lib/Analysis/models/gen-inline-oz-test-model.py

"""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)