Skip to content

GitLab

Commit e4112de7 authored by Jun Liu's avatar Jun Liu
Browse files

Merge branch 'develop' into amd-develop

parents a6ef5c39 fd72380a
Expand all Hide whitespace changes
Inline Side-by-side
Showing with 2813 additions and 736 deletions
CMakeLists.txt
View file @ e4112de7
......@@ -23,7 +23,7 @@ endif()
set(version 1.1.0)
# Check support for CUDA/HIP in Cmake
project(composable_kernel VERSION ${version} LANGUAGES CXX)
project(composable_kernel VERSION ${version} LANGUAGES CXX HIP)
include(CTest)
find_package(Python3 3.6 COMPONENTS Interpreter REQUIRED)
......@@ -112,7 +112,7 @@ message("checking which targets are supported")
#Setting GPU_TARGETS on command line will override this list
if(NOT PROFILER_ONLY)
rocm_check_target_ids(DEFAULT_GPU_TARGETS
TARGETS "gfx908;gfx90a;gfx940;gfx941;gfx942;gfx1030;gfx1100;gfx1101;gfx1102")
TARGETS "gfx900;gfx906;gfx908;gfx90a;gfx940;gfx941;gfx942;gfx1030;gfx1100;gfx1101;gfx1102")
else()
add_definitions(-DPROFILER_ONLY)
set(GPU_TARGETS "" CACHE STRING "" FORCE)
......@@ -135,12 +135,10 @@ endif()
message("Supported GPU_TARGETS= ${DEFAULT_GPU_TARGETS}")
set(AMDGPU_TARGETS "${DEFAULT_GPU_TARGETS}" CACHE STRING " " FORCE)
if(GPU_TARGETS)
message("Building CK for the following targets: ${GPU_TARGETS}")
else()
message("Building CK for the following targets: ${AMDGPU_TARGETS}")
message("Building CK for the default targets: ${DEFAULT_GPU_TARGETS}")
endif()
if (GPU_TARGETS)
......@@ -225,7 +223,13 @@ link_libraries(Threads::Threads)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
set(CMAKE_CXX_EXTENSIONS OFF)
message("CMAKE_CXX_COMPILER_ID: ${CMAKE_CXX_COMPILER_ID}")
message("CMAKE_CXX_COMPILER: ${CMAKE_CXX_COMPILER}")
## HIP
set(CMAKE_HIP_PLATFORM amd)
set(CMAKE_HIP_COMPILER ${CMAKE_CXX_COMPILER})
set(CMAKE_HIP_EXTENSIONS ON)
message("CMAKE_HIP_COMPILER: ${CMAKE_HIP_COMPILER}")
## OpenMP
if(CMAKE_CXX_COMPILER_ID MATCHES "Clang")
......
client_example/07_grouped_convnd_fwd/CMakeLists.txt
View file @ e4112de7
......@@ -4,4 +4,22 @@ if(GPU_TARGETS MATCHES "gfx9")
add_executable(client_grouped_conv1d_fwd grouped_conv1d_fwd.cpp)
target_link_libraries(client_grouped_conv1d_fwd PRIVATE composable_kernel::device_conv_operations)
endif()
\ No newline at end of file
if((DTYPES MATCHES "fp8") OR NOT DEFINED DTYPES)
add_executable(client_grouped_conv3d_fwd_fp8 grouped_conv3d_fwd_fp8.cpp)
target_link_libraries(client_grouped_conv3d_fwd_fp8 PRIVATE composable_kernel::device_conv_operations)
endif()
if((DTYPES MATCHES "bf8") OR NOT DEFINED DTYPES)
add_executable(client_grouped_conv3d_fwd_bf8 grouped_conv3d_fwd_bf8.cpp)
target_link_libraries(client_grouped_conv3d_fwd_bf8 PRIVATE composable_kernel::device_conv_operations)
endif()
if((DTYPES MATCHES "fp8" AND DTYPES MATCHES "bf8") OR NOT DEFINED DTYPES)
add_executable(client_grouped_conv3d_fwd_fp8_bf8 grouped_conv3d_fwd_fp8_bf8.cpp)
target_link_libraries(client_grouped_conv3d_fwd_fp8_bf8 PRIVATE composable_kernel::device_conv_operations)
add_executable(client_grouped_conv3d_fwd_bf8_fp8 grouped_conv3d_fwd_bf8_fp8.cpp)
target_link_libraries(client_grouped_conv3d_fwd_bf8_fp8 PRIVATE composable_kernel::device_conv_operations)
endif()
endif()
client_example/07_grouped_convnd_fwd/common.hpp 0 → 100644
View file @ e4112de7
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include <iomanip>
#include <iostream>
#include <iterator>
#include <numeric>
#include <string>
#include <vector>
#include "ck/ck.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_conv_fwd_multiple_abd.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
struct SimpleDeviceMem
{
SimpleDeviceMem() = delete;
SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
{
(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
}
void* GetDeviceBuffer() { return p_mem_; }
~SimpleDeviceMem() { (void)hipFree(p_mem_); }
void* p_mem_;
};
template <ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
std::size_t
GetFlops(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& output_lengths,
const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& weights_lengths)
{
// 2 * G * N * K * C * <output spatial lengths product> * <filter spatial lengths product>
ck::index_t G = weights_lengths[0];
ck::index_t N = output_lengths[1];
ck::index_t K = weights_lengths[1];
ck::index_t C = weights_lengths[2];
return static_cast<std::size_t>(2) * G * N * K * C *
std::accumulate(std::next(std::begin(output_lengths), NumNonSpatialDim),
std::end(output_lengths),
static_cast<std::size_t>(1),
std::multiplies<>()) *
std::accumulate(std::next(std::begin(weights_lengths), NumNonSpatialDim),
std::end(weights_lengths),
static_cast<std::size_t>(1),
