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Unverified Commit 9de63596 authored by Illia Silin's avatar Illia Silin Committed by GitHub
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Merge pull request #48 from ROCm/merge_from_public 

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client_example/23_grouped_convnd_fwd_scaleadd_scaleadd_relu/grouped_conv_fwd_scaleadd_scaleadd_relu_bf16.cpp → client_example/24_grouped_conv_activation/grouped_convnd_fwd_scaleadd_scaleadd_relu/grouped_conv_fwd_scaleadd_scaleadd_relu_bf16.cpp
View file @ 9de63596
// 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 <tuple>
......
client_example/23_grouped_convnd_fwd_scaleadd_scaleadd_relu/grouped_conv_fwd_scaleadd_scaleadd_relu_fp16.cpp → client_example/24_grouped_conv_activation/grouped_convnd_fwd_scaleadd_scaleadd_relu/grouped_conv_fwd_scaleadd_scaleadd_relu_fp16.cpp
View file @ 9de63596
// 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 <tuple>
......
client_example/23_grouped_convnd_fwd_scaleadd_scaleadd_relu/grouped_conv_fwd_scaleadd_scaleadd_relu_fp32.cpp → client_example/24_grouped_conv_activation/grouped_convnd_fwd_scaleadd_scaleadd_relu/grouped_conv_fwd_scaleadd_scaleadd_relu_fp32.cpp
View file @ 9de63596
// 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 <tuple>
......
client_example/23_grouped_convnd_fwd_scaleadd_scaleadd_relu/grouped_conv_fwd_scaleadd_scaleadd_relu_int8.cpp → client_example/24_grouped_conv_activation/grouped_convnd_fwd_scaleadd_scaleadd_relu/grouped_conv_fwd_scaleadd_scaleadd_relu_int8.cpp
View file @ 9de63596
// 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 <tuple>
......
client_example/24_grouped_convnd_fwd_scaleadd_ab/CMakeLists.txt deleted 100644 → 0
View file @ e60c5aea
add_executable(client_grouped_convnd_fwd_scaleadd_ab_fp32 grouped_conv_fwd_scaleadd_ab_fp32.cpp)
target_link_libraries(client_grouped_convnd_fwd_scaleadd_ab_fp32 PRIVATE composable_kernel::device_conv_operations)
add_executable(client_grouped_convnd_fwd_scaleadd_ab_fp16 grouped_conv_fwd_scaleadd_ab_fp16.cpp)
target_link_libraries(client_grouped_convnd_fwd_scaleadd_ab_fp16 PRIVATE composable_kernel::device_conv_operations)
add_executable(client_grouped_convnd_fwd_scaleadd_ab_bf16 grouped_conv_fwd_scaleadd_ab_bf16.cpp)
target_link_libraries(client_grouped_convnd_fwd_scaleadd_ab_bf16 PRIVATE composable_kernel::device_conv_operations)
add_executable(client_grouped_convnd_fwd_scaleadd_ab_int8 grouped_conv_fwd_scaleadd_ab_int8.cpp)
target_link_libraries(client_grouped_convnd_fwd_scaleadd_ab_int8 PRIVATE composable_kernel::device_conv_operations)
client_example/25_wrapper/CMakeLists.txt
View file @ 9de63596
......@@ -2,3 +2,11 @@ add_executable(client_tensor_transform_using_wrapper tensor_transform_using_wrap
target_link_libraries(client_tensor_transform_using_wrapper PRIVATE composable_kernel::device_other_operations)
add_executable(client_wrapper_img2col wrapper_img2col.cpp)
target_link_libraries(client_wrapper_img2col PRIVATE composable_kernel::device_other_operations)
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_executable(client_wrapper_basic_gemm wrapper_basic_gemm.cpp)
target_link_libraries(client_wrapper_basic_gemm PRIVATE composable_kernel::device_other_operations)
add_executable(client_wrapper_optimized_gemm wrapper_optimized_gemm.cpp)
target_link_libraries(client_wrapper_optimized_gemm PRIVATE composable_kernel::device_other_operations)
endif()
client_example/25_wrapper/README.md 0 → 100644
View file @ 9de63596
# Composable Kernel wrapper GEMM tutorial
This tutorial demonstrates how to implement matrix multiplication using Composable Kernel (CK)
wrapper. We present the base version of GEMM without most of the available optimizations; however,
it's worth noting that CK has kernels with different optimizations.
To implement these optimizations, you can use the CK wrapper or directly use available instances in
CK. You can also refer to the
[optimized GEMM example](https://github.com/ROCm/composable_kernel/blob/develop/client_example/25_wrapper/wrapper_optimized_gemm.cpp),
that uses CK wrapper based on the
[`gridwise_gemm_xdlops_v2r3`](https://github.com/ROCm/composable_kernel/blob/develop/include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v2r3.hpp) implementation.
The kernel definition should look similar to:
```cpp
template <typename DataType,
typename GemmTraits,
ck::index_t scalar_per_vector,
typename BlockShape,
typename ThreadLayout>
__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)
```
We pass pointers to global memory and matrix dimensions via arguments. Additionally, we pass
selected lengths of processed data through each block (`tile_shape`) and thread layout
(`thread_layout`). For compilation time parameters, we define the data type,
[traits for the GEMM operation](https://github.com/ROCm/composable_kernel/blob/develop/include/ck/wrapper/traits/blockwise_gemm_xdl_traits.hpp)
and scalar per vector value during copy.
Step 1: Create layouts for global and LDS memory.
```cpp
// Specify layouts for global memory.
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));
// Specify layouts for tiles.
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>{}));
// Apply padding for global memory.
auto a_global_layout_padded = ck::wrapper::pad(a_global_layout, shape(a_tile_layout));
auto b_global_layout_padded = ck::wrapper::pad(b_global_layout, shape(b_tile_layout));
auto c_global_layout_padded = ck::wrapper::pad(c_global_layout, shape(c_tile_layout));
```
We pad layouts for global tensors in case M, N, and K are not divisible by `MPerBlock`, `NPerBlock`, or
`KPerBlock`.
