Accelerate speculative decoding by pruning drafter vocabulary to high-frequency tokens. Achieves 16% speedup in memory-bound settings by eliminating unused vocabulary entries without retraining.
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Accelerate speculative decoding by pruning drafter vocabulary to high-frequency tokens. Achieves 16% speedup in memory-bound settings by eliminating unused vocabulary entries without retraining.
VocabTrim: Memory-Efficient Vocabulary Pruning for Speculative Decoding
Speculative decoding uses a small drafter model to propose multiple tokens per inference step, which the target verifier model accepts or rejects. This approach can 2-3× speedup inference when the drafter is fast. However, the drafter's language modeling head (the final layer outputting logits over all vocabulary tokens) becomes a memory bottleneck. For Llama-3 with 128K vocabulary tokens, computing logits over all 128K tokens at every step wastes memory and computation even though the drafter only samples from a tiny subset of frequently-occurring tokens.
VocabTrim solves this by reconstructing the drafter's vocabulary to contain only the high-frequency tokens it actually samples during inference. The insight is that drafters are biased toward "easy-to-predict" tokens (common words, punctuation) and rarely sample rare tokens. By trimming the vocabulary to the most frequent 25-50K tokens, you eliminate 60-75% of the LM head computation with negligible impact on acceptance rates.
Core Concept
VocabTrim works on a simple principle: replace the drafter's full vocabulary with a smaller one containing only tokens it frequently generates. The key insights are:
Drafters naturally focus on high-frequency, predictable tokens because they're trying to be fast and accurate
Target models have access to the full vocabulary during verification, so drafter coverage doesn't need to be complete
Token remapping can translate drafter outputs back to full vocabulary indices during generation
The method is training-free: measure token frequencies from any calibration data and reconstruct the LM head
The result is a smaller drafter that produces fewer logits per step (reduced memory transfers) and faster matrix multiplication (fewer output dimensions), with only minimal increase in rejection rate (1-3%).
Architecture Overview
VocabTrim modifies only the drafter's language modeling head:
Token Frequency Measurement: Run the drafter on calibration data and count which tokens are sampled during generation
Vocabulary Selection: Extract the top-K most frequently sampled tokens across the calibration set
LM Head Reconstruction: Create a new smaller LM head matrix containing only weights for selected token indices
Token Remapping: During inference, map drafter predictions (indices in small vocabulary) back to full vocabulary indices for the verifier
Verifier Unchanged: The target model receives the full vocabulary indices and operates without modification
Implementation
Step 1: Measure token frequencies on calibration data
Sample from the drafter on representative data and count which tokens appear most frequently in the output distribution.
defmeasure_drafter_token_frequencies(drafter, tokenizer, calibration_data,
num_samples=100000, sample_length=256):
"""
Run the drafter on calibration data and measure which tokens
are sampled most frequently. This identifies which tokens matter for inference speed.
"""
token_counts = {}
total_tokens_sampled = 0for batch_idx, batch inenumerate(calibration_data):
input_ids = tokenizer.encode(batch, return_tensors='pt').to(device)
# Generate from drafter, collecting all sampled tokenswith torch.no_grad():
for step inrange(sample_length):
outputs = drafter(input_ids)
logits = outputs.logits[:, -1, :] # Last position logits# Sample from the distribution (how the drafter would generate in practice)
probs = torch.softmax(logits, dim=-1)
sampled_tokens = torch.multinomial(probs, num_samples=1)
# Count token occurrencesfor token_id in sampled_tokens.flatten().tolist():
token_counts[token_id] = token_counts.get(token_id, 0) + 1
total_tokens_sampled += 1
input_ids = torch.cat([input_ids, sampled_tokens], dim=1)
if (batch_idx + 1) * sample_length >= num_samples:
break# Sort by frequency
sorted_tokens = sorted(token_counts.items(),
key=lambda x: x[1], reverse=True)
returndict(sorted_tokens)
Step 2: Select vocabulary and create remapping
Choose the top-K tokens and create an index mapping from small vocabulary to full vocabulary.
defcreate_vocab_mapping(token_frequencies, target_vocab_size=32000):
"""
Select the most frequent tokens and create a mapping.
This tells us which full vocabulary indices to keep.
"""# Select top-K tokens by frequency
top_tokens = sorted(token_frequencies.items(),
key=lambda x: x[1], reverse=True)[:target_vocab_size]
# Create two mappings:# 1. full_to_trimmed: maps full vocabulary indices to trimmed indices# 2. trimmed_to_full: maps trimmed indices back to full vocabulary
full_to_trimmed = {}
trimmed_to_full = {}
for trimmed_idx, (full_token_id, count) inenumerate(top_tokens):
full_to_trimmed[full_token_id] = trimmed_idx
trimmed_to_full[trimmed_idx] = full_token_id
# Calculate coverage: what percentage of real samples are covered
total_count = sum(token_frequencies.values())
selected_count = sum(count for _, count in top_tokens)
coverage = selected_count / total_count
return {
'full_to_trimmed': full_to_trimmed,
'trimmed_to_full': trimmed_to_full,
'vocab_size': target_vocab_size,
'coverage': coverage
}
Step 3: Reconstruct the drafter's LM head
Create a new, smaller language modeling head by extracting only the necessary weight rows.
defreconstruct_lm_head(drafter, vocab_mapping):
"""
Extract the drafter's LM head weights for selected tokens only.
This creates a new head with fewer output dimensions.
"""
original_head = drafter.lm_head
original_weight = original_head.weight # Shape: [vocab_size, hidden_dim]# Select only rows corresponding to kept tokens
selected_indices = torch.tensor(
[vocab_mapping['trimmed_to_full'][i]
for i inrange(vocab_mapping['vocab_size'])],
device=original_weight.device
)
# Extract selected rows
trimmed_weight = original_weight[selected_indices, :]
# Create new head with smaller output vocabulary
trimmed_head = torch.nn.Linear(
original_head.in_features,
vocab_mapping['vocab_size'],
bias=(original_head.bias isnotNone)
)
# Copy weightswith torch.no_grad():
trimmed_head.weight.copy_(trimmed_weight)
if original_head.bias isnotNone:
original_bias = original_head.bias
selected_bias = original_bias[selected_indices]
trimmed_head.bias.copy_(selected_bias)
return trimmed_head
Step 4: Implement token remapping in the speculative decoding loop
During generation, convert drafter indices (from trimmed vocabulary) back to full vocabulary indices.
defspeculative_decode_with_trimmed_vocab(target_model, drafter,
input_ids, vocab_mapping,
num_steps=256, draft_length=4):
"""
Run speculative decoding with a trimmed-vocabulary drafter.
The drafter outputs indices in the small vocabulary,
which we remap to the full vocabulary before verification.
