tensorflow/python/ops/ragged/ragged_tensor.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.
# ==============================================================================
"""Classes for storing ragged tensors and their values."""
import functools
import operator
import typing
import numpy as np
from tensorflow.core.protobuf import struct_pb2
from tensorflow.python import tf2
from tensorflow.python.client import session
from tensorflow.python.framework import composite_tensor
from tensorflow.python.framework import composite_tensor_gradient
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.framework import tensor as tensor_lib
from tensorflow.python.framework import tensor_conversion
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
from tensorflow.python.framework import type_spec
from tensorflow.python.framework import type_spec_registry
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import array_ops_stack
from tensorflow.python.ops import check_ops
from tensorflow.python.ops import cond
from tensorflow.python.ops import control_flow_assert
from tensorflow.python.ops import gen_ragged_conversion_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops.ragged import ragged_config
from tensorflow.python.ops.ragged import ragged_tensor_value
from tensorflow.python.ops.ragged import ragged_util
from tensorflow.python.ops.ragged.row_partition import RowPartition
from tensorflow.python.saved_model import nested_structure_coder
from tensorflow.python.types import core as core_types
from tensorflow.python.types import internal as internal_types
from tensorflow.python.util import dispatch
from tensorflow.python.util.tf_export import tf_export
from tensorflow.tools.docs import doc_controls
# pylint: disable=protected-access
_convert_row_partition = RowPartition._convert_row_partition
# pylint: enable=protected-access
# ===============================================================================
# RaggedTensor
# ===============================================================================
@tf_export("RaggedTensor")
class RaggedTensor(
composite_tensor.CompositeTensor,
internal_types.NativeObject,
internal_types.RaggedTensor,
):
"""Represents a ragged tensor.
A `RaggedTensor` is a tensor with one or more *ragged dimensions*, which are
dimensions whose slices may have different lengths. For example, the inner
(column) dimension of `rt=[[3, 1, 4, 1], [], [5, 9, 2], [6], []]` is ragged,
since the column slices (`rt[0, :]`, ..., `rt[4, :]`) have different lengths.
Dimensions whose slices all have the same length are called *uniform
dimensions*. The outermost dimension of a `RaggedTensor` is always uniform,
since it consists of a single slice (and so there is no possibility for
differing slice lengths).
The total number of dimensions in a `RaggedTensor` is called its *rank*,
and the number of ragged dimensions in a `RaggedTensor` is called its
*ragged-rank*. A `RaggedTensor`'s ragged-rank is fixed at graph creation
time: it can't depend on the runtime values of `Tensor`s, and can't vary
dynamically for different session runs.
Note that the `__init__` constructor is private. Please use one of the
following methods to construct a `RaggedTensor`:
* `tf.RaggedTensor.from_row_lengths`
* `tf.RaggedTensor.from_value_rowids`
* `tf.RaggedTensor.from_row_splits`
* `tf.RaggedTensor.from_row_starts`
* `tf.RaggedTensor.from_row_limits`
* `tf.RaggedTensor.from_nested_row_splits`
* `tf.RaggedTensor.from_nested_row_lengths`
* `tf.RaggedTensor.from_nested_value_rowids`
### Potentially Ragged Tensors
Many ops support both `Tensor`s and `RaggedTensor`s
(see [tf.ragged](https://www.tensorflow.org/api_docs/python/tf/ragged) for a
full listing). The term "potentially ragged tensor" may be used to refer to a
tensor that might be either a `Tensor` or a `RaggedTensor`. The ragged-rank
of a `Tensor` is zero.
### Documenting RaggedTensor Shapes
When documenting the shape of a RaggedTensor, ragged dimensions can be
indicated by enclosing them in parentheses. For example, the shape of
a 3-D `RaggedTensor` that stores the fixed-size word embedding for each
word in a sentence, for each sentence in a batch, could be written as
`[num_sentences, (num_words), embedding_size]`. The parentheses around
`(num_words)` indicate that dimension is ragged, and that the length
of each element list in that dimension may vary for each item.
### Component Tensors
Internally, a `RaggedTensor` consists of a concatenated list of values that
are partitioned into variable-length rows. In particular, each `RaggedTensor`
consists of:
* A `values` tensor, which concatenates the variable-length rows into a
flattened list. For example, the `values` tensor for
`[[3, 1, 4, 1], [], [5, 9, 2], [6], []]` is `[3, 1, 4, 1, 5, 9, 2, 6]`.
* A `row_splits` vector, which indicates how those flattened values are
divided into rows. In particular, the values for row `rt[i]` are stored
in the slice `rt.values[rt.row_splits[i]:rt.row_splits[i+1]]`.
Example:
>>> print(tf.RaggedTensor.from_row_splits(
... values=[3, 1, 4, 1, 5, 9, 2, 6],
... row_splits=[0, 4, 4, 7, 8, 8]))
<tf.RaggedTensor [[3, 1, 4, 1], [], [5, 9, 2], [6], []]>
### Alternative Row-Partitioning Schemes
In addition to `row_splits`, ragged tensors provide support for five other
row-partitioning schemes:
* `row_lengths`: a vector with shape `[nrows]`, which specifies the length
of each row.
* `value_rowids` and `nrows`: `value_rowids` is a vector with shape
`[nvals]`, corresponding one-to-one with `values`, which specifies
each value's row index. In particular, the row `rt[row]` consists of the
values `rt.values[j]` where `value_rowids[j]==row`. `nrows` is an
integer scalar that specifies the number of rows in the
`RaggedTensor`. (`nrows` is used to indicate trailing empty rows.)
* `row_starts`: a vector with shape `[nrows]`, which specifies the start
offset of each row. Equivalent to `row_splits[:-1]`.
* `row_limits`: a vector with shape `[nrows]`, which specifies the stop
offset of each row. Equivalent to `row_splits[1:]`.
* `uniform_row_length`: A scalar tensor, specifying the length of every
row. This row-partitioning scheme may only be used if all rows have
the same length.
Example: The following ragged tensors are equivalent, and all represent the
nested list `[[3, 1, 4, 1], [], [5, 9, 2], [6], []]`.
>>> values = [3, 1, 4, 1, 5, 9, 2, 6]
>>> RaggedTensor.from_row_splits(values, row_splits=[0, 4, 4, 7, 8, 8])
<tf.RaggedTensor [[3, 1, 4, 1], [], [5, 9, 2], [6], []]>
>>> RaggedTensor.from_row_lengths(values, row_lengths=[4, 0, 3, 1, 0])
<tf.RaggedTensor [[3, 1, 4, 1], [], [5, 9, 2], [6], []]>
>>> RaggedTensor.from_value_rowids(
... values, value_rowids=[0, 0, 0, 0, 2, 2, 2, 3], nrows=5)
<tf.RaggedTensor [[3, 1, 4, 1], [], [5, 9, 2], [6], []]>
>>> RaggedTensor.from_row_starts(values, row_starts=[0, 4, 4, 7, 8])
<tf.RaggedTensor [[3, 1, 4, 1], [], [5, 9, 2], [6], []]>
>>> RaggedTensor.from_row_limits(values, row_limits=[4, 4, 7, 8, 8])
<tf.RaggedTensor [[3, 1, 4, 1], [], [5, 9, 2], [6], []]>
>>> RaggedTensor.from_uniform_row_length(values, uniform_row_length=2)
<tf.RaggedTensor [[3, 1], [4, 1], [5, 9], [2, 6]]>
### Multiple Ragged Dimensions
`RaggedTensor`s with multiple ragged dimensions can be defined by using
a nested `RaggedTensor` for the `values` tensor. Each nested `RaggedTensor`
adds a single ragged dimension.
>>> inner_rt = RaggedTensor.from_row_splits( # =rt1 from above
... values=[3, 1, 4, 1, 5, 9, 2, 6], row_splits=[0, 4, 4, 7, 8, 8])
>>> outer_rt = RaggedTensor.from_row_splits(
... values=inner_rt, row_splits=[0, 3, 3, 5])
>>> print(outer_rt.to_list())
[[[3, 1, 4, 1], [], [5, 9, 2]], [], [[6], []]]
>>> print(outer_rt.ragged_rank)
2
The factory function `RaggedTensor.from_nested_row_splits` may be used to
construct a `RaggedTensor` with multiple ragged dimensions directly, by
providing a list of `row_splits` tensors:
>>> RaggedTensor.from_nested_row_splits(
... flat_values=[3, 1, 4, 1, 5, 9, 2, 6],
... nested_row_splits=([0, 3, 3, 5], [0, 4, 4, 7, 8, 8])).to_list()
[[[3, 1, 4, 1], [], [5, 9, 2]], [], [[6], []]]
### Uniform Inner Dimensions
`RaggedTensor`s with uniform inner dimensions can be defined
by using a multidimensional `Tensor` for `values`.
>>> rt = RaggedTensor.from_row_splits(values=tf.ones([5, 3], tf.int32),
... row_splits=[0, 2, 5])
>>> print(rt.to_list())
[[[1, 1, 1], [1, 1, 1]],
[[1, 1, 1], [1, 1, 1], [1, 1, 1]]]
>>> print(rt.shape)
(2, None, 3)
### Uniform Outer Dimensions
`RaggedTensor`s with uniform outer dimensions can be defined by using
one or more `RaggedTensor` with a `uniform_row_length` row-partitioning
tensor. For example, a `RaggedTensor` with shape `[2, 2, None]` can be
constructed with this method from a `RaggedTensor` values with shape
`[4, None]`:
>>> values = tf.ragged.constant([[1, 2, 3], [4], [5, 6], [7, 8, 9, 10]])
>>> print(values.shape)
(4, None)
>>> rt6 = tf.RaggedTensor.from_uniform_row_length(values, 2)
>>> print(rt6)
<tf.RaggedTensor [[[1, 2, 3], [4]], [[5, 6], [7, 8, 9, 10]]]>
>>> print(rt6.shape)
(2, 2, None)
Note that `rt6` only contains one ragged dimension (the innermost
dimension). In contrast, if `from_row_splits` is used to construct a similar
`RaggedTensor`, then that `RaggedTensor` will have two ragged dimensions:
>>> rt7 = tf.RaggedTensor.from_row_splits(values, [0, 2, 4])
>>> print(rt7.shape)
(2, None, None)
Uniform and ragged outer dimensions may be interleaved, meaning that a
tensor with any combination of ragged and uniform dimensions may be created.
For example, a RaggedTensor `t4` with shape `[3, None, 4, 8, None, 2]` could
be constructed as follows:
```python
t0 = tf.zeros([1000, 2]) # Shape: [1000, 2]
t1 = RaggedTensor.from_row_lengths(t0, [...]) # [160, None, 2]
t2 = RaggedTensor.from_uniform_row_length(t1, 8) # [20, 8, None, 2]
t3 = RaggedTensor.from_uniform_row_length(t2, 4) # [5, 4, 8, None, 2]
t4 = RaggedTensor.from_row_lengths(t3, [...]) # [3, None, 4, 8, None, 2]
```
"""
#=============================================================================
# Constructor (private)
#=============================================================================
@doc_controls.do_not_generate_docs
def __init__(self, values, row_partition, internal=False):
"""Creates a `RaggedTensor` with a specified partitioning for `values`.
This constructor is private -- please use one of the following ops to
build `RaggedTensor`s:
* `tf.RaggedTensor.from_row_lengths`
* `tf.RaggedTensor.from_value_rowids`
* `tf.RaggedTensor.from_row_splits`
* `tf.RaggedTensor.from_row_starts`
* `tf.RaggedTensor.from_row_limits`
* `tf.RaggedTensor.from_nested_row_splits`
* `tf.RaggedTensor.from_nested_row_lengths`
* `tf.RaggedTensor.from_nested_value_rowids`
Args:
values: A potentially ragged tensor of any dtype and shape `[nvals, ...]`.
row_partition: A `RowPartition` object, representing the arrangement of
the lists at the top level.
internal: True if the constructor is being called by one of the factory
methods. If false, an exception will be raised.
Raises:
ValueError: If internal = False. Note that this method is intended only
for internal use.
TypeError: If values is not a `RaggedTensor` or `Tensor`, or
row_partition is not a `RowPartition`.
"""
if not internal:
raise ValueError("RaggedTensor constructor is private; please use one "
"of the factory methods instead (e.g., "
"RaggedTensor.from_row_lengths())")
_assert_is_supported_ragged_values_type(values)
if not isinstance(row_partition, RowPartition):
raise TypeError(f"Argument `row_partition` must be a RowPartition. "
f"Received {row_partition}.")
# Validate shapes.
values.shape.with_rank_at_least(1)
if isinstance(values, RaggedTensor):
# pylint: disable=protected-access
assert row_partition.dtype == values._row_partition.dtype
self._values = values
self._row_partition = row_partition
#=============================================================================
# Factory Methods
#=============================================================================
@classmethod
def _from_row_partition(cls, values, row_partition, validate=True):
"""Creates a `RaggedTensor` with a row partition.
This is used as a way for RaggedTensors to share row partitions.
The outer dimension of values must be equal to `partition.nvals()`.
Args:
values: A potentially ragged tensor.
row_partition: a `RowPartition`: can be shared between tensors.
validate: If true, then use assertions to check that the arguments form a
valid `RaggedTensor`.
Returns:
A `RaggedTensor`. `result.rank = values.rank + 1`.
`result.ragged_rank = values.ragged_rank + 1`.
Raises:
ValueError: If partition.nvals() != _nrows(values)
"""
if not isinstance(row_partition, RowPartition):
raise TypeError(f"Argument `row_partition` must be a RowPartition. "
f"Received {row_partition}.")
if not isinstance(validate, bool):
raise TypeError(f"Argument `validate` must have type bool. "
f"Received {validate}.")
values, row_partition = cls._convert_values_and_partition(
values, row_partition, "partition")
if row_partition._has_precomputed_value_rowids(): # pylint: disable=protected-access
value_rowids_shape = row_partition.value_rowids().shape
values.shape[:1].assert_is_compatible_with(value_rowids_shape)
if validate:
msg = "Arguments to _from_row_partition do not form a valid RaggedTensor"
nvals = _nrows(values, row_partition.dtype)
checks = [
check_ops.assert_equal(
math_ops.cast(row_partition.nvals(), row_partition.dtype),
nvals,
message=msg),
]
if not isinstance(values, RaggedTensor):
checks.append(check_ops.assert_rank_at_least(values, 1))
row_partition = row_partition._with_dependencies(checks) # pylint: disable=protected-access
return cls(values=values, internal=True, row_partition=row_partition)
@classmethod
@dispatch.add_dispatch_support
def from_value_rowids(cls,
values,
value_rowids,
nrows=None,
name=None,
validate=True):
"""Creates a `RaggedTensor` with rows partitioned by `value_rowids`.
