path: root/tensorflow/compiler/xla/shape_util.cc

/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include "tensorflow/compiler/xla/shape_util.h"

#include <algorithm>
#include <functional>
#include <numeric>
#include <utility>
#include <vector>

#include "tensorflow/compiler/xla/index_util.h"
#include "tensorflow/compiler/xla/layout_util.h"
#include "tensorflow/compiler/xla/primitive_util.h"
#include "tensorflow/compiler/xla/status_macros.h"
#include "tensorflow/compiler/xla/types.h"
#include "tensorflow/compiler/xla/util.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/lib/core/stringpiece.h"
#include "tensorflow/core/lib/gtl/iterator_range.h"
#include "tensorflow/core/lib/gtl/optional.h"
#include "tensorflow/core/lib/strings/numbers.h"
#include "tensorflow/core/lib/strings/str_util.h"
#include "tensorflow/core/lib/strings/strcat.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/protobuf.h"
#include "tensorflow/core/platform/regexp.h"

namespace xla {

string ShapeIndex::ToString() const {
  return tensorflow::strings::StrCat(
      "{", tensorflow::str_util::Join(indices_, ","), "}");
}

string ShapeIndexView::ToString() const {
  return tensorflow::strings::StrCat(
      "{",
      tensorflow::str_util::Join(tensorflow::gtl::make_range(begin_, end_),
                                 ","),
      "}");
}

std::ostream& operator<<(std::ostream& out, const ShapeIndex& shape_index) {
  out << shape_index.ToString();
  return out;
}

namespace {

// Recursive helper for comparing the equality of two shapes. Returns true if
// the shapes are the same. If compare_layouts is true, then layouts must also
// match.
bool CompareShapes(const Shape& lhs, const Shape& rhs, bool compare_layouts) {
  if (ShapeUtil::IsTuple(lhs)) {
    return ShapeUtil::IsTuple(rhs) &&
           ContainersEqual(lhs.tuple_shapes(), rhs.tuple_shapes(),
                           [=](const Shape& l, const Shape& r) {
                             return CompareShapes(l, r, compare_layouts);
                           });
  }
  // Explicitly compare the fields rather than using MessageDifferencer because
  // we want empty layouts to be treated identically to missing layouts.
  if (compare_layouts) {
    if (!ContainersEqual(lhs.layout().minor_to_major(),
                         rhs.layout().minor_to_major())) {
      VLOG(3) << "CompareShapes: lhs layout != rhs layout";
      return false;
    }
    if (!ContainersEqual(lhs.layout().padded_dimensions(),
                         rhs.layout().padded_dimensions())) {
      VLOG(3)
          << "CompareShapes: lhs padded_dimensions != rhs padded_dimensions";
      return false;
    }
    if (lhs.layout().padding_value() != rhs.layout().padding_value()) {
      VLOG(3) << "CompareShapes: lhs padding value != rhs padding_value";
      return false;
    }
  }

  if (!ShapeUtil::SameDimensions(lhs, rhs)) {
    VLOG(3) << "CompareShapes: lhs dimensions != rhs dimensions";
    return false;
  }
  if (!ShapeUtil::SameElementType(lhs, rhs)) {
    VLOG(3) << "CompareShapes: lhs element type != rhs element type";
    return false;
  }
  return true;
}

// Constructs and returns the new shape with the given minor_to_major order in
// its Layout.
StatusOr<Shape> MakeShapeWithLayoutInternal(
    PrimitiveType element_type, tensorflow::gtl::ArraySlice<int64> dimensions,
    tensorflow::gtl::ArraySlice<int64> minor_to_major) {
  if (dimensions.size() != minor_to_major.size()) {
    return InvalidArgument("Dimensions size is %ld, but layout size is %ld.",
                           dimensions.size(), minor_to_major.size());
  }
  if (element_type == OPAQUE || element_type == TUPLE) {
    return InvalidArgument("Unsupported element type: %s",
                           PrimitiveType_Name(element_type).c_str());
  }
  Shape shape = ShapeUtil::MakeShape(element_type, dimensions);
  auto min2maj = shape.mutable_layout()->mutable_minor_to_major();
  min2maj->Clear();
  for (int64 value : minor_to_major) {
    min2maj->Add(value);
  }
  if (!shape.has_layout()) {
    return InvalidArgument("Shape has no layout.");
  }
  TF_RETURN_IF_ERROR(ShapeUtil::ValidateShape(shape));
  return shape;
}

}  // namespace

/* static */ bool ShapeUtil::Equal(const Shape& lhs, const Shape& rhs) {
  bool equal = CompareShapes(lhs, rhs, /*compare_layouts=*/true);
  if (!equal && VLOG_IS_ON(3)) {
    VLOG(3) << "ShapeUtil::Equal differ: lhs = " << lhs.ShortDebugString()
            << ", rhs = " << rhs.ShortDebugString();
  }

  return equal;
}

/* static */ int64 ShapeUtil::Rank(const Shape& shape) {
  CHECK(!ShapeUtil::IsTuple(shape)) << "Tuples do not have a rank";
  return shape.dimensions_size();
}

/* static */ int64 ShapeUtil::TrueRank(const Shape& shape) {
  int64 accum = 0;
  for (int64 dimension : shape.dimensions()) {
    // We do not count zero dimensions.
    if (dimension != 1) {
      accum += 1;
    }
  }
  return accum;
}

/* static */ ProgramShape ShapeUtil::MakeProgramShape(
    std::initializer_list<Shape> parameters, Shape result) {
  ProgramShape program_shape;
  for (const auto& shape : parameters) {
    *program_shape.add_parameters() = shape;
  }
  *program_shape.mutable_result() = std::move(result);
  return program_shape;
}

/* static */ Shape ShapeUtil::MakeShape(
    PrimitiveType element_type, tensorflow::gtl::ArraySlice<int64> dimensions) {
  DCHECK_NE(TUPLE, element_type);
  DCHECK_NE(OPAQUE, element_type);
  Shape result;
  PopulateShape(element_type, dimensions, &result);
  return result;
}

/* static */ Shape ShapeUtil::MakeShapeWithLayout(
    PrimitiveType element_type, tensorflow::gtl::ArraySlice<int64> dimensions,
    tensorflow::gtl::ArraySlice<int64> minor_to_major) {
  return MakeShapeWithLayoutInternal(element_type, dimensions, minor_to_major)
      .ValueOrDie();
}

/* static */ Shape ShapeUtil::MakeShapeWithMonotonicDim0MajorLayout(
    PrimitiveType element_type, tensorflow::gtl::ArraySlice<int64> dimensions) {
  std::vector<int64> layout(dimensions.size());
  std::iota(layout.rbegin(), layout.rend(), static_cast<int64>(0));
  return MakeShapeWithLayout(element_type, dimensions, layout);
}

/* static */ Shape ShapeUtil::NormalizeShapeToMonotonicDim0MajorLayout(
    const Shape& shape) {
  std::vector<int64> dims(shape.dimensions_size());
  for (int i = 0; i < shape.dimensions_size(); ++i) {
    dims[i] = shape.dimensions(LayoutUtil::Major(shape.layout(), i));
  }
  return MakeShapeWithMonotonicDim0MajorLayout(shape.element_type(), dims);
}

/* static */ void ShapeUtil::PopulateShape(
    PrimitiveType element_type, tensorflow::gtl::ArraySlice<int64> dimensions,
    Shape* shape) {
  shape->Clear();
  shape->set_element_type(element_type);
  for (int64 dimension : dimensions) {
    shape->add_dimensions(dimension);
  }
  LayoutUtil::SetToDefaultLayout(shape);
  TF_DCHECK_OK(ValidateShape(*shape));
}

/* static */ Shape ShapeUtil::MakeTupleShape(
    tensorflow::gtl::ArraySlice<Shape> shapes) {
  Shape result;
  result.set_element_type(TUPLE);
  for (const auto& shape : shapes) {
    AppendShapeToTuple(shape, &result);
  }
  TF_DCHECK_OK(ValidateShapeWithOptionalLayout(result));
  return result;
}

/* static */ Shape ShapeUtil::MakeOpaqueShape() {
  Shape result;
  result.set_element_type(OPAQUE);
  TF_DCHECK_OK(ValidateShapeWithOptionalLayout(result));
  return result;
}

/* static */ void ShapeUtil::AppendShapeToTuple(const Shape& shape,
                                                Shape* tuple_shape) {
  TF_DCHECK_OK(ValidateShapeWithOptionalLayout(shape));
  *tuple_shape->add_tuple_shapes() = shape;
}

/* static */ void ShapeUtil::AppendMajorDimension(int bound, Shape* shape) {
  shape->mutable_layout()->add_minor_to_major(Rank(*shape));
  shape->add_dimensions(bound);
  TF_DCHECK_OK(ValidateShape(*shape));
}

/* static */ bool ShapeUtil::ElementIsIntegral(const Shape& shape) {
  return primitive_util::IsIntegralType(shape.element_type());
}

/* static */ bool ShapeUtil::ElementIsIntegralWithBits(const Shape& shape,
                                                       int32 bits) {
  return ElementIsIntegral(shape) && ElementHasBitWidth(shape, bits);
}

/* static */ bool ShapeUtil::ElementHasBitWidth(const Shape& shape, int bits) {
  if (shape.element_type() == TUPLE || shape.element_type() == OPAQUE) {
    return false;
  }
  return primitive_util::BitWidth(shape.element_type()) == bits;
}

/* static */ bool ShapeUtil::ElementIsSigned(const Shape& shape) {
  switch (shape.element_type()) {
    case S8:
    case S16:
    case S32:
    case S64:
    case F16:
    case BF16:
    case F32:
    case F64:
      return true;

    case PRED:
    case U8:
    case U16:
    case U32:
    case U64:
    case C64:
    case TUPLE:
    case OPAQUE:
      return false;

    default:
      LOG(FATAL) << "Unhandled element type " << shape.element_type();
  }
}

/* static */ bool ShapeUtil::ElementIsComplex(const Shape& shape) {
  return primitive_util::IsComplexType(shape.element_type());
}

/* static */ bool ShapeUtil::ElementIsFloating(const Shape& shape) {
  return primitive_util::IsFloatingPointType(shape.element_type());
}

/* static */ bool ShapeUtil::IsNestedTuple(const Shape& shape) {
  return IsTuple(shape) && std::any_of(shape.tuple_shapes().begin(),
                                       shape.tuple_shapes().end(), IsTuple);
}

/* static */ bool ShapeUtil::IsEmptyTuple(const Shape& shape) {
  return IsTuple(shape) && TupleElementCount(shape) == 0;
}

/* static */ bool ShapeUtil::IsNil(const Shape& shape) {
  return IsTuple(shape) ? IsEmptyTuple(shape) : HasZeroElements(shape);
}

/* static */ int64 ShapeUtil::TupleElementCount(const Shape& shape) {
  CHECK(IsTuple(shape)) << HumanString(shape);
  return shape.tuple_shapes_size();
}

/* static */ const Shape& ShapeUtil::GetTupleElementShape(const Shape& shape,
                                                          int64 index) {
  CHECK(IsTuple(shape));
  CHECK_GT(TupleElementCount(shape), index);
  TF_DCHECK_OK(ValidateShapeWithOptionalLayout(shape.tuple_shapes(index)));
  return shape.tuple_shapes(index);
}

/* static */ Shape ShapeUtil::SliceTuple(const Shape& tuple, int64 start,
                                         int64 limit) {
  TF_DCHECK_OK(ValidateShapeWithOptionalLayout(tuple));
  CHECK(IsTuple(tuple));
  CHECK_LE(start, TupleElementCount(tuple));
  CHECK_LE(limit, TupleElementCount(tuple));

  std::vector<Shape> new_elements(tuple.tuple_shapes().begin() + start,
                                  tuple.tuple_shapes().begin() + limit);
  return MakeTupleShape(new_elements);
}

/* static */ bool ShapeUtil::ShapeIs(const Shape& shape,
                                     PrimitiveType element_type,
                                     std::initializer_list<int64> dimensions) {
  return Equal(shape, MakeShape(element_type, dimensions));
}

/* static */ int64 ShapeUtil::ElementsIn(const Shape& shape) {
  CHECK(!IsTuple(shape));
  CHECK_EQ(shape.dimensions_size(), Rank(shape));
  return std::accumulate<decltype(shape.dimensions().begin()), int64>(
      shape.dimensions().begin(), shape.dimensions().end(), 1LL,
      std::multiplies<int64>());
}

/* static */ bool ShapeUtil::HasZeroElements(const Shape& shape) {
  return ElementsIn(shape) == 0;
}

/* static */ bool ShapeUtil::IsScalarF32(const Shape& shape) {
  return shape.element_type() == F32 && Rank(shape) == 0;
}

/* static */ string ShapeUtil::HumanString(const Shape& shape) {
  if (shape.element_type() == TUPLE) {
    string text = "(";
    const char* prefix = "";
    for (const Shape& elem_shape : shape.tuple_shapes()) {
      tensorflow::strings::StrAppend(&text, prefix, HumanString(elem_shape));
      prefix = ", ";
    }
    text += ")";
    return text;
  } else {
    return tensorflow::strings::StrCat(
        tensorflow::str_util::Lowercase(
            PrimitiveType_Name(shape.element_type())),
        "[", tensorflow::str_util::Join(shape.dimensions(), ","), "]");
  }
}

namespace {

// Class to memoize the computation of
//   tensorflow::str_util::Lowercase(PrimitiveType_Name(p))
// for all PrimitiveType values "p"
class PrimitiveTypeNameGenerator {
 public:
  PrimitiveTypeNameGenerator() {
    for (int i = 0; i < PrimitiveType_ARRAYSIZE; i++) {
      if (PrimitiveType_IsValid(i)) {
        lowercase_name_[i] = tensorflow::str_util::Lowercase(
            PrimitiveType_Name(static_cast<PrimitiveType>(i)));
      }
    }
  }
  const string& LowercaseName(PrimitiveType t) {
    return lowercase_name_[static_cast<int>(t)];
  }

 private:
  string lowercase_name_[PrimitiveType_ARRAYSIZE];
};

const string& LowercasePrimitiveTypeName(PrimitiveType s) {
  static PrimitiveTypeNameGenerator* gen = new PrimitiveTypeNameGenerator();
  return gen->LowercaseName(s);
}
}  // namespace

/* static */ string ShapeUtil::HumanStringWithLayout(const Shape& shape) {
  if (shape.element_type() == TUPLE) {
    string text = "(";
    const char* prefix = "";
    for (const Shape& elem_shape : shape.tuple_shapes()) {
      tensorflow::strings::StrAppend(&text, prefix,
                                     HumanStringWithLayout(elem_shape));
      prefix = ", ";
    }
    text += ")";
    return text;
  } else {
    string result = tensorflow::strings::StrCat(
        LowercasePrimitiveTypeName(shape.element_type()), "[");
    for (int i = 0; i < shape.dimensions().size(); i++) {
      tensorflow::strings::StrAppend(&result, (i > 0) ? "," : "",
                                     shape.dimensions(i));
    }
    result += "]";
    if (!IsScalar(shape) && !IsOpaque(shape)) {
      if (LayoutUtil::HasLayout(shape)) {
        tensorflow::strings::StrAppend(&result,
                                       LayoutUtil::HumanString(shape.layout()));
      } else {
        tensorflow::strings::StrAppend(&result, "{no layout}");
      }
    }
    return result;
  }
}

/* static */ string ShapeUtil::HumanString(const ProgramShape& program_shape) {
  std::vector<string> parameters;
  for (auto& shape : program_shape.parameters()) {
    const int i = parameters.size();
    parameters.push_back(
        tensorflow::strings::StrCat(i < program_shape.parameter_names_size()
                                        ? program_shape.parameter_names(i)
                                        : "(unknown)",
                                    ": ", HumanString(shape)));
  }
  return tensorflow::strings::StrCat(
      "(", tensorflow::str_util::Join(parameters, ", "), ") -> ",
      HumanString(program_shape.result()));
}

namespace {
// Parses shapes with simple recursive descent structure -- consumes from the
// front of s and passes that view recursively as required.
StatusOr<Shape> ParseShapeStringInternal(tensorflow::StringPiece* s) {
  tensorflow::str_util::RemoveLeadingWhitespace(s);

  if (s->Consume("(")) {  // Tuple.
    std::vector<Shape> shapes;
    bool must_end = false;
    while (true) {
      if (s->Consume(")")) {
        break;
      } else if (must_end) {
        return InvalidArgument("Expected end of tuple; got: \"%s\"",
                               s->ToString().c_str());
      }
      shapes.emplace_back();
      TF_ASSIGN_OR_RETURN(shapes.back(), ParseShapeStringInternal(s));
      tensorflow::str_util::RemoveLeadingWhitespace(s);
      must_end = !s->Consume(",");
    }
    return ShapeUtil::MakeTupleShape(shapes);
  }

  string element_type_string;
  string dimensions_string;
  string layout_string;
  // tensorflow::StringPiece is not compatible with internal RE2 StringPiece, so
  // we convert in to the RE2-consumable type and then consume the corresponding
  // amount from our StringPiece type.
  tensorflow::RegexpStringPiece s_consumable(s->data(), s->size());
  if (RE2::Consume(&s_consumable,
                   "^(\\w*\\d*)\\[([\\d,]*)\\](?:\\s*{([\\d,]*)})?",
                   &element_type_string, &dimensions_string, &layout_string)) {
    size_t consumed = s->size() - s_consumable.size();
    s->remove_prefix(consumed);
    auto comma_list_to_int64s =
        [&s](const string& input) -> StatusOr<std::vector<int64>> {
      std::vector<int64> results;
      for (const string& piece : tensorflow::str_util::Split(input, ',')) {
        int64 element;
        if (!tensorflow::strings::safe_strto64(piece.c_str(), &element)) {
          return InvalidArgument(
              "Invalid s64 value in parsed shape string: \"%s\" in \"%s\"",
              piece.c_str(), s->ToString().c_str());
        }
        results.push_back(element);
      }
      return results;
    };

    // Extract the dimensions.
    TF_ASSIGN_OR_RETURN(std::vector<int64> dimensions,
                        comma_list_to_int64s(dimensions_string));

