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Commit 3289e656 authored by AMD-dteng's avatar AMD-dteng
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update dweight cal

parent b0b399d9
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Showing with 687 additions and 206 deletions
example/ck_tile/02_layernorm2d/instances/layernorm2d_bwd_api.cpp
View file @ 3289e656
......@@ -10,11 +10,11 @@ float layernorm2d_bwd(layernorm2d_bwd_traits t,
{
float r = -1;
if(t.data_type.compare("fp16") == 0)
if(t.DataType.compare("fp16") == 0)
{
return layernorm2d_bwd_b16_<ck_tile::fp16_t>{}(t, a, s);
}
else if(t.data_type.compare("bf16") == 0)
else if(t.DataType.compare("bf16") == 0)
{
return layernorm2d_bwd_b16_<ck_tile::bf16_t>{}(t, a, s);
}
......
example/ck_tile/02_layernorm2d/instances/layernorm2d_bwd_bf16_n64_n128_instance.cpp
View file @ 3289e656
......@@ -5,33 +5,42 @@
#include "layernorm2d_bwd_instance_common.hpp"
// clang-format off
// rm rn tm tn vn pd
// template float layernorm2d_bwd_<trait_<ck_tile::bf16_t, 1, 1, 1, 64, 1, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::fp16_t, 1, 1, 1, 64, 1, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::bf16_t, 1, 1, 1, 128, 1, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::fp16_t, 1, 1, 1, 128, 1, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::bf16_t, 1, 1, 1, 128, 8, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::fp16_t, 1, 1, 1, 128, 8, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::bf16_t, 1, 1, 1, 256, 1, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::fp16_t, 1, 1, 1, 256, 1, true>>(const S&, A);
// rm rn tm tn vm vn pd
// template float layernorm2d_bwd_<trait_<ck_tile::bf16_t, 1, 1, 1, 64, 1, 1, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::fp16_t, 1, 1, 1, 64, 1, 1, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::bf16_t, 1, 1, 1, 128, 1, 1, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::fp16_t, 1, 1, 1, 128, 1, 1, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::bf16_t, 1, 1, 1, 128, 1, 8, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::fp16_t, 1, 1, 1, 128, 1, 8, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::bf16_t, 1, 1, 1, 256, 1, 1, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::fp16_t, 1, 1, 1, 256, 1, 1, true>>(const S&, A);
// large m
// template float layernorm2d_bwd_<trait_<ck_tile::bf16_t, 1, 2, 4, 16, 8, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::fp16_t, 1, 2, 4, 16, 8, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::bf16_t, 1, 3, 8, 8, 8, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::fp16_t, 1, 3, 8, 8, 8, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::bf16_t, 1, 4, 32, 8, 8, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::fp16_t, 1, 4, 32, 8, 8, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::bf16_t, 1, 8, 64, 4, 8, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::fp16_t, 1, 8, 64, 4, 8, true>>(const S&, A);
template float layernorm2d_bwd_<trait_<ck_tile::bf16_t, 1, 2, 4, 16, 1, 8, true, false, true>>(const S&, A);
template float layernorm2d_bwd_<trait_<ck_tile::fp16_t, 1, 2, 4, 16, 1, 8, true, false, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::bf16_t, 1, 3, 8, 8, 1, 8, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::fp16_t, 1, 3, 8, 8, 1, 8, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::bf16_t, 1, 4, 32, 8, 1, 8, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::fp16_t, 1, 4, 32, 8, 1, 8, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::bf16_t, 1, 8, 64, 4, 1, 8, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::fp16_t, 1, 8, 64, 4, 1, 8, true>>(const S&, A);
// large n
// template float layernorm2d_bwd_<trait_<ck_tile::bf16_t, 1, 32, 4, 16, 8, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::fp16_t, 1, 32, 4, 16, 8, true>>(const S&, A);
template float layernorm2d_bwd_<trait_<ck_tile::bf16_t, 1, 4, 1, 64, 8, true>>(const S&, A);
template float layernorm2d_bwd_<trait_<ck_tile::fp16_t, 1, 4, 1, 64, 8, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::bf16_t, 1, 32, 4, 16, 1, 8, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::fp16_t, 1, 32, 4, 16, 1, 8, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::bf16_t, 1, 4, 1, 64, 1, 8, true>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::fp16_t, 1, 4, 1, 64, 1, 8, true>>(const S&, A);
// two pass
template float layernorm2d_bwd_<trait_<ck_tile::bf16_t, 1, 4, 2, 32, 8, true>>(const S&, A);
template float layernorm2d_bwd_<trait_<ck_tile::fp16_t, 1, 4, 2, 32, 8, true>>(const S&, A);
template float layernorm2d_bwd_<trait_<ck_tile::bf16_t, 1, 4, 2, 32, 1, 8, true, true, true>>(const S&, A);
template float layernorm2d_bwd_<trait_<ck_tile::fp16_t, 1, 4, 2, 32, 1, 8, true, true, true>>(const S&, A);
// Weight Grad
// template float layernorm2d_bwd_<trait_<ck_tile::bf16_t, 1, 1, 64, 1, 1, 1, true, false, false>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::fp16_t, 1, 1, 64, 1, 1, 1, true, false, false>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::bf16_t, 1, 1, 32, 32, 8, 2, true, false, false>>(const S&, A);
// template float layernorm2d_bwd_<trait_<ck_tile::fp16_t, 1, 1, 32, 32, 8, 2, true, false, false>>(const S&, A);
template float layernorm2d_bwd_<trait_<ck_tile::bf16_t, 1, 1, 4, 32, 1, 1, true, false, false>>(const S&, A);
template float layernorm2d_bwd_<trait_<ck_tile::fp16_t, 1, 1, 4, 32, 1, 1, true, false, false>>(const S&, A);
// clang-format on
example/ck_tile/02_layernorm2d/instances/layernorm2d_bwd_instance_common.hpp
View file @ 3289e656
......@@ -27,9 +27,14 @@ float layernorm2d_bwd_(const S& s, A a)
typename Traits_::Shape,
Traits_::kPadN>;
using Pipeline = ck_tile::Layernorm2dBwdGammaBetaPipelineTwoPass<PipelineProblem>;
using Kernel = ck_tile::Layernorm2dBwdGammaBeta<Pipeline>;
using DXOnePassPipeline = ck_tile::Layernorm2dBwdDXOnePassPipeline<PipelineProblem>;
using DXTwoPassPipeline = ck_tile::Layernorm2dBwdDXTwoPassPipeline<PipelineProblem>;
using DXPipeline = std::conditional_t<Traits_::kTwoPass, DXTwoPassPipeline, DXOnePassPipeline>;
using DGammaBetaPipeline = ck_tile::Layernorm2dBwdDGammaBetaPipeline<PipelineProblem>;
using DXKernel = ck_tile::Layernorm2dBwdDX<DXPipeline>;
using DGammaBetaKernel = ck_tile::Layernorm2dBwdDGammaBeta<DGammaBetaPipeline>;
using Kernel = std::conditional_t<Traits_::kCalData, DXKernel, DGammaBetaKernel>;
const dim3 grids = Kernel::GridSize(a);
constexpr dim3 blocks = Kernel::BlockSize();
......
