Updates
This commit is contained in:
parent
6c1fd745d7
commit
f8350d00ed
@ -6,10 +6,11 @@
|
||||
# python exps/GMOA/lfna.py --env_version v1 --device cuda --lr 0.002 --seq_length 16 --meta_batch 128
|
||||
# python exps/GMOA/lfna.py --env_version v1 --device cuda --lr 0.002 --seq_length 24 --time_dim 32 --meta_batch 128
|
||||
#####################################################
|
||||
import pdb, sys, time, copy, torch, random, argparse
|
||||
import sys, time, copy, torch, random, argparse
|
||||
from tqdm import tqdm
|
||||
from copy import deepcopy
|
||||
from pathlib import Path
|
||||
from torch.nn import functional as F
|
||||
|
||||
lib_dir = (Path(__file__).parent / ".." / "..").resolve()
|
||||
print("LIB-DIR: {:}".format(lib_dir))
|
||||
@ -103,7 +104,7 @@ def online_evaluate(env, meta_model, base_model, criterion, args, logger, save=F
|
||||
meta_model.eval()
|
||||
base_model.eval()
|
||||
_, [future_container], time_embeds = meta_model(
|
||||
future_time.to(args.device).view(1, 1), None, True
|
||||
future_time.to(args.device).view(1, 1), None, False
|
||||
)
|
||||
if save:
|
||||
w_containers[idx] = future_container.no_grad_clone()
|
||||
@ -159,50 +160,57 @@ def pretrain_v2(base_model, meta_model, criterion, xenv, args, logger):
|
||||
left_time = "Time Left: {:}".format(
|
||||
convert_secs2time(per_epoch_time.avg * (args.epochs - iepoch), True)
|
||||
)
|
||||
total_meta_v1_losses, total_meta_v2_losses, total_match_losses = [], [], []
|
||||
total_future_losses, total_present_losses, total_regu_losses = [], [], []
|
||||
optimizer.zero_grad()
|
||||
for ibatch in range(args.meta_batch):
|
||||
rand_index = random.randint(0, meta_model.meta_length - 1)
|
||||
timestamp = meta_model.meta_timestamps[rand_index]
|
||||
meta_embed = meta_model.super_meta_embed[rand_index]
|
||||
|
||||
_, [container], time_embed = meta_model(
|
||||
torch.unsqueeze(timestamp, dim=0), None, True
|
||||
torch.unsqueeze(timestamp, dim=0), None, False
|
||||
)
|
||||
_, (inputs, targets) = xenv(timestamp.item())
|
||||
inputs, targets = inputs.to(device), targets.to(device)
|
||||
# generate models one step ahead
|
||||
predictions = base_model.forward_with_container(inputs, container)
|
||||
total_meta_v1_losses.append(criterion(predictions, targets))
|
||||
# the matching loss
|
||||
match_loss = criterion(torch.squeeze(time_embed, dim=0), meta_embed)
|
||||
total_match_losses.append(match_loss)
|
||||
total_future_losses.append(criterion(predictions, targets))
|
||||
# randomly sample
|
||||
rand_index = random.randint(0, meta_model.meta_length - 1)
|
||||
timestamp = meta_model.meta_timestamps[rand_index]
|
||||
meta_embed = meta_model.super_meta_embed[rand_index]
|
||||
|
||||
time_embed = meta_model(torch.unsqueeze(timestamp, dim=0), None, True)
|
||||
total_regu_losses.append(
|
||||
F.mse_loss(
|
||||
torch.squeeze(time_embed, dim=0), meta_embed, reduction="mean"
|
||||
)
|
||||
)
|
||||
# generate models via memory
|
||||
_, [container], _ = meta_model(None, meta_embed.view(1, 1, -1), True)
|
||||
_, [container], _ = meta_model(None, meta_embed.view(1, 1, -1), False)
|
||||
predictions = base_model.forward_with_container(inputs, container)
|
||||
total_meta_v2_losses.append(criterion(predictions, targets))
|
||||
total_present_losses.append(criterion(predictions, targets))
|
||||
with torch.no_grad():
|
||||
meta_std = torch.stack(total_meta_v1_losses).std().item()
|
||||
meta_v1_loss = torch.stack(total_meta_v1_losses).mean()
|
||||
meta_v2_loss = torch.stack(total_meta_v2_losses).mean()
|
||||
match_loss = torch.stack(total_match_losses).mean()
|
||||
total_loss = meta_v1_loss + meta_v2_loss + match_loss
|
||||
meta_std = torch.stack(total_future_losses).std().item()
|
||||
loss_future = torch.stack(total_future_losses).mean()
|
||||
loss_present = torch.stack(total_present_losses).mean()
|
||||
regularization_loss = torch.stack(total_regu_losses).mean()
|
||||
total_loss = loss_future + loss_present + regularization_loss
|
||||
total_loss.backward()
|
||||
optimizer.step()
|
||||
# success
|
||||
success, best_score = meta_model.save_best(-total_loss.item())
|
||||
logger.log(
|
||||
"{:} [Pre-V2 {:04d}/{:}] loss : {:.4f} +- {:.4f} = {:.4f} + {:.4f} + {:.4f} (match)".format(
|
||||
"{:} [Pre-V2 {:04d}/{:}] loss : {:.4f} +- {:.4f} = {:.4f} + {:.4f} + {:.4f}".format(
|
||||
time_string(),
|
||||
iepoch,
|
||||
args.epochs,
|
||||
total_loss.item(),
|
||||
meta_std,
|
||||
meta_v1_loss.item(),
|
||||
meta_v2_loss.item(),
|
||||
match_loss.item(),
|
||||
loss_future.item(),
|
||||
loss_present.item(),
|
||||
regularization_loss.item(),
|
||||
)
|
||||
+ ", batch={:}".format(len(total_meta_v1_losses))
|
||||
+ ", batch={:}".format(len(total_future_losses))
|
||||
+ ", success={:}, best={:.4f}".format(success, -best_score)
|
||||
+ ", LS={:}/{:}".format(iepoch - last_success_epoch, early_stop_thresh)
|
||||
+ ", {:}".format(left_time)
|
||||
|
@ -34,7 +34,7 @@ class MetaModelV1(super_core.SuperModule):
|
||||
assert interval is not None
|
||||
self._interval = interval
|
||||
self._seq_length = seq_length
|
||||
self._thresh = interval * 30 if thresh is None else thresh
|
||||
self._thresh = interval * 50 if thresh is None else thresh
|
||||
|
||||
self.register_parameter(
|
||||
"_super_layer_embed",
|
||||
@ -183,7 +183,7 @@ class MetaModelV1(super_core.SuperModule):
|
||||
)
|
||||
return timestamp_embeds
|
||||
|
||||
def forward_raw(self, timestamps, time_embeds, get_seq_last):
|
||||
def forward_raw(self, timestamps, time_embeds, tembed_only=False):
|
||||
if time_embeds is None:
|
||||
time_seq = timestamps.view(-1, 1) + self._time_interval.view(1, -1)
|
||||
B, S = time_seq.shape
|
||||
@ -193,41 +193,23 @@ class MetaModelV1(super_core.SuperModule):
|
||||
B, S, _ = time_embeds.shape
|
||||
# create joint embed
|
||||
num_layer, _ = self._super_layer_embed.shape
|
||||
if get_seq_last:
|
||||
time_embeds = time_embeds[:, -1, :]
|
||||
# The shape of `joint_embed` is batch * num-layers * input-dim
|
||||
joint_embeds = torch.cat(
|
||||
(
|
||||
time_embeds.view(B, 1, -1).expand(-1, num_layer, -1),
|
||||
self._super_layer_embed.view(1, num_layer, -1).expand(B, -1, -1),
|
||||
),
|
||||
dim=-1,
|
||||
)
|
||||
else:
|
||||
# The shape of `joint_embed` is batch * seq * num-layers * input-dim
|
||||
joint_embeds = torch.cat(
|
||||
(
|
||||
time_embeds.view(B, S, 1, -1).expand(-1, -1, num_layer, -1),
|
||||
self._super_layer_embed.view(1, 1, num_layer, -1).expand(
|
||||
B, S, -1, -1
|
||||
),
|
||||
),
|
||||
dim=-1,
|
||||
)
|
||||
time_embeds = time_embeds[:, -1, :]
|
||||
if tembed_only:
|
||||
return time_embeds
|
||||
# The shape of `joint_embed` is batch * num-layers * input-dim
|
||||
joint_embeds = torch.cat(
|
||||
(
|
||||
time_embeds.view(B, 1, -1).expand(-1, num_layer, -1),
|
||||
self._super_layer_embed.view(1, num_layer, -1).expand(B, -1, -1),
|
||||
),
|
||||
dim=-1,
|
||||
)
|
||||
batch_weights = self._generator(joint_embeds)
|
||||
batch_containers = []
|
||||
for weights in torch.split(batch_weights, 1):
|
||||
if get_seq_last:
|
||||
batch_containers.append(
|
||||
self._shape_container.translate(torch.split(weights.squeeze(0), 1))
|
||||
)
|
||||
else:
|
||||
seq_containers = []
|
||||
for ws in torch.split(weights.squeeze(0), 1):
|
||||
seq_containers.append(
|
||||
self._shape_container.translate(torch.split(ws.squeeze(0), 1))
|
||||
)
|
||||
batch_containers.append(seq_containers)
|
||||
batch_containers.append(
|
||||
self._shape_container.translate(torch.split(weights.squeeze(0), 1))
|
||||
)
|
||||
return time_seq, batch_containers, time_embeds
|
||||
|
||||
def forward_candidate(self, input):
|
||||
@ -241,7 +223,9 @@ class MetaModelV1(super_core.SuperModule):
|
||||
with torch.set_grad_enabled(True):
|
||||
new_param = self.create_meta_embed()
|
||||
|
||||
optimizer = torch.optim.Adam([new_param], lr=lr, weight_decay=1e-5, amsgrad=True)
|
||||
optimizer = torch.optim.Adam(
|
||||
[new_param], lr=lr, weight_decay=1e-5, amsgrad=True
|
||||
)
|
||||
timestamp = torch.Tensor([timestamp]).to(new_param.device)
|
||||
self.replace_append_learnt(timestamp, new_param)
|
||||
self.train()
|
||||
@ -255,10 +239,10 @@ class MetaModelV1(super_core.SuperModule):
|
||||
best_new_param = new_param.detach().clone()
|
||||
for iepoch in range(epochs):
|
||||
optimizer.zero_grad()
|
||||
_, [_], time_embed = self(timestamp.view(1, 1), None, True)
|
||||
_, [_], time_embed = self(timestamp.view(1, 1), None)
|
||||
match_loss = criterion(new_param, time_embed)
|
||||
|
||||
_, [container], time_embed = self(None, new_param.view(1, 1, -1), True)
|
||||
_, [container], time_embed = self(None, new_param.view(1, 1, -1))
|
||||
y_hat = base_model.forward_with_container(x, container)
|
||||
meta_loss = criterion(y_hat, y)
|
||||
loss = meta_loss + match_loss
|
||||
|
@ -1,51 +1,49 @@
|
||||
#####################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.05 #
|
||||
#####################################################
|
||||
import math
|
||||
from .synthetic_utils import TimeStamp
|
||||
from .synthetic_env import SyntheticDEnv
|
||||
from .math_core import LinearFunc
|
||||
from .math_core import DynamicLinearFunc
|
||||
from .math_core import DynamicQuadraticFunc
|
||||
from .math_core import ConstantFunc, ComposedSinFunc
|
||||
from .math_core import ConstantFunc, ComposedSinFunc as SinFunc
|
||||
from .math_core import GaussianDGenerator
|
||||
|
||||
|
||||
__all__ = ["TimeStamp", "SyntheticDEnv", "get_synthetic_env"]
|
||||
|
||||
|
||||
def get_synthetic_env(total_timestamp=1000, num_per_task=1000, mode=None, version="v1"):
|
||||
if version == "v0":
|
||||
def get_synthetic_env(total_timestamp=1600, num_per_task=1000, mode=None, version="v1"):
|
||||
max_time = math.pi * 10
|
||||
if version == "v1":
|
||||
mean_generator = ConstantFunc(0)
|
||||
std_generator = ConstantFunc(1)
|
||||
data_generator = GaussianDGenerator(
|
||||
[mean_generator], [[std_generator]], (-2, 2)
|
||||
)
|
||||
time_generator = TimeStamp(
|
||||
min_timestamp=0, max_timestamp=math.pi * 8, num=total_timestamp, mode=mode
|
||||
min_timestamp=0, max_timestamp=max_time, num=total_timestamp, mode=mode
|
||||
)
|
||||
oracle_map = DynamicLinearFunc(
|
||||
params={
|
||||
0: ComposedSinFunc(params={0: 2.0, 1: 1.0, 2: 2.2}),
|
||||
1: ConstantFunc(0),
|
||||
0: SinFunc(params={0: 2.0, 1: 1.0, 2: 2.2}), # 2 sin(t) + 2.2
|
||||
1: SinFunc(params={0: 1.5, 1: 0.6, 2: 1.8}), # 1.5 sin(0.6t) + 1.8
|
||||
}
|
||||
)
|
||||
dynamic_env = SyntheticDEnv(
|
||||
data_generator, oracle_map, time_generator, num_per_task
|
||||
)
|
||||
elif version == "v1":
|
||||
elif version == "v2":
|
||||
mean_generator = ConstantFunc(0)
|
||||
std_generator = ConstantFunc(1)
|
||||
data_generator = GaussianDGenerator(
|
||||
[mean_generator], [[std_generator]], (-2, 2)
|
||||
)
|
||||
time_generator = TimeStamp(
|
||||
min_timestamp=0, max_timestamp=math.pi * 8, num=total_timestamp, mode=mode
|
||||
min_timestamp=0, max_timestamp=max_time, num=total_timestamp, mode=mode
|
||||
)
|
||||
oracle_map = DynamicLinearFunc(
|
||||
oracle_map = DynamicQuadraticFunc(
|
||||
params={
|
||||
0: ComposedSinFunc(params={0: 2.0, 1: 1.0, 2: 2.2}),
|
||||
1: ComposedSinFunc(params={0: 1.5, 1: 0.6, 2: 1.8}),
|
||||
0: LinearFunc(params={0: 0.1, 1: 0}), # 0.1 * t
|
||||
1: SinFunc(params={0: 1, 1: 1, 2: 0}), # sin(t)
|
||||
}
|
||||
)
|
||||
dynamic_env = SyntheticDEnv(
|
||||
|
Loading…
Reference in New Issue
Block a user