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Mojo function

block_scaled_matmul_with_epilogue

def block_scaled_matmul_with_epilogue[c_type: DType, a_type: DType, b_type: DType, scales_dtype: DType, //, *, SF_VECTOR_SIZE: Int, transpose_b: Bool = True, elementwise_lambda_fn: Optional[def[dtype: DType, width: SIMDSize, *, alignment: Int = Int(1)](IndexList[Int(2)], SIMD[dtype, width]) capturing -> None] = None](c: TileTensor[c_type, Storage=c.Storage, address_space=c.address_space, linear_idx_type=c.linear_idx_type, element_size=c.element_size], a: TileTensor[a_type, Storage=a.Storage, address_space=a.address_space, linear_idx_type=a.linear_idx_type, element_size=a.element_size], b: TileTensor[b_type, Storage=b.Storage, address_space=b.address_space, linear_idx_type=b.linear_idx_type, element_size=b.element_size], a_scales: TileTensor[scales_dtype, Storage=a_scales.Storage, address_space=a_scales.address_space, linear_idx_type=a_scales.linear_idx_type, element_size=a_scales.element_size], b_scales: TileTensor[scales_dtype, Storage=b_scales.Storage, address_space=b_scales.address_space, linear_idx_type=b_scales.linear_idx_type, element_size=b_scales.element_size], tensor_sf: Float32, ctx: DeviceContext)

Our sm100 block scaled matmul kernel still does not support fusion of elementwise operations. This is a temporary implementation that uses our sm100 block scaled matmul kernel and dispatch a separate epilogue kernel to apply the elementwise operations. Callers must allocate c; when an elementwise_lambda_fn is supplied the matmul result is written into c and then read back by the lambda.