Age | Commit message (Collapse) | Author |
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Allow the user to specify an activation function for conv2d rewrites
Enable automatic detection of most common activation function in rewrite in the case that the user does not specify one
Resolves: MLIA-1163
Signed-off-by: Nathan Bailey <nathan.bailey@arm.com>
Change-Id: Icbf6f4c6f8eaba6d78b88bdf62448f1d30aed1ae
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Adds support for rewrite-specific parameters
Resolves: MLIA-1114
Signed-off-by: Nathan Bailey <nathan.bailey@arm.com>
Change-Id: I290c326af3356033a916a43b28027819c876c3dd
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Rework doctrings in rewrite functions based on recent changes
Resolves MLIA-944
Signed-off-by: Nathan Bailey <nathan.bailey@arm.com>
Change-Id: I31a37e17a296f8a16d0db408d48c6de65c05300e
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Enable clustering and fully connected rewrites for conv2D layers.
Resolves: MLIA-1159 and MLIA-1160
Signed-off-by: Nathan Bailey <nathan.bailey@arm.com>
Change-Id: I640b8a7e79e455b12fb68d02ac1c33213b8de9c6
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Implements CLI and API changes for the new conv2d rewrite targets
Resolves: MLIA-1157
Signed-off-by: Nathan Bailey <nathan.bailey@arm.com>
Change-Id: I03c7a3a536d2f0a805b4689a9d96b95f8b4ab86c
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- Adds model.evaluate stage to retrieve correct MAE
- Sets augmentations to none by default
- Enables MAE calculations using dequantized data (if needed)
Resolves: MLIA-972
Signed-off-by: Nathan Bailey <nathan.bailey@arm.com>
Change-Id: Ia838b2d86caf7b10ad6e4d87bf6aa9e27c80bb72
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Implements a clustering rewrite for fully connected layers for int8 models
Resolves: MLIA-1080
Signed-off-by: Nathan Bailey <nathan.bailey@arm.com>
Change-Id: If48efb22764187a382e5b84bbb5c3b75a6e71b75
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Implements a clustering rewrite for fully connected layers for fp32 models
Resolves: MLIA-1079
Signed-off-by: Nathan Bailey <nathan.bailey@arm.com>
Change-Id: I4c12f0bf911219b4066f0760976e424ebe900a0b
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Adds API changes for a fully-connected-clustering rewrite
Resolves: MLIA-1077
Signed-off-by: Nathan Bailey <nathan.bailey@arm.com>
Change-Id: I845796a391c5020e66472456b97ecad5ee8139a8
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- Implement pruning-preserving quantisation aware training
- Rework the training logic to avoid duplication
- Remove the DynamicallyLoadedRewrite class as it is now unused
Resolves: MLIA-1003
Signed-off-by: Madeleine Dunn <madeleine.dunn@arm.com>
Change-Id: Ia7a4acf5f477a27963cffa88180cca085b32ffe4
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- Update the existing placeholder with code to prune the given model
Resolves: MLIA-1002
Signed-off-by: Madeleine Dunn <madeleine.dunn@arm.com>
Change-Id: I76b0e0bfe81be5e57d518cd7bb588eef76a11641
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- The placeholder currently duplicates the existing fc target
Resolves: MLIA-1000
Signed-off-by: Madeleine Dunn <madeleine.dunn@arm.com>
Change-Id: I0df5d47e61dafa567e212566bbcb0f1639fe7642
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- Add a placeholder file and registry option for sparsity
Resolves: MLIA-999
Signed-off-by: Madeleine Dunn <madeleine.dunn@arm.com>
Change-Id: I273192ba6813309f5226e3d8e0b686ce87ee6b79
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Fixes the checkpoint feature in training and also completes unit tests for it
Resolves: MLIA-1111
Signed-off-by: Nathan Bailey <nathan.bailey@arm.com>
Change-Id: Ic2b84b4b045db5ba3cb299fcd137ae9d31df5298
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Updates Vela Version to 3.11.0 and TensorFlow version to 2.15.1
Required keras import to change:
from keras.api._v2 import keras needed instead of calling tf.keras
Subsequently tf.keras.X needed to change to keras.X
Resolves: MLIA-1107
Signed-off-by: Nathan Bailey <nathan.bailey@arm.com>
Change-Id: I53bcaa9cdad58b0e6c311c8c6490393d33cb18bc
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- Rename "fully_connected" to "fully-connected"
- This will resolve issues with upstreaming rewrite library changes
Signed-off-by: Madeleine Dunn <madeleine.dunn@arm.com>
Change-Id: I2f24ae4917a556fd0bd44f0db6ee4e0f7a68cd24
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TFLiteConverter was producing log messages in the output that was not
possible to capture and redirect to logging.
The solution/workaround is to run it as a subprocess.
This change required some refactoring around existing invocations of
the converter.
Change-Id: I394bd0d49d36e6686cfcb9d658e4aad05326cb87
Signed-off-by: Gergely Nagy <gergely.nagy@arm.com>
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Fixed an artifact that crept in during the upstream rebase process,
change a6ae703b6e41c73 was being taken out,
but 3cd84481fa25 reintroduced the first part of a try/except block
which caused a syntax error.
Change-Id: I7fc2e18a227a30ebaaee0763450ee68646611add
Signed-off-by: Benjamin Klimczak <benjamin.klimczak@arm.com>
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If the input model for rewriting is quantized:
- Record de-quantized TFRecords
- enable writing de-quantized calibration data for the training
- re-generate augmented training data, if needed
- Use quantization-aware training (QAT) to train the replacement models
- Check if replacement model is quantized:
If source model is quantized, we make sure rewrite's output model
is quantized too. Right now, only int8 is supported so raising
an error if any other datatype is present in the output.
Resolves: MLIA-907, MLIA-908, MLIA-927
Signed-off-by: Benjamin Klimczak <benjamin.klimczak@arm.com>
Change-Id: Icb4070a9e6f1fdb5ce36120d73823986e89ac955
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- List available rewrites
- Refactor/rename 'Rewrite' class to 'RewritingOptimizer'
- Introduce a registry for rewrite functions
- Refactor 'Rewriter' to use the registry to look up rewrite functions
- Remove mentions of hardcoded "fully_connected" from CLI help and
error messages, using the registry instead
- Add unit tests
- Enable rewrites for all targets:
Extract optimization (including rewrite specific code) from the
Ethos-U-specific data collector into OptimizingDataCollector.
This is reused in other targets' collectors, such as TOSA
and Cortex-A.
- Add more logging for rewrite
- add display of MAE and NRMSE values for the trained result
- add total model MAE and NRMSE metric
Resolves: MLIA-891, MLIA-899, MLIA-906
Change-Id: Ie798749e1ed60cab14fdb6d9c2271c833960e93f
Signed-off-by: Benjamin Klimczak <benjamin.klimczak@arm.com>
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- Fix input shape of rewrite replacement:
During and after training of the replacement model for a rewrite the
Keras model is converted and saved in TensorFlow Lite format. If the
input shape does not match the teacher model exactly, e.g. if the
batch size is undefined, the TFLiteConverter adds extra operators
during conversion.
- Fix rewritten model output
- Save the model output with the rewritten operator in the output dir
- Log MAE and NRMSE of the rewrite
- Remove 'verbose' flag from rewrite module and rely on the logging
mechanism to control verbose output.
- Re-factor utility classes for rewrites
- Merge the two TFLiteModel classes
- Move functionality to load/save TensorFlow Lite flatbuffers to
nn/tensorflow/tflite_graph
- Fix issue with unknown shape in datasets
After upgrading to TensorFlow 2.12 the unknown shape of the
TFRecordDataset is causing problems when training the replacement models
for rewrites. By explicitly setting the right shape of the tensors we
can work around the issue.
- Adapt default parameters for rewrites. The training steps especially
had to be increased significantly to be effective.
Resolves: MLIA-895, MLIA-907, MLIA-946, MLIA-979
Signed-off-by: Benjamin Klimczak <benjamin.klimczak@arm.com>
Change-Id: I887ad165aed0f2c6e5a0041f64cec5e6c5ab5c5c
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* Define replacement function fully_connected layer
* Define RewriteConfiguration and Rewriter to integrate
rewrite module into mlia optimize command
* Fix a bug in the ethos_u/data_collection.py file
* Fix a bug in join.py
* Remove diff_stats and use diff instead, added related
changes around this to ensure e2e tests passing
* Add unit tests for all changes
* Fix bug in diff_stats function
* The bug was caused by a dividing by numpy array
of all zeros. The previous way of handling it
did not consider the all zeros case but only
dealt with partially zeros
* unit tests added.
* Fix the bug in rewrite/core/graph_edit/join.py
* Remove the possibility of passing None to append_relabel
function because it is immutable
* The bug happened when empty dictionary was passed in the
append_relabel function and the function overwrites the
reference of operator_map which caused the dictionary
was not updated after the function call
Resolves: MLIA-749, MLIA-864, MLIA-866
Change-Id: I1ab426996232f182345e6e98033d5dcb32aea08c
Signed-off-by: Benjamin Klimczak <benjamin.klimczak@arm.com>
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- Fix imports
- Update variable names
- Refactor helper functions
- Add licence headers
- Add docstrings
- Use f-strings rather than % notation
- Create type annotations in rewrite module
- Migrate from tqdm to rich progress bar
- Use logging module in rewrite module: All print statements are
replaced with logging module
Resolves: MLIA-831, MLIA-842, MLIA-844, MLIA-846
Signed-off-by: Benjamin Klimczak <benjamin.klimczak@arm.com>
Change-Id: Idee37538d72b9f01128a894281a8d10155f7c17c
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Note: The unit tests mostly call the main functions from the respective
modules only.
Change-Id: Ib2ce5c53d0c3eb222b8b8be42fba33ac8e007574
Signed-off-by: Benjamin Klimczak <benjamin.klimczak@arm.com>
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* Add flags for rewrite (--rewrite, --rewrite-start,
--rewrite-end, --rewrite-target)
* Refactor CLI interfaces to accept tflite models with optimize for
rewrite, keras models with optimize for clustering and pruning
* Refactor and move common.py and select.py out of the folder
nn/tensorflow/optimizations
* Add file nn/rewrite/core/rewrite.py as placeholder
* Update/add unit tests
* Refactor OptimizeModel in ethos_u/data_collection.py
for accepting tflite model case
* Extend the logic so that if "--rewrite" is specified, we don't add
pruning to also accept TFLite models.
* Update README.md
Resolves: MLIA-750, MLIA-854, MLIA-865
Signed-off-by: Benjamin Klimczak <benjamin.klimczak@arm.com>
Change-Id: I67d85f71fa253d2bad4efe304ad8225970b9622c
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- Add required files for rewriting of TensorFlow Lite graphs
- Adapt rewrite dependency paths and project name
- Add license headers
Change-Id: I19c5f63215fe2af2fa7d7d44af08144c6c5f911c
Signed-off-by: Benjamin Klimczak <benjamin.klimczak@arm.com>
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