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Commit f0759faf authored by Jun Liu's avatar Jun Liu
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Merge branch 'develop' into amd-develop

parents 20ddaeba 764164b4
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Showing with 1679 additions and 37 deletions
client_example/30_gemm_multi_abd/CMakeLists.txt → client_example/30_gemm_bf16Aint8B/CMakeLists.txt
View file @ f0759faf
......@@ -10,4 +10,7 @@ if(GPU_TARGETS MATCHES "gfx9" AND ((DTYPES MATCHES "int8" AND DTYPES MATCHES "bf
add_executable(client_gemm_bf16_i8_bf16 gemm_xdl_bf16_i8.cpp)
target_link_libraries(client_gemm_bf16_i8_bf16 PRIVATE composable_kernel::device_gemm_operations)
add_executable(client_gemm_multiply_bf16_i8_bf16 gemm_xdl_multiply_bf16_i8.cpp)
target_link_libraries(client_gemm_multiply_bf16_i8_bf16 PRIVATE composable_kernel::device_gemm_operations)
endif()
client_example/30_gemm_multi_abd/gemm_bias_fastgelu_xdl_bf16_i8.cpp → client_example/30_gemm_bf16Aint8B/gemm_bias_fastgelu_xdl_bf16_i8.cpp
View file @ f0759faf
......@@ -38,19 +38,19 @@ using EDataType = BF16;
using A0Layout = Row;
using AsLayout = ck::Tuple<A0Layout>;
using B0Layout = Col;
using B0Layout = Row;
using B1Layout = B0Layout;
using BsLayout = ck::Tuple<B0Layout, B1Layout>;
using D0Layout = Row;
using DsLayout = ck::Tuple<D0Layout>;
using ELayout = Row;
using Scales = ck::tensor_operation::element_wise::Scales;
using Multiply = ck::tensor_operation::element_wise::Multiply;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using AddFastGelu = ck::tensor_operation::element_wise::AddFastGelu;
using AElementOp = PassThrough;
using BElementOp = Scales;
using BElementOp = Multiply;
using CDEElementOp = AddFastGelu;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
......
client_example/30_gemm_multi_abd/gemm_bias_xdl_bf16_i8.cpp → client_example/30_gemm_bf16Aint8B/gemm_bias_xdl_bf16_i8.cpp
View file @ f0759faf
......@@ -36,7 +36,7 @@ using D0DataType = BF16;
using DsDataType = ck::Tuple<D0DataType>;
using EDataType = BF16;
using A0Layout = Col;
using A0Layout = Row;
using AsLayout = ck::Tuple<A0Layout>;
using B0Layout = Row;
using B1Layout = B0Layout;
......@@ -45,12 +45,12 @@ using D0Layout = Row;
using DsLayout = ck::Tuple<D0Layout>;
using ELayout = Row;
using Scales = ck::tensor_operation::element_wise::Scales;
using Multiply = ck::tensor_operation::element_wise::Multiply;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using Add = ck::tensor_operation::element_wise::Add;
using AElementOp = PassThrough;
using BElementOp = Scales;
using BElementOp = Multiply;
using CDEElementOp = Add;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
......
client_example/30_gemm_multi_abd/gemm_xdl_bf16_i8.cpp → client_example/30_gemm_bf16Aint8B/gemm_xdl_bf16_i8.cpp
View file @ f0759faf
......@@ -37,19 +37,19 @@ using EDataType = BF16;
using A0Layout = Row;
using AsLayout = ck::Tuple<A0Layout>;
using B0Layout = Col;
using B0Layout = Row;
using B1Layout = B0Layout;
using BsLayout = ck::Tuple<B0Layout, B1Layout>;
using D0Layout = Row;
using DsLayout = ck::Tuple<>;
using ELayout = Row;
using Scales = ck::tensor_operation::element_wise::Scales;
using Multiply = ck::tensor_operation::element_wise::Multiply;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using Add = ck::tensor_operation::element_wise::Add;
using AElementOp = PassThrough;
using BElementOp = Scales;
using BElementOp = Multiply;
using CDEElementOp = PassThrough;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
......@@ -74,12 +74,12 @@ struct SimpleDeviceMem
int main(int argc, char* argv[])
{
// GEMM shape
ck::index_t M = 64;
ck::index_t N = 1024;
ck::index_t K = 512;
ck::index_t M = 4096;
ck::index_t N = 768;
ck::index_t K = 6144;
ck::index_t StrideA = K;
ck::index_t StrideB = N;
ck::index_t StrideB = K;
ck::index_t StrideE = N;
if(argc == 1)
......
client_example/30_gemm_multi_abd/gemm_xdl_gelu_bf16_i8.cpp → client_example/30_gemm_bf16Aint8B/gemm_xdl_gelu_bf16_i8.cpp
View file @ f0759faf
......@@ -37,19 +37,19 @@ using EDataType = BF16;
using A0Layout = Row;
using AsLayout = ck::Tuple<A0Layout>;
using B0Layout = Col;
using B0Layout = Row;
using B1Layout = B0Layout;
using BsLayout = ck::Tuple<B0Layout, B1Layout>;
using D0Layout = Row;
using DsLayout = ck::Tuple<>;
using ELayout = Row;
using Scales = ck::tensor_operation::element_wise::Scales;
using Multiply = ck::tensor_operation::element_wise::Multiply;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using FastGelu = ck::tensor_operation::element_wise::FastGelu;
using AElementOp = PassThrough;
using BElementOp = Scales;
using BElementOp = Multiply;
using CDEElementOp = FastGelu;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
......
