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Commit fa335f31 authored by feifei14119's avatar feifei14119
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remove debug 9.8 tflops

parent 888317e6
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example/ck_tile/18_flatmm_uk/CMakeLists.txt 0 → 100644
View file @ fa335f31
set(TILE_EXAPMLE_FLATMM_UK "tile_example_flatmm_uk")
# not using add_example_executable() to add this target, since we don't want this to have
# to be included in "make all/install/check"
message("adding ${TILE_EXAPMLE_FLATMM_UK}")
file(GLOB INSTANCE_SRCS instances/*.cpp)
add_executable(${TILE_EXAPMLE_FLATMM_UK} EXCLUDE_FROM_ALL main.cpp)
target_include_directories(${TILE_EXAPMLE_FLATMM_UK} PRIVATE ${CMAKE_CURRENT_LIST_DIR})
target_sources(${TILE_EXAPMLE_FLATMM_UK} PRIVATE ${INSTANCE_SRCS})
set(TILE_EXAPMLE_FLATMM_UK_COMPILE_OPTIONS)
# NOTE: we turn off undefined-func-template to let source compile without explicit declare function specializations
list(APPEND TILE_EXAPMLE_FLATMM_UK_COMPILE_OPTIONS -Wno-undefined-func-template -Wno-float-equal)
list(APPEND TILE_EXAPMLE_FLATMM_UK_COMPILE_OPTIONS -DCK_TILE_BUFFER_LOAD_AGPR=1) # TODO: enable load to a
list(APPEND TILE_EXAPMLE_FLATMM_UK_COMPILE_OPTIONS -DCK_TILE_FLOAT_TO_BFLOAT16_DEFAULT=4) # rta
# list(APPEND TILE_EXAPMLE_FLATMM_UK_COMPILE_OPTIONS -mllvm -greedy-reverse-local-assignment=1)
# list(APPEND TILE_EXAPMLE_FLATMM_UK_COMPILE_OPTIONS -v --save-temps -Wno-gnu-line-marker)
target_compile_options(${TILE_EXAPMLE_FLATMM_UK} PRIVATE ${TILE_EXAPMLE_FLATMM_UK_COMPILE_OPTIONS})
example/ck_tile/18_flatmm_uk/flatmm_uk.hpp 0 → 100644
View file @ fa335f31
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/host/kernel_launch.hpp"
#include "ck_tile/ops/flatmm_uk.hpp"
#include <string>
// this is only a convenient structure for creating an example
// this is not part of the host API
template <typename I, typename W, typename O, typename ST, typename SW, typename SQ, typename KW>
struct FlatmmUkTypeConfig;
template <typename ST, typename SW, typename SQ, typename KW>
struct FlatmmUkTypeConfig<ck_tile::bf16_t, ck_tile::bf16_t, ck_tile::bf16_t, ST, SW, SQ, KW>
{
using ADataType = ck_tile::bf16_t;
using GDataType = ck_tile::bf16_t;
using DDataType = ck_tile::bf16_t;
using AccDataType = float;
using ODataType = ck_tile::bf16_t;
using AScaleDataType = ck_tile::remove_cvref_t<ST>;
using GScaleDataType = ck_tile::remove_cvref_t<SW>;
using DScaleDataType = ck_tile::remove_cvref_t<SW>;
using YSmoothScaleDataType = ck_tile::remove_cvref_t<SQ>;
using TopkWeightDataType = ck_tile::remove_cvref_t<KW>;
using IndexDataType = ck_tile::index_t;
};
template <typename ST, typename SW, typename SQ, typename KW>
struct FlatmmUkTypeConfig<ck_tile::fp16_t, ck_tile::fp16_t, ck_tile::fp16_t, ST, SW, SQ, KW>
{
using ADataType = ck_tile::fp16_t;
using GDataType = ck_tile::fp16_t;
using DDataType = ck_tile::fp16_t;
using AccDataType = float;
using ODataType = ck_tile::fp16_t;
using AScaleDataType = ck_tile::remove_cvref_t<ST>;
using GScaleDataType = ck_tile::remove_cvref_t<SW>;
using DScaleDataType = ck_tile::remove_cvref_t<SW>;
using YSmoothScaleDataType = ck_tile::remove_cvref_t<SQ>;
using TopkWeightDataType = ck_tile::remove_cvref_t<KW>;
using IndexDataType = ck_tile::index_t;
};
template <typename ST, typename SW, typename SQ, typename KW>
struct FlatmmUkTypeConfig<ck_tile::int8_t, ck_tile::int8_t, ck_tile::bf16_t, ST, SW, SQ, KW>
{
using ADataType = ck_tile::int8_t;
using GDataType = ck_tile::int8_t;
using DDataType = ck_tile::int8_t;
using AccDataType = int32_t;
using ODataType = ck_tile::bf16_t;
using AScaleDataType = ck_tile::remove_cvref_t<ST>;
using GScaleDataType = ck_tile::remove_cvref_t<SW>;
using DScaleDataType = ck_tile::remove_cvref_t<SW>;
using YSmoothScaleDataType = ck_tile::remove_cvref_t<SQ>;
using TopkWeightDataType = ck_tile::remove_cvref_t<KW>;
using IndexDataType = ck_tile::index_t;
};
struct flatmm_uk_args
{
const void* a_ptr; // [m, k], input token
const void* b_ptr; // [m, k], input token
const void* c_ptr; // [m, k], output token (no need to do zeroing)
void* d_ptr; // [m, k], output token (no need to do zeroing)
void* dbg_int_ptr; // [m, k], output token (no need to do zeroing)
void* dbg_bf16_ptr; // [m, k], output token (no need to do zeroing)
void* dbg_fp32_ptr; // [m, k], output token (no need to do zeroing)
ck_tile::index_t block_m; // block_m, used to devide the input
ck_tile::index_t hidden_size; // k
ck_tile::index_t intermediate_size; // n / TP, for Gate. if Gate+Up, Down need divide by 2
ck_tile::index_t num_tokens; // input number of tokens for current iteration
ck_tile::index_t num_experts; // number of groups
ck_tile::index_t topk; // need this?
