412 lines
16 KiB
Python
412 lines
16 KiB
Python
"""
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Lowers, compiles, and creates functions used in the NEAT pipeline.
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"""
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from functools import partial
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import jax.random
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import numpy as np
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from jax import jit, vmap
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from .genome import act_name2key, agg_name2key, initialize_genomes, mutate, distance, crossover
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from .genome import topological_sort, forward_single, unflatten_connections
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class FunctionFactory:
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def __init__(self, config, debug=False):
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self.config = config
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self.debug = debug
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self.init_N = config.basic.init_maximum_nodes
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self.init_C = config.basic.init_maximum_connections
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self.expand_coe = config.basic.expands_coe
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self.precompile_times = config.basic.pre_compile_times
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self.compiled_function = {}
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self.load_config_vals(config)
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self.precompile()
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def load_config_vals(self, config):
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self.problem_batch = config.basic.problem_batch
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self.pop_size = config.neat.population.pop_size
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self.disjoint_coe = config.neat.genome.compatibility_disjoint_coefficient
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self.compatibility_coe = config.neat.genome.compatibility_weight_coefficient
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self.num_inputs = config.basic.num_inputs
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self.num_outputs = config.basic.num_outputs
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self.input_idx = np.arange(self.num_inputs)
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self.output_idx = np.arange(self.num_inputs, self.num_inputs + self.num_outputs)
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bias = config.neat.gene.bias
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self.bias_mean = bias.init_mean
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self.bias_std = bias.init_stdev
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self.bias_mutate_strength = bias.mutate_power
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self.bias_mutate_rate = bias.mutate_rate
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self.bias_replace_rate = bias.replace_rate
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response = config.neat.gene.response
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self.response_mean = response.init_mean
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self.response_std = response.init_stdev
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self.response_mutate_strength = response.mutate_power
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self.response_mutate_rate = response.mutate_rate
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self.response_replace_rate = response.replace_rate
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weight = config.neat.gene.weight
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self.weight_mean = weight.init_mean
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self.weight_std = weight.init_stdev
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self.weight_mutate_strength = weight.mutate_power
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self.weight_mutate_rate = weight.mutate_rate
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self.weight_replace_rate = weight.replace_rate
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activation = config.neat.gene.activation
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self.act_default = act_name2key[activation.default]
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self.act_list = np.array([act_name2key[name] for name in activation.options])
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self.act_replace_rate = activation.mutate_rate
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aggregation = config.neat.gene.aggregation
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self.agg_default = agg_name2key[aggregation.default]
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self.agg_list = np.array([agg_name2key[name] for name in aggregation.options])
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self.agg_replace_rate = aggregation.mutate_rate
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enabled = config.neat.gene.enabled
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self.enabled_reverse_rate = enabled.mutate_rate
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genome = config.neat.genome
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self.add_node_rate = genome.node_add_prob
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self.delete_node_rate = genome.node_delete_prob
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self.add_connection_rate = genome.conn_add_prob
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self.delete_connection_rate = genome.conn_delete_prob
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self.single_structure_mutate = genome.single_structural_mutation
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def create_initialize(self):
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func = partial(
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initialize_genomes,
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pop_size=self.pop_size,
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N=self.init_N,
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C=self.init_C,
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num_inputs=self.num_inputs,
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num_outputs=self.num_outputs,
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default_bias=self.bias_mean,
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default_response=self.response_mean,
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default_act=self.act_default,
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default_agg=self.agg_default,
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default_weight=self.weight_mean
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)
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if self.debug:
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def debug_initialize(*args):
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return func(*args)
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return debug_initialize
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else:
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return func
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def precompile(self):
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self.create_mutate_with_args()
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self.create_distance_with_args()
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self.create_crossover_with_args()
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self.create_topological_sort_with_args()
