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Add more functions for synthetic env

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D-X-Y 2021-05-13 15:32:44 +08:00
parent c925cf1969
commit a2b1d0d227
7 changed files with 307 additions and 1 deletions
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0
exps/LFNA/lfna-fix-init.py → exps/LFNA/backup/lfna-fix-init.py
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exps/LFNA/lfna-ttss-hpnet.py → exps/LFNA/backup/lfna-ttss-hpnet.py
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exps/LFNA/lfna-v1.py → exps/LFNA/backup/lfna-v1.py
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230
exps/LFNA/lfna.py Normal file
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@ -0,0 +1,230 @@
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
#####################################################
# python exps/LFNA/lfna.py --env_version v1 --hidden_dim 16 --layer_dim 32 --epochs 50000
#####################################################
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_v2 import HyperNet
def main(args):
logger, env_info, model_kwargs = lfna_setup(args)
dynamic_env = env_info["dynamic_env"]
model = get_model(**model_kwargs)
model = model.to(args.device)
criterion = torch.nn.MSELoss()
logger.log("There are {:} weights.".format(model.get_w_container().numel()))
# meta_train_range = (dynamic_env.min_timestamp, (dynamic_env.min_timestamp + dynamic_env.max_timestamp) / 2)
# meta_train_interval = dynamic_env.timestamp_interval
shape_container = model.get_w_container().to_shape_container()
# pre-train the hypernetwork
timestamps = list(
dynamic_env.get_timestamp(index) for index in range(len(dynamic_env) // 2)
)
hypernet = HyperNet(shape_container, args.layer_dim, args.task_dim, timestamps)
hypernet = hypernet.to(args.device)
import pdb
pdb.set_trace()
# task_embed = torch.nn.Parameter(torch.Tensor(env_info["total"], args.task_dim))
total_bar = 16
task_embeds = []
for i in range(total_bar):
tensor = torch.Tensor(1, args.task_dim).to(args.device)
task_embeds.append(torch.nn.Parameter(tensor))
for task_embed in task_embeds:
trunc_normal_(task_embed, std=0.02)
model.train()
hypernet.train()
parameters = list(hypernet.parameters()) + task_embeds
# optimizer = torch.optim.Adam(parameters, lr=args.init_lr, amsgrad=True)
optimizer = torch.optim.Adam(parameters, lr=args.init_lr, weight_decay=1e-5)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=[
int(args.epochs * 0.8),
int(args.epochs * 0.9),
],
gamma=0.1,
)
# total_bar = env_info["total"] - 1
# LFNA meta-training
loss_meter = AverageMeter()
per_epoch_time, start_time = AverageMeter(), time.time()
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
)
losses = []
# for ibatch in range(args.meta_batch):
for cur_time in range(total_bar):
# cur_time = random.randint(0, total_bar)
cur_task_embed = task_embeds[cur_time]
cur_container = hypernet(cur_task_embed)
cur_x = env_info["{:}-x".format(cur_time)].to(args.device)
cur_y = env_info["{:}-y".format(cur_time)].to(args.device)
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())
if iepoch % 100 == 0:
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_embed": task_embed,
"lr_scheduler": lr_scheduler.state_dict(),
"iepoch": iepoch,
},
logger.path("model"),
logger,
)
loss_meter.reset()
per_epoch_time.update(time.time() - start_time)
start_time = time.time()
print(model)
print(hypernet)
w_container_per_epoch = dict()
for idx in range(0, total_bar):
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])
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(".")
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,
required=True,
help="The hidden dimension.",
)
parser.add_argument(
"--layer_dim",
type=int,
required=True,
help="The hidden dimension.",
)
#####
parser.add_argument(
"--init_lr",
type=float,
default=0.1,
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(
"--epochs",
type=int,
default=2000,
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.layer_dim
args.save_dir = "{:}-{:}-d{:}".format(
args.save_dir, args.env_version, args.hidden_dim
)
main(args)

72
exps/LFNA/lfna_models_v2.py Normal file
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@ -0,0 +1,72 @@
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
#####################################################
import copy
import torch
import torch.nn.functional as F
from xlayers import super_core
from xlayers import trunc_normal_
from models.xcore import get_model
class HyperNet(super_core.SuperModule):
"""The hyper-network."""
def __init__(
self,
shape_container,
layer_embeding,
task_embedding,
meta_timestamps,
return_container: bool = True,
):
super(HyperNet, self).__init__()
self._shape_container = shape_container
self._num_layers = len(shape_container)
self._numel_per_layer = []
for ilayer in range(self._num_layers):
self._numel_per_layer.append(shape_container[ilayer].numel())
self.register_parameter(
"_super_layer_embed",
torch.nn.Parameter(torch.Tensor(self._num_layers, layer_embeding)),
)
trunc_normal_(self._super_layer_embed, std=0.02)
model_kwargs = dict(
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,
act_cls="gelu",
norm_cls="layer_norm_1d",
dropout=0.1,
)
import pdb
pdb.set_trace()
self._generator = get_model(**model_kwargs)
self._return_container = return_container
print("generator: {:}".format(self._generator))
def forward_raw(self, task_embed):
# task_embed = F.normalize(task_embed, dim=-1, p=2)
# layer_embed = F.normalize(self._super_layer_embed, dim=-1, p=2)
layer_embed = self._super_layer_embed
task_embed = task_embed.view(1, -1).expand(self._num_layers, -1)
joint_embed = torch.cat((task_embed, layer_embed), dim=-1)
weights = self._generator(joint_embed)
if self._return_container:
weights = torch.split(weights, 1)
return self._shape_container.translate(weights)
else:
return weights
def forward_candidate(self, input):
raise NotImplementedError
def extra_repr(self) -> str:
return "(_super_layer_embed): {:}".format(list(self._super_layer_embed.shape))

4
lib/datasets/synthetic_env.py
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@ -55,6 +55,10 @@ class SyntheticDEnv(data.Dataset):
def timestamp_interval(self):
return self._timestamp_generator.interval
def get_timestamp(self, index):
index, timestamp = self._timestamp_generator[index]
return timestamp
def set_oracle_map(self, functor):
self._oracle_map = functor

2
lib/datasets/synthetic_utils.py
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@ -60,7 +60,7 @@ class TimeStamp(UnifiedSplit, data.Dataset):
@property
def max_timestamp(self):
return self._max_timestamp
@property
def interval(self):
return self._interval