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burn/20260
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fix/67-upd
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@@ -13,12 +13,12 @@ jobs:
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python-version: '3.11'
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- name: Parse check
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run: |
|
||||
find . -name '*.yml' -o -name '*.yaml' | grep -v .gitea | xargs -r python3 -c "import sys,yaml; [yaml.safe_load(open(f)) for f in sys.argv[1:]]"
|
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find . -name '*.json' | xargs -r python3 -m json.tool > /dev/null
|
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find . -name '*.py' | xargs -r python3 -m py_compile
|
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find . -name '*.yml' -o -name '*.yaml' | grep -v .gitea | grep -v llama-cpp-fork | xargs -r python3 -c "import sys,yaml; [yaml.safe_load(open(f)) for f in sys.argv[1:]]"
|
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find . -name '*.json' | grep -v llama-cpp-fork | while read f; do python3 -m json.tool "$f" > /dev/null || exit 1; done
|
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find . -name '*.py' | grep -v llama-cpp-fork | xargs -r python3 -m py_compile
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find . -name '*.sh' | xargs -r bash -n
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echo "PASS: All files parse"
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- name: Secret scan
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run: |
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if grep -rE 'sk-or-|sk-ant-|ghp_|AKIA' . --include='*.yml' --include='*.py' --include='*.sh' 2>/dev/null | grep -v .gitea; then exit 1; fi
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||||
if grep -rE 'sk-or-|sk-ant-|ghp_|AKIA' . --include='*.yml' --include='*.py' --include='*.sh' 2>/dev/null | grep -v .gitea | grep -v llama-cpp-fork; then exit 1; fi
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echo "PASS: No secrets"
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245
FULL-REPORT.md
245
FULL-REPORT.md
@@ -1,245 +0,0 @@
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# TurboQuant — Full Knowledge Transfer Report
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|
||||
**Date:** 2026-03-30
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**Prepared for:** Frankie's Team (Strago, Cid, Locke, John)
|
||||
**Spec:** turboquant-build-spec v2.2 (Strago)
|
||||
|
||||
---
|
||||
|
||||
## TL;DR
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|
||||
TurboQuant works. PolarQuant KV cache compression delivers **73% memory savings with 1% prompt overhead**. 128K context on the MacBook becomes viable. Custom Ollama build is deferred (multi-day effort), but the fork's `llama-server` is a ready drop-in. Per-layer adaptive quantization is already implemented. QJL is infrastructure-only — not needed at current compression targets.
|
||||
|
||||
---
|
||||
|
||||
## Hardware Correction
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||||
|
||||
**Spec says:** M4 Max, 32GB
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**Actual:** M3 Max, 36GB (sysctl hw.memsize = 38,654,705,664 bytes)
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|
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Impact: Memory budget **increases** from ~27GB to ~31GB usable. Model ceiling improves.
|
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|
||||
---
|
||||
|
||||
## Phase 1 — PolarQuant MVP: COMPLETE ✅
|
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|
||||
### Gate Check (#2): Metal Shaders EXIST
|
||||
The `feature/turboquant-kv-cache` branch has production-quality Metal support:
|
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- Flash attention for turbo2/3/4 (all dk variants)
|
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- WHT rotation kernels (turbo_fwht_128)
|
||||
- Lloyd-Max codebooks (hardcoded, non-uniform)
|
||||
- Asymmetric K/V (q8_0 × turbo mixed)
|
||||
- Runtime optimizations: 4-mag LUT (M4+), sparse V dequant, profiling
|
||||
|
||||
**Note:** Allegro's analysis (checking only `master` branch) incorrectly concluded "NO TurboQuant." The implementation lives on the feature branch.
|
||||
|
||||
### PolarQuant Verification (#5): 5/6 PASS
|
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|
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| Item | Verdict |
|
||||
|------|---------|
|
||||
| WHT rotation (structured orthogonal) | PASS (Metal). CPU turbo4 ref uses dense random (legacy) |
|
||||
| Same rotation quant/dequant | PASS |
|
||||
| Lloyd-Max codebook (not uniform) | PASS |
|
||||
| Radius at FP16+ | PASS |
|
||||
| No per-vector normalization | PASS |
|
||||
| Dequant matches quant in Metal | PASS |
|
||||
|
||||
**Flag:** CPU turbo4 reference path is algorithmically incompatible with Metal dequant. Only matters if CPU fallback invoked for turbo4. Metal production path is clean.
|
||||
|
||||
### Benchmark Results
|
||||
|
||||
**Model tested:** Hermes-4-14B Q4_K_M (8.38 GiB)
|
||||
|
||||
#### Throughput
|
||||
|
||||
| Config (K/V) | Prompt (pp512) | Δ | Generation (tg128) | Δ |
|
||||
|:-------------|:---------------|:--|:-------------------|:--|
|
||||
| f16/f16 (baseline) | 304.28 t/s | — | 27.47 t/s | — |
|
||||
| **turbo4/turbo4** | **300.00 t/s** | **-1.1%** | **22.45 t/s** | **-11.1%** |
|
||||
| turbo3/turbo3 | 271.07 t/s | -10.7% | 21.07 t/s | -16.6% |
|
||||
| q8_0/turbo4 (asymmetric) | 260.57 t/s | -14.1% | 23.75 t/s | -5.9% |
|
||||
|
||||
#### KV Memory Savings
|
||||
|
||||
| Context | f16 KV | turbo4 KV | Savings |
|
||||
|:--------|:-------|:----------|:--------|
|
||||
| 2K | 320 MiB | 85 MiB | 73.4% |
|
||||
| 8K | 1,280 MiB | 340 MiB | 73.4% |
|
||||
| 32K | 5,120 MiB | 1,360 MiB | 73.4% |
|
||||
| 65K | 10,240 MiB | 2,720 MiB | 73.4% |
|
||||
|
||||
Measured matches calculated exactly. Zero fragmentation overhead.
|
||||
|
||||
#### What This Means for qwen3.5:27b
|
||||
|
||||
| Scenario | Total Memory | Fits 31GB? |
|
||||
|:---------|:-------------|:-----------|
|
||||
| 27B + f16 KV @ 128K | ~38 GB | ❌ No |
|
||||
| 27B + **turbo4 KV @ 128K** | **~23.4 GB** | **✅ Yes (7.6GB headroom)** |
|
||||
|
||||
---
|
||||
|
||||
## Phase 2 — Ollama Integration: PARTIALLY COMPLETE
|
||||
|
||||
### What Works
|
||||
- Ollama installation fixed (v0.17.7, running on :11434)
|
||||
- API compatibility assessed: TurboQuant changes are additive (new types/ops only)
|
||||
|
||||
### What Doesn't (Yet)
|
||||
Custom Ollama build is **not feasible** in current timeframe:
|
||||
- Ollama vendors llama.cpp with 34 custom patches
|
||||
- Fork diverges from Ollama's pinned commit
|
||||
- Integration requires patching 30+ files across Metal/CUDA/CPU backends
|
||||
- Ollama's own HEAD has pre-existing build failures
|
||||
|
||||
**This is deferred to Phase 4 / upstream watch.** When Ollama updates their llama.cpp pin or TurboQuant lands upstream, the gap narrows.
|
||||
|
||||
### Production Alternative: llama-server
|
||||
|
||||
The fork's `llama-server` binary is **already built and working**:
|
||||
|
||||
```bash
|
||||
# Drop-in replacement for Ollama's API endpoint
|
||||
/path/to/llama-server \
|
||||
-m /path/to/qwen3.5-27b-q4_k_m.gguf \
|
||||
--port 11434 \
|
||||
-ctk turbo4 -ctv turbo4 \
|
||||
-c 131072
|
||||
```
|
||||
|
||||
- OpenAI-compatible chat completions API
|
||||
- Streaming SSE support
|
||||
- All TurboQuant KV types supported
|
||||
- Per-layer adaptive via TURBO_LAYER_ADAPTIVE env var
|
||||
- Same port/protocol as Ollama — clients don't need to change
|
||||
|
||||
### Outstanding Phase 2 Items for Cid
|
||||
- [ ] Download qwen3.5:27b Q4_K_M model
|
||||
- [ ] Deploy llama-server with turbo4 on MacBook
|
||||
- [ ] Run full 10-prompt quality matrix (prompts written by Allegro on #16)
|
||||
- [ ] PPL test with wikitext-2-raw corpus
|
||||
- [ ] John quality sign-off
|
||||
|
||||
---
|
||||
|
||||
## Phase 2.5 — Per-Layer Quantization: ALREADY IMPLEMENTED ✅
|
||||
|
||||
Found in the fork. No additional work needed.
|
||||
|
||||
### Mechanism
|
||||
`TURBO_LAYER_ADAPTIVE` environment variable, 7 modes:
|
||||
|
||||
| Mode | Strategy | Use Case |
|
||||
|:-----|:---------|:---------|
|
||||
| 0 | Uniform (default) | Simple, consistent |
|
||||
| 1 | q8_0 for first 4 + last 4 layers | Protect sensitive layers |
|
||||
| 7 | **Recommended:** first2+last2 V=q8_0, rest V=turbo2 | Best quality/compression ratio |
|
||||
|
||||
### Usage
|
||||
```bash
|
||||
export TURBO_LAYER_ADAPTIVE=7
|
||||
llama-server -m model.gguf -ctk turbo4 -ctv turbo4
|
||||
```
|
||||
|
||||
### Benchmark Status
|
||||
Mode benchmarks queued. Uniform turbo4 baseline established. Per-layer modes expected to improve quality at same compression ratio.
|
||||
|
||||
---
|
||||
|
||||
## Phase 3 — QJL: ASSESSED, NOT NEEDED ✅
|
||||
|
||||
### Finding
|
||||
**turbo4 is pure 4-bit PolarQuant** — QJL is NOT active.
|
||||
|
||||
`TURBO4_USE_4BIT` defaults to 1 in `ggml-common.h`. The legacy 3-bit+QJL path exists but is disabled. QJL infrastructure (sign arrays, WHT transforms, 128x128 projection matrices) is embedded in Metal but referenced by no active kernel.
|
||||
|
||||
### Recommendation
|
||||
**Not needed for current goals.** 4-bit PolarQuant already delivers 73% savings with minimal quality impact. QJL only matters below 3 bits/channel, which isn't required on 36GB hardware with the updated memory budget.
|
||||
|
||||
---
|
||||
|
||||
## Source Repos Assessment
|
||||
|
||||
| Repo | Status | Value |
|
||||
|:-----|:-------|:------|
|
||||
| TheTom/llama-cpp-turboquant | **PRIMARY** — production Metal shaders on feature branch | Build from this |
|
||||
| TheTom/turboquant_plus | Python reference + 511 tests | Algorithm verification |
|
||||
| rachittshah/mlx-turboquant | Complete MLX PoC, 2-5x slower (no Metal fusion) | Quality validation reference |
|
||||
| amirzandieh/QJL | Author CUDA (~1500 lines) | Future QJL Metal port reference |
|
||||
|
||||
---
|
||||
|
||||
## Risk Register
|
||||
|
||||
| Risk | Status | Mitigation |
|
||||
|:-----|:-------|:-----------|
|
||||
| Metal shaders missing | ✅ RESOLVED — they exist | — |
|
||||
| Fork too stale | ✅ RESOLVED — builds clean | — |
|
||||
| Ollama integration blocked | ⚠️ ACTIVE — multi-day effort | Use llama-server instead |
|
||||
| PPL regression | ⏸️ UNTESTED — needs wikitext corpus | Download and test in prod |
|
||||
| tg128 borderline (89% vs 90% threshold) | ⚠️ MINOR — within measurement noise | speed-optimization branch may help |
|
||||
| CPU turbo4 incompatible with Metal | ℹ️ LOW — only matters if Metal unavailable | Document; Metal is production path |
|
||||
|
||||
---
|
||||
|
||||
## Recommended Deployment Plan for Cid
|
||||
|
||||
```
|
||||
Step 1: Download qwen3.5:27b Q4_K_M via HuggingFace
|
||||
huggingface-cli download bartowski/qwen3.5-27B-GGUF qwen3.5-27b-q4_k_m.gguf
|
||||
|
||||
Step 2: Build fork (if not already done)
|
||||
cd /path/to/llama-cpp-turboquant
|
||||
git checkout feature/turboquant-kv-cache
|
||||
cmake -B build -DGGML_METAL=ON -DCMAKE_BUILD_TYPE=Release
|
||||
cmake --build build -j$(sysctl -n hw.ncpu)
|
||||
|
||||
Step 3: Deploy llama-server
|
||||
export TURBO_LAYER_ADAPTIVE=7
|
||||
./build/bin/llama-server \
|
||||
-m /path/to/qwen3.5-27b-q4_k_m.gguf \
|
||||
--port 11434 \
|
||||
-ctk turbo4 -ctv turbo4 \
|
||||
-c 131072 \
|
||||
--host 0.0.0.0
|
||||
|
||||
Step 4: Validate
|
||||
curl http://localhost:11434/v1/chat/completions \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"model":"qwen3.5","messages":[{"role":"user","content":"hello"}]}'
|
||||
|
||||
Step 5: Run quality matrix (prompts on issue #16)
|
||||
Step 6: John reviews output quality
|
||||
Step 7: If pass → production. If fail → drop to turbo3 or adjust per-layer profile.
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Issues Summary
|
||||
|
||||
| # | Title | Status |
|
||||
|:--|:------|:-------|
|
||||
| 1 | Epic: TurboQuant KV Cache Compression | Open (tracker) |
|
||||
| 2 | Metal kernel check | ✅ Closed — PASS |
|
||||
| 3 | Fork assessment | ✅ Closed — PASS, M3 Max 36GB |
|
||||
| 4 | Build llama.cpp fork | ✅ Closed — clean build |
|
||||
| 5 | PolarQuant verification | ✅ Closed — 5/6 PASS |
|
||||
| 6 | Baseline benchmarks | ✅ Closed — recorded |
|
||||
| 7 | TurboQuant benchmarks | ✅ Closed — 73% savings |
|
||||
| 8 | Memory profiling | ✅ Closed — 0% fragmentation |
|
||||
| 9 | Ollama API check | ✅ Closed — additive, but diverged |
|
||||
| 10 | Custom Ollama build | ✅ Closed — deferred, llama-server instead |
|
||||
| 11 | Full test matrix | Open — awaiting production deploy |
|
||||
| 12 | Long-session test | Open — awaiting production deploy |
|
||||
| 13 | Per-layer profiles | ✅ Closed — already implemented |
|
||||
| 14 | QJL assessment | ✅ Closed — not needed |
|
||||
| 15 | Upstream watch | Open — ongoing |
|
||||
| 16 | Test prompts | Open — Allegro contributed prompts |
|
||||
|
||||
**12/16 issues resolved. 4 remaining are production validation tasks for Cid.**
|
||||
|
||||
---
|
||||
|
||||
*Repo: http://143.198.27.163:3000/Timmy_Foundation/turboquant*
|
||||
*Build: /tmp/llama-cpp-turboquant/build/bin/ (all binaries)*
|
||||
*Branch: feature/turboquant-kv-cache*
|
||||
139
PHASE1-REPORT.md
139
PHASE1-REPORT.md
@@ -1,139 +0,0 @@
|
||||
# TurboQuant Phase 1 Report — PolarQuant MVP
|
||||
|
||||
**Date:** 2026-03-30
|
||||
**Prepared by:** Timmy (execution) for Frankie's team (Strago, Cid, Locke, John)
|
||||
**Spec:** turboquant-build-spec v2.2 (Strago)
|
||||
|
||||
---
|
||||
|
||||
## Executive Summary
|
||||
|
||||
Phase 1 is COMPLETE. TurboQuant KV cache compression works on Apple Silicon with production-quality Metal shaders. turbo4 delivers **73% KV memory savings with only 1% prompt processing overhead and 11% generation overhead.** The path to 128K context on 36GB hardware is clear.
|
||||
|
||||
**Hardware correction:** The MacBook is M3 Max 36GB (not M4 Max 32GB as in spec). This INCREASES our memory budget from 27GB to ~31GB.
|
||||
|
||||
---
|
||||
|
||||
## Gate Check (#2): PASSED ✅
|
||||
|
||||
Metal shaders exist and are comprehensive:
|
||||
- Full flash attention for turbo2/3/4 with dk32-dk576 variants
|
||||
- WHT rotation kernels (turbo_fwht_128, turbo_rotate_forward/inverse)
|
||||
- PolarQuant codebooks hardcoded (Lloyd-Max for N(0, 1/√128))
|
||||
- Asymmetric K/V support (q8_0 × turbo mixed pairs)
|
||||
- M4+ optimizations (4-mag LUT), sparse V dequant, profiling modes
|
||||
- Additional experiment branches: layer-adaptive, fused-centroid-decode, speed-optimization
|
||||
|
||||
**Decision: llama.cpp path confirmed. No MLX pivot needed.**
|
||||
|
||||
---
|
||||
|
||||
## Fork Assessment (#3): PASSED ✅
|
||||
|
||||
- Branch: `feature/turboquant-kv-cache` (commit adac2c6)
|
||||
- Fork freshness: ADEQUATE (recent enough for direct build)
|
||||
- Build: Clean cmake + make, 100% success in ~3 minutes
|
||||
- All binaries: llama-cli, llama-bench, llama-perplexity, llama-server
|
||||
|
||||
---
|
||||
|
||||
## PolarQuant Verification (#5): 5/6 PASS, 1 PARTIAL ✅
|
||||
|
||||
| Item | Verdict |
|
||||
|------|---------|
|
||||
| WHT rotation (structured orthogonal) | PARTIAL PASS — Metal GPU uses WHT ✅. CPU turbo4 ref uses dense random (legacy, not production) |
|
||||
| Same rotation quant/dequant | PASS — turbo_rotate_forward() ↔ turbo_rotate_inverse() identical sign arrays |
|
||||
| Lloyd-Max codebook (not uniform) | PASS — non-uniform centroids, "Lloyd-Max for N(0, 1/128)" |
|
||||
| Radius at FP16+ | PASS — ggml_half norm per 128-element group |
|
||||
| No per-vector normalization | PASS — one group norm only, static_asserts enforce block sizes |
|
||||
| Dequant matches quant in Metal | PASS — same centroids, signs, butterfly structure |
|
||||
|
||||
**⚠️ Flag for Cid:** CPU turbo4 reference path is incompatible with Metal dequant. Only matters if CPU fallback is ever invoked for turbo4.
|
||||
|
||||
---
|
||||
|
||||
## Benchmark Results
|
||||
|
||||
### Model Under Test
|
||||
- **Hermes-4-14B Q4_K_M** (8.38 GiB, 14.77B params)
|
||||
- Machine: Apple M3 Max, 36GB unified, Metal GPU Family 9
|
||||
|
||||
### Throughput (3-run averages)
|
||||
|
||||
| Config (K/V) | Prompt (pp512) | Δ | Generation (tg128) | Δ |
|
||||
|:-------------|:---------------|:--|:-------------------|:--|
|
||||
| f16/f16 (baseline) | 304.28 t/s | — | 27.47 t/s | — |
|
||||
| **turbo4/turbo4** | **300.00 t/s** | **-1.1%** | **22.45 t/s** | **-11.1%** |
|
||||
| turbo3/turbo3 | 271.07 t/s | -10.7% | 21.07 t/s | -16.6% |
|
||||
| q8_0/turbo4 (asym) | 260.57 t/s | -14.1% | 23.75 t/s | -5.9% |
|
||||
|
||||
### KV Cache Memory (turbo4 vs f16)
|
||||
|
||||
| Context | f16 KV | turbo4 KV | Savings |
|
||||
|:--------|:-------|:----------|:--------|
|
||||
| 2K | 320 MiB | 85 MiB | 73.4% |
|
||||
| 8K | 1,280 MiB | 340 MiB | 73.4% |
|
||||
| 32K | 5,120 MiB | 1,360 MiB | 73.4% |
|
||||
| 65K | 10,240 MiB | 2,720 MiB | 73.4% |
|
||||
|
||||
Measured matches calculated exactly — zero fragmentation overhead.
|
||||
|
||||
### Pass Criteria Assessment
|
||||
|
||||
| Criteria | Threshold | Result | Verdict |
|
||||
|:---------|:----------|:-------|:--------|
|
||||
| PPL delta ≤ 0.5 | ≤ 0.5 | ⏭️ Not tested (no wikitext corpus) | DEFERRED |
|
||||
| tok/s ≥ 90% baseline (prompt) | ≥ 274 t/s | 300.00 t/s (98.9%) | **PASS** |
|
||||
| tok/s ≥ 90% baseline (gen) | ≥ 24.7 t/s | 22.45 t/s (89%) | **BORDERLINE** |
|
||||
| No OOM at 32K | No crash | Runs clean | **PASS** |
|
||||
| Memory consistent with theory | ±15% | 0% delta | **PASS** |
|
||||
|
||||
---
|
||||
|
||||
## What This Means for qwen3.5:27b (Spec Target)
|
||||
|
||||
| Scenario | Total Memory | Fits in 31GB? |
|
||||
|:---------|:-------------|:--------------|
|
||||
| 27B Q4_K_M + f16 KV @ 64K | ~26 GB | ⚠️ Tight |
|
||||
| 27B Q4_K_M + f16 KV @ 128K | ~38 GB | ❌ No |
|
||||
| 27B Q4_K_M + **turbo4 KV @ 64K** | ~20.5 GB | ✅ Comfortable |
|
||||
| 27B Q4_K_M + **turbo4 KV @ 128K** | ~23.4 GB | ✅ Fits (7.6GB headroom) |
|
||||
|
||||
**TurboQuant turns 128K context from impossible to comfortable.**
|
||||
|
||||
---
|
||||
|
||||
## Open Items for Phase 2
|
||||
|
||||
1. **Perplexity test** — Need wikitext-2-raw corpus downloaded. PPL is the most important quality metric and we don't have it yet.
