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make it a bit easier to choose backend / algo from command line #15

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make it a bit easier to choose backend / algo from command line #15

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133 changes: 56 additions & 77 deletions engine.py
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Original file line number Diff line number Diff line change
@@ -1,9 +1,9 @@
from lcztools import load_network, LeelaBoard
from lcztools import LeelaBoard, list_backends
import search
import chess
import chess.pgn
import sys

from leela_lite import get_search_algos

logfile = open("leelalite.log", "w")
LOG = False

Expand Down Expand Up @@ -40,77 +40,56 @@ def process_position(tokens):

return board

if len(sys.argv) == 3:
weights = sys.argv[1]
nodes = int(sys.argv[2])
type = "uct"
elif len(sys.argv) == 4:
weights = sys.argv[1]
nodes = int(sys.argv[2])
if sys.argv[3] == 'minimax':
type = 'minimax'
else:
type = 'uct'
else:
print("Usage: python3 engine.py <weights file or network server ID> <nodes>")
print(len(sys.argv))
exit(1)

network_id = None
try:
# If the parameter is an integer, assume it's a network server ID
network_id = int(weights)
weights = None
except:
pass

def load_leela_network():
global net, nn
if network_id is not None:
net = load_network(backend='net_client', network_id=network_id, policy_softmax_temp=2.2)
else:
net = load_network(backend='pytorch_cuda', filename=weights, policy_softmax_temp=2.2)
nn = search.NeuralNet(net=net, lru_size=max(5000, nodes))


send("Leela Lite")
board = LeelaBoard()
net = None
nn = None



while True:
line = sys.stdin.readline()
line = line.rstrip()
log("<{}".format(line))
tokens = line.split()
if len(tokens) == 0:
continue

if tokens[0] == "uci":
send('id name Leela Lite')
send('id author Dietrich Kappe')
send('uciok')
elif tokens[0] == "quit":
exit(0)
elif tokens[0] == "isready":
load_leela_network()
send("readyok")
elif tokens[0] == "ucinewgame":
board = LeelaBoard()
elif tokens[0] == 'position':
board = process_position(tokens)
elif tokens[0] == 'go':
my_nodes = nodes
if (len(tokens) == 3) and (tokens[1] == 'nodes'):
my_nodes = int(tokens[2])
if nn == None:
load_leela_network()
if type == 'uct':
best, node = search.UCT_search(board, my_nodes, net=nn, C=3.0)
else:
best, node = search.MinMax_search(board, my_nodes, net=nn, C=3.0)
send("bestmove {}".format(best))

logfile.close()
def uci(args):
from leela_lite import load_leela_network

send("Leela Lite")
board = LeelaBoard()

nodes = args.nodes
net = load_leela_network(args.weights, args.backend)
nn = search.NeuralNet(net=net, lru_size=min(5000, nodes))

search_func = get_search_algos()[args.algo]

while True:
line = sys.stdin.readline()
line = line.rstrip()
log("<{}".format(line))
tokens = line.split()
if len(tokens) == 0:
continue

if tokens[0] == "uci":
send('id name Leela Lite')
send('id author Dietrich Kappe')
send('uciok')
elif tokens[0] == "quit":
exit(0)
elif tokens[0] == "isready":
send("readyok")
elif tokens[0] == "ucinewgame":
board = LeelaBoard()
elif tokens[0] == 'position':
board = process_position(tokens)
elif tokens[0] == 'go':
my_nodes = nodes
if (len(tokens) == 3) and (tokens[1] == 'nodes'):
my_nodes = int(tokens[2])
best, node = search_func(board, my_nodes, net=nn, C=3.0)
send("bestmove {}".format(best))

logfile.close()

if __name__ == '__main__':
algos = get_search_algos().keys()

import argparse
parser = argparse.ArgumentParser(description='')
parser.add_argument('weights', help='weights file or network server ID')
parser.add_argument('nodes', type=int, help='nodes per move')
parser.add_argument('-a', '--algo', default='uct', help='search algo to use', choices=algos)
parser.add_argument('-b', '--backend', default='pytorch_cuda', choices=list_backends(), help='nn backend')

args = parser.parse_args()
uci(args)
110 changes: 59 additions & 51 deletions leela_lite.py
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Original file line number Diff line number Diff line change
@@ -1,66 +1,74 @@
#!/usr/bin/python3
from lcztools import load_network, LeelaBoard
from lcztools import load_network, LeelaBoard, list_backends
import search
import chess
import chess.pgn
import sys
import time


if len(sys.argv) != 3:
print("Usage: python3 leela_lite.py <weights file or network server ID> <nodes>")
print(len(sys.argv))
exit(1)
else:
weights = sys.argv[1]
nodes = int(sys.argv[2])
def load_leela_network(weights, backend):
if backend == 'net_client':
network_id = int(weights)
return load_network(backend='net_client', network_id=network_id, policy_softmax_temp=2.2)

network_id = None
try:
# If the parameter is an integer, assume it's a network server ID
network_id = int(weights)
weights = None
except:
pass
return load_network(backend=backend, filename=weights, policy_softmax_temp=2.2)

def load_leela_network():
global net, nn
if network_id is not None:
net = load_network(backend='net_client', network_id=network_id, policy_softmax_temp=2.2)
else:
net = load_network(backend='pytorch_cuda', filename=weights, policy_softmax_temp=2.2)
def get_search_algos():
from inspect import getmembers, isfunction

methods = {}
for key, value in getmembers(search, isfunction):
if key.endswith('_search'):
methods[key.rsplit('_',1)[0].lower()] = value
return methods

def play(args):
nodes = args.nodes
net = load_leela_network(args.weights, args.backend)
nn = search.NeuralNet(net=net, lru_size=min(5000, nodes))


load_leela_network()
search_func = get_search_algos()[args.algo]

SELFPLAY = True
SELFPLAY = True

board = LeelaBoard()
while True:
if not SELFPLAY:
print(board.unicode())
print("Enter move: ", end='')
sys.stdout.flush()
line = sys.stdin.readline()
line = line.rstrip()
board.push_uci(line)
print(board.unicode())
print("thinking...")
start = time.time()
best, node = search.UCT_search(board, nodes, net=nn, C=3.4)
elapsed = time.time() - start
print("best: ", best)
print("Time: {:.3f} nps".format(nodes/elapsed))
print(nn.evaluate.cache_info())
board.push_uci(best)
if board.pc_board.is_game_over() or board.is_draw():
result = board.pc_board.result(claim_draw=True)
print("Game over... result is {}".format(result))
board = LeelaBoard()
while True:
if not SELFPLAY:
print(board.unicode())
print("Enter move: ", end='')
sys.stdout.flush()
line = sys.stdin.readline()
line = line.rstrip()
board.push_uci(line)
print(board.unicode())
print()
pgn_game = chess.pgn.Game.from_board(board.pc_board)
pgn_game.headers['Result'] = result
print(pgn_game)
break

print("thinking...")
start = time.time()
best, node = search_func(board, nodes, net=nn, C=3.4)
elapsed = time.time() - start
print("best: ", best)
print("Time: {:.3f} nps".format(nodes/elapsed))
print(nn.evaluate.cache_info())
board.push_uci(best)
if board.pc_board.is_game_over() or board.is_draw():
result = board.pc_board.result(claim_draw=True)
print("Game over... result is {}".format(result))
print(board.unicode())
print()
pgn_game = chess.pgn.Game.from_board(board.pc_board)
pgn_game.headers['Result'] = result
print(pgn_game)
break

if __name__ == '__main__':
algos = get_search_algos().keys()

import argparse
parser = argparse.ArgumentParser(description='')
parser.add_argument('weights', help='weights file or network server ID')
parser.add_argument('nodes', type=int, help='nodes per move')
parser.add_argument('-a', '--algo', default='uct', help='search algo to use', choices=algos)
parser.add_argument('-b', '--backend', default='pytorch_cuda', choices=list_backends(), help='nn backend')

args = parser.parse_args()
play(args)