ML Inference Advisor
Project description
ML Inference Advisor - Introduction
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.).
Inclusive language commitment
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.
Releases
Release notes can be found in MLIA releases.
Getting support
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.
Reporting vulnerabilities
Information on reporting security issues can be found in Reporting vulnerabilities.
License
ML Inference Advisor is licensed under Apache License 2.0.
Trademarks and copyrights
- Arm®, Arm® Ethos™-U, Arm® Cortex®-A, Arm® Cortex®-M, Arm® Corstone™ are registered trademarks or trademarks of Arm® Limited (or its subsidiaries) in the U.S. and/or elsewhere.
- TensorFlow™ is a trademark of Google® LLC.
- Keras™ is a trademark by François Chollet.
- Linux® is the registered trademark of Linus Torvalds in the U.S. and elsewhere.
- Python® is a registered trademark of the PSF.
- Ubuntu® is a registered trademark of Canonical.
- Microsoft and Windows are trademarks of the Microsoft group of companies.
General usage
Prerequisites and dependencies
It is recommended to use a virtual environment for MLIA installation, and a typical setup for MLIA requires:
- Ubuntu® 20.04.03 LTS (other OSs may work, the ML Inference Advisor has been tested on this one specifically)
- Python® >= 3.8.1
- Ethos™-U Vela dependencies (Linux® only)
- For more details, please refer to the prerequisites of Vela
Installation
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.
First steps
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:
- "check": perform compatibility or performance checks on the model
- "optimize": apply specified optimizations
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.
Sub-commands
This section gives an overview of the available sub-commands for MLIA.
check
compatibility
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
performance
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 option
mlia check ~/models/ds_cnn_large_fully_quantized_int8.tflite \
--target-profile ethos-u65-512 \
--performance \
--backend "vela" \
--backend "corstone-300"
# Get help and further information
mlia check --help
optimize
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:
- pruning: Sets insignificant model weights to zero until the specified sparsity is reached.
- clustering: Groups the weights into the specified number of clusters and then replaces the weight values with the cluster centroids.
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
Target profiles
The targets currently supported are described in the sections below. All MLIA sub-commands require a target profile as input parameter. That target profile can be either a name of a built-in target profile or a custom file. MLIA saves the target profile that was used for a run in the output directory.
The support of the above sub-commands for different targets is provided via backends that need to be installed separately, see Backend installation section.
Ethos-U
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:
Cortex-A
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.
TOSA
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:
Custom target profiles
For the custom target profiles, the configuration file for a custom
target profile is passed as path and needs to conform to the TOML file format.
Each target in MLIA has a pre-defined set of parameters which need to be present
in the config file. The built-in target profiles (in src/mlia/resources/target_profiles
)
can be used to understand what parameters apply for each target.
Example:
# for custom profiles
mlia ops --target-profile ~/my_custom_profile.toml sample_model.tflite
Backend installation
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:
- install
- uninstall
- list
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.
Available backends
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 |
+----------------------------------------------------------------------------+
Arm NN TensorFlow Lite delegate
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
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
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.
- For access to AVH for Corstone-310 please refer to: https://developer.arm.com/Processors/Corstone-310
- Please use the examples of MLIA using Corstone-310 here to get started: https://github.com/ARM-software/open-iot-sdk
TOSA Checker
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:
- Source code: https://review.mlplatform.org/admin/repos/tosa/tosa_checker
- PyPi package https://pypi.org/project/tosa-checker/
Vela
The Vela backend provides performance metrics for Ethos-U based systems. It comes pre-installed with MLIA.
Additional resources:
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