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()`.