Element-wise BLAS APIs & new Tensor for Python: ⬆️ 450 kernels #220
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SimSIMD becomes more similar to BLAS with every commit! New operations are: - Element-wise Sum: `a[i] + b[i]` - Scale & Shift: `alpha * a[i] + beta` Those are similar to `axpy` and `scal` in BLAS.
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Tests & CI
Without defining the executables as tests automatic tools like "ctest" will not find tests.
New classes are added to the Python SDK: public NDArray and NDIndex and internal BufferOrScalarArgument. Those can be used for high-dimensional tensor processing with up to 64 dimensions, as opposed to 32 in NumPy. The new interface handles mixed-precision much better, allowing to override the type spec of every tensor individually. That feature is in preview until the actual implementation in subsequent commits. No LibC is needed anymore for rounding floats to 8-bit integers in elementwise ops. New 16-, 32-, and 64-bit integer element-wise kernels are added for compatibility with NumPy. Serial for now. SimSIMD implementation is supposed to be more efficient when at least a few continuous dimensions are present.
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This entry is largely unnecessary, and its computation in linearization procedure depends on the value at the previous dim, making it hard to parallelize with SIMD.
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…mSIMD into main-elementwise
The reason to rename is the addition of new trigonometric APIs that will cause a confusion among users.
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It started as a straightforward optimization request from the @albumentations-team: to improve the special case of the
wsum(Weighted Sum) operation for the "non-weighted" scenario and to add APIs for scalar multiplication and addition. This update introduces new public APIs in both C and Python:scale: Implementssum: ComputesRecognizing the value of consistency with widely-used libraries, we’ve also added "aliases" aligned with names familiar to developers using NumPy and OpenCV for element-wise addition and multiplication across vectors and scalars:
np.addcv.addsimd.addnp.multiplycv.multiplysimd.multiplyThe real excitement came when we realized that larger projects would take time to adopt emerging numeric types like
bfloat16andfloat8, which are well-known in AI circles. To bridge this gap, SimSIMD now introduces anAnyTensortype designed for maximum interoperability via CPython's Buffer Protocol and beyond, setting it apart from similar types in NumPy, PyTorch, TensorFlow, and JAX.Tensor Class for C, Python, and Rust 🦀
Element-wise Operations 🧮
Geospatial Operations 🛰️
If you have any feedback regarding the limitations of current array-processing software in a single- or multi-node AI training settings, I am all ears 👂