TFLite model analyser & memory optimizer.
The tool is able to produce a short analysis of a Tensorflow Lite (v3) models, which includes:
- Information about intermediate tensors that need to be present in RAM (excludes weights, as they can be read directly from the model file.)
- Operator evaluation schedule (as given by the operator order in the model file), along with tensors that need to present at every step of execution and the amount of memory occupied by them.
- Plot memory usage during evaluation, detailing sizes of input and output tensors for each operator, as well as other tensors that are present in memory (see example image at the end of 'Example output' section).
The analysis can be printed to the standard output or to a set of CSV files using the --csv option.
Additionally, the tool can:
- Modify the model to minimise peak memory usage by reordering operators in the model file (
--optimizeoption). - Simulate code-book quantization by clustering the weights into
ncentroids, and replacing each weight with the closest centroid value. Note that this is done for each weight matrix separately and biases are left untouched.
The tool also offers an API through the TFLiteModel class --- see def main() in tflite_tools.py for example
usage.
The tool requires Python 3.6+ and a few dependencies, as described in Pipfile.
To create a new virtual environment with correct dependencies, run the following the root of the repository:
pipenv install
(requires pipenv, which you can install through your system's package manager or via pip: pip install pipenv)
% pipenv shell
% python tflite_tools.py --help
usage: tflite_tools.py [-h] [-i INPUT_PATH] [-o OUTPUT_PATH]
[--clusters CLUSTERS] [--optimize]
TFLite model analyser & memory optimizer
optional arguments:
-h, --help show this help message and exit
-i INPUT_PATH input model file (.tflite)
-o OUTPUT_PATH output model file (.tflite)
--clusters CLUSTERS cluster weights into n-many values (simulate code-book
quantization)
--optimize optimize peak working set size
--csv CSV_OUTPUT_FOLDER
output model analysis in CSV format into the specified
folder
--plot PLOT_FILE plot memory usage for each operator during the
execution
% python tflite_tools.py -i quantized_model.tflite -o quantized_model_optimized.tflite
Tensor information (weights excluded):
+----+-----------------------+-----------------+-----------------+
| Id | Tensor | Shape | Size in RAM (B) |
+----+-----------------------+-----------------+-----------------+
| 1 | Conv1/Relu | (1, 30, 30, 16) | 14,400 |
| 2 | Conv1_input | (1, 32, 32, 3) | 3,072 |
| 4 | Conv2/Relu | (1, 28, 28, 16) | 12,544 |
| 5 | FC2/BiasAdd | (1, 10) | 10 |
| 7 | FC2/Softmax | (1, 10) | 10 |
| 8 | activation/Relu | (1, 128) | 128 |
| 9 | max_pooling2d/MaxPool | (1, 14, 14, 16) | 3,136 |
+----+-----------------------+-----------------+-----------------+
Operator execution schedule:
+------------------------+-------------------------+----------------+
| Operator (output name) | Tensors in memory (IDs) | Memory use (B) |
+------------------------+-------------------------+----------------+
| Conv1/Relu | [1, 2] | 17,472 |
| Conv2/Relu | [1, 4] | 26,944 |
| max_pooling2d/MaxPool | [4, 9] | 15,680 |
| activation/Relu | [8, 9] | 3,264 |
| FC2/BiasAdd | [8, 5] | 138 |
| FC2/Softmax | [5, 7] | 20 |
+------------------------+-------------------------+----------------+
Current peak memory usage: 26,944 B
% python tflite_tools.py -i example_model.tflite --plot example_working_set.png
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