| commit | 5800fc990ed1e36ce7d06670f911fbb12a0ec771 | [log] [tgz] |
|---|---|---|
| author | Raul Farkas <raul.farkas@arm.com> | Tue Nov 29 13:29:04 2022 +0000 |
| committer | Raul Farkas <raul.farkas@arm.com> | Tue Jan 10 10:46:07 2023 +0000 |
| tree | 294605295cd2624ba63e6ad3df335a2a4b2700ab | |
| parent | dcd0bd31985c27e1d07333351b26cf8ad12ad1fd [diff] |
MLIA-650 Implement new CLI changes Breaking change in the CLI and API: Sub-commands "optimization", "operators", and "performance" were replaced by "check", which incorporates compatibility and performance checks, and "optimize" which is used for optimization. "get_advice" API was adapted to these CLI changes. API changes: * Remove previous advice category "all" that would perform all three operations (when possible). Replace them with the ability to pass a set of the advice categories. * Update api.get_advice method docstring to reflect new changes. * Set default advice category to COMPATIBILITY * Update core.common.AdviceCategory by changing the "OPERATORS" advice category to "COMPATIBILITY" and removing "ALL" enum type. Update all subsequent methods that previously used "OPERATORS" to use "COMPATIBILITY". * Update core.context.ExecutionContext to have "COMPATIBILITY" as default advice_category instead of "ALL". * Remove api.generate_supported_operators_report and all related functions from cli.commands, cli.helpers, cli.main, cli.options, core.helpers * Update tests to reflect new API changes. CLI changes: * Update README.md to contain information on the new CLI * Remove the ability to generate supported operators support from MLIA CLI * Replace `mlia ops` and `mlia perf` with the new `mlia check` command that can be used to perform both operations. * Replace `mlia opt` with the new `mlia optimize` command. * Replace `--evaluate-on` flag with `--backend` flag * Replace `--verbose` flag with `--debug` flag (no behaviour change). * Remove the ability for the user to select MLIA working directory. Create and use a temporary directory in /temp instead. * Change behaviour of `--output` flag to not format the content automatically based on file extension anymore. Instead it will simply redirect to a file. * Add the `--json` flag to specfy that the format of the output should be json. * Add command validators that are used to validate inter-dependent flags (e.g. backend validation based on target_profile). * Add support for selecting built-in backends for both `check` and `optimize` commands. * Add new unit tests and update old ones to test the new CLI changes. * Update RELEASES.md * Update copyright notice Change-Id: Ia6340797c7bee3acbbd26601950e5a16ad5602db
The ML Inference Advisor (MLIA) is used to help AI developers design and optimize neural network models for efficient inference on Arm® targets (see supported targets) by enabling performance analysis and providing actionable advice early in the model development cycle. The final advice can cover supported operators, performance analysis and suggestions for model optimization (e.g. pruning, clustering, etc.).
This product conforms to Arm's inclusive language policy and, to the best of our knowledge, does not contain any non-inclusive language.
If you find something that concerns you, email terms@arm.com.
Release notes can be found in MLIA releases.
In case you need support or want to report an issue, give us feedback or simply ask a question about MLIA, please send an email to mlia@arm.com.
Alternatively, use the AI and ML forum to get support by marking your post with the MLIA tag.
Information on reporting security issues can be found in Reporting vulnerabilities.
ML Inference Advisor is licensed under Apache License 2.0.
It is recommended to use a virtual environment for MLIA installation, and a typical setup for MLIA requires:
MLIA can be installed with pip using the following command:
pip install mlia
It is highly recommended to create a new virtual environment to install MLIA.
After the installation, you can check that MLIA is installed correctly by opening your terminal, activating the virtual environment and typing the following command that should print the help text:
mlia --help
The ML Inference Advisor works with sub-commands, i.e. in general a MLIA command would look like this:
mlia [sub-command] [arguments]
Where the following sub-commands are available:
Detailed help about the different sub-commands can be shown like this:
mlia [sub-command] --help
The following sections go into further detail regarding the usage of MLIA.
This section gives an overview of the available sub-commands for MLIA.
Default check that MLIA runs. It lists the model's operators with information about their compatibility with the specified target.
Examples:
# List operator compatibility with Ethos-U55 with 256 MAC mlia check ~/models/mobilenet_v1_1.0_224_quant.tflite --target-profile ethos-u55-256 # List operator compatibility with Cortex-A mlia check ~/models/mobilenet_v1_1.0_224_quant.tflite --target-profile cortex-a # Get help and further information mlia check --help
Estimate the model's performance on the specified target and print out statistics.
Examples:
# Use default parameters mlia check ~/models/mobilenet_v1_1.0_224_quant.tflite \ --target-profile ethos-u55-256 \ --performance # Explicitly specify the target profile and backend(s) to use with --backend mlia check ~/models/ds_cnn_large_fully_quantized_int8.tflite \ --target-profile ethos-u65-512 \ --performance \ --backend "Vela" "Corstone-310" # Get help and further information mlia check --help
This sub-command applies optimizations to a Keras model (.h5 or SavedModel) and shows the performance improvements compared to the original unoptimized model.
There are currently two optimization techniques available to apply:
More information about these techniques can be found online in the TensorFlow documentation, e.g. in the TensorFlow model optimization guides.
Note: A Keras model (.h5 or SavedModel) is required as input to perform the optimizations. Models in the TensorFlow Lite format are not supported.
Examples:
# Custom optimization parameters: pruning=0.6, clustering=16 mlia optimize ~/models/ds_cnn_l.h5 \ --target-profile ethos-u55-256 \ --pruning \ --pruning-target 0.6 \ --clustering \ --clustering-target 16 # Get help and further information mlia optimize --help
All sub-commands require the name of a target profile as input parameter. The profiles currently available are described in the following sections.
The support of the above sub-commands for different targets is provided via backends that need to be installed separately, see Backend installation section.
There are a number of predefined profiles for Ethos-U with the following attributes:
+--------------------------------------------------------------------+ | Profile name | MAC | System config | Memory mode | +===================================================================== | ethos-u55-256 | 256 | Ethos_U55_High_End_Embedded | Shared_Sram | +--------------------------------------------------------------------- | ethos-u55-128 | 128 | Ethos_U55_High_End_Embedded | Shared_Sram | +--------------------------------------------------------------------- | ethos-u65-512 | 512 | Ethos_U65_High_End | Dedicated_Sram | +--------------------------------------------------------------------- | ethos-u65-256 | 256 | Ethos_U65_High_End | Dedicated_Sram | +--------------------------------------------------------------------+
Example:
mlia check ~/model.tflite --target-profile ethos-u65-512 --performance
Ethos-U is supported by these backends:
The profile cortex-a can be used to get the information about supported operators for Cortex-A CPUs when using the Arm NN TensorFlow Lite delegate. Please, find more details in the section for the corresponding backend.
The target profile tosa can be used for TOSA compatibility checks of your model. It requires the TOSA Checker backend.
For more information, see TOSA Checker's:
The ML Inference Advisor is designed to use backends to provide different metrics for different target hardware. Some backends come pre-installed with MLIA, but others can be added and managed using the command mlia-backend, that provides the following functionality:
Examples:
# List backends installed and available for installation mlia-backend list # Install Corstone-300 backend for Ethos-U mlia-backend install Corstone-300 --path ~/FVP_Corstone_SSE-300/ # Uninstall the Corstone-300 backend mlia-backend uninstall Corstone-300 # Get help and further information mlia-backend --help
Note: Some, but not all, backends can be automatically downloaded, if no path is provided.
This section lists available backends. As not all backends work on any platform the following table shows some compatibility information:
+----------------------------------------------------------------------------+ | Backend | Linux | Windows | Python | +============================================================================= | Arm NN | | | | | TensorFlow | x86_64 | Windows 10 | Python>=3.8 | | Lite delegate | | | | +----------------------------------------------------------------------------- | Corstone-300 | x86_64 | Not compatible | Python>=3.8 | +----------------------------------------------------------------------------- | Corstone-310 | x86_64 | Not compatible | Python>=3.8 | +----------------------------------------------------------------------------- | TOSA checker | x86_64 (manylinux2014) | Not compatible | 3.7<=Python<=3.9 | +----------------------------------------------------------------------------- | Vela | x86_64 | Windows 10 | Python~=3.7 | +----------------------------------------------------------------------------+
This backend provides general information about the compatibility of operators with the Arm NN TensorFlow Lite delegate for Cortex-A. It comes pre-installed with MLIA.
For more information see:
Corstone-300 is a backend that provides performance metrics for systems based on Cortex-M55 and Ethos-U. It is only available on the Linux platform.
Examples:
# Download and install Corstone-300 automatically mlia-backend install Corstone-300 # Point to a local version of Corstone-300 installed using its installation script mlia-backend install Corstone-300 --path YOUR_LOCAL_PATH_TO_CORSTONE_300
For further information about Corstone-300 please refer to: https://developer.arm.com/Processors/Corstone-300
Corstone-310 is a backend that provides performance metrics for systems based on Cortex-M85 and Ethos-U. It is available as Arm Virtual Hardware (AVH) only, i.e. it can not be downloaded automatically.
The TOSA Checker backend provides operator compatibility checks against the TOSA specification.
Please, install it into the same environment as MLIA using this command:
mlia-backend install tosa-checker
Additional resources:
The Vela backend provides performance metrics for Ethos-U based systems. It comes pre-installed with MLIA.
Additional resources: