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Update xlayers

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This commit is contained in:
D-X-Y
2021-05-22 23:04:24 +08:00
parent 5b09f059fd
commit 8109ed166a
6 changed files with 104 additions and 33 deletions
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14
CHANGE-LOG.md
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@@ -1,12 +1,12 @@
# This file shows the major updates of this repo.
- [2020.04.11] [4ef9531] Add change log as `CHANGE-LOG.md`.
- [2019.12.20] [69ca086] Release NAS-Bench-201.
- [2019.09.28] [f8f3f38] TAS and SETN codes were publicly released.
- [2019.01.31] [13e908f] GDAS codes were publicly released.
- [2020.07.01] [a45808b] Upgrade NAS-API to the 2.0 version.
- [2020.09.16] [7052265] Create NATS-BENCH.
- [2020.04.11] [4ef9531](https://github.com/D-X-Y/AutoDL-Projects/tree/4ef9531) Add change log as `CHANGE-LOG.md`.
- [2019.12.20] [69ca086](https://github.com/D-X-Y/AutoDL-Projects/tree/69ca086) Release NAS-Bench-201.
- [2019.09.28] [f8f3f38](https://github.com/D-X-Y/AutoDL-Projects/tree/f8f3f38) TAS and SETN codes were publicly released.
- [2019.01.31] [13e908f](https://github.com/D-X-Y/AutoDL-Projects/tree/13e908f) GDAS codes were publicly released.
- [2020.07.01] [a45808b](https://github.com/D-X-Y/AutoDL-Projects/tree/a45808b) Upgrade NAS-API to the 2.0 version.
- [2020.09.16] [7052265](https://github.com/D-X-Y/AutoDL-Projects/tree/7052265) Create NATS-BENCH.
- [2020.10.15] [446262a](https://github.com/D-X-Y/AutoDL-Projects/tree/446262a) Update NATS-BENCH to version 1.0
- [2020.12.20] [dae387a](https://github.com/D-X-Y/AutoDL-Projects/tree/dae387a) Update NATS-BENCH to version 1.1
- [2021.05.18] [98fadf8](https://github.com/D-X-Y/AutoDL-Projects/tree/98fadf8) Before moving to `xautodl`
- [2021.05.21] [b4e8eae](https://github.com/D-X-Y/AutoDL-Projects/tree/b4e8eae) `xautodl` is close to ready
- [2021.05.21] [5b09f05](https://github.com/D-X-Y/AutoDL-Projects/tree/5b09f05) `xautodl` is close to ready

33
exps/LFNA/lfna.py
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@@ -106,8 +106,13 @@ def pretrain_v2(base_model, meta_model, criterion, xenv, args, logger):
logger.log("Using the optimizer: {:}".format(optimizer))
meta_model.set_best_dir(logger.path(None) / "ckps-pretrain-v2")
final_best_name = "final-pretrain-{:}.pth".format(args.rand_seed)
if meta_model.has_best(final_best_name):
meta_model.load_best(final_best_name)
return
meta_model.set_best_name("pretrain-{:}.pth".format(args.rand_seed))
last_success_epoch = 0
last_success_epoch, early_stop_thresh = 0, args.pretrain_early_stop_thresh
per_epoch_time, start_time = AverageMeter(), time.time()
for iepoch in range(args.epochs):
left_time = "Time Left: {:}".format(
@@ -164,14 +169,21 @@ def pretrain_v2(base_model, meta_model, criterion, xenv, args, logger):
)
+ ", batch={:}".format(len(total_meta_losses))
+ ", success={:}, best_score={:.4f}".format(success, -best_score)
+ " {:}".format(left_time)
+ ", LS={:}/{:}".format(last_success_epoch, early_stop_thresh)
+ ", {:}".format(left_time)
)
if iepoch - last_success_epoch >= args.early_stop_thresh * 5:
if success:
last_success_epoch = iepoch
if iepoch - last_success_epoch >= early_stop_thresh:
logger.log("Early stop the pre-training at {:}".format(iepoch))
break
per_epoch_time.update(time.time() - start_time)
start_time = time.time()
meta_model.load_best()
# save to the final model
meta_model.set_best_name(final_best_name)
success, _ = meta_model.save_best(best_score + 1e-6)
assert success
def pretrain_v1(base_model, meta_model, criterion, xenv, args, logger):
@@ -189,7 +201,7 @@ def pretrain_v1(base_model, meta_model, criterion, xenv, args, logger):
meta_model.set_best_dir(logger.path(None) / "ckps-pretrain-v1")
meta_model.set_best_name("pretrain-{:}.pth".format(args.rand_seed))
per_epoch_time, start_time = AverageMeter(), time.time()
last_success_epoch = 0
last_success_epoch, early_stop_thresh = 0, args.pretrain_early_stop_thresh
for iepoch in range(args.epochs):
left_time = "Time Left: {:}".format(
convert_secs2time(per_epoch_time.avg * (args.epochs - iepoch), True)
@@ -232,9 +244,12 @@ def pretrain_v1(base_model, meta_model, criterion, xenv, args, logger):
)
+ ", batch={:}".format(len(losses))
+ ", success={:}, best_score={:.4f}".format(success, -best_score)
+ ", LS={:}/{:}".format(last_success_epoch, early_stop_thresh)
+ " {:}".format(left_time)
)
if iepoch - last_success_epoch >= args.early_stop_thresh * 5:
if success:
last_success_epoch = iepoch
if iepoch - last_success_epoch >= early_stop_thresh:
logger.log("Early stop the pre-training at {:}".format(iepoch))
break
per_epoch_time.update(time.time() - start_time)
@@ -521,7 +536,7 @@ if __name__ == "__main__":
parser.add_argument(
"--refine_lr",
type=float,
default=0.005,
default=0.001,
help="The learning rate for the optimizer, during refine",
)
parser.add_argument(
@@ -533,6 +548,12 @@ if __name__ == "__main__":
default=20,
help="The #epochs for early stop.",
)
parser.add_argument(
"--pretrain_early_stop_thresh",
type=int,
default=200,
help="The #epochs for early stop.",
)
parser.add_argument(
"--seq_length", type=int, default=10, help="The sequence length."
)

18
exps/LFNA/lfna_meta_model.py
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@@ -70,6 +70,7 @@ class LFNA_Meta(super_core.SuperModule):
dropout,
norm_affine=False,
order=super_core.LayerOrder.PostNorm,
use_mask=True,
)
)
layers.append(super_core.SuperLinear(time_embedding * 2, time_embedding))
@@ -162,11 +163,14 @@ class LFNA_Meta(super_core.SuperModule):
def _obtain_time_embed(self, timestamps):
# 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(self.meta_timestamps.view(1, -1))
timestamp_v_embed = self.super_meta_embed.unsqueeze(dim=0)
timestamp_k_embed = self._tscalar_embed(meta_timestamps.view(1, -1))
timestamp_v_embed = meta_embeds.unsqueeze(dim=0)
# create the mask
mask = torch.unsqueeze(timestamps, dim=-1) <= meta_timestamps.view(1, 1, -1)
timestamp_embeds = self._trans_att(
timestamp_q_embed, timestamp_k_embed, timestamp_v_embed
timestamp_q_embed, timestamp_k_embed, timestamp_v_embed, mask
)
# relative_timestamps = timestamps - timestamps[:, :1]
# relative_pos_embeds = self._tscalar_embed(relative_timestamps)
@@ -186,8 +190,12 @@ class LFNA_Meta(super_core.SuperModule):
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)
joint_embed = torch.cat(
(meta_embed, layer_embed), dim=-1
) # batch, seq, num-layers, input-dim
batch_weights = self._generator(
joint_embed
) # batch, seq, num-layers, num-weights
batch_containers = []
for seq_weights in torch.split(batch_weights, 1):
seq_containers = []

