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segment_reduction_op.cc
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segment_reduction_op.cc
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#include "caffe2/operators/segment_reduction_op.h"
namespace caffe2 {
OpSchema::Cost CostInferenceForSparseLengths(
const OperatorDef& def,
const vector<TensorShape>& inputs,
bool use_weight) {
int min_num_of_inputs = 3 + use_weight;
CAFFE_ENFORCE_GE(
inputs.size(),
min_num_of_inputs,
def.type() + " requires at least " + c10::to_string(min_num_of_inputs));
const TensorShape data = inputs[0];
const TensorShape indices = inputs[1 + use_weight];
const TensorShape lengths = inputs[2 + use_weight];
OpSchema::Cost c;
CAFFE_ENFORCE_GT(data.dims_size(), 0, "data requires at least 1 dimension");
uint64_t N = data.dims(0);
if (N == 0) {
return c;
}
uint64_t D = nElemFromDim(data, 1);
CAFFE_ENFORCE_GT(
lengths.dims_size(), 0, "lengths requires at least 1 dimension");
uint64_t M = lengths.dims(0);
uint64_t indices_size = nElemFromDim(indices);
c.flops = indices_size * D;
c.bytes_read = indices_size *
(D * sizeof(data.data_type()) + sizeof(indices.data_type())) +
M * sizeof(lengths.data_type());
c.params_bytes = N * D * sizeof(data.data_type());
if (use_weight) {
const TensorShape weights = inputs[1];
c.flops += indices_size * D;
c.bytes_read += indices_size * sizeof(weights.data_type());
}
return c;
}
// registering 5 input gradient with main output
// gradient of SparseLengthsWeightedSum
OPERATOR_SCHEMA(SparseLengthsIndicesInGradientWeightedSumWithMainInputGradient)
.NumInputs(5)
.NumOutputs(2);
REGISTER_CPU_OPERATOR(
SparseLengthsIndicesInGradientWeightedSumWithMainInputGradient,
AbstractLengthsWithMainInputGradientOp<
float,
float,
int,
CPUContext,
WeightedSumReducerDef::template ReducerGradient<float, CPUContext>,
true /*SparseFused*/,
true /*GradientNeedIndices*/>);
// registering 4 input version
OPERATOR_SCHEMA(SparseLengthsIndicesInGradientWeightedSumGradient)
.NumInputs(4)
.NumOutputs(1);
REGISTER_CPU_OPERATOR(
SparseLengthsIndicesInGradientWeightedSumGradient,
AbstractLengthsGradientOp<
float,
int,
CPUContext,
WeightedSumReducerDef::template ReducerGradient<float, CPUContext>,
true /*GradientNeedIndices*/>);
// registering 3 input version
// gradient of SparseLengthsSum
OPERATOR_SCHEMA(SparseLengthsIndicesInGradientSumGradient)
.NumInputs(3)
.NumOutputs(1);
REGISTER_CPU_OPERATOR(
SparseLengthsIndicesInGradientSumGradient,
AbstractLengthsGradientOp<
float,
int,
CPUContext,
SumReducerDef::template ReducerGradient<float, CPUContext>,
true /*GradientNeedIndices*/>);
// gradient of LengthsSum
OPERATOR_SCHEMA(LengthsIndicesInGradientSumGradient).NumInputs(3).NumOutputs(1);
REGISTER_CPU_OPERATOR(
LengthsIndicesInGradientSumGradient,
AbstractLengthsGradientOp<
float,
int,
CPUContext,
SumReducerDef::template ReducerGradient<float, CPUContext>,
true /*GradientNeedIndices*/>);
// registering 3 input version
// gradient of SparseLengthsMean
OPERATOR_SCHEMA(SparseLengthsIndicesInGradientMeanGradient)
.NumInputs(3)
.NumOutputs(1);
REGISTER_CPU_OPERATOR(
SparseLengthsIndicesInGradientMeanGradient,
AbstractLengthsGradientOp<
float,
int,
CPUContext,
MeanReducerDef::template ReducerGradient<float, CPUContext>,
true /*GradientNeedIndices*/>);
// gradient of LengthsMean
OPERATOR_SCHEMA(LengthsIndicesInGradientMeanGradient)
.NumInputs(3)
.NumOutputs(1);
REGISTER_CPU_OPERATOR(
LengthsIndicesInGradientMeanGradient,
AbstractLengthsGradientOp<
float,
int,
CPUContext,
MeanReducerDef::template ReducerGradient<float, CPUContext>,
true /*GradientNeedIndices*/>);
namespace {
static const char* kLengthsMaxExtra = R"DOC(
The *LengthsMax* op takes two inputs *DATA* and *LENGTHS*, and produces a single output *OUTPUT*. The op finds the maximum value in each of the segments of *DATA*, where segments are defined by their lengths.
For example, if $DATA = [2,4,3,1,2,10]$ and $LENGTHS = [2,3,1]$ then $OUTPUT = [max([2,4]), max([3,1,2]), max([10])] = [4,3,10]$.
Github Link:
- https://github.com/caffe2/caffe2/blob/master/caffe2/operators/segment_reduction_op.cc
<details>
<summary> <b>Example</b> </summary>
**Code**
```
workspace.ResetWorkspace()
op = core.CreateOperator(
"LengthsMax",
["DATA", "LENGTHS"],
["OUTPUT"],
)
workspace.FeedBlob("DATA", np.array([2,4,3,1,2,10]).astype(np.float32))
print("DATA:\n", workspace.FetchBlob("DATA"))
workspace.FeedBlob("LENGTHS", np.array([2,3,1]).astype(np.int32))
print("LENGTHS:\n", workspace.FetchBlob("LENGTHS"))
workspace.RunOperatorOnce(op)
print("OUTPUT: \n", workspace.FetchBlob("OUTPUT"))
```
**Result**
```
DATA:
[ 2. 4. 3. 1. 2. 10.]
LENGTHS:
[2 3 1]
OUTPUT:
[ 4. 3. 10.]
```
</details>
)DOC";
static const char* kLengthsMeanExtra = R"DOC(
The *LengthsMean* op takes two inputs *DATA* and *LENGTHS*, and produces a single output *OUTPUT*. The op finds the mean value in each of the segments of *DATA*, where segments are defined by their lengths.
For example, if $DATA = [2,4,3,1,2,10]$ and $LENGTHS = [2,3,1]$ then $OUTPUT = [mean([2,4]), mean([3,1,2]), mean([10])] = [3,2,10]$.
Github Link:
- https://github.com/caffe2/caffe2/blob/master/caffe2/operators/segment_reduction_op.cc
<details>
<summary> <b>Example</b> </summary>
**Code**
```
workspace.ResetWorkspace()
op = core.CreateOperator(
"LengthsMean",
["DATA", "LENGTHS"],
["OUTPUT"],
)
workspace.FeedBlob("DATA", np.array([2,4,3,1,2,10]).astype(np.float32))
print("DATA:\n", workspace.FetchBlob("DATA"))
workspace.FeedBlob("LENGTHS", np.array([2,3,1]).astype(np.int32))
print("LENGTHS:\n", workspace.FetchBlob("LENGTHS"))
workspace.RunOperatorOnce(op)
print("OUTPUT: \n", workspace.FetchBlob("OUTPUT"))
```
**Result**
```
DATA:
[ 2. 4. 3. 1. 2. 10.]
LENGTHS:
[2 3 1]
OUTPUT:
[ 3. 2. 10.]
```
</details>
)DOC";
static const char* kLengthsSumExtra = R"DOC(
The *LengthsSum* op takes two inputs *DATA* and *LENGTHS*, and produces a single output *OUTPUT*. The op finds the sum in each of the segments of *DATA*, where segments are defined by their lengths.
For example, if $DATA = [2,4,3,1,2,10]$ and $LENGTHS = [2,3,1]$ then $OUTPUT = [sum([2,4]), sum([3,1,2]), sum([10])] = [6,6,10]$.
Github Link:
- https://github.com/caffe2/caffe2/blob/master/caffe2/operators/segment_reduction_op.cc
<details>
<summary> <b>Example</b> </summary>
**Code**
```
workspace.ResetWorkspace()
op = core.CreateOperator(
"LengthsSum",
["DATA", "LENGTHS"],
["OUTPUT"],
)
workspace.FeedBlob("DATA", np.array([2,4,3,1,2,10]).astype(np.float32))
print("DATA:\n", workspace.FetchBlob("DATA"))
workspace.FeedBlob("LENGTHS", np.array([2,3,1]).astype(np.int32))
print("LENGTHS:\n", workspace.FetchBlob("LENGTHS"))
workspace.RunOperatorOnce(op)
print("OUTPUT: \n", workspace.FetchBlob("OUTPUT"))
```
**Result**
```
DATA:
[ 2. 4. 3. 1. 2. 10.]
LENGTHS:
[2 3 1]
OUTPUT:
[ 6. 6. 10.]
```
</details>
)DOC";
static const char* kLengthsWeightedSumExtra = R"DOC(
The *LengthsWeightedSum* op takes three inputs *DATA*, *LENGTHS*, and *SCALARS*, and produces a single output *OUTPUT*. The op finds the weighted sum in each of the segments of *DATA*, where segments are defined by their lengths. Before calculating the sums, the input *DATA* is weighted by the contents of *SCALARS*.
For example, if $DATA = [2,4,3,1,2,10]$, $SCALARS = [8, 2, 1, 4, 1, 0.6]$, and $LENGTHS = [2,3,1]$, then $OUTPUT = [sum([8*2,2*4]), sum([1*3,4*1,1*2]), sum([0.6*10])] = [24,9,6]$.
Github Link:
- https://github.com/caffe2/caffe2/blob/master/caffe2/operators/segment_reduction_op.cc
<details>
<summary> <b>Example</b> </summary>
**Code**
```
workspace.ResetWorkspace()
op = core.CreateOperator(
"LengthsWeightedSum",
["DATA", "SCALARS","LENGTHS"],
["OUTPUT"],
)
workspace.FeedBlob("DATA", np.array([2,4,3,1,2,10]).astype(np.float32))
print("DATA:\n", workspace.FetchBlob("DATA"))
workspace.FeedBlob("SCALARS", np.array([8, 2, 1, 4, 1, 0.6]).astype(np.float32))
print("SCALARS:\n", workspace.FetchBlob("SCALARS"))
workspace.FeedBlob("LENGTHS", np.array([2,3,1]).astype(np.int32))
print("LENGTHS:\n", workspace.FetchBlob("LENGTHS"))
workspace.RunOperatorOnce(op)
print("OUTPUT: \n", workspace.FetchBlob("OUTPUT"))
```
**Result**
```
DATA:
[ 2. 4. 3. 1. 2. 10.]
SCALARS:
[8. 2. 1. 4. 1. 0.6]
LENGTHS:
[2 3 1]
OUTPUT:
[24. 9. 6.]
```
</details>
)DOC";
template <typename Def>
string FormatDoc() {
string doc = Def::doc;
c10::ReplaceAll(doc, "{op}", Def::OpDef::name);
c10::ReplaceAll(doc, "{op_doc}", Def::OpDef::doc);
if (strcmp(Def::OpDef::name, "Max") == 0) {
c10::ReplaceAll(doc, "{extra}", kLengthsMaxExtra);
} else if (strcmp(Def::OpDef::name, "Mean") == 0) {
c10::ReplaceAll(doc, "{extra}", kLengthsMeanExtra);
} else if (strcmp(Def::OpDef::name, "Sum") == 0) {
c10::ReplaceAll(doc, "{extra}", kLengthsSumExtra);
} else if (strcmp(Def::OpDef::name, "WeightedSum") == 0) {
c10::ReplaceAll(doc, "{extra}", kLengthsWeightedSumExtra);
} else {
c10::ReplaceAll(doc, "{extra}", " ");
}
return doc;
}
// Helper function to enforce naming conventions at compile time.
