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Unverified Commit 2a30cfdd authored by arai713's avatar arai713 Committed by GitHub
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Merge branch 'develop' into codegen-enable-hiprtc

parents 9533a172 78195ccc
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Showing with 831 additions and 242 deletions
include/ck_tile/host.hpp
View file @ 2a30cfdd
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/host/arg_parser.hpp"
#include "ck_tile/host/check_err.hpp"
#include "ck_tile/host/concat.hpp"
#include "ck_tile/host/convolution_host_tensor_descriptor_helper.hpp"
#include "ck_tile/host/convolution_parameter.hpp"
#include "ck_tile/host/device_memory.hpp"
......@@ -20,6 +21,7 @@
#include "ck_tile/host/reference/reference_batched_masking.hpp"
#include "ck_tile/host/reference/reference_batched_rotary_position_embedding.hpp"
#include "ck_tile/host/reference/reference_batched_softmax.hpp"
#include "ck_tile/host/reference/reference_batched_transpose.hpp"
#include "ck_tile/host/reference/reference_elementwise.hpp"
#include "ck_tile/host/reference/reference_fused_moe.hpp"
#include "ck_tile/host/reference/reference_gemm.hpp"
......
include/ck_tile/host/arg_parser.hpp
View file @ 2a30cfdd
......@@ -15,11 +15,14 @@
namespace ck_tile {
/*
* a host side utility, arg parser for
* -[key0]=[value0] -[key1]=[value1] ...
* a host side utility, arg parser for, either
* -[key0] = [value0, value1, value2]
* or
* -[key0]=[value0] -[key1]=[value1] ...
*/
class ArgParser
{
public:
class Arg
{
......@@ -187,6 +190,45 @@ class ArgParser
return value;
}
std::vector<std::string> get_string_vec(const std::string& name,
const std::string& delimiter = ",") const
{
if(get_str(name).empty())
{
return {};
}
std::string s = get_str(name);
std::vector<std::string> tokens;
size_t pos = 0;
std::string token;
while((pos = s.find(delimiter)) != std::string::npos)
{
token = s.substr(0, pos);
tokens.push_back(token);
s.erase(0, pos + delimiter.length());
}
tokens.push_back(s);
return tokens;
}
std::vector<int> get_int_vec(const std::string& name, const std::string& delimiter = ",") const
{
if(get_str(name).empty())
{
return {};
}
const std::vector<std::string> args = get_string_vec(name, delimiter);
std::vector<int> tokens;
tokens.reserve(static_cast<int>(args.size()));
for(const std::string& token : args)
{
int value = atoi(token.c_str());
tokens.push_back(value);
}
return tokens;
}
private:
std::unordered_map<std::string, Arg> input_map;
std::vector<std::string> keys;
......
include/ck_tile/host/check_err.hpp
View file @ 2a30cfdd
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -18,6 +18,114 @@
namespace ck_tile {
template <typename ComputeDataType, typename OutDataType, typename AccDataType = ComputeDataType>
double get_relative_threshold(const int number_of_accumulations = 1)
{
using F8 = ck_tile::fp8_t;
using BF8 = ck_tile::bf8_t;
using F16 = ck_tile::half_t;
using BF16 = ck_tile::bf16_t;
using F32 = float;
using I8 = int8_t;
using I32 = int32_t;
static_assert(is_any_of<ComputeDataType, F8, BF8, F16, BF16, F32, I8, I32, int>::value,
"Warning: Unhandled ComputeDataType for setting up the relative threshold!");
double compute_error = 0;
if constexpr(is_any_of<ComputeDataType, I8, I32, int>::value)
{
return 0;
}
else
{
compute_error = std::pow(2, -numeric_traits<ComputeDataType>::mant) * 0.5;
}
static_assert(is_any_of<OutDataType, F8, BF8, F16, BF16, F32, I8, I32, int>::value,
"Warning: Unhandled OutDataType for setting up the relative threshold!");
double output_error = 0;
if constexpr(is_any_of<OutDataType, I8, I32, int>::value)
{
return 0;
}
else
{
output_error = std::pow(2, -numeric_traits<OutDataType>::mant) * 0.5;
}
double midway_error = std::max(compute_error, output_error);
static_assert(is_any_of<AccDataType, F8, BF8, F16, BF16, F32, I8, I32, int>::value,
"Warning: Unhandled AccDataType for setting up the relative threshold!");
double acc_error = 0;
if constexpr(is_any_of<AccDataType, I8, I32, int>::value)
{
return 0;
}
else
{
acc_error = std::pow(2, -numeric_traits<AccDataType>::mant) * 0.5 * number_of_accumulations;
}
return std::max(acc_error, midway_error);
}
template <typename ComputeDataType, typename OutDataType, typename AccDataType = ComputeDataType>
double get_absolute_threshold(const double max_possible_num, const int number_of_accumulations = 1)
{
using F8 = ck_tile::fp8_t;
using BF8 = ck_tile::bf8_t;
using F16 = ck_tile::half_t;
using BF16 = ck_tile::bf16_t;
using F32 = float;
using I8 = int8_t;
using I32 = int32_t;
static_assert(is_any_of<ComputeDataType, F8, BF8, F16, BF16, F32, I8, I32, int>::value,
"Warning: Unhandled ComputeDataType for setting up the absolute threshold!");
auto expo = std::log2(std::abs(max_possible_num));
double compute_error = 0;
if constexpr(is_any_of<ComputeDataType, I8, I32, int>::value)
{
return 0;
}
else
{
compute_error = std::pow(2, expo - numeric_traits<ComputeDataType>::mant) * 0.5;
}
static_assert(is_any_of<OutDataType, F8, BF8, F16, BF16, F32, I8, I32, int>::value,
"Warning: Unhandled OutDataType for setting up the absolute threshold!");
double output_error = 0;
if constexpr(is_any_of<OutDataType, I8, I32, int>::value)
{
return 0;
}
else
{
output_error = std::pow(2, expo - numeric_traits<OutDataType>::mant) * 0.5;
}
double midway_error = std::max(compute_error, output_error);
static_assert(is_any_of<AccDataType, F8, BF8, F16, BF16, F32, I8, I32, int>::value,
"Warning: Unhandled AccDataType for setting up the absolute threshold!");
double acc_error = 0;
if constexpr(is_any_of<AccDataType, I8, I32, int>::value)
{
return 0;
}
else
{
acc_error =
std::pow(2, expo - numeric_traits<AccDataType>::mant) * 0.5 * number_of_accumulations;
}
return std::max(acc_error, midway_error);
}
template <typename T>
std::ostream& operator<<(std::ostream& os, const std::vector<T>& v)
{
......@@ -337,7 +445,11 @@ std::enable_if_t<(std::is_same_v<ranges::range_value_t<Range>, ranges::range_val
}
if(!res)
{
std::cerr << std::setw(12) << std::setprecision(7) << "max err: " << max_err << std::endl;
const float error_percent =
static_cast<float>(err_count) / static_cast<float>(out.size()) * 100.f;
std::cerr << "max err: " << max_err;
std::cerr << ", number of errors: " << err_count;
std::cerr << ", " << error_percent << "% wrong values" << std::endl;
}
return res;
}
......@@ -391,7 +503,11 @@ std::enable_if_t<(std::is_same_v<ranges::range_value_t<Range>, ranges::range_val
}
if(!res)
{
std::cerr << std::setw(12) << std::setprecision(7) << "max err: " << max_err << std::endl;
const float error_percent =
static_cast<float>(err_count) / static_cast<float>(out.size()) * 100.f;
std::cerr << "max err: " << max_err;
std::cerr << ", number of errors: " << err_count;
std::cerr << ", " << error_percent << "% wrong values" << std::endl;
}
return res;
}
......
include/ck_tile/host/concat.hpp 0 → 100644
View file @ 2a30cfdd
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_scheduler.hpp"
namespace ck_tile {
template <typename T>
struct IsCharArray : std::false_type
{
};
template <std::size_t N>
struct IsCharArray<char[N]> : std::true_type
{
};
template <std::size_t N>
struct IsCharArray<const char[N]> : std::true_type
{
};
template <std::size_t N>
struct IsCharArray<char (&)[N]> : std::true_type
{
};
template <std::size_t N>
struct IsCharArray<const char (&)[N]> : std::true_type
{
};
template <typename... Ts>
inline constexpr bool AllConvertibleToStringView = ((std::is_convertible_v<Ts, std::string_view> ||
IsCharArray<Ts>::value ||
std::is_same_v<Ts, char>)&&...);
template <typename... Ts>
[[nodiscard]] auto concat(const Ts&... xs)
-> std::enable_if_t<!AllConvertibleToStringView<Ts...>, std::string>
{
using ::operator<<;
thread_local std::ostringstream oss;
oss.str("");
(oss << ... << xs);
return oss.str();
}
template <std::size_t N>
[[nodiscard]] constexpr inline std::size_t getSize(char (&)[N]) noexcept
{
return N;
}
template <std::size_t N>
[[nodiscard]] constexpr inline std::size_t getSize(const char (&)[N]) noexcept
{
return N;
}
[[nodiscard]] constexpr inline std::size_t getSize(const char* s) noexcept
{
const char* end = s;
while(*end++ != 0) {}
return end - s - 1;
}
[[nodiscard]] constexpr inline std::size_t getSize(const char&) noexcept { return 1; }
[[nodiscard]] inline std::size_t getSize(const std::string& s) noexcept { return s.size(); }
[[nodiscard]] constexpr inline std::size_t getSize(const std::string_view& s) noexcept
{
return s.size();
}
template <typename... Ts>
auto concatInto(std::string& result, const Ts&... xs)
-> std::enable_if_t<AllConvertibleToStringView<Ts...>, void>
{
const std::size_t space = (1 + ... + getSize(xs));
result.reserve(result.size() + space);
((result += xs), ...);
}
template <typename... Ts>
[[nodiscard]] auto concat(const Ts&... xs)
-> std::enable_if_t<AllConvertibleToStringView<Ts...>, std::string>
{
std::string result;
concatInto(result, xs...);
return result;
}
// Function for types convertible to std::string_view
template <typename Sep, typename First, typename... Rest>
[[nodiscard]] auto concat(Sep sep, const First& first, const Rest&... rest)
-> std::enable_if_t<AllConvertibleToStringView<First, Rest...>, std::string>
{
std::string result;
result += first;
((result += sep, result += rest), ...);
return result;
}
// Function for other types
template <typename Sep, typename First, typename... Rest>
[[nodiscard]] auto concat(Sep sep, const First& first, const Rest&... rest)
-> std::enable_if_t<!AllConvertibleToStringView<First, Rest...>, std::string>
{
using ::operator<<;
thread_local std::ostringstream oss;
oss.str("");
oss << first;
((oss << sep << rest), ...);
return oss.str();
}
} // namespace ck_tile
include/ck_tile/host/convolution_host_tensor_descriptor_helper.hpp
View file @ 2a30cfdd
......@@ -14,57 +14,41 @@ namespace detail {
template <typename OldLayout>
CK_TILE_HOST std::vector<std::size_t> get_layout_transpose_gnchw_to_old()
{
if constexpr(std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNCW> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GKCX> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNKW>)
using namespace ck_tile::tensor_layout::convolution;
if constexpr(is_any_of<OldLayout, GNCW, GKCX, GNKW>::value)
{
return {0, 1, 2, 3};
}
else if constexpr(std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNCHW> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GKCYX> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNKHW>)
else if constexpr(is_any_of<OldLayout, GNCHW, GKCYX, GNKHW>::value)
{
return {0, 1, 2, 3, 4};
}
else if constexpr(std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNCDHW> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GKCZYX> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNKDHW>)
else if constexpr(is_any_of<OldLayout, GNCDHW, GKCZYX, GNKDHW>::value)
{
return {0, 1, 2, 3, 4, 5};
}
if constexpr(std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNWC> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GKXC> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNWK>)
if constexpr(is_any_of<OldLayout, GNWC, GKXC, GNWK>::value)
{
return {0, 1, 3, 2};
}
else if constexpr(std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNHWC> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GKYXC> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNHWK>)
else if constexpr(is_any_of<OldLayout, GNHWC, GKYXC, GNHWK>::value)
{
return {0, 1, 4, 2, 3};
}
else if constexpr(std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNDHWC> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GKZYXC> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNDHWK>)
else if constexpr(is_any_of<OldLayout, GNDHWC, GKZYXC, GNDHWK>::value)
{
return {0, 1, 5, 2, 3, 4};
}
else if constexpr(std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::NWGC> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::KXGC> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::NWGK>)
else if constexpr(is_any_of<OldLayout, NWGC, KXGC, NWGK>::value)
{
return {2, 0, 3, 1};
}
else if constexpr(std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::NHWGC> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::KYXGC> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::NHWGK>)
else if constexpr(is_any_of<OldLayout, NHWGC, KYXGC, NHWGK>::value)
{
return {3, 0, 4, 1, 2};
}
else if constexpr(std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::NDHWGC> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::KZYXGC> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::NDHWGK>)
else if constexpr(is_any_of<OldLayout, NDHWGC, KZYXGC, NDHWGK>::value)
{
return {4, 0, 5, 1, 2, 3};
}
......@@ -83,11 +67,11 @@ template <typename InLayout>
CK_TILE_HOST HostTensorDescriptor
make_input_host_tensor_descriptor_g_n_c_wis_packed(const ck_tile::conv::ConvParam& param)
{
using namespace ck_tile::tensor_layout::convolution;
std::vector<std::size_t> physical_lengths;
if constexpr(std::is_same_v<InLayout, ck_tile::tensor_layout::convolution::GNCW> ||
std::is_same_v<InLayout, ck_tile::tensor_layout::convolution::GNCHW> ||
std::is_same_v<InLayout, ck_tile::tensor_layout::convolution::GNCDHW>)
if constexpr(is_any_of<InLayout, GNCW, GNCHW, GNCDHW>::value)
{
physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.G_),
static_cast<std::size_t>(param.N_),
......@@ -97,9 +81,7 @@ make_input_host_tensor_descriptor_g_n_c_wis_packed(const ck_tile::conv::ConvPara
param.input_spatial_lengths_.begin(),
param.input_spatial_lengths_.begin() + param.num_dim_spatial_);
}
else if constexpr(std::is_same_v<InLayout, ck_tile::tensor_layout::convolution::GNWC> ||
std::is_same_v<InLayout, ck_tile::tensor_layout::convolution::GNHWC> ||
std::is_same_v<InLayout, ck_tile::tensor_layout::convolution::GNDHWC>)
else if constexpr(is_any_of<InLayout, GNWC, GNHWC, GNDHWC>::value)
{
physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.G_),
static_cast<std::size_t>(param.N_),
......@@ -109,9 +91,7 @@ make_input_host_tensor_descriptor_g_n_c_wis_packed(const ck_tile::conv::ConvPara
param.input_spatial_lengths_.begin(),
param.input_spatial_lengths_.begin() + param.num_dim_spatial_);
}
else if constexpr(std::is_same_v<InLayout, ck_tile::tensor_layout::convolution::NWGC> ||
std::is_same_v<InLayout, ck_tile::tensor_layout::convolution::NHWGC> ||
std::is_same_v<InLayout, ck_tile::tensor_layout::convolution::NDHWGC>)
else if constexpr(is_any_of<InLayout, NWGC, NHWGC, NDHWGC>::value)
{
physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.N_),
static_cast<std::size_t>(param.G_),
......@@ -139,11 +119,11 @@ template <typename WeiLayout>
CK_TILE_HOST HostTensorDescriptor
make_weight_host_tensor_descriptor_g_k_c_xs_packed(const ck_tile::conv::ConvParam& param)
{
using namespace ck_tile::tensor_layout::convolution;
std::vector<std::size_t> physical_lengths;
if constexpr(std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::KXC> ||
std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::KYXC> ||
std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::KZYXC>)
if constexpr(is_any_of<WeiLayout, KXC, KYXC, KZYXC>::value)
{
if(param.G_ != 1)
{
......@@ -157,9 +137,7 @@ make_weight_host_tensor_descriptor_g_k_c_xs_packed(const ck_tile::conv::ConvPara
param.filter_spatial_lengths_.begin(),
param.filter_spatial_lengths_.begin() + param.num_dim_spatial_);
}
else if constexpr(std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::GKCX> ||
std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::GKCYX> ||
std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::GKCZYX>)
else if constexpr(is_any_of<WeiLayout, GKCX, GKCYX, GKCZYX>::value)
{
physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.G_),
static_cast<std::size_t>(param.K_),
......@@ -169,9 +147,7 @@ make_weight_host_tensor_descriptor_g_k_c_xs_packed(const ck_tile::conv::ConvPara
param.filter_spatial_lengths_.begin(),
param.filter_spatial_lengths_.begin() + param.num_dim_spatial_);
}
else if constexpr(std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::GKXC> ||
std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::GKYXC> ||
std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::GKZYXC>)
else if constexpr(is_any_of<WeiLayout, GKXC, GKYXC, GKZYXC>::value)
{
physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.G_),
static_cast<std::size_t>(param.K_),
......@@ -181,9 +157,7 @@ make_weight_host_tensor_descriptor_g_k_c_xs_packed(const ck_tile::conv::ConvPara
param.filter_spatial_lengths_.begin(),
param.filter_spatial_lengths_.begin() + param.num_dim_spatial_);
}
else if constexpr(std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::KXGC> ||
std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::KYXGC> ||
std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::KZYXGC>)
else if constexpr(is_any_of<WeiLayout, KXGC, KYXGC, KZYXGC>::value)
{
physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.K_),
static_cast<std::size_t>(param.G_),
......@@ -211,11 +185,11 @@ template <typename OutLayout>
CK_TILE_HOST HostTensorDescriptor
make_output_host_tensor_descriptor_g_n_k_wos_packed(const ck_tile::conv::ConvParam& param)
{
using namespace ck_tile::tensor_layout::convolution;
std::vector<std::size_t> physical_lengths;
if constexpr(std::is_same_v<OutLayout, ck_tile::tensor_layout::convolution::GNKW> ||
std::is_same_v<OutLayout, ck_tile::tensor_layout::convolution::GNKHW> ||
std::is_same_v<OutLayout, ck_tile::tensor_layout::convolution::GNKDHW>)
if constexpr(is_any_of<OutLayout, GNKW, GNKHW, GNKDHW>::value)
{
physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.G_),
static_cast<std::size_t>(param.N_),
......@@ -226,9 +200,7 @@ make_output_host_tensor_descriptor_g_n_k_wos_packed(const ck_tile::conv::ConvPar
param.output_spatial_lengths_.begin() + param.num_dim_spatial_);
}
// separate from legacy code above
else if constexpr(std::is_same_v<OutLayout, ck_tile::tensor_layout::convolution::GNWK> ||
std::is_same_v<OutLayout, ck_tile::tensor_layout::convolution::GNHWK> ||
std::is_same_v<OutLayout, ck_tile::tensor_layout::convolution::GNDHWK>)
else if constexpr(is_any_of<OutLayout, GNWK, GNHWK, GNDHWK>::value)
{
physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.G_),
static_cast<std::size_t>(param.N_),
......@@ -238,9 +210,7 @@ make_output_host_tensor_descriptor_g_n_k_wos_packed(const ck_tile::conv::ConvPar
param.output_spatial_lengths_.begin(),
param.output_spatial_lengths_.begin() + param.num_dim_spatial_);
}
else if constexpr(std::is_same_v<OutLayout, ck_tile::tensor_layout::convolution::NWGK> ||
std::is_same_v<OutLayout, ck_tile::tensor_layout::convolution::NHWGK> ||
std::is_same_v<OutLayout, ck_tile::tensor_layout::convolution::NDHWGK>)
else if constexpr(is_any_of<OutLayout, NWGK, NHWGK, NDHWGK>::value)
{
physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.N_),
static_cast<std::size_t>(param.G_),
......
include/ck_tile/host/host_tensor.hpp
View file @ 2a30cfdd
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -678,4 +678,43 @@ struct HostTensor
Descriptor mDesc;
Data mData;
};
template <bool is_row_major>
auto host_tensor_descriptor(std::size_t row,
std::size_t col,
std::size_t stride,
bool_constant<is_row_major>)
{
using namespace ck_tile::literals;
if constexpr(is_row_major)
{
return HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return HostTensorDescriptor({row, col}, {1_uz, stride});
}
}
template <bool is_row_major>
auto get_default_stride(std::size_t row,
std::size_t col,
std::size_t stride,
bool_constant<is_row_major>)
{
if(stride == 0)
{
if constexpr(is_row_major)
{
return col;
}
else
{
return row;
}
}
else
return stride;
}
} // namespace ck_tile
include/ck_tile/host/reference/reference_batched_transpose.hpp 0 → 100644
View file @ 2a30cfdd
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/host/host_tensor.hpp"
#include <thread>
namespace ck_tile {
template <typename Type>
CK_TILE_HOST void reference_batched_transpose(const HostTensor<Type>& x,
HostTensor<Type>& y,
std::string layout_in = "NCHW",
std::string layout_out = "NHWC")
{
const int N = x.mDesc.get_lengths()[0];
auto f = [&](auto batch) {
if(layout_in == "NCHW" && layout_out == "NHWC")
{
const int C = x.mDesc.get_lengths()[1];
const int H = x.mDesc.get_lengths()[2];
const int W = x.mDesc.get_lengths()[3];
for(int c = 0; c < C; ++c)
{
for(int h = 0; h < H; ++h)
{
for(int w = 0; w < W; ++w)
{
Type v_x = x(batch, c, h, w);
y(batch, h, w, c) = v_x;
}
}
}
}
else if(layout_in == "NHWC" && layout_out == "NCHW")
{
const int H = x.mDesc.get_lengths()[1];
const int W = x.mDesc.get_lengths()[2];
const int C = x.mDesc.get_lengths()[3];
for(int h = 0; h < H; ++h)
{
for(int w = 0; w < W; ++w)
{
for(int c = 0; c < C; ++c)
{
Type v_x = x(batch, h, w, c);
y(batch, c, h, w) = v_x;
}
}
}
}
};
make_ParallelTensorFunctor(f, N)(std::thread::hardware_concurrency());
}
} // namespace ck_tile
include/ck_tile/host/reference/reference_fused_moe.hpp
View file @ 2a30cfdd
......@@ -73,7 +73,7 @@ void reference_fused_moe(
ck_tile::index_t tokens,
ck_tile::index_t experts,
ck_tile::index_t hidden_size,
ck_tile::index_t intermediate_size, // this size is for gate/up
ck_tile::index_t intermediate_size, // this size is for gate/up/down
ck_tile::index_t topk,
ck_tile::index_t gate_only)
{
......@@ -82,19 +82,8 @@ void reference_fused_moe(
assert(sorted_expert_ids_host.get_num_of_dimension() == 1);
assert(num_sorted_tiles_host.get_element_size() == 1);
ck_tile::index_t num_sorted_tiles = num_sorted_tiles_host.mData[0] / block_m;
ck_tile::index_t intermediate_size_0 = intermediate_size;
ck_tile::index_t intermediate_size_1 = intermediate_size / (gate_only ? 1 : 2);
// TODO: better remove this in the future, or modify the token_id value
auto get_topk_id = [&](ck_tile::index_t token_id_, ck_tile::index_t expert_id_) {
for(ck_tile::index_t i_ = 0; i_ < topk; i_++)
{
if(token_ids_host(token_id_, i_) == expert_id_)
return i_;
}
throw std::runtime_error("not correct token/expert pair\n");
return -1; // TODO: not correct!!
};
ck_tile::index_t intermediate_size_0 = intermediate_size * (gate_only ? 1 : 2);
ck_tile::index_t intermediate_size_1 = intermediate_size;
ck_tile::HostTensor<AccDataType> out_topk_tokens({tokens, topk, hidden_size});
......@@ -105,11 +94,31 @@ void reference_fused_moe(
if(i_tile >= num_sorted_tiles)
return;
ck_tile::index_t i_expert = sorted_expert_ids_host.mData[i_tile];
ck_tile::index_t i_token = sorted_token_ids_host.mData[i_flatten];
#if CK_TILE_REFERENCE_MOE_SORTING_MOCK_ID
ck_tile::index_t i_token = sorted_token_ids_host.mData[i_flatten];
ck_tile::index_t i_topk = i_token >> 24;
i_token &= 0xffffff;
if(i_token >= tokens)
return;
(void)token_ids_host;
#else
// TODO: better remove this in the future, or modify the token_id value
auto get_topk_id = [&](ck_tile::index_t token_id_, ck_tile::index_t expert_id_) {
for(ck_tile::index_t i_ = 0; i_ < topk; i_++)
{
if(token_ids_host(token_id_, i_) == expert_id_)
return i_;
}
throw std::runtime_error("not correct token/expert pair\n");
return -1; // TODO: not correct!!
