| import time | |
| import torch | |
| from hpc_rll.origin.upgo import upgo_loss | |
| from hpc_rll.rl_utils.upgo import UPGO | |
| from testbase import mean_relative_error, times | |
| assert torch.cuda.is_available() | |
| use_cuda = True | |
| T = 256 | |
| B = 256 | |
| N = 256 | |
| def upgo_val(): | |
| ori_target_output = torch.randn(T, B, N) | |
| ori_rhos = torch.randn(T, B) | |
| ori_action = torch.randint( | |
| 0, N, size=( | |
| T, | |
| B, | |
| ) | |
| ) | |
| ori_rewards = torch.randn(T, B) | |
| ori_bootstrap_values = torch.randn(T + 1, B) | |
| hpc_target_output = ori_target_output.clone().detach() | |
| hpc_rhos = ori_rhos.clone().detach() | |
| hpc_action = ori_action.clone().detach() | |
| hpc_rewards = ori_rewards.clone().detach() | |
| hpc_bootstrap_values = ori_bootstrap_values.clone().detach() | |
| hpc_upgo = UPGO(T, B, N) | |
| if use_cuda: | |
| ori_target_output = ori_target_output.cuda() | |
| ori_rhos = ori_rhos.cuda() | |
| ori_action = ori_action.cuda() | |
| ori_rewards = ori_rewards.cuda() | |
| ori_bootstrap_values = ori_bootstrap_values.cuda() | |
| hpc_target_output = hpc_target_output.cuda() | |
| hpc_rhos = hpc_rhos.cuda() | |
| hpc_action = hpc_action.cuda() | |
| hpc_rewards = hpc_rewards.cuda() | |
| hpc_bootstrap_values = hpc_bootstrap_values.cuda() | |
| hpc_upgo = hpc_upgo.cuda() | |
| ori_target_output.requires_grad_(True) | |
| ori_loss = upgo_loss(ori_target_output, ori_rhos, ori_action, ori_rewards, ori_bootstrap_values) | |
| ori_loss = ori_loss.mean() | |
| ori_loss.backward() | |
| if use_cuda: | |
| torch.cuda.synchronize() | |
| hpc_target_output.requires_grad_(True) | |
| hpc_loss = hpc_upgo(hpc_target_output, hpc_rhos, hpc_action, hpc_rewards, hpc_bootstrap_values) | |
| 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("upgo fp mean_relative_error: " + str(mre)) | |
| mre = mean_relative_error( | |
| torch.flatten(ori_target_output.grad).cpu().detach().numpy(), | |
| torch.flatten(hpc_target_output.grad).cpu().detach().numpy() | |
| ) | |
| print("upgo bp mean_relative_error: " + str(mre)) | |
| def upgo_perf(): | |
| ori_target_output = torch.randn(T, B, N) | |
| ori_rhos = torch.randn(T, B) | |
| ori_action = torch.randint( | |
| 0, N, size=( | |
| T, | |
| B, | |
| ) | |
| ) | |
| ori_rewards = torch.randn(T, B) | |
| ori_bootstrap_values = torch.randn(T + 1, B) | |
| hpc_target_output = ori_target_output.clone().detach() | |
| hpc_rhos = ori_rhos.clone().detach() | |
| hpc_action = ori_action.clone().detach() | |
| hpc_rewards = ori_rewards.clone().detach() | |
| hpc_bootstrap_values = ori_bootstrap_values.clone().detach() | |
| hpc_upgo = UPGO(T, B, N) | |
| if use_cuda: | |
| ori_target_output = ori_target_output.cuda() | |
| ori_rhos = ori_rhos.cuda() | |
| ori_action = ori_action.cuda() | |
| ori_rewards = ori_rewards.cuda() | |
| ori_bootstrap_values = ori_bootstrap_values.cuda() | |
| hpc_target_output = hpc_target_output.cuda() | |
| hpc_rhos = hpc_rhos.cuda() | |
| hpc_action = hpc_action.cuda() | |
| hpc_rewards = hpc_rewards.cuda() | |
| hpc_bootstrap_values = hpc_bootstrap_values.cuda() | |
| hpc_upgo = hpc_upgo.cuda() | |
| ori_target_output.requires_grad_(True) | |
| for i in range(times): | |
| t = time.time() | |
| ori_loss = upgo_loss(ori_target_output, ori_rhos, ori_action, ori_rewards, ori_bootstrap_values) | |
| ori_loss = ori_loss.mean() | |
| ori_loss.backward() | |
| if use_cuda: | |
| torch.cuda.synchronize() | |
| print('epoch: {}, original upgo cost time: {}'.format(i, time.time() - t)) | |
| hpc_target_output.requires_grad_(True) | |
| for i in range(times): | |
| t = time.time() | |
| hpc_loss = hpc_upgo(hpc_target_output, hpc_rhos, hpc_action, hpc_rewards, hpc_bootstrap_values) | |
| hpc_loss = hpc_loss.mean() | |
| hpc_loss.backward() | |
| if use_cuda: | |
| torch.cuda.synchronize() | |
| print('epoch: {}, hpc upgo cost time: {}'.format(i, time.time() - t)) | |
| if __name__ == '__main__': | |
| print("target problem: T = {}, B = {}, N = {}".format(T, B, N)) | |
| print("================run upgo validation test================") | |
| upgo_val() | |
| print("================run upgo performance test================") | |
| upgo_perf() | |