refactor checkpointing: remove date and run id

open_diloco/train_fsdp.py

@@ -22,6 +22,7 @@
 from datasets.distributed import split_dataset_by_node
 from fsspec.generic import GenericFileSystem
 from torch.distributed import destroy_process_group, init_process_group
+
 from torchdata.stateful_dataloader import StatefulDataLoader
 from transformers import (
     AutoTokenizer,
@@ -69,26 +70,18 @@ def log(message):
     logger.info(f"[rank {os.environ['LOCAL_RANK']}] {message}")
 
 
-def get_ckpt_folder(checkpoint_path, training_date, project, run_id):
-    return os.path.join(checkpoint_path, training_date, project, run_id)
-
-
-def check_checkpoint_path_access(checkpoint_path: str, training_date, project, run_id, rank):
-    dummy_file_path = os.path.join(
-        get_ckpt_folder(
-            checkpoint_path=checkpoint_path,
-            training_date=training_date,
-            project=project,
-            run_id=run_id,
-        ),
-        f"dummy_file_{rank}.txt",
-    )
+def check_checkpoint_path_access(checkpoint_path: str, rank: int):
+    dummy_file_path = os.path.join(checkpoint_path, f"dummy_file_{rank}.txt")
     with fsspec.open(dummy_file_path, "w") as f:
         f.write("This is a dummy file for testing access.")
     gfs = GenericFileSystem()
     gfs.rm(dummy_file_path)
 
 
+def get_diloco_rank_dir_name(world_rank_diloco: int) -> str:
+    return f"diloco_rank_{world_rank_diloco}"
+
+
 class HvConfig(BaseConfig):
     outer_lr: float = 0.7
     local_steps: int = 500
@@ -202,10 +195,6 @@ def train(config: Config):
     assert batch_size % config.per_device_train_batch_size == 0
     gradient_accumulation_steps = batch_size // config.per_device_train_batch_size
 
-    training_date = datetime.datetime.now().strftime(
-        "%Y-%m-%d"
-    )  # we define the data at the beginning of training in case the training take several days
-
     if config.hv is not None:
         sharding_strategy = ShardingStrategy.NO_SHARD
         log("Hivemind is used, ShardingStrategy.NO_SHARD is used")
@@ -232,7 +221,7 @@ def train(config: Config):
         log_visible_maddrs(dht.get_visible_maddrs(), only_p2p=False)
 
     if local_rank == 0:
-        check_checkpoint_path_access(config.checkpoint_path, training_date, config.project, run_id, rank)
+        check_checkpoint_path_access(config.checkpoint_path, rank)
 
     # DataLoader preparation
     tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=True)
@@ -290,7 +279,9 @@ def scheduler_fn(opt):
             # Otherwise the world messenger will get lonely and hang
             fake_optimizer = inner_optimizer(model.parameters())
             last_loss = load_checkpoint(
-                checkpoint_path=config.resume_from_checkpoint,
+                checkpoint_path=os.path.join(
+                    config.resume_from_checkpoint, get_diloco_rank_dir_name(config.hv.world_rank)
+                ),
                 model=model,
                 optimizer=fake_optimizer,
             )
@@ -329,7 +320,9 @@ def scheduler_fn(opt):
 
         if config.resume_from_checkpoint:
             last_loss = load_checkpoint(
-                checkpoint_path=config.resume_from_checkpoint,
+                checkpoint_path=os.path.join(
+                    config.resume_from_checkpoint, get_diloco_rank_dir_name(config.hv.world_rank)
+                ),
                 model=model,
                 optimizer=optimizer.inner_optimizer,
                 scheduler=scheduler,
@@ -470,16 +463,13 @@ def scheduler_fn(opt):
             # Save checkpoint every 'checkpoint_interval' steps
             if config.checkpoint_interval is not None and real_step % config.checkpoint_interval == 0:
                 log(f"saving at step {real_step}, step {step+1}")
-                ckpt_path = os.path.join(
-                    get_ckpt_folder(config.checkpoint_path, training_date, config.project, run_id),
-                    f"model_step_{int(real_step)}",
-                )
+                ckpt_path = os.path.join(config.checkpoint_path, f"model_step_{int(real_step)}")
 
                 if world_messenger_hv:
                     assert isinstance(optimizer, DiLoCoOptimizer)
                     with optimizer.tracker.pause_updates():
                         save_checkpoint(
-                            checkpoint_path=ckpt_path,
+                            checkpoint_path=os.path.join(ckpt_path, get_diloco_rank_dir_name(config.hv.world_rank)),
                             model=model,
                             optimizer=optimizer.inner_optimizer,
                             scheduler=scheduler,