The returned `RaggedTensor` corresponds with the python list defined by:
```python
result = [[values[i] for i in range(len(values)) if value_rowids[i] == row]
for row in range(nrows)]
```
Args:
values: A potentially ragged tensor with shape `[nvals, ...]`.
value_rowids: A 1-D integer tensor with shape `[nvals]`, which corresponds
one-to-one with `values`, and specifies each value's row index. Must be
nonnegative, and must be sorted in ascending order.
nrows: An integer scalar specifying the number of rows. This should be
specified if the `RaggedTensor` may containing empty training rows. Must
be greater than `value_rowids[-1]` (or zero if `value_rowids` is empty).
Defaults to `value_rowids[-1] + 1` (or zero if `value_rowids` is empty).
name: A name prefix for the RaggedTensor (optional).
validate: If true, then use assertions to check that the arguments form
a valid `RaggedTensor`. Note: these assertions incur a runtime cost,
since they must be checked for each tensor value.
Returns:
A `RaggedTensor`. `result.rank = values.rank + 1`.
`result.ragged_rank = values.ragged_rank + 1`.
Raises:
ValueError: If `nrows` is incompatible with `value_rowids`.
#### Example:
>>> print(tf.RaggedTensor.from_value_rowids(
... values=[3, 1, 4, 1, 5, 9, 2, 6],
... value_rowids=[0, 0, 0, 0, 2, 2, 2, 3],
... nrows=5))
<tf.RaggedTensor [[3, 1, 4, 1], [], [5, 9, 2], [6], []]>
"""
if not isinstance(validate, bool):
raise TypeError(f"Argument `validate` must have type bool. "
f"Received {validate}.")
with ops.name_scope(name, "RaggedFromValueRowIds",
[values, value_rowids, nrows]):
row_partition = RowPartition.from_value_rowids(
value_rowids=value_rowids,
nrows=nrows,
validate=validate,
dtype_hint=_get_optional_partition_dtype(values))
return cls._from_row_partition(values, row_partition, validate=validate)
@classmethod
@dispatch.add_dispatch_support
def from_row_splits(cls, values, row_splits, name=None, validate=True):
"""Creates a `RaggedTensor` with rows partitioned by `row_splits`.
The returned `RaggedTensor` corresponds with the python list defined by:
```python
result = [values[row_splits[i]:row_splits[i + 1]]
for i in range(len(row_splits) - 1)]
```
Args:
values: A potentially ragged tensor with shape `[nvals, ...]`.
row_splits: A 1-D integer tensor with shape `[nrows+1]`. Must not be
empty, and must be sorted in ascending order. `row_splits[0]` must be
zero and `row_splits[-1]` must be `nvals`.
name: A name prefix for the RaggedTensor (optional).
validate: If true, then use assertions to check that the arguments form
a valid `RaggedTensor`. Note: these assertions incur a runtime cost,
since they must be checked for each tensor value.
Returns:
A `RaggedTensor`. `result.rank = values.rank + 1`.
`result.ragged_rank = values.ragged_rank + 1`.
Raises:
ValueError: If `row_splits` is an empty list.
#### Example:
>>> print(tf.RaggedTensor.from_row_splits(
... values=[3, 1, 4, 1, 5, 9, 2, 6],
... row_splits=[0, 4, 4, 7, 8, 8]))
<tf.RaggedTensor [[3, 1, 4, 1], [], [5, 9, 2], [6], []]>
"""
if not isinstance(validate, bool):
raise TypeError(f"Argument `validate` must have type bool. "
f"Received {validate}.")
with ops.name_scope(name, "RaggedFromRowSplits", [values, row_splits]):
row_partition = RowPartition.from_row_splits(
row_splits=row_splits,
validate=validate,
dtype_hint=_get_optional_partition_dtype(values))
return cls._from_row_partition(values, row_partition, validate=validate)
@classmethod
@dispatch.add_dispatch_support
def from_row_lengths(cls, values, row_lengths, name=None, validate=True):
"""Creates a `RaggedTensor` with rows partitioned by `row_lengths`.
The returned `RaggedTensor` corresponds with the python list defined by:
```python
result = [[values.pop(0) for i in range(length)]
for length in row_lengths]
```
Args:
values: A potentially ragged tensor with shape `[nvals, ...]`.
row_lengths: A 1-D integer tensor with shape `[nrows]`. Must be
nonnegative. `sum(row_lengths)` must be `nvals`.
name: A name prefix for the RaggedTensor (optional).
validate: If true, then use assertions to check that the arguments form
a valid `RaggedTensor`. Note: these assertions incur a runtime cost,
since they must be checked for each tensor value.
Returns:
A `RaggedTensor`. `result.rank = values.rank + 1`.
`result.ragged_rank = values.ragged_rank + 1`.
#### Example:
>>> print(tf.RaggedTensor.from_row_lengths(
... values=[3, 1, 4, 1, 5, 9, 2, 6],
... row_lengths=[4, 0, 3, 1, 0]))
<tf.RaggedTensor [[3, 1, 4, 1], [], [5, 9, 2], [6], []]>
"""
if not isinstance(validate, bool):
raise TypeError(f"Argument `validate` must have type bool. "
f"Received {validate}.")
with ops.name_scope(name, "RaggedFromRowLengths", [values, row_lengths]):
row_partition = RowPartition.from_row_lengths(
row_lengths=row_lengths,
validate=validate,
dtype_hint=_get_optional_partition_dtype(values))
return cls._from_row_partition(values, row_partition, validate=validate)
@classmethod
@dispatch.add_dispatch_support
def from_row_starts(cls, values, row_starts, name=None, validate=True):
"""Creates a `RaggedTensor` with rows partitioned by `row_starts`.
Equivalent to: `from_row_splits(values, concat([row_starts, nvals]))`.
Args:
values: A potentially ragged tensor with shape `[nvals, ...]`.
row_starts: A 1-D integer tensor with shape `[nrows]`. Must be
nonnegative and sorted in ascending order. If `nrows>0`, then
`row_starts[0]` must be zero.
name: A name prefix for the RaggedTensor (optional).
validate: If true, then use assertions to check that the arguments form
a valid `RaggedTensor`. Note: these assertions incur a runtime cost,
since they must be checked for each tensor value.
Returns:
A `RaggedTensor`. `result.rank = values.rank + 1`.
`result.ragged_rank = values.ragged_rank + 1`.
#### Example:
>>> print(tf.RaggedTensor.from_row_starts(
... values=[3, 1, 4, 1, 5, 9, 2, 6],
... row_starts=[0, 4, 4, 7, 8]))
<tf.RaggedTensor [[3, 1, 4, 1], [], [5, 9, 2], [6], []]>
"""
if not isinstance(validate, bool):
raise TypeError(f"Argument `validate` must have type bool. "
f"Received {validate}.")
with ops.name_scope(name, "RaggedFromRowStarts", [values, row_starts]):
values = _convert_to_ragged_tensor_values(values)
row_partition = RowPartition.from_row_starts(
row_starts=row_starts,
nvals=_nrows(values),
validate=validate,
dtype_hint=_get_optional_partition_dtype(values))
return cls._from_row_partition(values, row_partition, validate=validate)
@classmethod
@dispatch.add_dispatch_support
def from_row_limits(cls, values, row_limits, name=None, validate=True):
"""Creates a `RaggedTensor` with rows partitioned by `row_limits`.
Equivalent to: `from_row_splits(values, concat([0, row_limits]))`.
Args:
values: A potentially ragged tensor with shape `[nvals, ...]`.
row_limits: A 1-D integer tensor with shape `[nrows]`. Must be sorted in
ascending order. If `nrows>0`, then `row_limits[-1]` must be `nvals`.
name: A name prefix for the RaggedTensor (optional).
validate: If true, then use assertions to check that the arguments form
a valid `RaggedTensor`. Note: these assertions incur a runtime cost,
since they must be checked for each tensor value.
Returns:
A `RaggedTensor`. `result.rank = values.rank + 1`.
`result.ragged_rank = values.ragged_rank + 1`.
#### Example:
>>> print(tf.RaggedTensor.from_row_limits(
... values=[3, 1, 4, 1, 5, 9, 2, 6],
... row_limits=[4, 4, 7, 8, 8]))
<tf.RaggedTensor [[3, 1, 4, 1], [], [5, 9, 2], [6], []]>
"""
if not isinstance(validate, bool):
raise TypeError(f"Argument `validate` must have type bool. "
f"Received {validate}.")
with ops.name_scope(name, "RaggedFromRowLimits", [values, row_limits]):
values = _convert_to_ragged_tensor_values(values)
row_partition = RowPartition.from_row_limits(
row_limits=row_limits,
validate=validate,
dtype_hint=_get_optional_partition_dtype(values))
return cls._from_row_partition(values, row_partition, validate=validate)
@classmethod
@dispatch.add_dispatch_support
def from_uniform_row_length(cls,
values,
uniform_row_length,
nrows=None,
validate=True,
name=None):
"""Creates a `RaggedTensor` with rows partitioned by `uniform_row_length`.
This method can be used to create `RaggedTensor`s with multiple uniform
outer dimensions. For example, a `RaggedTensor` with shape `[2, 2, None]`
can be constructed with this method from a `RaggedTensor` values with shape
`[4, None]`:
>>> values = tf.ragged.constant([[1, 2, 3], [4], [5, 6], [7, 8, 9, 10]])
>>> print(values.shape)
(4, None)
>>> rt1 = tf.RaggedTensor.from_uniform_row_length(values, 2)
>>> print(rt1)
<tf.RaggedTensor [[[1, 2, 3], [4]], [[5, 6], [7, 8, 9, 10]]]>
>>> print(rt1.shape)
(2, 2, None)
Note that `rt1` only contains one ragged dimension (the innermost
dimension). In contrast, if `from_row_splits` is used to construct a similar
`RaggedTensor`, then that `RaggedTensor` will have two ragged dimensions:
>>> rt2 = tf.RaggedTensor.from_row_splits(values, [0, 2, 4])
>>> print(rt2.shape)
(2, None, None)
Args:
values: A potentially ragged tensor with shape `[nvals, ...]`.
uniform_row_length: A scalar integer tensor. Must be nonnegative. The
size of the outer axis of `values` must be evenly divisible by
`uniform_row_length`.
nrows: The number of rows in the constructed RaggedTensor. If not
specified, then it defaults to `nvals/uniform_row_length` (or `0` if
`uniform_row_length==0`). `nrows` only needs to be specified if
`uniform_row_length` might be zero. `uniform_row_length*nrows` must be
`nvals`.
validate: If true, then use assertions to check that the arguments form
a valid `RaggedTensor`. Note: these assertions incur a runtime cost,
since they must be checked for each tensor value.
name: A name prefix for the RaggedTensor (optional).
Returns:
A `RaggedTensor` that corresponds with the python list defined by:
```python
result = [[values.pop(0) for i in range(uniform_row_length)]
for _ in range(nrows)]
```
`result.rank = values.rank + 1`.
`result.ragged_rank = values.ragged_rank + 1`.
"""
if not isinstance(validate, bool):
raise TypeError(f"Argument `validate` must have type bool. "
f"Received {validate}.")
with ops.name_scope(name, "RaggedFromUniformRowLength",
[values, uniform_row_length, nrows]):
values = _convert_to_ragged_tensor_values(values)
uniform_row_length = _convert_row_partition(
uniform_row_length, "UniformRowLength",
_get_optional_partition_dtype(values))
nvals = _nvals_uniform_row_length(values, uniform_row_length)
row_partition = RowPartition.from_uniform_row_length(
uniform_row_length=uniform_row_length,
nvals=nvals,
nrows=nrows,
validate=validate,
dtype_hint=_get_optional_partition_dtype(values))
return cls._from_row_partition(values, row_partition, validate=validate)
@classmethod
@dispatch.add_dispatch_support
def from_nested_value_rowids(cls,
flat_values,
nested_value_rowids,
nested_nrows=None,
name=None,
validate=True):
"""Creates a `RaggedTensor` from a nested list of `value_rowids` tensors.
Equivalent to:
```python
result = flat_values
for (rowids, nrows) in reversed(zip(nested_value_rowids, nested_nrows)):
result = from_value_rowids(result, rowids, nrows)
```
Args:
flat_values: A potentially ragged tensor.
nested_value_rowids: A list of 1-D integer tensors. The `i`th tensor is
used as the `value_rowids` for the `i`th ragged dimension.
nested_nrows: A list of integer scalars. The `i`th scalar is used as the
`nrows` for the `i`th ragged dimension.
name: A name prefix for the RaggedTensor (optional).
validate: If true, then use assertions to check that the arguments form
a valid `RaggedTensor`. Note: these assertions incur a runtime cost,
since they must be checked for each tensor value.
Returns:
A `RaggedTensor` (or `flat_values` if `nested_value_rowids` is empty).
Raises:
ValueError: If `len(nested_values_rowids) != len(nested_nrows)`.
"""
if not isinstance(validate, bool):
raise TypeError(f"Argument `validate` must have type bool. "
f"Received {validate}.")
if isinstance(nested_value_rowids, tensor_lib.Tensor):
raise TypeError(f"Argument `nested_value_rowids` must be a list of "
f"Tensors. Received {nested_value_rowids}.")
if nested_nrows is None:
nested_nrows = [None] * len(nested_value_rowids)
else:
if isinstance(nested_nrows, tensor_lib.Tensor):
raise TypeError(f"Argument `nested_nrows` must be a list of "
f"Tensors. Received {nested_nrows}.")
if len(nested_nrows) != len(nested_value_rowids):
raise ValueError(
f"Argument `nested_nrows` must have the same length as "
f"argument `nested_value_rowids`. len(nested_nrows) = "
f"{len(nested_nrows)} vs. len(nested_values_rowids) = "
f"{len(nested_value_rowids)}.")
with ops.name_scope(name, "RaggedFromNestedValueRowIds", [flat_values] +
list(nested_value_rowids) + list(nested_nrows)):
result = flat_values
for value_rowids, nrows in reversed(
list(zip(nested_value_rowids, nested_nrows))):
result = cls.from_value_rowids(
result, value_rowids, nrows, validate=validate)
return result
@classmethod
@dispatch.add_dispatch_support
def from_nested_row_splits(cls,
flat_values,
nested_row_splits,
name=None,
validate=True):
"""Creates a `RaggedTensor` from a nested list of `row_splits` tensors.
Equivalent to:
```python
result = flat_values
for row_splits in reversed(nested_row_splits):
result = from_row_splits(result, row_splits)
```
Args:
flat_values: A potentially ragged tensor.
nested_row_splits: A list of 1-D integer tensors. The `i`th tensor is
used as the `row_splits` for the `i`th ragged dimension.
name: A name prefix for the RaggedTensor (optional).
validate: If true, then use assertions to check that the arguments form
a valid `RaggedTensor`. Note: these assertions incur a runtime cost,
since they must be checked for each tensor value.
Returns:
A `RaggedTensor` (or `flat_values` if `nested_row_splits` is empty).
