How Speculative Decoding Works

Step-by-Step Workflow

1. Draft Generation:

• ‍The draft model generates a sequence of possible outputs based on the input prompt.
  
• These outputs are sampled in parallel, reducing latency.

2. Verification and Refinement:

• The refinement model evaluates the outputs from the draft model.
  
• It retains the most probable tokens while discarding or adjusting lower-quality predictions.

3. Output Assembly:

• The final output is constructed from the refined tokens, ensuring coherence and fluency.
  

Mathematical Foundation

Let's denote:

• The draft model as as Pd(x|y)
  
• The refinement model as Pr(x|y)
  ‍

For each token xt, the speculative decoding process aims to maximise the likelihood ratio: Tokens with higher scores are selected, balancing computational efficiency with output quality, the speculative decoding process aims to maximise the likelihood ratio:

Example Use Case: Voice AI

Voice AI applications, such as virtual assistants and text-to-speech (TTS) systems, demand real-time, high-quality output. Speculative decoding can significantly enhance these systems.
‍

Problem Statement

Consider a voice assistant generating spoken responses to user queries. The assistant needs to produce: 1/Fluent and coherent sentences ,
2/ Responses within milliseconds to maintain interactivity.
‍

Implementation Steps

1. Train Two Models:

• Draft model: A smaller, lightweight transformer.
• Refinement model: A larger transformer fine-tuned for fluency and accuracy.

2. Generate Draft Responses:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the draft model
draft_tokenizer = AutoTokenizer.from_pretrained("gpt-draft-model")
draft_model = AutoModelForCausalLM.from_pretrained("gpt-draft-model")

input_prompt = "What is the weather like today?"
input_ids = draft_tokenizer(input_prompt, return_tensors="pt").input_ids

# Generate speculative drafts
draft_outputs = draft_model.generate(input_ids, max_length=50, num_return_sequences=5, do_sample=True)
draft_sentences = [draft_tokenizer.decode(ids, skip_special_tokens=True) for ids in draft_outputs]

3. Refine Outputs:

# Load the refinement model
refinement_tokenizer = AutoTokenizer.from_pretrained("gpt-refinement-model")
refinement_model = AutoModelForCausalLM.from_pretrained("gpt-refinement-model")

refined_outputs = []
for draft in draft_sentences:
    draft_ids = refinement_tokenizer(draft, return_tensors="pt").input_ids
    refined_output = refinement_model.generate(draft_ids, max_length=50)
    refined_sentence = refinement_tokenizer.decode(refined_output[0], skip_special_tokens=True)
    refined_outputs.append(refined_sentence)

4. Select the Best Response: Evaluate the refined outputs using a scoring function or user feedback loop.

Diagram: Workflow of Speculative Decoding

Implementation with Amazon SageMaker

Amazon’s Bedrock and SageMaker platforms simplify deploying speculative decoding at scale, offering robust infrastructure for model hosting and inference.

Step-by-Step Guide

1. Prepare Your Models: Train and fine-tune your draft and refinement models locally or using SageMaker’s training jobs.

2. Deploy the Draft Model:

import boto3

sagemaker_client = boto3.client('sagemaker')

# Specify the model artifact and endpoint configuration
draft_model_artifact = "s3://your-bucket/draft-model.tar.gz"
endpoint_config_name = "draft-model-config"
endpoint_name = "draft-model-endpoint"

# Create endpoint configuration
sagemaker_client.create_endpoint_config(
    EndpointConfigName=endpoint_config_name,
    ProductionVariants=[{
        'VariantName': 'AllTraffic',
        'ModelName': 'draft-model',
        'InitialInstanceCount': 1,
        'InstanceType': 'ml.m5.large'
    }]
)

# Deploy the endpoint
sagemaker_client.create_endpoint(
    EndpointName=endpoint_name,
    EndpointConfigName=endpoint_config_name
)

3. Deploy the Refinement Model: Repeat the above steps for the refinement model, adjusting artifact paths and endpoint names.

4. Integrate the workflow:

import json
import requests

def speculative_decoding(prompt):
    # Query the draft model
    draft_response = requests.post(
        f"https://runtime.sagemaker.{region}.amazonaws.com/endpoints/draft-model-endpoint/invocations",
        json={"prompt": prompt}
    ).json()

    drafts = draft_response["generated_sequences"]

    refined_responses = []
    for draft in drafts:
        refinement_response = requests.post(
            f"https://runtime.sagemaker.{region}.amazonaws.com/endpoints/refinement-model-endpoint/invocations",
            json={"prompt": draft}
        ).json()
        refined_responses.append(refinement_response["generated_text"])

    return max(refined_responses, key=lambda x: len(x))

# Example usage
print(speculative_decoding("Tell me about speculative decoding"))

5. Optimise for Latency:
‍

• Use SageMaker’s multi-model endpoints to host both models on the same instance.
• Enable auto-scaling for high-demand scenarios.

Conclusion

Speculative decoding is an innovative approach that bridges the gap between efficiency and quality in generative AI. Its potential applications, especially in Voice AI, promise a future of more responsive and reliable AI systems. By leveraging this technique, developers can push the boundaries of what’s possible in generative tasks.

Whether you’re building a voice assistant or an advanced chatbot, speculative decoding is worth exploring to enhance both performance and user satisfaction.