    // Extract the primitive element type.
    PrimitiveType primitive_type = PRIMITIVE_TYPE_INVALID;
    for (PrimitiveType i =
             static_cast<PrimitiveType>(PRIMITIVE_TYPE_INVALID + 1);
         i < TUPLE; i = static_cast<PrimitiveType>(i + 1)) {
      if (tensorflow::str_util::Lowercase(PrimitiveType_Name(i)) ==
          element_type_string) {
        primitive_type = i;
        break;
      }
    }
    if (primitive_type == PRIMITIVE_TYPE_INVALID) {
      return InvalidArgument("Invalid element type string: \"%s\".",
                             element_type_string.c_str());
    }

    Shape result;
    if (layout_string.empty()) {
      // Create a shape without a layout set.
      result = ShapeUtil::MakeShape(primitive_type, dimensions);
    } else {
      // Extract the layout minor-to-major and set it.
      TF_ASSIGN_OR_RETURN(std::vector<int64> min2maj,
                          comma_list_to_int64s(layout_string));
      TF_ASSIGN_OR_RETURN(result, MakeShapeWithLayoutInternal(
                                      primitive_type, dimensions, min2maj));
    }
    TF_RETURN_IF_ERROR(ShapeUtil::ValidateShape(result));
    return std::move(result);
  }

  return InvalidArgument("Invalid shape string to parse: \"%s\"",
                         s->ToString().c_str());
}
}  // namespace

/* static */ StatusOr<Shape> ShapeUtil::ParseShapeString(
    tensorflow::StringPiece s) {
  return ParseShapeStringInternal(&s);
}

/* static */ bool ShapeUtil::SameDimensions(const Shape& lhs,
                                            const Shape& rhs) {
  return ContainersEqual(lhs.dimensions(), rhs.dimensions());
}

/* static */ bool ShapeUtil::Compatible(const Shape& lhs, const Shape& rhs) {
  if (lhs.element_type() == TUPLE) {
    return rhs.element_type() == TUPLE &&
           ContainersEqual(lhs.tuple_shapes(), rhs.tuple_shapes(), Compatible);
  }
  return SameDimensions(lhs, rhs) && SameElementType(lhs, rhs);
}

/* static */ int64 ShapeUtil::GetDimension(const Shape& shape,
                                           int64 dimension_number) {
  return shape.dimensions(GetDimensionNumber(shape, dimension_number));
}

/* static */ int64 ShapeUtil::GetDimensionNumber(const Shape& shape,
                                                 int64 dimension_number) {
  if (dimension_number < 0) {
    dimension_number += Rank(shape);
  }
  CHECK_GE(dimension_number, 0);
  return dimension_number;
}

/* static */ int64 ShapeUtil::ByteSizeOfPrimitiveType(
    PrimitiveType primitive_type) {
  switch (primitive_type) {
    case PRED:
      return sizeof(int8);
    case TUPLE:
      LOG(FATAL) << "tuples have no definitive size";
    case OPAQUE:
      LOG(FATAL) << "opaque have no definitive size";
    case S8:
      return sizeof(int8);
    case S16:
      return sizeof(int16);
    case S32:
      return sizeof(int32);
    case S64:
      return sizeof(int64);
    case U8:
      return sizeof(uint8);
    case U16:
      return sizeof(uint16);
    case U32:
      return sizeof(uint32);
    case U64:
      return sizeof(uint64);
    case F16:
      return sizeof(float) / 2;
    case F32:
      return sizeof(float);
    case F64:
      return sizeof(double);
    case C64:
      return sizeof(complex64);
    default:
      LOG(FATAL) << "Unhandled primitive type " << primitive_type;
  }
}

/* static */ int64 ShapeUtil::ByteSizeOf(const Shape& shape,
                                         int64 pointer_size) {
  TF_DCHECK_OK(ValidateShape(shape));
  DCHECK_NE(OPAQUE, shape.element_type());
  if (shape.element_type() == TUPLE) {
    CHECK_GT(pointer_size, 0);
    return pointer_size * shape.tuple_shapes_size();
  }
  int64 allocated_element_count;
  if (shape.layout().padded_dimensions_size() > 0) {
    CHECK_EQ(Rank(shape), shape.layout().padded_dimensions_size());
    allocated_element_count = 1;
    for (int64 dimension_size : shape.layout().padded_dimensions()) {
      allocated_element_count *= dimension_size;
    }
  } else {
    allocated_element_count = ElementsIn(shape);
  }
  return allocated_element_count *
         ByteSizeOfPrimitiveType(shape.element_type());
}

/* static */ Status ShapeUtil::ValidateShapeWithOptionalLayoutInternal(
    const Shape& shape) {
  if (shape.element_type() == TUPLE) {
    if (shape.dimensions_size() != 0) {
      return InvalidArgument("tuples must not have dimensions specified");
    }
    for (auto& element_shape : shape.tuple_shapes()) {
      TF_RETURN_IF_ERROR(
          ValidateShapeWithOptionalLayoutInternal(element_shape));
    }
    return Status::OK();
  }

  // Non-tuple shape.
  if (shape.tuple_shapes_size() > 0) {
    return InvalidArgument("non-tuple shape has tuple_shapes field");
  }
  if (shape.element_type() == PRIMITIVE_TYPE_INVALID) {
    return InvalidArgument("shape has invalid element type: %s",
                           shape.ShortDebugString().c_str());
  }
  if (Rank(shape) != shape.dimensions_size()) {
    return InvalidArgument(
        "shape's rank is mismatched with dimension count; rank=%lld "
        "dimensions_size=%d",
        Rank(shape), shape.dimensions_size());
  }
  for (int64 i = 0; i < Rank(shape); ++i) {
    int64 dimension = shape.dimensions(i);
    if (dimension < 0) {
      return InvalidArgument(
          "shape's dimensions must not be < 0; dimension at index %lld was "
          "%lld",
          i, dimension);
    }
  }

  return Status::OK();
}

/* static */ Status ShapeUtil::ValidateShapeWithOptionalLayout(
    const Shape& shape) {
  if (LayoutUtil::HasLayout(shape)) {
    // Since a layout is present, upgrade to the full set of invariant checks.
    return ValidateShape(shape);
  }
  return ValidateShapeWithOptionalLayoutInternal(shape);
}