};
ck_tile::index_t i_token = sorted_token_ids_host.mData[i_flatten];
if(i_token >= tokens)
return;
ck_tile::index_t i_topk = get_topk_id(i_token, i_expert); // TODO: ugly
auto weight = sorted_weight_host.mData[i_flatten];
#endif
auto weight = sorted_weight_host.mData[i_flatten];
ck_tile::HostTensor<AccDataType> acc_0({1, intermediate_size_0});
// first gemm
......
include/ck_tile/host/reference/reference_gemm.hpp
View file @ 2a30cfdd
......@@ -80,13 +80,14 @@ __global__ void naive_gemm_kernel(ADataType* A,
int b_index = (std::is_same_v<LayoutB, tensor_layout::gemm::ColumnMajor>)
? col * strideB + k
: k * strideB + col;
acc += static_cast<AccDataType>(A[a_index]) * static_cast<AccDataType>(B[b_index]);
acc += ck_tile::type_convert<AccDataType>(A[a_index]) *
ck_tile::type_convert<AccDataType>(B[b_index]);
}
int c_index = (std::is_same_v<LayoutC, tensor_layout::gemm::RowMajor>)
? row * strideC + col
: col * strideC + row;
C[c_index] = acc;
C[c_index] = ck_tile::type_convert<CDataType>(acc);
}
}
......@@ -97,9 +98,9 @@ template <typename ADataType,
typename LayoutA,
typename LayoutB,
typename LayoutC>
void reference_gemm_gpu(DeviceMem& a_device,
DeviceMem& b_device,
DeviceMem& c_device,
void reference_gemm_gpu(ADataType* a_ptr,
BDataType* b_ptr,
CDataType* c_ptr,
index_t M,
index_t N,
index_t K,
......@@ -107,79 +108,13 @@ void reference_gemm_gpu(DeviceMem& a_device,
index_t stride_b,
index_t stride_c)
{
ADataType* d_A;
BDataType* d_B;
CDataType* d_C;
hipError_t errA = hipMalloc(&d_A, M * K * sizeof(ADataType));
hipError_t errB = hipMalloc(&d_B, N * K * sizeof(BDataType));
hipError_t errC = hipMalloc(&d_C, M * N * sizeof(CDataType));
if(errA != hipSuccess)
{
std::cerr << "Error allocating device memory for A: " << hipGetErrorString(errA)
<< std::endl;
return; // Early exit on error
}
if(errB != hipSuccess)
{
std::cerr << "Error allocating device memory for B: " << hipGetErrorString(errB)
<< std::endl;
return; // Early exit on error
}
if(errC != hipSuccess)
{
std::cerr << "Error allocating device memory for C: " << hipGetErrorString(errC)
<< std::endl;
return; // Early exit on error
}
errA = hipMemcpy(
d_A, a_device.GetDeviceBuffer(), M * K * sizeof(ADataType), hipMemcpyHostToDevice);
if(errA != hipSuccess)
{
std::cerr << "Error copying A to device: " << hipGetErrorString(errA) << std::endl;
}
errB = hipMemcpy(
d_B, b_device.GetDeviceBuffer(), N * K * sizeof(BDataType), hipMemcpyHostToDevice);
if(errB != hipSuccess)
{
std::cerr << "Error copying B to device: " << hipGetErrorString(errB) << std::endl;
}
int totalElements = M * N;
int numThreadsPerBlock = 256; // Common choice for threads per block
int numBlocks = (totalElements + numThreadsPerBlock - 1) / numThreadsPerBlock;
naive_gemm_kernel<ADataType, BDataType, AccDataType, CDataType, LayoutA, LayoutB, LayoutC>
<<<numBlocks, numThreadsPerBlock>>>(d_A, d_B, d_C, M, N, K, stride_a, stride_b, stride_c);
errC = hipMemcpy(
c_device.GetDeviceBuffer(), d_C, M * N * sizeof(CDataType), hipMemcpyDeviceToHost);
if(errC != hipSuccess)
{
std::cerr << "Error copying C to device: " << hipGetErrorString(errC) << std::endl;
}
errA = hipFree(d_A);
if(errA != hipSuccess)
{
std::cerr << "Error free the A memory: " << hipGetErrorString(errA) << std::endl;
}
errB = hipFree(d_B);
if(errB != hipSuccess)
{
std::cerr << "Error free the B memory: " << hipGetErrorString(errB) << std::endl;
}
errC = hipFree(d_C);
if(errC != hipSuccess)
{
std::cerr << "Error free the C memory: " << hipGetErrorString(errC) << std::endl;
}
<<<numBlocks, numThreadsPerBlock>>>(
a_ptr, b_ptr, c_ptr, M, N, K, stride_a, stride_b, stride_c);
return;
}
......@@ -191,9 +126,9 @@ template <typename ADataType,
typename LayoutA,
typename LayoutB,
typename LayoutC>
void reference_batched_gemm_gpu(DeviceMem& a_device,
DeviceMem& b_device,
DeviceMem& c_device,
void reference_batched_gemm_gpu(ADataType* a_ptr,
BDataType* b_ptr,
CDataType* c_ptr,
index_t M,
index_t N,
index_t K,
......@@ -205,94 +140,20 @@ void reference_batched_gemm_gpu(DeviceMem& a_device,
index_t batch_stride_C,
index_t batch_count)
{
ADataType* d_A;
BDataType* d_B;
CDataType* d_C;
hipError_t errA = hipMalloc(&d_A, batch_count * M * K * sizeof(ADataType));
hipError_t errB = hipMalloc(&d_B, batch_count * N * K * sizeof(BDataType));
hipError_t errC = hipMalloc(&d_C, batch_count * M * N * sizeof(CDataType));
if(errA != hipSuccess)
{
std::cerr << "Error allocating device memory for A: " << hipGetErrorString(errA)
<< std::endl;
return; // Early exit on error
}
if(errB != hipSuccess)
{
std::cerr << "Error allocating device memory for B: " << hipGetErrorString(errB)
<< std::endl;
return; // Early exit on error
}
if(errC != hipSuccess)
{
std::cerr << "Error allocating device memory for C: " << hipGetErrorString(errC)
<< std::endl;
return; // Early exit on error
}
errA = hipMemcpy(d_A,
a_device.GetDeviceBuffer(),
batch_count * M * K * sizeof(ADataType),
hipMemcpyHostToDevice);
if(errA != hipSuccess)
{
std::cerr << "Error copying A to device: " << hipGetErrorString(errA) << std::endl;
}
errB = hipMemcpy(d_B,
b_device.GetDeviceBuffer(),
batch_count * N * K * sizeof(BDataType),
hipMemcpyHostToDevice);
if(errB != hipSuccess)
{
std::cerr << "Error copying B to device: " << hipGetErrorString(errB) << std::endl;
}
int totalElements = M * N;
int numThreadsPerBlock = 256; // Common choice for threads per block
int numBlocks = (totalElements + numThreadsPerBlock - 1) / numThreadsPerBlock;
