EVOLUTION-MANAGER

Edit File: ragged_tensor_shape.py

# Copyright 2018 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. # ============================================================================== """Shapes & broadcasting for RaggedTensors.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.ragged import ragged_array_ops from tensorflow.python.ops.ragged import ragged_config from tensorflow.python.ops.ragged import ragged_tensor from tensorflow.python.ops.ragged import ragged_util class RaggedTensorDynamicShape(object): """A collection of tensors encoding the shape of a potentially ragged tensor. Each `RaggedTensorDynamicShape` consists of an ordered list of dimension sizes. There are two dimension types: * "Uniform dimensions" are dimensions where all slices have the same length. `RaggedTensorDynamicShape` records the size of each uniform dimension using a single scalar integer. * "Ragged dimensions" are dimensions whose slices may have different lengths. `RaggedTensorDynamicShape` records the size of each ragged dimension using an integer vector containing the slice lengths for all the slices across that dimension. Furthermore, there are two ways a dimension might be encoded: * "Partitioned dimensions" are dimensions that are encoded using a `RaggedTensor`'s `nested_row_splits`. The outermostmost partitioned dimension must be uniform, and the innermost partitioned dimension must be ragged. * "Inner dimensions" are dimensions that are encoded using a `RaggedTensor`'s `flat_values`. Inner dimensions are always uniform. The sizes of partitioned dimensions are recorded using `partitioned_dim_sizes` and `inner_dim_sizes`: * `partitioned_dim_sizes` is a list of tensors (one for each partitioned dimension). * For uniform dimensions, the tensor is an integer scalar specifying the size of all slices across that dimension. * For ragged dimensions, the tensor is an integer vector specifying the size of each slice across that dimension. * `inner_dim_sizes` is a single integer vector, where each element specifies the size of a single inner dimension. Examples: Tensor | Ragged | Partitioned Dim Sizes | Inner Dim : Rank : : Sizes ------------------------------ | ------ | ---------------------- | ---------- `[[1, 2, 3], [4, 5, 6]]` | 0 | | `2, 3` `[[1, 2], [], [3, 4, 5]]` | 1 | `3, (2, 0, 3)` | `[[[1, 2], [3, 4]], [[5, 6]]]` | 1 | `2, (2, 1)` | 2 `[[[1, 2], [3]], [[4, 5]]]` | 2 | `2, (2, 1), (2, 1, 2)` | """ def __init__(self, partitioned_dim_sizes, inner_dim_sizes, dim_size_dtype=None): """Creates a RaggedTensorDynamicShape. Args: partitioned_dim_sizes: A `list` of 0-D or 1-D integer `Tensor`, one for each partitioned dimension. If dimension `d` is uniform, then `partitioned_dim_sizes[d]` must be an integer scalar, specifying the size of all slices across dimension `d`. If dimension `d` is ragged, then `partitioned_dim_sizes[d]` must be an integer vector, specifying the size of each slice across dimension `d`. inner_dim_sizes: A 1-D integer `Tensor`, whose length is equal to the number of inner dimensions. `inner_dim_sizes[n]` is the size of all slices across the `n`th inner dimension (which is the `(len(partitioned_dim_sizes)+n)`th dimension in the overall tensor. dim_size_dtype: dtype for dimension sizes. If not specified, then it is chosen based on the dtypes of `partitioned_dim_sizes` and `inner_dim_sizes`. """ assert isinstance(partitioned_dim_sizes, (list, tuple)) with ops.name_scope(None, 'RaggedTensorDynamicShape', (partitioned_dim_sizes, inner_dim_sizes)): partitioned_dim_sizes = tuple( ops.convert_to_tensor(size, name='partitioned_dimension_size_%d' % i) for (i, size) in enumerate(partitioned_dim_sizes)) inner_dim_sizes = ops.convert_to_tensor( inner_dim_sizes, name='inner_dim_sizes') # Validate shapes. if partitioned_dim_sizes: for axis, dimension_size in enumerate(partitioned_dim_sizes): if dimension_size.shape.ndims is None: raise ValueError( 'rank of partitioned_dim_sizes[%d] is unknown' % axis) dimension_size.shape.with_rank_at_most(1) if partitioned_dim_sizes[0].shape.ndims == 1: raise ValueError('outermost partitioned dimension must be uniform') if partitioned_dim_sizes[-1].shape.ndims == 0: raise ValueError('innermost partitioned dimension must be ragged') inner_dim_sizes.shape.assert_has_rank(1) # Convert dimension size tensors to a single dtype. if dim_size_dtype is None: dim_size_dtypes = set( p.dtype for p in partitioned_dim_sizes if p.shape.ndims == 1) if not dim_size_dtypes: dim_size_dtype = dtypes.int64 elif len(dim_size_dtypes) == 1: dim_size_dtype = dim_size_dtypes.pop() else: if not ragged_config.auto_cast_partition_dtype(): raise ValueError('partitioned_dim_sizes must have matching dtypes') dim_size_dtype = dtypes.int64 partitioned_dim_sizes = tuple(math_ops.cast(p, dim_size_dtype) for p in partitioned_dim_sizes) inner_dim_sizes = math_ops.cast(inner_dim_sizes, dim_size_dtype) self._partitioned_dim_sizes = partitioned_dim_sizes self._inner_dim_sizes = inner_dim_sizes def __repr__(self): return ('RaggedTensorDynamicShape' '(partitioned_dim_sizes=%r, inner_dim_sizes=%r)' % (self._partitioned_dim_sizes, self._inner_dim_sizes)) @staticmethod def from_dim_sizes(dim_sizes): """Constructs a ragged shape from a list of dimension sizes. This list contains a single tensor for each dimension, where the tensor is a scalar if the dimension is uniform, or a vector if the dimension is ragged. Args: dim_sizes: List of int32 or int64 scalars or vectors. Returns: A RaggedTensorDynamicShape. """ with ops.name_scope(None, 'RaggedTensorDynamicShapeFromDimensionSizes', [dim_sizes]): dim_sizes = tuple( ops.convert_to_tensor(size, preferred_dtype=dtypes.int64, name='dim_sizes') for size in dim_sizes) # Split the dimensions into partitioned & inner dimensions. inner_split = 0 for dim, dim_size in enumerate(dim_sizes): if dim_size.shape.ndims == 1: inner_split = dim + 1 elif dim_size.shape.ndims != 0: raise ValueError('Each dim_size must be a scalar or a vector') return RaggedTensorDynamicShape(dim_sizes[:inner_split], dim_sizes[inner_split:]) @classmethod def from_tensor(cls, rt_input, dim_size_dtype=None): """Constructs a ragged shape for a potentially ragged tensor.""" with ops.name_scope(None, 'RaggedTensorDynamicShapeFromTensor', [rt_input]): rt_input = ragged_tensor.convert_to_tensor_or_ragged_tensor(rt_input) if not ragged_tensor.is_ragged(rt_input): return cls([], array_ops.shape(rt_input)) else: partitioned_dim_sizes = ( (rt_input.nrows(),) + rt_input.nested_row_lengths()) return RaggedTensorDynamicShape( partitioned_dim_sizes, array_ops.shape(rt_input.flat_values)[1:], dim_size_dtype=dim_size_dtype) def dimension_size(self, axis): """Returns the size of slices across the specified dimension.""" if not isinstance(axis, int): raise TypeError('axis must be an integer') partitioned_ndims = len(self._partitioned_dim_sizes) if axis < partitioned_ndims: return self._partitioned_dim_sizes[axis] else: return self._inner_dim_sizes[axis - partitioned_ndims] def is_ragged(self, axis): """Returns true if the indicated dimension is ragged.""" if not isinstance(axis, int): raise TypeError('axis must be an integer') rank = self.rank if axis < 0: raise ValueError('Negative axis values are not supported') elif rank is not None and axis >= rank: raise ValueError('Expected axis=%s < rank=%s' % (axis, rank)) else: return (axis > 0 