autodl-projects/exps/LFNA/lfna_meta_model.py
2021-05-22 11:02:29 +00:00

209 lines
7.9 KiB
Python

#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
#####################################################
import copy
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 LFNA_Meta(super_core.SuperModule):
"""Learning to Forecast Neural Adaptation (Meta Model Design)."""
def __init__(
self,
shape_container,
layer_embedding,
time_embedding,
meta_timestamps,
mha_depth: int = 2,
dropout: float = 0.1,
):
super(LFNA_Meta, 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
self.register_parameter(
"_super_layer_embed",
torch.nn.Parameter(torch.Tensor(self._num_layers, layer_embedding)),
)
self.register_parameter(
"_super_meta_embed",
torch.nn.Parameter(torch.Tensor(len(meta_timestamps), time_embedding)),
)
self.register_buffer("_meta_timestamps", torch.Tensor(meta_timestamps))
self._time_embed_dim = time_embedding
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_embedding, scale=100
)
# build transformer
self._trans_att = super_core.SuperQKVAttention(
time_embedding,
time_embedding,
time_embedding,
time_embedding,
4,
True,
attn_drop=None,
proj_drop=dropout,
)
layers = []
for ilayer in range(mha_depth):
layers.append(
super_core.SuperTransformerEncoderLayer(
time_embedding * 2,
4,
True,
4,
dropout,
norm_affine=False,
order=super_core.LayerOrder.PostNorm,
)
)
layers.append(super_core.SuperLinear(time_embedding * 2, time_embedding))
self._meta_corrector = super_core.SuperSequential(*layers)
model_kwargs = dict(
config=dict(model_type="dual_norm_mlp"),
input_dim=layer_embedding + time_embedding,
output_dim=max(self._numel_per_layer),
hidden_dims=[(layer_embedding + time_embedding) * 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, meta_corrector, generator):
parameters = []
if time_embed:
parameters.append(self._super_meta_embed)
if meta_corrector:
parameters.extend(list(self._trans_att.parameters()))
parameters.extend(list(self._meta_corrector.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 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 _obtain_time_embed(self, timestamps):
# timestamps is a batch of sequence of timestamps
batch, seq = timestamps.shape
timestamp_q_embed = self._tscalar_embed(timestamps)
timestamp_k_embed = self._tscalar_embed(self.meta_timestamps.view(1, -1))
timestamp_v_embed = self.super_meta_embed.unsqueeze(dim=0)
timestamp_embeds = self._trans_att(
timestamp_q_embed, timestamp_k_embed, timestamp_v_embed
)
# relative_timestamps = timestamps - timestamps[:, :1]
# relative_pos_embeds = self._tscalar_embed(relative_timestamps)
init_timestamp_embeds = torch.cat((timestamp_q_embed, timestamp_embeds), dim=-1)
corrected_embeds = self._meta_corrector(init_timestamp_embeds)
return corrected_embeds
def forward_raw(self, timestamps, time_embed):
if time_embed is None:
batch, seq = timestamps.shape
time_embed = self._obtain_time_embed(timestamps)
else:
batch, seq, _ = time_embed.shape
# create joint embed
num_layer, _ = self._super_layer_embed.shape
meta_embed = time_embed.view(batch, seq, 1, -1).expand(-1, -1, num_layer, -1)
layer_embed = self._super_layer_embed.view(1, 1, num_layer, -1).expand(
batch, seq, -1, -1
)
joint_embed = torch.cat((meta_embed, layer_embed), dim=-1)
batch_weights = self._generator(joint_embed)
batch_containers = []
for seq_weights in torch.split(batch_weights, 1):
seq_containers = []
for weights in torch.split(seq_weights.squeeze(0), 1):
weights = torch.split(weights.squeeze(0), 1)
seq_containers.append(self._shape_container.translate(weights))
batch_containers.append(seq_containers)
return batch_containers, time_embed
def forward_candidate(self, input):
raise NotImplementedError
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),
)