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ggml : add AArch64 optimized GEMV and GEMM Q4 kernels (#5780)

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* Arm AArch64: optimized GEMV and GEMM kernels for q4_0_q8_0, and q8_0_q8_0 quantization

* Arm AArch64: add optimized GEMV and GEMM asm kernels for q4_0_q8_0 quantization and refactor code to address llama.cpp pr#5780 suggestions

* Arm AArch64: add optimized GEMV and GEMM asm kernels for q4_0_q8_0 quantization and refactor code to address llama.cpp pr#5780 suggestions

* Arm AArch64: add optimized GEMV and GEMM asm kernels for q4_0_q8_0 quantization and refactor code to address llama.cpp pr#5780 suggestions

* Arm AArch64: add optimized GEMV and GEMM asm kernels for q4_0_q8_0 quantization and refactor code to address llama.cpp pr#5780 suggestions

* Arm AArch64: add copyright claim only to ggml-aarch64.cpp and ggml-aarch64.h files

* Arm AArch64: minor code refactoring for rebase

* Arm AArch64: minor code refactoring for resolving a build issue with cmake

* Arm AArch64: minor code refactoring to split the Q4_0_AARC64 type into three separate types: Q4_0_4_4, Q4_0_4_8, and Q4_0_8_8

* Arm AArch64: minor code change for resolving a build issue with server-windows

* retrigger checks

* Arm AArch64: minor code changes for rebase

* Arm AArch64: minor changes to skip the pr#7433 vec_dot code for arm cpus with SVE VL not equal to 256 bits

* Arm AArch64: remove stale LLAMA_QKK_64 from CMakeLists.txt and delete build.zig

* Arm AArch64: add reference scalar gemm and gemv, and avoid dynamic memory allocations during quantization for Q4_0_4_4, Q4_0_4_8, and Q4_0_8_8

* Arm AArch64: add multithreaded quantization support for the new types: Q4_0_4_4, Q4_0_4_8, and Q4_0_8_8

* Arm AArch64: minor code refactoring

* Arm AArch64: simplify logic for calling gemm and gemv functions in ggml_compute_forward_mul_mat

* Arm AArch64: minimize changes in ggml_compute_forward_mul_mat

* Arm AArch64: minor code refactoring, and add reference scalar code to quantize routines for new quant types

* Arm AArch64: minor code refactoring

* Arm AArch64: minor code refactoring

* Arm AArch64: minor code refactoring

* rebase on the latest master commit 3fd62a6 and adapt to the new directory structure

* Arm AArch64: remove a redundant comment

* Arm AArch64: add pragma in ggml-aarch64.c to turn -Woverlength-strings warning off

* Arm AArch64: use __aarch64__ check to guard 64-bit neon kernels

* Arm AArch64: update docs/build.md README to include compile time flags for buiilding the Q4_0_4_4 quant type
This commit is contained in:
Dibakar Gope 2024-07-10 07:14:51 -05:00 committed by GitHub
parent 83321c6958
commit 0f1a39f343
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
14 changed files with 2534 additions and 53 deletions
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10
Makefile
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@ -835,7 +835,8 @@ OBJ_GGML += \
ggml/src/ggml.o \
ggml/src/ggml-alloc.o \
ggml/src/ggml-backend.o \
ggml/src/ggml-quants.o
ggml/src/ggml-quants.o \
ggml/src/ggml-aarch64.o
OBJ_LLAMA = \
src/llama.o \
@ -969,6 +970,13 @@ ggml/src/ggml-quants.o: \
ggml/src/ggml-common.h
$(CC) $(CFLAGS) -c $< -o $@
ggml/src/ggml-aarch64.o: \
ggml/src/ggml-aarch64.c \
ggml/include/ggml.h \
ggml/src/ggml-aarch64.h \
ggml/src/ggml-common.h
$(CC) $(CFLAGS) -c $< -o $@
ggml/src/ggml-blas.o: \
ggml/src/ggml-blas.cpp \
ggml/include/ggml-blas.h

1
Package.swift
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@ -10,6 +10,7 @@ var sources = [
"ggml/src/ggml-alloc.c",
"ggml/src/ggml-backend.c",
"ggml/src/ggml-quants.c",
"ggml/src/ggml-aarch64.c",
]
var resources: [Resource] = []

