ModelGraph Class

This page documents our ModelGraph class usage. You can generate generate an instance of this class through hls4ml’s API, for example by converting a Keras model:

import hls4ml

# Generate a simple configuration from keras model
config = hls4ml.utils.config_from_keras_model(keras_model, granularity='name')

# Convert to a ModelGraph instance (hls_model)
hls_model = hls4ml.converters.convert_from_keras_model(keras_model, hls_config=config, output_dir='test_prj')

This object can be used to perform common simulation and firmware-generation tasks. Here is a list of important user-facing methods:

• write

• compile

• predict

• build

• trace

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write method

Write the ModelGraph to the output directory specified in the config:

hls_model.write()

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compile method

Compiles the written C++/HLS code and links it into the Python runtime. Compiled model can be used to evaluate performance (accuracy) through predict() method.

hls_model.compile()

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predict method

Similar to keras‘s predict API, you can get the predictions just by supplying an input numpy array:

# Suppose that you already have input array X
# Note that you have to do hls_model.compile() before using predict

y = hls_model.predict(X)

This is similar to doing csim simulation, without creating the testbench and supplying data. It’s very helpful when you want to quickly prototype different configurations for your model.

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build method

This method “builds” the generated HLS project. The parameters of build are backend-specific and usually include simulation and synthesis. Refer to each backend for a complete list of supported parameters to build().

report = hls_model.build()

#You can also read the report of the build
hls4ml.report.read_vivado_report('hls4ml_prj')

The returned report object will contain the result of build step, which may include C-simulation results, HLS synthesis estimates, co-simulation latency etc, depending on the backend used.

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trace method

The trace method is an advanced version of the predict method. It’s used to trace individual outputs from each layer of the hls_model. This is useful for debugging and setting the appropriate configuration.

Return: A dictionary where the keys are the names of the layers, and its values are the layers’s outputs.

predict_ouputs, trace_outputs =  hls_model.trace(X)

#We also support a similar function for keras
keras_trace = hls4ml.model.profiling.get_ymodel_keras(keras_model, X)