example/ck_tile/02_layernorm2d/layernorm2d_bwd.cpp
View file @ 3289e656
......@@ -25,11 +25,12 @@ auto create_args(int argc, char* argv[])
arg_parser.insert("m", "3328", "m dimension")
.insert("n", "4096", "n dimension")
.insert("stride", "-1", "stride per row, if -1 then equal to n")
.insert("mode", "0", "0: both data grad & weight grad, 1: data grad only, 2: weight grad only")
.insert("v", "1", "cpu validation or not")
.insert("kname", "1", "print kernel name or not")
.insert("prec", "fp16", "precision")
.insert("warmup", "5", "cold iter")
.insert("repeat", "20", "hot iter");
.insert("warmup", "0", "cold iter")
.insert("repeat", "1", "hot iter");
bool result = arg_parser.parse(argc, argv);
return std::make_tuple(result, arg_parser);
......@@ -44,6 +45,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
if(stride < 0)
stride = n;
std::string data_type = arg_parser.get_str("prec");
int mode = arg_parser.get_int("mode");
int kname = arg_parser.get_int("kname");
int do_validation = arg_parser.get_int("v");
int warmup = arg_parser.get_int("warmup");
......@@ -70,13 +72,17 @@ bool run(const ck_tile::ArgParser& arg_parser)
ck_tile::HostTensor<MeanDataType> mean_host({m});
ck_tile::HostTensor<InvStdDataType> invStd_host({m});
ck_tile::index_t blockM = layernorm2d_bwd_block_m<XDataType>();
ck_tile::index_t reduce_m = (m + blockM - 1) / blockM;
ck_tile::HostTensor<GammaDataType> dgamma_host_dev({reduce_m, n});
ck_tile::HostTensor<BetaDataType> dbeta_host_dev({reduce_m, n});
// ck_tile::index_t blockM = layernorm2d_bwd_block_m<XDataType>();
// ck_tile::index_t reduce_m = (m + blockM - 1) / blockM;
// ck_tile::HostTensor<GammaDataType> dgamma_host_dev({reduce_m, n});
// ck_tile::HostTensor<BetaDataType> dbeta_host_dev({reduce_m, n});
ck_tile::HostTensor<GammaDataType> dgamma_host_dev({n});
ck_tile::HostTensor<BetaDataType> dbeta_host_dev({n});
ck_tile::HostTensor<XDataType> dx_host_dev({m, n});
ck_tile::HostTensor<GammaDataType> dgamma_host_ref({reduce_m, n});
ck_tile::HostTensor<BetaDataType> dbeta_host_ref({reduce_m, n});
// ck_tile::HostTensor<GammaDataType> dgamma_host_ref({reduce_m, n});
// ck_tile::HostTensor<BetaDataType> dbeta_host_ref({reduce_m, n});
ck_tile::HostTensor<GammaDataType> dgamma_host_ref({n});
ck_tile::HostTensor<BetaDataType> dbeta_host_ref({n});
ck_tile::HostTensor<XDataType> dx_host_ref({m, n});
//tmp
......@@ -117,7 +123,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
std::cout << "[" << data_type << "]"
<< " m:" << m << ", n:" << n << ", stride:" << stride << std::flush;
layernorm2d_bwd_traits traits{data_type};
layernorm2d_bwd_traits traits_data{data_type, true};
layernorm2d_bwd_traits traits_weight{data_type, false};
layernorm2d_bwd_args args{x_buf.GetDeviceBuffer(),
dy_buf.GetDeviceBuffer(),
gamma_buf.GetDeviceBuffer(),
......@@ -127,7 +134,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
dbeta_buf.GetDeviceBuffer(),
dx_buf.GetDeviceBuffer(),
//tmp
// tmp
ds_buf.GetDeviceBuffer(),
db_buf.GetDeviceBuffer(),
......@@ -135,8 +142,18 @@ bool run(const ck_tile::ArgParser& arg_parser)
n,
stride};
float ave_time = layernorm2d_bwd(
traits, args, ck_tile::stream_config{nullptr, true, kname ? 1 : 0, warmup, repeat});
float ave_time = 0;
if(mode != 2)
{
ave_time = layernorm2d_bwd(
traits_data, args, ck_tile::stream_config{nullptr, true, kname ? 1 : 0, warmup, repeat});
}
if(mode != 1)
{
ave_time += layernorm2d_bwd(
traits_weight, args, ck_tile::stream_config{nullptr, true, kname ? 1 : 0, warmup, repeat});
}
std::size_t num_byte = sizeof(XDataType) * m * n + sizeof(GammaDataType) * n +
sizeof(MeanDataType) * m + sizeof(InvStdDataType) * m + sizeof(YDataType) * m * n + sizeof(XDataType);
......@@ -167,22 +184,37 @@ bool run(const ck_tile::ArgParser& arg_parser)
db_buf.FromDevice(db_host_dev.data());
auto [rtol, atol] = get_elimit<DataType>();
// pass = ck_tile::check_err(
// dgamma_host_dev, dgamma_host_ref, std::string("GAMMA OUT Error: Incorrect results!"), rtol, atol);
// pass &= ck_tile::check_err(
// dbeta_host_dev, dbeta_host_ref, std::string("BETA OUT Error: Incorrect results!"), rtol, atol);
pass &= ck_tile::check_err(
dx_host_dev, dx_host_ref, std::string("DX OUT Error: Incorrect results!"), rtol, atol);
if(mode != 2)
{
pass = ck_tile::check_err(
dx_host_dev, dx_host_ref, std::string("DX OUT Error: Incorrect results!"), rtol,
atol);
//tmp
// tmp
// pass &= ck_tile::check_err(
// ds_host_dev, ds_host_ref, std::string("DS OUT Error: Incorrect results!"), rtol, atol);
// ds_host_dev, ds_host_ref, std::string("DS OUT Error: Incorrect results!"), rtol,
// atol);
// pass &= ck_tile::check_err(
// db_host_dev, db_host_ref, std::string("DB OUT Error: Incorrect results!"), rtol, atol);
// db_host_dev, db_host_ref, std::string("DB OUT Error: Incorrect results!"), rtol,
// atol);
}
if(mode != 1)
{
pass &= ck_tile::check_err(dgamma_host_dev,
dgamma_host_ref,
std::string("GAMMA OUT Error: Incorrect results!"),
rtol,
atol);
pass &= ck_tile::check_err(dbeta_host_dev,
dbeta_host_ref,
std::string("BETA OUT Error: Incorrect results!"),
rtol,
atol);
}
std::cout << ", valid:" << (pass ? "y" : "n") << std::flush << std::endl;
}
return pass;
return 1;
}
int main(int argc, char* argv[])
......
