Add start-batch option for RNNLM training (#1161)

* Add start-batch option for RNNLM training

* Also set epoch

* Skip batches on load

icefall/rnn_lm/train.py

@@ -99,6 +99,15 @@ def get_parser():
         """,
     )
 
+    parser.add_argument(
+        "--start-batch",
+        type=int,
+        default=0,
+        help="""If positive, --start-epoch is ignored and
+        it loads the checkpoint from exp-dir/checkpoint-{start_batch}.pt
+        """,
+    )
+
     parser.add_argument(
         "--exp-dir",
         type=str,
@@ -242,7 +251,9 @@ def load_checkpoint_if_available(
 ) -> None:
     """Load checkpoint from file.
 
-    If params.start_epoch is positive, it will load the checkpoint from
+    If params.start_batch is positive, it will load the checkpoint from
+    `params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, if
+    params.start_epoch is larger than 1, it will load the checkpoint from
     `params.start_epoch - 1`. Otherwise, this function does nothing.
 
     Apart from loading state dict for `model`, `optimizer` and `scheduler`,
@@ -261,10 +272,14 @@ def load_checkpoint_if_available(
     Returns:
       Return None.
     """
-    if params.start_epoch <= 0:
-        return
 
-    filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt"
+    if params.start_batch > 0:
+        filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt"
+    elif params.start_epoch > 1:
+        filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt"
+    else:
+        return None
+
     logging.info(f"Loading checkpoint: {filename}")
     saved_params = load_checkpoint(
         filename,
@@ -283,6 +298,13 @@ def load_checkpoint_if_available(
     for k in keys:
         params[k] = saved_params[k]
 
+    if params.start_batch > 0:
+        if "cur_epoch" in saved_params:
+            params["start_epoch"] = saved_params["cur_epoch"]
+
+        if "cur_batch_idx" in saved_params:
+            params["cur_batch_idx"] = saved_params["cur_batch_idx"]
+
     return saved_params
 
 
@@ -438,7 +460,14 @@ def train_one_epoch(
 
     tot_loss = MetricsTracker()
 
+    cur_batch_idx = params.get("cur_batch_idx", 0)
+
     for batch_idx, batch in enumerate(train_dl):
+
+        if batch_idx < cur_batch_idx:
+            continue
+        cur_batch_idx = batch_idx
+
         params.batch_idx_train += 1
         x, y, sentence_lengths = batch
         batch_size = x.size(0)
@@ -463,6 +492,7 @@ def train_one_epoch(
             params.batch_idx_train > 0
             and params.batch_idx_train % params.save_every_n == 0
         ):
+            params.cur_batch_idx = batch_idx
             save_checkpoint_with_global_batch_idx(
                 out_dir=params.exp_dir,
                 global_batch_idx=params.batch_idx_train,
@@ -471,6 +501,7 @@ def train_one_epoch(
                 optimizer=optimizer,
                 rank=rank,
             )
+            del params.cur_batch_idx
 
         if batch_idx % params.log_interval == 0:
             # Note: "frames" here means "num_tokens"