tinygrad/extra/optimization/test_net.py

66 lines
2.1 KiB
Python

import numpy as np
import math
import random
np.set_printoptions(suppress=True)
from copy import deepcopy
from tinygrad.helpers import getenv, colored
from tinygrad.tensor import Tensor
from tinygrad.nn.state import get_parameters, get_state_dict, safe_save, safe_load, load_state_dict
from tinygrad.engine.search import bufs_from_lin, time_linearizer, actions, get_linearizer_actions
from extra.optimization.helpers import load_worlds, ast_str_to_lin, lin_to_feats
from extra.optimization.extract_policynet import PolicyNet
from extra.optimization.pretrain_valuenet import ValueNet
VALUE = getenv("VALUE")
if __name__ == "__main__":
if VALUE:
net = ValueNet()
load_state_dict(net, safe_load("/tmp/valuenet.safetensors"))
else:
net = PolicyNet()
load_state_dict(net, safe_load("/tmp/policynet.safetensors"))
ast_strs = load_worlds()
# real randomness
random.seed()
random.shuffle(ast_strs)
wins = 0
for ep_num,ast_str in enumerate(ast_strs):
print("\nEPISODE", ep_num, f"win {wins*100/max(1,ep_num):.2f}%")
lin = ast_str_to_lin(ast_str)
rawbufs = bufs_from_lin(lin)
linhc = deepcopy(lin)
linhc.hand_coded_optimizations()
tmhc = time_linearizer(linhc, rawbufs)
print(f"{tmhc*1e6:10.2f} HC ", linhc.colored_shape())
pred_time = float('nan')
tm = float('inf')
while 1:
if VALUE:
acts,feats = [], []
for k,v in get_linearizer_actions(lin).items():
acts.append(k)
feats.append(lin_to_feats(v))
preds = net(Tensor(feats))
pred_time = math.exp(preds.numpy().min())
act = acts[preds.numpy().argmin()]
else:
probs = net(Tensor([lin_to_feats(lin)]))
dist = probs.exp().numpy()
act = dist.argmax()
if act == 0: break
try:
lin.apply_opt(actions[act-1])
except Exception:
print("FAILED")
break
tm = time_linearizer(lin, rawbufs)
print(f"{tm*1e6:10.2f} {pred_time*1e6:10.2f}", lin.colored_shape())
print(f"{colored('BEAT', 'green') if tm < tmhc else colored('lost', 'red')} hand coded {tmhc/tm:5.2f}x")
wins += int(tm < tmhc)