Add _resolve_execution_config helper function to qnode.py

[andrijapau]

Context:

The goal is to streamline the process of updating the execution configuration with non-device-specific settings. This approach consolidates the setup and validation of the execution configuration in one spot in the code, before calling qml.execute in qnode.py.

Description of the Change:

This refactor organizes the setup of the initial config by moving it into a dedicated helper function. It also includes the setup and validation code for handling the MCMConfig.

Benefits: Improves code cleaniness in qnode.py and allows for better testing of internal pipeline.

Possible Drawbacks: None

[sc-72086]

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[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 99.33%. Comparing base (9c2993a) to head (2e0fee8).
Report is 2 commits behind head on master.

Additional details and impacted files

@@            Coverage Diff             @@
##           master    #6498      +/-   ##
==========================================
+ Coverage   99.28%   99.33%   +0.04%     
==========================================
  Files         452      451       -1     
  Lines       43059    43022      -37     
==========================================
- Hits        42752    42734      -18     
+ Misses        307      288      -19     

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[albi3ro]

Potentially worth relying on something else so that we can deprecate get_gradient_fn.

[andrijapau]

Yeah ... I tried to use the new qml.workflow._get_gradient_fn earlier but it caused some tests to fail. I'm taking a look at it now and trying to make it work. 😄

The problem is with this branch in _get_gradient_fn,

...
    if diff_method == "best":
        qn = qml.QNode(lambda: None, device, diff_method=None)
        return qml.workflow.get_best_diff_method(qn)()
...

Since get_best_diff_method has no direct access to the tape provided to _get_gradient_fn it incorrectly returns backprop when it should be parameter-shift for finite shots. I tried setting the tape with qn._tape = tape but that didn't work.

Any ideas?

[andrijapau]

I think the best way forward would be to modify the qml.workflow._get_gradient_fn to not rely on qml.workflow.get_best_diff_method and instead replace that block of code with the necessary logic from the latter,

...
    if diff_method == "best":
        config = _make_execution_config(None, "best")

        if device.supports_derivatives(config, circuit=tape):
            new_config = device.preprocess(config)[1]
            return new_config.gradient_method

        if tape and any(isinstance(o, qml.operation.CV) for o in tape):
            return qml.gradients.param_shift_cv
            
        return qml.gradients.param_shift
 ...

[lillian542]

I think you just need to do something like pass shots from the tape in when calling the output of get_best_diff_method inside _get_gradient_fn and that should resolve.

I.e. something along the lines of return qml.workflow.get_best_diff_method(qn)(shots=tape.shots)

[andrijapau]

Interesting, I can try that.

Although, this might actually be an improvement to _get_gradient_fn since get_best_diff_method is designed to be user-friendly - returning strings rather than transform functions. Seems kind of weird for an internal developer function to rely on a user-designed function?

By extracting the relevant code from get_best_diff_method, we could simply return the transform function directly.

[lillian542]

I can see that it might be nice if _get_gradient_fn returned a function instead of a string. I don't have a strong opinion either way - since we are allowing users to pass in diff_method, we are going to have a mix of strings and functions to define our gradients up to some point, and I think at least in our current implementation we still have a mix at this point.

[lillian542]

I don't think we're compatible with the latest version of tensorflow.

But this is an internal function that should only ever be used if we've already established that the interface is tensorflow, so do we need to raise a "nice" error here instead of the standard import error?

[andrijapau]

I see what you mean but I was just thinking of the edge case where the user doesn't have tensorflow installed in their environment and they choose to use interface="tf". I think having a response akin to the jax error would be helpful.

I don't think we're compatible with the latest version of tensorflow.

Good point, I can update the error message to,

raise qml.QuantumFunctionError(  # pragma: no cover
            "tensorflow not found. Please install the latest "  # pragma: no cover
            "version of tensorflow supported by Pennylane " # pragma: no cover
            "to enable the 'tensorflow' interface."  # pragma: no cover
        ) from e  # pragma: no cover

[lillian542]

Is this the error that is raised if someone does that? I would hope we hit a user-facing error sooner than that (pretty much immediately), but I guess this is quite early in the pipeline. Either way, suggestion above looks good to me!

[lillian542]

I think you just need to do something like pass shots from the tape in when calling the output of get_best_diff_method inside _get_gradient_fn and that should resolve.

I.e. something along the lines of return qml.workflow.get_best_diff_method(qn)(shots=tape.shots)

[albi3ro]

Potentially we can move the if isinstance(diff_method, str) block below:

        raise qml.QuantumFunctionError(
            f"Differentiation method {diff_method} not recognized. Allowed "
            f"options are {tuple(get_args(SupportedDiffMethods))}."
        )

That way we aren't left with a hanging ending to the function.

[albi3ro]

Since the whole purpose of _get_gradient_fn is to be a step inside of _resolve_execution_config, we can update the call signature to:

def _resolve_diff_method(initial_config, device, tape):

Then we can determine whether or not the device supports the diff method for the full initial config, and not just the temporary version created for the time being.

We could even update it to be:

def _resolve_diff_method(initial_config, device, tape) -> "ExecutionConfig":

Where it does the device setup, and if the device doesn't support the diff method, it fills in gradient_method.