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Unverified Commit f84e2020 authored by Rostyslav Geyyer's avatar Rostyslav Geyyer Committed by GitHub
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Merge branch 'develop' into lwpck-1815

parents 408534d4 25935b57
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example/12_reduce/reduce_example_common.hpp
View file @ f84e2020
// 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
......@@ -38,7 +38,8 @@ struct ReduceShape
static constexpr ck::index_t NumReduceDim_ = NumReduceDim;
};
using reduce_shape_instances = std::tuple<ReduceShape<3, 1>,
using reduce_shape_instances = std::tuple<ReduceShape<12, 3>,
ReduceShape<3, 1>,
ReduceShape<3, 2>,
ReduceShape<4, 1>,
ReduceShape<4, 2>,
......
example/20_grouped_conv_bwd_weight/common.hpp
View file @ f84e2020
......@@ -23,12 +23,8 @@
using BF16 = ck::bhalf_t;
using F16 = ck::half_t;
using F32 = float;
#ifdef CK_ENABLE_FP8
using F8 = ck::f8_t;
#endif
#ifdef CK_ENABLE_BF8
using BF8 = ck::bf8_t;
#endif
using F8 = ck::f8_t;
using BF8 = ck::bf8_t;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
......
example/62_convnd_activ/CMakeLists.txt
View file @ f84e2020
......@@ -3,6 +3,7 @@ add_subdirectory(convinvscale)
add_subdirectory(convscale)
add_subdirectory(convscale_relu)
add_subdirectory(convscale_add)
add_subdirectory(convscale_reduce)
add_subdirectory(multi_AB)
add_subdirectory(unary)
......
example/62_convnd_activ/convscale_reduce/CMakeLists.txt 0 → 100644
View file @ f84e2020
list(APPEND gpu_list gfx908 gfx90a gfx940 gfx941 gfx942)
set(target 0)
foreach(gpu IN LISTS GPU_TARGETS)
if(gpu IN_LIST gpu_list AND target EQUAL 0)
add_custom_target(example_convnd_activ_xdl_convscale_reduce)
add_example_executable(example_convnd_fwd_xdl_convscale_relu_amax_fp8 convnd_fwd_xdl_convscale_relu_amax_fp8.cpp)
add_example_dependencies(example_convnd_activ_xdl_convscale_reduce example_convnd_fwd_xdl_convscale_relu_amax_fp8)
add_example_executable(example_convnd_fwd_xdl_convscale_amax_fp8 convnd_fwd_xdl_convscale_amax_fp8.cpp)
add_example_dependencies(example_convnd_activ_xdl_convscale_reduce example_convnd_fwd_xdl_convscale_amax_fp8)
set(target 1)
endif()
endforeach()
example/62_convnd_activ/convscale_reduce/convnd_fwd_xdl_convscale_amax_fp8.cpp 0 → 100644
View file @ f84e2020
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "convnd_fwd_convscale_reduce_common.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_abd_xdl_cshuffle.hpp"
using InDataType = ck::f8_t;
using WeiDataType = ck::f8_t;
using AccDataType = float;
using CShuffleDataType = float;
using ConvOutDataType = float; // data type of convolution result
using OutDataType = ck::f8_t; // data type of final result
using AComputeDataType = ck::f8_t;
using BComputeDataType = ck::f8_t;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using InElementOp = PassThrough;
using WeiElementOp = PassThrough;
using OutElementOp = ConvScale;
static constexpr auto ConvSpec =
ck::tensor_operation::device::ConvolutionForwardSpecialization::Default;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
template <ck::index_t NDimSpatial, typename InLayout, typename WeiLayout, typename OutLayout>
using DeviceGroupedConvNDFwdInstance =
ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<
NDimSpatial,
InLayout,
WeiLayout,
ck::Tuple<>,
OutLayout,
InDataType,
WeiDataType,
AccDataType,
CShuffleDataType,
ck::Tuple<>,
ConvOutDataType,
InElementOp,
WeiElementOp,
OutElementOp,
ConvSpec, // ConvForwardSpecialization
GemmSpec, // GemmSpecialization
1, //
256, // BlockSize
128, // MPerBlock
256, // NPerBlock
32, // KPerBlock
8, // AK1
8, // BK1
32, // MPerXdl
32, // NPerXdl
2, // MXdlPerWave
4, // NXdlPerWave
S<4, 64, 1>, // ABlockTransferThreadClusterLengths_AK0_M_AK1
S<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // ABlockTransferSrcAccessOrder
2, // ABlockTransferSrcVectorDim
8, // ABlockTransferSrcScalarPerVector
8, // ABlockTransferDstScalarPerVector_AK1
1, // ABlockLdsExtraM
S<4, 64, 1>, // BBlockTransferThreadClusterLengths_BK0_N_BK1
S<1, 0, 2>, // BBlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // BBlockTransferSrcAccessOrder
2, // BBlockTransferSrcVectorDim
8, // BBlockTransferSrcScalarPerVector
8, // BBlockTransferDstScalarPerVector_BK1
1, // BBlockLdsExtraN
1,
1,
S<1, 32, 1, 8>,
8,
AComputeDataType,
BComputeDataType>;
#include "run_convnd_fwd_example.inc"
int main(int argc, char* argv[]) { return run_convnd_fwd_example(argc, argv) ? 0 : 1; }
example/62_convnd_activ/convscale_reduce/convnd_fwd_xdl_convscale_relu_amax_fp8.cpp 0 → 100644
View file @ f84e2020
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "convnd_fwd_convscale_reduce_common.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_abd_xdl_cshuffle.hpp"
using InDataType = ck::f8_t;
using WeiDataType = ck::f8_t;
using AccDataType = float;
using CShuffleDataType = float;
using ConvOutDataType = float; // data type of convolution result
using OutDataType = ck::f8_t; // data type of final result
using AComputeDataType = ck::f8_t;
using BComputeDataType = ck::f8_t;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using InElementOp = PassThrough;
using WeiElementOp = PassThrough;
using OutElementOp = ConvScaleRelu;
static constexpr auto ConvSpec =
ck::tensor_operation::device::ConvolutionForwardSpecialization::Default;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
template <ck::index_t NDimSpatial, typename InLayout, typename WeiLayout, typename OutLayout>
using DeviceGroupedConvNDFwdInstance =
ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<
NDimSpatial,
InLayout,
WeiLayout,
ck::Tuple<>,
OutLayout,
InDataType,
WeiDataType,
AccDataType,
CShuffleDataType,
ck::Tuple<>,
ConvOutDataType,
InElementOp,
WeiElementOp,
OutElementOp,
ConvSpec, // ConvForwardSpecialization
GemmSpec, // GemmSpecialization
1, //
256, // BlockSize
128, // MPerBlock
256, // NPerBlock
32, // KPerBlock
8, // AK1
8, // BK1
32, // MPerXdl
32, // NPerXdl
2, // MXdlPerWave
4, // NXdlPerWave
S<4, 64, 1>, // ABlockTransferThreadClusterLengths_AK0_M_AK1
S<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // ABlockTransferSrcAccessOrder
2, // ABlockTransferSrcVectorDim
8, // ABlockTransferSrcScalarPerVector
8, // ABlockTransferDstScalarPerVector_AK1
1, // ABlockLdsExtraM
S<4, 64, 1>, // BBlockTransferThreadClusterLengths_BK0_N_BK1
S<1, 0, 2>, // BBlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // BBlockTransferSrcAccessOrder
2, // BBlockTransferSrcVectorDim
8, // BBlockTransferSrcScalarPerVector
8, // BBlockTransferDstScalarPerVector_BK1
1, // BBlockLdsExtraN
1,
1,
S<1, 32, 1, 8>,
8,
AComputeDataType,
BComputeDataType>;
#include "run_convnd_fwd_example.inc"
int main(int argc, char* argv[]) { return run_convnd_fwd_example(argc, argv) ? 0 : 1; }
example/62_convnd_activ/convscale_reduce/run_convnd_fwd_example.inc 0 → 100644
View file @ f84e2020
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
bool run_convnd_fwd_example(int argc, char* argv[])
{
print_helper_msg();
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
ck::utils::conv::ConvParam conv_param{
2, 1, 128, 256, 192, {3, 3}, {71, 71}, {2, 2}, {1, 1}, {1, 1}, {1, 1}};
if(argc == 1)
{
// use default
}
else if(argc == 4)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
const ck::index_t num_dim_spatial = std::stoi(argv[4]);
conv_param = ck::utils::conv::parse_conv_param(num_dim_spatial, 5, argv);
}
// instantiate in and wei element ops, will
// instantiate out_element_op below for every iteration
const auto in_element_op = InElementOp{};
const auto wei_element_op = WeiElementOp{};
const auto run = [&](auto ndim_spatial, auto in_layout, auto wei_layout, auto out_layout) {
constexpr ck::index_t ndim_spatial_value = ndim_spatial.value;
using InLayout = decltype(in_layout);
using WeiLayout = decltype(wei_layout);
using OutLayout = decltype(out_layout);
const auto in_g_n_c_wis_desc =
ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(
conv_param);
const auto wei_g_k_c_xs_desc =
ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(
conv_param);
const auto out_g_n_k_wos_desc =
ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(
conv_param);
return run_grouped_conv_fwd<
ndim_spatial_value,
InDataType,
WeiDataType,
ConvOutDataType,
OutDataType,
InElementOp,
WeiElementOp,
OutElementOp,
DeviceGroupedConvNDFwdInstance<ndim_spatial_value, InLayout, WeiLayout, OutLayout>>(
do_verification,
init_method,
time_kernel,
conv_param,
in_g_n_c_wis_desc,
wei_g_k_c_xs_desc,
out_g_n_k_wos_desc,
in_element_op,
wei_element_op);
};
namespace ctc = ck::tensor_layout::convolution;
if(conv_param.num_dim_spatial_ == 1)
{
return run(ck::Number<1>{}, ctc::GNWC{}, ctc::GKXC{}, ctc::GNWK{});
}
else if(conv_param.num_dim_spatial_ == 2)
{
return run(ck::Number<2>{}, ctc::GNHWC{}, ctc::GKYXC{}, ctc::GNHWK{});
}
else if(conv_param.num_dim_spatial_ == 3)
{
return run(ck::Number<3>{}, ctc::GNDHWC{}, ctc::GKZYXC{}, ctc::GNDHWK{});
}
return true;
}
example/65_gemm_multiply_multiply/gemm_add_add_xdl_fp16.cpp
View file @ f84e2020
......@@ -208,6 +208,7 @@ int main(int argc, char* argv[])
StrideB,
std::array<ck::index_t, NumDTensor>{StrideD, StrideD},
StrideE,
1,
a_element_op,
b_element_op,
cde_element_op);
......
example/65_gemm_multiply_multiply/gemm_multiply_multiply_xdl_fp8.cpp
View file @ f84e2020
......@@ -69,7 +69,7 @@ using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CDEElementOp = MultiplyMultiply;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::Default;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNPadding;
using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultiD_Xdl_CShuffle_V3
// clang-format off
......@@ -99,6 +99,8 @@ int main(int argc, char* argv[])
ck::index_t StrideD = 0;
ck::index_t StrideE = N;
ck::index_t KBatch = 1;
if(argc == 1)
{
// use default case
......@@ -109,7 +111,7 @@ int main(int argc, char* argv[])
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 11)
else if(argc == 12)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
......@@ -123,13 +125,16 @@ int main(int argc, char* argv[])
StrideB = std::stoi(argv[8]);
StrideD = std::stoi(argv[9]);
StrideE = std::stoi(argv[10]);
KBatch = std::stoi(argv[11]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=no, 1=yes)\n");
printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideD, StrideE\n");
printf(
"arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideD, StrideE, KBatch\n");
exit(0);
}
......@@ -212,6 +217,7 @@ int main(int argc, char* argv[])
StrideB,
std::array<ck::index_t, NumDTensor>{I0, I0},
StrideE,
KBatch,
a_element_op,
b_element_op,
cde_element_op);
......@@ -236,10 +242,12 @@ int main(int argc, char* argv[])
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
<< std::endl;
e_device_buf.FromDevice(e_m_n_device_result.mData.data());
if(do_verification)
{
invoker.Run(argument, StreamConfig{nullptr, false});
e_device_buf.FromDevice(e_m_n_device_result.mData.data());
Tensor<CShuffleDataType> c_m_n({M, N});
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<A0DataType,
......
