/
change_mp.py
159 lines (150 loc) · 6.88 KB
/
change_mp.py
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
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
import sys
import os
import torch
import copy
checkpoint = sys.argv[1]
target_mp = int(sys.argv[2])
assert os.path.isdir(checkpoint)
iteration_file = os.path.join(checkpoint, 'latest_checkpointed_iteration.txt')
if not os.path.exists(iteration_file):
iteration_file = os.path.join(checkpoint, 'latest')
if os.path.exists(iteration_file):
with open(iteration_file) as fin:
iteration = int(fin.read().strip())
checkpoint = os.path.join(checkpoint, str(iteration))
else:
iteration = None
filenames = os.listdir(checkpoint)
filenames = [filename for filename in filenames if filename.startswith("mp_rank_")]
filenames = sorted(filenames,
key=lambda x: int(x.split('_')[2]))
filenames = [os.path.join(checkpoint, x) for x in filenames]
if target_mp == len(filenames):
print("MP size keeps the same.")
exit(0)
if sys.argv[1][-1] == '/':
new_checkpoint = sys.argv[1][:-1] + '_MP' + sys.argv[2]
else:
new_checkpoint = sys.argv[1] + '_MP' + sys.argv[2]
if not os.path.exists(new_checkpoint):
os.mkdir(new_checkpoint)
if iteration is not None:
with open(os.path.join(new_checkpoint, 'latest_checkpointed_iteration.txt'), 'w') as fout:
fout.write("{}\n".format(iteration))
with open(os.path.join(new_checkpoint, 'latest'), 'w') as fout:
fout.write("{}\n".format(iteration))
new_checkpoint = os.path.join(new_checkpoint, str(iteration))
if not os.path.exists(new_checkpoint):
os.mkdir(new_checkpoint)
preserve_keys = [
"lr_scheduler",
"skipped_steps",
"global_steps",
"global_samples",
"dp_world_size",
"iteration",
"client_lr_scheduler",
"np_rng_state",
"random_rng_state",
"torch_rng_state",
"cuda_rng_state",
"rng_tracker_states",
]
if target_mp < len(filenames):
print("Decrease MP size.")
assert len(filenames) % target_mp == 0
ratio = len(filenames) // target_mp
for i in range(target_mp):
start = ratio * i
end = ratio * (i + 1)
d = torch.load(filenames[start],
map_location='cpu')
for k in d.keys():
if k != 'module':
if k in preserve_keys:
pass
elif k == "mp_world_size":
d[k] = target_mp
else:
d[k] = None
for j in range(start + 1, end):
d_new = torch.load(filenames[j],
map_location='cpu')
for k, v in d_new['module'].items():
assert len(v.shape) < 3
if len(v.shape) == 2 and 'position' not in k:
if 'query' in k:
size_1 = d['module'][k].shape[0] // 3
size_2 = v.shape[0] // 3
target = d['module'][k]
d['module'][k] = torch.cat([
target[:size_1, :], v[:size_2, :],
target[size_1:size_1 * 2, :], v[size_2:size_2 * 2, :],
target[size_1 * 2:, :], v[size_2 * 2:, :]], 0)
elif 'word' in k or 'h_to_4h' in k or 'relative' in k or "r_w_bias" in k or "r_r_bias" in k:
d['module'][k] = torch.cat([d['module'][k], v], 0)
else:
d['module'][k] = torch.cat([d['module'][k], v], 1)
elif len(v.shape) == 1 and 'query_key_value' in k:
size_1 = d['module'][k].shape[0] // 3
size_2 = v.shape[0] // 3
target = d['module'][k]
d['module'][k] = torch.cat([
target[:size_1], v[:size_2],
target[size_1:size_1 * 2], v[size_2:size_2 * 2],
target[size_1 * 2:], v[size_2 * 2:]], 0)
elif len(v.shape) == 1 and ('dense_h_to_4h' in k or "attention.relative" in k):
d['module'][k] = torch.cat([d['module'][k], v], 0)
filename = os.path.join(new_checkpoint, "mp_rank_{:02d}_model_states.pt".format(i))
torch.save(d, filename)
if target_mp > len(filenames):
print("Increase MP size.")
assert target_mp % len(filenames) == 0
ratio = target_mp // len(filenames)
for i in range(len(filenames)):
start = ratio * i
end = ratio * (i + 1)
d = torch.load(filenames[i],
map_location='cpu')
for j in range(start, end):
d_new = {}
shift = j - start
for k, v in d.items():
if k != 'module':
if k in preserve_keys:
d_new[k] = copy.deepcopy(d[k])
elif k == "mp_world_size":
d_new[k] = target_mp
else:
d_new[k] = None
d_new['module'] = {}
with torch.no_grad():
for k, v in d['module'].items():
assert len(v.shape) < 3
if len(v.shape) == 2 and 'position' not in k:
if 'query' in k:
part = v.shape[0] // ratio // 3
d_new['module'][k] = torch.cat([v[shift * part:(shift + 1) * part, :].clone(),
v[(shift + ratio) * part:(shift + 1 + ratio) * part,
:].clone(),
v[(shift + 2 * ratio) * part:(shift + 1 + 2 * ratio) * part,
:].clone()], 0)
elif 'word' in k or 'h_to_4h' in k or 'relative' in k or "r_w_bias" in k or "r_r_bias" in k:
part = v.shape[0] // ratio
d_new['module'][k] = v[shift * part:(shift + 1) * part, :].clone()
else:
part = v.shape[1] // ratio
d_new['module'][k] = v[:, shift * part:(shift + 1) * part].clone()
elif len(v.shape) == 1 and ('dense_h_to_4h' in k or "attention.relative" in k):
part = v.shape[0] // ratio
d_new['module'][k] = v[shift * part:(shift + 1) * part].clone()
elif len(v.shape) == 1 and 'query_key_value' in k:
part = v.shape[0] // ratio // 3
d_new['module'][k] = torch.cat(
[v[shift * part:(shift + 1) * part].clone(),
v[(shift + ratio) * part:(shift + 1 + ratio) * part].clone(),
v[(shift + 2 * ratio) * part:(shift + 1 + 2 * ratio) * part].clone()], 0)
else:
d_new['module'][k] = v.clone()
filename = os.path.join(new_checkpoint, "mp_rank_{:02d}_model_states.pt".format(j))
torch.save(d_new, filename)