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Commit 1f9546e0 authored by root's avatar root
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Merge branch 'develop' into gemm_bf16_sk_muozturk

parents 78394194 86990558
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example/62_convnd_activ/dynamic_unary/convnd_fwd_xdl_dynamic_softrelu_fp16.cpp 0 → 100644
View file @ 1f9546e0
// SPDX-License-Identifier: MIT
// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "convnd_fwd_activ_dynamic_unary_common.hpp"
#include "../run_convnd_activ_dynamic_example.inc"
int main(int argc, char* argv[])
{
ck::tensor_operation::element_wise::SoftRelu out_element_op;
return !run_convnd_example(argc, argv, out_element_op);
}
example/62_convnd_activ/dynamic_unary/convnd_fwd_xdl_dynamic_swish_fp16.cpp 0 → 100644
View file @ 1f9546e0
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "convnd_fwd_activ_dynamic_unary_common.hpp"
#include "../run_convnd_activ_dynamic_example.inc"
int main(int argc, char* argv[])
{
ck::tensor_operation::element_wise::Swish out_element_op(1.0f);
return !run_convnd_example(argc, argv, out_element_op);
}
example/62_convnd_activ/dynamic_unary/convnd_fwd_xdl_dynamic_tanh_fp16.cpp 0 → 100644
View file @ 1f9546e0
// SPDX-License-Identifier: MIT
// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "convnd_fwd_activ_dynamic_unary_common.hpp"
#include "../run_convnd_activ_dynamic_example.inc"
int main(int argc, char* argv[])
{
ck::tensor_operation::element_wise::TanH out_element_op;
return !run_convnd_example(argc, argv, out_element_op);
}
example/62_convnd_activ/run_convnd_activ_dynamic_example.inc 0 → 100644
View file @ 1f9546e0
// SPDX-License-Identifier: MIT
// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
void print_helper_msg()
{
std::cout << "arg1: verification (0=no, 1=yes)\n"
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
<< "arg3: time kernel (0=no, 1=yes)\n"
<< ck::utils::conv::get_conv_param_parser_helper_msg() << std::endl;
}
template <typename OutElementOp>
bool run_convnd_example(int argc, char* argv[], const OutElementOp& out_element_op)
{
print_helper_msg();
bool do_verification = true;
// Use floats for SoftRelu by default to avoid overflow after e^x.
int init_method =
std::is_same_v<OutElementOp, ck::tensor_operation::element_wise::SoftRelu> ? 2 : 1;
bool time_kernel = false;
// Following shapes are selected to avoid overflow. Expect inf in case of
// size increase for some elementwise ops.
ck::utils::conv::ConvParam conv_param{
3, 2, 16, 128, 8, {3, 3, 3}, {17, 17, 17}, {2, 2, 2}, {1, 1, 1}, {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);
}
const auto in_element_op = InElementOp{};
const auto wei_element_op = WeiElementOp{};
const auto run = [&]() {
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<NDimSpatial,
InDataType,
WeiDataType,
OutDataType,
InElementOp,
WeiElementOp,
OutElementOp,
DeviceGroupedConvNDActivInstance>(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,
out_element_op);
};
if(conv_param.num_dim_spatial_ == 3)
{
return run();
}
return false;
}
example/65_gemm_multiply_multiply/CMakeLists.txt
View file @ 1f9546e0
add_example_executable(example_gemm_multiply_multiply_xdl_fp8 gemm_multiply_multiply_xdl_fp8.cpp)
add_example_executable(example_gemm_multiply_multiply_xdl_fp8_ab_scale gemm_multiply_multiply_xdl_fp8_ab_scale.cpp)
add_example_executable(example_gemm_add_add_xdl_fp16 gemm_add_add_xdl_fp16.cpp)
add_example_executable(example_gemm_multiply_multiply_xdl_int8 gemm_multiply_multiply_xdl_int8.cpp)
\ No newline at end of file
example/65_gemm_multiply_multiply/gemm_multiply_multiply_xdl_fp8_ab_scale.cpp
View file @ 1f9546e0
......@@ -205,7 +205,6 @@ int main(int argc, char* argv[])
a1_device_buf.ToDevice(a1_m_k.mData.data());
b0_device_buf.ToDevice(b0_k_n.mData.data());
b1_device_buf.ToDevice(b1_k_n.mData.data());
e_device_buf.ToDevice(e_m_n_device_result.mData.data());
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
......@@ -253,8 +252,6 @@ int main(int argc, char* argv[])
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<AccDataType> c_m_n({M, N});
......
example/65_gemm_multiply_multiply/gemm_multiply_multiply_xdl_int8.cpp 0 → 100644
View file @ 1f9546e0
// 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/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 I8 = int8_t;
using I32 = int;
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 = I8;
using B0DataType = I8;
using AccDataType = I32;
using CShuffleDataType = I32;
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);
}
template <>
__host__ __device__ constexpr void operator()<ck::half_t, int, float, float>(
ck::half_t& e, const int& c, const float& d0, const float& d1) const
{
const float x0_f =
ck::type_convert<float>(c) * ck::type_convert<float>(d0) * ck::type_convert<float>(d1);
e = ck::type_convert<ck::half_t>(x0_f);
}
template <>
__host__ __device__ constexpr void operator()<ck::bhalf_t, int, float, float>(
ck::bhalf_t& e, const int& c, const float& d0, const float& d1) const
{
const float x0_f =
ck::type_convert<float>(c) * ck::type_convert<float>(d0) * ck::type_convert<float>(d1);
e = ck::type_convert<ck::bhalf_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::MNPadding;
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, I8>;
///###### 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, I8>;
// 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;
ck::index_t KBatch = 1;
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 == 12)
{
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]);
KBatch = std::stoi(argv[11]);
}
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, KBatch\n");
exit(0);
}
do_verification = false;
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,
KBatch,
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;
if(do_verification)
{
invoker.Run(argument, StreamConfig{nullptr, false});
e_device_buf.FromDevice(e_m_n_device_result.mData.data());
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/66_complex_contraction_bilinear/run_complex_contraction_bilinear_example.inc 100755 → 100644
View file @ 1f9546e0
......@@ -127,44 +127,47 @@ int run_complex_contraction_bilinear_example(int argc, char* argv[])
switch(init_method)
{
case 0: break;
case 1:
case 0: break;
case 1:
a_ms_ks_re.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
b_ns_ks_re.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
d_ms_ns_re.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
a_ms_ks_re.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
b_ns_ks_re.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
d_ms_ns_re.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
a_ms_ks_img.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
b_ns_ks_img.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
d_ms_ns_img.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
break;
a_ms_ks_img.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
b_ns_ks_img.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
d_ms_ns_img.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
break;
default:
a_ms_ks_re.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
b_ns_ks_re.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
d_ms_ns_re.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
default:
a_ms_ks_re.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
b_ns_ks_re.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
d_ms_ns_re.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
a_ms_ks_img.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
b_ns_ks_img.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
d_ms_ns_img.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
a_ms_ks_img.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
b_ns_ks_img.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
d_ms_ns_img.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
break;
break;
}
DeviceMem a_device_buf_re(sizeof(ADataType) * a_ms_ks_re.mDesc.GetElementSpaceSize());
DeviceMem b_device_buf_re(sizeof(BDataType) * b_ns_ks_re.mDesc.GetElementSpaceSize());
DeviceMem d_device_buf_re(sizeof(DDataType) * d_ms_ns_re.mDesc.GetElementSpaceSize());
DeviceMem e_device_buf_re(sizeof(EDataType) * e_ms_ns_device_result_re.mDesc.GetElementSpaceSize());
DeviceMem e_device_buf_re(sizeof(EDataType) *
e_ms_ns_device_result_re.mDesc.GetElementSpaceSize());
DeviceMem a_device_buf_img(sizeof(ADataType) * a_ms_ks_img.mDesc.GetElementSpaceSize());
DeviceMem b_device_buf_img(sizeof(BDataType) * b_ns_ks_img.mDesc.GetElementSpaceSize());
DeviceMem d_device_buf_img(sizeof(DDataType) * d_ms_ns_img.mDesc.GetElementSpaceSize());
DeviceMem e_device_buf_img(sizeof(EDataType) * e_ms_ns_device_result_img.mDesc.GetElementSpaceSize());
DeviceMem e_device_buf_img(sizeof(EDataType) *
e_ms_ns_device_result_img.mDesc.GetElementSpaceSize());
// Intermediate Value For E Real and Img
DeviceMem e_device_buf_re1(sizeof(EDataType) * e_ms_ns_device_result_re.mDesc.GetElementSpaceSize());
DeviceMem e_device_buf_img1(sizeof(EDataType) * e_ms_ns_device_result_img.mDesc.GetElementSpaceSize());
DeviceMem e_device_buf_re1(sizeof(EDataType) *
e_ms_ns_device_result_re.mDesc.GetElementSpaceSize());
DeviceMem e_device_buf_img1(sizeof(EDataType) *
e_ms_ns_device_result_img.mDesc.GetElementSpaceSize());