ck_tile::index_t stride_token; // for input/output, stride for each row, should >= hidden_size
};
// This is the public API, will be generated by script
struct flatmm_uk_traits
{
std::string prec_i; // input precision
std::string prec_w; // weight precision
std::string prec_o; // output precision
std::string prec_st; // token scale data type
std::string prec_sw; // weight scale data type
std::string prec_sq; // smooth quant scale
std::string prec_kw; // topk-weight data type
int block_m;
int gate_only;
int fused_quant; // 0:no-sweep, 1:smooth-dynamic-quant, 2:dynamic-quant
};
float flatmm_uk(flatmm_uk_traits, flatmm_uk_args, const ck_tile::stream_config&);
example/ck_tile/18_flatmm_uk/instances/flatmm_uk_api.cpp 0 → 100644
View file @ fa335f31
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "flatmm_uk.hpp"
#include "flatmm_uk_api.hpp"
#include "ck_tile/ops/flatmm_uk.hpp"
#include <iostream>
template <ck_tile::index_t... Is>
using S = ck_tile::sequence<Is...>;
// do not the define of this tepmlate function inside the _api.cpp, otherwise will block make -j
template <typename Ts_>
float flatmm_uk_(const ck_tile::stream_config& s_, flatmm_uk_args_ a_)
{
printf("[FF] ======= fused_moegemm_() ======= \n \tget moe arg in a_ <flatmm_uk_args>, get "
"config in Ts_\n");
using f_traits = ck_tile::FusedMoeGemmTraits<Ts_::GateOnly, Ts_::FusedQuant == 1, 1 /*atomic*/>;
using f_shape = ck_tile::FusedMoeGemmShape<typename Ts_::BlockTile_0,
typename Ts_::WarpPerBlock_0,
typename Ts_::WarpTile_0,
typename Ts_::BlockTile_1,
typename Ts_::WarpPerBlock_0,
typename Ts_::WarpTile_0>;
printf("[FF] --- fused_moegemm_(): <FusedMoeGemmShape> --- \n");
printf("[FF] f_shape::BlockSize = %d\n", static_cast<uint32_t>(f_shape::BlockSize));
printf("[FF] f_shape::NumWarps = %d\n", static_cast<uint32_t>(f_shape::NumWarps));
printf("[FF] --------- \n");
printf("[FF] f_shape::Block_M0 = %d\n", static_cast<uint32_t>(f_shape::Block_M0));
printf("[FF] f_shape::Block_N0 = %d\n", static_cast<uint32_t>(f_shape::Block_N0));
printf("[FF] f_shape::Block_K0 = %d\n", static_cast<uint32_t>(f_shape::Block_K0));
printf("[FF] f_shape::WarpPerBlock_M0 = %d\n", static_cast<uint32_t>(f_shape::WarpPerBlock_M0));
printf("[FF] f_shape::WarpPerBlock_N0 = %d\n", static_cast<uint32_t>(f_shape::WarpPerBlock_N0));
printf("[FF] f_shape::WarpPerBlock_K0 = %d\n", static_cast<uint32_t>(f_shape::WarpPerBlock_K0));
printf("[FF] f_shape::Warp_M0 = %d\n", static_cast<uint32_t>(f_shape::Warp_M0));
printf("[FF] f_shape::Warp_N0 = %d\n", static_cast<uint32_t>(f_shape::Warp_N0));
printf("[FF] f_shape::Warp_K0 = %d\n", static_cast<uint32_t>(f_shape::Warp_K0));
printf("[FF] f_shape::ThreadPerBlock_M0 = %d\n",
static_cast<uint32_t>(f_shape::ThreadPerBlock_M0));
printf("[FF] f_shape::ThreadPerBlock_N0 = %d\n",
static_cast<uint32_t>(f_shape::ThreadPerBlock_N0));
printf("[FF] f_shape::ThreadPerBlock_K0 = %d\n",
static_cast<uint32_t>(f_shape::ThreadPerBlock_K0));
printf("[FF] f_shape::Repeat_M0 = %d\n", static_cast<uint32_t>(f_shape::Repeat_M0));
printf("[FF] f_shape::Repeat_N0 = %d\n", static_cast<uint32_t>(f_shape::Repeat_N0));
printf("[FF] f_shape::Repeat_K0 = %d\n", static_cast<uint32_t>(f_shape::Repeat_K0));
printf("[FF] f_shape::Block_W0 = %d\n", static_cast<uint32_t>(f_shape::Block_W0));
printf("[FF] f_shape::Block_Nr0 = %d\n", static_cast<uint32_t>(f_shape::Block_Nr0));
printf("[FF] f_shape::Block_Kr0 = %d\n", static_cast<uint32_t>(f_shape::Block_Kr0));
printf("[FF] --------- \n");
printf("[FF] f_shape::Block_M1 = %d\n", static_cast<uint32_t>(f_shape::Block_M1));
printf("[FF] f_shape::Block_N1 = %d\n", static_cast<uint32_t>(f_shape::Block_N1));
printf("[FF] f_shape::Block_K1 = %d\n", static_cast<uint32_t>(f_shape::Block_K1));
printf("[FF] f_shape::WarpPerBlock_M1 = %d\n", static_cast<uint32_t>(f_shape::WarpPerBlock_M1));
printf("[FF] f_shape::WarpPerBlock_N1 = %d\n", static_cast<uint32_t>(f_shape::WarpPerBlock_N1));
printf("[FF] f_shape::WarpPerBlock_K1 = %d\n", static_cast<uint32_t>(f_shape::WarpPerBlock_K1));
printf("[FF] f_shape::Warp_M1 = %d\n", static_cast<uint32_t>(f_shape::Warp_M1));
printf("[FF] f_shape::Warp_N1 = %d\n", static_cast<uint32_t>(f_shape::Warp_N1));
printf("[FF] f_shape::Warp_K1 = %d\n", static_cast<uint32_t>(f_shape::Warp_K1));
printf("[FF] f_shape::ThreadPerBlock_M1 = %d\n",
static_cast<uint32_t>(f_shape::ThreadPerBlock_M1));
printf("[FF] f_shape::ThreadPerBlock_N1 = %d\n",
static_cast<uint32_t>(f_shape::ThreadPerBlock_N1));
printf("[FF] f_shape::ThreadPerBlock_K1 = %d\n",
static_cast<uint32_t>(f_shape::ThreadPerBlock_K1));
printf("[FF] f_shape::Repeat_M1 = %d\n", static_cast<uint32_t>(f_shape::Repeat_M1));
