polish

applications/ColossalChat/coati/dataset/loader.py

@@ -356,6 +356,14 @@ def apply_chat_template_and_mask(
     truncation: bool = True,
     ignore_idx: int = -100,
 ) -> Dict[str, torch.Tensor]:
+
+    system_prompt = "You are a helpful assistant. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and<answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>. Now the user asks you to solve a math problem that involves reasoning. After thinking, when you finally reach a conclusion, clearly output the final answer without explanation within the <answer> </answer> tags, your final answer should be a integer without unit, currency mark, thousands separator or other text. i.e., <answer> 123 </answer>.\n"
+
+    system_element = {
+        "role": "system",
+        "content": system_prompt,
+    }
+
     # Format for RL.
     gt_answer = None
     if "messages" in chat and "gt_answer" in chat:
@@ -365,7 +373,7 @@ def apply_chat_template_and_mask(
     tokens = []
     assistant_mask = []
     for i, msg in enumerate(chat):
-        msg_tokens = tokenizer.apply_chat_template([msg], tokenize=True)
+        msg_tokens = tokenizer.apply_chat_template([system_element, msg], tokenize=True, add_generation_prompt=True)
         # remove unexpected bos token
         if i > 0 and msg_tokens[0] == tokenizer.bos_token_id:
             msg_tokens = msg_tokens[1:]
@@ -378,14 +386,15 @@ def apply_chat_template_and_mask(
     if max_length is not None:
         if padding and len(tokens) < max_length:
             to_pad = max_length - len(tokens)
-            if tokenizer.padding_side == "right":
-                tokens.extend([tokenizer.pad_token_id] * to_pad)
-                assistant_mask.extend([False] * to_pad)
-                attention_mask.extend([0] * to_pad)
-            else:
-                tokens = [tokenizer.pad_token_id] * to_pad + tokens
-                assistant_mask = [False] * to_pad + assistant_mask
-                attention_mask = [0] * to_pad + attention_mask
+            # Left padding for generation.
+            # if tokenizer.padding_side == "right":
+            #     tokens.extend([tokenizer.pad_token_id] * to_pad)
+            #     assistant_mask.extend([False] * to_pad)
+            #     attention_mask.extend([0] * to_pad)
+            # else:
+            tokens = [tokenizer.pad_token_id] * to_pad + tokens
+            assistant_mask = [False] * to_pad + assistant_mask
+            attention_mask = [0] * to_pad + attention_mask
         if truncation and len(tokens) > max_length:
             tokens = tokens[:max_length]
             assistant_mask = assistant_mask[:max_length]

applications/ColossalChat/coati/distributed/grpo_consumer.py

@@ -9,7 +9,7 @@ from coati.distributed.loss import PolicyLoss
 from coati.distributed.reward.reward_fn import math_reward_fn
 from coati.distributed.reward.verifiable_reward import VerifiableReward
 from coati.distributed.utils import calc_action_log_probs
-from coati.trainer.utils import all_reduce_mean, is_rank_0
+from coati.trainer.utils import all_reduce_mean
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 from colossalai.nn.optimizer import HybridAdam
@@ -77,8 +77,15 @@ class GRPOConsumer(BaseConsumer):
         )
 
         self.policy_loss_fn = PolicyLoss()
-        if is_rank_0():
-            self.run = wandb.init(project="Colossal-GRPO-Test4")
+        self.global_step = 0
+        if self.rank == 0:
+            self.wandb_run = wandb.init(project="Colossal-GRPO-Test6", sync_tensorboard=True)
+            # import os
+            # import time
+
+            # log_dir = self.wandb_run.dir
+            # # log_dir = os.path.join(log_dir, time.strftime("%Y-%m-%d_%H:%M:%S", time.localtime()))
+            # # self.writer = SummaryWriter(log_dir=log_dir)
 
     def setup(self):
         super().setup()
@@ -115,10 +122,11 @@ class GRPOConsumer(BaseConsumer):
             )["logits"]
             action_log_probs = calc_action_log_probs(policy_model_logits, data["input_ids"], num_action)
 
