I’ve been iterating with a model for a while, and I’m running out of GPU memory. When I run the function below, I get a big long list of Parameter and Tensor objects. How do I tell PyTorch to release these objects? Running torch.cuda.empty_cache() doesn’t actually free up any memory.
def pretty_size(size):
"""Pretty prints a torch.Size object"""
assert(isinstance(size, torch.Size))
return " × ".join(map(str, size))
def dump_tensors(gpu_only=True):
"""Prints a list of the Tensors being tracked by the garbage collector."""
import gc
total_size = 0
for obj in gc.get_objects():
try:
if torch.is_tensor(obj):
if not gpu_only or obj.is_cuda:
print("%s:%s%s %s" % (type(obj).__name__,
" GPU" if obj.is_cuda else "",
" pinned" if obj.is_pinned else "",
pretty_size(obj.size())))
total_size += obj.numel()
elif hasattr(obj, "data") and torch.is_tensor(obj.data):
if not gpu_only or obj.is_cuda:
print("%s → %s:%s%s%s%s %s" % (type(obj).__name__,
type(obj.data).__name__,
" GPU" if obj.is_cuda else "",
" pinned" if obj.data.is_pinned else "",
" grad" if obj.requires_grad else "",
" volatile" if obj.volatile else "",
pretty_size(obj.data.size())))
total_size += obj.data.numel()
except Exception as e:
pass
print("Total size:", total_size)
dump_tensors()