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danyao12
2024-05-09 17:08:08 +08:00
parent bbd2e1eae3
commit e1a21655ae
36 changed files with 7275 additions and 429 deletions
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33
example/ck_tile/01_fmha/CMakeLists.txt
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@@ -1,17 +1,29 @@
# generate a list of kernels, but not actually emit files at config stage
execute_process(
COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py
--list_blobs ${CMAKE_CURRENT_BINARY_DIR}/blob_list.txt
--direction fwd --list_blobs ${CMAKE_CURRENT_BINARY_DIR}/fwd_blob_list.txt
)
# NOTE: for cmake, the FMHA_FWD_GEN_BLOBS files must be in the same directory
execute_process(
COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py
--direction bwd --list_blobs ${CMAKE_CURRENT_BINARY_DIR}/bwd_blob_list.txt
)
# NOTE: for cmake, the FMHA_FWD_GEN_BLOBS/FMHA_BWD_GEN_BLOBS files must be in the same directory
# as current cmake list, otherwise will not figure out the dependency properly
file(STRINGS ${CMAKE_CURRENT_BINARY_DIR}/blob_list.txt FMHA_FWD_GEN_BLOBS)
file(STRINGS ${CMAKE_CURRENT_BINARY_DIR}/fwd_blob_list.txt FMHA_FWD_GEN_BLOBS)
file(STRINGS ${CMAKE_CURRENT_BINARY_DIR}/bwd_blob_list.txt FMHA_BWD_GEN_BLOBS)
add_custom_command(
OUTPUT ${FMHA_FWD_GEN_BLOBS}
COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py
--output_dir ${CMAKE_CURRENT_BINARY_DIR}
--direction fwd --output_dir ${CMAKE_CURRENT_BINARY_DIR}
)
add_custom_command(
OUTPUT ${FMHA_BWD_GEN_BLOBS}
COMMAND ${Python3_EXECUTABLE} ${CMAKE_CURRENT_LIST_DIR}/generate.py
--direction bwd --output_dir ${CMAKE_CURRENT_BINARY_DIR}
)
set(EXAMPLE_FMHA_FWD "tile_example_fmha_fwd")
@@ -22,6 +34,14 @@ add_executable(${EXAMPLE_FMHA_FWD} EXCLUDE_FROM_ALL fmha_fwd.cpp)
target_include_directories(${EXAMPLE_FMHA_FWD} PRIVATE ${CMAKE_CURRENT_LIST_DIR})
target_sources(${EXAMPLE_FMHA_FWD} PRIVATE ${FMHA_FWD_GEN_BLOBS})
set(EXAMPLE_FMHA_BWD "tile_example_fmha_bwd")
# 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_example ${EXAMPLE_NAME}")
add_executable(${EXAMPLE_FMHA_BWD} EXCLUDE_FROM_ALL fmha_bwd.cpp)
target_include_directories(${EXAMPLE_FMHA_BWD} PRIVATE ${CMAKE_CURRENT_LIST_DIR})
target_sources(${EXAMPLE_FMHA_BWD} PRIVATE ${FMHA_BWD_GEN_BLOBS})
# NOTE: this is dangerous since will change the whole kernel to flush denormals
# WIP with compiler team for an exp2 intrinsic..., then remove this
if(NOT DEFINED FMHA_FWD_FAST_EXP2)
@@ -29,16 +49,21 @@ if(NOT DEFINED FMHA_FWD_FAST_EXP2)
endif()
set(EXAMPLE_FMHA_FWD_COMPILE_OPTIONS)
set(EXAMPLE_FMHA_BWD_COMPILE_OPTIONS)
# NOTE: we turn off undefined-func-template to let source compile without explicit declare function specializations
# ... because they are auto-generated
if(FMHA_FWD_FAST_EXP2)
list(APPEND EXAMPLE_FMHA_FWD_COMPILE_OPTIONS -Wno-undefined-func-template -DCK_TILE_FMHA_FWD_FAST_EXP2=1 -fgpu-flush-denormals-to-zero)
list(APPEND EXAMPLE_FMHA_BWD_COMPILE_OPTIONS -Wno-undefined-func-template -DCK_TILE_FMHA_FWD_FAST_EXP2=1 -fgpu-flush-denormals-to-zero)
else()
list(APPEND EXAMPLE_FMHA_FWD_COMPILE_OPTIONS -Wno-undefined-func-template -DCK_TILE_FMHA_FWD_FAST_EXP2=0)
list(APPEND EXAMPLE_FMHA_BWD_COMPILE_OPTIONS -Wno-undefined-func-template -DCK_TILE_FMHA_FWD_FAST_EXP2=0)
endif()
# Allow comparing floating points directly in order to check sentinel values
list(APPEND EXAMPLE_FMHA_FWD_COMPILE_OPTIONS -Wno-float-equal)
list(APPEND EXAMPLE_FMHA_BWD_COMPILE_OPTIONS -Wno-float-equal)
target_compile_options(${EXAMPLE_FMHA_FWD} PRIVATE ${EXAMPLE_FMHA_FWD_COMPILE_OPTIONS})
target_compile_options(${EXAMPLE_FMHA_BWD} PRIVATE ${EXAMPLE_FMHA_BWD_COMPILE_OPTIONS})

510
example/ck_tile/01_fmha/fmha_bwd.cpp
View File

@@ -1,6 +1,11 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "fmha_bwd.hpp"
#include "ck_tile/host.hpp"
#include "mask.hpp"
#include "utils.hpp"
#include <array>
#include <cstring>
#include <functional>
@@ -9,35 +14,28 @@
#include <string>
#include <tuple>
#include <utility>
#include <vector>
#include "ck/ck.hpp"
#include "ck/host_utility/device_prop.hpp"
#include "ck/tensor/tensor_view.hpp"
#include "ck/tensor_description/tensor_descriptor_helper.hpp"
#include "ck/tensor_description/cluster_descriptor.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/utility/common_header.hpp"
template <typename T>
std::ostream& operator<<(std::ostream& os, const std::vector<T>& v)
{
using size_type = typename std::vector<T>::size_type;
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/fill.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "common/arg_parser.hpp"
#include "fmha_bwd.hpp"
#include "mask.hpp"
#include "reference/reference_batched_elementwise.hpp"
#include "reference/reference_batched_gemm.hpp"
#include "reference/reference_batched_masking.hpp"
#include "reference/reference_batched_softmax.hpp"
#include "reference/reference_batched_dropout.hpp"
#include "utils.hpp"
os << "[";
for(size_type idx = 0; idx < v.size(); ++idx)
{
if(0 < idx)
{
os << ", ";
}
os << v[idx];
}
return os << "]";
}
auto create_args(int argc, char* argv[])
{
ArgParser arg_parser;
ck_tile::ArgParser arg_parser;
arg_parser.insert("v", "1", "weather do CPU validation or not")
.insert("mode", "0", "kernel mode. 0:batch, 1:group")
.insert("b", "2", "batch size")
@@ -69,11 +67,11 @@ auto create_args(int argc, char* argv[])
"'t:l,r', top-left sliding window attn(swa) with FA style left right size\n"
"'b:l,r', bottom-r sliding window attn(swa) with FA style left right size\n"
"'xt:window_size', xformer style masking from top-left, window_size negative is "
"causal, possitive is swa\n"
"causal, positive is swa\n"
"'xb:window_size', xformer style masking from bottom-r, window_size negative is "
"causal, possitive is swa\n"
"causal, positive is swa\n"
"'g:y,x', generic attention mask coordinate with y/x size (only debug purpose for "
"now)\n")
"now)")
.insert("kname", "0", "if set to 1 will print kernel name")
.insert("init", "1", "init method. 0:random int, 1:random float, 2:trig float")
.insert("seed",
@@ -96,18 +94,18 @@ auto get_elimit(int /*init_method*/)
{
double rtol = 1e-2;
double atol = 1e-2;
return ck::make_tuple(rtol, atol);
return ck_tile::make_tuple(rtol, atol);
}
template <typename DataType>
bool run(const ArgParser& arg_parser)
bool run(const ck_tile::ArgParser& arg_parser)
{
std::string data_type = arg_parser.get_str("prec");
int do_validation = arg_parser.get_int("v");
auto mode = static_cast<mode_enum>(arg_parser.get_uint32("mode"));
ck::index_t batch = arg_parser.get_int("b");
ck::index_t nhead = arg_parser.get_int("h");
ck::index_t nhead_k = arg_parser.get_int("h_k");
std::string data_type = arg_parser.get_str("prec");
int do_validation = arg_parser.get_int("v");
auto mode = static_cast<mode_enum>(arg_parser.get_uint32("mode"));
ck_tile::index_t batch = arg_parser.get_int("b");
ck_tile::index_t nhead = arg_parser.get_int("h");
ck_tile::index_t nhead_k = arg_parser.get_int("h_k");
if(nhead_k == 0)
nhead_k = nhead;
@@ -117,12 +115,12 @@ bool run(const ArgParser& arg_parser)
return false;
}
ck::index_t seqlen_q = arg_parser.get_int("s");
ck::index_t seqlen_k = arg_parser.get_int("s_k");
ck_tile::index_t seqlen_q = arg_parser.get_int("s");
ck_tile::index_t seqlen_k = arg_parser.get_int("s_k");
if(seqlen_k == 0)
seqlen_k = seqlen_q;
ck::index_t hdim_q = arg_parser.get_int("d");
ck::index_t hdim_v = arg_parser.get_int("d_v");
ck_tile::index_t hdim_q = arg_parser.get_int("d");
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)
@@ -136,7 +134,7 @@ bool run(const ArgParser& arg_parser)
float scale = arg_parser.get_float("scale");
if(scale == .0f)
scale = 1.0 / ck::math::sqrt(static_cast<float>(hdim_q));
scale = 1.0 / ck_tile::sqrt(static_cast<float>(hdim_q));
bool use_bias = arg_parser.get_bool("bias");
bool use_dbias = arg_parser.get_bool("dbias");
@@ -178,7 +176,7 @@ bool run(const ArgParser& arg_parser)
int stream_repeat = arg_parser.get_int("repeat");
bool kname = arg_parser.get_bool("kname");
StreamConfig stream_config{
ck_tile::stream_config stream_config{
nullptr, true, /* log_level = */ (kname ? 1 : 0), stream_warmup, stream_repeat};
const auto seqstart_q_host = generate_seqstarts(mode, batch, seqlen_q);
@@ -209,7 +207,7 @@ bool run(const ArgParser& arg_parser)
auto max_seqlen_k =
std::numeric_limits<int32_t>::min(); // we will use max seqlen to decide grid size
{
for(ck::index_t wb = 0; wb < batch; ++wb)
for(ck_tile::index_t wb = 0; wb < batch; ++wb)
{
const int32_t real_seqlen_q = seqstart_q_host[wb + 1] - seqstart_q_host[wb];
const int32_t real_seqlen_k = seqstart_k_host[wb + 1] - seqstart_k_host[wb];
@@ -224,11 +222,10 @@ bool run(const ArgParser& arg_parser)
max_seqlen_k = real_seqlen_k;
}
using namespace ck::literals;
flop += nhead *
(3_uz * 2_uz * real_seqlen_q * real_seqlen_k * hdim_q + // Q@K/dS^T@Q^T/dS@K^T
2_uz * 2_uz * real_seqlen_q * real_seqlen_k * hdim_v); // dO@V/P^T@dO^T
flop += nhead * (static_cast<std::size_t>(3) * static_cast<std::size_t>(2) *
real_seqlen_q * real_seqlen_k * hdim_q + // Q@K/dS^T@Q^T/dS@K^T
static_cast<std::size_t>(2) * static_cast<std::size_t>(2) *
real_seqlen_q * real_seqlen_k * hdim_v); // dO@V/P^T@dO^T
num_byte += nhead * (sizeof(QDataType) * real_seqlen_q * hdim_q +
sizeof(KDataType) * real_seqlen_k * hdim_q +
@@ -243,85 +240,97 @@ bool run(const ArgParser& arg_parser)
}
auto get_lengths = [&](bool permute,
ck::index_t b /*batch*/,
ck::index_t h /*nhead*/,
ck::index_t s /*seqlen*/,
ck::index_t d /*hdim*/) {
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::index_t, 4>{b, h, s, d};
return std::array<ck_tile::index_t, 4>{b, h, s, d};
else
return std::array<ck::index_t, 4>{b, s, h, d};
return std::array<ck_tile::index_t, 4>{b, s, h, d};
};
// host memory for storing all the tensor elements
const ck::index_t shape_batch = (mode == mode_enum::batch ? batch : 1);
const ck::index_t shape_seqlen_q =
const ck_tile::index_t shape_batch = (mode == mode_enum::batch ? batch : 1);
const ck_tile::index_t shape_seqlen_q =
(mode == mode_enum::batch ? seqlen_q : seqstart_q_host.back());
const ck::index_t shape_seqlen_k =
const ck_tile::index_t shape_seqlen_k =
(mode == mode_enum::batch ? seqlen_k : seqstart_k_host.back());
Tensor<QDataType> q_host(get_lengths(i_perm, shape_batch, nhead, shape_seqlen_q, hdim_q));
Tensor<KDataType> k_host(get_lengths(i_perm, shape_batch, nhead_k, shape_seqlen_k, hdim_q));
Tensor<VDataType> v_host(get_lengths(i_perm, shape_batch, nhead_k, shape_seqlen_k, hdim_v));
// use bias shape = [1, 1, shape_seqlen_q, shape_seqlen_k]. if use_bias=false, the bias_host
ck_tile::HostTensor<QDataType> q_host(
get_lengths(i_perm, shape_batch, nhead, shape_seqlen_q, hdim_q));
ck_tile::HostTensor<KDataType> k_host(
get_lengths(i_perm, shape_batch, nhead_k, shape_seqlen_k, hdim_q));
ck_tile::HostTensor<VDataType> v_host(
get_lengths(i_perm, shape_batch, nhead_k, shape_seqlen_k, hdim_v));
// use bias shape = [1, 1, shape_seqlen_q, max_seqlen_k]. if use_bias=false, the bias_host
// will not be used for verification at all (but will be copied to device anyway).
Tensor<BiasDataType> bias_host(
use_bias ? get_lengths(i_perm, 1, 1, shape_seqlen_q, shape_seqlen_k)
: std::array<ck::index_t, 4>{1, 1, 1, 1} /* dummy shape for simplifying code */);
Tensor<ODataType> o_host(get_lengths(o_perm, shape_batch, nhead, shape_seqlen_q, hdim_v));
Tensor<LSEDataType> lse_host(std::array<ck::index_t, 3>{batch, nhead, max_seqlen_q});
Tensor<DDataType> d_host(std::array<ck::index_t, 3>{batch, nhead, max_seqlen_q});
Tensor<RandValOutputDataType> randval_host(
ck_tile::HostTensor<BiasDataType> bias_host(
use_bias
? get_lengths(i_perm, 1, 1, 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<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});
ck_tile::HostTensor<DDataType> d_host(
std::array<ck_tile::index_t, 3>{batch, nhead, max_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::index_t, 4>{1, 1, 1, 1});
Tensor<QGradDataType> dq_host(get_lengths(i_perm, shape_batch, nhead, shape_seqlen_q, hdim_q));
Tensor<KGradDataType> dk_host(get_lengths(i_perm, shape_batch, nhead, shape_seqlen_k, hdim_q));
Tensor<VGradDataType> dv_host(get_lengths(i_perm, shape_batch, nhead, shape_seqlen_k, hdim_v));
Tensor<OGradDataType> do_host(get_lengths(o_perm, shape_batch, nhead, shape_seqlen_q, hdim_v));
Tensor<BiasGradDataType> dbias_host(
use_dbias ? get_lengths(i_perm, shape_batch, nhead, shape_seqlen_q, shape_seqlen_k)
: std::array<ck::index_t, 4>{1, 1, 1, 1} /* dummy shape for simplifying code */);
: std::array<ck_tile::index_t, 4>{1, 1, 1, 1});
ck_tile::HostTensor<QGradDataType> dq_host(
get_lengths(i_perm, shape_batch, nhead, shape_seqlen_q, hdim_q));
ck_tile::HostTensor<KGradDataType> dk_host(
get_lengths(i_perm, shape_batch, nhead, shape_seqlen_k, hdim_q));
ck_tile::HostTensor<VGradDataType> dv_host(
get_lengths(i_perm, shape_batch, nhead, shape_seqlen_k, hdim_v));
ck_tile::HostTensor<OGradDataType> do_host(
get_lengths(o_perm, shape_batch, nhead, shape_seqlen_q, hdim_v));
ck_tile::HostTensor<BiasGradDataType> dbias_host(
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 */);
if(init_method == 0)
{
ck::utils::FillUniformDistributionIntegerValue<QDataType>{-2.f, 2.f, seed}(q_host);
ck::utils::FillUniformDistributionIntegerValue<KDataType>{-2.f, 2.f, seed}(k_host);
ck::utils::FillUniformDistributionIntegerValue<VDataType>{-2.f, 2.f, seed}(v_host);
ck::utils::FillUniformDistributionIntegerValue<BiasDataType>{-2.f, 2.f, seed}(bias_host);
ck::utils::FillUniformDistributionIntegerValue<OGradDataType>{-2.f, 2.f, seed}(do_host);
ck_tile::FillUniformDistributionIntegerValue<QDataType>{-2.f, 2.f, seed}(q_host);
ck_tile::FillUniformDistributionIntegerValue<KDataType>{-2.f, 2.f, seed}(k_host);
ck_tile::FillUniformDistributionIntegerValue<VDataType>{-2.f, 2.f, seed}(v_host);
ck_tile::FillUniformDistributionIntegerValue<BiasDataType>{-2.f, 2.f, seed}(bias_host);
ck_tile::FillUniformDistributionIntegerValue<OGradDataType>{-2.f, 2.f, seed}(do_host);
}
else if(init_method == 1)
{
ck::utils::FillUniformDistribution<QDataType>{0.f, 1.f, seed}(q_host);
ck::utils::FillUniformDistribution<KDataType>{0.f, 1.f, seed}(k_host);
ck::utils::FillUniformDistribution<VDataType>{0.f, 1.f, seed}(v_host);
ck::utils::FillUniformDistribution<BiasDataType>{0.f, 1.f, seed}(bias_host);
ck::utils::FillUniformDistribution<OGradDataType>{0.f, 1.f, seed}(do_host);
ck_tile::FillUniformDistribution<QDataType>{0.f, 1.f, seed}(q_host);
ck_tile::FillUniformDistribution<KDataType>{0.f, 1.f, seed}(k_host);
ck_tile::FillUniformDistribution<VDataType>{0.f, 1.f, seed}(v_host);
ck_tile::FillUniformDistribution<BiasDataType>{0.f, 1.f, seed}(bias_host);
ck_tile::FillUniformDistribution<OGradDataType>{0.f, 1.f, seed}(do_host);
}
else if(init_method == 2)
{
ck::utils::FillTrigValue<QDataType>{}(q_host);
ck::utils::FillTrigValue<KDataType>{}(k_host);
ck::utils::FillTrigValue<VDataType>{}(v_host);
ck::utils::FillTrigValue<BiasDataType>{}(bias_host);
ck::utils::FillTrigValue<OGradDataType>{}(do_host);
ck_tile::FillTrigValue<QDataType>{}(q_host);
ck_tile::FillTrigValue<KDataType>{}(k_host);
ck_tile::FillTrigValue<VDataType>{}(v_host);
ck_tile::FillTrigValue<BiasDataType>{}(bias_host);
ck_tile::FillTrigValue<OGradDataType>{}(do_host);
}
DeviceMem q_buf(q_host.GetElementSpaceSizeInBytes());
DeviceMem k_buf(k_host.GetElementSpaceSizeInBytes());
DeviceMem v_buf(v_host.GetElementSpaceSizeInBytes());
DeviceMem bias_buf(bias_host.GetElementSpaceSizeInBytes());
DeviceMem o_buf(o_host.GetElementSpaceSizeInBytes());
DeviceMem lse_buf(lse_host.GetElementSpaceSizeInBytes());
DeviceMem d_buf(d_host.GetElementSpaceSizeInBytes());
DeviceMem randval_buf(randval_host.GetElementSpaceSizeInBytes());
DeviceMem dq_buf(dq_host.GetElementSpaceSizeInBytes());
DeviceMem dk_buf(dk_host.GetElementSpaceSizeInBytes());
DeviceMem dv_buf(dv_host.GetElementSpaceSizeInBytes());
DeviceMem do_buf(do_host.GetElementSpaceSizeInBytes());
DeviceMem dbias_buf(dbias_host.GetElementSpaceSizeInBytes());
DeviceMem seqstart_q(seqstart_q_host.size() * sizeof(int32_t));
DeviceMem seqstart_k(seqstart_k_host.size() * sizeof(int32_t));
ck_tile::DeviceMem q_buf(q_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem k_buf(k_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem v_buf(v_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem bias_buf(bias_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem o_buf(o_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem lse_buf(lse_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem d_buf(d_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem randval_buf(randval_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem dq_buf(dq_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem dk_buf(dk_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem dv_buf(dv_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem do_buf(do_host.get_element_space_size_in_bytes());
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));
q_buf.ToDevice(q_host.data());
k_buf.ToDevice(k_host.data());
@@ -363,40 +372,39 @@ bool run(const ArgParser& arg_parser)
/// seqlen_k] in this example, hence both the 'batch_stride_bias' &
/// 'nhead_stride_bias' are 0.
// setup stride_* arguments
const ck::index_t stride_q = (i_perm ? hdim_q : nhead * hdim_q);
const ck::index_t stride_k = (i_perm ? hdim_q : nhead_k * hdim_q);
const ck::index_t stride_v = (i_perm ? hdim_v : nhead_k * hdim_v);
const ck::index_t stride_bias = (i_perm ? shape_seqlen_k : 1 * shape_seqlen_k);
const ck::index_t stride_o = (o_perm ? hdim_v : nhead * hdim_v);
const ck::index_t stride_randval = (max_seqlen_k);
const ck::index_t stride_do = (o_perm ? hdim_v : nhead * hdim_v);
const ck::index_t stride_dk = (i_perm ? hdim_q : nhead * hdim_q);
const ck::index_t stride_dv = (i_perm ? hdim_v : nhead * hdim_v);
const ck::index_t stride_dbias = (i_perm ? shape_seqlen_k : nhead * shape_seqlen_k);
const ck_tile::index_t stride_q = (i_perm ? hdim_q : nhead * hdim_q);
const ck_tile::index_t stride_k = (i_perm ? hdim_q : nhead_k * hdim_q);
const ck_tile::index_t stride_v = (i_perm ? hdim_v : nhead_k * hdim_v);
const ck_tile::index_t stride_bias = (max_seqlen_k);
const ck_tile::index_t stride_o = (o_perm ? hdim_v : nhead * hdim_v);
const ck_tile::index_t stride_randval = (max_seqlen_k);
const ck_tile::index_t stride_do = (o_perm ? hdim_v : nhead * hdim_v);
const ck_tile::index_t stride_dk = (i_perm ? hdim_q : nhead * hdim_q);
const ck_tile::index_t stride_dv = (i_perm ? hdim_v : nhead * hdim_v);
const ck_tile::index_t stride_dbias = (i_perm ? max_seqlen_k : nhead * max_seqlen_k);
// setup nhead_stride_* arguments
const ck::index_t nhead_stride_q = (i_perm ? shape_seqlen_q * hdim_q : hdim_q);
const ck::index_t nhead_stride_k = (i_perm ? shape_seqlen_k * hdim_q : hdim_q);
const ck::index_t nhead_stride_v = (i_perm ? shape_seqlen_k * hdim_v : hdim_v);
const ck::index_t nhead_stride_bias =
(i_perm ? 0 * shape_seqlen_q * shape_seqlen_k : 0 * shape_seqlen_k);
const ck::index_t nhead_stride_o = (o_perm ? shape_seqlen_q * hdim_v : hdim_v);
const ck::index_t nhead_stride_randval = (shape_seqlen_q * max_seqlen_k);
const ck::index_t nhead_stride_do = (o_perm ? shape_seqlen_q * hdim_v : hdim_v);
const ck::index_t nhead_stride_lsed = max_seqlen_q;
const ck::index_t nhead_stride_dbias =
(i_perm ? shape_seqlen_q * shape_seqlen_k : shape_seqlen_k);
const ck_tile::index_t nhead_stride_q = (i_perm ? shape_seqlen_q * hdim_q : hdim_q);
const ck_tile::index_t nhead_stride_k = (i_perm ? shape_seqlen_k * hdim_q : hdim_q);
const ck_tile::index_t nhead_stride_v = (i_perm ? shape_seqlen_k * hdim_v : hdim_v);
const ck_tile::index_t nhead_stride_bias = 0;
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_dbias =
(i_perm ? shape_seqlen_q * max_seqlen_k : max_seqlen_k);
// setup batch_stride_* arguments
const ck::index_t batch_stride_q = (nhead * shape_seqlen_q * hdim_q);
const ck::index_t batch_stride_k = (nhead_k * shape_seqlen_k * hdim_q);
const ck::index_t batch_stride_v = (nhead_k * shape_seqlen_k * hdim_v);
const ck::index_t batch_stride_bias = (0 * nhead * shape_seqlen_q * shape_seqlen_k);
const ck::index_t batch_stride_o = (nhead * shape_seqlen_q * hdim_v);
const ck::index_t batch_stride_randval = (nhead * shape_seqlen_q * max_seqlen_k);
const ck::index_t batch_stride_do = (nhead * shape_seqlen_q * hdim_v);
const ck::index_t batch_stride_lsed = (nhead * max_seqlen_q);
const ck::index_t batch_stride_dk = (nhead * shape_seqlen_k * hdim_q);
const ck::index_t batch_stride_dv = (nhead * shape_seqlen_k * hdim_v);
const ck::index_t batch_stride_dbias = (nhead * shape_seqlen_q * shape_seqlen_k);
const ck_tile::index_t batch_stride_q = (nhead * shape_seqlen_q * hdim_q);
const ck_tile::index_t batch_stride_k = (nhead_k * shape_seqlen_k * hdim_q);
const ck_tile::index_t batch_stride_v = (nhead_k * shape_seqlen_k * hdim_v);
const ck_tile::index_t batch_stride_bias = 0;
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_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);
return fmha_bwd_args{q_buf.GetDeviceBuffer(),
k_buf.GetDeviceBuffer(),
@@ -456,7 +464,7 @@ bool run(const ArgParser& arg_parser)
batch_stride_dbias,
mask.left,
mask.right,
static_cast<ck::index_t>(mask.type),
static_cast<ck_tile::index_t>(mask.type),
p_drop,
p_undrop,
s_randval,
@@ -486,42 +494,43 @@ bool run(const ArgParser& arg_parser)
bool pass = true;
std::vector<Tensor<QDataType>> q_host_refs;
std::vector<Tensor<KDataType>> k_host_refs;
std::vector<Tensor<VDataType>> v_host_refs;
std::vector<Tensor<ODataType>> o_host_refs;
std::vector<Tensor<RandValOutputDataType>> randval_host_refs;
std::vector<Tensor<AccDataType>> p_hp_host_refs;
std::vector<Tensor<GemmDataType>> p_lp_host_refs;
std::vector<ck_tile::HostTensor<QDataType>> q_host_refs;
std::vector<ck_tile::HostTensor<KDataType>> k_host_refs;
std::vector<ck_tile::HostTensor<VDataType>> v_host_refs;
std::vector<ck_tile::HostTensor<ODataType>> o_host_refs;
std::vector<ck_tile::HostTensor<RandValOutputDataType>> randval_host_refs;
std::vector<ck_tile::HostTensor<AccDataType>> p_hp_host_refs;
std::vector<ck_tile::HostTensor<GemmDataType>> p_lp_host_refs;
randval_buf.FromDevice(randval_host.data());
for(ck::index_t wb = 0; wb < batch; ++wb)
for(ck_tile::index_t wb = 0; wb < batch; ++wb)
{
const ck::index_t real_seqlen_q = seqstart_q_host[wb + 1] - seqstart_q_host[wb];
const ck::index_t real_seqlen_k = seqstart_k_host[wb + 1] - seqstart_k_host[wb];
const ck_tile::index_t real_seqlen_q = seqstart_q_host[wb + 1] - seqstart_q_host[wb];
const ck_tile::index_t real_seqlen_k = seqstart_k_host[wb + 1] - seqstart_k_host[wb];
// adjust matrix index according to the mode
const ck::index_t b = (mode == mode_enum::batch ? wb : 0);
const ck::index_t query_offset = (mode == mode_enum::batch ? 0 : seqstart_q_host[wb]);
const ck::index_t key_offset = (mode == mode_enum::batch ? 0 : seqstart_k_host[wb]);
const ck_tile::index_t b = (mode == mode_enum::batch ? wb : 0);
const ck_tile::index_t query_offset = (mode == mode_enum::batch ? 0 : seqstart_q_host[wb]);
const ck_tile::index_t key_offset = (mode == mode_enum::batch ? 0 : seqstart_k_host[wb]);
Tensor<QDataType> q_host_ref({nhead, real_seqlen_q, hdim_q}); // q_g_m_k
Tensor<KDataType> k_host_ref({nhead, real_seqlen_k, hdim_q}); // k_g_n_k
Tensor<VDataType> v_host_ref({nhead, hdim_v, real_seqlen_k}); // v_g_o_n
Tensor<ODataType> o_host_ref({nhead, real_seqlen_q, hdim_v}); // o_g_m_o
Tensor<LSEDataType> lse_host_ref({nhead, real_seqlen_q}); // lse_g_m
Tensor<RandValOutputDataType> randval_host_ref(
{nhead, real_seqlen_q, real_seqlen_k}); // randval_g_m_n
Tensor<AccDataType> s_host_ref({nhead, real_seqlen_q, real_seqlen_k}); // s_g_m_n
Tensor<AccDataType> p_hp_host_ref(
ck_tile::HostTensor<QDataType> q_host_ref({nhead, real_seqlen_q, hdim_q}); // q_g_m_k
ck_tile::HostTensor<KDataType> k_host_ref({nhead, real_seqlen_k, hdim_q}); // k_g_n_k
ck_tile::HostTensor<VDataType> v_host_ref({nhead, hdim_v, real_seqlen_k}); // v_g_o_n
ck_tile::HostTensor<ODataType> o_host_ref({nhead, real_seqlen_q, hdim_v}); // o_g_m_o
ck_tile::HostTensor<LSEDataType> lse_host_ref({nhead, real_seqlen_q}); // lse_g_m
ck_tile::HostTensor<RandValOutputDataType> randval_host_ref(
{nhead, real_seqlen_q, real_seqlen_k}); // randval_g_m_n
ck_tile::HostTensor<AccDataType> s_host_ref(
{nhead, real_seqlen_q, real_seqlen_k}); // s_g_m_n
ck_tile::HostTensor<AccDataType> p_hp_host_ref(
{nhead, real_seqlen_q, real_seqlen_k}); // p_hp_g_m_n high precision
Tensor<AccDataType> p_dropped_hp_host_ref(
ck_tile::HostTensor<AccDataType> p_dropped_hp_host_ref(
{nhead, real_seqlen_q, real_seqlen_k}); // p_dropped_hp_g_m_n high precision
Tensor<GemmDataType> p_lp_host_ref(
ck_tile::HostTensor<GemmDataType> p_lp_host_ref(
{nhead, real_seqlen_q, real_seqlen_k}); // p_lp_g_m_n low precision
ck::index_t nr = nhead / nhead_k;
ck_tile::index_t nr = nhead / nhead_k;
// clang-format off
// permute
@@ -539,62 +548,68 @@ bool run(const ArgParser& arg_parser)
// reference
// S = scale * Q * K^T
reference_batched_gemm<QDataType, KDataType, AccDataType, AccDataType>(
q_host_ref, k_host_ref, s_host_ref, ck::identity{}, ck::identity{}, [&](AccDataType x) {
return scale * x;
}); // s_g_m_n = scale * q_g_m_k@k_g_n_k
ck_tile::reference_batched_gemm<QDataType, KDataType, AccDataType, AccDataType>(
q_host_ref,
k_host_ref,
s_host_ref,
ck_tile::identity{},
ck_tile::identity{},
ck_tile::scales(scale)); // s_g_m_n = scale * q_g_m_k@k_g_n_k
if(use_bias)
{
// clang-format off
Tensor<BiasDataType> bias_host_ref({1, real_seqlen_q, real_seqlen_k});
ck_tile::HostTensor<BiasDataType> bias_host_ref({1, real_seqlen_q, real_seqlen_k});
if(i_perm)
bias_host_ref.ForEach([&](auto& self, auto i) { self(i) = bias_host(0, 0, i[1] + query_offset, i[2] + key_offset); });
bias_host_ref.ForEach([&](auto& self, auto i) { self(i) = bias_host(0, 0, i[1] + query_offset, i[2]); });
else
bias_host_ref.ForEach([&](auto& self, auto i) { self(i) = bias_host(0, i[1] + query_offset, 0, i[2] + key_offset); });
bias_host_ref.ForEach([&](auto& self, auto i) { self(i) = bias_host(0, i[1] + query_offset, 0, i[2]); });
// clang-format on
// broadcast from [1, real_seqlen_q, real_seqlen_k] to [nhead, real_seqlen_q,
// real_seqlen_k]
reference_batched_elementwise<AccDataType, BiasDataType, AccDataType, AccDataType>(
s_host_ref, bias_host_ref, s_host_ref);
ck_tile::
