bug help!!!!! #222
Description
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File "/home/zl/miniconda3/envs/pytorch_gpu/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/home/zl/quantum_molecular_generate/architecture/torchquantum_model.py", line 75, in forward
tqf.cnot(self.device, wires=[k, 0])
File "/home/zl/miniconda3/envs/pytorch_gpu/lib/python3.8/site-packages/torchquantum-0.1.7-py3.8.egg/torchquantum/functional/functionals.py", line 2072, in cnot
gate_wrapper(
File "/home/zl/miniconda3/envs/pytorch_gpu/lib/python3.8/site-packages/torchquantum-0.1.7-py3.8.egg/torchquantum/functional/functionals.py", line 372, in gate_wrapper
q_device.states = apply_unitary_bmm(state, matrix, wires)
File "/home/zl/miniconda3/envs/pytorch_gpu/lib/python3.8/site-packages/torchquantum-0.1.7-py3.8.egg/torchquantum/functional/functionals.py", line 251, in apply_unitary_bmm
new_state = mat.expand(expand_shape).bmm(permuted)
File "/home/zl/miniconda3/envs/pytorch_gpu/lib/python3.8/site-packages/torch/fx/traceback.py", line 57, in format_stack
return traceback.format_stack()
(Triggered internally at ../torch/csrc/autograd/python_anomaly_mode.cpp:114.)
Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
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Traceback (most recent call last):
File "/home/zl/quantum_molecular_generate/architecture/train.py", line 61, in <module>
loss.backward()
File "/home/zl/miniconda3/envs/pytorch_gpu/lib/python3.8/site-packages/torch/_tensor.py", line 488, in backward
torch.autograd.backward(
File "/home/zl/miniconda3/envs/pytorch_gpu/lib/python3.8/site-packages/torch/autograd/__init__.py", line 197, in backward
Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
RuntimeError: Trying to backward through the graph a second time (or directly access saved tensors after they have already been freed). Saved intermediate values of the graph are freed when you call .backward() or autograd.grad(). Specify retain_graph=True if you need to backward through the graph a second time or if you need to access saved tensors after calling backward.
while I run code, it will happen. And I don't want to use this method retain_graph=True to solve it, because it will lead to slower and slower performance. So what am I supposed to do