feat: proper checkpoint resume in CLM fine-tune script

training/colossalai/run_clm.py

@@ -25,6 +25,7 @@ https://huggingface.co/models?filter=text-generation
 import datetime
 import math
 import os
+import re
 import signal
 import time
 from itertools import chain
@@ -219,7 +220,7 @@ def parse_args():
         default=None,
         help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
     )
-    parser.add_argument(
+    parser.add_argument("-r",
         "--resume_from_checkpoint",
         type=str,
         default=None,
@@ -563,9 +564,9 @@ def main():
     starting_epoch = 0
     global_step = 0
 
-    # FIXME(11b): One needs to manually update this when resuming from a
-    # checkpoint. Not ideal.
     step_from_checkpoint = 0
+    if args.resume_from_checkpoint is not None:
+        step_from_checkpoint = int(re.findall(r"epoch_\d+_step_(\d+).pt", args.resume_from_checkpoint)[0])
 
     for epoch in range(starting_epoch, args.num_train_epochs):
 
@@ -577,6 +578,13 @@ def main():
             if step < step_from_checkpoint:
                 completed_steps += 1
                 global_step += 1
+                progress_bar.update(1)
+                progress_bar.refresh()
+
+                # Apparently ColossalAI's checkpoint utilities don't work
+                # correctly for saving/restore the LR scheduler? So we "step" it
+                # manually here.
+                lr_scheduler.step()
                 continue
 
             batch = {k: v.cuda() for k, v in batch.items()}