261 lines
9.6 KiB
Python
261 lines
9.6 KiB
Python
#
|
|
# This is used for the ablation studies:
|
|
# The meta-model in this file uses the traditional attention in
|
|
# transformer.
|
|
#
|
|
import torch
|
|
|
|
import torch.nn.functional as F
|
|
|
|
from xautodl.xlayers import super_core
|
|
from xautodl.xlayers import trunc_normal_
|
|
from xautodl.models.xcore import get_model
|
|
|
|
|
|
class MetaModel_TraditionalAtt(super_core.SuperModule):
|
|
"""Learning to Generate Models One Step Ahead (Meta Model Design)."""
|
|
|
|
def __init__(
|
|
self,
|
|
shape_container,
|
|
layer_dim,
|
|
time_dim,
|
|
meta_timestamps,
|
|
dropout: float = 0.1,
|
|
seq_length: int = None,
|
|
interval: float = None,
|
|
thresh: float = None,
|
|
):
|
|
super(MetaModel_TraditionalAtt, 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._raw_meta_timestamps = meta_timestamps
|
|
assert interval is not None
|
|
self._interval = interval
|
|
self._thresh = interval * seq_length if thresh is None else thresh
|
|
|
|
self.register_parameter(
|
|
"_super_layer_embed",
|
|
torch.nn.Parameter(torch.Tensor(self._num_layers, layer_dim)),
|
|
)
|
|
self.register_parameter(
|
|
"_super_meta_embed",
|
|
torch.nn.Parameter(torch.Tensor(len(meta_timestamps), time_dim)),
|
|
)
|
|
self.register_buffer("_meta_timestamps", torch.Tensor(meta_timestamps))
|
|
self._time_embed_dim = time_dim
|
|
self._append_meta_embed = dict(fixed=None, learnt=None)
|
|
self._append_meta_timestamps = dict(fixed=None, learnt=None)
|
|
|
|
self._tscalar_embed = super_core.SuperDynamicPositionE(
|
|
time_dim, scale=1 / interval
|
|
)
|
|
|
|
# build transformer
|
|
self._trans_att = super_core.SuperQKVAttention(
|
|
in_q_dim=time_dim,
|
|
in_k_dim=time_dim,
|
|
in_v_dim=time_dim,
|
|
num_heads=4,
|
|
proj_dim=time_dim,
|
|
qkv_bias=True,
|
|
attn_drop=None,
|
|
proj_drop=dropout,
|
|
)
|
|
|
|
model_kwargs = dict(
|
|
config=dict(model_type="dual_norm_mlp"),
|
|
input_dim=layer_dim + time_dim,
|
|
output_dim=max(self._numel_per_layer),
|
|
hidden_dims=[(layer_dim + time_dim) * 2] * 3,
|
|
act_cls="gelu",
|
|
norm_cls="layer_norm_1d",
|
|
dropout=dropout,
|
|
)
|
|
self._generator = get_model(**model_kwargs)
|
|
|
|
# initialization
|
|
trunc_normal_(
|
|
[self._super_layer_embed, self._super_meta_embed],
|
|
std=0.02,
|
|
)
|
|
|
|
def get_parameters(self, time_embed, attention, generator):
|
|
parameters = []
|
|
if time_embed:
|
|
parameters.append(self._super_meta_embed)
|
|
if attention:
|
|
parameters.extend(list(self._trans_att.parameters()))
|
|
if generator:
|
|
parameters.append(self._super_layer_embed)
|
|
parameters.extend(list(self._generator.parameters()))
|
|
return parameters
|
|
|
|
@property
|
|
def meta_timestamps(self):
|
|
with torch.no_grad():
|
|
meta_timestamps = [self._meta_timestamps]
|
|
for key in ("fixed", "learnt"):
|
|
if self._append_meta_timestamps[key] is not None:
|
|
meta_timestamps.append(self._append_meta_timestamps[key])
|
|
return torch.cat(meta_timestamps)
|
|
|
|
@property
|
|
def super_meta_embed(self):
|
|
meta_embed = [self._super_meta_embed]
|
|
for key in ("fixed", "learnt"):
|
|
if self._append_meta_embed[key] is not None:
|
|
meta_embed.append(self._append_meta_embed[key])
|
|
return torch.cat(meta_embed)
|
|
|
|
def create_meta_embed(self):
|
|
param = torch.Tensor(1, self._time_embed_dim)
|
|
trunc_normal_(param, std=0.02)
|
|
param = param.to(self._super_meta_embed.device)
|
|
param = torch.nn.Parameter(param, True)
|
|
return param
|
|
|
|
def get_closest_meta_distance(self, timestamp):
|
|
with torch.no_grad():
|
|
distances = torch.abs(self.meta_timestamps - timestamp)
|
|
return torch.min(distances).item()
|
|
|
|
def replace_append_learnt(self, timestamp, meta_embed):
|
|
self._append_meta_timestamps["learnt"] = timestamp
|
|
self._append_meta_embed["learnt"] = meta_embed
|
|
|
|
@property
|
|
def meta_length(self):
|
|
return self.meta_timestamps.numel()
|
|
|
|
def clear_fixed(self):
|
|
self._append_meta_timestamps["fixed"] = None
|
|
self._append_meta_embed["fixed"] = None
|
|
|
|
def clear_learnt(self):
|
|
self.replace_append_learnt(None, None)
|
|
|
|
def append_fixed(self, timestamp, meta_embed):
|
|
with torch.no_grad():
|
|
device = self._super_meta_embed.device
|
|
timestamp = timestamp.detach().clone().to(device)
|
|
meta_embed = meta_embed.detach().clone().to(device)
|
|
if self._append_meta_timestamps["fixed"] is None:
|
|
self._append_meta_timestamps["fixed"] = timestamp
|
|
else:
|
|
self._append_meta_timestamps["fixed"] = torch.cat(
|
|
(self._append_meta_timestamps["fixed"], timestamp), dim=0
|
|
)
|
|
if self._append_meta_embed["fixed"] is None:
|
|
self._append_meta_embed["fixed"] = meta_embed
|
|
else:
|
|
self._append_meta_embed["fixed"] = torch.cat(
|
|
(self._append_meta_embed["fixed"], meta_embed), dim=0
|
|
)
|
|
|
|
def gen_time_embed(self, timestamps):
|
|
# timestamps is a batch of timestamps
|
|
[B] = timestamps.shape
|
|
# batch, seq = timestamps.shape
|
|
timestamps = timestamps.view(-1, 1)
|
|
meta_timestamps, meta_embeds = self.meta_timestamps, self.super_meta_embed
|
|
timestamp_v_embed = meta_embeds.unsqueeze(dim=0)
|
|
timestamp_q_embed = self._tscalar_embed(timestamps)
|
|
timestamp_k_embed = self._tscalar_embed(meta_timestamps.view(1, -1))
|
|
|
|
# create the mask
|
|
mask = (
|
|
torch.unsqueeze(timestamps, dim=-1) <= meta_timestamps.view(1, 1, -1)
|
|
) | (
|
|
torch.abs(
|
|
torch.unsqueeze(timestamps, dim=-1) - meta_timestamps.view(1, 1, -1)
|
|
)
|
|
> self._thresh
|
|
)
|
|
timestamp_embeds = self._trans_att(
|
|
timestamp_q_embed, timestamp_k_embed, timestamp_v_embed, mask
|
|
)
|
|
return timestamp_embeds[:, -1, :]
|
|
|
|
def gen_model(self, time_embeds):
|
|
B, _ = time_embeds.shape
|
|
# create joint embed
|
|
num_layer, _ = self._super_layer_embed.shape
|
|
# 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):
|
|
batch_containers.append(
|
|
self._shape_container.translate(torch.split(weights.squeeze(0), 1))
|
|
)
|
|
return batch_containers
|
|
|
|
def forward_raw(self, timestamps, time_embeds, tembed_only=False):
|
|
raise NotImplementedError
|
|
|
|
def forward_candidate(self, input):
|
|
raise NotImplementedError
|
|
|
|
def easy_adapt(self, timestamp, time_embed):
|
|
with torch.no_grad():
|
|
timestamp = torch.Tensor([timestamp]).to(self._meta_timestamps.device)
|
|
self.replace_append_learnt(None, None)
|
|
self.append_fixed(timestamp, time_embed)
|
|
|
|
def adapt(self, base_model, criterion, timestamp, x, y, lr, epochs, init_info):
|
|
distance = self.get_closest_meta_distance(timestamp)
|
|
if distance + self._interval * 1e-2 <= self._interval:
|
|
return False, None
|
|
x, y = x.to(self._meta_timestamps.device), y.to(self._meta_timestamps.device)
|
|
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
|
|
)
|
|
timestamp = torch.Tensor([timestamp]).to(new_param.device)
|
|
self.replace_append_learnt(timestamp, new_param)
|
|
self.train()
|
|
base_model.train()
|
|
if init_info is not None:
|
|
best_loss = init_info["loss"]
|
|
new_param.data.copy_(init_info["param"].data)
|
|
else:
|
|
best_loss = 1e9
|
|
with torch.no_grad():
|
|
best_new_param = new_param.detach().clone()
|
|
for iepoch in range(epochs):
|
|
optimizer.zero_grad()
|
|
time_embed = self.gen_time_embed(timestamp.view(1))
|
|
match_loss = F.l1_loss(new_param, time_embed)
|
|
|
|
[container] = self.gen_model(new_param.view(1, -1))
|
|
y_hat = base_model.forward_with_container(x, container)
|
|
meta_loss = criterion(y_hat, y)
|
|
loss = meta_loss + match_loss
|
|
loss.backward()
|
|
optimizer.step()
|
|
if meta_loss.item() < best_loss:
|
|
with torch.no_grad():
|
|
best_loss = meta_loss.item()
|
|
best_new_param = new_param.detach().clone()
|
|
self.easy_adapt(timestamp, best_new_param)
|
|
return True, best_loss
|
|
|
|
def extra_repr(self) -> str:
|
|
return "(_super_layer_embed): {:}, (_super_meta_embed): {:}, (_meta_timestamps): {:}".format(
|
|
list(self._super_layer_embed.shape),
|
|
list(self._super_meta_embed.shape),
|
|
list(self._meta_timestamps.shape),
|
|
)
|