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Commit 3dc5db72 authored by Jun Liu's avatar Jun Liu
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Merge branch 'amd-develop' into amd-master

parents b924e330 e547c141
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Showing with 482 additions and 80 deletions
example/ck_tile/01_fmha/codegen/ops/fmha_fwd_splitkv.py
View file @ 3dc5db72
......@@ -600,8 +600,8 @@ def get_fwd_splitkv_blobs(kernel_filter : Optional[str], receipt, mask_impl) ->
# TODO: use async pipeline when compiler is more stable
if hdim == 256 or hdim in [32, 64, 128]:
# if True:
pipelines.append(Pipeline('qr', 'row', 'f', 'f', 'f', 'f', bias, lse, squant, pagedkv, mask))
pipelines.append(Pipeline('qr', 'col', 'f', 'f', 'f', 'f', bias, lse, squant, pagedkv, mask))
pipelines.append(Pipeline('qr', 'row', 'f', 't', 'f', 'f', bias, lse, squant, pagedkv, mask))
pipelines.append(Pipeline('qr', 'col', 'f', 't', 'f', 'f', bias, lse, squant, pagedkv, mask))
pipelines.append(Pipeline('qr', 'row', 't', 't', 't', 't', bias, lse, squant, pagedkv, mask))
pipelines.append(Pipeline('qr', 'col', 't', 't', 't', 't', bias, lse, squant, pagedkv, mask))
......
example/ck_tile/01_fmha/fmha_bwd.cpp
View file @ 3dc5db72
......@@ -85,6 +85,9 @@ auto create_args(int argc, char* argv[])
.insert("p_drop", "0", "0~1 probability of dropout")
.insert("drop_seed", "1", "seed for random number generator")
.insert("drop_offset", "0", "offset for random number generator")
.insert("drop_prefs",
"0",
"seed and offset values are present on GPU; 0 - host, 1 - device/GPU")
.insert("timer", "gpu", "gpu:gpu timer, cpu:cpu timer")
.insert("warmup", "5", "number of iterations before benchmark the kernel")
.insert("repeat", "20", "number of iterations to benchmark the kernel")
......@@ -99,10 +102,23 @@ auto create_args(int argc, char* argv[])
// different threshold for different dtype
template <typename DataType>
auto get_elimit(int /*init_method*/)
auto get_elimit(ck_tile::index_t /*hdim_q*/, ck_tile::index_t /*hdim_v*/)
{
double rtol = 1e-2;
double atol = 1e-2;
return ck_tile::make_tuple(rtol, atol);
}
template <>
auto get_elimit<ck_tile::bf16_t>(ck_tile::index_t hdim_q, ck_tile::index_t hdim_v)
{
double rtol = 1e-2;
double atol = 1e-2;
if(hdim_q > 128 && hdim_v > 128) // 3.2 for RTZ/1.5 for RTN
{
rtol = 3.2e-2;
atol = 3.2e-2;
}
return ck_tile::make_tuple(rtol, atol);
}
......@@ -145,6 +161,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
float p_drop = arg_parser.get_float("p_drop");
uint64_t drop_seed = arg_parser.get_uint64("drop_seed");
uint64_t drop_offset = arg_parser.get_uint64("drop_offset");
bool drop_prefs = arg_parser.get_bool("drop_prefs");
if(use_dbias && bias.type != bias_enum::elementwise_bias)
{
std::cerr << "dbias only exists when bias type is elementwise" << std::endl;
......@@ -368,6 +386,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
ck_tile::DeviceMem dbias_buf(dbias_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem seqstart_q(seqstart_q_host.size() * sizeof(int32_t));
ck_tile::DeviceMem seqstart_k(seqstart_k_host.size() * sizeof(int32_t));
ck_tile::DeviceMem drop_seed_buf(drop_prefs ? sizeof(uint64_t) : 0);
ck_tile::DeviceMem drop_offset_buf(drop_prefs ? sizeof(uint64_t) : 0);
ck_tile::DeviceMem alibi_slope_buf(alibi_slope_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem dq_acc_buf(dq_acc_host.get_element_space_size_in_bytes());
......@@ -378,6 +398,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
do_buf.ToDevice(do_host.data());
seqstart_q.ToDevice(seqstart_q_host.data());
seqstart_k.ToDevice(seqstart_k_host.data());
drop_seed_buf.ToDevice(drop_prefs ? &drop_seed : nullptr);
drop_offset_buf.ToDevice(drop_prefs ? &drop_offset : nullptr);
alibi_slope_buf.ToDevice(alibi_slope_host.data());
// clang-format off
......@@ -459,6 +481,18 @@ bool run(const ck_tile::ArgParser& arg_parser)
const ck_tile::index_t split_stride_dq_acc =
(shape_batch * nhead * shape_seqlen_q * hdim_q);
const auto drop_seed_offset = [&]() -> decltype(fmha_bwd_args::drop_seed_offset) {
if(drop_prefs)
{
return std::make_pair(drop_seed_buf.GetDeviceBuffer(),
drop_offset_buf.GetDeviceBuffer());
}
else
{
return std::make_pair(drop_seed, drop_offset);
}
}();
return fmha_bwd_args{q_buf.GetDeviceBuffer(),
k_buf.GetDeviceBuffer(),
v_buf.GetDeviceBuffer(),
......@@ -532,7 +566,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
static_cast<ck_tile::index_t>(mask.type),
p_drop,
p_undrop,
{drop_seed, drop_offset}};
drop_seed_offset};
}();
float ave_time = fmha_bwd(fmha_traits, fmha_args, stream_config);
......@@ -899,7 +933,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
}
// clang-format on
auto [rtol, atol] = get_elimit<DataType>(init_method);
auto [rtol, atol] = get_elimit<DataType>(hdim_q, hdim_v);
bool dq_cur_pass = ck_tile::check_err(dq_host_result,
dq_host_ref,
std::string("Error: QGrad Incorrect results!"),
......
example/ck_tile/01_fmha/fmha_bwd.hpp
View file @ 3dc5db72
......@@ -9,7 +9,10 @@
#include "ck_tile/ops/epilogue.hpp"
#include "mask.hpp"
#include "bias.hpp"
#include <type_traits>
#include <utility>
#include <variant>
template <typename DataType>
struct FmhaBwdTypeConfig;
......@@ -135,7 +138,8 @@ struct fmha_bwd_args
ck_tile::index_t mask_type;
float p_drop;
float p_undrop;
std::tuple<uint64_t, uint64_t> drop_seed_offset;
std::variant<std::pair<uint64_t, uint64_t>, std::pair<const void*, const void*>>
drop_seed_offset;
};
template <typename FmhaBwdDQDKDVKernel>
......
example/ck_tile/01_fmha/fmha_fwd.cpp
View file @ 3dc5db72
......@@ -122,6 +122,9 @@ auto create_args(int argc, char* argv[])
.insert("p_drop", "0", "0~1 probability of dropout")
.insert("drop_seed", "1", "seed for random number generator")
.insert("drop_offset", "0", "offset for random number generator")
.insert("drop_prefs",
"0",
"seed and offset values are present on GPU; 0 - host, 1 - device/GPU")
.insert("timer", "gpu", "gpu:gpu timer, cpu:cpu timer")
.insert(
"rotary_dim", "0", "RoPE rotary dimension. rotary_dim <= 0 means not apply RoPE at all")
......@@ -442,6 +445,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
float p_drop = arg_parser.get_float("p_drop");
uint64_t drop_seed = arg_parser.get_uint64("drop_seed");
uint64_t drop_offset = arg_parser.get_uint64("drop_offset");
bool drop_prefs = arg_parser.get_bool("drop_prefs");
if(p_drop < 0.0f || p_drop > 1.0f)
{
std::cerr << "The value of p_drop should be 0~1" << std::endl;
......@@ -552,16 +557,33 @@ bool run(const ck_tile::ArgParser& arg_parser)
}
#endif
auto get_lengths = [&](bool permute,
ck_tile::index_t b /*batch*/,
ck_tile::index_t h /*nhead*/,
ck_tile::index_t s /*seqlen*/,
ck_tile::index_t d /*hdim*/) {
if(permute)
return std::array<ck_tile::index_t, 4>{b, h, s, d};
else
return std::array<ck_tile::index_t, 4>{b, s, h, d};
};
struct
{
auto operator()(bool permute,
ck_tile::index_t b /*batch*/,
ck_tile::index_t h /*nhead*/,
ck_tile::index_t s /*seqlen*/,
ck_tile::index_t d /*hdim*/)
{
if(permute)
return std::array<ck_tile::index_t, 4>{b, h, s, d};
else
return std::array<ck_tile::index_t, 4>{b, s, h, d};
}
auto operator()(bool permute,
ck_tile::index_t ns /*num_splits*/,
ck_tile::index_t b /*batch*/,
ck_tile::index_t h /*nhead*/,
ck_tile::index_t s /*seqlen*/,
ck_tile::index_t d /*hdim*/)
{
if(permute)
return std::array<ck_tile::index_t, 5>{ns, b, h, s, d};
else
return std::array<ck_tile::index_t, 5>{ns, b, s, h, d};
}
} get_lengths;
bool is_v_rowmajor = vlayout == std::string("r");
......@@ -617,7 +639,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
: std::array<ck_tile::index_t, 4>{1, 1, 1, 1});
ck_tile::HostTensor<OaccDataType> o_acc_host(
1 < num_splits || use_kvcache
? std::array<ck_tile::index_t, 5>{num_splits, batch, nhead, max_seqlen_q, hdim_v}
? get_lengths(o_perm, num_splits, shape_batch, nhead, shape_seqlen_q, hdim_v)
: std::array<ck_tile::index_t, 5>{1, 1, 1, 1, 1});
// batch mode of lse data layout is [batch, nhead, seqlen_q]
......@@ -739,6 +761,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
need_append_kvcache ? cache_seqlen_ks.size() * sizeof(int32_t) : 0);
ck_tile::DeviceMem rotary_cos_buf(rotary_cos_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem rotary_sin_buf(rotary_sin_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem drop_seed_buf(drop_prefs ? sizeof(uint64_t) : 0);
ck_tile::DeviceMem drop_offset_buf(drop_prefs ? sizeof(uint64_t) : 0);
ck_tile::DeviceMem randval_buf(randval_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem alibi_slope_buf(alibi_slope_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem block_table_buf(block_table_host.get_element_space_size_in_bytes());
......@@ -757,6 +781,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
cache_seqlen_k_buf.ToDevice(need_append_kvcache ? cache_seqlen_ks.data() : nullptr);
rotary_cos_buf.ToDevice(rotary_cos_host.data());
rotary_sin_buf.ToDevice(rotary_sin_host.data());
drop_seed_buf.ToDevice(drop_prefs ? &drop_seed : nullptr);
drop_offset_buf.ToDevice(drop_prefs ? &drop_offset : nullptr);
alibi_slope_buf.ToDevice(alibi_slope_host.data());
block_table_buf.ToDevice(block_table_host.data());
cache_batch_idx_buf.ToDevice(cache_batch_idx_host.data());
......@@ -854,7 +880,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
}();
const ck_tile::index_t stride_bias = (i_perm ? shape_seqlen_k : 1 * shape_seqlen_k);
const ck_tile::index_t stride_randval = (max_seqlen_k);
const ck_tile::index_t stride_o_acc = hdim_v;
const ck_tile::index_t stride_o_acc = (o_perm ? hdim_v : nhead * hdim_v);
const ck_tile::index_t stride_o = (o_perm ? hdim_v : nhead * hdim_v);
// setup nhead_stride_* arguments
const ck_tile::index_t nhead_stride_q = (i_perm ? shape_seqlen_q * hdim_q : hdim_q);
......@@ -881,7 +907,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
const ck_tile::index_t nhead_stride_randval = (shape_seqlen_q * max_seqlen_k);
const ck_tile::index_t nhead_stride_lse = shape_seqlen_q;
const ck_tile::index_t nhead_stride_lse_acc = shape_seqlen_q;
const ck_tile::index_t nhead_stride_o_acc = (max_seqlen_q * hdim_v);
const ck_tile::index_t nhead_stride_o_acc = (o_perm ? shape_seqlen_q * hdim_v : hdim_v);
const ck_tile::index_t nhead_stride_o = (o_perm ? shape_seqlen_q * hdim_v : hdim_v);
// setup batch_stride_* arguments
const ck_tile::index_t batch_stride_q = (nhead * shape_seqlen_q * hdim_q);
......@@ -897,12 +923,12 @@ bool run(const ck_tile::ArgParser& arg_parser)
const ck_tile::index_t batch_stride_randval = (nhead * shape_seqlen_q * max_seqlen_k);
const ck_tile::index_t batch_stride_lse = (nhead * shape_seqlen_q);
const ck_tile::index_t batch_stride_lse_acc = (nhead * shape_seqlen_q);
const ck_tile::index_t batch_stride_o_acc = (nhead * max_seqlen_q * hdim_v);
const ck_tile::index_t batch_stride_o_acc = (nhead * shape_seqlen_q * hdim_v);
const ck_tile::index_t batch_stride_o = (nhead * shape_seqlen_q * hdim_v);
const ck_tile::index_t batch_stride_block_table = (max_num_page_blocks / batch);
// setup split_stride_* arguments (only used in split-kv kernel)
const ck_tile::index_t split_stride_lse_acc = (shape_batch * nhead * shape_seqlen_q);
const ck_tile::index_t split_stride_o_acc = (batch * nhead * max_seqlen_q * hdim_v);
const ck_tile::index_t split_stride_o_acc = (shape_batch * nhead * shape_seqlen_q * hdim_v);
args.q_ptr = q_buf.GetDeviceBuffer();
args.k_ptr = k_buf.GetDeviceBuffer();
......@@ -996,9 +1022,17 @@ bool run(const ck_tile::ArgParser& arg_parser)
args.nhead_stride_randval = nhead_stride_randval;
args.batch_stride_randval = batch_stride_randval;
args.p_drop = p_drop;
args.s_randval = s_randval;
args.drop_seed_offset = std::tie(drop_seed, drop_offset);
args.p_drop = p_drop;
args.s_randval = s_randval;
if(drop_prefs)
{
args.drop_seed_offset = std::make_pair(drop_seed_buf.GetDeviceBuffer(),
drop_offset_buf.GetDeviceBuffer());
}
else
{
args.drop_seed_offset = std::make_pair(drop_seed, drop_offset);
}
}
else if constexpr(std::is_same_v<fmha_fwd_splitkv_args, std::decay_t<decltype(args)>>)
{
......
