Source code for pennylane.compiler.qjit_api

# Copyright 2023 Xanadu Quantum Technologies Inc.

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"""QJIT compatible quantum and compilation operations API"""

from .compiler import (
    AvailableCompilers,
    CompileError,
    _check_compiler_version,
    active_compiler,
    available,
)


[docs]def qjit(fn=None, *args, compiler="catalyst", **kwargs): # pylint:disable=keyword-arg-before-vararg """A decorator for just-in-time compilation of hybrid quantum programs in PennyLane. This decorator enables both just-in-time and ahead-of-time compilation, depending on the compiler package and whether function argument type hints are provided. .. note:: Currently, only two compilers are supported; the :doc:`Catalyst <catalyst:index>` hybrid quantum-classical compiler, which works with the JAX interface, and CUDA Quantum. For more details on Catalyst, see the :doc:`Catalyst documentation <catalyst:index>` and :func:`catalyst.qjit`. .. note:: Catalyst supports compiling QNodes that use ``lightning.qubit``, ``lightning.kokkos``, ``braket.local.qubit``, and ``braket.aws.qubit`` devices. It does not support ``default.qubit``. Please see the :doc:`Catalyst documentation <catalyst:index>` for more details on supported devices, operations, and measurements. CUDA Quantum supports ``softwareq.qpp``, ``nvidida.custatevec``, and ``nvidia.cutensornet``. Args: fn (Callable): Hybrid (quantum-classical) function to compile compiler (str): Name of the compiler to use for just-in-time compilation. Available options include ``catalyst`` and ``cuda_quantum``. autograph (bool): Experimental support for automatically converting Python control flow statements to Catalyst-compatible control flow. Currently supports Python ``if``, ``elif``, ``else``, and ``for`` statements. Note that this feature requires an available TensorFlow installation. See the :doc:`AutoGraph guide <catalyst:dev/autograph>` for more information. keep_intermediate (bool): Whether or not to store the intermediate files throughout the compilation. The files are stored at the location where the Python script is called. If ``True``, intermediate representations are available via the :attr:`~.QJIT.mlir`, :attr:`~.QJIT.jaxpr`, and :attr:`~.QJIT.qir`, representing different stages in the optimization process. verbosity (bool): If ``True``, the tools and flags used by Catalyst behind the scenes are printed out. logfile (TextIOWrapper): File object to write verbose messages to (default is ``sys.stderr``) pipelines (List[Tuple[str, List[str]]]): A list of pipelines to be executed. The elements of this list are named sequences of MLIR passes to be executed. A ``None`` value (the default) results in the execution of the default pipeline. This option is considered to be used by advanced users for low-level debugging purposes. static_argnums(int or Seqence[Int]): an index or a sequence of indices that specifies the positions of static arguments. abstracted_axes (Sequence[Sequence[str]] or Dict[int, str] or Sequence[Dict[int, str]]): An experimental option to specify dynamic tensor shapes. This option affects the compilation of the annotated function. Function arguments with ``abstracted_axes`` specified will be compiled to ranked tensors with dynamic shapes. For more details, please see the Dynamically-shaped Arrays section below. Returns: catalyst.QJIT: A class that, when executed, just-in-time compiles and executes the decorated function Raises: FileExistsError: Unable to create temporary directory PermissionError: Problems creating temporary directory OSError: Problems while creating folder for intermediate files AutoGraphError: Raised if there was an issue converting the given the function(s). ImportError: Raised if AutoGraph is turned on and TensorFlow could not be found. **Example** In just-in-time (JIT) mode, the compilation is triggered at the call site the first time the quantum function is executed. For example, ``circuit`` is compiled as early as the first call. .. code-block:: python dev = qml.device("lightning.qubit", wires=2) @qml.qjit @qml.qnode(dev) def circuit(theta): qml.Hadamard(wires=0) qml.RX(theta, wires=1) qml.CNOT(wires=[0,1]) return qml.expval(qml.Z(1)) >>> circuit(0.5) # the first call, compilation occurs here array(0.) >>> circuit(0.5) # the precompiled quantum function is called array(0.) :func:`~.qjit` compiled programs also support nested container types as inputs and outputs of compiled functions. This includes lists and dictionaries, as well as any data structure implementing the `JAX PyTree <https://jax.readthedocs.io/en/latest/pytrees.html>`__. .. code-block:: python dev = qml.device("lightning.qubit", wires=2) @qml.qjit @qml.qnode(dev) def f(x): qml.RX(x["rx_param"], wires=0) qml.RY(x["ry_param"], wires=0) qml.CNOT(wires=[0, 1]) return { "XY": qml.expval(qml.X(0) @ qml.Y(1)), "X": qml.expval(qml.X(0)), } >>> x = {"rx_param": 0.5, "ry_param": 0.54} >>> f(x) {'X': array(-0.75271018), 'XY': array(1.)} For more details on using the :func:`~.qjit` decorator and Catalyst with PennyLane, please refer to 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, and how to best structure your workflows to improve performance when using Catalyst. .. details:: :title: Static arguments ``static_argnums`` defines which elements should be treated as static. If it takes an integer, it means the argument whose index is equal to the integer is static. If it takes an iterable of integers, arguments whose index is contained in the iterable are static. Changing static arguments will trigger re-compilation. A valid static argument must be hashable and its ``__hash__`` method must be able to reflect any changes of its attributes. .. code-block:: python @dataclass class MyClass: val: int def __hash__(self): return hash(str(self)) @qjit(static_argnums=1) def f( x: int, y: MyClass, ): return x + y.val f(1, MyClass(5)) f(1, MyClass(6)) # re-compilation f(2, MyClass(5)) # no re-compilation In the example above, ``y`` is static. Note that the second function call triggers re-compilation since the input object is different from the previous one. However, the third function call directly uses the previous compiled one and does not introduce re-compilation. .. code-block:: python @dataclass class MyClass: val: int def __hash__(self): return hash(str(self)) @qjit(static_argnums=(1, 2)) def f( x: int, y: MyClass, z: MyClass, ): return x + y.val + z.val my_obj_1 = MyClass(5) my_obj_2 = MyClass(6) f(1, my_obj_1, my_obj_2) my_obj_1.val = 7 f(1, my_obj_1, my_obj_2) # re-compilation In the example above, ``y`` and ``z`` are static. The second function will cause function ``f`` to re-compile because ``my_obj_1`` is changed. This requires that the mutation is properly reflected in the hash value. Note that when ``static_argnums`` is used in conjunction with type hinting, ahead-of-time compilation will not be possible since the static argument values are not yet available. Instead, compilation will be just-in-time. .. details:: :title: Dynamically-shaped arrays There are three ways to use ``abstracted_axes``; by passing a sequence of tuples, a dictionary, or a sequence of dictionaries. Passing a sequence of tuples: .. code-block:: python abstracted_axes=((), ('n',), ('m', 'n')) Each tuple in the sequence corresponds to one of the arguments in the annotated function. Empty tuples can be used and correspond to parameters with statically known shapes. Non-empty tuples correspond to parameters with dynamically known shapes. In this example above, - the first argument will have a statically known shape, - the second argument will have dynamic shape ``n`` for the zeroth axis, and - the third argument will have dynamic shape ``m`` for its zeroth axis and dynamic shape ``n`` for its first axis. Passing a dictionary: .. code-block:: python abstracted_axes={0: 'n'} This approach allows a concise expression of the relationships between axes for different function arguments. In this example, it specifies that for all function arguments, the zeroth axis will have dynamic shape ``n``. Passing a sequence of dictionaries: .. code-block:: python abstracted_axes=({}, {0: 'n'}, {1: 'm', 0: 'n'}) The example here is a more verbose version of the tuple example. This convention allows axes to be omitted from the list of abstracted axes. Using ``abstracted_axes`` can help avoid the cost of recompilation. By using ``abstracted_axes``, a more general version of the compiled function will be generated. This more general version is parametrized over the abstracted axes and allows results to be computed over tensors independently of their axes lengths. For example: .. code-block:: python @qjit def sum(arr): return jnp.sum(arr) sum(jnp.array([1])) # Compilation happens here. sum(jnp.array([1, 1])) # And here! The ``sum`` function would recompile each time an array of different size is passed as an argument. .. code-block:: python @qjit(abstracted_axes={0: "n"}) def sum_abstracted(arr): return jnp.sum(arr) sum(jnp.array([1])) # Compilation happens here. sum(jnp.array([1, 1])) # No need to recompile. The ``sum_abstracted`` function would only compile once and its definition would be reused for subsequent function calls. """ if not available(compiler): raise CompileError(f"The {compiler} package is not installed.") # pragma: no cover # Check the minimum version of 'compiler' if installed _check_compiler_version(compiler) compilers = AvailableCompilers.names_entrypoints qjit_loader = compilers[compiler]["qjit"].load() return qjit_loader(fn=fn, *args, **kwargs)
