bench/ConvUnifiedBenchmark.cc (449 lines of code) (raw):
/*
* Copyright (c) Meta Platforms, Inc. and affiliates.
* All rights reserved.
* This source code is licensed under the BSD-style license found in the
* LICENSE file in the root directory of this source tree.
*/
#include <algorithm>
#include <chrono>
#include <cmath>
#include <iomanip>
#include <iostream>
#include <numeric>
#include <random>
#include <vector>
#ifdef _OPENMP
#include <omp.h>
#endif
#include "./BenchUtils.h"
#include "fbgemm/Fbgemm.h"
#include "src/RefImplementations.h"
using namespace std;
using namespace fbgemm;
// clang-format off
// 1D conv shapes
vector<conv_param_t<1>> shapes_1d = {
// MB, IC, OC, IW, G, KW, stride_w, pad_w_left, pad_w_right,
// (dilation, output_padding_w, tranpose)
// regular
conv_param_t<1>(1, 600, 100, {1}, 1, {3}, {1}, {2, 2}),
conv_param_t<1>(1, 600, 100, {2}, 1, {3}, {1}, {2, 2}),
conv_param_t<1>(1, 600, 100, {3}, 1, {3}, {1}, {2, 2}),
conv_param_t<1>(1, 200, 162, {1}, 1, {3}, {1}, {2, 2}),
conv_param_t<1>(1, 600, 100, {4}, 1, {3}, {1}, {2, 2}),
// deconv
conv_param_t<1>(1, 384, 192, {74}, 1, {16}, {8}, {4, 4}, {1}, {0}, true),
conv_param_t<1>(1, 192, 96, {74}, 1, {8}, {4}, {2, 2}, {1}, {0}, true),
conv_param_t<1>(1, 96, 48, {74}, 1, {4}, {2}, {1, 1}, {1}, {0}, true),
conv_param_t<1>(1, 96, 48, {74}, 1, {4}, {2}, {1, 1}, {1}, {1}, true),
};
// 2D conv shapes
vector<conv_param_t<2>> shapes_2d = {
// MB, IC, OC, IH, IW, G, KH, KW, stride_h, stride_w,
// pad_h_top, pad_w_left, pad_h_bottom, pad_w_right,
// (dilation_h, dilation_w, output_padding_h, output_padding_w, tranpose)
// 2D convolutions
// regular
conv_param_t<>(1, 128, 128, {56, 56}, 1, {3, 3},
{1, 1}, {1, 1, 1, 1}),
conv_param_t<>(1, 128, 128, {56, 56}, 1, {3, 3},
{1, 1}, {1, 1, 1, 1}, {1, 1}, {0}, true),
conv_param_t<>(1, 128, 128, {56, 56}, 1, {3, 3},
{1, 1}, {1, 1, 1, 1}, {1, 1}, {1}, true),
// regular with dilation
conv_param_t<>(1, 128, 128, {56, 56}, 1, {3, 3},
{1, 1}, {1, 1, 1, 1}, {2, 2}),
conv_param_t<>(1, 128, 128, {56, 56}, 1, {3, 3},
{1, 1}, {1, 1, 1, 1}, {2, 2}, {0}, true),
conv_param_t<>(1, 128, 128, {56, 56}, 1, {3, 3},
{1, 1}, {1, 1, 1, 1}, {2, 2}, {1}, true),
// groupwise
conv_param_t<>(1, 128, 128, {56, 56}, 32, {3, 3},
{1, 1}, {1, 1, 1, 1}),
conv_param_t<>(1, 128, 128, {56, 56}, 32, {3, 3},
{1, 1}, {1, 1, 1, 1}, {1, 1}, {0}, true),
conv_param_t<>(1, 128, 128, {56, 56}, 32, {3, 3},