Step 2: Create tensors for global and LDS memory.
```cpp
// Make tensors for global memory.
auto a_global_tensor = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Global>(
static_cast<const DataType*>(p_a), a_global_layout_padded);
auto b_global_tensor = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Global>(
static_cast<const DataType*>(p_b), b_global_layout_padded);
auto c_global_tensor = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Global>(
static_cast<DataType*>(p_c), c_global_layout_padded);
// Allocate LDS memory.
__shared__ DataType lds_a[ck::wrapper::size(a_tile_layout)];
__shared__ DataType lds_b[ck::wrapper::size(b_tile_layout)];
// Make tensors for lds memory.
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);
```
We must specify parameters for copy and convert block indexes to tuple:
```cpp
// Specify block index as tuple.
const auto block_idxs = ck::make_tuple(static_cast<ck::index_t>(blockIdx.x),
static_cast<ck::index_t>(blockIdx.y),
ck::wrapper::slice());
// Specify access parameters for copy.
using DimAccessOrder = ck::Tuple<ck::Number<0>, ck::Number<1>>;
constexpr ck::index_t vector_dim = 1;
```
We create a local tile (per block) and local partitions (per thread) for the global memory (`C`). We also
define and clear an output register (`c_vgpr_reg`) for the accumulation.
```cpp
auto c_global_local_tile = ck::wrapper::make_local_tile(
c_global_tensor,
tile_shape,
block_idxs,
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);
// Create C vgpr to accumulate results.
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>();
// Clear C vgpr.
ck::wrapper::clear(c_vgpr_reg);
```
We use two specific functions for `blockwise_gemm`: `make_blockwise_gemm_xdl_c_local_partition` and
`make_blockwise_gemm_xdl_c_vgpr`. This helps to choose the appropriate partition for the `C` output
and define tensors with specific layouts for `blockwise_gemm`. In the following step, we use only
generic functions for the CK wrapper.
Step 3: Create the compute loop.
```cpp
const ck::index_t num_loop = ck::math::integer_divide_ceil(K, KPerBlock);
ck::index_t i = 0;
do
{
// Get KPerBlock slice.
const auto k_slice = ck::wrapper::slice(i * KPerBlock, (i + 1) * KPerBlock);
auto a_global_tensor_k_slice = a_global_tensor(ck::wrapper::slice(), k_slice);
auto b_global_tensor_k_slice = b_global_tensor(ck::wrapper::slice(), k_slice);
// Create local tiles for A and B.
auto a_global_local_tile = ck::wrapper::make_local_tile(
a_global_tensor_k_slice,
tile_shape,
block_idxs,
make_tuple(ck::Number<1>{}, ck::wrapper::slice(N), ck::Number<1>{}));
auto b_global_local_tile = ck::wrapper::make_local_tile(
b_global_tensor_k_slice,
tile_shape,
block_idxs,
make_tuple(ck::wrapper::slice(M), ck::Number<1>{}, ck::Number<1>{}));
// Copy from global to LDS.
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);
// Synchronize lds.
ck::block_sync_lds();
// Execute blockwise GEMM.
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);
```
Loop iterate over `K / KPerBlock`. Each time a local tile is created for A and B tensors (tensor per block),
data is copied from global memory to LDS. The `blockwise_gemm` function performs the GEMM
operation on `a_lds_tensor` and `b_lds_tensor`, and stores results in `c_vgpr_reg`.
The end result from `c_vgpr_reg` is stored in the `C` local partition (tensor per thread):
```cpp
ck::wrapper::copy(c_vgpr_reg, c_global_local_partition);
```
If you want to dive deep into the details, you can find the entire example
[here](https://github.com/ROCm/composable_kernel/blob/develop/client_example/25_wrapper/wrapper_basic_gemm.cpp).
test/wrapper/test_gemm.cpp → client_example/25_wrapper/wrapper_basic_gemm.cpp
View file @ 9de63596
......@@ -6,13 +6,9 @@
#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"
......@@ -23,94 +19,88 @@
#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)
struct SimpleDeviceMem
{
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ReferenceGemmInstance = ck::tensor_operation::host::
ReferenceGemm<DataType, DataType, DataType, float, PassThrough, PassThrough, PassThrough>;
SimpleDeviceMem() = delete;
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}));
SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
{
(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
}
a_m_k.mData = a_data;
b_k_n.mData = b_data;
void* GetDeviceBuffer() { return p_mem_; }
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{});
~SimpleDeviceMem() { (void)hipFree(p_mem_); }
ref_invoker.Run(ref_argument);
EXPECT_TRUE(ck::utils::check_err(c_m_n_device_result, c_m_n_host_result.mData));
}
void* p_mem_;
};
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)
typename ThreadLayout>
__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);
// Specify layouts for global memory.
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));
// Specify layouts for tiles.
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>{}));
// Apply padding for global memory.
auto a_global_layout_padded = ck::wrapper::pad(a_global_layout, shape(a_tile_layout));
auto b_global_layout_padded = ck::wrapper::pad(b_global_layout, shape(b_tile_layout));
auto c_global_layout_padded = ck::wrapper::pad(c_global_layout, shape(c_tile_layout));
// Make tensors for global memory.
auto a_global_tensor = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Global>(
static_cast<const DataType*>(p_a), a_global_layout);
static_cast<const DataType*>(p_a), a_global_layout_padded);
auto b_global_tensor = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Global>(
static_cast<const DataType*>(p_b), b_global_layout);
static_cast<const DataType*>(p_b), b_global_layout_padded);
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));
static_cast<DataType*>(p_c), c_global_layout_padded);
// Allocate lds memory.
