diff --git a/doc/source/api-reference/qibo.rst b/doc/source/api-reference/qibo.rst index 30b6c1e422..593b9f2ed5 100644 --- a/doc/source/api-reference/qibo.rst +++ b/doc/source/api-reference/qibo.rst @@ -2597,6 +2597,7 @@ For a complete list of available backends, refer to the :ref:`Packages ` section of the documentation. Note that if the Stochastic Gradient Descent optimizer is used then the user has to use a backend based on tensorflow or pytorch primitives and not the default custom backend, as custom operators currently do not support automatic differentiation. -To switch the backend one can do ``qibo.set_backend("tensorflow")`` or ``qibo.set_backend("pytorch")``. +To switch the backend one can do ``qibo.set_backend(backend="qiboml", platform="tensorflow")`` +or ``qibo.set_backend(backend="qiboml", platform="pytorch")``, after ensuring the +``qiboml`` package has been installed. Check the :ref:`How to use automatic differentiation? ` section for more details. @@ -849,7 +851,7 @@ using the ``pytorch`` framework. import torch from qibo import Circuit, gates, set_backend - set_backend("pytorch") + set_backend(backend="qiboml", platform="pytorch") # Optimization parameters nepochs = 1000 @@ -1550,8 +1552,10 @@ combined with the readout mitigation: Clifford Data Regression (CDR) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -For CDR instead, you don't need to define anything additional. However, keep in mind that the input -circuit is expected to be decomposed in the set of primitive gates :math:`RX(\frac{\pi}{2}), CNOT, X` and :math:`RZ(\theta)`. +For CDR instead, you don't need to define anything additional. +However, keep in mind that the input circuit is expected to be +decomposed in the set of primitive gates +:math:`RX(\frac{\pi}{2}), CNOT, X` and :math:`RZ(\theta)`. .. testcode:: @@ -1575,14 +1579,16 @@ circuit is expected to be decomposed in the set of primitive gates :math:`RX(\fr ... -Again, the mitigated expected value improves over the noisy one and is also slightly better compared to ZNE. +Again, the mitigated expected value improves over the noisy one +and is also slightly better compared to ZNE. Variable Noise CDR (vnCDR) ^^^^^^^^^^^^^^^^^^^^^^^^^^ -Being a combination of ZNE and CDR, vnCDR requires you to define the noise levels as done in ZNE, and the same -caveat about the input circuit for CDR is valid here as well. +Being a combination of ZNE and CDR, vnCDR requires you to define +the noise levels as done in ZNE, and the same caveat about the +input circuit for CDR is valid here as well. .. testcode:: @@ -1608,8 +1614,10 @@ caveat about the input circuit for CDR is valid here as well. ... -The result is similar to the one obtained by CDR. Usually, one would expect slightly better results for vnCDR, -however, this can substantially vary depending on the circuit and the observable considered and, therefore, it is hard to tell +The result is similar to the one obtained by CDR. +Usually, one would expect slightly better results for vnCDR. +However, this can substantially vary depending on the circuit +and the observable considered and, therefore, it is hard to tell a priori. @@ -1640,8 +1648,11 @@ The use of iCS is straightforward, analogous to CDR and vnCDR. ... -Again, the mitigated expected value improves over the noisy one and is also slightly better compared to ZNE. -This was just a basic example usage of the three methods, for all the details about them you should check the API-reference page :ref:`Error Mitigation `. +Again, the mitigated expected value improves over the noisy +one and is also slightly better compared to ZNE. +This was just a basic example usage of the three methods, +for all the details about them you should check the API-reference page +:ref:`Error Mitigation `. .. _timeevol-example: diff --git a/doc/source/code-examples/examples.rst b/doc/source/code-examples/examples.rst index bcfdd8511e..e9d04eb7af 100644 --- a/doc/source/code-examples/examples.rst +++ b/doc/source/code-examples/examples.rst @@ -39,9 +39,9 @@ evaluation performance, e.g.: .. code-block:: python import numpy as np - # switch backend to "tensorflow" + # switch backend to "tensorflow" through the Qiboml provider import qibo - qibo.set_backend("tensorflow") + qibo.set_backend(backend="qiboml", platform="tensorflow") from qibo import Circuit, gates circuit = Circuit(2) @@ -54,7 +54,7 @@ evaluation performance, e.g.: init_state = np.ones(4) / 2.0 + i circuit(init_state) -Note that compiling is only supported when the native ``tensorflow`` backend is +Note that compiling is only supported when the ``tensorflow`` backend is used. This backend is much slower than ``qibojit`` which uses custom operators to apply gates. @@ -226,7 +226,7 @@ For applications that require the state vector to be collapsed during measuremen we refer to the :ref:`How to collapse state during measurements? ` The measured shots are obtained using pseudo-random number generators of the -underlying backend (numpy or Tensorflow). If the user has installed a custom +underlying backend. If the user has installed a custom backend (eg. qibojit) and asks for frequencies with more than 100000 shots, a custom Metropolis algorithm will be used to obtain the corresponding samples, for increase performance. The user can change the threshold for which this diff --git a/doc/source/getting-started/installation.rst b/doc/source/getting-started/installation.rst index 8c356b2592..a6a9213a69 100644 --- a/doc/source/getting-started/installation.rst +++ b/doc/source/getting-started/installation.rst @@ -7,15 +7,15 @@ Operating systems support In the table below we summarize the status of *pre-compiled binaries distributed with pypi* for the packages listed above. -+------------------+------+---------+------------+ -| Operating System | qibo | qibojit | tensorflow | -+==================+======+=========+============+ -| Linux x86 | Yes | Yes | Yes | -+------------------+------+---------+------------+ -| MacOS >= 10.15 | Yes | Yes | Yes | -+------------------+------+---------+------------+ -| Windows | Yes | Yes | Yes | -+------------------+------+---------+------------+ ++------------------+------+---------+-----------+---------+ +| Operating System | qibo | qibojit |Tensorflow | Pytorch | ++==================+======+=========+===========+=========+ +| Linux x86 | Yes | Yes | Yes | Yes | ++------------------+------+---------+-----------+---------+ +| MacOS >= 10.15 | Yes | Yes | Yes | Yes | ++------------------+------+---------+-----------+---------+ +| Windows | Yes | Yes | Yes | Yes | ++------------------+------+---------+-----------+---------+ .. note:: All packages are supported for Python >= 3.9. @@ -148,35 +148,6 @@ Then proceed with the ``qibojit`` installation using ``pip`` _______________________ -.. _installing-tensorflow: - -tensorflow -^^^^^^^^^^ - -If the `TensorFlow `_ package is installed Qibo -will detect and provide to the user the possibility to use ``tensorflow`` -backend. - -This backend is used by default if ``qibojit`` is not installed, however, if -needed, in order to switch to the ``tensorflow`` backend please do: - -.. code-block:: python - - import qibo - qibo.set_backend("tensorflow") - -In order to install the package, we recommend the installation using: - -.. code-block:: bash - - pip install qibo tensorflow - -.. note:: - TensorFlow can be installed following its `documentation - `_. - -_______________________ - .. _installing-numpy: numpy @@ -197,26 +168,45 @@ please do: _______________________ -.. _installing-pytorch: +.. _installing-qml-backends: -pytorch -^^^^^^^ +Backends with automatic differentiation support +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +If you need automatic differentiation support, for tracing gradients of your +quantum algorithm or for building some quantum machine learning routine, +the right backends for you are those provided by the `Qiboml `__ +package. -If the `PyTorch `_ package is installed Qibo -will detect and provide to the user the possibility to use ``pytorch`` -backend. +In particular, Qiboml currently support `Pytorch `_ and +`Tensorflow `_ interfaces, integrating the qibo functionalities +into these well-known machine learning frameworks. Quantum layers can be constructed +and added to your Pytorch or Tensorflow models, and trained using any supported +optimization routine. -In order to switch to the ``pytorch`` backend please do: +In order to use these quantum machine learning backends please make sure the +preferred package is installed following `Tensorflow's `_ +or `Pytorch's `_ installation instructions. + +To switch to Tensorflow or Pytorch backend please do: .. code-block:: python import qibo - qibo.set_backend("pytorch") + # in case of Tensorflow + qibo.set_backend(backend="qiboml", platform="tensorflow") + # in case of Pytorch + qibo.set_backend(backend="qiboml", platform="pytorch") -In order to install the package, we recommend the installation using: +In order to start using automatic differentiation tools with Qibo, +we recommend the