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Add optimized blockwise gemm using ck wrapper (#1157)

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* Add optimized blockwise gemm using ck wrapper

* Add basic gemm example

* Update docs

* Add tutorial for gemm using ck wrapper

* Add perf note

* edits

* Fix cmake

* Fixes

---------

Co-authored-by: Lisa Delaney <lisa.delaney@amd.com>
This commit is contained in:
Bartłomiej Kocot
2024-02-13 17:04:36 +01:00
committed by GitHub
parent bf98b47697
commit 1e73adbc28
20 changed files with 1597 additions and 512 deletions
Download Patch File Download Diff File Expand all files Collapse all files

27
test/wrapper/CMakeLists.txt
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@@ -1,14 +1,21 @@
add_gtest_executable(test_layout test_layout.cpp)
target_link_libraries(test_layout PRIVATE utility)
add_gtest_executable(test_tensor test_tensor.cpp)
target_link_libraries(test_tensor PRIVATE utility)
add_gtest_executable(test_copy test_copy.cpp)
target_link_libraries(test_copy PRIVATE utility)
add_gtest_executable(test_partition test_partition.cpp)
target_link_libraries(test_partition PRIVATE utility)
add_custom_target(test_wrapper)
add_gtest_executable(test_wrapper_layout test_wrapper_layout.cpp)
target_link_libraries(test_wrapper_layout PRIVATE utility)
add_dependencies(test_wrapper test_wrapper_layout)
add_gtest_executable(test_wrapper_tensor test_wrapper_tensor.cpp)
target_link_libraries(test_wrapper_tensor PRIVATE utility)
add_dependencies(test_wrapper test_wrapper_tensor)
add_gtest_executable(test_wrapper_copy test_wrapper_copy.cpp)
target_link_libraries(test_wrapper_copy PRIVATE utility)
add_dependencies(test_wrapper test_wrapper_copy)
add_gtest_executable(test_wrapper_partition test_wrapper_partition.cpp)
target_link_libraries(test_wrapper_partition PRIVATE utility)
add_dependencies(test_wrapper test_wrapper_partition)
if(GPU_TARGETS MATCHES "gfx908" OR GPU_TARGETS MATCHES "gfx90a" OR
GPU_TARGETS MATCHES "gfx940" OR GPU_TARGETS MATCHES "gfx941" OR
GPU_TARGETS MATCHES "gfx942")
add_gtest_executable(test_gemm test_gemm.cpp)
target_link_libraries(test_gemm PRIVATE utility)
add_gtest_executable(test_wrapper_gemm test_wrapper_gemm.cpp)
target_link_libraries(test_wrapper_gemm PRIVATE utility)
add_dependencies(test_wrapper test_wrapper_gemm)
endif()

