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D-X-Y 2021-05-26 02:41:36 +00:00
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exps/GMOA/lfna-debug-hpnet.py
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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
#####################################################
# python exps/LFNA/lfna-debug-hpnet.py --env_version v1 --hidden_dim 16 --meta_batch 64 --device cuda
#####################################################
import sys, time, copy, torch, random, argparse
from tqdm import tqdm
from copy import deepcopy
from pathlib import Path
lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
if str(lib_dir) not in sys.path:
sys.path.insert(0, str(lib_dir))
from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint
from log_utils import time_string
from log_utils import AverageMeter, convert_secs2time
from utils import split_str2indexes
from procedures.advanced_main import basic_train_fn, basic_eval_fn
from procedures.metric_utils import SaveMetric, MSEMetric, ComposeMetric
from datasets.synthetic_core import get_synthetic_env
from models.xcore import get_model
from xlayers import super_core, trunc_normal_
from lfna_utils import lfna_setup, train_model, TimeData
from lfna_models import HyperNet
def main(args):
logger, env_info, model_kwargs = lfna_setup(args)
dynamic_env = env_info["dynamic_env"]
model = get_model(**model_kwargs)
criterion = torch.nn.MSELoss()
shape_container = model.get_w_container().to_shape_container()
hypernet = HyperNet(
shape_container, args.hidden_dim, args.task_dim, len(dynamic_env)
)
hypernet = hypernet.to(args.device)
logger.log(
"{:} There are {:} weights in the base-model.".format(
time_string(), model.numel()
)
)
logger.log(
"{:} There are {:} weights in the meta-model.".format(
time_string(), hypernet.numel()
)
)
for i in range(len(dynamic_env)):
env_info["{:}-x".format(i)] = env_info["{:}-x".format(i)].to(args.device)
env_info["{:}-y".format(i)] = env_info["{:}-y".format(i)].to(args.device)
logger.log("{:} Convert to device-{:} done".format(time_string(), args.device))
optimizer = torch.optim.Adam(
hypernet.parameters(), lr=args.init_lr, weight_decay=1e-5, amsgrad=True
)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=[
int(args.epochs * 0.8),
int(args.epochs * 0.9),
],
gamma=0.1,
)
# LFNA meta-training
per_epoch_time, start_time = AverageMeter(), time.time()
last_success_epoch = 0
for iepoch in range(args.epochs):
need_time = "Time Left: {:}".format(
convert_secs2time(per_epoch_time.avg * (args.epochs - iepoch), True)
)
head_str = (
"[{:}] [{:04d}/{:04d}] ".format(time_string(), iepoch, args.epochs)
+ need_time
)
# One Epoch
loss_meter = AverageMeter()
for istep in range(args.per_epoch_step):
losses = []
for ibatch in range(args.meta_batch):
cur_time = random.randint(0, len(dynamic_env) - 1)
cur_container = hypernet(cur_time)
cur_x = env_info["{:}-x".format(cur_time)]
cur_y = env_info["{:}-y".format(cur_time)]
cur_dataset = TimeData(cur_time, cur_x, cur_y)
preds = model.forward_with_container(cur_dataset.x, cur_container)
optimizer.zero_grad()
loss = criterion(preds, cur_dataset.y)
losses.append(loss)
final_loss = torch.stack(losses).mean()
final_loss.backward()
optimizer.step()
lr_scheduler.step()
loss_meter.update(final_loss.item())
success, best_score = hypernet.save_best(-loss_meter.avg)
if success:
logger.log("Achieve the best with best_score = {:.3f}".format(best_score))
last_success_epoch = iepoch
if iepoch - last_success_epoch >= args.early_stop_thresh:
logger.log("Early stop at {:}".format(iepoch))
break
logger.log(
head_str
+ " meta-loss: {:.4f} ({:.4f}) :: lr={:.5f}, batch={:}".format(
loss_meter.avg,
loss_meter.val,
min(lr_scheduler.get_last_lr()),
len(losses),
)
)
save_checkpoint(
{
"hypernet": hypernet.state_dict(),
"lr_scheduler": lr_scheduler.state_dict(),
"iepoch": iepoch,
},
logger.path("model"),
logger,
)
per_epoch_time.update(time.time() - start_time)
start_time = time.time()
print(model)
print(hypernet)
hypernet.load_best()
w_container_per_epoch = dict()
for idx in range(0, env_info["total"]):
future_x = env_info["{:}-x".format(idx)]
future_y = env_info["{:}-y".format(idx)]
future_container = hypernet(idx)
w_container_per_epoch[idx] = future_container.no_grad_clone()
with torch.no_grad():
future_y_hat = model.forward_with_container(
future_x, w_container_per_epoch[idx]
)
future_loss = criterion(future_y_hat, future_y)
logger.log("meta-test: [{:03d}] -> loss={:.4f}".format(idx, future_loss.item()))
save_checkpoint(
{"w_container_per_epoch": w_container_per_epoch},
logger.path(None) / "final-ckp.pth",
logger,
)
logger.log("-" * 200 + "\n")
logger.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser("Use the data in the past.")
parser.add_argument(
"--save_dir",
type=str,
default="./outputs/lfna-synthetic/lfna-debug-hpnet",
help="The checkpoint directory.",
)
parser.add_argument(
"--env_version",
type=str,
required=True,
help="The synthetic enviornment version.",
)
parser.add_argument(
"--hidden_dim",
type=int,
required=True,
help="The hidden dimension.",
)
#####
parser.add_argument(
"--init_lr",
type=float,
default=0.01,
help="The initial learning rate for the optimizer (default is Adam)",
)
parser.add_argument(
"--meta_batch",
type=int,
default=64,
help="The batch size for the meta-model",
)
parser.add_argument(
"--early_stop_thresh",
type=int,
default=100,
help="The maximum epochs for early stop.",
)
parser.add_argument(
"--epochs",
type=int,
default=2000,
help="The total number of epochs.",
)
parser.add_argument(
"--per_epoch_step",
type=int,
default=20,
help="The total number of epochs.",
)
parser.add_argument(
"--device",
type=str,
default="cpu",
help="",
)
# Random Seed
parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")
args = parser.parse_args()
if args.rand_seed is None or args.rand_seed < 0:
args.rand_seed = random.randint(1, 100000)
assert args.save_dir is not None, "The save dir argument can not be None"
args.task_dim = args.hidden_dim
args.save_dir = "{:}-{:}-d{:}".format(
args.save_dir, args.env_version, args.hidden_dim
)
main(args)

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exps/GMOA/lfna-debug.py
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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
#####################################################
# python exps/LFNA/lfna-debug.py --env_version v1 --workers 0
# python exps/LFNA/lfna.py --env_version v1 --device cuda --lr 0.001
# python exps/LFNA/lfna.py --env_version v1 --device cuda --lr 0.002
#####################################################
import sys, time, copy, torch, random, argparse
from tqdm import tqdm
from copy import deepcopy
from pathlib import Path
lib_dir = (Path(__file__).parent / ".." / "..").resolve()
print("LIB-DIR: {:}".format(lib_dir))
if str(lib_dir) not in sys.path:
sys.path.insert(0, str(lib_dir))
from xautodl.procedures import (
prepare_seed,
prepare_logger,
save_checkpoint,
copy_checkpoint,
)
from xautodl.log_utils import time_string
from xautodl.log_utils import AverageMeter, convert_secs2time
from xautodl.utils import split_str2indexes
from xautodl.procedures.advanced_main import basic_train_fn, basic_eval_fn
from xautodl.procedures.metric_utils import SaveMetric, MSEMetric, ComposeMetric
from xautodl.datasets.synthetic_core import get_synthetic_env, EnvSampler
from xautodl.models.xcore import get_model
from xautodl.xlayers import super_core, trunc_normal_
from lfna_utils import lfna_setup, train_model, TimeData
from lfna_meta_model import LFNA_Meta
def epoch_train(loader, meta_model, base_model, optimizer, criterion, device, logger):
base_model.train()
meta_model.train()
loss_meter = AverageMeter()
for ibatch, batch_data in enumerate(loader):
timestamps, (batch_seq_inputs, batch_seq_targets) = batch_data
timestamps = timestamps.squeeze(dim=-1).to(device)
batch_seq_inputs = batch_seq_inputs.to(device)
batch_seq_targets = batch_seq_targets.to(device)
optimizer.zero_grad()
batch_seq_containers = meta_model(timestamps)
losses = []
for seq_containers, seq_inputs, seq_targets in zip(
batch_seq_containers, batch_seq_inputs, batch_seq_targets
):
for container, inputs, targets in zip(
seq_containers, seq_inputs, seq_targets
):
predictions = base_model.forward_with_container(inputs, container)
loss = criterion(predictions, targets)
losses.append(loss)
final_loss = torch.stack(losses).mean()