installation using: .. code-block:: bash - pip install qibo torch + pip install qibo qiboml tensorflow -_______________________ +or + +.. code-block:: bash + + pip install qibo 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qibo.config import log, raise_error -QIBO_NATIVE_BACKENDS = ("numpy", "pytorch", "qulacs") +QIBO_NATIVE_BACKENDS = ("numpy", "qulacs") class MissingBackend(ValueError): @@ -40,9 +39,6 @@ def load(backend: str, **kwargs) -> Backend: + f"The native qibo backends are {QIBO_NATIVE_BACKENDS + ('clifford',)}", ) - if backend == "pytorch": - return PyTorchBackend() - if backend == "clifford": engine = kwargs.pop("platform", None) kwargs["engine"] = engine @@ -80,7 +76,7 @@ class _Global: {"backend": "qibojit", "platform": "numba"}, {"backend": "numpy"}, {"backend": "qiboml", "platform": "tensorflow"}, - {"backend": "pytorch"}, + {"backend": "qiboml", "platform": "pytorch"}, ] @classmethod diff --git a/src/qibo/backends/pytorch.py b/src/qibo/backends/pytorch.py deleted file mode 100644 index 48404248d2..0000000000 --- a/src/qibo/backends/pytorch.py +++ /dev/null @@ -1,281 +0,0 @@ -"""PyTorch backend.""" - -from typing import Union - -import numpy as np - -from qibo import __version__ -from qibo.backends.npmatrices import NumpyMatrices -from qibo.backends.numpy import NumpyBackend - - -class TorchMatrices(NumpyMatrices): - """Matrix representation of every gate as a torch Tensor. - - Args: - dtype (torch.dtype): Data type of the matrices. - """ - - def __init__(self, dtype): - import torch # pylint: disable=import-outside-toplevel # type: ignore - - super().__init__(dtype) - self.np = torch - self.dtype = dtype - - def _cast(self, x, dtype): - flattened = [item for sublist in x for item in sublist] - tensor_list = [self.np.as_tensor(i, dtype=dtype) for i in flattened] - return self.np.stack(tensor_list).reshape(len(x), len(x)) - - def Unitary(self, u): - return self._cast(u, dtype=self.dtype) - - -class PyTorchBackend(NumpyBackend): - def __init__(self): - super().__init__() - import torch # pylint: disable=import-outside-toplevel # type: ignore - - self.np = torch - - self.name = "pytorch" - self.versions = { - "qibo": __version__, - "numpy": np.__version__, - "torch": self.np.__version__, - } - - # Default data type used for the gate matrices is complex128 - self.dtype = self._torch_dtype(self.dtype) - # Default data type used for the real gate parameters is float64 - self.parameter_dtype = self._torch_dtype("float64") - self.matrices = TorchMatrices(self.dtype) - self.device = self.np.device("cuda:0" if torch.cuda.is_available() else "cpu") - self.nthreads = 0 - self.tensor_types = (self.np.Tensor, np.ndarray) - - # These functions in Torch works in a different way than numpy or have different names - self.np.transpose = self.np.permute - self.np.copy = self.np.clone - self.np.power = self.np.pow - self.np.expand_dims = self.np.unsqueeze - self.np.mod = self.np.remainder - self.np.right_shift = self.np.bitwise_right_shift - self.np.sign = self.np.sgn - self.np.flatnonzero = lambda x: self.np.nonzero(x).flatten() - - def _torch_dtype(self, dtype): - if dtype == "float": - dtype += "32" - return getattr(self.np, dtype) - - def set_device(self, device): # pragma: no cover - self.device = device - - def cast( - self, - x, - dtype=None, - copy: bool = False, - ): - """Casts input as a Torch tensor of the specified dtype. - - This method supports casting of single tensors or lists of tensors - as for the :class:`qibo.backends.PyTorchBackend`. - - Args: - x (Union[torch.Tensor, list[torch.Tensor], np.ndarray, list[np.ndarray], int, float, complex]): - Input to be casted. - dtype (Union[str, torch.dtype, np.dtype, type]): Target data type. - If ``None``, the default dtype of the backend is used. - Defaults to ``None``. - copy (bool, optional): If ``True``, the input tensor is copied before casting. - Defaults to ``False``. - """ - - if dtype is None: - dtype = self.dtype - elif isinstance(dtype, type): - dtype = self._torch_dtype(dtype.__name__) - elif not isinstance(dtype, self.np.dtype): - dtype = self._torch_dtype(str(dtype)) - - if isinstance(x, self.np.Tensor): - x = x.to(dtype) - elif ( - isinstance(x, list) - and len(x) > 0 - and all(isinstance(row, self.np.Tensor) for row in x) - ): - x = self.np.stack(x) - else: - x = self.np.tensor(x, dtype=dtype) - - if copy: - return x.clone() - return x - - def matrix_parametrized(self, gate): - """Convert a parametrized gate to its matrix representation in the computational basis.""" - name = gate.