1. change the variable name from align_var_to_block to slice_var_up

2. replace split_method with slice_var_up in func init_splited_variables

python/paddle/fluid/tests/unittests/test_simple_dist_transpiler.py

@@ -72,7 +72,7 @@ class TestSimpleDistTranspiler(TranspilerTest):
             program=main,
             pservers=self.pserver_eps,
             trainers=self.trainers,
-            align_var_to_block=False)
+            slice_var_up=False)
         return t
 
 

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -71,7 +71,7 @@ def same_or_split_var(p_name, var_name):
     return p_name == var_name or p_name.startswith(var_name + ".block")
 
 
-def split_variable(var_list, service_count, min_block_size=8192):
+def slice_variable(var_list, slice_count, min_block_size=8192):
     """
     We may need to split dense tensor to one or more blocks and put
     them equally onto parameter server. One block is a sub-tensor
@@ -83,8 +83,8 @@ def split_variable(var_list, service_count, min_block_size=8192):
 
     Args:
         var_list (list): List of variables.
-        service_count (int): Numel of pserver services. A pserver may have two
-            or more listening ports.
+        slice_count (int): Numel of count that variables will be sliced, which
+            could be the pserver services' count.
         min_block_size (int): Minimum splitted block size.
     Returns:
         blocks (list[(varname, block_id, current_block_size)]): A list
@@ -92,12 +92,12 @@ def split_variable(var_list, service_count, min_block_size=8192):
     """
     blocks = []
     for var in var_list:
-        split_count = service_count
+        split_count = slice_count
         var_numel = reduce(lambda x, y: x * y, var.shape)
         max_pserver_count = int(math.floor(var_numel / float(min_block_size)))
         if max_pserver_count == 0:
             max_pserver_count = 1
-        if max_pserver_count < service_count:
+        if max_pserver_count < slice_count:
             split_count = max_pserver_count
         block_size = int(math.ceil(var_numel / float(split_count)))
 
@@ -178,7 +178,7 @@ class DistributeTranspiler:
                     for index in range(len(self.pserver_endpoints))
                 ]
 
-    def _init_splited_vars(self, split_method, align_var_to_block=True):
+    def _init_splited_vars(self, slice_var_up):
         # update these mappings for further transpile:
         # 1. param_var_mapping: param var name -> [splited params vars]
         # 2. grad_var_mapping: grad var name -> [splited grads vars]
@@ -197,16 +197,19 @@ class DistributeTranspiler:
         self._update_dist_lookup_table_vars(param_list, grad_list,
                                             self.params_grads)
 
-        if align_var_to_block:
-            grad_blocks = split_variable(grad_list, len(self.pserver_endpoints))
-            param_blocks = split_variable(param_list,
+        if slice_var_up:
+            # when we slice var up into blocks, we will slice the var according to
+            # pserver services' count. A pserver may have two or more listening ports.
+            grad_blocks = slice_variable(grad_list, len(self.pserver_endpoints))
+            param_blocks = slice_variable(param_list,
                                           len(self.pserver_endpoints))
         else:
-            # when we do NOT align var to block, we will always split params
+            # when we do NOT slice var up into blocks, we will always slice params
             # grads into one block.
-            grad_blocks = split_variable(grad_list, 1)
-            param_blocks = split_variable(param_list, 1)
+            grad_blocks = slice_variable(grad_list, 1)
+            param_blocks = slice_variable(param_list, 1)
         assert (len(grad_blocks) == len(param_blocks))
+
         # origin_varname -> [splited_var]
         self.param_var_mapping = self._create_vars_from_blocklist(
             self.origin_program, param_blocks)
@@ -237,7 +240,7 @@ class DistributeTranspiler:
                   program=None,
                   pservers="127.0.0.1:6174",
                   trainers=1,
-                  align_var_to_block=True,
+                  slice_var_up=True,
                   split_method=RoundRobin,
                   sync_mode=True):
         """
@@ -271,7 +274,7 @@ class DistributeTranspiler:
         self.has_distributed_lookup_table = self._has_distributed_lookup_table()
 
         # split and create vars, then put splited vars in dicts for later use.
-        self._init_splited_vars(split_method, align_var_to_block)
+        self._init_splited_vars(slice_var_up)
 
         # step 3.1: insert send op to send gradient vars to parameter servers
         ps_dispatcher.reset()
@@ -283,13 +286,13 @@ class DistributeTranspiler:
         #       fc_b@GRAD_trainer_0, fc_b@GRAD_trainer_1 --> pserver2
         # shuffle the map will avoid the uneven distribution above
         grad_var_mapping_items = self.grad_var_mapping.items()
-        if not align_var_to_block:
+        if not slice_var_up:
             np.random.shuffle(grad_var_mapping_items)
 
         for orig_varname, splited_vars in grad_var_mapping_items:
             eplist = ps_dispatcher.dispatch(splited_vars)
 
-            if not align_var_to_block:
+            if not slice_var_up:
                 assert (len(splited_vars) == 1)
 
             if len(splited_vars) == 1: