[Auto parallel] align infer accuracy for ernie generator mode (#40077)

* [Auto Parallel] Support the auto completion of while_op
* align infer  accuracy

python/paddle/distributed/auto_parallel/operators/dist_fill_constant_batch_size_like.py

@@ -65,7 +65,8 @@ class DistributedFillConstantBatchSizeLikeImpl0(DistributedOperatorImpl):
         if (not self.is_input_compatible(dist_op)) or \
             (not self.is_output_compatible(dist_op)):
             return False
-
+        op_desc = dist_op.serial_op.desc
+        op_dist_attr = dist_op.dist_attr
         out_name = op_desc.output('Out')[0]
         out_dims_mapping = op_dist_attr.get_output_dims_mapping(out_name)
         in_name = op_desc.input('Input')[0]
@@ -78,7 +79,7 @@ class DistributedFillConstantBatchSizeLikeImpl0(DistributedOperatorImpl):
         changed = False
         op_desc = dist_op.serial_op.desc
         op_dist_attr = dist_op.dist_attr
-        x_name = op_desc.input('X')[0]
+        x_name = op_desc.input('Input')[0]
         out_name = op_desc.output('Out')[0]
         x_dims_mapping = op_dist_attr.get_input_dims_mapping(x_name)
         out_dims_mapping = op_dist_attr.get_output_dims_mapping(out_name)

python/paddle/distributed/auto_parallel/operators/dist_matmul.py

@@ -1837,6 +1837,8 @@ class DistributedMulImpl1(DistributedOperatorImpl):
                                 out_var_dist_attr)
 
         intermediate_var_0 = main_block.create_var(
+            name=unique_name.generate_with_ignorable_key(".".join(
+                ["c_allreduce_sum", 'tmp'])),
             shape=Out_var.shape,
             dtype=Out_var.dtype,
             type=Out_var.type,
@@ -1936,7 +1938,6 @@ class DistributedMulImpl2(DistributedOperatorImpl):
         if is_valid_list_index(x_dims_mapping,
                                -2) and is_dim_shard(x_dims_mapping[-2]):
             return False
-
         if is_dim_shard(y_dims_mapping[-1]):
             return False
         if is_valid_list_index(y_dims_mapping,

python/paddle/distributed/auto_parallel/partitioner.py

@@ -329,13 +329,7 @@ def _partition_parameter(dist_context, src_var, dst_block, dst_varname,
         belong_to_optimizer=src_var.belong_to_optimizer,
         **copied_kwargs)
 
-    # set dist attr uid
-    # distributed_attr_uid = src_var.desc.get_distributed_attr_uid()
-    # param.desc.set_distributed_attr_uid(distributed_attr_uid)
-    dist_attr = copy.deepcopy(
-        dist_context.get_tensor_dist_attr_for_program(src_var))
-    assert dist_attr is not None
-    dist_context.set_tensor_dist_attr_for_program(param, dist_attr)
+    return param
 
 
 def _partition_intermediate_var(dist_context, src_var, dst_block, dst_varname,
@@ -352,13 +346,7 @@ def _partition_intermediate_var(dist_context, src_var, dst_block, dst_varname,
         is_data=src_var.is_data,
         belong_to_optimizer=src_var.belong_to_optimizer)
 
-    # set dist attr uid
-    # distributed_attr_uid = src_var.desc.get_distributed_attr_uid()
-    # var.desc.set_distributed_attr_uid(distributed_attr_uid)
-    dist_attr = copy.deepcopy(
-        dist_context.get_tensor_dist_attr_for_program(src_var))
-    assert dist_attr is not None
-    dist_context.set_tensor_dist_attr_for_program(var, dist_attr)
+    return var
 
 
 def _partition_var(dist_context, src_block, dst_block, src_varname,
@@ -369,7 +357,7 @@ def _partition_var(dist_context, src_block, dst_block, src_varname,
     src_var = src_block.var(src_varname)
 
     if src_var.type in __not_shape_var_type__:
-        dst_block.create_var(
+        new_var = dst_block.create_var(
             type=src_var.type,
             name=dst_varname,
             persistable=True,
@@ -380,11 +368,17 @@ def _partition_var(dist_context, src_block, dst_block, src_varname,
         target_shape = _get_dist_shape(src_var, dist_attr)
 
         if isinstance(src_var, Parameter):
-            _partition_parameter(dist_context, src_var, dst_block, dst_varname,
-                                 target_shape)
+            new_var = _partition_parameter(dist_context, src_var, dst_block,
+                                           dst_varname, target_shape)
         else:
-            _partition_intermediate_var(dist_context, src_var, dst_block,
-                                        dst_varname, target_shape)
+            new_var = _partition_intermediate_var(
+                dist_context, src_var, dst_block, dst_varname, target_shape)
+
+    dist_attr = copy.deepcopy(
+        dist_context.get_tensor_dist_attr_for_program(src_var))
+    assert dist_attr is not None
+    dist_context.set_tensor_dist_attr_for_program(new_var, dist_attr)
+
     return target_shape