ikawrakow/ik_llama.cpp
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Iwan Kawrakow
2024-11-15 15:59:49 +02:00
parent 4cf82e7e2f
commit e338e0a0cd
1 changed files with 29 additions and 86 deletions
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115
ggml/src/ggml-cuda/dmmv.cu
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@@ -15,13 +15,34 @@
static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2");
#endif
static __global__ void dequantize_mul_mat_vec_iq2_kt(const void * __restrict__ vx, const dfloat * __restrict__ yy, float * __restrict__ dst,
const int ncols, int nrows, int64_t row_size) {
static __device__ __forceinline__ uint32_t trellis_next(uint32_t& val) {
constexpr uint32_t ka = 89226354;
constexpr uint32_t kb = 64248484;
constexpr uint32_t kmask = 0x8fff8fff;
constexpr uint32_t km32 = 0x3b603b60;
val = ka*val + kb;
return (val & kmask) ^ km32;
}
static __device__ __forceinline__ void trellis_accum(uint32_t& val1, uint32_t& val2, uint32_t* s, const dfloat2* y, dfloat2& bdot1, dfloat2& bdot2) {
const half * h = (const half *)s;
s[0] = trellis_next(val1);
s[1] = trellis_next(val1);
s[2] = trellis_next(val2);
s[3] = trellis_next(val2);
#ifdef GGML_CUDA_F16
bdot1 = __hfma2(y[ 0], {h[0]+h[1], h[2]+h[3]}, bdot1);
bdot2 = __hfma2(y[64], {h[4]+h[5], h[6]+h[7]}, bdot2);
#else
bdot1.x += y[ 0].x * (float)(h[0] + h[1]);
bdot1.y += y[ 0].y * (float)(h[2] + h[3]);
bdot2.x += y[64].x * (float)(h[4] + h[5]);
bdot2.y += y[64].y * (float)(h[6] + h[7]);
#endif
}
static __global__ void dequantize_mul_mat_vec_iq2_kt(const void * __restrict__ vx, const dfloat * __restrict__ yy, float * __restrict__ dst,
const int ncols, int nrows, int64_t row_size) {
const int row = blockIdx.x*blockDim.y + threadIdx.y;
if (row > nrows) return;
@@ -38,7 +59,6 @@ static __global__ void dequantize_mul_mat_vec_iq2_kt(const void * __restrict__ v
const int ix = threadIdx.x%2;
uint32_t s[4];
const half * h = (const half *)&s;
for (int i = ix; i < num_blocks_per_row; i += 2) {
const dfloat2 * y = (const dfloat2 *)(yy + i * QK_K + 8*it);
@@ -52,19 +72,7 @@ static __global__ void dequantize_mul_mat_vec_iq2_kt(const void * __restrict__ v
uint32_t val1 = ql[it+ 0] + 4096;
uint32_t val2 = ql[it+16] + 4096;
for (int k = 0; k < 4; ++k) {
val1 = ka*val1 + kb; s[0] = (val1 & kmask) ^ km32;
val1 = ka*val1 + kb; s[1] = (val1 & kmask) ^ km32;
val2 = ka*val2 + kb; s[2] = (val2 & kmask) ^ km32;
val2 = ka*val2 + kb; s[3] = (val2 & kmask) ^ km32;
#ifdef GGML_CUDA_F16
bdot1 = __hfma2(y[k+ 0], {h[0]+h[1], h[2]+h[3]}, bdot1);
bdot2 = __hfma2(y[k+64], {h[4]+h[5], h[6]+h[7]}, bdot2);
#else
bdot1.x += y[k+ 0].x * (float)(h[0] + h[1]);
bdot1.y += y[k+ 0].y * (float)(h[2] + h[3]);