"""
if not isinstance(validate, bool):
raise TypeError(f"Argument `validate` must have type bool. "
f"Received {validate}.")
if isinstance(nested_row_splits, tensor_lib.Tensor):
raise TypeError(f"Argument `nested_row_splits` must be a list of "
f"Tensors. Received {nested_row_splits}.")
with ops.name_scope(name, "RaggedFromNestedRowSplits",
[flat_values] + list(nested_row_splits)):
result = flat_values
for splits in reversed(nested_row_splits):
result = cls.from_row_splits(result, splits, validate=validate)
return result
@classmethod
@dispatch.add_dispatch_support
def from_nested_row_lengths(cls,
flat_values,
nested_row_lengths,
name=None,
validate=True):
"""Creates a `RaggedTensor` from a nested list of `row_lengths` tensors.
Equivalent to:
```python
result = flat_values
for row_lengths in reversed(nested_row_lengths):
result = from_row_lengths(result, row_lengths)
```
Args:
flat_values: A potentially ragged tensor.
nested_row_lengths: A list of 1-D integer tensors. The `i`th tensor is
used as the `row_lengths` for the `i`th ragged dimension.
name: A name prefix for the RaggedTensor (optional).
validate: If true, then use assertions to check that the arguments form
a valid `RaggedTensor`. Note: these assertions incur a runtime cost,
since they must be checked for each tensor value.
Returns:
A `RaggedTensor` (or `flat_values` if `nested_row_lengths` is empty).
"""
if not isinstance(validate, bool):
raise TypeError(f"Argument `validate` must have type bool. "
f"Received {validate}.")
if isinstance(nested_row_lengths, tensor_lib.Tensor):
raise TypeError(f"Argument `nested_row_lengths` must be a list of "
f"Tensors. Received {nested_row_lengths}.")
with ops.name_scope(name, "RaggedFromNestedRowlengths",
[flat_values] + list(nested_row_lengths)):
result = flat_values
for lengths in reversed(nested_row_lengths):
result = cls.from_row_lengths(result, lengths, validate=validate)
return result
@classmethod
def _from_nested_row_partitions(cls,
flat_values,
nested_row_partitions,
name=None,
validate=True):
"""Creates a `RaggedTensor` from a nested list of row partitions.
Equivalent to:
```python
result = flat_values
for row_partition in reversed(nested_row_partitions):
result = _from_row_partition(result, row_partition)
```
Args:
flat_values: A potentially ragged tensor.
nested_row_partitions: A list of row partitions. The `i`th element is
used as the row partition for the `i`th ragged dimension.
name: A name prefix for the RaggedTensor (optional).
validate: If true, then use assertions to check that the arguments form
a valid `RaggedTensor`. Note: these assertions incur a runtime cost,
since they must be checked for each tensor value.
Returns:
A `RaggedTensor` (or `flat_values` if `nested_row_lengths` is empty).
"""
if not isinstance(validate, bool):
raise TypeError(f"Argument `validate` must have type bool. "
f"Received {validate}.")
if isinstance(nested_row_partitions, RowPartition):
raise TypeError(f"Argument `nested_row_partitions` must be a list of "
f"RowPartitions. Received {nested_row_partitions}.")
if isinstance(nested_row_partitions, tensor_lib.Tensor):
raise TypeError(f"Argument `nested_row_partitions` must be a list of "
f"RowPartitions. Received {nested_row_partitions}.")
with ops.name_scope(name, "RaggedFromNestedRowPartitions",
[flat_values] + list(nested_row_partitions)):
result = flat_values
for partition in reversed(nested_row_partitions):
result = cls._from_row_partition(result, partition, validate=validate)
return result
@classmethod
def _convert_values_and_partition(cls, values, row_partition, name):
"""Converts `values` and `partition` to Tensors.
If `values` is a `RaggedTensor`, then converts `values` and `partition`
to have compatible row-partitioning dtypes. In particular, if any of the
row partitioning tensors are `int64`, then all of the other row
partitioning tensors wil be cast to `int64` (if auto_cast_partition_dtype()
is true) or an error will be raised (if auto_cast_partition_dtype() is
false).
Args:
values: The `values` for the `RaggedTensor` being constructed.
row_partition: A RowPartition object for the `RaggedTensor` being
constructed.
name: The name of the RowPartition object.
Returns:
A tuple (values, partition).
"""
if not isinstance(row_partition, RowPartition):
raise TypeError(f"Argument `row_partition` must be a RowPartition. "
f"Received {row_partition}.")
if isinstance(values, RaggedTensor):
# pylint: disable=protected-access
if values._row_partition.dtype != row_partition.dtype:
if not ragged_config.auto_cast_partition_dtype():
# pylint: disable=protected-access
# TODO(edloper): get rid of the `name` parameter.
raise ValueError(
f"Argument `row_partition` of RaggedTensor with name: {name} "
f"must have same dtype as Argument `values`. "
f"({row_partition.dtype} vs. {values._row_partition.dtype}).")
values = values.with_row_splits_dtype(row_partition.dtype)
else:
values = _convert_to_ragged_tensor_values(values)
return (values, row_partition)
#=============================================================================
# Accessors
#=============================================================================
@property
def dtype(self):
"""The `DType` of values in this tensor."""
return self._values.dtype
@property
def shape(self):
"""The statically known shape of this ragged tensor.
Returns:
A `TensorShape` containing the statically known shape of this ragged
tensor. Ragged dimensions have a size of `None`.
Examples:
>>> tf.ragged.constant([[0], [1, 2]]).shape
TensorShape([2, None])
>>> tf.ragged.constant([[[0, 1]], [[1, 2], [3, 4]]], ragged_rank=1).shape
TensorShape([2, None, 2])
"""
nrows = self._row_partition.static_nrows
ncols = self._row_partition.static_uniform_row_length
value_shape = self._values.shape[1:]
return tensor_shape.TensorShape([nrows, ncols]).concatenate(value_shape)
def get_shape(self) -> tensor_shape.TensorShape:
"""The statically known shape of this ragged tensor.
Returns:
A `TensorShape` containing the statically known shape of this ragged
tensor. Ragged dimensions have a size of `None`.
Alias for `shape` property.
Examples:
>>> tf.ragged.constant([[0], [1, 2]]).get_shape()
TensorShape([2, None])
>>> tf.ragged.constant(
... [[[0, 1]], [[1, 2], [3, 4]]], ragged_rank=1).get_shape()
TensorShape([2, None, 2])
"""
return self.shape
@property
def ragged_rank(self):
"""The number of times the RaggedTensor's flat_values is partitioned.
Examples:
>>> values = tf.ragged.constant([[1, 2, 3], [4], [5, 6], [7, 8, 9, 10]])
>>> values.ragged_rank
1
>>> rt = tf.RaggedTensor.from_uniform_row_length(values, 2)
>>> rt.ragged_rank
2
Returns:
A Python `int` indicating the number of times the underlying `flat_values`
Tensor has been partitioned to add a new dimension.
I.e., `tf.rank(rt) = tf.rank(rt.flat_values) + rt.ragged_rank`.
"""
values_is_ragged = isinstance(self._values, RaggedTensor)
return self._values.ragged_rank + 1 if values_is_ragged else 1
@property
def values(self):
"""The concatenated rows for this ragged tensor.
`rt.values` is a potentially ragged tensor formed by flattening the two
outermost dimensions of `rt` into a single dimension.
`rt.values.shape = [nvals] + rt.shape[2:]` (where `nvals` is the
number of items in the outer two dimensions of `rt`).
`rt.ragged_rank = self.ragged_rank - 1`
Returns:
A potentially ragged tensor.
#### Example:
>>> rt = tf.ragged.constant([[3, 1, 4, 1], [], [5, 9, 2], [6], []])
>>> print(rt.values)
tf.Tensor([3 1 4 1 5 9 2 6], shape=(8,), dtype=int32)
"""
return self._values
@property
def _nested_row_partitions(self):
"""Returns the row partitions for this `RaggedTensor`."""
partitions = [self._row_partition]
rt_values = self.values
while isinstance(rt_values, RaggedTensor):
# pylint: disable=protected-access
partitions.append(rt_values._row_partition)
rt_values = rt_values.values
return tuple(partitions)
@property
def row_splits(self):
"""The row-split indices for this ragged tensor's `values`.
`rt.row_splits` specifies where the values for each row begin and end in
`rt.values`. In particular, the values for row `rt[i]` are stored in
the slice `rt.values[rt.row_splits[i]:rt.row_splits[i+1]]`.
Returns:
A 1-D integer `Tensor` with shape `[self.nrows+1]`.
The returned tensor is non-empty, and is sorted in ascending order.
`self.row_splits[0]` is zero, and `self.row_splits[-1]` is equal to
`self.values.shape[0]`.
#### Example:
>>> rt = tf.ragged.constant([[3, 1, 4, 1], [], [5, 9, 2], [6], []])
>>> print(rt.row_splits) # indices of row splits in rt.values
tf.Tensor([0 4 4 7 8 8], shape=(6,), dtype=int64)
"""
return self._row_partition.row_splits()
@property
def uniform_row_length(self):
"""The length of each row in this ragged tensor, or None if rows are ragged.
>>> rt1 = tf.ragged.constant([[1, 2, 3], [4], [5, 6], [7, 8, 9, 10]])
>>> print(rt1.uniform_row_length) # rows are ragged.
None
>>> rt2 = tf.RaggedTensor.from_uniform_row_length(
... values=rt1, uniform_row_length=2)
>>> print(rt2)
<tf.RaggedTensor [[[1, 2, 3], [4]], [[5, 6], [7, 8, 9, 10]]]>
>>> print(rt2.uniform_row_length) # rows are not ragged (all have size 2).
tf.Tensor(2, shape=(), dtype=int64)
A RaggedTensor's rows are only considered to be uniform (i.e. non-ragged)
if it can be determined statically (at graph construction time) that the
rows all have the same length.
Returns:
A scalar integer `Tensor`, specifying the length of every row in this
ragged tensor (for ragged tensors whose rows are uniform); or `None`
(for ragged tensors whose rows are ragged).
"""
return self._row_partition.uniform_row_length()
@property
def flat_values(self):
"""The innermost `values` tensor for this ragged tensor.
Concretely, if `rt.values` is a `Tensor`, then `rt.flat_values` is
`rt.values`; otherwise, `rt.flat_values` is `rt.values.flat_values`.
Conceptually, `flat_values` is the tensor formed by flattening the
outermost dimension and all of the ragged dimensions into a single
dimension.
`rt.flat_values.shape = [nvals] + rt.shape[rt.ragged_rank + 1:]`
(where `nvals` is the number of items in the flattened dimensions).
Returns:
A `Tensor`.
#### Example:
>>> rt = tf.ragged.constant([[[3, 1, 4, 1], [], [5, 9, 2]], [], [[6], []]])
>>> print(rt.flat_values)
tf.Tensor([3 1 4 1 5 9 2 6], shape=(8,), dtype=int32)
"""
rt_values = self.values
while isinstance(rt_values, RaggedTensor):
rt_values = rt_values.values
return rt_values
@property
def nested_row_splits(self):
"""A tuple containing the row_splits for all ragged dimensions.
`rt.nested_row_splits` is a tuple containing the `row_splits` tensors for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_row_splits = (rt.row_splits,) + value_splits` where:
* `value_splits = ()` if `rt.values` is a `Tensor`.
* `value_splits = rt.values.nested_row_splits` otherwise.
Returns:
A `tuple` of 1-D integer `Tensor`s.
#### Example:
>>> rt = tf.ragged.constant(
... [[[[3, 1, 4, 1], [], [5, 9, 2]], [], [[6], []]]])
>>> for i, splits in enumerate(rt.nested_row_splits):
... print('Splits for dimension %d: %s' % (i+1, splits.numpy()))
Splits for dimension 1: [0 3]
Splits for dimension 2: [0 3 3 5]
Splits for dimension 3: [0 4 4 7 8 8]
"""
rt_nested_splits = [self.row_splits]
rt_values = self.values
while isinstance(rt_values, RaggedTensor):
rt_nested_splits.append(rt_values.row_splits)
rt_values = rt_values.values
return tuple(rt_nested_splits)
def value_rowids(self, name=None):
"""Returns the row indices for the `values` in this ragged tensor.
`rt.value_rowids()` corresponds one-to-one with the outermost dimension of
`rt.values`, and specifies the row containing each value. In particular,
the row `rt[row]` consists of the values `rt.values[j]` where
`rt.value_rowids()[j] == row`.
Args:
name: A name prefix for the returned tensor (optional).
Returns:
A 1-D integer `Tensor` with shape `self.values.shape[:1]`.
The returned tensor is nonnegative, and is sorted in ascending order.
#### Example:
>>> rt = tf.ragged.constant([[3, 1, 4, 1], [], [5, 9, 2], [6], []])
>>> print(rt.values)
tf.Tensor([3 1 4 1 5 9 2 6], shape=(8,), dtype=int32)
>>> print(rt.value_rowids()) # corresponds 1:1 with rt.values
tf.Tensor([0 0 0 0 2 2 2 3], shape=(8,), dtype=int64)
"""
with ops.name_scope(name, "RaggedValueRowIds", [self]):
return self._row_partition.value_rowids()
def nested_value_rowids(self, name=None):
"""Returns a tuple containing the value_rowids for all ragged dimensions.
`rt.nested_value_rowids` is a tuple containing the `value_rowids` tensors
for
all ragged dimensions in `rt`, ordered from outermost to innermost. In
particular, `rt.nested_value_rowids = (rt.value_rowids(),) + value_ids`
where:
* `value_ids = ()` if `rt.values` is a `Tensor`.
* `value_ids = rt.values.nested_value_rowids` otherwise.
Args:
name: A name prefix for the returned tensors (optional).
Returns:
A `tuple` of 1-D integer `Tensor`s.
#### Example:
>>> rt = tf.ragged.constant(
... [[[[3, 1, 4, 1], [], [5, 9, 2]], [], [[6], []]]])
>>> for i, ids in enumerate(rt.nested_value_rowids()):
... print('row ids for dimension %d: %s' % (i+1, ids.numpy()))
row ids for dimension 1: [0 0 0]
row ids for dimension 2: [0 0 0 2 2]
row ids for dimension 3: [0 0 0 0 2 2 2 3]
"""
with ops.name_scope(name, "RaggedNestedValueRowIds", [self]):
rt_nested_ids = [self.value_rowids()]
rt_values = self.values
while isinstance(rt_values, RaggedTensor):
rt_nested_ids.append(rt_values.value_rowids())
rt_values = rt_values.values
return tuple(rt_nested_ids)
def nrows(self, out_type=None, name=None):
"""Returns the number of rows in this ragged tensor.
I.e., the size of the outermost dimension of the tensor.
Args:
out_type: `dtype` for the returned tensor. Defaults to
`self.row_splits.dtype`.
name: A name prefix for the returned tensor (optional).
Returns:
A scalar `Tensor` with dtype `out_type`.
#### Example:
>>> rt = tf.ragged.constant([[3, 1, 4, 1], [], [5, 9, 2], [6], []])
>>> print(rt.nrows()) # rt has 5 rows.
tf.Tensor(5, shape=(), dtype=int64)
"""
with ops.name_scope(name, "RaggedNRows", [self]):
if out_type is None:
return self._row_partition.nrows()
else:
return math_ops.cast(self._row_partition.nrows(), dtype=out_type)
def row_starts(self, name=None):
"""Returns the start indices for rows in this ragged tensor.
These indices specify where the values for each row begin in
`self.values`. `rt.row_starts()` is equal to `rt.row_splits[:-1]`.
Args:
name: A name prefix for the returned tensor (optional).
Returns:
A 1-D integer Tensor with shape `[nrows]`.
The returned tensor is nonnegative, and is sorted in ascending order.
#### Example:
>>> rt = tf.ragged.constant([[3, 1, 4, 1], [], [5, 9, 2], [6], []])
>>> print(rt.values)
tf.Tensor([3 1 4 1 5 9 2 6], shape=(8,), dtype=int32)
>>> print(rt.row_starts()) # indices of row starts in rt.values
tf.Tensor([0 4 4 7 8], shape=(5,), dtype=int64)
"""
with ops.name_scope(name, "RaggedRowStarts", [self]):
return self._row_partition.row_starts()
def row_limits(self, name=None):
"""Returns the limit indices for rows in this ragged tensor.
These indices specify where the values for each row end in
`self.values`. `rt.row_limits(self)` is equal to `rt.row_splits[:-1]`.
Args:
name: A name prefix for the returned tensor (optional).
Returns:
A 1-D integer Tensor with shape `[nrows]`.
The returned tensor is nonnegative, and is sorted in ascending order.
#### Example:
>>> rt = tf.ragged.constant([[3, 1, 4, 1], [], [5, 9, 2], [6], []])
>>> print(rt.values)
tf.Tensor([3 1 4 1 5 9 2 6], shape=(8,), dtype=int32)
>>> print(rt.row_limits()) # indices of row limits in rt.values
tf.Tensor([4 4 7 8 8], shape=(5,), dtype=int64)
"""
with ops.name_scope(name, "RaggedRowLimits", [self]):
return self._row_partition.row_limits()
def row_lengths(self, axis=1, name=None):
"""Returns the lengths of the rows in this ragged tensor.
`rt.row_lengths()[i]` indicates the number of values in the
`i`th row of `rt`.
Args:
axis: An integer constant indicating the axis whose row lengths should be
returned.
name: A name prefix for the returned tensor (optional).
Returns:
A potentially ragged integer Tensor with shape `self.shape[:axis]`.
Raises:
ValueError: If `axis` is out of bounds.
#### Example:
>>> rt = tf.ragged.constant(
... [[[3, 1, 4], [1]], [], [[5, 9], [2]], [[6]], []])
>>> print(rt.row_lengths()) # lengths of rows in rt
tf.Tensor([2 0 2 1 0], shape=(5,), dtype=int64)
>>> print(rt.row_lengths(axis=2)) # lengths of axis=2 rows.
<tf.RaggedTensor [[3, 1], [], [2, 1], [1], []]>
"""
if axis == 0:
return self._row_partition.nrows()
if axis == 1:
return self._row_partition.row_lengths()
with ops.name_scope(name, "RaggedRowLengths", [self]):
axis = array_ops.get_positive_axis(
axis, self.shape.rank, ndims_name="rank(self)")
if axis == 0:
return self.nrows()
elif axis == 1:
splits = self.row_splits
return splits[1:] - splits[:-1]
elif isinstance(self.values, RaggedTensor):
return self.with_values(self.values.row_lengths(axis - 1))
else:
shape = array_ops.shape(self.values, out_type=self._row_partition.dtype)
return self.with_values(
array_ops.ones(shape[:axis - 1], self._row_partition.dtype) *
shape[axis - 1])
def nested_row_lengths(self, name=None):
"""Returns a tuple containing the row_lengths for all ragged dimensions.
`rt.nested_row_lengths()` is a tuple containing the `row_lengths` tensors
for all ragged dimensions in `rt`, ordered from outermost to innermost.
Args:
name: A name prefix for the returned tensors (optional).
Returns:
A `tuple` of 1-D integer `Tensors`. The length of the tuple is equal to
`self.ragged_rank`.
"""
with ops.name_scope(name, "RaggedNestedRowLengths", [self]):
rt_nested_row_lengths = []
rt = self
while isinstance(rt, RaggedTensor):
rt_nested_row_lengths.append(rt.row_lengths())
rt = rt.values
return tuple(rt_nested_row_lengths)
def bounding_shape(self, axis=None, name=None, out_type=None):
"""Returns the tight bounding box shape for this `RaggedTensor`.
Args:
axis: An integer scalar or vector indicating which axes to return the
bounding box for. If not specified, then the full bounding box is
returned.
name: A name prefix for the returned tensor (optional).
out_type: `dtype` for the returned tensor. Defaults to
`self.row_splits.dtype`.
Returns:
An integer `Tensor` (`dtype=self.row_splits.dtype`). If `axis` is not
specified, then `output` is a vector with
`output.shape=[self.shape.ndims]`. If `axis` is a scalar, then the
`output` is a scalar. If `axis` is a vector, then `output` is a vector,
where `output[i]` is the bounding size for dimension `axis[i]`.
#### Example:
>>> rt = tf.ragged.constant([[1, 2, 3, 4], [5], [], [6, 7, 8, 9], [10]])
>>> rt.bounding_shape().numpy()
array([5, 4])
"""
if out_type is None:
out_type = self._row_partition.dtype
else:
out_type = dtypes.as_dtype(out_type)
with ops.name_scope(name, "RaggedBoundingBox", [self, axis]):
nested_splits = self.nested_row_splits
rt_flat_values = self.flat_values
# Optimized special cases for when axis=0 or axis=1:
if isinstance(axis, int):
if axis == 0:
return array_ops.shape(nested_splits[0], out_type=out_type)[0] - 1
elif axis == 1:
result = math_ops.maximum(math_ops.reduce_max(self.row_lengths()), 0)
if out_type != self._row_partition.dtype:
result = math_ops.cast(result, out_type)
return result
splits_shape = array_ops.shape(self.row_splits, out_type=out_type)
flat_values_shape = array_ops.shape(rt_flat_values, out_type=out_type)
ragged_dimensions = [splits_shape[0] - 1] + [
math_ops.maximum(math_ops.reduce_max(splits[1:] - splits[:-1]), 0)
for splits in nested_splits
]
inner_dimensions = flat_values_shape[1:]
if out_type != self._row_partition.dtype:
ragged_dimensions = [
math_ops.cast(d, out_type) for d in ragged_dimensions
]
bbox = array_ops.concat(
[array_ops_stack.stack(ragged_dimensions), inner_dimensions], axis=0)
return bbox if axis is None else array_ops.gather(bbox, axis)
#=============================================================================
# Transformation
#=============================================================================
def with_values(self, new_values):
"""Returns a copy of `self` with `values` replaced by `new_value`.
Preserves cached row-partitioning tensors such as `self.cached_nrows` and
`self.cached_value_rowids` if they have values.
Args:
new_values: Potentially ragged tensor to use as the `values` for the
returned `RaggedTensor`. Must have `rank > 0`, and must have the same
number of rows as `self.values`.
Returns:
A `RaggedTensor`. `result.rank = 1 + new_values.rank`.
`result.ragged_rank = 1 + new_values.ragged_rank`
"""
new_values = _convert_to_ragged_tensor_values(new_values)
new_values.shape.with_rank_at_least(1)
self.values.shape[:1].assert_is_compatible_with(new_values.shape[:1])
if (isinstance(new_values, RaggedTensor) and
self._row_partition.dtype != new_values.row_splits.dtype):
if not ragged_config.auto_cast_partition_dtype():
raise ValueError("self and new_values have mismatched row_splits "
"dtypes; use RaggedTensor.with_row_splits_dtype() to "
"convert them to compatible dtypes.")
new_values = new_values.with_row_splits_dtype(dtypes.int64)
return self.with_row_splits_dtype(dtypes.int64).with_values(new_values)
return RaggedTensor(
values=new_values, row_partition=self._row_partition, internal=True)
def with_flat_values(self, new_values):
"""Returns a copy of `self` with `flat_values` replaced by `new_value`.
Preserves cached row-partitioning tensors such as `self.cached_nrows` and
`self.cached_value_rowids` if they have values.
Args:
new_values: Potentially ragged tensor that should replace
`self.flat_values`. Must have `rank > 0`, and must have the same number
of rows as `self.flat_values`.
Returns:
A `RaggedTensor`.
`result.rank = self.ragged_rank + new_values.rank`.
`result.ragged_rank = self.ragged_rank + new_values.ragged_rank`.
"""
if isinstance(self._values, RaggedTensor):
return self.with_values(self.values.with_flat_values(new_values))
else:
new_values = _convert_to_ragged_tensor_values(new_values)
return self.with_values(new_values)
def with_row_splits_dtype(self, dtype):
"""Returns a copy of this RaggedTensor with the given `row_splits` dtype.
For RaggedTensors with multiple ragged dimensions, the `row_splits` for all
nested `RaggedTensor` objects are cast to the given dtype.
Args:
dtype: The dtype for `row_splits`. One of `tf.int32` or `tf.int64`.
Returns:
A copy of this RaggedTensor, with the `row_splits` cast to the given
type.
"""
dtype = dtypes.as_dtype(dtype)
if dtype not in (dtypes.int32, dtypes.int64):
raise ValueError(f"Argument `row_splits` dtype must be int32 or int64. "
f"Received {dtype}.")
if self._row_partition.dtype == dtype:
return self
current_values = self._values
if isinstance(current_values, RaggedTensor):
return RaggedTensor(
values=current_values.with_row_splits_dtype(dtype),
row_partition=self._row_partition.with_dtype(dtype),
internal=True)
else:
return RaggedTensor(
values=current_values,
row_partition=self._row_partition.with_dtype(dtype),
internal=True)
def merge_dims(self, outer_axis, inner_axis):
"""Merges outer_axis...inner_axis into a single dimension.
Returns a copy of this RaggedTensor with the specified range of dimensions
flattened into a single dimension, with elements in row-major order.
#### Examples:
>>> rt = tf.ragged.constant([[[1, 2], [3]], [[4, 5, 6]]])
>>> print(rt.merge_dims(0, 1))
<tf.RaggedTensor [[1, 2], [3], [4, 5, 6]]>
>>> print(rt.merge_dims(1, 2))
<tf.RaggedTensor [[1, 2, 3], [4, 5, 6]]>
>>> print(rt.merge_dims(0, 2))
tf.Tensor([1 2 3 4 5 6], shape=(6,), dtype=int32)
To mimic the behavior of `np.flatten` (which flattens all dimensions), use
`rt.merge_dims(0, -1). To mimic the behavior of `tf.layers.Flatten` (which
flattens all dimensions except the outermost batch dimension), use
`rt.merge_dims(1, -1)`.
Args:
outer_axis: `int`: The first dimension in the range of dimensions to
merge. May be negative if `self.shape.rank` is statically known.
inner_axis: `int`: The last dimension in the range of dimensions to merge.
May be negative if `self.shape.rank` is statically known.
Returns:
A copy of this tensor, with the specified dimensions merged into a
single dimension. The shape of the returned tensor will be
`self.shape[:outer_axis] + [N] + self.shape[inner_axis + 1:]`, where `N`
is the total number of slices in the merged dimensions.
"""
outer_axis = array_ops.get_positive_axis(
outer_axis,
self.shape.rank,
axis_name="outer_axis",
ndims_name="rank(self)")
inner_axis = array_ops.get_positive_axis(
inner_axis,
self.shape.rank,
axis_name="inner_axis",
ndims_name="rank(self)")
if not outer_axis <= inner_axis:
raise ValueError(f"Expected outer_axis ({outer_axis}) to be less than or "
f"equal to inner_axis ({inner_axis}).")
return merge_dims(self, outer_axis, inner_axis)
def _set_shape(self, shape):
"""Updates the static shape of `self` to be `shape`.
* If a dimension of `shape` has known rank, and is encoded via
partitioning, then this will update the corresponding partition to
define `_uniform_row_length` and `nrows`.
* If a dimension of `shape` has a known rank, and is encoded as one
of the `flat_values` dimensions, then `flat_values.set_shape()` will
be used to update its shape.
Warning: Using this method to assert an incorrect shape for a RaggedTensor
(i.e., one that's not consistent with its actual shape) can cause
segmentation faults and very difficult-to-diagnose behavior. Only use this
method if you are certain that the shape is correct.
Args:
shape: `tf.TensorShape` specifying the shape for this `RaggedTensor`.
"""
# TODO(edloper): Refactor this to not directly access private members
# of RowPartition.
# pylint: disable=protected-access
shape = tensor_shape.as_shape(shape)
if shape.rank is None:
return # Nothing to do.
shape = shape.as_list()
# Outermost dimension
if shape[0] is not None:
self._row_partition._row_splits.set_shape(shape[0] + 1)
# Partitioned dimensions
dtype = self._row_partition.dtype
for i, partition in enumerate(self._nested_row_partitions):
size = shape[i + 1]
if size is not None:
if partition._uniform_row_length is not None:
old_row_length = tensor_util.constant_value(
partition._uniform_row_length)
if old_row_length is not None:
if size == old_row_length:
continue # already have shape info for this axis.
else:
raise ValueError(f"Inconsistent size for axis {i + 1}: "
f"{old_row_length} vs. {size}.")
partition._uniform_row_length = ops.convert_to_tensor(size, dtype)
if partition._nrows is None:
partition._nrows = array_ops.size(
partition._row_splits, out_type=dtype) - 1
# self.flat_values could be a CompositeTensor and doesn't have set_shape.
if hasattr(self.flat_values, "set_shape"):
# Inner dimensions
flat_shape = tensor_shape.as_shape([None] + shape[self.ragged_rank + 1:])
self.flat_values.set_shape(flat_shape)
#=============================================================================
# Tensor Type Conversions
#=============================================================================
@classmethod
@dispatch.add_dispatch_support
def from_tensor(cls,
tensor,
lengths=None,
padding=None,
ragged_rank=1,
name=None,
row_splits_dtype=dtypes.int64):
"""Converts a `tf.Tensor` into a `RaggedTensor`.
The set of absent/default values may be specified using a vector of lengths
or a padding value (but not both). If `lengths` is specified, then the
output tensor will satisfy `output[row] = tensor[row][:lengths[row]]`. If
'lengths' is a list of lists or tuple of lists, those lists will be used
as nested row lengths. If `padding` is specified, then any row *suffix*
consisting entirely of `padding` will be excluded from the returned
`RaggedTensor`. If neither `lengths` nor `padding` is specified, then the
returned `RaggedTensor` will have no absent/default values.
Examples:
>>> dt = tf.constant([[5, 7, 0], [0, 3, 0], [6, 0, 0]])
>>> tf.RaggedTensor.from_tensor(dt)
<tf.RaggedTensor [[5, 7, 0], [0, 3, 0], [6, 0, 0]]>
>>> tf.RaggedTensor.from_tensor(dt, lengths=[1, 0, 3])
<tf.RaggedTensor [[5], [], [6, 0, 0]]>
>>> tf.RaggedTensor.from_tensor(dt, padding=0)
<tf.RaggedTensor [[5, 7], [0, 3], [6]]>
>>> dt = tf.constant([[[5, 0], [7, 0], [0, 0]],
... [[0, 0], [3, 0], [0, 0]],
... [[6, 0], [0, 0], [0, 0]]])
>>> tf.RaggedTensor.from_tensor(dt, lengths=([2, 0, 3], [1, 1, 2, 0, 1]))
<tf.RaggedTensor [[[5], [7]], [], [[6, 0], [], [0]]]>
Args:
tensor: The `Tensor` to convert. Must have rank `ragged_rank + 1` or
higher.
lengths: An optional set of row lengths, specified using a 1-D integer
`Tensor` whose length is equal to `tensor.shape[0]` (the number of rows
in `tensor`). If specified, then `output[row]` will contain
`tensor[row][:lengths[row]]`. Negative lengths are treated as zero. You
may optionally pass a list or tuple of lengths to this argument, which
will be used as nested row lengths to construct a ragged tensor with
multiple ragged dimensions.
padding: An optional padding value. If specified, then any row suffix
consisting entirely of `padding` will be excluded from the returned
RaggedTensor. `padding` is a `Tensor` with the same dtype as `tensor`
and with `shape=tensor.shape[ragged_rank + 1:]`.
ragged_rank: Integer specifying the ragged rank for the returned
`RaggedTensor`. Must be greater than zero.
name: A name prefix for the returned tensors (optional).
row_splits_dtype: `dtype` for the returned `RaggedTensor`'s `row_splits`
tensor. One of `tf.int32` or `tf.int64`.
Returns:
A `RaggedTensor` with the specified `ragged_rank`. The shape of the
returned ragged tensor is compatible with the shape of `tensor`.
Raises:
ValueError: If both `lengths` and `padding` are specified.
ValueError: If the rank of `tensor` is 0 or 1.
"""
row_splits_dtype = dtypes.as_dtype(row_splits_dtype)
if lengths is not None and padding is not None:
raise ValueError("Specify argument `lengths` or `padding`, but not both.")
if not isinstance(ragged_rank, int):
raise TypeError(f"Argument `ragged_rank` must be an int. "
f"Received {ragged_rank}.")
if ragged_rank <= 0:
raise ValueError(f"Argument `ragged_rank` must be greater than 0. "
f"Received {ragged_rank}.")
with ops.name_scope(name, "RaggedFromTensor", [tensor, lengths, padding]):
tensor = ops.convert_to_tensor(tensor, name="tensor")
if tensor.shape.rank is not None and tensor.shape.rank < 2:
raise ValueError(f"The rank of a RaggedTensor must be greater than 1, "
f"i.e., a list of scalars won't have ragged "
f"dimensions. Received argument `tensor` with rank "
f"{tensor.shape.rank}.")
tensor.shape.with_rank_at_least(ragged_rank + 1)
input_shape = array_ops.shape(tensor, out_type=row_splits_dtype)
ncols = input_shape[1]
# Handle nested row lengths.
if (lengths is not None and isinstance(lengths, (list, tuple)) and
len(lengths) and not isinstance(lengths[0], (int, float))):
if ragged_rank not in (1, len(lengths)):
# Note: we accept `ragged_rank=1` here because it's the default value;
# i.e., if the user passes in a tuple of lengths, but doesn't specify
# ragged_rank, then we should use that tuple to determine ragged_rank.
# We only want to complain if they pass in an explicit ragged_rank
# that doesn't match len(lengths).
raise ValueError(f"If Argument `lengths` is a tuple of row_lengths, "
f"argument `ragged_rank` must be "
f"len(lengths): {len(lengths)}. Received "
f"ragged_rank: {ragged_rank}.")
# Rather than reconstructing the tensor mask directly, we can
# recreate it as a boolean RaggedTensor, then densify that and use
# that as the mask to clear out the unused data in the passed tensor.
tensor.shape.with_rank_at_least(len(lengths) + 1)
num_tokens = math_ops.reduce_sum(lengths[-1])
ones_mask = array_ops.ones([num_tokens], dtype=dtypes.bool)
ragged_mask = cls.from_nested_row_lengths(
ones_mask, lengths, validate=False)
dense_ragged_mask = ragged_mask.to_tensor(default_value=False)
masked_data = array_ops.boolean_mask(tensor, dense_ragged_mask)
return cls.from_nested_row_lengths(masked_data, lengths, validate=False)
# Handle ragged_rank>1 via recursion:
# If the output should have multiple ragged dimensions, then first
# flatten the tensor to eliminate all but the last ragged dimension,
# and recursively convert that flattened tensor. Then add on the splits
# for the dimensions that we flattened out.
if ragged_rank > 1:
if tensor.shape.is_fully_defined():
input_shape = tensor.shape.as_list()
# The total number of elements in each dimension. E.g., if
# input_shape=[3, 4, 5, 6], then dim[2] has 3*4*5 elements in total.
dim_size = np.cumprod(input_shape)
new_shape = [dim_size[ragged_rank - 1]] + input_shape[ragged_rank:]
else:
dim_size = math_ops.cumprod(input_shape)
new_shape = array_ops.concat(
[[dim_size[ragged_rank - 1]], input_shape[ragged_rank:]], axis=0)
flattened = array_ops.reshape(tensor, new_shape)
result = cls.from_tensor(
flattened, lengths, padding, row_splits_dtype=row_splits_dtype)
for axis in range(ragged_rank - 1, 0, -1):
dim_len = tensor_shape.dimension_at_index(tensor.shape, axis).value
if dim_len is None:
dim_len = input_shape[axis]
else:
dim_len = constant_op.constant(dim_len, row_splits_dtype)
result = RaggedTensor.from_uniform_row_length(
values=result,
uniform_row_length=dim_len,
nrows=dim_size[axis - 1],
validate=False)
return result
# If padding was specified, then use it to find row lengths.
if padding is not None:
padding = ops.convert_to_tensor(
padding, name="padding", dtype=tensor.dtype)
padding.shape.assert_is_compatible_with(tensor.shape[2:])
# Find places where the padding is equal to the tensor. (This will
# broadcast `padding` across the outermost 2 dimensions of `tensor`,
# so `has_default_value.shape = tensor.shape`.)
has_default_value = math_ops.equal(padding, tensor)
# If the padding isn't a scalar, then require that all values in the
# padding match each item in the tensor. After this block of code,
# `has_default.shape = tensor.shape[:2]`. (Unfortunately, we can't just
# use reduce_all for both cases, becaue when you pass an empty `axis`
# list to reduce_all, it reduces all axes; but we want it to reduce no
# axes -- i.e., to be a no-op.)
tensor_rank = array_ops.rank(tensor)
reduce_axis = math_ops.range(2, tensor_rank)
has_default = cond.cond(
tensor_rank > 2,
lambda: math_ops.reduce_all(has_default_value, axis=reduce_axis),
lambda: has_default_value)
has_default.set_shape(tensor_shape.TensorShape([None, None]))
has_default.set_shape(tensor.shape[:2])
# Use has_default to find the length of each row: for each
# non-default item in a row, calculate the length that the row needs to
# have to include that item; and then take the max of those values
# (across each row).
has_nondefault = math_ops.logical_not(has_default)
has_nondefault = math_ops.cast(has_nondefault, row_splits_dtype)
length_for_nondefault_value = (
has_nondefault *
array_ops.expand_dims(math_ops.range(1, ncols + 1), 0))
lengths = math_ops.reduce_max(length_for_nondefault_value, axis=1)
if lengths is not None:
# If we have lengths (either directly supplied, or computed from
# paddings), then use those to construct splits; and then use masking
# to get the corresponding values.
lengths = ragged_util.convert_to_int_tensor(lengths, "lengths",
row_splits_dtype)
lengths.shape.assert_has_rank(1)
lengths = math_ops.minimum(lengths, ncols)
lengths = math_ops.maximum(lengths, 0)
limits = math_ops.cumsum(lengths)
splits = array_ops.concat(
[array_ops.zeros([1], row_splits_dtype), limits], axis=0)
mask = array_ops.sequence_mask(lengths, maxlen=ncols)
values = array_ops.boolean_mask(tensor, mask)
return cls.from_row_splits(values, splits, validate=False)
# If neither padding nor lengths were specified, then create a splits
# vector that contains no default values, and reshape the input tensor
# to form the values for the RaggedTensor.
values_shape = array_ops.concat(
[[input_shape[0] * input_shape[1]], input_shape[2:]], axis=0)
values = array_ops.reshape(tensor, values_shape)
const_nrows = tensor_shape.dimension_at_index(tensor.shape, 0).value
const_ncols = tensor_shape.dimension_at_index(tensor.shape, 1).value
if const_nrows is not None:
nrows = constant_op.constant(const_nrows, row_splits_dtype)
else:
nrows = input_shape[0]
if const_ncols is not None:
ncols = constant_op.constant(const_ncols, row_splits_dtype)
else:
ncols = input_shape[1]
return RaggedTensor.from_uniform_row_length(
values=values, uniform_row_length=ncols, nrows=nrows, validate=False)
def to_tensor(self, default_value=None, name=None, shape=None):
"""Converts this `RaggedTensor` into a `tf.Tensor`.
If `shape` is specified, then the result is padded and/or truncated to
the specified shape.
Examples:
>>> rt = tf.ragged.constant([[9, 8, 7], [], [6, 5], [4]])
>>> print(rt.to_tensor())
tf.Tensor(
[[9 8 7] [0 0 0] [6 5 0] [4 0 0]], shape=(4, 3), dtype=int32)
>>> print(rt.to_tensor(shape=[5, 2]))
tf.Tensor(
[[9 8] [0 0] [6 5] [4 0] [0 0]], shape=(5, 2), dtype=int32)
Args:
default_value: Value to set for indices not specified in `self`. Defaults
to zero. `default_value` must be broadcastable to
`self.shape[self.ragged_rank + 1:]`.
name: A name prefix for the returned tensors (optional).
shape: The shape of the resulting dense tensor. In particular,
`result.shape[i]` is `shape[i]` (if `shape[i]` is not None), or
`self.bounding_shape(i)` (otherwise).`shape.rank` must be `None` or
equal to `self.rank`.
Returns:
A `Tensor` with shape `ragged.bounding_shape(self)` and the
values specified by the non-empty values in `self`. Empty values are
assigned `default_value`.
"""
with ops.name_scope(name, "RaggedToTensor", [self, default_value, shape]):
if default_value is not None:
default_value = ops.convert_to_tensor(
default_value, name="default_value", dtype=self.dtype)
type_tensor_pairs = _get_row_partition_type_tensor_pairs(self)
row_partition_types = [x[0] for x in type_tensor_pairs]
row_partition_tensors = [x[1] for x in type_tensor_pairs]
if default_value is None:
default_value = array_ops.zeros((), self.dtype)
if (isinstance(shape, (list, tuple)) and
any(isinstance(v, tensor_lib.Tensor) for v in shape) and
all(isinstance(v, (int, tensor_lib.Tensor)) for v in shape)):
shape = array_ops_stack.stack(shape)
shape_tensor = _shape_as_tensor(shape, row_partition_tensors[0].dtype)
tensor = gen_ragged_conversion_ops.ragged_tensor_to_tensor(
shape=shape_tensor,
values=self.flat_values,
default_value=default_value,
row_partition_types=row_partition_types,
row_partition_tensors=row_partition_tensors,
)
ragged_shape = self.shape
if ragged_shape.rank is not None and not isinstance(
shape, tensor_lib.Tensor
):
# Merged self.shape and shape, favoring the second one as it takes
# into account potential padding added to the output.
shape = tensor_shape.as_shape(shape)
if shape.rank is None:
output_shape = ragged_shape
else:
# At this point we can assume that hshape.rank == ragged_shape.rank
# because otherwise it would have failed earlier.
output_shape = [
s1 if s1 is not None else s2
for (s1, s2) in zip(shape.as_list(), ragged_shape.as_list())
]
tensor.set_shape(output_shape)
return tensor
@classmethod
@dispatch.add_dispatch_support
def from_sparse(cls, st_input, name=None, row_splits_dtype=dtypes.int64):
"""Converts a 2D `tf.sparse.SparseTensor` to a `RaggedTensor`.
Each row of the `output` `RaggedTensor` will contain the explicit values
from the same row in `st_input`. `st_input` must be ragged-right. If not
it is not ragged-right, then an error will be generated.
Example:
>>> indices = [[0, 0], [0, 1], [0, 2], [1, 0], [3, 0]]
>>> st = tf.sparse.SparseTensor(indices=indices,
... values=[1, 2, 3, 4, 5],
... dense_shape=[4, 3])
>>> tf.RaggedTensor.from_sparse(st).to_list()
[[1, 2, 3], [4], [], [5]]
Currently, only two-dimensional `SparseTensors` are supported.
Args:
st_input: The sparse tensor to convert. Must have rank 2.
name: A name prefix for the returned tensors (optional).
row_splits_dtype: `dtype` for the returned `RaggedTensor`'s `row_splits`
tensor. One of `tf.int32` or `tf.int64`.
Returns:
A `RaggedTensor` with the same values as `st_input`.
`output.ragged_rank = rank(st_input) - 1`.
`output.shape = [st_input.dense_shape[0], None]`.
Raises:
ValueError: If the number of dimensions in `st_input` is not known
statically, or is not two.
"""
row_splits_dtype = dtypes.as_dtype(row_splits_dtype)
if not sparse_tensor.is_sparse(st_input):
raise TypeError(f"Argument `st_input` must be of type SparseTensor, but "
f"is of type {type(st_input).__name__}.")
with ops.name_scope(name, "RaggedFromSparse", [st_input]):
st_input = sparse_tensor.convert_to_tensor_or_sparse_tensor(
st_input, name="st_input")
if st_input.dense_shape.shape.ndims is None:
static_rank_from_dense_shape = None
else:
static_rank_from_dense_shape = st_input.dense_shape.shape.dims[0].value
if st_input.indices.shape.ndims is None:
static_rank_from_indices = None
else:
static_rank_from_indices = st_input.indices.shape.dims[1].value
if static_rank_from_dense_shape != 2 and static_rank_from_indices != 2:
raise ValueError("rank(st_input) must be 2.")
with ops.control_dependencies(
_assert_sparse_indices_are_ragged_right(st_input.indices)):
# Treat sparse row indices as segment ids to generate a splits tensor
# thta we can pair with the sparse tensor values. (Ignore sparse column
# indices.)
segment_ids = math_ops.cast(st_input.indices[:, 0], row_splits_dtype)
num_segments = math_ops.cast(st_input.dense_shape[0], row_splits_dtype)
return cls.from_value_rowids(
st_input.values, segment_ids, num_segments, validate=False)
def to_sparse(self, name=None):
"""Converts this `RaggedTensor` into a `tf.sparse.SparseTensor`.
