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Commit 129e58ae authored by Adam Osewski's avatar Adam Osewski
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Merge remote-tracking branch 'origin/develop' into aosewski/ggemm_multi_d2

parents 9bebfd42 cb0645be
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example/62_convnd_activ/convscale/convnd_fwd_xdl_convscale_fp8.cpp 0 → 100644
View file @ 129e58ae
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "convnd_fwd_convscale_common.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_abd_xdl_cshuffle.hpp"
#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
using InDataType = ck::f8_t;
using WeiDataType = ck::f8_t;
using AccDataType = float;
using CShuffleDataType = float;
using DsDataType = ck::Tuple<>;
using OutDataType = ck::f8_t;
using AComputeDataType = ck::f8_t;
using BComputeDataType = ck::f8_t;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using InElementOp = PassThrough;
using WeiElementOp = PassThrough;
using OutElementOp = ConvScale;
static constexpr auto ConvSpec =
ck::tensor_operation::device::ConvolutionForwardSpecialization::Default;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
template <ck::index_t NDimSpatial,
typename InLayout,
typename WeiLayout,
typename DsLayout,
typename OutLayout>
using DeviceGroupedConvNDFwdInstance =
ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<
NDimSpatial,
InLayout,
WeiLayout,
DsLayout,
OutLayout,
InDataType,
WeiDataType,
AccDataType,
CShuffleDataType,
DsDataType,
OutDataType,
InElementOp,
WeiElementOp,
OutElementOp,
ConvSpec, // ConvForwardSpecialization
GemmSpec, // GemmSpecialization
1, //
256, // BlockSize
128, // MPerBlock
256, // NPerBlock
32, // KPerBlock
8, // AK1
8, // BK1
32, // MPerXdl
32, // NPerXdl
2, // MXdlPerWave
4, // NXdlPerWave
S<4, 64, 1>, // ABlockTransferThreadClusterLengths_AK0_M_AK1
S<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // ABlockTransferSrcAccessOrder
2, // ABlockTransferSrcVectorDim
8, // ABlockTransferSrcScalarPerVector
8, // ABlockTransferDstScalarPerVector_AK1
1, // ABlockLdsExtraM
S<4, 64, 1>, // BBlockTransferThreadClusterLengths_BK0_N_BK1
S<1, 0, 2>, // BBlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // BBlockTransferSrcAccessOrder
2, // BBlockTransferSrcVectorDim
8, // BBlockTransferSrcScalarPerVector
8, // BBlockTransferDstScalarPerVector_BK1
1, // BBlockLdsExtraN
1,
1,
S<1, 32, 1, 8>,
8,
AComputeDataType,
BComputeDataType>;
#include "run_convnd_fwd_convscale_example.inc"
int main(int argc, char* argv[]) { return run_convnd_fwd_example(argc, argv) ? 0 : 1; }
example/62_convnd_activ/convscale/run_convnd_fwd_convscale_example.inc 0 → 100644
View file @ 129e58ae
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
bool run_convnd_fwd_example(int argc, char* argv[])
{
print_helper_msg();
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
ck::utils::conv::ConvParam conv_param{
2, 1, 128, 256, 192, {3, 3}, {71, 71}, {2, 2}, {1, 1}, {1, 1}, {1, 1}};
if(argc == 1)
{
// use default
}
else if(argc == 4)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
const ck::index_t num_dim_spatial = std::stoi(argv[4]);
conv_param = ck::utils::conv::parse_conv_param(num_dim_spatial, 5, argv);
}
// instantiate in and wei element ops, will
// instantiate out_element_op below for every iteration
const auto in_element_op = InElementOp{};
const auto wei_element_op = WeiElementOp{};
const auto run =
[&](auto ndim_spatial, auto in_layout, auto wei_layout, auto ds_layout, auto out_layout) {
constexpr ck::index_t ndim_spatial_value = ndim_spatial.value;
using InLayout = decltype(in_layout);
using WeiLayout = decltype(wei_layout);
using DsLayout = decltype(ds_layout);
using OutLayout = decltype(out_layout);
const auto in_g_n_c_wis_desc =
ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(
conv_param);
const auto wei_g_k_c_xs_desc =
ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(
conv_param);
const auto out_g_n_k_wos_desc =
ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(
conv_param);
return run_grouped_conv_fwd<ndim_spatial_value,
InDataType,
WeiDataType,
CShuffleDataType,
DsDataType,
OutDataType,
InElementOp,
WeiElementOp,
OutElementOp,
DeviceGroupedConvNDFwdInstance<ndim_spatial_value,
InLayout,
WeiLayout,
DsLayout,
OutLayout>>(
do_verification,
init_method,
time_kernel,
conv_param,
in_g_n_c_wis_desc,
wei_g_k_c_xs_desc,
out_g_n_k_wos_desc,
in_element_op,
wei_element_op);
};
namespace ctc = ck::tensor_layout::convolution;
if(conv_param.num_dim_spatial_ == 1)
{
return run(ck::Number<1>{}, ctc::GNWC{}, ctc::GKXC{}, ck::Tuple<>{}, ctc::GNWK{});
}
else if(conv_param.num_dim_spatial_ == 2)
{
return run(ck::Number<2>{}, ctc::GNHWC{}, ctc::GKYXC{}, ck::Tuple<>{}, ctc::GNHWK{});
}
else if(conv_param.num_dim_spatial_ == 3)
{
return run(ck::Number<3>{}, ctc::GNDHWC{}, ctc::GKZYXC{}, ck::Tuple<>{}, ctc::GNDHWK{});
}
return true;
}
example/65_gemm_multiply_multiply/CMakeLists.txt 0 → 100644
View file @ 129e58ae
add_example_executable(example_gemm_multiply_multiply_xdl_fp16 gemm_multiply_multiply_xdl_fp16.cpp)
example/65_gemm_multiply_multiply/gemm_multiply_multiply_xdl_fp16.cpp 0 → 100644
View file @ 129e58ae
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, 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/impl/device_gemm_multiple_d_xdl_cshuffle_v3.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.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.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 FP8 = ck::f8_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using A0DataType = FP8;
using B0DataType = FP8;
using AccDataType = F32;
using CShuffleDataType = F32;
using D0DataType = F32;
using D1DataType = F32;
using DsDataType = ck::Tuple<D0DataType, D1DataType>;
using EDataType = F16;
using A0Layout = Row;
using B0Layout = Col;
using D0Layout = Row;
using D1Layout = Col;
using DsLayout = ck::Tuple<D0Layout, D1Layout>;
using ELayout = Row;
struct MultiplyMultiply
{
template <typename E, typename C, typename D0, typename D1>
__host__ __device__ constexpr void
operator()(E& e, const C& c, const D0& d0, const D1& d1) const;
template <>
__host__ __device__ constexpr void operator()<ck::half_t, float, float, float>(
ck::half_t& e, const float& c, const float& d0, const float& d1) const
{
const float x0_f = c * d0 * d1;
e = ck::type_convert<ck::half_t>(x0_f);
}
};
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CDEElementOp = MultiplyMultiply;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::Default;
using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultiD_Xdl_CShuffle_V3
// clang-format off
///######| ALayout| BLayout| DsLayout| ELayout| AData| BData| DsData| EData| AccData| CShuffle| A| B| CDE| GEMM| 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| Type| Type| DataType| Elementwise| Elementwise| Elementwise| Spacialization| 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| | | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
///######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | S<C, D0, D1>|
///###### RRR
///< Row, Row, DsLayout, ELayout, A0DataType, B0DataType, DsDataType, EDataType, AccDataType, CShuffleDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 256, 256, 128, 64, 16, 4, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<16, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 4, 0, 1, 1, S<1, 32, 1, 8>, S<8, 8, 1>, ck::BlockGemmPipelineScheduler::Interwave, ck::BlockGemmPipelineVersion::v1, FP8>;
///###### RCR
< Row, Col, DsLayout, ELayout, A0DataType, B0DataType, DsDataType, EDataType, AccDataType, CShuffleDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 256, 256, 128, 64, 16, 16, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 32, 1, 8>, S<8, 8, 1>, ck::BlockGemmPipelineScheduler::Interwave, ck::BlockGemmPipelineVersion::v1, FP8>;
// clang-format on
int main(int argc, char* argv[])
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
// GEMM shape
ck::index_t M = 3840;
ck::index_t N = 4096;
ck::index_t K = 4096;
ck::index_t StrideA = K;
ck::index_t StrideB = K;
ck::index_t StrideD = 0;
ck::index_t StrideE = N;
if(argc == 1)
{
// use default case
}
else if(argc == 4)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 11)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);
StrideA = std::stoi(argv[7]);
StrideB = std::stoi(argv[8]);
StrideD = std::stoi(argv[9]);
StrideE = std::stoi(argv[10]);
}
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");
printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideD, StrideE\n");
exit(0);
}
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});
}
};
Tensor<A0DataType> a0_m_k(f_host_tensor_descriptor(M, K, StrideA, A0Layout{}));
Tensor<B0DataType> b0_k_n(f_host_tensor_descriptor(K, N, StrideB, B0Layout{}));
Tensor<D0DataType> d0_m_n(f_host_tensor_descriptor(M, N, StrideD, D0Layout{}));
Tensor<D1DataType> d1_m_n(f_host_tensor_descriptor(M, N, StrideD, D1Layout{}));
Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
std::cout << "a0_m_k: " << a0_m_k.mDesc << std::endl;
std::cout << "b0_k_n: " << b0_k_n.mDesc << std::endl;
std::cout << "d1_m_n: " << d1_m_n.mDesc << std::endl;
std::cout << "d0_m_n: " << d0_m_n.mDesc << std::endl;
std::cout << "e_m_n: " << e_m_n_host_result.mDesc << std::endl;
switch(init_method)
{
case 0: break;
case 1:
a0_m_k.GenerateTensorValue(GeneratorTensor_2<A0DataType>{-2, 2});
b0_k_n.GenerateTensorValue(GeneratorTensor_2<B0DataType>{0, 2});
d0_m_n.GenerateTensorValue(GeneratorTensor_2<D0DataType>{0, 2});
d1_m_n.GenerateTensorValue(GeneratorTensor_2<D1DataType>{0, 2});
break;
default:
a0_m_k.GenerateTensorValue(GeneratorTensor_3<A0DataType>{0.0, 1.0});
b0_k_n.GenerateTensorValue(GeneratorTensor_3<B0DataType>{-0.5, 0.5});
d0_m_n.GenerateTensorValue(GeneratorTensor_3<D0DataType>{-0.5, 0.5});
d1_m_n.GenerateTensorValue(GeneratorTensor_3<D1DataType>{-0.5, 0.5});
}
DeviceMem a0_device_buf(sizeof(A0DataType) * a0_m_k.mDesc.GetElementSpaceSize());
DeviceMem b0_device_buf(sizeof(B0DataType) * b0_k_n.mDesc.GetElementSpaceSize());
DeviceMem d0_device_buf(sizeof(D0DataType) * d0_m_n.mDesc.GetElementSpaceSize());
DeviceMem d1_device_buf(sizeof(D1DataType) * d1_m_n.mDesc.GetElementSpaceSize());
DeviceMem e_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());
a0_device_buf.ToDevice(a0_m_k.mData.data());
b0_device_buf.ToDevice(b0_k_n.mData.data());
d0_device_buf.ToDevice(d0_m_n.mData.data());
d1_device_buf.ToDevice(d1_m_n.mData.data());
e_device_buf.ToDevice(e_m_n_device_result.mData.data());
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto cde_element_op = CDEElementOp{};
constexpr ck::index_t NumDTensor = DsDataType::Size();
constexpr auto I0 = ck::Number<0>{};
// do GEMM
auto device_op = DeviceOpInstance{};
auto invoker = device_op.MakeInvoker();
auto argument =
device_op.MakeArgument(a0_device_buf.GetDeviceBuffer(),
b0_device_buf.GetDeviceBuffer(),
std::array<const void*, NumDTensor>{d0_device_buf.GetDeviceBuffer(),
d1_device_buf.GetDeviceBuffer()},
e_device_buf.GetDeviceBuffer(),
M,
N,
K,
StrideA,
StrideB,
std::array<ck::index_t, NumDTensor>{I0, I0},
StrideE,
a_element_op,
b_element_op,
cde_element_op);
if(!device_op.IsSupportedArgument(argument))
{
throw std::runtime_error(
"wrong! device_gemm with the specified compilation parameters does "
"not support this GEMM problem");
}
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel, 20, 50});
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: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
<< std::endl;
e_device_buf.FromDevice(e_m_n_device_result.mData.data());
if(do_verification)
{
Tensor<CShuffleDataType> c_m_n({M, N});
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<A0DataType,
B0DataType,
CShuffleDataType,
AccDataType,
PassThrough,
PassThrough,
PassThrough>;
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(
a0_m_k, b0_k_n, c_m_n, PassThrough{}, PassThrough{}, PassThrough{});
ref_invoker.Run(ref_argument);
for(int m = 0; m < M; ++m)
{
for(int n = 0; n < N; ++n)
{
cde_element_op(e_m_n_host_result(m, n), c_m_n(m, n), d0_m_n(m, n), d1_m_n(m, n));
}
}
e_device_buf.FromDevice(e_m_n_device_result.mData.data());
return ck::utils::check_err(e_m_n_device_result, e_m_n_host_result) ? 0 : 1;
}
return 0;
}
example/CMakeLists.txt
View file @ 129e58ae
...@@ -44,6 +44,13 @@ function(add_example_executable EXAMPLE_NAME FILE_NAME) ...@@ -44,6 +44,13 @@ function(add_example_executable EXAMPLE_NAME FILE_NAME)
endif() endif()
endforeach() endforeach()
endif() endif()
if(INSTANCES_ONLY)
set(EX_TARGETS ${DEFAULT_GPU_TARGETS})
else()
set(EX_TARGETS ${GPU_TARGETS})
endif()
#Do not build any DL examples if DL_KERNELS not set #Do not build any DL examples if DL_KERNELS not set
foreach(source IN LISTS FILE_NAME) foreach(source IN LISTS FILE_NAME)
if(NOT DEFINED DL_KERNELS AND source MATCHES "_dl") if(NOT DEFINED DL_KERNELS AND source MATCHES "_dl")
...@@ -53,23 +60,30 @@ function(add_example_executable EXAMPLE_NAME FILE_NAME) ...@@ -53,23 +60,30 @@ function(add_example_executable EXAMPLE_NAME FILE_NAME)
endforeach() endforeach()
#Do not build any XDL examples if gfx9 targets are not on the list #Do not build any XDL examples if gfx9 targets are not on the list
foreach(source IN LISTS FILE_NAME) foreach(source IN LISTS FILE_NAME)
if(NOT GPU_TARGETS MATCHES "gfx9" AND source MATCHES "_xdl") if(NOT EX_TARGETS MATCHES "gfx9" AND source MATCHES "_xdl")
message("removing xdl example ${source} ") message("removing xdl example ${source} ")
list(REMOVE_ITEM FILE_NAME "${source}") list(REMOVE_ITEM FILE_NAME "${source}")
endif() endif()
endforeach() endforeach()
#Do not build any WMMA examples if gfx11 targets are not on the list #Do not build any WMMA examples if gfx11 targets are not on the list
foreach(source IN LISTS FILE_NAME) foreach(source IN LISTS FILE_NAME)
if(NOT GPU_TARGETS MATCHES "gfx11" AND source MATCHES "_wmma") if(NOT EX_TARGETS MATCHES "gfx11" AND source MATCHES "_wmma")
message("removing wmma example ${source} ") message("removing wmma example ${source} ")
list(REMOVE_ITEM FILE_NAME "${source}") list(REMOVE_ITEM FILE_NAME "${source}")
endif() endif()
endforeach() endforeach()
#only continue if there are some source files left on the list #only continue if there are some source files left on the list
if(FILE_NAME) if(FILE_NAME)
if(FILE_NAME MATCHES "_xdl")
list(REMOVE_ITEM EX_TARGETS gfx1030 gfx1100 gfx1101 gfx1102 gfx1103)
elseif(FILE_NAME MATCHES "_wmma")
list(REMOVE_ITEM EX_TARGETS gfx908 gfx90a gfx940 gfx941 gfx942 gfx1030)
endif()
set_source_files_properties(${FILE_NAME} PROPERTIES LANGUAGE HIP)
add_executable(${EXAMPLE_NAME} ${FILE_NAME}) add_executable(${EXAMPLE_NAME} ${FILE_NAME})
target_link_libraries(${EXAMPLE_NAME} PRIVATE utility) target_link_libraries(${EXAMPLE_NAME} PRIVATE utility)
add_test(NAME ${EXAMPLE_NAME} COMMAND $<TARGET_FILE:${EXAMPLE_NAME}> ${ARGN}) add_test(NAME ${EXAMPLE_NAME} COMMAND $<TARGET_FILE:${EXAMPLE_NAME}> ${ARGN})
set_property(TARGET ${EXAMPLE_NAME} PROPERTY HIP_ARCHITECTURES ${EX_TARGETS} )
add_dependencies(examples ${EXAMPLE_NAME}) add_dependencies(examples ${EXAMPLE_NAME})
add_dependencies(check ${EXAMPLE_NAME}) add_dependencies(check ${EXAMPLE_NAME})
rocm_install(TARGETS ${EXAMPLE_NAME} COMPONENT examples) rocm_install(TARGETS ${EXAMPLE_NAME} COMPONENT examples)
...@@ -118,6 +132,12 @@ function(add_example_executable_no_testing EXAMPLE_NAME FILE_NAME) ...@@ -118,6 +132,12 @@ function(add_example_executable_no_testing EXAMPLE_NAME FILE_NAME)
endif() endif()
endforeach() endforeach()
endif() endif()
if(INSTANCES_ONLY)
set(EX_TARGETS ${DEFAULT_GPU_TARGETS})
else()
set(EX_TARGETS ${GPU_TARGETS})
endif()
#Do not build any DL examples if DL_KERNELS not set #Do not build any DL examples if DL_KERNELS not set
foreach(source IN LISTS FILE_NAME) foreach(source IN LISTS FILE_NAME)
if(NOT DEFINED DL_KERNELS AND source MATCHES "_dl") if(NOT DEFINED DL_KERNELS AND source MATCHES "_dl")
...@@ -127,23 +147,30 @@ function(add_example_executable_no_testing EXAMPLE_NAME FILE_NAME) ...@@ -127,23 +147,30 @@ function(add_example_executable_no_testing EXAMPLE_NAME FILE_NAME)
endforeach() endforeach()
#Do not build any XDL examples if gfx9 targets are not on the list #Do not build any XDL examples if gfx9 targets are not on the list
foreach(source IN LISTS FILE_NAME) foreach(source IN LISTS FILE_NAME)
if(NOT GPU_TARGETS MATCHES "gfx9" AND source MATCHES "_xdl") if(NOT EX_TARGETS MATCHES "gfx9" AND source MATCHES "_xdl")
message("removing xdl example ${source} ") message("removing xdl example ${source} ")
list(REMOVE_ITEM FILE_NAME "${source}") list(REMOVE_ITEM FILE_NAME "${source}")
endif() endif()
endforeach() endforeach()
#Do not build any WMMA examples if gfx11 targets are not on the list #Do not build any WMMA examples if gfx11 targets are not on the list
foreach(source IN LISTS FILE_NAME) foreach(source IN LISTS FILE_NAME)
if(NOT GPU_TARGETS MATCHES "gfx11" AND source MATCHES "_wmma") if(NOT EX_TARGETS MATCHES "gfx11" AND source MATCHES "_wmma")
message("removing wmma example ${source} ") message("removing wmma example ${source} ")
list(REMOVE_ITEM FILE_NAME "${source}") list(REMOVE_ITEM FILE_NAME "${source}")
endif() endif()
endforeach() endforeach()
#only continue if there are some source files left on the list #only continue if there are some source files left on the list
if(FILE_NAME) if(FILE_NAME)
if(FILE_NAME MATCHES "_xdl")
list(REMOVE_ITEM EX_TARGETS gfx900 gfx906 gfx1030 gfx1100 gfx1101 gfx1102 gfx1103)
elseif(FILE_NAME MATCHES "_wmma")
list(REMOVE_ITEM EX_TARGETS gfx900 gfx906 gfx908 gfx90a gfx940 gfx941 gfx942 gfx1030)
endif()
set_source_files_properties(${FILE_NAME} PROPERTIES LANGUAGE HIP)
add_executable(${EXAMPLE_NAME} ${FILE_NAME}) add_executable(${EXAMPLE_NAME} ${FILE_NAME})
target_link_libraries(${EXAMPLE_NAME} PRIVATE utility) target_link_libraries(${EXAMPLE_NAME} PRIVATE utility)
add_dependencies(examples ${EXAMPLE_NAME}) add_dependencies(examples ${EXAMPLE_NAME})
set_property(TARGET ${EXAMPLE_NAME} PROPERTY HIP_ARCHITECTURES ${EX_TARGETS} )
rocm_install(TARGETS ${EXAMPLE_NAME} COMPONENT examples) rocm_install(TARGETS ${EXAMPLE_NAME} COMPONENT examples)
set(result 0) set(result 0)
endif() endif()
......