example/CMakeLists.txt
View file @ f84e2020
......@@ -72,10 +72,24 @@ function(add_example_executable EXAMPLE_NAME FILE_NAME)
list(REMOVE_ITEM FILE_NAME "${source}")
endif()
endforeach()
#Do not build any FP8 examples if CK_ENABLE_FP8 not set
foreach(source IN LISTS FILE_NAME)
if(NOT DEFINED CK_ENABLE_FP8 AND source MATCHES "_fp8")
message("removing fp8 example ${source} ")
list(REMOVE_ITEM FILE_NAME "${source}")
endif()
endforeach()
#Do not build any BF8 examples if CK_ENABLE_BF8 not set
foreach(source IN LISTS FILE_NAME)
if(NOT DEFINED CK_ENABLE_BF8 AND source MATCHES "_bf8")
message("removing bf8 example ${source} ")
list(REMOVE_ITEM FILE_NAME "${source}")
endif()
endforeach()
#only continue if there are some source files left on the list
if(FILE_NAME)
if(FILE_NAME MATCHES "_xdl")
list(REMOVE_ITEM EX_TARGETS gfx1030 gfx1100 gfx1101 gfx1102 gfx1103)
list(REMOVE_ITEM EX_TARGETS gfx1030 gfx1100 gfx1101 gfx1102 gfx1103 gfx1200 gfx1201)
elseif(FILE_NAME MATCHES "_wmma")
list(REMOVE_ITEM EX_TARGETS gfx908 gfx90a gfx940 gfx941 gfx942 gfx1030)
endif()
......@@ -162,7 +176,7 @@ function(add_example_executable_no_testing EXAMPLE_NAME FILE_NAME)
#only continue if there are some source files left on the list
if(FILE_NAME)
if(FILE_NAME MATCHES "_xdl")
list(REMOVE_ITEM EX_TARGETS gfx900 gfx906 gfx1030 gfx1100 gfx1101 gfx1102 gfx1103)
list(REMOVE_ITEM EX_TARGETS gfx900 gfx906 gfx1030 gfx1100 gfx1101 gfx1102 gfx1103 gfx1200 gfx1201)
elseif(FILE_NAME MATCHES "_wmma")
list(REMOVE_ITEM EX_TARGETS gfx900 gfx906 gfx908 gfx90a gfx940 gfx941 gfx942 gfx1030)
endif()
......
example/ck_tile/01_fmha/CMakeLists.txt
View file @ f84e2020
......@@ -6,7 +6,7 @@ execute_process(
execute_process(
COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py
--api bwd --list_blobs ${CMAKE_CURRENT_BINARY_DIR}/bwd_blob_list.txt
--api bwd --list_blobs ${CMAKE_CURRENT_BINARY_DIR}/bwd_blob_list.txt --receipt 3
)
# NOTE: for cmake, the FMHA_FWD_GEN_BLOBS/FMHA_BWD_GEN_BLOBS files must be in the same directory
......@@ -23,7 +23,7 @@ add_custom_command(
add_custom_command(
OUTPUT ${FMHA_BWD_GEN_BLOBS}
COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py
--api bwd --output_dir ${CMAKE_CURRENT_BINARY_DIR}
--api bwd --output_dir ${CMAKE_CURRENT_BINARY_DIR} --receipt 3
)
set(EXAMPLE_FMHA_FWD "tile_example_fmha_fwd")
......@@ -55,11 +55,10 @@ set(EXAMPLE_FMHA_BWD_COMPILE_OPTIONS)
# ... because they are auto-generated
if(FMHA_FWD_FAST_EXP2)
list(APPEND EXAMPLE_FMHA_FWD_COMPILE_OPTIONS -Wno-undefined-func-template -DCK_TILE_FMHA_FWD_FAST_EXP2=1 -fgpu-flush-denormals-to-zero)
list(APPEND EXAMPLE_FMHA_BWD_COMPILE_OPTIONS -Wno-undefined-func-template -DCK_TILE_FMHA_FWD_FAST_EXP2=1 -fgpu-flush-denormals-to-zero)
else()
list(APPEND EXAMPLE_FMHA_FWD_COMPILE_OPTIONS -Wno-undefined-func-template -DCK_TILE_FMHA_FWD_FAST_EXP2=0)
list(APPEND EXAMPLE_FMHA_BWD_COMPILE_OPTIONS -Wno-undefined-func-template -DCK_TILE_FMHA_FWD_FAST_EXP2=0)
endif()
list(APPEND EXAMPLE_FMHA_BWD_COMPILE_OPTIONS -Wno-undefined-func-template -fgpu-flush-denormals-to-zero)
# Allow comparing floating points directly in order to check sentinel values
list(APPEND EXAMPLE_FMHA_FWD_COMPILE_OPTIONS -Wno-float-equal)
......
example/ck_tile/01_fmha/codegen/cpp_symbol_map.py
View file @ f84e2020
......@@ -66,6 +66,22 @@ BIAS_CHECK_MAP = {
"alibi" : "bias_enum::alibi"
}
DROPOUT_MAP = {
"no" : "ck_tile::BlockDropoutBwd<false, true, false>",
"dropout_wg32" : "ck_tile::BlockDropoutBwd<true, true, false>",
"dropout_wg32_storerandval" : "ck_tile::BlockDropoutBwd<true, true, true >",
"dropout_wg16" : "ck_tile::BlockDropoutBwd<true, false, false>",
"dropout_wg16_storerandval" : "ck_tile::BlockDropoutBwd<true, false, true >"
}
DROPOUT_CHECK_MAP = {
"no" : "t.has_dropout == false",
"dropout_wg32" : "t.has_dropout == true && t.is_store_randval == false",
"dropout_wg32_storerandval" : "t.has_dropout == true && t.is_store_randval == true",
"dropout_wg16" : "t.has_dropout == true && t.is_store_randval == false",
"dropout_wg16_storerandval" : "t.has_dropout == true && t.is_store_randval == true",
}
MODE_MAP = {
"batch" : "false",
"group" : "true"
......