2
docs/build.md
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@ -28,6 +28,7 @@ In order to build llama.cpp you have four different options.
```
- Notes:
- For `Q4_0_4_4` quantization type build, add the `GGML_NO_LLAMAFILE=1` flag. For example, use `make GGML_NO_LLAMAFILE=1`.
- For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `make -j 8` will run 8 jobs in parallel.
- For faster repeated compilation, install [ccache](https://ccache.dev/).
- For debug builds, run `make LLAMA_DEBUG=1`
@ -41,6 +42,7 @@ In order to build llama.cpp you have four different options.
**Notes**:
- For `Q4_0_4_4` quantization type build, add the `-DGGML_LLAMAFILE=OFF` cmake option. For example, use `cmake -B build -DGGML_LLAMAFILE=OFF`.
- For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `cmake --build build --config Release -j 8` will run 8 jobs in parallel.
- For faster repeated compilation, install [ccache](https://ccache.dev/).
- For debug builds, there are two cases:

3
examples/quantize/quantize.cpp
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@ -46,6 +46,9 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
{ "Q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M, " 5.33G, +0.0569 ppl @ Llama-3-8B", },
{ "Q6_K", LLAMA_FTYPE_MOSTLY_Q6_K, " 6.14G, +0.0217 ppl @ Llama-3-8B", },
{ "Q8_0", LLAMA_FTYPE_MOSTLY_Q8_0, " 7.96G, +0.0026 ppl @ Llama-3-8B", },
{ "Q4_0_4_4", LLAMA_FTYPE_MOSTLY_Q4_0_4_4, " 4.34G, +0.4685 ppl @ Llama-3-8B", },
{ "Q4_0_4_8", LLAMA_FTYPE_MOSTLY_Q4_0_4_8, " 4.34G, +0.4685 ppl @ Llama-3-8B", },
{ "Q4_0_8_8", LLAMA_FTYPE_MOSTLY_Q4_0_8_8, " 4.34G, +0.4685 ppl @ Llama-3-8B", },
{ "F16", LLAMA_FTYPE_MOSTLY_F16, "14.00G, +0.0020 ppl @ Mistral-7B", },
{ "BF16", LLAMA_FTYPE_MOSTLY_BF16, "14.00G, -0.0050 ppl @ Mistral-7B", },
{ "F32", LLAMA_FTYPE_ALL_F32, "26.00G @ 7B", },

17
ggml/include/ggml.h
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@ -383,6 +383,9 @@ extern "C" {
GGML_TYPE_F64 = 28,
GGML_TYPE_IQ1_M = 29,
GGML_TYPE_BF16 = 30,
GGML_TYPE_Q4_0_4_4 = 31,
GGML_TYPE_Q4_0_4_8 = 32,
GGML_TYPE_Q4_0_8_8 = 33,
GGML_TYPE_COUNT,
};
@ -424,6 +427,9 @@ extern "C" {
GGML_FTYPE_MOSTLY_IQ4_XS = 22, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ1_M = 23, // except 1d tensors
GGML_FTYPE_MOSTLY_BF16 = 24, // except 1d tensors
GGML_FTYPE_MOSTLY_Q4_0_4_4 = 25, // except 1d tensors
GGML_FTYPE_MOSTLY_Q4_0_4_8 = 26, // except 1d tensors
GGML_FTYPE_MOSTLY_Q4_0_8_8 = 27, // except 1d tensors
};
// available tensor operations:
@ -2406,6 +2412,12 @@ extern "C" {
typedef void (*ggml_from_float_t)(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
typedef void (*ggml_vec_dot_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, size_t bx,
const void * GGML_RESTRICT y, size_t by, int nrc);
typedef void (*ggml_from_float_to_mat_t)(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t nr,
int64_t k, int64_t bx);
typedef void (*ggml_gemv_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x,
const void * GGML_RESTRICT y, int nr, int nc);
typedef void (*ggml_gemm_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x,
const void * GGML_RESTRICT y, int nr, int nc);
typedef struct {
const char * type_name;
@ -2418,6 +2430,11 @@ extern "C" {
ggml_vec_dot_t vec_dot;
enum ggml_type vec_dot_type;
int64_t nrows; // number of rows to process simultaneously;
int64_t ncols; // number of columns to process simultaneously;
int64_t interleave_blcksize; // interleave elements in blocks of interleave_blcksize;
ggml_from_float_to_mat_t from_float_to_mat;
ggml_gemv_t gemv;
ggml_gemm_t gemm;
} ggml_type_traits_t;
GGML_API ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type);

1
ggml/src/CMakeLists.txt
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@ -1153,6 +1153,7 @@ add_library(ggml
${GGML_SOURCES_ROCM} ${GGML_HEADERS_ROCM}
${GGML_SOURCES_BLAS} ${GGML_HEADERS_BLAS}
${GGML_SOURCES_LLAMAFILE} ${GGML_HEADERS_LLAMAFILE}
ggml-aarch64.c ggml-aarch64.h
)
if (EMSCRIPTEN)

2187
ggml/src/ggml-aarch64.c Normal file
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39
ggml/src/ggml-aarch64.h Normal file
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@ -0,0 +1,39 @@
// SPDX-FileCopyrightText: Copyright 2024 Arm Ltd.
#pragma once
#define GGML_COMMON_DECL_C
#include "ggml-common.h"
#include "ggml.h"
// GGML internal header
#ifdef __cplusplus
extern "C" {
#endif
// Quantization
void quantize_q8_0_4x4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
void quantize_q8_0_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
void quantize_mat_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t nrows, int64_t n_per_row, int64_t interleave_blcksize);
// Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization")
size_t quantize_q4_0_4x4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
size_t quantize_q4_0_4x8(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
size_t quantize_q4_0_8x8(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
// GEMV
void ggml_gemv_q4_0_4x4_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemv_q4_0_4x8_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemv_q4_0_8x8_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
// GEMM
void ggml_gemm_q4_0_4x4_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemm_q4_0_4x8_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemm_q4_0_8x8_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
#ifdef __cplusplus
}
#endif

24
ggml/src/ggml-common.h
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@ -199,6 +199,30 @@ typedef struct {
} block_q8_1;
static_assert(sizeof(block_q8_1) == 2*sizeof(ggml_half) + QK8_1, "wrong q8_1 block size/padding");
typedef struct {
ggml_half d[4]; // deltas for 4 q4_0 blocks
uint8_t qs[QK4_0 * 2]; // nibbles / quants for 4 q4_0 blocks
} block_q4_0x4;
static_assert(sizeof(block_q4_0x4) == 4 * sizeof(ggml_half) + QK4_0 * 2, "wrong q4_0x4 block size/padding");
typedef struct {
ggml_half d[8]; // deltas for 8 q4_0 blocks
uint8_t qs[QK4_0 * 4]; // nibbles / quants for 8 q4_0 blocks
} block_q4_0x8;
static_assert(sizeof(block_q4_0x8) == 8 * sizeof(ggml_half) + QK4_0 * 4, "wrong q4_0x8 block size/padding");
typedef struct {
ggml_half d[4]; // deltas for 4 q8_0 blocks
int8_t qs[QK8_0 * 4]; // quants for 4 q8_0 blocks
} block_q8_0x4;
static_assert(sizeof(block_q8_0x4) == 4 * sizeof(ggml_half) + QK8_0 * 4, "wrong q8_0x4 block size/padding");
typedef struct {
ggml_half d[8]; // deltas for 8 q8_0 blocks
int8_t qs[QK8_0 * 8]; // quants for 8 q8_0 blocks
} block_q8_0x8;
static_assert(sizeof(block_q8_0x8) == 8 * sizeof(ggml_half) + QK8_0 * 8, "wrong q8_0x8 block size/padding");
//
// Super-block quantization structures
//

4
ggml/src/ggml-impl.h
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@ -609,6 +609,10 @@ static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
#endif // defined(__ARM_NEON) && (!defined(__MSC_VER)
#ifdef __ARM_FEATURE_SVE
#include <arm_sve.h>
#endif // __ARM_FEATURE_SVE
// precomputed f32 table for f16 (256 KB)
// defined in ggml.c, initialized in ggml_init()
extern float ggml_table_f32_f16[1 << 16];

122
ggml/src/ggml-quants.c
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@ -3814,43 +3814,47 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
}
#endif
#if defined(__ARM_FEATURE_SVE)
const svbool_t ptrueh = svptrue_pat_b8(SV_VL16);
const svbool_t ptruel = svnot_b_z(svptrue_b8(), ptrueh);
if (svcntb() == QK8_0) {
const svbool_t ptrueh = svptrue_pat_b8(SV_VL16);
const svbool_t ptruel = svnot_b_z(svptrue_b8(), ptrueh);
svfloat32_t sumv0 = svdup_n_f32(0.0f);
svfloat32_t sumv1 = svdup_n_f32(0.0f);
svfloat32_t sumv0 = svdup_n_f32(0.0f);
svfloat32_t sumv1 = svdup_n_f32(0.0f);
assert(nb % 2 == 0); // TODO: handle odd nb
assert(nb % 2 == 0); // TODO: handle odd nb
for (int i = 0; i < nb; i += 2) {
const block_q4_0 * restrict x0 = &x[i + 0];
const block_q4_0 * restrict x1 = &x[i + 1];
const block_q8_0 * restrict y0 = &y[i + 0];
const block_q8_0 * restrict y1 = &y[i + 1];
for (int i = 0; i < nb; i += 2) {
const block_q4_0 * restrict x0 = &x[i + 0];
const block_q4_0 * restrict x1 = &x[i + 1];
const block_q8_0 * restrict y0 = &y[i + 0];
const block_q8_0 * restrict y1 = &y[i + 1];
// load x
const svuint8_t qx0r = svld1rq_u8(svptrue_b8(), x0->qs);
const svuint8_t qx1r = svld1rq_u8(svptrue_b8(), x1->qs);
// load x
const svuint8_t qx0r = svld1rq_u8(svptrue_b8(), x0->qs);
const svuint8_t qx1r = svld1rq_u8(svptrue_b8(), x1->qs);
// 4-bit -> 8-bit
const svint8_t qx0 = svreinterpret_s8_u8(svlsr_n_u8_m(ptruel, svand_n_u8_m(ptrueh, qx0r, 0x0F), 0x04));
const svint8_t qx1 = svreinterpret_s8_u8(svlsr_n_u8_m(ptruel, svand_n_u8_m(ptrueh, qx1r, 0x0F), 0x04));
// 4-bit -> 8-bit
const svint8_t qx0 = svreinterpret_s8_u8(svlsr_n_u8_m(ptruel, svand_n_u8_m(ptrueh, qx0r, 0x0F), 0x04));
const svint8_t qx1 = svreinterpret_s8_u8(svlsr_n_u8_m(ptruel, svand_n_u8_m(ptrueh, qx1r, 0x0F), 0x04));
// sub 8
const svint8_t qx0s = svsub_n_s8_x(svptrue_b8(), qx0, 8);
const svint8_t qx1s = svsub_n_s8_x(svptrue_b8(), qx1, 8);
// sub 8
const svint8_t qx0s = svsub_n_s8_x(svptrue_b8(), qx0, 8);
const svint8_t qx1s = svsub_n_s8_x(svptrue_b8(), qx1, 8);
// load y
const svint8_t qy0 = svld1_s8(svptrue_b8(), y0->qs);
const svint8_t qy1 = svld1_s8(svptrue_b8(), y1->qs);
// load y
const svint8_t qy0 = svld1_s8(svptrue_b8(), y0->qs);
const svint8_t qy1 = svld1_s8(svptrue_b8(), y1->qs);
// dot product
sumv0 = svmla_n_f32_x(svptrue_b32(), sumv0, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx0s, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
sumv1 = svmla_n_f32_x(svptrue_b32(), sumv1, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx1s, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
// dot product
sumv0 = svmla_n_f32_x(svptrue_b32(), sumv0, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx0s, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
sumv1 = svmla_n_f32_x(svptrue_b32(), sumv1, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx1s, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
}
*s = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1));
return;
}
*s = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1));
#elif defined(__ARM_NEON)
#endif
#if defined(__ARM_NEON)
float32x4_t sumv0 = vdupq_n_f32(0.0f);
float32x4_t sumv1 = vdupq_n_f32(0.0f);
@ -5422,31 +5426,35 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r
}
#endif
#if defined(__ARM_FEATURE_SVE)
svfloat32_t sumv0 = svdup_n_f32(0.0f);
svfloat32_t sumv1 = svdup_n_f32(0.0f);
if (svcntb() == QK8_0) {
svfloat32_t sumv0 = svdup_n_f32(0.0f);
svfloat32_t sumv1 = svdup_n_f32(0.0f);
assert(nb % 2 == 0); // TODO: handle odd nb
assert(nb % 2 == 0); // TODO: handle odd nb
for (int i = 0; i < nb; i += 2) {
const block_q8_0 * restrict x0 = &x[i + 0];
const block_q8_0 * restrict x1 = &x[i + 1];
const block_q8_0 * restrict y0 = &y[i + 0];
const block_q8_0 * restrict y1 = &y[i + 1];
for (int i = 0; i < nb; i += 2) {
const block_q8_0 * restrict x0 = &x[i + 0];
const block_q8_0 * restrict x1 = &x[i + 1];
const block_q8_0 * restrict y0 = &y[i + 0];
const block_q8_0 * restrict y1 = &y[i + 1];
// load x
const svint8_t qx0 = svld1_s8(svptrue_b8(), x0->qs);
const svint8_t qx1 = svld1_s8(svptrue_b8(), x1->qs);
// load x
const svint8_t qx0 = svld1_s8(svptrue_b8(), x0->qs);
const svint8_t qx1 = svld1_s8(svptrue_b8(), x1->qs);
// load y
const svint8_t qy0 = svld1_s8(svptrue_b8(), y0->qs);
const svint8_t qy1 = svld1_s8(svptrue_b8(), y1->qs);
// load y
const svint8_t qy0 = svld1_s8(svptrue_b8(), y0->qs);
const svint8_t qy1 = svld1_s8(svptrue_b8(), y1->qs);
sumv0 = svmla_n_f32_x(svptrue_b32(), sumv0, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx0, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
sumv1 = svmla_n_f32_x(svptrue_b32(), sumv1, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx1, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
sumv0 = svmla_n_f32_x(svptrue_b32(), sumv0, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx0, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
sumv1 = svmla_n_f32_x(svptrue_b32(), sumv1, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx1, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
}
*s = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1));
return;
}
*s = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1));
#elif defined(__ARM_NEON)
#endif
#if defined(__ARM_NEON)
float32x4_t sumv0 = vdupq_n_f32(0.0f);
float32x4_t sumv1 = vdupq_n_f32(0.0f);
@ -14760,6 +14768,16 @@ static bool validate_fp16(ggml_fp16_t f, size_t i) {
} \
}
#define VALIDATE_ROW_DATA_DVEC_F16_IMPL(type, data, nb, nr) \
const type * q = (const type *) (data); \
for (size_t i = 0; i < (nb); ++i) { \
for (size_t j = 0; j < (nr); ++j) { \
if (!validate_fp16(q[i].d[j], i)) { \
return false; \
} \
} \
}
bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbytes) {
if (type < 0 || type >= GGML_TYPE_COUNT) {
fprintf(stderr, "%s: invalid type %d\n", __func__, type);
@ -14977,6 +14995,16 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_iq4_nl, data, nb);
} break;
case GGML_TYPE_Q4_0_4_4:
case GGML_TYPE_Q4_0_4_8:
{
VALIDATE_ROW_DATA_DVEC_F16_IMPL(block_q4_0x4, data, nbytes / sizeof(block_q4_0x4), 4);
} break;
case GGML_TYPE_Q4_0_8_8:
{
VALIDATE_ROW_DATA_DVEC_F16_IMPL(block_q4_0x8, data, nbytes / sizeof(block_q4_0x8), 8);
} break;
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:

150
ggml/src/ggml.c
View File

@ -4,6 +4,7 @@
#include "ggml-impl.h"
#include "ggml-quants.h"
#include "ggml.h"
#include "ggml-aarch64.h"
#if defined(_MSC_VER) || defined(__MINGW32__)
@ -37,7 +38,7 @@
#include <unistd.h>
#endif
#ifdef __ARM_FEATURE_MATMUL_INT8
#if defined(__ARM_FEATURE_SVE) || defined(__ARM_FEATURE_MATMUL_INT8)
#undef GGML_USE_LLAMAFILE
#endif
@ -692,6 +693,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
#else
.nrows = 1,
#endif
.from_float_to_mat = quantize_mat_q8_0,
},
[GGML_TYPE_Q8_1] = {
.type_name = "q8_1",
@ -889,6 +891,54 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.vec_dot = (ggml_vec_dot_t) ggml_vec_dot_bf16,
.vec_dot_type = GGML_TYPE_BF16,
.nrows = 1,
},
[GGML_TYPE_Q4_0_4_4] = {
.type_name = "q4_0_4x4",
.blck_size = QK4_0,
.type_size = sizeof(block_q4_0),
.is_quantized = true,
.to_float = NULL,
.from_float = NULL,
.from_float_reference = NULL,
.vec_dot = NULL,
.vec_dot_type = GGML_TYPE_Q8_0,
.nrows = 1,
.ncols = 4,
.interleave_blcksize = 4,
.gemv = ggml_gemv_q4_0_4x4_q8_0,
.gemm = ggml_gemm_q4_0_4x4_q8_0,
},
[GGML_TYPE_Q4_0_4_8] = {
.type_name = "q4_0_4x8",
.blck_size = QK4_0,
.type_size = sizeof(block_q4_0),
.is_quantized = true,
.to_float = NULL,
.from_float = NULL,
.from_float_reference = NULL,
.vec_dot = NULL,
.vec_dot_type = GGML_TYPE_Q8_0,
.nrows = 1,
.ncols = 4,
.interleave_blcksize = 8,
.gemv = ggml_gemv_q4_0_4x8_q8_0,
.gemm = ggml_gemm_q4_0_4x8_q8_0,
},
[GGML_TYPE_Q4_0_8_8] = {
.type_name = "q4_0_8x8",
.blck_size = QK4_0,
.type_size = sizeof(block_q4_0),
.is_quantized = true,
.to_float = NULL,
.from_float = NULL,
.from_float_reference = NULL,
.vec_dot = NULL,
.vec_dot_type = GGML_TYPE_Q8_0,
.nrows = 1,
.ncols = 8,
.interleave_blcksize = 8,
.gemv = ggml_gemv_q4_0_8x8_q8_0,
.gemm = ggml_gemm_q4_0_8x8_q8_0,
}
};
