mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-02 04:31:25 +00:00
6a3960c1e1
* sync with function interface of cshuffleepiloge,fix flatmm build fail * move code from solin/flatmm which add mfma16*16*32fp8 and optimize flatmm --------- Co-authored-by: solin <bingzhou@amd.com>
295 lines
13 KiB
C++
295 lines
13 KiB
C++
// SPDX-License-Identifier: MIT
|
|
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
|
|
#pragma once
|
|
#include <type_traits>
|
|
|
|
template <typename T>
|
|
constexpr const char* DataTypeToString() {
|
|
if constexpr (std::is_same_v<T, ck_tile::half_t>) {
|
|
return "fp16";
|
|
} else if constexpr (std::is_same_v<T, ck_tile::fp8_t>) {
|
|
return "fp8";
|
|
} else if constexpr (std::is_same_v<T, ck_tile::bf8_t>) {
|
|
return "bf8";
|
|
} else {
|
|
return "unknown";
|
|
}
|
|
}
|
|
|
|
template <typename Layout>
|
|
static constexpr inline auto is_row_major(Layout layout_)
|
|
{
|
|
return ck_tile::bool_constant<std::is_same_v<ck_tile::remove_cvref_t<decltype(layout_)>,
|
|
ck_tile::tensor_layout::gemm::RowMajor>>{};
|
|
}
|
|
|
|
// mfma_type, 0:32x32, 1:16x16
|
|
template <typename T>
|
|
auto shuffle_b(const ck_tile::HostTensor<T>& t, std::string mfma_dtype, int mfma_type)
|
|
{
|
|
assert(t.get_lengths().size() == 2);
|
|
int n_ = t.get_lengths()[1];
|
|
int k_ = t.get_lengths()[0];
|
|
|
|
if((mfma_dtype == "bf16" || mfma_dtype == "fp16") && mfma_type == 0)
|
|
{
|
|
ck_tile::HostTensor<T> t_view({n_ / 32, 32, k_ / 16, 2, 8});
|
|
std::copy(t.begin(), t.end(), t_view.begin());
|
|
return ck_tile::reference_permute(t_view, {0, 2, 3, 1, 4});
|
|
}
|
|
else if((mfma_dtype == "bf16" || mfma_dtype == "fp16") && mfma_type == 1)
|
|
{
|
|
ck_tile::HostTensor<T> t_view({n_ / 16, 16, k_ / 32, 4, 8});
|
|
std::copy(t.begin(), t.end(), t_view.begin());
|
|
return ck_tile::reference_permute(t_view, {0, 2, 3, 1, 4});
|
|
}
|
|
else if((mfma_dtype == "int8" || mfma_dtype == "fp8" || mfma_dtype == "bf8") && mfma_type == 0)
|
|
{
|
|
ck_tile::HostTensor<T> t_view({n_ / 32, 32, k_ / 32, 2, 16});
|
|
std::copy(t.begin(), t.end(), t_view.begin());
|
|
return ck_tile::reference_permute(t_view, {0, 2, 3, 1, 4});
|
|
}
|
|
else if((mfma_dtype == "int8" || mfma_dtype == "fp8" || mfma_dtype == "bf8") && mfma_type == 1)
|
|
{
|
|
ck_tile::HostTensor<T> t_view({n_ / 16, 16, k_ / 64, 4, 16});
|
|
std::copy(t.begin(), t.end(), t_view.begin());
|
|
return ck_tile::reference_permute(t_view, {0, 2, 3, 1, 4});
|
|
}
|
|
return t;
|
|
}
|
|
|
|
template <typename ADataType, typename BDataType, typename AccDataType, typename CDataType>
|
|
auto calculate_rtol_atol(const ck_tile::index_t K,
|
|
const ck_tile::index_t kbatch,
|
|
const float max_accumulated_value)
|
|
{
|
|
using ComputeType =
|
|
std::conditional_t<sizeof(ADataType) < sizeof(BDataType), ADataType, BDataType>;
|
|
// Calculate thresholds
|
|
