113 lines
4.9 KiB
Python
113 lines
4.9 KiB
Python
"""分支决策路由: 按决策树推导分支, 重叠区由端到端时延模型仲裁.
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决策树 (v0.98 §3.3):
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1. 前置归约: BatchA=1 或 BatchB=1 -> 转Matmul (本期仅标注, 详实现后续迭代)
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K=0 / K=1 -> 特殊分支 (本期仅标注)
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2. B >= C: 切B -> IterBatch 与 MergeBatch 仲裁
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仲裁规则 (v1.1 §4.5 统一分界):
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MergeBatch 最优 <=> K截断(k_L1=K) 且 b_core > b0*(T_comp+T_write)/T_cmd
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L1 绑定时 MergeBatch 恒劣于 IterBatch;
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两分支同时合法时用端到端时延模型 T_total 仲裁.
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3. B < C: 切 M/N -> ASW_Basic (后续迭代)
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4. B/M/N 都买不满: 切 K -> StreamK (后续迭代)
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"""
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from __future__ import annotations
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from .hardware import NpuSpec, ASCEND950PR
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from .models import BmmCase, ImplPlan, HardwareTiming
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from .branches.base import BranchResult
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from .branches.merge_batch import MergeBatchBranch
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from .branches.iter_batch import IterBatchBranch
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BRANCH_TO_MATMUL = "转Matmul"
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BRANCH_SPECIAL = "特殊分支"
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BRANCH_ASW = "ASW_Basic"
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BRANCH_STREAMK = "StreamK"
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class BranchRouter:
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"""case -> 理论最优分支 + 方案 + 时延评估."""
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def __init__(self, spec: NpuSpec = ASCEND950PR):
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self.spec = spec
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self.merge_batch = MergeBatchBranch(spec)
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self.iter_batch = IterBatchBranch(spec)
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# ------------------------------------------------------------------
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def route(self, case: BmmCase) -> dict:
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"""返回 {branch, plan, timing, arbitration, candidates}."""
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s = self.spec
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# 1) 前置归约
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if case.batch_a == 1 or case.batch_b == 1:
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return self._stub(case, BRANCH_TO_MATMUL,
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"BatchA=1或BatchB=1, 折叠转普通Matmul (该分支详实现待后续迭代)")
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if case.k <= 1:
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return self._stub(case, BRANCH_SPECIAL,
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f"K={case.k}, Cube 无用, 走 AIV 向量通路 (该分支详实现待后续迭代)")
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# 2) B >= C: IterBatch / MergeBatch
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if case.batch_c >= s.aic_num and case.batch_a == case.batch_b:
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return self._route_split_b(case)
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# 3/4) 兜底标注
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return self._stub(case, BRANCH_ASW,
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"B<C 或切B分支条件不满足 -> ASW_Basic/StreamK (该分支详实现待后续迭代)")
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# ------------------------------------------------------------------
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def _route_split_b(self, case: BmmCase) -> dict:
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mb = self.merge_batch.analyze(case)
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ib = self.iter_batch.analyze(case)
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candidates = []
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if mb.capable:
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candidates.append((self.merge_batch.name, mb))
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if ib.capable:
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candidates.append((self.iter_batch.name, ib))
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arbitration = ""
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if mb.capable and ib.capable:
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# 统一分界条件 + 端到端时延仲裁双保险
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mb_win, detail = self.merge_batch.beats_iterbatch(case)
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t_mb = mb.timing.t_total
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t_ib = ib.timing.t_total
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lat_win = self.merge_batch.name if t_mb <= t_ib else self.iter_batch.name
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win = self.merge_batch.name if mb_win else self.iter_batch.name
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arbitration = (
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f"两分支均合法, 仲裁:\n"
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f" [分界条件] MergeBatch最优={mb_win} ({detail})\n"
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f" [时延模型] T_MergeBatch={t_mb*1e6:.2f}us vs T_IterBatch={t_ib*1e6:.2f}us "
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f"-> {lat_win}更优\n"
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f" [裁决] {win}" + ("" if win == lat_win else f" (分界条件与时延模型不一致, 以时延模型为准: {lat_win})")
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)
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if win != lat_win:
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win = lat_win # 时延模型为最终裁决
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elif candidates:
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win = candidates[0][0]
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arbitration = f"仅 {win} 条件满足"
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else:
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return self._stub(
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case, BRANCH_ASW,
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"IterBatch/MergeBatch 进入条件均不满足 (如负载均衡/搬移效率不达标), "
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"回落 ASW_Basic (该分支详实现待后续迭代); "
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f"IterBatch未过: {ib.failed_conditions()}; "
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f"MergeBatch未过: {mb.failed_conditions()}")
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chosen = mb if win == self.merge_batch.name else ib
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return {
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"branch": win,
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"plan": chosen.plan,
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"timing": chosen.timing,
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"arbitration": arbitration,
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"candidates": {n: r.capable for n, r in
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[(self.merge_batch.name, mb), (self.iter_batch.name, ib)]},
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}
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# ------------------------------------------------------------------
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def _stub(self, case: BmmCase, branch: str, note: str) -> dict:
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plan = ImplPlan(case_id=case.case_id, branch=branch,
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used_core_num=self.spec.aic_num, note=note)
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return {"branch": branch, "plan": plan, "timing": None,
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"arbitration": note, "candidates": {}}
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