Add handling of namespaces to library generator (#2391)

When using custom ops, sometimes PyTorch will insert namespaces to the
abstract interpretation function name in the format:
`__torch__.{namespace_1}.{namespace_2}...{op_name}`.  The extra
namespaces are not part of the abstract interpretation function name,
so it needs to be removed before generating the library of MLIR
snippets of abstract interpretation functions. This commit adds
support for removing the namespace information.

python/torch_mlir/dialects/torch/importer/jit_ir/build_tools/library_generator.py

@@ -6,6 +6,7 @@
 import inspect
 import re
 from typing import List, Optional, Union, Any, Dict
+import codecs
 
 import torch
 
@@ -234,10 +235,22 @@ def generate_library(functions: Dict[str, Any]) -> str:
     # defined symbols. Since all of our shape functions conveniently have
     # a `〇` in them, we replace the torch namespace with our prefix. E.g.:
     # __torch__.aten〇add〇Scalar -> __torch_mlir_shape_fn.aten〇add〇Scalar
-    asm = re.sub(r"__torch__\.([^.(]+)\\E3\\80\\87([^.(]+)\\E3\\80\\A1([^.(\"]+)",
+
-                 r"__torch_mlir_\3_fn.\1\\E3\\80\\87\2",
+    # Encoding for: 〇
+    circle = r"\\E3\\80\\87"
+    # Encoding for: 〡
+    line = r"\\E3\\80\\A1"
+    name = r"[^.(]+"
+    # Sometimes PyTorch will insert namespaces to the function name in
+    # the format: `__torch__.{namespace_1}.{namespace_2}...{op_name}`
+    # The extra namespaces are not part of the abstract interpretation
+    # function name, so here we simply drop the extra namespaces.
+    namespace = fr"(?:{name}\.)"
+
+    asm = re.sub(fr'@"__torch__\.{namespace}*({name}){circle}({name}){line}({name})"',
+                 fr'@"__torch_mlir_\3_fn.\1{circle}\2"',
                  asm)
 
     # Put the `〇` back to a regular `.`.
-    asm = asm.replace("\\E3\\80\\87", ".")
+    asm = asm.replace(codecs.decode(circle, "unicode_escape"), ".")
     return asm

test/python/custom_op_shape_dtype_fn.py

@@ -3,6 +3,7 @@ import tempfile
 from typing import List, Tuple
 
 import torch
+import torch.multiprocessing as mp
 import torch.utils.cpp_extension
 import torch_mlir
 from torch_mlir_e2e_test.annotations import export, annotate_args
@@ -51,6 +52,10 @@ class CustomOpExampleModule(torch.nn.Module):
 mod = CustomOpExampleModule()
 mod.eval()
 
+def run():
+    mod = CustomOpExampleModule()
+    mod.eval()
+
     module = torch_mlir.compile(
         mod,
         torch.ones(3, 4),
@@ -61,6 +66,27 @@ module = torch_mlir.compile(
 
     print(module)
 
+run()
+
+# CHECK:    module attributes {torch.debug_module_name = "CustomOpExampleModule"} {
+# CHECK:      func.func @forward(%{{.*}}: !torch.vtensor<[3,4],f32>) -> !torch.vtensor<[3,4],f32> {
+# CHECK:        %{{.*}} = torch.constant.int 2
+# CHECK:        %{{.*}} = torch.aten.mul.Scalar %{{.*}}, %{{.*}} : !torch.vtensor<[3,4],f32>, !torch.int -> !torch.vtensor<[3,4],f32>
+# CHECK:        %{{.*}} = torch.operator "goofy.identity"(%{{.*}}) : (!torch.vtensor<[3,4],f32>) -> !torch.vtensor<[3,4],f32>
+# CHECK:        return %1 : !torch.vtensor<[3,4],f32>
+# CHECK:      }
+# CHECK:    }
+
+# Using `torch.multiprocessing` adds extra namespaces to the abstract
+# interpretation functions when they are imported into MLIR:
+#   `func @"__torch__.__mp_main__.{name}...`
+# This tests that the extra namespaces are removed correctly.
+if __name__ == "__main__":
+    mp.set_start_method("spawn")
+    p = mp.Process(target=run, args=())
+    p.start()
+    p.join()
+
 # CHECK:    module attributes {torch.debug_module_name = "CustomOpExampleModule"} {
 # CHECK:      func.func @forward(%{{.*}}: !torch.vtensor<[3,4],f32>) -> !torch.vtensor<[3,4],f32> {
 # CHECK:        %{{.*}} = torch.constant.int 2