reference_batched_elementwise<AccDataType, BiasDataType, AccDataType, AccDataType>(
s_host_ref, bias_host_ref, s_host_ref);
}
if(mask.type == mask_enum::no_mask)
{
reference_batched_masking<AccDataType>(s_host_ref,
FmhaMasks::NoMask{real_seqlen_q, real_seqlen_k});
ck_tile::reference_batched_masking<AccDataType>(
s_host_ref, FmhaMasks::NoMask{real_seqlen_q, real_seqlen_k});
}
else if(mask.type == mask_enum::window_generic)
{
reference_batched_masking<AccDataType>(
s_host_ref, FmhaMasks::GenericMask{mask.y, mask.x, real_seqlen_q, real_seqlen_k});
ck_tile::reference_batched_masking<AccDataType>(
s_host_ref,
ck_tile::make_generic_attention_mask_from_lr_window<FmhaMasks::GenericMask>(
mask.left, mask.right, real_seqlen_q, real_seqlen_k));
}
else
{
// if left window size is negative, means causal
// else means generic (for current batch)
if(mask.left < 0)
reference_batched_masking<AccDataType>(
ck_tile::reference_batched_masking<AccDataType>(
s_host_ref,
ck::make_generic_attention_mask_from_lr_window<FmhaMasks::CausalMask>(
ck_tile::make_generic_attention_mask_from_lr_window<FmhaMasks::CausalMask>(
mask.left,
mask.right,
real_seqlen_q,
real_seqlen_k,
mask.type == mask_enum::mask_top_left));
else
reference_batched_masking<AccDataType>(
ck_tile::reference_batched_masking<AccDataType>(
s_host_ref,
ck::make_generic_attention_mask_from_lr_window<FmhaMasks::GenericMask>(
ck_tile::make_generic_attention_mask_from_lr_window<FmhaMasks::GenericMask>(
mask.left,
mask.right,
real_seqlen_q,
real_seqlen_k,
mask.type == mask_enum::mask_top_left));
}
reference_batched_softmax<AccDataType, LSEDataType, AccDataType>(
s_host_ref, p_hp_host_ref, lse_host_ref);
ck_tile::reference_batched_softmax<AccDataType, LSEDataType, AccDataType>(
s_host_ref, p_hp_host_ref, ck_tile::identity{}, lse_host_ref);
if(p_drop > 0)
{
@@ -603,21 +618,21 @@ bool run(const ArgParser& arg_parser)
randval_host_ref.ForEach([&](auto& self, auto idx) {
self(idx) = randval_host(b, idx[0], idx[1] + query_offset, idx[2]);
});
reference_batched_dropout(
ck_tile::reference_batched_dropout(
p_dropped_hp_host_ref, randval_host_ref, p_undrop_in_uint8_t, rp_undrop);
p_dropped_hp_host_ref.ForEach([&](auto& self, auto idx) {
p_lp_host_ref(idx) = ck::type_convert<GemmDataType>(self(idx));
p_lp_host_ref(idx) = ck_tile::type_convert<GemmDataType>(self(idx));
});
}
else
{
p_hp_host_ref.ForEach([&](auto& self, auto idx) {
p_lp_host_ref(idx) = ck::type_convert<GemmDataType>(self(idx));
p_lp_host_ref(idx) = ck_tile::type_convert<GemmDataType>(self(idx));
});
}
// O = P * V
reference_batched_gemm<GemmDataType, VDataType, AccDataType, ODataType>(
ck_tile::reference_batched_gemm<GemmDataType, VDataType, AccDataType, ODataType>(
p_lp_host_ref, v_host_ref, o_host_ref); // o_g_m_o = p_lp_g_m_n@v_g_o_n
// clang-format off
@@ -652,28 +667,28 @@ bool run(const ArgParser& arg_parser)
dv_buf.FromDevice(dv_host.data());
dbias_buf.FromDevice(dbias_host.data());
for(ck::index_t wb = 0; wb < batch; ++wb)
for(ck_tile::index_t wb = 0; wb < batch; ++wb)
{
const ck::index_t real_seqlen_q = seqstart_q_host[wb + 1] - seqstart_q_host[wb];
const ck::index_t real_seqlen_k = seqstart_k_host[wb + 1] - seqstart_k_host[wb];
const ck_tile::index_t real_seqlen_q = seqstart_q_host[wb + 1] - seqstart_q_host[wb];
const ck_tile::index_t real_seqlen_k = seqstart_k_host[wb + 1] - seqstart_k_host[wb];
// adjust matrix index according to the mode
const ck::index_t b = (mode == mode_enum::batch ? wb : 0);
const ck::index_t query_offset = (mode == mode_enum::batch ? 0 : seqstart_q_host[wb]);
const ck::index_t key_offset = (mode == mode_enum::batch ? 0 : seqstart_k_host[wb]);
const ck_tile::index_t b = (mode == mode_enum::batch ? wb : 0);
const ck_tile::index_t query_offset = (mode == mode_enum::batch ? 0 : seqstart_q_host[wb]);
const ck_tile::index_t key_offset = (mode == mode_enum::batch ? 0 : seqstart_k_host[wb]);
Tensor<OGradDataType> do_host_ref({nhead, real_seqlen_q, hdim_v}); // do_g_m_o
Tensor<AccDataType> ds_hp_host_ref(
ck_tile::HostTensor<OGradDataType> do_host_ref({nhead, real_seqlen_q, hdim_v}); // do_g_m_o
ck_tile::HostTensor<AccDataType> ds_hp_host_ref(
{nhead, real_seqlen_q, real_seqlen_k}); // ds_g_m_n high precision
Tensor<GemmDataType> ds_lp_host_ref(
ck_tile::HostTensor<GemmDataType> ds_lp_host_ref(
{nhead, real_seqlen_q, real_seqlen_k}); // ds_g_m_n low precision
Tensor<AccDataType> dp_hp_host_ref(
ck_tile::HostTensor<AccDataType> dp_hp_host_ref(
{nhead, real_seqlen_q, real_seqlen_k}); // dp_g_m_n high precision
Tensor<BiasGradDataType> dbias_host_ref(
{nhead, real_seqlen_q, real_seqlen_k}); // dbias_g_m_n
Tensor<QGradDataType> dq_host_ref({nhead, real_seqlen_q, hdim_q}); // dq_g_m_k
Tensor<KGradDataType> dk_host_ref({nhead, real_seqlen_k, hdim_q}); // dk_g_n_k
Tensor<VGradDataType> dv_host_ref({nhead, real_seqlen_k, hdim_v}); // dv_g_n_o
ck_tile::HostTensor<BiasGradDataType> dbias_host_ref(
{nhead, real_seqlen_q, real_seqlen_k}); // dbias_g_m_n
ck_tile::HostTensor<QGradDataType> dq_host_ref({nhead, real_seqlen_q, hdim_q}); // dq_g_m_k
ck_tile::HostTensor<KGradDataType> dk_host_ref({nhead, real_seqlen_k, hdim_q}); // dk_g_n_k
ck_tile::HostTensor<VGradDataType> dv_host_ref({nhead, real_seqlen_k, hdim_v}); // dv_g_n_o
// clang-format off
if(o_perm) do_host_ref.ForEach([&](auto& self, auto i) { self(i) = do_host(b, i[0], i[1] + query_offset, i[2]); });
@@ -683,12 +698,12 @@ bool run(const ArgParser& arg_parser)
// dP = dO@V x Z w/ dropout
// dP = dO@V w/o dropout
auto v_t_host_ref = v_host_refs[wb].Transpose({0, 2, 1}); // v_g_o_n -> v_g_n_o
reference_batched_gemm<OGradDataType, VDataType, AccDataType, AccDataType>(
ck_tile::reference_batched_gemm<OGradDataType, VDataType, AccDataType, AccDataType>(
do_host_ref, v_t_host_ref, dp_hp_host_ref); // dp_g_m_n = do_g_m_o@v_g_n_o
if(p_drop > 0)
{
reference_batched_dropout(
ck_tile::reference_batched_dropout(
dp_hp_host_ref, randval_host_refs[wb], p_undrop_in_uint8_t, rp_undrop);
}
@@ -699,56 +714,59 @@ bool run(const ArgParser& arg_parser)
{
auto idx_gmo = idx_gmn;
idx_gmo[2] = o;
do_dot_o += ck::type_convert<AccDataType>(do_host_ref(idx_gmo)) *
ck::type_convert<AccDataType>(o_host_refs[wb](idx_gmo));
do_dot_o += ck_tile::type_convert<AccDataType>(do_host_ref(idx_gmo)) *
ck_tile::type_convert<AccDataType>(o_host_refs[wb](idx_gmo));
}
self(idx_gmn) = ck::type_convert<AccDataType>(p_hp_host_refs[wb](idx_gmn) *
(dp_hp_host_ref(idx_gmn) - do_dot_o));
self(idx_gmn) = ck_tile::type_convert<AccDataType>(
p_hp_host_refs[wb](idx_gmn) * (dp_hp_host_ref(idx_gmn) - do_dot_o));
});
if(use_dbias)
{
ds_hp_host_ref.ForEach([&](auto& self, auto idx) {
dbias_host_ref(idx) = ck::type_convert<BiasGradDataType>(self(idx));
dbias_host_ref(idx) = ck_tile::type_convert<BiasGradDataType>(self(idx));
});
}
ds_hp_host_ref.ForEach([&](auto& self, auto idx) {
ds_lp_host_ref(idx) = ck::type_convert<GemmDataType>(self(idx));
ds_lp_host_ref(idx) = ck_tile::type_convert<GemmDataType>(self(idx));
});
// dV = P_drop^T@dO^T
// dV = P^T@dO^T w/o dropout
auto p_t_lp_host_ref = p_lp_host_refs[wb].Transpose({0, 2, 1}); // p_lp_g_m_n -> p_lp_g_n_m
auto do_t_host_ref = do_host_ref.Transpose({0, 2, 1}); // do_g_m_o -> do_g_o_m
reference_batched_gemm<GemmDataType, OGradDataType, AccDataType, VGradDataType>(
ck_tile::reference_batched_gemm<GemmDataType, OGradDataType, AccDataType, VGradDataType>(
p_t_lp_host_ref, do_t_host_ref, dv_host_ref); // dv_g_n_o = p_lp_g_n_m@do_g_o_m
// dQ = scale * dS@K^T
auto k_t_host_ref = k_host_refs[wb].Transpose({0, 2, 1}); // k_g_n_k -> k_g_k_n
reference_batched_gemm<GemmDataType, KDataType, AccDataType, QGradDataType>(
ck_tile::reference_batched_gemm<GemmDataType, KDataType, AccDataType, QGradDataType>(
ds_lp_host_ref,
k_t_host_ref,
dq_host_ref,
ck::identity{},
ck::identity{},
[&scale](const AccDataType& x) { return scale * x; }); // dq_g_m_k = ds_g_m_n@k_g_k_n
ck_tile::identity{},
ck_tile::identity{},
ck_tile::scales(scale)); // dq_g_m_k = ds_g_m_n@k_g_k_n
// dK = scale * dS^T@Q^T
auto ds_t_lp_host_ref = ds_lp_host_ref.Transpose({0, 2, 1}); // ds_g_m_n -> ds_g_n_m
auto q_t_host_ref = q_host_refs[wb].Transpose({0, 2, 1}); // q_g_m_k -> q_g_k_m
reference_batched_gemm<GemmDataType, QDataType, AccDataType, KGradDataType>(
ck_tile::reference_batched_gemm<GemmDataType, QDataType, AccDataType, KGradDataType>(
ds_t_lp_host_ref,
q_t_host_ref,
dk_host_ref,
ck::identity{},
ck::identity{},
[&scale](const AccDataType& x) { return scale * x; }); // dk_g_n_k = ds_g_n_m@q_g_k_m
ck_tile::identity{},
ck_tile::identity{},
ck_tile::scales(scale)); // dk_g_n_k = ds_g_n_m@q_g_k_m
Tensor<QGradDataType> dq_host_result({nhead, real_seqlen_q, hdim_q}); // dq_g_m_k
Tensor<KGradDataType> dk_host_result({nhead, real_seqlen_k, hdim_q}); // dk_g_n_k
Tensor<VGradDataType> dv_host_result({nhead, real_seqlen_k, hdim_v}); // dv_g_n_o
Tensor<BiasGradDataType> dbias_host_result(
ck_tile::HostTensor<QGradDataType> dq_host_result(
{nhead, real_seqlen_q, hdim_q}); // dq_g_m_k
ck_tile::HostTensor<KGradDataType> dk_host_result(
{nhead, real_seqlen_k, hdim_q}); // dk_g_n_k
ck_tile::HostTensor<VGradDataType> dv_host_result(
{nhead, real_seqlen_k, hdim_v}); // dv_g_n_o
ck_tile::HostTensor<BiasGradDataType> dbias_host_result(
