MatmulOptions#

class nvmath.linalg.MatmulOptions( *, allocator: ~nvmath.memory.BaseCUDAMemoryManager | ~nvmath.memory.BaseCUDAMemoryManagerAsync | None = None, blocking: ~typing.Literal[True, 'auto'] = 'auto', logger: ~logging.Logger = <factory>, inplace: bool = False, )[source]#

A dataclass for providing options to a Matmul object.

allocator#

An object that supports the BaseCUDAMemoryManager protocol, used to draw device memory. If an allocator is not provided, a memory allocator from the library package will be used (torch.cuda.caching_allocator_alloc() for PyTorch operands, cupy.cuda.alloc() otherwise).

Type:: nvmath.memory.BaseCUDAMemoryManager | nvmath.memory.BaseCUDAMemoryManagerAsync | None

blocking#

A flag specifying the behavior of the stream-ordered functions and methods. When blocking is True, the stream-ordered methods do not return until the operation is complete. When blocking is "auto", the methods return immediately when the inputs are on the GPU. The stream-ordered methods always block when the operands are on the CPU to ensure that the user doesn’t inadvertently use the result before it becomes available. The default is "auto".

Type:: Literal[True, ‘auto’]

inplace#

Whether the matrix multiplication is performed in-place (operand C is overwritten).

Type:: bool

logger#

Python Logger object. The root logger will be used if a logger object is not provided.

Type:: logging.Logger