Skip to content

GitLab

Switch branch/tag
Find file Normal viewHistoryPermalink
test_dcnv4.py 4.01 KB
Newer Older
wangkx1's avatar
init  
wangkx1 committed
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136 
# --------------------------------------------------------
# DCNv4
# Copyright (c) 2024 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------

from __future__ import absolute_import
from __future__ import print_function
from __future__ import division

import time
import torch
import torch.nn as nn
import math
from torch.autograd import gradcheck
import pandas as pd
from easydict import EasyDict as edict

from torch.cuda import Event

# from functions.dcnv3_func import DCNv3Function, dcnv3_core_pytorch
from functions.dcnv4_func import DCNv4Function
torch.set_printoptions(threshold=10000)

H_in, W_in = 56, 56
N, M, D = 64, 4, 32

# H_in, W_in = 28, 28
# N, M, D = 64, 8, 32

# H_in, W_in = 14, 14
# N, M, D = 64, 16, 32

# H_in, W_in = 7, 7
# N, M, D = 64, 32, 32

# H_in, W_in = 8, 8
# N, M, D = 128, 4, 16


Kh, Kw = 3, 3
remove_center = False
P = Kh * Kw - remove_center
offset_scale = 2.0
pad = 1
dilation = 1
stride = 1
H_out = (H_in + 2 * pad - (dilation * (Kh - 1) + 1)) // stride + 1
W_out = (W_in + 2 * pad - (dilation * (Kw - 1) + 1)) // stride + 1

torch.manual_seed(3)

#@torch.no_grad()
def speed_test(func, args, inputs, name='Unknown'):

    tic = Event(enable_timing=True)
    toc = Event(enable_timing=True)
    # warmup
    for i in range(args.warmup_num):
        func(*inputs)

    total_time = 0
    tic.record()
    for i in range(args.test_num):
        o = func(*inputs)
        torch.cuda.synchronize()
    toc.record()

    avg_time = tic.elapsed_time(toc) / args.test_num
    print(
        f'>>> {name: <10} finished {args.test_num} running, avg_time: {avg_time:.6f} ms')
    return avg_time

@torch.no_grad()
def check_forward_equal_with_pytorch_half():
    input = torch.rand(N, H_in, W_in, M*D).cuda()
    print(input.shape)
    offset = (torch.rand(N, H_out, W_out, M*P*2).cuda() * 2 - 1)*10
    # offset = (torch.rand(N, H_out, W_out, M*P*2).cuda() * 2 - 1)*0
    mask_origin = torch.rand(N, H_out, W_out, M, P).cuda() + 1e-5
    mask_origin = mask_origin.half()
    mask = mask_origin
    # mask = torch.nn.functional.softmax(mask_origin, dim=-1)
    offset_mask = torch.cat([offset.unflatten(-1, (M, P * 2)), mask_origin.detach()], dim=-1).flatten(-2)

    im2col_step = 128

    input = input.half()
    offset = offset.half()
    mask = mask.half()
    offset_mask = offset_mask.half()


    input1 = input.detach()

    def pad(om):
        padded_zero = int(math.ceil(om.shape[3]/8)*8) - om.shape[3]
        padded = torch.zeros(om.shape[0], om.shape[1], om.shape[2], padded_zero).to(om)
        return torch.cat([om, padded], dim=-1)

    dcnv4_args = [
        input1, pad(offset_mask),
        Kh, Kw, stride, stride, Kh // 2, Kw // 2, dilation, dilation, M, D, offset_scale,
        im2col_step, remove_center, 8, 512, 2, 256, True, True,
    ]
    output_flash_cuda = DCNv4Function.apply(*dcnv4_args)
    print(f"test success")

    # fwdok = torch.allclose(output_flash_cuda, output_pytorch, rtol=1e-2, atol=1e-3)
    # max_abs_err = (output_flash_cuda - output_pytorch).abs().max()
    # max_rel_err = ((output_flash_cuda - output_pytorch).abs() /
    #                (output_pytorch.abs()+ 1e-3)).max()
    # print('>>> forward half')
    # print(f'* {fwdok} check_forward_equal_with_pytorch_float: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')
    # assert(fwdok)

    # test_args = edict({'warmup_num': 1000, 'test_num': 1000})
    
    # exp_time_dcnv4 = speed_test(DCNv4Function.apply, test_args, dcnv4_args, name='exp')
    # torch.cuda.synchronize()

    # results = [{}]
    # results[0]['dcnv3_time'] = exp_time_dcnv3
    # results[0]['dcnv4_time'] = exp_time_dcnv4
    # columns = list(results[0].keys())

    # outputs = pd.DataFrame(results, columns=columns)
    # with pd.option_context(
    #     'display.max_rows', None, 'display.max_columns', None,
    #     'display.max_colwidth', None, 'display.width', None,
    #     'display.precision', 4, ):
    #     print(outputs)


if __name__ == '__main__':
    check_forward_equal_with_pytorch_half()