don't save for backward pass if we don't need to. add purple edges to graph
@@ -30,12 +30,22 @@ class ProfileOp:
return self
def __exit__(self, *junk):
if GRAPH:
- for y in self.output:
+ saved_tensors = filter(lambda x: any([isinstance(x, v) for v in Device.buffers.values()]), self.ctx.saved_tensors)
for x in self.x:
+ for y in self.output:
G.add_edge(id(x.data), id(y.data), label=self.name, color="blue" if self.backward else "black")
G.nodes[id(x.data)]['label'], G.nodes[id(y.data)]['label'] = str(x.shape), str(y.shape)
- for x in filter(lambda x: any([isinstance(x, v) for v in Device.buffers.values()]) and self.backward, self.ctx.saved_tensors):
+ # which saved tensors does this backward depend on?
+ if self.backward:
+ for x in saved_tensors:
+ for y in self.output:
G.add_edge(id(x), id(y.data), label=self.name, color="red")
+ # did this forward create any intermediate tensors?
+ if not self.backward:
+ for x in self.x:
+ for y in saved_tensors:
+ if id(x.data) != id(y):
+ G.add_edge(id(x.data), id(y), label=self.name, color="purple")
if DEBUG:
self.output[0].data.toCPU()
et = (time.time()-self.st)*1000.
@@ -348,9 +358,11 @@ class Function:
def __init__(self, *tensors):
self.parents = tensors
+ self.requires_grad = any([t.requires_grad for t in tensors])
self.saved_tensors = []
def save_for_backward(self, *x):
+ if self.requires_grad:
self.saved_tensors.extend(x)
def apply(self, *x, **kwargs):