client_example/30_gemm_bf16Aint8B/gemm_xdl_multiply_bf16_i8.cpp 0 → 100644
View file @ f0759faf
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <iomanip>
#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_gemm_multiple_abd.hpp"
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/library/tensor_operation_instance/gpu/gemm_multi_abd.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using BF16 = ck::bhalf_t;
using I8 = int8_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using A0DataType = BF16;
using AsDataType = ck::Tuple<A0DataType>;
using B0DataType = I8;
using B1DataType = BF16;
using BsDataType = ck::Tuple<B0DataType>;
using AccDataType = F32;
using CShuffleDataType = BF16;
using DsDataType = ck::Tuple<B1DataType>;
using EDataType = BF16;
using A0Layout = Row;
using AsLayout = ck::Tuple<A0Layout>;
using B0Layout = Row;
using B1Layout = B0Layout;
using BsLayout = ck::Tuple<B0Layout>;
using D0Layout = Row;
using DsLayout = ck::Tuple<B1Layout>;
using ELayout = Row;
using Multiply = ck::tensor_operation::element_wise::Multiply;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CDEElementOp = Multiply;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
struct SimpleDeviceMem
{
SimpleDeviceMem() = delete;
SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
{
(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
}
void* GetDeviceBuffer() { return p_mem_; }
~SimpleDeviceMem() { (void)hipFree(p_mem_); }
void* p_mem_;
};
// clang-format on
int main(int argc, char* argv[])
{
// GEMM shape
ck::index_t M = 4096;
ck::index_t N = 768;
ck::index_t K = 6144;
ck::index_t StrideA = K;
ck::index_t StrideB = K;
ck::index_t StrideE = N;
if(argc == 1)
{
// use default case
}
else if(argc == 7)
{
M = std::stoi(argv[1]);
N = std::stoi(argv[2]);
K = std::stoi(argv[3]);
StrideA = std::stoi(argv[4]);
StrideB = std::stoi(argv[5]);
StrideE = std::stoi(argv[6]);
}
else
{
printf("arg1 to 7: M, N, K, StrideA, StrideB, StrideE\n");
exit(0);
}
auto f_matrix_space_size =
[](std::size_t nRow, std::size_t nCol, std::size_t stride, auto layout) {
using Layout = decltype(layout);
if constexpr(std::is_same<Layout, Row>::value)
{
return (nRow - 1) * stride + nCol;
}
else
{
return (nCol - 1) * stride + nRow;
}
};
SimpleDeviceMem a0_device_buf(sizeof(A0DataType) *
f_matrix_space_size(M, K, StrideA, A0Layout{}));
SimpleDeviceMem b0_device_buf(sizeof(B0DataType) *
f_matrix_space_size(K, N, StrideB, B0Layout{}));
SimpleDeviceMem b1_device_buf(sizeof(B1DataType) * f_matrix_space_size(K, N, 0, B1Layout{}));
SimpleDeviceMem e_device_buf(sizeof(EDataType) * f_matrix_space_size(M, N, StrideE, ELayout{}));
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto cde_element_op = CDEElementOp{};
constexpr ck::index_t NumATensor = 1;
constexpr ck::index_t NumBTensor = 1;
constexpr ck::index_t NumDTensor = 1;
using DeviceOp = ck::tensor_operation::device::DeviceGemmMultipleABD<AsLayout,
BsLayout,
DsLayout,
Row,
AsDataType,
BsDataType,
DsDataType,
BF16,
AElementOp,
BElementOp,
CDEElementOp>;
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
std::string best_op_name;
bool found = false;
int best_op_id = -1;
float best_ave_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
// profile device operation instances
std::cout << "Run all instances and do timing" << std::endl;
for(int i = 0; i < op_ptrs.size(); ++i)
{
auto& op_ptr = op_ptrs[i];
auto argument_ptr = op_ptr->MakeArgumentPointer(
std::array<const void*, NumATensor>{a0_device_buf.GetDeviceBuffer()},
std::array<const void*, NumBTensor>{b0_device_buf.GetDeviceBuffer()},
std::array<const void*, NumDTensor>{b1_device_buf.GetDeviceBuffer()},
e_device_buf.GetDeviceBuffer(),
M,
N,
K,
std::array<ck::index_t, NumATensor>{StrideA},
std::array<ck::index_t, NumBTensor>{StrideB},
std::array<ck::index_t, NumDTensor>{0},
StrideE,
a_element_op,
b_element_op,
cde_element_op);
auto invoker_ptr = op_ptr->MakeInvokerPointer();
std::string op_name = op_ptr->GetTypeString();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_btype =
sizeof(A0DataType) * M * K + sizeof(B0DataType) * K * N + sizeof(EDataType) * 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: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
<< gb_per_sec << " GB/s, " << op_name << std::endl;
if(tflops > best_tflops)
{
found = true;
best_op_id = i;
best_op_name = op_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
}
}
else
{
std::cout << op_name << " does not support this problem" << std::endl;
}
}
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
<< best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
return 0;
}
client_example/31_grouped_gemm_multi_abd/CMakeLists.txt → client_example/31_grouped_gemm_bf16Aint8B/CMakeLists.txt
View file @ f0759faf
......@@ -4,4 +4,13 @@ if(GPU_TARGETS MATCHES "gfx9" AND ((DTYPES MATCHES "int8" AND DTYPES MATCHES "bf
add_executable(client_grouped_gemm_fastgelu_bf16_i8_bf16 grouped_gemm_fastgelu_xdl_bf16_i8.cpp)
target_link_libraries(client_grouped_gemm_fastgelu_bf16_i8_bf16 PRIVATE composable_kernel::device_gemm_operations)
add_executable(client_grouped_gemm_multiply_bf16_i8_bf16 grouped_gemm_multiply_xdl_bf16_i8.cpp)
target_link_libraries(client_grouped_gemm_multiply_bf16_i8_bf16 PRIVATE composable_kernel::device_gemm_operations)
add_executable(client_grouped_gemm_multiply_bias_fastgelu_bf16_i8_bf16 grouped_gemm_multiply_bias_fastgelu_xdl_bf16_i8.cpp)
target_link_libraries(client_grouped_gemm_multiply_bias_fastgelu_bf16_i8_bf16 PRIVATE composable_kernel::device_gemm_operations)
add_executable(client_grouped_gemm_bf16_i8_bf16 grouped_gemm_xdl_bf16_i8.cpp)
target_link_libraries(client_grouped_gemm_bf16_i8_bf16 PRIVATE composable_kernel::device_gemm_operations)
endif()
client_example/31_grouped_gemm_multi_abd/grouped_gemm_bias_fastgelu_xdl_bf16_i8.cpp → client_example/31_grouped_gemm_bf16Aint8B/grouped_gemm_bias_fastgelu_xdl_bf16_i8.cpp
View file @ f0759faf
......@@ -38,19 +38,19 @@ using EDataType = BF16;
using A0Layout = Row;
using AsLayout = ck::Tuple<A0Layout>;
using B0Layout = Col;
using B0Layout = Row;
using B1Layout = B0Layout;
using BsLayout = ck::Tuple<B0Layout, B1Layout>;
using D0Layout = Row;
using DsLayout = ck::Tuple<D0Layout>;
using ELayout = Row;
using Scales = ck::tensor_operation::element_wise::Scales;
using Multiply = ck::tensor_operation::element_wise::Multiply;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using AddFastGelu = ck::tensor_operation::element_wise::AddFastGelu;
using AElementOp = PassThrough;
using BElementOp = Scales;
using BElementOp = Multiply;
using CDEElementOp = AddFastGelu;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
......
client_example/31_grouped_gemm_multi_abd/grouped_gemm_fastgelu_xdl_bf16_i8.cpp → client_example/31_grouped_gemm_bf16Aint8B/grouped_gemm_fastgelu_xdl_bf16_i8.cpp
View file @ f0759faf
......@@ -15,6 +15,8 @@
#include "ck/library/tensor_operation_instance/gpu/grouped_gemm_multi_abd_fixed_nk.hpp"
#include "ck/host_utility/hip_check_error.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
......@@ -36,7 +38,7 @@ using D0DataType = BF16;
using DsDataType = ck::Tuple<>;
using EDataType = BF16;
using A0Layout = Col;
using A0Layout = Row;
using AsLayout = ck::Tuple<A0Layout>;
using B0Layout = Row;
using B1Layout = B0Layout;
......@@ -45,12 +47,12 @@ using D0Layout = Row;
using DsLayout = ck::Tuple<>;
using ELayout = Row;
using Scales = ck::tensor_operation::element_wise::Scales;
using Multiply = ck::tensor_operation::element_wise::Multiply;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using FastGelu = ck::tensor_operation::element_wise::FastGelu;
using AElementOp = PassThrough;
using BElementOp = Scales;
using BElementOp = Multiply;
using CDEElementOp = FastGelu;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
......
