add FusedLamb in __init__

apex/contrib/optimizers/__init__.py

 from .fp16_optimizer import FP16_Optimizer
 from .fused_adam import FusedAdam
+from .fused_lamb import FusedLamb

apex/contrib/optimizers/fused_lamb.py

@@ -14,9 +14,9 @@ class FusedLAMB(torch.optim.Optimizer):
       * Fusion of the LAMB update's elementwise operations
       * A multi-tensor apply launch that batches the elementwise updates applied to all the model's parameters into one or a few kernel launches.
 
-    :class:`apex.optimizers.FusedLAMB`'s usage is identical to any ordinary Pytorch optimizer::
+    :class:`apex.contrib.optimizers.FusedLAMB`'s usage is identical to any ordinary Pytorch optimizer::
 
-        opt = apex.optimizers.FusedLAMB(model.parameters(), lr = ....)
+        opt = apex.contrib.optimizers.FusedLAMB(model.parameters(), lr = ....)
         ...
         opt.step()
 
@@ -70,7 +70,8 @@ class FusedLAMB(torch.optim.Optimizer):
                         betas=betas, eps=eps, weight_decay=weight_decay,
                         grad_averaging=grad_averaging,
                         max_grad_norm=max_grad_norm)
-        super(FusedLAMB, self).__init__(params, defaults)
+        s
+        per(FusedLAMB, self).__init__(params, defaults)
         if multi_tensor_applier.available:
             fused_lamb_cuda = importlib.import_module("fused_lamb_cuda")
             self.multi_tensor_lamb = fused_lamb_cuda.lamb
@@ -80,6 +81,7 @@ class FusedLAMB(torch.optim.Optimizer):
 
         self.adam_w_mode = 1 if adam_w_mode else 0
         self.set_grad_none = set_grad_none
+        print("using apex.contrib.optimizers.FusedLamb")
 
     def zero_grad(self):
         if self.set_grad_none: