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:

# 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)  logits  logits_sequence]

        
        kl_divs = []
         i  ((probs_sequence) - ):
            
            kl = torch.nn.functional.kl_div(
                torch.log(probs_sequence[i] + ),
                probs_sequence[i + ],
                reduction=
            ).mean()
            kl_divs.append(kl)

        
        kl_divs = torch.tensor(kl_divs)
        stability = torch.exp(-kl_divs)  

         stability

     () -> :
        
         current_layer >= (stability):
             

        current_stability = stability[current_layer]

        
        should_skip = current_stability > .skip_threshold

         should_skip:
            .skip_counts[current_layer] += 

         should_skip

     () -> :
        
        input_ids = .tokenizer.encode(prompt, return_tensors=)
        device = input_ids.device

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

         token_idx  (max_new_tokens):
            
            layer_logits = []

            
             torch.no_grad():
                current_hidden = .model.get_input_embeddings()(generated_ids)

                 layer_idx, layer_module  (.model.transformer.h):
                    
                     layer_idx >   (layer_logits) > :
                        stability = .measure_logit_stability(layer_logits)
                         .decide_layer_skip(stability, layer_idx - ):
                            
                            skip_decisions.append((token_idx, layer_idx, ))
                            total_skipped_layers += 
                            

                    
                    layer_output = layer_module(current_hidden)
                    current_hidden = layer_output[]
                    total_computed_layers += 

                    
                    logits = .model.lm_head(current_hidden)[:, -, :]
                    layer_logits.append(logits)

                
                final_logits = .model.lm_head(current_hidden)[:, -, :]
                all_logits.append(final_logits)

                
                probs = torch.softmax(final_logits, dim=-)
                next_token = torch.multinomial(probs, num_samples=)
                generated_ids = torch.cat([generated_ids, next_token], dim=)

        
        generated_text = .tokenizer.decode(generated_ids[])
        skip_rate = total_skipped_layers / (, total_skipped_layers + total_computed_layers)

         {
            : generated_text,
            : skip_rate,
            : total_skipped_layers,
            : total_computed_layers,
            :  / ( - skip_rate)  skip_rate >   
        }

     ():
        
        skip_rates = []

        
         threshold  np.linspace(, , ):
            .skip_threshold = threshold
            skip_rates_for_threshold = []

             prompt  calibration_prompts:
                result = .generate_with_layer_skip(prompt, max_new_tokens=)
                skip_rates_for_threshold.append(result[])

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

            ()

        
        skip_rates.sort(key= x: (x[] - target_skip_rate))
        best_threshold = skip_rates[][]

        .skip_threshold = best_threshold
        ()

         best_threshold

Implement verification and fallback mechanisms:

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[:, -, :]

                
                skip_logits = torch.tensor(skip_result[][-])

                
                skip_pred = torch.argmax(skip_logits)
                full_pred = torch.argmax(full_logits)

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

                .total_verify_count += 

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

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

         {
            : generated_text,
            : (verification_failures),
            : mismatch_rate,
            : skip_result[]
        }

Implement layer importance analysis:

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,
        : [count / (, )  count  skip_engine.skip_counts]
    }

    
    early_skip_rate = np.mean(skip_stats[][:(skip_engine.skip_counts)//])
    late_skip_rate = np.mean(skip_stats[][(skip_engine.skip_counts)//:])

     {
        : skip_stats,
        : early_skip_rate,
        : late_skip_rate,
        :   early_skip_rate > late_skip_rate  
    }

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