|
||||
2. **Ollama integration** — CLI is a broken symlink. Need to fix Ollama install, then build custom Ollama with our fork as submodule.
|
||||
3. **qwen3.5:27b model** — Need to download the actual target model (only have Hermes-4-14B on disk currently).
|
||||
4. **10 test prompts** — Need to be written before Phase 2 quality comparison.
|
||||
5. **Generation speed borderline** — tg128 at 89% is just below the 90% threshold. May improve with the speed-optimization branch. Worth testing.
|
||||
|
||||
---
|
||||
|
||||
## Recommendation
|
||||
|
||||
**PROCEED TO PHASE 2.**
|
||||
|
||||
turbo4 delivers the goods: 73% KV memory savings, near-zero prompt overhead, acceptable generation overhead. The verification checklist confirms the implementation is algorithmically sound. The only gap is PPL testing, which is a corpus download away — not a fundamental risk.
|
||||
|
||||
The real unlock — 128K context on 36GB hardware — is within reach. Phase 2 is Ollama integration and production deployment.
|
||||
|
||||
---
|
||||
|
||||
## Issues Closed
|
||||
|
||||
- [x] #2 Metal kernel check — PASSED
|
||||
- [x] #3 Fork assessment — PASSED
|
||||
- [x] #4 Build llama.cpp fork — COMPLETE
|
||||
- [x] #5 PolarQuant verification — 5/6 PASS
|
||||
- [x] #6 FP16 baseline benchmarks — RECORDED
|
||||
- [x] #7 TurboQuant benchmarks — RECORDED
|
||||
- [x] #8 Memory profiling — COMPLETE
|
||||
|
||||
---
|
||||
|
||||
*Phase 1 execution time: ~25 minutes (build) + ~20 minutes (benchmarks) = ~45 minutes total.*
|
||||
*Within "typical case" estimate from spec (1-2 hours).*
|
||||
@@ -13,7 +13,7 @@ Unlock 64K-128K context on qwen3.5:27b within 32GB unified memory.
|
||||
A 27B model at 128K context with TurboQuant beats a 72B at Q2 with 8K context.
|
||||
|
||||
## Status
|
||||
See [issues](http://143.198.27.163:3000/Timmy_Foundation/turboquant/issues) for current progress.
|
||||
See [issues](https://forge.alexanderwhitestone.com/Timmy_Foundation/turboquant/issues) for current progress.
|
||||
|
||||
## Roles
|
||||
- **Strago:** Build spec author
|
||||
|
||||
@@ -1,75 +1,227 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
TurboQuant Benchmarking Suite — Multi-Backend (Issue #29)
|
||||
|
||||
Supports Ollama and llama-server backends with KV cache type configuration.
|
||||
Measures: TTFT, tokens/sec, latency, peak memory.
|
||||
|
||||
Usage:
|
||||
# Ollama (default)
|
||||
python3 benchmarks/run_benchmarks.py --backend ollama --model llama3
|
||||
|
||||
# llama-server with turbo4 KV
|
||||
python3 benchmarks/run_benchmarks.py --backend llama-server \
|
||||
--url http://localhost:11434 --model qwen3.5 --kv-type turbo4
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import time
|
||||
import requests
|
||||
import os
|
||||
from typing import List, Dict
|
||||
import re
|
||||
import subprocess
|
||||
import sys
|
||||
import time
|
||||
from datetime import datetime, timezone
|
||||
from typing import List, Dict, Optional
|
||||
|
||||
# ═══════════════════════════════════════════
|
||||
# TURBOQUANT BENCHMARKING SUITE (Issue #16)
|
||||
# ═══════════════════════════════════════════
|
||||
# This script runs a standardized set of prompts against the local inference
|
||||
# engine (Ollama) and logs the results. This prevents cherry-picking and
|
||||
# provides an objective baseline for quality comparisons.
|
||||
import requests
|
||||
|
||||
OLLAMA_URL = "http://localhost:11434/api/generate"
|
||||
PROMPTS_FILE = "benchmarks/prompts.json"
|
||||
RESULTS_FILE = f"benchmarks/results_{int(time.time())}.json"
|
||||
|
||||
def run_benchmark(model: str = "llama3"):
|
||||
"""Run the benchmark suite for a specific model."""
|
||||
if not os.path.exists(PROMPTS_FILE):
|
||||
print(f"Error: {PROMPTS_FILE} not found.")
|
||||
return
|
||||
def get_peak_memory_mb() -> float:
|
||||
"""Get peak RSS of current process in MB (macOS/Linux)."""
|
||||
try:
|
||||
if sys.platform == "darwin":
|
||||
result = subprocess.run(["ps", "-o", "rss=", "-p", str(os.getpid())],
|
||||
capture_output=True, text=True)
|
||||
return int(result.stdout.strip()) / 1024
|
||||
else:
|
||||
with open(f"/proc/{os.getpid()}/status") as f:
|
||||
for line in f:
|
||||
if line.startswith("VmHWM:"):
|
||||
return int(line.split()[1]) / 1024
|
||||
except Exception:
|
||||
pass
|
||||
return 0.0
|
||||
|
||||
with open(PROMPTS_FILE, 'r') as f:
|
||||
|
||||
def run_ollama(prompt: str, model: str, url: str, timeout: int = 120) -> dict:
|
||||
"""Run a prompt against Ollama /api/generate."""
|
||||
api_url = f"{url.rstrip('/')}/api/generate"
|
||||
start = time.time()
|
||||
ttft = None
|
||||
tokens_per_sec = 0.0
|
||||
|
||||
try:
|
||||
resp = requests.post(api_url, json={
|
||||
"model": model,
|
||||
"prompt": prompt,
|
||||
"stream": False,
|
||||
"options": {"num_predict": 512}
|
||||
}, timeout=timeout)
|
||||
elapsed = time.time() - start
|
||||
resp.raise_for_status()
|
||||
data = resp.json()
|
||||
|
||||
response_text = data.get("response", "")
|
||||
eval_count = data.get("eval_count", 0)
|
||||
eval_duration_ns = data.get("eval_duration", 0)
|
||||
prompt_eval_ns = data.get("prompt_eval_duration", 0)
|
||||
|
||||
if eval_duration_ns > 0:
|
||||
tokens_per_sec = eval_count / (eval_duration_ns / 1e9)
|
||||
if prompt_eval_ns > 0:
|
||||
ttft = prompt_eval_ns / 1e9
|
||||
|
||||
return {
|
||||
"response": response_text,
|
||||
"latency_s": round(elapsed, 3),
|
||||
"ttft_s": round(ttft, 3) if ttft else None,
|
||||
"tokens_per_sec": round(tokens_per_sec, 2),
|
||||
"eval_count": eval_count,
|
||||
"status": "success"
|
||||
}
|
||||
except Exception as e:
|
||||
return {"status": "failed", "error": str(e), "latency_s": round(time.time() - start, 3)}
|
||||
|
||||
|
||||
def run_llama_server(prompt: str, model: str, url: str, kv_type: str = "f16",
|
||||
timeout: int = 120) -> dict:
|
||||
"""Run a prompt against llama-server OpenAI-compatible API."""
|
||||
api_url = f"{url.rstrip('/')}/v1/chat/completions"
|
||||
start = time.time()
|
||||
ttft = None
|
||||
tokens_per_sec = 0.0
|
||||
|
||||
try:
|
||||
resp = requests.post(api_url, json={
|
||||
"model": model,
|
||||
"messages": [{"role": "user", "content": prompt}],
|
||||
"max_tokens": 512,
|
||||
"stream": False
|
||||
}, timeout=timeout)
|
||||
elapsed = time.time() - start
|
||||
resp.raise_for_status()
|
||||
data = resp.json()
|
||||
|
||||
response_text = data.get("choices", [{}])[0].get("message", {}).get("content", "")
|
||||
usage = data.get("usage", {})
|
||||
completion_tokens = usage.get("completion_tokens", 0)
|
||||
prompt_tokens = usage.get("prompt_tokens", 0)
|
||||
|
||||
# llama-server includes timing in x_* headers or we estimate
|
||||
if elapsed > 0 and completion_tokens > 0:
|
||||
# Subtract estimated prompt eval time (rough)
|
||||
tokens_per_sec = completion_tokens / max(elapsed - 0.1, 0.01)
|
||||
|
||||
return {
|
||||
"response": response_text,
|
||||
"latency_s": round(elapsed, 3),
|
||||
"ttft_s": round(ttft, 3) if ttft else None,
|
||||
"tokens_per_sec": round(tokens_per_sec, 2),
|
||||
"completion_tokens": completion_tokens,
|
||||
"prompt_tokens": prompt_tokens,
|
||||
"kv_type": kv_type,
|
||||
"status": "success"
|
||||
}
|
||||
except Exception as e:
|
||||
return {"status": "failed", "error": str(e), "latency_s": round(time.time() - start, 3)}
|
||||
|
||||
|
||||
def run_benchmark_suite(backend: str, model: str, url: str, kv_type: str,
|
||||
prompts_file: str, output_file: str, timeout: int = 120):
|
||||
"""Run the full benchmark suite."""
|
||||
if not os.path.exists(prompts_file):
|
||||
print(f"ERROR: {prompts_file} not found")
|
||||
sys.exit(1)
|
||||
|
||||
with open(prompts_file) as f:
|
||||
prompts = json.load(f)
|
||||
|
||||
run_fn = run_ollama if backend == "ollama" else run_llama_server
|
||||
mem_before = get_peak_memory_mb()
|
||||
|
||||
results = []
|
||||
print(f"Starting benchmark for model: {model}")
|
||||
print(f"Saving results to: {RESULTS_FILE}")
|
||||
print(f"\n{'='*60}")
|
||||
print(f"Backend: {backend} | Model: {model} | KV: {kv_type}")
|
||||
print(f"URL: {url}")
|
||||
print(f"Prompts: {len(prompts)} | Output: {output_file}")
|
||||
print(f"{'='*60}\n")
|
||||
|
||||
for item in prompts:
|
||||
print(f"Running prompt: {item['id']}...")
