PaddlePaddle
diff --git a/‎python/paddle/distributed/auto_parallel/static/engine.py
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+1-1Lines changed: 1 addition & 1 deletion b/‎python/paddle/distributed/auto_parallel/static/engine.py
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+1-1Lines changed: 1 addition & 1 deletion
diff --git a/‎python/paddle/distributed/auto_parallel/static/operators/common.py
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+13Lines changed: 13 additions & 0 deletions b/‎python/paddle/distributed/auto_parallel/static/operators/common.py
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+13Lines changed: 13 additions & 0 deletions
@@ -729,7 +729,7 @@ def _optimization_tuning(self, mode, dataset, batch_size):
     def _plan(self, mode):
         if self._planned_mode is None:
             self._planned_mode = mode
-        else:
+        elif self._strategy.auto_mode != "semi":
             self._init_dist_context(mode)
 
         self._planners[mode] = Planner(mode, self._dist_contexts[mode])
 
@@ -25,6 +25,7 @@
     _get_corresponding_rank,
     compute_compatible_dims_mapping,
     is_optimize_op,
+    set_dist_op_desc_original_id,
 )
 
 _logger = get_logger(
@@ -718,3 +719,15 @@ def get_default_distributed_operator_impl():
     num_impls = len(dist_op_default_impl_container.impls)
     assert num_impls == 1, f"Default dist op has [{num_impls}] impls"
     return dist_op_default_impl_container.get_impl(0)
+
+
+def copy_op_without_infer_shape(src_op, block, ctx, varname_kwargs):
+    new_op = block.append_op(type='nop')
+    new_op_desc = new_op.desc
+    new_op_desc.copy_from(src_op.desc)
+    set_dist_op_desc_original_id(new_op_desc, src_op.desc, ctx)
+    for input_name in src_op.desc.input_names():
+        new_op_desc.set_input(input_name, varname_kwargs[input_name])
+    for output_name in src_op.desc.output_names():
+        new_op_desc.set_output(output_name, varname_kwargs[output_name])
+    return new_op