- Run models on XCORE.AI
- Run models via Python on host
- Examples
- Graph transformer command-line options
- Transforming Pytorch models
- FAQ
- Changelog
- Advanced topics
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.
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-flash-image-file" option.
xf.convert("example_int8_model.tflite", "xcore_optimised_int8_flash_model.tflite", {
"xcore-flash-image-file ": "./xcore_params.params",
})
Some of the commonly used configuration options are described here
from xmos_ai_tools.xinterpreters import xcore_tflm_host_interpreter
ie = xcore_tflm_host_interpreter()
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 = []
for i in range(num_of_outputs):
xformer_outputs.append(ie.get_tensor(ie.get_output_details()[i]['index']))