add kto

Former-commit-id: ec51986cf70b0bdd79b8141e45916670fb97a08e

src/llamafactory/train/kto/trainer.py

@@ -0,0 +1,206 @@
+from collections import defaultdict
+from contextlib import nullcontext
+from types import MethodType
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
+
+import torch
+from transformers import Trainer
+from trl import KTOTrainer
+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
+
+    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",
+        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.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 parameter
+        self.beta = finetuning_args.kto_beta
+        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"):
+            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 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()
+
+
+    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
+
+        completion_logits = model(
+            batch["input_ids"],
+            attention_mask=batch["attention_mask"],
+        ).logits
+
+        completion_logps = self.get_batch_logps(
+            completion_logits,
+            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"],
+            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]]
+
+        chosen_logps = completion_logps[chosen_idx, ...]
+        rejected_logps = completion_logps[rejected_idx, ...]
+
+        chosen_logits = completion_logits[chosen_idx, ...]
+        rejected_logits = completion_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."""
+        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,
+        ) = self.forward(model, batch)
+
+        with torch.no_grad():
+            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 ref_context:
+                (
+                    reference_chosen_logps,
+                    reference_rejected_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,
+            reference_chosen_logps,
+            reference_rejected_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']])
+
+
+        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