make onnx package to be optional. (#653)

* putting onnx package to be optional

* update the ci.yml

* add more message of missing ONNX package

.pipelines/ci.yml

@@ -89,8 +89,7 @@ stages:
         python -m pip install --upgrade pip
         python -m pip install --upgrade setuptools
         python -m pip install onnxruntime==$(ort.version)
-        python -m pip install -r requirements.txt
-      displayName: Install requirements.txt
+      displayName: Install requirements
 
     - script: |
         CPU_NUMBER=8 python -m pip install .
@@ -283,8 +282,7 @@ stages:
         python -m pip install --upgrade setuptools
         python -m pip install --upgrade wheel
         python -m pip install onnxruntime==$(ort.version)
-        python -m pip install -r requirements.txt
-      displayName: Install requirements.txt
+      displayName: Install requirements
 
     - script: |
         python -c "import onnxruntime;print(onnxruntime.__version__)"
@@ -419,7 +417,6 @@ stages:
         call activate pyenv
         python -m pip install --upgrade pip
         python -m pip install onnxruntime==$(ort.version)
-        python -m pip install -r requirements.txt
         python -m pip install -r requirements-dev.txt
       displayName: Install requirements{-dev}.txt and cmake python modules
 
@@ -653,7 +650,6 @@ stages:
                 python3 -m pip install --upgrade pip; \
                 python3 -m pip install --upgrade setuptools; \
                 python3 -m pip install onnxruntime-gpu==$(ORT_VERSION); \
-                python3 -m pip install -r requirements.txt; \
                 python3 -m pip install -v --config-settings "ortx-user-option=use-cuda" . ; \
                 python3 -m pip install $(TORCH_VERSION) ; \
                 python3 -m pip install -r requirements-dev.txt; \

README.md

@@ -31,12 +31,15 @@ python -m pip install git+https://github.com/microsoft/onnxruntime-extensions.gi
 
 ## Usage
 
-## 1. Generate the pre-/post- processing ONNX model
-With onnxruntime-extensions Python package, you can easily get the ONNX processing graph by converting them from Huggingface transformer data processing classes, check the following API for details.
+## 1. Generation of Pre-/Post-Processing ONNX Model
+The `onnxruntime-extensions` Python package provides a convenient way to generate the ONNX processing graph. This can be achieved by converting the Huggingface transformer data processing classes into the desired format. For more detailed information, please refer to the API below:
+
 ```python
 help(onnxruntime_extensions.gen_processing_models)
 ```
-### NOTE: These data processing model can be merged into other model [onnx.compose](https://onnx.ai/onnx/api/compose.html) if needed.
+### NOTE:
+The generation of model processing requires the **ONNX** package to be installed. The data processing models generated in this manner can be merged with other models using the [onnx.compose](https://onnx.ai/onnx/api/compose.html) if needed.
+
 ## 2. Using Extensions for ONNX Runtime inference
 
 ### Python

onnxruntime_extensions/__init__.py

@@ -10,37 +10,70 @@ This enables more flexibility and control over model execution, thus expanding t
 
 __author__ = "Microsoft"
 
-__all__ = [
-    'gen_processing_models',
-    'ort_inference',
-    'get_library_path',
-    'Opdef', 'onnx_op', 'PyCustomOpDef', 'PyOp',
-    'enable_py_op',
-    'expand_onnx_inputs',
-    'hook_model_op',
-    'default_opset_domain',
-    'OrtPyFunction', 'PyOrtFunction',
-    'optimize_model',
-    'make_onnx_model',
-    'ONNXRuntimeError',
-    'hash_64',
-    '__version__',
-]
 
 from ._version import __version__
 from ._ocos import get_library_path
 from ._ocos import Opdef, PyCustomOpDef
 from ._ocos import hash_64
 from ._ocos import enable_py_op
-from ._ocos import expand_onnx_inputs
-from ._ocos import hook_model_op
 from ._ocos import default_opset_domain
-from ._cuops import *  # noqa
-from ._ortapi2 import OrtPyFunction as PyOrtFunction  # backward compatibility
-from ._ortapi2 import OrtPyFunction, ort_inference, optimize_model, make_onnx_model
-from ._ortapi2 import ONNXRuntimeError, ONNXRuntimeException
-from .cvt import gen_processing_models
+
+
+_lib_only = False
+
+try:
+    import onnx  # noqa
+    import onnxruntime  # noqa
+except ImportError:
+    _lib_only = True
+    pass
+
+
+_offline_api = [
+    "gen_processing_models",
+    "ort_inference",
+    "OrtPyFunction",
+    "PyOrtFunction",
+    "optimize_model",
+    "make_onnx_model",
+    "ONNXRuntimeError",
+]
+
+__all__ = [
+    "get_library_path",
+    "Opdef",
+    "onnx_op",
+    "PyCustomOpDef",
+    "PyOp",
+    "enable_py_op",
+    "expand_onnx_inputs",
+    "hook_model_op",
+    "default_opset_domain",
+    "hash_64",
+    "__version__",
+]
 
 # rename the implementation with a more formal name
 onnx_op = Opdef.declare
 PyOp = PyCustomOpDef
+
+
+if _lib_only:
+
+    def _unimplemented(*args, **kwargs):
+        raise NotImplementedError("ONNX or ONNX Runtime is not installed")
+
+    gen_processing_models = _unimplemented
+    OrtPyFunction = _unimplemented
+    ort_inference = _unimplemented
+
+else:
+    __all__ += _offline_api
+
+    from ._cuops import *  # noqa
+    from ._ortapi2 import hook_model_op
+    from ._ortapi2 import expand_onnx_inputs
+    from ._ortapi2 import OrtPyFunction, ort_inference, optimize_model, make_onnx_model
+    from ._ortapi2 import OrtPyFunction as PyOrtFunction  # backward compatibility
+    from ._ortapi2 import ONNXRuntimeError, ONNXRuntimeException  # noqa
+    from .cvt import gen_processing_models

onnxruntime_extensions/_ocos.py

@@ -7,34 +7,36 @@ _ocos.py: PythonOp implementation
 """
 import os
 import sys
-import copy
 import glob
-import onnx
-from onnx import helper
 
 
 def _search_cuda_dir():
-    paths = os.getenv('PATH', '').split(os.pathsep)
+    paths = os.getenv("PATH", "").split(os.pathsep)
     for path in paths:
-        for filename in glob.glob(os.path.join(path, 'cudart64*.dll')):
+        for filename in glob.glob(os.path.join(path, "cudart64*.dll")):
             return os.path.dirname(filename)
 
     return None
 
 
-if sys.platform == 'win32':
+if sys.platform == "win32":
     from . import _version  # noqa: E402
-    if hasattr(_version, 'cuda'):
+
+    if hasattr(_version, "cuda"):
         cuda_path = _search_cuda_dir()
         if cuda_path is None:
-            raise RuntimeError(
-                "Cannot locate CUDA directory in the environment variable for GPU package")
+            raise RuntimeError("Cannot locate CUDA directory in the environment variable for GPU package")
 
         os.add_dll_directory(cuda_path)
 
 
 from ._extensions_pydll import (  # noqa
-    PyCustomOpDef, enable_py_op, add_custom_op, hash_64, default_opset_domain)
+    PyCustomOpDef,
+    enable_py_op,
+    add_custom_op,
+    hash_64,
+    default_opset_domain,
+)
 
 
 def get_library_path():
@@ -42,12 +44,11 @@ def get_library_path():
     The custom operator library binary path
     :return: A string of this library path.
     """
-    mod = sys.modules['onnxruntime_extensions._extensions_pydll']
+    mod = sys.modules["onnxruntime_extensions._extensions_pydll"]
     return mod.__file__
 
 
 class Opdef:
-
     _odlist = {}
 
     def __init__(self, op_type, func):
@@ -57,14 +58,14 @@ class Opdef:
 
     @staticmethod
     def declare(*args, **kwargs):
-        if len(args) > 0 and hasattr(args[0], '__call__'):
+        if len(args) > 0 and hasattr(args[0], "__call__"):
             raise RuntimeError("Unexpected arguments {}.".format(args))
             # return Opdef._create(args[0])
         return lambda f: Opdef.create(f, *args, **kwargs)
 
     @staticmethod
     def create(func, *args, **kwargs):
-        name = kwargs.get('op_type', None)
+        name = kwargs.get("op_type", None)
         op_type = name or func.__name__
         opdef = Opdef(op_type, func)
         od_id = id(opdef)
@@ -76,15 +77,15 @@ class Opdef:
         opdef._nativedef.op_type = op_type
         opdef._nativedef.obj_id = od_id
 
-        inputs = kwargs.get('inputs', None)
+        inputs = kwargs.get("inputs", None)
         if inputs is None:
             inputs = [PyCustomOpDef.dt_float]
         opdef._nativedef.input_types = inputs
-        outputs = kwargs.get('outputs', None)
+        outputs = kwargs.get("outputs", None)
         if outputs is None:
             outputs = [PyCustomOpDef.dt_float]
         opdef._nativedef.output_types = outputs
-        attrs = kwargs.get('attrs', None)
+        attrs = kwargs.get("attrs", None)
         if attrs is None:
             attrs = {}
         elif isinstance(attrs, (list, tuple)):
@@ -106,16 +107,15 @@ class Opdef:
             elif self._nativedef.attrs[k] == PyCustomOpDef.dt_string:
                 res[k] = v
             else:
-                raise RuntimeError("Unsupported attribute type {}.".format(
-                    self._nativedef.attrs[k]))
+                raise RuntimeError("Unsupported attribute type {}.".format(self._nativedef.attrs[k]))
         return res
 
 
 def _on_pyop_invocation(k_id, feed, attributes):
     if k_id not in Opdef._odlist:
         raise RuntimeError(
-            "Unable to find function id={}. "
-            "Did you decorate the operator with @onnx_op?.".format(k_id))
+            "Unable to find function id={}. " "Did you decorate the operator with @onnx_op?.".format(k_id)
+        )
     op_ = Opdef._odlist[k_id]
     rv = op_.body(*feed, **op_.cast_attributes(attributes))
     if isinstance(rv, tuple):
@@ -127,86 +127,7 @@ def _on_pyop_invocation(k_id, feed, attributes):
         res = tuple(res)
     else:
         res = (rv.shape, rv.flatten().tolist())
-    return (k_id, ) + res
-
-
-def _ensure_opset_domain(model):
-    op_domain_name = default_opset_domain()
-    domain_missing = True
-    for oi_ in model.opset_import:
-        if oi_.domain == op_domain_name:
-            domain_missing = False
-
-    if domain_missing:
-        model.opset_import.extend(
-            [helper.make_operatorsetid(op_domain_name, 1)])
-
-    return model
-
-
-def expand_onnx_inputs(model, target_input, extra_nodes, new_inputs):
-    """
-    Replace the existing inputs of a model with the new inputs, plus some extra nodes
-    :param model: The ONNX model loaded as ModelProto
-    :param target_input: The input name to be replaced
-    :param extra_nodes: The extra nodes to be added
-    :param new_inputs: The new input (type: ValueInfoProto) sequence
-    :return: The ONNX model after modification
-    """
-    graph = model.graph
-    new_inputs = [n for n in graph.input if n.name !=
-                  target_input] + new_inputs
-    new_nodes = list(model.graph.node) + extra_nodes
-    new_graph = helper.make_graph(
-        new_nodes, graph.name, new_inputs, list(graph.output), list(graph.initializer))
-
-    new_model = copy.deepcopy(model)
-    new_model.graph.CopyFrom(new_graph)
-
-    return _ensure_opset_domain(new_model)
-
-
-def hook_model_op(model, node_name, hook_func, input_types):
-    """
-    Add a hook function node in the ONNX Model, which could be used for the model diagnosis.
-    :param model: The ONNX model loaded as ModelProto
-    :param node_name: The node name where the hook will be installed
-    :param hook_func: The hook function, callback on the model inference
-    :param input_types: The input types as a list
-    :return: The ONNX model with the hook installed
-    """
-
-    # onnx.shape_inference is very unstable, useless.
-    # hkd_model = shape_inference.infer_shapes(model)
-    hkd_model = model
-
-    n_idx = 0
-    hnode, nnode = (None, None)
-    nodes = list(hkd_model.graph.node)
-    brkpt_name = node_name + '_hkd'
-    optype_name = "op_{}_{}".format(hook_func.__name__, node_name)
-    for n_ in nodes:
-        if n_.name == node_name:
-            input_names = list(n_.input)
-            brk_output_name = [i_ + '_hkd' for i_ in input_names]
-            hnode = onnx.helper.make_node(
-                optype_name, n_.input, brk_output_name, name=brkpt_name, domain=default_opset_domain())
-            nnode = n_
-            del nnode.input[:]
-            nnode.input.extend(brk_output_name)
-            break
-        n_idx += 1
-
-    if hnode is None:
-        raise ValueError("{} is not an operator node name".format(node_name))
-
-    repacked = nodes[:n_idx] + [hnode, nnode] + nodes[n_idx+1:]
-    del hkd_model.graph.node[:]
-    hkd_model.graph.node.extend(repacked)
-
-    Opdef.create(hook_func, op_type=optype_name,
-                 inputs=input_types, outputs=input_types)
-    return _ensure_opset_domain(hkd_model)
+    return (k_id,) + res
 
 
 PyCustomOpDef.install_hooker(_on_pyop_invocation)

onnxruntime_extensions/_ortapi2.py

@@ -7,8 +7,9 @@
 _ortapi2.py: ONNXRuntime-Extensions Python API
 """
 
+import copy
 import numpy as np
-from ._ocos import default_opset_domain, get_library_path  # noqa
+from ._ocos import default_opset_domain, get_library_path, Opdef
 from ._cuops import onnx, onnx_proto, SingleOpGraph
 
 _ort_check_passed = False
@@ -25,6 +26,82 @@ if not _ort_check_passed:
     raise RuntimeError("please install ONNXRuntime/ONNXRuntime-GPU >= 1.10.0")
 
 
+def _ensure_opset_domain(model):
+    op_domain_name = default_opset_domain()
+    domain_missing = True
+    for oi_ in model.opset_import:
+        if oi_.domain == op_domain_name:
+            domain_missing = False
+
+    if domain_missing:
+        model.opset_import.extend([onnx.helper.make_operatorsetid(op_domain_name, 1)])
+
+    return model
+
+
+def hook_model_op(model, node_name, hook_func, input_types):
+    """
+    Add a hook function node in the ONNX Model, which could be used for the model diagnosis.
+    :param model: The ONNX model loaded as ModelProto
+    :param node_name: The node name where the hook will be installed
+    :param hook_func: The hook function, callback on the model inference
+    :param input_types: The input types as a list
+    :return: The ONNX model with the hook installed
+    """
+
+    # onnx.shape_inference is very unstable, useless.
+    # hkd_model = shape_inference.infer_shapes(model)
+    hkd_model = model
+
+    n_idx = 0
+    hnode, nnode = (None, None)
+    nodes = list(hkd_model.graph.node)
+    brkpt_name = node_name + "_hkd"
+    optype_name = "op_{}_{}".format(hook_func.__name__, node_name)
+    for n_ in nodes:
+        if n_.name == node_name:
+            input_names = list(n_.input)
+            brk_output_name = [i_ + "_hkd" for i_ in input_names]
+            hnode = onnx.helper.make_node(
+                optype_name, n_.input, brk_output_name, name=brkpt_name, domain=default_opset_domain()
+            )
+            nnode = n_
+            del nnode.input[:]
+            nnode.input.extend(brk_output_name)
+            break
+        n_idx += 1
+
+    if hnode is None:
+        raise ValueError("{} is not an operator node name".format(node_name))
+
+    repacked = nodes[:n_idx] + [hnode, nnode] + nodes[n_idx + 1 :]
+    del hkd_model.graph.node[:]
+    hkd_model.graph.node.extend(repacked)
+
+    Opdef.create(hook_func, op_type=optype_name, inputs=input_types, outputs=input_types)
+    return _ensure_opset_domain(hkd_model)
+
+
+def expand_onnx_inputs(model, target_input, extra_nodes, new_inputs):
+    """
+    Replace the existing inputs of a model with the new inputs, plus some extra nodes
+    :param model: The ONNX model loaded as ModelProto
+    :param target_input: The input name to be replaced
+    :param extra_nodes: The extra nodes to be added
+    :param new_inputs: The new input (type: ValueInfoProto) sequence
+    :return: The ONNX model after modification
+    """
+    graph = model.graph
+    new_inputs = [n for n in graph.input if n.name != target_input] + new_inputs
+    new_nodes = list(model.graph.node) + extra_nodes
+    new_graph = onnx.helper.make_graph(new_nodes, graph.name, new_inputs, list(graph.output), list(graph.initializer))
+
+    new_model = copy.deepcopy(model)
+    new_model.graph.CopyFrom(new_graph)
+
+    return _ensure_opset_domain(new_model)
+
+
 def get_opset_version_from_ort():
     _ORT_OPSET_SUPPORT_TABLE = {
         "1.5": 11,
@@ -37,10 +114,10 @@ def get_opset_version_from_ort():
         "1.12": 17,
         "1.13": 17,
         "1.14": 18,
-        "1.15": 18
+        "1.15": 18,
     }
 
-    ort_ver_string = '.'.join(_ort.__version__.split('.')[0:2])
+    ort_ver_string = ".".join(_ort.__version__.split(".")[0:2])
     max_ver = max(_ORT_OPSET_SUPPORT_TABLE, key=_ORT_OPSET_SUPPORT_TABLE.get)
     if ort_ver_string > max_ver:
         ort_ver_string = max_ver
@@ -50,19 +127,18 @@ def get_opset_version_from_ort():
 def make_onnx_model(graph, opset_version=0, extra_domain=default_opset_domain(), extra_opset_version=1):
     if opset_version == 0:
         opset_version = get_opset_version_from_ort()
-    fn_mm = onnx.helper.make_model_gen_version if hasattr(onnx.helper, 'make_model_gen_version'
-                                                          ) else onnx.helper.make_model
-    model = fn_mm(graph, opset_imports=[
-        onnx.helper.make_operatorsetid('ai.onnx', opset_version)])
-    model.opset_import.extend(
-        [onnx.helper.make_operatorsetid(extra_domain, extra_opset_version)])
+    fn_mm = (
+        onnx.helper.make_model_gen_version if hasattr(onnx.helper, "make_model_gen_version") else onnx.helper.make_model
+    )
+    model = fn_mm(graph, opset_imports=[onnx.helper.make_operatorsetid("ai.onnx", opset_version)])
+    model.opset_import.extend([onnx.helper.make_operatorsetid(extra_domain, extra_opset_version)])
     return model
 
 
 class OrtPyFunction:
     """
     OrtPyFunction is a convenience class that serves as a wrapper around the ONNXRuntime InferenceSession,
-    equipped with registered onnxruntime-extensions. This allows execution of an ONNX model as if it were a 
+    equipped with registered onnxruntime-extensions. This allows execution of an ONNX model as if it were a
     standard Python function. The order of the function arguments correlates directly with
     the sequence of the input/output in the ONNX graph.
     """
@@ -78,10 +154,10 @@ class OrtPyFunction:
         self._onnx_model = None
         self.ort_session = None
         self.default_inputs = {}
-        self.execution_providers = ['CPUExecutionProvider']
+        self.execution_providers = ["CPUExecutionProvider"]
         if not cpu_only:
-            if _ort.get_device() == 'GPU':
-                self.execution_providers = ['CUDAExecutionProvider']
+            if _ort.get_device() == "GPU":
+                self.execution_providers = ["CUDAExecutionProvider"]
         self.extra_session_options = {}
         mpath = None
         if isinstance(path_or_model, str):
@@ -99,8 +175,8 @@ class OrtPyFunction:
 
     def add_default_input(self, **kwargs):
         inputs = {
-            ky_: val_ if isinstance(val_, (np.ndarray, np.generic)) else
-            np.asarray(list(val_), dtype=np.uint8) for ky_, val_ in kwargs.items()
+            ky_: val_ if isinstance(val_, (np.ndarray, np.generic)) else np.asarray(list(val_), dtype=np.uint8)
+            for ky_, val_ in kwargs.items()
         }
 
         self.default_inputs.update(inputs)
@@ -124,30 +200,29 @@ class OrtPyFunction:
         self._oxml = oxml
         if model_path is not None:
             self.ort_session = _ort.InferenceSession(
-                model_path, self.get_ort_session_options(),
-                self.execution_providers)
+                model_path, self.get_ort_session_options(), self.execution_providers
+            )
         return self
 
     def _ensure_ort_session(self):
         if self.ort_session is None:
             sess = _ort.InferenceSession(
-                self.onnx_model.SerializeToString(), self.get_ort_session_options(),
-                self.execution_providers)
+                self.onnx_model.SerializeToString(), self.get_ort_session_options(), self.execution_providers
+            )
             self.ort_session = sess
 
         return self.ort_session
 
     @staticmethod
     def _get_kwarg_device(kwargs):
-        cpuonly = kwargs.get('cpu_only', None)
+        cpuonly = kwargs.get("cpu_only", None)
         if cpuonly is not None:
-            del kwargs['cpu_only']
+            del kwargs["cpu_only"]
         return cpuonly
 
     @classmethod
     def from_customop(cls, op_type, *args, **kwargs):
-        return (cls(cpu_only=cls._get_kwarg_device(kwargs))
-                .create_from_customop(op_type, *args, **kwargs))
+        return cls(cpu_only=cls._get_kwarg_device(kwargs)).create_from_customop(op_type, *args, **kwargs)
 
     @classmethod
     def from_model(cls, path_or_model, *args, **kwargs):
@@ -165,9 +240,9 @@ class OrtPyFunction:
             x = args[idx]
             ts_x = np.array(x) if isinstance(x, (int, float, bool)) else x
             # numpy by default is int32 in some platforms, sometimes it is int64.
-            feed[i_.name] = \
-                ts_x.astype(
-                    np.int64) if i_.type.tensor_type.elem_type == onnx_proto.TensorProto.INT64 else ts_x
+            feed[i_.name] = (
+                ts_x.astype(np.int64) if i_.type.tensor_type.elem_type == onnx_proto.TensorProto.INT64 else ts_x
+            )
             idx += 1
 
         feed.update(kwargs)
@@ -175,8 +250,7 @@ class OrtPyFunction:
 
     def __call__(self, *args, **kwargs):
         self._ensure_ort_session()
-        outputs = self.ort_session.run(
-            None, self._argument_map(*args, **kwargs))
+        outputs = self.ort_session.run(None, self._argument_map(*args, **kwargs))
         return outputs[0] if len(outputs) == 1 else tuple(outputs)
 
 
@@ -191,8 +265,9 @@ def optimize_model(model_or_file, output_file):
     sess_options = OrtPyFunction().get_ort_session_options()
     sess_options.graph_optimization_level = _ort.GraphOptimizationLevel.ORT_ENABLE_BASIC
     sess_options.optimized_model_filepath = output_file
-    _ort.InferenceSession(model_or_file if isinstance(model_or_file, str)
-                          else model_or_file.SerializeToString(), sess_options)
+    _ort.InferenceSession(
+        model_or_file if isinstance(model_or_file, str) else model_or_file.SerializeToString(), sess_options
+    )
 
 
 ONNXRuntimeError = _ort.capi.onnxruntime_pybind11_state.Fail

requirements-dev.txt

@@ -1,8 +1,7 @@
-# include requirements.txt so pip has context to avoid installing incompatible dependencies
--r requirements.txt
 pytest
-# multiple versions of onnxruntime are supported, but only one can be installed at a time
+onnx             >= 1.9.0
 protobuf         < 4.0.0
+# multiple versions of onnxruntime are supported, but only one can be installed at a time
 onnxruntime      >=1.12.0
 transformers     >=4.9.2
 tensorflow_text  >=2.5.0;python_version < '3.11'

requirements.txt

@@ -1 +0,0 @@
-onnx>=1.9.0

setup.py

@@ -9,14 +9,13 @@ import sys
 import pathlib
 import setuptools
 
-from textwrap import dedent
 from setuptools import setup, find_packages
 
 TOP_DIR = os.path.dirname(__file__) or os.getcwd()
-PACKAGE_NAME = 'onnxruntime_extensions'
+PACKAGE_NAME = "onnxruntime_extensions"
 
 # setup.py cannot be debugged in pip command line, so the command classes are refactored into another file
-cmds_dir = pathlib.Path(TOP_DIR) / '.pyproject'
+cmds_dir = pathlib.Path(TOP_DIR) / ".pyproject"
 sys.path.append(str(cmds_dir))
 # noinspection PyUnresolvedReferences
 import cmdclass as _cmds  # noqa: E402
@@ -24,62 +23,50 @@ import cmdclass as _cmds  # noqa: E402
 _cmds.prepare_env(TOP_DIR)
 
 
-def read_requirements():
-    with open(os.path.join(TOP_DIR, "requirements.txt"), "r", encoding="utf-8") as f:
-        requirements = [_ for _ in [dedent(_) for _ in f.readlines()] if _ is not None]
-    return requirements
-
-
 # read version from the package file.
 def read_version():
-    version_str = '1.0.0'
-    with (open(os.path.join(TOP_DIR, 'version.txt'), "r")) as f:
+    version_str = "0.1.0"
+    with open(os.path.join(TOP_DIR, "version.txt"), "r") as f:
         version_str = f.readline().strip()
 
     # special handling for Onebranch building
-    if os.getenv('BUILD_SOURCEBRANCHNAME', "").startswith('rel-'):
+    if os.getenv("BUILD_SOURCEBRANCHNAME", "").startswith("rel-"):
         return version_str
 
