mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-02 03:32:50 +08:00
251 lines
11 KiB
Python
251 lines
11 KiB
Python
from collections import defaultdict
|
|
from contextlib import nullcontext
|
|
from types import MethodType
|
|
from typing import TYPE_CHECKING, Dict, Literal, Optional, Tuple, Union
|
|
|
|
import torch
|
|
import torch.nn.functional as F
|
|
from transformers import Trainer
|
|
from trl import DPOTrainer
|
|
from trl.trainer.utils import disable_dropout_in_model
|
|
|
|
from ...extras.constants import IGNORE_INDEX
|
|
from ..utils import create_custom_optimzer, create_custom_scheduler
|
|
|
|
|
|
if TYPE_CHECKING:
|
|
from transformers import PreTrainedModel, ProcessorMixin
|
|
|
|
from ...hparams import FinetuningArguments
|
|
|
|
|
|
class CustomDPOTrainer(DPOTrainer):
|
|
def __init__(
|
|
self,
|
|
model: Union["PreTrainedModel", torch.nn.Module],
|
|
ref_model: Optional[Union["PreTrainedModel", torch.nn.Module]],
|
|
finetuning_args: "FinetuningArguments",
|
|
processor: Optional["ProcessorMixin"],
|
|
disable_dropout: bool = True,
|
|
**kwargs,
|
|
):
|
|
if disable_dropout:
|
|
disable_dropout_in_model(model)
|
|
if ref_model is not None:
|
|
disable_dropout_in_model(ref_model)
|
|
|
|
self.finetuning_args = finetuning_args
|
|
self.processor = processor
|
|
self.reference_free = False
|
|
self.use_dpo_data_collator = True # hack to avoid warning
|
|
self.generate_during_eval = False # disable at evaluation
|
|
self.label_pad_token_id = IGNORE_INDEX
|
|
self.padding_value = 0
|
|
self.is_encoder_decoder = model.config.is_encoder_decoder
|
|
self.precompute_ref_log_probs = False
|
|
self._precomputed_train_ref_log_probs = False
|
|
self._precomputed_eval_ref_log_probs = False
|
|
self._peft_has_been_casted_to_bf16 = False
|
|
|
|
self.ref_model = ref_model
|
|
self._stored_metrics = defaultdict(lambda: defaultdict(list))
|
|
|
|
# dpo hyperparams
|
|
self.beta = finetuning_args.pref_beta
|
|
self.loss_type = finetuning_args.pref_loss
|
|
self.ftx_gamma = finetuning_args.pref_ftx
|
|
self.label_smoothing = finetuning_args.dpo_label_smoothing
|
|
self.simpo_gamma = finetuning_args.simpo_gamma
|
|
|
|
Trainer.__init__(self, model=model, **kwargs)
|
|
if not hasattr(self, "accelerator"):
|
|
raise AttributeError("Please update `transformers`.")
|
|
|
|
if ref_model is not None:
|
|
if self.is_deepspeed_enabled:
|
|
if not (
|
|
getattr(ref_model, "is_loaded_in_8bit", False) or getattr(ref_model, "is_loaded_in_4bit", False)
|
|
): # quantized models are already set on the correct device
|
|
self.ref_model = self._prepare_deepspeed(self.ref_model)
|
|
else:
|
|
self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)
|
|
|
|
if finetuning_args.use_badam:
|
|
from badam import clip_grad_norm_for_sparse_tensor
|
|
|
|
self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_for_sparse_tensor, self.accelerator)
|
|
|
|
def create_optimizer(self) -> "torch.optim.Optimizer":
|
|
if self.optimizer is None:
|
|
self.optimizer = create_custom_optimzer(self.model, self.args, self.finetuning_args)
|
|
return super().create_optimizer()
|
|
|
|
def create_scheduler(
|
|
self, num_training_steps: int, optimizer: Optional["torch.optim.Optimizer"] = None
|
|
) -> "torch.optim.lr_scheduler.LRScheduler":
|
|
create_custom_scheduler(self.args, num_training_steps, optimizer)
|
|
return super().create_scheduler(num_training_steps, optimizer)
|
|
|
|
def _save(self, output_dir: Optional[str] = None, state_dict: Optional[Dict[str, "torch.Tensor"]] = None) -> None:
|
|
super()._save(output_dir, state_dict)
|
|
if self.processor is not None:
|
|
output_dir = output_dir if output_dir is not None else self.args.output_dir
|
|
getattr(self.processor, "image_processor").save_pretrained(output_dir)
|
|
|
|
def sft_loss(self, batch: Dict[str, "torch.Tensor"], chosen_logits: "torch.FloatTensor") -> "torch.Tensor":
|
|
r"""
|
|
Computes supervised cross-entropy loss of given labels under the given logits.
