Source code for bentoml._internal.frameworks.onnx

from __future__ import annotations

import logging
import os
import typing as t
from types import ModuleType
from typing import TYPE_CHECKING

import attr

import bentoml
from bentoml import Tag
from bentoml.exceptions import BentoMLException
from bentoml.exceptions import MissingDependencyException
from bentoml.exceptions import NotFound
from bentoml.models import ModelContext
from bentoml.models import ModelOptions

from ..utils.pkg import PackageNotFoundError
from ..utils.pkg import get_pkg_version

    from bentoml.types import ModelSignature
    from bentoml.types import ModelSignatureDict

    from .utils.onnx import ONNXArgCastedType
    from .utils.onnx import ONNXArgType

    ProvidersType = list[str | tuple[str, dict[str, t.Any]]]

    import onnx
    from google.protobuf.json_format import MessageToDict

    from .utils.onnx import gen_input_casting_func
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"

    import onnxruntime as ort
except ImportError:  # pragma: no cover
    raise MissingDependencyException(
        "'onnxruntime' or 'onnxruntime-gpu' is required by 'bentoml.onnx', install onnxruntime or onnxruntime-gpu with 'pip install onnxruntime' or 'pip install onnxruntime-gpu'. For more information, refer to"

MODULE_NAME = "bentoml.onnx"
MODEL_FILENAME = "saved_model.onnx"

logger = logging.getLogger(__name__)

def flatten_providers_list(lst: ProvidersType) -> list[str]:
    return [k[0] if isinstance(k, (list, tuple)) else k for k in lst]

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: ProvidersType = attr.field(default=None)
    session_options: t.Optional["ort.SessionOptions"] = attr.field(default=None)

