Practice: Benchmarking a Quantized LLM

Benchmarking a quantized Large Language Model against its original counterpart is essential for understanding the impact of quantization. This hands-on experience will demonstrate how to measure this impact on both performance (speed, memory) and accuracy, using relevant evaluation metrics and strategies.

As we discussed, quantization is fundamentally about trade-offs. We aim to reduce the computational resources required (like memory footprint $M$ and inference latency $L$) while minimizing the impact on the model's predictive quality, often measured by accuracy or perplexity. This exercise involves quantifying these exact trade-offs.

Setting Up Your Environment

Before we begin, ensure you have the necessary libraries installed. We'll primarily use Hugging Face libraries for model loading, generation, and evaluation. You'll also need torch for core functionalities and potentially bitsandbytes if you're loading models quantized with it, or optimum for GPTQ/GGUF formats.

pip install transformers torch accelerate datasets evaluate optimum bitsandbytes sentencepiece
# If using GGUF models via ctransformers backend in optimum:
pip install optimum[exporters,ctransformers]
# Or if using AutoGPTQ backend in optimum:
pip install optimum[exporters,auto-gptq]

For this exercise, you'll need two versions of an LLM:

1. Base Model: The original, higher-precision model (e.g., FP16 or FP32).
2. Quantized Model: The lower-precision version (e.g., INT8, INT4) obtained through PTQ (like basic static/dynamic PTQ, GPTQ, or loaded via bitsandbytes) or even QAT.

You can either use a model you quantized in a previous exercise or load pre-quantized models available on the Hugging Face Hub. For consistency and faster execution, consider using a smaller model like gpt2, distilgpt2, or a small variant of Mistral/Llama if available in both original and quantized forms. Ensure you have access to a CUDA-enabled GPU for meaningful performance measurements.

Let's define our model identifiers. Replace these with the actual Hugging Face Hub IDs or local paths for your chosen models.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import time
import numpy as np

# --- Configuration ---
base_model_id = "gpt2"  # Example: Replace with your base model (e.g., "meta-llama/Llama-2-7b-hf")
quantized_model_id = "gpt2" # Example: Replace with your quantized model ID or path
# If loading 4-bit with bitsandbytes, the ID might be the same, but loading differs
load_in_4bit = True # Set to True if using bitsandbytes 4-bit quantization on load

device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")

# --- Load Tokenizer (usually the same for both) ---
tokenizer = AutoTokenizer.from_pretrained(base_model_id)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token # Set pad token if missing

# --- Load Base Model ---
print(f"Loading base model: {base_model_id}")
# Load in half-precision if using GPU and model supports it
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.float16 if device == "cuda" else torch.float32,
    device_map=device # Simple loading to the target device
)
base_model.eval() # Set to evaluation mode
print("Base model loaded.")

# --- Load Quantized Model ---
print(f"Loading quantized model: {quantized_model_id}")
if load_in_4bit and device == "cuda":
    from transformers import BitsAndBytesConfig
    # Example: Loading with bitsandbytes 4-bit quantization
    bnb_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_compute_dtype=torch.float16 # Optional: compute dtype
    )
    quantized_model = AutoModelForCausalLM.from_pretrained(
        quantized_model_id,
        quantization_config=bnb_config,
        device_map=device # bitsandbytes handles device placement
    )
    print("Quantized model loaded using bitsandbytes 4-bit config.")
# Add alternative loading methods here if needed (e.g., using Optimum for GPTQ/GGUF)
# elif quantized_model_id.endswith(".gguf"): # Example using Optimum with CTransformers
#     from optimum.quanto import CtransformersModelForCausalLM
#     quantized_model = CtransformersModelForCausalLM.from_pretrained(quantized_model_id, device_map=device)
#     print("Quantized GGUF model loaded using Optimum.")
else:
    # Assume loading a standard Hugging Face model (e.g., if QAT was used or it's pre-quantized differently)
    # Adjust dtype and loading if necessary based on the specific quantized format
    quantized_model = AutoModelForCausalLM.from_pretrained(
        quantized_model_id,
        torch_dtype=torch.float16 if device == "cuda" else torch.float32, # May need adjustment
        device_map=device
    )
    print("Quantized model loaded (adjust loading logic if not using bitsandbytes 4-bit).")

quantized_model.eval() # Set to evaluation mode

Make sure to adapt the loading logic for the quantized_model based on how it was quantized and saved. The example shows loading via bitsandbytes. If you have a GPTQ or GGUF model, you would typically use libraries like optimum with the appropriate backend (auto-gptq or ctransformers).

Benchmarking Performance

Now, let's measure speed and memory usage. We'll look at latency (time per generation) and peak memory consumption.

Measuring Latency

Latency is the time taken to process a single input. We can measure it by timing the generate function. For accurate GPU timing, it's important to synchronize before starting and stopping the timer.

# --- Latency Benchmark ---
prompt = "The future of AI is"
inputs = tokenizer(prompt, return_tensors="pt").to(device)

# Warm-up run (important for CUDA)
_ = base_model.generate(**inputs, max_new_tokens=50)
_ = quantized_model.generate(**inputs, max_new_tokens=50)
if device == "cuda":
    torch.cuda.synchronize()

# Measure Base Model Latency
start_time = time.perf_counter()
_ = base_model.generate(**inputs, max_new_tokens=50, pad_token_id=tokenizer.pad_token_id)
if device == "cuda":
    torch.cuda.synchronize()
end_time = time.perf_counter()
base_latency = end_time - start_time
print(f"Base Model Latency: {base_latency:.4f} seconds")

# Measure Quantized Model Latency
start_time = time.perf_counter()
_ = quantized_model.generate(**inputs, max_new_tokens=50, pad_token_id=tokenizer.pad_token_id)
if device == "cuda":
    torch.cuda.synchronize()
end_time = time.perf_counter()
quantized_latency = end_time - start_time
print(f"Quantized Model Latency: {quantized_latency:.4f} seconds")

if base_latency > 0:
    speedup = base_latency / quantized_latency
    print(f"Speedup: {speedup:.2f}x")
else:
    print("Base latency was zero, cannot calculate speedup.")

Run this multiple times or average over several prompts for more stable results. You should observe lower latency for the quantized model, especially on hardware with native support for lower-precision operations.

Measuring Memory Usage

We can estimate peak GPU memory usage during inference using PyTorch's memory management functions.

# --- Memory Benchmark ---
def get_peak_memory_mb(model, inputs):
    """Measures peak GPU memory usage for a model generation."""
    if device != "cuda":
        print("Memory measurement only available for CUDA devices.")
        return 0
    torch.cuda.empty_cache()
    torch.cuda.reset_peak_memory_stats()
    try:
        _ = model.generate(**inputs, max_new_tokens=50, pad_token_id=tokenizer.pad_token_id)
        torch.cuda.synchronize()
        peak_memory = torch.cuda.max_memory_allocated() / (1024**2) # Convert bytes to MB
    except Exception as e:
        print(f"Error during memory measurement: {e}")
        peak_memory = 0
    torch.cuda.empty_cache()
    return peak_memory

# Measure Base Model Memory
base_memory_mb = get_peak_memory_mb(base_model, inputs)
print(f"Base Model Peak Memory: {base_memory_mb:.2f} MB")

# Measure Quantized Model Memory
quantized_memory_mb = get_peak_memory_mb(quantized_model, inputs)
print(f"Quantized Model Peak Memory: {quantized_memory_mb:.2f} MB")

if base_memory_mb > 0:
    memory_reduction = base_memory_mb / quantized_memory_mb
    print(f"Memory Reduction Factor: {memory_reduction:.2f}x")
    print(f"Memory Savings: {(1 - (quantized_memory_mb / base_memory_mb)) * 100:.2f}%")
else:
    print("Base memory usage was zero or could not be measured.")

The quantized model should exhibit significantly lower peak memory usage, directly correlating with the reduced precision of its weights (and potentially activations, depending on the method).

Benchmarking Accuracy

Performance gains are only valuable if the model remains sufficiently accurate for its intended task. We need to evaluate the quantized model's quality using appropriate metrics.

