bug fix in benchmark ckpt loading and megatron hf save

Author: chenyushuo

trinity/manager/synchronizer.py

@@ -44,7 +44,10 @@ def __init__(self, config: Config, module_ref: ray.actor.ActorHandle):
         self._modules = {module_ref}
         self._modules_lock = asyncio.Lock()
         asyncio.create_task(self._check_modules())
-        if self.config.synchronizer.sync_method == SyncMethod.CHECKPOINT:
+        if (
+            self.config.mode != "bench"
+            and self.config.synchronizer.sync_method == SyncMethod.CHECKPOINT
+        ):
             asyncio.create_task(self._find_latest_state_dict())
 
     async def add_module(self, module_ref: ray.actor.ActorHandle) -> None:

trinity/trainer/verl/megatron_checkpoint_manager.py

@@ -233,45 +233,51 @@ def save_checkpoint(  # noqa: C901
                     json.dump(transformer_config_dict, f, indent=2)
 
         if self.should_save_hf_model or save_as_hf:
-            # wait for everyone to dump to local
-            state_dict = self.weight_saver(
-                self.model,
-                self.hf_config,
-                dtype=self.param_dtype,
-                is_value_model=self.is_value_model,
-                tie_word_embeddings=self.share_embeddings_and_output_weights,
-            )
+            try:
+                # wait for everyone to dump to local
+                state_dict = self.weight_saver(
+                    self.model,
+                    self.hf_config,
+                    dtype=self.param_dtype,
+                    is_value_model=self.is_value_model,
+                    tie_word_embeddings=self.share_embeddings_and_output_weights,
+                )
 
-            torch.distributed.barrier()
-            if self.rank == 0:
-                # TODO: async save or use mbridge to save hf model
-                hf_model_ckpt_path = get_hf_model_checkpoint_path(local_path)
-                import warnings
+                torch.distributed.barrier()
+                if self.rank == 0:
+                    # TODO: async save or use mbridge to save hf model
+                    hf_model_ckpt_path = get_hf_model_checkpoint_path(local_path)
+                    import warnings
 
-                from accelerate import init_empty_weights
+                    from accelerate import init_empty_weights
 
-                with init_empty_weights(), warnings.catch_warnings():
-                    warnings.simplefilter("ignore")
-                    if "mistral7b-rm" in self.config.model.path:
-                        from transformers import MistralForSequenceClassification
+                    with init_empty_weights(), warnings.catch_warnings():
+                        warnings.simplefilter("ignore")
+                        if "mistral7b-rm" in self.config.model.path:
+                            from transformers import MistralForSequenceClassification
 
-                        model = MistralForSequenceClassification.from_pretrained(
-                            self.config.model.path
-                        )  # use score head instead of lm_head
-                        state_dict["score.weight"] = state_dict["score.weight"]
-                    else:
-                        from transformers import AutoModelForCausalLM
+                            model = MistralForSequenceClassification.from_pretrained(
+                                self.config.model.path
+                            )  # use score head instead of lm_head
+                            state_dict["score.weight"] = state_dict["score.weight"]
+                        else:
+                            from transformers import AutoModelForCausalLM
 
-                        model = AutoModelForCausalLM.from_pretrained(
-                            self.config.model.path, torch_dtype="auto"
-                        )
-                model.save_pretrained(hf_model_ckpt_path, state_dict=state_dict)
-                log_with_rank(
-                    f"Saved Huggingface config and tokenizer to {hf_model_ckpt_path}",
-                    rank=self.rank,
-                    logger=logger,
-                    log_only_rank_0=True,
+                            model = AutoModelForCausalLM.from_pretrained(
+                                self.config.model.path, torch_dtype="auto"
+                            )
+                    model.save_pretrained(hf_model_ckpt_path, state_dict=state_dict)
+                    log_with_rank(
+                        f"Saved Huggingface config and tokenizer to {hf_model_ckpt_path}",
+                        rank=self.rank,
+                        logger=logger,
+                        log_only_rank_0=True,
+                    )
+            except Exception as e:
+                logger.error(
+                    f"Failed to save Huggingface model to {local_path}, you can try to set `use_mbridge=true` to save it."
                 )
+                logger.error(e)
 
         ray.get(
             self.checkpoint_monitor.register_thread_count.remote(