File size: 7,722 Bytes
86fe6bc | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 | """
解码策略模块 — Person D 负责实现
功能要求:
1. greedy_decode: 贪心解码
2. beam_search_decode: 束搜索解码
3. sample_decode: 采样解码 (temperature, top-k, top-p)
技术要点:
- Beam Search 是翻译任务最常用的解码策略
- 需要高效处理批量解码
- 支持长度惩罚 (length penalty) 和重复惩罚 (no_repeat_ngram)
- 对于预训练模型，可以直接使用 model.generate()
"""
from __future__ import annotations
import logging
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
logger = logging.getLogger(__name__)
def _apply_no_repeat_ngram(
logits: torch.Tensor,
generated_tokens: torch.Tensor,
ngram_size: int,
) -> torch.Tensor:
"""
防止生成重复的 n-gram。
"""
if ngram_size <= 1:
return logits
batch_size, vocab_size = logits.size()
for batch_idx in range(batch_size):
tokens = generated_tokens[batch_idx].tolist()
if len(tokens) < ngram_size - 1:
continue
banned_tokens: set[int] = set()
ngram_map: dict[tuple[int, ...], set[int]] = {}
for i in range(len(tokens) - ngram_size + 1):
prefix = tuple(tokens[i : i + ngram_size - 1])
next_token = tokens[i + ngram_size - 1]
ngram_map.setdefault(prefix, set()).add(next_token)
prefix = tuple(tokens[-(ngram_size - 1) :])
if prefix in ngram_map:
banned_tokens = ngram_map[prefix]
logits[batch_idx, list(banned_tokens)] = float("-inf")
return logits
@torch.no_grad()
def greedy_decode(
model: nn.Module,
src_ids: torch.Tensor,
src_padding_mask: torch.BoolTensor,
bos_id: int,
eos_id: int,
max_len: int = 256,
) -> torch.Tensor:
"""
贪心解码。
"""
encoder_output = model.encode(src_ids, src_padding_mask)
batch_size = src_ids.size(0)
device = src_ids.device
generated = torch.full((batch_size, 1), bos_id, dtype=torch.long, device=device)
finished = torch.zeros(batch_size, dtype=torch.bool, device=device)
for _ in range(max_len):
logits = model.decode_step(generated, encoder_output, src_padding_mask)
next_token = logits.argmax(dim=-1, keepdim=True)
generated = torch.cat([generated, next_token], dim=1)
finished = finished | next_token.squeeze(-1).eq(eos_id)
if finished.all():
break
return generated
@torch.no_grad()
def beam_search_decode(
model: nn.Module,
src_ids: torch.Tensor,
src_padding_mask: torch.BoolTensor,
bos_id: int,
eos_id: int,
beam_size: int = 5,
max_len: int = 256,
length_penalty: float = 1.0,
no_repeat_ngram_size: int = 0,
) -> torch.Tensor:
"""
束搜索解码。
"""
batch_size, seq_len = src_ids.size()
device = src_ids.device
encoder_output = model.encode(src_ids, src_padding_mask)
encoder_output = encoder_output.unsqueeze(1).expand(batch_size, beam_size, -1, -1)
encoder_output = encoder_output.reshape(batch_size * beam_size, seq_len, -1)
src_padding_mask = src_padding_mask.unsqueeze(1).expand(batch_size, beam_size, seq_len)
src_padding_mask = src_padding_mask.reshape(batch_size * beam_size, seq_len)
beam_scores = torch.full((batch_size, beam_size), float("-inf"), device=device)
beam_scores[:, 0] = 0.0
generated = torch.full((batch_size, beam_size, 1), bos_id, dtype=torch.long, device=device)
finished = torch.zeros((batch_size, beam_size), dtype=torch.bool, device=device)
for _ in range(max_len):
flat_generated = generated.view(batch_size * beam_size, -1)
logits = model.decode_step(flat_generated, encoder_output, src_padding_mask)
log_probs = F.log_softmax(logits, dim=-1)
if no_repeat_ngram_size > 0:
log_probs = _apply_no_repeat_ngram(log_probs, flat_generated, no_repeat_ngram_size)
finished_flat = finished.view(batch_size * beam_size)
if finished_flat.any():
log_probs[finished_flat] = float("-inf")
log_probs[finished_flat, eos_id] = 0.0
vocab_size = log_probs.size(-1)
scores = beam_scores.unsqueeze(-1) + log_probs.view(batch_size, beam_size, vocab_size)
scores_flat = scores.view(batch_size, -1)
topk_scores, topk_indices = scores_flat.topk(beam_size, dim=-1)
beam_indices = topk_indices // vocab_size
token_indices = topk_indices % vocab_size
next_generated = []
next_finished = []
for batch_idx in range(batch_size):
selected_beams = beam_indices[batch_idx]
selected_tokens = token_indices[batch_idx]
next_seq = generated[batch_idx, selected_beams]
next_seq = torch.cat([next_seq, selected_tokens.unsqueeze(-1)], dim=-1)
next_generated.append(next_seq)
next_finished.append(
finished[batch_idx, selected_beams]
| selected_tokens.eq(eos_id)
)
generated = torch.stack(next_generated, dim=0)
finished = torch.stack(next_finished, dim=0)
beam_scores = topk_scores
if finished.all():
break
length = generated.size(1)
penalty = float(length) ** float(length_penalty)
final_scores = beam_scores / penalty
best_indices = final_scores.argmax(dim=-1)
output = generated[torch.arange(batch_size, device=device), best_indices]
return output
@torch.no_grad()
def sample_decode(
model: nn.Module,
src_ids: torch.Tensor,
src_padding_mask: torch.BoolTensor,
bos_id: int,
eos_id: int,
max_len: int = 256,
temperature: float = 1.0,
top_k: int = 0,
top_p: float = 1.0,
) -> torch.Tensor:
"""
采样解码 (支持 temperature, top-k, top-p/nucleus sampling)。
"""
encoder_output = model.encode(src_ids, src_padding_mask)
batch_size = src_ids.size(0)
device = src_ids.device
generated = torch.full((batch_size, 1), bos_id, dtype=torch.long, device=device)
finished = torch.zeros(batch_size, dtype=torch.bool, device=device)
for _ in range(max_len):
logits = model.decode_step(generated, encoder_output, src_padding_mask)
logits = logits / max(temperature, 1e-8)
if top_k > 0:
top_k = min(top_k, logits.size(-1))
values, indices = torch.topk(logits, top_k, dim=-1)
mask = torch.full_like(logits, float("-inf"))
logits = mask.scatter(-1, indices, values)
if 0.0 < top_p < 1.0:
sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1)
probs = F.softmax(sorted_logits, dim=-1)
cumulative_probs = torch.cumsum(probs, dim=-1)
cutoff = cumulative_probs > top_p
cutoff[:, 1:] = cutoff[:, :-1].clone()
cutoff[:, 0] = False
sorted_logits[cutoff] = float("-inf")
logits = torch.zeros_like(logits).scatter(-1, sorted_indices, sorted_logits)
probs = F.softmax(logits, dim=-1)
next_token = torch.multinomial(probs, num_samples=1)
next_token = next_token.clamp(min=0)
next_token = torch.where(finished.unsqueeze(-1), torch.full_like(next_token, eos_id), next_token)
generated = torch.cat([generated, next_token], dim=1)
finished = finished | next_token.squeeze(-1).eq(eos_id)
if finished.all():
break
return generated
|