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Update LFNA

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This commit is contained in:
D-X-Y
2021-05-23 08:21:31 +00:00
parent 2a864ae705
commit 25dc78a7ce
5 changed files with 152 additions and 20 deletions
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15
exps/LFNA/lfna.py
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@@ -107,11 +107,20 @@ def online_evaluate(env, meta_model, base_model, criterion, args, logger):
base_model.eval()
time_seqs = torch.Tensor(time_seqs).view(1, -1).to(args.device)
[seq_containers], _ = meta_model(time_seqs, None)
future_container = seq_containers[-2]
_, (future_x, future_y) = env(time_seqs[0, -2].item())
# For Debug
for idx in range(time_seqs.numel()):
future_container = seq_containers[idx]
_, (future_x, future_y) = env(time_seqs[0, idx].item())
future_x, future_y = future_x.to(args.device), future_y.to(args.device)
future_y_hat = base_model.forward_with_container(future_x, future_container)
future_y_hat = base_model.forward_with_container(
future_x, future_container
)
future_loss = criterion(future_y_hat, future_y)
logger.log(
"--> time={:.4f} -> loss={:.4f}".format(
time_seqs[0, idx].item(), future_loss.item()
)
)
logger.log(
"[ONLINE] [{:03d}/{:03d}] loss={:.4f}".format(
idx, len(env), future_loss.item()

33
exps/LFNA/lfna_meta_model.py
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@@ -47,17 +47,17 @@ class LFNA_Meta(super_core.SuperModule):
self._append_meta_timestamps = dict(fixed=None, learnt=None)
self._tscalar_embed = super_core.SuperDynamicPositionE(
time_embedding, scale=100
time_embedding, scale=500
)
# build transformer
self._trans_att = super_core.SuperQKVAttention(
time_embedding,
time_embedding,
time_embedding,
time_embedding,
4,
True,
self._trans_att = super_core.SuperQKVAttentionV2(
qk_att_dim=time_embedding,
in_v_dim=time_embedding,
hidden_dim=time_embedding,
num_heads=4,
proj_dim=time_embedding,
qkv_bias=True,
attn_drop=None,
proj_drop=dropout,
)
@@ -166,9 +166,12 @@ class LFNA_Meta(super_core.SuperModule):
# timestamps is a batch of sequence of timestamps
batch, seq = timestamps.shape
meta_timestamps, meta_embeds = self.meta_timestamps, self.super_meta_embed
timestamp_q_embed = self._tscalar_embed(timestamps)
timestamp_k_embed = self._tscalar_embed(meta_timestamps.view(1, -1))
# timestamp_q_embed = self._tscalar_embed(timestamps)
# timestamp_k_embed = self._tscalar_embed(meta_timestamps.view(1, -1))
timestamp_v_embed = meta_embeds.unsqueeze(dim=0)
timestamp_qk_att_embed = self._tscalar_embed(
torch.unsqueeze(timestamps, dim=-1) - meta_timestamps
)
# create the mask
mask = (
torch.unsqueeze(timestamps, dim=-1) <= meta_timestamps.view(1, 1, -1)
@@ -179,11 +182,13 @@ class LFNA_Meta(super_core.SuperModule):
> self._thresh
)
timestamp_embeds = self._trans_att(
timestamp_q_embed, timestamp_k_embed, timestamp_v_embed, mask
timestamp_qk_att_embed, timestamp_v_embed, mask
)
relative_timestamps = timestamps - timestamps[:, :1]
relative_pos_embeds = self._tscalar_embed(relative_timestamps)
init_timestamp_embeds = torch.cat(
(timestamp_embeds, relative_pos_embeds), dim=-1
)
# 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

2
xautodl/xlayers/super_attention.py
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@@ -238,7 +238,7 @@ class SuperQKVAttention(SuperModule):
return root_node
def apply_candidate(self, abstract_child: spaces.VirtualNode):
super(SuperQVKAttention, self).apply_candidate(abstract_child)
super(SuperQKVAttention, self).apply_candidate(abstract_child)
if "q_fc" in abstract_child:
self.q_fc.apply_candidate(abstract_child["q_fc"])
if "k_fc" in abstract_child:

117
xautodl/xlayers/super_attention_v2.py Normal file
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@@ -0,0 +1,117 @@
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
#####################################################
from __future__ import division
from __future__ import print_function
import math
from functools import partial
from typing import Optional, Text
import torch
import torch.nn as nn
import torch.nn.functional as F
from xautodl import spaces
from .super_module import SuperModule
from .super_module import IntSpaceType
from .super_module import BoolSpaceType
from .super_linear import SuperLinear
class SuperQKVAttentionV2(SuperModule):
"""The super model for attention layer."""
def __init__(
self,
qk_att_dim: int,
in_v_dim: int,
hidden_dim: int,
num_heads: int,
proj_dim: int,
qkv_bias: bool = False,
attn_drop: Optional[float] = None,
proj_drop: Optional[float] = None,
):
super(SuperQKVAttentionV2, self).__init__()
self._in_v_dim = in_v_dim
self._qk_att_dim = qk_att_dim
self._proj_dim = proj_dim
self._hidden_dim = hidden_dim
self._num_heads = num_heads
self._qkv_bias = qkv_bias
self.qk_fc = SuperLinear(qk_att_dim, num_heads, bias=qkv_bias)
self.v_fc = SuperLinear(in_v_dim, hidden_dim * num_heads, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop or 0.0)
self.proj = SuperLinear(hidden_dim * num_heads, proj_dim)
self.proj_drop = nn.Dropout(proj_drop or 0.0)
self._infinity = 1e9
@property
def num_heads(self):
return spaces.get_max(self._num_heads)
@property
def in_v_dim(self):
return spaces.get_max(self._in_v_dim)
@property
def qk_att_dim(self):
return spaces.get_max(self._qk_att_dim)
@property
def hidden_dim(self):
return spaces.get_max(self._hidden_dim)
@property
def proj_dim(self):
return spaces.get_max(self._proj_dim)
@property
def abstract_search_space(self):
root_node = spaces.VirtualNode(id(self))
raise NotImplementedError
def apply_candidate(self, abstract_child: spaces.VirtualNode):
super(SuperQKVAttentionV2, self).apply_candidate(abstract_child)
raise NotImplementedError
def forward_qkv(
self, qk_att_tensor, v_tensor, num_head: int, mask=None
) -> torch.Tensor:
qk_att = self.qk_fc(qk_att_tensor)
B, N, S, _ = qk_att.shape
assert _ == num_head
attn_v1 = qk_att.permute(0, 3, 1, 2)
if mask is not None:
mask = torch.unsqueeze(mask, dim=1)
attn_v1 = attn_v1.masked_fill(mask, -self._infinity)
attn_v1 = attn_v1.softmax(dim=-1) # B * #head * N * S
attn_v1 = self.attn_drop(attn_v1)
v = self.v_fc(v_tensor)
B0, _, _ = v.shape
v_v1 = v.reshape(B0, S, num_head, -1).permute(0, 2, 1, 3)
feats_v1 = (attn_v1 @ v_v1).permute(0, 2, 1, 3).reshape(B, N, -1)
return feats_v1
def forward_candidate(self, qk_att_tensor, v_tensor, mask=None) -> torch.Tensor:
return self.forward_raw(qk_att_tensor, v_tensor, mask)
def forward_raw(self, qk_att_tensor, v_tensor, mask=None) -> torch.Tensor:
feats = self.forward_qkv(qk_att_tensor, v_tensor, self.num_heads, mask)
outs = self.proj(feats)
outs = self.proj_drop(outs)
return outs
def extra_repr(self) -> str:
return "input_dim={:}, hidden_dim={:}, proj_dim={:}, num_heads={:}, infinity={:}".format(
(self.qk_att_dim, self.in_v_dim),
self._hidden_dim,
self._proj_dim,
self._num_heads,
self._infinity,
)

1
xautodl/xlayers/super_core.py
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@@ -26,6 +26,7 @@ super_name2norm = {
from .super_attention import SuperSelfAttention
from .super_attention import SuperQKVAttention
from .super_attention_v2 import SuperQKVAttentionV2
from .super_transformer import SuperTransformerEncoderLayer
from .super_activations import SuperReLU