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Add pooling example (#257)

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* Add example for computing LayerNorm mean and meansquare

* Refactor the pool2d_fwd example and add example for float type testing

* Revert "Add example for computing LayerNorm mean and meansquare"

This reverts commit df52e6f9d8.

* Tiny fix in pool2d_fwd_common.hpp
This commit is contained in:
Qianfeng
2022-05-26 23:01:12 +08:00
committed by GitHub
parent 82d7d9938f
commit 97c4d486f4
5 changed files with 303 additions and 102 deletions
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4
example/13_pool2d_fwd/CMakeLists.txt
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@@ -1 +1,3 @@
add_example_executable(example_pool2d_fwd pool2d_fwd.cpp)
add_example_executable(example_pool2d_fwd_fp16 pool2d_fwd_fp16.cpp)
add_example_executable(example_pool2d_fwd_fp32 pool2d_fwd_fp32.cpp)

27
example/13_pool2d_fwd/README.md
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@@ -1,12 +1,12 @@
# Instructions for ```example_pool2d_fwd``` Example
# Instructions for ```example_pool2d_fwd``` Examples
## Run ```example_pool2d_fwd```
## Run ```example_pool2d_fwd_fp16```
```bash
#arg1: verification (0=no, 1=yes)
#arg2: initialization (0=no init, 1=single integer value, 2=scope integer value, 3=decimal value)
#arg3: time kernel (0=no, 1=yes)
#arg4 to 15: N, C, Y, X, Hi, Wi, Sy, Sx, LeftPy, LeftPx, RightPy, RightPx
./bin/example_pool2d_fwd 1 1 1
./bin/example_pool2d_fwd_fp16 1 1 1
```
Result
@@ -18,3 +18,24 @@ Warm up 1 time
Start running 10 times...
Perf: 0.397436 ms, 1.44252 TFlops, 783.713 GB/s
```
## Run ```example_pool2d_fwd_fp32```
```bash
#arg1: verification (0=no, 1=yes)
#arg2: initialization (0=no init, 1=single integer value, 2=scope integer value, 3=decimal value)
#arg3: time kernel (0=no, 1=yes)
#arg4 to 15: N, C, Y, X, Hi, Wi, Sy, Sx, LeftPy, LeftPx, RightPy, RightPx
./bin/example_pool2d_fwd_fp32 1 1 1
```
Result
```
./bin/example_pool2d_fwd_fp32 1 1 1
in_n_c_hi_wi: dim 4, lengths {128, 192, 71, 71}, strides {967872, 1, 13632, 192}
out_n_c_ho_wo: dim 4, lengths {128, 192, 36, 36}, strides {248832, 1, 6912, 192}
launch_and_time_kernel: grid_dim {124416, 1, 1}, block_dim {64, 1, 1}
Warm up 1 time
Start running 10 times...
Perf: 1.01823 ms, 0.563045 TFlops, 611.8 GB/s
```

142
example/13_pool2d_fwd/pool2d_fwd.cpp → example/13_pool2d_fwd/pool2d_fwd_common.hpp
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@@ -1,8 +1,6 @@
#pragma once
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include <stdlib.h>
#include "check_err.hpp"
#include "config.hpp"
@@ -13,44 +11,13 @@
#include "host_reduce_util.hpp"
#include "device_tensor.hpp"
#include "tensor_layout.hpp"
#include "reduction_operator.hpp"
#include "reduction_enums.hpp"
#include "device_pool2d_fwd_nhwc_nhwc.hpp"
using InDataType = ck::half_t;
using OutDataType = ck::half_t;
using AccDataType = float;
using IndexDataType = int32_t;
using InLayout = ck::tensor_layout::convolution::NHWC;
using OutLayout = ck::tensor_layout::convolution::NHWC;
#if 1
static constexpr auto ReduceOpId = ck::ReduceTensorOp::MAX;
#else
static constexpr auto ReduceOpId = ck::ReduceTensorOp::AVG;
#endif
static constexpr bool OutputIndex = false;
static constexpr bool PropagateNan = false;
using DevicePoolFwdInstance =
ck::tensor_operation::device::DevicePool2dFwd_Input_N_Hi_Wi_C_Output_N_Ho_Wo_C<
InDataType, // InDataType
OutDataType, // OutDataType
AccDataType, // AccDataType
ReduceOpId,
OutputIndex,
64, // BlockSize
64, // ReduceMThreadClusterSize
1, // ReduceKThreadClusterSize
4, // ReduceMThreadSliceSize
1, // ReduceKThreadSliceSize
4>; // InSrcOutDstVectorSize
template <typename InDataType,
typename OutDataType,
typename AccDataType,
typename IndexDataType,