and axis < len(self._partitioned_dim_sizes) and self._partitioned_dim_sizes[axis].shape.ndims == 1) @property def rank(self): """The number of dimensions in this shape, or None if unknown.""" inner_ndims = tensor_shape.dimension_value(self._inner_dim_sizes.shape[0]) if inner_ndims is None: return None else: return len(self._partitioned_dim_sizes) + inner_ndims @property def partitioned_dim_sizes(self): """The partitioned dimension sizes for this shape. Returns: A `list` of 0-D or 1-D integer `Tensor`. """ return self._partitioned_dim_sizes @property def inner_dim_sizes(self): """The inner dimension sizes for this shape. Returns: A 1-D integer `Tensor`. """ return self._inner_dim_sizes @property def num_partitioned_dimensions(self): """The number of partitioned dimensions in this shape.""" return len(self._partitioned_dim_sizes) @property def num_inner_dimensions(self): """The number of inner dimensions, or `None` if not statically known.""" return tensor_shape.dimension_value(self._inner_dim_sizes.shape[0]) @property def dim_size_dtype(self): """DType used by this shape for dimension sizes.""" return self._inner_dim_sizes.dtype def broadcast_to_rank(self, rank): """Adds leading size-1 dimensions to broadcast `self` to the given rank. E.g., if `shape1` is `[3, (D2), 4]`, then `shape1.broadcast_to_rank(5)` is `[1, 1, 3, (D2), 4]`. Args: rank: The rank for the returned shape. Returns: A RaggedTensorDynamicShape with `rank` dimensions, whose inner dimensions have the same size as `self` and whose outer dimensions have size `1`. Raises: ValueError: If `self.rank` is unknown or greater than `rank`. """ if self.rank is None: raise ValueError('Unable to broadcast: self.rank is unknown') dims_to_add = rank - self.rank if dims_to_add < 0: raise ValueError('Unable to broadcast: rank=%d must be greater than ' 'self.rank=%d.' % (rank, self.rank)) elif dims_to_add == 0: return self elif self._partitioned_dim_sizes: partitioned_dims = (1,) * dims_to_add + self._partitioned_dim_sizes return RaggedTensorDynamicShape(partitioned_dims, self._inner_dim_sizes) else: inner_dims = array_ops.concat( [array_ops.ones([dims_to_add], self.dim_size_dtype), self.inner_dim_sizes], axis=0) return RaggedTensorDynamicShape([], inner_dims) def broadcast_dimension(self, axis, lengths): """Returns a shape that is broadcast-compatible with self & lengths. * If dimension[axis] is uniform and lengths is a scalar, the check that either lengths==1 or axis==1 or lengths==axis, and tile dimension[axis] with tf.where(lengths==axis, 1, axis) repeats. * If dimension[axis] is uniform and lengths is a vector, then check that dimension[axis]==1, and raggedly tile dimension[axis] with lengths repeats. (we can skip tiling if we statically know that slice_lengths == 1??) * If dimension[axis] is ragged and lengths is a scalar, then check that lengths==1. * If dimension[axis] is ragged and lengths is a vector, then check that self.dimension_size(axis) == lengths. Args: axis: `int`. The dimension to broadcast. lengths: 0-D or 1-D integer `Tensor`. Returns: A `RaggedTensorDynamicShape`. """ lengths = ragged_util.convert_to_int_tensor( lengths, name='lengths', dtype=self.dim_size_dtype) # Check whether lengths is a scalar (for uniform dimensions) or # vector (for ragged dimensions). if lengths.shape.ndims is None: raise ValueError('lengths must have a known rank.') elif lengths.shape.ndims > 1: raise ValueError('lengths must be a scalar or vector') else: lengths_is_scalar = (lengths.shape.ndims == 0) # Verify that the shapes are compatible. if self.is_ragged(axis): if lengths_is_scalar: condition = math_ops.equal(lengths, 1) else: condition = math_ops.reduce_all( math_ops.equal(lengths, self.dimension_size(axis))) else: axis_dim_size = self.dimension_size(axis) if lengths_is_scalar: condition = ( math_ops.equal(lengths, 1) | math_ops.equal(axis_dim_size, 1) | math_ops.equal(axis_dim_size, lengths)) else: condition = math_ops.equal(axis_dim_size, 1) broadcast_err = [ 'Unable to broadcast: dimension size mismatch in dimension', axis, 'lengths=', lengths, 'dim_size=', self.dimension_size(axis) ] broadcast_check = control_flow_ops.Assert( condition, data=broadcast_err, summarize=10) with ops.control_dependencies([broadcast_check]): # Partitioned dimensions: if axis < self.num_partitioned_dimensions: if self.is_ragged(axis): # Use an identity op to make sure the check actually gets run. return RaggedTensorDynamicShape( self._partitioned_dim_sizes, array_ops.identity(self.inner_dim_sizes)) else: return self._broadcast_uniform_partitioned_dimension(axis, lengths) # Inner dimensions: else: if lengths_is_scalar: return self._broadcast_inner_dimension_to_uniform(axis, lengths) else: if axis == 0: raise ValueError('Unable to broadcast: ' 'outermost dimension must be uniform.') return self._broadcast_inner_dimension_to_ragged(axis, lengths) def num_slices_in_dimension(self, axis): """Returns the total number of slices across the indicated dimension.""" if axis < 0: return constant_op.constant(1, dtype=self.dim_size_dtype) elif self.is_ragged(axis): return math_ops.reduce_sum(self._partitioned_dim_sizes[axis]) else: return self.dimension_size(axis) * self.num_slices_in_dimension(axis - 1) def _broadcast_uniform_partitioned_dimension(self, axis, lengths): """Broadcasts the partitioned dimension `axis` to match `lengths`.""" axis_dim_size = self.dimension_size(axis) partitioned_sizes = list(self._partitioned_dim_sizes[:axis]) if lengths.shape.ndims == 0: lengths = array_ops.where( math_ops.equal(axis_dim_size, 1), lengths, axis_dim_size) repeats = array_ops.where(math_ops.equal(axis_dim_size, 1), lengths, 1) splits = array_ops.stack([0, self.num_slices_in_dimension(axis)]) else: splits = math_ops.range( array_ops.size(lengths, out_type=self.dim_size_dtype) + 1) repeats = lengths partitioned_sizes.append(lengths) for dim_size in self._partitioned_dim_sizes[axis + 1:]: if dim_size.shape.ndims == 0: partitioned_sizes.append(dim_size) splits *= dim_size else: partitioned_sizes.append( ragged_util.repeat_ranges(dim_size, splits, repeats)) splits = array_ops.gather( ragged_util.lengths_to_splits(dim_size), splits) inner_sizes = self._inner_dim_sizes return RaggedTensorDynamicShape(partitioned_sizes, inner_sizes) def _broadcast_inner_dimension_to_uniform(self, axis, length): """Broadcasts the inner dimension `axis` to match `lengths`.""" dim_size = self.dimension_size(axis) axis_in_inner_dims = axis - self.num_partitioned_dimensions partitioned_sizes = self._partitioned_dim_sizes inner_sizes = array_ops.concat([ self._inner_dim_sizes[:axis_in_inner_dims], [array_ops.where(math_ops.equal(dim_size, 1), length, dim_size)], self._inner_dim_sizes[axis_in_inner_dims + 1:] ], axis=0) return RaggedTensorDynamicShape(partitioned_sizes, inner_sizes) def _broadcast_inner_dimension_to_ragged(self, axis, lengths): axis_in_inner_dims = axis - self.num_partitioned_dimensions partitioned_sizes = ( self._partitioned_dim_sizes + tuple([ self._inner_dim_sizes[i] for i in range(axis_in_inner_dims) ]) + (lengths,)) inner_sizes = self._inner_dim_sizes[axis_in_inner_dims + 1:] return RaggedTensorDynamicShape(partitioned_sizes, inner_sizes) def with_dim_size_dtype(self, dtype): if dtype not in (dtypes.int32, dtypes.int64): raise ValueError('dtype must be int32 or int64') if self.dim_size_dtype == dtype: return self return RaggedTensorDynamicShape( [math_ops.cast(p, dtype) for p in self._partitioned_dim_sizes], math_ops.cast(self._inner_dim_sizes, dtype)) def broadcast_dynamic_shape(shape_x, shape_y): """Returns the shape formed by broadcasting two shapes to be compatible. Args: shape_x: A `RaggedTensorDynamicShape` shape_y: A `RaggedTensorDynamicShape` Returns: A `RaggedTensorDynamicShape`. Raises: ValueError: If `shape_x` and `shape_y` are not broadcast-compatible. """ if not isinstance(shape_x, RaggedTensorDynamicShape): raise TypeError('shape_x must be a RaggedTensorDynamicShape') if not isinstance(shape_y, RaggedTensorDynamicShape): raise TypeError('shape_y must be a RaggedTensorDynamicShape') # Broadcast both shapes to have the same rank. if shape_x.rank is None or shape_y.rank is None: raise ValueError('Unable to broadcast: unknown rank') broadcast_rank = max(shape_x.rank, shape_y.rank) shape_x = shape_x.broadcast_to_rank(broadcast_rank) shape_y = shape_y.broadcast_to_rank(broadcast_rank) # Broadcast dimensions one at a time, starting from the outermost dimension. for axis in range(broadcast_rank): shape_x = shape_x.broadcast_dimension(axis, shape_y.dimension_size(axis)) shape_y = shape_y.broadcast_dimension(axis, shape_x.dimension_size(axis)) return shape_x def broadcast_to(rt_input, shape, broadcast_inner_dimensions=True): """Broadcasts a potentially ragged tensor to a ragged shape. Tiles `rt_input` as necessary to match the given shape. Behavior is undefined if `rt_input` is not broadcast-compatible with `shape`. Args: rt_input: The potentially ragged tensor to broadcast. shape: A `RaggedTensorDynamicShape` broadcast_inner_dimensions: If false, then inner dimensions will not be tiled. Returns: A potentially ragged tensor whose values are taken from `rt_input`, and whose shape matches `shape`. """ if not isinstance(shape, RaggedTensorDynamicShape): raise TypeError('shape must be a RaggedTensorDynamicShape') rt_input = ragged_tensor.convert_to_tensor_or_ragged_tensor(rt_input) # Broadcasting to a uniform shape. if shape.num_partitioned_dimensions == 0: return _broadcast_to_uniform_shape(rt_input, shape, broadcast_inner_dimensions) else: return _broadcast_to_ragged_shape(rt_input, shape, broadcast_inner_dimensions) def _broadcast_to_uniform_shape(rt_input, shape, broadcast_inner_dimensions): """Broadcasts rt_input to the uniform shape `shape`.""" if isinstance(rt_input, ragged_tensor.RaggedTensor): raise ValueError('Incompatible with shape: ragged rank mismatch') if broadcast_inner_dimensions: return array_ops.broadcast_to(rt_input, shape.inner_dim_sizes) else: return rt_input def _broadcast_to_ragged_shape(rt_input, dst_shape, broadcast_inner_dimensions): """Broadcasts rt_input to the ragged shape `dst_shape`.""" # Check that rt_input and dst_shape have the same row_splits dtype. if (isinstance(rt_input, ragged_tensor.RaggedTensor) and rt_input.row_splits.dtype != dst_shape.dim_size_dtype): if not ragged_config.auto_cast_partition_dtype(): raise ValueError('rt_input and dst_shape have different row_split ' 'dtypes; use RaggedTensor.with_row_splits_dtype() or ' 'RaggedTensorDynamicShape.with_dim_size_dtype() to ' 'convert to a compatible dtype.') rt_input = rt_input.with_row_splits_dtype(dtypes.int64) dst_shape = dst_shape.with_dim_size_dtype(dtypes.int64) # dst_shape's rank and ragged_rank must be greater than or equal to rt_input's if rt_input.shape.ndims is None or dst_shape.rank is None: raise ValueError('Unable to broadcast: unknown rank') if rt_input.shape.ndims > dst_shape.rank: raise ValueError('Incompatible with shape: rank mismatch') if (isinstance(rt_input, ragged_tensor.RaggedTensor) and rt_input.ragged_rank >= dst_shape.num_partitioned_dimensions): raise ValueError('Incompatible with shape: ragged rank mismatch') src_shape = RaggedTensorDynamicShape.from_tensor(rt_input) src_shape = src_shape.broadcast_to_rank(dst_shape.rank) # Add dimensions to rt_input so its rank and ragged_rank matches dst_shape. if dst_shape.rank > rt_input.shape.ndims: if rt_input.shape.ndims < dst_shape.num_inner_dimensions + 1: rt_input = array_ops.reshape( rt_input, array_ops.concat([[-1], dst_shape.inner_dim_sizes], axis=0)) for _ in range(dst_shape.rank - rt_input.shape.ndims): if ragged_tensor.is_ragged(rt_input): nrows = rt_input.nrows() else: nrows = array_ops.shape(rt_input, out_type=dst_shape.dim_size_dtype)[0] rt_input = ragged_tensor.RaggedTensor.from_row_lengths(rt_input, [nrows], validate=False) # Add ragged dimensions to match dst_shape. if ragged_tensor.is_ragged(rt_input): inner_rank_diff = ( rt_input.flat_values.shape.ndims - 1 - dst_shape.num_inner_dimensions) if inner_rank_diff > 0: rt_input = rt_input.with_flat_values( ragged_tensor.RaggedTensor.from_tensor( rt_input.flat_values, ragged_rank=inner_rank_diff, row_splits_dtype=dst_shape.dim_size_dtype)) else: rt_input = ragged_tensor.RaggedTensor.from_tensor( rt_input, ragged_rank=dst_shape.num_partitioned_dimensions - 1, row_splits_dtype=dst_shape.dim_size_dtype) # Do broadcasting for any dimensions that will remain uniform. We can do # these all at once, since they're independent of one another. multiples = [1] * dst_shape.rank for axis in range(dst_shape.num_partitioned_dimensions): if not src_shape.is_ragged(axis) and not dst_shape.is_ragged(axis): src_size = src_shape.dimension_size(axis) dst_size = dst_shape.dimension_size(axis) if ((tensor_util.constant_value(src_size) in (1, None)) and (tensor_util.constant_value(dst_size) != 1)): multiples[axis] = array_ops.where( math_ops.equal(src_size, 1), dst_size, 1) if not all(isinstance(v, int) and v == 1 for v in multiples): multiples = array_ops.stack(multiples, axis=0) rt_input = ragged_array_ops.tile(rt_input, multiples) if broadcast_inner_dimensions: new_shape = array_ops.broadcast_dynamic_shape( array_ops.shape( rt_input.flat_values, out_type=dst_shape.dim_size_dtype), array_ops.concat([[1], dst_shape.inner_dim_sizes], axis=0)) rt_input = rt_input.with_flat_values( array_ops.broadcast_to(rt_input.flat_values, new_shape)) # Do broadcasting for dimensions that become ragged. We must do these from # outermost to innermost. for axis in range(dst_shape.num_partitioned_dimensions): if not src_shape.is_ragged(axis) and dst_shape.is_ragged(axis): dst_size = dst_shape.dimension_size(axis) rt_input = _ragged_tile_axis(rt_input, axis, dst_size, dst_shape.dim_size_dtype) return rt_input def _ragged_tile_axis(rt_input, axis, repeats, row_splits_dtype): """Tile a dimension of a RaggedTensor to match a ragged shape.""" assert axis > 0 # Outermost dimension may not be ragged. if not ragged_tensor.is_ragged(rt_input): rt_input = ragged_tensor.RaggedTensor.from_tensor( rt_input, ragged_rank=1, row_splits_dtype=row_splits_dtype) if axis > 1: return rt_input.with_values( _ragged_tile_axis(rt_input.values, axis - 1, repeats, row_splits_dtype)) else: src_row_splits = rt_input.nested_row_splits src_row_lengths = rt_input.nested_row_lengths() splits = src_row_splits[0] dst_row_lengths = [repeats] for i in range(1, len(src_row_lengths)): dst_row_lengths.append( ragged_util.repeat_ranges(src_row_lengths[i], splits, repeats)) splits = array_ops.gather(src_row_splits[i], splits) dst_values = ragged_util.repeat_ranges(rt_input.flat_values, splits, repeats) return ragged_tensor.RaggedTensor.from_nested_row_lengths( dst_values, dst_row_lengths, validate=False)