perf: dispatch linear gemv by decode batch size and unify extension style
- replace the per-shape auto tables in the linear backend with an M-banded rule (M in [2,4] on compute capability 8.0+) that measured at the HBM bandwidth floor across every family, and fold the capability check into the capable guard - drop the unreachable swiglu auto shape-table machinery so both backends share one env-mode ladder via the new dispatch.env_mode helper - add __all__ across extension modules, name the rotary registration records, and unify typing to the typing-module style - rewrite test_linear_dispatch.py around behavioral routing assertions and document the M-banded policy in the developer docs - Benchmark: L20 SM89, Python dispatch overhead 2.9us to 1.5us, auto now covers every projection shape at M in [2,4].
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@@ -21,3 +21,6 @@ def bf16_gemv(
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fallback or model-level dispatch.
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"""
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return get_module("bf16_gemv").bf16_gemv(x, weight, bias)
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__all__ = ["bf16_gemv"]
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