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Re-org debug codes

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This commit is contained in:
D-X-Y 2021-05-13 08:39:19 +00:00
parent 0138e71cf2
commit 17955123a0
4 changed files with 96 additions and 67 deletions
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0
exps/LFNA/lfna-test-hpnet.py → exps/LFNA/backup/lfna-test-hpnet.py
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6
exps/LFNA/basic-same.py
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@ -1,8 +1,8 @@
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
#####################################################
# python exps/LFNA/basic-same.py --env_version v1 --hidden_dim 16
# python exps/LFNA/basic-same.py --srange 1-999 --env_version v2 --hidden_dim
# python exps/LFNA/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
#####################################################
import sys, time, copy, torch, random, argparse
from tqdm import tqdm
@ -58,7 +58,6 @@ def main(args):
# build model
model = get_model(**model_kwargs)
print(model)
model.analyze_weights()
# build optimizer
optimizer = torch.optim.Adam(model.parameters(), lr=args.init_lr, amsgrad=True)
criterion = torch.nn.MSELoss()
@ -85,6 +84,7 @@ def main(args):
best_loss = loss.item()
best_param = copy.deepcopy(model.state_dict())
model.load_state_dict(best_param)
model.analyze_weights()
with torch.no_grad():
train_metric(preds, historical_y)
train_results = train_metric.get_info()

153
exps/LFNA/lfna-tall-hpnet.py → exps/LFNA/lfna-debug-hpnet.py
View File

@ -1,7 +1,7 @@
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
#####################################################
# python exps/LFNA/lfna-tall-hpnet.py --env_version v1 --hidden_dim 16 --epochs 100000 --meta_batch 64
# 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
@ -26,7 +26,6 @@ from xlayers import super_core, trunc_normal_
from lfna_utils import lfna_setup, train_model, TimeData
# from lfna_models import HyperNet_VX as HyperNet
from lfna_models import HyperNet
@ -36,19 +35,31 @@ def main(args):
model = get_model(**model_kwargs)
criterion = torch.nn.MSELoss()
logger.log("There are {:} weights.".format(model.numel()))
shape_container = model.get_w_container().to_shape_container()
hypernet = HyperNet(shape_container, args.hidden_dim, args.task_dim)
total_bar = env_info["total"] - 1
task_embeds = []
for i in range(env_info["total"]):
task_embeds.append(torch.nn.Parameter(torch.Tensor(1, args.task_dim)))
for task_embed in task_embeds:
trunc_normal_(task_embed, std=0.02)
hypernet = HyperNet(
shape_container, args.hidden_dim, args.task_dim, len(dynamic_env)
)
hypernet = hypernet.to(args.device)
parameters = list(hypernet.parameters()) + task_embeds
optimizer = torch.optim.Adam(parameters, lr=args.init_lr, amsgrad=True)
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=[
@ -59,8 +70,8 @@ def main(args):
)
# LFNA meta-training
loss_meter = AverageMeter()
per_epoch_time, start_time = AverageMeter(), time.time()
last_success_epoch = 0
for iepoch in range(args.epochs):
need_time = "Time Left: {:}".format(
@ -70,65 +81,65 @@ def main(args):
"[{:}] [{: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)
limit_bar = float(iepoch + 1) / args.epochs * total_bar
limit_bar = min(max(32, int(limit_bar)), total_bar)
losses = []
for ibatch in range(args.meta_batch):
cur_time = random.randint(0, limit_bar)
cur_task_embed = task_embeds[cur_time]
cur_container = hypernet(cur_task_embed)
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)
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()
torch.nn.utils.clip_grad_norm_(parameters, 1.0)
optimizer.step()
lr_scheduler.step()
loss_meter.update(final_loss.item())
if iepoch % 200 == 0:
logger.log(
head_str
+ " meta-loss: {:.4f} ({:.4f}) :: lr={:.5f}, batch={:}, limit={:}".format(
loss_meter.avg,
loss_meter.val,
min(lr_scheduler.get_last_lr()),
len(losses),
limit_bar,
)
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(),
"task_embeds": task_embeds,
"lr_scheduler": lr_scheduler.state_dict(),
"iepoch": iepoch,
},
logger.path("model"),
logger,
)
loss_meter.reset()
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_time = env_info["{:}-timestamp".format(idx)]
future_x = env_info["{:}-x".format(idx)]
future_y = env_info["{:}-y".format(idx)]
future_container = hypernet(task_embeds[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(
@ -152,7 +163,7 @@ if __name__ == "__main__":
parser.add_argument(
"--save_dir",
type=str,
default="./outputs/lfna-synthetic/lfna-tall-hpnet",
default="./outputs/lfna-synthetic/lfna-debug-hpnet",
help="The checkpoint directory.",
)
parser.add_argument(
@ -171,7 +182,7 @@ if __name__ == "__main__":
parser.add_argument(
"--init_lr",
type=float,
default=0.1,
default=0.01,
help="The initial learning rate for the optimizer (default is Adam)",
)
parser.add_argument(
@ -180,12 +191,30 @@ if __name__ == "__main__":
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()

4
exps/LFNA/lfna_models_v2.py
View File

@ -39,10 +39,10 @@ class HyperNet(super_core.SuperModule):
config=dict(model_type="dual_norm_mlp"),
input_dim=layer_embeding + task_embedding,
output_dim=max(self._numel_per_layer),
hidden_dims=[layer_embeding * 4] * 3,
hidden_dims=[(layer_embeding + task_embedding) * 2] * 3,
act_cls="gelu",
norm_cls="layer_norm_1d",
dropout=0.1,
dropout=0.2,
)
import pdb