example/ck_tile/01_fmha/fmha_bwd.cpp
View file @ f84e2020
......@@ -87,7 +87,11 @@ auto create_args(int argc, char* argv[])
.insert("drop_offset", "0", "offset for random number generator")
.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");
.insert("repeat", "20", "number of iterations to benchmark the kernel")
.insert("deterministic",
"0",
"if set to 1 will use multi-buffer reduction strategy for dq, atomic opeartion "
"will not be used");
bool result = arg_parser.parse(argc, argv);
return std::make_tuple(result, arg_parser);
......@@ -128,11 +132,6 @@ bool run(const ck_tile::ArgParser& arg_parser)
ck_tile::index_t hdim_v = arg_parser.get_int("d_v");
if(hdim_v < 0)
hdim_v = hdim_q;
if(hdim_q % 2 != 0 || hdim_v % 2 != 0)
{
std::cerr << "FMHA Bwd kernel currently only supports even headdim" << std::endl;
return false;
}
bool i_perm = arg_parser.get_bool("iperm"); // if true, will be batch * nhead * seqlen * hdim
bool o_perm = arg_parser.get_bool("operm"); // if false, will be batch * seqlen * nhead * hdim
......@@ -177,9 +176,10 @@ bool run(const ck_tile::ArgParser& arg_parser)
seed.reset();
}
int stream_warmup = arg_parser.get_int("warmup");
int stream_repeat = arg_parser.get_int("repeat");
bool kname = arg_parser.get_bool("kname");
int stream_warmup = arg_parser.get_int("warmup");
int stream_repeat = arg_parser.get_int("repeat");
bool kname = arg_parser.get_bool("kname");
bool deterministic = arg_parser.get_bool("deterministic");
ck_tile::stream_config stream_config{nullptr,
true,
......@@ -265,6 +265,9 @@ bool run(const ck_tile::ArgParser& arg_parser)
(mode == mode_enum::batch ? seqlen_q : seqstart_q_host.back());
const ck_tile::index_t shape_seqlen_k =
(mode == mode_enum::batch ? seqlen_k : seqstart_k_host.back());
const ck_tile::index_t kN0 = (hdim_q <= 128) ? 128 : 64;
const ck_tile::index_t nsplits =
deterministic ? ck_tile::integer_divide_ceil(max_seqlen_k, kN0) : 1;
ck_tile::HostTensor<QDataType> q_host(
get_lengths(i_perm, shape_batch, nhead, shape_seqlen_q, hdim_q));
......@@ -284,9 +287,9 @@ bool run(const ck_tile::ArgParser& arg_parser)
ck_tile::HostTensor<ODataType> o_host(
get_lengths(o_perm, shape_batch, nhead, shape_seqlen_q, hdim_v));
ck_tile::HostTensor<LSEDataType> lse_host(
std::array<ck_tile::index_t, 3>{batch, nhead, max_seqlen_q});
std::array<ck_tile::index_t, 3>{shape_batch, nhead, shape_seqlen_q});
ck_tile::HostTensor<DDataType> d_host(
std::array<ck_tile::index_t, 3>{batch, nhead, max_seqlen_q});
std::array<ck_tile::index_t, 3>{shape_batch, nhead, shape_seqlen_q});
ck_tile::HostTensor<RandValOutputDataType> randval_host(
p_drop > 0 ? get_lengths(true, shape_batch, nhead, shape_seqlen_q, max_seqlen_k)
: std::array<ck_tile::index_t, 4>{1, 1, 1, 1});
......@@ -302,6 +305,10 @@ bool run(const ck_tile::ArgParser& arg_parser)
use_dbias
? get_lengths(i_perm, shape_batch, nhead, shape_seqlen_q, max_seqlen_k)
: std::array<ck_tile::index_t, 4>{1, 1, 1, 1} /* dummy shape for simplifying code */);
ck_tile::HostTensor<AccDataType> dq_acc_host(
i_perm
? std::array<ck_tile::index_t, 5>{nsplits, shape_batch, nhead, shape_seqlen_q, hdim_q}
: std::array<ck_tile::index_t, 5>{nsplits, shape_batch, shape_seqlen_q, nhead, hdim_q});
if(init_method == 0)
{
......@@ -362,6 +369,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
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 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());
q_buf.ToDevice(q_host.data());
k_buf.ToDevice(k_host.data());
......@@ -387,8 +395,17 @@ bool run(const ck_tile::ArgParser& arg_parser)
std::cout << "[" << prec << "|" << mode << "|" << io_layout(i_perm, o_perm) << "] b:" << batch
<< ", h:" << nhead << "/" << nhead_k << ", s:" << seqlen_q << "/" << seqlen_k
<< ", d:" << hdim_q << "/" << hdim_v << ", scale:" << scale << ", bias:" << bias
<< ", dbias:" << use_dbias << ", p_drop:" << p_drop << ", mask:" << mask
<< std::flush;
<< ", dbias:" << use_dbias << ", p_drop:" << p_drop << ", s_randval:" << s_randval
<< ", deterministic:" << deterministic << ", mask:" << mask << std::flush;
std::size_t workspace_size =
