mirror of
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SOTA 3-bit quants (#5196)
* iq3_xxs: quantize/dequantize RMSE seems a bit high-ish at about half-way between q2_K and q3_K, so need to check more. * iq3_xxs: CUDA dequantize works * iq2_xxs: tuning quantization * iq3_xxs: starting to look better PPL on wiki.test.raw LLaMA-v1-7B: 6.4218 LLaMA-v2-7B: 6.3560 Mistral-7B : 6.0717 This is better than Q3_K_XS, with a 5% reduction in quantized model size. * iq3_xxs: CUDA dot product We have PP-512: 5891 t/s TG-128: 143.9 t/s * iq3_xxs: scalar and AVX2 dot products * iq3_xxs: ARM_NEON and Metal Metal performance is decent, ARM_NEON is pathetic * iq3_xxs: slightly better grid points * Faster iq3_xxs and iq2_xs dot products on CUDA * iq3_xxs: add some quant mix * iq3_xxs: fix failing quantization test Dot product still fails. Is this real? * iq3_xxs: hopefully fix ROCm * iq3_xxs: failing tests This time the dot product accuracy did find an actual bug in the AVX2 implementation. * Add IQ3_XXS to test-backend-ops --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow
GitHub
parent
2256f36b79
commit
f4d7e54974
@ -378,6 +378,8 @@ int main(int argc, char ** argv) {
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printf("testing %s ...\n", ggml_type_name(type));
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}
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ggml_quantize_init(type);
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error_stats global_stats {};
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for (const auto& kv_tensor : tensors) {
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@ -25,6 +25,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
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{ "IQ2_XS", LLAMA_FTYPE_MOSTLY_IQ2_XS, " 2.31 bpw quantization", },
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{ "Q2_K", LLAMA_FTYPE_MOSTLY_Q2_K, " 2.63G, +0.6717 ppl @ LLaMA-v1-7B", },
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{ "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.16G, +9.0634 ppl @ LLaMA-v1-7B", },
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{ "IQ3_XXS",LLAMA_FTYPE_MOSTLY_IQ3_XXS," 3.06 bpw quantization", },
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{ "Q3_K", LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M" },
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{ "Q3_K_XS",LLAMA_FTYPE_MOSTLY_Q3_K_XS,"3-bit extra small quantization" , },
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{ "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 2.75G, +0.5551 ppl @ LLaMA-v1-7B", },
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200
ggml-cuda.cu
200
ggml-cuda.cu
@ -191,6 +191,10 @@ static __device__ __forceinline__ int __vsubss4(const int a, const int b) {
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#endif // __has_builtin(__builtin_elementwise_sub_sat)
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}
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static __device__ __forceinline__ int __vsub4(const int a, const int b) {
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return __vsubss4(a, b);
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}
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static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) {
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#if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__)
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c = __builtin_amdgcn_sdot4(a, b, c, false);
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@ -505,6 +509,14 @@ typedef struct {
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} block_iq2_xs;
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static_assert(sizeof(block_iq2_xs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t) + QK_K/32, "wrong iq2_xs block size/padding");
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#define QR3_XXS 8
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#define QI3_XXS (QK_K / (4*QR3_XXS))
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typedef struct {
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half d;
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uint8_t qs[3*(QK_K/8)];
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} block_iq3_xxs;
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static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong iq3_xxs block size/padding");
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#define WARP_SIZE 32
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#define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses
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@ -1613,6 +1625,41 @@ static const __device__ uint64_t iq2xs_grid[512] = {
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0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b,
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};
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static const __device__ uint32_t iq3xxs_grid[256] = {
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0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414,
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0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14,
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0x040c140c, 0x040c142c, 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404,
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0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, 0x04141c1c, 0x04141c3e,
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0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c,
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0x041c3e04, 0x04240c1c, 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c,
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0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, 0x043e0c24, 0x043e0c34,
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0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c,
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0x0c041c04, 0x0c041c14, 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c,
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0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, 0x0c14140c, 0x0c141c04,
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0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c,
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0x0c24042c, 0x0c242c04, 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414,
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0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, 0x14041414, 0x14041434,
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0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c,
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0x140c1c04, 0x140c341c, 0x140c343e, 0x140c3e04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3e,
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0x14141404, 0x14141414, 0x14141c3e, 0x14142404, 0x14142c2c, 0x141c040c, 0x141c0c04, 0x141c0c24,
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0x141c3e04, 0x141c3e24, 0x14241c2c, 0x14242c1c, 0x142c041c, 0x142c143e, 0x142c240c, 0x142c3e24,
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0x143e040c, 0x143e041c, 0x143e0c34, 0x143e242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c,
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0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043e14, 0x1c0c0404, 0x1c0c0414, 0x1c0c1404, 0x1c0c1c0c,
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0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143e14,
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0x1c1c0c0c, 0x1c1c1c1c, 0x1c241c04, 0x1c24243e, 0x1c243e14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414,
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0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3e1c1c, 0x1c3e3404, 0x24040424, 0x24040c3e,
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0x24041c2c, 0x24041c3e, 0x24042c1c, 0x24042c3e, 0x240c3e24, 0x24141404, 0x24141c3e, 0x24142404,
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0x24143404, 0x24143434, 0x241c043e, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c,
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0x242c241c, 0x242c3e04, 0x243e042c, 0x243e0c04, 0x243e0c14, 0x243e1c04, 0x2c040c14, 0x2c04240c,
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0x2c043e04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143e14,
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0x2c1c0414, 0x2c1c2c1c, 0x2c240c04, 0x2c24141c, 0x2c24143e, 0x2c243e14, 0x2c2c0414, 0x2c2c1c0c,
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0x2c342c04, 0x2c3e1424, 0x2c3e2414, 0x34041424, 0x34042424, 0x34042434, 0x34043424, 0x340c140c,
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0x340c340c, 0x34140c3e, 0x34143424, 0x341c1c04, 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14,
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0x34341c1c, 0x343e041c, 0x343e140c, 0x3e04041c, 0x3e04042c, 0x3e04043e, 0x3e040c04, 0x3e041c14,
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0x3e042c14, 0x3e0c1434, 0x3e0c2404, 0x3e140c14, 0x3e14242c, 0x3e142c14, 0x3e1c0404, 0x3e1c0c2c,
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0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04,
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};
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static const __device__ uint8_t ksigns_iq2xs[128] = {
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0, 129, 130, 3, 132, 5, 6, 135, 136, 9, 10, 139, 12, 141, 142, 15,
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144, 17, 18, 147, 20, 149, 150, 23, 24, 153, 154, 27, 156, 29, 30, 159,
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@ -1624,6 +1671,43 @@ static const __device__ uint8_t ksigns_iq2xs[128] = {
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240, 113, 114, 243, 116, 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255,
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};
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//#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
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static const __device__ uint64_t ksigns64[128] = {
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0x0000000000000000, 0xff000000000000ff, 0xff0000000000ff00, 0x000000000000ffff,
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0xff00000000ff0000, 0x0000000000ff00ff, 0x0000000000ffff00, 0xff00000000ffffff,
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0xff000000ff000000, 0x00000000ff0000ff, 0x00000000ff00ff00, 0xff000000ff00ffff,
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0x00000000ffff0000, 0xff000000ffff00ff, 0xff000000ffffff00, 0x00000000ffffffff,
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0xff0000ff00000000, 0x000000ff000000ff, 0x000000ff0000ff00, 0xff0000ff0000ffff,
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0x000000ff00ff0000, 0xff0000ff00ff00ff, 0xff0000ff00ffff00, 0x000000ff00ffffff,
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0x000000ffff000000, 0xff0000ffff0000ff, 0xff0000ffff00ff00, 0x000000ffff00ffff,
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0xff0000ffffff0000, 0x000000ffffff00ff, 0x000000ffffffff00, 0xff0000ffffffffff,
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0xff00ff0000000000, 0x0000ff00000000ff, 0x0000ff000000ff00, 0xff00ff000000ffff,
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0x0000ff0000ff0000, 0xff00ff0000ff00ff, 0xff00ff0000ffff00, 0x0000ff0000ffffff,
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0x0000ff00ff000000, 0xff00ff00ff0000ff, 0xff00ff00ff00ff00, 0x0000ff00ff00ffff,
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0xff00ff00ffff0000, 0x0000ff00ffff00ff, 0x0000ff00ffffff00, 0xff00ff00ffffffff,
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0x0000ffff00000000, 0xff00ffff000000ff, 0xff00ffff0000ff00, 0x0000ffff0000ffff,
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0xff00ffff00ff0000, 0x0000ffff00ff00ff, 0x0000ffff00ffff00, 0xff00ffff00ffffff,
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0xff00ffffff000000, 0x0000ffffff0000ff, 0x0000ffffff00ff00, 0xff00ffffff00ffff,
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0x0000ffffffff0000, 0xff00ffffffff00ff, 0xff00ffffffffff00, 0x0000ffffffffffff,
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0xffff000000000000, 0x00ff0000000000ff, 0x00ff00000000ff00, 0xffff00000000ffff,
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0x00ff000000ff0000, 0xffff000000ff00ff, 0xffff000000ffff00, 0x00ff000000ffffff,
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0x00ff0000ff000000, 0xffff0000ff0000ff, 0xffff0000ff00ff00, 0x00ff0000ff00ffff,
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0xffff0000ffff0000, 0x00ff0000ffff00ff, 0x00ff0000ffffff00, 0xffff0000ffffffff,
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0x00ff00ff00000000, 0xffff00ff000000ff, 0xffff00ff0000ff00, 0x00ff00ff0000ffff,
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0xffff00ff00ff0000, 0x00ff00ff00ff00ff, 0x00ff00ff00ffff00, 0xffff00ff00ffffff,
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0xffff00ffff000000, 0x00ff00ffff0000ff, 0x00ff00ffff00ff00, 0xffff00ffff00ffff,
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0x00ff00ffffff0000, 0xffff00ffffff00ff, 0xffff00ffffffff00, 0x00ff00ffffffffff,
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0x00ffff0000000000, 0xffffff00000000ff, 0xffffff000000ff00, 0x00ffff000000ffff,
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0xffffff0000ff0000, 0x00ffff0000ff00ff, 0x00ffff0000ffff00, 0xffffff0000ffffff,
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0xffffff00ff000000, 0x00ffff00ff0000ff, 0x00ffff00ff00ff00, 0xffffff00ff00ffff,
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0x00ffff00ffff0000, 0xffffff00ffff00ff, 0xffffff00ffffff00, 0x00ffff00ffffffff,
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0xffffffff00000000, 0x00ffffff000000ff, 0x00ffffff0000ff00, 0xffffffff0000ffff,
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0x00ffffff00ff0000, 0xffffffff00ff00ff, 0xffffffff00ffff00, 0x00ffffff00ffffff,
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0x00ffffffff000000, 0xffffffffff0000ff, 0xffffffffff00ff00, 0x00ffffffff00ffff,
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0xffffffffffff0000, 0x00ffffffffff00ff, 0x00ffffffffffff00, 0xffffffffffffffff,
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};
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//#endif
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static const __device__ uint8_t kmask_iq2xs[8] = {1, 2, 4, 8, 16, 32, 64, 128};
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inline bool ggml_cuda_supports_mmq(enum ggml_type type) {
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@ -1690,6 +1774,34 @@ static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst
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}
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template<typename dst_t>
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static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
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const int i = blockIdx.x;
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const block_iq3_xxs * x = (const block_iq3_xxs *) vx;
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const int tid = threadIdx.x;
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#if QK_K == 256
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const int il = tid/8; // 0...3
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const int ib = tid%8; // 0...7
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dst_t * y = yy + i*QK_K + 32*ib + 8*il;
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const uint8_t * q3 = x[i].qs + 8*ib;
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const uint16_t * gas = (const uint16_t *)(x[i].qs + QK_K/4) + 2*ib;
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const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*il+0]);
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const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*il+1]);
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const uint32_t aux32 = gas[0] | (gas[1] << 16);
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const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.5f;
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const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127];
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for (int j = 0; j < 4; ++j) {
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y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f);
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y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
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}
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#else
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assert(false);
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#endif
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}
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static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
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static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
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@ -4313,6 +4425,7 @@ static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1(
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static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1(
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const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
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#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
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#if QK_K == 256
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const block_iq2_xs * bq2 = (const block_iq2_xs *) vbq;
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@ -4323,20 +4436,22 @@ static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1(
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const uint8_t ls2 = bq2->scales[ib32] >> 4;
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int sumi1 = 0;
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for (int l = 0; l < 2; ++l) {
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const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511));
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const uint8_t signs = ksigns_iq2xs[q2[l] >> 9];
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for (int j = 0; j < 8; ++j) {
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sumi1 += q8[j] * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
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}
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const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511));
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const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9));
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const int grid_l = __vsub4(grid[0] ^ signs[0], signs[0]);
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const int grid_h = __vsub4(grid[1] ^ signs[1], signs[1]);
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sumi1 = __dp4a(grid_l, *((const int *)q8 + 0), sumi1);
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sumi1 = __dp4a(grid_h, *((const int *)q8 + 1), sumi1);
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q8 += 8;
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}
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int sumi2 = 0;
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for (int l = 2; l < 4; ++l) {
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const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511));
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const uint8_t signs = ksigns_iq2xs[q2[l] >> 9];
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for (int j = 0; j < 8; ++j) {
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sumi2 += q8[j] * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
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}
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const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511));
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const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9));
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const int grid_l = __vsub4(grid[0] ^ signs[0], signs[0]);
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const int grid_h = __vsub4(grid[1] ^ signs[1], signs[1]);
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sumi2 = __dp4a(grid_l, *((const int *)q8 + 0), sumi2);
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sumi2 = __dp4a(grid_h, *((const int *)q8 + 1), sumi2);
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q8 += 8;
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}
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const float d = (float)bq2->d * __low2float(bq8_1[ib32].ds) * 0.25f;
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@ -4345,6 +4460,45 @@ static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1(
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assert(false);
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return 0.f;
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#endif
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#else
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assert(false);
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return 0.f;
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#endif
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}
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static __device__ __forceinline__ float vec_dot_iq3_xxs_q8_1(
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const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
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#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
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#if QK_K == 256
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const block_iq3_xxs * bq2 = (const block_iq3_xxs *) vbq;
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const int ib32 = iqs;
|
||||
const uint8_t * q3 = bq2->qs + 8*ib32;
|
||||
const uint16_t * gas = (const uint16_t *)(bq2->qs + QK_K/4) + 2*ib32;
|
||||
const int8_t * q8 = bq8_1[ib32].qs;
|
||||
uint32_t aux32 = gas[0] | (gas[1] << 16);
|
||||
int sumi = 0;
|
||||
for (int l = 0; l < 4; ++l) {
|
||||
const uint32_t * grid1 = iq3xxs_grid + q3[2*l+0];
|
||||
const uint32_t * grid2 = iq3xxs_grid + q3[2*l+1];
|
||||
const uint32_t * signs = (const uint32_t *)(ksigns64 + (aux32 & 127));
|
||||
const int grid_l = __vsub4(grid1[0] ^ signs[0], signs[0]);
|
||||
const int grid_h = __vsub4(grid2[0] ^ signs[1], signs[1]);
|
||||
sumi = __dp4a(grid_l, *((int *)q8+0), sumi);
|
||||
sumi = __dp4a(grid_h, *((int *)q8+1), sumi);
|
||||
q8 += 8;
|
||||
aux32 >>= 7;
|
||||
}
|
||||
const float d = (float)bq2->d * (0.5f + aux32) * __low2float(bq8_1[ib32].ds) * 0.5f;
|
||||
return d * sumi;
|
||||
#else
|
||||
assert(false);
|
||||
return 0.f;
|
||||
#endif
|
||||
#else
|
||||
assert(false);
|
||||
return 0.f;
|
||||
#endif
|
||||
}
|
||||
|
||||
template <int qk, int qr, int qi, bool need_sum, typename block_q_t, int mmq_x, int mmq_y, int nwarps,
|
||||
@ -6381,6 +6535,12 @@ static void dequantize_row_iq2_xs_cuda(const void * vx, dst_t * y, const int k,
|
||||
dequantize_block_iq2_xs<<<nb, 32, 0, stream>>>(vx, y);
|
||||
}
|
||||
|
||||
template<typename dst_t>
|
||||
static void dequantize_row_iq3_xxs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
|
||||
const int nb = k / QK_K;
|
||||
dequantize_block_iq3_xxs<<<nb, 32, 0, stream>>>(vx, y);
|
||||
}
|
||||
|
||||
template <typename src_t, typename dst_t>
|
||||
static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) {
|
||||
const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
|
||||
@ -6418,6 +6578,8 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
|
||||
return dequantize_row_iq2_xxs_cuda;
|
||||
case GGML_TYPE_IQ2_XS:
|
||||
return dequantize_row_iq2_xs_cuda;
|
||||
case GGML_TYPE_IQ3_XXS:
|
||||
return dequantize_row_iq3_xxs_cuda;
|
||||
case GGML_TYPE_F32:
|
||||
return convert_unary_cuda<float>;
|
||||
default:
|
||||
@ -6451,6 +6613,8 @@ static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
|
||||
return dequantize_row_iq2_xxs_cuda;
|
||||
case GGML_TYPE_IQ2_XS:
|
||||
return dequantize_row_iq2_xs_cuda;
|
||||
case GGML_TYPE_IQ3_XXS:
|
||||
return dequantize_row_iq3_xxs_cuda;
|
||||
case GGML_TYPE_F16:
|
||||
return convert_unary_cuda<half>;
|
||||
default:
|
||||
@ -6663,6 +6827,15 @@ static void mul_mat_vec_iq2_xs_q8_1_cuda(const void * vx, const void * vy, float
|
||||
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
|
||||
}
|
||||
|
||||
static void mul_mat_vec_iq3_xxs_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
||||
GGML_ASSERT(ncols % QK_K == 0);
|
||||
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
|
||||
const dim3 block_nums(block_num_y, 1, 1);
|
||||
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
|
||||
mul_mat_vec_q<QK_K, QI3_XXS, block_iq3_xxs, 1, vec_dot_iq3_xxs_q8_1>
|
||||
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
|
||||
}
|
||||
|
||||
static void ggml_mul_mat_q4_0_q8_1_cuda(
|
||||
const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
|
||||
const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
|
||||
@ -8213,6 +8386,7 @@ static int64_t get_row_rounding(ggml_type type, const std::array<float, GGML_CUD
|
||||
case GGML_TYPE_Q6_K:
|
||||
case GGML_TYPE_IQ2_XXS:
|
||||
case GGML_TYPE_IQ2_XS:
|
||||
case GGML_TYPE_IQ3_XXS:
|
||||
return max_compute_capability >= CC_RDNA2 ? 128 : 64;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
@ -8235,6 +8409,7 @@ static int64_t get_row_rounding(ggml_type type, const std::array<float, GGML_CUD
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_IQ2_XXS:
|
||||
case GGML_TYPE_IQ2_XS:
|
||||
case GGML_TYPE_IQ3_XXS:
|
||||
return max_compute_capability >= CC_VOLTA ? 128 : 64;
|
||||
case GGML_TYPE_Q6_K:
|
||||
return 64;
|
||||
@ -8306,6 +8481,9 @@ static void ggml_cuda_op_mul_mat_vec_q(
|
||||
case GGML_TYPE_IQ2_XS:
|
||||
mul_mat_vec_iq2_xs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
|
||||
break;
|
||||
case GGML_TYPE_IQ3_XXS:
|
||||
mul_mat_vec_iq3_xxs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
|
||||
break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
break;
|
||||
@ -10934,7 +11112,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
|
||||
return false;
|
||||
}
|
||||
ggml_type a_type = a->type;
|
||||
if (a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS) {
|
||||
if (a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS || a_type == GGML_TYPE_IQ3_XXS) {
|
||||
if (b->ne[1] == 1 && ggml_nrows(b) > 1) {
|
||||
return false;
|
||||
}
|
||||
|
||||
35
ggml-metal.m
35
ggml-metal.m
@ -60,6 +60,7 @@ enum ggml_metal_kernel_type {
|
||||
GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K,
|
||||
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS,
|
||||
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS,
|
||||
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS,
|
||||
GGML_METAL_KERNEL_TYPE_GET_ROWS_I32,
|
||||
GGML_METAL_KERNEL_TYPE_RMS_NORM,
|
||||
GGML_METAL_KERNEL_TYPE_GROUP_NORM,
|
||||
@ -81,6 +82,7 @@ enum ggml_metal_kernel_type {
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32,
|
||||
//GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32,
|
||||
@ -98,6 +100,7 @@ enum ggml_metal_kernel_type {
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32,
|
||||
@ -112,6 +115,7 @@ enum ggml_metal_kernel_type {
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32,
|
||||
@ -126,6 +130,7 @@ enum ggml_metal_kernel_type {
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_ROPE_F32,
|
||||
GGML_METAL_KERNEL_TYPE_ROPE_F16,
|
||||
GGML_METAL_KERNEL_TYPE_ALIBI_F32,
|
||||
@ -422,6 +427,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K, get_rows_q6_K, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS, get_rows_iq2_xxs, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, get_rows_iq2_xs, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS, get_rows_iq3_xxs, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, get_rows_i32, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM, rms_norm, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM, group_norm, ctx->support_simdgroup_reduction);
|
||||
@ -443,6 +449,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32, mul_mv_q6_K_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32, mul_mv_iq2_xxs_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, mul_mv_iq2_xs_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, mul_mv_iq3_xxs_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, mul_mv_id_f32_f32, ctx->support_simdgroup_reduction);
|
||||
//GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, mul_mv_id_f16_f16, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, mul_mv_id_f16_f32, ctx->support_simdgroup_reduction);
|
||||
@ -460,6 +467,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32, mul_mv_id_q6_K_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32, mul_mv_id_iq2_xxs_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, mul_mv_id_iq2_xs_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, mul_mv_id_iq3_xxs_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, mul_mm_f32_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, mul_mm_f16_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, mul_mm_q4_0_f32, ctx->support_simdgroup_mm);
|
||||
@ -474,6 +482,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32, mul_mm_q6_K_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32, mul_mm_iq2_xxs_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, mul_mm_iq2_xs_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, mul_mm_iq3_xxs_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, mul_mm_id_f32_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, mul_mm_id_f16_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, mul_mm_id_q4_0_f32, ctx->support_simdgroup_mm);
|
||||
@ -488,6 +497,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32, mul_mm_id_q6_K_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32, mul_mm_id_iq2_xxs_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, mul_mm_id_iq2_xs_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, mul_mm_id_iq3_xxs_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32, rope_f32, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16, rope_f16, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ALIBI_F32, alibi_f32, true);
|
||||
@ -1260,6 +1270,7 @@ static bool ggml_metal_graph_compute(
|
||||
case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32 ].pipeline; break;
|
||||
case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32].pipeline; break;
|
||||
case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32 ].pipeline; break;
|
||||
case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32].pipeline; break;
|
||||
default: GGML_ASSERT(false && "MUL MAT-MAT not implemented");
|
||||
}
|
||||
|
||||
@ -1388,6 +1399,12 @@ static bool ggml_metal_graph_compute(
|
||||
nth1 = 16;
|
||||
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32].pipeline;
|
||||
} break;
|
||||
case GGML_TYPE_IQ3_XXS:
|
||||
{
|
||||
nth0 = 4;
|
||||
nth1 = 16;
|
||||
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32].pipeline;
|
||||
} break;
|
||||
default:
|
||||
{
|
||||
GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src0t);
|
||||
@ -1430,6 +1447,11 @@ static bool ggml_metal_graph_compute(
|
||||
[encoder setThreadgroupMemoryLength:mem_size atIndex:0];
|
||||
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
|
||||
}
|
||||
else if (src0t == GGML_TYPE_IQ3_XXS) {
|
||||
const int mem_size = 256*4+128;
|
||||
[encoder setThreadgroupMemoryLength:mem_size atIndex:0];
|
||||
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
|
||||
}
|
||||
else if (src0t == GGML_TYPE_Q4_K) {
|
||||
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
|
||||
}
|
||||
@ -1524,6 +1546,7 @@ static bool ggml_metal_graph_compute(
|
||||
case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32 ].pipeline; break;
|
||||
case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32].pipeline; break;
|
||||
case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32 ].pipeline; break;
|
||||
case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32].pipeline; break;
|
||||
default: GGML_ASSERT(false && "MUL_MAT_ID not implemented");
|
||||
}
|
||||
|
||||
@ -1655,6 +1678,12 @@ static bool ggml_metal_graph_compute(
|
||||
nth1 = 16;
|
||||
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32].pipeline;
|
||||
} break;
|
||||
case GGML_TYPE_IQ3_XXS:
|
||||
{
|
||||
nth0 = 4;
|
||||
nth1 = 16;
|
||||
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32].pipeline;
|
||||
} break;
|
||||
default:
|
||||
{
|
||||
GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src2t);
|
||||
@ -1713,6 +1742,11 @@ static bool ggml_metal_graph_compute(
|
||||
[encoder setThreadgroupMemoryLength:mem_size atIndex:0];
|
||||
[encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
|
||||
}
|
||||
else if (src2t == GGML_TYPE_IQ3_XXS) {
|
||||
const int mem_size = 256*4+128;
|
||||
[encoder setThreadgroupMemoryLength:mem_size atIndex:0];
|
||||
[encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
|
||||
}
|
||||
else if (src2t == GGML_TYPE_Q4_K) {
|
||||
[encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
|
||||
}
|
||||
@ -1753,6 +1787,7 @@ static bool ggml_metal_graph_compute(
|
||||
case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K ].pipeline; break;
|
||||
case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS].pipeline; break;
|
||||
case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS ].pipeline; break;
|
||||
case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS].pipeline; break;
|
||||
case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32 ].pipeline; break;
|
||||
default: GGML_ASSERT(false && "not implemented");
|
||||
}
|
||||
|
||||
274
ggml-metal.metal
274
ggml-metal.metal
@ -2459,6 +2459,12 @@ typedef struct {
|
||||
} block_iq2_xs;
|
||||
// 74 bytes / block for QK_K = 256, so 2.3125 bpw
|
||||
|
||||
typedef struct {
|
||||
half d;
|
||||
uint8_t qs[3*QK_K/8];
|
||||
} block_iq3_xxs;
|
||||
// 98 bytes / block for QK_K = 256, so 3.0625 bpw
|
||||
|
||||
//====================================== dot products =========================
|
||||
|
||||
void kernel_mul_mv_q2_K_f32_impl(
|
||||
@ -3681,6 +3687,42 @@ constexpr constant static uint64_t iq2xs_grid[512] = {
|
||||
0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b,
|
||||
};
|
||||
|
||||
constexpr constant static uint32_t iq3xxs_grid[256] = {
|
||||
0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3c, 0x04041404, 0x04041414,
|
||||
0x04041c0c, 0x04042414, 0x04043c1c, 0x04043c2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14,
|
||||
0x040c140c, 0x040c142c, 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3c04, 0x04140404,
|
||||
0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, 0x04141c1c, 0x04141c3c,
|
||||
0x04142c0c, 0x04142c3c, 0x04143c2c, 0x041c040c, 0x041c043c, 0x041c0c04, 0x041c0c14, 0x041c142c,
|
||||
0x041c3c04, 0x04240c1c, 0x04241c3c, 0x04242424, 0x04242c3c, 0x04243c1c, 0x04243c2c, 0x042c040c,
|
||||
0x042c043c, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043c0c04, 0x043c0c24, 0x043c0c34,
|
||||
0x043c241c, 0x043c340c, 0x0c04040c, 0x0c04041c, 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c,
|
||||
0x0c041c04, 0x0c041c14, 0x0c041c24, 0x0c04243c, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c,
|
||||
0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, 0x0c14140c, 0x0c141c04,
|
||||
0x0c143c14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c,
|
||||
0x0c24042c, 0x0c242c04, 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3c0c, 0x0c34042c, 0x0c3c1414,
|
||||
0x0c3c2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, 0x14041414, 0x14041434,
