v8.3.3: Fix Blis crashes, widen numpy pin

• Update blis pin to v1.1. This updates the vendored blis code to 1.1, which should fix crashes from the previously vendored v0.9 code on Windows.
• Widen numpy pin, allowing versions across v1 and v2. Previously I had thought that if I build against numpy v2, I couldn't also have v1 as a runtime dependency. This is actually incorrect, so we can widen the numpy pin
• Set flag on loading PyTorch models to improve safety of loading PyTorch models.

v8.3.2: Fix regression to torch training, update ARM dependency

• Fix regression to torch training introduced in v8.3.1
• Restore MacOS ARM wheels, which were missing from previous builds
• Fix compatibility with thinc-apple-ops

v8.3.1: Fix torch deprecation warning

torch.cuda.amp is deprecated (Pytorch 2.4). This PR updates shims pytorch.py to use torch.amp.autocast instead of torch.cuda.amp.autocast.

Thanks to @Atlogit for the patch.

v9.1.1: Restore wheels for MacOS ARM 64

Previously we used a complicated build process that used self-hosted runners to build wheels for platforms Github Actions did not support. Github Actions has been adding support for ARM recently, so we've simplified the CI process to rely only on it exclusively.

This release adds back support for MacOS ARM64 wheels that were missing from the previous release. Linux ARM wheels are still pending, as Linux ARM architectures are currently only supported for private repos. Cross-compilation with QEMU is possible in theory, but in practice the build timed out after several hours.

v9.1.0: Depend on numpy 2.0.0

Numpy is a build dependency of Thinc, and numpy 2.0 is not binary compatible with numpy 1.0 (fair enough). This means we can't have a version that's compatible across numpy v1 and numpy v2.

This release updates v9 by pinning to numpy 2.0, and builds against it. No other changes are made, so that we have paired versions that only differ in their dependencies.

v8.3.0: Depend on numpy 2.0

Numpy is a build dependency of Thinc, and numpy 2.0 is not binary compatible with numpy 1.0 (fair enough). This means we can't have a version that's compatible across numpy v1 and numpy v2.

This release updates the pins to numpy 2.0 and builds against it. No other changes are made, so that we have paired versions that only differ in their dependencies.

v8.2.5: Restrict numpy pin to <2.0.0

Numpy v2.0 isn't binary compatible with v1 (understandably). We build against numpy so we need to restrict the pin.

v8.2.4: Relaxing `nbconvert` and `typing_extensions` upper pins

✨ New features and improvements

• Bump nbconvert pin
• Bump typing_extensions pin for Python 3.7
• Updates to the test suite

👥 Contributors

@honnibal, @ines, @svlandeg

v9.0.0: better learning rate schedules, integration of thinc-apple-ops

The main new feature of Thinc v9 is the support for learning rate schedules that can take the training dynamics into account. For example, the new plateau.v1 schedule scales the learning rate when no progress has been found after a given number of evaluation steps. Another visible change is that AppleOps is now part of Thinc, so it is not necessary anymore to install thinc-apple-ops to use the AMX units on Apple Silicon.

✨ New features and improvements

• Learning rate schedules can now take the training step as well as an arbitrary set of keyword arguments. This makes it possible to pass information such a the parameter name and last evaluation score to determine the learning rate (#804).
• Added the plateau.v1 schedule (#842). This schedule scales the learning rate if training was found to be stagnant for a given period.
• The functionality of thinc-apple-ops is integrated into Thinc (#927). Starting with this version of Thinc, it is not necessary anymore to install thinc-apple-ops.

🔴 Bug fixes

• Fix the use of thread-local storage (#917).

⚠️ Backwards incompatibilities

• Thinc v9.0.0 only support Python 3.9 and later.
• Schedules are not generators anymore, but implementations of the Schedule class (#804).
• thinc.backends.linalg has been removed (#742). The same functionality is provided by implementations in BLAS that are better tested and more performant.
• thinc.extra.search has been removed (#743). The beam search functionality in this module was strongly coupled to the spaCy transition parser and has therefore moved to spaCy in v4.

👥 Contributors

@adrianeboyd, @danieldk, @honnibal, @ines, @kadarakos, @shadeMe, @svlandeg

v8.2.3: Fix CuPy compatibility and fix strings2arrays for sequences of inequal length

🔴 Bug fixes

• Make strings2arrays work again for sequences of inequal length (#918).
• Fix cupy.cublas import (#921).

👥 Contributors

@danieldk, @honnibal, @ines, @svlandeg