vanshnawander/structured-diffusion-eval

Kaggle, Colab and other GPU evaluation

Download kaggle_lm_eval.ipynb from the public dataset https://huggingface.co/datasets/vanshnawander/structured-diffusion-eval and import it into Kaggle. Enable Internet and GPU in notebook settings. No Modal account or HF token is needed to download the 27 public pruned models.

The notebook defaults to one selected checkpoint. Change MODEL_INDEX or REPOS for each GPU session. EVALUATE_ALL_MODELS=True selects all 27 original pruned checkpoints. NUM_WORKERS and WORKER_INDEX partition checkpoint/task pairs exactly once between independent sessions. All seven benchmarks (ARC-Easy, Winogrande, PIQA, ARC-Challenge, HellaSwag, BoolQ, GSM8K) and both WikiText-2/C4 perplexities are enabled. The full grid is 189 benchmark tasks plus 54 PPL datasets.

The authoritative config is copied from Pruning-LLMs/diffusion_eval_and_vis/config.py. The notebook prints the complete effective config, adapter arguments and assignments before loading weights. MC samples remain 128; generation stays at 256 steps, LLaDA length/block 1024, Dream length 256, seed 42, original guidance/remasking, five-shot GSM8K and zero-shot other tasks. It does not reduce dataset sizes or generation lengths.

The shared profile keeps BF16 and MC mini-batch 32. The explicit kaggle profile uses mini-batch 1 with dtype=auto (FP16 on T4, BF16 on supporting hardware). This changes precision and the stochastic mini-batch grouping. Use the same profile for all comparisons. Profiles/revisions have separate output directories and merge groups. CPU/disk offload preserves model weights; there is no quantization. Auto device mapping can use both GPUs in a T4 x2 session. One evaluation is model-sharded; this is not two independent replicas.

Model snapshots/offload weights go in /kaggle/temp, outside saved outputs. Only evaluation results, response caches and RNG/PPL recovery state are saved in /kaggle/working/results. Likelihood saves each response in the notebook; generation saves each answer; perplexity saves each completed sequence. SESSION_HOURS defaults to 8. The runner commits progress and returns before the platform deadline; time budgets are checked between requests/sequences, so one outstanding request may finish after the budget.

Use Save Version > Save & Run All for background execution on Kaggle. The final cell produces evaluation_results_worker-.zip. Download/save outputs before discarding an interactive session. Attach that output to the next notebook, set RESTORE_ZIP to the archive path under /kaggle/input, and rerun with the same config and SHA. Scores already computed are replayed without another forward pass. Resumption retains RNG state; hardware/precision changes can still alter floating-point results.

Existing Modal results/partial responses are available in modal_progress.zip. IMPORT_MODAL_PROGRESS=True imports them into the original BF16/32 profile. They are not reused for FP16/1 evaluations. Saved original pruning models and unfinished LoRA recovery files remain preserved. No paid Modal work runs.

Original SliceGPT uploads are pruned baselines. None completed the requested sequential LoRA before the budget stop. Select a final fine-tuned repository and enable REQUIRE_FINETUNED_SLICE to enforce post-LoRA evaluation; this guard refuses a baseline without finetune_manifest.json. Baseline SliceGPT scores must not be presented as fine-tuned scores.

For a terminal, extract portable_eval.zip and install requirements.txt:

python portable_eval/runner.py --show-config --repo MODEL
python portable_eval/run_suite.py --repos MODEL --batch-size 1 --dtype auto --output results --time-budget-seconds 28800
python portable_eval/run_suite.py --repos MODEL --num-workers 3 --worker-index 0 --batch-size 1 --dtype auto --output results
python portable_eval/runner.py --repo MODEL --units ppl:c4 --batch-size 1 --dtype auto --output results
python portable_eval/runner.py --repo MODEL --tasks piqa --limit 2 --skip-ppl --batch-size 1 --dtype auto --output smoke_results

Omit --repos to assign the entire 27-model grid. Use --plan to preview without a GPU. Full evaluations omit --limit. Smoke outputs stay separate from full metrics. To combine worker outputs, extract ZIPs into a common directory:

python portable_eval/runner.py --merge combined/results --output merged_results.json

The merger includes benchmark and PPL metrics, rejects conflicting duplicates, and never combines different checkpoint SHAs/effective settings into one row.

Verification uses local tiny LLaDA/Dream models for each pruning method, real lm-eval scoring, sliced precision conversion/offload, and uninterrupted versus resumed PPL equality. This is not a full 7B/8B run inside a Kaggle account.

References: https://www.kaggle.com/docs/notebooks https://github.com/EleutherAI/lm-evaluation-harness https://huggingface.co/docs/accelerate/concept_guides/big_model_inference

The companion kaggle_slicegpt_lora.ipynb completes the nine unfinished sequential fine-tunes using the same diffusion objective and LoRA hyperparameters. Choose one original SliceGPT source per session, use both T4 GPUs or a larger GPU, and keep all weights on GPUs. The trainer refuses CPU/disk training offload. FP16 training is explicitly recorded as a separate precision profile; matching BF16 training requires supporting hardware. It checkpoints and exits at the session time budget, relocates recovery paths when restoring notebook output, merges/reloads/verifies, and can upload a public HF checkpoint using HF_TOKEN from Kaggle Secrets. Evaluation follows in a separate notebook with the uploaded repo and REQUIRE_FINETUNED_SLICE=True. This guard is enabled by default in the evaluation notebook, so original SliceGPT baseline scores require an explicit opt-out.

The two paid BF16 LoRA recovery states are downloadable as modal_training_recovery.zip. RESTORE_MODAL_BF16=True in the training notebook imports the chosen saved optimizer/adapter state, relocates its paths and requires the unchanged BF16 training configuration. Sources which never started have no recovery state. FP16 training intentionally starts a separate run.