adu2021 SeerAttention-R: Sparse Attention for Reasoning
Core Concept
SeerAttention-R enables efficient long reasoning by learning which tokens are most important for each attention computation. A lightweight gating mechanism learns sparsity patterns during training, allowing models to focus computational resources on relevant tokens. The approach maintains near-lossless reasoning performance while achieving 9x speedup.
Architecture Overview
- Self-distilled gating mechanism: Learn token importance without external labels
- Plug-in design: Integrates with existing models without parameter modification
- Large sparse blocks: 64/128 token blocks maintain coherence while enabling speedups
- Minimal retraining: Effective training on just 400M tokens
- Optimized kernels: TileLang for near-theoretical speedup on H100
- Autoregressive compatible: Seamlessly works with sequential token generation
Implementation
Step 1: Design Sparse Attention Gate
Create lightweight selector for important tokens:
class SparseAttentionGate(torch.nn.Module):
def __init__(self, hidden_dim: int = 768,
sparsity: float = 0.9):
super().__init__()
self.hidden_dim = hidden_dim
self.sparsity = sparsity
# Gating network (lightweight)
self.gate = torch.nn.Sequential(
torch.nn.Linear(hidden_dim, 256),
torch.nn.ReLU(),
torch.nn.Linear(256, 1)
)
# Temperature for sparsity control
self.temperature = torch.nn.Parameter(
torch.tensor(1.0)
)
def forward(self, query: torch.Tensor,
key: torch.Tensor) -> torch.Tensor:
"""Compute sparse mask indicating important keys."""
batch_size, query_len, _ = query.shape
key_len = key.shape[1]
# Compute importance scores
scores = self.gate(key) # (batch, key_len, 1)
# Create mask based on sparsity threshold
# Keep top (1 - sparsity) fraction of tokens
k = max(1, int(key_len * (1 - self.sparsity)))
# Top-k selection
topk_scores, topk_indices = torch.topk(
scores.squeeze(-1),
k,
dim=-1
)
# Create sparse mask
mask = torch.zeros(batch_size, key_len,
device=scores.device)
mask.scatter_(1, topk_indices, 1.0)
return mask, topk_indices
def forward_with_temperature(self, query: torch.Tensor,
key: torch.Tensor
) -> tuple:
"""Sparse selection with temperature annealing."""
# Compute importance with temperature scaling
scores = self.gate(key) / self.temperature
# Soften selection during early training
mask_soft = torch.sigmoid(scores)
# Hard selection with straight-through estimator
mask = (mask_soft > 0.5).float()
return mask, mask_soft
Step 2: Implement Self-Distillation Training
Train gating without external labels using model self-distillation:
class SelfDistillationTrainer:
def __init__(self, model_with_gate):
self.model = model_with_gate
self.optimizer = torch.optim.Adam(
self.model.gate.parameters(),
lr=1e-4
)
def compute_self_distillation_loss(self,
logits_full: torch.Tensor,
logits_sparse: torch.Tensor,
temperature: float = 4.0
) -> torch.Tensor:
"""KL divergence between full and sparse attention logits."""
# Compute soft probabilities
full_probs = torch.softmax(
logits_full / temperature,
dim=-1
)
sparse_probs = torch.softmax(
logits_sparse / temperature,
dim=-1
)
# KL divergence
kl_loss = torch.nn.functional.kl_div(
torch.log(sparse_probs),
full_probs,
reduction='mean'
)
return kl_loss
def train_step(self, input_ids: torch.Tensor,
max_tokens: int = 4096) -> dict:
"""Single self-distillation training step."""
batch_size, seq_len = input_ids.shape
# Forward pass with full attention (teacher)
with torch.no_grad():
output_full = self.model(
input_ids,
use_sparse=False,
output_hidden_states=True
)
logits_full = output_full.logits
# Forward pass with sparse attention (student)
output_sparse = self.model(
input_ids,
use_sparse=True,
output_hidden_states=True
)
logits_sparse = output_sparse.logits
# Compute distillation loss
loss = self.compute_self_distillation_loss(
logits_full,
logits_sparse
)
# Update sparse gate only
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
return {"distillation_loss": loss.item()}
Step 3: Integrate Sparse Attention into Model
Add sparse attention as plug-in to reasoning model:
class ReasoningModelWithSparseAttention(torch.nn.Module):
def __init__(self, base_model,
sparsity: float = 0.9,
block_size: int = 128):
super().__init__()
self.base_model = base_model
self.sparsity = sparsity
self.block_size = block_size
# Add gates for each attention head
num_heads = base_model.config.num_attention_heads
self.sparse_gates = torch.nn.ModuleList([
SparseAttentionGate(
hidden_dim=base_model.config.hidden_size,
sparsity=sparsity
)
for _ in range(num_heads)
])
def forward(self, input_ids: torch.Tensor,
use_sparse: bool = True) -> dict:
"""Forward pass with optional sparse attention."""
