Fu DaiandClaude Opus 4.8
1a585693be
FastAPI service wrapping the Surya-OCR-2 model (datalab-to) served through vLLM: legacy /v1/api/ai/* endpoints, an OpenAI-compatible /v1/chat/completions endpoint, a coalescing request batcher, a local OCR CLI, Docker packaging, multilingual example outputs, and quantization/concurrency benchmarks. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
30 lines
1.2 KiB
Python
30 lines
1.2 KiB
Python
import scripts.quant.run_all as ra
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from scripts.quant.aggregate import SUMMARY_FIELDS
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def test_run_method_failure_becomes_failed_row(monkeypatch):
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def boom(*a, **k):
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raise RuntimeError("OOM at load")
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monkeypatch.setattr(ra, "_measure_method", boom)
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row = ra.run_method("gptq", base_model="b", work_dir=ra.Path("/tmp/x"),
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eval_images=[], reference_dir=ra.Path("/tmp/ref"))
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assert row["method"] == "gptq"
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assert row["status"] == "failed"
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assert "OOM at load" in row["error"]
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assert set(row.keys()) == set(SUMMARY_FIELDS)
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def test_run_method_success_passes_through_metrics(monkeypatch):
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def fake_measure(method, base_model, work_dir, eval_images, reference_dir):
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return {"mean_cer": 0.01, "mean_bbox_iou": 0.98, "mean_latency_s": 4.2,
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"model_size_mb": 512.0}
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monkeypatch.setattr(ra, "_measure_method", fake_measure)
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row = ra.run_method("awq", base_model="b", work_dir=ra.Path("/tmp/x"),
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eval_images=[], reference_dir=ra.Path("/tmp/ref"))
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assert row["status"] == "ok"
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assert row["mean_cer"] == 0.01
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assert row["t4_deployable"] is True
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assert row["mean_latency_s"] == 4.2
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