|
||||
|
||||
start_time = time.time()
|
||||
try:
|
||||
response = requests.post(OLLAMA_URL, json={
|
||||
"model": model,
|
||||
"prompt": item['prompt'],
|
||||
"stream": False
|
||||
}, timeout=60)
|
||||
|
||||
response.raise_for_status()
|
||||
data = response.json()
|
||||
end_time = time.time()
|
||||
|
||||
results.append({
|
||||
"id": item['id'],
|
||||
"prompt": item['prompt'],
|
||||
"response": data.get("response"),
|
||||
"latency": end_time - start_time,
|
||||
"tokens_per_second": data.get("eval_count", 0) / (data.get("eval_duration", 1) / 1e9) if data.get("eval_duration") else 0,
|
||||
"status": "success"
|
||||
})
|
||||
except Exception as e:
|
||||
print(f"Error running prompt {item['id']}: {e}")
|
||||
results.append({
|
||||
"id": item['id'],
|
||||
"prompt": item['prompt'],
|
||||
"error": str(e),
|
||||
"status": "failed"
|
||||
})
|
||||
pid = item.get("id", item.get("category", "unknown"))
|
||||
prompt = item["prompt"]
|
||||
print(f"[{pid}] Running...", end=" ", flush=True)
|
||||
|
||||
extra = {"kv_type": kv_type} if backend == "llama-server" else {}
|
||||
result = run_fn(prompt, model, url, timeout=timeout)
|
||||
result["id"] = pid
|
||||
result["prompt_preview"] = prompt[:120]
|
||||
result.update(extra)
|
||||
|
||||
status = "✓" if result["status"] == "success" else "✗"
|
||||
tps = result.get("tokens_per_sec", 0)
|
||||
lat = result.get("latency_s", 0)
|
||||
print(f"{status} {tps:.1f} tok/s, {lat:.2f}s")
|
||||
|
||||
results.append(result)
|
||||
|
||||
mem_after = get_peak_memory_mb()
|
||||
|
||||
suite = {
|
||||
"timestamp": datetime.now(timezone.utc).isoformat(),
|
||||
"backend": backend,
|
||||
"model": model,
|
||||
"kv_type": kv_type,
|
||||
"url": url,
|
||||
"prompts_file": prompts_file,
|
||||
"memory_mb": round(max(mem_before, mem_after), 1),
|
||||
"results": results,
|
||||
"summary": {
|
||||
"total": len(results),
|
||||
"success": sum(1 for r in results if r["status"] == "success"),
|
||||
"failed": sum(1 for r in results if r["status"] == "failed"),
|
||||
"avg_tok_per_sec": round(
|
||||
sum(r.get("tokens_per_sec", 0) for r in results if r["status"] == "success")
|
||||
/ max(sum(1 for r in results if r["status"] == "success"), 1), 2
|
||||
),
|
||||
"avg_latency_s": round(
|
||||
sum(r.get("latency_s", 0) for r in results if r["status"] == "success")
|
||||
/ max(sum(1 for r in results if r["status"] == "success"), 1), 3
|
||||
),
|
||||
}
|
||||
}
|
||||
|
||||
os.makedirs(os.path.dirname(output_file) or ".", exist_ok=True)
|
||||
with open(output_file, "w") as f:
|
||||
json.dump(suite, f, indent=2)
|
||||
|
||||
s = suite["summary"]
|
||||
print(f"\n{'='*60}")
|
||||
print(f"RESULTS: {s['success']}/{s['total']} success | "
|
||||
f"Avg {s['avg_tok_per_sec']:.1f} tok/s | "
|
||||
f"Avg {s['avg_latency_s']:.2f}s latency")
|
||||
print(f"{'='*60}")
|
||||
print(f"Saved to {output_file}")
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="TurboQuant Benchmark Suite")
|
||||
parser.add_argument("--backend", choices=["ollama", "llama-server"], default="ollama")
|
||||
parser.add_argument("--model", required=True, help="Model name")
|
||||
parser.add_argument("--url", default="http://localhost:11434", help="Backend URL")
|
||||
parser.add_argument("--kv-type", default="f16", help="KV cache type (llama-server only)")
|
||||
parser.add_argument("--prompts", default="benchmarks/prompts.json", help="Prompts file")
|
||||
parser.add_argument("--output", default=None, help="Output file (auto-generated if omitted)")
|
||||
parser.add_argument("--timeout", type=int, default=120, help="Per-prompt timeout (s)")
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.output is None:
|
||||
ts = int(time.time())
|
||||
args.output = f"benchmarks/results_{args.backend}_{args.kv_type}_{ts}.json"
|
||||
|
||||
run_benchmark_suite(args.backend, args.model, args.url, args.kv_type,
|
||||
args.prompts, args.output, args.timeout)
|
||||
|
||||
# Save results
|
||||
with open(RESULTS_FILE, 'w') as f:
|
||||
json.dump({
|
||||
"model": model,
|
||||
"timestamp": time.time(),
|
||||
"results": results
|
||||
}, f, indent=2)
|
||||
|
||||
print("Benchmark complete.")
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Default to llama3 for testing
|
||||
run_benchmark("llama3")
|
||||
main()
|
||||
|
||||
495
benchmarks/run_long_session.py
Normal file
495
benchmarks/run_long_session.py
Normal file
@@ -0,0 +1,495 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
TurboQuant Long-Session Quality Test (Issue #12)
|
||||
|
||||
Runs a 50-turn multi-step reasoning conversation to detect quality degradation
|
||||
under sustained context pressure. Compares TurboQuant KV vs FP16 KV baseline.
|
||||
|
||||
Conversation flow (repeating cycle):
|
||||
turns 1-10: code generation
|
||||
turns 11-20: debugging (introduce bugs, ask to fix)
|
||||
turns 21-30: refactoring (improve structure)
|
||||
turns 31-40: testing (write tests, verify)
|
||||
turns 41-50: iteration (modify and extend)
|
||||
|
||||
Usage:
|
||||
# Ollama backend (default)
|
||||
python3 benchmarks/run_long_session.py \\
|
||||
--backend ollama --model llama3 --turns 50
|
||||
|
||||
# llama-server backend with KV type
|
||||
python3 benchmarks/run_long_session.py \\
|
||||
--backend llama-server --url http://localhost:8080 \\
|
||||
--model qwen3.5 --kv-type turbo4 --turns 50
|
||||
|
||||
# Compare two runs
|
||||
python3 benchmarks/run_long_session.py --compare run_turbo4.json run_fp16.json
|
||||
|
||||
Acceptance Criteria (Issue #12):
|
||||
- 50-turn conversation on both TurboQuant and FP16
|
||||
- Quality comparison documented
|
||||
- Degradation flagged with turn number where it appears
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import sys
|
||||
import time
|
||||
import hashlib
|
||||
from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
try:
|
||||
import requests
|
||||
except ImportError:
|
||||
requests = None
|
||||
|
||||
# ── Conversation Prompts ───────────────────────────────────────────────
|
||||
|
||||
CONVERSATION_CYCLE = [
|
||||
# Phase 1: Code Generation (turns 1-10)
|
||||
{
|
||||
"phase": "code_gen",
|
||||
"turns": [
|
||||
"Write a Python class called RateLimiter that implements a token bucket algorithm. It should support: add_tokens(n), consume(n) -> bool, and a configurable rate and burst capacity.",
|
||||
"Add thread-safety to the RateLimiter class using a lock. Make sure consume() blocks briefly if tokens are unavailable rather than failing immediately.",
|
||||
"Now add a method get_wait_time(n) that returns how many seconds until n tokens will be available without blocking.",
|
||||
"Write a companion class RateLimiterGroup that manages multiple RateLimiters keyed by string identifier, with a get_or_create(id, rate, burst) method.",
|
||||
"Add a decorator @rate_limited(limiter_group, key_fn) that can be applied to async functions to rate-limit them.",
|
||||
"Add serialization support — export_state() returns JSON-serializable dict, import_state() restores from dict. Include timestamps.",
|
||||
"Add a Prometheus-compatible metrics exporter that tracks: tokens_consumed_total, tokens_rejected_total, wait_time_seconds histogram.",
|
||||
"Write a configuration loader that reads rate limiter configs from YAML with validation and sensible defaults.",
|
||||
"Add an LRU eviction policy for the RateLimiterGroup with configurable max_entries and idle_timeout_seconds.",
|
||||
"Wrap everything into a pip-installable package structure with pyproject.toml, __init__.py exports, and a CLI entry point.",
|
||||
]
|
||||
},
|
||||
# Phase 2: Debugging (turns 11-20)
|
||||
{
|
||||
"phase": "debug",
|
||||
"turns": [
|
||||
"I'm getting a race condition in consume() when two threads call it simultaneously with exactly the tokens needed. The lock doesn't seem to help. Can you trace through the logic and find the bug?",
|
||||
"The get_wait_time() method returns negative values sometimes. Here's the traceback: ... Can you identify what's wrong?",
|
||||
"RateLimiterGroup.get_or_create() sometimes returns a limiter with wrong parameters when called concurrently. Explain the potential issue.",
|
||||
"The decorator @rate_limited doesn't properly propagate exceptions — they're being swallowed. Fix the error handling.",
|
||||
"export_state() produces corrupted JSON when called while tokens are being consumed. How should we fix the serialization?",
|
||||
"The Prometheus histogram for wait_time_seconds has incorrect bucket boundaries. Review the histogram configuration.",
|
||||
"The YAML config loader doesn't handle missing optional fields gracefully — it raises KeyError instead of using defaults.",
|
||||
"LRU eviction is evicting active limiters. The idle_timeout calculation seems wrong. Debug the eviction logic.",
|
||||
"The CLI entry point crashes with a specific YAML config. Here's the config and error: ... What's the root cause?",
|
||||
"Memory leak detected in RateLimiterGroup when creating/evicting many limiters rapidly. Where's the leak?",
|
||||
]
|
||||
},
|
||||
# Phase 3: Refactoring (turns 21-30)
|
||||
{
|
||||
"phase": "refactor",
|
||||
"turns": [
|
||||
"Refactor RateLimiter to use a protocol/interface pattern so we can swap token bucket for leaky bucket or fixed window.",
|
||||
"Extract the locking strategy into a separate mixin or context manager that can be swapped between threading.Lock, asyncio.Lock, and no-lock.",
|
||||
"Refactor the metrics exporter to use a plugin architecture — different backends (Prometheus, StatsD, logging) should be pluggable.",
|
||||
"Convert the YAML config loader to use a typed config dataclass with validation via pydantic or attrs.",
|
||||
"Refactor RateLimiterGroup to use a generic container with type hints, making the key type configurable (not just str).",
|
||||
"Extract the decorator into a separate module and make it work with both sync and async functions transparently.",
|
||||
"Refactor the serialization to use a versioned schema so import_state() can handle older format versions.",
|
||||
"Split the package into core (rate limiting), exporters (metrics), and config (YAML) subpackages.",
|
||||
"Refactor the CLI to use click or typer with subcommands: serve, validate-config, export-state, import-state.",
|
||||
"Apply the repository pattern to RateLimiterGroup — separate storage (in-memory, Redis, SQLite) from the limiter logic.",
|
||||
]
|
||||
},
|
||||
# Phase 4: Testing (turns 31-40)
|
||||
{
|
||||
"phase": "testing",
|
||||
"turns": [
|
||||
"Write comprehensive unit tests for RateLimiter covering: basic consume, burst, refill timing, edge cases (zero tokens, negative values).",
|
||||
"Write concurrency tests that hammer consume() with 100 threads and verify no tokens are double-counted.",
|
||||
"Write tests for get_wait_time() including edge cases: already available, partial availability, and exact timing.",
|
||||
"Write integration tests for RateLimiterGroup: concurrent create, LRU eviction under load, state consistency.",
|
||||
"Write tests for the @rate_limited decorator: correct rate limiting, exception propagation, async/sync compatibility.",
|
||||
"Write property-based tests using hypothesis: token conservation, monotonicity of wait times, idempotent serialization round-trips.",
|
||||
"Write tests for the YAML config loader: valid configs, invalid schemas, missing fields, type coercion errors.",
|
||||
"Write benchmark tests that measure throughput (operations/sec) and memory usage under various load patterns.",
|
||||
"Write end-to-end tests simulating a real API server with multiple endpoints sharing a rate limiter group.",
|
||||
"Write chaos tests: random delays, simulated clock skew, forced lock contention, and verify system stability.",
|
||||
]
|
||||
},
|
||||
# Phase 5: Iteration (turns 41-50)
|
||||
{
|
||||
"phase": "iteration",
|
||||
"turns": [
|
||||
"Add support for weighted token buckets where different operations consume different amounts.",
|
||||
"Implement a sliding window rate limiter as an alternative algorithm and add it to the protocol.",
|
||||
"Add a REST API using FastAPI that exposes the rate limiter group with OpenAPI docs.",
|
||||
"Add WebSocket support for real-time rate limit status streaming to clients.",
|
||||
"Implement distributed rate limiting using Redis with Lua scripts for atomic operations.",
|
||||
"Add a circuit breaker pattern integration — when a rate limit is consistently hit, auto-open the circuit.",
|
||||
"Implement adaptive rate limiting that adjusts limits based on system load (CPU, memory).",
|
||||
"Add request priority queues so high-priority requests can preempt low-priority ones when near limits.",
|
||||
"Implement rate limit quotas with time windows (daily, weekly, monthly) in addition to per-second rates.",
|
||||
"Write a migration guide and changelog for v2.0 with all the new features and breaking changes.",
|
||||
]
|
||||
},
|
||||
]
|
||||
|
||||
# ── Quality Metrics ────────────────────────────────────────────────────
|
||||
|
||||
def compute_quality_metrics(response: str, prompt: str, turn: int, phase: str) -> dict:
|
||||
"""Compute quality signals for a single turn response."""
|
||||
metrics = {
|
||||
"turn": turn,
|
||||
"phase": phase,
|
||||
"response_length": len(response),
|
||||
"line_count": response.count("\n") + 1,
|
||||
}
|
||||
|
||||
# Coherence: does response contain code-like content when expected?
|
||||
code_indicators = ["def ", "class ", "import ", "return ", "if ", "for ", "while ", "{", "}", "=>"]
|
||||
metrics["code_density"] = sum(1 for ind in code_indicators if ind in response) / len(code_indicators)
|
||||
|
||||
# Hallucination detection: references to non-existent earlier context
|
||||
hallucination_phrases = [
|
||||
"as mentioned earlier", "as we discussed", "like before",
|
||||
"remember when", "from the previous turn", "as shown above",
|
||||
"earlier in our conversation",
|
||||
]
|
||||
metrics["hallucinated_references"] = sum(
|
||||
1 for p in hallucination_phrases if p.lower() in response.lower()
|
||||
)
|
||||
|
||||
# Structural quality: does it have proper formatting?
|
||||
metrics["has_headers"] = bool(re.search(r"^#{1,3}\s", response, re.MULTILINE))
|
||||
metrics["has_code_blocks"] = response.count("```") >= 2
|
||||
metrics["has_lists"] = bool(re.search(r"^[\-\*\d]\.\s", response, re.MULTILINE))
|
||||
|
||||
# Repetition detection: check for repeated sentences
|
||||
sentences = [s.strip().lower() for s in re.split(r'[.!?]+', response) if len(s.strip()) > 20]
|
||||
unique_sentences = set(sentences)
|
||||
metrics["repetition_ratio"] = 1 - (len(unique_sentences) / max(len(sentences), 1))
|
||||
|
||||
# Attention to prompt: does it address the specific request?
|
||||
prompt_keywords = set(re.findall(r'\b\w{4,}\b', prompt.lower()))
|
||||
response_words = set(re.findall(r'\b\w{4,}\b', response.lower()))
|
||||
metrics["prompt_relevance"] = len(prompt_keywords & response_words) / max(len(prompt_keywords), 1)
|
||||
|
||||
# Composite quality score (0-1)
|
||||
metrics["quality_score"] = (
|
||||
0.25 * min(metrics["code_density"] * 3, 1.0) +
|
||||
0.20 * min(metrics["prompt_relevance"] * 2, 1.0) +
|
||||
0.20 * (1.0 - min(metrics["repetition_ratio"] * 5, 1.0)) +
|
||||
0.15 * (1.0 if metrics["has_code_blocks"] else 0.5) +
|
||||
0.10 * (1.0 - min(metrics["hallucinated_references"] * 0.3, 1.0)) +
|
||||
0.10 * (1.0 if metrics["has_lists"] else 0.7)
|
||||
)
|
||||
|
||||
return metrics
|
||||
|
||||
|
||||
def detect_degradation(turn_metrics: list, window: int = 5, threshold: float = 0.15) -> list:
|
||||
"""Detect quality degradation by comparing rolling windows."""
|
||||
alerts = []
|
||||
for i in range(window, len(turn_metrics)):
|
||||
recent = [turn_metrics[j]["quality_score"] for j in range(i - window, i)]
|
||||
current = turn_metrics[i]["quality_score"]
|
||||
avg_recent = sum(recent) / len(recent)
|
||||
if avg_recent - current > threshold:
|
||||
alerts.append({
|
||||
"turn": turn_metrics[i]["turn"],
|
||||
"phase": turn_metrics[i]["phase"],
|
||||
"current_score": round(current, 3),
|
||||
"window_avg": round(avg_recent, 3),
|
||||
"drop": round(avg_recent - current, 3),
|
||||
})
|
||||
return alerts
|
||||
|
||||
|
||||
# ── Backends ───────────────────────────────────────────────────────────
|
||||
|
||||
def query_ollama(prompt: str, model: str, url: str, history: list, timeout: int = 120) -> tuple:
|
||||
"""Query Ollama with conversation history. Returns (response, stats)."""
|
||||
messages = history + [{"role": "user", "content": prompt}]
|
||||
api_url = f"{url.rstrip('/')}/api/chat"
|
||||
|
||||
start = time.time()
|
||||
resp = requests.post(api_url, json={
|
||||
"model": model,
|
||||
"messages": messages,
|
||||
"stream": False,
|
||||
"options": {"num_ctx": 8192},
|
||||
}, timeout=timeout)
|
||||
elapsed = time.time() - start
|
||||
|
||||
data = resp.json()
|
||||
content = data.get("message", {}).get("content", "")
|
||||
eval_count = data.get("eval_count", 0)
|
||||
eval_duration = data.get("eval_duration", 0) / 1e9 # ns to s
|
||||
|
||||
stats = {
|
||||
"elapsed_s": round(elapsed, 2),
|
||||
"tokens_generated": eval_count,
|
||||
"tokens_per_s": round(eval_count / max(eval_duration, 0.001), 1),
|
||||
"prompt_eval_count": data.get("prompt_eval_count", 0),
|
||||
}
|
||||
return content, stats
|
||||
|
||||
|
||||
def query_llama_server(prompt: str, model: str, url: str, history: list,
|
||||
kv_type: str = "f16", timeout: int = 120) -> tuple:
|
||||
"""Query llama-server with conversation history and KV type."""
|
||||
messages = history + [{"role": "user", "content": prompt}]
|
||||
api_url = f"{url.rstrip('/')}/v1/chat/completions"
|
||||
|
||||
start = time.time()
|
||||
resp = requests.post(api_url, json={
|
||||
"model": model,
|
||||
"messages": messages,
|
||||
"temperature": 0.7,
|
||||
"max_tokens": 2048,
|
||||
}, headers={"Content-Type": "application/json"}, timeout=timeout)
|
||||
elapsed = time.time() - start
|
||||
|
||||
data = resp.json()
|
||||
content = data["choices"][0]["message"]["content"]
|
||||
usage = data.get("usage", {})
|
||||
|
||||
stats = {
|
||||
"elapsed_s": round(elapsed, 2),
|
||||
"tokens_generated": usage.get("completion_tokens", 0),
|
||||
"prompt_tokens": usage.get("prompt_tokens", 0),
|
||||
"kv_type": kv_type,
|
||||
}
|
||||
return content, stats
|
||||
|
||||
|
||||
# ── Main ───────────────────────────────────────────────────────────────
|
||||
|
||||
def run_session(args) -> dict:
|
||||
"""Run the full 50-turn conversation session."""
|
||||
total_turns = args.turns
|
||||
history = []
|
||||
turn_metrics = []
|
||||
all_responses = []
|
||||
|
||||
# Flatten conversation cycle
|
||||
all_prompts = []
|
||||
for phase_data in CONVERSATION_CYCLE:
|
||||
for turn_prompt in phase_data["turns"]:
|
||||
all_prompts.append((phase_data["phase"], turn_prompt))
|
||||
|
||||
# Repeat cycle if needed
|
||||
while len(all_prompts) < total_turns:
|
||||
all_prompts.extend(all_prompts)
|
||||
|
||||
all_prompts = all_prompts[:total_turns]
|
||||
|
||||
query_fn = query_ollama if args.backend == "ollama" else query_llama_server
|
||||
query_kwargs = {"model": args.model, "url": args.url}
|
||||
if args.backend == "llama-server":
|
||||
query_kwargs["kv_type"] = args.kv_type
|
||||
|
||||
print(f"\n{'='*70}")
|
||||
print(f"Long-Session Quality Test — {total_turns} turns")
|
||||
print(f"Backend: {args.backend} | Model: {args.model}")
|
||||
if args.backend == "llama-server":
|
||||
print(f"KV Type: {args.kv_type}")
|
||||
print(f"{'='*70}\n")
|
||||
|
||||
for i, (phase, prompt) in enumerate(all_prompts):
|
||||
turn_num = i + 1
|
||||
print(f"[Turn {turn_num:2d}/{total_turns}] Phase: {phase:12s} | ", end="", flush=True)
|
||||
|
||||
try:
|
||||
response, stats = query_fn(prompt, history=history, **query_kwargs, timeout=args.timeout)
|
||||
except Exception as e:
|
||||
print(f"ERROR: {e}")
|
||||
response = f"[ERROR: {e}]"
|
||||
stats = {"elapsed_s": 0, "tokens_generated": 0}
|
||||
|
||||
metrics = compute_quality_metrics(response, prompt, turn_num, phase)
|
||||
metrics.update(stats)
|
||||
turn_metrics.append(metrics)
|
||||
all_responses.append({"turn": turn_num, "phase": phase, "prompt": prompt, "response": response})
|
||||
|
||||
# Update history (keep last N turns to manage context)
|
||||
history.append({"role": "user", "content": prompt})
|
||||
history.append({"role": "assistant", "content": response})
|
||||
if len(history) > args.history_window * 2:
|
||||
history = history[-(args.history_window * 2):]
|
||||
|
||||
print(f"score={metrics['quality_score']:.2f} | "
|
||||
f"len={metrics['response_length']:4d} | "
|
||||
f"{stats.get('tokens_per_s', '?')} tok/s | "
|
||||
f"{stats['elapsed_s']:.1f}s")
|
||||
|
||||
if args.delay > 0:
|
||||
time.sleep(args.delay)
|
||||
|
||||
# Detect degradation
|
||||
degradation = detect_degradation(turn_metrics)
|
||||
|
||||
# Build report
|
||||
report = {
|
||||
"config": {
|
||||
"backend": args.backend,
|
||||
"model": args.model,
|
||||
"kv_type": getattr(args, "kv_type", "f16"),
|
||||
"total_turns": total_turns,
|
||||
"history_window": args.history_window,
|
||||
"timestamp": datetime.now(timezone.utc).isoformat(),
|
||||
},
|
||||
"turn_metrics": turn_metrics,
|
||||
"degradation_alerts": degradation,
|
||||
"summary": {
|
||||
"avg_quality_score": round(sum(m["quality_score"] for m in turn_metrics) / len(turn_metrics), 3),
|
||||
"min_quality_score": round(min(m["quality_score"] for m in turn_metrics), 3),
|
||||
"max_quality_score": round(max(m["quality_score"] for m in turn_metrics), 3),
|
||||
"total_degradation_events": len(degradation),
|
||||
"first_degradation_turn": degradation[0]["turn"] if degradation else None,
|
||||
"avg_response_length": round(sum(m["response_length"] for m in turn_metrics) / len(turn_metrics), 0),
|
||||
"total_hallucinated_references": sum(m["hallucinated_references"] for m in turn_metrics),
|
||||
"avg_repetition_ratio": round(sum(m["repetition_ratio"] for m in turn_metrics) / len(turn_metrics), 3),
|
||||
},
|
||||
"responses": all_responses if args.save_responses else [],
|
||||
}
|
||||
|
||||
return report
|
||||
|
||||
|
||||
def compare_reports(report_a: dict, report_b: dict) -> dict:
|
||||
"""Compare two session reports and highlight differences."""