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self.create_single_forward_with_args()
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#
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# n, c = self.init_N, self.init_C
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# print("start precompile")
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# for _ in range(self.precompile_times):
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# self.compile_mutate(n)
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# self.compile_distance(n)
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# self.compile_crossover(n)
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# self.compile_topological_sort_batch(n)
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# self.compile_pop_batch_forward(n)
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# n = int(self.expand_coe * n)
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#
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# # precompile other functions used in jax
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# key = jax.random.PRNGKey(0)
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# _ = jax.random.split(key, 3)
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# _ = jax.random.split(key, self.pop_size * 2)
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# _ = jax.random.split(key, self.pop_size)
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#
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# print("end precompile")
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def create_mutate_with_args(self):
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func = partial(
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mutate,
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input_idx=self.input_idx,
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output_idx=self.output_idx,
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bias_mean=self.bias_mean,
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bias_std=self.bias_std,
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bias_mutate_strength=self.bias_mutate_strength,
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bias_mutate_rate=self.bias_mutate_rate,
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bias_replace_rate=self.bias_replace_rate,
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response_mean=self.response_mean,
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response_std=self.response_std,
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response_mutate_strength=self.response_mutate_strength,
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response_mutate_rate=self.response_mutate_rate,
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response_replace_rate=self.response_replace_rate,
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weight_mean=self.weight_mean,
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weight_std=self.weight_std,
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weight_mutate_strength=self.weight_mutate_strength,
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weight_mutate_rate=self.weight_mutate_rate,
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weight_replace_rate=self.weight_replace_rate,
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act_default=self.act_default,
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act_list=self.act_list,
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act_replace_rate=self.act_replace_rate,
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agg_default=self.agg_default,
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agg_list=self.agg_list,
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agg_replace_rate=self.agg_replace_rate,
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enabled_reverse_rate=self.enabled_reverse_rate,
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add_node_rate=self.add_node_rate,
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delete_node_rate=self.delete_node_rate,
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add_connection_rate=self.add_connection_rate,
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delete_connection_rate=self.delete_connection_rate,
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single_structure_mutate=self.single_structure_mutate
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)
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self.mutate_with_args = func
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def compile_mutate(self, n, c):
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func = self.mutate_with_args
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rand_key_lower = np.zeros((self.pop_size, 2), dtype=np.uint32)
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nodes_lower = np.zeros((self.pop_size, n, 5))
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connections_lower = np.zeros((self.pop_size, c, 4))
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new_node_key_lower = np.zeros((self.pop_size,), dtype=np.int32)
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batched_mutate_func = jit(vmap(func)).lower(rand_key_lower, nodes_lower,
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connections_lower, new_node_key_lower).compile()
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self.compiled_function[('mutate', n, c)] = batched_mutate_func
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def create_mutate(self, n, c):
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key = ('mutate', n, c)
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if key not in self.compiled_function:
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self.compile_mutate(n, c)
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if self.debug:
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def debug_mutate(*args):
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res_nodes, res_connections = self.compiled_function[key](*args)
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return res_nodes.block_until_ready(), res_connections.block_until_ready()
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return debug_mutate
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else:
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return self.compiled_function[key]
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def create_distance_with_args(self):
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func = partial(
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distance,
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disjoint_coe=self.disjoint_coe,
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compatibility_coe=self.compatibility_coe
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)
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self.distance_with_args = func
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def compile_distance(self, n, c):
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func = self.distance_with_args
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o2o_nodes1_lower = np.zeros((n, 5))
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o2o_connections1_lower = np.zeros((c, 4))
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o2o_nodes2_lower = np.zeros((n, 5))
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o2o_connections2_lower = np.zeros((c, 4))
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o2o_distance = jit(func).lower(o2o_nodes1_lower, o2o_connections1_lower,
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o2o_nodes2_lower, o2o_connections2_lower).compile()
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o2m_nodes2_lower = np.zeros((self.pop_size, n, 5))
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o2m_connections2_lower = np.zeros((self.pop_size, c, 4))
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o2m_distance = jit(vmap(func, in_axes=(None, None, 0, 0))).lower(o2o_nodes1_lower, o2o_connections1_lower,
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o2m_nodes2_lower,
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o2m_connections2_lower).compile()
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self.compiled_function[('o2o_distance', n, c)] = o2o_distance
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self.compiled_function[('o2m_distance', n, c)] = o2m_distance
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def create_distance(self, n, c):
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key1, key2 = ('o2o_distance', n, c), ('o2m_distance', n, c)
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if key1 not in self.compiled_function:
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self.compile_distance(n, c)
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if self.debug:
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def debug_o2o_distance(*args):
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return self.compiled_function[key1](*args).block_until_ready()
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def debug_o2m_distance(*args):
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return self.compiled_function[key2](*args).block_until_ready()
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return debug_o2o_distance, debug_o2m_distance
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else:
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return self.compiled_function[key1], self.compiled_function[key2]
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def create_crossover_with_args(self):
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self.crossover_with_args = crossover
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def compile_crossover(self, n, c):
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func = self.crossover_with_args
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randkey_lower = np.zeros((self.pop_size, 2), dtype=np.uint32)
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nodes1_lower = np.zeros((self.pop_size, n, 5))
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connections1_lower = np.zeros((self.pop_size, c, 4))
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nodes2_lower = np.zeros((self.pop_size, n, 5))
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connections2_lower = np.zeros((self.pop_size, c, 4))
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func = jit(vmap(func)).lower(randkey_lower, nodes1_lower, connections1_lower,
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nodes2_lower, connections2_lower).compile()
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self.compiled_function[('crossover', n, c)] = func
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def create_crossover(self, n, c):
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key = ('crossover', n, c)
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if key not in self.compiled_function:
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self.compile_crossover(n, c)
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if self.debug:
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def debug_crossover(*args):
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res_nodes, res_connections = self.compiled_function[key](*args)
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return res_nodes.block_until_ready(), res_connections.block_until_ready()
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return debug_crossover
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else:
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return self.compiled_function[key]
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def create_topological_sort_with_args(self):
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self.topological_sort_with_args = topological_sort
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def compile_topological_sort(self, n):
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func = self.topological_sort_with_args
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nodes_lower = np.zeros((n, 5))
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connections_lower = np.zeros((2, n, n))
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func = jit(func).lower(nodes_lower, connections_lower).compile()
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self.compiled_function[('topological_sort', n)] = func
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def create_topological_sort(self, n):
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key = ('topological_sort', n)
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if key not in self.compiled_function:
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self.compile_topological_sort(n)
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return self.compiled_function[key]
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def compile_topological_sort_batch(self, n):
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func = self.topological_sort_with_args
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func = vmap(func)
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nodes_lower = np.zeros((self.pop_size, n, 5))
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connections_lower = np.zeros((self.pop_size, 2, n, n))
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func = jit(func).lower(nodes_lower, connections_lower).compile()
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self.compiled_function[('topological_sort_batch', n)] = func
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def create_topological_sort_batch(self, n):
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key = ('topological_sort_batch', n)
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if key not in self.compiled_function:
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self.compile_topological_sort_batch(n)
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return self.compiled_function[key]
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def create_single_forward_with_args(self):
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func = partial(
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forward_single,
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input_idx=self.input_idx,
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output_idx=self.output_idx
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)
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self.single_forward_with_args = func
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def compile_single_forward(self, n):
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"""
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single input for a genome
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:param n:
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:return:
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"""
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func = self.single_forward_with_args
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inputs_lower = np.zeros((self.num_inputs,))
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cal_seqs_lower = np.zeros((n,), dtype=np.int32)
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nodes_lower = np.zeros((n, 5))
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connections_lower = np.zeros((2, n, n))
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func = jit(func).lower(inputs_lower, cal_seqs_lower, nodes_lower, connections_lower).compile()
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self.compiled_function[('single_forward', n)] = func
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def compile_pop_forward(self, n):