__shared__ DataType lds_a[ck::wrapper::size(a_tile_layout)];
__shared__ DataType lds_b[ck::wrapper::size(b_tile_layout)];
// Make tensors for lds memory.
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);
// Specify block index as tuple.
const auto block_idxs = ck::make_tuple(static_cast<ck::index_t>(blockIdx.x),
static_cast<ck::index_t>(blockIdx.y),
ck::wrapper::slice());
// Specify access parameters for copy.
using DimAccessOrder = ck::Tuple<ck::Number<0>, ck::Number<1>>;
constexpr ck::index_t vector_dim = 1;
// Create tile and partition for C. Use specific function for blockwise_gemm to assign the
// appropriate partitions.
auto c_global_local_tile = ck::wrapper::make_local_tile(
c_padded_global_tensor,
c_global_tensor,
tile_shape,
block_idx,
block_idxs,
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,
......@@ -118,42 +108,49 @@ __global__ void DeviceGemm(const void* p_a,
decltype(b_tile_layout),
ck::wrapper::size(thread_layout),
GemmTraits>(c_global_local_tile);
// Create C vgpr to accumulate results.
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>();
// Clear C vgpr.
ck::wrapper::clear(c_vgpr_reg);
// Iterate over K with KPerBlock step.
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,
// Get KPerBlock slice.
const auto k_slice = ck::wrapper::slice(i * KPerBlock, (i + 1) * KPerBlock);
auto a_global_tensor_k_slice = a_global_tensor(ck::wrapper::slice(), k_slice);
auto b_global_tensor_k_slice = b_global_tensor(ck::wrapper::slice(), k_slice);
// Create local tiles for A and B.
auto a_global_local_tile = ck::wrapper::make_local_tile(
a_global_tensor_k_slice,
tile_shape,
block_idx,
block_idxs,
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,
b_global_tensor_k_slice,
tile_shape,
block_idx,
block_idxs,
make_tuple(ck::wrapper::slice(M), ck::Number<1>{}, ck::Number<1>{}));
// Copy from global to lds.
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);
// Synchronize lds.
ck::block_sync_lds();
// Execute blockwise gemm.
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);
// Copy vgpr results to C global memory.
ck::wrapper::copy(c_vgpr_reg, c_global_local_partition);
}
......@@ -161,97 +158,58 @@ template <typename DataType,
typename GemmTraits,
ck::index_t scalar_per_vector,
typename BlockShape,
typename ThreadLayoutShape>
typename ThreadLayout>
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)
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));
SimpleDeviceMem a_mem(M * K * sizeof(DataType));
SimpleDeviceMem b_mem(K * N * sizeof(DataType));
SimpleDeviceMem 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)) *
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, 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);
DeviceGemm<DataType, GemmTraits, scalar_per_vector, BlockShape, ThreadLayout>;
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;
}
TEST(TestGemm, Float_2x4_4x2_XdlPerWave)
int main(int argc, char* argv[])
{
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);
using DataType = ck::half_t;
const auto thread_layout =
ck::wrapper::make_layout(ck::make_tuple(ck::Number<64>{}, ck::Number<4>{}),
ck::make_tuple(ck::Number<4>{}, ck::Number<1>{}));
const auto tile_shape = ck::make_tuple(ck::Number<256>{}, ck::Number<128>{}, ck::Number<32>{});
PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_4x2XdlPerWave_8K1, 8>(
3840, 4096, 4096, tile_shape, thread_layout);
return 0;
}
client_example/25_wrapper/wrapper_img2col.cpp
View file @ 9de63596
......@@ -15,6 +15,7 @@
#include "ck/wrapper/layout.hpp"
#include "ck/wrapper/tensor.hpp"
#include "ck/wrapper/operations/copy.hpp"
#include "ck/wrapper/utils/kernel_utils.hpp"
static constexpr ck::index_t NumDimSpatial = 3;
using DataType = float;
......@@ -36,21 +37,20 @@ struct SimpleDeviceMem
void* p_mem_;
};
// Test copy from Global to Global through LDS and VGPR
template <typename InputTensor,
typename OutputTensor,
typename BlockShape,
typename ThreadLayoutShape>
__global__ void DeviceImageToColumnPad0(InputTensor input_tensor,
OutputTensor output_tensor,
const BlockShape tile_shape,
const ThreadLayoutShape thread_layout)
template <typename InputTensor, typename OutputTensor, typename BlockShape, typename ThreadLayout>
__global__ void __CK_WRAPPER_LAUNCH_BOUNDS__
DeviceImageToColumnPad0(InputTensor input_tensor,
OutputTensor output_tensor,
const BlockShape tile_shape,
const ThreadLayout thread_layout)
{
const ck::index_t block_idx = static_cast<ck::index_t>(blockIdx.x);
// grid layout (dim1, dim0)
const auto block_idxs =
ck::make_tuple(static_cast<ck::index_t>(blockIdx.y), static_cast<ck::index_t>(blockIdx.x));
// Get local tiles for global memory
auto input_local_tile = ck::wrapper::make_local_tile(input_tensor, tile_shape, block_idx);
auto output_local_tile = ck::wrapper::make_local_tile(output_tensor, tile_shape, block_idx);
auto input_local_tile = ck::wrapper::make_local_tile(input_tensor, tile_shape, block_idxs);
auto output_local_tile = ck::wrapper::make_local_tile(output_tensor, tile_shape, block_idxs);
// Get partition per thread
const auto input_local_partition =
......@@ -112,9 +112,11 @@ void PerformImageToColumnPad0(const ck::index_t G,
SimpleDeviceMem out_buf(ck::wrapper::size(out_layout) * sizeof(DataType));
// User can choose appropriate number of threads and sizes per block
const auto thread_layout = ck::make_tuple(ck::Number<8>{}, ck::Number<16>{});
const auto thread_layout =
ck::wrapper::make_layout(ck::make_tuple(ck::Number<8>{}, ck::Number<16>{}),
ck::make_tuple(ck::Number<16>{}, ck::Number<1>{}));
// This example doesn't support padding, user should select tile sizes
// which divides the shape completely
// which are divisible by the shape.
const auto tile_shape = ck::make_tuple(ck::Number<32>{}, ck::Number<64>{});
// Create buffers for global memory
......@@ -123,10 +125,11 @@ void PerformImageToColumnPad0(const ck::index_t G,
auto output_tensor_global = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Global>(
static_cast<DataType*>(out_buf.GetDeviceBuffer()), out_layout);
const ck::index_t grid_size = ck::math::integer_divide_ceil(ck::wrapper::size<0>(in_layout),
ck::wrapper::size<0>(tile_shape)) *
ck::math::integer_divide_ceil(ck::wrapper::size<1>(in_layout),
ck::wrapper::size<1>(tile_shape));
// grid layout (dim1, dim0)
const ck::index_t grid_size_x = ck::math::integer_divide_ceil(ck::wrapper::size<1>(in_layout),
ck::wrapper::size<1>(tile_shape));
const ck::index_t grid_size_y = ck::math::integer_divide_ceil(ck::wrapper::size<0>(in_layout),
ck::wrapper::size<0>(tile_shape));
const auto kernel = DeviceImageToColumnPad0<decltype(input_tensor_global),
decltype(output_tensor_global),
......@@ -134,7 +137,7 @@ void PerformImageToColumnPad0(const ck::index_t G,
decltype(thread_layout)>;
const float avg_time = launch_and_time_kernel(StreamConfig{nullptr, true},
kernel,
dim3(grid_size),
dim3(grid_size_x, grid_size_y, 1),
dim3(ck::wrapper::size(thread_layout)),
0,
input_tensor_global,
......@@ -178,3 +181,4 @@ int main(int argc, char* argv[])
{1, 1, 1} /*filter_dilations*/);
return 0;
}
// MI100 Perf: 0.255178 ms, 1698.9 GB/s,
client_example/25_wrapper/wrapper_optimized_gemm.cpp 0 → 100644
View file @ 9de63596
// 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 "ck/library/utility/host_tensor.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"
struct SimpleDeviceMem
{
SimpleDeviceMem() = delete;
SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
{
(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
}
void* GetDeviceBuffer() { return p_mem_; }
~SimpleDeviceMem() { (void)hipFree(p_mem_); }
void* p_mem_;
};
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);
// Create layouts for global memory
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));
// Apply padding
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);
// Create tensors for global memory
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);
// Create layouts and tensors for lds memory.
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) + K0PerBlock];
__shared__ DataType lds_b[ck::wrapper::size(b_tile_layout) + K0PerBlock];
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;
// Create tile and partition for C global memory. Use specific gemm
// functions to get appropriate layouts.
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);
// Define and clear c vgpr register
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);
// Local partitions for lds memory
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);
// Lamda to slice tensor, then create local tile and partition
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>{})));
// Copy first values to lds
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);
// Pipeline loop
const ck::index_t num_loop =
__builtin_amdgcn_readfirstlane(ck::math::integer_divide_ceil(K, KPerBlock));
// Skip if only tile should be processed
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);
// Copy data to A vgpr.
ck::wrapper::copy<DimAccessOrder, vector_dim, scalar_per_vector>(
a_global_local_partition_i, a_vgpr_tensor);
// Synchronize.
ck::block_sync_lds();
// Copy data to B vgpr.
ck::wrapper::copy<DimAccessOrder, vector_dim, scalar_per_vector>(
b_global_local_partition_i, b_vgpr_tensor);
// Perform gemm.
ck::wrapper::blockwise_gemm_xdl<DataType, ck::wrapper::size(thread_layout), GemmTraits>(
a_lds_tensor, b_lds_tensor, c_vgpr_reg);
// Synchronize
ck::block_sync_lds();
// Copy data to A and B lds tiles.
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));
}
// Handle tail.
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);
// Store data from C vgpr to C global memory.
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
SimpleDeviceMem a_mem(M * K * sizeof(DataType));
SimpleDeviceMem b_mem(K * N * sizeof(DataType));
SimpleDeviceMem c_mem(M * N * sizeof(DataType));
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;
}
int main(int argc, char* argv[])
{
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<256>{}, ck::Number<128>{}, ck::Number<32>{});
PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_4x2XdlPerWave_8K1, 8, false>(
3840, 4096, 4096, tile_shape, thread_layout);
return 0;
}
cmake/Embed.cmake 0 → 100644
View file @ 9de63596
#####################################################################################
# The MIT License (MIT)
#
# Copyright (c) 2015-2024 Advanced Micro Devices, Inc. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#####################################################################################
if(WIN32)
set(EMBED_USE RC CACHE STRING "Use RC or CArrays to embed data files")
set_property(CACHE EMBED_USE PROPERTY STRINGS "RC;CArrays")
else()
if(BUILD_SHARED_LIBS)
set(EMBED_USE LD CACHE STRING "Use LD or CArrays to embed data files")
else()
set(EMBED_USE CArrays CACHE STRING "Use LD or CArrays to embed data files")
endif()
set_property(CACHE EMBED_USE PROPERTY STRINGS "LD;CArrays")
endif()
if(EMBED_USE STREQUAL "LD")
find_program(EMBED_LD ld REQUIRED)
find_program(EMBED_OBJCOPY objcopy REQUIRED)
endif()
function(embed_wrap_string)
set(options)
set(oneValueArgs VARIABLE AT_COLUMN)
set(multiValueArgs)
cmake_parse_arguments(PARSE "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
string(LENGTH ${${PARSE_VARIABLE}} string_length)
math(EXPR offset "0")
while(string_length GREATER 0)
if(string_length GREATER ${PARSE_AT_COLUMN})
math(EXPR length "${PARSE_AT_COLUMN}")
else()
math(EXPR length "${string_length}")
endif()
string(SUBSTRING ${${PARSE_VARIABLE}} ${offset} ${length} line)
set(lines "${lines}\n${line}")
math(EXPR string_length "${string_length} - ${length}")
math(EXPR offset "${offset} + ${length}")
endwhile()
set(${PARSE_VARIABLE} "${lines}" PARENT_SCOPE)
endfunction()
function(generate_embed_source EMBED_NAME EMBED_DIR BASE_DIRECTORY)
set(options)
set(oneValueArgs)
set(multiValueArgs SYMBOLS FILES)
cmake_parse_arguments(PARSE "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
set(RESOURCE_ID 100)
list(LENGTH PARSE_SYMBOLS SYMBOLS_LEN)
list(LENGTH PARSE_FILES FILES_LEN)
if(NOT ${SYMBOLS_LEN} EQUAL ${FILES_LEN})
message(FATAL_ERROR "Symbols and objects dont match: ${SYMBOLS_LEN} != ${FILES_LEN}")
endif()
math(EXPR LEN "${SYMBOLS_LEN} - 1")
foreach(idx RANGE ${LEN})
list(GET PARSE_SYMBOLS ${idx} SYMBOL)
list(GET PARSE_FILES ${idx} FILE)
file(RELATIVE_PATH BASE_NAME "${BASE_DIRECTORY}" ${FILE})
if(EMBED_USE STREQUAL "RC")
string(TOUPPER "${SYMBOL}" SYMBOL)
string(APPEND FILE_IDS "#define IDR_${SYMBOL} ${RESOURCE_ID}\n")
file(TO_NATIVE_PATH "${FILE}" NATIVE_FILE)
string(REPLACE "\\" "\\\\" NATIVE_FILE "${NATIVE_FILE}")
string(APPEND RC_FILE_MAPPING "IDR_${SYMBOL} TEXTFILE \"${NATIVE_FILE}\"\n")
string(APPEND INIT_KERNELS "\n {\"${BASE_NAME}\", resource::read(IDR_${SYMBOL})},")
math(EXPR RESOURCE_ID "${RESOURCE_ID} + 1" OUTPUT_FORMAT DECIMAL)
else()
set(START_SYMBOL "_binary_${SYMBOL}_start")
set(LENGTH_SYMBOL "_binary_${SYMBOL}_length")
if(EMBED_USE STREQUAL "LD")
string(APPEND EXTERNS "
extern const char ${START_SYMBOL}[];
extern const size_t _binary_${SYMBOL}_size;
const auto ${LENGTH_SYMBOL} = reinterpret_cast<size_t>(&_binary_${SYMBOL}_size);
")
else()
string(APPEND EXTERNS "
extern const char ${START_SYMBOL}[];
extern const size_t ${LENGTH_SYMBOL};
")
endif()
string(APPEND INIT_KERNELS "
{ \"${BASE_NAME}\", { ${START_SYMBOL}, ${LENGTH_SYMBOL}} },")
endif()
endforeach()
if(EMBED_USE STREQUAL "RC")
file(WRITE "${EMBED_DIR}/include/resource.h" "
#define TEXTFILE 256
${FILE_IDS}
")
file(WRITE "${EMBED_DIR}/resource.rc" "
#include \"resource.h\"
${RC_FILE_MAPPING}
")
set(EXTERNS "
#include <Windows.h>
#include \"resource.h\"
namespace resource {
std::string_view read(int id)
{
HMODULE handle = GetModuleHandle(nullptr);
HRSRC rc = FindResource(handle, MAKEINTRESOURCE(id), MAKEINTRESOURCE(TEXTFILE));
HGLOBAL data = LoadResource(handle, rc);
return {static_cast<const char*>(LockResource(data)), SizeofResource(handle, rc)};
}
}
")
set(EMBED_FILES ${EMBED_DIR}/include/resource.h ${EMBED_DIR}/resource.rc)
endif()
file(WRITE "${EMBED_DIR}/include/${EMBED_NAME}.hpp" "
#include <string_view>
#include <unordered_map>
#include <utility>
std::unordered_map<std::string_view, std::string_view> ${EMBED_NAME}();
")
file(WRITE "${EMBED_DIR}/${EMBED_NAME}.cpp" "
#include <${EMBED_NAME}.hpp>
${EXTERNS}
std::unordered_map<std::string_view, std::string_view> ${EMBED_NAME}()
{
static std::unordered_map<std::string_view, std::string_view> result = {${INIT_KERNELS}
};
return result;
}
")
list(APPEND EMBED_FILES ${EMBED_DIR}/${EMBED_NAME}.cpp ${EMBED_DIR}/include/${EMBED_NAME}.hpp)
set(EMBED_FILES ${EMBED_FILES} PARENT_SCOPE)
endfunction()
function(embed_file FILE BASE_DIRECTORY)
message(STATUS " ${FILE}")
file(RELATIVE_PATH REL_FILE "${BASE_DIRECTORY}" ${FILE})
string(MAKE_C_IDENTIFIER "${REL_FILE}" OUTPUT_SYMBOL)
get_filename_component(OUTPUT_FILE_DIR "${REL_FILE}" DIRECTORY)
file(MAKE_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/${OUTPUT_FILE_DIR}")
if(EMBED_USE STREQUAL "LD")
set(OUTPUT_FILE "${CMAKE_CURRENT_BINARY_DIR}/${REL_FILE}.o")
add_custom_command(
OUTPUT "${OUTPUT_FILE}"
COMMAND ${EMBED_LD} -r -o "${OUTPUT_FILE}" -z noexecstack --format=binary "${REL_FILE}"
COMMAND ${EMBED_OBJCOPY} --rename-section .data=.rodata,alloc,load,readonly,data,contents "${OUTPUT_FILE}"
WORKING_DIRECTORY "${BASE_DIRECTORY}"
DEPENDS "${FILE}"
VERBATIM)
set(OUTPUT_FILE ${OUTPUT_FILE} PARENT_SCOPE)
elseif(EMBED_USE STREQUAL "CArrays")
set_property(DIRECTORY APPEND PROPERTY CMAKE_CONFIGURE_DEPENDS ${FILE})
set(OUTPUT_FILE "${CMAKE_CURRENT_BINARY_DIR}/${REL_FILE}.cpp")
# reads source file contents as hex string
file(READ ${FILE} HEX_STRING HEX)
# wraps the hex string into multiple lines
embed_wrap_string(VARIABLE HEX_STRING AT_COLUMN 80)
# adds '0x' prefix and comma suffix before and after every byte respectively
string(REGEX REPLACE "([0-9a-f][0-9a-f])" "0x\\1, " ARRAY_VALUES ${HEX_STRING})
# removes trailing comma
string(REGEX REPLACE ", $" "" ARRAY_VALUES ${ARRAY_VALUES})
file(WRITE "${OUTPUT_FILE}" "
#include <cstddef>
extern const char _binary_${OUTPUT_SYMBOL}_start[] = { ${ARRAY_VALUES} };
extern const size_t _binary_${OUTPUT_SYMBOL}_length = sizeof(_binary_${OUTPUT_SYMBOL}_start);
")
set(OUTPUT_FILE ${OUTPUT_FILE} PARENT_SCOPE)
endif()
set(OUTPUT_SYMBOL ${OUTPUT_SYMBOL} PARENT_SCOPE)
endfunction()
function(add_embed_library EMBED_NAME)
set(options)
set(oneValueArgs RELATIVE)
set(multiValueArgs)
cmake_parse_arguments(PARSE "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
set(EMBED_DIR ${CMAKE_CURRENT_BINARY_DIR}/embed/${EMBED_NAME})
file(MAKE_DIRECTORY ${EMBED_DIR})
message(STATUS "Embedding kernel files:")
foreach(FILE ${PARSE_UNPARSED_ARGUMENTS})
embed_file(${FILE} ${PARSE_RELATIVE})
list(APPEND OUTPUT_FILES ${OUTPUT_FILE})
list(APPEND SYMBOLS ${OUTPUT_SYMBOL})
endforeach()
message(STATUS "Generating embedding library '${EMBED_NAME}'")
generate_embed_source(${EMBED_NAME} ${EMBED_DIR} "${PARSE_RELATIVE}" SYMBOLS ${SYMBOLS} FILES ${PARSE_UNPARSED_ARGUMENTS})
set(INTERNAL_EMBED_LIB embed_lib_${EMBED_NAME})
if(EMBED_USE STREQUAL "LD")
add_library(${INTERNAL_EMBED_LIB} STATIC ${EMBED_FILES} ${OUTPUT_FILES})
else()
add_library(${INTERNAL_EMBED_LIB} OBJECT ${EMBED_FILES})
endif()
if(EMBED_USE STREQUAL "CArrays")
target_sources(${INTERNAL_EMBED_LIB} PRIVATE ${OUTPUT_FILES})
endif()
target_include_directories(${INTERNAL_EMBED_LIB} PRIVATE "${EMBED_DIR}/include")
target_compile_options(${INTERNAL_EMBED_LIB} PRIVATE -Wno-reserved-identifier -Wno-extern-initializer -Wno-missing-variable-declarations)
set_target_properties(${INTERNAL_EMBED_LIB} PROPERTIES POSITION_INDEPENDENT_CODE On)
add_library(${EMBED_NAME} INTERFACE)
if(EMBED_USE STREQUAL "RC")
target_link_libraries(${EMBED_NAME} INTERFACE $<TARGET_OBJECTS:${INTERNAL_EMBED_LIB}>)
elseif(EMBED_USE STREQUAL "LD")
target_link_libraries(${EMBED_NAME} INTERFACE ${INTERNAL_EMBED_LIB})
else()
target_sources(${EMBED_NAME} INTERFACE $<TARGET_OBJECTS:${INTERNAL_EMBED_LIB}>)
endif()
target_include_directories(${EMBED_NAME} INTERFACE "${EMBED_DIR}/include")
endfunction()
codegen/CMakeLists.txt 0 → 100644
View file @ 9de63596
cmake_minimum_required(VERSION 3.16)
project(composable_kernel_host)
set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib)
set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib)
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
set(CK_ROOT ${CMAKE_CURRENT_SOURCE_DIR}/..)
