Support evaluation during training

applications/ColossalChat/coati/distributed/grpo_consumer.py

@@ -40,6 +40,7 @@ class GRPOConsumer(BaseConsumer):
         project_name=None,
         save_interval: int = 100,
         save_dir="./model",
+        eval_interval: int = -1,
     ):
         print(f"Using GRPO config: {grpo_config}")
         if grpo_config.get("loss_variation", "sample_level") == "token_level":
@@ -72,6 +73,7 @@ class GRPOConsumer(BaseConsumer):
             minibatch_size,
             save_interval=save_interval,
             save_dir=save_dir,
+            eval_interval=eval_interval,
         )
         path = model_config.pop("path")
         self.policy_model = AutoModelForCausalLM.from_pretrained(path, **model_config)
@@ -528,4 +530,5 @@ class GRPOConsumer(BaseConsumer):
         self.policy_model._force_wait_all_gather()
         model = self.policy_model.unwrap()
         state_dict = model.state_dict()
+        state_dict["consumer_global_step"] = torch.tensor([self.global_step], device=self.device)
         return state_dict