fix(writer): fix tensorboard resume bug (#229)

internlm/core/trainer.py

@@ -38,6 +38,11 @@ class TrainState:
         # Total step count
         self.total_steps: int = config.data.total_steps
 
+        # resume tensorboard folder, need load from checkpoint or set manually.
+        self.resume_tb_folder = config.resume_tb_folder
+
+        self.tensorboard_folder = config.tensorboard_folder
+
     def init_batch_sampler(self, train_dl):
         # Copy of the batch sampler from the DataLoader
         self.batch_sampler = train_dl.batch_sampler.copy()
@@ -76,6 +81,9 @@ class TrainState:
         self.batch_sampler = train_dl.batch_sampler.copy()
         self.batch_sampler_iter = iter(self.batch_sampler)
 
+        # resume tensorboard from older tensorboard_folder
+        self.resume_tb_folder = other_stuffs.get("tensorboard_folder", None)
+
     def state_dict(self):
         return {
             "batch_count": self.batch_count,
@@ -83,6 +91,7 @@ class TrainState:
             "num_consumed_tokens": self.num_consumed_tokens,
             "inf_nan_skip_batches": self.inf_nan_skip_batches,
             "step_count": self.step_count,
+            "tensorboard_folder": self.tensorboard_folder,
         }
 
 

internlm/initialize/launch.py

@@ -200,6 +200,10 @@ and 'load_given_ckpt' is True, so internlm will load from 'load_ckpt_folder'"
             "resume_tb_folder", os.environ["resume_tb_folder"] if "resume_tb_folder" in os.environ else None
         )
 
+    if gpc.is_rank_for_log():
+        logger.info(f"tensorboard_folder: {gpc.config.tensorboard_folder}")
+        logger.info(f"resume_tb_folder: {gpc.config.resume_tb_folder}")
+
     # cudnn
     torch.backends.cudnn.benchmark = gpc.config.get("cudnn_benchmark", False)
     torch.backends.cudnn.deterministic = gpc.config.get("cudnn_deterministic", False)

internlm/utils/writer.py

@@ -38,10 +38,21 @@ def init_tb_writer(
         tb_folder = tensorboard_folder
 
     if gpc.get_global_rank() == 0:
+        # If we don't load ckpt, 'resume_tb_folder' is set as the tensorboard
+        # dir of the last task by 'make_launch_script.sh'.
+        # If we load ckpt, 'resume_tb_folder' will be overwritten as the
+        # reloaded 'train_state.resume_tb_folder'.s
         if resume_tb_folder is not None:
-            logger.info(f"Try mv tensorboard logs: {resume_tb_folder} to {tb_folder}...")
+            assert len(resume_tb_folder) > 0 and resume_tb_folder != "/"
-            os.system(f"cp -r {resume_tb_folder}/* {tb_folder}/")
+            if not os.path.exists(resume_tb_folder):
-            os.system(f"chmod -R +w {tb_folder}/")
+                logger.error(
+                    f"Can't found resume_tb_folder{resume_tb_folder}, \
+please make sure this folder is located at local file system."
+                )
+            else:
+                logger.info(f"Try mv tensorboard logs: {resume_tb_folder} to {tb_folder}... ")
+                os.system(f"cp -r {resume_tb_folder}/* {tb_folder}/")
+                os.system(f"chmod -R +w {tb_folder}/")
         else:
             logger.info(f"Login tensorboard logs to: {tb_folder}")
 

train.py

@@ -96,20 +96,6 @@ def main(args):
     # initialize customed llm logger
     uniscale_logger = initialize_llm_logger(start_time=current_time)
 
-    # initialize customed llm writer
-    with open(args.config, "r") as f:
-        config_lines = f.readlines()
-    writer = Writer(
-        job_name=gpc.config.JOB_NAME,
-        launch_time=current_time,
-        file_name=get_parallel_log_file_name(),
-        tensorboard_folder=gpc.config.tensorboard_folder,
-        resume_tb_folder=gpc.config.resume_tb_folder,
-        config=config_lines,
-        logger=logger,
-        enable_tb=gpc.config.enable_tb,
-    )
-
     # initialize and resume train state
     train_state = TrainState(gpc.config)
 
@@ -139,6 +125,21 @@ def main(args):
     # Loading other persistent training states.
     ckpt_manager.try_resume_training(lr_scheduler, optimizer, lr, train_state, train_dl)
 
+    # initialize customed llm writer
+    with open(args.config, "r") as f:
+        config_lines = f.readlines()
+    writer = Writer(
+        job_name=gpc.config.JOB_NAME,
+        launch_time=current_time,
+        file_name=get_parallel_log_file_name(),
+        tensorboard_folder=gpc.config.tensorboard_folder,
+        resume_tb_folder=train_state.resume_tb_folder,  # resume from ckpt.
+        step_count=train_state.step_count,  # resume from ckpt.
+        config=config_lines,
+        logger=logger,
+        enable_tb=gpc.config.enable_tb,
+    )
+
     # initialize metric for calculating accuracy and perplexity
     metric = AccPerplex(
         device=torch.cuda.current_device(),