fix: inverse WHT in test-turbo-quant.c round-trip (#59)#63
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seanrasch wants to merge 153 commits intoTheTom:feature/turboquant-kv-cachefrom
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fix: inverse WHT in test-turbo-quant.c round-trip (#59)#63seanrasch wants to merge 153 commits intoTheTom:feature/turboquant-kv-cachefrom
seanrasch wants to merge 153 commits intoTheTom:feature/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>
…eTom#21 - 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>
…m#23 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>
…heTom#26 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>
…heTom#23 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>
…om#23 Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…heTom#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>
…m#23 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>
…om#28 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) TheTom#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>
…m#23 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>
…heTom#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 TheTom#3 (TURBO_D). TheTom#1 and TheTom#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>
…Tom#30 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>
…heTom#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>
…eTom#31 TheTom#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>
- 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>
…Gemma 4 - Introduced a new function to check if TQ source tensors mutate during operations. - Updated matrix multiplication logic to handle TQ weights more effectively, ensuring correct concurrency behavior. - Adjusted Metal kernel definitions to support TQ weights with improved dispatch parameters. - Enhanced comments for clarity on concurrency issues related to TQ weights.
… and TQ4_1S quantization types with corresponding sizes in GGML_QUANT_SIZES.
…AM than q8_0 Autoresearch-discovered optimizations for TQ4_1S weight mul_mat_vec kernel. Native TQ4_1S at 5.0 bpv now runs 36% FASTER than the q8_0 load-time conversion (240 vs 176 t/s) while using 1.7× LESS VRAM (4.5 vs 7.5 GiB). Key optimizations (found via 86 automated experiments): 1. fp16 activation buffer — halves activation bandwidth (the bottleneck) 2. Shared-memory centroid LUT — eliminates constant memory serialization on divergent lane access (+89% single change) 3. Half2 arithmetic + strided block processing — 2× arithmetic density 4. Vectorized 128-bit loads — uint32×4 weights, int4 activations (+45%) 5. Register __byte_perm centroid decode — zero-memory centroid lookup 6. NWARPS 8→4 Also: - Load-time q8_0 conversion now opt-in (GGML_TQ_CONVERT_Q8=1) instead of default. Native kernel is strictly better on both speed and VRAM. - Autoresearch harness gains coherence testing (server API + factual Q&A) to catch silent corruption that PPL alone misses. Benchmarks (RTX 5090, Qwen2.5-7B-Instruct TQ4_1S): Upstream V12 runtime: 67 t/s (4.5 GiB VRAM) q8_0 conversion: 176 t/s (7.5 GiB VRAM) Native optimized: 240 t/s (4.5 GiB VRAM) ← this PR Quality (vs f16 baseline): PPL: 7.54 (f16: 7.18, q8_0 conv: 7.55) Mean KLD: 0.056 (q8_0 conv: 0.057, q4_0: 0.078) NIAH: 5/5 Coherence: 4/4 (Paris, 4, print, Shakespeare)
Replace per-element generic dequant template (which repeats the full 32-element WHT butterfly 16 times per block) with a warp-cooperative version using __shfl_xor_sync. One WHT per block instead of 16. Note: this improves the dequant kernel itself but doesn't fix the prefill gap (5.9K vs 13.3K). The bottleneck is cuBLAS fp32 GEMM vs the q8_0 conversion path's native int8 tensor core GEMM. The dequant was never the slow part — the GEMM dispatch is fundamentally different. For prefill-heavy workloads, load-time q8_0 conversion remains the recommended path (default ON). GGML_TQ_NATIVE=1 for decode-heavy interactive chat where the +29% decode speed matters more.
- Multi-token dp4a kernel for ne[1]≤8 (speculative decoding, small batches) loads weight data once per block, reuses across all ncols_dst tokens - Runtime TQ4_1S→fp16 dequant + cuBLAS for ne[1]>8 prefill - Fix multi-GPU crash: replace static global CUDA buffers with per-device pool allocations from ctx.pool(id), matching mmvq.cu pattern - Fix static build: TURBO_IQ_API wrapped in #ifdef GGML_BACKEND_SHARED
Enhance Metal operations for TQ weights and concurrency handling
extern "C" GGML_API creates double extern on paths where GGML_API
expands to 'extern'. Wrap in extern "C" {} block instead.
Reported by Madreag on RTX 5090 WSL2.
Co-Authored-By: Tom Turney <tturney1@gmail.com>
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Metal MoE support: - Add kernel_mul_mm_id_map0 instantiations for ne20 = 32, 60, 64, 128, 160, 256 - Covers Yuan, Qwen1.5-MoE, OLMoE, Qwen2/3-MoE, Mistral Small 4, Llama 4 Maverick, DeepSeek-V2/V3, Qwen3.5-35B/122B - Note: ne02=256 (Qwen3.5-35B-A3B) hits shmem assert in llama-server with flash attention — needs chunked map0 dispatch (follow-up) Backend tests: - Add TQ3_1S and TQ4_1S to all_types array in test-backend-ops - Enables GET_ROWS and MUL_MAT coverage for WHT-rotated weight types Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Graph reservation passes worst-case ne20=ne02 (256x256x2=128KB), exceeding the 32KB threadgroup memory limit on Apple Silicon. At runtime ne20 is the actual n_expert_used (e.g. 8), so shmem = 256*8*2 = 4KB, well within limits. Cap the reservation shmem to 32KB to prevent the assert from firing. Tested on Qwen3.5-35B-A3B (256 experts) with llama-server + flash attention — previously crashed during warmup, now runs at 22 t/s. Fixes TheTom#58 Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The dp4a int8 kernel is optimized for NVIDIA Turing+ dp4a throughput (240 t/s on 5090). On RDNA4, sudot4 has different throughput characteristics and the q8_1 activation quantization adds overhead, causing a regression vs the V12 float kernel (101 vs 135 t/s on RX 9070 XT). Fix: check GGML_CUDA_CC_IS_AMD(cc) at dispatch time and route AMD GPUs to a scalar half-precision kernel (same pattern as TQ3_1S). NVIDIA continues using the dp4a path. Changes: - Add mul_mat_tq4_1s_scalar_multi kernel: pre-rotated half activations, shmem centroid LUT, scalar dot product (no dp4a/byte_perm) - Dispatch: use_dp4a = !AMD && TQ4_1S. AMD falls through to scalar path. - LAUNCH_SCALAR macro unifies TQ4_1S/TQ3_1S scalar dispatch Expected RDNA4 result: restore V12-level decode (135 t/s, 130% of Q8_0) instead of dp4a regression (101 t/s, 60% of Q8_0). Co-Authored-By: Tom Turney <tturney1@gmail.com> Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
TurboQuant KV cache compression (turbo2/turbo3/turbo4) builds and runs correctly on AMD Instinct MI300X with ROCm 7.0.2. Zero code changes required — existing CUDA kernels compile via HIP translation. Test results (Qwen2.5-1.5B Q4_K_M, single MI300X): - WHT roundtrip: PASS (max error 2.98e-07) - turbo3 prefill: +3% vs f16 (25,200 vs 24,453 tok/s) - turbo3 decode: 88% of f16 (160 vs 181 tok/s) - turbo4 prefill: +4% vs f16 (25,427 vs 24,453 tok/s) - turbo4 decode: 89% of f16 (161 vs 181 tok/s) MI355X (gfx950) compiles but needs gfx950 added to llama.cpp's MMQ kernel dispatch (upstream issue, not TurboQuant-specific). Tested-by: Andy Luo <andyluo7@users.noreply.github.com>
Add AMD Instinct MI355X (gfx950) architecture support: Code changes: - vendors/hip.h: Add CDNA4 define for __gfx950__, include in CDNA family - common.cuh: Add GGML_CUDA_CC_CDNA4 constant and IS_CDNA4 macro - mma.cuh: Route CDNA4 to compatible MFMA instructions * bf16: mfma_f32_16x16x16bf16_1k (same as CDNA3) * int8: mfma_i32_16x16x32_i8 (same as CDNA3) * f32: mfma_f32_16x16x4f32 (CDNA2 path, NOT xf32 which doesn't exist on gfx950) - mmq.cuh: Include CDNA4 in stream-k dispatch - common.cuh: Exclude CDNA4 from CDNA3-specific e4m3_fnuz FP8 path (gfx950 uses standard e4m3fn) MI355X test results (Qwen2.5-1.5B Q4_K_M, single GPU): - turbo3: 39,140 tok/s prefill (98% of f16), 162 tok/s decode (64%) - turbo4: 39,232 tok/s prefill (98% of f16), 214 tok/s decode (84%) - WHT roundtrip: PASS (max error 2.98e-07) Note: non-FA MMQ path crashes on gfx950 (xf32 MFMA unsupported). TurboQuant types force FA and work correctly. Tested-by: Andy Luo <andyluo7@users.noreply.github.com>
perf: TQ4_1S native kernel 3.5× faster — 240 t/s, less VRAM than q8_0 conversion
Full turbo3 quantize/dequant pipeline for Vulkan backend: - types.glsl: block_turbo3_0 struct (norm + qs[8] + signs[4]) - dequant_turbo3_0.comp: standalone dequant shader (3-bit index reconstruction from 2-bit qs + 1-bit signs, centroid lookup) - dequant_funcs.glsl: inline dequant for get_rows/mul_mat paths - dequant_funcs_cm2.glsl: cooperative matrix 2 FA path support - copy_to_quant.comp: quantize function with norm correction - vulkan-shaders-gen.cpp: turbo3_0 type registration - ggml-vulkan.cpp: pipeline creation and supports_op dispatch Tested on AMD 7900 XTX (RADV): 243 pp / 25.8 tg t/s with turbo3 KV. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
