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Fix bugs in xlayers

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This commit is contained in:
D-X-Y
2021-05-22 16:41:54 +08:00
parent 97717d826e
commit bc42ab3c08
7 changed files with 197 additions and 39 deletions
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3
exps/LFNA/lfna.py
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@@ -10,7 +10,8 @@ from tqdm import tqdm
from copy import deepcopy
from pathlib import Path
lib_dir = (Path(__file__).parent / "..").resolve()
lib_dir = (Path(__file__).parent / ".." / "..").resolve()
print("LIB-DIR: {:}".format(lib_dir))
if str(lib_dir) not in sys.path:
sys.path.insert(0, str(lib_dir))

54
exps/LFNA/lfna_meta_model.py
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@@ -20,7 +20,7 @@ class LFNA_Meta(super_core.SuperModule):
layer_embedding,
time_embedding,
meta_timestamps,
mha_depth: int = 2,
mha_depth: int = 1,
dropout: float = 0.1,
):
super(LFNA_Meta, self).__init__()
@@ -44,8 +44,21 @@ class LFNA_Meta(super_core.SuperModule):
self._append_meta_embed = dict(fixed=None, learnt=None)
self._append_meta_timestamps = dict(fixed=None, learnt=None)
self._time_prob_drop = super_core.SuperDrop(dropout, (-1, 1), recover=False)
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(
@@ -74,15 +87,9 @@ class LFNA_Meta(super_core.SuperModule):
self._generator = get_model(**model_kwargs)
# print("generator: {:}".format(self._generator))
# unknown token
self.register_parameter(
"_unknown_token",
torch.nn.Parameter(torch.Tensor(1, time_embedding)),
)
# initialization
trunc_normal_(
[self._super_layer_embed, self._super_meta_embed, self._unknown_token],
[self._super_layer_embed, self._super_meta_embed],
std=0.02,
)
@@ -136,28 +143,21 @@ class LFNA_Meta(super_core.SuperModule):
(self._append_meta_embed["fixed"], meta_embed), dim=0
)
def forward_raw(self, timestamps):
def _obtain_time_embed(self, timestamps):
# timestamps is a batch of sequence of timestamps
batch, seq = timestamps.shape
timestamps = timestamps.unsqueeze(dim=-1)
meta_timestamps = self.meta_timestamps.view(1, 1, -1)
time_diffs = timestamps - meta_timestamps
time_match_v, time_match_i = torch.min(torch.abs(time_diffs), dim=-1)
# select corresponding meta-knowledge
meta_match = torch.index_select(
self.super_meta_embed, dim=0, index=time_match_i.view(-1)
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
)
meta_match = meta_match.view(batch, seq, -1)
# create the probability
time_probs = (1 / torch.exp(time_match_v * 10)).view(batch, seq, 1)
corrected_embeds = self.meta_corrector(timestamp_embeds)
return corrected_embeds
x_time_probs = self._time_prob_drop(time_probs)
# if self.training:
# time_probs[:, -1, :] = 0
unknown_token = self._unknown_token.view(1, 1, -1)
raw_meta_embed = x_time_probs * meta_match + (1 - x_time_probs) * unknown_token
meta_embed = self.meta_corrector(raw_meta_embed)
def forward_raw(self, timestamps):
batch, seq = timestamps.shape
meta_embed = self._obtain_time_embed(timestamps)
# create joint embed
num_layer, _ = self._super_layer_embed.shape
meta_embed = meta_embed.view(batch, seq, 1, -1).expand(-1, -1, num_layer, -1)

2
setup.py
View File

@@ -16,7 +16,7 @@
#
# TODO(xuanyidong): upload it to conda
#
# [2021.05.18] v1.0
# [2021.05.21] v0.9.9
import os
from setuptools import setup, find_packages

