[BUG] Differentiation does not work with dynamic_one_shot

[mudit2812]

Expected behavior

I expect to be able to differentiate arbitrary circuits when using the dynamic_one_shot transform.

Actual behavior

I get an error.

Additional information

Originally from this forum discussion

Source code

import pennylane as qml
import torch

dev = qml.device("default.qubit", shots=10)

@qml.qnode(dev, interface='torch') # switch to torch interface
def f(x):
    qml.RX(x, 0) # remove extraneous instructions
    return qml.expval(qml.measure(0)) # REPLACE PauliX with measure

x = torch.tensor(0.4, requires_grad=True) # switch to torch tensor
result = f(x) 
result.backward() # replace with torch gradient computation
x.grad

Tracebacks

/usr/local/lib/python3.10/dist-packages/autoray/autoray.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  return func(*args, **kwargs)
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-4-879285d05025> in <cell line: 13>()
     11 x = torch.tensor(0.4, requires_grad=True) # switch to torch tensor
     12 result = f(x)
---> 13 result.backward() # replace with torch gradient computation
     14 x.grad

1 frames
/usr/local/lib/python3.10/dist-packages/torch/_tensor.py in backward(self, gradient, retain_graph, create_graph, inputs)
    520                 inputs=inputs,
    521             )
--> 522         torch.autograd.backward(
    523             self, gradient, retain_graph, create_graph, inputs=inputs
    524         )

/usr/local/lib/python3.10/dist-packages/torch/autograd/__init__.py in backward(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)
    264     # some Python versions print out the first line of a multi-line function
    265     # calls in the traceback and some print out the last line
--> 266     Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
    267         tensors,
    268         grad_tensors_,

RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn

System information

Dev. Using Pennylane branch dos-interfaces

Existing GitHub issues

• I have searched existing GitHub issues to make sure the issue does not already exist.