Fix Summary for Environment | Generated by AI

Home 2026.07

It works! The model generates coherent output. Here’s a summary of everything.

Summary of Fixes

All issues were caused by version incompatibilities with your environment. Here’s what was fixed:

Files Modified

  1. nanovllm/layers/attention.py — Replaced flash-attn with PyTorch’s native scaled_dot_product_attention, replaced triton store_kvcache with pure PyTorch indexing, fixed decode attention mask (was using is_causal=True which creates wrong mask when q_len=1 and k_len>1)
  2. nanovllm/layers/layernorm.py — Removed @torch.compile decorators (caused segfaults with your torch/CUDA versions)
  3. nanovllm/layers/activation.py — Same
  4. nanovllm/layers/sampler.py — Same
  5. nanovllm/layers/rotary_embedding.py — Same
  6. pyproject.toml — Made flash-attn optional

How to Run

cd /mnt/data/nano-vllm

# Ensure dependencies are installed
pip3.11 install -e .

# Run the example (generates ~256 tokens with temperature 0.6)
python3.11 example.py

Note: Decode speed is slow (~7 tok/s) because the fallback SDPA attention gathers KV cache token-by-token in Python loops. The original code used flash-attn’s paged attention for fast decode. For faster inference, install flash-attn with a compatible CUDA version, or restore the triton kernel with matching CUDA toolkit.

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