metal: MUL_MAT kernels for turbo3/turbo4 + dual-LUT dequant (port of PR #22)#49
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TheTom wants to merge 135 commits intofeature/turboquant-kv-cachefrom
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metal: MUL_MAT kernels for turbo3/turbo4 + dual-LUT dequant (port of PR #22)#49TheTom wants to merge 135 commits intofeature/turboquant-kv-cachefrom
TheTom wants to merge 135 commits intofeature/turboquant-kv-cachefrom
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New types: GGML_TYPE_TURBO3_0 (3-bit) and GGML_TYPE_TURBO4_0 (4-bit) Implements PolarQuant + QJL compression per the ICLR 2026 paper. Block size = 128 (matching head_dim for optimal rotation Gaussianization) turbo3: 52 bytes per 128 values = 3.25 bits/value (4.9× vs fp16) turbo4: 68 bytes per 128 values = 4.25 bits/value (3.8× vs fp16) Status: - ✅ Type definitions in ggml.h - ✅ Block structures in ggml-common.h - ✅ Quantize/dequantize C implementation in ggml-turbo-quant.c - ✅ Registered in ggml.c type traits - ✅ Added to kv_cache_types in arg.cpp - ✅ Builds successfully - ✅ Shows in --help output - ❌ Metal SET_ROWS kernel not implemented (blocks GPU inference) - ❌ Needs Metal dequantize kernels for attention computation Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Added Metal shader implementations: - quantize_turbo3_0 / quantize_turbo4_0 (per-block quantization) - dequantize_turbo3_0 / dequantize_turbo4_0 (type4x4 and type4 variants) - kernel_set_rows_turbo template (128-element block size) - Flash attention instantiations for all dk/dv variants Added TURBO3_0/TURBO4_0 to Metal device SET_ROWS validation. Builds successfully. Testing with Qwen 3.5 35B-A3B MoE on M5 Max. Note: Initial version uses simplified quantization (no rotation matrix) for Metal compatibility. Full rotation requires custom kernel with extra buffer bindings — tracked for follow-up. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Embedded pre-computed 128×128 rotation and QJL matrices (256KB constant memory) directly in the Metal shader. Both quantize and dequantize now perform the full TurboQuant algorithm: Quantize: normalize → rotate → codebook → inverse rotate → residual → QJL Dequantize: codebook → inverse rotate → QJL correction → rescale Previous version (no rotation) produced garbage. This should produce meaningful output since the rotation Gaussianizes the KV distribution. Note: dequantize does full 128-element rotation per chunk (8× work). Optimization possible with caching or restructured kernel in follow-up. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Inlined turbo-matrices.h directly into ggml-metal.metal (256KB)
to fix JIT compilation failure with #include
- Added C round-trip test (test-turbo-quant.c):
turbo3 cosine=0.906, turbo4 cosine=0.966 — matches Python prototype
- Metal library loads successfully ("loaded in 5.9 sec")
- Model runs on Metal but output quality needs debugging
(Metal quantize/dequantize may have a bug vs the working C version)
C round-trip PROVES the algorithm works in C. Metal shader needs
debugging — likely an issue with the dequantize chunk addressing
or the large constant arrays in thread-local memory.
