[FSDP] Zero 3 Optimization Support

.circleci/config.yml

@@ -219,26 +219,26 @@ commands:
             - setupcuda
       - fixgit
       - restore_cache:
-          key: deps-20221130-<< parameters.cachename >>-{{ checksum "requirements.txt" }}
+          key: deps-20221202-<< parameters.cachename >>-{{ checksum "requirements.txt" }}
       - setup
       - installdeps
       - << parameters.more_installs >>
       - save_cache:
-          key: deps-20221130-<< parameters.cachename >>-{{ checksum "requirements.txt" }}
+          key: deps-20221202-<< parameters.cachename >>-{{ checksum "requirements.txt" }}
           paths:
             - "~/venv/bin"
             - "~/venv/lib"
       - findtests:
           marker: << parameters.marker >>
       - restore_cache:
-          key: data-20221130-<< parameters.cachename >>-{{ checksum "teststorun.txt" }}
+          key: data-20221202-<< parameters.cachename >>-{{ checksum "teststorun.txt" }}
       - run:
           name: Run tests
           no_output_timeout: 60m
           command: |
             coverage run -m pytest -m << parameters.marker >> << parameters.pytest_flags >> --junitxml=test-results/junit.xml
       - save_cache:
-          key: data-20221130-<< parameters.cachename >>-{{ checksum "teststorun.txt" }}
+          key: data-20221202-<< parameters.cachename >>-{{ checksum "teststorun.txt" }}
           paths:
             - "~/ParlAI/data"
       - codecov
@@ -255,12 +255,12 @@ commands:
       - checkout
       - fixgit
       - restore_cache:
-          key: deps-20221130-bw-{{ checksum "requirements.txt" }}
+          key: deps-20221202-bw-{{ checksum "requirements.txt" }}
       - setup
       - installdeps
       - installtorchgpu
       - save_cache:
-          key: deps-20221130-bw-{{ checksum "requirements.txt" }}
+          key: deps-20221202-bw-{{ checksum "requirements.txt" }}
           paths:
             - "~/venv/bin"
             - "~/venv/lib"

parlai/agents/hugging_face/t5.py

@@ -27,7 +27,7 @@
 from parlai.core.params import ParlaiParser
 from parlai.core.torch_agent import Batch, TorchAgent
 from parlai.core.torch_generator_agent import TorchGeneratorAgent, TorchGeneratorModel
-from parlai.utils.fsdp import is_fsdp
+from parlai.utils.fsdp import is_fsdp, delay_halving
 
 
 def check_hf_version(v: Tuple[int, int]) -> bool:
@@ -41,7 +41,9 @@ def check_hf_version(v: Tuple[int, int]) -> bool:
 def build_t5(opt: Opt) -> T5ForConditionalGeneration:
     if not check_hf_version(HF_VERSION):
         raise RuntimeError('Must use transformers package >= 4.3 to use t5')
-    torch_dtype = torch.float16 if opt['fp16'] else torch.float32
+    torch_dtype = (
+        torch.float16 if (opt['fp16'] and not delay_halving(opt)) else torch.float32
+    )
     try:
         return T5ForConditionalGeneration.from_pretrained(
             opt['t5_model_arch'],
@@ -369,7 +371,7 @@ def output(self, tensor):
         """
         # Taken directly from HuggingFace
         # Rescale output before projecting on vocab
-        # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
+        # See https://github.com/tensorflow/mesh/blob/fa19d69/mesh_tensorflow/transformer/transformer.py#L586
         tensor = tensor * (self.t5.model_dim**-0.5)
         lm_logits = self.t5.lm_head(tensor)
         return lm_logits

parlai/core/params.py

@@ -774,11 +774,11 @@ def add_distributed_training_args(self):
         )
         grp.add_argument(
             '--ddp-backend',
-            # TODO: add in zero3. https://github.com/facebookresearch/ParlAI/issues/3753
-            choices=['ddp', 'zero2'],
+            choices=['ddp', 'zero2', 'zero3'],
             default='ddp',
             help=(
                 'Distributed backend. Zero2 can be faster but is more experimental. '
+                'Zero3 significantly reduces memory pressure. '
                 'DDP is the most tested.'
             ),
         )

parlai/core/torch_agent.py

@@ -36,7 +36,13 @@
 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.fsdp import (
+    should_sync_gradnorm,
+    is_fsdp,
+    DEFAULT_DDP_BACKEND,
+    FSDP_AVAILABLE,
+    get_state_dict,
+)
 from parlai.utils.fp16 import (
     SafeFP16Optimizer,
     MemoryEfficientFP16Optimizer,
@@ -1981,8 +1987,11 @@ def state_dict(self):
             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()
+            elif is_fsdp(self.model) and FSDP_AVAILABLE:
+                # FSDP Model; use fancy saving
+                states['model'] = get_state_dict(self.model)
             else:
-                # regular model or FSDP
+                # regular model
                 states['model'] = self.model.state_dict()
 
         if hasattr(self, 'optimizer'):

parlai/core/torch_generator_agent.py

@@ -34,7 +34,7 @@
 from parlai.utils.misc import warn_once
 from parlai.utils.io import PathManager
 import parlai.utils.logging as logging
-from parlai.core.metrics import Metric, SumMetric, AverageMetric, FairseqBleuMetric
+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 (
@@ -516,8 +516,10 @@ 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()
+
+            self.model = self.build_model()
             with fsdp_utils.maybe_fsdp_wrap(opt):
-                self.model = fsdp_utils.fsdp_wrap(self.build_model())
+                self.model = fsdp_utils.fsdp_wrap(self.model)
                 if self.fp16 and not fsdp_utils.delay_halving(opt):
                     self.model = self.model.half()
 
@@ -2054,7 +2056,7 @@ def select_paths(self, logprobs, prior_scores, current_length) -> _PathSelection
 
 class FactualNucleusSampling(NucleusSampling):
     """
-    Factual Nucleus Sampling
+    Factual Nucleus Sampling.
 
