[BUG]PennyLane-SF 0.29.1 AttributeError: ‘NoneType’ object has no attribute ‘_trunc’

[RenXinZhaoSunny]

Expected behavior

I hope to build my own photonic neural network and successfully start training it

Actual behavior

In fact, once I ran it, I got the following error and was unable to train the standard NN in time to build it.

Additional information

No response

Source code

def TrainQKSAN():
    opt = qml.GradientDescentOptimizer(stepsize=0.4)
    batch_size = 15
    weights = params_init
    #bias = np.array(0.0, requires_grad=True)
    his_acc_val = []
    his_acc_train = []
    his_cost = []

    for it in range(30):

        # Update the weights by one optimizer step
        batch_index = np.random.randint(0, 2 * train_size, (batch_size,))
        feats_train_batch = feats_train[batch_index]
        Y_train_batch = Y_train[batch_index]
        #cost(params, bias, features, labels):
        weights, _, _, _cost = opt.step(cost, weights, feats_train_batch, Y_train_batch)

        # Compute predictions on train and validation set
        predictions_train = [np.sign(PGKSAN(inputs, weights)) for inputs in feats_train]
                                     
        predictions_val = [np.sign(PGKSAN(inputs, weights)) for inputs in feats_val]
        # Compute accuracy on train and validation set
        acc_train = accuracy(Y_train, predictions_train)

        acc_val = accuracy(Y_val, predictions_val)

        his_acc_val.append(acc_val)
        his_acc_train.append(acc_train)
        c = _cost
        his_cost.append(c)
        print("Iter: {:5d} | Cost: {:0.7f} | Acc train: {:0.7f} | Acc validation: {:0.7f} "
            "".format(it + 1, c, acc_train, acc_val))
        
    return his_acc_val, his_acc_train, np.array(his_cost)

Tracebacks

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[10], line 1
----> 1 his_acc_val1, his_acc_train1, his_cost1 = TrainQKSAN(params_init)
      3 print(his_acc_val1, his_acc_train1, his_cost1)

Cell In[9], line 17, in TrainQKSAN(params_init)
     15 Y_train_batch = Y_train[batch_index]
     16 #cost(params, bias, features, labels):
---> 17 weights, _, _ = opt.step(cost, weights, feats_train_batch, Y_train_batch)
     19 # Compute predictions on train and validation set
     20 predictions_train = [np.sign(PGKSAN(inputs, weights)) for inputs in feats_train]

File /opt/conda/envs/penny-sf/lib/python3.9/site-packages/pennylane/optimize/gradient_descent.py:88, in GradientDescentOptimizer.step(self, objective_fn, grad_fn, *args, **kwargs)
     70 def step(self, objective_fn, *args, grad_fn=None, **kwargs):
     71     """Update trainable arguments with one step of the optimizer.
     72 
     73     Args:
   (...)
     85         If single arg is provided, list [array] is replaced by array.
     86     """
---> 88     g, _ = self.compute_grad(objective_fn, args, kwargs, grad_fn=grad_fn)
     89     new_args = self.apply_grad(g, args)
     91     # unwrap from list if one argument, cleaner return

File /opt/conda/envs/penny-sf/lib/python3.9/site-packages/pennylane/optimize/nesterov_momentum.py:71, in NesterovMomentumOptimizer.compute_grad(self, objective_fn, args, kwargs, grad_fn)
     68         shifted_args[index] = args[index] - self.momentum * self.accumulation[index]
     70 g = get_gradient(objective_fn) if grad_fn is None else grad_fn
---> 71 grad = g(*shifted_args, **kwargs)
     72 forward = getattr(g, "forward", None)
     74 grad = (grad,) if len(trainable_indices) == 1 else grad

File /opt/conda/envs/penny-sf/lib/python3.9/site-packages/pennylane/_grad.py:115, in grad.__call__(self, *args, **kwargs)
    112     self._forward = self._fun(*args, **kwargs)
    113     return ()
--> 115 grad_value, ans = grad_fn(*args, **kwargs)
    116 self._forward = ans
    118 return grad_value

File /opt/conda/envs/penny-sf/lib/python3.9/site-packages/autograd/wrap_util.py:20, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f(*args, **kwargs)
     18 else:
     19     x = tuple(args[i] for i in argnum)
---> 20 return unary_operator(unary_f, x, *nary_op_args, **nary_op_kwargs)

File /opt/conda/envs/penny-sf/lib/python3.9/site-packages/pennylane/_grad.py:133, in grad._grad_with_forward(fun, x)
    127 @staticmethod
    128 @unary_to_nary
    129 def _grad_with_forward(fun, x):
    130     """This function is a replica of ``autograd.grad``, with the only
    131     difference being that it returns both the gradient *and* the forward pass
    132     value."""
--> 133     vjp, ans = _make_vjp(fun, x)
    135     if not vspace(ans).size == 1:
    136         raise TypeError(
    137             "Grad only applies to real scalar-output functions. "
    138             "Try jacobian, elementwise_grad or holomorphic_grad."
    139         )

File /opt/conda/envs/penny-sf/lib/python3.9/site-packages/autograd/core.py:10, in make_vjp(fun, x)
      8 def make_vjp(fun, x):
      9     start_node = VJPNode.new_root()
---> 10     end_value, end_node =  trace(start_node, fun, x)
     11     if end_node is None:
     12         def vjp(g): return vspace(x).zeros()

File /opt/conda/envs/penny-sf/lib/python3.9/site-packages/autograd/tracer.py:10, in trace(start_node, fun, x)
      8 with trace_stack.new_trace() as t:
      9     start_box = new_box(x, t, start_node)
---> 10     end_box = fun(start_box)
     11     if isbox(end_box) and end_box._trace == start_box._trace:
     12         return end_box._value, end_box._node

File /opt/conda/envs/penny-sf/lib/python3.9/site-packages/autograd/wrap_util.py:15, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f.<locals>.unary_f(x)
     13 else:
     14     subargs = subvals(args, zip(argnum, x))
---> 15 return fun(*subargs, **kwargs)

Cell In[8], line 32, in cost(params, features, labels)
     31 def cost(params, features, labels):
---> 32     predictions = [PGKSAN(inputs, params) for inputs in features]
     33     return square_loss(labels, predictions)

Cell In[8], line 32, in <listcomp>(.0)
     31 def cost(params, features, labels):
---> 32     predictions = [PGKSAN(inputs, params) for inputs in features]
     33     return square_loss(labels, predictions)

File /opt/conda/envs/penny-sf/lib/python3.9/site-packages/pennylane/qnode.py:889, in QNode.__call__(self, *args, **kwargs)
    885     self._update_original_device()
    887     return res
--> 889 res = qml.execute(
    890     [self.tape],
    891     device=self.device,
    892     gradient_fn=self.gradient_fn,
    893     interface=self.interface,
    894     gradient_kwargs=self.gradient_kwargs,
    895     override_shots=override_shots,
    896     **self.execute_kwargs,
    897 )
    899 if old_interface == "auto":
    900     self.interface = "auto"

File /opt/conda/envs/penny-sf/lib/python3.9/site-packages/pennylane/interfaces/execution.py:729, in execute(tapes, device, gradient_fn, interface, mode, gradient_kwargs, cache, cachesize, max_diff, override_shots, expand_fn, max_expansion, device_batch_transform)
    723 except ImportError as e:
    724     raise qml.QuantumFunctionError(
    725         f"{mapped_interface} not found. Please install the latest "
    726         f"version of {mapped_interface} to enable the '{mapped_interface}' interface."
    727     ) from e
--> 729 res = _execute(
    730     tapes, device, execute_fn, gradient_fn, gradient_kwargs, _n=1, max_diff=max_diff, mode=_mode
    731 )
    733 return batch_fn(res)

File /opt/conda/envs/penny-sf/lib/python3.9/site-packages/pennylane/interfaces/autograd.py:81, in execute(tapes, device, execute_fn, gradient_fn, gradient_kwargs, _n, max_diff, mode)
     75 # pylint misidentifies autograd.builtins as a dict
     76 # pylint: disable=no-member
     77 parameters = autograd.builtins.tuple(
     78     [autograd.builtins.list(t.get_parameters()) for t in tapes]
     79 )
---> 81 return _execute(
     82     parameters,
     83     tapes=tapes,
     84     device=device,
     85     execute_fn=execute_fn,
     86     gradient_fn=gradient_fn,
     87     gradient_kwargs=gradient_kwargs,
     88     _n=_n,
     89     max_diff=max_diff,
     90 )[0]

File /opt/conda/envs/penny-sf/lib/python3.9/site-packages/autograd/tracer.py:44, in primitive.<locals>.f_wrapped(*args, **kwargs)
     42 parents = tuple(box._node for _     , box in boxed_args)
     43 argnums = tuple(argnum    for argnum, _   in boxed_args)
---> 44 ans = f_wrapped(*argvals, **kwargs)
     45 node = node_constructor(ans, f_wrapped, argvals, kwargs, argnums, parents)
     46 return new_box(ans, trace, node)

File /opt/conda/envs/penny-sf/lib/python3.9/site-packages/autograd/tracer.py:48, in primitive.<locals>.f_wrapped(*args, **kwargs)
     46     return new_box(ans, trace, node)
     47 else:
---> 48     return f_raw(*args, **kwargs)

File /opt/conda/envs/penny-sf/lib/python3.9/site-packages/pennylane/interfaces/autograd.py:125, in _execute(parameters, tapes, device, execute_fn, gradient_fn, gradient_kwargs, _n, max_diff)
    104 """Autodifferentiable wrapper around ``Device.batch_execute``.
    105 
    106 The signature of this function is designed to work around Autograd restrictions.
   (...)
    122 understand the consequences!
    123 """
    124 with qml.tape.Unwrap(*tapes):
--> 125     res, jacs = execute_fn(tapes, **gradient_kwargs)
    127 for i, r in enumerate(res):
    128     if any(isinstance(m, CountsMP) for m in tapes[i].measurements):

File /opt/conda/envs/penny-sf/lib/python3.9/site-packages/pennylane/interfaces/execution.py:205, in cache_execute.<locals>.wrapper(tapes, **kwargs)
    201         return (res, []) if return_tuple else res
    203 else:
    204     # execute all unique tapes that do not exist in the cache
--> 205     res = fn(execution_tapes.values(), **kwargs)
    207 final_res = []
    209 for i, tape in enumerate(tapes):

File /opt/conda/envs/penny-sf/lib/python3.9/site-packages/pennylane/interfaces/execution.py:131, in cache_execute.<locals>.fn(tapes, **kwargs)
    129 def fn(tapes: Sequence[QuantumTape], **kwargs):  # pylint: disable=function-redefined
    130     tapes = [expand_fn(tape) for tape in tapes]
--> 131     return original_fn(tapes, **kwargs)

File /opt/conda/envs/penny-sf/lib/python3.9/contextlib.py:79, in ContextDecorator.__call__.<locals>.inner(*args, **kwds)
     76 @wraps(func)
     77 def inner(*args, **kwds):
     78     with self._recreate_cm():
---> 79         return func(*args, **kwds)

File /opt/conda/envs/penny-sf/lib/python3.9/site-packages/pennylane/_device.py:528, in Device.batch_execute(self, circuits)
    524 results = []
    525 for circuit in circuits:
    526     # we need to reset the device here, else it will
    527     # not start the next computation in the zero state
--> 528     self.reset()
    530     res = self.execute(circuit.operations, circuit.observables)
    531     results.append(res)

File /opt/conda/envs/penny-sf/lib/python3.9/site-packages/pennylane_sf/simulator.py:175, in StrawberryFieldsSimulator.reset(self)
    172 sf.hbar = self.hbar
    174 if self.eng is not None:
--> 175     self.eng.reset()
    176     self.eng = None
    178 if self.state is not None:

File /opt/conda/envs/penny-sf/lib/python3.9/site-packages/strawberryfields/engine.py:416, in LocalEngine.reset(self, backend_options)
    414 backend_options = backend_options or {}
    415 super().reset(backend_options)
--> 416 self.backend.reset(**self.backend_options)

File /opt/conda/envs/penny-sf/lib/python3.9/site-packages/strawberryfields/backends/fockbackend/backend.py:145, in FockBackend.reset(self, pure, **kwargs)
    144 def reset(self, pure=True, **kwargs):
--> 145     cutoff = kwargs.get("cutoff_dim", self.circuit._trunc)
    146     self._modemap.reset()
    147     self.circuit.reset(pure, num_subsystems=self._init_modes, cutoff_dim=cutoff)

AttributeError: 'NoneType' object has no attribute '_trunc'

System information

Name: PennyLane
Version: 0.29.1
Summary: PennyLane is a Python quantum machine learning library by Xanadu Inc.
Home-page: https://github.com/XanaduAI/pennylane
Author: 
Author-email: 
License: Apache License 2.0
Location: d:\miniconda3\envs\penny\lib\site-packages
Requires: appdirs, autograd, autoray, cachetools, networkx, numpy, pennylane-lightning, requests, retworkx, scipy, semantic-version, toml
Required-by: PennyLane-Lightning, PennyLane-SF

Platform info:           Windows-10-10.0.22000-SP0
Python version:          3.9.19
Numpy version:           1.23.5
Scipy version:           1.13.1
Installed devices:
- strawberryfields.fock (PennyLane-SF-0.29.1)
- strawberryfields.gaussian (PennyLane-SF-0.29.1)
- strawberryfields.gbs (PennyLane-SF-0.29.1)
- strawberryfields.remote (PennyLane-SF-0.29.1)
- strawberryfields.tf (PennyLane-SF-0.29.1)
- default.gaussian (PennyLane-0.29.1)
- default.mixed (PennyLane-0.29.1)
- default.qubit (PennyLane-0.29.1)
- default.qubit.autograd (PennyLane-0.29.1)
- default.qubit.jax (PennyLane-0.29.1)
- default.qubit.tf (PennyLane-0.29.1)
- default.qubit.torch (PennyLane-0.29.1)
- default.qutrit (PennyLane-0.29.1)
- null.qubit (PennyLane-0.29.1)
- lightning.qubit (PennyLane-Lightning-0.30.0)

Existing GitHub issues

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

[CatalinaAlbornoz]

Hi @RenXinZhaoSunny ,

Could you please share a minimal reproducible example so that we can look for the root of the problem? A minimal reproducible example (or minimal working example) is the simplest version of the code that reproduces the problem. It should be self-contained, including all necessary imports, data, functions, etc., so that we can copy-paste the code and reproduce the problem. However it shouldn't contain any unnecessary data, functions, ..., for example gates and functions that can be removed to simplify the code.

If you’re not sure what this means then please make sure to check out [this video](https://www.youtube.com/watch?v=snmOfreSPeY).

Let me know if you have any questions.