a_device_buf_re.ToDevice(a_ms_ks_re.mData.data());
b_device_buf_re.ToDevice(b_ns_ks_re.mData.data());
......@@ -181,7 +184,7 @@ int run_complex_contraction_bilinear_example(int argc, char* argv[])
// set zero for intermediate values
e_device_buf_re1.SetZero();
e_device_buf_img1.SetZero();
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto cde_element_op = CDEElementOp{alpha, beta};
......@@ -189,23 +192,24 @@ int run_complex_contraction_bilinear_example(int argc, char* argv[])
// device operation
// For real Intermediate Value re_1
auto op = DeviceOpInstance{};
auto invoker = op.MakeInvoker();
auto argument_re1 = op.MakeArgument(a_device_buf_re.GetDeviceBuffer(),
b_device_buf_re.GetDeviceBuffer(),
std::array<const void*, 1>{d_device_buf_re.GetDeviceBuffer()},
e_device_buf_re1.GetDeviceBuffer(),
a_ms_ks_lengths,
a_ms_ks_strides,
b_ns_ks_lengths,
b_ns_ks_strides,
std::array<std::vector<ck::index_t>, 1>{d_ms_ns_lengths},
std::array<std::vector<ck::index_t>, 1>{d_ms_ns_strides},
e_ms_ns_lengths,
e_ms_ns_strides,
a_element_op,
b_element_op,
cde_element_op);
auto op = DeviceOpInstance{};
auto invoker = op.MakeInvoker();
auto argument_re1 =
op.MakeArgument(a_device_buf_re.GetDeviceBuffer(),
b_device_buf_re.GetDeviceBuffer(),
std::array<const void*, 1>{d_device_buf_re.GetDeviceBuffer()},
e_device_buf_re1.GetDeviceBuffer(),
a_ms_ks_lengths,
a_ms_ks_strides,
b_ns_ks_lengths,
b_ns_ks_strides,
std::array<std::vector<ck::index_t>, 1>{d_ms_ns_lengths},
std::array<std::vector<ck::index_t>, 1>{d_ms_ns_strides},
e_ms_ns_lengths,
e_ms_ns_strides,
a_element_op,
b_element_op,
cde_element_op);
if(!op.IsSupportedArgument(argument_re1))
{
......@@ -216,7 +220,6 @@ int run_complex_contraction_bilinear_example(int argc, char* argv[])
float ave_time_re1 = invoker.Run(argument_re1, StreamConfig{nullptr, time_kernel});
alpha = -1.f;
beta = 1.f;
......@@ -228,21 +231,22 @@ int run_complex_contraction_bilinear_example(int argc, char* argv[])
// For real Intermediate Value re_2
// auto op = DeviceOpInstance{};
// auto invoker = op.MakeInvoker();
auto argument_re2 = op.MakeArgument(a_device_buf_img.GetDeviceBuffer(),
b_device_buf_img.GetDeviceBuffer(),
std::array<const void*, 1>{e_device_buf_re1.GetDeviceBuffer()},
e_device_buf_re.GetDeviceBuffer(),
a_ms_ks_lengths,
a_ms_ks_strides,
b_ns_ks_lengths,
b_ns_ks_strides,
std::array<std::vector<ck::index_t>, 1>{d_ms_ns_lengths},
std::array<std::vector<ck::index_t>, 1>{d_ms_ns_strides},
e_ms_ns_lengths,
e_ms_ns_strides,
a_element_op,
b_element_op,
cde_element_op);
auto argument_re2 =
op.MakeArgument(a_device_buf_img.GetDeviceBuffer(),
b_device_buf_img.GetDeviceBuffer(),
std::array<const void*, 1>{e_device_buf_re1.GetDeviceBuffer()},
e_device_buf_re.GetDeviceBuffer(),
a_ms_ks_lengths,
a_ms_ks_strides,
b_ns_ks_lengths,
b_ns_ks_strides,
std::array<std::vector<ck::index_t>, 1>{d_ms_ns_lengths},
std::array<std::vector<ck::index_t>, 1>{d_ms_ns_strides},
e_ms_ns_lengths,
e_ms_ns_strides,
a_element_op,
b_element_op,
cde_element_op);
if(!op.IsSupportedArgument(argument_re2))
{
......@@ -253,7 +257,6 @@ int run_complex_contraction_bilinear_example(int argc, char* argv[])
float ave_time_re2 = invoker.Run(argument_re2, StreamConfig{nullptr, time_kernel});
alpha = 1.f;
beta = 1.f;
......@@ -261,22 +264,22 @@ int run_complex_contraction_bilinear_example(int argc, char* argv[])
b_element_op = BElementOp{};
cde_element_op = CDEElementOp{alpha, beta};
auto argument_img1 = op.MakeArgument(a_device_buf_re.GetDeviceBuffer(),
b_device_buf_img.GetDeviceBuffer(),
std::array<const void*, 1>{d_device_buf_img.GetDeviceBuffer()},
e_device_buf_img1.GetDeviceBuffer(),
a_ms_ks_lengths,
a_ms_ks_strides,
b_ns_ks_lengths,
b_ns_ks_strides,
std::array<std::vector<ck::index_t>, 1>{d_ms_ns_lengths},
std::array<std::vector<ck::index_t>, 1>{d_ms_ns_strides},
e_ms_ns_lengths,
e_ms_ns_strides,
a_element_op,
b_element_op,
cde_element_op);
auto argument_img1 =
op.MakeArgument(a_device_buf_re.GetDeviceBuffer(),
b_device_buf_img.GetDeviceBuffer(),
std::array<const void*, 1>{d_device_buf_img.GetDeviceBuffer()},
e_device_buf_img1.GetDeviceBuffer(),
a_ms_ks_lengths,
a_ms_ks_strides,
b_ns_ks_lengths,
b_ns_ks_strides,
std::array<std::vector<ck::index_t>, 1>{d_ms_ns_lengths},
std::array<std::vector<ck::index_t>, 1>{d_ms_ns_strides},
e_ms_ns_lengths,
e_ms_ns_strides,
a_element_op,
b_element_op,
cde_element_op);
if(!op.IsSupportedArgument(argument_img1))
{
......@@ -290,23 +293,22 @@ int run_complex_contraction_bilinear_example(int argc, char* argv[])
alpha = 1.f;
beta = 1.f;
auto argument_img2 = op.MakeArgument(a_device_buf_img.GetDeviceBuffer(),
b_device_buf_re.GetDeviceBuffer(),
std::array<const void*, 1>{e_device_buf_img1.GetDeviceBuffer()},
e_device_buf_img.GetDeviceBuffer(),
a_ms_ks_lengths,
a_ms_ks_strides,
b_ns_ks_lengths,
b_ns_ks_strides,
std::array<std::vector<ck::index_t>, 1>{d_ms_ns_lengths},
std::array<std::vector<ck::index_t>, 1>{d_ms_ns_strides},
e_ms_ns_lengths,
e_ms_ns_strides,
a_element_op,
b_element_op,
cde_element_op);
auto argument_img2 =
op.MakeArgument(a_device_buf_img.GetDeviceBuffer(),
b_device_buf_re.GetDeviceBuffer(),
std::array<const void*, 1>{e_device_buf_img1.GetDeviceBuffer()},
e_device_buf_img.GetDeviceBuffer(),
a_ms_ks_lengths,
a_ms_ks_strides,
b_ns_ks_lengths,
b_ns_ks_strides,
std::array<std::vector<ck::index_t>, 1>{d_ms_ns_lengths},
std::array<std::vector<ck::index_t>, 1>{d_ms_ns_strides},
e_ms_ns_lengths,
e_ms_ns_strides,
a_element_op,
b_element_op,
cde_element_op);
if(!op.IsSupportedArgument(argument_img2))
{
......@@ -317,7 +319,6 @@ int run_complex_contraction_bilinear_example(int argc, char* argv[])
float ave_time_img2 = invoker.Run(argument_img2, StreamConfig{nullptr, time_kernel});
ck::index_t M =
ck::accumulate_n<ck::index_t>(e_ms_ns_lengths.begin(), NumDimM, 1, std::multiplies<>{});
......@@ -331,9 +332,9 @@ int run_complex_contraction_bilinear_example(int argc, char* argv[])
std::size_t num_btype = sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
sizeof(DDataType) * M * N + sizeof(EDataType) * M * N * 2;
float ave_time = ave_time_img2 + ave_time_img1 + ave_time_re2 + ave_time_re1 ;
float ave_time = ave_time_img2 + ave_time_img1 + ave_time_re2 + ave_time_re1;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
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, "
......@@ -343,7 +344,7 @@ int run_complex_contraction_bilinear_example(int argc, char* argv[])
e_device_buf_img.FromDevice(e_ms_ns_device_result_img.mData.data());
auto isRealOk = 0;
auto isImgOk = 0;
auto isImgOk = 0;
if(do_verification)
{
......@@ -366,17 +367,16 @@ int run_complex_contraction_bilinear_example(int argc, char* argv[])
auto ref_op = ReferenceOpInstance{};
auto ref_invoker = ref_op.MakeInvoker();
auto ref_argument_re =
ref_op.MakeArgument(a_ms_ks_re, b_ns_ks_re, c_ms_ns_host_result_re, a_element_op, b_element_op);
auto ref_argument_re = ref_op.MakeArgument(
a_ms_ks_re, b_ns_ks_re, c_ms_ns_host_result_re, a_element_op, b_element_op);
ref_invoker.Run(ref_argument_re);
alpha = 1.f;
beta = 1.f;
cde_element_op = CDEElementOp{alpha, beta};
for(size_t m0 = 0; m0 < e_ms_ns_host_result_re.mDesc.GetLengths()[0]; ++m0)
{
for(size_t m1 = 0; m1 < e_ms_ns_host_result_re.mDesc.GetLengths()[1]; ++m1)
......@@ -395,11 +395,11 @@ int run_complex_contraction_bilinear_example(int argc, char* argv[])
alpha = 1.f;
beta = -1.f;
cde_element_op = CDEElementOp{alpha, beta};
auto ref_argument_re1 =
ref_op.MakeArgument(a_ms_ks_img, b_ns_ks_img, c_ms_ns_host_result_re1, a_element_op, b_element_op);
auto ref_argument_re1 = ref_op.MakeArgument(
a_ms_ks_img, b_ns_ks_img, c_ms_ns_host_result_re1, a_element_op, b_element_op);
ref_invoker.Run(ref_argument_re1);
......@@ -419,23 +419,20 @@ int run_complex_contraction_bilinear_example(int argc, char* argv[])
}
}
isRealOk = ck::utils::check_err(e_ms_ns_device_result_re, e_ms_ns_host_result_re) ? 0 : 1;
isRealOk = ck::utils::check_err(e_ms_ns_device_result_re, e_ms_ns_host_result_re) ? 0 : 1;
// Img Part Verification
Tensor<CShuffleDataType> c_ms_ns_host_result_img(e_ms_ns_lengths, e_ms_ns_strides);
Tensor<CShuffleDataType> c_ms_ns_host_result_img1(e_ms_ns_lengths, e_ms_ns_strides);
auto ref_argument_img =
ref_op.MakeArgument(a_ms_ks_re, b_ns_ks_img, c_ms_ns_host_result_img, a_element_op, b_element_op);
auto ref_argument_img = ref_op.MakeArgument(
a_ms_ks_re, b_ns_ks_img, c_ms_ns_host_result_img, a_element_op, b_element_op);
ref_invoker.Run(ref_argument_img);
alpha = 1.f;
beta = 1.f;
cde_element_op = CDEElementOp{alpha, beta};
for(size_t m0 = 0; m0 < e_ms_ns_host_result_img.mDesc.GetLengths()[0]; ++m0)
......@@ -454,9 +451,9 @@ int run_complex_contraction_bilinear_example(int argc, char* argv[])
}
}
auto ref_argument_img1 =
ref_op.MakeArgument(a_ms_ks_img, b_ns_ks_re, c_ms_ns_host_result_img1, a_element_op, b_element_op);
auto ref_argument_img1 = ref_op.MakeArgument(
a_ms_ks_img, b_ns_ks_re, c_ms_ns_host_result_img1, a_element_op, b_element_op);
ref_invoker.Run(ref_argument_img1);
for(size_t m0 = 0; m0 < e_ms_ns_host_result_img.mDesc.GetLengths()[0]; ++m0)
......@@ -475,7 +472,7 @@ int run_complex_contraction_bilinear_example(int argc, char* argv[])
}
}
isImgOk = ck::utils::check_err(e_ms_ns_device_result_re, e_ms_ns_host_result_re) ? 0 : 1;
isImgOk = ck::utils::check_err(e_ms_ns_device_result_re, e_ms_ns_host_result_re) ? 0 : 1;
return (isRealOk && isImgOk);
}
......
example/CMakeLists.txt
View file @ 1f9546e0
......@@ -54,6 +54,13 @@ function(add_example_executable EXAMPLE_NAME FILE_NAME)
list(REMOVE_ITEM FILE_NAME "${source}")
endif()
endforeach()
#Do not build any DPP examples if DL_KERNELS not set
foreach(source IN LISTS FILE_NAME)
if(NOT DEFINED DL_KERNELS AND source MATCHES "_dpp")
message("removing dpp example ${source} ")
list(REMOVE_ITEM FILE_NAME "${source}")
endif()
endforeach()
#Do not build any XDL examples if gfx9 targets are not on the list
foreach(source IN LISTS FILE_NAME)
if(NOT EX_TARGETS MATCHES "gfx9" AND source MATCHES "_xdl")
......@@ -85,9 +92,9 @@ function(add_example_executable EXAMPLE_NAME FILE_NAME)
#only continue if there are some source files left on the list
if(FILE_NAME)
if(FILE_NAME MATCHES "_xdl")
list(REMOVE_ITEM EX_TARGETS gfx1030 gfx1100 gfx1101 gfx1102 gfx1103 gfx1200 gfx1201)
list(REMOVE_ITEM EX_TARGETS gfx900 gfx906 gfx906:xnack- gfx1030 gfx1100 gfx1101 gfx1102 gfx1103 gfx1200 gfx1201 gfx10.3-generic gfx11-generic gfx12-generic)
elseif(FILE_NAME MATCHES "_wmma")
list(REMOVE_ITEM EX_TARGETS gfx908 gfx90a gfx940 gfx941 gfx942 gfx1030)
list(REMOVE_ITEM EX_TARGETS gfx900 gfx906 gfx906:xnack- gfx908:xnack+ gfx908:xnack- gfx90a:xnack+ gfx90a:xnack- gfx908 gfx90a gfx940 gfx941 gfx942 gfx1030)
endif()
set_source_files_properties(${FILE_NAME} PROPERTIES LANGUAGE HIP)
add_executable(${EXAMPLE_NAME} ${FILE_NAME})
......@@ -169,9 +176,9 @@ function(add_example_executable_no_testing EXAMPLE_NAME FILE_NAME)
#only continue if there are some source files left on the list
if(FILE_NAME)
if(FILE_NAME MATCHES "_xdl")
list(REMOVE_ITEM EX_TARGETS gfx900 gfx906 gfx1030 gfx1100 gfx1101 gfx1102 gfx1103 gfx1200 gfx1201)
list(REMOVE_ITEM EX_TARGETS gfx900 gfx906 gfx906:xnack- gfx1030 gfx1100 gfx1101 gfx1102 gfx1103 gfx1200 gfx1201 gfx10.3-generic gfx11-generic gfx12-generic)
elseif(FILE_NAME MATCHES "_wmma")
list(REMOVE_ITEM EX_TARGETS gfx900 gfx906 gfx908 gfx90a gfx940 gfx941 gfx942 gfx1030)
list(REMOVE_ITEM EX_TARGETS gfx900 gfx906 gfx906:xnack- gfx908:xnack+ gfx908:xnack- gfx90a:xnack+ gfx90a:xnack- gfx908 gfx90a gfx940 gfx941 gfx942 gfx1030)
endif()
set_source_files_properties(${FILE_NAME} PROPERTIES LANGUAGE HIP)
add_executable(${EXAMPLE_NAME} ${FILE_NAME})
......
example/README.md 0 → 100644
View file @ 1f9546e0
[Back to the main page](../README.md)
# Composable Kernel examples
\ No newline at end of file
example/ck_tile/01_fmha/codegen/ops/fmha_fwd.py
View file @ 1f9546e0
......@@ -21,6 +21,14 @@ DTYPE_BITS = {
"bf8" : 8
}
K0_MAX_SUBMAX_MAP = {
32 : 32,
64 : 64,
96 : 128,
128: 128,
256: 256
}
TILE_PARTITIONER_MAP = {
"shb" : "ck_tile::FmhaFwdTilePartitioner_SHB",
"hbs" : "ck_tile::FmhaFwdTilePartitioner_HBS",
......@@ -35,14 +43,13 @@ FMHA_FWD_KERNEL_HEADER = """// SPDX-License-Identifier: MIT
FMHA_FWD_KERNEL_BODY="""
using fmha_dtype_{F_idx} = {F_dtype};
using fmha_block_tile_{F_idx} = ck_tile::sequence<{F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0blen}>;
using fmha_block_warps_{F_idx} = ck_tile::sequence<{F_rm}, {F_rn}, {F_rk}>;
using fmha_block_tile_{F_idx} = ck_tile::sequence<{F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0max}>;
using fmha_warp_tile_{F_idx} = ck_tile::sequence<{F_wm}, {F_wn}, {F_wk}>;
using fmha_shape_{F_idx} = ck_tile::TileFmhaShape<fmha_block_tile_{F_idx},
fmha_block_warps_{F_idx},
ck_tile::sequence<{F_rm0}, {F_rn0}, {F_rk0}>,
fmha_warp_tile_{F_idx},
fmha_block_warps_{F_idx},
ck_tile::sequence<{F_rm1}, {F_rn1}, {F_rk1}>,
fmha_warp_tile_{F_idx},
{F_vlayout}>;
......@@ -88,7 +95,7 @@ using fmha_kernel_{F_idx} =
fmha_pipeline_{F_idx},
fmha_epilogue_{F_idx}>;
using trait_{F_idx} = fmha_fwd_traits_<{F_hdim}, {F_dtype}, {F_mode},{F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0blen}, {F_vlayout},
using trait_{F_idx} = fmha_fwd_traits_<{F_hdim}, {F_dtype}, {F_mode},{F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0max}, {F_vlayout},
{F_pipeline_enum}, fmha_mask_{F_idx}, {F_bias}, {F_lse}, {F_dropout}, {F_squant}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}>;
#include <iostream>
......@@ -126,7 +133,7 @@ FMHA_FWD_API_PER_HDIM_CASE=""" {F_if} (t.hdim_q <= {F_hdim} && t.hdim_v <
FMHA_FWD_API_INNER_DISPATCH=""" {F_if}((t.is_group_mode == {F_mode}) && (t.is_v_rowmajor == {F_vlayout}) && ({F_mask_check}) && (t.bias_type == {F_bias_check}) && (t.has_lse == {F_lse}) && (t.has_dropout == {F_dropout}) && (t.do_fp8_static_quant == {F_squant}) &&
({F_scheck}) && ({F_skcheck}) && ({F_dcheck}) && ({F_dvcheck})) {{
using trait_ = fmha_fwd_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0blen}, {F_vlayout}, {F_pipeline_enum}, {F_mask}, {F_bias}, {F_lse}, {F_dropout}, {F_squant}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}>;
using trait_ = fmha_fwd_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0max}, {F_vlayout}, {F_pipeline_enum}, {F_mask}, {F_bias}, {F_lse}, {F_dropout}, {F_squant}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}>;
return fmha_fwd_<trait_>(s, a);
}}
"""
......@@ -143,7 +150,7 @@ class FmhaFwdApiTrait:
bk0 : int # tile size along qk gemm unroll
bn1 : int # tile size along v head_dim
bk1 : int # tile size along kv gemm unroll
bk0blen : int
bk0max : int
vlayout : str
mask : str
bias : str #
......@@ -157,7 +164,7 @@ class FmhaFwdApiTrait:
@property
def name(self) -> str:
return f'{self.hdim}-{self.dtype}-{self.mode}-{self.bm0}-{self.bn0}-{self.bk0}-{self.bn0}-{self.bk1}-{self.bk0blen}-'+\
return f'{self.hdim}-{self.dtype}-{self.mode}-{self.bm0}-{self.bn0}-{self.bk0}-{self.bn0}-{self.bk1}-{self.bk0max}-'+\
f'{self.vlayout}-{self.mask}-{self.bias}-{self.lse}-{self.dropout}-{self.squant}-{self.spad}-{self.skpad}-{self.dpad}-{self.dvpad}'
@property
......@@ -189,8 +196,9 @@ class FmhaFwdApiTrait:
if self.dpad == 't': return f'a.hdim_q % {vec} == 0'
else : assert False
elif self.pipeline_tag in ['qr']:
if self.dpad == 't': return f'true /*a.hdim_q % {self.bk0blen} != 0*/' # TODO: order of get_pipelines() matters! (ugly)
else : return f'a.hdim_q % {self.bk0blen} == 0'
bk0submax = K0_MAX_SUBMAX_MAP[self.bk0max]
if self.dpad == 't': return f'true /*a.hdim_q % {bk0submax} != 0*/' # TODO: order of get_pipelines() matters! (ugly)
else : return f'a.hdim_q % {bk0submax} == 0'
else: assert False
@property
......@@ -200,8 +208,9 @@ class FmhaFwdApiTrait:
if self.dvpad == 't': return f'a.hdim_v % {vec} == 0'
else : assert False
elif self.pipeline_tag in ['qr']:
if self.dvpad == 't': return f'true /*a.hdim_v % {self.bk0blen} != 0*/' # TODO: order of get_pipelines() matters! (ugly)
else : return f'a.hdim_v % {self.bk0blen} == 0'
bk0submax = K0_MAX_SUBMAX_MAP[self.bk0max]
if self.dvpad == 't': return f'true /*a.hdim_v % {bk0submax} != 0*/' # TODO: order of get_pipelines() matters! (ugly)
else : return f'a.hdim_v % {bk0submax} == 0'
else: assert False
@dataclass
......@@ -272,7 +281,7 @@ class FmhaFwdApiPool:
F_lse=BOOL_MAP[trait.lse], F_dropout=BOOL_MAP[trait.dropout] ,
F_squant=BOOL_MAP[trait.squant], F_scheck=trait.scheck, F_skcheck=trait.skcheck, F_dcheck=trait.dcheck, F_dvcheck=trait.dvcheck,
F_spad=BOOL_MAP[trait.spad], F_skpad=BOOL_MAP[trait.skpad], F_dpad=BOOL_MAP[trait.dpad], F_dvpad=BOOL_MAP[trait.dvpad],
F_bm0=trait.bm0, F_bn0=trait.bn0, F_bk0=trait.bk0, F_bn1=trait.bn1, F_bk1=trait.bk1, F_bk0blen=trait.bk0blen,
F_bm0=trait.bm0, F_bn0=trait.bn0, F_bk0=trait.bk0, F_bn1=trait.bn1, F_bk1=trait.bk1, F_bk0max=trait.bk0max,
F_hdim=hdim, F_dtype=DTYPE_MAP[dtype])
if_j = 'if' if j == 0 else 'else if'
per_hdim_case = per_hdim_case + FMHA_FWD_API_PER_HDIM_CASE.format(F_if=if_j, F_hdim=hdim, F_inner_dispatch=inners)
......@@ -290,19 +299,22 @@ class FmhaFwdTileSize:
F_bk0 : int # tile size along qk gemm unroll
F_bn1 : int # tile size along v head_dim
F_bk1 : int # tile size along kv gemm unroll
F_bk0blen : int # total length of K0, used for pipeline that need load Q at once (or repeately load Q as a whole tile)
F_rm : int # number of warps along q seqlen (block warps)
F_rn : int # number of warps along k seqlen(not used)
F_rk : int # number of warps along gemm-k(not used)
F_bk0max : int # total length of K0, used for pipeline that need load Q at once (or repeately load Q as a whole tile)
F_rm0 : int # number of warps for gemm0 along q seqlen
F_rn0 : int # number of warps for gemm0 along k seqlen
F_rk0 : int # number of warps for gemm0 along head dim q (not used)
F_rm1 : int # number of warps for gemm1 along q seqlen
F_rn1 : int # number of warps for gemm1 along head dim v
F_rk1 : int # number of warps for gemm1 along k seqlen (not used)
F_wm : int # warp size along m (warp size)
F_wn : int # warp size along n
F_wk : int # warp size along k
