diff --git a/.flake8 b/.flake8
index 2e04877a90..dc0d54120d 100644
--- a/.flake8
+++ b/.flake8
@@ -18,3 +18,6 @@ exclude =
   .eggs,
   **test/models/*.py,
   **onnx_backend_test_code/*.py,
+
+per-file-ignores =
+  onnxscript/function_libs/torch_aten/ops/*:E501
diff --git a/onnxscript/function_libs/tools/torch_aten/generate_aten_signatures.py b/onnxscript/function_libs/tools/torch_aten/generate_aten_signatures.py
index 3f1272b3d4..cfe11f646f 100644
--- a/onnxscript/function_libs/tools/torch_aten/generate_aten_signatures.py
+++ b/onnxscript/function_libs/tools/torch_aten/generate_aten_signatures.py
@@ -1,5 +1,334 @@
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+# --------------------------------------------------------------------------
+
 """Generates the ATen signatures for the ONNX ATen operator set using torch.ops."""
+from __future__ import annotations
+
+import argparse
+import ast
+import logging
+import os
+import textwrap
+import typing
+from typing import Any, Sequence
+
+import torchgen.gen
+import torchgen.model
+import yaml
+
+import opgen.pygen as cg
+
+
+def load_native_function_yaml(yaml_path: str):
+    with open(yaml_path, encoding="utf-8") as f:
+        yaml_str = f.read()
+    with open(yaml_path, encoding="utf-8") as f:
+        all_functions = yaml.safe_load(f)
+    valid_tags = set()
+    # Mark all tags as valid, since we don't want to validate them.
+    for func in all_functions:
+        if "tags" not in func:
+            continue
+        valid_tags.add(func["tags"])
+
+    return yaml_str, valid_tags
+
+
+def parse_native_functions_yaml(yaml_path: str) -> tuple[Any, Any]:
+    """Parses the native_functions.yaml file."""
+    yaml_str, valid_tags = load_native_function_yaml(yaml_path)
+    yaml_struct = yaml.load(yaml_str, Loader=torchgen.gen.LineLoader)  # noqa: DUO109
+    parsed = torchgen.gen.parse_native_yaml_struct(
+        yaml_struct, valid_tags, path=yaml_path, skip_native_fns_gen=True
+    )
+    return parsed.native_functions, parsed.backend_indices
+
+
+def create_list_type(arg: torchgen.model.Argument) -> cg.TypeRef:
+    assert isinstance(arg.type, torchgen.model.ListType), f"arg: {arg}"
+    arg_type = arg_type_to_str(arg.type)
+    if type_is_builtin(arg_type):
+        return cg.TypingRefs.Sequence(cg.BuiltinTypeRef(arg_type))
+    if arg_type == "TensorType":
+        return cg.TypingRefs.Sequence(cg.TypeRef("onnxscript.onnx_types", "TensorType"))
+    return cg.TypeRef("onnxscript", arg_type)
+
+    # TODO(justinchuby): Enable this when generics are better supported
+    # if arg.type.size is None:
+    #     # INT64[...]
+    #     return cg.TypeRef("onnxscript", arg_type, cg.EllipsisTypeRef())
+    # # INT64[3]
+    # return cg.TypeRef("onnxscript", arg_type, *[cg.TypeRef(None, f"{arg.type.size}")])
+
+
+def arg_type_to_str(arg_type: torchgen.model.Type) -> str:
+    if arg_type.is_base_ty_like(torchgen.model.BaseTy.Tensor):
+        return "TensorType"
+    elif arg_type.is_base_ty_like(torchgen.model.BaseTy.SymInt):
+        return "INT64"
+    elif arg_type.is_base_ty_like(torchgen.model.BaseTy.Scalar):
+        return "float"
+    elif arg_type.is_base_ty_like(torchgen.model.BaseTy.float):
+        return "float"
+    elif arg_type.is_base_ty_like(torchgen.model.BaseTy.int):
+        return "int"
+    elif arg_type.is_base_ty_like(torchgen.model.BaseTy.bool):
+        return "bool"
+    elif arg_type.is_base_ty_like(torchgen.model.BaseTy.str):
+        return "str"
+    elif arg_type.is_base_ty_like(torchgen.model.BaseTy.ScalarType):
+        return "int"
+    else:
+        # Anything unhandled is a string option.
+        return "str"
+
+
+def type_is_builtin(arg_type: str) -> bool:
+    """Returns whether the given type is a python builtin type (that we care about)."""
+    return arg_type in {"float", "int", "bool", "str"}
+
+
+def get_argument_type(arg: torchgen.model.Argument) -> cg.TypeRef:
+    """Returns the Python type for the given argument."""
