0;
for(int k0 = 0; k0 < K0; ++k0)
{
AccDataType v_a;
AccDataType v_b;
arg.a_element_op_(
v_a,
ck::type_convert<const AccDataType>(arg.a_gs_ms_ks_(g0, g1, m0, m1, k0)));
arg.b_element_op_(
v_b,
ck::type_convert<const AccDataType>(arg.b_gs_ns_ks_(g0, g1, n0, n1, k0)));
v_acc += v_a * v_b;
}
AccDataType v_c;
arg.cde_element_op_(v_c, v_acc);
arg.e_gs_ms_ns_(g0, g1, m0, m1, n0, n1) = v_c;
};
make_ParallelTensorFunctor(f_ms_ns,
arg.e_gs_ms_ns_.mDesc.GetLengths()[0],
arg.e_gs_ms_ns_.mDesc.GetLengths()[1],
arg.e_gs_ms_ns_.mDesc.GetLengths()[2],
arg.e_gs_ms_ns_.mDesc.GetLengths()[3],
arg.e_gs_ms_ns_.mDesc.GetLengths()[4],
arg.e_gs_ms_ns_.mDesc.GetLengths()[5])(
std::thread::hardware_concurrency());
return 0;
}
float Run(const ck::tensor_operation::device::BaseArgument* p_arg,
const StreamConfig& /* stream_config */ = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg));
}
};
static constexpr bool IsValidCompilationParameter()
{
// TODO: properly implement this check
return true;
}
bool IsSupportedArgument(const ck::tensor_operation::device::BaseArgument*) override
{
return true;
}
static auto MakeArgument(const Tensor<ADataType>& a_gs_ms_ks,
const Tensor<BDataType>& b_gs_ns_ks,
Tensor<EDataType>& e_gs_ms_ns,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation cde_element_op)
{
return Argument{
a_gs_ms_ks, b_gs_ns_ks, e_gs_ms_ns, a_element_op, b_element_op, cde_element_op};
}
static auto MakeInvoker() { return Invoker{}; }
virtual std::unique_ptr<ck::tensor_operation::device::BaseInvoker> MakeInvokerPointer()
{
return std::make_unique<Invoker>(Invoker{});
}
std::string GetTypeString() const override
{
auto str = std::stringstream();
// clang-format off
str << "ReferenceContraction_G2_M2_N2_K1"
<< std::endl;
// clang-format on
return str.str();
}
};
int main(int argc, char* argv[])
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
int split_k = 1;
ck::index_t G0 = 1;
ck::index_t G1 = 2;
ck::index_t M0 = 4;
ck::index_t M1 = 256;
ck::index_t N0 = 16;
ck::index_t N1 = 128;
ck::index_t K0 = 64 * 2;
// A[G0, G1, M0, M1, K0]
std::vector<ck::index_t> a_gs_ms_ks_lengths{G0, G1, M0, M1, K0};
std::vector<ck::index_t> a_gs_ms_ks_strides{G1 * M0 * M1 * K0, M0 * M1 * K0, M1 * K0, K0, 1};
// B[G0, G1, N0, N1, K0]
std::vector<ck::index_t> b_gs_ns_ks_lengths{G0, G1, N0, N1, K0};
std::vector<ck::index_t> b_gs_ns_ks_strides{G1 * N0 * N1 * K0, N0 * N1 * K0, N1 * K0, K0, 1};
// D[G0, G1, M0, N0, M1, N1]
std::vector<ck::index_t> d_gs_ms_ns_lengths{G0, G1, M0, M1, N0, N1};
std::vector<ck::index_t> d_gs_ms_ns_strides{G1 * N0 * N1, N0 * N1, 0, 0, N1, 1};
// E[G0, G1, M0, N0, M1, N1]
std::vector<ck::index_t> e_gs_ms_ns_lengths{G0, G1, M0, M1, N0, N1};
std::vector<ck::index_t> e_gs_ms_ns_strides{
G1 * M0 * N0 * M1 * N1, M0 * N0 * M1 * N1, N0 * M1 * N1, N1, M1 * N1, 1};
if(argc == 1)
{
// use default case
}
else if(argc == 5)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
split_k = std::stoi(argv[4]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=no, 1=yes)\n");
exit(0);
}
Tensor<ADataType> a_gs_ms_ks(
std::vector<std::size_t>(a_gs_ms_ks_lengths.begin(), a_gs_ms_ks_lengths.end()),
std::vector<std::size_t>(a_gs_ms_ks_strides.begin(), a_gs_ms_ks_strides.end()));
Tensor<BDataType> b_gs_ns_ks(
std::vector<std::size_t>(b_gs_ns_ks_lengths.begin(), b_gs_ns_ks_lengths.end()),
std::vector<std::size_t>(b_gs_ns_ks_strides.begin(), b_gs_ns_ks_strides.end()));
Tensor<DDataType> d_gs_ms_ns(
std::vector<std::size_t>(d_gs_ms_ns_lengths.begin(), d_gs_ms_ns_lengths.end()),
std::vector<std::size_t>(d_gs_ms_ns_strides.begin(), d_gs_ms_ns_strides.end()));
Tensor<EDataType> e_gs_ms_ns_host_result(
std::vector<std::size_t>(e_gs_ms_ns_lengths.begin(), e_gs_ms_ns_lengths.end()),
std::vector<std::size_t>(e_gs_ms_ns_strides.begin(), e_gs_ms_ns_strides.end()));
Tensor<EDataType> e_gs_ms_ns_device_result(
std::vector<std::size_t>(e_gs_ms_ns_lengths.begin(), e_gs_ms_ns_lengths.end()),
std::vector<std::size_t>(e_gs_ms_ns_strides.begin(), e_gs_ms_ns_strides.end()));
std::cout << "a_gs_ms_ks: " << a_gs_ms_ks.mDesc << std::endl;
std::cout << "b_gs_ns_ks: " << b_gs_ns_ks.mDesc << std::endl;
std::cout << "d_gs_ms_ns: " << d_gs_ms_ns.mDesc << std::endl;
std::cout << "e_gs_ms_ns: " << e_gs_ms_ns_host_result.mDesc << std::endl;
switch(init_method)
{
case 0: break;
case 1:
a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
b_gs_ns_ks.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
d_gs_ms_ns.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
break;
case 2:
a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
b_gs_ns_ks.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
d_gs_ms_ns.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
break;
default:
a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
b_gs_ns_ks.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
d_gs_ms_ns.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
break;
}
DeviceMem a_device_buf(sizeof(ADataType) * a_gs_ms_ks.mDesc.GetElementSpaceSize());
DeviceMem b_device_buf(sizeof(BDataType) * b_gs_ns_ks.mDesc.GetElementSpaceSize());
DeviceMem d_device_buf(sizeof(DDataType) * d_gs_ms_ns.mDesc.GetElementSpaceSize());
DeviceMem e_device_buf(sizeof(EDataType) *
e_gs_ms_ns_device_result.mDesc.GetElementSpaceSize());
a_device_buf.ToDevice(a_gs_ms_ks.mData.data());
b_device_buf.ToDevice(b_gs_ns_ks.mData.data());
d_device_buf.ToDevice(d_gs_ms_ns.mData.data());
// set zero
e_device_buf.SetZero();
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto cde_element_op = CDEElementOp{};
// device operation
auto op = DeviceOpInstance{};
auto invoker = op.MakeInvoker();
auto argument = op.MakeArgument(a_device_buf.GetDeviceBuffer(),
b_device_buf.GetDeviceBuffer(),
std::array<const void*, 1>{d_device_buf.GetDeviceBuffer()},
e_device_buf.GetDeviceBuffer(),
a_gs_ms_ks_lengths,
a_gs_ms_ks_strides,
b_gs_ns_ks_lengths,
b_gs_ns_ks_strides,
std::array<std::vector<ck::index_t>, 1>{d_gs_ms_ns_lengths},
std::array<std::vector<ck::index_t>, 1>{d_gs_ms_ns_strides},
e_gs_ms_ns_lengths,
e_gs_ms_ns_strides,
a_element_op,
b_element_op,
cde_element_op,
split_k);
if(!op.IsSupportedArgument(argument))
{
std::cout << op.GetTypeString() << " does not support this problem" << std::endl;
return 0;
}
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
ck::index_t G = std::accumulate(e_gs_ms_ns_lengths.begin(),
e_gs_ms_ns_lengths.begin() + NumDimG,
ck::index_t{1},
std::multiplies<ck::index_t>{});
ck::index_t M = std::accumulate(e_gs_ms_ns_lengths.begin() + NumDimG,
e_gs_ms_ns_lengths.begin() + NumDimG + NumDimM,
ck::index_t{1},
std::multiplies<ck::index_t>{});
ck::index_t N = std::accumulate(e_gs_ms_ns_lengths.begin() + NumDimG + NumDimM,
e_gs_ms_ns_lengths.begin() + NumDimG + NumDimM + NumDimN,
ck::index_t{1},
std::multiplies<ck::index_t>{});
ck::index_t K = std::accumulate(a_gs_ms_ks_lengths.begin() + NumDimG + NumDimM,
a_gs_ms_ks_lengths.begin() + NumDimG + NumDimM + NumDimK,
ck::index_t{1},
std::multiplies<ck::index_t>{});
std::size_t flop = std::size_t(2) * G * M * N * K;
std::size_t num_btype = sizeof(ADataType) * G * M * K + sizeof(BDataType) * G * K * N +
sizeof(DDataType) * G * M * N + sizeof(EDataType) * G * 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, "
<< op.GetTypeString() << std::endl;
e_device_buf.FromDevice(e_gs_ms_ns_device_result.mData.data());
if(do_verification)
{
Tensor<CShuffleDataType> c_ms_ns_host_result(
std::vector<std::size_t>(e_gs_ms_ns_lengths.begin(), e_gs_ms_ns_lengths.end()),
std::vector<std::size_t>(e_gs_ms_ns_strides.begin(), e_gs_ms_ns_strides.end()));
using ReferenceOpInstance = ReferenceContraction_G2_M2_N2_K1<NumDimG,
NumDimM,
NumDimN,
NumDimK,
ADataType,
BDataType,
CShuffleDataType,
AccDataType,
AElementOp,
BElementOp,
PassThrough>;
auto ref_gemm = ReferenceOpInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(
a_gs_ms_ks, b_gs_ns_ks, c_ms_ns_host_result, a_element_op, b_element_op, PassThrough{});
ref_invoker.Run(ref_argument);
e_gs_ms_ns_host_result.ForEach([&](auto&, auto idx) {
cde_element_op(e_gs_ms_ns_host_result(idx), c_ms_ns_host_result(idx), d_gs_ms_ns(idx));
});
return ck::utils::check_err(e_gs_ms_ns_device_result.mData, e_gs_ms_ns_host_result.mData)
? 0
: 1;
}
return 0;
}
example/44_elementwise_permute/CMakeLists.txt 0 → 100644
View file @ 5b7c2432
add_example_executable(example_elementwise_permute_4D_fp16 elementwise_permute_4D_fp16.cpp)
example/44_elementwise_permute/elementwise_permute_4D_fp16.cpp 0 → 100644
View file @ 5b7c2432
#include <iostream>
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_elementwise.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
using F16 = ck::half_t;
using F32 = float;
using ADataType = F16;
using BDataType = F16;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using DeviceElementwisePermuteInstance =
ck::tensor_operation::device::DeviceElementwise<ck::Tuple<ADataType>,
ck::Tuple<BDataType>,
PassThrough,
4,
8,
ck::Sequence<8>,
ck::Sequence<1>>;
template <typename HostTensorA, typename HostTensorB, typename Functor>
void host_elementwise4D(HostTensorB& B_nhwc, const HostTensorA& A_nchw, Functor functor)
{
for(std::size_t n = 0; n < A_nchw.mDesc.GetLengths()[0]; ++n)
for(std::size_t c = 0; c < A_nchw.mDesc.GetLengths()[1]; ++c)
for(std::size_t h = 0; h < A_nchw.mDesc.GetLengths()[2]; ++h)
for(std::size_t w = 0; w < A_nchw.mDesc.GetLengths()[3]; ++w)
{
auto a_val = A_nchw(n, c, h, w);
functor(B_nhwc(n, h, w, c), a_val);
}
}
int main()
{
bool do_verification = true;
bool time_kernel = true;
std::vector<std::size_t> nchw = {16, 128, 32, 64};
std::vector<std::size_t> nhwc = {16, 32, 64, 128};
Tensor<ADataType> a(nchw);
Tensor<BDataType> b(nhwc);
a.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
DeviceMem a_device_buf(sizeof(ADataType) * a.mDesc.GetElementSpaceSize());
DeviceMem b_device_buf(sizeof(BDataType) * b.mDesc.GetElementSpaceSize());
a_device_buf.ToDevice(a.mData.data());
std::array<const void*, 1> input = {a_device_buf.GetDeviceBuffer()};
