I have added support for QAT to my model. When I wanted to compare the performance between PTQ and QAT on my model. I saw that PTQ is doing better than QAT and also it is doing better than TensorFlow model itself.
So here, I have 2 main questions:
- How could PTQ be better than the model itself?
- Is it possible that PTQ can be better than QAT?
The difference I am seeing between the model and its PTQ is about 3% (for PTQ) and the difference I am seeing between PTQ and QAT is about 6% (for PTQ).
Finally the difference I am seeing between the model and QAT is about ~3/4% (for the model)
I would like to get some thoughts about that and how can be possible? or if there is something wrong that could lead to that?
[Google DeepMind Assisted]
Your observations about Post-Training Quantization (PTQ) performing better than Quantization-Aware Training (QAT) and even the original TensorFlow model are quite intriguing and somewhat unusual, as conventional wisdom often suggests that QAT should outperform PTQ in most scenarios due to its more granular approach to quantization. Let’s explore possible reasons for your findings:
PTQ Outperforming the Original Model
Regularization Effect: Quantization can sometimes act as a form of regularization, reducing overfitting on the training data. This is particularly true in models that are complex and have a tendency to overfit. PTQ could be simplifying the model in a way that it generalizes better to unseen data.
Model Sensitivity: Some models might be less sensitive to the reduced precision of weights and activations, meaning that the quantization process does not significantly degrade performance. In some cases, it might even inadvertently correct small errors or biases in the model, leading to improved performance.
Data Distribution: If your validation/testing data distribution slightly differs from the training data, the quantized model might be aligning better with the validation/testing data characteristics due to its constrained representation space.
PTQ Outperforming QAT
Training Procedure: QAT modifies the training process to simulate quantization effects during training. If not carefully managed, this can sometimes introduce noise or convergence issues, leading to suboptimal training outcomes. The success of QAT heavily depends on the hyperparameters and the way quantization is introduced during training.
Model Architecture Sensitivity: Some architectures might be more amenable to PTQ than QAT. For instance, models with certain types of layers or activation functions might respond better to PTQ’s straightforward quantization approach, as opposed to the simulated quantization in QAT.
Calibration Data: The effectiveness of PTQ can depend significantly on the choice of calibration data. If the calibration dataset used for PTQ is highly representative of the true data distribution, PTQ can yield surprisingly good results.
Investigating the Anomalies
To understand why PTQ is outperforming both the original TensorFlow model and the QAT version, consider the following steps:
Review Quantization Process: Double-check the quantization process for both PTQ and QAT. Ensure that QAT is being applied correctly throughout the entire training process and that the model is fully trained with quantization-aware operations.
Evaluate Calibration Data: For PTQ, examine the calibration dataset used for quantization. Ensure it is representative of the model’s deployment environment. An unrepresentative calibration dataset can lead to poor quantization results.
Model Complexity and Overfitting: Assess the complexity of your model. If your model is highly complex and possibly overfitting, quantization might be providing a beneficial regularization effect. Consider experimenting with other regularization techniques to see if they yield similar improvements.
Hyperparameter Tuning for QAT: QAT requires careful tuning of hyperparameters, including learning rate, quantization intervals, and possibly others, depending on the specific implementation. It’s possible that the QAT model has not been optimally tuned.
Hardware Considerations: Consider the hardware you’re using for evaluation. Some hardware might have optimizations for quantized models that could affect performance measurements.
Statistical Significance: Finally, ensure that the performance differences you’re observing are statistically significant and consistent across multiple runs and datasets.
It’s worth noting that while these scenarios can provide some explanation, the specific reasons for your observations could be a combination of factors unique to your model, dataset, and quantization implementation. Experimentation and a detailed analysis of your model’s behavior during and after quantization will be key to understanding these results.
Thanks for that answer! So I am training my model just for 1 epoch, then PTQ and finally I apply QAT on the trained model (1 epoch too). I believe 1 epoch is not enough at all and this is the principal reason that could lead to this observations, right?