Avoid calling logging.basicConfig (pytorch#86959)

Fixes pytorch#85952

Pull Request resolved: pytorch#86959
Approved by: https://github.com/xwang233, https://github.com/davidberard98

torch/fx/passes/backends/nvfuser.py

@@ -17,8 +17,8 @@
 
 import logging
 
-logging.basicConfig(level=logging.WARNING)
 logger = logging.getLogger(__name__)
+logger.setLevel(logging.WARNING)
 
 def aten_to_dtype(self, dtype: torch.dtype, **kwargs):
     if len(kwargs) > 0 or not dtype:
@@ -244,26 +244,26 @@ def lower_to_prims_and_execute(self, graph_module: GraphModule, *args, **kwargs)
         # "lowering to prims" and "trace execution" are grouped into this function, as they are both input dependent
 
         if graph_module in self.prim_decomp_cache:
-            logging.debug("prim_decomp_cache hit!")
+            logger.debug("prim_decomp_cache hit!")
             prim_module = self.prim_decomp_cache[graph_module]
         else:
             prim_graph = torch.fx.Graph()
             DecompositionInterpreter(graph_module, prim_graph, decomposition_table=aten2prim_decomp).run(*args, **kwargs)
             prim_module = torch.fx.GraphModule(graph_module, prim_graph)
             self.prim_decomp_cache[graph_module] = prim_module
 
-            logging.debug("Lower to prims graph: ", prim_module.code)
+            logger.debug("Lower to prims graph: ", prim_module.code)
 
         # invokes trace executor for running the prim graph
         return execute(prim_module, *args, executor="nvfuser")
 
     def compile(self, graph_module: GraphModule) -> GraphModule:
         # entry function for nvFuser backend
-        logging.debug("Compiling graph_module: ", graph_module.code)
+        logger.debug("Compiling graph_module: ", graph_module.code)
 
         # FX graph based partitioning based on nvfuser supported ops
         if graph_module in self.partitioner_cache:
-            logging.debug("partitioner_cache hit!")
+            logger.debug("partitioner_cache hit!")
             fused_graph_module = self.partitioner_cache[graph_module]
         else:
             partitioner = CapabilityBasedPartitioner(

torch/fx/passes/infra/partitioner.py

@@ -11,8 +11,8 @@
 import logging
 import itertools
 
-logging.basicConfig(level=logging.WARNING)
 logger = logging.getLogger(__name__)
+logger.setLevel(logging.WARNING)
 
 class Partition:
     def __init__(self, id: int = None, nodes: Iterable[Node] = None):
@@ -120,7 +120,7 @@ def assign(node: Node, id: Optional[int] = None):
                 else:
                     partitions_by_id[id].add_node(node)
 
-        logging.debug("Proposing partitions...")
+        logger.debug("Proposing partitions...")
 
         # visit candidates in reversed topological order
         for node in reversed(candidates):
@@ -210,14 +210,14 @@ def assign(node: Node, id: Optional[int] = None):
             for id in partitions_to_remove:
                 del partitions_by_id[id]
 
-        logging.debug("Partitions proposed:")
+        logger.debug("Partitions proposed:")
         for id, partition in partitions_by_id.items():
-            logging.debug(f"partition #{id}", [node.name for node in partition.nodes])
+            logger.debug(f"partition #{id}", [node.name for node in partition.nodes])
 
         return list(partitions_by_id.values())
 
     def fuse_partitions(self, partitions: List[Partition]) -> GraphModule:
-        logging.debug("Fusing partitions...")
+        logger.debug("Fusing partitions...")
         # fuse_by_partitions expects partitions in List[List[Node]]: [ [node0, node1], [node2, node3] ]
         return fuse_by_partitions(self.graph_module, [list(partition.nodes) for partition in partitions])