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| # Copyright 2024 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 transformers.utils.versions import require_version | |
| from ...extras.logging import get_logger | |
| if TYPE_CHECKING: | |
| from transformers import PretrainedConfig | |
| from ...hparams import ModelArguments | |
| logger = 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 == "auto" or model_args.flash_attn == "fa2": | |
| if is_flash_attn_2_available(): | |
| require_version("transformers>=4.42.4", "To fix: pip install transformers>=4.42.4") | |
| require_version("flash_attn>=2.6.0", "To fix: pip install flash_attn>=2.6.0") | |
| logger.warning("Gemma-2 should use flash attention 2, change `flash_attn` to fa2.") | |
| model_args.flash_attn = "fa2" | |
| else: | |
| logger.warning("Gemma-2 should use eager attention, change `flash_attn` to disabled.") | |
| model_args.flash_attn = "disabled" | |
| elif model_args.flash_attn == "sdpa": | |
| logger.warning("Gemma-2 should use soft-capping attention, while the SDPA attention does not support it.") | |
| if model_args.flash_attn == "auto": | |
| return | |
| elif model_args.flash_attn == "disabled": | |
| requested_attn_implementation = "eager" | |
| elif model_args.flash_attn == "sdpa": | |
| if not is_torch_sdpa_available(): | |
| logger.warning("torch>=2.1.1 is required for SDPA attention.") | |
| return | |
| requested_attn_implementation = "sdpa" | |
| elif model_args.flash_attn == "fa2": | |
| if not is_flash_attn_2_available(): | |
| logger.warning("FlashAttention-2 is not installed.") | |
| return | |
| requested_attn_implementation = "flash_attention_2" | |
| else: | |
| raise NotImplementedError("Unknown attention type: {}".format(model_args.flash_attn)) | |
| if getattr(config, "model_type", None) == "internlm2": # special case for custom models | |
| setattr(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("Using FlashAttention-2 for faster training and inference.") | |
| elif attn_implementation == "sdpa": | |
| logger.info("Using torch SDPA for faster training and inference.") | |
| else: | |
| logger.info("Using vanilla attention implementation.") | |