dq_acc_host.get_element_space_size_in_bytes() * sizeof(AccDataType) / (1024 * 1024);
if(deterministic == 1)
{
std::cout << "\nDeterministic mode ON: " << workspace_size
<< " MByte memory workspace allocated" << std::endl;
}
auto fmha_traits = fmha_bwd_traits{hdim_q,
hdim_v,
......@@ -397,7 +414,9 @@ bool run(const ck_tile::ArgParser& arg_parser)
mask.type,
bias.type,
use_dbias,
p_drop > 0.0f};
p_drop > 0.0f,
s_randval,
deterministic};
auto fmha_args = [&]() {
assert(nhead % nhead_k == 0);
/// NOTE: we broadcast bias from [1, 1, seqlen_q, seqlen_k] to [batch, nhead, seqlen_q,
......@@ -422,7 +441,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
const ck_tile::index_t nhead_stride_o = (o_perm ? shape_seqlen_q * hdim_v : hdim_v);
const ck_tile::index_t nhead_stride_randval = (shape_seqlen_q * max_seqlen_k);
const ck_tile::index_t nhead_stride_do = (o_perm ? shape_seqlen_q * hdim_v : hdim_v);
const ck_tile::index_t nhead_stride_lsed = max_seqlen_q;
const ck_tile::index_t nhead_stride_lsed = shape_seqlen_q;
const ck_tile::index_t nhead_stride_dbias =
(i_perm ? shape_seqlen_q * max_seqlen_k : max_seqlen_k);
// setup batch_stride_* arguments
......@@ -433,10 +452,12 @@ bool run(const ck_tile::ArgParser& arg_parser)
const ck_tile::index_t batch_stride_o = (nhead * shape_seqlen_q * hdim_v);
const ck_tile::index_t batch_stride_randval = (nhead * shape_seqlen_q * max_seqlen_k);
const ck_tile::index_t batch_stride_do = (nhead * shape_seqlen_q * hdim_v);
const ck_tile::index_t batch_stride_lsed = (nhead * max_seqlen_q);
const ck_tile::index_t batch_stride_lsed = (nhead * shape_seqlen_q);
const ck_tile::index_t batch_stride_dk = (nhead * shape_seqlen_k * hdim_q);
const ck_tile::index_t batch_stride_dv = (nhead * shape_seqlen_k * hdim_v);
const ck_tile::index_t batch_stride_dbias = (nhead * shape_seqlen_q * max_seqlen_k);
const ck_tile::index_t split_stride_dq_acc =
(shape_batch * nhead * shape_seqlen_q * hdim_q);
return fmha_bwd_args{q_buf.GetDeviceBuffer(),
k_buf.GetDeviceBuffer(),
......@@ -452,6 +473,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
dk_buf.GetDeviceBuffer(),
dv_buf.GetDeviceBuffer(),
dbias_buf.GetDeviceBuffer(),
dq_acc_buf.GetDeviceBuffer(),
seqstart_q.GetDeviceBuffer(),
seqstart_k.GetDeviceBuffer(),
nullptr,
......@@ -473,6 +495,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
stride_o,
stride_randval,
stride_do,
stride_q, // stride_dq_acc
stride_q, // stride_dq
stride_dk,
stride_dv,
stride_dbias,
......@@ -484,6 +508,10 @@ bool run(const ck_tile::ArgParser& arg_parser)
nhead_stride_randval,
nhead_stride_do,
nhead_stride_lsed,
nhead_stride_q, // nhead_stride_dq_acc
nhead_stride_q, // nhead_stride_dq
nhead_stride_k, // nhead_stride_dk
nhead_stride_v, // nhead_stride_dv
nhead_stride_dbias,
batch_stride_q,
batch_stride_k,
......@@ -493,15 +521,17 @@ bool run(const ck_tile::ArgParser& arg_parser)
batch_stride_randval,
batch_stride_do,
batch_stride_lsed,
batch_stride_q, // batch_stride_dq_acc
batch_stride_q, // batch_stride_dq
batch_stride_dk,
batch_stride_dv,
batch_stride_dbias,
split_stride_dq_acc,
mask.left,
mask.right,
static_cast<ck_tile::index_t>(mask.type),
p_drop,
p_undrop,
s_randval,
{drop_seed, drop_offset}};
}();
......@@ -719,7 +749,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
if(o_perm) o_host_ref.ForEach([&](auto& self, auto idx) { o_host(b, idx[0], idx[1] + query_offset, idx[2]) = self(idx); });
else o_host_ref.ForEach([&](auto& self, auto idx) { o_host(b, idx[1] + query_offset, idx[0], idx[2]) = self(idx); });
lse_host_ref.ForEach([&](auto& self, auto idx) { lse_host(wb, idx[0], idx[1]) = self(idx); });
lse_host_ref.ForEach([&](auto& self, auto idx) { lse_host(b, idx[0], idx[1] + query_offset) = self(idx); });
// clang-format on
q_host_refs.push_back(q_host_ref);
......@@ -738,6 +768,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
lse_buf.ToDevice(lse_host.data());
dq_buf.SetZero();
dbias_buf.SetZero();
dq_acc_buf.SetZero();
ck_tile::stream_config stream_config_v{
nullptr, true, 0, 0, 1, arg_parser.get_str("timer") == std::string("gpu")};
......
