[torchtrain] add gradient clipping

train.py

@@ -1,7 +1,7 @@
 import argparse
 import os
 from dataclasses import dataclass, field
-from typing import List
+from typing import List, Union
 
 # torch imports
 import torch
@@ -113,6 +113,8 @@ def main(args):
             input_ids = input_ids.cuda()
             labels = labels.cuda()
 
+            optimizer.zero_grad()
+
             # forward
             pred = model(input_ids)
             tok_loss = F.cross_entropy(
@@ -123,12 +125,14 @@ def main(args):
             # backward on scaled loss to create scaled gradients
             scaler.scale(loss).backward()
 
+            # clip gradients (after unscaling gradients of the optimizer's params)
+            scaler.unscale_(optimizer)
+            model.clip_grad_norm_(args.max_norm)
+
             # optimizer step
-            # scaler.step() first unscales gradients of the optimizer's params.
-            # If gradients don't contain infs/NaNs, optimizer.step() is then called,
+            # If gradients don't contain infs/NaNs, optimizer.step() is then called;
             # otherwise, optimizer.step() is skipped.
             scaler.step(optimizer)
-            optimizer.zero_grad()
 
             # updates the scale for next iteration
             scaler.update()
@@ -168,6 +172,9 @@ def main(args):
         "--optimizer", type=str, default="AdamW", help="optimizer to use"
     )
     parser.add_argument("--lr", type=float, default=2e-5, help="learning rate to use")
+    parser.add_argument(
+        "--max_norm", type=Union[float, int], default=1.0, help="max norm for gradient clipping"
+    )
     parser.add_argument(
         "--steps", type=int, default=-1, help="how many train steps to run"
     )