257
test/wrapper/test_gemm.cpp
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@@ -1,257 +0,0 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include <numeric>
#include <cstdlib>
#include <iostream>
#include <initializer_list>
#include <vector>
#include <gtest/gtest.h>
#include "ck/library/utility/host_tensor.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "ck/host_utility/kernel_launch.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/utility/common_header.hpp"
#include "ck/library/utility/fill.hpp"
#include "ck/wrapper/layout.hpp"
#include "ck/wrapper/tensor.hpp"
#include "ck/wrapper/operations/copy.hpp"
#include "ck/wrapper/operations/gemm.hpp"
template <typename DataType>
void CheckResult(const std::vector<DataType>& a_data,
const std::vector<DataType>& b_data,
std::vector<DataType>& c_m_n_device_result,
const ck::index_t M,
const ck::index_t N,
const ck::index_t K)
{
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ReferenceGemmInstance = ck::tensor_operation::host::
ReferenceGemm<DataType, DataType, DataType, float, PassThrough, PassThrough, PassThrough>;
Tensor<DataType> a_m_k(HostTensorDescriptor({M, K}));
Tensor<DataType> b_k_n(HostTensorDescriptor({K, N}, {1, K}));
Tensor<DataType> c_m_n_host_result(HostTensorDescriptor({M, N}));
a_m_k.mData = a_data;
b_k_n.mData = b_data;
auto ref_op = ReferenceGemmInstance{};
auto ref_invoker = ref_op.MakeInvoker();
auto ref_argument = ref_op.MakeArgument(
a_m_k, b_k_n, c_m_n_host_result, PassThrough{}, PassThrough{}, PassThrough{});
ref_invoker.Run(ref_argument);
EXPECT_TRUE(ck::utils::check_err(c_m_n_device_result, c_m_n_host_result.mData));
}
template <typename DataType,
typename GemmTraits,
ck::index_t scalar_per_vector,
typename BlockShape,
typename ThreadLayoutShape>
__global__ void DeviceGemm(const void* p_a,
const void* p_b,
void* p_c,
const ck::index_t M,
const ck::index_t N,
const ck::index_t K,
const BlockShape tile_shape,
const ThreadLayoutShape thread_layout)
{
constexpr auto MPerBlock = ck::wrapper::size<0>(tile_shape);
constexpr auto NPerBlock = ck::wrapper::size<1>(tile_shape);
constexpr auto KPerBlock = ck::wrapper::size<2>(tile_shape);
const auto a_global_layout =
ck::wrapper::make_layout(ck::make_tuple(M, K), ck::make_tuple(K, 1));
const auto b_global_layout =
ck::wrapper::make_layout(ck::make_tuple(N, K), ck::make_tuple(K, 1));
const auto c_global_layout =
ck::wrapper::make_layout(ck::make_tuple(M, N), ck::make_tuple(N, 1));
constexpr auto a_tile_layout = ck::wrapper::make_layout(
ck::make_tuple(MPerBlock, KPerBlock), ck::make_tuple(KPerBlock, ck::Number<1>{}));
constexpr auto b_tile_layout = ck::wrapper::make_layout(
ck::make_tuple(NPerBlock, KPerBlock), ck::make_tuple(KPerBlock, ck::Number<1>{}));
constexpr auto c_tile_layout = ck::wrapper::make_layout(
ck::make_tuple(MPerBlock, NPerBlock), ck::make_tuple(NPerBlock, ck::Number<1>{}));
auto a_global_tensor = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Global>(
static_cast<const DataType*>(p_a), a_global_layout);
auto b_global_tensor = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Global>(
static_cast<const DataType*>(p_b), b_global_layout);
auto c_global_tensor = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Global>(
static_cast<DataType*>(p_c), c_global_layout);
auto a_padded_global_tensor = ck::wrapper::pad(a_global_tensor, shape(a_tile_layout));
auto b_padded_global_tensor = ck::wrapper::pad(b_global_tensor, shape(b_tile_layout));
auto c_padded_global_tensor = ck::wrapper::pad(c_global_tensor, shape(c_tile_layout));
__shared__ DataType lds_a[ck::wrapper::size(a_tile_layout)];
__shared__ DataType lds_b[ck::wrapper::size(b_tile_layout)];
auto a_lds_tensor = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Lds>(
static_cast<DataType*>(lds_a), a_tile_layout);
auto b_lds_tensor = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Lds>(
static_cast<DataType*>(lds_b), b_tile_layout);
const ck::index_t block_idx = static_cast<ck::index_t>(blockIdx.x);
using DimAccessOrder = ck::Tuple<ck::Number<0>, ck::Number<1>>;
constexpr ck::index_t vector_dim = 1;
auto c_global_local_tile = ck::wrapper::make_local_tile(
c_padded_global_tensor,
tile_shape,
block_idx,
make_tuple(ck::Number<1>{}, ck::Number<1>{}, ck::wrapper::slice(KPerBlock)));
auto c_global_local_partition =
ck::wrapper::make_blockwise_gemm_xdl_c_local_partition<DataType,
decltype(a_tile_layout),
decltype(b_tile_layout),
ck::wrapper::size(thread_layout),
GemmTraits>(c_global_local_tile);
auto c_vgpr_reg = ck::wrapper::make_blockwise_gemm_xdl_c_vgpr<DataType,
decltype(a_tile_layout),
decltype(b_tile_layout),
ck::wrapper::size(thread_layout),
GemmTraits>();
ck::wrapper::clear(c_vgpr_reg);
