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Commit 45c6c584 authored by mtgu0705's avatar mtgu0705
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Modify the kernel to 128x128x64, and use mfma_32x32x4 

Add int4+scale based on Zhang, Jing pk_i4. Compile pass, function pass.
parent f03dda48
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Showing with 5156 additions and 57 deletions
example/01_gemm/CMakeLists.txt
View file @ 45c6c584
...@@ -30,6 +30,7 @@ add_example_executable(example_gemm_xdl_fp8_v3 gemm_xdl_fp8_v3.cpp) ...@@ -30,6 +30,7 @@ add_example_executable(example_gemm_xdl_fp8_v3 gemm_xdl_fp8_v3.cpp)
add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp8_v3) add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp8_v3)
add_example_executable(example_gemm_xdl_fp16_fp8_v3 gemm_xdl_fp16_fp8_v3.cpp) add_example_executable(example_gemm_xdl_fp16_fp8_v3 gemm_xdl_fp16_fp8_v3.cpp)
add_example_executable(example_gemm_xdl_fp16_pk_i4_v3 gemm_xdl_fp16_pk_i4_v3.cpp) add_example_executable(example_gemm_xdl_fp16_pk_i4_v3 gemm_xdl_fp16_pk_i4_v3.cpp)
add_example_executable(example_gemm_xdl_fp16_pk_i4_v3_b_scale gemm_xdl_fp16_pk_i4_v3_b_scale.cpp)
add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp16_fp8_v3) add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp16_fp8_v3)
add_example_executable(example_gemm_xdl_bf16_v3 gemm_xdl_bf16_v3.cpp) add_example_executable(example_gemm_xdl_bf16_v3 gemm_xdl_bf16_v3.cpp)
add_example_dependencies(example_gemm_xdl example_gemm_xdl_bf16_v3) add_example_dependencies(example_gemm_xdl example_gemm_xdl_bf16_v3)
......
example/01_gemm/gemm_xdl_fp16_pk_i4_v3.cpp
View file @ 45c6c584
...@@ -23,7 +23,7 @@ static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecializa ...@@ -23,7 +23,7 @@ static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecializa
static constexpr bool PermuteB = true; static constexpr bool PermuteB = true;
static constexpr ck::index_t KPerBlock = 128; static constexpr ck::index_t KPerBlock = 64;
// clang-format off // clang-format off
using DeviceGemmV2Instance = using DeviceGemmV2Instance =
...@@ -43,28 +43,28 @@ using DeviceGemmV2Instance = ...@@ -43,28 +43,28 @@ using DeviceGemmV2Instance =
2, 32, 32, 0, 2, 32, 32, 0,
1, 1, S<1, 16, 1, 8>, 4, 1, 1, S<1, 16, 1, 8>, 4,
#else #else
128, 256,
16, 64, 128, 128,
KPerBlock, 8, 32, KPerBlock, 8, 32,
16, 16, 32, 32,
1, 2, 2, 2,
S<16, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>,
2, 8, 8, 0, 2, 8, 8, 0,
S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, S<2, 128, 1>, S<1, 0, 2>, S<1, 0, 2>,
2, 32, 32, 0, 2, 32, 32, 0,
1, 1, S<1, 16, 1, 8>, 4, 1, 1, S<1, 16, 1, 8>, 4,
#endif #endif
ck::BlockGemmPipelineScheduler::Interwave, ck::BlockGemmPipelineVersion::v2, CDataType, CDataType, false, PermuteB>; ck::BlockGemmPipelineScheduler::Intrawave, ck::BlockGemmPipelineVersion::v3, CDataType, CDataType, false, PermuteB>;
// clang-format on // clang-format on
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType, using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType, BDataType,
CDataType, CDataType,
AccDataType, AccDataType,
PassThrough, PassThrough,
PassThrough, PassThrough,
PassThrough>; PassThrough>;
template <typename ProblemType> template <typename ProblemType>
bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config) bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
{ {
......
