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Unverified Commit a11cf2c6 authored by arai713's avatar arai713 Committed by GitHub
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Merge branch 'develop' into codegen_hiprtc

parents a72e9efa 64d5c4d6
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example/ck_tile/15_fused_moe/instances/fused_moe_api.cpp
View file @ a11cf2c6
......@@ -41,6 +41,7 @@ float fused_moe(fused_moe_traits t, fused_moe_args a, const ck_tile::stream_conf
t.prec_sq,
t.prec_kw,
t.block_m,
t.activation,
t.gate_only,
t.fused_quant};
auto a1 = fused_moegemm_args{
......
example/ck_tile/15_fused_moe/instances/fused_moegemm_api.cpp
View file @ a11cf2c6
......@@ -17,15 +17,67 @@ float fused_moegemm(fused_moegemm_traits t, fused_moegemm_args a, const ck_tile:
// clang-format off
float r = -1;
if(t.prec_i == "bf16" && t.prec_w == "bf16" && t.prec_o == "bf16" && t.prec_st == "fp32" &&
t.prec_sw == "fp32" && t.prec_sq == "fp32" && t.prec_kw == "fp32" && t.block_m == 32 && t.gate_only == 1)
t.prec_sw == "fp32" && t.prec_sq == "fp32" && t.prec_kw == "fp32" && t.block_m == 32 && t.gate_only == 1 && t.activation == 0)
{
using t_ = fmoe_<ck_tile::bf16_t, ck_tile::bf16_t, ck_tile::bf16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, 1, 0>;
constexpr ck_tile::index_t act_ = 0;
constexpr ck_tile::index_t go_ = 1;
using t_ = fmoe_<ck_tile::bf16_t, ck_tile::bf16_t, ck_tile::bf16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, act_, go_, 0>;
r = fused_moegemm_<t_>(s, a);
}
else if(t.prec_i == "bf16" && t.prec_w == "bf16" && t.prec_o == "bf16" && t.prec_st == "fp32" &&
t.prec_sw == "fp32" && t.prec_sq == "fp32" && t.prec_kw == "fp32" && t.block_m == 32 && t.gate_only == 0 && t.activation == 0)
{
constexpr ck_tile::index_t act_ = 0;
constexpr ck_tile::index_t go_ = 0;
using t_ = fmoe_<ck_tile::bf16_t, ck_tile::bf16_t, ck_tile::bf16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, act_, go_, 0>;
r = fused_moegemm_<t_>(s, a);
}
else if(t.prec_i == "fp16" && t.prec_w == "fp16" && t.prec_o == "fp16" && t.prec_st == "fp32" &&
t.prec_sw == "fp32" && t.prec_sq == "fp32" && t.prec_kw == "fp32" && t.block_m == 32 && t.gate_only == 1 && t.activation == 0)
{
constexpr ck_tile::index_t act_ = 0;
constexpr ck_tile::index_t go_ = 1;
using t_ = fmoe_<ck_tile::fp16_t, ck_tile::fp16_t, ck_tile::fp16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, act_, go_, 0>;
r = fused_moegemm_<t_>(s, a);
}
else if(t.prec_i == "fp16" && t.prec_w == "fp16" && t.prec_o == "fp16" && t.prec_st == "fp32" &&
t.prec_sw == "fp32" && t.prec_sq == "fp32" && t.prec_kw == "fp32" && t.block_m == 32 && t.gate_only == 0 && t.activation == 0)
{
constexpr ck_tile::index_t act_ = 0;
constexpr ck_tile::index_t go_ = 0;
using t_ = fmoe_<ck_tile::fp16_t, ck_tile::fp16_t, ck_tile::fp16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, act_, go_, 0>;
r = fused_moegemm_<t_>(s, a);
}
else if(t.prec_i == "bf16" && t.prec_w == "bf16" && t.prec_o == "bf16" && t.prec_st == "fp32" &&
t.prec_sw == "fp32" && t.prec_sq == "fp32" && t.prec_kw == "fp32" && t.block_m == 32 && t.gate_only == 1 && t.activation == 1)
{
constexpr ck_tile::index_t act_ = 1;
constexpr ck_tile::index_t go_ = 1;
using t_ = fmoe_<ck_tile::bf16_t, ck_tile::bf16_t, ck_tile::bf16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, act_, go_, 0>;
r = fused_moegemm_<t_>(s, a);
}
else if(t.prec_i == "bf16" && t.prec_w == "bf16" && t.prec_o == "bf16" && t.prec_st == "fp32" &&
t.prec_sw == "fp32" && t.prec_sq == "fp32" && t.prec_kw == "fp32" && t.block_m == 32 && t.gate_only == 0 && t.activation == 1)
{
constexpr ck_tile::index_t act_ = 1;
constexpr ck_tile::index_t go_ = 0;
using t_ = fmoe_<ck_tile::bf16_t, ck_tile::bf16_t, ck_tile::bf16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, act_, go_, 0>;
r = fused_moegemm_<t_>(s, a);
}
else if(t.prec_i == "fp16" && t.prec_w == "fp16" && t.prec_o == "fp16" && t.prec_st == "fp32" &&
t.prec_sw == "fp32" && t.prec_sq == "fp32" && t.prec_kw == "fp32" && t.block_m == 32 && t.gate_only == 1 && t.activation == 1)
{
constexpr ck_tile::index_t act_ = 1;
constexpr ck_tile::index_t go_ = 1;
using t_ = fmoe_<ck_tile::fp16_t, ck_tile::fp16_t, ck_tile::fp16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, act_, go_, 0>;
r = fused_moegemm_<t_>(s, a);
}
else if(t.prec_i == "fp16" && t.prec_w == "fp16" && t.prec_o == "fp16" && t.prec_st == "fp32" &&
t.prec_sw == "fp32" && t.prec_sq == "fp32" && t.prec_kw == "fp32" && t.block_m == 32 && t.gate_only == 1)
t.prec_sw == "fp32" && t.prec_sq == "fp32" && t.prec_kw == "fp32" && t.block_m == 32 && t.gate_only == 0 && t.activation == 1)
{
using t_ = fmoe_<ck_tile::fp16_t, ck_tile::fp16_t, ck_tile::fp16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, 1, 0>;
constexpr ck_tile::index_t act_ = 1;
constexpr ck_tile::index_t go_ = 0;
using t_ = fmoe_<ck_tile::fp16_t, ck_tile::fp16_t, ck_tile::fp16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, act_, go_, 0>;
r = fused_moegemm_<t_>(s, a);
}
// clang-format on
......
example/ck_tile/15_fused_moe/instances/fused_moegemm_api_internal.hpp
View file @ a11cf2c6
......@@ -21,21 +21,31 @@ float fused_moegemm_(const ck_tile::stream_config& s, fused_moegemm_args a)
typename Ts_::BlockTile_1,
typename Ts_::WarpPerBlock_0,
typename Ts_::WarpTile_0>;
using f_problem =
ck_tile::FusedMoeGemmPipelineProblem<typename Ts_::ADataType,
typename Ts_::GDataType,
typename Ts_::DDataType,
typename Ts_::AccDataType,
typename Ts_::ODataType,
typename Ts_::AScaleDataType,
typename Ts_::GScaleDataType,
typename Ts_::DScaleDataType,
typename Ts_::YSmoothScaleDataType,
typename Ts_::TopkWeightDataType,
typename Ts_::IndexDataType,
ck_tile::element_wise::FastGeluAsm, // TODO: hardcoded
f_shape,
f_traits>;
constexpr auto get_activation_ = []() {
if constexpr(Ts_::Activation == 0)
{
return ck_tile::element_wise::FastGeluAsm{};
}
else
return ck_tile::element_wise::Silu{};
};
using f_act_ = ck_tile::remove_cvref_t<decltype(get_activation_())>;
using f_problem = ck_tile::FusedMoeGemmPipelineProblem<typename Ts_::ADataType,
typename Ts_::GDataType,
typename Ts_::DDataType,
typename Ts_::AccDataType,
typename Ts_::ODataType,
typename Ts_::AScaleDataType,
typename Ts_::GScaleDataType,
typename Ts_::DScaleDataType,
typename Ts_::YSmoothScaleDataType,
typename Ts_::TopkWeightDataType,
typename Ts_::IndexDataType,
f_act_, // TODO: hardcoded
f_shape,
f_traits>;
// using f_pipeline = ck_tile::FusedMoeGemmPipeline_FlatmmEx<f_problem>;
using f_pipeline = ck_tile::FusedMoeGemmPipeline_FlatmmUk<f_problem>;
......
