| 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() | |