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Update LFNA

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This commit is contained in:
D-X-Y 2021-05-12 20:32:50 +08:00
parent 06f4a1f1cf
commit 0b1ca45c44
8 changed files with 121 additions and 15 deletions
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6
exps/LFNA/lfna-tall-hpnet.py
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@ -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 16
# python exps/LFNA/lfna-tall-hpnet.py --env_version v1 --hidden_dim 16 --epochs 100000 --meta_batch 64
#####################################################
import sys, time, copy, torch, random, argparse
from tqdm import tqdm
@ -33,7 +33,7 @@ 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(dict(model_type="simple_mlp"), **model_kwargs)
model = get_model(**model_kwargs)
criterion = torch.nn.MSELoss()
logger.log("There are {:} weights.".format(model.get_w_container().numel()))
@ -72,7 +72,7 @@ def main(args):
)
limit_bar = float(iepoch + 1) / args.epochs * total_bar
limit_bar = min(max(0, int(limit_bar)), 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)

44
exps/LFNA/lfna-test-hpnet.py
View File

@ -1,7 +1,7 @@
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
#####################################################
# python exps/LFNA/lfna-test-hpnet.py --env_version v1 --hidden_dim 16
# python exps/LFNA/lfna-test-hpnet.py --env_version v1 --hidden_dim 16 --layer_dim 32 --epochs 50000
#####################################################
import sys, time, copy, torch, random, argparse
from tqdm import tqdm
@ -33,17 +33,17 @@ 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(dict(model_type="simple_mlp"), **model_kwargs)
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()))
shape_container = model.get_w_container().to_shape_container()
hypernet = HyperNet(shape_container, args.hidden_dim, args.task_dim)
hypernet = HyperNet(shape_container, args.layer_dim, args.task_dim)
hypernet = hypernet.to(args.device)
# task_embed = torch.nn.Parameter(torch.Tensor(env_info["total"], args.task_dim))
total_bar = 10
total_bar = 16
task_embeds = []
for i in range(total_bar):
tensor = torch.Tensor(1, args.task_dim).to(args.device)
@ -51,8 +51,12 @@ def main(args):
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, amsgrad=True)
optimizer = torch.optim.Adam(parameters, lr=args.init_lr, weight_decay=1e-5)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=[
@ -98,7 +102,7 @@ def main(args):
lr_scheduler.step()
loss_meter.update(final_loss.item())
if iepoch % 200 == 0:
if iepoch % 100 == 0:
logger.log(
head_str
+ " meta-loss: {:.4f} ({:.4f}) :: lr={:.5f}, batch={:}".format(
@ -126,6 +130,26 @@ def main(args):
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()
@ -150,6 +174,12 @@ if __name__ == "__main__":
required=True,
help="The hidden dimension.",
)
parser.add_argument(
"--layer_dim",
type=int,
required=True,
help="The hidden dimension.",
)
#####
parser.add_argument(
"--init_lr",
@ -181,7 +211,7 @@ if __name__ == "__main__":
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.task_dim = args.layer_dim
args.save_dir = "{:}-{:}-d{:}".format(
args.save_dir, args.env_version, args.hidden_dim
)

2
exps/LFNA/lfna-ttss-hpnet.py
View File

@ -31,7 +31,7 @@ from lfna_models import HyperNet_VX as HyperNet
def main(args):
logger, env_info, model_kwargs = lfna_setup(args)
dynamic_env = env_info["dynamic_env"]
model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
model = get_model(**model_kwargs)
total_time = env_info["total"]
for i in range(total_time):

14
exps/LFNA/lfna_models.py
View File

@ -4,6 +4,8 @@
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
@ -29,13 +31,15 @@ class HyperNet(super_core.SuperModule):
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] * 4,
hidden_dims=[layer_embeding * 4] * 3,
act_cls="gelu",
norm_cls="layer_norm_1d",
dropout=0.1,
)
self._generator = get_model(dict(model_type="norm_mlp"), **model_kwargs)
self._generator = get_model(**model_kwargs)
"""
model_kwargs = dict(
input_dim=layer_embeding + task_embedding,
@ -50,8 +54,12 @@ class HyperNet(super_core.SuperModule):
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, self._super_layer_embed), dim=-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)

16
lib/models/xcore.py
View File

@ -11,6 +11,7 @@ __all__ = ["get_model"]
from xlayers.super_core import SuperSequential
from xlayers.super_core import SuperLinear
from xlayers.super_core import SuperDropout
from xlayers.super_core import super_name2norm
from xlayers.super_core import super_name2activation
@ -47,7 +48,20 @@ def get_model(config: Dict[Text, Any], **kwargs):
last_dim = hidden_dim
sub_layers.append(SuperLinear(last_dim, kwargs["output_dim"]))
model = SuperSequential(*sub_layers)
elif model_type == "dual_norm_mlp":
act_cls = super_name2activation[kwargs["act_cls"]]
norm_cls = super_name2norm[kwargs["norm_cls"]]
sub_layers, last_dim = [], kwargs["input_dim"]
for i, hidden_dim in enumerate(kwargs["hidden_dims"]):
if i > 0:
sub_layers.append(norm_cls(last_dim, elementwise_affine=False))
sub_layers.append(SuperLinear(last_dim, hidden_dim))
sub_layers.append(SuperDropout(kwargs["dropout"]))
sub_layers.append(SuperLinear(hidden_dim, hidden_dim))
sub_layers.append(act_cls())
last_dim = hidden_dim
sub_layers.append(SuperLinear(last_dim, kwargs["output_dim"]))
model = SuperSequential(*sub_layers)
else:
raise TypeError("Unkonwn model type: {:}".format(model_type))
return model

1
lib/xlayers/super_core.py
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@ -14,6 +14,7 @@ from .super_norm import SuperSimpleNorm
from .super_norm import SuperLayerNorm1D
from .super_norm import SuperSimpleLearnableNorm
from .super_norm import SuperIdentity
from .super_dropout import SuperDropout
super_name2norm = {
"simple_norm": SuperSimpleNorm,

40
lib/xlayers/super_dropout.py Normal file
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@ -0,0 +1,40 @@
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
#####################################################
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Optional, Callable
import spaces
from .super_module import SuperModule
from .super_module import IntSpaceType
from .super_module import BoolSpaceType
class SuperDropout(SuperModule):
"""Applies a the dropout function element-wise."""
def __init__(self, p: float = 0.5, inplace: bool = False) -> None:
super(SuperDropout, self).__init__()
self._p = p
self._inplace = inplace
@property
def abstract_search_space(self):
return spaces.VirtualNode(id(self))
def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
return self.forward_raw(input)
def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
return F.dropout(input, self._p, self.training, self._inplace)
def forward_with_container(self, input, container, prefix=[]):
return self.forward_raw(input)
def extra_repr(self) -> str:
xstr = "inplace=True" if self._inplace else ""
return "p={:}".format(self._p) + ", " + xstr

13
lib/xlayers/super_norm.py
View File

@ -74,6 +74,19 @@ class SuperLayerNorm1D(SuperModule):
def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
return F.layer_norm(input, (self.in_dim,), self.weight, self.bias, self.eps)
def forward_with_container(self, input, container, prefix=[]):
super_weight_name = ".".join(prefix + ["weight"])
if container.has(super_weight_name):
weight = container.query(super_weight_name)
else:
weight = None
super_bias_name = ".".join(prefix + ["bias"])
if container.has(super_bias_name):
bias = container.query(super_bias_name)
else:
bias = None
return F.layer_norm(input, (self.in_dim,), weight, bias, self.eps)
def extra_repr(self) -> str:
return (
"shape={in_dim}, eps={eps}, elementwise_affine={elementwise_affine}".format(