[mlir] Python: Parse ModuleOp from file path (#126572)

For extremely large models, it may be inefficient to load the model into
memory in Python prior to passing it to the MLIR C APIs for
deserialization. This change adds an API to parse a ModuleOp directly
from a file path.

Re-lands
[4e14b8a](https://github.com/llvm/llvm-project/commit/4e14b8afb44af58ab7073bb8c0b52875599b0ae1).

1 files changed, 19 insertions, 0 deletions

diff --git a/mlir/test/python/ir/module.py b/mlir/test/python/ir/module.py
index ecafcb46af21..6065e59fd6ed 100644
--- a/mlir/test/python/ir/module.py
+++ b/mlir/test/python/ir/module.py
@@ -1,6 +1,7 @@
 # RUN: %PYTHON %s | FileCheck %s
 
 import gc
+from tempfile import NamedTemporaryFile
 from mlir.ir import *
 
 
@@ -27,6 +28,24 @@ def testParseSuccess():
     print(str(module))
 
 
+# Verify successful parse from file.
+# CHECK-LABEL: TEST: testParseFromFileSuccess
+# CHECK: module @successfulParse
+@run
+def testParseFromFileSuccess():
+    ctx = Context()
+    with NamedTemporaryFile(mode="w") as tmp_file:
+        tmp_file.write(r"""module @successfulParse {}""")
+        tmp_file.flush()
+        module = Module.parseFile(tmp_file.name, ctx)
+        assert module.context is ctx
+        print("CLEAR CONTEXT")
+        ctx = None  # Ensure that module captures the context.
+        gc.collect()
+        module.operation.verify()
+        print(str(module))
+
+
 # Verify parse error.
 # CHECK-LABEL: TEST: testParseError
 # CHECK: testParseError: <