Co-Authored-By: tturney@psyguard.ai
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Codex review found: 1. Stale duplicate code in dequantize_turbo3_0_t4 (compile would fail) 2. thread static is risky/non-portable in MSL Fixed: removed thread static caching, using plain thread locals. Speed unchanged (2.4 tok/s) — the static caching wasn't actually working on Metal. True optimization needs architectural change in flash attention kernel to dequantize once per block, not per chunk. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Massive reduction in constant memory and compute: - 256KB of dense matrices → 512 bytes of sign arrays - O(d²) = 16,384 ops → O(d log d) = 896 ops per rotation - Metal shader file: 1.5MB → 432KB Speed: still 2.4 tok/s. WHT reduced per-rotation cost but the bottleneck is redundant calls (8-32× per block from flash attention). The dequantize function is called per 4/16-element chunk, each time doing the full 128-element WHT. Need to modify the flash attention kernel to dequantize once per block. Quality: WHT+signs gives BETTER quality than dense QR on real KV tensors (cosine 0.94 vs 0.79 at 2-bit). Sub-Gaussian distribution (kurtosis 1.53) means fewer outliers hitting extreme centroids. Reviewed by Codex: WHT butterfly correct, inverse order verified, QJL correction matches reference C implementation. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Root cause analysis: 8-32× redundant full-block dequantize per block from flash attention template. Four approaches documented with expected speedups and risk levels. Plan: D (reduce overhead) → A/B (eliminate redundant calls) Target: 2.4 tok/s → 20-40 tok/s Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…23 No-op dequant test: even returning all zeros from dequantize, turbo3 runs at 2.4 tok/s (same as with full WHT rotation). The bottleneck is NOT in the attention dequantize path. New hypothesis: the SET_ROWS (quantize) path is the bottleneck. The Metal quantize_turbo3_0 function does 3 WHT rotations per KV write, totaling ~3200 ops per block × 224 blocks per token. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
CRITICAL BUG: The #include "turbo-wht.h" caused Metal JIT compilation to fail at runtime. The model silently fell back to CPU for ALL ops. ALL previous benchmarks (2.4 tok/s) were measuring CPU, not Metal GPU. After inlining the header: - MoE gen: 2.4 → 10.7 tok/s (4.5× improvement, now actually on Metal) - MoE prompt: 4.2 → 60.9 tok/s (14.5× improvement) Remaining gap vs q8_0: 85 → 10.7 tok/s (8× slower, down from 35×) This is the SAME bug we hit with turbo-matrices.h earlier. Rule: NEVER use #include in ggml-metal.metal — always inline. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Previous 2.4 tok/s was CPU fallback. Real Metal numbers: MoE: 10.7 tok/s gen (8× slower than q8_0, was thought to be 35×) Qwopus: 5.3 tok/s gen (3.3× slower than q8_0) Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Full investigation log with all tests, results, and the root cause. Upstream TurboQuant activity tracked in #27. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Key findings from Dejan.ai, unixsysdev, and mudler: 1. QJL naively added back destroys quality (cosine 0.69) 2. Pre-rotate queries eliminates rotation from dequant path 3. WHT abandoned by everyone — dense QR or no rotation preferred 4. unixsysdev gets -0.8% speed loss with fused CUDA kernel 5. We're the only Metal implementation Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…in) #23 Removing WHT rotation from dequant (quality broken, speed test only): gen: 10.7 → 49.1 tok/s (4.6× improvement, 57% of q8_0) prompt: 67.3 → 162.6 tok/s Confirms pre-rotate-queries would deliver ~49 tok/s. Remaining gap (49 vs 85) is block size + QJL overhead. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Speed ceiling confirmed: stripping rotation from dequant gives 49.1 tok/s (vs 10.7 with rotation, vs 85.5 q8_0 baseline). Implementation plan: store rotation matrix in KV cache, apply to Q in graph builder, strip from Metal dequant. 