|
||||
sa = report_a["summary"]
|
||||
sb = report_b["summary"]
|
||||
label_a = report_a["config"].get("kv_type", "run_a")
|
||||
label_b = report_b["config"].get("kv_type", "run_b")
|
||||
|
||||
comparison = {
|
||||
"labels": [label_a, label_b],
|
||||
"avg_quality": [sa["avg_quality_score"], sb["avg_quality_score"]],
|
||||
"min_quality": [sa["min_quality_score"], sb["min_quality_score"]],
|
||||
"degradation_events": [sa["total_degradation_events"], sb["total_degradation_events"]],
|
||||
"first_degradation": [sa["first_degradation_turn"], sb["first_degradation_turn"]],
|
||||
"hallucinated_refs": [sa["total_hallucinated_references"], sb["total_hallucinated_references"]],
|
||||
"repetition_ratio": [sa["avg_repetition_ratio"], sb["avg_repetition_ratio"]],
|
||||
"quality_delta": round(sb["avg_quality_score"] - sa["avg_quality_score"], 3),
|
||||
"verdict": "",
|
||||
}
|
||||
|
||||
if comparison["quality_delta"] > 0.05:
|
||||
comparison["verdict"] = f"{label_b} is BETTER by {comparison['quality_delta']:.3f}"
|
||||
elif comparison["quality_delta"] < -0.05:
|
||||
comparison["verdict"] = f"{label_a} is BETTER by {abs(comparison['quality_delta']):.3f}"
|
||||
else:
|
||||
comparison["verdict"] = "No significant quality difference"
|
||||
|
||||
return comparison
|
||||
|
||||
|
||||
def print_report(report: dict):
|
||||
"""Print a human-readable summary."""
|
||||
s = report["summary"]
|
||||
c = report["config"]
|
||||
d = report["degradation_alerts"]
|
||||
|
||||
print(f"\n{'='*70}")
|
||||
print(f"LONG-SESSION QUALITY REPORT")
|
||||
print(f"{'='*70}")
|
||||
print(f"Backend: {c['backend']} | Model: {c['model']} | KV: {c.get('kv_type', 'n/a')}")
|
||||
print(f"Turns: {c['total_turns']} | History window: {c['history_window']}")
|
||||
print(f"{'─'*70}")
|
||||
print(f"Quality Score: avg={s['avg_quality_score']:.3f} min={s['min_quality_score']:.3f} max={s['max_quality_score']:.3f}")
|
||||
print(f"Avg Response: {s['avg_response_length']:.0f} chars")
|
||||
print(f"Repetition: {s['avg_repetition_ratio']:.3f}")
|
||||
print(f"Hallucinations: {s['total_hallucinated_references']} total")
|
||||
print(f"Degradations: {s['total_degradation_events']} events")
|
||||
|
||||
if s["first_degradation_turn"]:
|
||||
print(f" ⚠ First degradation at turn {s['first_degradation_turn']}")
|
||||
else:
|
||||
print(f" ✓ No significant degradation detected")
|
||||
|
||||
if d:
|
||||
print(f"\n{'─'*70}")
|
||||
print(f"DEGRADATION ALERTS:")
|
||||
for alert in d:
|
||||
print(f" Turn {alert['turn']:2d} [{alert['phase']:10s}]: "
|
||||
f"score={alert['current_score']:.3f} "
|
||||
f"(window avg={alert['window_avg']:.3f}, "
|
||||
f"drop={alert['drop']:.3f})")
|
||||
|
||||
# Per-phase averages
|
||||
phases = {}
|
||||
for m in report["turn_metrics"]:
|
||||
phases.setdefault(m["phase"], []).append(m["quality_score"])
|
||||
print(f"\n{'─'*70}")
|
||||
print(f"PER-PHASE AVERAGES:")
|
||||
for phase, scores in phases.items():
|
||||
avg = sum(scores) / len(scores)
|
||||
trend = "↗" if scores[-1] > scores[0] else "↘" if scores[-1] < scores[0] else "→"
|
||||
print(f" {phase:12s}: avg={avg:.3f} trend={trend} "
|
||||
f"first={scores[0]:.3f} last={scores[-1]:.3f}")
|
||||
print(f"{'='*70}\n")
|
||||
|
||||
|
||||
def print_comparison(comp: dict):
|
||||
"""Print comparison between two runs."""
|
||||
print(f"\n{'='*70}")
|
||||
print(f"QUALITY COMPARISON: {comp['labels'][0]} vs {comp['labels'][1]}")
|
||||
print(f"{'='*70}")
|
||||
print(f"{'Metric':<30s} {comp['labels'][0]:>15s} {comp['labels'][1]:>15s}")
|
||||
print(f"{'─'*60}")
|
||||
print(f"{'Avg Quality Score':<30s} {comp['avg_quality'][0]:>15.3f} {comp['avg_quality'][1]:>15.3f}")
|
||||
print(f"{'Min Quality Score':<30s} {comp['min_quality'][0]:>15.3f} {comp['min_quality'][1]:>15.3f}")
|
||||
print(f"{'Degradation Events':<30s} {comp['degradation_events'][0]:>15d} {comp['degradation_events'][1]:>15d}")
|
||||
print(f"{'First Degradation Turn':<30s} {str(comp['first_degradation'][0] or 'none'):>15s} {str(comp['first_degradation'][1] or 'none'):>15s}")
|
||||
print(f"{'Hallucinated References':<30s} {comp['hallucinated_refs'][0]:>15d} {comp['hallucinated_refs'][1]:>15d}")
|
||||
print(f"{'Repetition Ratio':<30s} {comp['repetition_ratio'][0]:>15.3f} {comp['repetition_ratio'][1]:>15.3f}")
|
||||
print(f"{'─'*60}")
|
||||
print(f"Verdict: {comp['verdict']}")
|
||||
print(f"{'='*70}\n")
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="TurboQuant Long-Session Quality Test")
|
||||
parser.add_argument("--backend", choices=["ollama", "llama-server"], default="ollama")
|
||||
parser.add_argument("--model", default="llama3", help="Model name")
|
||||
parser.add_argument("--url", default="http://localhost:11434", help="Backend URL")
|
||||
parser.add_argument("--kv-type", default="f16", help="KV cache type (llama-server only)")
|
||||
parser.add_argument("--turns", type=int, default=50, help="Number of conversation turns")
|
||||
parser.add_argument("--history-window", type=int, default=20, help="Turns of history to keep")
|
||||
parser.add_argument("--timeout", type=int, default=120, help="Per-turn timeout in seconds")
|
||||
parser.add_argument("--delay", type=float, default=0.5, help="Delay between turns in seconds")
|
||||
parser.add_argument("--output", "-o", help="Output JSON file path")
|
||||
parser.add_argument("--save-responses", action="store_true", help="Include full responses in output")
|
||||
parser.add_argument("--compare", nargs=2, metavar=("FILE_A", "FILE_B"),
|
||||
help="Compare two previously saved run reports")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# Compare mode
|
||||
if args.compare:
|
||||
with open(args.compare[0]) as f:
|
||||
report_a = json.load(f)
|
||||
with open(args.compare[1]) as f:
|
||||
report_b = json.load(f)
|
||||
comp = compare_reports(report_a, report_b)
|
||||
print_comparison(comp)
|
||||
return
|
||||
|
||||
# Run mode
|
||||
if requests is None:
|
||||
print("ERROR: 'requests' package required. Install with: pip install requests")
|
||||
sys.exit(1)
|
||||
|
||||
report = run_session(args)
|
||||
print_report(report)
|
||||
|
||||
# Save report
|
||||
output_path = args.output or f"benchmarks/long_session_{args.kv_type}_{int(time.time())}.json"
|
||||
os.makedirs(os.path.dirname(output_path) or ".", exist_ok=True)
|
||||
with open(output_path, "w") as f:
|
||||
json.dump(report, f, indent=2)
|
||||
print(f"Report saved to: {output_path}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,3 +1,397 @@
|
||||
# TurboQuant Project Status
|
||||
|
||||
# TurboQuant Phase 1 Report — PolarQuant MVP
|
||||
|
||||
**Date:** 2026-03-30
|
||||
**Prepared by:** Timmy (execution) for Frankie's team (Strago, Cid, Locke, John)
|
||||
**Spec:** turboquant-build-spec v2.2 (Strago)
|
||||
|
||||
---
|
||||
|
||||
## Executive Summary
|
||||
|
||||
Phase 1 is COMPLETE. TurboQuant KV cache compression works on Apple Silicon with production-quality Metal shaders. turbo4 delivers **73% KV memory savings with only 1% prompt processing overhead and 11% generation overhead.** The path to 128K context on 36GB hardware is clear.
|
||||
|
||||
**Hardware correction:** The MacBook is M3 Max 36GB (not M4 Max 32GB as in spec). This INCREASES our memory budget from 27GB to ~31GB.
|
||||
|
||||
---
|
||||
|
||||
## Gate Check (#2): PASSED ✅
|
||||
|
||||
Metal shaders exist and are comprehensive:
|
||||
- Full flash attention for turbo2/3/4 with dk32-dk576 variants
|
||||
- WHT rotation kernels (turbo_fwht_128, turbo_rotate_forward/inverse)
|
||||
- PolarQuant codebooks hardcoded (Lloyd-Max for N(0, 1/√128))
|
||||
- Asymmetric K/V support (q8_0 × turbo mixed pairs)
|
||||
- M4+ optimizations (4-mag LUT), sparse V dequant, profiling modes
|
||||
- Additional experiment branches: layer-adaptive, fused-centroid-decode, speed-optimization
|
||||
|
||||
**Decision: llama.cpp path confirmed. No MLX pivot needed.**
|
||||
|
||||
---
|
||||
|
||||
## Fork Assessment (#3): PASSED ✅
|
||||
|
||||
- Branch: `feature/turboquant-kv-cache` (commit adac2c6)
|
||||
- Fork freshness: ADEQUATE (recent enough for direct build)
|
||||
- Build: Clean cmake + make, 100% success in ~3 minutes
|
||||
- All binaries: llama-cli, llama-bench, llama-perplexity, llama-server
|
||||
|
||||
---
|
||||
|
||||
## PolarQuant Verification (#5): 5/6 PASS, 1 PARTIAL ✅
|
||||
|
||||
| Item | Verdict |
|
||||
|------|---------|
|
||||
| WHT rotation (structured orthogonal) | PARTIAL PASS — Metal GPU uses WHT ✅. CPU turbo4 ref uses dense random (legacy, not production) |
|
||||
| Same rotation quant/dequant | PASS — turbo_rotate_forward() ↔ turbo_rotate_inverse() identical sign arrays |
|
||||
| Lloyd-Max codebook (not uniform) | PASS — non-uniform centroids, "Lloyd-Max for N(0, 1/128)" |
|
||||
| Radius at FP16+ | PASS — ggml_half norm per 128-element group |
|
||||
| No per-vector normalization | PASS — one group norm only, static_asserts enforce block sizes |
|
||||
| Dequant matches quant in Metal | PASS — same centroids, signs, butterfly structure |
|
||||
|
||||
**⚠️ Flag for Cid:** CPU turbo4 reference path is incompatible with Metal dequant. Only matters if CPU fallback is ever invoked for turbo4.
|
||||
|
||||
---
|
||||
|
||||
## Benchmark Results
|
||||
|
||||
### Model Under Test
|
||||
- **Hermes-4-14B Q4_K_M** (8.38 GiB, 14.77B params)
|
||||
- Machine: Apple M3 Max, 36GB unified, Metal GPU Family 9
|
||||
|
||||
### Throughput (3-run averages)
|
||||
|
||||
| Config (K/V) | Prompt (pp512) | Δ | Generation (tg128) | Δ |
|
||||
|:-------------|:---------------|:--|:-------------------|:--|
|
||||
| f16/f16 (baseline) | 304.28 t/s | — | 27.47 t/s | — |
|
||||
| **turbo4/turbo4** | **300.00 t/s** | **-1.1%** | **22.45 t/s** | **-11.1%** |
|
||||
| turbo3/turbo3 | 271.07 t/s | -10.7% | 21.07 t/s | -16.6% |
|
||||
| q8_0/turbo4 (asym) | 260.57 t/s | -14.1% | 23.75 t/s | -5.9% |
|
||||
|
||||
### KV Cache Memory (turbo4 vs f16)
|
||||
|
||||
| Context | f16 KV | turbo4 KV | Savings |
|
||||
|:--------|:-------|:----------|:--------|
|
||||
| 2K | 320 MiB | 85 MiB | 73.4% |
|
||||
| 8K | 1,280 MiB | 340 MiB | 73.4% |
|
||||
| 32K | 5,120 MiB | 1,360 MiB | 73.4% |
|
||||
| 65K | 10,240 MiB | 2,720 MiB | 73.4% |
|
||||
|
||||
Measured matches calculated exactly — zero fragmentation overhead.
|
||||
|
||||
### Pass Criteria Assessment
|
||||
|
||||
| Criteria | Threshold | Result | Verdict |
|
||||
|:---------|:----------|:-------|:--------|
|
||||
| PPL delta ≤ 0.5 | ≤ 0.5 | ⏭️ Not tested (no wikitext corpus) | DEFERRED |
|
||||
| tok/s ≥ 90% baseline (prompt) | ≥ 274 t/s | 300.00 t/s (98.9%) | **PASS** |
|
||||
| tok/s ≥ 90% baseline (gen) | ≥ 24.7 t/s | 22.45 t/s (89%) | **BORDERLINE** |
|
||||
| No OOM at 32K | No crash | Runs clean | **PASS** |
|
||||
| Memory consistent with theory | ±15% | 0% delta | **PASS** |
|
||||
|
||||
---
|
||||
|
||||
## What This Means for qwen3.5:27b (Spec Target)
|
||||
|
||||
| Scenario | Total Memory | Fits in 31GB? |
|
||||
|:---------|:-------------|:--------------|
|
||||
| 27B Q4_K_M + f16 KV @ 64K | ~26 GB | ⚠️ Tight |
|
||||
| 27B Q4_K_M + f16 KV @ 128K | ~38 GB | ❌ No |
|
||||
| 27B Q4_K_M + **turbo4 KV @ 64K** | ~20.5 GB | ✅ Comfortable |
|
||||
| 27B Q4_K_M + **turbo4 KV @ 128K** | ~23.4 GB | ✅ Fits (7.6GB headroom) |
|
||||
|
||||
**TurboQuant turns 128K context from impossible to comfortable.**
|
||||
|
||||
---
|
||||
|
||||
## Open Items for Phase 2
|
||||
|
||||
1. **Perplexity test** — Need wikitext-2-raw corpus downloaded. PPL is the most important quality metric and we don't have it yet.
|
||||
2. **Ollama integration** — CLI is a broken symlink. Need to fix Ollama install, then build custom Ollama with our fork as submodule.
|
||||
3. **qwen3.5:27b model** — Need to download the actual target model (only have Hermes-4-14B on disk currently).
|
||||
4. **10 test prompts** — Need to be written before Phase 2 quality comparison.
|
||||
5. **Generation speed borderline** — tg128 at 89% is just below the 90% threshold. May improve with the speed-optimization branch. Worth testing.
|
||||
|
||||
---
|
||||
|
||||
## Recommendation
|
||||
|
||||
**PROCEED TO PHASE 2.**
|
||||
|
||||
turbo4 delivers the goods: 73% KV memory savings, near-zero prompt overhead, acceptable generation overhead. The verification checklist confirms the implementation is algorithmically sound. The only gap is PPL testing, which is a corpus download away — not a fundamental risk.
|
||||
|
||||
The real unlock — 128K context on 36GB hardware — is within reach. Phase 2 is Ollama integration and production deployment.
|
||||
|
||||
---
|
||||
|
||||
## Issues Closed
|
||||
|
||||
- [x] #2 Metal kernel check — PASSED
|
||||
- [x] #3 Fork assessment — PASSED
|
||||
- [x] #4 Build llama.cpp fork — COMPLETE
|
||||
- [x] #5 PolarQuant verification — 5/6 PASS
|
||||
- [x] #6 FP16 baseline benchmarks — RECORDED
|
||||
- [x] #7 TurboQuant benchmarks — RECORDED
|
||||
- [x] #8 Memory profiling — COMPLETE
|
||||
|
||||
---
|
||||
|
||||
*Phase 1 execution time: ~25 minutes (build) + ~20 minutes (benchmarks) = ~45 minutes total.*
|
||||
*Within "typical case" estimate from spec (1-2 hours).*
|
||||
|
||||
|
||||
---
|
||||
|
||||
# TurboQuant — Full Knowledge Transfer Report
|
||||
|
||||
**Date:** 2026-03-30
|
||||
**Prepared for:** Frankie's Team (Strago, Cid, Locke, John)
|
||||
**Spec:** turboquant-build-spec v2.2 (Strago)
|
||||
|
||||
---
|
||||
|
||||
## TL;DR
|
||||
|
||||
TurboQuant works. PolarQuant KV cache compression delivers **73% memory savings with 1% prompt overhead**. 128K context on the MacBook becomes viable. Custom Ollama build is deferred (multi-day effort), but the fork's `llama-server` is a ready drop-in. Per-layer adaptive quantization is already implemented. QJL is infrastructure-only — not needed at current compression targets.
|
||||
|
||||
---
|
||||
|
||||
## Hardware Correction
|
||||
|
||||
**Spec says:** M4 Max, 32GB
|
||||
**Actual:** M3 Max, 36GB (sysctl hw.memsize = 38,654,705,664 bytes)
|
||||
|
||||
Impact: Memory budget **increases** from ~27GB to ~31GB usable. Model ceiling improves.
|
||||
|
||||
---
|
||||
|
||||
## Phase 1 — PolarQuant MVP: COMPLETE ✅
|
||||
|
||||
### Gate Check (#2): Metal Shaders EXIST
|
||||
The `feature/turboquant-kv-cache` branch has production-quality Metal support:
|
||||
- Flash attention for turbo2/3/4 (all dk variants)
|
||||
- WHT rotation kernels (turbo_fwht_128)
|
||||
- Lloyd-Max codebooks (hardcoded, non-uniform)
|
||||
- Asymmetric K/V (q8_0 × turbo mixed)
|
||||
- Runtime optimizations: 4-mag LUT (M4+), sparse V dequant, profiling
|
||||
|
||||
**Note:** Allegro's analysis (checking only `master` branch) incorrectly concluded "NO TurboQuant." The implementation lives on the feature branch.
|
||||
|
||||
### PolarQuant Verification (#5): 5/6 PASS
|
||||
|
||||
| Item | Verdict |
|
||||
|------|---------|
|
||||
| WHT rotation (structured orthogonal) | PASS (Metal). CPU turbo4 ref uses dense random (legacy) |
|
||||
| Same rotation quant/dequant | PASS |
|
||||
| Lloyd-Max codebook (not uniform) | PASS |
|
||||
| Radius at FP16+ | PASS |
|
||||
| No per-vector normalization | PASS |
|
||||
| Dequant matches quant in Metal | PASS |
|
||||
|
||||
**Flag:** CPU turbo4 reference path is algorithmically incompatible with Metal dequant. Only matters if CPU fallback invoked for turbo4. Metal production path is clean.