F_occupancy : int # occupancy, -1 will let pipeline decide the occupancy, other value will overwrite occupancy
@property
def name(self) -> str:
return f"b{self.F_bm0}x{self.F_bn0}x{self.F_bk0}x{self.F_bn1}x{self.F_bk1}x{self.F_bk0blen}" +\
f"_r{self.F_rm}x{self.F_rn}x{self.F_rk}_w{self.F_wm}x{self.F_wn}x{self.F_wk}" +\
("" if self.F_occupancy == -1 else f"_o{self.F_occupancy}")
return f"b{self.F_bm0}x{self.F_bn0}x{self.F_bk0}x{self.F_bn1}x{self.F_bk1}x{self.F_bk0max}" +\
f"_r{self.F_rm0}x{self.F_rn0}x{self.F_rk0}_r{self.F_rm1}x{self.F_rn1}x{self.F_rk1}" +\
f"_w{self.F_wm}x{self.F_wn}x{self.F_wk}" + ("" if self.F_occupancy == -1 else f"_o{self.F_occupancy}")
@dataclass
class FmhaFwdKernel:
......@@ -333,10 +345,13 @@ class FmhaFwdKernel:
F_bk0 = self.F_tile.F_bk0,
F_bn1 = self.F_tile.F_bn1,
F_bk1 = self.F_tile.F_bk1,
F_bk0blen = self.F_tile.F_bk0blen,
F_rm = self.F_tile.F_rm,
F_rn = self.F_tile.F_rn,
F_rk = self.F_tile.F_rk,
F_bk0max = self.F_tile.F_bk0max,
F_rm0 = self.F_tile.F_rm0,
F_rn0 = self.F_tile.F_rn0,
F_rk0 = self.F_tile.F_rk0,
F_rm1 = self.F_tile.F_rm1,
F_rn1 = self.F_tile.F_rn1,
F_rk1 = self.F_tile.F_rk1,
F_wm = self.F_tile.F_wm,
F_wn = self.F_tile.F_wn,
F_wk = self.F_tile.F_wk,
......@@ -377,7 +392,7 @@ class FmhaFwdKernel:
bk0=self.F_tile.F_bk0,
bn1=self.F_tile.F_bn1,
bk1=self.F_tile.F_bk1,
bk0blen=self.F_tile.F_bk0blen,
bk0max=self.F_tile.F_bk0max,
vlayout=self.F_pipeline.F_vlayout,
mask=self.F_pipeline.F_mask,
bias=self.F_pipeline.F_bias,
......@@ -394,16 +409,17 @@ class FmhaFwdKernel:
def get_fmha_fwd_tile_dict_from_dtype(dtype : str) -> Optional[dict]:
if dtype == 'fp16' or dtype == 'bf16':
return {
'32' : FmhaFwdTileSize(128, 64, 16, 32, 32, 32, 2, 1, 1, 32, 32, 16, -1),
'64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 4, 1, 1, 32, 32, 16, -1),
'128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 32, 32, 16, -1),
'256' : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 32, 32, 16, -1),
'32' : FmhaFwdTileSize(128, 64, 16, 32, 32, 32, 2, 1, 1, 2, 1, 1, 32, 32, 16, -1),
'64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 4, 1, 1, 4, 1, 1, 32, 32, 16, -1),
## '96' : FmhaFwdTileSize(128, 128, 32, 128, 32, 96, 4, 1, 1, 4, 1, 1, 32, 32, 16, -1),
'128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 32, 32, 16, -1),
'256' : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 32, 32, 16, -1),
}
elif dtype == 'fp8' or dtype == 'bf8':
return {
'64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 2, 1, 1, 32, 32, 32, -1),
'128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 32, 32, 32, -1),
'256' : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 32, 32, 32, -1)
'64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 2, 1, 1, 2, 1, 1, 32, 32, 32, -1),
'128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 32, 32, 32, -1),
'256' : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 32, 32, 32, -1)
}
else:
return None
......@@ -505,4 +521,4 @@ def list_blobs(file_path : Path, kernel_filter : Optional[str], receipt, mask_im
_, kernels = get_fwd_blobs(kernel_filter, receipt, mask_impl)
for kernel in kernels:
f.write(str(file_path.parent / GEN_DIR / kernel.filename) + "\n")
f.write(str(file_path.parent / GEN_DIR / FMHA_FWD_API_FILENAME) + "\n")
\ No newline at end of file
f.write(str(file_path.parent / GEN_DIR / FMHA_FWD_API_FILENAME) + "\n")
example/ck_tile/01_fmha/codegen/ops/fmha_fwd_splitkv.py
View file @ 1f9546e0
......@@ -29,6 +29,14 @@ DTYPE_BITS = {
"bf8" : 8
}
K0_MAX_SUBMAX_MAP = {
32 : 32,
64 : 64,
96 : 128,
128: 128,
256: 256
}
FMHA_FWD_SPLITKV_PIPELINE_MAP = {
"qr" : "ck_tile::BlockFmhaFwdSplitKVPipelineQRKSVS",
"qr_async" : "ck_tile::BlockFmhaFwdSplitKVPipelineQRKSVSAsync",
......@@ -41,14 +49,13 @@ using fmha_mask_{F_idx} = {F_mask};
namespace {{
template <bool kHasUnevenSplits>
struct kernel_runner {{
using fmha_block_tile = ck_tile::sequence<{F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0blen}>;
using fmha_block_warps = ck_tile::sequence<{F_rm}, {F_rn}, {F_rk}>;
using fmha_block_tile = ck_tile::sequence<{F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0max}>;
using fmha_warp_tile = ck_tile::sequence<{F_wm}, {F_wn}, {F_wk}>;
using fmha_shape = ck_tile::TileFmhaShape<fmha_block_tile,
fmha_block_warps,
ck_tile::sequence<{F_rm0}, {F_rn0}, {F_rk0}>,
fmha_warp_tile,
fmha_block_warps,
ck_tile::sequence<{F_rm1}, {F_rn1}, {F_rk1}>,
fmha_warp_tile,
{F_vlayout}>;
......@@ -104,7 +111,7 @@ static void run(const ck_tile::stream_config& s, fmha_fwd_splitkv_args a)
}};
}}
using trait_{F_idx} = fmha_fwd_splitkv_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0blen}, {F_vlayout},
using trait_{F_idx} = fmha_fwd_splitkv_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0max}, {F_vlayout},
{F_pipeline_enum}, fmha_mask_{F_idx}, {F_bias}, {F_lse}, {F_squant}, {F_pagedkv}, {F_spad}, {F_skpad}, {F_dpad},
{F_dvpad}>;
......@@ -162,10 +169,12 @@ using fmha_pipeline_problem = ck_tile::BlockFmhaSplitKVCombinePipelineProblem<
using fmha_pipeline = ck_tile::BlockFmhaFwdSplitKVCombinePipeline<
fmha_pipeline_problem>;
/// FIXME: use {F_spad}/{F_dvpad} as kPadM/kPadN parameters after solving
/// store_tile_raw() data corruption issue
using fmha_epilogue =
ck_tile::Default2DEpilogue<ck_tile::Default2DEpilogueProblem<typename FmhaFwdTypeConfig<{F_dtype}>::OaccDataType,
typename FmhaFwdTypeConfig<{F_dtype}>::ODataType,
{F_spad}, {F_dvpad}>>;
false, false>>;
using fmha_kernel =
ck_tile::FmhaFwdSplitKVCombineKernel<ck_tile::FmhaFwdSplitKVCombineTilePartitioner<{F_bm0}, {F_bn1}>,
......@@ -191,7 +200,9 @@ using trait_{F_idx} = fmha_fwd_splitkv_combine_traits_<{F_hdim}, {F_dtype}, {F_m
template<>
void fmha_fwd_splitkv_combine_oneshot_<trait_{F_idx}>(const ck_tile::stream_config& s, fmha_fwd_splitkv_args a)
{{
if (a.num_splits <= 16) {{
if (a.num_splits <= 8) {{
kernel_runner<3>::run(s, a);
}} else if (a.num_splits <= 16) {{
kernel_runner<4>::run(s, a);
}} else if (a.num_splits <= 32) {{
kernel_runner<5>::run(s, a);
......@@ -236,12 +247,22 @@ float fmha_fwd_splitkv(fmha_fwd_splitkv_traits t, fmha_fwd_splitkv_args a, const
}}
"""
FMHA_FWD_SPLITKV_API_INNER_DISPATCH=""" {F_if}((t.is_group_mode == {F_mode}) && (t.is_v_rowmajor == {F_vlayout}) && ({F_mask_check}) && (t.bias_type == {F_bias_check}) && (t.has_lse == {F_lse}) && (t.do_fp8_static_quant == {F_squant}) &&
FMHA_FWD_SPLITKV_API_INNER_DISPATCH=""" {F_if}((t.is_group_mode == {F_mode}) && (t.is_v_rowmajor == {F_vlayout}) && ({F_mask_check}) && (t.bias_type == {F_bias_check}) && (t.do_fp8_static_quant == {F_squant}) &&
((a.block_table_ptr != nullptr) == {F_pagedkv}) && ({F_scheck}) && ({F_skcheck}) && ({F_dcheck}) && ({F_dvcheck})) {{
using traits_ = fmha_fwd_splitkv_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0blen}, {F_vlayout}, {F_pipeline_enum}, {F_mask}, {F_bias}, {F_lse}, {F_squant}, {F_pagedkv}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}>;
using traits2_ = fmha_fwd_splitkv_combine_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}/2, {F_bn1}, {F_lse}, {F_squant}, {F_spad}, {F_dvpad}>;
return fmha_fwd_splitkv_<traits_, traits2_>(s, a);
using traits_ = fmha_fwd_splitkv_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}, {F_bn0}, {F_bk0}, {F_bn1}, {F_bk1}, {F_bk0max}, {F_vlayout}, {F_pipeline_enum}, {F_mask}, {F_bias}, true, {F_squant}, {F_pagedkv}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}>;
if (t.has_lse) {{
if constexpr (std::is_same_v<{F_dtype}, ck_tile::fp8_t>) {{
return -1;
}} else {{
using traits2_ = fmha_fwd_splitkv_combine_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}/2, {F_bn1}/2, true, {F_squant}, {F_spad}, {F_dvpad}>;
return fmha_fwd_splitkv_<traits_, traits2_>(s, a);
}}
}} else {{
using traits2_ = fmha_fwd_splitkv_combine_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_bm0}/2, {F_bn1}/2, false, {F_squant}, {F_spad}, {F_dvpad}>;
return fmha_fwd_splitkv_<traits_, traits2_>(s, a);
}}
}}
"""
......@@ -257,7 +278,7 @@ class FmhaFwdSplitKVApiTrait:
bk0 : int # tile size along qk gemm unroll
bn1 : int # tile size along v head_dim
bk1 : int # tile size along kv gemm unroll
bk0blen : int
bk0max : int
vlayout : str
mask : str
bias : str #
......@@ -267,11 +288,11 @@ class FmhaFwdSplitKVApiTrait:
skpad : str
dpad : str
dvpad : str
pagedkv : str
pagedkv : str
@property
def name(self) -> str:
return f'{self.hdim}-{self.dtype}-{self.mode}-{self.bm0}-{self.bn0}-{self.bk0}-{self.bn0}-{self.bk1}-{self.bk0blen}-'+\
return f'{self.hdim}-{self.dtype}-{self.mode}-{self.bm0}-{self.bn0}-{self.bk0}-{self.bn0}-{self.bk1}-{self.bk0max}-'+\
f'{self.vlayout}-{self.mask}-{self.bias}-{self.lse}-{self.squant}-{self.spad}-{self.skpad}-{self.dpad}-'+\
f'{self.dvpad}-{self.pagedkv}'
