Documentation

Index

• Run models on XCORE.AI
• Run models via Python on host
• Examples
• Graph transformer command-line options
• Transforming Pytorch models
• FAQ
• Changelog
• Advanced topics
  • Detailed background to deploying on the edge using XCORE.AI
  • Building the graph transformer and xmos-ai-tools package

Installing xmos-ai-tools

xmos-ai-tools is available on PyPI. It includes:

• the MLIR-based XCore optimizer(xformer) to optimize Tensorflow Lite models for XCore
• the XCore tflm interpreter to run the transformed models on host

Perform the following steps once:

# Create a virtual environment with
python3 -m venv <name_of_virtualenv>

# Activate the virtual environment
# On Windows, run:
<name_of_virtualenv>\Scripts\activate.bat
# On Linux and MacOS, run:
source <name_of_virtualenv>/bin/activate

# Install xmos-ai-tools from PyPI
pip3 install xmos-ai-tools --upgrade

Use pip3 install xmos-ai-tools --pre --upgrade instead if you want to install the latest development version.

Using xmos-ai-tools from Python

from xmos_ai_tools import xformer as xf

# Optimizes the source model for xcore
# The main method in xformer is convert, which requires a path to an input model,
# an output path, and a list of configuration parameters.
# The list of parameters should be a dictionary of options and their values.
#
# Generates -
#   * An optimized model which can be run on the host interpreter
#   * C++ source and header which can be compiled for xcore target
#   * Optionally generates flash image for model weights
xf.convert("source model path", "converted model path", params=None)

# Returns the tensor arena size required for the optimized model
# Only valid after conversion is done
xf.tensor_arena_size()

# Prints xformer output
# Useful for inspecting optimization warnings, if any
# Only valid after conversion is done
xf.print_optimization_report()

# To see all available parameters
# To see hidden options, run `print_help(show_hidden=True)`
xf.print_help()

For example:

from xmos_ai_tools import xformer as xf

xf.convert("example_int8_model.tflite", "xcore_optimised_int8_model.tflite", {
    "xcore-thread-count": "5",
})

To create a parameters file and a tflite model suitable for loading to flash, use the "xcore-weights-file" option.

xf.convert("example_int8_model.tflite", "xcore_optimised_int8_flash_model.tflite", {
    "xcore-weights-file ": "./xcore_params.params",
})

Some of the commonly used configuration options are described here

Running the xcore model on host interpreter

from xmos_ai_tools.xinterpreters import TFLMHostInterpreter

input_data = ... # define your input data

ie = TFLMHostInterpreter()
ie.set_model(model_path='path_to_xcore_model', params_path='path_to_xcore_params')
ie.set_tensor(ie.get_input_details()[0]['index'], value=input_data)
ie.invoke()

xformer_outputs = []
num_of_outputs = len(ie.get_output_details())
for i in range(num_of_outputs):
    xformer_outputs.append(ie.get_tensor(ie.get_output_details()[i]['index'])).

# Note: use ie.close() or "with TFLMHostInterpreter() as ie:" to free resources
ie.close()