find_package(ROCM)
include(ROCMInstallTargets)
include(ROCMTest)
list(APPEND CMAKE_MODULE_PATH ${CK_ROOT}/cmake)
include(Embed)
file(GLOB_RECURSE KERNEL_FILES CONFIGURE_DEPENDS
${CK_ROOT}/include/ck/*.hpp)
message(STATUS "KERNEL_FILES: ${KERNEL_FILES}")
message(STATUS "RELATIVE: ${CK_ROOT}/include")
add_embed_library(ck_headers ${KERNEL_FILES} RELATIVE ${CK_ROOT}/include)
add_definitions(-std=c++17)
file(GLOB SOURCES CONFIGURE_DEPENDS src/*.cpp)
# TODO: Use object library
add_library(ck_host STATIC ${SOURCES})
target_link_libraries(ck_host PRIVATE ck_headers)
set_target_properties(ck_host PROPERTIES
LINKER_LANGUAGE CXX
POSITION_INDEPENDENT_CODE ON)
target_include_directories(ck_host PUBLIC
$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
)
add_executable(ck-template-driver driver/main.cpp)
target_link_libraries(ck-template-driver ck_host)
rocm_install(
TARGETS ck_host ck_headers
EXPORT ck_hostTargets
)
rocm_install(DIRECTORY include/ck DESTINATION ${CMAKE_INSTALL_INCLUDEDIR})
if(BUILD_TESTING)
add_subdirectory(test)
endif()
codegen/driver/main.cpp 0 → 100644
View file @ 9de63596
#include <functional>
#include <iostream>
#include <string>
#include <unordered_map>
#include <vector>
#include "ck/host/device_gemm_multiple_d/operation.hpp"
#include "ck/host/stringutils.hpp"
using ck::host::Transform;
struct Emitters
{
std::unordered_map<std::string, std::function<std::vector<std::string>()>> m;
template <class T>
void Register(const std::string& name)
{
m[name] = [] {
auto configs = T::CreateOperations();
return Transform(configs, [](const auto& ops) { return ToTuple(ops); });
};
}
template <class T>
static std::string ToTuple(const T& ops)
{
auto templates = Transform(
ops, [](const auto& op) { return " " + op.ToSolution().ToTemplateString(); });
return "std::tuple<\n" + ck::host::JoinStrings(templates, ",\n") + ">";
}
std::string Emit(const std::string& name) { return ck::host::JoinStrings(m.at(name)(), "\n"); }
std::vector<std::string> List() const
{
return Transform(m, [](auto&& p) { return p.first; });
}
};
int main(int argc, const char* argv[])
{
std::string prog = argv[0];
std::vector<std::string> args(argv + 1, argv + argc);
Emitters e;
e.Register<ck::host::device_gemm_multiple_d::Operation_Xdl_CShuffle>(
"DeviceGemmMultipleD_Xdl_CShuffle");
if(args.empty() or std::any_of(args.begin(), args.end(), [](auto arg) {
return arg == "-h" or arg == "--help";
}))
{
std::cout << "USAGE:" << std::endl;
std::cout << " " << prog << " [TEMPLATE]" << std::endl;
std::cout << std::endl;
std::cout << "FLAGS:" << std::endl;
std::cout << " -h, --help Show help" << std::endl;
std::cout << std::endl;
std::cout << "TEMPLATES:" << std::endl;
for(auto x : e.List())
std::cout << " " << x << std::endl;
std::cout << std::endl;
return 0;
}
for(auto name : args)
std::cout << e.Emit(name) << std::endl;
return 0;
}
codegen/include/ck/host/device_gemm_multiple_d.hpp 0 → 100644
View file @ 9de63596
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <cstdlib>
#include <vector>
#include <memory>
#include <sstream>
#include <iterator>
#include <numeric>
#include "ck/host/types.hpp"
namespace ck {
namespace host {
namespace device_gemm_multiple_d {
struct Problem
{
std::size_t M = 0;
std::size_t N = 0;
std::size_t K = 0;
bool TransA = false;
bool TransB = false;
bool TransE = false;
std::vector<bool> DsTrans = {};
DataType ADataType = DataType::Half;
DataType BDataType = DataType::Half;
DataType EDataType = DataType::Half;
std::vector<DataType> DsDataType = {};
std::string AElementOp = "ck::tensor_operation::element_wise::PassThrough";
std::string BElementOp = "ck::tensor_operation::element_wise::PassThrough";
std::string CDEElementOp = "ck::Tuple<>";
std::string GetIncludeHeader() const;
std::vector<Solution> GetSolutions(const std::string& arch) const;
};
} // namespace device_gemm_multiple_d
} // namespace host
} // namespace ck
codegen/include/ck/host/device_gemm_multiple_d/operation.hpp 0 → 100644
View file @ 9de63596
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <cstdlib>
#include <vector>
#include <string>
#include "ck/host/types.hpp"
#include "ck/host/operation/gemm.hpp"
#include "ck/host/device_gemm_multiple_d/problem.hpp"
namespace ck {
namespace host {
namespace device_gemm_multiple_d {
struct Operation_Xdl_CShuffle
{
static std::vector<std::vector<Operation_Xdl_CShuffle>> CreateOperations();
static std::vector<Operation_Xdl_CShuffle> CreateOperations(const Problem& prob);
TensorDesc A{};
TensorDesc B{};
DataType acc = DataType::Float;
DataType cs_type = DataType::Half;
std::vector<TensorDesc> Ds = {};
TensorDesc E{};
std::string a_elem_op = PassThrough;
std::string b_elem_op = PassThrough;
std::string cde_elem_op = Bilinear;
std::string gemm_specialization = "ck::tensor_operation::device::GemmSpecialization::Default";
operation::TileDesc tile_desc{};
operation::BlockTransferDesc a_block_transfer{};
operation::BlockTransferDesc b_block_transfer{};
operation::CShuffleDesc cshuffle{};
operation::CBlockTransferDesc c_block_transfer{};
Solution ToSolution() const;
};
} // namespace device_gemm_multiple_d
} // namespace host
} // namespace ck
codegen/include/ck/host/device_gemm_multiple_d/problem.hpp 0 → 100644
View file @ 9de63596
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <cstdlib>
#include <vector>
#include <string>
#include "ck/host/types.hpp"
namespace ck {
namespace host {
namespace device_gemm_multiple_d {
struct Problem
{
std::size_t M = 0;
std::size_t N = 0;
std::size_t K = 0;
bool TransA = false;
bool TransB = false;
bool TransE = false;
std::vector<bool> DsTrans = {};
DataType ADataType = DataType::Half;
DataType BDataType = DataType::Half;
DataType EDataType = DataType::Half;
std::vector<DataType> DsDataType = {};
std::string AElementOp = PassThrough;
std::string BElementOp = PassThrough;
