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/* Copyright 2015 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.
==============================================================================*/
#ifndef TENSORFLOW_CORE_FRAMEWORK_TENSOR_SHAPE_H_
#define TENSORFLOW_CORE_FRAMEWORK_TENSOR_SHAPE_H_
#include <string>
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
#include "tensorflow/core/framework/tensor_shape.pb.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/lib/core/status.h"
#include "tensorflow/core/lib/core/stringpiece.h"
#include "tensorflow/core/lib/gtl/array_slice.h"
#include "tensorflow/core/lib/gtl/inlined_vector.h"
#include "tensorflow/core/lib/strings/strcat.h"
#include "tensorflow/core/platform/logging.h"
namespace tensorflow {
// START_SKIP_DOXYGEN
template <class Shape>
class TensorShapeIter;
class TensorShape;
class PartialTensorShape;
// END_SKIP_DOXYGEN
/// Internal representation for both TensorShape and PartialTensorShape.
class TensorShapeRep {
public:
~TensorShapeRep();
/// Copy the specified shape
TensorShapeRep(const TensorShapeRep& b);
void operator=(const TensorShapeRep& b);
/// Move the specified shape. After moving, <b> is safe for destruction and
// can be reassigned into, but its dimensions and number of elements can be
// nonsensical (e.g., negative dimension sizes, or number of elements not
// properly recomputed).
TensorShapeRep(TensorShapeRep&& b);
void operator=(TensorShapeRep&& b);
/// Clear a tensor shape, producing the scalar shape.
void Clear();
// Maximum number of dimensions in a tensor.
// It's 254 because 255 = kUnknownRank is used to represent unknown rank.
static constexpr int MaxDimensions() { return 254; }
/// \brief Returns the number of elements in the tensor.
///
/// We use `int64` and not `size_t` to be compatible with `Eigen::Tensor`
/// which uses `ptrdiff_t`. For PartialTensorShape, -1 means not fully
/// defined.
int64 num_elements() const { return num_elements_; }
/// For error messages.
string DebugString() const;
static string DebugString(const TensorShapeProto& proto);
void DumpRep() const; // XXX
protected:
// Constructable only via TensorShapeBase
TensorShapeRep() = default;
void ClearAllButDataType();
// We use 16 bytes to represent a TensorShape. Because we need to
// be able to support full 64-bit dimension sizes and an arbitrary
// number of dimensions for a Tensor, but most tensor dimensions are
// significantly smaller than 64 bits and most tensors are 1, 2, or 3
// dimensions, we have several representations.
// Rep16: Supports up to 6 dimensions where each dimension is < 2^16 - 1
// Rep32: Supports up to 3 dimensions where each dimension is < 2^32 - 1
// Rep64: Supports arbitrary dimensionality, 64-bit dimensions using
// an out of line vector.
// For PartialTensorShape, a dimension of static_cast<uint??>(-1) is unknown.
// This value is not allowed in TensorShape either for format compatibility.
struct Rep16 {
uint16 dims_[6];
};
struct Rep32 {
uint32 dims_[3];
};
struct Rep64 {
gtl::InlinedVector<int64, 4>* dims_;
};
// We use the max value of uint16 or uint32 to represent unknown shapes, so
// the maximum representable valid shape in these representations is one less.
static const int64 kMaxRep16 = std::numeric_limits<uint16>::max() - 1;
static const int64 kMaxRep32 = std::numeric_limits<uint32>::max() - 1;
static const uint16 kUnknownRep16 = std::numeric_limits<uint16>::max();
static const uint32 kUnknownRep32 = std::numeric_limits<uint32>::max();
Rep16* as16() { return reinterpret_cast<Rep16*>(buf()); }
Rep32* as32() { return reinterpret_cast<Rep32*>(buf()); }
Rep64* as64() { return reinterpret_cast<Rep64*>(buf()); }
const Rep16* as16() const { return reinterpret_cast<const Rep16*>(buf()); }
const Rep32* as32() const { return reinterpret_cast<const Rep32*>(buf()); }
const Rep64* as64() const { return reinterpret_cast<const Rep64*>(buf()); }
enum RepTag { REP16 = 0, REP32 = 1, REP_OUT_OF_LINE = 2 };
// Since we have a convenient extra byte available, we allow the
// Tensor class to store an 8-bit value in this extra storage. This
// allows it to store the Tensor's datatype enum value here and avoid
// an extra word of storage.
friend class Tensor;
friend class TensorShapeTestHelper;
DataType data_type() const { return static_cast<DataType>(buf()[13]); }
void set_data_type(DataType dt) {
// We only have 8 bits available to store DataType, so make sure it fits
DCHECK_LT(static_cast<uint32>(dt), 256u);
buf()[13] = static_cast<uint8>(dt);
}
// We store the number of dimensions in byte 14, and the RepTag in byte 15.
// Bytes [0..13] vary depending on the representation.
// A value of 255 indicates unknown rank in the PartialTensorShape case.
static const uint8 kUnknownRank = 255;
uint8 ndims_byte() const { return buf()[14]; }
void set_ndims_byte(uint8 nd) { buf()[14] = nd; }
RepTag tag() const { return static_cast<RepTag>(buf()[15]); }
void set_tag(RepTag tag) { buf()[15] = static_cast<uint8>(tag); }
void set_num_elements(int64 n) { num_elements_ = n; }
private:
void DestructorOutOfLine();
void SlowCopyFrom(const TensorShapeRep& b);
uint8* buf() { return &u_.buf[0]; }
const uint8* buf() const { return &u_.buf[0]; }
union {
uint8 buf[16];
// Force data to be aligned enough for a pointer.
Rep64* unused_aligner;
} u_;
int64 num_elements_;
};
/// Base class for TensorShape and PartialTensorShape.
/// The class is templatized by either TensorShape or PartialTensorShape to
/// allow skipping known/unknown checks in the TensorShape case, but the
/// representation is shared exactly for fast conversion.
template <class Shape>
class TensorShapeBase : public TensorShapeRep {
public:
/// \brief Construct a `TensorShapeBase` from the provided sizes.
/// REQUIRES: `dim_sizes[i] >= 0` (or >= -1 for PartialTensorShape)
explicit TensorShapeBase(gtl::ArraySlice<int64> dim_sizes);
TensorShapeBase(std::initializer_list<int64> dim_sizes)
: TensorShapeBase(gtl::ArraySlice<int64>(dim_sizes)) {}
/// Construct an empty TensorShape, or an unknown rank PartialTensorShape
TensorShapeBase();
TensorShapeBase(const TensorShapeProto& proto);
/// Returns `true` iff `proto` is a valid tensor shape.
// For TensorShape, the proto shape must be fully defined.
static bool IsValid(const TensorShapeProto& proto);
/// Returns `OK` iff `proto` is a valid tensor shape, and a descriptive error
/// status otherwise.
static Status IsValidShape(const TensorShapeProto& proto);
/// \brief Add a dimension to the end ("inner-most").
/// REQUIRES: `size >= 0`
void AddDim(int64 size);
/// Appends all the dimensions from `shape`.
void AppendShape(const TensorShapeBase& shape);
// Maximum number of dimensions in a tensor.
static constexpr int MaxDimensions() { return 254; }
/// \brief Insert a dimension somewhere in the `TensorShape`.
/// REQUIRES: `0 <= d <= dims()`
/// REQUIRES: `size >= 0`
void InsertDim(int d, int64 size);
/// \brief Modifies the size of the dimension `d` to be `size`
/// REQUIRES: `0 <= d < dims()`
/// REQUIRES: `size >= 0`
void set_dim(int d, int64 size);
/// \brief Removes dimension `d` from the `TensorShape`.
/// REQUIRES: `0 <= d < dims()`
void RemoveDim(int d);
/// Return whether the rank is unknown
bool unknown_rank() const {
return kIsPartial && ndims_byte() == kUnknownRank;
}
/// Return the number of dimensions in the tensor.
/// Can be -1 meaning unknown rank for PartialTensorShape.
int dims() const {
uint8 dims = ndims_byte();
return kIsPartial && dims == kUnknownRank ? -1 : dims;
}
/// \brief Returns the number of elements in dimension `d`.
