# 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.
# =============================================================================
"""Functional operations."""
import re
from tensorflow.python.autograph.core import ag_ctx as autograph_ctx
from tensorflow.python.autograph.impl import api as autograph
from tensorflow.python.eager import context
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_spec
from tensorflow.python.framework import type_spec
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import tensor_array_ops
from tensorflow.python.ops import variable_scope as vs
from tensorflow.python.ops.ragged import ragged_tensor
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util import deprecation
from tensorflow.python.util import nest
from tensorflow.python.util.tf_export import tf_export
[文档]@tf_export(v1=["map_fn"])
@deprecation.deprecated_args(None, "Use fn_output_signature instead", "dtype")
def map_fn(fn,
elems,
dtype=None,
parallel_iterations=None,
back_prop=True,
swap_memory=False,
infer_shape=True,
name=None,
fn_output_signature=None):
"""Transforms `elems` by applying `fn` to each element unstacked on axis 0.
See also `tf.scan`.
`map_fn` unstacks `elems` on axis 0 to obtain a sequence of elements;
calls `fn` to transform each element; and then stacks the transformed
values back together.
#### Mapping functions with single-Tensor inputs and outputs
If `elems` is a single tensor and `fn`'s signature is `tf.Tensor->tf.Tensor`,
then `map_fn(fn, elems)` is equivalent to
`tf.stack([fn(elem) for elem in tf.unstack(elems)])`. E.g.:
>>> tf.map_fn(fn=lambda t: tf.range(t, t + 3), elems=tf.constant([3, 5, 2]))
<tf.Tensor: shape=(3, 3), dtype=int32, numpy=
array([[3, 4, 5],
[5, 6, 7],
[2, 3, 4]], dtype=int32)>
`map_fn(fn, elems).shape = [elems.shape[0]] + fn(elems[0]).shape`.
#### Mapping functions with multi-arity inputs and outputs
`map_fn` also supports functions with multi-arity inputs and outputs:
* If `elems` is a tuple (or nested structure) of tensors, then those tensors
must all have the same outer-dimension size (`num_elems`); and `fn` is
used to transform each tuple (or structure) of corresponding slices from
`elems`. E.g., if `elems` is a tuple `(t1, t2, t3)`, then `fn` is used to
transform each tuple of slices `(t1[i], t2[i], t3[i])`
(where `0 <= i < num_elems`).
* If `fn` returns a tuple (or nested structure) of tensors, then the
result is formed by stacking corresponding elements from those structures.
#### Specifying `fn`'s output signature
If `fn`'s input and output signatures are different, then the output
signature must be specified using `fn_output_signature`. (The input and
output signatures are differ if their structures, dtypes, or tensor types do
not match). E.g.:
>>> tf.map_fn(fn=tf.strings.length, # input & output have different dtypes
... elems=tf.constant(["hello", "moon"]),
... fn_output_signature=tf.int32)
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([5, 4], dtype=int32)>
>>> tf.map_fn(fn=tf.strings.join, # input & output have different structures
... elems=[tf.constant(['The', 'A']), tf.constant(['Dog', 'Cat'])],
... fn_output_signature=tf.string)
<tf.Tensor: shape=(2,), dtype=string,
numpy=array([b'TheDog', b'ACat'], dtype=object)>
`fn_output_signature` can be specified using any of the following:
* A `tf.DType` or `tf.TensorSpec` (to describe a `tf.Tensor`)
* A `tf.RaggedTensorSpec` (to describe a `tf.RaggedTensor`)
* A `tf.SparseTensorSpec` (to describe a `tf.sparse.SparseTensor`)
* A (possibly nested) tuple, list, or dict containing the above types.
#### RaggedTensors
`map_fn` supports `tf.RaggedTensor` inputs and outputs. In particular:
* If `elems` is a `RaggedTensor`, then `fn` will be called with each
row of that ragged tensor.
* If `elems` has only one ragged dimension, then the values passed to
`fn` will be `tf.Tensor`s.
* If `elems` has multiple ragged dimensions, then the values passed to
`fn` will be `tf.RaggedTensor`s with one fewer ragged dimension.
* If the result of `map_fn` should be a `RaggedTensor`, then use a
`tf.RaggedTensorSpec` to specify `fn_output_signature`.
