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import time
import torch
from hpc_rll.origin.td import q_nstep_td_error, q_nstep_td_data
from hpc_rll.rl_utils.td import QNStepTD
from testbase import mean_relative_error, times
assert torch.cuda.is_available()
use_cuda = True
T = 1024
B = 64
N = 64
gamma = 0.95
def qntd_val():
ori_q = torch.randn(B, N)
ori_next_n_q = torch.randn(B, N)
ori_action = torch.randint(0, N, size=(B, ))
ori_next_n_action = torch.randint(0, N, size=(B, ))
ori_reward = torch.randn(T, B)
ori_done = torch.randn(B)
ori_weight = torch.randn(B)
hpc_q = ori_q.clone().detach()
hpc_next_n_q = ori_next_n_q.clone().detach()
hpc_action = ori_action.clone().detach()
hpc_next_n_action = ori_next_n_action.clone().detach()
hpc_reward = ori_reward.clone().detach()
hpc_done = ori_done.clone().detach()
hpc_weight = ori_weight.clone().detach()
hpc_qntd = QNStepTD(T, B, N)
if use_cuda:
ori_q = ori_q.cuda()
ori_next_n_q = ori_next_n_q.cuda()
ori_action = ori_action.cuda()
ori_next_n_action = ori_next_n_action.cuda()
ori_reward = ori_reward.cuda()
ori_done = ori_done.cuda()
ori_weight = ori_weight.cuda()
hpc_q = hpc_q.cuda()
hpc_next_n_q = hpc_next_n_q.cuda()
hpc_action = hpc_action.cuda()
hpc_next_n_action = hpc_next_n_action.cuda()
hpc_reward = hpc_reward.cuda()
hpc_done = hpc_done.cuda()
hpc_weight = hpc_weight.cuda()
hpc_qntd = hpc_qntd.cuda()
ori_q.requires_grad_(True)
ori_loss, _ = q_nstep_td_error(
q_nstep_td_data(ori_q, ori_next_n_q, ori_action, ori_next_n_action, ori_reward, ori_done, ori_weight), gamma, T
)
ori_loss = ori_loss.mean()
ori_loss.backward()
if use_cuda:
torch.cuda.synchronize()
hpc_q.requires_grad_(True)
hpc_loss, _ = hpc_qntd(hpc_q, hpc_next_n_q, hpc_action, hpc_next_n_action, hpc_reward, hpc_done, hpc_weight, gamma)
hpc_loss = hpc_loss.mean()
hpc_loss.backward()
if use_cuda:
torch.cuda.synchronize()
mre = mean_relative_error(
torch.flatten(ori_loss).cpu().detach().numpy(),
torch.flatten(hpc_loss).cpu().detach().numpy()
)
print("qntd fp mean_relative_error: " + str(mre))
mre = mean_relative_error(
torch.flatten(ori_q.grad).cpu().detach().numpy(),
torch.flatten(hpc_q.grad).cpu().detach().numpy()
)
print("qntd bp mean_relative_error: " + str(mre))
def qntd_perf():
ori_q = torch.randn(B, N)
ori_next_n_q = torch.randn(B, N)
ori_action = torch.randint(0, N, size=(B, ))
ori_next_n_action = torch.randint(0, N, size=(B, ))
ori_reward = torch.randn(T, B)
ori_done = torch.randn(B)
ori_weight = torch.randn(B)
hpc_q = ori_q.clone().detach()
hpc_next_n_q = ori_next_n_q.clone().detach()
hpc_action = ori_action.clone().detach()
hpc_next_n_action = ori_next_n_action.clone().detach()
hpc_reward = ori_reward.clone().detach()
hpc_done = ori_done.clone().detach()
hpc_weight = ori_weight.clone().detach()
hpc_qntd = QNStepTD(T, B, N)
if use_cuda:
ori_q = ori_q.cuda()
ori_next_n_q = ori_next_n_q.cuda()
ori_action = ori_action.cuda()
ori_next_n_action = ori_next_n_action.cuda()
ori_reward = ori_reward.cuda()
ori_done = ori_done.cuda()
ori_weight = ori_weight.cuda()
hpc_q = hpc_q.cuda()
hpc_next_n_q = hpc_next_n_q.cuda()
hpc_action = hpc_action.cuda()
hpc_next_n_action = hpc_next_n_action.cuda()
hpc_reward = hpc_reward.cuda()
hpc_done = hpc_done.cuda()
hpc_weight = hpc_weight.cuda()
hpc_qntd = hpc_qntd.cuda()
ori_q.requires_grad_(True)
for i in range(times):
t = time.time()
ori_loss, _ = q_nstep_td_error(
q_nstep_td_data(ori_q, ori_next_n_q, ori_action, ori_next_n_action, ori_reward, ori_done, ori_weight),
gamma, T
)
ori_loss = ori_loss.mean()
ori_loss.backward()
if use_cuda:
torch.cuda.synchronize()
print('epoch: {}, original qntd cost time: {}'.format(i, time.time() - t))
hpc_q.requires_grad_(True)
for i in range(times):
t = time.time()
hpc_loss, _ = hpc_qntd(
hpc_q, hpc_next_n_q, hpc_action, hpc_next_n_action, hpc_reward, hpc_done, hpc_weight, gamma
)
hpc_loss = hpc_loss.mean()
hpc_loss.backward()
if use_cuda:
torch.cuda.synchronize()
print('epoch: {}, hpc qntd cost time: {}'.format(i, time.time() - t))
if __name__ == '__main__':
print("target problem: T = {}, B = {}, N = {}, gamma = {}".format(T, B, N, gamma))
print("================run qntd validation test================")
qntd_val()
print("================run qntd performance test================")
qntd_perf()
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