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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.
import inspect
from typing import TYPE_CHECKING
from ...extras.logging import get_logger
if TYPE_CHECKING:
from transformers import PretrainedConfig
from ...hparams import ModelArguments
logger = get_logger(__name__)
def apply_liger_kernel(
config: "PretrainedConfig",
model_args: "ModelArguments",
is_trainable: bool,
require_logits: bool,
) -> None:
if not is_trainable or not model_args.enable_liger_kernel:
return
model_type = getattr(config, "model_type", None)
if model_type == "gemma":
from liger_kernel.transformers import apply_liger_kernel_to_gemma as apply_liger_kernel
elif model_type == "gemma2":
from liger_kernel.transformers import apply_liger_kernel_to_gemma2 as apply_liger_kernel
elif model_type == "llama":
from liger_kernel.transformers import apply_liger_kernel_to_llama as apply_liger_kernel
elif model_type == "mistral":
from liger_kernel.transformers import apply_liger_kernel_to_mistral as apply_liger_kernel
elif model_type == "mixtral":
from liger_kernel.transformers import apply_liger_kernel_to_mixtral as apply_liger_kernel
elif model_type == "phi3":
from liger_kernel.transformers import apply_liger_kernel_to_phi3 as apply_liger_kernel
elif model_type == "qwen2":
from liger_kernel.transformers import apply_liger_kernel_to_qwen2 as apply_liger_kernel
elif model_type == "qwen2_vl":
from liger_kernel.transformers import apply_liger_kernel_to_qwen2_vl as apply_liger_kernel
else:
logger.warning("Current model does not support liger kernel.")
return
if require_logits and "fused_linear_cross_entropy" in inspect.signature(apply_liger_kernel).parameters:
logger.info("Current training stage does not support chunked cross entropy.")
kwargs = {"fused_linear_cross_entropy": False}
else:
kwargs = {}
apply_liger_kernel(**kwargs)
logger.info("Liger kernel has been applied to the model.")
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