llvm/lib/Analysis/models/inlining/config.py

"""Inlining Training config."""

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