whisper.cpp/ggml-cuda/mmq.cuh

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#pragma once
#include "common.cuh"
#include "vecdotq.cuh"
#include "mma.cuh"
#include <climits>
#include <cstdint>
#define MMQ_TILE_Y_K (WARP_SIZE + WARP_SIZE/QI8_1)
typedef void (*load_tiles_mmq_t)(
const char * __restrict__ x, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride);
typedef void (*vec_dot_mmq_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, float * __restrict__ sum, const int & k0);
typedef void (*mmq_write_back_t)(const float * __restrict__ sum, float * __restrict__ dst, const int & ne0, const int & ne1);
struct block_q8_1_mmq {
half2 ds[4];
int8_t qs[4*QK8_1];
};
static_assert(sizeof(block_q8_1_mmq) == 4*QK8_1 + 4*sizeof(half2), "Unexpected block_q8_1_mmq size");
static_assert(sizeof(block_q8_1_mmq) == 4*sizeof(block_q8_1), "Unexpected block_q8_1_mmq size");
struct tile_x_sizes {
int ql;
int dm;
int qh;
int sc;
};
// get_mmq_x_max_host is in common.cuh so that it can be used to determine the correct way to round for --split-mode row
static constexpr __device__ int get_mmq_x_max_device() {
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
return 64;
#else
#if __CUDA_ARCH__ >= CC_VOLTA
#ifdef CUDA_USE_TENSOR_CORES
return MMQ_MAX_BATCH_SIZE;
#else
return 128;
#endif // CUDA_USE_TENSOR_CORES
#else
return 64;
#endif // __CUDA_ARCH__ >= CC_VOLTA
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
}
// get_mmq_y_host is in common.cuh so that it can be used to determine the correct way to round for --split-mode row
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
static constexpr __device__ int get_mmq_y_device(int mmq_x) {
return mmq_x >= 32 ? 128 : 64;
}
#else
#if __CUDA_ARCH__ >= CC_VOLTA
static constexpr __device__ int get_mmq_y_device(int mmq_x) {
return mmq_x >= 32 ? 128 : 64;
}
#else
static constexpr __device__ int get_mmq_y_device(int /*mmq_x*/) {
return 64;
}
#endif // __CUDA_ARCH__ >= CC_VOLTA
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
#define TILE_X_SIZES_Q4_0 tile_x_sizes{mmq_y*WARP_SIZE + mmq_y, mmq_y*WARP_SIZE/QI4_0 + mmq_y/QI4_0, 0, 0}
#define TILE_X_SIZES_Q4_1 tile_x_sizes{mmq_y*WARP_SIZE + mmq_y, mmq_y*WARP_SIZE/QI4_1 + mmq_y/QI4_1, 0, 0}
#define TILE_X_SIZES_Q5_0 tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE/QI5_0 + mmq_y/QI5_0, 0, 0}
#define TILE_X_SIZES_Q5_1 tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE/QI5_1 + mmq_y/QI5_1, 0, 0}
#define TILE_X_SIZES_Q8_0 tile_x_sizes{mmq_y*WARP_SIZE + mmq_y, mmq_y*WARP_SIZE/QI8_0 + mmq_y/QI8_0, 0, 0}
#define TILE_X_SIZES_Q2_K tile_x_sizes{mmq_y*WARP_SIZE + mmq_y, mmq_y*WARP_SIZE/QI2_K + mmq_y/QI2_K, 0, mmq_y*WARP_SIZE/4 + mmq_y/4}
#define TILE_X_SIZES_Q3_K tile_x_sizes{mmq_y*WARP_SIZE + mmq_y, mmq_y*WARP_SIZE/QI3_K + mmq_y/QI3_K, mmq_y*WARP_SIZE/2 + mmq_y/2, mmq_y*WARP_SIZE/4 + mmq_y/4}
#define TILE_X_SIZES_Q4_K tile_x_sizes{mmq_y*WARP_SIZE + mmq_y, mmq_y*WARP_SIZE/QI4_K + mmq_y/QI4_K, 0, mmq_y*WARP_SIZE/8 + mmq_y/8}
#define TILE_X_SIZES_Q5_K tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE/QI5_K + mmq_y/QI5_K, 0, mmq_y*WARP_SIZE/8 + mmq_y/8}
#define TILE_X_SIZES_Q6_K tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE/QI6_K + mmq_y/QI6_K, 0, mmq_y*WARP_SIZE/8 + mmq_y/8}
#define GET_TILE_X_SIZES_BODY \
return type == GGML_TYPE_Q4_0 ? TILE_X_SIZES_Q4_0 : \
type == GGML_TYPE_Q4_1 ? TILE_X_SIZES_Q4_1 : \
type == GGML_TYPE_Q5_0 ? TILE_X_SIZES_Q5_0 : \
type == GGML_TYPE_Q5_1 ? TILE_X_SIZES_Q5_1 : \
type == GGML_TYPE_Q8_0 ? TILE_X_SIZES_Q8_0 : \
type == GGML_TYPE_Q2_K ? TILE_X_SIZES_Q2_K : \
type == GGML_TYPE_Q3_K ? TILE_X_SIZES_Q3_K : \
type == GGML_TYPE_Q4_K ? TILE_X_SIZES_Q4_K : \
type == GGML_TYPE_Q5_K ? TILE_X_SIZES_Q5_K : \
type == GGML_TYPE_Q6_K ? TILE_X_SIZES_Q6_K : \
tile_x_sizes{0, 0, 0, 0}
static tile_x_sizes get_tile_x_sizes_host(const ggml_type type, const int mmq_y) {
GET_TILE_X_SIZES_BODY;
}
template <int mmq_y>
static constexpr __device__ tile_x_sizes get_tile_x_sizes_device(ggml_type type) {
GET_TILE_X_SIZES_BODY;
}
// ------------------------------------------------------------
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_0(
const char * __restrict__ x, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) {
GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
const int kbx = threadIdx.x / QI4_0;
const int kqsx = threadIdx.x % QI4_0;
float * x_dmf = (float *) x_dm;
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
int i = i0 + threadIdx.y;
if (need_check) {
i = min(i, i_max);
}
const block_q4_0 * bxi = (const block_q4_0 *) x + kbx0 + i*stride + kbx;
x_ql[i * (WARP_SIZE + 1) + threadIdx.x] = get_int_from_uint8(bxi->qs, kqsx);
}
const int blocks_per_tile_x_row = WARP_SIZE / QI4_0;
const int kbxd = threadIdx.x % blocks_per_tile_x_row;
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_0) {
int i = i0 + threadIdx.y * QI4_0 + threadIdx.x / blocks_per_tile_x_row;
if (need_check) {
i = min(i, i_max);
}
const block_q4_0 * bxi = (const block_q4_0 *) x + kbx0 + i*stride + kbxd;
x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbxd] = bxi->d;
}
}
template <int mmq_x, int mmq_y, int nwarps>
static __device__ __forceinline__ void vec_dot_q4_0_q8_1_dp4a(
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
const float * x_df = (const float *) x_dm;
const int * y_qs = (const int *) y + 4;
const half2 * y_ds = (const half2 *) y;
#pragma unroll
for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
const int j = j0 + threadIdx.y;
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
const int i = i0 + threadIdx.x;
const int kyqs = k0 % (QI8_1/2) + QI8_1 * (k0 / (QI8_1/2));
int u[2*VDR_Q4_0_Q8_1_MMQ];
#pragma unroll
for (int l = 0; l < VDR_Q4_0_Q8_1_MMQ; ++l) {
u[2*l+0] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l) % WARP_SIZE];
u[2*l+1] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l + QI4_0) % WARP_SIZE];
}
sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMQ>
(&x_ql[i*(WARP_SIZE + 1) + k0], u, x_df[i*(WARP_SIZE/QI4_0) + i/QI4_0 + k0/QI4_0],
y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]);
}
}
}
template <int mmq_x, int mmq_y, int nwarps>
static __device__ __forceinline__ void vec_dot_q4_0_q8_1_mma(
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
typedef mma_int_A_I16K8 mma_A;
typedef mma_int_B_J8K8 mma_B;
typedef mma_int_C_I16J8 mma_C;
const float * x_df = (const float *) x_dm;
const int * y_qs = (const int *) y + 4;
const half2 * y_ds = (const half2 *) y;
mma_A A;
float dA[mma_C::ne/2];
const int i0 = threadIdx.y*mma_A::I;
static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y");
#pragma unroll
for (int l = 0; l < mma_A::ne; ++l) {
const int i = i0 + mma_A::get_i(l);
const int k = k0 + mma_A::get_k(l) % QI4_0;
const int shift = 4*(mma_A::get_k(l) / QI4_0);
A.x[l] = __vsubss4((x_ql[i*(WARP_SIZE + 1) + k] >> shift) & 0x0F0F0F0F, 0x08080808);
}
#pragma unroll
for (int l = 0; l < mma_C::ne/2; ++l) {
const int i = i0 + mma_C::get_i(2*l);
dA[l] = x_df[i*(WARP_SIZE/QI4_0) + i/QI4_0 + k0/QI4_0];
}
for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) {
mma_C C;
mma_B B;
half2 dsB[mma_C::ne/2];
#pragma unroll
for (int l = 0; l < mma_B::ne; ++l) {
const int j = j0 + mma_B::get_j(l);
const int k = (2*k0 + mma_B::get_k(l)) % WARP_SIZE;