client_example/31_grouped_gemm_bf16Aint8B/grouped_gemm_multiply_bias_fastgelu_xdl_bf16_i8.cpp 0 → 100644
View file @ f0759faf
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <iomanip>
#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_grouped_gemm_multi_abd.hpp"
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_gemm_tile_loop_multply.hpp"
#include "ck/host_utility/hip_check_error.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using BF16 = ck::bhalf_t;
using I8 = int8_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using A0DataType = BF16;
using B0DataType = I8;
using B1DataType = BF16;
using AccDataType = F32;
using CShuffleDataType = F32;
using D0DataType = BF16;
using DsDataType = ck::Tuple<B1DataType, D0DataType>;
using EDataType = BF16;
using A0Layout = Row;
using B0Layout = Row;
using B1Layout = B0Layout;
using D0Layout = Row;
using DsLayout = ck::Tuple<B0Layout, D0Layout>;
using ELayout = Row;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using MultiplyAddFastGelu = ck::tensor_operation::element_wise::MultiplyAddFastGelu;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CDEElementOp = MultiplyAddFastGelu;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
struct SimpleDeviceMem
{
SimpleDeviceMem() = delete;
SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
{
(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
}
void* GetDeviceBuffer() { return p_mem_; }
~SimpleDeviceMem() { (void)hipFree(p_mem_); }
void* p_mem_;
};
struct ProblemSize final
{
std::vector<ck::index_t> Ms;
std::vector<ck::index_t> Ns;
std::vector<ck::index_t> Ks;
std::vector<ck::index_t> stride_As;
std::vector<ck::index_t> stride_Bs;
std::vector<ck::index_t> stride_Cs;
ck::index_t group_count;
};
struct ExecutionConfig final
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
int k_batch = 1;
};
bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& config)
{
auto group_count = problem_size.group_count;
// GEMM shape
std::vector<ck::tensor_operation::device::GemmDesc> gemm_descs;
gemm_descs.reserve(group_count);
int sum_of_m = 0;
using DeviceMemPtr = std::unique_ptr<SimpleDeviceMem>;
std::vector<DeviceMemPtr> a0_tensors_device, b0_tensors_device, b1_tensors_device,
d0_tensors_device, c_tensors_device;
a0_tensors_device.reserve(group_count);
b0_tensors_device.reserve(group_count);
b1_tensors_device.reserve(group_count);
d0_tensors_device.reserve(group_count);
c_tensors_device.reserve(group_count);
std::size_t flop = 0, num_btype = 0;
for(int i = 0; i < group_count; i++)
{
sum_of_m += problem_size.Ms[i];
}
constexpr ck::index_t NumDTensor = 2;
using GroupedGemmKernelArgument =
ck::tensor_operation::device::GroupedGemmTileLoopKernelArguments<NumDTensor>;
std::vector<GroupedGemmKernelArgument> grouped_gemm_kernel_args_;
grouped_gemm_kernel_args_.reserve(group_count);
for(int i = 0; i < group_count; i++)
{
a0_tensors_device.emplace_back(std::make_unique<SimpleDeviceMem>(
sizeof(A0DataType) * problem_size.Ms[i] * problem_size.Ks[i]));
b0_tensors_device.emplace_back(std::make_unique<SimpleDeviceMem>(
sizeof(B0DataType) * problem_size.Ns[i] * problem_size.Ks[i]));
b1_tensors_device.emplace_back(
std::make_unique<SimpleDeviceMem>(sizeof(B1DataType) * problem_size.Ns[i]));
c_tensors_device.emplace_back(std::make_unique<SimpleDeviceMem>(
sizeof(EDataType) * problem_size.Ms[i] * problem_size.Ns[i]));
d0_tensors_device.emplace_back(
std::make_unique<SimpleDeviceMem>(sizeof(D0DataType) * problem_size.Ns[i]));
gemm_descs.push_back({problem_size.Ms[i],
problem_size.Ns[i],
problem_size.Ks[i],
problem_size.stride_As[i],
problem_size.stride_Bs[i],
problem_size.stride_Cs[i],
{0, 0}});
grouped_gemm_kernel_args_.push_back(
{a0_tensors_device[i]->GetDeviceBuffer(),
b0_tensors_device[i]->GetDeviceBuffer(),
{b1_tensors_device[i]->GetDeviceBuffer(), d0_tensors_device[i]->GetDeviceBuffer()},
c_tensors_device[i]->GetDeviceBuffer(),
problem_size.Ms[i],
problem_size.Ns[i],
problem_size.Ks[i],
problem_size.stride_As[i],
problem_size.stride_Bs[i],
{0, 0},
problem_size.stride_Cs[i]});
}
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto cde_element_op = CDEElementOp{};
using DeviceOp = ck::tensor_operation::device::DeviceGroupedGemmTileLoop<A0Layout,
B0Layout,
DsLayout,
ELayout,
A0DataType,
B0DataType,
DsDataType,
EDataType,
AElementOp,
BElementOp,
CDEElementOp>;
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
std::string best_op_name;
bool found = false;
int best_op_id = -1;
float best_ave_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
// profile device operation instances
std::cout << "Run all instances and do timing" << std::endl;
for(int i = 0; i < op_ptrs.size(); ++i)
{
auto& op_ptr = op_ptrs[i];
std::vector<const void*> p_As = {};
std::vector<const void*> p_Bs = {};
std::vector<std::array<const void*, NumDTensor>> p_Ds = {};
std::vector<void*> p_Cs = {};
auto argument_ptr = op_ptr->MakeArgumentPointer(
p_As, p_Bs, p_Ds, p_Cs, gemm_descs, a_element_op, b_element_op, cde_element_op);
auto invoker_ptr = op_ptr->MakeInvokerPointer();
std::string op_name = op_ptr->GetTypeString();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
SimpleDeviceMem gemm_kernel_args_dev(
op_ptr->GetDeviceKernelArgSize(argument_ptr.get()));
hip_check_error(hipMemcpy(gemm_kernel_args_dev.GetDeviceBuffer(),
grouped_gemm_kernel_args_.data(),
op_ptr->GetDeviceKernelArgSize(argument_ptr.get()),
hipMemcpyHostToDevice));
op_ptr->SetDeviceKernelArgs(argument_ptr.get(), gemm_kernel_args_dev.GetDeviceBuffer());
float ave_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true, 0, 20, 50});
std::size_t flop = std::size_t(2) * sum_of_m * problem_size.Ns[0] * problem_size.Ks[0];
std::size_t num_btype = sizeof(A0DataType) * sum_of_m * problem_size.Ks[0] +
sizeof(B0DataType) * problem_size.Ks[0] * problem_size.Ns[0] +
sizeof(EDataType) * sum_of_m * problem_size.Ns[0];
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
<< gb_per_sec << " GB/s, " << op_name << std::endl;
if(tflops > best_tflops)
{
found = true;
best_op_id = i;
best_op_name = op_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
}
}
else
{
std::cout << op_name << " does not support this problem" << std::endl;
}
}
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
<< best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
return true;
}
int main(int argc, char* argv[])
{
ProblemSize problem_size;
ExecutionConfig config;
problem_size.group_count = 16;
for(int i = 0; i < problem_size.group_count; i++)
{
problem_size.Ms.push_back(1 + rand() % 1024);
problem_size.Ns.push_back(6144);
problem_size.Ks.push_back(4096);
problem_size.stride_As.push_back(problem_size.Ks[i]);
problem_size.stride_Bs.push_back(problem_size.Ns[i]);
problem_size.stride_Cs.push_back(problem_size.Ns[i]);
std::cout << " M = " << problem_size.Ms[i] << " N = " << problem_size.Ns[i] << " K "
<< problem_size.Ks[i] << std::endl;
}
return !run_grouped_gemm(problem_size, config);
}
client_example/31_grouped_gemm_bf16Aint8B/grouped_gemm_multiply_xdl_bf16_i8.cpp 0 → 100644
View file @ f0759faf
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <iomanip>
#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_grouped_gemm_multi_abd.hpp"
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_gemm_tile_loop_multply.hpp"
#include "ck/host_utility/hip_check_error.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using BF16 = ck::bhalf_t;
using I8 = int8_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using A0DataType = BF16;
using B0DataType = I8;
using B1DataType = BF16;
using AccDataType = F32;
using CShuffleDataType = BF16;
using D0DataType = BF16;
using DsDataType = ck::Tuple<B1DataType>;
using EDataType = BF16;
using A0Layout = Row;
using B0Layout = Row;
using B1Layout = B0Layout;
using D0Layout = Row;
using DsLayout = ck::Tuple<B1Layout>;
using ELayout = Row;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using Multiply = ck::tensor_operation::element_wise::Multiply;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CDEElementOp = Multiply;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
struct SimpleDeviceMem
{
SimpleDeviceMem() = delete;
SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
{
(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
}
void* GetDeviceBuffer() { return p_mem_; }
~SimpleDeviceMem() { (void)hipFree(p_mem_); }
void* p_mem_;
};
struct ProblemSize final
{
std::vector<ck::index_t> Ms;
std::vector<ck::index_t> Ns;
std::vector<ck::index_t> Ks;
std::vector<ck::index_t> stride_As;
std::vector<ck::index_t> stride_Bs;
std::vector<ck::index_t> stride_Cs;
ck::index_t group_count;
};
struct ExecutionConfig final
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
int k_batch = 1;
};
bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& config)
{
auto group_count = problem_size.group_count;
// GEMM shape
std::vector<ck::tensor_operation::device::GemmDesc> gemm_descs;
gemm_descs.reserve(group_count);
int sum_of_m = 0;
using DeviceMemPtr = std::unique_ptr<SimpleDeviceMem>;
std::vector<DeviceMemPtr> a0_tensors_device, b0_tensors_device, b1_tensors_device,
c_tensors_device;
a0_tensors_device.reserve(group_count);
b0_tensors_device.reserve(group_count);
b1_tensors_device.reserve(group_count);
c_tensors_device.reserve(group_count);
std::size_t flop = 0, num_btype = 0;
for(int i = 0; i < group_count; i++)
{
sum_of_m += problem_size.Ms[i];
}
constexpr ck::index_t NumDTensor = 1;
using GroupedGemmKernelArgument =
ck::tensor_operation::device::GroupedGemmTileLoopKernelArguments<NumDTensor>;
std::vector<GroupedGemmKernelArgument> grouped_gemm_kernel_args_;
grouped_gemm_kernel_args_.reserve(group_count);
for(int i = 0; i < group_count; i++)
{
a0_tensors_device.emplace_back(std::make_unique<SimpleDeviceMem>(
sizeof(A0DataType) * problem_size.Ms[i] * problem_size.Ks[i]));
b0_tensors_device.emplace_back(std::make_unique<SimpleDeviceMem>(
sizeof(B0DataType) * problem_size.Ns[i] * problem_size.Ks[i]));
b1_tensors_device.emplace_back(
std::make_unique<SimpleDeviceMem>(sizeof(B1DataType) * problem_size.Ns[i]));
c_tensors_device.emplace_back(std::make_unique<SimpleDeviceMem>(
sizeof(EDataType) * problem_size.Ms[i] * problem_size.Ns[i]));
gemm_descs.push_back({problem_size.Ms[i],
problem_size.Ns[i],
problem_size.Ks[i],
problem_size.stride_As[i],
problem_size.stride_Bs[i],
problem_size.stride_Cs[i],
{0}});
grouped_gemm_kernel_args_.push_back({a0_tensors_device[i]->GetDeviceBuffer(),
b0_tensors_device[i]->GetDeviceBuffer(),
{b1_tensors_device[i]->GetDeviceBuffer()},
c_tensors_device[i]->GetDeviceBuffer(),
problem_size.Ms[i],
problem_size.Ns[i],
problem_size.Ks[i],
problem_size.stride_As[i],
problem_size.stride_Bs[i],
{0},
problem_size.stride_Cs[i]});
}
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto cde_element_op = CDEElementOp{};
using DeviceOp = ck::tensor_operation::device::DeviceGroupedGemmTileLoop<A0Layout,
B0Layout,
DsLayout,
ELayout,
A0DataType,
B0DataType,
DsDataType,
EDataType,
AElementOp,
BElementOp,
CDEElementOp>;
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
std::string best_op_name;
bool found = false;
int best_op_id = -1;
float best_ave_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
// profile device operation instances
std::cout << "Run all instances and do timing" << std::endl;
for(int i = 0; i < op_ptrs.size(); ++i)
{
auto& op_ptr = op_ptrs[i];
std::vector<const void*> p_As = {};
std::vector<const void*> p_Bs = {};
std::vector<std::array<const void*, NumDTensor>> p_Ds = {};
std::vector<void*> p_Cs = {};
auto argument_ptr = op_ptr->MakeArgumentPointer(
p_As, p_Bs, p_Ds, p_Cs, gemm_descs, a_element_op, b_element_op, cde_element_op);
auto invoker_ptr = op_ptr->MakeInvokerPointer();
std::string op_name = op_ptr->GetTypeString();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
SimpleDeviceMem gemm_kernel_args_dev(
op_ptr->GetDeviceKernelArgSize(argument_ptr.get()));
hip_check_error(hipMemcpy(gemm_kernel_args_dev.GetDeviceBuffer(),
grouped_gemm_kernel_args_.data(),
op_ptr->GetDeviceKernelArgSize(argument_ptr.get()),
hipMemcpyHostToDevice));
op_ptr->SetDeviceKernelArgs(argument_ptr.get(), gemm_kernel_args_dev.GetDeviceBuffer());
float ave_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true, 0, 20, 50});
std::size_t flop = std::size_t(2) * sum_of_m * problem_size.Ns[0] * problem_size.Ks[0];
std::size_t num_btype = sizeof(A0DataType) * sum_of_m * problem_size.Ks[0] +
sizeof(B0DataType) * problem_size.Ks[0] * problem_size.Ns[0] +
sizeof(EDataType) * sum_of_m * problem_size.Ns[0];
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
<< gb_per_sec << " GB/s, " << op_name << std::endl;
if(tflops > best_tflops)
{
found = true;
best_op_id = i;
best_op_name = op_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
}
}
else
{
std::cout << op_name << " does not support this problem" << std::endl;
}
}
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
<< best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
return true;
}
int main(int argc, char* argv[])
{
ProblemSize problem_size;
ExecutionConfig config;
problem_size.group_count = 16;
for(int i = 0; i < problem_size.group_count; i++)
{
problem_size.Ms.push_back(1 + rand() % 1024);
problem_size.Ns.push_back(4096);
problem_size.Ks.push_back(4096);
problem_size.stride_As.push_back(problem_size.Ks[i]);
problem_size.stride_Bs.push_back(problem_size.Ns[i]);
problem_size.stride_Cs.push_back(problem_size.Ns[i]);
std::cout << " M = " << problem_size.Ms[i] << " N = " << problem_size.Ns[i] << " K "
<< problem_size.Ks[i] << std::endl;
}
return !run_grouped_gemm(problem_size, config);
}
client_example/31_grouped_gemm_bf16Aint8B/grouped_gemm_xdl_bf16_i8.cpp 0 → 100644
View file @ f0759faf
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <iomanip>
#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_grouped_gemm_multi_abd.hpp"
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_gemm_multi_abd_fixed_nk.hpp"
#include "ck/host_utility/hip_check_error.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using BF16 = ck::bhalf_t;
using I8 = int8_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using A0DataType = BF16;
using AsDataType = ck::Tuple<A0DataType>;
using B0DataType = I8;
using B1DataType = BF16;
using BsDataType = ck::Tuple<B0DataType, B1DataType>;
using AccDataType = F32;
using CShuffleDataType = BF16;
using D0DataType = BF16;
using DsDataType = ck::Tuple<>;
using EDataType = BF16;
using A0Layout = Row;
using AsLayout = ck::Tuple<A0Layout>;
using B0Layout = Row;
using B1Layout = B0Layout;
using BsLayout = ck::Tuple<B0Layout, B1Layout>;
using D0Layout = Row;
using DsLayout = ck::Tuple<>;
using ELayout = Row;
using Multiply = ck::tensor_operation::element_wise::Multiply;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using AElementOp = PassThrough;
using BElementOp = Multiply;
using CDEElementOp = PassThrough;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
struct SimpleDeviceMem
{
SimpleDeviceMem() = delete;
SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
{
(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
}
void* GetDeviceBuffer() { return p_mem_; }
~SimpleDeviceMem() { (void)hipFree(p_mem_); }
void* p_mem_;
};
struct ProblemSize final
{
std::vector<ck::index_t> Ms;
std::vector<ck::index_t> Ns;
std::vector<ck::index_t> Ks;
std::vector<ck::index_t> stride_As;
std::vector<ck::index_t> stride_Bs;
std::vector<ck::index_t> stride_Cs;
ck::index_t group_count;
};
struct ExecutionConfig final
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
int k_batch = 1;
};
bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& config)
{
auto group_count = problem_size.group_count;
// GEMM shape
std::vector<ck::tensor_operation::device::GemmMultiABDDesc> gemm_descs;
gemm_descs.reserve(group_count);
int sum_of_m = 0;
using DeviceMemPtr = std::unique_ptr<SimpleDeviceMem>;
std::vector<DeviceMemPtr> a0_tensors_device, b0_tensors_device, b1_tensors_device,
c_tensors_device;
a0_tensors_device.reserve(group_count);
b0_tensors_device.reserve(group_count);
b1_tensors_device.reserve(group_count);
c_tensors_device.reserve(group_count);
std::size_t flop = 0, num_btype = 0;
for(int i = 0; i < group_count; i++)
{
sum_of_m += problem_size.Ms[i];
}
constexpr ck::index_t NumATensor = 1;
constexpr ck::index_t NumBTensor = 2;
constexpr ck::index_t NumDTensor = 0;
using GroupedGemmKernelArgument = ck::tensor_operation::device::
GroupedGemmMultiABDKernelArgument<NumATensor, NumBTensor, NumDTensor>;
std::vector<GroupedGemmKernelArgument> grouped_gemm_kernel_args_;
grouped_gemm_kernel_args_.reserve(group_count);
for(int i = 0; i < group_count; i++)
{
a0_tensors_device.emplace_back(
std::make_unique<SimpleDeviceMem>(sizeof(A0DataType) * sum_of_m * problem_size.Ks[i]));
b0_tensors_device.emplace_back(std::make_unique<SimpleDeviceMem>(
sizeof(B0DataType) * problem_size.Ns[i] * problem_size.Ks[i]));
b1_tensors_device.emplace_back(
std::make_unique<SimpleDeviceMem>(sizeof(B1DataType) * problem_size.Ns[i]));
c_tensors_device.emplace_back(
std::make_unique<SimpleDeviceMem>(sizeof(EDataType) * sum_of_m * problem_size.Ns[i]));
gemm_descs.push_back(
{sum_of_m, problem_size.Ns[i], problem_size.Ks[i], {1}, {1, 1}, {}, 1});
grouped_gemm_kernel_args_.push_back(
{std::array<const void*, NumATensor>{a0_tensors_device[i]->GetDeviceBuffer()},
std::array<const void*, NumBTensor>{b0_tensors_device[i]->GetDeviceBuffer(),
b1_tensors_device[i]->GetDeviceBuffer()},
std::array<const void*, NumDTensor>{},
c_tensors_device[i]->GetDeviceBuffer(),
problem_size.Ms[i],
problem_size.Ns[i],
problem_size.Ks[i],
std::array<ck::index_t, NumATensor>{problem_size.stride_As[i]},
std::array<ck::index_t, NumBTensor>{problem_size.stride_Bs[i], 0},
std::array<ck::index_t, NumDTensor>{},
problem_size.stride_Cs[i]});
}
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto cde_element_op = CDEElementOp{};
using DeviceOp = ck::tensor_operation::device::DeviceGroupedGemmMultiABDFixedNK<AsLayout,
BsLayout,
DsLayout,
Row,
AsDataType,
BsDataType,
DsDataType,
BF16,
AElementOp,
BElementOp,
CDEElementOp>;
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
std::string best_op_name;
bool found = false;
int best_op_id = -1;
float best_ave_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
// profile device operation instances
std::cout << "Run all instances and do timing" << std::endl;
for(int i = 0; i < op_ptrs.size(); ++i)
{
auto& op_ptr = op_ptrs[i];
std::vector<std::array<const void*, NumATensor>> p_As = {};
std::vector<std::array<const void*, NumBTensor>> p_Bs = {};
std::vector<std::array<const void*, NumDTensor>> p_Ds = {};
std::vector<void*> p_Cs = {};
auto argument_ptr = op_ptr->MakeArgumentPointer(p_As, p_Bs, p_Ds, p_Cs, gemm_descs);
auto invoker_ptr = op_ptr->MakeInvokerPointer();
std::string op_name = op_ptr->GetTypeString();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
SimpleDeviceMem gemm_kernel_args_dev(
op_ptr->GetDeviceKernelArgSize(argument_ptr.get()));
hip_check_error(hipMemcpy(gemm_kernel_args_dev.GetDeviceBuffer(),
grouped_gemm_kernel_args_.data(),
op_ptr->GetDeviceKernelArgSize(argument_ptr.get()),
hipMemcpyHostToDevice));
op_ptr->SetDeviceKernelArgs(argument_ptr.get(), gemm_kernel_args_dev.GetDeviceBuffer());
op_ptr->SetElementwiseOps(
argument_ptr.get(), a_element_op, b_element_op, cde_element_op);
float ave_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true, 0, 20, 50});
std::size_t flop = std::size_t(2) * sum_of_m * problem_size.Ns[0] * problem_size.Ks[0];
std::size_t num_btype = sizeof(A0DataType) * sum_of_m * problem_size.Ks[0] +
sizeof(B0DataType) * problem_size.Ks[0] * problem_size.Ns[0] +
sizeof(EDataType) * sum_of_m * problem_size.Ns[0];
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
<< gb_per_sec << " GB/s, " << op_name << std::endl;
if(tflops > best_tflops)
{
found = true;
best_op_id = i;
best_op_name = op_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
}
}
else
{
std::cout << op_name << " does not support this problem" << std::endl;
}
}
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
<< best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
return true;
}
int main(int argc, char* argv[])
{
ProblemSize problem_size;
ExecutionConfig config;
problem_size.group_count = 16;
for(int i = 0; i < problem_size.group_count; i++)
{
problem_size.Ms.push_back(1 + rand() % 1024);
problem_size.Ns.push_back(4096);
problem_size.Ks.push_back(4096);
problem_size.stride_As.push_back(problem_size.Ks[i]);
problem_size.stride_Bs.push_back(problem_size.Ns[i]);
problem_size.stride_Cs.push_back(problem_size.Ns[i]);
std::cout << " M = " << problem_size.Ms[i] << " N = " << problem_size.Ns[i] << " K "
<< problem_size.Ks[i] << std::endl;
}
return !run_grouped_gemm(problem_size, config);
}
example/01_gemm/CMakeLists.txt
View file @ f0759faf
......@@ -28,6 +28,8 @@ 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_executable(example_gemm_xdl_fp16_fp8_v3 gemm_xdl_fp16_fp8_v3.cpp)
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_dependencies(example_gemm_xdl example_gemm_xdl_bf16_v3)
add_example_executable(example_gemm_xdl_wavelet_fp16 gemm_xdl_wavelet_fp16.cpp)
add_example_dependencies(example_gemm_xdl example_gemm_xdl_wavelet_fp16)
......