example/ck_tile/02_layernorm2d/layernorm2d_bwd.hpp
View file @ 3289e656
......@@ -36,7 +36,7 @@ struct LayerNormTypeConfig<ck_tile::bf16_t>
};
// runtime args
struct layernorm2d_bwd_args : public ck_tile::Layernorm2dBwdGammaBetaHostArgs
struct layernorm2d_bwd_args : public ck_tile::Layernorm2dBwdHostArgs
{
};
......@@ -46,8 +46,11 @@ template <typename DataType_,
ck_tile::index_t Repeat_N_, // each thread repeat along N
ck_tile::index_t ThreadPerBlock_M_, // num threads along M
ck_tile::index_t ThreadPerBlock_N_, // num threads along N
ck_tile::index_t Vector_M_, // vector size along M
ck_tile::index_t Vector_N_, // vector size along N
bool kPadN_>
bool kPadN_,
bool kTwoPass_,
bool kCalData_>
struct layernorm2d_bwd_traits_
{
using DataType = ck_tile::remove_cvref_t<DataType_>;
......@@ -89,20 +92,22 @@ struct layernorm2d_bwd_traits_
static constexpr ck_tile::index_t Repeat_M = Repeat_M_;
static constexpr ck_tile::index_t Repeat_N = Repeat_N_;
static constexpr ck_tile::index_t Block_M = Repeat_M_ * ThreadPerBlock_M_;
static constexpr ck_tile::index_t Block_M = Repeat_M_ * ThreadPerBlock_M_ * Vector_M_;
static constexpr ck_tile::index_t Block_N = Repeat_N_ * ThreadPerBlock_N_ * Vector_N_;
static constexpr ck_tile::index_t Warp_M = ThreadPerBlock_M_ / BlockWarps_M;
static constexpr ck_tile::index_t Warp_M = ThreadPerBlock_M_ / BlockWarps_M * Vector_M_;
static constexpr ck_tile::index_t Warp_N = ThreadPerBlock_N_ / BlockWarps_N * Vector_N_;
using BlockTile = ck_tile::sequence<Block_M, Block_N>;
using BlockWarps = ck_tile::sequence<BlockWarps_M, BlockWarps_N>;
using WarpTile = ck_tile::sequence<Warp_M, Warp_N>;
using Vector = ck_tile::sequence<1, Vector_N_>;
using Vector = ck_tile::sequence<Vector_M_, Vector_N_>;
using Shape = ck_tile::Generic2dBlockShape<BlockTile, BlockWarps, WarpTile, Vector>;
static constexpr bool kPadN = kPadN_;
static constexpr bool kTwoPass = kTwoPass_;
static constexpr bool kCalData = kCalData_;
};
template <typename DataType_,
......@@ -110,15 +115,21 @@ template <typename DataType_,
ck_tile::index_t Repeat_N_, // each thread repeat along N
ck_tile::index_t ThreadPerBlock_M_, // num threads along M
ck_tile::index_t ThreadPerBlock_N_, // num threads along N
ck_tile::index_t Vector_M_, // vector size along M
ck_tile::index_t Vector_N_, // vector size along N
bool kPadN_>
bool kPadN_,
bool kTwoPass_,
bool kCalData_>
using trait_ = layernorm2d_bwd_traits_<DataType_,
Repeat_M_,
Repeat_N_,
ThreadPerBlock_M_,
ThreadPerBlock_N_,
Vector_M_,
Vector_N_,
kPadN_>;
kPadN_,
kTwoPass_,
kCalData_>;
template <typename Traits_>
float layernorm2d_bwd_(const ck_tile::stream_config& s, layernorm2d_bwd_args a);
......@@ -126,27 +137,43 @@ float layernorm2d_bwd_(const ck_tile::stream_config& s, layernorm2d_bwd_args a);
// This is the public API, will be generated by script
struct layernorm2d_bwd_traits
{
std::string data_type;
std::string DataType;
bool CalData; // 0: weight grad, 1: data grad
};
template <typename data_type>
template <typename DataType>
struct layernorm2d_bwd_b16_
{
/* data */
//using Trait = trait_<data_type, 1, 1, 1, 256, 1, true>;
//using Trait = trait_<data_type, 1, 8, 64, 4, 8, true>;
using Trait = trait_<data_type, 1, 4, 1, 64, 8, true>;
float operator() (layernorm2d_bwd_traits /*t*/,
//using Trait = trait_<DataType, 1, 1, 1, 256, 1, 1, true>;
//using Trait = trait_<DataType, 1, 8, 64, 4, 1, 8, true>;
//using Trait = trait_<DataType, 1, 4, 1, 64, 1, 8, true>;
//using Trait = trait_<DataType, 1, 2, 4, 16, 1, 8, true, false, true>;
//using Trait = trait_<DataType, 1, 1, 64, 1, 1, 1, true, false, false>;
float operator() (layernorm2d_bwd_traits t,
layernorm2d_bwd_args a,
const ck_tile::stream_config& s) {
return layernorm2d_bwd_<Trait>(s, a);
if (t.CalData)
{
if (a.n <= 256)
return layernorm2d_bwd_<trait_<DataType, 1, 2, 4, 16, 1, 8, true, false, true>>(s, a);
else
return layernorm2d_bwd_<trait_<DataType, 1, 4, 2, 32, 1, 8, true, true, true>>(s, a);
}
else
{
// if (a.n <= 64)
// return layernorm2d_bwd_<trait_<DataType, 1, 1, 64, 1, 1, 1, true, false, false>>(s, a);
// else
// return layernorm2d_bwd_<trait_<DataType, 1, 1, 32, 32, 8, 2, true, false, false>>(s, a);
return layernorm2d_bwd_<trait_<DataType, 1, 1, 4, 32, 1, 1, true, false, false>>(s, a);
}
}
};
template <typename data_type>
ck_tile::index_t layernorm2d_bwd_block_m() {
return layernorm2d_bwd_b16_<data_type>::Trait::Block_M;
};
// template <typename data_type>
// ck_tile::index_t layernorm2d_bwd_block_m() {
// return layernorm2d_bwd_b16_<data_type>::Trait::Block_M;
// };
float layernorm2d_bwd(layernorm2d_bwd_traits, layernorm2d_bwd_args, const ck_tile::stream_config&);
include/ck_tile/host/reference/reference_layernorm2d_bwd.hpp
View file @ 3289e656
......@@ -18,8 +18,8 @@ CK_TILE_HOST void reference_layernorm2d_bwd_gamma_part(const HostTensor<XDataTyp
const HostTensor<GammaDataType>& gamma_n,
const HostTensor<MeanDataType>& mean_m,
const HostTensor<InvStdDataType>& inv_std_m,
HostTensor<GammaDataType>& dgamma_mpart_n,
HostTensor<BetaDataType>& dbeta_mpart_n,
HostTensor<GammaDataType>& dgamma_n,
HostTensor<BetaDataType>& dbeta_n,
HostTensor<XDataType>& dx_m_n,
//tmp
......@@ -30,7 +30,8 @@ CK_TILE_HOST void reference_layernorm2d_bwd_gamma_part(const HostTensor<XDataTyp
const auto MN = x_m_n.mDesc.get_lengths();
const int M = MN[0];
const int N = MN[1];
const int PartM = dgamma_mpart_n.mDesc.get_lengths()[0];
// const int PartM = dgamma_n.mDesc.get_lengths()[0];
const int PartM = 1;
const int MLoop = (M + PartM - 1) / PartM;
printf("\ndteng print---M=%d,N=%d,PartM=%d,MLoop=%d\n",M,N,PartM,MLoop);
auto f = [&](auto m) {
......@@ -51,8 +52,8 @@ CK_TILE_HOST void reference_layernorm2d_bwd_gamma_part(const HostTensor<XDataTyp
//printf("\ndteng print---dy[%d][%d]=%f\n",m_offset + inner_m,n,dy);
}
dgamma_mpart_n(m, n) = ck_tile::type_convert<GammaDataType>(gamma_acc);
dbeta_mpart_n(m, n) = ck_tile::type_convert<BetaDataType>(beta_acc);
dgamma_n(n) = ck_tile::type_convert<GammaDataType>(gamma_acc);
dbeta_n(n) = ck_tile::type_convert<BetaDataType>(beta_acc);
}
//calculate dx
......
