Specify retain_graph=True when calling backward the first time.千万别改成loss.backward(retain_graph=True)，会导致显卡内存随着训练⼀直增加直到OOM：RuntimeError: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 10.73 GiB total capacity; 9.79 GiB already allocated; 13.62 MiB free;...
/pytorch/aten/src/ATen/native/cuda/Loss.cu:115: operator(): block: [235,0,0], thread: [21,0,0] Assertion `input_val >= zero && input_val <= one` failed. /pytorch/aten/src/ATen/native/cuda/Loss.cu:115: operator(): block: [235,0,0], thread: [22,0,0] Assertion `input_va...
torch.autograd.backward(self, gradient, retain_graph, create_graph) File "/home/xyz/anaconda3/envs/ml_torch/lib/python3.7/site-packages/torch/autograd/init.py", line 90, in backward allow_unreachable=True) # allow_unreachable flag RuntimeError: CUDA error: out of memory ...
index=torch.unsqueeze(labels, dim=1), value=0.9) score = model(images) log_prob = torch.nn.functional.log_softmax(score, dim=1) loss = -torch.sum(log_prob * smoothed_labels) / N optimizer.zero_grad() loss.backward() optimizer.step() ...
(dense): Linear(in_features=4096, out_features=4096, bias=False) ) (post_attention_layernorm): RMSNorm() (mlp): MLP( (dense_h_to_4h): Linear(in_features=4096, out_features=27392, bias=False) (dense_4h_to_h): Linear(in_features=13696, out_features=4096, bias=False) ...
with autocast(device_type='cuda', dtype=torch.float16): output = model(input) loss = loss_fn(output, target) # Scales loss. Calls backward() on scaled loss to create scaled gradients. # Backward passes under autocast are not recommended. # Backward ops run in the same dtype autocast ...