Fully Sharded Data Parallel

parlai/agents/transformer/modules/decoder.py

@@ -25,6 +25,7 @@
 from parlai.core.opt import Opt
 from parlai.utils.misc import warn_once
 from parlai.utils.torch import PipelineHelper
+from parlai.utils.fsdp import fsdp_wrap
 
 
 @swappable(
@@ -277,16 +278,15 @@ def _default(val, default):
     def build_layers(self) -> nn.ModuleList:
         layers = nn.ModuleList()
         for _ in range(self.n_layers):
-            layers.append(
-                self.swappables.layer(
-                    self.opt,
-                    attention_dropout=self.opt.get('attention_dropout', 0.0),
-                    relu_dropout=self.opt.get('relu_dropout', 0.0),
-                    dropout=self.opt.get('dropout', 0.0),
-                    activation=self.activation,
-                    variant=self.variant,
-                )  # type: ignore
+            layer = self.swappables.layer(
+                self.opt,
+                attention_dropout=self.opt.get('attention_dropout', 0.0),
+                relu_dropout=self.opt.get('relu_dropout', 0.0),
+                dropout=self.opt.get('dropout', 0.0),
+                activation=self.activation,
+                variant=self.variant,
             )
+            layers.append(fsdp_wrap(layer))  # type: ignore
         return layers
 
     def forward_embedding(

parlai/agents/transformer/modules/encoder.py

@@ -25,6 +25,7 @@
 from parlai.core.opt import Opt
 from parlai.utils.misc import warn_once
 from parlai.utils.torch import PipelineHelper
+from parlai.utils.fsdp import fsdp_wrap
 
 
 @swappable(self_attention=MultiHeadAttention, feedforward=TransformerFFN)
@@ -227,16 +228,15 @@ def _default(val, default):
     def build_layers(self) -> nn.ModuleList:
         layers = nn.ModuleList()
         for _ in range(self.n_layers):
-            layers.append(
-                self.swappables.layer(  # type: ignore
-                    self.opt,
-                    attention_dropout=self.opt.get('attention_dropout', 0.0),
-                    relu_dropout=self.opt.get('relu_dropout', 0.0),
-                    dropout=self.dropout_frac,
-                    variant=self.variant,
-                    activation=self.activation,
-                )
+            layer = self.swappables.layer(  # type: ignore
+                self.opt,
+                attention_dropout=self.opt.get('attention_dropout', 0.0),
+                relu_dropout=self.opt.get('relu_dropout', 0.0),
+                dropout=self.dropout_frac,
+                variant=self.variant,
+                activation=self.activation,
             )
+            layers.append(fsdp_wrap(layer))
         return layers
 
     def forward_embedding(

parlai/core/params.py

@@ -772,6 +772,16 @@ def add_distributed_training_args(self):
         grp.add_argument(
             '--distributed-world-size', type=int, help='Number of workers.'
         )
+        grp.add_argument(
+            '--ddp-backend',
+            # TODO: add in zero3. https://github.com/facebookresearch/ParlAI/issues/3753
+            choices=['ddp', 'zero2'],
+            default='ddp',
+            help=(
+                'Distributed backend. Zero2 can be faster but is more experimental. '
+                'DDP is the most tested.'
+            ),
+        )
         return grp
 
     def add_model_args(self):

parlai/core/torch_agent.py

@@ -36,6 +36,7 @@
 from parlai.utils.distributed import is_distributed
 from parlai.utils.misc import AttrDict, warn_once
 from parlai.utils.io import PathManager
+from parlai.utils.fsdp import should_sync_gradnorm, is_fsdp, DEFAULT_DDP_BACKEND
 from parlai.utils.fp16 import (
     SafeFP16Optimizer,
     MemoryEfficientFP16Optimizer,
@@ -1052,7 +1053,9 @@ def init_optim(
         self.optimizer = optim_class(params, **kwargs)
         if self.fp16:
             if self.fp16_impl == 'safe':
-                self.optimizer = SafeFP16Optimizer(self.optimizer)
+                self.optimizer = SafeFP16Optimizer(
+                    self.optimizer, should_sync_gradnorm(opt)
+                )
             else:
                 # Using memory efficient optimizer
                 opt_name = opt['optimizer']
@@ -1064,7 +1067,9 @@ def init_optim(
                         'with Memory Efficient FP16. Please select from among this '
                         f'list:\n{compatible_list}'
                     )
-                self.optimizer = MemoryEfficientFP16Optimizer(self.optimizer)
+                self.optimizer = MemoryEfficientFP16Optimizer(
+                    self.optimizer, should_sync_gradnorm(opt)
+                )
 
         if is_finetune:
             logging.warning('Detected a fine-tune run. Resetting the optimizer.')
@@ -1969,10 +1974,11 @@ def state_dict(self):
         """
         states = {}
         if hasattr(self, 'model'):  # save model params
-            if hasattr(self.model, 'module'):
-                # did we wrap in a DistributedDataParallel
+            if hasattr(self.model, 'module') and not is_fsdp(self.model):
+                # did we wrap in a DistributedDataParallel or DataParallel
                 states['model'] = self.model.module.state_dict()
             else:
+                # regular model or FSDP
                 states['model'] = self.model.state_dict()
 
         if hasattr(self, 'optimizer'):
@@ -1992,6 +1998,16 @@ def state_dict(self):
 
         return states
 
+    def save_nonprimary(self, path=None):
+        """
+        Save model parameters, when you are working on the non-primary worker.
+
+        For models or optimizers that shard parameters, this ensures we sync.
+        """
+        if self.opt.get('ddp_backend', DEFAULT_DDP_BACKEND) in ('zero2', 'zero3'):
+            # make sure we call the state dict
+            self.state_dict()
+
     def save(self, path=None):
         """
         Save model parameters to path (or default to model_file arg).

parlai/core/torch_generator_agent.py

@@ -35,6 +35,7 @@
 import parlai.utils.logging as logging
 from parlai.core.metrics import SumMetric, AverageMetric, FairseqBleuMetric
 from parlai.utils.fp16 import FP16SafeCrossEntropy
+import parlai.utils.fsdp as fsdp_utils
 from parlai.utils.torch import (
     neginf,
     total_parameters,
@@ -479,8 +480,10 @@ def __init__(self, opt: Opt, shared=None):
         else:
             # this is not a shared instance of this class, so do full init
             self.criterion = self.build_criterion()
-            # ensure all distributed copies will always be in sync
-            self.model = self.build_model()
+            with fsdp_utils.maybe_fsdp_wrap(opt):
+                self.model = fsdp_utils.fsdp_wrap(self.build_model())
+                if self.fp16 and not fsdp_utils.should_use_fsdp(opt):
+                    self.model = self.model.half()
 
             # load the block_list for beam search
             self.beam_block_list = self._load_beam_block_list()
@@ -498,16 +501,15 @@ def __init__(self, opt: Opt, shared=None):
                     self.model.cuda()
                 self.criterion.cuda()
 
-            sync_parameters(self.model)
+            if not fsdp_utils.is_fsdp(self.model):
+                sync_parameters(self.model)
+
             train_params = trainable_parameters(self.model)
             total_params = total_parameters(self.model)
             logging.info(
                 f"Total parameters: {total_params:,d} ({train_params:,d} trainable)"
             )
 
-            if self.fp16:
-                self.model = self.model.half()
-
             if init_model is not None:
                 # load model parameters if available
                 logging.info(f'Loading existing model params from {init_model}')
@@ -530,7 +532,11 @@ def __init__(self, opt: Opt, shared=None):
                 logging.warning("Optimizer was reset. Also resetting LR scheduler.")
             self.build_lr_scheduler(states, hard_reset=is_finetune or was_reset)
 
-        if shared is None and is_distributed():
+        if (
+            shared is None
+            and is_distributed()
+            and opt.get('ddp_backend', fsdp_utils.DEFAULT_DDP_BACKEND) == 'ddp'
+        ):
             device_ids = None if self.model_parallel else [self.opt['gpu']]
             self.model = torch.nn.parallel.DistributedDataParallel(
                 self.model, device_ids=device_ids, broadcast_buffers=False

parlai/scripts/multiprocessing_eval.py

@@ -23,7 +23,6 @@
 """
 
 import torch
-import random
 import os
 import signal
 import parlai.utils.distributed as distributed_utils
@@ -88,7 +87,7 @@ def setup_args(cls):
         return setup_args()
 
     def run(self):
-        port = random.randint(32000, 48000)
+        port = distributed_utils.find_free_port()
         return launch_and_eval(self.opt, port)
 
 

parlai/scripts/multiprocessing_train.py

@@ -24,7 +24,6 @@
 """
 
 import torch
-import random
 import os
 import signal
 import traceback
@@ -55,10 +54,12 @@ def multiprocess_train(
             raise
 
 
-def launch_and_train(opt, port):
+def launch_and_train(opt, port=None):
     """
     Perform a fork() to many processes.
     """
+    if port is None:
+        port = distributed_utils.find_free_port()
     # Launch multiple subprocesses
     spawncontext = torch.multiprocessing.start_processes(
         multiprocess_train,
@@ -99,7 +100,7 @@ def setup_args(cls):
 
     def run(self):
         if self.opt['port'] is None:
-            port = random.randint(32000, 48000)
+            port = None
         else:
             port = self.opt['port']
         return launch_and_train(self.opt, port)

parlai/scripts/train_model.py

@@ -442,17 +442,20 @@ def save_model(self, suffix=None):
         """
         Save the model to disk, possibly with a suffix.
         """
-        if not is_primary_worker():
-            # never do IO as a non-primary worker
-            return
-
         if not self.opt.get('model_file'):
             # nothing to save to, just exit
             return
 
         fn = self.opt['model_file']
         if suffix:
             fn += suffix
+
+        if not is_primary_worker():
+            # never do IO as a non-primary worker
+            if hasattr(self.agent, 'save_nonprimary'):
+                self.agent.save_nonprimary(fn)
+            return
+
         while True:
             # don't ever let a ctrl-c interrupt saving
             try:
@@ -543,7 +546,7 @@ def validate(self):
             )
             self.best_valid = new_valid
             self.impatience = 0
-            if opt.get('model_file') and is_primary_worker():
+            if opt.get('model_file'):
                 logging.info(f"saving best valid model: {opt['model_file']}")
                 self.save_model()
                 self.saved = True
@@ -566,11 +569,7 @@ def validate(self):
         self.validate_time.reset()
 
         # saving
-        if (
-            opt.get('model_file')
-            and opt.get('save_after_valid')
-            and is_primary_worker()
-        ):
+        if opt.get('model_file') and opt.get('save_after_valid'):
             logging.info(f"saving model checkpoint: {opt['model_file']}.checkpoint")
             self.save_model('.checkpoint')
 
@@ -720,24 +719,26 @@ def _get_time(self, world: World) -> Tuple[float, float, float]:
             self._total_epochs = self._preempted_epochs + sum(
                 all_gather_list(world.get_total_epochs())
             )
-            train_time, log_time, validate_time = sync_object(
+            train_time, log_time, validate_time, save_time = sync_object(
                 (
                     self.train_time.time(),
                     self.log_time.time(),
                     self.validate_time.time(),
+                    self.save_time.time(),
                 )
             )
         else:
-            train_time, log_time, validate_time = (
+            train_time, log_time, validate_time, save_time = (
                 self.train_time.time(),
                 self.log_time.time(),
                 self.validate_time.time(),
+                self.save_time.time(),
             )
             self._total_epochs = self._preempted_epochs + (
                 num_workers() * world.get_total_epochs()
             )
 
-        return train_time, log_time, validate_time
+        return train_time, log_time, validate_time, save_time
 
     def log(self):
         """
@@ -810,7 +811,7 @@ def train_steps(self):
                 self._last_log_steps += 1 / self.update_freq
 
                 # the following additionally updates self._total_epochs
-                train_time, log_time, validate_time = self._get_time(world)
+                train_time, log_time, validate_time, save_time = self._get_time(world)
                 # get the total training examples done, compute epochs
                 exs_per_epoch = world.num_examples()
                 self._total_exs = int(np.round(self._total_epochs * exs_per_epoch))
@@ -859,11 +860,7 @@ def train_steps(self):
                         break
                     # make sure metrics are clean before we log
                     world.reset_metrics()
-                if (
-                    self.save_time.time() > self.save_every_n_secs
-                    and opt.get('model_file')
-                    and is_primary_worker()
-                ):
+                if save_time > self.save_every_n_secs and opt.get('model_file'):
                     logging.info(
                         f"saving model checkpoint: {opt['model_file']}.checkpoint"
                     )
@@ -872,7 +869,7 @@ def train_steps(self):
                     self.save_model('.checkpoint')
                     self.save_time.reset()
 
-        if not self.saved and is_primary_worker():
+        if not sync_object(self.saved):
             # save agent
             self.save_model()
 

parlai/utils/distributed.py

@@ -296,6 +296,19 @@ def distributed_context(
             dist.destroy_process_group()
 
 
+def get_dist_group():
+    """
+    Find the default pytorch distributed group.
+
+    Used within FSDP to mark which workers are participating. Important to manually call
+    this because FSDP will cache old groups, but our test suite will instantiate new
+    groups per test.
+    """
+    from torch.distributed.distributed_c10d import _get_default_group
+
+    return _get_default_group()
+
+
 @contextlib.contextmanager
 def slurm_distributed_context(opt):
     """
@@ -346,3 +359,15 @@ def slurm_distributed_context(opt):
     except FileNotFoundError:
         # Slurm is not installed
         raise RuntimeError('SLURM does not appear to be installed.')
+
+
+def find_free_port() -> int:
+    """
+    Find a free port we can bind to locally.
+
+    Credit: https://stackoverflow.com/questions/1365265/on-localhost-how-do-i-pick-a-free-port-number
+    """
+    with contextlib.closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s:
+        s.bind(('', 0))
+        s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+        return s.getsockname()[1]