vbatts/llama.cpp
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Merge 'origin/master' into hipblas

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Henri Vasserman 2023-04-28 10:08:41 +03:00
commit 3b4a53138f
10 changed files with 862 additions and 56 deletions
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2
.devops/tools.sh
View file

@ -23,7 +23,7 @@ elif [[ $arg1 == '--all-in-one' || $arg1 == '-a' ]]; then
echo "Skip model quantization, it already exists: ${i/f16/q4_0}"
else
echo "Converting PTH to GGML: $i into ${i/f16/q4_0}..."
./quantize "$i" "${i/f16/q4_0}" 2
./quantize "$i" "${i/f16/q4_0}" q4_0
fi
done
else

1
.gitignore vendored
View file

@ -15,6 +15,7 @@ build-em/
build-debug/
build-release/
build-static/
build-cublas/
build-no-accel/
build-sanitize-addr/
build-sanitize-thread/

123
README.md
View file

@ -7,31 +7,27 @@
Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
**Warnings**
- `Q4_2` and `Q4_3` are still in development. Do not expect any kind of backward compatibility until they are finalized
**Hot topics:**
- [New quantization methods](https://github.com/ggerganov/llama.cpp#quantization)
- [Added LoRA support](https://github.com/ggerganov/llama.cpp/pull/820)
- [Add GPU support to ggml](https://github.com/ggerganov/llama.cpp/discussions/915)
- [Roadmap Apr 2023](https://github.com/ggerganov/llama.cpp/discussions/784)
## Description
The main goal of llama.cpp is to run the llama model using 4-bit quantization on a MacBook.
The main goal of `llama.cpp` is to run the LLaMA model using 4-bit integer quantization on a MacBook
- Plain C/C++ implementation without dependencies
- Apple silicon first-class citizen - optimized via ARM NEON and Accelerate framework
- AVX2 support for x86 architectures
- Mixed F16 / F32 precision
- 4-bit quantization support
- 4-bit integer quantization support
- Runs on the CPU
This was [hacked in an evening](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022) - I have no idea if it works correctly.
Please do not make conclusions about the models based on the results from this implementation.
For all I know, it can be completely wrong. This project is for educational purposes.
New features will probably be added mostly through community contributions.
The original implementation of `llama.cpp` was [hacked in an evening](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022).
Since then, the project has improved significantly thanks to many contributions. This project is for educational purposes and serves
as the main playground for developing new features for the [ggml](https://github.com/ggerganov/ggml) library.
**Supported platforms:**
@ -167,15 +163,27 @@ cd llama.cpp
### Build
Note: For Windows, CMake or Zig can be used.
In order to build llama.cpp you have three different options.
1. Use `make`
- Using `make`:
- On Linux or MacOS:
```bash
make
```
```bash
make
```
1. Use CMake
- On Windows:
1. Download the latest fortran version of [w64devkit](https://github.com/seeto/w64devkit/releases).
2. Extract `w64devkit` on your pc.
3. Run `w64devkit.exe`.
4. Use the `cd` command to reach the `llama.cpp` folder.
5. From here you can run:
```bash
make
```
- Using `CMake`:
```bash
mkdir build
@ -184,12 +192,71 @@ Note: For Windows, CMake or Zig can be used.
cmake --build . --config Release
```
1. Use Zig
- Using `Zig`:
```bash
zig build -Drelease-fast
```
### BLAS Build
Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). BLAS doesn't affect the normal generation performance. There are currently three different implementations of it:
- Accelerate Framework:
This is only available on Mac PCs and it's enabled by default. You can just build using the normal instructions.
- OpenBLAS:
This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS installed on your machine.
- Using `make`:
- On Linux:
```bash
make LLAMA_OPENBLAS=1
```
Note: In order to build on Arch Linux with OpenBLAS support enabled you must edit the Makefile adding at the end of the line 105: `-lcblas`
- On Windows:
1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
2. Download the latest version of [OpenBLAS for Windows](https://github.com/xianyi/OpenBLAS/releases).
3. Extract `w64devkit` on your pc.
4. From the OpenBLAS zip that you just downloaded copy `libopenblas.a`, located inside the `lib` folder, inside `w64devkit\x86_64-w64-mingw32\lib`.
5. From the same OpenBLAS zip copy the content of the `include` folder inside `w64devkit\x86_64-w64-mingw32\include`.
6. Run `w64devkit.exe`.
7. Use the `cd` command to reach the `llama.cpp` folder.
8. From here you can run:
```bash
make LLAMA_OPENBLAS=1
```
- Using `CMake` on Linux:
```bash
mkdir build
cd build
cmake .. -DLLAMA_OPENBLAS=ON
cmake --build . --config Release
```
- cuBLAS
This provides BLAS acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads).
- Using `make`:
```bash
make LLAMA_CUBLAS=1
```
- Using `CMake`:
```bash
mkdir build
cd build
cmake .. -DLLAMA_CUBLAS=ON
cmake --build . --config Release
```
### Prepare Data & Run
```bash
@ -203,8 +270,8 @@ python3 -m pip install -r requirements.txt
# convert the 7B model to ggml FP16 format
python3 convert.py models/7B/
# quantize the model to 4-bits (using method 2 = q4_0)
./quantize ./models/7B/ggml-model-f16.bin ./models/7B/ggml-model-q4_0.bin 2
# quantize the model to 4-bits (using q4_0 method)
./quantize ./models/7B/ggml-model-f16.bin ./models/7B/ggml-model-q4_0.bin q4_0
# run the inference
./main -m ./models/7B/ggml-model-q4_0.bin -n 128
@ -223,6 +290,24 @@ As the models are currently fully loaded into memory, you will need adequate dis
| 30B | 60 GB | 19.5 GB |
| 65B | 120 GB | 38.5 GB |
### Quantization
Several quantization methods are supported. They differ in the resulting model disk size and inference speed.
Model | F16 | Q4_0 | Q4_1 | Q4_2 | Q4_3 | Q5_0 | Q5_1 | Q8_0
-- | -- | -- | -- | -- | -- | -- | -- | --
7B (ppl) | 5.9565 | 6.2103 | 6.1286 | 6.1698 | 6.0617 | 6.0139 | 5.9934 | 5.9571
7B (size) | 13.0G | 4.0G | 4.8G | 4.0G | 4.8G | 4.4G | 4.8G | 7.1G
7B (ms/tok @ 4th) | 128 | 56 | 61 | 84 | 91 | 91 | 95 | 75
7B (ms/tok @ 8th) | 128 | 47 | 55 | 48 | 53 | 53 | 59 | 75
7B (bpw) | 16.0 | 5.0 | 6.0 | 5.0 | 6.0 | 5.5 | 6.0 | 9.0
-- | -- | -- | -- | -- | -- | -- | -- | --
13B (ppl) | 5.2455 | 5.3748 | 5.3471 | 5.3433 | 5.3234 | 5.2768 | 5.2582 | 5.2458
13B (size) | 25.0G | 7.6G | 9.1G | 7.6G | 9.1G | 8.4G | 9.1G | 14G
13B (ms/tok @ 4th) | 239 | 104 | 113 | 160 | 175 | 176 | 185 | 141
13B (ms/tok @ 8th) | 240 | 85 | 99 | 97 | 114 | 108 | 117 | 147
13B (bpw) | 16.0 | 5.0 | 6.0 | 5.0 | 6.0 | 5.5 | 6.0 | 9.0
### Interactive mode
If you want a more ChatGPT-like experience, you can run in interactive mode by passing `-i` as a parameter.

