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import time
import torch
from hpc_rll.origin.td import dist_nstep_td_error, dist_nstep_td_data
from hpc_rll.rl_utils.td import DistNStepTD
from testbase import mean_relative_error, times
assert torch.cuda.is_available()
use_cuda = True
T = 128
B = 128
N = 128
gamma = 0.95
v_min = -10.0
v_max = 10.0
n_atom = 51
def dntd_val():
ori_dist = torch.randn(B, N, n_atom).abs()
ori_next_n_dist = torch.randn(B, N, n_atom).abs()
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_dist = ori_dist.clone().detach()
hpc_next_n_dist = ori_next_n_dist.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_dntd = DistNStepTD(T, B, N, n_atom)
if use_cuda:
ori_dist = ori_dist.cuda()
ori_next_n_dist = ori_next_n_dist.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_dist = hpc_dist.cuda()
hpc_next_n_dist = hpc_next_n_dist.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_dntd = hpc_dntd.cuda()
ori_dist.requires_grad_(True)
ori_loss, ori_td_err = dist_nstep_td_error(
dist_nstep_td_data(ori_dist, ori_next_n_dist, ori_action, ori_next_n_action, ori_reward, ori_done, ori_weight),
gamma, v_min, v_max, n_atom, T
)
ori_loss = ori_loss.mean()
ori_loss.backward()
hpc_dist.requires_grad_(True)
hpc_loss, hpc_td_err = hpc_dntd(
hpc_dist, hpc_next_n_dist, hpc_action, hpc_next_n_action, hpc_reward, hpc_done, hpc_weight, gamma, v_min, v_max
)
hpc_loss = hpc_loss.mean()
hpc_loss.backward()
mre = mean_relative_error(
torch.flatten(ori_loss).cpu().detach().numpy(),
torch.flatten(hpc_loss).cpu().detach().numpy()
)
print("dntd fp mean_relative_error: " + str(mre))
mre = mean_relative_error(
torch.flatten(ori_td_err).cpu().detach().numpy(),
torch.flatten(hpc_td_err).cpu().detach().numpy()
)
print("dntd fp td_err mean_relative_error: " + str(mre))
mre = mean_relative_error(
torch.flatten(ori_dist.grad).cpu().detach().numpy(),
torch.flatten(hpc_dist.grad).cpu().detach().numpy()
)
print("dntd bp mean_relative_error: " + str(mre))
def dntd_perf():
ori_dist = torch.randn(B, N, n_atom).abs()
ori_next_n_dist = torch.randn(B, N, n_atom).abs()
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_dist = ori_dist.clone().detach()
hpc_next_n_dist = ori_next_n_dist.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_dntd = DistNStepTD(T, B, N, n_atom)
if use_cuda:
ori_dist = ori_dist.cuda()
ori_next_n_dist = ori_next_n_dist.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_dist = hpc_dist.cuda()
hpc_next_n_dist = hpc_next_n_dist.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_dntd = hpc_dntd.cuda()
ori_dist.requires_grad_(True)
for i in range(times):
t = time.time()
ori_loss, ori_td_err = dist_nstep_td_error(
dist_nstep_td_data(
ori_dist, ori_next_n_dist, ori_action, ori_next_n_action, ori_reward, ori_done, ori_weight
), gamma, v_min, v_max, n_atom, T
)
ori_loss = ori_loss.mean()
ori_loss.backward()
if use_cuda:
torch.cuda.synchronize()
print('epoch: {}, origin dntd cost time: {}'.format(i, time.time() - t))
hpc_dist.requires_grad_(True)
for i in range(times):
t = time.time()
hpc_loss, hpc_td_err = hpc_dntd(
hpc_dist, hpc_next_n_dist, hpc_action, hpc_next_n_action, hpc_reward, hpc_done, hpc_weight, gamma, v_min,
v_max
)
hpc_loss = hpc_loss.mean()
hpc_loss.backward()
if use_cuda:
torch.cuda.synchronize()
print('epoch: {}, hpc dntd cost time: {}'.format(i, time.time() - t))
if __name__ == '__main__':
print(
"target problem: T = {}, B = {}, N = {}, gamma = {}, v_min = {}, v_max = {}, n_atom = {}".format(
T, B, N, gamma, v_min, v_max, n_atom
)
)
print("================run dntd validation test================")
dntd_val()
print("================run dntd performance test================")
dntd_perf()
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