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TensorFlow Lite delegate
========================
Mesa contains a TensorFlow Lite delegate that can make use of NPUs to accelerate ML inference. It is implemented in the form of a *external delegate*, a shared library that the TensorFlow Lite runtime can load at startup. See https://www.tensorflow.org/api_docs/python/tf/lite/experimental/load_delegate.
.. list-table:: Supported acceleration hardware
:header-rows: 1
* - Gallium driver
- NPU supported
- Hardware tested
* - Etnaviv
- ``VeriSilicon VIPNano-QI.7120``
- ``Amlogic A311D on Libre Computer AML-A311D-CC Alta and Khadas VIM3``
* - Etnaviv
- ``VeriSilicon VIPNano-SI+.8002``
- ``NXP iMX8M Plus on Toradex Verdin SoM``
.. list-table:: Tested models
:header-rows: 1
* - Model name
- Data type
- Link (may be outdated)
- Status
- Inference speed on AML-A311D-CC Alta
- Inference speed on Verdin iMX8M Plus
* - MobileNet V1
- UINT8
- http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz
- Fully supported
- ~6.6 ms
- ~7.9 ms
* - MobileNet V2
- UINT8
- https://storage.googleapis.com/mobilenet_v2/checkpoints/quantized_v2_224_100.tgz
- Fully supported
- ~6.9 ms
- ~8.0 ms
* - SSDLite MobileDet
- UINT8
- https://raw.githubusercontent.com/google-coral/test_data/master/ssdlite_mobiledet_coco_qat_postprocess.tflite
- Fully supported
- ~24.8 ms
- ~24.4 ms
Build
-----
Build Mesa as usual, with the -Dteflon=true argument.
Example instructions:
.. code-block:: console
# Install build dependencies
~ # apt-get -y build-dep mesa
~ # apt-get -y install git cmake
# Download sources
~ $ git clone https://gitlab.freedesktop.org/mesa/mesa.git
# Build Mesa
~ $ cd mesa
mesa $ meson setup build -Dgallium-drivers=etnaviv -Dvulkan-drivers= -Dteflon=true
mesa $ meson compile -C build
Install runtime dependencies
----------------------------
Your board should have booted into a mainline 6.7 (6.8 for the i.MX8MP) or greater kernel.
.. code-block:: console
# Install Python 3.10 and dependencies (as root)
~ # echo deb-src http://deb.debian.org/debian testing main >> /etc/apt/sources.list
~ # echo deb http://deb.debian.org/debian unstable main >> /etc/apt/sources.list
~ # echo 'APT::Default-Release "testing";' >> /etc/apt/apt.conf
~ # apt-get update
~ # apt-get -y install python3.10 python3-pytest python3-exceptiongroup
# Install TensorFlow Lite Python package (as non-root)
~ $ python3.10 -m pip install --break-system-packages tflite-runtime==2.13.0
# For the classification.py script mentioned below, you will need PIL
~ $ python3.10 -m pip install --break-system-packages pillow
Do some inference with MobileNetV1
----------------------------------
Run the above for a quick way of checking that the setup is correct and the NPU is accelerating the inference. It assumes you have followed the steps above so Python 3.10 and dependencies have been installed, and assumes that Mesa was built to the ``./build`` directory.
You can use any image that prominently features one of the objects in the ``src/gallium/frontends/teflon/tests/labels_mobilenet_quant_v1_224.txt`` file.
This example script has been based from the code in https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/examples/python.
.. code-block:: console
~ $ cd mesa/
mesa $ TEFLON_DEBUG=verbose ETNA_MESA_DEBUG=ml_dbgs python3.10 src/gallium/frontends/teflon/tests/classification.py \
-i ~/tensorflow/assets/grace_hopper.bmp \
-m src/gallium/targets/teflon/tests/mobilenet_v1_1.0_224_quant.tflite \
-l src/gallium/frontends/teflon/tests/labels_mobilenet_quant_v1_224.txt \
-e build/src/gallium/targets/teflon/libteflon.so
Loading external delegate from build/src/gallium/targets/teflon/libteflon.so with args: {}
Teflon delegate: loaded etnaviv driver
teflon: compiling graph: 89 tensors 28 operations
idx scale zp has_data size
=======================================
0 0.023528 0 no 1x1x1x1024
1 0.166099 42 no 1x1x1x1001
2 0.000117 0 yes 1001x0x0x0
3 0.004987 4a yes 1001x1x1x1024
4 0.166099 42 no 1x1001x0x0
5 0.166099 42 yes 2x0x0x0
6 0.000171 0 yes 32x0x0x0
7 0.023528 0 no 1x112x112x32
8 0.021827 97 yes 32x3x3x3
9 0.023528 0 no 1x14x14x512
...
idx type in out operation type-specific
================================================================================================
0 CONV 88 7 w: 8 b: 6 stride: 2 pad: SAME
1 DWCONV 7 33 w: 35 b: 34 stride: 1 pad: SAME
2 CONV 33 37 w: 38 b: 36 stride: 1 pad: SAME
3 DWCONV 37 39 w: 41 b: 40 stride: 2 pad: SAME
4 CONV 39 43 w: 44 b: 42 stride: 1 pad: SAME
5 DWCONV 43 45 w: 47 b: 46 stride: 1 pad: SAME
6 CONV 45 49 w: 50 b: 48 stride: 1 pad: SAME
7 DWCONV 49 51 w: 53 b: 52 stride: 2 pad: SAME
8 CONV 51 55 w: 56 b: 54 stride: 1 pad: SAME
9 DWCONV 55 57 w: 59 b: 58 stride: 1 pad: SAME
10 CONV 57 61 w: 62 b: 60 stride: 1 pad: SAME
11 DWCONV 61 63 w: 65 b: 64 stride: 2 pad: SAME
12 CONV 63 67 w: 68 b: 66 stride: 1 pad: SAME
13 DWCONV 67 69 w: 71 b: 70 stride: 1 pad: SAME
14 CONV 69 73 w: 74 b: 72 stride: 1 pad: SAME
15 DWCONV 73 75 w: 77 b: 76 stride: 1 pad: SAME
16 CONV 75 79 w: 80 b: 78 stride: 1 pad: SAME
17 DWCONV 79 81 w: 83 b: 82 stride: 1 pad: SAME
18 CONV 81 85 w: 86 b: 84 stride: 1 pad: SAME
19 DWCONV 85 9 w: 11 b: 10 stride: 1 pad: SAME
20 CONV 9 13 w: 14 b: 12 stride: 1 pad: SAME
21 DWCONV 13 15 w: 17 b: 16 stride: 1 pad: SAME
22 CONV 15 19 w: 20 b: 18 stride: 1 pad: SAME
23 DWCONV 19 21 w: 23 b: 22 stride: 2 pad: SAME
24 CONV 21 25 w: 26 b: 24 stride: 1 pad: SAME
25 DWCONV 25 27 w: 29 b: 28 stride: 1 pad: SAME
26 CONV 27 31 w: 32 b: 30 stride: 1 pad: SAME
27 POOL 31 0 filter: 0x0 stride: 0 pad: VALID
teflon: compiled graph, took 10307 ms
teflon: invoked graph, took 21 ms
teflon: invoked graph, took 17 ms
teflon: invoked graph, took 17 ms
teflon: invoked graph, took 17 ms
teflon: invoked graph, took 16 ms
0.866667: military uniform
0.031373: Windsor tie
0.015686: mortarboard
0.007843: bow tie
0.007843: academic
|