const auto rtol = ck_tile::get_relative_threshold<ComputeType, CDataType, AccDataType>(
|
|
ck_tile::integer_divide_ceil(K, kbatch));
|
|
const auto atol = ck_tile::get_absolute_threshold<ComputeType, CDataType, AccDataType>(
|
|
max_accumulated_value / kbatch, ck_tile::integer_divide_ceil(K, kbatch));
|
|
// Calculate error due to split_k accumulation
|
|
const auto rtol_split_k =
|
|
ck_tile::get_relative_threshold<CDataType, CDataType, CDataType>(kbatch);
|
|
const auto atol_split_k = ck_tile::get_absolute_threshold<CDataType, CDataType, CDataType>(
|
|
max_accumulated_value, kbatch);
|
|
// Use higher threshold
|
|
return ck_tile::make_tuple(std::max(rtol, rtol_split_k), std::max(atol, atol_split_k));
|
|
}
|
|
|
|
template <typename ADataType,
|
|
typename BDataType,
|
|
typename AccDataType,
|
|
typename CDataType,
|
|
typename ALayout,
|
|
typename BLayout,
|
|
typename CLayout>
|
|
float invoke_flatmm(ck_tile::DeviceMem& a_dev_buf,
|
|
ck_tile::DeviceMem& b_shuffle_dev_buf,
|
|
ck_tile::DeviceMem& c_dev_buf,
|
|
ck_tile::index_t M,
|
|
ck_tile::index_t N,
|
|
ck_tile::index_t K,
|
|
ck_tile::index_t stride_A,
|
|
ck_tile::index_t stride_B,
|
|
ck_tile::index_t stride_C,
|
|
ck_tile::index_t kbatch,
|
|
int n_warmup,
|
|
int n_repeat)
|
|
{
|
|
ck_tile::FlatmmHostArgs args;
|
|
args.a_ptr = a_dev_buf.GetDeviceBuffer();
|
|
args.b_shuffle_ptr = b_shuffle_dev_buf.GetDeviceBuffer();
|
|
args.c_ptr = c_dev_buf.GetDeviceBuffer();
|
|
|
|
args.k_batch = kbatch;
|
|
args.M = M;
|
|
args.N = N;
|
|
args.K = K;
|
|
args.stride_A = stride_A;
|
|
args.stride_B = stride_B;
|
|
args.stride_C = stride_C;
|
|
|
|
float ave_time = flatmm_calc<ADataType, BDataType, AccDataType, CDataType, ALayout, BLayout, CLayout>(
|
|
args, ck_tile::stream_config{nullptr, true, 1, n_warmup, n_repeat});
|
|
|
|
std::size_t flop = std::size_t(2) * M * N * K;
|
|
std::size_t num_byte =
|
|
sizeof(ADataType) * M * K + sizeof(BDataType) * N * K + sizeof(CDataType) * M * N;
|
|
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
|
|
float gb_per_sec = num_byte / 1.E6 / ave_time;
|
|
|
|
std::cout << "Run Flatmm kernel with DataType = " << DataTypeToString<ADataType>() << " M =" << M << " N =" << N << " K =" << K
|
|
<< " StrideA =" << stride_A << " StrideB =" << stride_B << " StrideC =" << stride_C
|
|
<< " : " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
|
|
<< std::endl;
|
|
|
|
return ave_time;
|
|
}
|
|
|
|
template <typename PrecType,
|
|
typename ALayout,
|
|
typename BLayout,
|
|
typename CLayout>
|
|
int run_flatmm_example_with_layouts(int argc,
|
|
char* argv[],
|
|
const ALayout a_layout = ALayout{},
|
|
const BLayout b_layout = BLayout{},
|
|
[[maybe_unused]] const CLayout c_layout = CLayout{})
|
|
{
|
|
auto [result, arg_parser] = create_args(argc, argv);
|
|
if(!result)
|
|
return -1;
|
|
|
|
using ADataType = typename GemmBasicTypeConfig<PrecType>::ADataType;
|
|