Example:
>>> rt = tf.ragged.constant([[1, 2, 3], [4], [], [5, 6]])
>>> print(rt.to_sparse())
SparseTensor(indices=tf.Tensor(
[[0 0] [0 1] [0 2] [1 0] [3 0] [3 1]],
shape=(6, 2), dtype=int64),
values=tf.Tensor([1 2 3 4 5 6], shape=(6,), dtype=int32),
dense_shape=tf.Tensor([4 3], shape=(2,), dtype=int64))
Args:
name: A name prefix for the returned tensors (optional).
Returns:
A SparseTensor with the same values as `self`.
"""
with ops.name_scope(name, "RaggedToSparse", [self]):
result = gen_ragged_conversion_ops.ragged_tensor_to_sparse(
self.nested_row_splits, self.flat_values, name=name)
return sparse_tensor.SparseTensor(result.sparse_indices,
result.sparse_values,
result.sparse_dense_shape)
@classmethod
def _from_variant(cls,
variant,
dtype,
output_ragged_rank,
input_ragged_rank=None,
row_splits_dtype=dtypes.int64,
name=None):
"""Converts a `variant` Tensor into a `RaggedTensor`.
The input `variant` could be a scalar, meaning it encodes a single
`RaggedTensor` with ragged_rank `output_ragged_rank`. Alternatively it could
have an arbitrary rank, in which case each element is decoded into a
`RaggedTensor` with ragged_rank `input_ragged_rank` and these are then
stacked according to the input shape to output a single `RaggedTensor`
with ragged_rank `output_ragged_rank`. If `input_ragged_rank` is not
provided, it is inferred dynamically as `output_ragged_rank` -
`rank(variant)`. If `input_ragged_rank` is provided, the following must be
true: `output_ragged_rank` = `input_ragged_rank` + `rank(variant)`.
Example:
>>> rt = tf.ragged.constant([[0], [1, 2]])
>>> et = rt._to_variant()
>>> stacked_et = tf.stack([et, et])
>>> tf.RaggedTensor._from_variant( # scalar input.
... et, dtype=tf.int32, output_ragged_rank=1).to_list()
[[0], [1, 2]]
>>> tf.RaggedTensor._from_variant( # batched input.
... stacked_et, dtype=tf.int32, output_ragged_rank=2).to_list()
[[[0], [1, 2]], [[0], [1, 2]]]
Args:
variant: A `variant` Tensor representing an encoded (possibly
nested-batched) `RaggedTensor`.
dtype: The dtype of the encoded `RaggedTensor`.
output_ragged_rank: The expected ragged rank of the output `RaggedTensor`.
input_ragged_rank: The ragged rank of each encoded `RaggedTensor`. This is
optional and inferred dynamically if not provided.
row_splits_dtype: `dtype` for the RaggedTensor's `row_splits` tensor. One
of `tf.int32` or `tf.int64`.
name: A name prefix for the returned tensors (optional).
Returns:
A `RaggedTensor` of dtype `dtype` and ragged rank `output_ragged_rank`.
Raises:
ValueError: If the input rank is known, `input_ragged_rank` is provided
and `output_ragged_rank` = `input_ragged_rank` + `rank(variant)` does
not hold.
"""
variant = ops.convert_to_tensor(
variant, name="variant", dtype=dtypes.variant)
if (variant.shape.ndims is not None and input_ragged_rank is not None and
output_ragged_rank != input_ragged_rank + variant.shape.ndims):
raise ValueError(
f"Argument `output_ragged_rank` ({output_ragged_rank}) must be equal "
f"to `input_ragged_rank` + `variant.shape.ndims` "
f"({input_ragged_rank} + {variant.shape.ndims}).")
input_ragged_rank = -1 if input_ragged_rank is None else input_ragged_rank
with ops.name_scope(
name, "RaggedFromVariant",
[variant, dtype, input_ragged_rank, output_ragged_rank]):
result = gen_ragged_conversion_ops.ragged_tensor_from_variant(
variant, input_ragged_rank, max(output_ragged_rank, 0), dtype,
row_splits_dtype, name)
return cls.from_nested_row_splits(
result.output_dense_values,
result.output_nested_splits,
validate=False)
def _to_variant(self, batched_input=False, name=None):
"""Converts this `RaggedTensor` into a `variant` Tensor.
If `batched_input` is `True`, then the `RaggedTensor` is unbatched along the
zero-th dimension, each component `RaggedTensor` is encoded into a scalar
`variant` Tensor, and these are stacked to return a 1-D `variant` Tensor.
If `batched_input` is `False`, then the `RaggedTensor` is encoded as is and
a scalar `variant` Tensor is returned.
Example:
>>> rt = tf.ragged.constant([[[0]], [[1]], [[2]]])
>>> rt._to_variant().shape.as_list()
[]
>>> rt._to_variant(batched_input=True).shape.as_list()
[3]
Args:
batched_input: If `True`, the `RaggedTensor` is unbatched and converted to
a `variant` vector. Set to `False` by default.
name: A name prefix for the returned tensors (optional).
Returns:
A `variant` Tensor that encodes this `RaggedTensor`.
"""
with ops.name_scope(name, "RaggedToVariant", [self, batched_input]):
return gen_ragged_conversion_ops.ragged_tensor_to_variant(
self.nested_row_splits, self.flat_values, batched_input, name)
#=============================================================================
# String Encoding
#=============================================================================
def __repr__(self):
if self._is_eager():
# The np.array2string in _formatter provides a separator argument, but
# doesn't handle recursive calls correctly. The np.printoptions handles
# recursive calls correctly, but doesn't provide a separator argument.
# Combines them together to print elements separated by comma, while
# avoiding the redundant array prefixes and dtypes. For example,
# the value of tf.ragged.constant([[1, 2], [3, 4]]) will look like
#
# [[1, 2],
# [3, 4]]
with np.printoptions(formatter={"all": _formatter}):
value_text = _formatter(self.numpy())
return f"<tf.RaggedTensor {value_text}>"
else:
return "tf.RaggedTensor(values=%s, row_splits=%s)" % (self.values,
self.row_splits)
#=============================================================================
# Eager Execution Mode
#=============================================================================
def numpy(self):
"""Returns a numpy `array` with the values for this `RaggedTensor`.
Requires that this `RaggedTensor` was constructed in eager execution mode.
Ragged dimensions are encoded using numpy `arrays` with `dtype=object` and
`rank=1`, where each element is a single row.
#### Examples
In the following example, the value returned by `RaggedTensor.numpy()`
contains three numpy `array` objects: one for each row (with `rank=1` and
`dtype=int64`), and one to combine them (with `rank=1` and `dtype=object`):
>>> tf.ragged.constant([[1, 2, 3], [4, 5]], dtype=tf.int64).numpy()
array([array([1, 2, 3]), array([4, 5])], dtype=object)
Uniform dimensions are encoded using multidimensional numpy `array`s. In
the following example, the value returned by `RaggedTensor.numpy()` contains
a single numpy `array` object, with `rank=2` and `dtype=int64`:
>>> tf.ragged.constant([[1, 2, 3], [4, 5, 6]], dtype=tf.int64).numpy()
array([[1, 2, 3], [4, 5, 6]])
Returns:
A numpy `array`.
"""
if not self._is_eager():
raise ValueError("RaggedTensor.numpy() is only supported in eager mode.")
values = self.values.numpy()
splits = self.row_splits.numpy()
rows = [values[splits[i]:splits[i + 1]] for i in range(len(splits) - 1)]
if not rows:
return np.zeros((0, 0) + values.shape[1:], dtype=values.dtype)
# Note: if `rows` have ragged lengths, then they will be stored in a
# np.ndarray with dtype=object and rank=1. If they have uniform lengths,
# they will be combined into a single np.ndarray with dtype=row.dtype and
# rank=row.rank+1.
#
# Manually set dtype as numpy now complains when given ragged rows.
has_variable_length_rows = any(len(row) != len(rows[0]) for row in rows)
dtype = np.object_ if has_variable_length_rows else None
return np.array(rows, dtype=dtype)
def to_list(self):
"""Returns a nested Python `list` with the values for this `RaggedTensor`.
Requires that `rt` was constructed in eager execution mode.
Returns:
A nested Python `list`.
"""
if not isinstance(self.row_splits, ops.EagerTensor):
raise ValueError("to_list can only be used in eager mode.")
row_splits = self.row_splits.numpy().tolist()
values = self.values
if isinstance(values, RaggedTensor):
return [
values[row_splits[i]:row_splits[i + 1]].to_list()
for i in range(len(row_splits) - 1)
]
else:
# Convert values to a Python list.
if hasattr(values, "numpy"):
values_as_list = values.numpy().tolist()
elif hasattr(values, "to_list"):
values_as_list = values.to_list()
else:
raise ValueError("values must be convertible to a list")
return [
values_as_list[row_splits[i]:row_splits[i + 1]]
for i in range(len(row_splits) - 1)
]
def _eager_value(self):
"""Returns a RaggedTensorValue for self. Requires self._is_eager()=true."""
value = self.flat_values.numpy()
for row_splits in reversed(self.nested_row_splits):
value = ragged_tensor_value.RaggedTensorValue(value, row_splits.numpy())
return value
def _is_eager(self):
"""Returns True if values & row_splits Tensors are all `EagerTensor`s."""
rt = self
while isinstance(rt, RaggedTensor):
if not isinstance(rt.row_splits, ops.EagerTensor):
return False
rt = rt.values
return isinstance(rt, ops.EagerTensor)
#=============================================================================
# Operators
#=============================================================================
# To avoid circular dependencies, we define stub methods for operators here,
# and then override them when the ragged_operators module is imported.
def _overloaded_operator(name): # pylint: disable=no-self-argument
def stub(*args, **kwargs):
del args, kwargs
raise ValueError(
f"You must import 'tensorflow.python.ops.ragged.ragged_ops' "
f"before using RaggedTensor.{name}.")
return stub
__getitem__ = _overloaded_operator("__getitem__")
__ge__ = _overloaded_operator("__ge__")
__gt__ = _overloaded_operator("__gt__")
__le__ = _overloaded_operator("__le__")
__lt__ = _overloaded_operator("__lt__")
__and__ = _overloaded_operator("__and__")
__rand__ = _overloaded_operator("__rand__")
__invert__ = _overloaded_operator("__invert__")
__ror__ = _overloaded_operator("__ror__")
__or__ = _overloaded_operator("__or__")
__xor__ = _overloaded_operator("__xor__")
__rxor__ = _overloaded_operator("__rxor__")
__abs__ = _overloaded_operator("__abs__")
__add__ = _overloaded_operator("__add__")
__radd__ = _overloaded_operator("__radd__")
__div__ = _overloaded_operator("__div__")
__rdiv__ = _overloaded_operator("__rdiv__")
__floordiv__ = _overloaded_operator("__floordiv__")
__rfloordiv__ = _overloaded_operator("__rfloordiv__")
__mod__ = _overloaded_operator("__mod__")
__rmod__ = _overloaded_operator("__rmod__")
__mul__ = _overloaded_operator("__mul__")
__rmul__ = _overloaded_operator("__rmul__")
__neg__ = _overloaded_operator("__neg__")
__pow__ = _overloaded_operator("__pow__")
__rpow__ = _overloaded_operator("__rpow__")
__sub__ = _overloaded_operator("__sub__")
__rsub__ = _overloaded_operator("__rsub__")
__truediv__ = _overloaded_operator("__truediv__")
__rtruediv__ = _overloaded_operator("__rtruediv__")
del _overloaded_operator
#=============================================================================
# Name Scope
#=============================================================================
# This private function is used by ops.name_scope to ensure that all of the
# input tensors for the scope belong to the same graph. Defining this means
# that you may include `RaggedTensor` objects in the name_scope `values`
# list.
def _as_graph_element(self):
"""Convert `self` to a graph element."""
values = self.values
while isinstance(values, RaggedTensor):
values = values.values
return values
#=============================================================================
# Composite Tensor
#=============================================================================
@property
def _type_spec(self):
return RaggedTensorSpec.from_value(self)
def _shape_invariant_to_type_spec(self, shape):
return RaggedTensorSpec(shape, self.dtype, self.ragged_rank,
self.row_splits.dtype)
def consumers(self):
return self._consumers()
__composite_gradient__ = (
composite_tensor_gradient.WithValuesCompositeTensorGradient())
def is_ragged(value):
"""Returns true if `value` is a ragged tensor or ragged tensor value."""
return isinstance(value,
(RaggedTensor, ragged_tensor_value.RaggedTensorValue))
def match_row_splits_dtypes(*tensors, **kwargs):
"""Return a copy of `tensors` with row_splits all having the same dtype.
Args:
*tensors: A list of Tensors or RaggedTensors.
**kwargs: If 'return_dtype=True', then return a tuple (dtype, tensors),
where `dtype` is the data type used by row-splits, and `tensors` is the
converted list of `Tensors` and `RaggedTensors`.
Returns:
The converted list of `Tensors` and `RaggedTensors`.
"""
return_dtype = kwargs.pop("return_dtype", False)
if kwargs:
raise ValueError(f"Unexpected keyword args {kwargs}.")
has_int32 = False
has_int64 = False
for tensor in tensors:
if isinstance(tensor, RaggedTensor):
if tensor.row_splits.dtype == dtypes.int32:
has_int32 = True
else:
has_int64 = True
if has_int32 and has_int64:
if not ragged_config.auto_cast_partition_dtype():
raise ValueError("Input RaggedTensors have mismatched row_splits dtypes; "
"use RaggedTensor.with_row_splits_dtype() to convert "
"them to compatible dtypes.")
dtype = dtypes.int64
tensors = tuple(
t.with_row_splits_dtype(dtypes.int64) if isinstance(t, RaggedTensor
) else t
for t in tensors)
elif has_int32:
dtype = dtypes.int32
else:
dtype = dtypes.int64
if return_dtype:
return (dtype, tensors)
else:
return tensors
# ===============================================================================
# RaggedTensorSpec
# ===============================================================================
@tf_export("RaggedTensorSpec")
@type_spec_registry.register("tf.RaggedTensorSpec")
class RaggedTensorSpec(
type_spec.BatchableTypeSpec, internal_types.RaggedTensorSpec):
"""Type specification for a `tf.RaggedTensor`."""
__slots__ = [
"_shape", "_dtype", "_ragged_rank", "_row_splits_dtype",
"_flat_values_spec"
]
@property
def dtype(self):
"""The `tf.dtypes.DType` specified by this type for the RaggedTensor.
Examples:
>>> rt = tf.ragged.constant([["a"], ["b", "c"]], dtype=tf.string)
>>> tf.type_spec_from_value(rt).dtype
tf.string
Returns:
A `tf.dtypes.DType` of the values in the RaggedTensor.
"""
return self._dtype
@property
def shape(self):
"""The statically known shape of the RaggedTensor.
Examples:
>>> rt = tf.ragged.constant([[0], [1, 2]])
>>> tf.type_spec_from_value(rt).shape
TensorShape([2, None])
>>> rt = tf.ragged.constant([[[0, 1]], [[1, 2], [3, 4]]], ragged_rank=1)
>>> tf.type_spec_from_value(rt).shape
TensorShape([2, None, 2])
Returns:
A `tf.TensorShape` containing the statically known shape of the
RaggedTensor. Ragged dimensions have a size of `None`.
"""
return self._shape
@property
def ragged_rank(self):
"""The number of times the RaggedTensor's flat_values is partitioned.
Defaults to `shape.ndims - 1`.
Examples:
>>> values = tf.ragged.constant([[1, 2, 3], [4], [5, 6], [7, 8, 9, 10]])
>>> tf.type_spec_from_value(values).ragged_rank
1
>>> rt1 = tf.RaggedTensor.from_uniform_row_length(values, 2)
>>> tf.type_spec_from_value(rt1).ragged_rank
2
Returns:
A Python `int` indicating the number of times the underlying `flat_values`
Tensor has been partitioned to add a new dimension.
I.e., `tf.rank(rt) = tf.rank(rt.flat_values) + rt.ragged_rank`.
"""
return self._ragged_rank
@property
def row_splits_dtype(self):
"""The `tf.dtypes.DType` of the RaggedTensor's `row_splits`.
Examples:
>>> rt = tf.ragged.constant([[1, 2, 3], [4]], row_splits_dtype=tf.int64)
>>> tf.type_spec_from_value(rt).row_splits_dtype
tf.int64
Returns:
A `tf.dtypes.DType` for the RaggedTensor's `row_splits` tensor. One
of `tf.int32` or `tf.int64`.
"""
return self._row_splits_dtype
@property
def flat_values_spec(self):
"""The `TypeSpec` of the flat_values of RaggedTensor.
Returns:
- The TypeSpec of flat_values.
- None when the flat_values is a Tensor.
"""
return self._flat_values_spec
@property
def value_type(self):
return RaggedTensor if self._ragged_rank > 0 else tensor_lib.Tensor
def __init__(self,
shape=None,
dtype=dtypes.float32,
ragged_rank=None,
row_splits_dtype=dtypes.int64,
flat_values_spec=None):
"""Constructs a type specification for a `tf.RaggedTensor`.
Args:
shape: The shape of the RaggedTensor, or `None` to allow any shape. If a
shape is specified, then all ragged dimensions must have size `None`.
dtype: `tf.DType` of values in the RaggedTensor.
ragged_rank: Python integer, the number of times the RaggedTensor's
flat_values is partitioned. Defaults to `shape.ndims - 1`.
row_splits_dtype: `dtype` for the RaggedTensor's `row_splits` tensor. One
of `tf.int32` or `tf.int64`.
flat_values_spec: TypeSpec for flat_value of the RaggedTensor. It shall be
provided when the flat_values is a CompositeTensor rather then Tensor.
If both `dtype` and `flat_values_spec` and are provided, `dtype` must
be the same as `flat_values_spec.dtype`. (experimental)
"""
self._shape = tensor_shape.as_shape(shape)
self._row_splits_dtype = dtypes.as_dtype(row_splits_dtype)
if flat_values_spec is not None:
if dtype is None:
dtype = flat_values_spec.dtype
elif dtype != flat_values_spec.dtype:
raise ValueError("dtype must be the same as flat_values_spec.dtype")
elif dtype is None:
raise ValueError(
"At least one of dtype or flat_values_spec must be provided")
self._dtype = dtypes.as_dtype(dtype)
self._flat_values_spec = flat_values_spec
rank = self._shape.ndims
if ragged_rank is None:
if rank is None:
raise ValueError("Must specify ragged_rank or "
"a shape with a known rank.")
ragged_rank = rank - 1
self._ragged_rank = ragged_rank
if not isinstance(self._ragged_rank, int):
raise TypeError(f"Argument `ragged_rank` must be an int. "
f"Received {ragged_rank}.")
if rank is not None:
if ragged_rank >= rank:
raise ValueError(f"Argument `ragged_rank` ({ragged_rank}) must be less "
f"than rank ({rank}).")
def is_compatible_with(self, spec_or_value):
# RaggedTensor with ragged_rank 0 can be compatible with raw flat_values.
if self._ragged_rank == 0:
if self._flat_values_spec is None:
if isinstance(
spec_or_value, (tensor_lib.Tensor, tensor_lib.TensorSpec)):
return tensor_lib.TensorSpec(
self._shape, self._dtype).is_compatible_with(spec_or_value)
elif not isinstance(spec_or_value, (RaggedTensor, RaggedTensorSpec)):
return self._flat_values_spec.is_compatible_with(spec_or_value)
return super(RaggedTensorSpec, self).is_compatible_with(spec_or_value)
def _serialize(self):
if self._flat_values_spec is None:
return (self._shape, self._dtype, self._ragged_rank,
self._row_splits_dtype)
else:
return (self._shape, self._dtype, self._ragged_rank,
self._row_splits_dtype, self._flat_values_spec)
@property
def _component_specs(self):
if self._ragged_rank <= 0:
if self._flat_values_spec is not None:
return [self._flat_values_spec]
else:
return [tensor_lib.TensorSpec(self._shape, self._dtype)]
flat_values_spec = self._flat_values_spec
if flat_values_spec is None:
flat_values_shape = tensor_shape.TensorShape([None]).concatenate(
self._shape[self._ragged_rank + 1:])
flat_values_spec = tensor_lib.TensorSpec(flat_values_shape, self._dtype)
outer_dim = tensor_shape.dimension_at_index(self._shape, 0)
outer_splits_shape = [None if outer_dim is None else outer_dim + 1]
inner_splits_spec = tensor_lib.TensorSpec([None], self._row_splits_dtype)
specs = ([
flat_values_spec,
tensor_lib.TensorSpec(outer_splits_shape, self._row_splits_dtype)
] + [inner_splits_spec for _ in range(self._ragged_rank - 1)])
return specs
def _to_components(self, value):
if is_ragged(value):
return [value.flat_values] + list(value.nested_row_splits)
else:
return [value]
def _from_components(self, tensor_list):
result = tensor_list[0]
if (all(isinstance(t, np.ndarray) for t in tensor_list) and
not tf2.enabled()):
for row_splits in reversed(tensor_list[1:]):
result = ragged_tensor_value.RaggedTensorValue(result, row_splits)
else:
if isinstance(tensor_list[0], np.ndarray):
tensor_list = [ops.convert_to_tensor(t) for t in tensor_list]
result = tensor_list[0]
for row_splits in reversed(tensor_list[1:]):
result = RaggedTensor(
result,
RowPartition.from_row_splits(row_splits, validate=False),
internal=True)
if self._shape.ndims is not None:
if isinstance(result, RaggedTensor):
result._set_shape(self._shape) # pylint: disable=protected-access
# TODO(xjun): MaskedTensor doesn't implement set_shape.
if self.flat_values_spec is not None and hasattr(result.flat_values,
"set_shape"):
result.flat_values.set_shape(self.flat_values_spec.shape)
elif isinstance(result, tensor_lib.Tensor):
result.set_shape(self._shape)
return result
# The RaggedTensorSpec tensor_list encoding uses to/from_variant ops
# to (un)box the component tensors in a way that allows for batching &
# unbatching.
@property
def _flat_tensor_specs(self):
# NOTE(mishragaurav): The default flat shape of a boxed `RaggedTensor` is
# `[]` (scalar), but a `RaggedTensorSpec` can also represent a batch of
# boxed `RaggedTensor` objects with shape `(...)` (and batches of batches,
# etc.), so the flat shape must be unknown.
return [tensor_lib.TensorSpec(None, dtypes.variant)]
def _to_tensor_list(self, value):
# TODO(edloper): Update gen_ragged_conversion_ops that convert to and
# from variant to include all of the row-partitioning tensors.
if self._flat_values_spec is not None:
raise ValueError("Customized value_type is not supported.")
if isinstance(value, RaggedTensor):
if value.ragged_rank != self._ragged_rank:
raise ValueError(
f"Ragged rank of value {value.ragged_rank} does not match "
f"ragged rank of type {self._ragged_rank}.")
# pylint: disable=protected-access
return [value._to_variant(batched_input=False)]
else:
if self._ragged_rank > 0:
raise ValueError(
f"Expected a RaggedTensor if ragged rank={self._ragged_rank}"
f" but got {type(value).__name__}."
)
return [
gen_ragged_conversion_ops.ragged_tensor_to_variant(
(), value, batched_input=False)
]
def _to_batched_tensor_list(self, value):
if self._flat_values_spec is not None:
raise ValueError("Customized value_type is not supported.")
if isinstance(value, RaggedTensor):
if value.ragged_rank != self._ragged_rank:
raise ValueError(
f"Ragged rank of value {value.ragged_rank} does not match "
f"ragged rank of type {self._ragged_rank}.")
# pylint: disable=protected-access
return [value._to_variant(batched_input=True)]
else:
if self._ragged_rank > 0:
raise ValueError(
f"Expected a RaggedTensor if ragged rank={self._ragged_rank}"
f" but got {type(value).__name__}."
)
return [
gen_ragged_conversion_ops.ragged_tensor_to_variant(
rt_nested_splits=(), rt_dense_values=value, batched_input=True)
]
def _from_compatible_tensor_list(self, tensor_list):
if self._flat_values_spec is not None:
raise ValueError("Customized value_type is not supported.")
result = RaggedTensor._from_variant( # pylint: disable=protected-access
tensor_list[0],
dtype=self._dtype,
row_splits_dtype=self._row_splits_dtype,
output_ragged_rank=self._ragged_rank)
if self._shape.ndims is not None:
if isinstance(result, RaggedTensor):
result._set_shape(self._shape) # pylint: disable=protected-access
# TODO(xjun): MaskedTensor doesn't implement set_shape.
if self.flat_values_spec is not None and hasattr(self.flat_values,
"set_shape"):
result.flat_values.set_shape(self.flat_values_spec.shape)
else:
result.set_shape(self._shape)
return result
def _batch(self, batch_size):
if self._flat_values_spec is not None:
raise ValueError("Customized value_type is not supported.")
return RaggedTensorSpec(
tensor_shape.TensorShape([batch_size]).concatenate(self._shape),
self._dtype, self._ragged_rank + 1, self._row_splits_dtype)
def _unbatch(self):
if self._flat_values_spec is not None:
raise ValueError("Customized value_type is not supported.")
# Note: Negative ragged_rank is allowed here because the dataset could be
# subsequently batched again. If ragged_rank > 1, assume row_splits_dtype is
# consistent. Errors are handled in
# RaggedTensorSpec._from_compatible_tensor_list()
return RaggedTensorSpec(self._shape[1:], self._dtype, self._ragged_rank - 1,
self._row_splits_dtype)
def _to_legacy_output_types(self):
return self._dtype
def _to_legacy_output_shapes(self):
return self._shape
def _to_legacy_output_classes(self):
return self
@classmethod
def from_value(cls, value):
if (isinstance(value, ragged_tensor_value.RaggedTensorValue) or
isinstance(value.flat_values, tensor_lib.Tensor)):
return cls(
shape=value.shape,
dtype=value.values.dtype,
ragged_rank=value.ragged_rank,
row_splits_dtype=value.row_splits.dtype)
else:
flat_values_spec = type_spec.type_spec_from_value(value.flat_values)
# Relax shape[0] to None, as it is connected to dynamic ragged shapes.
flat_values_spec = flat_values_spec._unbatch()._batch(None) # pylint: disable=protected-access
return cls(
shape=value.shape,
dtype=value.values.dtype,
ragged_rank=value.ragged_rank,
row_splits_dtype=value.row_splits.dtype,
flat_values_spec=flat_values_spec)
nested_structure_coder.register_codec(
nested_structure_coder.BuiltInTypeSpecCodec(
RaggedTensorSpec, struct_pb2.TypeSpecProto.RAGGED_TENSOR_SPEC
)
)
type_spec.register_type_spec_from_value_converter(
ragged_tensor_value.RaggedTensorValue, RaggedTensorSpec.from_value)
# ===============================================================================
# Convert value -> tensor
# ===============================================================================
def convert_to_tensor_or_ragged_tensor(value,
dtype=None,
preferred_dtype=None,
name=None):
"""Converts value to a `RaggedTensor` or `Tensor`.
* If `value` is a `RaggedTensor`, then return it as-is.
* If `value` is a `RaggedTensorValue`, return a corresponding constant
`RaggedTensor`.
* Otherwise, use `convert_to_tensor` to convert `value` to a `Tensor`.
Args:
value: A `RaggedTensor`, a `RaggedTensorValue`, or an object whose type has
a registered `Tensor` conversion function.
dtype: Optional element type for the returned tensor. If missing the type
is inferred from the type of `value`.
preferred_dtype: Optional element type for the returned tensor, used when
dtype is None. This argument has no effect if `value` is already a
tensor, or when conversion is not possible.
name: Optional name to use if a new `Tensor` is created.
Returns:
A `Tensor` or `RaggedTensor`.
"""
if isinstance(value, RaggedTensor):
if dtype and not dtype.is_compatible_with(value.dtype):
raise ValueError(f"Tensor conversion requested dtype {dtype.name} for "
f"RaggedTensor with dtype {value.dtype.name}: {value}.")
return value
elif isinstance(value, ragged_tensor_value.RaggedTensorValue):
with ops.name_scope(name, "ConvertToTensorOrRaggedTensor", []):
flat_values = ops.convert_to_tensor(
value=value.flat_values,
dtype=dtype,
dtype_hint=preferred_dtype,
name="flat_values")
return RaggedTensor.from_nested_row_splits(
flat_values, value.nested_row_splits, validate=False)
else:
return tensor_conversion.convert_to_tensor_v2_with_dispatch(
value=value, dtype=dtype, dtype_hint=preferred_dtype, name=name
)
def _convert_to_ragged_tensor_values(value):
"""Converts value to supported RaggedTensor value.
* If `value` is an object of supported value type, then return it as-is.
* Otherwise convert it to Tensor or RaggedTensor.
Args:
value: An object of `Tensor`, `RaggedTensor` or registerred RaggedTensor
value types, or an object whose type has a registered `Tensor` conversion
function.
Returns:
An object of `Tensor`, `RaggedTensor` or registerred RaggedTensor
value types
"""
if _is_supported_ragged_values_type(value):
return value
else:
return convert_to_tensor_or_ragged_tensor(value, name="values")
# ===============================================================================
# Register RaggedTensor for use with session.run.
# ===============================================================================
def _ragged_tensor_value_from_components(components):
components = list(components)
value = components.pop()
while components:
value = ragged_tensor_value.RaggedTensorValue(value, components.pop())
return value
def _ragged_tensor_session_fetch(rt):
components = rt.nested_row_splits + (rt.flat_values,)
return (components, _ragged_tensor_value_from_components)
def _ragged_tensor_session_feed(feed_key, feed_val):
key_components = feed_key.nested_row_splits + (feed_key.flat_values,)
val_components = feed_val.nested_row_splits + (feed_val.flat_values,)
return zip(key_components, val_components)
def _ragged_tensor_session_feed_for_partial_run(feed_key):
return feed_key.nested_row_splits + (feed_key.flat_values,)
session.register_session_run_conversion_functions(
RaggedTensor, _ragged_tensor_session_fetch, _ragged_tensor_session_feed,
_ragged_tensor_session_feed_for_partial_run)
# ===============================================================================
# RaggedTensorType
# ===============================================================================
class RaggedTensorType:
"""Encoding of a static type for a `RaggedTensor`.
Use this type to express/declare that an output must have the type of
`RaggedTensor`.
"""
def __init__(self, dtype, ragged_rank, row_splits_dtype=dtypes.int64):
"""Initializes a RaggedTensorType object.
Args:
dtype: data type of the `RaggedTensor`'s inner values.
ragged_rank: ragged_rank of the declared `RaggedTensor`.
row_splits_dtype: data type for the `RaggedTensor`'s row splits.
One of: `tf.int32` or `tf.int64`.
"""
row_splits_dtype = dtypes.as_dtype(row_splits_dtype)
self._dtype = dtype
self._ragged_rank = ragged_rank
self._row_splits_dtype = row_splits_dtype
dtype = property(lambda self: self._dtype)
ragged_rank = property(lambda self: self._ragged_rank)
row_splits_dtype = property(lambda self: self._row_splits_dtype)
def __repr__(self):
return "RaggedTensorType(%r, %r, %r)" % (self.dtype, self.ragged_rank,
self.row_splits_dtype)
# ===============================================================================
# Helper Functions
# ===============================================================================
def _assert_sparse_indices_are_ragged_right(indices):
"""Checks that the given SparseTensor.indices tensor is ragged-right.
Example: `indices = [[0, 0], [0, 1], [2, 0], [3, 1]]` is not ragged right
because the entry `[3, 1]` skips a cell.
Args:
indices: The SparseTensor indices to check.
Returns:
A list of control dependency op tensors.
"""
index_prefix = indices[:, :-1]
index_suffix = indices[:, -1]
# Check whether each index is starting a new row in the innermost dimension
# (prefix[i] != prefix[i-1]) or continuing a row (prefix[i] == prefix[i-1]).
# (Note: this skips the first index; we will check that separately below.)
index_prefix_changed = math_ops.reduce_any(
math_ops.not_equal(index_prefix[1:], index_prefix[:-1]), axis=1)
# Check two cases:
# * For indices that start a new row: index_suffix[i] must be zero.
# * For indices that continue a row: index_suffix[i] must be equal to
# index_suffix[i-1]+1.
index_ok = array_ops.where(
index_prefix_changed, math_ops.equal(index_suffix[1:], 0),
math_ops.equal(index_suffix[1:], index_suffix[:-1] + 1))
# Also check that the very first index didn't skip any cells. The first
# index starts a new row (by definition), so its suffix should be zero.
sparse_indices_are_ragged_right = math_ops.logical_and(
math_ops.reduce_all(math_ops.equal(index_suffix[:1], 0)),
math_ops.reduce_all(index_ok))
message = [
"SparseTensor is not right-ragged", "SparseTensor.indices =", indices
]
return [control_flow_assert.Assert(sparse_indices_are_ragged_right, message)]
@ops.RegisterGradient("RaggedTensorToSparse")
def _ragged_tensor_to_sparse_gradient(op, unused_sparse_indices_grad,
sparse_values_grad,
unused_sparse_shape_grad):
"""Gradient for RaggedTensorToSparse."""
op_inputs_nested_row_splits = op.inputs[:-1]
op_inputs_flat_values = op.inputs[-1]
# No gradient for the RaggedTensor's nested_row_splits.
nested_row_splits_gradient = [None] * len(op_inputs_nested_row_splits)
# Gradient for the RaggedTensor's flat_values is formed by reshaping
# the gradient for the SparseTensor's values.
flat_values_shape = array_ops.shape(op_inputs_flat_values)
flat_values_gradient = array_ops.reshape(sparse_values_grad,
flat_values_shape)
return nested_row_splits_gradient + [flat_values_gradient]
def _assert_monotonic_increasing(tensor, message=None):
return check_ops.assert_non_negative(
tensor[1:] - tensor[:-1], message=message)
def _assert_zero(tensor, message=None):
return check_ops.assert_equal(
tensor, constant_op.constant(0, dtype=tensor.dtype), message=message)
def _nrows(tensor, out_type=dtypes.int32):
if isinstance(tensor, RaggedTensor):
return tensor.nrows(out_type=out_type)
else:
return array_ops.shape(tensor, out_type=out_type)[0]
def merge_dims(value, outer_axis, inner_axis):
"""Merges value[outer_axis...inner_axis] into a single dimension.
See `RaggedTensor.merge_dims()` for more details. This helper differs from
`RaggedTensor.merge_dims()` in that `value` may be a dense or ragged tensor.
Args:
value: A `RaggedTensor` or `Tensor`
outer_axis: `int`
inner_axis: `int`
Returns:
A flattened `RaggedTensor` or `Tensor`.
"""
if outer_axis == inner_axis:
return value
# Flatten outer dimensions of a RaggedTensor by just taking its values.
while outer_axis == 0 and isinstance(value, RaggedTensor):
value = value.values
inner_axis -= 1
if inner_axis == 0:
return value
# Flatten non-Ragged tensors using tf.reshape().
if not isinstance(value, RaggedTensor):
if value.shape.is_fully_defined():
old_shape = value.shape.as_list()
new_shape = old_shape[:outer_axis] + [-1] + old_shape[inner_axis + 1:]
else:
old_shape = array_ops.shape(value)
new_shape = array_ops.concat(
[old_shape[:outer_axis], [-1], old_shape[inner_axis + 1:]], axis=0)
return array_ops.reshape(value, new_shape)
# Handle outer_axis>1 via recursion.
if outer_axis > 1:
return value.with_values(
merge_dims(value.values, outer_axis - 1, inner_axis - 1))
# At this point, we know outer_axis == 1, and value is a RaggedTensor.
# So we need to flatten the values and build a corresponding splits tensor.
new_values = value.values
new_splits = value.row_splits
for axis in range(outer_axis, inner_axis):
if isinstance(new_values, RaggedTensor):
# Flatten a single ragged dimension.
new_splits = array_ops.gather(new_values.row_splits, new_splits)
new_values = new_values.values
else:
# Flatten all remaining dense dimensions.
shape_split = inner_axis - axis + 1
if new_values.shape.is_fully_defined():
old_shape = new_values.shape.as_list()
new_shape = [-1] + old_shape[shape_split:]
flat_size = _prod(old_shape[1:shape_split])
else:
old_shape = array_ops.shape(new_values)
new_shape = array_ops.concat([[-1], old_shape[shape_split:]], axis=0)
flat_size = math_ops.cast(
math_ops.reduce_prod(old_shape[1:shape_split]), new_splits.dtype)
new_values = array_ops.reshape(new_values, new_shape)
new_splits = new_splits * flat_size
break
return RaggedTensor.from_row_splits(new_values, new_splits)
def _prod(lst):
"""Returns the product of the numbers in a list."""
return functools.reduce(operator.mul, lst, 1)
def _get_row_partition_type_tensor_pairs_tail(partition):
"""Gets a row partition type tensor pair for the tail.
If value_rowid is defined, then it is used. Otherwise, row_splits
are used.
Args:
partition: a RowPartition.
Returns:
A list of (row_partition_type, row_partition_tensor) pairs.
"""
if partition._has_precomputed_value_rowids(): # pylint: disable=protected-access
return ("VALUE_ROWIDS", partition.value_rowids())
else:
return ("ROW_SPLITS", partition.row_splits())
def _get_row_partition_type_tensor_pairs(rt_input):
"""Gets a list of the row partitions for rt_input.
If value_rowids are defined, then they are used. Otherwise, row_splits
are used. If the outermost level has value_rowids defind, then nrows is
also added.
Args:
rt_input: a ragged tensor.
Returns:
A list of (row_partition_type, row_partition_tensor) pairs.
"""
partitions = rt_input._nested_row_partitions # pylint: disable=protected-access
tail = [_get_row_partition_type_tensor_pairs_tail(x) for x in partitions[1:]]
if partitions[0]._value_rowids is not None: # pylint: disable=protected-access
return [("FIRST_DIM_SIZE", partitions[0].nrows()),
("VALUE_ROWIDS", partitions[0].value_rowids())] + tail
else:
return [("ROW_SPLITS", partitions[0].row_splits())] + tail
def _shape_as_tensor(shape, dtype):
"""Takes shape and coerces it to a shape as a tensor.
If the object is already a tensor, simply passes it on (result is guaranteed
to be int64 or int32, but not necessarily dtype).
If not, creates a tensor of type dtype.
Result is either a scalar equal to -1 if the shape is unknown_rank.
Otherwise, it is a vector, where unknown dimensions are represented with a
value of -1.
In C++, see TensorShapeFromTensor for parsing shapes in kernels, and
InferenceContext::MakeShapeFromShapeTensorTreatScalarAsUnknownShape, for
use in the shape inference function.
Args:
shape: input to coerce from TensorShape, Tensor, None, List[Optional[Int]],
Tuple[Optional[Int]].
dtype: tf.int64 or tf.int32
Returns:
a scalar or vector tensor of dtype tf.int32 or tf.int64.
"""
if dtype != dtypes.int64 and dtype != dtypes.int32:
raise ValueError(f"Expected int64 or int32 for dtype: got {dtype}.")
if isinstance(shape, tensor_lib.Tensor):
if shape.dtype != dtypes.int64 and shape.dtype != dtypes.int32:
return math_ops.cast(shape, dtype)
return shape
shape = tensor_shape.as_shape(shape)
if not shape:
# Imply rank is unknown using a -1 scalar.
return constant_op.constant(-1, dtype=dtype)
shape = [(-1 if x is None else x) for x in shape.as_list()]
# At this point, shape is List[Int].
return constant_op.constant(shape, dtype=dtype)
def _nvals_uniform_row_length(values, uniform_row_length):
"""Get the number of values for uniform row length constructor."""
const_nvals = tensor_shape.dimension_at_index(values.shape, 0).value
if const_nvals is not None:
nvals = constant_op.constant(const_nvals, uniform_row_length.dtype)
elif isinstance(values, RaggedTensor):
nvals = values.nrows(out_type=uniform_row_length.dtype)
else:
nvals = array_ops.shape(values, out_type=uniform_row_length.dtype)[0]
return nvals
def _get_optional_partition_dtype(values):
"""Returns the partition dtype, or None if None exists."""
if isinstance(values, RaggedTensor):
# pylint: disable=protected-access
return values._row_partition.dtype
return None
_SUPPORTED_RAGGED_VALUE_TYPES = (tensor_lib.Tensor, RaggedTensor)
# TODO(edloper): Consider whether we should change the registry to be on
# TypeSpecs rather than ValueTypes.
def _add_supported_value_type(cls):
"""Register the `cls` as supported value type of RaggedTenosr.
The cls must be a subclass of CompositeTensor, and must support:
- Spec:
The Spec must be a `BatchableTypeSpec`
- Properties:
- x.shape
- x.dtype
- Methods:
- x.__getitem__(idx) (method: returns a supported value type)
- x.set_shape(shape)
- Ops:
- tf.shape(x) -- tf.shape(x)[0] must be a tf.Tensor.
- tf.tile(x)
- assert_rank_at_least(x)
- tf.ones_like(x)
- tf.gather(params=x, indices=Tensor)
- tf.add(x, y)
- tf.boolean_mask(x, ...)
- @TODO(edloper): Complete this list
Note: the following RaggedTensor, RaggedTensorSpec methods & ops are not
currently supported unless `rt.values` is a RaggedTensor or a tf.Tensor:
- rt.to_tensor()
- rt.to_sparse_tensor()
- rt._to_variant()
- rt._from_variant()
- tf.ragged.cross([rt])
- tf.gather(params=x, indices=rt) # rt used for indices
- RaggedTensorSpec methods:
- _batch
- _unbatch
- _to_tensor_list
- _to_batched_tensor_list
- _from_compatible_tensor_list
Args:
cls: The type to be added to supported value types.
"""
if not issubclass(cls, composite_tensor.CompositeTensor):
raise ValueError(f"cls ({cls}) must be a subclass of CompositeTensor.")
if not hasattr(cls, "shape"):
raise ValueError("cls must support the `shape` property.")
if not hasattr(cls, "dtype"):
raise ValueError("cls must support the `dtype` property.")
global _SUPPORTED_RAGGED_VALUE_TYPES
_SUPPORTED_RAGGED_VALUE_TYPES += (cls,)
def _is_supported_ragged_values_type(value):
return isinstance(value, _SUPPORTED_RAGGED_VALUE_TYPES)
def _assert_is_supported_ragged_values_type(value):
if not _is_supported_ragged_values_type(value):
ok_types = ", ".join(cls.__name__ for cls in _SUPPORTED_RAGGED_VALUE_TYPES)
raise TypeError(f"type(values) must be one of: {ok_types}, got {value}.")
def _formatter(x):
"""Separate Numpy array elements with comma."""
if isinstance(x, np.ndarray):
if x.size != 0:
return np.array2string(x, separator=", ")
else:
# When x.size==0, np.array2string always returns `[]`. This isn't always
# what we want. E.g., if `x.shape=[0, 3]`, then we want `[[], [], []]`.
return repr(x.tolist())
else:
return str(x)
# Type annotation indicating that a value is ragged. Includes RaggedTensor
# as well as the (deprecated) RaggedTensorValue class from TF 1.x.
Ragged = typing.Union[RaggedTensor, ragged_tensor_value.RaggedTensorValue]
# Type annotation indicating that a value is a ragged tensor, a dense tensor,
# or a value that can be converted to a tensor (e.g. np.array).
# TODO(edloper): Add Variable to TensorLike, and remove it from here.
RaggedOrDense = typing.Union[Ragged, core_types.TensorLike]
# RaggedTensor must import ragged_ops to ensure that all dispatched ragged ops
# are registered. Ragged ops import RaggedTensor, so import at bottom of the
# file to avoid a partially-initialized module error.
from tensorflow.python.ops.ragged import ragged_ops # pylint: disable=unused-import, g-bad-import-order, g-import-not-at-top