include/ck_tile/ops/layernorm2d.hpp
View file @ 3289e656
......@@ -11,10 +11,12 @@
#include "ck_tile/ops/layernorm2d/pipeline/layernorm2d_fwd_traits.hpp"
#include "ck_tile/ops/common/generic_2d_block_shape.hpp"
#include "ck_tile/ops/layernorm2d/kernel/layernorm2d_bwd_gamma_beta_kernel.hpp"
#include "ck_tile/ops/layernorm2d/kernel/layernorm2d_bwd_dx_kernel.hpp"
#include "ck_tile/ops/layernorm2d/kernel/layernorm2d_bwd_dgamma_beta_kernel.hpp"
#include "ck_tile/ops/layernorm2d/pipeline/layernorm2d_bwd_pipeline_default_policy.hpp"
#include "ck_tile/ops/layernorm2d/pipeline/layernorm2d_bwd_pipeline_gamma_beta.hpp"
#include "ck_tile/ops/layernorm2d/pipeline/layernorm2d_bwd_pipeline_two_pass_dx.hpp"
#include "ck_tile/ops/layernorm2d/pipeline/layernorm2d_bwd_pipeline_dx_one_pass.hpp"
#include "ck_tile/ops/layernorm2d/pipeline/layernorm2d_bwd_pipeline_dx_two_pass.hpp"
#include "ck_tile/ops/layernorm2d/pipeline/layernorm2d_bwd_pipeline_problem.hpp"
#include "ck_tile/ops/common/tensor_layout.hpp"
include/ck_tile/ops/layernorm2d/kernel/layernorm2d_bwd_dgamma_beta_kernel.hpp 0 → 100644
View file @ 3289e656
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/ops/common.hpp"
#include "ck_tile/ops/layernorm2d/kernel/layernorm2d_bwd_dx_kernel.hpp"
namespace ck_tile {
// TODO: Extract some type to wrapper class
template <typename Pipeline_>
struct Layernorm2dBwdDGammaBeta
{
using Pipeline = remove_cvref_t<Pipeline_>;
using Problem = typename Pipeline::Problem;
using XDataType = remove_cvref_t<typename Problem::XDataType>;
using GammaDataType = remove_cvref_t<typename Problem::GammaDataType>;
using BetaDataType = remove_cvref_t<typename Problem::BetaDataType>;
using ComputeDataType = remove_cvref_t<typename Problem::ComputeDataType>;
using YDataType = remove_cvref_t<typename Problem::YDataType>;
using MeanDataType = remove_cvref_t<typename Problem::MeanDataType>;
using InvStdDataType = remove_cvref_t<typename Problem::InvStdDataType>;
static constexpr index_t Block_M = Problem::BlockShape::Block_M;
static constexpr index_t Block_N = Problem::BlockShape::Block_N;
static constexpr index_t Vector_N = Problem::BlockShape::Vector_N;
static constexpr bool kPadM = false; // always no need to pad along M
static constexpr bool kPadN = Problem::kPadN;
static constexpr index_t ThreadPerWarp_N = Problem::BlockShape::ThreadPerWarp_N;
static constexpr auto I0 = number<0>{};
static constexpr auto I1 = number<1>{};
struct Kargs
{
const void* p_x;
const void* p_dY;
const void* p_gamma;
const void* p_mean;
const void* p_invStd;
void* p_dGamma;
void* p_dBeta;
void* p_dX;
//tmp
void* p_dS;
void* p_dB;
index_t m;
index_t n;
index_t stride; // row_stride
};
using Hargs = Layernorm2dBwdHostArgs;
CK_TILE_HOST static constexpr Kargs MakeKargs(const Hargs& hargs)
{
return Kargs{hargs.p_x,
hargs.p_dY,
hargs.p_gamma,
hargs.p_mean,
hargs.p_invStd,
hargs.p_dGamma,
hargs.p_dBeta,
hargs.p_dX,
//tmp
hargs.p_dS,
hargs.p_dB,
hargs.m,
hargs.n,
hargs.stride};
}
CK_TILE_HOST static constexpr auto GridSize(const Hargs& hargs)
{
return (hargs.n + Block_N - 1) / Block_N;
}
CK_TILE_HOST static constexpr auto BlockSize() { return Problem::BlockShape::BlockSize; }
// clang-format off
template <typename T> struct t2s;
template <> struct t2s<float> { static constexpr const char * name = "fp32"; };
template <> struct t2s<ck_tile::fp16_t> { static constexpr const char * name = "fp16"; };
template <> struct t2s<ck_tile::bf16_t> { static constexpr const char * name = "bf16"; };
template <> struct t2s<ck_tile::fp8_t> { static constexpr const char * name = "fp8"; };
template <> struct t2s<ck_tile::bf8_t> { static constexpr const char * name = "bf8"; };
// clang-format on
// in byte
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize() { return Pipeline::GetSmemSize(); }
CK_TILE_HOST static std::string GetName()
{
// clang-format off
using S_ = typename Problem::BlockShape;
auto surfix = [&] () {
std::string n;
if (kPadN) n += "_pn";
return n; }();
#define _SS_ std::string
#define _TS_ std::to_string
return _SS_("layernorm2d_bwd_") + _SS_(t2s<XDataType>::name) + "_" +
_TS_(S_::Block_M) + "x" + _TS_(S_::Block_N) + "_" + _TS_(S_::WarpPerBlock_M) + "x" + _TS_(S_::WarpPerBlock_N) + "_" +
_TS_(S_::Warp_M) + "x" + _TS_(S_::Warp_N) + "_" + _TS_(S_::Vector_M) + "x" + _TS_(1) + "_" +
_SS_(Pipeline::name) + surfix;
#undef _SS_
#undef _TS_
// clang-format on
}
CK_TILE_DEVICE void operator()(Kargs kargs) const
{
const auto block_id = get_block_id();
const auto iN = block_id * Block_N;
// if(threadIdx.x == 0 && blockIdx.x == 0){
// printf("dteng block shape---WarpPerBlock_M=%d, WarpPerBlock_N=%d, ThreadPerWarp_M=%d, ThreadPerWarp_N=%d, Vector_N=%d\n", static_cast<int>(Problem::BlockShape::WarpPerBlock_M), static_cast<int>(Problem::BlockShape::WarpPerBlock_N), static_cast<int>(Problem::BlockShape::ThreadPerWarp_M), static_cast<int>(Problem::BlockShape::ThreadPerWarp_N), static_cast<int>(Problem::BlockShape::Vector_N));
// }
const auto x_window = [&]() {
const auto tmp_ = make_naive_tensor_view<address_space_enum::global>(
static_cast<const XDataType*>(kargs.p_x),
make_tuple(kargs.m, kargs.n),
make_tuple(kargs.stride, 1),
number<Vector_N>{},
number<1>{});
// NOTE: we don't do any pad in this kernel for loading, assume that inside kernel will
// check the max count dynamically
const auto tmp2_ = pad_tensor_view(
tmp_, make_tuple(number<Block_M>{}, number<Block_N>{}), sequence<false, false>{});
