287 lines
13 KiB
Python
287 lines
13 KiB
Python
"""Context policy — the window ladder for managed local models.
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One contract: any model runs at any window up to its native max; hardware
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and session depth only change tokens/s. Constants, not knobs — nothing in
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this module reads config.
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The policy encodes behavior measured on real hardware (llama.cpp,
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discrete NVIDIA GPUs on Windows/WDDM, and unified-memory devices):
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- Windows never over-allocates VRAM ahead of need. On WDDM, allocating
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past residency slows decode roughly 9x even at identical conversation
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depth — the driver silently demotes pages instead of failing. Every
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window grant therefore re-fits against live memory at grant time.
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- Models launch at the largest window that fits entirely in GPU memory
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(zero-spill) and grow toward their native max as the session needs
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room, at request boundaries only.
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- Growth re-prefills the conversation into the larger window. Measured
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cost is comparable to save/restore on discrete GPUs, and recurrent or
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hybrid-attention models cannot rewind mid-sequence anyway, so
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re-prefill is the only mechanism that works for every architecture.
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- Every recommended model gets at least a 64K window. When weights alone
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exceed VRAM, the fit deliberately spills weights to host RAM to
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protect that floor (measured: an explicit context size makes the fit
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spill weights and hold the window rather than shrink it).
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- Below ~6 tok/s decode, growth stops and compression becomes the
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default; deeper context is an explicit per-session choice. The deepest
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measured host-spilled configuration bottomed out near this rate.
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- Spilled mixture-of-experts configs pin expert/FFN weights to host so
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attention and KV stay GPU-resident — measured ~1.75x faster than
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spilling layers naively at the same host byte count.
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- Speculative decoding (MTP) defaults on only for spilled configs, where
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its speedup is largest (measured 1.43x spilled vs 1.35x resident).
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from hermes_cli.local_runtime.estimator import (
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HardwareBudget,
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ModelProfile,
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PhysicsRefusal,
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ctx_bytes,
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physics_check,
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)
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FLOOR = 64 * 1024 # = target; one internal constant
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_LADDER_GROWTH = 1.5
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_GROW_AT_OCCUPANCY = 0.85 # of the current window, at turn boundary
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SPEED_FLOOR_TOK_S = 6.0 # deepest measured spill bottomed near this
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_EARLY_COST_CTX_FRACTION = 0.15 # bounded early cost when weights spill
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# TARGET_WINDOW: the smallest ladder rung at which compression becomes the
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# exception rather than the routine. Measured over 161 real agentic
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# sessions: 66% complete uncompressed in 64K, 82% in 96K, 91% in 144K —
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# and the marginal gain past 144K (+6 points for 216K) falls below the
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# quality cost of stepping down another quant. Quant selection prefers
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# the best build that reaches this; the FLOOR remains the guarantee.
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TARGET_WINDOW = 144 * 1024
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# What a load really costs beyond weights + KV: CUDA contexts and compute
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# buffers at the DEFAULT microbatch (-ub 512, no MTP). Measured on a
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# 32 GiB card: a model estimated at 29.3 GiB (weights+KV) loaded at
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# ~31.2 GiB resident and the server's own fit still shaved a layer to
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# CPU. Microbatch/MTP logits buffers are priced separately per model
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# (ub_logits_bytes — they scale with the model's vocab and doubled once
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# packed a card 3.9 GiB past this constant). Callers add mmproj bytes on
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# top.
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RUNTIME_OVERHEAD_BYTES = int(1.5 * (1 << 30))
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def ladder(native: int) -> list[int]:
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"""64K -> 96K -> 128K -> ... -> native (native always the last rung)."""
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rungs: list[int] = []
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step = float(FLOOR)
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while step < native:
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rungs.append(int(step))
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step *= _LADDER_GROWTH
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rungs.append(native)
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return rungs
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@dataclass
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class WindowDecision:
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window: int
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spill_bytes: int # weights displaced to host at this window
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kv_on_gpu: bool
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reasons: list[str] = field(default_factory=list)
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@property
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def spilled(self) -> bool:
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return self.spill_bytes > 0
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def initial_window(profile: ModelProfile, budget: HardwareBudget,
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*, flash_attention: bool = True,
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overhead_bytes: int = 0) -> WindowDecision | PhysicsRefusal:
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"""The launch decision: largest cheap rung, never below the floor.
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Zero-spill rung: weights + ctx + overhead fit usable VRAM entirely.
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Bounded-early-cost rung: weights already exceed VRAM; take the largest
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rung whose ctx stays <= ~15% of usable VRAM.
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Floor everywhere, capped at native.
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``overhead_bytes``: runtime cost beyond weights+KV (RUNTIME_OVERHEAD
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plus the vision projector when one loads). Zero keeps this function
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pure physics for decision-table tests; production callers pass it.
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"""
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refusal = physics_check(profile, budget, FLOOR, flash_attention=flash_attention)
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if refusal:
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return refusal
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native = profile.n_ctx_train or FLOOR
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rungs = ladder(native)
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reasons: list[str] = []
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best_zero_spill: int | None = None
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for rung in rungs:
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need = (profile.weights_bytes + overhead_bytes
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+ ctx_bytes(profile, rung, flash_attention=flash_attention))
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if need <= budget.usable_vram_bytes:
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best_zero_spill = rung
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else:
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break
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if best_zero_spill is not None and best_zero_spill >= min(FLOOR, native):
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window = best_zero_spill
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reasons.append(f"largest zero-spill rung ({window // 1024}K)")
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else:
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# Weights spill from turn one (steep-curve model on a small card) —
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# hold the floor, bound the early ctx cost.
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cap = int(budget.usable_vram_bytes * _EARLY_COST_CTX_FRACTION)
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window = min(FLOOR, native)
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for rung in rungs:
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if rung < window:
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continue
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if ctx_bytes(profile, rung, flash_attention=flash_attention) <= cap:
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window = rung
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else:
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break
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reasons.append(f"floor held at {window // 1024}K; weights spill (deliberate price of the guarantee)")
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kv = ctx_bytes(profile, window, flash_attention=flash_attention)
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spill = max(0, profile.weights_bytes + kv - budget.usable_vram_bytes)
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return WindowDecision(window=window, spill_bytes=spill,
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kv_on_gpu=kv <= budget.usable_vram_bytes,
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reasons=reasons)
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@dataclass
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class GrowthDecision:
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action: str # "grow" | "hold" | "compress-default"
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next_window: int | None = None
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reason: str = ""
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def growth_decision(profile: ModelProfile, budget: HardwareBudget, *,
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current_window: int, session_tokens: int,
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measured_decode_tok_s: float | None,
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server_idle: bool,
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flash_attention: bool = True,
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occupancy_confirmed: bool = False) -> GrowthDecision:
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"""One growth evaluation, END-OF-TURN ONLY (caller guarantees the turn
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boundary; recurrent state cannot rewind mid-sequence).
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Gate ordering:
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1. occupancy (~85%) — nothing to do before the edge;
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2. native cap — the contract tops out at trained context;
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3. idleness — growth re-grants only on an otherwise-idle
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server (concurrency design);
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4. speed floor — below it, compression becomes the default and deeper
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is an explicit user choice;
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5. re-fit against LIVE free memory (the rung must fit residency
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NOW, not at launch time — over-allocation is the slow path).
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``occupancy_confirmed``: the caller has independently established that
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the session is at its window's edge (the agent's compression gate fired
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on its own threshold). Skips gate 1 so two separately-derived edge
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definitions can't deadlock into compress-before-grow.
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"""
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if not occupancy_confirmed and session_tokens < current_window * _GROW_AT_OCCUPANCY:
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return GrowthDecision("hold", reason="session below growth occupancy")
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native = profile.n_ctx_train or current_window
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if current_window >= native:
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return GrowthDecision("compress-default",
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reason="at native window; compression is the only move")
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if not server_idle:
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return GrowthDecision("hold", reason="server busy; re-grant deferred to idle")
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if measured_decode_tok_s is not None and measured_decode_tok_s < SPEED_FLOOR_TOK_S:
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return GrowthDecision(
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"compress-default",
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reason=(f"decode {measured_decode_tok_s:.1f} tok/s below the "
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f"~{SPEED_FLOOR_TOK_S:.0f} tok/s floor; growth is now an "
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"explicit per-session choice"))
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next_rung = next((r for r in ladder(native) if r > current_window), native)
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# Re-fit against live free memory: allocation beyond residency is the
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# slow path, so a rung that no longer fits doesn't get granted.