6 files to modify. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Instead of inverse-rotating every K during dequant, rotate Q once before attention. Math: <q, R^T*c[idx]> = <R*q, c[idx]>. Changes: - Store rotation matrix (R^T) in KV cache, filled after buffer clear - Apply ggml_mul_mat(R_T, q) in build_attn_mha after permute - Strip turbo_rotate_inverse from Metal dequant - Dynamic cast to access rotation from mctx Results: - MoE gen: 10.7 → 51.4 tok/s (4.8× speedup) - MoE prompt: 67.3 → 160.3 tok/s (2.4× speedup) - Now at 60% of q8_0 speed with 4.9× compression - Model produces coherent output Codex review: fixed buffer clear ordering (was zeroing rotation after init). Verified: rotation point is correct (after 4d reshape + permute, ne[0]=128). Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…23 Full investigation log documenting every test, every dead end, and every breakthrough. 21× total improvement from CPU fallback to pre-rotate-queries. Key lessons: no #include in Metal, no-op testing, pre-rotate-queries, buffer clear ordering, codex+roast catch real bugs. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Validated on real Qwen3 KV tensors: cosine sim 0.9508 → 0.9831 (+3.2%) MSE-only better on 99.3% of vectors including p1 tails. 3-bit index split: lower 2 bits in qs[], upper 1 bit in signs[]. No QJL stage in quantize or dequant. Results: - MoE gen: 51.4 → 62.2 tok/s (73% of q8_0, was 60%) - MoE prompt: 160 → 200 tok/s (90% of q8_0) - Qwopus gen: 14.6 → 15.5 tok/s (88% of q8_0, was 83%) - Qwopus prompt: 67 → 83 tok/s (100% of q8_0!) Codex verified: bit packing correct, quantize/dequant consistent. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Speed ceiling without Q rotation: 61.3 tok/s (vs 62.2 with it). The 128×128 ggml_mul_mat adds <1% overhead on Metal. Remaining gap is structural (block size + dequant complexity). Final: MoE 62.2 tok/s (73%), Qwopus 15.5 tok/s (88%). Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Diagnostic benchmark proves the 26% gap is entirely from block size 128. q4_0 (block 32, 4-bit quantization) runs at 84.2 tok/s = identical to q8_0. Next: turbo3 with block size 32. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Changed QK_TURBO3 from 128 to 32 (storage block size). Rotation still operates on 128-element groups (QK_TURBO3_GROUP=128). SET_ROWS kernel processes 4 blocks per rotation group. Flash attention nl_k changed from 32 to 8 (matching q4_0). Block struct: 14 bytes per 32 values = 3.5 bits/val → 4.6× compression. Results: - MoE gen: 62.2 → 77.7 tok/s (91% of q8_0 at 85.5) - MoE prompt: 200 → 218.5 tok/s (98% of q8_0) - Qwopus gen: 15.5 → 17.0 tok/s (97% of q8_0 at 17.6) - Qwopus prompt: 83 → 89.5 tok/s (108% of q8_0 — FASTER) Target was 75+ tok/s. Exceeded. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Codex post-commit review found: 1. TURBO_D was QK_TURBO3 (now 32) — broke turbo4 C array sizes 2. SET_ROWS kernel turbo3-specific but instantiated for turbo4 3. Tail block drop for non-128 head dims Fixed #3 (TURBO_D). #1 and #2 don't affect turbo3+dk128 path. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Perplexity benchmarking reveals catastrophic quality failure: - f16: 6.121, q8_0: 6.111, q4_0: 6.142 - turbo3: 165.6 (27× worse) Speed benchmarks were meaningless — fast garbage. Root cause investigation needed before any quality claims. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