constexpr bool equal(
char const* lhs,
char const* rhs1,
char const* rhs2,
char const* rhs3 = "") {
return (*lhs == 0 && *rhs1 == 0 && *rhs2 == 0 && *rhs3 == 0) ||
(*rhs1 != 0 && *lhs == *rhs1 && equal(lhs + 1, rhs1 + 1, rhs2, rhs3)) ||
(*rhs1 == 0 && *rhs2 != 0 && *lhs == *rhs2 &&
equal(lhs + 1, rhs1, rhs2 + 1, rhs3)) ||
(*rhs1 == 0 && *rhs2 == 0 && *rhs3 != 0 && *lhs == *rhs3 &&
equal(lhs + 1, rhs1, rhs2, rhs3 + 1));
}
// Helper macro when the main op is defined elsewhere, and we only need to
// define the schema, and the gradient op.
// TODO: enable input fillers
#define REGISTER_SEGMENT_DEF_SCHEMA_GRADIENT_ONLY( \
segment_name, gradient_name, ...) \
static_assert( \
equal(#segment_name, __VA_ARGS__::basename, __VA_ARGS__::OpDef::name), \
#segment_name); \
static_assert( \
equal( \
#gradient_name, \
__VA_ARGS__::basename, \
__VA_ARGS__::OpDef::name, \
"Gradient"), \
#gradient_name); \
OPERATOR_SCHEMA(segment_name) \
.NumInputs(__VA_ARGS__::ForwardOp::kNumInputs) \
.NumOutputs(1) \
.DisallowInputFillers() \
.SetDoc(FormatDoc<__VA_ARGS__>()) \
.Output(0, "OUTPUT", "Aggregated tensor") \
.FillUsing(__VA_ARGS__::PopulateSchema); \
REGISTER_CPU_OPERATOR_STR(string(#gradient_name), __VA_ARGS__::BackwardOp); \
OPERATOR_SCHEMA(gradient_name) \
.NumInputs(__VA_ARGS__::BackwardOp::kNumInputs) \
.NumOutputs(1) \
.DisallowInputFillers(); \
REGISTER_GRADIENT_STR(string(#segment_name), __VA_ARGS__::GetGradient)
#define REGISTER_SEGMENT_DEF(segment_name, gradient_name, ...) \
static_assert( \
equal(#segment_name, __VA_ARGS__::basename, __VA_ARGS__::OpDef::name), \
#segment_name); \
REGISTER_CPU_OPERATOR_STR(string(#segment_name), __VA_ARGS__::ForwardOp); \
REGISTER_SEGMENT_DEF_SCHEMA_GRADIENT_ONLY( \
segment_name, gradient_name, __VA_ARGS__)
REGISTER_SEGMENT_DEF(
SortedSegmentRangeSum,
SortedSegmentRangeSumGradient,
AbstractSortedSegmentRangeDef<float, int, CPUContext, SumRangeReducerDef>);
REGISTER_SEGMENT_DEF(
SortedSegmentRangeLogSumExp,
SortedSegmentRangeLogSumExpGradient,
AbstractSortedSegmentRangeDef<
float,
int,
CPUContext,
LogSumExpRangeReducerDef>);
REGISTER_SEGMENT_DEF(
SortedSegmentRangeLogMeanExp,
SortedSegmentRangeLogMeanExpGradient,
AbstractSortedSegmentRangeDef<
float,
int,
CPUContext,
LogMeanExpRangeReducerDef>);
REGISTER_SEGMENT_DEF(
SortedSegmentRangeMean,
SortedSegmentRangeMeanGradient,
AbstractSortedSegmentRangeDef<float, int, CPUContext, MeanRangeReducerDef>);
REGISTER_SEGMENT_DEF(
SortedSegmentRangeMax,
SortedSegmentRangeMaxGradient,
AbstractSortedSegmentRangeDef<float, int, CPUContext, MaxRangeReducerDef>);
REGISTER_SEGMENT_DEF(
SortedSegmentSum,
SortedSegmentSumGradient,
AbstractSortedSegmentDef<float, int, CPUContext, SumReducerDef>);
REGISTER_SEGMENT_DEF(
SparseSortedSegmentSum,
SparseSortedSegmentSumGradient,
