-
Notifications
You must be signed in to change notification settings - Fork 4
/
trainer.py
201 lines (174 loc) · 9.29 KB
/
trainer.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
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
import time
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import DataLoader
import dist
from models import MVAR, VQVAE, VectorQuantizer2
from utils.amp_sc import AmpOptimizer
from utils.misc import MetricLogger, TensorboardLogger
Ten = torch.Tensor
FTen = torch.Tensor
ITen = torch.LongTensor
BTen = torch.BoolTensor
class MVARTrainer(object):
def __init__(
self, device, patch_nums: Tuple[int, ...], resos: Tuple[int, ...],
vae_local: VQVAE, mvar_wo_ddp: MVAR, mvar: DDP,
mvar_opt: AmpOptimizer, label_smooth: float,
):
super(MVARTrainer, self).__init__()
self.mvar, self.vae_local, self.quantize_local = mvar, vae_local, vae_local.quantize
self.quantize_local: VectorQuantizer2
self.mvar_wo_ddp: MVAR = mvar_wo_ddp # after torch.compile
self.mvar_opt = mvar_opt
del self.mvar_wo_ddp.rng
self.mvar_wo_ddp.rng = torch.Generator(device=device)
self.label_smooth = label_smooth
self.train_loss = nn.CrossEntropyLoss(label_smoothing=label_smooth, reduction='none')
self.val_loss = nn.CrossEntropyLoss(label_smoothing=0.0, reduction='mean')
self.L = sum(pn * pn for pn in patch_nums)
self.last_l = patch_nums[-1] * patch_nums[-1]
self.loss_weight = torch.ones(1, self.L, device=device) / self.L
self.patch_nums, self.resos = patch_nums, resos
self.begin_ends = []
cur = 0
for i, pn in enumerate(patch_nums):
self.begin_ends.append((cur, cur + pn * pn))
cur += pn*pn
self.prog_it = 0
self.last_prog_si = -1
self.first_prog = True
@torch.no_grad()
def eval_ep(self, ld_val: DataLoader):
tot = 0
L_mean, L_tail, acc_mean, acc_tail = 0, 0, 0, 0
stt = time.time()
training = self.mvar_wo_ddp.training
self.mvar_wo_ddp.eval()
for inp_B3HW, label_B in ld_val:
B, V = label_B.shape[0], self.vae_local.vocab_size
inp_B3HW = inp_B3HW.to(dist.get_device(), non_blocking=True)
label_B = label_B.to(dist.get_device(), non_blocking=True)
gt_idx_Bl: List[ITen] = self.vae_local.img_to_idxBl(inp_B3HW)
gt_BL = torch.cat(gt_idx_Bl, dim=1)
x_BLCv_wo_first_l: Ten = self.quantize_local.idxBl_to_var_input(gt_idx_Bl)
logits_BLV = self.mvar_wo_ddp(label_B, x_BLCv_wo_first_l)
L_mean += self.val_loss(logits_BLV.data.view(-1, V), gt_BL.view(-1)) * B
L_tail += self.val_loss(logits_BLV.data[:, -self.last_l:].reshape(-1, V), gt_BL[:, -self.last_l:].reshape(-1)) * B
acc_mean += (logits_BLV.data.argmax(dim=-1) == gt_BL).sum() * (100/gt_BL.shape[1])
acc_tail += (logits_BLV.data[:, -self.last_l:].argmax(dim=-1) == gt_BL[:, -self.last_l:]).sum() * (100 / self.last_l)
tot += B
self.mvar_wo_ddp.train(training)
stats = L_mean.new_tensor([L_mean.item(), L_tail.item(), acc_mean.item(), acc_tail.item(), tot])
dist.allreduce(stats)
tot = round(stats[-1].item())
stats /= tot
L_mean, L_tail, acc_mean, acc_tail, _ = stats.tolist()
return L_mean, L_tail, acc_mean, acc_tail, tot, time.time()-stt
def train_step(
self, it: int, g_it: int, stepping: bool, metric_lg: MetricLogger, tb_lg: TensorboardLogger,
inp_B3HW: FTen, label_B: Union[ITen, FTen], prog_si: int, prog_wp_it: float,
) -> Tuple[Optional[Union[Ten, float]], Optional[float]]:
# if progressive training
self.mvar_wo_ddp.prog_si = self.vae_local.quantize.prog_si = prog_si
if self.last_prog_si != prog_si:
if self.last_prog_si != -1: self.first_prog = False
self.last_prog_si = prog_si
self.prog_it = 0
self.prog_it += 1
prog_wp = max(min(self.prog_it / prog_wp_it, 1), 0.01)
if self.first_prog: prog_wp = 1 # no prog warmup at first prog stage, as it's already solved in wp
if prog_si == len(self.patch_nums) - 1: prog_si = -1 # max prog, as if no prog
# forward
B, V = label_B.shape[0], self.vae_local.vocab_size
self.mvar.require_backward_grad_sync = stepping
