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"""Generate a mock model for LLVM tests.
The generated model is not a neural net - it is just a tf.function with the
correct input and output parameters. By construction, the mock model will always
output 1.
"""
import os
import importlib.util
import sys
import tensorflow as tf
def get_output_spec_path(path):
return os.path.join(path, 'output_spec.json')
def build_mock_model(path, signature):
"""Build and save the mock model with the given signature"""
module = tf.Module()
# We have to set this useless variable in order for the TF C API to correctly
# intake it
module.var = tf.Variable(0.)
def action(*inputs):
s = tf.reduce_sum([tf.cast(x, tf.float32) for x in tf.nest.flatten(inputs)])
return {signature['output']: float('inf') + s + module.var}
module.action = tf.function()(action)
action = {'action': module.action.get_concrete_function(signature['inputs'])}
tf.saved_model.save(module, path, signatures=action)
output_spec_path = get_output_spec_path(path)
with open(output_spec_path, 'w') as f:
print(f'Writing output spec to {output_spec_path}.')
f.write(signature['output_spec'])
def get_external_signature(config_path):
"""Get the signature for the desired model.
We manually import the python file at config_path to avoid adding a gin
dependency to the LLVM build.
"""
spec = importlib.util.spec_from_file_location('config', config_path)
config = importlib.util.module_from_spec(spec)
spec.loader.exec_module(config)
return {
'inputs': config.get_input_signature(),
'output': config.get_output_signature(),
'output_spec': config.get_output_spec()
}
def main(argv):
assert len(argv) == 3
config_path = argv[1]
model_path = argv[2]
print(f'Using config file at [{argv[1]}]')
signature = get_external_signature(config_path)
build_mock_model(model_path, signature)
if __name__ == '__main__':
main(sys.argv)
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