     See https://arxiv.org/pdf/2206.04624.pdf for more information
     """

parlai/scripts/distributed_eval.py

@@ -31,10 +31,10 @@
   -m seq2seq -t convai2 --dict-file /path/to/dict-file
 ```
 """
-
-import parlai.scripts.eval_model as eval_model
 from parlai.core.script import ParlaiScript
+import parlai.scripts.eval_model as eval_model
 import parlai.utils.distributed as distributed_utils
+import parlai.utils.fsdp as fsdp_utils
 
 
 def setup_args():
@@ -51,7 +51,9 @@ def setup_args(cls):
 
     def run(self):
         with distributed_utils.slurm_distributed_context(self.opt) as opt:
-            return eval_model.eval_model(opt)
+            self.evaluator = fsdp_utils.JoinableEvaluator(opt)
+            with fsdp_utils.fsdp_join(self.evaluator):
+                return self.evaluator.eval_model()
 
 
 if __name__ == '__main__':

parlai/scripts/distributed_train.py

@@ -35,6 +35,7 @@
 import parlai.scripts.train_model as single_train
 from parlai.core.script import ParlaiScript
 import parlai.utils.distributed as distributed_utils
+import parlai.utils.fsdp as fsdp_utils
 
 
 def setup_args():
@@ -51,8 +52,9 @@ def setup_args(cls):
 
     def run(self):
         with distributed_utils.slurm_distributed_context(self.opt) as opt:
-            self.train_loop = single_train.TrainLoop(opt)
-            return self.train_loop.train()
+            self.train_loop = fsdp_utils.JoinableTrainLoop(opt)
+            with fsdp_utils.fsdp_join(self.train_loop):
+                return self.train_loop.train()
 
 
 if __name__ == '__main__':

parlai/scripts/eval_model.py

@@ -24,6 +24,7 @@
     aggregate_unnamed_reports,
     Metric,
 )
+from parlai.core.opt import Opt
 from parlai.core.worlds import create_task
 from parlai.utils.misc import TimeLogger, nice_report
 from parlai.utils.world_logging import WorldLogger
@@ -77,7 +78,10 @@ def setup_args(parser=None):
         '-auc',
         type=int,
         default=-1,
-        help='a positive number indicates to calculate the area under the roc curve and it also determines how many decimal digits of the predictions to keep (higher numbers->more precise); also used to determine whether or not to calculate the AUC metric',
+        help='a positive number indicates to calculate the area under the '
+        'roc curve and it also determines how many decimal digits of the '
+        'predictions to keep (higher numbers->more precise); also used '
+        'to determine whether or not to calculate the AUC metric',
     )
     parser.add_argument(
         '--area-under-curve-class',
@@ -291,14 +295,23 @@ def eval_model(opt):
     return report
 
 
+class Evaluator:
+    def __init__(self, opt: Opt):
+        self.opt = opt
+
+    def eval_model(self):
+        return eval_model(self.opt)
+
+
 @register_script('eval_model', aliases=['em', 'eval'])
 class EvalModel(ParlaiScript):
     @classmethod
     def setup_args(cls):
         return setup_args()
 
     def run(self):
-        return eval_model(self.opt)
+        self.evaluator = Evaluator(self.opt)
+        return self.evaluator.eval_model()
 
 
 if __name__ == '__main__':

parlai/scripts/multiprocessing_eval.py

@@ -25,8 +25,9 @@
 import torch
 import os
 import signal
-import parlai.utils.distributed as distributed_utils
 import parlai.scripts.eval_model as eval_model
+import parlai.utils.distributed as distributed_utils
+import parlai.utils.fsdp as fsdp_utils
 from parlai.core.script import ParlaiScript, register_script
 
 
@@ -43,7 +44,9 @@ def multiprocess_eval(
         rank, opt, rank_offset, gpu, init_method=init_method
     ) as opt:
         opt['multiprocessing'] = True
-        return eval_model.eval_model(opt)
+        evaluator = fsdp_utils.JoinableEvaluator(opt)
+        with fsdp_utils.fsdp_join(evaluator):
+            return evaluator.eval_model()
 
 
 def launch_and_eval(opt, port):

parlai/scripts/multiprocessing_train.py

@@ -29,6 +29,7 @@
 import traceback
 import parlai.scripts.train_model as single_train
 import parlai.utils.distributed as distributed_utils
+import parlai.utils.fsdp as fsdp_utils
 from parlai.core.script import ParlaiScript, register_script
 
 
@@ -41,8 +42,10 @@ def multiprocess_train(
     ) as opt:
         # Run the actual training
         opt['multiprocessing'] = True
+        loop = fsdp_utils.JoinableTrainLoop(opt)
         try:
-            return single_train.TrainLoop(opt).train()
+            with fsdp_utils.fsdp_join(loop):
+                return loop.train()
         except Exception:
             import parlai.utils.logging as logging