mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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227 lines
9.4 KiB
Python
227 lines
9.4 KiB
Python
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
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#
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# This code is inspired by the HuggingFace's TRL library.
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# https://github.com/huggingface/trl/blob/v0.8.0/trl/trainer/kto_trainer.py
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import warnings
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from collections import defaultdict
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from contextlib import nullcontext
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from types import MethodType
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from typing import TYPE_CHECKING, Dict, Literal, Optional, Tuple, Union
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import torch
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from transformers import Trainer
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from trl import KTOTrainer
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from trl.trainer import disable_dropout_in_model
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from ...extras.constants import IGNORE_INDEX
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from ..callbacks import SaveProcessorCallback
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from ..trainer_utils import create_custom_optimizer, create_custom_scheduler, get_batch_logps
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if TYPE_CHECKING:
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import torch.utils.data
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from transformers import PreTrainedModel, ProcessorMixin
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from ...hparams import FinetuningArguments
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class CustomKTOTrainer(KTOTrainer):
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def __init__(
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self,
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model: Union["PreTrainedModel", torch.nn.Module],
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ref_model: Optional[Union["PreTrainedModel", torch.nn.Module]],
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finetuning_args: "FinetuningArguments",
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processor: Optional["ProcessorMixin"],
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disable_dropout: bool = True,
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**kwargs,
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):
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if disable_dropout:
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disable_dropout_in_model(model)
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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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self.label_pad_token_id = IGNORE_INDEX
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self.padding_value = 0
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self.is_encoder_decoder = model.config.is_encoder_decoder
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self.precompute_ref_log_probs = False
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self._precomputed_train_ref_log_probs = False
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self._precomputed_eval_ref_log_probs = False
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self._peft_has_been_casted_to_bf16 = False
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self.ref_model = ref_model
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self._stored_metrics = defaultdict(lambda: defaultdict(list))
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# kto hyperparams
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self.beta = finetuning_args.pref_beta
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self.desirable_weight = finetuning_args.kto_chosen_weight
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self.undesirable_weight = finetuning_args.kto_rejected_weight
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self.ftx_gamma = finetuning_args.pref_ftx
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Trainer.__init__(self, model=model, **kwargs)
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if not hasattr(self, "accelerator"):
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raise AttributeError("Please update `transformers`.")
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warnings.simplefilter("ignore") # remove gc warnings on ref model
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if ref_model is not None:
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if self.is_deepspeed_enabled:
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if not (
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getattr(ref_model, "is_loaded_in_8bit", False) or getattr(ref_model, "is_loaded_in_4bit", False)
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): # quantized models are already set on the correct device
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self.ref_model = self._prepare_deepspeed(self.ref_model)
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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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self.ref_model.eval()
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if processor is not None:
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self.add_callback(SaveProcessorCallback(processor))
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if finetuning_args.use_badam:
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from badam import BAdamCallback, clip_grad_norm_old_version
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self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
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self.add_callback(BAdamCallback)
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def create_optimizer(self) -> "torch.optim.Optimizer":
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if self.optimizer is None:
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self.optimizer = create_custom_optimizer(self.model, self.args, self.finetuning_args)
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return super().create_optimizer()
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def create_scheduler(
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self, num_training_steps: int, optimizer: Optional["torch.optim.Optimizer"] = None
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) -> "torch.optim.lr_scheduler.LRScheduler":
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create_custom_scheduler(self.args, num_training_steps, optimizer)
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return super().create_scheduler(num_training_steps, optimizer)
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def _get_train_sampler(self) -> Optional["torch.utils.data.Sampler"]:
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r"""
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Replaces the sequential sampler of KTO Trainer created by trl with the random sampler.
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"""
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return Trainer._get_train_sampler(self)
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def forward(
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self, model: "PreTrainedModel", batch: Dict[str, "torch.Tensor"], prefix: Literal["", "kl_"] = ""
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) -> Tuple["torch.Tensor", "torch.Tensor"]:
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r"""
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Runs forward pass and computes the log probabilities.
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"""
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batch = {k: v.detach().clone() for k, v in batch.items()} # avoid error
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model_inputs = {
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"input_ids": batch["{}input_ids".format(prefix)],
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"attention_mask": batch["{}attention_mask".format(prefix)],
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}
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if "pixel_values" in batch:
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model_inputs["pixel_values"] = batch["pixel_values"]
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if "image_grid_thw" in batch:
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model_inputs["image_grid_thw"] = batch["image_grid_thw"]
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if "{}token_type_ids".format(prefix) in batch:
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model_inputs["token_type_ids"] = batch["{}token_type_ids".format(prefix)]
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logits = model(**model_inputs, return_dict=True, use_cache=False).logits.to(torch.float32)
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logps, valid_length = get_batch_logps(logits=logits, labels=batch["{}labels".format(prefix)])
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return logps, logps / valid_length
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def concatenated_forward(
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self, model: "PreTrainedModel", batch: Dict[str, "torch.Tensor"]
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) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor"]:
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target_logps, target_logps_avg = self.forward(model, batch)
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with torch.no_grad():
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kl_logps, _ = self.forward(model, batch, prefix="kl_")
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if len(target_logps) != len(batch["kto_tags"]):
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raise ValueError("Mismatched shape of inputs and labels.")
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chosen_logps = target_logps[batch["kto_tags"]]
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rejected_logps = target_logps[~batch["kto_tags"]]
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chosen_logps_avg = target_logps_avg[batch["kto_tags"]]
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return chosen_logps, rejected_logps, kl_logps, chosen_logps_avg
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def compute_reference_log_probs(
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self, model: "PreTrainedModel", batch: Dict[str, "torch.Tensor"]
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) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor"]:
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r"""
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Computes log probabilities of the reference model.
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"""
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if self.ref_model is None:
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ref_model = model
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ref_context = self.accelerator.unwrap_model(model).disable_adapter()
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else:
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ref_model = self.ref_model
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ref_context = nullcontext()
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with torch.no_grad(), ref_context:
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reference_chosen_logps, reference_rejected_logps, reference_kl_logps, _ = self.concatenated_forward(
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ref_model, batch
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)
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return reference_chosen_logps, reference_rejected_logps, reference_kl_logps
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def get_batch_loss_metrics(
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self,
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model: "PreTrainedModel",
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batch: Dict[str, "torch.Tensor"],
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) -> Tuple["torch.Tensor", Dict[str, "torch.Tensor"]]:
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r"""
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Computes the DPO loss and other metrics for the given batch of inputs for train or test.
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"""
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metrics = {}
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policy_chosen_logps, policy_rejected_logps, policy_kl_logps, policy_chosen_logps_avg = (
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self.concatenated_forward(model, batch)
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)
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reference_chosen_logps, reference_rejected_logps, reference_kl_logps = self.compute_reference_log_probs(
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model, batch
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)
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losses, chosen_rewards, rejected_rewards, kl = self.kto_loss(
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policy_chosen_logps,
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policy_rejected_logps,
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policy_kl_logps,
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reference_chosen_logps,
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reference_rejected_logps,
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reference_kl_logps,
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)
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losses = losses.nanmean()
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if self.ftx_gamma > 1e-6 and len(policy_chosen_logps) > 0: # remember to rescale
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sft_loss = -policy_chosen_logps_avg
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losses += self.ftx_gamma * sft_loss.nanmean() / len(policy_chosen_logps) * len(batch["labels"])
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num_chosen = torch.Tensor([len(chosen_rewards)]).to(self.accelerator.device)
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num_rejected = torch.Tensor([len(rejected_rewards)]).to(self.accelerator.device)
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all_num_chosen = self.accelerator.gather(num_chosen).sum().item()
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all_num_rejected = self.accelerator.gather(num_rejected).sum().item()
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if all_num_chosen > 0:
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metrics["rewards/chosen_sum"] = self.accelerator.gather(chosen_rewards.nansum()).nansum().item()
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metrics["logps/chosen_sum"] = self.accelerator.gather(policy_chosen_logps.nansum()).nansum().item()
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metrics["count/chosen"] = all_num_chosen
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if all_num_rejected > 0:
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metrics["rewards/rejected_sum"] = self.accelerator.gather(rejected_rewards.nansum()).nansum().item()
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metrics["logps/rejected_sum"] = self.accelerator.gather(policy_rejected_logps.nansum()).nansum().item()
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metrics["count/rejected"] = all_num_rejected
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metrics["kl"] = kl.item()
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return losses, metrics
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