Add extra_library kwarg to torch_mlir.compile (llvm#1986)

This commit adds the ability to specify extra abstract interpretation
functions in `torch_mlir.compile` to use during type refinement. This
allows users to easily add custom ops without having to interact with
MLIR or C++ directly.

python/torch_mlir/__init__.py

@@ -3,18 +3,20 @@
 # SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 # Also available under a BSD-style license. See LICENSE.
 
-from typing import Optional, Sequence, Union, List, Dict, Tuple
+from typing import Optional, Sequence, Union, List, Dict, Tuple, Callable, Iterable
 from enum import Enum
 
 import sys
 from io import StringIO
+import tempfile
 
 from torch._functorch.compile_utils import strip_overloads
 import torch
 
 from torch_mlir.passmanager import PassManager
 from .compiler_utils import run_pipeline_with_repro_report
 from torch_mlir.dialects.torch.importer.jit_ir import ClassAnnotator, ImportOptions, ModuleBuilder
+from torch_mlir.dialects.torch.importer.jit_ir.build_tools.library_generator import generate_library
 
 
 class OutputType(Enum):
@@ -252,7 +254,7 @@ def compile(model: torch.nn.Module,
             use_tracing: bool = False,
             ignore_traced_shapes=False,
             backend_legal_ops: Optional[Sequence[str]] = None,
-            _completely_unsupported_in_progress_extra_library: Optional[str] = None,
+            extra_library: Iterable[Callable] = [],
             verbose: bool = False):
     """Convert a PyTorch model to MLIR.
 
@@ -278,12 +280,28 @@ def compile(model: torch.nn.Module,
         backend_legal_ops: A list of ops that should be considered legal for
             the backend. An op that is considered legal will not be decomposed.
             This option is only valid with the `"torch"` output type.
+        extra_library: List of abstract interpretation functions to splice
+            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.
 
     Returns:
         An MLIR module that contains the converted model in the specified
         output type.
     """
+    extra_library_file_name = ""
+    if len(extra_library) != 0:
+        extra_library_dict = {}
+        for library_func in extra_library:
+            extra_library_dict[library_func.__name__] = library_func
+        mlir_library = generate_library(extra_library_dict)
+
+        extra_library_file_name = \
+            tempfile.gettempdir() + "/custom_op_extra_library.mlir"
+        with open(extra_library_file_name, "w") as f:
+            f.write(mlir_library)
+
     output_type = OutputType.get(output_type)
     example_args = ExampleArgs.get(example_args)
     if ignore_traced_shapes and not use_tracing:
@@ -368,11 +386,8 @@ def compile(model: torch.nn.Module,
     if output_type == OutputType.RAW:
         return mb.module
 
-    option_string = "{backend-legal-ops=" + ",".join(backend_legal_ops) + (
-        (" extra-library=" + _completely_unsupported_in_progress_extra_library)
-        if (_completely_unsupported_in_progress_extra_library is not None)
-        else ""
-    ) + "}"
+    option_string = "{backend-legal-ops=" + ",".join(backend_legal_ops) + \
+        " extra-library=" + extra_library_file_name + "}"
     run_pipeline_with_repro_report(
         mb.module,
         f"builtin.module(torchscript-module-to-torch-backend-pipeline{option_string})",

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

@@ -5,7 +5,7 @@
 
 import inspect
 import re
-from typing import List, Optional, Union
+from typing import List, Optional, Union, Any, Dict
 
 import torch
 
@@ -138,25 +138,30 @@ def _verify_signature_matches_registry(f, registry: Registry):
     atoms = function_name.split("〇")
     if len(atoms) == 2:
         atoms += [""]
-    operator = registry.get_by_triple(tuple(atoms))
+    try:
+        operator = registry.get_by_triple(tuple(atoms))
+    except KeyError as e:
+        raise ValueError(f"Unable to find op {'.'.join(atoms)} in registry")
     if function_kind == "shape":
         expected_signature = operator.get_shape_function_signature()
     elif function_kind == "dtype":
         expected_signature = operator.get_dtype_function_signature()
     elif function_kind == "decomposition":
         expected_signature = operator.get_decomposition_function_signature()
+    elif function_kind == "has_value_semantics":
+        expected_signature = operator.get_has_value_semantics_function_signature()
     else:
         raise ValueError(f"Invalid Op signature function kind: '{function_kind}'")
     if signature != expected_signature:
         raise ValueError(f"Signature mismatch for {f.__name__!r}: expected {expected_signature!r}, got {signature!r}")
 