AbstractSparseSortedSegmentDef<float, int, CPUContext, SumReducerDef>);
REGISTER_SEGMENT_DEF(
UnsortedSegmentSum,
UnsortedSegmentSumGradient,
AbstractUnsortedSegmentDef<float, int, CPUContext, SumReducerDef>);
REGISTER_SEGMENT_DEF(
SparseUnsortedSegmentSum,
SparseUnsortedSegmentSumGradient,
AbstractSparseUnsortedSegmentDef<float, int, CPUContext, SumReducerDef>);
REGISTER_SEGMENT_DEF(
LengthsSum,
LengthsSumGradient,
AbstractLengthsDef<float, int, CPUContext, SumReducerDef, true>);
REGISTER_SEGMENT_DEF(
SortedSegmentMean,
SortedSegmentMeanGradient,
AbstractSortedSegmentDef<float, int, CPUContext, MeanReducerDef>);
REGISTER_SEGMENT_DEF(
SparseSortedSegmentMean,
SparseSortedSegmentMeanGradient,
AbstractSparseSortedSegmentDef<float, int, CPUContext, MeanReducerDef>);
REGISTER_SEGMENT_DEF(
UnsortedSegmentMean,
UnsortedSegmentMeanGradient,
AbstractUnsortedSegmentDef<float, int, CPUContext, MeanReducerDef>);
REGISTER_SEGMENT_DEF(
SparseUnsortedSegmentMean,
SparseUnsortedSegmentMeanGradient,
AbstractSparseUnsortedSegmentDef<float, int, CPUContext, MeanReducerDef>);
REGISTER_SEGMENT_DEF(
LengthsMean,
LengthsMeanGradient,
AbstractLengthsDef<float, int, CPUContext, MeanReducerDef, true>);
REGISTER_SEGMENT_DEF(
ReduceFrontWeightedSum,
ReduceFrontWeightedSumGradient,
AbstractReduceFrontDef<float, CPUContext, WeightedSumReducerDef>);
REGISTER_SEGMENT_DEF(
SortedSegmentWeightedSum,
SortedSegmentWeightedSumGradient,
AbstractSortedSegmentDef<float, int, CPUContext, WeightedSumReducerDef>);
REGISTER_SEGMENT_DEF(
SparseSortedSegmentWeightedSum,
SparseSortedSegmentWeightedSumGradient,
AbstractSparseSortedSegmentDef<
float,
int,
CPUContext,
WeightedSumReducerDef>);
REGISTER_SEGMENT_DEF(
UnsortedSegmentWeightedSum,
UnsortedSegmentWeightedSumGradient,
AbstractUnsortedSegmentDef<float, int, CPUContext, WeightedSumReducerDef>);
REGISTER_SEGMENT_DEF(
SparseUnsortedSegmentWeightedSum,
SparseUnsortedSegmentWeightedSumGradient,
AbstractSparseUnsortedSegmentDef<
float,
int,
CPUContext,
WeightedSumReducerDef>);
REGISTER_SEGMENT_DEF(
LengthsWeightedSum,
LengthsWeightedSumGradient,
AbstractLengthsDef<float, int, CPUContext, WeightedSumReducerDef, false>);
// Auxiliary output gradients are currently implemented only for Lengths version
#define REGISTER_GRADIENT_WITH_MAIN_INPUT(gradient_name, ...) \
static_assert( \
equal( \
#gradient_name, \
__VA_ARGS__::basename, \
__VA_ARGS__::OpDef::name, \
"WithMainInputGradient"), \
#gradient_name); \
REGISTER_CPU_OPERATOR_STR( \
string(#gradient_name), __VA_ARGS__::WithMainInputBackwardOp); \
OPERATOR_SCHEMA(gradient_name) \
.NumInputs(__VA_ARGS__::WithMainInputBackwardOp::kNumInputs) \
.NumOutputs(1, INT_MAX)
REGISTER_GRADIENT_WITH_MAIN_INPUT(
LengthsWeightedSumWithMainInputGradient,
AbstractLengthsDef<float, int, CPUContext, WeightedSumReducerDef>);
REGISTER_GRADIENT_WITH_MAIN_INPUT(
SparseLengthsWeightedSumWithMainInputGradient,
AbstractSparseLengthsDef<float, int, CPUContext, WeightedSumReducerDef>);
} // namespace
#define REGISTER_GRADIENT_WITH_MAIN_INPUT_AND_FORWARD_OUTPUT( \
gradient_name, ...) \
static_assert( \
equal( \
#gradient_name, \
__VA_ARGS__::basename, \
__VA_ARGS__::OpDef::name, \
"WithMainInputAndForwardOutputGradient"), \
#gradient_name); \
REGISTER_CPU_OPERATOR_STR( \
string(#gradient_name), \
__VA_ARGS__::WithMainInputAndForwardOutputBackwardOp); \
OPERATOR_SCHEMA(gradient_name) \
.NumInputs( \
__VA_ARGS__::WithMainInputAndForwardOutputBackwardOp::kNumInputs) \
.NumOutputs(1, INT_MAX)
#define REGISTER_SEGMENT_DEF_MAIN_INPUT_AND_FORWARD_OUTPUT_GRADIENT( \
segment_name, gradient_name, ...) \
static_assert( \
equal(#segment_name, __VA_ARGS__::basename, __VA_ARGS__::OpDef::name), \
#segment_name); \
OPERATOR_SCHEMA(segment_name) \
.NumInputs(__VA_ARGS__::ForwardOp::kNumInputs) \
.NumOutputs(1) \
.SetDoc(FormatDoc<__VA_ARGS__>()) \
.Output(0, "OUTPUT", "Aggregated tensor") \
.FillUsing(__VA_ARGS__::PopulateSchema); \
REGISTER_GRADIENT_WITH_MAIN_INPUT_AND_FORWARD_OUTPUT( \
gradient_name, __VA_ARGS__); \
REGISTER_GRADIENT_STR(string(#segment_name), __VA_ARGS__::GetGradient)
// This implements and registers a length op with a gradient which requires
// the main input as well as the output of the forward output.
#define REGISTER_LENGTHS_OPS_MAIN_INPUT_AND_FORWARD_OUTPUT_GRADIENT( \
segment_name, gradient_name, ...) \
static_assert( \
equal(#segment_name, __VA_ARGS__::basename, __VA_ARGS__::OpDef::name), \
#segment_name); \
REGISTER_CPU_OPERATOR_STR(string(#segment_name), __VA_ARGS__::ForwardOp); \
REGISTER_SEGMENT_DEF_MAIN_INPUT_AND_FORWARD_OUTPUT_GRADIENT( \
segment_name, gradient_name, __VA_ARGS__)
REGISTER_LENGTHS_OPS_MAIN_INPUT_AND_FORWARD_OUTPUT_GRADIENT(
LengthsMax,
LengthsMaxWithMainInputAndForwardOutputGradient,
AbstractLengthsDef<float, int, CPUContext, MaxReducerDef>);
} // namespace caffe2
// Macro doesn't like comma
using LengthsSumCPUOp = caffe2::AbstractLengthsDef<
float,
int,
caffe2::CPUContext,
caffe2::SumReducerDef,
true>::ForwardOp;
using LengthsMeanCPUOp = caffe2::AbstractLengthsDef<
float,
int,
caffe2::CPUContext,
caffe2::MeanReducerDef,
true>::ForwardOp;
using LengthsMaxCPUOp = caffe2::AbstractLengthsDef<
float,
int,
caffe2::CPUContext,
caffe2::MaxReducerDef,
true>::ForwardOp;
C10_EXPORT_CAFFE2_OP_TO_C10_CPU(
LengthsSum,
"_caffe2::LengthsSum(Tensor data, Tensor lengths) -> Tensor",
LengthsSumCPUOp);
C10_EXPORT_CAFFE2_OP_TO_C10_CPU(
LengthsMean,
"_caffe2::LengthsMean(Tensor data, Tensor lengths) -> Tensor",
LengthsMeanCPUOp);
C10_EXPORT_CAFFE2_OP_TO_C10_CPU(
LengthsMax,
"_caffe2::LengthsMax(Tensor data, Tensor lengths) -> Tensor",
LengthsMaxCPUOp);