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1433160a36
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/66742 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: D31705366 fbshipit-source-id: be58222426c192406a7f93c21582c3f6f2082401
141 lines
3.8 KiB
C++
141 lines
3.8 KiB
C++
#ifndef CAFFE2_OPERATORS_MINMAX_OPS_H_
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#define CAFFE2_OPERATORS_MINMAX_OPS_H_
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#include "caffe2/core/context.h"
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#include "caffe2/core/logging.h"
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#include "caffe2/core/operator.h"
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#include "caffe2/core/types.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 <typename T, class Context>
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class MaxOp final : public Operator<Context> {
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public:
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USE_OPERATOR_CONTEXT_FUNCTIONS;
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USE_SIMPLE_CTOR_DTOR(MaxOp)
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bool RunOnDevice() override {
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const auto& X0 = Input(0);
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auto* Y = Output(0);
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Y->ResizeLike(X0);
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const T* X0_data = X0.template data<T>();
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T* Y_data = Y->template mutable_data<T>();
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const int N = X0.numel();
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if (InputSize() == 1) {
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if (Y != &X0) {
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context_.template CopySameDevice<T>(N, X0_data, Y_data);
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}
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return true;
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}
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const auto& X1 = Input(1);
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CAFFE_ENFORCE_EQ(
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X0.sizes(),
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Y->sizes(),
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"Description: Input #1, input dimension:",
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X1.sizes(),
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" should match output dimension: ",
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Y->sizes());
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const T* X1_data = X1.template data<T>();
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math::Max<T, Context>(N, X0_data, X1_data, Y_data, &context_);
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for (const auto i : c10::irange(2, InputSize())) {
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const auto& Xi = Input(i);
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CAFFE_ENFORCE_EQ(
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Xi.sizes(),
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Y->sizes(),
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"Description: Input #",
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i,
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", input dimension:",
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Input(i).sizes(),
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" should match output dimension: ",
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Y->sizes());
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const T* Xi_data = Xi.template data<T>();
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math::Max<T, Context>(N, Y_data, Xi_data, Y_data, &context_);
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}
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return true;
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}
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};
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template <typename T, class Context>
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class MinOp final : public Operator<Context> {
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public:
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USE_OPERATOR_CONTEXT_FUNCTIONS;
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USE_SIMPLE_CTOR_DTOR(MinOp)
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bool RunOnDevice() override {
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const auto& X0 = Input(0);
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auto* Y = Output(0);
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Y->ResizeLike(X0);
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const T* X0_data = X0.template data<T>();
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T* Y_data = Y->template mutable_data<T>();
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const int N = X0.numel();
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if (InputSize() == 1) {
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if (Y != &X0) {
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context_.template CopySameDevice<T>(N, X0_data, Y_data);
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}
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return true;
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}
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const auto& X1 = Input(1);
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CAFFE_ENFORCE_EQ(
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X0.sizes(),
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Y->sizes(),
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"Description: Input #1, input dimension:",
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X1.sizes(),
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" should match output dimension: ",
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Y->sizes());
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const T* X1_data = X1.template data<T>();
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math::Min<T, Context>(N, X0_data, X1_data, Y_data, &context_);
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for (const auto i : c10::irange(2, InputSize())) {
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const auto& Xi = Input(i);
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CAFFE_ENFORCE_EQ(
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Xi.sizes(),
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Y->sizes(),
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"Description: Input #",
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i,
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", input dimension:",
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Input(i).sizes(),
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" should match output dimension: ",
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Y->sizes());
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const T* Xi_data = Xi.template data<T>();
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math::Min<T, Context>(N, Y_data, Xi_data, Y_data, &context_);
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}
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return true;
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}
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};
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template <typename T, class Context>
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class SelectGradientOpBase : public Operator<Context> {
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public:
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USE_OPERATOR_CONTEXT_FUNCTIONS;
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USE_SIMPLE_CTOR_DTOR(SelectGradientOpBase)
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bool RunOnDevice() override;
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};
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template <typename T, class Context>
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class MaxGradientOp final : public SelectGradientOpBase<T, Context> {
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public:
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template <class... Args>
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explicit MaxGradientOp(Args&&... args)
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: SelectGradientOpBase<T, Context>(std::forward<Args>(args)...) {}
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~MaxGradientOp() = default;
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};
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template <typename T, class Context>
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class MinGradientOp final : public SelectGradientOpBase<T, Context> {
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public:
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template <class... Args>
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explicit MinGradientOp(Args&&... args)
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: SelectGradientOpBase<T, Context>(std::forward<Args>(args)...) {}
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~MinGradientOp() = default;
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};
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} // namespace caffe2
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#endif // CAFFE2_OPERATORS_MINMAX_OPS_H_
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