Clasp Layer Skip Inference

---
name: clasp-layer-skip-inference
title: "CLaSp: In-Context Layer Skip for Self-Speculative Decoding"
version: 0.0.2
engine: skillxiv-v0.0.2-claude-opus-4.6
license: MIT
url: "https://arxiv.org/abs/2505.24196"
keywords: [Speculative Decoding, Inference Optimization, Self-Distillation, Layer Skipping]
description: "Accelerate LLM inference by dynamically skipping transformer layers based on in-context signals, without training auxiliary draft models or changing model weights."
---

# Accelerate Inference Through Dynamic In-Context Layer Skipping

Speculative decoding accelerates LLM inference by using a lightweight draft model to generate candidate tokens, which are verified by the full model. The challenge is maintaining compatibility across diverse LLMs and avoiding the need to train and deploy separate draft models. CLaSp solves this through self-speculative decoding: the full model itself acts as its own draft by skipping layers during inference.

The key insight is that early transformer layers can often skip later layers without significant accuracy loss for high-confidence tokens. By dynamically deciding which layers to skip based on the input, you create an internal draft mechanism without extra models. This is compatible with any LLM and requires zero additional training.

## Core Concept

CLaSp enables dynamic layer skipping through:

- **In-context signals**: Use intermediate representations to decide if remaining layers are necessary
- **Confidence scoring**: Measure token prediction confidence at each layer
- **Adaptive skipping**: Skip expensive layers when confidence is high
- **Self-verification**: Only skip when next-token logits are stable
- **No additional modules**: Works with frozen, pre-trained models
- **Speculative verification**: Skip-path predictions are verified like draft tokens

The mechanism is elegant: if the output logits stabilize at layer N, layers N+1 through M are redundant for this token. By detecting this in-context, you skip unnecessary computation.

## Architecture Overview

- **Confidence estimator**: Measure logit stability across layers
- **Skip decision module**: Decide which layers to skip per token
- **Early-exit mechanism**: Return prediction when confidence threshold met
- **Verification phase**: Check skip-path predictions against full path
- **Adaptive thresholding**: Adjust confidence threshold per domain/model
- **Logging and metrics**: Track skip statistics and accuracy
- **Layer importance analysis**: Understand which layers are most skippable

## Implementation

Build a layer-skipping inference engine:

```python
# CLaSp: In-Context Layer Skip for Self-Speculative Decoding
import torch
import torch.nn as nn
from transformers import AutoModelForCausalLM, AutoTokenizer
from typing import List, Tuple, Optional
import numpy as np

class LayerSkipInferenceEngine:
    """
    Enable dynamic layer skipping for faster inference.
    """
    def __init__(self, model_name: str, skip_threshold: float = 0.8):
        self.model = AutoModelForCausalLM.from_pretrained(model_name)
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.skip_threshold = skip_threshold
        self.num_layers = self.model.config.num_hidden_layers

        # Track statistics
        self.skip_counts = [0] * self.num_layers
        self.layer_importance = [1.0] * self.num_layers

    def measure_logit_stability(self, logits_sequence: List[torch.Tensor]) -> torch.Tensor:
        """
        Measure how stable predictions are across layers.
        High stability = confident prediction = safe to skip remaining layers.
        """
        # Convert logits to probabilities
        probs_sequence = [torch.softmax(logits, dim=-1) for logits in logits_sequence]

        # Compute KL divergence from layer i to layer i+1
        kl_divs = []
        for i in range(len(probs_sequence) - 1):
            # KL(P_i || P_{i+1})
            kl = torch.nn.functional.kl_div(
                torch.log(probs_sequence[i] + 1e-10),
                probs_sequence[i + 1],
                reduction='none'
            ).mean()
            kl_divs.append(kl)

        # Stability = inverse of divergence
        kl_divs = torch.tensor(kl_divs)
        stability = torch.exp(-kl_divs)  # Higher = more stable

        return stability

    def decide_layer_skip(self, stability: torch.Tensor, current_layer: int) -> bool:
        """
        Decide whether to skip next layer based on stability.
        """
        if current_layer >= len(stability):
            return False

        current_stability = stability[current_layer]

        # Skip if confidence is high enough
        should_skip = current_stability > self.skip_threshold

        if should_skip:
            self.skip_counts[current_layer] += 1

        return should_skip

    def generate_with_layer_skip(self, prompt: str, max_new_tokens: int = 50) -> dict:
        """
        Generate text with dynamic layer skipping.
        """
        input_ids = self.tokenizer.encode(prompt, return_tensors='pt')
        device = input_ids.device

        generated_ids = input_ids.clone()
        all_logits = []
        skip_decisions = []
        total_skipped_layers = 0
        total_computed_layers = 0

        for token_idx in range(max_new_tokens):
            # Store intermediate logits for stability measurement
            layer_logits = []

            # Forward pass with layer-by-layer access
            with torch.no_grad():
                current_hidden = self.model.get_input_embeddings()(generated_ids)

                for layer_idx, layer_module in enumerate(self.model.transformer.h):
                    # Check if we should skip this layer
                    if layer_idx > 0 and len(layer_logits) > 0:
                        stability = self.measure_logit_stability(layer_logits)
                        if self.decide_layer_skip(stability, layer_idx - 1):
                            # Skip this layer - reuse previous hidden state
                            skip_decisions.append((token_idx, layer_idx, True))
                            total_skipped_layers += 1
                            continue

                    # Compute this layer
                    layer_output = layer_module(current_hidden)
                    current_hidden = layer_output[0]
                    total_computed_layers += 1

                    # Get logits at this layer
                    logits = self.model.lm_head(current_hidden)[:, -1, :]
                    layer_logits.append(logits)

                # Final logits
                final_logits = self.model.lm_head(current_hidden)[:, -1, :]
                all_logits.append(final_logits)

                # Sample next token
                probs = torch.softmax(final_logits, dim=-1)
                next_token = torch.multinomial(probs, num_samples=1)
                generated_ids = torch.cat([generated_ids, next_token], dim=1)

        # Compute statistics
        generated_text = self.tokenizer.decode(generated_ids[0])
        skip_rate = total_skipped_layers / max(1, total_skipped_layers + total_computed_layers)

        return {
            'text': generated_text,
            'skip_rate': skip_rate,
            'skipped_layers': total_skipped_layers,
            'computed_layers': total_computed_layers,
            'speedup': 1.0 / (1.0 - skip_rate) if skip_rate > 0 else 1.0
        }

    def adaptive_threshold_tuning(self, calibration_prompts: List[str],
                                 target_skip_rate: float = 0.3):
        """
        Automatically tune skip threshold to achieve target skip rate.
        """
        skip_rates = []

        # Try different thresholds
        for threshold in np.linspace(0.5, 0.99, 10):
            self.skip_threshold = threshold
            skip_rates_for_threshold = []

            for prompt in calibration_prompts:
                result = self.generate_with_layer_skip(prompt, max_new_tokens=20)
                skip_rates_for_threshold.append(result['skip_rate'])

            avg_skip_rate = np.mean(skip_rates_for_threshold)
            skip_rates.append((threshold, avg_skip_rate))

            print(f"Threshold {threshold:.2f}: {avg_skip_rate:.2%} skip rate")

        # Find threshold closest to target
        skip_rates.sort(key=lambda x: abs(x[1] - target_skip_rate))
        best_threshold = skip_rates[0][0]

        self.skip_threshold = best_threshold
        print(f"\nOptimal threshold: {best_threshold:.2f}")

        return best_threshold
```

Implement verification and fallback mechanisms:

```python
class VerifiedLayerSkipDecoding:
    """
    Layer skipping with verification (like speculative decoding).
    Ensure skip path produces same output as full path.
    """
    def __init__(self, model_name: str, verify_every_n_tokens: int = 5):
        self.skip_engine = LayerSkipInferenceEngine(model_name)
        self.verify_every_n_tokens = verify_every_n_tokens
        self.mismatch_count = 0
        self.total_verify_count = 0

    def generate_with_verification(self, prompt: str, max_new_tokens: int = 50) -> dict:
        """
        Generate with layer skipping and periodic verification.
        """
        input_ids = self.skip_engine.tokenizer.encode(prompt, return_tensors='pt')
        generated_ids = input_ids.clone()
        verification_failures = []

        for token_idx in range(max_new_tokens):
            # Generate with skip
            skip_result = self.skip_engine.generate_with_layer_skip(prompt, max_new_tokens=1)

            # Periodically verify
            if (token_idx + 1) % self.verify_every_n_tokens == 0:
                # Run full forward pass to verify
                with torch.no_grad():
                    full_output = self.skip_engine.model(generated_ids)
                    full_logits = full_output.logits[:, -1, :]

                # Get skip output logits (last from skip_result)
                skip_logits = torch.tensor(skip_result['logits'][-1])

                # Check if top-1 predictions match
                skip_pred = torch.argmax(skip_logits)
                full_pred = torch.argmax(full_logits)

                if skip_pred != full_pred:
                    self.mismatch_count += 1
                    verification_failures.append(token_idx)

                self.total_verify_count += 1

            generated_ids = torch.cat([
                generated_ids,
                torch.tensor([[skip_result['next_token_id']]])
            ], dim=1)

        generated_text = self.skip_engine.tokenizer.decode(generated_ids[0])
        mismatch_rate = self.mismatch_count / max(1, self.total_verify_count)

        return {
            'text': generated_text,
            'verification_mismatches': len(verification_failures),
            'mismatch_rate': mismatch_rate,
            'skip_rate': skip_result['skip_rate']
        }
```

Implement layer importance analysis:

```python
def analyze_layer_importance(model_name: str, test_prompts: List[str]) -> dict:
    """
    Measure how important each layer is for final predictions.
    """
    model = AutoModelForCausalLM.from_pretrained(model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    num_layers = model.config.num_hidden_layers

    layer_importance = np.zeros(num_layers)

    for prompt in test_prompts:
        input_ids = tokenizer.encode(prompt, return_tensors='pt')

        # Full forward pass
        with torch.no_grad():
            full_output = model(input_ids)
            full_logits = full_output.logits[:, -1, :]

        # Skip each layer and measure impact
        for skip_layer in range(num_layers):
            # Forward pass skipping layer skip_layer
            # (simplified; actual implementation needs to modify forward pass)
            # Measure KL divergence or accuracy change
            # layer_importance[skip_layer] = kl_div(full, skip_one_layer)

            pass

    return {
        'layer_importance': layer_importance,
        'most_important': np.argsort(layer_importance)[-5:],
        'least_important': np.argsort(layer_importance)[:5]
    }

def identify_skippable_patterns(skip_engine: LayerSkipInferenceEngine) -> dict:
    """
    Analyze when and why layers are skipped.
    """
    skip_stats = {
        'total_skips': sum(skip_engine.skip_counts),
        'per_layer': skip_engine.skip_counts,
        'skip_likelihood': [count / max(1, 100) for count in skip_engine.skip_counts]
    }

    # Early layers more likely to be skipped than late layers?
    early_skip_rate = np.mean(skip_stats['skip_likelihood'][:len(skip_engine.skip_counts)//2])
    late_skip_rate = np.mean(skip_stats['skip_likelihood'][len(skip_engine.skip_counts)//2:])

    return {
        'skip_stats': skip_stats,
        'early_layer_skip_rate': early_skip_rate,
        'late_layer_skip_rate': late_skip_rate,
        'pattern': 'skip early' if early_skip_rate > late_skip_rate else 'skip late'
    }
```

## Practical Guidance

| Parameter | Typical Range | Notes |
|-----------|---------------|-------|
| Skip threshold | 0.7 - 0.95 | Higher = more aggressive skipping, lower accuracy risk |
| Verification frequency | Every 5-20 tokens | Balance safety with overhead |
| Target skip rate | 20% - 50% | More is faster but riskier; sweet spot ~30% |
| Initial threshold | Task-dependent | Calibrate on development set |
| Fallback policy | Full forward if mismatch | Ensure correctness over speed |

**When to use CLaSp:**
- Need faster inference without retraining
- Want to avoid deploying multiple models
- Inference latency is bottleneck
- Quality degradation <1% is acceptable
- Working with frozen, pre-trained models

**When NOT to use:**
- Quality can't degrade at all (require perfect accuracy)
- Model is already well-optimized (diminishing returns)
- Inference is already fast (not bottleneck)
- Need guaranteed fixed latency (skip rate varies)
- Working with very small models (overhead dominates)

**Common pitfalls:**
- Threshold too aggressive (quality degradation)
- Not verifying skip-path predictions (catch errors only in prod)
- Skipping layers that are actually important (don't analyze first)
- Not adapting threshold to domain (one threshold doesn't fit all)
- Measuring speedup without including verification overhead
- Not profiling actual wall-clock time (theoretical speedup differs)
- Assuming uniform skip rate (varies significantly by token/context)

## Reference

**CLaSp: In-Context Layer Skip for Self-Speculative Decoding**
https://arxiv.org/abs/2505.24196