     # is it a dev build or release?
-    rel_br, cid = _cmds.read_git_refs(TOP_DIR) if os.path.isdir(
-        os.path.join(TOP_DIR, '.git')) else (True, None)
+    rel_br, cid = _cmds.read_git_refs(TOP_DIR) if os.path.isdir(os.path.join(TOP_DIR, ".git")) else (True, None)
 
     if rel_br:
         return version_str
 
-    build_id = os.getenv('BUILD_BUILDID', None)
+    build_id = os.getenv("BUILD_BUILDID", None)
     if build_id is not None:
-        version_str += '.{}'.format(build_id)
+        version_str += ".{}".format(build_id)
     else:
-        version_str += '+' + cid[:7]
+        version_str += "+" + cid[:7]
     return version_str
 
 
 def write_py_version(ext_version):
-    text = ["# Generated by setup.py, DON'T MANUALLY UPDATE IT!\n",
-            "__version__ = \"{}\"\n".format(ext_version)]
-    with (open(os.path.join(TOP_DIR, 'onnxruntime_extensions/_version.py'), "w")) as _fver:
+    text = ["# Generated by setup.py, DON'T MANUALLY UPDATE IT!\n", '__version__ = "{}"\n'.format(ext_version)]
+    with open(os.path.join(TOP_DIR, "onnxruntime_extensions/_version.py"), "w") as _fver:
         _fver.writelines(text)
 
 
-ext_modules = [
-    setuptools.extension.Extension(
-        name=str('onnxruntime_extensions._extensions_pydll'),
-        sources=[])
-]
+ext_modules = [setuptools.extension.Extension(name=str("onnxruntime_extensions._extensions_pydll"), sources=[])]
 
 packages = find_packages()
-package_dir = {k: os.path.join('.', k.replace(".", "/")) for k in packages}
+package_dir = {k: os.path.join(".", k.replace(".", "/")) for k in packages}
 package_data = {
     "onnxruntime_extensions": ["*.so", "*.pyd"],
 }
 
-long_description = ''
-with open(os.path.join(TOP_DIR, "README.md"), 'r', encoding="utf-8") as _f:
+long_description = ""
+with open(os.path.join(TOP_DIR, "README.md"), "r", encoding="utf-8") as _f:
     long_description += _f.read()
-    start_pos = long_description.find('# Introduction')
+    start_pos = long_description.find("# Introduction")
     start_pos = 0 if start_pos < 0 else start_pos
-    end_pos = long_description.find('# Contributing')
+    end_pos = long_description.find("# Contributing")
     long_description = long_description[start_pos:end_pos]
 ortx_version = read_version()
 write_py_version(ortx_version)
@@ -92,25 +79,25 @@ setup(
     package_data=package_data,
     description="ONNXRuntime Extensions",
     long_description=long_description,
-    long_description_content_type='text/markdown',
-    license='MIT License',
-    author='Microsoft Corporation',
-    author_email='onnxruntime@microsoft.com',
-    url='https://github.com/microsoft/onnxruntime-extensions',
+    long_description_content_type="text/markdown",
+    license="MIT License",
+    author="Microsoft Corporation",
+    author_email="onnxruntime@microsoft.com",
+    url="https://github.com/microsoft/onnxruntime-extensions",
     ext_modules=ext_modules,
     cmdclass=_cmds.ortx_cmdclass,
     include_package_data=True,
-    install_requires=read_requirements(),
+    install_requires=[],
     classifiers=[
-        'Development Status :: 4 - Beta',
-        'Environment :: Console',
-        'Intended Audience :: Developers',
-        'Operating System :: MacOS :: MacOS X',
-        'Operating System :: Microsoft :: Windows',
-        'Operating System :: POSIX :: Linux',
+        "Development Status :: 4 - Beta",
+        "Environment :: Console",
+        "Intended Audience :: Developers",
+        "Operating System :: MacOS :: MacOS X",
+        "Operating System :: Microsoft :: Windows",
+        "Operating System :: POSIX :: Linux",
         "Programming Language :: C++",
-        'Programming Language :: Python',
+        "Programming Language :: Python",
         "Programming Language :: Python :: Implementation :: CPython",
-        'License :: OSI Approved :: MIT License'
-    ]
+        "License :: OSI Approved :: MIT License",
+    ],
 )