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ggml : sync latest repo (mostly refactoring changes)

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Georgi Gerganov
2023-07-02 21:45:27 +03:00
parent 85ed71aaec
commit d6509bf78d
11 changed files with 2154 additions and 1677 deletions
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501
ggml-cuda.cu
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@@ -117,7 +117,13 @@ static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 blo
//================================= k-quants
#ifdef GGML_QKK_64
#define QK_K 64
#define K_SCALE_SIZE 4
#else
#define QK_K 256
#define K_SCALE_SIZE 12
#endif
typedef struct {
uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
@@ -128,13 +134,25 @@ typedef struct {
static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding");
typedef struct {
uint8_t hmask[QK_K/8];
uint8_t qs[QK_K/4]; // nibbles / quants
uint8_t scales[3*QK_K/64];
half d;
uint8_t hmask[QK_K/8]; // quants - high bit
uint8_t qs[QK_K/4]; // quants - low 2 bits
#ifdef GGML_QKK_64
uint8_t scales[2]; // scales, quantized with 8 bits
#else
uint8_t scales[K_SCALE_SIZE]; // scales, quantized with 6 bits
#endif
half d; // super-block scale
} block_q3_K;
static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + 11 * QK_K / 64, "wrong q3_K block size/padding");
//static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + K_SCALE_SIZE, "wrong q3_K block size/padding");
#ifdef GGML_QKK_64
typedef struct {
half d[2]; // super-block scales/mins
uint8_t scales[2]; // 4-bit block scales/mins
uint8_t qs[QK_K/2]; // 4--bit quants
} block_q4_K;
static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + QK_K/2 + 2, "wrong q4_K block size/padding");
#else
typedef struct {
half d; // super-block scale for quantized scales
half dmin; // super-block scale for quantized mins
@@ -142,15 +160,26 @@ typedef struct {
uint8_t qs[QK_K/2]; // 4--bit quants
} block_q4_K;
static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + 3*QK_K/64 + QK_K/2, "wrong q4_K block size/padding");
#endif
#ifdef GGML_QKK_64
typedef struct {
half d; // super-block scale for quantized scales
half dmin; // super-block scale for quantized mins
uint8_t scales[3*QK_K/64]; // scales, quantized with 6 bits
half d; // super-block scale
int8_t scales[QK_K/16]; // block scales
uint8_t qh[QK_K/8]; // quants, high bit
uint8_t qs[QK_K/2]; // quants, low 4 bits
} block_q5_K;
static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding");
#else
typedef struct {
half d; // super-block scale for quantized scales
half dmin; // super-block scale for quantized mins
uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
uint8_t qh[QK_K/8]; // quants, high bit
uint8_t qs[QK_K/2]; // quants, low 4 bits
} block_q5_K;
static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + 3*QK_K/64 + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
#endif
typedef struct {
uint8_t ql[QK_K/2]; // quants, lower 4 bits
@@ -185,6 +214,11 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_
static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2");
#endif
struct ggml_tensor_extra_gpu {
void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors
cudaEvent_t events[GGML_CUDA_MAX_DEVICES]; // events for synchronizing multiple GPUs
};
static __global__ void add_f32(const float * x, const float * y, float * dst, const int k) {
const int i = blockDim.x*blockIdx.x + threadIdx.x;
@@ -194,6 +228,15 @@ static __global__ void add_f32(const float * x, const float * y, float * dst, co
dst[i] = x[i] + y[i];
}
static __global__ void add_f16_f32_f16(const half * x, const float * y, half * dst, const int k) {
const int i = blockDim.x*blockIdx.x + threadIdx.x;
if (i >= k) {
return;
}
dst[i] = __hadd(x[i], __float2half(y[i]));
}
static __global__ void mul_f32(const float * x, const float * y, float * dst, const int kx, const int ky) {
const int i = blockDim.x*blockIdx.x + threadIdx.x;
@@ -349,13 +392,14 @@ static __device__ __forceinline__ void dequantize_q8_0(const void * vx, const in
static __global__ void dequantize_block_q2_K(const void * vx, float * yy) {
const int i = blockIdx.x;
const block_q2_K * x = (const block_q2_K *) vx;
const int tid = threadIdx.x;
#if QK_K == 256
const int n = tid/32;
const int l = tid - 32*n;
const int is = 8*n + l/16;
const block_q2_K * x = (const block_q2_K *) vx;
const uint8_t q = x[i].qs[32*n + l];
float * y = yy + i*QK_K + 128*n;
@@ -365,21 +409,32 @@ static __global__ void dequantize_block_q2_K(const void * vx, float * yy) {
y[l+32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is+2] >> 4);
y[l+64] = dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4);
y[l+96] = dall * (x[i].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is+6] >> 4);
#else
const int is = tid/16; // 0 or 1
const int il = tid%16; // 0...15
const uint8_t q = x[i].qs[il] >> (2*is);
float * y = yy + i*QK_K + 16*is + il;
float dall = x[i].d;
float dmin = x[i].dmin;
y[ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
y[32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+2] >> 4);
#endif
}
static __global__ void dequantize_block_q3_K(const void * vx, float * yy) {
int r = threadIdx.x/4;
int i = blockIdx.x;
int tid = r/2;
int is0 = r%2;
int l0 = 16*is0 + 4*(threadIdx.x%4);
int n = tid / 4;
int j = tid - 4*n;
const int i = blockIdx.x;
const block_q3_K * x = (const block_q3_K *) vx;
#if QK_K == 256
const int r = threadIdx.x/4;
const int tid = r/2;
const int is0 = r%2;
const int l0 = 16*is0 + 4*(threadIdx.x%4);
const int n = tid / 4;
const int j = tid - 4*n;
uint8_t m = 1 << (4*n + j);
int is = 8*n + 2*j + is0;
int shift = 2*j;
@@ -396,9 +451,31 @@ static __global__ void dequantize_block_q3_K(const void * vx, float * yy) {
const uint8_t * hm = x[i].hmask;
for (int l = l0; l < l0+4; ++l) y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4));
#else
const int tid = threadIdx.x;
const int is = tid/16; // 0 or 1
const int il = tid%16; // 0...15
const int im = il/8; // 0...1
const int in = il%8; // 0...7
float * y = yy + i*QK_K + 16*is + il;
const uint8_t q = x[i].qs[il] >> (2*is);
const uint8_t h = x[i].hmask[in] >> (2*is + im);
const float d = (float)x[i].d;
if (is == 0) {
y[ 0] = d * ((x[i].scales[0] & 0xF) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4));
y[32] = d * ((x[i].scales[1] & 0xF) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4));
} else {
y[ 0] = d * ((x[i].scales[0] >> 4) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4));
y[32] = d * ((x[i].scales[1] >> 4) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4));
}
#endif
}
#if QK_K == 256
static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) {
if (j < 4) {
d = q[j] & 63; m = q[j + 4] & 63;
@@ -407,19 +484,14 @@ static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t
m = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4);
}
}
#endif
static __global__ void dequantize_block_q4_K(const void * vx, float * yy) {
const block_q4_K * x = (const block_q4_K *) vx;
const int i = blockIdx.x;
//// assume 64 threads - this is very slightly better than the one below
//const int tid = threadIdx.x;
//const int il = tid/16;
//const int ir = tid%16;
//const int is = 2*il;
//const int n = 2;
#if QK_K == 256
// assume 32 threads
const int tid = threadIdx.x;
const int il = tid/8;
@@ -443,6 +515,15 @@ static __global__ void dequantize_block_q4_K(const void * vx, float * yy) {
y[l + 0] = d1 * (q[l] & 0xF) - m1;
y[l +32] = d2 * (q[l] >> 4) - m2;
}
#else
const int tid = threadIdx.x;
const uint8_t * q = x[i].qs;
float * y = yy + i*QK_K;
const float d = (float)x[i].d[0];
const float m = (float)x[i].d[1];
y[tid+ 0] = d * (x[i].scales[0] & 0xF) * (q[tid] & 0xF) - m * (x[i].scales[0] >> 4);
y[tid+32] = d * (x[i].scales[1] & 0xF) * (q[tid] >> 4) - m * (x[i].scales[1] >> 4);
#endif
}
static __global__ void dequantize_block_q5_K(const void * vx, float * yy) {
@@ -450,6 +531,7 @@ static __global__ void dequantize_block_q5_K(const void * vx, float * yy) {
const int i = blockIdx.x;
#if QK_K == 256
// assume 64 threads - this is very slightly better than the one below
const int tid = threadIdx.x;
const int il = tid/16; // il is in 0...3
@@ -476,12 +558,25 @@ static __global__ void dequantize_block_q5_K(const void * vx, float * yy) {
hm <<= 1;
y[32] = d2 * ((ql[ 0] >> 4) + (qh[ 0] & hm ? 16 : 0)) - m2;
y[33] = d2 * ((ql[ 1] >> 4) + (qh[ 1] & hm ? 16 : 0)) - m2;
#else
const int tid = threadIdx.x;
const uint8_t q = x[i].qs[tid];
const int im = tid/8; // 0...3
const int in = tid%8; // 0...7
const int is = tid/16; // 0 or 1
const uint8_t h = x[i].qh[in] >> im;
const float d = x[i].d;
float * y = yy + i*QK_K + tid;
y[ 0] = d * x[i].scales[is+0] * ((q & 0xF) - ((h >> 0) & 1 ? 0 : 16));
y[32] = d * x[i].scales[is+2] * ((q >> 4) - ((h >> 4) & 1 ? 0 : 16));
#endif
}
static __global__ void dequantize_block_q6_K(const void * vx, float * yy) {
const block_q6_K * x = (const block_q6_K *) vx;
const int i = blockIdx.x;
#if QK_K == 256
// assume 64 threads - this is very slightly better than the one below
const int tid = threadIdx.x;
@@ -501,6 +596,24 @@ static __global__ void dequantize_block_q6_K(const void * vx, float * yy) {
y[32] = d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32);
y[64] = d * sc[4] * ((int8_t)((ql[ 0] >> 4) | (((qh >> 4) & 3) << 4)) - 32);
y[96] = d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh >> 6) & 3) << 4)) - 32);
#else
// assume 32 threads
const int tid = threadIdx.x;
const int ip = tid/16; // 0 or 1
const int il = tid - 16*ip; // 0...15
float * y = yy + i*QK_K + 16*ip + il;
const float d = x[i].d;
const uint8_t ql = x[i].ql[16*ip + il];
const uint8_t qh = x[i].qh[il] >> (2*ip);
const int8_t * sc = x[i].scales;
y[ 0] = d * sc[ip+0] * ((int8_t)((ql & 0xF) | (((qh >> 0) & 3) << 4)) - 32);
y[32] = d * sc[ip+2] * ((int8_t)((ql >> 4) | (((qh >> 4) & 3) << 4)) - 32);
#endif
}
static __global__ void dequantize_mul_mat_vec_q2_k(const void * vx, const float * yy, float * dst, const int ncols, int nrows) {
@@ -515,6 +628,9 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * vx, const float
const block_q2_K * x = (const block_q2_K *)vx + ib0;
float tmp = 0; // partial sum for thread in warp
#if QK_K == 256
const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...15
const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1
@@ -528,8 +644,6 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * vx, const float
const int s_offset = 8*im;
const int y_offset = 128*im + l0;
float tmp = 0; // partial sum for thread in warp
uint32_t aux[4];
const uint8_t * d = (const uint8_t *)aux;
const uint8_t * m = (const uint8_t *)(aux + 2);
@@ -565,6 +679,39 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * vx, const float
tmp += dall * sum1 - dmin * sum2;
}
#else
const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 or 0...7
const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0....1 or 0...3
const int offset = tid * K_QUANTS_PER_ITERATION;
uint32_t uaux[2];
const uint8_t * d = (const uint8_t *)uaux;
for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
const float * y = yy + i * QK_K + offset;
const uint8_t * q = x[i].qs + offset;
const uint32_t * s = (const uint32_t *)x[i].scales;
uaux[0] = s[0] & 0x0f0f0f0f;
uaux[1] = (s[0] >> 4) & 0x0f0f0f0f;
const half2 * dh = (const half2 *)&x[i].d;
const float2 dall = __half22float2(dh[0]);
float sum1 = 0, sum2 = 0;
for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
const uint8_t ql = q[l];
sum1 += y[l+ 0] * d[0] * ((ql >> 0) & 3)
+ y[l+16] * d[1] * ((ql >> 2) & 3)
+ y[l+32] * d[2] * ((ql >> 4) & 3)
+ y[l+48] * d[3] * ((ql >> 6) & 3);
sum2 += y[l+0] * d[4] + y[l+16] * d[5] + y[l+32] * d[6] + y[l+48] * d[7];
}
tmp += dall.x * sum1 - dall.y * sum2;
}
#endif
// sum up partial sums and write back result
__syncthreads();
@@ -573,16 +720,13 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * vx, const float
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
}
if (tid == 0) {
if (threadIdx.x == 0) {
dst[row] = tmp;
}
}
static __global__ void dequantize_mul_mat_vec_q3_k(const void * vx, const float * yy, float * dst, const int ncols, int nrows) {
const uint16_t kmask1 = 0x0303;
const uint16_t kmask2 = 0x0f0f;
const int row = blockIdx.y*blockDim.y + threadIdx.y;
if (row > nrows) return;
@@ -591,6 +735,13 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * vx, const float
const block_q3_K * x = (const block_q3_K *)vx + ib0;
float tmp = 0; // partial sum for thread in warp
#if QK_K == 256
const uint16_t kmask1 = 0x0303;
const uint16_t kmask2 = 0x0f0f;
const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16
const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1
@@ -610,8 +761,6 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * vx, const float
const uint16_t s_shift = 4*im;
float tmp = 0; // partial sum for thread in warp
for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
const float * y = yy + i * QK_K + y_offset;
@@ -640,6 +789,34 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * vx, const float
tmp += d * sum;
}
#else
const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 or 0...7
const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0....1 or 0...3
const int offset = tid * K_QUANTS_PER_ITERATION; // 0...15 or 0...14
const int in = offset/8; // 0 or 1
const int im = offset%8; // 0...7
for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
const float * y = yy + i * QK_K + offset;
const uint8_t * q = x[i].qs + offset;
const uint8_t * s = x[i].scales;
const float dall = (float)x[i].d;
float sum = 0;
for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
const uint8_t hl = x[i].hmask[im+l] >> in;
const uint8_t ql = q[l];
sum += y[l+ 0] * dall * ((s[0] & 0xF) - 8) * ((int8_t)((ql >> 0) & 3) - ((hl >> 0) & 1 ? 0 : 4))
+ y[l+16] * dall * ((s[0] >> 4) - 8) * ((int8_t)((ql >> 2) & 3) - ((hl >> 2) & 1 ? 0 : 4))
+ y[l+32] * dall * ((s[1] & 0xF) - 8) * ((int8_t)((ql >> 4) & 3) - ((hl >> 4) & 1 ? 0 : 4))
+ y[l+48] * dall * ((s[1] >> 4) - 8) * ((int8_t)((ql >> 6) & 3) - ((hl >> 6) & 1 ? 0 : 4));
}
tmp += sum;
}
#endif
// sum up partial sums and write back result
__syncthreads();
@@ -648,22 +825,25 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * vx, const float
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
}
if (tid == 0) {
if (threadIdx.x == 0) {
dst[row] = tmp;
}
}
static __global__ void dequantize_mul_mat_vec_q4_k(const void * vx, const float * yy, float * dst, const int ncols, int nrows) {
const uint16_t kmask1 = 0x3f3f;
const uint16_t kmask2 = 0x0f0f;
const uint16_t kmask3 = 0xc0c0;
const int row = blockIdx.y*blockDim.y + threadIdx.y;
if (row > nrows) return;
const int num_blocks_per_row = ncols / QK_K;
const int ib0 = row*num_blocks_per_row;
const block_q4_K * x = (const block_q4_K *)vx + ib0;
#if QK_K == 256
const uint16_t kmask1 = 0x3f3f;
const uint16_t kmask2 = 0x0f0f;
const uint16_t kmask3 = 0xc0c0;
const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16
const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1
@@ -683,8 +863,6 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * vx, const float
uint16_t aux[4];
const uint8_t * sc = (const uint8_t *)aux;
const block_q4_K * x = (const block_q4_K *)vx + ib0;
float tmp = 0; // partial sum for thread in warp
for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
@@ -713,6 +891,36 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * vx, const float
tmp += dall * (s.x * sc[0] + s.y * sc[1] + s.z * sc[4] + s.w * sc[5]) - dmin * smin;
}
#else
const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15
const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION);
const int step = tid * K_QUANTS_PER_ITERATION;
uint16_t aux16[2];
const uint8_t * s = (const uint8_t *)aux16;
float tmp = 0;
for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
const uint8_t * q = x[i].qs + step;
const float * y = yy + i*QK_K + step;
const uint16_t * a = (const uint16_t *)x[i].scales;
aux16[0] = a[0] & 0x0f0f;
aux16[1] = (a[0] >> 4) & 0x0f0f;
const float d = (float)x[i].d[0];
const float m = (float)x[i].d[1];
float sum = 0.f;
for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
sum += y[j+ 0] * (d * s[0] * (q[j+ 0] & 0xF) - m * s[2])
+ y[j+16] * (d * s[0] * (q[j+16] & 0xF) - m * s[2])
+ y[j+32] * (d * s[1] * (q[j+ 0] >> 4) - m * s[3])
+ y[j+48] * (d * s[1] * (q[j+16] >> 4) - m * s[3]);
}
tmp += sum;
}
#endif
// sum up partial sums and write back result
__syncthreads();
@@ -728,15 +936,19 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * vx, const float
static __global__ void dequantize_mul_mat_vec_q5_k(const void * vx, const float * yy, float * dst, const int ncols) {
const uint16_t kmask1 = 0x3f3f;
const uint16_t kmask2 = 0x0f0f;
const uint16_t kmask3 = 0xc0c0;
//const int row = blockIdx.x*blockDim.y + threadIdx.y;
const int row = blockIdx.x;
const int num_blocks_per_row = ncols / QK_K;
const int ib0 = row*num_blocks_per_row;
const block_q5_K * x = (const block_q5_K *)vx + ib0;
float tmp = 0; // partial sum for thread in warp
#if QK_K == 256
const uint16_t kmask1 = 0x3f3f;
const uint16_t kmask2 = 0x0f0f;
const uint16_t kmask3 = 0xc0c0;
const int tid = threadIdx.x/2; // 0...15
const int ix = threadIdx.x%2;
@@ -757,10 +969,6 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * vx, const float
uint16_t aux[4];
const uint8_t * sc = (const uint8_t *)aux;
const block_q5_K * x = (const block_q5_K *)vx + ib0;
float tmp = 0; // partial sum for thread in warp
for (int i = ix; i < num_blocks_per_row; i += 2) {
const uint8_t * ql1 = x[i].qs + q_offset;
@@ -793,9 +1001,32 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * vx, const float
+ (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7];
}
tmp += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin;
}
#else
const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15
const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION);
const int step = tid * K_QUANTS_PER_ITERATION;
const int im = step/8;
const int in = step%8;
for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
const uint8_t * q = x[i].qs + step;
const int8_t * s = x[i].scales;
const float * y = yy + i*QK_K + step;
const float d = x[i].d;
float sum = 0.f;
for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
const uint8_t h = x[i].qh[in+j] >> im;
sum += y[j+ 0] * d * s[0] * ((q[j+ 0] & 0xF) - ((h >> 0) & 1 ? 0 : 16))
+ y[j+16] * d * s[1] * ((q[j+16] & 0xF) - ((h >> 2) & 1 ? 0 : 16))
+ y[j+32] * d * s[2] * ((q[j+ 0] >> 4) - ((h >> 4) & 1 ? 0 : 16))
+ y[j+48] * d * s[3] * ((q[j+16] >> 4) - ((h >> 6) & 1 ? 0 : 16));
}
tmp += sum;
}
#endif
// sum up partial sums and write back result
__syncthreads();
#pragma unroll
@@ -803,7 +1034,7 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * vx, const float
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
}
if (tid == 0) {
if (threadIdx.x == 0) {
dst[row] = tmp;
}
}
@@ -820,6 +1051,8 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * vx, const float
const block_q6_K * x = (const block_q6_K *)vx + ib0;
#if QK_K == 256
const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16
const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1
@@ -874,6 +1107,37 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * vx, const float
}
#else
const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...7
const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0...3
const int step = tid * K_QUANTS_PER_ITERATION;
float tmp = 0; // partial sum for thread in warp
for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
const float * y = yy + i * QK_K + step;
const uint8_t * ql = x[i].ql + step;
const uint8_t * qh = x[i].qh + step;
const int8_t * s = x[i].scales;
const float d = x[i+0].d;
float sum = 0;
for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
sum += y[j+ 0] * s[0] * d * ((int8_t)((ql[j+ 0] & 0xF) | ((qh[j] & 0x03) << 4)) - 32)
+ y[j+16] * s[1] * d * ((int8_t)((ql[j+16] & 0xF) | ((qh[j] & 0x0c) << 2)) - 32)
+ y[j+32] * s[2] * d * ((int8_t)((ql[j+ 0] >> 4) | ((qh[j] & 0x30) >> 0)) - 32)
+ y[j+48] * s[3] * d * ((int8_t)((ql[j+16] >> 4) | ((qh[j] & 0xc0) >> 2)) - 32);
}
tmp += sum;
}
#endif
// sum up partial sums and write back result
__syncthreads();
#pragma unroll
@@ -985,7 +1249,7 @@ static __global__ void dequantize_mul_mat_vec(const void * vx, const dfloat * y,
}
static __global__ void mul_mat_p021_f16_f32(const void * vx, const float * y, float * dst, const int ncols_x, const int nrows_x, const int nchannels_x) {
const half * x = (half *) vx;
const half * x = (const half *) vx;
const int row_x = blockDim.y*blockIdx.y + threadIdx.y;
const int channel = blockDim.z*blockIdx.z + threadIdx.z;
@@ -1033,9 +1297,9 @@ static __global__ void mul_mat_p021_f16_f32(const void * vx, const float * y, fl
static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous
const void * vx, const float * y, float * dst, const int ncols_x, const int nrows_x,
const int row_stride_x, const int nchannels_x, const int channel_stride_x) {
const int row_stride_x, const int channel_stride_x) {
const half * x = (half *) vx;
const half * x = (const half *) vx;
const int row_x = blockDim.y*blockIdx.y + threadIdx.y;
const int channel = blockDim.z*blockIdx.z + threadIdx.z;
@@ -1078,14 +1342,14 @@ static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous
}
static __device__ void cpy_1_f32_f32(const char * cxi, char * cdsti) {
const float * xi = (float *) cxi;
const float * xi = (const float *) cxi;
float * dsti = (float *) cdsti;
*dsti = *xi;
}
static __device__ void cpy_1_f32_f16(const char * cxi, char * cdsti) {
const float * xi = (float *) cxi;
const float * xi = (const float *) cxi;
half * dsti = (half *) cdsti;
*dsti = __float2half(*xi);
@@ -1209,6 +1473,11 @@ static void add_f32_cuda(const float * x, const float * y, float * dst, const in
add_f32<<<num_blocks, CUDA_ADD_BLOCK_SIZE, 0, stream>>>(x, y, dst, k);
}
static void add_f16_f32_f16_cuda(const half * x, const float * y, half * dst, const int k, cudaStream_t stream) {
const int num_blocks = (k + CUDA_ADD_BLOCK_SIZE - 1) / CUDA_ADD_BLOCK_SIZE;
add_f16_f32_f16<<<num_blocks, CUDA_ADD_BLOCK_SIZE, 0, stream>>>(x, y, dst, k);
}
static void mul_f32_cuda(const float * x, const float * y, float * dst, const int kx, const int ky, cudaStream_t stream) {
const int num_blocks = (kx + CUDA_MUL_BLOCK_SIZE - 1) / CUDA_MUL_BLOCK_SIZE;
mul_f32<<<num_blocks, CUDA_MUL_BLOCK_SIZE, 0, stream>>>(x, y, dst, kx, ky);
@@ -1252,12 +1521,20 @@ static void dequantize_row_q8_0_cuda(const void * vx, float * y, const int k, cu
static void dequantize_row_q2_K_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
const int nb = k / QK_K;
#if QK_K == 256
dequantize_block_q2_K<<<nb, 64, 0, stream>>>(vx, y);
#else
dequantize_block_q2_K<<<nb, 32, 0, stream>>>(vx, y);
#endif
}
static void dequantize_row_q3_K_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
const int nb = k / QK_K;
#if QK_K == 256
dequantize_block_q3_K<<<nb, 64, 0, stream>>>(vx, y);
#else
dequantize_block_q3_K<<<nb, 32, 0, stream>>>(vx, y);
#endif
}
static void dequantize_row_q4_K_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
@@ -1267,12 +1544,20 @@ static void dequantize_row_q4_K_cuda(const void * vx, float * y, const int k, cu
static void dequantize_row_q5_K_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
const int nb = k / QK_K;
#if QK_K == 256
dequantize_block_q5_K<<<nb, 64, 0, stream>>>(vx, y);
#else
dequantize_block_q5_K<<<nb, 32, 0, stream>>>(vx, y);
#endif
}
static void dequantize_row_q6_K_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
const int nb = k / QK_K;
#if QK_K == 256
dequantize_block_q6_K<<<nb, 64, 0, stream>>>(vx, y);
#else
dequantize_block_q6_K<<<nb, 32, 0, stream>>>(vx, y);
#endif
}
static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
@@ -1418,7 +1703,7 @@ static void ggml_mul_mat_vec_nc_f16_f32_cuda(
const dim3 block_nums(1, nrows_x, nchannels_x);
const dim3 block_dims(WARP_SIZE, 1, 1);
mul_mat_vec_nc_f16_f32<<<block_nums, block_dims, 0, stream>>>
(vx, y, dst, ncols_x, nrows_x, row_stride_x, nchannels_x, channel_stride_x);
(vx, y, dst, ncols_x, nrows_x, row_stride_x, channel_stride_x);
}
static void ggml_cpy_f32_f32_cuda(
@@ -1675,7 +1960,7 @@ inline void ggml_cuda_op_add(
float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1,
cudaStream_t & cudaStream_main){
GGML_ASSERT(src0_ddf_i != nullptr);
GGML_ASSERT(src0_ddq_i != nullptr || src0_ddf_i != nullptr);
GGML_ASSERT(src1_ddf_i != nullptr);
GGML_ASSERT(dst_ddf_i != nullptr);
@@ -1683,8 +1968,13 @@ inline void ggml_cuda_op_add(
const int64_t i01_diff = i01_high - i01_low;
// compute
add_f32_cuda(src0_ddf_i, src1_ddf_i, dst_ddf_i, ne0*i01_diff, cudaStream_main);
CUDA_CHECK(cudaGetLastError());
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
add_f32_cuda(src0_ddf_i, src1_ddf_i, dst_ddf_i, ne0*i01_diff, cudaStream_main);
} else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
add_f16_f32_f16_cuda((half *) src0_ddq_i, src1_ddf_i, (half *) dst_ddf_i, ne0*i01_diff, cudaStream_main);
} else {
GGML_ASSERT(false);
}
(void) src1;
(void) dst;
@@ -1716,7 +2006,6 @@ inline void ggml_cuda_op_mul(
// compute
mul_f32_cuda(src0_ddf_i01, src1_ddf_i01, dst_ddf_i01, ne00, ne10, cudaStream_main);
CUDA_CHECK(cudaGetLastError());
}
(void) dst;
@@ -1737,7 +2026,6 @@ inline void ggml_cuda_op_silu(
// compute
silu_f32_cuda(src0_ddf_i, dst_ddf_i, ne00*i01_diff, cudaStream_main);
CUDA_CHECK(cudaGetLastError());
(void) src1;
(void) dst;
@@ -1760,7 +2048,6 @@ inline void ggml_cuda_op_rms_norm(
// compute
rms_norm_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, cudaStream_main);
CUDA_CHECK(cudaGetLastError());
(void) src1;
(void) dst;
@@ -1839,7 +2126,6 @@ inline void ggml_cuda_op_dequantize_mul_mat_vec(
GGML_ASSERT(false);
break;
}
CUDA_CHECK(cudaGetLastError());
#ifdef GGML_CUDA_DMMV_F16
if (src1_convert_f16) {
@@ -1916,7 +2202,6 @@ inline void ggml_cuda_op_rope(
// compute
rope_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, p, theta_scale, cudaStream_main);
CUDA_CHECK(cudaGetLastError());
(void) dst;
(void) src0_ddq_i;
@@ -1940,7 +2225,6 @@ inline void ggml_cuda_op_diag_mask_inf(
// compute
diag_mask_inf_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, ne01, n_past, cudaStream_main);
CUDA_CHECK(cudaGetLastError());
(void) dst;
(void) src0_ddq_i;
@@ -1962,7 +2246,6 @@ inline void ggml_cuda_op_soft_max(
// compute
soft_max_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, cudaStream_main);
CUDA_CHECK(cudaGetLastError());
(void) src1;
(void) dst;
@@ -2058,10 +2341,11 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm
size_t src1_asf[GGML_CUDA_MAX_DEVICES] = {0};
size_t dst_asf[GGML_CUDA_MAX_DEVICES] = {0};
// if multiple GPUs are used they need to wait for the main GPU to finish
// if multiple devices are used they need to wait for the main device
// here an event is recorded that signifies that the main device has finished calculating the input data
if (split && g_device_count > 1) {
CUDA_CHECK(cudaSetDevice(g_main_device));
CUDA_CHECK(cudaDeviceSynchronize());
CUDA_CHECK(cudaEventRecord(src0_extra->events[g_main_device], g_cudaStreams_main[g_main_device]));
}
for (int id = 0; id < g_device_count; ++id) {
@@ -2087,6 +2371,12 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm
int64_t row_diff = row_high - row_low;
cudaSetDevice(id);
cudaStream_t cudaStream_main = g_cudaStreams_main[id];
// wait for main GPU data if necessary
if (split && id != g_main_device) {
CUDA_CHECK(cudaStreamWaitEvent(cudaStream_main, src0_extra->events[g_main_device]));
}
if (src0_on_device && src0_is_contiguous) {
if (src0_is_f32) {
@@ -2162,8 +2452,6 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm
}
const int64_t i11 = i13*ne12 + i12;
cudaStream_t cudaStream_main = g_cudaStreams_main[id];
// for split tensors the data begins at i0 == i0_offset_low
char * src0_ddq_i = src0_ddq[id] + (i0 - i0_offset_low)*src0_stride*src0_ts/src0_bs;
float * src0_ddf_i = src0_ddf[id] + (i0 - i0_offset_low)*src0_stride;
@@ -2223,6 +2511,7 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm
// do the computation
op(src0, src1, dst, src0_ddq_i, src0_ddf_i, src1_ddf_i, dst_ddf_i, i02, i01_low, i01_high, i11, cudaStream_main);
CUDA_CHECK(cudaGetLastError());
// copy dst to host or other device if necessary
if (!dst_on_device) {
@@ -2252,6 +2541,11 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm
CUDA_CHECK(cudaMemcpyAsync(dhf_dst_i, dst_ddf_i, dst_stride*sizeof(float), kind, cudaStream_main));
}
}
// signify to main device that other device is done
if (split && g_device_count > 1 && id != g_main_device) {
CUDA_CHECK(cudaEventRecord(src0_extra->events[id], cudaStream_main));
}
}
}
}
@@ -2263,7 +2557,6 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm
}
CUDA_CHECK(cudaSetDevice(id));
CUDA_CHECK(cudaDeviceSynchronize());
if (src0_asq[id] > 0) {
ggml_cuda_pool_free(src0_ddq[id], src0_asq[id]);
@@ -2278,11 +2571,32 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm
ggml_cuda_pool_free(dst_ddf[id], dst_asf[id]);
}
}
// main device waits for all other devices to be finished
if (split && g_device_count > 1) {
CUDA_CHECK(cudaSetDevice(g_main_device));
for (int id = 0; id < g_device_count; ++id) {
if (id != g_main_device) {
CUDA_CHECK(cudaStreamWaitEvent(g_cudaStreams_main[g_main_device], src0_extra->events[id]));
}
}
}
if (dst->backend == GGML_BACKEND_CPU) {
CUDA_CHECK(cudaSetDevice(g_main_device));
CUDA_CHECK(cudaDeviceSynchronize());
}
}
void ggml_cuda_add(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
ggml_cuda_op(src0, src1, dst, ggml_cuda_op_add, true, true);
// ggml_cuda_add permits f16 dst even though this could in theory cause problems with the pointer arithmetic in ggml_cuda_op.
// Due to flatten_rows == true this does in practice not make a difference however.
// Better solution would be nice but right now that would require disproportionate changes.
GGML_ASSERT(
(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16) &&
src1->type == GGML_TYPE_F32 &&
(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16));
ggml_cuda_op(src0, src1, dst, ggml_cuda_op_add, false, true);
}
void ggml_cuda_mul(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
@@ -2511,6 +2825,10 @@ void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor) {
cudaMemcpy(buf, buf_host, size, cudaMemcpyHostToDevice);
extra->data_device[id] = buf;
if (backend == GGML_BACKEND_GPU_SPLIT) {
CUDA_CHECK(cudaEventCreateWithFlags(&extra->events[id], cudaEventDisableTiming));
}
}
tensor->extra = extra;
@@ -2524,18 +2842,21 @@ void ggml_cuda_free_data(struct ggml_tensor * tensor) {
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
for (int id = 0; id < g_device_count; ++id) {
if (extra->data_device[id] == nullptr) {
continue;
if (extra->data_device[id] != nullptr) {
CUDA_CHECK(cudaSetDevice(id));
CUDA_CHECK(cudaFree(extra->data_device[id]));
}
CUDA_CHECK(cudaSetDevice(id));
CUDA_CHECK(cudaFree(extra->data_device[id]));
if (extra->events[id] != nullptr) {
CUDA_CHECK(cudaSetDevice(id));
CUDA_CHECK(cudaEventDestroy(extra->events[id]));
}
}
delete extra;
}
void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch) {
void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch, bool force_inplace) {
if (scratch && g_scratch_size == 0) {
return;
}
@@ -2544,22 +2865,24 @@ void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch) {
if (tensor->src0 != nullptr && tensor->src0->backend == GGML_BACKEND_CPU) {
const ggml_op src0_op = tensor->src0->op;
if (src0_op == GGML_OP_RESHAPE || src0_op == GGML_OP_TRANSPOSE || src0_op == GGML_OP_VIEW) {
ggml_cuda_assign_buffers_impl(tensor->src0, scratch);
ggml_cuda_assign_buffers_impl(tensor->src0, scratch, force_inplace);
}
}
if (tensor->op == GGML_OP_CPY && tensor->src1->backend == GGML_BACKEND_CPU) {
ggml_cuda_assign_buffers_impl(tensor->src1, scratch);
ggml_cuda_assign_buffers_impl(tensor->src1, scratch, force_inplace);
}
tensor->backend = GGML_BACKEND_GPU;
struct ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu;
memset(extra, 0, sizeof(*extra));
const bool inplace = (tensor->src0 != nullptr && tensor->src0->data == tensor->data) ||
tensor->op == GGML_OP_VIEW;
tensor->op == GGML_OP_VIEW ||
force_inplace;
const size_t size = ggml_nbytes(tensor);
CUDA_CHECK(cudaSetDevice(g_main_device));
if (inplace && tensor->src0->backend == GGML_BACKEND_GPU) {
if (inplace && (tensor->src0->backend == GGML_BACKEND_GPU || tensor->src0->backend == GGML_BACKEND_GPU_SPLIT)) {
struct ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src0->extra;
char * src0_ddc = (char *) src0_extra->data_device[g_main_device];
size_t offset = 0;
@@ -2598,11 +2921,15 @@ void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch) {
}
void ggml_cuda_assign_buffers(struct ggml_tensor * tensor) {
ggml_cuda_assign_buffers_impl(tensor, true);
ggml_cuda_assign_buffers_impl(tensor, true, false);
}
void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor) {
ggml_cuda_assign_buffers_impl(tensor, false);
ggml_cuda_assign_buffers_impl(tensor, false, false);
}
void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor) {
ggml_cuda_assign_buffers_impl(tensor, false, true);
}
void ggml_cuda_set_main_device(int main_device) {
@@ -2635,7 +2962,7 @@ void ggml_cuda_free_scratch() {
bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor){
ggml_cuda_func_t func;
const bool any_on_device = tensor->backend == GGML_BACKEND_GPU
|| tensor->src0->backend == GGML_BACKEND_GPU || tensor->src0->backend == GGML_BACKEND_GPU_SPLIT
|| (tensor->src0 != nullptr && (tensor->src0->backend == GGML_BACKEND_GPU || tensor->src0->backend == GGML_BACKEND_GPU_SPLIT))
|| (tensor->src1 != nullptr && tensor->src1->backend == GGML_BACKEND_GPU);
switch (tensor->op) {