######################################################################
+
+def _flatparam(model, whole, already=[], offset=0):
+ for v in model._parameters:
+ p = model._parameters[v]
+ e = p.numel()
+ s = p.size()
+ model._parameters[v] = whole[offset : offset + e].view(s)
+ with torch.no_grad():
+ model._parameters[v].copy_(p)
+ offset += e
+ already.append(model)
+ for m in model.modules():
+ if m not in already:
+ offset = _flatparam(m, whole, already, offset)
+ return offset
+
+
def flatparam(model):
- with torch.no_grad():
- n = sum(p.numel() for p in model.parameters())
- big = next(model.parameters()).new(n) # Get same device and dtype
- k = 0
- for p in model.parameters():
- tmp = p.new(0).set_(p)
- p.set_(big.storage(), k, p.size()).copy_(tmp)
- k += p.numel()
+ n = sum(p.numel() for p in model.parameters())
+ whole = next(model.parameters()).new(n) # Get same device and dtype
+ whole.requires_grad_()
+ _flatparam(model, whole)
+ model.parameters = lambda: iter([whole])
+
######################################################################
model = nn.Sequential(
- nn.Linear(2, 10), nn.ReLU(), nn.Linear(10, 2)
+ nn.Linear(2, 4),
+ nn.ReLU(),
+ nn.Sequential(nn.Linear(4, 4), nn.ReLU(), nn.Linear(4, 2)),
)
-print('Before:')
+######################################################################
+
+print("Before:")
for p in model.parameters():
print(p.size(), p.storage().size())
flatparam(model)
-print('After:')
+print("After:")
for p in model.parameters():
print(p.size(), p.storage().size())
######################################################################
-print('Check:')
+print("Check:")
input = torch.rand(100, 2)
targets = torch.rand(100, 2)
-optimizer = torch.optim.SGD(model.parameters(), lr = 1e-2)
+optimizer = torch.optim.SGD(model.parameters(), lr=1e-2)
mse = nn.MSELoss()
for e in range(10):