final_loss.backward()
optimizer.step()
loss_meter.update(final_loss.item())
return loss_meter
def epoch_evaluate(loader, meta_model, base_model, criterion, device, logger):
with torch.no_grad():
base_model.eval()
meta_model.eval()
loss_meter = AverageMeter()
for ibatch, batch_data in enumerate(loader):
timestamps, (batch_seq_inputs, batch_seq_targets) = batch_data
timestamps = timestamps.squeeze(dim=-1).to(device)
batch_seq_inputs = batch_seq_inputs.to(device)
batch_seq_targets = batch_seq_targets.to(device)
batch_seq_containers = meta_model(timestamps)
losses = []
for seq_containers, seq_inputs, seq_targets in zip(
batch_seq_containers, batch_seq_inputs, batch_seq_targets
):
for container, inputs, targets in zip(
seq_containers, seq_inputs, seq_targets
):
predictions = base_model.forward_with_container(inputs, container)
loss = criterion(predictions, targets)
losses.append(loss)
final_loss = torch.stack(losses).mean()
loss_meter.update(final_loss.item())
return loss_meter
def pretrain(base_model, meta_model, criterion, xenv, args, logger):
base_model.train()
meta_model.train()
optimizer = torch.optim.Adam(
meta_model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay,
amsgrad=True,
)
logger.log("Pre-train the meta-model")
logger.log("Using the optimizer: {:}".format(optimizer))
meta_model.set_best_dir(logger.path(None) / "ckps-basic-pretrain")
rand_index = random.randint(0, meta_model.meta_length - xenv.seq_length - 1)
for iepoch in range(args.epochs):
left_time = "Time Left: {:}".format(
convert_secs2time(per_epoch_time.avg * (args.epochs - iepoch), True)
)
losses = []
for ibatch in range(args.meta_batch):
timestamps = meta_model.meta_timestamps[
rand_index : rand_index + xenv.seq_length
]
seq_timestamps, (seq_inputs, seq_targets) = xenv.seq_call(timestamps)
time_embeds = meta_model.super_meta_embed[
rand_index : rand_index + xenv.seq_length
]
[seq_containers], time_embeds = meta_model(
None, torch.unsqueeze(time_embeds, dim=0)
)
seq_inputs, seq_targets = seq_inputs.to(args.device), seq_targets.to(
args.device
)
for container, inputs, targets in zip(
seq_containers, seq_inputs, seq_targets
):
predictions = base_model.forward_with_container(inputs, container)
loss = criterion(predictions, targets)
losses.append(loss)
final_loss = torch.stack(losses).mean()
final_loss.backward()
optimizer.step()
# success
success, best_score = meta_model.save_best(-final_loss.item())
logger.log(
"{:} [{:04d}/{:}] loss : {:.5f}".format(
time_string(),
iepoch,
args.epochs,
final_loss.item(),
)
+ ", batch={:}".format(len(losses))
+ ", success={:}, best_score={:.4f}".format(success, -best_score)
+ " {:}".format(left_time)
)
per_epoch_time.update(time.time() - start_time)
start_time = time.time()
def main(args):
logger, env_info, model_kwargs = lfna_setup(args)
train_env = get_synthetic_env(mode="train", version=args.env_version)
valid_env = get_synthetic_env(mode="valid", version=args.env_version)
logger.log("training enviornment: {:}".format(train_env))
logger.log("validation enviornment: {:}".format(valid_env))
base_model = get_model(**model_kwargs)
base_model = base_model.to(args.device)
criterion = torch.nn.MSELoss()
shape_container = base_model.get_w_container().to_shape_container()
# pre-train the hypernetwork
timestamps = train_env.get_timestamp(None)
meta_model = LFNA_Meta(shape_container, args.layer_dim, args.time_dim, timestamps)
meta_model = meta_model.to(args.device)
logger.log("The base-model has {:} weights.".format(base_model.numel()))
logger.log("The meta-model has {:} weights.".format(meta_model.numel()))
batch_sampler = EnvSampler(train_env, args.meta_batch, args.sampler_enlarge)
train_env.reset_max_seq_length(args.seq_length)
valid_env.reset_max_seq_length(args.seq_length)
valid_env_loader = torch.utils.data.DataLoader(
valid_env,
batch_size=args.meta_batch,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
)
train_env_loader = torch.utils.data.DataLoader(
train_env,
batch_sampler=batch_sampler,
num_workers=args.workers,
pin_memory=True,
)
optimizer = torch.optim.Adam(
meta_model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay,
amsgrad=True,
)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=[1, 2, 3, 4, 5],
gamma=0.2,
)
logger.log("The base-model is\n{:}".format(base_model))
logger.log("The meta-model is\n{:}".format(meta_model))
logger.log("The optimizer is\n{:}".format(optimizer))
logger.log("The scheduler is\n{:}".format(lr_scheduler))
logger.log("Per epoch iterations = {:}".format(len(train_env_loader)))
pretrain(base_model, meta_model, criterion, train_env, args, logger)
if logger.path("model").exists():
ckp_data = torch.load(logger.path("model"))
base_model.load_state_dict(ckp_data["base_model"])
meta_model.load_state_dict(ckp_data["meta_model"])
optimizer.load_state_dict(ckp_data["optimizer"])
lr_scheduler.load_state_dict(ckp_data["lr_scheduler"])
last_success_epoch = ckp_data["last_success_epoch"]
start_epoch = ckp_data["iepoch"] + 1
check_strs = [
"epochs",
"env_version",
"hidden_dim",
"lr",
"layer_dim",
"time_dim",
"seq_length",
]
for xstr in check_strs:
cx = getattr(args, xstr)
px = getattr(ckp_data["args"], xstr)
assert cx == px, "[{:}] {:} vs {:}".format(xstr, cx, ps)
success, _ = meta_model.save_best(ckp_data["cur_score"])
logger.log("Load ckp from {:}".format(logger.path("model")))
if success:
logger.log(
"Re-save the best model with score={:}".format(ckp_data["cur_score"])
)
else:
start_epoch, last_success_epoch = 0, 0
# LFNA meta-train
meta_model.set_best_dir(logger.path(None) / "checkpoint")
per_epoch_time, start_time = AverageMeter(), time.time()
for iepoch in range(start_epoch, args.epochs):
head_str = "[{:}] [{:04d}/{:04d}] ".format(
time_string(), iepoch, args.epochs
) + "Time Left: {:}".format(
convert_secs2time(per_epoch_time.avg * (args.epochs - iepoch), True)
)
loss_meter = epoch_train(
train_env_loader,
meta_model,
base_model,
optimizer,
criterion,
args.device,
logger,
)
valid_loss_meter = epoch_evaluate(
valid_env_loader, meta_model, base_model, criterion, args.device, logger
)
logger.log(
head_str
+ " meta-train-loss: {meter.avg:.4f} ({meter.count:.0f})".format(
meter=loss_meter
)
+ " meta-valid-loss: {meter.val:.4f}".format(meter=valid_loss_meter)
+ " :: lr={:.5f}".format(min(lr_scheduler.get_last_lr()))
+ " :: last-success={:}".format(last_success_epoch)
)
success, best_score = meta_model.save_best(-loss_meter.avg)
if success:
logger.log("Achieve the best with best-score = {:.5f}".format(best_score))
last_success_epoch = iepoch
save_checkpoint(
{
"meta_model": meta_model.state_dict(),
"base_model": base_model.state_dict(),
"optimizer": optimizer.state_dict(),
"lr_scheduler": lr_scheduler.state_dict(),
"last_success_epoch": last_success_epoch,
"cur_score": -loss_meter.avg,
"iepoch": iepoch,
"args": args,
},
logger.path("model"),
logger,
)
if iepoch - last_success_epoch >= args.early_stop_thresh:
if lr_scheduler.last_epoch > 4:
logger.log("Early stop at {:}".format(iepoch))
break
else:
last_success_epoch = iepoch
lr_scheduler.step()
logger.log("Decay the lr [{:}]".format(lr_scheduler.last_epoch))
per_epoch_time.update(time.time() - start_time)
start_time = time.time()
# meta-test
meta_model.load_best()
eval_env = env_info["dynamic_env"]
w_container_per_epoch = dict()