example/ck_tile/15_fused_moe/instances/fused_moegemm_api_traits.hpp
View file @ a11cf2c6
......@@ -15,7 +15,8 @@ template <typename I,
typename KW,
typename BlockTIle_, // seq<b_token, b_interm, b_hidden, b_down>
typename WarpPerBlock_,
typename WarpTile_, // seq<*,*,*>, used to select mfma
typename WarpTile_, // seq<*,*,*>, used to select mfma
ck_tile::index_t Activation_ = 0, // 0: Gelu 1: Silu
ck_tile::index_t GateOnly_ = 0,
ck_tile::index_t FusedQuant_ = 0>
struct fmoe_ // traits, ugly name, only used for internal
......@@ -44,10 +45,11 @@ struct fmoe_ // traits, ugly name, only used for internal
using WarpPerBlock_0 = ck_tile::remove_cvref_t<WarpPerBlock_>;
using WarpTile_0 = ck_tile::remove_cvref_t<WarpTile_>;
using BlockTile_1 = ck_tile::sequence<BT_, BD_, BI_ / (GateOnly_ ? 1 : 2)>;
using BlockTile_1 = ck_tile::sequence<BT_, BD_, BI_>;
using WarpPerBlock_1 = ck_tile::remove_cvref_t<WarpPerBlock_>;
using WarpTile_1 = ck_tile::remove_cvref_t<WarpTile_>;
static constexpr ck_tile::index_t Activation = Activation_; // 0: Gelu 1: Silu
static constexpr ck_tile::index_t GateOnly = GateOnly_;
static constexpr ck_tile::index_t FusedQuant = FusedQuant_;
};
example/ck_tile/15_fused_moe/instances/fused_moegemm_bf16_m32.cpp
View file @ a11cf2c6
......@@ -8,7 +8,18 @@
// clang-format off
template float fused_moegemm_<
fmoe_<ck_tile::bf16_t, ck_tile::bf16_t, ck_tile::bf16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, 1, 0>
fmoe_<ck_tile::bf16_t, ck_tile::bf16_t, ck_tile::bf16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, 0, 0, 0>
>(const ck_tile::stream_config& s, fused_moegemm_args a);
template float fused_moegemm_<
fmoe_<ck_tile::bf16_t, ck_tile::bf16_t, ck_tile::bf16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, 0, 1, 0>
>(const ck_tile::stream_config& s, fused_moegemm_args a);
template float fused_moegemm_<
fmoe_<ck_tile::bf16_t, ck_tile::bf16_t, ck_tile::bf16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, 1, 0, 0>
>(const ck_tile::stream_config& s, fused_moegemm_args a);
template float fused_moegemm_<
fmoe_<ck_tile::bf16_t, ck_tile::bf16_t, ck_tile::bf16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, 1, 1, 0>
>(const ck_tile::stream_config& s, fused_moegemm_args a);
// clang-format on
example/ck_tile/15_fused_moe/instances/fused_moegemm_fp16_m32.cpp
View file @ a11cf2c6
......@@ -8,7 +8,19 @@
// clang-format off
template float fused_moegemm_<
fmoe_<ck_tile::fp16_t, ck_tile::fp16_t, ck_tile::fp16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, 1, 0>
fmoe_<ck_tile::fp16_t, ck_tile::fp16_t, ck_tile::fp16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, 0, 0, 0>
>(const ck_tile::stream_config& s, fused_moegemm_args a);
template float fused_moegemm_<
fmoe_<ck_tile::fp16_t, ck_tile::fp16_t, ck_tile::fp16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, 0, 1, 0>
>(const ck_tile::stream_config& s, fused_moegemm_args a);
template float fused_moegemm_<
fmoe_<ck_tile::fp16_t, ck_tile::fp16_t, ck_tile::fp16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, 1, 0, 0>
>(const ck_tile::stream_config& s, fused_moegemm_args a);
template float fused_moegemm_<
fmoe_<ck_tile::fp16_t, ck_tile::fp16_t, ck_tile::fp16_t, float, float, float, float, S<32, 512, 128, 128>, S<1, 4, 1>, S<16, 16, 32>, 1, 1, 0>
>(const ck_tile::stream_config& s, fused_moegemm_args a);
// clang-format on
example/ck_tile/15_fused_moe/main.cpp
View file @ a11cf2c6
......@@ -108,12 +108,14 @@ auto create_args(int argc, char* argv[])
.insert(
"gate_only", "1", "w0(gate/up) style, 0:gate+up will double interm size, 1:only gate")
.insert("api", "0", "benchmark api set: 0:fused-moe(moe-gemm+moe-sorting), 1:moe-gemm")
.insert("act", "0", "activation after first gemm. 0:gelu, 1:silu")
.insert("balance",
"0",
"if set to 1, will try balance the expert in topk-ids(convenient for testing)")
.insert("init",
"2",
"init method. 0:random stepped float(fast). 1: random uniform, 2:rand normalized"
"1",
"init method. 0:random stepped float(fast). 1: random uniform[-0.5, 0.5], 2:rand "
"normalized[0, 1]"
"normalized(slow)")
.insert("seed", "11939", "seed used to do random")
.insert("warmup", "5", "cold iter")
......@@ -135,6 +137,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
ck_tile::index_t intermediate_size = arg_parser.get_int("i");
ck_tile::index_t stride = arg_parser.get_int("stride");
ck_tile::index_t block_m = arg_parser.get_int("bm");
ck_tile::index_t activation = arg_parser.get_int("act");
if(stride < 0)
stride = hidden_size;
std::string prec_i = arg_parser.get_str("prec_i");
......@@ -194,11 +197,14 @@ bool run(const ck_tile::ArgParser& arg_parser)
return std::string(", st:") + std::to_string(stride);
}();
std::cout << "[" << api_str << "|" << prec_str << "]"
<< " t:" << tokens << ", e:" << experts << ", k:" << topk << stride_str
<< ", hidden:" << hidden_size << ", interm:" << intermediate_size << ", tp:" << tp
<< ", shrd_interm:" << shared_intermediate_size_0 << "|" << shared_intermediate_size_1
<< ", go:" << gate_only << ", q:" << fused_quant << std::flush;
std::cout
<< "[" << api_str << "|" << prec_str << "]"
<< " t:" << tokens << ", e:" << experts << ", k:" << topk << stride_str
<< ", hidden:" << hidden_size << ", interm:" << intermediate_size << ", tp:" << tp
<< ", act:"
<< activation
// << ", shrd_interm:" << shared_intermediate_size_0 << "|" << shared_intermediate_size_1
<< (gate_only ? ", g1u0" : ", g1u1") << ", q:" << fused_quant << std::flush;
using TypeConfig = FusedMoeGemmTypeConfig<I, W, O, ST, SW, SQ, KW>;
using ADataType = typename TypeConfig::ADataType;
......@@ -370,6 +376,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
prec_sq,
prec_kw,
block_m,
activation,
gate_only,
fused_quant};
......@@ -389,7 +396,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
num_sorted_tiles_buf.GetDeviceBuffer(),
block_m,
hidden_size,
shared_intermediate_size_0,
intermediate_size / tp,
tokens,
experts,
topk,
......@@ -408,6 +415,28 @@ bool run(const ck_tile::ArgParser& arg_parser)
<< cal_tbps(ave_time) << " TB/s" << std::flush;
bool pass = true;
#define CPU_FUSED_MOE(act_type_) \
ck_tile::reference_fused_moe<AccDataType, act_type_>(a_host, \
g_host, \
d_host, \
sa_host, \
sg_host, \
sd_host, \
sy_host, \
o_host, \
sorted_token_ids_host, \
sorted_weight_host, \
sorted_expert_ids_host, \
num_sorted_tiles_host, \
topk_ids_host, \
block_m, \
tokens, \
experts, \
hidden_size, \
intermediate_size / tp, \
topk, \
gate_only)
if(do_validation)
{
ck_tile::reference_moe_sorting<TopkWeightDataType, IndexDataType>(
......@@ -419,28 +448,14 @@ bool run(const ck_tile::ArgParser& arg_parser)
num_sorted_tiles_host.mData[0],
experts,
block_m);
ck_tile::reference_fused_moe<AccDataType, ck_tile::element_wise::Gelu>(
a_host,
g_host,
d_host,
sa_host,
sg_host,
sd_host,
sy_host,
o_host,
sorted_token_ids_host,
sorted_weight_host,
sorted_expert_ids_host,
num_sorted_tiles_host,
topk_ids_host,
block_m,
tokens,
experts,
hidden_size,
shared_intermediate_size_0,
topk,
gate_only);
if(activation == 0)
{
CPU_FUSED_MOE(ck_tile::element_wise::Gelu);
}
else
{
CPU_FUSED_MOE(ck_tile::element_wise::Silu);
}
auto o_dev = o_buf.ToHost<ODataType>();
// o_dev.savetxt("gpu-out.txt", "float");
......@@ -491,6 +506,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
prec_sq,
prec_kw,
block_m,
activation,
gate_only,
fused_quant};
......@@ -507,7 +523,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
sorted_expert_ids_buf.GetDeviceBuffer(),
num_sorted_tiles_buf.GetDeviceBuffer(),
hidden_size,
shared_intermediate_size_0,
intermediate_size / tp,
tokens,
experts,
topk,
......@@ -529,27 +545,14 @@ bool run(const ck_tile::ArgParser& arg_parser)
if(do_validation)
{
ck_tile::reference_fused_moe<AccDataType, ck_tile::element_wise::Gelu>(
a_host,
g_host,
d_host,
sa_host,
sg_host,
sd_host,
sy_host,
o_host,
sorted_token_ids_host,
sorted_weight_host,
sorted_expert_ids_host,
num_sorted_tiles_host,
topk_ids_host,
block_m,
tokens,
experts,
hidden_size,
shared_intermediate_size_0,
topk,
gate_only);
if(activation == 0)
{
CPU_FUSED_MOE(ck_tile::element_wise::Gelu);
}
else
{
CPU_FUSED_MOE(ck_tile::element_wise::Silu);
}
auto o_dev = o_buf.ToHost<ODataType>();
// o_dev.savetxt("gpu-out.txt", "float");
......
example/ck_tile/16_batched_gemm/batched_gemm.cpp
View file @ a11cf2c6
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
#include <hip/hip_runtime.h>
......@@ -51,7 +51,7 @@ float batched_gemm(const ck_tile::BatchedGemmHostArgs& args, const ck_tile::stre
ck_tile::sequence<M_Warp, N_Warp, K_Warp>,
ck_tile::sequence<M_Warp_Tile, N_Warp_Tile, K_Warp_Tile>>;
using TilePartitioner = ck_tile::GemmTilePartitioner<CodegenGemmShape>;
using TilePartitioner = ck_tile::GemmTile2DPartitioner<CodegenGemmShape>;
using GemmEpilogue = std::conditional_t<
CShuffleEpilogue,
......@@ -63,8 +63,8 @@ float batched_gemm(const ck_tile::BatchedGemmHostArgs& args, const ck_tile::stre
kOutputRank,
1,
0,
TilePartitioner::kM,
TilePartitioner::kN>>,
TilePartitioner::MPerBlock,
TilePartitioner::NPerBlock>>,
ck_tile::Default2DEpilogue<
ck_tile::Default2DEpilogueProblem<AccDataType, CDataType, kPadM, kPadN>>>;
......
example/ck_tile/16_batched_gemm/run_batched_gemm_example.inc
View file @ a11cf2c6
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
template <typename Layout>
static constexpr inline auto is_row_major(Layout layout_)
{
return ck_tile::bool_constant<std::is_same_v<ck_tile::remove_cvref_t<decltype(layout_)>,
ck_tile::tensor_layout::gemm::RowMajor>>{};
}
auto calculate_rtol_atol(const ck_tile::index_t K,
const ck_tile::index_t kbatch,
const float max_accumulated_value)
{
using ComputeType =
std::conditional_t<sizeof(ADataType) < sizeof(BDataType), ADataType, BDataType>;
// Calculate thresholds
const auto rtol = ck_tile::get_relative_threshold<ComputeType, CDataType, AccDataType>(
ck_tile::integer_divide_ceil(K, kbatch));
const auto atol = ck_tile::get_absolute_threshold<ComputeType, CDataType, AccDataType>(
max_accumulated_value / kbatch, ck_tile::integer_divide_ceil(K, kbatch));
// Calculate error due to split_k accumulation
const auto rtol_split_k =
ck_tile::get_relative_threshold<CDataType, CDataType, CDataType>(kbatch);
const auto atol_split_k = ck_tile::get_absolute_threshold<CDataType, CDataType, CDataType>(
max_accumulated_value, kbatch);
// Use higher threshold
return ck_tile::make_tuple(std::max(rtol, rtol_split_k), std::max(atol, atol_split_k));
}
template <typename ALayout, typename BLayout, typename CLayout>
float invoke_batched_gemm(ck_tile::DeviceMem& a_m_k_dev_buf,
ck_tile::DeviceMem& b_k_n_dev_buf,
......@@ -86,56 +113,16 @@ int run_batched_gemm_example_with_layouts(int argc,
int n_warmup = arg_parser.get_int("warmup");
int n_repeat = arg_parser.get_int("repeat");
using namespace ck_tile::literals;
auto f_host_tensor_descriptor = [](std::size_t batch_count_,
std::size_t row,
std::size_t col,
std::size_t stride,
std::size_t batch_stride,
auto layout) {
if constexpr(std::is_same_v<decltype(layout), ck_tile::tensor_layout::gemm::RowMajor>)
{
return ck_tile::HostTensorDescriptor({batch_count_, row, col},
{batch_stride, stride, 1_uz});
}
else
{
return ck_tile::HostTensorDescriptor({batch_count_, row, col},
{batch_stride, 1_uz, stride});
}
};
auto f_get_default_stride = [](std::size_t row,
std::size_t col,
std::size_t stride,
auto layout) {
if(stride == 0)
{
// give a chance if stride is zero, return a default packed stride
if constexpr(std::is_same_v<decltype(layout), ck_tile::tensor_layout::gemm::RowMajor>)
{
return col;
}
else
{
return row;
}
}
else
return stride;
};
stride_A = f_get_default_stride(M, K, stride_A, a_layout);
stride_B = f_get_default_stride(K, N, stride_B, b_layout);
stride_C = f_get_default_stride(M, N, stride_C, c_layout);
ck_tile::HostTensor<ADataType> a_m_k(
f_host_tensor_descriptor(batch_count, M, K, stride_A, batch_stride_A, a_layout));
ck_tile::HostTensor<BDataType> b_k_n(
f_host_tensor_descriptor(batch_count, K, N, stride_B, batch_stride_B, b_layout));
ck_tile::HostTensor<CDataType> c_m_n_dev_result(
f_host_tensor_descriptor(batch_count, M, N, stride_C, batch_stride_C, c_layout));
stride_A = ck_tile::get_default_stride(M, K, stride_A, is_row_major(a_layout));
stride_B = ck_tile::get_default_stride(K, N, stride_B, is_row_major(b_layout));
stride_C = ck_tile::get_default_stride(M, N, stride_C, is_row_major(c_layout));
ck_tile::HostTensor<ADataType> a_m_k(ck_tile::host_tensor_descriptor(
batch_count, M, K, stride_A, batch_stride_A, is_row_major(a_layout)));
ck_tile::HostTensor<BDataType> b_k_n(ck_tile::host_tensor_descriptor(
batch_count, K, N, stride_B, batch_stride_B, is_row_major(b_layout)));
ck_tile::HostTensor<CDataType> c_m_n_dev_result(ck_tile::host_tensor_descriptor(
batch_count, M, N, stride_C, batch_stride_C, is_row_major(c_layout)));
ck_tile::FillUniformDistribution<ADataType>{-5.f, 5.f}(a_m_k);
ck_tile::FillUniformDistribution<BDataType>{-5.f, 5.f}(b_k_n);
......@@ -171,23 +158,33 @@ int run_batched_gemm_example_with_layouts(int argc,
if(arg_parser.get_int("v") == 1)
{
ck_tile::HostTensor<CDataType> c_m_n_host_ref(
f_host_tensor_descriptor(batch_count, M, N, stride_C, batch_stride_C, CLayout{}));
ck_tile::HostTensor<CDataType> c_m_n_host_ref(ck_tile::host_tensor_descriptor(
batch_count, M, N, stride_C, batch_stride_C, is_row_major(CLayout){}));
c_m_n_host_ref.SetZero();
const auto b_n_k = b_k_n.transpose({0, 2, 1});
ck_tile::reference_batched_gemm<ADataType, BDataType, AccDataType, CDataType>(
a_m_k, b_n_k, c_m_n_host_ref);
pass = ck_tile::check_err(c_m_n_dev_result, c_m_n_host_ref);
const float max_accumulated_value =
*std::max_element(c_m_n_host_ref.mData.begin(), c_m_n_host_ref.mData.end());
const auto rtol_atol = calculate_rtol_atol(K, kbatch, max_accumulated_value);
pass = ck_tile::check_err(c_m_n_dev_result,
c_m_n_host_ref,
"Error: Incorrect results!",
rtol_atol.at(ck_tile::number<0>{}),
rtol_atol.at(ck_tile::number<1>{}));
std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
<< " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
<< std::endl;
std::cout << "The CPU veification result is:" << (pass ? "correct" : "fail") << std::endl;
}
else if(arg_parser.get_int("v") == 2)
{
ck_tile::HostTensor<CDataType> c_m_n_gpu_ref(
f_host_tensor_descriptor(batch_count, M, N, stride_C, batch_stride_C, CLayout{}));
ck_tile::HostTensor<CDataType> c_m_n_gpu_ref(ck_tile::host_tensor_descriptor(
batch_count, M, N, stride_C, batch_stride_C, is_row_major(CLayout){}));
ck_tile::DeviceMem c_m_n_gpu_buf_ref(c_m_n_gpu_ref.get_element_space_size_in_bytes());
c_m_n_gpu_ref.SetZero();
c_m_n_gpu_buf_ref.SetZero();
......@@ -240,7 +237,18 @@ int run_batched_gemm_example_with_layouts(int argc,
ck_tile::hip_check_error(hipFree(d_C));
c_m_n_gpu_buf_ref.FromDevice(c_m_n_gpu_ref.data());
pass = ck_tile::check_err(c_m_n_dev_result, c_m_n_gpu_ref);
const float max_accumulated_value =
*std::max_element(c_m_n_gpu_ref.mData.begin(), c_m_n_gpu_ref.mData.end());
const auto rtol_atol = calculate_rtol_atol(K, kbatch, max_accumulated_value);
pass = ck_tile::check_err(c_m_n_dev_result,
c_m_n_gpu_ref,
"Error: Incorrect results!",
rtol_atol.at(ck_tile::number<0>{}),
rtol_atol.at(ck_tile::number<1>{}));
std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
<< " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
<< std::endl;
std::cout << "The GPU verification result is: " << (pass ? "correct" : "fail") << std::endl;
}
......
example/ck_tile/17_grouped_gemm/grouped_gemm.cpp
View file @ a11cf2c6
......@@ -15,7 +15,6 @@
#include "ck_tile/ops/gemm.hpp"
#include "ck_tile/host.hpp"
#include "grouped_gemm.hpp"
#include "utils.hpp"
namespace {
......@@ -102,7 +101,7 @@ using Kernel = ck_tile::GroupedGemmKernel<TilePartitioner,
GemmEpilogue<CLayout>>;
}; // namespace
std::size_t GetWorkspaceSize(const std::vector<grouped_gemm_kargs>& gemm_descs)
std::size_t get_workspace_size(const std::vector<grouped_gemm_kargs>& gemm_descs)
{
return ::Kernel<std::nullptr_t, std::nullptr_t, std::nullptr_t>::GetWorkSpaceSize(gemm_descs);
}
......
example/ck_tile/17_grouped_gemm/grouped_gemm.hpp
View file @ a11cf2c6
......@@ -52,8 +52,8 @@ auto create_args(int argc, char* argv[])
return std::make_tuple(result, arg_parser);
}
std::size_t GetWorkspaceSize(const std::vector<grouped_gemm_kargs>& gemm_descs);
std::size_t get_workspace_size(const std::vector<grouped_gemm_kargs>& gemm_descs);
float grouped_gemm_calc(const std::vector<grouped_gemm_kargs>& gemm_descs,
const ck_tile::stream_config& s,
void* p_workspace_);
float grouped_gemm(const std::vector<grouped_gemm_kargs>& gemm_descs,
const ck_tile::stream_config& s,
void* p_workspace_);
example/ck_tile/17_grouped_gemm/run_grouped_gemm_example.inc
View file @ a11cf2c6
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
template <typename Layout>
static constexpr inline auto is_row_major(Layout layout_)
{
return ck_tile::bool_constant<std::is_same_v<ck_tile::remove_cvref_t<decltype(layout_)>,
ck_tile::tensor_layout::gemm::RowMajor>>{};
}
auto calculate_rtol_atol(const ck_tile::index_t K,
const ck_tile::index_t kbatch,
const float max_accumulated_value)
{
using ComputeType =
std::conditional_t<sizeof(ADataType) < sizeof(BDataType), ADataType, BDataType>;
// Calculate thresholds
const auto rtol = ck_tile::get_relative_threshold<ComputeType, CDataType, AccDataType>(
ck_tile::integer_divide_ceil(K, kbatch));
const auto atol = ck_tile::get_absolute_threshold<ComputeType, CDataType, AccDataType>(
max_accumulated_value / kbatch, ck_tile::integer_divide_ceil(K, kbatch));
// Calculate error due to split_k accumulation
const auto rtol_split_k =
ck_tile::get_relative_threshold<CDataType, CDataType, CDataType>(kbatch);
const auto atol_split_k = ck_tile::get_absolute_threshold<CDataType, CDataType, CDataType>(
max_accumulated_value, kbatch);
// Use higher threshold
return ck_tile::make_tuple(std::max(rtol, rtol_split_k), std::max(atol, atol_split_k));
}
template <typename ALayout, typename BLayout, typename CLayout>
float invoke_gemm(int n_warmup,
int n_repeat,
......@@ -11,7 +38,7 @@ float invoke_gemm(int n_warmup,
{
ck_tile::DeviceMem gemm_workspace;
gemm_workspace.Realloc(GetWorkspaceSize(args));
gemm_workspace.Realloc(get_workspace_size(args));
float ave_time = grouped_gemm<ALayout, BLayout, CLayout>(
args,
......@@ -108,16 +135,19 @@ int run_grouped_gemm_example_with_layouts(int argc,
const ck_tile::index_t N = Ns[i];
const ck_tile::index_t K = Ks[i];
stride_As[i] = f_get_default_stride(M, N, stride_As[i], a_layout);
stride_Bs[i] = f_get_default_stride(K, N, stride_Bs[i], b_layout);
stride_Cs[i] = f_get_default_stride(M, N, stride_Cs[i], CLayout{});
a_m_k_tensors.push_back(
ck_tile::HostTensor<ADataType>(f_host_tensor_descriptor(M, K, stride_As[i], a_layout)));
b_k_n_tensors.push_back(
ck_tile::HostTensor<BDataType>(f_host_tensor_descriptor(K, N, stride_Bs[i], b_layout)));
stride_As[i] =
ck_tile::get_default_stride(M, N, stride_As[i], is_row_major(a_layout));
stride_Bs[i] =
ck_tile::get_default_stride(K, N, stride_Bs[i], is_row_major(b_layout));
stride_Cs[i] =
ck_tile::get_default_stride(M, N, stride_Cs[i], is_row_major(CLayout{}));
a_m_k_tensors.push_back(ck_tile::HostTensor<ADataType>(
ck_tile::host_tensor_descriptor(M, K, stride_As[i], is_row_major(a_layout))));
b_k_n_tensors.push_back(ck_tile::HostTensor<BDataType>(
ck_tile::host_tensor_descriptor(K, N, stride_Bs[i], is_row_major(b_layout))));
c_m_n_tensors.push_back(ck_tile::HostTensor<CDataType>(
f_host_tensor_descriptor(M, N, stride_Cs[i], CLayout{})));
ck_tile::host_tensor_descriptor(M, N, stride_Cs[i], is_row_major(CLayout{}))));
std::cout << "gemm[" << i << "]"
<< " a_m_k: " << a_m_k_tensors[i].mDesc << " b_k_n: " << b_k_n_tensors[i].mDesc
......@@ -157,12 +187,23 @@ int run_grouped_gemm_example_with_layouts(int argc,
{
for(int i = 0; i < group_count; ++i)
{
ck_tile::HostTensor<CDataType> c_m_n_host_ref(
f_host_tensor_descriptor(Ms[i], Ns[i], stride_Cs[i], CLayout{}));
ck_tile::HostTensor<CDataType> c_m_n_host_ref(ck_tile::host_tensor_descriptor(
Ms[i], Ns[i], stride_Cs[i], is_row_major(CLayout{})));
c_m_n_host_ref.SetZero();
ck_tile::reference_gemm<ADataType, BDataType, AccDataType, CDataType>(
a_m_k_tensors[i], b_k_n_tensors[i], c_m_n_host_ref);
pass &= ck_tile::check_err(c_m_n_tensors[i], c_m_n_host_ref);
const float max_accumulated_value =
*std::max_element(c_m_n_host_ref.mData.begin(), c_m_n_host_ref.mData.end());
const auto rtol_atol = calculate_rtol_atol(Ks[i], 1 /*kbatch*/, max_accumulated_value);
pass &= ck_tile::check_err(c_m_n_tensors[i],
c_m_n_host_ref,
"Error: Incorrect results!",
rtol_atol.at(ck_tile::number<0>{}),
rtol_atol.at(ck_tile::number<1>{}));
std::cout << "gemm[" << i
<< "] Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
<< " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
<< std::endl;
}
std::cout << "The CPU veification result is:" << (pass ? "correct" : "fail") << std::endl;
}
......
