284 lines
9.7 KiB
Python
284 lines
9.7 KiB
Python
import warnings
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from typing import Callable
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import jax, jax.numpy as jnp
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import sympy as sp
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from utils import (
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unflatten_conns,
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topological_sort,
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topological_sort_python,
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I_INF,
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extract_node_attrs,
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extract_conn_attrs,
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set_node_attrs,
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set_conn_attrs,
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attach_with_inf,
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SYMPY_FUNCS_MODULE_NP,
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SYMPY_FUNCS_MODULE_JNP,
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)
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from . import BaseGenome
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from ..gene import BaseNodeGene, BaseConnGene, DefaultNodeGene, DefaultConnGene
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from ..ga import BaseMutation, BaseCrossover, DefaultMutation, DefaultCrossover
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class DefaultGenome(BaseGenome):
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"""Default genome class, with the same behavior as the NEAT-Python"""
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network_type = "feedforward"
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def __init__(
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self,
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num_inputs: int,
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num_outputs: int,
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max_nodes=5,
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max_conns=4,
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node_gene: BaseNodeGene = DefaultNodeGene(),
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conn_gene: BaseConnGene = DefaultConnGene(),
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mutation: BaseMutation = DefaultMutation(),
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crossover: BaseCrossover = DefaultCrossover(),
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output_transform: Callable = None,
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):
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super().__init__(
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num_inputs,
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num_outputs,
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max_nodes,
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max_conns,
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node_gene,
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conn_gene,
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mutation,
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crossover,
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)
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if output_transform is not None:
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try:
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_ = output_transform(jnp.zeros(num_outputs))
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except Exception as e:
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raise ValueError(f"Output transform function failed: {e}")
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self.output_transform = output_transform
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def transform(self, state, nodes, conns):
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u_conns = unflatten_conns(nodes, conns)
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conn_exist = u_conns != I_INF
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seqs = topological_sort(nodes, conn_exist)
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return seqs, nodes, conns, u_conns
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def restore(self, state, transformed):
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seqs, nodes, conns, u_conns = transformed
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return nodes, conns
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def forward(self, state, transformed, inputs):
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cal_seqs, nodes, conns, u_conns = transformed
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ini_vals = jnp.full((self.max_nodes,), jnp.nan)
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ini_vals = ini_vals.at[self.input_idx].set(inputs)
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nodes_attrs = jax.vmap(extract_node_attrs)(nodes)
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conns_attrs = jax.vmap(extract_conn_attrs)(conns)
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def cond_fun(carry):
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values, idx = carry
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return (idx < self.max_nodes) & (cal_seqs[idx] != I_INF)
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def body_func(carry):
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values, idx = carry
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i = cal_seqs[idx]
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def input_node():
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z = self.node_gene.input_transform(state, nodes_attrs[i], values[i])
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new_values = values.at[i].set(z)
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return new_values
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def otherwise():
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conn_indices = u_conns[:, i]
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hit_attrs = attach_with_inf(conns_attrs, conn_indices)
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ins = jax.vmap(self.conn_gene.forward, in_axes=(None, 0, 0))(
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state, hit_attrs, values
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)
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z = self.node_gene.forward(
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state,
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nodes_attrs[i],
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ins,
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is_output_node=jnp.isin(i, self.output_idx),
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)
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new_values = values.at[i].set(z)
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return new_values
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values = jax.lax.cond(jnp.isin(i, self.input_idx), input_node, otherwise)
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return values, idx + 1
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vals, _ = jax.lax.while_loop(cond_fun, body_func, (ini_vals, 0))
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if self.output_transform is None:
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return vals[self.output_idx]
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else:
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return self.output_transform(vals[self.output_idx])
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def update_by_batch(self, state, batch_input, transformed):
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cal_seqs, nodes, conns, u_conns = transformed
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batch_size = batch_input.shape[0]
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batch_ini_vals = jnp.full((batch_size, self.max_nodes), jnp.nan)
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batch_ini_vals = batch_ini_vals.at[:, self.input_idx].set(batch_input)
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nodes_attrs = jax.vmap(extract_node_attrs)(nodes)
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conns_attrs = jax.vmap(extract_conn_attrs)(conns)
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def cond_fun(carry):
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batch_values, nodes_attrs_, conns_attrs_, idx = carry
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return (idx < self.max_nodes) & (cal_seqs[idx] != I_INF)
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def body_func(carry):
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batch_values, nodes_attrs_, conns_attrs_, idx = carry
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i = cal_seqs[idx]
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def input_node():
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batch, new_attrs = self.node_gene.update_input_transform(
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state, nodes_attrs_[i], batch_values[:, i]
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)
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return (
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batch_values.at[:, i].set(batch),
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nodes_attrs_.at[i].set(new_attrs),
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conns_attrs_,