{nhead, real_seqlen_q, real_seqlen_k}); // dbias_g_m_n
// clang-format off
@@ -764,36 +782,36 @@ bool run(const ArgParser& arg_parser)
if(use_dbias)
{
if(i_perm) dbias_host_result.ForEach([&](auto& self, auto idx) {self(idx) = dbias_host(b, idx[0], idx[1] + query_offset, idx[2] + key_offset); });
else dbias_host_result.ForEach([&](auto& self, auto idx) {self(idx) = dbias_host(b, idx[1] + query_offset, idx[0], idx[2] + key_offset); });
if(i_perm) dbias_host_result.ForEach([&](auto& self, auto idx) {self(idx) = dbias_host(b, idx[0], idx[1] + query_offset, idx[2]); });
else dbias_host_result.ForEach([&](auto& self, auto idx) {self(idx) = dbias_host(b, idx[1] + query_offset, idx[0], idx[2]); });
}
// clang-format on
auto [rtol, atol] = get_elimit<DataType>(init_method);
bool dq_cur_pass = ck::utils::check_err(dq_host_result,
dq_host_ref,
std::string("Error: QGrad Incorrect results!"),
rtol,
atol);
bool dk_cur_pass = ck::utils::check_err(dk_host_result,
dk_host_ref,
std::string("Error: KGrad Incorrect results!"),
rtol,
atol);
bool dv_cur_pass = ck::utils::check_err(dv_host_result,
dv_host_ref,
std::string("Error: VGrad Incorrect results!"),
rtol,
atol);
bool dq_cur_pass = ck_tile::check_err(dq_host_result,
dq_host_ref,
std::string("Error: QGrad Incorrect results!"),
rtol,
atol);
bool dk_cur_pass = ck_tile::check_err(dk_host_result,
dk_host_ref,
std::string("Error: KGrad Incorrect results!"),
rtol,
atol);
bool dv_cur_pass = ck_tile::check_err(dv_host_result,
dv_host_ref,
std::string("Error: VGrad Incorrect results!"),
rtol,
atol);
bool dbias_cur_pass = true;
if(use_dbias)
{
dbias_cur_pass = ck::utils::check_err(dbias_host_result,
dbias_host_ref,
std::string("Error: BiasGrad Incorrect results!"),
rtol,
atol);
dbias_cur_pass = ck_tile::check_err(dbias_host_result,
dbias_host_ref,
std::string("Error: BiasGrad Incorrect results!"),
rtol,
atol);
}
pass &= (dq_cur_pass & dk_cur_pass & dv_cur_pass & dbias_cur_pass);
if(!(dq_cur_pass & dk_cur_pass & dv_cur_pass & dbias_cur_pass))
@@ -822,11 +840,11 @@ int main(int argc, char* argv[])
const std::string data_type = arg_parser.get_str("prec");
if(data_type == "fp16")
{
return run<ck::half_t>(arg_parser) ? 0 : -2;
return run<ck_tile::half_t>(arg_parser) ? 0 : -2;
}
else if(data_type == "bf16")
{
return run<ck::bhalf_t>(arg_parser) ? 0 : -2;
return run<ck_tile::bf16_t>(arg_parser) ? 0 : -2;
}
return -3;

346
example/ck_tile/01_fmha/fmha_bwd.hpp Normal file
View File

@@ -0,0 +1,346 @@
// 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/fmha.hpp"
#include "ck_tile/ops/epilogue.hpp"
#include "mask.hpp"
#include <type_traits>
template <typename DataType>
struct FmhaBwdTypeConfig;
template <>
struct FmhaBwdTypeConfig<ck_tile::half_t>
{
using QDataType = ck_tile::half_t;
using KDataType = ck_tile::half_t;
using VDataType = ck_tile::half_t;
using GemmDataType = ck_tile::half_t;
using BiasDataType = ck_tile::half_t;
using LSEDataType = float;
using AccDataType = float; // data type for gemm accumulation
using DDataType = float;
using RandValOutputDataType = uint8_t;
using ODataType = ck_tile::half_t;
using OGradDataType = ck_tile::half_t;
using QGradDataType = ck_tile::half_t;
using KGradDataType = ck_tile::half_t;
using VGradDataType = ck_tile::half_t;
using BiasGradDataType = ck_tile::half_t;
};
template <>
struct FmhaBwdTypeConfig<ck_tile::bf16_t>
{
using QDataType = ck_tile::bf16_t;
using KDataType = ck_tile::bf16_t;
using VDataType = ck_tile::bf16_t;
using GemmDataType = ck_tile::bf16_t;
using BiasDataType = ck_tile::bf16_t;
using LSEDataType = float;
using AccDataType = float; // data type for gemm accumulation
using DDataType = float;
using RandValOutputDataType = uint8_t;
using ODataType = ck_tile::bf16_t;
using OGradDataType = ck_tile::bf16_t;
using QGradDataType = ck_tile::bf16_t;
using KGradDataType = ck_tile::bf16_t;
using VGradDataType = ck_tile::bf16_t;
using BiasGradDataType = ck_tile::bf16_t;
};
struct FmhaMasks
{
using NoMask = ck_tile::GenericAttentionMask<false>;
using GenericMask = ck_tile::GenericAttentionMask<true, true>;
using CausalMask = ck_tile::GenericAttentionMask<true, false>;
};
// runtime args, some will passed to karg, some will used to compute grids/blocks
struct fmha_bwd_args
{
const void* q_ptr;
const void* k_ptr;
const void* v_ptr;
const void* bias_ptr;
const void* o_ptr;
const void* lse_ptr;
const void* do_ptr;
void* d_ptr;
void* rand_val_ptr;
void* dq_ptr;
void* dk_ptr;
void* dv_ptr;
void* dbias_ptr;
const void* seqstart_q_ptr;
const void* seqstart_k_ptr;
const void* seqlen_k_ptr;
ck_tile::index_t seqlen_q;
ck_tile::index_t seqlen_k;
ck_tile::index_t batch;
ck_tile::index_t max_seqlen_q;
ck_tile::index_t max_seqlen_k;
ck_tile::index_t hdim_q;
ck_tile::index_t hdim_v;
ck_tile::index_t nhead_q;
ck_tile::index_t nhead_k;
float scale;
ck_tile::index_t stride_q;
ck_tile::index_t stride_k;
ck_tile::index_t stride_v;
ck_tile::index_t stride_bias;
ck_tile::index_t stride_o;
ck_tile::index_t stride_randval;
ck_tile::index_t stride_do;
ck_tile::index_t stride_dk;
ck_tile::index_t stride_dv;
ck_tile::index_t stride_dbias;
ck_tile::index_t nhead_stride_q;
ck_tile::index_t nhead_stride_k;
ck_tile::index_t nhead_stride_v;
ck_tile::index_t nhead_stride_bias;
ck_tile::index_t nhead_stride_o;
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_dbias;
ck_tile::index_t batch_stride_q;
ck_tile::index_t batch_stride_k;
ck_tile::index_t batch_stride_v;
ck_tile::index_t batch_stride_bias;
ck_tile::index_t batch_stride_o;
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_dk;
ck_tile::index_t batch_stride_dv;
ck_tile::index_t batch_stride_dbias;
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;
};
template <typename FmhaBwdDQDKDVKernel>
auto fmha_bwd_dq_dk_dv_create_kargs_and_grids(fmha_bwd_args args)
{
assert(args.nhead_q % args.nhead_k == 0);
auto kargs = [&] {
// create group mode kernel arguments
if constexpr(FmhaBwdDQDKDVKernel::kIsGroupMode)
{
return FmhaBwdDQDKDVKernel::MakeKargs(args.q_ptr,
args.k_ptr,
args.v_ptr,
args.bias_ptr,
args.lse_ptr,
args.do_ptr,
args.d_ptr,
args.rand_val_ptr,
args.dq_ptr,
args.dk_ptr,
args.dv_ptr,
args.dbias_ptr,
args.seqstart_q_ptr,
args.seqstart_k_ptr,
args.seqlen_k_ptr,
args.hdim_q,
args.hdim_v,
args.nhead_q,
args.nhead_q / args.nhead_k,
args.scale,
args.stride_q,
args.stride_k,
args.stride_v,
args.stride_bias,
args.stride_randval,
args.stride_do,
args.stride_dk,
args.stride_dv,
args.stride_dbias,
args.nhead_stride_q,
args.nhead_stride_k,
args.nhead_stride_v,
args.nhead_stride_bias,
args.nhead_stride_randval,
args.nhead_stride_do,
args.nhead_stride_lsed,
args.nhead_stride_dbias,
args.batch_stride_lsed,
args.window_size_left,
args.window_size_right,
args.mask_type,
args.p_drop,
args.s_randval,
args.drop_seed_offset);
}
else
{ // create batch mode kernel arguments
return FmhaBwdDQDKDVKernel::MakeKargs(args.q_ptr,
args.k_ptr,
args.v_ptr,
args.bias_ptr,
args.lse_ptr,
args.do_ptr,
args.d_ptr,
args.rand_val_ptr,
args.dq_ptr,
args.dk_ptr,
args.dv_ptr,
args.dbias_ptr,
args.seqlen_q,
args.seqlen_k,
args.hdim_q,
args.hdim_v,
args.nhead_q,
args.nhead_q / args.nhead_k,
args.scale,
args.stride_q,
args.stride_k,
args.stride_v,
args.stride_bias,
args.stride_randval,
args.stride_do,
args.stride_dk,
args.stride_dv,
args.stride_dbias,
args.nhead_stride_q,
args.nhead_stride_k,
args.nhead_stride_v,
args.nhead_stride_bias,
args.nhead_stride_randval,
args.nhead_stride_do,
args.nhead_stride_lsed,
args.nhead_stride_dbias,
args.batch_stride_q,
args.batch_stride_k,
args.batch_stride_v,
args.batch_stride_bias,
args.batch_stride_randval,
args.batch_stride_do,
args.batch_stride_lsed,
args.batch_stride_dk,
args.batch_stride_dv,
args.batch_stride_dbias,
args.window_size_left,
args.window_size_right,
args.mask_type,
args.p_drop,
args.s_randval,
args.drop_seed_offset);
}
}();
dim3 grids = FmhaBwdDQDKDVKernel::GridSize(args.batch, args.nhead_q, args.max_seqlen_k);
return ck_tile::make_tuple(kargs, grids);
}
template <typename FmhaBwdOGradDotOKernel>
auto fmha_bwd_dot_do_o_create_kargs_and_grids(fmha_bwd_args args)
{
auto kargs = [&] {
// create group mode kernel arguments
if constexpr(FmhaBwdOGradDotOKernel::kIsGroupMode)
{
return FmhaBwdOGradDotOKernel::MakeKargs(args.o_ptr,
args.do_ptr,
args.d_ptr,
args.p_undrop,
args.seqstart_q_ptr,
args.hdim_v,
args.stride_do,
args.stride_o,
args.nhead_stride_do,
args.nhead_stride_o,
args.nhead_stride_lsed,
args.batch_stride_lsed);
}
else
{ // create batch mode kernel arguments
return FmhaBwdOGradDotOKernel::MakeKargs(args.o_ptr,
args.do_ptr,
args.d_ptr,
args.p_undrop,
args.seqlen_q,
args.hdim_v,
args.stride_do,
args.stride_o,
args.nhead_stride_do,
args.nhead_stride_o,
args.nhead_stride_lsed,
args.batch_stride_do,
args.batch_stride_o,
args.batch_stride_lsed);
}
}();
dim3 grids = FmhaBwdOGradDotOKernel::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_,
bool kHasBias_,
bool kHasBiasGrad_,
bool kHasDropout_,
bool kPadS_,
bool kPadSK_,
bool kPadD_,
bool kPadDv_>
struct fmha_bwd_dq_dk_dv_traits_
{
static constexpr ck_tile::index_t HDim = HDim_;
using DataType = ck_tile::remove_cvref_t<DataType_>;
static constexpr bool kIsGroupMode = kIsGroupMode_;
static constexpr auto FmhaBwdPipelineEnum = FmhaBwdPipelineEnum_;
using FmhaMask = ck_tile::remove_cvref_t<FmhaMask_>;
static constexpr bool kHasBias = kHasBias_;
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_;
};
template <typename Traits_>
float fmha_bwd_dq_dk_dv_(const ck_tile::stream_config&, fmha_bwd_args);
template <ck_tile::index_t HDim_, typename DataType_, bool kIsGroupMode_, bool kPadS_, bool kPadDv_>
struct fmha_bwd_dot_do_o_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 kPadDv = kPadDv_;
};
template <typename Traits_>
float fmha_bwd_dot_do_o_(const ck_tile::stream_config&, fmha_bwd_args);
// This is the public API, will be generated by script
struct fmha_bwd_traits
{
int hdim_q;
int hdim_v;
std::string data_type;
bool is_group_mode;
mask_enum mask_type;
bool has_bias;
bool has_dbias;
bool has_dropout;
// TODO: padding check is inside this api
};
float fmha_bwd(fmha_bwd_traits, fmha_bwd_args, const ck_tile::stream_config&);

568
example/ck_tile/01_fmha/generate.py
View File

@@ -65,7 +65,6 @@ BOOL_MAP = {
"f" : "false"
}
DIRECTIONS = ["fwd"]
GEN_DIR = "" # in Cmake, have to generate files in same folder
FMHA_FWD_KERNEL_HEADER = """// SPDX-License-Identifier: MIT
@@ -469,7 +468,7 @@ def get_fmha_fwd_tile_dict_from_dtype(direction : str, dtype : str) -> Optional[
else:
return None
def get_blobs(kernel_filter : Optional[str], receipt, mask_impl) -> Tuple[FmhaFwdApiPool, List[FmhaFwdKernel]]:
def get_fwd_blobs(kernel_filter : Optional[str], receipt, mask_impl) -> Tuple[FmhaFwdApiPool, List[FmhaFwdKernel]]:
# TODO: we don't support tuning yet, so pick up one value for vlayout/pipeline/pad
# support this in future
def get_pipelines(dtype, hdim) -> List[FmhaFwdPipeline]:
@@ -507,7 +506,7 @@ def get_blobs(kernel_filter : Optional[str], receipt, mask_impl) -> Tuple[FmhaFw
gen = list()
api_pool = FmhaFwdApiPool(mask_impl)
for direction, dtype in itertools.product(DIRECTIONS, DTYPE_MAP.keys()):
for direction, dtype in itertools.product(["fwd"], DTYPE_MAP.keys()):
d = get_fmha_fwd_tile_dict_from_dtype(direction, dtype)
if d == None:
continue
@@ -536,39 +535,574 @@ def get_blobs(kernel_filter : Optional[str], receipt, mask_impl) -> Tuple[FmhaFw
return (api_pool, gen)
def write_single_kernel(kernel: FmhaFwdKernel, autogen_dir: Path) -> None:
BWD_DQDKDV_PIPELINE_MAP = {
"ks_kts_vr" : "ck_tile::BlockFmhaBwdDQDKDVPipelineKSKTSVR",
"qs_ks_vr_dos" : "ck_tile::BlockFmhaBwdDQDKDVPipelineQSKSVROGradS",
"ks_vr" : "ck_tile::BlockFmhaBwdDQDKDVPipelineKSVR",
}
BWD_DQDKDV_PIPELINE_ENUM_MAP = {
"ks_kts_vr" : "ck_tile::BlockFmhaBwdPipelineEnum::KSKTSVR",
"qs_ks_vr_dos" : "ck_tile::BlockFmhaBwdPipelineEnum::QSKSVROGradS",
"ks_vr" : "ck_tile::BlockFmhaBwdPipelineEnum::KSVR",
}
FMHA_BWD_KERNEL_HEADER = """// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.\n
// auto generated by generate.py
#include "fmha_bwd.hpp"
"""
FMHA_BWD_DQ_DK_DV_KERNEL_BODY="""
using fmha_dtype_{F_idx} = {F_dtype};
using fmha_block_tile_{F_idx} = ck_tile::sequence<{F_bm0}, {F_bn0}, {F_bk0}, {F_bk1}, {F_bk2}, {F_bk3}, {F_bk4}, {F_bhdq}, {F_bhdv}>;
using fmha_block_warps0_{F_idx} = ck_tile::sequence<{F_rm0}, {F_rn0}, {F_rk0}>;
using fmha_block_warps1_{F_idx} = ck_tile::sequence<{F_rm1}, {F_rn1}, {F_rk1}>;
using fmha_block_warps2_{F_idx} = ck_tile::sequence<{F_rm2}, {F_rn2}, {F_rk2}>;
using fmha_warp_tile_{F_idx} = ck_tile::sequence<{F_wm}, {F_wn}, {F_wk}>;
// TODO: simplify Gemm0~4BlockWarps in TileFmhaBwdShape
// G0&G2 -> GSdP
// G1&G3 -> GdKV
// G4 -> GdQ
using fmha_bwd_shape_{F_idx} = ck_tile::TileFmhaBwdShape<fmha_block_tile_{F_idx},
fmha_block_warps0_{F_idx},
fmha_warp_tile_{F_idx},
fmha_block_warps1_{F_idx},
fmha_warp_tile_{F_idx},
fmha_block_warps0_{F_idx},
fmha_warp_tile_{F_idx},
fmha_block_warps1_{F_idx},
fmha_warp_tile_{F_idx},
fmha_block_warps2_{F_idx},
fmha_warp_tile_{F_idx}>;
using fmha_bwd_trait_{F_idx} = ck_tile::TileFmhaTraits<{F_spad},
{F_skpad},
{F_dpad},
{F_dvpad},
{F_bias},
{F_dbias},
false,
{F_dropout},
false,
{F_occupancy}>;
using fmha_mask_{F_idx} = {F_mask};
using fmha_bwd_pipeline_problem_{F_idx} = ck_tile::BlockFmhaBwdPipelineProblem<
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::QDataType,
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::KDataType,
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::VDataType,
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::GemmDataType,
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::LSEDataType,
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::AccDataType,
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::DDataType,
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::BiasDataType,
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::RandValOutputDataType,
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::ODataType,
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::OGradDataType,
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::QGradDataType,
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::KGradDataType,
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::VGradDataType,
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::BiasGradDataType,
fmha_bwd_shape_{F_idx},
{F_mode},
fmha_mask_{F_idx},
fmha_bwd_trait_{F_idx}>;
using fmha_bwd_pipeline_{F_idx} = {F_pipeline}<
fmha_bwd_pipeline_problem_{F_idx}>;
using fmha_bwd_epilogue_{F_idx} =
ck_tile::FmhaBwdEpilogue<ck_tile::FmhaBwdEpilogueProblem<typename FmhaBwdTypeConfig<{F_dtype}>::AccDataType,
typename FmhaBwdTypeConfig<{F_dtype}>::KGradDataType,
typename FmhaBwdTypeConfig<{F_dtype}>::VGradDataType>>;
using fmha_bwd_dq_dk_dv_kernel_{F_idx} =
ck_tile::FmhaBwdDQDKDVKernel<ck_tile::FmhaBwdTilePartitioner<fmha_bwd_shape_{F_idx}>,
fmha_bwd_pipeline_{F_idx},
fmha_bwd_epilogue_{F_idx}>;
using dq_dk_dv_trait_{F_idx} = fmha_bwd_dq_dk_dv_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_pipeline_enum}, fmha_mask_{F_idx}, {F_bias}, {F_dbias}, {F_dropout}, {F_spad}, {F_skpad}, {F_dpad}, {F_dvpad}>;
#include <iostream>
template<>
float fmha_bwd_dq_dk_dv_<dq_dk_dv_trait_{F_idx}>(const ck_tile::stream_config& s, fmha_bwd_args a)
{{
using k_ = fmha_bwd_dq_dk_dv_kernel_{F_idx};
if(s.log_level_ > 0)
std::cout << ", " << k_::GetName() << std::flush;
auto [kargs, grids] = fmha_bwd_dq_dk_dv_create_kargs_and_grids<k_>(a);
constexpr dim3 blocks = k_::BlockSize();
constexpr ck_tile::index_t kBlockPerCu = k_::kBlockPerCu;
return ck_tile::launch_kernel<blocks.x, kBlockPerCu>(s, k_{{}}, grids, blocks, 0, kargs);
}}
"""
FMHA_BWD_API_FILENAME="fmha_bwd_api.cpp"
FMHA_BWD_API="""
float fmha_bwd(fmha_bwd_traits t, fmha_bwd_args a, const ck_tile::stream_config& s){{
float r = -1;
{F_dispatch}
return r;
}}
"""
FMHA_BWD_API_PER_DTYPE=""" {F_if}(t.data_type.compare(\"{F_dtype}\") == 0){{
{F_hdim_case}
}}
"""
FMHA_BWD_API_PER_HDIM_CASE=""" {F_if} (t.hdim_q <= {F_hdim} && t.hdim_v <= {F_hdim}) {{
{F_inner_dispatch}
}}
"""
FMHA_BWD_API_INNER_DISPATCH=""" {F_if}((t.is_group_mode == {F_mode}) && ({F_mask_check}) && (t.has_bias == {F_bias}) && (t.has_dbias == {F_dbias}) && (t.has_dropout == {F_dropout}) &&
({F_scheck}) && ({F_skcheck}) && ({F_dcheck}) && ({F_dvcheck})) {{
using dq_dk_dv_trait_ = fmha_bwd_dq_dk_dv_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_pipeline_enum}, {F_mask}, {F_bias}, {F_dbias}, {F_dropout}, {F_spad0}, {F_skpad}, {F_dpad}, {F_dvpad}>;
using dot_do_o_trait_ = fmha_bwd_dot_do_o_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_spad1}, {F_dvpad}>;
r = fmha_bwd_dot_do_o_<dot_do_o_trait_>(s, a);
r += fmha_bwd_dq_dk_dv_<dq_dk_dv_trait_>(s, a);
return r;
}}
"""
@dataclass
class FmhaBwdDQDKDVApiTrait:
pipeline : str
# sync with fmha_bwd_traits<>, to generate fallback calls
hdim : str
dtype : str # data type
mode : str # value from MODE_MAP
bm0 : int # tile size along q seqlen (block size)
bn0 : int # tile size along k seqlen
bhdq : int # q head_dim
bhdv : int # v head_dim
mask : str
bias : str # true/false
dbias : str
dropout : str
spad : str
skpad : str
dpad : str
dvpad : str
@property
def name(self) -> str:
return f'{self.pipeline}-{self.hdim}-{self.dtype}-{self.mode}-{self.mask}-{self.bias}-{self.dbias}-{self.dropout}-{self.spad}-{self.skpad}-{self.dpad}-{self.dvpad}'
def scheck(self, spad1 : str) -> str:
if self.mode == 'group':
return 'true' # always support
elif self.spad == 't' and spad1 == 't':
return f'a.seqlen_q % {self.bm0} != 0'
elif self.spad == 'f' and spad1 == 't':
return f'a.seqlen_q % {self.bm0} == 0 and a.seqlen_q % 256 != 0' # BlockSize
else: # self.skpad == 'f' and skpad1 == 'f'
return f'a.seqlen_q % 256 == 0' # BlockSize
@property
def skcheck(self) -> str:
if self.mode == 'group':
return 'true' # always support
elif self.skpad == 't':
return f'a.seqlen_k % {self.bn0} != 0'
else:
return f'a.seqlen_k % {self.bn0} == 0'
@property
def dcheck(self) -> str:
if self.dpad == 't': return f'a.hdim_q % {self.bhdq} != 0'
else : return f'a.hdim_q % {self.bhdq} == 0'
@property
def dvcheck(self) -> str:
if self.dvpad == 't': return f'a.hdim_v % {self.bhdv} != 0'
else : return f'a.hdim_v % {self.bhdv} == 0'
class FmhaBwdApiPool:
def __init__(self, mask_impl):
self.dq_dk_dv_pool = dict()
self.mask_impl = mask_impl
def register_dq_dk_dv_traits(self, trait : FmhaBwdDQDKDVApiTrait) -> None:
