Common Issues and Troubleshooting

Common Issues and Troubleshooting

Solutions to frequently encountered problems with BigCode Evaluation Harness.

Installation Issues

Issue: PyTorch Version Conflicts

Symptom: Import errors or CUDA incompatibility after installation.

Solution: Install PyTorch separately BEFORE installing the harness:

# Check your CUDA version
nvidia-smi

# Install matching PyTorch (example for CUDA 11.8)
pip install torch --index-url https://download.pytorch.org/whl/cu118

# Then install harness
pip install -e .

Issue: DS-1000 Specific Requirements

Symptom: Errors when running DS-1000 benchmark.

Solution: DS-1000 requires Python 3.7.10 specifically:

# Create conda environment
conda create -n ds1000 python=3.7.10
conda activate ds1000

# Install specific dependencies
pip install -e ".[ds1000]"
pip install torch==1.12.1+cu116 --extra-index-url https://download.pytorch.org/whl/cu116

# Set environment variables
export TF_CPP_MIN_LOG_LEVEL=3
export TF_FORCE_GPU_ALLOW_GROWTH=true

Issue: HuggingFace Authentication

Symptom: 401 Unauthorized when accessing gated models/datasets.

Solution:

# Login to HuggingFace
huggingface-cli login

# Use auth token in command
accelerate launch main.py \
  --model meta-llama/CodeLlama-7b-hf \
  --use_auth_token \
  ...

Memory Issues

Issue: CUDA Out of Memory

Symptom: torch.cuda.OutOfMemoryError: CUDA out of memory

Solutions:

1. Use quantization:

# 8-bit quantization (saves ~50% memory)
accelerate launch main.py \
  --model bigcode/starcoder2-15b \
  --load_in_8bit \
  ...

# 4-bit quantization (saves ~75% memory)
accelerate launch main.py \
  --model bigcode/starcoder2-15b \
  --load_in_4bit \
  ...

2. Reduce batch size:

--batch_size 1

3. Set memory limits:

--max_memory_per_gpu "20GiB"
# OR
--max_memory_per_gpu auto

4. Use half precision:

--precision fp16
# OR
--precision bf16

Issue: Running Out of RAM During Evaluation

Symptom: Process killed, system becomes unresponsive.

Solution: Reduce number of samples being held in memory:

# Save intermediate results
--save_every_k_tasks 10

# Evaluate subset at a time
--limit 50 --limit_start 0
# Then
--limit 50 --limit_start 50

Execution Issues

Issue: Code Execution Not Allowed

Symptom: Error about code execution being disabled.

Solution: Add the execution flag:

accelerate launch main.py \
  --model ... \
  --tasks humaneval \
  --allow_code_execution  # Required for unit test benchmarks

Issue: Execution Timeout/Hang

Symptom: Evaluation hangs indefinitely or times out.

Solutions:

1. Use Docker for isolation:

# Generate without execution
accelerate launch main.py \
  --model ... \
  --tasks humaneval \
  --generation_only \
  --save_generations \
  --save_generations_path generations.json

# Evaluate in Docker
docker run -v $(pwd)/generations.json:/app/generations.json:ro \
  -it evaluation-harness python3 main.py \
  --tasks humaneval \
  --load_generations_path /app/generations.json \
  --allow_code_execution

2. Use subsets for debugging:

--limit 10  # Only evaluate first 10 problems

Issue: MultiPL-E Language Runtime Errors

Symptom: Errors executing code in non-Python languages.

Solution: Use the MultiPL-E specific Docker image:

docker pull ghcr.io/bigcode-project/evaluation-harness-multiple
docker run -it evaluation-harness-multiple ...

Result Discrepancies

Issue: Results Don't Match Paper/Leaderboard

Symptom: Your pass@k scores differ from reported values.

Common causes and fixes:

1. Wrong n_samples:

# For accurate pass@k estimation, use n_samples >= 200
--n_samples 200

2. Wrong temperature:

# Papers often use different temperatures
# For pass@1: temperature 0.2 (near-greedy)
# For pass@10, pass@100: temperature 0.8 (more sampling)
--temperature 0.8

3. Task name mismatch:

# Use exact task names
--tasks humaneval      # Correct
--tasks human_eval     # Wrong
--tasks HumanEval      # Wrong

4. Prompting differences:

# Some models need instruction formatting
--instruction_tokens "<s>[INST],</s>,[/INST]"

# Or specific prompt types for HumanEvalPack
--prompt instruct

5. Postprocessing differences:

# Enable/disable postprocessing
--postprocess True  # Default

Issue: Inconsistent Results Across Runs

Symptom: Different scores each time you run.

Solution: For reproducibility:

# Use greedy decoding for deterministic results
--do_sample False
--temperature 0.0

# OR set seeds (if using sampling)
# Note: Sampling inherently has variance
# Use high n_samples to reduce noise
--n_samples 200

Model Loading Issues

Issue: Model with Custom Code

Symptom: ValueError: ... requires you to execute the configuration file

Solution:

--trust_remote_code

Issue: Private/Gated Model Access

Symptom: 401 Unauthorized or 403 Forbidden

Solution:

# First login
huggingface-cli login

# Then use auth token
--use_auth_token

Issue: PEFT/LoRA Adapter Loading

Symptom: Can't load fine-tuned adapter.

Solution:

--model base-model-name \
--peft_model path/to/adapter

Issue: Seq2Seq Model Not Generating

Symptom: Empty or truncated outputs with encoder-decoder models.

Solution:

--modeltype seq2seq

Task-Specific Issues

Issue: Low MBPP Scores with Instruction Models

Symptom: Instruction-tuned models score poorly on MBPP.

Solution: MBPP prompts are plain text, not instruction format. Consider:

1. Using instruct-humaneval for instruction models
2. Creating custom instruction-formatted prompts

Issue: APPS Taking Too Long

Symptom: APPS evaluation runs for hours.

Solutions:

# Use subset
--limit 100

# Reduce samples
--n_samples 10

# Use introductory level only
--tasks apps-introductory

Issue: GSM8K Wrong max_length

Symptom: Truncated outputs, low scores on math tasks.

Solution: GSM8K needs longer context for 8-shot prompts:

--max_length_generation 2048  # Not default 512

Docker Issues

Issue: Docker Image Pull Fails

Symptom: Error response from daemon: manifest unknown

Solution: Build locally:

# Clone repo
git clone https://github.com/bigcode-project/bigcode-evaluation-harness.git
cd bigcode-evaluation-harness

# Build image
sudo make DOCKERFILE=Dockerfile all

# For MultiPL-E
sudo make DOCKERFILE=Dockerfile-multiple all

Issue: Docker Can't Access GPU

Symptom: No GPU available inside container.

Solution: Use nvidia-docker:

docker run --gpus all -it evaluation-harness ...

Debugging Tips

Enable Verbose Output

# Check what's being generated
--save_generations
--save_references

# Inspect a few samples
--limit 5

Test Reference Solutions

# Verify test cases pass with ground truth
--check_references

Inspect Intermediate Results

# Save progress periodically
--save_every_k_tasks 10
--save_generations_path intermediate_generations.json

Common Debug Workflow

# 1. Test with tiny subset
accelerate launch main.py \
  --model your-model \
  --tasks humaneval \
  --limit 3 \
  --n_samples 1 \
  --save_generations \
  --allow_code_execution

# 2. Inspect generations
cat generations.json | python -m json.tool | head -100

# 3. If looks good, scale up
accelerate launch main.py \
  --model your-model \
  --tasks humaneval \
  --n_samples 200 \
  --allow_code_execution

Getting Help

1. Check existing issues: https://github.com/bigcode-project/bigcode-evaluation-harness/issues
2. Search closed issues: Often contains solutions
3. Open new issue with:
   • Full command used
   • Error message
   • Environment details (Python version, PyTorch version, GPU)
   • Model being evaluated