[autockpt] make solver work on resnet.

colossalai/auto_parallel/passes/comm_metainfo_pass.py

@@ -54,16 +54,16 @@ def _construct_meta_info(node: Node, origin_sharding_spec: ShardingSpec,
     return meta_info
 
 
-def _runtime_apply_meta_info(node: Node, original_sharding_spec_dict, sharding_spec_dict) -> MetaInfo:
+def _runtime_apply_meta_info(node: Node, origin_spec_dict, sharding_spec_dict) -> MetaInfo:
     """
     This method is used to construct `MetaInto` for shape consistency node
     """
 
     # extract node index and user node index
     args = node.args
     node_index, user_node_index = args[3], args[4]
-    origin_sharding_spec, target_sharding_spec = original_sharding_spec_dict[node_index], sharding_spec_dict[
-        node_index][user_node_index]
+    origin_sharding_spec, target_sharding_spec = origin_spec_dict[node_index], sharding_spec_dict[node_index][
+        user_node_index]
 
     return _construct_meta_info(node, origin_sharding_spec, target_sharding_spec)
 
@@ -98,16 +98,16 @@ def _runtime_comm_spec_apply_meta_info(node: Node, comm_actions_dict: Dict) -> M
     return meta_info
 
 
-def comm_metainfo_pass(gm: GraphModule, sharding_spec_dict: Dict, original_sharding_spec_dict: Dict,
-                       comm_actions_dict: Dict):
+def comm_metainfo_pass(gm: GraphModule, sharding_spec_dict: Dict, origin_spec_dict: Dict,
+                       comm_actions_dict: Dict) -> GraphModule:
     """
     The method manages all the metainfo of the communication node (run_time_apply, runtime_comm_spec_apply) in the graph.
     """
     for node in gm.graph.nodes:
         if node.target == runtime_apply:
-            setattr(node, 'best_metainfo',
-                    _runtime_apply_meta_info(node, original_sharding_spec_dict, sharding_spec_dict))
+            setattr(node, 'best_metainfo', _runtime_apply_meta_info(node, origin_spec_dict, sharding_spec_dict))
         elif node.target == runtime_comm_spec_apply:
             setattr(node, 'best_metainfo', _runtime_comm_spec_apply_meta_info(node, comm_actions_dict))
         else:
             pass
+    return gm

colossalai/auto_parallel/tensor_shard/node_handler/binary_elementwise_handler.py

@@ -16,7 +16,7 @@
 
 
 @operator_registry.register(BCAST_FUNC_OP)
-class BinaryElementwiseHandler(NodeHandler):
+class BinaryElementwiseHandler(MetaInfoNodeHandler):
     """
     An BinaryBcastOpHandler is a node handler which deals with operations which have two
     operands and broadcasting occurs such as torch.add.

colossalai/auto_parallel/tensor_shard/node_handler/reshape_handler.py

@@ -3,7 +3,7 @@
 import torch
 
 from ..sharding_strategy import OperationData, OperationDataType
-from .node_handler import NodeHandler
+from .node_handler import MetaInfoNodeHandler, NodeHandler
 from .registry import operator_registry
 from .strategy import ReshapeGenerator, StrategyGenerator
 
@@ -13,7 +13,7 @@
 @operator_registry.register(torch.flatten)
 @operator_registry.register(torch.Tensor.unsqueeze)
 @operator_registry.register(torch.nn.AdaptiveAvgPool2d)
-class ReshapeHandler(NodeHandler):
+class ReshapeHandler(MetaInfoNodeHandler):
     """
     A ReshapeHandler which deals with the sharding strategies for Reshape Op, such as torch.reshape.
     """

colossalai/auto_parallel/tensor_shard/node_handler/unary_elementwise_handler.py

@@ -3,7 +3,7 @@
 import torch
 
 from ..sharding_strategy import OperationData, OperationDataType
-from .node_handler import NodeHandler
+from .node_handler import MetaInfoNodeHandler, NodeHandler
 from .registry import operator_registry
 from .strategy import StrategyGenerator, UnaryElementwiseGenerator
 
@@ -19,7 +19,7 @@
 @operator_registry.register(torch.nn.modules.dropout.Dropout)
 @operator_registry.register(torch.Tensor.contiguous)
 @operator_registry.register(torch.nn.functional.dropout)
-class UnaryElementwiseHandler(NodeHandler):
+class UnaryElementwiseHandler(MetaInfoNodeHandler):
     """
     A UnaryElementwiseHandler which deals with the sharding strategies for UnaryElementwise Op.
     """