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Mojo function

gemv_gpu

def gemv_gpu[transpose_b: Bool = False, elementwise_lambda_fn: Optional[def[dtype: DType, width: SIMDLength, *, alignment: Int = Int(1)](IndexList[Int(2)], SIMD[dtype, width]) capturing thin -> None] = None, pdl_level: PDLLevel = PDLLevel.ON](c: TileTensor[Engine=c.Engine, address_space=c.address_space, linear_idx_type=c.linear_idx_type], a: TileTensor[Engine=a.Engine, address_space=a.address_space, linear_idx_type=a.linear_idx_type], b: TileTensor[Engine=b.Engine, address_space=b.address_space, linear_idx_type=b.linear_idx_type], ctx: DeviceContext)

Selects and dispatches the appropriate GPU GEMV kernel based on shape and hardware.

Examines runtime dimensions M, N, K and static shape information to choose among GEMV_KERNEL, GEMV_KERNEL_VECTOR, GEMV_SPLIT_K, GEVM_KERNEL, and MATMUL_NAIVE variants.

Parameters:

• ​transpose_b (Bool): When True, B is treated as transposed (N, K) row-major.
• ​elementwise_lambda_fn (Optional[def[dtype: DType, width: SIMDLength, *, alignment: Int = Int(1)](IndexList[Int(2)], SIMD[dtype, width]) capturing thin -> None]): Optional epilogue applied element-wise to each output.
• ​pdl_level (PDLLevel): Programmatic dependent launch level for PDL barriers.

Args:

• ​c (TileTensor[Engine=c.Engine, address_space=c.address_space, linear_idx_type=c.linear_idx_type]): Rank-2 output TileTensor.
• ​a (TileTensor[Engine=a.Engine, address_space=a.address_space, linear_idx_type=a.linear_idx_type]): Rank-2 input matrix TileTensor.
• ​b (TileTensor[Engine=b.Engine, address_space=b.address_space, linear_idx_type=b.linear_idx_type]): Rank-2 input vector or matrix TileTensor.
• ​ctx (DeviceContext): Device context for kernel launch.

def gemv_gpu[EpilogueFnType: ElementwiseEpilogueFn, //, transpose_b: Bool = False, pdl_level: PDLLevel = PDLLevel.ON](c: TileTensor[Engine=c.Engine, address_space=c.address_space, linear_idx_type=c.linear_idx_type], a: TileTensor[Engine=a.Engine, address_space=a.address_space, linear_idx_type=a.linear_idx_type], b: TileTensor[Engine=b.Engine, address_space=b.address_space, linear_idx_type=b.linear_idx_type], epilogue_fn: EpilogueFnType, ctx: DeviceContext)

Selects and dispatches a GPU GEMV kernel that stores through epilogue_fn.

Parameters:

• ​EpilogueFnType (ElementwiseEpilogueFn): Type of epilogue_fn (inferred).
• ​transpose_b (Bool): When True, B is treated as transposed (N, K) row-major.
• ​pdl_level (PDLLevel): Programmatic dependent launch level for PDL barriers.

Args:

• ​c (TileTensor[Engine=c.Engine, address_space=c.address_space, linear_idx_type=c.linear_idx_type]): Rank-2 output TileTensor. Only its shape is read; epilogue_fn stores the result.
• ​a (TileTensor[Engine=a.Engine, address_space=a.address_space, linear_idx_type=a.linear_idx_type]): Rank-2 input matrix TileTensor.
• ​b (TileTensor[Engine=b.Engine, address_space=b.address_space, linear_idx_type=b.linear_idx_type]): Rank-2 input vector or matrix TileTensor.
• ​epilogue_fn (EpilogueFnType): Stores each output element.
• ​ctx (DeviceContext): Device context for kernel launch.