perf: refine onnx implementation

.github/workflows/frameworks.yml

@@ -381,7 +381,7 @@ jobs:
       - name: Install dependencies
         run: |
           pip install .
-          pip install onnx onnxruntime
+          pip install onnx onnxruntime skl2onnx
           pip install -r requirements/tests-requirements.txt
 
       - name: Run tests and generate coverage report

src/bentoml/_internal/frameworks/onnx.py

@@ -7,7 +7,6 @@
 from typing import TYPE_CHECKING
 
 import attr
-import numpy as np
 
 import bentoml
 from bentoml import Tag
@@ -17,26 +16,25 @@
 from bentoml.exceptions import BentoMLException
 from bentoml.exceptions import MissingDependencyException
 
-from ..types import LazyType
 from ..utils.pkg import get_pkg_version
 from ..utils.pkg import PackageNotFoundError
-from ..runner.utils import Params
+from ..utils.onnx import gen_input_casting_func
 
 if TYPE_CHECKING:
-    import torch  # noqa
 
     from bentoml.types import ModelSignature
     from bentoml.types import ModelSignatureDict
 
-    from .. import external_typing as ext
-    from ..external_typing import tensorflow as tf_ext  # noqa
+    from ..utils.onnx import ONNXArgType
+    from ..utils.onnx import ONNXArgCastedType
 
     ProvidersType = list[str | tuple[str, dict[str, t.Any]]]
-    ONNXArgType = ext.NpNDArray | ext.PdDataFrame | torch.Tensor | tf_ext.Tensor
 
 
 try:
     import onnx
+    from google.protobuf.json_format import MessageToDict
+
 except ImportError:  # pragma: no cover
     raise MissingDependencyException(
         "onnx is required in order to use module 'bentoml.onnx', install onnx with 'pip install onnx'. For more information, refer to https://onnx.ai/get-started.html"
@@ -51,7 +49,7 @@
 
 MODULE_NAME = "bentoml.onnx"
 MODEL_FILENAME = "saved_model.onnx"
-API_VERSION = "v1"
+API_VERSION = "v2"
 
 logger = logging.getLogger(__name__)
 
@@ -76,6 +74,8 @@ def flatten_list(lst: t.List[t.Any]) -> t.List[str]:  # pragma: no cover
 class ONNXOptions(ModelOptions):
     """Options for the ONNX model"""
 
+    input_specs: dict[str, list[dict[str, t.Any]]] = attr.field(factory=dict)
+    output_specs: dict[str, list[dict[str, t.Any]]] = attr.field(factory=dict)
     providers: t.Optional[list[str]] = attr.field(default=None)
     session_options: t.Optional["ort.SessionOptions"] = attr.field(default=None)
 
@@ -106,6 +106,16 @@ def get(tag_like: str | Tag) -> bentoml.Model:
     return model
 
 
+def _load_raw_model(bento_model: str | Tag | bentoml.Model) -> onnx.ModelProto:
+
+    if not isinstance(bento_model, bentoml.Model):
+        bento_model = get(bento_model)
+
+    model_path = bento_model.path_of(MODEL_FILENAME)
+    raw_model = onnx.load(model_path)
+    return raw_model
+
+
 def load_model(
     bento_model: str | Tag | bentoml.Model,
     *,
@@ -162,7 +172,7 @@ def save_model(
     name: str,
     model: onnx.ModelProto,
     *,
-    signatures: dict[str, ModelSignatureDict | ModelSignature] | None = None,
+    signatures: dict[str, ModelSignatureDict] | dict[str, ModelSignature] | None = None,
     labels: dict[str, str] | None = None,
     custom_objects: dict[str, t.Any] | None = None,
     external_modules: t.List[ModuleType] | None = None,
@@ -265,6 +275,9 @@ def forward(self, x, bias):
     ), "Failed to find onnxruntime package version."
 
     assert _onnxruntime_version is not None, "onnxruntime is not installed"
+    if not isinstance(model, onnx.ModelProto):
+        raise TypeError(f"Given model ({model}) is not a onnx.ModelProto.")
+
     context = ModelContext(
         framework_name="onnx",
         framework_versions={
@@ -289,15 +302,12 @@ def forward(self, x, bias):
                 f"Provided method names {[m for m  in provided_methods if m != 'run']} are invalid. 'bentoml.onnx' will load ONNX model into an 'onnxruntime.InferenceSession' for inference, so the only supported method name is 'run'."
             )
 