feat: Vulkan compute shader support for turbo3 (experimental)
Two-pass block-parallel attention kernel optimized for turbo3 V cache decode on Apple Silicon Metal. Supports both q8_0-K (asymmetric) and turbo3-K (symmetric) configurations via compile-time function constant. Architecture: - Pass 1: 32-thread SIMD group per (query-head, block) pair - Each lane handles DK/32 interleaved dimensions - Q loaded to per-lane registers, K dequant via q8_0 or turbo3 path - K scoring via simd_sum dot product - turbo3 V unpack with register codebook (8 centroids) - Online softmax (m/l/o state) entirely in registers - Zero shared memory in pass 1 - Pass 2: merge partial results across blocks - Online softmax correction with global max/sum - Inverse WHT via simd_shuffle_xor (stages 0-4) + shared memory (stages 5-6) - Eliminates 5 of 7 threadgroup barriers vs naive butterfly Auto-detection: activates for single-token decode (ne01==1) when V is turbo3 and K is q8_0 or turbo3. Controllable via TURBO_FLASH env var (0=off, 1=force). Block size B=64 (proven optimal on Apple Silicon). Benchmarks (Qwen2.5-7B Q8_0, asymmetric q8_0-K/turbo3-V): - M5 Max 128GB: +1.5% decode at 8K (56.82 vs 56.00 tok/s), 93% of q8_0 - M2 Pro 32GB: +0.6% decode at 8K (20.55 vs 20.42 tok/s) - Advantage scales with context (+7.3% at 32K) Inspired by Eric Kryski's TurboFlash architecture (mlx-swift-lm). Co-Authored-By: tturney@psyguard.ai Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
feat: AMD Instinct MI300X + MI355X (gfx942/gfx950) ROCm support
The existing C round-trip test compared input vectors in the original domain against output vectors still in the WHT-rotated domain, because dequantize_row_turbo*_0 intentionally leave the result rotated (Q is also rotated in the graph, so <Q_rot, K_rot> computes the correct attention score without an explicit inverse on K). As a result the reported MSE and cosine similarity were meaningless. For Test 1 the output was uniform [-0.088, -0.088, -0.088, -0.088] against input [1, 0, 0, 0], yielding cosine ~= -0.088 instead of ~1. Add turbo_cpu_fwht_inverse() — same butterfly as the forward pass, but with s1 and s2 swapped — and call it after dequant in each test so the comparison happens in a common space. Math: the forward pass is y = D(s2) * N * H * D(s1) * x where N = 1/sqrt(group_size) and H is the unnormalized Hadamard butterfly with H*H = group_size * I. Then (N*H) is self-inverse and s1, s2 are ±1 diagonals, also self-inverse, so the inverse is D(s1) * N * H * D(s2) — structurally identical with s1 and s2 swapped. Verified in isolation: round-trip MSE 2.78e-17 on a basis vector and 6.94e-13 on an arbitrary vector (fp32 machine epsilon), orthonormality ratio exactly 1.000000. Fixes TheTom#59. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Fixes #59.
The existing C round-trip test compared input vectors in the original
domain against output vectors still in the WHT-rotated domain, because
dequantize_row_turbo*_0intentionally leave the result rotated (Q isalso rotated in the graph, so
<Q_rot, K_rot>computes the correctattention score without an explicit inverse on K). The MSE and cosine
metrics in the test were therefore meaningless — Test 1 reported cosine
~-0.088 on a basis-vector round trip that should yield ~1.0.
Change
Add
turbo_cpu_fwht_inverse()alongside the existingturbo_cpu_fwht()in
ggml-turbo-quant.c, and call it on the dequant output in each ofthe three tests before computing metrics.
The forward pass is
y = D(s2) · N · H · D(s1) · xwhereN = 1/sqrt(group_size)andHis the unnormalized Hadamard butterfly withH·H = group_size·I.(N·H)is self-inverse ands1,s2are ±1diagonals (also self-inverse), so the inverse is
D(s1) · N · H · D(s2)— structurally identical, withs1ands2swapped.
Verification
Extracted the forward + inverse pair into a standalone C program and
ran on the exact `s1`/`s2` sign vectors used here:
```
basis: MSE=2.78e-17 max_abs_err=5.96e-08 x[0]=1.000000
arb: MSE=6.94e-13 max_abs_err=1.91e-06
norm: in=79.6926 fwht_out=79.6926 ratio=1.000000
```
Round-trip recovery is at fp32 machine epsilon for both a basis vector
and an arbitrary mixed-frequency vector, and the forward pass is
confirmed orthonormal.
Disclosure: drafted with Claude Opus assistance. I understand the math
and the code and am happy to discuss either.