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PiperOrigin-RevId: 289974538
Change-Id: Ie67bf5810f8c529916a302100cc94b4883252c1b
This commit is contained in:
A. Unique TensorFlower 2020-01-15 17:50:24 -08:00 committed by TensorFlower Gardener
parent 8ff1179b74
commit fc7e43de1f
2 changed files with 0 additions and 85 deletions
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83
tensorflow/core/util/sparse/sparse_tensor.cc
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@ -108,83 +108,6 @@ SparseTensor::SparseTensor(Tensor ix, Tensor vals, const VarDimArray shape,
DCHECK_EQ(shape.size(), dims_) << "Shape rank must be SparseTensor rank.";
}
// Optimized version of `IndicesValid()` with the following requirements:
// * The sparse tensor is two-dimensional.
// * The tensor's indices are in the "standard" (lexicographic) order.
// * All of the tensor's indices fit within the range of a signed int32.
//
// Returns true if the indices are valid, otherwise false.
// NOTE(mrry): If this method returns false, call IndicesValidHelper<true>()
// to obtain a meaningful error message.
bool SparseTensor::IndicesValid32BitFastPath() const {
const auto ix_t = ix_.matrix<int64>();
const int64* const shape_ptr = shape_.data();
DCHECK_EQ(shape_.size(), 2);
DCHECK_EQ(order_[0], 0);
DCHECK_EQ(order_[1], 1);
DCHECK_LE(shape_ptr[0], std::numeric_limits<int32>::max());
DCHECK_LE(shape_ptr[1], std::numeric_limits<int32>::max());
const int32 max_rows = static_cast<int32>(shape_ptr[0]);
const int32 max_cols = static_cast<int32>(shape_ptr[1]);
// We maintain separate bools for each validation predicate to enable
// vectorization across loop iterations.
bool row_zeros_valid = true;
bool row_in_range_valid = true;
bool col_zeros_valid = true;
bool col_in_range_valid = true;
bool order_valid = true;
int64 prev_index = -1;
// Points to the beginning of the current row of the indices matrix.
// Each row has two int64 elements, but we use an int32 pointer to access
// the low and high 32 bits of each element separately. This means that our
// stride per row is 4 elements.
const int32* index_ptr = reinterpret_cast<const int32*>(ix_t.data());
const size_t kInt32ElementsPerRow = 4;
for (std::size_t n = 0; n < ix_t.dimension(0); ++n) {
index_ptr += kInt32ElementsPerRow;
// Unpack the values on the current row of the indices matrix.
#if __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__
const int32 row_zeros = index_ptr[0];
const int32 row_32 = index_ptr[1];
const int32 col_zeros = index_ptr[2];
const int32 col_32 = index_ptr[3];
#else
const int32 row_32 = index_ptr[0];
const int32 row_zeros = index_ptr[1];
const int32 col_32 = index_ptr[2];
const int32 col_zeros = index_ptr[3];
#endif
// Validate that the high 32 bits of the row and column indices are zero.
row_zeros_valid = row_zeros_valid & (row_zeros == 0);
col_zeros_valid = col_zeros_valid & (col_zeros == 0);
// Validate that the low 32 bits of the row and column indices are within
// range of the shape.
row_in_range_valid =
row_in_range_valid & (row_32 >= 0) & (row_32 < max_rows);
col_in_range_valid =
col_in_range_valid & (col_32 >= 0) & (col_32 < max_cols);
// Interpret the row and column as a concatenated 64-bit integer, and
// validate that the concatenated indices are in strictly increasing order.
const int64 concatenated_index =
(static_cast<int64>(row_32) << 32) + col_32;
order_valid = order_valid & (concatenated_index > prev_index);
prev_index = concatenated_index;
}
return row_zeros_valid & row_in_range_valid & col_zeros_valid &
col_in_range_valid & order_valid;
}
template <bool standard_order>
Status SparseTensor::IndicesValidHelper() const {
const auto ix_t = ix_.matrix<int64>();
@ -251,12 +174,6 @@ Status SparseTensor::IndicesValid() const {
}
if (standard_order) {
if (shape_.size() == 2 && shape_[0] <= std::numeric_limits<int32>::max() &&
shape_[1] <= std::numeric_limits<int32>::max()) {
if (IndicesValid32BitFastPath()) {
return Status::OK();
}
}
return IndicesValidHelper<true>();
} else {
return IndicesValidHelper<false>();

2
tensorflow/core/util/sparse/sparse_tensor.h
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@ -201,8 +201,6 @@ class SparseTensor {
return vec;
}
bool IndicesValid32BitFastPath() const;
template <bool standard_order>
Status IndicesValidHelper() const;