[docs]def while_loop(cond_fn): """A :func:`~.qjit` compatible while-loop for PennyLane programs. .. note:: This function only supports the Catalyst compiler. See :func:`catalyst.while_loop` 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. This decorator provides a functional version of the traditional while loop, similar to `jax.lax.while_loop <https://jax.readthedocs.io/en/latest/_autosummary/jax.lax.while_loop.html>`__. That is, any variables that are modified across iterations need to be provided as inputs and outputs to the loop body function: - Input arguments contain the value of a variable at the start of an iteration - Output arguments contain the value at the end of the iteration. The outputs are then fed back as inputs to the next iteration. The final iteration values are also returned from the transformed function. The semantics of ``while_loop`` are given by the following Python pseudo-code: .. code-block:: python def while_loop(cond_fn, body_fn, *args): while cond_fn(*args): args = body_fn(*args) return args Args: cond_fn (Callable): the condition function in the while loop Returns: Callable: A wrapper around the while-loop function. Raises: CompileError: if the compiler is not installed .. seealso:: :func:`~.for_loop`, :func:`~.qjit` **Example** .. code-block:: python dev = qml.device("lightning.qubit", wires=1) @qml.qjit @qml.qnode(dev) def circuit(x: float): @qml.while_loop(lambda x: x < 2.0) def loop_rx(x): # perform some work and update (some of) the arguments qml.RX(x, wires=0) return x ** 2 # apply the while loop final_x = loop_rx(x) return qml.expval(qml.Z(0)), final_x >>> circuit(1.6) (array(-0.02919952), array(2.56)) """ if active_jit := active_compiler(): compilers = AvailableCompilers.names_entrypoints ops_loader = compilers[active_jit]["ops"].load() return ops_loader.while_loop(cond_fn) raise CompileError("There is no active compiler package.") # pragma: no cover
[docs]def for_loop(lower_bound, upper_bound, step): """A :func:`~.qjit` compatible for-loop for PennyLane programs. .. note:: This function only supports the Catalyst compiler. See :func:`catalyst.for_loop` 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. This decorator provides a functional version of the traditional for-loop, similar to `jax.cond.fori_loop <https://jax.readthedocs.io/en/latest/_autosummary/jax.lax.fori_loop.html>`__. That is, any variables that are modified across iterations need to be provided as inputs/outputs to the loop body function: - Input arguments contain the value of a variable at the start of an iteration. - output arguments contain the value at the end of the iteration. The outputs are then fed back as inputs to the next iteration. The final iteration values are also returned from the transformed function. The semantics of ``for_loop`` are given by the following Python pseudo-code: .. code-block:: python def for_loop(lower_bound, upper_bound, step, loop_fn, *args): for i in range(lower_bound, upper_bound, step): args = loop_fn(i, *args) return args Unlike ``jax.cond.fori_loop``, the step can be negative if it is known at tracing time (i.e., constant). If a non-constant negative step is used, the loop will produce no iterations. Args: lower_bound (int): starting value of the iteration index upper_bound (int): (exclusive) upper bound of the iteration index step (int): increment applied to the iteration index at the end of each iteration Returns: Callable[[int, ...], ...]: A wrapper around the loop body function. Note that the loop body function must always have the iteration index as its first argument, which can be used arbitrarily inside the loop body. As the value of the index across iterations is handled automatically by the provided loop bounds, it must not be returned from the function. Raises: CompileError: if the compiler is not installed .. seealso:: :func:`~.while_loop`, :func:`~.qjit` **Example** .. code-block:: python dev = qml.device("lightning.qubit", wires=1) @qml.qjit @qml.qnode(dev) def circuit(n: int, x: float): @qml.for_loop(0, n, 1) def loop_rx(i, x): # perform some work and update (some of) the arguments qml.RX(x, wires=0) # update the value of x for the next iteration return jnp.sin(x) # apply the for loop final_x = loop_rx(x) return qml.expval(qml.Z(0)), final_x >>> circuit(7, 1.6) (array(0.97926626), array(0.55395718)) """ if active_jit := active_compiler(): compilers = AvailableCompilers.names_entrypoints ops_loader = compilers[active_jit]["ops"].load() return ops_loader.for_loop(lower_bound, upper_bound, step) raise CompileError("There is no active compiler package.") # pragma: no cover