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https://github.com/hiyouga/LlamaFactory.git
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5e5fc337f9
* add liger kernel to qwen2_5 vl * fix patch * fix patch Former-commit-id: 828776d155986166498dfc907194f64436571106
105 lines
4.0 KiB
Python
105 lines
4.0 KiB
Python
# Copyright 2025 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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import inspect
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from typing import TYPE_CHECKING
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from ...extras import logging
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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 apply_liger_kernel_to_qwen2_5_vl(
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rope: bool = True,
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cross_entropy: bool = False,
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fused_linear_cross_entropy: bool = True,
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rms_norm: bool = True,
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swiglu: bool = True,
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) -> None:
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from liger_kernel.transformers import LigerCrossEntropyLoss, LigerRMSNorm, LigerSwiGLUMLP
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from liger_kernel.transformers.model.qwen2_vl import lce_forward as qwen2_vl_lce_forward
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from liger_kernel.transformers.qwen2vl_mrope import liger_multimodal_rotary_pos_emb
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from transformers.models.qwen2_5_vl import modeling_qwen2_5_vl
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def get_dtype(self: "modeling_qwen2_5_vl.Qwen2_5_VisionTransformerPretrainedModel"):
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return self.dtype
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modeling_qwen2_5_vl.Qwen2_5_VisionTransformerPretrainedModel.get_dtype = get_dtype
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if rope:
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modeling_qwen2_5_vl.apply_multimodal_rotary_pos_emb = liger_multimodal_rotary_pos_emb
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if rms_norm:
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modeling_qwen2_5_vl.Qwen2RMSNorm = LigerRMSNorm
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if cross_entropy:
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modeling_qwen2_5_vl.CrossEntropyLoss = LigerCrossEntropyLoss
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if fused_linear_cross_entropy:
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modeling_qwen2_5_vl.Qwen2_5_VLForConditionalGeneration.forward = qwen2_vl_lce_forward
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if swiglu:
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modeling_qwen2_5_vl.Qwen2MLP = LigerSwiGLUMLP
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def apply_liger_kernel(
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config: "PretrainedConfig",
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model_args: "ModelArguments",
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is_trainable: bool,
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require_logits: bool,
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) -> 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 == "mllama":
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from liger_kernel.transformers import apply_liger_kernel_to_mllama 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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elif model_type == "qwen2_5_vl":
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apply_liger_kernel = apply_liger_kernel_to_qwen2_5_vl
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else:
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logger.warning_rank0("Current model does not support liger kernel.")
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return
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if require_logits and "fused_linear_cross_entropy" in inspect.signature(apply_liger_kernel).parameters:
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logger.info_rank0("Current training stage does not support chunked cross entropy.")
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kwargs = {"fused_linear_cross_entropy": False, "cross_entropy": True}
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else:
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kwargs = {}
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apply_liger_kernel(**kwargs)
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logger.info_rank0("Liger kernel has been applied to the model.")
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