/* static */ Status ShapeUtil::ValidateShape(const Shape& shape) {
  TF_RETURN_IF_ERROR(ValidateShapeWithOptionalLayoutInternal(shape));

  return LayoutUtil::ValidateLayoutInShape(shape);
}

/* static */ Shape ShapeUtil::ChangeElementType(const Shape& shape,
                                                PrimitiveType type) {
  Shape new_shape = shape;
  new_shape.set_element_type(type);
  return new_shape;
}

/* static */ const Shape& ShapeUtil::GetSubshape(const Shape& shape,
                                                 ShapeIndexView index) {
  const Shape* return_shape = &shape;
  for (auto i : index) {
    CHECK(IsTuple(*return_shape));
    return_shape = &return_shape->tuple_shapes(i);
  }
  return *return_shape;
}

/* static */ Shape* ShapeUtil::GetMutableSubshape(Shape* shape,
                                                  ShapeIndexView index) {
  Shape* return_shape = shape;
  for (auto i : index) {
    CHECK(IsTuple(*return_shape));
    return_shape = return_shape->mutable_tuple_shapes(i);
  }
  return return_shape;
}

/* static */
bool ShapeUtil::IsLeafIndex(const Shape& shape, const ShapeIndex& index) {
  return !IsTuple(GetSubshape(shape, index));
}

/* static */ Shape ShapeUtil::StripDegenerateDimensions(const Shape& 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 =
      shape.has_layout() ? MakeShapeWithLayout(shape.element_type(),
                                               dimension_sizes, minor_to_major)
                         : MakeShape(shape.element_type(), dimension_sizes);

  VLOG(10) << "Original_shape: " << HumanStringWithLayout(shape);
  VLOG(10) << "Stripped_shape: " << HumanStringWithLayout(stripped_shape);
  return stripped_shape;
}

namespace {

// Helper for ForEachSubshape which visits the subshapes of the given shape in
// DFS pre-order starting with the index.
Status ForEachSubshapeHelper(const Shape& shape,
                             const ShapeUtil::StatusVisitorFunction& func,
                             ShapeIndex* index) {
  TF_RETURN_IF_ERROR(func(shape, *index));
  if (ShapeUtil::IsTuple(shape)) {
    for (int64 i = 0; i < ShapeUtil::TupleElementCount(shape); ++i) {
      index->push_back(i);
      TF_RETURN_IF_ERROR(ForEachSubshapeHelper(
          ShapeUtil::GetTupleElementShape(shape, i), func, index));
      index->pop_back();
    }
  }
  return Status::OK();
}

// Helper for ForEachMutableSubshape which visits the subshapes of the given
// shape in DFS pre-order starting with the index.
Status ForEachMutableSubshapeHelper(
    Shape* shape, const ShapeUtil::MutatingStatusVisitorFunction& func,
    ShapeIndex* index) {
  TF_RETURN_IF_ERROR(func(shape, *index));
  if (ShapeUtil::IsTuple(*shape)) {
    for (int64 i = 0; i < ShapeUtil::TupleElementCount(*shape); ++i) {
      index->push_back(i);
      TF_RETURN_IF_ERROR(ForEachMutableSubshapeHelper(
          shape->mutable_tuple_shapes(i), func, index));
      index->pop_back();
    }
  }
  return Status::OK();
}

}  // namespace

/* static */ void ShapeUtil::ForEachSubshape(const Shape& shape,
                                             const VisitorFunction& func) {
  ShapeIndex index;
  ForEachSubshapeHelper(
      shape,
      [&func](const Shape& subshape, const ShapeIndex& index) {
        func(subshape, index);
        return Status::OK();
      },
      &index)
      .IgnoreError();
}

/* static */ void ShapeUtil::ForEachMutableSubshape(
    Shape* shape, const MutatingVisitorFunction& func) {
  ShapeIndex index;
  ForEachMutableSubshapeHelper(
      shape,
      [&func](Shape* subshape, const ShapeIndex& index) {
        func(subshape, index);
        return Status::OK();
      },
      &index)
      .IgnoreError();
}

/* static */ Status ShapeUtil::ForEachSubshapeWithStatus(
    const Shape& shape, const StatusVisitorFunction& func) {
  ShapeIndex index;
  return ForEachSubshapeHelper(shape, func, &index);
}

/* static */ Status ShapeUtil::ForEachMutableSubshapeWithStatus(
    Shape* shape, const MutatingStatusVisitorFunction& func) {
  ShapeIndex index;
  return ForEachMutableSubshapeHelper(shape, func, &index);
}

/* static */ Shape ShapeUtil::PermuteDimensions(
    tensorflow::gtl::ArraySlice<int64> permutation, const Shape& shape) {
  Shape new_shape = shape;
  new_shape.clear_dimensions();
  for (auto dim : Permute(permutation, shape.dimensions())) {
    new_shape.add_dimensions(dim);
  }
  if (shape.has_layout()) {
    Layout* new_layout = new_shape.mutable_layout();
    new_layout->clear_minor_to_major();
    for (auto index : Permute(permutation, shape.layout().minor_to_major())) {
      new_layout->add_minor_to_major(index);
    }
    if (shape.layout().padded_dimensions_size() > 0) {
      new_layout->clear_padded_dimensions();
      for (auto dim :
           Permute(permutation, shape.layout().padded_dimensions())) {
        new_layout->add_padded_dimensions(dim);
      }
    }
  }
  return new_shape;
}

/* static */ std::tuple<bool, std::vector<int64>, std::vector<int64>>
ShapeUtil::InsertedOrDeleted1SizedDimensions(const Shape& shape_pre,
                                             const Shape& shape_post) {
  auto nil = std::make_tuple(false, std::vector<int64>(), std::vector<int64>());

  std::vector<int64> deleted_indices;
  std::vector<int64> inserted_indices;
  // Returns false if any input/output index between prior_unmodified_dim_pair
  // and unmodified_dim_pair have size >1. Otherwise, returns true and appends
  // the degerenate input/output dimensions in the gap to
  // deleted_indices/inserted_indices respectively.
  auto check_modified_dims =
      [&shape_pre, &shape_post, &deleted_indices, &inserted_indices](
          std::pair<int64, int64> prior_unmodified_dim_pair,
          std::pair<int64, int64> unmodified_dim_pair) {
        for (int64 modified_input_dim = prior_unmodified_dim_pair.first + 1;
             modified_input_dim < unmodified_dim_pair.first;
             ++modified_input_dim) {
          if (shape_pre.dimensions(modified_input_dim) > 1) {
            return false;
          }
          deleted_indices.push_back(modified_input_dim);
        }
        for (int64 modified_output_dim = prior_unmodified_dim_pair.second + 1;
             modified_output_dim < unmodified_dim_pair.second;
             ++modified_output_dim) {
          if (shape_post.dimensions(modified_output_dim) > 1) {
            return false;
          }
          inserted_indices.push_back(modified_output_dim);
        }
        return true;
      };

  std::vector<std::pair<int64, int64>> unmodified_dims =
      DimensionsUnmodifiedByReshape(shape_pre, shape_post);
  // Returns nil if the reshape modifies any non-degenerate input/output
  // dimension. DimensionsUnmodifiedByReshape gives us all unmodified
  // dimensions, so we only need to check whether dimensions in the gaps (thus
  // modified) have size >1.
  for (size_t i = 0; i <= unmodified_dims.size(); ++i) {
    // Check (modified) dimensions between unmodified_dims[i-1] and
    // unmodified_dims[i].
    auto prior_unmodified_dim_pair =
        i > 0 ? unmodified_dims[i - 1] : std::make_pair(-1LL, -1LL);
    auto unmodified_dim_pair =
        i < unmodified_dims.size()
            ? unmodified_dims[i]
            : std::make_pair(Rank(shape_pre), Rank(shape_post));
    if (!check_modified_dims(prior_unmodified_dim_pair, unmodified_dim_pair)) {
      return nil;
    }
  }

  return std::make_tuple(true, deleted_indices, inserted_indices);
}

/* static */ std::vector<std::pair<int64, int64>>
ShapeUtil::DimensionsUnmodifiedByReshape(const Shape& input_shape,
                                         const Shape& output_shape) {
  // Unmodified dimensions are merely common factors of rank 1.
  auto common_factors = CommonFactors(AsInt64Slice(input_shape.dimensions()),
                                      AsInt64Slice(output_shape.dimensions()));
  for (size_t i = 0; i < common_factors.size() - 1;) {
    if (1 != common_factors[i + 1].first - common_factors[i].first ||
        1 != common_factors[i + 1].second - common_factors[i].second) {
      common_factors.erase(common_factors.begin() + i);
    } else {
      ++i;
    }
  }
  // `CommonFactors(a, b).back() == (a.rank, b.rank)` so we must pop it.
  common_factors.pop_back();
  return common_factors;
}

/* static */ bool ShapeUtil::TransposeIsBitcast(
    const Shape& input_shape, const Shape& output_shape,
    tensorflow::gtl::ArraySlice<int64> dimension_mapping) {
  // Can't insert bitcasts without layout information.
  if (!LayoutUtil::HasLayout(input_shape) &&
      !LayoutUtil::HasLayout(output_shape)) {
    return false;
  }

  // Padding is not handled.
  if (LayoutUtil::IsPadded(input_shape) && LayoutUtil::IsPadded(output_shape)) {
    return false;
  }

  // Check the reshape permutes the positions of each dimension in the
  // minor-to-major order. positions[i]=k means dimension `i` is k-th minor.
  //   input_positions = apply(dimension_mapping, output_positions)
  //
  // Because the positions of each dimension are the inverse permutation of the
  // minor-to-major order, the above check is equivalent to
  //   inverse(input_dimensions) =
  //       apply(dimension_mapping, inverse(output_dimensions))
  //   # `I` indicates identity permutation.
  //   apply(input_dimensions, I) =
  //       apply(dimension_mapping, apply(output_dimensions, I))
  //   apply(input_dimensions, I) =
  //       apply((dimension_mapping * output_dimensions), I)
  //   input_dimensions = dimension_mapping * output_dimensions
  return ContainersEqual(
      ComposePermutations(dimension_mapping,
                          AsInt64Slice(output_shape.layout().minor_to_major())),
      input_shape.layout().minor_to_major());
}

/* static */ bool ShapeUtil::ReshapeIsBitcast(const Shape& input_shape,
                                              const Shape& output_shape) {
  // Can't convert reshapes into bitcasts without layout information.
  if (!LayoutUtil::HasLayout(input_shape) ||
      !LayoutUtil::HasLayout(output_shape)) {
    return false;
  }

  // Padding is not handled.
  if (LayoutUtil::IsPadded(input_shape) || LayoutUtil::IsPadded(output_shape)) {
    return false;
  }

  CHECK_EQ(ElementsIn(input_shape), ElementsIn(output_shape));
  if (ElementsIn(input_shape) == 0) {
    return true;
  }

  // TL;DR: The rest of the method checks that the reshape does not change the
  // physical location of any unit input or output index. Unit indices have
  // exactly one dimension that equals 1 and other dimensions 0. This condition
  // is necessary for the reshape to be a bitcast, because a bitcast-equivalent
  // reshape shouldn't change the physical location of any element. It is also a
  // sufficient condition as is proved below (note: many details are omitted for
  // space).
  //
  // Definitions:
  //
  // * Denote the input shape by IS and output shape by OS. IS[i] or OS[i] means
  // the size of i-th least significant dimension of IS or OS (this is opposite
  // to how we define the index of Shape::dimensions()).
  //
  // * Given an input or output index I, denote by p(I) I's physical linear
  // index (or physical index for short) and l(I) I's logical linear index (or
  // logical index for short).
  //
  // * Given a logical index k, denote by II(k) the input index whose linear
  // index is k, and OI(k) the corresponding output index.
  //
  // * Denote by IT[i] the increment of physical index if i-th dimension of the
  // input index is increased by 1. Similarly, OT[i] means the increment if i-th
  // dimension of the output index is increased by 1. Note that IT[i] or OT[i]
  // is a function of IS or OS and the layout, and not dependent on the specific
  // input or output index.
  //
  // To prove the reshape from IS to OS is a bitcast, it is sufficient to prove
  // that, for any linear index k, p(II(k))=p(OI(k)). We prove this by
  // induction. We know p(II(0))=p(OI(0)) is trivially true, so what's left is
  // to prove, with every increment on k, the above formula still holds.
  //
  // First, suppose reshaping from IS to OS is non-factorizable (we discuss
  // refactorizable reshapes later). A reshape from IS to OS is factorizable, if
  // there exists (i,j) such that
  //
  //   0<=i<=|IS|
  //   0<=j<=|OS|
  //   |IS|-i+|OS|-j > 0 (i.e., i,j mustn't both point to the end)
  //   product(IS[i], IS[i+1], ..., IS[|IS|-1])
  //     = product(OS[j], OS[j+1], ..., OS[|OS|-1])
  //
  // p(II(k))=p(OI(k)) is trivially true for k=0 because p(II(0)) and p(OI(0))
  // are both 0. It's also trivially true for k=1, because II(1) and OI(1) are
  // unit indices which are already tested. This also means IT[0]=OT[0]
  // because p(II(1))=IT[0] and p(OI(1))=OT[0].
  //
  // Furthermore, p(II(k))=p(OI(k)) for k<min(IS[0],OS[0]), because each
  // increment of k adds IT[0] to the input physical and OT[0] (same as IT[0])
  // to the output physical.
  //
  // When k=min(IS[0],OS[0]), the first wrap happens. Without losing generality,
  // suppose IS[0]<OS[0] and thus k=IS[0]. Similar proof applies to IS[0]>OS[0].
  // Note that IS[0]!=OS[0] because the reshape is non-factorizable. From
  // logical index k-1 to logical index k, dimension 1 of the input index
  // is increased by 1 and dimension 0 is reset to 0 thus decreased by
  // IS[0]-1. Therefore, the physical input index is increased by
  //
  //   p(II(k)) - p(II(k-1)) = IT[1] - (IS[0]-1) * IT[0]
  //
  // Because IS[0]<OS[0], the only change to the output index is that its
  // dimension 0 is increased by one. Therefore,
  //
  //   p(OI(k)) - p(OI(k-1)) = OT[0] = IT[0]
  //
  // Because II(k) is an unit index -- (0,..,0,1,0), we already tested that
  // p(II(k))=p(OI(k)). Therefore,
  //   IT[1] - (IS[0]-1) * IT[0] = IT[0]
  //   IT[1] = IS[0] * IT[0]
  // In other words, input dimension 1 is immediately more major than input
  // dimension 0. We can now conceptually collapse these two dimensions because
  // an increment in the logical index affecting only these two dimensions maps
  // to IT[0] in the physical index.
  //
  // By induction (omitted here), we can prove IT[i]=IS[i-1]*IT[i-1] and
  // OT[i]=OS[i-1]*OT[i-1]. Therefore, both IS and OS are row-major and bitwise
  // identical.
  //
  // A factorizable reshape can be factorized into a list of non-factorizable
  // sub-reshapes, each of which can be handled similarly to the proof above.
  // For example,
  //
  //   [7x9x2x15] -> [63x6x5]
  //
  // can be factorized into
  //
  //   [7x9] -> [63] and [2x15] -> [6x5].
  //
  // Suppose input index I=(x3,x2,x1,x0) and output index O=(y2,y1,y0) have the
  // same logical linear index. According to the factorization, we know
  // l(x3,x2,0,0)=l(y2,0,0) and l(0,0,x1,x0)=l(0,y1,y0). Using the proof for
  // non-factorizable reshapes, we can prove p(0,0,x1,x0)=p(0,y1,y0). Using a
  // similar proof, with the increment of the logical index set to
  // IS[1]*IS[0]=OS[1]*OS[0]=30 instead of 1, we can prove
  // p(x3,x2,0,0)=p(y2,0,0) too. Therefore,
  //
  //   p(x3,x2,x1,x0) = p(x3,x2,0,0) + p(0,0,x1,x0)
  //                  = p(y2,0,0) + p(0,0,y1,y0)
  //                  = p(y2,y1,y0)
  //
  // check_input_unit_indices checks one way of the condition: each input unit
  // index is mapped to an output index with the same physical location. This
  // lambda will be called again with input_shape and output_shape reversed to
  // check the other way.
  auto check_input_unit_indices = [](const Shape& input_shape,
                                     const Shape& output_shape) {
    // input_shape_dim0_major/output_shape_dim0_major has the same "dimensions"
    // as input_shape/output_shape and the dimension-0-major layout. These two
    // shapes are used for conversion between logical linear indices and
    // multi-dimensional indices.
    Shape input_shape_dim0_major = MakeShapeWithMonotonicDim0MajorLayout(
        input_shape.element_type(), AsInt64Slice(input_shape.dimensions()));
    Shape output_shape_dim0_major = MakeShapeWithMonotonicDim0MajorLayout(
        output_shape.element_type(), AsInt64Slice(output_shape.dimensions()));

    for (int64 input_dim = 0; input_dim < Rank(input_shape); ++input_dim) {
      if (input_shape.dimensions(input_dim) <= 1) {
        continue;
      }

      std::vector<int64> input_unit_index(Rank(input_shape), 0);
      input_unit_index[input_dim] = 1;
      int64 logical_linear_index =
          IndexUtil::MultidimensionalIndexToLinearIndex(input_shape_dim0_major,
                                                        input_unit_index);
      // output_index has the same logical linear index as input_unit_index.
      std::vector<int64> output_index =
          IndexUtil::LinearIndexToMultidimensionalIndex(output_shape_dim0_major,
                                                        logical_linear_index);
      // Check input_unit_index and output_index have the same physical linear
      // index.
      if (IndexUtil::MultidimensionalIndexToLinearIndex(input_shape,
                                                        input_unit_index) !=
          IndexUtil::MultidimensionalIndexToLinearIndex(output_shape,
                                                        output_index)) {
        return false;
      }
    }
    return true;
  };
  return check_input_unit_indices(input_shape, output_shape) &&
         check_input_unit_indices(output_shape, input_shape);
}

/* static */ tensorflow::gtl::optional<Shape> ShapeUtil::AlignLayouts(
    const Shape& input_shape, const Shape& output_shape) {
  int64 input_rank = Rank(input_shape);
  int64 output_rank = Rank(output_shape);

  // First, calculate an alignment of the dimensions. A consecutive sequence of
  // input dimensions and output dimensions belong to the same alignment part if
  // the products of their dimension bounds are the same. In the easiest case,
  // an alignment part consists of one input dimension and one output dimension
  // which both have the same dimension bound. An alignment part specifies which
  // dimensions need to be kept together in a physical layout if we want a
  // reshape to be a bitcast. The order of the alignment parts is defined by the
  // physical layout of the input shape, so when we construct the layout for the
  // output shape we just process the alignment parts in this order, and then
  // layout the dimensions belonging to each part in descending (major to minor)
  // order.