* If `fn` returns `tf.Tensor`s with varying sizes, then use a
`tf.RaggedTensorSpec` with `ragged_rank=0` to combine them into a
single ragged tensor (which will have ragged_rank=1).
* If `fn` returns `tf.RaggedTensor`s, then use a `tf.RaggedTensorSpec`
with the same `ragged_rank`.
>>> # Example: RaggedTensor input
>>> rt = tf.ragged.constant([[1, 2, 3], [], [4, 5], [6]])
>>> tf.map_fn(tf.reduce_sum, rt, fn_output_signature=tf.int32)
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([6, 0, 9, 6], dtype=int32)>
>>> # Example: RaggedTensor output
>>> elems = tf.constant([3, 5, 0, 2])
>>> tf.map_fn(tf.range, elems,
... fn_output_signature=tf.RaggedTensorSpec(shape=[None],
... dtype=tf.int32))
<tf.RaggedTensor [[0, 1, 2], [0, 1, 2, 3, 4], [], [0, 1]]>
Note: `map_fn` should only be used if you need to map a function over the
*rows* of a `RaggedTensor`. If you wish to map a function over the
individual values, then you should use:
* `tf.ragged.map_flat_values(fn, rt)`
(if fn is expressible as TensorFlow ops)
* `rt.with_flat_values(map_fn(fn, rt.flat_values))`
(otherwise)
E.g.:
>>> rt = tf.ragged.constant([[1, 2, 3], [], [4, 5], [6]])
>>> tf.ragged.map_flat_values(lambda x: x + 2, rt)
<tf.RaggedTensor [[3, 4, 5], [], [6, 7], [8]]>
#### SparseTensors
`map_fn` supports `tf.sparse.SparseTensor` inputs and outputs. In particular:
* If `elems` is a `SparseTensor`, then `fn` will be called with each row
of that sparse tensor. In particular, the value passed to `fn` will be a
`tf.sparse.SparseTensor` with one fewer dimension than `elems`.
* If the result of `map_fn` should be a `SparseTensor`, then use a
`tf.SparseTensorSpec` to specify `fn_output_signature`. The individual
`SparseTensor`s returned by `fn` will be stacked into a single
`SparseTensor` with one more dimension.
>>> # Example: SparseTensor input
>>> st = tf.sparse.SparseTensor([[0, 0], [2, 0], [2, 1]], [2, 3, 4], [4, 4])
>>> tf.map_fn(tf.sparse.reduce_sum, st, fn_output_signature=tf.int32)
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([2, 0, 7, 0], dtype=int32)>
>>> # Example: SparseTensor output
>>> tf.sparse.to_dense(
... tf.map_fn(tf.sparse.eye, tf.constant([2, 3]),
... fn_output_signature=tf.SparseTensorSpec(None, tf.float32)))
<tf.Tensor: shape=(2, 3, 3), dtype=float32, numpy=
array([[[1., 0., 0.],
[0., 1., 0.],
[0., 0., 0.]],
[[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]]], dtype=float32)>
Note: `map_fn` should only be used if you need to map a function over the
*rows* of a `SparseTensor`. If you wish to map a function over the nonzero
values, then you should use:
* If the function is expressible as TensorFlow ops, use:
```python
tf.sparse.SparseTensor(st.indices, fn(st.values), st.dense_shape)
```
* Otherwise, use:
```python
tf.sparse.SparseTensor(st.indices, tf.map_fn(fn, st.values),
st.dense_shape)
```
#### `map_fn` vs. vectorized operations
`map_fn` will apply the operations used by `fn` to each element of `elems`,
resulting in `O(elems.shape[0])` total operations. This is somewhat
mitigated by the fact that `map_fn` can process elements in parallel.
However, a transform expressed using `map_fn` is still typically less
efficient than an equivalent transform expressed using vectorized operations.
`map_fn` should typically only be used if one of the following is true:
* It is difficult or expensive to express the desired transform with
vectorized operations.
* `fn` creates large intermediate values, so an equivalent vectorized
transform would take too much memory.
* Processing elements in parallel is more efficient than an equivalent
vectorized transform.
* Efficiency of the transform is not critical, and using `map_fn` is
more readable.
