Source code for pennylane.tracker

# Copyright 2018-2021 Xanadu Quantum Technologies Inc.

# Licensed under the Apache License, Version 2.0 (the "License");
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This module contains a class for updating and recording information about device executions.

# pylint: disable=attribute-defined-outside-init

from numbers import Number

[docs]class Tracker: """This class stores information about device executions and allows users to interact with that data upon individual executions and batches, even within parameter-shift gradients and optimization steps. The information is stored in three class attribute dictionaries: ``totals``, ``history``, and ``latest``: * ``latest`` tracks the last set of information passed to the tracker. * ``history`` stores a list of values passed for each keyword. * ``totals`` keeps a running sum per keyword when the values are numeric. Standard devices will track the number of executions, number of shots, number of batch executions, and batch execution length, but plugins may store additional information with no changes to this class. Information is only stored when the class attribute ``active`` is set to ``True``. This attribute can be toggled via a context manager and Python's ``with`` statement. Upon entering a context, the stored information is reset, unless ``persistent=True``. Tracking mode can also be manually triggered by setting `` = True`` without the use of a context manager. Args: dev (Device): A PennyLane compatible device callback=None (callable or None): A function of the keywords ``totals``, ``history`` and ``latest``. Run on each ``record`` call with current values of the corresponding attributes. persistent=False (bool): Whether to reset stored information upon entering a runtime context. **Example** Using a ``with`` statement to toggle the active mode, we can see the number of executions and shots used to calculate a parameter-shift derivative. .. code-block:: python dev = qml.device('default.qubit', wires=1, shots=100) @qml.qnode(dev, diff_method="parameter-shift") def circuit(x): qml.RX(x, wires=0) return qml.expval(qml.PauliZ(0)) x = np.array(0.1, requires_grad=True) with qml.Tracker(dev) as tracker: qml.grad(circuit)(x) You can then access the tabulated information through ``totals``, ``history``, and ``latest``: >>> tracker.totals {'executions': 3, 'shots': 300, 'batches': 1, 'batch_len': 2} >>> tracker.history {'executions': [1, 1, 1], 'shots': [100, 100, 100], 'batches': [1], 'batch_len': [2]} >>> tracker.latest {'batches': 1, 'batch_len': 2} We can see that calculating the gradient of ``circuit`` takes three total evaluations: one forward pass and one batch of length two for the derivative of ``qml.RX``. .. details:: :title: Usage Details .. note:: With backpropagation, this function should take ``qnode.device`` instead of the device used to create the QNode. Users can pass a custom callback function to the ``callback`` keyword. This function is run each time the ``record()`` method is called, which occurs near the end of a device's ``execute`` and ``batch_execute`` methods. Using ``print`` or logging, users can monitor completion during a long set of jobs. The function passed must accept ``totals``, ``history``, and ``latest`` as keyword arguments. The dictionary ``latest`` will contain different keywords based on whether whether ``execute`` or ``batch_execute`` last performed an update. >>> def shots_info(totals, history, latest): ... if 'shots' in latest: ... print("Total shots: ", totals['shots']) >>> x = np.array(0.1, requires_grad=True) >>> with qml.Tracker(circuit.device, callback=shots_info) as tracker: ... qml.grad(circuit)(x) Total shots: 100 Total shots: 200 Total shots: 300 By specifying ``persistent=False``, you can reuse the same tracker across multiple contexts. >>> with qml.Tracker(circuit.device, persistent=False) as tracker: ... circuit(0.1) >>> with tracker: ... circuit(0.2) >>> tracker.totals['executions'] 2 """ def __init__(self, dev=None, callback=None, persistent=False): self.persistent = persistent self.callback = callback self.reset() = False if dev is not None: if not hasattr(dev, "tracker"): raise Exception(f"Device '{dev.short_name}' does not support device tracking") dev.tracker = self def __enter__(self): if not self.persistent: self.reset() = True return self def __exit__(self, exc_type, exc_value, exc_traceback): = False
[docs] def update(self, **kwargs): """Store passed keyword-value pairs into ``totals``,``history``, and ``latest`` attributes. There is no restriction on the key-value pairs passed, but in the standard devices, the device ``execute`` method will pass ``executions`` and ``shots``, and the ``batch_execute`` method will pass ``batches`` and ``batch_len``. Only numeric values will be added to ``totals``. >>> tracker.update(a=1, b=2, c="c") >>> tracker.latest {"a":1, "b":2, "c":"c"} >>> tracker.history {"a": [1], "b": [2], "c": ["c"]} >>> tracker.totals {"a": 1, "b": 2} """ self.latest = kwargs for key, value in kwargs.items(): # update history if key in self.history: self.history[key].append(value) else: self.history[key] = [value] # updating totals if value is not None: # Only total numeric values if isinstance(value, Number): self.totals[key] = value + self.totals.get(key, 0)
[docs] def reset(self): """Resets stored information.""" self.totals = {} self.history = {} self.latest = {}
[docs] def record(self): """This method allows users to interact with the stored data. While it's intended purpose is monitoring large jobs through ``print`` statements or logging, the function is completely flexible and customizable. If a user provided a ``callback`` function during initialization, that function is called with the current ``totals``, ``history``, and ``latest`` data variables as keyword arguments. """ if self.callback is not None: self.callback(totals=self.totals, history=self.history, latest=self.latest)