/// REQUIRES: `0 <= d < dims()`
// TODO(touts): Rename to `dimension()` to match
// `Eigen::Tensor::dimension()`?
int64 dim_size(int d) const;
/// Returns sizes of all dimensions.
// Returns an empty list for unknown rank PartialTensorShape.
gtl::InlinedVector<int64, 4> dim_sizes() const;
/// Return true iff the rank and all of the dimensions are well defined
// TODO(irving): Rename to is_fully_defined now that it's fast.
bool IsFullyDefined() const { return !kIsPartial || num_elements() != -1; }
/// Fill `*proto` from `*this`.
void AsProto(TensorShapeProto* proto) const;
/// For iterating through the dimensions.
TensorShapeIter<Shape> begin() const;
TensorShapeIter<Shape> end() const;
private:
void RecomputeNumElements();
// True for PartialTensorShape, false for TensorShape
static constexpr bool kIsPartial =
std::is_same<Shape, PartialTensorShape>::value;
static_assert(kIsPartial || std::is_same<Shape, TensorShape>::value,
"Shape is neither TensorShape nor PartialTensorShape");
// Used by AddDim and MakeShapeHelper. Does no error checking.
void UnsafeAddDim(int64 size, int64 new_num_elements);
// For use by TensorShapeUtils::MakeShape
template <class T, class S>
friend Status MakeShapeHelper(const T*, int64, S*);
};
/// Represents the shape of a Tensor.
///
/// A tensor's shape is denoted by its number of dimensions and a size for each
/// dimension. For example, a Tensor represented by a 3 x 4 matrix would have
/// a shape of 2-D, [3,4].
///
/// If you know the exact shape of your Tensor when you create the TensorShape
/// object, you can specify it then, or you can create a TensorShape with
/// zero dimensions and one element, and call AddDim() to add dimensions later.
class TensorShape : public TensorShapeBase<TensorShape> {
public:
using TensorShapeBase<TensorShape>::TensorShapeBase;
/// Allow a TensorShape to be used as a PartialTensorShape without copying
operator const PartialTensorShape&() const; // NOLINT(runtime/explicit)
/// Returns true if `*this` and `b` have the same sizes. Ignores
/// dimension names.
bool IsSameSize(const TensorShape& b) const;
bool operator==(const TensorShape& b) const { return IsSameSize(b); }
bool operator!=(const TensorShape& b) const { return !IsSameSize(b); }
/// Fill `*dsizes` from `*this`.
template <int NDIMS>
Eigen::DSizes<Eigen::DenseIndex, NDIMS> AsEigenDSizes() const;
/// Same as `AsEigenDSizes()` but allows for `NDIMS > dims()` -- in
/// which case we pad the rest of the sizes with 1.
template <int NDIMS>
Eigen::DSizes<Eigen::DenseIndex, NDIMS> AsEigenDSizesWithPadding() const;
private:
// These CHECK fail to ease debugging.
// REQUIRES: dims() == NDIMS
void CheckDimsEqual(int NDIMS) const;
// REQUIRES: dims() >= NDIMS
void CheckDimsAtLeast(int NDIMS) const;
};
/// Represents the value of one dimension in a TensorShape.
struct TensorShapeDim {
explicit TensorShapeDim(int64 s) : size(s) {}
int64 size;
};
// START_SKIP_DOXYGEN
template <class Shape>
class TensorShapeIter {
public:
TensorShapeIter(const Shape* shape, int d) : shape_(shape), d_(d) {}
bool operator==(const TensorShapeIter& rhs) {
DCHECK(shape_ == rhs.shape_);
return d_ == rhs.d_;
}
bool operator!=(const TensorShapeIter& rhs) {
DCHECK(shape_ == rhs.shape_);
return d_ != rhs.d_;
}
void operator++() { ++d_; }
TensorShapeDim operator*() { return TensorShapeDim(shape_->dim_size(d_)); }
private:
const Shape* shape_;
int d_;
};
// END_SKIP_DOXYGEN
/// \brief Static helper routines for `TensorShape`. Includes a few common
/// predicates on a tensor shape.