B.x[l] = y_qs[j*MMQ_TILE_Y_K + k];
}
#pragma unroll
for (int l = 0; l < mma_C::ne/2; ++l) {
const int j = j0 + mma_C::get_j(l);
dsB[l] = y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)];
}
C.mma_K8(A, B);
#pragma unroll
for (int l = 0; l < mma_C::ne; ++l) {
sum[(j0/B.J)*C.ne + l] += dA[l/2]*__low2float(dsB[l%2])*C.x[l];
}
}
}
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_1(
const char * __restrict__ x, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) {
GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
const int kbx = threadIdx.x / QI4_1;
const int kqsx = threadIdx.x % QI4_1;
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
int i = i0 + threadIdx.y;
if (need_check) {
i = min(i, i_max);
}
const block_q4_1 * bxi = (const block_q4_1 *) x + kbx0 + i*stride + kbx;
x_ql[i * (WARP_SIZE + 1) + threadIdx.x] = get_int_from_uint8_aligned(bxi->qs, kqsx);
}
const int blocks_per_tile_x_row = WARP_SIZE / QI4_1;
const int kbxd = threadIdx.x % blocks_per_tile_x_row;
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_1) {
int i = i0 + threadIdx.y * QI4_1 + threadIdx.x / blocks_per_tile_x_row;
if (need_check) {
i = min(i, i_max);
}
const block_q4_1 * bxi = (const block_q4_1 *) x + kbx0 + i*stride + kbxd;
x_dm[i * (WARP_SIZE/QI4_1) + i / QI4_1 + kbxd] = bxi->dm;
}
}
template <int mmq_x, int mmq_y, int nwarps>
static __device__ __forceinline__ void vec_dot_q4_1_q8_1_dp4a(
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
const int * y_qs = (const int *) y + 4;
const half2 * y_ds = (const half2 *) y;
#pragma unroll
for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
const int j = j0 + threadIdx.y;
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
const int i = i0 + threadIdx.x;
const int kyqs = k0 % (QI8_1/2) + QI8_1 * (k0 / (QI8_1/2));
int u[2*VDR_Q4_1_Q8_1_MMQ];
#pragma unroll
for (int l = 0; l < VDR_Q4_1_Q8_1_MMQ; ++l) {
u[2*l+0] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l) % WARP_SIZE];
u[2*l+1] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l + QI4_1) % WARP_SIZE];
}
sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMQ>
(&x_ql[i*(WARP_SIZE + 1) + k0], u, x_dm[i*(WARP_SIZE/QI4_1) + i/QI4_1 + k0/QI4_1],
y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]);
}
}
}
template <int mmq_x, int mmq_y, int nwarps>
static __device__ __forceinline__ void vec_dot_q4_1_q8_1_mma(
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
typedef mma_int_A_I16K8 mma_A;
typedef mma_int_B_J8K8 mma_B;
typedef mma_int_C_I16J8 mma_C;
const int * y_qs = (const int *) y + 4;
const half2 * y_ds = (const half2 *) y;
mma_A A;
half2 dmA[mma_C::ne/2];
const int i0 = threadIdx.y*mma_A::I;
static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y");
#pragma unroll
for (int l = 0; l < mma_A::ne; ++l) {
const int i = i0 + mma_A::get_i(l);
const int k = k0 + mma_A::get_k(l) % QI4_0;
const int shift = 4*(mma_A::get_k(l) / QI4_0);
A.x[l] = (x_ql[i*(WARP_SIZE + 1) + k] >> shift) & 0x0F0F0F0F;
}
#pragma unroll
for (int l = 0; l < mma_C::ne/2; ++l) {
const int i = i0 + mma_C::get_i(2*l);
dmA[l] = x_dm[i*(WARP_SIZE/QI4_0) + i/QI4_0 + k0/QI4_0];
}
for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) {
mma_C C;
mma_B B;
half2 dsB[mma_C::ne/2];
#pragma unroll
for (int l = 0; l < mma_B::ne; ++l) {
const int j = j0 + mma_B::get_j(l);
const int k = (2*k0 + mma_B::get_k(l)) % WARP_SIZE;
B.x[l] = y_qs[j*MMQ_TILE_Y_K + k];
}
#pragma unroll
for (int l = 0; l < mma_C::ne/2; ++l) {
const int j = j0 + mma_C::get_j(l);
dsB[l] = y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)];
}
C.mma_K8(A, B);
#pragma unroll
for (int l = 0; l < mma_C::ne; ++l) {
const half2 dmA_dsB = dmA[l/2]*dsB[l%2];
sum[(j0/B.J)*C.ne + l] += __low2float(dmA_dsB)*C.x[l] + __high2float(dmA_dsB);
}
}
}
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_0(
const char * __restrict__ x, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) {
GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
const int kbx = threadIdx.x / QI5_0;
const int kqsx = threadIdx.x % QI5_0;
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
int i = i0 + threadIdx.y;
if (need_check) {
i = min(i, i_max);
}
const block_q5_0 * bxi = (const block_q5_0 *) x + kbx0 + i*stride + kbx;
const int ql = get_int_from_uint8(bxi->qs, kqsx);
const int qh = get_int_from_uint8(bxi->qh, 0) >> (4 * (threadIdx.x % 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*threadIdx.x+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*threadIdx.x+1] = qs1;
}
const int blocks_per_tile_x_row = WARP_SIZE / QI5_0;
const int kbxd = threadIdx.x % blocks_per_tile_x_row;
float * x_dmf = (float *) x_dm;
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_0) {
int i = i0 + threadIdx.y * QI5_0 + threadIdx.x / blocks_per_tile_x_row;
if (need_check) {
i = min(i, i_max);
}
const block_q5_0 * bxi = (const block_q5_0 *) x + kbx0 + i*stride + kbxd;
x_dmf[i * (WARP_SIZE/QI5_0) + i / QI5_0 + kbxd] = bxi->d;
}
}
template <int mmq_x, int mmq_y, int nwarps>
static __device__ __forceinline__ void vec_dot_q5_0_q8_1_dp4a(
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
const float * x_dmf = (const float *) x_dm;
const int * y_qs = (const int *) y + 4;
const float * y_df = (const float *) y;
#pragma unroll
for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
const int j = j0 + threadIdx.y;
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
const int i = i0 + threadIdx.x;
const int kyqs = k0 % (QI8_1/2) + QI8_1 * (k0 / (QI8_1/2));
const int index_bx = i*(WARP_SIZE/QI5_0) + i/QI5_0 + k0/QI5_0;
int u[2*VDR_Q5_0_Q8_1_MMQ];
#pragma unroll
for (int l = 0; l < VDR_Q5_0_Q8_1_MMQ; ++l) {
u[2*l+0] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l) % WARP_SIZE];
u[2*l+1] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l + QI5_0) % WARP_SIZE];
}
sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q8_0_q8_1_impl<float, QR5_0*VDR_Q5_0_Q8_1_MMQ>
(&x_ql[i*(2*WARP_SIZE + 1) + 2*k0], u, x_dmf[index_bx], y_df[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]);
}
}
}
template <int mmq_x, int mmq_y, int nwarps>
static __device__ __forceinline__ void vec_dot_q5_0_q8_1_mma(
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
typedef mma_int_A_I16K8 mma_A;
typedef mma_int_B_J8K8 mma_B;
typedef mma_int_C_I16J8 mma_C;
const float * x_df = (const float *) x_dm;
const int * y_qs = (const int *) y + 4;
const float * y_df = (const float *) y;
mma_A A;
float dA[mma_C::ne/2];
const int i0 = threadIdx.y*mma_A::I;
static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y");
#pragma unroll
for (int l = 0; l < mma_A::ne; ++l) {
const int i = i0 + mma_A::get_i(l);
const int k = 2*(k0 + mma_A::get_k(l) % QI5_0) + mma_A::get_k(l) / QI5_0;
A.x[l] = x_ql[i*(2*WARP_SIZE + 1) + k];
}
#pragma unroll
for (int l = 0; l < mma_C::ne/2; ++l) {
const int i = i0 + mma_C::get_i(2*l);
dA[l] = x_df[i*(WARP_SIZE/QI5_0) + i/QI5_0 + k0/QI5_0];
}
for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) {
mma_C C;
mma_B B;
float dB[mma_C::ne/2];
#pragma unroll
for (int l = 0; l < mma_B::ne; ++l) {
const int j = j0 + mma_B::get_j(l);
const int k = (2*k0 + mma_B::get_k(l)) % WARP_SIZE;
B.x[l] = y_qs[j*MMQ_TILE_Y_K + k];
}
#pragma unroll
for (int l = 0; l < mma_C::ne/2; ++l) {
const int j = j0 + mma_C::get_j(l);
dB[l] = y_df[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)];
}
C.mma_K8(A, B);
#pragma unroll
for (int l = 0; l < mma_C::ne; ++l) {
sum[(j0/B.J)*C.ne + l] += dA[l/2]*dB[l%2]*C.x[l];
}
}
}
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_1(
const char * __restrict__ x, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) {
GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
const int kbx = threadIdx.x / QI5_1;
const int kqsx = threadIdx.x % QI5_1;
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
int i = i0 + threadIdx.y;
if (need_check) {
i = min(i, i_max);
}
const block_q5_1 * bxi = (const block_q5_1 *) x + kbx0 + i*stride + kbx;
const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
const int qh = get_int_from_uint8_aligned(bxi->qh, 0) >> (4 * (threadIdx.x % 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*threadIdx.x+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*threadIdx.x+1] = qs1;
}
const int blocks_per_tile_x_row = WARP_SIZE / QI5_1;
const int kbxd = threadIdx.x % blocks_per_tile_x_row;
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_1) {
int i = i0 + threadIdx.y * QI5_1 + threadIdx.x / blocks_per_tile_x_row;
if (need_check) {
i = min(i, i_max);
}
const block_q5_1 * bxi = (const block_q5_1 *) x + kbx0 + i*stride + kbxd;
x_dm[i * (WARP_SIZE/QI5_1) + i / QI5_1 + kbxd] = bxi->dm;
}
}
template <int mmq_x, int mmq_y, int nwarps>
static __device__ __forceinline__ void vec_dot_q5_1_q8_1_dp4a(
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
const int * y_qs = (const int *) y + 4;
const half2 * y_ds = (const half2 *) y;
#pragma unroll
for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
const int j = j0 + threadIdx.y;
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
const int i = i0 + threadIdx.x;
const int kyqs = k0 % (QI8_1/2) + QI8_1 * (k0 / (QI8_1/2));
const int index_bx = i*(WARP_SIZE/QI5_1) + i/QI5_1 + k0/QI5_1;
int u[2*VDR_Q5_1_Q8_1_MMQ];
#pragma unroll
for (int l = 0; l < VDR_Q5_1_Q8_1_MMQ; ++l) {
u[2*l+0] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l) % WARP_SIZE];
u[2*l+1] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l + QI5_1) % WARP_SIZE];
}
sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q8_1_q8_1_impl<QR5_1*VDR_Q5_1_Q8_1_MMQ>
(&x_ql[i*(2*WARP_SIZE + 1) + 2*k0], u, x_dm[index_bx], y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]);
}
}
}
template <int mmq_x, int mmq_y, int nwarps>
static __device__ __forceinline__ void vec_dot_q5_1_q8_1_mma(
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
typedef mma_int_A_I16K8 mma_A;
typedef mma_int_B_J8K8 mma_B;
typedef mma_int_C_I16J8 mma_C;
const int * y_qs = (const int *) y + 4;
const half2 * y_ds = (const half2 *) y;
mma_A A;
half2 dmA[mma_C::ne/2];
const int i0 = threadIdx.y*mma_A::I;
static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y");
#pragma unroll
for (int l = 0; l < mma_A::ne; ++l) {
const int i = i0 + mma_A::get_i(l);
const int k = 2*(k0 + mma_A::get_k(l) % QI5_1) + mma_A::get_k(l) / QI5_1;
A.x[l] = x_ql[i*(2*WARP_SIZE + 1) + k];
}
#pragma unroll
for (int l = 0; l < mma_C::ne/2; ++l) {
const int i = i0 + mma_C::get_i(2*l);
dmA[l] = x_dm[i*(WARP_SIZE/QI5_1) + i/QI5_1 + k0/QI5_1];
}
for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) {
mma_C C;
mma_B B;
half2 dsB[mma_C::ne/2];
#pragma unroll
for (int l = 0; l < mma_B::ne; ++l) {
const int j = j0 + mma_B::get_j(l);
const int k = (2*k0 + mma_B::get_k(l)) % WARP_SIZE;
B.x[l] = y_qs[j*MMQ_TILE_Y_K + k];
}
#pragma unroll
for (int l = 0; l < mma_C::ne/2; ++l) {
const int j = j0 + mma_C::get_j(l);
dsB[l] = y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)];
}
C.mma_K8(A, B);
#pragma unroll
for (int l = 0; l < mma_C::ne; ++l) {
const half2 dmA_dsB = dmA[l/2]*dsB[l%2];
sum[(j0/B.J)*C.ne + l] += __low2float(dmA_dsB)*C.x[l] + __high2float(dmA_dsB);
}
}
}
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q8_0(
const char * __restrict__ x, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) {
GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
const int kbx = threadIdx.x / QI8_0;
const int kqsx = threadIdx.x % QI8_0;
float * x_dmf = (float *) x_dm;
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
int i = i0 + threadIdx.y;
if (need_check) {
i = min(i, i_max);
}
const block_q8_0 * bxi = (const block_q8_0 *) x + kbx0 + i*stride + kbx;
x_ql[i * (WARP_SIZE + 1) + threadIdx.x] = get_int_from_int8(bxi->qs, kqsx);
}
const int blocks_per_tile_x_row = WARP_SIZE / QI8_0;
const int kbxd = threadIdx.x % blocks_per_tile_x_row;
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI8_0) {
int i = i0 + threadIdx.y * QI8_0 + threadIdx.x / blocks_per_tile_x_row;
if (need_check) {
i = min(i, i_max);
}
const block_q8_0 * bxi = (const block_q8_0 *) x + kbx0 + i*stride + kbxd;
x_dmf[i * (WARP_SIZE/QI8_0) + i / QI8_0 + kbxd] = bxi->d;
}
}
template <int mmq_x, int mmq_y, int nwarps>
static __device__ __forceinline__ void vec_dot_q8_0_q8_1_dp4a(
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
const float * x_dmf = (const float *) x_dm;
const int * y_qs = (const int *) y + 4;
const float * y_df = (const float *) y;
#pragma unroll
for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
const int j = j0 + threadIdx.y;
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
const int i = i0 + threadIdx.x;
sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q8_0_q8_1_impl<float, VDR_Q8_0_Q8_1_MMQ>
(&x_ql[i*(WARP_SIZE + 1) + k0], &y_qs[j*MMQ_TILE_Y_K + k0], x_dmf[i*(WARP_SIZE/QI8_0) + i/QI8_0 + k0/QI8_0],
y_df[j*MMQ_TILE_Y_K + k0/QI8_1]);
}
}
}
template <int mmq_x, int mmq_y, int nwarps>
static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mma(
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
typedef mma_int_A_I16K8 mma_A;
typedef mma_int_B_J8K8 mma_B;
typedef mma_int_C_I16J8 mma_C;
const float * x_df = (const float *) x_dm;
const int * y_qs = (const int *) y + 4;
const float * y_df = (const float *) y;
mma_A A;
float dA[mma_C::ne/2];
const int i0 = threadIdx.y*mma_A::I;
static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y");
#pragma unroll
for (int l = 0; l < mma_A::ne; ++l) {
const int i = i0 + mma_A::get_i(l);
const int k = k0 + mma_A::get_k(l);
A.x[l] = x_ql[i*(WARP_SIZE + 1) + k];
}
#pragma unroll
for (int l = 0; l < mma_C::ne/2; ++l) {
const int i = i0 + mma_C::get_i(2*l);
dA[l] = x_df[i*(WARP_SIZE/QI8_0) + i/QI8_0 + k0/QI8_0];
}
for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) {
mma_C C;
mma_B B;
float dB[mma_C::ne/2];
#pragma unroll
for (int l = 0; l < mma_B::ne; ++l) {
const int j = j0 + mma_B::get_j(l);
const int k = k0 + mma_B::get_k(l);
B.x[l] = y_qs[j*MMQ_TILE_Y_K + k];
}
#pragma unroll
for (int l = 0; l < mma_C::ne/2; ++l) {
const int j = j0 + mma_C::get_j(l);
dB[l] = y_df[j*MMQ_TILE_Y_K + k0/QI8_1];
}
C.mma_K8(A, B);
#pragma unroll
for (int l = 0; l < mma_C::ne; ++l) {
sum[(j0/B.J)*C.ne + l] += C.x[l]*dA[l/2]*dB[l%2];
}
}
}
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q2_K(
const char * __restrict__ x, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) {
GGML_UNUSED(x_qh);
const int kbx = threadIdx.x / QI2_K;
const int kqsx = threadIdx.x % QI2_K;
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