|
||||
0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c,
|
||||
0x140c1c04, 0x140c341c, 0x140c343c, 0x140c3c04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3c,
|
||||
0x14141404, 0x14141414, 0x14141c3c, 0x14142404, 0x14142c2c, 0x141c040c, 0x141c0c04, 0x141c0c24,
|
||||
0x141c3c04, 0x141c3c24, 0x14241c2c, 0x14242c1c, 0x142c041c, 0x142c143c, 0x142c240c, 0x142c3c24,
|
||||
0x143c040c, 0x143c041c, 0x143c0c34, 0x143c242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c,
|
||||
0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043c14, 0x1c0c0404, 0x1c0c0414, 0x1c0c1404, 0x1c0c1c0c,
|
||||
0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143c14,
|
||||
0x1c1c0c0c, 0x1c1c1c1c, 0x1c241c04, 0x1c24243c, 0x1c243c14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414,
|
||||
0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3c1c1c, 0x1c3c3404, 0x24040424, 0x24040c3c,
|
||||
0x24041c2c, 0x24041c3c, 0x24042c1c, 0x24042c3c, 0x240c3c24, 0x24141404, 0x24141c3c, 0x24142404,
|
||||
0x24143404, 0x24143434, 0x241c043c, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c,
|
||||
0x242c241c, 0x242c3c04, 0x243c042c, 0x243c0c04, 0x243c0c14, 0x243c1c04, 0x2c040c14, 0x2c04240c,
|
||||
0x2c043c04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143c14,
|
||||
0x2c1c0414, 0x2c1c2c1c, 0x2c240c04, 0x2c24141c, 0x2c24143c, 0x2c243c14, 0x2c2c0414, 0x2c2c1c0c,
|
||||
0x2c342c04, 0x2c3c1424, 0x2c3c2414, 0x34041424, 0x34042424, 0x34042434, 0x34043424, 0x340c140c,
|
||||
0x340c340c, 0x34140c3c, 0x34143424, 0x341c1c04, 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14,
|
||||
0x34341c1c, 0x343c041c, 0x343c140c, 0x3c04041c, 0x3c04042c, 0x3c04043c, 0x3c040c04, 0x3c041c14,
|
||||
0x3c042c14, 0x3c0c1434, 0x3c0c2404, 0x3c140c14, 0x3c14242c, 0x3c142c14, 0x3c1c0404, 0x3c1c0c2c,
|
||||
0x3c1c1c1c, 0x3c1c3404, 0x3c24140c, 0x3c24240c, 0x3c2c0404, 0x3c2c0414, 0x3c2c1424, 0x3c341c04,
|
||||
};
|
||||
|
||||
|
||||
constexpr constant static uint8_t ksigns_iq2xs[128] = {
|
||||
0, 129, 130, 3, 132, 5, 6, 135, 136, 9, 10, 139, 12, 141, 142, 15,
|
||||
144, 17, 18, 147, 20, 149, 150, 23, 24, 153, 154, 27, 156, 29, 30, 159,
|
||||
@ -3970,6 +4012,143 @@ kernel void kernel_mul_mv_iq2_xs_f32(
|
||||
kernel_mul_mv_iq2_xs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
|
||||
}
|
||||
|
||||
void kernel_mul_mv_iq3_xxs_f32_impl(
|
||||
device const void * src0,
|
||||
device const float * src1,
|
||||
device float * dst,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
constant int64_t & ne02,
|
||||
constant int64_t & ne10,
|
||||
constant int64_t & ne12,
|
||||
constant int64_t & ne0,
|
||||
constant int64_t & ne1,
|
||||
constant uint & r2,
|
||||
constant uint & r3,
|
||||
threadgroup int8_t * shared_values [[threadgroup(0)]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint tiisg[[thread_index_in_simdgroup]],
|
||||
uint sgitg[[simdgroup_index_in_threadgroup]]) {
|
||||
|
||||
const int nb = ne00/QK_K;
|
||||
const int r0 = tgpig.x;
|
||||
const int r1 = tgpig.y;
|
||||
const int im = tgpig.z;
|
||||
|
||||
const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST;
|
||||
const int ib_row = first_row * nb;
|
||||
|
||||
const uint i12 = im%ne12;
|
||||
const uint i13 = im/ne12;
|
||||
|
||||
const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
|
||||
|
||||
device const block_iq3_xxs * x = (device const block_iq3_xxs *) src0 + ib_row + offset0;
|
||||
device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1;
|
||||
|
||||
float yl[32];
|
||||
float sumf[N_DST]={0.f}, all_sum;
|
||||
|
||||
const int nb32 = nb * (QK_K / 32);
|
||||
|
||||
threadgroup uint32_t * values = (threadgroup uint32_t *)shared_values;
|
||||
threadgroup uint8_t * shared_signs = (threadgroup uint8_t *)(values + 256);
|
||||
{
|
||||
int nval = 4;
|
||||
int pos = (32*sgitg + tiisg)*nval;
|
||||
for (int i = 0; i < nval; ++i) values[pos + i] = iq3xxs_grid[pos + i];
|
||||
nval = 2;
|
||||
pos = (32*sgitg + tiisg)*nval;
|
||||
for (int i = 0; i < nval; ++i) shared_signs[pos+i] = ksigns_iq2xs[pos+i];
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
}
|
||||
|
||||
#if QK_K == 256
|
||||
const int ix = tiisg;
|
||||
|
||||
device const float * y4 = y + 32 * ix;
|
||||
|
||||
for (int ib32 = ix; ib32 < nb32; ib32 += 32) {
|
||||
|
||||
for (int i = 0; i < 32; ++i) {
|
||||
yl[i] = y4[i];
|
||||
}
|
||||
|
||||
const int ibl = ib32 / (QK_K / 32);
|
||||
const int ib = ib32 % (QK_K / 32);
|
||||
|
||||
device const block_iq3_xxs * xr = x + ibl;
|
||||
device const uint8_t * q3 = xr->qs + 8 * ib;
|
||||
device const uint16_t * gas = (device const uint16_t *)(xr->qs + QK_K/4) + 2 * ib;
|
||||
device const half * dh = &xr->d;
|
||||
|
||||
for (int row = 0; row < N_DST; row++) {
|
||||
|
||||
const float db = dh[0];
|
||||
const uint32_t aux32 = gas[0] | (gas[1] << 16);
|
||||
const float d = db * (0.5f + (aux32 >> 28));
|
||||
|
||||
float2 sum = {0};
|
||||
for (int l = 0; l < 4; ++l) {
|
||||
const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(values + q3[2*l+0]);
|
||||
const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(values + q3[2*l+1]);
|
||||
const uint8_t signs = shared_signs[(aux32 >> 7*l) & 127];
|
||||
for (int j = 0; j < 4; ++j) {
|
||||
sum[0] += yl[8*l + j + 0] * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f);
|
||||
sum[1] += yl[8*l + j + 4] * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
|
||||
}
|
||||
}
|
||||
sumf[row] += d * (sum[0] + sum[1]);
|
||||
|
||||
dh += nb*sizeof(block_iq3_xxs)/2;
|
||||
q3 += nb*sizeof(block_iq3_xxs);
|
||||
gas += nb*sizeof(block_iq3_xxs)/2;
|
||||
}
|
||||
|
||||
y4 += 32 * 32;
|
||||
}
|
||||
#else
|
||||
// TODO
|
||||
#endif
|
||||
|
||||
for (int row = 0; row < N_DST; ++row) {
|
||||
all_sum = simd_sum(sumf[row]);
|
||||
if (tiisg == 0) {
|
||||
dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum * 0.5f;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
[[host_name("kernel_mul_mv_iq3_xxs_f32")]]
|
||||
kernel void kernel_mul_mv_iq3_xxs_f32(
|
||||
device const void * src0,
|
||||
device const float * src1,
|
||||
device float * dst,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
constant int64_t & ne02,
|
||||
constant uint64_t & nb00,
|
||||
constant uint64_t & nb01,
|
||||
constant uint64_t & nb02,
|
||||
constant int64_t & ne10,
|
||||
constant int64_t & ne11,
|
||||
constant int64_t & ne12,
|
||||
constant uint64_t & nb10,
|
||||
constant uint64_t & nb11,
|
||||
constant uint64_t & nb12,
|
||||
constant int64_t & ne0,
|
||||
constant int64_t & ne1,
|
||||
constant uint & r2,
|
||||
constant uint & r3,
|
||||
threadgroup int8_t * shared_values [[threadgroup(0)]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint tiisg[[thread_index_in_simdgroup]],
|
||||
uint sgitg[[simdgroup_index_in_threadgroup]]) {
|
||||
|
||||
kernel_mul_mv_iq3_xxs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
|
||||
}
|
||||
|
||||
|
||||
//============================= templates and their specializations =============================
|
||||
|
||||
// NOTE: this is not dequantizing - we are simply fitting the template
|
||||
@ -4287,6 +4466,33 @@ void dequantize_iq2_xs(device const block_iq2_xs * xb, short il, thread type4x4
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq3_xxs(device const block_iq3_xxs * xb, short il, thread type4x4 & reg) {
|
||||
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
|
||||
const float d = xb->d;
|
||||
const int ib32 = il/2;
|
||||
il = il%2;
|
||||
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
|
||||
device const uint8_t * q3 = xb->qs + 8*ib32;
|
||||
device const uint16_t * gas = (device const uint16_t *)(xb->qs + QK_K/4) + 2*ib32;
|
||||
const uint32_t aux32 = gas[0] | (gas[1] << 16);
|
||||
const float dl = d * (0.5f + (aux32 >> 28)) * 0.5f;
|
||||
constant uint8_t * grid1 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+0]);
|
||||
constant uint8_t * grid2 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+1]);
|
||||
uint8_t signs = ksigns_iq2xs[(aux32 >> 14*il) & 127];
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
reg[0][i] = dl * grid1[i] * (signs & kmask_iq2xs[i+0] ? -1.f : 1.f);
|
||||
reg[1][i] = dl * grid2[i] * (signs & kmask_iq2xs[i+4] ? -1.f : 1.f);
|
||||
}
|
||||
grid1 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+2]);
|
||||
grid2 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+3]);
|
||||
signs = ksigns_iq2xs[(aux32 >> (14*il+7)) & 127];
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
reg[2][i] = dl * grid1[i] * (signs & kmask_iq2xs[i+0] ? -1.f : 1.f);
|
||||
reg[3][i] = dl * grid2[i] * (signs & kmask_iq2xs[i+4] ? -1.f : 1.f);
|
||||
}
|
||||
}
|
||||
|
||||
template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread float4x4 &)>
|
||||
kernel void kernel_get_rows(
|
||||
device const void * src0,
|
||||
@ -4828,6 +5034,7 @@ template [[host_name("kernel_get_rows_q5_K")]] kernel get_rows_t kernel_get_rows
|
||||
template [[host_name("kernel_get_rows_q6_K")]] kernel get_rows_t kernel_get_rows<block_q6_K, QK_NL, dequantize_q6_K>;
|
||||
template [[host_name("kernel_get_rows_iq2_xxs")]] kernel get_rows_t kernel_get_rows<block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
|
||||
template [[host_name("kernel_get_rows_iq2_xs")]] kernel get_rows_t kernel_get_rows<block_iq2_xs, QK_NL, dequantize_iq2_xs>;
|
||||
template [[host_name("kernel_get_rows_iq3_xxs")]] kernel get_rows_t kernel_get_rows<block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
|
||||
|
||||
//
|
||||
// matrix-matrix multiplication
|
||||
@ -4866,6 +5073,7 @@ template [[host_name("kernel_mul_mm_q5_K_f32")]] kernel mat_mm_t kernel_mul_mm<b
|
||||
template [[host_name("kernel_mul_mm_q6_K_f32")]] kernel mat_mm_t kernel_mul_mm<block_q6_K, QK_NL, dequantize_q6_K>;
|
||||
template [[host_name("kernel_mul_mm_iq2_xxs_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
|
||||
template [[host_name("kernel_mul_mm_iq2_xs_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq2_xs, QK_NL, dequantize_iq2_xs>;
|
||||
template [[host_name("kernel_mul_mm_iq3_xxs_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
|
||||
|
||||
//
|
||||
// indirect matrix-matrix multiplication
|
||||
@ -4916,6 +5124,7 @@ template [[host_name("kernel_mul_mm_id_q5_K_f32")]] kernel mat_mm_id_t kernel_mu
|
||||
template [[host_name("kernel_mul_mm_id_q6_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_q6_K, QK_NL, dequantize_q6_K>;
|
||||
template [[host_name("kernel_mul_mm_id_iq2_xxs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
|
||||
template [[host_name("kernel_mul_mm_id_iq2_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq2_xs, QK_NL, dequantize_iq2_xs>;
|
||||
template [[host_name("kernel_mul_mm_id_iq3_xxs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
|
||||
|
||||
//
|
||||
// matrix-vector multiplication
|
||||
@ -5818,3 +6027,68 @@ kernel void kernel_mul_mv_id_iq2_xs_f32(
|
||||
tiisg,
|
||||
sgitg);
|
||||
}
|
||||
|
||||
[[host_name("kernel_mul_mv_id_iq3_xxs_f32")]]
|
||||
kernel void kernel_mul_mv_id_iq3_xxs_f32(
|
||||
device const char * ids,
|
||||
device const char * src1,
|
||||
device float * dst,
|
||||
constant uint64_t & nbi1,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
constant int64_t & ne02,
|
||||
constant uint64_t & nb00,
|
||||
constant uint64_t & nb01,
|
||||
constant uint64_t & nb02,
|
||||
constant int64_t & ne10,
|
||||
constant int64_t & ne11,
|
||||
constant int64_t & ne12,
|
||||
constant int64_t & ne13,
|
||||
constant uint64_t & nb10,
|
||||
constant uint64_t & nb11,
|
||||
constant uint64_t & nb12,
|
||||
constant int64_t & ne0,
|
||||
constant int64_t & ne1,
|
||||
constant uint64_t & nb1,
|
||||
constant uint & r2,
|
||||
constant uint & r3,
|
||||
constant int & idx,
|
||||
device const char * src00,
|
||||
device const char * src01,
|
||||
device const char * src02,
|
||||
device const char * src03,
|
||||
device const char * src04,
|
||||
device const char * src05,
|
||||
device const char * src06,
|
||||
device const char * src07,
|
||||
threadgroup int8_t * shared_values [[threadgroup(0)]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint tiitg[[thread_index_in_threadgroup]],
