support logging rollouts to wandb

Author: YeAnbang

applications/ColossalChat/coati/distributed/producer.py

@@ -49,6 +49,7 @@ def __init__(
         project_name: str = None,
         run_name: str = None,
         wandb_group_name: str = None,
+        wandb_log_rollout_interval: int = 20,
     ):
         self.producer_idx = producer_idx
         self.num_producers = num_producers
@@ -58,7 +59,7 @@ def __init__(
         self.microbatch_size = microbatch_size
         assert batch_size % microbatch_size == 0
         self.num_microbatches = batch_size // microbatch_size
-        self.lastest_eval_step = -1
+        self.latest_eval_step = -1
 
         self.train_dataset_config = train_dataset_config
         self.model_config = model_config
@@ -68,6 +69,10 @@ def __init__(
         self.eval_interval = eval_interval
         self.eval_save_dir = eval_save_dir
         self.consumer_global_step = 0
+        self.eval_mode = False
+        self.wandb_rollout_data = []
+        self.wandb_log_rollout_interval = wandb_log_rollout_interval
+        self.latest_rollout_log_step = -1
         if self.producer_idx == 0:
             self.wandb_run = wandb.init(
                 project=project_name,
@@ -77,7 +82,7 @@ def __init__(
                 group=wandb_group_name,
             )
 
-        if os.path.exists(self.eval_save_dir):
+        if os.path.exists(self.eval_save_dir) and self.eval_interval > 0:
             raise ValueError(f"Eval save dir {self.eval_save_dir} already exists. Please delete it or change the name.")
 
         # init tokenizer
@@ -180,10 +185,11 @@ def loop(self) -> None:
                     break
                 if self.eval_interval > 0 and self.eval_dataset_config is not None:
                     if (
-                        self.consumer_global_step - self.lastest_eval_step >= self.eval_interval
-                        and self.consumer_global_step > self.lastest_eval_step
-                    ):
+                        self.consumer_global_step - self.latest_eval_step >= self.eval_interval
+                        and self.consumer_global_step > self.latest_eval_step
+                    ) or self.latest_eval_step == -1:
                         to_log_msg = {}
+                        self.eval_mode = True
                         for eval_task_name in self.eval_dataloaders:
                             if self.producer_idx == 0:
                                 print(
@@ -227,7 +233,8 @@ def loop(self) -> None:
 
                         if self.producer_idx == 0:
                             self.wandb_run.log(to_log_msg, step=self.consumer_global_step)
-                        self.lastest_eval_step = self.consumer_global_step
+                        self.eval_mode = False
+                        self.latest_eval_step = self.consumer_global_step
                 outputs = self.rollout(**batch)
 
                 print(f"[P{self.producer_idx}] Send data {[(k, v.shape) for k, v in outputs.items()]}")
@@ -345,9 +352,26 @@ def __init__(
     @torch.no_grad()
     def rollout(self, input_ids, attention_mask, **kwargs):
         rollouts = self.model.generate(input_ids, attention_mask, **kwargs)
-        # if self.producer_idx == 1:
-        #     print("Rollout example:\n", self.tokenizer.decode(rollouts["input_ids"][0][0], skip_special_tokens=True))
-
+        if self.producer_idx == 0 and not self.eval_mode:
+            wandb_rollout_data = self.wandb_rollout_data + [
+                [
+                    str(self.consumer_global_step),
+                    str(self.tokenizer.decode(rollouts["input_ids"][0][0], skip_special_tokens=True)),
+                ]
+            ]
+            if (
+                self.consumer_global_step - self.latest_rollout_log_step >= self.wandb_log_rollout_interval
+                or self.latest_rollout_log_step == -1
+            ):
+                self.wandb_rollout_data = wandb_rollout_data
+                self.latest_rollout_log_step = self.consumer_global_step
+            self.wandb_run.log(
+                {
+                    "rollout/rollout_examples": wandb.Table(
+                        columns=["train_step", "rollout_examples"], data=wandb_rollout_data
+                    )
+                }
+            )
         return rollouts
 
     def load_state_dict(self, state_dict):