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Commit 6252d207 authored by carlushuang's avatar carlushuang
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Merge remote-tracking branch 'origin/develop' into ck_tile/fav3_fwd_sept

parents eed60199 e07f1108
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profiler/include/profiler/profile_pool2d_fwd_impl.hpp 0 → 100644
View file @ 6252d207
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iomanip>
#include "ck/ck.hpp"
#include "ck/library/tensor_operation_instance/gpu/pool2d_fwd.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_pool_fwd.hpp"
namespace ck {
namespace profiler {
template <typename InDataType,
typename OutDataType,
typename ComputeDataType,
typename IndexDataType,
typename InLayout,
typename OutLayout,
ck::ReduceTensorOp ReduceOpId,
bool PropagateNan,
bool OutputIndex>
bool profile_pool2d_fwd_impl(int do_verification,
int init_method,
bool do_log,
bool time_kernel,
std::vector<index_t> in_length, // NCHW
std::vector<index_t> window_spatial_lengths,
std::vector<index_t> window_strides,
std::vector<index_t> window_dilations,
std::vector<index_t> input_left_pads,
std::vector<index_t> input_right_pads)
{
constexpr index_t InOutRank = 4;
constexpr index_t WindowRank = 2;
if(in_length.size() != InOutRank || window_spatial_lengths.size() != WindowRank ||
window_strides.size() != WindowRank || window_dilations.size() != WindowRank ||
input_left_pads.size() != WindowRank || input_right_pads.size() != WindowRank)
return false;
std::vector<index_t> out_length(InOutRank);
int N = in_length[0];
int C = in_length[1];
out_length[0] = N;
out_length[1] = C;
// Calculate Ho, Wo
for(int i = 2; i < InOutRank; ++i)
{
auto pad1 = input_left_pads[i - 2];
auto pad2 = input_right_pads[i - 2];
auto windows_size = window_spatial_lengths[i - 2];
auto windows_stride = window_strides[i - 2];
auto windows_dilation = window_dilations[i - 2];
auto eff = (windows_size - 1) * windows_dilation + 1;
out_length[i] = (in_length[i] + pad1 + pad2 - eff) / windows_stride + 1;
}
int Hi = in_length[2];
int Wi = in_length[3];
int Ho = out_length[2];
int Wo = out_length[3];
auto f_host_tensor_descriptor =
[](std::size_t N_, std::size_t C_, std::size_t H, std::size_t W) {
using namespace ck::literals;
return HostTensorDescriptor({N_, C_, H, W}, {C_ * H * W, 1_uz, W * C_, C_});
};
Tensor<InDataType> in_n_c_hi_wi(f_host_tensor_descriptor(N, C, Hi, Wi));
Tensor<OutDataType> out_n_c_ho_wo_host(f_host_tensor_descriptor(N, C, Ho, Wo));
Tensor<IndexDataType> out_indices_n_c_ho_wo_host(f_host_tensor_descriptor(N, C, Ho, Wo));
Tensor<OutDataType> out_n_c_ho_wo_device(f_host_tensor_descriptor(N, C, Ho, Wo));
Tensor<IndexDataType> out_indices_n_c_ho_wo_device(f_host_tensor_descriptor(N, C, Ho, Wo));
switch(init_method)
{
case 0: in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_1<InDataType>{}); break;
case 1: in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5}); break;
default: in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_3<InDataType>{-0.5, 0.5});
}
DeviceMem in_device_buf(sizeof(InDataType) * in_n_c_hi_wi.mDesc.GetElementSpaceSize());
DeviceMem out_device_buf(sizeof(OutDataType) *
out_n_c_ho_wo_device.mDesc.GetElementSpaceSize());
DeviceMem out_indices_device_buf(sizeof(IndexDataType) *
out_indices_n_c_ho_wo_device.mDesc.GetElementSpaceSize());
in_device_buf.ToDevice(in_n_c_hi_wi.mData.data());
// add device normalization instances
using DeviceOp = ck::tensor_operation::device::DevicePoolFwd<InOutRank,
WindowRank,
InDataType,
OutDataType,
IndexDataType,
InLayout,
OutLayout,
ReduceOpId,
OutputIndex>;
// get device op instances
const auto instance_ptrs =
ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << instance_ptrs.size() << " instances" << std::endl;
std::string best_instance_name;
float best_avg_time = std::numeric_limits<float>::max();
float best_gb_per_sec = 0;
if(do_verification)
{
using ReferenceInstance = ck::tensor_operation::host::ReferencePoolingFwd<InOutRank,
WindowRank,
InDataType,
OutDataType,
ComputeDataType,
IndexDataType,
ReduceOpId,
PropagateNan,
OutputIndex>;
ReferenceInstance ref;
auto ref_argument = ref.MakeArgument(in_n_c_hi_wi,
out_n_c_ho_wo_host,
out_indices_n_c_ho_wo_host,
