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Added Multi_ABD support into Gemm and GroupedGemmFixedNK (#978)

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* added an example grouped_gemm_multi_abd

* fixed ci

* add setElementwiseOp

* changed API

* clean code: add multiA into example

* fixed v7r2 copy

* add transpose

* clean

* fixed vector_load check

* Update example/15_grouped_gemm/grouped_gemm_multi_abd_xdl_fixed_nk_bias_fp16.cpp

Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>

* Update example/15_grouped_gemm/grouped_gemm_multi_abd_xdl_fixed_nk_bias_fp16.cpp

Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>

* Update example/15_grouped_gemm/grouped_gemm_multi_abd_xdl_fixed_nk_bias_fp16.cpp

Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>

* Update include/ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_abd_xdl_cshuffle.hpp

Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>

* Update include/ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_abd_xdl_cshuffle.hpp

Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>

* Update include/ck/tensor_operation/gpu/device/device_grouped_gemm_multi_abd.hpp

Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>

* Update include/ck/tensor_operation/gpu/device/device_grouped_gemm_multi_abd.hpp

Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>

* Update include/ck/tensor_operation/gpu/device/device_grouped_gemm_multi_abd.hpp

Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>

* Update include/ck/tensor_operation/gpu/device/device_grouped_gemm_multi_abd.hpp

Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>

* Update include/ck/tensor_operation/gpu/device/device_grouped_gemm_multi_abd.hpp

Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>

* Update include/ck/tensor_operation/gpu/device/device_grouped_gemm_multi_abd.hpp

Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>

* Update include/ck/tensor_operation/gpu/device/device_grouped_gemm_multi_abd_fixed_nk.hpp

Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>

* Update include/ck/tensor_operation/gpu/device/device_grouped_gemm_multi_abd_fixed_nk.hpp

Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>

* add reduce

* testing

* add example_b16_i8

* refactor example

* clean

* add mpading

* disable reduce for kbatch = 1

* seperate reduce device op

* add reduce op

* add guard for workspace_size

* add instances

* format

* fixed

* add client example

* add a colmajor

* add instances

* Update cmake-ck-dev.sh

* Update profile_gemm_splitk.cpp

* Update gridwise_gemm_xdlops_v2r4r2.hpp

* format

* Update profile_gemm_splitk.cpp

* fixed

* fixed

* adjust test

* adjust precision loss

* adjust test

* fixed

* add bf16_i8 scale bias

* fixed scale

* fixed scale elementwise_op

* revert contraction deviceop changes

* fixed

* Add AddFastGelu

* Revert "Merge branch 'jizhan/gemm_splitk_reduce' into grouped_gemm_multi_abd_fixed_nk_example"

This reverts commit 3b5d001efd, reversing
changes made to 943199a991.

* add Scales into elementwise

* add gemm_multi_abd client example

* add client examples

* add rcr and crr

* add grouped gemm client example

* add grouped gemm client example

* add instance for rcr crr

* format

* fixed

* fixed cmake

* fixed

* fixed client_example

* format

* fixed contraction isSupport

* Update include/ck/tensor_operation/gpu/device/device_grouped_gemm_multi_abd_fixed_nk.hpp

Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>

* Update device_reduce_threadwise.hpp

* clean

* Fixes

* Fix example

---------

Co-authored-by: Jing Zhang <jizha@amd.com>
Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>

[ROCm/composable_kernel commit: 12865fbf28]
This commit is contained in:
zjing14
2024-04-15 21:09:45 -05:00
committed by GitHub
parent bb5dbf2f06
commit 4ddb546fe5
45 changed files with 6345 additions and 199 deletions
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98
include/ck/tensor_operation/gpu/device/device_grouped_gemm_multi_abd.hpp Normal file
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@@ -0,0 +1,98 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iostream>
#include <vector>
#include "device_base.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
struct GemmMultiABDDesc
{
ck::index_t M_, N_, K_;
std::vector<ck::index_t> stride_As_;
std::vector<ck::index_t> stride_Bs_;
std::vector<ck::index_t> stride_Ds_;
ck::index_t stride_C_;
};
/*
* \brief Grouped Gemm Multi ABD
*
* C = a_op(A, A1...) * b_op(B, B1...)
* E = cde_op(C, D0, D1, ...)
*
* \tparam AsLayout A layouts (tuple).
* \tparam BsLayout B layouts (tuple).
* \tparam DsLayout Ds layouts (tuple).
* \tparam ELayout Output layout.
* \tparam AsDataType A data types (tuple).
* \tparam BsDataType B data types (tuple).
* \tparam DsDataType D data types (tuple).
* \tparam EDataType Output data type.
* \tparam AElementwiseOperation A elementwise operation.
* \tparam BElementwiseOperation B elementwise operation.
* \tparam CDEElementwiseOperation C elementwise operation.
*/
template <typename AsLayout,
typename BsLayout,
typename DsLayout,
typename ELayout,
typename AsDataType,
typename BsDataType,
typename DsDataType,
typename EDataType,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CDEElementwiseOperation>
struct DeviceGroupedGemmMultiABD : public BaseOperator
{
static constexpr index_t NumATensor = AsDataType::Size();
static constexpr index_t NumBTensor = BsDataType::Size();
static constexpr index_t NumDTensor = DsDataType::Size();
static_assert(AsLayout::Size() == AsDataType::Size(), "wrong! inconsistent NumATensor");
static_assert(BsLayout::Size() == BsDataType::Size(), "wrong! inconsistent NumBTensor");
static_assert(DsLayout::Size() == DsDataType::Size(), "wrong! inconsistent NumDTensor");
/*
* \brief Make argument pointer for grouped gemm multi abd.
*
* \param p_as A pointers to the A.
* \param p_bs A pointers to the B.
* \param p_ds A pointers to the Ds.
* \param p_e A pointers to the E.
* \param gemm_desc Gemm descriptors for each group.
* \param a_element_op A elementwise operation object.
* \param b_element_op B elementwise operation object.
* \param cde_element_op CDE elementwise operation object.
* \return Pointer to the argument.
*/
virtual std::unique_ptr<BaseArgument>
MakeArgumentPointer(std::vector<std::array<const void*, NumATensor>>& p_as,
std::vector<std::array<const void*, NumBTensor>>& p_bs,
std::vector<std::array<const void*, NumDTensor>>& p_ds,
std::vector<void*>& p_e,
std::vector<GemmMultiABDDesc>& gemm_desc,
AElementwiseOperation a_element_op = AElementwiseOperation{},
BElementwiseOperation b_element_op = BElementwiseOperation{},
CDEElementwiseOperation c_element_op = CDEElementwiseOperation{}) = 0;
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
virtual void SetElementwiseOps(BaseArgument* p_arg,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation cde_element_op) const = 0;
};
} // namespace device
} // namespace tensor_operation
} // namespace ck

81
include/ck/tensor_operation/gpu/device/device_grouped_gemm_multi_abd_fixed_nk.hpp Normal file
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@@ -0,0 +1,81 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iostream>
#include <array>
#include "device_grouped_gemm_multi_abd.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
template <index_t NumATensor = 1, index_t NumBTensor = 1, index_t NumDTensor = 0>
struct GroupedGemmMultiABDKernelArgument
{
std::array<const void*, NumATensor> p_as_grid;
std::array<const void*, NumBTensor> p_bs_grid;
std::array<const void*, NumDTensor> p_ds_grid;
void* p_e_grid;
index_t M;
index_t N;
index_t K;
std::array<index_t, NumATensor> StrideAs;
std::array<index_t, NumBTensor> StrideBs;
std::array<index_t, NumDTensor> StrideDs;
index_t StrideE;
};
/*
* \brief Grouped Gemm Multi ABD Fixed NK
*
* C = a_op(A, A1...) * b_op(B, B1...)
* E = cde_op(C, D0, D1, ...)
*
* \tparam AsLayout A layouts (tuple).
* \tparam BsLayout B layouts (tuple).
* \tparam DsLayout Ds layouts (tuple).
* \tparam ELayout Output layout.
* \tparam AsDataType A data types (tuple).
* \tparam BsDataType B data types (tuple).
* \tparam DsDataType D data types (tuple).
* \tparam EDataType Output data type.
* \tparam AElementwiseOperation A elementwise operation.
* \tparam BElementwiseOperation B elementwise operation.
* \tparam CDEElementwiseOperation C elementwise operation.
*/
template <typename AsLayout,
typename BsLayout,
typename DsLayout,
typename ELayout,
typename AsDataType,
typename BsDataType,
typename DsDataType,
typename EDataType,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CElementwiseOperation>
struct DeviceGroupedGemmMultiABDFixedNK : DeviceGroupedGemmMultiABD<AsLayout,
BsLayout,
DsLayout,
ELayout,
AsDataType,
BsDataType,
DsDataType,
EDataType,
AElementwiseOperation,
BElementwiseOperation,
CElementwiseOperation>
{
virtual void SetDeviceKernelArgs(BaseArgument* p_arg, const void* kernel_args) const = 0;
virtual size_t GetDeviceKernelArgSize(const BaseArgument* p_arg) const = 0;
virtual void SetKBatch(BaseArgument* p_arg, index_t k_batch) const = 0;
};
} // namespace device
} // namespace tensor_operation
} // namespace ck

