update llvm tag to a2620e00. (#1567)

- also update MHLO to 57ba12a2(branch greencommit/2022-11-07-a2620e00)
- change -pass-pipeline format to make tests pass.

externals/llvm-project

@@ -1 +1 @@
-Subproject commit 74fb770de9399d7258a8eda974c93610cfde698e
+Subproject commit a2620e00ffa232a406de3a1d8634beeda86956fd

externals/mlir-hlo

@@ -1 +1 @@
-Subproject commit 36238f16441cd1a884af988d4400d2ebb0c75bbc
+Subproject commit 57ba12a2a1934c3c9fc3cd1580f28f0c233f41d4

python/torch_mlir/__init__.py

@@ -346,7 +346,7 @@ PyTorch TorchScript module -> torch-mlir Object Graph IR import failed with:
     option_string = "{backend-legal-ops=" + ",".join(backend_legal_ops) + "}"
     run_pipeline_with_repro_report(
         mb.module,
-        f"torchscript-module-to-torch-backend-pipeline{option_string}",
+        f"builtin.module(torchscript-module-to-torch-backend-pipeline{option_string})",
         "Lowering TorchScript IR -> Torch Backend IR",
     )
 
@@ -361,7 +361,7 @@ PyTorch TorchScript module -> torch-mlir Object Graph IR import failed with:
     if output_type == OutputType.TOSA:
         run_pipeline_with_repro_report(
             mb.module,
-            "torch-backend-to-tosa-backend-pipeline",
+            "builtin.module(torch-backend-to-tosa-backend-pipeline)",
             "Lowering Torch Backend IR -> TOSA Backend IR")
         if verbose:
             print("\n====================")
@@ -372,7 +372,7 @@ PyTorch TorchScript module -> torch-mlir Object Graph IR import failed with:
     if output_type == OutputType.LINALG_ON_TENSORS:
         run_pipeline_with_repro_report(
             mb.module,
-            "torch-backend-to-linalg-on-tensors-backend-pipeline",
+            "builtin.module(torch-backend-to-linalg-on-tensors-backend-pipeline)",
             "Lowering Torch Backend IR -> Linalg-on-Tensors Backend IR")
         if verbose:
             print("\n====================")
@@ -383,7 +383,7 @@ PyTorch TorchScript module -> torch-mlir Object Graph IR import failed with:
     elif output_type == OutputType.MHLO:
         run_pipeline_with_repro_report(
             mb.module,
-            "torch-backend-to-mhlo-backend-pipeline",
+            "builtin.module(torch-backend-to-mhlo-backend-pipeline)",
             "Lowering Torch Backend IR -> MHLO Backend IR")
         if verbose:
             print("\n====================")

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

@@ -1261,7 +1261,7 @@ def main(args):
     for function in torch.jit._state._python_cu.get_functions():
         mb.import_function(function)
     # Clean up the IR a bit before writing it out.
-    pm = PassManager.parse("canonicalize", context=mb.module.context)
+    pm = PassManager.parse("builtin.module(canonicalize)", context=mb.module.context)
     pm.run(mb.module)
     # Munge the IR a bit to make it more systematically accessible.
     asm = mb.module.operation.get_asm()

python/torch_mlir_e2e_test/eager_backends/refbackend.py

@@ -67,7 +67,7 @@ class EagerModeRefBackend(TorchMLIREagerBackend):
         if module_hash not in self.module_to_refbackend_invoker:
             run_pipeline_with_repro_report(
                 imported_module,
-                "torch-function-to-torch-backend-pipeline,torch-backend-to-linalg-on-tensors-backend-pipeline",
+                "builtin.module(torch-function-to-torch-backend-pipeline,torch-backend-to-linalg-on-tensors-backend-pipeline)",
                 "EagerMode",
             )
             self.module_to_refbackend_invoker[module_hash] = _ref_backend.load(

python/torch_mlir_e2e_test/linalg_on_tensors_backends/refbackend.py

@@ -114,7 +114,7 @@ class RefBackendInvoker:
         return invoke
 
 
-LOWERING_PIPELINE = ",".join([
+LOWERING_PIPELINE = "builtin.module(" + ",".join([
     "func.func(refback-generalize-tensor-pad)",
     # Bufferize.
     "func.func(scf-bufferize)",
@@ -152,7 +152,7 @@ LOWERING_PIPELINE = ",".join([
     "convert-func-to-llvm",
     "convert-cf-to-llvm",
     "reconcile-unrealized-casts",
-])
+]) + ")"
 
 
 class RefBackendLinalgOnTensorsBackend(LinalgOnTensorsBackend):

python/torch_mlir_e2e_test/mhlo_backends/linalg_on_tensors.py

@@ -36,7 +36,7 @@ class LinalgOnTensorsMhloBackend(MhloBackend):
         """
         run_pipeline_with_repro_report(
             imported_module,
-            "func.func(symbolic-shape-optimization),func.func(hlo-legalize-to-linalg),func.func(canonicalize)",
+            "builtin.module(func.func(symbolic-shape-optimization),func.func(hlo-legalize-to-linalg),func.func(canonicalize))",
             "Lowering MLIR-HLO to Linalg-on-Tensors")
         return self.refbackend.compile(imported_module)
 

python/torch_mlir_e2e_test/tosa_backends/linalg_on_tensors.py

@@ -43,20 +43,20 @@ class LinalgOnTensorsTosaBackend(TosaBackend):
         # that depend on TOSA as well as TOSA-to-Standard.  
         run_pipeline_with_repro_report(
             imported_module,
-            "func.func(tosa-to-arith)",
+            "builtin.module(func.func(tosa-to-arith))",
             "Lowering TOSA to Arith")
 
         # Named ops must be legalized prior to general tosa-to-linalg
         run_pipeline_with_repro_report(
             imported_module,
-            "func.func(tosa-to-linalg-named)",
+            "builtin.module(func.func(tosa-to-linalg-named))",
             "Lowering TOSA to Linalg-on-Tensors for Named Ops")
 
         # TOSA-to-LinAlg may generate tosa.const() ops, so we want to lower them
         # to arith.constants here before proceeding further.
         run_pipeline_with_repro_report(
             imported_module,
-            "func.func(tosa-to-tensor),func.func(tosa-to-linalg),func.func(tosa-to-arith)",
+            "builtin.module(func.func(tosa-to-tensor),func.func(tosa-to-linalg),func.func(tosa-to-arith))",
             "Lowering TOSA to Linalg-on-Tensors")
 
         return self.refbackend.compile(imported_module)

test/Dialect/Torch/torch-function-to-torch-backend-pipeline.mlir

@@ -1,4 +1,4 @@
-// RUN: torch-mlir-opt -pass-pipeline='torch-function-to-torch-backend-pipeline{backend-legal-ops=torch.aten.square,torch.aten.argmax}' -split-input-file %s | FileCheck %s
+// RUN: torch-mlir-opt -pass-pipeline='builtin.module(torch-function-to-torch-backend-pipeline{backend-legal-ops=torch.aten.square,torch.aten.argmax})' -split-input-file %s | FileCheck %s
 
 // CHECK-LABEL: func.func @torch.aten.square
 func.func @torch.aten.square(%arg0: !torch.vtensor<[?,?,?],f32>) -> !torch.vtensor<[?,?,?],f32> {