__class__.__name__ - _matrix = getattr(self.matrices, name) - if name == "GeneralizedRBS": - for parameter in ["theta", "phi"]: - if not isinstance(gate.init_kwargs[parameter], self.np.Tensor): - gate.init_kwargs[parameter] = self._cast_parameter( - gate.init_kwargs[parameter], trainable=gate.trainable - ) - - _matrix = _matrix( - qubits_in=gate.init_args[0], - qubits_out=gate.init_args[1], - theta=gate.init_kwargs["theta"], - phi=gate.init_kwargs["phi"], - ) - return _matrix - else: - new_parameters = [] - for parameter in gate.parameters: - if not isinstance(parameter, self.np.Tensor): - parameter = self._cast_parameter( - parameter, trainable=gate.trainable - ) - elif parameter.requires_grad: - gate.trainable = True - new_parameters.append(parameter) - gate.parameters = tuple(new_parameters) - _matrix = _matrix(*gate.parameters) - return _matrix - - def _cast_parameter(self, x, trainable): - """Cast a gate parameter to a torch tensor. - - Args: - x (Union[int, float, complex]): Parameter to be casted. - trainable (bool): If ``True``, the tensor requires gradient. - """ - if isinstance(x, int) and trainable: - return self.np.tensor(x, dtype=self.parameter_dtype, requires_grad=True) - if isinstance(x, float): - return self.np.tensor( - x, dtype=self.parameter_dtype, requires_grad=trainable - ) - return self.np.tensor(x, dtype=self.dtype, requires_grad=trainable) - - def is_sparse(self, x): - if isinstance(x, self.np.Tensor): - return x.is_sparse - - return super().is_sparse(x) - - def to_numpy(self, x): - if isinstance(x, list): - return np.asarray([self.to_numpy(i) for i in x]) - - if isinstance(x, self.np.Tensor): - return x.numpy(force=True) - - return x - - def _order_probabilities(self, probs, qubits, nqubits): - """Arrange probabilities according to the given ``qubits`` ordering.""" - if probs.dim() == 0: # pragma: no cover - return probs - unmeasured, reduced = [], {} - for i in range(nqubits): - if i in qubits: - reduced[i] = i - len(unmeasured) - else: - unmeasured.append(i) - return self.np.transpose(probs, [reduced.get(i) for i in qubits]) - - def calculate_probabilities(self, state, qubits, nqubits): - rtype = self.np.real(state).dtype - unmeasured_qubits = tuple(i for i in range(nqubits) if i not in qubits) - state = self.np.reshape(self.np.abs(state) ** 2, nqubits * (2,)) - if len(unmeasured_qubits) == 0: - probs = self.cast(state, dtype=rtype) - else: - probs = self.np.sum(self.cast(state, dtype=rtype), axis=unmeasured_qubits) - return self._order_probabilities(probs, qubits, nqubits).ravel() - - def set_seed(self, seed): - self.np.manual_seed(seed) - np.random.seed(seed) - - def sample_shots(self, probabilities, nshots): - return self.np.multinomial( - self.cast(probabilities, dtype="float"), nshots, replacement=True - ) - - def calculate_eigenvalues(self, matrix, k: int = 6, hermitian: bool = True): - if hermitian: - return self.np.linalg.eigvalsh(matrix) # pylint: disable=not-callable - return self.np.linalg.eigvals(matrix) # pylint: disable=not-callable - - def calculate_eigenvectors(self, matrix, k: int = 6, hermitian: int = True): - if hermitian: - return self.np.linalg.eigh(matrix) # pylint: disable=not-callable - return self.np.linalg.eig(matrix) # pylint: disable=not-callable - - def calculate_matrix_exp(self, a, matrix, eigenvectors=None, eigenvalues=None): - if eigenvectors is None or self.is_sparse(matrix): - return self.np.linalg.matrix_exp( # pylint: disable=not-callable - -1j * a * matrix - ) - expd = self.np.diag(self.np.exp(-1j * a * eigenvalues)) - ud = self.np.conj(eigenvectors).T - return self.np.matmul(eigenvectors, self.np.matmul(expd, ud)) - - def calculate_matrix_power( - self, - matrix, - power: Union[float, int], - precision_singularity: float = 1e-14, - ): - copied = self.cast(matrix, copy=True) - copied = self.to_numpy(copied) if power >= 0.0 else copied.detach() - copied = super().calculate_matrix_power(copied, power, precision_singularity) - return self.cast(copied, dtype=copied.dtype) - - def calculate_jacobian_matrix( - self, circuit, parameters=None, initial_state=None, return_complex: bool = True - ): - copied = circuit.copy(deep=True) - - def func(parameters): - """torch requires object(s) to be wrapped in a function.""" - copied.set_parameters(parameters) - state = self.execute_circuit(copied, initial_state=initial_state).state() - if return_complex: - return self.np.real(state), self.np.imag(state) - return self.np.real(state) - - return self.np.autograd.functional.jacobian(func, parameters) - - def _test_regressions(self, name): - if name == "test_measurementresult_apply_bitflips": - return [ - [0, 0, 0, 0, 2, 3, 0, 0, 0, 0], - [0, 0, 0, 0, 2, 3, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 1, 0, 0, 0, 0], - [0, 0, 0, 0, 2, 0, 0, 0, 0, 0], - ] - - if name == "test_probabilistic_measurement": - if self.device == "cuda": # pragma: no cover - return {0: 273, 1: 233, 2: 242, 3: 252} - return {1: 270, 2: 248, 3: 244, 0: 238} - - if name == "test_unbalanced_probabilistic_measurement": - if self.device == "cuda": # pragma: no cover - return {0: 196, 1: 153, 2: 156, 3: 495} - return {3: 492, 2: 176, 0: 168, 1: 164} - - if name == "test_post_measurement_bitflips_on_circuit": - return [ - {5: 30}, - {5: 17, 4: 5, 7: 4, 1: 2, 6: 2}, - {4: 9, 2: 5, 5: 5, 3: 4, 6: 4, 0: 1, 1: 1, 7: 1}, - ] diff --git a/src/qibo/hamiltonians/hamiltonians.py b/src/qibo/hamiltonians/hamiltonians.py index 7bf98865bf..0016ddb05b 100644 --- a/src/qibo/hamiltonians/hamiltonians.py +++ b/src/qibo/hamiltonians/hamiltonians.py @@ -7,7 +7,6 @@ import numpy as np import sympy -from qibo.backends import PyTorchBackend, _check_backend from qibo.config import EINSUM_CHARS, log, raise_error from qibo.hamiltonians.abstract import AbstractHamiltonian from qibo.symbols import Z @@ -266,7 +265,7 @@ def __mul__(self, o): if self.backend.np.real(o) >= 0: # TODO: check for side effects K.qnp r._eigenvalues = o * self._eigenvalues elif not self.backend.is_sparse(self.matrix): - axis = (0,) if isinstance(self.backend, PyTorchBackend) else 0 + axis = (0,) if (self.backend.platform == "pytorch") else 0 r._eigenvalues = o * self.backend.np.flip(self._eigenvalues, axis) if self._eigenvectors is not None: if self.backend.np.real(o) > 0: # TODO: see above diff --git a/src/qibo/models/error_mitigation.py b/src/qibo/models/error_mitigation.py index ac876e84ea..fe7b0bb307 100644 --- a/src/qibo/models/error_mitigation.py +++ b/src/qibo/models/error_mitigation.py @@ -328,7 +328,7 @@ def _curve_fit( Returns: ndarray: the optimal parameters. """ - if backend.name == "pytorch": + if backend.platform == "pytorch": # pytorch has some problems with the `scipy.optim.curve_fit` function # thus we use a `torch.optim` optimizer params.requires_grad = True diff --git a/src/qibo/optimizers.py b/src/qibo/optimizers.py index 0914800f34..65e107e9a9 100644 --- a/src/qibo/optimizers.py +++ b/src/qibo/optimizers.py @@ -282,7 +282,7 @@ def sgd( callback=callback, ) - if backend.name == "pytorch": + if backend.platform == "pytorch": if compile: log.warning( "PyTorch does not support compilation of the optimization graph." diff --git a/src/qibo/quantum_info/basis.py b/src/qibo/quantum_info/basis.py index 7efab48547..673f7e48ca 100644 --- a/src/qibo/quantum_info/basis.py +++ b/src/qibo/quantum_info/basis.py @@ -105,7 +105,7 @@ def pauli_basis( basis_full = basis_single if vectorize and sparse: - if backend.name == "pytorch": + if backend.platform == "pytorch": nonzero = lambda x: backend.np.nonzero(x, as_tuple=True) else: nonzero = backend.np.nonzero diff --git a/tests/conftest.py b/tests/conftest.py index c6bd642750..344accf983 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -13,11 +13,11 @@ # backends to be tested BACKENDS = [ "numpy", - "pytorch", "qibojit-numba", "qibojit-cupy", "qibojit-cuquantum", "qiboml-tensorflow", + "qiboml-pytorch", ] # multigpu configurations to be tested (only with qibojit-cupy) ACCELERATORS = [ diff --git a/tests/test_backends.py b/tests/test_backends.py index 85fb18f3a3..383fdb7c17 100644 --- a/tests/test_backends.py +++ b/tests/test_backends.py @@ -113,19 +113,17 @@ def test_construct_backend(backend): def test_list_available_backends(): - tensorflow = False if platform.system() == "Windows" else True qulacs = ( False if platform.system() == "Darwin" and sys.version_info[1] == 9 else True ) available_backends = { "numpy": True, - "pytorch": True, "qulacs": qulacs, "qibojit": {"numba": True, "cupy": False, "cuquantum": False}, "qibolab": False, "qibo-cloud-backends": False, "qibotn": {"cutensornet": False, "qutensornet": True}, - "qiboml": {"tensorflow": tensorflow}, + "qiboml": {"tensorflow": False, "pytorch": True}, } assert available_backends == list_available_backends( "qibojit", "qibolab", "qibo-cloud-backends", "qibotn", "qiboml" diff --git a/tests/test_backends_clifford.py b/tests/test_backends_clifford.py index 6e0faf27cc..6cd1cd9e2c 100644 --- a/tests/test_backends_clifford.py +++ b/tests/test_backends_clifford.py @@ -4,10 +4,10 @@ import numpy as np import