using BDataType = typename GemmBasicTypeConfig<PrecType>::BDataType;
|
|
using CDataType = typename GemmBasicTypeConfig<PrecType>::CDataType;
|
|
using AccDataType = typename GemmBasicTypeConfig<PrecType>::AccDataType;
|
|
|
|
ck_tile::index_t M = arg_parser.get_int("m");
|
|
ck_tile::index_t N = arg_parser.get_int("n");
|
|
ck_tile::index_t K = arg_parser.get_int("k");
|
|
|
|
ck_tile::index_t stride_A = arg_parser.get_int("stride_a");
|
|
ck_tile::index_t stride_B = arg_parser.get_int("stride_b");
|
|
ck_tile::index_t stride_C = arg_parser.get_int("stride_c");
|
|
|
|
ck_tile::index_t kbatch = arg_parser.get_int("split_k");
|
|
int n_warmup = arg_parser.get_int("warmup");
|
|
int n_repeat = arg_parser.get_int("repeat");
|
|
|
|
stride_A = ck_tile::get_default_stride(M, K, stride_A, is_row_major(a_layout));
|
|
stride_B = ck_tile::get_default_stride(K, N, stride_B, is_row_major(b_layout));
|
|
stride_C = ck_tile::get_default_stride(M, N, stride_C, is_row_major(CLayout{}));
|
|
|
|
ck_tile::HostTensor<ADataType> a_host(
|
|
ck_tile::host_tensor_descriptor(M, K, stride_A, is_row_major(a_layout)));
|
|
ck_tile::HostTensor<BDataType> b_origin_host(
|
|
ck_tile::host_tensor_descriptor(K, N, stride_B, is_row_major(b_layout)));
|
|
ck_tile::HostTensor<CDataType> c_rslt_host(
|
|
ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
|
|
|
|
// TODO: add different init types
|
|
ck_tile::FillUniformDistribution<ADataType>{-.5f, .5f}(a_host);
|
|
ck_tile::FillUniformDistribution<BDataType>{-.5f, .5f}(b_origin_host);
|
|
|
|
ck_tile::DeviceMem a_dev_buf(a_host.get_element_space_size_in_bytes());
|
|
ck_tile::DeviceMem c_dev_buf(c_rslt_host.get_element_space_size_in_bytes());
|
|
|
|
a_dev_buf.ToDevice(a_host.data());
|
|
c_rslt_host.SetZero();
|
|
|
|
// do pre-shuffle
|
|
std::string mfma = arg_parser.get_str("prec");
|
|
#if defined(USING_MFMA_16x16x32) && defined(ENABLE_FP8)
|
|
ck_tile::index_t mfma_type = 1;
|
|
#else
|
|
ck_tile::index_t mfma_type = 0;
|
|
#endif
|
|
ck_tile::HostTensor<BDataType> b_shuffle_host = shuffle_b(b_origin_host, mfma, mfma_type);
|
|
ck_tile::DeviceMem b_shuffle_dev_buf(b_shuffle_host.get_element_space_size_in_bytes());
|
|
b_shuffle_dev_buf.ToDevice(b_shuffle_host.data());
|
|
|
|
invoke_flatmm<ADataType, BDataType, AccDataType, CDataType, ALayout, BLayout, CLayout>(
|
|
a_dev_buf,
|
|
b_shuffle_dev_buf,
|
|
c_dev_buf,
|
|
M,
|
|
N,
|
|
K,
|
|
stride_A,
|
|
stride_B,
|
|
stride_C,
|
|
kbatch,
|
|
n_warmup,
|
|
n_repeat);
|
|
|
|
c_dev_buf.FromDevice(c_rslt_host.data());
|
|
bool pass = true;
|
|
|
|
if(arg_parser.get_int("v") == 1)
|
|
{
|
|
ck_tile::HostTensor<CDataType> c_ref_host(
|
|
ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
|
|
c_ref_host.SetZero();
|
|
|
|
ck_tile::reference_gemm<ADataType, BDataType, AccDataType, CDataType>(
|
|
a_host, b_origin_host, c_ref_host);
|
|
const float max_accumulated_value =
