mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-02 11:42:49 +08:00
91 lines
3.4 KiB
Python
91 lines
3.4 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, List
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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, PreTrainedModel, PreTrainedTokenizer
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logger = get_logger(__name__)
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def find_all_linear_modules(model: "PreTrainedModel", freeze_vision_tower: bool) -> List[str]:
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r"""
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Finds all available modules to apply lora or galore.
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"""
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forbidden_modules = {"lm_head"}
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if model.config.model_type == "chatglm":
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forbidden_modules.add("output_layer")
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elif model.config.model_type == "internlm2":
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forbidden_modules.add("output")
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elif model.config.model_type in ["llava", "paligemma"]:
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forbidden_modules.add("multi_modal_projector")
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elif model.config.model_type in ["qwen2_vl"]:
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forbidden_modules.add("merger")
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if freeze_vision_tower:
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forbidden_modules.add("vision_tower")
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module_names = set()
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for name, module in model.named_modules():
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if any(forbidden_module in name for forbidden_module in forbidden_modules):
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continue
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if "Linear" in module.__class__.__name__ and "Embedding" not in module.__class__.__name__:
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module_names.add(name.split(".")[-1])
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logger.info("Found linear modules: {}".format(",".join(module_names)))
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return list(module_names)
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def find_expanded_modules(model: "PreTrainedModel", target_modules: List[str], num_layer_trainable: int) -> List[str]:
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r"""
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Finds the modules in the expanded blocks to apply lora.
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"""
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num_layers = getattr(model.config, "num_hidden_layers", None)
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if not num_layers:
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raise ValueError("Model was not supported.")
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if num_layers % num_layer_trainable != 0:
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raise ValueError(
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"`num_layers` {} should be divisible by `num_layer_trainable` {}.".format(num_layers, num_layer_trainable)
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)
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stride = num_layers // num_layer_trainable
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trainable_layer_ids = range(stride - 1, num_layers + stride - 1, stride)
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trainable_layers = [".{:d}.".format(idx) for idx in trainable_layer_ids]
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module_names = []
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for name, _ in model.named_modules():
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if any(target_module in name for target_module in target_modules) and any(
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trainable_layer in name for trainable_layer in trainable_layers
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):
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module_names.append(name)
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logger.info("Apply lora to layers: {}".format(",".join(map(str, trainable_layer_ids))))
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return module_names
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def register_autoclass(config: "PretrainedConfig", model: "PreTrainedModel", tokenizer: "PreTrainedTokenizer"):
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if "AutoConfig" in getattr(config, "auto_map", {}):
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config.__class__.register_for_auto_class()
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if "AutoModelForCausalLM" in getattr(config, "auto_map", {}):
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model.__class__.register_for_auto_class()
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if "AutoTokenizer" in tokenizer.init_kwargs.get("auto_map", {}):
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tokenizer.__class__.register_for_auto_class()
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