int i = i0 + threadIdx.y;
if (need_check) {
i = min(i, i_max);
}
const block_q2_K * bxi = (const block_q2_K *) x + kbx0 + i*stride + kbx;
x_ql[i * (WARP_SIZE + 1) + threadIdx.x] = get_int_from_uint8_aligned(bxi->qs, kqsx);
}
const int blocks_per_tile_x_row = WARP_SIZE / QI2_K;
const int kbxd = threadIdx.x % blocks_per_tile_x_row;
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI2_K) {
int i = (i0 + threadIdx.y * QI2_K + threadIdx.x / blocks_per_tile_x_row) % mmq_y;
if (need_check) {
i = min(i, i_max);
}
const block_q2_K * bxi = (const block_q2_K *) x + kbx0 + i*stride + kbxd;
x_dm[i * (WARP_SIZE/QI2_K) + i / QI2_K + kbxd] = bxi->dm;
}
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
int i = i0 + threadIdx.y * 4 + threadIdx.x / (WARP_SIZE/4);
if (need_check) {
i = min(i, i_max);
}
const block_q2_K * bxi = (const block_q2_K *) x + kbx0 + i*stride + (threadIdx.x % (WARP_SIZE/4)) / (QI2_K/4);
x_sc[i * (WARP_SIZE/4) + i / 4 + threadIdx.x % (WARP_SIZE/4)] = get_int_from_uint8_aligned(bxi->scales, threadIdx.x % (QI2_K/4));
}
}
template <int mmq_x, int mmq_y, int nwarps>
static __device__ __forceinline__ void vec_dot_q2_K_q8_1_mul_mat(
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
GGML_UNUSED(x_qh);
const int * y_qs = (const int *) y + 4;
const float * y_df = (const float *) y;
#pragma unroll
for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
const int j = j0 + threadIdx.y;
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
const int i = i0 + threadIdx.x;
const int kbx = k0 / QI2_K;
const int ky = (k0 % QI2_K) * QR2_K;
int v[QR2_K*VDR_Q2_K_Q8_1_MMQ];
const int kqsx = i*(WARP_SIZE + 1) + kbx*QI2_K + (QI2_K/2) * (ky/(2*QI2_K)) + ky % (QI2_K/2);
const int shift = 2 * ((ky % (2*QI2_K)) / (QI2_K/2));
#pragma unroll
for (int l = 0; l < QR2_K*VDR_Q2_K_Q8_1_MMQ; ++l) {
v[l] = (x_ql[kqsx + l] >> shift) & 0x03030303;
}
const uint8_t * scales = ((const uint8_t *) &x_sc[i*(WARP_SIZE/4) + i/4 + kbx*4]) + ky/4;
sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q2_K_q8_1_impl_mmq(
v, &y_qs[j*MMQ_TILE_Y_K + (QR2_K*k0) % WARP_SIZE], scales,
x_dm[i*(WARP_SIZE/QI2_K) + i/QI2_K + kbx], y_df[j*MMQ_TILE_Y_K + ((QR2_K*k0) % WARP_SIZE)/QI8_1]);
}
}
}
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q3_K(
const char * __restrict__ x, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) {
const int kbx = threadIdx.x / QI3_K;
const int kqsx = threadIdx.x % QI3_K;
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
int i = i0 + threadIdx.y;
if (need_check) {
i = min(i, i_max);
}
const block_q3_K * bxi = (const block_q3_K *) x + kbx0 + i*stride + kbx;
x_ql[i * (WARP_SIZE + 1) + threadIdx.x] = get_int_from_uint8(bxi->qs, kqsx);
}
const int blocks_per_tile_x_row = WARP_SIZE / QI3_K;
const int kbxd = threadIdx.x % blocks_per_tile_x_row;
float * x_dmf = (float *) x_dm;
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI3_K) {
int i = (i0 + threadIdx.y * QI3_K + threadIdx.x / blocks_per_tile_x_row) % mmq_y;
if (need_check) {
i = min(i, i_max);
}
const block_q3_K * bxi = (const block_q3_K *) x + kbx0 + i*stride + kbxd;
x_dmf[i * (WARP_SIZE/QI3_K) + i / QI3_K + kbxd] = bxi->d;
}
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 2) {
int i = i0 + threadIdx.y * 2 + threadIdx.x / (WARP_SIZE/2);
if (need_check) {
i = min(i, i_max);
}
const block_q3_K * bxi = (const block_q3_K *) x + kbx0 + i*stride + (threadIdx.x % (WARP_SIZE/2)) / (QI3_K/2);
// invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
x_qh[i * (WARP_SIZE/2) + i / 2 + threadIdx.x % (WARP_SIZE/2)] = ~get_int_from_uint8(bxi->hmask, threadIdx.x % (QI3_K/2));
}
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
int i = i0 + threadIdx.y * 4 + threadIdx.x / (WARP_SIZE/4);
if (need_check) {
i = min(i, i_max);
}
const block_q3_K * bxi = (const block_q3_K *) x + kbx0 + i*stride + (threadIdx.x % (WARP_SIZE/4)) / (QI3_K/4);
const int ksc = threadIdx.x % (QI3_K/4);
const int ksc_low = ksc % (QI3_K/8);
const int shift_low = 4 * (ksc / (QI3_K/8));
const int sc_low = (get_int_from_uint8(bxi->scales, ksc_low) >> shift_low) & 0x0F0F0F0F;
const int ksc_high = QI3_K/8;
const int shift_high = 2 * ksc;
const int sc_high = ((get_int_from_uint8(bxi->scales, ksc_high) >> shift_high) << 4) & 0x30303030;
const int sc = __vsubss4(sc_low | sc_high, 0x20202020);
x_sc[i * (WARP_SIZE/4) + i / 4 + threadIdx.x % (WARP_SIZE/4)] = sc;
}
}
template <int mmq_x, int mmq_y, int nwarps>
static __device__ __forceinline__ void vec_dot_q3_K_q8_1_mul_mat(
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
const float * x_dmf = (const float *) x_dm;
const int * y_qs = (const int *) y + 4;
const float * y_df = (const float *) y;
#pragma unroll
for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
const int j = j0 + threadIdx.y;
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
const int i = i0 + threadIdx.x;
const int kbx = k0 / QI3_K;
const int ky = (k0 % QI3_K) * QR3_K;
const int8_t * scales = ((const int8_t *) (x_sc + i * (WARP_SIZE/4) + i/4 + kbx*4)) + ky/4;
int v[QR3_K*VDR_Q3_K_Q8_1_MMQ];
#pragma unroll
for (int l = 0; l < QR3_K*VDR_Q3_K_Q8_1_MMQ; ++l) {
const int kqsx = i*(WARP_SIZE + 1) + kbx*QI3_K + (QI3_K/2) * (ky/(2*QI3_K)) + ky % (QI3_K/2);
const int shift = 2 * ((ky % 32) / 8);
const int vll = (x_ql[kqsx + l] >> shift) & 0x03030303;
const int vh = x_qh[i*(WARP_SIZE/2) + i/2 + kbx * (QI3_K/2) + (ky+l)%8] >> ((ky+l) / 8);
const int vlh = (vh << 2) & 0x04040404;
v[l] = __vsubss4(vll, vlh);
}
sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q3_K_q8_1_impl_mmq(
v, &y_qs[j*MMQ_TILE_Y_K + (k0*QR3_K) % WARP_SIZE], scales,
x_dmf[i*(WARP_SIZE/QI3_K) + i/QI3_K + kbx], y_df[j*MMQ_TILE_Y_K + ((k0*QR3_K) % WARP_SIZE)/QI8_1]);
}
}
}
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_K(
const char * __restrict__ x, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) {
GGML_UNUSED(x_qh);
const int kbx = 0; // threadIdx.x / QI4_K
const int kqsx = threadIdx.x; // threadIdx.x % QI4_K
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
int i = i0 + threadIdx.y;
if (need_check) {
i = min(i, i_max);
}
const block_q4_K * bxi = (const block_q4_K *) x + kbx0 + i*stride + kbx;
x_ql[i * (WARP_SIZE + 1) + threadIdx.x] = get_int_from_uint8_aligned(bxi->qs, kqsx);
}
const int blocks_per_tile_x_row = WARP_SIZE / QI4_K; // == 1 if QK_K == 256
const int kbxd = threadIdx.x % blocks_per_tile_x_row; // == 0 if QK_K == 256
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_K) {
int i = (i0 + threadIdx.y * QI4_K + threadIdx.x / blocks_per_tile_x_row) % mmq_y;
if (need_check) {
i = min(i, i_max);
}
const block_q4_K * bxi = (const block_q4_K *) x + kbx0 + i*stride + kbxd;
x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = bxi->dm;
}
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
int i = (i0 + threadIdx.y * 8 + threadIdx.x / (WARP_SIZE/8)) % mmq_y;
if (need_check) {
i = min(i, i_max);
}
const block_q4_K * bxi = (const block_q4_K *) x + kbx0 + i*stride + (threadIdx.x % (WARP_SIZE/8)) / (QI4_K/8);
const int * scales = (const int *) bxi->scales;
const int ksc = threadIdx.x % (WARP_SIZE/8);
// scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
scales8 |= (scales[ksc/2] >> (2 * (ksc % 2))) & 0x30303030; // upper 2 bits
