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Try a different model / LFNA

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This commit is contained in:
D-X-Y 2021-05-23 23:09:14 +08:00
parent 25dc78a7ce
commit 9135667cc1
2 changed files with 123 additions and 74 deletions
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88
exps/LFNA/lfna.py
View File

@ -99,7 +99,7 @@ def online_evaluate(env, meta_model, base_model, criterion, args, logger):
future_time = timestamp.item() future_time = timestamp.item()
time_seqs = [ time_seqs = [
future_time - iseq * env.timestamp_interval future_time - iseq * env.timestamp_interval
for iseq in range(args.seq_length * 2) for iseq in range(args.seq_length)
] ]
time_seqs.reverse() time_seqs.reverse()
with torch.no_grad(): with torch.no_grad():
@ -107,30 +107,26 @@ def online_evaluate(env, meta_model, base_model, criterion, args, logger):
base_model.eval() base_model.eval()
time_seqs = torch.Tensor(time_seqs).view(1, -1).to(args.device) time_seqs = torch.Tensor(time_seqs).view(1, -1).to(args.device)
[seq_containers], _ = meta_model(time_seqs, None) [seq_containers], _ = meta_model(time_seqs, None)
# For Debug future_container = seq_containers[-1]
for idx in range(time_seqs.numel()): future_x, future_y = future_x.to(args.device), future_y.to(args.device)
future_container = seq_containers[idx] future_y_hat = base_model.forward_with_container(future_x, future_container)
_, (future_x, future_y) = env(time_seqs[0, idx].item()) future_loss = criterion(future_y_hat, future_y)
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_loss = criterion(future_y_hat, future_y)
logger.log(
"--> time={:.4f} -> loss={:.4f}".format(
time_seqs[0, idx].item(), future_loss.item()
)
)
logger.log( logger.log(
"[ONLINE] [{:03d}/{:03d}] loss={:.4f}".format( "[ONLINE] [{:03d}/{:03d}] loss={:.4f}".format(
idx, len(env), future_loss.item() idx, len(env), future_loss.item()
) )
) )
meta_model.adapt(
future_time,
future_x,
future_y,
env.timestamp_interval,
args.refine_lr,
args.refine_epochs,
)
import pdb import pdb
pdb.set_trace() pdb.set_trace()
for iseq in range(args.seq_length):
time_seqs.append(future_time - iseq * eval_env.timestamp_interval)
print("-") print("-")
@ -156,6 +152,7 @@ def pretrain_v2(base_model, meta_model, criterion, xenv, args, logger):
meta_model.set_best_name("pretrain-{:}.pth".format(args.rand_seed)) meta_model.set_best_name("pretrain-{:}.pth".format(args.rand_seed))
last_success_epoch, early_stop_thresh = 0, args.pretrain_early_stop_thresh last_success_epoch, early_stop_thresh = 0, args.pretrain_early_stop_thresh
per_epoch_time, start_time = AverageMeter(), time.time() per_epoch_time, start_time = AverageMeter(), time.time()
device = args.device
for iepoch in range(args.epochs): for iepoch in range(args.epochs):
left_time = "Time Left: {:}".format( left_time = "Time Left: {:}".format(
convert_secs2time(per_epoch_time.avg * (args.epochs - iepoch), True) convert_secs2time(per_epoch_time.avg * (args.epochs - iepoch), True)
@ -163,32 +160,38 @@ def pretrain_v2(base_model, meta_model, criterion, xenv, args, logger):
total_meta_v1_losses, total_meta_v2_losses, total_match_losses = [], [], [] total_meta_v1_losses, total_meta_v2_losses, total_match_losses = [], [], []
optimizer.zero_grad() optimizer.zero_grad()
for ibatch in range(args.meta_batch): for ibatch in range(args.meta_batch):
rand_index = random.randint(0, meta_model.meta_length - 1)
timestamp = meta_model.meta_timestamps[rand_index]
meta_embed = meta_model.super_meta_embed[rand_index]
timestamps, [container], time_embeds = meta_model(
torch.unsqueeze(timestamp, dim=0), None, True
)
_, (inputs, targets) = xenv(timestamp.item())
inputs, targets = inputs.to(device), targets.to(device)
# generate models one step ahead
predictions = base_model.forward_with_container(inputs, container)
total_meta_v1_losses.append(criterion(predictions, targets))
# the matching loss
match_loss = criterion(torch.squeeze(time_embeds, dim=0), meta_embed)
total_match_losses.append(match_loss)
# generate models via memory
rand_index = random.randint(0, meta_model.meta_length - xenv.seq_length - 1) rand_index = random.randint(0, meta_model.meta_length - xenv.seq_length - 1)
_, [seq_containers], _ = meta_model(
None,
torch.unsqueeze(
meta_model.super_meta_embed[
rand_index : rand_index + xenv.seq_length
],
dim=0,
),
False,
)
timestamps = meta_model.meta_timestamps[ timestamps = meta_model.meta_timestamps[
