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Perplexity Evaluation Tool

Overview

This tool evaluates the perplexity of ONNX Runtime GenAI models and HuggingFace models using the WikiText-2 dataset. Perplexity is a standard metric for language models: lower values indicate better predictive performance.

Attribution

This script is originally based on perplexity_metrics.py from the Microsoft ONNX Runtime GenAI repository. It has been modified to handle:

  • Multiple context lengths
  • Configurable chunk sizes
  • Enhanced prefill chunking handling
  • HuggingFace model evaluation support

Scripts

  • perplexity_metrics.py: Core evaluation logic for computing perplexity.
  • run_perplexity.py: Command-line utility for evaluating one or more models and saving results to CSV.

Requirements

  • Python 3.8+

  • CUDA 12.x (if using GPU acceleration)

  • Install dependencies:

    For CUDA 12.x (recommended for CUDA 12.1-12.9):

    pip install -r requirements.txt

  • Install ONNX Runtime GenAI (required for ONNX model evaluation):

    pip install onnxruntime-genai

  • HuggingFace CLI login is required to access the WikiText-2 dataset:

    huggingface-cli login

Supported Models

ONNX Runtime GenAI Models

  • Any ONNX Runtime GenAI model exported with a compatible genai_config.json and tokenizer.
  • Supported architectures include: Gemma, Llama, Mistral, Phi (language + vision), Qwen.
  • Supported execution providers: CPU, DirectML, CUDA, NvTensorRtRtx.

HuggingFace Models

  • Any HuggingFace causal language model (e.g., meta-llama/Llama-2-7b-hf, gpt2, mistralai/Mistral-7B-v0.1).
  • Models are automatically downloaded from the HuggingFace Hub if not cached locally.
  • Supports custom data types (float16, bfloat16, float32) for efficient inference.

How to Run

Evaluate ONNX Models

Single Model

python run_perplexity.py --models /path/to/model

Multiple Models

python run_perplexity.py --models /path/to/model1 /path/to/model2

Custom Input Sequence Length(s)

You can specify the input sequence length(s) to evaluate using the --i argument:

python run_perplexity.py --models /path/to/model --i 1024,2048,4096,8192,12288

Custom Prefill Chunk Size

You can specify the prefill chunk size to evaluate using the --chunk_size argument:

python run_perplexity.py --models /path/to/model --i 1024,2048,4096,8192,12288 --chunk_size=1024

Evaluate HuggingFace Models

Basic HuggingFace Model Evaluation

python run_perplexity.py --hf_model meta-llama/Llama-2-7b-hf --i 1024

With Custom Data Type (Recommended for Performance)

python run_perplexity.py --hf_model meta-llama/Llama-2-7b-hf --hf_dtype float16 --i 1024

With Multiple Input Lengths

python run_perplexity.py --hf_model meta-llama/Llama-2-7b-hf --hf_dtype float16 --i 1024,2048,4096

On CPU (if no GPU available)

python run_perplexity.py --hf_model gpt2 --hf_device cpu --i 1024

Evaluate Both ONNX and HuggingFace Models Together

Compare ONNX and HuggingFace models side-by-side:

python run_perplexity.py \
  --models /path/to/onnx_model \
  --hf_model meta-llama/Llama-2-7b-hf \
  --hf_dtype float16 \
  --i 1024 \
  --output comparison_results.csv

HuggingFace Model Arguments

  • --hf_model: HuggingFace model name or local path (e.g., meta-llama/Llama-2-7b-hf)
  • --hf_device: Device to run on (cuda, cpu, cuda:0, etc.) - default: cuda
  • --hf_dtype: Data type for model weights - options: float16, bfloat16, float32, fp16, bf16, fp32 - default: model default (usually float32)

Custom Output File

python run_perplexity.py --models /path/to/model --output results.csv

Expected Output

Expected scores often fall between 2 and 1000; lower is better. See ranges below.

Perplexity Configuration Setting (for ONNX models)

  • If kv_chunking is enabled in the model configuration (i.e., "chunk_size" is present in the "search" section of genai_config.json), then:
    • max_input_seq_length is set to 8192
    • stride is set to the value of chunk_size
  • If kv_chunking is not enabled (default):
    • max_input_seq_length is 1024
    • stride is 512

For HuggingFace Models

  • Default max_length is 1024
  • Default stride is 512 (or chunk_size if specified)

Console Output

============================================================
Evaluating HuggingFace model: meta-llama/Llama-2-7b-hf
============================================================
[INFO] Loading Wikitext-2 'test' split ...
[TOKENIZER] Tokenizing ...

[RESULT] Perplexity of meta-llama/Llama-2-7b-hf: 5.47

HuggingFace perplexity evaluation completed

============================================================
Evaluating perplexity for: /path/to/onnx_model
============================================================
[INFO] Loading Wikitext-2 'test' split ...
[TOKENIZER] Tokenizing ...

[RESULT] Perplexity of /path/to/onnx_model: 5.48

Perplexity evaluation completed successfully

CSV Output

Generated file contains:

  • Model Path (model directory or HuggingFace model name)
  • Model Type (ONNX or HuggingFace)
  • Input Length
  • Perplexity score
  • Status (Success/Failed)
  • Error details (if any)

Debug Mode

Set DEBUG = True in perplexity_metrics.py for detailed logs.

Typical Perplexity Ranges

  • Excellent: 2-20
  • Good: 20-40
  • OK: 40-80
  • Poor: 100+

Common Use Cases

Compare ONNX vs. HuggingFace Model

Verify that your ONNX exported model has similar perplexity to the original HuggingFace model:

python run_perplexity.py \
  --models /path/to/exported_onnx_model \
  --hf_model meta-llama/Llama-2-7b-hf \
  --hf_dtype float16 \
  --i 1024 \
  --output validation_results.csv

Evaluate Small Models (for quick testing)

python run_perplexity.py --hf_model gpt2 --hf_dtype float16 --i 1024

Benchmark Multiple Quantization Variants

python run_perplexity.py \
  --models /path/to/fp16_model /path/to/int8_model /path/to/int4_model \
  --hf_model original/model-name \
  --hf_dtype float16 \
  --i 2048 \
  --output quantization_comparison.csv