bdot2.x += y[k+64].x * (float)(h[4] + h[5]);
bdot2.y += y[k+64].y * (float)(h[6] + h[7]);
#endif
trellis_accum(val1, val2, s, y+k, bdot1, bdot2);
}
#ifdef GGML_CUDA_F16
tmp = __hfma2(dl1, bdot1, tmp);
@@ -86,11 +94,6 @@ static __global__ void dequantize_mul_mat_vec_iq2_kt(const void * __restrict__ v
static __global__ void dequantize_mul_mat_vec_iq3_kt(const void * __restrict__ vx, const dfloat * __restrict__ yy, float * __restrict__ dst,
const int ncols, int nrows, int64_t row_size) {
constexpr uint32_t ka = 89226354;
constexpr uint32_t kb = 64248484;
constexpr uint32_t kmask = 0x8fff8fff;
constexpr uint32_t km32 = 0x3b603b60;
const int row = blockIdx.x*blockDim.y + threadIdx.y;
if (row > nrows) return;
@@ -106,7 +109,6 @@ static __global__ void dequantize_mul_mat_vec_iq3_kt(const void * __restrict__ v
const int ix = threadIdx.x%2;
uint32_t s[4];
const half * h = (const half *)s;
for (int i = ix; i < num_blocks_per_row; i += 2) {
const dfloat2 * y = (const dfloat2 *)(yy + i * QK_K + 8*it);
@@ -121,36 +123,12 @@ static __global__ void dequantize_mul_mat_vec_iq3_kt(const void * __restrict__ v
uint32_t val1 = ql[2*it+ 0] + ((qh[2*it+0] << 8) & 0xf00) + 4096;
uint32_t val2 = ql[2*it+32] + ((qh[2*it+0] << 4) & 0xf00) + 4096;
for (int k = 0; k < 2; ++k) {
val1 = ka*val1 + kb; s[0] = (val1 & kmask) ^ km32;
val1 = ka*val1 + kb; s[1] = (val1 & kmask) ^ km32;
val2 = ka*val2 + kb; s[2] = (val2 & kmask) ^ km32;
val2 = ka*val2 + kb; s[3] = (val2 & kmask) ^ km32;
#ifdef GGML_CUDA_F16
bdot1 = __hfma2(y[k+ 0], {h[0]+h[1], h[2]+h[3]}, bdot1);
bdot2 = __hfma2(y[k+64], {h[4]+h[5], h[6]+h[7]}, bdot2);
#else
bdot1.x += y[k+ 0].x * (float)(h[0] + h[1]);
bdot1.y += y[k+ 0].y * (float)(h[2] + h[3]);
bdot2.x += y[k+64].x * (float)(h[4] + h[5]);
bdot2.y += y[k+64].y * (float)(h[6] + h[7]);
#endif
trellis_accum(val1, val2, s, y+k, bdot1, bdot2);
}
val1 = ql[2*it+ 1] + ((qh[2*it+1] << 8) & 0xf00) + 4096;
val2 = ql[2*it+33] + ((qh[2*it+1] << 4) & 0xf00) + 4096;
for (int k = 2; k < 4; ++k) {
val1 = ka*val1 + kb; s[0] = (val1 & kmask) ^ km32;
val1 = ka*val1 + kb; s[1] = (val1 & kmask) ^ km32;
val2 = ka*val2 + kb; s[2] = (val2 & kmask) ^ km32;
val2 = ka*val2 + kb; s[3] = (val2 & kmask) ^ km32;
#ifdef GGML_CUDA_F16
bdot1 = __hfma2(y[k+ 0], {h[0]+h[1], h[2]+h[3]}, bdot1);
bdot2 = __hfma2(y[k+64], {h[4]+h[5], h[6]+h[7]}, bdot2);
#else
bdot1.x += y[k+ 0].x * (float)(h[0] + h[1]);
bdot1.y += y[k+ 0].y * (float)(h[2] + h[3]);
bdot2.x += y[k+64].x * (float)(h[4] + h[5]);
bdot2.y += y[k+64].y * (float)(h[6] + h[7]);
#endif
trellis_accum(val1, val2, s, y+k, bdot1, bdot2);
}
#ifdef GGML_CUDA_F16
tmp = __hfma2(dl1, bdot1, tmp);
@@ -172,10 +150,6 @@ static __global__ void dequantize_mul_mat_vec_iq3_kt(const void * __restrict__ v
