Merge branch 'main' into xiaowu/fixBug(embedding_bag)

.lintrunner.toml

@@ -70,7 +70,7 @@ init_command = [
 ]
 
 [[linter]]
-code = 'BLACK-ISORT'
+code = 'RUFF-FORMAT'
 include_patterns = [
     '**/*.py',
 ]
@@ -82,7 +82,7 @@ command = [
     '-m',
     'lintrunner_adapters',
     'run',
-    'black_isort_linter',
+    'ruff_format_linter',
     '--',
     '@{{PATHSFILE}}'
 ]

docs/examples/04_plot_eager_mode_evaluation.py

@@ -18,9 +18,7 @@
 
 
 @script()
-def linear(
-    A: FLOAT["N", "K"], W: FLOAT["K", "M"], Bias: FLOAT["M"]
-) -> FLOAT["N", "M"]:  # noqa: F821
+def linear(A: FLOAT["N", "K"], W: FLOAT["K", "M"], Bias: FLOAT["M"]) -> FLOAT["N", "M"]:  # noqa: F821
     T1 = op.MatMul(A, W)
     T2 = op.Add(T1, Bias)
     Y = op.Relu(T2)

noxfile.py

@@ -27,8 +27,8 @@
     "pyyaml",
 )
 ONNX = "onnx==1.14.1"
-ONNX_RUNTIME = "onnxruntime==1.16.0"
-PYTORCH = "torch==2.0.1"
+ONNX_RUNTIME = "onnxruntime==1.16.1"
+PYTORCH = "torch==2.1.0"
 ONNX_RUNTIME_NIGHTLY_DEPENDENCIES = (
     "flatbuffers",
     "coloredlogs",

onnxscript/_internal/param_manipulation.py

@@ -61,8 +61,7 @@ def separate_input_attributes_from_arguments(
             else:
                 onnx_attributes[param.name] = kwargs[param.name]
         elif (
-            param.is_attribute
-            and param.default is not values._EmptyDefault  # pylint: disable=protected-access
+            param.is_attribute and param.default is not values._EmptyDefault  # pylint: disable=protected-access
         ):
             # User did not provide the attribute
             if fill_defaults:

onnxscript/converter.py

@@ -21,9 +21,8 @@
 import onnx
 
 import onnxscript
-from onnxscript import irbuilder, onnx_types, sourceinfo
+from onnxscript import irbuilder, onnx_types, sourceinfo, values
 from onnxscript import type_annotation as ta
-from onnxscript import values
 from onnxscript._internal import analysis, ast_utils, autocast, param_manipulation
 
 PY_VERSION_GE_39 = ast_utils.PY_VERSION_GE_39

onnxscript/converter_test.py

@@ -17,6 +17,7 @@
 import numpy as np
 import onnx
 import onnxruntime as ort
+import pytest
 from onnxruntime.capi.onnxruntime_pybind11_state import (
     Fail,
     InvalidArgument,
@@ -270,7 +271,10 @@ def test_renaming(self):
 
         self.validate_save(renaming, shape_inference=False)
 
-    @unittest.skip(reason="TypeError: val must be numeric not <class 'NoneType'>")
+    @pytest.mark.xfail(
+        strict=True,
+        reason="default_opset must be specified in script for functions that do not contain any use of an ONNX op",
+    )
     def test_opt_output(self):
         from onnxscript.tests.models import opt_output
 

onnxscript/function_libs/tools/torch_lib/deduce_type_constraints_test.py

@@ -30,7 +30,6 @@ class TestDeduceTypeConstraints(unittest.TestCase):
         "_aten_embedding_bag_onnx",
         "_aten_embedding_bag_1d_padding_idx_onnx",
     )
-    _SKIP_FUNCTIONS_WITH_NESTED_FUNCTION = ()
 
     @parameterized.parameterized.expand(
         ((op,) for op in torch_lib_onnx_functions_from_registry()),
@@ -41,11 +40,13 @@ def test_deduce_type_constraints_does_not_crash_for_onnx_function(
     ):
         if onnx_function.name in self._SKIP_FUNCTIONS_WITH_LOOP_OR_SCAN:
             self.skipTest("Unimplemented: function contains loop or scan node.")
-        if onnx_function.name in self._SKIP_FUNCTIONS_WITH_NESTED_FUNCTION:
-            self.skipTest("Unimplemented: function contains nested function.")
-        signature_type_constraint = deduce_type_constraints.deduce_type_constraints(
-            onnx_function
-        )
+        try:
+            signature_type_constraint = deduce_type_constraints.deduce_type_constraints(
+                onnx_function
+            )
+        except NotImplementedError as e:
+            if "Nested function" in str(e):
+                self.skipTest("Unimplemented: function contains nested function.")
         logger.info(
             "Original signature: %s%s",
             onnx_function.name,

onnxscript/function_libs/tools/torch_lib/generate_prims_signatures.py

@@ -12,11 +12,8 @@
 import os
 import re
 import textwrap
-from pathlib import Path
 from typing import Any, Dict, List, Sequence
 
