ONNX Runtime: TreeEnsemble Post_transform Issue

by Alex Johnson 48 views

Introduction

In the realm of machine learning and specifically within the ONNX (Open Neural Network Exchange) ecosystem, the TreeEnsemble operator plays a crucial role. This operator is designed to represent ensemble-based machine learning models, such as gradient-boosted decision trees, which are widely used for their predictive power and versatility. One of the key aspects of the TreeEnsemble operator is the post_transform attribute, which dictates how the raw output of the ensemble is transformed into a final prediction. However, a discrepancy in the implementation of post_transform options within ONNX Runtime has come to light, potentially affecting the accuracy and reliability of models utilizing this operator.

Understanding the TreeEnsemble Operator and post_transform

The TreeEnsemble operator in ONNX allows for various post-transformation methods to be applied to the ensemble's output. These transformations are essential for adapting the raw scores produced by the trees into meaningful predictions, such as probabilities or class labels. According to the ONNX specifications, the following post_transform options should be available:

  • NONE (0): No transformation is applied.
  • SOFTMAX (1): Applies the softmax function, converting outputs into probabilities.
  • LOGISTIC (2): Applies the logistic sigmoid function, suitable for binary classification.
  • SOFTMAX_ZERO (3): Similar to softmax but with special handling for zero values.
  • PROBIT (4): Applies the probit function, another method for converting outputs into probabilities.

The post_transform attribute is critical because it ensures that the output of the TreeEnsemble operator is in a format that is appropriate for the specific machine learning task. For instance, in classification problems, SOFTMAX or LOGISTIC transformations are commonly used to produce probability scores for each class. However, as discovered in a recent issue, ONNX Runtime's implementation does not fully support all of these options.

The Discrepancy in ONNX Runtime

The issue at hand revolves around the fact that ONNX Runtime, a popular inference engine for ONNX models, does not fully implement all the post_transform options for the TreeEnsemble operator. Specifically, the implementation in tree_ensemble_aggregator.h only provides explicit support for PROBIT and NONE options. This means that if a model uses other post_transform options like SOFTMAX or LOGISTIC, ONNX Runtime silently treats them as NONE, leading to incorrect predictions.

This discrepancy can have significant implications for users who rely on ONNX Runtime to deploy their machine learning models. If a model is designed to use a specific post_transform function, such as LOGISTIC for binary classification, the lack of proper implementation in ONNX Runtime can result in the model producing raw, untransformed scores instead of probabilities. This not only affects the interpretability of the model's output but also its accuracy.

Reproducing the Issue

To demonstrate the issue, a minimal example of a TreeEnsemble model with post_transform set to logistic has been provided. This example can be used to reproduce the incorrect behavior in ONNX Runtime. The steps to reproduce the issue are as follows:

  1. Download the tree_ensemble.onnx file, which contains the ONNX model with post_transform set to LOGISTIC.
  2. Use the ONNX Runtime Python API to load the model and run inference.
  3. Observe the output of the model.

The provided Python code snippet illustrates how to reproduce the issue:

import onnxruntime as ort

session = ort.InferenceSession("tree_ensemble.onnx")
print(session.run(None, {"X": [[1, 0, 1, 0]]}))

When this code is executed, the output is [array([[10.3366785]], dtype=float32)]. This result is not a probability between 0 and 1, as would be expected from a logistic transformation. Instead, it is a raw score, indicating that the LOGISTIC post_transform was not correctly applied.

Impact and Urgency

The impact of this issue can be substantial, especially for applications that require accurate probability estimates. For example, in medical diagnosis or fraud detection, where decisions are based on the likelihood of an event, incorrect probability scores can lead to serious consequences. Therefore, addressing this issue is of high importance to ensure the reliability of ONNX Runtime for deploying machine learning models.

The urgency of the issue depends on the specific use case and the reliance on the affected post_transform options. For users who heavily depend on SOFTMAX or LOGISTIC transformations, this issue requires immediate attention. While a workaround may involve manually applying the transformations after the inference, this adds complexity and potential for errors. A proper fix within ONNX Runtime is necessary for a seamless and accurate deployment process.

Platforms and Versions Affected

The issue has been reported on a Mac platform, specifically version 26.1. However, it is likely that the issue affects other platforms as well, as the implementation discrepancy exists within the core ONNX Runtime code. The ONNX Runtime version in which the issue was identified is 1.23.2, but it is possible that earlier versions are also affected.

The issue was observed using the Python API of ONNX Runtime, but it is reasonable to assume that other APIs (e.g., C++, Java) are similarly affected. The architecture reported is ARM64, but the issue is not architecture-specific and should be present on other architectures as well.

The execution provider used was the default CPU provider, which means that the issue is not specific to any particular hardware acceleration backend. It is a general implementation issue within ONNX Runtime's TreeEnsemble operator.

Potential Causes and Solutions

The root cause of the issue is the incomplete implementation of post_transform options in the tree_ensemble_aggregator.h file within the ONNX Runtime codebase. This could be due to several reasons, such as oversight, prioritization of certain options over others, or incomplete testing.

To resolve the issue, the following steps need to be taken:

  1. Identify the Missing Implementations: Clearly identify which post_transform options are not fully implemented in the code.
  2. Implement the Missing Transformations: Add the necessary code to implement the SOFTMAX, LOGISTIC, and SOFTMAX_ZERO transformations within the tree_ensemble_aggregator.h file.
  3. Test the Implementation: Thoroughly test the implementation with various models and input data to ensure that the transformations are applied correctly.
  4. Release a Fix: Release a new version of ONNX Runtime with the fix included.

In the meantime, users who are affected by this issue can consider the following workarounds:

  • Manual Post-processing: Apply the required post_transform manually after obtaining the raw output from ONNX Runtime. For example, if the model uses LOGISTIC, the user can apply the sigmoid function to the output using libraries like NumPy or SciPy.
  • Model Modification: If possible, modify the model to use the NONE post_transform option and include the transformation logic within the model itself. This would ensure that the transformation is applied correctly by ONNX Runtime.

Conclusion

The discrepancy in the implementation of post_transform options for the TreeEnsemble operator in ONNX Runtime is a significant issue that can affect the accuracy and reliability of machine learning models. The lack of support for options like SOFTMAX and LOGISTIC can lead to incorrect predictions, especially in classification tasks where probability estimates are crucial. Addressing this issue requires a comprehensive fix within ONNX Runtime, including implementing the missing transformations and thorough testing.

In the interim, users can employ workarounds such as manual post-processing or model modification. However, a proper resolution within ONNX Runtime is essential for a seamless and accurate deployment experience. By addressing this issue, ONNX Runtime can further solidify its position as a robust and reliable inference engine for ONNX models.

For more information on ONNX and ONNX Runtime, please visit the official ONNX website.