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191 lines
7 KiB
ReStructuredText
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===========
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API Summary
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===========
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Summary of public functions and classes exposed
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in *ONNX Runtime*.
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.. contents::
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:local:
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OrtValue
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=========
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*ONNX Runtime* works with native Python data structures which are mapped into ONNX data formats :
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Numpy arrays (tensors), dictionaries (maps), and a list of Numpy arrays (sequences).
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The data backing these are on CPU.
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*ONNX Runtime* supports a custom data structure that supports all ONNX data formats that allows users
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to place the data backing these on a device, for example, on a CUDA supported device. This allows for
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interesting *IOBinding* scenarios (discussed below). In addition, *ONNX Runtime* supports directly
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working with *OrtValue* (s) while inferencing a model if provided as part of the input feed.
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Below is an example showing creation of an *OrtValue* from a Numpy array while placing its backing memory
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on a CUDA device:
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.. code-block:: python
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#X is numpy array on cpu, create an OrtValue and place it on cuda device id = 0
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ortvalue = onnxruntime.OrtValue.ortvalue_from_numpy(X, 'cuda', 0)
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ortvalue.device_name() # 'cuda'
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ortvalue.shape() # shape of the numpy array X
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ortvalue.data_type() # 'tensor(float)'
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ortvalue.is_tensor() # 'True'
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np.array_equal(ortvalue.numpy(), X) # 'True'
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#ortvalue can be provided as part of the input feed to a model
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ses = onnxruntime.InferenceSession('model.onnx')
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res = sess.run(["Y"], {"X": ortvalue})
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IOBinding
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=========
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By default, *ONNX Runtime* always places input(s) and output(s) on CPU, which
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is not optimal if the input or output is consumed and produced on a device
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other than CPU because it introduces data copy between CPU and the device.
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*ONNX Runtime* provides a feature, *IO Binding*, which addresses this issue by
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enabling users to specify which device to place input(s) and output(s) on.
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Here are scenarios to use this feature.
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(In the following code snippets, *model.onnx* is the model to execute,
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*X* is the input data to feed, and *Y* is the output data.)
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Scenario 1:
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A graph is executed on a device other than CPU, for instance CUDA. Users can
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use IOBinding to put input on CUDA as the follows.
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.. code-block:: python
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#X is numpy array on cpu
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session = onnxruntime.InferenceSession('model.onnx')
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io_binding = session.io_binding()
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# OnnxRuntime will copy the data over to the CUDA device if 'input' is consumed by nodes on the CUDA device
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io_binding.bind_cpu_input('input', X)
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io_binding.bind_output('output')
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session.run_with_iobinding(io_binding)
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Y = io_binding.copy_outputs_to_cpu()[0]
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Scenario 2:
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The input data is on a device, users directly use the input. The output data is on CPU.
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.. code-block:: python
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#X is numpy array on cpu
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X_ortvalue = onnxruntime.OrtValue.ortvalue_from_numpy(X, 'cuda', 0)
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session = onnxruntime.InferenceSession('model.onnx')
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io_binding = session.io_binding()
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io_binding.bind_input(name='input', device_type=X_ortvalue.device_name(), device_id=0, element_type=np.float32, shape=X_ortvalue.shape(), buffer_ptr=X_ortvalue.data_ptr())
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io_binding.bind_output('output')
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session.run_with_iobinding(io_binding)
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Y = io_binding.copy_outputs_to_cpu()[0]
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Scenario 3:
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The input data and output data are both on a device, users directly use the input and also place output on the device.