@ -3188,6 +3238,9 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
case GGML_FTYPE_MOSTLY_IQ4_XS: wtype = GGML_TYPE_IQ4_XS; break;
case GGML_FTYPE_MOSTLY_IQ3_S: wtype = GGML_TYPE_IQ3_S; break;
case GGML_FTYPE_MOSTLY_IQ2_S: wtype = GGML_TYPE_IQ2_S; break;
case GGML_FTYPE_MOSTLY_Q4_0_4_4: wtype = GGML_TYPE_Q4_0_4_4; break;
case GGML_FTYPE_MOSTLY_Q4_0_4_8: wtype = GGML_TYPE_Q4_0_4_8; break;
case GGML_FTYPE_MOSTLY_Q4_0_8_8: wtype = GGML_TYPE_Q4_0_8_8; break;
case GGML_FTYPE_UNKNOWN: wtype = GGML_TYPE_COUNT; break;
case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16: wtype = GGML_TYPE_COUNT; break;
}
@ -9432,6 +9485,9 @@ static void ggml_compute_forward_add(
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_Q4_0_4_4:
case GGML_TYPE_Q4_0_4_8:
case GGML_TYPE_Q4_0_8_8:
{
ggml_compute_forward_add_q_f32(params, dst);
} break;
@ -9807,6 +9863,9 @@ static void ggml_compute_forward_add1(
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_Q4_0_4_4:
case GGML_TYPE_Q4_0_4_8:
case GGML_TYPE_Q4_0_8_8:
{
ggml_compute_forward_add1_q_f32(params, dst);
} break;
@ -9932,6 +9991,9 @@ static void ggml_compute_forward_acc(
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_Q4_0_4_4:
case GGML_TYPE_Q4_0_4_8:
case GGML_TYPE_Q4_0_8_8:
default:
{
GGML_ASSERT(false);
@ -12134,6 +12196,12 @@ static void ggml_compute_forward_mul_mat(
enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type;
ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
int64_t const vec_dot_num_rows = type_traits[type].nrows;
int64_t const matmul_num_cols = type_traits[type].ncols;
int64_t const interleave_blcksize = type_traits[type].interleave_blcksize;
ggml_from_float_to_mat_t const from_float_to_mat
= type_traits[vec_dot_type].from_float_to_mat;
ggml_gemv_t const gemv = type_traits[type].gemv;
ggml_gemm_t const gemm = type_traits[type].gemm;
GGML_ASSERT(ne0 == ne01);
GGML_ASSERT(ne1 == ne11);
@ -12192,7 +12260,16 @@ UseGgmlGemm1:;
for (int64_t i13 = 0; i13 < ne13; ++i13) {
for (int64_t i12 = 0; i12 < ne12; ++i12) {
for (int64_t i11 = ith; i11 < ne11; i11 += nth) {
int64_t i11_processed = 0;
if ((ggml_n_dims(src1) == 2) && from_float_to_mat && gemm) {
for (int64_t i11 = ith * 4; i11 < ne11 - ne11 % 4; i11 += nth * 4) {
from_float_to_mat((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11),
(void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1),
4, ne10, interleave_blcksize);
}
i11_processed = ne11 - ne11 % 4;
}
for (int64_t i11 = i11_processed + ith; i11 < ne11; i11 += nth) {
from_float_to_vec_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11),
(void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1),
ne10);
@ -12273,6 +12350,28 @@ UseGgmlGemm2:;
const int64_t dr0 = (nr0 + nchunk0 - 1) / nchunk0;
const int64_t dr1 = (nr1 + nchunk1 - 1) / nchunk1;
if ((ggml_n_dims(src0) == 2) && gemv) {
const void * src1_wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata;
const size_t src1_col_stride = ggml_is_contiguous(src1) || src1->type != vec_dot_type ? ggml_row_size(vec_dot_type, ne10) : nb11;
int64_t src0_start = (ith * ne01) / nth;
int64_t src0_end = ((ith + 1) * ne01) / nth;
src0_start = (src0_start % matmul_num_cols) ? src0_start + matmul_num_cols - (src0_start % matmul_num_cols): src0_start;
src0_end = (src0_end % matmul_num_cols) ? src0_end + matmul_num_cols - (src0_end % matmul_num_cols): src0_end;
if (src0_start >= src0_end) return;
// If there are more than three rows in src1, use gemm; otherwise, use gemv.
if (gemm && (ne11 > 3)) {
gemm(ne00, (float *)((char *) dst->data) + src0_start, ne01, (const char *) src0->data + src0_start * nb01,
(const char *) src1_wdata, ne11 - ne11 % 4, src0_end - src0_start);
}
for (int iter = gemm ? ne11 - ne11 % 4 : 0; iter < ne11; iter++) {
gemv(ne00, (float *)((char *) dst->data + (iter * nb1)) + src0_start, ne01,