example/ck_tile/01_fmha/CMakeLists.txt
View file @ 129e58ae
# generate a list of kernels, but not actually emit files at config stage # generate a list of kernels, but not actually emit files at config stage
execute_process( execute_process(
COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py
--list_blobs ${CMAKE_CURRENT_BINARY_DIR}/blob_list.txt --direction fwd --list_blobs ${CMAKE_CURRENT_BINARY_DIR}/fwd_blob_list.txt
) )
# NOTE: for cmake, the FMHA_FWD_GEN_BLOBS files must be in the same directory execute_process(
COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py
--direction bwd --list_blobs ${CMAKE_CURRENT_BINARY_DIR}/bwd_blob_list.txt
)
# NOTE: for cmake, the FMHA_FWD_GEN_BLOBS/FMHA_BWD_GEN_BLOBS files must be in the same directory
# as current cmake list, otherwise will not figure out the dependency properly # as current cmake list, otherwise will not figure out the dependency properly
file(STRINGS ${CMAKE_CURRENT_BINARY_DIR}/blob_list.txt FMHA_FWD_GEN_BLOBS) file(STRINGS ${CMAKE_CURRENT_BINARY_DIR}/fwd_blob_list.txt FMHA_FWD_GEN_BLOBS)
file(STRINGS ${CMAKE_CURRENT_BINARY_DIR}/bwd_blob_list.txt FMHA_BWD_GEN_BLOBS)
add_custom_command( add_custom_command(
OUTPUT ${FMHA_FWD_GEN_BLOBS} OUTPUT ${FMHA_FWD_GEN_BLOBS}
COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py
--output_dir ${CMAKE_CURRENT_BINARY_DIR} --direction fwd --output_dir ${CMAKE_CURRENT_BINARY_DIR}
)
add_custom_command(
OUTPUT ${FMHA_BWD_GEN_BLOBS}
COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py
--direction bwd --output_dir ${CMAKE_CURRENT_BINARY_DIR}
) )
set(EXAMPLE_FMHA_FWD "tile_example_fmha_fwd") set(EXAMPLE_FMHA_FWD "tile_example_fmha_fwd")
...@@ -22,6 +34,14 @@ add_executable(${EXAMPLE_FMHA_FWD} EXCLUDE_FROM_ALL fmha_fwd.cpp) ...@@ -22,6 +34,14 @@ add_executable(${EXAMPLE_FMHA_FWD} EXCLUDE_FROM_ALL fmha_fwd.cpp)
target_include_directories(${EXAMPLE_FMHA_FWD} PRIVATE ${CMAKE_CURRENT_LIST_DIR}) target_include_directories(${EXAMPLE_FMHA_FWD} PRIVATE ${CMAKE_CURRENT_LIST_DIR})
target_sources(${EXAMPLE_FMHA_FWD} PRIVATE ${FMHA_FWD_GEN_BLOBS}) target_sources(${EXAMPLE_FMHA_FWD} PRIVATE ${FMHA_FWD_GEN_BLOBS})
set(EXAMPLE_FMHA_BWD "tile_example_fmha_bwd")
# not using add_example_executable() to add this target, since we don't want this to have
# to be included in "make all/install/check"
message("adding example ${EXAMPLE_FMHA_BWD}")
add_executable(${EXAMPLE_FMHA_BWD} EXCLUDE_FROM_ALL fmha_bwd.cpp)
target_include_directories(${EXAMPLE_FMHA_BWD} PRIVATE ${CMAKE_CURRENT_LIST_DIR})
target_sources(${EXAMPLE_FMHA_BWD} PRIVATE ${FMHA_BWD_GEN_BLOBS})
# NOTE: this is dangerous since will change the whole kernel to flush denormals # NOTE: this is dangerous since will change the whole kernel to flush denormals
# WIP with compiler team for an exp2 intrinsic..., then remove this # WIP with compiler team for an exp2 intrinsic..., then remove this
if(NOT DEFINED FMHA_FWD_FAST_EXP2) if(NOT DEFINED FMHA_FWD_FAST_EXP2)
...@@ -29,16 +49,27 @@ if(NOT DEFINED FMHA_FWD_FAST_EXP2) ...@@ -29,16 +49,27 @@ if(NOT DEFINED FMHA_FWD_FAST_EXP2)
endif() endif()
set(EXAMPLE_FMHA_FWD_COMPILE_OPTIONS) set(EXAMPLE_FMHA_FWD_COMPILE_OPTIONS)
set(EXAMPLE_FMHA_BWD_COMPILE_OPTIONS)
# NOTE: we turn off undefined-func-template to let source compile without explicit declare function specializations # NOTE: we turn off undefined-func-template to let source compile without explicit declare function specializations
# ... because they are auto-generated # ... because they are auto-generated
if(FMHA_FWD_FAST_EXP2) if(FMHA_FWD_FAST_EXP2)
list(APPEND EXAMPLE_FMHA_FWD_COMPILE_OPTIONS -Wno-undefined-func-template -DCK_TILE_FMHA_FWD_FAST_EXP2=1 -fgpu-flush-denormals-to-zero) list(APPEND EXAMPLE_FMHA_FWD_COMPILE_OPTIONS -Wno-undefined-func-template -DCK_TILE_FMHA_FWD_FAST_EXP2=1 -fgpu-flush-denormals-to-zero)
list(APPEND EXAMPLE_FMHA_BWD_COMPILE_OPTIONS -Wno-undefined-func-template -DCK_TILE_FMHA_FWD_FAST_EXP2=1 -fgpu-flush-denormals-to-zero)
else() else()
list(APPEND EXAMPLE_FMHA_FWD_COMPILE_OPTIONS -Wno-undefined-func-template -DCK_TILE_FMHA_FWD_FAST_EXP2=0) list(APPEND EXAMPLE_FMHA_FWD_COMPILE_OPTIONS -Wno-undefined-func-template -DCK_TILE_FMHA_FWD_FAST_EXP2=0)
list(APPEND EXAMPLE_FMHA_BWD_COMPILE_OPTIONS -Wno-undefined-func-template -DCK_TILE_FMHA_FWD_FAST_EXP2=0)
endif() endif()
# Allow comparing floating points directly in order to check sentinel values # Allow comparing floating points directly in order to check sentinel values
list(APPEND EXAMPLE_FMHA_FWD_COMPILE_OPTIONS -Wno-float-equal) list(APPEND EXAMPLE_FMHA_FWD_COMPILE_OPTIONS -Wno-float-equal)
list(APPEND EXAMPLE_FMHA_BWD_COMPILE_OPTIONS -Wno-float-equal)
target_compile_options(${EXAMPLE_FMHA_FWD} PRIVATE ${EXAMPLE_FMHA_FWD_COMPILE_OPTIONS}) target_compile_options(${EXAMPLE_FMHA_FWD} PRIVATE ${EXAMPLE_FMHA_FWD_COMPILE_OPTIONS})
target_compile_options(${EXAMPLE_FMHA_BWD} PRIVATE ${EXAMPLE_FMHA_BWD_COMPILE_OPTIONS})
# TODO: we have to turn off this global prop, otherwise the progress bar generated
# by cmake will print too many files, execvp: /bin/sh: Argument list too long
# however, this property may affect global
# TODO: consider codegen a makefile by us
set_property(GLOBAL PROPERTY RULE_MESSAGES OFF)
example/ck_tile/01_fmha/README.md
View file @ 129e58ae
...@@ -34,6 +34,7 @@ args: ...@@ -34,6 +34,7 @@ args:
if not equal to h, then this is GQA/MQA case if not equal to h, then this is GQA/MQA case
-s seqlen_q. if group-mode, means the average value of seqlen_q (default:3328) -s seqlen_q. if group-mode, means the average value of seqlen_q (default:3328)
total_seqlen_q = seqlen_q * batch, and seqlen_q per batch may vary total_seqlen_q = seqlen_q * batch, and seqlen_q per batch may vary
also with "-s=s0,s1,s2..." comma seperated int to set per batch seqlen(group-mode)
-s_k seqlen_k, -1 means equal to s (default:-1) -s_k seqlen_k, -1 means equal to s (default:-1)
-d head dim for q, k (default:128) -d head dim for q, k (default:128)
-d_v head dim for v, -1 means equal to d (default:-1) -d_v head dim for v, -1 means equal to d (default:-1)
......