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.. code-block:: python
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#X is numpy array on cpu
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X_ortvalue = onnxruntime.OrtValue.ortvalue_from_numpy(X, 'cuda', 0)
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Y_ortvalue = onnxruntime.OrtValue.ortvalue_from_shape_and_type([3, 2], np.float32, 'cuda', 0) # Change the shape to the actual shape of the output being bound
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session = onnxruntime.InferenceSession('model.onnx')
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io_binding = session.io_binding()
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io_binding.bind_input(name='input', device_type=X_ortvalue.device_name(), device_id=0, element_type=np.float32, shape=X_ortvalue.shape(), buffer_ptr=X_ortvalue.data_ptr())
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io_binding.bind_output(name='output', device_type=Y_ortvalue.device_name(), device_id=0, element_type=np.float32, shape=Y_ortvalue.shape(), buffer_ptr=Y_ortvalue.data_ptr())
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session.run_with_iobinding(io_binding)
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Scenario 4:
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Users can request *ONNX Runtime* to allocate an output on a device. This is particularly useful for dynamic shaped outputs.
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Users can use the *get_outputs()* API to get access to the *OrtValue* (s) corresponding to the allocated output(s).
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Users can thus consume the *ONNX Runtime* allocated memory for the output as an *OrtValue*.
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.. code-block:: python
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#X is numpy array on cpu
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X_ortvalue = onnxruntime.OrtValue.ortvalue_from_numpy(X, 'cuda', 0)
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session = onnxruntime.InferenceSession('model.onnx')
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io_binding = session.io_binding()
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io_binding.bind_input(name='input', device_type=X_ortvalue.device_name(), device_id=0, element_type=np.float32, shape=X_ortvalue.shape(), buffer_ptr=X_ortvalue.data_ptr())
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#Request ONNX Runtime to bind and allocate memory on CUDA for 'output'
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io_binding.bind_output('output', 'cuda')
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session.run_with_iobinding(io_binding)
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# The following call returns an OrtValue which has data allocated by ONNX Runtime on CUDA
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ort_output = io_binding.get_outputs()[0]
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Scenario 5:
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Users can bind *OrtValue* (s) directly.
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.. code-block:: python
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#X is numpy array on cpu
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#X is numpy array on cpu
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X_ortvalue = onnxruntime.OrtValue.ortvalue_from_numpy(X, 'cuda', 0)
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Y_ortvalue = onnxruntime.OrtValue.ortvalue_from_shape_and_type([3, 2], np.float32, 'cuda', 0) # Change the shape to the actual shape of the output being bound
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session = onnxruntime.InferenceSession('model.onnx')
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io_binding = session.io_binding()
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io_binding.bind_ortvalue_input('input', X_ortvalue)
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io_binding.bind_ortvalue_output('output', Y_ortvalue)
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session.run_with_iobinding(io_binding)
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Device
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======
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The package is compiled for a specific device, GPU or CPU.
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The CPU implementation includes optimizations
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such as MKL (Math Kernel Libary). The following function
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indicates the chosen option:
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.. autofunction:: onnxruntime.get_device
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Examples and datasets
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=====================
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The package contains a few models stored in ONNX format
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used in the documentation. These don't need to be downloaded
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as they are installed with the package.
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.. autofunction:: onnxruntime.datasets.get_example
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Load and run a model
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====================
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*ONNX Runtime* reads a model saved in ONNX format.
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The main class *InferenceSession* wraps these functionalities
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in a single place.
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.. autoclass:: onnxruntime.ModelMetadata
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:members:
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.. autoclass:: onnxruntime.InferenceSession
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:members:
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.. autoclass:: onnxruntime.NodeArg
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:members:
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.. autoclass:: onnxruntime.RunOptions
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:members:
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.. autoclass:: onnxruntime.SessionOptions
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:members:
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Backend
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=======
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In addition to the regular API which is optimized for performance and usability,
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*ONNX Runtime* also implements the
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`ONNX backend API <https://github.com/onnx/onnx/blob/master/docs/ImplementingAnOnnxBackend.md>`_
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for verification of *ONNX* specification conformance.
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The following functions are supported:
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.. autofunction:: onnxruntime.backend.is_compatible
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.. autofunction:: onnxruntime.backend.prepare
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.. autofunction:: onnxruntime.backend.run
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.. autofunction:: onnxruntime.backend.supports_device
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