......@@ -304,8 +325,9 @@ class FmhaFwdSplitKVApiTrait:
if self.dpad == 't': return f'a.hdim_q % {vec} == 0'
else : assert False
elif self.pipeline_tag in ['qr']:
if self.dpad == 't': return f'true /*a.hdim_q % {self.bk0blen} != 0*/' # TODO: order of get_pipelines() matters! (ugly)
else : return f'a.hdim_q % {self.bk0blen} == 0'
bk0submax = K0_MAX_SUBMAX_MAP[self.bk0max]
if self.dpad == 't': return f'true /*a.hdim_q % {bk0submax} != 0*/' # TODO: order of get_pipelines() matters! (ugly)
else : return f'a.hdim_q % {bk0submax} == 0'
else: assert False
@property
......@@ -315,8 +337,9 @@ class FmhaFwdSplitKVApiTrait:
if self.dvpad == 't': return f'a.hdim_v % {vec} == 0'
else : assert False
elif self.pipeline_tag in ['qr']:
if self.dvpad == 't': return f'true /*a.hdim_v % {self.bk0blen} != 0*/' # TODO: order of get_pipelines() matters! (ugly)
else : return f'a.hdim_v % {self.bk0blen} == 0'
bk0submax = K0_MAX_SUBMAX_MAP[self.bk0max]
if self.dvpad == 't': return f'true /*a.hdim_v % {bk0submax} != 0*/' # TODO: order of get_pipelines() matters! (ugly)
else : return f'a.hdim_v % {bk0submax} == 0'
else: assert False
@dataclass
......@@ -411,7 +434,7 @@ class FmhaFwdSplitKVApiPool:
F_lse=BOOL_MAP[trait.lse], F_squant=BOOL_MAP[trait.squant], F_pagedkv=BOOL_MAP[trait.pagedkv],
F_scheck=trait.scheck, F_skcheck=trait.skcheck, F_dcheck=trait.dcheck, F_dvcheck=trait.dvcheck,
F_spad=BOOL_MAP[trait.spad], F_skpad=BOOL_MAP[trait.skpad], F_dpad=BOOL_MAP[trait.dpad], F_dvpad=BOOL_MAP[trait.dvpad],
F_bm0=trait.bm0, F_bn0=trait.bn0, F_bk0=trait.bk0, F_bn1=trait.bn1, F_bk1=trait.bk1, F_bk0blen=trait.bk0blen,
F_bm0=trait.bm0, F_bn0=trait.bn0, F_bk0=trait.bk0, F_bn1=trait.bn1, F_bk1=trait.bk1, F_bk0max=trait.bk0max,
F_hdim=hdim, F_dtype=DTYPE_MAP[dtype])
if_j = 'if' if j == 0 else 'else if'
per_hdim_case = per_hdim_case + FMHA_FWD_API_PER_HDIM_CASE.format(F_if=if_j, F_hdim=hdim, F_inner_dispatch=inners)
......@@ -455,10 +478,13 @@ class FmhaFwdSplitKVKernel:
F_bk0 = self.F_tile.F_bk0,
F_bn1 = self.F_tile.F_bn1,
F_bk1 = self.F_tile.F_bk1,
F_bk0blen = self.F_tile.F_bk0blen,
F_rm = self.F_tile.F_rm,
F_rn = self.F_tile.F_rn,
F_rk = self.F_tile.F_rk,
F_bk0max = self.F_tile.F_bk0max,
F_rm0 = self.F_tile.F_rm0,
F_rn0 = self.F_tile.F_rn0,
F_rk0 = self.F_tile.F_rk0,
F_rm1 = self.F_tile.F_rm1,
F_rn1 = self.F_tile.F_rn1,
F_rk1 = self.F_tile.F_rk1,
F_wm = self.F_tile.F_wm,
F_wn = self.F_tile.F_wn,
F_wk = self.F_tile.F_wk,
......@@ -498,7 +524,7 @@ class FmhaFwdSplitKVKernel:
bk0=self.F_tile.F_bk0,
bn1=self.F_tile.F_bn1,
bk1=self.F_tile.F_bk1,
bk0blen=self.F_tile.F_bk0blen,
bk0max=self.F_tile.F_bk0max,
vlayout=self.F_pipeline.F_vlayout,
mask=self.F_pipeline.F_mask,
bias=self.F_pipeline.F_bias,
......@@ -551,16 +577,17 @@ class FmhaFwdSplitKVCombineKernel:
def get_fmha_fwd_tile_dict_from_dtype(dtype : str) -> Optional[dict]:
if dtype == 'fp16' or dtype == 'bf16':
return {
'32' : FmhaFwdTileSize(128, 64, 16, 32, 32, 32, 2, 1, 1, 32, 32, 16, -1),
'64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 4, 1, 1, 32, 32, 16, -1),
'128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 32, 32, 16, -1),
'256' : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 32, 32, 16, -1),
'32' : FmhaFwdTileSize(32, 64, 16, 32, 32, 32, 2, 1, 1, 2, 1, 1, 16, 16, 16, -1),
'64' : FmhaFwdTileSize(64, 64, 32, 64, 32, 64, 4, 1, 1, 4, 1, 1, 16, 16, 16, -1),
## '96' : FmhaFwdTileSize(64, 128, 32, 128, 32, 96, 4, 1, 1, 4, 1, 1, 16, 16, 16, -1),
'128' : FmhaFwdTileSize(64, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 16, 16, 16, -1),
'256' : FmhaFwdTileSize(64, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 16, 16, 16, -1),
}
elif dtype == 'fp8' or dtype == 'bf8':
return {
'64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 2, 1, 1, 32, 32, 32, -1),
'128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 32, 32, 32, -1),
'256' : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 32, 32, 32, -1)
'64' : FmhaFwdTileSize(128, 64, 32, 64, 32, 64, 2, 1, 1, 2, 1, 1, 32, 32, 32, -1),
'128' : FmhaFwdTileSize(128, 128, 32, 128, 32, 128, 4, 1, 1, 4, 1, 1, 32, 32, 32, -1),
'256' : FmhaFwdTileSize(128, 128, 32, 256, 32, 256, 4, 1, 1, 4, 1, 1, 32, 32, 32, -1)
}
else:
return None
......@@ -568,16 +595,17 @@ def get_fmha_fwd_tile_dict_from_dtype(dtype : str) -> Optional[dict]:
def get_fmha_fwd_splitkv_combine_tile_dict_from_dtype(dtype : str) -> Optional[dict]:
if dtype == 'fp16' or dtype == 'bf16':
return {
'32' : FmhaFwdSplitKVCombineTileSize(64, 32, -1),
'64' : FmhaFwdSplitKVCombineTileSize(64, 64, -1),
'128' : FmhaFwdSplitKVCombineTileSize(64, 128, -1),
'256' : FmhaFwdSplitKVCombineTileSize(64, 256, -1),
'32' : FmhaFwdSplitKVCombineTileSize(16, 16, -1),
'64' : FmhaFwdSplitKVCombineTileSize(32, 32, -1),
## '96' : FmhaFwdSplitKVCombineTileSize(32, 64, -1),
'128' : FmhaFwdSplitKVCombineTileSize(32, 64, -1),
'256' : FmhaFwdSplitKVCombineTileSize(32, 128, -1),
}
elif dtype == 'fp8' or dtype == 'bf8':
return {
'64' : FmhaFwdSplitKVCombineTileSize(64, 64, -1),
'128' : FmhaFwdSplitKVCombineTileSize(64, 128, -1),
'256' : FmhaFwdSplitKVCombineTileSize(64, 256, -1),
'64' : FmhaFwdSplitKVCombineTileSize(64, 32, -1),
'128' : FmhaFwdSplitKVCombineTileSize(64, 64, -1),
'256' : FmhaFwdSplitKVCombineTileSize(64, 128, -1),
}
else:
return None
......@@ -596,27 +624,26 @@ def get_fwd_splitkv_blobs(kernel_filter : Optional[str], receipt, mask_impl) ->
squant = 't' if dtype == 'fp8' else 'f'
pipelines = []
if dtype in ['fp16', 'bf16']:
for mask, bias, lse, pagedkv in itertools.product(get_mask_map(mask_impl).keys(), BIAS_MAP.keys(), ["t", "f"], ["t", "f"]):
for mask, bias, pagedkv in itertools.product(get_mask_map(mask_impl).keys(), BIAS_MAP.keys(), ["t", "f"]):
# TODO: use async pipeline when compiler is more stable
if hdim == 256 or hdim in [32, 64, 128]:
if hdim == 256 or hdim in [32, 64, 128]: ### [32, 64, 96, 128]:
# if True:
pipelines.append(Pipeline('qr', 'row', 'f', 't', 'f', 'f', bias, lse, squant, pagedkv, mask))
pipelines.append(Pipeline('qr', 'col', 'f', 't', 'f', 'f', bias, lse, squant, pagedkv, mask))
pipelines.append(Pipeline('qr', 'row', 'f', 't', 'f', 'f', bias, 't', squant, pagedkv, mask))
pipelines.append(Pipeline('qr', 'col', 'f', 't', 'f', 'f', bias, 't', squant, pagedkv, mask))
pipelines.append(Pipeline('qr', 'row', 't', 't', 't', 't', bias, lse, squant, pagedkv, mask))
pipelines.append(Pipeline('qr', 'col', 't', 't', 't', 't', bias, lse, squant, pagedkv, mask))
pipelines.append(Pipeline('qr', 'row', 't', 't', 't', 't', bias, 't', squant, pagedkv, mask))
pipelines.append(Pipeline('qr', 'col', 't', 't', 't', 't', bias, 't', squant, pagedkv, mask))
else:
pipelines.append(Pipeline('qr_async', 'row', 't', 'f', 't', 't', bias, lse, squant, pagedkv, mask))
pipelines.append(Pipeline('qr_async', 'row', 't', 't', 't', 't', bias, lse, squant, pagedkv, mask))
pipelines.append(Pipeline('qr_async', 'col', 't', 'f', 't', 't', bias, lse, squant, pagedkv, mask))
pipelines.append(Pipeline('qr_async', 'col', 't', 't', 't', 't', bias, lse, squant, pagedkv, mask))
pipelines.append(Pipeline('qr_async', 'row', 't', 'f', 't', 't', bias, 't', squant, pagedkv, mask))
pipelines.append(Pipeline('qr_async', 'row', 't', 't', 't', 't', bias, 't', squant, pagedkv, mask))
pipelines.append(Pipeline('qr_async', 'col', 't', 'f', 't', 't', bias, 't', squant, pagedkv, mask))
pipelines.append(Pipeline('qr_async', 'col', 't', 't', 't', 't', bias, 't', squant, pagedkv, mask))
if receipt == 1:
pipelines.append(Pipeline('qr', 'row', 't', 't', 't', 't', bias, lse, squant, pagedkv, mask)) # TODO: cover arbitraty hdim
pipelines.append(Pipeline('qr', 'col', 't', 'f', 't', 't', bias, lse, squant, pagedkv, mask)) # TODO: cover arbitraty hdim
pipelines.append(Pipeline('qr', 'row', 't', 't', 't', 't', bias, 't', squant, pagedkv, mask)) # TODO: cover arbitraty hdim
pipelines.append(Pipeline('qr', 'col', 't', 'f', 't', 't', bias, 't', squant, pagedkv, mask)) # TODO: cover arbitraty hdim
elif dtype in ['fp8', 'bf8']:
# no need lse/paged-kv kernels
for mask, bias in itertools.product(get_mask_map(mask_impl).keys(), BIAS_MAP.keys()):
pipelines.append(Pipeline('qr', 'col', 'f', 'f', 'f', 'f', bias, 'f', squant, 'f', mask))
pipelines.append(Pipeline('qr', 'col', 'f', 'f', 'f', 'f', bias, 't', squant, 'f', mask))
else:
assert False
return pipelines
......@@ -637,9 +664,6 @@ def get_fwd_splitkv_blobs(kernel_filter : Optional[str], receipt, mask_impl) ->
if pipeline.F_spad != 't' or pipeline.F_skpad != 't':
# in group mode, spad/skpad must be true, since we can't predict if seqlen of current batch need pad or not
continue
if pipeline.F_pagedkv == 't':
# we only use batch mode kernels to handle (paged-) kvcache problems
continue
k = Kernel(F_idx=0,
F_hdim=hdim,
F_dtype=dtype,
......@@ -737,4 +761,4 @@ def list_blobs(file_path : Path, kernel_filter : Optional[str], receipt, mask_im
_, kernels = get_fwd_splitkv_blobs(kernel_filter, receipt, mask_impl)
for kernel in kernels:
f.write(str(file_path.parent / GEN_DIR / kernel.filename) + "\n")
f.write(str(file_path.parent / GEN_DIR / FMHA_FWD_SPLITKV_API_FILENAME) + "\n")
\ No newline at end of file
f.write(str(file_path.parent / GEN_DIR / FMHA_FWD_SPLITKV_API_FILENAME) + "\n")
example/ck_tile/01_fmha/fmha_bwd.hpp
View file @ 1f9546e0
......@@ -150,113 +150,113 @@ auto fmha_bwd_dq_dk_dv_create_kargs_and_grids(fmha_bwd_args args)
// 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.dk_ptr,
args.dv_ptr,
args.dbias_ptr,
args.dq_acc_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_dq_acc,
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_dq_acc,
args.nhead_stride_dk,
args.nhead_stride_dv,
args.nhead_stride_dbias,
args.split_stride_dq_acc,
args.window_size_left,
args.window_size_right,
args.mask_type,
args.p_drop,
args.drop_seed_offset);
return FmhaBwdDQDKDVKernel::MakeKargsImpl(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.dk_ptr,
args.dv_ptr,
args.dbias_ptr,
args.dq_acc_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_dq_acc,
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_dq_acc,
args.nhead_stride_dk,
args.nhead_stride_dv,
args.nhead_stride_dbias,
args.split_stride_dq_acc,
args.window_size_left,
args.window_size_right,
args.mask_type,
args.p_drop,
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.dk_ptr,
args.dv_ptr,
args.dbias_ptr,
args.dq_acc_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_dq_acc,
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_dq_acc,
args.nhead_stride_dk,
args.nhead_stride_dv,
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_dq_acc,
args.batch_stride_dk,
args.batch_stride_dv,
args.batch_stride_dbias,
args.split_stride_dq_acc,
args.window_size_left,
args.window_size_right,
args.mask_type,
args.p_drop,
args.drop_seed_offset);
return FmhaBwdDQDKDVKernel::MakeKargsImpl(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.dk_ptr,
args.dv_ptr,
args.dbias_ptr,
args.dq_acc_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_dq_acc,
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_dq_acc,
args.nhead_stride_dk,
args.nhead_stride_dv,
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_dq_acc,
args.batch_stride_dk,
args.batch_stride_dv,
args.batch_stride_dbias,
args.split_stride_dq_acc,
args.window_size_left,
args.window_size_right,
args.mask_type,
args.p_drop,
args.drop_seed_offset);
}
}();
......
example/ck_tile/01_fmha/fmha_fwd.cpp
View file @ 1f9546e0
......@@ -62,7 +62,7 @@ auto create_args(int argc, char* argv[])
"-1 to choose s_knew in [1, s] randomly.")
.insert("s_kpad",
"-1",
"seqlen_k stride between 2 tokens, currently used in group-mode only\n"
"seqlen_k stride between 2 batches, currently used in group-mode only\n"
"for kv-cache case, each batch [1,s,h,d]/[1,h,s,d] can have a stride\n"
"along seqlen, instead of packed. same as xformer kv_padding")
.insert("d", "128", "head dim for q, k")
......@@ -294,7 +294,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
#if !CK_TILE_FMHA_FWD_APPENDKV_API
if(seqlen_knew != 0)
{
std::cerr << "kvcache is not supported. ignoring the 's_knew' option" << std::endl;
std::cerr << "fmha_fwd_appendkv() is not enabled. ignoring the 's_knew' option"
<< std::endl;
seqlen_knew = 0;
}
#endif
......@@ -321,6 +322,13 @@ bool run(const ck_tile::ArgParser& arg_parser)
rotary_dim = 0;
}
#endif
// to use fmha_fwd_appendkv(), make sure it's in batch mode
const bool need_append_kvcache = (0 < seqlen_knew || 0 < rotary_dim);
if(need_append_kvcache && mode == mode_enum::group)
{
std::cerr << "fmha_fwd_appendkv() will be invoked. ignoring the 'mode' option" << std::endl;
mode = mode_enum::batch;
}
if(!(rotary_dim <= hdim_q))
{
std::cerr << "rotary_dim should be less than or equal to head dim for q" << std::endl;
......@@ -356,22 +364,26 @@ bool run(const ck_tile::ArgParser& arg_parser)
<< std::endl;
use_cache_batch_idx = false;
}
#endif
if(0 < page_block_size && use_cache_batch_idx)
#else
if(use_cache_batch_idx)
{
std::cerr << "paged-kvcache does not support cache_batch_idx. ignoring the "
"'cache_batch_idx' option"
<< std::endl;
use_cache_batch_idx = false;
if(0 < page_block_size)
{
std::cerr << "paged-kvcache does not support cache_batch_idx. ignoring the "
"'cache_batch_idx' option"
<< std::endl;
use_cache_batch_idx = false;
}
else if(mode == mode_enum::group)
{
std::cerr << "group mode will not use cache_batch_idx. ignoring the "
"'cache_batch_idx' option"
<< std::endl;
use_cache_batch_idx = false;
}
}
// the input tensor layout for kvcache is same as batch mode
const bool need_append_kvcache = (0 < seqlen_knew || 0 < rotary_dim);
#endif
const bool use_kvcache = (need_append_kvcache || use_cache_batch_idx || 0 < page_block_size);
if(use_kvcache && mode != mode_enum::batch)
{
std::cerr << "kvcache enabled. ignoring the 'mode' option" << std::endl;
mode = mode_enum::batch;
}
auto [seqlen_qs, seqlen_ks, seqlen_kpads] =
decode_seqlen(mode,
......@@ -380,7 +392,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
arg_parser.get_str("s_k"),
arg_parser.get_str("s_kpad"),
/*seqlen_k_min=*/0 < seqlen_knew ? seqlen_knew : 0,
use_kvcache);
need_append_kvcache);
// compute kvcache seqlen_k (before appending knew/vnew)
auto cache_seqlen_ks = seqlen_ks;
std::transform(cache_seqlen_ks.begin(),
......@@ -557,33 +569,16 @@ bool run(const ck_tile::ArgParser& arg_parser)
}
#endif
struct
{
auto operator()(bool permute,
ck_tile::index_t b /*batch*/,
ck_tile::index_t h /*nhead*/,
ck_tile::index_t s /*seqlen*/,
ck_tile::index_t d /*hdim*/)
{
if(permute)
return std::array<ck_tile::index_t, 4>{b, h, s, d};
else
return std::array<ck_tile::index_t, 4>{b, s, h, d};
}
auto operator()(bool permute,
ck_tile::index_t ns /*num_splits*/,
ck_tile::index_t b /*batch*/,
ck_tile::index_t h /*nhead*/,
ck_tile::index_t s /*seqlen*/,
ck_tile::index_t d /*hdim*/)
{
if(permute)
return std::array<ck_tile::index_t, 5>{ns, b, h, s, d};
else
return std::array<ck_tile::index_t, 5>{ns, b, s, h, d};
}
} get_lengths;
static const auto get_lengths = [](bool permute,
ck_tile::index_t b /*batch*/,
ck_tile::index_t h /*nhead*/,
ck_tile::index_t s /*seqlen*/,
ck_tile::index_t d /*hdim*/) {
if(permute)
return std::array<ck_tile::index_t, 4>{b, h, s, d};
else
return std::array<ck_tile::index_t, 4>{b, s, h, d};
};
bool is_v_rowmajor = vlayout == std::string("r");
......@@ -635,12 +630,15 @@ bool run(const ck_tile::ArgParser& arg_parser)
ck_tile::HostTensor<LSEDataType> lse_acc_host(
1 < num_splits || use_kvcache
? std::array<ck_tile::index_t, 4>{num_splits, shape_batch, nhead, shape_seqlen_q}
? std::array<ck_tile::index_t, 4>{shape_batch, nhead, num_splits, shape_seqlen_q}
: std::array<ck_tile::index_t, 4>{1, 1, 1, 1});
ck_tile::HostTensor<OaccDataType> o_acc_host(
1 < num_splits || use_kvcache
? get_lengths(o_perm, num_splits, shape_batch, nhead, shape_seqlen_q, hdim_v)
: std::array<ck_tile::index_t, 5>{1, 1, 1, 1, 1});
1 < num_splits || use_kvcache ? std::array<ck_tile::index_t, 5>{shape_batch,
nhead,
num_splits,
shape_seqlen_q,
hdim_v}
: std::array<ck_tile::index_t, 5>{1, 1, 1, 1, 1});
// batch mode of lse data layout is [batch, nhead, seqlen_q]
// group mode of lse data layout is [nhead, total_seqlen_q]
......@@ -755,8 +753,10 @@ bool run(const ck_tile::ArgParser& arg_parser)
ck_tile::DeviceMem o_buf(o_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem seqstart_q(seqstart_q_host.size() * sizeof(int32_t));
ck_tile::DeviceMem seqstart_k(seqstart_k_host.size() * sizeof(int32_t));
ck_tile::DeviceMem seqlen_k_buf(
use_kvcache || 0 <= seqlen_kpads[0] ? seqlen_ks.size() * sizeof(int32_t) : 0);
ck_tile::DeviceMem seqlen_k_buf((mode == mode_enum::batch && use_kvcache) ||
0 <= seqlen_kpads[0]
? seqlen_ks.size() * sizeof(int32_t)
: 0);
ck_tile::DeviceMem cache_seqlen_k_buf(
need_append_kvcache ? cache_seqlen_ks.size() * sizeof(int32_t) : 0);
ck_tile::DeviceMem rotary_cos_buf(rotary_cos_host.get_element_space_size_in_bytes());
......@@ -777,7 +777,9 @@ bool run(const ck_tile::ArgParser& arg_parser)
seqstart_q.ToDevice(seqstart_q_host.data());
seqstart_k.ToDevice(seqlen_kpads[0] < 0 ? seqstart_k_host.data()
: seqstart_k_with_padding_host.data());
seqlen_k_buf.ToDevice(use_kvcache || 0 <= seqlen_kpads[0] ? seqlen_ks.data() : nullptr);
seqlen_k_buf.ToDevice((mode == mode_enum::batch && use_kvcache) || 0 <= seqlen_kpads[0]
? seqlen_ks.data()
: nullptr);
