Remove dead code

apex/contrib/optimizers/distributed_fused_adam.py

-import types
 import math
 import torch
 import importlib
-from apex.multi_tensor_apply import multi_tensor_applier
 
 class DistributedFusedAdam(torch.optim.Optimizer):
 
@@ -202,13 +200,7 @@ class DistributedFusedAdam(torch.optim.Optimizer):
 
     def _pipeline_block_step(self, block_id, flat_grads, new_params):
         start = block_id * self._block_size
-        end = start + self._block_size
-        grad_block = flat_grads[start:end]
-        grad_shards = [grad_block[i*self._shard_size:(i+1)*self._shard_size] for i in range(self._group_size)]
         new_params_shards = [new_params[start+shard_i*self._shard_size:start+(shard_i+1)*self._shard_size] for shard_i in range(self._group_size)]
-        shard_start = start + self._rank_in_group * self._shard_size
-        shard_end = shard_start + self._shard_size
-        block_id = start // self._block_size
         self._partial_step_single_shard(block_id)
         if self._pg_supports_no_copy:
             work = torch.distributed.all_gather(new_params_shards,new_params_shards[self._rank_in_group],group=self._ag_pg[block_id%len(self._ag_pg)],async_op=True,no_copy=True)
@@ -418,8 +410,6 @@ class DistributedFusedAdam(torch.optim.Optimizer):
     def _do_compute_L2_grad_norm(self):
         partial_sum = torch.zeros([]).cuda()
         for block in range(self._num_blocks):
-            start = block * self._block_size
-            end = start + self._block_size
             grad_block = self._flat_grads[block*self._block_size:(block+1)*self._block_size]
             grad_shards = [grad_block[i*self._shard_size:(i+1)*self._shard_size] for i in range(self._group_size)]
             shard_grad_norm = grad_shards[self._rank_in_group].float().norm()