feat: add additional TurboQuant kernel templates for enhanced flash a…#2
Merged
Merged
Conversation
…ttention support Added new kernel templates for 512x512 dimensions across TurboQuant configurations (turbo2, turbo3, turbo4) to improve flash attention capabilities. This enhancement allows for better performance and flexibility in handling larger input sizes.
Vect0rM
pushed a commit
that referenced
this pull request
Apr 21, 2026
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>
Vect0rM
pushed a commit
that referenced
this pull request
Apr 21, 2026
Complete experiment log: #1 4-mag LUT: 15.1 at 8K (BEST, +38%) #2 Batched extract: 13.7 (+25%) #3 Inline FA block: 13.5 (I-cache pressure) #4 Deferred norm: 12.9 (loses ILP) #5 2-pair half2: 12.0 (ternary overhead) #6 Select chain: 11.9 (branches kill) #7 Bit-arithmetic: 11.6 (ALU too heavy) #8 FMA branchless: 11.4 (ALU still too heavy) #9 Named-reg ternary: 10.3 (branches worst) #10 Main (8-LUT): 10.95 (baseline) #11 Non-vec FA: 10.2 (wrong kernel) Ceiling: 24.5 (no dequant) Apple8 hardware truth: 1 divergent constant read < 7 ALU ops (even with fma) Branches cost MORE than divergent constant reads Array indexing ALWAYS spills on Metal 4 constant addresses is the sweet spot The 4-mag LUT is the dequant-level ceiling on Apple Silicon. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> Co-Authored-By: tturney@psyguard.ai
Vect0rM
pushed a commit
that referenced
this pull request
Apr 21, 2026
* vulkan: add TQ4_1S weight compression support
Adds Vulkan shader support for TQ4_1S (4-bit WHT-rotated weight
compression with 16 Lloyd-Max centroids, 32-element blocks).
Shaders:
- dequant_tq4_1s.comp: standalone dequant with WHT inverse via
subgroupShuffleXor (32-thread workgroup, 5-stage butterfly)
- mul_mat_vec_tq4_1s.comp: specialized MUL_MAT_VEC with inline
activation pre-rotation (forward RHT on activation, centroid*scale
dequant without inverse RHT)
- copy_from_quant.comp: TQ4_1S dequant path with full WHT inverse
- copy_to_quant.comp: TQ4_1S SET_ROWS quantization path with forward
RHT, dual half-block RMS scales, 16-centroid quantization
- types.glsl: block_tq4_1s struct (d0, d1, qs[16])
- dequant_funcs.glsl: TQ4_1S centroid*scale dequant (no RHT)
Pipeline wiring (ggml-vulkan.cpp):
- MUL_MAT, SET_ROWS, CPY supports_op
- pipeline_dequant, pipeline_set_rows, pipeline_cpy_quant_f32
- Specialized MUL_MAT_VEC with forced subgroup workgroup size
Tests:
- test_set_rows_tq4_1s: SET_ROWS round-trip validation
* vulkan: add fused mul_mat_vec kernel for TQ4_1S
Adds a specialised MUL_MAT_VEC shader for TQ4_1S weights so the
per-decode-step matrix-vector product no longer has to dequant the
full weight tensor to f16 and then go through the generic matmul
path. The kernel pre-rotates the activation via a forward
Walsh-Hadamard Transform in shared memory and dot-products against
the raw centroid*scale stored weights, folding the inverse-WHT on
the weight side into the activation by the symmetry H = H^T.
Math:
w[k] = sign[k] * INV_SQRT32 * (H @ stored)[k]
sum_k w[k] * a[k] = INV_SQRT32 * sum_j stored[j] * (H @ (sign * a))[j]
Portability choices:
- Workgroup size is pinned to 32 threads regardless of the
DMMV_WG_SIZE bucket the rest of the mul_mat_vec family picks for
the current architecture. The butterfly operates on 32-element
blocks with one element per thread; that contract is fixed by the
quantization format, not by the GPU. Earlier revisions used
`gl_WorkGroupSize.x` as the stride unit, which silently skipped
half the work on Intel drivers that force the subgroup to 16
(tests passed via NMSE tolerance while real inference output was
garbage).