const ck::index_t num_loop = ck::math::integer_divide_ceil(K, KPerBlock);
ck::index_t i = 0;
do
{
const auto k_slice = ck::wrapper::slice(i * KPerBlock, (i + 1) * KPerBlock);
auto a_padded_global_tensor_k_slice = a_padded_global_tensor(ck::wrapper::slice(), k_slice);
auto b_padded_global_tensor_k_slice = b_padded_global_tensor(ck::wrapper::slice(), k_slice);
auto a_global_local_tile = ck::wrapper::make_local_tile(
a_padded_global_tensor_k_slice,
tile_shape,
block_idx,
make_tuple(ck::Number<1>{}, ck::wrapper::slice(N), ck::Number<1>{}));
auto b_global_local_tile = ck::wrapper::make_local_tile(
b_padded_global_tensor_k_slice,
tile_shape,
block_idx,
make_tuple(ck::wrapper::slice(M), ck::Number<1>{}, ck::Number<1>{}));
ck::wrapper::blockwise_copy<DimAccessOrder, vector_dim, scalar_per_vector>(
a_global_local_tile, a_lds_tensor, thread_layout);
ck::wrapper::blockwise_copy<DimAccessOrder, vector_dim, scalar_per_vector>(
b_global_local_tile, b_lds_tensor, thread_layout);
ck::block_sync_lds();
ck::wrapper::blockwise_gemm_xdl<DataType, ck::wrapper::size(thread_layout), GemmTraits>(
a_lds_tensor, b_lds_tensor, c_vgpr_reg);
++i;
} while(i < num_loop);
ck::wrapper::copy(c_vgpr_reg, c_global_local_partition);
}
template <typename DataType,
typename GemmTraits,
ck::index_t scalar_per_vector,
typename BlockShape,
typename ThreadLayoutShape>
void PerformGemm(const ck::index_t M,
const ck::index_t N,
const ck::index_t K,
const BlockShape& tile_shape,
const ThreadLayoutShape& thread_layout)
{
// Global memory buffers
DeviceMem a_mem(M * K * sizeof(DataType));
DeviceMem b_mem(K * N * sizeof(DataType));
DeviceMem c_mem(M * N * sizeof(DataType));
std::vector<DataType> a_data(M * K);
std::vector<DataType> b_data(K * N);
ck::utils::FillUniformDistributionIntegerValue<DataType>{-5.f, 5.f}(a_data);
ck::utils::FillUniformDistributionIntegerValue<DataType>{-5.f, 5.f}(b_data);
a_mem.ToDevice(a_data.data());
b_mem.ToDevice(b_data.data());
c_mem.SetZero();
const ck::index_t grid_size =
ck::math::integer_divide_ceil(M, ck::wrapper::size<0>(tile_shape)) *
ck::math::integer_divide_ceil(N, ck::wrapper::size<1>(tile_shape));
const auto kernel =
DeviceGemm<DataType, GemmTraits, scalar_per_vector, BlockShape, ThreadLayoutShape>;
launch_and_time_kernel(StreamConfig{nullptr},
kernel,
dim3(grid_size),
dim3(ck::wrapper::size(thread_layout)),
0,
a_mem.GetDeviceBuffer(),
b_mem.GetDeviceBuffer(),
c_mem.GetDeviceBuffer(),
M,
N,
K,
tile_shape,
thread_layout);
std::vector<DataType> c_data(M * N);
c_mem.FromDevice(c_data.data());
CheckResult<DataType>(a_data, b_data, c_data, M, N, K);
}
TEST(TestGemm, Float)
{
using DataType = float;
const auto thread_layout = ck::make_tuple(ck::Number<16>{}, ck::Number<16>{});
const auto tile_shape = ck::make_tuple(ck::Number<128>{}, ck::Number<128>{}, ck::Number<64>{});
PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_2x2XdlPerWave_4K1, 4>(
512, 512, 128, tile_shape, thread_layout);
// Irregular case
PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_2x2XdlPerWave_4K1, 1>(
129, 129, 67, tile_shape, thread_layout);
}
TEST(TestGemm, Int8)
{
using DataType = int8_t;
const auto thread_layout = ck::make_tuple(ck::Number<64>{}, ck::Number<4>{});
const auto tile_shape = ck::make_tuple(ck::Number<128>{}, ck::Number<128>{}, ck::Number<64>{});
PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_2x2XdlPerWave_16K1, 16>(
512, 512, 128, tile_shape, thread_layout);
// Irregular case
PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_2x2XdlPerWave_16K1, 1>(
129, 129, 67, tile_shape, thread_layout);
}
TEST(TestGemm, Half)
{
using DataType = ck::half_t;
const auto thread_layout = ck::make_tuple(ck::Number<32>{}, ck::Number<8>{});
const auto tile_shape = ck::make_tuple(ck::Number<128>{}, ck::Number<128>{}, ck::Number<64>{});
PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_2x2XdlPerWave_8K1, 8>(
512, 512, 128, tile_shape, thread_layout);
// Irregular case
PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_2x2XdlPerWave_8K1, 1>(
129, 129, 67, tile_shape, thread_layout);
}
TEST(TestGemm, Float_2x4_4x2_XdlPerWave)
{
using DataType = float;
const auto thread_layout_4x2_xdl_per_wave = ck::make_tuple(ck::Number<16>{}, ck::Number<8>{});
const auto thread_layout_2x4_xdl_per_wave = ck::make_tuple(ck::Number<8>{}, ck::Number<16>{});
const auto tile_shape = ck::make_tuple(ck::Number<128>{}, ck::Number<128>{}, ck::Number<64>{});
PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_4x2XdlPerWave_4K1, 4>(
512, 512, 128, tile_shape, thread_layout_4x2_xdl_per_wave);
PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_2x4XdlPerWave_4K1, 4>(
512, 512, 128, tile_shape, thread_layout_2x4_xdl_per_wave);
}

27
test/wrapper/test_copy.cpp → test/wrapper/test_wrapper_copy.cpp
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@@ -20,23 +20,25 @@
template <typename InputTensor,
typename OutputTensor,
typename BlockShape,
typename ThreadLayoutShape,
typename ThreadLayout,
bool UseOptimizedCopy>
__global__ void TestCopyDevice(const InputTensor input_tensor,
OutputTensor output_tensor,
const BlockShape tile_shape,
const ThreadLayoutShape thread_layout)
const ThreadLayout thread_layout)
{
__shared__ ck::index_t p_shared[ck::wrapper::size(tile_shape)];
const auto tensor_lds = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Lds>(
p_shared, ck::wrapper::make_layout(tile_shape));
const auto block_idx = static_cast<ck::index_t>(blockIdx.x);
const auto block_idxs =
ck::make_tuple(static_cast<ck::index_t>(blockIdx.x), static_cast<ck::index_t>(blockIdx.y));
// Get local tiles for global memory
const auto input_local_tile = ck::wrapper::make_local_tile(input_tensor, tile_shape, block_idx);
const auto input_local_tile =
ck::wrapper::make_local_tile(input_tensor, tile_shape, block_idxs);
const auto output_local_tile =
ck::wrapper::make_local_tile(output_tensor, tile_shape, block_idx);
ck::wrapper::make_local_tile(output_tensor, tile_shape, block_idxs);
// Get partition per thread
const auto input_local_partition =
@@ -49,7 +51,7 @@ __global__ void TestCopyDevice(const InputTensor input_tensor,
// Allocate VGPR
auto tensor_vgpr =
ck::wrapper::make_register_tensor<ck::wrapper::MemoryTypeEnum::Vgpr, ck::index_t>(
layout(lds_local_partition));
ck::wrapper::make_layout(shape(lds_local_partition)));
// Perform copy
if constexpr(UseOptimizedCopy)
@@ -99,11 +101,14 @@ void PerformCopyGlobalToGlobalViaLDS()
auto output_tensor_global = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Global>(
static_cast<ck::index_t*>(out_buf.GetDeviceBuffer()), layout);
const auto thread_layout = ck::make_tuple(ck::Number<1>{}, ck::Number<32>{});
const auto tile_shape = ck::make_tuple(ck::Number<4>{}, ck::Number<64>{});
const auto thread_layout =
ck::wrapper::make_layout(ck::make_tuple(ck::Number<1>{}, ck::Number<32>{}));
const auto tile_shape = ck::make_tuple(ck::Number<4>{}, ck::Number<64>{});
const ck::index_t grid_size = ck::math::integer_divide_ceil(
ck::wrapper::size(input_tensor_global), ck::wrapper::size(tile_shape));
const ck::index_t grid_size_x = ck::math::integer_divide_ceil(
ck::wrapper::size<0>(input_tensor_global), ck::wrapper::size<0>(tile_shape));
const ck::index_t grid_size_y = ck::math::integer_divide_ceil(
ck::wrapper::size<1>(input_tensor_global), ck::wrapper::size<1>(tile_shape));
const auto kernel = TestCopyDevice<decltype(input_tensor_global),
decltype(output_tensor_global),
@@ -112,7 +117,7 @@ void PerformCopyGlobalToGlobalViaLDS()
UseOptimizedCopy>;
launch_and_time_kernel(StreamConfig{},
kernel,
dim3(grid_size),
dim3(grid_size_x, grid_size_y, 1),
dim3(ck::wrapper::size(thread_layout)),
0,
input_tensor_global,

376
test/wrapper/test_wrapper_gemm.cpp Normal file
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@@ -0,0 +1,376 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include <numeric>
#include <cstdlib>
#include <iostream>
#include <initializer_list>
#include <vector>
#include <gtest/gtest.h>
#include "ck/library/utility/host_tensor.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "ck/host_utility/kernel_launch.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/utility/common_header.hpp"
#include "ck/library/utility/fill.hpp"
#include "ck/wrapper/layout.hpp"
#include "ck/wrapper/tensor.hpp"
#include "ck/wrapper/operations/copy.hpp"
#include "ck/wrapper/operations/gemm.hpp"
#include "ck/wrapper/utils/kernel_utils.hpp"
template <typename DataType>
void CheckResult(const std::vector<DataType>& a_data,
const std::vector<DataType>& b_data,
std::vector<DataType>& c_m_n_device_result,
const ck::index_t M,
const ck::index_t N,
const ck::index_t K)
{
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ReferenceGemmInstance = ck::tensor_operation::host::
ReferenceGemm<DataType, DataType, DataType, float, PassThrough, PassThrough, PassThrough>;
Tensor<DataType> a_m_k(HostTensorDescriptor({M, K}));
Tensor<DataType> b_k_n(HostTensorDescriptor({K, N}, {1, K}));
Tensor<DataType> c_m_n_host_result(HostTensorDescriptor({M, N}));
a_m_k.mData = a_data;
b_k_n.mData = b_data;
auto ref_op = ReferenceGemmInstance{};
auto ref_invoker = ref_op.MakeInvoker();
auto ref_argument = ref_op.MakeArgument(
a_m_k, b_k_n, c_m_n_host_result, PassThrough{}, PassThrough{}, PassThrough{});
ref_invoker.Run(ref_argument);
EXPECT_TRUE(ck::utils::check_err(c_m_n_device_result, c_m_n_host_result.mData));
}
template <bool DoPad, typename Layout, typename PaddingDims>
__device__ auto ApplyPadding(const Layout& layout, const PaddingDims& padding_dims)
{
if constexpr(DoPad)
{
return ck::wrapper::pad(layout, padding_dims);
}
else
{
return layout;
}
}
template <typename DataType,
typename GemmTraits,
ck::index_t scalar_per_vector,
typename BlockShape,
typename ThreadLayout,
bool DoPadding>
__global__ void __CK_WRAPPER_LAUNCH_BOUNDS__ DeviceGemm(const void* p_a,
const void* p_b,
void* p_c,
const ck::index_t M,
const ck::index_t N,
const ck::index_t K,
const BlockShape tile_shape,
const ThreadLayout thread_layout)
{
constexpr auto MPerBlock = ck::wrapper::size<0>(tile_shape);
constexpr auto NPerBlock = ck::wrapper::size<1>(tile_shape);
constexpr auto KPerBlock = ck::wrapper::size<2>(tile_shape);
constexpr auto K1 = GemmTraits::K1;
constexpr auto K0PerBlock = KPerBlock / K1;
const auto K0 = ck::math::integer_divide_ceil(K, K1);
const auto tile_shape_k0_m_n_k1 = ck::make_tuple(K0PerBlock, MPerBlock, NPerBlock, K1);
const auto a_global_layout =
ck::wrapper::make_layout(ck::make_tuple(M, K), ck::make_tuple(K, 1));
const auto b_global_layout =
ck::wrapper::make_layout(ck::make_tuple(N, K), ck::make_tuple(K, 1));
const auto c_global_layout =
ck::wrapper::make_layout(ck::make_tuple(M, N), ck::make_tuple(N, 1));
auto a_padded_global_layout =
ApplyPadding<DoPadding>(a_global_layout, ck::make_tuple(MPerBlock, KPerBlock));
auto b_padded_global_layout =
ApplyPadding<DoPadding>(b_global_layout, ck::make_tuple(NPerBlock, KPerBlock));
auto c_padded_global_layout =
ApplyPadding<DoPadding>(c_global_layout, ck::make_tuple(MPerBlock, NPerBlock));
// Reshape from M,K to K0,M,K1
const auto reshaped_dims_idxs =
ck::make_tuple(ck::Number<1>{}, ck::make_tuple(ck::Number<0>{}, ck::Number<2>{}));
auto a_padded_unmerged_global_layout =
ck::wrapper::unmerge<1>(a_padded_global_layout, ck::make_tuple(K0, K1), reshaped_dims_idxs);
auto b_padded_unmerged_global_layout =
ck::wrapper::unmerge<1>(b_padded_global_layout, ck::make_tuple(K0, K1), reshaped_dims_idxs);
auto a_global_tensor = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Global>(
static_cast<const DataType*>(p_a), a_padded_unmerged_global_layout);
auto b_global_tensor = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Global>(
static_cast<const DataType*>(p_b), b_padded_unmerged_global_layout);
auto c_global_tensor = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Global>(
static_cast<DataType*>(p_c), c_padded_global_layout);
// Add extra M and N
constexpr auto a_tile_layout = ck::wrapper::make_layout(
ck::make_tuple(K0PerBlock, MPerBlock, K1),
ck::make_tuple((MPerBlock + ck::Number<1>{}) * K1, K1, ck::Number<1>{}));
constexpr auto b_tile_layout = ck::wrapper::make_layout(
ck::make_tuple(K0PerBlock, NPerBlock, K1),