example/ck_tile/01_fmha/fmha_fwd.hpp
View file @ 3dc5db72
......@@ -13,6 +13,8 @@
#include "rotary.hpp"
#include <type_traits>
#include <utility>
#include <variant>
template <typename DataType>
struct FmhaFwdTypeConfig;
......@@ -144,7 +146,9 @@ struct fmha_fwd_args
float p_drop;
bool s_randval;
std::tuple<uint64_t, uint64_t> drop_seed_offset;
std::variant<std::pair<uint64_t, uint64_t>, std::pair<const void*, const void*>>
drop_seed_offset;
};
struct fmha_fwd_splitkv_args
......@@ -398,10 +402,8 @@ auto fmha_fwd_splitkv_create_kargs_and_grids(fmha_fwd_splitkv_args args)
args.nhead_stride_bias,
args.nhead_stride_lse_acc,
args.nhead_stride_o_acc,
args.batch_stride_k,
args.batch_stride_v,
args.batch_stride_lse_acc,
args.batch_stride_o_acc,
args.batch_stride_k, // only used for paged-kvcache
args.batch_stride_v, // only used for paged-kvcache
args.split_stride_lse_acc,
args.split_stride_o_acc,
args.window_size_left,
......@@ -475,7 +477,6 @@ auto fmha_fwd_splitkv_combine_create_kargs_and_grids(fmha_fwd_splitkv_args args)
args.lse_ptr,
args.o_ptr,
args.batch,
args.max_seqlen_q,
args.seqstart_q_ptr,
args.hdim_v,
args.num_splits,
......@@ -486,7 +487,6 @@ auto fmha_fwd_splitkv_combine_create_kargs_and_grids(fmha_fwd_splitkv_args args)
args.nhead_stride_o_acc,
args.nhead_stride_lse,
args.nhead_stride_o,
args.batch_stride_o_acc,
args.split_stride_lse_acc,
args.split_stride_o_acc);
}
......@@ -497,7 +497,6 @@ auto fmha_fwd_splitkv_combine_create_kargs_and_grids(fmha_fwd_splitkv_args args)
args.lse_ptr,
args.o_ptr,
args.batch,
args.max_seqlen_q,
args.seqlen_q,
args.hdim_v,
args.num_splits,
......
example/ck_tile/02_layernorm2d/README.md
View file @ 3dc5db72
......@@ -6,7 +6,8 @@ This folder contains example for Layernorm2D forward using ck_tile tile-programm
```
# in the root of ck_tile
mkdir build && cd build
sh ../script/cmake-ck-dev.sh ../ <arch> # you can replace this <arch> to gfx90a, gfx942...
# you can replace <arch> with the appropriate architecture (for example gfx90a or gfx942) or leave it blank
sh ../script/cmake-ck-dev.sh ../ <arch>
make tile_example_layernorm2d_fwd -j
```
This will result in an executable `build/bin/tile_example_layernorm2d_fwd`
......
example/ck_tile/02_layernorm2d/layernorm2d_fwd.cpp
View file @ 3dc5db72
......@@ -35,7 +35,9 @@ float layernorm2d_fwd(layernorm2d_fwd_traits t,
YDataType,
MeanDataType,
InvStdDataType,
Shape>;
Shape,
true,
true>;
using Kernel = ck_tile::Layernorm2dFwd<PipelineProblem>;
......
example/ck_tile/03_gemm/README.md
View file @ 3dc5db72
......@@ -6,7 +6,8 @@ This folder contains example for GEMM using ck_tile tile-programming implementat
```
# in the root of ck_tile
mkdir build && cd build
sh ../script/cmake-ck-dev.sh ../ <arch> # you can replace this <arch> to gfx90a, gfx942...
# you can replace <arch> with the appropriate architecture (for example gfx90a or gfx942) or leave it blank
sh ../script/cmake-ck-dev.sh ../ <arch>
make tile_example_gemm_basic -j
```
This will result in an executable `build/bin/tile_example_gemm_basic`
......@@ -14,10 +15,17 @@ This will result in an executable `build/bin/tile_example_gemm_basic`
## example
```
args:
-m m dimension (default:3328)
-n m dimension (default:4096)
-b batch size (default:1)
-m m dimension (default:1024)
-n n dimension (default:2048)
-k k dimension (default:64)
-e epsilon (default:1e-5)
-v cpu validation or not (default:1)
-prec precision (default:fp16)
-stride_a Tensor A stride (default:0)
-stride_b Tensor B stride (default:0)
-stride_c Tensor C stride (default:0)
-v 0. No validation, 1. Validation on CPU, 2. Validation on GPU (default:2)
-e Absolute error tolerance (default:1e-5)
-prec data type. fp16/bf16/fp8/bf8 (default:fp16)
-warmup number of iterations before benchmark the kernel (default:10)
-repeat number of iterations to benchmark the kernel (default:100)
-timer gpu:gpu timer, cpu:cpu timer (default:gpu)
```
example/ck_tile/03_gemm/gemm_basic.cpp
View file @ 3dc5db72
......@@ -41,18 +41,39 @@ template <typename LayoutA,
float gemm_calc(const gemm_basic_args& args, const ck_tile::stream_config& s)
{
// The kPadA, kPadB, kPadC & kBlockPerCu should also come from the Codegen part.
constexpr bool kPadA = true;
constexpr bool kPadB = true;
constexpr bool kPadA = true;
constexpr bool kPadB = true;
constexpr bool kTilePermute = false;
constexpr int kBlockPerCu = 1;
using TilePartitioner = ck_tile::GemmTilePartitioner<GemmShape>;
using GemmEpilogue = ck_tile::Default2DEpilogue<
ck_tile::Default2DEpilogueProblem<AccDataType, CDataType, kPadA, kPadB>>;
// The rank and permutation will also be generate out by the CodeGen part.
constexpr ck_tile::index_t kOutputRank = 2;
// Whether doing the CShuffle (transpose before the global memory), depending on the output
// layout.
constexpr bool CShuffleEpilogue =
std::is_same_v<LayoutC, ck_tile::tensor_layout::gemm::ColumnMajor>;
using GemmEpilogue = std::conditional_t<
CShuffleEpilogue,
ck_tile::CShuffleEpilogue<ck_tile::CShuffleEpilogueProblem<AccDataType,
CDataType,
kPadA,
kPadB,
kTilePermute,
kOutputRank,
1,
0,
TilePartitioner::kM,
TilePartitioner::kN>>,
ck_tile::Default2DEpilogue<
ck_tile::Default2DEpilogueProblem<AccDataType, CDataType, kPadA, kPadB>>>;
// ToDo: Will add the codegen part to test different pipeline policies in GEMM.
