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b0d3165cc8
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17084 clangr codemod Reviewed By: ezyang Differential Revision: D14078507 fbshipit-source-id: ed02d772890b30196302b6830f541f054b7e95c8
58 lines
1.6 KiB
C++
58 lines
1.6 KiB
C++
#pragma once
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#include "caffe2/core/operator.h"
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#include "caffe2/utils/eigen_utils.h"
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#include "caffe2/utils/math.h"
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namespace caffe2 {
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template <typename T, class Context>
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class EnsureClippedOp final : public Operator<Context> {
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public:
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USE_OPERATOR_CONTEXT_FUNCTIONS;
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template <class... Args>
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explicit EnsureClippedOp(Args&&... args)
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: Operator<Context>(std::forward<Args>(args)...),
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min_(std::numeric_limits<T>::lowest()),
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max_(std::numeric_limits<T>::max()) {
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if (HasArgument("min")) {
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min_ = static_cast<T>(this->template GetSingleArgument<float>("min", 0));
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}
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if (HasArgument("max")) {
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max_ = static_cast<T>(this->template GetSingleArgument<float>("max", 0));
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}
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}
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bool RunOnDevice() override {
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if (InputSize() > INDICES) {
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// spares gradient, selective checking clipping
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CAFFE_ENFORCE_EQ(
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Input(PARAM).size_from_dim(1),
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Input(GRAD).size_from_dim(Input(INDICES).dim()));
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return DispatchHelper<TensorTypes<int32_t, int64_t>>::call(
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this, Input(INDICES));
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} else {
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auto& X = Input(PARAM);
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auto* Y = Output(OUTPUT_PARAM, X.sizes(), at::dtype<float>());
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EigenVectorMap<float>(Y->template mutable_data<float>(), Y->numel()) =
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ConstEigenVectorMap<float>(X.template data<float>(), X.numel())
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.cwiseMax(min_)
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.cwiseMin(max_);
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return true;
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}
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}
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template <typename SIndex>
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bool DoRunWithType();
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protected:
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T min_;
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T max_;
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INPUT_TAGS(PARAM, INDICES, GRAD);
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OUTPUT_TAGS(OUTPUT_PARAM);
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};
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} // namespace caffe2
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