1. V cache returns rotated-space values (cosine=0.02 vs correct 0.987) 2. dynamic_cast to llama_kv_cache_context fails for MoE models (uses llama_memory_hybrid_context, not kv_cache_context) → Q rotation and V inverse rotation NEVER executed Fix: store rotation tensors in llm_graph_context, not KV cache. Or access through hybrid memory interface. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…31 Block 128: PPL=165.6 (same as block 32) Disabled Q rotation: PPL=165.6 (same) Root cause: dynamic_cast fails for MoE hybrid memory context. Q rotation and V inverse rotation never execute. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…#30 ROOT CAUSE: pre-rotate-queries never executed because: 1. Q ne[0]=256 (GQA concatenated heads), rotation matrix ne[0]=128 2. mctx dynamic_cast failed for MoE hybrid memory FIX: put inverse WHT rotation back in dequantize_full_block. This is slower (10.7 tok/s vs 77.7) but produces CORRECT results. PERPLEXITY RESULTS: - f16: 6.121 - q8_0: 6.111 - q4_0: 6.142 - turbo3: 6.194 (+1.2% vs q8_0) ✅ The speed optimization (pre-rotate-queries) needs to be reimplemented to work with GQA head layout and hybrid memory types. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Quality confirmed: PPL 6.194 (+1.4% of q8_0) Speed: 10.7 tok/s (inverse rotation in dequant, no pre-rotate-queries) Previous speed claims (51-77 tok/s) were invalid — measured garbage output speed. Key lessons documented for future reference. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…w-up) state_write_data and state_read_data used hparams.n_embd_k_gqa (576) for ggml_row_size, but turbo types zero-pad to 640. For turbo4 (QK=128), 576 % 128 != 0 → ggml_row_size assertion failure during prompt cache save on llama-server slot reuse. Fix: use k->ne[0] / v->ne[0] (actual padded tensor width) instead of hparams values in all four serialization paths (K write, K read, V write, V read). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…rnels Port TheTom's warp-cooperative turbo3 SET_ROWS kernel and turbo2/turbo3 flash attention templates to HIP/ROCm (7900 XTX, gfx1100). HIP vendor header fixes: - Add cudaMemcpyToSymbol/FromSymbol -> hipMemcpyToSymbol/FromSymbol - Add cudaMemcpyHostToDevice/DeviceToHost mappings - Fix __shfl_sync, __shfl_xor_sync, __shfl_up_sync, __shfl_down_sync to support both 3-arg and 4-arg calls (CUDA allows defaulting width to warpSize, HIP macros required 4 args) - Add __ballot_sync -> __ballot with uint32_t cast (HIP returns 64-bit on wave64 platforms, turbo code expects 32-bit) HIP CMakeLists: - Add turbo3 and turbo2 flash attention template instances (same files as CUDA CMakeLists, were missing from HIP build) Tested: Mistral-Small-24B turbo3 PPL = 5.28 (+2.4% vs F16 baseline 5.16) Previously showed catastrophic PPL ~15000 due to CPU quantize stub bug (fixed by TheTom in 53f1298). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
One norm per rotation group instead of four identical copies.
Eliminates 6 bytes of redundant storage per 128-element group.
turbo3: 3.50 -> 3.125 bits/value, 4.57x -> 5.12x compression
turbo2: 2.50 -> 2.125 bits/value, 6.4x -> 7.53x compression
Zero PPL regression validated across:
- Asymmetric q8_0-K + turbo{2,3}-V
- Symmetric turbo3/turbo3
- Boundary V (LA-V7)
- 3 architectures (dense, Qwen, MoE)
- 3 context lengths (512, 8K, 32K)
- 2 Apple Silicon platforms (M5 Max, M2 Pro)
- NIAH 3/3 pass
+3-7% decode on tested M2 Pro setup. No regression on M5.
Also adds derived NL_TURBO3/NL_TURBO2 macros replacing ~250
hardcoded FA template nl values. Block size is now a one-line
edit in ggml-common.h.
Credit to @AmesianX whose block_size=256 CUDA implementation
prompted this investigation.
Co-Authored-By: tturney@psyguard.ai
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The block_size=128 change (adac2c6) broke CUDA quantization: with QK=128, blocks_per_group=1, but the warp-cooperative packing still used blk_base+warp_id, causing warps 1-3 to write OOB. Fix: compute elem_in_block = j % QK_TURBO_N and use it for block pointer (j / QK_TURBO_N) and byte offsets (elem_in_block / 4 for qs, elem_in_block / 8 for signs). Works for both QK=32 and QK=128. Validated on RTX 3090 (sm_86), llama3.1:8b Q4_K_M, q8_0/turbo3: PPL = 7.587 (matches QK=32 baseline exactly).
