For the complete documentation index, see llms.txt. Markdown versions of all pages are available by appending .md to any URL (e.g. /get-started.md).
Mojo function
grouped_matmul_block_scaled_amd
def grouped_matmul_block_scaled_amd[out_dtype: DType](c: TileTensor[out_dtype, Storage=c.Storage, address_space=c.address_space, linear_idx_type=c.linear_idx_type], a: TileTensor[DType.uint8, Storage=a.Storage, address_space=a.address_space, linear_idx_type=a.linear_idx_type], b: TileTensor[DType.uint8, Storage=b.Storage, address_space=b.address_space, linear_idx_type=b.linear_idx_type], a_scales: TileTensor[DType.float8_e8m0fnu, Storage=a_scales.Storage, address_space=a_scales.address_space, linear_idx_type=a_scales.linear_idx_type], b_scales: TileTensor[DType.float8_e8m0fnu, Storage=b_scales.Storage, address_space=b_scales.address_space, linear_idx_type=b_scales.linear_idx_type], row_offsets: TileTensor[DType.uint32, Storage=row_offsets.Storage, address_space=row_offsets.address_space, linear_idx_type=row_offsets.linear_idx_type], expert_ids: TileTensor[DType.int32, Storage=expert_ids.Storage, address_space=expert_ids.address_space, linear_idx_type=expert_ids.linear_idx_type], num_active_experts: Int, ctx: DeviceContext)
Launches the grouped per-expert MXFP4 block-scaled matmul kernel on AMD CDNA4.
Enqueues grouped_matmul_block_scaled_amd_kernel over a 2D grid
sized by the output M and N dimensions, emitting a trace event for
profiling.
Parameters:
- out_dtype (
DType): Element type of the output matrix.