feat(task): modality-aware detect_task unifies detection across commands#807
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timenick wants to merge 3 commits into
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feat(task): modality-aware detect_task unifies detection across commands#807timenick wants to merge 3 commits into
timenick wants to merge 3 commits into
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Add loader/task.py::detect_task — a single offline, config-only detector returning a modality-aware WinMLTask (e.g. image-feature-extraction) instead of Optimum's lossy feature-extraction. The D2 vision heuristic (top-level image_size/patch_size) is applied to the returned task only; internal model-class resolution keeps the Optimum-canonical task, so no Optimum API receives image-feature-extraction. Flip detection consumers: inspect resolver.detect_task delegates to the loader detector; eval _resolve_task uses detect_task and drops the reverse io_config reconstruction (to_hf_pipeline_task / HF_TASK_NAME_MAPPING) — an explicit --task is now surfaced verbatim. config / perf / build inherit via resolve_task_and_model_class Case 1. Source-level fix for #778: image-embedding models (DINOv2) auto-detect image-feature-extraction everywhere, so calibration datasets and evaluators pick the image asset directly. Part of #800.
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…ger branch Add an explanatory comment to the empty 'except ValueError: pass' so it reads as deliberate control flow (fall through to HF_TASK_DEFAULTS) rather than a swallowed error. Resolves the CodeQL py/empty-except alert on #807.
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What
PR2 of #800. Add a single, offline, modality-aware task detector and route every detection consumer through it, so the same model resolves to the same WinMLTask everywhere. Builds on #801 (the
to_optimum_taskboundary), now onmain.Changes
loader/task.py::detect_task(config) -> (WinMLTask, source)— new single detector. Config-only dispatch (HF_MODEL_CLASS_MAPPING -> wrapped-library -> TasksManager -> HF_TASK_DEFAULTS), then a D2 vision heuristic (top-levelimage_size/patch_sizeupgradesfeature-extraction->image-feature-extraction) applied to the returned task only. Internal model-class resolution keeps the Optimum-canonical task, so no Optimum API receivesimage-feature-extraction.inspect/resolver.py::detect_taskdelegates to the loader detector (drops its own dispatch + now-dead imports).eval/evaluate.py::_resolve_taskusesdetect_taskand drops the reverse io_config reconstruction (to_hf_pipeline_task/HF_TASK_NAME_MAPPING); an explicit--taskis surfaced verbatim.config/perf/buildinherit modality-aware detection viaresolve_task_and_model_classCase 1 — no code change.tests/unit/loader/test_detect_task.pyandtests/integration/test_task_consistency.py(cross-resolver agreement on real configs);tests/unit/eval/test_eval.pyupdated.Behavior
image-feature-extractionacross inspect / config / export / eval / perf / build — source-level fix for winml build and eval command fails on image embedding model like facebook/dinov2-base #778, so calibration datasets and evaluators pick the image asset directly.image_sizeis nested -> D2 does not fire).winml eval --task feature-extractionon a vision model no longer auto-upgrades; per the canonical rule it is out-of-domain and surfaced as-is (fails downstream rather than being silently rewritten). A clean early rejection would need model-aware validation (out of scope).