Evaluating Perplexity

Perplexity is a common intrinsic metric for language models. It measures how well a model predicts a sequence of text. Lower perplexity generally indicates a better model. We can use the evaluate library and a standard dataset like WikiText.

import evaluate
from datasets import load_dataset
from tqdm import tqdm

# --- Perplexity Evaluation ---
try:
    perplexity_metric = evaluate.load("perplexity", module_type="metric")
    # Use a small subset for faster evaluation in this example
    dataset = load_dataset("wikitext", "wikitext-2-raw-v1", split="test[:1%]")
    # Or use the full test set: split="test"

    # Preprocess text data (concatenate and chunk) for perplexity calculation
    # This approach processes the entire dataset text as one sequence
    encodings = tokenizer("\n\n".join(dataset["text"]), return_tensors="pt")
    max_length = base_model.config.n_positions # Use model's max sequence length
    stride = 512 # How much overlap between chunks

    seq_len = encodings.input_ids.size(1)
    nlls_base = []
    nlls_quantized = []
    prev_end_loc = 0

    print("Calculating perplexity...")
    for begin_loc in tqdm(range(0, seq_len, stride)):
        end_loc = min(begin_loc + max_length, seq_len)
        trg_len = end_loc - prev_end_loc # may be different from stride on last loop
        input_ids = encodings.input_ids[:, begin_loc:end_loc].to(device)
        target_ids = input_ids.clone()
        target_ids[:, :-trg_len] = -100 # Ignore loss calculation for overlapping tokens

        with torch.no_grad():
            # Base model
            outputs_base = base_model(input_ids, labels=target_ids)
            neg_log_likelihood_base = outputs_base.loss * trg_len # Scale loss by target length
            nlls_base.append(neg_log_likelihood_base)

            # Quantized model
            outputs_quantized = quantized_model(input_ids, labels=target_ids)
            neg_log_likelihood_quantized = outputs_quantized.loss * trg_len # Scale loss by target length
            nlls_quantized.append(neg_log_likelihood_quantized)

        prev_end_loc = end_loc
        if end_loc == seq_len:
            break

    # Calculate Perplexity
    ppl_base = torch.exp(torch.stack(nlls_base).sum() / end_loc)
    ppl_quantized = torch.exp(torch.stack(nlls_quantized).sum() / end_loc)

    print(f"\nBase Model Perplexity: {ppl_base.item():.4f}")
    print(f"Quantized Model Perplexity: {ppl_quantized.item():.4f}")
    print(f"Perplexity Increase: {ppl_quantized.item() - ppl_base.item():.4f}")

except ImportError:
    print("Please install 'evaluate' and 'datasets' to run perplexity benchmark.")
except Exception as e:
    print(f"Could not run perplexity evaluation: {e}")

This perplexity calculation uses a sliding window approach common for causal LMs. Note that calculating perplexity over a large dataset can be time-consuming. Using a smaller subset (split="test[:1%]") provides a quick estimate.

Evaluating on a Downstream Task (Optional)

Alternatively, or additionally, you can evaluate the models on a specific task they might be used for, such as question answering, summarization, or sentiment analysis. This often provides a more direct measure of practical utility. Frameworks like the lm-evaluation-harness are designed for this purpose, offering standardized evaluation setups across many tasks. Setting up lm-evaluation-harness is more involved and outside the scope of this basic practical, but it's the standard for rigorous LLM evaluation.

For a simpler task-based evaluation within this notebook, you could adapt the generation loop to run on a task-specific dataset (e.g., BoolQ for yes/no questions) and compute accuracy.

Analyzing the Results

Now, consolidate your findings. A simple table is often effective:

Metric	Base Model	Quantized Model	Change
Latency (seconds)	base_latency	quant_latency	speedupx faster
Peak Memory (MB)	base_memory	quant_memory	reductionx smaller
Perplexity	ppl_base	ppl_quantized	increase higher
Task Accuracy (%)	(if measured)	(if measured)	(difference)

(Replace placeholder values with your measured results)

Visualize the trade-off. A simple scatter plot comparing speedup/memory reduction against the accuracy drop (e.g., percentage increase in perplexity) can be insightful.

Performance metrics like latency and memory reduction vs. evaluation metrics like perplexity increase. The example shows a 1.85x speedup with a 5.2% increase in perplexity for a 4-bit quantized model. Replace values with your own measurements.

Interpret the results:

• Did quantization meet performance goals? Was the speedup or memory reduction significant?
• Was the accuracy impact acceptable? Did perplexity increase substantially? If you measured task accuracy, did it drop below an acceptable threshold for your application?
• Is this specific quantization method suitable? Based on the trade-off, would you deploy this quantized model, or explore other methods (e.g., a less aggressive quantization level, advanced PTQ like GPTQ, or QAT)?

This hands-on benchmarking process provides concrete data to guide decisions about deploying quantized models, ensuring you balance efficiency gains with the required level of predictive performance.