12
include/ck/tensor_operation/gpu/device/impl/device_contraction_multiple_abd_xdl_cshuffle.hpp
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@@ -663,7 +663,8 @@ struct DeviceContractionMultipleABD_Xdl_CShuffle
const bool valid_a_access_dim_k =
ABlockTransferSrcVectorDim == 2 && arg.as_kz_consecutive_[i];
const bool valid_a_access_dim = valid_a_access_dim_m || valid_a_access_dim_k;
if(!(valid_a_vector_size && valid_a_access_dim))
if(!((valid_a_vector_size && valid_a_access_dim) ||
ABlockTransferSrcScalarPerVector == 1))
{
valid_as_access = false;
}
@@ -682,7 +683,8 @@ struct DeviceContractionMultipleABD_Xdl_CShuffle
const bool valid_b_access_dim_k =
BBlockTransferSrcVectorDim == 2 && arg.bs_kz_consecutive_[i];
const bool valid_b_access_dim = valid_b_access_dim_n || valid_b_access_dim_k;
if(!(valid_b_vector_size && valid_b_access_dim))
if(!((valid_b_vector_size && valid_b_access_dim) ||
BBlockTransferSrcScalarPerVector == 1))
{
valid_bs_access = false;
}
@@ -698,7 +700,8 @@ struct DeviceContractionMultipleABD_Xdl_CShuffle
arg.ds_max_read_elems_[i] % CDEBlockTransferScalarPerVector_NPerBlock == 0;
// Vector read of Ds is always on N dimension.
const bool valid_d_access_dim = arg.ds_nz_consecutive_[i];
if(!(valid_d_vector_size && valid_d_access_dim))
if(!((valid_d_vector_size && valid_d_access_dim) ||
CDEBlockTransferScalarPerVector_NPerBlock == 1))
{
valid_ds_access = false;
}
@@ -712,7 +715,8 @@ struct DeviceContractionMultipleABD_Xdl_CShuffle
arg.e_max_write_elems_ % CDEBlockTransferScalarPerVector_NPerBlock == 0;
// Vector write of E is always on N dimension.
const bool valid_e_access_dim = arg.e_nz_consecutive_;
if(!(valid_e_vector_size && valid_e_access_dim))
if(!((valid_e_vector_size && valid_e_access_dim) ||
CDEBlockTransferScalarPerVector_NPerBlock == 1))
{
return false;
}

106
include/ck/tensor_operation/gpu/device/impl/device_gemm_multiple_abd_xdl_cshuffle.hpp
View File

@@ -169,78 +169,6 @@ struct DeviceGemmMultipleABD_Xdl_CShuffle : public DeviceGemmMultipleABD<AsLayou
static constexpr auto I2 = Number<2>{};
static constexpr auto I3 = Number<3>{};
#if 0
static constexpr auto matrix_padder =
MatrixPadder<GemmSpec, index_t, index_t, index_t>{MPerBlock, NPerBlock, KPerBlock};
static auto MakeAGridDescriptor_M_K(index_t MRaw, index_t KRaw, index_t StrideAs)
{
const auto a_grid_desc_mraw_kraw = [&]() {
if constexpr(is_same_v<tensor_layout::gemm::RowMajor, AsLayout>)
{
return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
make_tuple(StrideAs, I1));
}
else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, AsLayout>)
{
return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
make_tuple(I1, StrideAs));
}
}();
return matrix_padder.PadADescriptor_M_K(a_grid_desc_mraw_kraw);
}
static auto MakeBGridDescriptor_N_K(index_t KRaw, index_t NRaw, index_t StrideBs)
{
const auto b_grid_desc_nraw_kraw = [&]() {
if constexpr(is_same<tensor_layout::gemm::RowMajor, BsLayout>::value)
{
return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
make_tuple(I1, StrideBs));
}
else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BsLayout>::value)
{
return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
make_tuple(StrideBs, I1));
}
}();
return matrix_padder.PadBDescriptor_N_K(b_grid_desc_nraw_kraw);
}
template <typename ELay>
static auto MakeEGridDescriptor_M_N(index_t MRaw, index_t NRaw, index_t StrideE)
{
const auto e_grid_desc_mraw_nraw = [&]() {
if constexpr(is_same<tensor_layout::gemm::RowMajor, ELay>::value)
{
return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
make_tuple(StrideE, I1));
}
else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, ELay>::value)
{
return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
make_tuple(I1, StrideE));
}
}();
return matrix_padder.PadCDescriptor_M_N(e_grid_desc_mraw_nraw);
}
static auto MakeDsGridDescriptor_M_N(const std::array<index_t, NumDTensor>& MRaws,
const std::array<index_t, NumDTensor>& NRaws,
const std::array<index_t, NumDTensor>& DsStride)
{
return generate_tuple(
[&](auto i) {
using DLayout = remove_cvref_t<tuple_element_t<i.value, DsLayout>>;
return DeviceOp::MakeEGridDescriptor_M_N<DLayout>(MRaws[i], NRaws[i], DsStride[i]);
},
Number<NumDTensor>{});
}
#endif
using ComputeDataType = EDataType;
// GridwiseGemm
@@ -384,7 +312,7 @@ struct DeviceGemmMultipleABD_Xdl_CShuffle : public DeviceGemmMultipleABD<AsLayou
// B desc
bs_grid_desc_n_k_(i) =
GridwiseGemm::template MakeBGridDescriptor_N_K<BLayout, GemmSpec>(
KRaw, NRaw, StrideBs[i]);
NRaw, KRaw, StrideBs[i]);
});
// populate pointer, desc for Ds
@@ -424,15 +352,6 @@ struct DeviceGemmMultipleABD_Xdl_CShuffle : public DeviceGemmMultipleABD<AsLayou
}
}
void Print() const
{
// std::cout << "A[M, K]: " << as_grid_desc_m_k_ << std::endl;
// std::cout << "B[N, K]: " << bs_grid_desc_n_k_ << std::endl;
// static_for<0, NumDTensor, 1>{}(
//[&](auto i) { std::cout << "Ds[M, N]: " << ds_grid_desc_m_n_[i] << std::endl; });
// std::cout << "E[M, N]: " << e_grid_desc_m_n_ << std::endl;
}
// private:
// pointers
typename GridwiseGemm::AsGridPointer p_as_grid_;
@@ -578,7 +497,10 @@ struct DeviceGemmMultipleABD_Xdl_CShuffle : public DeviceGemmMultipleABD<AsLayou
}
else
{
all_valid = false;
if(ABlockTransferSrcScalarPerVector != 1)
{
all_valid = false;
}
}
});
@@ -602,13 +524,15 @@ struct DeviceGemmMultipleABD_Xdl_CShuffle : public DeviceGemmMultipleABD<AsLayou
}
else
{
all_valid = false;
if(BBlockTransferSrcScalarPerVector != 1)
{
all_valid = false;
}
}
});
// check vector load of Ds
// only support RowMajor for now
static_for<0, NumDTensor, 1>{}([&](auto i) {
using DLayout = remove_cvref_t<tuple_element_t<i.value, DsLayout>>;
@@ -618,21 +542,21 @@ struct DeviceGemmMultipleABD_Xdl_CShuffle : public DeviceGemmMultipleABD<AsLayou
}
});
if(!all_valid)
{
return false;
}
// check vector store of E
// only support RowMajor for now
if constexpr(is_same_v<ELayout, Row>)
{
if(arg.NRaw_ % CDEBlockTransferScalarPerVector_NPerBlock != 0)
{
return false;
all_valid = false;
}
}
else
{
all_valid = false;
}
if(!all_valid)
{
return false;
}

