Add MIT LICENSE file#4
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[WIP] Add MIT license file to repository
Add MIT LICENSE file
Feb 27, 2026
This was referenced Jun 17, 2026
timenick
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Jun 18, 2026
…accessor - Add _composite_registry(): the single registry-load trigger the three readers (resolve_composite / composite_pipeline_tasks / _composite_components_for_task) now share. It raises RuntimeError when COMPOSITE_MODEL_REGISTRY is empty, so a moved/renamed registration fails loudly instead of silently returning []/None and disabling the composite feature unnoticed. - Fix a mypy break the main merge surfaced: #896 re-tightened the _composite_components_for_task annotation to type[WinMLCompositeModel] while the prior commit had removed that import; re-add it under TYPE_CHECKING. - Test: the accessor raises loudly when the registry is empty.
DingmaomaoBJTU
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Jun 22, 2026
…sampling, promote_findings) Aligns the autoconfig POC code with docs/self-evolution-design.html section 4. Implements the components still marked TODO; champion-config, arch pruning (build_insight) and the feature-gaps log were already done. bench_utils.py (Fix #1/#2/#5): - run_perf_session: atomic single-session perf primitive - paired_ab_bench / adaptive_paired_ab_bench: interleaved baseline-vs-hypothesis A/B so DVFS drift cancels in the within-pair ratio; adaptive variant samples until the 95% CI is decisive (KEEP/DISCARD band) or MAX_PAIRS, returns verdict + CI - thermal_classify: COOL/WARM/HOT_RUN from a cold reference latency - session_cv: shared between-session noise-floor helper promote_findings.py (Fix #4, NEW): reads catalog-*-sweep/*/results.json, applies the L1->L4 confidence ladder (L2 = effect-size gate, L3 = >=2 models/arch, L4 = >=3 arch classes), writes ep_knowledge/_auto_promoted.json as a draft sink that never clobbers curated KB. Tolerant of both QNN (full.p50s_ms) and GPU/CPU (full_p50s_ms) schemas. catalog_qnn_sweep.py (Fix #1 wiring): opt-in --paired-ab flag (default off) that runs adaptive paired A/B per hypothesis against the baseline ONNX and records verdict + CI; sequential Phase B remains the default path. README: document the self-evolution tooling and refresh the directory layout.
DingmaomaoBJTU
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Jun 24, 2026
…g improvements catalog_sweep.py: - _op_signature/_same_graph: read winml-analyze op inventory (total_operators, operator_counts, opset) to diff each hypothesis graph against baseline - NO-OP short-circuit: a non-baseline hypothesis whose built graph is identical to baseline (flag never fired) is marked NOOP_SKIPPED and skips screen+full bench, reusing baseline perf — saves one screen + N full sessions per dead hyp - benched hypotheses now record op_signature + graph_changed; per-model noop_hypotheses roll-up - general build_flags passthrough (enables --no-analyze hypothesis) - _prune_runnable_except_best + enlarged QNN timeouts (disk/timeout fixes) ep_device_knowledge: - qnn_npu.json: finding npu-011 (fusions that fire but yield no perf benefit must be benefit-gated; op-count diff separates applied-but-useless from never-applied, with BERT evidence); h1 repurposed to no-analyze; larger timeouts - cpu_cpu/dml_gpu/qnn_gpu.json: add no-analyze hypothesis README: feature gap #4 — benefit-gated fusion in winml analyze
DingmaomaoBJTU
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Jun 26, 2026
…gistry, non-mutating model_type override - registry: replace decorator/register API with a plain QUANT_FINALIZERS dict; get_quant_finalizer lazily imports + instantiates (review #1). - quantizer: resolve+apply the model-type-specific quant policy inside quantize_onnx from config.model_type, a single seam shared by all callers; drop the duplicated dispatch blocks in commands/build.py and build/hf.py (review #2). - loader: thread an explicit model_type as model_type_override through resolve_task instead of mutating hf_config.model_type, so exporters/patchers keep the architecture's native type while the loader config surfaces the build variant (review #4).
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