static __global__ void dequantize_mul_mat_vec_iq4_kt(const void * __restrict__ vx, const dfloat * __restrict__ yy, float * __restrict__ dst,
const int ncols, int nrows, int64_t row_size) {
constexpr uint32_t ka = 89226354;
constexpr uint32_t kb = 64248484;
constexpr uint32_t kmask = 0x8fff8fff;
constexpr uint32_t km32 = 0x3b603b60;
constexpr int kNumGroups = 64;
const int row = blockIdx.x*blockDim.y + threadIdx.y;
@@ -198,7 +172,6 @@ static __global__ void dequantize_mul_mat_vec_iq4_kt(const void * __restrict__ v
const int jj = ib32*8 + 2*ig; // 0...30 in steps of 2
uint32_t s[4];
const half * h = (const half *)s;
for (int i = ix; i < num_blocks_per_row; i += 2) {
const dfloat2 * y = (const dfloat2 *)(yy + i * QK_K + 8*it);
@@ -216,42 +189,12 @@ static __global__ void dequantize_mul_mat_vec_iq4_kt(const void * __restrict__ v
uint32_t val1 = ql[jj+ 0] + ((qh[jj] << 8) & 0xf00) + (((shb[ib32+0] >> (8 + 6*ig+0)) & 7) << 12) + offset1;
uint32_t val2 = ql[jj+32] + ((qh[jj] << 4) & 0xf00) + (((shb[ib32+4] >> (8 + 6*ig+0)) & 7) << 12) + offset2;
for (int k = 0; k < 2; ++k) {
val1 = ka*val1 + kb; s[0] = (val1 & kmask) ^ km32;
val1 = ka*val1 + kb; s[1] = (val1 & kmask) ^ km32;
val2 = ka*val2 + kb; s[2] = (val2 & kmask) ^ km32;
val2 = ka*val2 + kb; s[3] = (val2 & kmask) ^ km32;
#ifdef GGML_CUDA_F16
bdot1 = __hfma2(y[k+ 0], {h[0]+h[1], h[2]+h[3]}, bdot1);
bdot2 = __hfma2(y[k+64], {h[4]+h[5], h[6]+h[7]}, bdot2);
tmp2 += y[k] + y[k+64];
#else
bdot1.x += y[k+ 0].x * (float)(h[0] + h[1]);
bdot1.y += y[k+ 0].y * (float)(h[2] + h[3]);
bdot2.x += y[k+64].x * (float)(h[4] + h[5]);
bdot2.y += y[k+64].y * (float)(h[6] + h[7]);
tmp2.x += y[k].x + y[k+64].x;
tmp2.y += y[k].y + y[k+64].y;
#endif
trellis_accum(val1, val2, s, y+k, bdot1, bdot2);
}
val1 = ql[jj+ 1] + ((qh[jj+1] << 8) & 0xf00) + (((shb[ib32+0] >> (8 + 6*ig+3)) & 7) << 12) + offset1;
val2 = ql[jj+33] + ((qh[jj+1] << 4) & 0xf00) + (((shb[ib32+4] >> (8 + 6*ig+3)) & 7) << 12) + offset2;
for (int k = 2; k < 4; ++k) {
val1 = ka*val1 + kb; s[0] = (val1 & kmask) ^ km32;
val1 = ka*val1 + kb; s[1] = (val1 & kmask) ^ km32;
val2 = ka*val2 + kb; s[2] = (val2 & kmask) ^ km32;
val2 = ka*val2 + kb; s[3] = (val2 & kmask) ^ km32;
#ifdef GGML_CUDA_F16
bdot1 = __hfma2(y[k+ 0], {h[0]+h[1], h[2]+h[3]}, bdot1);
bdot2 = __hfma2(y[k+64], {h[4]+h[5], h[6]+h[7]}, bdot2);
tmp2 += y[k] + y[k+64];
#else
bdot1.x += y[k+ 0].x * (float)(h[0] + h[1]);
bdot1.y += y[k+ 0].y * (float)(h[2] + h[3]);
bdot2.x += y[k+64].x * (float)(h[4] + h[5]);
bdot2.y += y[k+64].y * (float)(h[6] + h[7]);
tmp2.x += y[k].x + y[k+64].x;
tmp2.y += y[k].y + y[k+64].y;
#endif
trellis_accum(val1, val2, s, y+k, bdot1, bdot2);
}
#ifdef GGML_CUDA_F16
tmp1 = __hfma2(dl1, bdot1, tmp1);