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Fix bug in FsaFromTensor for empty FSA; make index select from ragged… #481

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Dec 9, 2020
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Fix bug in FsaFromTensor for empty FSA; make index select from ragged… #481

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4 changes: 4 additions & 0 deletions k2/csrc/fsa.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -238,6 +238,10 @@ Fsa FsaFromArray1(Array1<Arc> &array, bool *error) {
const Arc *arcs_data = array.Data();
ContextPtr &c = array.Context();
int32_t num_arcs = array.Dim();
// We choose to return an Fsa with no states and no arcs. We could also have
// chosen to return an Fsa with 2 states and no arcs.
if (num_arcs == 0)
return Fsa(EmptyRaggedShape(c, 2), Array1<Arc>(c, 0));
*error = false;

// If the FSA has arcs entering the final state, that will
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12 changes: 7 additions & 5 deletions k2/csrc/fsa.h
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Expand Up @@ -200,13 +200,17 @@ std::ostream &operator<<(std::ostream &os, const DenseFsaVec &dfsavec);
*/
Fsa FsaFromTensor(Tensor &t, bool *error);


Fsa FsaFromArray1(Array1<Arc> &arc, bool *error);

/*
Returns a single Tensor that represents the FSA; this is just the vector of
Arc reinterpreted as num_arcs by 4 Tensor of int32_t. It can be converted
back to an equivalent FSA using `FsaFromTensor`. Notice: this is not the
same format as we use to serialize FsaVec.
Arc reinterpreted as a (num_arcs by 4) Tensor of int32_t. It can be converted
back to an equivalent FSA using `FsaFromTensor`. Notice: this is not the same
format as we use to serialize FsaVec. Also the round-trip conversion to
Tensor and back may not preserve the number of states for FSAs that had no
arcs entering the final-state, since we have to guess the number of states in
this case.

It is an error if `fsa.NumAxes() != 2`.
*/
Expand Down Expand Up @@ -266,8 +270,6 @@ Tensor FsaVecToTensor(const FsaVec &fsa_vec);
*/
FsaVec FsaVecFromTensor(Tensor &t, bool *error);

FsaVec FsaVecFromArray1(Array1<Arc> &arc, bool *error); // TODO: implement it

/*
Return one Fsa in an FsaVec. Note, this has to make copies of the
row offsets and strides but can use a sub-array of the arcs array
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25 changes: 19 additions & 6 deletions k2/python/csrc/torch/index_select.cu
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Expand Up @@ -173,7 +173,6 @@ static torch::Tensor SimpleRaggedIndexSelect1D(torch::Tensor src,
int64_t src_stride = src.strides()[0];
int64_t ans_stride = ans.strides()[0];

#if !defined(NDEBUG)
// check if there is at most one non-zero element in src for each sub-list
Ragged<int32_t> non_zero_elems(indexes.shape,
Array1<int32_t>(context, indexes_num_elems));
Expand All @@ -189,15 +188,29 @@ static torch::Tensor SimpleRaggedIndexSelect1D(torch::Tensor src,
Array1<int32_t> counts(context, indexes_dim0);
SumPerSublist(non_zero_elems, 0, &counts);
const int32_t *counts_data = counts.Data();
Array1<int32_t> status(context, 1, 0);
Array1<int32_t> status(context, 1, 0); // 0 -> success; otherwise 1 + row_id
// of bad row in `indexes`
int32_t *status_data = status.Data();
K2_EVAL(
context, counts.Dim(), lambda_check_status, (int32_t i)->void {
if (counts_data[i] > 1) status_data[0] = 1;
if (counts_data[i] > 1) status_data[0] = 1 + i;
});
K2_CHECK_EQ(status[0], 0) << "There must be at most one non-zero "
"element in src for any sub-list in indexes";
#endif
int32_t s = status[0];
if (s != 0) {
Array1<T> indexed_values(context, indexes_num_elems);
T *indexed_values_data = indexed_values.Data();
K2_EVAL(context, indexes_num_elems, lambda_set_values, (int32_t i) -> void {
int32_t src_index = indexes_data[i];
indexed_values_data[i] = src_data[src_index * src_stride];
});
Array1<int32_t> row_splits = indexes.RowSplits(1);

K2_LOG(FATAL) << "There must be at most one non-zero "
"element in src for any sub-list in indexes; sub-list "
<< (s-1) << " has too many elements: "
<< indexed_values.Arange(row_splits[s-1],
row_splits[s]);
}

K2_EVAL(
context, indexes_num_elems, lambda_set_ans_data, (int32_t i)->void {
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3 changes: 2 additions & 1 deletion k2/python/k2/fsa_algo.py
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Expand Up @@ -299,7 +299,8 @@ def determinize(fsa: Fsa) -> Fsa:
'''Determinize the input Fsa.

Caution:
It only works on for CPU and doesn't support autograd.
It only works on for CPU and doesn't support autograd (for now;
this is not a fundamental limitation).

Args:
fsa:
Expand Down