mem: reduce PaddleOCR rec_batch_num from 6 to 1 (#4295)
Reduce PaddleOCR `rec_batch_num` from 6 (default) to 1. Paddle's native
inference engine allocates 500 MiB memory arena chunks proportional to
recognition batch size. With batch_num=6, four chunks are allocated
during text recognition. Setting it to 1 reduces this to one chunk.
| Setting | Peak memory |
|---------|------------|
| `rec_batch_num=6` | 7,184 MiB |
| `rec_batch_num=1` | 2,684 MiB |
| **Delta** | **-4,500 MiB (-62.6%)** |
Measured with `memray run` on `layout-parser-paper-with-table.pdf`
through `partition()` with hi_res + PaddleOCR table OCR. On CPU, batch
processing doesn't parallelize — it's sequential within
`predictor.run()`. Smaller batches just allocate less workspace memory.
## Reproduce
Requires `unstructured[pdf]`, `paddlepaddle`, `unstructured-paddleocr`,
and `memray`.
```bash
cat > /tmp/bench_paddle.py << 'SCRIPT'
from unstructured.partition.auto import partition
elements = partition(
filename="example-docs/layout-parser-paper.pdf",
strategy="hi_res",
pdf_infer_table_structure=True,
ocr_agent="unstructured.partition.utils.ocr_models.paddle_ocr.OCRAgentPaddle",
)
print(f"Partitioned: {len(elements)} elements")
SCRIPT
# Baseline (main branch, rec_batch_num=6):
git checkout main
memray run --native --trace-python-allocators -o /tmp/paddle_baseline.bin /tmp/bench_paddle.py
memray stats /tmp/paddle_baseline.bin | grep "Peak memory"
# With this change (rec_batch_num=1):
git checkout mem/paddle-rec-batch-num
memray run --native --trace-python-allocators -o /tmp/paddle_opt.bin /tmp/bench_paddle.py
memray stats /tmp/paddle_opt.bin | grep "Peak memory"
```