window_spatial_lengths,
window_strides,
window_dilations,
input_left_pads,
input_right_pads);
auto ref_invoker = ref.MakeInvoker();
ref_invoker.Run(ref_argument);
}
int num_kernel = 0;
for(auto& inst_ptr : instance_ptrs)
{
auto argument_ptr = inst_ptr->MakeArgumentPointer(
static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
static_cast<IndexDataType*>(out_indices_device_buf.GetDeviceBuffer()),
in_length,
window_spatial_lengths,
out_length,
{C * Hi * Wi, 1, Wi * C, C},
{C * Ho * Wo, 1, Wo * C, C},
{C * Ho * Wo, 1, Wo * C, C},
window_strides,
window_dilations,
input_left_pads,
input_right_pads,
{2, 3});
if(inst_ptr->IsSupportedArgument(argument_ptr.get()))
{
++num_kernel;
}
else
{
if(time_kernel)
{
std::cout << inst_ptr->GetTypeString() << " skipped due to unsupported argument: ";
LogRange(std::cout << "input lengths = ", in_length, ", ") << std::endl;
}
continue;
}
auto invoker_ptr = inst_ptr->MakeInvokerPointer();
float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
std::size_t num_bytes = in_n_c_hi_wi.mDesc.GetElementSize() * sizeof(InDataType) +
out_n_c_ho_wo_host.mDesc.GetElementSize() * sizeof(OutDataType);
if constexpr(OutputIndex)
num_bytes += out_indices_n_c_ho_wo_host.mDesc.GetElementSize() * sizeof(IndexDataType);
float gb_per_sec = num_bytes / 1.E6 / avg_time;
if(time_kernel)
std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << gb_per_sec << " GB/s, "
<< inst_ptr->GetTypeString() << std::endl;
if(avg_time < best_avg_time)
{
best_instance_name = inst_ptr->GetTypeString();
best_avg_time = avg_time;
best_gb_per_sec = gb_per_sec;
}
if(do_verification)
{
out_device_buf.FromDevice(out_n_c_ho_wo_device.mData.data());
bool pass = ck::utils::check_err(out_n_c_ho_wo_device.mData,
out_n_c_ho_wo_host.mData,
"Error: Incorrect results",
1e-3,
1e-3);
if constexpr(OutputIndex)
{
out_indices_device_buf.FromDevice(out_indices_n_c_ho_wo_device.mData.data());
pass = pass && ck::utils::check_err(out_indices_n_c_ho_wo_device,
out_indices_n_c_ho_wo_host);
}
if(do_log)
{
LogRangeAsType<float>(std::cout << "in_n_c_hi_wi : ", in_n_c_hi_wi.mData, ",")
<< std::endl;
LogRangeAsType<float>(
std::cout << "out_n_c_ho_wo_host : ", out_n_c_ho_wo_host.mData, ",")
<< std::endl;
LogRangeAsType<float>(
std::cout << "out_n_c_ho_wo_device : ", out_n_c_ho_wo_device.mData, ",")
<< std::endl;
if constexpr(OutputIndex)
LogRangeAsType<float>(std::cout << "out_indices_n_c_ho_wo_device : ",
out_indices_n_c_ho_wo_device.mData,
",")
<< std::endl;
}
if(!pass)
{
std::cout << inst_ptr->GetTypeString() << " failed verification: ";
LogRange(std::cout << "lengths = [", in_length, ", ") << "]." << std::endl;
return false;
}
else
{
if(time_kernel)
std::cout << "pass" << std::endl;
}
}
}
if(time_kernel)
{
LogRange(std::cout << "length = ", in_length, ",") << std::endl;
std::cout << "best perf = " << best_avg_time << " ms, " << best_gb_per_sec << " GB/s, "
<< best_instance_name << std::endl;
}
if(num_kernel == 0)
{
std::cout << "Error: No kernel is applicable" << std::endl;
return false;
}
return true;
}
} // namespace profiler
} // namespace ck
profiler/src/CMakeLists.txt 100755 → 100644
View file @ 6252d207
......@@ -9,9 +9,12 @@ set(PROFILER_SOURCES
profile_layernorm_bwd_gamma_beta.cpp
profile_groupnorm_bwd_gamma_beta.cpp
profile_layernorm_fwd.cpp
profile_max_pool2d_fwd.cpp
profile_max_pool3d_fwd.cpp
profile_avg_pool3d_bwd.cpp
profile_max_pool3d_bwd.cpp
profile_avg_pool2d_bwd.cpp
profile_max_pool2d_bwd.cpp
profile_softmax.cpp
profile_batchnorm_fwd.cpp
profile_batchnorm_bwd.cpp
......@@ -98,7 +101,9 @@ target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_normalization_bwd_ga
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_softmax_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_reduce_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batchnorm_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_pool2d_fwd_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_pool3d_fwd_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_avg_pool2d_bwd_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_avg_pool3d_bwd_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_max_pool_bwd_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_image_to_column_instance)
......
profiler/src/profile_avg_pool2d_bwd.cpp 0 → 100644
View file @ 6252d207
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <vector>
#include <unordered_map>
#include "profiler/data_type_enum.hpp"
#include "profiler/profile_avg_pool2d_bwd_impl.hpp"
#include "profiler_operation_registry.hpp"
using ck::index_t;
struct maxPoolbwdArgParser
{
std::unordered_map<std::string, std::vector<int>> long_opts = {{"length", {}},
{"wsize", {}},
{"wstride", {}},
{"wdilation", {}},
{"pad1", {}},
{"pad2", {}}};
bool parse_opt(int argc, char* argv[], const std::string& key, int i)
{
if(std::string("--") + key == argv[i])
{
int pos = i;
while(++i < argc && argv[i][0] != '-') {}
int end = i;
for(int j = pos + 1; j < end; j++)
{
long_opts[key].push_back(std::stoi(argv[j]));
}
return true;
}
return false;
}
void operator()(int argc, char* argv[])
{
for(auto& kv : long_opts)
{
for(int i = 1; i < argc; i++)
{
if(parse_opt(argc, argv, kv.first, i))
break;
}
}
}
};
void print_help_avg_pool2d_bwd()
{
std::cout << "arg1: data type (0: fp16; 1: fp32; 3: int8; 5: bf16, 7: Float8)\n"
<< "arg2: verification (0: no; 1: yes)\n"
<< "arg3: initialization (0: no init; 1: integer value; 2: decimal value)\n"
<< "arg4: print tensor value (0: no; 1: yes)\n"
<< "arg5: time kernel (0=no, 1=yes)\n"
<< "--length: input tensor length for NCHW(e.g, --length 2 32 30 30) \n"
<< "--wsize: window size for YX (e.g, --wsize 2 2) \n"
<< "--wstride: window stride for HW (e.g, --wstride 2 2) \n"
<< "--wdilation: window dilation for HW (e.g, --wdilation 1 1) \n"
<< "--pad1: left side of padding in HW (e.g, --pad1 1 1) \n"
<< "--pad2: right side of padding in HW (e.g, --pad2 1 1) \n"
<< "eg: ckProfiler avg_pool2d_bwd 0 1 2 0 --length 2 32 30 30 --wsize 2 2 "
"--wstride 2 2 --wdilation 1 1 --pad1 1 1 --pad2 1 1"
<< std::endl;
}
int profile_avg_pool2d_bwd(int argc, char* argv[])
{
ck::DataTypeEnum data_type = ck::DataTypeEnum::Float8;
bool do_verification = true;
int init_method = 2;
bool do_log = false;
bool time_kernel = true;
std::vector<index_t> in_length = {2, 32, 30, 30};
std::vector<index_t> wsize = {2, 2};
std::vector<index_t> wstride = {2, 2};
std::vector<index_t> wdilation = {1, 1};
std::vector<index_t> pad1 = {1, 1};
std::vector<index_t> pad2 = {1, 1};
if(argc != 2 && argc != 33)
{
print_help_avg_pool2d_bwd();
return 0;
}
else if(argc == 33)
{
data_type = static_cast<ck::DataTypeEnum>(std::stoi(argv[2]));
do_verification = std::stoi(argv[3]);
init_method = std::stoi(argv[4]);
do_log = std::stoi(argv[5]);
time_kernel = std::stoi(argv[6]);
maxPoolbwdArgParser arg_parser;
arg_parser(argc, argv);
in_length = arg_parser.long_opts["length"];
wsize = arg_parser.long_opts["wsize"];
wstride = arg_parser.long_opts["wstride"];
wdilation = arg_parser.long_opts["wdilation"];
pad1 = arg_parser.long_opts["pad1"];
pad2 = arg_parser.long_opts["pad2"];
}
using F16 = ck::half_t;
using BF16 = ck::bhalf_t;
using F8 = ck::f8_t;
using F32 = float;
using I8 = int8_t;
using NHWC = ck::tensor_layout::convolution::NHWC;
if(data_type == ck::DataTypeEnum::Half)
{
ck::profiler::profile_avg_pool2d_bwd_impl<F16, F16, NHWC, NHWC>(do_verification,
init_method,
do_log,
time_kernel,
in_length,
wsize,
wstride,
wdilation,
pad1,
pad2);
}
else if(data_type == ck::DataTypeEnum::BFloat16)
{
ck::profiler::profile_avg_pool2d_bwd_impl<BF16, BF16, NHWC, NHWC>(do_verification,
init_method,
do_log,
time_kernel,
in_length,
wsize,
wstride,
wdilation,
pad1,
pad2);
}
else if(data_type == ck::DataTypeEnum::Float)
{
ck::profiler::profile_avg_pool2d_bwd_impl<F32, F32, NHWC, NHWC>(do_verification,
init_method,
do_log,
time_kernel,
in_length,
wsize,
wstride,
wdilation,
pad1,
pad2);
}
else if(data_type == ck::DataTypeEnum::Float8)
{
ck::profiler::profile_avg_pool2d_bwd_impl<F8, F8, NHWC, NHWC>(do_verification,
init_method,
do_log,
time_kernel,
in_length,
wsize,
wstride,
wdilation,
pad1,
pad2);
}
else if(data_type == ck::DataTypeEnum::Int8)
{
ck::profiler::profile_avg_pool2d_bwd_impl<I8, I8, NHWC, NHWC>(do_verification,
init_method,
do_log,
time_kernel,
in_length,
wsize,
wstride,
wdilation,
pad1,
pad2);
}
else
{
throw std::runtime_error("not implemented yet");
}
return 0;
}
REGISTER_PROFILER_OPERATION("avg_pool2d_bwd", "avg_pool2d bwd", profile_avg_pool2d_bwd);
profiler/src/profile_max_pool2d_bwd.cpp 0 → 100644
View file @ 6252d207
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <vector>
#include <unordered_map>
#include "profiler/data_type_enum.hpp"
#include "profiler/profile_max_pool2d_bwd_impl.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "profiler_operation_registry.hpp"
using ck::index_t;
struct maxPoolbwdArgParser
{
std::unordered_map<std::string, std::vector<int>> long_opts = {{"length", {}},
{"wsize", {}},
{"wstride", {}},
{"wdilation", {}},
{"pad1", {}},
{"pad2", {}}};
bool parse_opt(int argc, char* argv[], const std::string& key, int i)
{
if(std::string("--") + key == argv[i])
{
int pos = i;
while(++i < argc && argv[i][0] != '-') {}
int end = i;
for(int j = pos + 1; j < end; j++)
{
long_opts[key].push_back(std::stoi(argv[j]));
}
return true;
}
return false;
}
void operator()(int argc, char* argv[])
{
for(auto& kv : long_opts)
{
for(int i = 1; i < argc; i++)
{
if(parse_opt(argc, argv, kv.first, i))
break;
}
}
}
};
void print_help_max_pool2d_bwd()
{
std::cout << "arg1: data type (0: fp16; 1: fp32; 3: int8; 5: bf16)\n"
<< "arg2: verification (0: no; 1: yes)\n"
<< "arg3: initialization (0: no init; 1: integer value; 2: decimal value)\n"
<< "arg4: print tensor value (0: no; 1: yes)\n"
<< "arg5: time kernel (0=no, 1=yes)\n"
<< "--length: input tensor length for NCHW(e.g, --length 2 32 30 30) \n"
<< "--wsize: window size for YX (e.g, --wsize 2 2) \n"
<< "--wstride: window stride for HW (e.g, --wstride 2 2) \n"
<< "--wdilation: window dilation for HW (e.g, --wdilation 1 1) \n"
<< "--pad1: left side of padding in HW (e.g, --pad1 1 1) \n"
<< "--pad2: right side of padding in HW (e.g, --pad2 1 1) \n"
<< "eg: ckProfiler max_pool2d_bwd 0 1 2 0 --length 2 32 30 30 --wsize 2 2 "
"--wstride 2 2 --wdilation 1 1 --pad1 1 1 --pad2 1 1"
<< std::endl;
}
int profile_max_pool2d_bwd(int argc, char* argv[])
{
ck::DataTypeEnum data_type = ck::DataTypeEnum::Half;
bool do_verification = true;
int init_method = 2;
bool do_log = false;
bool time_kernel = true;
std::vector<index_t> in_length = {2, 32, 30, 30};
std::vector<index_t> wsize = {2, 2};
std::vector<index_t> wstride = {2, 2};
std::vector<index_t> wdilation = {1, 1};
std::vector<index_t> pad1 = {1, 1};
std::vector<index_t> pad2 = {1, 1};
if(argc != 2 && argc != 33)
{
print_help_max_pool2d_bwd();
return 0;
}
else if(argc == 33)
{
data_type = static_cast<ck::DataTypeEnum>(std::stoi(argv[2]));
do_verification = std::stoi(argv[3]);
init_method = std::stoi(argv[4]);
do_log = std::stoi(argv[5]);
time_kernel = std::stoi(argv[6]);
// parse the long options
maxPoolbwdArgParser arg_parser;
arg_parser(argc, argv);
in_length = arg_parser.long_opts["length"];
wsize = arg_parser.long_opts["wsize"];
wstride = arg_parser.long_opts["wstride"];
wdilation = arg_parser.long_opts["wdilation"];
pad1 = arg_parser.long_opts["pad1"];
pad2 = arg_parser.long_opts["pad2"];
}
using F16 = ck::half_t;
using BF16 = ck::bhalf_t;
using F32 = float;
using I8 = int8_t;
using I32 = int32_t;
if(data_type == ck::DataTypeEnum::Half)
{
ck::profiler::profile_max_pool2d_bwd_impl<F16, F16, I32, F16, F16, false>(do_verification,
init_method,
do_log,
time_kernel,
in_length,
wsize,
wstride,
wdilation,
pad1,
pad2);
}
else if(data_type == ck::DataTypeEnum::BFloat16)
{
ck::profiler::profile_max_pool2d_bwd_impl<BF16, BF16, I32, BF16, BF16, false>(
do_verification,
init_method,
do_log,
time_kernel,
in_length,
wsize,
wstride,
wdilation,
pad1,
pad2);
}
else if(data_type == ck::DataTypeEnum::Float)
{
ck::profiler::profile_max_pool2d_bwd_impl<F32, F32, I32, F32, F32, false>(do_verification,
init_method,
do_log,
time_kernel,
in_length,
wsize,
wstride,
wdilation,
pad1,
pad2);
}
else if(data_type == ck::DataTypeEnum::Int8)
{
ck::profiler::profile_max_pool2d_bwd_impl<I8, I8, I32, I8, I8, false>(do_verification,
init_method,
do_log,
time_kernel,
in_length,
wsize,
wstride,
wdilation,
pad1,
pad2);
}
else
{
throw std::runtime_error("not implemented yet");
}
return 0;
}
REGISTER_PROFILER_OPERATION("max_pool2d_bwd", "max_pool2d bwd", profile_max_pool2d_bwd);
profiler/src/profile_max_pool2d_fwd.cpp 0 → 100644
View file @ 6252d207
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <vector>
#include <unordered_map>
#include "profiler/data_type_enum.hpp"