851
include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_multi_abd_xdl_fixed_nk.hpp Normal file
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@@ -0,0 +1,851 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iostream>
#include <sstream>
#include "ck/utility/common_header.hpp"
#include "ck/tensor_description/tensor_descriptor.hpp"
#include "ck/tensor_description/tensor_descriptor_helper.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_gemm_multi_abd_fixed_nk.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_abd_xdl_cshuffle.hpp"
#include "ck/host_utility/device_prop.hpp"
#include "ck/host_utility/kernel_launch.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
template <typename GridwiseGemm,
typename GemmDesc,
GemmSpecialization GemmSpec,
typename AsLayout,
typename BsLayout,
typename DsLayout,
typename ELayout,
typename Block2ETileMap,
typename GroupedGemmBlock2ETileMap,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CDEElementwiseOperation,
InMemoryDataOperationEnum EGlobalMemoryDataOperation,
bool HasMainKBlockLoop>
__global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
kernel_grouped_gemm_xdl_fixed_nk(const void CK_CONSTANT_ADDRESS_SPACE* gemm_descs_const,
const index_t group_count,
const index_t grid_size_grp,
const AElementwiseOperation a_element_op,
const BElementwiseOperation b_element_op,
const CDEElementwiseOperation cde_element_op)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__))
__shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
const index_t KBatch = 1;
const index_t block_id = get_block_1d_id();
const auto gemm_desc_ptr =
reinterpret_cast<const GemmDesc*>(cast_pointer_to_generic_address_space(gemm_descs_const));
const index_t group_id = block_id / grid_size_grp;
if(group_id >= group_count)
return;
const index_t M = gemm_desc_ptr[group_id].M;
const index_t N = gemm_desc_ptr[group_id].N;
const index_t K = gemm_desc_ptr[group_id].K;
if(M * N * K == 0)
return;
const auto StrideAs = gemm_desc_ptr[group_id].StrideAs;
const auto StrideBs = gemm_desc_ptr[group_id].StrideBs;
const auto StrideDs = gemm_desc_ptr[group_id].StrideDs;
const auto StrideE = gemm_desc_ptr[group_id].StrideE;
const auto e_grid_desc_m_n =
GridwiseGemm::template MakeEGridDescriptor_M_N<ELayout, GemmSpec>(M, N, StrideE);
const index_t BlockStart = group_id * grid_size_grp;
const auto local_b2e_tile_map = Block2ETileMap{e_grid_desc_m_n, KBatch};
const auto local_grid_size = local_b2e_tile_map.CalculateGridSize(e_grid_desc_m_n);
constexpr auto NumATensor = GridwiseGemm::AsGridPointer::Size();
constexpr auto NumBTensor = GridwiseGemm::BsGridPointer::Size();
constexpr auto NumDTensor = GridwiseGemm::DsGridPointer::Size();
typename GridwiseGemm::AsGridPointer p_as_grid_;
typename GridwiseGemm::BsGridPointer p_bs_grid_;
typename GridwiseGemm::DsGridPointer p_ds_grid_;
static_for<0, NumATensor, 1>{}([&](auto i) {
using ADataType = remove_cvref_t<decltype(p_as_grid_(i))>;
p_as_grid_(i) = static_cast<ADataType>(gemm_desc_ptr[group_id].p_as_grid[i]);
});
static_for<0, NumBTensor, 1>{}([&](auto i) {
using BDataType = remove_cvref_t<decltype(p_bs_grid_(i))>;
p_bs_grid_(i) = static_cast<BDataType>(gemm_desc_ptr[group_id].p_bs_grid[i]);
});
static_for<0, NumDTensor, 1>{}([&](auto i) {
using DDataType = remove_cvref_t<decltype(p_ds_grid_(i))>;
p_ds_grid_(i) = static_cast<DDataType>(gemm_desc_ptr[group_id].p_ds_grid[i]);
});
index_t id_off = 0;
index_t id_local = get_block_1d_id() - BlockStart;
while(id_local < local_grid_size)
{
const auto block_2_etile_map =
GroupedGemmBlock2ETileMap(local_b2e_tile_map, BlockStart, id_off);
GridwiseGemm::
template Run<HasMainKBlockLoop, GemmSpec, AsLayout, BsLayout, DsLayout, ELayout>(
p_as_grid_,
p_bs_grid_,
p_ds_grid_,
gemm_desc_ptr[group_id].p_e_grid,
p_shared,
a_element_op,
b_element_op,
cde_element_op,
M,
N,
K,
StrideAs,
StrideBs,
StrideDs,
StrideE,
block_2_etile_map);
id_off += grid_size_grp;
id_local += grid_size_grp;
}
#else
ignore = gemm_descs_const;
ignore = group_count;
ignore = grid_size_grp;
ignore = a_element_op;
ignore = b_element_op;
ignore = cde_element_op;
#endif
}
template <typename AsLayout,
typename BsLayout,
typename DsLayout,
typename ELayout,
typename AsDataType,
typename BsDataType,
typename AccDataType,
typename CShuffleDataType,
typename DsDataType,
typename EDataType,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CDEElementwiseOperation,
GemmSpecialization GemmSpec,
ck::index_t NumPrefetch,
ck::index_t BlockSize,
ck::index_t MPerBlock,
ck::index_t NPerBlock,
ck::index_t KPerBlock,
ck::index_t AK1,
ck::index_t BK1,
ck::index_t MPerXDL,
ck::index_t NPerXDL,
ck::index_t MXdlPerWave,
ck::index_t NXdlPerWave,
typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
typename ABlockTransferThreadClusterArrangeOrder,
typename ABlockTransferSrcAccessOrder,
ck::index_t ABlockTransferSrcVectorDim,
ck::index_t ABlockTransferSrcScalarPerVector,
ck::index_t ABlockTransferDstScalarPerVector_AK1,
bool ABlockLdsExtraM,
typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
typename BBlockTransferThreadClusterArrangeOrder,
typename BBlockTransferSrcAccessOrder,
ck::index_t BBlockTransferSrcVectorDim,
ck::index_t BBlockTransferSrcScalarPerVector,
ck::index_t BBlockTransferDstScalarPerVector_BK1,
bool BBlockLdsExtraN,
index_t CShuffleMXdlPerWavePerShuffle,
index_t CShuffleNXdlPerWavePerShuffle,
typename CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
index_t CDEBlockTransferScalarPerVector_NPerBlock,
typename ComputeType = EDataType,
LoopScheduler LoopSched = make_default_loop_scheduler()>
struct DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK
: public DeviceGroupedGemmMultiABDFixedNK<AsLayout,
BsLayout,
DsLayout,
ELayout,
AsDataType,
BsDataType,
DsDataType,
EDataType,
AElementwiseOperation,
BElementwiseOperation,
CDEElementwiseOperation>
{
using DeviceOp = DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK;
static constexpr index_t NumATensor = AsDataType::Size();
static constexpr index_t NumBTensor = BsDataType::Size();
static constexpr index_t NumDTensor = DsDataType::Size();
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
static constexpr auto I2 = Number<2>{};
static constexpr index_t NumGemmKPrefetchStage = 1;
// GridwiseGemm
using GridwiseGemm = GridwiseGemmMultipleABD_xdl_cshuffle<
AsDataType,
BsDataType,
ComputeType,
AccDataType,
CShuffleDataType,
DsDataType,
EDataType,
AElementwiseOperation,
BElementwiseOperation,
CDEElementwiseOperation,
InMemoryDataOperationEnum::Set,
NumGemmKPrefetchStage,
BlockSize,
MPerBlock,
NPerBlock,
KPerBlock,
AK1,
BK1,
MPerXDL,
NPerXDL,
MXdlPerWave,
NXdlPerWave,
ABlockTransferThreadClusterLengths_AK0_M_AK1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_AK1,
false,
ABlockLdsExtraM,
BBlockTransferThreadClusterLengths_BK0_N_BK1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_BK1,
false,
BBlockLdsExtraN,
CShuffleMXdlPerWavePerShuffle,
CShuffleNXdlPerWavePerShuffle,
CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
CDEBlockTransferScalarPerVector_NPerBlock,
LoopSched>;
template <typename UnderlyingBlockToCTileMap>
struct OffsettedBlockToCTileMapMLoops
{
using underlying_type = UnderlyingBlockToCTileMap;
__host__ __device__ OffsettedBlockToCTileMapMLoops(
UnderlyingBlockToCTileMap block_to_ctile_map, index_t block_start, index_t id_off = 0)
{
block_to_ctile_map_ = block_to_ctile_map;
block_start_ = block_start;
id_off_ = id_off;
}
template <typename TopIdx>
__host__ __device__ constexpr auto CalculateBottomIndex(const TopIdx& idx_top) const
{
auto idx_bot = block_to_ctile_map_.CalculateBottomIndex(
make_multi_index(idx_top[Number<0>{}] - block_start_ + id_off_));
return make_tuple(
// idx_bot[Number<0>{}],
idx_bot[Number<1>{}],
idx_bot[Number<2>{}]);
}
template <typename CTileIdx, typename CTileDim>
__host__ __device__ bool ValidCTileIndex(const CTileIdx& c_tile_idx,
const CTileDim& c_tile_dim) const
{
return block_to_ctile_map_.ValidCTileIndex(c_tile_idx, c_tile_dim);
}
template <typename CGridDesc_M_N>
__host__ bool CheckValidity(const CGridDesc_M_N& c_grid_desc_m_n) const
{
return block_to_ctile_map_.CheckValidity(c_grid_desc_m_n);
}
template <typename CGridDesc_M_N>
__host__ constexpr index_t CalculateGridSize(const CGridDesc_M_N& c_grid_desc_m_n) const
{
return block_to_ctile_map_.CalculateGridSize(c_grid_desc_m_n);
}
UnderlyingBlockToCTileMap block_to_ctile_map_;
index_t block_start_;
index_t id_off_;
};
template <index_t MPerBlock_, index_t NPerBlock_>
struct BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops
{
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
__host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops() = default;
__host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops(
const BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops&) = default;
__host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops(
BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops&&) = default;
__host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops&
operator=(const BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops&) = default;
__host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops&
operator=(BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops&&) = default;
__host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops(index_t M,
index_t N,
index_t KBatch,
index_t M01 = 8)
: M_(M), N_(N), KBatch_(KBatch), M01_(M01)
{
}
template <typename CGridDesc_M_N>
__host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops(
const CGridDesc_M_N& c_grid_desc_m_n, index_t KBatch, index_t M01 = 8)
: BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops(
c_grid_desc_m_n.GetLength(I0), c_grid_desc_m_n.GetLength(I1), KBatch, M01)
{
}
__host__ __device__ constexpr index_t CalculateGridSize(index_t M, index_t N) const
{
const auto M0 = math::integer_divide_ceil(M, MPerBlock);
const auto N0 = math::integer_divide_ceil(N, NPerBlock);
return M0 * N0 * KBatch_;
}
template <typename CGridDesc_M_N>
__host__ __device__ constexpr index_t
CalculateGridSize(const CGridDesc_M_N& c_grid_desc_m_n) const
{
return CalculateGridSize(c_grid_desc_m_n.GetLength(I0), c_grid_desc_m_n.GetLength(I1));
}
template <typename CGridDesc_M_N>
__host__ bool CheckValidity(const CGridDesc_M_N& /* c_grid_desc_m_n */) const
{
return true;
}
template <typename TopIdx>
__host__ __device__ constexpr auto CalculateBottomIndex(const TopIdx& idx_top) const
{
auto block_1d_id = idx_top[I0];
const auto M0 = math::integer_divide_ceil(M_, MPerBlock_);
const auto N0 = math::integer_divide_ceil(N_, NPerBlock_);
block_1d_id = block_1d_id % (M0 * N0 * KBatch_); // hide groups
const index_t idx_ksplit = block_1d_id / (M0 * N0);
block_1d_id = block_1d_id % (M0 * N0);
index_t idx_N0 = block_1d_id % N0;
index_t idx_M0 = block_1d_id / N0;
const auto M01_adapt = (idx_M0 < M0 - M0 % M01_) ? M01_ : M0 % M01_;
index_t idx_M00 = idx_M0 / M01_;
index_t idx_M01 = idx_M0 % M01_;
index_t idx_N0_M01_local = idx_N0 + idx_M01 * N0;
return make_tuple(idx_ksplit,
idx_N0_M01_local % M01_adapt + idx_M00 * M01_,
idx_N0_M01_local / M01_adapt);
}
template <typename CTileIdx, typename CTileDim>
__host__ __device__ bool ValidCTileIndex(const CTileIdx& /* c_tile_idx */,
const CTileDim& /* c_tile_dim */) const
{
return true; // always valid provided that user gets grid size from CalculateGridSize()
}
private:
index_t M_;
index_t N_;
index_t KBatch_;
index_t M01_;
};
using Block2ETileMap = BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops<MPerBlock, NPerBlock>;
using GroupedGemmBlock2ETileMap = OffsettedBlockToCTileMapMLoops<Block2ETileMap>;
struct GemmBiasTransKernelArg
{
// pointers
std::array<const void*, NumATensor> as_ptr_;
std::array<const void*, NumBTensor> bs_ptr_;
std::array<const void*, NumDTensor> ds_ptr_;
void* e_ptr_;
index_t M_, N_, K_;
std::array<index_t, NumATensor> StrideAs_;
std::array<index_t, NumBTensor> StrideBs_;
std::array<index_t, NumDTensor> StrideDs_;
index_t StrideE_;
};
// Argument
struct Argument : public BaseArgument
{
void UpdateKBatch(index_t) {}
Argument(std::vector<std::array<const void*, NumATensor>>&,
std::vector<std::array<const void*, NumBTensor>>&,
std::vector<std::array<const void*, NumDTensor>>&,
std::vector<void*>&,
std::vector<GemmMultiABDDesc>& gemm_descs,
AElementwiseOperation a_element_op = AElementwiseOperation{},
BElementwiseOperation b_element_op = BElementwiseOperation{},
CDEElementwiseOperation c_element_op = CDEElementwiseOperation{})
: a_element_op_{a_element_op}, b_element_op_{b_element_op}, c_element_op_{c_element_op}
{
grid_size_ = 0;
k_batch_ = 1;
grouped_gemm_kernel_args_dev = nullptr;
group_count_ = ck::type_convert<ck::index_t>(gemm_descs.size());
gemm_desc_kernel_arg_.reserve(group_count_);
index_t group_id = 0;
sum_of_m = gemm_descs[0].M_;
const index_t AverM = math::integer_divide_ceil(sum_of_m, group_count_);
const index_t N = gemm_descs[0].N_;
const index_t K = gemm_descs[0].K_;
for(std::size_t i = 0; i < gemm_descs.size(); i++)
{
if(sum_of_m != gemm_descs[i].M_ || N != gemm_descs[i].N_ || K != gemm_descs[i].K_)
{
throw std::runtime_error("wrong! M/N/K is not identical");
}
a_mtx_mraw_kraw_.emplace_back(sum_of_m, K);
b_mtx_nraw_kraw_.emplace_back(N, K);
// pointer
std::array<const void*, NumATensor> p_as_grid;
std::array<const void*, NumBTensor> p_bs_grid;
std::array<const void*, NumDTensor> p_ds_grid;
static_for<0, NumATensor, 1>{}([&](auto j) { p_as_grid[j] = nullptr; });
static_for<0, NumBTensor, 1>{}([&](auto j) { p_bs_grid[j] = nullptr; });
static_for<0, NumDTensor, 1>{}([&](auto j) { p_ds_grid[j] = nullptr; });
std::array<index_t, NumATensor> StrideAs;
std::array<index_t, NumBTensor> StrideBs;
std::array<index_t, NumDTensor> StrideDs;
const index_t StrideE = gemm_descs[i].stride_C_;
if(gemm_descs[i].stride_As_.size() != NumATensor)
{
throw std::runtime_error(
"wrong! gemm_descs[i].stride_As_.size() does not match NumATensor");
}
static_for<0, NumATensor, 1>{}(
[&](auto j) { StrideAs[j] = gemm_descs[i].stride_As_[j]; });
if(gemm_descs[i].stride_Bs_.size() != NumBTensor)
{
throw std::runtime_error(
"wrong! gemm_descs[i].stride_Bs_.size() does not match NumBTensor");
}
static_for<0, NumBTensor, 1>{}(
[&](auto j) { StrideBs[j] = gemm_descs[i].stride_Bs_[j]; });
if(gemm_descs[i].stride_Ds_.size() != NumDTensor)
{
throw std::runtime_error(
"wrong! gemm_descs[i].stride_Ds_.size() does not match NumDTensor");
}
static_for<0, NumDTensor, 1>{}(
[&](auto j) { StrideDs[j] = gemm_descs[i].stride_Ds_[j]; });
const auto e_grid_desc_m_n =
GridwiseGemm::template MakeEGridDescriptor_M_N<ELayout, GemmSpec>(
AverM, N, StrideE);
// block-to-e-tile map
const auto local_b2c_tile_map = Block2ETileMap{e_grid_desc_m_n, k_batch_};
grid_size_grp_ = local_b2c_tile_map.CalculateGridSize(e_grid_desc_m_n);
if(group_id * grid_size_grp_ != grid_size_)
{
throw std::runtime_error("wrong! grid_size_grp_ is not identical!");
}
grid_size_ += grid_size_grp_;
// check block-to-E-tile
if(!local_b2c_tile_map.CheckValidity(e_grid_desc_m_n))
{
throw std::runtime_error("wrong! block_2_etile_map validation failed");
}
gemm_desc_kernel_arg_.push_back(GemmBiasTransKernelArg{
p_as_grid,
p_bs_grid,
p_ds_grid,
nullptr,
AverM,
N,
K,
StrideAs,
StrideBs,
StrideDs,
StrideE,
});
group_id++;
}
const auto e_grid_desc_sum_m_n =
GridwiseGemm::template MakeEGridDescriptor_M_N<ELayout, GemmSpec>(
sum_of_m, gemm_desc_kernel_arg_[0].N_, gemm_desc_kernel_arg_[0].StrideE_);
