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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
75 lines
2.3 KiB
C++
75 lines
2.3 KiB
C++
#include "sparse_to_dense_op.h"
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#include "caffe2/core/context.h"
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namespace caffe2 {
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REGISTER_CPU_OPERATOR(SparseToDense, SparseToDenseOp<CPUContext>);
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OPERATOR_SCHEMA(SparseToDense)
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.NumInputs(2, 3)
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.NumOutputs(1)
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.TensorInferenceFunction([](const OperatorDef& def,
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const vector<TensorShape>& in) {
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vector<TensorShape> out(1);
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if (in.size() == 3) {
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out[0].add_dims(in[2].dims(0));
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} else {
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out[0].set_unknown_shape(true);
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return out;
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}
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for (int i = 1; i < in[1].dims().size(); i++) {
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out[0].add_dims(in[1].dims(i));
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}
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out[0].set_data_type(in[1].data_type());
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return out;
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})
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.SetDoc(R"DOC(
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Convert sparse representations to dense with given indices.
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Transforms a sparse representation of map<id, value> represented as `indices`
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vector and `values` tensor into a compacted tensor where the first dimension
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is determined by the first dimension of the 3rd input if it is given or the
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max index. Missing values are filled with zeros.
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The op supports duplicated indices and performs summation over corresponding
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values. This behavior is useful for converting GradientSlices into dense
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representation.
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After running this op:
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output[indices[i], :] += values[i] // sum over all indices[i] equal to the index
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output[j, ...] = 0 if j not in indices
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)DOC")
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.Input(0, "indices", "1-D int32/int64 tensor of concatenated ids of data")
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.Input(
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1,
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"values",
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"Data tensor, first dimension has to match `indices`, "
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"basic numeric types are supported")
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.Input(
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2,
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"data_to_infer_dim",
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"Optional: if provided, the first dimension of output is the first "
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"dimension of this tensor.")
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.Output(
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0,
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"output",
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"Output tensor of the same type as `values` of shape `[len(lengths), "
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"len(mask)] + shape(default_value)` (if `lengths` is not provided the "
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"first dimension is omitted)");
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namespace {
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class GetSparseToDenseGradient : public GradientMakerBase {
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using GradientMakerBase::GradientMakerBase;
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vector<OperatorDef> GetGradientDefs() override {
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return SingleGradientDef(
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"Gather", "", vector<string>{GO(0), I(0)}, vector<string>{GI(1)});
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}
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};
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REGISTER_GRADIENT(SparseToDense, GetSparseToDenseGradient);
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}
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} // namespace caffe2
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