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LLaDA Model Evaluation Guide

This document provides detailed instructions for evaluating the LLaDA model on GSM8K math problem solving and HumanEval code generation tasks.

Environment Setup

Before running any evaluation, set the following environment variables:

export HF_ALLOW_CODE_EVAL=1
export HF_DATASETS_TRUST_REMOTE_CODE=true

GSM8K Evaluation

GSM8K is a dataset of 8,000 grade school math problems designed to evaluate mathematical reasoning capabilities.

Common Parameters

task=gsm8k
length=256
block_length=32
num_fewshot=5
steps=$((length / block_length))

Evaluation Methods

  1. Baseline
accelerate launch eval_llada.py --tasks ${task} --num_fewshot ${num_fewshot} \
--confirm_run_unsafe_code --model llada_dist \
--model_args model_path='GSAI-ML/LLaDA-8B-Instruct',gen_length=${length},steps=${length},block_length=${block_length},show_speed=True
  1. Prefix Cache
accelerate launch eval_llada.py --tasks ${task} --num_fewshot ${num_fewshot} \
--confirm_run_unsafe_code --model llada_dist \
--model_args model_path='GSAI-ML/LLaDA-8B-Instruct',gen_length=${length},steps=${length},block_length=${block_length},use_cache=True,show_speed=True
  1. Parallel Generation
accelerate launch eval_llada.py --tasks ${task} --num_fewshot ${num_fewshot} \
--confirm_run_unsafe_code --model llada_dist \
--model_args model_path='GSAI-ML/LLaDA-8B-Instruct',gen_length=${length},steps=${steps},block_length=${block_length},threshold=0.9,show_speed=True
  1. Prefix Cache + Parallel
accelerate launch eval_llada.py --tasks ${task} --num_fewshot ${num_fewshot} \
--confirm_run_unsafe_code --model llada_dist \
--model_args model_path='GSAI-ML/LLaDA-8B-Instruct',gen_length=${length},steps=${steps},block_length=${block_length},use_cache=True,threshold=0.9,show_speed=True
  1. Dual Cache + Parallel
accelerate launch eval_llada.py --tasks ${task} --num_fewshot ${num_fewshot} \
--confirm_run_unsafe_code --model llada_dist \
--model_args model_path='GSAI-ML/LLaDA-8B-Instruct',gen_length=${length},steps=${steps},block_length=${block_length},use_cache=True,dual_cache=True,threshold=0.9,show_speed=True

Parameter Descriptions

  • task: Evaluation task (gsm8k)
  • length: Generation length
  • block_length: Block size for parallel generation
  • num_fewshot: Number of few-shot examples
  • steps: Number of generation steps
  • use_cache: Enable prefix cache
  • dual_cache: Enable dual cache
  • threshold: Confidence threshold for parallel generation
  • show_speed: Display speed metrics

HumanEval Evaluation

HumanEval is a dataset of 164 Python programming problems designed to evaluate code generation capabilities.

Common Parameters

task=humaneval
length=256
block_length=32
steps=$((length / block_length))

Evaluation Methods

  1. Baseline
accelerate launch eval_llada.py --tasks ${task} \
--confirm_run_unsafe_code --model llada_dist \
--model_args model_path='GSAI-ML/LLaDA-8B-Instruct',gen_length=${length},steps=${length},block_length=${block_length},show_speed=True \
--output_path evals_results/baseline/humaneval-ns0-${length} --log_samples
  1. Prefix Cache
accelerate launch eval_llada.py --tasks ${task} \
--confirm_run_unsafe_code --model llada_dist \
--model_args model_path='GSAI-ML/LLaDA-8B-Instruct',gen_length=${length},steps=${length},block_length=${block_length},use_cache=True,show_speed=True \
--output_path evals_results/prefix_cache/humaneval-ns0-${length} --log_samples
  1. Parallel Generation
accelerate launch eval_llada.py --tasks ${task} \
--confirm_run_unsafe_code --model llada_dist \
--model_args model_path='GSAI-ML/LLaDA-8B-Instruct',gen_length=${length},steps=${steps},block_length=${block_length},threshold=0.9,show_speed=True \
--output_path evals_results/parallel/humaneval-ns0-${length} --log_samples
  1. Prefix Cache + Parallel
accelerate launch eval_llada.py --tasks ${task} \
--confirm_run_unsafe_code --model llada_dist \
--model_args model_path='GSAI-ML/LLaDA-8B-Instruct',gen_length=${length},steps=${steps},block_length=${block_length},use_cache=True,threshold=0.9,show_speed=True \
--output_path evals_results/cache_parallel/humaneval-ns0-${length} --log_samples
  1. Dual Cache + Parallel
accelerate launch eval_llada.py --tasks ${task} \
--confirm_run_unsafe_code --model llada_dist \
--model_args model_path='GSAI-ML/LLaDA-8B-Instruct',gen_length=${length},steps=${steps},block_length=${block_length},use_cache=True,dual_cache=True,threshold=0.9,show_speed=True \
--output_path evals_results/dual_cache_parallel/humaneval-ns0-${length} --log_samples

Post-processing

For HumanEval evaluation, post-processing is required:

python postprocess_code.py {the samples_xxx.jsonl file under output_path}

Notes

  1. All evaluations use the LLaDA-8B-Instruct model
  2. Results are saved in the evals_results directory
  3. For HumanEval, samples are logged for post-processing
  4. Speed metrics are shown for all evaluations
  5. Different optimization strategies can be combined: