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[XLA] Delete StripDegenerateDimensions()

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This is unused, and, as it turns out, is broken for sparse shapes.

PiperOrigin-RevId: 200313641
This commit is contained in:
Michael Kuperstein 2018-06-12 17:53:23 -07:00 committed by TensorFlower Gardener
parent 213810a0d6
commit bbc2c612da
3 changed files with 0 additions and 92 deletions
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62
tensorflow/compiler/xla/shape_util.cc
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@ -939,68 +939,6 @@ bool ShapeUtil::IsLeafIndex(const Shape& shape, const ShapeIndex& index) {
return leaves; return leaves;
} }
/* static */ Shape ShapeUtil::StripDegenerateDimensions(const Shape& shape) {
CHECK(IsArray(shape));
std::vector<int64> dimension_sizes;
std::vector<int64> degenerate_dimensions;
for (int64 i = 0; i < shape.dimensions_size(); ++i) {
if (shape.dimensions(i) == 1) {
degenerate_dimensions.push_back(i);
} else {
dimension_sizes.push_back(shape.dimensions(i));
}
}
// Construct minor_to_major of stripped shape. The order of the non-degenerate
// dimensions should be preserved from the original shape. First, create
// vector of the non-degenerate dimensions from the original minor_to_major
// array.
std::vector<int64> minor_to_major;
for (int64 i : shape.layout().minor_to_major()) {
if (std::find(degenerate_dimensions.begin(), degenerate_dimensions.end(),
i) == degenerate_dimensions.end()) {
minor_to_major.push_back(i);
}
}
// The dimensions in minor_to_major need to be renumbered to account for the
// degenerate dimensions which have removed. Decrement each dimension number
// once for each degenerate dimension which has a smaller number.
for (int i = 0; i < minor_to_major.size(); ++i) {
int adjustment = 0;
for (int64 dim : degenerate_dimensions) {
if (minor_to_major[i] > dim) {
adjustment++;
}
}
minor_to_major[i] -= adjustment;
}
{
std::vector<int64> dims(minor_to_major.size());
std::iota(dims.begin(), dims.end(), 0);
DCHECK(minor_to_major.size() == dims.size() &&
std::is_permutation(minor_to_major.begin(), minor_to_major.end(),
dims.begin()));
}
Shape stripped_shape;
if (LayoutUtil::IsDenseArray(shape)) {
stripped_shape = MakeShapeWithLayout(shape.element_type(), dimension_sizes,
minor_to_major);
} else if (LayoutUtil::IsSparseArray(shape)) {
stripped_shape =
MakeShapeWithSparseLayout(shape.element_type(), dimension_sizes,
shape.layout().max_sparse_elements());
} else {
stripped_shape = MakeShape(shape.element_type(), dimension_sizes);
}
VLOG(10) << "Original_shape: " << HumanStringWithLayout(shape);
VLOG(10) << "Stripped_shape: " << HumanStringWithLayout(stripped_shape);
return stripped_shape;
}
namespace { namespace {
// Helper for ForEachSubshape which visits the subshapes of the given shape in // Helper for ForEachSubshape which visits the subshapes of the given shape in

20
tensorflow/compiler/xla/shape_util.h
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@ -510,26 +510,6 @@ class ShapeUtil {
static Status ForEachMutableSubshapeWithStatus( static Status ForEachMutableSubshapeWithStatus(
Shape* shape, const MutatingStatusVisitorFunction& func); Shape* shape, const MutatingStatusVisitorFunction& func);
// Removes all degenerate dimensions (size one) from the given shape. The
// stripped minor_to_major preserves the relative ordering of non-degenerate
// dimensions. The stripped shape has the property that the underlying
// representation (bits in memory) for the stripped shape is the same as the
// original shape modulo padding. Examples:
//
// input shape: F32 [1, 2, 1], minor_to_major = {0, 1, 2}
// stripped shape: F32 [2], minor_to_major = {0}
//
// input shape: F32 [6, 1, 5], minor_to_major = {2, 0, 1}
// stripped shape: F32 [6, 5], minor_to_major = {1, 0}
//
// input shape: F32 [1, 7, 1, 6, 5, 1], minor_to_major = {0, 2, 5, 4, 3, 1}
// stripped shape: F32 [7, 6, 5], minor_to_major = {0, 2, 1}
//
// input shape: F32 [1, 1], minor_to_major = {0, 1}
// stripped shape: F32 [], minor_to_major = {}
// Precondition: !ShapeUtil::IsOpaque(shape) && !ShapeUtil::IsTuple(shape)
static Shape StripDegenerateDimensions(const Shape& shape);
// Permutes the dimensions by the given permutation, so // Permutes the dimensions by the given permutation, so
// return_value.dimensions[permutation[i]] = argument.dimensions[i] // return_value.dimensions[permutation[i]] = argument.dimensions[i]
static Shape PermuteDimensions(tensorflow::gtl::ArraySlice<int64> permutation, static Shape PermuteDimensions(tensorflow::gtl::ArraySlice<int64> permutation,

10
tensorflow/compiler/xla/shape_util_test.cc
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@ -742,16 +742,6 @@ TEST(ShapeUtilTest, ReshapeIsBitcast_3x2x2_6x2_Dim1IsMostMinor) {
ShapeUtil::MakeShapeWithLayout(F32, {6, 2}, {0, 1}))); ShapeUtil::MakeShapeWithLayout(F32, {6, 2}, {0, 1})));
} }
TEST(ShapeUtilTest, StripDegenerateDimensions) {
EXPECT_TRUE(ShapeUtil::Equal(ShapeUtil::StripDegenerateDimensions(
ShapeUtil::MakeShape(F32, {3, 1, 2})),
ShapeUtil::MakeShape(F32, {3, 2})));
EXPECT_TRUE(ShapeUtil::Equal(
ShapeUtil::StripDegenerateDimensions(
ShapeUtil::MakeShapeWithSparseLayout(F32, {3, 1, 2}, 10)),
ShapeUtil::MakeShapeWithSparseLayout(F32, {3, 2}, 10)));
}
TEST(AlgebraicSimplifierTest, ReshapeIsBitcast_3x2x2_6x2_Dim0IsMostMinor) { TEST(AlgebraicSimplifierTest, ReshapeIsBitcast_3x2x2_6x2_Dim0IsMostMinor) {
EXPECT_FALSE(ShapeUtil::ReshapeIsBitcast( EXPECT_FALSE(ShapeUtil::ReshapeIsBitcast(
ShapeUtil::MakeShapeWithLayout(F32, {3, 2, 2}, {0, 1, 2}), ShapeUtil::MakeShapeWithLayout(F32, {3, 2, 2}, {0, 1, 2}),