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| # Copyright 2025 the LlamaFactory team. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| from typing import TYPE_CHECKING | |
| from transformers.utils import is_flash_attn_2_available, is_torch_sdpa_available | |
| from ...extras import logging | |
| from ...extras.constants import AttentionFunction | |
| if TYPE_CHECKING: | |
| from transformers import PretrainedConfig | |
| from ...hparams import ModelArguments | |
| logger = logging.get_logger(__name__) | |
| def configure_attn_implementation( | |
| config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool | |
| ) -> None: | |
| if getattr(config, "model_type", None) == "gemma2" and is_trainable: | |
| if model_args.flash_attn == AttentionFunction.AUTO or model_args.flash_attn == AttentionFunction.FA2: | |
| if is_flash_attn_2_available(): | |
| if model_args.flash_attn != AttentionFunction.FA2: | |
| logger.warning_rank0("Gemma 2 should use flash attention 2, change `flash_attn` to fa2.") | |
| model_args.flash_attn = AttentionFunction.FA2 | |
| else: | |
| logger.warning_rank0("FlashAttention-2 is not installed, use eager attention.") | |
| model_args.flash_attn = AttentionFunction.DISABLED | |
| elif model_args.flash_attn == AttentionFunction.SDPA: | |
| logger.warning_rank0( | |
| "Gemma-2 should use soft-capping attention, while the SDPA attention does not support it." | |
| ) | |
| if model_args.flash_attn == AttentionFunction.AUTO: | |
| return | |
| elif model_args.flash_attn == AttentionFunction.DISABLED: | |
| requested_attn_implementation = "eager" | |
| elif model_args.flash_attn == AttentionFunction.SDPA: | |
| if not is_torch_sdpa_available(): | |
| logger.warning_rank0("torch>=2.1.1 is required for SDPA attention.") | |
| return | |
| requested_attn_implementation = "sdpa" | |
| elif model_args.flash_attn == AttentionFunction.FA2: | |
| if not is_flash_attn_2_available(): | |
| logger.warning_rank0("FlashAttention-2 is not installed.") | |
| return | |
| requested_attn_implementation = "flash_attention_2" | |
| else: | |
| raise NotImplementedError(f"Unknown attention type: {model_args.flash_attn}") | |
| if getattr(config, "model_type", None) == "internlm2": # special case for custom models | |
| setattr(config, "attn_implementation", requested_attn_implementation) | |
| elif getattr(config, "model_type", None) == "kimi_vl": | |
| setattr(config.vision_config, "_attn_implementation", requested_attn_implementation) | |
| setattr(config.text_config, "_attn_implementation", requested_attn_implementation) | |
| else: | |
| setattr(config, "_attn_implementation", requested_attn_implementation) | |
| def print_attn_implementation(config: "PretrainedConfig") -> None: | |
| if getattr(config, "model_type", None) == "internlm2": # special case for custom models | |
| attn_implementation = getattr(config, "attn_implementation", None) | |
| else: | |
| attn_implementation = getattr(config, "_attn_implementation", None) | |
| if attn_implementation == "flash_attention_2": | |
| logger.info_rank0("Using FlashAttention-2 for faster training and inference.") | |
| elif attn_implementation == "sdpa": | |
| logger.info_rank0("Using torch SDPA for faster training and inference.") | |
| else: | |
| logger.info_rank0("Using vanilla attention implementation.") | |