E.g., the example given above that maps `fn=lambda t: tf.range(t, t + 3)`
across `elems` could be rewritten more efficiently using vectorized ops:
>>> elems = tf.constant([3, 5, 2])
>>> tf.range(3) + tf.expand_dims(elems, 1)
<tf.Tensor: shape=(3, 3), dtype=int32, numpy=
array([[3, 4, 5],
[5, 6, 7],
[2, 3, 4]], dtype=int32)>
In some cases, `tf.vectorized_map` can be used to automatically convert a
function to a vectorized equivalent.
#### Eager execution
When executing eagerly, `map_fn` does not execute in parallel even if
`parallel_iterations` is set to a value > 1. You can still get the
performance benefits of running a function in parallel by using the
`tf.function` decorator:
>>> fn=lambda t: tf.range(t, t + 3)
>>> @tf.function
... def func(elems):
... return tf.map_fn(fn, elems, parallel_iterations=3)
>>> func(tf.constant([3, 5, 2]))
<tf.Tensor: shape=(3, 3), dtype=int32, numpy=
array([[3, 4, 5],
[5, 6, 7],
[2, 3, 4]], dtype=int32)>
Note: if you use the `tf.function` decorator, any non-TensorFlow Python
code that you may have written in your function won't get executed. See
`tf.function` for more details. The recommendation would be to debug without
`tf.function` but switch to it to get performance benefits of running `map_fn`
in parallel.
Args:
fn: The callable to be performed. It accepts one argument, which will have
the same (possibly nested) structure as `elems`. Its output must have the
same structure as `fn_output_signature` if one is provided; otherwise it
must have the same structure as `elems`.
elems: A tensor or (possibly nested) sequence of tensors, each of which will
be unstacked along their first dimension. `fn` will be applied to the
nested sequence of the resulting slices. `elems` may include ragged and
sparse tensors. `elems` must consist of at least one tensor.
dtype: Deprecated: Equivalent to `fn_output_signature`.
parallel_iterations: (optional) The number of iterations allowed to run in
parallel. When graph building, the default value is 10. While executing
eagerly, the default value is set to 1.
back_prop: (optional) False disables support for back propagation.
swap_memory: (optional) True enables GPU-CPU memory swapping.
infer_shape: (optional) False disables tests for consistent output shapes.
name: (optional) Name prefix for the returned tensors.
fn_output_signature: The output signature of `fn`. Must be specified if
`fn`'s input and output signatures are different (i.e., if their
structures, dtypes, or tensor types do not match).
`fn_output_signature` can be specified using any of the following:
* A `tf.DType` or `tf.TensorSpec` (to describe a `tf.Tensor`)
* A `tf.RaggedTensorSpec` (to describe a `tf.RaggedTensor`)
* A `tf.SparseTensorSpec` (to describe a `tf.sparse.SparseTensor`)
* A (possibly nested) tuple, list, or dict containing the above types.
Returns:
A tensor or (possibly nested) sequence of tensors. Each tensor stacks the
results of applying `fn` to tensors unstacked from `elems` along the first
dimension, from first to last. The result may include ragged and sparse
tensors.
Raises:
TypeError: if `fn` is not callable or the structure of the output of
`fn` and `fn_output_signature` do not match.
ValueError: if the lengths of the output of `fn` and `fn_output_signature`
do not match, or if the `elems` does not contain any tensor.
Examples:
>>> elems = np.array([1, 2, 3, 4, 5, 6])
>>> tf.map_fn(lambda x: x * x, elems)
<tf.Tensor: shape=(6,), dtype=int64, numpy=array([ 1, 4, 9, 16, 25, 36])>
>>> elems = (np.array([1, 2, 3]), np.array([-1, 1, -1]))
>>> tf.map_fn(lambda x: x[0] * x[1], elems, fn_output_signature=tf.int64)
<tf.Tensor: shape=(3,), dtype=int64, numpy=array([-1, 2, -3])>
>>> elems = np.array([1, 2, 3])
>>> tf.map_fn(lambda x: (x, -x), elems,
... fn_output_signature=(tf.int64, tf.int64))
(<tf.Tensor: shape=(3,), dtype=int64, numpy=array([1, 2, 3])>,
<tf.Tensor: shape=(3,), dtype=int64, numpy=array([-1, -2, -3])>)
"""
# This function uses a `while_loop` to call `fn` on each value of the input
# tensor(s) (unstacked on dimension 0). The following sequence of variables
# are used to transform the input tensor(s) (`elems`) into the output
# tensor(s) (`result`):
#
# - Preparing and unstacking input values for the while_loop:
# - elems: The input tensor(s) to map_fn. May include composite tensors.
# - elems_flat: Flattened list of tensors from elems (using nest.flatten)
# May include composite tensors.
# - elems_batchable: Concatenation of "batchable tensor lists" for each
# tensor in elems_flat. This "boxes" composite tensors
# into sliceable tf.Tensor objects. For more info see:
# TensorSpec._to_batched_tensor_list
# - elems_batchable_ta: List of TensorArrays used to unstack each Tensor
# in elems_batchable into elems_value_batchable.
#
# - Calling `fn` on each unstacked value in the body of the while_loop:
# - elems_value_batchable: Single unstacked value from elems_batchable.
# - elems_value_flat: Single unstacked value from elems_flat,
# constructed from elems_value_batchable (using
# TensorSpec._from_tensor_list).
# - elems_value: Single unstacked value from elems (the input to fn).
# - result_value: Result of calling `fn(elems_value)`. May contain
# composite tensors.
# - result_value_flat: Flattened list of tensors from result_value.
# May contain composite tensors.
# - result_value_batchable: Concatenation of batchable tensor lists for
# each tensor in result_value_flat
# (using TensorSpec._to_tensor_list).
#
# - Collecting and stacking output values from the while_loop:
# - result_batchable_ta: List of TensorArrays used to stack each tensor
# ta result_value_batchable into result_batchable.
# - result_batchable: Stacked tensors from result_batchable_ta.
# - result_flat: Flat list of tensors for the result, constructed from
# results bactchable (using TensorSpec._from_tensor_list).
# - result: Structured result value packed from results flat
# (using nest.pack_sequence_as).
if fn_output_signature is None:
fn_output_signature = dtype
if not callable(fn):
raise TypeError(f"The provided function {fn.__name__} is not callable."
"fn must be callable.")
in_graph_mode = not context.executing_eagerly()
# Set the default number of parallel_iterations depending on graph/eager mode.
if in_graph_mode and not parallel_iterations:
parallel_iterations = 10
elif not in_graph_mode and not parallel_iterations:
parallel_iterations = 1
elif not in_graph_mode and parallel_iterations > 1:
logging.log_first_n(
logging.WARN, "Setting parallel_iterations > 1 has no "
"effect when executing eagerly. Consider calling map_fn"
" with tf.function to execute fn in "
"parallel.", 1)
parallel_iterations = 1
# Flatten the input tensors, and get the TypeSpec for each one.
elems_flat = nest.flatten(elems)
# Check in case this is an empty list
if len(elems_flat) == 0:
raise ValueError(
"elems must be a Tensor or (possibly nested) sequence of Tensors. "
"Got {}, which does not contain any Tensors.".format(elems))
elems_flat_signature = [type_spec.type_spec_from_value(e) for e in elems_flat]
elems_unflatten = lambda x: nest.pack_sequence_as(elems, x)
# Flatten fn's output signature.
if fn_output_signature is None:
# If fn_output_signature was not specified, then assume that it matches the
# input signature.
result_flat_signature = [
_most_general_compatible_type(s)._unbatch() # pylint: disable=protected-access
for s in elems_flat_signature
]
result_unflatten = elems_unflatten
else:
result_flat_signature = [
_dtype_to_spec(d) for d in nest.flatten(fn_output_signature)
]
result_unflatten = lambda x: nest.pack_sequence_as(fn_output_signature, x)
with ops.name_scope(name, "map", elems_flat):
# TODO(akshayka): Remove the in_graph_mode check once caching devices are
# supported in Eager
if in_graph_mode:
# Any get_variable calls in fn will cache the first call locally
# and not issue repeated network I/O requests for each iteration.
varscope = vs.get_variable_scope()
varscope_caching_device_was_none = False
if varscope.caching_device is None:
# TODO(ebrevdo): Change to using colocate_with here and in other
# methods.
varscope.set_caching_device(lambda op: op.device)
varscope_caching_device_was_none = True
elems_flat = [
ops.convert_to_tensor_or_composite(t, name="elem") for t in elems_flat
]
# Check that inputs are not scalars.
first_elem = elems_flat[0]
if hasattr(first_elem, "shape"):
elems_static_shape = first_elem.shape
if elems_static_shape.ndims is not None and elems_static_shape.ndims < 1:
raise ValueError(
"Elements in elems must be 1+ dimensional Tensors, not scalars")
# Box any composite tensors into tensor lists.
elems_batchable = _elems_flat_to_batchable(elems_flat)
# Find the number of iterations, n. (may be known statically.)
n_static = tensor_shape.Dimension(
tensor_shape.dimension_value(
elems_batchable[0].get_shape().with_rank_at_least(1)[0]))
for tensor in elems_batchable[1:]:
n_static.assert_is_compatible_with(
tensor_shape.Dimension(
tensor_shape.dimension_value(
tensor.get_shape().with_rank_at_least(1)[0])))
n = n_static.value or array_ops.shape(elems_batchable[0])[0]
# Convert elems to tensor array.
# TODO(edloper): Should we set infer_shape=False for composite tensors?
elems_batchable_ta = [
tensor_array_ops.TensorArray(
dtype=t.dtype, size=n, dynamic_size=False, infer_shape=True)
for t in elems_batchable
]
# Unpack elements
elems_batchable_ta = [
ta.unstack(t) for (ta, t) in zip(elems_batchable_ta, elems_batchable)
]
i = constant_op.constant(0)
# Prepare result tensor array.
# TODO(edloper): Should we set infer_shape=False for composite tensors?
result_batchable_tensor_spec = (
_result_flat_signature_to_batchable_tensor_spec(result_flat_signature))
result_batchable_ta = []
for spec in result_batchable_tensor_spec:
result_batchable_ta.append(
tensor_array_ops.TensorArray(
dtype=spec.dtype, size=n, dynamic_size=False,
infer_shape=infer_shape, element_shape=spec.shape))
def compute(i, tas):
"""The loop body of map_fn.
Args:
i: the loop counter
tas: the flat TensorArray accumulator list
Returns:
(i + 1, tas): the updated counter + updated TensorArrays
Raises:
TypeError: if fn_output_signature and result_value structure don't match
ValueType: if fn_output_signature and result_value lengths don't match
"""
elems_value_batchable = [ta.read(i) for ta in elems_batchable_ta]
elems_value_flat = _elems_value_batchable_to_flat(elems_value_batchable,
elems_flat_signature)
elems_value = elems_unflatten(elems_value_flat)
ag_ctx = autograph_ctx.control_status_ctx()
autographed_fn = autograph.tf_convert(fn, ag_ctx)
result_value = autographed_fn(elems_value)
nest.assert_same_structure(fn_output_signature or elems, result_value)
result_value_flat = nest.flatten(result_value)
result_value_batchable = _result_value_flat_to_batchable(
result_value_flat, result_flat_signature)
tas = [
ta.write(i, value) for (ta, value) in zip(tas, result_value_batchable)
]
return (i + 1, tas)
_, r_a = control_flow_ops.while_loop(
lambda i, _: i < n,
compute, (i, result_batchable_ta),
parallel_iterations=parallel_iterations,
back_prop=back_prop,
swap_memory=swap_memory,
maximum_iterations=n)
result_batchable = [r.stack() for r in r_a]
# Update each output tensor w/ static shape info about the outer dimension.
for r in result_batchable:
r.set_shape(tensor_shape.TensorShape(n_static).concatenate(
r.get_shape()[1:]))
# TODO(akshayka): Remove the in_graph_mode check once caching devices are
# supported in Eager
if in_graph_mode and varscope_caching_device_was_none:
varscope.set_caching_device(None)
result_flat = _result_batchable_to_flat(result_batchable,
result_flat_signature,
n_static)
result = result_unflatten(result_flat)
return result
def _dtype_to_spec(d):
if not isinstance(d, type_spec.TypeSpec):
d = tensor_spec.TensorSpec(None, d)
return d
def _most_general_compatible_type(spec):
"""Returns the most general TypeSpec compatible with `spec`."""
