Training with onnxruntime¶
onnxruntime offers the possibility to compute a gradient. Then with some extra lines, it is possible to implement a gradient descent.
Training capabilities are part of the same repository but released under a different package onnxruntime-training. It is not an extension, it replaces onnxruntime and has the same import name. It can be built with different compilation settings or downloaded from pypi. There are two versions to keep a low size for the version which only does inference.
Two training API are available. The first one assumes the loss is part of the graph to train. It can be trained as a whole. The second API assumes the graph is only a piece or a layer in a model trained by another framework or at least a logic which updates the weights. This mechanism is convenient when a model is trained with pytorch.
First API: TrainingSession¶
TrainingSession is used by class
OrtGradientOptimizer in example
Train a linear regression with onnxruntime-training to show how it could be wrappped
to train a model. Example Train a linear regression with onnxruntime-training in details digs
into the details of the implementation. It goes through the following
express the loss with ONNX operators
select all initializers to train
fill an instance of TrainingParameters
create an instance of TrainingSession
That’s what method
does. It does not implement a training algorithm, only an iteration
- forward + backward - with the expected label, the learning rate and the features
as inputs. The class updates its weights. When the training ends, the user
must collect the updated weights and create a new ONNX file with the
onnxruntime-training does not implement loss functions.
That must be done independently. That’s what function
It implements a couple of usual losses in ONNX.
guesses all the coefficients of an ONNX graph if the user does not specify any.
Another common use not implemented in onnxruntime-training.
GPU is no different. It changes the syntax because data has to be moved on this device first. Example Train a linear regression with onnxruntime-training on GPU in details adapts previous example to this configuration. Finally, a last example compares this approach against scikit-learn in the same conditions.
Second API: TrainingAgent¶
TrainingAgent is used by class
to train the same model. The training is split into the
forward step, the backward step (gradient computation), the weight
updating step. TrainingAgent implement forward and backward.
Everything else must be explicitely implemented outside of this class
or be taken care of by an existing framework such as this one
or pytorch. First, forward, backward with TrainingAgent.
To build it, the following steps are needed:
fill an instance of OrtModuleGraphBuilderConfiguration
create the training graph with OrtModuleGraphBuilder
retrieve the training graph
create an instance of InferenceSession with this graph
create an instance of TrainingAgent
That’s what method
does. Forward and backward steps must be called separately.
It is not trivial to guess how to call them (a forward step can be
called to predict or to train if followed by a backward step).
implements those two steps with the proper API. Next lines gives an
idea on how it can be done. First the forward step.
def forward(self, inputs, training=False): forward_inputs = cls.input_to_ort( inputs, cls._devices, cls._debug) if training: forward_outputs = OrtValueVector() state = PartialGraphExecutionState() self.states_.append(state) cls._training_agent.run_forward( forward_inputs, forward_outputs, state, cls._cache) return forward_outputs else: iobinding = SessionIOBinding(cls._sess_eval._sess) for name, inp in zip( cls._grad_input_names, forward_inputs): iobinding.bind_ortvalue_input(name, inp) for name, dev in zip( cls._output_names, cls._fw_no_grad_output_device_info): iobinding.bind_output(name, dev) cls._sess_eval._sess.run_with_iobinding( iobinding, cls._run_options) return iobinding.get_outputs()
Then the backward step.
def backward(self, grad_outputs): cls = self.__class__ inputs = self.saved_tensors state = self.states_.pop() backward_inputs = cls.input_to_ort( grad_outputs, cls._bw_outputs_device_info, cls._debug) backward_outputs = OrtValueVector() cls._training_agent.run_backward( backward_inputs, backward_outputs, state) return backward_outputs
The API implemented by class TrainingAgent does not use named inputs, only a list of inputs, the features followed by the current weights. Initializers must be be given names in alphabetical order to avoid any confusion with that API.
Train a linear regression with forward backward changes the previous example
to use class
explains the details of the implementation. This example is the best
place to continue if using the raw API of onnxruntime-training
is the goal. Then the same
example is changed to use GPU: Forward backward on a neural network on GPU.
And finally a benchmark to compare this approach with
scikit-learn: Benchmark, comparison scikit-learn - forward-backward.
Beside forward and backard, the training needs three elements
to be complete.
a loss: a square loss for example
SquareLearningLossbut it could be
a way to update the weight: a simple learning rate for a stockastic gradient descent
LearningRateSGDor something more complex such as
a regularization applied to the weight, it could be seen as an extension of the loss but this design seemed more simple as it does not mix the gradient applied to the output and the gradient due to the regularization, the most simple regularization is no regularization with
NoLearningPenalty, but it could be L1 or L2 penalty as well with
These parts can easily be replaced by the same pieces
implemented in pytorch. That’s what wrapper
class ORTModule offers except it starts from a pytorch
model then converted into ONNX. That’s what shows example
Benchmark, comparison torch - forward-backward. Class
directly starts with the ONNX graph and adds the pieces not implemented