# tf.while_loop

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/while_loop.py#L35-L251) |

Repeat `body` while the condition `cond` is true. (deprecated argument values)

    tf.while_loop(
        cond,
        body,
        loop_vars,
        shape_invariants=None,
        parallel_iterations=10,
        back_prop=True,
        swap_memory=False,
        maximum_iterations=None,
        name=None
    )

> [!WARNING]
> **Deprecated:** SOME ARGUMENT VALUES ARE DEPRECATED: `(back_prop=False)`. They will be removed in a future version. Instructions for updating: back_prop=False is deprecated. Consider using tf.stop_gradient instead. Instead of: results = tf.while_loop(c, b, vars, back_prop=False) Use: results = tf.nest.map_structure(tf.stop_gradient, tf.while_loop(c, b, vars))

> [!NOTE]
> **Note:** This op is automatically used in a [`tf.function`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/function) to convert Python for- and while- loops when the loop variable is a [`tf.Tensor`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/Tensor), unless `autograph=False` is explicitly specified in [`tf.function`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/function) args. For example, the following are equivalent:

    @tf.function
    def sumSquare(n):
      i, result = tf.constant(0), tf.constant(0)
      while i < n: # AutoGraph converts while-loop to tf.while_loop().
        result += i * i
        i += 1
      return result
    sumSquare(10).numpy()
    285

    @tf.function
    def sumSquare2(n):
      i, result = tf.constant(0), tf.constant(0)
      c = lambda i, _: tf.less(i, n)
      b = lambda i, result: (i + 1, result + i * i)
      return tf.while_loop(c, b, [i, result])[1]
    sumSquare2(10).numpy()
    285

For more information, see [tf.function and AutoGraph guide](https://www.tensorflow.org/guide/function#autograph_transformations).

`cond` is a callable returning a boolean scalar tensor. `body` is a callable
returning a (possibly nested) tuple, namedtuple or list of tensors of the same
arity (length and structure) and types as `loop_vars`. `loop_vars` is a
(possibly nested) tuple, namedtuple or list of tensors that is passed to both
`cond` and `body`. `cond` and `body` both take as many arguments as there are
`loop_vars`.

In addition to regular Tensors or IndexedSlices, the body may accept and
return TensorArray objects. The flows of the TensorArray objects will
be appropriately forwarded between loops and during gradient calculations.

Note that `while_loop` calls `cond` and `body` *exactly once* (inside the
call to `while_loop`, and not at all during `Session.run()`). `while_loop`
stitches together the graph fragments created during the `cond` and `body`
calls with some additional graph nodes to create the graph flow that
repeats `body` until `cond` returns false.

For correctness, [`tf.while_loop()`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/while_loop) strictly enforces shape invariants for
the loop variables. A shape invariant is a (possibly partial) shape that
is unchanged across the iterations of the loop. An error will be raised
if the shape of a loop variable after an iteration is determined to be more
general than or incompatible with its shape invariant. For example, a shape
of `[11, None]` is more general than a shape of `[11, 17]`, and `[11, 21]` is
not compatible with `[11, 17]`. By default (if the argument `shape_invariants`
is not specified), it is assumed that the initial shape of each tensor in
`loop_vars` is the same in every iteration. The `shape_invariants` argument
allows the caller to specify a less specific shape invariant for each loop
variable, which is needed if the shape varies between iterations. The
[`tf.Tensor.set_shape`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/Tensor#set_shape)
function may also be used in the `body` function to indicate that
the output loop variable has a particular shape. The shape invariant for
SparseTensor and IndexedSlices are treated specially as follows:

a) If a loop variable is a SparseTensor, the shape invariant must be
`TensorShape([r])` where `r` is the rank of the dense tensor represented
by the sparse tensor. It means the shapes of the three tensors of the
SparseTensor are `([None], [None, r], [r])`. NOTE: The shape invariant here
is the shape of the SparseTensor.dense_shape property. It must be the shape of
a vector.

b) If a loop variable is an IndexedSlices, the shape invariant must be
a shape invariant of the values tensor of the IndexedSlices. It means
the shapes of the three tensors of the IndexedSlices are `(shape, [shape[0]],
[shape.ndims])`.

`while_loop` implements non-strict semantics, enabling multiple iterations
to run in parallel. The maximum number of parallel iterations can be
controlled by `parallel_iterations`, which gives users some control over
memory consumption and execution order. For correct programs, `while_loop`
should return the same result for any `parallel_iterations > 0`.

