zheng-da commented on a change in pull request #11566: [MXNET-626] Add
while_loop
URL: https://github.com/apache/incubator-mxnet/pull/11566#discussion_r201929531
##########
File path: python/mxnet/symbol/contrib.py
##########
@@ -336,3 +336,219 @@ def check_data(inputs, in_type, msg):
states = states[0]
return (outs, states)
+
+def while_loop(cond, func, loop_vars, max_iterations=None, name="while_loop"):
+ """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 Symbols 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 symbol,
+ 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) => Symbol`.
+
+ `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[Symbol] step_output, List[Symbol]
new_loop_vars)`.
+
+ `max_iterations` is a scalar that defines the maximum number of iterations
allowed.
+
+ This function returns two lists as a tuple.
+ 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: Even if `cond` is never satisfied,
+ while_loop returns a list of outputs with inferred dtype and shape.
+ This is different from the NDArray version,
+ where in this case `step_outputs` are assumed as an empty list.
+
+ Warning 2: The output shape along axis 0 is `max_iteration`,
+ which is different from the NDArray version,
+ where it is the actual number of steps taken.
+
+ Parameters
+ ----------
+ cond: a Python function.
+ The loop condition.
+ func: a Python function.
+ The loop body.
+ loop_vars: list of Symbol.
+ The initial values of the loop variables.
+ max_iteration: a python int.
+ Maximum number of iterations.
+
+ Returns
+ -------
+ outputs: a tuple of two lists, which both contains 0, 1 or more Symbols.
+ 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.sym.var('i'), mx.sym.var('s'))
+ >>> outputs, states = mx.sym.contrib.while_loop(cond, func, loop_vars,
max_iterations=10)
+ """
+ def _to_python_scalar(inputs, type_, name):
+ """Converts "inputs", possibly typed mxnet NDArray, a numpy ndarray,
other python types,
+ to the given type
+ """
+ if hasattr(inputs, "asscalar"):
+ inputs = inputs.asscalar()
+ try:
+ inputs = type_(inputs)
+ except:
+ raise ValueError("Cannot convert %s to python %s" % (name,
type_.__name__))
+ return inputs
+
+ def _to_symbol_tuple(inputs, name):
+ """Converts "inputs", possibly a single mxnet Symbol, a list of mxnet
Symbol,
+ a tuple of mxnet Symbol, into a tuple of Symbol
+ """
+ if isinstance(inputs, list):
+ inputs = tuple(inputs)
+ if isinstance(inputs, Symbol):
+ inputs = (inputs, )
+ if not isinstance(inputs, tuple):
+ raise ValueError("%s must be a Symbol, or a tuple or list of
Symbol" % (name, ))
+ for item in inputs:
+ if not isinstance(item, Symbol):
+ raise ValueError("%s must be a Symbol, or a tuple or list of
Symbol" % (name, ))
+ return inputs
+
+ def _cond_wrapper(loop_vars):
+ result = cond(*loop_vars)
+ if not isinstance(result, Symbol):
+ raise ValueError("Return of cond must be a Symbol")
+ return [], [result]
+
+ 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 -> (list 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_symbol_tuple(step_output, "step_output")
+ new_loop_vars = _to_symbol_tuple(new_loop_vars, "new_loop_vars")
+ if len(loop_vars) != len(new_loop_vars):
+ raise ValueError("The number of loop_vars should be consistent
during the loop")
+ return list(step_output), list(new_loop_vars)
+
+ def _create_subgraph(graph_vars, graph_func, subgraph_name):
+ with AttrScope(__subgraph_name__=subgraph_name):
Review comment:
probably not. the C code that cuts the subgraph looks for nodes with the
attribute of `__subgraph_name__`
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