nvmath-python Bindings — NVIDIA nvmath-python 0.1.0 documentation (2024)

Overview¶

Low-level Python bindings for C APIs from NVIDIA Math Libraries are exposed under the corresponding modules in nvmath.bindings.To access the Python bindings, use the modules for the corrsponding libraries.Under the hood, nvmath-python handles the run-time linking to the libraries for you lazily.

The currently supported libraries along with the corresponding module names are listed as follows:

Support for more libraries will be added in the future.

Naming & Calling Convention¶

Inside each of the modules, all public APIs of the corresponding NVIDIA Math library are exposed following the PEP 8 style guide along with the following changes:

• All library name prefixes are stripped

• The function names are broken by words and follow the camel case

  See Also

  The Circle Class Has An Internal Variable Radius. It Has A Constructor: Circle( Double R ) { Radius =r;}NumPy 1.26 中文文档（四十三）-腾讯云开发者社区-腾讯云Packt+ | Advance your knowledge in tech

• The first letter in each word in the enum names are capitalized

• Each enum’s name prefix is stripped from its values’ names

• Whenever applicable, the outputs are stripped away from the function arguments and returned directly as Python objects

• Pointers are passed as Python int

• Exceptions are raised instead of returning the C error code

Below is a non-exhaustive list of examples of such C-to-Python mappings:

• Function: cublasDgemm -> cublas.dgemm().

• Function: curandSetGeneratorOrdering -> curand.set_generator_ordering()

• Enum type: cublasLtMatmulTile_t -> cublasLt.MatmulTile

• Enum type: cufftXtSubFormat -> cufft.XtSubFormat

• Enum value name: CUSOLVER_EIG_MODE_NOVECTOR -> cusolver.EigMode.NOVECTOR

• Enum value name: CUSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED -> cusparse.Status.MATRIX_TYPE_NOT_SUPPORTED

• Returns: The outputs of cusolverDnXpotrf_bufferSize are the workspace sizes on device and host, which are wrapped as a 2-tuple in the corresponding cusolverDn.xpotrf_buffer_size() Python API.

There may be exceptions for the above rules, but they would be self-evident and will be properly documented. In the next section we discusspointer passing in Python.

Memory management¶

# create a host buffer to hold 5 intbuf = numpy.empty((5,), dtype=numpy.int32)# pass buf's pointer to the wrapper# buf could get modified in-place if the function writes to itmy_func(..., buf.ctypes.data, ...)# examine/use buf's dataprint(buf)# create a device buffer to hold 10 doublebuf = cupy.empty((10,), dtype=cupy.float64)# pass buf's pointer to the wrapper# buf could get modified in-place if the function writes to itmy_func(..., buf.data.ptr, ...)# examine/use buf's dataprint(buf)# create an untyped device buffer of 128 bytesbuf = cupy.cuda.alloc(128)# pass buf's pointer to the wrapper# buf could get modified in-place if the function writes to itmy_func(..., buf.ptr, ...)# buf is automatically destroyed when going out of scope

# passing a host buffer of int type can be done like thisbuf = numpy.array([0, 1, 3, 5, 6], dtype=numpy.int32)my_func(..., buf.ctypes.data, ...)# or just thisbuf = [0, 1, 3, 5, 6]my_func(..., buf, ...) # the underlying data type is determined by the C API

API Reference¶

This reference describes all nvmath-python’s math primitives.