- Butterfly implementation is shared memory only. A subgroup-shuffle
variant (`subgroupShuffleXor`) was prototyped and measured on Intel
Arc A380 with Mesa Xe HPG: it ran ~60-85 %% slower than the
explicit shared-memory butterfly, because Mesa emulates subgroup
shuffles via LDS and ends up doing the same LDS traffic with extra
driver overhead. The shared-memory butterfly is correct on every
device regardless of subgroup-op support, is the fastest path on
every device we can actually measure, and leaves the
`pipeline_dequant_mul_mat_vec_f32_f32[w][TQ4_1S]` slot uniform
across all DMMV_WG_SIZE buckets.
- Reduction is the shared-memory tree reduction (no subgroupAdd), for
the same reason: on Intel Arc the subgroupAdd is also LDS-backed
and the hybrid reduction path was measurably slower. Future
vendor-specific heuristics can switch to the hybrid or pure-subgroup
reduction variants on NVIDIA / AMD RDNA if hardware subgroup ops
turn out to beat the LDS roundtrip there; the existing reduction
modes in `mul_mat_vec_base.glsl` already provide the necessary
variants.
- NUM_ROWS is 8 so the butterfly cost amortises across 8 output rows
per workgroup. Each thread holds one position of each of the 8
weight blocks and pairs them with the shared rotated activation.
- `mul_mm` and `flash_attn_cm2` shader generation is skipped for
TQ4_1S because it is a weight-only format that never reaches the
coopmat2 matmul or the KV cache flash-attention paths.
Tests:
- `test-backend-ops` MUL_MAT tolerance tightened from 2.0 to 0.01
NMSE so real defects can't hide behind a loose check.
- Added Gemma-4 E2B, Qwen, Phi and Llama dimensional coverage
(k in {1536, 2048, 2304, 3072, 4096}, m in {256, 1152, 1536,
2048, 5120, 6144}, n in {1..8, 16, 64, 256}). 148 MUL_MAT test
cases total.
Verification (Intel Arc A380, 6 GB VRAM, Vulkan ANV / Mesa Xe HPG,
`llama-bench -p 512 -n 128 -r 3` and `llama-perplexity -c 512
--chunks 20 wiki.test.raw`):
| Model | Config | Size | Reduction | PPL Δ | pp512/Q8 | tg128/Q8 |
|---------------|---------|----------:|----------:|-------:|---------:|---------:|
| Qwen2.5-1.5B | I | 1570→1082 | -31.1% | +4.66% | 53.9% | 107.5% |
| Phi-3.5-mini | I | 3873→2839 | -26.7% | +5.36% | 57.6% | 52.8% |
| Llama-3.2-3B | hybrid | 3263→2147 | -34.2% | +2.03% | 82.4% | 84.2% |
| Llama-3.2-3B | premium | 3263→2577 | -21.0% | +0.98% | 71.3% | 67.3% |
Qwen2.5-1.5B is faster than its own Q8_0 baseline with Config I:
the compressed model fits in less VRAM, and on a small model the
TQ4_1S compute cost is offset by the reduced memory traffic.
All four models produce coherent output end-to-end and the
reductions line up with the TurboQuant paper's validation matrix
(§5.8). The remaining gap to Q8_0 on the bigger models is
compute-bound on the A380; it closes further on GPUs with more raw
throughput.
* vulkan: restructure TQ4_1S inner loop for cross-row smem reuse
Splits the dequant+accumulate phase into two sub-loops:
1. Pre-compute w_vals[n] for all NUM_ROWS rows (centroid lookup +
scale multiply, reads from weight buffer only).
2. Read the rotated activation from shared memory ONCE per column,
then FMA across all rows in a tight register loop.
This is the Vulkan analogue of the 'hot loop load dedup' from the
CUDA kernel (PR TheTom#57 optimisation #2). It makes the shared memory
read explicitly loop-invariant across rows, which helps compilers
that don't auto-hoist LDS loads out of unrolled loops.
Measured effect on Intel Arc A380 (Llama-3.2-3B premium,
llama-bench tg128, r=5): 15.50 -> 15.78 t/s (+1.8%, within noise
but not a regression). The structure is cleaner regardless and
should benefit architectures with higher LDS latency.
9 tasks
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
2 participants
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Overview
Additional information
https://github.com/TheTom/llama-cpp-turboquant/compare/experiment/turbo4-quality-investigation...AtomicBot-ai:atomic-llama-cpp-turboquant:feature/sdf?expand=1
Requirements