-import black
-import isort
 import torch
 import torchgen.gen
 import torchgen.model
@@ -319,15 +316,6 @@ def main(args: argparse.Namespace) -> None:
             )
             py_module.accept(cg.PythonWriter(f))
 
-    # Format the generated files so that they pass linting.
-    # line_length=95 is to match the lintrunner rules.
-    isort.file(output_path)
-    black.format_file_in_place(
-        Path(output_path),
-        fast=True,
-        mode=black.Mode(line_length=95),
-        write_back=black.WriteBack.YES,
-    )
     print("Done.")
 
 

onnxscript/function_libs/torch_lib/_constants.py

@@ -0,0 +1,3 @@
+"""Shared constants for the library."""
+
+DOMAIN = "pkg.onnxscript.torch_lib"

onnxscript/function_libs/torch_lib/_flags.py

@@ -0,0 +1,41 @@
+"""Experimental flags.
+
+NOTE: These flags are experimental only. Any flag here can be removed at any
+time without notice.
+"""
+
+import logging
+import os
+
+logger = logging.getLogger(__name__)
+
+
+def _load_boolean_flag(
+    name: str,
+    *,
+    this_will: str,
+    deprecated: bool = False,
+) -> bool:
+    """Load a boolean flag from environment variable.
+
+    Args:
+        name: The name of the environment variable.
+        this_will: A string that describes what this flag will do.
+        deprecated: Whether this flag is deprecated.
+    """
+    state = os.getenv(name) == "1"
+    if state:
+        if deprecated:
+            logger.error(
+                "Experimental flag %s is deprecated. Please remove it from your environment.",
+                name,
+            )
+        else:
+            logger.warning("Experimental flag %s is enabled. This will %s.", name, this_will)
+    return state
+
+
+EXPERIMENTAL_INITIALIZERS_AS_INPUTS: bool = _load_boolean_flag(
+    "TORCHLIB_EXPERIMENTAL_INITIALIZERS_AS_INPUTS",
+    this_will="make initializers as inputs to the model graph",
+)

onnxscript/function_libs/torch_lib/graph_building.py

@@ -21,6 +21,8 @@
 from onnxscript import evaluator
 from onnxscript import tensor as onnxscript_tensor
 from onnxscript._internal import param_manipulation, runtime_typing
+from onnxscript.function_libs.torch_lib import _flags
+from onnxscript.function_libs.torch_lib.ops import common as common_ops
 
 __all__ = [
     "TorchScriptTensor",
@@ -198,7 +200,7 @@ def symbolic_value(self) -> torch.Value:
 def _unwrap_tensor_to_torch_value(
     value: Union[
         ValidArgumentType, Mapping[str, ValidArgumentType], Sequence[ValidArgumentType]
-    ]
+    ],
 ) -> Union[
     ValidTorchValueType,
     Dict[str, ValidTorchValueType],
@@ -363,6 +365,16 @@ def _tensor_rawdata_size(tensor: torch.Tensor) -> int:
     return tensor.numel() * tensor.element_size()
 
 
+def _shared_functions() -> list[onnx.FunctionProto]:
+    """Hack to always include the share ops."""
+
+    # TODO: Remove after https://github.com/microsoft/onnxscript/issues/834 is fixed
+    return [
+        common_ops.Rank.to_function_proto(),
+        common_ops.IsScalar.to_function_proto(),
+    ]
+
+
 class TorchScriptGraph:
     def __init__(
         self,
@@ -717,7 +729,6 @@ def to_function_proto(self, opset_version: int, function_name: str) -> onnx.Func
             opset_imports=onnx_model.opset_import,
             doc_string=onnx_model.doc_string,
         )
-        # TODO: onnx.checker.check_function(onnx_function)?
         return onnx_function
 
     @runtime_typing.checked
@@ -740,13 +751,15 @@ def to_model_proto(
         large_model = initializers_size > _LARGE_MODEL_SIZE_THRESHOLD
 
         export_kwargs: dict[str, Any] = dict(
-            initializers=self.initializers if include_initializers else {},
+            initializers=self.initializers
+            if include_initializers and not _flags.EXPERIMENTAL_INITIALIZERS_AS_INPUTS
+            else {},
             onnx_opset_version=opset_version,
             dynamic_axes={},
             defer_weight_export=False,
             operator_export_type=torch.onnx.OperatorExportTypes.ONNX,
             strip_doc_string=False,
-            keep_initializers_as_inputs=False,
+            keep_initializers_as_inputs=_flags.EXPERIMENTAL_INITIALIZERS_AS_INPUTS,
             custom_opsets={},
             add_node_names=True,
             node_attr_to_name={},
@@ -786,6 +799,7 @@ def to_model_proto(
             onnx_model = onnx.load_from_string(proto)
 
         onnx_model.functions.extend(function_proto_dict.values())
+        onnx_model.functions.extend(_shared_functions())
 
         # `_export_onnx` only exports opset_imports that is visible to it. It does not
         # export opset_imports for nested functions, since it does not have access to
@@ -800,6 +814,13 @@ def to_model_proto(
                 for domain, version in unique_custom_domains.items()
             ]
         )
+        # Include the library shared opset domain
+        # TODO: Remove after https://github.com/microsoft/onnxscript/issues/834 is fixed
+        onnx_model.opset_import.append(
+            onnx.helper.make_opsetid(
+                common_ops.common_opset.domain, common_ops.common_opset.version
+            )
+        )
 
         try:
             if not cache_model_to_disk:

onnxscript/function_libs/torch_lib/graph_building_test.py

@@ -11,11 +11,9 @@
 import onnxscript.testing
 from onnxscript import FLOAT, evaluator
 from onnxscript import opset18 as op
-from onnxscript._internal import version_utils
 from onnxscript.function_libs.torch_lib import graph_building, ops
 
 
-@unittest.skipIf(version_utils.torch_older_than("2.0"), "torchscript in 1.13 not supported")
 class TestTorchScriptTracingEvaluator(unittest.TestCase):
     def setUp(self):
         self.opset_version = 18

onnxscript/function_libs/torch_lib/ops/common.py

@@ -0,0 +1,56 @@
+"""Common operators shared in the torchlib library."""
+
+import onnxscript
+import onnxscript.values
+from onnxscript import BOOL, INT64
+from onnxscript import opset18 as op
+from onnxscript.function_libs.torch_lib import _constants, tensor_typing
+from onnxscript.function_libs.torch_lib.tensor_typing import RealType
+from onnxscript.onnx_types import COMPLEX64, COMPLEX128, DOUBLE, FLOAT
+
+COMPLEX64_TYPE = COMPLEX64.dtype
+COMPLEX128_TYPE = COMPLEX128.dtype
+
+DOMAIN = f"{_constants.DOMAIN}.common"
+
+common_opset = onnxscript.values.Opset(domain=DOMAIN, version=1)
+
+
+@onnxscript.script(common_opset)
+def Rank(input: tensor_typing.TTensor) -> INT64:
+    """Take the rank of the input tensor."""
+
+    return op.Size(op.Shape(input))
+
+
+@onnxscript.script(common_opset)
+def IsScalar(input: tensor_typing.TTensor) -> BOOL:
+    """Return whether the input has rank 0, or is a scalar."""
+
+    return op.Equal(op.Size(op.Shape(input)), op.Constant(value_int=0))
+
+
+def cast_to(a: RealType, dtype: int) -> RealType:
+    """Cast input to dtype while handling complex types."""
+
+    # Traced function because different if branches return different dtypes
+    # which is not supported in an ONNX function
+    if dtype == COMPLEX128_TYPE:
+        # Cast to the real representation of the complex type
+        casted = op.Cast(a, to=DOUBLE.dtype)
+        # Create a complex number
+        real_part = op.Unsqueeze(casted, axes=[-1])
+        imag_part = op.Expand(op.Cast(0.0, to=DOUBLE.dtype), op.Shape(real_part))
+        result = op.Concat(real_part, imag_part, axis=-1)
+    elif dtype == COMPLEX64_TYPE:
+        # Cast to the real representation of the complex type
+        casted = op.Cast(a, to=FLOAT.dtype)
+        # Create a complex number
+        real_part = op.Unsqueeze(casted, axes=[-1])
+        imag_part = op.Expand(0.0, op.Shape(real_part))
+        result = op.Concat(real_part, imag_part, axis=-1)
+    else:
+        # Cast to real numbers
+        result = op.Cast(a, to=dtype)
+
+    return result