test_gpu_speed.py

import torch
import warpctc_pytorch_change1 as warp_ctc_new
import warpctc_pytorch as warp_ctc
import time


def test_compare_cpu(repeat_num=20):
    probs = torch.FloatTensor([
        [[0.1, 0.6, 0.1, 0.1, 0.1], [0.1, 0.1, 0.6, 0.1, 0.1]],
        [[0.6, 0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.5, 0.2, 0.1]]
    ]).contiguous()
    labels = torch.IntTensor([1, 2])
    label_sizes = torch.IntTensor([2, 0])
    sizes = torch.IntTensor([2, 2])
    minibatch_size = probs.size(1)
    costs = torch.zeros(minibatch_size)
    grads = torch.zeros(probs.size())

    time_st = time.perf_counter()
    # 1.运行老版本 CPU
    for i in range(repeat_num):
        probs_old = probs.clone()
        costs_old = costs.clone()
        grads_old = grads.clone()
        warp_ctc.cpu_ctc(probs_old, grads_old, labels, label_sizes, sizes, minibatch_size, costs_old, 0)
        if i == 0:
            print('CPU_costs_old: %f' % costs_old.sum())
            print('CPU probs_old={}\ngrads_old={}\ncosts_old={}'.format(probs_old, grads_old, costs_old))
    time_used = (time.perf_counter() - time_st) / repeat_num
    print('CPU warp_ctc old version using time: ', time_used)

    time_st = time.perf_counter()
    # 2.运行新版本 CPU
    for i in range(repeat_num):
        probs_new = probs.clone()
        costs_new = costs.clone()
        grads_new = grads.clone()
        warp_ctc_new.cpu_ctc(probs_new, grads_new, labels, label_sizes, sizes, minibatch_size, costs_new, 0)
        if i == 0:
            print('CPU_costs_new: %f' % costs_new.sum())
            print('CPU probs={}\ngrads_new={}\ncosts_new={}'.format(probs_new, grads_new, costs_new))
    time_used = (time.perf_counter() - time_st) / repeat_num
    print('CPU warp_ctc new version using time: ', time_used)


def test_compare_gpu():
    probs0 = torch.FloatTensor([
        [[0.1, 0.6, 0.1, 0.1, 0.1], [0.1, 0.1, 0.6, 0.1, 0.1]],
        [[0.6, 0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.5, 0.2, 0.1]]
    ]).contiguous().cuda()
    labels = torch.IntTensor([1, 2])
    label_sizes = torch.IntTensor([2, 0])
    sizes = torch.IntTensor([2, 2])
    minibatch_size = probs0.size(1)

    # 1.运行新版本 CPU
    probs_new = probs0.clone().cuda()
    costs_new = torch.zeros(minibatch_size)
    grads_new = torch.zeros(probs0.size())
    warp_ctc_new.cpu_ctc(probs_new, grads_new, labels, label_sizes, sizes, minibatch_size, costs_new, 0)
    print('CPU_costs_new: %f' % costs_new.sum())
    print('CPU probs_new={}\ngrads_new={}\ncosts_new={}'.format(probs_new, grads_new, costs_new))
    # 2.运行老版本 CPU
    probs = probs0.clone().cuda()
    costs = torch.zeros(minibatch_size)
    grads = torch.zeros(probs0.size())
    warp_ctc.cpu_ctc(probs0, grads, labels, label_sizes, sizes, minibatch_size, costs, 0)
    print('CPU_cost: %f' % costs.sum())
    print('CPU probs={}\ngrads={}\ncosts={}'.format(probs, grads, costs))


if __name__ == '__main__':
    print('torch.cuda.is_available() ', torch.cuda.is_available())
    test_compare_cpu()
    test_compare_gpu()