std::multiplies<>());
}
template <typename InDataType, ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
std::size_t
GetInputByte(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& input_lengths)
{
// sizeof(InDataType) * (G * N * C * <input spatial lengths product>) +
return sizeof(InDataType) * std::accumulate(std::begin(input_lengths),
std::end(input_lengths),
static_cast<std::size_t>(1),
std::multiplies<>());
}
template <typename WeiDataType, ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
std::size_t
GetWeightByte(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& weights_lengths)
{
// sizeof(WeiDataType) * (G * K * C * <filter spatial lengths product>) +
return sizeof(WeiDataType) * std::accumulate(std::begin(weights_lengths),
std::end(weights_lengths),
static_cast<std::size_t>(1),
std::multiplies<>());
}
template <typename OutDataType, ck::index_t NumDimSpatial, ck::index_t NumNonSpatialDim = 3>
std::size_t
GetOutputByte(const std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>& output_lengths)
{
// sizeof(OutDataType) * (G * N * K * <output spatial lengths product>);
return sizeof(OutDataType) * std::accumulate(std::begin(output_lengths),
std::end(output_lengths),
static_cast<std::size_t>(1),
std::multiplies<std::size_t>());
}
template <ck::index_t NumDimSpatial,
typename InDataType,
typename WeiDataType,
typename OutDataType,
typename InLayout,
typename WeiLayout,
typename OutLayout,
ck::index_t NumNonSpatialDim = 3,
typename AComputeType = InDataType,
typename BComputeType = AComputeType>
bool run_grouped_conv_fwd(std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> in_lengths,
std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> wei_lengths,
std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> out_lengths)
{
std::size_t in_mem_size = GetInputByte<InDataType, NumDimSpatial>(in_lengths);
std::size_t wei_mem_size = GetWeightByte<WeiDataType, NumDimSpatial>(wei_lengths);
std::size_t out_mem_size = GetOutputByte<OutDataType, NumDimSpatial>(out_lengths);
SimpleDeviceMem in(in_mem_size);
SimpleDeviceMem wei(wei_mem_size);
SimpleDeviceMem out(out_mem_size);
std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> in_strides;
std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> wei_strides;
std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim> out_strides;
in_strides.fill(0);
wei_strides.fill(0);
out_strides.fill(0);
in_strides.back() = 1;
wei_strides.back() = 1;
out_strides.back() = 1;
std::partial_sum(rbegin(in_lengths),
std::prev(rend(in_lengths)),
std::next(rbegin(in_strides)),
std::multiplies<>{});
std::partial_sum(rbegin(wei_lengths),
std::prev(rend(wei_lengths)),
std::next(rbegin(wei_strides)),
std::multiplies<>{});
std::partial_sum(rbegin(out_lengths),
std::prev(rend(out_lengths)),
std::next(rbegin(out_strides)),
std::multiplies<>{});
// transpose NDHWGC/KZYXGC/NDHWGK to GNDHWC/GKZYXC/GNDHWK to GNCDHW/GKCZYX/GNKDHW
std::rotate(std::next(rbegin(in_lengths)), std::next(rbegin(in_lengths), 2), rend(in_lengths));
std::rotate(rbegin(in_lengths),
std::next(rbegin(in_lengths)),
std::next(rbegin(in_lengths), NumDimSpatial + 1));
std::rotate(std::next(rbegin(in_strides)), std::next(rbegin(in_strides), 2), rend(in_strides));
std::rotate(rbegin(in_strides),
std::next(rbegin(in_strides)),
std::next(rbegin(in_strides), NumDimSpatial + 1));
std::rotate(rbegin(wei_lengths),
std::next(rbegin(wei_lengths)),
std::next(rbegin(wei_lengths), NumDimSpatial + 1));
std::rotate(rbegin(wei_strides),
std::next(rbegin(wei_strides)),
std::next(rbegin(wei_strides), NumDimSpatial + 1));
std::rotate(
std::next(rbegin(out_lengths)), std::next(rbegin(out_lengths), 2), rend(out_lengths));
std::rotate(rbegin(out_lengths),
std::next(rbegin(out_lengths)),
std::next(rbegin(out_lengths), NumDimSpatial + 1));
std::rotate(
std::next(rbegin(out_strides)), std::next(rbegin(out_strides), 2), rend(out_strides));
std::rotate(rbegin(out_strides),
std::next(rbegin(out_strides)),
std::next(rbegin(out_strides), NumDimSpatial + 1));
std::array<ck::index_t, NumDimSpatial> conv_filter_strides;
std::array<ck::index_t, NumDimSpatial> conv_filter_dilations;
std::array<ck::index_t, NumDimSpatial> input_left_pads;
std::array<ck::index_t, NumDimSpatial> input_right_pads;
conv_filter_strides.fill(1);
conv_filter_dilations.fill(1);
input_left_pads.fill(1);
input_right_pads.fill(1);
std::size_t flop = GetFlops<NumDimSpatial>(out_lengths, wei_lengths);
std::size_t num_bytes = in_mem_size + wei_mem_size + out_mem_size;
using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD<NumDimSpatial,
InLayout,
WeiLayout,
ck::Tuple<>,
OutLayout,
InDataType,