for idx in range(args.seq_length, len(eval_env)):
# build-timestamp
future_time = env_info["{:}-timestamp".format(idx)].item()
time_seqs = []
for iseq in range(args.seq_length):
time_seqs.append(future_time - iseq * eval_env.timestamp_interval)
time_seqs.reverse()
with torch.no_grad():
meta_model.eval()
base_model.eval()
time_seqs = torch.Tensor(time_seqs).view(1, -1).to(args.device)
[seq_containers] = meta_model(time_seqs)
future_container = seq_containers[-1]
w_container_per_epoch[idx] = future_container.no_grad_clone()
# evaluation
future_x = env_info["{:}-x".format(idx)].to(args.device)
future_y = env_info["{:}-y".format(idx)].to(args.device)
future_y_hat = base_model.forward_with_container(
future_x, w_container_per_epoch[idx]
)
future_loss = criterion(future_y_hat, future_y)
logger.log(
"meta-test: [{:03d}] -> loss={:.4f}".format(idx, future_loss.item())
)
# creating the new meta-time-embedding
distance = meta_model.get_closest_meta_distance(future_time)
if distance < eval_env.timestamp_interval:
continue
#
new_param = meta_model.create_meta_embed()
optimizer = torch.optim.Adam(
[new_param], lr=args.refine_lr, weight_decay=1e-5, amsgrad=True
)
meta_model.replace_append_learnt(
torch.Tensor([future_time]).to(args.device), new_param
)
meta_model.eval()
base_model.train()
for iepoch in range(args.refine_epochs):
optimizer.zero_grad()
[seq_containers] = meta_model(time_seqs)
future_container = seq_containers[-1]
future_y_hat = base_model.forward_with_container(future_x, future_container)
future_loss = criterion(future_y_hat, future_y)
future_loss.backward()
optimizer.step()
logger.log(
"post-meta-test: [{:03d}] -> loss={:.4f}".format(idx, future_loss.item())
)
with torch.no_grad():
meta_model.replace_append_learnt(None, None)
meta_model.append_fixed(torch.Tensor([future_time]), new_param)
save_checkpoint(
{"w_container_per_epoch": w_container_per_epoch},
logger.path(None) / "final-ckp.pth",
logger,
)
logger.log("-" * 200 + "\n")
logger.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser(".")
parser.add_argument(
"--save_dir",
type=str,
default="./outputs/lfna-synthetic/lfna-battle",
help="The checkpoint directory.",
)
parser.add_argument(
"--env_version",
type=str,
required=True,
help="The synthetic enviornment version.",
)
parser.add_argument(
"--hidden_dim",
type=int,
default=16,
help="The hidden dimension.",
)
parser.add_argument(
"--layer_dim",
type=int,
default=16,
help="The layer chunk dimension.",
)
parser.add_argument(
"--time_dim",
type=int,
default=16,
help="The timestamp dimension.",
)
#####
parser.add_argument(
"--lr",
type=float,
default=0.002,
help="The initial learning rate for the optimizer (default is Adam)",
)
parser.add_argument(
"--weight_decay",
type=float,
default=0.00001,
help="The weight decay for the optimizer (default is Adam)",
)
parser.add_argument(
"--meta_batch",
type=int,
default=64,
help="The batch size for the meta-model",
)
parser.add_argument(
"--sampler_enlarge",
type=int,
default=5,
help="Enlarge the #iterations for an epoch",
)
parser.add_argument("--epochs", type=int, default=10000, help="The total #epochs.")
parser.add_argument(
"--refine_lr",
type=float,
default=0.005,
help="The learning rate for the optimizer, during refine",
)
parser.add_argument(
"--refine_epochs", type=int, default=1000, help="The final refine #epochs."
)
parser.add_argument(
"--early_stop_thresh",
type=int,
default=20,
help="The #epochs for early stop.",
)
parser.add_argument(
"--seq_length", type=int, default=10, help="The sequence length."
)
parser.add_argument(
"--workers", type=int, default=4, help="The number of workers in parallel."
)
parser.add_argument(
"--device",
type=str,
default="cpu",
help="",
)
# Random Seed
parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")
args = parser.parse_args()
if args.rand_seed is None or args.rand_seed < 0:
args.rand_seed = random.randint(1, 100000)
assert args.save_dir is not None, "The save dir argument can not be None"
args.save_dir = "{:}-d{:}_{:}_{:}-s{:}-lr{:}-wd{:}-e{:}-env{:}".format(
args.save_dir,
args.hidden_dim,
args.layer_dim,
args.time_dim,
args.seq_length,
args.lr,
args.weight_decay,
args.epochs,
args.env_version,
)
main(args)

0
exps/GMOA/basic-his.py → exps/GeMOSA/basic-his.py
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0
exps/GMOA/basic-maml.py → exps/GeMOSA/basic-maml.py
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0
exps/GMOA/basic-prev.py → exps/GeMOSA/basic-prev.py
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4
exps/GMOA/basic-same.py → exps/GeMOSA/basic-same.py
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@ -1,8 +1,8 @@
##################################################### #####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 # # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
##################################################### #####################################################
# python exps/LFNA/basic-same.py --env_version v1 --hidden_dim 16 --epochs 500 --init_lr 0.1 # python exps/GeMOSA/basic-same.py --env_version v1 --hidden_dim 16 --epochs 500 --init_lr 0.1
# python exps/LFNA/basic-same.py --env_version v2 --hidden_dim 16 --epochs 1000 --init_lr 0.05 # python exps/GeMOSA/basic-same.py --env_version v2 --hidden_dim 16 --epochs 1000 --init_lr 0.05
##################################################### #####################################################
import sys, time, copy, torch, random, argparse import sys, time, copy, torch, random, argparse
from tqdm import tqdm from tqdm import tqdm

11
exps/GMOA/lfna_meta_model.py → exps/GeMOSA/lfna_meta_model.py
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@ -181,21 +181,20 @@ class MetaModelV1(super_core.SuperModule):
timestamp_v_embed, timestamp_v_embed,
mask, mask,
) )
return timestamp_embeds return timestamp_embeds[:, -1, :]
def forward_raw(self, timestamps, time_embeds, tembed_only=False): def forward_raw(self, timestamps, time_embeds, tembed_only=False):
if time_embeds is None: if time_embeds is None:
time_seq = timestamps.view(-1, 1) + self._time_interval.view(1, -1) time_seq = timestamps.view(-1, 1) + self._time_interval.view(1, -1)
B, S = time_seq.shape B, S = time_seq.shape
time_embeds = self._obtain_time_embed(time_seq) time_embeds = self._obtain_time_embed(time_seq)
else: else: # use the hyper-net only
time_seq = None time_seq = None
B, S, _ = time_embeds.shape B, _ = time_embeds.shape
# create joint embed
num_layer, _ = self._super_layer_embed.shape
time_embeds = time_embeds[:, -1, :]
if tembed_only: if tembed_only:
return time_embeds return time_embeds
# create joint embed
num_layer, _ = self._super_layer_embed.shape
# The shape of `joint_embed` is batch * num-layers * input-dim # The shape of `joint_embed` is batch * num-layers * input-dim
joint_embeds = torch.cat( joint_embeds = torch.cat(
( (

0
exps/GMOA/lfna_models.py → exps/GeMOSA/lfna_models.py
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0
exps/GMOA/lfna_utils.py → exps/GeMOSA/lfna_utils.py
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123
exps/GMOA/lfna.py → exps/GeMOSA/main.py
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@ -1,10 +1,10 @@
##################################################### #####################################################
# Learning to Generate Model One Step Ahead # # Learning to Generate Model One Step Ahead #
##################################################### #####################################################
