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#include "feature_maps_ops.h"
#include "caffe2/core/context.h"
namespace caffe2 {
namespace {
const std::string doc = R"DOC(
Single-feature representation:
- scalar features:
<feature full name> T
- list features:
<feature full name>.lengths int32
<feature full name>.values T
- map features:
<feature full name>.lengths int32
<feature full name>.keys K
<feature full name>.values V
Missing values are set to zero, and value presence flag is set accordingly:
<feature full name>.presence bool
Multi-feature representation:
- scalar features:
<feature type>.lengths int32
<feature type>.keys int64
<feature type>.values T
- list features:
<feature type>.lengths int32
<feature type>.keys int64
<feature type>.values.lengths int32
<feature type>.values.values T
- map features:
<feature type>.lengths int32
<feature type>.keys int64
<feature type>.values.lengths int32
<feature type>.values.keys K
<feature type>.values.values V
You can read more about representing batches of lists and maps here:
https://our.intern.facebook.com/intern/dex/caffe2/sparse-operations/
)DOC";
REGISTER_CPU_OPERATOR(
MergeDenseFeatureTensors,
MergeDenseFeatureTensorsOp<CPUContext>);
OPERATOR_SCHEMA(MergeDenseFeatureTensors)
.SetDoc(
"Merge given multi-feature dense tensors into one "
"multi-feature tensor." +
doc)
.NumInputs(2)
.NumOutputs(3)
.Input(0, "in1", "")
.Input(1, "in1_presence", ".presence")
.Output(0, "out_lengths", ".lengths")
.Output(1, "out_keys", ".keys")
.Output(2, "out_values", ".values")
.Arg("feature_ids", "feature ids");
REGISTER_CPU_OPERATOR(
MergeSingleScalarFeatureTensors,
MergeSingleScalarFeatureTensorsOp<CPUContext>);
OPERATOR_SCHEMA(MergeSingleScalarFeatureTensors)
.SetDoc(
"Merge given single-feature tensors with scalar features into one "
"multi-feature tensor." +
doc)
.NumInputs([](int n) { return n >= 2 && n % 2 == 0; })
.NumOutputs(3)
.Input(0, "in1", "")
.Input(1, "in1_presence", ".presence")
.Output(0, "out_lengths", ".lengths")
.Output(1, "out_keys", ".keys")
.Output(2, "out_values", ".values")
.Arg("feature_ids", "feature ids");
class GetMergeSingleScalarFeatureTensorsGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
vector<string> input_blob_names{};
vector<string> output_blob_names{};
for (int inputIdx = 0; inputIdx < def_.input_size() / 2; ++inputIdx) {
input_blob_names.push_back(I(inputIdx * 2 + 1));
output_blob_names.push_back(GI(inputIdx * 2));
}
input_blob_names.push_back(GO(2));
return SingleGradientDef(
"MergeSingleScalarFeatureTensorsGradient",
"", /* name */
input_blob_names,
output_blob_names);
}
};
REGISTER_CPU_OPERATOR(
MergeSingleScalarFeatureTensorsGradient,
MergeSingleScalarFeatureTensorsGradientOp<CPUContext>);
OPERATOR_SCHEMA(MergeSingleScalarFeatureTensorsGradient)
.SetDoc(
"Explode multi-feature tensor of scalar features into one or more"
"single-feature tensors" +
doc)
.NumInputs([](int n) { return n >= 2; })
.NumOutputs([](int n) { return n >= 1; })
.Input(0, "in1_presence", ".presence")
.Input(1, ".values_grad", ".values_grad")
.Output(0, "in1_grad", "_grad of inputs");
REGISTER_GRADIENT(
MergeSingleScalarFeatureTensors,
GetMergeSingleScalarFeatureTensorsGradient);
