mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-12-14 19:06:26 +08:00
79 lines
3.0 KiB
Python
79 lines
3.0 KiB
Python
from typing import TYPE_CHECKING, List
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import torch
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from ...extras.logging import get_logger
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from .quantization import QuantizationMethod
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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") -> 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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quantization_method = getattr(model, "quantization_method", None)
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if quantization_method is None:
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linear_cls = torch.nn.Linear
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elif quantization_method == QuantizationMethod.BITS_AND_BYTES:
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import bitsandbytes as bnb
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linear_cls = bnb.nn.Linear4bit if getattr(model, "is_loaded_in_4bit", False) else bnb.nn.Linear8bitLt
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else:
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raise ValueError("Finding linear modules for {} models is not supported.".format(quantization_method))
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output_layer_names = ["lm_head"]
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if model.config.model_type == "chatglm":
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output_layer_names.append("output_layer")
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elif model.config.model_type == "internlm2":
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output_layer_names.append("output")
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module_names = set()
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for name, module in model.named_modules():
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if isinstance(module, linear_cls) and not any(output_layer in name for output_layer in output_layer_names):
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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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