def local_transform(node):
if node in nodes_seen:
return False
# importing Scan into module scope would be circular
from theano.scan_module.scan_op import Scan
from theano.compile import OpFromGraph
if isinstance(node.op, (Scan, OpFromGraph)):
# recurse on the inner graph
(new_inner_inputs, new_outer_inputs,
new_inner_outputs) = _map_variables_inner(
wrapped_replacer,
inner_inputs=node.op.inputs,
outer_inputs=node.inputs,
inner_outputs=node.op.outputs,
containing_op=node.op)
# reinstantiate the op
if isinstance(node.op, Scan):
new_op = Scan(
new_inner_inputs,
new_inner_outputs,
node.op.info,
# FIXME: infer this someday?
typeConstructor=None)
elif isinstance(node.op, OpFromGraph):
new_op = OpFromGraph(new_inner_inputs, new_inner_outputs,
**node.op.kwargs)
# make a new node to replace the old one
new_node = new_op.make_node(*new_outer_inputs)
nodes_seen.add(new_node)
return new_node.outputs
else:
nodes_seen.add(node)
return list(map(wrapped_replacer, node.outputs))
def local_transform(node):
if node in nodes_seen:
return False
# importing Scan into module scope would be circular
from theano.scan_module.scan_op import Scan
from theano.compile import OpFromGraph
if isinstance(node.op, (Scan, OpFromGraph)):
# recurse on the inner graph
(new_inner_inputs, new_outer_inputs, new_inner_outputs) = _map_variables_inner(
wrapped_replacer,
inner_inputs=node.op.inputs,
outer_inputs=node.inputs,
inner_outputs=node.op.outputs,
containing_op=node.op,
)
# reinstantiate the op
if isinstance(node.op, Scan):
new_op = Scan(
new_inner_inputs,
new_inner_outputs,
node.op.info,
# FIXME: infer this someday?
typeConstructor=None,
)
elif isinstance(node.op, OpFromGraph):
new_op = OpFromGraph(new_inner_inputs, new_inner_outputs, **node.op.kwargs)
# make a new node to replace the old one
new_node = new_op.make_node(*new_outer_inputs)
nodes_seen.add(new_node)
return new_node.outputs
else:
nodes_seen.add(node)
return list(map(wrapped_replacer, node.outputs))
def construct_scan(scan_args):
scan_op = Scan(scan_args.inner_inputs, scan_args.inner_outputs,
scan_args.info)
scan_out = scan_op(*scan_args.outer_inputs)
if not isinstance(scan_out, list):
scan_out = [scan_out]
return scan_out
def export(node, extra_inner_outputs):
assert isinstance(node.op, Scan)
# this is ugly but we can't use scan_utils.scan_args because that
# clones the inner graph and then extra_inner_outputs aren't in
# there anymore
old_inner_inputs = node.op.inputs
old_inner_outputs = node.op.outputs
old_outer_inputs = node.inputs
new_inner_inputs = list(old_inner_inputs)
new_inner_outputs = list(old_inner_outputs)
new_outer_inputs = list(old_outer_inputs)
new_info = copy.deepcopy(node.op.info)
# put the new inner outputs in the right place in the output list and
# update info
new_info["n_nit_sot"] += len(extra_inner_outputs)
yuck = len(old_inner_outputs) - new_info["n_shared_outs"]
new_inner_outputs[yuck:yuck] = extra_inner_outputs
# in step 8, theano.scan() adds an outer input (being the actual
# number of steps) for each nitsot. we need to do the same thing.
# note these don't come with corresponding inner inputs.
offset = (1 + node.op.n_seqs + node.op.n_mit_mot + node.op.n_mit_sot +
node.op.n_sit_sot + node.op.n_shared_outs)
# the outer input is just the actual number of steps, which is
# always available as the first outer input.
new_outer_inputs[offset:offset] = [new_outer_inputs[0]
] * len(extra_inner_outputs)
new_op = Scan(new_inner_inputs, new_inner_outputs, new_info)
outer_outputs = new_op(*new_outer_inputs)
# grab the outputs we actually care about
extra_outer_outputs = outer_outputs[yuck:yuck + len(extra_inner_outputs)]
return extra_outer_outputs
def get_population_outputs(batch_outputs, popstats):
replacements = []
visited_scan_ops = set()
for var in theano.gof.graph.ancestors(batch_outputs):
if hasattr(var.tag, "bn_statistic"):
# can't rely on object identity because scan_args clones; use original_id
popstat = next(popstat for batchstat, popstat in popstats.items()
if batchstat.tag.original_id == var.tag.original_id)
replacements.append(
(var, T.patternbroadcast(popstat, var.broadcastable)))
# descend into Scan
try:
op = var.owner.op
except:
continue
if isinstance(op, Scan):
# this would cause multiple replacements for this variable
assert not hasattr(var.tag, "bn_statistic")
if op in visited_scan_ops:
continue
visited_scan_ops.add(op)
print "descending into", var
node = var.owner
sa = scan_utils.scan_args(outer_inputs=node.inputs,
outer_outputs=node.outputs,
_inner_inputs=node.op.inputs,
_inner_outputs=node.op.outputs,
info=node.op.info)
# add subscript as sequence
# TODO check if this integer input drops the scan to cpu, if so use float and cast back in subtensor expression
indices = T.arange(sa.n_steps)
index = scan_utils.safe_new(indices[0])
sa.outer_in_seqs.append(indices)
sa.inner_in_seqs.append(index)
# add popstats as nonsequences (because they may be shorter than len(indices))
inner_popstats = {}
for batchstat, outer_popstat in popstats.items():
# this can't be subscripted hence won't appear in the inner graph
if outer_popstat.ndim == 0:
continue
inner_popstat = scan_utils.safe_new(outer_popstat)
sa.outer_in_non_seqs.append(outer_popstat)
sa.inner_in_non_seqs.append(inner_popstat)
inner_popstats[batchstat] = theano.ifelse.ifelse(
index < inner_popstat.shape[0], inner_popstat[index],
inner_popstat[-1])
# recurse on inner graph
new_inner_outputs = sa.inner_outputs
new_inner_outputs = get_population_outputs(new_inner_outputs,
inner_popstats)
# construct new scan node
new_op = Scan(sa.inner_inputs, new_inner_outputs, sa.info)
new_outer_outputs = new_op(*sa.outer_inputs)
# there is one-to-one correspondence between old outer
# inputs and new_outer_inputs; replace one-to-one
replacements.extend(equizip(node.outputs, new_outer_outputs))
print "replacements", replacements
population_outputs = scan_utils.clone(batch_outputs, replace=replacements)
return population_outputs