// Now we only use the BlockGemmASmemBSmemCRegV1DefaultPolicy.
using Kernel =
ck_tile::GemmKernel<TilePartitioner, GemmPipeline, GemmEpilogue, LayoutA, LayoutB, LayoutC>;
using Kernel = ck_tile::GemmKernel<TilePartitioner, GemmPipeline, GemmEpilogue>;
auto kargs = Kernel::MakeKargs(args.p_a,
args.p_b,
......@@ -255,15 +276,13 @@ int main(int argc, char* argv[])
ck_tile::sequence<M_Warp, N_Warp, K_Warp>,
ck_tile::sequence<M_Warp_Tile, N_Warp_Tile, K_Warp_Tile>>;
using CodegenPipelineProblem = ck_tile::BlockGemmPipelineProblem<ADataType,
BDataType,
AccDataType,
CodegenGemmShape,
kPadA,
kPadB,
kPadC>;
using CodegenGemmTraits = ck_tile::
TileGemmTraits<kPadA, kPadB, kPadC, matrix_a_layout, matrix_b_layout, matrix_c_layout>;
using CodegenPipelineProblem = ck_tile::
GemmPipelineProblem<ADataType, BDataType, AccDataType, CodegenGemmShape, CodegenGemmTraits>;
using CodegenGemmPipeline = ck_tile::BlockGemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem>;
using CodegenGemmPipeline = ck_tile::GemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem>;
invoke_gemm<ck_tile::half_t,
matrix_a_layout,
......@@ -341,7 +360,13 @@ int main(int argc, char* argv[])
ck_tile::HostTensor<CDataType> c_host_gpu_ref(c_dimensions);
ck_tile::DeviceMem c_gpu_buf(c_host_gpu_ref.get_element_space_size_in_bytes());
ck_tile::reference_gemm_gpu<ADataType, BDataType, AccDataType, CDataType>(
ck_tile::reference_gemm_gpu<ADataType,
BDataType,
AccDataType,
CDataType,
matrix_a_layout,
matrix_b_layout,
matrix_c_layout>(
a_buf, b_buf, c_gpu_buf, M, N, K, stride_a, stride_b, stride_c);
c_buf.FromDevice(c_host_gpu_ref.data());
......
example/ck_tile/04_img2col/CMakeLists.txt 0 → 100644
View file @ 3dc5db72
# 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"
add_executable(tile_example_img2col EXCLUDE_FROM_ALL image_to_column.cpp)
example/ck_tile/04_img2col/README.md 0 → 100644
View file @ 3dc5db72
# Image to Column
This folder contains example for Image to Column using ck_tile tile-programming implementation.
## build
```
# in the root of ck_tile
mkdir build && cd build
# you can replace <arch> with the appropriate architecture (for example gfx90a or gfx942) or leave it blank
sh ../script/cmake-ck-dev.sh ../ <arch>
make tile_example_img2col -j
```
This will result in an executable `build/bin/tile_example_img2col`
example/ck_tile/04_img2col/image_to_column.cpp 0 → 100644
View file @ 3dc5db72
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include <algorithm>
#include <cstring>
#include "ck_tile/host.hpp"
#include "image_to_column.hpp"
// Host API implementation
template <>
float image_to_column(const image_to_column_traits& traits,
const image_to_column_args<2>& args,
const ck_tile::stream_config& stream_conf)
{
if(traits.data_type.compare("fp16") == 0)
{
constexpr ck_tile::index_t NDimSpatial = 2;
constexpr ck_tile::index_t VectorSize = 8;
using thread_tile = ck_tile::sequence<8, 8>;
using warp_tile = ck_tile::sequence<64, 64>;
using block_tile = ck_tile::sequence<128, 128>;
using Shape = ck_tile::TileImageToColumnShape<thread_tile, warp_tile, block_tile>;
using InDataType = ck_tile::half_t;
using OutDataType = ck_tile::half_t;
using PipelineProblem = ck_tile::BlockImageToColumnProblem<InDataType,
OutDataType,
Shape,
NDimSpatial,
VectorSize,
VectorSize>;
using Kernel = ck_tile::ImageToColumn<PipelineProblem>;
auto kargs = Kernel::MakeKargs(args.p_in,
args.p_out,
args.G,
args.N,
args.C,
args.input_spatial_lengths,
args.filter_spatial_lengths,
args.output_spatial_lengths,
args.image_g_n_c_wis_strides,
args.gemm_g_m_k_strides,
args.conv_filter_strides,
args.conv_filter_dilations,
args.input_left_pads,
args.input_right_pads);
const dim3 grids = Kernel::GridSize(
args.N * args.output_spatial_lengths[0] * args.output_spatial_lengths[1],
args.filter_spatial_lengths[0] * args.filter_spatial_lengths[1] * args.C,
args.G);
constexpr dim3 blocks = Kernel::BlockSize();
constexpr ck_tile::index_t kBlockPerCu = 2;
float ave_time = ck_tile::launch_kernel(
stream_conf,
ck_tile::make_kernel<blocks.x, kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
