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1622546050
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/70248 Modified loops in files under fbsource/fbcode/caffe2/ from the format ``` for(TYPE var=x0;var<x_max;x++) ``` to the format ``` for(const auto var: irange(xmax)) ``` This was achieved by running r-barnes's loop upgrader script (D28874212) with some modification to exclude all files under /torch/jit and a number of reversions or unused variable suppression warnings added by hand. Test Plan: Sandcastle Reviewed By: malfet Differential Revision: D32813863 fbshipit-source-id: 527244b4a2b220fdfe7f17dee3599603f492a2ca
78 lines
2.0 KiB
C++
78 lines
2.0 KiB
C++
#ifndef CAFFE2_OPERATORS_TRANSPOSE_H_
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#define CAFFE2_OPERATORS_TRANSPOSE_H_
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#include <algorithm>
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#include <vector>
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#include "caffe2/core/context.h"
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#include "caffe2/core/operator.h"
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#include "caffe2/utils/math.h"
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#include "c10/util/irange.h"
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namespace caffe2 {
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template <class Context>
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class TransposeOp : public Operator<Context> {
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public:
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USE_OPERATOR_CONTEXT_FUNCTIONS;
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USE_DISPATCH_HELPER;
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template <class... Args>
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explicit TransposeOp(Args&&... args)
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: Operator<Context>(std::forward<Args>(args)...),
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axes_(this->template GetRepeatedArgument<int>("axes")) {
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// We will check the legality of axes_: it should be from 0 to axes_.size().
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std::vector<int> axes_sorted = axes_;
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std::sort(axes_sorted.begin(), axes_sorted.end());
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for (const auto i : c10::irange(axes_sorted.size())) {
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if (axes_sorted[i] != static_cast<int64_t>(i)) {
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CAFFE_THROW("Axes should be a permutation of 0 to ndim.");
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}
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}
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}
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bool RunOnDevice() override {
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// Do the actual transpose, which is implemented in DoRunWithType().
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return DispatchHelper<TensorTypes<float, double, int, int64_t>>::call(
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this, Input(0));
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}
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protected:
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template <typename T>
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void TransposeImpl(const Tensor& X, Tensor* Y) {
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const int ndim = X.dim();
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if (axes_.empty()) {
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axes_.resize(ndim);
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std::iota(axes_.rbegin(), axes_.rend(), 0);
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} else {
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CAFFE_ENFORCE_EQ(ndim, axes_.size());
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}
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const at::IntArrayRef X_dims = X.sizes();
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std::vector<std::int64_t> Y_dims(ndim);
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for (const auto i : c10::irange(ndim)) {
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Y_dims[i] = X_dims[axes_[i]];
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}
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Y->Resize(Y_dims);
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math::Transpose<std::int64_t, T, Context>(
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X_dims.size(),
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X_dims.data(),
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axes_.data(),
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X.template data<T>(),
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Y->template mutable_data<T>(),
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&context_);
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}
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private:
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template <typename T>
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bool DoRunWithType() {
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TransposeImpl<T>(Input(0), Output(0));
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return true;
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}
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std::vector<int> axes_;
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};
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} // namespace caffe2
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#endif // CAFFE2_OPERATORS_TRANSPOSE_H_
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