const auto local_b2c_tile_map = Block2ETileMap{e_grid_desc_sum_m_n, 1};
barrier_size_grp_ = local_b2c_tile_map.CalculateGridSize(e_grid_desc_sum_m_n);
}
// private:
index_t group_count_;
AElementwiseOperation a_element_op_;
BElementwiseOperation b_element_op_;
CDEElementwiseOperation c_element_op_;
std::vector<GemmBiasTransKernelArg> gemm_desc_kernel_arg_;
std::vector<Tuple<index_t, index_t>> a_mtx_mraw_kraw_;
std::vector<Tuple<index_t, index_t>> b_mtx_nraw_kraw_;
const void* grouped_gemm_kernel_args_dev;
index_t grid_size_;
index_t grid_size_grp_;
index_t barrier_size_grp_;
index_t sum_of_m;
index_t k_batch_ = 1;
};
// Invoker
struct Invoker : public BaseInvoker
{
using Argument = DeviceOp::Argument;
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
bool has_main_k_block_loop = true;
for(std::size_t i = 0; i < arg.gemm_desc_kernel_arg_.size(); i++)
{
if(GridwiseGemm::CalculateHasMainKBlockLoop(arg.gemm_desc_kernel_arg_[i].K_) !=
has_main_k_block_loop)
{
throw std::runtime_error("wrong! not all gemm has_main_k_block_loop");
}
}
if(arg.grouped_gemm_kernel_args_dev == nullptr)
{
throw std::runtime_error("wrong! grouped_gemm_kernel_args_dev is nullpr");
}
float ave_time = 0;
auto launch_kernel = [&](auto has_main_k_block_loop_, auto e_global_memory_operation_) {
const auto kernel = kernel_grouped_gemm_xdl_fixed_nk<
GridwiseGemm,
GroupedGemmMultiABDKernelArgument<NumATensor, NumBTensor, NumDTensor>,
GemmSpec,
AsLayout,
BsLayout,
DsLayout,
ELayout,
Block2ETileMap,
GroupedGemmBlock2ETileMap,
AElementwiseOperation,
BElementwiseOperation,
CDEElementwiseOperation,
e_global_memory_operation_,
has_main_k_block_loop_>;
return launch_and_time_kernel(
stream_config,
kernel,
dim3(arg.grid_size_),
dim3(BlockSize),
0,
cast_pointer_to_constant_address_space(arg.grouped_gemm_kernel_args_dev),
arg.gemm_desc_kernel_arg_.size(),
arg.grid_size_grp_,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_);
};
constexpr auto AtomicAdd = InMemoryDataOperationEnum::AtomicAdd;
constexpr auto Set = InMemoryDataOperationEnum::Set;
if(arg.k_batch_ > 1)
{
if(has_main_k_block_loop)
{
ave_time =
launch_kernel(integral_constant<bool, true>{},
integral_constant<InMemoryDataOperationEnum, AtomicAdd>{});
}
else
{
ave_time =
launch_kernel(integral_constant<bool, false>{},
integral_constant<InMemoryDataOperationEnum, AtomicAdd>{});
}
}
else
{
if(has_main_k_block_loop)
{
ave_time = launch_kernel(integral_constant<bool, true>{},
integral_constant<InMemoryDataOperationEnum, Set>{});
}
else
{
ave_time = launch_kernel(integral_constant<bool, false>{},
integral_constant<InMemoryDataOperationEnum, Set>{});
}
}
return ave_time;
}
// polymorphic
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};
static bool IsSupportedArgument(const Argument& arg)
{
if(ck::type_convert<ck::index_t>(arg.gemm_desc_kernel_arg_.size()) != arg.group_count_)
{
return false;
}
bool supported = true;
// If we use padding we do not support vector loads for dimensions not divisible by vector
// load size.
if constexpr(GemmSpec != GemmSpecialization::Default)
{
// [A|B]BlockTransferSrcVectorDim value define dimension in the block {K0,M,K1} layout,
// thus we have to adapt it to the {M,K} or {N,K} layout.
const auto a_raw_vector_dim = ABlockTransferSrcVectorDim != 1 ? 1 : 0;
const auto b_raw_vector_dim = BBlockTransferSrcVectorDim != 1 ? 1 : 0;
for(index_t i = 0; i < arg.group_count_; ++i)
{
const auto a_vector_dim = arg.a_mtx_mraw_kraw_[i].At(Number<a_raw_vector_dim>{});
const auto b_vector_dim = arg.b_mtx_nraw_kraw_[i].At(Number<b_raw_vector_dim>{});
supported = supported & (a_vector_dim % ABlockTransferSrcScalarPerVector == 0);
supported = supported & (b_vector_dim % BBlockTransferSrcScalarPerVector == 0);
}
}
return supported;
}
// polymorphic
bool IsSupportedArgument(const BaseArgument* p_arg) override
{
return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
}
static auto MakeArgument(std::vector<std::array<const void*, NumATensor>>& p_As,
std::vector<std::array<const void*, NumBTensor>>& p_Bs,
std::vector<std::array<const void*, NumDTensor>>& p_Ds,
std::vector<void*>& p_Es,
std::vector<GemmMultiABDDesc> gemm_descs,
AElementwiseOperation a_element_op = AElementwiseOperation{},
BElementwiseOperation b_element_op = BElementwiseOperation{},
CDEElementwiseOperation c_element_op = CDEElementwiseOperation{})
{
return Argument{
p_As, p_Bs, p_Ds, p_Es, gemm_descs, a_element_op, b_element_op, c_element_op};
}
static auto MakeInvoker() { return Invoker{}; }
// polymorphic
std::unique_ptr<BaseArgument>
MakeArgumentPointer(std::vector<std::array<const void*, NumATensor>>& p_As,
std::vector<std::array<const void*, NumBTensor>>& p_Bs,
std::vector<std::array<const void*, NumDTensor>>& p_Ds,
std::vector<void*>& p_Es,
std::vector<GemmMultiABDDesc>& gemm_descs,
AElementwiseOperation a_element_op = AElementwiseOperation{},
BElementwiseOperation b_element_op = BElementwiseOperation{},
CDEElementwiseOperation c_element_op = CDEElementwiseOperation{}) override
{
return std::make_unique<Argument>(
p_As, p_Bs, p_Ds, p_Es, gemm_descs, a_element_op, b_element_op, c_element_op);
}
// polymorphic
std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
{
return std::make_unique<Invoker>(Invoker{});
}
// polymorphic
std::string GetTypeString() const override
{
auto str = std::stringstream();
// clang-format off
str << "DeviceGroupedGemm_Xdl_Fixed_NK"
<< "<"
<< BlockSize << ", "
<< MPerBlock << ", "
<< NPerBlock << ", "
<< KPerBlock << ", "
<< AK1 << ", "
<< BK1 << ", "
<< MPerXDL << ", "
<< NPerXDL << ", "
<< MXdlPerWave << ", "
<< NXdlPerWave << ", "
<< ABlockTransferSrcScalarPerVector << ", "
<< BBlockTransferSrcScalarPerVector << ", "
<< CShuffleMXdlPerWavePerShuffle << ", "
<< CShuffleNXdlPerWavePerShuffle << ", "
<< getGemmSpecializationString(GemmSpec)
<< ">";
// clang-format on
return str.str();
}
static void SetElementwiseOps(Argument& arg,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation c_element_op)
{
arg.a_element_op_ = a_element_op;
arg.b_element_op_ = b_element_op;
arg.c_element_op_ = c_element_op;
}
static void SetDeviceKernelArgs(Argument& arg, const void* kernel_args)
{
arg.grouped_gemm_kernel_args_dev = kernel_args;
}
// polymorphic
void SetDeviceKernelArgs(BaseArgument* p_arg, const void* kernel_args) const override
{
return SetDeviceKernelArgs(*dynamic_cast<Argument*>(p_arg), kernel_args);
}
void SetElementwiseOps(BaseArgument* p_arg,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation c_element_op) const override
{
SetElementwiseOps(
*dynamic_cast<Argument*>(p_arg), a_element_op, b_element_op, c_element_op);
}
size_t GetDeviceKernelArgSize(const BaseArgument* p_arg) const override
{
auto arg = *dynamic_cast<const Argument*>(p_arg);
return arg.group_count_ *
sizeof(GroupedGemmMultiABDKernelArgument<NumATensor, NumBTensor, NumDTensor>);
}
#if 0
size_t GetWorkSpaceSize(const BaseArgument* p_arg) const override
{
auto arg = *dynamic_cast<const Argument*>(p_arg);
return arg.group_count_ * arg.barrier_size_grp_ * sizeof(uint32_t);
}
void SetWorkSpacePointer(BaseArgument* p_arg,
void* p_workspace,
const StreamConfig& stream_config = StreamConfig{}) const override
{
auto p_arg_ = dynamic_cast<Argument*>(p_arg);
p_arg_->p_workspace_ = p_workspace;
hip_check_error(
hipMemsetAsync(p_workspace, 0, GetWorkSpaceSize(p_arg), stream_config.stream_id_));
}
#endif
static void SetKBatch(Argument& arg, index_t k_batch) { arg.UpdateKBatch(k_batch); }
// polymorphic
void SetKBatch(BaseArgument* p_arg, index_t k_batch) const override
{
return SetKBatch(*dynamic_cast<Argument*>(p_arg), k_batch);
}
};
} // namespace device
} // namespace tensor_operation
} // namespace ck