printf("[FF] f_shape::Repeat_N1 = %d\n", static_cast<uint32_t>(f_shape::Repeat_N1));
printf("[FF] f_shape::Repeat_K1 = %d\n", static_cast<uint32_t>(f_shape::Repeat_K1));
printf("[FF] f_shape::Block_W1 = %d\n", static_cast<uint32_t>(f_shape::Block_W1));
printf("[FF] f_shape::Block_Nr1 = %d\n", static_cast<uint32_t>(f_shape::Block_Nr1));
printf("[FF] f_shape::Block_Kr1 = %d\n", static_cast<uint32_t>(f_shape::Block_Kr1));
using f_problem =
ck_tile::FusedMoeGemmPipelineProblem<typename Ts_::ADataType,
typename Ts_::GDataType,
typename Ts_::DDataType,
typename Ts_::AccDataType,
typename Ts_::ODataType,
typename Ts_::AScaleDataType,
typename Ts_::GScaleDataType,
typename Ts_::DScaleDataType,
typename Ts_::YSmoothScaleDataType,
typename Ts_::TopkWeightDataType,
typename Ts_::IndexDataType,
ck_tile::element_wise::FastGeluAsm, // TODO: hardcoded
f_shape,
f_traits>;
// using f_pipeline = ck_tile::FusedMoeGemmPipeline_FlatmmEx<f_problem>;
using f_pipeline = ck_tile::GemmPipeline_FlatmmUk<f_problem>;
using f_kernel = ck_tile::FlatmmUkKernel<f_pipeline, void>;
const dim3 grids = f_kernel::GridSize(a_);
constexpr dim3 blocks = f_kernel::BlockSize();
constexpr ck_tile::index_t kBlockPerCu = 1;
printf("[FF] grids = [%d, %d, %d]\n", grids.x, grids.y, grids.z);
printf("[FF] blocks = [%d, %d, %d]\n", blocks.x, blocks.y, blocks.z);
static int printed = 0;
auto kargs = f_kernel::MakeKargs(a_);
f_kernel kernel{};
auto lambda_kenrel =
ck_tile::make_kernel<blocks.x, kBlockPerCu>(kernel, grids, blocks, 0, kargs);
if(s_.log_level_ > 0 && printed == 10)
{
// std::cout << ", " << f_kernel::GetName() << std::flush;
printed = 1;
}
return ck_tile::launch_kernel(
s_, lambda_kenrel
// ck_tile::make_kernel<blocks.x, kBlockPerCu>(f_kernel{}, grids, blocks, 0, kargs)
);
}
float flatmm_uk(flatmm_uk_traits t, flatmm_uk_args a, const ck_tile::stream_config& s)
{
// auto s_ = ck_tile::stream_config{s.stream_id_, false, s.log_level_, 0, 1};
auto s_ = s;
auto t_ = flatmm_uk_traits_{t.prec_i,
t.prec_w,
t.prec_o,
t.prec_st,
t.prec_sw,
t.prec_sq,
t.prec_kw,
t.block_m,
t.gate_only,
t.fused_quant};
auto a_ = flatmm_uk_args_{
a.a_ptr, // const void* a_ptr;
a.b_ptr, // const void* a_ptr;
a.c_ptr, // void* o_ptr;
a.d_ptr, // void* o_ptr;
a.dbg_int_ptr,
a.dbg_bf16_ptr,
a.dbg_fp32_ptr,
a.hidden_size, // index_t hidden_size;
a.intermediate_size, // index_t intermediate_size;
a.num_tokens, // index_t num_tokens;
a.num_experts, // index_t num_experts;
a.topk, // index_t topk;
a.stride_token // index_t stride_token;
};
float r = -1;
if(t_.prec_i == "bf16" && t_.prec_w == "bf16" && t_.prec_o == "bf16" && t_.prec_st == "fp32" &&
t_.prec_sw == "fp32" && t_.prec_sq == "fp32" && t_.prec_kw == "fp32" && t_.block_m == 32 &&
t_.gate_only == 1)
{
using t_ = fmoe_<ck_tile::bf16_t,
ck_tile::bf16_t,
ck_tile::bf16_t,
float,
float,
float,
float,
S<32, 512, 128, 128>,
S<1, 4, 1>,
S<16, 16, 32>,
1,
0>;
r = flatmm_uk_<t_>(s_, a_);
}
else if(t_.prec_i == "fp16" && t_.prec_w == "fp16" && t_.prec_o == "fp16" &&
t_.prec_st == "fp32" && t_.prec_sw == "fp32" && t_.prec_sq == "fp32" &&
t_.prec_kw == "fp32" && t_.block_m == 32 && t_.gate_only == 1)
{
using t_ = fmoe_<ck_tile::fp16_t,
ck_tile::fp16_t,
ck_tile::fp16_t,
float,
float,
float,
float,
S<32, 512, 128, 128>,
S<1, 4, 1>,
S<16, 16, 32>,
1,
0>;
r = flatmm_uk_<t_>(s_, a_);
}
// keep unsupported case return negative
if(r < 0)
return -1;
return r;
}
example/ck_tile/18_flatmm_uk/instances/flatmm_uk_api.hpp 0 → 100644
View file @ fa335f31
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/host/kernel_launch.hpp"
#include "ck_tile/ops/flatmm_uk.hpp"
#include <string>
// runtime args
struct flatmm_uk_args_ : public ck_tile::FlatmmUkHostArgs
{
};
// This is the public API, will be generated by script
struct flatmm_uk_traits_
{
std::string prec_i; // input precision
std::string prec_w; // weight precision
std::string prec_o; // output precision
std::string prec_st; // token scale data type
std::string prec_sw; // weight scale data type
std::string prec_sq; // smooth quant scale
std::string prec_kw; // topk-weight data type
int block_m;
int gate_only;
int fused_quant; // 0:no-sweep, 1:smooth-dynamic-quant, 2:dynamic-quant
};
// this is used to pattern-match internl kernel implementation, not to instantiate kernel
template <typename I,
typename W,
typename O,
typename ST,
typename SW,
typename SQ,
typename KW,
typename BlockTIle_, // seq<b_token, b_interm, b_hidden, b_down>
typename WarpPerBlock_,
typename WarpTile_, // seq<*,*,*>, used to select mfma
ck_tile::index_t GateOnly_ = 0,
ck_tile::index_t FusedQuant_ = 0>
struct fmoe_ // traits, ugly name, only used for internal
{
using TypeConfig = FlatmmUkTypeConfig<I, W, O, ST, SW, SQ, KW>;
using ADataType = ck_tile::remove_cvref_t<typename TypeConfig::ADataType>;
using GDataType = ck_tile::remove_cvref_t<typename TypeConfig::GDataType>;
using DDataType = ck_tile::remove_cvref_t<typename TypeConfig::DDataType>;
using AccDataType = ck_tile::remove_cvref_t<typename TypeConfig::AccDataType>;
using ODataType = ck_tile::remove_cvref_t<typename TypeConfig::ODataType>;
using AScaleDataType = ck_tile::remove_cvref_t<typename TypeConfig::AScaleDataType>;
using GScaleDataType = ck_tile::remove_cvref_t<typename TypeConfig::GScaleDataType>;
using DScaleDataType = ck_tile::remove_cvref_t<typename TypeConfig::DScaleDataType>;
using YSmoothScaleDataType = ck_tile::remove_cvref_t<typename TypeConfig::YSmoothScaleDataType>;
using TopkWeightDataType = ck_tile::remove_cvref_t<typename TypeConfig::TopkWeightDataType>;
using IndexDataType = ck_tile::remove_cvref_t<typename TypeConfig::IndexDataType>;
static constexpr ck_tile::index_t BT_ = BlockTIle_::at(ck_tile::number<0>{}); // block token
static constexpr ck_tile::index_t BI_ =
BlockTIle_::at(ck_tile::number<1>{}); // block intermediate
static constexpr ck_tile::index_t BH_ = BlockTIle_::at(ck_tile::number<2>{}); // block hidden
static constexpr ck_tile::index_t BD_ = BlockTIle_::at(ck_tile::number<3>{}); // block down
using BlockTile_0 = ck_tile::sequence<BT_, BI_, BH_>;
using WarpPerBlock_0 = ck_tile::remove_cvref_t<WarpPerBlock_>;
using WarpTile_0 = ck_tile::remove_cvref_t<WarpTile_>;
using BlockTile_1 = ck_tile::sequence<BT_, BD_, BI_ / (GateOnly_ ? 1 : 2)>;
using WarpPerBlock_1 = ck_tile::remove_cvref_t<WarpPerBlock_>;
using WarpTile_1 = ck_tile::remove_cvref_t<WarpTile_>;
static constexpr ck_tile::index_t GateOnly = GateOnly_;
static constexpr ck_tile::index_t FusedQuant = FusedQuant_;
};
example/ck_tile/18_flatmm_uk/main.cpp 0 → 100644
View file @ fa335f31
This diff is collapsed.
example/ck_tile/CMakeLists.txt
View file @ fa335f31
......@@ -17,3 +17,4 @@ add_subdirectory(14_moe_smoothquant)
add_subdirectory(15_fused_moe)
add_subdirectory(16_batched_gemm)
add_subdirectory(17_grouped_gemm)
add_subdirectory(18_flatmm_uk)
include/ck_tile/ops/flatmm_uk.hpp 0 → 100644
View file @ fa335f31
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/ops/flatmm/block/flatmm_uk_config.hpp"
#include "ck_tile/ops/flatmm/block/flatmm_32x512x128_1x4x1_16x16x32.hpp"
#include "ck_tile/ops/flatmm/block/flatmm_ff_32x512x128_1x4x1_16x16x32.hpp"
#include "ck_tile/ops/fused_moe/kernel/flatmm_uk_kernel.hpp"
#include "ck_tile/ops/fused_moe/kernel/fused_moegemm_shape.hpp"
#include "ck_tile/ops/fused_moe/kernel/fused_moegemm_tile_partitioner.hpp"
#include "ck_tile/ops/fused_moe/pipeline/flatmm_uk_pipeline_policy.hpp"
#include "ck_tile/ops/fused_moe/pipeline/flatmm_uk_pipeline.hpp"
#include "ck_tile/ops/fused_moe/pipeline/fused_moegemm_pipeline_problem.hpp"
#include "ck_tile/ops/fused_moe/pipeline/fused_moegemm_traits.hpp"
#include "ck_tile/ops/common/generic_2d_block_shape.hpp"
#include "ck_tile/ops/common/tensor_layout.hpp"
include/ck_tile/ops/fused_moe/kernel/flatmm_uk_kernel.hpp 0 → 100644
View file @ fa335f31
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/ops/common.hpp"
#include "ck_tile/ops/elementwise.hpp"
#include <string>
#include <type_traits>
// clang-format off
// [indexing implementation-1]
// using M_a as constexpr block_size to partition all tokens into different slices
// each slice map to one expert, and one expert can have multiple slices
// e.g. num_experts = 6, topk=3, M_a = 4, input_tokens = 5
// before sort, topk_ids is : [[0, 3, 5], [2, 3, 5], [1, 3, 5], [1, 2, 3], [1, 3, 5]]
// tok-0 tok-1 tok-2 tok-3 tok-4
// topk_weight is : [[a, b, c], [d, e, f], [g, h, i], [j, k, l], [m, n, o]] (some float number)
//
// token_id_per_expert is : [[0], [2, 3, 4], [1, 3], [0, 1, 2, 3, 4], [], [0, 1, 2, 5]]
// (only for reference) exp-0 exp-1 exp-2 exp-3 exp-4 exp-5
// weight_id_per_expert is: [[a], [g, j, m], [d, k], [b, e, h, l, n], [], [c, f, i, o]]
//
// max_num_tokens_padded : topk * input_tokens + num_experts * (M_a - 1)
// * this could be larger than actual, since actual tokens are on GPU
//
// sorted_token_ids_ptr : [0, 6, 6, 6, 2, 3, 4, 6, 1, 3, 6, 6, 0, 1, 2, 3, 4, 6, 6, 6, 6, 6, 6, 6, 0, 1, 2, 5]
// |- exp-0 -|- exp-1 -|- exp-2 -|- exp-3 -|- exp-4 -|- exp-5 -|
// sorted_weight_ptr : [a, *, *, *, g, j, m, *, d, k, *, *, b, e, h, l, n, *, *, *, *, *, *, *, c, f, i, o]
//
// * length is max_num_tokens_padded, actual size is num_tokens_post_padded_ptr
//
// * Note on token_id_per_expert/sorted_token_ids_ptr data:
// currently we do not have topk information from the data of token_id_per_expert/sorted_token_ids_ptr.
// In some cases(like smooth-quant), we need topk information to indexing into tokens quant from
// different expert smooth quant. So we modify the number stored inside token_id_per_expert/sorted_token_ids_ptr
//
// 32bit 0........23 24.....31 bit
// (data) -> (token_id | topk_id)
// low 24 bit is for token id, top 8 bit is for topk id
//
// the input after smooth-quant is [token, topk, hidden_dim], originally it is [token, hidden_dim]
// the input scale for token is [topk, token, 1], the smooth-quant scale for first gemm is [expert, interm_dim]
//
// sorted_expert_ids_ptr : [0, 1, 2, 3, 3, 4, 5]
// * length is (max_num_tokens_padded + block_size - 1) / block_size
//
// num_tokens_post_padded_ptr : [28]
// num_sorted_tiles_ptr : [7]
//
// * different from vLLM
// 1) token_id stored in sorted_token_ids_ptr is actual token_id, not token_id*top_K expanded id
// 2）need sorted_weight_ptr
// 3) use num_sorted_tiles_ptr, already divided by M_a
//
// * below used for indexing
// 1) sorted_token_ids_ptr [max_num_tokens_padded]
// 2) sorted_weight_ptr
// 3) sorted_expert_ids_ptr
// 4）num_tokens_post_padded_ptr/num_sorted_tiles_ptr (select one)
//
// max_num_tokens_padded: opk_ids.numel() + num_experts * (block_size - 1)
//
// [indexing implementation-2]
// before sort, topk_ids is : [[0, 3, 5], [2, 3, 5], [1, 3, 5], [1, 2, 3], [1, 3, 5]]
// tok-0 tok-1 tok-2 tok-3 tok-4
// topk_weight is : [[a, b, c], [d, e, f], [g, h, i], [j, k, l], [m, n, o]] (some float number)
//
// we generate original rol/col id as
// topk_rc_ids : [[0, 5, A], [1, 6, B], [2, 7, C], [3, 8, D], [4, 9, E]]
// let x be one element of above, we can get:
// tpok_row_id(token_id) = x % num_tokens(5)
// tpok_col_id(expert_Id) = x / num_tokens
// topk_row_id/col_id can be used to access original topk_ids/topk_weight
//
// token_id_per_expert is : [[0], [2, 3, 4], [1, 3], [0, 1, 2, 3, 4], [], [0, 1, 5, 5]]
// (only for reference) exp-0 exp-1 exp-2 exp-3 exp-4 exp-5
// weight_id_per_expert is: [[a], [g, j, m], [d, k], [b, e, h, l, n], [], [c, f, i, o]]
//
// we can get permuted_rc_ids:
// [[0], [2, 3, 4], [1, 8], [5, 6, 7, D, 9], [], [A, B, C, E]]
//
//
// clang-format on
//
namespace ck_tile {
// m: num_tokens (or token*input-batch)
// k: intermediate_size
// n: intermediate_size used between 2 FC (TP slice this)
// e: num expert
// if doing pre-shuffle
// nr : n / Block_Nr
// kr : k / Block_Kr
// w : fattened 1d wave buffer
struct FlatmmUkHostArgs
{
const void* a_ptr; // [m, k], input token
const void* b_ptr; // [m, k], input token
const void* c_ptr; // [m, k], output token
void* d_ptr; // [m, k], output token
void* dbg_int_ptr; // [m, k], output token
void* dbg_bf16_ptr; // [m, k], output token
void* dbg_fp32_ptr; // [m, k], output token
index_t hidden_size; // K
index_t intermediate_size; // N
index_t num_tokens; // M
index_t num_experts; // number of groups
index_t topk; // need this?
index_t stride_token; // for input/output, stride for each row, should >= hidden_size
};
// This is scatter/gather b2b group-gemm
template <typename Pipeline_, typename Epilogue_>
struct FlatmmUkKernel
{
using Pipeline = remove_cvref_t<Pipeline_>;
using Epilogue = remove_cvref_t<Epilogue_>; // TODO: not used
// static constexpr index_t kBlockPerCu = Pipeline::kBlockPerCu;
// static_assert(kBlockPerCu > 0);
using BlockShape = typename Pipeline::BlockShape; // this is FusedMoeGemmShape
static constexpr index_t BlockSize_ = BlockShape::BlockSize;
using ADataType = typename Pipeline::Problem::ADataType;
using GDataType = typename Pipeline::Problem::GDataType;
using DDataType = typename Pipeline::Problem::AccDataType;
using AccDataType = typename Pipeline::Problem::AccDataType;
using ODataType = typename Pipeline::Problem::ODataType;
using AScaleDataType = typename Pipeline::Problem::AScaleDataType;
using GScaleDataType = typename Pipeline::Problem::GScaleDataType;
using DScaleDataType = typename Pipeline::Problem::DScaleDataType;
using YSmoothScaleDataType = typename Pipeline::Problem::YSmoothScaleDataType;
using TopkWeightDataType = typename Pipeline::Problem::TopkWeightDataType;
using IndexDataType = typename Pipeline::Problem::IndexDataType;
using YDataType = typename Pipeline::Problem::YDataType;
using Traits = typename Pipeline::Problem::Traits;
static constexpr bool UseUK = true;
static constexpr bool IsGateOnly = Traits::IsGateOnly;
static constexpr bool UseSmoothQuant = Traits::UseSmoothQuant;
static constexpr bool PadHiddenSize = Traits::PadHiddenSize;
static constexpr bool PadIntermediateSize = Traits::PadIntermediateSize;
// clang-format off
template <typename T> struct t2s;
template <> struct t2s<float> { static constexpr const char * name = "fp32"; };
template <> struct t2s<fp16_t> { static constexpr const char * name = "fp16"; };
template <> struct t2s<bf16_t> { static constexpr const char * name = "bf16"; };
template <> struct t2s<fp8_t> { static constexpr const char * name = "fp8"; };
template <> struct t2s<bf8_t> { static constexpr const char * name = "bf8"; };
template <> struct t2s<int8_t> { static constexpr const char * name = "int8"; };
// clang-format on
CK_TILE_HOST static std::string GetName()
{
#define _SS_ std::string
#define _TS_ std::to_string
// clang-format off
using S_ = BlockShape;
auto prec_str = [&] () {
std::string base_str = _SS_(t2s<ADataType>::name);
if (!std::is_same_v<ADataType, GDataType>) {
base_str += _SS_("_") + _SS_(t2s<GDataType>::name);
}
return base_str;
}();
return _SS_("fused_moe_") + _SS_(prec_str) + "_" +
_TS_(S_::Block_M0) + "x" + _TS_(S_::Block_N0) + "x" + _TS_(S_::Block_K0) + "x" + _TS_(S_::Block_N1) + "_" +
_TS_(S_::WarpPerBlock_M0) + "x" + _TS_(S_::WarpPerBlock_N0) + "x" + _TS_(S_::WarpPerBlock_K0) + "_" +
_TS_(S_::Warp_M0) + "x" + _TS_(S_::Warp_N0) + "x" + _TS_(S_::Warp_K0) + "_" + _SS_(Pipeline::name);
#undef _SS_
#undef _TS_
// clang-format on
}
struct FusedMoeGemmKargs
{
const void* a_ptr; // [m, k], input token
const void* b_ptr; // [m, k], input token
const void* c_ptr; // [m, k], output token
void* d_ptr; // [m, k], output token
void* dbg_int_ptr; // [m, k], output token
void* dbg_bf16_ptr; // [m, k], output token
void* dbg_fp32_ptr; // [m, k], output token
index_t hidden_size; // K
index_t intermediate_size; // N
index_t num_tokens; // M
index_t num_experts; // number of groups
index_t topk; // need this?
index_t stride_token; // for input/output, stride for each row, should >= hidden_size
};
// TODO: switch karg based on
using Kargs = FusedMoeGemmKargs;
using Hargs = FlatmmUkHostArgs;
CK_TILE_HOST static constexpr Kargs MakeKargs(const Hargs& hargs)
{
// TODO: hargs/kargs not guranteed to be the same
return bit_cast<Kargs>(hargs);
}
CK_TILE_HOST static constexpr auto GridSize(const Hargs& hargs)
{
index_t ms = ck_tile::integer_divide_ceil(hargs.num_tokens, BlockShape::Block_M0);
index_t ns = ck_tile::integer_divide_ceil(hargs.intermediate_size, BlockShape::Block_N0);
return dim3(ns, ms, 1);
}
CK_TILE_HOST static constexpr auto BlockSize() { return dim3(BlockSize_); }
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize() { return Pipeline::GetSmemSize(); }
CK_TILE_DEVICE void operator()(Kargs kargs) const
{
#if 0
if(threadIdx.x == 0 && blockIdx.x == 0 && threadIdx.y == 0 && blockIdx.y == 0)
{
printf("[KERNEL] FlatmmUkKernel =====\n");
printf("[KERNEL] blockDim: [%d, %d], gridDim: [%d, %d]\n",
static_cast<int>(blockDim.x),
static_cast<int>(blockDim.y),
static_cast<int>(gridDim.x),
static_cast<int>(gridDim.y));
printf("[KERNEL] lds = %.3f (KB)\n", GetSmemSize() / 1024.0f);
}
[[maybe_unused]] uint32_t tidx = threadIdx.x; // 0~255
[[maybe_unused]] uint32_t tidy = threadIdx.y; // 0~0
[[maybe_unused]] uint32_t bidx = blockIdx.x; // 0~1
[[maybe_unused]] uint32_t bidy = blockIdx.y; // 0~51
[[maybe_unused]] uint32_t bdmx = blockDim.x; // 256
[[maybe_unused]] uint32_t bdmy = blockDim.y; // 1
[[maybe_unused]] uint32_t gdmx = gridDim.x; // 2
[[maybe_unused]] uint32_t gdmy = gridDim.y; // 52
[[maybe_unused]] uint32_t gid = ((bdmx * bdmy) * gdmx) * bidy
+ (bdmx * bdmy) * bidx
+ bdmx * tidy
+ tidx;
[[maybe_unused]]int * dbg_int = static_cast<int*>(kargs.dbg_int_ptr);
[[maybe_unused]]short * dbg_bf16 = static_cast<short*>(kargs.dbg_bf16_ptr);
[[maybe_unused]]float * dbg_fp32 = static_cast<float*>(kargs.dbg_fp32_ptr);
dbg_int[gid] = -1;
dbg_fp32[gid] = -1.0f;
#endif
__shared__ CK_TILE_LDS_ADDR ADataType smem[GetSmemSize()];
Pipeline{}(kargs, smem);
}
};
} // namespace ck_tile
include/ck_tile/ops/fused_moe/pipeline/flatmm_uk_pipeline.hpp 0 → 100644
View file @ fa335f31
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/ops/common/tensor_layout.hpp"
#include "ck_tile/ops/fused_moe/pipeline/flatmm_uk_pipeline_policy.hpp"
namespace ck_tile {
/*
This pipeline deal with a gemm(actually 2 gemm) with one very small(token), one very big(weight)
we need to design the pipeline such that all waves along gemm-N dim (gemm-m only 1 wave)
<----- gemm-N ------>
+----+----+----+----+
| w0 | w1 | w2 | w3 | gemm-m
+----+----+----+----+
*/
template <typename Problem_, typename Policy_ = GemmPipelineFlatmmPolicy>
struct GemmPipeline_FlatmmUk
{
using Problem = remove_cvref_t<Problem_>;
using Policy = remove_cvref_t<Policy_>;
using BlockShape = typename Problem::BlockShape; // this is FusedMoeGemmShape
using ADataType = typename Problem::ADataType;
using GDataType = typename Problem::GDataType;
using DDataType = typename Problem::AccDataType;
using AccDataType = typename Problem::AccDataType;
using ODataType = typename Problem::ODataType;
using AScaleDataType = typename Problem::AScaleDataType;
using GScaleDataType = typename Problem::GScaleDataType;
using DScaleDataType = typename Problem::DScaleDataType;
using YSmoothScaleDataType = typename Problem::YSmoothScaleDataType;
using TopkWeightDataType = typename Problem::TopkWeightDataType;
using IndexDataType = typename Problem::IndexDataType;
using YDataType = typename Problem::YDataType;
using Traits = typename Problem::Traits;
static constexpr bool IsGateOnly = Traits::IsGateOnly;
static constexpr bool UseSmoothQuant = Traits::UseSmoothQuant;
static constexpr bool PadHiddenSize = Traits::PadHiddenSize;
static constexpr bool PadIntermediateSize = Traits::PadIntermediateSize;
static constexpr index_t kAlignmentA = Policy::template GetAlignment_A<Problem>();
static constexpr index_t kAlignmentG = Policy::template GetAlignment_G<Problem>();
static constexpr index_t kAlignmentD = Policy::template GetAlignment_D<Problem>();
static constexpr index_t kAlignmentO = Policy::template GetAlignment_O<Problem>();
static constexpr index_t SLD_A = static_cast<index_t>(FusedMoeGemmPipelineSequencerEnum::SLD_A);
static constexpr index_t GLD_A = static_cast<index_t>(FusedMoeGemmPipelineSequencerEnum::GLD_A);
static constexpr index_t GLD_B = static_cast<index_t>(FusedMoeGemmPipelineSequencerEnum::GLD_B);
static constexpr index_t GST_O = static_cast<index_t>(FusedMoeGemmPipelineSequencerEnum::GST_O);
static constexpr index_t kBlockPerCu = []() {
if constexpr(Problem::kBlockPerCu != -1)
return Problem::kBlockPerCu;
else
{
// minimize occupancy
return 2;
}
}();
static constexpr const char* name = "flatmm_uk";
CK_TILE_HOST_DEVICE static constexpr ck_tile::index_t GetSmemSize()
{
constexpr index_t smem_0 = Policy::template GetUK_0<Problem>().GetSmemSize();
constexpr index_t smem_bridge =
BlockShape::Block_M0 * BlockShape::Block_N0 * sizeof(YDataType);
return max(smem_0, smem_bridge);
}
// this is the thread-offset along row/col
CK_TILE_HOST_DEVICE static auto GetACoord()
{
constexpr auto a_dist = Policy::template MakeGlobalTileDistribution_A<Problem>();
const auto a_coord = a_dist.calculate_index();
return a_coord;
}
// this is the thread-offset along row/col
CK_TILE_HOST_DEVICE static auto GetOCoord()
{
constexpr auto o_dist = Policy::template MakeOGlobalTileDistribution<Problem>();
const auto o_coord = o_dist.calculate_index();
return o_coord;
}
CK_TILE_DEVICE constexpr auto GetNumRowCoords_A()
{
constexpr index_t KLans = BlockShape::Block_K0 / kAlignmentA;
constexpr index_t MLans = BlockShape::BlockSize / KLans;
constexpr index_t MRepeat = BlockShape::Block_M0 / MLans;
return MRepeat;
}
// TODO: properlly support scatter/gather
CK_TILE_DEVICE auto GetRowCoords_A(index_t base_offset)
{
constexpr index_t KLans = BlockShape::Block_K0 / kAlignmentA;
constexpr index_t MLans = BlockShape::BlockSize / KLans;
constexpr index_t MRepeat = BlockShape::Block_M0 / MLans;
auto base_coord = threadIdx.x / KLans + base_offset;
array<index_t, MRepeat> coords;
static_for<0, MRepeat, 1>{}([&](auto i) { coords.at(i) = base_coord + i * MLans; });
return coords;
}
CK_TILE_DEVICE auto GetRowCoords_O2(index_t base_offset)
{
constexpr index_t NLans = BlockShape::Block_N0 / kAlignmentO;
constexpr index_t MLans = BlockShape::BlockSize / NLans;
constexpr index_t MRepeat = BlockShape::Block_M0 / MLans;
auto base_coord = threadIdx.x / NLans + base_offset;
array<index_t, MRepeat> coords;
static_for<0, MRepeat, 1>{}([&](auto i) { coords.at(i) = base_coord + i * MLans; });
return coords;
}
template <typename ROW_COORDS>
CK_TILE_DEVICE auto GetRowID(const ROW_COORDS coords, const IndexDataType* sorted_token_ids_ptr)
{
constexpr index_t n_size = coords.size();
array<index_t, n_size> row_ids;
static_for<0, n_size, 1>{}([&](auto i) {
row_ids.at(i) = sorted_token_ids_ptr[coords[i]]; // base_coord + i * MLans;
});
return row_ids;
}
template <typename ROW_COORDS>
CK_TILE_DEVICE auto GetWeightScale(const ROW_COORDS coords,
const TopkWeightDataType* sorted_weight_ptr)
{
constexpr index_t n_size = coords.size();
array<TopkWeightDataType, n_size> w;
static_for<0, n_size, 1>{}([&](auto i) {
w.at(i) = sorted_weight_ptr[coords[i]]; // base_coord + i * MLans;
});
return w;
}
// TODO: this row id is before shuffle atomic, need use acc distribution
CK_TILE_DEVICE auto GetRowCoords_O(index_t base_offset)
{
constexpr index_t MLanes = BlockShape::Warp_M1;
constexpr index_t Repeat_M = BlockShape::Repeat_M1;
auto base_coord = threadIdx.x % MLanes + base_offset;
array<index_t, Repeat_M> coords;
static_for<0, Repeat_M, 1>{}([&](auto i) { coords.at(i) = base_coord + i * MLanes; });
return coords;
}
template <typename Karg>
CK_TILE_DEVICE auto operator()(const Karg& kargs, CK_TILE_LDS_ADDR void* smem)
{
#if 0
if(threadIdx.x == 0 && blockIdx.x == 0 && threadIdx.y == 0 && blockIdx.y == 0)
{
printf("[PIPE] GemmPipeline_FlatmmUk =====\n");
}
[[maybe_unused]] uint32_t tidx = threadIdx.x; // 0~255
[[maybe_unused]] uint32_t tidy = threadIdx.y; // 0~0
[[maybe_unused]] uint32_t bidx = blockIdx.x; // 0~1
[[maybe_unused]] uint32_t bidy = blockIdx.y; // 0~51
[[maybe_unused]] uint32_t bdmx = blockDim.x; // 256
[[maybe_unused]] uint32_t bdmy = blockDim.y; // 1
[[maybe_unused]] uint32_t gdmx = gridDim.x; // 2
[[maybe_unused]] uint32_t gdmy = gridDim.y; // 52
[[maybe_unused]] uint32_t gid = ((bdmx * bdmy) * gdmx) * bidy
+ (bdmx * bdmy) * bidx
+ bdmx * tidy
+ tidx;
#endif
[[maybe_unused]] int* dbg_int = static_cast<int*>(kargs.dbg_int_ptr);
[[maybe_unused]] short* dbg_bf16 = static_cast<short*>(kargs.dbg_bf16_ptr);
[[maybe_unused]] float* dbg_fp32 = static_cast<float*>(kargs.dbg_fp32_ptr);
ck_tile::index_t shared_intermediate_size_0 = kargs.intermediate_size; // N
index_t nr_0 = shared_intermediate_size_0 / BlockShape::Warp_N0; // divide N in W
index_t kr_0 = kargs.hidden_size / BlockShape::Warp_K0; // divide K in W
index_t interm_idx_nr0 = __builtin_amdgcn_readfirstlane(
blockIdx.x * BlockShape::Block_Nr0); // intermediate_tile_id * Block_N / (N in W)
// ----------------------------------------------------------------------------
// a
auto a_res =
make_wave_buffer_resource(reinterpret_cast<const ADataType*>(kargs.a_ptr),
kargs.num_tokens * kargs.hidden_size * sizeof(ADataType));
auto row_ids_a = GetRowCoords_A(blockIdx.y * BlockShape::Block_M0);
auto a_coords = generate_tuple(
[&](auto i) {
return row_ids_a[i] * kargs.hidden_size +
threadIdx.x % (BlockShape::Block_K0 / kAlignmentA) * kAlignmentA;
},
number<row_ids_a.size()>{});
// ----------------------------------------------------------------------------
// b
auto b_win = [&]() {
const GDataType* b_ptr = reinterpret_cast<const GDataType*>(kargs.b_ptr) +
interm_idx_nr0 * kr_0 * BlockShape::Block_W0;
auto b_view_ = make_naive_tensor_view<address_space_enum::global>(
b_ptr,
make_tuple(nr_0, kr_0, number<BlockShape::Block_W0>{}),
make_tuple(kr_0 * BlockShape::Block_W0, number<BlockShape::Block_W0>{}, 1),
number<kAlignmentG>{},
number<1>{});
auto b_window_ = make_tile_window_linear_raw(
b_view_,
make_tuple(number<BlockShape::Block_Nr0>{},
number<BlockShape::Block_Kr0>{},
number<BlockShape::Block_W0>{}),
{0, 0, 0},
Policy::template MakeGlobalTileDistribution_G<Problem>(),
sequence<0, 1, 1>{});
return b_window_;
}();
auto b_res = b_win.get_bottom_tensor_view().get_buffer_view().cached_buf_res_;
auto b_coords = generate_tuple([&](auto i) { return b_win.cached_coords_[i].get_offset(); },
number<decltype(b_win)::NumAccess_NonLinear>{});
// ----------------------------------------------------------------------------
// core
auto uk_0 = Policy::template GetUK_0<Problem>();
auto acc_0 = uk_0(a_res,
a_coords,
b_res,
b_coords,
smem,
kargs.hidden_size,
BlockShape::Block_K0, // tile offset for B matrix each unroll
BlockShape::Block_Kr0 *
BlockShape::Block_W0, // tile offset for B matrix each unroll
dbg_int,
dbg_bf16,
dbg_fp32);
// ----------------------------------------------------------------------------
{
int tid = threadIdx.x;
float srdfp32 = 0.f;
float* smemfp32 = static_cast<float*>(smem);
// ----------------------------------------------------------------------------
// store to lds
for(uint32_t accIdx = 0; accIdx < 16; accIdx++)
{
float* accSmem = smemfp32 + 4 * blockDim.x * accIdx;
for(int xyzw = 0; xyzw < 4; xyzw++)
{
accSmem[tid * 4 + xyzw] = acc_0.get_thread_buffer()[accIdx * 4 + xyzw];
}
}
block_sync_lds();
// ----------------------------------------------------------------------------
// read from lds
int sldIdx = 0;
// int MLn = 15;
// int Nln = tid / MLn;
int tidInWave = tid % 64;
int waveId = tid / 64;
// sldIdx = (tid64 % 16 * 16 + tid64 / 16) % 64
// + tid / 64;
sldIdx = (tidInWave % 16 * 16 + tidInWave / 16) + waveId * 4;
const int accNLane = 16;
const int NLaneCnt = BlockShape::Block_N0 / 4; // xyzw 512 / 4 = 128
const int accBlkSize = blockDim.x;
int accInnerId = tid % accNLane; // 0~15
int accNIdx = tid / NLaneCnt; // 0~127 = 0; 128~255 = 1
int acc01BlkIdx = tid % NLaneCnt / 16; // 0 ~ 7
int accBlkIdx = acc01BlkIdx * 2; // 0, 2, 4, ..., 14
int acc4Id = accBlkIdx * accBlkSize //
+ accNIdx * accBlkSize + accInnerId * 16;
sldIdx = acc4Id;
float* d_buf = static_cast<float*>(kargs.d_ptr);
int c_blk_offset = blockIdx.y * BlockShape::Block_M0 * kargs.intermediate_size / 4 +
blockIdx.x * BlockShape::Block_N0 / 4;
for(uint32_t accIdx = 0; accIdx < 16; accIdx++)
{
for(int xyzw = 0; xyzw < 4; xyzw++)
{
srdfp32 = smemfp32[accIdx * (1 * 4) + sldIdx * 4 + xyzw];
acc_0.get_thread_buffer()[accIdx * 4 + xyzw] = srdfp32;
}
// ----------------------------------------------------------------------------
// store to vmem
int c_m_idx_offset = (accIdx + accNIdx * 16) * kargs.intermediate_size / 4;
int c_idx_offset = c_blk_offset + c_m_idx_offset + (tid % NLaneCnt);
for(int xyzw = 0; xyzw < 4; xyzw++)
{
srdfp32 = acc_0.get_thread_buffer()[accIdx * 4 + xyzw];
d_buf[c_idx_offset * 4 + xyzw] = srdfp32;
}
}
}
#if 0
// ----------------------------------------------------------------------------
// debug
for(uint32_t dbgi = 0; dbgi < 64; dbgi++)
{
dbg_fp32[gid * 64 + dbgi] = acc_0.get_thread_buffer()[dbgi];
}
#endif
}
};
} // namespace ck_tile
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