example/ck_tile/17_grouped_gemm/utils.hpp deleted 100644 → 0
View file @ a72e9efa
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
template <typename TLayout>
constexpr auto
f_host_tensor_descriptor(std::size_t row, std::size_t col, std::size_t stride, TLayout layout)
{
using namespace ck_tile::literals;
if constexpr(std::is_same_v<decltype(layout), ck_tile::tensor_layout::gemm::RowMajor>)
{
return ck_tile::HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return ck_tile::HostTensorDescriptor({row, col}, {1_uz, stride});
}
}
template <typename TLayout>
constexpr auto
f_get_default_stride(std::size_t row, std::size_t col, std::size_t stride, TLayout layout)
{
if(stride == 0)
{
if constexpr(std::is_same_v<decltype(layout), ck_tile::tensor_layout::gemm::RowMajor>)
{
return col;
}
else
{
return row;
}
}
else
return stride;
}
include/ck/ck.hpp
View file @ a11cf2c6
......@@ -17,7 +17,9 @@ CK_DECLARE_ENV_VAR_BOOL(CK_LOGGING)
#endif
// to do: add various levels of logging with CK_LOG_LEVEL
#ifndef CK_TIME_KERNEL
#define CK_TIME_KERNEL 1
#endif
// constant address space for kernel parameter
// https://llvm.org/docs/AMDGPUUsage.html#address-spaces
......@@ -155,6 +157,9 @@ CK_DECLARE_ENV_VAR_BOOL(CK_LOGGING)
// LDS direct loads using inline assembly
#define CK_USE_AMD_LDS_DIRECT_LOAD_INLINE_ASM 0
// set rounding to nearest even as default for bf16 conversions
#define CK_USE_RNE_BF16_CONVERSION 1
// set rounding to nearest even as default for f8 conversions
#define CK_USE_SR_F8_CONVERSION 0
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_multiple_abd_xdl_cshuffle.hpp
View file @ a11cf2c6
// SPDX-License-Identifier: MIT
// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2023-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -122,19 +122,6 @@ __global__ void
static_for<0, NumDTensor, 1>{}(
[&](auto i) { p_ds_grid_grp(i) = p_ds_grid[i] + ds_group_offset[i]; });
if constexpr(is_same_v<AElementwiseOperation, element_wise::DynamicUnaryOp>)
{
a_element_op.InitUnaryOpPtrOnDevice();
}
if constexpr(is_same_v<BElementwiseOperation, element_wise::DynamicUnaryOp>)
{
b_element_op.InitUnaryOpPtrOnDevice();
}
if constexpr(is_same_v<CDEElementwiseOperation, element_wise::DynamicUnaryOp>)
{
cde_element_op.InitUnaryOpPtrOnDevice();
}
if constexpr(isMultiA || isMultiB)
{
AsPointer p_as_grid_grp;
......
include/ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp
View file @ a11cf2c6
......@@ -247,32 +247,6 @@ struct DequantPack8
constexpr const static bool is_pack8_invocable = true;
};
#pragma clang diagnostic push
#pragma clang diagnostic ignored "-Wnon-virtual-dtor"
struct UnaryOpBase
{
public:
__host__ __device__ ~UnaryOpBase() = default;
__host__ __device__ constexpr UnaryOpBase() = default;
__host__ __device__ constexpr UnaryOpBase(const UnaryOpBase&) = default;
__host__ __device__ constexpr UnaryOpBase(UnaryOpBase&&) = default;
__host__ __device__ UnaryOpBase& operator=(const UnaryOpBase&) = default;
__host__ __device__ UnaryOpBase& operator=(UnaryOpBase&&) = default;
__host__ __device__ virtual inline void operator()(float& y, const float& x) const = 0;
__host__ __device__ virtual inline void operator()(double& y, const double& x) const = 0;
__host__ __device__ virtual inline void operator()(int32_t& y, const int32_t& x) const = 0;
__host__ __device__ virtual inline void operator()(int8_t& y, const int8_t& x) const = 0;
__host__ __device__ virtual inline void operator()(half_t& y, const half_t& x) const = 0;
__host__ __device__ virtual inline void operator()(bhalf_t& y, const bhalf_t& x) const = 0;
};
struct PassThroughPack2
{
template <typename Y, typename X>
......@@ -304,27 +278,8 @@ struct PassThroughPack2
constexpr const static bool is_pack2_invocable = true;
};
struct PassThrough final : public UnaryOpBase
struct PassThrough
{
__host__ __device__ constexpr PassThrough() = default;
__host__ __device__ constexpr PassThrough(const PassThrough&) = default;
__host__ __device__ constexpr PassThrough(PassThrough&&) = default;
__host__ __device__ PassThrough& operator=(const PassThrough&) = default;
__host__ __device__ PassThrough& operator=(PassThrough&&) = default;
__host__ __device__ ~PassThrough() = default;
__host__ __device__ inline void operator()(float& y, const float& x) const final { y = x; }
__host__ __device__ inline void operator()(double& y, const double& x) const final { y = x; }
__host__ __device__ inline void operator()(int32_t& y, const int32_t& x) const final { y = x; }
__host__ __device__ inline void operator()(int8_t& y, const int8_t& x) const final { y = x; }
__host__ __device__ inline void operator()(half_t& y, const half_t& x) const final { y = x; }
__host__ __device__ inline void operator()(bhalf_t& y, const bhalf_t& x) const final { y = x; }
template <typename Y, typename X>
__host__ __device__ void operator()(Y& y, const X& x) const;
......@@ -334,6 +289,12 @@ struct PassThrough final : public UnaryOpBase
y = x;
}
template <>
__host__ __device__ void operator()<double, double>(double& y, const double& x) const
{
y = x;
}
template <>
__host__ __device__ void operator()<float, double>(float& y, const double& x) const
{
......@@ -346,12 +307,36 @@ struct PassThrough final : public UnaryOpBase
y = type_convert<double>(x);
}
template <>
__host__ __device__ void operator()<float, float>(float& y, const float& x) const
{
y = x;
}
template <>
__host__ __device__ void operator()<half_t, half_t>(half_t& y, const half_t& x) const
{
y = x;
}
template <>
__host__ __device__ void operator()<half_t, float>(half_t& y, const float& x) const
{
y = type_convert<half_t>(x);
}
template <>
__host__ __device__ void operator()<bhalf_t, bhalf_t>(bhalf_t& y, const bhalf_t& x) const
{
y = x;
}
template <>
__host__ __device__ void operator()<int32_t, int32_t>(int32_t& y, const int32_t& x) const
{
y = x;
}
template <>
__host__ __device__ void operator()<bhalf_t, float>(bhalf_t& y, const float& x) const
{
......@@ -376,6 +361,12 @@ struct PassThrough final : public UnaryOpBase
y = type_convert<float>(x);
}
template <>
__host__ __device__ void operator()<int8_t, int8_t>(int8_t& y, const int8_t& x) const
{
y = x;
}
template <>
__host__ __device__ void operator()<half_t, int8_t>(half_t& y, const int8_t& x) const
{
......@@ -675,45 +666,21 @@ struct UnarySquare
};
};
struct UnaryAbs final : public UnaryOpBase
struct UnaryAbs
{
__host__ __device__ constexpr UnaryAbs() = default;
__host__ __device__ constexpr UnaryAbs(const UnaryAbs&) = default;
__host__ __device__ constexpr UnaryAbs(UnaryAbs&&) = default;
__host__ __device__ UnaryAbs& operator=(const UnaryAbs&) = default;
__host__ __device__ UnaryAbs& operator=(UnaryAbs&&) = default;
__host__ __device__ ~UnaryAbs() = default;
__host__ __device__ inline void operator()(float& y, const float& x) const final
{
y = ck::math::abs(x);
}
__host__ __device__ inline void operator()(double& y, const double& x) const final
{
y = ck::math::abs(x);
}
__host__ __device__ inline void operator()(int32_t& y, const int32_t& x) const final
template <typename T>
__host__ __device__ void operator()(T& y, const T& x) const
{
y = ck::math::abs(x);
}
__host__ __device__ inline void operator()(int8_t& y, const int8_t& x) const final
{
y = ck::math::abs(x);
}
static_assert(is_same<T, float>::value || is_same<T, double>::value ||
is_same<T, half_t>::value || is_same<T, int32_t>::value ||
is_same<T, int8_t>::value,
"Data type is not supported by this operation!");
__host__ __device__ inline void operator()(half_t& y, const half_t& x) const final
{
y = math::abs(x);
}
__host__ __device__ inline void operator()(bhalf_t& y, const bhalf_t& x) const final
{
y = ck::math::abs(x);
}
};
template <>
__host__ __device__ void operator()(f8_t& y, const f8_t& x) const
{
y = ck::type_convert<f8_t>(ck::math::abs(ck::type_convert<float>(x)));
......@@ -732,41 +699,20 @@ struct UnarySqrt
};
};
struct Relu final : public UnaryOpBase
struct Relu
{
__host__ __device__ constexpr Relu() = default;
__host__ __device__ constexpr Relu(const Relu&) = default;
__host__ __device__ constexpr Relu(Relu&&) = default;
__host__ __device__ Relu& operator=(const Relu&) = default;
__host__ __device__ Relu& operator=(Relu&&) = default;
__host__ __device__ ~Relu() = default;
__host__ __device__ inline void operator()(float& y, const float& x) const final
{
y = x > 0 ? x : 0;
}
__host__ __device__ inline void operator()(double& y, const double& x) const final
{
y = x > 0 ? x : 0;
}
__host__ __device__ inline void operator()(int32_t& y, const int32_t& x) const final
{
y = x > 0 ? x : 0;
}
__host__ __device__ inline void operator()(int8_t& y, const int8_t& x) const final
{
y = x > 0 ? x : 0;
}
__host__ __device__ inline void operator()(half_t& y, const half_t& x) const final
template <typename T>
__host__ __device__ void operator()(T& y, const T& x) const
{
static_assert(is_same<T, float>::value || is_same<T, double>::value ||
is_same<T, half_t>::value || is_same<T, int32_t>::value ||
is_same<T, int8_t>::value,
"Data type is not supported by this operation!");
y = x > 0 ? x : 0;
}
__host__ __device__ inline void operator()(bhalf_t& y, const bhalf_t& x) const final
template <>
__host__ __device__ void operator()(bhalf_t& y, const bhalf_t& x) const
{
float x_f32 = type_convert<float>(x);
float y_f32 = x_f32 > 0 ? x_f32 : 0;
......@@ -913,52 +859,18 @@ struct Gelu
}
};
struct Sigmoid final : public UnaryOpBase
struct Sigmoid
{
__host__ __device__ constexpr Sigmoid() = default;
__host__ __device__ constexpr Sigmoid(const Sigmoid&) = default;
__host__ __device__ constexpr Sigmoid(Sigmoid&&) = default;
__host__ __device__ Sigmoid& operator=(const Sigmoid&) = default;
__host__ __device__ Sigmoid& operator=(Sigmoid&&) = default;
__host__ __device__ ~Sigmoid() = default;
__host__ __device__ inline void operator()(float& y, const float& x) const final
{
constexpr float one = type_convert<float>(1);
y = one / (one + math::exp(-x));
}
__host__ __device__ inline void operator()(double& y, const double& x) const final
{
constexpr double one = type_convert<double>(1);
y = one / (one + ck::math::exp(-x));
}
__host__ __device__ inline void operator()(int32_t& y, const int32_t& x) const final
{
constexpr int32_t one = type_convert<int32_t>(1);
y = one / (one + ck::math::exp(-x));
}
__host__ __device__ inline void operator()(int8_t& y, const int8_t& x) const final
{
constexpr int8_t one = type_convert<int8_t>(1);
y = one / (one + ck::math::exp(-x));
}
__host__ __device__ inline void operator()(half_t& y, const half_t& x) const final
{
constexpr half_t one = type_convert<half_t>(1);
y = one / (one + math::exp(-x));
}
__host__ __device__ inline void operator()(bhalf_t& y, const bhalf_t& x) const final
template <typename T>
__host__ __device__ void operator()(T& y, const T& x) const
{
constexpr float one = type_convert<float>(1);
float x_f32 = ck::type_convert<float>(x);
float y_f32 = one / (one + ck::math::exp(x_f32));
y = ck::type_convert<bhalf_t>(y_f32);
}
static_assert(is_same<T, float>::value || is_same<T, double>::value ||
is_same<T, ck::half_t>::value || is_same<T, int8_t>::value ||
is_same<T, int32_t>::value,
"Data type is not supported by this operation!");
constexpr T one = type_convert<T>(1);
y = one / (one + math::exp(-x));
};
};
struct Silu
......@@ -974,44 +886,18 @@ struct Silu
};
};
struct TanH final : public UnaryOpBase
struct TanH
{
__host__ __device__ constexpr TanH() = default;
__host__ __device__ constexpr TanH(const TanH&) = default;
__host__ __device__ constexpr TanH(TanH&&) = default;
__host__ __device__ TanH& operator=(const TanH&) = default;
__host__ __device__ TanH& operator=(TanH&&) = default;
__host__ __device__ ~TanH() = default;
__host__ __device__ inline void operator()(float& y, const float& x) const final
{
y = math::tanh(x);
}
__host__ __device__ inline void operator()(double& y, const double& x) const final
{
y = ck::math::tanh(x);
}
__host__ __device__ inline void operator()(int32_t& y, const int32_t& x) const final
{
y = ck::math::tanh(x);
}
__host__ __device__ inline void operator()(int8_t& y, const int8_t& x) const final
{
y = ck::math::tanh(x);
}
__host__ __device__ inline void operator()(half_t& y, const half_t& x) const final
template <typename T>
__host__ __device__ void operator()(T& y, const T& x) const
{
y = ck::math::tanh(x);
}
static_assert(is_same<T, float>::value || is_same<T, double>::value ||
is_same<T, ck::half_t>::value || is_same<T, int8_t>::value ||
is_same<T, int32_t>::value,
"Data type is not supported by this operation!");
__host__ __device__ inline void operator()(bhalf_t& y, const bhalf_t& x) const final
{
y = ck::math::tanh(x);
}
y = math::tanh(x);
};
};
struct ACos
......@@ -1252,418 +1138,138 @@ struct Rcp
};
};
struct Swish final : public UnaryOpBase
struct Swish
{
__host__ __device__ constexpr Swish(const Swish&) = default;
__host__ __device__ constexpr Swish(Swish&&) = default;
__host__ __device__ ~Swish() = default;
__host__ __device__ Swish(float beta = 1.0f) : beta_(beta) {}
__host__ __device__ float get_beta() const { return beta_; }
const float beta_;
__host__ __device__ inline void operator()(float& y, const float& x) const final
{
float bx = -beta_ * type_convert<float>(x);
y = type_convert<float>(x / (1.f + ck::math::exp(bx)));
}
__host__ __device__ inline void operator()(double& y, const double& x) const final
{
float bx = -beta_ * type_convert<float>(x);
y = type_convert<double>(x / (1.f + ck::math::exp(bx)));
}
__host__ __device__ inline void operator()(int32_t& y, const int32_t& x) const final
{
float bx = -beta_ * type_convert<float>(x);
y = type_convert<int32_t>(x / (1.f + ck::math::exp(bx)));
}
__host__ __device__ inline void operator()(int8_t& y, const int8_t& x) const final
{
float bx = -beta_ * type_convert<float>(x);
y = type_convert<int8_t>(x / (1.f + ck::math::exp(bx)));
}
__host__ __device__ inline void operator()(half_t& y, const half_t& x) const final
{
float bx = -beta_ * type_convert<float>(x);
y = type_convert<half_t>(x / (1.f + ck::math::exp(bx)));
}
__host__ __device__ inline void operator()(bhalf_t& y, const bhalf_t& x) const final
{
float bx = -beta_ * type_convert<float>(x);
y = type_convert<bhalf_t>(x / (1.f + ck::math::exp(bx)));
}
Swish(float beta = 1.0f) : beta_(beta) {}
template <typename Y, typename X>
__host__ __device__ void operator()(Y& y, const X& x) const
{
static_assert(is_same<X, float>::value || is_same<X, double>::value ||
is_same<X, half_t>::value,
is_same<X, ck::half_t>::value || is_same<X, int8_t>::value,
"Data type is not supported by this operation!");
static_assert(is_same<Y, float>::value || is_same<Y, double>::value ||
is_same<Y, half_t>::value,
is_same<Y, ck::half_t>::value || is_same<Y, int8_t>::value,
"Data type is not supported by this operation!");
float bx = -beta_ * type_convert<float>(x);
y = type_convert<Y>(x / (1.f + math::exp(bx)));
}
};
const float beta_;
};
struct SoftRelu final : public UnaryOpBase
struct SoftRelu
{
__host__ __device__ constexpr SoftRelu(const SoftRelu&) = default;
__host__ __device__ constexpr SoftRelu(SoftRelu&&) = default;
__host__ __device__ ~SoftRelu() = default;
__host__ __device__ SoftRelu(float alpha = 1.0f) : alpha_(alpha) {}
__host__ __device__ float get_alpha() const { return alpha_; }
const float alpha_;
__host__ __device__ inline void operator()(float& y, const float& x) const final
{
float casted_alpha = type_convert<float>(alpha_);
constexpr float one = type_convert<float>(1);
y = ck::math::log(one + ck::math::exp(x * casted_alpha)) / casted_alpha;
}
__host__ __device__ inline void operator()(double& y, const double& x) const final
{
double casted_alpha = type_convert<double>(alpha_);
constexpr double one = type_convert<double>(1);
y = ck::math::log(one + ck::math::exp(x * casted_alpha)) / casted_alpha;
}
__host__ __device__ inline void operator()(int32_t& y, const int32_t& x) const final
{
int32_t casted_alpha = type_convert<int32_t>(alpha_);
constexpr int32_t one = type_convert<int32_t>(1);
y = ck::math::log(one + ck::math::exp(x * casted_alpha)) / casted_alpha;
}
__host__ __device__ inline void operator()(int8_t& y, const int8_t& x) const final
{
int8_t casted_alpha = type_convert<int8_t>(alpha_);
constexpr int8_t one = type_convert<int8_t>(1);
y = ck::math::log(one + ck::math::exp(x * casted_alpha)) / casted_alpha;
}
SoftRelu(float alpha = 1.f) : alpha_(alpha){};
__host__ __device__ inline void operator()(half_t& y, const half_t& x) const final
{
half_t casted_alpha = type_convert<half_t>(alpha_);
constexpr half_t one = type_convert<half_t>(1);
y = math::log(one + math::exp(x * casted_alpha)) / casted_alpha;
}
__host__ __device__ inline void operator()(bhalf_t& y, const bhalf_t& x) const final
template <typename T>
__host__ __device__ void operator()(T& y, const T& x) const
{
bhalf_t casted_alpha = type_convert<bhalf_t>(alpha_);
constexpr bhalf_t one = type_convert<bhalf_t>(1);
y = ck::math::log(one + ck::math::exp(x * casted_alpha)) / casted_alpha;
static_assert(is_same<T, float>::value || is_same<T, double>::value ||
is_same<T, half_t>::value || is_same<T, int32_t>::value ||
is_same<T, int8_t>::value,
"Data type is not supported by this operation!");
T casted_alpha = type_convert<T>(alpha_);
constexpr T one = type_convert<T>(1);
y = math::log(one + math::exp(x * casted_alpha)) / casted_alpha;
}
const float alpha_;
};
struct Power final : public UnaryOpBase
struct Power
{
__host__ __device__ constexpr Power(const Power&) = default;
__host__ __device__ constexpr Power(Power&&) = default;
__host__ __device__ ~Power() = default;
Power(float alpha = 0.f, float beta = 1.f, float gamma = 2.f)
: alpha_(alpha), beta_(beta), gamma_(gamma){};
__host__ __device__ Power(float alpha = 0.f, float beta = 1.f, float gamma = 2.f)
: alpha_(alpha), beta_(beta), gamma_(gamma)
template <typename T>
__host__ __device__ void operator()(T& y, const T& x) const
{
static_assert(is_same<T, float>::value || is_same<T, double>::value ||
is_same<T, half_t>::value || is_same<T, int32_t>::value ||
is_same<T, int8_t>::value,
"Data type is not supported by this operation!");
T casted_alpha = type_convert<T>(alpha_);
T casted_beta = type_convert<T>(beta_);
T casted_gamma = type_convert<T>(gamma_);
T shifted_scaled_x = casted_alpha + casted_beta * x;
y = math::pow(shifted_scaled_x, casted_gamma);
}
__host__ __device__ float get_alpha() const { return alpha_; }
__host__ __device__ float get_beta() const { return beta_; }
__host__ __device__ float get_gamma() const { return gamma_; }
const float alpha_;
const float beta_;
const float gamma_;
__host__ __device__ inline void operator()(float& y, const float& x) const final
{
float casted_alpha = type_convert<float>(alpha_);
float casted_beta = type_convert<float>(beta_);
float casted_gamma = type_convert<float>(gamma_);
float shifted_scaled_x = casted_alpha + casted_beta * x;
y = ck::math::pow(shifted_scaled_x, casted_gamma);
}
__host__ __device__ inline void operator()(double& y, const double& x) const final
{
double casted_alpha = type_convert<double>(alpha_);
double casted_beta = type_convert<double>(beta_);
double casted_gamma = type_convert<double>(gamma_);
double shifted_scaled_x = casted_alpha + casted_beta * x;
y = ck::math::pow(shifted_scaled_x, casted_gamma);
}
__host__ __device__ inline void operator()(int32_t& y, const int32_t& x) const final
{
int32_t casted_alpha = type_convert<int32_t>(alpha_);
int32_t casted_beta = type_convert<int32_t>(beta_);
int32_t casted_gamma = type_convert<int32_t>(gamma_);
int32_t shifted_scaled_x = casted_alpha + casted_beta * x;
y = ck::math::pow(shifted_scaled_x, casted_gamma);
}
__host__ __device__ inline void operator()(int8_t& y, const int8_t& x) const final
{
int8_t casted_alpha = type_convert<int8_t>(alpha_);
int8_t casted_beta = type_convert<int8_t>(beta_);
int8_t casted_gamma = type_convert<int8_t>(gamma_);
int8_t shifted_scaled_x = casted_alpha + casted_beta * x;
y = ck::math::pow(shifted_scaled_x, casted_gamma);
}
__host__ __device__ inline void operator()(half_t& y, const half_t& x) const final
{
half_t casted_alpha = type_convert<half_t>(alpha_);
half_t casted_beta = type_convert<half_t>(beta_);
half_t casted_gamma = type_convert<half_t>(gamma_);
half_t shifted_scaled_x = casted_alpha + casted_beta * x;
y = math::pow(shifted_scaled_x, casted_gamma);
}
__host__ __device__ inline void operator()(bhalf_t& y, const bhalf_t& x) const final
{
bhalf_t casted_alpha = type_convert<bhalf_t>(alpha_);
bhalf_t casted_beta = type_convert<bhalf_t>(beta_);
bhalf_t casted_gamma = type_convert<bhalf_t>(gamma_);
bhalf_t shifted_scaled_x = casted_alpha + casted_beta * x;
y = ck::math::pow(shifted_scaled_x, casted_gamma);
}
};
struct ClippedRelu final : public UnaryOpBase
struct ClippedRelu
{
__host__ __device__ constexpr ClippedRelu(const ClippedRelu&) = default;
__host__ __device__ constexpr ClippedRelu(ClippedRelu&&) = default;
__host__ __device__ ~ClippedRelu() = default;
ClippedRelu(float alpha = 0.f, float beta = 1.f) : alpha_(alpha), beta_(beta){};
__host__ __device__ ClippedRelu(float alpha = 0.f, float beta = 1.f)
: alpha_(alpha), beta_(beta)
template <typename T>
__host__ __device__ void operator()(T& y, const T& x) const
{
static_assert(is_same<T, float>::value || is_same<T, double>::value ||
is_same<T, half_t>::value || is_same<T, int32_t>::value ||
is_same<T, int8_t>::value,
"Data type is not supported by this operation!");
T casted_alpha = type_convert<T>(alpha_);
T casted_beta = type_convert<T>(beta_);
y = math::min(casted_beta, math::max(casted_alpha, x));
}
__host__ __device__ float get_alpha() const { return alpha_; }
__host__ __device__ float get_beta() const { return beta_; }
const float alpha_;
const float beta_;
__host__ __device__ inline void operator()(float& y, const float& x) const final
{
float casted_alpha = type_convert<float>(alpha_);
float casted_beta = type_convert<float>(beta_);
y = ck::math::min(casted_beta, ck::math::max(casted_alpha, x));
}
__host__ __device__ inline void operator()(double& y, const double& x) const final
{
double casted_alpha = type_convert<double>(alpha_);
double casted_beta = type_convert<double>(beta_);
y = ck::math::min(casted_beta, ck::math::max(casted_alpha, x));
}
__host__ __device__ inline void operator()(int32_t& y, const int32_t& x) const final
{
int32_t casted_alpha = type_convert<int32_t>(alpha_);
int32_t casted_beta = type_convert<int32_t>(beta_);
y = ck::math::min(casted_beta, ck::math::max(casted_alpha, x));
}
__host__ __device__ inline void operator()(int8_t& y, const int8_t& x) const final
{
int8_t casted_alpha = type_convert<int8_t>(alpha_);
int8_t casted_beta = type_convert<int8_t>(beta_);
y = ck::math::min(casted_beta, ck::math::max(casted_alpha, x));
}
__host__ __device__ inline void operator()(half_t& y, const half_t& x) const final
{
half_t casted_alpha = type_convert<half_t>(alpha_);
half_t casted_beta = type_convert<half_t>(beta_);
y = math::min(casted_beta, math::max(casted_alpha, x));
}
__host__ __device__ inline void operator()(bhalf_t& y, const bhalf_t& x) const final
{
bhalf_t casted_alpha = type_convert<bhalf_t>(alpha_);
bhalf_t casted_beta = type_convert<bhalf_t>(beta_);
y = ck::math::min(casted_beta, ck::math::max(casted_alpha, x));
}
};
struct LeakyRelu final : public UnaryOpBase
struct LeakyRelu
{
__host__ __device__ constexpr LeakyRelu(const LeakyRelu&) = default;
__host__ __device__ constexpr LeakyRelu(LeakyRelu&&) = default;
__host__ __device__ ~LeakyRelu() = default;
__host__ __device__ LeakyRelu(float alpha = 0.f) : alpha_(alpha) {}
__host__ __device__ float get_alpha() const { return alpha_; }
const float alpha_;
__host__ __device__ inline void operator()(float& y, const float& x) const final
{
float casted_alpha = type_convert<float>(alpha_);
y = x >= 0 ? x : x * casted_alpha;
}
__host__ __device__ inline void operator()(double& y, const double& x) const final
{
double casted_alpha = type_convert<double>(alpha_);
y = x >= 0 ? x : x * casted_alpha;
}
__host__ __device__ inline void operator()(int32_t& y, const int32_t& x) const final
{
int32_t casted_alpha = type_convert<int32_t>(alpha_);
y = x >= 0 ? x : x * casted_alpha;
}
__host__ __device__ inline void operator()(int8_t& y, const int8_t& x) const final
{
int8_t casted_alpha = type_convert<int8_t>(alpha_);
y = x >= 0 ? x : x * casted_alpha;
}
__host__ __device__ inline void operator()(half_t& y, const half_t& x) const final
{
half_t casted_alpha = type_convert<half_t>(alpha_);
y = x >= 0 ? x : x * casted_alpha;
}
LeakyRelu(float alpha = 0.01f) : alpha_(alpha){};
__host__ __device__ inline void operator()([[maybe_unused]] bhalf_t& y,
[[maybe_unused]] const bhalf_t& x) const final
template <typename T>
__host__ __device__ void operator()(T& y, const T& x) const
{
static_assert(is_same<T, float>::value || is_same<T, double>::value ||
is_same<T, half_t>::value || is_same<T, int32_t>::value ||
is_same<T, int8_t>::value,
"Data type is not supported by this operation!");
T casted_alpha = type_convert<T>(alpha_);
y = x >= 0 ? x : x * casted_alpha;
}
const float alpha_;
};
struct Elu final : public UnaryOpBase
struct Elu
{
__host__ __device__ constexpr Elu(const Elu&) = default;
__host__ __device__ constexpr Elu(Elu&&) = default;
__host__ __device__ ~Elu() = default;
__host__ __device__ Elu(float alpha = 1.f) : alpha_(alpha) {}
__host__ __device__ float get_alpha() const { return alpha_; }
const float alpha_;
__host__ __device__ inline void operator()(float& y, const float& x) const final
{
float casted_alpha = type_convert<float>(alpha_);
y = x > 0 ? x : casted_alpha * ck::math::expm1(x);
}
__host__ __device__ inline void operator()(double& y, const double& x) const final
{
double casted_alpha = type_convert<double>(alpha_);
y = x > 0 ? x : casted_alpha * ck::math::expm1(x);
}
__host__ __device__ inline void operator()(int32_t& y, const int32_t& x) const final
{
int32_t casted_alpha = type_convert<int32_t>(alpha_);
y = x > 0 ? x : casted_alpha * ck::math::expm1(x);
}
__host__ __device__ inline void operator()(int8_t& y, const int8_t& x) const final
{
int8_t casted_alpha = type_convert<int8_t>(alpha_);
y = x > 0 ? x : casted_alpha * ck::math::expm1(x);
}
__host__ __device__ inline void operator()(half_t& y, const half_t& x) const final
{
half_t casted_alpha = type_convert<half_t>(alpha_);
y = x > 0 ? x : casted_alpha * math::expm1(x);
}
Elu(float alpha = 1.f) : alpha_(alpha){};
__host__ __device__ inline void operator()(bhalf_t& y, const bhalf_t& x) const final
template <typename T>
__host__ __device__ void operator()(T& y, const T& x) const
{
bhalf_t casted_alpha = type_convert<bhalf_t>(alpha_);
y = x > 0 ? x : casted_alpha * ck::math::expm1(x);
static_assert(is_same<T, float>::value || is_same<T, double>::value ||
is_same<T, half_t>::value || is_same<T, int32_t>::value ||
is_same<T, int8_t>::value,
"Data type is not supported by this operation!");
T casted_alpha = type_convert<T>(alpha_);
y = x > 0 ? x : casted_alpha * math::expm1(x);
}
const float alpha_;
};
struct Logistic final : public UnaryOpBase
struct Logistic
{
__host__ __device__ constexpr Logistic(const Logistic&) = default;
__host__ __device__ constexpr Logistic(Logistic&&) = default;
__host__ __device__ ~Logistic() = default;
__host__ __device__ Logistic(float alpha = 1.0f) : alpha_(alpha) {}
__host__ __device__ float get_alpha() const { return alpha_; }
const float alpha_;
__host__ __device__ inline void operator()(float& y, const float& x) const final
{
float casted_alpha = type_convert<float>(alpha_);
constexpr float one = type_convert<float>(1);
y = casted_alpha / (one + ck::math::exp(-x) * casted_alpha);
}
__host__ __device__ inline void operator()(double& y, const double& x) const final
{
double casted_alpha = type_convert<double>(alpha_);
constexpr double one = type_convert<double>(1);
y = casted_alpha / (one + ck::math::exp(-x) * casted_alpha);
}
__host__ __device__ inline void operator()(int32_t& y, const int32_t& x) const final
{
int32_t casted_alpha = type_convert<int32_t>(alpha_);
constexpr int32_t one = type_convert<int32_t>(1);
y = casted_alpha / (one + ck::math::exp(-x) * casted_alpha);
}
Logistic(float alpha = 1.f) : alpha_(alpha){};
__host__ __device__ inline void operator()(int8_t& y, const int8_t& x) const final
{
int8_t casted_alpha = type_convert<int8_t>(alpha_);
constexpr int8_t one = type_convert<int8_t>(1);
y = casted_alpha / (one + ck::math::exp(-x) * casted_alpha);
}
__host__ __device__ inline void operator()(half_t& y, const half_t& x) const final
{
half_t casted_alpha = type_convert<half_t>(alpha_);
constexpr half_t one = type_convert<half_t>(1);
y = casted_alpha / (one + ck::math::exp(-x) * casted_alpha);
}
__host__ __device__ inline void operator()(bhalf_t& y, const bhalf_t& x) const final
template <typename T>
__host__ __device__ void operator()(T& y, const T& x) const
{
bhalf_t casted_alpha = type_convert<bhalf_t>(alpha_);
constexpr bhalf_t one = type_convert<bhalf_t>(1);
y = casted_alpha / (one + ck::math::exp(-x) * casted_alpha);
static_assert(is_same<T, float>::value || is_same<T, double>::value ||
is_same<T, half_t>::value || is_same<T, int32_t>::value ||
is_same<T, int8_t>::value,
"Data type is not supported by this operation!");
T casted_alpha = type_convert<T>(alpha_);
constexpr T one = type_convert<T>(1);
y = casted_alpha / (one + ck::math::exp(-x) * casted_alpha);
}
const float alpha_;
};
struct ConvInvscale
......@@ -1728,7 +1334,7 @@ struct ConvScaleRelu
__host__ __device__ void operator()<f8_t, float>(f8_t& e, const float& c) const
{
float x;
Relu{}(x, c * scale_in_ * scale_wei_);
Relu{}.template operator()<float>(x, c * scale_in_ * scale_wei_);
e = type_convert<f8_t>(x * scale_out_);
};
......@@ -1809,225 +1415,138 @@ struct FastNumericArrayConverter<uint8_t, half_t, N>
struct DynamicUnaryOp
{
DynamicUnaryOp& operator=(const DynamicUnaryOp& other)
{
if(this != &other)
{
unary_op_ptr_ = other.unary_op_ptr_;
unary_op_type_ = other.unary_op_type_;
}
return *this;
}
__host__ __device__ DynamicUnaryOp() = delete;
__host__ __device__ DynamicUnaryOp(const Swish& swish)
: unary_op_type_(UnaryOpType::Swish), swish_{swish.beta_}
{
unary_op_type_ = UnaryOpType::Swish;
beta = swish.get_beta();
}
__host__ __device__ DynamicUnaryOp(const Swish&& swish)
: unary_op_type_(UnaryOpType::Swish), swish_{swish.beta_}
{
unary_op_type_ = UnaryOpType::Swish;
beta = swish.get_beta();
}
__host__ __device__ DynamicUnaryOp(const Sigmoid&) { unary_op_type_ = UnaryOpType::Sigmoid; }
__host__ __device__ DynamicUnaryOp(const Sigmoid&) : unary_op_type_(UnaryOpType::Sigmoid) {}
__host__ __device__ DynamicUnaryOp(const Sigmoid&&) { unary_op_type_ = UnaryOpType::Sigmoid; }
__host__ __device__ DynamicUnaryOp(const Sigmoid&&) : unary_op_type_(UnaryOpType::Sigmoid) {}
__host__ __device__ DynamicUnaryOp(const PassThrough&)
: unary_op_type_(UnaryOpType::PassThrough)
{
unary_op_type_ = UnaryOpType::PassThrough;
}
__host__ __device__ DynamicUnaryOp(const PassThrough&&)
: unary_op_type_(UnaryOpType::PassThrough)
{
unary_op_type_ = UnaryOpType::PassThrough;
}
__host__ __device__ DynamicUnaryOp(const Logistic& logistic)
: unary_op_type_(UnaryOpType::Logistic), logistic_{logistic.alpha_}
{
unary_op_type_ = UnaryOpType::Logistic;
alpha = logistic.get_alpha();
}
__host__ __device__ DynamicUnaryOp(const Logistic&& logistic)
: unary_op_type_(UnaryOpType::Logistic), logistic_{logistic.alpha_}
{
unary_op_type_ = UnaryOpType::Logistic;
alpha = logistic.get_alpha();
}
__host__ __device__ DynamicUnaryOp(const TanH&) { unary_op_type_ = UnaryOpType::TanH; }
__host__ __device__ DynamicUnaryOp(const TanH&) : unary_op_type_(UnaryOpType::TanH) {}
__host__ __device__ DynamicUnaryOp(const TanH&&) { unary_op_type_ = UnaryOpType::TanH; }
__host__ __device__ DynamicUnaryOp(const TanH&&) : unary_op_type_(UnaryOpType::TanH) {}
__host__ __device__ DynamicUnaryOp(const Relu&) { unary_op_type_ = UnaryOpType::Relu; }
__host__ __device__ DynamicUnaryOp(const Relu&) : unary_op_type_(UnaryOpType::Relu) {}
__host__ __device__ DynamicUnaryOp(const Relu&&) { unary_op_type_ = UnaryOpType::Relu; }
__host__ __device__ DynamicUnaryOp(const Relu&&) : unary_op_type_(UnaryOpType::Relu) {}
__host__ __device__ DynamicUnaryOp(const SoftRelu& softrelu)
: unary_op_type_(UnaryOpType::SoftRelu), soft_relu_{softrelu.alpha_}
{
unary_op_type_ = UnaryOpType::SoftRelu;
alpha = softrelu.get_alpha();
}
__host__ __device__ DynamicUnaryOp(const SoftRelu&& softrelu)
: unary_op_type_(UnaryOpType::SoftRelu), soft_relu_{softrelu.alpha_}
{
unary_op_type_ = UnaryOpType::SoftRelu;
alpha = softrelu.get_alpha();
}
__host__ __device__ DynamicUnaryOp(const UnaryAbs&) { unary_op_type_ = UnaryOpType::UnaryAbs; }
__host__ __device__ DynamicUnaryOp(const UnaryAbs&) : unary_op_type_(UnaryOpType::UnaryAbs) {}
__host__ __device__ DynamicUnaryOp(const UnaryAbs&&) { unary_op_type_ = UnaryOpType::UnaryAbs; }
__host__ __device__ DynamicUnaryOp(const UnaryAbs&&) : unary_op_type_(UnaryOpType::UnaryAbs) {}
__host__ __device__ DynamicUnaryOp(const Power& pow)
: unary_op_type_(UnaryOpType::Power), power_(pow.alpha_, pow.beta_, pow.gamma_)
{
unary_op_type_ = UnaryOpType::Power;
alpha = pow.get_alpha();
beta = pow.get_beta();
gamma = pow.get_gamma();
}
__host__ __device__ DynamicUnaryOp(const Power&& pow)
: unary_op_type_(UnaryOpType::Power), power_(pow.alpha_, pow.beta_, pow.gamma_)
{
unary_op_type_ = UnaryOpType::Power;
alpha = pow.get_alpha();
beta = pow.get_beta();
gamma = pow.get_gamma();
}
__host__ __device__ DynamicUnaryOp(const ClippedRelu& clippedrelu)
: unary_op_type_(UnaryOpType::ClippedRelu),
clipped_relu_{clippedrelu.alpha_, clippedrelu.beta_}
{
unary_op_type_ = UnaryOpType::ClippedRelu;
alpha = clippedrelu.get_alpha();
beta = clippedrelu.get_beta();
}
__host__ __device__ DynamicUnaryOp(const ClippedRelu&& clippedrelu)
: unary_op_type_(UnaryOpType::ClippedRelu),
clipped_relu_{clippedrelu.alpha_, clippedrelu.beta_}
{
unary_op_type_ = UnaryOpType::ClippedRelu;
alpha = clippedrelu.get_alpha();
beta = clippedrelu.get_beta();
}
__host__ __device__ DynamicUnaryOp(const LeakyRelu& leakyrelu)
: unary_op_type_(UnaryOpType::LeakyRelu), leaky_relu_{leakyrelu.alpha_}
{
unary_op_type_ = UnaryOpType::LeakyRelu;
alpha = leakyrelu.get_alpha();
}
__host__ __device__ DynamicUnaryOp(const LeakyRelu&& leakyrelu)
: unary_op_type_(UnaryOpType::LeakyRelu), leaky_relu_{leakyrelu.alpha_}
{
unary_op_type_ = UnaryOpType::LeakyRelu;
alpha = leakyrelu.get_alpha();
}
__host__ __device__ DynamicUnaryOp(const Elu& elu)
: unary_op_type_(UnaryOpType::Elu), elu_{elu.alpha_}
{
unary_op_type_ = UnaryOpType::Elu;
alpha = elu.get_alpha();
}
__host__ __device__ DynamicUnaryOp(const Elu&& elu)
: unary_op_type_(UnaryOpType::Elu), elu_{elu.alpha_}
{
unary_op_type_ = UnaryOpType::Elu;
alpha = elu.get_alpha();
}
__host__ __device__ DynamicUnaryOp(const DynamicUnaryOp& dynamic_op)
: unary_op_type_(dynamic_op.unary_op_type_),
unary_op_ptr_(dynamic_op.unary_op_ptr_),
alpha(dynamic_op.alpha),
beta(dynamic_op.beta),
gamma(dynamic_op.gamma)
{
}
__host__ __device__ DynamicUnaryOp(const DynamicUnaryOp& dynamic_op) = default;
__host__ __device__ ~DynamicUnaryOp()
{
switch(unary_op_type_)
{
case(UnaryOpType::Swish): delete static_cast<Swish*>(unary_op_ptr_); break;
case(UnaryOpType::Sigmoid): delete static_cast<Sigmoid*>(unary_op_ptr_); break;
case(UnaryOpType::PassThrough): delete static_cast<PassThrough*>(unary_op_ptr_); break;
case(UnaryOpType::Logistic): delete static_cast<Logistic*>(unary_op_ptr_); break;
case(UnaryOpType::TanH): delete static_cast<TanH*>(unary_op_ptr_); break;
case(UnaryOpType::Relu): delete static_cast<Relu*>(unary_op_ptr_); break;
case(UnaryOpType::SoftRelu): delete static_cast<SoftRelu*>(unary_op_ptr_); break;
case(UnaryOpType::UnaryAbs): delete static_cast<UnaryAbs*>(unary_op_ptr_); break;
case(UnaryOpType::Power): delete static_cast<Power*>(unary_op_ptr_); break;
case(UnaryOpType::ClippedRelu): delete static_cast<ClippedRelu*>(unary_op_ptr_); break;
case(UnaryOpType::LeakyRelu): delete static_cast<LeakyRelu*>(unary_op_ptr_); break;
case(UnaryOpType::Elu): delete static_cast<Elu*>(unary_op_ptr_); break;
default: break;
}
}
__device__ void InitUnaryOpPtrOnDevice()
{
switch(unary_op_type_)
{
case(UnaryOpType::Swish): unary_op_ptr_ = new Swish(beta); break;
case(UnaryOpType::Sigmoid): unary_op_ptr_ = new Sigmoid; break;
case(UnaryOpType::PassThrough): unary_op_ptr_ = new PassThrough; break;
case(UnaryOpType::Logistic): unary_op_ptr_ = new Logistic(alpha); break;
case(UnaryOpType::TanH): unary_op_ptr_ = new TanH; break;
case(UnaryOpType::Relu): unary_op_ptr_ = new Relu; break;
case(UnaryOpType::SoftRelu): unary_op_ptr_ = new SoftRelu(alpha); break;
case(UnaryOpType::UnaryAbs): unary_op_ptr_ = new UnaryAbs; break;
case(UnaryOpType::Power): unary_op_ptr_ = new Power(alpha, beta, gamma); break;
case(UnaryOpType::ClippedRelu): unary_op_ptr_ = new ClippedRelu(alpha, beta); break;
case(UnaryOpType::LeakyRelu): unary_op_ptr_ = new LeakyRelu(alpha); break;
case(UnaryOpType::Elu): unary_op_ptr_ = new Elu(alpha); break;
default: unary_op_ptr_ = nullptr; break;
}
}
template <typename Y, typename X>
__device__ void operator()(Y& y, const X& x) const
{
isSupported<X, Y>();
unary_op_ptr_->operator()(y, x);
}
__host__ __device__ ~DynamicUnaryOp() {}
template <typename Y, typename X>
__host__ void operator()(Y& y, const X& x) const
__host__ __device__ void operator()(Y& y, const X& x) const
{
isSupported<X, Y>();
switch(unary_op_type_)
{
case(UnaryOpType::Swish): Swish{}.operator()(y, x); break;
case(UnaryOpType::Sigmoid): Sigmoid{}.operator()(y, x); break;
case(UnaryOpType::PassThrough): PassThrough{}.operator()(y, x); break;
case(UnaryOpType::Logistic): Logistic{}.operator()(y, x); break;
case(UnaryOpType::TanH): TanH{}.operator()(y, x); break;
case(UnaryOpType::Relu): Relu{}.operator()(y, x); break;
case(UnaryOpType::SoftRelu): SoftRelu{}.operator()(y, x); break;
case(UnaryOpType::UnaryAbs): UnaryAbs{}.operator()(y, x); break;
case(UnaryOpType::Power): Power{}.operator()(y, x); break;
case(UnaryOpType::ClippedRelu): ClippedRelu{}.operator()(y, x); break;
case(UnaryOpType::LeakyRelu): LeakyRelu{}.operator()(y, x); break;
case(UnaryOpType::Elu): Elu{}.operator()(y, x); break;
case(UnaryOpType::Swish): swish_(y, x); break;
case(UnaryOpType::Sigmoid): sigmoid_(y, x); break;
case(UnaryOpType::PassThrough): pass_through_(y, x); break;
case(UnaryOpType::Logistic): logistic_(y, x); break;
case(UnaryOpType::TanH): tanh_(y, x); break;
case(UnaryOpType::Relu): relu_(y, x); break;
case(UnaryOpType::SoftRelu): soft_relu_(y, x); break;
case(UnaryOpType::UnaryAbs): unary_abs_(y, x); break;
case(UnaryOpType::Power): power_(y, x); break;
case(UnaryOpType::ClippedRelu): clipped_relu_(y, x); break;
case(UnaryOpType::LeakyRelu): leaky_relu_(y, x); break;
case(UnaryOpType::Elu): elu_(y, x); break;
default: break;
}
}
template <typename X, typename Y>
__device__ __host__ constexpr void isSupported() const
template <>
__host__ __device__ void operator()<bhalf_t, bhalf_t>(bhalf_t& y, const bhalf_t& x) const
{
static_assert(std::is_same<X, Y>::value, "X and Y must be of the same type");
static_assert(is_same<X, float>::value || is_same<X, double>::value ||
is_same<X, bhalf_t>::value || is_same<X, half_t>::value ||
is_same<X, int32_t>::value || is_same<X, int8_t>::value,
"Data type is not supported by this operation!");
float y_float;
float x_float = type_convert<float>(x);
this->operator()(y_float, x_float);
y = type_convert<bhalf_t>(y_float);
}
private:
......@@ -2049,12 +1568,20 @@ struct DynamicUnaryOp
public:
UnaryOpType unary_op_type_;
UnaryOpBase* unary_op_ptr_ = nullptr;
float alpha;
float beta;
float gamma;
Swish swish_;
Sigmoid sigmoid_;
PassThrough pass_through_;
Logistic logistic_;
TanH tanh_;
Relu relu_;
SoftRelu soft_relu_;
UnaryAbs unary_abs_;
Power power_;
ClippedRelu clipped_relu_;
LeakyRelu leaky_relu_;
Elu elu_;
};
#pragma clang diagnostic pop
} // namespace element_wise
} // namespace tensor_operation
......
