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swarm.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
from typing import List, Optional, Dict, Any
import asyncio
import shortuuid
import numpy as np
import torch
import copy
from swarm.environment.operations.final_decision import FinalDecision, MergingStrategy
from swarm.optimizer.edge_optimizer.parameterization import EdgeWiseDistribution
from swarm.memory import GlobalMemory
from swarm.graph.composite_graph import CompositeGraph
from swarm.utils.log import logger
from swarm.environment.agents import AgentRegistry
from swarm.environment.operations.operation_registry import OperationRegistry
class Swarm:
"""
A class representing a swarm in the GPTSwarm framework.
Attributes:
"""
def __init__(self,
agent_names: List[str],
domain: str, # No default, we want the user to be aware of what domain they select.
model_name: Optional[str] = None, # None is mapped to "gpt-4-1106-preview".
open_graph_as_html: bool = False,
final_node_class: str = "FinalDecision",
final_node_kwargs: Dict[str, Any] = {'strategy': MergingStrategy.OutputsAsReferences},
edge_optimize: bool = False,
node_optimize: bool = False,
init_connection_probability: float = 0.5,
connect_output_nodes_to_final_node: bool = False,
include_inner_agent_connections: bool = True,
):
self.id = shortuuid.ShortUUID().random(length=4)
self.agent_names = agent_names
self.domain = domain
self.model_name = model_name
self.open_graph_as_html = open_graph_as_html
self.memory = GlobalMemory.instance()
self.final_node_class = final_node_class
self.final_node_kwargs = final_node_kwargs
self.edge_optimize = edge_optimize
self.node_optimize = node_optimize
self.init_connection_probability = init_connection_probability
self.connect_output_nodes_to_final_node = connect_output_nodes_to_final_node
self.organize(include_inner_agent_connections)
def organize(self, include_inner_agent_connections: bool = True):
self.used_agents = []
decision_method = OperationRegistry.get(self.final_node_class, self.domain, self.model_name, **self.final_node_kwargs)
self.composite_graph = CompositeGraph(decision_method,
self.domain, self.model_name)
potential_connections = []
for agent_name in self.agent_names:
if agent_name in AgentRegistry.registry:
agent_instance = AgentRegistry.get(agent_name,
self.domain, self.model_name)
if not include_inner_agent_connections:
for node in agent_instance.nodes:
for successor in agent_instance.nodes[node].successors:
potential_connections.append((node, successor.id))
agent_instance.nodes[node].successors = []
self.composite_graph.add_graph(agent_instance)
self.used_agents.append(agent_instance)
else:
logger.error(f"Cannot find {agent_name} in the list of registered agents "
f"({list(AgentRegistry.keys())})")
potential_connections = []
if self.edge_optimize:
# Add bi-directional connections between all nodes of all agents (except for the decision nodes).
for agent1 in self.used_agents:
for agent2 in self.used_agents:
if agent1 != agent2:
for node1 in agent1.nodes:
for node2 in agent2.nodes:
potential_connections.append((node1, node2)) # (from, to)
# Add only forward connections from all agents' nodes to the final decision node.
for agent in self.used_agents:
for node in agent.nodes:
if (self.connect_output_nodes_to_final_node and
node in [output_node.id for output_node in agent.output_nodes]):
agent.nodes[node].add_successor(decision_method)
else:
potential_connections.append((node, decision_method.id)) # (from, to)
else:
# Connect all output nodes to the decision method if edge optimization is not enabled
for agent in self.used_agents:
for node in agent.nodes:
if node in [output_node.id for output_node in agent.output_nodes]:
agent.nodes[node].add_successor(decision_method)
self.connection_dist = EdgeWiseDistribution(potential_connections, self.init_connection_probability)
self.potential_connections = potential_connections
def visualize_adj_matrix_distribution(self, logits):
probs = torch.sigmoid(logits)
matrix = np.zeros((self.composite_graph.num_nodes, self.composite_graph.num_nodes))
num_nodes_per_agent = np.array([len(agent.nodes) for agent in self.used_agents])
for i in range(len(num_nodes_per_agent)):
matrix[num_nodes_per_agent[:i].sum():num_nodes_per_agent[:i+1].sum(), num_nodes_per_agent[:i].sum():num_nodes_per_agent[:i+1].sum()] \
= self.used_agents[i].adj_matrix
probs_idx = 0
for i in range(len(self.used_agents)):
for j in range(len(self.used_agents)):
if i != j:
for k in range(num_nodes_per_agent[i]):
for l in range(num_nodes_per_agent[j]):
matrix[k + num_nodes_per_agent[:i].sum(), l + num_nodes_per_agent[:j].sum()] = probs[probs_idx]
probs_idx += 1
node_idx = 0
for agent in self.used_agents:
for node in agent.nodes:
if node in [output_node.id for output_node in agent.output_nodes] and self.connect_output_nodes_to_final_node:
matrix[node_idx, -1] = 1
else:
matrix[node_idx, -1] = probs[probs_idx]
probs_idx += 1
node_idx += 1
return matrix
def run(self,
inputs: Dict[str, Any],
realized_graph: Optional[CompositeGraph] = None,
display: bool = False,
):
if realized_graph is None:
_graph, _ = self.connection_dist.realize(self.composite_graph)
else:
_graph = copy.deepcopy(realized_graph)
if display:
_graph.display(draw=self.open_graph_as_html)
final_answer = asyncio.run(_graph.run(inputs))
return final_answer
async def arun(self,
inputs: Dict[str, Any],
realized_graph: Optional[CompositeGraph] = None,
):
if realized_graph is None:
_graph, _ = self.connection_dist.realize(self.composite_graph)
else:
_graph = copy.deepcopy(realized_graph)
_graph.display(draw=self.open_graph_as_html)
final_answer = await _graph.run(inputs)
return final_answer