-def generate_library(globals_) -> str:
-    """Convert all op functions in `globals()` into MLIR."""
+def generate_library(functions: Dict[str, Any]) -> str:
+    """Convert all op functions in `functions` into MLIR."""
     mb = ModuleBuilder()
     # We use the registry to ensure that the shape functions are consistent
     # with the ops.
     registry = Registry.load()
-    for k, v in globals_.items():
+    for k, v in functions.items():
         if "〇" not in k:
             continue
         if not hasattr(v, "_not_present_in_registry"):

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

@@ -267,6 +267,23 @@ def ret_decl_builder(arg: "SIG_ATTR_TYPE") -> str:
         return self._get_function_signature(
             "decomposition", parameter_decl_builder, ret_decl_builder)
 
+    def get_has_value_semantics_function_signature(self):
+        """Gets the Python function signature for this op's has_value_semantics function.
+
+        While this is technically debug-only output, it is useful to copy-paste
+        it from the debug dump into the library definitions, as many
+        ops have extra default arguments and stuff that are tedious to write out
+        right.
+        """
+        def parameter_decl_builder(arg: "SIG_ATTR_TYPE") -> str:
+            return ""
+
+        def ret_decl_builder(arg: "SIG_ATTR_TYPE") -> str:
+            return "None"
+
+        return self._get_function_signature(
+            "has_value_semantics", parameter_decl_builder, ret_decl_builder)
+
     def __repr__(self):
         f = io.StringIO()
         emitter = TextEmitter(f)

test/python/custom_op_shape_dtype_fn.py

@@ -1,5 +1,6 @@
 import os
 import tempfile
+from typing import List, Tuple
 
 import torch
 import torch.utils.cpp_extension
@@ -18,6 +19,18 @@ def identity(x: torch.Tensor):
 goofy_lib.define("identity(Tensor t) -> Tensor")
 goofy_lib.impl("identity", identity)
 
+def goofy〇identity〡shape(t: List[int]) -> List[int]:
+    return t
+
+def goofy〇identity〡dtype(t_rank_dtype: Tuple[int, int]) -> int:
+    t_rank, t_dtype = t_rank_dtype
+    return t_dtype
+
+def goofy〇identity〡has_value_semantics() -> None:
+    return
+
+extra_library = [
+    goofy〇identity〡shape, goofy〇identity〡dtype, goofy〇identity〡has_value_semantics]
 
 class CustomOpExampleModule(torch.nn.Module):
     def __init__(self):
@@ -38,26 +51,12 @@ def forward(self, a):
 mod = CustomOpExampleModule()
 mod.eval()
 
-abstract_interp_src = """\
-func.func @__torch_mlir_shape_fn.goofy.identity(%arg0: !torch.list<int>) -> !torch.list<int> {
-  return %arg0 : !torch.list<int>
-}
-func.func @__torch_mlir_dtype_fn.goofy.identity(%arg0 : !torch.tuple<int, int>) -> !torch.int {
-  %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int
-  return %0#1 : !torch.int
-}
-func.func @__torch_mlir_has_value_semantics_fn.goofy.identity() { return }
-"""
-
-with open("/tmp/custom_op_shape_dtype_fn.mlir", "w") as tmp:
-    tmp.write(abstract_interp_src)
-
 module = torch_mlir.compile(
     mod,
     torch.ones(3, 4),
     output_type="torch",
     backend_legal_ops=["goofy.identity"],
-    _completely_unsupported_in_progress_extra_library="/tmp/custom_op_shape_dtype_fn.mlir",
+    extra_library=extra_library,
 )
 
 print(module)