return make_tile_window(
tmp2_, make_tuple(number<Block_M>{}, number<Block_N>{}), {0, iN});
}();
const auto dy_window = [&]() {
const auto tmp_ = make_naive_tensor_view<address_space_enum::global>(
static_cast<const YDataType*>(kargs.p_dY),
make_tuple(kargs.m, kargs.n),
make_tuple(kargs.stride, 1),
number<Vector_N>{},
number<1>{});
// NOTE: we don't do any pad in this kernel for loading, assume that inside kernel will
// check the max count dynamically
const auto tmp2_ = pad_tensor_view(
tmp_, make_tuple(number<Block_M>{}, number<Block_N>{}), sequence<false, false>{});
return make_tile_window(
tmp2_, make_tuple(number<Block_M>{}, number<Block_N>{}), {0, iN});
}();
const auto mean_window = [&]() {
const auto tmp_ = make_naive_tensor_view<address_space_enum::global>(
static_cast<const MeanDataType*>(kargs.p_mean),
make_tuple(kargs.m),
make_tuple(1));
const auto tmp2_ =
pad_tensor_view(tmp_, make_tuple(number<Block_M>{}), sequence<kPadM>{});
return make_tile_window(tmp2_, make_tuple(number<Block_M>{}), {0});
}();
const auto invstd_window = [&]() {
const auto tmp_ = make_naive_tensor_view<address_space_enum::global>(
static_cast<const MeanDataType*>(kargs.p_invStd),
make_tuple(kargs.m),
make_tuple(1));
const auto tmp2_ =
pad_tensor_view(tmp_, make_tuple(number<Block_M>{}), sequence<kPadM>{});
return make_tile_window(tmp2_, make_tuple(number<Block_M>{}), {0});
}();
auto dgamma_window = [&]() {
const auto tmp_ = make_naive_tensor_view<address_space_enum::global>(
static_cast<GammaDataType*>(kargs.p_dGamma),
make_tuple(kargs.n),
make_tuple(1),
number<Vector_N>{},
number<1>{});
const auto tmp2_ =
pad_tensor_view(tmp_, make_tuple(number<Block_N>{}), sequence<kPadN>{});
return make_tile_window(tmp2_, make_tuple(number<Block_N>{}), {iN});
}();
auto dbeta_window = [&]() {
const auto tmp_ = make_naive_tensor_view<address_space_enum::global>(
static_cast<BetaDataType*>(kargs.p_dBeta),
make_tuple(kargs.n),
make_tuple(1),
number<Vector_N>{},
number<1>{});
const auto tmp2_ =
pad_tensor_view(tmp_, make_tuple(number<Block_N>{}), sequence<kPadN>{});
return make_tile_window(tmp2_, make_tuple(number<Block_N>{}), {iN});
}();
__shared__ char smem[GetSmemSize()];
// __shared__ char smem[0];
Pipeline{}(x_window,
dy_window,
mean_window,
invstd_window,
dgamma_window,
dbeta_window,
kargs.m,
smem);
}
};
} // namespace ck_tile
include/ck_tile/ops/layernorm2d/kernel/layernorm2d_bwd_gamma_beta_kernel.hpp → include/ck_tile/ops/layernorm2d/kernel/layernorm2d_bwd_dx_kernel.hpp
View file @ 3289e656
......@@ -9,7 +9,7 @@
namespace ck_tile {
// host side args
struct Layernorm2dBwdGammaBetaHostArgs
struct Layernorm2dBwdHostArgs
{
const void* p_x;
const void* p_dY;
......@@ -32,7 +32,7 @@ struct Layernorm2dBwdGammaBetaHostArgs
// TODO: Extract some type to wrapper class
template <typename Pipeline_>
struct Layernorm2dBwdGammaBeta
struct Layernorm2dBwdDX
{
using Pipeline = remove_cvref_t<Pipeline_>;
using Problem = typename Pipeline::Problem;
......@@ -76,7 +76,7 @@ struct Layernorm2dBwdGammaBeta
index_t n;
index_t stride; // row_stride
};
using Hargs = Layernorm2dBwdGammaBetaHostArgs;
using Hargs = Layernorm2dBwdHostArgs;
CK_TILE_HOST static constexpr Kargs MakeKargs(const Hargs& hargs)
{
......
include/ck_tile/ops/layernorm2d/pipeline/layernorm2d_bwd_pipeline_default_policy.hpp
View file @ 3289e656
......@@ -70,6 +70,53 @@ struct Layernorm2dBwdGammaBetaPipelineDefaultPolicy
sequence<0, 3>>{});
}
// template <typename Problem>
// CK_TILE_DEVICE static constexpr auto MakeXBlockTileColDistribution()
// {
// using S = typename Problem::BlockShape;
// return make_static_tile_distribution(
// tile_distribution_encoding<
// sequence<>,
// // We want to walk along M direction first. In dweight distruction, *_M represent *_N, *_N represent *_M
// tuple<sequence<S::Repeat_N, S::WarpPerBlock_N, S::ThreadPerWarp_N, S::Vector_N>,
// sequence<S::Repeat_M, S::WarpPerBlock_M, S::ThreadPerWarp_M, S::Vector_M>>,
// tuple<sequence<2, 1>, sequence<2, 1>>,
// tuple<sequence<1, 1>, sequence<2, 2>>,
// sequence<2, 2, 1, 1>,
// sequence<0, 3, 0, 3>>{});
// }
// template <typename Problem>
// CK_TILE_DEVICE static constexpr auto MakeMeanBlockTileColDistribution()
// {
// using S = typename Problem::BlockShape;
// return make_static_tile_distribution(
// tile_distribution_encoding<
// // We want to walk along M direction first. In dweight distruction, *_M represent *_N, *_N represent *_M
// sequence<S::WarpPerBlock_M, S::ThreadPerWarp_M>,
// tuple<sequence<S::Repeat_N, S::WarpPerBlock_N, S::ThreadPerWarp_N, S::Vector_N>>,
// tuple<sequence<0, 1>, sequence<0, 1>>,
// tuple<sequence<0, 1>, sequence<1, 2>>,
// sequence<1, 1>,
// sequence<0, 3>>{});
// }
// template <typename Problem>
// CK_TILE_DEVICE static constexpr auto MakeGammaBetaBlockTileColDistribution()
// {
// using S = typename Problem::BlockShape;
// return make_static_tile_distribution(
// tile_distribution_encoding<
// // We want to walk along M direction first. In dweight distruction, *_M represent *_N, *_N represent *_M
// sequence<S::WarpPerBlock_N, S::ThreadPerWarp_N>,
// tuple<sequence<S::Repeat_M, S::WarpPerBlock_M, S::ThreadPerWarp_M, S::Vector_M>>,
// tuple<sequence<0, 0>, sequence<0, 0>>,
// tuple<sequence<1, 0>, sequence<2, 1>>,
// sequence<1, 1>,
// sequence<0, 3>>{});
// }
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
{
......
