Source code for pennylane._grad
# Copyright 2018-2021 Xanadu Quantum Technologies Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This module contains the autograd wrappers :class:`grad` and :func:`jacobian`
"""
import warnings
from autograd import jacobian as _jacobian
from autograd.core import make_vjp as _make_vjp
from autograd.numpy.numpy_boxes import ArrayBox
from autograd.extend import vspace
from autograd.wrap_util import unary_to_nary
from pennylane.compiler import compiler
from pennylane.compiler.compiler import CompileError
make_vjp = unary_to_nary(_make_vjp)
[docs]class grad:
"""Returns the gradient as a callable function of hybrid quantum-classical functions.
:func:`~.qjit` and Autograd compatible.
By default, gradients are computed for arguments which contain
the property ``requires_grad=True``. Alternatively, the ``argnum`` keyword argument
can be specified to compute gradients for function arguments without this property,
such as scalars, lists, tuples, dicts, or vanilla NumPy arrays. Setting
``argnum`` to the index of an argument with ``requires_grad=False`` will raise
a ``NonDifferentiableError``.
When the output gradient function is executed, both the forward pass
*and* the backward pass will be performed in order to compute the gradient.
The value of the forward pass is available via the :attr:`~.forward` property.
.. warning::
``grad`` is intended to be used with the Autograd interface only.
.. note::
When used with :func:`~.qjit`, this function currently only supports the
Catalyst compiler. See :func:`catalyst.grad` for more details.
Please see the Catalyst :doc:`quickstart guide <catalyst:dev/quick_start>`,
as well as the :doc:`sharp bits and debugging tips <catalyst:dev/sharp_bits>`
page for an overview of the differences between Catalyst and PennyLane.
Args:
func (function): a plain QNode, or a Python function that contains
a combination of quantum and classical nodes
argnum (int, list(int), None): Which argument(s) to take the gradient
with respect to. By default, the arguments themselves are used
to determine differentiability, by examining the ``requires_grad``
property.
method (str): Specifies the gradient method when used with the :func:`~.qjit`
decorator. Outside of :func:`~.qjit`, this keyword argument
has no effect and should not be set. In just-in-time (JIT) mode,
this can be any of ``["auto", "fd"]``, where:
- ``"auto"`` represents deferring the quantum differentiation to the method
specified by the QNode, while the classical computation is differentiated
using traditional auto-diff. Catalyst supports ``"parameter-shift"`` and
``"adjoint"`` on internal QNodes. QNodes with ``diff_method="finite-diff"``
are not supported with ``"auto"``.
- ``"fd"`` represents first-order finite-differences for the entire hybrid
function.
h (float): The step-size value for the finite-difference (``"fd"``) method within
:func:`~.qjit` decorated functions. This value has
no effect in non-compiled functions.
Returns:
function: The function that returns the gradient of the input
function with respect to the differentiable arguments, or, if specified,
the arguments in ``argnum``.
"""
def __new__(cls, func, argnum=None, method=None, h=None):
"""Patch to the proper grad function"""
if active_jit := compiler.active_compiler():
available_eps = compiler.AvailableCompilers.names_entrypoints
ops_loader = available_eps[active_jit]["ops"].load()
return ops_loader.grad(func, method=method, h=h, argnum=argnum)
if method or h: # pragma: no cover
raise ValueError(
f"Invalid values for 'method={method}' and 'h={h}' in interpreted mode"
)
return super().__new__(cls)
def __init__(self, func, argnum=None):
self._forward = None
self._grad_fn = None
self._fun = func
self._argnum = argnum
if self._argnum is not None:
# If the differentiable argnum is provided, we can construct
# the gradient function at once during initialization.
# Known pylint issue with function signatures and decorators:
# pylint:disable=unexpected-keyword-arg,no-value-for-parameter
self._grad_fn = self._grad_with_forward(func, argnum=argnum)
def _get_grad_fn(self, args):
"""Get the required gradient function.
* If the differentiable argnum was provided on initialization,
this has been pre-computed and is available via self._grad_fn
* Otherwise, we must dynamically construct the gradient function by
inspecting as to which of the parameter arguments are marked
as differentiable.
"""
if self._grad_fn is not None:
return self._grad_fn, self._argnum
# Inspect the arguments for differentiability, and
# compute the autograd gradient function with required argnums
# dynamically.
argnum = []
for idx, arg in enumerate(args):
trainable = getattr(arg, "requires_grad", None) or isinstance(arg, ArrayBox)
if trainable:
if arg.dtype.name[:3] == "int":
raise ValueError("Autograd does not support differentiation of ints.")
argnum.append(idx)
if len(argnum) == 1:
argnum = argnum[0]
# Known pylint issue with function signatures and decorators:
# pylint:disable=unexpected-keyword-arg,no-value-for-parameter
return self._grad_with_forward(self._fun, argnum=argnum), argnum
def __call__(self, *args, **kwargs):
"""Evaluates the gradient function, and saves the function value
calculated during the forward pass in :attr:`.forward`."""
grad_fn, argnum = self._get_grad_fn(args)
if not isinstance(argnum, int) and not argnum:
warnings.warn(
"Attempted to differentiate a function with no trainable parameters. "
"If this is unintended, please add trainable parameters via the "
"'requires_grad' attribute or 'argnum' keyword."
)
self._forward = self._fun(*args, **kwargs)
return ()
grad_value, ans = grad_fn(*args, **kwargs) # pylint: disable=not-callable
self._forward = ans
return grad_value
@property
def forward(self):
"""float: The result of the forward pass calculated while performing
backpropagation. Will return ``None`` if the backpropagation has not yet
been performed."""
return self._forward
@staticmethod
@unary_to_nary
def _grad_with_forward(fun, x):
"""This function is a replica of ``autograd.grad``, with the only
difference being that it returns both the gradient *and* the forward pass
value."""
vjp, ans = _make_vjp(fun, x) # pylint: disable=redefined-outer-name
if vspace(ans).size != 1:
raise TypeError(
"Grad only applies to real scalar-output functions. "
"Try jacobian, elementwise_grad or holomorphic_grad."
)
grad_value = vjp(vspace(ans).ones())
return grad_value, ans
[docs]def jacobian(func, argnum=None, method=None, h=None):
"""Returns the Jacobian as a callable function of vector-valued (functions of) QNodes.
:func:`~.qjit` and Autograd compatible.
.. note::
When used with :func:`~.qjit`, this function currently only supports the
Catalyst compiler. See :func:`catalyst.jacobian` for more details.
Please see the Catalyst :doc:`quickstart guide <catalyst:dev/quick_start>`,
as well as the :doc:`sharp bits and debugging tips <catalyst:dev/sharp_bits>`
page for an overview of the differences between Catalyst and PennyLane.
Args:
func (function): A vector-valued Python function or QNode that contains
a combination of quantum and classical nodes. The output of the computation
must consist of a single NumPy array (if classical) or a tuple of
expectation values (if a quantum node)
argnum (int or Sequence[int]): Which argument to take the gradient
with respect to. If a sequence is given, the Jacobian corresponding
to all marked inputs and all output elements is returned.
method (str): Specifies the gradient method when used with the :func:`~.qjit`
decorator. Outside of :func:`~.qjit`, this keyword argument
has no effect and should not be set. In just-in-time (JIT) mode,
this can be any of ``["auto", "fd"]``, where:
- ``"auto"`` represents deferring the quantum differentiation to the method
specified by the QNode, while the classical computation is differentiated
using traditional auto-diff. Catalyst supports ``"parameter-shift"`` and
``"adjoint"`` on internal QNodes. QNodes with ``diff_method="finite-diff"``
are not supported with ``"auto"``.
- ``"fd"`` represents first-order finite-differences for the entire hybrid
function.
h (float): The step-size value for the finite-difference (``"fd"``) method within
:func:`~.qjit` decorated functions. This value has no effect in non-compiled
functions.
Returns:
function: the function that returns the Jacobian of the input function with respect to the
arguments in argnum
.. note::
Due to a limitation in Autograd, this function can only differentiate built-in scalar
or NumPy array arguments.
For ``argnum=None``, the trainable arguments are inferred dynamically from the arguments
passed to the function. The returned function takes the same arguments as the original
function and outputs a ``tuple``. The ``i`` th entry of the ``tuple`` has shape
``(*output shape, *shape of args[argnum[i]])``.
If a single trainable argument is inferred, or if a single integer
is provided as ``argnum``, the tuple is unpacked and its only entry is returned instead.
