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#include <algorithm>
#include <sstream>
#include <unordered_map>
#include <vector>
#include "caffe2/core/blob_serialization.h"
#include "caffe2/core/operator.h"
#include "caffe2/core/tensor.h"
#include "caffe2/utils/eigen_utils.h"
#include "caffe2/utils/math.h"
namespace caffe2 {
namespace {
void RowwiseMaxAndArg(
const float* mat,
int32_t N,
int32_t D,
float* rowMax,
int32_t* argMax) {
auto eigenMat = ConstEigenMatrixMap<float>(mat, D, N);
for (auto i = 0; i < D; i++) {
// eigenMat.row(i) is equivalent to column i in mat
rowMax[i] = eigenMat.row(i).maxCoeff(argMax + i);
}
}
void ColwiseMaxAndArg(
const float* mat,
int32_t N,
int32_t D,
float* colMax,
int32_t* argMax) {
auto eigenMat = ConstEigenMatrixMap<float>(mat, D, N);
for (auto i = 0; i < N; i++) {
// eigenMat.col(i) is equivalent to row i in mat
colMax[i] = eigenMat.col(i).maxCoeff(argMax + i);
}
}
class ViterbiPathOp : public Operator<CPUContext> {
public:
template <class... Args>
explicit ViterbiPathOp(Args&&... args)
: Operator(std::forward<Args>(args)...) {}
void GatherRow(
const TensorCPU& data,
int32_t rowIndex,
int32_t block_size,
int32_t block_bytesize,
TensorCPU* outRow) {
CAFFE_ENFORCE(
0 <= rowIndex && rowIndex < data.size(0),
"rowIndex is out of DATA bounds");
auto out = static_cast<char*>(outRow->raw_mutable_data(data.dtype()));
auto src_base = static_cast<const char*>(data.raw_data());
auto src = src_base + rowIndex * block_bytesize;
context_.CopyItemsSameDevice(data.dtype(), block_size, src, out);
}
void
AddColToMat(const TensorCPU& mat, const TensorCPU& col, TensorCPU* result) {
float* resultData = result->template mutable_data<float>();
const float* colData = col.template data<float>();
// Initialize the columns of the result to be = the input col
for (auto i = 0; i < result->dim32(1); i++) {
for (auto j = 0; j < result->dim32(0); j++) {
resultData[i * result->dim32(0) + j] = colData[i];
}
}
// Element-wise add of the result and the input matrix
math::Add<float, CPUContext>(
mat.numel(),
resultData,
mat.template data<float>(),
resultData,
&context_);
}
bool RunOnDevice() override {
auto& predictions = Input(0);
auto& transitions = Input(1);
CAFFE_ENFORCE(
predictions.dim() == 2 && transitions.dim() == 2,
"Predictions and transitions hould 2D matrices");
CAFFE_ENFORCE(
predictions.size(1) == transitions.size(0),
"Predictions and transitions dimensions not matching");
auto seqLen = predictions.dim32(0);
auto* viterbiPath = Output(0, {seqLen}, at::dtype<int32_t>());
auto block_size = predictions.numel() / predictions.size(0);
auto block_bytesize =
predictions.size_from_dim(1) * predictions.dtype().itemsize();
Tensor backpointers(CPU);
backpointers.ResizeLike(predictions);
Tensor trellis(std::vector<int64_t>{block_size}, CPU);
Tensor dpMat(CPU);
dpMat.ResizeLike(transitions);
Tensor dpMax(std::vector<int64_t>{block_size}, CPU);
GatherRow(predictions, 0, block_size, block_bytesize, &trellis);
for (auto i = 1; i < seqLen; i++) {
AddColToMat(transitions, trellis, &dpMat);
RowwiseMaxAndArg(
dpMat.template data<float>(),
dpMat.size(0),
dpMat.size(1),
dpMax.template mutable_data<float>(),
backpointers.template mutable_data<int32_t>() + (i * block_size));
GatherRow(predictions, i, block_size, block_bytesize, &trellis);