-    if not isinstance(model, onnx.ModelProto):
-        raise TypeError(f"Given model ({model}) is not a onnx.ModelProto.")
-
-    if len(model.graph.output) > 1:
-        logger.warning(
-            "The model you are attempting to save has more than one output. The ONNX runner will only return the first output."
-        )
+    run_input_specs = [MessageToDict(inp) for inp in model.graph.input]
+    run_output_specs = [MessageToDict(out) for out in model.graph.output]
+    input_specs = {"run": run_input_specs}
+    output_specs = {"run": run_output_specs}
 
-    options = ONNXOptions()
+    options = ONNXOptions(input_specs=input_specs, output_specs=output_specs)
 
     with bentoml.models.create(
         name,
@@ -321,6 +331,25 @@ def get_runnable(bento_model: bentoml.Model) -> t.Type[bentoml.Runnable]:
     Private API: use :obj:`~bentoml.Model.to_runnable` instead.
     """
 
+    # backward compatibility for v1, load raw model to infer
+    # input_specs/output_specs for onnx model
+    if bento_model.info.api_version == "v1":
+
+        raw_model: onnx.ModelProto | None = None
+        options = t.cast(ONNXOptions, bento_model.info.options)
+
+        if not options.input_specs:
+            raw_model = _load_raw_model(bento_model)
+            run_input_specs = [MessageToDict(inp) for inp in raw_model.graph.input]
+            input_specs = {"run": run_input_specs}
+            bento_model = bento_model.with_options(input_specs=input_specs)
+
+        if not options.output_specs:
+            raw_model = raw_model or _load_raw_model(bento_model)
+            run_output_specs = [MessageToDict(out) for out in raw_model.graph.output]
+            output_specs = {"run": run_output_specs}
+            bento_model = bento_model.with_options(output_specs=output_specs)
+
     class ONNXRunnable(bentoml.Runnable):
         SUPPORTED_RESOURCES = ("nvidia.com/gpu", "cpu")
         SUPPORTS_CPU_MULTI_THREADING = True
@@ -376,48 +405,50 @@ def __init__(self):
             for method_name in bento_model.info.signatures:
                 self.predict_fns[method_name] = getattr(self.model, method_name)
 
-    def _mapping(item: ONNXArgType) -> ext.NpNDArray:
-
-        # currently ort only support np.float32
-        # https://onnxruntime.ai/docs/api/python/auto_examples/plot_common_errors.html
-        # TODO @larme: what dtype of input to use if we use FP16? ref: onnxruntime issue 11768
-        if LazyType["ext.NpNDArray"]("numpy.ndarray").isinstance(item):
-            if item.dtype != np.float32:
-                item = item.astype(np.float32, copy=False)
-        elif LazyType["ext.PdDataFrame"]("pandas.DataFrame").isinstance(item):
-            item = item.to_numpy(dtype=np.float32)
-        elif LazyType["tf.Tensor"]("tensorflow.Tensor").isinstance(item):
-            item = np.array(memoryview(item)).astype(np.float32, copy=False)
-        elif LazyType["torch.Tensor"]("torch.Tensor").isinstance(item):
-            item = item.numpy().astype(np.float32, copy=False)
+    def add_runnable_method(
+        method_name: str,
+        signatures: ModelSignature,
+        input_specs: list[dict[str, t.Any]],
+        output_specs: list[dict[str, t.Any]],
+    ):
+
+        casting_funcs = [gen_input_casting_func(spec) for spec in input_specs]
+
+        if len(output_specs) > 1:
+
+            def _process_output(outs):
+                return tuple(outs)
+
         else:
-            raise TypeError(
-                "'run' of ONNXRunnable only takes 'numpy.ndarray' or 'pd.DataFrame', 'tf.Tensor', or 'torch.Tensor' as input parameters."
-            )
-        # This cast is safe since we have already checked the type of item
-        return t.cast("ext.NpNDArray", item)
 
-    def add_runnable_method(method_name: str, options: ModelSignature):
+            def _process_output(outs):
+                return outs[0]
+
         def _run(self: ONNXRunnable, *args: ONNXArgType) -> t.Any:
-            params = Params["ONNXArgType"](*args)
-            params = params.map(_mapping)
+            casted_args = [
+                casting_funcs[idx](args[idx]) for idx in range(len(casting_funcs))
+            ]
 
