[onnx] Add torch-mlir-import-onnx tool. (#2637)

Simple Python console script to import an ONNX protobuf to the torch
dialect for additional processing.

For installed wheels, this can be used with something like:

```
torch-mlir-import-onnx test/python/onnx_importer/LeakyReLU.onnx
```

Or from a dev setup:

```
python -m torch_mlir.tools.import_onnx ...
```

python/CMakeLists.txt

@@ -38,6 +38,13 @@ declare_mlir_python_sources(TorchMLIRPythonSources.Importers
     extras/onnx_importer.py
 )
 
+declare_mlir_python_sources(TorchMLIRPythonSources.Tools
+  ROOT_DIR "${TORCH_MLIR_PYTHON_ROOT_DIR}"
+  ADD_TO_PARENT TorchMLIRPythonSources
+  SOURCES
+    tools/import_onnx/__main__.py
+)
+
 ################################################################################
 # Extensions
 ################################################################################

python/torch_mlir/tools/import_onnx/__main__.py

@@ -0,0 +1,77 @@
+# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+# See https://llvm.org/LICENSE.txt for license information.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+# Also available under a BSD-style license. See LICENSE.
+
+"""Console tool for converting an ONNX proto to torch IR.
+
+Typically, when installed from a wheel, this can be invoked as:
+
+  torch-mlir-import-onnx some.pb
+
+Or from Python:
+  
+  python -m torch_mlir.tools.import_onnx ...
+"""
+import argparse
+from pathlib import Path
+import sys
+
+import onnx
+
+from ...extras import onnx_importer
+
+from ...dialects import torch as torch_d
+from ...ir import (
+    Context,
+)
+
+
+def main(args):
+    model_proto = load_onnx_model(args.input_file)
+    context = Context()
+    torch_d.register_dialect(context)
+    model_info = onnx_importer.ModelInfo(model_proto)
+    m = model_info.create_module(context=context)
+    imp = onnx_importer.NodeImporter.define_function(model_info.main_graph, m)
+    imp.import_all()
+    if not args.no_verify:
+        m.verify()
+
+    # TODO: This isn't very efficient output. If these files ever
+    # get large, enable bytecode and direct binary emission to save
+    # some copies.
+    if args.output_file and args.output_file != "-":
+        with open(args.output_file, "wt") as f:
+            print(m.get_asm(assume_verified=not args.no_verify), file=f)
+    else:
+        print(m.get_asm(assume_verified=not args.no_verify))
+
+
+def load_onnx_model(file_path: Path) -> onnx.ModelProto:
+    raw_model = onnx.load(file_path)
+    inferred_model = onnx.shape_inference.infer_shapes(raw_model)
+    return inferred_model
+
+
+def parse_arguments(argv=None):
+    parser = argparse.ArgumentParser(description="Torch-mlir ONNX import tool")
+    parser.add_argument("input_file", help="ONNX protobuf input", type=Path)
+    parser.add_argument(
+        "-o", dest="output_file", help="Output path (or '-' for stdout)"
+    )
+    parser.add_argument(
+        "--no-verify",
+        action="store_true",
+        help="Disable verification prior to printing",
+    )
+    args = parser.parse_args(argv)
+    return args
+
+
+def _cli_main():
+    sys.exit(main(parse_arguments()))
+
+
+if __name__ == "__main__":
+    _cli_main()

setup.py

@@ -186,6 +186,11 @@ setup(
         "onnx": [
             "onnx>=1.15.0",
         ],
-    }
+    },
+    entry_points={
+        "console_scripts": [
+            "torch-mlir-import-onnx = torch_mlir.tools.import_onnx:_cli_main",
+        ],
+    },
     zip_safe=False,
 )

test/lit.cfg.py

@@ -24,7 +24,7 @@ config.name = 'TORCH_MLIR'
 config.test_format = lit.formats.ShTest(not llvm_config.use_lit_shell)
 
 # suffixes: A list of file extensions to treat as test files.
-config.suffixes = ['.mlir', '.py']
+config.suffixes = ['.mlir', '.py', '.runlit']
 
 # test_source_root: The root path where tests are located.
 config.test_source_root = os.path.dirname(__file__)

test/python/onnx_importer/LeakyReLU.onnx

@@ -0,0 +1,15 @@
+pytorch0.3:h
+"
+
0
1"	LeakyRelu*
+alpha
+×#< 

torch-jit-exportZ
+
0
+

+
+
+b
+
1
+

+
+
+B

test/python/onnx_importer/import_onnx_tool.runlit

@@ -0,0 +1,3 @@
+# RUN: %PYTHON -m torch_mlir.tools.import_onnx %S/LeakyReLU.onnx | FileCheck %s
+
+# CHECK: torch.operator "onnx.LeakyRelu"