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Unverified Commit 85aeb428 authored by Michael Yang's avatar Michael Yang Committed by GitHub
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Merge pull request #270 from jmorganca/update-llama-cpp 

update llama.cpp
parents f0b365a4 c5bcf328
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Showing with 623 additions and 298 deletions
llama/ggml-alloc.c
View file @ 85aeb428
/**
* llama.cpp - git c574bddb368424b5996cbee2ec45ec050967d404
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
*
* MIT License
*
......
llama/ggml-alloc.h
View file @ 85aeb428
/**
* llama.cpp - git c574bddb368424b5996cbee2ec45ec050967d404
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
*
* MIT License
*
......
llama/ggml-cuda.cu
View file @ 85aeb428
/**
* llama.cpp - git c574bddb368424b5996cbee2ec45ec050967d404
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
*
* MIT License
*
......@@ -188,7 +188,7 @@ typedef float (*vec_dot_q_cuda_t)(const void * __restrict__ vbq, const block_q8_
typedef void (*allocate_tiles_cuda_t)(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc);
typedef void (*load_tiles_cuda_t)(
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & i_offset, const int & k, const int & blocks_per_row);
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row);
typedef float (*vec_dot_q_mul_mat_cuda_t)(
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
const int * __restrict__ y_qs, const half2 * __restrict__ y_ms, const int & i, const int & j, const int & k);
......@@ -1388,22 +1388,185 @@ static __global__ void dequantize_block(const void * __restrict__ vx, float * __
}
// VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called
// MMVQ = mul_mat_vec_q, MMQ = mul_mat_q
#define VDR_q4_0_q8_1 1
#define VDR_Q4_0_Q8_1_MMVQ 2
#define VDR_Q4_0_Q8_1_MMQ 4
static __device__ __forceinline__ float vec_dot_q4_0_q8_1_impl(
const int & vi, const int & ui0, const int & ui1, const half & d4, const half2 & ds8) {
template <int vdr> static __device__ __forceinline__ float vec_dot_q4_0_q8_1_impl(
const int * v, const int * u, const float & d4, const half2 & ds8) {
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
// subtract 8 from each quantized value
const int vi0 = (vi >> 0) & 0x0F0F0F0F;
const int vi1 = (vi >> 4) & 0x0F0F0F0F;
int sumi = 0;
// SIMD dot product of quantized values
int sumi = __dp4a(vi0, ui0, 0);
sumi = __dp4a(vi1, ui1, sumi);
#pragma unroll
for (int i = 0; i < vdr; ++i) {
const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
// SIMD dot product of quantized values
sumi = __dp4a(vi0, u[2*i+0], sumi);
sumi = __dp4a(vi1, u[2*i+1], sumi);
}
// second part effectively subtracts 8 from each quant value
return d4 * (sumi * __half2float(ds8.x) - (8*vdr/QI4_0) * __half2float(ds8.y));
#else
return 0.0f; // only to satisfy the compiler
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
}
#define VDR_Q4_1_Q8_1_MMVQ 2
#define VDR_Q4_1_Q8_1_MMQ 4
template <int vdr> static __device__ __forceinline__ float vec_dot_q4_1_q8_1_impl(
const int * v, const int * u, const half2 & dm4, const half2 & ds8) {
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
int sumi = 0;
#pragma unroll
for (int i = 0; i < vdr; ++i) {
const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
// SIMD dot product of quantized values
sumi = __dp4a(vi0, u[2*i+0], sumi);
sumi = __dp4a(vi1, u[2*i+1], sumi);
}
return __half2float(d4) * (sumi * __half2float(ds8.x) - (8/QI4_0) * __half2float(ds8.y));
#ifdef GGML_CUDA_F16
const half2 tmp = __hmul2(dm4, ds8);
const float d4d8 = __half2float(tmp.x);
const float m4s8 = __half2float(tmp.y);
#else
const float d4d8 = __half2float(dm4.x) * __half2float(ds8.x);
const float m4s8 = __half2float(dm4.y) * __half2float(ds8.y);
#endif // GGML_CUDA_F16
// scale second part of sum by QI8_1/(vdr * QR4_1) to compensate for multiple threads adding it
return sumi * d4d8 + m4s8 / (QI8_1 / (vdr * QR4_1));
#else
return 0.0f; // only to satisfy the compiler
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
}
#define VDR_Q5_0_Q8_1_MMVQ 2
#define VDR_Q5_0_Q8_1_MMQ 4
template <int vdr> static __device__ __forceinline__ float vec_dot_q5_0_q8_1_impl(
const int * vl, const int * vh, const int * u, const float & d5, const half2 & ds8) {
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
int sumi = 0;
for (int i = 0; i < vdr; ++i) {
int vi0 = (vl[i] >> 0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
vi0 |= (vh[i] << 4) & 0x00000010; // 0 -> 4
vi0 |= (vh[i] << 11) & 0x00001000; // 1 -> 12
vi0 |= (vh[i] << 18) & 0x00100000; // 2 -> 20
vi0 |= (vh[i] << 25) & 0x10000000; // 3 -> 28
sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values
int vi1 = (vl[i] >> 4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
vi1 |= (vh[i] >> 12) & 0x00000010; // 16 -> 4
vi1 |= (vh[i] >> 5) & 0x00001000; // 17 -> 12
vi1 |= (vh[i] << 2) & 0x00100000; // 18 -> 20
vi1 |= (vh[i] << 9) & 0x10000000; // 19 -> 28
sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values
}
// second part effectively subtracts 16 from each quant value
return d5 * (sumi*__half2float(ds8.x) - (16*vdr/QI5_0) * __half2float(ds8.y));
#else
return 0.0f; // only to satisfy the compiler
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
}
#define VDR_Q5_1_Q8_1_MMVQ 2
#define VDR_Q5_1_Q8_1_MMQ 4
template <int vdr> static __device__ __forceinline__ float vec_dot_q5_1_q8_1_impl(
const int * vl, const int * vh, const int * u, const half2 & dm5, const half2 & ds8) {
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
int sumi = 0;
for (int i = 0; i < vdr; ++i) {
int vi0 = (vl[i] >> 0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
vi0 |= (vh[i] << 4) & 0x00000010; // 0 -> 4
vi0 |= (vh[i] << 11) & 0x00001000; // 1 -> 12
vi0 |= (vh[i] << 18) & 0x00100000; // 2 -> 20
vi0 |= (vh[i] << 25) & 0x10000000; // 3 -> 28
sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values
int vi1 = (vl[i] >> 4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
vi1 |= (vh[i] >> 12) & 0x00000010; // 16 -> 4
vi1 |= (vh[i] >> 5) & 0x00001000; // 17 -> 12
vi1 |= (vh[i] << 2) & 0x00100000; // 18 -> 20
vi1 |= (vh[i] << 9) & 0x10000000; // 19 -> 28
sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values
}
#ifdef GGML_CUDA_F16
const half2 tmp = __hmul2(dm5, ds8);
const float d5d8 = __half2float(tmp.x);
const float m5s8 = __half2float(tmp.y);
#else
const float d5d8 = __half2float(dm5.x) * __half2float(ds8.x);
const float m5s8 = __half2float(dm5.y) * __half2float(ds8.y);
#endif // GGML_CUDA_F16
// scale second part of sum by QI5_1 / vdr to compensate for multiple threads adding it
return sumi*d5d8 + m5s8 / (QI5_1 / vdr);
#else
return 0.0f; // only to satisfy the compiler
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
}
#define VDR_Q8_0_Q8_1_MMVQ 2
#define VDR_Q8_0_Q8_1_MMQ 8
template <int vdr> static __device__ __forceinline__ float vec_dot_q8_0_q8_1_impl(
const int * v, const int * u, const float & d8_0, const half2 & ds8_1) {
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
int sumi = 0;
for (int i = 0; i < vdr; ++i) {
// SIMD dot product of quantized values
sumi = __dp4a(v[i], u[i], sumi);
}
return sumi * d8_0 * __half2float(ds8_1.x);
#else
return 0.0f; // only to satisfy the compiler
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
}
template <int vdr> static __device__ __forceinline__ float vec_dot_q8_1_q8_1_impl(
const int * v, const int * u, const half2 & dm8, const half2 & ds8) {
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
int sumi = 0;
for (int i = 0; i < vdr; ++i) {
// SIMD dot product of quantized values
sumi = __dp4a(v[i], u[i], sumi);
}
#ifdef GGML_CUDA_F16
const half2 tmp = __hmul2(dm8, ds8);
const float d8d8 = __half2float(tmp.x);
const float m8s8 = __half2float(tmp.y);
#else
const float d8d8 = __half2float(dm8.x) * __half2float(ds8.x);
const float m8s8 = __half2float(dm8.y) * __half2float(ds8.y);
#endif // GGML_CUDA_F16
// scale second part of sum by QI8_1/ vdr to compensate for multiple threads adding it
return sumi*d8d8 + m8s8 / (QI8_1 / vdr);
#else
return 0.0f; // only to satisfy the compiler
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
......@@ -1414,25 +1577,31 @@ static __device__ __forceinline__ float vec_dot_q4_0_q8_1(
const block_q4_0 * bq4_0 = (const block_q4_0 *) vbq;
const int vi = get_int_from_uint8(bq4_0->qs, iqs);
const int ui0 = get_int_from_int8_aligned(bq8_1->qs, iqs);
const int ui1 = get_int_from_int8_aligned(bq8_1->qs, iqs + QI4_0);
int v[VDR_Q4_0_Q8_1_MMVQ];
int u[2*VDR_Q4_0_Q8_1_MMVQ];
#pragma unroll
for (int i = 0; i < VDR_Q4_0_Q8_1_MMVQ; ++i) {
v[i] = get_int_from_uint8(bq4_0->qs, iqs + i);
u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_0);
}
return vec_dot_q4_0_q8_1_impl(vi, ui0, ui1, bq4_0->d, bq8_1->ds);
return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMVQ>(v, u, bq4_0->d, bq8_1->ds);
}
static __device__ __forceinline__ void allocate_tiles_q4_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
__shared__ int tile_x_qs[GGML_CUDA_MMQ_Y * (WARP_SIZE) + GGML_CUDA_MMQ_Y];
__shared__ half2 tile_x_d[GGML_CUDA_MMQ_Y * (WARP_SIZE/QI4_0) + GGML_CUDA_MMQ_Y/QI4_0];
__shared__ float tile_x_d[GGML_CUDA_MMQ_Y * (WARP_SIZE/QI4_0) + GGML_CUDA_MMQ_Y/QI4_0];
*x_ql = tile_x_qs;
*x_dm = tile_x_d;
*x_dm = (half2 *) tile_x_d;
}
static __device__ __forceinline__ void load_tiles_q4_0(
template <bool need_check> static __device__ __forceinline__ void load_tiles_q4_0(
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & i_offset, const int & k, const int & blocks_per_row) {
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
__builtin_assume(i_offset >= 0);
__builtin_assume(i_offset < 8);
......@@ -1444,14 +1613,20 @@ static __device__ __forceinline__ void load_tiles_q4_0(
const block_q4_0 * bx0 = (block_q4_0 *) vx;
float * x_dmf = (float *) x_dm;
#pragma unroll
for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8) {
const int i = i0 + i_offset;
int i = i0 + i_offset;
if (need_check) {
i = min(i, i_max);
}
const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbx;
x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
x_dm[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbx].x = bxi->d;
x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbx] = bxi->d;
}
// const int blocks_per_tile_x_row = WARP_SIZE / QI4_0;
......@@ -1459,6 +1634,7 @@ static __device__ __forceinline__ void load_tiles_q4_0(
// #pragma unroll
// for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * QI4_0) {
// FIXME out-of-bounds
// const int i = i0 + i_offset * QI4_0 + k / blocks_per_tile_x_row;
// if (i >= GGML_CUDA_MMQ_Y) {
......@@ -1483,39 +1659,19 @@ static __device__ __forceinline__ float vec_dot_q4_0_q8_1_mul_mat(
__builtin_assume(k < WARP_SIZE);
const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
const float * x_dmf = (float *) x_dm;
return vec_dot_q4_0_q8_1_impl(
x_ql[i * (WARP_SIZE + 1) + k], y_qs[j * (2*WARP_SIZE) + kyqs], y_qs[j * (2*WARP_SIZE) + kyqs + (QI8_1/2)],
x_dm[i * (WARP_SIZE/QI4_0) + i/QI4_0 + k/QI4_0].x, y_ds[j * (2*WARP_SIZE/QI8_1) + 2*k/QI8_1]);
}
int u[2*VDR_Q4_0_Q8_1_MMQ];
#define VDR_q4_1_q8_1 1
static __device__ __forceinline__ float vec_dot_q4_1_q8_1_impl(
const int & vi, const int & ui0, const int & ui1, const half2 & dm4, const half2 & ds8) {
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
const int vi0 = (vi >> 0) & 0x0F0F0F0F;
const int vi1 = (vi >> 4) & 0x0F0F0F0F;
// SIMD dot product of quantized values
int sumi = __dp4a(vi0, ui0, 0);
sumi = __dp4a(vi1, ui1, sumi);
#ifdef GGML_CUDA_F16
const half2 tmp = __hmul2(dm4, ds8);
const float d4d8 = __half2float(tmp.x);
const float m4s8 = __half2float(tmp.y);
#else
const float d4d8 = __half2float(dm4.x) * __half2float(ds8.x);
const float m4s8 = __half2float(dm4.y) * __half2float(ds8.y);
#endif // GGML_CUDA_F16
#pragma unroll
for (int l = 0; l < VDR_Q4_0_Q8_1_MMQ; ++l) {
u[2*l+0] = y_qs[j * (2*WARP_SIZE) + kyqs + l];
u[2*l+1] = y_qs[j * (2*WARP_SIZE) + kyqs + l + QI4_0];
}
// scale second part of sum by QI8_1/QR4_1 to compensate for multiple threads adding it
return sumi * d4d8 + m4s8 / (QI8_1 / QR4_1);
#else
return 0.0f; // only to satisfy the compiler
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMQ>
(&x_ql[i * (WARP_SIZE + 1) + k], u, x_dmf[i * (WARP_SIZE/QI4_0) + i/QI4_0 + k/QI4_0],
y_ds[j * (2*WARP_SIZE/QI8_1) + 2*k/QI8_1]);
}
static __device__ __forceinline__ float vec_dot_q4_1_q8_1(
......@@ -1523,11 +1679,17 @@ static __device__ __forceinline__ float vec_dot_q4_1_q8_1(
const block_q4_1 * bq4_1 = (const block_q4_1 *) vbq;
const int vi = get_int_from_uint8_aligned(bq4_1->qs, iqs);
const int ui0 = get_int_from_int8_aligned(bq8_1->qs, iqs);
const int ui1 = get_int_from_int8_aligned(bq8_1->qs, iqs + QI4_1);
int v[VDR_Q4_1_Q8_1_MMVQ];
int u[2*VDR_Q4_1_Q8_1_MMVQ];
#pragma unroll
for (int i = 0; i < VDR_Q4_1_Q8_1_MMVQ; ++i) {
v[i] = get_int_from_uint8_aligned(bq4_1->qs, iqs + i);
u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_1);
}
return vec_dot_q4_1_q8_1_impl(vi, ui0, ui1, bq4_1->dm, bq8_1->ds);
return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMVQ>(v, u, bq4_1->dm, bq8_1->ds);
}
static __device__ __forceinline__ void allocate_tiles_q4_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
......@@ -1539,9 +1701,9 @@ static __device__ __forceinline__ void allocate_tiles_q4_1(int ** x_ql, half2 **
*x_dm = tile_x_dm;
}
static __device__ __forceinline__ void load_tiles_q4_1(
template <bool need_check> static __device__ __forceinline__ void load_tiles_q4_1(
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & i_offset, const int & k, const int & blocks_per_row) {
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
__builtin_assume(i_offset >= 0);
__builtin_assume(i_offset < 8);
......@@ -1555,7 +1717,11 @@ static __device__ __forceinline__ void load_tiles_q4_1(
#pragma unroll
for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8) {
const int i = i0 + i_offset;
int i = i0 + i_offset;
if (need_check) {
i = min(i, i_max);
}
const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbx;
......@@ -1567,7 +1733,11 @@ static __device__ __forceinline__ void load_tiles_q4_1(
#pragma unroll
for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * QI4_1) {
const int i = i0 + i_offset * QI4_1 + k / blocks_per_tile_x_row;
int i = i0 + i_offset * QI4_1 + k / blocks_per_tile_x_row;
if (need_check) {
i = min(i, i_max);
}
const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbxd;
......@@ -1588,35 +1758,17 @@ static __device__ __forceinline__ float vec_dot_q4_1_q8_1_mul_mat(
const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
return vec_dot_q4_1_q8_1_impl(
x_ql[i * (WARP_SIZE + 1) + k], y_qs[j * (2*WARP_SIZE) + kyqs], y_qs[j * (2*WARP_SIZE) + kyqs + (QI8_1/2)],
x_dm[i * (WARP_SIZE/QI4_1) + i/QI4_1 + k/QI4_1], y_ds[j * (2*WARP_SIZE/QI8_1) + 2*k/QI8_1]);
}
#define VDR_q5_0_q8_1 1
int u[2*VDR_Q4_1_Q8_1_MMQ];
static __device__ __forceinline__ float vec_dot_q5_0_q8_1_impl(
const int & qs, const int & qh, const int & ui0, const int & ui1, const half & d5, const half2 & ds8) {
#pragma unroll
for (int l = 0; l < VDR_Q4_1_Q8_1_MMQ; ++l) {
u[2*l+0] = y_qs[j * (2*WARP_SIZE) + kyqs + l];
u[2*l+1] = y_qs[j * (2*WARP_SIZE) + kyqs + l + QI4_1];
}
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
int vi0 = (qs >> 0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
vi0 |= (qh << 4) & 0x00000010; // 0 -> 4
vi0 |= (qh << 11) & 0x00001000; // 1 -> 12
vi0 |= (qh << 18) & 0x00100000; // 2 -> 20
vi0 |= (qh << 25) & 0x10000000; // 3 -> 28
int sumi = __dp4a(vi0, ui0, 0); // SIMD dot product of quantized values
int vi1 = (qs >> 4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
vi1 |= (qh >> 12) & 0x00000010; // 16 -> 4
vi1 |= (qh >> 5) & 0x00001000; // 17 -> 12
vi1 |= (qh << 2) & 0x00100000; // 18 -> 20
vi1 |= (qh << 9) & 0x10000000; // 19 -> 28
sumi = __dp4a(vi1, ui1, sumi); // SIMD dot product of quantized values
return __half2float(d5) * (sumi*__half2float(ds8.x) - (16/QI5_0) * __half2float(ds8.y));
#else
return 0.0f; // only to satisfy the compiler
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMQ>
(&x_ql[i * (WARP_SIZE + 1) + k], u, x_dm[i * (WARP_SIZE/QI4_1) + i/QI4_1 + k/QI4_1],
y_ds[j * (2*WARP_SIZE/QI8_1) + 2*k/QI8_1]);
}
static __device__ __forceinline__ float vec_dot_q5_0_q8_1(
......@@ -1624,28 +1776,33 @@ static __device__ __forceinline__ float vec_dot_q5_0_q8_1(
const block_q5_0 * bq5_0 = (const block_q5_0 *) vbq;
const int qs = get_int_from_uint8(bq5_0->qs, iqs);
const int qh = get_int_from_uint8(bq5_0->qh, 0) >> (4 * iqs);
const int ui0 = get_int_from_int8_aligned(bq8_1->qs, iqs);
const int ui1 = get_int_from_int8_aligned(bq8_1->qs, iqs + QI5_0);
int vl[VDR_Q5_0_Q8_1_MMVQ];
int vh[VDR_Q5_0_Q8_1_MMVQ];
int u[2*VDR_Q5_0_Q8_1_MMVQ];
return vec_dot_q5_0_q8_1_impl(qs, qh, ui0, ui1, bq5_0->d, bq8_1->ds);
#pragma unroll
for (int i = 0; i < VDR_Q5_0_Q8_1_MMVQ; ++i) {
vl[i] = get_int_from_uint8(bq5_0->qs, iqs + i);
vh[i] = get_int_from_uint8(bq5_0->qh, 0) >> (4 * (iqs + i));
u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_0);
}
return vec_dot_q5_0_q8_1_impl<VDR_Q5_0_Q8_1_MMVQ>(vl, vh, u, bq5_0->d, bq8_1->ds);
}
static __device__ __forceinline__ void allocate_tiles_q5_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
__shared__ int tile_x_ql[GGML_CUDA_MMQ_Y * (WARP_SIZE) + GGML_CUDA_MMQ_Y];
__shared__ int tile_x_qh[GGML_CUDA_MMQ_Y * (WARP_SIZE/QI5_0) + GGML_CUDA_MMQ_Y/QI5_0];
__shared__ half2 tile_x_d[GGML_CUDA_MMQ_Y * (WARP_SIZE/QI5_0) + GGML_CUDA_MMQ_Y/QI5_0];
__shared__ int tile_x_ql[GGML_CUDA_MMQ_Y * (2*WARP_SIZE) + GGML_CUDA_MMQ_Y];
__shared__ float tile_x_d[GGML_CUDA_MMQ_Y * (WARP_SIZE/QI5_0) + GGML_CUDA_MMQ_Y/QI5_0];
*x_ql = tile_x_ql;
*x_qh = tile_x_qh;
*x_dm = tile_x_d;
*x_dm = (half2 *) tile_x_d;
}
static __device__ __forceinline__ void load_tiles_q5_0(
template <bool need_check> static __device__ __forceinline__ void load_tiles_q5_0(
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & i_offset, const int & k, const int & blocks_per_row) {
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
__builtin_assume(i_offset >= 0);
__builtin_assume(i_offset < 8);
......@@ -1659,24 +1816,51 @@ static __device__ __forceinline__ void load_tiles_q5_0(
#pragma unroll
for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8) {
const int i = i0 + i_offset;
int i = i0 + i_offset;
if (need_check) {
i = min(i, i_max);
}
const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbx;
x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
const int ql = get_int_from_uint8(bxi->qs, kqsx);
const int qh = get_int_from_uint8(bxi->qh, 0) >> (4 * (k % QI5_0));
int qs0 = (ql >> 0) & 0x0F0F0F0F;
qs0 |= (qh << 4) & 0x00000010; // 0 -> 4
qs0 |= (qh << 11) & 0x00001000; // 1 -> 12
qs0 |= (qh << 18) & 0x00100000; // 2 -> 20
qs0 |= (qh << 25) & 0x10000000; // 3 -> 28
qs0 = __vsubss4(qs0, 0x10101010); // subtract 16
x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;
int qs1 = (ql >> 4) & 0x0F0F0F0F;
qs1 |= (qh >> 12) & 0x00000010; // 16 -> 4
qs1 |= (qh >> 5) & 0x00001000; // 17 -> 12
qs1 |= (qh << 2) & 0x00100000; // 18 -> 20
qs1 |= (qh << 9) & 0x10000000; // 19 -> 28
qs1 = __vsubss4(qs1, 0x10101010); // subtract 16
x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
}
const int blocks_per_tile_x_row = WARP_SIZE / QI5_0;
const int kbxd = k % blocks_per_tile_x_row;
float * x_dmf = (float *) x_dm;
#pragma unroll
for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * QI5_0) {
const int i = i0 + i_offset * QI5_0 + k / blocks_per_tile_x_row;
int i = i0 + i_offset * QI5_0 + k / blocks_per_tile_x_row;
if (need_check) {
i = min(i, i_max);
}
const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbxd;
x_qh[i * (WARP_SIZE/QI5_0) + i / QI5_0 + kbxd] = get_int_from_uint8(bxi->qh, 0);
x_dm[i * (WARP_SIZE/QI5_0) + i / QI5_0 + kbxd].x = bxi->d;
x_dmf[i * (WARP_SIZE/QI5_0) + i / QI5_0 + kbxd] = bxi->d;
}
}
......@@ -1693,46 +1877,18 @@ static __device__ __forceinline__ float vec_dot_q5_0_q8_1_mul_mat(
const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
const int index_bx = i * (WARP_SIZE/QI5_0) + i/QI5_0 + k/QI5_0;
const float * x_dmf = (float *) x_dm;
return vec_dot_q5_0_q8_1_impl(
x_ql[i * (WARP_SIZE + 1) + k], x_qh[index_bx] >> (4 * (k % QI5_0)), y_qs[j * (2*WARP_SIZE) + kyqs],
y_qs[j * (2*WARP_SIZE) + kyqs + (QI8_1/2)], x_dm[index_bx].x, y_ds[j * (2*WARP_SIZE/QI8_1) + 2*k/QI8_1]);
}
#define VDR_q5_1_q8_1 1
static __device__ __forceinline__ float vec_dot_q5_1_q8_1_impl(
const int & qs, const int & qh, const int & ui0, const int & ui1, const half2 & dm5, const half2 & ds8) {
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
int vi0 = (qs >> 0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh0 as 5th bits
vi0 |= (qh << 4) & 0x00000010; // 0 -> 4
vi0 |= (qh << 11) & 0x00001000; // 1 -> 12
vi0 |= (qh << 18) & 0x00100000; // 2 -> 20
vi0 |= (qh << 25) & 0x10000000; // 3 -> 28
int sumi = __dp4a(vi0, ui0, 0); // SIMD dot product of quantized values
int vi1 = (qs >> 4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh1 as 5th bits
vi1 |= (qh >> 12) & 0x00000010; // 16 -> 4
vi1 |= (qh >> 5) & 0x00001000; // 17 -> 12
vi1 |= (qh << 2) & 0x00100000; // 18 -> 20
vi1 |= (qh << 9) & 0x10000000; // 19 -> 28
sumi = __dp4a(vi1, ui1, sumi); // SIMD dot product of quantized values
int u[2*VDR_Q5_0_Q8_1_MMQ];
#ifdef GGML_CUDA_F16
const half2 tmp = __hmul2(dm5, ds8);
const float d5d8 = __half2float(tmp.x);
const float m5s8 = __half2float(tmp.y);
#else
const float d5d8 = __half2float(dm5.x) * __half2float(ds8.x);
const float m5s8 = __half2float(dm5.y) * __half2float(ds8.y);
#endif // GGML_CUDA_F16
return sumi*d5d8 + m5s8/QI5_1; // scale sum by QI5_1 because there are QI5_1 threads working on this block
#pragma unroll
for (int l = 0; l < VDR_Q5_0_Q8_1_MMQ; ++l) {
u[2*l+0] = y_qs[j * (2*WARP_SIZE) + kyqs + l];
u[2*l+1] = y_qs[j * (2*WARP_SIZE) + kyqs + l + QI5_0];
}
#else
return 0.0f; // only to satisfy the compiler
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
return vec_dot_q8_0_q8_1_impl<QR5_0*VDR_Q5_0_Q8_1_MMQ>
(&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dmf[index_bx], y_ds[j * (2*WARP_SIZE/QI8_1) + 2*k/QI8_1]);
}
static __device__ __forceinline__ float vec_dot_q5_1_q8_1(
......@@ -1740,28 +1896,33 @@ static __device__ __forceinline__ float vec_dot_q5_1_q8_1(
const block_q5_1 * bq5_1 = (const block_q5_1 *) vbq;
const int qs = get_int_from_uint8_aligned(bq5_1->qs, iqs);
const int qh = get_int_from_uint8_aligned(bq5_1->qh, 0) >> (4 * iqs);
const int ui0 = get_int_from_int8_aligned(bq8_1->qs, iqs);
const int ui1 = get_int_from_int8_aligned(bq8_1->qs, iqs + QI5_1);
int vl[VDR_Q5_1_Q8_1_MMVQ];
int vh[VDR_Q5_1_Q8_1_MMVQ];
int u[2*VDR_Q5_1_Q8_1_MMVQ];
return vec_dot_q5_1_q8_1_impl(qs, qh, ui0, ui1, bq5_1->dm, bq8_1->ds);
#pragma unroll
for (int i = 0; i < VDR_Q5_1_Q8_1_MMVQ; ++i) {
vl[i] = get_int_from_uint8_aligned(bq5_1->qs, iqs + i);