return ave_time;
}
return 0;
}
int main(int argc, char* argv[])
{
constexpr ck_tile::index_t NDimSpatial = 2;
ExecutionConfig config;
ck_tile::conv::ConvParam conv_params = DefaultConvParams;
if(!parse_cmd_args(argc, argv, config, conv_params))
{
return EXIT_FAILURE;
}
if(conv_params.num_dim_spatial_ != NDimSpatial)
{
std::cerr << "unsupported # of spatial dimensions" << std::endl;
return EXIT_FAILURE;
}
using InDataType = ck_tile::half_t;
using OutDataType = ck_tile::half_t;
using ImLayout = ck_tile::tensor_layout::convolution::NHWGC;
const auto G = conv_params.G_;
const auto N = conv_params.N_;
const auto C = conv_params.C_;
const ck_tile::long_index_t NHoWo =
N * std::accumulate(conv_params.output_spatial_lengths_.begin(),
std::next(conv_params.output_spatial_lengths_.begin(), NDimSpatial),
1,
std::multiplies<>());
const ck_tile::long_index_t CYX =
C * std::accumulate(conv_params.filter_spatial_lengths_.begin(),
std::next(conv_params.filter_spatial_lengths_.begin(), NDimSpatial),
1,
std::multiplies<>());
const auto in_desc =
ck_tile::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<ImLayout>(conv_params);
const auto out_desc = ck_tile::HostTensorDescriptor({G, NHoWo, CYX});
// host verify
ck_tile::HostTensor<InDataType> in(in_desc);
ck_tile::HostTensor<OutDataType> out_device(out_desc);
ck_tile::HostTensor<OutDataType> out_host(out_desc);
switch(config.init_method)
{
case 0: break;
case 1: ck_tile::FillUniformDistributionIntegerValue<InDataType>{-5.f, 5.f}(in); break;
default: ck_tile::FillUniformDistribution<InDataType>{-0.5, 0.5}(in); break;
}
ck_tile::DeviceMem in_device_buf(in.get_element_space_size_in_bytes());
ck_tile::DeviceMem out_device_buf(out_device.get_element_space_size_in_bytes());
in_device_buf.ToDevice(in.data());
image_to_column_traits traits{"fp16"};
image_to_column_args<NDimSpatial> args{
in_device_buf.GetDeviceBuffer(),
out_device_buf.GetDeviceBuffer(),
G,
N,
C,
ck_tile::to_array<ck_tile::long_index_t, NDimSpatial>(conv_params.input_spatial_lengths_),
ck_tile::to_array<ck_tile::long_index_t, NDimSpatial>(conv_params.filter_spatial_lengths_),
ck_tile::to_array<ck_tile::long_index_t, NDimSpatial>(conv_params.output_spatial_lengths_),
ck_tile::to_array<ck_tile::long_index_t, NDimSpatial + 3>(in_desc.get_strides()),
ck_tile::to_array<ck_tile::long_index_t, 3>(out_desc.get_strides()),
ck_tile::to_array<ck_tile::long_index_t, NDimSpatial>(conv_params.conv_filter_strides_),
ck_tile::to_array<ck_tile::long_index_t, NDimSpatial>(conv_params.conv_filter_dilations_),
ck_tile::to_array<ck_tile::long_index_t, NDimSpatial>(conv_params.input_left_pads_),
ck_tile::to_array<ck_tile::long_index_t, NDimSpatial>(conv_params.input_right_pads_)};
float ave_time =
image_to_column(traits, args, ck_tile::stream_config{nullptr, config.time_kernel});
std::size_t num_btype = G * NHoWo * CYX * (sizeof(OutDataType) + sizeof(InDataType));
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << gb_per_sec << " GB/s" << std::endl;
bool pass = true;
if(config.do_verification)
{
// reference
ck_tile::reference_im2col<InDataType, OutDataType, NDimSpatial>(in, out_host, conv_params);
out_device_buf.FromDevice(out_device.data());
pass = ck_tile::check_err(out_device, out_host);
std::cout << "valid:" << (pass ? "y" : "n") << std::endl;
}
return !pass;
}
example/ck_tile/04_img2col/image_to_column.hpp 0 → 100644
View file @ 3dc5db72
// 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/image_to_column.hpp"
#include <string>
#define DefaultConvParams \
ck_tile::conv::ConvParam \
{ \
2, 2, 32, 32, 32, {4, 4}, {64, 64}, {1, 1}, {1, 1}, {0, 0}, { 0, 0 } \
}
struct ExecutionConfig final
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
};
inline void print_help_msg()
{
std::cerr << "arg1: verification (0=no, 1=yes)\n"
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
<< "arg3: time kernel (0=no, 1=yes)\n"
<< ck_tile::conv::get_conv_param_parser_helper_msg() << std::endl;
}
inline bool parse_cmd_args(int argc,
char* argv[],
ExecutionConfig& config,
ck_tile::conv::ConvParam& conv_params)
{
constexpr int num_execution_config_args =
3; // arguments for do_verification, init_method, time_kernel
constexpr int num_conv_param_leading_args = 5; // arguments for num_dim_spatial_, G_, N_, K_, C_
constexpr int threshold_to_catch_partial_args = 1 + num_execution_config_args;
constexpr int threshold_to_catch_all_args =
threshold_to_catch_partial_args + num_conv_param_leading_args;
if(argc == 1)
{
// use default
config = ExecutionConfig{};
}
// catch only ExecutionConfig arguments
else if(argc == threshold_to_catch_partial_args)
{
config.do_verification = std::stoi(argv[1]);
config.init_method = std::stoi(argv[2]);
config.time_kernel = std::stoi(argv[3]);
}
// catch both ExecutionConfig & ConvParam arguments
else if(threshold_to_catch_all_args < argc && ((argc - threshold_to_catch_all_args) % 3 == 0))
{
config.do_verification = std::stoi(argv[1]);
config.init_method = std::stoi(argv[2]);
config.time_kernel = std::stoi(argv[3]);
const ck_tile::index_t num_dim_spatial = std::stoi(argv[4]);
conv_params =
ck_tile::conv::parse_conv_param(num_dim_spatial, threshold_to_catch_partial_args, argv);
}
else
{
print_help_msg();
return false;
}
return true;
}
struct image_to_column_traits
{
std::string data_type;
};
template <ck_tile::index_t NDimSpatial>
struct image_to_column_args
{
const void* p_in;
void* p_out;
const ck_tile::long_index_t G;
const ck_tile::long_index_t N;
const ck_tile::long_index_t C;
const ck_tile::array<ck_tile::long_index_t, NDimSpatial> input_spatial_lengths;
const ck_tile::array<ck_tile::long_index_t, NDimSpatial> filter_spatial_lengths;
const ck_tile::array<ck_tile::long_index_t, NDimSpatial> output_spatial_lengths;
const ck_tile::array<ck_tile::long_index_t, NDimSpatial + 3> image_g_n_c_wis_strides;
const ck_tile::array<ck_tile::long_index_t, 3> gemm_g_m_k_strides;
const ck_tile::array<ck_tile::long_index_t, NDimSpatial> conv_filter_strides;
const ck_tile::array<ck_tile::long_index_t, NDimSpatial> conv_filter_dilations;
const ck_tile::array<ck_tile::long_index_t, NDimSpatial> input_left_pads;
const ck_tile::array<ck_tile::long_index_t, NDimSpatial> input_right_pads;
};
// host API
template <ck_tile::index_t NDimSpatial>
float image_to_column(const image_to_column_traits&,
const image_to_column_args<NDimSpatial>&,
const ck_tile::stream_config&);
example/ck_tile/CMakeLists.txt
View file @ 3dc5db72
......@@ -5,3 +5,4 @@ include_directories(AFTER
add_subdirectory(01_fmha)
add_subdirectory(02_layernorm2d)
add_subdirectory(03_gemm)
add_subdirectory(04_img2col)
include/ck/config.h.in
View file @ 3dc5db72
......@@ -97,13 +97,6 @@
#cmakedefine CK_ENABLE_DL_KERNELS @CK_ENABLE_DL_KERNELS@
#endif
//
// Instances supports in the current CK build
//
#ifndef CK_ENABLE_INSTANCES_ONLY
#cmakedefine CK_ENABLE_INSTANCES_ONLY @CK_ENABLE_INSTANCES_ONLY@
#endif
//
// CK kernels which support XDL (MI series)
//
......
include/ck/host_utility/kernel_launch.hpp
View file @ 3dc5db72
......@@ -66,6 +66,9 @@ float launch_and_time_kernel(const StreamConfig& stream_config,
hip_check_error(hipEventElapsedTime(&total_time, start, stop));
hip_check_error(hipEventDestroy(start));
hip_check_error(hipEventDestroy(stop));
return total_time / nrepeat;
}
else
......@@ -143,6 +146,9 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config,
hip_check_error(hipEventElapsedTime(&total_time, start, stop));
hip_check_error(hipEventDestroy(start));
hip_check_error(hipEventDestroy(stop));
return total_time / nrepeat;
}
else
......
include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops.hpp
View file @ 3dc5db72
......@@ -406,7 +406,7 @@ struct BlockwiseGemmXdlops_pipeline_v4
}
template <>
__device__ static constexpr auto TailScheduler<1>()
__device__ constexpr auto TailScheduler<1>()
{
// schedule
constexpr auto num_ds_read_inst =
......@@ -433,7 +433,7 @@ struct BlockwiseGemmXdlops_pipeline_v4
}
template <>
__device__ static constexpr auto TailScheduler<2>()
__device__ constexpr auto TailScheduler<2>()
{
// schedule
constexpr auto num_ds_read_inst =
......
