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@ -486,7 +486,9 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
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#### Use Huggingface Accelerate
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```bash
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accelerate launch --config_file config.yaml src/train_bash.py # arguments (same as above)
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accelerate launch --config_file config.yaml src/train_bash.py \
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--ddp_timeout 180000000 \
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... # arguments (same as above)
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```
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<details><summary>Example config.yaml for LoRA training</summary>
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@ -519,8 +521,8 @@ use_cpu: false
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```bash
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deepspeed --num_gpus 8 src/train_bash.py \
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--deepspeed ds_config.json \
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--ddp_timeout 180000000 \
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--deepspeed ds_config.json \
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--ddp_timeout 180000000 \
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... # arguments (same as above)
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```
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@ -485,7 +485,9 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
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#### 使用 Huggingface Accelerate
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```bash
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accelerate launch --config_file config.yaml src/train_bash.py # 参数同上
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accelerate launch --config_file config.yaml src/train_bash.py \
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--ddp_timeout 180000000 \
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... # 参数同上
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```
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<details><summary>使用 Accelerate 进行 LoRA 训练的 config.yaml 示例</summary>
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@ -519,9 +521,8 @@ use_cpu: false
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```bash
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deepspeed --num_gpus 8 src/train_bash.py \
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--deepspeed ds_config.json \
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--ddp_timeout 180000000 \
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--ddp_timeout 180000000 \
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... # 参数同上
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```
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<details><summary>使用 DeepSpeed ZeRO-2 进行全参数训练的 ds_config.json 示例</summary>
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@ -8,11 +8,14 @@ from trl import DPOTrainer
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from trl.trainer.utils import disable_dropout_in_model
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from ...extras.constants import IGNORE_INDEX
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from ..utils import create_custom_optimzer
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if TYPE_CHECKING:
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from transformers import PreTrainedModel
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from ...hparams import FinetuningArguments
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class CustomDPOTrainer(DPOTrainer):
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def __init__(
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@ -21,6 +24,7 @@ class CustomDPOTrainer(DPOTrainer):
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loss_type: Literal["sigmoid", "hinge", "ipo", "kto_pair"],
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ftx_gamma: float,
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model: Union["PreTrainedModel", torch.nn.Module],
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finetuning_args: "FinetuningArguments",
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ref_model: Optional[Union["PreTrainedModel", torch.nn.Module]] = None,
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disable_dropout: bool = True,
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**kwargs,
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@ -30,6 +34,7 @@ class CustomDPOTrainer(DPOTrainer):
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if ref_model is not None:
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disable_dropout_in_model(ref_model)
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self.finetuning_args = finetuning_args
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self.reference_free = False
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self.use_dpo_data_collator = True # hack to avoid warning
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self.generate_during_eval = False # disable at evaluation
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@ -61,6 +66,13 @@ class CustomDPOTrainer(DPOTrainer):
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else:
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self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)
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def create_optimizer_and_scheduler(self, num_training_steps: int) -> None:
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self.optimizer = create_custom_optimzer(self.model, self.args, self.finetuning_args, num_training_steps)
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if self.optimizer is None:
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self.create_optimizer()
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self.create_scheduler(num_training_steps=num_training_steps, optimizer=self.optimizer)
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def sft_loss(self, chosen_logits: torch.FloatTensor, chosen_labels: torch.LongTensor) -> torch.Tensor:
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r"""
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Computes supervised cross-entropy loss of given labels under the given logits.
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@ -7,7 +7,7 @@ from ...extras.constants import IGNORE_INDEX
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from ...extras.ploting import plot_loss
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from ...hparams import ModelArguments
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from ...model import load_model, load_tokenizer
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from ..utils import create_custom_optimzer, create_modelcard_and_push, create_ref_model
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from ..utils import create_modelcard_and_push, create_ref_model
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from .collator import DPODataCollatorWithPadding
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from .trainer import CustomDPOTrainer
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@ -44,18 +44,17 @@ def run_dpo(
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training_args.remove_unused_columns = False # important for pairwise dataset
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# Initialize our Trainer
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optimizer = create_custom_optimzer(model, dataset, training_args, finetuning_args)
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trainer = CustomDPOTrainer(
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beta=finetuning_args.dpo_beta,
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loss_type=finetuning_args.dpo_loss,
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ftx_gamma=finetuning_args.dpo_ftx,
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finetuning_args=finetuning_args,
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model=model,
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ref_model=ref_model,
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args=training_args,
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tokenizer=tokenizer,
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data_collator=data_collator,
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callbacks=callbacks,
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optimizers=(optimizer, None),
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**split_dataset(dataset, data_args, training_args),
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)
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@ -64,16 +64,16 @@ def run_ppo(
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)
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# Create optimizer and scheduler
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optimizer = create_custom_optimzer(model, dataset, training_args, finetuning_args)
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if optimizer is None:
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optimizer = AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=training_args.learning_rate)
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if training_args.max_steps > 0:
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num_training_steps = training_args.max_steps
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else:
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total_train_batch_size = backward_batch_size * finetuning_args.ppo_buffer_size * training_args.world_size
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num_training_steps = training_args.num_train_epochs * math.ceil(len(dataset) / total_train_batch_size)
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optimizer = create_custom_optimzer(model, training_args, finetuning_args, num_training_steps)
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if optimizer is None:
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optimizer = AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=training_args.learning_rate)
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lr_scheduler = get_scheduler(
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training_args.lr_scheduler_type,
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optimizer=optimizer,
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30
src/llmtuner/train/pt/trainer.py
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30
src/llmtuner/train/pt/trainer.py
Normal file
@ -0,0 +1,30 @@
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from typing import TYPE_CHECKING
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from transformers import Trainer
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from ...extras.logging import get_logger
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from ..utils import create_custom_optimzer
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if TYPE_CHECKING:
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from ...hparams import FinetuningArguments
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logger = get_logger(__name__)
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class CustomTrainer(Trainer):
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r"""
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Inherits Trainer for custom optimizer.
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"""
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def __init__(self, finetuning_args: "FinetuningArguments", **kwargs) -> None:
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super().__init__(**kwargs)
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self.finetuning_args = finetuning_args
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def create_optimizer_and_scheduler(self, num_training_steps: int) -> None:
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self.optimizer = create_custom_optimzer(self.model, self.args, self.finetuning_args, num_training_steps)
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if self.optimizer is None:
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self.create_optimizer()
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self.create_scheduler(num_training_steps=num_training_steps, optimizer=self.optimizer)
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@ -3,12 +3,13 @@
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import math
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from typing import TYPE_CHECKING, List, Optional
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from transformers import DataCollatorForLanguageModeling, Trainer
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from transformers import DataCollatorForLanguageModeling
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from ...data import get_dataset, split_dataset
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from ...extras.ploting import plot_loss
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from ...model import load_model, load_tokenizer
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from ..utils import create_custom_optimzer, create_modelcard_and_push
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from ..utils import create_modelcard_and_push
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from .trainer import CustomTrainer
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if TYPE_CHECKING:
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@ -30,14 +31,13 @@ def run_pt(
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data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
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# Initialize our Trainer
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optimizer = create_custom_optimzer(model, dataset, training_args, finetuning_args)
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trainer = Trainer(
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trainer = CustomTrainer(
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model=model,
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args=training_args,
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finetuning_args=finetuning_args,
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tokenizer=tokenizer,
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data_collator=data_collator,
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callbacks=callbacks,
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optimizers=(optimizer, None),
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**split_dataset(dataset, data_args, training_args),
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)
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@ -6,25 +6,36 @@ import torch
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from transformers import Trainer
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from ...extras.logging import get_logger
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from ..utils import create_custom_optimzer
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if TYPE_CHECKING:
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from transformers.modeling_utils import PreTrainedModel
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from transformers.trainer import PredictionOutput
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from ...hparams import FinetuningArguments
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logger = get_logger(__name__)
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class PairwiseTrainer(Trainer):
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r"""
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Inherits PeftTrainer to compute pairwise loss.