WeiDataType,
ck::Tuple<>,
OutDataType,
PassThrough,
PassThrough,
PassThrough,
AComputeType,
BComputeType>;
// get device op instances
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
std::string best_op_name;
int best_op_id = -1;
float best_avg_time = std::numeric_limits<float>::max();
float best_gb_per_sec = 0;
float best_tflops = 0;
// profile device operation instances
std::cout << "Run all instances and do timing" << std::endl;
for(int i = 0; i < op_ptrs.size(); ++i)
{
auto& op_ptr = op_ptrs[i];
auto argument_ptr = op_ptr->MakeArgumentPointer(
in.GetDeviceBuffer(),
wei.GetDeviceBuffer(),
std::array<const void*, 0>{},
out.GetDeviceBuffer(),
in_lengths,
in_strides,
wei_lengths,
wei_strides,
std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 0>{{}},
std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 0>{{}},
out_lengths,
out_strides,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
PassThrough{},
PassThrough{},
PassThrough{});
auto invoker_ptr = op_ptr->MakeInvokerPointer();
std::string op_name = op_ptr->GetTypeString();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
float gb_per_sec = num_bytes / 1.E6 / avg_time;
std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << tflops << " TFlops, "
<< gb_per_sec << " GB/s, " << op_name << std::endl;
if(tflops > best_tflops)
{
best_op_id = i;
best_op_name = op_name;
best_avg_time = avg_time;
best_gb_per_sec = gb_per_sec;
best_tflops = tflops;
}
}
else
{
std::cerr << op_name << " does not support this problem" << std::endl;
}
}
if(best_op_id < 0)
{
std::cerr << "no suitable instance" << std::endl;
return false;
}
std::cout << "Best Perf: " << std::setw(10) << best_avg_time << " ms, " << best_tflops
<< " TFlops, " << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
// run the best intance
{
auto& op_ptr = op_ptrs[best_op_id];
std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
<< std::endl;
auto argument_ptr = op_ptr->MakeArgumentPointer(
in.GetDeviceBuffer(),
wei.GetDeviceBuffer(),
std::array<const void*, 0>{},
out.GetDeviceBuffer(),
in_lengths,
in_strides,
wei_lengths,
wei_strides,
std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 0>{{}},
std::array<std::array<ck::index_t, NumDimSpatial + NumNonSpatialDim>, 0>{{}},
out_lengths,
out_strides,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
PassThrough{},
PassThrough{},
PassThrough{});
auto invoker_ptr = op_ptr->MakeInvokerPointer();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
}
std::cout << "Done" << std::endl;
}
return true;
}
client_example/07_grouped_convnd_fwd/grouped_conv1d_fwd.cpp
View file @ e4112de7
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include <iomanip>
#include <iostream>
#include <iterator>
#include <numeric>
#include <vector>
#include "common.hpp"
#include "ck/ck.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
using InDataType = ck::half_t;
using WeiDataType = ck::half_t;
......@@ -31,199 +24,16 @@ static constexpr ck::index_t X = 3;
static constexpr ck::index_t Wi = 28;
static constexpr ck::index_t Wo = 28;
struct SimpleDeviceMem
{
SimpleDeviceMem() = delete;
SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
{
(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
}
void* GetDeviceBuffer() { return p_mem_; }
~SimpleDeviceMem() { (void)hipFree(p_mem_); }
void* p_mem_;
};
int main()
{
std::array<ck::index_t, NumDimSpatial + 3> in_lengths{G, N, Wi, C};
std::array<ck::index_t, NumDimSpatial + 3> in_strides{0, 0, 0, 1};
std::array<ck::index_t, NumDimSpatial + 3> wei_lengths{G, K, X, C};
std::array<ck::index_t, NumDimSpatial + 3> wei_strides{0, 0, 0, 1};
std::array<ck::index_t, NumDimSpatial + 3> out_lengths{G, N, Wo, K};
std::array<ck::index_t, NumDimSpatial + 3> out_strides{0, 0, 0, 1};
std::partial_sum(rbegin(in_lengths),
std::prev(rend(in_lengths)),
std::next(rbegin(in_strides)),
std::multiplies<>{});
std::partial_sum(rbegin(wei_lengths),
std::prev(rend(wei_lengths)),
std::next(rbegin(wei_strides)),
std::multiplies<>{});
std::partial_sum(rbegin(out_lengths),
std::prev(rend(out_lengths)),
std::next(rbegin(out_strides)),
std::multiplies<>{});
// transpose GNWC/GKXC/GNWK to GNCW/GKCX/GNCW
std::rotate(rbegin(in_lengths),
std::next(rbegin(in_lengths)),
std::next(rbegin(in_lengths), NumDimSpatial + 1));
std::rotate(rbegin(in_strides),
std::next(rbegin(in_strides)),
std::next(rbegin(in_strides), NumDimSpatial + 1));
std::rotate(rbegin(wei_lengths),
std::next(rbegin(wei_lengths)),
std::next(rbegin(wei_lengths), NumDimSpatial + 1));
std::rotate(rbegin(wei_strides),
std::next(rbegin(wei_strides)),
std::next(rbegin(wei_strides), NumDimSpatial + 1));
std::rotate(rbegin(out_lengths),