**Example**
Consider the QNode
.. code-block::
dev = qml.device("default.qubit", wires=2)
@qml.qnode(dev)
def circuit(weights):
qml.RX(weights[0, 0, 0], wires=0)
qml.RY(weights[0, 0, 1], wires=1)
qml.RZ(weights[1, 0, 2], wires=0)
return qml.probs()
weights = np.array(
[[[0.2, 0.9, -1.4]], [[0.5, 0.2, 0.1]]], requires_grad=True
)
It has a single array-valued QNode argument with shape ``(2, 1, 3)`` and outputs
the probability of each 2-wire basis state, of which there are ``2**num_wires`` = 4.
Therefore, the Jacobian of this QNode will be a single array with shape ``(2, 2, 1, 3)``:
>>> qml.jacobian(circuit)(weights).shape
(4, 2, 1, 3)
On the other hand, consider the following QNode for the same circuit
structure:
.. code-block::
dev = qml.device("default.qubit", wires=2)
@qml.qnode(dev)
def circuit(x, y, z):
qml.RX(x, wires=0)
qml.RY(y, wires=1)
qml.RZ(z, wires=0)
return tuple(qml.expval(qml.Z(w)) for w in dev.wires)
x = np.array(0.2, requires_grad=True)
y = np.array(0.9, requires_grad=True)
z = np.array(-1.4, requires_grad=True)
It has three scalar QNode arguments and outputs the probability for each of
the 4 basis states. Consequently, its Jacobian will be a three-tuple of
arrays with the shape ``(4,)``:
>>> jac = qml.jacobian(circuit)(x, y, z)
>>> type(jac)
tuple
>>> for sub_jac in jac:
... print(sub_jac.shape)
(4,)
(4,)
(4,)
For a more advanced setting of QNode arguments, consider the QNode
.. code-block::
dev = qml.device("default.qubit", wires=3)
@qml.qnode(dev)
def circuit(x, y):
qml.RX(x[0], wires=0)
qml.RY(y[0, 3], wires=1)
qml.RX(x[1], wires=2)
return [qml.expval(qml.Z(w)) for w in [0, 1, 2]]
x = np.array([0.1, 0.5], requires_grad=True)
y = np.array([[-0.3, 1.2, 0.1, 0.9], [-0.2, -3.1, 0.5, -0.7]], requires_grad=True)
If we do not provide ``argnum``, ``qml.jacobian`` will correctly identify both,
``x`` and ``y``, as trainable function arguments:
>>> jac = qml.jacobian(circuit)(x, y)
>>> print(type(jac), len(jac))
<class 'tuple'> 2
>>> qml.math.shape(jac[0])
(8, 2)
>>> qml.math.shape(jac[1])
(8, 2, 4)
As we can see, there are two entries in the output, one Jacobian for each
QNode argument. The shape ``(8, 2)`` of the first Jacobian is the combination
of the QNode output shape (``(8,)``) and the shape of ``x`` (``(2,)``).
Similarly, the shape ``(2, 4)`` of ``y`` leads to a Jacobian shape ``(8, 2, 4)``.
Instead we may choose the output to contain only one of the two
entries by providing an iterable as ``argnum``:
>>> jac = qml.jacobian(circuit, argnum=[1])(x, y)
>>> print(type(jac), len(jac))
<class 'tuple'> 1
>>> qml.math.shape(jac)
(1, 8, 2, 4)
Here we included the size of the tuple in the shape analysis, corresponding to the
first dimension of size ``1``.
Finally, we may want to receive the single entry above directly, not as a tuple
with a single entry. This is done by providing a single integer as ``argnum``
>>> jac = qml.jacobian(circuit, argnum=1)(x, y)
>>> print(type(jac), len(jac))
<class 'numpy.ndarray'> 8
>>> qml.math.shape(jac)
(8, 2, 4)
As expected, the tuple was unpacked and we directly received the Jacobian of the
QNode with respect to ``y``.
We can also compute the Jacobian transformation inside a :func:`~.qjit` decorated program:
.. code-block::
dev = qml.device("lightning.qubit", wires=1)
@qml.qjit
def workflow(x):
@qml.qnode(dev)
def circuit(x):
qml.RX(np.pi * x[0], wires=0)
qml.RY(x[1], wires=0)
return qml.probs()
g = qml.jacobian(circuit)
return g(x)
>>> workflow(np.array([2.0, 1.0]))
array([[-1.32116540e-07, 1.33781874e-07],
[-4.20735506e-01, 4.20735506e-01]])
You can further compute the Jacobian transformation using other supported differentiation
methods by :func:`catalyst.jacobian`.
.. code-block::
@qml.qjit
def workflow(x):
@qml.qnode(dev)
def circuit(x):
qml.RX(np.pi * x[0], wires=0)
qml.RY(x[1], wires=0)
return qml.probs()
g = qml.jacobian(circuit, method="fd", h=0.3)
return g(x)
>>> qml.qjit(workflow)(np.array([2.0, 1.0]))
array([[-0.37120096, -0.45467246],
[0.37120096, 0.45467246]])
"""
# pylint: disable=no-value-for-parameter
if active_jit := compiler.active_compiler():
available_eps = compiler.AvailableCompilers.names_entrypoints
ops_loader = available_eps[active_jit]["ops"].load()
return ops_loader.jacobian(func, method=method, h=h, argnum=argnum)
if method or h:
raise ValueError(f"Invalid values for 'method={method}' and 'h={h}' in interpreted mode")
def _get_argnum(args):
"""Inspect the arguments for differentiability and return the
corresponding indices."""
argnum = []
for idx, arg in enumerate(args):
trainable = getattr(arg, "requires_grad", None) or isinstance(arg, ArrayBox)
if trainable:
if arg.dtype.name[:3] == "int":
raise ValueError("Autograd does not support differentiation of ints.")
argnum.append(idx)
return argnum
def _jacobian_function(*args, **kwargs):
"""Compute the autograd Jacobian.
This wrapper function is returned to the user instead of autograd.jacobian,
so that we can take into account cases where the user computes the
jacobian function once, but then calls it with arguments that change
in differentiability.
"""
if argnum is None:
# Infer which arguments to consider trainable
_argnum = _get_argnum(args)
# Infer whether to unpack from the inferred argnum
unpack = len(_argnum) == 1
else:
# For a single integer as argnum, unpack the Jacobian tuple
unpack = isinstance(argnum, int)
_argnum = [argnum] if unpack else argnum
if not _argnum:
warnings.warn(
"Attempted to differentiate a function with no trainable parameters. "
"If this is unintended, please add trainable parameters via the "
"'requires_grad' attribute or 'argnum' keyword."
)
jac = tuple(_jacobian(func, arg)(*args, **kwargs) for arg in _argnum)
return jac[0] if unpack else jac
return _jacobian_function
# pylint: disable=too-many-arguments
[docs]def vjp(f, params, cotangents, method=None, h=None, argnum=None):
"""A :func:`~.qjit` compatible Vector-Jacobian product of PennyLane programs.
This function allows the Vector-Jacobian Product of a hybrid quantum-classical function to be
computed within the compiled program.
.. warning::
``vjp`` is intended to be used with :func:`~.qjit` only.
.. note::
When used with :func:`~.qjit`, this function only supports the Catalyst compiler.
See :func:`catalyst.vjp` for more details.
Please see the Catalyst :doc:`quickstart guide <catalyst:dev/quick_start>`,
as well as the :doc:`sharp bits and debugging tips <catalyst:dev/sharp_bits>`
page for an overview of the differences between Catalyst and PennyLane.
Args:
f(Callable): Function-like object to calculate VJP for
params(List[Array]): List (or a tuple) of arguments for `f` specifying the point to calculate
VJP at. A subset of these parameters are declared as
differentiable by listing their indices in the ``argnum`` parameter.
cotangents(List[Array]): List (or a tuple) of tangent values to use in VJP. The list size
and shapes must match the size and shape of ``f`` outputs.
method(str): Differentiation method to use, same as in :func:`~.grad`.
h (float): the step-size value for the finite-difference (``"fd"``) method
argnum (Union[int, List[int]]): the params' indices to differentiate.
Returns:
Tuple[Array]: Return values of ``f`` paired with the VJP values.
Raises:
TypeError: invalid parameter types
ValueError: invalid parameter values
.. seealso:: :func:`~.grad`, :func:`~.jvp`, :func:`~.jacobian`
**Example**
.. code-block:: python
@qml.qjit
def vjp(params, cotangent):
def f(x):
y = [jnp.sin(x[0]), x[1] ** 2, x[0] * x[1]]
return jnp.stack(y)
return qml.vjp(f, [params], [cotangent])
>>> x = jnp.array([0.1, 0.2])
>>> dy = jnp.array([-0.5, 0.1, 0.3])
>>> vjp(x, dy)
[array([0.09983342, 0.04 , 0.02 ]),
array([-0.43750208, 0.07000001])]
"""
if active_jit := compiler.active_compiler():
available_eps = compiler.AvailableCompilers.names_entrypoints
ops_loader = available_eps[active_jit]["ops"].load()
return ops_loader.vjp(f, params, cotangents, method=method, h=h, argnum=argnum)
raise CompileError("Pennylane does not support the VJP function without QJIT.")
# pylint: disable=too-many-arguments
[docs]def jvp(f, params, tangents, method=None, h=None, argnum=None):
"""A :func:`~.qjit` compatible Jacobian-vector product of PennyLane programs.
This function allows the Jacobian-vector Product of a hybrid quantum-classical function to be
computed within the compiled program.
.. warning::
``jvp`` is intended to be used with :func:`~.qjit` only.
.. note::
When used with :func:`~.qjit`, this function only supports the Catalyst compiler;
see :func:`catalyst.jvp` for more details.
Please see the Catalyst :doc:`quickstart guide <catalyst:dev/quick_start>`,
as well as the :doc:`sharp bits and debugging tips <catalyst:dev/sharp_bits>`
page for an overview of the differences between Catalyst and PennyLane.
Args:
f (Callable): Function-like object to calculate JVP for
params (List[Array]): List (or a tuple) of the function arguments specifying the point
to calculate JVP at. A subset of these parameters are declared as
differentiable by listing their indices in the ``argnum`` parameter.
tangents(List[Array]): List (or a tuple) of tangent values to use in JVP. The list size and
shapes must match the ones of differentiable params.
method(str): Differentiation method to use, same as in :func:`~.grad`.
h (float): the step-size value for the finite-difference (``"fd"``) method
argnum (Union[int, List[int]]): the params' indices to differentiate.
Returns:
Tuple[Array]: Return values of ``f`` paired with the JVP values.
Raises:
TypeError: invalid parameter types
ValueError: invalid parameter values
.. seealso:: :func:`~.grad`, :func:`~.vjp`, :func:`~.jacobian`
**Example 1 (basic usage)**
.. code-block:: python
@qml.qjit
def jvp(params, tangent):
def f(x):
y = [jnp.sin(x[0]), x[1] ** 2, x[0] * x[1]]
return jnp.stack(y)
return qml.jvp(f, [params], [tangent])
>>> x = jnp.array([0.1, 0.2])
>>> tangent = jnp.array([0.3, 0.6])
>>> jvp(x, tangent)
[array([0.09983342, 0.04 , 0.02 ]),
array([0.29850125, 0.24000006, 0.12 ])]
**Example 2 (argnum usage)**
Here we show how to use ``argnum`` to ignore the non-differentiable parameter ``n`` of the
target function. Note that the length and shapes of tangents must match the length and shape of
primal parameters, which we mark as differentiable by passing their indices to ``argnum``.
.. code-block:: python
@qml.qjit
@qml.qnode(qml.device("lightning.qubit", wires=2))
def circuit(n, params):
qml.RX(params[n, 0], wires=n)
qml.RY(params[n, 1], wires=n)
return qml.expval(qml.Z(1))
@qml.qjit
def workflow(primals, tangents):
return qml.jvp(circuit, [1, primals], [tangents], argnum=[1])
>>> params = jnp.array([[0.54, 0.3154], [0.654, 0.123]])
>>> dy = jnp.array([[1.0, 1.0], [1.0, 1.0]])
>>> workflow(params, dy)
[array(0.78766064), array(-0.7011436)]
"""
if active_jit := compiler.active_compiler():
available_eps = compiler.AvailableCompilers.names_entrypoints
ops_loader = available_eps[active_jit]["ops"].load()
return ops_loader.jvp(f, params, tangents, method=method, h=h, argnum=argnum)
raise CompileError("Pennylane does not support the JVP function without QJIT.")
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