# python exps/GMOA/lfna.py --env_version v1 --workers 0 # python exps/GeMOSA/lfna.py --env_version v1 --workers 0
# python exps/GMOA/lfna.py --env_version v1 --device cuda --lr 0.001 # python exps/GeMOSA/lfna.py --env_version v1 --device cuda --lr 0.001
# python exps/GMOA/lfna.py --env_version v1 --device cuda --lr 0.002 --seq_length 16 --meta_batch 128 # python exps/GeMOSA/main.py --env_version v1 --device cuda --lr 0.002 --seq_length 16 --meta_batch 128
# python exps/GMOA/lfna.py --env_version v1 --device cuda --lr 0.002 --seq_length 24 --time_dim 32 --meta_batch 128 # python exps/GeMOSA/lfna.py --env_version v1 --device cuda --lr 0.002 --seq_length 24 --time_dim 32 --meta_batch 128
##################################################### #####################################################
import sys, time, copy, torch, random, argparse import sys, time, copy, torch, random, argparse
from tqdm import tqdm from tqdm import tqdm
@ -38,63 +38,6 @@ from lfna_utils import lfna_setup, train_model, TimeData
from lfna_meta_model import MetaModelV1 from lfna_meta_model import MetaModelV1
def epoch_train(loader, meta_model, base_model, optimizer, criterion, device, logger):
base_model.train()
meta_model.train()
loss_meter = AverageMeter()
for ibatch, batch_data in enumerate(loader):
timestamps, (batch_seq_inputs, batch_seq_targets) = batch_data
timestamps = timestamps.squeeze(dim=-1).to(device)
batch_seq_inputs = batch_seq_inputs.to(device)
batch_seq_targets = batch_seq_targets.to(device)
optimizer.zero_grad()
batch_seq_containers = meta_model(timestamps)
losses = []
for seq_containers, seq_inputs, seq_targets in zip(
batch_seq_containers, batch_seq_inputs, batch_seq_targets
):
for container, inputs, targets in zip(
seq_containers, seq_inputs, seq_targets
):
predictions = base_model.forward_with_container(inputs, container)
loss = criterion(predictions, targets)
losses.append(loss)
final_loss = torch.stack(losses).mean()
final_loss.backward()
optimizer.step()
loss_meter.update(final_loss.item())
return loss_meter
def epoch_evaluate(loader, meta_model, base_model, criterion, device, logger):
with torch.no_grad():
base_model.eval()
meta_model.eval()
loss_meter = AverageMeter()
for ibatch, batch_data in enumerate(loader):
timestamps, (batch_seq_inputs, batch_seq_targets) = batch_data
timestamps = timestamps.squeeze(dim=-1).to(device)
batch_seq_inputs = batch_seq_inputs.to(device)
batch_seq_targets = batch_seq_targets.to(device)
batch_seq_containers = meta_model(timestamps)
losses = []
for seq_containers, seq_inputs, seq_targets in zip(
batch_seq_containers, batch_seq_inputs, batch_seq_targets
):
for container, inputs, targets in zip(
seq_containers, seq_inputs, seq_targets
):
predictions = base_model.forward_with_container(inputs, container)
loss = criterion(predictions, targets)
losses.append(loss)
final_loss = torch.stack(losses).mean()
loss_meter.update(final_loss.item())
return loss_meter
def online_evaluate(env, meta_model, base_model, criterion, args, logger, save=False): def online_evaluate(env, meta_model, base_model, criterion, args, logger, save=False):
logger.log("Online evaluate: {:}".format(env)) logger.log("Online evaluate: {:}".format(env))
loss_meter = AverageMeter() loss_meter = AverageMeter()
@ -133,7 +76,7 @@ def online_evaluate(env, meta_model, base_model, criterion, args, logger, save=F
return w_containers, loss_meter return w_containers, loss_meter
def pretrain_v2(base_model, meta_model, criterion, xenv, args, logger): def meta_train_procedure(base_model, meta_model, criterion, xenv, args, logger):
base_model.train() base_model.train()
meta_model.train() meta_model.train()
optimizer = torch.optim.Adam( optimizer = torch.optim.Adam(
@ -152,6 +95,7 @@ def pretrain_v2(base_model, meta_model, criterion, xenv, args, logger):
logger.log("Directly load the best model from {:}".format(final_best_name)) logger.log("Directly load the best model from {:}".format(final_best_name))
return return
total_indexes = list(range(meta_model.meta_length))
meta_model.set_best_name("pretrain-{:}.pth".format(args.rand_seed)) meta_model.set_best_name("pretrain-{:}.pth".format(args.rand_seed))
last_success_epoch, early_stop_thresh = 0, args.pretrain_early_stop_thresh last_success_epoch, early_stop_thresh = 0, args.pretrain_early_stop_thresh
per_epoch_time, start_time = AverageMeter(), time.time() per_epoch_time, start_time = AverageMeter(), time.time()
@ -160,47 +104,50 @@ def pretrain_v2(base_model, meta_model, criterion, xenv, args, logger):
