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Semantic-Conditioned Decoder.
input text
-> Semantic-Lite-2 (FROZEN)
Data A: (B, 256) -> proj_a -> prefix token prepended to decoder
Data B: (B, L, 2048) -> proj_b -> cross-attention key/value
-> TransformerDecoderLayer (d_model=1024, nhead=16, FFN=1024) [TRAINABLE]
-> output_proj (1024 -> 2048) + tied embedding (131072 vocab)
Only proj_a, proj_b, dec_embed_proj, the decoder layer, output_proj and
pos_embed are trained. The encoder and its embedding table are frozen.
TIED EMBEDDING
--------------
The output head reuses the encoder's frozen embedding matrix instead of
learning a 131072 x 2048 matrix. That saves ~132M parameters (and the VRAM to
hold them), at the cost of forcing the output geometry to match an embedding
table that was never trained for generation. Whether that trade is worth it is
an open question — see NOTES.md.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
DATA_A_DIM = 256
DATA_B_DIM = 2048
class SemanticConditionedDecoder(nn.Module):
def __init__(self, encoder, tokenizer, d_model=1024, nhead=16,
num_decoder_layers=1, dim_feedforward=1024, dropout=0.1):
super().__init__()
self.encoder = encoder # frozen
self.tokenizer = tokenizer
self.d_model = d_model
self.vocab_size = tokenizer.vocab_size
self.hidden_size = encoder.config.hidden_size # 2048
self.proj_a = nn.Linear(DATA_A_DIM, d_model)
self.proj_b = nn.Linear(DATA_B_DIM, d_model)
self.dec_embed_proj = nn.Linear(self.hidden_size, d_model, bias=False)
decoder_layer = nn.TransformerDecoderLayer(
d_model=d_model,
nhead=nhead,
dim_feedforward=dim_feedforward,
dropout=dropout,
batch_first=True,
activation="gelu",
)
self.decoder = nn.TransformerDecoder(decoder_layer, num_layers=num_decoder_layers)
self.output_proj = nn.Linear(d_model, self.hidden_size, bias=False)
self.pos_embed = nn.Embedding(2048, d_model)
self.bos_id = tokenizer.bos_token_id
self.eos_id = tokenizer.eos_token_id
self.pad_id = tokenizer.pad_token_id
def _logits_from_hidden(self, hidden):
"""hidden (..., d_model) -> logits (..., vocab_size) via tied embedding."""
h = self.output_proj(hidden) # (..., 2048)
emb = self.encoder.backbone.embedding.weight # (131072, 2048), frozen
return F.linear(h, emb)
def encode(self, input_ids, attention_mask):
"""Encode input -> Data A (B, 256) + Data B (B, L, 2048)."""
with torch.no_grad():
data_a = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
out_b = self.encoder.backbone(input_ids=input_ids, attention_mask=attention_mask)
data_b = out_b.last_hidden_state
return data_a, data_b
def _embed_target(self, decoder_input_ids):
"""Token ids -> d_model embeddings + learned positional embedding."""
emb = self.encoder.backbone.embedding(decoder_input_ids) # (B, L, 2048)
emb = self.dec_embed_proj(emb) # (B, L, d_model)
pos = torch.arange(decoder_input_ids.shape[1], device=decoder_input_ids.device)
return emb + self.pos_embed(pos).unsqueeze(0)
@staticmethod
def _causal_mask(length, device):
"""
Bool causal mask: True above the diagonal = "not allowed to attend".
Bool (not float -inf) so that it matches the dtype of
tgt_key_padding_mask. Mixing a float attn_mask with a bool
key_padding_mask is deprecated in recent PyTorch and emits a warning.
"""
return torch.triu(
torch.ones(length, length, dtype=torch.bool, device=device), diagonal=1
)
def forward(self, input_ids, attention_mask, decoder_input_ids):
"""
input_ids: (B, L_src) source text (the `problem` column)
attention_mask: (B, L_src)
decoder_input_ids: (B, L_tgt) shifted-right target (BOS + thinking + solution)
"""
B = input_ids.shape[0]
data_a, data_b = self.encode(input_ids, attention_mask)
a_proj = self.proj_a(data_a) # (B, d_model)
b_proj = self.proj_b(data_b) # (B, L_src, d_model)
dec_emb = self._embed_target(decoder_input_ids)
# Data A as a prefix token, so the global meaning is visible at every step.
dec_emb = torch.cat([a_proj.unsqueeze(1), dec_emb], dim=1) # (B, 1+L_tgt, d_model)
L_dec = dec_emb.shape[1]
tgt_mask = self._causal_mask(L_dec, dec_emb.device)