include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v1_ab_scale.hpp
View file @ 3dc5db72
......@@ -308,7 +308,7 @@ struct BlockwiseGemmXdlops_pipeline_v1_ab_scale<BlockGemmPipelineScheduler::Intr
typename vector_type<ComputeDataType,
xdlops_gemm.K1PerXdlops>::type;
xdlops_gemm.template Run(
xdlops_gemm.template Run<>(
a_thread_vec.template AsType<mfma_input_type>(),
b_thread_vec.template AsType<mfma_input_type>(),
c_thread_buf_per_scale.GetVectorTypeReference(I0));
......@@ -390,9 +390,10 @@ struct BlockwiseGemmXdlops_pipeline_v1_ab_scale<BlockGemmPipelineScheduler::Intr
using mfma_input_type =
typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
xdlops_gemm.template Run(a_thread_vec.template AsType<mfma_input_type>(),
b_thread_vec.template AsType<mfma_input_type>(),
c_thread_buf_per_scale.GetVectorTypeReference(I0));
xdlops_gemm.template Run<>(
a_thread_vec.template AsType<mfma_input_type>(),
b_thread_vec.template AsType<mfma_input_type>(),
c_thread_buf_per_scale.GetVectorTypeReference(I0));
});
static_for<0, xdlops_gemm.GetRegSizePerXdlops(), 1>{}([&](auto t) {
constexpr index_t c_offset =
......
include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v2_ab_scale.hpp
View file @ 3dc5db72
......@@ -350,7 +350,7 @@ struct BlockwiseGemmXdlops_pipeline_v2_ab_scale<BlockGemmPipelineScheduler::Intr
typename vector_type<ComputeDataType,
xdlops_gemm.K1PerXdlops>::type;
xdlops_gemm.template Run(
xdlops_gemm.template Run<>(
a_thread_vec.template AsType<mfma_input_type>(),
b_thread_vec.template AsType<mfma_input_type>(),
c_thread_buf_per_scale.GetVectorTypeReference(I0));
......@@ -443,7 +443,7 @@ struct BlockwiseGemmXdlops_pipeline_v2_ab_scale<BlockGemmPipelineScheduler::Intr
typename vector_type<ComputeDataType,
xdlops_gemm.K1PerXdlops>::type;
xdlops_gemm.template Run(
xdlops_gemm.template Run<>(
a_thread_vec.template AsType<mfma_input_type>(),
b_thread_vec.template AsType<mfma_input_type>(),
c_thread_buf_per_scale.GetVectorTypeReference(I0));
......@@ -518,9 +518,10 @@ struct BlockwiseGemmXdlops_pipeline_v2_ab_scale<BlockGemmPipelineScheduler::Intr
using mfma_input_type =
typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
xdlops_gemm.template Run(a_thread_vec.template AsType<mfma_input_type>(),
b_thread_vec.template AsType<mfma_input_type>(),
c_thread_buf_per_scale.GetVectorTypeReference(I0));
xdlops_gemm.template Run<>(
a_thread_vec.template AsType<mfma_input_type>(),
b_thread_vec.template AsType<mfma_input_type>(),
c_thread_buf_per_scale.GetVectorTypeReference(I0));
});
static_for<0, xdlops_gemm.GetRegSizePerXdlops(), 1>{}([&](auto t) {
constexpr index_t c_offset =
......@@ -575,9 +576,10 @@ struct BlockwiseGemmXdlops_pipeline_v2_ab_scale<BlockGemmPipelineScheduler::Intr
using mfma_input_type =
typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
xdlops_gemm.template Run(a_thread_vec.template AsType<mfma_input_type>(),
b_thread_vec.template AsType<mfma_input_type>(),
c_thread_buf_per_scale.GetVectorTypeReference(I0));
xdlops_gemm.template Run<>(
a_thread_vec.template AsType<mfma_input_type>(),
b_thread_vec.template AsType<mfma_input_type>(),
c_thread_buf_per_scale.GetVectorTypeReference(I0));
});
static_for<0, xdlops_gemm.GetRegSizePerXdlops(), 1>{}([&](auto t) {
constexpr index_t c_offset =
......
include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v3_ab_scale.hpp
View file @ 3dc5db72
......@@ -427,7 +427,7 @@ struct BlockwiseGemmXdlops_pipeline_v3_ab_scale<BlockGemmPipelineScheduler::Intr
typename vector_type<ComputeDataType,
xdlops_gemm.K1PerXdlops>::type;
xdlops_gemm.template Run(
xdlops_gemm.template Run<>(
a_thread_vec.template AsType<mfma_input_type>(),
b_thread_vec.template AsType<mfma_input_type>(),
c_thread_buf_per_scale.GetVectorTypeReference(I0));
......@@ -504,9 +504,10 @@ struct BlockwiseGemmXdlops_pipeline_v3_ab_scale<BlockGemmPipelineScheduler::Intr
using mfma_input_type =
typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
xdlops_gemm.template Run(a_thread_vec.template AsType<mfma_input_type>(),
b_thread_vec.template AsType<mfma_input_type>(),
c_thread_buf_per_scale.GetVectorTypeReference(I0));
xdlops_gemm.template Run<>(
a_thread_vec.template AsType<mfma_input_type>(),
b_thread_vec.template AsType<mfma_input_type>(),
c_thread_buf_per_scale.GetVectorTypeReference(I0));
});
static_for<0, xdlops_gemm.GetRegSizePerXdlops(), 1>{}([&](auto t) {
constexpr index_t c_offset =
......
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