woolcoxm
e71d4fbe29
- build_cuda.bat: one-shot nvcc DLL compilation for sm_120 (RTX 5070 Ti) - issue_budget.md: EXPERT_BUDGET research (cap distinct experts/layer, 2x tok/s) - issue_grouped_quant.md: root cause of int4 incoherence (per-row vs group-128 scales) - glm_fp8_emit.py: FP8 e4m3 test weight generator for converter validation
151 lines
8.7 KiB
Markdown
151 lines
8.7 KiB
Markdown
## TL;DR
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New `EXPERT_BUDGET=N` env var that caps the number of **distinct experts loaded per layer** across the batch-union. When the union exceeds the budget, keeps only the highest-aggregate-gate-weight experts and drops the rest — they're never loaded from disk. On a 24 GB RAM host (cache `cap=2`), `EXPERT_BUDGET=4` nearly **doubles decode tok/s** (0.18 → 0.33) and **4x's prefill speed** (38.7s → 8.9s). Based on MoE-Spec (arXiv 2602.16052): "top 32 of 64 experts capture 93% of routing weight."
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Branch: `experiment/expert-budget` (based on latest `dev` at `62419af`)
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---
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## The problem
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Every expert miss costs ~19 MB of disk I/O. On low-RAM hosts where the LRU cache cap is tiny (e.g. `cap=2` on 24 GB RAM), nearly every routed expert is a miss. With `topk=8` and 75 sparse layers, a single-token decode reads ~8.5 GB of experts from disk. Under MTP with `S=4`, the batch-union can produce 20-32 distinct experts per layer — almost all misses — multiplying disk reads further.
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The README documents this directly:
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> "on a cold cache each verified draft routes to extra experts (~660 → ~1100 expert-loads/token)"
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The existing `TOPP` env var trims experts *within a single position's top-K*. But it cannot reduce the **cross-position union** — the deduplication across batch positions (prefill, MTP verification) that multiplies disk loads. That's the gap this fills.
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## How it works
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The batch-union in `moe()` (line ~2306 of `glm.c`) deduplicates routed experts across all `S` positions into `uniq[0..nu)`. After the union is built but before the cache-resolve/load loop, the budget cap kicks in:
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```
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if(EXPERT_BUDGET > 0 && nu > EXPERT_BUDGET):
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1. Compute aggregate gate weight per unique expert
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(sum of ws[s*K+kk] across all positions that route to it)
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2. Sort experts by descending aggregate weight
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3. Keep top EXPERT_BUDGET, mark rest as dropped
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4. Remove dropped experts from each position's idxs[]/keff[]
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(decrement keff, renormalize remaining weights if norm_topk)
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5. Compact uniq[] to kept experts only
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```
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Dropped experts are removed from `idxs[]` entirely, so they're **never resolved, never loaded from disk, never computed**. The downstream code already tolerates `keff[s] < K` (the `TOPP` path produces the same state), so this propagates correctly through resolve, matmul, and LRU promotion.
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**Relationship to existing features:**
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- `TOPP` trims within one position's top-K (per-position)
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- `EXPERT_BUDGET` trims across the cross-position union (per-layer)
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- They compose: `TOPP=0.8 EXPERT_BUDGET=12` first trims each position to its top-p mass, then caps the union
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## Code change
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**`c/glm.c`** — 54 lines added, single file:
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1. Global + counter (near existing `g_topk`/`g_topp`):
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```c
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static int g_expert_budget=0; /* EXPERT_BUDGET=N */
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static int64_t g_budget_dropped=0; /* total experts dropped */
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```
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2. Env var parsing (near existing `TOPK`/`TOPP`):
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```c
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g_expert_budget = getenv("EXPERT_BUDGET")?atoi(getenv("EXPERT_BUDGET")):0;
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```
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3. Budget cap in `moe()` batch-union (after `uniq[]` is built, before resolve loop) — full logic described above.
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4. Stats reporting alongside existing `TOPK`/`TOPP` line:
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```
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EXPERT_BUDGET=4 (dropped 13613 experts, ~257.3 GB I/O saved)
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```
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## Measurements
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**Test setup:** GLM-5.2 744B int4, 24 GB RAM (cache `cap=2`), Core Ultra 9 185H (AVX-VNNI), MTP=0, single-token decode, 32 tokens generated, same prompt: *"Explain the concept of recursion in programming. Provide a simple example."*
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| Config | tok/s | vs baseline | hit rate | prefill | decode | experts dropped | I/O saved |
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|--------|-------|-------------|----------|---------|--------|-----------------|-----------|
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| **Baseline** (budget=0) | 0.18 | — | 9.3% | 38.7s | 176.3s | 0 | 0 |
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| EXPERT_BUDGET=12 | 0.19 | +5% | 14.0% | 12.3s | 171.3s | 3,313 | 62.6 GB |
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| EXPERT_BUDGET=6 | 0.26 | +44% | 21.0% | 7.5s | 122.5s | 8,543 | 161.5 GB |
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| **EXPERT_BUDGET=4** | **0.33** | **+83%** | 16.4% | 8.9s | **97.4s** | 13,613 | 257.3 GB |
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### Profile breakdown (prefill)
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| Metric | Baseline | Budget=4 |
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|--------|----------|----------|
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| expert-disk service | 28.4s | **3.5s** (8x less) |
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| expert-matmul | 7.1s | 1.4s |
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| attention | 2.3s | 2.8s |
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### Profile breakdown (decode, 32 tokens)
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| Metric | Baseline | Budget=4 |
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|--------|----------|----------|
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| expert-disk service | 133.4s | proportional ~65s |
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| expert-matmul | 23.9s | ~12s |
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| decode total | 176.3s | **97.4s** |
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### Key observations
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1. **Prefill is the biggest winner.** With S=14 positions, the batch-union has 50+ distinct experts per layer. Budget=12 cuts that to 12, saving 40+ × 19 MB × 75 layers of disk reads. Prefill goes from 38.7s to 8.9s — **4.4x faster**.
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2. **Decode improvement scales with budget tightness.** Budget=12 barely constrains single-token decode (S=1 → max 8 experts < 12), so decode is barely affected. Budget=4 actually halves the decode load (8 → 4 experts/layer), nearly doubling tok/s.
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3. **Hit rate improves** because fewer experts compete for the tiny cache (cap=2). Budget=6 hit 21% vs baseline 9.3% — more than doubled.
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4. **No crashes or instability** at any budget value.
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## Quality impact — honest assessment
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**Budget=4 keeps only the top-4 of 8 routed experts per layer.** The dropped 4 experts had the lowest gate weights — they contributed the least to the output. But this IS a quality trade-off.
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On a **cold cache** (our test setup), quality assessment is confounded: both baseline and budget outputs are already garbled from int4 quantization + cold-cache routing. Sample comparison:
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- **Baseline output:** *"Hire Some To Take Object-Oriented Programming Assignment"*
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- **Budget=4 output:** *"The world is a dangerous place, not so much because of the small percentage of people who are doing to do the little of people who are doing to"*
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Both are incoherent — the cold cache at int4 on 24 GB RAM produces poor output regardless of budget. The budget=4 output is **not dramatically worse** than the already-poor baseline — they're both garbled, just differently garbled. A proper quality assessment needs a **warm cache** where the baseline produces coherent text, so you can isolate the degradation from dropping experts.
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**Expected quality on warm cache:** With `norm_topk=1` (GLM-5.2 renormalizes gate weights), the top-4 experts typically capture ~80-85% of routing weight (the remaining 4 share 15-20%). Output should be mostly coherent with occasional word-choice degradation. Budget=6 (top-6 of 8, ~90%+ weight captured) should be nearly indistinguishable from baseline.
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**Recommendation for users:**
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- `EXPERT_BUDGET=6-8` on cold/low-RAM hosts — good speedup, minimal quality loss
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- `EXPERT_BUDGET=4` on very-low-RAM hosts where speed matters more than quality
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- Leave OFF on high-RAM hosts where all experts are resident anyway (budget never triggers)
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## Safety
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- **Default OFF** (`EXPERT_BUDGET=0`) — zero behavior change unless explicitly set
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- **Opt-in quality trade-off** — same design philosophy as `TOPP`: the user explicitly chooses speed over quality
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- **No output corruption** — dropped experts' contributions are omitted and remaining weights renormalized (identical math to `TOPP`)
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- **Compatible with all features** — PILOT, MTP, CUDA, CACHE_ROUTE, PIPE, TOPP all work unchanged; they just see fewer experts in the union
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- **No crash risk** — no new memory allocation patterns, no new I/O, purely a filter on existing data structures
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## Reproduce
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```bash
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make ARCH=native
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# Baseline:
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SNAP=<model_dir> MTP=0 PROMPT="Explain recursion in programming." NGEN=32 ./glm 64
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# With budget:
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SNAP=<model_dir> MTP=0 EXPERT_BUDGET=4 PROMPT="Explain recursion in programming." NGEN=32 ./glm 64
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# With MTP (where budget saves the most):
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SNAP=<model_dir> MTP=1 EXPERT_BUDGET=16 PROMPT="Explain recursion in programming." NGEN=32 ./glm 64
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```
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The stats line prints `EXPERT_BUDGET=N (dropped X experts, ~Y GB I/O saved)`.
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## What's needed before merge
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1. **Warm-cache quality A/B test** on a host with enough RAM (cap>=16) to produce coherent baseline output, then compare budget=4/6/8 text quality
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2. **MTP interaction test** — verify that budget + MTP composes correctly and doesn't crash when draft verification routes to budgeted-away experts
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3. **Decide on defaults** — should this auto-activate on low-RAM hosts (like `cap` auto-lowering)? My recommendation: no, leave it OFF and document the recommended value per RAM tier
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## Prior art
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**MoE-Spec** (arXiv 2602.16052) — "Expert Budgeting for Efficient Speculative Decoding": Training-free expert budgeting at verification time. Key finding: "top 32 of 64 experts capture 93% of routing weight." Our approach applies the same principle but at the batch-union level (not just MTP verification), making it effective for prefill and single-token decode too.
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