# TODO(edloper): Consider adding most_general_compatible_type to TypeSpec API
if isinstance(spec, tensor_spec.TensorSpec):
return tensor_spec.TensorSpec(None, spec.dtype)
elif isinstance(spec, ragged_tensor.RaggedTensorSpec):
# pylint: disable=protected-access
return ragged_tensor.RaggedTensorSpec(None, spec._dtype, spec._ragged_rank,
spec._row_splits_dtype)
elif isinstance(spec, sparse_tensor.SparseTensorSpec):
# pylint: disable=protected-access
return sparse_tensor.SparseTensorSpec(None, spec.dtype)
else:
return spec
def _result_flat_signature_to_batchable_tensor_spec(result_flat_signature):
"""Converts result_flat_signature -> result_batchable_tensor_specs."""
tensor_specs = []
for spec in result_flat_signature:
if not isinstance(spec, type_spec.BatchableTypeSpec):
raise TypeError("map_fn can not generate %s outputs" % (spec,))
tensor_specs.extend(spec._flat_tensor_specs) # pylint: disable=protected-access
return tensor_specs
def _elems_flat_to_batchable(elems_flat):
"""Converts elems_flat -> elems_batchable."""
elems_batchable = []
for elems_tensor in elems_flat:
spec = type_spec.type_spec_from_value(elems_tensor)
if not isinstance(spec, type_spec.BatchableTypeSpec):
raise TypeError("map_fn can not consume %s inputs: got %r" %
(spec, elems_tensor))
# pylint: disable=protected-access
elems_batchable.extend(spec._to_batched_tensor_list(elems_tensor))
return elems_batchable
def _elems_value_batchable_to_flat(elems_value_batchable, elems_flat_signature):
"""Converts elems_value_batchable -> elems_value_flat."""
elems_value_flat = []
i = 0
for spec in elems_flat_signature:
# pylint: disable=protected-access
spec = spec._unbatch()
tensor_list = elems_value_batchable[i:i + len(spec._flat_tensor_specs)]
elems_value_flat.append(spec._from_compatible_tensor_list(tensor_list))
i += len(tensor_list)
assert i == len(elems_value_batchable)
return elems_value_flat
def _result_value_flat_to_batchable(result_value_flat, result_flat_signature):
"""Converts result_value_flat -> result_value_batchable."""
result_value_batchable = []
for (r_value, r_spec) in zip(result_value_flat, result_flat_signature):
if isinstance(r_spec, tensor_spec.TensorSpec):
result_value_batchable.append(r_value)
else:
if not r_spec.is_compatible_with(r_value):
raise ValueError(
"Error in map_fn:\n Expected `fn` to return a:\n %s\n"
" But it returned a:\n %s\n (value=%s)\n"
" To fix, update the `fn_output_signature` (or `dtype`) "
"argument to `map_fn`." %
(r_spec, type_spec.type_spec_from_value(r_value), r_value))
result_value_batchable.extend(r_spec._to_tensor_list(r_value)) # pylint: disable=protected-access
return result_value_batchable
def _result_batchable_to_flat(result_batchable, result_flat_signature,
batch_size):
"""Converts result_batchable -> result_flat."""
result_flat = []
i = 0
for spec in result_flat_signature:
# pylint: disable=protected-access
num_tensors = len(spec._flat_tensor_specs)
result_flat.append(
spec._batch(batch_size)._from_compatible_tensor_list(
result_batchable[i:i + num_tensors]))
i += num_tensors
assert i == len(result_batchable)
return result_flat
@tf_export("map_fn", v1=[])
@deprecation.deprecated_arg_values(
None,
"""back_prop=False is deprecated. Consider using tf.stop_gradient instead.
Instead of:
results = tf.map_fn(fn, elems, back_prop=False)
Use:
results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))""",
warn_once=True,
back_prop=False)
@deprecation.deprecated_args(None, "Use fn_output_signature instead", "dtype")
def map_fn_v2(fn,
elems,
dtype=None,
parallel_iterations=None,
back_prop=True,
swap_memory=False,
infer_shape=True,
name=None,
fn_output_signature=None):
"""Transform `elems` by applying `fn` to each element unstacked on axis 0."""
if fn_output_signature is None:
fn_output_signature = dtype
return map_fn(
fn=fn,
elems=elems,
fn_output_signature=fn_output_signature,
parallel_iterations=parallel_iterations,
back_prop=back_prop,
swap_memory=swap_memory,
infer_shape=infer_shape,
name=name)
# Docstring for v2 is the same as v1, except that back_prop is deprecated.
map_fn_v2.__doc__ = re.sub(
r"( back_prop: \(optional\) )(.*)",
r"\1Deprecated: prefer using `tf.stop_gradient` instead. \2",
map_fn.__doc__)
assert "prefer using `tf.stop_gradient` instead" in map_fn_v2.__doc__