mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-10-15 16:18:10 +08:00
56 lines
2.2 KiB
Python
56 lines
2.2 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 ...extras.logging import get_logger
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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 = get_logger(__name__)
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def configure_liger_kernel(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
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if not is_trainable or not model_args.enable_liger_kernel:
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return
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model_type = getattr(config, "model_type", None)
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if model_type == "gemma":
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from liger_kernel.transformers import apply_liger_kernel_to_gemma as apply_liger_kernel
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elif model_type == "gemma2":
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from liger_kernel.transformers import apply_liger_kernel_to_gemma2 as apply_liger_kernel
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elif model_type == "llama":
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from liger_kernel.transformers import apply_liger_kernel_to_llama as apply_liger_kernel
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elif model_type == "mistral":
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from liger_kernel.transformers import apply_liger_kernel_to_mistral as apply_liger_kernel
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elif model_type == "mixtral":
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from liger_kernel.transformers import apply_liger_kernel_to_mixtral as apply_liger_kernel
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elif model_type == "phi3":
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from liger_kernel.transformers import apply_liger_kernel_to_phi3 as apply_liger_kernel
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elif model_type == "qwen2":
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from liger_kernel.transformers import apply_liger_kernel_to_qwen2 as apply_liger_kernel
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elif model_type == "qwen2_vl":
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from liger_kernel.transformers import apply_liger_kernel_to_qwen2_vl as apply_liger_kernel
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else:
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logger.warning("Current model does not support liger kernel.")
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return
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apply_liger_kernel()
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logger.info("Liger kernel has been applied to the model.")
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