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// See docs in ../ops/array_ops.cc.
#define EIGEN_USE_THREADS
#include <limits>
#include "tensorflow/core/common_runtime/device.h"
#include "tensorflow/core/framework/register_types.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/lib/gtl/edit_distance.h"
#include "tensorflow/core/public/status.h"
#include "tensorflow/core/util/sparse/sparse_tensor.h"
#include "tensorflow/core/platform/logging.h"
namespace tensorflow {
namespace {
Status ValidateShapes(OpKernelContext* ctx, const Tensor& hypothesis_indices,
const Tensor& hypothesis_values,
const Tensor& hypothesis_shape,
const Tensor& truth_indices, const Tensor& truth_values,
const Tensor& truth_shape) {
if (!TensorShapeUtils::IsMatrix(hypothesis_indices.shape()))
return errors::InvalidArgument(
"hypothesis_indices should be a matrix, but got shape: ",
hypothesis_indices.shape().DebugString());
if (!TensorShapeUtils::IsMatrix(truth_indices.shape()))
return errors::InvalidArgument(
"truth_indices should be a matrix, but got shape: ",
truth_indices.shape().DebugString());
if (!TensorShapeUtils::IsVector(hypothesis_values.shape()))
return errors::InvalidArgument(
"hypothesis_values should be a vector, but got shape: ",
hypothesis_values.shape().DebugString());
if (!TensorShapeUtils::IsVector(truth_values.shape()))
return errors::InvalidArgument(
"truth_values should be a vector, but got shape: ",
truth_values.shape().DebugString());
if (!TensorShapeUtils::IsVector(hypothesis_shape.shape()))
return errors::InvalidArgument(
"hypothesis_shape should be a vector, but got shape: ",
hypothesis_shape.shape().DebugString());
if (!TensorShapeUtils::IsVector(truth_shape.shape()))
return errors::InvalidArgument(
"truth_shape should be a vector, but got shape: ",
truth_shape.shape().DebugString());
if (hypothesis_shape.NumElements() != hypothesis_indices.dim_size(1))
return errors::InvalidArgument(
"Expected hypothesis_shape.NumElements == "
"#cols(hypothesis_indices), their shapes are: ",
hypothesis_shape.shape().DebugString(), " and ",
hypothesis_indices.shape().DebugString());
if (truth_shape.NumElements() < 2)
return errors::InvalidArgument(
"Input SparseTensors must have rank at least 2, but truth_shape "
"rank is: ",
truth_shape.NumElements());
if (truth_shape.NumElements() != truth_indices.dim_size(1))
return errors::InvalidArgument(
"Expected truth_shape.NumElements == "
"#cols(truth_indices), their shapes are: ",
truth_shape.shape().DebugString(), " and ",
truth_indices.shape().DebugString());
if (truth_shape.NumElements() != hypothesis_shape.NumElements())
return errors::InvalidArgument(
"Expected truth and hypothesis to have matching ranks, but "
"their shapes are: ",
truth_shape.shape().DebugString(), " and ",
hypothesis_shape.shape().DebugString());
return Status::OK();
}
} // namespace
template <typename T>
class EditDistanceOp : public OpKernel {
public:
explicit EditDistanceOp(OpKernelConstruction* ctx) : OpKernel(ctx) {
OP_REQUIRES_OK(ctx, ctx->GetAttr("normalize", &normalize_));
}
void Compute(OpKernelContext* ctx) override {
const Tensor* hypothesis_indices;
const Tensor* hypothesis_values;
const Tensor* hypothesis_shape;
const Tensor* truth_indices;
const Tensor* truth_values;
const Tensor* truth_shape;
OP_REQUIRES_OK(ctx, ctx->input("hypothesis_indices", &hypothesis_indices));
OP_REQUIRES_OK(ctx, ctx->input("hypothesis_values", &hypothesis_values));
OP_REQUIRES_OK(ctx, ctx->input("hypothesis_shape", &hypothesis_shape));
OP_REQUIRES_OK(ctx, ctx->input("truth_indices", &truth_indices));
OP_REQUIRES_OK(ctx, ctx->input("truth_values", &truth_values));
OP_REQUIRES_OK(ctx, ctx->input("truth_shape", &truth_shape));
OP_REQUIRES_OK(
ctx, ValidateShapes(ctx, *hypothesis_indices, *hypothesis_values,
*hypothesis_shape, *truth_indices, *truth_values,
*truth_shape));
TensorShape hypothesis_st_shape = TensorShapeUtils::MakeShape(
hypothesis_shape->vec<int64>().data(), hypothesis_shape->NumElements());
TensorShape truth_st_shape = TensorShapeUtils::MakeShape(
truth_shape->vec<int64>().data(), truth_shape->NumElements());
// Assume indices are sorted in row-major order.