|
||||
uint tiisg[[thread_index_in_simdgroup]],
|
||||
uint sgitg[[simdgroup_index_in_threadgroup]]) {
|
||||
device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
|
||||
|
||||
const int64_t bid = tgpig.z/(ne12*ne13);
|
||||
|
||||
tgpig.z = tgpig.z%(ne12*ne13);
|
||||
|
||||
const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
|
||||
|
||||
kernel_mul_mv_iq3_xxs_f32_impl(
|
||||
src0[id],
|
||||
(device const float *) (src1 + bid*nb11),
|
||||
dst + bid*ne0,
|
||||
ne00,
|
||||
ne01,
|
||||
ne02,
|
||||
ne10,
|
||||
ne12,
|
||||
ne0,
|
||||
ne1,
|
||||
r2,
|
||||
r3,
|
||||
shared_values,
|
||||
tgpig,
|
||||
tiisg,
|
||||
sgitg);
|
||||
}
|
||||
|
||||
630
ggml-quants.c
630
ggml-quants.c
@ -3441,6 +3441,41 @@ static const uint64_t iq2xs_grid[512] = {
|
||||
0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b,
|
||||
};
|
||||
|
||||
static const uint32_t iq3xxs_grid[256] = {
|
||||
0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414,
|
||||
0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14,
|
||||
0x040c140c, 0x040c142c, 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404,
|
||||
0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, 0x04141c1c, 0x04141c3e,
|
||||
0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c,
|
||||
0x041c3e04, 0x04240c1c, 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c,
|
||||
0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, 0x043e0c24, 0x043e0c34,
|
||||
0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c,
|
||||
0x0c041c04, 0x0c041c14, 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c,
|
||||
0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, 0x0c14140c, 0x0c141c04,
|
||||
0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c,
|
||||
0x0c24042c, 0x0c242c04, 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414,
|
||||
0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, 0x14041414, 0x14041434,
|
||||
0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c,
|
||||
0x140c1c04, 0x140c341c, 0x140c343e, 0x140c3e04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3e,
|
||||
0x14141404, 0x14141414, 0x14141c3e, 0x14142404, 0x14142c2c, 0x141c040c, 0x141c0c04, 0x141c0c24,
|
||||
0x141c3e04, 0x141c3e24, 0x14241c2c, 0x14242c1c, 0x142c041c, 0x142c143e, 0x142c240c, 0x142c3e24,
|
||||
0x143e040c, 0x143e041c, 0x143e0c34, 0x143e242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c,
|
||||
0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043e14, 0x1c0c0404, 0x1c0c0414, 0x1c0c1404, 0x1c0c1c0c,
|
||||
0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143e14,
|
||||
0x1c1c0c0c, 0x1c1c1c1c, 0x1c241c04, 0x1c24243e, 0x1c243e14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414,
|
||||
0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3e1c1c, 0x1c3e3404, 0x24040424, 0x24040c3e,
|
||||
0x24041c2c, 0x24041c3e, 0x24042c1c, 0x24042c3e, 0x240c3e24, 0x24141404, 0x24141c3e, 0x24142404,
|
||||
0x24143404, 0x24143434, 0x241c043e, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c,
|
||||
0x242c241c, 0x242c3e04, 0x243e042c, 0x243e0c04, 0x243e0c14, 0x243e1c04, 0x2c040c14, 0x2c04240c,
|
||||
0x2c043e04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143e14,
|
||||
0x2c1c0414, 0x2c1c2c1c, 0x2c240c04, 0x2c24141c, 0x2c24143e, 0x2c243e14, 0x2c2c0414, 0x2c2c1c0c,
|
||||
0x2c342c04, 0x2c3e1424, 0x2c3e2414, 0x34041424, 0x34042424, 0x34042434, 0x34043424, 0x340c140c,
|
||||
0x340c340c, 0x34140c3e, 0x34143424, 0x341c1c04, 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14,
|
||||
0x34341c1c, 0x343e041c, 0x343e140c, 0x3e04041c, 0x3e04042c, 0x3e04043e, 0x3e040c04, 0x3e041c14,
|
||||
0x3e042c14, 0x3e0c1434, 0x3e0c2404, 0x3e140c14, 0x3e14242c, 0x3e142c14, 0x3e1c0404, 0x3e1c0c2c,
|
||||
0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04,
|
||||
};
|
||||
|
||||
static const uint8_t ksigns_iq2xs[128] = {
|
||||
0, 129, 130, 3, 132, 5, 6, 135, 136, 9, 10, 139, 12, 141, 142, 15,
|
||||
144, 17, 18, 147, 20, 149, 150, 23, 24, 153, 154, 27, 156, 29, 30, 159,
|
||||
@ -3507,6 +3542,38 @@ void dequantize_row_iq2_xs(const block_iq2_xs * restrict x, float * restrict y,
|
||||
}
|
||||
}
|
||||
|
||||
// ====================== 3.0625 bpw (de)-quantization
|
||||
|
||||
void dequantize_row_iq3_xxs(const block_iq3_xxs * restrict x, float * restrict y, int k) {
|
||||
assert(k % QK_K == 0);
|
||||
const int nb = k / QK_K;
|
||||
|
||||
uint32_t aux32;
|
||||
|
||||
for (int i = 0; i < nb; i++) {
|
||||
|
||||
const float d = GGML_FP16_TO_FP32(x[i].d);
|
||||
const uint8_t * qs = x[i].qs;
|
||||
const uint8_t * scales_and_signs = qs + QK_K/4;
|
||||
|
||||
for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
|
||||
memcpy(&aux32, scales_and_signs + 4*ib32, sizeof(uint32_t));
|
||||
const float db = d * (0.5f + (aux32 >> 28)) * 0.5f;
|
||||
for (int l = 0; l < 4; ++l) {
|
||||
const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*l) & 127];
|
||||
const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + qs[2*l+0]);
|
||||
const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + qs[2*l+1]);
|
||||
for (int j = 0; j < 4; ++j) {
|
||||
y[j+0] = db * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f);
|
||||
y[j+4] = db * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
|
||||
}
|
||||
y += 8;
|
||||
}
|
||||
qs += 8;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
//===================================== Q8_K ==============================================
|
||||
|
||||
void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k) {
|
||||
@ -8551,6 +8618,136 @@ void ggml_vec_dot_iq2_xs_q8_K(const int n, float * restrict s, const void * rest
|
||||
#endif
|
||||
}
|
||||
|
||||
// TODO
|
||||
void ggml_vec_dot_iq3_xxs_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
|
||||
assert(n % QK_K == 0);
|
||||
|
||||
const block_iq3_xxs * restrict x = vx;
|
||||
const block_q8_K * restrict y = vy;
|
||||
|
||||
const int nb = n / QK_K;
|
||||
|
||||
#if defined(__ARM_NEON)
|
||||
|
||||
const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs;
|
||||
|
||||
uint32_t aux32[2];
|
||||
|
||||
ggml_int8x16x4_t q3s;
|
||||
ggml_int8x16x4_t q8b;
|
||||
|
||||
float sumf = 0;
|
||||
for (int i = 0; i < nb; ++i) {
|
||||
const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
|
||||
const uint8_t * restrict q3 = x[i].qs;
|
||||
const uint8_t * restrict gas = x[i].qs + QK_K/4;
|
||||
const int8_t * restrict q8 = y[i].qs;
|
||||
float sumf1 = 0, sumf2 = 0;
|
||||
for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
|
||||
q8b = ggml_vld1q_s8_x4(q8); q8 += 64;
|
||||
memcpy(aux32, gas, 2*sizeof(uint32_t)); gas += 2*sizeof(uint32_t);
|
||||
const uint32x4_t aux32x4_0 = {iq3xxs_grid[q3[ 0]], iq3xxs_grid[q3[ 1]], iq3xxs_grid[q3[ 2]], iq3xxs_grid[q3[ 3]]};
|
||||
const uint32x4_t aux32x4_1 = {iq3xxs_grid[q3[ 4]], iq3xxs_grid[q3[ 5]], iq3xxs_grid[q3[ 6]], iq3xxs_grid[q3[ 7]]};
|
||||
const uint32x4_t aux32x4_2 = {iq3xxs_grid[q3[ 8]], iq3xxs_grid[q3[ 9]], iq3xxs_grid[q3[10]], iq3xxs_grid[q3[11]]};
|
||||
const uint32x4_t aux32x4_3 = {iq3xxs_grid[q3[12]], iq3xxs_grid[q3[13]], iq3xxs_grid[q3[14]], iq3xxs_grid[q3[15]]};
|
||||
q3 += 16;
|
||||
q3s.val[0] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[0] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[0] >> 7) & 127))));
|
||||
q3s.val[1] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[0] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[0] >> 21) & 127))));
|
||||
q3s.val[2] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >> 7) & 127))));
|
||||
q3s.val[3] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >> 21) & 127))));
|
||||
q3s.val[0] = vmulq_s8(q3s.val[0], vreinterpretq_s8_u32(aux32x4_0));
|
||||
q3s.val[1] = vmulq_s8(q3s.val[1], vreinterpretq_s8_u32(aux32x4_1));
|
||||
q3s.val[2] = vmulq_s8(q3s.val[2], vreinterpretq_s8_u32(aux32x4_2));
|
||||
q3s.val[3] = vmulq_s8(q3s.val[3], vreinterpretq_s8_u32(aux32x4_3));
|
||||
const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[0], q8b.val[0]), q3s.val[1], q8b.val[1]);
|
||||
const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[2], q8b.val[2]), q3s.val[3], q8b.val[3]);
|
||||
sumf1 += vaddvq_s32(p1) * (0.5f + (aux32[0] >> 28));
|
||||
sumf2 += vaddvq_s32(p2) * (0.5f + (aux32[1] >> 28));
|
||||
}
|
||||
sumf += d*(sumf1 + sumf2);
|
||||
}
|
||||
*s = 0.5f * sumf;
|
||||
|
||||
#elif defined(__AVX2__)
|
||||
|
||||
const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs;
|
||||
|
||||
uint32_t aux32[2];
|
||||
|
||||
__m256 accumf = _mm256_setzero_ps();
|
||||
for (int i = 0; i < nb; ++i) {
|
||||
const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
|
||||
const uint8_t * restrict q3 = x[i].qs;
|
||||
const uint8_t * restrict gas = x[i].qs + QK_K/4;
|
||||
const int8_t * restrict q8 = y[i].qs;
|
||||
__m256i sumi1 = _mm256_setzero_si256();
|
||||
__m256i sumi2 = _mm256_setzero_si256();
|
||||
for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
|
||||
const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
|
||||
const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
|
||||
const __m256i q2_1 = _mm256_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]],
|
||||
iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]);
|
||||
q3 += 8;
|
||||
const __m256i q2_2 = _mm256_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]],
|
||||
iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]);
|
||||
q3 += 8;
|
||||
memcpy(aux32, gas, 8); gas += 8;
|
||||
const __m256i s2_1 = _mm256_set_epi64x(signs64[(aux32[0] >> 21) & 127], signs64[(aux32[0] >> 14) & 127],
|
||||
signs64[(aux32[0] >> 7) & 127], signs64[(aux32[0] >> 0) & 127]);
|
||||
const __m256i s2_2 = _mm256_set_epi64x(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127],
|
||||
signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]);
|
||||
const __m256i q8s_1 = _mm256_sign_epi8(q8_1, s2_1);
|
||||
const __m256i q8s_2 = _mm256_sign_epi8(q8_2, s2_2);
|
||||
const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1);
|
||||
const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2);
|
||||
const uint16_t ls1 = aux32[0] >> 28;
|
||||
const uint16_t ls2 = aux32[1] >> 28;
|
||||
const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*ls1+1));
|
||||
const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*ls2+1));
|
||||
sumi1 = _mm256_add_epi32(sumi1, p1);
|
||||
sumi2 = _mm256_add_epi32(sumi2, p2);
|
||||
}
|
||||
|
||||
accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf);
|
||||
|
||||
}
|
||||
|
||||
*s = 0.25f * hsum_float_8(accumf);
|
||||
|
||||
#else
|
||||
|
||||
uint32_t aux32;
|
||||
|
||||
float sumf = 0.f;
|
||||
for (int i = 0; i < nb; ++i) {
|
||||
const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
|
||||
const uint8_t * restrict q3 = x[i].qs;
|
||||
const uint8_t * restrict gas = x[i].qs + QK_K/4;
|
||||
const int8_t * restrict q8 = y[i].qs;
|
||||
int32_t bsum = 0;
|
||||
for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
|
||||
memcpy(&aux32, gas, sizeof(uint32_t)); gas += sizeof(uint32_t);
|
||||
const uint32_t ls = 2*(aux32 >> 28) + 1;
|
||||
int32_t sumi = 0;
|
||||
for (int l = 0; l < 4; ++l) {
|
||||
const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*l+0]);
|
||||
const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*l+1]);
|
||||
const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*l) & 127];
|
||||
for (int j = 0; j < 4; ++j) {
|
||||
sumi += grid1[j] * q8[j+0] * (signs & kmask_iq2xs[j+0] ? -1 : 1);
|
||||
sumi += grid2[j] * q8[j+4] * (signs & kmask_iq2xs[j+4] ? -1 : 1);
|
||||
}
|
||||
q8 += 8;
|
||||
}
|
||||
q3 += 8;
|
||||
bsum += sumi * ls;
|
||||
}
|
||||
sumf += d * bsum;
|
||||
}
|
||||
*s = 0.25f * sumf;
|
||||
#endif
|
||||
}
|
||||
|
||||
// ================================ IQ2 quantization =============================================
|
||||
|
||||
typedef struct {
|
||||
@ -9189,3 +9386,436 @@ size_t quantize_iq2_xs(const float * src, void * dst, int nrow, int n_per_row, i
|
||||
return nrow * nblock * sizeof(block_iq2_xs);
|
||||
}
|
||||
|
||||
//
|
||||
// ============================================= 3-bit using D4 lattice
|
||||
//
|
||||
|
||||
typedef struct {
|
||||
uint32_t * grid;
|
||||