include/ck/utility/data_type.hpp
View file @ a11cf2c6
......@@ -31,8 +31,6 @@ struct pk_i4_t
type data;
__host__ __device__ constexpr pk_i4_t() : data{type{}} {}
__host__ __device__ constexpr pk_i4_t(type init) : data{init} {}
__host__ __device__ constexpr operator float() const { return static_cast<int8_t>(data); }
};
inline constexpr auto next_pow2(uint32_t x)
......
include/ck/utility/dynamic_buffer.hpp
View file @ a11cf2c6
......@@ -29,6 +29,13 @@ struct DynamicBuffer
ElementSpaceSize element_space_size_;
T invalid_element_value_ = T{0};
static constexpr index_t PackedSize = []() {
if constexpr(is_same_v<remove_cvref_t<T>, pk_i4_t>)
return 2;
else
return 1;
}();
__host__ __device__ constexpr DynamicBuffer(T* p_data, ElementSpaceSize element_space_size)
: p_data_{p_data}, element_space_size_{element_space_size}
{
......@@ -82,14 +89,18 @@ struct DynamicBuffer
return amd_buffer_load_invalid_element_return_zero<remove_cvref_t<T>,
t_per_x,
coherence>(
p_data_, i, is_valid_element, element_space_size_);
p_data_, i, is_valid_element, element_space_size_ / PackedSize);
}
else
{
return amd_buffer_load_invalid_element_return_customized_value<remove_cvref_t<T>,
t_per_x,
coherence>(
p_data_, i, is_valid_element, element_space_size_, invalid_element_value_);
p_data_,
i,
is_valid_element,
element_space_size_ / PackedSize,
invalid_element_value_);
}
}
else
......@@ -191,7 +202,7 @@ struct DynamicBuffer
dst_buf.p_data_,
dst_offset,
is_valid_element,
element_space_size_);
element_space_size_ / PackedSize);
}
template <typename X,
......@@ -226,7 +237,7 @@ struct DynamicBuffer
constexpr index_t t_per_x = scalar_per_x_vector / scalar_per_t_vector;
amd_buffer_store<remove_cvref_t<T>, t_per_x, coherence>(
x, p_data_, i, is_valid_element, element_space_size_);
x, p_data_, i, is_valid_element, element_space_size_ / PackedSize);
}
else if constexpr(GetAddressSpace() == AddressSpaceEnum::Lds &&
is_same<typename scalar_type<remove_cvref_t<T>>::type, int8_t>::value &&
......@@ -378,7 +389,7 @@ struct DynamicBuffer
constexpr index_t t_per_x = scalar_per_x_vector / scalar_per_t_vector;
amd_buffer_atomic_add<remove_cvref_t<T>, t_per_x>(
x, p_data_, i, is_valid_element, element_space_size_);
x, p_data_, i, is_valid_element, element_space_size_ / PackedSize);
}
else
{
......@@ -417,7 +428,7 @@ struct DynamicBuffer
constexpr index_t t_per_x = scalar_per_x_vector / scalar_per_t_vector;
amd_buffer_atomic_max<remove_cvref_t<T>, t_per_x>(
x, p_data_, i, is_valid_element, element_space_size_);
x, p_data_, i, is_valid_element, element_space_size_ / PackedSize);
}
else if(is_valid_element)
{
......
include/ck/utility/type_convert.hpp
View file @ a11cf2c6
......@@ -14,6 +14,41 @@ namespace ck {
#define __gfx94__
#endif
// Declare a template function for bf16 conversion using RTN
template <typename Y, typename X>
__host__ __device__ constexpr Y bf16_convert_rtn(X x);
// Convert fp32 to bf16 with RTN if higher precision is needed
template <>
inline __host__ __device__ constexpr bhalf_t bf16_convert_rtn<bhalf_t, float>(float x)
{
// Nan check
if(x != x)
{
return uint16_t(0x7FC0);
}
union
{
float fp32;
uint32_t int32;
} u = {x};
const uint32_t first_bf16_mantisa_bit = ((u.int32 >> 16) & 1);
constexpr uint32_t rounding_bias = uint32_t((1 << 15) - 1);
return uint16_t((u.int32 + first_bf16_mantisa_bit + rounding_bias) >> 16);
}
// convert fp16 to bfp16 via fp32 with RTN if higher precision is needed
template <>
inline __host__ __device__ constexpr bhalf_t bf16_convert_rtn<bhalf_t, half_t>(half_t x)
{
float x_fp32 = static_cast<float>(x);
return bf16_convert_rtn<bhalf_t>(x_fp32);
}
// Convert X to Y, both X and Y are non-const data types.
template <typename Y,
typename X,
......@@ -51,17 +86,15 @@ inline __host__ __device__ constexpr float type_convert<float, bhalf_t>(bhalf_t
return u.fp32;
}
// convert fp32 to bfp16
// convert fp32 to bfp16, round to nearest even
template <>
inline __host__ __device__ constexpr bhalf_t type_convert<bhalf_t, float>(float x)
{
union
{
float fp32;
uint32_t int32;
} u = {x};
#if CK_USE_RNE_BF16_CONVERSION
return bf16_convert_rtn<bhalf_t>(x);
#else
return uint16_t(u.int32 >> 16);
#endif
}
// convert bfp16 to fp16 via fp32
......@@ -635,60 +668,4 @@ inline __host__ __device__ void array_convert(Array<Y, NumElems>& y, const Array
}
}
// Declare a template function for bf16 conversion using RTN
template <typename Y, typename X>
__host__ __device__ constexpr Y bf16_convert_rtn(X x);
// Convert fp32 to bf16 with RTN if higher precision is needed
template <>
inline __host__ __device__ constexpr bhalf_t bf16_convert_rtn<bhalf_t, float>(float x)
{
union
{
float fp32;
uint32_t int32;
} u = {x};
// When the exponent bits are not all 1s, then the value is zero, normal,
// or subnormal. We round the bfloat16 mantissa up by adding 0x7FFF, plus
// 1 if the least significant bit of the bfloat16 mantissa is 1 (odd).
// This causes the bfloat16's mantissa to be incremented by 1 if the 16
// least significant bits of the float mantissa are greater than 0x8000,
// or if they are equal to 0x8000 and the least significant bit of the
// bfloat16 mantissa is 1 (odd). This causes it to be rounded to even when
// the lower 16 bits are exactly 0x8000. If the bfloat16 mantissa already
// has the value 0x7f, then incrementing it causes it to become 0x00 and
// the exponent is incremented by one, which is the next higher FP value
// to the unrounded bfloat16 value. When the bfloat16 value is subnormal
// with an exponent of 0x00 and a mantissa of 0x7f, it may be rounded up
// to a normal value with an exponent of 0x01 and a mantissa of 0x00.
// When the bfloat16 value has an exponent of 0xFE and a mantissa of 0x7F,
// incrementing it causes it to become an exponent of 0xFF and a mantissa
// of 0x00, which is Inf, the next higher value to the unrounded value.
bool flag0 = ~u.int32 & 0x7f800000;
// When all of the exponent bits are 1, the value is Inf or NaN.
// Inf is indicated by a zero mantissa. NaN is indicated by any nonzero
// mantissa bit. Quiet NaN is indicated by the most significant mantissa
// bit being 1. Signaling NaN is indicated by the most significant
// mantissa bit being 0 but some other bit(s) being 1. If any of the
// lower 16 bits of the mantissa are 1, we set the least significant bit
// of the bfloat16 mantissa, in order to preserve signaling NaN in case
// the bfloat16's mantissa bits are all 0.
bool flag1 = !flag0 && (u.int32 & 0xffff);
u.int32 += flag0 ? 0x7fff + ((u.int32 >> 16) & 1) : 0; // Round to nearest, round to even
u.int32 |= flag1 ? 0x10000 : 0x0; // Preserve signaling NaN
return uint16_t(u.int32 >> 16);
}
// convert fp16 to bfp16 via fp32 with RTN if higher precision is needed
template <>
inline __host__ __device__ constexpr bhalf_t bf16_convert_rtn<bhalf_t, half_t>(half_t x)
{
float x_fp32 = static_cast<float>(x);
return bf16_convert_rtn<bhalf_t>(x_fp32);
}
} // namespace ck
include/ck_tile/core.hpp
View file @ a11cf2c6
......@@ -54,7 +54,6 @@
#include "ck_tile/core/tensor/tile_window_linear.hpp"
#include "ck_tile/core/tensor/tile_window_utils.hpp"
#include "ck_tile/core/tensor/update_tile.hpp"
#include "ck_tile/core/utility/amd_address_space.hpp"
#include "ck_tile/core/utility/bit_cast.hpp"
#include "ck_tile/core/utility/functional.hpp"
#include "ck_tile/core/utility/functional_with_tuple.hpp"
......
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