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a9b0a921d5
Summary: As GoogleTest `TEST` macro is non-compliant with it as well as `DEFINE_DISPATCH` All changes but the ones to `.clang-tidy` are generated using following script: ``` for i in `find . -type f -iname "*.c*" -or -iname "*.h"|xargs grep cppcoreguidelines-avoid-non-const-global-variables|cut -f1 -d:|sort|uniq`; do sed -i "/\/\/ NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)/d" $i; done ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/62008 Reviewed By: driazati, r-barnes Differential Revision: D29838584 Pulled By: malfet fbshipit-source-id: 1b2f8602c945bd4ce50a9bfdd204755556e31d13
108 lines
3.4 KiB
C++
108 lines
3.4 KiB
C++
#include "caffe2/operators/pack_rnn_sequence_op.h"
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namespace caffe2 {
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namespace {
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REGISTER_CPU_OPERATOR(PackRNNSequence, PackRNNSequenceOpBase<CPUContext, true>);
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REGISTER_CPU_OPERATOR(
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UnpackRNNSequence,
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PackRNNSequenceOpBase<CPUContext, false>);
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OPERATOR_SCHEMA(PackRNNSequence)
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.NumInputs(2)
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.NumOutputs(1)
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.SetDoc(R"DOC(
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Pack values based on the length blob. Each number from length blob represents
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the corresponding values that need to be packed. The dimension for each pack
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is the same as the maximum number from the length blob (padding with zero is
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implemented for smaller length value). The overall output dimension is:
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T * N * D, where T is the max number of lengths, N is the size of lengths,
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and D is the dimension of each feature value. The following example shows
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the input and output of this operator:
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Given:
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values = [v1, v2, v3, v4, v5, v6, v7, v8]
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lengths = [2, 3, 1, 2];
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Output:
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output = [
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[v1, v3, v6, v7],
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[v2, v4, 0, v8],
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[0, v5, 0, 0 ],
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]
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One application for this operator is the transfer data into the format that is
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used for RNN models. Note that the gradient operator of PackRNNSequence is
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UnpackRNNSequence.
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)DOC")
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.Input(0, "values", "Data tensor, contains a sequence of features")
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.Input(1, "lengths", "lengths with each number representing the pack size.")
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.Output(0, "output", "Output tensor after packing");
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OPERATOR_SCHEMA(UnpackRNNSequence)
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.NumInputs(2)
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.NumOutputs(1)
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.SetDoc(R"DOC(
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This is the reverse operator for PackRNNSequence. It maps the packed values
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back to sequence values based on the length blob. Each number from length blob
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represents the corresponding values that has been grouped. The dimension
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for each pack is the same as the maximum number from the length blob (padding
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with zero was implemented for smaller length value). The overall output
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dimension is: M * D, where M is the sum of lengths, and D is the dimension of
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each feature value. The following example shows the input and output of
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this operator:
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Given:
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values = [
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[v1, v3, v6, v7],
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[v2, v4, 0, v8],
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[0, v5, 0, 0 ],
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]
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lengths = [2, 3, 1, 2]
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Output:
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output = [v1, v2, v3, v4, v5, v6, v7, v8];
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One application for this operator is the transfer data from the format of RNN
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back to sequence values. Note that the gradient operator of
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UnpackRNNSequence is PackRNNSequence.
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)DOC")
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.Input(0, "values", "Data tensor, contains the packed features")
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.Input(1, "lengths", "lengths with each number representing the pack size.")
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.Output(0, "output", "Output tensor before packing");
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class GetPackRNNSequenceGradient : public GradientMakerBase {
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using GradientMakerBase::GradientMakerBase;
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vector<OperatorDef> GetGradientDefs() override {
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CAFFE_ENFORCE_EQ(def_.input_size(), 2);
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return SingleGradientDef(
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"UnpackRNNSequence",
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"",
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vector<string>{GO(0), I(1)},
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vector<string>{GI(0)});
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}
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};
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class GetUnpackRNNSequenceGradient : public GradientMakerBase {
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using GradientMakerBase::GradientMakerBase;
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vector<OperatorDef> GetGradientDefs() override {
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CAFFE_ENFORCE_EQ(def_.input_size(), 2);
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return SingleGradientDef(
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"PackRNNSequence",
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"",
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vector<string>{GO(0), I(1)},
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vector<string>{GI(0)});
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}
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};
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REGISTER_GRADIENT(PackRNNSequence, GetPackRNNSequenceGradient);
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REGISTER_GRADIENT(UnpackRNNSequence, GetUnpackRNNSequenceGradient);
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} // namespace
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} // namespace caffe2
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