vh[i] = get_int_from_uint8_aligned(bq5_1->qh, 0) >> (4 * (iqs + i));
u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_1);
}
return vec_dot_q5_1_q8_1_impl<VDR_Q5_1_Q8_1_MMVQ>(vl, vh, u, bq5_1->dm, bq8_1->ds);
}
static __device__ __forceinline__ void allocate_tiles_q5_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
__shared__ int tile_x_ql[GGML_CUDA_MMQ_Y * (WARP_SIZE ) + GGML_CUDA_MMQ_Y];
__shared__ int tile_x_qh[GGML_CUDA_MMQ_Y * (WARP_SIZE/QI5_1) + GGML_CUDA_MMQ_Y/QI5_1];
__shared__ int tile_x_ql[GGML_CUDA_MMQ_Y * (2*WARP_SIZE) + GGML_CUDA_MMQ_Y];
__shared__ half2 tile_x_dm[GGML_CUDA_MMQ_Y * (WARP_SIZE/QI5_1) + GGML_CUDA_MMQ_Y/QI5_1];
*x_ql = tile_x_ql;
*x_qh = tile_x_qh;
*x_dm = tile_x_dm;
}
static __device__ __forceinline__ void load_tiles_q5_1(
template <bool need_check> static __device__ __forceinline__ void load_tiles_q5_1(
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & i_offset, const int & k, const int & blocks_per_row) {
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
__builtin_assume(i_offset >= 0);
__builtin_assume(i_offset < 8);
......@@ -1775,11 +1936,32 @@ static __device__ __forceinline__ void load_tiles_q5_1(
#pragma unroll
for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8) {
const int i = i0 + i_offset;
int i = i0 + i_offset;
if (need_check) {
i = min(i, i_max);
}
const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbx;
x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
const int qh = get_int_from_uint8_aligned(bxi->qh, 0) >> (4 * (k % QI5_1));
int qs0 = (ql >> 0) & 0x0F0F0F0F;
qs0 |= (qh << 4) & 0x00000010; // 0 -> 4
qs0 |= (qh << 11) & 0x00001000; // 1 -> 12
qs0 |= (qh << 18) & 0x00100000; // 2 -> 20
qs0 |= (qh << 25) & 0x10000000; // 3 -> 28
x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;
int qs1 = (ql >> 4) & 0x0F0F0F0F;
qs1 |= (qh >> 12) & 0x00000010; // 16 -> 4
qs1 |= (qh >> 5) & 0x00001000; // 17 -> 12
qs1 |= (qh << 2) & 0x00100000; // 18 -> 20
qs1 |= (qh << 9) & 0x10000000; // 19 -> 28
x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
}
const int blocks_per_tile_x_row = WARP_SIZE / QI5_1;
......@@ -1787,11 +1969,14 @@ static __device__ __forceinline__ void load_tiles_q5_1(
#pragma unroll
for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * QI5_1) {
const int i = i0 + i_offset * QI5_1 + k / blocks_per_tile_x_row;
int i = i0 + i_offset * QI5_1 + k / blocks_per_tile_x_row;
if (need_check) {
i = min(i, i_max);
}
const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbxd;
x_qh[i * (WARP_SIZE/QI5_1) + i / QI5_1 + kbxd] = get_int_from_uint8_aligned(bxi->qh, 0);
x_dm[i * (WARP_SIZE/QI5_1) + i / QI5_1 + kbxd] = bxi->dm;
}
}
......@@ -1810,24 +1995,16 @@ static __device__ __forceinline__ float vec_dot_q5_1_q8_1_mul_mat(
const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
const int index_bx = i * (WARP_SIZE/QI5_1) + + i/QI5_1 + k/QI5_1;
return vec_dot_q5_1_q8_1_impl(
x_ql[i * (WARP_SIZE + 1) + k], x_qh[index_bx] >> (4 * (k % QI5_1)), y_qs[j * (2*WARP_SIZE) + kyqs],
y_qs[j * (2*WARP_SIZE) + kyqs + (QI8_1/2)], x_dm[index_bx], y_ds[j * (2*WARP_SIZE/QI8_1) + 2*k/QI8_1]);
}
#define VDR_q8_0_q8_1 1
static __device__ __forceinline__ float vec_dot_q8_0_q8_1_impl(
const int & vi, const int & ui, const half & d8_0, const half2 & ds8_1) {
int u[2*VDR_Q5_1_Q8_1_MMQ];
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
// SIMD dot product of quantized values
const int sumi = __dp4a(vi, ui, 0);
#pragma unroll
for (int l = 0; l < VDR_Q5_1_Q8_1_MMQ; ++l) {
u[2*l+0] = y_qs[j * (2*WARP_SIZE) + kyqs + l];
u[2*l+1] = y_qs[j * (2*WARP_SIZE) + kyqs + l + QI5_1];
}
return sumi * __half2float(d8_0) * __half2float(ds8_1.x);
#else
return 0.0f; // only to satisfy the compiler
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
return vec_dot_q8_1_q8_1_impl<QR5_1*VDR_Q5_1_Q8_1_MMQ>
(&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dm[index_bx], y_ds[j * (2*WARP_SIZE/QI8_1) + 2*k/QI8_1]);
}
static __device__ __forceinline__ float vec_dot_q8_0_q8_1(
......@@ -1835,24 +2012,29 @@ static __device__ __forceinline__ float vec_dot_q8_0_q8_1(
const block_q8_0 * bq8_0 = (const block_q8_0 *) vbq;
const int vi = get_int_from_int8(bq8_0->qs, iqs);
const int ui = get_int_from_int8_aligned(bq8_1->qs, iqs);
int v[VDR_Q8_0_Q8_1_MMVQ];
int u[VDR_Q8_0_Q8_1_MMVQ];
for (int i = 0; i < VDR_Q8_0_Q8_1_MMVQ; ++i) {
v[i] = get_int_from_int8(bq8_0->qs, iqs + i);
u[i] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
}
return vec_dot_q8_0_q8_1_impl(vi, ui, bq8_0->d, bq8_1->ds);
return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMVQ>(v, u, bq8_0->d, bq8_1->ds);
}
static __device__ __forceinline__ void allocate_tiles_q8_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
__shared__ int tile_x_qs[GGML_CUDA_MMQ_Y * (WARP_SIZE) + GGML_CUDA_MMQ_Y];
__shared__ half2 tile_x_d[GGML_CUDA_MMQ_Y * (WARP_SIZE/QI8_0) + GGML_CUDA_MMQ_Y/QI8_0];
__shared__ float tile_x_d[GGML_CUDA_MMQ_Y * (WARP_SIZE/QI8_0) + GGML_CUDA_MMQ_Y/QI8_0];
*x_ql = tile_x_qs;
*x_dm = tile_x_d;
*x_dm = (half2 *) tile_x_d;
}
static __device__ __forceinline__ void load_tiles_q8_0(
template <bool need_check> static __device__ __forceinline__ void load_tiles_q8_0(
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & i_offset, const int & k, const int & blocks_per_row) {
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
__builtin_assume(i_offset >= 0);
__builtin_assume(i_offset < 8);
......@@ -1861,17 +2043,22 @@ static __device__ __forceinline__ void load_tiles_q8_0(
const int kbx = k / QI8_0;
const int kqsx = k % QI8_0;
float * x_dmf = (float *) x_dm;
const block_q8_0 * bx0 = (block_q8_0 *) vx;
#pragma unroll
for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8) {
const int i = i0 + i_offset;
int i = i0 + i_offset;
if (need_check) {
i = min(i, i_max);
}
const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbx;
x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_int8(bxi->qs, kqsx);
x_dm[i * (WARP_SIZE/QI8_0) + i / QI8_0 + kbx].x = bxi->d;
x_dmf[i * (WARP_SIZE/QI8_0) + i / QI8_0 + kbx] = bxi->d;
}
// const int blocks_per_tile_x_row = WARP_SIZE / QI8_0;
......@@ -1879,6 +2066,7 @@ static __device__ __forceinline__ void load_tiles_q8_0(
// #pragma unroll
// for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * QI8_0) {
// FIXME out-of-bounds
// const int i = i0 + i_offset * QI8_0 + k / blocks_per_tile_x_row;
// #if GGML_CUDA_MMQ_Y < 64
......@@ -1904,9 +2092,11 @@ static __device__ __forceinline__ float vec_dot_q8_0_q8_1_mul_mat(
__builtin_assume(k >= 0);
__builtin_assume(k < WARP_SIZE);
return vec_dot_q8_0_q8_1_impl(
x_ql[i * (WARP_SIZE + 1) + k], y_qs[j*WARP_SIZE + k],
x_dm[i * (WARP_SIZE/QI8_0) + i/QI8_0 + k/QI8_0].x, y_ds[j * (WARP_SIZE/QI8_1) + k/QI8_1]);
const float * x_dmf = (float *) x_dm;
return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMQ>
(&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[j * WARP_SIZE + k], x_dmf[i * (WARP_SIZE/QI8_0) + i/QI8_0 + k/QI8_0],
y_ds[j * (WARP_SIZE/QI8_1) + k/QI8_1]);
}
#define VDR_q2_K_q8_1 1
......@@ -1973,9 +2163,9 @@ static __device__ __forceinline__ void allocate_tiles_q2_K(int ** x_ql, half2 **
*x_sc = tile_x_sc;
}
static __device__ __forceinline__ void load_tiles_q2_K(
template <bool need_check> static __device__ __forceinline__ void load_tiles_q2_K(
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & i_offset, const int & k, const int & blocks_per_row) {
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
__builtin_assume(i_offset >= 0);
__builtin_assume(i_offset < 8);
......@@ -1989,7 +2179,11 @@ static __device__ __forceinline__ void load_tiles_q2_K(
