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Update LFNA with train/valid

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This commit is contained in:
D-X-Y
2021-05-17 07:39:24 +00:00
parent de8cf677d9
commit 5c851ac25a
5 changed files with 123 additions and 26 deletions
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89
exps/LFNA/lfna.py
View File

@@ -2,7 +2,8 @@
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
#####################################################
# python exps/LFNA/lfna.py --env_version v1 --workers 0
# python exps/LFNA/lfna.py --env_version v1 --device cuda
# 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
@@ -58,9 +59,40 @@ def epoch_train(loader, meta_model, base_model, optimizer, criterion, device, lo
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 main(args):
logger, env_info, model_kwargs = lfna_setup(args)
dynamic_env = get_synthetic_env(mode="train", version=args.env_version)
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()
@@ -68,26 +100,25 @@ def main(args):
shape_container = base_model.get_w_container().to_shape_container()
# pre-train the hypernetwork
timestamps = dynamic_env.get_timestamp(None)
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(dynamic_env, args.meta_batch, args.sampler_enlarge)
dynamic_env.reset_max_seq_length(args.seq_length)
"""
env_loader = torch.utils.data.DataLoader(
dynamic_env,
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,
)
"""
env_loader = torch.utils.data.DataLoader(
dynamic_env,
train_env_loader = torch.utils.data.DataLoader(
train_env,
batch_sampler=batch_sampler,
num_workers=args.workers,
pin_memory=True,
@@ -95,7 +126,7 @@ def main(args):
optimizer = torch.optim.Adam(
meta_model.parameters(),
lr=args.init_lr,
lr=args.lr,
weight_decay=args.weight_decay,
amsgrad=True,
)
@@ -108,7 +139,7 @@ def main(args):
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(env_loader)))
logger.log("Per epoch iterations = {:}".format(len(train_env_loader)))
if logger.path("model").exists():
ckp_data = torch.load(logger.path("model"))
@@ -122,7 +153,7 @@ def main(args):
"epochs",
"env_version",
"hidden_dim",
"init_lr",
"lr",
"layer_dim",
"time_dim",
"seq_length",
@@ -152,7 +183,7 @@ def main(args):
)
loss_meter = epoch_train(
env_loader,
train_env_loader,
meta_model,
base_model,
optimizer,
@@ -160,9 +191,16 @@ def main(args):
args.device,
logger,
)
valid_loss_meter = epoch_evaluate(
valid_env_loader, meta_model, base_model, criterion, args.device, logger
)
logger.log(
head_str
+ " meta-loss: {meter.avg:.4f} ({meter.count:.0f})".format(meter=loss_meter)
+ " 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)
)
@@ -231,14 +269,14 @@ def main(args):
#
new_param = meta_model.create_meta_embed()
optimizer = torch.optim.Adam(
[new_param], lr=args.init_lr, weight_decay=1e-5, amsgrad=True
[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.epochs):
for iepoch in range(args.refine_epochs):
optimizer.zero_grad()
[seq_containers] = meta_model(time_seqs)
future_container = seq_containers[-1]
@@ -297,7 +335,7 @@ if __name__ == "__main__":
)
#####
parser.add_argument(
"--init_lr",
"--lr",
type=float,
default=0.005,
help="The initial learning rate for the optimizer (default is Adam)",
@@ -321,10 +359,19 @@ if __name__ == "__main__":
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=50,
default=20,
help="The #epochs for early stop.",
)
parser.add_argument(
@@ -350,7 +397,7 @@ if __name__ == "__main__":
args.hidden_dim,
args.layer_dim,
args.time_dim,
args.init_lr,
args.lr,
args.weight_decay,
args.epochs,
args.env_version,

9
exps/LFNA/lfna_meta_model.py
View File

@@ -44,6 +44,7 @@ class LFNA_Meta(super_core.SuperModule):
self._append_meta_embed = dict(fixed=None, learnt=None)
self._append_meta_timestamps = dict(fixed=None, learnt=None)
self._time_prob_drop = super_core.SuperDrop(dropout, (-1, 1), recover=False)
# build transformer
layers = []
for ilayer in range(mha_depth):
@@ -149,10 +150,12 @@ class LFNA_Meta(super_core.SuperModule):
meta_match = meta_match.view(batch, seq, -1)
# create the probability
time_probs = (1 / torch.exp(time_match_v * 10)).view(batch, seq, 1)
if self.training:
time_probs[:, -1, :] = 0
x_time_probs = self._time_prob_drop(time_probs)
# if self.training:
# time_probs[:, -1, :] = 0
unknown_token = self._unknown_token.view(1, 1, -1)
raw_meta_embed = time_probs * meta_match + (1 - time_probs) * unknown_token
raw_meta_embed = x_time_probs * meta_match + (1 - x_time_probs) * unknown_token
meta_embed = self.meta_corrector(raw_meta_embed)
# create joint embed

5
lib/datasets/synthetic_env.py
View File

@@ -151,12 +151,15 @@ class SyntheticDEnv(data.Dataset):
return len(self._timestamp_generator)
def __repr__(self):
return "{name}({cur_num:}/{total} elements, ndim={ndim}, num_per_task={num_per_task})".format(
return "{name}({cur_num:}/{total} elements, ndim={ndim}, num_per_task={num_per_task}, range=[{xrange_min:.5f}~{xrange_max:.5f}], mode={mode})".format(
name=self.__class__.__name__,
cur_num=len(self),
total=len(self._timestamp_generator),
ndim=self._ndim,
num_per_task=self._num_per_task,
xrange_min=self.min_timestamp,
xrange_max=self.max_timestamp,
mode=self._timestamp_generator.mode,
)

1
lib/xlayers/super_core.py
View File

@@ -15,6 +15,7 @@ from .super_norm import SuperLayerNorm1D
from .super_norm import SuperSimpleLearnableNorm
from .super_norm import SuperIdentity
from .super_dropout import SuperDropout
from .super_dropout import SuperDrop
super_name2norm = {
"simple_norm": SuperSimpleNorm,

45
lib/xlayers/super_dropout.py
View File

@@ -6,7 +6,7 @@ import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Optional, Callable
from typing import Optional, Callable, Tuple
import spaces
from .super_module import SuperModule
@@ -38,3 +38,46 @@ class SuperDropout(SuperModule):
def extra_repr(self) -> str:
xstr = "inplace=True" if self._inplace else ""
return "p={:}".format(self._p) + ", " + xstr
class SuperDrop(SuperModule):
"""Applies a the drop-path function element-wise."""
def __init__(self, p: float, dims: Tuple[int], recover: bool = True) -> None:
super(SuperDrop, self).__init__()
self._p = p
self._dims = dims
self._recover = recover
@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:
if not self.training or self._p <= 0:
return input
keep_prob = 1 - self._p
shape = [input.shape[0]] + [
x if y == -1 else y for x, y in zip(input.shape[1:], self._dims)
]
random_tensor = keep_prob + torch.rand(
shape, dtype=input.dtype, device=input.device
)
random_tensor.floor_() # binarize
if self._recover:
return input.div(keep_prob) * random_tensor
else:
return input * random_tensor # as masks
def forward_with_container(self, input, container, prefix=[]):
return self.forward_raw(input)
def extra_repr(self) -> str:
return (
"p={:}".format(self._p)
+ ", dims={:}".format(self._dims)
+ ", recover={:}".format(self._recover)
)