# Copyright (c) ModelScope Contributors. All rights reserved. from __future__ import annotations from typing import Any from swift.utils.logger import get_logger logger = get_logger() _ORIGINAL_MINDSPEED_TE_CP_CLASS = None def patch_mindspeed_te_cp_implementation(megatron_args: dict[str, Any]) -> None: """ Route NPU CP to the legacy MindSpeed TE adaptor when the new strategy factory only supports kvallgather. """ # MindSpeed 0.15.3 replaced the TE context-parallel attention class with a # new implementation. That new class does not yet cover all CP algorithms, # so the default non-kvallgather path can fail during Megatron training. # For those algorithms, temporarily route TE attention back to the legacy # MindSpeedCPDotProductAttention adaptor. Once MindSpeed's new CP class has # feature parity, this compatibility patch can be removed. try: import mindspeed.te.pytorch.attention.dot_product_attention.dot_product_attention as ms_te_dpa from mindspeed.core.context_parallel.adaptor import MindSpeedCPDotProductAttention except ImportError as e: logger.warning(f'Failed to import MindSpeed CP modules before repatch: {e}') return global _ORIGINAL_MINDSPEED_TE_CP_CLASS if _ORIGINAL_MINDSPEED_TE_CP_CLASS is None: _ORIGINAL_MINDSPEED_TE_CP_CLASS = getattr(ms_te_dpa, 'MindSpeedTEDotProductAttention', None) if _ORIGINAL_MINDSPEED_TE_CP_CLASS is None: logger.warning('MindSpeedTEDotProductAttention is unavailable before repatch; skip CP workaround.') return cp_algo = megatron_args.get('context_parallel_algo', 'megatron_cp_algo') use_legacy_cp_te = int(megatron_args.get('context_parallel_size', 1)) > 1 and cp_algo != 'kvallgather_cp_algo' target_cls = MindSpeedCPDotProductAttention if use_legacy_cp_te else _ORIGINAL_MINDSPEED_TE_CP_CLASS if getattr(ms_te_dpa, 'MindSpeedTEDotProductAttention', None) is target_cls: return ms_te_dpa.MindSpeedTEDotProductAttention = target_cls logger.info( 'Patched MindSpeedTEDotProductAttention to %s for context_parallel_size=%s, context_parallel_algo=%s.', target_cls.__name__, megatron_args.get('context_parallel_size', 1), cp_algo, )