example/ck_tile/01_fmha/fmha_bwd.cpp 0 → 100644
View file @ 129e58ae
This diff is collapsed.
example/ck_tile/01_fmha/fmha_bwd.hpp 0 → 100644
View file @ 129e58ae
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/host/kernel_launch.hpp"
#include "ck_tile/ops/fmha.hpp"
#include "ck_tile/ops/epilogue.hpp"
#include "mask.hpp"
#include "bias.hpp"
#include <type_traits>
template <typename DataType>
struct FmhaBwdTypeConfig;
template <>
struct FmhaBwdTypeConfig<ck_tile::half_t>
{
using QDataType = ck_tile::half_t;
using KDataType = ck_tile::half_t;
using VDataType = ck_tile::half_t;
using GemmDataType = ck_tile::half_t;
using BiasDataType = ck_tile::half_t;
using LSEDataType = float;
using AccDataType = float; // data type for gemm accumulation
using DDataType = float;
using RandValOutputDataType = uint8_t;
using ODataType = ck_tile::half_t;
using OGradDataType = ck_tile::half_t;
using QGradDataType = ck_tile::half_t;
using KGradDataType = ck_tile::half_t;
using VGradDataType = ck_tile::half_t;
using BiasGradDataType = ck_tile::half_t;
};
template <>
struct FmhaBwdTypeConfig<ck_tile::bf16_t>
{
using QDataType = ck_tile::bf16_t;
using KDataType = ck_tile::bf16_t;
using VDataType = ck_tile::bf16_t;
using GemmDataType = ck_tile::bf16_t;
using BiasDataType = ck_tile::bf16_t;
using LSEDataType = float;
using AccDataType = float; // data type for gemm accumulation
using DDataType = float;
using RandValOutputDataType = uint8_t;
using ODataType = ck_tile::bf16_t;
using OGradDataType = ck_tile::bf16_t;
using QGradDataType = ck_tile::bf16_t;
using KGradDataType = ck_tile::bf16_t;
using VGradDataType = ck_tile::bf16_t;
using BiasGradDataType = ck_tile::bf16_t;
};
struct FmhaMasks
{
using NoMask = ck_tile::GenericAttentionMask<false>;
using GenericMask = ck_tile::GenericAttentionMask<true, true>;
using CausalMask = ck_tile::GenericAttentionMask<true, false>;
};
// runtime args, some will passed to karg, some will used to compute grids/blocks
struct fmha_bwd_args
{
const void* q_ptr;
const void* k_ptr;
const void* v_ptr;
const void* bias_ptr; // bias or alibi_slope pointer
const void* o_ptr;
const void* lse_ptr;
const void* do_ptr;
void* d_ptr;
void* rand_val_ptr;
void* dq_ptr;
void* dk_ptr;
void* dv_ptr;
void* dbias_ptr;
const void* seqstart_q_ptr;
const void* seqstart_k_ptr;
const void* seqlen_k_ptr;
ck_tile::index_t seqlen_q;
ck_tile::index_t seqlen_k;
ck_tile::index_t batch;
ck_tile::index_t max_seqlen_q;
ck_tile::index_t max_seqlen_k;
ck_tile::index_t hdim_q;
ck_tile::index_t hdim_v;
ck_tile::index_t nhead_q;
ck_tile::index_t nhead_k;
float scale;
ck_tile::index_t stride_q;
ck_tile::index_t stride_k;
ck_tile::index_t stride_v;
ck_tile::index_t stride_bias; // if alibi, b*h need set this to h, 1*h need set this to 0
ck_tile::index_t stride_o;
ck_tile::index_t stride_randval;
ck_tile::index_t stride_do;
ck_tile::index_t stride_dk;
ck_tile::index_t stride_dv;
ck_tile::index_t stride_dbias;
ck_tile::index_t nhead_stride_q;
ck_tile::index_t nhead_stride_k;
ck_tile::index_t nhead_stride_v;
ck_tile::index_t nhead_stride_bias;
ck_tile::index_t nhead_stride_o;
ck_tile::index_t nhead_stride_randval;
ck_tile::index_t nhead_stride_do;
ck_tile::index_t nhead_stride_lsed;
ck_tile::index_t nhead_stride_dbias;
ck_tile::index_t batch_stride_q;
ck_tile::index_t batch_stride_k;
ck_tile::index_t batch_stride_v;
ck_tile::index_t batch_stride_bias;
ck_tile::index_t batch_stride_o;
ck_tile::index_t batch_stride_randval;
ck_tile::index_t batch_stride_do;
ck_tile::index_t batch_stride_lsed;
ck_tile::index_t batch_stride_dk;
ck_tile::index_t batch_stride_dv;
ck_tile::index_t batch_stride_dbias;
ck_tile::index_t window_size_left;
ck_tile::index_t window_size_right;
ck_tile::index_t mask_type;
float p_drop;
float p_undrop;
bool s_randval;
std::tuple<uint64_t, uint64_t> drop_seed_offset;
};
template <typename FmhaBwdDQDKDVKernel>
auto fmha_bwd_dq_dk_dv_create_kargs_and_grids(fmha_bwd_args args)
{
assert(args.nhead_q % args.nhead_k == 0);
auto kargs = [&] {
// create group mode kernel arguments
if constexpr(FmhaBwdDQDKDVKernel::kIsGroupMode)
{
return FmhaBwdDQDKDVKernel::MakeKargs(args.q_ptr,
args.k_ptr,
args.v_ptr,
args.bias_ptr,
args.lse_ptr,
args.do_ptr,
args.d_ptr,
args.rand_val_ptr,
args.dq_ptr,
args.dk_ptr,
args.dv_ptr,
args.dbias_ptr,
args.seqstart_q_ptr,
args.seqstart_k_ptr,
args.seqlen_k_ptr,
args.hdim_q,
args.hdim_v,
args.nhead_q,
args.nhead_q / args.nhead_k,
args.scale,
args.stride_q,
args.stride_k,
args.stride_v,
args.stride_bias,
args.stride_randval,
args.stride_do,
args.stride_dk,
args.stride_dv,
args.stride_dbias,
args.nhead_stride_q,
args.nhead_stride_k,
args.nhead_stride_v,
args.nhead_stride_bias,
args.nhead_stride_randval,
args.nhead_stride_do,
args.nhead_stride_lsed,
args.nhead_stride_dbias,
args.batch_stride_lsed,
args.window_size_left,
args.window_size_right,
args.mask_type,
args.p_drop,
args.s_randval,
args.drop_seed_offset);
}
else
{ // create batch mode kernel arguments
return FmhaBwdDQDKDVKernel::MakeKargs(args.q_ptr,
args.k_ptr,
args.v_ptr,
args.bias_ptr,
args.lse_ptr,
args.do_ptr,
args.d_ptr,
args.rand_val_ptr,
args.dq_ptr,
args.dk_ptr,
args.dv_ptr,
args.dbias_ptr,
args.seqlen_q,
args.seqlen_k,
args.hdim_q,
args.hdim_v,
args.nhead_q,
args.nhead_q / args.nhead_k,
args.scale,
args.stride_q,
args.stride_k,
args.stride_v,
args.stride_bias,
args.stride_randval,
args.stride_do,
args.stride_dk,
args.stride_dv,
args.stride_dbias,
args.nhead_stride_q,
args.nhead_stride_k,
args.nhead_stride_v,
args.nhead_stride_bias,
args.nhead_stride_randval,
args.nhead_stride_do,
args.nhead_stride_lsed,
args.nhead_stride_dbias,
args.batch_stride_q,
args.batch_stride_k,
args.batch_stride_v,
args.batch_stride_bias,
args.batch_stride_randval,
args.batch_stride_do,
args.batch_stride_lsed,
args.batch_stride_dk,
args.batch_stride_dv,
args.batch_stride_dbias,
args.window_size_left,
args.window_size_right,
args.mask_type,
args.p_drop,
args.s_randval,
args.drop_seed_offset);
}
}();
dim3 grids = FmhaBwdDQDKDVKernel::GridSize(args.batch, args.nhead_q, args.max_seqlen_k);
return ck_tile::make_tuple(kargs, grids);
}
template <typename FmhaBwdOGradDotOKernel>
auto fmha_bwd_dot_do_o_create_kargs_and_grids(fmha_bwd_args args)
{
auto kargs = [&] {
// create group mode kernel arguments
if constexpr(FmhaBwdOGradDotOKernel::kIsGroupMode)
{
return FmhaBwdOGradDotOKernel::MakeKargs(args.o_ptr,
args.do_ptr,
args.d_ptr,
args.p_undrop,
args.seqstart_q_ptr,
args.hdim_v,
args.stride_do,
args.stride_o,
args.nhead_stride_do,
args.nhead_stride_o,
args.nhead_stride_lsed,
args.batch_stride_lsed);
}
else
{ // create batch mode kernel arguments
return FmhaBwdOGradDotOKernel::MakeKargs(args.o_ptr,
args.do_ptr,
args.d_ptr,
args.p_undrop,
args.seqlen_q,
args.hdim_v,
args.stride_do,
args.stride_o,
args.nhead_stride_do,
args.nhead_stride_o,
args.nhead_stride_lsed,
args.batch_stride_do,
args.batch_stride_o,
args.batch_stride_lsed);
}
}();
dim3 grids = FmhaBwdOGradDotOKernel::GridSize(args.batch, args.nhead_q, args.max_seqlen_q);
return ck_tile::make_tuple(kargs, grids);
}
// this is used to pattern-match internl kernel implementation, not to instantiate kernel
template <ck_tile::index_t HDim_,
typename DataType_,
bool kIsGroupMode_,
ck_tile::BlockFmhaBwdPipelineEnum FmhaBwdPipelineEnum_,
typename FmhaMask_,
ck_tile::BlockAttentionBiasEnum BiasEnum_,
bool kHasBiasGrad_,
bool kHasDropout_,
bool kPadS_,
bool kPadSK_,
bool kPadD_,
bool kPadDv_>
struct fmha_bwd_dq_dk_dv_traits_
{
static constexpr ck_tile::index_t HDim = HDim_;
using DataType = ck_tile::remove_cvref_t<DataType_>;
static constexpr bool kIsGroupMode = kIsGroupMode_;
static constexpr auto FmhaBwdPipelineEnum = FmhaBwdPipelineEnum_;
using FmhaMask = ck_tile::remove_cvref_t<FmhaMask_>;
static constexpr auto BiasEnum = BiasEnum_;
static constexpr bool kHasBiasGrad = kHasBiasGrad_;
static constexpr bool kHasDropout = kHasDropout_;
static constexpr bool kPadS = kPadS_;
static constexpr bool kPadSK = kPadSK_;
static constexpr bool kPadD = kPadD_;
static constexpr bool kPadDv = kPadDv_;
};
template <typename Traits_>
float fmha_bwd_dq_dk_dv_(const ck_tile::stream_config&, fmha_bwd_args);
template <typename Traits_>
void fmha_bwd_dq_dk_dv_oneshot_(const ck_tile::stream_config&, fmha_bwd_args);
template <typename Traits_>
std::string fmha_bwd_dq_dk_dv_get_name_();
template <ck_tile::index_t HDim_, typename DataType_, bool kIsGroupMode_, bool kPadS_, bool kPadDv_>
struct fmha_bwd_dot_do_o_traits_
{
static constexpr ck_tile::index_t HDim = HDim_;
using DataType = ck_tile::remove_cvref_t<DataType_>;
static constexpr bool kIsGroupMode = kIsGroupMode_;
static constexpr bool kPadS = kPadS_;
static constexpr bool kPadDv = kPadDv_;
};
template <typename Traits_>
float fmha_bwd_dot_do_o_(const ck_tile::stream_config&, fmha_bwd_args);
template <typename Traits_>
void fmha_bwd_dot_do_o_oneshot_(const ck_tile::stream_config&, fmha_bwd_args);
template <typename Traits_>
std::string fmha_bwd_dot_do_o_get_name_();
// This is the public API, will be generated by script
struct fmha_bwd_traits
{
int hdim_q;
int hdim_v;
std::string data_type;
bool is_group_mode;
mask_enum mask_type;
bias_enum bias_type; // 0:no bias, 1:elementwise bias, 2:alibi. sync with BlockAttentionBiasEnum
bool has_dbias;
bool has_dropout;
// TODO: padding check is inside this api
};
float fmha_bwd(fmha_bwd_traits, fmha_bwd_args, const ck_tile::stream_config&);
example/ck_tile/01_fmha/fmha_fwd.cpp
View file @ 129e58ae
This diff is collapsed.
example/ck_tile/01_fmha/fmha_fwd.hpp
View file @ 129e58ae
...@@ -17,61 +17,65 @@ struct FmhaFwdTypeConfig; ...@@ -17,61 +17,65 @@ struct FmhaFwdTypeConfig;
template <> template <>
struct FmhaFwdTypeConfig<ck_tile::half_t> struct FmhaFwdTypeConfig<ck_tile::half_t>
{ {
using QDataType = ck_tile::half_t; using QDataType = ck_tile::half_t;
using KDataType = ck_tile::half_t; using KDataType = ck_tile::half_t;
using VDataType = ck_tile::half_t; using VDataType = ck_tile::half_t;
using BiasDataType = ck_tile::half_t; using BiasDataType = ck_tile::half_t;
using LSEDataType = float; // data type for lse(logsumexp L_j = max_j + log(l_j)) using RandValOutputDataType = uint8_t;
using SaccDataType = float; // data type for first gemm accumulation using LSEDataType = float; // data type for lse(logsumexp L_j = max_j + log(l_j))
using SMPLComputeDataType = float; // data type for reduction, softmax using SaccDataType = float; // data type for first gemm accumulation
using PDataType = ck_tile::half_t; // data type for A matrix of second gemm using SMPLComputeDataType = float; // data type for reduction, softmax
using OaccDataType = float; // data type for second gemm accumulation using PDataType = ck_tile::half_t; // data type for A matrix of second gemm
using ODataType = ck_tile::half_t; using OaccDataType = float; // data type for second gemm accumulation
using ODataType = ck_tile::half_t;
}; };
template <> template <>
struct FmhaFwdTypeConfig<ck_tile::bf16_t> struct FmhaFwdTypeConfig<ck_tile::bf16_t>
{ {
using QDataType = ck_tile::bf16_t; using QDataType = ck_tile::bf16_t;
using KDataType = ck_tile::bf16_t; using KDataType = ck_tile::bf16_t;
using VDataType = ck_tile::bf16_t; using VDataType = ck_tile::bf16_t;
using BiasDataType = ck_tile::bf16_t; using BiasDataType = ck_tile::bf16_t;
using LSEDataType = float; // data type for lse(logsumexp L_j = max_j + log(l_j)) using RandValOutputDataType = uint8_t;
using SaccDataType = float; // data type for first gemm accumulation using LSEDataType = float; // data type for lse(logsumexp L_j = max_j + log(l_j))
using SMPLComputeDataType = float; // data type for reduction, softmax using SaccDataType = float; // data type for first gemm accumulation
using PDataType = ck_tile::bf16_t; // data type for A matrix of second gemm using SMPLComputeDataType = float; // data type for reduction, softmax
using OaccDataType = float; // data type for second gemm accumulation using PDataType = ck_tile::bf16_t; // data type for A matrix of second gemm
using ODataType = ck_tile::bf16_t; using OaccDataType = float; // data type for second gemm accumulation
using ODataType = ck_tile::bf16_t;
}; };
template <> template <>
struct FmhaFwdTypeConfig<ck_tile::fp8_t> struct FmhaFwdTypeConfig<ck_tile::fp8_t>
{ {
using QDataType = ck_tile::fp8_t; using QDataType = ck_tile::fp8_t;
using KDataType = ck_tile::fp8_t; using KDataType = ck_tile::fp8_t;
using VDataType = ck_tile::fp8_t; using VDataType = ck_tile::fp8_t;
using BiasDataType = float; using BiasDataType = float;
using LSEDataType = float; // data type for lse(logsumexp L_j = max_j + log(l_j)) using RandValOutputDataType = uint8_t;
using SaccDataType = float; // data type for first gemm accumulation using LSEDataType = float; // data type for lse(logsumexp L_j = max_j + log(l_j))
using SMPLComputeDataType = float; // data type for reduction, softmax using SaccDataType = float; // data type for first gemm accumulation
using PDataType = ck_tile::fp8_t; // data type for A matrix of second gemm using SMPLComputeDataType = float; // data type for reduction, softmax
using OaccDataType = float; // data type for second gemm accumulation using PDataType = ck_tile::fp8_t; // data type for A matrix of second gemm
using ODataType = ck_tile::fp8_t; using OaccDataType = float; // data type for second gemm accumulation
using ODataType = ck_tile::fp8_t;
}; };
template <> template <>
struct FmhaFwdTypeConfig<ck_tile::bf8_t> struct FmhaFwdTypeConfig<ck_tile::bf8_t>
{ {
using QDataType = ck_tile::bf8_t; using QDataType = ck_tile::bf8_t;
using KDataType = ck_tile::bf8_t; using KDataType = ck_tile::bf8_t;
using VDataType = ck_tile::bf8_t; using VDataType = ck_tile::bf8_t;
using BiasDataType = ck_tile::bf8_t; using BiasDataType = ck_tile::bf8_t;
using LSEDataType = float; // data type for lse(logsumexp L_j = max_j + log(l_j)) using RandValOutputDataType = uint8_t;
using SaccDataType = float; // data type for first gemm accumulation using LSEDataType = float; // data type for lse(logsumexp L_j = max_j + log(l_j))
using SMPLComputeDataType = float; // data type for reduction, softmax using SaccDataType = float; // data type for first gemm accumulation
using PDataType = ck_tile::bf8_t; // data type for A matrix of second gemm using SMPLComputeDataType = float; // data type for reduction, softmax
using OaccDataType = float; // data type for second gemm accumulation using PDataType = ck_tile::bf8_t; // data type for A matrix of second gemm
using ODataType = ck_tile::bf8_t; using OaccDataType = float; // data type for second gemm accumulation
using ODataType = ck_tile::bf8_t;
}; };
struct FmhaMasks struct FmhaMasks
...@@ -88,6 +92,7 @@ struct fmha_fwd_args ...@@ -88,6 +92,7 @@ struct fmha_fwd_args
const void* k_ptr; const void* k_ptr;
const void* v_ptr; const void* v_ptr;
const void* bias_ptr; // bias or alibi_slope pointer const void* bias_ptr; // bias or alibi_slope pointer
void* rand_val_ptr;
void* lse_ptr; void* lse_ptr;
void* o_ptr; void* o_ptr;
const void* seqstart_q_ptr; const void* seqstart_q_ptr;
...@@ -108,22 +113,28 @@ struct fmha_fwd_args ...@@ -108,22 +113,28 @@ struct fmha_fwd_args
ck_tile::index_t stride_k; ck_tile::index_t stride_k;
ck_tile::index_t stride_v; ck_tile::index_t stride_v;
ck_tile::index_t stride_bias; // if alibi, b*h need set this to h, 1*h need set this to 0 ck_tile::index_t stride_bias; // if alibi, b*h need set this to h, 1*h need set this to 0
ck_tile::index_t stride_randval;
ck_tile::index_t stride_o; ck_tile::index_t stride_o;
ck_tile::index_t nhead_stride_q; ck_tile::index_t nhead_stride_q;
