Remove use of dtype aliases to be removed in Numpy 2 #10890
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All of these are direct substitutions; the forms changed in this commit are just straight aliases, and Numpy 2 will remove them.
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All of these are direct substitutions; the forms changed in this commit are just straight aliases, and Numpy 2 will remove them. (cherry picked from commit cecb788)
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All of these are direct substitutions; the forms changed in this commit are just straight aliases, and Numpy 2 will remove them. (cherry picked from commit cecb788) Co-authored-by: Jake Lishman <jake.lishman@ibm.com> Co-authored-by: Luciano Bello <bel@zurich.ibm.com>
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This commit brings the Qiskit test suite to a passing state (with all optionals installed) with Numpy 2.0.0b1, building on previous commits that handled much of the rest of the changing requirements: - Qiskitgh-10890 - Qiskitgh-10891 - Qiskitgh-10892 - Qiskitgh-10897 - Qiskitgh-11023 Notably, this commit did not actually require a rebuild of Qiskit, despite us compiling against Numpy; it seems to happen that the C API stuff we use via `rust-numpy` (which loads the Numpy C extensions dynamically during module initialisation) hasn't changed.
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This commit brings the Qiskit test suite to a passing state (with all optionals installed) with Numpy 2.0.0b1, building on previous commits that handled much of the rest of the changing requirements: - Qiskitgh-10890 - Qiskitgh-10891 - Qiskitgh-10892 - Qiskitgh-10897 - Qiskitgh-11023 Notably, this commit did not actually require a rebuild of Qiskit, despite us compiling against Numpy; it seems to happen that the C API stuff we use via `rust-numpy` (which loads the Numpy C extensions dynamically during module initialisation) hasn't changed. The main changes are: - adapting to the changed `copy=None` and `copy=False` semantics in `array` and `asarray`. - making sure all our implementers of `__array__` accept both `dtype` and `copy` arguments. Co-authored-by: Lev S. Bishop <18673315+levbishop@users.noreply.github.com>
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This commit brings the Qiskit test suite to a passing state (with all optionals installed) with Numpy 2.0.0b1, building on previous commits that handled much of the rest of the changing requirements: - Qiskitgh-10890 - Qiskitgh-10891 - Qiskitgh-10892 - Qiskitgh-10897 - Qiskitgh-11023 Notably, this commit did not actually require a rebuild of Qiskit, despite us compiling against Numpy; it seems to happen that the C API stuff we use via `rust-numpy` (which loads the Numpy C extensions dynamically during module initialisation) hasn't changed. The main changes are: - adapting to the changed `copy=None` and `copy=False` semantics in `array` and `asarray`. - making sure all our implementers of `__array__` accept both `dtype` and `copy` arguments. Co-authored-by: Lev S. Bishop <18673315+levbishop@users.noreply.github.com>
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* Finalise support for Numpy 2.0 This commit brings the Qiskit test suite to a passing state (with all optionals installed) with Numpy 2.0.0b1, building on previous commits that handled much of the rest of the changing requirements: - gh-10890 - gh-10891 - gh-10892 - gh-10897 - gh-11023 Notably, this commit did not actually require a rebuild of Qiskit, despite us compiling against Numpy; it seems to happen that the C API stuff we use via `rust-numpy` (which loads the Numpy C extensions dynamically during module initialisation) hasn't changed. The main changes are: - adapting to the changed `copy=None` and `copy=False` semantics in `array` and `asarray`. - making sure all our implementers of `__array__` accept both `dtype` and `copy` arguments. Co-authored-by: Lev S. Bishop <18673315+levbishop@users.noreply.github.com> * Update `__array__` methods for Numpy 2.0 compatibility As of Numpy 2.0, implementers of `__array__` are expected and required to have a signature def __array__(self, dtype=None, copy=None): ... In Numpys before 2.0, the `copy` argument will never be passed, and the expected signature was def __array__(self, dtype=None): ... Because of this, we have latitude to set `copy` in our implementations to anything we like if we're running against Numpy 1.x, but we should default to `copy=None` if we're running against Numpy 2.0. The semantics of the `copy` argument to `np.array` changed in Numpy 2.0. Now, `copy=False` means "raise a `ValueError` if a copy is required" and `copy=None` means "copy only if required". In Numpy 1.x, `copy=False` meant "copy only if required". In _both_ Numpy 1.x and 2.0, `ndarray.astype` takes a `copy` argument, and in both, `copy=False` means "copy only if required". In Numpy 2.0 only, `np.asarray` gained a `copy` argument with the same semantics as the `np.array` copy