ck::ReduceTensorOp ReduceOpId,
bool PropagateNan,
bool OutputIndex>
@@ -147,68 +114,46 @@ static void pool_host_verify(const Tensor<InDataType>& in,
};
}
int main(int argc, char* argv[])
template <typename InDataType,
typename OutDataType,
typename AccDataType,
typename IndexDataType,
typename InLayout,
typename OutLayout,
ck::ReduceTensorOp ReduceOpId,
bool PropagateNan,
bool OutputIndex>
bool pool_test(bool do_verification,
int init_method,
bool time_kernel,
ck::index_t N,
ck::index_t C,
ck::index_t Y,
ck::index_t X,
ck::index_t Hi,
ck::index_t Wi,
ck::index_t window_stride_h,
ck::index_t window_stride_w,
ck::index_t in_left_pad_h,
ck::index_t in_left_pad_w,
ck::index_t in_right_pad_h,
ck::index_t in_right_pad_w)
{
using namespace ck::host_reduce;
bool do_verification;
int init_method;
bool time_kernel;
// Pool shape
ck::index_t N = 128;
ck::index_t C = 192;
ck::index_t Y = 3;
ck::index_t X = 3;
ck::index_t Hi = 71;
ck::index_t Wi = 71;
ck::index_t window_stride_h = 2;
ck::index_t window_stride_w = 2;
ck::index_t in_left_pad_h = 1;
ck::index_t in_left_pad_w = 1;
ck::index_t in_right_pad_h = 1;
ck::index_t in_right_pad_w = 1;
if(argc == 1)
{
do_verification = true;
init_method = 1;
time_kernel = true;
}
else if(argc == 4)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = static_cast<bool>(std::stoi(argv[3]));
}
else if(argc == 16)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = static_cast<bool>(std::stoi(argv[3]));
N = std::stoi(argv[4]);
C = std::stoi(argv[5]);
Y = std::stoi(argv[6]);
X = std::stoi(argv[7]);
Hi = std::stoi(argv[8]);
Wi = std::stoi(argv[9]);
window_stride_h = std::stoi(argv[10]);
window_stride_w = std::stoi(argv[11]);
in_left_pad_h = std::stoi(argv[12]);
in_left_pad_w = std::stoi(argv[13]);
in_right_pad_h = std::stoi(argv[14]);
in_right_pad_w = std::stoi(argv[15]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=no, 1=yes)\n");
printf("arg4 to 15: N, C, Y, X, Hi, Wi, Sy, Sx, LeftPy, LeftPx, RightPy, "
"RightPx\n");
exit(0);
}
using DevicePoolFwdInstance =
ck::tensor_operation::device::DevicePool2dFwd_Input_N_Hi_Wi_C_Output_N_Ho_Wo_C<
InDataType, // InDataType
OutDataType, // OutDataType
AccDataType, // AccDataType
ReduceOpId,
OutputIndex,
64, // BlockSize
64, // ReduceMThreadClusterSize
1, // ReduceKThreadClusterSize
4, // ReduceMThreadSliceSize
1, // ReduceKThreadSliceSize
4>; // InSrcOutDstVectorSize
const ck::index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - Y) / window_stride_h + 1;
const ck::index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - X) / window_stride_w + 1;
@@ -302,6 +247,7 @@ int main(int argc, char* argv[])
pool_host_verify<InDataType,
OutDataType,
AccDataType,
IndexDataType,
ReduceOpId,
PropagateNan,
OutputIndex>(in_n_c_hi_wi,
@@ -325,5 +271,5 @@ int main(int argc, char* argv[])
};
}
return (pass ? 0 : 1);
}
return (pass);
};

116
example/13_pool2d_fwd/pool2d_fwd_fp16.cpp Normal file
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@@ -0,0 +1,116 @@
#include <iostream>
#include <cstdlib>
#include "config.hpp"
#include "tensor_layout.hpp"
#include "reduction_enums.hpp"
#include "pool2d_fwd_common.hpp"
using InDataType = ck::half_t;
using OutDataType = ck::half_t;
using AccDataType = float;
using IndexDataType = int32_t;
using InLayout = ck::tensor_layout::convolution::NHWC;
using OutLayout = ck::tensor_layout::convolution::NHWC;
#if 1
static constexpr auto ReduceOpId = ck::ReduceTensorOp::MAX;
#else
static constexpr auto ReduceOpId = ck::ReduceTensorOp::AVG;
#endif
static constexpr bool OutputIndex = false;
static constexpr bool PropagateNan = false;
int main(int argc, char* argv[])
{
using namespace ck::host_reduce;
bool do_verification;
int init_method;
bool time_kernel;
// Pool shape
ck::index_t N = 128;
ck::index_t C = 192;