for(index_t batch_id = 0; batch_id < batch_count; ++batch_id)
{
ADataType* d_ATemp = d_A + batch_id * batch_stride_A;
BDataType* d_BTemp = d_B + batch_id * batch_stride_B;
CDataType* d_CTemp = d_C + batch_id * batch_stride_C;
ADataType* d_ATemp = a_ptr + batch_id * batch_stride_A;
BDataType* d_BTemp = b_ptr + batch_id * batch_stride_B;
CDataType* d_CTemp = c_ptr + batch_id * batch_stride_C;
naive_gemm_kernel<ADataType, BDataType, AccDataType, CDataType, LayoutA, LayoutB, LayoutC>
<<<numBlocks, numThreadsPerBlock>>>(
d_ATemp, d_BTemp, d_CTemp, M, N, K, stride_a, stride_b, stride_c);
}
errC = hipMemcpy(c_device.GetDeviceBuffer(),
d_C,
batch_count * M * N * sizeof(CDataType),
hipMemcpyDeviceToHost);
if(errC != hipSuccess)
{
std::cerr << "Error copying C to device: " << hipGetErrorString(errC) << std::endl;
}
errA = hipFree(d_A);
if(errA != hipSuccess)
{
std::cerr << "Error free the A memory: " << hipGetErrorString(errA) << std::endl;
}
errB = hipFree(d_B);
if(errB != hipSuccess)
{
std::cerr << "Error free the B memory: " << hipGetErrorString(errB) << std::endl;
}
errC = hipFree(d_C);
if(errC != hipSuccess)
{
std::cerr << "Error free the C memory: " << hipGetErrorString(errC) << std::endl;
}
return;
}
} // namespace ck_tile
include/ck_tile/host/reference/reference_moe_sorting.hpp
View file @ 2a30cfdd
......@@ -14,12 +14,15 @@ namespace ck_tile {
template <typename WeightType, typename IndexType = index_t>
CK_TILE_HOST void reference_moe_sorting(const HostTensor<IndexType>& topk_ids,
const HostTensor<WeightType>& weights,
const HostTensor<IndexType>& local_expert_mask,
HostTensor<IndexType>& p_sorted_token_ids,
HostTensor<WeightType>& sorted_weight,
HostTensor<IndexType>& sorted_expert_ids,
index_t& unit_cnt,
const index_t experts,
const index_t unit_size)
const index_t unit_size,
bool local_expert_masking,
bool skip_experts_with_zero_token = true)
{
const index_t num_token = topk_ids.mDesc.get_lengths()[0];
const index_t topk = topk_ids.mDesc.get_lengths()[1];
......@@ -33,8 +36,11 @@ CK_TILE_HOST void reference_moe_sorting(const HostTensor<IndexType>& topk_ids,
#endif
std::vector<std::vector<WeightType>> expert_token_weights(
experts, std::vector<WeightType>(unit_size, 0));
// count number of unit-size slices in this expert
std::vector<IndexType> expert_slices(experts, 1);
// count the tokens used in this expert
std::vector<IndexType> expert_slice_idxs(experts, 0);
// TODO: above 2 buffer seems duplicated
for(index_t t = 0; t < num_token; t++)
{
......@@ -72,8 +78,23 @@ CK_TILE_HOST void reference_moe_sorting(const HostTensor<IndexType>& topk_ids,
IndexType* out_tokens = p_sorted_token_ids.data();
WeightType* out_weights = sorted_weight.data();
IndexType* out_expert_id = sorted_expert_ids.data();
int curr_expert_id = 0;
for(index_t e = 0; e < experts; e++)
{
if(local_expert_masking)
{
if(local_expert_mask(e) == 0)
continue;
}
if(skip_experts_with_zero_token)
{
if(expert_slice_idxs[e] == 0)
{
curr_expert_id++;
continue;
}
}
memcpy(out_tokens, expert_tokens[e].data(), sizeof(index_t) * expert_slices[e] * unit_size);
out_tokens += expert_slices[e] * unit_size;
memcpy(out_weights,
......@@ -83,10 +104,11 @@ CK_TILE_HOST void reference_moe_sorting(const HostTensor<IndexType>& topk_ids,
for(index_t s = 0; s < expert_slices[e]; s++)
{
out_expert_id[s] = e;
out_expert_id[s] = curr_expert_id;
unit_cnt++;
}
out_expert_id += expert_slices[e];
curr_expert_id++;
}
unit_cnt *= unit_size;
return;
......
include/ck_tile/host/reference/reference_rmsnorm2d_fwd.hpp
View file @ 2a30cfdd
......@@ -8,16 +8,40 @@
namespace ck_tile {
// Note: for simplicity, each functor only care about single M
struct reference_rmsnorm2d_default_epilogue
{
template <typename OutDataType, typename AccDataType>
void operator()(int m, HostTensor<OutDataType>& o, const HostTensor<AccDataType>& acc)
{
const int N = acc.mDesc.get_lengths()[1];
for(int n = 0; n < N; ++n)
{
o(m, n) = ck_tile::type_convert<OutDataType>(acc(m, n));
}
}
template <typename OutDataType, typename AccDataType>
auto operator()(int m, const HostTensor<AccDataType>& acc)
{
HostTensor<OutDataType> o(acc.get_lengths(), acc.get_strides());
operator()(m, o, acc);
return o;
}
};
template <typename XDataType,
typename GammaDataType,
typename ComputeDataType,
typename YDataType,
typename InvRmsDataType>
typename InvRmsDataType,
typename Epilogue = reference_rmsnorm2d_default_epilogue>
void reference_rmsnorm2d_fwd(const HostTensor<XDataType>& x_m_n,
const HostTensor<GammaDataType>& gamma_n,
HostTensor<YDataType>& y_m_n,
HostTensor<InvRmsDataType>& invRms_m,
ComputeDataType epsilon)
ComputeDataType epsilon,
Epilogue epilogue_functor = {})
{
auto rmsnorm2d_fwd_func = [&](auto m) {
const int N = x_m_n.mDesc.get_lengths()[1];
......@@ -37,13 +61,15 @@ void reference_rmsnorm2d_fwd(const HostTensor<XDataType>& x_m_n,
if constexpr(!std::is_same_v<InvRmsDataType, ck_tile::null_type>)
invRms_m(m) = ck_tile::type_convert<InvRmsDataType>(divisor);
HostTensor<ComputeDataType> acc(x_m_n.get_lengths(), x_m_n.get_strides());
for(int n = 0; n < N; ++n)
{
ComputeDataType x = ck_tile::type_convert<ComputeDataType>(x_m_n(m, n));
ComputeDataType gamma = ck_tile::type_convert<ComputeDataType>(gamma_n(n));
auto y = x * divisor * gamma;
y_m_n(m, n) = ck_tile::type_convert<YDataType>(y);
acc(m, n) = x * divisor * gamma;
}
epilogue_functor(m, y_m_n, acc);
};
make_ParallelTensorFunctor(rmsnorm2d_fwd_func, invRms_m.mDesc.get_lengths()[0])(
......
