File size: 6,635 Bytes
8594de8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 | """generator.py — Inferência autoregressiva para BiGRU_T_version.
Implementa geração greedy + top-k sampling + streaming, reaproveitando a
filosofia do GRURingV139.generate() da fonte (xavante_work/flexnet/gru_ring_v13_9.py).
O UnifiedModel produz (batch, vocab_size) — 1 logit por forward. Para geração
autoregressiva, alimentamos a sequência crescente e usamos o último logit.
"""
from __future__ import annotations
import logging
import math
from typing import Iterator, List, Optional, Tuple
import torch
import torch.nn.functional as F
logger = logging.getLogger(__name__)
class BiGRUTGenerator:
"""Gerador autoregressivo para UnifiedModel.
Suporta:
- greedy decoding
- top-k sampling com temperatura
- repetition penalty
- max_new_tokens + EOS stop
- streaming (yield token a token)
Args:
model: UnifiedModel treinado
eos_token_id: ID do token de fim (para parar)
pad_token_id: ID do padding
max_seq_len: comprimento máximo de contexto (trunca à esquerda)
"""
def __init__(
self,
model,
eos_token_id: int = 2,
pad_token_id: int = 1,
max_seq_len: int = 64,
):
self.model = model
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.max_seq_len = max_seq_len
self.device = next(model.parameters()).device
@torch.no_grad()
def generate(
self,
input_ids: torch.Tensor,
max_new_tokens: int = 32,
temperature: float = 1.0,
top_k: int = 0,
repetition_penalty: float = 1.0,
do_sample: bool = False,
) -> torch.Tensor:
"""Gera tokens autoregressivamente.
Args:
input_ids: (batch, T) tokens de prompt
max_new_tokens: máx. tokens a gerar
temperature: temperatura do sampling (1.0 = sem escala)
top_k: se > 0, amostra apenas dos top-k tokens
repetition_penalty: penaliza tokens já gerados (1.0 = sem pena)
do_sample: se False, greedy decoding
Returns:
generated_ids: (batch, T + max_new_tokens)
"""
self.model.eval()
batch_size = input_ids.size(0)
generated = input_ids.clone().to(self.device)
for _step in range(max_new_tokens):
# Trunca à esquerda se exceder max_seq_len
if generated.size(1) > self.max_seq_len:
context = generated[:, -self.max_seq_len:]
else:
context = generated
# Forward (sem hipótese — inferência usa só TrainT)
out = self.model(context, temperature=1.0, use_hypothesis=False)
y_hat = out[0] if isinstance(out, tuple) else out # (batch, vocab)
# Último logit
logits = y_hat # já é (batch, vocab) — 1 logit por forward
# Repetition penalty
if repetition_penalty != 1.0:
for b in range(batch_size):
for prev_token in generated[b].tolist():
if logits[b, prev_token] > 0:
logits[b, prev_token] /= repetition_penalty
else:
logits[b, prev_token] *= repetition_penalty
if do_sample and temperature > 0:
# Top-k sampling
if top_k > 0:
top_k = min(top_k, logits.size(-1))
values, _ = torch.topk(logits, top_k, dim=-1)
min_val = values[:, -1:].unsqueeze(-1)
logits = torch.where(
logits < min_val,
torch.full_like(logits, float("-inf")),
logits,
)
# Temperatura
logits = logits / max(temperature, 1e-8)
probs = F.softmax(logits, dim=-1)
next_token = torch.multinomial(probs, num_samples=1)
else:
# Greedy
next_token = logits.argmax(dim=-1, keepdim=True)
# Concatena
generated = torch.cat([generated, next_token], dim=1)
# Para se todos geraram EOS
if (next_token == self.eos_token_id).all():
break
return generated
@torch.no_grad()
def stream_generate(
self,
input_ids: torch.Tensor,
max_new_tokens: int = 32,
temperature: float = 1.0,
top_k: int = 0,
repetition_penalty: float = 1.0,
do_sample: bool = False,
) -> Iterator[torch.Tensor]:
"""Geração streaming — yields um token por vez.
Args: mesmos de generate()
Yields:
next_token: (batch, 1) tensor a cada iteração
"""
self.model.eval()
batch_size = input_ids.size(0)
generated = input_ids.clone().to(self.device)
for _step in range(max_new_tokens):
if generated.size(1) > self.max_seq_len:
context = generated[:, -self.max_seq_len:]
else:
context = generated
out = self.model(context, temperature=1.0, use_hypothesis=False)
y_hat = out[0] if isinstance(out, tuple) else out
logits = y_hat
if repetition_penalty != 1.0:
for b in range(batch_size):
for prev_token in generated[b].tolist():
if logits[b, prev_token] > 0:
logits[b, prev_token] /= repetition_penalty
else:
logits[b, prev_token] *= repetition_penalty
if do_sample and temperature > 0:
if top_k > 0:
top_k = min(top_k, logits.size(-1))
values, _ = torch.topk(logits, top_k, dim=-1)
min_val = values[:, -1:].unsqueeze(-1)
logits = torch.where(
logits < min_val,
torch.full_like(logits, float("-inf")),
logits,
)
logits = logits / max(temperature, 1e-8)
probs = F.softmax(logits, dim=-1)
next_token = torch.multinomial(probs, num_samples=1)
else:
next_token = logits.argmax(dim=-1, keepdim=True)
generated = torch.cat([generated, next_token], dim=1)
yield next_token
if (next_token == self.eos_token_id).all():
break
__all__ = ["BiGRUTGenerator"]
|