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Table of Contents

Tensor Naming Scheme
Tensor Encoding Scheme PR
Tensor Encoding Scheme Mapping
Where to find the structure of these tensors in the code?

Blocks
Superblocks

How are these tensors packed?

block_q4_K.scales packing example

There isn't really a real writeup of all the mapping, but this should hopefully be a good central starting point if any maintainers needs some understanding of when each feature was added and the general specs of each. Updating it with more accurate information is greatly appreciated

Tensor Naming Scheme

This is not definitive, but is helpful when reading sourcecode or console output to understand what each means typically.

<Encoding>_<Variants>
- <Encoding> : This defines the most common encoding of individual weights in the model
  - Floating Point Formats:
    - BF16: 16-bit bfloat16 Google Brain truncated form of 32-bit IEEE 754 (1 sign bit, 8 exponent bits, 7 fractional bits)
    - F64: 64-bit IEEE 754 floats per weight (1 sign bit, 11 exponent bits, 52 fractional bits)
    - F32: 32-bit IEEE 754 floats per weight (1 sign bit, 8 exponent bits, 23 fractional bits)
    - F16: 16-bit IEEE 754 floats per weight (1 sign bit, 5 exponent bits, 10 fractional bits)
  - Integer formats:
    - I<X>: X bits per weight, where X could be 4 (for 4 bits) or 8 (for 8 bits) etc...
  - Quantized formats:
    - Q<X>: X bits per weight, where X could be 4 (for 4 bits) or 8 (for 8 bits) etc...
    - KQ<X> (or Q<X>_K) : k-quant based models. X bits per weight, where X could be 4 (for 4 bits) or 8 (for 8 bits) etc...
    - IQ<X>: i-quant based models. X bits per weight, where X could be 4 (for 4 bits) or 8 (for 8 bits) etc...
- <Variants>: This represents different strategies of packing quantized weights into a gguf file. This is because we may want a mix of different bit sizes for weights of varying importance, or we may be encoding a general offset to a block or super-block. This may be omitted if trivial or initial attempt, refer to encoding scheme name table for details.

Tensor Encoding Scheme PR

PR Filter of all Tensor Encoding Scheme Related Pull Requests
- This is useful for locating Pull Requests where we merged in a new encoding scheme (or fix a bug with encoding etc...)

Tensor Encoding Scheme Mapping

Scheme	`ggml_ftype` C enumeration name	`ggml_type` C enum name	Bits/Weight	Data Type	Block Configuration	Quantized Weight Formula	Initial Commits Or Pull Request Sources (of `ggml_type`)
BF16	GGML_FTYPE_MOSTLY_BF16	GGML_TYPE_BF16	16	bfloat16 (trunc 32b IEEE754)	Homogonous Array Of Floating Weights	-	llama.cpp PR: Introduce bfloat16 support #6412
F16	GGML_FTYPE_MOSTLY_F16	GGML_TYPE_F16	16	16-bit IEEE 754	Homogonous Array Of Floating Weights	-	llama.cpp CM: Initial Release
F32	GGML_FTYPE_ALL_F32	GGML_TYPE_F32	32	32-bit IEEE 754	Homogonous Array Of Floating Weights	-	llama.cpp CM: Initial Release
F64	-	GGML_TYPE_F64	64	64-bit IEEE 754	Homogonous Array Of Floating Weights	-	llama.cpp CM: Add support for I64 and F64 arrays #6062
I8	-	GGML_TYPE_I8	8	(signed?) integer	-	-	llama.cpp PR: Designate enum vals for integer types #6050
I16	-	GGML_TYPE_I16	16	(signed?) integer	-	-	llama.cpp PR: Designate enum vals for integer types #6050
I32	-	GGML_TYPE_I32	32	(signed?) integer	-	-	llama.cpp PR: Designate enum vals for integer types #6050
I64	-	GGML_TYPE_I64	64	(signed?) integer	-	-	llama.cpp PR: Add support for I64 and F64 arrays #6062
Q4_0	GGML_FTYPE_MOSTLY_Q4_0	GGML_TYPE_Q4_0	4	round to nearest quantization	Each block has 32 weights	w = q * block_scale	llama.cpp CM: Initial Release
Q4_1	GGML_FTYPE_MOSTLY_Q4_1	GGML_TYPE_Q4_1	4	round to nearest quantization	Each block has 32 weights	w = q * block_scale + block_minimum	llama.cpp CM: Initial Release
Q4_1_F16	GGML_FTYPE_MOSTLY_Q4_1_SOME_F16	-	4	round to nearest quantization	Each block has 32 weights (token embedding and output weights are F16)	w = q * block_scale + block_minimum	llama.cpp CM: add Q5 WASM SIMD + GGML_FTYPE
Q8_0	GGML_FTYPE_MOSTLY_Q8_0	GGML_TYPE_Q8_0	8	round to nearest quantization	Each block has 32 weights	w = q * block_scale	llama.cpp PR: Add Q8_0 quantization format (rename the old one to Q8_1) (ARM NEON) #1179
Q8_1	-	GGML_TYPE_Q8_1	8	round to nearest quantization	Each block has 32 weights	w = q * block_scale + block_minimum	llama.cpp PR: Add Q8_0 quantization for intermediate results #951 (Note: Renamed to Q8_1 in later commit)
Q5_0	GGML_FTYPE_MOSTLY_Q5_0	GGML_TYPE_Q5_0	5	round to nearest quantization	Each block has 32 weights	w = q * block_scale	llama.cpp PR: Add Q5_0 and Q5_1 quantization #1187
Q5_1	GGML_FTYPE_MOSTLY_Q5_1	GGML_TYPE_Q5_1	5	round to nearest quantization	Each block has 32 weights	w = q * block_scale + block_minimum	llama.cpp PR: Add Q5_0 and Q5_1 quantization #1187
Q2_K	GGML_FTYPE_MOSTLY_Q2_K	GGML_TYPE_Q2_K	2.5625	k-quantization	Superblocks has 16 blocks ( 16 weights per block)	w = q * block_scale (4-bit) + block_min (4-bit)	llama.cpp PR: k-quants #1684
Q3_K	GGML_FTYPE_MOSTLY_Q3_K	GGML_TYPE_Q3_K	3.4375	k-quantization	Superblocks has 16 blocks ( 16 weights per block)	w = q * block_scale (6-bit)	llama.cpp PR: k-quants #1684
Q4_K	GGML_FTYPE_MOSTLY_Q4_K	GGML_TYPE_Q4_K	4.5	k-quantization	Superblocks has 8 blocks ( 32 weights per block)	w = q * block_scale (6-bit) + block_min (6-bit)	llama.cpp PR: k-quants #1684
Q5_K	GGML_FTYPE_MOSTLY_Q5_K	GGML_TYPE_Q5_K	5.5	k-quantization	Superblocks has 8 blocks ( 32 weights per block)	w = q * block_scale (6-bit) + block_min (6-bit)	llama.cpp PR: k-quants #1684
Q6_K	GGML_FTYPE_MOSTLY_Q6_K	GGML_TYPE_Q6_K	6.5625	k-quantization	Superblocks has 16 blocks ( 16 weights per block)	w = q * block_scale (8-bit)	llama.cpp PR: k-quants #1684
Q8_K	-	GGML_TYPE_Q8_K	8.0	k-quantization	Superblocks has 1 blocks (256 weights per block) (Only used for intermediate quants)	w = q * block_scale (8-bit)	llama.cpp PR: k-quants #1684
IQ1_S	GGML_FTYPE_MOSTLY_IQ1_S	GGML_TYPE_IQ1_S	1.5	i-quantization	Superblocks has 8 blocks ( 32 weights per block)	w = func(superblock_scale, importance_matrix)	llama.cpp PR: 1.5 bit quantization #5453
IQ1_M	GGML_FTYPE_MOSTLY_IQ1_M	GGML_TYPE_IQ1_M	1.75	i-quantization	Superblocks has 16 blocks ( 16 weights per block)	w = func(superblock_scale, importance_matrix)	llama.cpp PR: IQ1_M: 1.75 bpw quantization #6302
IQ2_XXS	GGML_FTYPE_MOSTLY_IQ2_XXS	GGML_TYPE_IQ2_XXS	2.0625	i-quantization	Superblocks has 8 blocks ( 32 weights per block)	w = func(superblock_scale, importance_matrix)	llama.cpp PR: SOTA 2-bit quants #4773
IQ2_XS	GGML_FTYPE_MOSTLY_IQ2_XS	GGML_TYPE_IQ2_XS	2.31	i-quantization	Superblocks has 16 blocks ( 16 weights per block)	w = func(superblock_scale, importance_matrix)	llama.cpp PR: SOTA 2-bit quants - part 2 #4856
IQ2_S	GGML_FTYPE_MOSTLY_IQ2_S	GGML_TYPE_IQ2_S	2.5	i-quantization	?	w = func(superblock_scale, importance_matrix)	llama.cpp PR: Adding IQ2_S and IQ2_M to complete coverage of the 2-3 bit quantization range #5721
IQ3_S	GGML_FTYPE_MOSTLY_IQ3_S	GGML_TYPE_IQ3_S	3.4375	i-quantization	?	w = func(superblock_scale, importance_matrix)	llama.cpp PR: IQ3_S: a much better alternative to Q3_K #5676
IQ3_XXS	GGML_FTYPE_MOSTLY_IQ3_XXS	GGML_TYPE_IQ3_XXS	3.0625	i-quantization	Superblocks has 8 blocks ( 32 weights per block)	w = func(superblock_scale, importance_matrix)	llama.cpp PR: SOTA 3-bit quants #5196
IQ4_NL	GGML_FTYPE_MOSTLY_IQ4_NL	GGML_TYPE_IQ4_NL	4.5	i-quantization	Superblocks has 16 blocks ( 16 weights per block)	w = [non linear mapping of quants to weights]	llama.cpp PR: IQ4_NL: 4-bit non-linear quants with blocks of 32 #5590
IQ4_XS	GGML_FTYPE_MOSTLY_IQ4_XS	GGML_TYPE_IQ4_XS	4.25	i-quantization	Superblocks has 8 blocks ( 32 weights per block)	w = func(superblock_scale, importance_matrix)	llama.cpp PR: IQ4_XS: a 4.25 bpw quantization #5747

All superblocks have fp16 scaling factor and contains up to 256 weights. Number of weights in a block must be divisible by 256. (To be confirmed)

Where to find the structure of these tensors in the code?

You would find it all usually in ggml-common.h where it typically be of this form

Blocks

#define QK4_0 32
typedef struct {
    ggml_half d;           // delta
    uint8_t qs[QK4_0 / 2]; // nibbles / quants
} block_q4_0;
static_assert(sizeof(block_q4_0) == sizeof(ggml_half) + QK4_0 / 2, "wrong q4_0 block size/padding");

Superblocks

//
// Super-block quantization structures
//

// 2-bit quantization
// weight is represented as x = a * q + b
// 16 blocks of 16 elements each
// Effectively 2.625 bits per weight
typedef struct {
    uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
    uint8_t qs[QK_K/4];      // quants
    union {
        struct {
            ggml_half d;    // super-block scale for quantized scales
            ggml_half dmin; // super-block scale for quantized mins
        } GGML_COMMON_AGGR;
        ggml_half2 dm;
    };
} block_q2_K;
static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_half) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding");

How are these tensors packed?

This is as explained by compilade in this thread.

Regarding how to find the bit pattern structure of a packed tensor block in the gguf file... there isn't a consistent encoding scheme for each block as sometimes a single field in the structs stores multiple types of values, like in Q4_K where block_q4_K.scales stores 6-bit scales and mins in some pattern. The easiest way to understand what the bits mean is to have a look at the respective dequantize_row function of each type.

block_q4_K.scales packing example

The 12 bytes in Q4_K .scales are packed a bit like this, where the uppercased letters are bits for the scales and lowercased letters are the bits of the mins as seen below, which corresponds to this function as shown here:

0: EEAAAAAA
1: FFBBBBBB
2: GGCCCCCC
3: HHDDDDDD
4: eeaaaaaa
5: ffbbbbbb
6: ggcccccc
7: hhdddddd
8: eeeeEEEE
9: ffffFFFF
10: ggggGGGG
11: hhhhHHHH

Note that this is packing a 6bit scale and mins but split across multiple bytes. This use of byte offsets and bitwise operations is likely done to be more friendlier for SIMD processing. As compilade noted, he believes that the indexing is only done at the byte level, hence the packing and unpacking of the 6-bit values in this block will require bitwise operations. In his anecdotal experience he also noticed that when making the vec_dot of Q1_3, that shuffles are surprisingly as fast as additions in SIMD.

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