32
examples/quantize/quantize.cpp
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@ -2,8 +2,19 @@
#include "llama.h"
#include <cstdio>
#include <map>
#include <string>
static const std::map<std::string, enum llama_ftype> LLAMA_FTYPE_MAP = {
{"q4_0", LLAMA_FTYPE_MOSTLY_Q4_0},
{"q4_1", LLAMA_FTYPE_MOSTLY_Q4_1},
{"q4_2", LLAMA_FTYPE_MOSTLY_Q4_2},
{"q4_3", LLAMA_FTYPE_MOSTLY_Q4_3},
{"q5_0", LLAMA_FTYPE_MOSTLY_Q5_0},
{"q5_1", LLAMA_FTYPE_MOSTLY_Q5_1},
{"q8_0", LLAMA_FTYPE_MOSTLY_Q8_0},
};
// usage:
// ./quantize models/llama/ggml-model.bin models/llama/ggml-model-quant.bin type
//
@ -12,11 +23,9 @@ int main(int argc, char ** argv) {
if (argc < 4) {
fprintf(stderr, "usage: %s model-f32.bin model-quant.bin type [nthread]\n", argv[0]);
fprintf(stderr, " type = %d - q4_0\n", LLAMA_FTYPE_MOSTLY_Q4_0);
fprintf(stderr, " type = %d - q4_1\n", LLAMA_FTYPE_MOSTLY_Q4_1);
fprintf(stderr, " type = %d - q4_2\n", LLAMA_FTYPE_MOSTLY_Q4_2);
fprintf(stderr, " type = %d - q4_3\n", LLAMA_FTYPE_MOSTLY_Q4_3);
fprintf(stderr, " type = %d - q8_0\n", LLAMA_FTYPE_MOSTLY_Q8_0);
for (auto it = LLAMA_FTYPE_MAP.begin(); it != LLAMA_FTYPE_MAP.end(); it++) {
fprintf(stderr, " type = \"%s\" or %d\n", it->first.c_str(), it->second);
}
return 1;
}
@ -30,7 +39,18 @@ int main(int argc, char ** argv) {
const std::string fname_inp = argv[1];
const std::string fname_out = argv[2];
const enum llama_ftype ftype = (enum llama_ftype)atoi(argv[3]);
enum llama_ftype ftype;
if (argv[3][0] == 'q') {
auto it = LLAMA_FTYPE_MAP.find(argv[3]);
if (it == LLAMA_FTYPE_MAP.end()) {
fprintf(stderr, "%s: unknown ftype '%s'\n", __func__, argv[3]);
return 1;
}
ftype = it->second;
} else {
ftype = (enum llama_ftype)atoi(argv[3]);
}
int nthread = argc > 4 ? atoi(argv[4]) : 0;
const int64_t t_main_start_us = ggml_time_us();

85
ggml-cuda.cu
View file

@ -41,6 +41,23 @@ typedef struct {
} block_q4_3;
static_assert(sizeof(block_q4_3) == 2 * sizeof(ggml_fp16_t) + QK4_3 / 2, "wrong q4_3 block size/padding");
#define QK5_0 32
typedef struct {
__half d; // delta
uint8_t qh[4]; // 5-th bit of quants
uint8_t qs[QK5_0 / 2]; // nibbles / quants
} block_q5_0;
static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding");
#define QK5_1 32
typedef struct {
__half d; // delta
__half m; // min
uint32_t qh; // 5-th bit of quants
uint8_t qs[QK5_1 / 2]; // nibbles / quants
} block_q5_1;
static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");
#define QK8_0 32
typedef struct {
float d; // delta
@ -142,6 +159,64 @@ static __global__ void dequantize_block_q4_3(const void * vx, float * y) {
}
}
static __global__ void dequantize_block_q5_0(const void * vx, float * y) {
const block_q5_0 * x = (const block_q5_0 *) vx;
const int i = blockIdx.x;
const float d = x[i].d;
const uint8_t * pp = x[i].qs;
uint32_t qh;
memcpy(&qh, x[i].qh, sizeof(qh));
for (int l = 0; l < QK5_0; l += 2) {
const uint8_t vi = pp[l/2];
const int8_t vh0 = ((qh & (1 << (l + 0))) >> (l + 0)) << 4;
const int8_t vh1 = ((qh & (1 << (l + 1))) >> (l + 1)) << 4;
const int8_t vi0 = ((vi & 0xf) | vh0);
const int8_t vi1 = ((vi >> 4) | vh1);
const float v0 = (vi0 - 16)*d;
const float v1 = (vi1 - 16)*d;
y[i*QK5_0 + l + 0] = v0;
y[i*QK5_0 + l + 1] = v1;
}
}
static __global__ void dequantize_block_q5_1(const void * vx, float * y) {
const block_q5_1 * x = (const block_q5_1 *) vx;
const int i = blockIdx.x;
const float d = x[i].d;
const float m = x[i].m;
const uint8_t * pp = x[i].qs;
const uint32_t qh = x[i].qh;
for (int l = 0; l < QK5_1; l += 2) {
const uint8_t vi = pp[l/2];
const int8_t vh0 = ((qh & (1 << (l + 0))) >> (l + 0)) << 4;
const int8_t vh1 = ((qh & (1 << (l + 1))) >> (l + 1)) << 4;
const int8_t vi0 = (vi & 0xf) | vh0;
const int8_t vi1 = (vi >> 4) | vh1;
const float v0 = vi0*d + m;
const float v1 = vi1*d + m;
y[i*QK5_1 + l + 0] = v0;
y[i*QK5_1 + l + 1] = v1;
}
}
static __global__ void dequantize_block_q8_0(const void * vx, float * y) {
const block_q8_0 * x = (const block_q8_0 *) vx;
@ -178,6 +253,16 @@ void dequantize_row_q4_3_cuda(const void * vx, float * y, int k, cudaStream_t st
dequantize_block_q4_3<<<nb, 1, 0, stream>>>(vx, y);
}
void dequantize_row_q5_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK5_0;
dequantize_block_q5_0<<<nb, 1, 0, stream>>>(vx, y);
}
void dequantize_row_q5_1_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK5_1;
dequantize_block_q5_1<<<nb, 1, 0, stream>>>(vx, y);
}
void dequantize_row_q8_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK8_0;
dequantize_block_q8_0<<<nb, 1, 0, stream>>>(vx, y);

2
ggml-cuda.h
View file

@ -77,6 +77,8 @@ void dequantize_row_q4_0_cuda(const void * vx, float * y, int k, cudaStream_t st
void dequantize_row_q4_1_cuda(const void * vx, float * y, int k, cudaStream_t stream);
void dequantize_row_q4_2_cuda(const void * vx, float * y, int k, cudaStream_t stream);
void dequantize_row_q4_3_cuda(const void * vx, float * y, int k, cudaStream_t stream);
void dequantize_row_q5_0_cuda(const void * vx, float * y, int k, cudaStream_t stream);
void dequantize_row_q5_1_cuda(const void * vx, float * y, int k, cudaStream_t stream);
void dequantize_row_q8_0_cuda(const void * vx, float * y, int k, cudaStream_t stream);
#ifdef __cplusplus

645
ggml.c
View file

@ -328,6 +328,20 @@ static ggml_fp16_t table_exp_f16[1 << 16];
// precomputed f32 table for f16 (256 KB)
static float table_f32_f16[1 << 16];
#if defined(__ARM_NEON)
#define B1(c,s,n) 0x ## n ## c , 0x ## n ## s
#define B2(c,s,n) B1(c,s,n ## c), B1(c,s,n ## s)
#define B3(c,s,n) B2(c,s,n ## c), B2(c,s,n ## s)
#define B4(c,s,n) B3(c,s,n ## c), B3(c,s,n ## s)
#define B5(c,s,n) B4(c,s,n ## c), B4(c,s,n ## s)
#define B6(c,s,n) B5(c,s,n ## c), B5(c,s,n ## s)
#define B7(c,s,n) B6(c,s,n ## c), B6(c,s,n ## s)
#define B8(c,s ) B7(c,s, c), B7(c,s, s)
// precomputed tables for expanding 8bits to 8 bytes (shl 4)
static const uint64_t table_b2b_u[1 << 8] = { B8(00, 10) };
#endif
// On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32,
// so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON.
// This is also true for POWER9.
@ -477,6 +491,19 @@ static inline int hsum_i32_4(const __m128i a) {
}
#if __AVX2__ || __AVX512F__
// spread 32 bits to 32 bytes { 0x00, 0xFF }
static inline __m256i bytes_from_bits_32(const uint8_t * x) {
uint32_t x32;
memcpy(&x32, x, sizeof(uint32_t));
const __m256i shuf_mask = _mm256_set_epi64x(
0x0303030303030303, 0x0202020202020202,
0x0101010101010101, 0x0000000000000000);
__m256i bytes = _mm256_shuffle_epi8(_mm256_set1_epi32(x32), shuf_mask);
const __m256i bit_mask = _mm256_set1_epi64x(0x7fbfdfeff7fbfdfe);
bytes = _mm256_or_si256(bytes, bit_mask);
return _mm256_cmpeq_epi8(bytes, _mm256_set1_epi64x(-1));
}
// Unpack 32 4-bit fields into 32 bytes
// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval
static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi)
@ -673,6 +700,23 @@ typedef struct {
} block_q4_3;
static_assert(sizeof(block_q4_3) == 2 * sizeof(ggml_fp16_t) + QK4_3 / 2, "wrong q4_3 block size/padding");
#define QK5_0 32
typedef struct {
ggml_fp16_t d; // delta
uint8_t qh[4]; // 5-th bit of quants
uint8_t qs[QK5_0 / 2]; // nibbles / quants
} block_q5_0;
static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding");
#define QK5_1 32
typedef struct {
ggml_fp16_t d; // delta
ggml_fp16_t m; // min
uint8_t qh[4]; // 5-th bit of quants
uint8_t qs[QK5_1 / 2]; // nibbles / quants
} block_q5_1;
static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");
#define QK8_0 32
typedef struct {
float d; // delta
@ -1288,6 +1332,103 @@ static void quantize_row_q4_3(const float * restrict x, void * restrict vy, int
quantize_row_q4_3_reference(x, y, k);
}
static void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict y, int k) {
assert(k % QK5_0 == 0);
const int nb = k / QK5_0;
for (int i = 0; i < nb; i++) {
float amax = 0.0f; // absolute max
float max = 0.0f;
for (int l = 0; l < QK5_0; l++) {
const float v = x[i*QK5_0 + l];
if (amax < fabsf(v)) {
amax = fabsf(v);
max = v;
}
}
const float d = max / -16;
const float id = d ? 1.0f/d : 0.0f;
y[i].d = GGML_FP32_TO_FP16(d);
uint32_t qh = 0;
for (int l = 0; l < QK5_0; l += 2) {
const float v0 = x[i*QK5_0 + l + 0]*id;
const float v1 = x[i*QK5_0 + l + 1]*id;
const uint32_t vi0 = MIN(31, (int) (v0 + 16.5f));
const uint32_t vi1 = MIN(31, (int) (v1 + 16.5f));
y[i].qs[l/2] = (vi0 & 0x0F) | ((vi1 & 0x0F) << 4);
// get the 5-th bit and store it in qh at the right position
qh |= ((vi0 & 0x10) >> 4) << (l + 0);
qh |= ((vi1 & 0x10) >> 4) << (l + 1);
}
memcpy(&y[i].qh, &qh, sizeof(y[i].qh));
}
}
static void quantize_row_q5_0(const float * restrict x, void * restrict vy, int k) {
assert(k % QK5_0 == 0);
block_q5_0 * restrict y = vy;
quantize_row_q5_0_reference(x, y, k);
}
static void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict y, int k) {
assert(k % QK5_1 == 0);
const int nb = k / QK5_1;
for (int i = 0; i < nb; i++) {
float min = FLT_MAX;
float max = -FLT_MAX;
for (int l = 0; l < QK5_1; l++) {
const float v = x[i*QK5_1 + l];
if (v < min) min = v;
if (v > max) max = v;
}
const float d = (max - min) / ((1 << 5) - 1);
const float id = d ? 1.0f/d : 0.0f;
y[i].d = GGML_FP32_TO_FP16(d);
y[i].m = GGML_FP32_TO_FP16(min);
uint32_t qh = 0;
for (int l = 0; l < QK5_1; l += 2) {
const float v0 = (x[i*QK5_1 + l + 0] - min)*id;
const float v1 = (x[i*QK5_1 + l + 1] - min)*id;
const uint32_t vi0 = (int) (v0 + 0.5f);
const uint32_t vi1 = (int) (v1 + 0.5f);
y[i].qs[l/2] = (vi0 & 0x0F) | ((vi1 & 0x0F) << 4);
// get the 5-th bit and store it in qh at the right position
qh |= ((vi0 & 0x10) >> 4) << (l + 0);
qh |= ((vi1 & 0x10) >> 4) << (l + 1);
}
memcpy(&y[i].qh, &qh, sizeof(y[i].qh));
}
}
static void quantize_row_q5_1(const float * restrict x, void * restrict vy, int k) {
assert(k % QK5_1 == 0);
block_q5_1 * restrict y = vy;
quantize_row_q5_1_reference(x, y, k);
}
// reference implementation for deterministic creation of model files
static void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int k) {
assert(k % QK8_0 == 0);
@ -1571,7 +1712,7 @@ static void dequantize_row_q4_0(const void * restrict vx, float * restrict y, in
const uint8x8_t v8 = vld1_u8(pp + l/2);
// Expand 4-bit qs to 8-bit bytes
const uint8x8_t v0 = vand_u8(v8, vdup_n_u8(0x0f));
const uint8x8_t v0 = vand_u8(v8, vdup_n_u8(0x0F));
const uint8x8_t v1 = vshr_n_u8(v8, 4);
// Convert to signed 8-bit integers
@ -1621,7 +1762,7 @@ static void dequantize_row_q4_0(const void * restrict vx, float * restrict y, in
for (int l = 0; l < QK4_0; l += 2) {
const uint8_t vi = pp[l/2];
const int8_t vi0 = vi & 0xf;
const int8_t vi0 = vi & 0x0F;
const int8_t vi1 = vi >> 4;
const float v0 = (vi0 - 8)*d;
@ -1687,7 +1828,7 @@ static void dequantize_row_q4_1(const void * restrict vx, float * restrict y, in
const uint8x8_t v8 = vld1_u8(pp + l/2);
// Expand 4-bit qs to 8-bit bytes
const uint8x8_t v0 = vand_u8(v8, vdup_n_u8(0x0f));
const uint8x8_t v0 = vand_u8(v8, vdup_n_u8(0x0F));