For training, TensorFlow stores the tensors that are produced in the
forward inference and are needed in back propagation. These tensors are a
main source of memory consumption and often cause OOM errors when training
on GPUs. When the flag swap_memory is true, we swap out these tensors from
GPU to CPU. This for example allows us to train RNN models with very long
sequences and large batches.

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| ## Args ||
|---|---|
| `cond` | A callable that represents the termination condition of the loop. |
| `body` | A callable that represents the loop body. |
| `loop_vars` | A (possibly nested) tuple, namedtuple or list of numpy array, `Tensor`, and `TensorArray` objects. |
| `shape_invariants` | The shape invariants for the loop variables. |
| `parallel_iterations` | The number of iterations allowed to run in parallel. It must be a positive integer. |
| `back_prop` | (optional) Deprecated. False disables support for back propagation. Prefer using [`tf.stop_gradient`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/stop_gradient) instead. |
| `swap_memory` | Whether GPU-CPU memory swap is enabled for this loop. |
| `maximum_iterations` | Optional maximum number of iterations of the while loop to run. If provided, the `cond` output is AND-ed with an additional condition ensuring the number of iterations executed is no greater than `maximum_iterations`. |
| `name` | Optional name prefix for the returned tensors. |

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| ## Returns ||
|---|---|
| The output tensors for the loop variables after the loop. The return value has the same structure as `loop_vars`. ||

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| ## Raises ||
|---|---|
| `TypeError` | if `cond` or `body` is not callable. |
| `ValueError` | if `loop_vars` is empty. |

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#### Example:

    i = tf.constant(0)
    c = lambda i: tf.less(i, 10)
    b = lambda i: (tf.add(i, 1), )
    r = tf.while_loop(c, b, [i])[0]
    r.numpy()
    10

Example with nesting and a namedtuple:

    import collections
    Pair = collections.namedtuple('Pair', 'j, k')
    ijk_0 = (tf.constant(0), Pair(tf.constant(1), tf.constant(2)))
    c = lambda i, p: i < 10
    b = lambda i, p: (i + 1, Pair((p.j + p.k), (p.j - p.k)))
    ijk_final = tf.while_loop(c, b, ijk_0)[1]
    ijk_final[0].numpy(), ijk_final[1].numpy()
    (32, 64)

Example using shape_invariants:

    i0 = tf.constant(0)
    m0 = tf.ones([2, 2])
    c = lambda i, m: i < 10
    b = lambda i, m: [i+1, tf.concat([m, m], axis=0)]
    tf.while_loop(
        c, b, loop_vars=[i0, m0],
        shape_invariants=[i0.get_shape(), tf.TensorShape([None, 2])])[1]
    <tf.Tensor: shape=(2048, 2), dtype=float32, numpy=...>

Example which demonstrates non-strict semantics: In the following
example, the final value of `counter` does not depend on `x`. So
the `while_loop` can increment the counter parallel to updates of `x`.
However, because the loop counter at one loop iteration depends
on the value at the previous iteration, the loop counter itself cannot
be incremented in parallel. Hence if we just want the final value of the
counter (which we print on the line `print(sess.run(i))`), then
`x` will never be incremented, but the counter will be updated on a
single thread. Conversely, if we want the value of the output (which we
print on the line `print(sess.run(out).shape)`), then the counter may be
incremented on its own thread, while `x` can be incremented in
parallel on a separate thread. In the extreme case, it is conceivable
that the thread incrementing the counter runs until completion before
`x` is incremented even a single time. The only thing that can never
happen is that the thread updating `x` can never get ahead of the
counter thread because the thread incrementing `x` depends on the value
of the counter.

    with tf.compat.v1.Session() as sess:
      n = 10
      c = lambda i, x: i < n
      b = lambda i, x: (
          tf.compat.v1.Print(i + 1, [i], "Updating i based on i == "),
          # Let x depend on i
          tf.compat.v1.Print(x + i, [i], "Updating x based on i == "))

      # Make x to be a big matrix so its updating thread would run slowly
      x = tf.zeros([1000, 100], dtype=tf.int32)
      counter = tf.constant(0)
      counter_out, x_out = tf.while_loop(c, b, (counter, x))

      # The following line may increment the counter and x in parallel.
      # The counter thread may get ahead of the x thread, but not the
      # other way around. For example, the log may contain these messages:
      #

... # Updating i based on i == \[9\]
... # Updating x based on i == \[3\]
... # `... # meaning that the counter(i) thread is on iteration 9,
... # while the x thread is on iteration 3.
... print(sess.run(x_out).shape)
(1000, 100)`