mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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90 lines
3.5 KiB
Python
90 lines
3.5 KiB
Python
# Copyright 2024 the LlamaFactory team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import TYPE_CHECKING
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from transformers.utils import is_flash_attn_2_available, is_torch_sdpa_available
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from ...extras import logging
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from ...extras.misc import check_version
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if TYPE_CHECKING:
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from transformers import PretrainedConfig
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from ...hparams import ModelArguments
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logger = logging.get_logger(__name__)
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def configure_attn_implementation(
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config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool
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) -> None:
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if getattr(config, "model_type", None) == "gemma2" and is_trainable:
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if model_args.flash_attn == "auto" or model_args.flash_attn == "fa2":
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if is_flash_attn_2_available():
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check_version("transformers>=4.42.4")
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check_version("flash_attn>=2.6.3")
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if model_args.flash_attn != "fa2":
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logger.warning_rank0("Gemma-2 should use flash attention 2, change `flash_attn` to fa2.")
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model_args.flash_attn = "fa2"
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else:
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logger.warning_rank0("FlashAttention-2 is not installed, use eager attention.")
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model_args.flash_attn = "disabled"
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elif model_args.flash_attn == "sdpa":
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logger.warning_rank0(
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"Gemma-2 should use soft-capping attention, while the SDPA attention does not support it."
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)
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if model_args.flash_attn == "auto":
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return
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elif model_args.flash_attn == "disabled":
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requested_attn_implementation = "eager"
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elif model_args.flash_attn == "sdpa":
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if not is_torch_sdpa_available():
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logger.warning_rank0("torch>=2.1.1 is required for SDPA attention.")
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return
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requested_attn_implementation = "sdpa"
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elif model_args.flash_attn == "fa2":
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if not is_flash_attn_2_available():
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logger.warning_rank0("FlashAttention-2 is not installed.")
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return
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requested_attn_implementation = "flash_attention_2"
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else:
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raise NotImplementedError(f"Unknown attention type: {model_args.flash_attn}")
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if getattr(config, "model_type", None) == "internlm2": # special case for custom models
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setattr(config, "attn_implementation", requested_attn_implementation)
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else:
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setattr(config, "_attn_implementation", requested_attn_implementation)
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def print_attn_implementation(config: "PretrainedConfig") -> None:
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if getattr(config, "model_type", None) == "internlm2": # special case for custom models
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attn_implementation = getattr(config, "attn_implementation", None)
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else:
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attn_implementation = getattr(config, "_attn_implementation", None)
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if attn_implementation == "flash_attention_2":
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logger.info_rank0("Using FlashAttention-2 for faster training and inference.")
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elif attn_implementation == "sdpa":
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logger.info_rank0("Using torch SDPA for faster training and inference.")
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else:
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logger.info_rank0("Using vanilla attention implementation.")
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