new architecture
This commit is contained in:
wls2002
@@ -1,87 +1,38 @@
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from typing import Type
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import jax, jax.numpy as jnp
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from utils import State
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from .. import BaseAlgorithm
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from .genome import *
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from .species import *
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from .ga import *
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import jax
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from jax import numpy as jnp
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import numpy as np
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class NEAT(BaseAlgorithm):
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from config import Config
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from core import Algorithm, State, Gene, Genome
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from .ga import create_next_generation
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from .species import SpeciesInfo, update_species, speciate
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def __init__(
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self,
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genome: BaseGenome,
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species: BaseSpecies,
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mutation: BaseMutation = DefaultMutation(),
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crossover: BaseCrossover = DefaultCrossover(),
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):
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self.genome = genome
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self.species = species
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self.mutation = mutation
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self.crossover = crossover
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class NEAT(Algorithm):
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def __init__(self, config: Config, gene_type: Type[Gene]):
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self.config = config
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self.gene = gene_type(config.gene)
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self.forward_func = None
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self.tell_func = None
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def setup(self, randkey, state: State = State()):
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"""initialize the state of the algorithm"""
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input_idx = np.arange(self.config.neat.inputs)
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output_idx = np.arange(self.config.neat.inputs,
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self.config.neat.inputs + self.config.neat.outputs)
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state = state.update(
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P=self.config.basic.pop_size,
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N=self.config.neat.max_nodes,
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C=self.config.neat.max_conns,
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S=self.config.neat.max_species,
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NL=1 + len(self.gene.node_attrs), # node length = (key) + attributes
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CL=3 + len(self.gene.conn_attrs), # conn length = (in, out, key) + attributes
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max_stagnation=self.config.neat.max_stagnation,
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species_elitism=self.config.neat.species_elitism,
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spawn_number_change_rate=self.config.neat.spawn_number_change_rate,
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genome_elitism=self.config.neat.genome_elitism,
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survival_threshold=self.config.neat.survival_threshold,
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compatibility_threshold=self.config.neat.compatibility_threshold,
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compatibility_disjoint=self.config.neat.compatibility_disjoint,
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compatibility_weight=self.config.neat.compatibility_weight,
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input_idx=input_idx,
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output_idx=output_idx,
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def setup(self, randkey):
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k1, k2 = jax.random.split(randkey, 2)
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return State(
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randkey=k1,
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generation=0,
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next_node_key=max(*self.genome.input_idx, *self.genome.output_idx) + 2,
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# inputs nodes, output nodes, 1 hidden node
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species=self.species.setup(k2),
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)
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state = self.gene.setup(state)
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pop_genomes = self._initialize_genomes(state)
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species_info = SpeciesInfo.initialize(state)
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idx2species = jnp.zeros(state.P, dtype=jnp.float32)
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center_nodes = jnp.full((state.S, state.N, state.NL), jnp.nan, dtype=jnp.float32)
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center_conns = jnp.full((state.S, state.C, state.CL), jnp.nan, dtype=jnp.float32)
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center_genomes = Genome(center_nodes, center_conns)
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center_genomes = center_genomes.set(0, pop_genomes[0])
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generation = 0
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next_node_key = max(*state.input_idx, *state.output_idx) + 2
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next_species_key = 1
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state = state.update(
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randkey=randkey,
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pop_genomes=pop_genomes,
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species_info=species_info,
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idx2species=idx2species,
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center_genomes=center_genomes,
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# avoid jax auto cast from int to float. that would cause re-compilation.
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generation=jnp.asarray(generation, dtype=jnp.int32),
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next_node_key=jnp.asarray(next_node_key, dtype=jnp.float32),
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next_species_key=jnp.asarray(next_species_key, dtype=jnp.float32),
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)
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return jax.device_put(state)
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def ask_algorithm(self, state: State):
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return state.pop_genomes
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def tell_algorithm(self, state: State, fitness):
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state = self.gene.update(state)
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def ask(self, state: State):
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return self.species.ask(state)
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def tell(self, state: State, fitness):
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k1, k2, randkey = jax.random.split(state.randkey, 3)
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state = state.update(
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@@ -89,46 +40,55 @@ class NEAT(Algorithm):
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randkey=randkey
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)
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state, winner, loser, elite_mask = update_species(state, k1, fitness)
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state, winner, loser, elite_mask = self.species.update_species(state, fitness, state.generation)
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state = create_next_generation(self.config.neat, self.gene, state, k2, winner, loser, elite_mask)
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state = self.create_next_generation(k2, state, winner, loser, elite_mask)
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state = speciate(self.gene, state)
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state = self.species.speciate(state, state.generation)
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return state
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def forward_transform(self, state: State, genome: Genome):
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return self.gene.forward_transform(state, genome)
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def transform(self, state: State):
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"""transform the genome into a neural network"""
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raise NotImplementedError
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def forward(self, state: State, inputs, genome: Genome):
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return self.gene.forward(state, inputs, genome)
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def forward(self, inputs, transformed):
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raise NotImplementedError
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def _initialize_genomes(self, state):
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o_nodes = np.full((state.N, state.NL), np.nan, dtype=np.float32) # original nodes
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o_conns = np.full((state.C, state.CL), np.nan, dtype=np.float32) # original connections
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def create_next_generation(self, randkey, state, winner, loser, elite_mask):
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# prepare random keys
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pop_size = self.species.pop_size
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new_node_keys = jnp.arange(pop_size) + state.species.next_node_key
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input_idx = state.input_idx
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output_idx = state.output_idx
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new_node_key = max([*input_idx, *output_idx]) + 1
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k1, k2 = jax.random.split(randkey, 2)
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crossover_rand_keys = jax.random.split(k1, pop_size)
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mutate_rand_keys = jax.random.split(k2, pop_size)
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o_nodes[input_idx, 0] = input_idx
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o_nodes[output_idx, 0] = output_idx
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o_nodes[new_node_key, 0] = new_node_key
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o_nodes[np.concatenate([input_idx, output_idx]), 1:] = self.gene.new_node_attrs(state)
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o_nodes[new_node_key, 1:] = self.gene.new_node_attrs(state)
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wpn, wpc = state.species.pop_nodes[winner], state.species.pop_conns[winner]
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lpn, lpc = state.species.pop_nodes[loser], state.species.pop_conns[loser]
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input_conns = np.c_[input_idx, np.full_like(input_idx, new_node_key)]
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o_conns[input_idx, 0:2] = input_conns # in key, out key
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o_conns[input_idx, 2] = True # enabled
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o_conns[input_idx, 3:] = self.gene.new_conn_attrs(state)
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# batch crossover
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n_nodes, n_conns = (jax.vmap(self.crossover, in_axes=(0, None, 0, 0, 0, 0))
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(crossover_rand_keys, self.genome, wpn, wpc, lpn, lpc))
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output_conns = np.c_[np.full_like(output_idx, new_node_key), output_idx]
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o_conns[output_idx, 0:2] = output_conns # in key, out key
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o_conns[output_idx, 2] = True # enabled
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o_conns[output_idx, 3:] = self.gene.new_conn_attrs(state)
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# batch mutation
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m_n_nodes, m_n_conns = (jax.vmap(self.mutation, in_axes=(0, None, 0, 0, 0))
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(mutate_rand_keys, self.genome, n_nodes, n_conns, new_node_keys))
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# repeat origin genome for P times to create population
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pop_nodes = np.tile(o_nodes, (state.P, 1, 1))
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pop_conns = np.tile(o_conns, (state.P, 1, 1))
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# elitism don't mutate
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pop_nodes = jnp.where(elite_mask[:, None, None], n_nodes, m_n_nodes)
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pop_conns = jnp.where(elite_mask[:, None, None], n_conns, m_n_conns)
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# update next node key
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all_nodes_keys = pop_nodes[:, :, 0]
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max_node_key = jnp.max(jnp.where(jnp.isnan(all_nodes_keys), -jnp.inf, all_nodes_keys))
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next_node_key = max_node_key + 1
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return state.update(
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species=state.species.update(
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pop_nodes=pop_nodes,
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pop_conns=pop_conns,
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),
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next_node_key=next_node_key,
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)
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return Genome(pop_nodes, pop_conns)
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