def replace(self, r, new_r, reason=None, verbose=None):
"""
This is the main interface to manipulate the subgraph in FunctionGraph.
For every node that uses r as input, makes it use new_r instead.
"""
if verbose is None:
verbose = config.optimizer_verbose
if verbose:
print(reason, r, new_r)
if hasattr(r, "fgraph") and r.fgraph is not self:
raise Exception(
"Cannot replace %s because it does not belong "
"to this FunctionGraph" % r,
str(reason),
)
if r.type != new_r.type:
new_r2 = r.type.convert_variable(new_r)
# We still make sure that the type converts correctly
if new_r2 is None or new_r2.type != r.type:
done = dict()
used_ids = dict()
old = theano.compile.debugmode.debugprint(
r,
prefix=" ",
depth=6,
file=StringIO(),
done=done,
print_type=True,
used_ids=used_ids,
).getvalue()
new = theano.compile.debugmode.debugprint(
new_r,
prefix=" ",
depth=6,
file=StringIO(),
done=done,
print_type=True,
used_ids=used_ids,
).getvalue()
raise toolbox.BadOptimization(
r,
new_r,
None,
None,
str(reason) +
". The type of the replacement must be the same.",
old,
new,
)
new_r = new_r2
if r not in self.variables:
# this variable isn't in the graph... don't raise an
# exception here, just return silently because it makes it
# easier to implement some optimizations for
# multiple-output ops
return
if theano.config.compute_test_value != "off":
try:
tval = theano.gof.op.get_test_value(r)
new_tval = theano.gof.op.get_test_value(new_r)
except TestValueError:
pass
else:
tval_shape = getattr(tval, "shape", None)
new_tval_shape = getattr(new_tval, "shape", None)
if tval_shape != new_tval_shape:
raise AssertionError(
"The replacement variable has a test value with "
"a shape different from the original variable's "
"test value. Original: %s, new: %s" %
(tval_shape, new_tval_shape),
r,
new_r,
str(reason),
)
for node, i in list(r.clients): # copy the client list for iteration
assert (node == "output"
and self.outputs[i] is r) or (node.inputs[i] is r)
self.change_input(node, i, new_r, reason=reason)
def replace(self, var, new_var, reason=None, verbose=None):
"""Replace a variable in the `FunctionGraph`.
This is the main interface to manipulate the subgraph in `FunctionGraph`.
For every node that uses `var` as input, makes it use `new_var` instead.
Parameters:
----------
var : theano.gof.graph.Variable
The variable to be replaced.
new_var : theano.gof.graph.Variable
The variable to replace `var`.
reason : str
The name of the optimization or operation in progress.
verbose : bool
Print `reason`, `var`, and `new_var`.
"""
if verbose is None:
verbose = config.optimizer_verbose
if verbose:
print(reason, var, new_var)
if var.type != new_var.type:
new_var_2 = var.type.convert_variable(new_var)
# We still make sure that the type converts correctly
if new_var_2 is None or new_var_2.type != var.type:
done = dict()
used_ids = dict()
old = theano.compile.debugmode.debugprint(
var,
prefix=" ",
depth=6,
file=StringIO(),
done=done,
print_type=True,
used_ids=used_ids,
).getvalue()
new = theano.compile.debugmode.debugprint(
new_var,
prefix=" ",
depth=6,
file=StringIO(),
done=done,
print_type=True,
used_ids=used_ids,
).getvalue()
raise toolbox.BadOptimization(
var,
new_var,
None,
None,
str(reason) +
". The type of the replacement must be the same.",
old,
new,
)
new_var = new_var_2
if var not in self.variables:
# this variable isn't in the graph... don't raise an
# exception here, just return silently because it makes it
# easier to implement some optimizations for
# multiple-output ops
return
if theano.config.compute_test_value != "off":
try:
tval = theano.gof.op.get_test_value(var)
new_tval = theano.gof.op.get_test_value(new_var)
except TestValueError:
pass
else:
tval_shape = getattr(tval, "shape", None)
new_tval_shape = getattr(new_tval, "shape", None)
if tval_shape != new_tval_shape:
raise AssertionError(
"The replacement variable has a test value with "
"a shape different from the original variable's "
f"test value. Original: {tval_shape}, new: {new_tval_shape}"
)
for node, i in list(self.clients[var]):
assert (node == "output"
and self.outputs[i] is var) or (node.inputs[i] is var)
self.change_input(node, i, new_var, reason=reason)
