Agent Solution

TASK OBJECTIVE

Python

How do you fix python /app/evalbench/evaluate.py so padded and packed causal-LM batches match across padding, order, and cache state?

python /app/evalbench/evaluate.pyTerminal and artifact builds

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01

TASK STATEMENT

shipped agent instruction

Repair `/app/evalbench/` so batched evaluation matches the single-example semantics of the local causal-LM-like model in `/app/model/`.

This CLI must work for any valid shard:

```bash
python /app/evalbench/evaluate.py --data /app/data/public_eval.jsonl --batch-size 8 --padding-side left --batch-mode packed --cache-dir /app/cache --out /app/results.json
```

`--batch-mode` is `padded` or `packed`, defaulting to `padded`; both modes must produce identical results. Outputs must also be deterministic and invariant to batch size, padding side, and input order. Correctness must not depend on prior cache contents.

The full evaluator contract is documented in `/app/evalbench/SPEC.md`; it is authoritative for rendering, scoring, generation, output rows, and metrics. `/app/data/public_runtime.jsonl` is available as a shared-prefix performance smoke shard for checking that packed evaluation and cache reuse are not falling back to slow per-example recomputation.

The repaired evaluator must run with the provided Python 3.11 standard library and NumPy environment; do not require extra runtime packages.

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Primary terms

python /app/evalbench/evaluate.py/app/results.json/app/data/public_eval.jsonlPythonpaddedpacked/app/evalbench/SPEC.md/app/data/public_runtime.jsonlevalbench/packing.pyevalbench/prefix_cache.pyevalbench/byte_stops.pyevalbench/scheduler.py

Technical objective

a repaired evaluator tree under /app/evalbench/, driven by the documented python /app/evalbench/evaluate.py ... CLI.

Success condition

for any valid shard, batched evaluation must reproduce the single-example semantics of the local causal-LM-like model in /app/model/; padded and packed batch modes must agree; results must be deterministic and invariant to batch size, padding side, and input order; correctness must not depend on pre-existing cache contents; and the run must stay inside the provided Python 3.11 standard library plus NumPy environment. /app/evalbench/SPEC.md is named as the authoritative contract for rendering, scoring, generation, output rows, and metrics.

Search fingerprints

  • Command: python /app/evalbench/evaluate.py
  • Deliverable: /app/results.json
  • Input: /app/data/public_eval.jsonl
  • Stack: Python
  • Command line: python /app/evalbench/evaluate.py --data /app/data/public_eval.jsonl --batch-size 8 --padding-side left --batch-mode packed --cache-dir /app/cache --out /app/results.json
  • Flags: --batch-mode (padded | packed), --padding-side, --batch-size, --cache-dir, --out
  • Contract file: /app/evalbench/SPEC.md
  • Data shards: /app/data/public_eval.jsonl, /app/data/public_runtime.jsonl
  • Module names: evalbench/packing.py, evalbench/prefix_cache.py, evalbench/byte_stops.py, evalbench/scheduler.py, evalbench/rendering.py, evalbench/scoring.py, evalbench/metrics.py, evalbench/generation.py, evalbench/spans.py, evalbench/tokenizer.py,…
  • Model fixture: /app/model/config.json, /app/model/vocab.json, /app/model/weights.json
  • Technologies: Python 3.11, NumPy, causal language model, left/right padding, sequence packing, prefix KV cache
  • Upstream metadata tags: evaluation, batching, python, debugging

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