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lengths_reducer_ops.cc
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lengths_reducer_ops.cc
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#include "caffe2/operators/lengths_reducer_ops.h"
#include "caffe2/core/context.h"
#include "caffe2/core/operator.h"
#include "caffe2/operators/segment_reduction_op.h"
#include "caffe2/utils/math.h"
namespace caffe2 {
// Use _STR option because the schema is declared using _STR version too in
// generic fashion. Otherwise it'd break schema declaration check.
// TODO(dzhulgakov): remove _STR when all lengths ops are off generic version.
using SparseLengthsSumOp =
CPUSparseLengthsReductionOp<float, TensorTypes<float, at::Half>, 0, 0>;
using SparseLengthsWeightedSumOp =
CPUSparseLengthsReductionOp<float, TensorTypes<float, at::Half>, 1, 0>;
using SparseLengthsMeanOp =
CPUSparseLengthsReductionOp<float, TensorTypes<float, at::Half>, 0, 1>;
REGISTER_CPU_OPERATOR(SparseLengthsSum, SparseLengthsSumOp);
REGISTER_CPU_OPERATOR(SparseLengthsWeightedSum, SparseLengthsWeightedSumOp);
REGISTER_CPU_OPERATOR(SparseLengthsMean, SparseLengthsMeanOp);
OPERATOR_SCHEMA(SparseLengthsPositionalWeightedSum)
.NumInputs(4)
.NumOutputs(1)
.SetDoc(R"DOC(
Variation of SparseLengthsWeightedSum operator, where, for each row,
weights are accessed by indices [0..L-1], where L is the length of given row.
This is basically a fused operator of LengthsRangeFill + Gather +
SparseWeightedSum
)DOC")
.Input(
0,
"DATA",
"uint8 tensor obtained with "
"operator FloatToRowwiseQuantized8Bits")
.Input(
1,
"WEIGHT",
"Scalar multipliers for the input slices. Must "
"be a vector with the length matching the length of DATA")
.Input(
2,
"INDICES",
"Integer vector containing indices of the first "
"dimension of DATA for the slices that are being aggregated")
.Input(
3,
"LENGTHS",
"Vector with the same sum of elements as the first dimension of DATA")
.Output(0, "output", "output");
REGISTER_CPU_OPERATOR_STR(
"SparseLengthsPositionalWeightedSum",
CPUSparseLengthsReductionOp<float, TensorTypes<float, at::Half>, 1, 0, 1>);
template <typename Def>
string FormatDoc() {
string doc = Def::doc;
c10::ReplaceAll(doc, "{op}", Def::OpDef::name);
c10::ReplaceAll(doc, "{op_doc}", Def::OpDef::doc);
auto replaced = c10::ReplaceAll(doc, "{extra}", "");
CAFFE_ENFORCE_EQ(replaced, 0);
return doc;
}
using SparseLengthsSumDef = AbstractSparseLengthsDef<
float,
int,
CPUContext,
SumReducerDef,
true /*GradientNeedIndices*/>;
OPERATOR_SCHEMA(SparseLengthsSum)
.NumInputs(SparseLengthsSumDef::ForwardOp::kNumInputs)
.NumOutputs(1)
.ValueKeyLengthInputFillers(
SparseLengthsSumOp::DATA,
SparseLengthsSumOp::INDICES,
SparseLengthsSumOp::LENGTHS)
.SetDoc(FormatDoc<SparseLengthsSumDef>())
.Output(0, "OUTPUT", "Aggregated tensor")
.FillUsing(SparseLengthsSumDef::PopulateSchema)
.InheritOnnxSchema();
REGISTER_CPU_OPERATOR(
SparseLengthsSumGradient,
SparseLengthsSumDef::BackwardOp);
OPERATOR_SCHEMA(SparseLengthsSumGradient)
.NumInputs(SparseLengthsSumDef::BackwardOp::kNumInputs)
.NumOutputs(1)
.DisallowInputFillers();
REGISTER_GRADIENT(SparseLengthsSum, SparseLengthsSumDef::GetGradient)
using SparseLengthsWeightedSumDef = AbstractSparseLengthsDef<
float,
int,
CPUContext,
WeightedSumReducerDef,
true /*GradientNeedIndices*/>;
OPERATOR_SCHEMA(SparseLengthsWeightedSum)
.NumInputs(SparseLengthsWeightedSumDef::ForwardOp::kNumInputs)
.NumOutputs(1)
.WeightedValueKeyLengthInputFillers(
SparseLengthsWeightedSumOp::DATA,
SparseLengthsWeightedSumOp::INDICES,
SparseLengthsWeightedSumOp::LENGTHS,
SparseLengthsWeightedSumOp::WEIGHT)
.SetDoc(FormatDoc<SparseLengthsWeightedSumDef>())
.Output(0, "OUTPUT", "Aggregated tensor")
.FillUsing(SparseLengthsWeightedSumDef::PopulateSchema)
.InheritOnnxSchema();
REGISTER_CPU_OPERATOR(
SparseLengthsWeightedSumGradient,
SparseLengthsWeightedSumDef::BackwardOp);
OPERATOR_SCHEMA(SparseLengthsWeightedSumGradient)
.NumInputs(SparseLengthsWeightedSumDef::BackwardOp::kNumInputs)
.NumOutputs(1)
.DisallowInputFillers();
REGISTER_GRADIENT(
SparseLengthsWeightedSum,
SparseLengthsWeightedSumDef::GetGradient)
using SparseLengthsMeanDef = AbstractSparseLengthsDef<
float,
int,
CPUContext,
MeanReducerDef,
true /*GradientNeedIndices*/>;
OPERATOR_SCHEMA(SparseLengthsMean)
.NumInputs(SparseLengthsMeanDef::ForwardOp::kNumInputs)
.NumOutputs(1)
.ValueKeyLengthInputFillers(
SparseLengthsMeanOp::DATA,
SparseLengthsMeanOp::INDICES,
SparseLengthsMeanOp::LENGTHS)
.SetDoc(FormatDoc<SparseLengthsMeanDef>())
.Output(0, "OUTPUT", "Aggregated tensor")
.FillUsing(SparseLengthsMeanDef::PopulateSchema);
REGISTER_CPU_OPERATOR(
SparseLengthsMeanGradient,
SparseLengthsMeanDef::BackwardOp);
OPERATOR_SCHEMA(SparseLengthsMeanGradient)
.NumInputs(SparseLengthsMeanDef::BackwardOp::kNumInputs)
.NumOutputs(1)
.DisallowInputFillers();
REGISTER_GRADIENT(SparseLengthsMean, SparseLengthsMeanDef::GetGradient)
} // namespace caffe2