ck::make_tuple((NPerBlock + ck::Number<1>{}) * K1, K1, ck::Number<1>{}));
__shared__ DataType lds_a[ck::wrapper::size(a_tile_layout) + NPerBlock];
__shared__ DataType lds_b[ck::wrapper::size(b_tile_layout) + NPerBlock];
auto a_lds_tensor = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Lds>(
static_cast<DataType*>(lds_a), a_tile_layout);
auto b_lds_tensor = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Lds>(
static_cast<DataType*>(lds_b), b_tile_layout);
const auto block_idxs = ck::make_tuple(ck::wrapper::slice(),
static_cast<ck::index_t>(blockIdx.x),
static_cast<ck::index_t>(blockIdx.y),
ck::wrapper::slice());
using DimAccessOrder = ck::Tuple<ck::Number<1>, ck::Number<0>, ck::Number<2>>;
constexpr ck::index_t vector_dim = 2;
auto c_global_local_tile =
ck::wrapper::make_local_tile(c_global_tensor,
tile_shape_k0_m_n_k1,
block_idxs,
make_tuple(ck::wrapper::slice(K0PerBlock),
ck::Number<1>{},
ck::Number<1>{},
ck::wrapper::slice(K1)));
auto c_global_local_partition =
ck::wrapper::make_blockwise_gemm_xdl_c_local_partition<DataType,
decltype(a_tile_layout),
decltype(b_tile_layout),
ck::wrapper::size(thread_layout),
GemmTraits>(c_global_local_tile);
auto c_vgpr_reg = ck::wrapper::make_blockwise_gemm_xdl_c_vgpr<DataType,
decltype(a_tile_layout),
decltype(b_tile_layout),
ck::wrapper::size(thread_layout),
GemmTraits>();
ck::wrapper::clear(c_vgpr_reg);
auto a_lds_tensor_local_partition =
ck::wrapper::make_local_partition(a_lds_tensor, thread_layout, threadIdx.x);
auto b_lds_tensor_local_partition =
ck::wrapper::make_local_partition(b_lds_tensor, thread_layout, threadIdx.x);
auto make_global_partition = [&](auto tensor, auto projection, ck::index_t i) {
const auto k_slice =
ck::make_tuple(ck::wrapper::slice(i * K0PerBlock, (i + 1) * K0PerBlock),
ck::wrapper::slice(),
ck::wrapper::slice());
auto local_tile = ck::wrapper::make_local_tile(
tensor(k_slice), tile_shape_k0_m_n_k1, block_idxs, projection);
return ck::wrapper::make_local_partition(local_tile, thread_layout, threadIdx.x);
};
auto a_global_local_partition = make_global_partition(
a_global_tensor,
make_tuple(ck::Number<1>{}, ck::Number<1>{}, ck::wrapper::slice(N), ck::Number<1>{}),
0);
auto b_global_local_partition = make_global_partition(
b_global_tensor,
make_tuple(ck::Number<1>{}, ck::wrapper::slice(M), ck::Number<1>{}, ck::Number<1>{}),
0);
// (row-major vgpr layout)
auto a_vgpr_tensor =
ck::wrapper::make_register_tensor<ck::wrapper::MemoryTypeEnum::Vgpr, DataType>(
ck::wrapper::make_layout(
shape(a_global_local_partition),
ck::make_tuple(ck::wrapper::size<1>(a_global_local_partition) *
ck::wrapper::size<2>(a_global_local_partition),
ck::wrapper::size<2>(a_global_local_partition),
ck::Number<1>{})));
auto b_vgpr_tensor =
ck::wrapper::make_register_tensor<ck::wrapper::MemoryTypeEnum::Vgpr, DataType>(
ck::wrapper::make_layout(
shape(b_global_local_partition),
ck::make_tuple(ck::wrapper::size<1>(a_global_local_partition) *
ck::wrapper::size<2>(a_global_local_partition),
ck::wrapper::size<2>(a_global_local_partition),
ck::Number<1>{})));
ck::wrapper::copy<DimAccessOrder, vector_dim, scalar_per_vector>(a_global_local_partition,
a_vgpr_tensor);
ck::wrapper::copy<DimAccessOrder, vector_dim, scalar_per_vector>(b_global_local_partition,
b_vgpr_tensor);
ck::wrapper::copy<DimAccessOrder, vector_dim, scalar_per_vector>(a_vgpr_tensor,
a_lds_tensor_local_partition);
ck::wrapper::copy<DimAccessOrder, vector_dim, scalar_per_vector>(b_vgpr_tensor,
b_lds_tensor_local_partition);
const ck::index_t num_loop =
__builtin_amdgcn_readfirstlane(ck::math::integer_divide_ceil(K, KPerBlock));
if(num_loop > 1)
{
ck::index_t i = 0;
do
{
auto a_global_local_partition_i = make_global_partition(
a_global_tensor,
make_tuple(
ck::Number<1>{}, ck::Number<1>{}, ck::wrapper::slice(N), ck::Number<1>{}),
i + 1);
auto b_global_local_partition_i = make_global_partition(
b_global_tensor,
make_tuple(
ck::Number<1>{}, ck::wrapper::slice(M), ck::Number<1>{}, ck::Number<1>{}),
i + 1);
ck::wrapper::copy<DimAccessOrder, vector_dim, scalar_per_vector>(
a_global_local_partition_i, a_vgpr_tensor);
ck::block_sync_lds();
ck::wrapper::copy<DimAccessOrder, vector_dim, scalar_per_vector>(
b_global_local_partition_i, b_vgpr_tensor);
ck::wrapper::blockwise_gemm_xdl<DataType, ck::wrapper::size(thread_layout), GemmTraits>(
a_lds_tensor, b_lds_tensor, c_vgpr_reg);
ck::block_sync_lds();
ck::wrapper::copy<DimAccessOrder, vector_dim, scalar_per_vector>(
a_vgpr_tensor, a_lds_tensor_local_partition);
ck::wrapper::copy<DimAccessOrder, vector_dim, scalar_per_vector>(