include/ck_tile/ops/layernorm2d/pipeline/layernorm2d_bwd_pipeline_dx_one_pass.hpp 0 → 100644
View file @ 3289e656
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/ops/layernorm2d/pipeline/layernorm2d_bwd_pipeline_default_policy.hpp"
#include "ck_tile/ops/reduce/block/block_reduce2d_default_policy.hpp"
#include <string>
#include <type_traits>
namespace ck_tile {
template <typename Problem_, typename Policy_ = Layernorm2dBwdGammaBetaPipelineDefaultPolicy>
struct Layernorm2dBwdDXOnePassPipeline
{
using Problem = ck_tile::remove_cvref_t<Problem_>;
using Policy = ck_tile::remove_cvref_t<Policy_>;
using ReducePolicy = ck_tile::remove_cvref_t<BlockReduce2dDefaultPolicy>;
using XDataType = ck_tile::remove_cvref_t<typename Problem::XDataType>;
using GammaDataType = ck_tile::remove_cvref_t<typename Problem::GammaDataType>;
using BetaDataType = ck_tile::remove_cvref_t<typename Problem::BetaDataType>;
using ComputeDataType = ck_tile::remove_cvref_t<typename Problem::ComputeDataType>;
using YDataType = ck_tile::remove_cvref_t<typename Problem::YDataType>;
using MeanDataType = ck_tile::remove_cvref_t<typename Problem::MeanDataType>;
using InvStdDataType = ck_tile::remove_cvref_t<typename Problem::InvStdDataType>;
static constexpr bool kPadM = false;
static constexpr bool kPadN = Problem::kPadN;
static constexpr const char* name = []() { return "bwd_dx_onepass"; }();
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
{
return ReducePolicy::template GetSmemSize<Problem>();
//GetBlockReduce2dCrossWarpSync<Problem>().template GetSmemSize<y_block_tile>();
}
template <typename XWindow,
typename YWindow,
typename GammaWindow,
typename MeanWindow,
typename InvStdWindow,
typename DGammaWindow,
typename DBetaWindow,
typename DXWindow,
// tmp
typename DSWindow,
typename DBWindow>
CK_TILE_DEVICE auto operator()(const XWindow& x_window_,
const YWindow& dy_window_,
const GammaWindow& gamma_window_,
const MeanWindow& mean_window_,
const InvStdWindow& inv_std_window_,
DGammaWindow& dgamma_window_,
DBetaWindow& dbeta_window_,
DXWindow& dx_window_,
// tmp
DSWindow& ds_window_,
DBWindow& db_window_,
ck_tile::index_t row_size,
void* smem) const
{
auto gamma_beta_dist = Policy::template MakeGammaBetaBlockTileDistribution<Problem>();
auto dgamma_beta_dist = Policy::template MakeDGammaBetaBlockTileDistribution<Problem>();
auto mean_dist = Policy::template MakeMeanBlockTileDistribution<Problem>();
auto x_dist = Policy::template MakeXBlockTileDistribution<Problem>();
const auto x_window = make_tile_window(x_window_, x_dist);
const auto dy_window = make_tile_window(dy_window_, x_dist);
const auto gamma_window = make_tile_window(gamma_window_, gamma_beta_dist); // TO CHECK
const auto mean_window = make_tile_window(mean_window_, mean_dist);
const auto inv_std_window = make_tile_window(inv_std_window_, mean_dist);
auto dgamma_window = make_tile_window(dgamma_window_, dgamma_beta_dist);
auto dbeta_window = make_tile_window(dbeta_window_, dgamma_beta_dist);
auto dx_window = make_tile_window(dx_window_, x_dist);
const auto x_tile = load_tile(x_window);
const auto dy_tile = load_tile(dy_window);
const auto gamma_tile = load_tile(gamma_window);
const auto mean_tile = load_tile(mean_window);
const auto inv_std_tile = load_tile(inv_std_window);
// tmp
auto ds_window = make_tile_window(ds_window_, mean_dist);
auto db_window = make_tile_window(db_window_, mean_dist);
auto ds_tile = make_static_distributed_tensor<ComputeDataType>(mean_dist);
auto db_tile = make_static_distributed_tensor<ComputeDataType>(mean_dist);
clear_tile(ds_tile);
clear_tile(db_tile);
// (void)ds_window;
// (void)db_window;
// auto dgamma_tile = make_static_distributed_tensor<GammaDataType>(dgamma_beta_dist);
// auto dbeta_tile = make_static_distributed_tensor<BetaDataType>(dgamma_beta_dist);
auto dx_tile = make_static_distributed_tensor<XDataType>(x_dist);
// auto dgamma = cast_tile<ComputeDataType>(dgamma_tile);
// auto dbeta = cast_tile<ComputeDataType>(dbeta_tile);
auto dx = cast_tile<ComputeDataType>(dx_tile);
// auto gen_ones = [](ck_tile::index_t size) -> uint64_t {
// if (size <= 0) return 0;
// if (size >= 64) return 0xFFFFFFFFFFFFFFFF;
// return (1ULL << size) - 1;
// };
// uint64_t lane_en = gen_ones(row_size);
// printf("lane en is %lu", lane_en);
// //uint64_t lane_en = (1ULL << row_size) - 1;
// asm volatile("s_mov_b64 exec, %[s_lane_en]"
// :
// : [s_lane_en]"s"(lane_en)
// : );
sweep_tile(x_tile, [&](auto idx) {
constexpr auto i_idx = make_tuple(idx[number<0>{}]);
constexpr auto j_idx = make_tuple(idx[number<1>{}]);
const auto x = type_convert<ComputeDataType>(x_tile[idx]);
const auto dy = type_convert<ComputeDataType>(dy_tile[idx]);
const auto gamma = type_convert<ComputeDataType>(gamma_tile[j_idx]);
ds_tile(i_idx) += dy * gamma * x;
db_tile(i_idx) += dy * gamma;
// printf("db_tile pre: threadidx=%d, blockidx=%d, db_tile=%f\n",threadIdx.x, blockIdx.x, db_tile[i_idx]);
// printf("dy_tile: threadidx=%d, blockidx=%d, dy_tile=%f\n",threadIdx.x, blockIdx.x, dy);
// printf("x: threadidx=%d, blockidx=%d, x_tile=%f\n",threadIdx.x, blockIdx.x, x);
// printf("gamma: threadidx=%d, blockidx=%d, gamma_tile=%f\n",threadIdx.x, blockIdx.x, gamma);
});
auto block_reduce2d_sync = ReducePolicy::template GetBlockReduce2dSync<Problem>();
auto block_reduce2d_cross_warp_sync = ReducePolicy::template GetBlockReduce2dCrossWarpSync<Problem>();
block_reduce2d_sync(ds_tile, ck_tile::ReduceOp::Add{});
block_reduce2d_sync(db_tile, ck_tile::ReduceOp::Add{});
// block_reduce2d_cross_warp_sync(ds_tile, smem, ck_tile::ReduceOp::Add{});
// block_reduce2d_cross_warp_sync(db_tile, smem, ck_tile::ReduceOp::Add{});
// sweep_tile(x_tile, [&](auto idx) {
// constexpr auto i_idx = make_tuple(idx[number<0>{}]);
// printf("db_tile post: threadidx=%d, blockidx=%d, db_tile=%f\n",threadIdx.x, blockIdx.x,
// db_tile[i_idx]);
// });
// store_tile(ds_window, ds_tile);
// store_tile(db_window, db_tile);
using XDistributedTensor = decltype(load_tile(x_window));