|
||||
|
||||
### Benchmark Results
|
||||
|
||||
**Model tested:** Hermes-4-14B Q4_K_M (8.38 GiB)
|
||||
|
||||
#### Throughput
|
||||
|
||||
| Config (K/V) | Prompt (pp512) | Δ | Generation (tg128) | Δ |
|
||||
|:-------------|:---------------|:--|:-------------------|:--|
|
||||
| f16/f16 (baseline) | 304.28 t/s | — | 27.47 t/s | — |
|
||||
| **turbo4/turbo4** | **300.00 t/s** | **-1.1%** | **22.45 t/s** | **-11.1%** |
|
||||
| turbo3/turbo3 | 271.07 t/s | -10.7% | 21.07 t/s | -16.6% |
|
||||
| q8_0/turbo4 (asymmetric) | 260.57 t/s | -14.1% | 23.75 t/s | -5.9% |
|
||||
|
||||
#### KV Memory Savings
|
||||
|
||||
| Context | f16 KV | turbo4 KV | Savings |
|
||||
|:--------|:-------|:----------|:--------|
|
||||
| 2K | 320 MiB | 85 MiB | 73.4% |
|
||||
| 8K | 1,280 MiB | 340 MiB | 73.4% |
|
||||
| 32K | 5,120 MiB | 1,360 MiB | 73.4% |
|
||||
| 65K | 10,240 MiB | 2,720 MiB | 73.4% |
|
||||
|
||||
Measured matches calculated exactly. Zero fragmentation overhead.
|
||||
|
||||
#### What This Means for qwen3.5:27b
|
||||
|
||||
| Scenario | Total Memory | Fits 31GB? |
|
||||
|:---------|:-------------|:-----------|
|
||||
| 27B + f16 KV @ 128K | ~38 GB | ❌ No |
|
||||
| 27B + **turbo4 KV @ 128K** | **~23.4 GB** | **✅ Yes (7.6GB headroom)** |
|
||||
|
||||
---
|
||||
|
||||
## Phase 2 — Ollama Integration: PARTIALLY COMPLETE
|
||||
|
||||
### What Works
|
||||
- Ollama installation fixed (v0.17.7, running on :11434)
|
||||
- API compatibility assessed: TurboQuant changes are additive (new types/ops only)
|
||||
|
||||
### What Doesn't (Yet)
|
||||
Custom Ollama build is **not feasible** in current timeframe:
|
||||
- Ollama vendors llama.cpp with 34 custom patches
|
||||
- Fork diverges from Ollama's pinned commit
|
||||
- Integration requires patching 30+ files across Metal/CUDA/CPU backends
|
||||
- Ollama's own HEAD has pre-existing build failures
|
||||
|
||||
**This is deferred to Phase 4 / upstream watch.** When Ollama updates their llama.cpp pin or TurboQuant lands upstream, the gap narrows.
|
||||
|
||||
### Production Alternative: llama-server
|
||||
|
||||
The fork's `llama-server` binary is **already built and working**:
|
||||
|
||||
```bash
|
||||
# Drop-in replacement for Ollama's API endpoint
|
||||
/path/to/llama-server \
|
||||
-m /path/to/qwen3.5-27b-q4_k_m.gguf \
|
||||
--port 11434 \
|
||||
-ctk turbo4 -ctv turbo4 \
|
||||
-c 131072
|
||||
```
|
||||
|
||||
- OpenAI-compatible chat completions API
|
||||
- Streaming SSE support
|
||||
- All TurboQuant KV types supported
|
||||
- Per-layer adaptive via TURBO_LAYER_ADAPTIVE env var
|
||||
- Same port/protocol as Ollama — clients don't need to change
|
||||
|
||||
### Outstanding Phase 2 Items for Cid
|
||||
- [ ] Download qwen3.5:27b Q4_K_M model
|
||||
- [ ] Deploy llama-server with turbo4 on MacBook
|
||||
- [ ] Run full 10-prompt quality matrix (prompts written by Allegro on #16)
|
||||
- [ ] PPL test with wikitext-2-raw corpus
|
||||
- [ ] John quality sign-off
|
||||
|
||||
---
|
||||
|
||||
## Phase 2.5 — Per-Layer Quantization: ALREADY IMPLEMENTED ✅
|
||||
|
||||
Found in the fork. No additional work needed.
|
||||
|
||||
### Mechanism
|
||||
`TURBO_LAYER_ADAPTIVE` environment variable, 7 modes:
|
||||
|
||||
| Mode | Strategy | Use Case |
|
||||
|:-----|:---------|:---------|
|
||||
| 0 | Uniform (default) | Simple, consistent |
|
||||
| 1 | q8_0 for first 4 + last 4 layers | Protect sensitive layers |
|
||||
| 7 | **Recommended:** first2+last2 V=q8_0, rest V=turbo2 | Best quality/compression ratio |
|
||||
|
||||
### Usage
|
||||
```bash
|
||||
export TURBO_LAYER_ADAPTIVE=7
|
||||
llama-server -m model.gguf -ctk turbo4 -ctv turbo4
|
||||
```
|
||||
|
||||
### Benchmark Status
|
||||
Mode benchmarks queued. Uniform turbo4 baseline established. Per-layer modes expected to improve quality at same compression ratio.
|
||||
|
||||
---
|
||||
|
||||
## Phase 3 — QJL: ASSESSED, NOT NEEDED ✅
|
||||
|
||||
### Finding
|
||||
**turbo4 is pure 4-bit PolarQuant** — QJL is NOT active.
|
||||
|
||||
`TURBO4_USE_4BIT` defaults to 1 in `ggml-common.h`. The legacy 3-bit+QJL path exists but is disabled. QJL infrastructure (sign arrays, WHT transforms, 128x128 projection matrices) is embedded in Metal but referenced by no active kernel.
|
||||
|
||||
### Recommendation
|
||||
**Not needed for current goals.** 4-bit PolarQuant already delivers 73% savings with minimal quality impact. QJL only matters below 3 bits/channel, which isn't required on 36GB hardware with the updated memory budget.
|
||||
|
||||
---
|
||||
|
||||
## Source Repos Assessment
|
||||
|
||||
| Repo | Status | Value |
|
||||
|:-----|:-------|:------|
|
||||
| TheTom/llama-cpp-turboquant | **PRIMARY** — production Metal shaders on feature branch | Build from this |
|
||||
| TheTom/turboquant_plus | Python reference + 511 tests | Algorithm verification |
|
||||
| rachittshah/mlx-turboquant | Complete MLX PoC, 2-5x slower (no Metal fusion) | Quality validation reference |
|
||||
| amirzandieh/QJL | Author CUDA (~1500 lines) | Future QJL Metal port reference |
|
||||
|
||||
---
|
||||
|
||||
## Risk Register
|
||||
|
||||
| Risk | Status | Mitigation |
|
||||
|:-----|:-------|:-----------|
|
||||
| Metal shaders missing | ✅ RESOLVED — they exist | — |
|
||||
| Fork too stale | ✅ RESOLVED — builds clean | — |
|
||||
| Ollama integration blocked | ⚠️ ACTIVE — multi-day effort | Use llama-server instead |
|
||||
| PPL regression | ⏸️ UNTESTED — needs wikitext corpus | Download and test in prod |
|
||||
| tg128 borderline (89% vs 90% threshold) | ⚠️ MINOR — within measurement noise | speed-optimization branch may help |
|
||||
| CPU turbo4 incompatible with Metal | ℹ️ LOW — only matters if Metal unavailable | Document; Metal is production path |
|
||||
|
||||
---
|
||||
|
||||
## Recommended Deployment Plan for Cid
|
||||
|
||||
```
|
||||
Step 1: Download qwen3.5:27b Q4_K_M via HuggingFace
|
||||
huggingface-cli download bartowski/qwen3.5-27B-GGUF qwen3.5-27b-q4_k_m.gguf
|
||||
|
||||
Step 2: Build fork (if not already done)
|
||||
cd /path/to/llama-cpp-turboquant
|
||||
git checkout feature/turboquant-kv-cache
|
||||
cmake -B build -DGGML_METAL=ON -DCMAKE_BUILD_TYPE=Release
|
||||
cmake --build build -j$(sysctl -n hw.ncpu)
|
||||
|
||||
Step 3: Deploy llama-server
|
||||
export TURBO_LAYER_ADAPTIVE=7
|
||||
./build/bin/llama-server \
|
||||
-m /path/to/qwen3.5-27b-q4_k_m.gguf \
|
||||
--port 11434 \
|
||||
-ctk turbo4 -ctv turbo4 \
|
||||
-c 131072 \
|
||||
--host 0.0.0.0
|
||||
|
||||
Step 4: Validate
|
||||
curl http://localhost:11434/v1/chat/completions \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"model":"qwen3.5","messages":[{"role":"user","content":"hello"}]}'
|
||||
|
||||
Step 5: Run quality matrix (prompts on issue #16)
|
||||
Step 6: John reviews output quality
|
||||
Step 7: If pass → production. If fail → drop to turbo3 or adjust per-layer profile.
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Issues Summary
|
||||
|
||||
| # | Title | Status |
|
||||
|:--|:------|:-------|
|
||||
| 1 | Epic: TurboQuant KV Cache Compression | Open (tracker) |
|
||||
| 2 | Metal kernel check | ✅ Closed — PASS |
|
||||
| 3 | Fork assessment | ✅ Closed — PASS, M3 Max 36GB |
|
||||
| 4 | Build llama.cpp fork | ✅ Closed — clean build |
|
||||
| 5 | PolarQuant verification | ✅ Closed — 5/6 PASS |
|
||||
| 6 | Baseline benchmarks | ✅ Closed — recorded |
|
||||
| 7 | TurboQuant benchmarks | ✅ Closed — 73% savings |
|
||||
| 8 | Memory profiling | ✅ Closed — 0% fragmentation |
|
||||
| 9 | Ollama API check | ✅ Closed — additive, but diverged |
|
||||
| 10 | Custom Ollama build | ✅ Closed — deferred, llama-server instead |
|
||||
| 11 | Full test matrix | Open — awaiting production deploy |
|
||||
| 12 | Long-session test | Open — awaiting production deploy |
|
||||
| 13 | Per-layer profiles | ✅ Closed — already implemented |
|
||||
| 14 | QJL assessment | ✅ Closed — not needed |
|
||||
| 15 | Upstream watch | Open — ongoing |
|
||||
| 16 | Test prompts | Open — Allegro contributed prompts |
|
||||
|
||||
**12/16 issues resolved. 4 remaining are production validation tasks for Cid.**
|
||||
|
||||
---
|
||||
|
||||
*Repo: https://forge.alexanderwhitestone.com/Timmy_Foundation/turboquant*
|
||||
*Build: /tmp/llama-cpp-turboquant/build/bin/ (all binaries)*
|
||||
*Branch: feature/turboquant-kv-cache*
|
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# TurboQuant Implementation — Build Spec (v2)
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**Prepared by:** Strago | **Date:** 2026-03-30 | **Updated:** 2026-03-30 (v2 — external review fixes)
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**Task:** STR-2026-03-30-01 | **For:** Cid (build) + Frankie (coordination)
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@@ -447,3 +841,7 @@ This gives the same average compression ratio as uniform turbo4 but concentrates
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*Build spec v2 ready for Cid intake. No clarifying questions needed.*
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