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import torch
import torch.nn as nn
import torch.nn.functional as F
from plot2eq.models.components import (
ConvNeXt1DEncoder,
DecoderLayer,
EncoderLayer,
LearnablePositionalEncoding,
RMSNorm,
precompute_freqs_cis,
)
class Plot2EqModel(nn.Module):
def __init__(
self,
vocab_size,
pad_idx,
d_model=512,
nhead=8,
num_enc_layers=4,
num_dec_layers=4,
max_seq_len=256,
dropout=0.1,
) -> None:
super().__init__()
self.d_model = d_model
self.pad_idx = pad_idx
self.cnn_encoder = ConvNeXt1DEncoder(in_channels=2, d_model=d_model)
self.encoder_pe = LearnablePositionalEncoding(d_model, max_len=64)
dim_feedforward = int(d_model * 8 / 3)
self.encoder_layers = nn.ModuleList(
[
EncoderLayer(d_model, nhead, dim_feedforward, dropout)
for _ in range(num_enc_layers)
]
)
self.enc_norm = RMSNorm(d_model)
self.target_emb = nn.Embedding(vocab_size, d_model, padding_idx=pad_idx)
self.emb_dropout = nn.Dropout(dropout)
nn.init.normal_(self.target_emb.weight, std=d_model**-0.5)
self.decoder_layers = nn.ModuleList(
[
DecoderLayer(d_model, nhead, dim_feedforward, dropout)
for _ in range(num_dec_layers)
]
)
self.dec_norm = RMSNorm(d_model)
self.fc_out = nn.Linear(d_model, vocab_size, bias=False)
self.fc_out.weight = self.target_emb.weight
head_dim = d_model // nhead
freqs_cos, freqs_sin = precompute_freqs_cis(head_dim, max_seq_len)
self.register_buffer("freqs_cos", freqs_cos, persistent=False)
self.register_buffer("freqs_sin", freqs_sin, persistent=False)
def forward(self, src, tgt):
# src shape : (B, 2, 256)
# target shape: (B, seq_len)
features = self.cnn_encoder(src)
memory = features.transpose(1, 2)
memory = self.encoder_pe(memory)
for layer in self.encoder_layers:
memory = layer(memory)
memory = self.enc_norm(memory)
tgt_embedded = self.target_emb(tgt) * math.sqrt(self.d_model)
tgt_embedded = self.emb_dropout(tgt_embedded)
seq_len = tgt.size(1)
head_dim = self.d_model // self.encoder_layers[0].self_attn.nhead # type: ignore
fc = self.freqs_cos[:seq_len].view(1, seq_len, 1, head_dim // 2) # type: ignore
fs = self.freqs_sin[:seq_len].view(1, seq_len, 1, head_dim // 2) # type: ignore
for layer in self.decoder_layers:
tgt_embedded, _ = layer(
x=tgt_embedded, memory=memory, freqs_cos=fc, freqs_sin=fs
)
tgt_embedded = self.dec_norm(tgt_embedded)
logits = self.fc_out(tgt_embedded)
return logits
@torch.no_grad()
def generate(self, src, sos_idx, eos_idx, max_len=128):
B = src.size(0)
device = src.device
features = self.cnn_encoder(src)
memory = features.transpose(1, 2)
memory = self.encoder_pe(memory)
for layer in self.encoder_layers:
memory = layer(memory)
memory = self.enc_norm(memory)
tokens = torch.full((B, 1), sos_idx, dtype=torch.long, device=device)
kv_caches = [None] * len(self.decoder_layers)
head_dim = self.d_model // self.decoder_layers[0].self_attn.nhead # type: ignore
for pos in range(max_len):
curr_token = tokens[:, -1:]
tgt_embedded = self.target_emb(curr_token) * math.sqrt(self.d_model)
fc = self.freqs_cos[pos : pos + 1].view(1, 1, 1, head_dim // 2) # type: ignore
fs = self.freqs_sin[pos : pos + 1].view(1, 1, 1, head_dim // 2) # type: ignore
new_caches = []
for i, layer in enumerate(self.decoder_layers):
tgt_embedded, new_layer_cache = layer(
x=tgt_embedded,
memory=memory,
freqs_cos=fc,
freqs_sin=fs,
kv_cache=kv_caches[i],
)
new_caches.append(new_layer_cache)
kv_caches = new_caches
tgt_embedded = self.dec_norm(tgt_embedded)
logits = self.fc_out(tgt_embedded) # (B, 1, vocab_size)
next_token = logits.argmax(dim=-1) # (B, 1)
tokens = torch.cat([tokens, next_token], dim=1)
if (tokens == eos_idx).any(dim=1).all():
break
return tokens
def beam_search(self, src, sos_idx, eos_idx, max_len=128, beam_size=5):
B = src.size(0)
device = src.device
features = self.cnn_encoder(src)
memory = features.transpose(1, 2)
memory = self.encoder_pe(memory)
for layer in self.encoder_layers:
memory = layer(memory)
memory = self.enc_norm(memory)
memory = memory.repeat_interleave(beam_size, dim=0)
tokens = torch.full(
(B * beam_size, 1), sos_idx, dtype=torch.long, device=device
)
scores = torch.full((B, beam_size), -float("inf"), device=device)
scores[:, 0] = 0.0
scores = scores.view(-1)
is_finished = torch.zeros(B * beam_size, dtype=torch.bool, device=device)
kv_caches = [None] * len(self.decoder_layers)
head_dim = self.d_model // self.decoder_layers[0].self_attn.nhead # type: ignore
for pos in range(max_len):
curr_token = tokens[:, -1:]
tgt_embedded = self.target_emb(curr_token) * math.sqrt(self.d_model)
fc = self.freqs_cos[pos : pos + 1].view(1, 1, 1, head_dim // 2) # type: ignore
fs = self.freqs_sin[pos : pos + 1].view(1, 1, 1, head_dim // 2) # type: ignore
new_caches = []
for i, layer in enumerate(self.decoder_layers):
tgt_embedded, new_layer_cache = layer(
x=tgt_embedded,
memory=memory,
freqs_cos=fc,
freqs_sin=fs,
kv_cache=kv_caches[i],
)
new_caches.append(new_layer_cache)
kv_caches = new_caches
tgt_embedded = self.dec_norm(tgt_embedded)
logits = self.fc_out(tgt_embedded).squeeze(
1
) # (B * beam_width, vocab_size)
vocab_size = logits.size(-1)
log_probs = F.log_softmax(logits, dim=-1)
log_probs[is_finished] = -float("inf")
log_probs[is_finished, eos_idx] = 0.0
next_scores = (
scores.unsqueeze(-1) + log_probs
) # (B * beam_width, vocab_size)
next_scores = next_scores.view(B, -1) # (B, beam_width * vocab_size)
topk_scores, topk_indices = torch.topk(next_scores, beam_size, dim=-1)
beam_indices = topk_indices // vocab_size
token_indices = topk_indices % vocab_size
batch_offsets = torch.arange(B, device=device).unsqueeze(1) * beam_size
global_beam_indices = (batch_offsets + beam_indices).view(-1)
tokens = torch.cat(
[tokens[global_beam_indices], token_indices.view(-1, 1)], dim=1
)
scores = topk_scores.view(-1)
is_finished = is_finished[global_beam_indices] | (
token_indices.view(-1) == eos_idx
)
reordered_caches = []
for sa_cache, ca_cache in kv_caches:
sa_k, sa_v = sa_cache
sa_k, sa_v = sa_k[global_beam_indices], sa_v[global_beam_indices]
ca_k, ca_v = ca_cache
ca_k, ca_v = ca_k[global_beam_indices], ca_v[global_beam_indices]
reordered_caches.append(((sa_k, sa_v), (ca_k, ca_v)))
kv_caches = reordered_caches
if is_finished.all():
break
all_candidates = tokens.reshape(B, beam_size, -1)
return all_candidates
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