Overcome the attention gap in sparse transformers by training with both full and sparse attention simultaneously, aligned through bidirectional losses that encourage naturally sparser distributions while maintaining learning capability, enabling efficient inference without capability degradation.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
Overcome the attention gap in sparse transformers by training with both full and sparse attention simultaneously, aligned through bidirectional losses that encourage naturally sparser distributions while maintaining learning capability, enabling efficient inference without capability degradation.
SSA: Sparse Sparse Attention
Sparse attention mechanisms reduce computational costs but suffer from two problems: the attention gap (learned distributions don't match sparse patterns) and the capability gap (sparse-only training underperforms). This skill demonstrates how to overcome both through dual-stream training that alternates between full and sparse attention, aligned via bidirectional losses that encourage naturally sparse distributions.
The core insight is that full and sparse attention should learn together—full attention guides learning while sparse attention optimizes for efficiency.
Core Concept
Sparse Sparse Attention (SSA) implements:
Dual-Stream Training: Randomly alternate between full and sparse attention during training
Bidirectional Alignment: Sparsity loss encourages full→sparse matching; commitment loss keeps sparse→full aligned
Natural Sparsity Emergence: Full attention gradually becomes sparse through training, reducing gap
Seamless Inference: Can run with either sparse (fast) or full (capable) mode without retraining
Architecture Overview
Full Attention Stream: Provides complete gradient signals for all tokens
Sparse Attention Stream: Operates on selected tokens for efficiency
Token Selection Mechanism: Learnable selection of important tokens
Sparsity Loss: Encourages full-attention outputs to match sparse-attention patterns
Implement training that switches between full/sparse attention.
deftrain_ssa_transformer(
model,
train_dataloader,
optimizer,
num_epochs=10,
sparsity_schedule='constant'):
"""
Training loop for SSA-enabled transformer.
Alternates between full and sparse attention streams.
Args:
model: SSA-enabled transformer
train_dataloader: Training data iterator
optimizer: PyTorch optimizer
num_epochs: Number of training epochs
sparsity_schedule: How sparsity changes over training (constant, increasing, etc.)
Returns:
loss_history: Training loss per step
"""
loss_history = []
for epoch inrange(num_epochs):
for batch_idx, batch inenumerate(train_dataloader):
input_ids, labels = batch
# Forward pass
logits = model(input_ids, training_mode=True)
# Task loss (e.g., next-token prediction)
task_loss = torch.nn.functional.cross_entropy(
logits.reshape(-1, logits.shape[-1]),
labels.reshape(-1)
)
# Alignment loss (encouraging sparse efficiency)
alignment_loss = 0.0for layer in model.layers:
_, layer_alignment = layer(input_ids, training_mode=True)
alignment_loss += layer_alignment
alignment_loss = alignment_loss / len(model.layers)
# Combined loss
total_loss = task_loss + 0.1 * alignment_loss
# Backward and update
optimizer.zero_grad()
total_loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
loss_history.append(total_loss.item())
if batch_idx % 100 == 0:
print(f"Epoch {epoch}, Batch {batch_idx}: Loss={total_loss:.4f}")
return loss_history
6. Inference with Sparse or Full Mode
Use trained model with flexible attention mode selection.
definference_with_ssa(model, input_ids, max_length=512, use_sparse=True):
"""
Generate text using SSA transformer.
Can switch between sparse (fast) or full (capable) attention at inference.
Args:
model: Trained SSA transformer
input_ids: (batch, seq_len) input token IDs
max_length: Maximum generation length
use_sparse: Whether to use sparse attention (True=fast, False=capable)
Returns:
generated_ids: (batch, max_length) generated token IDs
"""
model.eval()
with torch.no_grad():
for _ inrange(max_length - input_ids.shape[1]):
# Forward pass with specified attention modefor layer in model.layers:
normed = layer.norm1(hidden_states)
attention_output, _ = layer.attention(normed, use_sparse=use_sparse)
hidden_states = hidden_states + attention_output
# ... FFN ...# Predict next token
next_logits = model.output_head(hidden_states[:, -1])
next_token = torch.argmax(next_logits, dim=-1, keepdim=True)
# Append to sequence
input_ids = torch.cat([input_ids, next_token], dim=1)
return input_ids
Practical Guidance
When to Use SSA:
Transformer models where inference latency is critical
Long-sequence tasks (>2K tokens) where sparse attention provides significant speedup
Scenarios requiring both efficiency and quality without retraining
When NOT to Use:
Short sequences (<512 tokens) where sparsity overhead exceeds benefits
Tasks where attention pattern interpretability is essential
Models already heavily optimized for full attention
Key Hyperparameters:
sparsity_level: Fraction of tokens to prune (0.3-0.7 typical)
lambda_sparsity: Weight of sparsity loss (0.5-2.0)
lambda_commitment: Weight of commitment loss (0.1-0.5)
temperature: Softness of token selection (0.5-2.0)
Performance Impact:
Inference speedup: 2-3× with 50% sparsity
Training overhead: ~17% additional cost (dual-stream forward/backward)
Memory reduction: ~30-40% with sparse inference
Integration Pattern:
Drop-in replacement for standard transformer layers. Set use_sparse=False during training (gets alignment losses), then use_sparse=True during inference for efficiency.