int * map;
|
||||
uint16_t * neighbours;
|
||||
} iq3_entry_t;
|
||||
|
||||
static iq3_entry_t iq3_data[1] = {
|
||||
{NULL, NULL, NULL},
|
||||
};
|
||||
|
||||
static inline int iq3_data_index(int grid_size) {
|
||||
(void)grid_size;
|
||||
GGML_ASSERT(grid_size == 256);
|
||||
return 0;
|
||||
}
|
||||
|
||||
static int iq3_compare_func(const void * left, const void * right) {
|
||||
const int * l = (const int *)left;
|
||||
const int * r = (const int *)right;
|
||||
return l[0] < r[0] ? -1 : l[0] > r[0] ? 1 : l[1] < r[1] ? -1 : l[1] > r[1] ? 1 : 0;
|
||||
}
|
||||
|
||||
void iq3xs_init_impl(int grid_size) {
|
||||
const int gindex = iq3_data_index(grid_size);
|
||||
if (iq3_data[gindex].grid) {
|
||||
return;
|
||||
}
|
||||
static const uint16_t kgrid_256[256] = {
|
||||
0, 2, 4, 9, 11, 15, 16, 18, 25, 34, 59, 61, 65, 67, 72, 74,
|
||||
81, 85, 88, 90, 97, 108, 120, 128, 130, 132, 137, 144, 146, 153, 155, 159,
|
||||
169, 175, 189, 193, 199, 200, 202, 213, 248, 267, 287, 292, 303, 315, 317, 321,
|
||||
327, 346, 362, 413, 436, 456, 460, 462, 483, 497, 513, 515, 520, 522, 529, 531,
|
||||
536, 538, 540, 551, 552, 576, 578, 585, 592, 594, 641, 643, 648, 650, 657, 664,
|
||||
698, 704, 706, 720, 729, 742, 758, 769, 773, 808, 848, 852, 870, 889, 901, 978,
|
||||
992, 1024, 1026, 1033, 1035, 1040, 1042, 1046, 1049, 1058, 1089, 1091, 1093, 1096, 1098, 1105,
|
||||
1112, 1139, 1143, 1144, 1152, 1154, 1161, 1167, 1168, 1170, 1183, 1184, 1197, 1217, 1224, 1228,
|
||||
1272, 1276, 1309, 1323, 1347, 1367, 1377, 1404, 1473, 1475, 1486, 1509, 1537, 1544, 1546, 1553,
|
||||
1555, 1576, 1589, 1594, 1600, 1602, 1616, 1625, 1636, 1638, 1665, 1667, 1672, 1685, 1706, 1722,
|
||||
1737, 1755, 1816, 1831, 1850, 1856, 1862, 1874, 1901, 1932, 1950, 1971, 2011, 2032, 2052, 2063,
|
||||
2077, 2079, 2091, 2095, 2172, 2192, 2207, 2208, 2224, 2230, 2247, 2277, 2308, 2345, 2356, 2389,
|
||||
2403, 2424, 2501, 2504, 2506, 2520, 2570, 2593, 2616, 2624, 2630, 2646, 2669, 2700, 2714, 2746,
|
||||
2754, 2795, 2824, 2835, 2839, 2874, 2882, 2905, 2984, 3028, 3042, 3092, 3108, 3110, 3124, 3153,
|
||||
3185, 3215, 3252, 3288, 3294, 3364, 3397, 3434, 3483, 3523, 3537, 3587, 3589, 3591, 3592, 3610,
|
||||
3626, 3670, 3680, 3722, 3749, 3754, 3776, 3789, 3803, 3824, 3857, 3873, 3904, 3906, 3924, 3992,
|
||||
};
|
||||
const int kmap_size = 4096;
|
||||
const int nwant = 2;
|
||||
const uint16_t * kgrid = kgrid_256;
|
||||
uint32_t * kgrid_q3xs;
|
||||
int * kmap_q3xs;
|
||||
uint16_t * kneighbors_q3xs;
|
||||
|
||||
printf("================================================================= %s(grid_size = %d)\n", __func__, grid_size);
|
||||
uint32_t * the_grid = (uint32_t *)malloc(grid_size*sizeof(uint32_t));
|
||||
for (int k = 0; k < grid_size; ++k) {
|
||||
int8_t * pos = (int8_t *)(the_grid + k);
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
int l = (kgrid[k] >> 3*i) & 0x7;
|
||||
pos[i] = 2*l + 1;
|
||||
}
|
||||
}
|
||||
kgrid_q3xs = the_grid;
|
||||
iq3_data[gindex].grid = the_grid;
|
||||
kmap_q3xs = (int *)malloc(kmap_size*sizeof(int));
|
||||
iq3_data[gindex].map = kmap_q3xs;
|
||||
for (int i = 0; i < kmap_size; ++i) kmap_q3xs[i] = -1;
|
||||
uint32_t aux32;
|
||||
uint8_t * aux8 = (uint8_t *)&aux32;
|
||||
for (int i = 0; i < grid_size; ++i) {
|
||||
aux32 = kgrid_q3xs[i];
|
||||
uint16_t index = 0;
|
||||
for (int k=0; k<4; ++k) {
|
||||
uint16_t q = (aux8[k] - 1)/2;
|
||||
index |= (q << 3*k);
|
||||
}
|
||||
kmap_q3xs[index] = i;
|
||||
}
|
||||
int8_t pos[4];
|
||||
int * dist2 = (int *)malloc(2*grid_size*sizeof(int));
|
||||
int num_neighbors = 0, num_not_in_map = 0;
|
||||
for (int i = 0; i < kmap_size; ++i) {
|
||||
if (kmap_q3xs[i] >= 0) continue;
|
||||
++num_not_in_map;
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
int l = (i >> 3*k) & 0x7;
|
||||
pos[k] = 2*l + 1;
|
||||
}
|
||||
for (int j = 0; j < grid_size; ++j) {
|
||||
const int8_t * pg = (const int8_t *)(kgrid_q3xs + j);
|
||||
int d2 = 0;
|
||||
for (int k = 0; k < 4; ++k) d2 += (pg[k] - pos[k])*(pg[k] - pos[k]);
|
||||
dist2[2*j+0] = d2;
|
||||
dist2[2*j+1] = j;
|
||||
}
|
||||
qsort(dist2, grid_size, 2*sizeof(int), iq3_compare_func);
|
||||
int n = 0; int d2 = dist2[0];
|
||||
int nhave = 1;
|
||||
for (int j = 0; j < grid_size; ++j) {
|
||||
if (dist2[2*j] > d2) {
|
||||
if (nhave == nwant) break;
|
||||
d2 = dist2[2*j];
|
||||
++nhave;
|
||||
}
|
||||
++n;
|
||||
}
|
||||
num_neighbors += n;
|
||||
}
|
||||
printf("%s: %d neighbours in total\n", __func__, num_neighbors);
|
||||
kneighbors_q3xs = (uint16_t *)malloc((num_neighbors + num_not_in_map)*sizeof(uint16_t));
|
||||
iq3_data[gindex].neighbours = kneighbors_q3xs;
|
||||
int counter = 0;
|
||||
for (int i = 0; i < kmap_size; ++i) {
|
||||
if (kmap_q3xs[i] >= 0) continue;
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
int l = (i >> 3*k) & 0x7;
|
||||
pos[k] = 2*l + 1;
|
||||
}
|
||||
for (int j = 0; j < grid_size; ++j) {
|
||||
const int8_t * pg = (const int8_t *)(kgrid_q3xs + j);
|
||||
int d2 = 0;
|
||||
for (int k = 0; k < 4; ++k) d2 += (pg[k] - pos[k])*(pg[k] - pos[k]);
|
||||
dist2[2*j+0] = d2;
|
||||
dist2[2*j+1] = j;
|
||||
}
|
||||
qsort(dist2, grid_size, 2*sizeof(int), iq3_compare_func);
|
||||
kmap_q3xs[i] = -(counter + 1);
|
||||
int d2 = dist2[0];
|
||||
uint16_t * start = &kneighbors_q3xs[counter++];
|
||||
int n = 0, nhave = 1;
|
||||
for (int j = 0; j < grid_size; ++j) {
|
||||
if (dist2[2*j] > d2) {
|
||||
if (nhave == nwant) break;
|
||||
d2 = dist2[2*j];
|
||||
++nhave;
|
||||
}
|
||||
kneighbors_q3xs[counter++] = dist2[2*j+1];
|
||||
++n;
|
||||
}
|
||||
*start = n;
|
||||
}
|
||||
free(dist2);
|
||||
}
|
||||
|
||||
void iq3xs_free_impl(int grid_size) {
|
||||
GGML_ASSERT(grid_size == 256);
|
||||
const int gindex = iq3_data_index(grid_size);
|
||||
if (iq3_data[gindex].grid) {
|
||||
free(iq3_data[gindex].grid); iq3_data[gindex].grid = NULL;
|
||||
free(iq3_data[gindex].map); iq3_data[gindex].map = NULL;
|
||||
free(iq3_data[gindex].neighbours); iq3_data[gindex].neighbours = NULL;
|
||||
}
|
||||
}
|
||||
|
||||
static int iq3_find_best_neighbour(const uint16_t * restrict neighbours, const uint32_t * restrict grid,
|
||||
const float * restrict xval, const float * restrict weight, float scale, int8_t * restrict L) {
|
||||
int num_neighbors = neighbours[0];
|
||||
GGML_ASSERT(num_neighbors > 0);
|
||||
float best_d2 = FLT_MAX;
|
||||
int grid_index = -1;
|
||||
for (int j = 1; j <= num_neighbors; ++j) {
|
||||
const int8_t * pg = (const int8_t *)(grid + neighbours[j]);
|
||||
float d2 = 0;
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
float q = pg[i];
|
||||
float diff = scale*q - xval[i];
|
||||
d2 += weight[i]*diff*diff;
|
||||
}
|
||||
if (d2 < best_d2) {
|
||||
best_d2 = d2; grid_index = neighbours[j];
|
||||
}
|
||||
}
|
||||
GGML_ASSERT(grid_index >= 0);
|
||||
const int8_t * pg = (const int8_t *)(grid + grid_index);
|
||||
for (int i = 0; i < 4; ++i) L[i] = (pg[i] - 1)/2;
|
||||
return grid_index;
|
||||
}
|
||||
|
||||
static void quantize_row_iq3_xxs_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) {
|
||||
|
||||
const int gindex = iq3_data_index(256);
|
||||
|
||||
const uint32_t * kgrid_q3xs = iq3_data[gindex].grid;
|
||||
const int * kmap_q3xs = iq3_data[gindex].map;
|
||||
const uint16_t * kneighbors_q3xs = iq3_data[gindex].neighbours;
|
||||
|
||||
//GGML_ASSERT(quant_weights && "missing quantization weights");
|
||||
GGML_ASSERT(kgrid_q3xs && "forgot to call ggml_quantize_init()?");
|
||||
GGML_ASSERT(kmap_q3xs && "forgot to call ggml_quantize_init()?");
|
||||
GGML_ASSERT(kneighbors_q3xs && "forgot to call ggml_quantize_init()?");
|
||||
GGML_ASSERT(n%QK_K == 0);
|
||||
|
||||
const int kMaxQ = 8;
|
||||
|
||||
const int nbl = n/256;
|
||||
|
||||
block_iq3_xxs * y = vy;
|
||||
|
||||
float scales[QK_K/32];
|
||||
float weight[32];
|
||||
float xval[32];
|
||||
int8_t L[32];
|
||||
int8_t Laux[32];
|
||||
float waux[32];
|
||||
bool is_on_grid[8];
|
||||
bool is_on_grid_aux[8];
|
||||
uint8_t block_signs[8];
|
||||
uint8_t q3[3*(QK_K/8)];
|
||||
uint32_t * scales_and_signs = (uint32_t *)(q3 + QK_K/4);
|
||||
|
||||
for (int ibl = 0; ibl < nbl; ++ibl) {
|
||||
|
||||
y[ibl].d = GGML_FP32_TO_FP16(0.f);
|
||||
memset(q3, 0, 3*QK_K/8);
|
||||
|
||||
float max_scale = 0;
|
||||
|
||||
const float * xbl = x + QK_K*ibl;
|
||||
float sumx2 = 0;
|
||||
for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i];
|
||||
float sigma2 = sumx2/QK_K;
|
||||
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
const float * xb = xbl + 32*ib;
|
||||
if (quant_weights) {
|
||||
const float * qw = quant_weights + QK_K*ibl + 32*ib;
|
||||
for (int i = 0; i < 32; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
|
||||
} else {
|
||||
for (int i = 0; i < 32; ++i) weight[i] = xb[i]*xb[i];
|
||||
}
|
||||
for (int i = 0; i < 32; ++i) waux[i] = sqrtf(weight[i]);
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
int nflip = 0;
|
||||
uint8_t s = 0;
|
||||
for (int i = 0; i < 8; ++i) {
|
||||
if (xb[8*k + i] >= 0) xval[8*k + i] = xb[8*k + i];
|
||||
else {
|
||||
xval[8*k + i] = -xb[8*k + i]; ++nflip; s |= (1 << i);
|
||||
}
|
||||
}
|
||||
if (nflip%2) {
|
||||
int imin = 0; float min = weight[8*k+imin]*xb[8*k+imin]*xb[8*k+imin];
|
||||
for (int i = 1; i < 8; ++i) {
|
||||
float ax = weight[8*k+i]*xb[8*k+i]*xb[8*k+i];
|
||||
if (ax < min) {
|
||||
min = ax; imin = i;
|
||||
}
|
||||
}
|
||||
xval[8*k+imin] = -xval[8*k+imin];
|
||||
s ^= (1 << imin);
|
||||
}
|
||||
block_signs[k] = s & 127;
|
||||
}
|
||||
float max = xval[0];
|
||||
for (int i = 1; i < 32; ++i) max = MAX(max, xval[i]);
|
||||
if (!max) {
|
||||
scales[ib] = 0;
|
||||
memset(L, 0, 32);
|
||||
continue;
|
||||
}
|
||||
float best = 0;
|
||||
float scale = max/(2*kMaxQ-1);
|
||||
for (int is = -15; is <= 15; ++is) {
|
||||
float id = (2*kMaxQ-1+is*0.2f)/max;
|
||||
float this_scale = 1/id;
|
||||
for (int k = 0; k < 8; ++k) {
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
int l = nearest_int(0.5f*(id*xval[4*k+i]-1));
|
||||
Laux[4*k+i] = MAX(0, MIN(kMaxQ-1, l));
|
||||
}
|
||||
uint16_t u = 0;
|
||||
for (int i = 0; i < 4; ++i) u |= (Laux[4*k+i] << 3*i);
|
||||
int grid_index = kmap_q3xs[u];
|
||||
is_on_grid_aux[k] = true;
|
||||
if (grid_index < 0) {
|
||||
is_on_grid_aux[k] = false;
|
||||
const uint16_t * neighbours = kneighbors_q3xs - kmap_q3xs[u] - 1;
|
||||
grid_index = iq3_find_best_neighbour(neighbours, kgrid_q3xs, xval + 4*k, waux + 4*k, this_scale, Laux + 4*k);
|
||||
}
|
||||
}
|
||||
float sumqx = 0, sumq2 = 0;
|
||||
for (int i = 0; i < 32; ++i) {
|
||||
float w = weight[i];
|
||||
float q = 2*Laux[i] + 1;
|
||||
sumqx += w*xval[i]*q;
|
||||
sumq2 += w*q*q;
|
||||
}
|
||||
if (sumq2 > 0 && sumqx*sumqx > best*sumq2) {
|
||||
scale = sumqx/sumq2; best = scale*sumqx;
|
||||
for (int i = 0; i < 32; ++i) L[i] = Laux[i];
|
||||
for (int k = 0; k < 8; ++k) is_on_grid[k] = is_on_grid_aux[k];
|
||||
}
|
||||
}
|
||||
int n_not_ongrid = 0;
|
||||
for (int k = 0; k < 8; ++k) if (!is_on_grid[k]) ++n_not_ongrid;
|
||||
if (n_not_ongrid > 0 && scale > 0) {
|
||||
float id = 1/scale;
|
||||
for (int k = 0; k < 8; ++k) {
|
||||
if (is_on_grid[k]) continue;
|
||||
uint16_t u = 0;
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
int l = nearest_int(0.5f*(id*xval[4*k+i]-1));
|
||||
l = MAX(0, MIN(kMaxQ-1, l));
|
||||
u |= (l << 3*i);
|
||||
}
|
||||
int grid_index = kmap_q3xs[u];
|
||||
if (grid_index < 0) {
|
||||
const uint16_t * neighbours = kneighbors_q3xs - kmap_q3xs[u] - 1;
|
||||
grid_index = iq3_find_best_neighbour(neighbours, kgrid_q3xs, xval + 4*k, waux + 4*k, scale, L + 4*k);
|
||||
}
|
||||
const int8_t * pg = (const int8_t *)(kgrid_q3xs + grid_index);
|
||||
for (int i = 0; i < 4; ++i) L[4*k+i] = (pg[i] - 1)/2;
|
||||
}
|
||||
float sumqx = 0, sumq2 = 0;
|
||||
for (int i = 0; i < 32; ++i) {
|
||||
float w = weight[i];
|
||||
float q = 2*L[i] + 1;
|
||||
sumqx += w*xval[i]*q;
|
||||
sumq2 += w*q*q;
|
||||
}
|
||||
if (sumq2 > 0) scale = sumqx/sumq2;
|
||||
}
|
||||
if (scale < 0) {
|
||||
// This should never happen, but just in case, flip scale so that it is positive (we use uint's to encode the scale)
|
||||
// and correspondingly flip quant signs.