include/ck_tile/host/reference/reference_rowwise_quantization2d.hpp
View file @ 2a30cfdd
......@@ -22,7 +22,7 @@ CK_TILE_HOST void reference_rowwise_quantization2d(const HostTensor<XDataType>&
// scale = amax / 127 for int8
auto v_scale = type_convert<XDataType>(scale_m(m));
auto v_qx = v_x / v_scale;
qx_m_n(m, n) = saturates<QXDataType>{}(v_qx);
qx_m_n(m, n) = type_convert<QXDataType>(saturates<QXDataType>{}(v_qx));
}
};
......
include/ck_tile/ops/add_rmsnorm2d_rdquant.hpp
View file @ 2a30cfdd
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -10,3 +10,4 @@
#include "ck_tile/ops/add_rmsnorm2d_rdquant/pipeline/add_rmsnorm2d_rdquant_fwd_pipeline_three_pass.hpp"
#include "ck_tile/ops/common/generic_2d_block_shape.hpp"
#include "ck_tile/ops/common/tensor_layout.hpp"
#include "ck_tile/ops/common/utils.hpp"
include/ck_tile/ops/batched_transpose.hpp 0 → 100644
View file @ 2a30cfdd
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/ops/batched_transpose/kernel/batched_transpose_kernel.hpp"
#include "ck_tile/ops/batched_transpose/pipeline/batched_transpose_pipeline.hpp"
#include "ck_tile/ops/batched_transpose/pipeline/batched_transpose_policy.hpp"
#include "ck_tile/ops/batched_transpose/pipeline/batched_transpose_problem.hpp"
#include "ck_tile/ops/common/generic_2d_block_shape.hpp"
#include "ck_tile/ops/common/tensor_layout.hpp"
#include "ck_tile/ops/common/utils.hpp"
include/ck_tile/ops/batched_transpose/kernel/batched_transpose_kernel.hpp 0 → 100644
View file @ 2a30cfdd
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/ops/common.hpp"
#include "ck_tile/ops/elementwise.hpp"
#include "ck_tile/host/hip_check_error.hpp"
#include <string>
#include <type_traits>
namespace ck_tile {
struct BatchedTransposeHostArgs
{
const void* p_input;
void* p_output;
index_t batch;
index_t height;
index_t width;
// index_t dim_blocks;
index_t dim_stride;
index_t dim_block_h;
index_t dim_block_w;
};
template <typename Pipeline_>
struct BatchedTransposeKernel
{
using Pipeline = remove_cvref_t<Pipeline_>;
using Problem = remove_cvref_t<typename Pipeline::Problem>;
using Type = typename Problem::InputType;
struct BatchedTransposeKargs
{
const void* p_input;
void* p_output;
index_t batch;
index_t height;
index_t width;
index_t dim_stride;
};
using Kargs = BatchedTransposeKargs;
using Hargs = BatchedTransposeHostArgs;
CK_TILE_HOST static constexpr auto GridSize(const Hargs& h)
{
size_t grid_size_x = (h.width + h.dim_block_w - 1) / h.dim_block_w;
size_t grid_size_y = (h.height + h.dim_block_h - 1) / h.dim_block_h;
size_t grid_size_z = h.batch;
return dim3(grid_size_x, grid_size_y, grid_size_z);
}
CK_TILE_HOST static constexpr auto MakeKargs(const Hargs& h)
{
Kargs k;
k.p_input = h.p_input;
k.p_output = h.p_output;
k.batch = h.batch;
k.height = h.height;
k.width = h.width;
k.dim_stride = h.dim_stride;
return k;
}
CK_TILE_HOST_DEVICE static constexpr auto BlockSize() { return Problem::kBlockSize; }
CK_TILE_DEVICE void operator()(Kargs kargs) const
{
static constexpr ck_tile::index_t kMPerBlock = Problem::kMPerBlock;
static constexpr ck_tile::index_t kNPerBlock = Problem::kNPerBlock;
static constexpr bool kPadM = Problem::kPadM;
static constexpr bool kPadN = Problem::kPadN;
static constexpr ck_tile::index_t kMPerThread = Problem::kMPerThread;
static constexpr ck_tile::index_t kNPerThread = Problem::kNPerThread;
static_assert(kMPerThread == 1 && kNPerThread == 1);
const auto iDim = blockIdx.z;
const auto x_m_n = [&]() {
const auto x_dram_naive = make_naive_tensor_view<address_space_enum::global>(
static_cast<const Type*>(kargs.p_input) + iDim * kargs.dim_stride,
make_tuple(kargs.height, kargs.width),
make_tuple(kargs.width, 1),
number<kNPerThread>{}, // TODO thread load value
number<1>{});
return pad_tensor_view(x_dram_naive,
make_tuple(number<kMPerBlock>{}, number<kNPerBlock>{}),
sequence<kPadM, kPadN>{});
}();
const auto iM = __builtin_amdgcn_readfirstlane(blockIdx.x * kMPerBlock);
const auto iN = __builtin_amdgcn_readfirstlane(blockIdx.y * kNPerBlock);
const auto y_n_m = [&]() {
const auto y_dram_naive = make_naive_tensor_view<address_space_enum::global>(
static_cast<Type*>(kargs.p_output) + iDim * kargs.dim_stride,
make_tuple(kargs.width, kargs.height),
make_tuple(kargs.height, 1),
number<kMPerThread>{},
number<1>{});
return pad_tensor_view(y_dram_naive,
make_tuple(number<kNPerBlock>{}, number<kMPerBlock>{}),
sequence<kPadN, kPadM>{});
}();
auto x_block_window =
make_tile_window(x_m_n,
make_tuple(number<kMPerBlock>{}, number<kNPerBlock>{}),
{static_cast<ck_tile::index_t>(iM * kMPerBlock),
static_cast<ck_tile::index_t>(iN * kNPerBlock)});
auto y_block_window =
make_tile_window(y_n_m,
make_tuple(number<kNPerBlock>{}, number<kMPerBlock>{}),
{static_cast<ck_tile::index_t>(iN * kNPerBlock),
static_cast<ck_tile::index_t>(iM * kMPerBlock)});
Pipeline{}(x_block_window, y_block_window);
}
};
} // namespace ck_tile
include/ck_tile/ops/batched_transpose/pipeline/batched_transpose_pipeline.hpp 0 → 100644
View file @ 2a30cfdd
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/ops/batched_transpose/pipeline/batched_transpose_policy.hpp"
#include <string>
#include <type_traits>
namespace ck_tile {
template <typename Problem_, typename Policy_ = BatchedTransposePolicy>
struct BatchedTransposePipeline
{
// TODO: this kernel only support warp per row
using Problem = remove_cvref_t<Problem_>;
using Policy = remove_cvref_t<Policy_>;
using InputType = ck_tile::remove_cvref_t<typename Problem::InputType>;
static constexpr ck_tile::index_t kMPerBlock = Problem::kMPerBlock;
static constexpr ck_tile::index_t kNPerBlock = Problem::kNPerBlock;
static constexpr index_t AlignmentM = Problem::AlignmentM;
static constexpr index_t AlignmentN = Problem::AlignmentN;
static constexpr bool kPadM = Problem::kPadM;
static constexpr bool kPadN = Problem::kPadN;
template <typename InputWindow, typename OutputWindow>
CK_TILE_DEVICE auto operator()(const InputWindow& input_window, OutputWindow& out_window)
{
auto inp_win =
make_tile_window(input_window, Policy::template MakeInputDistribution<Problem>());
auto out_win =
make_tile_window(out_window, Policy::template MakeOutputDistribution<Problem>());
auto x = load_tile(inp_win); // x->thread input_win->block
auto y = make_static_distributed_tensor<InputType>(
Policy::template MakeOutputDistribution<Problem>());
constexpr auto span_2d_x = decltype(x)::get_distributed_spans();
sweep_tile_span(span_2d_x[number<0>{}], [&](auto idx0) {
sweep_tile_span(span_2d_x[number<1>{}], [&](auto idx1) {
constexpr auto i_j_idx = make_tuple(idx1, idx0);
y(i_j_idx) = x(i_j_idx);
});
});
store_tile(out_win, y);
}
};
} // namespace ck_tile
include/ck_tile/ops/batched_transpose/pipeline/batched_transpose_policy.hpp 0 → 100644
View file @ 2a30cfdd
// 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/softmax.hpp"
#include "ck_tile/ops/topk.hpp"
namespace ck_tile {
struct BatchedTransposePolicy
{
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeInputDistribution()
{
using S = Problem;
return make_static_tile_distribution(
tile_distribution_encoding<
sequence<>,
tuple<sequence<S::kMWarpPerBlock, S::kMThreadPerWarp, S::kMPerThread>,
sequence<S::kNWarpPerBlock, S::kNThreadPerWarp, S::kNPerThread>>,
tuple<sequence<1, 2>, sequence<1, 2>>,
tuple<sequence<0, 0>, sequence<1, 1>>,
sequence<1, 2>,
sequence<2, 2>>{});
}
template <typename Problem>
CK_TILE_HOST_DEVICE static constexpr auto MakeOutputDistribution()
{
using S = Problem;
return make_static_tile_distribution(
tile_distribution_encoding<
sequence<>,
tuple<sequence<S::kNWarpPerBlock, S::kNThreadPerWarp, S::kNPerThread>,
sequence<S::kMWarpPerBlock, S::kMThreadPerWarp, S::kMPerThread>>,
tuple<sequence<2, 1>, sequence<2, 1>>,
tuple<sequence<0, 0>, sequence<1, 1>>,
sequence<2, 1>,
sequence<2, 2>>{});
}
};
} // namespace ck_tile
include/ck_tile/ops/batched_transpose/pipeline/batched_transpose_problem.hpp 0 → 100644
View file @ 2a30cfdd
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/core.hpp"
#include <string>
#include <type_traits>
#define VectorLoadSize 16
namespace ck_tile {
template <typename InputType_,
typename BlockTile, // Sequence<...
typename WarpTile, // Sequence<...
typename ThreadTile, // Sequence<...
bool kPadM_ = true,
bool kPadN_ = true>
struct BatchedTransposeProblem
{
using InputType = remove_cvref_t<InputType_>;
static constexpr index_t kMPerThread = ThreadTile::at(number<0>{});
static constexpr index_t kNPerThread = ThreadTile::at(number<1>{});
static constexpr index_t kMPerWarp = WarpTile::at(number<0>{});
static constexpr index_t kNPerWarp = WarpTile::at(number<1>{});
static constexpr index_t kMThreadPerWarp = kMPerWarp / kMPerThread;
static constexpr index_t kNThreadPerWarp = kNPerWarp / kNPerThread;
static constexpr index_t kMPerBlock = BlockTile::at(number<0>{});
static constexpr index_t kNPerBlock = BlockTile::at(number<1>{});
static constexpr index_t kMWarpPerBlock = kMPerBlock / kMPerWarp;
static constexpr index_t kNWarpPerBlock = kNPerBlock / kNPerWarp;
static constexpr index_t kBlockSize =
kMThreadPerWarp * kNThreadPerWarp * kMWarpPerBlock * kNWarpPerBlock;
static constexpr bool kPadM = kPadM_;
static constexpr bool kPadN = kPadN_;
static constexpr index_t AlignmentM = kPadM ? VectorLoadSize / sizeof(InputType) : 1; // TODO
static constexpr index_t AlignmentN = kPadN ? VectorLoadSize / sizeof(InputType) : 1;
};
} // namespace ck_tile
include/ck_tile/ops/common.hpp
View file @ 2a30cfdd
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/ops/common/generic_2d_block_shape.hpp"
#include "ck_tile/ops/common/tensor_layout.hpp"
#include "ck_tile/ops/common/utils.hpp"
include/ck_tile/ops/common/utils.hpp 0 → 100644
View file @ 2a30cfdd
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iostream>
#include <string>
#include "ck_tile/core.hpp"
namespace ck_tile {
// clang-format off
template <typename T> struct typeToStr;
template <> struct typeToStr<float> { static constexpr const char * name = "fp32"; };
template <> struct typeToStr<fp16_t> { static constexpr const char * name = "fp16"; };
template <> struct typeToStr<bf16_t> { static constexpr const char * name = "bf16"; };
template <> struct typeToStr<fp8_t> { static constexpr const char * name = "fp8"; };
template <> struct typeToStr<bf8_t> { static constexpr const char * name = "bf8"; };
template <> struct typeToStr<int8_t> { static constexpr const char * name = "int8"; };
// clang-format on
template <typename ADataType_, typename BDataType_>
std::string gemm_prec_str()
{
std::string base_str = std::string(typeToStr<ADataType_>::name);
if(!std::is_same_v<ADataType_, BDataType_>)
{
base_str += "_" + std::string(typeToStr<BDataType_>::name);
}
return base_str;
}
} // namespace ck_tile
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