Updating public repo with latest changes.

utils.py

@@ -35,7 +35,34 @@ def print_rank_0(message):
         print(message, flush=True)
 
 
-def print_args(args):
+def enable_adlr_autoresume(args):
+    print_rank_0('enabling autoresume ...')
+    import sys
+    sys.path.append(os.environ.get('SUBMIT_SCRIPTS','.'))
+    try:
+        from userlib.auto_resume import AutoResume
+    except:
+        print_rank_0('ADLR autoresume is not available, exiting ...')
+        exit(0)
+    args.AutoResume = AutoResume
+    args.AutoResume.init()
+
+
+def check_adlr_autoresume_termination(iteration, model, optimizer,
+                                       lr_scheduler, args):
+    # Add barrier to ensure consistnecy.
+    torch.distributed.barrier()
+    if args.AutoResume.termination_requested():
+        if args.save:
+            save_checkpoint(iteration, model, optimizer, lr_scheduler, args)
+        print_rank_0(">>> autoresume termination request found!")
+        if torch.distributed.get_rank() == 0:
+            args.AutoResume.request_resume()
+        print_rank_0(">>> training terminated. Returning")
+        exit(0)
+
+
+def print_args(args, writer=None):
     """Print arguments."""
 
     print('arguments:', flush=True)
@@ -43,6 +70,8 @@ def print_args(args):
         dots = '.' * (29 - len(arg))
         print('  {} {} {}'.format(arg, dots, getattr(args, arg)), flush=True)
 
+        if writer:
+            writer.add_text(arg, str(getattr(args, arg)))
 
 def print_params_min_max_norm(optimizer, iteration):
     """Print min, max, and norm of all parameters."""
@@ -119,6 +148,16 @@ class Timers:
             self.timers[name] = self.Timer(name)
         return self.timers[name]
 
+    def write(self, names, writer, iteration, normalizer=1.0, reset=False):
+        """Write timers to a tensorboard writer"""
+        # currently when using add_scalars,
+        # torch.utils.add_scalars makes each timer its own run, which
+        # polutes the runs list, so we just add each as a scalar
+        assert normalizer > 0.0
+        for name in names:
+            value = self.timers[name].elapsed(reset=reset) / normalizer
+            writer.add_scalar(name + '_time', value, iteration)
+
     def log(self, names, normalizer=1.0, reset=True):
         """Log a group of timers."""
         assert normalizer > 0.0
@@ -144,13 +183,13 @@ def report_memory(name):
         torch.cuda.max_memory_cached()/ mega_bytes)
     print_rank_0(string)
 
-def get_checkpoint_name(checkpoints_path, iteration, release=False):
+def get_checkpoint_name(checkpoints_path, iteration, release=False, mp_rank=None):
     if release:
         d = 'release'
     else:
         d = 'iter_{:07d}'.format(iteration)
     return os.path.join(checkpoints_path, d,
-                        'mp_rank_{:02d}'.format(mpu.get_model_parallel_rank()),
+                        'mp_rank_{:02d}'.format(mpu.get_model_parallel_rank() if mp_rank is None else mp_rank),
                         'model_optim_rng.pt')
 
 
@@ -353,3 +392,14 @@ def move_weights(our, oai, dst2src=False):
 
     for our_layer, oai_layer in zip(our.transformer.layers, oai.transformer.h):
         load_transformer_layer(our_layer, oai_layer, dst2src)
+
+
+def merge_parallel_state_dicts(state_dicts):
+    temp_sd = {}
+    for sd in state_dicts:
+        for k, v in sd.items():
+            temp_sd[k].append()
+    pass
+
+def merge_parallel_checkpoints(checkpoint_dir, model_parallel_size):
+    pass