rand_index : rand_index + xenv.seq_length rand_index : rand_index + xenv.seq_length
] ]
meta_embeds = meta_model.super_meta_embed[
rand_index : rand_index + xenv.seq_length
]
_, (seq_inputs, seq_targets) = xenv.seq_call(timestamps) _, (seq_inputs, seq_targets) = xenv.seq_call(timestamps)
seq_inputs, seq_targets = seq_inputs.to(args.device), seq_targets.to( seq_inputs, seq_targets = seq_inputs.to(device), seq_targets.to(device)
args.device
)
# generate models one step ahead
[seq_containers], time_embeds = meta_model(
torch.unsqueeze(timestamps, dim=0), None
)
for container, inputs, targets in zip(
seq_containers, seq_inputs, seq_targets
):
predictions = base_model.forward_with_container(inputs, container)
total_meta_v1_losses.append(criterion(predictions, targets))
# the matching loss
match_loss = criterion(torch.squeeze(time_embeds, dim=0), meta_embeds)
total_match_losses.append(match_loss)
# generate models via memory
[seq_containers], _ = meta_model(None, torch.unsqueeze(meta_embeds, dim=0))
for container, inputs, targets in zip( for container, inputs, targets in zip(
seq_containers, seq_inputs, seq_targets seq_containers, seq_inputs, seq_targets
): ):
@ -250,7 +253,14 @@ def main(args):
# pre-train the hypernetwork # pre-train the hypernetwork
timestamps = train_env.get_timestamp(None) timestamps = train_env.get_timestamp(None)
meta_model = LFNA_Meta(shape_container, args.layer_dim, args.time_dim, timestamps) meta_model = LFNA_Meta(
shape_container,
args.layer_dim,
args.time_dim,
timestamps,
seq_length=args.seq_length,
interval=train_env.timestamp_interval,
)
meta_model = meta_model.to(args.device) meta_model = meta_model.to(args.device)
logger.log("The base-model has {:} weights.".format(base_model.numel())) logger.log("The base-model has {:} weights.".format(base_model.numel()))

109
exps/LFNA/lfna_meta_model.py
View File

@ -22,7 +22,9 @@ class LFNA_Meta(super_core.SuperModule):
meta_timestamps, meta_timestamps,
mha_depth: int = 2, mha_depth: int = 2,
dropout: float = 0.1, dropout: float = 0.1,
thresh: float = 0.05, seq_length: int = 10,
interval: float = None,
thresh: float = None,
): ):
super(LFNA_Meta, self).__init__() super(LFNA_Meta, self).__init__()
self._shape_container = shape_container self._shape_container = shape_container
@ -31,7 +33,10 @@ class LFNA_Meta(super_core.SuperModule):
for ilayer in range(self._num_layers): for ilayer in range(self._num_layers):
self._numel_per_layer.append(shape_container[ilayer].numel()) self._numel_per_layer.append(shape_container[ilayer].numel())
self._raw_meta_timestamps = meta_timestamps self._raw_meta_timestamps = meta_timestamps
self._thresh = thresh assert interval is not None
self._interval = interval
self._seq_length = seq_length
self._thresh = interval * 30 if thresh is None else thresh
self.register_parameter( self.register_parameter(
"_super_layer_embed", "_super_layer_embed",
@ -42,6 +47,10 @@ class LFNA_Meta(super_core.SuperModule):
torch.nn.Parameter(torch.Tensor(len(meta_timestamps), time_embedding)), torch.nn.Parameter(torch.Tensor(len(meta_timestamps), time_embedding)),
) )
self.register_buffer("_meta_timestamps", torch.Tensor(meta_timestamps)) self.register_buffer("_meta_timestamps", torch.Tensor(meta_timestamps))
# register a time difference buffer
time_interval = [-i * self._interval for i in range(self._seq_length)]
time_interval.reverse()
self.register_buffer("_time_interval", torch.Tensor(time_interval))
self._time_embed_dim = time_embedding self._time_embed_dim = time_embedding
self._append_meta_embed = dict(fixed=None, learnt=None) self._append_meta_embed = dict(fixed=None, learnt=None)
self._append_meta_timestamps = dict(fixed=None, learnt=None) self._append_meta_timestamps = dict(fixed=None, learnt=None)
@ -51,12 +60,12 @@ class LFNA_Meta(super_core.SuperModule):
) )
# build transformer # build transformer
self._trans_att = super_core.SuperQKVAttentionV2( self._trans_att = super_core.SuperQKVAttention(
qk_att_dim=time_embedding, time_embedding,
in_v_dim=time_embedding, time_embedding,
hidden_dim=time_embedding, time_embedding,
time_embedding,
num_heads=4, num_heads=4,
proj_dim=time_embedding,
qkv_bias=True, qkv_bias=True,
attn_drop=None, attn_drop=None,
proj_drop=dropout, proj_drop=dropout,
@ -166,12 +175,9 @@ class LFNA_Meta(super_core.SuperModule):
# timestamps is a batch of sequence of timestamps # timestamps is a batch of sequence of timestamps
batch, seq = timestamps.shape batch, seq = timestamps.shape
meta_timestamps, meta_embeds = self.meta_timestamps, self.super_meta_embed meta_timestamps, meta_embeds = self.meta_timestamps, self.super_meta_embed
# timestamp_q_embed = self._tscalar_embed(timestamps) timestamp_q_embed = self._tscalar_embed(timestamps)
# timestamp_k_embed = self._tscalar_embed(meta_timestamps.view(1, -1)) timestamp_k_embed = self._tscalar_embed(meta_timestamps.view(1, -1))
timestamp_v_embed = meta_embeds.unsqueeze(dim=0) 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 # create the mask
mask = ( mask = (
torch.unsqueeze(timestamps, dim=-1) <= meta_timestamps.view(1, 1, -1) torch.unsqueeze(timestamps, dim=-1) <= meta_timestamps.view(1, 1, -1)
@ -182,7 +188,7 @@ class LFNA_Meta(super_core.SuperModule):
> self._thresh > self._thresh
) )
timestamp_embeds = self._trans_att( timestamp_embeds = self._trans_att(
timestamp_qk_att_embed, timestamp_v_embed, mask timestamp_q_embed, timestamp_k_embed, timestamp_v_embed, mask
) )
relative_timestamps = timestamps - timestamps[:, :1] relative_timestamps = timestamps - timestamps[:, :1]
relative_pos_embeds = self._tscalar_embed(relative_timestamps) relative_pos_embeds = self._tscalar_embed(relative_timestamps)
@ -192,36 +198,69 @@ class LFNA_Meta(super_core.SuperModule):
corrected_embeds = self._meta_corrector(init_timestamp_embeds) corrected_embeds = self._meta_corrector(init_timestamp_embeds)
return corrected_embeds return corrected_embeds
def forward_raw(self, timestamps, time_embed): def forward_raw(self, timestamps, time_embeds, get_seq_last):
if time_embed is None: if time_embeds is None:
batch, seq = timestamps.shape time_seq = timestamps.view(-1, 1) + self._time_interval.view(1, -1)
time_embed = self._obtain_time_embed(timestamps) B, S = time_seq.shape
time_embeds = self._obtain_time_embed(time_seq)
else: else:
batch, seq, _ = time_embed.shape time_seq = None
B, S, _ = time_embeds.shape
# create joint embed # create joint embed
num_layer, _ = self._super_layer_embed.shape num_layer, _ = self._super_layer_embed.shape
meta_embed = time_embed.view(batch, seq, 1, -1).expand(-1, -1, num_layer, -1) if get_seq_last:
layer_embed = self._super_layer_embed.view(1, 1, num_layer, -1).expand( time_embeds = time_embeds[:, -1, :]
batch, seq, -1, -1 # The shape of `joint_embed` is batch * num-layers * input-dim
) joint_embeds = torch.cat(
joint_embed = torch.cat( (
(meta_embed, layer_embed), dim=-1 time_embeds.view(B, 1, -1).expand(-1, num_layer, -1),
) # batch, seq, num-layers, input-dim self._super_layer_embed.view(1, num_layer, -1).expand(B, -1, -1),
batch_weights = self._generator( ),
joint_embed dim=-1,
) # batch, seq, num-layers, num-weights )
else:
# The shape of `joint_embed` is batch * seq * num-layers * input-dim
joint_embeds = torch.cat(
(
time_embeds.view(B, S, 1, -1).expand(-1, -1, num_layer, -1),
self._super_layer_embed.view(1, 1, num_layer, -1).expand(
B, S, -1, -1
),
),
dim=-1,
)
batch_weights = self._generator(joint_embeds)
batch_containers = [] batch_containers = []
for seq_weights in torch.split(batch_weights, 1): for weights in torch.split(batch_weights, 1):
seq_containers = [] if get_seq_last:
for weights in torch.split(seq_weights.squeeze(0), 1): batch_containers.append(
weights = torch.split(weights.squeeze(0), 1) self._shape_container.translate(torch.split(weights.squeeze(0), 1))
seq_containers.append(self._shape_container.translate(weights)) )
batch_containers.append(seq_containers) else:
return batch_containers, time_embed seq_containers = []
for ws in torch.split(weights.squeeze(0), 1):
seq_containers.append(
self._shape_container.translate(torch.split(ws.squeeze(0), 1))
)
batch_containers.append(seq_containers)
return time_seq, batch_containers, time_embeds
def forward_candidate(self, input): def forward_candidate(self, input):
raise NotImplementedError raise NotImplementedError
def adapt(self, timestamp, x, y, threshold, lr, epochs):
if distance + threshold * 1e-2 <= threshold:
return False
with torch.set_grad_enabled(True):
new_param = self.create_meta_embed()
optimizer = torch.optim.Adam(
[new_param], lr=args.refine_lr, weight_decay=1e-5, amsgrad=True
)
import pdb
pdb.set_trace()
print("-")
def extra_repr(self) -> str: def extra_repr(self) -> str:
return "(_super_layer_embed): {:}, (_super_meta_embed): {:}, (_meta_timestamps): {:}".format( return "(_super_layer_embed): {:}, (_super_meta_embed): {:}, (_meta_timestamps): {:}".format(
list(self._super_layer_embed.shape), list(self._super_layer_embed.shape),