Linear Algebra

Overview

The Linear Algebra module nvmath.linalg in nvmath-python leverages various NVIDIA math libraries to support multiple linear algebra computations. As of the initial Beta release, we offer the specialized matrix multiplication API based on the cuBLASLt library.

API Reference

Generic Linear Algebra APIs (nvmath.linalg)

Generic APIs will be available in a later release.

Specialized Linear Algebra APIs (nvmath.linalg.advanced)

matmul(a, b, /[, c, alpha, beta, epilog, ...])	Perform the specified matrix multiplication computation \(F(\alpha a @ b + \beta c)\), where \(F\) is the epilog.
matrix_qualifiers_dtype	NumPy dtype object that encapsulates the matrix qualifiers in linalg.advanced.
Algorithm(algorithm)	An interface class to query algorithm capabilities and configure the algorithm.
Matmul(a, b, /[, c, alpha, beta, ...])	Create a stateful object encapsulating the specified matrix multiplication computation \(\alpha a @ b + \beta c\) and the required resources to perform the operation.
MatmulComputeType	alias of ComputeType
MatmulEpilog	alias of Epilogue
MatmulInnerShape(value[, names, module, ...])	See cublasLtMatmulInnerShape_t.
MatmulNumericalImplFlags(value[, names, ...])	These flags can be combined with the | operator: OP_TYPE_FMA | OP_TYPE_TENSOR_HMMA ...
MatmulReductionScheme	alias of ReductionScheme
MatmulOptions([compute_type, scale_type, ...])	A data class for providing options to the Matmul object and the wrapper function matmul().
MatmulPlanPreferences([...])	A data class for providing options to the Matmul.plan() method and the wrapper function matmul().