update trainers

src/llamafactory/api/chat.py

@@ -7,7 +7,7 @@ from typing import TYPE_CHECKING, AsyncGenerator, Dict, List, Optional, Tuple
 
 from ..data import Role as DataRole
 from ..extras.logging import get_logger
-from ..extras.packages import is_fastapi_available, is_pillow_available
+from ..extras.packages import is_fastapi_available, is_pillow_available, is_requests_available
 from .common import dictify, jsonify
 from .protocol import (
     ChatCompletionMessage,
@@ -29,10 +29,13 @@ if is_fastapi_available():
 
 
 if is_pillow_available():
-    import requests
     from PIL import Image
 
 
+if is_requests_available():
+    import requests
+
+
 if TYPE_CHECKING:
     from numpy.typing import NDArray
 

src/llamafactory/train/dpo/trainer.py

@@ -187,13 +187,7 @@ class CustomDPOTrainer(DPOTrainer):
             ref_context = nullcontext()
 
         with torch.no_grad(), ref_context:
-            (
-                reference_chosen_logps,
-                reference_rejected_logps,
-                _,
-                _,
-                _,
-            ) = self.concatenated_forward(ref_model, batch)
+            reference_chosen_logps, reference_rejected_logps, *_ = self.concatenated_forward(ref_model, batch)
 
         return reference_chosen_logps, reference_rejected_logps
 

src/llamafactory/train/kto/trainer.py

@@ -146,15 +146,8 @@ class CustomKTOTrainer(KTOTrainer):
         if len(target_logps) != len(batch["kto_tags"]):
             raise ValueError("Mismatched shape of inputs and labels.")
 
-        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_logps = target_logps[chosen_idx, ...]
-        rejected_logps = target_logps[rejected_idx, ...]
-
-        chosen_logits = target_logits[chosen_idx, ...]
-        rejected_logits = target_logits[rejected_idx, ...]
-
+        chosen_logps, rejected_logps = target_logps[batch["kto_tags"]], target_logps[~batch["kto_tags"]]
+        chosen_logits, rejected_logits = target_logits[batch["kto_tags"]], target_logits[~batch["kto_tags"]]
         return chosen_logps, rejected_logps, chosen_logits, rejected_logits, kl_logps
 
     def compute_reference_log_probs(

src/llamafactory/train/ppo/utils.py

@@ -8,13 +8,14 @@ from transformers.integrations import is_deepspeed_zero3_enabled
 from ...extras.packages import is_requests_available
 
 
+if is_requests_available():
+    import requests
+
+
 if TYPE_CHECKING:
     from transformers import PreTrainedModel
     from trl import AutoModelForCausalLMWithValueHead
 
-if is_requests_available():
-    import requests
-
 
 def get_rewards_from_server(server_url: str, messages: List[str]) -> List[torch.Tensor]:
     headers = {"Content-Type": "application/json"}