std::next(rbegin(out_lengths)),
std::next(rbegin(out_lengths), NumDimSpatial + 1));
std::rotate(rbegin(out_strides),
std::next(rbegin(out_strides)),
std::next(rbegin(out_strides), NumDimSpatial + 1));
std::array<ck::index_t, NumDimSpatial> filter_strides{1};
std::array<ck::index_t, NumDimSpatial> filter_dilations{1};
std::array<ck::index_t, NumDimSpatial> input_left_pads{1};
std::array<ck::index_t, NumDimSpatial> input_right_pads{1};
SimpleDeviceMem in(sizeof(InDataType) * G * N * Wi * C);
SimpleDeviceMem wei(sizeof(WeiDataType) * G * K * X * C);
SimpleDeviceMem out(sizeof(OutDataType) * G * N * Wo * K);
using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD<NumDimSpatial,
InLayout,
WeiLayout,
ck::Tuple<>,
OutLayout,
InDataType,
WeiDataType,
ck::Tuple<>,
OutDataType,
PassThrough,
PassThrough,
PassThrough>;
// get device op instances
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
std::string best_op_name;
int best_op_id = -1;
float best_avg_time = std::numeric_limits<float>::max();
float best_gb_per_sec = 0;
float best_tflops = 0;
// profile device operation instances
std::cout << "Run all instances and do timing" << std::endl;
for(int i = 0; i < op_ptrs.size(); ++i)
{
auto& op_ptr = op_ptrs[i];
auto argument_ptr = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
wei.GetDeviceBuffer(),
{},
out.GetDeviceBuffer(),
in_lengths,
in_strides,
wei_lengths,
wei_strides,
{},
{},
out_lengths,
out_strides,
filter_strides,
filter_dilations,
input_left_pads,
input_right_pads,
PassThrough{},
PassThrough{},
PassThrough{});
auto invoker_ptr = op_ptr->MakeInvokerPointer();
std::string op_name = op_ptr->GetTypeString();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
std::size_t flop = std::size_t(2) * G * N * K * C * Wo * X;
std::size_t num_bytes = sizeof(InDataType) * G * N * Wi * C +
sizeof(WeiDataType) * G * K * X * C +
sizeof(OutDataType) * G * N * Wo * K;
float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
float gb_per_sec = num_bytes / 1.E6 / avg_time;
std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << tflops << " TFlops, "
<< gb_per_sec << " GB/s, " << op_name << std::endl;
if(tflops > best_tflops)
{
best_op_id = i;
best_op_name = op_name;
best_avg_time = avg_time;
best_gb_per_sec = gb_per_sec;
best_tflops = tflops;
}
}
else
{
std::cerr << op_name << " does not support this problem" << std::endl;
}
}
if(best_op_id < 0)
{
std::cerr << "no suitable instance" << std::endl;
return EXIT_FAILURE;
}
std::cout << "Best Perf: " << std::setw(10) << best_avg_time << " ms, " << best_tflops
<< " TFlops, " << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
// run the best intance
{
auto& op_ptr = op_ptrs[best_op_id];
std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
<< std::endl;
auto argument_ptr = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
wei.GetDeviceBuffer(),
{},
out.GetDeviceBuffer(),
in_lengths,
in_strides,
wei_lengths,
wei_strides,
{},
{},
out_lengths,
out_strides,
filter_strides,
filter_dilations,
input_left_pads,
input_right_pads,
PassThrough{},
PassThrough{},
PassThrough{});
auto invoker_ptr = op_ptr->MakeInvokerPointer();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
}
std::cout << "Done" << std::endl;
}
return run_grouped_conv_fwd<NumDimSpatial,
InDataType,
WeiDataType,
OutDataType,
InLayout,
WeiLayout,
OutLayout,
3>({N, Wi, G, C}, {G, K, X, C}, {N, Wo, G, K})
? EXIT_SUCCESS
: EXIT_FAILURE;
}
client_example/07_grouped_convnd_fwd/grouped_conv2d_fwd.cpp
View file @ e4112de7
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include <iomanip>
#include <iostream>
#include <iterator>
#include <numeric>
#include <vector>
#include "common.hpp"
#include "ck/ck.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
using InDataType = ck::half_t;
using WeiDataType = ck::half_t;
......@@ -34,167 +27,16 @@ static constexpr ck::index_t Wi = 28; // input W
static constexpr ck::index_t Ho = 28; // output H
static constexpr ck::index_t Wo = 28; // output W
struct SimpleDeviceMem
{
SimpleDeviceMem() = delete;
SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
{
(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
}
void* GetDeviceBuffer() { return p_mem_; }
~SimpleDeviceMem() { (void)hipFree(p_mem_); }
void* p_mem_;
};
int main()
{
// We have NHWGC/GKYXC/NHWGK (x, weight, y) in memory space
// However, CK's API only accept length and stride with order of GNCHW/GKCYX/GNCHW
// Hence, we need to adjust the order of stride
std::array<ck::index_t, 5> in_lengths{G, N, C, Hi, Wi};
std::array<ck::index_t, 5> in_strides{C, Hi * Wi * G * C, 1, Wi * G * C, G * C};
std::array<ck::index_t, 5> wei_lengths{G, K, C, Y, X};
std::array<ck::index_t, 5> wei_strides{K * Y * X * C, Y * X * C, 1, X * C, C};
std::array<ck::index_t, 5> out_lengths{G, N, K, Ho, Wo};
std::array<ck::index_t, 5> out_strides{C, Ho * Wo * G * C, 1, Wo * G * C, G * C};
std::array<ck::index_t, NumDimSpatial> filter_strides{1, 1};
std::array<ck::index_t, NumDimSpatial> filter_dilations{1, 1};
std::array<ck::index_t, NumDimSpatial> input_left_pads{1, 1};
std::array<ck::index_t, NumDimSpatial> input_right_pads{1, 1};
SimpleDeviceMem in(sizeof(InDataType) * N * Hi * Wi * G * C);
SimpleDeviceMem wei(sizeof(WeiDataType) * G * K * Y * X * C);
SimpleDeviceMem out(sizeof(OutDataType) * N * Ho * Wo * G * K);
using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD<NumDimSpatial,
InLayout,
WeiLayout,
ck::Tuple<>,
OutLayout,
InDataType,
WeiDataType,
ck::Tuple<>,
OutDataType,
PassThrough,
PassThrough,
PassThrough>;
// get device op instances
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
std::string best_op_name;
int best_op_id = -1;
float best_avg_time = std::numeric_limits<float>::max();
float best_gb_per_sec = 0;
float best_tflops = 0;
// profile device operation instances
std::cout << "Run all instances and do timing" << std::endl;
for(int i = 0; i < op_ptrs.size(); ++i)
{
auto& op_ptr = op_ptrs[i];
auto argument_ptr = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
wei.GetDeviceBuffer(),
{},
out.GetDeviceBuffer(),
in_lengths,
in_strides,
wei_lengths,
wei_strides,
{},
{},
out_lengths,
out_strides,
filter_strides,
filter_dilations,
input_left_pads,
input_right_pads,
PassThrough{},
PassThrough{},
PassThrough{});
auto invoker_ptr = op_ptr->MakeInvokerPointer();
std::string op_name = op_ptr->GetTypeString();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
std::size_t flop = std::size_t(2) * G * N * K * C * Ho * Wo * Y * X;
std::size_t num_bytes = sizeof(InDataType) * N * Hi * Wi * G * C +
sizeof(WeiDataType) * G * K * Y * X * C +
sizeof(OutDataType) * N * Ho * Wo * G * K;
float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
float gb_per_sec = num_bytes / 1.E6 / avg_time;
std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << tflops << " TFlops, "
<< gb_per_sec << " GB/s, " << op_name << std::endl;
if(tflops > best_tflops)
{
best_op_id = i;
best_op_name = op_name;
best_avg_time = avg_time;
best_gb_per_sec = gb_per_sec;
best_tflops = tflops;
}
}
else
{
std::cerr << op_name << " does not support this problem" << std::endl;
}
}
if(best_op_id < 0)
{
std::cerr << "no suitable instance" << std::endl;
return EXIT_FAILURE;
}
std::cout << "Best Perf: " << std::setw(10) << best_avg_time << " ms, " << best_tflops
<< " TFlops, " << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
// run the best intance
{
auto& op_ptr = op_ptrs[best_op_id];
std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
<< std::endl;
auto argument_ptr = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
wei.GetDeviceBuffer(),
{},
out.GetDeviceBuffer(),
in_lengths,
in_strides,
wei_lengths,
wei_strides,
{},
{},
out_lengths,
out_strides,
filter_strides,
filter_dilations,
input_left_pads,
input_right_pads,
PassThrough{},
PassThrough{},
PassThrough{});
auto invoker_ptr = op_ptr->MakeInvokerPointer();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
}
std::cout << "Done" << std::endl;
}
return run_grouped_conv_fwd<NumDimSpatial,
InDataType,
WeiDataType,
OutDataType,
InLayout,
WeiLayout,
OutLayout,
3>({N, Hi, Wi, G, C}, {G, K, Y, X, C}, {N, Ho, Wo, G, K})
? EXIT_SUCCESS
: EXIT_FAILURE;
}
client_example/16_convnd_fwd/CMakeLists.txt
View file @ e4112de7
......@@ -7,22 +7,6 @@ endif()
if((DTYPES MATCHES "fp8") OR NOT DEFINED DTYPES)
add_executable(client_conv3d_fwd_fp16_comp_fp8 conv3d_fwd_fp16_comp_fp8.cpp)
target_link_libraries(client_conv3d_fwd_fp16_comp_fp8 PRIVATE composable_kernel::device_conv_operations)
add_executable(client_conv3d_fwd_fp8 conv3d_fwd_fp8.cpp)
target_link_libraries(client_conv3d_fwd_fp8 PRIVATE composable_kernel::device_conv_operations)
endif()
if((DTYPES MATCHES "bf8") OR NOT DEFINED DTYPES)
add_executable(client_conv3d_fwd_bf8 conv3d_fwd_bf8.cpp)
target_link_libraries(client_conv3d_fwd_bf8 PRIVATE composable_kernel::device_conv_operations)
endif()
if((DTYPES MATCHES "fp8" AND DTYPES MATCHES "bf8") OR NOT DEFINED DTYPES)
add_executable(client_conv3d_fwd_fp8_bf8 conv3d_fwd_fp8_bf8.cpp)
target_link_libraries(client_conv3d_fwd_fp8_bf8 PRIVATE composable_kernel::device_conv_operations)
add_executable(client_conv3d_fwd_bf8_fp8 conv3d_fwd_bf8_fp8.cpp)
target_link_libraries(client_conv3d_fwd_bf8_fp8 PRIVATE composable_kernel::device_conv_operations)
endif()
if((DTYPES MATCHES "fp32") OR NOT DEFINED DTYPES)
......
