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Update xlayers

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D-X-Y 2021-05-07 10:26:35 +08:00
parent f6a024a6ff
commit 80aaac4dfa
9 changed files with 333 additions and 83 deletions
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172
exps/LFNA/lfna-v1.py
View File

@ -21,6 +21,57 @@ 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
class LFNAmlp:
"""A LFNA meta-model that uses the MLP as delta-net."""
def __init__(self, obs_dim, hidden_sizes, act_name):
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
)
def adapt(self, model, criterion, w_container, xs, ys):
containers = [w_container]
for idx, (x, y) in enumerate(zip(xs, ys)):
y_hat = model.forward_with_container(x, containers[-1])
loss = criterion(y_hat, y)
gradients = torch.autograd.grad(loss, containers[-1].tensors)
with torch.no_grad():
flatten_w = containers[-1].flatten().view(-1, 1)
flatten_g = containers[-1].flatten(gradients).view(-1, 1)
input_statistics = torch.tensor([x.mean(), x.std()]).view(1, 2)
input_statistics = input_statistics.expand(flatten_w.numel(), -1)
delta_inputs = torch.cat((flatten_w, flatten_g, input_statistics), dim=-1)
delta = self.delta_net(delta_inputs).view(-1)
# delta = torch.clamp(delta, -0.5, 0.5)
unflatten_delta = containers[-1].unflatten(delta)
future_container = containers[-1].additive(unflatten_delta)
containers.append(future_container)
# containers = containers[1:]
meta_loss = []
for idx, (x, y) in enumerate(zip(xs, ys)):
if idx == 0:
continue
current_container = containers[idx]
y_hat = model.forward_with_container(x, current_container)
loss = criterion(y_hat, y)
meta_loss.append(loss)
meta_loss = sum(meta_loss)
meta_loss.backward()
self.meta_optimizer.step()
def zero_grad(self):
self.meta_optimizer.zero_grad()
self.delta_net.zero_grad()
class Population:
@ -28,11 +79,23 @@ class Population:
def __init__(self):
self._time2model = dict()
self._time2score = dict() # higher is better
def append(self, timestamp, model):
def append(self, timestamp, model, score):
if timestamp in self._time2model:
raise ValueError("This timestamp has been added.")
self._time2model[timestamp] = model
self._time2score[timestamp] = score
def query(self, timestamp):
closet_timestamp = None
for xtime, model in self._time2model.items():
if (
closet_timestamp is None
or timestamp - closet_timestamp >= timestamp - xtime
):
closet_timestamp = xtime
return self._time2model[closet_timestamp], closet_timestamp
def main(args):
@ -70,100 +133,39 @@ def main(args):
)
w_container = base_model.named_parameters_buffers()
criterion = torch.nn.MSELoss()
print("There are {:} weights.".format(w_container.numel()))
adaptor = LFNAmlp(4, (50, 20), "leaky_relu")
pool = Population()
pool.append(0, w_container)
# LFNA meta-training
per_epoch_time, start_time = AverageMeter(), time.time()
for iepoch in range(args.epochs):
import pdb
pdb.set_trace()
print("-")
for i, idx in enumerate(to_evaluate_indexes):
need_time = "Time Left: {:}".format(
convert_secs2time(
per_timestamp_time.avg * (len(to_evaluate_indexes) - i), True
)
convert_secs2time(per_epoch_time.avg * (args.epochs - iepoch), True)
)
logger.log(
"[{:}]".format(time_string())
+ " [{:04d}/{:04d}][{:04d}]".format(i, len(to_evaluate_indexes), idx)
+ " "
"[{:}] [{:04d}/{:04d}] ".format(time_string(), iepoch, args.epochs)
+ need_time
)
# train the same data
assert idx != 0
historical_x = env_info["{:}-x".format(idx)]
historical_y = env_info["{:}-y".format(idx)]
# build model
mean, std = historical_x.mean().item(), historical_x.std().item()
model_kwargs = dict(input_dim=1, output_dim=1, mean=mean, std=std)
model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
# build optimizer
optimizer = torch.optim.Adam(model.parameters(), lr=args.init_lr, amsgrad=True)
criterion = torch.nn.MSELoss()
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=[
int(args.epochs * 0.25),
int(args.epochs * 0.5),
int(args.epochs * 0.75),
],
gamma=0.3,
)
train_metric = MSEMetric()
best_loss, best_param = None, None
for _iepoch in range(args.epochs):
preds = model(historical_x)
optimizer.zero_grad()
loss = criterion(preds, historical_y)
loss.backward()
optimizer.step()
lr_scheduler.step()
# save best
if best_loss is None or best_loss > loss.item():
best_loss = loss.item()
best_param = copy.deepcopy(model.state_dict())
model.load_state_dict(best_param)
with torch.no_grad():
train_metric(preds, historical_y)
train_results = train_metric.get_info()
metric = ComposeMetric(MSEMetric(), SaveMetric())
eval_dataset = torch.utils.data.TensorDataset(
env_info["{:}-x".format(idx)], env_info["{:}-y".format(idx)]
)
eval_loader = torch.utils.data.DataLoader(
eval_dataset, batch_size=args.batch_size, shuffle=False, num_workers=0
)
results = basic_eval_fn(eval_loader, model, metric, logger)
log_str = (
"[{:}]".format(time_string())
+ " [{:04d}/{:04d}]".format(idx, env_info["total"])
+ " train-mse: {:.5f}, eval-mse: {:.5f}".format(
train_results["mse"], results["mse"]
)
)
logger.log(log_str)
for ibatch in range(args.meta_batch):
sampled_timestamp = random.randint(0, train_time_bar)
query_w_container, query_timestamp = pool.query(sampled_timestamp)
# def adapt(self, model, w_container, xs, ys):
xs, ys = [], []
for it in range(sampled_timestamp, sampled_timestamp + args.max_seq):
xs.append(env_info["{:}-x".format(it)])
ys.append(env_info["{:}-y".format(it)])
adaptor.adapt(base_model, criterion, query_w_container, xs, ys)
import pdb
save_path = logger.path(None) / "{:04d}-{:04d}.pth".format(
idx, env_info["total"]
)
save_checkpoint(
{
"model_state_dict": model.state_dict(),
"model": model,
"index": idx,
"timestamp": env_info["{:}-timestamp".format(idx)],
},
save_path,
logger,
)
pdb.set_trace()
print("-")
logger.log("")
per_timestamp_time.update(time.time() - start_time)
@ -188,10 +190,10 @@ if __name__ == "__main__":
help="The initial learning rate for the optimizer (default is Adam)",
)
parser.add_argument(
"--batch_size",
"--meta_batch",
type=int,
default=512,
help="The batch size",
default=2,
help="The batch size for the meta-model",
)
parser.add_argument(
"--epochs",
@ -199,6 +201,12 @@ if __name__ == "__main__":
default=1000,
help="The total number of epochs.",
)
parser.add_argument(
"--max_seq",
type=int,
default=5,
help="The maximum length of the sequence.",
)
parser.add_argument(
"--workers",
type=int,

1
lib/models/xcore.py
View File

@ -34,3 +34,4 @@ def get_model(config: Dict[Text, Any], **kwargs):
else:
raise TypeError("Unkonwn model type: {:}".format(model_type))
return model

26
lib/xlayers/super_activations.py
View File

@ -31,6 +31,9 @@ class SuperReLU(SuperModule):
def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
return F.relu(input, inplace=self._inplace)
def forward_with_container(self, input, container, prefix=[]):
return self.forward_raw(input)
def extra_repr(self) -> str:
return "inplace=True" if self._inplace else ""
@ -53,6 +56,29 @@ class SuperLeakyReLU(SuperModule):
def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
return F.leaky_relu(input, self._negative_slope, self._inplace)
def forward_with_container(self, input, container, prefix=[]):
return self.forward_raw(input)
def extra_repr(self) -> str:
inplace_str = "inplace=True" if self._inplace else ""
return "negative_slope={}{}".format(self._negative_slope, inplace_str)
class SuperTanh(SuperModule):
"""Applies a the Tanh function element-wise."""
def __init__(self) -> None:
super(SuperTanh, self).__init__()
@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 torch.tanh(input)
def forward_with_container(self, input, container, prefix=[]):
return self.forward_raw(input)

7
lib/xlayers/super_container.py
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@ -111,3 +111,10 @@ class SuperSequential(SuperModule):
for module in self:
input = module(input)
return input
def forward_with_container(self, input, container, prefix=[]):
for index, module in enumerate(self):
input = module.forward_with_container(
input, container, prefix + [str(index)]
)
return input

7
lib/xlayers/super_core.py
View File

@ -27,8 +27,13 @@ from .super_transformer import SuperTransformerEncoderLayer
from .super_activations import SuperReLU
from .super_activations import SuperLeakyReLU
from .super_activations import SuperTanh
super_name2activation = {"relu": SuperReLU, "leaky_relu": SuperLeakyReLU}
super_name2activation = {
"relu": SuperReLU,
"leaky_relu": SuperLeakyReLU,
"tanh": SuperTanh,
}
from .super_trade_stem import SuperAlphaEBDv1

10
lib/xlayers/super_linear.py
View File

@ -115,6 +115,16 @@ class SuperLinear(SuperModule):
self._in_features, self._out_features, self._bias
)
def forward_with_container(self, input, container, prefix=[]):
super_weight_name = ".".join(prefix + ["_super_weight"])
super_weight = container.query(super_weight_name)
super_bias_name = ".".join(prefix + ["_super_bias"])
if container.has(super_bias_name):
super_bias = container.query(super_bias_name)
else:
super_bias = None
return F.linear(input, super_weight, super_bias)
class SuperMLPv1(SuperModule):
"""An MLP layer: FC -> Activation -> Drop -> FC -> Drop."""

55
lib/xlayers/super_module.py
View File

@ -39,6 +39,41 @@ class TensorContainer:
self._param_or_buffers = []
self._name2index = dict()
def additive(self, tensors):
result = TensorContainer()
for index, name in enumerate(self._names):
new_tensor = self._tensors[index] + tensors[index]
result.append(name, new_tensor, self._param_or_buffers[index])
return result
def no_grad_clone(self):
result = TensorContainer()
with torch.no_grad():
for index, name in enumerate(self._names):
result.append(
name, self._tensors[index].clone(), self._param_or_buffers[index]
)
return result
@property
def tensors(self):
return self._tensors
def flatten(self, tensors=None):
if tensors is None:
tensors = self._tensors
tensors = [tensor.view(-1) for tensor in tensors]
return torch.cat(tensors)
def unflatten(self, tensor):
tensors, s = [], 0
for raw_tensor in self._tensors:
length = raw_tensor.numel()
x = torch.reshape(tensor[s : s + length], shape=raw_tensor.shape)
tensors.append(x)
s += length
return tensors
def append(self, name, tensor, param_or_buffer):
if not isinstance(tensor, torch.Tensor):
raise TypeError(
@ -54,6 +89,23 @@ class TensorContainer:
)
self._name2index[name] = len(self._names) - 1
def query(self, name):
if not self.has(name):
raise ValueError(
"The {:} is not in {:}".format(name, list(self._name2index.keys()))
)
index = self._name2index[name]
return self._tensors[index]
def has(self, name):
return name in self._name2index
def has_prefix(self, prefix):
for name, idx in self._name2index.items():
if name.startswith(prefix):
return name
return False
def numel(self):
total = 0
for tensor in self._tensors:
@ -181,3 +233,6 @@ class SuperModule(abc.ABC, nn.Module):
)
)
return outputs
def forward_with_container(self, inputs, container, prefix=[]):
raise NotImplementedError

18
lib/xlayers/super_norm.py
View File

@ -161,6 +161,21 @@ class SuperSimpleLearnableNorm(SuperModule):
mean, std = torch.unsqueeze(mean, dim=0), torch.unsqueeze(std, dim=0)
return tensor.sub_(mean).div_(std)
def forward_with_container(self, input, container, prefix=[]):
if not self._inplace:
tensor = input.clone()
else:
tensor = input
mean_name = ".".join(prefix + ["_mean"])
std_name = ".".join(prefix + ["_std"])
mean, std = (
container.query(mean_name).to(tensor.device),
torch.abs(container.query(std_name).to(tensor.device)) + self._eps,
)
while mean.ndim < tensor.ndim:
mean, std = torch.unsqueeze(mean, dim=0), torch.unsqueeze(std, dim=0)
return tensor.sub_(mean).div_(std)
def extra_repr(self) -> str:
return "mean={mean}, std={std}, inplace={inplace}".format(
mean=self._mean.item(), std=self._std.item(), inplace=self._inplace
@ -191,3 +206,6 @@ class SuperIdentity(SuperModule):
def extra_repr(self) -> str:
return "inplace={inplace}".format(inplace=self._inplace)
def forward_with_container(self, input, container, prefix=[]):
return self.forward_raw(input)

120
lib/xlayers/super_rl_actor.py Normal file
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@ -0,0 +1,120 @@
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
#####################################################
# DISABLED / NOT-FINISHED
#####################################################
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Optional, Callable
import spaces
from .super_container import SuperSequential
from .super_linear import SuperLinear
class SuperActor(SuperModule):
"""A Actor in RL."""
def _distribution(self, obs):
raise NotImplementedError
def _log_prob_from_distribution(self, pi, act):
raise NotImplementedError
def forward_candidate(self, **kwargs):
return self.forward_raw(**kwargs)
def forward_raw(self, obs, act=None):
# Produce action distributions for given observations, and
# optionally compute the log likelihood of given actions under
# those distributions.
pi = self._distribution(obs)
logp_a = None
if act is not None:
logp_a = self._log_prob_from_distribution(pi, act)
return pi, logp_a
class SuperLfnaMetaMLP(SuperModule):
def __init__(self, obs_dim, hidden_sizes, act_cls):
super(SuperLfnaMetaMLP).__init__()
self.delta_net = SuperSequential(
SuperLinear(obs_dim, hidden_sizes[0]),
act_cls(),
SuperLinear(hidden_sizes[0], hidden_sizes[1]),
act_cls(),
SuperLinear(hidden_sizes[1], 1),
)
class SuperLfnaMetaMLP(SuperModule):
def __init__(self, obs_dim, act_dim, hidden_sizes, act_cls):
super(SuperLfnaMetaMLP).__init__()
log_std = -0.5 * np.ones(act_dim, dtype=np.float32)
self.log_std = torch.nn.Parameter(torch.as_tensor(log_std))
self.mu_net = SuperSequential(
SuperLinear(obs_dim, hidden_sizes[0]),
act_cls(),
SuperLinear(hidden_sizes[0], hidden_sizes[1]),
act_cls(),
SuperLinear(hidden_sizes[1], act_dim),
)
def _distribution(self, obs):
mu = self.mu_net(obs)
std = torch.exp(self.log_std)
return Normal(mu, std)
def _log_prob_from_distribution(self, pi, act):
return pi.log_prob(act).sum(axis=-1)
def forward_candidate(self, **kwargs):
return self.forward_raw(**kwargs)
def forward_raw(self, obs, act=None):
# Produce action distributions for given observations, and
# optionally compute the log likelihood of given actions under
# those distributions.
pi = self._distribution(obs)
logp_a = None
if act is not None:
logp_a = self._log_prob_from_distribution(pi, act)
return pi, logp_a
class SuperMLPGaussianActor(SuperModule):
def __init__(self, obs_dim, act_dim, hidden_sizes, act_cls):
super(SuperMLPGaussianActor).__init__()
log_std = -0.5 * np.ones(act_dim, dtype=np.float32)
self.log_std = torch.nn.Parameter(torch.as_tensor(log_std))
self.mu_net = SuperSequential(
SuperLinear(obs_dim, hidden_sizes[0]),
act_cls(),
SuperLinear(hidden_sizes[0], hidden_sizes[1]),
act_cls(),
SuperLinear(hidden_sizes[1], act_dim),
)
def _distribution(self, obs):
mu = self.mu_net(obs)
std = torch.exp(self.log_std)
return Normal(mu, std)
def _log_prob_from_distribution(self, pi, act):
return pi.log_prob(act).sum(axis=-1)
def forward_candidate(self, **kwargs):
return self.forward_raw(**kwargs)
def forward_raw(self, obs, act=None):
# Produce action distributions for given observations, and
# optionally compute the log likelihood of given actions under
# those distributions.
pi = self._distribution(obs)
logp_a = None
if act is not None:
logp_a = self._log_prob_from_distribution(pi, act)
return pi, logp_a