example/ck_tile/01_fmha/fmha_bwd.hpp
View file @ f84e2020
......@@ -77,6 +77,7 @@ struct fmha_bwd_args
void* dk_ptr;
void* dv_ptr;
void* dbias_ptr;
void* dq_acc_ptr;
const void* seqstart_q_ptr;
const void* seqstart_k_ptr;
const void* seqlen_k_ptr;
......@@ -97,6 +98,8 @@ struct fmha_bwd_args
ck_tile::index_t stride_o;
ck_tile::index_t stride_randval;
ck_tile::index_t stride_do;
ck_tile::index_t stride_dq_acc;
ck_tile::index_t stride_dq;
ck_tile::index_t stride_dk;
ck_tile::index_t stride_dv;
ck_tile::index_t stride_dbias;
......@@ -108,6 +111,10 @@ struct fmha_bwd_args
ck_tile::index_t nhead_stride_randval;
ck_tile::index_t nhead_stride_do;
ck_tile::index_t nhead_stride_lsed;
ck_tile::index_t nhead_stride_dq_acc;
ck_tile::index_t nhead_stride_dq;
ck_tile::index_t nhead_stride_dk;
ck_tile::index_t nhead_stride_dv;
ck_tile::index_t nhead_stride_dbias;
ck_tile::index_t batch_stride_q;
ck_tile::index_t batch_stride_k;
......@@ -117,15 +124,17 @@ struct fmha_bwd_args
ck_tile::index_t batch_stride_randval;
ck_tile::index_t batch_stride_do;
ck_tile::index_t batch_stride_lsed;
ck_tile::index_t batch_stride_dq_acc;
ck_tile::index_t batch_stride_dq;
ck_tile::index_t batch_stride_dk;
ck_tile::index_t batch_stride_dv;
ck_tile::index_t batch_stride_dbias;
ck_tile::index_t split_stride_dq_acc;
ck_tile::index_t window_size_left;
ck_tile::index_t window_size_right;
ck_tile::index_t mask_type;
float p_drop;
float p_undrop;
bool s_randval;
std::tuple<uint64_t, uint64_t> drop_seed_offset;
};
......@@ -145,10 +154,10 @@ auto fmha_bwd_dq_dk_dv_create_kargs_and_grids(fmha_bwd_args args)
args.do_ptr,
args.d_ptr,
args.rand_val_ptr,
args.dq_ptr,
args.dk_ptr,
args.dv_ptr,
args.dbias_ptr,
args.dq_acc_ptr,
args.seqstart_q_ptr,
args.seqstart_k_ptr,
args.seqlen_k_ptr,
......@@ -163,6 +172,7 @@ auto fmha_bwd_dq_dk_dv_create_kargs_and_grids(fmha_bwd_args args)
args.stride_bias,
args.stride_randval,
args.stride_do,
args.stride_dq_acc,
args.stride_dk,
args.stride_dv,
args.stride_dbias,
......@@ -173,13 +183,15 @@ auto fmha_bwd_dq_dk_dv_create_kargs_and_grids(fmha_bwd_args args)
args.nhead_stride_randval,
args.nhead_stride_do,
args.nhead_stride_lsed,
args.nhead_stride_dq_acc,
args.nhead_stride_dk,
args.nhead_stride_dv,
args.nhead_stride_dbias,
args.batch_stride_lsed,
args.split_stride_dq_acc,
args.window_size_left,
args.window_size_right,
args.mask_type,
args.p_drop,
args.s_randval,
args.drop_seed_offset);
}
else
......@@ -192,10 +204,10 @@ auto fmha_bwd_dq_dk_dv_create_kargs_and_grids(fmha_bwd_args args)
args.do_ptr,
args.d_ptr,
args.rand_val_ptr,
args.dq_ptr,
args.dk_ptr,
args.dv_ptr,
args.dbias_ptr,
args.dq_acc_ptr,
args.seqlen_q,
args.seqlen_k,
args.hdim_q,
......@@ -209,6 +221,7 @@ auto fmha_bwd_dq_dk_dv_create_kargs_and_grids(fmha_bwd_args args)
args.stride_bias,
args.stride_randval,
args.stride_do,
args.stride_dq_acc,
args.stride_dk,
args.stride_dv,
args.stride_dbias,
......@@ -219,6 +232,9 @@ auto fmha_bwd_dq_dk_dv_create_kargs_and_grids(fmha_bwd_args args)
args.nhead_stride_randval,
args.nhead_stride_do,
args.nhead_stride_lsed,
args.nhead_stride_dq_acc,
args.nhead_stride_dk,
args.nhead_stride_dv,
args.nhead_stride_dbias,
args.batch_stride_q,
args.batch_stride_k,
......@@ -227,14 +243,15 @@ auto fmha_bwd_dq_dk_dv_create_kargs_and_grids(fmha_bwd_args args)
args.batch_stride_randval,
args.batch_stride_do,
args.batch_stride_lsed,
args.batch_stride_dq_acc,
args.batch_stride_dk,
args.batch_stride_dv,
args.batch_stride_dbias,
args.split_stride_dq_acc,
args.window_size_left,
args.window_size_right,
args.mask_type,
args.p_drop,
args.s_randval,
args.drop_seed_offset);
}
}();
......@@ -260,8 +277,7 @@ auto fmha_bwd_dot_do_o_create_kargs_and_grids(fmha_bwd_args args)
args.stride_o,
args.nhead_stride_do,
args.nhead_stride_o,
args.nhead_stride_lsed,
args.batch_stride_lsed);
args.nhead_stride_lsed);
}
else
{ // create batch mode kernel arguments
......@@ -286,19 +302,59 @@ auto fmha_bwd_dot_do_o_create_kargs_and_grids(fmha_bwd_args args)
return ck_tile::make_tuple(kargs, grids);
}
template <typename FmhaBwdConvertQGradKernel>
auto fmha_bwd_convert_dq_create_kargs_and_grids(fmha_bwd_args args)
{
auto kargs = [&] {
// create group mode kernel arguments
if constexpr(FmhaBwdConvertQGradKernel::kIsGroupMode)
{
return FmhaBwdConvertQGradKernel::MakeKargs(args.dq_acc_ptr,
args.dq_ptr,
args.seqstart_q_ptr,
args.seqstart_k_ptr,
args.hdim_q,
args.stride_dq,
args.stride_dq_acc,
args.nhead_stride_dq,
args.nhead_stride_dq_acc,
args.split_stride_dq_acc);
}
else
{ // create batch mode kernel arguments
return FmhaBwdConvertQGradKernel::MakeKargs(args.dq_acc_ptr,
args.dq_ptr,
args.seqlen_q,
args.seqlen_k,
args.hdim_q,
args.stride_dq,
args.stride_dq_acc,
args.nhead_stride_dq,
args.nhead_stride_dq_acc,
args.batch_stride_dq,
args.batch_stride_dq_acc,
args.split_stride_dq_acc);
}
}();
dim3 grids = FmhaBwdConvertQGradKernel::GridSize(args.batch, args.nhead_q, args.max_seqlen_q);
return ck_tile::make_tuple(kargs, grids);
}
// this is used to pattern-match internl kernel implementation, not to instantiate kernel
template <ck_tile::index_t HDim_,
typename DataType_,
bool kIsGroupMode_,
ck_tile::BlockFmhaBwdPipelineEnum FmhaBwdPipelineEnum_,
typename FmhaMask_,
typename FmhaDropout_,