std::string CDEElementOp = PassThrough;
std::string GetIncludeHeader() const;
std::vector<Solution> GetSolutions(const std::string& arch) const;
};
} // namespace device_gemm_multiple_d
} // namespace host
} // namespace ck
codegen/include/ck/host/headers.hpp 0 → 100644
View file @ 9de63596
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <string>
#include <string_view>
#include <utility>
#include <unordered_map>
#include <vector>
namespace ck {
namespace host {
std::unordered_map<std::string_view, std::string_view> GetHeaders();
} // namespace host
} // namespace ck
codegen/include/ck/host/operation/gemm.hpp 0 → 100644
View file @ 9de63596
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <string>
namespace ck {
namespace host {
namespace operation {
struct TileDesc
{
int block_size = 0;
int m_per_block = 0;
int n_per_block = 0;
int k_per_block = 0;
int ak1 = 0;
int bk1 = 0;
int m_per_XDL = 0;
int n_per_XDL = 0;
int m_Xdl_per_wave = 0;
int n_Xdl_per_wave = 0;
int num_gemmk_prefetch_stage = 0;
};
struct BlockTransferDesc
{
std::string thread_cluster_length = "";
std::string thread_cluster_arrange_order = "";
std::string src_access_order = "";
int src_vec_dim = 0;
int src_scalar_per_vector = 0;
int dst_scalar_per_vector_k1 = 0;
int lds_add_extra_dim = 0;
};
struct CShuffleDesc
{
int m_Xdl_per_wave_per_shuffle = 0;
int n_Xdl_per_wave_per_shuffle = 0;
};
struct CBlockTransferDesc
{
std::string cluster_lengths_m_block_m_wave_m_per_Xdl_n_block_n_wave_n_per_Xdl = "";
int scalar_per_vector_n_wave_n_per_Xdl = 0;
};
} // namespace operation
} // namespace host
} // namespace ck
codegen/include/ck/host/stringutils.hpp 0 → 100644
View file @ 9de63596
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <algorithm>
#include <cassert>
#include <numeric>
#include <string>
#include <utility>
#include <unordered_map>
#include <vector>
namespace ck {
namespace host {
template <class F>
std::string trim(const std::string& s, F f)
{
auto start = std::find_if_not(s.begin(), s.end(), f);
auto last = std::find_if_not(s.rbegin(), std::string::const_reverse_iterator(start), f).base();
return {start, last};
}
inline std::string trim(const std::string& s)
{
return trim(s, [](unsigned char c) { return std::isspace(c); });
}
template <class Strings>
inline std::string JoinStrings(Strings strings, const std::string& delim)
{
auto it = strings.begin();
if(it == strings.end())
return "";
auto nit = std::next(it);
return std::accumulate(nit, strings.end(), *it, [&](std::string x, std::string y) {
return std::move(x) + delim + std::move(y);
});
}
template <class F>
inline std::string
InterpolateString(const std::string& input, F f, std::string start = "${", std::string end = "}")
{
std::string result = "";
result.reserve(input.size());
auto it = input.begin();
while(it != input.end())
{
auto next_start = std::search(it, input.end(), start.begin(), start.end());
auto next_end = std::search(next_start, input.end(), end.begin(), end.end());
result.append(it, next_start);
if(next_start == input.end())
break;
if(next_end == input.end())
{
throw std::runtime_error("Unbalanced brackets");
}
auto r = f(next_start + start.size(), next_end);
result.append(r.begin(), r.end());
it = next_end + end.size();
}
return result;
}
inline std::string InterpolateString(const std::string& input,
const std::unordered_map<std::string, std::string>& vars,
std::string start = "${",
std::string end = "}")
{
return InterpolateString(
input,
[&](auto start_it, auto last_it) {
auto key = trim({start_it, last_it});
auto it = vars.find(key);
if(it == vars.end())
throw std::runtime_error("Unknown key: " + key);
return it->second;
},
std::move(start),
std::move(end));
}
template <class Range, class F>
inline auto Transform(const Range& r, F f) -> std::vector<decltype(f(*r.begin()))>
{
std::vector<decltype(f(*r.begin()))> result;
std::transform(r.begin(), r.end(), std::back_inserter(result), f);
return result;
}
template <class Range1, class Range2, class F>
inline auto Transform(const Range1& r1, const Range2& r2, F f)
-> std::vector<decltype(f(*r1.begin(), *r2.begin()))>
{
std::vector<decltype(f(*r1.begin(), *r2.begin()))> result;
assert(std::distance(r1.begin(), r1.end()) == std::distance(r2.begin(), r2.end()));
std::transform(r1.begin(), r1.end(), r2.begin(), std::back_inserter(result), f);
return result;
}
} // namespace host
} // namespace ck
codegen/include/ck/host/types.hpp 0 → 100644
View file @ 9de63596
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <string>
#include <sstream>
#include <utility>
#include <unordered_map>
#include <vector>
namespace ck {
namespace host {
struct Solution
{
Solution() = default;
Solution(std::string str, std::unordered_map<std::string, std::string> values);
std::string ToTemplateString() const;
std::string GetTemplateParameter(const std::string& name) const;
template <class T>
T GetTemplateParameter(const std::string& name) const
{
T result;
std::stringstream ss(GetTemplateParameter(name));
ss >> result;
return result;
}
private:
std::string template_str;
std::unordered_map<std::string, std::string> template_values;
};
enum class DataType
{
Half,
Float,
Int8,
Int32
};
std::string ToString(DataType dt);
enum class Layout
{
Row,
Column
};
std::string ToString(Layout dl);
enum class GemmType
{
Default
};
std::string ToString(GemmType gt);
struct TensorDesc
{
DataType element;
Layout layout;
};
std::string SequenceStr(const std::vector<int>& v);
std::string MakeTuple(const std::vector<std::string>& v);
template <int... xs>
const std::string S = SequenceStr({xs...});
constexpr const char* PassThrough = "ck::tensor_operation::element_wise::PassThrough";
constexpr const char* Bilinear = "ck::tensor_operation::element_wise::Bilinear";
} // namespace host
} // namespace ck
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