example/01_gemm/gemm_xdl_fp16_pk_i4_v3_b_scale.cpp 0 → 100644
View file @ 45c6c584
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "common.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_xdl_cshuffle_v3_b_scale.hpp"
using ADataType = ck::half_t;
using BDataType = ck::pk_i4_t;
using BScaleDataType = ck::half_t;
using AccDataType = float;
using CShuffleDataType = ck::half_t;
using CDataType = ck::half_t;
using ALayout = Row;
using BLayout = Col;
using CLayout = Row;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CElementOp = PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
static constexpr bool PermuteB = true;
static constexpr ck::index_t Scale_Block_N = 1;
static constexpr ck::index_t Scale_Block_K = 64;
static constexpr ck::index_t KPerBlock = 64;
// clang-format off
using DeviceGemmV2Instance =
ck::tensor_operation::device::DeviceGemm_Xdl_CShuffleV3<
ALayout, BLayout, CLayout,
ADataType, BDataType, BScaleDataType, CDataType, AccDataType, CShuffleDataType,
AElementOp, BElementOp, CElementOp, GemmDefault,
#if 0
128, Scale_Block_N, Scale_Block_K,
16, 128,
KPerBlock, 8, 32,
16, 16,
1, 4,
S<16, 8, 1>, S<1, 0, 2>, S<1, 0, 2>,
2, 8, 8, 0,
S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>,
2, 32, 32, 0,
1, 1, S<1, 16, 1, 8>, 4,
#else
256, Scale_Block_N, Scale_Block_K,
128, 128,
KPerBlock, 8, 32,
32, 32,
2, 2,
S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>,
2, 8, 8, 0,
S<2, 128, 1>, S<1, 0, 2>, S<1, 0, 2>,
2, 32, 32, 0,
1, 1, S<1, 16, 1, 8>, 4,
#endif
ck::BlockGemmPipelineScheduler::Intrawave, ck::BlockGemmPipelineVersion::v3, CDataType, CDataType, false, PermuteB>;
// clang-format on
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
AccDataType,
CDataType,
AccDataType,
PassThrough,
PassThrough,
PassThrough>;
template <typename ProblemType>
bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
{
using namespace ck::literals;
auto M = problem_size.M;
auto N = problem_size.N;
auto K = problem_size.K;
auto StrideA = problem_size.StrideA;
auto StrideB = problem_size.StrideB;
auto StrideC = problem_size.StrideC;
auto KBatch = problem_size.KBatch;
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
{
return HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return HostTensorDescriptor({row, col}, {1_uz, stride});
}
};
auto f_get_default_stride =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
if(stride == 0)
{
// give a chance if stride is zero, return a default packed stride
if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
{
return col;
}
else
{
return row;
}
}
else
return stride;
};
ck::index_t Scale_Stride_BN = (K + Scale_Block_K - 1) / Scale_Block_K;
StrideA = f_get_default_stride(M, K, StrideA, ALayout{});
StrideB = f_get_default_stride(K, N, StrideB, BLayout{});
StrideC = f_get_default_stride(M, N, StrideC, CLayout{});
Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
Tensor<BDataType> b_k_n_permute(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
Tensor<BScaleDataType> b1_k_n(f_host_tensor_descriptor((K + Scale_Block_K - 1) / Scale_Block_K,
(N + Scale_Block_N - 1) / Scale_Block_N,
Scale_Stride_BN,
BLayout{}));
switch(config.init_method)
{
case 0:
a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
b_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
b1_k_n.GenerateTensorValue(GeneratorTensor_1<BScaleDataType>{1});
break;
case 1:
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
b1_k_n.GenerateTensorValue(GeneratorTensor_2<BScaleDataType>{0, 1});
break;
case 2:
a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
b1_k_n.GenerateTensorValue(GeneratorTensor_1<BScaleDataType>{1});
break;
case 3:
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
b_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
b1_k_n.GenerateTensorValue(GeneratorTensor_1<BScaleDataType>{1});
break;
default:
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.5, 0.5});
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
b1_k_n.GenerateTensorValue(GeneratorTensor_3<BScaleDataType>{0, 1.0});
}
Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
std::cout << "b1_k_n: " << b1_k_n.mDesc << std::endl;
std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n_permute.mDesc.GetElementSpaceSize());
