keeping turboquant up to date v4#25
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InfernalDread merged 5 commits intoInfernalDread:turboquant_kv_cache_updated_v4from Apr 12, 2026
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keeping turboquant up to date v4#25InfernalDread merged 5 commits intoInfernalDread:turboquant_kv_cache_updated_v4from
InfernalDread merged 5 commits intoInfernalDread:turboquant_kv_cache_updated_v4from
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* docs: add guide on how to add multimodal support * nits
* mtmd: add Gemma 4 audio conformer encoder support Add audio processing for Gemma 4 E2B/E4B via a USM-style Conformer. Architecture: - 12-layer Conformer: FFN → Self-Attention → Causal Conv1D → FFN → Norm - Subsampling Conv Projection: 2x Conv2D(stride=2) with LayerNorm - Full self-attention with sinusoidal RPE and sliding window mask (24) - Logit softcapping at 50.0, ClippableLinear clamping - Output: 1024 → 1536 → RMSNorm → multimodal embedder Mel preprocessing (dedicated mtmd_audio_preprocessor_gemma4a): - HTK mel scale, 128 bins, magnitude STFT, mel_floor=1e-3 - Standard periodic Hann window (320 samples), zero-padded to FFT size - Semicausal left-padding (frame_length/2 samples) - Frame count matched to PyTorch (unfold formula) - No pre-emphasis, no Whisper-style normalization - Mel cosine similarity vs PyTorch: 0.9998 Key fixes: - Tensor loading dedup: prevent get_tensor() from creating duplicate entries in ctx_data. Fixed with std::set guard. - ClippableLinear clamp_info loading moved after per-layer tensors. - Sliding window mask (24 positions) matching PyTorch context_size. - Skip Whisper normalization for Gemma4 mel output. Tested on E2B and E4B with CPU and Vulkan backends. Transcribes: "Glad to see things are going well and business is starting to pick up" (matching ground truth). Ref: #21325
* mtmd: add gemma 4 test (vision + audio) * add to docs
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Mixed turbo3-K/turbo2-V and turbo2-K/turbo3-V had no CUDA FA kernel instances, causing ~11x prefill regression (falling back to CPU FA). Added VEC template instances for both cross-type pairs at D=64/128/256. Updated the mixed-type guard in get_best_fattn_kernel to allow any combination of turbo2, turbo3, and q8_0. Tested: turbo3/turbo2 and turbo2/turbo3 both run at full CUDA VEC speed (~170 t/s prefill, ~221 t/s decode on Qwen3.5 35B). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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…bug 1 Ports GGML_TYPE_TURBO4_0 to CUDA using the 4-bit PolarQuant format (16 centroids, nibble-packed, no QJL). Previously turbo4 crashed on CUDA with "cannot run the operation (SET_ROWS)". Changes TURBO4_USE_4BIT default from Metal-only to all backends. The 4-bit format (16 centroids) has better quality than the legacy 3-bit+QJL format and is simpler to implement (no residual projection). Full CUDA stack: - turbo-quant.cuh: 4-bit centroids, midpoints, nearest-centroid, dequant element, per-block quantize - set-rows.cu: k_set_rows_turbo4 kernel (128 threads, WHT rotation, 4-bit quantize, nibble pack via warp shuffle, corrected norm) - dequantize.cuh + convert.cu: turbo4 to f16/f32 - fattn-common.cuh: vec_dot_KQ_turbo4 + dequantize_V_turbo4 - fattn-vec.cuh + fattn.cu: VEC dispatch + all cross-type instances (turbo4×turbo4, turbo4×q8_0, turbo4×turbo3, turbo4×turbo2) - ggml-cpu.c: CPU FA vec_dot for turbo4 PPL (Qwen3.5, wikitext-2): 6.23 (+0.8% vs q8_0) at 3.8× compression Speed: 217 t/s decode (comparable to turbo3 222 t/s) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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