diff --git a/exps/LFNA/lfna-tall-hpnet.py b/exps/LFNA/lfna-tall-hpnet.py
index 0dccefd..7d2dbee 100644
--- a/exps/LFNA/lfna-tall-hpnet.py
+++ b/exps/LFNA/lfna-tall-hpnet.py
@@ -36,7 +36,7 @@ def main(args):
model = get_model(**model_kwargs)
criterion = torch.nn.MSELoss()
- logger.log("There are {:} weights.".format(model.get_w_container().numel()))
+ 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)
diff --git a/exps/LFNA/lfna-test-hpnet.py b/exps/LFNA/lfna-test-hpnet.py
index 2b815ed..1f14642 100644
--- a/exps/LFNA/lfna-test-hpnet.py
+++ b/exps/LFNA/lfna-test-hpnet.py
@@ -1,8 +1,8 @@
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
#####################################################
-# python exps/LFNA/lfna-test-hpnet.py --env_version v1 --hidden_dim 16 --layer_dim 32 --epochs 500000 --init_lr 0.01
-# python exps/LFNA/lfna-test-hpnet.py --env_version v1 --hidden_dim 16 --layer_dim 32 --epochs 500000 --init_lr 0.01 --device cuda
+# python exps/LFNA/lfna-test-hpnet.py --env_version v1 --hidden_dim 16 --layer_dim 16 --epochs 20000 --init_lr 0.01
+# python exps/LFNA/lfna-test-hpnet.py --env_version v1 --hidden_dim 16 --layer_dim 16 --epochs 10000 --init_lr 0.01 --device cuda
#####################################################
import sys, time, copy, torch, random, argparse
from tqdm import tqdm
@@ -39,7 +39,8 @@ def main(args):
criterion = torch.nn.MSELoss()
shape_container = model.get_w_container().to_shape_container()
- hypernet = HyperNet(shape_container, args.layer_dim, args.task_dim)
+ total_bar = 100
+ hypernet = HyperNet(shape_container, args.layer_dim, args.task_dim, total_bar)
hypernet = hypernet.to(args.device)
logger.log(
@@ -52,14 +53,6 @@ def main(args):
time_string(), hypernet.numel()
)
)
- # task_embed = torch.nn.Parameter(torch.Tensor(env_info["total"], args.task_dim))
- total_bar = 100
- 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)
for i in range(total_bar):
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)
@@ -67,9 +60,9 @@ def main(args):
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)
+ 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=[
@@ -97,10 +90,10 @@ def main(args):
# 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_task_embed = task_embeds[cur_time]
+ 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)
@@ -126,10 +119,14 @@ def main(args):
)
)
+ success, best_score = hypernet.save_best(-loss_meter.avg)
+ if success:
+ logger.log(
+ "Achieve the best with best_score = {:.3f}".format(best_score)
+ )
save_checkpoint(
{
"hypernet": hypernet.state_dict(),
- "task_embed": task_embed,
"lr_scheduler": lr_scheduler.state_dict(),
"iepoch": iepoch,
},
@@ -142,13 +139,15 @@ def main(args):
print(model)
print(hypernet)
+ hypernet.load_best()
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])
+ # 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(
diff --git a/exps/LFNA/lfna_models.py b/exps/LFNA/lfna_models.py
index f07ffb0..2133cb2 100644
--- a/exps/LFNA/lfna_models.py
+++ b/exps/LFNA/lfna_models.py
@@ -15,7 +15,12 @@ class HyperNet(super_core.SuperModule):
"""The hyper-network."""
