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Running on Zero
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import torch
import torch.nn as nn
class Vocab:
def __init__(self, char2idx=None, idx2char=None):
if char2idx is None:
char2idx = {
"<pad>": 0,
"<sos>": 1,
"<eos>": 2,
"<unk>": 3,
}
self.char2idx = {
char: int(index)
for char, index in char2idx.items()
}
if idx2char is None:
self.idx2char = {
index: char
for char, index in self.char2idx.items()
}
else:
self.idx2char = {
int(index): char
for index, char in idx2char.items()
}
def encode(self, text: str) -> List[int]:
unk_id = self.char2idx["<unk>"]
return [
self.char2idx.get(character, unk_id)
for character in text
]
def decode(self, ids: List[int]) -> str:
characters = []
eos_id = self.char2idx["<eos>"]
for index in ids:
index = int(index)
if index == eos_id:
break
if index > eos_id:
characters.append(
self.idx2char.get(index, "")
)
return "".join(characters)
class LemmaModel(nn.Module):
def __init__(
self,
vocab_size,
char_emb_dim=96,
hidden_size=128,
drop_prob=0.30,
num_heads=16,
max_gen_len=30,
):
super().__init__()
self.max_gen_len = max_gen_len
self.emb = nn.Embedding(
vocab_size,
char_emb_dim,
padding_idx=0,
)
self.dropout_enc = nn.Dropout(drop_prob)
self.dropout_dec = nn.Dropout(drop_prob)
self.dropout_att = nn.Dropout(drop_prob)
self.enc1 = nn.LSTM(
char_emb_dim,
hidden_size,
bidirectional=True,
batch_first=True,
)
self.enc2 = nn.LSTM(
hidden_size * 2,
hidden_size,
bidirectional=True,
batch_first=True,
)
self.attn = nn.MultiheadAttention(
hidden_size * 2,
num_heads,
batch_first=True,
)
self.dec = nn.LSTM(
char_emb_dim + hidden_size * 4,
hidden_size * 2,
batch_first=True,
)
self.dec_cross_attn = nn.MultiheadAttention(
embed_dim=hidden_size * 2,
num_heads=num_heads,
kdim=hidden_size * 4,
vdim=hidden_size * 4,
batch_first=True,
)
self.out = nn.Linear(
hidden_size * 2,
vocab_size,
bias=True,
)
def encode(self, src, src_lens):
embedded = self.emb(src)
packed1 = nn.utils.rnn.pack_padded_sequence(
embedded,
src_lens.cpu(),
batch_first=True,
enforce_sorted=False,
)
enc1_output, _ = self.enc1(packed1)
enc1_output, _ = (
nn.utils.rnn.pad_packed_sequence(
enc1_output,
batch_first=True,
)
)
enc1_output = self.dropout_enc(enc1_output)
packed2 = nn.utils.rnn.pack_padded_sequence(
enc1_output,
src_lens.cpu(),
batch_first=True,
enforce_sorted=False,
)
enc2_output, _ = self.enc2(packed2)
enc2_output, _ = (
nn.utils.rnn.pad_packed_sequence(
enc2_output,
batch_first=True,
)
)
enc2_output = self.dropout_enc(enc2_output)
attention_output, _ = self.attn(
enc1_output,
enc2_output,
enc2_output,
)
attention_output = self.dropout_att(
attention_output
)
return torch.cat(
[enc2_output, attention_output],
dim=-1,
)
def forward(self, src, src_lens, tgt):
encoder_combined = self.encode(
src,
src_lens,
)
decoder_target = self.emb(tgt[:, :-1])
target_len = decoder_target.size(1)
if encoder_combined.size(1) >= target_len:
combined_trimmed = encoder_combined[
:, :target_len, :
]
else:
padding = encoder_combined.new_zeros(
encoder_combined.size(0),
target_len - encoder_combined.size(1),
encoder_combined.size(2),
)
combined_trimmed = torch.cat(
[encoder_combined, padding],
dim=1,
)
decoder_input = torch.cat(
[decoder_target, combined_trimmed],
dim=-1,
)
decoder_output, _ = self.dec(decoder_input)
decoder_output = self.dropout_dec(decoder_output)
cross_output, _ = self.dec_cross_attn(
decoder_output,
encoder_combined,
encoder_combined,
)
cross_output = self.dropout_att(cross_output)
return self.out(cross_output)
def generate(
self,
src,
src_lens,
vocab,
max_len=None,
):
self.eval()
if max_len is None:
max_len = self.max_gen_len
batch_size = src.size(0)
eos_id = vocab.char2idx["<eos>"]
with torch.inference_mode():
encoder_combined = self.encode(
src,
src_lens,
)
source_len = encoder_combined.size(1)
current = torch.full(
(batch_size, 1),
vocab.char2idx["<sos>"],
device=src.device,
dtype=torch.long,
)
hidden = None
hypotheses = [
[]
for _ in range(batch_size)
]
finished = torch.zeros(
batch_size,
dtype=torch.bool,
device=src.device,
)
for step in range(max_len):
embedded = self.emb(current).squeeze(1)
combined_step = encoder_combined[
:,
min(step, source_len - 1),
:,
]
decoder_input = torch.cat(
[embedded, combined_step],
dim=-1,
).unsqueeze(1)
decoder_output, hidden = self.dec(
decoder_input,
hidden,
)
decoder_output = self.dropout_dec(
decoder_output
)
cross_output, _ = self.dec_cross_attn(
decoder_output,
encoder_combined,
encoder_combined,
)
cross_output = self.dropout_att(
cross_output
)
logits = self.out(cross_output)
next_ids = logits.argmax(dim=-1)
current = next_ids
for index in range(batch_size):
if not finished[index]:
token_id = int(
next_ids[index, 0].item()
)
hypotheses[index].append(token_id)
if token_id == eos_id:
finished[index] = True
if finished.all():
break
current = torch.where(
finished.unsqueeze(1),
torch.full_like(current, eos_id),
current,
)
return [
vocab.decode(hypothesis)
for hypothesis in hypotheses
] |