# TODO: do we need to check duplication?
if trait.dtype not in self.dq_dk_dv_pool.keys():
self.dq_dk_dv_pool[trait.dtype] = dict()
if trait.hdim not in self.dq_dk_dv_pool[trait.dtype].keys():
self.dq_dk_dv_pool[trait.dtype][trait.hdim] = list()
self.dq_dk_dv_pool[trait.dtype][trait.hdim].append(copy.copy(trait))
@property
def api(self) -> str:
per_dtypes=str()
for i, dtype in enumerate(self.dq_dk_dv_pool.keys()):
per_hdim_case=str()
for j, hdim in enumerate(self.dq_dk_dv_pool[dtype].keys()):
traits=self.dq_dk_dv_pool[dtype][hdim]
inners=str()
for k, trait in enumerate(traits):
if_k = 'if' if k == 0 else 'else if'
for spad1 in ["t", "f"]:
if ((spad1 == "f" and trait.spad == "t") or (trait.mode == "group" and spad1 == "f")):
continue
inners = inners + FMHA_BWD_API_INNER_DISPATCH.format(F_if=if_k, F_mode=MODE_MAP[trait.mode], F_mask=get_mask_map(self.mask_impl)[trait.mask], F_pipeline_enum=BWD_DQDKDV_PIPELINE_ENUM_MAP[trait.pipeline],
F_mask_check=get_mask_check_map(self.mask_impl)[trait.mask], F_bias=BOOL_MAP[trait.bias], F_dbias=BOOL_MAP[trait.dbias], F_dropout=BOOL_MAP[trait.dropout],
F_scheck=trait.scheck(spad1=spad1), F_skcheck=trait.skcheck, F_dcheck=trait.dcheck, F_dvcheck=trait.dvcheck, F_hdim=hdim, F_dtype=DTYPE_MAP[dtype],
F_spad0=BOOL_MAP[trait.spad], F_spad1=BOOL_MAP[spad1], F_skpad=BOOL_MAP[trait.skpad], F_dpad=BOOL_MAP[trait.dpad], F_dvpad=BOOL_MAP[trait.dvpad])
if_j = 'if' if j == 0 else 'else if'
per_hdim_case = per_hdim_case + FMHA_BWD_API_PER_HDIM_CASE.format(F_if=if_j, F_hdim=hdim, F_inner_dispatch=inners)
if_i = 'if' if i == 0 else 'else if'
per_dtypes = per_dtypes + FMHA_BWD_API_PER_DTYPE.format(F_if=if_i, F_dtype=dtype, F_hdim_case=per_hdim_case)
return FMHA_BWD_KERNEL_HEADER + FMHA_BWD_API.format(F_dispatch = per_dtypes)
# GEMM0: Q@K=S^T
# GEMM1: P^T@dO^T=dV(This was chosen as G1 to match fwd, but N1 must be equal to headdim_v)
# GEMM2: dO@V=dP^T(This was chosen as G2 because of the calculation order)
# GEMM3: dS^T@Q^T=dK(Similar to G1, but N3 must be equal to headdim_qk)
# GEMM4: dS@K^T=dQ(N4 must be equal to headdim_qk)
# Is it necessary to distinguish between K0~K4?
@dataclass
class FmhaBwdDQDKDVTileSize:
F_bm0 : int # tile size along q seqlen (block size)
F_bn0 : int # tile size along k seqlen
F_bk0 : int # tile size along gemm0 unroll(F_bhdq)
F_bk1 : int # tile size along gemm1 unroll(F_bm0)
F_bk2 : int # tile size along gemm2 unroll(F_bhdv)
F_bk3 : int # tile size along gemm3 unroll(F_bm0)
F_bk4 : int # tile size along gemm4 unroll(F_bn0)
F_bhdq : int # q head_dim
F_bhdv : int # v head_dim
F_rm0 : int # number of warps along q seqlen (block warps) in gemm0/gemm2
F_rn0 : int # number of warps along k seqlen (block warps) in gemm0/gemm2
F_rk0 : int # number of warps along gemm-k (not used) in gemm0/gemm2
F_rm1 : int # number of warps along k seqlen (block warps) in gemm1/gemm3
F_rn1 : int # number of warps along q seqlen (block warps) in gemm1/gemm3
F_rk1 : int # number of warps along gemm-k (not used) in gemm1/gemm3
F_rm2 : int # number of warps along k seqlen (block warps) in gemm4
F_rn2 : int # number of warps along q seqlen (block warps) in gemm4
F_rk2 : int # number of warps along gemm-k (not used) in gemm4
F_wm : int # warp size along m (warp size)
F_wn : int # warp size along n
F_wk : int # warp size along k
F_occupancy : int # occupancy
@property
def name(self) -> str:
return f"b{self.F_bm0}x{self.F_bn0}"
@dataclass
class FmhaBwdDQDKDVKernel:
direction : str
F_idx : int # this is not a tunable, but a counter to differentiate symbol
F_hdim : int # hdim
F_dtype : str # data type
F_tile : FmhaBwdDQDKDVTileSize
F_spad : str # true/false
F_skpad : str #
F_dpad : str #
F_dvpad : str #
F_bias : str #
F_dbias : str #
F_dropout : str #
F_mask : str # value from MASK_MAP
F_mode : str # value from MODE_MAP
F_pipeline : str
mask_impl : str
@property
def template(self) -> str:
return FMHA_BWD_KERNEL_HEADER + \
FMHA_BWD_DQ_DK_DV_KERNEL_BODY.format(
F_idx = self.F_idx,
F_hdim = self.F_hdim,
F_dtype = DTYPE_MAP[self.F_dtype],
F_bm0 = self.F_tile.F_bm0,
F_bn0 = self.F_tile.F_bn0,
F_bk0 = self.F_tile.F_bk0,
F_bk1 = self.F_tile.F_bk1,
F_bk2 = self.F_tile.F_bk2,
F_bk3 = self.F_tile.F_bk3,
F_bk4 = self.F_tile.F_bk4,
F_bhdq = self.F_tile.F_bhdq,
F_bhdv = self.F_tile.F_bhdv,
F_rm0 = self.F_tile.F_rm0,
F_rn0 = self.F_tile.F_rn0,
F_rk0 = self.F_tile.F_rk0,
F_rm1 = self.F_tile.F_rm1,
F_rn1 = self.F_tile.F_rn1,
F_rk1 = self.F_tile.F_rk1,
F_rm2 = self.F_tile.F_rm2,
F_rn2 = self.F_tile.F_rn2,
F_rk2 = self.F_tile.F_rk2,
F_wm = self.F_tile.F_wm,
F_wn = self.F_tile.F_wn,
F_wk = self.F_tile.F_wk,
F_spad = BOOL_MAP[self.F_spad],
F_skpad = BOOL_MAP[self.F_skpad],
F_dpad = BOOL_MAP[self.F_dpad],
F_dvpad = BOOL_MAP[self.F_dvpad],
F_bias = BOOL_MAP[self.F_bias],
F_dbias = BOOL_MAP[self.F_dbias],
F_dropout = BOOL_MAP[self.F_dropout],
F_occupancy = self.F_tile.F_occupancy,
F_mask = get_mask_map(self.mask_impl)[self.F_mask],
F_mode = MODE_MAP[self.F_mode],
F_pipeline_enum = BWD_DQDKDV_PIPELINE_ENUM_MAP[self.F_pipeline],
F_pipeline = BWD_DQDKDV_PIPELINE_MAP[self.F_pipeline])
@property
def name(self) -> str:
def mask_name() -> str:
n = ''
if self.F_mask[0:2] == 's_':
if self.F_mask == 's_mask': n += f'_mask'
else:
if self.F_mask != 'no' : n += f'_m{self.F_mask[0]}'
return n
# TODO: we don't encode idx here
mn = mask_name()
n = f"fmha_{self.direction}_d{self.F_hdim}_{self.F_dtype}_{self.F_mode}_" + self.F_tile.name +\
f"_p{BOOL_MAP[self.F_spad][0]}{BOOL_MAP[self.F_skpad][0]}{BOOL_MAP[self.F_dpad][0]}{BOOL_MAP[self.F_dvpad][0]}" +\
f"_b{BOOL_MAP[self.F_bias][0]}_db{BOOL_MAP[self.F_dbias][0]}_dp{BOOL_MAP[self.F_dropout][0]}"
if mn != '' : n += f'{mn}'
return n
@property
def filename(self) -> str:
return self.name + ".cpp"
def api_trait(self) -> FmhaBwdDQDKDVApiTrait:
return FmhaBwdDQDKDVApiTrait(pipeline=self.F_pipeline,
hdim=str(self.F_hdim),
dtype=self.F_dtype,
mode=self.F_mode,
bm0=self.F_tile.F_bm0,
bn0=self.F_tile.F_bn0,
bhdq=self.F_tile.F_bhdq,
bhdv=self.F_tile.F_bhdv,
mask=self.F_mask,
bias=self.F_bias,
dbias=self.F_dbias,
dropout=self.F_dropout,
spad=self.F_spad,
skpad=self.F_skpad,
dpad=self.F_dpad,
dvpad=self.F_dvpad)