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Inherits Trainer to compute pairwise loss.
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"""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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def __init__(self, finetuning_args: "FinetuningArguments", **kwargs) -> None:
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super().__init__(**kwargs)
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self.finetuning_args = finetuning_args
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self.can_return_loss = True # override property to return eval_loss
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def create_optimizer_and_scheduler(self, num_training_steps: int) -> None:
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self.optimizer = create_custom_optimzer(self.model, self.args, self.finetuning_args, num_training_steps)
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if self.optimizer is None:
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self.create_optimizer()
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self.create_scheduler(num_training_steps=num_training_steps, optimizer=self.optimizer)
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def compute_loss(
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self, model: "PreTrainedModel", inputs: Dict[str, torch.Tensor], return_outputs: bool = False
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) -> Union[torch.Tensor, Tuple[torch.Tensor, List[torch.Tensor]]]:
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@ -7,7 +7,7 @@ from ...extras.callbacks import FixValueHeadModelCallback
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from ...extras.misc import fix_valuehead_checkpoint
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from ...extras.ploting import plot_loss
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from ...model import load_model, load_tokenizer
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from ..utils import create_custom_optimzer, create_modelcard_and_push
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from ..utils import create_modelcard_and_push
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from .collator import PairwiseDataCollatorWithPadding
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from .metric import compute_accuracy
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from .trainer import PairwiseTrainer
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@ -35,14 +35,13 @@ def run_rm(
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training_args.remove_unused_columns = False # important for pairwise dataset
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# Initialize our Trainer
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optimizer = create_custom_optimzer(model, dataset, training_args, finetuning_args)
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trainer = PairwiseTrainer(
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model=model,
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args=training_args,
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finetuning_args=finetuning_args,
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tokenizer=tokenizer,
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data_collator=data_collator,
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callbacks=callbacks + [FixValueHeadModelCallback()],
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optimizers=(optimizer, None),
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compute_metrics=compute_accuracy,
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**split_dataset(dataset, data_args, training_args),
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)
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@ -4,28 +4,41 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
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import numpy as np
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import torch
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import torch.nn as nn
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from transformers import Seq2SeqTrainer
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from ...extras.constants import IGNORE_INDEX
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from ...extras.logging import get_logger
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from ..utils import create_custom_optimzer
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if TYPE_CHECKING:
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from transformers.trainer import PredictionOutput
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from ...hparams import FinetuningArguments
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logger = get_logger(__name__)
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class CustomSeq2SeqTrainer(Seq2SeqTrainer):
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r"""
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Inherits PeftTrainer to compute generative metrics such as BLEU and ROUGE.
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Inherits Seq2SeqTrainer to compute generative metrics such as BLEU and ROUGE.
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"""
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def __init__(self, finetuning_args: "FinetuningArguments", **kwargs) -> None:
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super().__init__(**kwargs)
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self.finetuning_args = finetuning_args
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def create_optimizer_and_scheduler(self, num_training_steps: int) -> None:
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self.optimizer = create_custom_optimzer(self.model, self.args, self.finetuning_args, num_training_steps)
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if self.optimizer is None:
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self.create_optimizer()
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self.create_scheduler(num_training_steps=num_training_steps, optimizer=self.optimizer)
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def prediction_step(
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self,
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model: nn.Module,
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model: "torch.nn.Module",
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inputs: Dict[str, Union[torch.Tensor, Any]],
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prediction_loss_only: bool,
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ignore_keys: Optional[List[str]] = None,
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@ -9,10 +9,9 @@ from ...extras.constants import IGNORE_INDEX
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from ...extras.misc import get_logits_processor
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from ...extras.ploting import plot_loss
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from ...model import load_model, load_tokenizer
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from ...train.sft.metric import ComputeMetrics
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from ...train.sft.trainer import CustomSeq2SeqTrainer
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from ...train.utils import create_modelcard_and_push
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from ..utils import create_custom_optimzer
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from ..utils import create_modelcard_and_push
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from .metric import ComputeMetrics
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from .trainer import CustomSeq2SeqTrainer
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if TYPE_CHECKING:
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@ -50,14 +49,13 @@ def run_sft(
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training_args.generation_num_beams = data_args.eval_num_beams or training_args.generation_num_beams
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# Initialize our Trainer
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optimizer = create_custom_optimzer(model, dataset, training_args, finetuning_args)
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trainer = CustomSeq2SeqTrainer(
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model=model,
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args=training_args,
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finetuning_args=finetuning_args,
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tokenizer=tokenizer,
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data_collator=data_collator,
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callbacks=callbacks,
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optimizers=(optimizer, None),
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compute_metrics=ComputeMetrics(tokenizer) if training_args.predict_with_generate else None,
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**split_dataset(dataset, data_args, training_args),
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)
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@ -1,4 +1,3 @@
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import math
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from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Union
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import torch
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@ -19,7 +18,6 @@ if is_galore_available():
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if TYPE_CHECKING:
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from datasets import Dataset, IterableDataset
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from transformers import Seq2SeqTrainingArguments
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from transformers.modeling_utils import PreTrainedModel
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from trl import AutoModelForCausalLMWithValueHead
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@ -156,9 +154,9 @@ def _get_decay_parameter_names(model: "PreTrainedModel") -> List[str]:
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def _create_galore_optimizer(
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model: "PreTrainedModel",
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dataset: Union["Dataset", "IterableDataset"],
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training_args: "Seq2SeqTrainingArguments",
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finetuning_args: "FinetuningArguments",
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max_steps: int,
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) -> "torch.optim.Optimizer":
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require_version("galore_torch", "To fix: pip install galore-torch")
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@ -209,12 +207,6 @@ def _create_galore_optimizer(
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if training_args.gradient_accumulation_steps != 1:
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raise ValueError("Per-layer GaLore does not support gradient accumulation.")
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if training_args.max_steps > 0:
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num_training_steps = training_args.max_steps
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else:
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total_train_batch_size = training_args.per_device_train_batch_size * training_args.world_size
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num_training_steps = training_args.num_train_epochs * math.ceil(len(dataset) / total_train_batch_size)
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optimizer_dict: Dict["torch.Tensor", "torch.optim.Optimizer"] = {}
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for param in nodecay_params:
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param_groups = [dict(params=[param])]
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@ -231,8 +223,8 @@ def _create_galore_optimizer(
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scheduler_dict[param] = get_scheduler(
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training_args.lr_scheduler_type,
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optimizer=optimizer_dict[param],
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num_warmup_steps=training_args.get_warmup_steps(num_training_steps) * 2,
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num_training_steps=num_training_steps * 2,
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num_warmup_steps=training_args.get_warmup_steps(max_steps) * 2,
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num_training_steps=max_steps * 2,
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)
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def optimizer_hook(param: "torch.Tensor"):
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@ -259,7 +251,6 @@ def _create_galore_optimizer(
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def _create_loraplus_optimizer(
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model: "PreTrainedModel",
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dataset: Union["Dataset", "IterableDataset"],
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training_args: "Seq2SeqTrainingArguments",
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finetuning_args: "FinetuningArguments",
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) -> "torch.optim.Optimizer":
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@ -302,12 +293,12 @@ def _create_loraplus_optimizer(
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def create_custom_optimzer(
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model: "PreTrainedModel",
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dataset: Union["Dataset", "IterableDataset"],
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training_args: "Seq2SeqTrainingArguments",
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finetuning_args: "FinetuningArguments",
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max_steps: int,
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) -> Optional["torch.optim.Optimizer"]:
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if finetuning_args.use_galore:
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return _create_galore_optimizer(model, dataset, training_args, finetuning_args)
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return _create_galore_optimizer(model, training_args, finetuning_args, max_steps)
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if finetuning_args.loraplus_lr_ratio is not None:
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return _create_loraplus_optimizer(model, dataset, training_args, finetuning_args)
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return _create_loraplus_optimizer(model, training_args, finetuning_args)
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