x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
}
}
template <int mmq_x, int mmq_y, int nwarps>
static __device__ __forceinline__ void vec_dot_q4_K_q8_1_dp4a(
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
GGML_UNUSED(x_qh);
const int * y_qs = (const int *) y + 4;
const half2 * y_ds = (const half2 *) y;
#pragma unroll
for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
const int j = j0 + threadIdx.y;
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
const int i = i0 + threadIdx.x;
const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/16]) + 2*((k0 % 16) / 8);
sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q4_K_q8_1_impl_mmq(
&x_ql[i*(WARP_SIZE + 1) + k0], &y_qs[j*MMQ_TILE_Y_K + (QR4_K*k0) % WARP_SIZE], sc, sc+8,
x_dm[i*(WARP_SIZE/QI4_K) + i/QI4_K], &y_ds[j*MMQ_TILE_Y_K + ((QR4_K*k0) % WARP_SIZE)/QI8_1]);
}
}
}
template <int mmq_x, int mmq_y, int nwarps>
static __device__ __forceinline__ void vec_dot_q4_K_q8_1_mma(
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
typedef mma_int_A_I16K8 mma_A;
typedef mma_int_B_J8K8 mma_B;
typedef mma_int_C_I16J8 mma_C;
const int * y_qs = (const int *) y + 4;
const half2 * y_ds = (const half2 *) y;
const int i0 = threadIdx.y*mma_A::I;
static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y");
mma_A A[2];
int scA[mma_C::ne/2][2];
int mA[mma_C::ne/2][2];
half2 dmA[mma_C::ne/2];
#pragma unroll
for (int kvdr = 0; kvdr < VDR_Q4_K_Q8_1_MMQ; kvdr += 4) {
#pragma unroll
for (int l = 0; l < mma_A::ne; ++l) {
const int i = i0 + mma_A::get_i(l);
const int k = k0 + mma_A::get_k(l);
A[kvdr/4].x[l] = (x_ql[i*(WARP_SIZE + 1) + k] >> kvdr) & 0x0F0F0F0F;
}
#pragma unroll
for (int l = 0; l < mma_C::ne/2; ++l) {
const int i = i0 + mma_C::get_i(2*l);
const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/16]) + 2 * ((k0 % 16) / 8);
const uint8_t * m = sc + 8;
scA[l][kvdr/4] = sc[kvdr/4];
mA[l][kvdr/4] = m[kvdr/4];
}
}
#pragma unroll
for (int l = 0; l < mma_C::ne/2; ++l) {
const int i = i0 + mma_C::get_i(2*l);
dmA[l] = x_dm[i*(WARP_SIZE/QI5_K) + i/QI5_K + k0/QI5_K];
}
#pragma unroll
for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) {
float tmpd[mma_C::ne] = {0.0f};
float tmpm[mma_C::ne] = {0.0f};
#pragma unroll
for (int kvdr = 0; kvdr < VDR_Q5_K_Q8_1_MMQ; kvdr += 4) {
mma_C C;
mma_B B;
half2 dsB[mma_C::ne/2];
#pragma unroll
for (int l = 0; l < mma_B::ne; ++l) {
const int j = j0 + mma_B::get_j(l);
const int k = (2*k0 + 2*kvdr + mma_B::get_k(l)) % WARP_SIZE;
B.x[l] = y_qs[j*MMQ_TILE_Y_K + k];
}
#pragma unroll
for (int l = 0; l < mma_C::ne/2; ++l) {
const int j = j0 + mma_C::get_j(l);
dsB[l] = y_ds[j*MMQ_TILE_Y_K + ((2*k0 + 2*kvdr)/QI8_1) % (WARP_SIZE/QI8_1)];
}
C.mma_K8(A[kvdr/4], B);
#pragma unroll
for (int l = 0; l < mma_C::ne; ++l) {
tmpd[l] += (C.x[l]*scA[l/2][kvdr/4]) * __low2float(dsB[l%2]);
tmpm[l] += mA[l/2][kvdr/4] * __high2float(dsB[l%2]);
}
}
#pragma unroll
for (int l = 0; l < mma_C::ne; ++l) {
sum[(j0/mma_B::J)*mma_C::ne + l] += __low2float(dmA[l/2])*tmpd[l] - __high2float(dmA[l/2])*tmpm[l];
}
}
}
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_K(
const char * __restrict__ x, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) {
GGML_UNUSED(x_qh);
const int kbx = 0; // threadIdx.x / QI5_K
const int kqsx = threadIdx.x; // threadIdx.x % QI5_K
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
int i = i0 + threadIdx.y;
if (need_check) {
i = min(i, i_max);
}
const block_q5_K * bxi = (const block_q5_K *) x + kbx0 + i*stride + kbx;
const int ky = QR5_K*kqsx;
const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
const int ql0 = (ql >> 0) & 0x0F0F0F0F;
const int ql1 = (ql >> 4) & 0x0F0F0F0F;
const int qh = get_int_from_uint8_aligned(bxi->qh, kqsx % (QI5_K/4));
const int qh0 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 0)) << 4) & 0x10101010;
const int qh1 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 1)) << 4) & 0x10101010;
const int kq0 = ky - ky % (QI5_K/2) + threadIdx.x % (QI5_K/4) + 0;
const int kq1 = ky - ky % (QI5_K/2) + threadIdx.x % (QI5_K/4) + (QI5_K/4);
x_ql[i * (2*WARP_SIZE + 1) + kq0] = ql0 | qh0;
x_ql[i * (2*WARP_SIZE + 1) + kq1] = ql1 | qh1;
}
const int blocks_per_tile_x_row = WARP_SIZE / QI5_K; // == 1 if QK_K == 256
const int kbxd = threadIdx.x % blocks_per_tile_x_row; // == 0 if QK_K == 256
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_K) {
int i = (i0 + threadIdx.y * QI5_K + threadIdx.x / blocks_per_tile_x_row) % mmq_y;
if (need_check) {
i = min(i, i_max);
}
const block_q5_K * bxi = (const block_q5_K *) x + kbx0 + i*stride + kbxd;
x_dm[i * (WARP_SIZE/QI5_K) + i / QI5_K + kbxd] = bxi->dm;
}
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
int i = (i0 + threadIdx.y * 8 + threadIdx.x / (WARP_SIZE/8)) % mmq_y;
if (need_check) {
i = min(i, i_max);
}
const block_q5_K * bxi = (const block_q5_K *) x + kbx0 + i*stride + (threadIdx.x % (WARP_SIZE/8)) / (QI5_K/8);
const int * scales = (const int *) bxi->scales;
const int ksc = threadIdx.x % (WARP_SIZE/8);
// scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
scales8 |= (scales[ksc/2] >> (2 * (ksc % 2))) & 0x30303030; // upper 2 bits
x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
}
}
template <int mmq_x, int mmq_y, int nwarps>
static __device__ __forceinline__ void vec_dot_q5_K_q8_1_dp4a(
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
GGML_UNUSED(x_qh);
const int * y_qs = (const int *) y + 4;
const half2 * y_ds = (const half2 *) y;
#pragma unroll
for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
const int j = j0 + threadIdx.y;
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
const int i = i0 + threadIdx.x;
const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/16]) + 2 * ((k0 % 16) / 8);
sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q5_K_q8_1_impl_mmq(
&x_ql[i*(QR5_K*WARP_SIZE + 1) + QR5_K*k0], &y_qs[j*MMQ_TILE_Y_K + (QR5_K*k0) % WARP_SIZE], sc, sc+8,
x_dm[i*(WARP_SIZE/QI5_K) + i/QI5_K], &y_ds[j*MMQ_TILE_Y_K + ((QR5_K*k0) % WARP_SIZE)/QI8_1]);
}
}
}
template <int mmq_x, int mmq_y, int nwarps>
static __device__ __forceinline__ void vec_dot_q5_K_q8_1_mma(
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
typedef mma_int_A_I16K8 mma_A;
typedef mma_int_B_J8K8 mma_B;
typedef mma_int_C_I16J8 mma_C;
const int * y_qs = (const int *) y + 4;
const half2 * y_ds = (const half2 *) y;
const int i0 = threadIdx.y*mma_A::I;
static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y");
mma_A A[2];
int scA[mma_C::ne/2][2];
int mA[mma_C::ne/2][2];
half2 dmA[mma_C::ne/2];
#pragma unroll
for (int kvdr = 0; kvdr < VDR_Q5_K_Q8_1_MMQ; kvdr += 4) {
#pragma unroll
for (int l = 0; l < mma_A::ne; ++l) {
const int i = i0 + mma_A::get_i(l);
const int k = QR5_K*k0 + QR5_K*kvdr + mma_A::get_k(l);
A[kvdr/4].x[l] = x_ql[i*(QR5_K*WARP_SIZE + 1) + k];
}
#pragma unroll
for (int l = 0; l < mma_C::ne/2; ++l) {
const int i = i0 + mma_C::get_i(2*l);
const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/16]) + 2 * ((k0 % 16) / 8);
const uint8_t * m = sc + 8;
scA[l][kvdr/4] = sc[kvdr/4];
mA[l][kvdr/4] = m[kvdr/4];
}
}
#pragma unroll
for (int l = 0; l < mma_C::ne/2; ++l) {
const int i = i0 + mma_C::get_i(2*l);
dmA[l] = x_dm[i*(WARP_SIZE/QI5_K) + i/QI5_K + k0/QI5_K];
}
#pragma unroll
