mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-03 20:22:49 +08:00
184 lines
7.7 KiB
Python
184 lines
7.7 KiB
Python
from typing import TYPE_CHECKING, Optional, Union
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import torch
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from transformers.utils.versions import require_version
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from ..extras.logging import get_logger
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from ..extras.packages import is_galore_available
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from ..hparams import FinetuningArguments, ModelArguments
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from ..model import load_model_and_tokenizer, load_valuehead_params
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if is_galore_available():
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from galore_torch import GaLoreAdafactor, GaLoreAdamW, GaLoreAdamW8bit
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if TYPE_CHECKING:
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from transformers import Seq2SeqTrainingArguments, Trainer
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from transformers.modeling_utils import PreTrainedModel
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from trl import AutoModelForCausalLMWithValueHead
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from ..hparams import DataArguments
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logger = get_logger(__name__)
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def create_modelcard_and_push(
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trainer: "Trainer",
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model_args: "ModelArguments",
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data_args: "DataArguments",
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training_args: "Seq2SeqTrainingArguments",
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finetuning_args: "FinetuningArguments",
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) -> None:
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kwargs = {
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"tasks": "text-generation",
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"finetuned_from": model_args.model_name_or_path,
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"dataset": [dataset.strip() for dataset in data_args.dataset.split(",")],
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"tags": ["llama-factory", finetuning_args.finetuning_type],
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}
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if not training_args.do_train:
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pass
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elif training_args.push_to_hub:
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trainer.push_to_hub(**kwargs)
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else:
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trainer.create_model_card(license="other", **kwargs) # prevent from connecting to hub
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def create_ref_model(
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model_args: "ModelArguments", finetuning_args: "FinetuningArguments", add_valuehead: Optional[bool] = False
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) -> Union["PreTrainedModel", "AutoModelForCausalLMWithValueHead"]:
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r"""
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Creates reference model for PPO/DPO training. Evaluation mode is not supported.
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The valuehead parameter is randomly initialized since it is useless for PPO training.
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"""
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if finetuning_args.ref_model is not None:
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ref_model_args_dict = model_args.to_dict()
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ref_model_args_dict.update(
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dict(
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model_name_or_path=finetuning_args.ref_model,
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adapter_name_or_path=finetuning_args.ref_model_adapters,
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quantization_bit=finetuning_args.ref_model_quantization_bit,
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)
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)
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ref_model_args = ModelArguments(**ref_model_args_dict)
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ref_finetuning_args = FinetuningArguments(finetuning_type="lora")
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ref_model, _ = load_model_and_tokenizer(
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ref_model_args, ref_finetuning_args, is_trainable=False, add_valuehead=add_valuehead
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)
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logger.info("Created reference model from {}".format(finetuning_args.ref_model))
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else:
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if finetuning_args.finetuning_type == "lora":
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ref_model = None
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else:
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ref_model, _ = load_model_and_tokenizer(
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model_args, finetuning_args, is_trainable=False, add_valuehead=add_valuehead
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)
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logger.info("Created reference model from the model itself.")
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return ref_model
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def create_reward_model(
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model: "AutoModelForCausalLMWithValueHead", model_args: "ModelArguments", finetuning_args: "FinetuningArguments"
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) -> "AutoModelForCausalLMWithValueHead":
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r"""
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Creates reward model for PPO training.
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"""
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if finetuning_args.reward_model_type == "api":
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assert finetuning_args.reward_model.startswith("http"), "Please provide full url."
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logger.info("Use reward server {}".format(finetuning_args.reward_model))
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return finetuning_args.reward_model
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elif finetuning_args.reward_model_type == "lora":
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model.pretrained_model.load_adapter(finetuning_args.reward_model, "reward")
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for name, param in model.named_parameters(): # https://github.com/huggingface/peft/issues/1090
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if "default" in name:
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param.data = param.data.to(torch.float32) # trainable params should in fp32
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vhead_params = load_valuehead_params(finetuning_args.reward_model, model_args)
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assert vhead_params is not None, "Reward model is not correctly loaded."
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model.register_buffer("reward_head_weight", vhead_params["v_head.summary.weight"], persistent=False)
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model.register_buffer("reward_head_bias", vhead_params["v_head.summary.bias"], persistent=False)
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model.register_buffer(
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"default_head_weight", torch.zeros_like(vhead_params["v_head.summary.weight"]), persistent=False
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)
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model.register_buffer(
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"default_head_bias", torch.zeros_like(vhead_params["v_head.summary.bias"]), persistent=False
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)
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logger.info("Loaded adapter weights of reward model from {}".format(finetuning_args.reward_model))
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return None
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else:
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reward_model_args_dict = model_args.to_dict()
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reward_model_args_dict.update(
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dict(
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model_name_or_path=finetuning_args.reward_model,
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adapter_name_or_path=finetuning_args.reward_model_adapters,
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quantization_bit=finetuning_args.reward_model_quantization_bit,
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)
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)
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reward_model_args = ModelArguments(**reward_model_args_dict)
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reward_finetuning_args = FinetuningArguments(finetuning_type="lora")
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reward_model, _ = load_model_and_tokenizer(
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reward_model_args, reward_finetuning_args, is_trainable=False, add_valuehead=True
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)
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logger.info("Loaded full weights of reward model from {}".format(finetuning_args.reward_model))
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logger.warning("Please ensure the ppo model and reward model share SAME tokenizer and vocabulary.")
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return reward_model
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def create_custom_optimzer(
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model: "PreTrainedModel", training_args: "Seq2SeqTrainingArguments", finetuning_args: "FinetuningArguments"
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) -> Optional["torch.optim.Optimizer"]:
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if not finetuning_args.use_galore:
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return None
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require_version("galore_torch", "To fix: pip install git+https://github.com/hiyouga/GaLore.git")
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galore_params = []
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galore_targets = finetuning_args.galore_target.split(",")
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for name, module in model.named_modules():
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if isinstance(module, torch.nn.Linear) and any(target in name for target in galore_targets):
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galore_params += list(filter(lambda p: p.requires_grad, module.parameters()))
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id_galore_params = [id(p) for p in galore_params]
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trainable_params = filter(lambda p: p.requires_grad, model.parameters())
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non_galore_params = [p for p in trainable_params if id(p) not in id_galore_params]
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# define param groups as galore_params and non_galore_params
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param_groups = [
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{"params": non_galore_params},
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{
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"params": galore_params,
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"rank": finetuning_args.galore_rank,
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"update_proj_gap": finetuning_args.galore_update_interval,
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"scale": finetuning_args.galore_scale,
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"proj_type": finetuning_args.galore_proj_type,
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},
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]
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if training_args.optim == "adamw_torch":
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optimizer = GaLoreAdamW(
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param_groups,
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lr=training_args.learning_rate,
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eps=training_args.adam_epsilon,
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betas=(training_args.adam_beta1, training_args.adam_beta2),
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)
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elif training_args.optim in ["adamw_bnb_8bit", "adamw_8bit", "paged_adamw_8bit"]:
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optimizer = GaLoreAdamW8bit(
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param_groups,
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lr=training_args.learning_rate,
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eps=training_args.adam_epsilon,
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betas=(training_args.adam_beta1, training_args.adam_beta2),
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optim_bits=8,
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is_paged="paged" in training_args.optim,
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)
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elif training_args.optim == "adafactor":
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optimizer = GaLoreAdafactor(
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param_groups,
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lr=training_args.learning_rate,
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)
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else:
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raise NotImplementedError("Unknow optim: {}".format(training_args.optim))
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logger.info("Using GaLore optimizer, may cause hanging at the start of training, wait patiently.")
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return optimizer
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