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| 1 | +package sk.ai.net.gguf |
| 2 | + |
| 3 | +/** |
| 4 | + * This is a kotlin gguf reader related logic interpreted from python code "gguf-py/gguf/constants.py" |
| 5 | + * of github repo "https://github.com/ggerganov/llama.cpp" |
| 6 | + */ |
| 7 | + |
| 8 | +//TODO convert the rest of file from constants.py |
| 9 | + |
| 10 | +const val GGUF_MAGIC = 0x46554747u |
| 11 | +const val GGUF_VERSION = 3 |
| 12 | +const val GGUF_DEFAULT_ALIGNMENT = 32 |
| 13 | + |
| 14 | +enum class GGMLQuantizationType(val value: Int) { |
| 15 | + F32(0), |
| 16 | + F16(1), |
| 17 | + Q4_0(2), |
| 18 | + Q4_1(3), |
| 19 | + Q5_0(6), |
| 20 | + Q5_1(7), |
| 21 | + Q8_0(8), |
| 22 | + Q8_1(9), |
| 23 | + Q2_K(10), |
| 24 | + Q3_K(11), |
| 25 | + Q4_K(12), |
| 26 | + Q5_K(13), |
| 27 | + Q6_K(14), |
| 28 | + Q8_K(15), |
| 29 | + IQ2_XXS(16), |
| 30 | + IQ2_XS(17), |
| 31 | + IQ3_XXS(18), |
| 32 | + IQ1_S(19), |
| 33 | + IQ4_NL(20), |
| 34 | + IQ3_S(21), |
| 35 | + IQ2_S(22), |
| 36 | + IQ4_XS(23), |
| 37 | + I8(24), |
| 38 | + I16(25), |
| 39 | + I32(26), |
| 40 | + I64(27), |
| 41 | + F64(28), |
| 42 | + IQ1_M(29), |
| 43 | + BF16(30), |
| 44 | + TQ1_0(34), |
| 45 | + TQ2_0(35); |
| 46 | + |
| 47 | + companion object { |
| 48 | + fun fromValue(value: Int): GGMLQuantizationType? { |
| 49 | + return values().find { it.value == value } |
| 50 | + } |
| 51 | + } |
| 52 | +} |
| 53 | + |
| 54 | +// Block size constant |
| 55 | +const val QK_K = 256 |
| 56 | + |
| 57 | +// Quantization type and corresponding sizes |
| 58 | +val GGML_QUANT_SIZES: Map<GGMLQuantizationType, Pair<Int, Int>> = mapOf( |
| 59 | + GGMLQuantizationType.F32 to (1 to 4), |
| 60 | + GGMLQuantizationType.F16 to (1 to 2), |
| 61 | + GGMLQuantizationType.Q4_0 to (32 to 2 + 16), |
| 62 | + GGMLQuantizationType.Q4_1 to (32 to 2 + 2 + 16), |
| 63 | + GGMLQuantizationType.Q5_0 to (32 to 2 + 4 + 16), |
| 64 | + GGMLQuantizationType.Q5_1 to (32 to 2 + 2 + 4 + 16), |
| 65 | + GGMLQuantizationType.Q8_0 to (32 to 2 + 32), |
| 66 | + GGMLQuantizationType.Q8_1 to (32 to 4 + 4 + 32), |
| 67 | + GGMLQuantizationType.Q2_K to (256 to 2 + 2 + QK_K / 16 + QK_K / 4), |
| 68 | + GGMLQuantizationType.Q3_K to (256 to 2 + QK_K / 4 + QK_K / 8 + 12), |
| 69 | + GGMLQuantizationType.Q4_K to (256 to 2 + 2 + QK_K / 2 + 12), |
| 70 | + GGMLQuantizationType.Q5_K to (256 to 2 + 2 + QK_K / 2 + QK_K / 8 + 12), |
| 71 | + GGMLQuantizationType.Q6_K to (256 to 2 + QK_K / 2 + QK_K / 4 + QK_K / 16), |
| 72 | + GGMLQuantizationType.Q8_K to (256 to 4 + QK_K + QK_K / 8), |
| 73 | + GGMLQuantizationType.IQ2_XXS to (256 to 2 + QK_K / 4), |
| 74 | + GGMLQuantizationType.IQ2_XS to (256 to 2 + QK_K / 4 + QK_K / 32), |
| 75 | + GGMLQuantizationType.IQ3_XXS to (256 to 2 + QK_K / 4 + QK_K / 8), |
| 76 | + GGMLQuantizationType.IQ1_S to (256 to 2 + QK_K / 8 + QK_K / 16), |
| 77 | + GGMLQuantizationType.IQ4_NL to (32 to 2 + 16), |
| 78 | + GGMLQuantizationType.IQ3_S to (256 to 2 + QK_K / 4 + QK_K / 8 + QK_K / 32 + 4), |
| 79 | + GGMLQuantizationType.IQ2_S to (256 to 2 + QK_K / 4 + QK_K / 16), |
| 80 | + GGMLQuantizationType.IQ4_XS to (256 to 2 + 2 + QK_K / 2 + QK_K / 64), |
| 81 | + GGMLQuantizationType.I8 to (1 to 1), |
| 82 | + GGMLQuantizationType.I16 to (1 to 2), |
| 83 | + GGMLQuantizationType.I32 to (1 to 4), |
| 84 | + GGMLQuantizationType.I64 to (1 to 8), |
| 85 | + GGMLQuantizationType.F64 to (1 to 8), |
| 86 | + GGMLQuantizationType.IQ1_M to (256 to QK_K / 8 + QK_K / 16 + QK_K / 32), |
| 87 | + GGMLQuantizationType.BF16 to (1 to 2), |
| 88 | + GGMLQuantizationType.TQ1_0 to (256 to 2 + 4 * 13), |
| 89 | + GGMLQuantizationType.TQ2_0 to (256 to 2 + 64) |
| 90 | +) |
| 91 | + |
| 92 | +enum class GGUFValueType(val value: Int) { |
| 93 | + UINT8(0), |
| 94 | + INT8(1), |
| 95 | + UINT16(2), |
| 96 | + INT16(3), |
| 97 | + UINT32(4), |
| 98 | + INT32(5), |
| 99 | + FLOAT32(6), |
| 100 | + BOOL(7), |
| 101 | + STRING(8), |
| 102 | + ARRAY(9), |
| 103 | + UINT64(10), |
| 104 | + INT64(11), |
| 105 | + FLOAT64(12) |
| 106 | +} |
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