|
|
*std::max_element(c_ref_host.mData.begin(), c_ref_host.mData.end());
|
|
const auto rtol_atol = calculate_rtol_atol<ADataType, BDataType, AccDataType, CDataType>(K, kbatch, max_accumulated_value);
|
|
pass = ck_tile::check_err(c_rslt_host,
|
|
c_ref_host,
|
|
"Error: Incorrect results!",
|
|
rtol_atol.at(ck_tile::number<0>{}),
|
|
rtol_atol.at(ck_tile::number<1>{}));
|
|
|
|
std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
|
|
<< " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
|
|
<< std::endl;
|
|
std::cout << "The CPU veification result is:" << (pass ? "correct" : "fail") << std::endl;
|
|
}
|
|
else if(arg_parser.get_int("v") == 2)
|
|
{
|
|
ck_tile::DeviceMem b_origin_dev_buf(b_origin_host.get_element_space_size_in_bytes());
|
|
b_origin_dev_buf.ToDevice(b_origin_host.data());
|
|
|
|
ck_tile::HostTensor<CDataType> c_gpu_ref_host(
|
|
ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
|
|
ck_tile::DeviceMem c_gpu_ref_dev_buf(c_gpu_ref_host.get_element_space_size_in_bytes());
|
|
c_gpu_ref_host.SetZero();
|
|
c_gpu_ref_dev_buf.SetZero();
|
|
|
|
ADataType* d_A;
|
|
BDataType* d_B;
|
|
CDataType* d_C;
|
|
|
|
ck_tile::hip_check_error(hipMalloc(&d_A, M * K * sizeof(ADataType)));
|
|
ck_tile::hip_check_error(hipMalloc(&d_B, N * K * sizeof(BDataType)));
|
|
ck_tile::hip_check_error(hipMalloc(&d_C, M * N * sizeof(CDataType)));
|
|
|
|
ck_tile::hip_check_error(hipMemcpy(
|
|
d_A, a_dev_buf.GetDeviceBuffer(), M * K * sizeof(ADataType), hipMemcpyHostToDevice));
|
|
ck_tile::hip_check_error(hipMemcpy(d_B,
|
|
b_origin_dev_buf.GetDeviceBuffer(),
|
|
N * K * sizeof(BDataType),
|
|
hipMemcpyHostToDevice));
|
|
|
|
ck_tile::reference_gemm_gpu<ADataType,
|
|
BDataType,
|
|
AccDataType,
|
|
CDataType,
|
|
ALayout,
|
|
BLayout,
|
|
CLayout>(d_A, d_B, d_C, M, N, K, stride_A, stride_B, stride_C);
|
|
|
|
ck_tile::hip_check_error(hipMemcpy(c_gpu_ref_dev_buf.GetDeviceBuffer(),
|
|
d_C,
|
|
M * N * sizeof(CDataType),
|
|
hipMemcpyDeviceToHost));
|
|
|
|
ck_tile::hip_check_error(hipFree(d_A));
|
|
ck_tile::hip_check_error(hipFree(d_B));
|
|
ck_tile::hip_check_error(hipFree(d_C));
|
|
|
|
c_gpu_ref_dev_buf.FromDevice(c_gpu_ref_host.data());
|
|
const float max_accumulated_value =
|
|
*std::max_element(c_gpu_ref_host.mData.begin(), c_gpu_ref_host.mData.end());
|
|
const auto rtol_atol = calculate_rtol_atol<ADataType, BDataType, AccDataType, CDataType>(K, kbatch, max_accumulated_value);
|
|
pass = ck_tile::check_err(c_rslt_host,
|
|
c_gpu_ref_host,
|
|
"Error: Incorrect results!",
|
|
rtol_atol.at(ck_tile::number<0>{}),
|
|
rtol_atol.at(ck_tile::number<1>{}));
|
|
|
|
std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
|
|
<< " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
|
|
<< std::endl;
|
|
std::cout << "The GPU veification result is: " << (pass ? "correct" : "fail") << std::endl;
|
|
}
|
|
|
|
return pass;
|
|
}
|