// ##########################################################
REGISTER_CPU_OPERATOR(
MergeSingleListFeatureTensors,
MergeSingleListFeatureTensorsOp<CPUContext>);
OPERATOR_SCHEMA(MergeSingleListFeatureTensors)
.SetDoc(
"Merge given single-feature tensors with list features into one "
"multi-feature tensor." +
doc)
.NumInputs([](int n) { return n >= 3 && n % 3 == 0; })
.NumOutputs(4)
.Input(0, "in1_lengths", ".lengths")
.Input(1, "in1_values", ".values")
.Input(2, "in1_presence", ".presence")
.Output(0, "out_lengths", ".lengths")
.Output(1, "out_keys", ".keys")
.Output(2, "out_values_lengths", ".values.lengths")
.Output(3, "out_values_values", ".values.values")
.Arg("feature_ids", "feature ids");
class GetMergeSingleListFeatureTensorsGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
vector<string> input_blob_names{};
vector<string> output_blob_names{};
for (int inputIdx = 0; inputIdx < def_.input_size() / 3; ++inputIdx) {
input_blob_names.push_back(I(inputIdx * 3));
input_blob_names.push_back(I(inputIdx * 3 + 2));
output_blob_names.push_back(GI(inputIdx * 3 + 1));
}
input_blob_names.push_back(GO(3));
return SingleGradientDef(
"MergeSingleListFeatureTensorsGradient",
"",
input_blob_names,
output_blob_names);
}
};
REGISTER_CPU_OPERATOR(
MergeSingleListFeatureTensorsGradient,
MergeSingleListOrMapFeatureTensorsGradientOp<CPUContext>);
OPERATOR_SCHEMA(MergeSingleListFeatureTensorsGradient)
.SetDoc(
"Explode multi-feature tensors with list features into "
"single-feature tensors." +
doc)
.NumInputs([](int n) { return n >= 3 && n % 2 == 1; })
.NumOutputs([](int n) { return n >= 1; })
.Input(0, "in1_lengths", ".lengths")
.Input(1, "in1_presence", ".presence")
.Input(2, "out_values_values", ".values.values_grad")
.Output(0, "out1_values", ".values_grad");
REGISTER_GRADIENT(
MergeSingleListFeatureTensors,
GetMergeSingleListFeatureTensorsGradient);
// ##########################################################
REGISTER_CPU_OPERATOR(
MergeSingleMapFeatureTensors,
MergeSingleMapFeatureTensorsOp<CPUContext>);
OPERATOR_SCHEMA(MergeSingleMapFeatureTensors)
.SetDoc(
"Merge given single-feature tensors with map features into one "
"multi-feature tensor." +
doc)
.NumInputs([](int n) { return n >= 4 && n % 4 == 0; })
.NumOutputs(5)
.Input(0, "in1_lengths", ".lengths")
.Input(1, "in1_keys", ".keys")
.Input(2, "in1_values", ".values")
.Input(3, "in1_presence", ".presence")
.Output(0, "out_lengths", ".lengths")
.Output(1, "out_keys", ".keys")
.Output(2, "out_values_lengths", ".values.lengths")
.Output(3, "out_values_keys", ".values.keys")
.Output(4, "out_values_values", ".values.values")
.Arg("feature_ids", "feature ids");
class GetMergeSingleMapFeatureTensorsGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
vector<string> input_blob_names{};
vector<string> output_blob_names{};
for (int inputIdx = 0; inputIdx < def_.input_size() / 4; ++inputIdx) {
input_blob_names.push_back(I(inputIdx * 4));
input_blob_names.push_back(I(inputIdx * 4 + 3));
output_blob_names.push_back(GI(inputIdx * 4 + 2));
}
input_blob_names.push_back(GO(4));
return SingleGradientDef(
"MergeSingleMapFeatureTensorsGradient",
"",
input_blob_names,
output_blob_names);
}
};
REGISTER_CPU_OPERATOR(
MergeSingleMapFeatureTensorsGradient,
