EVOLUTION-MANAGER

Edit File: map_fn.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. # ============================================================================= """Functional operations.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function 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 lazy_loader from tensorflow.python.util import nest from tensorflow.python.util.tf_export import tf_export np_arrays = lazy_loader.LazyLoader( "np_arrays", globals(), "tensorflow.python.ops.numpy_ops.np_arrays") @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 eqivalent. #### 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("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 isinstance(first_elem, np_arrays.ndarray): first_elem = first_elem.data elems_static_shape = first_elem.shape if elems_static_shape.ndims is not None and elems_static_shape.ndims < 1: if len(elems_flat) == 1: raise ValueError("elems must be a 1+ dimensional Tensor, not a scalar") else: 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.merge_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__