ck::index_t Y = 3;
ck::index_t X = 3;
ck::index_t Hi = 71;
ck::index_t Wi = 71;
ck::index_t window_stride_h = 2;
ck::index_t window_stride_w = 2;
ck::index_t in_left_pad_h = 1;
ck::index_t in_left_pad_w = 1;
ck::index_t in_right_pad_h = 1;
ck::index_t in_right_pad_w = 1;
if(argc == 1)
{
do_verification = true;
init_method = 1;
time_kernel = true;
}
else if(argc == 4)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = static_cast<bool>(std::stoi(argv[3]));
}
else if(argc == 16)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = static_cast<bool>(std::stoi(argv[3]));
N = std::stoi(argv[4]);
C = std::stoi(argv[5]);
Y = std::stoi(argv[6]);
X = std::stoi(argv[7]);
Hi = std::stoi(argv[8]);
Wi = std::stoi(argv[9]);
window_stride_h = std::stoi(argv[10]);
window_stride_w = std::stoi(argv[11]);
in_left_pad_h = std::stoi(argv[12]);
in_left_pad_w = std::stoi(argv[13]);
in_right_pad_h = std::stoi(argv[14]);
in_right_pad_w = std::stoi(argv[15]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=no, 1=yes)\n");
printf("arg4 to 15: N, C, Y, X, Hi, Wi, Sy, Sx, LeftPy, LeftPx, RightPy, "
"RightPx\n");
exit(0);
}
bool pass = pool_test<InDataType,
OutDataType,
AccDataType,
IndexDataType,
InLayout,
OutLayout,
ReduceOpId,
PropagateNan,
OutputIndex>(do_verification,
init_method,
time_kernel,
N,
C,
Y,
X,
Hi,
Wi,
window_stride_h,
window_stride_w,
in_left_pad_h,
in_left_pad_w,
in_right_pad_h,
in_right_pad_w);
return (pass ? 0 : 1);
}

116
example/13_pool2d_fwd/pool2d_fwd_fp32.cpp Normal file
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@@ -0,0 +1,116 @@
#include <iostream>
#include <cstdlib>
#include "config.hpp"
#include "tensor_layout.hpp"
#include "reduction_enums.hpp"
#include "pool2d_fwd_common.hpp"
using InDataType = float;
using OutDataType = float;
using AccDataType = float;
using IndexDataType = int32_t;
using InLayout = ck::tensor_layout::convolution::NHWC;
using OutLayout = ck::tensor_layout::convolution::NHWC;
#if 1
static constexpr auto ReduceOpId = ck::ReduceTensorOp::MAX;
#else
static constexpr auto ReduceOpId = ck::ReduceTensorOp::AVG;
#endif
static constexpr bool OutputIndex = false;
static constexpr bool PropagateNan = false;
int main(int argc, char* argv[])
{
using namespace ck::host_reduce;
bool do_verification;
int init_method;
bool time_kernel;
// Pool shape
ck::index_t N = 128;
ck::index_t C = 192;
ck::index_t Y = 3;
ck::index_t X = 3;
ck::index_t Hi = 71;
ck::index_t Wi = 71;
ck::index_t window_stride_h = 2;
ck::index_t window_stride_w = 2;
ck::index_t in_left_pad_h = 1;
ck::index_t in_left_pad_w = 1;
ck::index_t in_right_pad_h = 1;
ck::index_t in_right_pad_w = 1;
if(argc == 1)
{
do_verification = true;
init_method = 1;
time_kernel = true;
}
else if(argc == 4)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = static_cast<bool>(std::stoi(argv[3]));
}
else if(argc == 16)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = static_cast<bool>(std::stoi(argv[3]));
N = std::stoi(argv[4]);
C = std::stoi(argv[5]);
Y = std::stoi(argv[6]);
X = std::stoi(argv[7]);
Hi = std::stoi(argv[8]);
Wi = std::stoi(argv[9]);
window_stride_h = std::stoi(argv[10]);
window_stride_w = std::stoi(argv[11]);
in_left_pad_h = std::stoi(argv[12]);
in_left_pad_w = std::stoi(argv[13]);
in_right_pad_h = std::stoi(argv[14]);
in_right_pad_w = std::stoi(argv[15]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=no, 1=yes)\n");
printf("arg4 to 15: N, C, Y, X, Hi, Wi, Sy, Sx, LeftPy, LeftPx, RightPy, "
"RightPx\n");
exit(0);
}
bool pass = pool_test<InDataType,
OutDataType,
AccDataType,
IndexDataType,
InLayout,
OutLayout,
ReduceOpId,
PropagateNan,
OutputIndex>(do_verification,
init_method,
time_kernel,
N,
C,
Y,
X,
Hi,
Wi,
window_stride_h,
window_stride_w,
in_left_pad_h,
in_left_pad_w,
in_right_pad_h,
in_right_pad_w);
return (pass ? 0 : 1);
}