3486b1b736
Casting operator to make TFLite happy. Reviewed By: yundiqian Differential Revision: https://reviews.llvm.org/D131584
71 lines
1.9 KiB
Python
71 lines
1.9 KiB
Python
"""Generate a mock model for LLVM tests for Register Allocation.
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The generated model is not a neural net - it is just a tf.function with the
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correct input and output parameters. By construction, the mock model will always
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output the first liverange that can be evicted.
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"""
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import os
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import sys
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import tensorflow as tf
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POLICY_DECISION_LABEL = 'index_to_evict'
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POLICY_OUTPUT_SPEC = """
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[
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{
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"logging_name": "index_to_evict",
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"tensor_spec": {
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"name": "StatefulPartitionedCall",
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"port": 0,
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"type": "int64_t",
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"shape": [
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1
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]
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}
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}
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]
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"""
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PER_REGISTER_FEATURE_LIST = ['mask']
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NUM_REGISTERS = 33
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def get_input_signature():
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"""Returns (time_step_spec, action_spec) for LLVM register allocation."""
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inputs = dict(
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(key, tf.TensorSpec(dtype=tf.int64, shape=(NUM_REGISTERS), name=key))
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for key in PER_REGISTER_FEATURE_LIST)
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return inputs
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def get_output_spec_path(path):
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return os.path.join(path, 'output_spec.json')
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def build_mock_model(path):
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"""Build and save the mock model with the given signature."""
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module = tf.Module()
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# We have to set this useless variable in order for the TF C API to correctly
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# intake it
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module.var = tf.Variable(0, dtype=tf.int64)
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def action(*inputs):
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result = tf.math.argmax(
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tf.cast(inputs[0]['mask'], tf.int32), axis=-1) + module.var
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return {POLICY_DECISION_LABEL: result}
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module.action = tf.function()(action)
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action = {
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'action': module.action.get_concrete_function(get_input_signature())
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}
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tf.saved_model.save(module, path, signatures=action)
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output_spec_path = get_output_spec_path(path)
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with open(output_spec_path, 'w') as f:
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print(f'Writing output spec to {output_spec_path}.')
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f.write(POLICY_OUTPUT_SPEC)
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def main(argv):
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assert len(argv) == 2
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model_path = argv[1]
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build_mock_model(model_path)
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if __name__ == '__main__':
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main(sys.argv)
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