math::Add<float, CPUContext>(
trellis.numel(),
trellis.template data<float>(),
dpMax.template data<float>(),
trellis.template mutable_data<float>(),
&context_);
}
Tensor tMax(std::vector<int64_t>{1}, CPU);
Tensor tArgMax(std::vector<int64_t>{1}, CPU);
ColwiseMaxAndArg(
trellis.template data<float>(),
1,
trellis.numel(),
tMax.template mutable_data<float>(),
tArgMax.template mutable_data<int32_t>());
std::vector<int32_t> viterbiVec;
viterbiVec.push_back(tArgMax.template data<int32_t>()[0]);
Tensor bpEntry(std::vector<int64_t>{block_size}, CPU);
block_bytesize =
backpointers.size_from_dim(1) * backpointers.dtype().itemsize();
for (auto i = seqLen - 1; i > 0; i--) {
GatherRow(backpointers, i, block_size, block_bytesize, &bpEntry);
viterbiVec.push_back(bpEntry.template data<int32_t>()[viterbiVec.back()]);
}
std::reverse_copy(
viterbiVec.begin(),
viterbiVec.end(),
viterbiPath->template mutable_data<int32_t>());
return true;
}
};
class SwapBestPathOp : public Operator<CPUContext> {
public:
template <class... Args>
explicit SwapBestPathOp(Args&&... args)
: Operator(std::forward<Args>(args)...) {}
bool RunOnDevice() override {
auto& data = Input(0);
auto& newBestIdicies = Input(1);
CAFFE_ENFORCE(
data.dim() == 2 && newBestIdicies.dim() == 1,
"predictions should be a 2D matrix and bestPath should be 1D vector");
CAFFE_ENFORCE(
data.size(0) == newBestIdicies.size(0),
"predictions and bestPath dimensions not matching");
auto* updatedData = Output(0, data.sizes(), at::dtype<float>());
float* outData = updatedData->template mutable_data<float>();
context_.CopyItemsSameDevice(
data.dtype(), data.numel(), data.template data<float>(), outData);
Tensor bestScores(CPU);
bestScores.ResizeLike(newBestIdicies);
Tensor oldBestIndices(CPU);
oldBestIndices.ResizeLike(newBestIdicies);
ColwiseMaxAndArg(
data.template data<float>(),
data.size(0),
data.size(1),
bestScores.template mutable_data<float>(),
oldBestIndices.template mutable_data<int32_t>());
auto block_size = data.numel() / data.size(0);
const int32_t* oldBestIdx = oldBestIndices.template data<int32_t>();
const int32_t* newIdx = newBestIdicies.template data<int32_t>();
for (auto i = 0; i < data.dim32(0); i++) {
std::swap(
outData[i * block_size + newIdx[i]],
outData[i * block_size + oldBestIdx[i]]);
}
return true;
}
};
REGISTER_CPU_OPERATOR(ViterbiPath, ViterbiPathOp);
OPERATOR_SCHEMA(ViterbiPath)
.NumInputs(2)
.NumOutputs(1)
.SetDoc(R"DOC(
Given a predictions matrix and a transitions matrix, get the path with the best
score
)DOC")
.Input(0, "predictions", "N*D predictions matrix")
.Input(1, "transitions", "D*D transitions matrix")
.Output(0, "viterbi_path", "N*1 vector holds the best path indices");
NO_GRADIENT(ViterbiPath);
REGISTER_CPU_OPERATOR(SwapBestPath, SwapBestPathOp);
OPERATOR_SCHEMA(SwapBestPath)
.NumInputs(2)
.NumOutputs(1)
.SetDoc(R"DOC(
Given a sequence of indices and a matrix, enforce that these indices have the
best columnwise scores
score
)DOC")
.Input(0, "predictions", "N*D predictions matrix")
.Input(1, "bestPath", "N*1 vector holds the best path indices ")
.Output(0, "new_predictions", "N*D updated predictions matrix");
NO_GRADIENT(SwapBestPath);
} // namespace
} // namespace caffe2
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