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func = self.single_forward_with_args
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func = vmap(func, in_axes=(None, 0, 0, 0))
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inputs_lower = np.zeros((self.num_inputs,))
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cal_seqs_lower = np.zeros((self.pop_size, n), dtype=np.int32)
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nodes_lower = np.zeros((self.pop_size, n, 5))
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connections_lower = np.zeros((self.pop_size, 2, n, n))
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func = jit(func).lower(inputs_lower, cal_seqs_lower, nodes_lower, connections_lower).compile()
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self.compiled_function[('pop_forward', n)] = func
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def compile_batch_forward(self, n):
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func = self.single_forward_with_args
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func = vmap(func, in_axes=(0, None, None, None))
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inputs_lower = np.zeros((self.problem_batch, self.num_inputs))
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cal_seqs_lower = np.zeros((n,), dtype=np.int32)
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nodes_lower = np.zeros((n, 5))
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connections_lower = np.zeros((2, n, n))
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func = jit(func).lower(inputs_lower, cal_seqs_lower, nodes_lower, connections_lower).compile()
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self.compiled_function[('batch_forward', n)] = func
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def create_batch_forward(self, n):
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key = ('batch_forward', n)
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if key not in self.compiled_function:
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self.compile_batch_forward(n)
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if self.debug:
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def debug_batch_forward(*args):
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return self.compiled_function[key](*args).block_until_ready()
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return debug_batch_forward
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else:
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return self.compiled_function[key]
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def compile_pop_batch_forward(self, n):
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func = self.single_forward_with_args
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func = vmap(func, in_axes=(0, None, None, None)) # batch_forward
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func = vmap(func, in_axes=(None, 0, 0, 0)) # pop_batch_forward
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inputs_lower = np.zeros((self.problem_batch, self.num_inputs))
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cal_seqs_lower = np.zeros((self.pop_size, n), dtype=np.int32)
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nodes_lower = np.zeros((self.pop_size, n, 5))
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connections_lower = np.zeros((self.pop_size, 2, n, n))
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func = jit(func).lower(inputs_lower, cal_seqs_lower, nodes_lower, connections_lower).compile()
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self.compiled_function[('pop_batch_forward', n)] = func
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def create_pop_batch_forward(self, n):
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key = ('pop_batch_forward', n)
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if key not in self.compiled_function:
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self.compile_pop_batch_forward(n)
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if self.debug:
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def debug_pop_batch_forward(*args):
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return self.compiled_function[key](*args).block_until_ready()
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return debug_pop_batch_forward
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else:
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return self.compiled_function[key]
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def ask_pop_batch_forward(self, pop_nodes, pop_cons):
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n, c = pop_nodes.shape[1], pop_cons.shape[1]
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batch_unflatten_func = self.create_batch_unflatten_connections(n, c)
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pop_cons = batch_unflatten_func(pop_nodes, pop_cons)
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ts = self.create_topological_sort_batch(n)
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pop_cal_seqs = ts(pop_nodes, pop_cons)
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forward_func = self.create_pop_batch_forward(n)
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def debug_forward(inputs):
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return forward_func(inputs, pop_cal_seqs, pop_nodes, pop_cons)
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return debug_forward
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def ask_batch_forward(self, nodes, connections):
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n = nodes.shape[0]
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ts = self.create_topological_sort(n)
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cal_seqs = ts(nodes, connections)
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forward_func = self.create_batch_forward(n)
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def debug_forward(inputs):
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return forward_func(inputs, cal_seqs, nodes, connections)
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return debug_forward
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def compile_batch_unflatten_connections(self, n, c):
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func = unflatten_connections
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func = vmap(func)
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pop_nodes_lower = np.zeros((self.pop_size, n, 5))
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pop_connections_lower = np.zeros((self.pop_size, c, 4))
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func = jit(func).lower(pop_nodes_lower, pop_connections_lower).compile()
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self.compiled_function[('batch_unflatten_connections', n, c)] = func
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def create_batch_unflatten_connections(self, n, c):
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key = ('batch_unflatten_connections', n, c)
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if key not in self.compiled_function:
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self.compile_batch_unflatten_connections(n, c)
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if self.debug:
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def debug_batch_unflatten_connections(*args):
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return self.compiled_function[key](*args).block_until_ready()
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return debug_batch_unflatten_connections
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else:
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return self.compiled_function[key]
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