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# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's TRL library.
# https://github.com/huggingface/trl/blob/v0.8.0/trl/trainer/kto_trainer.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from collections import defaultdict
from contextlib import nullcontext
from types import MethodType
from typing import TYPE_CHECKING, Dict, Literal, Optional, Tuple, Union
import torch
from transformers import Trainer
from trl import KTOTrainer
from trl.trainer import disable_dropout_in_model
from ...extras.constants import IGNORE_INDEX
from ..trainer_utils import create_custom_optimzer, create_custom_scheduler, get_batch_logps
if TYPE_CHECKING:
import torch.utils.data
from transformers import PreTrainedModel, ProcessorMixin
from ...hparams import FinetuningArguments
class CustomKTOTrainer(KTOTrainer):
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))
# kto hyperparams
self.beta = finetuning_args.pref_beta
self.desirable_weight = finetuning_args.kto_chosen_weight
self.undesirable_weight = finetuning_args.kto_rejected_weight
self.ftx_gamma = finetuning_args.pref_ftx
Trainer.__init__(self, model=model, **kwargs)
if not hasattr(self, "accelerator"):
raise AttributeError("Please update `transformers`.")
warnings.simplefilter("ignore") # remove gc warnings on ref model
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)
self.ref_model.eval()
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 _get_train_sampler(self) -> Optional["torch.utils.data.Sampler"]:
r"""
Replaces the sequential sampler of KTO Trainer created by trl with the random sampler.
"""
return Trainer._get_train_sampler(self)
def _save(self, output_dir: Optional[str] = None, state_dict: Optional[Dict[str, "torch.Tensor"]] = None) -> None:
super()._save(output_dir, state_dict)
output_dir = output_dir if output_dir is not None else self.args.output_dir
if self.processor is not None:
getattr(self.processor, "image_processor").save_pretrained(output_dir)
def forward(
self, model: "PreTrainedModel", batch: Dict[str, "torch.Tensor"], prefix: Literal["", "kl_"] = ""
) -> Tuple["torch.Tensor", "torch.Tensor"]:
r"""
Runs forward pass and computes the log probabilities.
"""
batch = {k: v.detach().clone() for k, v in batch.items()} # avoid error
model_inputs = {
"input_ids": batch["{}input_ids".format(prefix)],
"attention_mask": batch["{}attention_mask".format(prefix)],
}
if "pixel_values" in batch:
model_inputs["pixel_values"] = batch["pixel_values"]
if "{}token_type_ids".format(prefix) in batch:
model_inputs["token_type_ids"] = batch["{}token_type_ids".format(prefix)]
logits = model(**model_inputs, return_dict=True, use_cache=False).logits.to(torch.float32)
logps, valid_length = get_batch_logps(logits=logits, labels=batch["{}labels".format(prefix)])
return logps, logps / valid_length
def concatenated_forward(
self, model: "PreTrainedModel", batch: Dict[str, "torch.Tensor"]
) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor"]:
target_logps, target_logps_avg = self.forward(model, batch)
with torch.no_grad():
kl_logps, _ = self.forward(model, batch, prefix="kl_")
if len(target_logps) != len(batch["kto_tags"]):
raise ValueError("Mismatched shape of inputs and labels.")
chosen_logps = target_logps[batch["kto_tags"]]
rejected_logps = target_logps[~batch["kto_tags"]]
chosen_logps_avg = target_logps_avg[batch["kto_tags"]]
return chosen_logps, rejected_logps, kl_logps, chosen_logps_avg
def compute_reference_log_probs(
self, model: "PreTrainedModel", batch: Dict[str, "torch.Tensor"]
) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor"]:
r"""
Computes log probabilities of the reference model.
"""
if self.ref_model is None:
ref_model = model
ref_context = self.accelerator.unwrap_model(model).disable_adapter()
else:
ref_model = self.ref_model
ref_context = nullcontext()
with torch.no_grad(), ref_context:
reference_chosen_logps, reference_rejected_logps, reference_kl_logps, _ = self.concatenated_forward(
ref_model, batch
)
return reference_chosen_logps, reference_rejected_logps, reference_kl_logps
def get_batch_loss_metrics(
self,
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 = {}
policy_chosen_logps, policy_rejected_logps, policy_kl_logps, policy_chosen_logps_avg = (
self.concatenated_forward(model, batch)
)
reference_chosen_logps, reference_rejected_logps, reference_kl_logps = self.compute_reference_log_probs(
model, batch
)
losses, chosen_rewards, rejected_rewards, kl = self.kto_loss(
policy_chosen_logps,
policy_rejected_logps,
policy_kl_logps,
reference_chosen_logps,
reference_rejected_logps,
reference_kl_logps,
)
losses = losses.nanmean()
if self.ftx_gamma > 1e-6 and len(policy_chosen_logps) > 0: # remember to rescale
sft_loss = -policy_chosen_logps_avg
losses += self.ftx_gamma * sft_loss.nanmean() / len(policy_chosen_logps) * 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)
all_num_chosen = self.accelerator.gather(num_chosen).sum().item()
all_num_rejected = self.accelerator.gather(num_rejected).sum().item()
if all_num_chosen > 0:
metrics["rewards/chosen_sum"] = self.accelerator.gather(chosen_rewards.nansum()).nansum().item()
metrics["logps/chosen_sum"] = self.accelerator.gather(policy_chosen_logps.nansum()).nansum().item()
metrics["count/chosen"] = all_num_chosen
if all_num_rejected > 0:
metrics["rewards/rejected_sum"] = self.accelerator.gather(rejected_rewards.nansum()).nansum().item()
metrics["logps/rejected_sum"] = self.accelerator.gather(policy_rejected_logps.nansum()).nansum().item()
metrics["count/rejected"] = all_num_rejected
metrics["kl"] = kl.item()
return losses, metrics
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