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https://github.com/zebrajr/pytorch.git
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85408e744f
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/10522 Move filler interface to operator schema to avoid extra code for caffe2 mobile. Reviewed By: dzhulgakov Differential Revision: D9312940 fbshipit-source-id: 77fb2406f0c6b171a1912a207e05e36da50c6966
97 lines
3.3 KiB
C++
97 lines
3.3 KiB
C++
/**
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* Copyright (c) 2016-present, Facebook, Inc.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "caffe2/experiments/operators/sparse_funhash_op.h"
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namespace caffe2 {
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namespace {
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REGISTER_CPU_OPERATOR(SparseFunHash, SparseFunHashOp<float, CPUContext>);
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REGISTER_CPU_OPERATOR(
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SparseFunHashGradient,
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SparseFunHashGradientOp<float, CPUContext>);
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OPERATOR_SCHEMA(SparseFunHash)
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.NumInputs(4, 5)
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.NumOutputs(1)
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.DisallowInputFillers() // TODO: enable the filler
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.SetDoc(R"DOC(
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This layer compresses a fully-connected layer for sparse inputs
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via hashing.
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It takes four required inputs and an option fifth input.
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The first three inputs `scalars`, `indices`, and `segment_ids` are
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the sparse segmented representation of sparse data, which are the
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same as the last three inputs of the `SparseSortedSegmentWeightedSum`
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operator. If the argument `num_segments` is specified, it would be used
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as the first dimension for the output; otherwise it would be derived
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from the maximum segment ID.
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The fourth input is a 1D weight vector. Each entry of the fully-connected
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layer would be randomly mapped from one of the entries in this vector.
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When the optional fifth input vector is present, each weight of the
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fully-connected layer would be the linear combination of K entries
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randomly mapped from the weight vector, provided the input
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(length-K vector) serves as the coefficients.
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)DOC")
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.Input(0, "scalars", "Values of the non-zero entries of the sparse data.")
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.Input(1, "indices", "Indices to the non-zero valued features.")
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.Input(
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2,
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"segment_ids",
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"Segment IDs corresponding to the non-zero entries.")
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.Input(3, "weight", "Weight vector")
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.Input(
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4,
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"alpha",
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"Optional coefficients for linear combination of hashed weights.")
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.Output(
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0,
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"output",
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"Output tensor with the first dimension equal to the number "
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"of segments.")
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.Arg("num_outputs", "Number of outputs")
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.Arg("num_segments", "Number of segments");
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OPERATOR_SCHEMA(SparseFunHashGradient)
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.NumInputs(5, 6)
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.NumOutputs(2, 3)
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.DisallowInputFillers();
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class GetSparseFunHashGradient : public GradientMakerBase {
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using GradientMakerBase::GradientMakerBase;
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vector<OperatorDef> GetGradientDefs() override {
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if (def_.input_size() == 4) {
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return SingleGradientDef(
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"SparseFunHashGradient",
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"",
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vector<string>{GO(0), I(0), I(1), I(2), I(3)},
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vector<string>{GI_V(3), GI_I(3)});
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}
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// def_.input_size() == 5
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return SingleGradientDef(
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"SparseFunHashGradient",
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"",
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vector<string>{GO(0), I(0), I(1), I(2), I(3), I(4)},
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vector<string>{GI_V(3), GI_I(3), GI(4)});
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}
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};
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REGISTER_GRADIENT(SparseFunHash, GetSparseFunHashGradient);
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} // namespace
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} // namespace caffe2
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