DeviceMem b1_scale_device_buf(sizeof(BScaleDataType) * b1_k_n.mDesc.GetElementSpaceSize());
DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpaceSize());
// weight permute
if constexpr(PermuteB)
{
int K1 = KPerBlock;
int K0 = K / KPerBlock;
// int K0, N, K1
for(int j = 0; j < K0; j++)
{
for(int i = 0; i < N; i++)
{
for(int jj = 0; jj < K1; jj++)
{
b_k_n_permute(j * N * K1 + i * K1 + jj) = b_k_n(i * K + (j * K1 + jj));
}
}
}
}
else
{
for(int i = 0; i < N; i++)
{
for(int j = 0; j < K; j++)
{
b_k_n_permute(i * K + j) = b_k_n(i * K + j);
}
}
}
// vector pk_i4x4 permute
for(int i = 0; i < N; i++)
{
for(int j = 0; j < K; j += 8)
{
int input[8];
for(int k = 0; k < 4; k++)
{
int i4x2 = b_k_n_permute(j + k * 2, i);
input[k * 2 + 0] = (i4x2 >> 4) & 0xf;
input[k * 2 + 1] = (i4x2 >> 0) & 0xf;
}
// permute 01234567->20643175
{
int hi = input[2];
int lo = input[0];
int i4x2 = (hi << 4) | lo;
b_k_n_permute(j + 0, i) = i4x2;
}
{
int hi = input[6];
int lo = input[4];
int i4x2 = (hi << 4) | lo;
b_k_n_permute(j + 2, i) = i4x2;
}
{
int hi = input[3];
int lo = input[1];
int i4x2 = (hi << 4) | lo;
b_k_n_permute(j + 4, i) = i4x2;
}
{
int hi = input[7];
int lo = input[5];
int i4x2 = (hi << 4) | lo;
b_k_n_permute(j + 6, i) = i4x2;
}
}
}
a_m_k_device_buf.ToDevice(a_m_k.mData.data());
b_k_n_device_buf.ToDevice(b_k_n_permute.mData.data());
b1_scale_device_buf.ToDevice(b1_k_n.mData.data());
DeviceMem workspace;
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto c_element_op = CElementOp{};
// do GEMM
auto gemm = DeviceGemmV2Instance{};
auto invoker = gemm.MakeInvoker();
float ave_time = 0;
auto argument =
gemm.MakeArgument(static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
M,
N,
K,
StrideA,
StrideB,
StrideC,
static_cast<BScaleDataType*>(b1_scale_device_buf.GetDeviceBuffer()),
KBatch,
a_element_op,
b_element_op,
c_element_op);
if(!gemm.IsSupportedArgument(argument))
{
std::cerr << gemm.GetTypeString() << " does not support this problem" << std::endl;
return true;
}
bool pass = true;
if(config.do_verification)
{
Tensor<float> b_k_n_pre({K, N});
float v_b = 0;
for(int n = 0; n < N; n++)
{
for(int k = 0; k < K; k++)
{
ck::pk_i4_t i4x2 = b_k_n(k, n);
int8_t i4 = 0;
if(k % 2 == 1)
i4 = (i4x2 >> 0) & 0xf;
else
i4 = (i4x2 >> 4) & 0xf;
i4 = i4 - 8;
v_b = ck::type_convert<float>(i4);
b_k_n_pre(k, n) =
ck::type_convert<float>(v_b) *
ck::type_convert<float>(b1_k_n(k / Scale_Block_K, n / Scale_Block_N));
}
}
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(
a_m_k, b_k_n_pre, c_m_n_host_result, PassThrough{}, PassThrough{}, PassThrough{});
ref_invoker.Run(ref_argument);
ave_time = invoker.Run(argument, StreamConfig{nullptr, false, 0});
c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
pass &= ck::utils::check_err(c_m_n_device_result,
c_m_n_host_result,
"Error: Incorrect results!",
get_rtol<CDataType>(),
get_atol<CDataType>());
#if 0
std::cout << "a_m_k: " << std::endl;
for(int i = 0; i < M; i++)
{
for(int j = 0; j < K; j++)
{
std::cout << ck::type_convert<float>(a_m_k(i, j)) << ",";
}
std::cout << std::endl;
}
std::cout << "b_k_n: " << std::endl;
for(int i = 0; i < N; i++)
{
for(int j = 0; j < K; j++)
{
ck::pk_i4_t i4x2 = b_k_n(j, i);
int8_t i4 = 0;
if( j % 2 == 1)
i4 = (i4x2 >> 0) & 0xf;
else
i4 = (i4x2 >> 4) & 0xf;
i4 = i4 - 8;
std::cout << ck::type_convert<float>(i4) << ",";
}
std::cout << std::endl;
}
std::cout << "c_m_n_device_result: " << std::endl;
for(int i = 0; i < M; i++)
{
for(int j = 0; j < N; j++)
{
std::cout << ck::type_convert<float>(c_m_n_device_result(i, j)) << ",";
}
std::cout << std::endl;
}
std::cout << "c_m_n_host_result: " << std::endl;
for(int i = 0; i < M; i++)
{
for(int j = 0; j < N; j++)
{
std::cout << ck::type_convert<float>(c_m_n_host_result(i, j)) << ",";
}
std::cout << std::endl;
}
#endif
}
if(config.time_kernel)
{
ave_time =
invoker.Run(argument, StreamConfig{nullptr, config.time_kernel, 0, 20, 50, true, 50});
std::size_t flop = 2_uz * M * N * K;
std::size_t num_btype =
sizeof(ADataType) * M * K +
sizeof(BDataType) * K * N /
(ck::is_same_v<ck::remove_cvref_t<BDataType>, ck::pk_i4_t> ? 2 : 1) +
sizeof(CDataType) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
<< " GB/s, " << gemm.GetTypeString() << std::endl;
}
return pass;
}
bool run_gemm_splitk_example(int argc, char* argv[])
{
ProblemSizeSplitK problem_size;
ExecutionConfig config;
return !parse_cmd_args(argc, argv, problem_size, config) || run_gemm(problem_size, config);
}
int main(int argc, char* argv[]) { return !run_gemm_splitk_example(argc, argv); }
include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_selector.hpp
View file @ 45c6c584
...@@ -4,8 +4,8 @@ ...@@ -4,8 +4,8 @@
#pragma once #pragma once
#include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v1.hpp" #include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v1.hpp"
#include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v2.hpp" #include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v2_b_scale.hpp"
#include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v3.hpp" #include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v3_b_scale.hpp"
#include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v4.hpp" #include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v4.hpp"
#include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v5.hpp" #include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v5.hpp"
...@@ -68,49 +68,49 @@ constexpr auto BlockGemmPipeline_Selector() ...@@ -68,49 +68,49 @@ constexpr auto BlockGemmPipeline_Selector()
} }
else if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v2) else if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v2)
{ {
return BlockwiseGemmXdlops_pipeline_v2<BlkGemmPipeSche, return BlockwiseGemmXdlops_pipeline_v2_b_scale<BlkGemmPipeSche,
BlockSize, BlockSize,
ADataType, ADataType,
BDataType, BDataType,
ComputeDataType, ComputeDataType,
AccDataType, AccDataType,
ATileDesc, ATileDesc,
BTileDesc, BTileDesc,
AMmaTileDesc, AMmaTileDesc,
BMmaTileDesc, BMmaTileDesc,
ABlockTransferSrcScalarPerVector, ABlockTransferSrcScalarPerVector,
BBlockTransferSrcScalarPerVector, BBlockTransferSrcScalarPerVector,
MPerBlock, MPerBlock,
NPerBlock, NPerBlock,
KPerBlock, KPerBlock,
MPerXDL, MPerXDL,
NPerXDL, NPerXDL,
MRepeat, MRepeat,
NRepeat, NRepeat,
KPack>{}; KPack>{};
} }
else if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v3) else if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v3)
{ {
return BlockwiseGemmXdlops_pipeline_v3<BlkGemmPipeSche, return BlockwiseGemmXdlops_pipeline_v3_b_scale<BlkGemmPipeSche,
BlockSize, BlockSize,
ADataType, ADataType,
BDataType, BDataType,
ComputeDataType, ComputeDataType,
AccDataType, AccDataType,
ATileDesc, ATileDesc,
BTileDesc, BTileDesc,
AMmaTileDesc, AMmaTileDesc,
BMmaTileDesc, BMmaTileDesc,
ABlockTransferSrcScalarPerVector, ABlockTransferSrcScalarPerVector,
BBlockTransferSrcScalarPerVector, BBlockTransferSrcScalarPerVector,
MPerBlock, MPerBlock,
NPerBlock, NPerBlock,
KPerBlock, KPerBlock,
MPerXDL, MPerXDL,
NPerXDL, NPerXDL,
MRepeat, MRepeat,
NRepeat, NRepeat,
KPack>{}; KPack>{};
} }
else if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v4) else if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v4)
{ {
......
include/ck/tensor_operation/gpu/device/device_gemm_v2.hpp
View file @ 45c6c584
...@@ -76,6 +76,42 @@ struct DeviceGemmV2R1 : public BaseOperator ...@@ -76,6 +76,42 @@ struct DeviceGemmV2R1 : public BaseOperator
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0; virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
}; };
template <typename ALayout,
typename BLayout,
typename CLayout,
typename ADataType,
typename BDataType,
typename BScaleType,
typename CDataType,
index_t ScaleBlockN,
index_t ScaleBlockK,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CElementwiseOperation>
struct DeviceGemmV2BScale : public BaseOperator
{
virtual std::unique_ptr<BaseArgument>
MakeArgumentPointer(const void* p_a,
const void* p_b,
void* p_c,
ck::index_t M,
ck::index_t N,
ck::index_t K,
ck::index_t StrideA,
ck::index_t StrideB,
ck::index_t StrideC,
const void* p_b_scale,
ck::index_t KSplit,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op) = 0;
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
virtual bool GetPermuteB() = 0;
virtual ck::index_t GetKPerBlock() = 0;
};
} // namespace device } // namespace device
} // namespace tensor_operation } // namespace tensor_operation
} // namespace ck } // namespace ck
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