std::array<void*, 1> output = {b_device_buf.GetDeviceBuffer()};
std::array<ck::index_t, 4> ab_lengths;
std::array<ck::index_t, 4> a_strides = {static_cast<int>(nchw[1] * nchw[2] * nchw[3]),
static_cast<int>(nchw[2] * nchw[3]),
static_cast<int>(nchw[3]),
1};
std::array<ck::index_t, 4> b_strides = {static_cast<int>(nhwc[1] * nhwc[2] * nhwc[3]),
1,
static_cast<int>(nhwc[2] * nhwc[3]),
static_cast<int>(nhwc[3])};
std::copy(nchw.begin(), nchw.end(), ab_lengths.begin());
auto broadcastPermute = DeviceElementwisePermuteInstance{};
auto argument = broadcastPermute.MakeArgumentPointer(
ab_lengths, {a_strides}, {b_strides}, input, output, PassThrough{});
if(!broadcastPermute.IsSupportedArgument(argument.get()))
{
throw std::runtime_error(
"The runtime parameters seems not supported by the device instance, exiting!");
};
std::cout << "A (nchw): " << a.mDesc << std::endl;
std::cout << "B (nhwc): " << b.mDesc << std::endl;
auto broadcastPermute_invoker_ptr = broadcastPermute.MakeInvokerPointer();
float ave_time =
broadcastPermute_invoker_ptr->Run(argument.get(), StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * nchw[0] * nchw[1] * nchw[2] * nchw[3];
std::size_t num_btype = sizeof(ADataType) * (nchw[0] * nchw[1] * nchw[2] * nchw[3]) +
sizeof(BDataType) * (nchw[0] * nchw[1] * nchw[2] * nchw[3]);
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"
<< std::endl;
bool pass = true;
if(do_verification)
{
b_device_buf.FromDevice(b.mData.data());
Tensor<BDataType> host_b(nhwc);
host_elementwise4D(host_b, a, PassThrough{});
pass &=
ck::utils::check_err(b.mData, host_b.mData, "Error: Incorrect results b", 1e-3, 1e-3);
}
return pass ? 0 : 1;
}
include/ck/tensor_operation/gpu/device/device_normalization.hpp
View file @ 5b7c2432
...@@ -11,33 +11,6 @@ ...@@ -11,33 +11,6 @@
namespace ck { namespace ck {
namespace tensor_operation { namespace tensor_operation {
namespace device { namespace device {
struct DeviceNormalization : public BaseOperator
{
// inLengths: input tensor extent(s) from high to low dimension
// inStrides: input tensor stride(s) from high to low dimension
// reduceDims: the dimension(s) the normalization operation is applied
// alpha: typeless pointer in host memory storing the alpha scaling value of type AccDataType
// beta: typeless pointer in host memory storing the beta scaling value of type AccDataType
// in_dev: typeless const pointer in device memory storing the input tensor
// out_dev: typeless pointer in device memory storing the output tensor
virtual std::unique_ptr<BaseArgument> MakeArgumentPointer(const std::vector<index_t> inLengths,
const std::vector<index_t> inStrides,
const std::vector<int> reduceDims,
const void* alpha,
const void* beta,
const void* in_dev,
void* out_dev) = 0;
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
virtual index_t GetRank() const = 0;
virtual index_t GetNumReduceDim() const = 0;
};
using DeviceNormalizationPtr = std::unique_ptr<DeviceNormalization>;
template <typename XDataType, template <typename XDataType,
typename GammaDataType, typename GammaDataType,
typename BetaDataType, typename BetaDataType,
...@@ -46,7 +19,7 @@ template <typename XDataType, ...@@ -46,7 +19,7 @@ template <typename XDataType,
typename AccElementwiseOperation, typename AccElementwiseOperation,
index_t Rank, index_t Rank,
index_t NumReduceDim> index_t NumReduceDim>
struct DeviceLayernorm : public BaseOperator struct DeviceNormalization : public BaseOperator
{ {
virtual std::unique_ptr<BaseArgument> virtual std::unique_ptr<BaseArgument>
MakeArgumentPointer(const std::vector<index_t> lengths, MakeArgumentPointer(const std::vector<index_t> lengths,
...@@ -73,14 +46,14 @@ template <typename XDataType, ...@@ -73,14 +46,14 @@ template <typename XDataType,
typename AccElementwiseOperation, typename AccElementwiseOperation,
index_t Rank, index_t Rank,
index_t NumReduceDim> index_t NumReduceDim>
using DeviceLayernormPtr = std::unique_ptr<DeviceLayernorm<XDataType, using DeviceNormalizationPtr = std::unique_ptr<DeviceNormalization<XDataType,
GammaDataType, GammaDataType,
BetaDataType, BetaDataType,
AccDataType, AccDataType,
YDataType, YDataType,
AccElementwiseOperation, AccElementwiseOperation,
Rank, Rank,
NumReduceDim>>; NumReduceDim>>;
} // namespace device } // namespace device
} // namespace tensor_operation } // namespace tensor_operation
......
include/ck/tensor_operation/gpu/device/device_splitk_contraction_multiple_d.hpp 0 → 100644
View file @ 5b7c2432
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iostream>
#include <vector>
#include "ck/tensor_operation/gpu/device/device_base.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
// Tensor Contraction:
// input : A
// input : B
// input : D0, D1, ...
// output : E
// C = a_op(A) * b_op(B)
// E = cde_op(C, D0, D1, ...)
// Assume:
// A[G0, G1, ..., M0, M1, M2, ..., K0, K1, K2, ...]
// B[G0, G1, ..., N0, N1, N2, ..., K0, K1, K2, ...]
// D[G0, G1, ..., M0, M1, M2, ..., N0, N1, N2, ...]
// E[G0, G1, ..., M0, M1, M2, ..., N0, N1, N2, ...]
template <index_t NumDimG,
index_t NumDimM,
index_t NumDimN,
index_t NumDimK,
typename ADataType,
typename BDataType,
typename DsDataType,
typename EDataType,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CDEElementwiseOperation>
struct DeviceSplitKContractionMultipleD : public BaseOperator
{
static constexpr index_t NumDTensor = DsDataType::Size();
virtual std::unique_ptr<BaseArgument>
MakeArgumentPointer(const void* p_a,
const void* p_b,
std::array<const void*, NumDTensor> p_ds,
void* p_e,
const std::vector<index_t>& a_gs_ms_ns_lengths,
const std::vector<index_t>& a_gs_ms_ks_strides,
const std::vector<index_t>& b_gs_ns_ks_lengths,
const std::vector<index_t>& b_gs_ns_ks_strides,
const std::array<std::vector<index_t>, NumDTensor>& ds_gs_ms_ns_lengths,
const std::array<std::vector<index_t>, NumDTensor>& ds_gs_ms_ns_strides,
const std::vector<index_t>& e_gs_ms_ns_lengths,
const std::vector<index_t>& e_gs_ms_ns_strides,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation cde_element_op,
index_t split_k) = 0;
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
};
} // namespace device
} // namespace tensor_operation
} // namespace ck
include/ck/tensor_operation/gpu/device/device_splitk_contraction_multiple_d_xdl_cshuffle.hpp 0 → 100644
View file @ 5b7c2432
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iostream>
#include <sstream>
#include "ck/utility/common_header.hpp"
#include "ck/tensor_description/tensor_descriptor.hpp"
#include "ck/tensor_description/tensor_descriptor_helper.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_splitk_contraction_multiple_d.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/tensor_specialization.hpp"
#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_split_k_multiple_d_xdl_cshuffle.hpp"
#include "ck/host_utility/device_prop.hpp"
#include "ck/host_utility/kernel_launch.hpp"
namespace ck {
template <typename GridwiseGemm,
typename FloatAB,
typename FloatDsPointer,
typename FloatE,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CDEElementwiseOperation,
typename AGridDesc_AKB_AK0_M_AK1,
typename BGridDesc_BKB_BK0_N_BK1,
typename DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
typename EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
typename ComputePtrOffsetOfBatch,
typename Block2ETileMap,
bool HasMainKBlockLoop>
__global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
kernel_contraction_multiple_d_xdl_cshuffle(
const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
FloatDsPointer p_ds_grid,
FloatE* __restrict__ p_e_grid,
const index_t batch_count,
const AElementwiseOperation a_element_op,
const BElementwiseOperation b_element_op,
const CDEElementwiseOperation cde_element_op,
const AGridDesc_AKB_AK0_M_AK1 a_grid_desc_akb_ak0_m_ak1,
const BGridDesc_BKB_BK0_N_BK1 b_grid_desc_bkb_bk0_n_bk1,
const DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
ds_grid_desc_mblock_mperblock_nblock_nperblock,
const EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
e_grid_desc_mblock_mperblock_nblock_nperblock,
const ComputePtrOffsetOfBatch compute_ptr_offset_of_batch,
const Block2ETileMap block_2_etile_map)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
__shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
const index_t num_blocks_per_batch =
__builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);
const long_index_t a_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx)));
const long_index_t b_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_ptr_offset_of_batch.GetBPtrOffset(g_idx)));
const long_index_t e_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_ptr_offset_of_batch.GetEPtrOffset(g_idx)));
const auto ds_batch_offset = compute_ptr_offset_of_batch.GetDsPtrOffset(g_idx);
FloatDsPointer p_ds_grid_grp;
static constexpr index_t NumDTensor =
DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock::Size();
static_for<0, NumDTensor, 1>{}(
[&](auto i) { p_ds_grid_grp(i) = p_ds_grid[i] + ds_batch_offset[i]; });
GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid + a_batch_offset,
p_b_grid + b_batch_offset,
p_ds_grid_grp,
p_e_grid + e_batch_offset,
p_shared,
a_element_op,
b_element_op,
cde_element_op,
a_grid_desc_akb_ak0_m_ak1,
b_grid_desc_bkb_bk0_n_bk1,
ds_grid_desc_mblock_mperblock_nblock_nperblock,
e_grid_desc_mblock_mperblock_nblock_nperblock,
block_2_etile_map);
#else
ignore = p_a_grid;
ignore = p_b_grid;
ignore = p_ds_grid;
ignore = p_e_grid;
ignore = batch_count;
ignore = a_element_op;
ignore = b_element_op;
ignore = cde_element_op;
ignore = a_grid_desc_akb_ak0_m_ak1;
ignore = b_grid_desc_bkb_bk0_n_bk1;
ignore = ds_grid_desc_mblock_mperblock_nblock_nperblock;
ignore = e_grid_desc_mblock_mperblock_nblock_nperblock;
ignore = block_2_etile_map;
ignore = compute_ptr_offset_of_batch;
#endif
}
} // namespace ck
namespace ck {
namespace tensor_operation {
namespace device {
// Tensor Contraction:
// input : A
// input : B
// input : D0, D1, ...