cache_seqlen_k_buf.ToDevice(need_append_kvcache ? cache_seqlen_ks.data() : nullptr);
rotary_cos_buf.ToDevice(rotary_cos_host.data());
rotary_sin_buf.ToDevice(rotary_sin_host.data());
......@@ -880,7 +882,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
}();
const ck_tile::index_t stride_bias = (i_perm ? shape_seqlen_k : 1 * shape_seqlen_k);
const ck_tile::index_t stride_randval = (max_seqlen_k);
const ck_tile::index_t stride_o_acc = (o_perm ? hdim_v : nhead * hdim_v);
const ck_tile::index_t stride_o_acc = (hdim_v);
const ck_tile::index_t stride_o = (o_perm ? hdim_v : nhead * hdim_v);
// setup nhead_stride_* arguments
const ck_tile::index_t nhead_stride_q = (i_perm ? shape_seqlen_q * hdim_q : hdim_q);
......@@ -906,8 +908,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
(i_perm ? 0 * shape_seqlen_q * shape_seqlen_k : 0 * shape_seqlen_k);
const ck_tile::index_t nhead_stride_randval = (shape_seqlen_q * max_seqlen_k);
const ck_tile::index_t nhead_stride_lse = shape_seqlen_q;
const ck_tile::index_t nhead_stride_lse_acc = shape_seqlen_q;
const ck_tile::index_t nhead_stride_o_acc = (o_perm ? shape_seqlen_q * hdim_v : hdim_v);
const ck_tile::index_t nhead_stride_lse_acc = (num_splits * shape_seqlen_q);
const ck_tile::index_t nhead_stride_o_acc = (num_splits * shape_seqlen_q * hdim_v);
const ck_tile::index_t nhead_stride_o = (o_perm ? shape_seqlen_q * hdim_v : hdim_v);
// setup batch_stride_* arguments
const ck_tile::index_t batch_stride_q = (nhead * shape_seqlen_q * hdim_q);
......@@ -922,13 +924,13 @@ bool run(const ck_tile::ArgParser& arg_parser)
const ck_tile::index_t batch_stride_bias = (0 * nhead * shape_seqlen_q * shape_seqlen_k);
const ck_tile::index_t batch_stride_randval = (nhead * shape_seqlen_q * max_seqlen_k);
const ck_tile::index_t batch_stride_lse = (nhead * shape_seqlen_q);
const ck_tile::index_t batch_stride_lse_acc = (nhead * shape_seqlen_q);
const ck_tile::index_t batch_stride_o_acc = (nhead * shape_seqlen_q * hdim_v);
const ck_tile::index_t batch_stride_o = (nhead * shape_seqlen_q * hdim_v);
const ck_tile::index_t batch_stride_lse_acc = (nhead * num_splits * shape_seqlen_q);
const ck_tile::index_t batch_stride_o_acc = (nhead * num_splits * shape_seqlen_q * hdim_v);
const ck_tile::index_t batch_stride_o = (nhead * shape_seqlen_q * hdim_v);
const ck_tile::index_t batch_stride_block_table = (max_num_page_blocks / batch);
// setup split_stride_* arguments (only used in split-kv kernel)
const ck_tile::index_t split_stride_lse_acc = (shape_batch * nhead * shape_seqlen_q);
const ck_tile::index_t split_stride_o_acc = (shape_batch * nhead * shape_seqlen_q * hdim_v);
const ck_tile::index_t split_stride_lse_acc = (shape_seqlen_q);
const ck_tile::index_t split_stride_o_acc = (shape_seqlen_q * hdim_v);
args.q_ptr = q_buf.GetDeviceBuffer();
args.k_ptr = k_buf.GetDeviceBuffer();
......@@ -990,8 +992,9 @@ bool run(const ck_tile::ArgParser& arg_parser)
(mode == mode_enum::group ? seqstart_q.GetDeviceBuffer() : nullptr);
args.seqstart_k_ptr =
(mode == mode_enum::group ? seqstart_k.GetDeviceBuffer() : nullptr);
args.seqlen_k_ptr =
(use_kvcache || 0 <= k_paddings_[0] ? seqlen_k_buf.GetDeviceBuffer() : nullptr);
args.seqlen_k_ptr = ((mode == mode_enum::batch && use_kvcache) || 0 <= k_paddings_[0]
? seqlen_k_buf.GetDeviceBuffer()
: nullptr);
args.seqlen_k = shape_seqlen_k; // unused in group mode (or kvcache enabled)
args.max_seqlen_q = max_seqlen_q;
......@@ -1043,6 +1046,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
(0 < page_block_size ? block_table_buf.GetDeviceBuffer() : nullptr);
args.batch_stride_block_table = batch_stride_block_table;
args.page_block_size = page_block_size;
args.is_gappy = false; // use 'false' for flash-attention integration
args.cache_batch_idx =
(use_cache_batch_idx ? cache_batch_idx_buf.GetDeviceBuffer() : nullptr);
......
example/ck_tile/01_fmha/fmha_fwd.hpp
View file @ 1f9546e0
......@@ -165,6 +165,8 @@ struct fmha_fwd_splitkv_args
void* block_table_ptr;
ck_tile::index_t batch_stride_block_table; // only used if 'block_table_ptr' is not nullptr
ck_tile::index_t page_block_size; // only used if 'block_table_ptr' is not nullptr
bool is_gappy; // differentiate seqstart_k_ptr usage. only used if 'block_table_ptr' is not
// nullptr.
const void* cache_batch_idx;
......@@ -173,9 +175,21 @@ struct fmha_fwd_splitkv_args
// seqlen_k = kargs.seqlen_k
// group mode: seqlen_q = kargs.seqstart_q_ptr[b + 1] - kargs.seqstart_q_ptr[b]
// seqlen_k = kargs.seqstart_k_ptr[b + 1] - kargs.seqstart_k_ptr[b]
// kvcache mode (use same kernel as batch mode):
// or kargs.seqlen_k_ptr[b]
//
// batch mode (kvcache):
// seqlen_q = kargs.seqlen_q
// seqlen_k = kargs.seqlen_k_ptr[b]
// group mode (kvcache):
// seqlen_q = kargs.seqstart_q_ptr[b + 1] - kargs.seqstart_q_ptr[b]
//
// when is_gappy=true:
// seqlen_k = kargs.seqlen_k_ptr[b]
// seqstart_k_ptr[b] now store local offset of each batch
//
// when is_gappy=false:
// seqlen_k = kargs.seqstart_k_ptr[b + 1] - kargs.seqstart_k_ptr[b]
// or kargs.seqlen_k_ptr[b]
const void* seqstart_q_ptr;
const void* seqstart_k_ptr;
const void* seqlen_k_ptr;
......@@ -251,7 +265,7 @@ struct fmha_fwd_appendkv_args
ck_tile::index_t batch_stride_block_table; // only used if 'block_table_ptr' is not nullptr
ck_tile::index_t page_block_size; // only used if 'block_table_ptr' is not nullptr
const void* cache_batch_idx;
const void* cache_batch_idx; // only used if block_table_ptr is nullptr -> batch mode (kvcache)
ck_tile::index_t stride_q;
ck_tile::index_t stride_k;
......@@ -278,87 +292,87 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args)
// create group mode kernel arguments
if constexpr(FmhaKernel::kIsGroupMode)
{
return FmhaKernel::MakeKargs(args.q_ptr,
args.k_ptr,
args.v_ptr,
args.bias_ptr,
args.rand_val_ptr,
args.lse_ptr,
args.o_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_s,
args.scale_p,
args.scale_o,
args.stride_q,
args.stride_k,
args.stride_v,
args.stride_bias,
args.stride_randval,
args.stride_o,
args.nhead_stride_q,
args.nhead_stride_k,
args.nhead_stride_v,
args.nhead_stride_bias,
args.nhead_stride_randval,
args.nhead_stride_lse,
args.nhead_stride_o,
args.window_size_left,
args.window_size_right,
args.mask_type,
args.p_drop,
args.s_randval,
args.drop_seed_offset);
return FmhaKernel::MakeKargsImpl(args.q_ptr,
args.k_ptr,
args.v_ptr,
args.bias_ptr,
args.rand_val_ptr,
args.lse_ptr,
args.o_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_s,
args.scale_p,
args.scale_o,
args.stride_q,
args.stride_k,
args.stride_v,
args.stride_bias,
args.stride_randval,
args.stride_o,
args.nhead_stride_q,
args.nhead_stride_k,
args.nhead_stride_v,
args.nhead_stride_bias,
args.nhead_stride_randval,
args.nhead_stride_lse,
args.nhead_stride_o,
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 FmhaKernel::MakeKargs(args.q_ptr,
args.k_ptr,
args.v_ptr,
args.bias_ptr,
args.rand_val_ptr,
args.lse_ptr,
args.o_ptr,
args.seqlen_q,
args.seqlen_k,
args.hdim_q,
args.hdim_v,
args.nhead_q,
args.nhead_q / args.nhead_k,
args.scale_s,
args.scale_p,
args.scale_o,
args.stride_q,
args.stride_k,
args.stride_v,
args.stride_bias,
args.stride_randval,
args.stride_o,
args.nhead_stride_q,
args.nhead_stride_k,
args.nhead_stride_v,
args.nhead_stride_bias,
args.nhead_stride_randval,
args.nhead_stride_lse,
args.nhead_stride_o,
args.batch_stride_q,
args.batch_stride_k,
args.batch_stride_v,
args.batch_stride_bias,
args.batch_stride_randval,
args.batch_stride_lse,
args.batch_stride_o,
args.window_size_left,
args.window_size_right,
args.mask_type,
args.p_drop,
args.s_randval,
args.drop_seed_offset);
return FmhaKernel::MakeKargsImpl(args.q_ptr,
args.k_ptr,
args.v_ptr,
args.bias_ptr,
args.rand_val_ptr,
args.lse_ptr,
args.o_ptr,
args.seqlen_q,
args.seqlen_k,
args.hdim_q,
args.hdim_v,
args.nhead_q,
args.nhead_q / args.nhead_k,
args.scale_s,
args.scale_p,
args.scale_o,
args.stride_q,
args.stride_k,
args.stride_v,
args.stride_bias,
args.stride_randval,
args.stride_o,
args.nhead_stride_q,
args.nhead_stride_k,
args.nhead_stride_v,
args.nhead_stride_bias,
args.nhead_stride_randval,
args.nhead_stride_lse,
args.nhead_stride_o,
args.batch_stride_q,
args.batch_stride_k,
args.batch_stride_v,
args.batch_stride_bias,
args.batch_stride_randval,
args.batch_stride_lse,
args.batch_stride_o,
args.window_size_left,
args.window_size_right,
args.mask_type,
args.p_drop,
args.s_randval,
args.drop_seed_offset);
}
}();
......@@ -389,6 +403,10 @@ auto fmha_fwd_splitkv_create_kargs_and_grids(fmha_fwd_splitkv_args args)
args.nhead_q,
args.nhead_q / args.nhead_k,
args.num_splits,
args.block_table_ptr,
args.batch_stride_block_table,
args.page_block_size,
args.is_gappy,
args.scale_s,
args.scale_p,
args.stride_q,
......
