Allow printing all IR in `torch_mlir.compile` (#2669)

This PR adds the `enable_ir_printing` option to `torch_mlir.compile`,
which can be used to print the IR for all intermediate passes.

When running the added test file via:
```shell
$ python test/python/compile.py 2> tiny.stderr
```
the file `tiny.stderr` is about 700 KB.

projects/pt1/python/torch_mlir/__init__.py

@@ -319,7 +319,8 @@ def compile(model: torch.nn.Module,
             backend_legal_ops: Optional[Sequence[str]] = None,
             extra_library: Iterable[Callable] = [],
             verbose: bool = False,
-            use_make_fx: bool = False):
+            use_make_fx: bool = False,
+            enable_ir_printing: bool = False):
     """Convert a PyTorch model to MLIR.
 
     Args:
@@ -348,7 +349,13 @@ def compile(model: torch.nn.Module,
             into the abstract interpretation library. See
             `docs/adding_abstract_interpretation_functions.md` for more info
             on the format the functions should have.
-        verbose: If true, print extra information about the conversion.
+        verbose: If true, print extra information about the conversion to
+            stdout.
+        enable_ir_printing: If true, print the IR before and after each pass to
+            stderr. This is equivalent to setting MLIR's `-print-ir-after-all`
+            flag. Note that this can easily generate many gigabytes of text,
+            so make sure to pipe stderr to a file (for example, run
+            `python tinymodel.py 2> tinymodel.stderr` on Linux).
 
     Returns:
         An MLIR module that contains the converted model in the specified
@@ -452,6 +459,7 @@ PyTorch TorchScript module -> torch-mlir Object Graph IR import failed with:
         mb.module,
         f"builtin.module(torchscript-module-to-torch-backend-pipeline{option_string})",
         "Lowering TorchScript IR -> Torch Backend IR",
+        enable_ir_printing=enable_ir_printing,
     )
 
     return _lower_mlir_module(verbose, output_type, mb.module)

projects/pt1/python/torch_mlir/compiler_utils.py

@@ -27,7 +27,8 @@ class TorchMlirCompilerError(Exception):
 
 def run_pipeline_with_repro_report(module,
                                    pipeline: str,
-                                   description: str):
+                                   description: str,
+                                   enable_ir_printing: bool = False):
     """Runs `pipeline` on `module`, with a nice repro report if it fails."""
     module_name = get_module_name_for_debug_dump(module)
     try:
@@ -36,8 +37,11 @@ def run_pipeline_with_repro_report(module,
         asm_for_error_report = module.operation.get_asm(
             large_elements_limit=10, enable_debug_info=True)
         # Lower module in place to make it ready for compiler backends.
-        with module.context:
+        with module.context as ctx:
             pm = PassManager.parse(pipeline)
+            if enable_ir_printing:
+                ctx.enable_multithreading(False)
+                pm.enable_ir_printing()
             pm.run(module.operation)
     except Exception as e:
         # TODO: More robust.

test/python/compile.py

@@ -0,0 +1,34 @@
+# RUN: %PYTHON -s %s 2>&1 | FileCheck %s
+
+import gc
+import sys
+import torch
+import torch_mlir
+
+
+def run_test(f):
+    print("TEST:", f.__name__, file=sys.stderr)
+    f()
+    gc.collect()
+
+
+class TinyModel(torch.nn.Module):
+    def __init__(self):
+        super(TinyModel, self).__init__()
+
+        self.linear = torch.nn.Linear(20, 30)
+
+    def forward(self, x):
+        x = self.linear(x)
+        return x
+
+
+# CHECK-LABEL: TEST: test_enable_ir_printing
+@run_test
+def test_enable_ir_printing():
+    torch_mlir.compile(TinyModel(),
+                       torch.ones(1, 3, 20, 20),
+                       output_type="linalg-on-tensors",
+                       enable_ir_printing=True)
+# CHECK: // -----// IR Dump Before Canonicalizer (canonicalize)
+# CHECK-NEXT: module attributes {torch.debug_module_name = "TinyModel"} {