// output : E
// C = a_op(A) * b_op(B)
// E = cde_op(C, D0, D1, ...)
// Assume:
// A[G0, G1, ..., M0, M1, M2, ..., K0, K1, K2, ...]
// B[G0, G1, ..., N0, N1, N2, ..., K0, K1, K2, ...]
// D[G0, G1, ..., M0, M1, M2, ..., N0, N1, N2, ...]
// E[G0, G1, ..., M0, M1, M2, ..., N0, N1, N2, ...]
template <index_t NumDimG,
index_t NumDimM,
index_t NumDimN,
index_t NumDimK,
typename ADataType,
typename BDataType,
typename AccDataType,
typename CShuffleDataType,
typename DsDataType,
typename EDataType,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CDEElementwiseOperation,
GemmSpecialization GemmSpec,
TensorSpecialization ASpec,
TensorSpecialization BSpec,
TensorSpecialization DESpec,
index_t NumGemmKPrefetchStage,
index_t BlockSize,
index_t MPerBlock,
index_t NPerBlock,
index_t KPerBlock,
index_t AK1,
index_t BK1,
index_t MPerXDL,
index_t NPerXDL,
index_t MXdlPerWave,
index_t NXdlPerWave,
typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
typename ABlockTransferThreadClusterArrangeOrder,
typename ABlockTransferSrcAccessOrder,
index_t ABlockTransferSrcVectorDim,
index_t ABlockTransferSrcScalarPerVector,
index_t ABlockTransferDstScalarPerVector_AK1,
bool ABlockLdsExtraM,
typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
typename BBlockTransferThreadClusterArrangeOrder,
typename BBlockTransferSrcAccessOrder,
index_t BBlockTransferSrcVectorDim,
index_t BBlockTransferSrcScalarPerVector,
index_t BBlockTransferDstScalarPerVector_BK1,
bool BBlockLdsExtraN,
index_t CShuffleMXdlPerWavePerShuffle,
index_t CShuffleNXdlPerWavePerShuffle,
typename CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
index_t CDEBlockTransferScalarPerVector_NPerBlock,
LoopScheduler LoopSched = make_default_loop_scheduler()>
struct DeviceSplitKContractionMultipleD_Xdl_CShuffle
: public DeviceSplitKContractionMultipleD<NumDimG,
NumDimM,
NumDimN,
NumDimK,
ADataType,
BDataType,
DsDataType,
EDataType,
AElementwiseOperation,
BElementwiseOperation,
CDEElementwiseOperation>
{
using DeviceOp = DeviceSplitKContractionMultipleD_Xdl_CShuffle;
static constexpr index_t NumDTensor = DsDataType::Size();
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
static constexpr auto I2 = Number<2>{};
static constexpr auto I3 = Number<3>{};
static constexpr auto matrix_padder =
MatrixPadder<GemmSpec, index_t, index_t, index_t>{MPerBlock, NPerBlock, KPerBlock};
// Assume: A[G0, G1, ..., M0, M1, M2, ..., K0, K1, K2, ...]
static auto MakeAGridDescriptor_M_K(const std::vector<index_t>& a_gs_ms_ks_lengths_vec,
const std::vector<index_t>& a_gs_ms_ks_strides_vec)
{
assert(a_gs_ms_ks_lengths_vec.size() == NumDimG + NumDimM + NumDimK &&
a_gs_ms_ks_strides_vec.size() == NumDimG + NumDimM + NumDimK);
const auto to_tuple = [&](auto& vec, auto start, auto end) {
return generate_tuple([&](auto i) { return vec[start + i]; }, Number<end - start>{});
};
const auto a_ms_ks_lengths = to_tuple(
a_gs_ms_ks_lengths_vec, Number<NumDimG>{}, Number<NumDimG + NumDimM + NumDimK>{});
const auto a_ms_ks_strides = to_tuple(
a_gs_ms_ks_strides_vec, Number<NumDimG>{}, Number<NumDimG + NumDimM + NumDimK>{});
// dimension Ids for M0, M1, ...
constexpr auto mDimIds = typename arithmetic_sequence_gen<0, NumDimM, 1>::type{};
// dimension Ids for K0, K1, ...
constexpr auto kDimIds =
typename arithmetic_sequence_gen<NumDimM, NumDimM + NumDimK, 1>::type{};
// lengths for M0, M1, ...
const auto mLengths = get_container_subset(a_ms_ks_lengths, mDimIds);
// lengths for K0, K1, ...
const auto kLengths = get_container_subset(a_ms_ks_lengths, kDimIds);
if constexpr(ASpec == TensorSpecialization::Packed)
{
auto M = container_reduce(mLengths, math::multiplies{}, Number<1>{});
auto K = container_reduce(kLengths, math::multiplies{}, Number<1>{});
const auto a_grid_desc_mraw_kraw = make_naive_tensor_descriptor(
make_tuple(M, K),
make_tuple(a_ms_ks_strides[Number<NumDimM - 1>{}],
a_ms_ks_strides[Number<NumDimM + NumDimK - 1>{}]));
return matrix_padder.PadADescriptor_M_K(a_grid_desc_mraw_kraw);
}
else
{
// naive tensor A[M0, M1, M2, ..., K0, K1, K2...]
const auto a_grid_desc_ms_ks =
make_naive_tensor_descriptor(a_ms_ks_lengths, a_ms_ks_strides);
// transformed tensor A[MRaw = M0 * M1 * M2 * ... , KRaw = K0 * K1 * K2 * ...]
const auto a_grid_desc_mraw_kraw = transform_tensor_descriptor(
a_grid_desc_ms_ks,
make_tuple(make_merge_transform(mLengths), make_merge_transform(kLengths)),
make_tuple(mDimIds, kDimIds),
make_tuple(Sequence<0>{}, Sequence<1>{}));
return matrix_padder.PadADescriptor_M_K(a_grid_desc_mraw_kraw);
}
}
// Assume: B[G0, G1, ..., N0, N1, N2, ..., K0, K1, K2, ...]
static auto MakeBGridDescriptor_N_K(const std::vector<index_t>& b_gs_ns_ks_lengths_vec,
const std::vector<index_t>& b_gs_ns_ks_strides_vec)
{
assert(b_gs_ns_ks_lengths_vec.size() == NumDimG + NumDimN + NumDimK &&
b_gs_ns_ks_strides_vec.size() == NumDimG + NumDimN + NumDimK);
const auto to_tuple = [&](auto& vec, auto start, auto end) {
return generate_tuple([&](auto i) { return vec[start + i]; }, Number<end - start>{});
};
const auto b_ns_ks_lengths = to_tuple(
b_gs_ns_ks_lengths_vec, Number<NumDimG>{}, Number<NumDimG + NumDimN + NumDimK>{});
const auto b_ns_ks_strides = to_tuple(
b_gs_ns_ks_strides_vec, Number<NumDimG>{}, Number<NumDimG + NumDimN + NumDimK>{});
// dimension Ids for N0, N1, ...
constexpr auto nDimIds = typename arithmetic_sequence_gen<0, NumDimN, 1>::type{};
// dimension Ids for K0, K1, ...
constexpr auto kDimIds =
typename arithmetic_sequence_gen<NumDimN, NumDimN + NumDimK, 1>::type{};
// lengths for K0, K1, ...
const auto kLengths = get_container_subset(b_ns_ks_lengths, kDimIds);
// lengths for N0, N1, ...
const auto nLengths = get_container_subset(b_ns_ks_lengths, nDimIds);
if constexpr(BSpec == TensorSpecialization::Packed)
{
auto N = container_reduce(nLengths, math::multiplies{}, Number<1>{});
auto K = container_reduce(kLengths, math::multiplies{}, Number<1>{});
const auto b_grid_desc_nraw_kraw = make_naive_tensor_descriptor(
make_tuple(N, K),
make_tuple(b_ns_ks_strides[Number<NumDimN - 1>{}],
b_ns_ks_strides[Number<NumDimN + NumDimK - 1>{}]));
return matrix_padder.PadBDescriptor_N_K(b_grid_desc_nraw_kraw);
}
else
{
// naive tensor B[N0, N1, N2, ..., K0, K1, K2, ...]
const auto b_grid_desc_ns_ks =
make_naive_tensor_descriptor(b_ns_ks_lengths, b_ns_ks_strides);
// transformed tensor B[NRaw = N0 * N1 * N2 * ..., KRaw = K0 * K1 * K2 * ...]
const auto b_grid_desc_nraw_kraw = transform_tensor_descriptor(
b_grid_desc_ns_ks,
make_tuple(make_merge_transform(nLengths), make_merge_transform(kLengths)),
make_tuple(nDimIds, kDimIds),
make_tuple(Sequence<0>{}, Sequence<1>{}));
return matrix_padder.PadBDescriptor_N_K(b_grid_desc_nraw_kraw);
}
}
// assume E[G0, G1, ..., M0, M1, M2, ..., N0, N1, N2...]
static auto MakeEGridDescriptor_M_N(const std::vector<index_t>& e_gs_ms_ns_lengths_vec,
const std::vector<index_t>& e_gs_ms_ns_strides_vec)
{
assert(e_gs_ms_ns_lengths_vec.size() == NumDimG + NumDimM + NumDimN &&
e_gs_ms_ns_strides_vec.size() == NumDimG + NumDimM + NumDimN);
const auto to_tuple = [&](auto& vec, auto start, auto end) {
return generate_tuple([&](auto i) { return vec[start + i]; }, Number<end - start>{});
};
const auto e_ms_ns_lengths = to_tuple(
e_gs_ms_ns_lengths_vec, Number<NumDimG>{}, Number<NumDimG + NumDimM + NumDimN>{});
const auto e_ms_ns_strides = to_tuple(
e_gs_ms_ns_strides_vec, Number<NumDimG>{}, Number<NumDimG + NumDimM + NumDimN>{});
// dimension Ids for M0, M1, ...
constexpr auto mDimIds = typename arithmetic_sequence_gen<0, NumDimM, 1>::type{};
// dimension Ids for N0, N1, ...
constexpr auto nDimIds =
typename arithmetic_sequence_gen<NumDimM, NumDimM + NumDimN, 1>::type{};
// lengths for M0, M1, ...
const auto mLengths = get_container_subset(e_ms_ns_lengths, mDimIds);
// lengths for K0, K1, ...
const auto nLengths = get_container_subset(e_ms_ns_lengths, nDimIds);
if constexpr(DESpec == TensorSpecialization::Packed)
{
auto M = container_reduce(mLengths, math::multiplies{}, Number<1>{});
auto N = container_reduce(nLengths, math::multiplies{}, Number<1>{});
const auto e_grid_desc_mraw_nraw = make_naive_tensor_descriptor(
make_tuple(M, N),
make_tuple(e_ms_ns_strides[Number<NumDimM - 1>{}],
e_ms_ns_strides[Number<NumDimM + NumDimN - 1>{}]));
return matrix_padder.PadCDescriptor_M_N(e_grid_desc_mraw_nraw);
}
else
{
// naive tensor E[M0, M1, M2, ..., N0, N1, N2...]
const auto e_grid_desc_ms_ns =
make_naive_tensor_descriptor(e_ms_ns_lengths, e_ms_ns_strides);
// transformed tensor E[MRaw = M0 * M1 * M2 * ... , NRaw = N0 * N1 * N2 * ...]
const auto e_grid_desc_mraw_nraw = transform_tensor_descriptor(
e_grid_desc_ms_ns,
make_tuple(make_merge_transform(mLengths), make_merge_transform(nLengths)),
make_tuple(mDimIds, nDimIds),
make_tuple(Sequence<0>{}, Sequence<1>{}));
return matrix_padder.PadCDescriptor_M_N(e_grid_desc_mraw_nraw);
}
}
// assume E[G0, G1, ..., M0, M1, M2, ..., N0, N1, N2...]
static auto MakeEGridDescriptor_G_M_N(const std::vector<index_t>& e_gs_ms_ns_lengths_vec,
const std::vector<index_t>& e_gs_ms_ns_strides_vec)
{
assert(e_gs_ms_ns_lengths_vec.size() == NumDimG + NumDimM + NumDimN &&
e_gs_ms_ns_strides_vec.size() == NumDimG + NumDimM + NumDimN);
const auto to_tuple = [&](auto& vec, auto start, auto end) {
return generate_tuple([&](auto i) { return vec[start + i]; }, Number<end - start>{});
};
const auto e_gs_ms_ns_lengths =
to_tuple(e_gs_ms_ns_lengths_vec, Number<0>{}, Number<NumDimG + NumDimM + NumDimN>{});
const auto e_gs_ms_ns_strides =
to_tuple(e_gs_ms_ns_strides_vec, Number<0>{}, Number<NumDimG + NumDimM + NumDimN>{});
// dimension Ids for G0, G1, ...
constexpr auto gDimIds = typename arithmetic_sequence_gen<0, NumDimG, 1>::type{};
// dimension Ids for M0, M1, ...
constexpr auto mDimIds =
typename arithmetic_sequence_gen<NumDimG, NumDimG + NumDimM, 1>::type{};
// dimension Ids for N0, N1, ...
constexpr auto nDimIds = typename arithmetic_sequence_gen<NumDimG + NumDimM,
NumDimG + NumDimM + NumDimN,
1>::type{};
// lengths for G0, G1, ...
const auto gLengths = get_container_subset(e_gs_ms_ns_lengths, gDimIds);
// lengths for M0, M1, ...
const auto mLengths = get_container_subset(e_gs_ms_ns_lengths, mDimIds);
// lengths for K0, K1, ...
const auto nLengths = get_container_subset(e_gs_ms_ns_lengths, nDimIds);
if constexpr(DESpec == TensorSpecialization::Packed)
{
auto G = container_reduce(gLengths, math::multiplies{}, Number<1>{});
auto M = container_reduce(mLengths, math::multiplies{}, Number<1>{});
auto N = container_reduce(nLengths, math::multiplies{}, Number<1>{});
const auto e_grid_desc_g_mraw_nraw = make_naive_tensor_descriptor(
make_tuple(G, M, N),
make_tuple(e_gs_ms_ns_strides[Number<NumDimG - 1>{}],
e_gs_ms_ns_strides[Number<NumDimG + NumDimM - 1>{}],
e_gs_ms_ns_strides[Number<NumDimG + NumDimM + NumDimN - 1>{}]));
// return matrix_padder.PadCDescriptor_M_N(e_grid_desc_g_mraw_nraw);
return e_grid_desc_g_mraw_nraw;
}
else
{
// naive tensor E[G0, G1, ..., M0, M1, M2, ..., N0, N1, N2...]
const auto e_grid_desc_gs_ms_ns =
make_naive_tensor_descriptor(e_gs_ms_ns_lengths, e_gs_ms_ns_strides);
// transformed tensor E[G = G0 * G1 * ..., MRaw = M0 * M1 * M2 * ... , NRaw = N0 * N1 *
// N2 * ...]
const auto e_grid_desc_g_mraw_nraw = transform_tensor_descriptor(
e_grid_desc_gs_ms_ns,
make_tuple(make_merge_transform(gLengths),
make_merge_transform(mLengths),
make_merge_transform(nLengths)),
make_tuple(gDimIds, mDimIds, nDimIds),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
// return matrix_padder.PadCDescriptor_M_N(e_grid_desc_g_mraw_nraw);
return e_grid_desc_g_mraw_nraw;
}
}
static auto MakeDsGridDescriptor_M_N(
const std::array<std::vector<index_t>, NumDTensor>& ds_gs_ms_ns_lengths_vec,
const std::array<std::vector<index_t>, NumDTensor>& ds_gs_ms_ns_strides_vec)
{
return generate_tuple(
[&](auto i) {
return DeviceOp::MakeEGridDescriptor_M_N(ds_gs_ms_ns_lengths_vec[i],
ds_gs_ms_ns_strides_vec[i]);
},
Number<NumDTensor>{});
}
static auto MakeDsGridDescriptor_G_M_N(
const std::array<std::vector<index_t>, NumDTensor>& ds_gs_ms_ns_lengths_vec,
const std::array<std::vector<index_t>, NumDTensor>& ds_gs_ms_ns_strides_vec)
{
return generate_tuple(
[&](auto i) {
return DeviceOp::MakeEGridDescriptor_G_M_N(ds_gs_ms_ns_lengths_vec[i],
ds_gs_ms_ns_strides_vec[i]);
},
Number<NumDTensor>{});
}
using AGridDesc_M_K = decltype(MakeAGridDescriptor_M_K({}, {}));
using BGridDesc_N_K = decltype(MakeBGridDescriptor_N_K({}, {}));
using DsGridDesc_M_N = remove_cvref_t<decltype(MakeDsGridDescriptor_M_N({{}}, {{}}))>;
using EGridDesc_M_N = decltype(MakeEGridDescriptor_M_N({}, {}));
using DsGridDesc_G_M_N = remove_cvref_t<decltype(MakeDsGridDescriptor_G_M_N({}, {}))>;
using EGridDesc_G_M_N = decltype(MakeEGridDescriptor_G_M_N({}, {}));
struct ComputePtrOffsetOfStridedBatch
{
ComputePtrOffsetOfStridedBatch(index_t batch_stride_A,
index_t batch_stride_B,
DsGridDesc_G_M_N ds_grid_desc_g_m_n,
EGridDesc_G_M_N e_grid_desc_g_m_n)
: batch_stride_A_(batch_stride_A),
batch_stride_B_(batch_stride_B),
ds_grid_desc_g_m_n_(ds_grid_desc_g_m_n),
e_grid_desc_g_m_n_(e_grid_desc_g_m_n)
{
}
__host__ __device__ constexpr long_index_t GetAPtrOffset(index_t g_idx) const
{
return g_idx * static_cast<long_index_t>(batch_stride_A_);
}
__host__ __device__ constexpr long_index_t GetBPtrOffset(index_t g_idx) const
{
return g_idx * static_cast<long_index_t>(batch_stride_B_);
}
__host__ __device__ constexpr auto GetDsPtrOffset(index_t g_idx) const
{
std::array<long_index_t, NumDTensor> ds_offset;
static_for<0, NumDTensor, 1>{}([&](auto i) {
ds_offset[i] = static_cast<long_index_t>(g_idx) *
ds_grid_desc_g_m_n_[i].CalculateOffset(make_multi_index(1, 0, 0));
});
return ds_offset;
}
__host__ __device__ constexpr long_index_t GetEPtrOffset(index_t g_idx) const
{
return static_cast<long_index_t>(g_idx) *
e_grid_desc_g_m_n_.CalculateOffset(make_multi_index(1, 0, 0));
}
private:
index_t batch_stride_A_;
index_t batch_stride_B_;
DsGridDesc_G_M_N ds_grid_desc_g_m_n_;
EGridDesc_G_M_N e_grid_desc_g_m_n_;
};
// GridwiseGemm
using GridwiseGemm = GridwiseGemmSplitKMultipleD_xdl_cshuffle<
ADataType, // TODO: distinguish A/B datatype
AccDataType,
CShuffleDataType,
DsDataType,
EDataType,
AElementwiseOperation,
BElementwiseOperation,
CDEElementwiseOperation,
InMemoryDataOperationEnum::Set,
AGridDesc_M_K,
BGridDesc_N_K,
DsGridDesc_M_N,
EGridDesc_M_N,
NumGemmKPrefetchStage,
BlockSize,
MPerBlock,
NPerBlock,
KPerBlock,
AK1,
BK1,
MPerXDL,
NPerXDL,
MXdlPerWave,
NXdlPerWave,
ABlockTransferThreadClusterLengths_AK0_M_AK1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_AK1,
false,
ABlockLdsExtraM,
BBlockTransferThreadClusterLengths_BK0_N_BK1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_BK1,
false,
BBlockLdsExtraN,
CShuffleMXdlPerWavePerShuffle,
CShuffleNXdlPerWavePerShuffle,
CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
CDEBlockTransferScalarPerVector_NPerBlock,
LoopSched>;
// GridwiseGemm
using GridwiseGemmAtomicAdd = GridwiseGemmSplitKMultipleD_xdl_cshuffle<
ADataType, // TODO: distinguish A/B datatype
AccDataType,
CShuffleDataType,
DsDataType,
EDataType,
AElementwiseOperation,
BElementwiseOperation,
CDEElementwiseOperation,
InMemoryDataOperationEnum::AtomicAdd,
AGridDesc_M_K,
BGridDesc_N_K,
DsGridDesc_M_N,
EGridDesc_M_N,
NumGemmKPrefetchStage,
BlockSize,
MPerBlock,
NPerBlock,
KPerBlock,
AK1,
BK1,
MPerXDL,
NPerXDL,
MXdlPerWave,
NXdlPerWave,
ABlockTransferThreadClusterLengths_AK0_M_AK1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_AK1,
false,
ABlockLdsExtraM,
BBlockTransferThreadClusterLengths_BK0_N_BK1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_BK1,
false,
BBlockLdsExtraN,
CShuffleMXdlPerWavePerShuffle,
CShuffleNXdlPerWavePerShuffle,
CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
CDEBlockTransferScalarPerVector_NPerBlock,
LoopSched>;
using AGridDesc_AKB_AK0_M_AK1 = remove_cvref_t<decltype(
GridwiseGemm::MakeDefaultAGridDescriptor_AKB_AK0_M_AK1(AGridDesc_M_K{}, 1))>;
using BGridDesc_BKB_BK0_N_BK1 = remove_cvref_t<decltype(
GridwiseGemm::MakeDefaultBGridDescriptor_BKB_BK0_N_BK1(BGridDesc_N_K{}, 1))>;
using Block2ETileMap = typename GridwiseGemm::DefaultBlock2ETileMap;
// Argument
struct Argument : public BaseArgument
{
Argument(const void* p_a_grid,
const void* p_b_grid,
std::array<const void*, NumDTensor> p_ds_grid,
void* p_e_grid,
const std::vector<index_t>& a_gs_ms_ns_lengths,
const std::vector<index_t>& a_gs_ms_ks_strides,
const std::vector<index_t>& b_gs_ns_ks_lengths,
const std::vector<index_t>& b_gs_ns_ks_strides,
const std::array<std::vector<index_t>, NumDTensor>& ds_gs_ms_ns_lengths,
const std::array<std::vector<index_t>, NumDTensor>& ds_gs_ms_ns_strides,
const std::vector<index_t>& e_gs_ms_ns_lengths,
const std::vector<index_t>& e_gs_ms_ns_strides,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation cde_element_op,
index_t split_k)
: p_a_grid_{static_cast<const ADataType*>(p_a_grid)},
p_b_grid_{static_cast<const BDataType*>(p_b_grid)},
p_ds_grid_{},
p_e_grid_{static_cast<EDataType*>(p_e_grid)},
a_grid_desc_m_k_{
DeviceOp::MakeAGridDescriptor_M_K(a_gs_ms_ns_lengths, a_gs_ms_ks_strides)},
b_grid_desc_n_k_{
DeviceOp::MakeBGridDescriptor_N_K(b_gs_ns_ks_lengths, b_gs_ns_ks_strides)},
ds_grid_desc_m_n_{},
e_grid_desc_m_n_{
DeviceOp::MakeEGridDescriptor_M_N(e_gs_ms_ns_lengths, e_gs_ms_ns_strides)},
ds_grid_desc_g_m_n_{
DeviceOp::MakeDsGridDescriptor_G_M_N(ds_gs_ms_ns_lengths, ds_gs_ms_ns_strides)},
e_grid_desc_g_m_n_{
DeviceOp::MakeEGridDescriptor_G_M_N(e_gs_ms_ns_lengths, e_gs_ms_ns_strides)},
a_grid_desc_akb_ak0_m_ak1_{GridwiseGemm::MakeDefaultAGridDescriptor_AKB_AK0_M_AK1(
a_grid_desc_m_k_, split_k)},
b_grid_desc_bkb_bk0_n_bk1_{GridwiseGemm::MakeDefaultBGridDescriptor_BKB_BK0_N_BK1(
b_grid_desc_n_k_, split_k)},
ds_grid_desc_mblock_mperblock_nblock_nperblock_{},
e_grid_desc_mblock_mperblock_nblock_nperblock_{},
block_2_etile_map_{
GridwiseGemm::MakeDefaultBlock2ETileMap(e_grid_desc_m_n_, split_k)},
a_element_op_{a_element_op},
b_element_op_{b_element_op},
cde_element_op_{cde_element_op},
a_mz_stride_{},
a_kz_stride_{},
b_nz_stride_{},
b_kz_stride_{},
ds_nz_stride_{},
e_nz_stride_{},
a_batch_stride_{a_gs_ms_ks_strides[NumDimG - 1]},
b_batch_stride_{b_gs_ns_ks_strides[NumDimG - 1]},
compute_ptr_offset_of_batch_{
a_batch_stride_, b_batch_stride_, ds_grid_desc_g_m_n_, e_grid_desc_g_m_n_},
split_k_{split_k}
{
static_assert(NumDimG > 0 && NumDimM > 0 && NumDimN > 0 && NumDimK > 0, "");
// populate pointer, batch stride, desc for Ds
static_for<0, NumDTensor, 1>{}([&](auto i) {
using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
// D pointer
p_ds_grid_(i) = static_cast<const DDataType*>(p_ds_grid[i]);
// D desc
ds_grid_desc_m_n_(i) = DeviceOp::MakeEGridDescriptor_M_N(ds_gs_ms_ns_lengths[i],
ds_gs_ms_ns_strides[i]);
});
// populate desc for Ds/E
if(GridwiseGemm::CheckValidity(a_grid_desc_akb_ak0_m_ak1_,
b_grid_desc_bkb_bk0_n_bk1_,
ds_grid_desc_m_n_,
e_grid_desc_m_n_,
block_2_etile_map_))
{
e_grid_desc_mblock_mperblock_nblock_nperblock_ =
GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
e_grid_desc_m_n_);
ds_grid_desc_mblock_mperblock_nblock_nperblock_ =
GridwiseGemm::MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
ds_grid_desc_m_n_);
}
// for sanity check of vector memory access
a_mz_stride_ = a_gs_ms_ks_strides[NumDimG + NumDimM - 1];
a_kz_stride_ = a_gs_ms_ks_strides[NumDimG + NumDimM + NumDimK - 1];
b_nz_stride_ = b_gs_ns_ks_strides[NumDimG + NumDimN - 1];
b_kz_stride_ = b_gs_ns_ks_strides[NumDimG + NumDimN + NumDimK - 1];
for(index_t i = 0; i < NumDTensor; ++i)
{
ds_nz_stride_[i] = ds_gs_ms_ns_strides[i][NumDimG + NumDimM + NumDimN - 1];
}
e_nz_stride_ = e_gs_ms_ns_strides[NumDimG + NumDimM + NumDimN - 1];
Print();
}
void Print() const
{
std::cout << "A[M, K]: " << a_grid_desc_m_k_.GetLength(I0) << ", "
<< a_grid_desc_m_k_.GetLength(I1) << std::endl;
std::cout << "B[N, K]: " << b_grid_desc_n_k_.GetLength(I0) << ", "
<< b_grid_desc_n_k_.GetLength(I1) << std::endl;
std::cout << "A[akb, ak0, m, ak1]: " << a_grid_desc_akb_ak0_m_ak1_.GetLength(I0) << ", "
<< a_grid_desc_akb_ak0_m_ak1_.GetLength(I1) << ", "
<< a_grid_desc_akb_ak0_m_ak1_.GetLength(I2) << ", "
<< a_grid_desc_akb_ak0_m_ak1_.GetLength(I3) << std::endl;
std::cout << "B[bkb, bk0, n, bk1]: " << b_grid_desc_bkb_bk0_n_bk1_.GetLength(I0) << ", "
<< b_grid_desc_bkb_bk0_n_bk1_.GetLength(I1) << ", "
<< b_grid_desc_bkb_bk0_n_bk1_.GetLength(I2) << ", "
<< b_grid_desc_bkb_bk0_n_bk1_.GetLength(I3) << std::endl;
static_for<0, NumDTensor, 1>{}([&](auto i) {
std::cout << "Ds[M, N]: " << ds_grid_desc_m_n_[i].GetLength(I0) << ", "
<< ds_grid_desc_m_n_[i].GetLength(I1) << std::endl;
});
std::cout << "E[M, N]: " << e_grid_desc_m_n_.GetLength(I0) << ", "
<< e_grid_desc_m_n_.GetLength(I1) << std::endl;
}
// private:
// pointers
const ADataType* p_a_grid_;
const BDataType* p_b_grid_;
typename GridwiseGemm::DsGridPointer p_ds_grid_;
EDataType* p_e_grid_;
// tensor descriptors for problem definiton
AGridDesc_M_K a_grid_desc_m_k_;
BGridDesc_N_K b_grid_desc_n_k_;
DsGridDesc_M_N ds_grid_desc_m_n_;
EGridDesc_M_N e_grid_desc_m_n_;
DsGridDesc_G_M_N ds_grid_desc_g_m_n_;
EGridDesc_G_M_N e_grid_desc_g_m_n_;
// tensor descriptors for block/thread-wise copy
AGridDesc_AKB_AK0_M_AK1 a_grid_desc_akb_ak0_m_ak1_;
BGridDesc_BKB_BK0_N_BK1 b_grid_desc_bkb_bk0_n_bk1_;
typename GridwiseGemm::DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
ds_grid_desc_mblock_mperblock_nblock_nperblock_;
typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
e_grid_desc_mblock_mperblock_nblock_nperblock_;
// block-to-e-tile map
Block2ETileMap block_2_etile_map_;
// element-wise op
AElementwiseOperation a_element_op_;
BElementwiseOperation b_element_op_;
CDEElementwiseOperation cde_element_op_;
// Strides for the last M/N/K dimensions of A/B/Ds/E
// for sanity check of vector load/store
index_t a_mz_stride_;
index_t a_kz_stride_;
index_t b_nz_stride_;
index_t b_kz_stride_;
std::array<index_t, NumDTensor> ds_nz_stride_;
index_t e_mz_stride_;
index_t e_nz_stride_;
index_t a_batch_stride_;
index_t b_batch_stride_;
ComputePtrOffsetOfStridedBatch compute_ptr_offset_of_batch_;
index_t split_k_;
};
// Invoker
struct Invoker : public BaseInvoker
{
using Argument = DeviceOp::Argument;
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_akb_ak0_m_ak1_,
arg.b_grid_desc_bkb_bk0_n_bk1_,
arg.ds_grid_desc_m_n_,
arg.e_grid_desc_m_n_,
arg.block_2_etile_map_))
{
throw std::runtime_error(
"wrong! GridwiseGemmMultipleD_xdl_cshuffle has invalid setting");
}
const index_t G = arg.e_grid_desc_g_m_n_.GetLength(I0);
const index_t grid_size =
arg.block_2_etile_map_.CalculateGridSize(arg.e_grid_desc_m_n_) * G;
const auto K = arg.a_grid_desc_akb_ak0_m_ak1_.GetLength(I1) *
arg.a_grid_desc_akb_ak0_m_ak1_.GetLength(I3);
auto launch_kernel = [&](auto has_main_k_block_loop) {
constexpr bool has_main_loop = has_main_k_block_loop.value;
const auto kernel = kernel_contraction_multiple_d_xdl_cshuffle<
GridwiseGemm,
ADataType, // TODO: distiguish A/B datatype
typename GridwiseGemm::DsGridPointer,
EDataType,
AElementwiseOperation,
BElementwiseOperation,
CDEElementwiseOperation,
DeviceOp::AGridDesc_AKB_AK0_M_AK1,
DeviceOp::BGridDesc_BKB_BK0_N_BK1,
typename GridwiseGemm::DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
ComputePtrOffsetOfStridedBatch,
typename GridwiseGemm::DefaultBlock2ETileMap,
has_main_loop>;
return launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_ds_grid_,
arg.p_e_grid_,
G,
arg.a_element_op_,
arg.b_element_op_,
arg.cde_element_op_,
arg.a_grid_desc_akb_ak0_m_ak1_,
arg.b_grid_desc_bkb_bk0_n_bk1_,
arg.ds_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.e_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.compute_ptr_offset_of_batch_,
arg.block_2_etile_map_);
};
auto launch_kernel_atomic_add = [&](auto has_main_k_block_loop) {
constexpr bool has_main_loop = has_main_k_block_loop.value;
const auto kernel = kernel_contraction_multiple_d_xdl_cshuffle<
GridwiseGemmAtomicAdd,
ADataType, // TODO: distiguish A/B datatype
typename GridwiseGemmAtomicAdd::DsGridPointer,
EDataType,
