Source code for pennylane.math.utils

# 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.
"""Utility functions"""
# pylint: disable=import-outside-toplevel
from autograd.numpy.numpy_boxes import ArrayBox
import autoray as ar
from autoray import numpy as np
import numpy as _np

from . import single_dispatch  # pylint:disable=unused-import


[docs]def allequal(tensor1, tensor2, **kwargs): """Returns True if two tensors are element-wise equal along a given axis. This function is equivalent to calling ``np.all(tensor1 == tensor2, **kwargs)``, but allows for ``tensor1`` and ``tensor2`` to differ in type. Args: tensor1 (tensor_like): tensor to compare tensor2 (tensor_like): tensor to compare **kwargs: Accepts any keyword argument that is accepted by ``np.all``, such as ``axis``, ``out``, and ``keepdims``. See the `NumPy documentation <https://numpy.org/doc/stable/reference/generated/numpy.all.html>`__ for more details. Returns: ndarray, bool: If ``axis=None``, a logical AND reduction is applied to all elements and a boolean will be returned, indicating if all elements evaluate to ``True``. Otherwise, a boolean NumPy array will be returned. **Example** >>> a = torch.tensor([1, 2]) >>> b = np.array([1, 2]) >>> allequal(a, b) True """ t1 = ar.to_numpy(tensor1) t2 = ar.to_numpy(tensor2) return np.all(t1 == t2, **kwargs)
[docs]def allclose(a, b, rtol=1e-05, atol=1e-08, **kwargs): """Wrapper around np.allclose, allowing tensors ``a`` and ``b`` to differ in type""" try: # Some frameworks may provide their own allclose implementation. # Try and use it if available. res = np.allclose(a, b, rtol=rtol, atol=atol, **kwargs) except (TypeError, AttributeError): # Otherwise, convert the input to NumPy arrays. # # TODO: replace this with a bespoke, framework agnostic # low-level implementation to avoid the NumPy conversion: # # np.abs(a - b) <= atol + rtol * np.abs(b) # t1 = ar.to_numpy(a) t2 = ar.to_numpy(b) res = np.allclose(t1, t2, rtol=rtol, atol=atol, **kwargs) return res
allclose.__doc__ = _np.allclose.__doc__
[docs]def cast(tensor, dtype): """Casts the given tensor to a new type. Args: tensor (tensor_like): tensor to cast dtype (str, np.dtype): Any supported NumPy dtype representation; this can be a string (``"float64"``), a ``np.dtype`` object (``np.dtype("float64")``), or a dtype class (``np.float64``). If ``tensor`` is not a NumPy array, the **equivalent** dtype in the dispatched framework is used. Returns: tensor_like: a tensor with the same shape and values as ``tensor`` and the same dtype as ``dtype`` **Example** We can use NumPy dtype specifiers: >>> x = torch.tensor([1, 2]) >>> cast(x, np.float64) tensor([1., 2.], dtype=torch.float64) We can also use strings: >>> x = tf.Variable([1, 2]) >>> cast(x, "complex128") <tf.Tensor: shape=(2,), dtype=complex128, numpy=array([1.+0.j, 2.+0.j])> """ if isinstance(tensor, (list, tuple)): tensor = np.asarray(tensor) if not isinstance(dtype, str): try: dtype = np.dtype(dtype).name except (AttributeError, TypeError): dtype = getattr(dtype, "name", dtype) return ar.astype(tensor, ar.to_backend_dtype(dtype, like=ar.infer_backend(tensor)))
[docs]def cast_like(tensor1, tensor2): """Casts a tensor to the same dtype as another. Args: tensor1 (tensor_like): tensor to cast tensor2 (tensor_like): tensor with corresponding dtype to cast to Returns: tensor_like: a tensor with the same shape and values as ``tensor1`` and the same dtype as ``tensor2`` **Example** >>> x = torch.tensor([1, 2]) >>> y = torch.tensor([3., 4.]) >>> cast_like(x, y) tensor([1., 2.]) """ dtype = ar.to_numpy(tensor2).dtype.type return cast(tensor1, dtype)
[docs]def convert_like(tensor1, tensor2): """Convert a tensor to the same type as another. Args: tensor1 (tensor_like): tensor to convert tensor2 (tensor_like): tensor with corresponding type to convert to Returns: tensor_like: a tensor with the same shape, values, and dtype as ``tensor1`` and the same type as ``tensor2``. **Example** >>> x = np.array([1, 2]) >>> y = tf.Variable([3, 4]) >>> convert_like(x, y) <tf.Tensor: shape=(2,), dtype=int64, numpy=array([1, 2])> """ interface = get_interface(tensor2) if interface == "torch": dev = tensor2.device return np.asarray(tensor1, device=dev, like=interface) return np.asarray(tensor1, like=interface)
[docs]def get_interface(tensor): """Returns the name of the package that any array/tensor manipulations will dispatch to. The returned strings correspond to those used for PennyLane :doc:`interfaces </introduction/interfaces>`. Args: tensor (tensor_like): tensor input Returns: str: name of the interface **Example** >>> x = torch.tensor([1., 2.]) >>> get_interface(x) 'torch' >>> from pennylane import numpy as np >>> x = np.array([4, 5], requires_grad=True) >>> get_interface(x) 'autograd' """ namespace = tensor.__class__.__module__.split(".")[0] if namespace in ("pennylane", "autograd"): return "autograd" res = ar.infer_backend(tensor) if res == "builtins": return "numpy" return res
[docs]def requires_grad(tensor, interface=None): """Returns True if the tensor is considered trainable. .. warning:: The implemetation depends on the contained tensor type, and may be context dependent. For example, Torch tensors and PennyLane tensors track trainability as a property of the tensor itself. TensorFlow, on the other hand, only tracks trainability if being watched by a gradient tape. Args: tensor (tensor_like): input tensor interface (str): The name of the interface. Will be determined automatically if not provided. **Example** Calling this function on a PennyLane NumPy array: >>> x = np.array([1., 5.], requires_grad=True) >>> requires_grad(x) True >>> x.requires_grad = False >>> requires_grad(x) False PyTorch has similar behaviour. With TensorFlow, the output is dependent on whether the tensor is currently being watched by a gradient tape: >>> x = tf.Variable([0.6, 0.1]) >>> requires_grad(x) False >>> with tf.GradientTape() as tape: ... print(requires_grad(x)) True While TensorFlow constants are by default not trainable, they can be manually watched by the gradient tape: >>> x = tf.constant([0.6, 0.1]) >>> with tf.GradientTape() as tape: ... print(requires_grad(x)) False >>> with tf.GradientTape() as tape: ... tape.watch([x]) ... print(requires_grad(x)) True """ interface = interface or get_interface(tensor) if interface == "tensorflow": import tensorflow as tf try: from tensorflow.python.eager.tape import should_record_backprop except ImportError: # pragma: no cover from tensorflow.python.eager.tape import should_record as should_record_backprop return should_record_backprop([tf.convert_to_tensor(tensor)]) if interface == "autograd": if isinstance(tensor, ArrayBox): return True # Currently, in the Autograd interface, we assume # that all objects are differentiable by default. return getattr(tensor, "requires_grad", True) if interface == "torch": return getattr(tensor, "requires_grad", False) if interface == "numpy": return False if interface == "jax": import jax return isinstance(tensor, jax.interpreters.ad.JVPTracer) raise ValueError(f"Argument {tensor} is an unknown object")