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engine_for_pretraining.py
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engine_for_pretraining.py
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# --------------------------------------------------------
# Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI
# By Wei-Bang Jiang
# Based on BEiT-v2, timm, DeiT, and DINO code bases
# https://github.com/microsoft/unilm/tree/master/beitv2
# https://github.com/rwightman/pytorch-image-models/tree/master/timm
# https://github.com/facebookresearch/deit/
# https://github.com/facebookresearch/dino
# ---------------------------------------------------------
from cgitb import enable
import math
import sys
from typing import Iterable
import torch
import torch.nn as nn
import torch.nn.functional as F
import utils
from einops import rearrange
from contextlib import nullcontext
def random_masking(x, mask_ratio):
"""
Perform per-sample random masking by per-sample shuffling.
Per-sample shuffling is done by argsort random noise.
x: [N, L, D], sequence
"""
N, L, D = x.shape # batch, length, dim
len_keep = int(L * (1 - mask_ratio))
noise = torch.rand(N, L, device=x.device) # noise in [0, 1]
# sort noise for each sample
ids_shuffle = torch.argsort(noise, dim=1) # ascend: small is keep, large is remove
ids_restore = torch.argsort(ids_shuffle, dim=1)
# keep the first subset
ids_keep = ids_shuffle[:, :len_keep]
x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))
# generate the binary mask: 0 is keep, 1 is remove
mask = torch.ones([N, L], device=x.device)
mask[:, :len_keep] = 0
# unshuffle to get the binary mask
mask = torch.gather(mask, dim=1, index=ids_restore)
# mask = np.hstack([
# np.zeros(len_keep),
# np.ones(L - len_keep),
# ])
# np.random.shuffle(mask)
return mask.to(torch.bool)
def train_one_epoch(model: torch.nn.Module, vqnsp: torch.nn.Module,
data_loader_list: Iterable, optimizer: torch.optim.Optimizer,
device: torch.device, epoch: int, loss_scaler, max_norm: float = 0,
log_writer=None, lr_scheduler=None, start_steps=None,
lr_schedule_values=None, wd_schedule_values=None, ch_names_list=None, args=None):
model.train()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
metric_logger.add_meter('min_lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
header = 'Epoch: [{}]'.format(epoch)
print_freq = 10
loss_fn = nn.CrossEntropyLoss()
step_loader = 0
for data_loader, ch_names in zip(data_loader_list, ch_names_list):
if len(data_loader) == 0:
continue
input_chans = utils.get_input_chans(ch_names)
for step, (batch) in enumerate(metric_logger.log_every(data_loader, print_freq * args.gradient_accumulation_steps, header)):
# assign learning rate & weight decay for each step
it = start_steps + step + step_loader # global training iteration
if lr_schedule_values is not None or wd_schedule_values is not None:
for i, param_group in enumerate(optimizer.param_groups):
if lr_schedule_values is not None:
param_group["lr"] = lr_schedule_values[it] * param_group["lr_scale"]
if wd_schedule_values is not None and param_group["weight_decay"] > 0:
param_group["weight_decay"] = wd_schedule_values[it]
samples = batch
samples = samples.float().to(device, non_blocking=True) / 100
samples = rearrange(samples, 'B N (A T) -> B N A T', T=200)
bool_masked_pos = random_masking(samples.flatten(1, 2), mask_ratio=0.5).to(device, non_blocking=True)
with torch.no_grad():
with torch.cuda.amp.autocast():
input_ids = vqnsp.get_codebook_indices(samples, input_chans)
labels = input_ids[bool_masked_pos]
labels_sym = input_ids[~bool_masked_pos]
my_context = model.no_sync if args.distributed and (step + 1) % args.gradient_accumulation_steps != 0 else nullcontext
with my_context():
with torch.cuda.amp.autocast(): # enabled=False
outputs = model(samples, input_chans, bool_masked_pos=bool_masked_pos)
x_rec, x_rec_sym = outputs
loss_rec = loss_fn(x_rec, labels)
loss_rec_sym = loss_fn(x_rec_sym, labels_sym)
loss = loss_rec + loss_rec_sym
loss_value = loss.item()
if not math.isfinite(loss_value):
print(f"Loss is {loss_value}, stopping training at rank {utils.get_rank()}", force=True)
sys.exit(1)
# this attribute is added by timm on one optimizer (adahessian)
is_second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order
grad_norm = loss_scaler(loss, optimizer, clip_grad=max_norm,
parameters=model.parameters(), create_graph=is_second_order, update_grad=(step + 1) % args.gradient_accumulation_steps == 0)
loss_scale_value = loss_scaler.state_dict()["scale"]
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.zero_grad()
torch.cuda.synchronize()
mlm_acc = (x_rec.max(-1)[1] == labels).float().mean().item()
mlm_acc_sym = (x_rec_sym.max(-1)[1] == labels_sym).float().mean().item()
metric_logger.update(mlm_acc=mlm_acc)
metric_logger.update(mlm_acc_sym=mlm_acc_sym)
metric_logger.update(loss_rec=loss_rec.item() / 2)
if log_writer is not None:
log_writer.update(mlm_acc=mlm_acc, head="loss")
log_writer.update(mlm_acc_sym=mlm_acc_sym, head="loss")
log_writer.update(loss_rec=loss_rec.item() / 2, head="loss")
metric_logger.update(loss=loss_value)
metric_logger.update(loss_scale=loss_scale_value)
min_lr = 10.
max_lr = 0.
for group in optimizer.param_groups:
min_lr = min(min_lr, group["lr"])
max_lr = max(max_lr, group["lr"])
metric_logger.update(lr=max_lr)
metric_logger.update(min_lr=min_lr)
weight_decay_value = None
for group in optimizer.param_groups:
if group["weight_decay"] > 0:
weight_decay_value = group["weight_decay"]
metric_logger.update(weight_decay=weight_decay_value)
metric_logger.update(grad_norm=grad_norm)
if log_writer is not None:
log_writer.update(loss=loss_value, head="loss")
log_writer.update(loss_scale=loss_scale_value, head="opt")
log_writer.update(lr=max_lr, head="opt")
log_writer.update(min_lr=min_lr, head="opt")
log_writer.update(weight_decay=weight_decay_value, head="opt")
log_writer.update(grad_norm=grad_norm, head="opt")
log_writer.set_step()
if lr_scheduler is not None:
lr_scheduler.step_update(start_steps + step + step_loader)
step_loader += step
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}