+    if isinstance(arg.type, torchgen.model.ListType):
+        inner_node = create_list_type(arg)
+    else:
+        arg_type_str = arg_type_to_str(arg.type)
+        if type_is_builtin(arg_type_str):
+            inner_node = cg.BuiltinTypeRef(arg_type_str)
+        elif arg_type_str == "TensorType":
+            inner_node = cg.TypeRef("onnxscript.onnx_types", "TensorType")
+        else:
+            inner_node = cg.TypeRef("onnxscript", arg_type_str)
+
+    if arg.type.is_nullable():
+        return cg.TypingRefs.Optional(inner_node)
+    if arg.default is not None and parse_default_value(arg) is None:
+        return cg.TypingRefs.Optional(inner_node)
+    return inner_node
+
+
+def should_generate_signature(func: torchgen.model.NativeFunction) -> bool:
+    """Returns whether the signature for the given function should be generated."""
+    if func.func.name.name.base.startswith("_"):
+        return False
+    if func.func.name.name.inplace:
+        return False
+    if func.func.name.overload_name and func.func.name.overload_name != "Tensor":
+        # Ignore overloads for now.
+        # Some ops only have overloaded versions, like aten::add.Tensor. And we
+        # want to generate the aten::add op.
+        return False
+    return True
+
+
+def get_op_name(func: torchgen.model.NativeFunction) -> str:
+    if func.func.name.overload_name and func.func.name.overload_name != "Tensor":
+        # Do not include the overload name if it is "Tensor", since ops like
+        # aten::add.Tensor is what we want for aten::add.
+        name = f"{func.func.name.name.base}_{func.func.name.overload_name}"
+    else:
+        name = f"{func.func.name.name.base}"
+
+    # Prefix with namespace to avoid name conflicts with other operators and arguments.
+    return f"{func.namespace}_{name}"
+
+
+def parse_default_value(arg: torchgen.model.Argument) -> Any:
+    default = arg.default
+    assert default is not None, f"arg: {arg}"
+    if default.startswith("[") and default.endswith("]"):
+        # Convert list to tuple
+        default_val = ast.literal_eval(default)
+        assert isinstance(default_val, list)
+        if not default_val:
+            # Empty list is represented as None.
+            return None
+        return tuple(default_val)
+    # Special case for reduction=Mean
+    if default == "Mean":
+        return 1
+
+    try:
+        value = ast.literal_eval(default)
+        if isinstance(value, int):
+            # Expand the value to a tuple if the type is a list.
+            if isinstance(arg.type, torchgen.model.ListType):
+                if arg.type.size is not None:
+                    return (value,) * arg.type.size
+                return (value,)
+        return value
+    except ValueError:
+        # Treat it as a string.
+        return default.lower()
+
+
+def create_return_type(returns: Sequence[torchgen.model.Return]) -> cg.TypeRef:
+    """Returns the Python type for the return value of the given function."""
+    if not returns:
+        return cg.TypingRefs.Any()
+    return_nodes = []
+    for return_val in returns:
+        return_type = return_val.type
+        return_type_str = arg_type_to_str(return_type)
+        if type_is_builtin(return_type_str):
+            # Python type
+            return_node: cg.TypeRef = cg.BuiltinTypeRef(return_type_str)
+        elif return_type_str == "TensorType":
+            return_node = cg.TypeRef("onnxscript.onnx_types", "TensorType")
+        else:
+            return_node = cg.TypeRef("onnxscript", arg_type_to_str(return_type))
+        if return_type.is_nullable():
+            return_node = cg.TypingRefs.Optional(return_node)
+        return_nodes.append(return_node)
+    if len(return_nodes) == 1:
+        return return_nodes[0]
+    return cg.BuiltinTypeRef("tuple", *return_nodes)
+
+
+def format_arg_name(arg: torchgen.model.Argument) -> str:
+    """Returns the python compatible name of the given argument."""
+    if arg.name == "from":
+        return f"{arg.name}_"
+    return arg.name  # type: ignore[no-any-return]
+
+
+def create_signature(func: torchgen.model.NativeFunction) -> cg.FunctionDef:
+    """Creates the signature for the given function."""
+    op_name = get_op_name(func)
+    args = [
+        arg.argument if isinstance(arg, torchgen.model.SelfArgument) else arg
+        for arg in func.func.arguments.positional
+    ]
+    kwargs = [
+        arg
+        for arg in func.func.arguments.kwarg_only
+        if not isinstance(arg, torchgen.model.TensorOptionsArguments)
+    ]
+
+    py_args = [
+        cg.Arg(
+            format_arg_name(arg),
+            get_argument_type(arg),
+            default_value=cg.Constant(parse_default_value(arg))
+            if arg.default is not None
+            else None,
+        )
+        for arg in args
+    ]
+    if kwargs:
+        # Arguments after this point are keyword-only.
+        py_args += [
+            cg.Arg(
+                format_arg_name(kwarg),
+                get_argument_type(kwarg),
+                default_value=cg.Constant(parse_default_value(kwarg))
+                if kwarg.default is not None
+                else None,
+                is_kwarg=True,
+            )
+            for kwarg in kwargs
+        ]
+
+    return cg.FunctionDef(
+        op_name,
+        *py_args,
+        return_type=create_return_type(func.func.returns),
+        body=[
+            cg.ThunkStmt(f"# {func.func}"),
+            cg.Raise(cg.Call(cg.Name("NotImplementedError"))),  # type: ignore[list-item]
+        ],
+    )
+
+
+def create_onnx_function_module(
+    functions: Sequence[torchgen.model.NativeFunction],
+) -> cg.Module:
+    """Creates the onnx function module."""
+    return cg.Module(
+        cg.ImportFrom("__future__", cg.Alias("annotations")),
+        *[create_signature(func) for func in functions if should_generate_signature(func)],
+    )
+
+
+def copyright_header() -> str:
+    """Creates the copyright header."""
+    dashline = f"# {'-' * 74}"
+    return textwrap.dedent(
+        f"""\
+        {dashline}
+        # Copyright (c) Microsoft Corporation. All rights reserved.
+        # Licensed under the MIT License.
+        {dashline}
+        # mypy: disable-error-code=misc
+        # mypy: disable-error-code=type-arg
+        # mypy: disable-error-code=valid-type
+        # mypy: disable-error-code=assignment
+        """
+    )
+
+
+def main(args: argparse.Namespace) -> None:
+    native_functions, _ = parse_native_functions_yaml(args.yaml)
+    functions: dict[str, dict[str, torchgen.model.NativeFunction]] = {}
+    for func in native_functions:
+        if not should_generate_signature(func):
+            continue
+
+        module_name = typing.cast(str, func.python_module)
+        if not module_name:
+            module_name = "core"
+        if module_name not in functions:
+            functions[module_name] = {}
+        op_name = get_op_name(func)
+        if op_name in functions[module_name]:
+            logging.warning(
+                "Duplicated function: %s, overload: %s", op_name, func.func.name.overload_name
+            )
+            continue
+        functions[module_name][op_name] = func
+
+    os.makedirs(args.outdir, exist_ok=True)
+
+    for module_name, module_functions in functions.items():
+        sorted_functions = sorted(module_functions.items(), key=lambda x: x[0])
+        py_module = create_onnx_function_module([func for _, func in sorted_functions])
+        py_module.accept(cg.ImportAdjuster())
+        py_module.accept(cg.DocCommentBuilder())
+        output_path = os.path.join(args.outdir, f"{module_name}.py")
+
+        print(f"Generating {output_path}")
+        with open(output_path, "w", encoding="utf-8") as f:
+            f.write(copyright_header())
+            # Add docstring
+            f.write(
+                textwrap.dedent(
+                    f'''\
+                    """torch.ops.aten operators under the `{module_name}` module.
+
+                    - No inplace operators.
+                    - All functions should not have the script() decorator. This is because
+                        we want to delay the compilation of the function.
+                    """
+                '''
+                )
+            )
+            py_module.accept(cg.PythonWriter(f))
+    print("Done.")
 
 
-def main():
-    pass
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--yaml",
+        type=str,
+        help="Path to PyTorch aten/src/ATen/native/native_functions.yaml",
+    )
+    parser.add_argument(
+        "--outdir",
+        type=str,
+        help="Output directory for generated modules",
+    )
+    main(parser.parse_args())
diff --git a/pyproject.toml b/pyproject.toml
index 8590188703..c0ac8b2d7a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -71,6 +71,7 @@ module = [
   "onnxruntime.*",
   "autopep8.*",
   "parameterized.*",
+  "torchgen.*",
 ]
 ignore_missing_imports = true
 
diff --git a/requirements-dev.txt b/requirements-dev.txt
index c51de1f16b..0ee6feff7a 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -11,6 +11,9 @@ sphinx<=5.3.0
 sphinx-gallery
 pydata_sphinx_theme
 
+# ATen lib
+types-PyYAML
+
 # Testing
 pytest!=7.1.0
 pytest-cov