(const char *) src0->data + src0_start * nb01, (const char *) src1_wdata + (src1_col_stride * iter), 1,
src0_end - src0_start);
}
return;
}
// The first chunk comes from our thread_id, the rest will get auto-assigned.
int current_chunk = ith;
@ -12318,6 +12417,8 @@ static void ggml_compute_forward_mul_mat_id(
ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot;
enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type;
ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
int64_t const matmul_num_cols = type_traits[type].ncols;
ggml_gemv_t const gemv = type_traits[type].gemv;
// we don't support permuted src0 or src1
GGML_ASSERT(nb00 == ggml_type_size(type));
@ -12403,6 +12504,34 @@ static void ggml_compute_forward_mul_mat_id(
const int64_t nr0 = ne01; // src0 rows
const int64_t nr1 = cne1; // src1 rows
if (((ggml_n_dims(src0) - 1) == 2) && gemv) {
int64_t src0_cur_start = (ith * ne01) / nth;
int64_t src0_cur_end = ((ith + 1) * ne01) / nth;
src0_cur_start = (src0_cur_start % matmul_num_cols) ? src0_cur_start + matmul_num_cols - (src0_cur_start % matmul_num_cols): src0_cur_start;
src0_cur_end = (src0_cur_end % matmul_num_cols) ? src0_cur_end + matmul_num_cols - (src0_cur_end % matmul_num_cols): src0_cur_end;
if (src0_cur_start >= src0_cur_end) return;
for (int ir1 = 0; ir1 < nr1; ir1++) {
struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, ir1);
const int id = row_mapping.i1; // selected expert index
const int64_t i11 = id % ne11;
const int64_t i12 = row_mapping.i2; // row index in src1
const int64_t i1 = id; // selected expert index
const int64_t i2 = i12; // row
const char * src1_col = (const char *) wdata +
(src1_cont || src1->type != vec_dot_type
? (i11 + i12 * ne11) * row_size
: (i11 * nb11 + i12 * nb12));
gemv(ne00, (float *)((char *) dst->data + (i1 * nb1 + i2 * nb2)) + src0_cur_start, ne01,
(const char *) src0_cur + src0_cur_start * nb01, src1_col, 1, src0_cur_end - src0_cur_start);
}
continue;
}
// distribute the thread work across the inner or outer loop based on which one is larger
const int64_t nth0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows
@ -12704,6 +12833,9 @@ static void ggml_compute_forward_out_prod(
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_Q4_0_4_4:
case GGML_TYPE_Q4_0_4_8:
case GGML_TYPE_Q4_0_8_8:
{
ggml_compute_forward_out_prod_q_f32(params, dst);
} break;
@ -12889,6 +13021,9 @@ static void ggml_compute_forward_set(
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_Q4_0_4_4:
case GGML_TYPE_Q4_0_4_8:
case GGML_TYPE_Q4_0_8_8:
default:
{
GGML_ASSERT(false);
@ -13148,6 +13283,9 @@ static void ggml_compute_forward_get_rows(
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_Q4_0_4_4:
case GGML_TYPE_Q4_0_4_8:
case GGML_TYPE_Q4_0_8_8:
{
ggml_compute_forward_get_rows_q(params, dst);
} break;
@ -13734,6 +13872,9 @@ static void ggml_compute_forward_clamp(
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_Q8_K:
case GGML_TYPE_Q4_0_4_4:
case GGML_TYPE_Q4_0_4_8:
case GGML_TYPE_Q4_0_8_8:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
@ -20457,6 +20598,9 @@ size_t ggml_quantize_chunk(
case GGML_TYPE_IQ1_M: result = quantize_iq1_m (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_IQ4_NL: result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_IQ4_XS: result = quantize_iq4_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_Q4_0_4_4: result = quantize_q4_0_4x4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_Q4_0_4_8: result = quantize_q4_0_4x8(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_Q4_0_8_8: result = quantize_q4_0_8x8(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_F16:
{
size_t elemsize = sizeof(ggml_fp16_t);
@ -21759,8 +21903,6 @@ int ggml_cpu_has_neon(void) {
int ggml_cpu_has_sve(void) {
#if defined(__ARM_FEATURE_SVE)
// TODO: Currently, SVE 256 bit is only supported.
GGML_ASSERT(svcntb() == QK8_0);
return 1;
#else
return 0;

3
include/llama.h
View File

@ -162,6 +162,9 @@ extern "C" {
LLAMA_FTYPE_MOSTLY_IQ4_XS = 30, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ1_M = 31, // except 1d tensors
LLAMA_FTYPE_MOSTLY_BF16 = 32, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_0_4_4 = 33, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_0_4_8 = 34, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_0_8_8 = 35, // except 1d tensors
LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
};

24
src/llama.cpp
View File

@ -3788,6 +3788,9 @@ struct llama_model_loader {
case GGML_TYPE_IQ4_NL: ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL; break;
case GGML_TYPE_IQ4_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS; break;
case GGML_TYPE_IQ3_S: ftype = LLAMA_FTYPE_MOSTLY_IQ3_S; break;
case GGML_TYPE_Q4_0_4_4: ftype = LLAMA_FTYPE_MOSTLY_Q4_0_4_4; break;
case GGML_TYPE_Q4_0_4_8: ftype = LLAMA_FTYPE_MOSTLY_Q4_0_4_8; break;
case GGML_TYPE_Q4_0_8_8: ftype = LLAMA_FTYPE_MOSTLY_Q4_0_8_8; break;
default:
{
LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max));
@ -4481,6 +4484,9 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
case LLAMA_FTYPE_MOSTLY_IQ4_XS: return "IQ4_XS - 4.25 bpw";
case LLAMA_FTYPE_MOSTLY_IQ3_S: return "IQ3_S - 3.4375 bpw";
case LLAMA_FTYPE_MOSTLY_IQ3_M: return "IQ3_S mix - 3.66 bpw";
case LLAMA_FTYPE_MOSTLY_Q4_0_4_4: return "Q4_0_4_4";
case LLAMA_FTYPE_MOSTLY_Q4_0_4_8: return "Q4_0_4_8";
case LLAMA_FTYPE_MOSTLY_Q4_0_8_8: return "Q4_0_8_8";
default: return "unknown, may not work";
}
@ -17768,6 +17774,10 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
new_type = GGML_TYPE_IQ3_S;
}
else if (new_type == GGML_TYPE_Q4_0_4_4 || new_type == GGML_TYPE_Q4_0_4_8 ||
new_type == GGML_TYPE_Q4_0_8_8) {
new_type = GGML_TYPE_Q4_0;
}
}
} else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
@ -18080,6 +18090,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
case LLAMA_FTYPE_MOSTLY_IQ4_XS: default_type = GGML_TYPE_IQ4_XS; break;
case LLAMA_FTYPE_MOSTLY_IQ3_S: default_type = GGML_TYPE_IQ3_S; break;
case LLAMA_FTYPE_MOSTLY_IQ3_M: default_type = GGML_TYPE_IQ3_S; break;
case LLAMA_FTYPE_MOSTLY_Q4_0_4_4: default_type = GGML_TYPE_Q4_0_4_4; break;
case LLAMA_FTYPE_MOSTLY_Q4_0_4_8: default_type = GGML_TYPE_Q4_0_4_8; break;
case LLAMA_FTYPE_MOSTLY_Q4_0_8_8: default_type = GGML_TYPE_Q4_0_8_8; break;
default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
}
@ -18390,6 +18403,14 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
f32_data = (float *) f32_conv_buf.data();
}
int chunk_size_multiplier = 1;
if (new_type == GGML_TYPE_Q4_0_4_4 || new_type == GGML_TYPE_Q4_0_4_8 || new_type == GGML_TYPE_Q4_0_8_8) {
if ((new_type == GGML_TYPE_Q4_0_8_8) && (tensor->ne[1] % 8 != 0)) new_type = GGML_TYPE_Q4_0;
else if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_Q4_0;
if (new_type == GGML_TYPE_Q4_0_8_8) chunk_size_multiplier = 8;
else if (new_type == GGML_TYPE_Q4_0_4_4 || new_type == GGML_TYPE_Q4_0_4_8) chunk_size_multiplier = 4;
}
LLAMA_LOG_INFO("converting to %s .. ", ggml_type_name(new_type));
fflush(stdout);
@ -18402,7 +18423,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
const int64_t nrows = tensor->ne[1];
static const int64_t min_chunk_size = 32 * 512;
const int64_t chunk_size = n_per_row >= min_chunk_size ? n_per_row : n_per_row * ((min_chunk_size + n_per_row - 1)/n_per_row);
const int64_t chunk_size = (n_per_row >= min_chunk_size ? n_per_row : n_per_row * ((min_chunk_size + n_per_row - 1)/n_per_row)) *
chunk_size_multiplier;
const int64_t nelements_matrix = tensor->ne[0] * tensor->ne[1];
const int64_t nchunk = (nelements_matrix + chunk_size - 1)/chunk_size;