{1, 1}, {1, 1, 1, 1}, {1, 1}, {0}, true),
// DW
conv_param_t<>(1, 272, 272, {47, 125}, 272, {3, 3},
{1, 1}, {1, 1, 1, 1}),
conv_param_t<>(1, 128, 256, {32, 100}, 128, {3, 3},
{1, 1}, {1, 1, 1, 1}),
// Pointwise
conv_param_t<>(1, 128, 128, {56, 56}, 1, {1, 1},
{1, 1}, {0, 0, 0, 0})
};
vector<conv_param_t<2>> shapes_2d_resnext_101 = {
// ResNext-101 (unique shapes only)
// conv_param_t<>(N, C, M, H, W, groups, /* kern */ {KH, KW}, /* stride */
// {stride_h, stride_w}, /* padding pad_l = pad_h */ {pad_l, pad_l, pad_l, pad_l}, /* dialation */
// {1, 1}, /* otpt_pad */ {0, 0}, /* trans */transpose)
conv_param_t<>(1, 3, 64, {224, 224}, 1, {7, 7},
{2, 2}, {3, 3, 3, 3}, {1, 1}, {0, 0}, false),
conv_param_t<>(1, 64, 128, {56, 56}, 1, {1, 1},
{1, 1}, {0, 0, 0, 0}, {1, 1}, {0, 0}, false),
conv_param_t<>(1, 128, 128, {56, 56}, 32, {3, 3},
{1, 1}, {1, 1, 1, 1}, {1, 1}, {0, 0}, false),
conv_param_t<>(1, 128, 256, {56, 56}, 1, {1, 1},
{1, 1}, {0, 0, 0, 0}, {1, 1}, {0, 0}, false),
conv_param_t<>(1, 64, 256, {56, 56}, 1, {1, 1},
{1, 1}, {0, 0, 0, 0}, {1, 1}, {0, 0}, false),
conv_param_t<>(1, 256, 128, {56, 56}, 1, {1, 1},
{1, 1}, {1, 1, 1, 1}, {1, 1}, {0, 0}, false),
conv_param_t<>(1, 256, 128, {56, 56}, 1, {1, 1},
{1, 1}, {0, 0, 0, 0}, {1, 1}, {0, 0}, false),
conv_param_t<>(1, 256, 256, {56, 56}, 1, {1, 1},
{1, 1}, {0, 0, 0, 0}, {1, 1}, {0, 0}, false),
conv_param_t<>(1, 256, 256, {56, 56}, 32, {3, 3},
{2, 2}, {1, 1, 1, 1}, {1, 1}, {0, 0}, false),
conv_param_t<>(1, 256, 512, {28, 28}, 1, {1, 1},
{1, 1}, {0, 0, 0, 0}, {1, 1}, {0, 0}, false),
conv_param_t<>(1, 256, 512, {56, 56}, 1, {1, 1},
{2, 2}, {0, 0, 0, 0}, {1, 1}, {0, 0}, false),
conv_param_t<>(1, 512, 256, {28, 28}, 1, {1, 1},
{1, 1}, {0, 0, 0, 0}, {1, 1}, {0, 0}, false),
conv_param_t<>(1, 256, 256, {28, 28}, 32, {3, 3},
{1, 1}, {1, 1, 1, 1}, {1, 1}, {0, 0}, false),
conv_param_t<>(1, 512, 512, {28, 28}, 1, {1, 1},
{1, 1}, {0, 0, 0, 0}, {1, 1}, {0, 0}, false),
conv_param_t<>(1, 512, 512, {28, 28}, 32, {3, 3},
{2, 2}, {1, 1, 1, 1}, {1, 1}, {0, 0}, false),
conv_param_t<>(1, 512, 1024, {14, 14}, 1, {1, 1},
{1, 1}, {0, 0, 0, 0}, {1, 1}, {0, 0}, false),
conv_param_t<>(1, 512, 1024, {28, 28}, 1, {1, 1},
{2, 2}, {0, 0, 0, 0}, {1, 1}, {0, 0}, false),
conv_param_t<>(1, 1024, 512, {14, 14}, 1, {1, 1},
{1, 1}, {0, 0, 0, 0}, {1, 1}, {0, 0}, false),
conv_param_t<>(1, 512, 512, {14, 14}, 32, {3, 3},
{1, 1}, {1, 1, 1, 1}, {1, 1}, {0, 0}, false),
conv_param_t<>(1, 1024, 1024, {14, 14}, 1, {1, 1},
{1, 1}, {0, 0, 0, 0}, {1, 1}, {0, 0}, false),
conv_param_t<>(1, 1024, 1024, {14, 14}, 32, {3, 3},
{2, 2}, {1, 1, 1, 1}, {1, 1}, {0, 0}, false),
conv_param_t<>(1, 1024, 2048, {7, 7}, 1, {1, 1},
{1, 1}, {0, 0, 0, 0}, {1, 1}, {0, 0}, false),
conv_param_t<>(1, 1024, 2048, {14, 14}, 1, {1, 1},
{2, 2}, {0, 0, 0, 0}, {1, 1}, {0, 0}, false),
conv_param_t<>(1, 2048, 1024, {7, 7}, 1, {1, 1},
{1, 1}, {0, 0, 0, 0}, {1, 1}, {0, 0}, false),
conv_param_t<>(1, 1024, 1024, {7, 7}, 32, {3, 3},
{1, 1}, {1, 1, 1, 1}, {1, 1}, {0, 0}, false)
};
// 3D conv shapes
vector<conv_param_t<3>> shapes_3d = {
// MB, IC, OC, {IT, IH, IW}, G, {KT, KH, KW}, {stride_t, stride_h,
// stride_w},
// {pad_prev, pad_h_top, pad_w_left, pad_next, pad_h_bottom, pad_w_right},
// ({dilation_t, dilation_h, dilation_w},
// {output_padding_t, output_padding_h, output_padding_w}, tranpose)
// Regular
conv_param_t<3>(1, 64, 64, {8, 14, 14}, 1, {3, 3, 3},
{1, 1, 1}, {1, 1, 1, 1, 1, 1}),
conv_param_t<3>(1, 64, 64, {8, 14, 14}, 1, {3, 3, 3},
{1, 1, 1}, {1, 1, 1, 1, 1, 1}, {1, 1, 1}, {0, 0, 0}, true),
conv_param_t<3>(1, 64, 64, {8, 14, 14}, 1, {3, 3, 3},
{1, 1, 1}, {1, 1, 1, 1, 1, 1}, {1, 1, 1}, {1, 1, 1}, true),
//With dilations
conv_param_t<3>(1, 64, 64, {8, 14, 14}, 1, {3, 3, 3},
{1, 1, 1}, {1, 1, 1, 1, 1, 1}, {2, 2, 2}),
conv_param_t<3>(1, 64, 64, {8, 14, 14}, 1, {3, 3, 3},
{1, 1, 1}, {1, 1, 1, 1, 1, 1}, {2, 2, 2}, {0, 0, 0}, true),
conv_param_t<3>(1, 64, 64, {8, 14, 14}, 1, {3, 3, 3},
{1, 1, 1}, {1, 1, 1, 1, 1, 1}, {2, 2, 2}, {1, 1, 1}, true),
// Groupwise
conv_param_t<3>(32, 192, 192, {2, 28, 28}, 96, {3, 3, 3},
{2, 2, 2}, {1, 1, 1, 1, 1, 1}),
conv_param_t<3>(32, 192, 192, {1, 14, 14}, 96, {3, 3, 3},
{1, 1, 1}, {1, 1, 1, 1, 1, 1}),
conv_param_t<3>(32, 384, 384, {1, 14, 14}, 192, {3, 3, 3},
{1, 2, 2}, {1, 1, 1, 1, 1, 1}),
conv_param_t<3>(32, 384, 384, {1, 7, 7}, 192, {3, 3, 3},
{1, 1, 1}, {1, 1, 1, 1, 1, 1}),
conv_param_t<3>(32, 16, 16, {4, 56, 56}, 8, {3, 3, 3},
{1, 1, 1}, {1, 1, 1, 1, 1, 1}),
conv_param_t<3>(32, 16, 16, {2, 28, 28}, 8, {3, 3, 3},
{1, 1, 1}, {1, 1, 1, 1, 1, 1}),
conv_param_t<3>(32, 32, 32, {4, 56, 56}, 16, {3, 3, 3},
{2, 2, 2}, {1, 1, 1, 1, 1, 1}),
conv_param_t<3>(32, 32, 32, {2, 28, 28}, 16, {3, 3, 3},
{1, 1, 1}, {1, 1, 1, 1, 1, 1}),
conv_param_t<3>(32, 32, 32, {2, 28, 28}, 16, {3, 3, 3},
{2, 2, 2}, {1, 1, 1, 1, 1, 1}),