AElementwiseOperation,
BElementwiseOperation,
CDEElementwiseOperation,
DeviceOp::AGridDesc_AKB_AK0_M_AK1,
DeviceOp::BGridDesc_BKB_BK0_N_BK1,
typename GridwiseGemmAtomicAdd::
DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
typename GridwiseGemmAtomicAdd::
EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
ComputePtrOffsetOfStridedBatch,
typename GridwiseGemmAtomicAdd::DefaultBlock2ETileMap,
has_main_loop>;
hipGetErrorString(hipMemset(
arg.p_e_grid_,
0,
arg.e_grid_desc_mblock_mperblock_nblock_nperblock_.GetElementSpaceSize() *
sizeof(EDataType)));
return launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_ds_grid_,
arg.p_e_grid_,
G,
arg.a_element_op_,
arg.b_element_op_,
arg.cde_element_op_,
arg.a_grid_desc_akb_ak0_m_ak1_,
arg.b_grid_desc_bkb_bk0_n_bk1_,
arg.ds_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.e_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.compute_ptr_offset_of_batch_,
arg.block_2_etile_map_);
};
if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
{
if(arg.split_k_ <= 1)
return launch_kernel(integral_constant<bool, true>{});
else
return launch_kernel_atomic_add(integral_constant<bool, true>{});
}
else
{
if(arg.split_k_ <= 1)
return launch_kernel(integral_constant<bool, false>{});
else
return launch_kernel_atomic_add(integral_constant<bool, false>{});
}
}
// polymorphic
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};
static bool IsSupportedArgument(const Argument& arg)
{
if(!(ck::get_device_name() == "gfx908" || ck::get_device_name() == "gfx90a"))
{
return false;
}
if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_akb_ak0_m_ak1_,
arg.b_grid_desc_bkb_bk0_n_bk1_,
arg.ds_grid_desc_m_n_,
arg.e_grid_desc_m_n_,
arg.block_2_etile_map_))
{
return false;
}
// check vector access
static_assert((ABlockTransferSrcVectorDim == 2 || ABlockTransferSrcVectorDim == 3) &&
(BBlockTransferSrcVectorDim == 2 || BBlockTransferSrcVectorDim == 3),
"wrong!");
// vector memory access of A: could be on M or AK1 dimension
if constexpr(ABlockTransferSrcVectorDim == 2)
{
if(!(arg.a_mz_stride_ == 1 &&
arg.a_grid_desc_akb_ak0_m_ak1_.GetLength(I2) % ABlockTransferSrcScalarPerVector ==
0))
{
return false;
}
}
else
{
if(!(arg.a_kz_stride_ == 1 &&
arg.a_grid_desc_akb_ak0_m_ak1_.GetLength(I3) % ABlockTransferSrcScalarPerVector ==
0))
{
return false;
}
}
// vector memory access of B: could be on N or BK1 dimension
if constexpr(BBlockTransferSrcVectorDim == 2)
{
if(!(arg.b_nz_stride_ == 1 &&
arg.b_grid_desc_bkb_bk0_n_bk1_.GetLength(I2) % BBlockTransferSrcScalarPerVector ==
0))
{
return false;
}
}
else
{
if(!(arg.b_kz_stride_ == 1 &&
arg.b_grid_desc_bkb_bk0_n_bk1_.GetLength(I3) % BBlockTransferSrcScalarPerVector ==
0))
{
return false;
}
}
// vector memory access of Ds: always on NPerBlock dimension
bool valid_d_access = true;
static_for<0, NumDTensor, 1>{}([&](auto i) {
if(!(arg.ds_nz_stride_[i] == 1 &&
arg.ds_grid_desc_mblock_mperblock_nblock_nperblock_[i].GetLength(I3) %
CDEBlockTransferScalarPerVector_NPerBlock ==
0))
{
valid_d_access = false;
}
});
if(valid_d_access == false)
{
return false;
}
// vector memory access of E: always on NPerBlock dimension
if(!((arg.e_nz_stride_ == 1 &&
arg.e_grid_desc_mblock_mperblock_nblock_nperblock_.GetLength(I3) %
CDEBlockTransferScalarPerVector_NPerBlock ==
0) ||
CDEBlockTransferScalarPerVector_NPerBlock == 1))
{
return false;
}
return true;
}
// polymorphic
bool IsSupportedArgument(const BaseArgument* p_arg) override
{
return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
}
static auto
MakeArgument(const void* p_a,
const void* p_b,
std::array<const void*, NumDTensor> p_ds,
void* p_e,
const std::vector<index_t>& a_gs_ms_ns_lengths,
const std::vector<index_t>& a_gs_ms_ks_strides,
const std::vector<index_t>& b_gs_ns_ks_lengths,
const std::vector<index_t>& b_gs_ns_ks_strides,
const std::array<std::vector<index_t>, NumDTensor>& ds_gs_ms_ns_lengths,
const std::array<std::vector<index_t>, NumDTensor>& ds_gs_ms_ns_strides,
const std::vector<index_t>& e_gs_ms_ns_lengths,
const std::vector<index_t>& e_gs_ms_ns_strides,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation cde_element_op,
index_t split_k)
{
return Argument{p_a,
p_b,
p_ds,
p_e,
a_gs_ms_ns_lengths,
a_gs_ms_ks_strides,
b_gs_ns_ks_lengths,
b_gs_ns_ks_strides,
ds_gs_ms_ns_lengths,
ds_gs_ms_ns_strides,
e_gs_ms_ns_lengths,
e_gs_ms_ns_strides,
a_element_op,
b_element_op,
cde_element_op,
split_k};
}
static auto MakeInvoker() { return Invoker{}; }
// polymorphic
std::unique_ptr<BaseArgument>
MakeArgumentPointer(const void* p_a,
const void* p_b,
std::array<const void*, NumDTensor> p_ds,
void* p_e,
const std::vector<index_t>& a_gs_ms_ns_lengths,
const std::vector<index_t>& a_gs_ms_ks_strides,
const std::vector<index_t>& b_gs_ns_ks_lengths,
const std::vector<index_t>& b_gs_ns_ks_strides,
const std::array<std::vector<index_t>, NumDTensor>& ds_gs_ms_ns_lengths,
const std::array<std::vector<index_t>, NumDTensor>& ds_gs_ms_ns_strides,
const std::vector<index_t>& e_gs_ms_ns_lengths,
const std::vector<index_t>& e_gs_ms_ns_strides,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation cde_element_op,
index_t split_k) override
{
return std::make_unique<Argument>(p_a,
p_b,
p_ds,
p_e,
a_gs_ms_ns_lengths,
a_gs_ms_ks_strides,
b_gs_ns_ks_lengths,
b_gs_ns_ks_strides,
ds_gs_ms_ns_lengths,
ds_gs_ms_ns_strides,
e_gs_ms_ns_lengths,
e_gs_ms_ns_strides,
a_element_op,
b_element_op,
cde_element_op,
split_k);
}
// polymorphic
std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
{
return std::make_unique<Invoker>(Invoker{});
}
// polymorphic
std::string GetTypeString() const override
{
auto str = std::stringstream();
// clang-format off
str << "DeviceSplitKContractionMultipleD_Xdl_CShuffle"
<< "<"
<< NumDimG << ", "
<< NumDimM << ", "
<< NumDimN << ", "
<< NumDimK << ", "
<< BlockSize << ", "
<< MPerBlock << ", "
<< NPerBlock << ", "
<< KPerBlock << ", "
<< AK1 << ", "
<< BK1 << ", "
<< ABlockTransferSrcVectorDim << ", "
<< BBlockTransferSrcVectorDim
<< ">";
// clang-format on
return str.str();
}
};
} // namespace device
} // namespace tensor_operation
} // namespace ck
include/ck/tensor_operation/gpu/device/impl/device_batched_gemm_e_permute_xdl.hpp 0 → 100644
View file @ 5b7c2432
#pragma once
#include <iostream>
#include <sstream>
#include "ck/utility/common_header.hpp"
#include "ck/tensor_description/tensor_descriptor.hpp"
#include "ck/tensor_description/tensor_descriptor_helper.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_batched_gemm_e_permute.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_d_xdl_cshuffle.hpp"
#include "ck/host_utility/device_prop.hpp"
#include "ck/host_utility/kernel_launch.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
/*
* \brief Wrapper function of GridwiseGemm::Run to realize BatchedGEMM.
*
* \tparam ComputePtrOffsetOfBatch Class that computes the base pointer offsets of A, B, C matrix
* given the batch. For example, ComputePtrOffsetOfStridedBatch() computes the offsets of evenly
* strided batched, but we can easily extend to other layouts. The returned offset can be either \p
* index_t or \p long_index_t. If it returns \p long_index_t, we are not subject to the 2GB
#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
* limitations.
*
* \tparam Block2ETileMap Block2ETileMap::CalculateBottomIndex() takes in id of a workgroup and
* returns the 2D index of the tile that it computes. \see
* GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3::Run().
* \note Using \p ComputePtrOffsetOfBatch gives us the flexibility that 2 workgroups can compute 2
* tiles from different matrices. Keep in mind that these 2 matrices can share the same grid
* descriptor (like in BatchedGEMM), or use their own grid descriptors (in GroupedGemm). \link
* impl/device_conv3d_fwd_xdl_ndhwc_kzyxc_ndhwk.hpp kernel_gemm_xdlops_v2r3_for_conv3d \endlink for
\link
* DeviceConv3d \endlink uses the same concept, but currently does NOT encapsulate the computing of
* pointer offset into \p ComputePtrOffsetOfStridedBatch.
*
* \note \p Block2ETileMap allows customized mapping between a workgroup and the C-tile it computes.
* Together with \p ComputePtrOffsetOfBatch, we can reuse GridwiseGemm (and GridwiseGemm fusion ) to
* realize BatchedGemmCPermute and GroupedGemm (and the corresponding GEMM fusion).
