[autoparallel] recover the merged node strategy index (#1613)

colossalai/auto_parallel/solver/solver.py

@@ -61,6 +61,23 @@ def __init__(self,
         # The last objective value of the best ILP solution.
         self.last_objective = None
 
+    def _recover_merged_node_strategy(self):
+        '''
+        During cost graph constructing, some nodes, such as unary element-wise node or ReshapeOp, were merged into the previous node.
+        Therefore, the index of those strategies are copied from the previous node. This method is used to recover the strategy index of those merged
+        node.
+        '''
+        for node_index, node in enumerate(self.graph.nodes):
+            if node.strategies_vector.check_merge():
+                # the merged node has only one input, and its strategies follow the input sharding strategy
+                input_strategies_vector = node.args[0].strategies_vector
+                input_best_strategy_index = self.last_s_val[node_index - 1]
+                input_sharding_spec = input_strategies_vector[input_best_strategy_index].output_sharding_spec
+                for strategy_index, strategy in enumerate(node.strategies_vector):
+                    if strategy.input_shardings[0].sharding_sequence == input_sharding_spec.sharding_sequence:
+                        self.last_s_val[node_index] = strategy_index
+                        break
+
     def _generate_node_index_dict(self) -> Dict[Node, int]:
         node_index_dict = {}
         for index, strategies_vector in enumerate(self.leaf_strategies):
@@ -411,13 +428,14 @@ def get_non_zero_index(binary_vector):
             if verbose and r[idx][e_val[idx]] > 0:
                 print(f"Edge cost {(i, j)} : {r[idx][e_val[idx]]}")
 
-        self.last_s_val = s_val
+        self.last_s_val = list(s_val)
+        self._recover_merged_node_strategy()
         self.last_objective = objective
 
         if objective > INFINITY_COST:
             warnings.warn("Detect unexpected behaviors in the auto-sharding pass.")
 
-        return s_val, e_val, objective, status
+        return self.last_s_val, e_val, self.last_objective, status
 
     def call_solver_serialized_args(self):
         """

tests/test_auto_parallel/test_solver_with_resnet.py

@@ -80,32 +80,27 @@ def test_cost_graph():
     gm.recompile()
     graph_analyser = GraphAnalyser(gm)
     liveness_list = graph_analyser.liveness_analysis()
-    # print(len(liveness_dict[0].unique_live_vars))
-    # assert False
     solver_options = SolverOptions(fast=True)
     strategies_constructor = StrategiesConstructor(graph, device_mesh, solver_options)
     strategies_constructor.build_strategies_and_cost()
 
     cost_graph = CostGraph(strategies_constructor.leaf_strategies)
     cost_graph.simplify_graph()
-    solver = Solver(gm.graph, strategies_constructor, cost_graph, graph_analyser, memory_budget=1620017824.0)
-    # solver = Solver(gm.graph, strategies_constructor, cost_graph, graph_analyser)
+    solver = Solver(gm.graph, strategies_constructor, cost_graph, graph_analyser)
 
     ret = solver.call_solver_serialized_args()
-    print(ret)
-    strategies_list = list(ret[0])
-    print(strategies_list)
+    print(ret[0])
+    solver._recover_merged_node_strategy()
+    print(solver.last_s_val)
+    strategies_list = solver.last_s_val
+
     computation_cost = 0
     communication_cost = 0
     communication_cost_bn = 0
     memory_cost = 0
     for index, node in enumerate(graph.nodes):
         if node.op == 'call_module':
             submod = node.graph.owning_module.get_submodule(node.target)
-            if type(submod) in ELEMENTWISE_MODULE_OP:
-                input_spec = node.args[0].strategies_vector[strategies_list[index]].output_sharding_spec
-                print(node.name, input_spec)
-                continue
             if type(submod) in BATCHNORM_MODULE_OP:
                 communication_cost_bn += node.strategies_vector[strategies_list[index]].communication_cost
         print(node.name, node.strategies_vector[strategies_list[index]].name)