[docs]def get(tag_like: str | Tag) -> bentoml.Model: """ Get the BentoML model with the given tag. Args: tag_like: The tag of the model to retrieve from the model store. Returns: :obj:`~bentoml.Model`: A BentoML :obj:`~bentoml.Model` with the matching tag. Example: .. code-block:: python import bentoml # target model must be from the BentoML model store model = bentoml.onnx.get("onnx_resnet50") """ model = bentoml.models.get(tag_like) if not in (MODULE_NAME, __name__): raise NotFound( f"Model {model.tag} was saved with module {}, not loading with {MODULE_NAME}." ) 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
[docs]def load_model( bento_model: str | Tag | bentoml.Model, *, providers: ProvidersType | None = None, session_options: ort.SessionOptions | None = None, ) -> ort.InferenceSession: """ Load the onnx model with the given tag from the local BentoML model store. Args: bento_model (``str`` ``|`` :obj:`~bentoml.Tag` ``|`` :obj:`~bentoml.Model`): Either the tag of the model to get from the store, or a BentoML `~bentoml.Model` instance to load the model from. providers (`List[Union[str, Tuple[str, Dict[str, Any]]`, `optional`, default to :code:`None`): Different providers provided by users. By default BentoML will use ``["CPUExecutionProvider"]`` when loading a model. session_options (`onnxruntime.SessionOptions`, `optional`, default to :code:`None`): SessionOptions per use case. If not specified, then default to :code:`None`. Returns: :obj:`onnxruntime.InferenceSession`: An instance of ONNX Runtime inference session created using ONNX model loaded from the model store. Example: .. code-block:: python import bentoml sess = bentoml.onnx.load_model("my_onnx_model") """ # noqa if not isinstance(bento_model, bentoml.Model): bento_model = get(bento_model) if not in (MODULE_NAME, __name__): raise NotFound( f"Model {bento_model.tag} was saved with module {}, not loading with {MODULE_NAME}." ) if providers: if not all( i in ort.get_all_providers() for i in flatten_providers_list(providers) ): raise BentoMLException(f"'{providers}' cannot be parsed by `onnxruntime`") else: providers = ["CPUExecutionProvider"] return ort.InferenceSession( bento_model.path_of(MODEL_FILENAME), sess_options=session_options, providers=providers, )
[docs]def save_model( name: Tag | str, model: onnx.ModelProto, *, 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, metadata: dict[str, t.Any] | None = None, ) -> bentoml.Model: """Save a onnx model instance to the BentoML model store. Args: name (``str``): The name to give to the model in the BentoML store. This must be a valid :obj:`~bentoml.Tag` name. model (:obj:`~onnx.ModelProto`): The ONNX model to be saved. signatures (``dict[str, ModelSignatureDict]``, optional): Signatures of predict methods to be used. If not provided, the signatures default to ``{"run": {"batchable": False}}``. See :obj:`~bentoml.types.ModelSignature` for more details. ``bentoml.onnx`` internally use ``onnxruntime.InferenceSession`` to run inference. When the original model is converted to ONNX format and loaded by ``onnxruntime.InferenceSession``, the inference method of the original model is converted to the ``run`` method of the ``onnxruntime.InferenceSession``. ``signatures`` here refers to the predict method of ``onnxruntime.InferenceSession``, hence the only allowed method name in ``signatures`` is ``run``. labels (``dict[str, str]``, optional): A default set of management labels to be associated with the model. An example is ``{"training-set": "data-1"}``. custom_objects (``dict[str, Any]``, optional): Custom objects to be saved with the model. An example is ``{"my-normalizer": normalizer}``. Custom objects are currently serialized with cloudpickle, but this implementation is subject to change. external_modules (:code:`List[ModuleType]`, `optional`, default to :code:`None`): user-defined additional python modules to be saved alongside the model or custom objects, e.g. a tokenizer module, preprocessor module, model configuration module metadata (``dict[str, Any]``, optional): Metadata to be associated with the model. An example is ``{"bias": 4}``. Metadata is intended for display in a model management UI and therefore must be a default Python type, such as ``str`` or ``int``. Returns: :obj:`~bentoml.Model`: A BentoML model containing the saved ONNX model instance. store. Example: .. code-block:: python import bentoml import torch import torch.nn as nn class ExtendedModel(nn.Module): def __init__(self, D_in, H, D_out): # In the constructor we instantiate two nn.Linear modules and assign them as # member variables. super(ExtendedModel, self).__init__() self.linear1 = nn.Linear(D_in, H) self.linear2 = nn.Linear(H, D_out) def forward(self, x, bias): # In the forward function we accept a Tensor of input data and an optional bias h_relu = self.linear1(x).clamp(min=0) y_pred = self.linear2(h_relu) return y_pred + bias N, D_in, H, D_out = 64, 1000, 100, 1 x = torch.randn(N, D_in) model = ExtendedModel(D_in, H, D_out) input_names = ["x", "bias"] output_names = ["output1"] tmpdir = "/tmp/model" model_path = os.path.join(tmpdir, "test_torch.onnx") torch.onnx.export( model, (x, torch.Tensor([1.0])), model_path, input_names=input_names, output_names=output_names, ) bento_model = bentoml.onnx.save_model("onnx_model", model_path, signatures={"run": {"batchable": True}}) """ # prefer "onnxruntime-gpu" over "onnxruntime" for framework versioning _onnxruntime_version = None _onnxruntime_pkg = None _PACKAGE = ["onnxruntime-gpu", "onnxruntime", "onnxruntime-silicon"] for p in _PACKAGE: try: _onnxruntime_version = get_pkg_version(p) _onnxruntime_pkg = p break except PackageNotFoundError: pass assert ( _onnxruntime_pkg is not None and _onnxruntime_version is not None ), "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={ "onnx": get_pkg_version("onnx"), _onnxruntime_pkg: _onnxruntime_version, }, ) if signatures is None: signatures = { "run": {"batchable": False}, } 'Using the default model signature for ONNX (%s) for model "%s".', signatures, name, ) else: provided_methods = list(signatures.keys()) if provided_methods != ["run"]: raise BentoMLException( 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'." ) 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(input_specs=input_specs, output_specs=output_specs) with bentoml.models.create( name, module=MODULE_NAME, api_version=API_VERSION, signatures=signatures, labels=labels, options=options, custom_objects=custom_objects, external_modules=external_modules, metadata=metadata, context=context, ) as bento_model:, bento_model.path_of(MODEL_FILENAME)) return bento_model
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 == "v1": raw_model: onnx.ModelProto | None = None options = t.cast(ONNXOptions, 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 = ("", "cpu") SUPPORTS_CPU_MULTI_THREADING = True def __init__(self): super().__init__() session_options = ( or ort.SessionOptions() ) # check for resources available_gpus = os.getenv("CUDA_VISIBLE_DEVICES") if available_gpus is not None and available_gpus not in ("", "-1"): # assign GPU resources providers = or [ "CUDAExecutionProvider", "CPUExecutionProvider", ] else: # assign CPU resources # If onnxruntime-gpu is installed, # CUDAExecutionProvider etc. will be available even no # GPU is presented in system, which may result some # error when initializing ort.InferenceSession providers = or [ "CPUExecutionProvider" ] # set CPUExecutionProvider parallelization options # TODO @larme: follow onnxruntime issue 11668 and # 10330 to decide best cpu parallelization strategy thread_count = int(os.getenv("BENTOML_NUM_THREAD", 1)) session_options.execution_mode = ort.ExecutionMode.ORT_PARALLEL if session_options.intra_op_num_threads != 0: logger.warning( "Overriding specified 'session_options.intra_op_num_threads'." ) session_options.intra_op_num_threads = thread_count if session_options.inter_op_num_threads != 0: logger.warning( "Overriding specified 'session_options.inter_op_num_threads'." ) session_options.inter_op_num_threads = thread_count self.model = load_model( bento_model, session_options=session_options, providers=providers ) self.predict_fns: dict[str, t.Callable[..., t.Any]] = {} for method_name in self.predict_fns[method_name] = getattr(self.model, method_name) 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: def _process_output(outs): return outs[0] def _run(self: ONNXRunnable, *args: ONNXArgType) -> t.Any: casted_args = [ casting_funcs[idx](args[idx]) for idx in range(len(casting_funcs)) ] input_names: dict[str, ONNXArgCastedType] = { val for i, val in zip(self.model.get_inputs(), casted_args) } output_names: list[str] = [ for o in self.model.get_outputs()] raw_outs = self.predict_fns[method_name](output_names, input_names) return _process_output(raw_outs) ONNXRunnable.add_method( _run, name=method_name, batchable=signatures.batchable, batch_dim=signatures.batch_dim, input_spec=signatures.input_spec, output_spec=signatures.output_spec, ) for method_name, signatures in options = t.cast(ONNXOptions, 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