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https://github.com/zebrajr/pytorch.git
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4c4525fa5c
Summary: Delete `-Wno-unused-variable` from top level `CMakeLists.txt` Still suppress those warnings for tests and `torch_python` Delete number of unused variables from caffe2 code Use `(void)var;` to suppress unused variable in range loops Use `C10_UNUSED` for global constructors and use `constexpr` instead of `static` for global constants Do not delete `caffe2::OperatorBase::Output` calls as they have side effects Pull Request resolved: https://github.com/pytorch/pytorch/pull/66041 Reviewed By: ngimel Differential Revision: D31360142 Pulled By: malfet fbshipit-source-id: 6fdfb9f91efdc49ca984a2f2a17ee377d28210c8
66 lines
2.0 KiB
C++
66 lines
2.0 KiB
C++
#include "caffe2/core/logging.h"
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#include "l2_minimization.h"
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#include <cassert>
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#include <cmath>
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namespace dnnlowp {
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TensorQuantizationParams P99::ChooseQuantizationParams(
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const Histogram& hist,
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bool preserve_sparsity,
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int precision) {
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// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
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float min, max;
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std::vector<float> bins_f(
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dnnlowp::adjust_hist_to_include_zero(hist, &min, &max));
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int nbins = bins_f.size();
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CAFFE_ENFORCE(min <= 0.f);
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CAFFE_ENFORCE(max >= 0.f);
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float org_max = max;
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float org_min = min;
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// NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
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float bin_width = (max - min) / nbins;
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double total_sum = 0;
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for (int i = 0; i < nbins; ++i) {
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total_sum += bins_f[i];
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}
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double sum = 0;
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std::vector<double> CDF(nbins, 0.f);
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for (int i = 0; i < nbins; ++i) {
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sum += bins_f[i];
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CDF[i] = (double)sum / total_sum;
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}
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CAFFE_ENFORCE(threshold_ > 0.5 && threshold_ < 1);
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double left_quantile = (1.0f - threshold_) / 2.0f;
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double right_quantile = 1.0f - left_quantile;
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int i_begin = 0;
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int i_end = nbins - 2;
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bool finished = false;
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while (i_begin <= i_end && !finished) {
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finished = true;
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if (CDF[i_begin] < left_quantile) {
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i_begin++;
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finished = false;
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}
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if (CDF[i_end] > right_quantile) {
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finished = false;
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i_end--;
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}
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}
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// NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
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min = i_begin * bin_width + org_min;
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// NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
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max = (i_end + 2) * bin_width + org_min;
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VLOG(2) << "Org min " << org_min << " org max " << org_max << " found min "
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<< min << " max " << max;
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QuantizationFactory* qfactory = QuantizationFactory::GetDefaultInstance();
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return qfactory->ChooseQuantizationParams(
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min, max, precision, preserve_sparsity);
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} // ChooseQuantizationParams
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} // namespace dnnlowp
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