---
id: wiki-2026-0508-parameter-sharing
title: Parameter Sharing
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Weight Sharing, Tied Weights]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [parameter-sharing, weight-tying, cnn, rnn, model-compression]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: pytorch
---

# Parameter Sharing

## 매 한 줄
> **"매 same weights, different positions"**. 매 single parameter set 가 multiple computations 에 reuse — translation invariance (CNN), temporal invariance (RNN), parameter efficiency (transformer FFN tied embeddings). 매 modern DL 의 fundamental design pattern.

## 매 핵심

### 매 motivation
- Parameter explosion: 매 fully connected layer on image → billions of params.
- Inductive bias: 매 weight sharing encodes prior (translation/time invariance).
- Generalization: 매 fewer params → better generalization (less overfit).
- Compute: 매 shared weights enable convolution / matmul optimization.

### 매 forms
- **Spatial sharing (CNN)**: 매 same conv kernel slid across image.
- **Temporal sharing (RNN/LSTM/GRU)**: 매 same recurrent weights at every timestep.
- **Cross-layer sharing**: 매 ALBERT, Universal Transformer — 매 same layer params reused L times.
- **Tied embeddings**: 매 input embedding == output projection (LM head).
- **Multi-head**: 매 NOT shared (each head has own W_q, W_k, W_v).

### 매 modern usage
- ALBERT (2019): cross-layer sharing for BERT compression (12× param reduction).
- ViT: spatial sharing via patch embedding.
- Mamba/SSM: temporal sharing via state-space recurrence.
- LoRA: 매 single low-rank delta shared across positions.

### 매 응용
1. CNN image classification (ResNet, ConvNeXt).
2. Sequence modeling (RNN, Transformer position embeddings).
3. Model compression (ALBERT, distillation).
4. Multi-task learning (shared encoder).

## 💻 패턴

### CNN spatial sharing
```python
import torch.nn as nn
# Single 3x3 kernel applied to every spatial position
conv = nn.Conv2d(3, 64, kernel_size=3, padding=1)
# Params: 3*64*3*3 + 64 = 1792 (independent of image size)
```

### Tied input/output embeddings
```python
class LanguageModel(nn.Module):
    def __init__(self, vocab_size, dim):
        super().__init__()
        self.embed = nn.Embedding(vocab_size, dim)
        # tie: lm_head.weight = embed.weight
        self.lm_head = nn.Linear(dim, vocab_size, bias=False)
        self.lm_head.weight = self.embed.weight  # share!

    def forward(self, x):
        h = self.embed(x)
        return self.lm_head(h)  # no extra params
```

### Cross-layer sharing (ALBERT-style)
```python
class SharedTransformer(nn.Module):
    def __init__(self, num_layers, dim):
        super().__init__()
        self.shared_layer = TransformerBlock(dim)  # ONE block
        self.num_layers = num_layers

    def forward(self, x):
        for _ in range(self.num_layers):
            x = self.shared_layer(x)  # reuse same params
        return x
```

### RNN temporal sharing (built-in)
```python
rnn = nn.GRU(input_size=128, hidden_size=256, num_layers=2)
# At every timestep t, same W_ih, W_hh applied
# Params independent of sequence length
```

### Detect shared params
```python
# Count unique parameter tensors
seen = set()
unique = 0
for p in model.parameters():
    if id(p) not in seen:
        seen.add(id(p))
        unique += p.numel()
print(f"Unique params: {unique}")
```

### Multi-task shared encoder
```python
class MultiTaskModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.encoder = ResNet50()  # SHARED
        self.classifier = nn.Linear(2048, 1000)
        self.detector = DetectionHead(2048)

    def forward(self, x, task):
        features = self.encoder(x)
        return self.classifier(features) if task == "cls" else self.detector(features)
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Image input | CNN spatial sharing |
| Sequence input | RNN or Transformer (positional sharing) |
| Memory constrained, many layers | Cross-layer sharing (ALBERT) |
| LM with large vocab | Tied embeddings (saves vocab*dim params) |
| Multi-task related | Shared encoder |
| Tasks unrelated | Don't force sharing — degrades quality |

**기본값**: tied embeddings + CNN spatial / Transformer positional sharing.

## 🔗 Graph
- 부모: [[Inductive-Bias]] · [[LLM_Optimization_and_Deployment_Strategies|Model-Compression]]
- 응용: [[CNN]] · [[데이터_사이언스_및_ML_엔지니어링|RNN]] · [[Transformer]]

## 🤖 LLM 활용
**언제**: 매 designing efficient architecture, debugging param count, applying inductive bias.
**언제 X**: 매 tasks/positions truly independent (forcing sharing hurts quality).

## ❌ 안티패턴
- **Over-sharing**: 매 ALL layers shared → severe quality drop on complex tasks.
- **No tied embeddings on small LM**: 매 vocab=50k, dim=512 → 25M wasted params.
- **Sharing across modalities**: 매 vision encoder ≠ text encoder weights (use CLIP-style separate).
- **Forgetting LayerNorm not shared**: 매 cross-layer share W matrices but keep LN per-layer.

## 🧪 검증 / 중복
- Verified (LeCun 1989 CNN, ALBERT paper, Press & Wolf 2017 tied embeddings).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — sharing forms, modern usage, patterns |