gt_idx_Bl: List[ITen] = self.vae_local.img_to_idxBl(inp_B3HW)
gt_BL = torch.cat(gt_idx_Bl, dim=1)
x_BLCv_wo_first_l: Ten = self.quantize_local.idxBl_to_var_input(gt_idx_Bl)
with self.mvar_opt.amp_ctx:
self.mvar_wo_ddp.forward
logits_BLV = self.mvar(label_B, x_BLCv_wo_first_l)
loss = self.train_loss(logits_BLV.view(-1, V), gt_BL.view(-1)).view(B, -1)
if prog_si >= 0: # in progressive training
bg, ed = self.begin_ends[prog_si]
assert logits_BLV.shape[1] == gt_BL.shape[1] == ed
lw = self.loss_weight[:, :ed].clone()
lw[:, bg:ed] *= min(max(prog_wp, 0), 1)
else: # not in progressive training
lw = self.loss_weight
loss = loss.mul(lw).sum(dim=-1).mean()
# backward
_, scale_log2 = self.mvar_opt.backward_clip_step(loss=loss, stepping=stepping)
# log
pred_BL = logits_BLV.data.argmax(dim=-1)
if it == 0 or it in metric_lg.log_iters:
Lmean = self.val_loss(logits_BLV.data.view(-1, V), gt_BL.view(-1)).item()
acc_mean = (pred_BL == gt_BL).float().mean().item() * 100
if prog_si >= 0: # in progressive training
Ltail = acc_tail = -1
else: # not in progressive training
Ltail = self.val_loss(logits_BLV.data[:, -self.last_l:].reshape(-1, V), gt_BL[:, -self.last_l:].reshape(-1)).item()
acc_tail = (pred_BL[:, -self.last_l:] == gt_BL[:, -self.last_l:]).float().mean().item() * 100
#grad_norm = grad_norm.item()
metric_lg.update(Lm=Lmean, Lt=Ltail, Accm=acc_mean, Acct=acc_tail, tnm=0)
# log to tensorboard
if g_it == 0 or (g_it + 1) % 500 == 0:
prob_per_class_is_chosen = pred_BL.view(-1).bincount(minlength=V).float()
dist.allreduce(prob_per_class_is_chosen)
prob_per_class_is_chosen /= prob_per_class_is_chosen.sum()
cluster_usage = (prob_per_class_is_chosen > 0.001 / V).float().mean().item() * 100
if dist.is_master():
if g_it == 0:
tb_lg.update(head='AR_iter_loss', z_voc_usage=cluster_usage, step=-10000)
tb_lg.update(head='AR_iter_loss', z_voc_usage=cluster_usage, step=-1000)
kw = dict(z_voc_usage=cluster_usage)
for si, (bg, ed) in enumerate(self.begin_ends):
if 0 <= prog_si < si: break
pred, tar = logits_BLV.data[:, bg:ed].reshape(-1, V), gt_BL[:, bg:ed].reshape(-1)
acc = (pred.argmax(dim=-1) == tar).float().mean().item() * 100
ce = self.val_loss(pred, tar).item()
kw[f'acc_{self.resos[si]}'] = acc
kw[f'L_{self.resos[si]}'] = ce
tb_lg.update(head='AR_iter_loss', **kw, step=g_it)
tb_lg.update(head='AR_iter_schedule', prog_a_reso=self.resos[prog_si], prog_si=prog_si, prog_wp=prog_wp, step=g_it)
self.mvar_wo_ddp.prog_si = self.vae_local.quantize.prog_si = -1
return 0., scale_log2
def get_config(self):
return {
'patch_nums': self.patch_nums, 'resos': self.resos,
'label_smooth': self.label_smooth,
'prog_it': self.prog_it, 'last_prog_si': self.last_prog_si, 'first_prog': self.first_prog,
}
def state_dict(self):
state = {'config': self.get_config()}
for k in ('mvar_wo_ddp', 'vae_local', 'mvar_opt'):
m = getattr(self, k)
if m is not None:
if hasattr(m, '_orig_mod'):
m = m._orig_mod
state[k] = m.state_dict()
return state
def load_state_dict(self, state, strict=True, skip_vae=False):
for k in ('mvar_wo_ddp', 'vae_local', 'mvar_opt'):
if skip_vae and 'vae' in k: continue
m = getattr(self, k)
if m is not None:
if hasattr(m, '_orig_mod'):
m = m._orig_mod
ret = m.load_state_dict(state[k], strict=strict)
if ret is not None:
missing, unexpected = ret
print(f'[MVARTrainer.load_state_dict] {k} missing: {missing}')
print(f'[MVARTrainer.load_state_dict] {k} unexpected: {unexpected}')
config: dict = state.pop('config', None)
self.prog_it = config.get('prog_it', 0)
self.last_prog_si = config.get('last_prog_si', -1)
self.first_prog = config.get('first_prog', True)
if config is not None:
for k, v in self.get_config().items():
if config.get(k, None) != v:
err = f'[MVAR.load_state_dict] config mismatch: this.{k}={v} (ckpt.{k}={config.get(k, None)})'
if strict: raise AttributeError(err)
else: print(err)