const uint8x8_t v1 = vshr_n_u8(v8, 4);
// Interleave and combine
@ -1729,7 +1870,7 @@ static void dequantize_row_q4_1(const void * restrict vx, float * restrict y, in
for (int l = 0; l < QK4_1; l += 2) {
const uint8_t vi = pp[l/2];
const int8_t vi0 = vi & 0xf;
const int8_t vi0 = vi & 0x0F;
const int8_t vi1 = vi >> 4;
const float v0 = vi0*d + m;
@ -1759,7 +1900,7 @@ static void dequantize_row_q4_2(const void * restrict vx, float * restrict y, in
for (int l = 0; l < QK4_2; l += 2) {
const uint8_t vi = pp[l/2];
const int8_t vi0 = vi & 0xf;
const int8_t vi0 = vi & 0x0F;
const int8_t vi1 = vi >> 4;
const float v0 = (vi0 - 8)*d;
@ -1789,7 +1930,7 @@ static void dequantize_row_q4_3(const void * restrict vx, float * restrict y, in
for (int l = 0; l < QK4_3; l += 2) {
const uint8_t vi = pp[l/2];
const int8_t vi0 = vi & 0xf;
const int8_t vi0 = vi & 0x0F;
const int8_t vi1 = vi >> 4;
const float v0 = vi0*d + m;
@ -1804,6 +1945,79 @@ static void dequantize_row_q4_3(const void * restrict vx, float * restrict y, in
}
}
static void dequantize_row_q5_0(const void * restrict vx, float * restrict y, int k) {
assert(k % QK5_0 == 0);
const int nb = k / QK5_0;
const block_q5_0 * restrict x = vx;
for (int i = 0; i < nb; i++) {
const float d = GGML_FP16_TO_FP32(x[i].d);
const uint8_t * restrict pp = x[i].qs;
uint32_t qh;
memcpy(&qh, x[i].qh, sizeof(qh));
for (int l = 0; l < QK5_0; l += 2) {
const uint8_t vi = pp[l/2];
// extract the 5-th bit from qh
const uint8_t vh0 = ((qh & (1 << (l + 0))) >> (l + 0)) << 4;
const uint8_t vh1 = ((qh & (1 << (l + 1))) >> (l + 1)) << 4;
const int8_t vi0 = (vi & 0x0F) | vh0;
const int8_t vi1 = (vi >> 4) | vh1;
const float v0 = (vi0 - 16)*d;
const float v1 = (vi1 - 16)*d;
y[i*QK5_0 + l + 0] = v0;
y[i*QK5_0 + l + 1] = v1;
assert(!isnan(y[i*QK5_0 + l + 0]));
assert(!isnan(y[i*QK5_0 + l + 1]));
}
}
}
static void dequantize_row_q5_1(const void * restrict vx, float * restrict y, int k) {
assert(k % QK5_1 == 0);
const int nb = k / QK5_1;
const block_q5_1 * restrict x = vx;
for (int i = 0; i < nb; i++) {
const float d = GGML_FP16_TO_FP32(x[i].d);
const float m = GGML_FP16_TO_FP32(x[i].m);
const uint8_t * restrict pp = x[i].qs;
uint32_t qh;
memcpy(&qh, x[i].qh, sizeof(qh));
for (int l = 0; l < QK5_1; l += 2) {
const uint8_t vi = pp[l/2];
// extract the 5-th bit from qh
const uint8_t vh0 = ((qh & (1 << (l + 0))) >> (l + 0)) << 4;
const uint8_t vh1 = ((qh & (1 << (l + 1))) >> (l + 1)) << 4;
const uint8_t vi0 = (vi & 0x0F) | vh0;
const uint8_t vi1 = (vi >> 4) | vh1;
const float v0 = vi0*d + m;
const float v1 = vi1*d + m;
y[i*QK5_1 + l + 0] = v0;
y[i*QK5_1 + l + 1] = v1;
assert(!isnan(y[i*QK5_1 + l + 0]));
assert(!isnan(y[i*QK5_1 + l + 1]));
}
}
}
static void dequantize_row_q8_0(const void * restrict vx, float * restrict y, int k) {
assert(k % QK8_0 == 0);
const int nb = k / QK8_0;
@ -1825,6 +2039,8 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
static void ggml_vec_dot_q4_2_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
static void ggml_vec_dot_q4_3_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
static const quantize_fns_t quantize_fns[GGML_TYPE_COUNT] = {
@ -1860,6 +2076,22 @@ static const quantize_fns_t quantize_fns[GGML_TYPE_COUNT] = {
.vec_dot_q = ggml_vec_dot_q4_3_q8_1,
.vec_dot_type = GGML_TYPE_Q8_1,
},
[GGML_TYPE_Q5_0] = {
.dequantize_row_q = dequantize_row_q5_0,
.quantize_row_q = quantize_row_q5_0,
.quantize_row_q_reference = (quantize_row_q_t) quantize_row_q5_0_reference,
.quantize_row_q_dot = quantize_row_q8_0,
.vec_dot_q = ggml_vec_dot_q5_0_q8_0,
.vec_dot_type = GGML_TYPE_Q8_0,
},
[GGML_TYPE_Q5_1] = {
.dequantize_row_q = dequantize_row_q5_1,
.quantize_row_q = quantize_row_q5_1,
.quantize_row_q_reference = (quantize_row_q_t) quantize_row_q5_1_reference,
.quantize_row_q_dot = quantize_row_q8_1,
.vec_dot_q = ggml_vec_dot_q5_1_q8_1,
.vec_dot_type = GGML_TYPE_Q8_1,
},
[GGML_TYPE_Q8_0] = {
.dequantize_row_q = dequantize_row_q8_0,
.quantize_row_q = quantize_row_q8_0,
@ -2496,7 +2728,7 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
const block_q8_0 * restrict y0 = &y[i + 0];
const block_q8_0 * restrict y1 = &y[i + 1];
const uint8x16_t m4b = vdupq_n_u8(0xf);
const uint8x16_t m4b = vdupq_n_u8(0x0F);
const int8x16_t s8b = vdupq_n_s8(0x8);
const uint8x16_t v0_0 = vld1q_u8(x0->qs);
@ -2632,8 +2864,8 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
for (int j = 0; j < QK8_0/2; j++) {
const uint8_t v0 = p0[j];
const int i0 = (int8_t) (v0 & 0xf) - 8;
const int i1 = (int8_t) (v0 >> 4) - 8;
const int i0 = (int8_t) (v0 & 0x0F) - 8;
const int i1 = (int8_t) (v0 >> 4) - 8;
const int i2 = p1[2*j + 0];
const int i3 = p1[2*j + 1];
@ -2670,7 +2902,7 @@ static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void *
summs += x0->m * (y0->s0 + y0->s1) + x1->m * (y1->s0 + y1->s1);
const uint8x16_t m4b = vdupq_n_u8(0xf);
const uint8x16_t m4b = vdupq_n_u8(0x0F);