b_vgpr_tensor, b_lds_tensor_local_partition);
++i;
} while(i < (num_loop - 1));
}
ck::block_sync_lds();
ck::wrapper::blockwise_gemm_xdl<DataType, ck::wrapper::size(thread_layout), GemmTraits>(
a_lds_tensor, b_lds_tensor, c_vgpr_reg);
ck::wrapper::copy(c_vgpr_reg, c_global_local_partition);
}
template <typename DataType,
typename GemmTraits,
ck::index_t scalar_per_vector,
bool DoPadding,
typename BlockShape,
typename ThreadLayout>
void PerformGemm(const ck::index_t M,
const ck::index_t N,
const ck::index_t K,
const BlockShape& tile_shape,
const ThreadLayout& thread_layout)
{
// Global memory buffers
DeviceMem a_mem(M * K * sizeof(DataType));
DeviceMem b_mem(K * N * sizeof(DataType));
DeviceMem c_mem(M * N * sizeof(DataType));
std::vector<DataType> a_data(M * K);
std::vector<DataType> b_data(K * N);
ck::utils::FillUniformDistributionIntegerValue<DataType>{-5.f, 5.f}(a_data);
ck::utils::FillUniformDistributionIntegerValue<DataType>{-5.f, 5.f}(b_data);
a_mem.ToDevice(a_data.data());
b_mem.ToDevice(b_data.data());
c_mem.SetZero();
const ck::index_t grid_size_x =
ck::math::integer_divide_ceil(M, ck::wrapper::size<0>(tile_shape));
const ck::index_t grid_size_y =
ck::math::integer_divide_ceil(N, ck::wrapper::size<1>(tile_shape));
const auto kernel =
DeviceGemm<DataType, GemmTraits, scalar_per_vector, BlockShape, ThreadLayout, DoPadding>;
const float avg_time = launch_and_time_kernel(StreamConfig{nullptr, true},
kernel,
dim3(grid_size_x, grid_size_y, 1),
dim3(ck::wrapper::size(thread_layout)),
0,
a_mem.GetDeviceBuffer(),
b_mem.GetDeviceBuffer(),
c_mem.GetDeviceBuffer(),
M,
N,
K,
tile_shape,
thread_layout);
std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_btype =
sizeof(DataType) * M * K + sizeof(DataType) * K * N + sizeof(DataType) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
float gb_per_sec = num_btype / 1.E6 / avg_time;
std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << tflops << " TFlops, "
<< gb_per_sec << " GB/s, " << std::endl;
std::vector<DataType> c_data(M * N);
c_mem.FromDevice(c_data.data());
CheckResult<DataType>(a_data, b_data, c_data, M, N, K);
}
TEST(TestGemm, Float)
{
using DataType = float;
// (dim1, dim2, dim0 thread layout)
const auto thread_layout =
ck::wrapper::make_layout(ck::make_tuple(ck::Number<4>{}, ck::Number<64>{}, ck::Number<1>{}),
ck::make_tuple(ck::Number<1>{}, ck::Number<4>{}, ck::Number<1>{}));
const auto tile_shape = ck::make_tuple(ck::Number<128>{}, ck::Number<128>{}, ck::Number<16>{});
PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_2x2XdlPerWave_4K1, 4, false>(
512, 512, 128, tile_shape, thread_layout);
// Irregular case
PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_2x2XdlPerWave_4K1, 1, true>(
129, 129, 67, tile_shape, thread_layout);
}
TEST(TestGemm, Int8)
{
using DataType = int8_t;
const auto thread_layout =
ck::wrapper::make_layout(ck::make_tuple(ck::Number<4>{}, ck::Number<64>{}, ck::Number<1>{}),
ck::make_tuple(ck::Number<1>{}, ck::Number<4>{}, ck::Number<1>{}));
const auto tile_shape = ck::make_tuple(ck::Number<128>{}, ck::Number<128>{}, ck::Number<64>{});
PerformGemm<DataType,
ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_2x2XdlPerWave_16K1,
16,
false>(512, 512, 128, tile_shape, thread_layout);
// Irregular case
PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_2x2XdlPerWave_16K1, 1, true>(
129, 129, 67, tile_shape, thread_layout);
}
TEST(TestGemm, Half)
{
using DataType = ck::half_t;
const auto thread_layout =
ck::wrapper::make_layout(ck::make_tuple(ck::Number<4>{}, ck::Number<64>{}, ck::Number<1>{}),
ck::make_tuple(ck::Number<1>{}, ck::Number<4>{}, ck::Number<1>{}));
const auto tile_shape = ck::make_tuple(ck::Number<128>{}, ck::Number<128>{}, ck::Number<32>{});
PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_2x2XdlPerWave_8K1, 8, false>(
512, 512, 128, tile_shape, thread_layout);
// Irregular case
PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_2x2XdlPerWave_8K1, 1, true>(
129, 129, 67, tile_shape, thread_layout);
}
TEST(TestGemm, Float_2x4_4x2_XdlPerWave)
{
using DataType = float;
const auto thread_layout =
ck::wrapper::make_layout(ck::make_tuple(ck::Number<4>{}, ck::Number<64>{}, ck::Number<1>{}),
ck::make_tuple(ck::Number<1>{}, ck::Number<4>{}, ck::Number<1>{}));
const auto tile_shape = ck::make_tuple(ck::Number<256>{}, ck::Number<128>{}, ck::Number<16>{});
PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_4x2XdlPerWave_4K1, 4, false>(
512, 512, 128, tile_shape, thread_layout);
}

2
test/wrapper/test_layout.cpp → test/wrapper/test_wrapper_layout.cpp
View File

@@ -1,5 +1,5 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include <iostream>

33
test/wrapper/test_partition.cpp → test/wrapper/test_wrapper_partition.cpp
View File

@@ -29,8 +29,11 @@ TEST(TestPartition, LocalPartition)
const auto tensor =
ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Generic>(data.data(), layout);
const auto thread_steps = ck::make_tuple(ck::Number<1>{}, ck::Number<8>{}, ck::Number<1>{});
const auto thread_layout = ck::make_tuple(ck::Number<4>{}, ck::Number<8>{}, ck::Number<1>{});
const auto thread_steps = ck::make_tuple(ck::Number<1>{}, ck::Number<8>{}, ck::Number<1>{});
// row-major thread layout
const auto thread_layout =
ck::wrapper::make_layout(ck::make_tuple(ck::Number<4>{}, ck::Number<8>{}, ck::Number<1>{}),
ck::make_tuple(ck::Number<8>{}, ck::Number<1>{}, ck::Number<1>{}));
// 3d partition on 2d shape (calculate partition on 3d thread layout, and then skip first dim)
const auto thread_projection =
ck::make_tuple(ck::wrapper::slice(4), ck::Number<1>{}, ck::Number<1>{});
@@ -70,29 +73,37 @@ TEST(TestPartition, LocalTile)
ck::make_tuple(ck::Number<2>{}, ck::Number<4>{}, ck::Number<2>{}, ck::Number<2>{});
const auto block_projection =
ck::make_tuple(ck::Number<1>{}, ck::Number<1>{}, ck::Number<1>{}, ck::wrapper::slice(2));
constexpr ck::index_t projection_block_dim = ck::Number<2>{};
const auto num_blocks =
const auto grid_shape =
ck::make_tuple(ck::wrapper::size<0>(shape) / ck::wrapper::size<0>(block_shape),
ck::wrapper::size<1>(shape) / ck::wrapper::size<1>(block_shape),
ck::wrapper::size<2>(shape) / ck::wrapper::size<2>(block_shape));
std::vector<ck::index_t> block_idxs(ck::wrapper::size(num_blocks));
std::iota(block_idxs.begin(), block_idxs.end(), 0);
std::vector<ck::Tuple<ck::index_t, ck::index_t, ck::index_t, ck::index_t>> block_idxs;
for(int i = 0; i < ck::wrapper::size<0>(grid_shape); i++)
{
for(int j = 0; j < ck::wrapper::size<1>(grid_shape); j++)
{
for(int k = 0; k < ck::wrapper::size<2>(grid_shape); k++)
{
block_idxs.emplace_back(i, j, k, 0);
}
}
}
for(auto block_idx : block_idxs)
{
constexpr ck::index_t projection_block_dim = ck::Number<2>{};
const auto packed_tile =
ck::wrapper::make_local_tile(tensor, block_shape, block_idx, block_projection);
const auto expected_tile_size = ck::wrapper::size(block_shape) / projection_block_dim;
auto expected_tile_first_val = (block_idx % ck::wrapper::size<2>(num_blocks)) *
auto expected_tile_first_val = ck::wrapper::size<2>(block_idx) *
ck::wrapper::size<2>(block_shape) *
ck::wrapper::size<2>(strides);
block_idx /= ck::wrapper::size<2>(num_blocks);
expected_tile_first_val += (block_idx % ck::wrapper::size<1>(num_blocks)) *
expected_tile_first_val += ck::wrapper::size<1>(block_idx) *
ck::wrapper::size<1>(block_shape) *
ck::wrapper::size<1>(strides);
block_idx /= ck::wrapper::size<1>(num_blocks);
expected_tile_first_val += (block_idx % ck::wrapper::size<0>(num_blocks)) *
expected_tile_first_val += ck::wrapper::size<0>(block_idx) *
ck::wrapper::size<0>(block_shape) *
ck::wrapper::size<0>(strides);

0
test/wrapper/test_tensor.cpp → test/wrapper/test_wrapper_tensor.cpp
View File