moe-policylang 1.1.2

A domain-specific language for Mixture-of-Experts scheduling policies

MoE-PolicyLang

A scheduling language for Mixture-of-Experts models.

Author: Jesse Pokora · License: MIT

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What Is This?

Large language models like Mixtral, DeepSeek, and Qwen use Mixture-of-Experts (MoE) — instead of one giant network, they have dozens of smaller "expert" networks and a router that picks which ones to use for each token. By design, only a fraction of experts are active at any time, so the rest are offloaded to CPU memory — this is intentional, not a limitation.

But managing that offloading is complex. Which experts to keep on GPU? When to prefetch the next ones? Where to run cache misses — wait for the GPU transfer, or fall back to CPU? And how to adapt as the workload shifts?

Every existing system hardcodes these decisions in hundreds of lines of C++/CUDA. MoE-PolicyLang replaces all of that with a small, declarative language.

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The Language

A MoE-PolicyLang policy is a .moe file with four composable blocks:

policy balanced {
    cache {
        capacity = 16
        eviction = lfu
        frequency_decay = 0.9
    }
    prefetch {
        strategy = history
        budget = 4
    }
    schedule { mode = hybrid }
    adapt {
        when hit_rate < 0.4 for 100 accesses
            { eviction = lru }
    }
}

Block	Controls	Options
cache	Which experts stay on GPU	LRU, LFU, score-based, frequency-threshold
prefetch	Proactive loading	History, affinity, lookahead
schedule	Where to run cache misses	GPU-only, CPU-fallback, hybrid
adapt	Runtime self-tuning	Conditional rules that hot-swap components

Switching from LRU to LFU? Change one word. Adding prefetching? Two lines.

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Two Lines to Attach

import moe_policylang
from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("allenai/OLMoE-1B-7B-0924")

# Auto-generate a tuned policy from your model + GPU, attach it
mgr = moe_policylang.auto_attach(model)
output = model.generate(...)
print(mgr.get_stats())  # hit rate, transfers, evictions

Or write a policy explicitly:

mgr = moe_policylang.attach(model, """
    policy aggressive {
        cache { capacity = 8  eviction = lru }
    }
""")

Or load a .moe file:

mgr = moe_policylang.attach(model, open("my_policy.moe").read())

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Why a Language?

Python dicts could configure this. The DSL adds three things they can't:

1. Static validation — 20 semantic rules catch bad policies at parse time, not mid-inference
2. Portability — .moe files are shareable, diffable, and tool-agnostic
3. Constraint — you can't write arbitrary code in a scheduling policy; the grammar limits you to what makes sense

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Results

The abstraction is effectively free

All policies add < 3.2% overhead on A100 (6–47 µs/layer vs. 1,459 µs for MoE forward pass).

14–40× less code than published systems

System	Their LOC	MoE-PolicyLang	Reduction
Fiddler	280	7 lines	40×
HybriMoE	~500	14 lines	36×
MoE-Infinity	520	16 lines	33×
vLLM	300	12 lines	25×
ExpertFlow	~400	16 lines	25×
FineMoE	~350	25 lines	14×

Bold LOC counts are measured from open-source repos (primary expert-management function or module — e.g., Fiddler's set_expert_loc() in src/fiddler/mixtral.py); others estimated from paper descriptions.

Policy selection produces measurable differences

Different policies → different real performance. Mixtral saturates quickly (8 experts); DeepSeek (64 experts) needs smarter strategies.

EPCB: Per-layer cache budgeting (with an honest negative result)

Not all layers see the same routing pattern — some concentrate on a few experts, others spread across many. Empirical Per-layer Cache Budgeting (EPCB) has two parts:

1. Per-layer cache structure is the load-bearing lever: replacing a single shared cache with per-layer caches at the same total budget yields +14.7pp hit rate on DeepSeek-V2-Lite in trace replay, and +540% wall-clock on A100 end-to-end (1.60 → 10.22 tok/s, eliminating all CPU↔GPU transfers in steady state). Bit-identical output verified against fully-resident baseline.

2. The allocation signal does not matter. We tested six signals (Shannon entropy, inverse top-k mass, inverse variance, inverse KL, inverse Gini, uniform) and none differentiates from uniform allocation by more than 2.5pp in hit rate at any budget, and all six collapse to within noise of uniform in wall-clock on two models tested end-to-end. We retain Shannon entropy as the default allocator for principled reasons, but recommend uniform allocation for simplicity in practice.

Strategy	Hit Rate	Δ vs shared	Wall-clock (A100)
Shared cache (32 slots)	48.6%	baseline	1.60 tok/s
Per-layer uniform (864 slots)	63.3%	+14.7pp	10.22 tok/s (+540%)
Per-layer Shannon entropy (864 slots)	65.5%	+16.9pp	10.17 tok/s (≈ uniform)

Regime caveat: per-layer caching only helps when the per-layer budget covers each layer's active working set. On 16 GB consumer hardware where the budget is tight (Qwen on RTX 5080), per-layer caching hurts throughput by 16%; flat shared caching is the better default in that regime.

Live inference on consumer GPU

OLMoE-1B-7B on RTX 5080 Laptop (16 GB VRAM):

Policy	Cap	Hit Rate	tok/s
Vanilla (no hooks)	—	—	39.2
Naive LRU	4	2.4%	34.6
LRU	16	26.3%	34.7
LFU+History	16	27.1%	33.8
EPCB	16	47.3%	33.6

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Installation

From PyPI:

pip install moe-policylang           # DSL only (no GPU deps)
pip install moe-policylang[gpu]      # + torch, transformers, accelerate
pip install moe-policylang[all]      # everything

From source (development):

git clone https://github.com/jesse-pokora/MoE-PolicyLang.git
cd MoE-PolicyLang
pip install -e ".[dev,gpu]"

Cython fast path (< 10 µs/layer):

pip install moe-policylang[cython]
python setup_cython.py build_ext --inplace

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Tested Models

MoE-PolicyLang auto-detects MoE structure from any HuggingFace model — no model-specific code required. We have evaluated on:

Model	Experts × Layers	Routing	Hardware
Mixtral-8×7B-Instruct	8 × 32	top-2	A100-80 GB
DeepSeek-V2-Lite	64 × 27	top-6	A100-80 GB
Qwen1.5-MoE-A2.7B	60 × 24	top-4	RTX 5080 (16 GB)
OLMoE-1B-7B	64 × 16	top-8	RTX 5080 (16 GB)

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Project Structure

moe_policylang/
├── grammar.lark           # Lark LALR grammar (62 productions)
├── parser.py              # Grammar → PolicyIR
├── ir.py                  # Intermediate representation
├── validator.py           # 20 semantic validation rules
├── compiler.py            # IR → CompiledPolicy
├── auto.py                # Auto-generate policies from model + GPU
├── dsl.py                 # Python eDSL (@sched.policy decorator)
├── adaptive.py            # Adaptive policies (adapt blocks)
├── autotuner.py           # Grid-search policy optimizer
├── cli.py                 # CLI: validate, compile, run
├── runtime/
│   ├── hooks.py           # 5-step per-layer dispatch protocol
│   ├── cache.py           # LRU / LFU / Score / FreqThreshold
│   ├── prefetch.py        # Affinity / History / Lookahead
│   ├── scheduler.py       # GPU-only / CPU-fallback / Hybrid
│   ├── per_layer.py       # EPCB — entropy-proportional caching
│   ├── triggers.py        # Memory-pressure & TTL eviction
│   └── _fast/             # Cython-accelerated paths
└── integrations/
    ├── __init__.py         # attach() — main user API
    ├── huggingface.py      # HuggingFace Transformers hooks
    ├── weight_placement.py # Expert offloading manager
    └── async_transfer.py   # CUDA stream async transfers

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Running Experiments

# Offline trace replay (no GPU needed)
python scripts/run_eval.py
python scripts/run_sweep.py

# Live inference on consumer GPU
python scripts/run_dsl_demo.py
python scripts/run_constrained_e2e.py

# Generate all paper figures
python scripts/generate_figures.py

# Benchmarks & evaluations (requires CUDA GPU + model weights)
python scripts/bench_qwen_multirun.py   # Qwen throughput (Table 4)
python scripts/bench_coldstart.py       # Cold-start throughput analysis
python scripts/bench_power.py           # Power/energy measurement
python scripts/eval_quality.py          # Perplexity evaluation (wikitext-2)
python scripts/ablation_epcb_sensitivity.py  # EPCB hyperparameter sweep
python scripts/plot_coldstart.py        # Generate cold-start figure

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Tests

python -m pytest tests/ -q

398+ tests covering parsing, validation, compilation, runtime dispatch, adaptive policies, per-layer EPCB, and integration hooks.

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Documentation

See docs/MANUAL.md for the full language reference, runtime API, and policy authoring guide.

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Citation

@misc{pokora2026moepolicylang,
  title={MoE-PolicyLang: A Domain-Specific Language for Mixture-of-Experts Scheduling Policies},
  author={Pokora, Jesse},
  year={2026},
  url={https://github.com/jesse-pokora/MoE-PolicyLang}
}

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License

MIT License — Copyright (c) 2026 Jesse Pokora