-            input_names: dict[str, ext.NpNDArray] = {
-                i.name: val for i, val in zip(self.model.get_inputs(), params.args)
+            input_names: dict[str, ONNXArgCastedType] = {
+                i.name: val for i, val in zip(self.model.get_inputs(), casted_args)
             }
             output_names: list[str] = [o.name for o in self.model.get_outputs()]
-            return self.predict_fns[method_name](output_names, input_names)[0]
+            raw_outs = self.predict_fns[method_name](output_names, input_names)
+            return _process_output(raw_outs)
 
         ONNXRunnable.add_method(
             _run,
             name=method_name,
-            batchable=options.batchable,
-            batch_dim=options.batch_dim,
-            input_spec=options.input_spec,
-            output_spec=options.output_spec,
+            batchable=signatures.batchable,
+            batch_dim=signatures.batch_dim,
+            input_spec=signatures.input_spec,
+            output_spec=signatures.output_spec,
         )
 
-    for method_name, options in bento_model.info.signatures.items():
-        add_runnable_method(method_name, options)
+    for method_name, signatures in bento_model.info.signatures.items():
+        options = t.cast(ONNXOptions, bento_model.info.options)
+        input_specs = options.input_specs[method_name]
+        output_specs = options.output_specs[method_name]
+        add_runnable_method(method_name, signatures, input_specs, output_specs)
 
