[pre-commit.ci] auto fixes from pre-commit.com hooks

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applications/ColossalChat/coati/distributed/consumer.py

@@ -13,8 +13,8 @@ from colossalai.booster import Booster
 from colossalai.booster.plugin import HybridParallelPlugin
 from colossalai.initialize import launch
 from colossalai.nn.optimizer import HybridAdam
-from colossalai.utils import get_current_device
 from colossalai.shardformer.policies.auto_policy import get_autopolicy
+from colossalai.utils import get_current_device
 
 from .comm import ray_broadcast_tensor_dict
 from .utils import bind_batch, post_recv, unbind_batch

applications/ColossalChat/coati/distributed/inference_backend.py

@@ -90,13 +90,13 @@ class TransformersInferenceBackend(BaseInferenceBackend):
             attention_mask = attention_mask.repeat_interleave(action_mask.size(0) // attention_mask.size(0), dim=0)
 
         attention_mask = torch.cat((attention_mask, action_mask), dim=1)
-        
+
         data = {
             "input_ids": out.sequences,
             "attention_mask": attention_mask,
             "action_log_probs": action_log_probs,
             "action_mask": action_mask,
-            "response_idx": response_idx
+            "response_idx": response_idx,
         }
         return data
 

applications/ColossalChat/coati/distributed/launch.py

@@ -7,11 +7,7 @@ from .grpo_consumer import GRPOConsumer
 from .ppo_consumer import PPOConsumer
 from .producer import SimpleProducer
 
-ALGO_MAP = {
-    "Simple": SimpleConsumer,
-    "GRPO": GRPOConsumer,
-    "PPO": PPOConsumer
-}
+ALGO_MAP = {"Simple": SimpleConsumer, "GRPO": GRPOConsumer, "PPO": PPOConsumer}
 
 
 def get_jsonl_size_fast(path: str) -> int:

applications/ColossalChat/coati/distributed/loss.py

@@ -72,4 +72,4 @@ class ValueLoss(nn.Module):
             loss = torch.mean(masked_mean(torch.max(surr1, surr2), action_mask))
         else:
             loss = torch.mean(torch.max(surr1, surr2))
-        return 0.5 * loss
+        return 0.5 * loss

applications/ColossalChat/coati/distributed/ppo_consumer.py

@@ -9,8 +9,8 @@ from coati.distributed.loss import PolicyLoss, ValueLoss
 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, compute_reward_ppo
-from coati.trainer.utils import all_reduce_mean
 from coati.models import Critic, disable_dropout
+from coati.trainer.utils import all_reduce_mean
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 from colossalai.nn.optimizer import HybridAdam
@@ -33,9 +33,9 @@ class PPOConsumer(BaseConsumer):
         plugin_config,
         microbatch_size=1,
         num_generations=1,
-        gamma:float=1.0,
-        lam:float=0.95,
-        kl_coef:float=0.05,
+        gamma: float = 1.0,
+        lam: float = 0.95,
+        kl_coef: float = 0.05,
         use_wandb=True,
     ):
         super().__init__(
@@ -55,7 +55,7 @@ class PPOConsumer(BaseConsumer):
         self.gamma = gamma
         self.lam = lam
         self.kl_coef = kl_coef
-        
+
         path = model_config.pop("path")
         self.policy_model = AutoModelForCausalLM.from_pretrained(path, **model_config)
         self.policy_model.train()
@@ -63,14 +63,14 @@ class PPOConsumer(BaseConsumer):
         self.critic_model = Critic(path, **model_config)
         self.critic_model.model.gradient_checkpointing_enable()
         self.critic_model.train()
-        
+
         # Disable dropout
         disable_dropout(self.policy_model)
         disable_dropout(self.critic_model)
-        
+
         self.optimizer = HybridAdam(self.policy_model.parameters(), lr=1e-6)
         self.critic_optimizer = HybridAdam(self.critic_model.parameters(), lr=1e-6)
-        
+
         self.accum_loss = torch.zeros(1, device=self.device)
         self.accum_reward = torch.zeros(1, device=self.device)
         self.accum_kl = torch.zeros(1, device=self.device)
@@ -152,15 +152,13 @@ class PPOConsumer(BaseConsumer):
                 input_ids=data["input_ids"],
                 attention_mask=data["attention_mask"],
             )
-            value = value[:, -num_action -1: -1] * action_mask
+            value = value[:, -num_action - 1 : -1] * action_mask
 
-            r = self.reward_model(
-                data["input_ids"], gt_answer=data["gt_answer"], response_idx=data["response_idx"]
-            )
+            r = self.reward_model(data["input_ids"], gt_answer=data["gt_answer"], response_idx=data["response_idx"])
             reward, kl = compute_reward_ppo(
                 r, self.kl_coef, old_action_log_probs, reference_action_log_probs, action_mask=action_mask
             )
-                
+
             # Calculate advantages
             # reference: https://github.com/huggingface/trl/blob/e3244d2d096ff1e2e248c931d06d39e165e20623/trl/trainer/ppo_trainer.py#L514C17-L523C46lastgaelam = 0
             lastgaelam = 0
@@ -172,7 +170,7 @@ class PPOConsumer(BaseConsumer):
                 advantage_reversed.append(lastgaelam)
             advantage = torch.stack(advantage_reversed[::-1], axis=1) * action_mask
             advantage = advantage.detach()
-            
+
             # KL divergence for logging
             per_token_kl = (
                 torch.exp(reference_action_log_probs - action_log_probs)
@@ -189,7 +187,7 @@ class PPOConsumer(BaseConsumer):
                 0,  # kl is already included in the advantage
                 action_mask,
             )
-            
+
             # Critic Loss
             # Hack: use the current value to approximate the old value, should be old value mathematically
             critic_loss = self.critic_loss_fn(
@@ -236,7 +234,9 @@ class PPOConsumer(BaseConsumer):
                             data["input_ids"][i], skip_special_tokens=True
                         )
                         response_reward_for_logging = r[i]
-                        print(f"###### Generation Sample {i} ######\nResponse: {response_decoded_for_logging}\nReward: {response_reward_for_logging}")
+                        print(
+                            f"###### Generation Sample {i} ######\nResponse: {response_decoded_for_logging}\nReward: {response_reward_for_logging}"
+                        )
                 self.wandb_run.log(
                     {
                         "train/loss": self.accum_loss.item() / self.accum_count,

applications/ColossalChat/coati/distributed/producer.py

@@ -94,11 +94,11 @@ class BaseProducer:
         print(
             f"[P{self.producer_idx}] num_valid_microbatches {num_valid_microbatches}, nmb: {self.num_microbatches}, dl: {len(self.dataloader)}"
         )
-        for episode in range(self.num_episodes): 
+        for episode in range(self.num_episodes):
             self.dataloader.sampler.set_epoch(episode)
             for i, batch in enumerate(self.dataloader):
                 if i >= num_valid_microbatches:
-                    break 
+                    break
                 outputs = self.rollout(**batch)
                 print(f"[P{self.producer_idx}] Send data {[(k, v.shape) for k, v in outputs.items()]}")
                 outputs = pre_send(outputs)

applications/ColossalChat/coati/distributed/utils.py

@@ -100,6 +100,7 @@ def masked_mean(tensor: torch.Tensor, mask: torch.Tensor, dim: int = 1) -> torch
     mean = tensor / (mask_sum + 1e-8)
     return mean
 
+
 def compute_reward_ppo(
     r: Union[torch.Tensor, float],
     kl_coef: float,
@@ -125,4 +126,4 @@ def compute_reward_ppo(
         assert action_mask[i].sum() > 0
         reward[i, : action_mask[i].sum()] += r_clip[i]
         reward[i, action_mask[i].sum() :] *= 0
-    return reward, ((log_ratio * (log_ratio < 10)).exp() - 1 - log_ratio) * action_mask
+    return reward, ((log_ratio * (log_ratio < 10)).exp() - 1 - log_ratio) * action_mask

applications/ColossalChat/rl_example.py

@@ -28,7 +28,7 @@ if __name__ == "__main__":
         top_p=0.8,
     )
 
-    if args.backend == "transformers":            
+    if args.backend == "transformers":
         inference_model_config.update(
             dict(
                 use_flash_attention_2=True,
@@ -43,13 +43,7 @@ if __name__ == "__main__":
             )
         )
         generate_config.update(
-            dict(
-                max_length=768,
-                do_sample=True,
-                max_new_tokens=None,
-                early_stopping=False,
-                stop_strings=["</answer>"]
-            )
+            dict(max_length=768, do_sample=True, max_new_tokens=None, early_stopping=False, stop_strings=["</answer>"])
         )
     elif args.backend == "vllm":
         inference_model_config.update(