fix #175

Former-commit-id: 85c2210452cc45470c228f17b2b0df09b47e9575

requirements.txt

@@ -3,7 +3,7 @@ transformers>=4.29.1
 datasets>=2.12.0
 accelerate>=0.19.0
 peft>=0.3.0
-trl>=0.4.4
+trl==0.4.4
 sentencepiece
 jieba
 rouge-chinese

src/llmtuner/chat/stream_chat.py

@@ -1,3 +1,4 @@
+import torch
 from typing import Any, Dict, Generator, List, Optional, Tuple
 from threading import Thread
 from transformers import TextIteratorStreamer
@@ -41,10 +42,10 @@ class ChatModel:
         gen_kwargs = self.generating_args.to_dict()
         gen_kwargs.update(dict(
             input_ids=inputs["input_ids"],
-            temperature=temperature if temperature else gen_kwargs["temperature"],
+            temperature=temperature or gen_kwargs["temperature"],
-            top_p=top_p if top_p else gen_kwargs["top_p"],
+            top_p=top_p or gen_kwargs["top_p"],
-            top_k=top_k if top_k else gen_kwargs["top_k"],
+            top_k=top_k or gen_kwargs["top_k"],
-            repetition_penalty=repetition_penalty if repetition_penalty else gen_kwargs["repetition_penalty"],
+            repetition_penalty=repetition_penalty or gen_kwargs["repetition_penalty"],
             logits_processor=get_logits_processor()
         ))
 
@@ -58,6 +59,7 @@ class ChatModel:
 
         return gen_kwargs, prompt_length
 
+    @torch.inference_mode()
     def chat(
         self, query: str, history: Optional[List[Tuple[str, str]]] = None, prefix: Optional[str] = None, **input_kwargs
     ) -> Tuple[str, Tuple[int, int]]:
@@ -68,6 +70,7 @@ class ChatModel:
         response_length = len(outputs)
         return response, (prompt_length, response_length)
 
+    @torch.inference_mode()
     def stream_chat(
         self, query: str, history: Optional[List[Tuple[str, str]]] = None, prefix: Optional[str] = None, **input_kwargs
     ) -> Generator[str, None, None]:

src/llmtuner/tuner/core/loader.py

@@ -28,7 +28,7 @@ check_min_version("4.29.1")
 require_version("datasets>=2.12.0", "To fix: pip install datasets>=2.12.0")
 require_version("accelerate>=0.19.0", "To fix: pip install accelerate>=0.19.0")
 require_version("peft>=0.3.0", "To fix: pip install peft>=0.3.0")
-require_version("trl>=0.4.4", "To fix: pip install trl>=0.4.4")
+require_version("trl==0.4.4", "To fix: pip install trl==0.4.4")
 
 
 def load_model_and_tokenizer(

src/llmtuner/tuner/ppo/trainer.py

@@ -153,7 +153,7 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
             if self.control.should_training_stop:
                 break
 
-    @torch.no_grad()
+    @torch.inference_mode()
     def generate(
         self,
         inputs: Dict[str, torch.Tensor],

src/llmtuner/tuner/sft/trainer.py

@@ -32,17 +32,40 @@ class Seq2SeqPeftTrainer(PeftTrainer):
         Subclass and override to inject custom behavior.
         """
         prompt_len, label_len = inputs["input_ids"].size(-1), inputs["labels"].size(-1)
-        if self.tokenizer.padding_side == "right": # pads the labels to the same length as the inputs
+        if prompt_len > label_len:
-            inputs["labels"] = torch.cat((inputs["labels"], torch.zeros_like(inputs["input_ids"])[:, label_len:]), dim=-1)
+            inputs["labels"] = self._pad_tensors_to_target_len(inputs["labels"], inputs["input_ids"])
-        else:
+        if label_len > prompt_len:
-            inputs["labels"] = torch.cat((torch.zeros_like(inputs["input_ids"])[:, label_len:], inputs["labels"]), dim=-1)
+            inputs["input_ids"] = self._pad_tensors_to_target_len(inputs["input_ids"], inputs["labels"])
+
         loss, generated_tokens, labels = super().prediction_step(
             model, inputs, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys
         )
-        generated_tokens = generated_tokens[:, prompt_len:] if generated_tokens is not None else None
+        generated_tokens = generated_tokens[:, max(prompt_len, label_len):] if generated_tokens is not None else None
 
         return (loss, generated_tokens, labels)
 
+    def _pad_tensors_to_target_len(self, src_tensor: torch.Tensor, tgt_tensor: torch.Tensor) -> torch.Tensor:
+        r"""
+        Pads the tensor to the same length as the target tensor.
+
+        Should only be called when predict_with_generate=True.
+        """
+        if self.tokenizer is not None and hasattr(self.tokenizer, "pad_token_id"):
+            assert self.tokenizer.padding_side == "left", "This method only accepts left-padded tensor."
+            # If PAD token is not defined at least EOS token has to be defined
+            pad_token_id = (
+                self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
+            )
+        else:
+            if self.model.config.pad_token_id is not None:
+                pad_token_id = self.model.config.pad_token_id
+            else:
+                raise ValueError("Pad_token_id must be set in the configuration of the model, in order to pad tensors")
+
+        padded_tensor = pad_token_id * torch.ones_like(tgt_tensor)
+        padded_tensor[:, -src_tensor.shape[-1]:] = src_tensor # adopt left-padding
+        return padded_tensor
+
     def save_predictions(
         self,
         predict_results: PredictionOutput