''' Date: 2024-11-08 02:46:47 LastEditors: djw LastEditTime: 2024-11-08 02:46:55 ''' """This file is used for /tests and /benchmarks""" from typing import Dict, List import numpy import torch # Precompute permutations for Marlin weight and scale shuffling # noqa: E501 # # Marlin works on [16,64] tiles. The goal of the permutations is to reorder the weight data so that it is compatible noqa: # noqa: E501 # with the tensor-core format that is described here: # https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#matrix-fragments-for-mma-m16n8k16-with-floating-point-type # noqa: E501 # # As a result of this reordering, the vector loads inside the kernel will get the data as it is needed for tensor-core # noqa: E501 # (without the need to use ldmatrix instructions) # noqa: E501 def get_perms(num_bits: int): perm_list: List[int] = [] for i in range(32): perm1: List[int] = [] col = i // 4 for block in [0, 1]: for row in [ 2 * (i % 4), 2 * (i % 4) + 1, 2 * (i % 4 + 4), 2 * (i % 4 + 4) + 1, ]: perm1.append(16 * row + col + 8 * block) for j in range(4): perm_list.extend([p + 256 * j for p in perm1]) perm = numpy.array(perm_list) if num_bits == 4: interleave = numpy.array([0, 2, 4, 6, 1, 3, 5, 7]) elif num_bits == 8: interleave = numpy.array([0, 2, 1, 3]) else: raise Exception("num_bits must be 4 or 8, got {}".format(num_bits)) perm = perm.reshape((-1, len(interleave)))[:, interleave].ravel() perm = torch.from_numpy(perm) scale_perm: List[int] = [] for i in range(8): scale_perm.extend([i + 8 * j for j in range(8)]) scale_perm_single: List[int] = [] for i in range(4): scale_perm_single.extend( [2 * i + j for j in [0, 1, 8, 9, 16, 17, 24, 25]]) return perm, scale_perm, scale_perm_single marlin_perm: Dict[int, torch.Tensor] = {} marlin_scale_perm: Dict[int, List[int]] = {} marlin_scale_perm_single: Dict[int, List[int]] = {} for num_bits in [4, 8]: perm, scale_perm, scale_perm_single = get_perms(num_bits) marlin_perm[num_bits] = perm marlin_scale_perm[num_bits] = scale_perm marlin_scale_perm_single[num_bits] = scale_perm_single