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trainer.py
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trainer.py
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"""
ADOBE CONFIDENTIAL
Copyright 2024 Adobe
All Rights Reserved.
NOTICE: All information contained herein is, and remains
the property of Adobe and its suppliers, if any. The intellectual
and technical concepts contained herein are proprietary to Adobe
and its suppliers and are protected by all applicable intellectual
property laws, including trade secret and copyright laws.
Dissemination of this information or reproduction of this material
is strictly forbidden unless prior written permission is obtained
from Adobe.
"""
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 torchvision
import numpy as np
import dist
from models import VAR, 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 VARTrainer(object):
def __init__(
self, device, patch_nums: Tuple[int, ...], resos: Tuple[int, ...],
vae_local: VQVAE, var_wo_ddp: VAR, var: DDP,
var_opt: AmpOptimizer, label_smooth: float,
):
super(VARTrainer, self).__init__()
self.var, self.vae_local, self.quantize_local = var, vae_local, vae_local.quantizes
self.quantize_local: VectorQuantizer2
self.var_wo_ddp: VAR = var_wo_ddp # after torch.compile
self.var_opt = var_opt
# del self.var_wo_ddp.rng
self.var_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.var_wo_ddp.training
self.var_wo_ddp.eval()
for inp_B3HW, label_B in ld_val:
B, V, N_pq = label_B.shape[0], self.vae_local.vocab_size, self.vae_local.product_quant
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 = self.vae_local.img_to_idxBl(inp_B3HW)
gt_BL_list = [torch.cat(gt_idx_Bl, dim=1) for gt_idx_Bl in gt_idx_Bl_list]
x_BLCv_wo_first_l: Ten = self.vae_local.idxBl_to_var_input(gt_idx_Bl_list)
self.var_wo_ddp.forward
logits_BLV = self.var_wo_ddp(label_B, x_BLCv_wo_first_l)
L_mean += sum([self.val_loss(logits, gt_BL.view(-1)).item() for logits, gt_BL in
zip(logits_BLV.view(-1, V).chunk(N_pq, dim=-1), gt_BL_list)]) * B / N_pq
L_tail += sum([self.val_loss(logits, gt_BL[:, -self.last_l:].reshape(-1)).item() for logits, gt_BL in
zip(logits_BLV[:, -self.last_l:].reshape(-1, V).chunk(N_pq, dim=-1), gt_BL_list)]) * B / N_pq
acc_mean += sum([(logits.data.argmax(dim=-1) == gt_BL).sum() * (100/gt_BL.shape[1]) for logits, gt_BL in
zip(logits_BLV.chunk(N_pq, dim=-1), gt_BL_list)]) / N_pq
acc_tail += sum([(logits.data.argmax(dim=-1)[:, -self.last_l:] == gt_BL[:, -self.last_l:]).sum() * (100 / self.last_l)
for logits, gt_BL in zip(logits_BLV.chunk(N_pq, dim=-1), gt_BL_list)]) / N_pq
tot += B
# visualize
class_labels = np.random.choice(1000, 8).tolist()
B, cfg, seed = len(class_labels), 5, 0
label_B = torch.tensor(class_labels, device=dist.get_device())
recon_B3HW = self.var_wo_ddp.autoregressive_infer_cfg(B=B, label_B=label_B, cfg=cfg, top_k=900, top_p=0.95, g_seed=seed, more_smooth=False)
chw = torchvision.utils.make_grid(recon_B3HW, nrow=8, padding=0, pad_value=1.0)
chw = chw.clone().permute(1, 2, 0).mul_(255).cpu().numpy()
self.var_wo_ddp.train(training)
stats = logits_BLV.new_tensor([L_mean, L_tail, acc_mean, acc_tail, 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, chw
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, p_drop_factor: float,
) -> Tuple[Optional[Union[Ten, float]], Optional[float]]:
# if progressive training
self.var_wo_ddp.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, N_pq = label_B.shape[0], self.vae_local.vocab_size, self.vae_local.product_quant
self.var.require_backward_grad_sync = stepping
gt_idx_Bl_list = self.vae_local.img_to_idxBl(inp_B3HW)
gt_BL_list = [torch.cat(gt_idx_Bl, dim=1) for gt_idx_Bl in gt_idx_Bl_list]
x_BLCv_wo_first_l: Ten = self.vae_local.idxBl_to_var_input(gt_idx_Bl_list)
with self.var_opt.amp_ctx:
self.var_wo_ddp.forward
logits_BLV = self.var(label_B, x_BLCv_wo_first_l, p_drop_factor)
# loss = sum([self.train_loss(logits, gt_BL.view(-1)).view(B, -1) for logits, gt_BL in
# zip(logits_BLV.view(-1, V).chunk(N_pq, dim=-1), gt_BL_list)]) / N_pq
loss = 0
for l in [self.train_loss(logits, gt_BL.view(-1)).view(B, -1) for logits, gt_BL in
zip(logits_BLV.view(-1, V).chunk(N_pq, dim=-1), gt_BL_list)]:
loss = loss + l
loss = loss / N_pq
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
grad_norm, scale_log2 = self.var_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()
Lmean = sum([self.val_loss(logits, gt_BL.view(-1)).item() for logits, gt_BL in
zip(logits_BLV.view(-1, V).chunk(N_pq, dim=-1), gt_BL_list)]) / N_pq
# acc_mean = (pred_BL == gt_BL).float().mean().item() * 100
acc_mean = sum([(logits.data.argmax(dim=-1) == gt_BL).float().mean().item() * 100 for logits, gt_BL in
zip(logits_BLV.chunk(N_pq, dim=-1), gt_BL_list)]) / N_pq
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()
Ltail = sum([self.val_loss(logits, gt_BL[:, -self.last_l:].reshape(-1)).item() for logits, gt_BL in
zip(logits_BLV[:, -self.last_l:].reshape(-1, V).chunk(N_pq, dim=-1), gt_BL_list)]) / N_pq
# acc_tail = (pred_BL[:, -self.last_l:] == gt_BL[:, -self.last_l:]).float().mean().item() * 100
acc_tail = sum([(logits.data.argmax(dim=-1)[:, -self.last_l:] == gt_BL[:, -self.last_l:]).float().mean().item() * 100
for logits, gt_BL in zip(logits_BLV.chunk(N_pq, dim=-1), gt_BL_list)]) / N_pq
grad_norm = grad_norm.item()
metric_lg.update(Lm=Lmean, Lt=Ltail, Accm=acc_mean, Acct=acc_tail, tnm=grad_norm)
# 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.var_wo_ddp.prog_si = self.vae_local.quantize.prog_si = -1
self.var_wo_ddp.prog_si = -1
return grad_norm, 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 ('var_wo_ddp', 'vae_local', 'var_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 ('var_wo_ddp', 'vae_local', 'var_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'[VARTrainer.load_state_dict] {k} missing: {missing}')
print(f'[VARTrainer.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'[VAR.load_state_dict] config mismatch: this.{k}={v} (ckpt.{k}={config.get(k, None)})'
if strict: raise AttributeError(err)
else: print(err)