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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.")
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