Fix buffer dtype in ` FSDP.state_dict()` when using mixed precision (#705)

* add failing test for buffer dtype

* fix buffer dtype issue

* update CHANGELOG

* fix

CHANGELOG.md

@@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ## NEXT - TBD
 ### Fixed
 - checkpointing: use dummy tensor to ensure backward pass is called [#701]
+- FSDP: fixed bug where buffers returned in `state_dict()` could still be half precision when `mixed_precision` is set to `True`.
 
 ### Added
 

fairscale/nn/data_parallel/fully_sharded_data_parallel.py

@@ -653,17 +653,21 @@ class FullyShardedDataParallel(nn.Module):
         if torch.cuda.is_available():
             torch.cuda.synchronize()
         self._lazy_init()
+
+        def maybe_cast_buffers(dtype: Optional[torch.dtype] = None) -> None:
             if self.mixed_precision:
-            # Buffers dtype stays consistent with parameters.
-            self._cast_buffers(dtype=torch.float32)
+                self._cast_buffers(dtype=dtype)
 
         if self._return_full_state_dict:
             if self.training_state != TrainingState.SUMMON_FULL_PARAMS:
                 with self.summon_full_params(recurse=False, volatile=True):
+                    maybe_cast_buffers(torch.float32)
                     state_dict = super().state_dict(*args, **kwargs)
             else:
+                maybe_cast_buffers(torch.float32)
                 state_dict = super().state_dict(*args, **kwargs)
         else:
+            maybe_cast_buffers(torch.float32)
             if self.flatten_parameters:
                 assert isinstance(self.module, FlattenParamsWrapper)
                 state_dict = self.module.flat_state_dict(*args, **kwargs)
@@ -674,9 +678,8 @@ class FullyShardedDataParallel(nn.Module):
             for k in state_dict.keys():
                 state_dict[k] = state_dict[k].cpu()
 
-        if self.mixed_precision:
         # In case we are in mixed precision, restore buffers back to buffer_dtype.
-            self._cast_buffers()
+        maybe_cast_buffers()
         return state_dict
 
     @typing.overload

tests/nn/data_parallel/test_fsdp_state_dict.py

@@ -101,8 +101,9 @@ class TestSaveLoadStateDict(DistributedTest):
     def _test_state_dict_before_forward(cls, config, rank, group):
         ddp_model = cls.get_wrapped_model(group, cuda_first=False, config=config)
         sd = ddp_model.state_dict()
-        wt = sd["embed_tokens.weight"]
-        assert wt.dtype == torch.float32, f"got dtype {wt.dtype} expected torch.float32"
+        for param_name in ("embed_tokens.weight", "vocab_bias"):
+            wt = sd[param_name]
+            assert wt.dtype == torch.float32, f"got dtype {wt.dtype} for {param_name}, expected torch.float32"
         cls._train_for_several_steps(ddp_model, 1, ddp_model.mixed_precision)
 
     @classmethod
@@ -232,15 +233,9 @@ class TestStateDictDeviceDtype(DistributedTest):
                 assert v.device.type == "cuda", v.device.type
 
         expected_dtype = torch.float16 if pure_fp16 else torch.float32
-        buffers = {
-            k.replace("_fsdp_wrapped_module.", "").replace("_fpw_module.", "") for k, _ in fsdp_model.named_buffers()
-        }
         for k, v in sd.items():
             if not torch.is_floating_point(v):
                 continue
-            if k in buffers:
-                assert v.dtype == fsdp_model.buffer_dtype, f"{v.dtype} != {fsdp_model.buffer_dtype}"
-            else:
             assert v.dtype == expected_dtype, f"{v.dtype} != {expected_dtype}"