std::vector<int64> sorted_order(truth_st_shape.dims());
std::iota(sorted_order.begin(), sorted_order.end(), 0);
sparse::SparseTensor hypothesis(*hypothesis_indices, *hypothesis_values,
hypothesis_st_shape, sorted_order);
sparse::SparseTensor truth(*truth_indices, *truth_values, truth_st_shape,
sorted_order);
// Group dims 0, 1, ..., RANK - 1. The very last dim is assumed
// to store the variable length sequences.
std::vector<int64> group_dims(truth_st_shape.dims() - 1);
std::iota(group_dims.begin(), group_dims.end(), 0);
TensorShape output_shape;
for (int d = 0; d < group_dims.size(); ++d) {
output_shape.AddDim(std::max(hypothesis_st_shape.dim_size(d),
truth_st_shape.dim_size(d)));
}
Tensor* output = nullptr;
OP_REQUIRES_OK(ctx, ctx->allocate_output("output", output_shape, &output));
auto output_t = output->flat<float>();
output_t.setZero();
std::vector<int64> output_strides(output_shape.dims());
output_strides[output_shape.dims() - 1] = 1;
for (int d = output_shape.dims() - 2; d >= 0; --d) {
output_strides[d] = output_strides[d + 1] * output_shape.dim_size(d + 1);
}
auto hypothesis_grouper = hypothesis.group(group_dims);
auto truth_grouper = truth.group(group_dims);
auto hypothesis_iter = hypothesis_grouper.begin();
auto truth_iter = truth_grouper.begin();
auto cmp = std::equal_to<T>();
while (hypothesis_iter != hypothesis_grouper.end() &&
truth_iter != truth_grouper.end()) {
sparse::Group truth_i = *truth_iter;
sparse::Group hypothesis_j = *hypothesis_iter;
std::vector<int64> g_truth = truth_i.group();
std::vector<int64> g_hypothesis = hypothesis_j.group();
auto truth_seq = truth_i.values<T>();
auto hypothesis_seq = hypothesis_j.values<T>();
if (g_truth == g_hypothesis) {
auto loc = std::inner_product(g_truth.begin(), g_truth.end(),
output_strides.begin(), 0);
output_t(loc) =
gtl::LevenshteinDistance<T>(truth_seq, hypothesis_seq, cmp);
if (normalize_) output_t(loc) /= truth_seq.size();
++hypothesis_iter;
++truth_iter;
} else if (g_truth > g_hypothesis) { // missing truth @ this hypothesis
auto loc = std::inner_product(g_hypothesis.begin(), g_hypothesis.end(),
output_strides.begin(), 0);
output_t(loc) = hypothesis_seq.size();
if (normalize_) output_t(loc) /= 0.0;
++hypothesis_iter;
} else { // missing hypothesis @ this truth
auto loc = std::inner_product(g_truth.begin(), g_truth.end(),
output_strides.begin(), 0);
output_t(loc) = (normalize_) ? 1.0 : truth_seq.size();
++truth_iter;
}
}
while (hypothesis_iter != hypothesis_grouper.end()) { // missing truths
sparse::Group hypothesis_j = *hypothesis_iter;
std::vector<int64> g_hypothesis = hypothesis_j.group();
auto hypothesis_seq = hypothesis_j.values<T>();
auto loc = std::inner_product(g_hypothesis.begin(), g_hypothesis.end(),
output_strides.begin(), 0);
output_t(loc) = hypothesis_seq.size();
if (normalize_) output_t(loc) /= 0.0;
++hypothesis_iter;
}
while (truth_iter != truth_grouper.end()) { // missing hypotheses
sparse::Group truth_i = *truth_iter;
std::vector<int64> g_truth = truth_i.group();
auto truth_seq = truth_i.values<T>();
auto loc = std::inner_product(g_truth.begin(), g_truth.end(),
output_strides.begin(), 0);
output_t(loc) = (normalize_) ? 1.0 : truth_seq.size();
++truth_iter;
}
}
private:
bool normalize_;
TF_DISALLOW_COPY_AND_ASSIGN(EditDistanceOp);
};
#define REGISTER_CPU_KERNEL(T) \
REGISTER_KERNEL_BUILDER( \
Name("EditDistance").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
EditDistanceOp<T>);
TF_CALL_ALL_TYPES(REGISTER_CPU_KERNEL);
#undef REGISTER_CPU_KERNEL
} // end namespace tensorflow
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