# TODO: design a more practical way to do it
# this is current supported tile size & pipeline.
def get_fmha_bwd_dq_dk_dv_tile_ppl_dict_from_dtype(direction : str, dtype : str) -> Optional[dict]:
if direction == 'bwd':
if dtype == 'fp16' or dtype == 'bf16':
return {
'32' : [FmhaBwdDQDKDVTileSize(128, 128, 32, 32, 32, 32, 32, 32, 32, 1, 4, 1, 4, 1, 1, 4, 1, 1, 32, 32, 16, 1),
"qs_ks_vr_dos"],
'64' : [FmhaBwdDQDKDVTileSize( 64, 128, 32, 32, 32, 32, 32, 64, 64, 1, 4, 1, 4, 1, 1, 2, 2, 1, 32, 32, 16, 1),
"ks_kts_vr"],
'128' : [FmhaBwdDQDKDVTileSize( 64, 128, 32, 32, 32, 32, 32, 128, 128, 1, 4, 1, 4, 1, 1, 2, 2, 1, 32, 32, 16, 1),
"ks_vr"]
}
else:
return None
else:
return None
def get_bwd_dq_dk_dv_blobs(kernel_filter : Optional[str], mask_impl) -> Tuple[FmhaBwdApiPool, List[FmhaBwdDQDKDVKernel]]:
# TODO: we don't support tuning yet, so pick up one value for pad
# support this in future
gen = list()
api_pool = FmhaBwdApiPool(mask_impl)
for direction, dtype in itertools.product(["bwd"], DTYPE_MAP.keys()):
d = get_fmha_bwd_dq_dk_dv_tile_ppl_dict_from_dtype(direction, dtype)
if d == None:
continue
for hdim_str, mode, mask, bias, dbias, dropout, spad, skpad, dpad, dvpad in itertools.product(d.keys(), MODE_MAP.keys(), get_mask_map(mask_impl).keys(), ["t", "f"], ["t", "f"], ["t", "f"], ["t", "f"], ["t", "f"], ["t", "f"], ["t", "f"]):
tile = d[hdim_str][0]
ppl = d[hdim_str][1]
hdim = int(hdim_str)
if (mode == "group") and (spad == "f" or skpad == "f"):
continue
if (bias == "f" and dbias == "t"):
continue
k = FmhaBwdDQDKDVKernel(direction=direction, F_idx=0, F_hdim=hdim, F_dtype=dtype, F_tile=tile,
F_spad=spad, F_skpad=skpad, F_dpad=dpad, F_dvpad=dvpad,
F_bias=bias, F_dbias=dbias, F_dropout=dropout, F_mask=mask, F_mode=mode,
F_pipeline=ppl, mask_impl=mask_impl)
if kernel_filter != None:
if not fnmatch.fnmatch(k.name, kernel_filter):
continue
api_pool.register_dq_dk_dv_traits(k.api_trait())
gen.append(k)
return (api_pool, gen)
FMHA_BWD_DOT_DO_O_KERNEL_BODY="""
using fmha_dtype_{F_idx} = {F_dtype};
using fmha_bwd_dot_do_o_trait_{F_idx} = ck_tile::TileFmhaBwdOGradDotOTraits<{F_spad},
{F_dvpad},
{F_occupancy}>;
using fmha_bwd_dot_do_o_pipeline_problem_{F_idx} = ck_tile::BlockFmhaBwdOGradDotOPipelineProblem<
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::ODataType,
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::OGradDataType,
typename FmhaBwdTypeConfig<fmha_dtype_{F_idx}>::DDataType,
/* BlockSize = */ 256,
{F_hdim},
{F_mode},
fmha_bwd_dot_do_o_trait_{F_idx}>;
using fmha_bwd_dot_do_o_{F_idx} = typename ck_tile::BlockFmhaBwdOGradDotO<
fmha_bwd_dot_do_o_pipeline_problem_{F_idx}>;
using fmha_bwd_dot_do_o_kernel_{F_idx} =
ck_tile::FmhaBwdOGradDotOKernel<ck_tile::FmhaBwdOGradDotOTilePartitioner</* BlockSize = */ 256>,
fmha_bwd_dot_do_o_{F_idx}>;
using dot_do_o_trait_{F_idx} = fmha_bwd_dot_do_o_traits_<{F_hdim}, {F_dtype}, {F_mode}, {F_spad}, {F_dvpad}>;
template<>
float fmha_bwd_dot_do_o_<dot_do_o_trait_{F_idx}>(const ck_tile::stream_config& s, fmha_bwd_args a)
{{
using k_ = fmha_bwd_dot_do_o_kernel_{F_idx};
auto [kargs, grids] = fmha_bwd_dot_do_o_create_kargs_and_grids<k_>(a);
constexpr dim3 blocks = k_::BlockSize();
constexpr ck_tile::index_t kBlockPerCu = k_::kBlockPerCu;
return ck_tile::launch_kernel<blocks.x, kBlockPerCu>(s, k_{{}}, grids, blocks, 0, kargs);
}}
"""
@dataclass
class FmhaBwdOGradDotOKernel:
direction : str
F_idx : int # this is not a tunable, but a counter to differentiate symbol
F_hdim : int # hdim
F_dtype : str # data type
F_spad : str # true/false
F_dvpad : str #
F_mode : str # value from MODE_MAP
F_occupancy : int
@property
def template(self) -> str:
return FMHA_BWD_KERNEL_HEADER + \
FMHA_BWD_DOT_DO_O_KERNEL_BODY.format(
F_idx = self.F_idx,
F_hdim = self.F_hdim,
F_dtype = DTYPE_MAP[self.F_dtype],
F_spad = BOOL_MAP[self.F_spad],
F_dvpad = BOOL_MAP[self.F_dvpad],
F_mode = MODE_MAP[self.F_mode],
F_occupancy = self.F_occupancy)
@property
def name(self) -> str:
# TODO: we don't encode idx here
return f"fmha_{self.direction}_d{self.F_hdim}_{self.F_dtype}_{self.F_mode}" +\
f"_p{BOOL_MAP[self.F_spad][0]}{BOOL_MAP[self.F_dvpad][0]}" +\
f"_o{self.F_occupancy}"
@property
def filename(self) -> str:
return self.name + ".cpp"
def get_bwd_dot_do_o_blobs() -> List[FmhaBwdOGradDotOKernel]:
# TODO: we don't support tuning yet, so pick up one value for pad/occupancy
# support this in future
def get_occupancy(dtype, hdim):
return 2
gen = list()
for direction, dtype in itertools.product(["bwd"], DTYPE_MAP.keys()):
d = get_fmha_bwd_dq_dk_dv_tile_ppl_dict_from_dtype(direction, dtype)
if d == None:
continue
for hdim_str, mode, spad, dvpad in itertools.product(d.keys(), MODE_MAP.keys(), ["t", "f"], ["t", "f"]):
hdim = int(hdim_str)
if (mode == "group" and spad == "f"):
continue
k = FmhaBwdOGradDotOKernel(direction=direction+"_dot_do_o", F_idx=0, F_hdim=hdim, F_dtype=dtype,
F_spad=spad, F_dvpad=dvpad, F_mode=mode,
F_occupancy=get_occupancy(dtype, hdim))
gen.append(k)
return gen
def write_single_fwd_kernel(kernel: FmhaFwdKernel, autogen_dir: Path) -> None:
(autogen_dir / kernel.filename).write_text(kernel.template)
def write_api(api_pool : FmhaFwdApiPool, autogen_dir: Path) -> None:
def write_fwd_api(api_pool : FmhaFwdApiPool, autogen_dir: Path) -> None:
(autogen_dir / FMHA_FWD_API_FILENAME).write_text(api_pool.api)
def write_blobs(output_dir : Optional[str], kernel_filter : Optional[str], receipt, mask_impl) -> None:
def write_single_bwd_dq_dk_dv_kernel(kernel: FmhaBwdDQDKDVKernel, autogen_dir: Path) -> None:
(autogen_dir / kernel.filename).write_text(kernel.template)
def write_single_bwd_dot_do_o_kernel(kernel: FmhaBwdOGradDotOKernel, autogen_dir: Path) -> None:
(autogen_dir / kernel.filename).write_text(kernel.template)
def write_bwd_api(api_pool : FmhaBwdApiPool, autogen_dir: Path) -> None:
(autogen_dir / FMHA_BWD_API_FILENAME).write_text(api_pool.api)
def write_blobs(output_dir: Optional[str], direction: str, kernel_filter : Optional[str], receipt, mask_impl) -> None:
if output_dir is None:
output_dir = Path(__file__).parent
else:
output_dir = Path(output_dir) / GEN_DIR
output_dir.mkdir(parents=True, exist_ok=True)
api_pool, kernels = get_blobs(kernel_filter, receipt, mask_impl)
for kernel in kernels:
write_single_kernel(kernel, output_dir)
write_api(api_pool, output_dir)
if direction == 'fwd':
api_pool, kernels = get_fwd_blobs(kernel_filter, receipt, mask_impl)
for kernel in kernels:
write_single_fwd_kernel(kernel, output_dir)
write_fwd_api(api_pool, output_dir)
else:
kernels = get_bwd_dot_do_o_blobs()
for kernel in kernels:
write_single_bwd_dot_do_o_kernel(kernel, output_dir)
api_pool, kernels = get_bwd_dq_dk_dv_blobs(kernel_filter, mask_impl)
for kernel in kernels:
write_single_bwd_dq_dk_dv_kernel(kernel, output_dir)
write_bwd_api(api_pool, output_dir)
# list all the files that will be generated
def list_blobs(output_file : Optional[str], kernel_filter : Optional[str], receipt, mask_impl) -> None:
def list_blobs(output_file : Optional[str], direction : str, kernel_filter : Optional[str], receipt, mask_impl) -> None:
assert output_file is not None
file_path = Path(output_file)
with file_path.open('a') as f:
_, kernels = get_blobs(kernel_filter, receipt, mask_impl)
for kernel in kernels:
f.write(str(file_path.parent / GEN_DIR / kernel.filename) + "\n")
f.write(str(file_path.parent / GEN_DIR / FMHA_FWD_API_FILENAME) + "\n")
if direction == 'fwd':
_, kernels = get_fwd_blobs(kernel_filter, receipt, mask_impl)
for kernel in kernels:
f.write(str(file_path.parent / GEN_DIR / kernel.filename) + "\n")
f.write(str(file_path.parent / GEN_DIR / FMHA_FWD_API_FILENAME) + "\n")
else:
kernels = get_bwd_dot_do_o_blobs()
for kernel in kernels:
f.write(str(file_path.parent / GEN_DIR / kernel.filename) + "\n")
_, kernels = get_bwd_dq_dk_dv_blobs(kernel_filter, mask_impl)
for kernel in kernels:
f.write(str(file_path.parent / GEN_DIR / kernel.filename) + "\n")
f.write(str(file_path.parent / GEN_DIR / FMHA_BWD_API_FILENAME) + "\n")
if __name__ == "__main__":
parser = argparse.ArgumentParser(
prog="generate",
description="gen api for CK fmha kernel",
)
parser.add_argument(
"-d",
"--direction",
default='fwd',
choices=['fwd', 'bwd'],
required=False,
help="choose the direction of kernels(default: fwd)"
)
parser.add_argument(
"-o",
"--output_dir",
@@ -608,6 +1142,6 @@ if __name__ == "__main__":
args = parser.parse_args()
if args.list_blobs is not None:
list_blobs(args.list_blobs, args.filter, args.receipt, mask_impl=args.mask)
list_blobs(args.list_blobs, args.direction, args.filter, args.receipt, mask_impl=args.mask)
else:
write_blobs(args.output_dir, args.filter, args.receipt, mask_impl=args.mask)
write_blobs(args.output_dir, args.direction, args.filter, args.receipt, mask_impl=args.mask)

21
example/ck_tile/01_fmha/script/benchmark_bwd.sh Normal file
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@@ -0,0 +1,21 @@
#!/bin/sh
# TODO: run this script from CK root
BUILD=build
EXE=$BUILD/bin/tile_example_fmha_bwd
VALID=0
for prec in "fp16" "bf16" ; do
for perm in 0 1 ; do
for hdim in 32 64 128 ; do
nhead=$((2048 / $hdim)) # follow fav2 setup
$EXE -prec=$prec -b=32 -h=$nhead -d=$hdim -s=512 -iperm=$perm -operm=$perm -kname=1 -v=$VALID ; sleep 3
$EXE -prec=$prec -b=16 -h=$nhead -d=$hdim -s=1024 -iperm=$perm -operm=$perm -kname=1 -v=$VALID ; sleep 3
$EXE -prec=$prec -b=8 -h=$nhead -d=$hdim -s=2048 -iperm=$perm -operm=$perm -kname=1 -v=$VALID ; sleep 3
$EXE -prec=$prec -b=4 -h=$nhead -d=$hdim -s=4096 -iperm=$perm -operm=$perm -kname=1 -v=$VALID ; sleep 3
$EXE -prec=$prec -b=2 -h=$nhead -d=$hdim -s=8192 -iperm=$perm -operm=$perm -kname=1 -v=$VALID ; sleep 3
$EXE -prec=$prec -b=1 -h=$nhead -d=$hdim -s=16384 -iperm=$perm -operm=$perm -kname=1 -v=$VALID ; sleep 3
done
done
done

33
example/ck_tile/01_fmha/script/smoke_test_bwd.sh Normal file
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@@ -0,0 +1,33 @@
#!/bin/sh
# TODO: run this script from CK root
BUILD=build
EXE=$BUILD/bin/tile_example_fmha_bwd
KNAME=1
export CK_WARMUP=0
export CK_REPEAT=1
COMMON_ARGS='-v=1 -warmup=0 -repeat=1'
for prec in "fp16" "bf16" ; do
for perm in 0 1 ; do
for hdim in 32 64 128 ; do
for mode in 0 1 ; do
for bias in 0 1 ; do
for dbias in 0 1 ; do
for p_drop in 0.0 0.2; 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
done
done
done
done
done
done
done