  // Stores the input and output dimension numbers where each alignment part
  // starts.
  std::vector<std::pair<int64, int64>> alignment;
  alignment.push_back({0, 0});

  // Stores a mapping from the input dimension to the alignment part it belongs
  // to.
  std::vector<int64> dimension_to_alignment_index(input_rank);
  int64 input_dimension_product = 1, output_dimension_product = 1;
  for (int64 i = 0, j = 0; i < input_rank || j < output_rank;) {
    // Check if we have reached the end of an alignment part.
    if (input_dimension_product == output_dimension_product &&
        input_dimension_product > 1) {
      alignment.push_back({i, j});
      input_dimension_product = output_dimension_product = 1;
    }
    if (input_dimension_product < output_dimension_product ||
        j == output_rank) {
      if (i == input_rank) {
        return tensorflow::gtl::nullopt;
      }
      dimension_to_alignment_index[i] = alignment.size() - 1;
      input_dimension_product *= input_shape.dimensions(i);
      ++i;
    } else {
      output_dimension_product *= output_shape.dimensions(j);
      ++j;
    }
  }
  if (input_dimension_product != output_dimension_product) {
    return tensorflow::gtl::nullopt;
  }
  // We also need to store an end element so that we know where the last
  // alignment part ends.
  alignment.push_back({input_rank, output_rank});

  // Now check if the physical layout can potentially be aligned to the output
  // shape by changing the physical layout of the output shape. We need to check
  // that all dimension numbers that belong to the same alignment part appear
  // consecutively, and are in descending order. However we can ignore any
  // trivial dimension bounds of 1, because they can be placed anywhere.
  auto input_dimension_numbers = input_shape.layout().minor_to_major();
  std::vector<int64> output_layout;
  output_layout.reserve(output_rank);
  for (int64 i = 0; i < input_rank;) {
    int64 current_dimension_number = input_dimension_numbers[i];

    // Skip trivial dimensions with a bound of 1.
    if (input_shape.dimensions(current_dimension_number) == 1) {
      ++i;
      continue;
    }

    // Calculate the number of non-trivial dimension bounds in the input shape
    // belonging to the current alignment part.
    const int64 current_alignment_index =
        dimension_to_alignment_index[current_dimension_number];
    // Because of the special end element that we added, we can be sure that
    // 'current_alignment_index' is < alignment.size() - 1.
    CHECK_LT(current_alignment_index, alignment.size() - 1);
    int64 num_non_trivial_dimensions_in_alignment_part = 0;
    for (int64 j = alignment[current_alignment_index].first;
         j < alignment[current_alignment_index + 1].first; ++j) {
      if (input_shape.dimensions(j) != 1) {
        ++num_non_trivial_dimensions_in_alignment_part;
      }
    }

    // Check that the following 'num_non_trivial_dimensions_in_alignment_part'
    // dimension numbers (ignoring dimension numbers with dimension bound 1) are
    // in descending order and belong to the current alignment part.
    for (int64 j = 0; j < num_non_trivial_dimensions_in_alignment_part;
         ++i, ++j) {
      if (i == input_rank) {
        return tensorflow::gtl::nullopt;
      }
      // Skip trivial dimensions with a bound of 1.
      if (input_shape.dimensions(input_dimension_numbers[i]) == 1) {
        --j;
        continue;
      }
      // If the current dimension number belongs to a different alignment part,
      // or the dimension numbers are not in descending order, we can return
      // early.
      if (dimension_to_alignment_index[input_dimension_numbers[i]] !=
              current_alignment_index ||
          input_dimension_numbers[i] > current_dimension_number) {
        return tensorflow::gtl::nullopt;
      }
      current_dimension_number = input_dimension_numbers[i];
    }

    // The output dimension numbers that belong to the current alignment part
    // need to appear in the same descending order as in the input. Again, we
    // can skip dimensions with a bound of 1.
    for (int64 j = alignment[current_alignment_index + 1].second - 1;
         j >= alignment[current_alignment_index].second; --j) {
      if (output_shape.dimensions(j) != 1) {
        output_layout.push_back(j);
      }
    }
  }
  // Now add all the dimensions with dimension bound 1 at the end of
  // 'output_layout'.
  for (int64 i = 0; i < output_rank; ++i) {
    if (output_shape.dimensions(i) == 1) {
      output_layout.push_back(i);
    }
  }
  CHECK_EQ(output_layout.size(), output_rank);
  Shape output_shape_with_layout = MakeShapeWithLayout(
      output_shape.element_type(), AsInt64Slice(output_shape.dimensions()),
      output_layout);
  CHECK(ReshapeIsBitcast(input_shape, output_shape_with_layout));
  return output_shape_with_layout;
}

/* static */ Shape ShapeUtil::DeleteDimension(int64 dim_to_delete,
                                              Shape shape) {
  shape.mutable_dimensions()->erase(shape.dimensions().begin() + dim_to_delete);
  if (LayoutUtil::HasLayout(shape)) {
    Layout* layout = shape.mutable_layout();
    for (size_t i = 0; i < layout->minor_to_major().size();) {
      if (layout->minor_to_major(i) == dim_to_delete) {
        layout->mutable_minor_to_major()->erase(
            layout->minor_to_major().begin() + i);
        continue;
      }
      if (layout->minor_to_major(i) > dim_to_delete) {
        (*layout->mutable_minor_to_major())[i] -= 1;
      }
      ++i;
    }
  }
  return shape;
}

/* static */ Shape ShapeUtil::FilterDimensions(
    const std::function<bool(int64)>& p, Shape shape) {
  std::vector<int64> dims_to_delete;
  for (int64 i = shape.dimensions().size() - 1; i >= 0; --i) {
    if (!p(i)) {
      dims_to_delete.push_back(i);
    }
  }
  for (int64 dim : dims_to_delete) {
    shape = DeleteDimension(dim, shape);
  }
  return shape;
}

}  // namespace xla