|
|
|
|
Returns:
|
|
A tensor of shape (batch_size,) containing the cross-entropy loss of each samples.
|
|
"""
|
|
batch_size = batch["input_ids"].size(0) // 2
|
|
chosen_labels, _ = batch["labels"].split(batch_size, dim=0)
|
|
chosen_logps = self.get_batch_logps(chosen_logits, chosen_labels, average_log_prob=True)
|
|
return -chosen_logps
|
|
|
|
def odds_ratio_loss(self, chosen_logps: "torch.Tensor", rejected_logps: "torch.Tensor") -> "torch.Tensor":
|
|
r"""
|
|
Computes ORPO's odds ratio (OR) loss for batched log probabilities of the policy model.
|
|
"""
|
|
log_odds = (chosen_logps - rejected_logps) - (
|
|
torch.log1p(-torch.exp(chosen_logps)) - torch.log1p(-torch.exp(rejected_logps))
|
|
)
|
|
sft_loss = -chosen_logps
|
|
odds_ratio_loss = -F.logsigmoid(log_odds)
|
|
orpo_loss = sft_loss + self.beta * odds_ratio_loss
|
|
return orpo_loss
|
|
|
|
def simpo_loss(self, chosen_logps: "torch.Tensor", rejected_logps: "torch.Tensor") -> "torch.Tensor":
|
|
r"""
|
|
Computes SimPO loss for batched log probabilities of the policy model.
|
|
"""
|
|
pi_logratios = chosen_logps - rejected_logps
|
|
gamma_logratios = self.simpo_gamma / self.beta
|
|
logits = pi_logratios - gamma_logratios
|
|
simpo_loss = -F.logsigmoid(self.beta * logits)
|
|
return simpo_loss
|
|
|
|
def compute_preference_loss(
|
|
self,
|
|
policy_chosen_logps: "torch.Tensor",
|
|
policy_rejected_logps: "torch.Tensor",
|
|
reference_chosen_logps: Optional["torch.Tensor"],
|
|
reference_rejected_logps: Optional["torch.Tensor"],
|
|
) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor"]:
|
|
r"""
|
|
Computes loss for preference learning.
|
|
"""
|
|
if not self.finetuning_args.use_ref_model:
|
|
if self.loss_type == "orpo":
|
|
losses = self.odds_ratio_loss(policy_chosen_logps, policy_rejected_logps)
|
|
elif self.loss_type == "simpo":
|
|
losses = self.simpo_loss(policy_chosen_logps, policy_rejected_logps)
|
|
else:
|
|
raise NotImplementedError("Unknown loss type: {}.".format(self.loss_type))
|
|
|
|
chosen_rewards = self.beta * policy_chosen_logps.to(self.accelerator.device).detach()
|
|
rejected_rewards = self.beta * policy_rejected_logps.to(self.accelerator.device).detach()
|
|
else:
|
|
losses, chosen_rewards, rejected_rewards = self.dpo_loss(
|
|
policy_chosen_logps, policy_rejected_logps, reference_chosen_logps, reference_rejected_logps
|
|
)
|
|
|
|
return losses, chosen_rewards, rejected_rewards
|
|
|
|
def concatenated_forward(
|
|
self, model: "PreTrainedModel", batch: Dict[str, "torch.Tensor"]
|
|
) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor"]:
|
|
r"""
|
|
Computes the sum log probabilities of the labels under the given logits if loss_type != IPO.
|
|
|
|
Otherwise the average log probabilities.
