Merge branch 'rescaling' into 'main'

Add support for signal-based dynamic checkpointing

See merge request ADLR/megatron-lm!361

megatron/__init__.py

@@ -17,6 +17,7 @@ import torch
 from .global_vars import get_args
 from .global_vars import get_current_global_batch_size
 from .global_vars import get_num_microbatches
+from .global_vars import get_signal_handler
 from .global_vars import update_num_microbatches
 from .global_vars import get_tokenizer
 from .global_vars import get_tensorboard_writer

megatron/arguments.py

@@ -377,6 +377,9 @@ def _add_logging_args(parser):
     group.add_argument('--log-memory-to-tensorboard',
                        action='store_true',
                        help='Enable memory logging to tensorboard.')
+    group.add_argument('--log-world-size-to-tensorboard',
+                       action='store_true',
+                       help='Enable world size logging to tensorboard.')
 
     return parser
 
@@ -472,6 +475,9 @@ def _add_training_args(parser):
                        'by this value.')
     group.add_argument('--exit-duration-in-mins', type=int, default=None,
                        help='Exit the program after this many minutes.')
+    group.add_argument('--exit-signal-handler', action='store_true',
+                       help='Dynamically save the checkpoint and shutdown the '
+                       'training if SIGTERM is received')
     group.add_argument('--tensorboard-dir', type=str, default=None,
                        help='Write TensorBoard logs to this directory.')
     group.add_argument('--no-masked-softmax-fusion',

megatron/dist_signal_handler.py

+import signal
+
+import torch
+
+
+def get_world_size():
+    if torch.distributed.is_available() and torch.distributed.is_initialized():
+        world_size = torch.distributed.get_world_size()
+    else:
+        world_size = 1
+    return world_size
+
+
+def get_device(local_rank=None):
+    backend = torch.distributed.get_backend()
+    if backend == 'nccl':
+        if local_rank is None:
+            device = torch.device('cuda')
+        else:
+            device = torch.device(f'cuda:{local_rank}')
+    elif backend == 'gloo':
+        device = torch.device('cpu')
+    else:
+        raise RuntimeError
+    return device
+
+
+def all_gather_item(item, dtype, group=None, async_op=False, local_rank=None):
+    if not torch.distributed.is_available() or \
+       not torch.distributed.is_initialized():
+        return [item]
+
+    device = get_device(local_rank)
+
+    if group is not None:
+        group_size = group.size()
+    else:
+        group_size = get_world_size()
+
+    tensor = torch.tensor([item], device=device, dtype=dtype)
+    output_tensors = [
+        torch.zeros(1, dtype=tensor.dtype, device=tensor.device)
+        for _ in range(group_size)
+    ]
+    torch.distributed.all_gather(output_tensors, tensor, group, async_op)
+    output = [elem.item() for elem in output_tensors]
+    return output
+
+
+class DistributedSignalHandler:
+    def __init__(self, sig=signal.SIGTERM):
+        self.sig = sig
+
+    def signals_received(self):
+        all_received = all_gather_item(
+            self._signal_received, dtype=torch.int32
+        )
+        return all_received
+
+    def __enter__(self):
+        self._signal_received = False
+        self.released = False
+        self.original_handler = signal.getsignal(self.sig)
+
+        def handler(signum, frame):
+            self._signal_received = True
+
+        signal.signal(self.sig, handler)
+
+        return self
+
+    def __exit__(self, type, value, tb):
+        self.release()
+
+    def release(self):
+        if self.released:
+            return False
+
+        signal.signal(self.sig, self.original_handler)
+        self.released = True
+        return True

megatron/global_vars.py

@@ -21,6 +21,7 @@ import time
 
 import torch
 
+from megatron import dist_signal_handler
 from megatron.tokenizer import build_tokenizer
 from .arguments import parse_args
 from .microbatches import build_num_microbatches_calculator
@@ -31,6 +32,7 @@ _GLOBAL_TOKENIZER = None
 _GLOBAL_TENSORBOARD_WRITER = None
 _GLOBAL_ADLR_AUTORESUME = None
 _GLOBAL_TIMERS = None
+_GLOBAL_SIGNAL_HANDLER = None
 
 
 def get_args():
@@ -75,6 +77,14 @@ def get_timers():
     _ensure_var_is_initialized(_GLOBAL_TIMERS, 'timers')
     return _GLOBAL_TIMERS
 
+def get_signal_handler():
+    _ensure_var_is_initialized(_GLOBAL_SIGNAL_HANDLER, 'signal handler')
+    return _GLOBAL_SIGNAL_HANDLER
+
+def _set_signal_handler():
+    global _GLOBAL_SIGNAL_HANDLER
+    _ensure_var_is_not_initialized(_GLOBAL_SIGNAL_HANDLER, 'signal handler')
+    _GLOBAL_SIGNAL_HANDLER = dist_signal_handler.DistributedSignalHandler().__enter__()
 
 def set_global_variables(extra_args_provider=None, args_defaults={},
                          ignore_unknown_args=False):
@@ -89,6 +99,9 @@ def set_global_variables(extra_args_provider=None, args_defaults={},
     _set_adlr_autoresume(args)
     _set_timers()
 
+    if args.exit_signal_handler:
+        _set_signal_handler()
+
 
 def _parse_args(extra_args_provider=None, defaults={},
                 ignore_unknown_args=False):

megatron/initialize.py

@@ -180,7 +180,7 @@ def _initialize_distributed():
     torch.distributed.init_process_group(
         backend=args.distributed_backend,
         world_size=args.world_size, rank=args.rank,
-        timeout=timedelta(days=7))
+        timeout=timedelta(minutes=10))
 
     # Set the tensor model-parallel, pipeline model-parallel, and
     # data-parallel communicators.

megatron/training.py

@@ -26,6 +26,7 @@ import torch
 from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP
 
 from megatron import get_args
+from megatron import get_signal_handler
 from megatron import get_timers
 from megatron import get_tensorboard_writer
 from megatron import get_current_global_batch_size
@@ -544,6 +545,10 @@ def training_log(loss_dict, total_loss_dict, learning_rate, iteration,
             writer.add_scalar('loss-scale', loss_scale, iteration)
             writer.add_scalar('loss-scale vs samples', loss_scale,
                               args.consumed_train_samples)
+        if args.log_world_size_to_tensorboard:
+            writer.add_scalar('world-size', args.world_size, iteration)
+            writer.add_scalar('world-size vs samples', args.world_size,
+                              args.consumed_train_samples)
         if grad_norm is not None:
             writer.add_scalar('grad-norm', grad_norm, iteration)
             writer.add_scalar('grad-norm vs samples', grad_norm,
@@ -698,6 +703,14 @@ def train(forward_step_func, model, optimizer, lr_scheduler,
 
         # Checkpointing
         saved_checkpoint = False
+        if args.exit_signal_handler:
+            signal_handler = get_signal_handler()
+            if any(signal_handler.signals_received()):
+                save_checkpoint_and_time(iteration, model, optimizer,
+                                         lr_scheduler)
+                print_datetime('exiting program after receiving SIGTERM.')
+                sys.exit()
+
         if args.save and args.save_interval and \
            iteration % args.save_interval == 0:
             save_checkpoint_and_time(iteration, model, optimizer,