conv_param_t<3>(32, 32, 32, {1, 14, 14}, 16, {3, 3, 3},
{1, 1, 1}, {1, 1, 1, 1, 1, 1}),
conv_param_t<3>(32, 128, 128, {2, 28, 28}, 32, {3, 3, 3},
{2, 2, 2}, {1, 1, 1, 1, 1, 1}),
conv_param_t<3>(32, 128, 128, {1, 14, 14}, 32, {3, 3, 3},
{1, 1, 1}, {1, 1, 1, 1, 1, 1}),
conv_param_t<3>(32, 256, 256, {1, 14, 14}, 64, {3, 3, 3},
{1, 2, 2}, {1, 1, 1, 1, 1, 1}),
conv_param_t<3>(32, 256, 256, {1, 7, 7}, 64, {3, 3, 3},
{1, 1, 1}, {1, 1, 1, 1, 1, 1}),
conv_param_t<3>(32, 128, 128, {2, 28, 28}, 32, {3, 3, 3},
{2, 2, 2}, {1, 1, 1, 1, 1, 1}, {1, 1, 1}, {0, 0, 0}, true),
conv_param_t<3>(32, 128, 128, {1, 14, 14}, 32, {3, 3, 3},
{1, 1, 1}, {1, 1, 1, 1, 1, 1}, {1, 1, 1}, {0, 0, 0}, true),
conv_param_t<3>(32, 128, 128, {1, 14, 14}, 32, {3, 3, 3},
{1, 1, 1}, {1, 1, 1, 1, 1, 1}, {1, 1, 1}, {1, 1, 1}, true),
// Depthwise
conv_param_t<3>(1, 64, 64, {8, 14, 14}, 64, {3, 3, 3},
{1, 1, 1}, {1, 1, 1, 1, 1, 1}),
conv_param_t<3>(1, 144, 144, {4, 28, 28}, 144, {3, 5, 5},
{1, 2, 2}, {1, 2, 2, 1, 2, 2}),
conv_param_t<3>(1, 288, 288, {4, 14, 14}, 288, {3, 5, 5},
{1, 1, 1}, {1, 2, 2, 1, 2, 2}),
// Pointwise
conv_param_t<3>(1, 128, 128, {8, 14, 14}, 1, {1, 1, 1},
{1, 1, 1}, {0, 0, 0, 0})
};
// clang-format on
template <int SPATIAL_DIM, typename Acc_t>
void performance_test(
const vector<conv_param_t<SPATIAL_DIM>>& shapes,
bool flush,
int repetitions) {
std::vector<char> llc;
if (flush) {
llc.resize(128 * 1024 * 1024, 1.0);
}
constexpr int NWARMUP = 4;
const int NITER = repetitions;
string header = "MB, IC, OC, ";
if (SPATIAL_DIM == 3) {
header += "IT, ";
}
if (SPATIAL_DIM > 1) {
header += "IH, ";
}
header += "IW, G, ";
if (SPATIAL_DIM == 3) {
header += "KT, ";
}
if (SPATIAL_DIM > 1) {
header += "KH, ";
}
header += "KW, ";
if (SPATIAL_DIM == 3) {
header += "stride_t, ";
}
if (SPATIAL_DIM > 1) {
header += "stride_h, ";
}
header += "stride_w, ";
if (SPATIAL_DIM == 3) {
header += "pad_t, ";
}
if (SPATIAL_DIM > 1) {
header += "pad_h, ";
}
header += "pad_w, ";
if (SPATIAL_DIM == 3) {
header += "dilation_t, ";
}
if (SPATIAL_DIM > 1) {
header += "dilation_h, ";
}
header += "dilation_w, ";
if (SPATIAL_DIM == 3) {
header += "output_padding_t, ";
}
if (SPATIAL_DIM > 1) {
header += "output_padding_h, ";
}
header += "output_padding_w, ";
header += "transposed, ";
header += "Type, M, N, K, ";
header += "#_ops, ";
#ifdef FBGEMM_MEASURE_TIME_BREAKDOWN
cout << "WARNING: the timer may be inaccurate when used by multiple threads."
