143 lines
3.6 KiB
Python
143 lines
3.6 KiB
Python
"""Generate a mock model for LLVM tests.
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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 1.
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"""
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import os
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import importlib.util
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import sys
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import tensorflow as tf
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POLICY_DECISION_LABEL = 'inlining_decision'
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POLICY_OUTPUT_SPEC = """
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[
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{
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"logging_name": "inlining_decision",
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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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# pylint: disable=g-complex-comprehension
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def get_input_signature():
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"""Returns the list of features for LLVM inlining."""
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# int64 features
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inputs = [
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tf.TensorSpec(dtype=tf.int64, shape=(), name=key) for key in [
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'caller_basic_block_count',
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'caller_conditionally_executed_blocks',
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'caller_users',
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'callee_basic_block_count',
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'callee_conditionally_executed_blocks',
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'callee_users',
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'nr_ctant_params',
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'node_count',
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'edge_count',
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'callsite_height',
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'cost_estimate',
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'inlining_default',
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'sroa_savings',
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'sroa_losses',
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'load_elimination',
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'call_penalty',
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'call_argument_setup',
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'load_relative_intrinsic',
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'lowered_call_arg_setup',
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'indirect_call_penalty',
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'jump_table_penalty',
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'case_cluster_penalty',
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'switch_penalty',
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'unsimplified_common_instructions',
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'num_loops',
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'dead_blocks',
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'simplified_instructions',
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'constant_args',
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'constant_offset_ptr_args',
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'callsite_cost',
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'cold_cc_penalty',
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'last_call_to_static_bonus',
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'is_multiple_blocks',
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'nested_inlines',
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'nested_inline_cost_estimate',
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'threshold',
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]
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]
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# float32 features
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inputs.extend([
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tf.TensorSpec(dtype=tf.float32, shape=(), name=key)
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for key in ['discount', 'reward']
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])
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# int32 features
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inputs.extend([
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tf.TensorSpec(dtype=tf.int32, shape=(), name=key)
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for key in ['step_type']
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])
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return inputs
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def get_output_signature():
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return POLICY_DECISION_LABEL
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def get_output_spec():
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return POLICY_OUTPUT_SPEC
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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, signature):
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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.)
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def action(*inputs):
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s = tf.reduce_sum([tf.cast(x, tf.float32) for x in tf.nest.flatten(inputs)])
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return {signature['output']: tf.cast(tf.divide((s + module.var), tf.abs(s + module.var)), tf.int64)}
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module.action = tf.function()(action)
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action = {'action': module.action.get_concrete_function(signature['inputs'])}
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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(signature['output_spec'])
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def get_signature():
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return {
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'inputs': get_input_signature(),
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'output': get_output_signature(),
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'output_spec': get_output_spec()
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}
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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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print(f'Output model to: [{argv[1]}]')
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signature = get_signature()
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build_mock_model(model_path, signature)
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if __name__ == '__main__':
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main(sys.argv)
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