ck_tile::BlockAttentionBiasEnum BiasEnum_,
bool kHasBiasGrad_,
bool kHasDropout_,
bool kPadS_,
bool kPadSK_,
bool kPadD_,
bool kPadDv_>
bool kPadDv_,
bool kIsDeterministic_>
struct fmha_bwd_dq_dk_dv_traits_
{
static constexpr ck_tile::index_t HDim = HDim_;
......@@ -306,13 +362,14 @@ struct fmha_bwd_dq_dk_dv_traits_
static constexpr bool kIsGroupMode = kIsGroupMode_;
static constexpr auto FmhaBwdPipelineEnum = FmhaBwdPipelineEnum_;
using FmhaMask = ck_tile::remove_cvref_t<FmhaMask_>;
using FmhaDropout = ck_tile::remove_cvref_t<FmhaDropout_>;
static constexpr auto BiasEnum = BiasEnum_;
static constexpr bool kHasBiasGrad = kHasBiasGrad_;
static constexpr bool kHasDropout = kHasDropout_;
static constexpr bool kPadS = kPadS_;
static constexpr bool kPadSK = kPadSK_;
static constexpr bool kPadD = kPadD_;
static constexpr bool kPadDv = kPadDv_;
static constexpr bool kIsDeterministic = kIsDeterministic_;
};
template <typename Traits_>
......@@ -343,6 +400,31 @@ void fmha_bwd_dot_do_o_oneshot_(const ck_tile::stream_config&, fmha_bwd_args);
template <typename Traits_>
std::string fmha_bwd_dot_do_o_get_name_();
template <ck_tile::index_t HDim_,
typename DataType_,
bool kIsGroupMode_,
bool kPadS_,
bool kPadD_,
bool kIsDeterministic_>
struct fmha_bwd_convert_dq_traits_
{
static constexpr ck_tile::index_t HDim = HDim_;
using DataType = ck_tile::remove_cvref_t<DataType_>;
static constexpr bool kIsGroupMode = kIsGroupMode_;
static constexpr bool kPadS = kPadS_;
static constexpr bool kPadD = kPadD_;
static constexpr bool kIsDeterministic = kIsDeterministic_;
};
template <typename Traits_>
float fmha_bwd_convert_dq_(const ck_tile::stream_config&, fmha_bwd_args);
template <typename Traits_>
void fmha_bwd_convert_dq_oneshot_(const ck_tile::stream_config&, fmha_bwd_args);
template <typename Traits_>
std::string fmha_bwd_convert_dq_get_name_();
// This is the public API, will be generated by script
struct fmha_bwd_traits
{
......@@ -354,6 +436,8 @@ struct fmha_bwd_traits
bias_enum bias_type; // 0:no bias, 1:elementwise bias, 2:alibi. sync with BlockAttentionBiasEnum
bool has_dbias;
bool has_dropout;
bool is_store_randval;
bool is_deterministic;
// TODO: padding check is inside this api
};
float fmha_bwd(fmha_bwd_traits, fmha_bwd_args, const ck_tile::stream_config&);
example/ck_tile/01_fmha/fmha_fwd.cpp
View file @ f84e2020
......@@ -479,16 +479,18 @@ bool run(const ck_tile::ArgParser& arg_parser)
: std::array<ck_tile::index_t, 2>{1, 1});
ck_tile::HostTensor<LSEDataType> lse_acc_host(
1 < num_splits ? std::array<ck_tile::index_t, 4>{num_splits, batch, nhead, max_seqlen_q}
: std::array<ck_tile::index_t, 4>{1, 1, 1, 1});
1 < num_splits
? std::array<ck_tile::index_t, 4>{num_splits, shape_batch, nhead, shape_seqlen_q}
: std::array<ck_tile::index_t, 4>{1, 1, 1, 1});
ck_tile::HostTensor<OaccDataType> o_acc_host(
1 < num_splits
? std::array<ck_tile::index_t, 5>{num_splits, batch, nhead, max_seqlen_q, hdim_v}
: std::array<ck_tile::index_t, 5>{1, 1, 1, 1, 1});
// self define lse data layout as [batch, nhead, max_seqlen_q]
// batch mode of lse data layout is [batch, nhead, seqlen_q]
// group mode of lse data layout is [nhead, total_seqlen_q]
ck_tile::HostTensor<LSEDataType> lse_host(
lse ? std::array<ck_tile::index_t, 3>{batch, nhead, max_seqlen_q}
lse ? std::array<ck_tile::index_t, 3>{shape_batch, nhead, shape_seqlen_q}
: std::array<ck_tile::index_t, 3>{1, 1, 1} /* dummy shape for simplifying code */);
ck_tile::HostTensor<ODataType> o_host(
......@@ -669,8 +671,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
const ck_tile::index_t nhead_stride_bias =
(i_perm ? 0 * shape_seqlen_q * shape_seqlen_k : 0 * shape_seqlen_k);
const ck_tile::index_t nhead_stride_randval = (shape_seqlen_q * max_seqlen_k);
const ck_tile::index_t nhead_stride_lse = max_seqlen_q;
const ck_tile::index_t nhead_stride_lse_acc = max_seqlen_q;
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 = (o_perm ? shape_seqlen_q * hdim_v : hdim_v);
// setup batch_stride_* arguments
......@@ -679,12 +681,12 @@ bool run(const ck_tile::ArgParser& arg_parser)
const ck_tile::index_t batch_stride_v = (nhead_k * hdim_v * shape_seqlen_k);
const ck_tile::index_t batch_stride_bias = (0 * nhead * shape_seqlen_q * shape_seqlen_k);
const ck_tile::index_t batch_stride_randval = (nhead * shape_seqlen_q * max_seqlen_k);
const ck_tile::index_t batch_stride_lse = (nhead * max_seqlen_q);
const ck_tile::index_t batch_stride_lse_acc = (nhead * max_seqlen_q);
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 = (nhead * shape_seqlen_q * hdim_v);
// setup split_stride_* arguments (only used in split-kv kernel)
const ck_tile::index_t split_stride_lse_acc = (batch * nhead * max_seqlen_q);
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);
return fmha_fwd_args{q_buf.GetDeviceBuffer(),
......@@ -996,8 +998,9 @@ bool run(const ck_tile::ArgParser& arg_parser)
if(lse)
{
ck_tile::HostTensor<SMPLComputeDataType> lse_host_result({nhead, real_seqlen_q});
lse_host_result.ForEach(
[&](auto& self, auto idx) { self(idx) = lse_host(wb, idx[0], idx[1]); });
lse_host_result.ForEach([&](auto& self, auto idx) {
self(idx) = lse_host(b, idx[0], idx[1] + query_offset);
});
cur_pass = ck_tile::check_err(lse_host_result,
lse_host_ref,
......