|
||||
scale = -scale;
|
||||
for (int k = 0; k < 4; ++k) block_signs[k] = (~block_signs[k]) & 127;
|
||||
}
|
||||
for (int k = 0; k < 8; ++k) {
|
||||
uint16_t u = 0;
|
||||
for (int i = 0; i < 4; ++i) u |= (L[4*k+i] << 3*i);
|
||||
int grid_index = kmap_q3xs[u];
|
||||
if (grid_index < 0) {
|
||||
printf("Oops: found point %u not on grid:", u);
|
||||
for (int i = 0; i < 4; ++i) printf(" %d", L[4*k+i]);
|
||||
printf("\n");
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
q3[8*ib+k] = grid_index;
|
||||
}
|
||||
scales_and_signs[ib] = block_signs[0] | (block_signs[1] << 7) | (block_signs[2] << 14) | (block_signs[3] << 21);
|
||||
GGML_ASSERT(scale >= 0);
|
||||
scales[ib] = scale;
|
||||
max_scale = MAX(max_scale, scale);
|
||||
}
|
||||
|
||||
if (!max_scale) {
|
||||
memset(y[ibl].qs, 0, 3*QK_K/8);
|
||||
continue;
|
||||
}
|
||||
|
||||
float d = max_scale/31;
|
||||
y[ibl].d = GGML_FP32_TO_FP16(d);
|
||||
float id = 1/d;
|
||||
float sumqx = 0, sumq2 = 0;
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
int l = nearest_int(0.5f*(id*scales[ib]-1));
|
||||
l = MAX(0, MIN(15, l));
|
||||
scales_and_signs[ib] |= ((uint32_t)l << 28);
|
||||
if (false) {
|
||||
const float * xb = xbl + 32*ib;
|
||||
if (quant_weights) {
|
||||
const float * qw = quant_weights + QK_K*ibl + 32*ib;
|
||||
for (int i = 0; i < 32; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
|
||||
} else {
|
||||
for (int i = 0; i < 32; ++i) weight[i] = xb[i]*xb[i];
|
||||
}
|
||||
const float db = 0.25f * d * (1 + 2*l);
|
||||
for (int k = 0; k < 8; ++k) {
|
||||
const int8_t * signs = keven_signs_q2xs + 8*((scales_and_signs[ib] >> 7*(k/2)) & 127) + 4*(k%2);
|
||||
const float * xk = xb + 4*k;
|
||||
const float * wk = weight + 4*k;
|
||||
//const uint8_t * grid = (const uint8_t *)(kgrid_q3xs + q3[8*ib+k]);
|
||||
const uint8_t * grid = (const uint8_t *)(iq3xxs_grid + q3[8*ib+k]);
|
||||
float best_mse = 0; int best_index = q3[8*ib+k];
|
||||
for (int j = 0; j < 4; ++j) {
|
||||
float diff = db * grid[j] * signs[j] - xk[j];
|
||||
best_mse += wk[j] * diff * diff;
|
||||
}
|
||||
for (int idx = 0; idx < 256; ++idx) {
|
||||
//grid = (const uint8_t *)(kgrid_q3xs + idx);
|
||||
grid = (const uint8_t *)(iq3xxs_grid + idx);
|
||||
float mse = 0;
|
||||
for (int j = 0; j < 4; ++j) {
|
||||
float diff = db * grid[j] * signs[j] - xk[j];
|
||||
mse += wk[j] * diff * diff;
|
||||
}
|
||||
if (mse < best_mse) {
|
||||
best_mse = mse; best_index = idx;
|
||||
}
|
||||
}
|
||||
q3[8*ib+k] = best_index;
|
||||
//grid = (const uint8_t *)(kgrid_q3xs + best_index);
|
||||
grid = (const uint8_t *)(iq3xxs_grid + best_index);
|
||||
for (int j = 0; j < 4; ++j) {
|
||||
float q = db * grid[j] * signs[j];
|
||||
sumqx += wk[j] * q * xk[j];
|
||||
sumq2 += wk[j] * q * q;
|
||||
}
|
||||
}
|
||||
if (sumq2 > 0) y[ibl].d = GGML_FP32_TO_FP16(d*sumqx/sumq2);
|
||||
}
|
||||
}
|
||||
memcpy(y[ibl].qs, q3, 3*QK_K/8);
|
||||
}
|
||||
}
|
||||
|
||||
size_t quantize_iq3_xxs(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) {
|
||||
(void)hist;
|
||||
GGML_ASSERT(n_per_row%QK_K == 0);
|
||||
int nblock = n_per_row/QK_K;
|
||||
char * qrow = (char *)dst;
|
||||
for (int row = 0; row < nrow; ++row) {
|
||||
quantize_row_iq3_xxs_impl(src, qrow, n_per_row, quant_weights);
|
||||
src += n_per_row;
|
||||
qrow += nblock*sizeof(block_iq3_xxs);
|
||||
}
|
||||
return nrow * nblock * sizeof(block_iq3_xxs);
|
||||
}
|
||||
|
||||
void quantize_row_iq3_xxs(const float * restrict x, void * restrict vy, int k) {
|
||||
assert(k % QK_K == 0);
|
||||
block_iq3_xxs * restrict y = vy;
|
||||
quantize_row_iq3_xxs_reference(x, y, k);
|
||||
}
|
||||
|
||||
void quantize_row_iq3_xxs_reference(const float * restrict x, block_iq3_xxs * restrict y, int k) {
|
||||
assert(k % QK_K == 0);
|
||||
quantize_row_iq3_xxs_impl(x, y, k, NULL);
|
||||
}
|
||||
|
||||
@ -166,7 +166,7 @@ typedef struct {
|
||||
static_assert(sizeof(block_q8_K) == sizeof(float) + QK_K + QK_K/16*sizeof(int16_t), "wrong q8_K block size/padding");
|
||||
|
||||
// (Almost) "true" 2-bit quantization.
|
||||
// Due to the need to use blocks as per ggml dsign, it ends up using
|
||||
// Due to the need to use blocks as per ggml design, it ends up using
|
||||
// 2.0625 bpw because of the 16-bit scale for each block of 256.
|
||||
typedef struct {
|
||||
ggml_fp16_t d;
|
||||
@ -182,6 +182,15 @@ typedef struct {
|
||||
} block_iq2_xs;
|
||||
static_assert(sizeof(block_iq2_xs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t) + QK_K/32, "wrong iq2_xs block size/padding");
|
||||
|
||||
// (Almost) "true" 3-bit quantization.
|
||||
// Due to the need to use blocks as per ggml design, it ends up using
|
||||
// 3.0625 bpw because of the 16-bit scale for each block of 256.
|
||||
typedef struct {
|
||||
ggml_fp16_t d;
|
||||
uint8_t qs[3*QK_K/8];
|
||||
} block_iq3_xxs;
|
||||
static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong iq3_xxs block size/padding");
|
||||
|
||||
// Quantization
|
||||
void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int k);
|
||||
void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int k);
|
||||
@ -196,6 +205,7 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict
|
||||
void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int k);
|
||||
void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k);
|
||||
void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k);
|
||||
void quantize_row_iq3_xxs_reference(const float * restrict x, block_iq3_xxs * restrict y, int k);
|
||||
|
||||
void quantize_row_q4_0(const float * restrict x, void * restrict y, int k);
|
||||
void quantize_row_q4_1(const float * restrict x, void * restrict y, int k);
|
||||
@ -210,6 +220,7 @@ void quantize_row_q4_K(const float * restrict x, void * restrict y, int k);
|
||||
void quantize_row_q5_K(const float * restrict x, void * restrict y, int k);
|
||||
void quantize_row_q6_K(const float * restrict x, void * restrict y, int k);
|
||||
void quantize_row_q8_K(const float * restrict x, void * restrict y, int k);
|
||||
void quantize_row_iq3_xxs(const float * restrict x, void * restrict y, int k);
|
||||
|
||||
// Dequantization
|
||||
void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int k);
|
||||
@ -227,6 +238,7 @@ void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int
|
||||
void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int k);
|
||||
void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y, int k);
|
||||
void dequantize_row_iq2_xs (const block_iq2_xs * restrict x, float * restrict y, int k);
|
||||
void dequantize_row_iq3_xxs(const block_iq3_xxs * restrict x, float * restrict y, int k);
|
||||
|
||||
// Dot product
|
||||
void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
|
||||
@ -242,12 +254,14 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, const void * restrict vx,
|
||||
void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
|
||||
void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
|
||||
void ggml_vec_dot_iq2_xs_q8_K (int n, float * restrict s, const void * restrict vx, const void * restrict vy);
|
||||
void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
|
||||
|
||||
//
|
||||
// Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization")
|
||||
//
|
||||
size_t quantize_iq2_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
|
||||
size_t quantize_iq2_xs (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
|
||||
size_t quantize_iq3_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
|
||||
size_t quantize_q2_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
|
||||
size_t quantize_q3_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
|
||||
size_t quantize_q4_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
|
||||
@ -260,3 +274,5 @@ size_t quantize_q5_1 (const float * src, void * dst, int nrows, int n_per_row,
|
||||
|
||||
void iq2xs_init_impl(int grid_size);
|
||||
void iq2xs_free_impl(int grid_size);
|
||||
void iq3xs_init_impl(int grid_size);
|
||||
void iq3xs_free_impl(int grid_size);
|
||||
|
||||
30
ggml.c
30
ggml.c
@ -599,6 +599,17 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
|
||||
.vec_dot = ggml_vec_dot_iq2_xs_q8_K,
|
||||
.vec_dot_type = GGML_TYPE_Q8_K,
|
||||
},
|
||||
[GGML_TYPE_IQ3_XXS] = {
|
||||
.type_name = "iq3_xxs",
|
||||
.blck_size = QK_K,
|
||||
.type_size = sizeof(block_iq3_xxs),
|
||||
.is_quantized = true,
|
||||
.to_float = (ggml_to_float_t) dequantize_row_iq3_xxs,
|
||||
.from_float = quantize_row_iq3_xxs,
|
||||
.from_float_reference = (ggml_from_float_t)quantize_row_iq3_xxs_reference,
|
||||
.vec_dot = ggml_vec_dot_iq3_xxs_q8_K,
|
||||
.vec_dot_type = GGML_TYPE_Q8_K,
|
||||
},
|
||||
[GGML_TYPE_Q8_K] = {
|
||||
.type_name = "q8_K",
|
||||
.blck_size = QK_K,
|
||||
@ -2144,6 +2155,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
|
||||
case GGML_FTYPE_MOSTLY_Q6_K: wtype = GGML_TYPE_Q6_K; break;
|
||||
case GGML_FTYPE_MOSTLY_IQ2_XXS: wtype = GGML_TYPE_IQ2_XXS; break;
|
||||
case GGML_FTYPE_MOSTLY_IQ2_XS: wtype = GGML_TYPE_IQ2_XS; break;
|
||||
case GGML_FTYPE_MOSTLY_IQ3_XXS: wtype = GGML_TYPE_IQ3_XXS; break;
|
||||
case GGML_FTYPE_UNKNOWN: wtype = GGML_TYPE_COUNT; break;
|
||||
case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16: wtype = GGML_TYPE_COUNT; break;
|
||||
}
|
||||
@ -7537,6 +7549,7 @@ static void ggml_compute_forward_add(
|
||||
case GGML_TYPE_Q6_K:
|
||||
case GGML_TYPE_IQ2_XXS:
|
||||
case GGML_TYPE_IQ2_XS:
|
||||
case GGML_TYPE_IQ3_XXS:
|
||||
{
|
||||
ggml_compute_forward_add_q_f32(params, src0, src1, dst);
|
||||
} break;
|
||||
@ -7803,6 +7816,7 @@ static void ggml_compute_forward_add1(
|
||||
case GGML_TYPE_Q6_K:
|
||||
case GGML_TYPE_IQ2_XXS:
|
||||
case GGML_TYPE_IQ2_XS:
|
||||
case GGML_TYPE_IQ3_XXS:
|
||||
{
|
||||
ggml_compute_forward_add1_q_f32(params, src0, src1, dst);
|
||||
} break;
|
||||
@ -7922,6 +7936,7 @@ static void ggml_compute_forward_acc(
|
||||
case GGML_TYPE_Q6_K:
|
||||
case GGML_TYPE_IQ2_XXS:
|
||||
case GGML_TYPE_IQ2_XS:
|
||||
case GGML_TYPE_IQ3_XXS:
|
||||
default:
|
||||
{
|
||||
GGML_ASSERT(false);
|
||||
@ -10673,6 +10688,7 @@ static void ggml_compute_forward_out_prod(
|
||||
case GGML_TYPE_Q6_K:
|
||||
case GGML_TYPE_IQ2_XXS:
|
||||
case GGML_TYPE_IQ2_XS:
|
||||
case GGML_TYPE_IQ3_XXS:
|
||||
{
|
||||
ggml_compute_forward_out_prod_q_f32(params, src0, src1, dst);
|
||||
} break;
|
||||
@ -10852,6 +10868,7 @@ static void ggml_compute_forward_set(
|
||||
case GGML_TYPE_Q6_K:
|
||||
case GGML_TYPE_IQ2_XXS:
|
||||
case GGML_TYPE_IQ2_XS:
|
||||
case GGML_TYPE_IQ3_XXS:
|
||||
default:
|
||||
{
|
||||
GGML_ASSERT(false);
|
||||
@ -11048,6 +11065,7 @@ static void ggml_compute_forward_get_rows(
|
||||
case GGML_TYPE_Q6_K:
|
||||
case GGML_TYPE_IQ2_XXS:
|
||||
case GGML_TYPE_IQ2_XS:
|
||||
case GGML_TYPE_IQ3_XXS:
|
||||
{
|
||||
ggml_compute_forward_get_rows_q(params, src0, src1, dst);
