✨ New features and improvements
* Thinc now vendorizes OpenBLAS's `cblas_sgemm` function, and delegates matrix multiplications to it by default. The provided function is single-threaded, making it easy to call Thinc from multiple processes. The default sgemm function can be overridden using the `THINC_BLAS` environment variable --- see below.
* `thinc.neural.util.get_ops` now understands device integers, e.g. `0` for GPU 0, as well as strings like `"cpu"` and `"cupy"`.
* Update `StaticVectors` model, to make use of spaCy v2.0's [`Vectors`](https://spacy.io/api/vectors) class.
* New `.gemm()` method on NumpyOps and CupyOps classes, allowing matrix and vector multiplication to be handled with a simple function. Example usage:
**Customizing the matrix multiplication backend**
Previous versions of Thinc have relied on numpy for matrix multiplications. When numpy is installed via wheel using pip (the default), numpy will usually be linked against a suboptimal matrix multiplication kernel. This made it difficult to ensure that Thinc was well optimized for the target machine.
To fix this, Thinc now provides its own matrix multiplications, by bundling the source code for OpenBLAS's sgemm kernel within the library. To change the default BLAS library, you can specify an environment variable, giving the location of the shared library you want to link against:
bash
THINC_BLAS=/opt/openblas/lib/libopenblas.so pip install thinc --no-cache-dir --no-binary
export LD_LIBRARY_PATH=/opt/openblas/lib
On OSX:
export DYLD_LIBRARY_PATH=/opt/openblas/lib
If you want to link against the Intel MKL instead of OpenBLAS, the easiest way is to install Miniconda. For instance, if you installed miniconda to `/opt/miniconda', the command to install Thinc linked against MKL would be:
bash
THINC_BLAS=/opt/miniconda/numpy-mkl/lib/libmkl_rt.so pip install thinc --no-cache-dir --no-binary
export LD_LIBRARY_PATH=/opt/miniconda/numpy-mkl/lib
On OSX:
export DYLD_LIBRARY_PATH=/opt/miniconda/numpy-mkl/lib
If the library file ends in a .a extension, it is linked statically; if it ends in .so, it's linked dynamically. Make sure you have the directory on your `LD_LIBRARY_PATH` at runtime if you use the dynamic linking.
🔴 Bug fixes
* Fix pickle support for `FeatureExtracter` class.
* Fix unicode error in Quora dataset loader.
* Fix batch normalization bugs. Now supports batch "renormalization" correctly.
* Models now reliably distinguish predict vs. train modes, using the convention `drop=None`. Previously, layers such as `BatchNorm` relied on having their `predict()` method called, which didn't work they were called by layers which didn't implement a `predict()` method. We now set `drop=None` to make this more reliable.
* Fix bug that caused incorrect data types to be produced by `FeatureExtracter`.
👥 Contributors
Thanks to dvsrepo, justindujardin, alephmelo and darkdreamingdan for the pull requests and contributions.