-            reference_model_logits = self.reference_model(
-                input_ids=data["input_ids"],
-                attention_mask=data["attention_mask"],
-            )["logits"]
+            with torch.no_grad():
+                reference_model_logits = self.reference_model(
+                    input_ids=data["input_ids"],
+                    attention_mask=data["attention_mask"],
+                )["logits"]
             reference_action_log_probs = calc_action_log_probs(reference_model_logits, data["input_ids"], num_action)
 
             # GRPO advantage calculation
@@ -126,7 +134,9 @@ class GRPOConsumer(BaseConsumer):
                 action_mask, dim=-1
             )
 
-            reward = self.reward_model(data["input_ids"], gt_answer=data["gt_answer"])
+            reward = self.reward_model(
+                data["input_ids"], gt_answer=data["gt_answer"], response_idx=data["response_idx"]
+            )
             reward = kl + reward
             # [batch_size, num_generations]
             group_reward = reward.view(-1, self.num_generations)
@@ -163,11 +173,19 @@ class GRPOConsumer(BaseConsumer):
             self.optimizer.step()
             self.optimizer.zero_grad()
             loss_scalar = self.accum_loss.item()
-            if is_rank_0():
+            if self.rank == 0:
                 print("Loss:", self.accum_loss.item(), "Reward:", self.accum_reward.item(), "KL:", self.accum_kl.item())
-                self.run.log(
-                    {"loss": self.accum_loss.item(), "reward": self.accum_reward.item(), "kl": self.accum_kl.item()}
+                self.wandb_run.log(
+                    {
+                        "train/loss": self.accum_loss.item(),
+                        "train/reward": self.accum_reward.item(),
+                        "train/kl": self.accum_kl.item(),
+                    }
                 )
+                # self.writer.add_scalar("train/loss", self.accum_loss.item(), self.global_step)
+                # self.writer.add_scalar("train/reward", self.accum_reward.item(), self.global_step)
+                # self.writer.add_scalar("train/kl", self.accum_kl.item(), self.global_step)
+                # self.global_step += 1
             self.accum_loss.zero_()
             self.accum_reward.zero_()
             self.accum_kl.zero_()

applications/ColossalChat/coati/distributed/inference_backend.py

@@ -154,6 +154,7 @@ class VLLMInferenceBackend(BaseInferenceBackend):
     )
     FORCE_GENERATE_CONFIG = dict(
         logprobs=0,
+        n=4,
     )
 
     def __init__(self, model_config: Dict[str, Any], generate_config: Dict[str, Any], tokenizer: PreTrainedTokenizer):
@@ -166,19 +167,24 @@ class VLLMInferenceBackend(BaseInferenceBackend):
         generate_config.update(self.FORCE_GENERATE_CONFIG)
         self.generate_config = SamplingParams(**generate_config)
         self.tokenizer = tokenizer
+        self.num_generations = self.FORCE_GENERATE_CONFIG["n"]
 
     @torch.no_grad()
     def generate(self, input_ids: torch.Tensor, attention_mask: torch.Tensor, **kwargs) -> Dict[str, torch.Tensor]:
+        micro_batch_size = input_ids.size(0)
+        response_start_idx = input_ids.size(1)
         outputs = self.llm.generate(
             prompt_token_ids=input_ids.tolist(), sampling_params=self.generate_config, use_tqdm=False
         )
         out_tokens = []
         out_len = []
         log_probs = []
+        response_idx = []
         for out in outputs:
             for output_i in out.outputs:
                 out_len.append(len(output_i.token_ids))
                 out_tokens.append(list(output_i.token_ids))
+                response_idx.append((response_start_idx, response_start_idx + len(output_i.token_ids) - 1))
                 assert len(output_i.logprobs) == len(output_i.token_ids)
                 p = [m[t].logprob for m, t in zip(output_i.logprobs, output_i.token_ids)]
                 log_probs.append(p)
@@ -195,6 +201,8 @@ class VLLMInferenceBackend(BaseInferenceBackend):
 
