Spaces:
Running
Running
File size: 3,966 Bytes
e81015c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 |
# 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.
import inspect
from typing import TYPE_CHECKING
from ...extras import logging
if TYPE_CHECKING:
from transformers import PretrainedConfig
from ...hparams import ModelArguments
logger = logging.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 == "gemma3":
from liger_kernel.transformers import apply_liger_kernel_to_gemma3 as apply_liger_kernel
elif model_type == "gemma3_text":
from liger_kernel.transformers import apply_liger_kernel_to_gemma3_text as apply_liger_kernel
elif model_type == "glm4":
from liger_kernel.transformers import apply_liger_kernel_to_glm4 as apply_liger_kernel
elif model_type == "granite":
from liger_kernel.transformers import apply_liger_kernel_to_granite 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 == "llava":
from liger_kernel.transformers import apply_liger_kernel_to_llava 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 == "mllama":
from liger_kernel.transformers import apply_liger_kernel_to_mllama as apply_liger_kernel
elif model_type == "olmo2":
from liger_kernel.transformers import apply_liger_kernel_to_olmo2 as apply_liger_kernel
elif model_type == "paligemma":
from liger_kernel.transformers import apply_liger_kernel_to_paligemma 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
elif model_type == "qwen2_5_vl":
from liger_kernel.transformers import apply_liger_kernel_to_qwen2_5_vl as apply_liger_kernel
elif model_type == "qwen3":
from liger_kernel.transformers import apply_liger_kernel_to_qwen3 as apply_liger_kernel
else:
logger.warning_rank0("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_rank0("Current training stage does not support chunked cross entropy.")
kwargs = {"fused_linear_cross_entropy": False, "cross_entropy": True}
else:
kwargs = {}
apply_liger_kernel(**kwargs)
logger.info_rank0("Liger kernel has been applied to the model.")
|