Merge pull request PaddlePaddle#14 from sandyhouse/revert-12-lilong/moe

Revert "upload assign pos op"

python/paddle/distributed/utils.py

@@ -50,68 +50,9 @@
            'pull_worker_log',
            'global_scatter',
            'global_gather',
-           'assign_pos',
 ]
 
 
-def assign_pos(x,
-               cum_count):
-    """
-    Assign pos decides which tokens should be fetched belong to 
-    specially expert orderingly.
-    
-    Args:
-        x (Tensor): Tensor. Every element in the list must be a Tensor whose data type
-            should be float16, float32, float64, int32 or int64.
-        cum_count (Tensor): The cumulative sum tokens of experts. Every element in the list must be a Tensor whose 
-            data type should be int64.
-  
-    Returns:
-        out (Tensor): Assemble tokens in the order of experts. 
-    
-    Examples:
-        .. code-block:: python
-
-            # required: distributed
-            import paddle
-            local_expert_count = [2, 0, 2, 0]
-            gate_idx = [
-                [0, 2],
-                [0, 2]
-            ]
-            local_expert_count = paddle.to_tensor(local_expert_count)
-            gate_idx = paddle.to_tensor(gate_idx, dtype="int32")
-            lec_cum = paddle.cumsum(local_expert_count)
-            pos = paddle.distributed.utils.assign_pos(x=gate_idx, cum_count=lec_cum)
-            print(pos) # the result: (2, 0, 3, 1)
-    """
-    if in_dygraph_mode():
-        return core.ops.assign_pos(x, cum_count, cum_count[-1])
-    else:
-        op_type = 'assign_pos'
-        # check_variable_and_dtype(
-        #     x, 'x', ['float16', 'float32', 'float64', 'int32', 'int64'],
-        #     'global_scatter')
-        # check_variable_and_dtype(local_count, 'local_count', ['int64'],
-        #                          'global_scatter')
-        # check_variable_and_dtype(global_count, 'global_count', ['int64'],
-        #                          'global_scatter')
-
-        helper = LayerHelper(op_type, **locals())
-        out = helper.create_variable_for_type_inference(dtype=cum_count.dtype)
-
-        helper.append_op(
-            type=op_type,
-            inputs={
-                'X': [x],
-                'cum_count': [cum_count],
-                "eff_gates_len": [cum_count[-1]]
-            },
-            outputs={'Out': [out]})
-        return out
-
-
-
 def global_scatter(x,
                    local_count,
                    global_count,