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)
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def otherwise():
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conn_indices = u_conns[:, i]
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hit_attrs = attach_with_inf(conns_attrs, conn_indices)
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batch_ins, new_conn_attrs = jax.vmap(
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self.conn_gene.update_by_batch,
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in_axes=(None, 0, 1),
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out_axes=(1, 0),
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)(state, hit_attrs, batch_values)
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batch_z, new_node_attrs = self.node_gene.update_by_batch(
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state,
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nodes_attrs_[i],
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batch_ins,
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is_output_node=jnp.isin(i, self.output_idx),
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)
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return (
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batch_values.at[:, i].set(batch_z),
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nodes_attrs_.at[i].set(new_node_attrs),
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conns_attrs_.at[conn_indices].set(new_conn_attrs),
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)
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# the val of input nodes is obtained by the task, not by calculation
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(batch_values, nodes_attrs_, conns_attrs_) = jax.lax.cond(
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jnp.isin(i, self.input_idx),
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input_node,
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otherwise,
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)
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return batch_values, nodes_attrs_, conns_attrs_, idx + 1
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batch_vals, nodes_attrs, conns_attrs, _ = jax.lax.while_loop(
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cond_fun, body_func, (batch_ini_vals, nodes_attrs, conns_attrs, 0)
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)
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nodes = jax.vmap(set_node_attrs)(nodes, nodes_attrs)
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conns = jax.vmap(set_conn_attrs)(conns, conns_attrs)
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new_transformed = (cal_seqs, nodes, conns, u_conns)
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if self.output_transform is None:
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return batch_vals[:, self.output_idx], new_transformed
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else:
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return (
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jax.vmap(self.output_transform)(batch_vals[:, self.output_idx]),
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new_transformed,
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)
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def sympy_func(self, state, network, sympy_output_transform=None, backend="jax"):
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assert backend in ["jax", "numpy"], "backend should be 'jax' or 'numpy'"
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module = SYMPY_FUNCS_MODULE_JNP if backend == "jax" else SYMPY_FUNCS_MODULE_NP
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if sympy_output_transform is None and self.output_transform is not None:
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warnings.warn(
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"genome.output_transform is not None but sympy_output_transform is None!"
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)
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input_idx = self.get_input_idx()
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output_idx = self.get_output_idx()
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order, _ = topological_sort_python(set(network["nodes"]), set(network["conns"]))
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hidden_idx = [i for i in network["nodes"] if i not in input_idx and i not in output_idx]
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symbols = {}
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for i in network["nodes"]:
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if i in input_idx:
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symbols[i] = sp.Symbol(f"i{i - min(input_idx)}")
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elif i in output_idx:
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symbols[i] = sp.Symbol(f"o{i - min(output_idx)}")
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else: # hidden
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symbols[i] = sp.Symbol(f"h{i - min(hidden_idx)}")
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nodes_exprs = {}
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args_symbols = {}
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for i in order:
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if i in input_idx:
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nodes_exprs[symbols[i]] = symbols[i]
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else:
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in_conns = [c for c in network["conns"] if c[1] == i]
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node_inputs = []
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for conn in in_conns:
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val_represent = symbols[conn[0]]
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# a_s -> args_symbols
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val, a_s = self.conn_gene.sympy_func(
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state,
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network["conns"][conn],
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val_represent,
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)
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args_symbols.update(a_s)
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node_inputs.append(val)
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nodes_exprs[symbols[i]], a_s = self.node_gene.sympy_func(
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state,
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network["nodes"][i],
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node_inputs,
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is_output_node=(i in output_idx),
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)
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args_symbols.update(a_s)
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if i in output_idx and sympy_output_transform is not None:
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nodes_exprs[symbols[i]] = sympy_output_transform(
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nodes_exprs[symbols[i]]
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)
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input_symbols = [v for k, v in symbols.items() if k in input_idx]
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reduced_exprs = nodes_exprs.copy()
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for i in order:
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reduced_exprs[symbols[i]] = reduced_exprs[symbols[i]].subs(reduced_exprs)
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output_exprs = [reduced_exprs[symbols[i]] for i in output_idx]
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lambdify_output_funcs = [
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sp.lambdify(
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input_symbols + list(args_symbols.keys()),
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exprs,
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modules=[backend, module],
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)
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for exprs in output_exprs
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]
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fixed_args_output_funcs = []
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for i in range(len(output_idx)):
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def f(inputs, i=i):
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return lambdify_output_funcs[i](*inputs, *args_symbols.values())
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fixed_args_output_funcs.append(f)
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forward_func = lambda inputs: jnp.array([f(inputs) for f in fixed_args_output_funcs])
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return (
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symbols,
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args_symbols,
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input_symbols,
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nodes_exprs,
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output_exprs,
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forward_func,
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)
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