for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) {
float tmpd[mma_C::ne] = {0.0f};
float tmpm[mma_C::ne] = {0.0f};
#pragma unroll
for (int kvdr = 0; kvdr < VDR_Q5_K_Q8_1_MMQ; kvdr += 4) {
mma_C C;
mma_B B;
half2 dsB[mma_C::ne/2];
#pragma unroll
for (int l = 0; l < mma_B::ne; ++l) {
const int j = j0 + mma_B::get_j(l);
const int k = (2*k0 + 2*kvdr + mma_B::get_k(l)) % WARP_SIZE;
B.x[l] = y_qs[j*MMQ_TILE_Y_K + k];
}
#pragma unroll
for (int l = 0; l < mma_C::ne/2; ++l) {
const int j = j0 + mma_C::get_j(l);
dsB[l] = y_ds[j*MMQ_TILE_Y_K + ((2*k0 + 2*kvdr)/QI8_1) % (WARP_SIZE/QI8_1)];
}
C.mma_K8(A[kvdr/4], B);
#pragma unroll
for (int l = 0; l < mma_C::ne; ++l) {
tmpd[l] += (C.x[l]*scA[l/2][kvdr/4]) * __low2float(dsB[l%2]);
tmpm[l] += mA[l/2][kvdr/4] * __high2float(dsB[l%2]);
}
}
#pragma unroll
for (int l = 0; l < mma_C::ne; ++l) {
sum[(j0/mma_B::J)*mma_C::ne + l] += __low2float(dmA[l/2])*tmpd[l] - __high2float(dmA[l/2])*tmpm[l];
}
}
}
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q6_K(
const char * __restrict__ x, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) {
GGML_UNUSED(x_qh);
const int kbx = 0; // threadIdx.x / QI6_K
const int kqsx = threadIdx.x; // threadIdx.x % QI6_K
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
int i = i0 + threadIdx.y;
if (need_check) {
i = min(i, i_max);
}
const block_q6_K * bxi = (const block_q6_K *) x + kbx0 + i*stride + kbx;
const int ky = QR6_K*kqsx;
const int ql = get_int_from_uint8(bxi->ql, kqsx);
const int ql0 = (ql >> 0) & 0x0F0F0F0F;
const int ql1 = (ql >> 4) & 0x0F0F0F0F;
const int qh = get_int_from_uint8(bxi->qh, (QI6_K/4) * (kqsx / (QI6_K/2)) + kqsx % (QI6_K/4));
const int qh0 = ((qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) << 4) & 0x30303030;
const int qh1 = (qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) & 0x30303030;
const int kq0 = ky - ky % QI6_K + threadIdx.x % (QI6_K/2) + 0;
const int kq1 = ky - ky % QI6_K + threadIdx.x % (QI6_K/2) + (QI6_K/2);
x_ql[i * (2*WARP_SIZE + 1) + kq0] = __vsubss4(ql0 | qh0, 0x20202020);
x_ql[i * (2*WARP_SIZE + 1) + kq1] = __vsubss4(ql1 | qh1, 0x20202020);
}
const int blocks_per_tile_x_row = WARP_SIZE / QI6_K; // == 1 if QK_K == 256
const int kbxd = threadIdx.x % blocks_per_tile_x_row; // == 0 if QK_K == 256
float * x_dmf = (float *) x_dm;
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI6_K) {
int i = (i0 + threadIdx.y * QI6_K + threadIdx.x / blocks_per_tile_x_row) % mmq_y;
if (need_check) {
i = min(i, i_max);
}
const block_q6_K * bxi = (const block_q6_K *) x + kbx0 + i*stride + kbxd;
x_dmf[i * (WARP_SIZE/QI6_K) + i / QI6_K + kbxd] = bxi->d;
}
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
int i = (i0 + threadIdx.y * 8 + threadIdx.x / (WARP_SIZE/8)) % mmq_y;
if (need_check) {
i = min(i, i_max);
}
const block_q6_K * bxi = (const block_q6_K *) x + kbx0 + i*stride + (threadIdx.x % (WARP_SIZE/8)) / 4;
x_sc[i * (WARP_SIZE/8) + i / 8 + threadIdx.x % (WARP_SIZE/8)] = get_int_from_int8(bxi->scales, threadIdx.x % (QI6_K/8));
}
}
template <int mmq_x, int mmq_y, int nwarps>
static __device__ __forceinline__ void vec_dot_q6_K_q8_1_dp4a(
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
GGML_UNUSED(x_qh);
const float * x_dmf = (const float *) x_dm;
const int * y_qs = (const int *) y + 4;
const float * y_df = (const float *) y;
#pragma unroll
for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
const int j = j0 + threadIdx.y;
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
const int i = i0 + threadIdx.x;
const int8_t * sc = ((const int8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/8]);
sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q6_K_q8_1_impl_mmq(
&x_ql[i*(QR6_K*WARP_SIZE + 1) + QR6_K*k0], &y_qs[j*MMQ_TILE_Y_K + (QR6_K*k0) % WARP_SIZE], sc,
x_dmf[i*(WARP_SIZE/QI6_K) + i/QI6_K], &y_df[j*MMQ_TILE_Y_K + ((QR6_K*k0) % WARP_SIZE)/QI8_1]);
}
}
}
template <int mmq_x, int mmq_y, int nwarps>
static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mma(
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
typedef mma_int_A_I16K4 mma_A;
typedef mma_int_B_J8K4 mma_B;
typedef mma_int_C_I16J8 mma_C;
const float * x_df = (const float *) x_dm;
const int * y_qs = (const int *) y + 4;
const float * y_df = (const float *) y;
const int i0 = threadIdx.y*mma_A::I;
static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y");
mma_A A[4];
int scA[mma_C::ne/2][4];
float dA[mma_C::ne/2];
#pragma unroll
for (int kvdr = 0; kvdr < VDR_Q6_K_Q8_1_MMQ; kvdr += 4) {
#pragma unroll
for (int l = 0; l < mma_A::ne; ++l) {
const int i = i0 + mma_A::get_i(l);
const int k = QR6_K*k0 + QR6_K*kvdr + mma_A::get_k(l);
A[kvdr/2 + 0].x[l] = x_ql[i*(QR6_K*WARP_SIZE + 1) + k + 0];
A[kvdr/2 + 1].x[l] = x_ql[i*(QR6_K*WARP_SIZE + 1) + k + mma_A::K];
}
#pragma unroll
for (int l = 0; l < mma_C::ne/2; ++l) {
const int i = i0 + mma_C::get_i(2*l);
const int8_t * sc = ((const int8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/8]);
scA[l][kvdr/2 + 0] = sc[kvdr/2 + 0];
scA[l][kvdr/2 + 1] = sc[kvdr/2 + 1];
}
}
#pragma unroll
for (int l = 0; l < mma_C::ne/2; ++l) {
const int i = i0 + mma_C::get_i(2*l);
dA[l] = x_df[i*(WARP_SIZE/QI6_K) + i/QI6_K + k0/QI6_K];
}
#pragma unroll
for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) {
float tmp[mma_C::ne] = {0.0f};
#pragma unroll
for (int kvdr = 0; kvdr < VDR_Q6_K_Q8_1_MMQ; kvdr += 4) {
mma_C C[2];
mma_B B[2];
float dB[mma_C::ne/2];
#pragma unroll
for (int l = 0; l < mma_B::ne; ++l) {
const int j = j0 + mma_B::get_j(l);
const int k = (2*k0 + 2*kvdr + mma_B::get_k(l)) % WARP_SIZE;
B[0].x[l] = y_qs[j*MMQ_TILE_Y_K + k + 0];
B[1].x[l] = y_qs[j*MMQ_TILE_Y_K + k + mma_B::K];
}
#pragma unroll
for (int l = 0; l < mma_C::ne/2; ++l) {
const int j = j0 + mma_C::get_j(l);
dB[l] = y_df[j*MMQ_TILE_Y_K + ((2*k0 + 2*kvdr)/QI8_1) % (WARP_SIZE/QI8_1)];
}
C[0].mma_K4(A[kvdr/2 + 0], B[0]);
C[1].mma_K4(A[kvdr/2 + 1], B[1]);
#pragma unroll
for (int l = 0; l < mma_C::ne; ++l) {
tmp[l] += (C[0].x[l]*scA[l/2][kvdr/2 + 0] + C[1].x[l]*scA[l/2][kvdr/2 + 1])*dB[l%2];
}
}
#pragma unroll
for (int l = 0; l < mma_C::ne; ++l) {
sum[(j0/mma_B::J)*mma_C::ne + l] += tmp[l]*dA[l/2];
}
}
}
template<int mmq_x, int mmq_y, int nwarps, bool need_check>
static __device__ __forceinline__ void mmq_write_back_dp4a(const float * __restrict__ sum, float * __restrict__ dst, const int & ne0, const int & ne1) {
#pragma unroll
for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
const int j = blockIdx.y*mmq_x + j0 + threadIdx.y;
if (j >= ne1) {
return;
}
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
const int i = blockIdx.x*mmq_y + i0 + threadIdx.x;
if (need_check && i >= ne0) {
continue;
}
dst[j*ne0 + i] = sum[(j0/nwarps) * (mmq_y/WARP_SIZE) + i0/WARP_SIZE];
}
}
}
template<int mmq_x, int mmq_y, int nwarps, bool need_check>
static __device__ __forceinline__ void mmq_write_back_mma(const float * __restrict__ sum, float * __restrict__ dst, const int & ne0, const int & ne1) {
typedef mma_int_C_I16J8 mma_C;
const int i0 = threadIdx.y*mma_C::I;
static_assert(nwarps*mma_C::I == mmq_y, "nwarps*mma_C::I != mmq_y");
#pragma unroll
for (int j0 = 0; j0 < mmq_x; j0 += mma_C::J) {
#pragma unroll
for (int l = 0; l < mma_C::ne; ++l) {
const int j = blockIdx.y*mmq_x + j0 + mma_C::get_j(l);
if (j >= ne1) {
continue;
}
const int i = blockIdx.x*mmq_y + i0 + mma_C::get_i(l);
if (need_check && i >= ne0) {
continue;
}
dst[j*ne0 + i] = sum[(j0/mma_C::J)*mma_C::ne + l];
}
}
}
// -------------------------------------------------------------------------------------------------------------------------------------
template <int mmq_x, int mmq_y, int nwarps, bool need_check, ggml_type type>
struct mmq_type_traits;
template <int mmq_x, int mmq_y, int nwarps, bool need_check>
struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q4_0> {
static constexpr int vdr = VDR_Q4_0_Q8_1_MMQ;
static constexpr load_tiles_mmq_t load_tiles = load_tiles_q4_0<mmq_y, nwarps, need_check>;
#ifdef INT8_MMA_AVAILABLE
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q4_0_q8_1_mma<mmq_x, mmq_y, nwarps>;
static constexpr mmq_write_back_t write_back = mmq_write_back_mma<mmq_x, mmq_y, nwarps, need_check>;
#else
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q4_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, nwarps, need_check>;
#endif // INT8_MMA_AVAILABLE
};
template <int mmq_x, int mmq_y, int nwarps, bool need_check>
struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q4_1> {
static constexpr int vdr = VDR_Q4_1_Q8_1_MMQ;
static constexpr load_tiles_mmq_t load_tiles = load_tiles_q4_1<mmq_y, nwarps, need_check>;
#ifdef INT8_MMA_AVAILABLE
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q4_1_q8_1_mma<mmq_x, mmq_y, nwarps>;
static constexpr mmq_write_back_t write_back = mmq_write_back_mma<mmq_x, mmq_y, nwarps, need_check>;
#else
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q4_1_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, nwarps, need_check>;
#endif // INT8_MMA_AVAILABLE
};
template <int mmq_x, int mmq_y, int nwarps, bool need_check>
struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q5_0> {
static constexpr int vdr = VDR_Q5_0_Q8_1_MMQ;
static constexpr load_tiles_mmq_t load_tiles = load_tiles_q5_0<mmq_y, nwarps, need_check>;
#ifdef INT8_MMA_AVAILABLE
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q5_0_q8_1_mma<mmq_x, mmq_y, nwarps>;
static constexpr mmq_write_back_t write_back = mmq_write_back_mma<mmq_x, mmq_y, nwarps, need_check>;
#else
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q5_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, nwarps, need_check>;
#endif // INT8_MMA_AVAILABLE
};
template <int mmq_x, int mmq_y, int nwarps, bool need_check>
struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q5_1> {
static constexpr int vdr = VDR_Q5_1_Q8_1_MMQ;
static constexpr load_tiles_mmq_t load_tiles = load_tiles_q5_1<mmq_y, nwarps, need_check>;
#ifdef INT8_MMA_AVAILABLE
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q5_1_q8_1_mma<mmq_x, mmq_y, nwarps>;
static constexpr mmq_write_back_t write_back = mmq_write_back_mma<mmq_x, mmq_y, nwarps, need_check>;
#else
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q5_1_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, nwarps, need_check>;
#endif // INT8_MMA_AVAILABLE
};
template <int mmq_x, int mmq_y, int nwarps, bool need_check>
struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q8_0> {
static constexpr int vdr = VDR_Q8_0_Q8_1_MMQ;
static constexpr load_tiles_mmq_t load_tiles = load_tiles_q8_0<mmq_y, nwarps, need_check>;
#ifdef INT8_MMA_AVAILABLE
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, nwarps>;
static constexpr mmq_write_back_t write_back = mmq_write_back_mma<mmq_x, mmq_y, nwarps, need_check>;
#else
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q8_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, nwarps, need_check>;
#endif // INT8_MMA_AVAILABLE
};
template <int mmq_x, int mmq_y, int nwarps, bool need_check>
struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q2_K> {
static constexpr int vdr = VDR_Q2_K_Q8_1_MMQ;
static constexpr load_tiles_mmq_t load_tiles = load_tiles_q2_K<mmq_y, nwarps, need_check>;
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q2_K_q8_1_mul_mat<mmq_x, mmq_y, nwarps>;
static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, nwarps, need_check>;
};
template <int mmq_x, int mmq_y, int nwarps, bool need_check>
struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q3_K> {
static constexpr int vdr = VDR_Q3_K_Q8_1_MMQ;
static constexpr load_tiles_mmq_t load_tiles = load_tiles_q3_K<mmq_y, nwarps, need_check>;
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q3_K_q8_1_mul_mat<mmq_x, mmq_y, nwarps>;
static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, nwarps, need_check>;
};
template <int mmq_x, int mmq_y, int nwarps, bool need_check>
struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q4_K> {
static constexpr int vdr = VDR_Q4_K_Q8_1_MMQ;
static constexpr load_tiles_mmq_t load_tiles = load_tiles_q4_K<mmq_y, nwarps, need_check>;
#ifdef INT8_MMA_AVAILABLE
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q4_K_q8_1_mma<mmq_x, mmq_y, nwarps>;
static constexpr mmq_write_back_t write_back = mmq_write_back_mma<mmq_x, mmq_y, nwarps, need_check>;
#else
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q4_K_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, nwarps, need_check>;
#endif // INT8_MMA_AVAILABLE
};
template <int mmq_x, int mmq_y, int nwarps, bool need_check>
struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q5_K> {
static constexpr int vdr = VDR_Q5_K_Q8_1_MMQ;
static constexpr load_tiles_mmq_t load_tiles = load_tiles_q5_K<mmq_y, nwarps, need_check>;
#ifdef INT8_MMA_AVAILABLE
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q5_K_q8_1_mma<mmq_x, mmq_y, nwarps>;
static constexpr mmq_write_back_t write_back = mmq_write_back_mma<mmq_x, mmq_y, nwarps, need_check>;
#else
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q5_K_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, nwarps, need_check>;
#endif // INT8_MMA_AVAILABLE
};
template <int mmq_x, int mmq_y, int nwarps, bool need_check>
struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q6_K> {
static constexpr int vdr = VDR_Q6_K_Q8_1_MMQ;
static constexpr load_tiles_mmq_t load_tiles = load_tiles_q6_K<mmq_y, nwarps, need_check>;
#ifdef INT8_MMA_AVAILABLE
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q6_K_q8_1_mma<mmq_x, mmq_y, nwarps>;
static constexpr mmq_write_back_t write_back = mmq_write_back_mma<mmq_x, mmq_y, nwarps, need_check>;
#else
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q6_K_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, nwarps, need_check>;
#endif // INT8_MMA_AVAILABLE
};
static int mmq_need_sum(const ggml_type type_x) {
switch (type_x) {
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
return true;
case GGML_TYPE_Q5_0:
return false;
case GGML_TYPE_Q5_1:
return true;
case GGML_TYPE_Q8_0:
case GGML_TYPE_Q2_K:
case GGML_TYPE_Q3_K:
return false;
case GGML_TYPE_Q4_K:
case GGML_TYPE_Q5_K:
return true;
case GGML_TYPE_Q6_K:
return false;
default:
GGML_ASSERT(false);
break;
}
return false;
}
template <ggml_type type, int mmq_x, int nwarps, bool need_check>
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
#if defined(RDNA3) || defined(RDNA2)
__launch_bounds__(WARP_SIZE*nwarps, 2)
#endif // defined(RDNA3) || defined(RDNA2)
#else
#if __CUDA_ARCH__ >= CC_VOLTA
__launch_bounds__(WARP_SIZE*nwarps, 1)
#else
