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Update LFNA ablation codes

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D-X-Y
2021-05-12 15:45:45 +08:00
parent 4da19d6efe
commit d51e5fdc7f
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280
exps/LFNA/backup/lfna-debug.py
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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
#####################################################
# python exps/LFNA/lfna-debug.py --env_version v1 --hidden_dim 16
#####################################################
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
from lfna_utils import lfna_setup, train_model, TimeData
from lfna_models import HyperNet
class LFNAmlp:
"""A LFNA meta-model that uses the MLP as delta-net."""
def __init__(self, obs_dim, hidden_sizes, act_name, criterion):
self.delta_net = super_core.SuperSequential(
super_core.SuperLinear(obs_dim, hidden_sizes[0]),
super_core.super_name2activation[act_name](),
super_core.SuperLinear(hidden_sizes[0], hidden_sizes[1]),
super_core.super_name2activation[act_name](),
super_core.SuperLinear(hidden_sizes[1], 1),
)
self.meta_optimizer = torch.optim.Adam(
self.delta_net.parameters(), lr=0.01, amsgrad=True
)
self.criterion = criterion
def adapt(self, model, seq_flatten_w):
delta_inputs = torch.stack(seq_flatten_w, dim=-1)
delta = self.delta_net(delta_inputs)
container = model.get_w_container()
unflatten_delta = container.unflatten(delta)
future_container = container.create_container(unflatten_delta)
return future_container
def step(self):
torch.nn.utils.clip_grad_norm_(self.delta_net.parameters(), 1.0)
self.meta_optimizer.step()
def zero_grad(self):
self.meta_optimizer.zero_grad()
self.delta_net.zero_grad()
def state_dict(self):
return dict(
delta_net=self.delta_net.state_dict(),
meta_optimizer=self.meta_optimizer.state_dict(),
)
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)
total_time = env_info["total"]
for i in range(total_time):
for xkey in ("timestamp", "x", "y"):
nkey = "{:}-{:}".format(i, xkey)
assert nkey in env_info, "{:} no in {:}".format(nkey, list(env_info.keys()))
train_time_bar = total_time // 2
criterion = torch.nn.MSELoss()
logger.log("There are {:} weights.".format(model.get_w_container().numel()))
adaptor = LFNAmlp(args.meta_seq, (200, 200), "leaky_relu", criterion)
# pre-train the model
dataset = init_dataset = TimeData(0, env_info["0-x"], env_info["0-y"])
shape_container = model.get_w_container().to_shape_container()
hypernet = HyperNet(shape_container, 16)
optimizer = torch.optim.Adam(hypernet.parameters(), lr=args.init_lr, amsgrad=True)
best_loss, best_param = None, None
for _iepoch in range(args.epochs):
container = hypernet(None)
preds = model.forward_with_container(dataset.x, container)
optimizer.zero_grad()
loss = criterion(preds, dataset.y)
loss.backward()
optimizer.step()
# save best
if best_loss is None or best_loss > loss.item():
best_loss = loss.item()
best_param = copy.deepcopy(model.state_dict())
print("hyper-net : best={:.4f}".format(best_loss))
init_loss = train_model(model, init_dataset, args.init_lr, args.epochs)
logger.log("The pre-training loss is {:.4f}".format(init_loss))
import pdb
pdb.set_trace()
all_past_containers = []
ground_truth_path = (
logger.path(None) / ".." / "use-same-timestamp-v1-d16" / "final-ckp.pth"
)
ground_truth_data = torch.load(ground_truth_path)
all_gt_containers = ground_truth_data["w_container_per_epoch"]
all_gt_flattens = dict()
for idx, container in all_gt_containers.items():
all_gt_flattens[idx] = container.no_grad_clone().flatten()
# LFNA meta-training
meta_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)
)
logger.log(
"[{:}] [{:04d}/{:04d}] ".format(time_string(), iepoch, args.epochs)
+ need_time
)
adaptor.zero_grad()
meta_losses = []
for ibatch in range(args.meta_batch):
future_timestamp = random.randint(args.meta_seq, train_time_bar)
future_dataset = TimeData(
future_timestamp,
env_info["{:}-x".format(future_timestamp)],
env_info["{:}-y".format(future_timestamp)],
)
seq_datasets = []
for iseq in range(args.meta_seq):
cur_time = future_timestamp - iseq - 1
cur_x = env_info["{:}-x".format(cur_time)]
cur_y = env_info["{:}-y".format(cur_time)]
seq_datasets.append(TimeData(cur_time, cur_x, cur_y))
seq_datasets.reverse()
seq_flatten_w = [
all_gt_flattens[dataset.timestamp] for dataset in seq_datasets
]
future_container = adaptor.adapt(network, seq_flatten_w)
"""
future_y_hat = network.forward_with_container(
future_dataset.x, future_container
)
future_loss = adaptor.criterion(future_y_hat, future_dataset.y)
"""
future_loss = adaptor.criterion(
future_container.flatten(), all_gt_flattens[future_timestamp]
)
# import pdb; pdb.set_trace()
meta_losses.append(future_loss)
meta_loss = torch.stack(meta_losses).mean()
meta_loss.backward()
adaptor.step()
meta_loss_meter.update(meta_loss.item())
logger.log(
"meta-loss: {:.4f} ({:.4f}) ".format(
meta_loss_meter.avg, meta_loss_meter.val
)
)
if iepoch % 200 == 0:
save_checkpoint(
{"adaptor": adaptor.state_dict(), "iepoch": iepoch},
logger.path("model"),
logger,
)
per_epoch_time.update(time.time() - start_time)
start_time = time.time()
w_container_per_epoch = dict()
# import pdb; pdb.set_trace()
for idx in range(1, env_info["total"]):
future_time = env_info["{:}-timestamp".format(idx)]
future_x = env_info["{:}-x".format(idx)]
future_y = env_info["{:}-y".format(idx)]
seq_datasets = []
for iseq in range(1, args.meta_seq + 1):
cur_time = future_timestamp - iseq - 1
if cur_time < 0:
cur_time = 0
cur_x = env_info["{:}-x".format(cur_time)]
cur_y = env_info["{:}-y".format(cur_time)]
seq_datasets.append(TimeData(cur_time, cur_x, cur_y))
seq_datasets.reverse()
seq_flatten_w = [all_gt_flattens[dataset.timestamp] for dataset in seq_datasets]
future_container = adaptor.adapt(network, seq_flatten_w)
w_container_per_epoch[idx] = future_container.no_grad_clone()
with torch.no_grad():
future_y_hat = network.forward_with_container(
future_x, w_container_per_epoch[idx]
)
future_loss = adaptor.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("Use the data in the past.")
parser.add_argument(
"--save_dir",
type=str,
default="./outputs/lfna-synthetic/lfna-debug",
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(
"--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=32,
help="The batch size for the meta-model",
)
parser.add_argument(
"--meta_seq",
type=int,
default=10,
help="The length of the sequence for meta-model.",
)
parser.add_argument(
"--epochs",
type=int,
default=2000,
help="The total number of epochs.",
)
# 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.save_dir = "{:}-{:}-d{:}".format(
args.save_dir, args.env_version, args.hidden_dim
)
main(args)

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exps/LFNA/lfna-test-hpnet.py Normal file
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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
#####################################################
# python exps/LFNA/lfna-test-hpnet.py --env_version v1 --hidden_dim 16
#####################################################
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 import HyperNet_VX as HyperNet
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)
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)
# task_embed = torch.nn.Parameter(torch.Tensor(env_info["total"], args.task_dim))
task_embed = torch.nn.Parameter(torch.Tensor(1, args.task_dim))
trunc_normal_(task_embed, std=0.02)
parameters = list(hypernet.parameters()) + [task_embed]
optimizer = torch.optim.Adam(parameters, lr=args.init_lr, amsgrad=True)
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
total_bar = 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_embed
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)
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 % 200 == 0:
logger.log(
head_str
+ "meta-loss: {:.4f} ({:.4f}) :: lr={:.5f}, batch={:}".format(
loss_meter.avg,
loss_meter.val,
min(lr_scheduler.get_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)
logger.log("-" * 200 + "\n")
logger.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser("Use the data in the past.")
parser.add_argument(
"--save_dir",
type=str,
default="./outputs/lfna-synthetic/lfna-test-hpnet",
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(
"--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.",
)
# 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.hidden_dim
args.save_dir = "{:}-{:}-d{:}".format(
args.save_dir, args.env_version, args.hidden_dim
)
main(args)

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exps/LFNA/lfna-ttss-hpnet.py Normal file
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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
#####################################################
# python exps/LFNA/lfna-ttss-hpnet.py --env_version v1 --hidden_dim 16
#####################################################
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
from lfna_utils import lfna_setup, train_model, TimeData
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)
total_time = env_info["total"]
for i in range(total_time):
for xkey in ("timestamp", "x", "y"):
nkey = "{:}-{:}".format(i, xkey)
assert nkey in env_info, "{:} no in {:}".format(nkey, list(env_info.keys()))
train_time_bar = total_time // 2
criterion = torch.nn.MSELoss()
logger.log("There are {:} weights.".format(model.get_w_container().numel()))
# pre-train the model
dataset = init_dataset = TimeData(0, env_info["0-x"], env_info["0-y"])
shape_container = model.get_w_container().to_shape_container()
hypernet = HyperNet(shape_container, 16)
print(hypernet)
optimizer = torch.optim.Adam(hypernet.parameters(), lr=args.init_lr, amsgrad=True)
best_loss, best_param = None, None
for _iepoch in range(args.epochs):
container = hypernet(None)
preds = model.forward_with_container(dataset.x, container)
optimizer.zero_grad()
loss = criterion(preds, dataset.y)
loss.backward()
optimizer.step()
# save best
if best_loss is None or best_loss > loss.item():
best_loss = loss.item()
best_param = copy.deepcopy(model.state_dict())
print("hyper-net : best={:.4f}".format(best_loss))
init_loss = train_model(model, init_dataset, args.init_lr, args.epochs)
logger.log("The pre-training loss is {:.4f}".format(init_loss))
print(model)
print(hypernet)
logger.log("-" * 200 + "\n")
logger.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser("Use the data in the past.")
parser.add_argument(
"--save_dir",
type=str,
default="./outputs/lfna-synthetic/lfna-debug",
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(
"--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=32,
help="The batch size for the meta-model",
)
parser.add_argument(
"--meta_seq",
type=int,
default=10,
help="The length of the sequence for meta-model.",
)
parser.add_argument(
"--epochs",
type=int,
default=2000,
help="The total number of epochs.",
)
# 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.save_dir = "{:}-{:}-d{:}".format(
args.save_dir, args.env_version, args.hidden_dim
)
main(args)

97
exps/LFNA/lfna_models.py Normal file
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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
#####################################################
import copy
import torch
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, return_container=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(
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 = task_embed.view(1, -1).expand(self._num_layers, -1)
joint_embed = torch.cat((task_embed, self._super_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))
class HyperNet_VX(super_core.SuperModule):
def __init__(self, shape_container, input_embeding, return_container=True):
super(HyperNet_VX, 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, input_embeding)),
)
trunc_normal_(self._super_layer_embed, std=0.02)
model_kwargs = dict(
input_dim=input_embeding,
output_dim=max(self._numel_per_layer),
hidden_dim=input_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, input):
weights = self._generator(self._super_layer_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))

1
lib/xlayers/super_core.py
View File

@@ -41,4 +41,5 @@ super_name2activation = {
from .super_trade_stem import SuperAlphaEBDv1
from .super_positional_embedding import SuperDynamicPositionE
from .super_positional_embedding import SuperPositionalEncoder

35
lib/xlayers/super_positional_embedding.py
View File

@@ -10,6 +10,41 @@ from .super_module import SuperModule
from .super_module import IntSpaceType
class SuperDynamicPositionE(SuperModule):
"""Applies a positional encoding to the input positions."""
def __init__(self, dimension: int, scale: float = 1.0) -> None:
super(SuperDynamicPositionE, self).__init__()
self._scale = scale
self._dimension = dimension
# weights to be optimized
self.register_buffer(
"_div_term",
torch.exp(
torch.arange(0, dimension, 2).float() * (-math.log(10000.0) / dimension)
),
)
@property
def abstract_search_space(self):
root_node = spaces.VirtualNode(id(self))
return root_node
def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
return self.forward_raw(input)
def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
import pdb
pdb.set_trace()
print("---")
return F.linear(input, self._super_weight, self._super_bias)
def extra_repr(self) -> str:
return "scale={:}, dim={:}".format(self._scale, self._dimension)
class SuperPositionalEncoder(SuperModule):
"""Attention Is All You Need: https://arxiv.org/pdf/1706.03762.pdf
https://github.com/pytorch/examples/blob/master/word_language_model/model.py#L65