"""
full_to_trimmed = vocab_mapping['full_to_trimmed']
trimmed_to_full = vocab_mapping['trimmed_to_full']
current_ids = input_ids
for step inrange(num_steps):
# Drafter generates draft tokens
draft_tokens = []
drafter_ids = current_ids
for draft_step inrange(draft_length):
with torch.no_grad():
# Forward pass through drafter with TRIMMED vocabulary
drafter_outputs = drafter(drafter_ids)
drafter_logits = drafter_outputs.logits[:, -1, :]
# Sample from trimmed vocabulary
trimmed_probs = torch.softmax(drafter_logits, dim=-1)
trimmed_sample = torch.multinomial(trimmed_probs, num_samples=1)
# REMAP: convert trimmed index to full vocabulary index
full_sample = torch.tensor(
[[trimmed_to_full[int(trimmed_sample[0, 0])]]],
device=trimmed_sample.device
)
draft_tokens.append(full_sample)
drafter_ids = torch.cat([drafter_ids, full_sample], dim=1)
# Concatenate draft tokens for verification
draft_ids = torch.cat(draft_tokens, dim=1)
candidate_ids = torch.cat([current_ids, draft_ids], dim=1)
# Target model verifies candidate tokens (with FULL vocabulary)with torch.no_grad():
target_outputs = target_model(candidate_ids)
target_logits = target_outputs.logits
# Verification: accept tokens while probabilities remain highfor accept_idx inrange(draft_length):
position = current_ids.shape[1] + accept_idx
candidate_token = draft_ids[0, accept_idx].item()
# Get target model's probability for this token
position_logits = target_logits[0, position - 1, :]
target_probs = torch.softmax(position_logits, dim=-1)
target_prob = target_probs[candidate_token].item()
# Accept or rejectif torch.rand(1).item() < target_prob:
# Accept: token is added permanently
current_ids = torch.cat([current_ids, draft_ids[:, accept_idx:accept_idx+1]], dim=1)
else:
# Reject: resample from target model
resampled = torch.multinomial(target_probs, num_samples=1)
current_ids = torch.cat([current_ids, resampled], dim=1)
breakreturn current_ids
Step 5: Evaluate speedup and coverage trade-off
Measure the actual inference speedup and how often the drafter misses tokens outside its vocabulary.
defevaluate_vocabtrim_performance(target_model, drafter, vocab_mapping,
eval_prompts, vocab_mapping_baseline):
"""
Compare trimmed drafter (VocabTrim) against baseline speculative decoding.
Key metrics: speedup, acceptance rate, coverage.
"""
results = {
'trimmed': [],
'baseline': []
}
for prompt in eval_prompts:
input_ids = tokenizer.encode(prompt, return_tensors='pt').to(device)
# Baseline: full vocabulary drafter
start_time = time.time()
output_baseline = speculative_decode_baseline(target_model, drafter,
input_ids, max_length=256)
time_baseline = time.time() - start_time
# Trimmed: VocabTrim drafter
start_time = time.time()
output_trimmed = speculative_decode_with_trimmed_vocab(
target_model, drafter, input_ids, vocab_mapping, num_steps=256
)
time_trimmed = time.time() - start_time
# Compute acceptance rate# (tokens where drafter was right and target accepted them)
acceptance_baseline = count_accepted_tokens(output_baseline)
acceptance_trimmed = count_accepted_tokens(output_trimmed)
results['baseline'].append({
'time': time_baseline,
'acceptance': acceptance_baseline
})
results['trimmed'].append({
'time': time_trimmed,
'acceptance': acceptance_trimmed
})
speedup = np.mean([r['time'] for r in results['baseline']]) / \
np.mean([r['time'] for r in results['trimmed']])
acceptance_degradation = (np.mean([r['acceptance'] for r in results['baseline']]) -
np.mean([r['acceptance'] for r in results['trimmed']])) / \
np.mean([r['acceptance'] for r in results['baseline']])
return {
'speedup': speedup,
'acceptance_degradation': acceptance_degradation,
'vocab_coverage': vocab_mapping['coverage']
}
Practical Guidance
Hyperparameter
Recommended Value
Notes
Target vocab size
25K-50K
Higher = more speedup but diminishing returns
Coverage threshold
85-95%
Ensure you're covering most sampled tokens
Calibration data size
10K-50K examples
Measure frequencies on representative data
Acceptance rate tolerance
97-99%
Can afford 1-3% degradation for 16% speedup
Remapping overhead
<1%
Negligible compared to inference speedup
When to use VocabTrim:
You're using speculative decoding with draft-verify architecture
Your drafter has a large vocabulary (Llama-3 with 128K tokens)
You're memory-bound (not compute-bound) during inference
You need 10-20% inference speedup without retraining
When NOT to use VocabTrim:
Your drafter already has a small vocabulary (less than 50K)
