fix generation bug #532

src/llmtuner/chat/stream_chat.py

@@ -3,7 +3,7 @@ from typing import Any, Dict, Generator, List, Optional, Tuple
 from threading import Thread
 from transformers import TextIteratorStreamer
 
-from llmtuner.extras.misc import dispatch_model, get_logits_processor, get_stopping_criteria
+from llmtuner.extras.misc import dispatch_model, get_logits_processor
 from llmtuner.extras.template import get_template_and_fix_tokenizer
 from llmtuner.tuner.core import get_infer_args, load_model_and_tokenizer
 
@@ -49,10 +49,9 @@ class ChatModel:
             top_p=top_p or gen_kwargs["top_p"],
             top_k=top_k or gen_kwargs["top_k"],
             repetition_penalty=repetition_penalty or gen_kwargs["repetition_penalty"],
-            eos_token_id=self.tokenizer.eos_token_id,
+            eos_token_id=[self.tokenizer.eos_token_id] + self.tokenizer.additional_special_tokens_ids,
             pad_token_id=self.tokenizer.pad_token_id,
-            logits_processor=get_logits_processor(),
-            stopping_criteria=get_stopping_criteria(self.tokenizer.additional_special_tokens_ids)
+            logits_processor=get_logits_processor()
         ))
 
         if max_length:

src/llmtuner/extras/misc.py

@@ -2,9 +2,8 @@ import torch
 from typing import TYPE_CHECKING, List, Optional, Tuple
 from transformers import (
     LogitsProcessor,
-    LogitsProcessorList,
-    StoppingCriteria,
-    StoppingCriteriaList
+    InfNanRemoveLogitsProcessor,
+    LogitsProcessorList
 )
 
 from llmtuner.extras.constants import LAYERNORM_NAMES
@@ -33,37 +32,12 @@ class AverageMeter:
         self.avg = self.sum / self.count
 
 
-class InvalidScoreLogitsProcessor(LogitsProcessor):
-
-    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
-        if torch.isnan(scores).any() or torch.isinf(scores).any():
-            scores.zero_()
-            scores[..., 0] = 1.0
-        return scores
-
-
 def get_logits_processor() -> LogitsProcessorList:
     logits_processor = LogitsProcessorList()
-    logits_processor.append(InvalidScoreLogitsProcessor())
+    logits_processor.append(InfNanRemoveLogitsProcessor())
     return logits_processor
 
 
-class StopWordsCriteria(StoppingCriteria):
-
-    def __init__(self, stop_ids: List[int]) -> None:
-        super().__init__()
-        self.stop_ids = stop_ids
-
-    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
-        return any([stop_id in input_ids[:, -1] for stop_id in self.stop_ids])
-
-
-def get_stopping_criteria(stop_ids: List[int]) -> StoppingCriteriaList:
-    stopping_criteria = StoppingCriteriaList()
-    stopping_criteria.append(StopWordsCriteria(stop_ids))
-    return stopping_criteria
-
-
 def count_parameters(model: torch.nn.Module) -> Tuple[int, int]:
     r"""
     Returns the number of trainable parameters and number of all parameters in the model.

src/llmtuner/tuner/ppo/trainer.py

@@ -10,7 +10,7 @@ from trl import PPOTrainer
 from trl.core import LengthSampler
 
 from llmtuner.extras.logging import get_logger
-from llmtuner.extras.misc import AverageMeter, count_parameters, get_logits_processor, get_stopping_criteria
+from llmtuner.extras.misc import AverageMeter, count_parameters, get_logits_processor
 from llmtuner.tuner.core.trainer import PeftTrainer
 from llmtuner.tuner.ppo.utils import cast_layernorm_dtype, replace_model
 
@@ -74,10 +74,9 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
 
         # Keyword arguments for `model.generate`
         gen_kwargs = self.generating_args.to_dict()
-        gen_kwargs["eos_token_id"] = self.tokenizer.eos_token_id
+        gen_kwargs["eos_token_id"] = [self.tokenizer.eos_token_id] + self.tokenizer.additional_special_tokens_ids
         gen_kwargs["pad_token_id"] = self.tokenizer.pad_token_id
         gen_kwargs["logits_processor"] = get_logits_processor()
-        gen_kwargs["stopping_criteria"] = get_stopping_criteria(self.tokenizer.additional_special_tokens_ids)
 
         length_sampler = LengthSampler(max_target_length // 2, max_target_length)
         unwrapped_model: "AutoModelForCausalLMWithValueHead" = self.accelerator.unwrap_model(self.model)

src/llmtuner/tuner/sft/trainer.py

@@ -50,9 +50,10 @@ class Seq2SeqPeftTrainer(PeftTrainer):
         loss, generated_tokens, labels = super().prediction_step(
             model, inputs, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys
         )
-        generated_tokens = (
-            generated_tokens[:, max(prompt_len, label_len):] if generated_tokens is not None else None
-        )
+        if generated_tokens is not None:
+            generated_tokens[:, :max(prompt_len, label_len)] = (
+                self.tokenizer.pad_token_id * torch.ones_like(generated_tokens[:, :max(prompt_len, label_len)])
+            )
 
         return (loss, generated_tokens, labels)
 
@@ -72,10 +73,7 @@ class Seq2SeqPeftTrainer(PeftTrainer):
                 assert self.tokenizer.padding_side == "left", "This method only accepts left-padded tensor."
                 pad_token_id = self.tokenizer.pad_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.")
+                raise ValueError("PAD token is required.")
 
         padded_tensor = pad_token_id * torch.ones_like(tgt_tensor)
         padded_tensor[:, -src_tensor.shape[-1]:] = src_tensor # adopt left-padding

src/llmtuner/tuner/sft/workflow.py

@@ -5,7 +5,7 @@ from transformers import DataCollatorForSeq2Seq
 
 from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset
 from llmtuner.extras.constants import IGNORE_INDEX
-from llmtuner.extras.misc import get_logits_processor, get_stopping_criteria
+from llmtuner.extras.misc import get_logits_processor
 from llmtuner.extras.ploting import plot_loss
 from llmtuner.tuner.core import load_model_and_tokenizer
 from llmtuner.tuner.sft.metric import ComputeMetrics
@@ -52,10 +52,9 @@ def run_sft(
 
     # Keyword arguments for `model.generate`
     gen_kwargs = generating_args.to_dict()
-    gen_kwargs["eos_token_id"] = tokenizer.eos_token_id
+    gen_kwargs["eos_token_id"] = [tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids
     gen_kwargs["pad_token_id"] = tokenizer.pad_token_id
     gen_kwargs["logits_processor"] = get_logits_processor()
-    gen_kwargs["stopping_criteria"] = get_stopping_criteria(tokenizer.additional_special_tokens_ids)
 
     # Training
     if training_args.do_train: