Add a new python script to auto-generate ATen op ODS definitions. (#43)

* Add a new python script to auto-generate ATen op ODS definitions.

* There is still some work on some of the ops to annotate correct types.
* The ODS is not actually included into the dialect yet, but I'd like to commit it so that we can track changes.
* Will reconcile this with the ops produced by the existing script in a followup. Still need to do some more iteration to reach parity.

python/npcomp/torch/opdefs/generate_ods.py

@@ -0,0 +1,199 @@
+#  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
+"""Generates ODS for a registry of ops."""
+
+from typing import TextIO
+
+import argparse
+from contextlib import contextmanager
+import importlib
+import logging
+import re
+import sys
+import textwrap
+
+from .registry import *
+
+_INDENT = "  "
+
+
+class OdsEmitter:
+  ods_prefix = "ATen_"
+  ods_suffix = "Op"
+  ods_value_template = "ATen_ImmutableTensorOp"
+  ods_ref_template = "ATen_RefTensorOp"
+  op_prefix = ""
+
+  def __init__(self, r: OpRegistry, out: TextIO):
+    super().__init__()
+    self.r = r
+    self.out = out
+    self.indent_level = 0
+
+  def emit_ods(self):
+    for op_m in self.r.mappings:
+      if isinstance(op_m, SimpleOpMapping):
+        self._emit_simple_op_mapping(op_m)
+      else:
+        logging.warn(f"Unrecognized op mapping type: {op_m!r}")
+
+  def _emit_simple_op_mapping(self, op_m: SimpleOpMapping):
+    identifier = (f"{self.ods_prefix}"
+                  f"{_snakecase_to_camelcase(op_m.mlir_operation_name)}"
+                  f"{self.ods_suffix}")
+    traits = []
+
+    if op_m.is_outref_form:
+      template_name = self.ods_ref_template
+      summary = "See non-inplace op variant."
+      description = ""
+    else:
+      template_name = self.ods_value_template
+      summary, description = _split_docstring(op_m.op_f.__doc__)
+
+    if not op_m.is_outref_form:
+      traits.append("NoSideEffect")
+    self.print(f"def {identifier}: {template_name}"
+               f"<{_quote(op_m.mlir_operation_name)}, ["
+               f"{', '.join(traits)}"
+               f"]> {{")
+
+    # Summary.
+    with self.indent():
+      self.print(f"let summary = {_quote(summary)};")
+
+    # Arguments.
+    with self.indent():
+      self.print("let arguments = (ins")
+      with self.indent():
+        operand_len = len(op_m.operand_map)
+        for index, (_, value_spec) in enumerate(op_m.operand_map):
+          is_last = index == operand_len - 1
+          self.print(f"{value_spec.mlir_ods_predicate}:${value_spec.name}",
+                     end="\n" if is_last else ",\n")
+      self.print(");")
+
+    # Results (omitted if an outref/inplace form).
+    with self.indent():
+      if op_m.is_outref_form:
+        self.print("let results = (outs);")
+      else:
+        self.print("let results = (outs")
+        with self.indent():
+          result_len = len(op_m.result_map)
+          for index, (_, value_spec) in enumerate(op_m.result_map):
+            is_last = index == result_len - 1
+            self.print(f"{value_spec.mlir_ods_predicate}:${value_spec.name}",
+                       end="\n" if is_last else ",\n")
+        self.print(");")
+
+    # Description and extra class declarations.
+    with self.indent():
+      if description:
+        quoted_description = _quote_multiline_docstring(
+            description, indent_level=self.indent_level)
+        self.print(f"let description = {quoted_description};")
+
+    self.print("}\n")
+
+  @contextmanager
+  def indent(self, level=1):
+    self.indent_level += level
+    yield
+    self.indent_level -= level
+    assert self.indent_level >= 0, "Unbalanced indentation"
+
+  def print(self, s, *, end="\n", indent=True):
+    if indent and self.indent_level:
+      self.out.write(_INDENT * self.indent_level)
+    self.out.write(s)
+    self.out.write(end)
+
+
+def _snakecase_to_camelcase(ident: str):
+  return "".join(x.capitalize() or "_" for x in re.split(r"[\._]", ident))
+
+
+def _quote(s: str):
+  s = s.replace(r'"', r'\\"')
+  return f'"{s}"'
+
+
+def _quote_multiline_docstring(s: str, indent_level: int = 0):
+  # TODO: Possibly find a python module to markdown the docstring for better
+  # document generation.
+  # Unlikely to contain the delimitter and since just a docstring, be safe.
+  s = s.replace("}]", "")
+  # Strip each line.
+  s = "\n".join([l.rstrip() for l in s.splitlines()])
+  indent = _INDENT * indent_level
+  s = textwrap.indent(s, indent + _INDENT)
+  return "[{\n" + s + "\n" + indent + "}]"
+
+
+def _split_docstring(docstring: str):
+  """Splits the docstring into a summary and description."""
+  lines = docstring.splitlines()
+  # Skip leading blank lines.
+  while lines and not lines[0]:
+    lines = lines[1:]
+  if len(lines) > 2:
+    return lines[0], "\n".join(lines[2:])
+  else:
+    return lines[0]
+
+
+def main(args):
+  r = OpRegistry()
+  # Populate from modules that provide a populate() function.
+  op_modules = [args.op_module]
+  for m_name in op_modules:
+    logging.info(f"Populating from module: {m_name}")
+    m = importlib.import_module(m_name, package=__package__)
+    f = getattr(m, "populate")
+    f(r)
+
+  out = sys.stdout
+
+  # Write file header.
+  module_name = sys.modules["__main__"].__loader__.name
+  banner_lines = [
+      "//===-------------------------------------------------------*- tablegen -*-===//",
+      "//",
+      "// This file is licensed 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",
+      "//",
+      "// Operation summaries and descriptions were systematically derived from public",
+      "// API docstrings and are licensed accordingly:",
+      "//   https://github.com/pytorch/pytorch/blob/master/LICENSE",
+      "//===----------------------------------------------------------------------===//",
+      "// This file is automatically generated.  Please do not edit.",
+      "// Generated via:",
+      f"//   python -m {module_name} {' '.join(sys.argv[1:])}",
+      "//===----------------------------------------------------------------------===//",
+      "",
+      "",
+  ]
+  banner_lines = [l.strip() for l in banner_lines]
+  out.write("\n".join(banner_lines))
+
+  emitter = OdsEmitter(r, out=out)
+  emitter.emit_ods()
+
+
+def _create_argparse():
+  parser = argparse.ArgumentParser(prog="generate_ods")
+  parser.add_argument(
+      "--op_module",
+      default=".aten_ops",
+      help="Name of a python module for populating the registry")
+  return parser
+
+
+if __name__ == "__main__":
+  logging.basicConfig(level=logging.DEBUG)
+  parser = _create_argparse()
+  args = parser.parse_args()
+  main(args)