1
include/ck/tensor_operation/gpu/device/impl/device_reduce_threadwise.hpp
View File

@@ -11,7 +11,6 @@
#include "ck/host_utility/kernel_launch.hpp"
#include "ck/tensor_operation/gpu/device/device_reduce.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_reduce_common.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_2d_reduction_multiblock.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_2d_reduction_threadwise.hpp"
namespace ck {

24
include/ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp
View File

@@ -4,7 +4,7 @@
#pragma once
#include "ck/utility/data_type.hpp"
#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
namespace ck {
namespace tensor_operation {
@@ -92,6 +92,15 @@ struct Add
};
};
struct Scales
{
template <typename Y, typename X0, typename X1>
__host__ __device__ constexpr void operator()(Y& y, const X0& x0, const X1& x1) const
{
y = ck::type_convert<Y>(ck::type_convert<float>(x0) * ck::type_convert<float>(x1));
}
};
struct Max
{
template <typename Y, typename X0, typename X1>
@@ -485,6 +494,19 @@ struct AddFastGelu
e = type_convert<half_t>(x1_f);
}
template <>
__host__ __device__ constexpr void
operator()<bhalf_t, bhalf_t, bhalf_t>(bhalf_t& e, const bhalf_t& c, const bhalf_t& d) const
{
const float x0_f = type_convert<float>(c) + type_convert<float>(d);
float x1_f = 0;
FastGelu{}.template operator()<float, float>(x1_f, x0_f);
e = type_convert<bhalf_t>(x1_f);
}
template <>
__host__ __device__ constexpr void
operator()<bhalf_t, float, bhalf_t>(bhalf_t& e, const float& c, const bhalf_t& d) const

9
include/ck/tensor_operation/gpu/element/combined_element_wise_operation.hpp
View File

@@ -14,6 +14,8 @@ namespace element_wise {
template <typename... UnaryOpsSet>
struct UnaryCombinedOp
{
__host__ __device__ UnaryCombinedOp() : unary_ops_() {}
__host__ __device__ UnaryCombinedOp(UnaryOpsSet... unary_ops) : unary_ops_(unary_ops...) {}
template <typename Y, typename X>
@@ -32,6 +34,8 @@ struct UnaryCombinedOp
template <typename BinaryOp, typename UnaryOp0, typename UnaryOp1>
struct BinaryWithUnaryCombinedOp
{
__host__ __device__ BinaryWithUnaryCombinedOp() : binary_op_(), unary_op0_(), unary_op1_() {}
__host__ __device__ BinaryWithUnaryCombinedOp(BinaryOp binary_op,
UnaryOp0 unary_op0,
UnaryOp1 unary_op1)
@@ -63,6 +67,11 @@ template <typename BinaryOp0,
typename UnaryOp2>
struct TrinaryWithUnaryCombinedOp
{
__host__ __device__ TrinaryWithUnaryCombinedOp()
: binary_op0_(), binary_op1_(), unary_op0_(), unary_op1_(), unary_op2_()
{
}
__host__ __device__ TrinaryWithUnaryCombinedOp(BinaryOp0 binary_op0,
BinaryOp0 binary_op1,
UnaryOp0 unary_op0,

37
include/ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp
View File

@@ -288,10 +288,13 @@ struct ConvertF8RNE
struct Scale
{
__host__ __device__ Scale(float scale) : scale_(scale) {}
__host__ __device__ Scale(float scale = 1.f) : scale_(scale) {}
template <typename Y, typename X>
__host__ __device__ void operator()(Y& y, const X& x) const;
__host__ __device__ void operator()(Y& y, const X& x) const
{
y = ck::type_convert<Y>(ck::type_convert<float>(x) * scale_);
}
template <>
__host__ __device__ void operator()<half_t, half_t>(half_t& y, const half_t& x) const
@@ -500,6 +503,36 @@ struct FastGelu
y = type_convert<half_t>(y_f);
}
template <>
__device__ void operator()<bhalf_t, float>(bhalf_t& y, const float& x) const
{
float y_f;
this->operator()<float, float>(y_f, x);
y = type_convert<bhalf_t>(y_f);
}
template <>
__device__ void operator()<bhalf_t, bhalf_t>(bhalf_t& y, const bhalf_t& x) const
{
float y_f;
this->operator()<float, float>(y_f, type_convert<float>(x));
y = type_convert<bhalf_t>(y_f);
}
template <>
__host__ void operator()<bhalf_t, bhalf_t>(bhalf_t& y, const bhalf_t& x) const
{
float y_f;
this->operator()<float, float>(y_f, type_convert<float>(x));
y = type_convert<bhalf_t>(y_f);
}
};
// https://paperswithcode.com/method/gelu

13
include/ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_abd_xdl_cshuffle.hpp
View File

@@ -439,7 +439,7 @@ struct GridwiseGemmMultipleABD_xdl_cshuffle
template <typename BLayout, GemmSpecialization GemmSpec>
__host__ __device__ static auto
MakeBGridDescriptor_N_K(index_t KRaw, index_t NRaw, index_t StrideB)
MakeBGridDescriptor_N_K(const index_t NRaw, const index_t KRaw, const index_t StrideB)
{
constexpr auto matrix_padder =
ck::tensor_operation::device::MatrixPadder<GemmSpec, index_t, index_t, index_t>{
@@ -463,15 +463,15 @@ struct GridwiseGemmMultipleABD_xdl_cshuffle
template <typename BsLayout, GemmSpecialization GemmSpec>
__host__ __device__ static auto
MakeBsGridDescriptor_N_K(const std::array<index_t, NumBTensor>& KRaws,
const std::array<index_t, NumBTensor>& NRaws,
MakeBsGridDescriptor_N_K(const std::array<index_t, NumBTensor>& NRaws,
const std::array<index_t, NumBTensor>& KRaws,
const std::array<index_t, NumBTensor>& BsStride)
{
return generate_tuple(
[&](auto i) {
using BLayout = remove_cvref_t<tuple_element_t<i.value, BsLayout>>;
return MakeBGridDescriptor_N_K<BLayout, GemmSpec>(KRaws[i], NRaws[i], BsStride[i]);
return MakeBGridDescriptor_N_K<BLayout, GemmSpec>(NRaws[i], KRaws[i], BsStride[i]);
},
Number<NumBTensor>{});
}
@@ -574,7 +574,6 @@ struct GridwiseGemmMultipleABD_xdl_cshuffle
{
return;
}
// HACK: this force m/n_block_data_idx_on_grid into SGPR
const index_t m_block_data_idx_on_grid =
__builtin_amdgcn_readfirstlane(block_work_idx[I0] * MPerBlock);
@@ -595,8 +594,10 @@ struct GridwiseGemmMultipleABD_xdl_cshuffle
generate_tuple([&](auto) { return make_multi_index(0, m_block_data_idx_on_grid, 0); },
Number<NumATensor>{});
#if 0
static_assert(ABlockTransferSrcScalarPerVector == ABlockTransferDstScalarPerVector_AK1,
"Src and Dst ScalarPerVector must be the same");
#endif
auto a_blockwise_copy = ThreadGroupTensorSliceTransfer_v7r2<
ThisThreadBlock,
@@ -626,8 +627,10 @@ struct GridwiseGemmMultipleABD_xdl_cshuffle
generate_tuple([&](auto) { return make_multi_index(0, n_block_data_idx_on_grid, 0); },
Number<NumBTensor>{});
#if 0
static_assert(BBlockTransferSrcScalarPerVector == BBlockTransferDstScalarPerVector_BK1,
"Src and Dst ScalarPerVector must be the same");
#endif
auto b_blockwise_copy = ThreadGroupTensorSliceTransfer_v7r2<
ThisThreadBlock,

33
include/ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp
View File

@@ -10,38 +10,9 @@
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer_util.hpp"
namespace ck {
// Do following things to avoid "alloca" in LLVM-IR, which would cause scratch memory
// and sometimes useless instructions:
// 1. Don't save a reference to tensor descriptor in class, pass in tensor descriptor as argument
// instead
// 2. Don't construct a new tensor coordinate everytime when using it, update and reuse the same
// tensor coordinate instead
// 3. Don't use a pointer to VGPR buffer, use vector instead
namespace detail {
// TODO: How to fix this? It uses an struct instead of lambda because lambda
// doesn't have constructor
template <index_t VectorDim, index_t ScalarPerVector>
struct lambda_scalar_per_access
{
__host__ __device__ constexpr auto operator()(index_t i) const
{
return (i == VectorDim) ? ScalarPerVector : 1;
}
};
template <index_t VectorDim>
struct lambda_scalar_step_in_vector
{
__host__ __device__ constexpr auto operator()(index_t i) const
{
return (i == VectorDim) ? 1 : 0;
}
};
} // namespace detail
// Assume:
// 1. src:
// 1. SrcDesc is known at compile-time

