Add GRPO and Support RLVR for PPO

.github/workflows/run_chatgpt_examples.yml

@@ -61,5 +61,6 @@ jobs:
           PRETRAINED_MODEL_PATH: ./models
           SFT_DATASET: ./sft_data
           PROMPT_DATASET: ./prompt_data
+          PROMPT_RLVR_DATASET: ./prompt_data
           PREFERENCE_DATASET: ./preference_data
           KTO_DATASET: ./kto_data

applications/ColossalChat/.gitignore

@@ -158,6 +158,7 @@ temp/
 applications/ColossalChat/logs
 applications/ColossalChat/models
 applications/ColossalChat/sft_data
+applications/ColossalChat/kto_data
 applications/ColossalChat/prompt_data
 applications/ColossalChat/preference_data
 applications/ColossalChat/temp

applications/ColossalChat/coati/dataset/conversation.py

@@ -141,7 +141,7 @@ def setup_conversation_template(
                 pass
             except ValueError as e:
                 raise ValueError(e)
-    if not dist.is_initialized() or dist.get_rank() == 0:
+    if save_path is not None and (not dist.is_initialized() or dist.get_rank() == 0):
         os.makedirs(os.path.dirname(save_path), exist_ok=True)
         with open(save_path, "w", encoding="utf8") as f:
             logger.info(f"Successfully generated a conversation tempalte config, save to {save_path}.")

applications/ColossalChat/coati/dataset/loader.py

@@ -155,13 +155,14 @@ def __call__(self, instances: Sequence[Dict[str, List[int]]]) -> Dict[str, torch
                 `input_ids`: `torch.Tensor` of shape (bsz, max_len);
                 `attention_mask`: `torch.BoolTensor` of shape (bsz, max_len);
         """
+        gt_answer = [ins.get("gt_answer", None) for ins in instances]
         instances = [{"input_ids": ins["input_ids"], "labels": ins["input_ids"]} for ins in instances]
         ret = super().__call__(instances=instances)
         input_ids = F.pad(
             ret["input_ids"], (self.max_length - ret["input_ids"].size(1), 0), value=self.tokenizer.pad_token_id
         )
         attention_mask = F.pad(ret["attention_mask"], (self.max_length - ret["attention_mask"].size(1), 0), value=False)
-        return {"input_ids": input_ids, "attention_mask": attention_mask}
+        return {"input_ids": input_ids, "attention_mask": attention_mask, "gt_answer": gt_answer}
 
 
 @dataclass

applications/ColossalChat/coati/dataset/tokenization_utils.py

@@ -147,7 +147,6 @@ def tokenize_prompt(
         ignore_index: the ignore index when calculate loss during training
         max_length: the maximum context length
     """
-
     messages = data_point["messages"]
     template = deepcopy(conversation_template)
     template.messages = []
@@ -167,7 +166,6 @@ def tokenize_prompt(
     if len(template.messages) % 2 != 1:
         # exclude the answer if provided. keep only the prompt
         template.messages = template.messages[:-1]
-
     # Prepare data
     prompt = template.get_prompt(length=len(template.messages), add_generation_prompt=True)
     tokenized = tokenizer([prompt], add_special_tokens=False)["input_ids"][0]
@@ -185,12 +183,21 @@ def tokenize_prompt(
         )
 
     # `inputs_decode` can be used to check whether the tokenization method is true.
-    return dict(
-        input_ids=tokenized,
-        inputs_decode=prompt,
-        seq_length=len(tokenized),
-        seq_category=data_point["category"] if "category" in data_point else "None",
-    )
+    if "gt_answer" in data_point:
+        return dict(
+            input_ids=tokenized,
+            inputs_decode=prompt,
+            seq_length=len(tokenized),
+            seq_category=data_point["category"] if "category" in data_point else "None",
+            gt_answer=data_point["gt_answer"],
+        )
+    else:
+        return dict(
+            input_ids=tokenized,
+            inputs_decode=prompt,
+            seq_length=len(tokenized),
+            seq_category=data_point["category"] if "category" in data_point else "None",
+        )
 
 
 def apply_rlhf_data_format(template: Conversation, tokenizer: Any):

applications/ColossalChat/coati/experience_buffer/naive.py

@@ -27,6 +27,8 @@ def __init__(self, sample_batch_size: int, limit: int = 0, cpu_offload: bool = T
         self.target_device = torch.device(f"cuda:{torch.cuda.current_device()}")
         # TODO(ver217): add prefetch
         self.items: List[BufferItem] = []
+        self.rng_sequence = []
+        self.ptr = 0
 
     @torch.no_grad()
     def append(self, experience: Experience) -> None:
@@ -40,6 +42,9 @@ def append(self, experience: Experience) -> None:
             if samples_to_remove > 0:
                 logger.warning(f"Experience buffer is full. Removing {samples_to_remove} samples.")
                 self.items = self.items[samples_to_remove:]
+        self.rng_sequence = [i for i in range(len(self.items))]
+        random.shuffle(self.rng_sequence)
+        self.ptr = 0
 
     def clear(self) -> None:
         self.items.clear()
@@ -52,7 +57,10 @@ def sample(self) -> Experience:
         Returns:
             A batch of sampled experiences.
         """
-        items = random.sample(self.items, self.sample_batch_size)
+        items = []
+        for _ in range(self.sample_batch_size):
+            self.ptr = (self.ptr + 1) % len(self.items)
+            items.append(self.items[self.rng_sequence[self.ptr]])
         experience = make_experience_batch(items)
         if self.cpu_offload:
             experience.to_device(self.target_device)