*
*/
template <typename GridwiseGemm,
typename ABDataType,
typename EDataType,
typename AGridDesc_AK0_M_AK1,
typename BGridDesc_BK0_N_BK1,
typename EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CDEElementwiseOperation,
typename ComputePtrOffsetOfBatch,
typename Block2ETileMap,
bool HasMainKBlockLoop>
__global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
kernel_batched_gemm_e_permute_xdl(const ABDataType* __restrict__ p_a_grid,
const ABDataType* __restrict__ p_b_grid,
EDataType* __restrict__ p_e_grid,
const index_t batch_count,
const AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1,
const BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1,
const EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock
e_grid_desc_mblock_mperblock_nblock_nperblock,
const AElementwiseOperation a_element_op,
const BElementwiseOperation b_element_op,
const CDEElementwiseOperation cde_element_op,
const ComputePtrOffsetOfBatch compute_ptr_offset_of_batch,
const Block2ETileMap block_2_etile_map)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
const index_t num_blocks_per_batch =
__builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);
const long_index_t a_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx)));
const long_index_t b_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_ptr_offset_of_batch.GetBPtrOffset(g_idx)));
const long_index_t e_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_ptr_offset_of_batch.GetCPtrOffset(g_idx)));
__shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid + a_batch_offset,
p_b_grid + b_batch_offset,
ck::Tuple<>{},
p_e_grid + e_batch_offset,
p_shared,
a_element_op,
b_element_op,
cde_element_op,
a_grid_desc_ak0_m_ak1,
b_grid_desc_bk0_n_bk1,
ck::Tuple<>{},
e_grid_desc_mblock_mperblock_nblock_nperblock,
block_2_etile_map);
#else
ignore = p_a_grid;
ignore = p_b_grid;
ignore = p_e_grid;
ignore = batch_count;
ignore = a_grid_desc_ak0_m_ak1;
ignore = b_grid_desc_bk0_n_bk1;
ignore = e_grid_desc_mblock_mperblock_nblock_nperblock;
ignore = a_element_op;
ignore = b_element_op;
ignore = cde_element_op;
ignore = compute_ptr_offset_of_batch;
ignore = block_2_etile_map;
#endif
}
template <typename ALayout,
typename BLayout,
typename ELayout,
typename ADataType,
typename BDataType,
typename AccDataType,
typename CShuffleDataType,
typename EDataType,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CDEElementwiseOperation,
GemmSpecialization GemmSpec,
index_t NumPrefetch,
index_t BlockSize,
index_t MPerBlock,
index_t NPerBlock,
index_t KPerBlock,
index_t AK1,
index_t BK1,
index_t MPerXDL,
index_t NPerXDL,
index_t MXdlPerWave,
index_t NXdlPerWave,
typename ABlockTransferThreadClusterLengths_K0_M_K1,
typename ABlockTransferThreadClusterArrangeOrder,
typename ABlockTransferSrcAccessOrder,
index_t ABlockTransferSrcVectorDim,
index_t ABlockTransferSrcScalarPerVector,
index_t ABlockTransferDstScalarPerVector_K1,
index_t ABlockLdsExtraM,
typename BBlockTransferThreadClusterLengths_K0_N_K1,
typename BBlockTransferThreadClusterArrangeOrder,
typename BBlockTransferSrcAccessOrder,
index_t BBlockTransferSrcVectorDim,
index_t BBlockTransferSrcScalarPerVector,
index_t BBlockTransferDstScalarPerVector_K1,
index_t BBlockLdsExtraN,
index_t CShuffleMXdlPerWavePerShuffle,
index_t CShuffleNXdlPerWavePerShuffle,
typename CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
index_t CDEBlockTransferScalarPerVector_NPerBlock,
LoopScheduler LoopSched = make_default_loop_scheduler()>
struct DeviceBatchedGemmEPermuteXdl : public DeviceBatchedGemmEPermute<ALayout,
BLayout,
ELayout,
ADataType,
BDataType,
EDataType,
AElementwiseOperation,
BElementwiseOperation,
CDEElementwiseOperation>
{
using DeviceOp = DeviceBatchedGemmEPermuteXdl;
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
static constexpr auto I2 = Number<2>{};
static constexpr auto matrix_padder =
MatrixPadder<GemmSpec, index_t, index_t, index_t>{MPerBlock, NPerBlock, KPerBlock};
static auto MakeAGridDescriptor_M_K(index_t MRaw, index_t KRaw, index_t StrideA)
{
const auto a_grid_desc_mraw_kraw = [&]() {
if constexpr(is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
{
return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
make_tuple(StrideA, I1));
}
else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
{
return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
make_tuple(I1, StrideA));
}
}();
return matrix_padder.PadADescriptor_M_K(a_grid_desc_mraw_kraw);
}
static auto MakeBGridDescriptor_N_K(index_t KRaw, index_t NRaw, index_t StrideB)
{
const auto b_grid_desc_nraw_kraw = [&]() {
if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
{
return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
make_tuple(I1, StrideB));
}
else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
{
return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
make_tuple(StrideB, I1));
}
}();
return matrix_padder.PadBDescriptor_N_K(b_grid_desc_nraw_kraw);
}
static auto
MakeEGridDescriptor_M_N(index_t MRaw, index_t NRaw, index_t stride_M, index_t stride_N)
{
const auto e_grid_desc_mraw_nraw =
make_naive_tensor_descriptor(make_tuple(MRaw, NRaw), make_tuple(stride_M, stride_N));
return matrix_padder.PadCDescriptor_M_N(e_grid_desc_mraw_nraw);
}
static auto MakeEGridDescriptor_G0_G1_M_N(index_t G0,
index_t G1,
index_t MRaw,
index_t NRaw,
index_t stride_G0,
index_t stride_G1,
index_t stride_M,
index_t stride_N)
{
const auto e_grid_desc_g0_g1_mraw_nraw = [&]() {
return make_naive_tensor_descriptor(
make_tuple(G0, G1, MRaw, NRaw),
make_tuple(stride_G0, stride_G1, stride_M, stride_N));
}();
const auto M = math::integer_divide_ceil(MRaw, MPerBlock) * MPerBlock;
const auto N = math::integer_divide_ceil(NRaw, NPerBlock) * NPerBlock;
const auto MPad = M - MRaw;
const auto NPad = N - NRaw;
if constexpr(GemmSpec == GemmSpecialization::MNPadding ||
GemmSpec == GemmSpecialization::MNKPadding)
{
// pad M and N
return transform_tensor_descriptor(
e_grid_desc_g0_g1_mraw_nraw,
make_tuple(make_pass_through_transform(G0),
make_pass_through_transform(G1),
make_right_pad_transform(MRaw, MPad),
make_right_pad_transform(NRaw, NPad)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
}
else if constexpr(GemmSpec == GemmSpecialization::MPadding ||
GemmSpec == GemmSpecialization::MKPadding)
{
// pad M, but not N
return transform_tensor_descriptor(
e_grid_desc_g0_g1_mraw_nraw,
make_tuple(make_pass_through_transform(G0),
make_pass_through_transform(G1),
make_right_pad_transform(MRaw, MPad),
make_pass_through_transform(NRaw)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
}
else if constexpr(GemmSpec == GemmSpecialization::NPadding ||
GemmSpec == GemmSpecialization::NKPadding)
{
// pad N, but not M
return transform_tensor_descriptor(
e_grid_desc_g0_g1_mraw_nraw,
make_tuple(make_pass_through_transform(G0),
make_pass_through_transform(G1),
make_pass_through_transform(MRaw),
make_right_pad_transform(NRaw, NPad)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
}
else
{
// not pad M or N
return e_grid_desc_g0_g1_mraw_nraw;
}
}
using AGridDesc_M_K = decltype(MakeAGridDescriptor_M_K(1, 1, 1));
using BGridDesc_N_K = decltype(MakeBGridDescriptor_N_K(1, 1, 1));
using EGridDesc_M_N = decltype(MakeEGridDescriptor_M_N(1, 1, 1, 1));
using EGridDesc_G0_G1_M_N = decltype(MakeEGridDescriptor_G0_G1_M_N(1, 1, 1, 1, 1, 1, 1, 1));
struct ComputePtrOffsetOfStridedBatch
{
ComputePtrOffsetOfStridedBatch(index_t Batchstride_A,
index_t Batchstride_B,
EGridDesc_G0_G1_M_N e_grid_desc_g0_g1_m_n)
: Batchstride_A_(Batchstride_A),
Batchstride_B_(Batchstride_B),
e_grid_desc_g0_g1_m_n_(e_grid_desc_g0_g1_m_n)
{
}
__host__ __device__ constexpr long_index_t GetAPtrOffset(index_t g_idx) const
{
return g_idx * static_cast<long_index_t>(Batchstride_A_);
}
__host__ __device__ constexpr long_index_t GetBPtrOffset(index_t g_idx) const
{
return g_idx * static_cast<long_index_t>(Batchstride_B_);
}
__host__ __device__ constexpr long_index_t GetCPtrOffset(index_t g_idx) const
{
const index_t G1 = e_grid_desc_g0_g1_m_n_.GetLength(I1);
index_t b0 = g_idx / G1;
index_t b1 = g_idx - b0 * G1; // g_idx % G1
return e_grid_desc_g0_g1_m_n_.CalculateOffset(make_multi_index(b0, b1, 0, 0));
}
private:
index_t Batchstride_A_;
index_t Batchstride_B_;
EGridDesc_G0_G1_M_N e_grid_desc_g0_g1_m_n_;
};
using GridwiseGemm = GridwiseGemmMultipleD_xdl_cshuffle<
ADataType, // TODO: distinguish A/B datatype
AccDataType,
CShuffleDataType,
ck::Tuple<>, // DsDataType,
EDataType, // EDataType,
AElementwiseOperation,
BElementwiseOperation,
CDEElementwiseOperation,
InMemoryDataOperationEnum::Set,
AGridDesc_M_K,
BGridDesc_N_K,
Tuple<>,
EGridDesc_M_N,
NumPrefetch,
BlockSize,
MPerBlock,
NPerBlock,
KPerBlock,
AK1,
BK1,
MPerXDL,
NPerXDL,
MXdlPerWave,
NXdlPerWave,
ABlockTransferThreadClusterLengths_K0_M_K1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_K1,
false, // AThreadTransferSrcResetCoordinateAfterRun,
ABlockLdsExtraM,
BBlockTransferThreadClusterLengths_K0_N_K1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_K1,
false, // BThreadTransferSrcResetCoordinateAfterRun,
BBlockLdsExtraN,
CShuffleMXdlPerWavePerShuffle,
CShuffleNXdlPerWavePerShuffle,
CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
CDEBlockTransferScalarPerVector_NPerBlock,
LoopSched>;
using AGridDesc_AK0_M_AK1 = remove_cvref_t<decltype(
GridwiseGemm::MakeDefaultAGridDescriptor_AK0_M_AK1(AGridDesc_M_K{}))>;
using BGridDesc_BK0_N_BK1 = remove_cvref_t<decltype(
GridwiseGemm::MakeDefaultBGridDescriptor_BK0_N_BK1(BGridDesc_N_K{}))>;
using EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock = decltype(
GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(EGridDesc_M_N{}));
using Block2ETileMap = typename GridwiseGemm::DefaultBlock2ETileMap;
// Argument
struct Argument : public BaseArgument
{
Argument(const ADataType* p_a_grid,
