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Mojo function

gemv_gpu_dispatch

def gemv_gpu_dispatch[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, tile_m: Int = Int(1)](kernel_func: GEMVAlgorithm, c: TileTensor[Storage=c.Storage, address_space=c.address_space, linear_idx_type=c.linear_idx_type], a: TileTensor[Storage=a.Storage, address_space=a.address_space, linear_idx_type=a.linear_idx_type], b: TileTensor[Storage=b.Storage, address_space=b.address_space, linear_idx_type=b.linear_idx_type], ctx: DeviceContext)

Launches the GPU GEMV kernel indicated by kernel_func with appropriate grid and block dims.

Translates a GEMVAlgorithm variant into a concrete kernel call with shape-derived launch parameters, handling input/output layout transformation where needed.

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.
tile_m (Int): Number of output rows processed per CTA (used by GEMV_SPLIT_K).

Args:

kernel_func (GEMVAlgorithm): The GEMV algorithm variant to launch.
c (TileTensor[Storage=c.Storage, address_space=c.address_space, linear_idx_type=c.linear_idx_type]): Rank-2 output TileTensor.
a (TileTensor[Storage=a.Storage, address_space=a.address_space, linear_idx_type=a.linear_idx_type]): Rank-2 input matrix TileTensor.
b (TileTensor[Storage=b.Storage, 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.