const uint8x16_t v0_0 = vld1q_u8(x0->qs);
const uint8x16_t v0_1 = vld1q_u8(x1->qs);
@ -2767,8 +2999,8 @@ static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void *
for (int j = 0; j < QK8_1/2; j++) {
const uint8_t v0 = p0[j];
const float f0 = d0*(v0 & 0xf) + m0;
const float f1 = d0*(v0 >> 4) + m0;
const float f0 = d0*(v0 & 0x0F) + m0;
const float f1 = d0*(v0 >> 4) + m0;
const float f2 = d1*p1[2*j + 0];
const float f3 = d1*p1[2*j + 1];
@ -2803,7 +3035,7 @@ static void ggml_vec_dot_q4_2_q8_0(const int n, float * restrict s, const void *
const block_q8_0 * restrict y0 = &y[i + 0];
const block_q8_0 * restrict y1 = &y[i + 1];
const uint8x16_t m4b = vdupq_n_u8(0xf);
const uint8x16_t m4b = vdupq_n_u8(0x0F);
const int8x16_t s8b = vdupq_n_s8(0x8);
const uint8x16_t v0_0 = vcombine_u8(vld1_u8(x0_0->qs), vld1_u8(x0_1->qs));
@ -2914,11 +3146,11 @@ static void ggml_vec_dot_q4_2_q8_0(const int n, float * restrict s, const void *
const uint8_t v0 = x0[j];
const uint8_t v1 = x1[j];
const int i0_0 = (int8_t) (v0 & 0xf) - 8;
const int i1_0 = (int8_t) (v0 >> 4) - 8;
const int i0_0 = (int8_t) (v0 & 0x0F) - 8;
const int i1_0 = (int8_t) (v0 >> 4) - 8;
const int i0_1 = (int8_t) (v1 & 0xf) - 8;
const int i1_1 = (int8_t) (v1 >> 4) - 8;
const int i0_1 = (int8_t) (v1 & 0x0F) - 8;
const int i1_1 = (int8_t) (v1 >> 4) - 8;
const int i2_0 = y0[2*j + 0];
const int i3_0 = y0[2*j + 1];
@ -2966,7 +3198,7 @@ static void ggml_vec_dot_q4_3_q8_1(const int n, float * restrict s, const void *
const uint8x16_t v0_0 = vcombine_u8(vld1_u8(x0_0->qs), vld1_u8(x0_1->qs));
// 4-bit -> 8-bit
const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, vdupq_n_u8(0xf)));
const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, vdupq_n_u8(0x0F)));
const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4));
// interleave
@ -3045,10 +3277,10 @@ static void ggml_vec_dot_q4_3_q8_1(const int n, float * restrict s, const void *
const uint8_t v0 = x0[j];
const uint8_t v1 = x1[j];
const int x0_0 = v0 & 0xf;
const int x0_0 = v0 & 0x0F;
const int x1_0 = v0 >> 4;
const int x0_1 = v1 & 0xf;
const int x0_1 = v1 & 0x0F;
const int x1_1 = v1 >> 4;
const int y0_0 = y0[2*j + 0];
@ -3067,6 +3299,271 @@ static void ggml_vec_dot_q4_3_q8_1(const int n, float * restrict s, const void *
#endif
}
static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
const int nb = n / QK8_0;
assert(n % QK8_0 == 0);
assert(nb % 2 == 0);
assert(QK8_0 == QK5_0);
const block_q5_0 * restrict x = vx;
const block_q8_0 * restrict y = vy;
#if defined(__ARM_NEON)
float32x4_t sumv = vdupq_n_f32(0.0f);
uint64_t tmp[4];
for (int i = 0; i < nb; ++i) {
const block_q5_0 * restrict x0 = &x[i];
const block_q8_0 * restrict y0 = &y[i];
const uint8x16_t m4b = vdupq_n_u8(0x0F);
const int8x16_t s16b = vdupq_n_s8(0x10);
// extract the 5th bit
uint32_t qh;
memcpy(&qh, x0->qh, sizeof(qh));
tmp[0] = table_b2b_u[(qh >> 0) & 0xFF];
tmp[1] = table_b2b_u[(qh >> 8) & 0xFF];
tmp[2] = table_b2b_u[(qh >> 16) & 0xFF];
tmp[3] = table_b2b_u[(qh >> 24) ];
const int8x16_t qhl = vld1q_s8((const int8_t *)(tmp + 0));
const int8x16_t qhh = vld1q_s8((const int8_t *)(tmp + 2));
const uint8x16_t v0 = vld1q_u8(x0->qs);
// 4-bit -> 8-bit
const int8x16_t v0l = vreinterpretq_s8_u8(vandq_u8 (v0, m4b));
const int8x16_t v0h = vreinterpretq_s8_u8(vshrq_n_u8(v0, 4));
// interleave
const int8x16_t v0lz = vzip1q_s8(v0l, v0h);
const int8x16_t v0hz = vzip2q_s8(v0l, v0h);
// add high bit and sub 16
const int8x16_t v0lf = vsubq_s8(vorrq_s8(v0lz, qhl), s16b);
const int8x16_t v0hf = vsubq_s8(vorrq_s8(v0hz, qhh), s16b);
// load y
const int8x16_t v1l = vld1q_s8(y0->qs);
const int8x16_t v1h = vld1q_s8(y0->qs + 16);
const float x0d = GGML_FP16_TO_FP32(x0->d);
#if defined(__ARM_FEATURE_DOTPROD)
sumv = vmlaq_n_f32(sumv, vcvtq_f32_s32(vaddq_s32(
vdotq_s32(vdupq_n_s32(0), v0lf, v1l),
vdotq_s32(vdupq_n_s32(0), v0hf, v1h))), x0d*y0->d);
#else
const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0lf), vget_low_s8 (v1l));
const int16x8_t pl0h = vmull_s8(vget_high_s8(v0lf), vget_high_s8(v1l));
const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0hf), vget_low_s8 (v1h));
const int16x8_t ph0h = vmull_s8(vget_high_s8(v0hf), vget_high_s8(v1h));
const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
sumv = vmlaq_n_f32(sumv, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), x0d*y0->d);
#endif
}
*s = vaddvq_f32(sumv);
#elif defined(__AVX2__)
// Initialize accumulator with zeros
__m256 acc = _mm256_setzero_ps();
// Main loop
for (int i = 0; i < nb; i++) {
/* Compute combined scale for the block */
const __m256 d = _mm256_mul_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d)), _mm256_broadcast_ss(&y[i].d));
__m256i bx = bytes_from_nibbles_32(x[i].qs);
__m256i bxhi = bytes_from_bits_32(x[i].qh);
bxhi = _mm256_andnot_si256(bxhi, _mm256_set1_epi8((char)0xF0));
bx = _mm256_or_si256(bx, bxhi);
__m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
const __m256 q = mul_sum_i8_pairs_float(bx, by);
/* Multiply q with scale and accumulate */
acc = _mm256_fmadd_ps(d, q, acc);
}
*s = hsum_float_8(acc);
#else
// scalar
float sumf = 0.0;
for (int i = 0; i < nb; i++) {
const uint8_t * restrict x0 = x[i].qs;
const int8_t * restrict y0 = y[i].qs;
uint32_t qh;
memcpy(&qh, x[i].qh, sizeof(qh));
const float d = GGML_FP16_TO_FP32(x[i].d);
int sxy = 0;
for (int j = 0; j < QK8_0/2; j++) {
const uint8_t v0 = x0[j];
const int x0_0h = ((qh & (1 << (2*j + 0))) >> (2*j + 0)) << 4;
const int x1_0h = ((qh & (1 << (2*j + 1))) >> (2*j + 1)) << 4;
const int x0_0 = ((v0 & 0x0F) | x0_0h) - 16;
const int x1_0 = ((v0 >> 4) | x1_0h) - 16;
const int y0_0 = y0[2*j + 0];
const int y1_0 = y0[2*j + 1];
sxy += x0_0*y0_0 + x1_0*y1_0;
}
sumf += (d*sxy)*y[i].d;
}
*s = sumf;
#endif
}
static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
const int nb = n / QK8_1;
assert(n % QK8_1 == 0);
assert(nb % 2 == 0);
assert(QK8_1 == QK5_1);
const block_q5_1 * restrict x = vx;
const block_q8_1 * restrict y = vy;
#if defined(__ARM_NEON)
float32x4_t sumv = vdupq_n_f32(0.0f);
float summs = 0.0f;
uint64_t tmp[4];
for (int i = 0; i < nb; ++i) {
const block_q5_1 * restrict x0 = &x[i];
const block_q8_1 * restrict y0 = &y[i];
summs += GGML_FP16_TO_FP32(x0->m) * (y0->s0 + y0->s1);
// extract the 5th bit
uint32_t qh;
memcpy(&qh, x0->qh, sizeof(qh));
tmp[0] = table_b2b_u[(qh >> 0) & 0xFF];
tmp[1] = table_b2b_u[(qh >> 8) & 0xFF];
tmp[2] = table_b2b_u[(qh >> 16) & 0xFF];
tmp[3] = table_b2b_u[(qh >> 24) ];
const int8x16_t qhl = vld1q_s8((const int8_t *)(tmp + 0));
const int8x16_t qhh = vld1q_s8((const int8_t *)(tmp + 2));
const uint8x16_t v0 = vld1q_u8(x0->qs);
// 4-bit -> 8-bit
const int8x16_t v0l = vreinterpretq_s8_u8(vandq_u8 (v0, vdupq_n_u8(0x0F)));
const int8x16_t v0h = vreinterpretq_s8_u8(vshrq_n_u8(v0, 4));
// interleave
const int8x16_t v0lz = vzip1q_s8(v0l, v0h);
const int8x16_t v0hz = vzip2q_s8(v0l, v0h);
// add
const int8x16_t v0lf = vorrq_s8(v0lz, qhl);
const int8x16_t v0hf = vorrq_s8(v0hz, qhh);
// load y
const int8x16_t v1l = vld1q_s8(y0->qs);
const int8x16_t v1h = vld1q_s8(y0->qs + 16);
const float x0d = GGML_FP16_TO_FP32(x0->d);
#if defined(__ARM_FEATURE_DOTPROD)
sumv = vmlaq_n_f32(sumv, vcvtq_f32_s32(vaddq_s32(
vdotq_s32(vdupq_n_s32(0), v0lf, v1l),
vdotq_s32(vdupq_n_s32(0), v0hf, v1h))), x0d*y0->d);
#else
const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0lf), vget_low_s8 (v1l));
const int16x8_t pl0h = vmull_s8(vget_high_s8(v0lf), vget_high_s8(v1l));
const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0hf), vget_low_s8 (v1h));
const int16x8_t ph0h = vmull_s8(vget_high_s8(v0hf), vget_high_s8(v1h));
const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
sumv = vmlaq_n_f32(sumv, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), x0d*y0->d);
#endif
}
*s = vaddvq_f32(sumv) + summs;
#elif defined(__AVX2__)
// Initialize accumulator with zeros
__m256 acc = _mm256_setzero_ps();
float summs = 0.0f;
// Main loop
for (int i = 0; i < nb; i++) {
const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d));
summs += GGML_FP16_TO_FP32(x[i].m) * (y[i].s0 + y[i].s1);
__m256i bx = bytes_from_nibbles_32(x[i].qs);
__m256i bxhi = bytes_from_bits_32(x[i].qh);
bxhi = _mm256_and_si256(bxhi, _mm256_set1_epi8(0x10));
bx = _mm256_or_si256(bx, bxhi);
const __m256 dy = _mm256_broadcast_ss(&y[i].d);
const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
const __m256 q = mul_sum_i8_pairs_float(bx, by);
acc = _mm256_fmadd_ps(q, _mm256_mul_ps(dx, dy), acc);
}
*s = hsum_float_8(acc) + summs;
#else
float sumf = 0.0;
for (int i = 0; i < nb; i++) {
const uint8_t * restrict x0 = x[i].qs;
const int8_t * restrict y0 = y[i].qs;
uint32_t qh;
memcpy(&qh, x[i].qh, sizeof(qh));
const float d = GGML_FP16_TO_FP32(x[i].d);
const float m = GGML_FP16_TO_FP32(x[i].m);
int sxy = 0;
for (int j = 0; j < QK8_1/2; j++) {
const uint8_t v0 = x0[j];
const int x0_0h = ((qh & (1 << (2*j + 0))) >> (2*j + 0)) << 4;
const int x1_0h = ((qh & (1 << (2*j + 1))) >> (2*j + 1)) << 4;
const int x0_0 = (v0 & 0x0F) | x0_0h;
const int x1_0 = (v0 >> 4) | x1_0h;
const int y0_0 = y0[2*j + 0];
const int y1_0 = y0[2*j + 1];
sxy += x0_0*y0_0 + x1_0*y1_0;
}
sumf += (d*sxy)*y[i].d + m*(y[i].s0 + y[i].s1);
}
*s = sumf;
#endif
}
static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
const int nb = n / QK8_0;
@ -3409,13 +3906,15 @@ static const int GGML_BLCK_SIZE[GGML_TYPE_COUNT] = {
[GGML_TYPE_Q4_1] = QK4_1,
[GGML_TYPE_Q4_2] = QK4_2,
[GGML_TYPE_Q4_3] = QK4_3,
[GGML_TYPE_Q5_0] = QK5_0,
[GGML_TYPE_Q5_1] = QK5_1,
[GGML_TYPE_Q8_0] = QK8_0,
[GGML_TYPE_Q8_1] = QK8_1,
[GGML_TYPE_I8] = 1,
[GGML_TYPE_I16] = 1,
[GGML_TYPE_I32] = 1,
};
static_assert(GGML_TYPE_COUNT == 11, "GGML_BLCK_SIZE is outdated");
static_assert(GGML_TYPE_COUNT == 13, "GGML_BLCK_SIZE is outdated");
static const size_t GGML_TYPE_SIZE[GGML_TYPE_COUNT] = {
[GGML_TYPE_F32] = sizeof(float),
@ -3424,13 +3923,15 @@ static const size_t GGML_TYPE_SIZE[GGML_TYPE_COUNT] = {
[GGML_TYPE_Q4_1] = sizeof(block_q4_1),
[GGML_TYPE_Q4_2] = sizeof(block_q4_2),
[GGML_TYPE_Q4_3] = sizeof(block_q4_3),
[GGML_TYPE_Q5_0] = sizeof(block_q5_0),
[GGML_TYPE_Q5_1] = sizeof(block_q5_1),
[GGML_TYPE_Q8_0] = sizeof(block_q8_0),
[GGML_TYPE_Q8_1] = sizeof(block_q8_1),
[GGML_TYPE_I8] = sizeof(int8_t),
[GGML_TYPE_I16] = sizeof(int16_t),
[GGML_TYPE_I32] = sizeof(int32_t),
};
static_assert(GGML_TYPE_COUNT == 11, "GGML_TYPE_SIZE is outdated");
static_assert(GGML_TYPE_COUNT == 13, "GGML_TYPE_SIZE is outdated");
static const char * GGML_TYPE_NAME[GGML_TYPE_COUNT] = {
@ -3440,13 +3941,15 @@ static const char * GGML_TYPE_NAME[GGML_TYPE_COUNT] = {
[GGML_TYPE_Q4_1] = "q4_1",
[GGML_TYPE_Q4_2] = "q4_2",
[GGML_TYPE_Q4_3] = "q4_3",
[GGML_TYPE_Q5_0] = "q5_0",
[GGML_TYPE_Q5_1] = "q5_1",
[GGML_TYPE_Q8_0] = "q8_0",
[GGML_TYPE_Q8_1] = "q8_1",
[GGML_TYPE_I8] = "i8",
[GGML_TYPE_I16] = "i16",
[GGML_TYPE_I32] = "i32",
};
static_assert(GGML_TYPE_COUNT == 11, "GGML_TYPE_NAME is outdated");
static_assert(GGML_TYPE_COUNT == 13, "GGML_TYPE_NAME is outdated");
static bool GGML_IS_QUANTIZED[GGML_TYPE_COUNT] = {
[GGML_TYPE_F32] = false,
@ -3455,13 +3958,15 @@ static bool GGML_IS_QUANTIZED[GGML_TYPE_COUNT] = {
[GGML_TYPE_Q4_1] = true,
[GGML_TYPE_Q4_2] = true,
[GGML_TYPE_Q4_3] = true,
[GGML_TYPE_Q5_0] = true,
[GGML_TYPE_Q5_1] = true,
[GGML_TYPE_Q8_0] = true,
[GGML_TYPE_Q8_1] = true,
[GGML_TYPE_I8] = false,
[GGML_TYPE_I16] = false,
[GGML_TYPE_I32] = false,
};
static_assert(GGML_TYPE_COUNT == 11, "GGML_IS_QUANTIZED is outdated");
static_assert(GGML_TYPE_COUNT == 13, "GGML_IS_QUANTIZED is outdated");
static const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
"NONE",
@ -6673,6 +7178,8 @@ static void ggml_compute_forward_add(
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q4_2:
case GGML_TYPE_Q4_3:
case GGML_TYPE_Q5_0:
case GGML_TYPE_Q5_1:
case GGML_TYPE_Q8_0:
{
ggml_compute_forward_add_q_f32(params, src0, src1, dst);
@ -8161,6 +8668,12 @@ static void ggml_compute_forward_mul_mat_q_f32(
else if (type == GGML_TYPE_Q4_3) {
dequantize_row_q_cuda = dequantize_row_q4_3_cuda;
}
else if (type == GGML_TYPE_Q5_0) {
dequantize_row_q_cuda = dequantize_row_q5_0_cuda;
}
else if (type == GGML_TYPE_Q5_1) {
dequantize_row_q_cuda = dequantize_row_q5_1_cuda;
}
else if (type == GGML_TYPE_Q8_0) {
dequantize_row_q_cuda = dequantize_row_q8_0_cuda;
}
@ -8318,6 +8831,8 @@ static void ggml_compute_forward_mul_mat(
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q4_2:
case GGML_TYPE_Q4_3:
case GGML_TYPE_Q5_0:
case GGML_TYPE_Q5_1:
case GGML_TYPE_Q8_0:
case GGML_TYPE_Q8_1:
{
@ -8548,6 +9063,8 @@ static void ggml_compute_forward_get_rows(
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q4_2:
case GGML_TYPE_Q4_3:
case GGML_TYPE_Q5_0:
case GGML_TYPE_Q5_1:
case GGML_TYPE_Q8_0:
case GGML_TYPE_Q8_1:
{
@ -12260,7 +12777,7 @@ size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t *
for (int i = 0; i < nb; i++) {
for (int l = 0; l < QK4_0; l += 2) {
const uint8_t vi0 = y[i].qs[l/2] & 0xF;
const uint8_t vi0 = y[i].qs[l/2] & 0x0F;
const uint8_t vi1 = y[i].qs[l/2] >> 4;
hist[vi0]++;
@ -12283,7 +12800,7 @@ size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t *
for (int i = 0; i < nb; i++) {
for (int l = 0; l < QK4_1; l += 2) {
const uint8_t vi0 = y[i].qs[l/2] & 0xF;
const uint8_t vi0 = y[i].qs[l/2] & 0x0F;
const uint8_t vi1 = y[i].qs[l/2] >> 4;
hist[vi0]++;
@ -12306,7 +12823,7 @@ size_t ggml_quantize_q4_2(const float * src, void * dst, int n, int k, int64_t *
for (int i = 0; i < nb; i++) {
for (int l = 0; l < QK4_2; l += 2) {
const uint8_t vi0 = y[i].qs[l/2] & 0xF;
const uint8_t vi0 = y[i].qs[l/2] & 0x0F;
const uint8_t vi1 = y[i].qs[l/2] >> 4;
hist[vi0]++;
@ -12329,7 +12846,7 @@ size_t ggml_quantize_q4_3(const float * src, void * dst, int n, int k, int64_t *
for (int i = 0; i < nb; i++) {
for (int l = 0; l < QK4_3; l += 2) {
const uint8_t vi0 = y[i].qs[l/2] & 0xF;
const uint8_t vi0 = y[i].qs[l/2] & 0x0F;
const uint8_t vi1 = y[i].qs[l/2] >> 4;
hist[vi0]++;
@ -12341,6 +12858,66 @@ size_t ggml_quantize_q4_3(const float * src, void * dst, int n, int k, int64_t *
return (n/QK4_3*sizeof(block_q4_3));
}
size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t * hist) {
assert(k % QK5_0 == 0);
const int nb = k / QK5_0;
for (int j = 0; j < n; j += k) {
block_q5_0 * restrict y = (block_q5_0 *)dst + j/QK5_0;
quantize_row_q5_0_reference(src + j, y, k);
for (int i = 0; i < nb; i++) {
uint32_t qh;
memcpy(&qh, &y[i].qh, sizeof(qh));
for (int l = 0; l < QK5_0; l += 2) {
const uint8_t vh0 = ((qh & (1 << (l + 0))) >> (l + 0)) << 4;
const uint8_t vh1 = ((qh & (1 << (l + 1))) >> (l + 1)) << 4;
// cast to 16 bins
const uint8_t vi0 = ((y[i].qs[l/2] & 0x0F) | vh0) / 2;
const uint8_t vi1 = ((y[i].qs[l/2] >> 4) | vh1) / 2;
hist[vi0]++;
hist[vi1]++;
}
}
}
return (n/QK5_0*sizeof(block_q5_0));
}
size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t * hist) {
assert(k % QK5_1 == 0);
const int nb = k / QK5_1;
for (int j = 0; j < n; j += k) {
block_q5_1 * restrict y = (block_q5_1 *)dst + j/QK5_1;
quantize_row_q5_1_reference(src + j, y, k);
for (int i = 0; i < nb; i++) {
uint32_t qh;
memcpy(&qh, &y[i].qh, sizeof(qh));
for (int l = 0; l < QK5_1; l += 2) {
const uint8_t vh0 = ((qh & (1 << (l + 0))) >> (l + 0)) << 4;
const uint8_t vh1 = ((qh & (1 << (l + 1))) >> (l + 1)) << 4;
// cast to 16 bins
const uint8_t vi0 = ((y[i].qs[l/2] & 0x0F) | vh0) / 2;
const uint8_t vi1 = ((y[i].qs[l/2] >> 4) | vh1) / 2;
hist[vi0]++;
hist[vi1]++;
}
}
}
return (n/QK5_1*sizeof(block_q5_1));
}
size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * hist) {
assert(k % QK8_0 == 0);
const int nb = k / QK8_0;
@ -12389,6 +12966,18 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i
block_q4_3 * block = (block_q4_3*)dst + start / QK4_3;
result = ggml_quantize_q4_3(src + start, block, n, n, hist);
} break;
case GGML_TYPE_Q5_0:
{
GGML_ASSERT(start % QK5_0 == 0);
block_q5_0 * block = (block_q5_0*)dst + start / QK5_0;
result = ggml_quantize_q5_0(src + start, block, n, n, hist);
} break;
case GGML_TYPE_Q5_1:
{
GGML_ASSERT(start % QK5_1 == 0);
block_q5_1 * block = (block_q5_1*)dst + start / QK5_1;
result = ggml_quantize_q5_1(src + start, block, n, n, hist);
} break;
case GGML_TYPE_Q8_0:
{
GGML_ASSERT(start % QK8_0 == 0);

8
ggml.h
View file

@ -222,8 +222,10 @@ extern "C" {
GGML_TYPE_Q4_1 = 3,
GGML_TYPE_Q4_2 = 4,
GGML_TYPE_Q4_3 = 5,
GGML_TYPE_Q8_0 = 6,
GGML_TYPE_Q8_1 = 7,
GGML_TYPE_Q5_0 = 6,
GGML_TYPE_Q5_1 = 7,
GGML_TYPE_Q8_0 = 8,
GGML_TYPE_Q8_1 = 9,
GGML_TYPE_I8,
GGML_TYPE_I16,
GGML_TYPE_I32,
@ -833,6 +835,8 @@ extern "C" {
GGML_API size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist);
GGML_API size_t ggml_quantize_q4_2(const float * src, void * dst, int n, int k, int64_t * hist);
GGML_API size_t ggml_quantize_q4_3(const float * src, void * dst, int n, int k, int64_t * hist);
GGML_API size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t * hist);
GGML_API size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t * hist);
GGML_API size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * hist);
GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist);

15
llama.cpp
View file

@ -484,6 +484,8 @@ struct llama_file_loader {
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q4_2:
case GGML_TYPE_Q4_3:
case GGML_TYPE_Q5_0:
case GGML_TYPE_Q5_1:
case GGML_TYPE_Q8_0:
break;
default: {
@ -559,6 +561,8 @@ struct llama_file_saver {
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q4_2:
case GGML_TYPE_Q4_3:
case GGML_TYPE_Q5_0:
case GGML_TYPE_Q5_1:
case GGML_TYPE_Q8_0:
break;
default: LLAMA_ASSERT(false);
@ -850,6 +854,8 @@ static const char *llama_ftype_name(enum llama_ftype ftype) {
return "mostly Q4_1, some F16";
case LLAMA_FTYPE_MOSTLY_Q4_2: return "mostly Q4_2";
case LLAMA_FTYPE_MOSTLY_Q4_3: return "mostly Q4_3";
case LLAMA_FTYPE_MOSTLY_Q5_0: return "mostly Q5_0";
case LLAMA_FTYPE_MOSTLY_Q5_1: return "mostly Q5_1";
case LLAMA_FTYPE_MOSTLY_Q8_0: return "mostly Q8_0";
default: return "unknown, may not work";
}
@ -1588,6 +1594,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
case LLAMA_FTYPE_MOSTLY_Q4_1: quantized_type = GGML_TYPE_Q4_1; break;
case LLAMA_FTYPE_MOSTLY_Q4_2: quantized_type = GGML_TYPE_Q4_2; break;
case LLAMA_FTYPE_MOSTLY_Q4_3: quantized_type = GGML_TYPE_Q4_3; break;
case LLAMA_FTYPE_MOSTLY_Q5_0: quantized_type = GGML_TYPE_Q5_0; break;
case LLAMA_FTYPE_MOSTLY_Q5_1: quantized_type = GGML_TYPE_Q5_1; break;
case LLAMA_FTYPE_MOSTLY_Q8_0: quantized_type = GGML_TYPE_Q8_0; break;
default: throw format("invalid output file type %d\n", ftype);
};
@ -2082,6 +2090,13 @@ int llama_get_kv_cache_token_count(struct llama_context * ctx) {
#define LLAMA_MAX_RNG_STATE 64*1024
void llama_set_rng_seed(struct llama_context * ctx, int seed) {
if (seed <= 0) {
seed = time(NULL);
}
ctx->rng.seed(seed);
}
// Returns the size of the state
size_t llama_get_state_size(struct llama_context * ctx) {
// we don't know size of rng until we actually serialize it. so reserve more than enough memory for its serialized state.

5
llama.h
View file

@ -75,6 +75,8 @@ extern "C" {
LLAMA_FTYPE_MOSTLY_Q4_2 = 5, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_3 = 6, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors
};
LLAMA_API struct llama_context_params llama_context_default_params();
@ -116,6 +118,9 @@ extern "C" {
// Returns the number of tokens in the KV cache
LLAMA_API int llama_get_kv_cache_token_count(struct llama_context * ctx);
// Sets the current rng seed.
LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, int seed);
// Returns the size in bytes of the state (rng, logits, embedding and kv_cache)
LLAMA_API size_t llama_get_state_size(struct llama_context * ctx);