class TensorShapeUtils {
public:
static bool IsScalar(const TensorShape& shape) { return shape.dims() == 0; }
static bool IsVector(const TensorShape& shape) { return shape.dims() == 1; }
static bool IsVectorOrHigher(const TensorShape& shape) {
return shape.dims() >= 1;
}
static bool IsMatrix(const TensorShape& shape) { return shape.dims() == 2; }
static bool IsSquareMatrix(const TensorShape& shape) {
return shape.dims() == 2 && shape.dim_size(0) == shape.dim_size(1);
}
static bool IsMatrixOrHigher(const TensorShape& shape) {
return shape.dims() >= 2;
}
/// \brief Returns a `TensorShape` whose dimensions are
/// `dims[0]`, `dims[1]`, ..., `dims[n-1]`.
static Status MakeShape(const int32* dims, int64 n, TensorShape* out);
static Status MakeShape(const int64* dims, int64 n, TensorShape* out);
static Status MakeShape(gtl::ArraySlice<int32> shape, TensorShape* out);
static Status MakeShape(gtl::ArraySlice<int64> shape, TensorShape* out);
static Status MakeShape(const int32* dims, int64 n, PartialTensorShape* out);
static Status MakeShape(const int64* dims, int64 n, PartialTensorShape* out);
static Status MakeShape(gtl::ArraySlice<int32> shape,
PartialTensorShape* out);
static Status MakeShape(gtl::ArraySlice<int64> shape,
PartialTensorShape* out);
static string ShapeListString(const gtl::ArraySlice<TensorShape>& shapes);
/// \brief Returns true iff `shape` starts with `prefix`.
static bool StartsWith(const TensorShape& shape, const TensorShape& prefix);
/// \brief Returns true iff `shape` ends with `suffix`.
static bool EndsWith(const TensorShape& shape, const TensorShape& suffix);
/// \brief Returns the product of values in an int64 array,
/// or a failing Status if the array represents a value larger than
/// a `TensorShape` can hold.
static Status NumElements(gtl::ArraySlice<int64> shape, int64* num_elements);
};
/// Manages the partially known dimensions of a Tensor and their sizes.
class PartialTensorShape : public TensorShapeBase<PartialTensorShape> {
public:
PartialTensorShape() {}
using TensorShapeBase<PartialTensorShape>::TensorShapeBase;
/// Add a dimension to the end ("inner-most"), returns a new
/// PartialTensorShape.
/// REQUIRES: `size >= -1`, where -1 means unknown
PartialTensorShape Concatenate(int64 size) const;
/// Appends all the dimensions from `shape`. Returns a new
/// PartialTensorShape.
PartialTensorShape Concatenate(const PartialTensorShape& shape) const;
/// Merges all the dimensions from `shape`. Returns
/// `InvalidArgument` error if either `shape` has a different rank
/// or if any of the dimensions are incompatible.
Status MergeWith(const PartialTensorShape& shape,
PartialTensorShape* result) const;
/// Exact equality test. Returns true iff the ranks match (i.e., both are
/// unknown, or both are known and equal), and all dimensions are equal (i.e.,
/// both dimensions are known, or both are known and equal). This is a
/// stronger condition that IsCompatibleWith.
bool IsIdenticalTo(const PartialTensorShape& shape) const;
/// Return true iff the ranks match, and if the
/// dimensions all either match or one is unknown.
bool IsCompatibleWith(const PartialTensorShape& shape) const;
// Fill `*shape` from `*this`.
// If `*this` is not fully defined, returns false and
// `*shape` is left in an intermediate state. Otherwise
// returns true.
bool AsTensorShape(TensorShape* shape) const;
/// \brief Returns a `PartialTensorShape` whose dimensions are
/// `dims[0]`, `dims[1]`, ..., `dims[n-1]`. Values of -1 are
/// considered "unknown".
template <class T>
static Status MakePartialShape(const T* dims, int n,
PartialTensorShape* out) {
return TensorShapeUtils::MakeShape(dims, n, out);
}
};
/// \brief Static helper routines for `PartialTensorShape`. Includes a few
/// common predicates on a partially known tensor shape.
class PartialTensorShapeUtils {
public:
static string PartialShapeListString(
const gtl::ArraySlice<PartialTensorShape>& shapes);
static bool AreIdentical(const gtl::ArraySlice<PartialTensorShape>& shapes0,
const gtl::ArraySlice<PartialTensorShape>& shapes1);
static bool AreCompatible(const gtl::ArraySlice<PartialTensorShape>& shapes0,
const gtl::ArraySlice<PartialTensorShape>& shapes1);
};
// ----------------------------------------------------------------------------
// Template method implementation details below
// ----------------------------------------------------------------------------
template <int NDIMS>
Eigen::DSizes<Eigen::DenseIndex, NDIMS> TensorShape::AsEigenDSizes() const {
CheckDimsEqual(NDIMS);
return AsEigenDSizesWithPadding<NDIMS>();
}
template <int NDIMS>
Eigen::DSizes<Eigen::DenseIndex, NDIMS> TensorShape::AsEigenDSizesWithPadding()
const {
CheckDimsAtLeast(NDIMS);
static_assert(NDIMS <= TensorShape::MaxDimensions(), "Too many dimensions");
Eigen::DSizes<Eigen::DenseIndex, NDIMS> dsizes;
for (int d = 0; d < dims(); d++) {
dsizes[d] = dim_size(d);
}
for (int d = dims(); d < NDIMS; d++) {
dsizes[d] = 1;
}
return dsizes;
}
// ----------------------------------------------------------------------------
// Inlining of some performance critical routines
// ----------------------------------------------------------------------------
inline TensorShapeRep::TensorShapeRep(const TensorShapeRep& b) {
num_elements_ = b.num_elements_;
if (b.tag() != REP_OUT_OF_LINE) {
memcpy(buf(), b.buf(), sizeof(u_.buf));
// memcpy above Implicitly does:
// set_ndims_byte(b.ndims_byte());
// set_tag(b.tag());
} else {
set_tag(REP16); // So that SlowCopyFrom does not try to deallocate
SlowCopyFrom(b);
}
}
inline TensorShapeRep::TensorShapeRep(TensorShapeRep&& b) {
num_elements_ = b.num_elements_;
memcpy(buf(), b.buf(), sizeof(u_.buf));
// memcpy above Implicitly does:
// set_ndims_byte(b.ndims_byte());
// set_tag(b.tag());
b.set_tag(REP16); // other shape no longer owns out-of-line data, if any.
}
inline TensorShapeRep::~TensorShapeRep() {
if (tag() == REP_OUT_OF_LINE) {
DestructorOutOfLine();
}
}
inline void TensorShapeRep::operator=(const TensorShapeRep& b) {
num_elements_ = b.num_elements_;
if (tag() != REP_OUT_OF_LINE && b.tag() != REP_OUT_OF_LINE) {
memcpy(buf(), b.buf(), sizeof(u_.buf));
// memcpy above implicitly also does:
// set_tag(b.tag());
// set_ndims_byte(b.ndims_byte());
} else {
SlowCopyFrom(b);
}
}
inline void TensorShapeRep::operator=(TensorShapeRep&& b) {
if (tag() == REP_OUT_OF_LINE) {
DestructorOutOfLine();
}
num_elements_ = b.num_elements_;
memcpy(buf(), b.buf(), sizeof(u_.buf));
// memcpy above Implicitly does:
// set_ndims_byte(b.ndims_byte());
// set_tag(b.tag());
b.set_tag(REP16); // other shape no longer owns out-of-line data, if any.
}
inline TensorShape::operator const PartialTensorShape&() const {
// Downcast to the shared representation and upcast to PartialTensorShape
const TensorShapeRep* rep = this;
return *static_cast<const PartialTensorShape*>(rep);
}
// Declare explicit instantiations in .cc file
extern template class TensorShapeBase<TensorShape>;
extern template class TensorShapeBase<PartialTensorShape>;
} // namespace tensorflow
#endif // TENSORFLOW_CORE_FRAMEWORK_TENSOR_SHAPE_H_