ck_tile::index_t nhead_stride_k; ck_tile::index_t nhead_stride_k;
ck_tile::index_t nhead_stride_v; ck_tile::index_t nhead_stride_v;
ck_tile::index_t nhead_stride_bias; ck_tile::index_t nhead_stride_bias;
ck_tile::index_t nhead_stride_randval;
ck_tile::index_t nhead_stride_lse; ck_tile::index_t nhead_stride_lse;
ck_tile::index_t nhead_stride_o; ck_tile::index_t nhead_stride_o;
ck_tile::index_t batch_stride_q; ck_tile::index_t batch_stride_q;
ck_tile::index_t batch_stride_k; ck_tile::index_t batch_stride_k;
ck_tile::index_t batch_stride_v; ck_tile::index_t batch_stride_v;
ck_tile::index_t batch_stride_bias; ck_tile::index_t batch_stride_bias;
ck_tile::index_t batch_stride_randval;
ck_tile::index_t batch_stride_lse; ck_tile::index_t batch_stride_lse;
ck_tile::index_t batch_stride_o; ck_tile::index_t batch_stride_o;
ck_tile::index_t window_size_left; ck_tile::index_t window_size_left;
ck_tile::index_t window_size_right; ck_tile::index_t window_size_right;
ck_tile::index_t mask_type; ck_tile::index_t mask_type;
float p_drop;
bool s_randval;
std::tuple<uint64_t, uint64_t> drop_seed_offset;
}; };
template <typename FmhaKernel> template <typename FmhaKernel>
...@@ -138,6 +149,7 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args) ...@@ -138,6 +149,7 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args)
args.k_ptr, args.k_ptr,
args.v_ptr, args.v_ptr,
args.bias_ptr, args.bias_ptr,
args.rand_val_ptr,
args.lse_ptr, args.lse_ptr,
args.o_ptr, args.o_ptr,
args.seqstart_q_ptr, args.seqstart_q_ptr,
...@@ -145,6 +157,7 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args) ...@@ -145,6 +157,7 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args)
args.seqlen_k_ptr, args.seqlen_k_ptr,
args.hdim_q, args.hdim_q,
args.hdim_v, args.hdim_v,
args.nhead_q,
args.nhead_q / args.nhead_k, args.nhead_q / args.nhead_k,
args.scale_s, args.scale_s,
args.scale_p, args.scale_p,
...@@ -153,16 +166,22 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args) ...@@ -153,16 +166,22 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args)
args.stride_k, args.stride_k,
args.stride_v, args.stride_v,
args.stride_bias, args.stride_bias,
args.stride_randval,
args.stride_o, args.stride_o,
args.nhead_stride_q, args.nhead_stride_q,
args.nhead_stride_k, args.nhead_stride_k,
args.nhead_stride_v, args.nhead_stride_v,
args.nhead_stride_bias, args.nhead_stride_bias,
args.nhead_stride_randval,
args.nhead_stride_lse, args.nhead_stride_lse,
args.nhead_stride_o, args.nhead_stride_o,
args.batch_stride_lse,
args.window_size_left, args.window_size_left,
args.window_size_right, args.window_size_right,
args.mask_type); args.mask_type,
args.p_drop,
args.s_randval,
args.drop_seed_offset);
} }
else else
{ // create batch mode kernel arguments { // create batch mode kernel arguments
...@@ -170,12 +189,14 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args) ...@@ -170,12 +189,14 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args)
args.k_ptr, args.k_ptr,
args.v_ptr, args.v_ptr,
args.bias_ptr, args.bias_ptr,
args.rand_val_ptr,
args.lse_ptr, args.lse_ptr,
args.o_ptr, args.o_ptr,
args.seqlen_q, args.seqlen_q,
args.seqlen_k, args.seqlen_k,
args.hdim_q, args.hdim_q,
args.hdim_v, args.hdim_v,
args.nhead_q,
args.nhead_q / args.nhead_k, args.nhead_q / args.nhead_k,
args.scale_s, args.scale_s,
args.scale_p, args.scale_p,
...@@ -184,22 +205,28 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args) ...@@ -184,22 +205,28 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args)
args.stride_k, args.stride_k,
args.stride_v, args.stride_v,
args.stride_bias, args.stride_bias,
args.stride_randval,
args.stride_o, args.stride_o,
args.nhead_stride_q, args.nhead_stride_q,
args.nhead_stride_k, args.nhead_stride_k,
args.nhead_stride_v, args.nhead_stride_v,
args.nhead_stride_bias, args.nhead_stride_bias,
args.nhead_stride_randval,
args.nhead_stride_lse, args.nhead_stride_lse,
args.nhead_stride_o, args.nhead_stride_o,
args.batch_stride_q, args.batch_stride_q,
args.batch_stride_k, args.batch_stride_k,
args.batch_stride_v, args.batch_stride_v,
args.batch_stride_bias, args.batch_stride_bias,
args.batch_stride_randval,
args.batch_stride_lse, args.batch_stride_lse,
args.batch_stride_o, args.batch_stride_o,
args.window_size_left, args.window_size_left,
args.window_size_right, args.window_size_right,
args.mask_type); args.mask_type,
args.p_drop,
args.s_randval,
args.drop_seed_offset);
} }
}(); }();
...@@ -222,6 +249,7 @@ template <ck_tile::index_t HDim_, ...@@ -222,6 +249,7 @@ template <ck_tile::index_t HDim_,
typename FmhaMask_, typename FmhaMask_,
ck_tile::BlockAttentionBiasEnum BiasEnum_, ck_tile::BlockAttentionBiasEnum BiasEnum_,
bool kStoreLse_, bool kStoreLse_,
bool kHasDropout_,
bool kDoFp8StaticQuant_, bool kDoFp8StaticQuant_,
bool kPadS_, bool kPadS_,
bool kPadSK_, bool kPadSK_,
...@@ -243,6 +271,7 @@ struct fmha_fwd_traits_ ...@@ -243,6 +271,7 @@ struct fmha_fwd_traits_
using FmhaMask = ck_tile::remove_cvref_t<FmhaMask_>; using FmhaMask = ck_tile::remove_cvref_t<FmhaMask_>;
static constexpr auto BiasEnum = BiasEnum_; static constexpr auto BiasEnum = BiasEnum_;
static constexpr bool kStoreLse = kStoreLse_; static constexpr bool kStoreLse = kStoreLse_;
static constexpr bool kHasDropout = kHasDropout_;
static constexpr bool kDoFp8StaticQuant = kDoFp8StaticQuant_; static constexpr bool kDoFp8StaticQuant = kDoFp8StaticQuant_;
static constexpr bool kPadS = kPadS_; static constexpr bool kPadS = kPadS_;
static constexpr bool kPadSK = kPadSK_; static constexpr bool kPadSK = kPadSK_;
...@@ -264,6 +293,7 @@ struct fmha_fwd_traits ...@@ -264,6 +293,7 @@ struct fmha_fwd_traits
mask_enum mask_type; mask_enum mask_type;
bias_enum bias_type; // 0:no bias, 1:elementwise bias, 2:alibi. sync with BlockAttentionBiasEnum bias_enum bias_type; // 0:no bias, 1:elementwise bias, 2:alibi. sync with BlockAttentionBiasEnum
bool has_lse; bool has_lse;
bool has_dropout;
bool do_fp8_static_quant; bool do_fp8_static_quant;
// TODO: padding check is inside this api // TODO: padding check is inside this api
}; };
......
example/ck_tile/01_fmha/generate.py
View file @ 129e58ae
This diff is collapsed.
