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improve KTO impl., replace datasets

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Former-commit-id: e56a57ddcf061de6e4acc8679f7dbf0b68364986
This commit is contained in:
hiyouga
2024-05-18 03:44:56 +08:00
parent e4570e28a8
commit 2bff90719b
53 changed files with 448 additions and 330 deletions
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105
src/llamafactory/train/kto/trainer.py
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@@ -1,7 +1,7 @@
from collections import defaultdict
from contextlib import nullcontext
from types import MethodType
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
from typing import TYPE_CHECKING, Dict, Optional, Tuple, Union
import torch
from transformers import Trainer
@@ -13,7 +13,7 @@ from ..utils import create_custom_optimzer, create_custom_scheduler
if TYPE_CHECKING:
from transformers import PreTrainedModel
from transformers import PreTrainedModel, ProcessorMixin
from ...hparams import FinetuningArguments
@@ -24,6 +24,7 @@ class CustomKTOTrainer(KTOTrainer):
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,
):
@@ -33,6 +34,7 @@ class CustomKTOTrainer(KTOTrainer):
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
@@ -43,15 +45,15 @@ class CustomKTOTrainer(KTOTrainer):
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))
# KTO parameter
# kto hyperparams
self.beta = finetuning_args.kto_beta
self.desirable_weight = finetuning_args.kto_chosen_weight
self.undesirable_weight = finetuning_args.kto_rejected_weight
self.ftx_gamma = finetuning_args.kto_ftx
self.desirable_weight = finetuning_args.kto_desirable_weight
self.undesirable_weight = finetuning_args.kto_undesirable_weight
Trainer.__init__(self, model=model, **kwargs)
if not hasattr(self, "accelerator"):
@@ -82,78 +84,85 @@ class CustomKTOTrainer(KTOTrainer):
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, chosen_logits: "torch.FloatTensor", chosen_labels: "torch.LongTensor") -> "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.
"""
all_logps = self.get_batch_logps(chosen_logits, chosen_labels, average_log_prob=True)
return -all_logps.nanmean()
return -all_logps
def forward(
self, model: "PreTrainedModel", batch: Dict[str, "torch.Tensor"]
) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor"]:
with torch.no_grad():
KL_logits = model(
batch["KL_completion_input_ids"],
attention_mask=batch["KL_completion_attention_mask"],
).logits
kl_logits = model(
input_ids=batch["kl_input_ids"],
attention_mask=batch["kl_attention_mask"],
return_dict=True,
use_cache=False,
).logits.to(torch.float32)
completion_logits = model(
batch["input_ids"],
target_logits = model(
input_ids=batch["input_ids"],
attention_mask=batch["attention_mask"],
).logits
return_dict=True,
use_cache=False,
).logits.to(torch.float32)
completion_logps = self.get_batch_logps(
completion_logits,
batch["labels"],
target_logps = self.get_batch_logps(
logits=target_logits,
labels=batch["labels"],
average_log_prob=False,
is_encoder_decoder=self.is_encoder_decoder,
label_pad_token_id=self.label_pad_token_id,
)
KL_logps = self.get_batch_logps(
KL_logits,
batch["kl_labels"],
kl_logps = self.get_batch_logps(
logits=kl_logits,
labels=batch["kl_labels"],
average_log_prob=False,
is_encoder_decoder=self.is_encoder_decoder,
label_pad_token_id=self.label_pad_token_id,
)
if completion_logps.shape[0] != len(batch["tag"]):
raise ValueError(
"There is a mismatch between the number of examples in this batch and the number of "
"examples for which an output sequence was predicted."
)
chosen_idx = [i for i in range(completion_logps.shape[0]) if batch["tag"][i]]
rejected_idx = [i for i in range(completion_logps.shape[0]) if not batch["tag"][i]]
if len(target_logps) != len(batch["kto_tags"]):
raise ValueError("Mismatched shape of inputs and labels.")
chosen_logps = completion_logps[chosen_idx, ...]
rejected_logps = completion_logps[rejected_idx, ...]
chosen_idx = [i for i in range(len(target_logps)) if batch["kto_tags"][i]]
rejected_idx = [i for i in range(len(target_logps)) if not batch["kto_tags"][i]]
chosen_logits = completion_logits[chosen_idx, ...]
rejected_logits = completion_logits[rejected_idx, ...]
chosen_logps = target_logps[chosen_idx, ...]
rejected_logps = target_logps[rejected_idx, ...]
return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, KL_logps)
chosen_logits = target_logits[chosen_idx, ...]
rejected_logits = target_logits[rejected_idx, ...]
return chosen_logps, rejected_logps, chosen_logits, rejected_logits, kl_logps
def get_batch_loss_metrics(
self,
model,
batch: Dict[str, Union[List, torch.LongTensor]],
):
"""Compute the KTO loss and other metrics for the given batch of inputs for train or test."""
model: "PreTrainedModel",
batch: Dict[str, "torch.Tensor"],
) -> 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 = {}
batch = {k: (v.to(self.accelerator.device) if isinstance(v, torch.Tensor) else v) for k, v in batch.items()}
(
policy_chosen_logps,
policy_rejected_logps,
policy_chosen_logits,
policy_rejected_logits,
policy_KL_logps,
_,
policy_kl_logps,
) = self.forward(model, batch)
with torch.no_grad():
@@ -163,27 +172,29 @@ class CustomKTOTrainer(KTOTrainer):
else:
ref_model = self.ref_model
ref_context = nullcontext()
with ref_context:
(
reference_chosen_logps,
reference_rejected_logps,
_,
_,
reference_KL_logps,
reference_kl_logps,
) = self.forward(ref_model, batch)
losses, chosen_rewards, rejected_rewards, kl = self.kto_loss(
policy_chosen_logps,
policy_rejected_logps,
policy_KL_logps,
policy_kl_logps,
reference_chosen_logps,
reference_rejected_logps,
reference_KL_logps,
reference_kl_logps,
)
losses = losses.nanmean()
if self.ftx_gamma > 1e-6 and len(batch["labels"][batch['tag']])>0:
losses += self.ftx_gamma * self.sft_loss(policy_chosen_logits, batch["labels"][batch['tag']])
if self.ftx_gamma > 1e-6 and len(policy_chosen_logps) > 0: # remember to rescale
sft_loss = self.sft_loss(policy_chosen_logits, batch["labels"][batch["kto_tags"]])
losses += self.ftx_gamma * sft_loss.nanmean() / len(policy_chosen_logits) * len(batch["labels"])
num_chosen = torch.Tensor([len(chosen_rewards)]).to(self.accelerator.device)
num_rejected = torch.Tensor([len(rejected_rewards)]).to(self.accelerator.device)
@@ -203,4 +214,4 @@ class CustomKTOTrainer(KTOTrainer):
metrics["kl"] = kl.item()
return losses, metrics
return losses, metrics