example/ck_tile/01_fmha/fmha_fwd.hpp
View file @ f84e2020
......@@ -185,7 +185,6 @@ auto fmha_fwd_create_kargs_and_grids(fmha_fwd_args args)
args.nhead_stride_randval,
args.nhead_stride_lse,
args.nhead_stride_o,
args.batch_stride_lse,
args.window_size_left,
args.window_size_right,
args.mask_type,
......@@ -284,7 +283,6 @@ auto fmha_fwd_splitkv_create_kargs_and_grids(fmha_fwd_args args)
args.nhead_stride_randval,
args.nhead_stride_lse_acc,
args.nhead_stride_o_acc,
args.batch_stride_lse_acc,
args.batch_stride_o_acc,
args.split_stride_lse_acc,
args.split_stride_o_acc,
......@@ -376,9 +374,7 @@ auto fmha_fwd_splitkv_combine_create_kargs_and_grids(fmha_fwd_args args)
args.nhead_stride_o_acc,
args.nhead_stride_lse,
args.nhead_stride_o,
args.batch_stride_lse_acc,
args.batch_stride_o_acc,
args.batch_stride_lse,
args.split_stride_lse_acc,
args.split_stride_o_acc);
}
......
example/ck_tile/01_fmha/script/smoke_test_bwd.sh
View file @ f84e2020
......@@ -11,18 +11,19 @@ COMMON_ARGS='-v=1'
set -x
for prec in "fp16" "bf16" ; do
for perm in 0 1 ; do
for hdim in 32 64 128 ; do
for hdim in 32 64 128 256 ; do
for mode in 0 1 ; do
for bias in "n" "e" "a"; do
for dbias in 0 1 ; do
for p_drop in 0.0 0.2; do
for bias in "n" "a" ; do
for dbias in 0 ; do
for p_drop in 0.0 0.2 ; do
for deterministic in 0 ; do
$EXE -prec=$prec -b=1 -h=4 -h_k=2 -d=$hdim -s=259 -bias=$bias -dbias=$dbias -p_drop=$p_drop -iperm=$perm -operm=$perm -v=1 -mode=$mode -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -b=2 -h=2 -d=$hdim -s=516 -s_k=253 -bias=$bias -dbias=$dbias -p_drop=$p_drop -iperm=$perm -operm=$perm -v=1 -mode=$mode -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -b=1 -h=4 -h_k=1 -d=$hdim -s=500 -s_k=251 -bias=$bias -dbias=$dbias -p_drop=$p_drop -iperm=$perm -operm=$perm -mask=1 -v=1 -mode=$mode -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -b=1 -h=2 -d=$hdim -s=900 -s_k=258 -bias=$bias -dbias=$dbias -p_drop=$p_drop -iperm=$perm -operm=$perm -mask=2 -v=1 -mode=$mode -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -b=2 -h=1 -d=$hdim -s=987 -s_k=219 -bias=$bias -dbias=$dbias -p_drop=$p_drop -iperm=$perm -operm=$perm -mask=t:128,30 -v=1 -mode=$mode -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -b=2 -h=3 -h_k=1 -d=$hdim -s=244 -s_k=499 -bias=$bias -dbias=$dbias -p_drop=$p_drop -iperm=$perm -operm=$perm -mask=b:4,35 -v=1 -mode=$mode -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -b=1 -h=4 -h_k=2 -d=$hdim -s=259 -bias=$bias -dbias=$dbias -p_drop=$p_drop -iperm=$perm -operm=$perm -deterministic=$deterministic -v=1 -mode=$mode -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -b=2 -h=2 -d=$hdim -s=516 -s_k=253 -bias=$bias -dbias=$dbias -p_drop=$p_drop -iperm=$perm -operm=$perm -deterministic=$deterministic -v=1 -mode=$mode -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -b=1 -h=4 -h_k=1 -d=$hdim -s=500 -s_k=251 -bias=$bias -dbias=$dbias -p_drop=$p_drop -iperm=$perm -operm=$perm -mask=1 -deterministic=$deterministic -v=1 -mode=$mode -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -b=1 -h=2 -d=$hdim -s=900 -s_k=258 -bias=$bias -dbias=$dbias -p_drop=$p_drop -iperm=$perm -operm=$perm -mask=2 -v=1 -deterministic=$deterministic -mode=$mode -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -b=2 -h=1 -d=$hdim -s=987 -s_k=219 -bias=$bias -dbias=$dbias -p_drop=$p_drop -iperm=$perm -operm=$perm -mask=t:128,30 -deterministic=$deterministic -v=1 -mode=$mode -kname=$KNAME $COMMON_ARGS
$EXE -prec=$prec -b=2 -h=3 -h_k=1 -d=$hdim -s=244 -s_k=499 -bias=$bias -dbias=$dbias -p_drop=$p_drop -iperm=$perm -operm=$perm -mask=b:4,35 -deterministic=$deterministic -v=1 -mode=$mode -kname=$KNAME $COMMON_ARGS
done
done
......@@ -31,4 +32,5 @@ done
done
done
done
done
set +x
include/ck/ck.hpp
View file @ f84e2020
// 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
......@@ -153,8 +153,8 @@ CK_DECLARE_ENV_VAR_BOOL(CK_LOGGING)
// LDS direct loads using inline assembly
#define CK_USE_AMD_LDS_DIRECT_LOAD_INLINE_ASM 0
// set stochastic rounding as default for f8 conversions
#define CK_USE_SR_F8_CONVERSION 1
// set rounding to nearest even as default for f8 conversions
#define CK_USE_SR_F8_CONVERSION 0
// block synchronization only s_wait lgkmcnt(0), not vmcnt(0)
#define CK_EXPERIMENTAL_BLOCK_SYNC_LDS_WITHOUT_SYNC_VMEM 1
......
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