MergeSingleListOrMapFeatureTensorsGradientOp<CPUContext>);
OPERATOR_SCHEMA(MergeSingleMapFeatureTensorsGradient)
.SetDoc(
"Explode given multi-feature tensors with map features into "
"multiple single-feature tensor." +
doc)
.NumInputs([](int n) { return n >= 3 && n % 2 == 1; })
.NumOutputs([](int n) { return n >= 1; })
.Input(0, "in1_lengths", ".lengths")
.Input(1, "in1_presence", ".presence")
.Input(2, "out_values_values_grad", ".values.values_grad")
.Output(0, "in1_values_grad", ".values_grad");
REGISTER_GRADIENT(
MergeSingleMapFeatureTensors,
GetMergeSingleMapFeatureTensorsGradient);
// ##########################################################
REGISTER_CPU_OPERATOR(
MergeMultiScalarFeatureTensors,
MergeMultiScalarFeatureTensorsOp<CPUContext>);
OPERATOR_SCHEMA(MergeMultiScalarFeatureTensors)
.SetDoc(
"Merge given multi-feature tensors with scalar features into one." +
doc)
.NumInputs([](int n) { return n >= 3 && n % 3 == 0; })
.NumOutputs(3)
.Input(0, "in1_lengths", ".lengths")
.Input(1, "in1_keys", ".keys")
.Input(2, "in1_values", ".values")
.Output(0, "out_lengths", ".lengths")
.Output(1, "out_keys", ".keys")
.Output(2, "out_values", ".values");
class GetMergeMultiScalarFeatureTensorsGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
vector<string> input_blob_names{};
vector<string> output_blob_names{};
for (int inputIdx = 0; inputIdx < def_.input_size() / kNumTensorsPerInput;
++inputIdx) {
input_blob_names.push_back(I(inputIdx * kNumTensorsPerInput));
output_blob_names.push_back(GI(inputIdx * kNumTensorsPerInput + 2));
}
input_blob_names.push_back(GO(2));
return SingleGradientDef(
"MergeMultiScalarFeatureTensorsGradient",
"",
input_blob_names,
output_blob_names);
}
private:
const int kNumTensorsPerInput = 3;
};
REGISTER_CPU_OPERATOR(
MergeMultiScalarFeatureTensorsGradient,
MergeMultiScalarFeatureTensorsGradientOp<CPUContext>);
OPERATOR_SCHEMA(MergeMultiScalarFeatureTensorsGradient)
.SetDoc(
"Explode given multi-feature tensors with scalar features into many." +
doc)
.NumInputs([](int n) { return n >= 2; })
.NumOutputs([](int n) { return n >= 1; })
.Input(0, "in1_lengths", ".lengths")
.Input(1, "out_values_grad", ".values_grad")
.Output(0, "in1_values_grad", ".values_grad");
REGISTER_GRADIENT(
MergeMultiScalarFeatureTensors,
GetMergeMultiScalarFeatureTensorsGradient);
// ##########################################################
REGISTER_CPU_OPERATOR(
MergeMultiListFeatureTensors,
MergeMultiListFeatureTensorsOp<CPUContext>);
REGISTER_CPU_OPERATOR(
MergeMultiListFeatureTensorsGradient,
MergeMultiListOrMapFeatureTensorsGradientOp<CPUContext>);
OPERATOR_SCHEMA(MergeMultiListFeatureTensors)
.SetDoc(
"Merge given multi-feature tensors with list features into one." + doc)
.NumInputs([](int n) { return n >= 4 && n % 4 == 0; })
.NumOutputs(4)
.Input(0, "in1_lengths", ".lengths")
.Input(1, "in1_keys", ".keys")
.Input(2, "in1_values_lengths", ".values.lengths")
.Input(3, "in1_values_values", ".values.values")
.Output(0, "out_lengths", ".lengths")
.Output(1, "out_keys", ".keys")
.Output(2, "out_values_lengths", ".values.lengths")
.Output(3, "out_values_values", ".values.values");
OPERATOR_SCHEMA(MergeMultiListFeatureTensorsGradient)
.SetDoc(