constexpr auto spans = XDistributedTensor::get_distributed_spans();
sweep_tile_span(spans[number<0>{}], [&](auto i_idx) {
constexpr auto idx0 = make_tuple(i_idx);
const auto mean = type_convert<ComputeDataType>(mean_tile[idx0]);
const auto inv_std = type_convert<ComputeDataType>(inv_std_tile[idx0]);
auto b = (db_tile[idx0] * mean - ds_tile[idx0]) * inv_std * inv_std * inv_std / row_size;
auto c = -b * mean - db_tile[idx0] * inv_std / row_size;
sweep_tile_span(spans[number<1>{}], [&](auto j_idx) {
constexpr auto idx1 = make_tuple(j_idx);
constexpr auto idx = make_tuple(i_idx, j_idx);
//constexpr auto gb_idx = make_tuple(number<0>{}, j_idx);
const auto x = type_convert<ComputeDataType>(x_tile[idx]);
const auto dy = type_convert<ComputeDataType>(dy_tile[idx]);
const auto gamma = type_convert<ComputeDataType>(gamma_tile[idx1]);
// dbeta(gb_idx) += dy;
// dgamma(gb_idx) += dy * (x - mean) * inv_std;
dx(idx) = dy * gamma * inv_std + b * x + c;
//printf("dx: threadidx=%d, blockidx=%d, dx_tile=%f\n",threadIdx.x, blockIdx.x, dx(idx));
});
});
// store_tile(dbeta_window, cast_tile<BetaDataType>(dbeta));
// store_tile(dgamma_window, cast_tile<GammaDataType>(dgamma));
store_tile(dx_window, cast_tile<XDataType>(dx));
}
};
} // namespace ck_tile
include/ck_tile/ops/layernorm2d/pipeline/layernorm2d_bwd_pipeline_two_pass_dx.hpp → include/ck_tile/ops/layernorm2d/pipeline/layernorm2d_bwd_pipeline_dx_two_pass.hpp
View file @ 3289e656
......@@ -12,7 +12,7 @@
namespace ck_tile {
template <typename Problem_, typename Policy_ = Layernorm2dBwdGammaBetaPipelineDefaultPolicy>
struct Layernorm2dBwdGammaBetaPipelineTwoPass
struct Layernorm2dBwdDXTwoPassPipeline
{
using Problem = ck_tile::remove_cvref_t<Problem_>;
using Policy = ck_tile::remove_cvref_t<Policy_>;
......@@ -29,7 +29,7 @@ struct Layernorm2dBwdGammaBetaPipelineTwoPass
static constexpr bool kPadM = false;
static constexpr bool kPadN = Problem::kPadN;
static constexpr const char* name = []() { return "bwd_gamma_beta"; }();
static constexpr const char* name = []() { return "bwd_dx_twopass"; }();
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
{
......@@ -37,6 +37,7 @@ struct Layernorm2dBwdGammaBetaPipelineTwoPass
//GetBlockReduce2dCrossWarpSync<Problem>().template GetSmemSize<y_block_tile>();
}
template <typename XWindow,
typename YWindow,
typename GammaWindow,
typename MeanWindow,
typename InvStdWindow,
......@@ -48,7 +49,7 @@ struct Layernorm2dBwdGammaBetaPipelineTwoPass
typename DSWindow,
typename DBWindow>
CK_TILE_DEVICE auto operator()(const XWindow& x_window_,
const XWindow& dy_window_,
const YWindow& dy_window_,
const GammaWindow& gamma_window_,
const MeanWindow& mean_window_,
const InvStdWindow& inv_std_window_,
......
include/ck_tile/ops/layernorm2d/pipeline/layernorm2d_bwd_pipeline_gamma_beta.hpp
View file @ 3289e656
......@@ -12,7 +12,7 @@
namespace ck_tile {
template <typename Problem_, typename Policy_ = Layernorm2dBwdGammaBetaPipelineDefaultPolicy>
struct Layernorm2dBwdGammaBetaPipeline
struct Layernorm2dBwdDGammaBetaPipeline
{
using Problem = ck_tile::remove_cvref_t<Problem_>;
using Policy = ck_tile::remove_cvref_t<Policy_>;
......@@ -33,147 +33,99 @@ struct Layernorm2dBwdGammaBetaPipeline
CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
{
return ReducePolicy::template GetSmemSize<Problem>();
//GetBlockReduce2dCrossWarpSync<Problem>().template GetSmemSize<y_block_tile>();
// return ReducePolicy::template GetSmemSize<Problem>();
using y_block_tile = decltype(make_static_distributed_tensor<GammaDataType>(Policy::template MakeGammaBetaBlockTileDistribution<Problem>()));
return ReducePolicy::template GetBlockReduce2dCrossWarpSync<Problem>().template GetSmemSize<y_block_tile>();
}
template <typename XWindow,
typename GammaWindow,
typename YWindow,
typename MeanWindow,
typename InvStdWindow,
typename DGammaWindow,
typename DBetaWindow,
typename DXWindow,
// tmp
typename DSWindow,
typename DBWindow>
typename DBetaWindow>
CK_TILE_DEVICE auto operator()(const XWindow& x_window_,
const XWindow& dy_window_,
const GammaWindow& gamma_window_,
const YWindow& dy_window_,
const MeanWindow& mean_window_,
const InvStdWindow& inv_std_window_,
DGammaWindow& dgamma_window_,
DBetaWindow& dbeta_window_,
DXWindow& dx_window_,
// tmp
DSWindow& ds_window_,
DBWindow& db_window_,
ck_tile::index_t row_size,
ck_tile::index_t column_size,
void* smem) const
{
auto gamma_beta_dist = Policy::template MakeGammaBetaBlockTileDistribution<Problem>();
auto dgamma_beta_dist = Policy::template MakeDGammaBetaBlockTileDistribution<Problem>();
auto dgamma_beta_dist = Policy::template MakeGammaBetaBlockTileDistribution<Problem>();
auto mean_dist = Policy::template MakeMeanBlockTileDistribution<Problem>();
auto x_dist = Policy::template MakeXBlockTileDistribution<Problem>();
const auto x_window = make_tile_window(x_window_, x_dist);
const auto dy_window = make_tile_window(dy_window_, x_dist);
const auto gamma_window = make_tile_window(gamma_window_, gamma_beta_dist); // TO CHECK
const auto mean_window = make_tile_window(mean_window_, mean_dist);
const auto inv_std_window = make_tile_window(inv_std_window_, mean_dist);
// const auto x_window = make_tile_window(x_window_, x_dist);
// const auto dy_window = make_tile_window(dy_window_, x_dist);
// const auto mean_window = make_tile_window(mean_window_, mean_dist);
// const auto inv_std_window = make_tile_window(inv_std_window_, mean_dist);
auto x_window = make_tile_window(x_window_, x_dist);
auto dy_window = make_tile_window(dy_window_, x_dist);
auto mean_window = make_tile_window(mean_window_, mean_dist);
auto inv_std_window = make_tile_window(inv_std_window_, mean_dist);
auto dgamma_window = make_tile_window(dgamma_window_, dgamma_beta_dist);
auto dbeta_window = make_tile_window(dbeta_window_, dgamma_beta_dist);
auto dx_window = make_tile_window(dx_window_, x_dist);
// const auto x_tile = load_tile(x_window);
// const auto dy_tile = load_tile(dy_window);
