Hi All,
We are trying to develop models and deploy on platforms. During this process we are trying to understand whether the same tflite model when run in Python and C++ should give bit exact output.
For example, what i see in examples/helloworld also is not bit exact between Python and C++
Is this expected. We have been hunting this for a while and any help is appreciated.
Thanks.
Regards,
Poornima
Hello Poornima,
Your observation regarding the difference in outputs between Python and C++ when running the same TensorFlow Lite (TFLite) model is not uncommon. While one would ideally expect bit-exact results for the same model across different languages or platforms, in practice, slight discrepancies can occur due to a few reasons:
- Numerical Precision and Floating-Point Arithmetic: Both Python and C++ adhere to IEEE floating-point standards, but the way each language or its libraries implement certain operations can lead to minor differences. These differences might be amplified in deep learning models due to the cumulative effect of many operations.
- TensorFlow Lite Kernels: The TFLite runtime uses optimized kernels for various operations. These optimizations can vary between implementations (e.g., the use of different libraries or platform-specific optimizations in C++ vs. Python bindings), leading to slight variations in results.
- Environment and Hardware: The underlying hardware and its configuration (like CPU architecture, instruction sets used, etc.) can influence the execution of operations, especially when optimizations like vectorization are applied. If the Python and C++ environments are running on slightly different hardware or configurations, this could introduce discrepancies.
- Threading and Parallelism: Differences in how multithreading is managed between Python and C++ implementations can lead to non-deterministic behaviors, especially in operations that can be parallelized. The order of execution and the way computations are split across threads can affect the final result due to floating-point rounding errors.
- Library Versions and Dependencies: Ensure that the TensorFlow Lite versions used in both Python and C++ environments are the same. Differences in library versions or in the dependencies that these libraries use can lead to variations in outputs.
To minimize these discrepancies, you can:
- Use Quantized Models: Quantization reduces the model to fixed-point arithmetic, which can help achieve more consistent results across different platforms.
- Control Parallelism: Limiting the number of threads or enforcing single-threaded execution might reduce non-determinism introduced by parallel computations.
- Environment Consistency: Ensure that the software environment (libraries, TensorFlow Lite versions, etc.) is as consistent as possible between Python and C++ implementations.
- Review Optimizations: Be aware of any platform-specific optimizations and try to align them as closely as possible between your Python and C++ environments.
It’s also valuable to engage with the TensorFlow community or check the official TensorFlow GitHub issues page to see if there are known issues or workarounds for ensuring bit-exact reproducibility across different platforms.
Understanding and accepting a certain level of variance might be necessary, but if the discrepancies are significant, it’s worth investigating further to ensure there aren’t deeper issues at play, such as incorrect model conversion or implementation bugs.
Thanks @Tim_Wolfe. Appreciate your detailed response. Couple of observations.
1.) The difference is not slight between platforms and what we observed was substantial difference. For example even the hello world didnot match between python and C. Incidentally when we mask the fully connected multiplier to 64bit the results were bit exact
2.) Even with the above change microspeech example was still different between python and C
3.) Is there a way to debug python source code. Are there any pointers available here.
Appreciate if you can help me on the same.
Thanks.
Regards,
poornima