fix #4120

README.md

@@ -298,7 +298,7 @@ huggingface-cli login
 | datasets     | 2.16.0  | 2.19.2    |
 | accelerate   | 0.30.1  | 0.30.1    |
 | peft         | 0.11.1  | 0.11.1    |
-| trl          | 0.9.3   | 0.9.3     |
+| trl          | 0.8.6   | 0.9.3     |
 
 | Optional     | Minimum | Recommend |
 | ------------ | ------- | --------- |

README_zh.md

@@ -298,7 +298,7 @@ huggingface-cli login
 | datasets     | 2.16.0  | 2.19.2    |
 | accelerate   | 0.30.1  | 0.30.1    |
 | peft         | 0.11.1  | 0.11.1    |
-| trl          | 0.9.3   | 0.9.3     |
+| trl          | 0.8.6   | 0.9.3     |
 
 | 可选项       | 至少     | 推荐      |
 | ------------ | ------- | --------- |

requirements.txt

@@ -2,7 +2,7 @@ transformers>=4.41.2
 datasets>=2.16.0
 accelerate>=0.30.1
 peft>=0.11.1
-trl>=0.9.3
+trl>=0.8.6
 gradio>=4.0.0
 scipy
 einops

src/llamafactory/extras/misc.py

@@ -65,7 +65,7 @@ def check_dependencies() -> None:
         require_version("datasets>=2.16.0", "To fix: pip install datasets>=2.16.0")
         require_version("accelerate>=0.30.1", "To fix: pip install accelerate>=0.30.1")
         require_version("peft>=0.11.1", "To fix: pip install peft>=0.11.1")
-        require_version("trl>=0.9.3", "To fix: pip install trl>=0.9.3")
+        require_version("trl>=0.8.6", "To fix: pip install trl>=0.8.6")
 
 
 def count_parameters(model: torch.nn.Module) -> Tuple[int, int]:

src/llamafactory/train/dpo/trainer.py

@@ -10,7 +10,7 @@ from trl import DPOTrainer
 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_ref_context
+from ..trainer_utils import create_custom_optimzer, create_custom_scheduler, get_batch_logps, get_ref_context
 
 
 if TYPE_CHECKING:
@@ -155,12 +155,7 @@ class CustomDPOTrainer(DPOTrainer):
 
         all_logits: "torch.Tensor" = model(**batch, return_dict=True, use_cache=False).logits.to(torch.float32)
 
-        all_logps, valid_length = self.get_batch_logps(
+        all_logps, valid_length = get_batch_logps(logits=all_logits, labels=batch["labels"])
-            logits=all_logits,
-            labels=batch["labels"],
-            is_encoder_decoder=self.is_encoder_decoder,
-            label_pad_token_id=self.label_pad_token_id,
-        )
         if self.loss_type in ["ipo", "orpo", "simpo"]:
             all_logps = all_logps / valid_length
 

src/llamafactory/train/kto/trainer.py

@@ -9,7 +9,7 @@ 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_ref_context
+from ..trainer_utils import create_custom_optimzer, create_custom_scheduler, get_batch_logps, get_ref_context
 
 
 if TYPE_CHECKING:
@@ -98,16 +98,6 @@ class CustomKTOTrainer(KTOTrainer):
             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
-
     def forward(
         self, model: "PreTrainedModel", batch: Dict[str, "torch.Tensor"], prefix: Literal["", "kl_"] = ""
     ) -> Tuple["torch.Tensor", "torch.Tensor"]:
@@ -127,28 +117,23 @@ class CustomKTOTrainer(KTOTrainer):
 
         logits = model(**model_inputs, return_dict=True, use_cache=False).logits.to(torch.float32)
 
-        logps = self.get_batch_logps(
+        logps, valid_length = get_batch_logps(logits=logits, labels=batch["{}labels".format(prefix)])
-            logits=logits,
+        return logps, logps / valid_length
-            labels=batch["{}labels".format(prefix)],
-            average_log_prob=False,
-            is_encoder_decoder=self.is_encoder_decoder,
-            label_pad_token_id=self.label_pad_token_id,
-        )
-        return logits, logps
 
     def concatenated_forward(
         self, model: "PreTrainedModel", batch: Dict[str, "torch.Tensor"]
-    ) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor"]:
+    ) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor"]:
-        target_logits, target_logps = self.forward(model, batch)
+        target_logps, target_logps_avg = self.forward(model, batch)
         with torch.no_grad():
-            _, kl_logps = self.forward(model, batch, prefix="kl_")
+            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, rejected_logps = target_logps[batch["kto_tags"]], target_logps[~batch["kto_tags"]]
+        chosen_logps = target_logps[batch["kto_tags"]]
-        chosen_logits, rejected_logits = target_logits[batch["kto_tags"]], target_logits[~batch["kto_tags"]]
+        rejected_logps = target_logps[~batch["kto_tags"]]
-        return chosen_logps, rejected_logps, chosen_logits, rejected_logits, kl_logps
+        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"]
@@ -164,13 +149,9 @@ class CustomKTOTrainer(KTOTrainer):
             ref_context = nullcontext()
 
         with torch.no_grad(), ref_context:
-            (
+            reference_chosen_logps, reference_rejected_logps, reference_kl_logps, _ = self.concatenated_forward(
-                reference_chosen_logps,
+                ref_model, batch
-                reference_rejected_logps,
+            )
-                _,
-                _,
-                reference_kl_logps,
-            ) = self.concatenated_forward(ref_model, batch)
 
         return reference_chosen_logps, reference_rejected_logps, reference_kl_logps
 
@@ -183,14 +164,9 @@ class CustomKTOTrainer(KTOTrainer):
         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 = (
-            policy_chosen_logps,
+            self.concatenated_forward(model, batch)
-            policy_rejected_logps,
+        )
-            policy_chosen_logits,
-            _,
-            policy_kl_logps,
-        ) = self.concatenated_forward(model, batch)
-
         reference_chosen_logps, reference_rejected_logps, reference_kl_logps = self.compute_reference_log_probs(
             model, batch
         )
@@ -205,8 +181,8 @@ class CustomKTOTrainer(KTOTrainer):
         losses = losses.nanmean()
 
         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"]])
+            sft_loss = -policy_chosen_logps_avg
-            losses += self.ftx_gamma * sft_loss.nanmean() / len(policy_chosen_logits) * len(batch["labels"])
+            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)

src/llamafactory/train/trainer_utils.py

@@ -1,5 +1,5 @@
 from contextlib import contextmanager
-from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Union
+from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Tuple, Union
 
 import torch
 from transformers import Trainer
@@ -7,6 +7,7 @@ from transformers.optimization import get_scheduler
 from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
 from transformers.trainer_pt_utils import get_parameter_names
 
+from ..extras.constants import IGNORE_INDEX
 from ..extras.logging import get_logger
 from ..extras.packages import is_galore_available
 from ..hparams import FinetuningArguments, ModelArguments
@@ -399,3 +400,24 @@ def create_custom_scheduler(
 
         for param in optimizer_dict.keys():
             param.register_post_accumulate_grad_hook(scheduler_hook)
+
+
+def get_batch_logps(
+    logits: "torch.Tensor", labels: "torch.Tensor", label_pad_token_id: int = IGNORE_INDEX
+) -> Tuple["torch.Tensor", "torch.Tensor"]:
+    r"""
+    Computes the log probabilities of the given labels under the given logits.
+
+    Returns:
+        logps: A tensor of shape (batch_size,) containing the sum of log probabilities.
+        valid_length: A tensor of shape (batch_size,) containing the number of non-masked tokens.
+    """
+    if logits.shape[:-1] != labels.shape:
+        raise ValueError("Logits (batchsize x seqlen) and labels must have the same shape.")
+
+    labels = labels[:, 1:].clone()
+    logits = logits[:, :-1, :]
+    loss_mask = labels != label_pad_token_id
+    labels[labels == label_pad_token_id] = 0  # dummy token
+    per_token_logps = torch.gather(logits.log_softmax(-1), dim=2, index=labels.unsqueeze(2)).squeeze(2)
+    return (per_token_logps * loss_mask).sum(-1), loss_mask.sum(-1)