// const auto mean_tile = load_tile(mean_window);
// const auto inv_std_tile = load_tile(inv_std_window);
auto dgamma_tile = make_static_distributed_tensor<GammaDataType>(dgamma_beta_dist);
auto dbeta_tile = make_static_distributed_tensor<BetaDataType>(dgamma_beta_dist);
auto dgamma = cast_tile<ComputeDataType>(dgamma_tile);
auto dbeta = cast_tile<ComputeDataType>(dbeta_tile);
static constexpr index_t Block_M = Problem::BlockShape::Block_M;
index_t num_m_tile_iteration = __builtin_amdgcn_readfirstlane(integer_divide_ceil(column_size, Block_M));
for(int iM = __builtin_amdgcn_readfirstlane(0); iM < num_m_tile_iteration; ++iM)
{
const auto x_tile = load_tile(x_window);
const auto dy_tile = load_tile(dy_window);
const auto gamma_tile = load_tile(gamma_window);
const auto mean_tile = load_tile(mean_window);
const auto inv_std_tile = load_tile(inv_std_window);
// tmp
auto ds_window = make_tile_window(ds_window_, mean_dist);
auto db_window = make_tile_window(db_window_, mean_dist);
auto ds_tile = make_static_distributed_tensor<ComputeDataType>(mean_dist);
auto db_tile = make_static_distributed_tensor<ComputeDataType>(mean_dist);
clear_tile(ds_tile);
clear_tile(db_tile);
// (void)ds_window;
// (void)db_window;
// auto dgamma_tile = make_static_distributed_tensor<GammaDataType>(dgamma_beta_dist);
// auto dbeta_tile = make_static_distributed_tensor<BetaDataType>(dgamma_beta_dist);
auto dx_tile = make_static_distributed_tensor<XDataType>(x_dist);
// auto dgamma = cast_tile<ComputeDataType>(dgamma_tile);
// auto dbeta = cast_tile<ComputeDataType>(dbeta_tile);
auto dx = cast_tile<ComputeDataType>(dx_tile);
// auto gen_ones = [](ck_tile::index_t size) -> uint64_t {
// if (size <= 0) return 0;
// if (size >= 64) return 0xFFFFFFFFFFFFFFFF;
// return (1ULL << size) - 1;
// };
// uint64_t lane_en = gen_ones(row_size);
// printf("lane en is %lu", lane_en);
// //uint64_t lane_en = (1ULL << row_size) - 1;
// asm volatile("s_mov_b64 exec, %[s_lane_en]"
// :
// : [s_lane_en]"s"(lane_en)
// : );
move_tile_window(x_window, {Block_M, 0});
move_tile_window(dy_window, {Block_M, 0});
move_tile_window(mean_window, {Block_M});
move_tile_window(inv_std_window, {Block_M});
sweep_tile(x_tile, [&](auto idx) {
constexpr auto i_idx = make_tuple(idx[number<0>{}]);
constexpr auto j_idx = make_tuple(idx[number<1>{}]);
const auto x = type_convert<ComputeDataType>(x_tile[idx]);
const auto dy = type_convert<ComputeDataType>(dy_tile[idx]);
const auto gamma = type_convert<ComputeDataType>(gamma_tile[j_idx]);
ds_tile(i_idx) += dy * gamma * x;
db_tile(i_idx) += dy * gamma;
// printf("db_tile pre: threadidx=%d, blockidx=%d, db_tile=%f\n",threadIdx.x, blockIdx.x, db_tile[i_idx]);
// printf("dy_tile: threadidx=%d, blockidx=%d, dy_tile=%f\n",threadIdx.x, blockIdx.x, dy);
// printf("x: threadidx=%d, blockidx=%d, x_tile=%f\n",threadIdx.x, blockIdx.x, x);
// printf("gamma: threadidx=%d, blockidx=%d, gamma_tile=%f\n",threadIdx.x, blockIdx.x, gamma);
const auto mean = type_convert<ComputeDataType>(mean_tile[i_idx]);
const auto inv_std = type_convert<ComputeDataType>(inv_std_tile[i_idx]);
dbeta(j_idx) += dy;
dgamma(j_idx) += dy * (x - mean) * inv_std;
printf("dy: threadidx=%d, blockidx=%d, x_tile=%f\n",threadIdx.x, blockIdx.x, dy);
});
}
auto block_reduce2d_sync = ReducePolicy::template GetBlockReduce2dSync<Problem>();
auto block_reduce2d_cross_warp_sync = ReducePolicy::template GetBlockReduce2dCrossWarpSync<Problem>();
block_reduce2d_sync(ds_tile, ck_tile::ReduceOp::Add{});
block_reduce2d_sync(db_tile, ck_tile::ReduceOp::Add{});
// block_reduce2d_cross_warp_sync(ds_tile, smem, ck_tile::ReduceOp::Add{});
// block_reduce2d_cross_warp_sync(db_tile, smem, ck_tile::ReduceOp::Add{});
// sweep_tile(x_tile, [&](auto idx) {
// constexpr auto i_idx = make_tuple(idx[number<0>{}]);
// printf("db_tile post: threadidx=%d, blockidx=%d, db_tile=%f\n",threadIdx.x, blockIdx.x,
// db_tile[i_idx]);
// });
// store_tile(ds_window, ds_tile);
// store_tile(db_window, db_tile);
using XDistributedTensor = decltype(load_tile(x_window));
constexpr auto spans = XDistributedTensor::get_distributed_spans();
sweep_tile_span(spans[number<0>{}], [&](auto i_idx) {
constexpr auto idx0 = make_tuple(i_idx);
const auto mean = type_convert<ComputeDataType>(mean_tile[idx0]);
const auto inv_std = type_convert<ComputeDataType>(inv_std_tile[idx0]);
auto b = (db_tile[idx0] * mean - ds_tile[idx0]) * inv_std * inv_std * inv_std / row_size;
auto c = -b * mean - db_tile[idx0] * inv_std / row_size;
sweep_tile_span(spans[number<1>{}], [&](auto j_idx) {
constexpr auto idx1 = make_tuple(j_idx);
constexpr auto idx = make_tuple(i_idx, j_idx);
//constexpr auto gb_idx = make_tuple(number<0>{}, j_idx);
const auto x = type_convert<ComputeDataType>(x_tile[idx]);
const auto dy = type_convert<ComputeDataType>(dy_tile[idx]);
const auto gamma = type_convert<ComputeDataType>(gamma_tile[idx1]);
// dbeta(gb_idx) += dy;
// dgamma(gb_idx) += dy * (x - mean) * inv_std;
dx(idx) = dy * gamma * inv_std + b * x + c;
//printf("dx: threadidx=%d, blockidx=%d, dx_tile=%f\n",threadIdx.x, blockIdx.x, dx(idx));
block_reduce2d_sync(dbeta, ck_tile::ReduceOp::Add{});
block_reduce2d_sync(dgamma, ck_tile::ReduceOp::Add{});
sweep_tile(dbeta, [&](auto idx) {
printf("dbeta pre: threadidx=%d, blockidx=%d, dbeta=%f\n",threadIdx.x, blockIdx.x,
dbeta[idx]);
});
block_reduce2d_cross_warp_sync(dbeta, smem, ck_tile::ReduceOp::Add{});
block_reduce2d_cross_warp_sync(dgamma, smem, ck_tile::ReduceOp::Add{});
sweep_tile(dbeta, [&](auto idx) {
printf("dbeta post: threadidx=%d, blockidx=%d, dbeta=%f\n",threadIdx.x, blockIdx.x,
dbeta[idx]);
});
// store_tile(dbeta_window, cast_tile<BetaDataType>(dbeta));
// store_tile(dgamma_window, cast_tile<GammaDataType>(dgamma));
store_tile(dx_window, cast_tile<XDataType>(dx));
store_tile(dbeta_window, cast_tile<BetaDataType>(dbeta));
store_tile(dgamma_window, cast_tile<GammaDataType>(dgamma));
}
};
} // namespace ck_tile
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