Speculative Decoding: Fast LLM Inference Without Quality Loss › Code Implementation
Michael BrenndoerferJanuary 16, 20267 min read
Language AI HandbookAll chapters Part 42 of 67Inference Optimization
Speculative Decoding: Fast LLM Inference Without Quality Loss › Code ImplementationOne section · Sections in this chapter10 / 14 ↓
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This section presents a complete speculative decoding implementation using the Hugging Face transformers library. We will use actual models to demonstrate the concept with measurable results. This implementation captures all the key components: draft generation, parallel verification, and the acceptance-correction procedure.
In[24]:
Codeuv pip install transformersOut[24]:
Console/private/tmp/mb-language-ai-modern-plots/books/_quarto_language-ai-handbook/.venv/bin/python: No module named uv
Note: you may need to restart the kernel to use updated packages.
In[5]:
Codeimport torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load draft model (smaller)
draft_model_name = "gpt2" # 124M parameters
draft_tokenizer = AutoTokenizer.from_pretrained(draft_model_name)
draft_model = AutoModelForCausalLM.from_pretrained(draft_model_name)
# Load target model (larger)
target_model_name = "gpt2-medium" # 355M parameters
target_tokenizer = AutoTokenizer.from_pretrained(target_model_name)
target_model = AutoModelForCausalLM.from_pretrained(target_model_name)
# Ensure same tokenizer (required for speculative decoding)
assert draft_tokenizer.vocab_size == target_tokenizer.vocab_size
# Move to GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
draft_model = draft_model.to(device).eval()
target_model = target_model.to(device).eval()Speculative Decoding Core
The core implementation encapsulates the three main operations: drafting candidate tokens, verifying them against the target model, and applying the acceptance/rejection logic. Each method is designed to be modular and clearly reflect the algorithm's structure. Notice how verify_tokens captures the key efficiency property: a single forward pass computes verification probabilities for all draft tokens simultaneously.
In[26]:
Codeclass SpeculativeDecoder:
def __init__(self, draft_model, target_model, tokenizer, device="cpu"):
self.draft_model = draft_model
self.target_model = target_model
self.tokenizer = tokenizer
self.device = device
@torch.no_grad()
def draft_tokens(self, input_ids, num_tokens, temperature=1.0):
"""Generate K draft tokens autoregressively."""
draft_tokens = []
draft_probs = []
current_ids = input_ids.clone()
for _ in range(num_tokens):
outputs = self.draft_model(current_ids)
logits = outputs.logits[:, -1, :] / temperature
probs = F.softmax(logits, dim=-1)
# Sample from draft distribution
token = torch.multinomial(probs, num_samples=1)
draft_tokens.append(token.item())
draft_probs.append(probs[0, token.item()].item())
current_ids = torch.cat([current_ids, token], dim=-1)
return draft_tokens, draft_probs
@torch.no_grad()
def verify_tokens(self, input_ids, draft_tokens, temperature=1.0):
"""Verify all draft tokens in one forward pass."""
# Build sequence with draft tokens
draft_tensor = torch.tensor([draft_tokens], device=self.device)
full_ids = torch.cat([input_ids, draft_tensor], dim=-1)
# Single forward pass
outputs = self.target_model(full_ids)
logits = outputs.logits / temperature
# Extract probabilities at verification positions
num_input = input_ids.shape[-1]
num_draft = len(draft_tokens)
target_probs = []
target_distributions = []
for i in range(num_draft):
pos = num_input + i - 1 # Position predicting draft token i
probs = F.softmax(logits[:, pos, :], dim=-1)
target_probs.append(probs[0, draft_tokens[i]].item())
target_distributions.append(probs[0].cpu().numpy())
return target_probs, target_distributions
def accept_reject(
self,
draft_tokens,
draft_probs,
target_probs,
target_distributions,
draft_distributions,
):
"""Determine which tokens to accept and generate correction if needed."""