# Prefix token is never padding, so prepend a False column.
tgt_key_padding_mask = torch.cat([
torch.zeros(B, 1, dtype=torch.bool, device=decoder_input_ids.device),
(decoder_input_ids == self.pad_id),
], dim=1)
dec_out = self.decoder(
tgt=dec_emb,
memory=b_proj,
tgt_mask=tgt_mask,
tgt_key_padding_mask=tgt_key_padding_mask,
memory_key_padding_mask=(attention_mask == 0),
)
dec_out = dec_out[:, 1:, :] # drop the prefix token
return self._logits_from_hidden(dec_out)
@torch.no_grad()
def generate(self, input_ids, attention_mask, max_new_tokens=256, temperature=1.0):
"""Autoregressive generation. Returns (B, 1 + n_generated) token ids."""
self.eval()
B, device = input_ids.shape[0], input_ids.device
data_a, data_b = self.encode(input_ids, attention_mask)
a_proj = self.proj_a(data_a)
memory = self.proj_b(data_b)
mem_pad_mask = (attention_mask == 0)
generated = torch.full((B, 1), self.bos_id, dtype=torch.long, device=device)
for _ in range(max_new_tokens):
dec_emb = self._embed_target(generated)
dec_emb = torch.cat([a_proj.unsqueeze(1), dec_emb], dim=1)
L_dec = dec_emb.shape[1]
tgt_mask = self._causal_mask(L_dec, device)
dec_out = self.decoder(
tgt=dec_emb, memory=memory, tgt_mask=tgt_mask,
memory_key_padding_mask=mem_pad_mask,
)
logits = self._logits_from_hidden(dec_out[:, -1:, :])
if temperature != 1.0:
logits = logits / temperature
probs = F.softmax(logits, dim=-1)
next_token = torch.multinomial(probs.squeeze(1), num_samples=1)
generated = torch.cat([generated, next_token], dim=1)
if (next_token == self.eos_id).all():
break
return generated
class DecoderNoConditioning(nn.Module):
"""
Ablation baseline: identical to SemanticConditionedDecoder except the
cross-attention memory is a learned constant instead of a function of the
input, and there is no Data A prefix.
proj_a / proj_b are kept (but unused) so the trainable parameter count
matches the conditioned arm exactly. The only variable that changes is
whether the memory carries information about the input.
"""
def __init__(self, encoder, tokenizer, d_model=1024, nhead=16,
num_decoder_layers=1, dim_feedforward=1024, dropout=0.1):
super().__init__()
self.encoder = encoder
self.tokenizer = tokenizer
self.d_model = d_model
self.vocab_size = tokenizer.vocab_size
self.hidden_size = encoder.config.hidden_size
# Parameter parity only — never used in forward().
self.proj_a = nn.Linear(DATA_A_DIM, d_model)
self.proj_b = nn.Linear(DATA_B_DIM, d_model)
self.dec_embed_proj = nn.Linear(self.hidden_size, d_model, bias=False)
decoder_layer = nn.TransformerDecoderLayer(
d_model=d_model, nhead=nhead, dim_feedforward=dim_feedforward,
dropout=dropout, batch_first=True, activation="gelu",
)
self.decoder = nn.TransformerDecoder(decoder_layer, num_layers=num_decoder_layers)
self.output_proj = nn.Linear(d_model, self.hidden_size, bias=False)
self.pos_embed = nn.Embedding(2048, d_model)
self.null_memory = nn.Parameter(torch.randn(1, 1, d_model) * 0.02)
self.bos_id = tokenizer.bos_token_id
self.eos_id = tokenizer.eos_token_id
self.pad_id = tokenizer.pad_token_id
def _logits_from_hidden(self, hidden):
h = self.output_proj(hidden)
return F.linear(h, self.encoder.backbone.embedding.weight)
def forward(self, input_ids, attention_mask, decoder_input_ids):
B = input_ids.shape[0]
memory = self.null_memory.expand(B, -1, -1) # (B, 1, d_model)
emb = self.encoder.backbone.embedding(decoder_input_ids)
emb = self.dec_embed_proj(emb)
pos = torch.arange(decoder_input_ids.shape[1], device=decoder_input_ids.device)
emb = emb + self.pos_embed(pos).unsqueeze(0)
L_dec = emb.shape[1]
tgt_mask = torch.triu(
torch.ones(L_dec, L_dec, dtype=torch.bool, device=emb.device), diagonal=1
)
dec_out = self.decoder(
tgt=emb, memory=memory, tgt_mask=tgt_mask,
tgt_key_padding_mask=(decoder_input_ids == self.pad_id),
)
return self._logits_from_hidden(dec_out)
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