"Explode given multi-feature tensors with list features "
"into many." +
doc)
.NumInputs([](int n) { return n >= 3 && n % 2 == 1; })
.NumOutputs([](int n) { return n >= 1; })
.Input(0, "in1_lengths", ".lengths")
.Input(1, "in1_values_lengths", ".values.lengths")
.Input(2, "out_values_values_grad", ".values.values_grad")
.Output(0, "in1_values_values_grad", ".values.values_grad");
class GetMergeMultiListFeatureTensorsGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
vector<string> input_blob_names{};
vector<string> output_blob_names{};
for (int inputIdx = 0; inputIdx < def_.input_size() / kNumTensorsPerInput;
++inputIdx) {
input_blob_names.push_back(I(inputIdx * kNumTensorsPerInput));
input_blob_names.push_back(I(inputIdx * kNumTensorsPerInput + 2));
output_blob_names.push_back(GI(inputIdx * kNumTensorsPerInput + 3));
}
input_blob_names.push_back(GO(3));
return SingleGradientDef(
"MergeMultiListFeatureTensorsGradient",
"",
input_blob_names,
output_blob_names);
}
private:
const int kNumTensorsPerInput = 4;
};
REGISTER_GRADIENT(
MergeMultiListFeatureTensors,
GetMergeMultiListFeatureTensorsGradient);
// ##########################################################
REGISTER_CPU_OPERATOR(
MergeMultiMapFeatureTensors,
MergeMultiMapFeatureTensorsOp<CPUContext>);
OPERATOR_SCHEMA(MergeMultiMapFeatureTensors)
.SetDoc(
"Merge given multi-feature tensors with map features into one." + doc)
.NumInputs([](int n) { return n >= 5 && n % 5 == 0; })
.NumOutputs(5)
.Input(0, "in1_lengths", ".lengths")
.Input(1, "in1_keys", ".keys")
.Input(2, "in1_values_lengths", ".values.lengths")
.Input(3, "in1_values_keys", ".values.keys")
.Input(4, "in1_values_values", ".values.values")
.Output(0, "out_lengths", ".lengths")
.Output(1, "out_keys", ".keys")
.Output(2, "out_values_lengths", ".values_lengths")
.Output(3, "out_values_keys", ".values.keys")
.Output(4, "out_values_values", ".values.values");
class GetMergeMultiMapFeatureTensorsGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
vector<string> input_blob_names{};
vector<string> output_blob_names{};
for (int inputIdx = 0; inputIdx < def_.input_size() / kNumTensorsPerInput;
++inputIdx) {
input_blob_names.push_back(I(inputIdx * kNumTensorsPerInput));
input_blob_names.push_back(I(inputIdx * kNumTensorsPerInput + 2));
output_blob_names.push_back(GI(inputIdx * kNumTensorsPerInput + 4));
}
input_blob_names.push_back(GO(4));
return SingleGradientDef(
"MergeMultiMapFeatureTensorsGradient",
"",
input_blob_names,
output_blob_names);
}
private:
const int kNumTensorsPerInput = 5;
};
REGISTER_CPU_OPERATOR(
MergeMultiMapFeatureTensorsGradient,
MergeMultiListOrMapFeatureTensorsGradientOp<CPUContext>);
OPERATOR_SCHEMA(MergeMultiMapFeatureTensorsGradient)
.SetDoc(
"Explode given multi-feature tensors with map features "
"into many." +
doc)
.NumInputs([](int n) { return n >= 3 && n % 2 == 1; })
.NumOutputs([](int n) { return n >= 1; })
.Input(0, "in1_lengths", ".lengths")
.Input(1, "in1_values_lengths", ".values.lengths")
.Input(2, "out_values_values_grad", ".values.values_grad")
.Output(0, "in1_values_values_grad", ".values.values_grad");
REGISTER_GRADIENT(
MergeMultiMapFeatureTensors,
GetMergeMultiMapFeatureTensorsGradient);
} // namespace
} // namespace caffe2
|