#pragma unroll
for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8) {
const int i = i0 + i_offset;
int i = i0 + i_offset;
if (need_check) {
i = min(i, i_max);
}
const block_q2_K * bxi = bx0 + i*blocks_per_row + kbx;
......@@ -2001,7 +2195,11 @@ static __device__ __forceinline__ void load_tiles_q2_K(
#pragma unroll
for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * QI2_K) {
const int i = (i0 + i_offset * QI2_K + k / blocks_per_tile_x_row) % GGML_CUDA_MMQ_Y;
int i = (i0 + i_offset * QI2_K + k / blocks_per_tile_x_row) % GGML_CUDA_MMQ_Y;
if (need_check) {
i = min(i, i_max);
}
const block_q2_K * bxi = bx0 + i*blocks_per_row + kbxd;
......@@ -2010,7 +2208,11 @@ static __device__ __forceinline__ void load_tiles_q2_K(
#pragma unroll
for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * 4) {
const int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
if (need_check) {
i = min(i, i_max);
}
const block_q2_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI2_K/4);
......@@ -2125,9 +2327,9 @@ static __device__ __forceinline__ void allocate_tiles_q3_K(int ** x_ql, half2 **
*x_sc = tile_x_sc;
}
static __device__ __forceinline__ void load_tiles_q3_K(
template <bool need_check> static __device__ __forceinline__ void load_tiles_q3_K(
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & i_offset, const int & k, const int & blocks_per_row) {
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
__builtin_assume(i_offset >= 0);
__builtin_assume(i_offset < 8);
......@@ -2141,7 +2343,11 @@ static __device__ __forceinline__ void load_tiles_q3_K(
#pragma unroll
for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8) {
const int i = i0 + i_offset;
int i = i0 + i_offset;
if (need_check) {
i = min(i, i_max);
}
const block_q3_K * bxi = bx0 + i*blocks_per_row + kbx;
......@@ -2153,7 +2359,11 @@ static __device__ __forceinline__ void load_tiles_q3_K(
#pragma unroll
for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * QI3_K) {
const int i = (i0 + i_offset * QI3_K + k / blocks_per_tile_x_row) % GGML_CUDA_MMQ_Y;
int i = (i0 + i_offset * QI3_K + k / blocks_per_tile_x_row) % GGML_CUDA_MMQ_Y;
if (need_check) {
i = min(i, i_max);
}
const block_q3_K * bxi = bx0 + i*blocks_per_row + kbxd;
......@@ -2162,7 +2372,11 @@ static __device__ __forceinline__ void load_tiles_q3_K(
#pragma unroll
for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * 2) {
const int i = i0 + i_offset * 2 + k / (WARP_SIZE/2);
int i = i0 + i_offset * 2 + k / (WARP_SIZE/2);
if (need_check) {
i = min(i, i_max);
}
const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/2)) / (QI3_K/2);
......@@ -2171,7 +2385,11 @@ static __device__ __forceinline__ void load_tiles_q3_K(
#pragma unroll
for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * 4) {
const int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
if (need_check) {
i = min(i, i_max);
}
const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI3_K/4);
......@@ -2252,15 +2470,15 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1(
int u[2*QR4_K];
float d8[QR4_K];
// iqs is in 0...15. bq8_offset = 2 * (iqs/4) -> bq8_offset = 0, 2, 4, 6
const int bq8_offset = QR4_K * (iqs / (QI8_1/2));
// iqs is in 0,2..30. bq8_offset = iqs/4 -> bq8_offset = 0, 2, 4, 6
const int bq8_offset = QR4_K * ((iqs/2) / (QI8_1/2));
// iqs = 0....3 -> bq8_offset = 0, want q4_offset = 0, 4, 8, 12
// iqs = 4....7 -> bq8_offset = 2, want q4_offset = 32, 36, 40, 44
// iqs = 8...11 -> bq8_offset = 4, want q4_offset = 64, 68, 72, 76
// iqs = 12..15 -> bq8_offset = 6, want q4_offset = 96, 100, 104, 108
const int * q4 = (const int *)(bq4_K->qs + 16 * bq8_offset + 4 * (iqs%4));
const int * q4 = (const int *)(bq4_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
v[0] = q4[0];
v[1] = q4[4];
......@@ -2281,7 +2499,7 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1(
const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
d8[i] = bq8i->ds.x;
const int * q8 = (const int *)bq8i->qs + (iqs%4);
const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
u[2*i+0] = q8[0];
u[2*i+1] = q8[4];
}
......@@ -2309,12 +2527,12 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1(
const float d8_1 = bq8_1[0].ds.x;
const float d8_2 = bq8_1[1].ds.x;
const int ui1 = *((const int *)bq8_1[0].qs + iqs);
const int ui2 = *((const int *)bq8_1[0].qs + iqs + 4);
const int ui3 = *((const int *)bq8_1[1].qs + iqs);
const int ui4 = *((const int *)bq8_1[1].qs + iqs + 4);
const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2));
const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4);
const int * q4 = (const int *)bq4_K->qs + iqs;
const int * q4 = (const int *)bq4_K->qs + (iqs/2);
const int v1 = q4[0];
const int v2 = q4[4];
......@@ -2346,9 +2564,9 @@ static __device__ __forceinline__ void allocate_tiles_q4_K(int ** x_ql, half2 **
*x_sc = tile_x_sc;
}
static __device__ __forceinline__ void load_tiles_q4_K(
template <bool need_check> static __device__ __forceinline__ void load_tiles_q4_K(
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & i_offset, const int & k, const int & blocks_per_row) {
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
__builtin_assume(i_offset >= 0);
__builtin_assume(i_offset < 8);
......@@ -2362,7 +2580,11 @@ static __device__ __forceinline__ void load_tiles_q4_K(
#pragma unroll
for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8) {
const int i = i0 + i_offset;
int i = i0 + i_offset;
if (need_check) {
i = min(i, i_max);
}
const block_q4_K * bxi = bx0 + i*blocks_per_row + kbx;
......@@ -2374,7 +2596,11 @@ static __device__ __forceinline__ void load_tiles_q4_K(
#pragma unroll
for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * QI4_K) {
const int i = (i0 + i_offset * QI4_K + k / blocks_per_tile_x_row) % GGML_CUDA_MMQ_Y;
int i = (i0 + i_offset * QI4_K + k / blocks_per_tile_x_row) % GGML_CUDA_MMQ_Y;
if (need_check) {
i = min(i, i_max);
}
const block_q4_K * bxi = bx0 + i*blocks_per_row + kbxd;
......@@ -2383,7 +2609,11 @@ static __device__ __forceinline__ void load_tiles_q4_K(
#pragma unroll
for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * 8) {
const int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % GGML_CUDA_MMQ_Y;
int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % GGML_CUDA_MMQ_Y;
if (need_check) {
i = min(i, i_max);
}
const block_q4_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI4_K/8);
......@@ -2409,11 +2639,11 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1_mul_mat(
int u[2*QR4_K];
float d8[QR4_K];
// iqs is in 0...15. bq8_offset = 2 * (iqs/4) -> bq8_offset = 0, 2, 4, 6
const int bq8_offset = QR4_K * (kqsx / (QI8_1/2));
// kqsx is in 0,2...30. bq8_offset = 2 * (kqsx/4) -> bq8_offset = 0, 2, 4, 6
const int bq8_offset = QR4_K * ((kqsx/2) / (QI8_1/2));
v[0] = x_ql[i * (WARP_SIZE + 1) + 4 * bq8_offset + kqsx % 4 + 0];
v[1] = x_ql[i * (WARP_SIZE + 1) + 4 * bq8_offset + kqsx % 4 + 4];
v[0] = x_ql[i * (WARP_SIZE + 1) + 4 * bq8_offset + (kqsx/2) % 4 + 0];
v[1] = x_ql[i * (WARP_SIZE + 1) + 4 * bq8_offset + (kqsx/2) % 4 + 4];
const uint16_t * scales = (const uint16_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + kbx * 4];
uint16_t aux[2];
......@@ -2429,7 +2659,7 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1_mul_mat(
const uint8_t * m = sc + 2;
for (int l = 0; l < QR4_K; ++l) {
const int kqsy = j * (QR4_K*WARP_SIZE) + kbx * (QR4_K*QI4_K) + (bq8_offset + l) * QI8_1 + kqsx % (QI8_1/2);
const int kqsy = j * (QR4_K*WARP_SIZE) + kbx * (QR4_K*QI4_K) + (bq8_offset + l) * QI8_1 + (kqsx/2) % (QI8_1/2);
u[2*l+0] = y_qs[kqsy + 0*(QI8_1/2)];
u[2*l+1] = y_qs[kqsy + 1*(QI8_1/2)];
d8[l] = y_ds[kqsy / QI8_1].x;
......@@ -2484,9 +2714,9 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1(
int u[2*QR5_K];
float d8[QR5_K];
const int bq8_offset = QR5_K * (iqs / (QI8_1/2));
const int * ql = (const int *)(bq5_K->qs + 16 * bq8_offset + 4 * (iqs%4));
const int * qh = (const int *)(bq5_K->qh + 4 * (iqs%4));
const int bq8_offset = QR5_K * ((iqs/2) / (QI8_1/2));
const int * ql = (const int *)(bq5_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
const int * qh = (const int *)(bq5_K->qh + 4 * ((iqs/2)%4));
vl[0] = ql[0];
vl[1] = ql[4];
......@@ -2511,7 +2741,7 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1(
const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
d8[i] = bq8i->ds.x;
const int * q8 = (const int *)bq8i->qs + (iqs%4);
const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
u[2*i+0] = q8[0];
u[2*i+1] = q8[4];
}
......@@ -2530,17 +2760,17 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1(
const float d8_1 = bq8_1[0].ds.x;
const float d8_2 = bq8_1[1].ds.x;
const int ui1 = *((const int *)bq8_1[0].qs + iqs);
const int ui2 = *((const int *)bq8_1[0].qs + iqs + 4);
const int ui3 = *((const int *)bq8_1[1].qs + iqs);
const int ui4 = *((const int *)bq8_1[1].qs + iqs + 4);
const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2));
const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4);
const int * ql = (const int *)bq5_K->qs + iqs;
const int * ql = (const int *)bq5_K->qs + (iqs/2);
const int vl1 = ql[0];
const int vl2 = ql[4];
const int step = 4 * iqs; // 0, 4, 8, 12
const int im = step/8; // = 0 for iqs = 0, 1, = 1 for iqs = 2, 3
const int step = 4 * (iqs/2); // 0, 4, 8, 12
const int im = step/8; // = 0 for iqs = 0, 2, = 1 for iqs = 4, 6
const int in = step%8; // 0, 4, 0, 4
const int vh = (*((const int *)(bq5_K->qh + in))) >> im;
......@@ -2574,9 +2804,9 @@ static __device__ __forceinline__ void allocate_tiles_q5_K(int ** x_ql, half2 **
*x_sc = tile_x_sc;
}
static __device__ __forceinline__ void load_tiles_q5_K(
template <bool need_check> static __device__ __forceinline__ void load_tiles_q5_K(
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & i_offset, const int & k, const int & blocks_per_row) {
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
__builtin_assume(i_offset >= 0);
__builtin_assume(i_offset < 8);
......@@ -2590,7 +2820,11 @@ static __device__ __forceinline__ void load_tiles_q5_K(
#pragma unroll
for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8) {
const int i = i0 + i_offset;
int i = i0 + i_offset;
if (need_check) {
i = min(i, i_max);
}
const block_q5_K * bxi = bx0 + i*blocks_per_row + kbx;
......@@ -2602,7 +2836,11 @@ static __device__ __forceinline__ void load_tiles_q5_K(
#pragma unroll
for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * QI5_K) {
const int i = (i0 + i_offset * QI5_K + k / blocks_per_tile_x_row) % GGML_CUDA_MMQ_Y;
int i = (i0 + i_offset * QI5_K + k / blocks_per_tile_x_row) % GGML_CUDA_MMQ_Y;
if (need_check) {
i = min(i, i_max);
}
const block_q5_K * bxi = bx0 + i*blocks_per_row + kbxd;
......@@ -2611,7 +2849,11 @@ static __device__ __forceinline__ void load_tiles_q5_K(
#pragma unroll
for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * 4) {
const int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
if (need_check) {
i = min(i, i_max);
}
const block_q5_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI5_K/4);
......@@ -2620,7 +2862,11 @@ static __device__ __forceinline__ void load_tiles_q5_K(
#pragma unroll
for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * 8) {
const int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % GGML_CUDA_MMQ_Y;
int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % GGML_CUDA_MMQ_Y;
if (need_check) {
i = min(i, i_max);
}
const block_q5_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI5_K/8);
......@@ -2647,13 +2893,13 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1_mul_mat(
int u[2*QR4_K];
float d8[QR4_K];
const int bq8_offset = QR5_K * (kqsx / (QI8_1/2));
const int bq8_offset = QR5_K * ((kqsx/2) / (QI8_1/2));
vl[0] = x_ql[i * (WARP_SIZE + 1) + 4 * bq8_offset + kqsx % 4 + 0];
vl[1] = x_ql[i * (WARP_SIZE + 1) + 4 * bq8_offset + kqsx % 4 + 4];
vl[0] = x_ql[i * (WARP_SIZE + 1) + 4 * bq8_offset + (kqsx/2) % 4 + 0];
vl[1] = x_ql[i * (WARP_SIZE + 1) + 4 * bq8_offset + (kqsx/2) % 4 + 4];
vh[0] = x_qh[i * (WARP_SIZE/4) + i/4 + kqsx % 4 + 0] >> bq8_offset;
vh[1] = x_qh[i * (WARP_SIZE/4) + i/4 + kqsx % 4 + 4] >> bq8_offset;
vh[0] = x_qh[i * (WARP_SIZE/4) + i/4 + (kqsx/2) % 4 + 0] >> bq8_offset;
vh[1] = x_qh[i * (WARP_SIZE/4) + i/4 + (kqsx/2) % 4 + 4] >> bq8_offset;
const uint16_t * scales = (const uint16_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + kbx * 4];
uint16_t aux[2];
......@@ -2669,7 +2915,7 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1_mul_mat(
const uint8_t * m = sc + 2;
for (int l = 0; l < QR5_K; ++l) {
const int kqsy = j * (QR5_K*WARP_SIZE) + kbx * (QR5_K*QI5_K) + (bq8_offset + l) * QI8_1 + kqsx % (QI8_1/2);
const int kqsy = j * (QR5_K*WARP_SIZE) + kbx * (QR5_K*QI5_K) + (bq8_offset + l) * QI8_1 + (kqsx/2) % (QI8_1/2);
u[2*l+0] = y_qs[kqsy + 0*(QI8_1/2)];
u[2*l+1] = y_qs[kqsy + 1*(QI8_1/2)];
d8[l] = y_ds[kqsy / QI8_1].x;
......@@ -2743,9 +2989,9 @@ static __device__ __forceinline__ void allocate_tiles_q6_K(int ** x_ql, half2 **
*x_sc = tile_x_sc;
}
static __device__ __forceinline__ void load_tiles_q6_K(
template <bool need_check> static __device__ __forceinline__ void load_tiles_q6_K(
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & i_offset, const int & k, const int & blocks_per_row) {
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
__builtin_assume(i_offset >= 0);
__builtin_assume(i_offset < 8);
......@@ -2759,7 +3005,11 @@ static __device__ __forceinline__ void load_tiles_q6_K(
#pragma unroll
for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8) {
const int i = i0 + i_offset;
int i = i0 + i_offset;
if (need_check) {
i = min(i, i_max);
}
const block_q6_K * bxi = bx0 + i*blocks_per_row + kbx;
......@@ -2771,7 +3021,11 @@ static __device__ __forceinline__ void load_tiles_q6_K(
#pragma unroll
for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * QI6_K) {
const int i = (i0 + i_offset * QI6_K + k / blocks_per_tile_x_row) % GGML_CUDA_MMQ_Y;
int i = (i0 + i_offset * QI6_K + k / blocks_per_tile_x_row) % GGML_CUDA_MMQ_Y;
if (need_check) {
i = min(i, i_max);
}
const block_q6_K * bxi = bx0 + i*blocks_per_row + kbxd;
......@@ -2780,7 +3034,11 @@ static __device__ __forceinline__ void load_tiles_q6_K(
#pragma unroll
for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * 2) {
const int i = i0 + i_offset * 2 + k / (WARP_SIZE/2);
int i = i0 + i_offset * 2 + k / (WARP_SIZE/2);
if (need_check) {
i = min(i, i_max);
}
const block_q6_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/2)) / (QI6_K/2);
......@@ -2789,7 +3047,11 @@ static __device__ __forceinline__ void load_tiles_q6_K(
#pragma unroll
for (int i0 = 0; i0 < GGML_CUDA_MMQ_Y; i0 += 8 * 8) {
const int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % GGML_CUDA_MMQ_Y;
int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % GGML_CUDA_MMQ_Y;
if (need_check) {
i = min(i, i_max);
}
const block_q6_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / 4;
......@@ -2875,7 +3137,7 @@ static __global__ void mul_mat_q(
for (int ib0 = 0; ib0 < blocks_per_row_x; ib0 += blocks_per_warp) {
load_tiles(x + row_x_0*blocks_per_row_x + ib0, tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc,
tid_y, tid_x, blocks_per_row_x);
tid_y, nrows_x-row_x_0-1, tid_x, blocks_per_row_x);
for (int ir = 0; ir < qr; ++ir) {
const int kqs = ir*WARP_SIZE + tid_x;