const BDataType* p_b_grid,
EDataType* p_e_grid,
index_t M,
index_t N,
index_t K,
index_t stride_A,
index_t stride_B,
index_t batch_stride_A,
index_t batch_stride_B,
BatchedGemmEPermuteDesc batched_gemm_e_permute_desc,
index_t BatchCount,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation cde_element_op)
: p_a_grid_{p_a_grid},
p_b_grid_{p_b_grid},
p_e_grid_{p_e_grid},
BatchCount_(BatchCount),
a_grid_desc_m_k_{DeviceOp::MakeAGridDescriptor_M_K(M, K, stride_A)},
b_grid_desc_n_k_{DeviceOp::MakeBGridDescriptor_N_K(K, N, stride_B)},
e_grid_desc_m_n_{
DeviceOp::MakeEGridDescriptor_M_N(batched_gemm_e_permute_desc.M_,
batched_gemm_e_permute_desc.N_,
batched_gemm_e_permute_desc.stride_M_,
batched_gemm_e_permute_desc.stride_N_)},
a_grid_desc_ak0_m_ak1_{
GridwiseGemm::MakeDefaultAGridDescriptor_AK0_M_AK1(a_grid_desc_m_k_)},
b_grid_desc_bk0_n_bk1_{
GridwiseGemm::MakeDefaultBGridDescriptor_BK0_N_BK1(b_grid_desc_n_k_)},
e_grid_desc_mblock_mperblock_nblock_nperblock{},
e_grid_desc_g0_g1_m_n_{
DeviceOp::MakeEGridDescriptor_G0_G1_M_N(batched_gemm_e_permute_desc.G0_,
batched_gemm_e_permute_desc.G1_,
batched_gemm_e_permute_desc.M_,
batched_gemm_e_permute_desc.N_,
batched_gemm_e_permute_desc.stride_G0_,
batched_gemm_e_permute_desc.stride_G1_,
batched_gemm_e_permute_desc.stride_M_,
batched_gemm_e_permute_desc.stride_N_)},
compute_ptr_offset_of_batch_{batch_stride_A, batch_stride_B, e_grid_desc_g0_g1_m_n_},
block_2_etile_map_{GridwiseGemm::MakeDefaultBlock2ETileMap(e_grid_desc_m_n_)},
a_element_op_{a_element_op},
b_element_op_{b_element_op},
cde_element_op_{cde_element_op}
{
if(GridwiseGemm::CheckValidity(a_grid_desc_m_k_,
b_grid_desc_n_k_,
ck::Tuple<>{},
e_grid_desc_m_n_,
block_2_etile_map_))
{
e_grid_desc_mblock_mperblock_nblock_nperblock =
GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
e_grid_desc_m_n_);
}
}
void Print() const
{
std::cout << "A[M, K]: " << a_grid_desc_m_k_ << std::endl;
std::cout << "B[N, K]: " << b_grid_desc_n_k_ << std::endl;
std::cout << "C[M, N]: " << e_grid_desc_m_n_ << std::endl;
}
// private:
// pointers
const ADataType* p_a_grid_;
const BDataType* p_b_grid_;
EDataType* p_e_grid_;
// batch count
index_t BatchCount_;
// tensor descriptors for problem definiton
AGridDesc_M_K a_grid_desc_m_k_;
BGridDesc_N_K b_grid_desc_n_k_;
EGridDesc_M_N e_grid_desc_m_n_;
// tensor descriptors for block/thread-wise copy
AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1_;
BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1_;
EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock e_grid_desc_mblock_mperblock_nblock_nperblock;
EGridDesc_G0_G1_M_N e_grid_desc_g0_g1_m_n_;
// for calculating Batch offset
ComputePtrOffsetOfStridedBatch compute_ptr_offset_of_batch_;
// block-to-e-tile map
Block2ETileMap block_2_etile_map_;
// element-wise op
AElementwiseOperation a_element_op_;
BElementwiseOperation b_element_op_;
CDEElementwiseOperation cde_element_op_;
};
// Invoker
struct Invoker : public BaseInvoker
{
using Argument = DeviceOp::Argument;
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_m_k_,
arg.b_grid_desc_n_k_,
ck::Tuple<>{},
arg.e_grid_desc_m_n_,
arg.block_2_etile_map_))
{
throw std::runtime_error(
"wrong! GridwiseBatchedGemmCPermute_km_kn_m0m1n0n1_xdlops_v2r3 has invalid "
"setting");
}
const index_t grid_size =
arg.block_2_etile_map_.CalculateGridSize(arg.e_grid_desc_m_n_) * arg.BatchCount_;
const auto K =
arg.a_grid_desc_ak0_m_ak1_.GetLength(I0) * arg.a_grid_desc_ak0_m_ak1_.GetLength(I2);
auto launch_kernel = [&](auto has_main_k_block_loop_) {
const auto kernel = kernel_batched_gemm_e_permute_xdl<
GridwiseGemm,
ADataType, // TODO: distiguish A/B datatype
EDataType,
remove_reference_t<DeviceOp::AGridDesc_AK0_M_AK1>,
remove_reference_t<DeviceOp::BGridDesc_BK0_N_BK1>,
typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
AElementwiseOperation,
BElementwiseOperation,
CDEElementwiseOperation,
ComputePtrOffsetOfStridedBatch,
remove_reference_t<Block2ETileMap>,
has_main_k_block_loop_>;
return launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_e_grid_,
arg.BatchCount_,
arg.a_grid_desc_ak0_m_ak1_,
arg.b_grid_desc_bk0_n_bk1_,
arg.e_grid_desc_mblock_mperblock_nblock_nperblock,
arg.a_element_op_,
arg.b_element_op_,
arg.cde_element_op_,
arg.compute_ptr_offset_of_batch_,
arg.block_2_etile_map_);
};
if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
{
return launch_kernel(integral_constant<bool, true>{});
}
else
{
return launch_kernel(integral_constant<bool, false>{});
}
}
// polymorphic
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};
static constexpr bool IsValidCompilationParameter()
{
// TODO: properly implement this check
return true;
}
static bool IsSupportedArgument(const Argument& arg)
{
return GridwiseGemm::CheckValidity(arg.a_grid_desc_m_k_,
arg.b_grid_desc_n_k_,
ck::Tuple<>{},
arg.e_grid_desc_m_n_,
arg.block_2_etile_map_);
}
// polymorphic
bool IsSupportedArgument(const BaseArgument* p_arg) override
{
return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
}
static auto MakeArgument(const ADataType* p_a,
const BDataType* p_b,
EDataType* p_e,
index_t M,
index_t N,
index_t K,
index_t stride_A,
index_t stride_B,
index_t batch_stride_A,
index_t batch_stride_B,
BatchedGemmEPermuteDesc batched_gemm_e_permute_desc,
index_t BatchCount,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation cde_element_op)
{
return Argument{p_a,
p_b,
p_e,
M,
N,
K,
stride_A,
stride_B,
batch_stride_A,
batch_stride_B,
batched_gemm_e_permute_desc,
BatchCount,
a_element_op,
b_element_op,
cde_element_op};
}
static auto MakeInvoker() { return Invoker{}; }
// polymorphic
std::unique_ptr<BaseArgument>
MakeArgumentPointer(const void* p_a,
const void* p_b,
void* p_e,
index_t M,
index_t N,
index_t K,
index_t stride_A,
index_t stride_B,
index_t batch_stride_A,
index_t batch_stride_B,
BatchedGemmEPermuteDesc batched_gemm_e_permute_desc,
index_t BatchCount,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation cde_element_op) override
{
return std::make_unique<Argument>(static_cast<const ADataType*>(p_a),
static_cast<const BDataType*>(p_b),
static_cast<EDataType*>(p_e),
M,
N,
K,
stride_A,
stride_B,
batch_stride_A,
batch_stride_B,
batched_gemm_e_permute_desc,
BatchCount,
a_element_op,
b_element_op,
cde_element_op);
}
// polymorphic
std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
{
return std::make_unique<Invoker>(Invoker{});
}
// polymorphic
std::string GetTypeString() const override
{
auto str = std::stringstream();
// clang-format off
str << "DeviceBatchedGemmEPermuteXdl"
<< "<"
<< BlockSize << ", "
<< MPerBlock << ", "
<< NPerBlock << ", "
<< KPerBlock
<< ">";
// clang-format on
return str.str();
}
};
} // namespace device
} // namespace tensor_operation
} // namespace ck
include/ck/tensor_operation/gpu/device/device_batched_gemm_multi_d_xdl.hpp → include/ck/tensor_operation/gpu/device/impl/device_batched_gemm_multi_d_xdl.hpp
View file @ 5b7c2432
...@@ -38,9 +38,9 @@ namespace device { ...@@ -38,9 +38,9 @@ namespace device {
* \note Using \p ComputePtrOffsetOfBatch gives us the flexibility that 2 workgroups can compute 2 * \note Using \p ComputePtrOffsetOfBatch gives us the flexibility that 2 workgroups can compute 2
* tiles from different matrices. Keep in mind that these 2 matrices can share the same grid * tiles from different matrices. Keep in mind that these 2 matrices can share the same grid
* descriptor (like in BatchedGEMM), or use their own grid descriptors (in GroupedGemm). \link * descriptor (like in BatchedGEMM), or use their own grid descriptors (in GroupedGemm). \link
* device_conv3d_fwd_xdl_ndhwc_kzyxc_ndhwk.hpp kernel_gemm_xdlops_v2r3_for_conv3d \endlink for \link * impl/device_conv3d_fwd_xdl_ndhwc_kzyxc_ndhwk.hpp kernel_gemm_xdlops_v2r3_for_conv3d \endlink for
* DeviceConv3d \endlink uses the same concept, but currently does NOT encapsulate the computing of * \link DeviceConv3d \endlink uses the same concept, but currently does NOT encapsulate the
* pointer offset into \p ComputePtrOffsetOfStridedBatch. * computing of pointer offset into \p ComputePtrOffsetOfStridedBatch.
* *
* \note \p Block2ETileMap allows customized mapping between a workgroup and the C-tile it computes. * \note \p Block2ETileMap allows customized mapping between a workgroup and the C-tile it computes.
* Together with \p ComputePtrOffsetOfBatch, we can reuse GridwiseGemm (and GridwiseGemm fusion ) to * Together with \p ComputePtrOffsetOfBatch, we can reuse GridwiseGemm (and GridwiseGemm fusion ) to
......
include/ck/tensor_operation/gpu/device/device_batched_gemm_softmax_gemm_permute_xdl_cshuffle.hpp → include/ck/tensor_operation/gpu/device/impl/device_batched_gemm_softmax_gemm_permute_xdl_cshuffle.hpp
View file @ 5b7c2432
...@@ -9,10 +9,10 @@ ...@@ -9,10 +9,10 @@
#include "ck/utility/common_header.hpp" #include "ck/utility/common_header.hpp"
#include "ck/tensor_description/tensor_descriptor.hpp" #include "ck/tensor_description/tensor_descriptor.hpp"
#include "ck/tensor_description/tensor_descriptor_helper.hpp" #include "ck/tensor_description/tensor_descriptor_helper.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_batched_gemm_softmax_gemm_permute.hpp" #include "ck/tensor_operation/gpu/device/device_batched_gemm_softmax_gemm_permute.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp" #include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/matrix_padder.hpp" #include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_batched_gemm_softmax_gemm_xdl_cshuffle_v1.hpp" #include "ck/tensor_operation/gpu/grid/gridwise_batched_gemm_softmax_gemm_xdl_cshuffle_v1.hpp"
#include "ck/host_utility/device_prop.hpp" #include "ck/host_utility/device_prop.hpp"
#include "ck/host_utility/kernel_launch.hpp" #include "ck/host_utility/kernel_launch.hpp"
......
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