67
include/ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer_util.hpp Normal file
View File

@@ -0,0 +1,67 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
namespace ck {
// Do following things to avoid "alloca" in LLVM-IR, which would cause scratch memory
// and sometimes useless instructions:
// 1. Don't save a reference to tensor descriptor in class, pass in tensor descriptor as argument
// instead
// 2. Don't construct a new tensor coordinate everytime when using it, update and reuse the same
// tensor coordinate instead
// 3. Don't use a pointer to VGPR buffer, use vector instead
namespace detail {
// TODO: How to fix this? It uses an struct instead of lambda because lambda
// doesn't have constructor
template <index_t VectorDim, index_t ScalarPerVector>
struct lambda_scalar_per_access
{
__host__ __device__ constexpr auto operator()(index_t i) const
{
return (i == VectorDim) ? ScalarPerVector : 1;
}
};
template <index_t VectorDim>
struct lambda_scalar_step_in_vector
{
__host__ __device__ constexpr auto operator()(index_t i) const
{
return (i == VectorDim) ? 1 : 0;
}
};
// TODO: How to fix this? It uses an struct instead of lambda because lambda
// doesn't have constructor
template <index_t SrcVectorDim,
index_t SrcScalarPerVector,
index_t DstVectorDim,
index_t DstScalarPerVector>
struct lambda_scalar_per_access_for_src_and_dst
{
__host__ __device__ constexpr auto operator()(index_t i) const
{
if(i == SrcVectorDim && i == DstVectorDim)
{
return math::lcm(SrcScalarPerVector, DstScalarPerVector);
}
else if(i == SrcVectorDim)
{
return SrcScalarPerVector;
}
else if(i == DstVectorDim)
{
return DstScalarPerVector;
}
else
{
return 1;
}
}
};
} // namespace detail
} // namespace ck

37
include/ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer_v3r1.hpp
View File

@@ -1,5 +1,5 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
@@ -7,44 +7,13 @@
#include "ck/tensor_description/tensor_descriptor.hpp"
#include "ck/tensor_description/tensor_descriptor_helper.hpp"
#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
#include "ck/tensor/static_tensor.hpp"
#include "ck/utility/is_detected.hpp"
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer_util.hpp"
namespace ck {
namespace detail {
// TODO: How to fix this? It uses an struct instead of lambda because lambda
// doesn't have constructor
template <index_t SrcVectorDim,
index_t SrcScalarPerVector,
index_t DstVectorDim,
index_t DstScalarPerVector>
struct lambda_scalar_per_access_for_src_and_dst
{
__host__ __device__ constexpr auto operator()(index_t i) const
{
if(i == SrcVectorDim && i == DstVectorDim)
{
return math::lcm(SrcScalarPerVector, DstScalarPerVector);
}
else if(i == SrcVectorDim)
{
return SrcScalarPerVector;
}
else if(i == DstVectorDim)
{
return DstScalarPerVector;
}
else
{
return 1;
}
}
};
} // namespace detail
// Assume:
// 1. src_desc and dst_desc are not known at compile-time
// 2. SrcBuffer and DstBuffer are DynamicBuffer

247
include/ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer_v7r2.hpp
View File