def __init__(
- self, shape_container, layer_embeding, task_embedding, return_container=True
+ self,
+ shape_container,
+ layer_embeding,
+ task_embedding,
+ num_tasks,
+ return_container=True,
):
super(HyperNet, self).__init__()
self._shape_container = shape_container
@@ -28,36 +33,33 @@ class HyperNet(super_core.SuperModule):
"_super_layer_embed",
torch.nn.Parameter(torch.Tensor(self._num_layers, layer_embeding)),
)
+ self.register_parameter(
+ "_super_task_embed",
+ torch.nn.Parameter(torch.Tensor(num_tasks, task_embedding)),
+ )
trunc_normal_(self._super_layer_embed, std=0.02)
+ trunc_normal_(self._super_task_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 * 2] * 3,
+ hidden_dims=[(layer_embeding + task_embedding) * 2] * 3,
act_cls="gelu",
norm_cls="layer_norm_1d",
- dropout=0.1,
+ dropout=0.2,
)
self._generator = get_model(**model_kwargs)
- """
- model_kwargs = dict(
- input_dim=layer_embeding + task_embedding,
- output_dim=max(self._numel_per_layer),
- hidden_dim=layer_embeding * 4,
- act_cls="sigmoid",
- norm_cls="identity",
- )
- self._generator = get_model(dict(model_type="simple_mlp"), **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)
+ def forward_raw(self, task_embed_id):
layer_embed = self._super_layer_embed
- task_embed = task_embed.view(1, -1).expand(self._num_layers, -1)
+ task_embed = (
+ self._super_task_embed[task_embed_id]
+ .view(1, -1)
+ .expand(self._num_layers, -1)
+ )
joint_embed = torch.cat((task_embed, layer_embed), dim=-1)
weights = self._generator(joint_embed)
diff --git a/lib/xlayers/super_module.py b/lib/xlayers/super_module.py
index 09b1e69..9beeb23 100644
--- a/lib/xlayers/super_module.py
+++ b/lib/xlayers/super_module.py
@@ -2,7 +2,9 @@
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
#####################################################
+import os
import abc
+import tempfile
import warnings
from typing import Optional, Union, Callable
import torch
@@ -16,6 +18,9 @@ from .super_utils import LayerOrder, SuperRunMode
from .super_utils import TensorContainer
from .super_utils import ShapeContainer
+BEST_DIR_KEY = "best_model_dir"
+BEST_SCORE_KEY = "best_model_score"
+
class SuperModule(abc.ABC, nn.Module):
"""This class equips the nn.Module class with the ability to apply AutoDL."""
@@ -25,6 +30,7 @@ class SuperModule(abc.ABC, nn.Module):
self._super_run_type = SuperRunMode.Default
self._abstract_child = None
self._verbose = False
+ self._meta_info = {}
def set_super_run_type(self, super_run_type):
def _reset_super_run(m):
@@ -84,6 +90,34 @@ class SuperModule(abc.ABC, nn.Module):
total += buf.numel()
return total
+ def save_best(self, score):
+ if BEST_DIR_KEY not in self._meta_info:
+ tempdir = tempfile.mkdtemp("-xlayers")
+ self._meta_info[BEST_DIR_KEY] = tempdir
+ if BEST_SCORE_KEY not in self._meta_info:
+ self._meta_info[BEST_SCORE_KEY] = None
+ best_score = self._meta_info[BEST_SCORE_KEY]
+ if best_score is None or best_score < score:
+ best_save_path = os.path.join(
+ self._meta_info[BEST_DIR_KEY],
+ "best-{:}.pth".format(self.__class__.__name__),
+ )
+ self._meta_info[BEST_SCORE_KEY] = score
+ torch.save(self.state_dict(), best_save_path)
+ return True, self._meta_info[BEST_SCORE_KEY]
+ else:
+ return False, self._meta_info[BEST_SCORE_KEY]
+
+ def load_best(self):
+ if BEST_DIR_KEY not in self._meta_info or BEST_SCORE_KEY not in self._meta_info:
+ raise ValueError("Please call save_best at first")
+ best_save_path = os.path.join(
+ self._meta_info[BEST_DIR_KEY],
+ "best-{:}.pth".format(self.__class__.__name__),
+ )
+ state_dict = torch.load(best_save_path)
+ self.load_state_dict(state_dict)
+
@property
def abstract_search_space(self):
raise NotImplementedError