|
||||
} break;
|
||||
@ -11695,6 +11713,7 @@ static void ggml_compute_forward_alibi(
|
||||
case GGML_TYPE_Q6_K:
|
||||
case GGML_TYPE_IQ2_XXS:
|
||||
case GGML_TYPE_IQ2_XS:
|
||||
case GGML_TYPE_IQ3_XXS:
|
||||
case GGML_TYPE_Q8_K:
|
||||
case GGML_TYPE_I8:
|
||||
case GGML_TYPE_I16:
|
||||
@ -11771,6 +11790,7 @@ static void ggml_compute_forward_clamp(
|
||||
case GGML_TYPE_Q6_K:
|
||||
case GGML_TYPE_IQ2_XXS:
|
||||
case GGML_TYPE_IQ2_XS:
|
||||
case GGML_TYPE_IQ3_XXS:
|
||||
case GGML_TYPE_Q8_K:
|
||||
case GGML_TYPE_I8:
|
||||
case GGML_TYPE_I16:
|
||||
@ -18827,6 +18847,7 @@ void ggml_quantize_init(enum ggml_type type) {
|
||||
switch (type) {
|
||||
case GGML_TYPE_IQ2_XXS: iq2xs_init_impl(256); break;
|
||||
case GGML_TYPE_IQ2_XS: iq2xs_init_impl(512); break;
|
||||
case GGML_TYPE_IQ3_XXS: iq3xs_init_impl(256); break;
|
||||
default: // nothing
|
||||
break;
|
||||
}
|
||||
@ -19089,6 +19110,15 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i
|
||||
result = quantize_iq2_xs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
|
||||
GGML_ASSERT(result == row_size * nrows);
|
||||
} break;
|
||||
case GGML_TYPE_IQ3_XXS:
|
||||
{
|
||||
GGML_ASSERT(start % QK_K == 0);
|
||||
GGML_ASSERT(start % n_per_row == 0);
|
||||
size_t start_row = start / n_per_row;
|
||||
size_t row_size = ggml_row_size(type, n_per_row);
|
||||
result = quantize_iq3_xxs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
|
||||
GGML_ASSERT(result == row_size * nrows);
|
||||
} break;
|
||||
case GGML_TYPE_F16:
|
||||
{
|
||||
size_t elemsize = sizeof(ggml_fp16_t);
|
||||
|
||||
2
ggml.h
2
ggml.h
@ -353,6 +353,7 @@ extern "C" {
|
||||
GGML_TYPE_Q8_K = 15,
|
||||
GGML_TYPE_IQ2_XXS = 16,
|
||||
GGML_TYPE_IQ2_XS = 17,
|
||||
GGML_TYPE_IQ3_XXS = 18,
|
||||
GGML_TYPE_I8,
|
||||
GGML_TYPE_I16,
|
||||
GGML_TYPE_I32,
|
||||
@ -389,6 +390,7 @@ extern "C" {
|
||||
GGML_FTYPE_MOSTLY_Q6_K = 14, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_IQ2_XXS = 15, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_IQ2_XS = 16, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_IQ3_XXS = 17, // except 1d tensors
|
||||
};
|
||||
|
||||
// available tensor operations:
|
||||
|
||||
24
llama.cpp
24
llama.cpp
@ -2367,6 +2367,7 @@ struct llama_model_loader {
|
||||
case GGML_TYPE_Q6_K: ftype = LLAMA_FTYPE_MOSTLY_Q6_K; break;
|
||||
case GGML_TYPE_IQ2_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XXS; break;
|
||||
case GGML_TYPE_IQ2_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XS; break;
|
||||
case GGML_TYPE_IQ3_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS; break;
|
||||
default:
|
||||
{
|
||||
LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max));
|
||||
@ -2715,6 +2716,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
|
||||
case LLAMA_FTYPE_MOSTLY_IQ2_XXS:return "IQ2_XSS - 2.0625 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ2_XS: return "IQ2_XS - 2.3125 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_Q3_K_XS:return "Q3_K - Extra small";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ3_XXS:return "IQ3_XSS - 3.0625 bpw";
|
||||
|
||||
default: return "unknown, may not work";
|
||||
}
|
||||
@ -9237,6 +9239,13 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
|
||||
else if (new_type != GGML_TYPE_Q8_0) {
|
||||
new_type = GGML_TYPE_Q6_K;
|
||||
}
|
||||
} else if (name == "token_embd.weight") {
|
||||
if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS) {
|
||||
new_type = GGML_TYPE_Q2_K;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
|
||||
new_type = GGML_TYPE_Q4_K;
|
||||
}
|
||||
} else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS) {
|
||||
if (name.find("attn_v.weight") != std::string::npos) {
|
||||
if (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q4_K;
|
||||
@ -9247,7 +9256,6 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
|
||||
if (qs.i_ffn_down < qs.n_ffn_down/8) new_type = GGML_TYPE_Q2_K;
|
||||
++qs.i_ffn_down;
|
||||
}
|
||||
else if (name == "token_embd.weight") new_type = GGML_TYPE_Q2_K;
|
||||
} else if (name.find("attn_v.weight") != std::string::npos) {
|
||||
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) {
|
||||
new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
|
||||
@ -9255,6 +9263,9 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && qs.model.hparams.n_gqa() >= 4) {
|
||||
new_type = GGML_TYPE_Q4_K;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS && qs.model.hparams.n_gqa() >= 4) {
|
||||
new_type = GGML_TYPE_Q4_K;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
|
||||
new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
|
||||
}
|
||||
@ -9292,6 +9303,9 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS) {
|
||||
if (i_layer < n_layer/8) new_type = GGML_TYPE_Q4_K;
|
||||
}
|
||||
//else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
|
||||
// if (i_layer < n_layer/8) new_type = GGML_TYPE_Q5_K;
|
||||
//}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
|
||||
new_type = i_layer < n_layer/16 ? GGML_TYPE_Q5_K
|
||||
: arch != LLM_ARCH_FALCON || use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q4_K
|
||||
@ -9323,13 +9337,14 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
|
||||
} else if (name.find("attn_output.weight") != std::string::npos) {
|
||||
if (arch != LLM_ARCH_FALCON) {
|
||||
if (qs.model.hparams.n_expert == 8) {
|
||||
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS ||
|
||||
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) {
|
||||
new_type = GGML_TYPE_Q5_K;
|
||||
}
|
||||
} else {
|
||||
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K ) new_type = GGML_TYPE_Q3_K;
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) new_type = GGML_TYPE_Q3_K;
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) new_type = GGML_TYPE_Q4_K;
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
|
||||
}
|
||||
@ -9372,7 +9387,8 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
|
||||
bool convert_incompatible_tensor = false;
|
||||
if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K ||
|
||||
new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K ||
|
||||
new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS) {
|
||||
new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS ||
|
||||
new_type == GGML_TYPE_IQ3_XXS) {
|
||||
int nx = tensor->ne[0];
|
||||
int ny = tensor->ne[1];
|
||||
if (nx % QK_K != 0) {
|
||||
@ -9386,6 +9402,7 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
|
||||
switch (new_type) {
|
||||
case GGML_TYPE_IQ2_XXS:
|
||||
case GGML_TYPE_IQ2_XS:
|
||||
case GGML_TYPE_IQ3_XXS:
|
||||
case GGML_TYPE_Q2_K: new_type = GGML_TYPE_Q4_0; break;
|
||||
case GGML_TYPE_Q3_K: new_type = GGML_TYPE_Q4_1; break;
|
||||
case GGML_TYPE_Q4_K: new_type = GGML_TYPE_Q5_0; break;
|
||||
@ -9427,6 +9444,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
case LLAMA_FTYPE_MOSTLY_Q6_K: quantized_type = GGML_TYPE_Q6_K; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ2_XXS:quantized_type = GGML_TYPE_IQ2_XXS; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ2_XS :quantized_type = GGML_TYPE_IQ2_XS; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ3_XXS:quantized_type = GGML_TYPE_IQ3_XXS; break;
|
||||
|
||||
default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
|
||||
}
|
||||
|
||||
1
llama.h
1
llama.h
@ -112,6 +112,7 @@ extern "C" {
|
||||
LLAMA_FTYPE_MOSTLY_IQ2_XS = 20, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q2_K_S = 21, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q3_K_XS = 22, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_IQ3_XXS = 23, // except 1d tensors
|
||||
|
||||
LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
|
||||
};
|
||||
|
||||
@ -1890,6 +1890,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
|
||||
GGML_TYPE_Q4_K, GGML_TYPE_Q5_K,
|
||||
GGML_TYPE_Q6_K,
|
||||
GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS,
|
||||
GGML_TYPE_IQ3_XXS,
|
||||
};
|
||||
|
||||
// unary ops
|
||||
|
||||
@ -17,7 +17,9 @@ constexpr float MAX_QUANTIZATION_REFERENCE_ERROR = 0.0001f;
|
||||
constexpr float MAX_QUANTIZATION_TOTAL_ERROR = 0.002f;
|
||||
constexpr float MAX_QUANTIZATION_TOTAL_ERROR_2BITS = 0.0075f;
|
||||
constexpr float MAX_QUANTIZATION_TOTAL_ERROR_3BITS = 0.0040f;
|
||||
constexpr float MAX_QUANTIZATION_TOTAL_ERROR_3BITS_XXS = 0.0050f;
|
||||
constexpr float MAX_DOT_PRODUCT_ERROR = 0.02f;
|
||||
constexpr float MAX_DOT_PRODUCT_ERROR_LOWBIT = 0.04f;
|
||||
|
||||
static const char* RESULT_STR[] = {"ok", "FAILED"};
|
||||
|
||||
@ -135,18 +137,21 @@ int main(int argc, char * argv[]) {
|
||||
}
|
||||
|
||||
const ggml_type ei = (ggml_type)i;
|
||||
|
||||
if (ei == GGML_TYPE_IQ2_XXS || ei == GGML_TYPE_IQ2_XS) {
|
||||
printf("Skip %s due to missing quantization functionality\n", ggml_type_name(ei));
|
||||
continue;
|
||||
}
|
||||
|
||||
printf("Testing %s\n", ggml_type_name((ggml_type) i));
|
||||
ggml_quantize_init(ei);
|
||||
|
||||
if (qfns.from_float && qfns.to_float) {
|
||||
const float total_error = total_quantization_error(qfns, test_size, test_data.data());
|
||||
const float max_quantization_error =
|
||||
type == GGML_TYPE_Q2_K ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS :
|
||||
type == GGML_TYPE_Q3_K ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS : MAX_QUANTIZATION_TOTAL_ERROR;
|
||||
type == GGML_TYPE_Q2_K ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS :
|
||||
type == GGML_TYPE_Q3_K ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS :
|
||||
type == GGML_TYPE_IQ3_XXS ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS_XXS : MAX_QUANTIZATION_TOTAL_ERROR;
|
||||
failed = !(total_error < max_quantization_error);
|
||||
num_failed += failed;
|
||||
if (failed || verbose) {
|
||||
@ -161,7 +166,9 @@ int main(int argc, char * argv[]) {
|
||||
}
|
||||
|
||||
const float vec_dot_error = dot_product_error(qfns, test_size, test_data.data(), test_data2.data());
|
||||
failed = !(vec_dot_error < MAX_DOT_PRODUCT_ERROR);
|
||||
const float max_allowed_error = type == GGML_TYPE_Q2_K || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ2_XXS ||
|
||||
type == GGML_TYPE_IQ3_XXS ? MAX_DOT_PRODUCT_ERROR_LOWBIT : MAX_DOT_PRODUCT_ERROR;
|
||||
failed = !(vec_dot_error < max_allowed_error);
|
||||
num_failed += failed;
|
||||
if (failed || verbose) {
|
||||
printf("%5s dot product error: %s (%f)\n", ggml_type_name(type), RESULT_STR[failed], vec_dot_error);
|
||||
|
||||
@ -278,6 +278,8 @@ int main(int argc, char * argv[]) {
|
||||
if (qfns.from_float && qfns.to_float) {
|
||||
printf("%s\n", ggml_type_name(type));
|
||||
|
||||
ggml_quantize_init(type);
|
||||
|
||||
if (params.op_quantize_row_q_reference) {
|
||||
printf(" quantize_row_q_reference\n");
|
||||
for (size_t size : params.test_sizes) {
|
||||
|
||||
Loading…
Reference in New Issue
Block a user