@@ -1,5 +1,5 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
@@ -8,9 +8,11 @@
#include "ck/tensor_description/tensor_descriptor_helper.hpp"
#include "ck/tensor_description/tensor_space_filling_curve.hpp"
#include "ck/utility/is_detected.hpp"
#include "ck/tensor/static_tensor.hpp"
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer_util.hpp"
namespace ck {
// Thread-level multi-source, multi-destination tensor slice data movement
// Assume:
// 1. All sources and destinations are DynamicBuffer
@@ -70,16 +72,18 @@ struct ThreadwiseTensorSliceTransfer_v7r2
static constexpr auto src_scalar_per_access = generate_sequence(
detail::lambda_scalar_per_access<SrcVectorDim, SrcScalarPerVector>{}, Number<nDim>{});
using SrcSpaceFillingCurve = SpaceFillingCurve<SliceLengths,
SrcDimAccessOrder,
remove_cv_t<decltype(src_scalar_per_access)>>;
static constexpr auto dst_scalar_per_access = generate_sequence(
detail::lambda_scalar_per_access<DstVectorDim, DstScalarPerVector>{}, Number<nDim>{});
using SrcSpaceFillingCurve = SpaceFillingCurve<SliceLengths,
SrcDimAccessOrder,
remove_cv_t<decltype(src_scalar_per_access)>,
false>;
using DstSpaceFillingCurve = SpaceFillingCurve<SliceLengths,
DstDimAccessOrder,
remove_cv_t<decltype(dst_scalar_per_access)>>;
remove_cv_t<decltype(dst_scalar_per_access)>,
false>;
__device__ constexpr ThreadwiseTensorSliceTransfer_v7r2(
const SrcDescs& src_descs,
@@ -139,9 +143,9 @@ struct ThreadwiseTensorSliceTransfer_v7r2
__device__ void RunRead(const SrcDescs& src_descs, const SrcBuffers& src_bufs)
{
// loop over space-filling curve
static_for<0, num_access, 1>{}([&](auto iAccess) {
static_for<0, src_num_access, 1>{}([&](auto iAccess) {
auto src_vectors = generate_vectors<SrcDatas, SrcScalarPerVector>();
auto dst_vectors = generate_vectors<DstDatas, DstScalarPerVector>();
auto elm_vectors = generate_vectors<DstDatas, SrcScalarPerVector>();
// copy data from src_bufs into src_vectors
static_for<0, nSrc, 1>{}([&](auto i) {
@@ -199,7 +203,7 @@ struct ThreadwiseTensorSliceTransfer_v7r2
using elem_op_vec_t = typename vector_type<DstData, elem_op_vec_len>::type;
return dst_vectors(iDst).template AsType<elem_op_vec_t>()(i);
return elm_vectors(iDst).template AsType<elem_op_vec_t>()(i);
},
Number<nDst>{});
@@ -214,10 +218,10 @@ struct ThreadwiseTensorSliceTransfer_v7r2
unpack2(element_op_, dst_data_refs, src_data_refs);
});
dst_vectors_tuple_(iAccess) = dst_vectors;
elm_vectors_tuple_(iAccess) = elm_vectors;
// move coordinate
if constexpr(iAccess.value != num_access - 1)
if constexpr(iAccess.value != src_num_access - 1)
{
constexpr auto forward_step = SrcSpaceFillingCurve::GetForwardStep(iAccess);
@@ -241,15 +245,113 @@ struct ThreadwiseTensorSliceTransfer_v7r2
});
}
__device__ void TransposeFromElmToDst()
{
using DstData = remove_cvref_t<decltype(DstDatas{}[I0])>;
using SrcThreadScratch =
StaticTensorTupleOfVectorBuffer<AddressSpaceEnum::Vgpr,
DstData,
SrcScalarPerVector,
decltype(GetSrcThreadScratchDescriptor()),
true>;
using DstThreadScratch =
StaticTensorTupleOfVectorBuffer<AddressSpaceEnum::Vgpr,
DstData,
DstScalarPerVector,
decltype(GetDstThreadScratchDescriptor()),
true>;
SrcThreadScratch elm_thread_scratch_;
DstThreadScratch dst_thread_scratch_;
elm_thread_scratch_.data_ =
bit_cast<decltype(elm_thread_scratch_.data_)>(elm_vectors_tuple_);
if constexpr(SrcVectorDim != DstVectorDim &&
((is_same<half_t, remove_cvref_t<DstData>>::value &&
SrcScalarPerVector % 2 == 0 && DstScalarPerVector % 2 == 0) ||
(is_same<int8_t, remove_cvref_t<DstData>>::value &&
SrcScalarPerVector % 4 == 0 && DstScalarPerVector % 4 == 0)))
{
// each transpose does
// DstScalarPerVector # of src vectors in src_thread_scratch_
// SrcScalarPerVector # of dst vectors in dst_thread_scratch_
constexpr index_t num_src_vector = Number<DstScalarPerVector>{};
constexpr index_t num_dst_vector = Number<SrcScalarPerVector>{};
// Assume SrcVectorDim is not the same as DstVectorDim, so we do transpose
// TODO: make this logic generic for all scenario
constexpr auto src_scalar_step_in_vector = generate_sequence(
detail::lambda_scalar_step_in_vector<SrcVectorDim>{}, Number<nDim>{});
constexpr auto dst_scalar_step_in_vector = generate_sequence(
detail::lambda_scalar_step_in_vector<DstVectorDim>{}, Number<nDim>{});
constexpr auto scalar_per_access = generate_sequence(
detail::lambda_scalar_per_access_for_src_and_dst<SrcVectorDim,
SrcScalarPerVector,
DstVectorDim,
DstScalarPerVector>{},
Number<nDim>{});
constexpr auto access_lengths = SliceLengths{} / scalar_per_access;
static_ford<decltype(access_lengths)>{}([&](auto access_idx) {
constexpr auto data_idx = access_idx * scalar_per_access;
constexpr auto data_idx_seq = generate_sequence_v2(
[&](auto i) { return Number<data_idx[i]>{}; }, Number<nDim>{});
using src_vector_t = vector_type_maker_t<DstData, SrcScalarPerVector>;
using dst_vector_t = vector_type_maker_t<DstData, DstScalarPerVector>;
// get DstScalarPerVector # of read-only references to src vectors from
// src_thread_scratch_
const auto src_vector_refs = generate_tie(
[&](auto i) -> const src_vector_t& {
// i increment corresponds to movement in DstVectorDim
return elm_thread_scratch_.GetVectorTypeReference(
data_idx_seq + i * dst_scalar_step_in_vector);
},
Number<num_src_vector>{});
// get SrcScalarPerVector # of references to dst vectors from dst_thread_scratch_
auto dst_vector_refs = generate_tie(
[&](auto i) -> dst_vector_t& {
// i increment corresponds to movement in SrcVectorDim
return dst_thread_scratch_.GetVectorTypeReference(
data_idx_seq + i * src_scalar_step_in_vector);
},
Number<num_dst_vector>{});
// do data transpose
transpose_vectors<DstData, DstScalarPerVector, SrcScalarPerVector>{}(
src_vector_refs, dst_vector_refs);
});
}
else
{
static_ford<SliceLengths>{}(
[&](auto idx) { dst_thread_scratch_(idx) = elm_thread_scratch_[idx]; });
}
dst_vectors_tuple_ = bit_cast<decltype(dst_vectors_tuple_)>(dst_thread_scratch_.data_);
}
// DstDescs: Tuple<const DstDesc0&, const DstDesc1&, ...>
// DstBuffers: Tuple<const DstBuffer0&, const DstBuffer1&, ...>
template <typename DstBuffers,
enable_if_t<DstDescs::Size() == DstBuffers::Size(), bool> = false>
enable_if_t<DstDescs::Size() == 1 && DstBuffers::Size() == 1, bool> = false>
__device__ void RunWrite(const DstDescs& dst_descs, DstBuffers dst_bufs)
{
TransposeFromElmToDst();
// loop over space-filling curve
static_for<0, num_access, 1>{}([&](auto iAccess) {
auto dst_vectors = dst_vectors_tuple_[iAccess];
static_for<0, dst_num_access, 1>{}([&](auto iAccess) {
auto dst_vectors = dst_vectors_tuple_[Number<iAccess>{}];
// copy data from buf_vectors into dst_bufs
static_for<0, nDst, 1>{}([&](auto i) {
@@ -269,7 +371,7 @@ struct ThreadwiseTensorSliceTransfer_v7r2
});
// move coordinate
if constexpr(iAccess.value != num_access - 1)
if constexpr(iAccess.value != dst_num_access - 1)
{
constexpr auto forward_step = DstSpaceFillingCurve::GetForwardStep(iAccess);
@@ -312,28 +414,126 @@ struct ThreadwiseTensorSliceTransfer_v7r2
__device__ static constexpr auto GetSrcCoordinateResetStep()
{
if constexpr(num_access == 0)
if constexpr(src_num_access == 0)
{
return typename SrcSpaceFillingCurve::Index{};
}
else
{
return SrcSpaceFillingCurve::GetStepBetween(Number<num_access - 1>{}, Number<0>{});
return SrcSpaceFillingCurve::GetStepBetween(Number<src_num_access - 1>{}, Number<0>{});
}
}
__device__ static constexpr auto GetDstCoordinateResetStep()
{
if constexpr(num_access == 0)
if constexpr(dst_num_access == 0)
{
return typename DstSpaceFillingCurve::Index{};
}
else
{
return DstSpaceFillingCurve::GetStepBetween(Number<num_access - 1>{}, Number<0>{});
return DstSpaceFillingCurve::GetStepBetween(Number<dst_num_access - 1>{}, Number<0>{});
}
}
__device__ static constexpr auto GetSrcThreadScratchDescriptor()
{
// constexpr auto src_scalar_per_access = generate_sequence(
// detail::lambda_scalar_per_access<SrcVectorDim, SrcScalarPerVector>{}, Number<nDim>{});
constexpr auto src_access_lengths = SliceLengths{} / src_scalar_per_access;
constexpr auto src_access_lengths_and_vector_length = container_push_back(
sequence_to_tuple_of_number(src_access_lengths), Number<SrcScalarPerVector>{});
// 1st stage of transforms
constexpr auto desc0 =
make_naive_tensor_descriptor_packed(src_access_lengths_and_vector_length);
// 2nd stage of transforms
constexpr auto transforms = generate_tuple(
[&](auto i) {
if constexpr(i == SrcVectorDim)
{
return make_merge_transform_v3_division_mod(
make_tuple(src_access_lengths_and_vector_length[i],
src_access_lengths_and_vector_length[Number<nDim>{}]));
}
else
{
return make_pass_through_transform(src_access_lengths_and_vector_length[i]);
}
},
Number<nDim>{});
constexpr auto low_dim_idss = generate_tuple(
[&](auto i) {
if constexpr(i == SrcVectorDim)
{
return Sequence<i.value, nDim>{};
}
else
{
return Sequence<i.value>{};
}
},
Number<nDim>{});
constexpr auto up_dim_idss =
generate_tuple([&](auto i) { return Sequence<i.value>{}; }, Number<nDim>{});
return transform_tensor_descriptor(desc0, transforms, low_dim_idss, up_dim_idss);
}
__device__ static constexpr auto GetDstThreadScratchDescriptor()
{
// 1st stage of transforms
// constexpr auto dst_scalar_per_access = generate_sequence(
// detail::lambda_scalar_per_access<DstVectorDim, DstScalarPerVector>{}, Number<nDim>{});
constexpr auto dst_access_lengths = SliceLengths{} / dst_scalar_per_access;
constexpr auto dst_access_lengths_and_vector_length = container_push_back(
sequence_to_tuple_of_number(dst_access_lengths), Number<DstScalarPerVector>{});
constexpr auto desc0 =
make_naive_tensor_descriptor_packed(dst_access_lengths_and_vector_length);
// 2nd stage of transforms
constexpr auto transforms = generate_tuple(
[&](auto i) {
if constexpr(i == DstVectorDim)
{
return make_merge_transform_v3_division_mod(
make_tuple(dst_access_lengths_and_vector_length[i],
dst_access_lengths_and_vector_length[Number<nDim>{}]));
}
else
{
return make_pass_through_transform(dst_access_lengths_and_vector_length[i]);
}
},
Number<nDim>{});
constexpr auto low_dim_idss = generate_tuple(
[&](auto i) {
if constexpr(i == DstVectorDim)
{
return Sequence<i.value, nDim>{};
}
else
{
return Sequence<i.value>{};
}
},
Number<nDim>{});
constexpr auto up_dim_idss =
generate_tuple([&](auto i) { return Sequence<i.value>{}; }, Number<nDim>{});
return transform_tensor_descriptor(desc0, transforms, low_dim_idss, up_dim_idss);
}
// src_slice_origin_step_idx need to be known at compile-time, for performance reason
template <index_t ISrc>
__device__ void MoveSrcSliceWindow(const SrcDescs& src_descs,
@@ -372,11 +572,14 @@ struct ThreadwiseTensorSliceTransfer_v7r2
private:
using SrcVectorsType = decltype(generate_vectors<SrcDatas, SrcScalarPerVector>());
using ElmVectorsType = decltype(generate_vectors<DstDatas, SrcScalarPerVector>());
using DstVectorsType = decltype(generate_vectors<DstDatas, DstScalarPerVector>());
static constexpr auto num_access = SrcSpaceFillingCurve::GetNumOfAccess();
static constexpr auto src_num_access = SrcSpaceFillingCurve::GetNumOfAccess();
static constexpr auto dst_num_access = DstSpaceFillingCurve::GetNumOfAccess();
StaticallyIndexedArray<DstVectorsType, num_access> dst_vectors_tuple_;
StaticallyIndexedArray<ElmVectorsType, src_num_access> elm_vectors_tuple_;
StaticallyIndexedArray<DstVectorsType, dst_num_access> dst_vectors_tuple_;
SrcCoords src_coords_;
DstCoords dst_coords_;