argument from Numpy 2.0. Further, the semantics of the `__array__` method changed in Numpy 2.0, particularly around copying. Now, Numpy will assume that it can pass `copy=True` and the implementer will handle this. If `copy=False` is given and a copy or calculation is required, then the implementer is required to raise `ValueError`. We have a few places where the `__array__` method may (or always does) calculate the array, so in all these, we must forbid `copy=False`. With all this in mind: this PR sets up all our implementers of `__array__` to either default to `copy=None` if they will never actually need to _use_ the `copy` argument within themselves (except perhaps to test if it was set by Numpy 2.0 to `False`, as Numpy 1.x will never set it), or to a compatibility shim `_numpy_compat.COPY_ONLY_IF_NEEDED` if they do naturally want to use it with those semantics. The pattern def __array__(self, dtype=None, copy=_numpy_compat.COPY_ONLY_IF_NEEDED): dtype = self._array.dtype if dtype is None else dtype return np.array(self._array, dtype=dtype, copy=copy) using `array` instead of `asarray` lets us achieve all the desired behaviour between the interactions of `dtype` and `copy` in a way that is compatible with both Numpy 1.x and 2.x. * fixing numerical issues on mac-arm * Change error to match Numpy --------- Co-authored-by: Lev S. Bishop <18673315+levbishop@users.noreply.github.com> Co-authored-by: Sebastian Brandhofer <148463728+sbrandhsn@users.noreply.github.com>
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* Finalise support for Numpy 2.0 This commit brings the Qiskit test suite to a passing state (with all optionals installed) with Numpy 2.0.0b1, building on previous commits that handled much of the rest of the changing requirements: - gh-10890 - gh-10891 - gh-10892 - gh-10897 - gh-11023 Notably, this commit did not actually require a rebuild of Qiskit, despite us compiling against Numpy; it seems to happen that the C API stuff we use via `rust-numpy` (which loads the Numpy C extensions dynamically during module initialisation) hasn't changed. The main changes are: - adapting to the changed `copy=None` and `copy=False` semantics in `array` and `asarray`. - making sure all our implementers of `__array__` accept both `dtype` and `copy` arguments. Co-authored-by: Lev S. Bishop <18673315+levbishop@users.noreply.github.com> * Update `__array__` methods for Numpy 2.0 compatibility As of Numpy 2.0, implementers of `__array__` are expected and required to have a signature def __array__(self, dtype=None, copy=None): ... In Numpys before 2.0, the `copy` argument will never be passed, and the expected signature was def __array__(self, dtype=None): ... Because of this, we have latitude to set `copy` in our implementations to anything we like if we're running against Numpy 1.x, but we should default to `copy=None` if we're running against Numpy 2.0. The semantics of the `copy` argument to `np.array` changed in Numpy 2.0. Now, `copy=False` means "raise a `ValueError` if a copy is required" and `copy=None` means "copy only if required". In Numpy 1.x, `copy=False` meant "copy only if required". In _both_ Numpy 1.x and 2.0, `ndarray.astype` takes a `copy` argument, and in both, `copy=False` means "copy only if required". In Numpy 2.0 only, `np.asarray` gained a `copy` argument with the same semantics as the `np.array` copy argument from Numpy 2.0. Further, the semantics of the `__array__` method changed in Numpy 2.0, particularly around copying. Now, Numpy will assume that it can pass `copy=True` and the implementer will handle this. If `copy=False` is given and a copy or calculation is required, then the implementer is required to raise `ValueError`. We have a few places where the `__array__` method may (or always does) calculate the array, so in all these, we must forbid `copy=False`. With all this in mind: this PR sets up all our implementers of `__array__` to either default to `copy=None` if they will never actually need to _use_ the `copy` argument within themselves (except perhaps to test if it was set by Numpy 2.0 to `False`, as Numpy 1.x will never set it), or to a compatibility shim `_numpy_compat.COPY_ONLY_IF_NEEDED` if they do naturally want to use it with those semantics. The pattern def __array__(self, dtype=None, copy=_numpy_compat.COPY_ONLY_IF_NEEDED): dtype = self._array.dtype if dtype is None else dtype return np.array(self._array, dtype=dtype, copy=copy) using `array` instead of `asarray` lets us achieve all the desired behaviour between the interactions of `dtype` and `copy` in a way that is compatible with both Numpy 1.x and 2.x. --------- Co-authored-by: Lev S. Bishop <18673315+levbishop@users.noreply.github.com> Co-authored-by: Raynel Sanchez <87539502+raynelfss@users.noreply.github.com>
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Summary
All of these are direct substitutions; the forms changed in this commit are just straight aliases, and Numpy 2 will remove them.
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