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from typing import Any
from typing import Callable
from typing import Dict
from typing import Iterable
from typing import Mapping
from typing import Optional
from typing import Sequence
import numpy
import onnx
import onnxruntime as ort
from apache_beam.io.filesystems import FileSystems
from apache_beam.ml.inference import utils
from apache_beam.ml.inference.base import ModelHandler
from apache_beam.ml.inference.base import PredictionResult
__all__ = ['OnnxModelHandlerNumpy']
NumpyInferenceFn = Callable[
[Sequence[numpy.ndarray], ort.InferenceSession, Optional[Dict[str, Any]]],
Iterable[PredictionResult]]
def default_numpy_inference_fn(
inference_session: ort.InferenceSession,
batch: Sequence[numpy.ndarray],
inference_args: Optional[Dict[str, Any]] = None) -> Any:
ort_inputs = {
inference_session.get_inputs()[0].name: numpy.stack(batch, axis=0)
}
if inference_args:
ort_inputs = {**ort_inputs, **inference_args}
ort_outs = inference_session.run(None, ort_inputs)[0]
return ort_outs
[docs]
class OnnxModelHandlerNumpy(ModelHandler[numpy.ndarray,
PredictionResult,
ort.InferenceSession]):
def __init__( #pylint: disable=dangerous-default-value
self,
model_uri: str,
session_options=None,
providers=['CUDAExecutionProvider', 'CPUExecutionProvider'],
provider_options=None,
*,
inference_fn: NumpyInferenceFn = default_numpy_inference_fn,
large_model: bool = False,
model_copies: Optional[int] = None,
min_batch_size: Optional[int] = None,
max_batch_size: Optional[int] = None,
max_batch_duration_secs: Optional[int] = None,
**kwargs):
""" Implementation of the ModelHandler interface for onnx
using numpy arrays as input.
Note that inputs to ONNXModelHandler should be of the same sizes
Example Usage::
pcoll | RunInference(OnnxModelHandler(model_uri="my_uri"))
Args:
model_uri: The URI to where the model is saved.
inference_fn: The inference function to use on RunInference calls.
default=default_numpy_inference_fn
large_model: set to true if your model is large enough to run into
memory pressure if you load multiple copies. Given a model that
consumes N memory and a machine with W cores and M memory, you should
set this to True if N*W > M.
model_copies: The exact number of models that you would like loaded
onto your machine. This can be useful if you exactly know your CPU or
GPU capacity and want to maximize resource utilization.
min_batch_size: the minimum batch size to use when batching inputs.
max_batch_size: the maximum batch size to use when batching inputs.
max_batch_duration_secs: the maximum amount of time to buffer a batch
before emitting; used in streaming contexts.
kwargs: 'env_vars' can be used to set environment variables
before loading the model.
"""
self._model_uri = model_uri
self._session_options = session_options
self._providers = providers
self._provider_options = provider_options
self._model_inference_fn = inference_fn
self._env_vars = kwargs.get('env_vars', {})
self._share_across_processes = large_model or (model_copies is not None)
self._model_copies = model_copies or 1
self._batching_kwargs = {}
if min_batch_size is not None:
self._batching_kwargs["min_batch_size"] = min_batch_size
if max_batch_size is not None:
self._batching_kwargs["max_batch_size"] = max_batch_size
if max_batch_duration_secs is not None:
self._batching_kwargs["max_batch_duration_secs"] = max_batch_duration_secs
[docs]
def load_model(self) -> ort.InferenceSession:
"""Loads and initializes an onnx inference session for processing."""
# when path is remote, we should first load into memory then deserialize
f = FileSystems.open(self._model_uri, "rb")
model_proto = onnx.load(f)
model_proto_bytes = model_proto
if not isinstance(model_proto, bytes):
if (hasattr(model_proto, "SerializeToString") and
callable(model_proto.SerializeToString)):
model_proto_bytes = model_proto.SerializeToString()
else:
raise TypeError(
"No SerializeToString method is detected on loaded model. " +
f"Type of model: {type(model_proto)}")
ort_session = ort.InferenceSession(
model_proto_bytes,
sess_options=self._session_options,
providers=self._providers,
provider_options=self._provider_options)
return ort_session
[docs]
def run_inference(
self,
batch: Sequence[numpy.ndarray],
inference_session: ort.InferenceSession,
inference_args: Optional[Dict[str, Any]] = None
) -> Iterable[PredictionResult]:
"""Runs inferences on a batch of numpy arrays.
Args:
batch: A sequence of examples as numpy arrays. They should
be single examples.
inference_session: An onnx inference session.
Must be runnable with input x where x is sequence of numpy array
inference_args: Any additional arguments for an inference.
Returns:
An Iterable of type PredictionResult.
"""
predictions = self._model_inference_fn(
inference_session, batch, inference_args)
return utils._convert_to_result(batch, predictions)
[docs]
def get_num_bytes(self, batch: Sequence[numpy.ndarray]) -> int:
"""
Returns:
The number of bytes of data for a batch.
"""
return sum((np_array.itemsize for np_array in batch))
[docs]
def get_metrics_namespace(self) -> str:
"""
Returns:
A namespace for metrics collected by the RunInference transform.
"""
return 'BeamML_Onnx'
[docs]
def share_model_across_processes(self) -> bool:
return self._share_across_processes
[docs]
def model_copies(self) -> int:
return self._model_copies
[docs]
def batch_elements_kwargs(self) -> Mapping[str, Any]:
return self._batching_kwargs