......@@ -2899,10 +3161,10 @@ static __global__ void mul_mat_q(
__syncthreads();
#if __CUDA_ARCH__ >= 700 // TODO: actually test this with compute capability 7.X cards
#if __CUDA_ARCH__ >= 700 // Unrolling the loop is slower on Pascal
#pragma unroll
#endif // __CUDA_ARCH__ >= 700
for (int k = 0; k < WARP_SIZE/vdr; ++k) {
for (int k = 0; k < WARP_SIZE; k += vdr) {
#pragma unroll
for (int j = 0; j < WARP_SIZE; j += 8) {
#pragma unroll
......@@ -2954,9 +3216,9 @@ static __global__ void mul_mat_vec_q(const void * __restrict__ vx, const void *
for (int i = 0; i < blocks_per_row; i += blocks_per_warp) {
const int ibx = row*blocks_per_row + i + threadIdx.x / (qi/vdr); // x block index
const int iby = (i + threadIdx.x / (qi/vdr)) * qk/QK8_1; // y block index that aligns with ibx
const int iby = (i + threadIdx.x / (qi/vdr)) * (qk/QK8_1); // y block index that aligns with ibx
const int iqs = threadIdx.x % (qi/vdr); // x block quant index when casting the quants to int
const int iqs = vdr * (threadIdx.x % (qi/vdr)); // x block quant index when casting the quants to int
tmp += vec_dot_q_cuda(&x[ibx], &y[iby], iqs);
}
......@@ -3499,7 +3761,7 @@ static void mul_mat_vec_q4_0_q8_1_cuda(const void * vx, const void * vy, float *
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(1, block_num_y, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK4_0, QI4_0, block_q4_0, VDR_q4_0_q8_1, vec_dot_q4_0_q8_1>
mul_mat_vec_q<QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
......@@ -3508,7 +3770,7 @@ static void mul_mat_vec_q4_1_q8_1_cuda(const void * vx, const void * vy, float *
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(1, block_num_y, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK4_0, QI4_1, block_q4_1, VDR_q4_1_q8_1, vec_dot_q4_1_q8_1>
mul_mat_vec_q<QK4_0, QI4_1, block_q4_1, VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
......@@ -3517,7 +3779,7 @@ static void mul_mat_vec_q5_0_q8_1_cuda(const void * vx, const void * vy, float *
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(1, block_num_y, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK5_0, QI5_0, block_q5_0, VDR_q5_0_q8_1, vec_dot_q5_0_q8_1>
mul_mat_vec_q<QK5_0, QI5_0, block_q5_0, VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
......@@ -3526,7 +3788,7 @@ static void mul_mat_vec_q5_1_q8_1_cuda(const void * vx, const void * vy, float *
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(1, block_num_y, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK5_1, QI5_1, block_q5_1, VDR_q5_1_q8_1, vec_dot_q5_1_q8_1>
mul_mat_vec_q<QK5_1, QI5_1, block_q5_1, VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
......@@ -3535,7 +3797,7 @@ static void mul_mat_vec_q8_0_q8_1_cuda(const void * vx, const void * vy, float *
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(1, block_num_y, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK8_0, QI8_0, block_q8_0, VDR_q8_0_q8_1, vec_dot_q8_0_q8_1>
mul_mat_vec_q<QK8_0, QI8_0, block_q8_0, VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
......@@ -3635,8 +3897,14 @@ static void ggml_mul_mat_q4_0_q8_1_cuda(
const int block_num_y = (ncols_y + WARP_SIZE - 1) / WARP_SIZE;
const dim3 block_nums(block_num_x, block_num_y, 1);
const dim3 block_dims(WARP_SIZE, WARP_SIZE/4, 1);
mul_mat_q<QK4_0, QR4_0, QI4_0, block_q4_0, allocate_tiles_q4_0, load_tiles_q4_0, VDR_q4_0_q8_1, vec_dot_q4_0_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
if (nrows_x % GGML_CUDA_MMQ_Y == 0) {
mul_mat_q<QK4_0, QR4_0, QI4_0, block_q4_0, allocate_tiles_q4_0, load_tiles_q4_0<false>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
} else {
mul_mat_q<QK4_0, QR4_0, QI4_0, block_q4_0, allocate_tiles_q4_0, load_tiles_q4_0<true>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
}
}
static void ggml_mul_mat_q4_1_q8_1_cuda(
......@@ -3647,8 +3915,14 @@ static void ggml_mul_mat_q4_1_q8_1_cuda(
const int block_num_y = (ncols_y + WARP_SIZE - 1) / WARP_SIZE;
const dim3 block_nums(block_num_x, block_num_y, 1);
const dim3 block_dims(WARP_SIZE, WARP_SIZE/4, 1);
mul_mat_q<QK4_1, QR4_1, QI4_1, block_q4_1, allocate_tiles_q4_1, load_tiles_q4_1, VDR_q4_1_q8_1, vec_dot_q4_1_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
if (nrows_x % GGML_CUDA_MMQ_Y == 0) {
mul_mat_q<QK4_1, QR4_1, QI4_1, block_q4_1, allocate_tiles_q4_1, load_tiles_q4_1<false>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
} else {
mul_mat_q<QK4_1, QR4_1, QI4_1, block_q4_1, allocate_tiles_q4_1, load_tiles_q4_1<true>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
}
}
static void ggml_mul_mat_q5_0_q8_1_cuda(
......@@ -3659,8 +3933,14 @@ static void ggml_mul_mat_q5_0_q8_1_cuda(
const int block_num_y = (ncols_y + WARP_SIZE - 1) / WARP_SIZE;
const dim3 block_nums(block_num_x, block_num_y, 1);
const dim3 block_dims(WARP_SIZE, WARP_SIZE/4, 1);
mul_mat_q<QK5_0, QR5_0, QI5_0, block_q5_0, allocate_tiles_q5_0, load_tiles_q5_0, VDR_q5_0_q8_1, vec_dot_q5_0_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
if (nrows_x % GGML_CUDA_MMQ_Y == 0) {
mul_mat_q<QK5_0, QR5_0, QI5_0, block_q5_0, allocate_tiles_q5_0, load_tiles_q5_0<false>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
} else {
mul_mat_q<QK5_0, QR5_0, QI5_0, block_q5_0, allocate_tiles_q5_0, load_tiles_q5_0<true>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
}
}
static void ggml_mul_mat_q5_1_q8_1_cuda(
......@@ -3671,8 +3951,14 @@ static void ggml_mul_mat_q5_1_q8_1_cuda(
const int block_num_y = (ncols_y + WARP_SIZE - 1) / WARP_SIZE;
const dim3 block_nums(block_num_x, block_num_y, 1);
const dim3 block_dims(WARP_SIZE, WARP_SIZE/4, 1);
mul_mat_q<QK5_1, QR5_1, QI5_1, block_q5_1, allocate_tiles_q5_1, load_tiles_q5_1, VDR_q5_1_q8_1, vec_dot_q5_1_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
if (nrows_x % GGML_CUDA_MMQ_Y == 0) {
mul_mat_q<QK5_1, QR5_1, QI5_1, block_q5_1, allocate_tiles_q5_1, load_tiles_q5_1<false>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
} else {
mul_mat_q<QK5_1, QR5_1, QI5_1, block_q5_1, allocate_tiles_q5_1, load_tiles_q5_1<true>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
}
}
static void ggml_mul_mat_q8_0_q8_1_cuda(
......@@ -3683,8 +3969,14 @@ static void ggml_mul_mat_q8_0_q8_1_cuda(
const int block_num_y = (ncols_y + WARP_SIZE - 1) / WARP_SIZE;
const dim3 block_nums(block_num_x, block_num_y, 1);
const dim3 block_dims(WARP_SIZE, WARP_SIZE/4, 1);
mul_mat_q<QK8_0, QR8_0, QI8_0, block_q8_0, allocate_tiles_q8_0, load_tiles_q8_0, VDR_q8_0_q8_1, vec_dot_q8_0_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
if (nrows_x % GGML_CUDA_MMQ_Y == 0) {
mul_mat_q<QK8_0, QR8_0, QI8_0, block_q8_0, allocate_tiles_q8_0, load_tiles_q8_0<false>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
} else {
mul_mat_q<QK8_0, QR8_0, QI8_0, block_q8_0, allocate_tiles_q8_0, load_tiles_q8_0<true>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
}
}
static void ggml_mul_mat_q2_K_q8_1_cuda(
......@@ -3695,8 +3987,14 @@ static void ggml_mul_mat_q2_K_q8_1_cuda(
const int block_num_y = (ncols_y + WARP_SIZE - 1) / WARP_SIZE;
const dim3 block_nums(block_num_x, block_num_y, 1);
const dim3 block_dims(WARP_SIZE, WARP_SIZE/4, 1);
mul_mat_q<QK_K, QR2_K, QI2_K, block_q2_K, allocate_tiles_q2_K, load_tiles_q2_K, VDR_q2_K_q8_1, vec_dot_q2_K_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
if (nrows_x % GGML_CUDA_MMQ_Y == 0) {
mul_mat_q<QK_K, QR2_K, QI2_K, block_q2_K, allocate_tiles_q2_K, load_tiles_q2_K<false>, VDR_q2_K_q8_1, vec_dot_q2_K_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
} else {
mul_mat_q<QK_K, QR2_K, QI2_K, block_q2_K, allocate_tiles_q2_K, load_tiles_q2_K<true>, VDR_q2_K_q8_1, vec_dot_q2_K_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
}
}
static void ggml_mul_mat_q3_K_q8_1_cuda(
......@@ -3707,8 +4005,14 @@ static void ggml_mul_mat_q3_K_q8_1_cuda(
const int block_num_y = (ncols_y + WARP_SIZE - 1) / WARP_SIZE;
const dim3 block_nums(block_num_x, block_num_y, 1);
const dim3 block_dims(WARP_SIZE, WARP_SIZE/4, 1);
mul_mat_q<QK_K, QR3_K, QI3_K, block_q3_K, allocate_tiles_q3_K, load_tiles_q3_K, VDR_q3_K_q8_1, vec_dot_q3_K_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
if (nrows_x % GGML_CUDA_MMQ_Y == 0) {
mul_mat_q<QK_K, QR3_K, QI3_K, block_q3_K, allocate_tiles_q3_K, load_tiles_q3_K<false>, VDR_q3_K_q8_1, vec_dot_q3_K_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
} else {
mul_mat_q<QK_K, QR3_K, QI3_K, block_q3_K, allocate_tiles_q3_K, load_tiles_q3_K<true>, VDR_q3_K_q8_1, vec_dot_q3_K_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
}
}
static void ggml_mul_mat_q4_K_q8_1_cuda(
......@@ -3719,8 +4023,14 @@ static void ggml_mul_mat_q4_K_q8_1_cuda(
const int block_num_y = (ncols_y + WARP_SIZE - 1) / WARP_SIZE;
const dim3 block_nums(block_num_x, block_num_y, 1);
const dim3 block_dims(WARP_SIZE, WARP_SIZE/4, 1);
mul_mat_q<QK_K, QR4_K, QI4_K, block_q4_K, allocate_tiles_q4_K, load_tiles_q4_K, VDR_q4_K_q8_1, vec_dot_q4_K_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
if (nrows_x % GGML_CUDA_MMQ_Y == 0) {
mul_mat_q<QK_K, QR4_K, QI4_K, block_q4_K, allocate_tiles_q4_K, load_tiles_q4_K<false>, VDR_q4_K_q8_1, vec_dot_q4_K_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
} else {
mul_mat_q<QK_K, QR4_K, QI4_K, block_q4_K, allocate_tiles_q4_K, load_tiles_q4_K<true>, VDR_q4_K_q8_1, vec_dot_q4_K_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
}
}
static void ggml_mul_mat_q5_K_q8_1_cuda(
......@@ -3731,8 +4041,14 @@ static void ggml_mul_mat_q5_K_q8_1_cuda(
const int block_num_y = (ncols_y + WARP_SIZE - 1) / WARP_SIZE;
const dim3 block_nums(block_num_x, block_num_y, 1);
const dim3 block_dims(WARP_SIZE, WARP_SIZE/4, 1);
mul_mat_q<QK_K, QR5_K, QI5_K, block_q5_K, allocate_tiles_q5_K, load_tiles_q5_K, VDR_q5_K_q8_1, vec_dot_q5_K_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
if (nrows_x % GGML_CUDA_MMQ_Y == 0) {
mul_mat_q<QK_K, QR5_K, QI5_K, block_q5_K, allocate_tiles_q5_K, load_tiles_q5_K<false>, VDR_q5_K_q8_1, vec_dot_q5_K_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
} else {
mul_mat_q<QK_K, QR5_K, QI5_K, block_q5_K, allocate_tiles_q5_K, load_tiles_q5_K<true>, VDR_q5_K_q8_1, vec_dot_q5_K_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
}
}
static void ggml_mul_mat_q6_K_q8_1_cuda(
......@@ -3743,8 +4059,14 @@ static void ggml_mul_mat_q6_K_q8_1_cuda(
const int block_num_y = (ncols_y + WARP_SIZE - 1) / WARP_SIZE;
const dim3 block_nums(block_num_x, block_num_y, 1);
const dim3 block_dims(WARP_SIZE, WARP_SIZE/4, 1);
mul_mat_q<QK_K, QR6_K, QI6_K, block_q6_K, allocate_tiles_q6_K, load_tiles_q6_K, VDR_q6_K_q8_1, vec_dot_q6_K_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
if (nrows_x % GGML_CUDA_MMQ_Y == 0) {
mul_mat_q<QK_K, QR6_K, QI6_K, block_q6_K, allocate_tiles_q6_K, load_tiles_q6_K<false>, VDR_q6_K_q8_1, vec_dot_q6_K_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
} else {
mul_mat_q<QK_K, QR6_K, QI6_K, block_q6_K, allocate_tiles_q6_K, load_tiles_q6_K<true>, VDR_q6_K_q8_1, vec_dot_q6_K_q8_1_mul_mat>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
}
}
static void ggml_mul_mat_p021_f16_f32_cuda(
......@@ -4690,8 +5012,12 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm
row_low = id == 0 ? 0 : nrows0*g_tensor_split[id];
row_low -= row_low % GGML_CUDA_MMQ_Y;
row_high = id == g_device_count - 1 ? nrows0 : nrows0*g_tensor_split[id + 1];
row_high -= row_high % GGML_CUDA_MMQ_Y;
if (id == g_device_count - 1) {
row_high = nrows0;
} else {
row_high = nrows0*g_tensor_split[id + 1];
row_high -= row_high % GGML_CUDA_MMQ_Y;
}
} else {
row_low = 0;
row_high = nrows0*i02_divisor;
......@@ -5171,8 +5497,12 @@ void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor) {
row_low = id == 0 ? 0 : nrows*g_tensor_split[id];
row_low -= row_low % GGML_CUDA_MMQ_Y;
row_high = id == g_device_count - 1 ? nrows : nrows*g_tensor_split[id + 1];
row_high -= row_high % GGML_CUDA_MMQ_Y;
if (id == g_device_count - 1) {
row_high = nrows;
} else {
row_high = nrows*g_tensor_split[id + 1];
row_high -= row_high % GGML_CUDA_MMQ_Y;
}
} else {
GGML_ASSERT(false);
}
......
llama/ggml-cuda.h
View file @ 85aeb428
/**
* llama.cpp - git c574bddb368424b5996cbee2ec45ec050967d404
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
*
* MIT License
*
......
llama/ggml-metal.h
View file @ 85aeb428
//go:build darwin
/**
* llama.cpp - git c574bddb368424b5996cbee2ec45ec050967d404
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
*
* MIT License
*
......
llama/ggml-metal.m
View file @ 85aeb428
//go:build darwin
/**
* llama.cpp - git c574bddb368424b5996cbee2ec45ec050967d404
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
*
* MIT License
*
......
llama/ggml-metal.metal
View file @ 85aeb428
//go:build darwin
/**
* llama.cpp - git c574bddb368424b5996cbee2ec45ec050967d404
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
*
* MIT License
*
......
llama/ggml-mpi.c
View file @ 85aeb428
//go:build mpi
/**
* llama.cpp - git c574bddb368424b5996cbee2ec45ec050967d404
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
*
* MIT License
*
......
llama/ggml-mpi.h
View file @ 85aeb428
//go:build mpi
/**
* llama.cpp - git c574bddb368424b5996cbee2ec45ec050967d404
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
*
* MIT License
*
......
llama/ggml-opencl.cpp
View file @ 85aeb428
//go:build opencl
/**
* llama.cpp - git c574bddb368424b5996cbee2ec45ec050967d404
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
*
* MIT License
*
......
llama/ggml-opencl.h
View file @ 85aeb428
//go:build opencl
/**
* llama.cpp - git c574bddb368424b5996cbee2ec45ec050967d404
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
*
* MIT License
*
......
llama/ggml.c
View file @ 85aeb428
/**
* llama.cpp - git c574bddb368424b5996cbee2ec45ec050967d404
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
*
* MIT License
*
......
llama/ggml.h
View file @ 85aeb428
/**
* llama.cpp - git c574bddb368424b5996cbee2ec45ec050967d404
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
*
* MIT License
*
......
llama/k_quants.c
View file @ 85aeb428
/**
* llama.cpp - git c574bddb368424b5996cbee2ec45ec050967d404
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
*
* MIT License
*
......
llama/k_quants.h
View file @ 85aeb428
/**
* llama.cpp - git c574bddb368424b5996cbee2ec45ec050967d404
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
*
* MIT License
*
......
llama/llama-util.h
View file @ 85aeb428
/**
* llama.cpp - git c574bddb368424b5996cbee2ec45ec050967d404
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
*
* MIT License
*
......
llama/llama.cpp
View file @ 85aeb428
/**
* llama.cpp - git c574bddb368424b5996cbee2ec45ec050967d404
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
*
* MIT License
*
......
llama/llama.go
View file @ 85aeb428
......@@ -128,11 +128,6 @@ func New(model string, opts api.Options) (*LLM, error) {
C.llama_backend_init(C.bool(llm.UseNUMA))
// TODO: GQA == 8 suggests 70B model which doesn't support metal
if llm.NumGQA == 8 {
llm.NumGPU = 0
}
params := C.llama_context_default_params()
params.seed = C.uint(llm.Seed)
params.n_ctx = C.int(llm.NumCtx)
......
llama/llama.h
View file @ 85aeb428
/**
* llama.cpp - git c574bddb368424b5996cbee2ec45ec050967d404
* llama.cpp - git 8183159cf3def112f6d1fe94815fce70e1bffa12
*
* MIT License
*
......
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