example/01_gemm/gemm_xdl_bf16_v3.cpp 0 → 100644
View file @ f0759faf
// 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.hpp"
using ADataType = ck::bhalf_t;
using BDataType = ck::bhalf_t;
using AccDataType = float;
using CShuffleDataType = ck::bhalf_t;
using CDataType = ck::bhalf_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;
// clang-format off
using DeviceGemmV2Instance =
ck::tensor_operation::device::DeviceGemm_Xdl_CShuffleV3<
ALayout, BLayout, CLayout,
ADataType, BDataType, CDataType, AccDataType, CShuffleDataType,
PassThrough, PassThrough, PassThrough, GemmDefault,
256,
128, 128,
64, 8, 8,
16, 16,
4, 4,
S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>,
2, 8, 8, 0,
S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>,
2, 8, 8, 0,
1, 2, S<1, 32, 1, 8>, 8,
ck::BlockGemmPipelineScheduler::Intrawave,ck::BlockGemmPipelineVersion::v3>;
// clang-format on
using ReferenceGemmInstance = ck::tensor_operation::host::
ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
#include "run_gemm_example_v2.inc"
int main(int argc, char* argv[]) { return !run_gemm_splitk_example(argc, argv); }
example/01_gemm/run_gemm_example_v2.inc
View file @ f0759faf
......@@ -3,6 +3,88 @@
#pragma once
template <typename DataType>
inline __host__ __device__ constexpr double get_rtol()
{
if constexpr(std::is_same_v<DataType, float>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, double>)
{
return 1e-6;
}
else if constexpr(std::is_same_v<DataType, ck::half_t>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
{
return 5e-2;
}
else if constexpr(std::is_same_v<DataType, int32_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, int8_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, ck::f8_t>)
{
return 1e-1; // 240 and 224 are acceptable
}
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
{
return 1.5e-1; // 57344 and 49152 are acceptable
}
else
{
return 1e-3;
}
}
template <typename DataType>
inline __host__ __device__ constexpr double get_atol()
{
if constexpr(std::is_same_v<DataType, float>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, double>)
{
return 1e-6;
}
else if constexpr(std::is_same_v<DataType, ck::half_t>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
{
return 5e-2;
}
else if constexpr(std::is_same_v<DataType, int32_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, int8_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, ck::f8_t>)
{
return 16.1; // 240 and 224 are acceptable
}
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
{
return 8192.1; // 57344 and 49152 are acceptable
}
else
{
return 1e-3;
}
}
template <typename ProblemType>
bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
{
......@@ -180,7 +262,11 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
#else
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);
pass &= ck::utils::check_err(c_m_n_device_result,
c_m_n_host_result,
"Error: Incorrect results!",
get_rtol<CDataType>(),
get_atol<CDataType>());
#endif
}
......
example/15_grouped_gemm/CMakeLists.txt
View file @ f0759faf
......@@ -26,6 +26,9 @@ add_example_dependencies(example_grouped_gemm_xdl example_grouped_gemm_xdl_int8)
add_example_executable(example_grouped_gemm_xdl_fixed_nk_fp16_fp8 grouped_gemm_xdl_fixed_nk_fp16_fp8.cpp)
add_example_dependencies(example_grouped_gemm_xdl example_grouped_gemm_xdl_fixed_nk_fp16_fp8)
add_example_executable(example_grouped_gemm_multiple_d_xdl_fp16 grouped_gemm_multiple_d_xdl_fp16.cpp)
add_example_dependencies(example_grouped_gemm_xdl example_grouped_gemm_multiple_d_xdl_fp16)
if(USE_BITINT_EXTENSION_INT4)
add_example_executable(example_grouped_gemm_xdl_int4 grouped_gemm_xdl_int4.cpp)
add_example_dependencies(example_grouped_gemm_xdl example_grouped_gemm_xdl_int4)
......
example/15_grouped_gemm/grouped_gemm_multiple_d_xdl_fp16.cpp 0 → 100644
View file @ f0759faf
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, 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/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_gemm_multiple_d_xdl_cshuffle_tile_loop.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_gemm_tile_loop.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include <ck/utility/data_type.hpp>
#include <ck/utility/tuple.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"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm_multiple_d.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using AddAdd = ck::tensor_operation::element_wise::AddAdd;
using ADataType = F16;
using BDataType = F16;
using AccDataType = F32;
using CShuffleDataType = F32;
using DDataType = F16;
using DsDataType = ck::Tuple<DDataType, DDataType>;
using EDataType = F16;
using ALayout = Row;
using BLayout = Col;
using DLayout = Row;
using DsLayout = ck::Tuple<DLayout, DLayout>;
using ELayout = Row;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CDEElementOp = AddAdd;
static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
static constexpr int NumDs = 2;
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceGroupedGemmMultipleDXdlCShuffleTileLoop
// clang-format off
//######| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//######| | | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| 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|
//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmMNKPadding, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 4>;
// clang-format on
struct ProblemSize final
{
std::vector<ck::index_t> Ms;
std::vector<ck::index_t> Ns;
std::vector<ck::index_t> Ks;
std::vector<ck::index_t> stride_As;
std::vector<ck::index_t> stride_Bs;
std::vector<std::vector<ck::index_t>> stride_Ds;
std::vector<ck::index_t> stride_Cs;
ck::index_t group_count;
};
struct ExecutionConfig final
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = true;
};
bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& config)
{
auto group_count = problem_size.group_count;
using KernelArguments = ck::tensor_operation::device::GroupedGemmTileLoopKernelArguments<NumDs>;
// GEMM shape
std::vector<ck::tensor_operation::device::GemmDesc> gemm_descs;
std::vector<KernelArguments> ggemm_kargs;
std::vector<void*> p_Cs;
std::vector<const void*> p_As;
std::vector<const void*> p_Bs;
std::vector<std::array<const void*, NumDs>> p_Ds = {};
gemm_descs.reserve(group_count);
ggemm_kargs.reserve(group_count);
p_As.reserve(group_count);
p_Bs.reserve(group_count);
p_Ds.reserve(group_count);
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
using namespace ck::literals;
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{
return HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return HostTensorDescriptor({row, col}, {1_uz, stride});
}
};
std::vector<Tensor<ADataType>> a_tensors;
std::vector<Tensor<BDataType>> b_tensors;
std::vector<std::array<Tensor<DDataType>, NumDs>> d_tensors;
std::vector<Tensor<EDataType>> c_host_tensors;
std::vector<Tensor<EDataType>> c_device_result_tensors;
a_tensors.reserve(group_count);
b_tensors.reserve(group_count);
d_tensors.reserve(group_count);
c_host_tensors.reserve(group_count);
c_device_result_tensors.reserve(group_count);
using DeviceMemPtr = std::unique_ptr<DeviceMem>;
std::vector<DeviceMemPtr> a_tensors_device, b_tensors_device, c_tensors_device;
std::vector<std::vector<DeviceMemPtr>> d_tensors_device;
a_tensors_device.reserve(group_count);
b_tensors_device.reserve(group_count);
d_tensors_device.reserve(group_count);
c_tensors_device.reserve(group_count);
std::size_t flop = 0, num_btype = 0;
for(int i = 0; i < group_count; i++)
{
a_tensors.push_back(Tensor<ADataType>(f_host_tensor_descriptor(
problem_size.Ms[i], problem_size.Ks[i], problem_size.stride_As[i], ALayout{})));
b_tensors.push_back(Tensor<BDataType>(f_host_tensor_descriptor(
problem_size.Ks[i], problem_size.Ns[i], problem_size.stride_Bs[i], BLayout{})));
auto d0_tensor = Tensor<DDataType>(f_host_tensor_descriptor(
problem_size.Ms[i], problem_size.Ns[i], problem_size.stride_Cs[i], DLayout{}));
auto d1_tensor = Tensor<DDataType>(f_host_tensor_descriptor(
problem_size.Ms[i], problem_size.Ns[i], problem_size.stride_Cs[i], DLayout{}));
std::array<Tensor<DDataType>, NumDs> d_tens = {d0_tensor, d1_tensor};
d_tensors.push_back(d_tens);
c_host_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
problem_size.Ms[i], problem_size.Ns[i], problem_size.stride_Cs[i], ELayout{})));
c_device_result_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
problem_size.Ms[i], problem_size.Ns[i], problem_size.stride_Cs[i], ELayout{})));
std::cout << "gemm[" << i << "] a_m_k: " << a_tensors[i].mDesc
<< " b_k_n: " << b_tensors[i].mDesc
<< " c_m_n: " << c_device_result_tensors[i].mDesc << std::endl;
flop += std::size_t(2) * problem_size.Ms[i] * problem_size.Ks[i] * problem_size.Ns[i];
num_btype += sizeof(ADataType) * a_tensors[i].GetElementSize() +
sizeof(BDataType) * b_tensors[i].GetElementSize() +
sizeof(DDataType) * d_tensors[i][0].GetElementSize() * NumDs +
sizeof(EDataType) * c_device_result_tensors[i].GetElementSize();
switch(config.init_method)
{
case 0: break;
case 1:
a_tensors[i].GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
b_tensors[i].GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
for(int j = 0; j < NumDs; ++j)
{
d_tensors[i][j].GenerateTensorValue(GeneratorTensor_2<DDataType>{-5, 5});
}
break;
case 2:
a_tensors[i].GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
b_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
for(int j = 0; j < NumDs; ++j)
{
d_tensors[i][j].GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
}
break;
default:
a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
for(int j = 0; j < NumDs; ++j)
{
d_tensors[i][j].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
}
}
}
for(int i = 0; i < group_count; i++)
{
a_tensors_device.emplace_back(
std::make_unique<DeviceMem>(a_tensors[i].GetElementSpaceSize() * sizeof(ADataType)));
b_tensors_device.emplace_back(
std::make_unique<DeviceMem>(b_tensors[i].GetElementSpaceSize() * sizeof(BDataType)));
c_tensors_device.emplace_back(std::make_unique<DeviceMem>(
c_device_result_tensors[i].GetElementSpaceSize() * sizeof(EDataType)));
for(int j = 0; j < NumDs; ++j)
{
d_tensors_device[i].emplace_back(std::make_unique<DeviceMem>(
d_tensors[i][j].GetElementSpaceSize() * sizeof(DDataType)));
}
a_tensors_device[i]->ToDevice(a_tensors[i].mData.data());
b_tensors_device[i]->ToDevice(b_tensors[i].mData.data());
for(int j = 0; j < NumDs; ++j)
{
d_tensors_device[i][j]->ToDevice(d_tensors[i][j].mData.data());
}
c_tensors_device[i]->SetZero();
p_As.push_back(a_tensors_device[i]->GetDeviceBuffer());
p_Bs.push_back(b_tensors_device[i]->GetDeviceBuffer());
p_Ds.push_back(
{d_tensors_device[i][0]->GetDeviceBuffer(), d_tensors_device[i][1]->GetDeviceBuffer()});
p_Cs.push_back(c_tensors_device[i]->GetDeviceBuffer());
// The device op does not have to know M problem size at lunch time.
gemm_descs.push_back({0,
problem_size.Ns[i],
problem_size.Ks[i],
problem_size.stride_As[i],
problem_size.stride_Bs[i],
problem_size.stride_Cs[i],
{problem_size.stride_Cs[i], problem_size.stride_Cs[i]}});
ggemm_kargs.push_back(
{a_tensors_device[i]->GetDeviceBuffer(),
b_tensors_device[i]->GetDeviceBuffer(),
{d_tensors_device[i][0]->GetDeviceBuffer(), d_tensors_device[i][1]->GetDeviceBuffer()},
c_tensors_device[i]->GetDeviceBuffer(),
problem_size.Ms[i],
problem_size.Ns[i],
problem_size.Ks[i],
problem_size.stride_As[i],
problem_size.stride_Bs[i],
{problem_size.stride_Cs[i], problem_size.stride_Cs[i]},
problem_size.stride_Cs[i]});
}
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto cde_element_op = CDEElementOp{};
auto gemm = DeviceGemmInstance{};
auto invoker = gemm.MakeInvoker();
// do GEMM
auto argument = gemm.MakeArgument(
p_As, p_Bs, p_Ds, p_Cs, gemm_descs, a_element_op, b_element_op, cde_element_op);
if(!gemm.IsSupportedArgument(argument))
{
throw std::runtime_error(
"wrong! device_gemm with the specified compilation parameters does "
"not support this GEMM problem");
}
DeviceMem gemm_arg_dev_mem(gemm.GetDeviceKernelArgSize(&argument));
hip_check_error(hipMemcpy(gemm_arg_dev_mem.GetDeviceBuffer(),
ggemm_kargs.data(),
gemm.GetDeviceKernelArgSize(&argument),
hipMemcpyHostToDevice));
gemm.SetDeviceKernelArgs(argument, gemm_arg_dev_mem.GetDeviceBuffer());
invoker.Run(argument, StreamConfig{nullptr, false, 1});
bool pass = true;
if(config.do_verification)
{
using ReferenceGemmInstance =
ck::tensor_operation::host::ReferenceGemmMultipleD<ADataType,
BDataType,
DsDataType,
EDataType,
AccDataType,
AElementOp,
BElementOp,
CDEElementOp>;
for(std::size_t i = 0; i < gemm_descs.size(); i++)
{
auto karg = ggemm_kargs[i];
auto dev_res_tensor =
Tensor<float>(f_host_tensor_descriptor(karg.M, karg.N, karg.StrideE, ELayout{}));
c_tensors_device[i]->FromDevice(c_device_result_tensors[i].mData.data());
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(a_tensors[i],
b_tensors[i],
d_tensors[i],
c_host_tensors[i],
a_element_op,
b_element_op,
cde_element_op);
ref_invoker.Run(ref_argument);
pass &= ck::utils::check_err(c_device_result_tensors[i], c_host_tensors[i]);
}
std::cout << "Verification: " << (pass ? "SUCCESS" : "FAILURE") << "!" << std::endl;
}
if(config.time_kernel)
{
float ave_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
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;
}
std::vector<int> argToIntArray(char* input)
{
std::vector<int> out;
std::istringstream in(input);
std::string item;
while(std::getline(in, item, ','))
{
out.push_back(std::stoi(item));
}
return out;
}
int main(int argc, char* argv[])
{
ProblemSize problem_size;
ExecutionConfig config;
if(argc < 10)
{
std::vector<ck::index_t> Ms{64, 127, 255, 129, 260, 190, 77};
problem_size.group_count = Ms.size();
for(int i = 0; i < problem_size.group_count; i++)
{
problem_size.Ms.push_back(Ms[i]);
problem_size.Ns.push_back(252);
problem_size.Ks.push_back(4608);
problem_size.stride_As.push_back(problem_size.Ks[i]);
problem_size.stride_Bs.push_back(problem_size.Ks[i]);
problem_size.stride_Cs.push_back(problem_size.Ns[i]);
problem_size.stride_Ds.push_back({});
for(int j = 0; j < NumDs; ++j)
{
problem_size.stride_Ds[i].push_back(problem_size.Ns[i]);
}
}
std::cout
<< "Usage:\n"
<< "arg1: verification (0=no, 1=yes)\n"
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
<< "arg3: time kernel (0=n0, 1=yes)\n"
<< "arg4 to 9: Ms, Ns, Ks, StrideAs, StrideBs, StrideCs (e.g., 256,256 128,128 64,64 "
"64,64 64,64 128,128)\n"
<< "... setting default values." << std::endl;
}
else
{
config.do_verification = std::stoi(argv[1]);
config.init_method = std::stoi(argv[2]);
config.time_kernel = std::stoi(argv[3]);
problem_size.Ms = argToIntArray(argv[4]);
problem_size.Ns = argToIntArray(argv[5]);
problem_size.Ks = argToIntArray(argv[6]);
problem_size.stride_As = argToIntArray(argv[7]);
problem_size.stride_Bs = argToIntArray(argv[8]);
problem_size.stride_Cs = argToIntArray(argv[9]);
for(int j = 0; j < NumDs; ++j)
{
problem_size.stride_Ds.push_back(problem_size.stride_Cs);
}
problem_size.group_count = problem_size.Ms.size();
}
return !run_grouped_gemm(problem_size, config);
}
example/59_grouped_gemm_multi_ABD/grouped_gemm_multi_abd_xdl_fixed_nk_bias_bf16_i8.cpp
View file @ f0759faf
......@@ -52,12 +52,12 @@ using BsLayout = ck::Tuple<B0Layout, B1Layout>;
using DsLayout = ck::Tuple<Row>;
using ELayout = Row;
using Scales = ck::tensor_operation::element_wise::Scales;
using Multiply = ck::tensor_operation::element_wise::Multiply;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using AddFastGelu = ck::tensor_operation::element_wise::AddFastGelu;
using AElementOp = PassThrough;
using BElementOp = Scales;
using BElementOp = Multiply;
using CDEElementOp = AddFastGelu;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
......
example/60_gemm_multi_ABD/CMakeLists.txt
View file @ f0759faf
add_example_executable(example_gemm_multi_ABD_xdl_fp16 gemm_multi_ABD_xdl_fp16.cpp)
add_example_executable(example_gemm_multi_ABD_xdl_bf16_i8 gemm_multi_ABD_xdl_bf16_i8.cpp)
\ No newline at end of file
add_example_executable(example_gemm_multi_ABD_xdl_bias_fastgelu_bf16_i8 gemm_multi_ABD_xdl_bias_fastgelu_bf16_i8.cpp)
add_example_executable(example_gemm_multi_ABD_xdl_multiply_bias_fastgelu_bf16_i8 gemm_multi_ABD_xdl_multiply_bias_fastgelu_bf16_i8.cpp)
add_example_executable(example_gemm_multi_ABD_xdl_fastgelu_bf16_i8 gemm_multi_ABD_xdl_fastgelu_bf16_i8.cpp)
example/60_gemm_multi_ABD/gemm_multi_ABD_xdl_bf16_i8.cpp → example/60_gemm_multi_ABD/gemm_multi_ABD_xdl_bias_fastgelu_bf16_i8.cpp
View file @ f0759faf
......@@ -18,9 +18,12 @@
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/utility/blkgemmpipe_scheduler.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using F16 = ck::half_t;
using BF16 = ck::bhalf_t;
using I8 = int8_t;
using F32 = float;
......@@ -41,22 +44,22 @@ using EDataType = BF16;
using A0Layout = Row;
using AsLayout = ck::Tuple<A0Layout>;
using B0Layout = Col;
using B0Layout = Row;
using B1Layout = B0Layout;
using BsLayout = ck::Tuple<B0Layout, B1Layout>;
using D0Layout = Row;
using DsLayout = ck::Tuple<D0Layout>;
using ELayout = Row;
using Scales = ck::tensor_operation::element_wise::Scales;
using Multiply = ck::tensor_operation::element_wise::Multiply;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using AddFastGelu = ck::tensor_operation::element_wise::AddFastGelu;
using AElementOp = PassThrough;
using BElementOp = Scales;
using BElementOp = Multiply;
using CDEElementOp = AddFastGelu;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::Default;
using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultipleABD_Xdl_CShuffle
// clang-format off
......@@ -64,9 +67,9 @@ using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultipleABD_Xdl
///######| | | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| 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|
///######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 128, 16, 128, 32, 8, 8, 16, 16, 1, 4, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, 1, 1, S<1, 16, 1, 8>, 1>;
< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 128, 128, 64, 8, 4, 32, 32, 2, 2, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, S<16, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 4, 0, 1, 1, S<1, 32, 1, 8>, 8, ck::BlockGemmPipelineScheduler::Intrawave, ck::BlockGemmPipelineVersion::v4>;
// clang-format on
int main(int argc, char* argv[])
{
bool do_verification = true;
......@@ -74,13 +77,13 @@ int main(int argc, char* argv[])
bool time_kernel = false;
// GEMM shape
ck::index_t M = 64;
ck::index_t N = 1024;
ck::index_t K = 512;
ck::index_t M = 4096;
ck::index_t N = 768;
ck::index_t K = 6144;
ck::index_t StrideA = K;
ck::index_t StrideB = K;
ck::index_t StrideD = N;
ck::index_t StrideB = N;
ck::index_t StrideD = 0;
ck::index_t StrideE = N;
if(argc == 1)
......
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