For the complete documentation index, see llms.txt. Markdown versions of all pages are available by appending .md to any URL (e.g. /max/get-started.md).

Mojo function

dispatch_sm100_batched_matmul

def dispatch_sm100_batched_matmul[c_type: DType, a_type: DType, b_type: DType, transpose_b: Bool, pdl_level: PDLLevel = PDLLevel.OFF](c: TileTensor[c_type, Storage=c.Storage, address_space=c.address_space, linear_idx_type=c.linear_idx_type], a: TileTensor[a_type, Storage=a.Storage, address_space=a.address_space, linear_idx_type=a.linear_idx_type], b: TileTensor[b_type, Storage=b.Storage, address_space=b.address_space, linear_idx_type=b.linear_idx_type], ctx: DeviceContext)

Dispatch batched matmul to SM100 kernel.

First, try to dispatch to a batched matmul config from the tuning table. Then try to find a optimized config for the given shape. If not found, then dispatch to a default config.

Parameters:

c_type (DType): Element type of the output tensor c.
a_type (DType): Element type of the LHS operand a.
b_type (DType): Element type of the RHS operand b.
transpose_b (Bool): Whether b is stored transposed.
pdl_level (PDLLevel): Programmatic dependent launch level for the dispatched kernel (defaults to PDLLevel.OFF).