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kv = ctx_bytes(profile, next_rung, flash_attention=flash_attention)
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total_need = profile.weights_bytes + kv
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if total_need > budget.usable_vram_bytes + budget.ram_available_bytes:
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return GrowthDecision("compress-default",
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reason="next rung exceeds physics; compression instead")
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return GrowthDecision("grow", next_window=next_rung,
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reason=f"rung {current_window // 1024}K -> {next_rung // 1024}K")
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def spill_overrides(profile: ModelProfile) -> list[str]:
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"""-ot placement for spilled configs: expert/FFN weights to host so
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attention + KV stay GPU-resident. MoE gets the expert pattern;
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hybrids push recurrent-layer FFNs (their n_head_kv==0 layers carry no
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KV worth protecting)."""
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if profile.moe:
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return ["-ot", r"blk\.\d+\.ffn_.*_exps\.weight=CPU"]
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if profile.recurrent_layer_count:
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return ["-ot", r"blk\.\d+\.ffn_.*\.weight=CPU"]
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return [] # dense: fit's back-to-front layer cut is the only axis
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def launch_args(profile: ModelProfile, decision: WindowDecision, *,
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flash_attention: bool = True,
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mtp_capable: bool = False,
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mtp_draft_depth: int = 3,
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uma: bool = False,
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mtp_prefill: bool = False) -> list[str]:
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"""Per-model launch flags from a window decision. Explicit -c puts fit
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into spill-weights-and-hold-ctx; q8 KV cache wherever flash attention
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exists; -ot placement on spilled configs — DISCRETE cards only.
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``uma``: on unified memory there is no bus to protect tensors from —
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"CPU" and "GPU" are the same silicon, and pinning FFN weights to the
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host path just forces CPU compute (measured well over 2x slower than
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letting the allocator place everything). The discrete
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~1.75x win the -ot pattern encodes does not transfer; a spilled UMA
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config runs unpinned.
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MTP and the large prefill microbatch both win, and whether they may
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STACK is a fit question, not a rule: backend sampling keeps a
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ubatch x vocab x fp32 logits buffer on the GPU and MTP's draft
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context doubles it, so the stacked posture costs a few GiB extra at
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large vocab. Where it fits, it measures best on both axes (Qwen3.8
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Q4 on a 32 GiB card: 93.3 tok/s decode vs 89.5 at ub512, prefill
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slightly better too); where it doesn't, ub512 keeps the decode win
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without packing the card. ``mtp_prefill`` is that fit verdict —
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presets decide it against the priced margin, and ub_logits_bytes()
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prices the same choice so the flag and its cost travel together."""
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args = ["-c", str(decision.window)]
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if mtp_capable:
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args += ["--spec-type", "draft-mtp",
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"--spec-draft-n-max", str(mtp_draft_depth),
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"--backend-sampling", "--spec-draft-backend-sampling"]
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if mtp_prefill:
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args += ["-b", "4096", "-ub", "2048"]
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else:
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args += ["-b", "2048", "-ub", "2048"]
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if flash_attention:
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args += ["-ctk", "q8_0", "-ctv", "q8_0", "-fa", "on"]
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if decision.spilled and not uma:
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args += spill_overrides(profile)
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return args
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def ub_logits_bytes(n_vocab: int, *, mtp_capable: bool,
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mtp_prefill: bool = False) -> int:
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"""GPU logits/compute-buffer cost of the microbatch posture chosen by
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launch_args, priced from the model's own vocab and calibrated against
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measured server RSS (Qwen3.8 Q4, both postures, three windows):
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stacked (MTP + ub2048): ubatch x vocab x fp32 x 1.5 (~2.9 GiB at
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248K vocab; fitted 2.5, rounded up)
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decode (MTP + ub512): ubatch x vocab x fp32 x 2 (~1.0 GiB)
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plain (ub2048): ubatch x vocab x fp32 (~1.9 GiB)
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Callers add this to RUNTIME_OVERHEAD per model — the flag and its
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price travel together or the fit lies."""
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v = max(0, int(n_vocab))
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if mtp_capable and mtp_prefill:
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return int(2048 * v * 4 * 1.5)
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if mtp_capable:
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return 512 * v * 4 * 2
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return 2048 * v * 4
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