     return ONNXRunnable

src/bentoml/_internal/utils/onnx.py

@@ -0,0 +1,160 @@
+from __future__ import annotations
+
+import typing as t
+import logging
+from typing import TYPE_CHECKING
+
+from bentoml.exceptions import BentoMLException
+
+from ..types import LazyType
+from .lazy_loader import LazyLoader
+
+if TYPE_CHECKING:
+    import onnx
+    import torch
+
+    from .. import external_typing as ext
+    from ..external_typing import tensorflow as tf_ext  # noqa
+
+    ONNXArgTensorType = (
+        ext.NpNDArray
+        | ext.PdDataFrame
+        | torch.Tensor
+        | tf_ext.Tensor
+        | list[int | float | str]
+    )
+    ONNXArgSequenceType = list["ONNXArgType"]
+    ONNXArgMapKeyType = int | str
+    ONNXArgMapType = dict[ONNXArgMapKeyType, "ONNXArgType"]
+    ONNXArgType = ONNXArgMapType | ONNXArgTensorType | ONNXArgSequenceType
+
+    ONNXArgCastedType = (
+        ext.NpNDArray
+        | list["ONNXArgCastedType"]
+        | dict[ONNXArgMapKeyType, "ONNXArgCastedType"]
+    )
+    ONNXArgCastingFuncType = t.Callable[[ONNXArgType], ONNXArgCastedType]
+    ONNXArgCastingFuncGeneratorType = t.Callable[
+        [dict[str, t.Any]], t.Callable[[ONNXArgType], ONNXArgCastedType]
+    ]
+
+else:
+    np = LazyLoader("np", globals(), "numpy")
+    onnx = LazyLoader(
+        "onnx",
+        globals(),
+        "onnx",
+        exc_msg="`onnx` is required to use bentoml.onnx module.",
+    )
+
+logger = logging.getLogger(__name__)
+
+TENSORPROTO_ELEMENT_TYPE_TO_NUMPY_TYPE: dict[int, str] = {
+    onnx.TensorProto.FLOAT: "float32",  # 1
+    onnx.TensorProto.UINT8: "uint8",  # 2
+    onnx.TensorProto.INT8: "int8",  # 3
+    onnx.TensorProto.UINT16: "uint16",  # 4
+    onnx.TensorProto.INT16: "int16",  # 5
+    onnx.TensorProto.INT32: "int32",  # 6
+    onnx.TensorProto.INT64: "int64",  # 7
+    onnx.TensorProto.STRING: "str",  # 8 or "unicode"?
+    onnx.TensorProto.BOOL: "bool",  # 9
+    onnx.TensorProto.FLOAT16: "float16",  # 10
+    onnx.TensorProto.DOUBLE: "double",  # 11
+    onnx.TensorProto.UINT32: "uint32",  # 12
+    onnx.TensorProto.UINT64: "uint64",  # 13
+    onnx.TensorProto.COMPLEX64: "csingle",  # 14
+    onnx.TensorProto.COMPLEX128: "cdouble",  # 15
+    # onnx.TensorProto.BFLOAT16: None,  # 16
+}
+
+
+CASTING_FUNC_DISPATCHER: dict[str, ONNXArgCastingFuncGeneratorType] = {
+    # type -> casting function generator
+}
+
+
+def gen_input_casting_func(spec: dict[str, t.Any]) -> ONNXArgCastingFuncType:
+    return _gen_input_casting_func(spec["type"])
+
+
+def _gen_input_casting_func(sig: dict[str, t.Any]) -> ONNXArgCastingFuncType:
+
+    input_types = list(sig.keys())
+    if len(input_types) != 1:
+        raise BentoMLException(
+            "onnx model input type dictionary should have only one key!"
+        )
+    input_type = input_types[0]
+    input_spec = sig[input_type]
+    return CASTING_FUNC_DISPATCHER[input_type](input_spec)
+
+
+def _gen_input_casting_func_for_tensor(
+    sig: dict[str, t.Any]
+) -> t.Callable[[ONNXArgTensorType], ext.NpNDArray]:
+
+    elem_type = sig["elemType"]
+    to_dtype = TENSORPROTO_ELEMENT_TYPE_TO_NUMPY_TYPE[elem_type]
+
+    def _mapping(item: ONNXArgTensorType) -> ext.NpNDArray:
+        if LazyType["ext.NpNDArray"]("numpy.ndarray").isinstance(item):
+            item = item.astype(to_dtype, copy=False)
+        elif isinstance(item, list):
+            item = np.array(item).astype(to_dtype, copy=False)
+        elif LazyType["ext.PdDataFrame"]("pandas.DataFrame").isinstance(item):
+            item = item.to_numpy(dtype=to_dtype)
+        elif LazyType["tf.Tensor"]("tensorflow.Tensor").isinstance(item):
+            item = np.array(memoryview(item)).astype(to_dtype, copy=False)  # type: ignore
+        elif LazyType["torch.Tensor"]("torch.Tensor").isinstance(item):
+            item = item.numpy().astype(to_dtype, copy=False)
+        else:
+            raise TypeError(
+                "`run` of ONNXRunnable only takes `numpy.ndarray`, `pd.DataFrame`, `tf.Tensor`, `torch.Tensor` or a list as input Tensor type"
+            )
+        return t.cast("ext.NpNDArray", item)
+
+    return _mapping
+
+
+CASTING_FUNC_DISPATCHER["tensorType"] = t.cast(
+    "ONNXArgCastingFuncGeneratorType", _gen_input_casting_func_for_tensor
+)
+
+
+def _gen_input_casting_func_for_map(
+    sig: dict[str, t.Any]
+) -> t.Callable[[ONNXArgMapType], dict[ONNXArgMapKeyType, ONNXArgCastedType]]:
+
+    map_value_sig = t.cast(dict[str, t.Any], sig["valueType"])
+    value_casting_func = _gen_input_casting_func(map_value_sig)
+
+    def _mapping(item: ONNXArgMapType) -> dict[ONNXArgMapKeyType, t.Any]:
+        new_item = {k: value_casting_func(v) for k, v in item.items()}
+        return new_item
+
+    return _mapping
+
+
+CASTING_FUNC_DISPATCHER["mapType"] = t.cast(
+    "ONNXArgCastingFuncGeneratorType", _gen_input_casting_func_for_map
+)
+
+
+def _gen_input_casting_func_for_sequence(
+    sig: dict[str, t.Any]
+) -> t.Callable[[ONNXArgSequenceType], list[t.Any]]:
+
+    seq_elem_sig = t.cast(dict[str, t.Any], sig["elemType"])
+    elem_casting_func = _gen_input_casting_func(seq_elem_sig)
+
+    def _mapping(item: ONNXArgSequenceType) -> list[t.Any]:
+        new_item = list(elem_casting_func(elem) for elem in item)
+        return new_item
+
+    return _mapping
+
+
+CASTING_FUNC_DISPATCHER["sequenceType"] = t.cast(
+    "ONNXArgCastingFuncGeneratorType", _gen_input_casting_func_for_sequence
+)