pytest -from qiboml.backends import TensorflowBackend +from qiboml.backends import PyTorchBackend, TensorflowBackend from qibo import Circuit, gates, get_backend, set_backend -from qibo.backends import CliffordBackend, NumpyBackend, PyTorchBackend +from qibo.backends import CliffordBackend, NumpyBackend from qibo.backends.clifford import _get_engine_name from qibo.noise import DepolarizingError, NoiseModel, PauliError from qibo.quantum_info.random_ensembles import random_clifford diff --git a/tests/test_backends_torch_gradients.py b/tests/test_backends_torch_gradients.py deleted file mode 100644 index 9dcd0f032a..0000000000 --- a/tests/test_backends_torch_gradients.py +++ /dev/null @@ -1,85 +0,0 @@ -import sys - -import numpy as np -import pytest - -from qibo import Circuit, gates -from qibo.backends import PyTorchBackend, construct_backend -from qibo.quantum_info import infidelity - - -def test_torch_gradients(): - backend = PyTorchBackend() - backend.np.manual_seed(42) - nepochs = 400 - optimizer = backend.np.optim.Adam - target_state = backend.np.rand(4, dtype=backend.np.complex128) - target_state = target_state / backend.np.norm(target_state) - params = backend.np.rand(4, dtype=backend.np.float64, requires_grad=True) - circuit = Circuit(2) - circuit.add(gates.RX(0, params[0])) - circuit.add(gates.RY(1, params[1])) - circuit.add(gates.U2(1, params[2], params[3])) - - initial_params = params.clone() - initial_loss = infidelity( - target_state, backend.execute_circuit(circuit).state(), backend=backend - ) - - optimizer = optimizer([params], lr=0.01) - for _ in range(nepochs): - optimizer.zero_grad() - circuit.set_parameters(params) - final_state = backend.execute_circuit(circuit).state() - loss = infidelity(target_state, final_state, backend=backend) - loss.backward() - grad = params.grad.clone().norm() - optimizer.step() - - assert initial_loss > loss - assert initial_params[0] != params[0] - assert grad.item() < 10e-3 - - -@pytest.mark.skipif( - sys.platform != "linux", reason="Tensorflow available only when testing on linux." -) -def test_torch_tensorflow_gradients(): - - backend = PyTorchBackend() - tf_backend = construct_backend(backend="qiboml", platform="tensorflow") - - target_state = backend.np.tensor([0.0, 1.0], dtype=backend.np.complex128) - param = backend.np.tensor([0.1], dtype=backend.np.float64, requires_grad=True) - circuit = Circuit(1) - circuit.add(gates.RX(0, param[0])) - - optimizer = backend.np.optim.SGD - optimizer = optimizer([param], lr=1) - circuit.set_parameters(param) - final_state = backend.execute_circuit(circuit).state() - loss = infidelity(target_state, final_state, backend=backend) - loss.backward() - torch_param_grad = param.grad.clone().item() - optimizer.step() - torch_param = param.clone().item() - - target_state = tf_backend.tf.constant([0.0, 1.0], dtype=tf_backend.tf.complex128) - param = tf_backend.tf.Variable([0.1], dtype=tf_backend.tf.float64) - circuit = Circuit(1) - circuit.add(gates.RX(0, param[0])) - - optimizer = tf_backend.tf.optimizers.SGD(learning_rate=1.0) - - with tf_backend.tf.GradientTape() as tape: - circuit.set_parameters(param) - final_state = tf_backend.execute_circuit(circuit).state() - loss = infidelity(target_state, final_state, backend=tf_backend) - - grads = tape.gradient(loss, [param]) - tf_param_grad = grads[0].numpy()[0] - optimizer.apply_gradients(zip(grads, [param])) - tf_param = param.numpy()[0] - - assert np.allclose(torch_param_grad, tf_param_grad, atol=1e-7, rtol=1e-7) - assert np.allclose(torch_param, tf_param, atol=1e-7, rtol=1e-7) diff --git a/tests/test_hamiltonians.py b/tests/test_hamiltonians.py index f6732c88f0..e66c824da2 100644 --- a/tests/test_hamiltonians.py +++ b/tests/test_hamiltonians.py @@ -69,10 +69,10 @@ def transformation_d(a, b, use_eye=False): H2 = hamiltonians.XXZ(nqubits=2, delta=1, backend=backend) mH1, mH2 = backend.to_numpy(H1.matrix), backend.to_numpy(H2.matrix) else: - if backend.platform == "tensorflow": - pytest.skip("Tensorflow does not support operations with sparse matrices.") - elif backend.name == "pytorch": - pytest.skip("Pytorch does not support operations with sparse matrices.") + if backend.platform in ["tensorflow", "pytorch"]: + pytest.skip( + "Tensorflow and Pytorch do not support operations with sparse matrices." + ) mH1 = random_sparse_matrix(backend, 64, sparse_type=sparse_type) mH2 = random_sparse_matrix(backend, 64, sparse_type=sparse_type) @@ -101,10 +101,10 @@ def test_hamiltonian_addition(backend, sparse_type): H1 = hamiltonians.Y(nqubits=3, backend=backend) H2 = hamiltonians.TFIM(nqubits=3, h=1.0, backend=backend) else: - if backend.platform == "tensorflow": - pytest.skip("Tensorflow does not support operations with sparse matrices.") - elif backend.name == "pytorch": - pytest.skip("Pytorch does not support operations with sparse matrices.") + if backend.platform in ["tensorflow", "pytorch"]: + pytest.skip( + "Tensorflow and Pytorch do not support operations with sparse matrices." + ) H1 = hamiltonians.Hamiltonian( 6, random_sparse_matrix(backend, 64, sparse_type=sparse_type), @@ -149,10 +149,10 @@ def test_hamiltonian_operation_errors(backend): @pytest.mark.parametrize("sparse_type", [None, "coo", "csr", "csc", "dia"]) def test_hamiltonian_matmul(backend, sparse_type): """Test matrix multiplication between Hamiltonians.""" - if backend.name == "pytorch": - pytest.skip("Pytorch does not support operations with sparse matrices.") - if backend.platform == "tensorflow": - pytest.skip("Tensorflow does not support operations with sparse matrices.") + if backend.platform in ["tensorflow", "pytorch"]: + pytest.skip( + "Tensorflow and Pytorch do not support operations with sparse matrices." + ) if sparse_type is None: nqubits = 3 H1 = hamiltonians.TFIM(nqubits, h=1.0, backend=backend) @@ -193,10 +193,10 @@ def test_hamiltonian_matmul_states(backend, sparse_type): nqubits = 3 H = hamiltonians.TFIM(nqubits, h=1.0, backend=backend) else: - if backend.platform == "tensorflow": - pytest.skip("Tensorflow does not support operations with sparse matrices.") - elif backend.name == "pytorch": - pytest.skip("Pytorch does not support operations with sparse matrices.") + if backend.platform in ["tensorflow", "pytorch"]: + pytest.skip( + "Tensorflow and Pytorch do not support operations with sparse matrices." + ) nqubits = 3 nstates = 2**nqubits matrix = random_sparse_matrix(backend, nstates, sparse_type) @@ -230,10 +230,10 @@ def test_hamiltonian_expectation(backend, dense, density_matrix, sparse_type): if sparse_type is None: h = hamiltonians.XXZ(nqubits=3, delta=0.5, dense=dense, backend=backend) else: - if backend.platform == "tensorflow": - pytest.skip("Tensorflow does not support operations with sparse matrices.") - elif backend.name == "pytorch": - pytest.skip("Pytorch does not support operations with sparse matrices.") + if backend.platform in ["tensorflow", "pytorch"]: + pytest.skip( + "Tensorflow and Pytorch do not support operations with sparse matrices." + ) h = hamiltonians.Hamiltonian( 6, random_sparse_matrix(backend, 64, sparse_type), backend=backend ) @@ -325,10 +325,10 @@ def test_hamiltonian_eigenvalues(backend, dtype, sparse_type, dense): if sparse_type is None: H1 = hamiltonians.XXZ(nqubits=2, delta=0.5, dense=dense, backend=backend) else: - if backend.platform == "tensorflow": - pytest.skip("Tensorflow does not support operations with sparse matrices.") - elif backend.name == "pytorch": - pytest.skip("Pytorch does not support operations with sparse matrices.") + if backend.platform in ["tensorflow", "pytorch"]: + pytest.skip( + "Tensorflow and Pytorch do not support operations with sparse matrices." + ) from scipy import sparse H1 = hamiltonians.XXZ(nqubits=5, delta=0.5, backend=backend) @@ -402,10 +402,10 @@ def test_hamiltonian_ground_state(backend, sparse_type, dense): if sparse_type is None: H = hamiltonians.XXZ(nqubits=2, delta=0.5, dense=dense, backend=backend) else: - if backend.platform == "tensorflow": - pytest.skip("Tensorflow does not support operations with sparse matrices.") - elif backend.name == "pytorch": - pytest.skip("Pytorch does not support operations with sparse matrices.") + if backend.platform in ["tensorflow", "pytorch"]: + pytest.skip( + "Tensorflow and Pytorch do not support operations with sparse matrices." + ) from scipy import sparse H = hamiltonians.XXZ(nqubits=5, delta=0.5, backend=backend) @@ -434,12 +434,10 @@ def construct_hamiltonian(): if sparse_type is None: return hamiltonians.XXZ(nqubits=2, delta=0.5, dense=dense, backend=backend) else: - if backend.platform == "tensorflow": + if backend.platform in ["tensorflow", "pytorch"]: pytest.skip( - "Tensorflow does not support operations with sparse matrices." + "Tensorflow and Pytorch do not support operations with sparse matrices." ) - elif backend.name == "pytorch": - pytest.skip("Pytorch does not support operations with sparse matrices.") from scipy import sparse ham = hamiltonians.XXZ(nqubits=5, delta=0.5, backend=backend) diff --git a/tests/test_models_variational.py b/tests/test_models_variational.py index 1c49f4bacf..08031b853f 100644 --- a/tests/test_models_variational.py +++ b/tests/test_models_variational.py @@ -109,13 +109,11 @@ def myloss(parameters, circuit, target): def test_vqe(backend, method, options, compile, filename): """Performs a VQE circuit minimization test.""" if (method == "sgd" or compile) and ( - (backend.name != "pytorch") or (backend.platform != "tensorflow") + backend.platform not in ["tensorflow", "pytorch"] ): pytest.skip("Skipping SGD test for unsupported backend.") - if method != "sgd" and backend.name == "pytorch": - pytest.skip("Skipping scipy optimizers for pytorch.") - if method != "sgd" and backend.platform == "tensorflow": - pytest.skip("Skipping scipy optimizers for tensorflow.") + if method != "sgd" and (backend.platform in ["tensorflow", "pytorch"]): + pytest.skip("Skipping scipy optimizers for pytorch and tensorflow.") n_threads = backend.nthreads backend.set_threads(1) nqubits = 3 @@ -138,7 +136,7 @@ def test_vqe(backend, method, options, compile, filename): initial_parameters = backend.cast( np.random.uniform(0, 2 * np.pi, 2 * nqubits * layers + nqubits), dtype="float64" ) - if backend.name == "pytorch": + if backend.platform == "pytorch": initial_parameters.requires_grad = True v = VQE(circuit, hamiltonian) @@ -281,14 +279,14 @@ def test_qaoa_errors(backend): @pytest.mark.parametrize(test_names, test_values) def test_qaoa_optimization(backend, method, options, dense, filename): - if (method == "sgd") and (backend.name not in ["tensorflow", "pytorch"]): + if (method == "sgd") and (backend.platform not in ["tensorflow", "pytorch"]): pytest.skip("Skipping SGD test for unsupported backend.") - if method != "sgd" and backend.name in ("tensorflow", "pytorch"): + if method != "sgd" and backend.platform in ("tensorflow", "pytorch"): pytest.skip("Skipping scipy optimizers for tensorflow and pytorch.") h = XXZ(3, dense=dense, backend=backend) qaoa = QAOA(h) initial_p = backend.cast([0.05, 0.06, 0.07, 0.08], dtype="float64") - if backend.name == "pytorch": + if backend.platform == "pytorch": initial_p.requires_grad = True best, params, _ = qaoa.minimize(initial_p, method=method, options=options) if filename is not None: diff --git a/tests/test_quantum_info_clifford.py b/tests/test_quantum_info_clifford.py index 0c90039c1a..e39232e264 100644 --- a/tests/test_quantum_info_clifford.py +++ b/tests/test_quantum_info_clifford.py @@ -3,10 +3,10 @@ import numpy as np import pytest -from qiboml.backends import TensorflowBackend +from qiboml.backends import PyTorchBackend, TensorflowBackend from qibo import Circuit, gates, matrices -from qibo.backends import CliffordBackend, PyTorchBackend +from qibo.backends import CliffordBackend from qibo.backends.clifford import _get_engine_name from qibo.quantum_info._clifford_utils import ( _cnot_cost, diff --git a/tests/test_quantum_info_metrics.py b/tests/test_quantum_info_metrics.py index 4e5b9ea849..a4c53dfdfa 100644 --- a/tests/test_quantum_info_metrics.py +++ b/tests/test_quantum_info_metrics.py @@ -394,7 +394,7 @@ def test_frame_potential(backend, nqubits, power_t, samples): @pytest.mark.parametrize("return_complex", [False, True]) @pytest.mark.parametrize("nqubits", [4, 8]) def test_qfim(backend, nqubits, return_complex, params_flag): - if backend.name == "pytorch" or backend.platform == "tensorflow": + if backend.platform in ["tensorflow", "pytorch"]: # QFIM from https://arxiv.org/abs/2405.20408 is known analytically data = np.random.rand(nqubits) data = backend.cast(data, dtype=data.dtype) diff --git a/tests/test_quantum_info_operations.py b/tests/test_quantum_info_operations.py index d25643f9f8..4791ff5df0 100644 --- a/tests/test_quantum_info_operations.py +++ b/tests/test_quantum_info_operations.py @@ -260,7 +260,7 @@ def test_singular_value_decomposition(backend): S_sorted = backend.np.sort(S) coeffs_sorted = backend.np.sort(coeffs) - if backend.name == "pytorch": + if backend.platform == "pytorch": S_sorted, coeffs_sorted = S_sorted[0], coeffs_sorted[0] backend.assert_allclose(S_sorted, coeffs_sorted) diff --git a/tests/test_quantum_info_random.py b/tests/test_quantum_info_random.py index d522f1458d..3d2b6d92dd 100644 --- a/tests/test_quantum_info_random.py +++ b/tests/test_quantum_info_random.py @@ -174,7 +174,7 @@ def test_random_unitary(backend, measure): matrix_dagger = backend.np.conj(matrix).T matrix_inv = ( backend.np.inverse(matrix) - if backend.name == "pytorch" + if backend.platform == "pytorch" else np.linalg.inv(matrix) ) norm = float(