Antigravity Agent
139 lines
4.8 KiB
Markdown
139 lines
4.8 KiB
Markdown
---
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id: wiki-2026-0508-encoder-decoder-inconsistency
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title: Encoder Decoder Inconsistency
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [encoder-decoder-mismatch, seq2seq-inconsistency]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [nlp, transformer, training, decoding]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: pytorch
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---
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# Encoder Decoder Inconsistency
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## 매 한 줄
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> **"매 encoder가 본 분포 ≠ decoder가 생성하는 분포"**. Seq2seq training 시 encoder는 ground-truth context를 보지만 decoder는 inference에서 자기 prediction을 다시 입력으로 받기 때문에 train/inference 간 distribution shift가 발생한다. 매 exposure bias 의 근본 원인.
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## 매 핵심
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### 매 정의
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- **Train**: decoder input = teacher-forced ground truth.
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- **Inference**: decoder input = previously generated token.
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- **Gap**: 매 error compounds along sequence — early mistake → later tokens conditioned on out-of-distribution prefix.
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### 매 표현
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- Exposure bias (Ranzato 2016).
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- Schedule sampling 의 motivation.
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- Hallucination 의 한 원인 (특히 long-form generation).
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### 매 응용
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1. NMT (Neural Machine Translation) — 매 long sentence translation degradation.
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2. Summarization — repetition / drift.
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3. Speech recognition — RNN-T vs CTC trade-off.
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4. Code generation — 매 long completion 의 syntax break.
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## 💻 패턴
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### Scheduled Sampling
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```python
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import torch
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import torch.nn.functional as F
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def scheduled_sampling_step(decoder, prev_token, hidden, gt_token, p_use_gt: float):
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"""p_use_gt 의 확률로 ground-truth, 아니면 model prediction 의 사용."""
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if torch.rand(1).item() < p_use_gt:
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input_tok = gt_token
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else:
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with torch.no_grad():
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logits, _ = decoder(prev_token, hidden)
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input_tok = logits.argmax(dim=-1)
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out_logits, hidden = decoder(input_tok, hidden)
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return out_logits, hidden
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```
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### Minimum Risk Training
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```python
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def mrt_loss(model, src, refs, n_samples=8):
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"""매 sequence-level loss 의 — 매 sampled hypotheses 에 대해 risk minimize."""
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hyps = [model.sample(src) for _ in range(n_samples)]
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risks = torch.tensor([1 - bleu(h, refs) for h in hyps])
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log_probs = torch.stack([model.log_prob(h, src) for h in hyps])
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weights = F.softmax(log_probs, dim=0)
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return (weights * risks).sum()
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```
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### Self-distillation Fix
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```python
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def self_distill(student, teacher, src, T=2.0):
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"""매 teacher 가 자기 생성한 sequence 의 사용 — 매 train/inference gap 축소."""
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with torch.no_grad():
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gen = teacher.generate(src, do_sample=True, top_p=0.9)
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teacher_logits = teacher(src, gen).logits
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student_logits = student(src, gen).logits
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return F.kl_div(
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F.log_softmax(student_logits / T, dim=-1),
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F.softmax(teacher_logits / T, dim=-1),
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reduction="batchmean",
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) * T * T
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```
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### Beam Search with Length Penalty
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```python
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def length_penalty(score, length, alpha=0.7):
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"""GNMT length penalty — 매 short hypothesis 의 bias 보정."""
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return score / ((5 + length) ** alpha / (5 + 1) ** alpha)
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```
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### Contrastive Decoding
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```python
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def contrastive_decode(big, small, prompt, alpha=0.5):
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"""매 large model logit − small model logit — 매 expert/amateur gap 의 강조."""
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big_logits = big(prompt).logits[:, -1]
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small_logits = small(prompt).logits[:, -1]
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return big_logits - alpha * small_logits
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```
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## 매 결정 기준
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| 상황 | Approach |
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| Short sequence (<32) | Teacher forcing 충분 |
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| Long sequence | Scheduled sampling / MRT |
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| Production NMT | Beam + length penalty + coverage |
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| LLM long-form | Contrastive decoding / self-distillation |
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**기본값**: teacher forcing + 1k step warmup 이후 scheduled sampling.
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## 🔗 Graph
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- 부모: [[Sequence-to-Sequence]] · [[Transformer]]
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- 변형: [[Scheduled-Sampling]] · [[Minimum-Risk-Training]]
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- 응용: [[Neural-Machine-Translation]] · [[Abstractive-Summarization]]
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- Adjacent: [[Exposure-Bias]] · [[Hallucination]] · [[Beam-Search]]
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## 🤖 LLM 활용
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**언제**: long-form generation 의 quality issue 분석 시. Train/eval BLEU gap 의 진단.
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**언제 X**: 매 short classification — 매 inconsistency 의 무관.
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## ❌ 안티패턴
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- **Pure teacher forcing forever**: 매 inference distribution 의 미본 채 deploy.
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- **Greedy decoding only**: 매 early mistake 의 lock-in.
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- **No length normalization**: beam 의 short hypothesis bias.
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## 🧪 검증 / 중복
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- Verified (Ranzato et al. 2016, Bengio et al. 2015 scheduled sampling).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — encoder/decoder distribution shift + scheduled sampling/MRT/contrastive decoding |
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