python/npcomp/torch/opdefs/registry.py

@@ -22,7 +22,7 @@ Example usage (fully automatic discovery):
        alpha=ScalarValue()).with_outref_variant()
 """
 
-from typing import Dict, List, Optional, Tuple
+from typing import Dict, List, Optional, Sequence, Tuple
 
 import logging
 import random
@@ -92,6 +92,10 @@ class ValueSpec:
     super().__init__()
     self.name = name
 
+  @property
+  def mlir_ods_predicate(self):
+    return "AnyType"
+
   def generate_example(self, index=0):
     """Generates an example value."""
     raise NotImplementedError()
@@ -109,6 +113,10 @@ class TensorValue(ValueSpec):
       example_size = (2, 3, 7)  # No significance.
     self.example_size = example_size
 
+  @property
+  def mlir_ods_predicate(self):
+    return "ATen_AnyTensor"
+
   def generate_example(self, index=0):
     return torch.rand(*self.example_size)
 
@@ -122,6 +130,10 @@ class TensorOutRef(ValueSpec):
       example_size = (2, 3, 7)  # No significance.
     self.example_size = example_size
 
+  @property
+  def mlir_ods_predicate(self):
+    return "ATen_AnyRefTensor"
+
   def generate_example(self, index=0):
     return torch.rand(*self.example_size)
 
@@ -133,13 +145,22 @@ class ScalarValue(ValueSpec):
     super().__init__(name=name)
     self.value = value
 
+  @property
+  def mlir_ods_predicate(self):
+    return "ATen_AnyScalar"
+
   def generate_example(self, index=0):
     if self.value is not None:
       return self.value
     return 1.0 + index  # Generates a stable value.
 
 
-class SimpleOpMapping:
+class OpMapping:
+  """Base class for things purporting to map an operation."""
+  pass
+
+
+class SimpleOpMapping(OpMapping):
   """Maps a PyTorch invocation to its MLIR representation."""
 
   def __init__(self, op_f, *op_args, **op_kwargs):
@@ -228,10 +249,17 @@ class SimpleOpMapping:
 
   def _set_default_mlir_operation_name(self):
     op_ns, op_name = self.op_kind.split("::", maxsplit=1)
-    default_name = op_ns + "." + op_name
+    # Since these are emitted into the "aten" dialect namespace, alias them
+    # to omit the prefix to distinguish from custom ops and others (which will
+    # have a prefix).
+    default_name = op_name if op_ns == "aten" else op_ns + "." + op_name
+    if op_ns == "aten":
+      default_name = op_name
+    else:
+      default_name = op_ns + "." + op_name
 
     if self.is_outref_form:
-      default_name += "_outref"
+      default_name += ".inplace"
     self.mlir_operation_name = default_name
 
   def _configure_from_example(self):
@@ -352,8 +380,12 @@ class OpRegistry:
     return m
 
   @property
-  def mappings(self):
-    """Returns the list of SimpleOpMappings."""
+  def mappings(self) -> Sequence[OpMapping]:
+    """Returns the list of OpMapping.
+
+    Returns:
+      Sequence of OpMapping concrete classes (most commonly SimpleOpMapping).
+    """
     self._finalize_pending()
     return self._mappings