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Ensure active return check doesn't break CI #483

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merged 5 commits into from
Aug 24, 2023
Merged

Ensure active return check doesn't break CI #483

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dce2717
Ensure active return check doesn't break CI
mlxd Aug 24, 2023
6b9d74e
Auto update version
github-actions[bot] Aug 24, 2023
fb9e4fa
Trigger CI
vincentmr Aug 24, 2023
35b728b
Fix codcov
vincentmr Aug 24, 2023
043ccc8
Update changelog.
vincentmr Aug 24, 2023
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3 changes: 3 additions & 0 deletions .github/CHANGELOG.md
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Expand Up @@ -25,6 +25,9 @@

### Bug fixes

* Ensure PennyLane has an `active_return` attribute before calling it.
[(#483)] (https://github.com/PennyLaneAI/pennylane-lightning/pull/483)

* Do no import `sqrt2_v` from `<numbers>` in `Util.hpp` to resolve issue with Lightning-GPU builds.
[(#479)](https://github.com/PennyLaneAI/pennylane-lightning/pull/479)

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2 changes: 1 addition & 1 deletion pennylane_lightning/core/_version.py
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Expand Up @@ -16,4 +16,4 @@
Version number (major.minor.patch[-label])
"""

__version__ = "0.32.0-dev11"
__version__ = "0.32.0-dev12"
4 changes: 3 additions & 1 deletion pennylane_lightning/lightning_kokkos/lightning_kokkos.py
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Expand Up @@ -721,7 +721,9 @@ def adjoint_jacobian(self, tape, starting_state=None, use_device_state=False):
jac = jac.reshape(-1, len(trainable_params))
jac_r = np.zeros((jac.shape[0], processed_data["all_params"]))
jac_r[:, processed_data["record_tp_rows"]] = jac
return self._adjoint_jacobian_processing(jac_r) if qml.active_return() else jac_r
if hasattr(qml, "active_return"): # pragma: no cover
return self._adjoint_jacobian_processing(jac_r) if qml.active_return() else jac_r
return self._adjoint_jacobian_processing(jac_r)

# pylint: disable=inconsistent-return-statements, line-too-long
def vjp(self, measurements, grad_vec, starting_state=None, use_device_state=False):
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4 changes: 3 additions & 1 deletion pennylane_lightning/lightning_qubit/lightning_qubit.py
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Expand Up @@ -689,7 +689,9 @@ def adjoint_jacobian(self, tape, starting_state=None, use_device_state=False):
jac = jac.reshape(-1, len(trainable_params))
jac_r = np.zeros((jac.shape[0], processed_data["all_params"]))
jac_r[:, processed_data["record_tp_rows"]] = jac
return self._adjoint_jacobian_processing(jac_r) if qml.active_return() else jac_r
if hasattr(qml, "active_return"): # pragma: no cover
return self._adjoint_jacobian_processing(jac_r) if qml.active_return() else jac_r
return self._adjoint_jacobian_processing(jac_r)

# pylint: disable=line-too-long, inconsistent-return-statements
def vjp(self, measurements, grad_vec, starting_state=None, use_device_state=False):
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