feat(ggml): add Q3_K and Q5_K dequantization (types 11 and 13)#103
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mvkorobkov wants to merge 2 commits into
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feat(ggml): add Q3_K and Q5_K dequantization (types 11 and 13)#103mvkorobkov wants to merge 2 commits into
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May 15, 2026 11:57
Implements scalar dequantize for Q3_K (110 B/block) and Q5_K (176 B/block) so that DeepSeek-R1-0528-Qwen3-8B-Q3_K_L and similar models can be converted via larql gguf-to-vindex. - q3_k.rs: unpack_q3k_scales (kmask1/kmask2 per llama.cpp), two-half-block loop with m-bitmask for high bits, signed-scale centred at 32. - q5_k.rs: reuses pub(super) unpack_q4k_scales from q4_k; u1/u2 mask walk for high bits, 4 iterations of 64 elements each. - mod.rs: Q3_K_BLOCK_BYTES=110, Q5_K_BLOCK_BYTES=176, dispatch in tensor_data_size() and dequantize(). - q4_k.rs: unpack_q4k_scales promoted to pub(super) for Q5_K reuse.
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What
Implements scalar dequantization for two missing K-quant formats:
Both
tensor_data_size()anddequantize()inmod.rsare wired up.Why
Without these,
larql convert gguf-to-vindexfails withunsupported type id 11/13on any model that uses Q3_K or Q5_K tensors. This includes:Implementation
q3_k.rs—dequantize_q3_k()hmask[0..32]·qs[32..96]·scales[96..108]·d[108..110]unpack_q3k_scales(): 12 bytes → 16 six-bit signed values using thekmask1=0x03030303/kmask2=0x0F0F0F0Fshuffle fromdequantize_row_q3_Kin llama.cppmbitmask walks throughhmask; clear bit → subtract 4 from q2 valueq5_k.rs—dequantize_q5_k()d[0..2]·dmin[2..4]·scales[4..16]·qh[16..48]·qs[48..176]pub(super) unpack_q4k_scales()fromq4_k.rs(same 12-byte format as Q4_K)u1/u2bitmask pair walks throughqh; set bit → add 16 to 4-bit nibbledequantize_row_q5_Kin llama.cppq4_k.rs—unpack_q4k_scalesvisibility changedfn→pub(super)so Q5_K can share it without duplication.Testing
Unit tests in each module:
q3_k: zero-scale all-zero output, hmask-clear subtracts 4, wrong-size errorq5_k: zero-scale all-zero output, high-bit adds 16, wrong-size errorEnd-to-end:
larql convert gguf-to-vindexonDeepSeek-R1-0528-Qwen3-8B-Q3_K_L.ggufcompletes through dequantization without errors (145 Q3_K + 108 Q5_K tensors dequantized cleanly).All 82 existing larql-models tests continue to pass.