#include "profiler/profile_pool2d_fwd_impl.hpp"
#include "profiler_operation_registry.hpp"
using ck::index_t;
struct maxPoolFwdArgParser
{
std::unordered_map<std::string, std::vector<int>> long_opts = {{"length", {}},
{"wsize", {}},
{"wstride", {}},
{"wdilation", {}},
{"pad1", {}},
{"pad2", {}}};
bool parse_opt(int argc, char* argv[], const std::string& key, int i)
{
if(std::string("--") + key == argv[i])
{
int pos = i;
while(++i < argc && argv[i][0] != '-') {}
int end = i;
for(int j = pos + 1; j < end; j++)
{
long_opts[key].push_back(std::stoi(argv[j]));
}
return true;
}
return false;
}
void operator()(int argc, char* argv[])
{
for(auto& kv : long_opts)
{
for(int i = 1; i < argc; i++)
{
if(parse_opt(argc, argv, kv.first, i))
break;
}
}
}
};
void print_help_max_pool2d_fwd()
{
std::cout << "arg1: data type (0: fp16; 1: fp32; 5: bf16)\n"
<< "arg2: verification (0: no; 1: yes)\n"
<< "arg3: initialization (0: no init; 1: integer value; 2: decimal value)\n"
<< "arg4: print tensor value (0: no; 1: yes)\n"
<< "arg5: time kernel (0=no, 1=yes)\n"
<< "arg6: return index (0=no, 1=yes)\n"
<< "--length: input tensor length for NCHW(e.g, --length 2 32 30 30) \n"
<< "--wsize: window size for YX (e.g, --wsize 2 2) \n"
<< "--wstride: window stride for HW (e.g, --wstride 2 2) \n"
<< "--wdilation: window dilation for HW (e.g, --wdilation 1 1) \n"
<< "--pad1: left side of padding in HW (e.g, --pad1 1 1) \n"
<< "--pad2: right side of padding in HW (e.g, --pad2 1 1) \n"
<< "eg: ckProfiler max_pool2d_fwd 0 1 2 0 1 0 --length 2 32 30 30 --wsize 2 2"
"--wstride 2 2 --wdilation 1 1 --pad1 1 1 --pad2 1 1"
<< std::endl;
}
int profile_max_pool2d_fwd(int argc, char* argv[])
{
ck::DataTypeEnum data_type = ck::DataTypeEnum::Half;
bool do_verification = true;
int init_method = 0;
bool do_log = false;
bool time_kernel = true;
bool return_index = false;
std::vector<index_t> in_length = {2, 32, 30, 30};
std::vector<index_t> wsize = {2, 2};
std::vector<index_t> wstride = {2, 2};
std::vector<index_t> wdilation = {1, 1};
std::vector<index_t> pad1 = {1, 1};
std::vector<index_t> pad2 = {1, 1};
if(argc != 2 && argc != 28)
{
print_help_max_pool2d_fwd();
return 0;
}
else if(argc == 28)
{
data_type = static_cast<ck::DataTypeEnum>(std::stoi(argv[2]));
do_verification = std::stoi(argv[3]);
init_method = std::stoi(argv[4]);
do_log = std::stoi(argv[5]);
time_kernel = std::stoi(argv[6]);
return_index = std::stoi(argv[7]);
// parse the long options
maxPoolFwdArgParser arg_parser;
arg_parser(argc, argv);
in_length = arg_parser.long_opts["length"];
wsize = arg_parser.long_opts["wsize"];
wstride = arg_parser.long_opts["wstride"];
wdilation = arg_parser.long_opts["wdilation"];
pad1 = arg_parser.long_opts["pad1"];
pad2 = arg_parser.long_opts["pad2"];
}
using F16 = ck::half_t;
using BF16 = ck::bhalf_t;
using F32 = float;
using I32 = int32_t;
using NHWC = ck::tensor_layout::convolution::NHWC;
constexpr auto ReduceOpId = ck::ReduceTensorOp::MAX;
if(data_type == ck::DataTypeEnum::Half)
{
if(return_index)
{
ck::profiler::
profile_pool2d_fwd_impl<F16, F16, F16, I32, NHWC, NHWC, ReduceOpId, false, true>(
do_verification,
init_method,
do_log,
time_kernel,
in_length,
wsize,
wstride,
wdilation,
pad1,
pad2);
}
else
{
ck::profiler::
profile_pool2d_fwd_impl<F16, F16, F16, I32, NHWC, NHWC, ReduceOpId, false, false>(
do_verification,
init_method,
do_log,
time_kernel,
in_length,
wsize,
wstride,
wdilation,
pad1,
pad2);
}
}
else if(data_type == ck::DataTypeEnum::BFloat16)
{
if(return_index)
{
ck::profiler::
profile_pool2d_fwd_impl<BF16, BF16, BF16, I32, NHWC, NHWC, ReduceOpId, false, true>(
do_verification,
init_method,
do_log,
time_kernel,
in_length,
wsize,
wstride,
wdilation,
pad1,
pad2);
}
else
{
ck::profiler::profile_pool2d_fwd_impl<BF16,
BF16,
BF16,
I32,
NHWC,
NHWC,
ReduceOpId,
false,
false>(do_verification,
init_method,
do_log,
time_kernel,
in_length,
wsize,
wstride,
wdilation,
pad1,
pad2);
}
}
else if(data_type == ck::DataTypeEnum::Float)
{
if(return_index)
{
ck::profiler::
profile_pool2d_fwd_impl<F32, F32, F32, I32, NHWC, NHWC, ReduceOpId, false, true>(
do_verification,
init_method,
do_log,
time_kernel,
in_length,
wsize,
wstride,
wdilation,
pad1,
pad2);
}
else
{
ck::profiler::
profile_pool2d_fwd_impl<F32, F32, F32, I32, NHWC, NHWC, ReduceOpId, false, false>(
do_verification,
init_method,
do_log,
time_kernel,
in_length,
wsize,
wstride,
wdilation,
pad1,
pad2);
}
}
else
{
throw std::runtime_error("not implemented yet");
}
return 0;
}
REGISTER_PROFILER_OPERATION("max_pool2d_fwd", "max_pool2d fwd", profile_max_pool2d_fwd);
test/pool/CMakeLists.txt
View file @ 6252d207
......@@ -4,13 +4,25 @@ add_gtest_executable(test_avg_pool3d_bwd test_avg_pool3d_bwd.cpp)
add_gtest_executable(test_max_pool3d_bwd test_max_pool3d_bwd.cpp)
add_gtest_executable(test_avg_pool3d_fwd test_avg_pool3d_fwd.cpp)
add_gtest_executable(test_max_pool3d_fwd test_max_pool3d_fwd.cpp)
add_gtest_executable(test_avg_pool2d_bwd test_avg_pool2d_bwd.cpp)
add_gtest_executable(test_max_pool2d_bwd test_max_pool2d_bwd.cpp)
add_gtest_executable(test_avg_pool2d_fwd test_avg_pool2d_fwd.cpp)
add_gtest_executable(test_max_pool2d_fwd test_max_pool2d_fwd.cpp)
target_link_libraries(test_avg_pool3d_bwd PRIVATE utility device_avg_pool3d_bwd_instance)
target_link_libraries(test_avg_pool2d_bwd PRIVATE utility device_avg_pool2d_bwd_instance)
target_link_libraries(test_max_pool2d_bwd PRIVATE utility device_max_pool_bwd_instance)
target_link_libraries(test_max_pool3d_bwd PRIVATE utility device_max_pool_bwd_instance)
target_link_libraries(test_avg_pool3d_fwd PRIVATE utility device_pool3d_fwd_instance)
target_link_libraries(test_max_pool3d_fwd PRIVATE utility device_pool3d_fwd_instance)
target_link_libraries(test_avg_pool2d_fwd PRIVATE utility device_pool2d_fwd_instance)
target_link_libraries(test_max_pool2d_fwd PRIVATE utility device_pool2d_fwd_instance)
add_dependencies(test_pool test_avg_pool3d_bwd)
add_dependencies(test_pool test_max_pool3d_bwd)
add_dependencies(test_pool test_avg_pool3d_fwd)
add_dependencies(test_pool test_max_pool3d_fwd)
add_dependencies(test_pool test_avg_pool2d_bwd)
add_dependencies(test_pool test_max_pool2d_bwd)
add_dependencies(test_pool test_avg_pool2d_fwd)
add_dependencies(test_pool test_max_pool2d_fwd)
test/pool/test_avg_pool2d_bwd.cpp 0 → 100644
View file @ 6252d207
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "gtest/gtest.h"
#include "profiler/profile_avg_pool2d_bwd_impl.hpp"
#include "test_pool_fwd_common.hpp"
template <typename T>
class AvgPool2dBWDTest : public ::testing::Test
{
protected:
using InDataType = std::tuple_element_t<0, T>;
using OutDataType = std::tuple_element_t<1, T>;
static std::vector<PoolingParam> params;
void Run()
{
for(auto param : this->params)
{
bool success =
ck::profiler::profile_avg_pool2d_bwd_impl<InDataType, OutDataType, NHWC, NHWC>(
true,
2,
false,
false,
param.length_,
param.window_spatial_lengths_,
param.window_strides_,
param.window_dilations_,
param.input_left_pads_,
param.input_right_pads_);
EXPECT_TRUE(success);
}
}
};
template <typename T>
std::vector<PoolingParam> AvgPool2dBWDTest<T>::params = {
{{1, 1, 1, 1}, {1, 1}, {1, 1}, {1, 1}, {0, 0}, {0, 0}},
{{1, 1, 64, 64}, {64, 64}, {1, 1}, {1, 1}, {0, 0}, {0, 0}},
{{1, 5, 7, 7}, {2, 2}, {2, 2}, {1, 1}, {2, 2}, {0, 0}},
{{1, 1, 8, 8}, {2, 2}, {2, 2}, {1, 1}, {2, 2}, {0, 0}},
{{1, 1, 8, 8}, {2, 2}, {1, 1}, {1, 1}, {1, 1}, {0, 0}},
{{2, 32, 30, 30}, {2, 2}, {2, 2}, {1, 1}, {1, 1}, {1, 1}},
{{1, 2, 30, 30}, {2, 2}, {2, 2}, {1, 1}, {0, 0}, {0, 0}}};
using Avg_Pool_2D_f32_types = ::testing::Types<std::tuple<F32, F32>>;
using Avg_Pool_2D_int8_types = ::testing::Types<std::tuple<I8, I8>>;
using Avg_Pool_2D_f16_types = ::testing::Types<std::tuple<F16, F16>>;
using Avg_Pool_2D_bf16_types = ::testing::Types<std::tuple<BF16, BF16>>;
using Avg_Pool_2D_f8_types = ::testing::Types<std::tuple<F8, F8>>;
template <typename TType>
class AvgPool2D_f32 : public AvgPool2dBWDTest<TType>
{
protected:
void SetUp() override
{
if(!CK_ENABLE_FP32)
{
GTEST_SKIP() << "Skipping AvgPool2D_f32 tests because CK_ENABLE_FP32 is not enabled";
}
}
};
template <typename TType>
class AvgPool2D_int8 : public AvgPool2dBWDTest<TType>
{
protected:
void SetUp() override
{
if(!CK_ENABLE_INT8)
{
GTEST_SKIP() << "Skipping AvgPool2D_int8 tests because CK_ENABLE_INT8 is not enabled";
}
}
};
template <typename TType>
class AvgPool2D_f16 : public AvgPool2dBWDTest<TType>
{
protected:
void SetUp() override
{
if(!CK_ENABLE_FP16)
{
GTEST_SKIP() << "Skipping AvgPool2D_f16 because CK_ENABLE_FP16 is not enabled";
}
}
};
template <typename TType>
class AvgPool2D_bf16 : public AvgPool2dBWDTest<TType>
{
protected:
void SetUp() override
{
if(!CK_ENABLE_BF16)
{
GTEST_SKIP() << "Skipping AvgPool2D_bf16 tests because CK_ENABLE_BF16 is not enabled";
}
}
};
template <typename TType>
class AvgPool2D_f8 : public AvgPool2dBWDTest<TType>
{
protected:
void SetUp() override
{
if(!CK_ENABLE_FP8)
{
GTEST_SKIP() << "Skipping AvgPool2D_f8 tests because CK_ENABLE_FP8 is not enabled";
}
}
};
TYPED_TEST_SUITE(AvgPool2D_f32, Avg_Pool_2D_f32_types);
TYPED_TEST_SUITE(AvgPool2D_int8, Avg_Pool_2D_int8_types);
TYPED_TEST_SUITE(AvgPool2D_f16, Avg_Pool_2D_f16_types);
TYPED_TEST_SUITE(AvgPool2D_bf16, Avg_Pool_2D_bf16_types);
TYPED_TEST_SUITE(AvgPool2D_f8, Avg_Pool_2D_f8_types);
TYPED_TEST(AvgPool2D_f32, AvgPool2DTest_f32) { this->Run(); }
TYPED_TEST(AvgPool2D_int8, AvgPool2DTest_int8) { this->Run(); }
TYPED_TEST(AvgPool2D_f16, AvgPool2DTest_f16) { this->Run(); }
TYPED_TEST(AvgPool2D_bf16, AvgPool2DTest_bf16) { this->Run(); }
TYPED_TEST(AvgPool2D_f8, AvgPool2DTest_f8) { this->Run(); }
test/pool/test_avg_pool2d_fwd.cpp 0 → 100644
View file @ 6252d207
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "gtest/gtest.h"
#include "profiler/profile_pool2d_fwd_impl.hpp"
#include "test_pool_fwd_common.hpp"
template <typename Tuple>
class TestAvgPool2dFwd : public ::testing::Test
{
protected:
using InDataType = std::tuple_element_t<0, Tuple>;
using OutDataType = std::tuple_element_t<1, Tuple>;
using ComputeDataType = std::tuple_element_t<2, Tuple>;
using IndexDataType = std::tuple_element_t<3, Tuple>;
std::vector<PoolingParam> params;
void Run()
{
for(auto param : params)
{
// avg pool
bool success =
ck::profiler::profile_pool2d_fwd_impl<InDataType,
OutDataType,
ComputeDataType,
IndexDataType,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::NHWC,
ck::ReduceTensorOp::AVG,
false,
false>(true,
2,
false,
false,
param.length_,
param.window_spatial_lengths_,
param.window_strides_,
param.window_dilations_,
param.input_left_pads_,
param.input_right_pads_);
EXPECT_TRUE(success);
}
}
};
using KernelTypes = std::conditional_t<
CK_ENABLE_FP16 && CK_ENABLE_BF16,
::testing::Types<std::tuple<F16, F16, F32, I32>,
std::tuple<F16, F16, F32, I32>,
std::tuple<BF16, BF16, F32, I32>,
std::tuple<BF16, BF16, F32, I32>,
std::tuple<F32, F32, F32, I32>,
std::tuple<F32, F32, F32, I32>>,
::testing::Types<std::tuple<F32, F32, F32, I32>, std::tuple<F32, F32, F32, I32>>>;
TYPED_TEST_SUITE(TestAvgPool2dFwd, KernelTypes);
TYPED_TEST(TestAvgPool2dFwd, Test_Pool)
{
// length, window_length, window_stride, window_dilation, left_pad, right_pad
this->params = {{{1, 1, 1, 1}, {1, 1}, {1, 1}, {1, 1}, {0, 0}, {0, 0}},
{{2, 16, 64, 64}, {64, 64}, {1, 1}, {1, 1}, {0, 0}, {0, 0}},
{{2, 16, 64, 64}, {4, 4}, {4, 4}, {2, 2}, {0, 0}, {0, 0}},
{{2, 32, 30, 30}, {2, 2}, {2, 2}, {1, 1}, {1, 1}, {1, 1}}};
this->Run();
}
test/pool/test_max_pool2d_bwd.cpp 0 → 100644
View file @ 6252d207
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "gtest/gtest.h"
#include "profiler/profile_max_pool2d_bwd_impl.hpp"
#include "test_pool_fwd_common.hpp"
template <typename T>
class MaxPool2dBWDTest : public ::testing::Test
{
protected:
using DOutDataType = std::tuple_element_t<0, T>;
using DInDataType = std::tuple_element_t<1, T>;
using IndexDataType = std::tuple_element_t<2, T>;
using InDataType = DInDataType;
using OutDataType = DOutDataType;
static std::vector<PoolingParam> params;
void Run()
{
for(auto param : this->params)
{
bool success =
ck::profiler::profile_max_pool2d_bwd_impl<InDataType,
OutDataType,
IndexDataType,
DOutDataType,
DInDataType,
false>(true,
2,
false,
false,
param.length_,
param.window_spatial_lengths_,
param.window_strides_,
param.window_dilations_,
param.input_left_pads_,
param.input_right_pads_);
EXPECT_TRUE(success);
}
}
};
template <typename T>
std::vector<PoolingParam> MaxPool2dBWDTest<T>::params = {
{{1, 1, 1, 1}, {1, 1}, {1, 1}, {1, 1}, {0, 0}, {0, 0}},
{{2, 16, 64, 64}, {64, 64}, {1, 1}, {1, 1}, {0, 0}, {0, 0}},
{{2, 16, 64, 64}, {4, 4}, {4, 4}, {2, 2}, {0, 0}, {0, 0}},
{{2, 32, 30, 30}, {2, 2}, {2, 2}, {1, 1}, {1, 1}, {1, 1}},
{{2, 2, 30, 30}, {2, 2}, {2, 2}, {1, 1}, {1, 1}, {1, 1}}};
using Max_Pool_2D_f32_types = ::testing::Types<std::tuple<F32, F32, I32>>;
using Max_Pool_2D_int8_types = ::testing::Types<std::tuple<I8, I8, I32>>;
using Max_Pool_2D_f16_types = ::testing::Types<std::tuple<F16, F16, I32>>;
using Max_Pool_2D_bf16_types = ::testing::Types<std::tuple<BF16, BF16, I32>>;
template <typename TType>
class MaxPool2D_f32 : public MaxPool2dBWDTest<TType>
{
protected:
void SetUp() override
{
if(!CK_ENABLE_FP32)
{
GTEST_SKIP() << "Skipping MaxPool2D_f32 tests because CK_ENABLE_FP32 is not enabled";
}
}
};
template <typename TType>
class MaxPool2D_int8 : public MaxPool2dBWDTest<TType>
{
protected:
void SetUp() override
{
if(!CK_ENABLE_INT8)
{
GTEST_SKIP() << "Skipping MaxPool2D_int8 tests because CK_ENABLE_INT8 is not enabled";
}
}
};
template <typename TType>
class MaxPool2D_f16 : public MaxPool2dBWDTest<TType>
{
protected:
void SetUp() override
{
if(!CK_ENABLE_FP16)
{
GTEST_SKIP() << "Skipping MaxPool2D_f16 because CK_ENABLE_FP16 is not enabled";
}
}
};
template <typename TType>
class MaxPool2D_bf16 : public MaxPool2dBWDTest<TType>
{
protected:
void SetUp() override
{
if(!CK_ENABLE_BF16)
{
GTEST_SKIP() << "Skipping MaxPool2D_bf16 tests because CK_ENABLE_BF16 is not enabled";
}
}
};
TYPED_TEST_SUITE(MaxPool2D_f32, Max_Pool_2D_f32_types);
TYPED_TEST_SUITE(MaxPool2D_int8, Max_Pool_2D_int8_types);
TYPED_TEST_SUITE(MaxPool2D_f16, Max_Pool_2D_f16_types);
TYPED_TEST_SUITE(MaxPool2D_bf16, Max_Pool_2D_bf16_types);
TYPED_TEST(MaxPool2D_f32, MaxPool2DTest_f32) { this->Run(); }
TYPED_TEST(MaxPool2D_int8, MaxPool2DTest_int8) { this->Run(); }
TYPED_TEST(MaxPool2D_f16, MaxPool2DTest_f16) { this->Run(); }
TYPED_TEST(MaxPool2D_bf16, MaxPool2DTest_bf16) { this->Run(); }
test/pool/test_max_pool2d_fwd.cpp 0 → 100644
View file @ 6252d207
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "gtest/gtest.h"
#include "profiler/profile_pool2d_fwd_impl.hpp"
#include "test_pool_fwd_common.hpp"
template <typename Tuple>
class TestMaxPool2dFwd : public ::testing::Test
{
protected:
using InDataType = std::tuple_element_t<0, Tuple>;
using OutDataType = std::tuple_element_t<1, Tuple>;
using ComputeDataType = std::tuple_element_t<2, Tuple>;
using IndexDataType = std::tuple_element_t<3, Tuple>;
static constexpr bool ReturnIndex = std::tuple_element_t<4, Tuple>::value;
std::vector<PoolingParam> params;
void Run()
{
for(auto param : params)
{
// max pool
bool success =
ck::profiler::profile_pool2d_fwd_impl<InDataType,
OutDataType,
ComputeDataType,
IndexDataType,
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::NHWC,
ck::ReduceTensorOp::MAX,
false,
ReturnIndex>(true,
2,
false,
false,
param.length_,
param.window_spatial_lengths_,
param.window_strides_,
param.window_dilations_,
param.input_left_pads_,
param.input_right_pads_);
EXPECT_TRUE(success);
}
}
};
using true_t = std::integral_constant<bool, true>;
using false_t = std::integral_constant<bool, false>;
using KernelTypes = std::conditional_t<CK_ENABLE_FP16 && CK_ENABLE_BF16,
::testing::Types<std::tuple<F16, F16, F32, I32, true_t>,
std::tuple<F16, F16, F32, I32, false_t>,
std::tuple<BF16, BF16, F32, I32, true_t>,
std::tuple<BF16, BF16, F32, I32, false_t>,
std::tuple<F32, F32, F32, I32, true_t>,
std::tuple<F32, F32, F32, I32, false_t>>,
::testing::Types<std::tuple<F32, F32, F32, I32, true_t>,
std::tuple<F32, F32, F32, I32, false_t>>>;
TYPED_TEST_SUITE(TestMaxPool2dFwd, KernelTypes);
TYPED_TEST(TestMaxPool2dFwd, Test_Pool)
{
// length, window_length, window_stride, window_dilation, left_pad, right_pad
this->params = {{{1, 1, 1, 1}, {1, 1}, {1, 1}, {1, 1}, {0, 0}, {0, 0}},
{{2, 16, 64, 64}, {64, 64}, {1, 1}, {1, 1}, {0, 0}, {0, 0}},
{{2, 16, 64, 64}, {4, 4}, {4, 4}, {2, 2}, {0, 0}, {0, 0}},
{{2, 32, 30, 30}, {2, 2}, {2, 2}, {1, 1}, {1, 1}, {1, 1}}};
this->Run();
}
test/pool/test_pool_fwd_common.hpp
View file @ 6252d207
// 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.
#include "gtest/gtest.h"
#include "ck/ck.hpp"
......@@ -8,8 +8,11 @@ using F16 = ck::half_t;
using BF16 = ck::bhalf_t;
using F32 = float;
using I32 = int32_t;
using I8 = int8_t;
using F8 = ck::f8_t;
using ck::index_t;
using NDHWC = ck::tensor_layout::convolution::NDHWC;
using NHWC = ck::tensor_layout::convolution::NHWC;
struct PoolingParam
{
......
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