Sparse V: now enabled by default on all Metal (was M5+ only). Validated across 30+ testers with zero PPL impact. Opt-out: TURBO_SPARSE_V=0. Boundary V: auto-enabled (mode 7) when -ctv turbo2 is set. Protects first 2 + last 2 layers with q8_0-V, rest turbo2-V. 37-91% quality recovery across 4 tested models. Opt-out: TURBO_LAYER_ADAPTIVE=0. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The HIP build was missing 9 turbo cross-type flash attention vec instantiations (turbo4 combos, turbo3/turbo2 cross-types) that were present in the CUDA CMakeLists but not mirrored to the HIP CMakeLists. Also guard the D>=576 tile kernel dispatch with #ifndef GGML_USE_HIP since those instance files are already excluded from the HIP build (they exceed HIP's 65536-byte local memory limit). Tested on: ROCm 6.4.4, gfx1151 (AMD Ryzen AI Max+ 395 / Strix Halo)
Leftover from 1-bit VX experiment. Causes -Werror build failure in CI. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Add WHT-rotated weight quantization types: - TQ3_1S (type 44): 3-bit, 8 Lloyd-Max centroids, 4.0 BPW - TQ4_1S (type 45): 4-bit, 16 Lloyd-Max centroids, 5.0 BPW Both use 32-element Randomized Hadamard Transform with dual half-block scales (d0/d1). Quantization: forward RHT → scale search → iterative refinement (6 iter) → pack indices. Metal optimization (V2.1 fused kernel): - Zero threadgroup memory for rotation (was 20KB+ on large models) - Cooperative SIMD rotation via simd_shuffle_xor (registers only) - Single simd_sum at end (not per-block) - NR0=8 rows per threadgroup (amortizes rotation cost) - Memory barriers between rotate/matmul/unrotate dispatches - MoE MUL_MAT_ID support with rotated expert dispatch Config I (recommended): attn+ffn_gate/up=TQ4_1S, ffn_down=Q4_K, boundary 2+2 Validated on Qwen2.5-1.5B, Qwen3.5-27B, Qwen3.5-35B-A3B MoE: - 27-41% model size reduction - +1.3-1.9% PPL (Qwen), 94-102% decode speed - NIAH pass, KLD comparable to turbo3 KV - Llama 3.1 70B shows +25% PPL — needs investigation Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Without this barrier, the GPU may start executing the next node's matmul while the unrotate kernel is still modifying src1. This causes data corruption when TQ and non-TQ tensors are mixed within the same layer's attention block. The barrier ensures the unrotate completes before any subsequent operation reads from the same activation buffer. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…uant) Upstream commit 744c0c7 added graph-level Hadamard rotation for KV cache quantization. This conflicts with our kernel-level WHT rotation and causes graph hash table overflow on Phi-4 and potentially other models. Disable by default since TurboQuant already handles rotation at the kernel level (more efficient, no extra graph nodes). Users can re-enable with LLAMA_ATTN_ROT_DISABLE=0 if needed. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…stration - ggml-cuda.cu: add TQ4_1S/TQ3_1S exclusion in ggml_cuda_should_fuse_ mul_mat_vec_q (was missing, causing ABORT in mmvq.cu) - tools/quantize/quantize.cpp: register TQ3_1S/TQ4_1S in allowed types Tested: Qwen2.5-7B TQ4_1S — correct output, PPL 8.82 (+1.1% vs Q2_K), 6510 t/s prefill, 20 t/s decode (cuBLAS dequant-to-f16 path). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Two-phase approach: pre-rotate activation once via warp shuffle WHT, then simple mmvq kernel reads pre-rotated values (centroid × scale only, zero WHT per block). Results (Qwen2.5-7B TQ4_1S, RTX 5090): Decode: 20.3 → 69 t/s (3.4x speedup) vs q8_0 (177 t/s): 39% PPL: 8.82 (identical) Comprehensive optimization log (13 versions tested): cuBLAS baseline: 20 t/s V1 per-warp WHT, 4 warps: 60 t/s (3.0x) V3 shmem activation cache: 33 t/s (syncthreads kills it) V5 multi-warp per row: 62 t/s V6 LUT (shmem): 37 t/s (sync overhead) V7 8 warps clean: 62 t/s V8 pre-rotation (2-phase): 69 t/s ← BEST (3.4x) V9 pre-rot + q8_1: 70 t/s (marginal) V10 4-elem/thread: 57 t/s V13 8-elem, 4-thread dot: 45 t/s NR0=2/4/8: all regressed (register spill / cache thrash) The gap to q4_0 (275 t/s) is from dp4a integer intrinsics and packed int32 processing — TQ4_1S requires float centroid lookup which can't use dp4a. The gap to TheTom's Metal (85-99% of q8_0) is from Apple Silicon's cooperative SIMD efficiency. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
MoE models use MUL_MAT_ID which calls cudaStreamSynchronize in the expert routing dispatch. This is incompatible with CUDA graph capture. TQ4_1S types now disable CUDA graphs for MUL_MAT_ID nodes, matching the existing behavior for non-quantized types. Also: use persistent buffer for activation pre-rotation scratch, with graph-capture-safe check. Tested: Qwen3.5-35B TQ4_1S — 47 t/s decode, PPL 6.42. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…ratch Replace two-phase approach (separate pre-rotation kernel + global memory round-trip) with single-phase kernel where all 8 warps cooperatively WHT-rotate activation into shared memory, then each warp processes one row reading from shmem (broadcast reads from L1). Key changes: - No global scratch buffer (eliminates CUDA graph incompatibility) - No separate kernel launch for pre-rotation - Activation stays in shmem (~14-32 KB) instead of global memory - Single __syncthreads between rotation and dot product (NOT in inner loop) - V8 two-phase fallback retained for ncols > 12288 (48 KB shmem limit) This avoids the NR0 regression that killed V3/V6/V11 — those had sync inside the dot product loop. V12's sync is between the two phases. Expected: 30-50% decode improvement on Ampere+ (shmem broadcast eliminates 2x activation bandwidth). Pascal improvement smaller (still bandwidth bound). NEEDS TESTING — apply and benchmark on Ampere/Ada/Blackwell. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Add _USE_MATH_DEFINES + M_PI fallback for MSVC (doesn't define M_PI) - Add GGML_API to turbo3_cpu_wht_group_size for DLL export - Move extern declaration to file scope with extern "C" GGML_API linkage to fix C vs C++ name mangling across DLL boundary All changes are no-ops on Linux/Mac. Fixes MSVC build errors: C2065: 'M_PI': undeclared identifier LNK2001: unresolved external symbol turbo3_cpu_wht_group_size Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Add missing CUDA->HIP stream capture API mappings (vendors/hip.h) - Add TURBO_IQ_API macro for cross-DLL symbol visibility (Windows + Linux) - Add fileno/isatty POSIX compat macros for clang on Windows No kernel changes needed. signalnine's fused mmvq-tq kernel uses __shfl_xor_sync which maps directly to HIP warp shuffle on RDNA 4. Tested: RX 9070 XT (gfx1201, RDNA 4), Qwen2.5-1.5B Config I. Result: 30% faster decode than Q8_0 (135 vs 104 t/s), +1.8% PPL. Metal regression: clean, no changes to non-Windows/non-HIP paths. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…port) Gemma 4 uses global_head_dim=512 for full attention layers. The turbo FA kernels were only instantiated up to dk256 for symmetric and cross-turbo combos. Missing dk512_dv512 caused pipeline compilation failure on Gemma 4 (and any future model with head_dim=512 + turbo KV). Added 18 template instantiations (9 non-vec + 9 vec) for all turbo type combinations at dk512_dv512. Asymmetric q8_0/turbo combos already had dk512 and were not affected. Tested: Gemma 4 31B on M5 Max, symmetric turbo3/turbo3 and asymmetric q8_0/turbo4 both produce correct bench results at dk512. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Stack-allocated float tmp[4096] buffers in CPU vec_dot functions crashed on models with intermediate_size > 4096 (e.g. TinyLlama 5632, Qwen 27B 18944). Replaced with heap allocation. Affects CPU-only inference fallback path. GPU users unaffected. Reported by @oemc1470 on RX 6600 (gfx1032) where broken HIP forced CPU fallback. Tested: Qwen3.5-27B Config I, CPU-only (-ngl 0), intermediate_size=18944. No crash, no assert. Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Fix/turbo4 wht dequant
Cherry-picked from signalnine (daf0484, dca057a). TQ4_1S tensors are converted to q8_0 at model load via fused CUDA kernel. Transparent to downstream code. 40% smaller on disk, TQ4_1S quality, q8_0 decode speed. signalnine's results (RTX 5090): 105 t/s converted vs 103 t/s native q8_0. PPL 7.608 vs 7.599 (0.009 requantization rounding). Also includes group_size=32 for ggml_turbo_wht (needed for TQ weight types). Metal regression: clean (tested Q8_0, Config I, turbo3 KV on M5 Max). Co-Authored-By: Gabe Ortiz <gabe@signalnine.net> Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
GCC 13.3 on Ubuntu 24.04 hard errors on the redundant `extern` in the
GGML_API macro when used inside `extern "C" {}` blocks. MSVC and Clang
accept it silently.