<< endl;
cout << header << "Im2Col (ms), "
<< "Packing (ms), "
<< "Kernel (ms), "
<< "Postprocessing (ms), "
<< "fbgemmPacked (ms), "
<< "Total (ms), "
<< "GOPS" << endl;
#else
cout << setw(6) << header << setw(5) << "GOPS" << endl;
#endif
chrono::time_point<chrono::high_resolution_clock> begin, end;
for (auto conv_p : shapes) {
if (conv_p.IC % conv_p.G != 0 || conv_p.OC % conv_p.G != 0) {
// invalid shapes
continue;
}
int im_in_dim = accumulate(
conv_p.IN_DIM.begin(), conv_p.IN_DIM.end(), 1, multiplies<int>());
aligned_vector<uint8_t> Aint8(conv_p.MB * im_in_dim * conv_p.IC);
int kernel_dim =
accumulate(conv_p.K.begin(), conv_p.K.end(), 1, multiplies<int>());
aligned_vector<int8_t> Bint8(
kernel_dim * conv_p.IC * (conv_p.OC / conv_p.G));
aligned_vector<int8_t> Bint8_tr(
kernel_dim * conv_p.IC * (conv_p.OC / conv_p.G));
int im_out_dim = accumulate(
conv_p.OUT_DIM.begin(), conv_p.OUT_DIM.end(), 1, multiplies<int>());
aligned_vector<int32_t> Cint32_ref(conv_p.MB * im_out_dim * conv_p.OC);
aligned_vector<uint8_t> Cint8_ref(Cint32_ref.size(), 0);
aligned_vector<int32_t> Cint32_fb(Cint32_ref.size());
aligned_vector<uint8_t> Cint8_fb(Cint32_ref.size(), 0);
aligned_vector<uint8_t> Cint8_fb2(Cint32_ref.size(), 0);
aligned_vector<int32_t> Cint32_fb2(Cint32_ref.size());
// A matrix (input activations)
randFill<uint8_t>(Aint8, 0, 5);
int32_t Aint8_zero_point = 4;
// B matrix (weights)
randFill<int8_t>(Bint8, -4, 4);
aligned_vector<int32_t> Bint8_zero_point(1);
randFill(Bint8_zero_point, -3, -1);
aligned_vector<float> C_multiplier(Bint8_zero_point.size());
randFill(C_multiplier, 0.1234f / 2, 0.1234f * 3 / 2);
int32_t C_zero_point = 5;
// reference implementation
// conv_ref expects weights to be in G (R S C/G) K/G
transposeConvWeights<SPATIAL_DIM>(conv_p, Bint8.data(), Bint8_tr.data());
conv_ref(
conv_p,
Aint8.data(),
Aint8_zero_point,
Bint8_tr.data(),
Cint32_ref.data());
// matrix dimensions after im2col
int MDim = conv_p.MB * im_out_dim;
int NDim = conv_p.OC / conv_p.G;
int KDim = kernel_dim * conv_p.IC;
int KDimPerGroup = KDim / conv_p.G;
int OC_per_G = conv_p.OC / conv_p.G;
// computing row offset
vector<int32_t> row_offsets(MDim);
vector<uint8_t> Aint8_im2col(MDim * KDim);
im2col_ref(conv_p, Aint8.data(), Aint8_zero_point, Aint8_im2col.data());
// computing column offset
vector<int32_t> col_offsets(conv_p.OC);
for (int g = 0; g < conv_p.G; ++g) {
col_offsets_with_zero_pt_s8acc32_ref(
KDimPerGroup,
OC_per_G,
OC_per_G,
Bint8_tr.data() + g * KDimPerGroup * OC_per_G,
Bint8_zero_point.data(),
col_offsets.data() + g * OC_per_G,
conv_p.OC);
}
for (int g = 0; g < conv_p.G; ++g) {
row_offsets_u8acc32_ref(
MDim,
KDimPerGroup,
KDim,
Aint8_im2col.data() + g * KDimPerGroup,
row_offsets.data());
requantize_u8acc32_ref(
MDim,
NDim,
conv_p.G * NDim,
Cint32_ref.data() + g * NDim,
Cint8_ref.data() + g * NDim,
C_multiplier.data() + g * NDim / conv_p.OC,
C_zero_point,
Aint8_zero_point,
Bint8_zero_point.data() + g * NDim / conv_p.OC,
row_offsets.data(),
col_offsets.data() + g * NDim,
nullptr,
conv_p.OC);
}
double nops = 2.0 * static_cast<double>(NITER) * MDim * NDim * KDim;
double ttot = 0.0;
string runType;
PackWeightsForConv<SPATIAL_DIM> packedB(conv_p, Bint8.data());
runType = "UniConv";
ttot = 0;
#ifdef FBGEMM_MEASURE_TIME_BREAKDOWN
double im2col_time = 0.0;
double total_im2col_time = 0.0;
double total_packing_time = 0.0;
double total_computing_time = 0.0;
double total_kernel_time = 0.0;
double total_postprocessing_time = 0.0;
double total_run_time = 0.0;
#endif
for (auto i = 0; i < NWARMUP + NITER; ++i) {
#ifdef FBGEMM_MEASURE_TIME_BREAKDOWN
packing_time = 0.0;
computing_time = 0.0;
kernel_time = 0.0;
postprocessing_time = 0.0;
run_time = 0.0;
#endif
llc_flush(llc);
begin = chrono::high_resolution_clock::now();
#ifdef _OPENMP
#pragma omp parallel
#endif
{
int num_threads = fbgemm_get_num_threads();
int tid = fbgemm_get_thread_num();
// no-op output process objects
DoNothing<> doNothingObj{};
ReQuantizeOutput<false, QuantizationGranularity::TENSOR> outputProcObj(
doNothingObj,
C_multiplier.data(),
C_zero_point,
Aint8_zero_point,
Bint8_zero_point.data(),
nullptr, // row offsets
col_offsets.data(),
nullptr, // bias
conv_p.OC,
conv_p.G);
fbgemmConv(
conv_p,
Aint8.data(),
packedB,
Cint8_fb.data(),
Cint32_fb.data(),
outputProcObj,
tid,
num_threads);
}
end = chrono::high_resolution_clock::now();
if (i >= NWARMUP) {
auto dur = chrono::duration_cast<chrono::nanoseconds>(end - begin);
ttot += dur.count();
#ifdef FBGEMM_MEASURE_TIME_BREAKDOWN
total_packing_time += packing_time;
total_computing_time += computing_time;
total_kernel_time += kernel_time;
total_postprocessing_time += postprocessing_time;
total_run_time += run_time;
#endif
}
}
cout << conv_p.MB << ", " << conv_p.IC << ", " << conv_p.OC << ", ";
for (int i = 0; i < SPATIAL_DIM; ++i) {
cout << conv_p.IN_DIM[i] << ", ";
}
cout << conv_p.G << ", ";
for (int i = 0; i < SPATIAL_DIM; ++i) {
cout << conv_p.K[i] << ", ";
}
for (int i = 0; i < SPATIAL_DIM; ++i) {
cout << conv_p.stride[i] << ", ";
}
for (int i = 0; i < SPATIAL_DIM; ++i) {
cout << conv_p.pad[i] << ", ";
}
for (int i = 0; i < SPATIAL_DIM; ++i) {
cout << conv_p.dilation[i] << ", ";
}
for (int i = 0; i < SPATIAL_DIM; ++i) {
cout << conv_p.output_pad[i] << ", ";
}
cout << conv_p.transposed;
cout << setw(13) << ", " << runType << ", " << setw(5) << fixed << setw(5)
<< setw(6) << MDim << ", " << setw(6) << NDim << ", " << setw(6)
<< KDim << ", " << nops << ", ";
#ifdef FBGEMM_MEASURE_TIME_BREAKDOWN
cout << fixed << setprecision(6) << setw(8) << 0 << ", "
<< total_packing_time / (double)NITER / 1e6 << ", "
<< total_kernel_time / (double)NITER / 1e6 << ", "
<< total_postprocessing_time / (double)NITER / 1e6 << ", "
<< total_run_time / (double)NITER / 1e6 << ", "
<< ttot / (double)NITER / 1e6 << ", ";
#endif
cout << setprecision(2) << nops / ttot << endl;
compare_buffers(
Cint8_ref.data(),
Cint8_fb.data(),
MDim,
NDim * conv_p.G,
NDim * conv_p.G,
5);
} // shapes
}
typedef struct {
bool no_flush; /* if true, llc won't be flushed inbetween benchmark iterations
*/
bool run_extended_shapes; /* if true, runs additional shapes on top of the
default set */
int benchmark_repetitions; /* specified number of timed benchmark iterations
*/
} user_args_t;
int main(int argc, const char* argv[]) {
user_args_t user_args;
user_args.benchmark_repetitions =
parseArgumentInt(argc, argv, "--repit=", 10, 10);
user_args.no_flush = parseArgumentBool(argc, argv, "--no-flush", false);
user_args.run_extended_shapes =
parseArgumentBool(argc, argv, "--run-extn-shapes", false);
#ifdef _OPENMP
// Use 1 thread unless OMP_NUM_THREADS is explicit set.
const char* val = getenv("OMP_NUM_THREADS");
if (val == nullptr || !*val) {
omp_set_num_threads(1);
}
#endif
// Add extra shapes to the runlist
if (user_args.run_extended_shapes) {
shapes_2d.insert(
shapes_2d.end(),
shapes_2d_resnext_101.begin(),
shapes_2d_resnext_101.end());
}
// performance_test<int16_t>();
performance_test<1, int32_t>(
shapes_1d, !user_args.no_flush, user_args.benchmark_repetitions);
performance_test<2, int32_t>(
shapes_2d, !user_args.no_flush, user_args.benchmark_repetitions);
performance_test<3, int32_t>(
shapes_3d, !user_args.no_flush, user_args.benchmark_repetitions);
return 0;
}