[fix] Dead code removal for OSS (#276)

* removing a dead call since ShardedDDP, small speedup
* unrelated, but filling in the changelog
* another nit

CHANGELOG.md

@@ -13,6 +13,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Fixed
 - AdaScale: smoothing factor value fixed when using gradient accumulation (#235)
 - Pipe: documentation on balancing functions (#243)
+- ShardedDDP: handle typical NLP models
+- ShardedDDP: better partitioning when finetuning
+
 
 ## [0.1.1] - 2020-12-01
 ### Fixed

benchmarks/oss.py

@@ -171,8 +171,7 @@ def train(
                         else:
                             final_loss = optimizer.step(closure)
 
-                        prof.export_chrome_trace(f"{optim_type}_trace_rank_{rank}.json")
-
+                prof.export_chrome_trace(f"{optim_type}_trace_rank_{rank}.json")
                 need_profiling = False  # only profile once
 
             else:

fairscale/optim/oss.py

@@ -208,9 +208,6 @@ class OSS(Optimizer):
         else:
             loss = self.optim.step(**kwargs)
 
-        # Depending on the DDP engine used, gradients specific to other ranks may still be loaded
-        self._free_other_grads()
-
         # Sync all the updated shards in between the ranks
         self._broadcast_params()
 
@@ -507,17 +504,6 @@ class OSS(Optimizer):
                 # Discard this tensor/rank, broadcast necessary for syncing
                 broadcast_object(empty_buffer, src_rank=global_rank, group=self.group, dist_device=self._device)
 
-    def _free_other_grads(self) -> None:
-        """Free all the gradients only useful for the other ranks
-        """
-        for rank, partition in enumerate(self.partition_parameters()):
-            if rank == self.rank:
-                continue
-
-            for p in partition:
-                for t in p["params"]:
-                    t.grad = None
-
     def _broadcast_params(self) -> None:
         """Helper function to broadcast all the parameters from a given device"""