szha commented on a change in pull request #11566: [MXNET-626] Add while_loop
URL: https://github.com/apache/incubator-mxnet/pull/11566#discussion_r203248876
##########
File path: python/mxnet/ndarray/contrib.py
##########
@@ -191,3 +191,170 @@ def check_input(inputs, in_type, msg):
if not_data_list and len(outputs) == 1:
outputs = outputs[0]
return (outputs, states)
+
+
+def while_loop(cond, func, loop_vars, max_iterations=None):
+ """Run a while loop with user-defined computation and loop condition.
+
+ This operator simulates a while loop which iterately does customized
computation
+ as long as the condition is satisfied.
+
+ `loop_vars` is a list of NDArrays on which the computation uses.
+
+ `cond` is a user-defined function, used as the loop condition.
+ It consumes `loop_vars`, and produces a scalar MXNet NDArray,
+ indicating the termination of the loop.
+ The loop ends when `cond` returns false (zero).
+ The `cond` is variadic, and its signature should be
+ `cond(*loop_vars) => NDArray`.
+
+ `func` is a user-defined function, used as the loop body.
+ It also consumes `loop_vars`, and produces `step_output` and
`new_loop_vars` at each step.
+ In each step, `step_output` should contain the same number elements.
+ Through all steps, the i-th element of `step_output` should have the same
shape and dtype.
+ Also, `new_loop_vars` should contain the same number of elements as
`loop_vars`,
+ and the corresponding element should have the same shape and dtype.
+ The `func` is variadic, and its signature should be
+ `func(*loop_vars) => (List[NDArray] step_output, List[NDArray]
new_loop_vars)`.
+
+ `max_iterations` is a scalar that defines the maximum number of iterations
allowed.
+
+ This function returns two lists.
+ The first list has the length of `|step_output|`,
+ in which the i-th element are all i-th elements of
+ `step_output` from all steps, stacked along axis 0.
+ The second list has the length of `|loop_vars|`,
+ which represents final states of loop variables.
+
+ Warning 1: for now, the axis 0 of all NDArrays in the first list are
`max_iterations`,
+ due to lack of dynamic shape inference.
+
+ Warning 2: when `cond` is never satisfied, we assume `step_output` is
empty,
+ because it cannot be inferred. This is different from the symbolic version.
+
+ Parameters
+ ----------
+ cond: a Python function.
+ The loop condition.
+ func: a Python function.
+ The loop body.
+ loop_vars: list of NDArrays.
+ The initial values of the loop variables.
+ max_iterations: a python int.
+ Maximum number of iterations.
+
+ Returns
+ -------
+ outputs: two lists, which both contains 0, 1 or more NDArrays.
+ The first list contains the stacked output from each step,
+ The second list contains the final state.
+
+ Examples
+ --------
+ >>> cond = lambda i, s: i <= 5
+ >>> func = lambda i, s: ([i + s], [i + 1, s + i])
+ >>> loop_vars = (mx.nd.array([0], dtype="int64"), mx.nd.array([1],
dtype="int64"))
+ >>> outputs, states = mx.nd.contrib.while_loop(cond, func, loop_vars,
max_iterations=10)
+ >>> outputs
+ [
+ [[ 1]
+ [ 2]
+ [ 4]
+ [ 7]
+ [11]
+ [16]
+ [...] # undefined value
+ [...]
+ [...]
+ [...]]
+ <NDArray 6x1 @cpu(0)>]
+ >>> states
+ [
+ [6]
+ <NDArray 1 @cpu(0)>,
+ [16]
+ <NDArray 1 @cpu(0)>]
+ """
+ def _to_python_scalar(inputs, type_, name):
+ """Converts "inputs", possibly typed mxnet NDArray, a numpy ndarray,
other python types,
+ to the given type
+ """
+ if isinstance(inputs, ndarray.NDArray):
+ inputs = inputs.asscalar()
+ try:
+ inputs = type_(inputs)
+ except:
+ raise ValueError("Cannot convert %s to python %s" % (name,
type_.__name__))
+ return inputs
+
+ def _to_ndarray_tuple(inputs, name):
+ """Converts "inputs", possibly a single mxnet NDArray, a list of mxnet
NDArray,
+ a tuple of mxnet NDArray, into a tuple of NDArray
+ """
+ if isinstance(inputs, list):
+ inputs = tuple(inputs)
+ if isinstance(inputs, ndarray.NDArray):
+ inputs = (inputs, )
+ if not isinstance(inputs, tuple):
+ raise ValueError("%s must be an NDArray, or a tuple or list of
NDArrays" % (name, ))
+ for item in inputs:
+ if not isinstance(item, ndarray.NDArray):
+ raise ValueError("%s must be an NDArray, or a tuple or list of
NDArrays" % (name, ))
+ return inputs
+
+ def _func_wrapper(loop_vars):
+ """This wrapper unifies
+ "func: loop_vars -> new_loop_vars"
+ and "func: loop_vars -> (step_output, new_loop_vars)"
+ into "func: loop_vars -> (None or tuple of step_outputs, tuple of
new_loop_vars)
+ """
+ step_output, new_loop_vars = func(*loop_vars)
+ if step_output is None:
+ step_output = []
+ if new_loop_vars is None:
+ new_loop_vars = []
+ step_output = _to_ndarray_tuple(step_output, "step_output")
+ new_loop_vars = _to_ndarray_tuple(new_loop_vars, "new_loop_vars")
+ if len(loop_vars) != len(new_loop_vars):
+ raise ValueError("The length of loop_vars should be consistent
during the loop")
+ return step_output, new_loop_vars
+
+ if max_iterations is None:
+ raise ValueError("max_iterations should be specified")
+ max_iterations = _to_python_scalar(max_iterations, int, "max_iteration")
+ loop_vars = _to_ndarray_tuple(loop_vars, "loop_vars")
+ # It should be work as fine if loop_vars are empty I guess,
+ # but it is semantically unnecessary to include this case.
+ if len(loop_vars) == 0:
+ raise ValueError("loop_vars should contain at least one element")
+
+ steps = 0
+ outputs = []
+ while steps < max_iterations and \
+ _to_python_scalar(cond(*loop_vars), bool, "Return value of cond"):
# loop condition
+ step_output, loop_vars = _func_wrapper(loop_vars)
+ outputs.append(step_output)
+ steps += 1
+ if len(outputs) != steps or len(step_output) != len(outputs[0]):
+ raise ValueError("Number of elements in step_output should be the
same in each step")
+ stacked_outputs = []
+ for i_th, items in enumerate(zip(*outputs), 1):
+ # `mx.ndarray.pad` only support 4-D or 5-D inputs for now
+ # so we could not use it.
+ items = [x.reshape([1] + list(x.shape)) for x in items]
+ if steps != max_iterations and items:
+ pad_shape = [max_iterations - steps] + list(items[0].shape[1: ])
+ pad = ndarray.empty(
Review comment:
🤦♂️
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