From 1a9e8a5718eb0134f0355b7ca3296a16976e7dd3 Mon Sep 17 00:00:00 2001
From: Fangjun Kuang <csukuangfj@gmail.com>
Date: Sun, 20 Mar 2022 19:42:20 +0800
Subject: [PATCH] WIP: Periodically saving checkpoint after processing given
 number of batches.

---
 icefall/checkpoint.py | 99 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 99 insertions(+)

diff --git a/icefall/checkpoint.py b/icefall/checkpoint.py
index dbe3c1315..b4c93f03a 100644
--- a/icefall/checkpoint.py
+++ b/icefall/checkpoint.py
@@ -15,7 +15,10 @@
 # limitations under the License.
 
 
+import glob
 import logging
+import os
+import re
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Union
 
@@ -151,3 +154,99 @@ def average_checkpoints(
             avg[k] //= n
 
     return avg
+
+
+def save_checkpoint_with_global_batch_idx(
+    out_dir: Path,
+    global_batch_idx: int,
+    model: Union[nn.Module, DDP],
+    params: Optional[Dict[str, Any]] = None,
+    optimizer: Optional[Optimizer] = None,
+    scheduler: Optional[_LRScheduler] = None,
+    scaler: Optional[GradScaler] = None,
+    rank: int = 0,
+):
+    """Save training info after processing given number of batches.
+
+    Args:
+      out_dir:
+        The directory to save the checkpoint.
+      global_batch_idx:
+        The number of batches processed so far from the very start of the
+        training. The saved checkpoint will have the following filename:
+
+            f'out_dir / checkpoint-{global_batch_idx}.pt'
+      model:
+        The neural network model whose `state_dict` will be saved in the
+        checkpoint.
+      params:
+        A dict of training configurations to be saved.
+      optimizer:
+        The optimizer used in the training. Its `state_dict` will be saved.
+      scheduler:
+        The learning rate scheduler used in the training. Its `state_dict` will
+        be saved.
+      scaler:
+        The scaler used for mix precision training. Its `state_dict` will
+        be saved.
+      rank:
+        The rank ID used in DDP training of the current node. Set it to 0
+        if DDP is not used.
+    """
+    out_dir = Path(out_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+    filename = out_dir / f"checkpoint-{global_batch_idx}.pt"
+    save_checkpoint(
+        filename=filename,
+        model=model,
+        params=params,
+        optimizer=optimizer,
+        scheduler=scheduler,
+        scaler=scaler,
+        rank=rank,
+    )
+
+
+def remove_checkpoints(
+    out_dir: Path,
+    topk: int,
+    rank: int = 0,
+):
+    """Remove checkpoints from the given directory.
+
+    We assume that checkpoint filename has the form `checkpoint-xxx.pt`
+    where xxx is a number, representing the number of processed batches
+    when saving that checkpoint. We sort checkpoints by filename and keep
+    only the `topk` checkpoints with the highest `xxx`.
+
+    Args:
+      out_dir:
+        The directory containing checkpoints to be removed.
+      topk:
+        Number of checkpoints to keep.
+      rank:
+        If using DDP for training, it is the rank of the current node.
+        Use 0 if no DDP is used for training.
+    """
+    assert topk >= 1, topk
+    if rank != 0:
+        return
+    checkpoints = list(glob.glob(f"{out_dir}/checkpoint-[0-9]*.pt"))
+
+    if len(checkpoints) == 0:
+        logging.warn(f"No checkpoints found in {out_dir}")
+        return
+
+    if len(checkpoints) <= topk:
+        return
+
+    pattern = re.compile(r"checkpoint-([0-9]+).pt")
+    idx_checkpoints = [
+        (int(pattern.search(c).group(1)), c) for c in checkpoints
+    ]
+
+    idx_checkpoints = sorted(idx_checkpoints, reverse=True, key=lambda x: x[0])
+    to_remove = idx_checkpoints[topk:]
+    to_remove = [ic[1] for ic in to_remove]
+    for c in to_remove:
+        os.remove(c)