|
|
"""
|
|
if self.finetuning_args.use_ref_model:
|
|
batch = {k: v.detach().clone() for k, v in batch.items()} # avoid error
|
|
|
|
all_logits: "torch.Tensor" = model(**batch, return_dict=True, use_cache=False).logits.to(torch.float32)
|
|
|
|
all_logps = self.get_batch_logps(
|
|
logits=all_logits,
|
|
labels=batch["labels"],
|
|
average_log_prob=(self.loss_type in ["ipo", "orpo", "simpo"]),
|
|
is_encoder_decoder=self.is_encoder_decoder,
|
|
label_pad_token_id=self.label_pad_token_id,
|
|
)
|
|
batch_size = batch["input_ids"].size(0) // 2
|
|
chosen_logps, rejected_logps = all_logps.split(batch_size, dim=0)
|
|
chosen_logits, rejected_logits = all_logits.split(batch_size, dim=0)
|
|
return chosen_logps, rejected_logps, chosen_logits, rejected_logits
|
|
|
|
def compute_reference_log_probs(
|
|
self, batch: Dict[str, "torch.Tensor"]
|
|
) -> Tuple[Optional["torch.Tensor"], Optional["torch.Tensor"]]:
|
|
r"""
|
|
Computes log probabilities of the reference model.
|
|
"""
|
|
if not self.finetuning_args.use_ref_model:
|
|
return None, None
|
|
|
|
if self.ref_model is None:
|
|
ref_model = self.model
|
|
ref_context = self.accelerator.unwrap_model(self.model).disable_adapter()
|
|
else:
|
|
ref_model = self.ref_model
|
|
ref_context = nullcontext()
|
|
|
|
with torch.no_grad(), ref_context:
|
|
(
|
|
reference_chosen_logps,
|
|
reference_rejected_logps,
|
|
_,
|
|
_,
|
|
) = self.concatenated_forward(ref_model, batch)
|
|
|
|
return reference_chosen_logps, reference_rejected_logps
|
|
|
|
def get_batch_loss_metrics(
|
|
self,
|
|
model: "PreTrainedModel",
|
|
batch: Dict[str, "torch.Tensor"],
|
|
train_eval: Literal["train", "eval"] = "train",
|
|
) -> Tuple["torch.Tensor", Dict[str, "torch.Tensor"]]:
|
|
r"""
|
|
Computes the DPO loss and other metrics for the given batch of inputs for train or test.
|
|
"""
|
|
metrics = {}
|
|
(
|
|
policy_chosen_logps,
|
|
policy_rejected_logps,
|
|
policy_chosen_logits,
|
|
policy_rejected_logits,
|
|
) = self.concatenated_forward(model, batch)
|
|
|
|
reference_chosen_logps, reference_rejected_logps = self.compute_reference_log_probs(batch)
|
|
losses, chosen_rewards, rejected_rewards = self.compute_preference_loss(
|
|
policy_chosen_logps,
|
|
policy_rejected_logps,
|
|
reference_chosen_logps,
|
|
reference_rejected_logps,
|
|
)
|
|
sft_loss = self.sft_loss(batch, policy_chosen_logits) # compute chosen_logps with masks
|
|
if self.ftx_gamma > 1e-6:
|
|
losses += self.ftx_gamma * sft_loss
|
|
|
|
reward_accuracies = (chosen_rewards > rejected_rewards).float()
|
|
|
|
prefix = "eval_" if train_eval == "eval" else ""
|
|
metrics["{}rewards/chosen".format(prefix)] = chosen_rewards.mean().cpu()
|
|
metrics["{}rewards/rejected".format(prefix)] = rejected_rewards.mean().cpu()
|
|
metrics["{}rewards/accuracies".format(prefix)] = reward_accuracies.mean().cpu()
|
|
metrics["{}rewards/margins".format(prefix)] = (chosen_rewards - rejected_rewards).mean().cpu()
|
|
metrics["{}logps/rejected".format(prefix)] = policy_rejected_logps.detach().mean().cpu()
|
|
metrics["{}logps/chosen".format(prefix)] = policy_chosen_logps.detach().mean().cpu()
|
|
metrics["{}logits/rejected".format(prefix)] = policy_rejected_logits.detach().mean().cpu()
|
|
metrics["{}logits/chosen".format(prefix)] = policy_chosen_logits.detach().mean().cpu()
|
|
if self.loss_type == "orpo":
|
|
metrics["{}sft_loss".format(prefix)] = sft_loss.detach().mean().cpu()
|
|
metrics["{}odds_ratio_loss".format(prefix)] = ((losses - sft_loss) / self.beta).detach().mean().cpu()
|
|
|
|
return losses.mean(), metrics
|