example/CMakeLists.txt
View file @ e4112de7
......@@ -44,6 +44,13 @@ function(add_example_executable EXAMPLE_NAME FILE_NAME)
endif()
endforeach()
endif()
if(INSTANCES_ONLY)
set(EX_TARGETS ${DEFAULT_GPU_TARGETS})
else()
set(EX_TARGETS ${GPU_TARGETS})
endif()
#Do not build any DL examples if DL_KERNELS not set
foreach(source IN LISTS FILE_NAME)
if(NOT DEFINED DL_KERNELS AND source MATCHES "_dl")
......@@ -53,23 +60,30 @@ function(add_example_executable EXAMPLE_NAME FILE_NAME)
endforeach()
#Do not build any XDL examples if gfx9 targets are not on the list
foreach(source IN LISTS FILE_NAME)
if(NOT GPU_TARGETS MATCHES "gfx9" AND source MATCHES "_xdl")
if(NOT EX_TARGETS MATCHES "gfx9" AND source MATCHES "_xdl")
message("removing xdl example ${source} ")
list(REMOVE_ITEM FILE_NAME "${source}")
endif()
endforeach()
#Do not build any WMMA examples if gfx11 targets are not on the list
foreach(source IN LISTS FILE_NAME)
if(NOT GPU_TARGETS MATCHES "gfx11" AND source MATCHES "_wmma")
if(NOT EX_TARGETS MATCHES "gfx11" AND source MATCHES "_wmma")
message("removing wmma example ${source} ")
list(REMOVE_ITEM FILE_NAME "${source}")
endif()
endforeach()
#only continue if there are some source files left on the list
if(FILE_NAME)
if(FILE_NAME MATCHES "_xdl")
list(REMOVE_ITEM EX_TARGETS gfx1030 gfx1100 gfx1101 gfx1102 gfx1103)
elseif(FILE_NAME MATCHES "_wmma")
list(REMOVE_ITEM EX_TARGETS gfx908 gfx90a gfx940 gfx941 gfx942 gfx1030)
endif()
set_source_files_properties(${FILE_NAME} PROPERTIES LANGUAGE HIP)
add_executable(${EXAMPLE_NAME} ${FILE_NAME})
target_link_libraries(${EXAMPLE_NAME} PRIVATE utility)
add_test(NAME ${EXAMPLE_NAME} COMMAND $<TARGET_FILE:${EXAMPLE_NAME}> ${ARGN})
set_property(TARGET ${EXAMPLE_NAME} PROPERTY HIP_ARCHITECTURES ${EX_TARGETS} )
add_dependencies(examples ${EXAMPLE_NAME})
add_dependencies(check ${EXAMPLE_NAME})
rocm_install(TARGETS ${EXAMPLE_NAME} COMPONENT examples)
......@@ -118,6 +132,12 @@ function(add_example_executable_no_testing EXAMPLE_NAME FILE_NAME)
endif()
endforeach()
endif()
if(INSTANCES_ONLY)
set(EX_TARGETS ${DEFAULT_GPU_TARGETS})
else()
set(EX_TARGETS ${GPU_TARGETS})
endif()
#Do not build any DL examples if DL_KERNELS not set
foreach(source IN LISTS FILE_NAME)
if(NOT DEFINED DL_KERNELS AND source MATCHES "_dl")
......@@ -127,23 +147,30 @@ function(add_example_executable_no_testing EXAMPLE_NAME FILE_NAME)
endforeach()
#Do not build any XDL examples if gfx9 targets are not on the list
foreach(source IN LISTS FILE_NAME)
if(NOT GPU_TARGETS MATCHES "gfx9" AND source MATCHES "_xdl")
if(NOT EX_TARGETS MATCHES "gfx9" AND source MATCHES "_xdl")
message("removing xdl example ${source} ")
list(REMOVE_ITEM FILE_NAME "${source}")
endif()
endforeach()
#Do not build any WMMA examples if gfx11 targets are not on the list
foreach(source IN LISTS FILE_NAME)
if(NOT GPU_TARGETS MATCHES "gfx11" AND source MATCHES "_wmma")
if(NOT EX_TARGETS MATCHES "gfx11" AND source MATCHES "_wmma")
message("removing wmma example ${source} ")
list(REMOVE_ITEM FILE_NAME "${source}")
endif()
endforeach()
#only continue if there are some source files left on the list
if(FILE_NAME)
if(FILE_NAME MATCHES "_xdl")
list(REMOVE_ITEM EX_TARGETS gfx900 gfx906 gfx1030 gfx1100 gfx1101 gfx1102 gfx1103)
elseif(FILE_NAME MATCHES "_wmma")
list(REMOVE_ITEM EX_TARGETS gfx900 gfx906 gfx908 gfx90a gfx940 gfx941 gfx942 gfx1030)
endif()
set_source_files_properties(${FILE_NAME} PROPERTIES LANGUAGE HIP)
add_executable(${EXAMPLE_NAME} ${FILE_NAME})
target_link_libraries(${EXAMPLE_NAME} PRIVATE utility)
add_dependencies(examples ${EXAMPLE_NAME})
set_property(TARGET ${EXAMPLE_NAME} PROPERTY HIP_ARCHITECTURES ${EX_TARGETS} )
rocm_install(TARGETS ${EXAMPLE_NAME} COMPONENT examples)
set(result 0)
endif()
......
include/ck/host_utility/flush_cache.hpp
View file @ e4112de7
......@@ -104,14 +104,19 @@ inline void flush_icache()
hip_check_error(hipGetLastError());
}
// if TimePrePress == false, return time does not include preprocess's time
template <bool TimePreprocess, typename Args, typename F, typename PreProcessFunc>
template <bool TimePreprocess,
typename GemmArgs,
typename... Args,
typename F,
typename PreProcessFunc>
float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config,
PreProcessFunc preprocess,
F kernel,
dim3 grid_dim,
dim3 block_dim,
std::size_t lds_byte,
Args& args)
GemmArgs& gemm_args,
Args... args)
{
#if CK_TIME_KERNEL
#define MEDIAN 1
......@@ -133,7 +138,7 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config,
// warm up
for(int i = 0; i < stream_config.cold_niters_; ++i)
{
kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(args);
kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(gemm_args, args...);
hip_check_error(hipGetLastError());
}
......@@ -172,7 +177,7 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config,
preprocess();
}
// run real kernel
kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(args);
kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(gemm_args, args...);
hip_check_error(hipGetLastError());
// end real kernel
......@@ -190,9 +195,9 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config,
{
std::cout << "i: " << i << " cur_time: " << cur_time << std::endl;
printf("args.p_a_grid: %p, args.p_b_grid:%p\n",
static_cast<const void*>(args.p_a_grid),
static_cast<const void*>(args.p_b_grid));
printf("gemm_args.p_a_grid: %p, gemm_args.p_b_grid:%p\n",
static_cast<const void*>(gemm_args.p_a_grid),
static_cast<const void*>(gemm_args.p_b_grid));
}
}
......@@ -216,13 +221,13 @@ float launch_and_time_kernel_with_preprocess(const StreamConfig& stream_config,
else
{
preprocess();
kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(args);
kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(gemm_args, args...);
hip_check_error(hipGetLastError());
return 0;
}
#else
kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(args);
kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(gemm_args, args...);
hip_check_error(hipGetLastError());
return 0;
......
include/ck/tensor_description/multi_index_transform.hpp
View file @ e4112de7
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -1952,7 +1952,7 @@ struct Modulo
}
};
template <typename LowLengths>
template <typename LowLengths, bool ApplyModulo>
struct Xor
{
using LowerIndex = MultiIndex<2>;
......@@ -1981,8 +1981,15 @@ struct Xor
idx_low(Number<0>{}) = idx_up[Number<0>{}];
idx_low(Number<1>{}) =
idx_up[Number<1>{}] ^ (idx_up[Number<0>{}] % up_lengths_[Number<1>{}]);
if constexpr(ApplyModulo)
{
idx_low(Number<1>{}) =
idx_up[Number<1>{}] ^ (idx_up[Number<0>{}] % up_lengths_[Number<1>{}]);
}
else
{
idx_low(Number<1>{}) = idx_up[Number<1>{}] ^ idx_up[Number<0>{}];
}
}
template <typename LowIdxDiff,
......
include/ck/tensor_description/multi_index_transform_helper.hpp
View file @ e4112de7
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -128,9 +128,15 @@ __host__ __device__ constexpr auto make_modulo_transform(const Modulus& modulus,
return Modulo<Modulus, UpLength>{modulus, up_length};
}
template <typename LowLengths>
__host__ __device__ constexpr auto make_xor_with_modulo_transform(const LowLengths& low_lengths)
{
return Xor<LowLengths, true /*ApplyModulo*/>{low_lengths};
}
template <typename LowLengths>
__host__ __device__ constexpr auto make_xor_transform(const LowLengths& low_lengths)
{
return Xor<LowLengths>{low_lengths};
return Xor<LowLengths, false /*ApplyModulo*/>{low_lengths};
}
} // namespace ck
include/ck/tensor_operation/gpu/device/impl/device_contraction_multiple_d_xdl_cshuffle.hpp
View file @ e4112de7
......@@ -53,8 +53,7 @@ __global__ void
e_grid_desc_mblock_mperblock_nblock_nperblock,
const Block2ETileMap block_2_etile_map)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
defined(__gfx94__))
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx9__))
__shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid,
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_multi_abd_xdl_fixed_nk.hpp
View file @ e4112de7
......@@ -45,8 +45,7 @@ __global__ void
const BElementwiseOperation b_element_op,
const CDEElementwiseOperation cde_element_op)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__))
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx9__))
__shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
const index_t KBatch = 1;
......
include/ck/tensor_operation/gpu/grid/gridwise_fpAintB_gemm_wmma.hpp
View file @ e4112de7
......@@ -50,8 +50,7 @@ __global__ void
const CElementwiseOperation c_element_op,
const Block2CTileMap block_2_ctile_map)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx1100__) || defined(__gfx1101__) || \
defined(__gfx1102__))
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx11__))
__shared__ char p_shared[GridwiseGemm::SharedMemTrait::lds_size];
GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid,
......@@ -80,7 +79,7 @@ __global__ void
ignore = b_element_op;
ignore = c_element_op;
ignore = block_2_ctile_map;
#endif // end of if (defined(__gfx1100__))
#endif // end of if (defined(__gfx11__))
}
// Assume B is Col-Major
......
include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdl_cshuffle_v3.hpp
View file @ e4112de7
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -34,8 +34,7 @@ __global__ void
// __attribute__((amdgpu_waves_per_eu(1, 1)))
kernel_gemm_xdl_cshuffle_v3(typename GridwiseGemm::Argument karg)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__))
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx9__))
__shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
auto splitk_batch_offset = typename GridwiseGemm::SplitKBatchOffset(karg);
......@@ -48,7 +47,7 @@ __global__ void
karg);
#else
ignore = karg;
#endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
#endif // end of if (defined(__gfx9__))
}
template <typename GridwiseGemm,
......@@ -63,8 +62,7 @@ __global__ void
// __attribute__((amdgpu_waves_per_eu(1, 1)))
kernel_gemm_xdl_cshuffle_v3_2lds(typename GridwiseGemm::Argument karg)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__))
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx9__))
// Pass two lds pointer is the key to tell compiler that ds_read/write
// operate on different lds chunk at same time without order dependecy
__shared__ char p_shared_0[GridwiseGemm::GetSharedMemoryNumberOfByte()];
......@@ -81,7 +79,7 @@ __global__ void
karg);
#else
ignore = karg;
#endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
#endif // end of if (defined(__gfx9__))
}
template <typename ALayout,
......@@ -605,8 +603,8 @@ struct GridwiseGemm_xdl_cshuffle_v3
constexpr auto a_lds_block_desc_permuted = transform_tensor_descriptor(
a_lds_block_desc,
make_tuple(make_xor_transform(make_tuple(Number<MPerBlock / MLdsLayer>{},
Number<AK0Number * MLdsLayer>{})),
make_tuple(make_xor_with_modulo_transform(make_tuple(
Number<MPerBlock / MLdsLayer>{}, Number<AK0Number * MLdsLayer>{})),
make_pass_through_transform(AK1Number)),
make_tuple(Sequence<1, 0>{}, Sequence<2>{}),
make_tuple(Sequence<1, 0>{}, Sequence<2>{}));
......@@ -671,7 +669,7 @@ struct GridwiseGemm_xdl_cshuffle_v3
make_tuple(
make_pass_through_transform(Number<KThreadWrite / kfold / KThreadReadPerm>{}),
make_pass_through_transform(Number<K0PerThreadWrite>{}),
make_xor_transform(
make_xor_with_modulo_transform(
make_tuple(Number<KThreadReadPerm * M1>{}, Number<kfold * M0 / mpair>{})),
make_pass_through_transform(Number<mpair>{}),
make_pass_through_transform(AK1Number)),
......@@ -742,8 +740,8 @@ struct GridwiseGemm_xdl_cshuffle_v3
constexpr auto b_lds_block_desc_permuted = transform_tensor_descriptor(
b_lds_block_desc,
make_tuple(make_xor_transform(make_tuple(Number<NPerBlock / NLdsLayer>{},
Number<BK0Number * NLdsLayer>{})),
make_tuple(make_xor_with_modulo_transform(make_tuple(
Number<NPerBlock / NLdsLayer>{}, Number<BK0Number * NLdsLayer>{})),
make_pass_through_transform(BK1Number)),
make_tuple(Sequence<1, 0>{}, Sequence<2>{}),
make_tuple(Sequence<1, 0>{}, Sequence<2>{}));
......@@ -805,7 +803,7 @@ struct GridwiseGemm_xdl_cshuffle_v3
make_tuple(
make_pass_through_transform(Number<KThreadWrite / kfold / KThreadReadPerm>{}),
make_pass_through_transform(Number<K0PerThreadWrite>{}),
make_xor_transform(
make_xor_with_modulo_transform(
make_tuple(Number<KThreadReadPerm * N1>{}, Number<kfold * N0 / npair>{})),
make_pass_through_transform(Number<npair>{}),
make_pass_through_transform(BK1Number)),
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment