blob: d8737f26e0d887159905e887f44a47134e1431d5 [file] [log] [blame]
"""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
POLICY_DECISION_LABEL = 'inlining_decision'
POLICY_OUTPUT_SPEC = """
[
{
"logging_name": "inlining_decision",
"tensor_spec": {
"name": "StatefulPartitionedCall",
"port": 0,
"type": "int64_t",
"shape": [
1
]
}
}
]
"""
# pylint: disable=g-complex-comprehension
def get_input_signature():
"""Returns the list of features for LLVM inlining."""
# int64 features
inputs = [
tf.TensorSpec(dtype=tf.int64, shape=(), name=key) for key in [
'caller_basic_block_count',
'caller_conditionally_executed_blocks',
'caller_users',
'callee_basic_block_count',
'callee_conditionally_executed_blocks',
'callee_users',
'nr_ctant_params',
'node_count',
'edge_count',
'callsite_height',
'cost_estimate',
'inlining_default',
'sroa_savings',
'sroa_losses',
'load_elimination',
'call_penalty',
'call_argument_setup',
'load_relative_intrinsic',
'lowered_call_arg_setup',
'indirect_call_penalty',
'jump_table_penalty',
'case_cluster_penalty',
'switch_penalty',
'unsimplified_common_instructions',
'num_loops',
'dead_blocks',
'simplified_instructions',
'constant_args',
'constant_offset_ptr_args',
'callsite_cost',
'cold_cc_penalty',
'last_call_to_static_bonus',
'is_multiple_blocks',
'nested_inlines',
'nested_inline_cost_estimate',
'threshold',
]
]
# float32 features
inputs.extend([
tf.TensorSpec(dtype=tf.float32, shape=(), name=key)
for key in ['discount', 'reward']
])
# int32 features
inputs.extend([
tf.TensorSpec(dtype=tf.int32, shape=(), name=key)
for key in ['step_type']
])
return inputs
def get_output_signature():
return POLICY_DECISION_LABEL
def get_output_spec():
return POLICY_OUTPUT_SPEC
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()
def action(*inputs):
return {signature['output']: tf.constant(value=1, dtype=tf.int64)}
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_signature():
return {
'inputs': get_input_signature(),
'output': get_output_signature(),
'output_spec': get_output_spec()
}
def main(argv):
assert len(argv) == 2
model_path = argv[1]
print(f'Output model to: [{argv[1]}]')
signature = get_signature()
build_mock_model(model_path, signature)
if __name__ == '__main__':
main(sys.argv)