fix master params dtype

colossalai/zero/sharded_optim/sharded_adam.py

@@ -26,7 +26,7 @@ class ShardedAdam(ColossalaiOptimizer):
 
     def __init__(self,
                  adam_optim: Optimizer,
-                 sharded_model: nn.Module,
+                 sharded_model: Union[nn.Module, ShardedModelV2],
                  cpu_offload: bool = False,
                  initial_scale: float = 2**32,
                  min_scale: float = 1,
@@ -61,9 +61,11 @@ class ShardedAdam(ColossalaiOptimizer):
             for p in group['params']:
                 if hasattr(p, 'ca_attr'):
                     assert p.ca_attr.is_sharded, 'ShardedAdam can be only used with sharded model'
-                    self.master_params[p] = p.ca_attr.payload(self.device).to(torch.float)
+                    self.master_params[p] = p.ca_attr.payload(self.device)
                 else:
-                    self.master_params[p] = p.data.to(torch.float)
+                    self.master_params[p] = p.data.to(device=self.device)
+                if torch.is_floating_point(self.master_params[p]) and self.master_params[p].dtype != torch.float:
+                    self.master_params[p] = self.master_params[p].to(torch.float)
 
     def step(self, *args, **kwargs):
         # unscale grads if scaled
@@ -85,8 +87,9 @@ class ShardedAdam(ColossalaiOptimizer):
         # Write master param to payload and set p.data to None
         for group in self.optim.param_groups:
             for p in group['params']:
-                # TODO: update payload
-                p.data = None
+                if hasattr(p, 'ca_attr'):
+                    # TODO: update payload
+                    p.data = None
         return ret
 
     def backward(self, loss: Tensor) -> None:
@@ -129,10 +132,7 @@ class ShardedAdam(ColossalaiOptimizer):
         # all-reduce over model parallel group
         dist.all_reduce(self._found_overflow, op=dist.ReduceOp.MAX, group=self.mp_process_group)
 
-        if self._found_overflow.item() > 0:
-            return True
-        else:
-            return False
+        return self._found_overflow.item() > 0
 
     def _unscale_grads(self):
         assert self.optim_state == OptimState.SCALED