example/ck_tile/01_fmha/script/benchmark_bwd.sh 0 → 100644
View file @ 129e58ae
#!/bin/sh
# TODO: run this script from CK root
BUILD=build
EXE=$BUILD/bin/tile_example_fmha_bwd
VALID=0
for prec in "fp16" "bf16" ; do
for perm in 0 1 ; do
for hdim in 32 64 128 ; do
nhead=$((2048 / $hdim)) # follow fav2 setup
$EXE -prec=$prec -b=32 -h=$nhead -d=$hdim -s=512 -iperm=$perm -operm=$perm -kname=1 -v=$VALID ; sleep 3
$EXE -prec=$prec -b=16 -h=$nhead -d=$hdim -s=1024 -iperm=$perm -operm=$perm -kname=1 -v=$VALID ; sleep 3
$EXE -prec=$prec -b=8 -h=$nhead -d=$hdim -s=2048 -iperm=$perm -operm=$perm -kname=1 -v=$VALID ; sleep 3
$EXE -prec=$prec -b=4 -h=$nhead -d=$hdim -s=4096 -iperm=$perm -operm=$perm -kname=1 -v=$VALID ; sleep 3
$EXE -prec=$prec -b=2 -h=$nhead -d=$hdim -s=8192 -iperm=$perm -operm=$perm -kname=1 -v=$VALID ; sleep 3
$EXE -prec=$prec -b=1 -h=$nhead -d=$hdim -s=16384 -iperm=$perm -operm=$perm -kname=1 -v=$VALID ; sleep 3
done
done
done
example/ck_tile/01_fmha/script/smoke_test_bwd.sh 0 → 100644
View file @ 129e58ae
#!/bin/sh
# TODO: run this script from CK root
BUILD=build
EXE=$BUILD/bin/tile_example_fmha_bwd
KNAME=1
export CK_WARMUP=0
export CK_REPEAT=1
COMMON_ARGS='-v=1'
for prec in "fp16" "bf16" ; do
for perm in 0 1 ; do
for hdim in 32 64 128 ; do
for mode in 0 1 ; do
for bias in "n" "e" "a"; do
for dbias in 0 1 ; do
for p_drop in 0.0 0.2; do
$EXE -prec=$prec -b=1 -h=4 -h_k=2 -d=$hdim -s=259 -bias=$bias -dbias=$dbias -p_drop=$p_drop -iperm=$perm -operm=$perm -v=1 -mode=$mode -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -b=2 -h=2 -d=$hdim -s=516 -s_k=253 -bias=$bias -dbias=$dbias -p_drop=$p_drop -iperm=$perm -operm=$perm -v=1 -mode=$mode -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -b=1 -h=4 -h_k=1 -d=$hdim -s=500 -s_k=251 -bias=$bias -dbias=$dbias -p_drop=$p_drop -iperm=$perm -operm=$perm -mask=1 -v=1 -mode=$mode -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -b=1 -h=2 -d=$hdim -s=900 -s_k=258 -bias=$bias -dbias=$dbias -p_drop=$p_drop -iperm=$perm -operm=$perm -mask=2 -v=1 -mode=$mode -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -b=2 -h=1 -d=$hdim -s=987 -s_k=219 -bias=$bias -dbias=$dbias -p_drop=$p_drop -iperm=$perm -operm=$perm -mask=t:128,30 -v=1 -mode=$mode -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -b=2 -h=3 -h_k=1 -d=$hdim -s=244 -s_k=499 -bias=$bias -dbias=$dbias -p_drop=$p_drop -iperm=$perm -operm=$perm -mask=b:4,35 -v=1 -mode=$mode -kname=$KNAME $COMMON_ARGS
done
done
done
done
done
done
done
example/ck_tile/01_fmha/script/smoke_test.sh → example/ck_tile/01_fmha/script/smoke_test_fwd.sh
View file @ 129e58ae
...@@ -17,17 +17,19 @@ for perm in 0 1 ; do ...@@ -17,17 +17,19 @@ for perm in 0 1 ; do
for vlayout in "r" "c" ; do for vlayout in "r" "c" ; do
for hdim in 32 64 128 256 ; do for hdim in 32 64 128 256 ; do
for lse in 0 1 ; do for lse in 0 1 ; do
for bias in "n" "e" "a"; do for bias in "n" "e" "a" ; do
for p_drop in 0.0 0.2; do
# $EXE -prec=$prec -mode=$mode -b=1 -h=1 -d=$hdim -s=1024 -bias=$bias -lse=$lse -iperm=$perm -operm=$perm -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS # $EXE -prec=$prec -mode=$mode -b=1 -h=1 -d=$hdim -s=1024 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=2 -h=2 -h_k=1 -d=16, -d_v=$hdim -s=55 -s_k=256 -bias=$bias -lse=$lse -iperm=$perm -operm=$perm -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS $EXE -prec=$prec -mode=$mode -b=2 -h=2 -h_k=1 -d=16, -d_v=$hdim -s=55 -s_k=256 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=1 -h=3 -d=$hdim -s=100 -s_k=51 -bias=$bias -lse=$lse -iperm=$perm -operm=$perm -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS $EXE -prec=$prec -mode=$mode -b=1 -h=3 -d=$hdim -s=100 -s_k=51 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=2 -h=1 -d=16 -d_v=$hdim -s=99 -s_k=256 -bias=$bias -lse=$lse -iperm=$perm -operm=$perm -mask=1 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS $EXE -prec=$prec -mode=$mode -b=2 -h=1 -d=16 -d_v=$hdim -s=99 -s_k=256 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=1 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=1 -h=2 -h_k=1 -d=$hdim -s=1024 -s_k=256 -bias=$bias -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS $EXE -prec=$prec -mode=$mode -b=1 -h=2 -h_k=1 -d=$hdim -s=1024 -s_k=256 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=2 -h=1 -d=$hdim -d_v=24 -s=3 -s_k=99 -bias=$bias -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS $EXE -prec=$prec -mode=$mode -b=2 -h=1 -d=$hdim -d_v=24 -s=3 -s_k=99 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=3 -h=2 -h_k=1 -d=$hdim -s=200 -s_k=520 -bias=$bias -lse=$lse -iperm=$perm -operm=$perm -mask=t:128,30 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS $EXE -prec=$prec -mode=$mode -b=3 -h=2 -h_k=1 -d=$hdim -s=200 -s_k=520 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=t:128,30 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=2 -h=1 -d=$hdim -s=99 -s_k=32 -bias=$bias -lse=$lse -iperm=$perm -operm=$perm -mask=b:4,35 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS $EXE -prec=$prec -mode=$mode -b=2 -h=1 -d=$hdim -s=99 -s_k=32 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=b:4,35 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=1 -h=2 -h_k=1 -d=$hdim -s=33 -s_k=0 -bias=$bias -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS $EXE -prec=$prec -mode=$mode -b=1 -h=2 -h_k=1 -d=$hdim -s=33 -s_k=0 -bias=$bias -p_drop=$p_drop -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -mode=$mode -b=1 -h=2 -h_k=1 -d=$hdim -s=1 -s_k=10 -s_kpad=32 -bias=$bias -lse=$lse -iperm=$perm -operm=$perm -mask=2 -vlayout=$vlayout -kname=$KNAME $COMMON_ARGS
done done
done done
...@@ -36,6 +38,7 @@ done ...@@ -36,6 +38,7 @@ done
done done
done done
done done
done
for perm in 0 1 ; do for perm in 0 1 ; do
for bias in "n" "e" "a" ; do for bias in "n" "e" "a" ; do
......
example/ck_tile/01_fmha/utils.hpp
View file @ 129e58ae
...@@ -4,12 +4,14 @@ ...@@ -4,12 +4,14 @@
#pragma once #pragma once
#include <cstdint> #include <cstdint>
#include <cstdlib>
#include <optional> #include <optional>
#include <ostream> #include <ostream>
#include <tuple> #include <tuple>
#include <utility> #include <utility>
#include <vector> #include <vector>
#include <functional> #include <functional>
#include <string>
#include "ck_tile/core/container/span.hpp" #include "ck_tile/core/container/span.hpp"
...@@ -37,12 +39,14 @@ std::vector<int32_t> to_seqstarts(ck_tile::span<const int32_t> seqlens) ...@@ -37,12 +39,14 @@ std::vector<int32_t> to_seqstarts(ck_tile::span<const int32_t> seqlens)
std::vector<int32_t> generate_seqlens(mode_enum mode, std::vector<int32_t> generate_seqlens(mode_enum mode,
unsigned count, unsigned count,
int32_t seqlens_sum, int32_t seqlen_avg,
int32_t seqlen_max = -1, // if not negative, clamp max
std::optional<unsigned> seed = std::nullopt) std::optional<unsigned> seed = std::nullopt)
{ {
assert(0 < count); assert(0 < count);
std::vector<int32_t> seqlens(count, seqlens_sum); std::vector<int32_t> seqlens(
count, seqlen_max > 0 ? (seqlen_avg < seqlen_max ? seqlen_avg : seqlen_max) : seqlen_avg);
if(mode == mode_enum::group && 1 < count) if(mode == mode_enum::group && 1 < count)
{ {
...@@ -55,7 +59,7 @@ std::vector<int32_t> generate_seqlens(mode_enum mode, ...@@ -55,7 +59,7 @@ std::vector<int32_t> generate_seqlens(mode_enum mode,
std::uniform_int_distribution<size_type> step_dist(1, count - 1); std::uniform_int_distribution<size_type> step_dist(1, count - 1);
auto next_step = std::bind(step_dist, std::ref(random_engine)); auto next_step = std::bind(step_dist, std::ref(random_engine));
for(unsigned repeat = seqlens_sum * (count / 2); 0 < repeat; --repeat) for(unsigned repeat = seqlen_avg * (count / 2); 0 < repeat; --repeat)
{ {
const size_type to_decrease = next_idx(); const size_type to_decrease = next_idx();
// make sure each elements of seqlens is always greater than 0 // make sure each elements of seqlens is always greater than 0
...@@ -66,6 +70,11 @@ std::vector<int32_t> generate_seqlens(mode_enum mode, ...@@ -66,6 +70,11 @@ std::vector<int32_t> generate_seqlens(mode_enum mode,
const size_type to_increase = (to_decrease + next_step()) % count; const size_type to_increase = (to_decrease + next_step()) % count;
if(seqlen_max > 0 && seqlens[to_increase] >= seqlen_max)
{
continue;
}
--seqlens[to_decrease]; --seqlens[to_decrease];
++seqlens[to_increase]; ++seqlens[to_increase];
} }
...@@ -76,10 +85,91 @@ std::vector<int32_t> generate_seqlens(mode_enum mode, ...@@ -76,10 +85,91 @@ std::vector<int32_t> generate_seqlens(mode_enum mode,
std::vector<int32_t> generate_seqstarts(mode_enum mode, std::vector<int32_t> generate_seqstarts(mode_enum mode,
unsigned count, unsigned count,
int32_t seqlens_sum, int32_t seqlen_avg,
int32_t seqlen_max = -1,
std::optional<unsigned> seed = std::nullopt) std::optional<unsigned> seed = std::nullopt)
{ {
return to_seqstarts(generate_seqlens(mode, count, seqlens_sum, seed)); return to_seqstarts(generate_seqlens(mode, count, seqlen_avg, seqlen_max, seed));
}
/*
* decode the seqlen string from cmdline
* example (assume batch=3)
* q_val=1,2,3 k_val=4,5,6 -> OK
* q_val=1,2,3 -> OK, k same as q
* q_val=1,2 -> OK, q will rand remaining 1 element, k same as q
* q_val=1,2 k_val=4,5 -> OK, q/k will rand remaining 1 element
* q_val=1,2,3,4 -> OK, but ignore exceed one
*
* q_val=1,2 k_val=4,5,6 -> not OK, k must have same splits with q
* q_val=1,2 k_val=4 -> not OK, k must have same splits with q
*/
std::tuple<std::vector<ck_tile::index_t>,
std::vector<ck_tile::index_t>,
std::vector<ck_tile::index_t>>
decode_seqlen(mode_enum mode,
ck_tile::index_t batch,
std::string q_val,
std::string k_val,
std::string k_pad_val,
std::optional<unsigned> seed = std::nullopt)
{
#define _S2I_(str_) static_cast<ck_tile::index_t>(std::atoi((str_).c_str()))
if(mode == mode_enum::batch)
{
ck_tile::index_t q = _S2I_(q_val);
ck_tile::index_t k = _S2I_(k_val);
auto s_q = std::vector<ck_tile::index_t>(batch, q);
auto s_k = std::vector<ck_tile::index_t>(batch, k < 0 ? q : k);
auto s_kpad = std::vector<ck_tile::index_t>(batch, -1); // TODO: batch not support k_padding
return std::make_tuple(s_q, s_k, s_kpad);
}
else
{
ck_tile::index_t idx = 0;
std::string::size_type pos_q = 0;
std::string::size_type pos_k = 0;
std::string::size_type pos_kp = 0;
std::vector<ck_tile::index_t> s_q;
std::vector<ck_tile::index_t> s_k;
std::vector<ck_tile::index_t> s_kpad;
while(true)
{
auto found_q = q_val.find(',', pos_q);
auto found_k = k_val.find(',', pos_k);
auto found_kp = k_pad_val.find(',', pos_kp);
ck_tile::index_t q = _S2I_(
q_val.substr(pos_q, found_q == std::string::npos ? found_q : found_q - pos_q));
ck_tile::index_t k = _S2I_(
k_val.substr(pos_k, found_k == std::string::npos ? found_k : found_k - pos_k));
ck_tile::index_t kp = _S2I_(k_pad_val.substr(
pos_kp, found_kp == std::string::npos ? found_kp : found_kp - pos_kp));
s_q.push_back(q);
s_k.push_back(k < 0 ? q : k);
s_kpad.push_back(kp);
idx++;
if(found_q == std::string::npos || idx >= batch)
{
break;
}
pos_q = found_q + 1;
pos_k = found_k == std::string::npos ? pos_k : found_k + 1;
pos_kp = found_kp == std::string::npos ? pos_kp : found_kp + 1;
}
if(idx < batch)
{
auto rem_q = generate_seqlens(mode, batch - idx, s_q.back(), s_kpad.back(), seed);
auto rem_k = generate_seqlens(mode, batch - idx, s_k.back(), s_kpad.back(), seed);
s_q.insert(s_q.end(), rem_q.begin(), rem_q.end());
s_k.insert(s_k.end(), rem_k.begin(), rem_k.end());
s_kpad.insert(s_kpad.end(), batch - idx, s_kpad.back());
}
return std::make_tuple(s_q, s_k, s_kpad);
}
#undef _S2I_
} }
int env_get_int(const char* var_name, int default_int) int env_get_int(const char* var_name, int default_int)
...@@ -87,6 +177,6 @@ int env_get_int(const char* var_name, int default_int) ...@@ -87,6 +177,6 @@ int env_get_int(const char* var_name, int default_int)
char* v = getenv(var_name); char* v = getenv(var_name);
int r = default_int; int r = default_int;
if(v) if(v)
r = atoi(v); r = std::atoi(v);
return r; return r;
} }
include/ck/host_utility/flush_cache.hpp
View file @ 129e58ae
...@@ -104,20 +104,25 @@ inline void flush_icache() ...@@ -104,20 +104,25 @@ inline void flush_icache()
hip_check_error(hipGetLastError()); hip_check_error(hipGetLastError());
} }
// if TimePrePress == false, return time does not include preprocess's time // if TimePrePress == false, return time does not include preprocess's time
template <bool TimePreprocess, typename Args, typename F, typename PreProcessFunc> template <bool TimePreprocess,
typename GemmArgs,
typename... Args,
typename F,
typename PreProcessFunc>
float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config, float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config,
PreProcessFunc preprocess, PreProcessFunc preprocess,
F kernel, F kernel,
dim3 grid_dim, dim3 grid_dim,
dim3 block_dim, dim3 block_dim,
std::size_t lds_byte, std::size_t lds_byte,
Args& args) GemmArgs& gemm_args,
Args... args)
{ {
#if CK_TIME_KERNEL #if CK_TIME_KERNEL
#define MEDIAN 1 #define MEDIAN 1
if(stream_config.time_kernel_) if(stream_config.time_kernel_)
{ {
if(ck::EnvIsEnabled(ENV(CK_LOGGING))) if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
{ {
printf("%s: grid_dim {%u, %u, %u}, block_dim {%u, %u, %u} \n", printf("%s: grid_dim {%u, %u, %u}, block_dim {%u, %u, %u} \n",
__func__, __func__,
...@@ -133,7 +138,7 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config, ...@@ -133,7 +138,7 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config,
// warm up // warm up
for(int i = 0; i < stream_config.cold_niters_; ++i) for(int i = 0; i < stream_config.cold_niters_; ++i)
{ {
kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(args); kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(gemm_args, args...);
hip_check_error(hipGetLastError()); hip_check_error(hipGetLastError());
} }
...@@ -142,7 +147,7 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config, ...@@ -142,7 +147,7 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config,
{ {
return 0.0; return 0.0;
} }
if(ck::EnvIsEnabled(ENV(CK_LOGGING))) if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
{ {
printf("Start running %d times...\n", nrepeat); printf("Start running %d times...\n", nrepeat);
} }
...@@ -172,7 +177,7 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config, ...@@ -172,7 +177,7 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config,
preprocess(); preprocess();
} }
// run real kernel // run real kernel
kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(args); kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(gemm_args, args...);
hip_check_error(hipGetLastError()); hip_check_error(hipGetLastError());
// end real kernel // end real kernel
...@@ -186,13 +191,13 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config, ...@@ -186,13 +191,13 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config,
total_time += cur_time; total_time += cur_time;
#endif #endif
if(ck::EnvIsEnabled(ENV(CK_LOGGING))) if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
{ {
std::cout << "i: " << i << " cur_time: " << cur_time << std::endl; std::cout << "i: " << i << " cur_time: " << cur_time << std::endl;
printf("args.p_a_grid: %p, args.p_b_grid:%p\n", printf("gemm_args.p_a_grid: %p, gemm_args.p_b_grid:%p\n",
static_cast<const void*>(args.p_a_grid), static_cast<const void*>(gemm_args.p_a_grid),
static_cast<const void*>(args.p_b_grid)); static_cast<const void*>(gemm_args.p_b_grid));
} }
} }
...@@ -216,13 +221,13 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config, ...@@ -216,13 +221,13 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config,
else else
{ {
preprocess(); preprocess();
kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(args); kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(gemm_args, args...);
hip_check_error(hipGetLastError()); hip_check_error(hipGetLastError());
return 0; return 0;
} }
#else #else
kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(args); kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(gemm_args, args...);
hip_check_error(hipGetLastError()); hip_check_error(hipGetLastError());
return 0; return 0;
......
include/ck/host_utility/kernel_launch.hpp
View file @ 129e58ae
...@@ -20,7 +20,7 @@ float launch_and_time_kernel(const StreamConfig& stream_config, ...@@ -20,7 +20,7 @@ float launch_and_time_kernel(const StreamConfig& stream_config,
#if CK_TIME_KERNEL #if CK_TIME_KERNEL
if(stream_config.time_kernel_) if(stream_config.time_kernel_)
{ {
if(ck::EnvIsEnabled(ENV(CK_LOGGING))) if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
{ {
printf("%s: grid_dim {%u, %u, %u}, block_dim {%u, %u, %u} \n", printf("%s: grid_dim {%u, %u, %u}, block_dim {%u, %u, %u} \n",
__func__, __func__,
...@@ -41,7 +41,7 @@ float launch_and_time_kernel(const StreamConfig& stream_config, ...@@ -41,7 +41,7 @@ float launch_and_time_kernel(const StreamConfig& stream_config,
} }
const int nrepeat = stream_config.nrepeat_; const int nrepeat = stream_config.nrepeat_;
if(ck::EnvIsEnabled(ENV(CK_LOGGING))) if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
{ {
printf("Start running %d times...\n", nrepeat); printf("Start running %d times...\n", nrepeat);
} }
...@@ -95,7 +95,7 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config, ...@@ -95,7 +95,7 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config,
#if CK_TIME_KERNEL #if CK_TIME_KERNEL
if(stream_config.time_kernel_) if(stream_config.time_kernel_)
{ {
if(ck::EnvIsEnabled(ENV(CK_LOGGING))) if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
{ {
printf("%s: grid_dim {%u, %u, %u}, block_dim {%u, %u, %u} \n", printf("%s: grid_dim {%u, %u, %u}, block_dim {%u, %u, %u} \n",
__func__, __func__,
...@@ -117,7 +117,7 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config, ...@@ -117,7 +117,7 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config,
} }
const int nrepeat = stream_config.nrepeat_; const int nrepeat = stream_config.nrepeat_;
if(ck::EnvIsEnabled(ENV(CK_LOGGING))) if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
{ {
printf("Start running %d times...\n", nrepeat); printf("Start running %d times...\n", nrepeat);
} }
......
include/ck/tensor_description/multi_index_transform.hpp
View file @ 129e58ae
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once #pragma once
...@@ -1952,7 +1952,7 @@ struct Modulo ...@@ -1952,7 +1952,7 @@ struct Modulo
} }
}; };
template <typename LowLengths> template <typename LowLengths, bool ApplyModulo>
struct Xor struct Xor
{ {
using LowerIndex = MultiIndex<2>; using LowerIndex = MultiIndex<2>;
...@@ -1981,8 +1981,15 @@ struct Xor ...@@ -1981,8 +1981,15 @@ struct Xor
idx_low(Number<0>{}) = idx_up[Number<0>{}]; idx_low(Number<0>{}) = idx_up[Number<0>{}];
idx_low(Number<1>{}) = if constexpr(ApplyModulo)
idx_up[Number<1>{}] ^ (idx_up[Number<0>{}] % up_lengths_[Number<1>{}]); {
idx_low(Number<1>{}) =
idx_up[Number<1>{}] ^ (idx_up[Number<0>{}] % up_lengths_[Number<1>{}]);
}
else
{
idx_low(Number<1>{}) = idx_up[Number<1>{}] ^ idx_up[Number<0>{}];
}
} }
template <typename LowIdxDiff, template <typename LowIdxDiff,
......
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