# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# --------------------------------------------------------------------------
import logging
from pathlib import Path
from typing import Any, ClassVar, Dict, List, Optional, Tuple, Union
import numpy as np
import onnx
from onnx import GraphProto, ModelProto
from olive.common.ort_inference import OrtSessionFallbackError, get_ort_inference_session
from olive.constants import Framework, ModelFileFormat
from olive.exception import OliveEvaluationError
from olive.hardware.accelerator import AcceleratorLookup, Device
from olive.model.config.registry import model_handler_registry
from olive.model.handler.base import OliveModelHandler
from olive.model.handler.mixin import OnnxEpValidateMixin, OnnxGraphMixin
from olive.model.utils.onnx_utils import get_additional_file_path, get_onnx_file_path
from olive.resource_path import OLIVE_RESOURCE_ANNOTATIONS
logger = logging.getLogger(__name__)
[docs]@model_handler_registry("ONNXModel")
class ONNXModelHandler(OliveModelHandler, OnnxEpValidateMixin, OnnxGraphMixin): # pylint: disable=too-many-ancestors
"""ONNX model handler.
Besides the model loading functionalities, the model handler also provider the onnx graph functionality by mixin
the mixin class OnnxEpValidateMixin is used to validate the execution providers.
the mixin class OnnxGraphMixin is used to support onnx graph operations.
"""
json_config_keys: Tuple[str, ...] = (
"onnx_file_name",
"inference_settings",
"use_ort_extensions",
"external_initializers_file_name",
"constant_inputs_file_name",
)
def __init__(
self,
model_path: OLIVE_RESOURCE_ANNOTATIONS = None,
onnx_file_name: Optional[str] = None,
inference_settings: Optional[dict] = None,
use_ort_extensions: bool = False,
model_attributes: Optional[Dict[str, Any]] = None,
external_initializers_file_name: Optional[str] = None,
constant_inputs_file_name: Optional[str] = None,
):
super().__init__(
framework=Framework.ONNX,
model_file_format=ModelFileFormat.ONNX,
model_path=model_path,
model_attributes=model_attributes,
)
self.inference_settings = inference_settings
self.use_ort_extensions = use_ort_extensions
self.onnx_file_name = onnx_file_name
self.external_initializers_file_name = external_initializers_file_name
self.constant_inputs_file_name = constant_inputs_file_name
self.graph = None
self.all_graphs: Optional[List[GraphProto]] = None
# check for file names since it will automatically validate the paths
# these call the property methods which in turn validate the paths using get_onnx_file_path
# and get_additional_file_path
to_check = ["model_path", "external_initializers_path", "constant_inputs_path"]
for attr in to_check:
getattr(self, attr)
@property
def model_path(self) -> str:
model_path = super().model_path
return get_onnx_file_path(model_path, self.onnx_file_name) if model_path else None
@property
def external_initializers_path(self) -> Optional[str]:
model_path = super().model_path
return get_additional_file_path(model_path, self.external_initializers_file_name) if model_path else None
@property
def constant_inputs_path(self) -> Optional[str]:
model_path = super().model_path
return get_additional_file_path(model_path, self.constant_inputs_file_name) if model_path else None
def load_model(self, rank: int = None) -> ModelProto:
return onnx.load(self.model_path)
def prepare_session(
self,
inference_settings: Optional[Dict[str, Any]] = None,
device: Device = Device.CPU,
execution_providers: Union[str, List[str]] = None,
rank: Optional[int] = None,
):
# user provided inference_settings > model's inference_settings > default settings
inference_settings = self.merge_inference_settings(inference_settings, execution_providers)
if not inference_settings["execution_provider"]:
# if no execution_providers are provided, use the default ones
inference_settings["execution_provider"] = self._get_default_execution_providers(device)
inference_settings["provider_options"] = None
# device id for ranked model
device_id = rank if device == Device.GPU else None
# load external initializers if available
external_initializers = np.load(self.external_initializers_path) if self.external_initializers_path else None
try:
return get_ort_inference_session(
self.model_path, inference_settings, self.use_ort_extensions, device_id, external_initializers
)
except OrtSessionFallbackError as e:
raise OliveEvaluationError(e) from e
def merge_inference_settings(
self, inference_settings: Optional[Dict[str, Any]] = None, execution_providers: List[str] = None
):
"""Merge user provided inference settings with model's inference settings.
user provided inference_settings > model's inference_settings > eps given by arguments
"""
inference_settings_merged = {"execution_provider": None, "provider_options": None}
if self.inference_settings:
# start with model's inference settings
inference_settings_merged.update(self.inference_settings)
if inference_settings:
# update with user provided inference settings
inference_settings_merged.update(inference_settings)
if inference_settings_merged.get("execution_provider") is None:
# use execution providers
inference_settings_merged["execution_provider"] = execution_providers
inference_settings_merged["provider_options"] = None
# execution_provider should be a list
if isinstance(inference_settings_merged["execution_provider"], (str, tuple)):
inference_settings_merged["execution_provider"] = [inference_settings_merged["execution_provider"]]
return inference_settings_merged
@property
def io_config(self):
"""Get input/output names, shapes, types of the onnx model without creating an ort session.
This function loads the onnx model and parses the graph to get the io config.
"""
if self._io_config:
return self._io_config
# save io_config
self._io_config = self.get_graph_io_config()
return self._io_config
def _get_default_execution_providers(self, device: Device):
# return available ep as ort default ep
available_providers = AcceleratorLookup.get_execution_providers_for_device(device)
eps = [ep for ep in available_providers if self.is_valid_ep(self.model_path, ep)]
if not eps:
eps.append("CPUExecutionProvider")
return eps
[docs]@model_handler_registry("DistributedOnnxModel")
class DistributedOnnxModelHandler(OliveModelHandler, OnnxEpValidateMixin):
json_config_keys: Tuple[str, ...] = (
"model_name_pattern",
"num_ranks",
"inference_settings",
"use_ort_extensions",
)
EXECUTION_PROVIDERS: ClassVar[dict] = {
"cpu": ["CPUExecutionProvider"],
"gpu": ["CUDAExecutionProvider", "CPUExecutionProvider"],
}
DEFAULT_RANKED_MODEL_NAME_FORMAT: ClassVar[str] = "model_{:02d}.onnx"
def __init__(
self,
model_path: OLIVE_RESOURCE_ANNOTATIONS,
model_name_pattern: str,
num_ranks: int,
inference_settings: Optional[dict] = None,
use_ort_extensions: bool = False,
model_attributes: Optional[Dict[str, Any]] = None,
):
super().__init__(
framework=Framework.ONNX,
model_file_format=ModelFileFormat.ONNX,
model_path=model_path,
model_attributes=model_attributes,
)
self.inference_settings = inference_settings
self.use_ort_extensions = use_ort_extensions
self.model_name_pattern = model_name_pattern
self.num_ranks = num_ranks
def ranked_model_name(self, rank: int) -> str:
return self.model_name_pattern.format(rank)
def ranked_model_path(self, rank: int) -> Union[Path, str]:
return Path(self.model_path) / self.ranked_model_name(rank)
def load_model(self, rank: int = None) -> ONNXModelHandler:
return ONNXModelHandler(
self.ranked_model_path(rank),
inference_settings=self.inference_settings,
use_ort_extensions=self.use_ort_extensions,
model_attributes=self.model_attributes,
)
def prepare_session(
self,
inference_settings: Optional[Dict[str, Any]] = None,
device: Device = Device.GPU, # pylint: disable=signature-differs
execution_providers: Union[str, List[str]] = None,
rank: Optional[int] = 0,
):
raise RuntimeError("DistributedOnnxModel doesn't have a session of its own")
def get_default_execution_providers_with_model(self, filepath: str, device: Device):
# return firstly available ep as ort default ep
available_providers = self.get_execution_providers(device)
for ep in available_providers:
if self.is_valid_ep(filepath, ep):
return [ep]
return ["CUDAExecutionProvider", "CPUExecutionProvider"]
@staticmethod
def get_execution_providers(device: Device):
import onnxruntime as ort
eps_per_device = DistributedOnnxModelHandler.EXECUTION_PROVIDERS.get(device)
available_providers = ort.get_available_providers()
return AcceleratorLookup.get_execution_providers(eps_per_device, available_providers)