o ?emm@sdZddlZddlmZddlmZddlm Z ddl m Z ddl m Z ddl m Z dd l mZdd l mZdd l mZdd lmZdd lmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZddl m!Z!e!dgde"ddd       d2ddZ#ddZ$dd Z%d!d"Z&d#d$Z'd%d&Z(d'd(Z)d)d*Z*e!dgdej+dd+ddd,e"ddd       d2d-d.Z,e-d/d0e#je,_d1e,jvsJdS)3zFunctional operations.N)ag_ctx)api)context) constant_op)ops) sparse_tensor) tensor_shape) tensor_spec) type_spec) array_ops)tensor_array_ops)variable_scope) while_loop) ragged_tensor) tf_logging) deprecation)nest)variable_utils) tf_exportmap_fn)v1zUse fn_output_signature insteaddtypeTFc  sdur|tstdjdt } | r|sd}n| s&|s&d}n| s6|dkr6ttjddd}t t } t | dkrMt dd d | Dfd d durhd d D} ndd t Dfdd } t|d| | rt} d} | jdur| dd d} dd | D} | d}t|dr|j}|jdur|jdkrt dt| }tt|ddd}|ddD]}|tt|ddq|jpt |ddfdd |Ddd t!|Dt"#d}t$}g}|D]}|%t&j'|j(d||jdqfdd}t)j)fdd |||f|||d\}}dd |D}|D]}|*t+|,|ddqP| rp| rp| dt-||}| |}|WdS1swYdS)a3-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])) `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.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) `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) >>> # 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)) 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) #### 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) >>> # 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))) 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) 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])) 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) >>> 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) >>> elems = np.array([1, 2, 3]) >>> tf.map_fn(lambda x: (x, -x), elems, ... fn_output_signature=(tf.int64, tf.int64)) (, ) NzThe provided function z% is not callable.fn must be callable. zSetting parallel_iterations > 1 has no effect when executing eagerly. 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