__launch_bounds__(WARP_SIZE*nwarps, type == GGML_TYPE_Q2_K ? 1 : 2)
#endif // __CUDA_ARCH__ >= CC_VOLTA
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
static __global__ void mul_mat_q(
const char * __restrict__ x, const char * __restrict__ yc, float * __restrict__ dst,
const int ne00, const int ne01, const int stride01, const int ne10, const int ne11, const int stride11, const int ne0) {
// Skip unused template specializations for faster compilation:
if (mmq_x > get_mmq_x_max_device()) {
NO_DEVICE_CODE;
return;
}
constexpr int qk = ggml_cuda_type_traits<type>::qk;
constexpr int qr = ggml_cuda_type_traits<type>::qr;
constexpr int qi = ggml_cuda_type_traits<type>::qi;
constexpr int mmq_y = get_mmq_y_device(mmq_x);
constexpr int vdr = mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, type>::vdr;
constexpr load_tiles_mmq_t load_tiles = mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, type>::load_tiles;
constexpr vec_dot_mmq_t vec_dot = mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, type>::vec_dot;
constexpr mmq_write_back_t write_back = mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, type>::write_back;
constexpr tile_x_sizes txs = get_tile_x_sizes_device<mmq_y>(type);
extern __shared__ char data_mul_mat_q[];
int * tile_x_ql = (int *) data_mul_mat_q;
half2 * tile_x_dm = (half2 *) (tile_x_ql + txs.ql);
int * tile_x_qh = (int *) (tile_x_dm + txs.dm);
int * tile_x_sc = (int *) (tile_x_qh + txs.qh);
int * tile_y = (int *) (tile_x_sc + txs.sc); // [mmq_x * (WARP_SIZE + WARP_SIZE/QI8_1)]
const int blocks_per_row_x = ne00 / qk;
const int blocks_per_warp = WARP_SIZE / qi;
const int & ne1 = ne11;
const int tile_x_max_i = ne01 - blockIdx.x*mmq_y - 1;
const int * y = (const int *) yc + blockIdx.y*(mmq_x*sizeof(block_q8_1_mmq)/sizeof(int));
float sum[mmq_x*mmq_y / (nwarps*WARP_SIZE)] = {0.0f};
for (int kb0 = 0; kb0 < blocks_per_row_x; kb0 += blocks_per_warp) {
load_tiles(x, tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc, stride01*blockIdx.x*mmq_y + kb0, tile_x_max_i, stride01);
#pragma unroll
for (int kr = 0; kr < qr; ++kr) {
const int * by0 = y + stride11*(kb0*(qk*sizeof(block_q8_1_mmq) / (4*QK8_1*sizeof(int))) + kr*sizeof(block_q8_1_mmq)/sizeof(int));
#pragma unroll
for (int l0 = 0; l0 < mmq_x*MMQ_TILE_Y_K; l0 += nwarps*WARP_SIZE) {
int l = l0 + threadIdx.y*WARP_SIZE + threadIdx.x;
tile_y[l] = by0[l];
}
__syncthreads();
// #pragma unroll // unrolling this loop causes too much register pressure
for (int k0 = kr*WARP_SIZE/qr; k0 < (kr+1)*WARP_SIZE/qr; k0 += vdr) {
vec_dot(tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc, tile_y, sum, k0);
}
__syncthreads();
}
}
write_back(sum, dst, ne0, ne1);
}
struct mmq_args {
const char * x; const char * y; float * dst;
int64_t ne00; int64_t ne01; int64_t stride01;
int64_t ne10; int64_t ne11; int64_t stride11;
int64_t ne0;
};
template <ggml_type type, int mmq_x, int nwarps>
static void launch_mul_mat_q(const mmq_args & args, cudaStream_t stream) {
const int id = ggml_cuda_get_device();
const int cc = ggml_cuda_info().devices[id].cc;
const int mmq_y = get_mmq_y_host(cc, mmq_x);
const int block_num_x = (args.ne01 + mmq_y - 1) / mmq_y;
const int block_num_y = (args.ne11 + mmq_x - 1) / mmq_x;
const dim3 block_nums(block_num_x, block_num_y, 1);
const dim3 block_dims(WARP_SIZE, nwarps, 1);
const tile_x_sizes txs = get_tile_x_sizes_host(type, mmq_y);
const int shmem_x = txs.ql*sizeof(int) + txs.dm*sizeof(half2) + txs.qh*sizeof(int) + txs.sc*sizeof(int);
const int shmem_y = mmq_x*WARP_SIZE*sizeof(int) + mmq_x*(WARP_SIZE/QI8_1)*sizeof(half2);
const int shmem = shmem_x + GGML_PAD(shmem_y, nwarps*WARP_SIZE*sizeof(int));
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
static bool shmem_limit_raised[GGML_CUDA_MAX_DEVICES] = {false};
if (!shmem_limit_raised[id]) {
CUDA_CHECK(cudaFuncSetAttribute(mul_mat_q<type, mmq_x, nwarps, false>, cudaFuncAttributeMaxDynamicSharedMemorySize, shmem));
CUDA_CHECK(cudaFuncSetAttribute(mul_mat_q<type, mmq_x, nwarps, true>, cudaFuncAttributeMaxDynamicSharedMemorySize, shmem));
shmem_limit_raised[id] = true;
}
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
if (args.ne01 % mmq_y == 0) {
const bool need_check = false;
mul_mat_q<type, mmq_x, nwarps, need_check><<<block_nums, block_dims, shmem, stream>>>
(args.x, args.y, args.dst, args.ne00, args.ne01, args.stride01, args.ne10, args.ne11, args.stride11, args.ne0);
} else {
const bool need_check = true;
mul_mat_q<type, mmq_x, nwarps, need_check><<<block_nums, block_dims, shmem, stream>>>
(args.x, args.y, args.dst, args.ne00, args.ne01, args.stride01, args.ne10, args.ne11, args.stride11, args.ne0);
}
}
template <ggml_type type>
void mul_mat_q_case(const mmq_args & args, cudaStream_t stream) {
const int id = ggml_cuda_get_device();
const int nsm = ggml_cuda_info().devices[id].nsm;
const int cc = ggml_cuda_info().devices[id].cc;
const int mmq_x_max = get_mmq_x_max_host(cc);
const int mmq_y = get_mmq_y_host(cc, mmq_x_max);
const int block_num_y = (args.ne01 + mmq_y - 1) / mmq_y;
int mmq_x_best = 0;
int nwaves_best = INT_MAX;
for (int mmq_x = 8; mmq_x <= mmq_x_max && nwaves_best > 1; mmq_x += 8) {
const int block_num_x = (args.ne11 + mmq_x - 1) / mmq_x;
const int nwaves = (block_num_x*block_num_y + nsm - 1) / nsm;
if (nwaves < nwaves_best) {
mmq_x_best = mmq_x;
nwaves_best = nwaves;
}
}
switch (mmq_x_best) {
case 8:
launch_mul_mat_q<type, 8, 4>(args, stream);
break;
case 16:
launch_mul_mat_q<type, 16, 4>(args, stream);
break;
case 24:
launch_mul_mat_q<type, 24, 4>(args, stream);
break;
case 32:
launch_mul_mat_q<type, 32, 8>(args, stream);
break;
case 40:
launch_mul_mat_q<type, 40, 8>(args, stream);
break;
case 48:
launch_mul_mat_q<type, 48, 8>(args, stream);
break;
case 56:
launch_mul_mat_q<type, 56, 8>(args, stream);
break;
case 64:
launch_mul_mat_q<type, 64, 8>(args, stream);
break;
case 72:
launch_mul_mat_q<type, 72, 8>(args, stream);
break;
case 80:
launch_mul_mat_q<type, 80, 8>(args, stream);
break;
case 88:
launch_mul_mat_q<type, 88, 8>(args, stream);
break;
case 96:
launch_mul_mat_q<type, 96, 8>(args, stream);
break;
case 104:
launch_mul_mat_q<type, 104, 8>(args, stream);
break;
case 112:
launch_mul_mat_q<type, 112, 8>(args, stream);
break;
case 120:
launch_mul_mat_q<type, 120, 8>(args, stream);
break;
case 128:
launch_mul_mat_q<type, 128, 8>(args, stream);
break;
default:
GGML_ASSERT(false);
break;
}
}
#define DECL_MMQ_CASE(type) \
template void mul_mat_q_case<type>(const mmq_args & args, cudaStream_t stream) \
extern DECL_MMQ_CASE(GGML_TYPE_Q4_0);
extern DECL_MMQ_CASE(GGML_TYPE_Q4_1);
extern DECL_MMQ_CASE(GGML_TYPE_Q5_0);
extern DECL_MMQ_CASE(GGML_TYPE_Q5_1);
extern DECL_MMQ_CASE(GGML_TYPE_Q8_0);
extern DECL_MMQ_CASE(GGML_TYPE_Q2_K);
extern DECL_MMQ_CASE(GGML_TYPE_Q3_K);
extern DECL_MMQ_CASE(GGML_TYPE_Q4_K);
extern DECL_MMQ_CASE(GGML_TYPE_Q5_K);
extern DECL_MMQ_CASE(GGML_TYPE_Q6_K);
// -------------------------------------------------------------------------------------------------------------------------
void ggml_cuda_op_mul_mat_q(
ggml_backend_cuda_context & ctx,
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
const int64_t src1_padded_row_size, cudaStream_t stream);
bool ggml_cuda_supports_mmq(enum ggml_type type);