if not use_sparse:
# Use standard model
return self.base_model(input_ids)
# Apply sparse attention via hooks
sparse_attention_fn = self._create_sparse_attention_hook(
use_sparse=True
)
# Register hooks for each attention layer
hooks = []
for layer in self.base_model.model.layers:
hook = layer.self_attn.register_forward_hook(
sparse_attention_fn
)
hooks.append(hook)
# Forward pass
output = self.base_model(input_ids)
# Clean up hooks
for hook in hooks:
hook.remove()
return output
def _create_sparse_attention_hook(self, use_sparse: bool):
"""Create hook for sparse attention modification."""
def sparse_attention_hook(module, input, output):
if not use_sparse:
return output
attn_output, attn_weights = output
# Reduce attention weights based on gate
# (This is simplified; in practice, modify QKV computation)
return (attn_output, attn_weights)
return sparse_attention_hook
def forward_with_sparse_blocks(self,
input_ids: torch.Tensor
) -> dict:
"""Process in large sparse blocks for efficiency."""
seq_len = input_ids.shape[1]
# Process in blocks
for block_start in range(0, seq_len, self.block_size):
block_end = min(block_start + self.block_size, seq_len)
block_ids = input_ids[:, block_start:block_end]
# Compute sparse attention mask for block
with torch.no_grad():
hidden = self.base_model.get_hidden(block_ids)
# Use gates to identify important tokens
masks = []
for gate in self.sparse_gates:
mask, _ = gate(hidden, hidden)
masks.append(mask)
output = self.base_model(input_ids)
return output
Step 4: Optimized Kernel Implementation
Implement efficient sparse attention computation:
class OptimizedSparseAttentionKernel:
"""Optimized kernel for sparse attention (pseudocode)."""
def __init__(self):
# Would use TileLang or CUDA kernels in production
pass
def sparse_attention_forward(self, Q: torch.Tensor,
K: torch.Tensor,
V: torch.Tensor,
sparse_mask: torch.Tensor,
block_size: int = 128
) -> torch.Tensor:
"""Compute attention only on sparse tokens."""
# Q: (batch, num_heads, query_len, head_dim)
# K, V: (batch, num_heads, key_len, head_dim)
# sparse_mask: (batch, num_heads, key_len)
batch_size, num_heads, query_len, head_dim = Q.shape
# Compute scores only for non-masked tokens
scores = torch.matmul(Q, K.transpose(-2, -1))
scores = scores / torch.sqrt(torch.tensor(head_dim))
# Apply sparse mask
mask_value = torch.tensor(float('-inf'))
masked_scores = scores.masked_fill(
sparse_mask.unsqueeze(2) == 0,
mask_value
)
# Compute attention weights
attn_weights = torch.softmax(masked_scores, dim=-1)
# Output (only use non-masked values)
output = torch.matmul(attn_weights, V)
return output
Step 5: Evaluate on Reasoning Tasks
Measure speedup and quality retention:
def evaluate_sparse_reasoning(model,
benchmark_dataset: list,
sparsity: float = 0.9
) -> dict:
"""Benchmark sparse attention on reasoning."""
import time
results = {
"full_attention": {"accuracy": 0.0, "time": 0.0},
"sparse_attention": {"accuracy": 0.0, "time": 0.0}
}
# Test full attention
start = time.time()
full_correct = 0
for sample in benchmark_dataset:
with torch.no_grad():
output = model(
sample["input_ids"],
use_sparse=False
)
full_correct += (output.predictions == sample["label"]).sum()
results["full_attention"]["accuracy"] = (
full_correct / len(benchmark_dataset)
)
results["full_attention"]["time"] = time.time() - start
# Test sparse attention
start = time.time()
sparse_correct = 0
for sample in benchmark_dataset:
with torch.no_grad():
output = model(
sample["input_ids"],
use_sparse=True
)
sparse_correct += (output.predictions == sample["label"]).sum()
results["sparse_attention"]["accuracy"] = (
sparse_correct / len(benchmark_dataset)
)
results["sparse_attention"]["time"] = time.time() - start
# Compute speedup
speedup = (results["full_attention"]["time"] /
results["sparse_attention"]["time"])
results["speedup"] = speedup
results["quality_retention"] = (
results["sparse_attention"]["accuracy"] /
results["full_attention"]["accuracy"]
)
return results
Practical Guidance
Sparsity Levels: 90% sparsity achieves 9x speedup while maintaining near-lossless reasoning performance. Adjust based on latency requirements.
Block Size: Large blocks (64/128 tokens) maintain coherence while enabling efficient computation. Smaller blocks lose context; larger blocks reduce efficiency gains.
Self-Distillation: Training with full model as teacher ensures sparse model preserves reasoning quality. This eliminates need for labeled data.
Minimal Retraining: 400M tokens sufficient for effective sparse gate training. Don't overtrain—risk of distributional shift.
Hardware Optimization: Speedup depends on optimized kernels. Generic sparse operations may be slower than dense—use TileLang or custom CUDA for H100 speedups.
When to Apply: Use SeerAttention-R when inference latency is critical for reasoning models, or when deploying on resource-constrained hardware.
Reference
SeerAttention-R learns to identify and focus on important tokens for reasoning via lightweight gating mechanisms. Key insight: self-distillation from the full model ensures sparse attention preserves reasoning quality while reducing computation. Achieves near-theoretical 9x speedup at 90% sparsity on modern hardware.