158
xautodl/xlayers/super_attention.py
View File

@@ -20,7 +20,7 @@ from .super_module import BoolSpaceType
from .super_linear import SuperLinear
class SuperAttention(SuperModule):
class SuperSelfAttention(SuperModule):
"""The super model for attention layer."""
def __init__(
@@ -32,7 +32,7 @@ class SuperAttention(SuperModule):
attn_drop: Optional[float] = None,
proj_drop: Optional[float] = None,
):
super(SuperAttention, self).__init__()
super(SuperSelfAttention, self).__init__()
self._input_dim = input_dim
self._proj_dim = proj_dim
self._num_heads = num_heads
@@ -150,3 +150,157 @@ class SuperAttention(SuperModule):
return "input_dim={:}, proj_dim={:}, num_heads={:}".format(
self._input_dim, self._proj_dim, self._num_heads
)
class SuperQKVAttention(SuperModule):
"""The super model for attention layer."""
def __init__(
self,
in_q_dim: IntSpaceType,
in_k_dim: IntSpaceType,
in_v_dim: IntSpaceType,
proj_dim: IntSpaceType,
num_heads: IntSpaceType,
qkv_bias: BoolSpaceType = False,
attn_drop: Optional[float] = None,
proj_drop: Optional[float] = None,
):
super(SuperQKVAttention, self).__init__()
self._in_v_dim = in_v_dim
self._in_q_dim = in_q_dim
self._in_k_dim = in_k_dim
self._proj_dim = proj_dim
self._num_heads = num_heads
self._qkv_bias = qkv_bias
self.q_fc = SuperLinear(in_q_dim, proj_dim, bias=qkv_bias)
self.k_fc = SuperLinear(in_k_dim, proj_dim, bias=qkv_bias)
self.v_fc = SuperLinear(in_v_dim, proj_dim, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop or 0.0)
self.proj = SuperLinear(proj_dim, proj_dim)
self.proj_drop = nn.Dropout(proj_drop or 0.0)
@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 in_q_dim(self):
return spaces.get_max(self._in_q_dim)
@property
def in_k_dim(self):
return spaces.get_max(self._in_k_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))
space_q = self.q_fc.abstract_search_space
space_k = self.k_fc.abstract_search_space
space_v = self.v_fc.abstract_search_space
space_proj = self.proj.abstract_search_space
if not spaces.is_determined(self._num_heads):
root_node.append("_num_heads", self._num_heads.abstract(reuse_last=True))
if not spaces.is_determined(space_q):
root_node.append("q_fc", space_q)
if not spaces.is_determined(space_k):
root_node.append("k_fc", space_k)
if not spaces.is_determined(space_v):
root_node.append("v_fc", space_v)
if not spaces.is_determined(space_proj):
root_node.append("proj", space_proj)
return root_node
def apply_candidate(self, abstract_child: spaces.VirtualNode):
super(SuperAttention, 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:
self.k_fc.apply_candidate(abstract_child["k_fc"])
if "v_fc" in abstract_child:
self.v_fc.apply_candidate(abstract_child["v_fc"])
if "proj" in abstract_child:
self.proj.apply_candidate(abstract_child["proj"])
def forward_qkv(self, q_tensor, k_tensor, v_tensor, num_head: int) -> torch.Tensor:
q = self.q_fc(q_tensor)
B, N, C = q.shape
k = self.k_fc(k_tensor)
B0, S, _ = k.shape
v = self.v_fc(v_tensor)
assert B0 == v.shape[0] and S == v.shape[1]
head_dim = C // num_head
if num_head > C:
raise ValueError("Invalid num_head [{:}] vs C [{:}]".format(num_head, C))
q_v1 = (
q[:, :, : num_head * head_dim]
.reshape(B, N, num_head, head_dim)
.permute(0, 2, 1, 3)
)
k_v1 = (
k[:, :, : num_head * head_dim]
.reshape(B0, S, num_head, head_dim)
.permute(0, 2, 1, 3)
)
# compute the attention map
attn_v1 = (q_v1 @ k_v1.transpose(-2, -1)) * math.sqrt(head_dim)
attn_v1 = attn_v1.softmax(dim=-1) # B * #head * N * S
attn_v1 = self.attn_drop(attn_v1)
v_v1 = (
v[:, :, : num_head * head_dim]
.reshape(B0, S, num_head, head_dim)
.permute(0, 2, 1, 3)
)
feats_v1 = (attn_v1 @ v_v1).permute(0, 2, 1, 3).reshape(B, N, -1)
# process the first [num_head * head_dim] part
if C == head_dim * num_head:
feats = feats_v1
else: # The channels can not be divided by num_head, the remainder forms an additional head
# [might have bugs, did not check yet]
q_v2 = q[:, :, num_head * head_dim :]
k_v2 = k[:, :, num_head * head_dim :]
v_v2 = v[:, :, num_head * head_dim :]
attn_v2 = (q_v2 @ k_v2.transpose(-2, -1)) * math.sqrt(q_v2.shape[-1])
attn_v2 = attn_v2.softmax(dim=-1)
attn_v2 = self.attn_drop(attn_v2)
feats_v2 = attn_v2 @ v_v2
feats = torch.cat([feats_v1, feats_v2], dim=-1)
return feats
def forward_candidate(self, q_tensor, k_tensor, v_tensor) -> torch.Tensor:
# check the num_heads:
if not spaces.is_determined(self._num_heads):
num_heads = self.abstract_child["_num_heads"].value
else:
num_heads = spaces.get_determined_value(self._num_heads)
feats = self.forward_qkv(q_tensor, k_tensor, v_tensor, num_heads)
outs = self.proj(feats)
outs = self.proj_drop(outs)
return outs
def forward_raw(self, q_tensor, k_tensor, v_tensor) -> torch.Tensor:
feats = self.forward_qkv(q_tensor, k_tensor, v_tensor, self.num_heads)
outs = self.proj(feats)
outs = self.proj_drop(outs)
return outs
def extra_repr(self) -> str:
return "input_dim={:}, proj_dim={:}, num_heads={:}".format(
(self.in_q_dim, self.in_k_dim, self.in_v_dim),
self._proj_dim,
self._num_heads,
)

3
xautodl/xlayers/super_core.py
View File

@@ -24,7 +24,8 @@ super_name2norm = {
"identity": SuperIdentity,
}
from .super_attention import SuperAttention
from .super_attention import SuperSelfAttention
from .super_attention import SuperQKVAttention
from .super_transformer import SuperTransformerEncoderLayer
from .super_activations import SuperReLU

12
xautodl/xlayers/super_positional_embedding.py
View File

@@ -35,11 +35,13 @@ class SuperDynamicPositionE(SuperModule):
return self.forward_raw(input)
def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
import pdb
pdb.set_trace()
print("---")
return F.linear(input, self._super_weight, self._super_bias)
positions = torch.unsqueeze(input * self._scale, dim=-1)
divisions = torch.reshape(
self._div_term, [1] * input.ndim + [self._div_term.numel()]
)
values = positions / divisions
embeds = torch.cat((torch.sin(values), torch.cos(values)), dim=-1)
return embeds
def extra_repr(self) -> str:
return "scale={:}, dim={:}".format(self._scale, self._dimension)

4
xautodl/xlayers/super_transformer.py
View File

@@ -19,7 +19,7 @@ from .super_module import LayerOrder
from .super_module import SuperModule
from .super_linear import SuperMLPv2
from .super_norm import SuperLayerNorm1D
from .super_attention import SuperAttention
from .super_attention import SuperSelfAttention
class SuperTransformerEncoderLayer(SuperModule):
@@ -47,7 +47,7 @@ class SuperTransformerEncoderLayer(SuperModule):
order: LayerOrder = LayerOrder.PreNorm,
):
super(SuperTransformerEncoderLayer, self).__init__()
mha = SuperAttention(
mha = SuperSelfAttention(
d_model,
d_model,
num_heads=num_heads,