accepted = []
for i, (token, q, p) in enumerate(
zip(draft_tokens, draft_probs, target_probs)
):
# Acceptance probability
accept_prob = min(1.0, p / q)
if torch.rand(1).item() < accept_prob:
accepted.append(token)
else:
# Rejection: sample from correction distribution
p_dist = target_distributions[i]
q_dist = draft_distributions[i]
# Correction distribution: max(0, p - q), normalized
correction = np.maximum(0, p_dist - q_dist)
correction_sum = correction.sum()
if correction_sum > 0:
correction = correction / correction_sum
correction_token = np.random.choice(
len(correction), p=correction
)
else:
correction_token = np.argmax(p_dist)
accepted.append(correction_token)
break # Stop at first rejection
return acceptedRunning Speculative Decoding
This function orchestrates the complete generation process, repeatedly running speculative decoding rounds until the desired number of tokens is reached. It also tracks statistics that help us evaluate the system's performance in practice.
In[27]:
Codedef speculative_generate(
decoder, prompt, max_tokens=50, num_draft=4, temperature=1.0
):
"""
Generate text using speculative decoding.
Returns generated text and statistics.
"""
input_ids = decoder.tokenizer.encode(prompt, return_tensors="pt").to(
decoder.device
)
generated_tokens = []
stats = {"rounds": 0, "drafted": 0, "accepted": 0}
while len(generated_tokens) < max_tokens:
stats["rounds"] += 1
# Build current context
if generated_tokens:
current_ids = torch.cat(
[
input_ids,
torch.tensor([generated_tokens], device=decoder.device),
],
dim=-1,
)
else:
current_ids = input_ids
# Draft phase
draft_tokens, draft_probs = decoder.draft_tokens(
current_ids, num_draft, temperature
)
stats["drafted"] += len(draft_tokens)
# Get draft distributions for correction
draft_distributions = []
temp_ids = current_ids.clone()
for token in draft_tokens:
outputs = decoder.draft_model(temp_ids)
probs = F.softmax(outputs.logits[:, -1, :] / temperature, dim=-1)
draft_distributions.append(probs[0].cpu().numpy())
temp_ids = torch.cat(
[temp_ids, torch.tensor([[token]], device=decoder.device)],
dim=-1,
)
# Verify phase
target_probs, target_distributions = decoder.verify_tokens(
current_ids, draft_tokens, temperature
)
# Accept/reject phase
accepted = decoder.accept_reject(
draft_tokens,
draft_probs,
target_probs,
target_distributions,
draft_distributions,
)
generated_tokens.extend(accepted)
stats["accepted"] += len(accepted)
# Check for EOS
if decoder.tokenizer.eos_token_id in accepted:
break
# Decode generated tokens
full_ids = torch.cat(
[
input_ids,
torch.tensor(
[generated_tokens[:max_tokens]], device=decoder.device
),
],
dim=-1,
)
generated_text = decoder.tokenizer.decode(
full_ids[0], skip_special_tokens=True
)
return generated_text, statsComparing with Standard Generation
To measure the benefit of speculative decoding, we need a baseline. This standard autoregressive generation function provides that baseline, using the same target model but generating one token at a time. The comparison between the two approaches reveals the practical speedup factor for your specific model pair and hardware.
In[28]:
Codedef standard_generate(
model, tokenizer, prompt, max_tokens=50, temperature=1.0, device="cpu"
):
"""Standard autoregressive generation for comparison."""
input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
generated = []
current_ids = input_ids
for _ in range(max_tokens):
with torch.no_grad():
outputs = model(current_ids)
logits = outputs.logits[:, -1, :] / temperature
probs = F.softmax(logits, dim=-1)
token = torch.multinomial(probs, num_samples=1)
generated.append(token.item())
current_ids = torch.cat([current_ids, token], dim=-1)
if token.item() == tokenizer.eos_token_id:
break
full_ids = torch.cat(
[input_ids, torch.tensor([generated], device=device)], dim=-1
)
return tokenizer.decode(full_ids[0], skip_special_tokens=True)Example Usage
Since loading actual models requires significant resources, we demonstrate the workflow with a simulation that captures the key dynamics. This simulation explores how different acceptance rates affect performance without requiring large language model weights.
In[29]:
Codeimport numpy as np
class SimulatedSpeculativeDecoder:
"""
Simulates speculative decoding to demonstrate the algorithm
without requiring actual model weights.
"""
def __init__(self, acceptance_rate=0.75, vocab_size=1000):
self.acceptance_rate = acceptance_rate
self.vocab_size = vocab_size
def simulate_round(self, num_draft=5):
"""Simulate one round of speculative decoding."""
accepted = 0
for i in range(num_draft):
# Simulate acceptance based on rate
if np.random.random() < self.acceptance_rate:
accepted += 1
else:
# Rejection - add correction token and stop
accepted += 1 # Correction always added
break
else:
# All accepted - add bonus token in real implementation
pass
return accepted
def simulate_generation(
self, total_tokens, num_draft=5, draft_time_ms=5, target_time_ms=100
):
"""Simulate generating total_tokens with speculative decoding."""
tokens_generated = 0
rounds = 0
total_time_ms = 0
while tokens_generated < total_tokens:
rounds += 1
accepted = self.simulate_round(num_draft)
tokens_generated += accepted
# Time for this round: draft all tokens + one target pass
round_time = num_draft * draft_time_ms + target_time_ms
total_time_ms += round_time
# Standard generation time for comparison
standard_time_ms = total_tokens * target_time_ms
return {
"tokens": tokens_generated,
"rounds": rounds,
"speculative_time_ms": total_time_ms,
"standard_time_ms": standard_time_ms,
"speedup": standard_time_ms / total_time_ms,
"tokens_per_round": tokens_generated / rounds,
}
# Run simulations for different acceptance rates
total_tokens = 100
num_simulations = 50
# Simulation parameters
draft_ms = 5
target_ms = 100
num_drafts = 5
acceptance_rates_sim = [0.5, 0.6, 0.7, 0.8, 0.9]
simulation_results = []
for rate in acceptance_rates_sim:
decoder = SimulatedSpeculativeDecoder(acceptance_rate=rate)
rate_stats = []
for _ in range(num_simulations):
stats = decoder.simulate_generation(
total_tokens,
num_draft=num_drafts,
draft_time_ms=draft_ms,
target_time_ms=target_ms,
)
rate_stats.append(stats)
simulation_results.append(
{
"rate": rate,
"speedup_mean": np.mean([s["speedup"] for s in rate_stats]),
"speedup_std": np.std([s["speedup"] for s in rate_stats]),
"tokens_mean": np.mean([s["tokens_per_round"] for s in rate_stats]),
"tokens_std": np.std([s["tokens_per_round"] for s in rate_stats]),
}
)Out[30]:
ConsoleSpeculative Decoding Simulation Results ============================================================ Generating 100 tokens, K=5 draft tokens Draft model: 5ms/token, Target model: 100ms/token Acceptance rate: 50% Average speedup: 1.60x (±0.13) Tokens per round: 2.01 (±0.16) Acceptance rate: 60% Average speedup: 1.86x (±0.16) Tokens per round: 2.36 (±0.21) Acceptance rate: 70% Average speedup: 2.16x (±0.17) Tokens per round: 2.73 (±0.20) Acceptance rate: 80% Average speedup: 2.61x (±0.18) Tokens per round: 3.31 (±0.23) Acceptance rate: 90% Average speedup: 3.32x (±0.24) Tokens per round: 4.23 (±0.31)
The simulation confirms our theoretical analysis. At 80% acceptance rate, we achieve approximately 2x speedup, generating over 3 tokens per round on average. This matches well with reported results from production speculative decoding systems, validating that our theoretical framework accurately predicts real-world performance. The standard deviation in speedup also narrows at higher acceptance rates, indicating more consistent performance as draft model quality improves.