         out_tokens = torch.tensor(out_tokens)
         log_probs = torch.tensor(log_probs)
+        response_idx = torch.tensor(response_idx)
+
         if attention_mask.size(0) != action_mask.size(0):
             assert action_mask.size(0) % attention_mask.size(0) == 0
             num_returns = action_mask.size(0) // attention_mask.size(0)
@@ -209,9 +217,14 @@ class VLLMInferenceBackend(BaseInferenceBackend):
             "attention_mask": attention_mask,
             "action_log_probs": log_probs,
             "action_mask": action_mask,
+            "response_idx": response_idx,
         }
+
+        data = {k: v.view(micro_batch_size, self.num_generations, v.size(-1)) for k, v in data.items()}
+
         if "gt_answer" in kwargs:
-            data["gt_answer"] = kwargs["gt_answer"]
+            # repeat gt_answer for each prompt.
+            data["gt_answer"] = kwargs["gt_answer"].repeat_interleave(self.num_generations, dim=1)
         data = {k: v.to(get_current_device()) for k, v in data.items()}
         return data
 

applications/ColossalChat/coati/distributed/reward/reward_fn.py

@@ -3,12 +3,14 @@ import torch
 from .reward_utils import extract_solution, validate_response_structure
 
 
-def math_reward_fn(input_ids, gt_answer, **kwargs):
+def math_reward_fn(input_ids, gt_answer, response_idx, **kwargs):
     tokenizer = kwargs["tokenizer"]
     reward = torch.tensor(0.0).to(input_ids.device)
+    s, e = response_idx[0], response_idx[1]
     if gt_answer is None:
         return reward
-    decoded_final_answer = tokenizer.decode(input_ids, skip_special_tokens=True)
+
+    decoded_final_answer = tokenizer.decode(input_ids[s : e + 1], skip_special_tokens=True)
     gt_answer = tokenizer.decode(gt_answer.squeeze(0))
     final_answer, processed_str = extract_solution(decoded_final_answer)
 
@@ -29,7 +31,7 @@ def gsm8k_reward_fn(input_ids, **kwargs):
     reward = torch.tensor(0.0).to(input_ids.device)
     if gt_answer is None:
         return reward
-    decoded_final_answer = tokenizer.decode(input_ids[s:e], skip_special_tokens=True)
+    decoded_final_answer = tokenizer.decode(input_ids[s : e + 1], skip_special_tokens=True)
     final_answer, processed_str = extract_solution(decoded_final_answer)
     is_valid = True
     try:

applications/ColossalChat/coati/distributed/reward/verifiable_reward.py

@@ -16,6 +16,7 @@ class VerifiableReward:
         self,
         input_ids: torch.LongTensor,
         gt_answer: List[torch.Tensor] = None,
+        response_idx: List[torch.Tensor] = None,
     ) -> torch.Tensor:
         # Get batch size
         bs = input_ids.size(0)
@@ -30,6 +31,7 @@ class VerifiableReward:
                     reward_fn(
                         input_ids[i],
                         gt_answer=gt_answer[i],
+                        response_idx=response_idx[i],
                         **self.kwargs,
                     )
                     for i in range(bs)

applications/ColossalChat/rl_example.py

@@ -51,13 +51,17 @@ if __name__ == "__main__":
     elif args.backend == "vllm":
         inference_model_config.update(
             dict(
-                gpu_memory_utilization=0.6,
+                gpu_memory_utilization=0.7,
             )
         )
         generate_config.update(
             dict(
-                max_tokens=256,
+                max_tokens=2048,
                 ignore_eos=True,
+                include_stop_str_in_output=True,
+                stop=["</answer>"],
+                temperature=0.2,
+                top_p=0.95,
             )
         )
     else: