tf.map_fn

Transforms elems by applying fn to each element unstacked on axis 0. (deprecated arguments)

tf.map_fn(
    fn, elems, dtype=None, parallel_iterations=None, back_prop=True,
    swap_memory=False, infer_shape=True, name=None, fn_output_signature=None
)

Warning: SOME ARGUMENTS ARE DEPRECATED: (dtype). They will be removed in a future version. Instructions for updating: Use fn_output_signature instead

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.Tensors.
  • If elems has multiple ragged dimensions, then the values passed to fn will be tf.RaggedTensors 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.Tensors 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.RaggedTensors, 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 SparseTensors 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:

tf.sparse.SparseTensor(st.indices, fn(st.values), st.dense_shape)

• Otherwise, use:

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.
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) Deprecated: prefer using tf.stop_gradient instead. 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.

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])>)