left_time = "Time Left: {:}".format( left_time = "Time Left: {:}".format(
convert_secs2time(per_epoch_time.avg * (args.epochs - iepoch), True) convert_secs2time(per_epoch_time.avg * (args.epochs - iepoch), True)
) )
total_future_losses, total_present_losses, total_regu_losses = [], [], []
optimizer.zero_grad() optimizer.zero_grad()
for ibatch in range(args.meta_batch):
rand_index = random.randint(0, meta_model.meta_length - 1)
timestamp = meta_model.meta_timestamps[rand_index]
_, [container], time_embed = meta_model( generated_time_embeds = meta_model(meta_model.meta_timestamps, None, True)
torch.unsqueeze(timestamp, dim=0), None, False
) batch_indexes = random.choices(total_indexes, k=args.meta_batch)
_, (inputs, targets) = xenv(timestamp.item())
raw_time_steps = meta_model.meta_timestamps[batch_indexes]
regularization_loss = F.l1_loss(
generated_time_embeds, meta_model.super_meta_embed, reduction="mean"
)
# future loss
total_future_losses, total_present_losses = [], []
_, future_containers, _ = meta_model(
None, generated_time_embeds[batch_indexes], False
)
_, present_containers, _ = meta_model(
None, meta_model.super_meta_embed[batch_indexes], False
)
for ibatch, time_step in enumerate(raw_time_steps.cpu().tolist()):
_, (inputs, targets) = xenv(time_step)
inputs, targets = inputs.to(device), targets.to(device) inputs, targets = inputs.to(device), targets.to(device)
# generate models one step ahead
predictions = base_model.forward_with_container(inputs, container)
total_future_losses.append(criterion(predictions, targets))
# randomly sample
rand_index = random.randint(0, meta_model.meta_length - 1)
timestamp = meta_model.meta_timestamps[rand_index]
meta_embed = meta_model.super_meta_embed[rand_index]
time_embed = meta_model(torch.unsqueeze(timestamp, dim=0), None, True) predictions = base_model.forward_with_container(
total_regu_losses.append( inputs, future_containers[ibatch]
F.mse_loss( )
torch.squeeze(time_embed, dim=0), meta_embed, reduction="mean" total_future_losses.append(criterion(predictions, targets))
)
predictions = base_model.forward_with_container(
inputs, present_containers[ibatch]
) )
# generate models via memory
_, [container], _ = meta_model(None, meta_embed.view(1, 1, -1), False)
predictions = base_model.forward_with_container(inputs, container)
total_present_losses.append(criterion(predictions, targets)) total_present_losses.append(criterion(predictions, targets))
with torch.no_grad(): with torch.no_grad():
meta_std = torch.stack(total_future_losses).std().item() meta_std = torch.stack(total_future_losses).std().item()
loss_future = torch.stack(total_future_losses).mean() loss_future = torch.stack(total_future_losses).mean()
loss_present = torch.stack(total_present_losses).mean() loss_present = torch.stack(total_present_losses).mean()
regularization_loss = torch.stack(total_regu_losses).mean()
total_loss = loss_future + loss_present + regularization_loss total_loss = loss_future + loss_present + regularization_loss
total_loss.backward() total_loss.backward()
optimizer.step() optimizer.step()
# success # success
success, best_score = meta_model.save_best(-total_loss.item()) success, best_score = meta_model.save_best(-total_loss.item())
logger.log( logger.log(
"{:} [Pre-V2 {:04d}/{:}] loss : {:.4f} +- {:.4f} = {:.4f} + {:.4f} + {:.4f}".format( "{:} [META {:04d}/{:}] loss : {:.4f} +- {:.4f} = {:.4f} + {:.4f} + {:.4f}".format(
time_string(), time_string(),
iepoch, iepoch,
args.epochs, args.epochs,
@ -264,7 +211,7 @@ def main(args):
logger.log("The base-model is\n{:}".format(base_model)) logger.log("The base-model is\n{:}".format(base_model))
logger.log("The meta-model is\n{:}".format(meta_model)) logger.log("The meta-model is\n{:}".format(meta_model))
pretrain_v2(base_model, meta_model, criterion, trainval_env, args, logger) meta_train_procedure(base_model, meta_model, criterion, trainval_env, args, logger)
# try to evaluate once # try to evaluate once
# online_evaluate(train_env, meta_model, base_model, criterion, args, logger) # online_evaluate(train_env, meta_model, base_model, criterion, args, logger)

0
exps/GMOA/vis-synthetic.py → exps/GeMOSA/vis-synthetic.py
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