40
xautodl/xlayers/super_attention.py
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@@ -31,12 +31,15 @@ class SuperSelfAttention(SuperModule):
qkv_bias: BoolSpaceType = False,
attn_drop: Optional[float] = None,
proj_drop: Optional[float] = None,
use_mask=False,
):
super(SuperSelfAttention, self).__init__()
self._input_dim = input_dim
self._proj_dim = proj_dim
self._num_heads = num_heads
self._qkv_bias = qkv_bias
self._use_mask = use_mask
self._infinity = 1e9
self.q_fc = SuperLinear(input_dim, input_dim, bias=qkv_bias)
self.k_fc = SuperLinear(input_dim, input_dim, bias=qkv_bias)
@@ -113,6 +116,12 @@ class SuperSelfAttention(SuperModule):
.permute(0, 2, 1, 3)
)
attn_v1 = (q_v1 @ k_v1.transpose(-2, -1)) * math.sqrt(head_dim)
if self._use_mask:
mask = torch.triu(
torch.ones((N, N), dtype=torch.bool, device=input.device), 1
)
mask = torch.unsqueeze(torch.unsqueeze(mask, dim=0), dim=0)
attn_v1 = attn_v1.masked_fill(mask, -self._infinity)
attn_v1 = attn_v1.softmax(dim=-1) # B * #head * N * N
attn_v1 = self.attn_drop(attn_v1)
feats_v1 = (attn_v1 @ v_v1).permute(0, 2, 1, 3).reshape(B, N, -1)
@@ -147,8 +156,14 @@ class SuperSelfAttention(SuperModule):
return outs
def extra_repr(self) -> str:
return "input_dim={:}, proj_dim={:}, num_heads={:}".format(
self._input_dim, self._proj_dim, self._num_heads
return (
"input_dim={:}, proj_dim={:}, num_heads={:}, mask={:}, infinity={:}".format(
self._input_dim,
self._proj_dim,
self._num_heads,
self._use_mask,
self._infinity,
)
)
@@ -181,6 +196,7 @@ class SuperQKVAttention(SuperModule):
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)
self._infinity = 1e9
@property
def num_heads(self):
@@ -232,7 +248,9 @@ class SuperQKVAttention(SuperModule):
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:
def forward_qkv(
self, q_tensor, k_tensor, v_tensor, num_head: int, mask=None
) -> torch.Tensor:
q = self.q_fc(q_tensor)
B, N, C = q.shape
@@ -257,6 +275,9 @@ class SuperQKVAttention(SuperModule):
)
# compute the attention map
attn_v1 = (q_v1 @ k_v1.transpose(-2, -1)) * math.sqrt(head_dim)
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)
@@ -281,26 +302,29 @@ class SuperQKVAttention(SuperModule):
feats = torch.cat([feats_v1, feats_v2], dim=-1)
return feats
def forward_candidate(self, q_tensor, k_tensor, v_tensor) -> torch.Tensor:
def forward_candidate(
self, q_tensor, k_tensor, v_tensor, mask=None
) -> 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)
feats = self.forward_qkv(q_tensor, k_tensor, v_tensor, num_heads, mask)
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)
def forward_raw(self, q_tensor, k_tensor, v_tensor, mask=None) -> torch.Tensor:
feats = self.forward_qkv(q_tensor, k_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={:}, proj_dim={:}, num_heads={:}".format(
return "input_dim={:}, proj_dim={:}, num_heads={:}, infinity={:}".format(
(self.in_q_dim, self.in_k_dim, self.in_v_dim),
self._proj_dim,
self._num_heads,
self._infinity,
)

30
xautodl/xlayers/super_module.py
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@@ -117,16 +117,32 @@ class SuperModule(abc.ABC, nn.Module):
else:
return False, self._meta_info[BEST_SCORE_KEY]
def load_best(self):
if BEST_DIR_KEY not in self._meta_info or BEST_SCORE_KEY not in self._meta_info:
raise ValueError("Please call save_best at first")
best_save_path = os.path.join(
self._meta_info[BEST_DIR_KEY],
"best-{:}.pth".format(self.__class__.__name__),
)
def load_best(self, best_save_path=None):
if best_save_path is None:
if (
BEST_DIR_KEY not in self._meta_info
or BEST_SCORE_KEY not in self._meta_info
):
raise ValueError("Please call save_best at first")
best_save_name = self._meta_info.get(
BEST_NAME_KEY, "best-{:}.pth".format(self.__class__.__name__)
)
best_save_path = os.path.join(self._meta_info[BEST_DIR_KEY], best_save_name)
state_dict = torch.load(best_save_path)
self.load_state_dict(state_dict)
def has_best(self, best_name=None):
if BEST_DIR_KEY not in self._meta_info:
raise ValueError("Please set BEST_DIR_KEY at first")
if best_name is None:
best_save_name = self._meta_info.get(
BEST_NAME_KEY, "best-{:}.pth".format(self.__class__.__name__)
)
else:
best_save_name = best_name
best_save_path = os.path.join(self._meta_info[BEST_DIR_KEY], best_save_name)
return os.path.exists(best_save_path)
@property
def abstract_search_space(self):
raise NotImplementedError

2
xautodl/xlayers/super_transformer.py
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@@ -45,6 +45,7 @@ class SuperTransformerEncoderLayer(SuperModule):
norm_affine: bool = True,
act_layer: Callable[[], nn.Module] = nn.GELU,
order: LayerOrder = LayerOrder.PreNorm,
use_mask: bool = False,
):
super(SuperTransformerEncoderLayer, self).__init__()
mha = SuperSelfAttention(
@@ -54,6 +55,7 @@ class SuperTransformerEncoderLayer(SuperModule):
qkv_bias=qkv_bias,
attn_drop=drop,
proj_drop=drop,
use_mask=use_mask,
)
mlp = SuperMLPv2(
d_model,