example/ck_tile/01_fmha/generate.py
View file @ 1f9546e0
......@@ -47,6 +47,9 @@ def list_blobs(output_file : Optional[str], api_list : List[str], kernel_filter
assert output_file is not None
file_path = Path(output_file)
# create an empty file / drop its contents if it exists
open(file_path, "w").close()
for api in api_list:
handler = handlers[api][HandlerId.LIST_BLOBS]
handler(file_path, kernel_filter, receipt, mask_impl)
......
example/ck_tile/01_fmha/script/smoke_test_fwd.sh
View file @ 1f9546e0
......@@ -29,14 +29,14 @@ while getopts ":sa" opt; do
done
run_fp16_bf16_tests() {
local NUM_SPLITS=(1)
local PAGE_BLOCK_SIZE=(0)
local CACHE_BATCH_IDX=(0)
local NUM_SPLITS="1"
local PAGE_BLOCK_SIZE="0"
local CACHE_BATCH_IDX="0"
if [ $TEST_SPLITKV -eq 1 ] ; then
NUM_SPLITS+=(2 3)
PAGE_BLOCK_SIZE+=(128)
CACHE_BATCH_IDX+=(1)
NUM_SPLITS="$NUM_SPLITS 2 3"
PAGE_BLOCK_SIZE="$PAGE_BLOCK_SIZE 128"
CACHE_BATCH_IDX="$CACHE_BATCH_IDX 1"
fi
for prec in "fp16" "bf16" ; do
......@@ -47,9 +47,9 @@ run_fp16_bf16_tests() {
for lse in 0 1 ; do
for bias in "n" "e" "a" ; do
for p_drop in 0.0 0.2 ; do
for num_splits in "${NUM_SPLITS[@]}" ; do
for page_block_size in "${PAGE_BLOCK_SIZE[@]}" ; do
for cache_batch_idx in "${CACHE_BATCH_IDX[@]}" ; do
for num_splits in $NUM_SPLITS ; do
for page_block_size in $PAGE_BLOCK_SIZE ; do
for cache_batch_idx in $CACHE_BATCH_IDX ; do
# $EXE -prec=$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 -num_splits=$num_splits -page_block_size=$page_block_size -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 -num_splits=$num_splits -page_block_size=$page_block_size -cache_batch_idx=$cache_batch_idx -kname=$KNAME $COMMON_ARGS
......@@ -103,4 +103,4 @@ if [ $TEST_APPENDKV -eq 1 ] ; then
run_fp16_appendkv_tests
fi
set +x
\ No newline at end of file
set +x
example/ck_tile/01_fmha/utils.hpp
View file @ 1f9546e0
......@@ -145,7 +145,7 @@ decode_seqlen(mode_enum mode,
std::string k_val,
std::string k_pad_val,
ck_tile::index_t seqlen_k_min = 0,
bool use_kvcache = false,
bool need_append_kvcache = false,
std::optional<unsigned> seed = std::nullopt)
{
#define _S2I_(str_) static_cast<ck_tile::index_t>(std::atoi((str_).c_str()))
......@@ -159,7 +159,7 @@ decode_seqlen(mode_enum mode,
const ck_tile::index_t seqlen_k_max = (k < 0 ? q : k);
std::vector<ck_tile::index_t> seqlen_ks(batch, seqlen_k_max);
if(1 < batch && use_kvcache)
if(1 < batch && need_append_kvcache)
{
// to keep the original s_k value, we always use seqlen_k_max in first batch
randints(std::next(seqlen_ks.begin()),
......
example/ck_tile/02_layernorm2d/CMakeLists.txt
View file @ 1f9546e0
# not using add_example_executable() to add this target, since we don't want this to have
# to be included in "make all/install/check"
add_executable(tile_example_layernorm2d_fwd EXCLUDE_FROM_ALL layernorm2d_fwd.cpp)
target_compile_options(tile_example_layernorm2d_fwd PRIVATE -DSAVE_MEAN_INV_STD)
\ No newline at end of file
set(LAYERNORM2D_FWD_KNOWN_APIS "fwd;bwd")
set(LAYERNORM2D_FWD_ENABLE_APIS "fwd" CACHE STRING
"semicolon-separated list of APIs to generate (${LAYERNORM2D_FWD_KNOWN_APIS}) & link, or \"all\".")
if(LAYERNORM2D_FWD_ENABLE_APIS STREQUAL "all")
set(LAYERNORM2D_FWD_ENABLE_APIS ${LAYERNORM2D_FWD_KNOWN_APIS})
endif()
# generate a list of kernels, but not actually emit files at config sta
execute_process(
COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py
--api ${LAYERNORM2D_FWD_ENABLE_APIS} --working_path ${CMAKE_CURRENT_BINARY_DIR} --list_blobs
RESULT_VARIABLE ret
)
if(ret AND NOT ret EQUAL 0)
message( FATAL_ERROR "Fail to generate kernels via Python. ${ret}")
endif()
file(STRINGS ${CMAKE_CURRENT_BINARY_DIR}/layernorm2d_fwd_blobs.txt LAYERNORM2D_FWD_GEN_BLOBS)
add_custom_command(
OUTPUT ${LAYERNORM2D_FWD_GEN_BLOBS}
COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py
--api ${LAYERNORM2D_FWD_ENABLE_APIS} --working_path ${CMAKE_CURRENT_BINARY_DIR} --gen_blobs
)
set(EXAMPLE_LAYERNORM2D_FWD "tile_example_layernorm2d_fwd")
message("adding example ${EXAMPLE_LAYERNORM2D_FWD}")
add_executable(${EXAMPLE_LAYERNORM2D_FWD} EXCLUDE_FROM_ALL layernorm2d_fwd.cpp)
target_include_directories(${EXAMPLE_LAYERNORM2D_FWD} PRIVATE ${CMAKE_CURRENT_LIST_DIR})
target_sources(${EXAMPLE_LAYERNORM2D_FWD} PRIVATE ${LAYERNORM2D_FWD_GEN_BLOBS})
set(EXAMPLE_LAYERNORM2D_FWD_COMPILE_OPTIONS)
# NOTE: we turn off undefined-func-template to let source compile without explicit declare function specializations
list(APPEND EXAMPLE_LAYERNORM2D_FWD_COMPILE_OPTIONS -Wno-undefined-func-template -Wno-float-equal)
target_compile_options(${EXAMPLE_LAYERNORM2D_FWD} PRIVATE ${EXAMPLE_LAYERNORM2D_FWD_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/02_layernorm2d/README.md
View file @ 1f9546e0
# Layernorm2D forward
This folder contains example for Layernorm2D forward using ck_tile tile-programming implementation.
This folder contains example for Layernorm2D forward using `ck_tile` tile-programming implementation.
# Implementation and feature support
## welford online algorithm
We use welfold algorithm to update `mean`/`variance` block by block. For `N <=4096` case we can compute `mean`/`var`/`normalization` within one loop, we call it `one-pass`. For large N case, it is hard to keep `mean`/`var` inside register/LDS and then computation `normalization`, so we need to load input twice, first time to compute `mean`/`var` block-by-block, then load input another time to compute the `normalization`. We call it `two-pass`.
## mean/variance save
In training case the mean/variance need to store out (TBD, not supported yet)
## prenorm/postnorm
![](misc/pnorm.png)
since [prenorm/postnorm](https://arxiv.org/pdf/1906.01787) is quite common in LLM blocks, this example boosts this feature by kernel fusion. Note that `prenorm`/`postnorm` always need to do elementwise-add a `shortcut` before the actual layernorm computation, and optionally store out the result to global. You can use `-fadd=1` to test `pre-add+store`, or `-fadd=2` to test `pre-add` without store out (not codegen by default).
## smooth-quant/dynamic-quant
we support smooth/dynamic quantization for `int8` output, by setting `-fquant=1` and `-prec_o=int8`. In this case the output will doing a rowwise dynamic quantization like below. Note that smooth-quant require input a `(1*N)` size per-channel scale(in fp32 in our example, though this is customizable), then elememt-wise multiply the tensor for each row, then compute the rowwise dynamic quant. if set `-fquant=2` will have the input per-channel scale stage, only the dynamic quant. This case is supported in our kernel but by default not generated (TBD: add some filter in generate.py support on-demand codegen)
![](misc/dquant.png)
```
# assume output int8, hidden_states is [m, n] shape and in fp16/bf16
# [m, 1]
per_token_amax, _ = torch.max(
input=torch.abs(hidden_states),
dim=-1,
keepdim=True
)
per_token_scale = per_token_amax.to(dtype=torch.float32) / 127.0
# quant hidden_states
hidden_states = (hidden_states / per_token_scale).to(dtype=torch.int8)
return hidden_states, per_token_scale
# hidden_states now is int8 will feed to next layer as intput
# per_token_scale will be used as dequant factor later layer
```
## build
```
# in the root of ck_tile
mkdir build && cd build
# you can replace <arch> with the appropriate architecture (for example gfx90a or gfx942) or leave it blank
sh ../script/cmake-ck-dev.sh ../ <arch>
sh ../script/cmake-ck-dev.sh ../ <arch> # you can replace this <arch> to gfx90a, gfx942...
make tile_example_layernorm2d_fwd -j
```
This will result in an executable `build/bin/tile_example_layernorm2d_fwd`
......@@ -16,8 +51,35 @@ This will result in an executable `build/bin/tile_example_layernorm2d_fwd`
```
args:
-m m dimension (default:3328)
-n m dimension (default:4096)
-n n dimension (default:4096)
-stride stride per row, if -1 then equal to n (default:-1)
-e epsilon (default:1e-5)
-save_mv save mean/variance(invstd) or not. set to 1 in training case (default:0)
-v cpu validation or not (default:1)
-prec precision (default:fp16)
```
\ No newline at end of file
-kname print kernel name or not (default:1)
-prec_i input precision (default:fp16)
-prec_o output precision, set auto will be the same as input (default:auto)
-prec_sx output quant scale type, set auto will be the same as input. used when fquant=1 (default:auto)
-prec_sy output quant scale type, set auto will be the same as input. used when fquant=1 or 2 (default:auto)
-fadd fused-add, 0:no fused add, 1:preadd+store, 2:preadd only (default:0)
-fquant fused-quant, 0:no, 1:smooth-dynamic-quant, 2:dynamic-quant (default:0)
-warmup cold iter (default:5)
-repeat hot iter (default:20)
```
## limitations
Note that `fquant=2`, `fadd=2`, `prec_sx/prec_sy` other than `fp32` are not by default generated. Though our kernel template suppor this. (TBD: add some flag in generate.py) to generate those instance on demand. Beside, `N>8192` case will by default using two-pass pipeline, and `-fquant=1/2` are not supported yet. If need suport `N>8192` and `fused+residual+store`, you can use this example together with `12_smoothquant`, to construct layernorm+residual, and smoothquant, 2 kernels for this purpose.
```
# some case
# standard fp16 layernorm 2d, m=10. n=1024
./build/bin/tile_example_layernorm2d_fwd -m=10 -n=1024
# standard fp16 layernorm 2d, m=10. n=1024, fused-smooth-quant, output in int8
./build/bin/tile_example_layernorm2d_fwd -m=10 -n=1024 -prec_o=int8 -fquant=1
# standard fp16 layernorm 2d, m=10. n=1024, fused-smooth-quant+fused-add-store, output in int8
./build/bin/tile_example_layernorm2d_fwd -m=10 -n=1024 -prec_o=int8 -fquant=1 -fadd=1
```
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