Reported independently by joemc1470 (RX 6600 HIP) and
christopheraleman1015 (WSL2 Ubuntu).
Co-Authored-By: tturney@psyguard.ai
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…ization Port of wxtry's Apple9 MUL_MAT additions (PR #22) onto current TOT, adapted for QK_TURBO3=128 (original was QK=32). turbo3 (full MUL_MAT pipeline): - kernel_mul_mv_turbo3_f32: hand-written matvec for single-token decode - kernel_mul_mv_ext_turbo3_f32_r1_{2..5}: batched matvec variants - kernel_mul_mv_id_turbo3_f32: indirect matvec (MoE) - kernel_mul_mm_turbo3_{f32,f16} + _id variants: simdgroup matmul turbo4 (mul_mm only, mul_mv still needed): - kernel_mul_mm_turbo4_{f32,f16} + _id variants Dual-LUT dequant: two 256-entry half4 LUTs replace per-element bit extraction in dequantize_turbo3_0_t4. 4KB constant memory, eliminates 12 scalar bit-extract ops per call. supports_op: turbo4 blocked from MUL_MAT (no mul_mv), both blocked from GET_ROWS (no kernel). Tested on M5 Max + M2 Mini, zero regressions on FA path. Based on work by wxtry (PR #22, commits 70e45b7..ceebfe0) Co-Authored-By: wxtry <wxtwxtry@gmail.com> Co-Authored-By: Tom Turney <tturney@psyguard.ai> Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Owner
Author
M2 Mini (Apple8, 16GB) — Qwen2.5-7B Q8_0Speed (pp512/tg128, 3 runs)
PPL (wikitext-2, 8 chunks)
NIAH (single needle, 4K + 8K, depths 0/50/100%)
KV Memory (8K context, from server logs)
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spiritbuun
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Apr 6, 2026
- turbo4 K+V results on Qwen3.5-27B (-0.32% vs q8_0) and Qwen3-14B (+6.3%) - Sparse V dequant benchmarks: MoE native dequant +10.9% at 8K - Gemma-3 turbo3 results post-iSWA fix (+3.3%) - KVLinC no-K-rotation negative result - Speculative decoding negative result - CUDA 13.2 compatibility verified - Experiments TheTom#31, TheTom#39, TheTom#42, TheTom#45, TheTom#49, TheTom#50, TheTom#51 status updates Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Summary
Port of @wxtry's Apple9 MUL_MAT additions from PR #22 onto current TOT (
feature/turboquant-kv-cache), adapted for our QK_TURBO3=128 block size.PR #22 was originally merged into
experiment/decode-speed-paritywhich has diverged significantly from TOT. Cherry-pick failed with 6 conflicts across Metal files. This is a clean manual port of the new code onto our production branch.What's new
turbo3 full MUL_MAT pipeline (non-FA fallback):
kernel_mul_mv_turbo3_f32— hand-written matvec for single-token decodekernel_mul_mv_ext_turbo3_f32_r1_{2..5}— batched matvec variantskernel_mul_mv_id_turbo3_f32— indirect matvec (MoE)kernel_mul_mm_turbo3_{f32,f16}+_idvariants — simdgroup matmul for prefillturbo4 partial MUL_MAT (mul_mm only, mul_mv still needed):
kernel_mul_mm_turbo4_{f32,f16}+_idvariantsDual-LUT dequant optimization:
half4LUTs replace per-element bit extraction indequantize_turbo3_0_t4supports_op fixes:
Internal test results
M5 Max (Apple10, Qwen2.5-1.5B Q4_K_M):
M2 Mini (Apple8, Qwen2.5-1.5B Q4_K_M):
Requesting community validation
Before merging, I want independent testing on your hardware. PR #22 was merged into the wrong branch and I'm being thorough about validating the port.
Please build from this branch and test:
Post your numbers for speed and PPL. Especially interested in M3 Ultra (@wxtry) since that's the hardware this was originally validated on.
Based on work by @wxtry (PR #22, commits 70e45b7..ceebfe0)
🤖 Generated with Claude Code