diff --git a/ggml-common.h b/ggml-common.h index 369a44b80..2bbf5c0f9 100644 --- a/ggml-common.h +++ b/ggml-common.h @@ -1059,6 +1059,7 @@ GGML_TABLE_END() #define NGRID_IQ1S 2048 #define IQ1S_DELTA 0.125f +#define IQ1M_DELTA 0.125f #if defined(GGML_COMMON_IMPL_C) GGML_TABLE_BEGIN(uint64_t, iq1s_grid, NGRID_IQ1S) 0xffffffffffffffff, 0xffffffffffffff01, 0xffffffffffff0000, 0xffffffffffff01ff, diff --git a/ggml-quants.c b/ggml-quants.c index 32f42bbcd..fdc8fdbe4 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -11913,20 +11913,240 @@ size_t quantize_iq1_s(const float * restrict src, void * restrict dst, int nrow, return nrow * nblock * sizeof(block_iq1_s); } +static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights, + float * scales, + float * weight, + float * pairs, + int8_t * L, + uint16_t * index, + int8_t * shifts) { + + const int gindex = iq2_data_index(GGML_TYPE_IQ1_M); + + const uint64_t * kgrid_q2xs = iq2_data[gindex].grid; + const int * kmap_q2xs = iq2_data[gindex].map; + const uint16_t * kneighbors_q2xs = iq2_data[gindex].neighbours; + + GGML_ASSERT(quant_weights && "missing quantization weights"); + GGML_ASSERT(kgrid_q2xs && "forgot to call ggml_quantize_init()?"); + GGML_ASSERT(kmap_q2xs && "forgot to call ggml_quantize_init()?"); + GGML_ASSERT(kneighbors_q2xs && "forgot to call ggml_quantize_init()?"); + GGML_ASSERT(n%QK_K == 0); + + block_iq1_m * y = vy; + + const int nbl = n/QK_K; + + const int block_size = IQ1M_BLOCK_SIZE; + + const float x_p[3] = {-1 + IQ1M_DELTA, IQ1M_DELTA, 1 + IQ1M_DELTA}; + const float x_m[3] = {-1 - IQ1M_DELTA, -IQ1M_DELTA, 1 - IQ1M_DELTA}; + const uint8_t masks[4] = {0x00, 0x80, 0x08, 0x88}; + + int * idx = (int *)(pairs + 1); + + float sumqx[4], sumq2[4]; + + for (int ibl = 0; ibl < nbl; ++ibl) { + + y[ibl].d = GGML_FP32_TO_FP16(0.f); + memset(y[ibl].qs, 0, QK_K/8); + memset(y[ibl].qh, 0, QK_K/16); + memset(y[ibl].scales, 0, QK_K/32); + + float max_scale = 0; + + const float * xbl = x + QK_K*ibl; + float sumx2 = 0; + for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i]; + float sigma2 = 2*sumx2/QK_K; + + for (int ib = 0; ib < QK_K/block_size; ++ib) { + const float * xb = xbl + block_size*ib; + const float * qw = quant_weights + QK_K*ibl + block_size*ib; + for (int i = 0; i < block_size; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]); + float max = fabsf(xb[0]); + for (int i = 1; i < block_size; ++i) max = MAX(max, fabsf(xb[i])); + if (!max) { + scales[ib] = 0; + memset(L, 1, block_size); + continue; + } + // Here we solve exactly the sum of squared difference (SSD) weighted minimization problem. + // With just 3 allowed quant values (-1, 0, 1), we can search exhaustively for the two + // boundaries that split the weights xb[i] into 3 groups. To do so, we sort the weights + // in ascending order, compute Si = sum[weight[j] xb[j], j = 0...i] and + // Wi = sum[weight[j], j = 0...i], and use these to quckly get get the optimum scale + // for each possible and score for each split. + for (int j = 0; j < block_size; ++j) { + pairs[2*j] = xb[j]; + idx[2*j] = j; + } + qsort(pairs, block_size, 2*sizeof(float), iq1_sort_helper); + float best_score = 0, scale = max; + int besti1 = -1, besti2 = -1, best_k = -1; + // 0: +, + + // 1: +, - + // 2: -, + + // 3: -, - + for (int i1 = 0; i1 <= block_size; ++i1) { + for (int i2 = i1; i2 <= block_size; ++i2) { + memset(sumqx, 0, 4*sizeof(float)); + memset(sumq2, 0, 4*sizeof(float)); + for (int j = 0; j < i1; ++j) { + int i = idx[2*j]; + if (i < block_size/2) { + sumqx[0] += weight[i]*x_p[0]*xb[i]; + sumqx[1] += weight[i]*x_p[0]*xb[i]; + sumqx[2] += weight[i]*x_m[0]*xb[i]; + sumqx[3] += weight[i]*x_m[0]*xb[i]; + sumq2[0] += weight[i]*x_p[0]*x_p[0]; + sumq2[1] += weight[i]*x_p[0]*x_p[0]; + sumq2[2] += weight[i]*x_m[0]*x_m[0]; + sumq2[3] += weight[i]*x_m[0]*x_m[0]; + } else { + sumqx[0] += weight[i]*x_p[0]*xb[i]; + sumqx[2] += weight[i]*x_p[0]*xb[i]; + sumqx[1] += weight[i]*x_m[0]*xb[i]; + sumqx[3] += weight[i]*x_m[0]*xb[i]; + sumq2[0] += weight[i]*x_p[0]*x_p[0]; + sumq2[2] += weight[i]*x_p[0]*x_p[0]; + sumq2[1] += weight[i]*x_m[0]*x_m[0]; + sumq2[3] += weight[i]*x_m[0]*x_m[0]; + } + } + for (int j = i1; j < i2; ++j) { + int i = idx[2*j]; + if (i < block_size/2) { + sumqx[0] += weight[i]*x_p[1]*xb[i]; + sumqx[1] += weight[i]*x_p[1]*xb[i]; + sumqx[2] += weight[i]*x_m[1]*xb[i]; + sumqx[3] += weight[i]*x_m[1]*xb[i]; + sumq2[0] += weight[i]*x_p[1]*x_p[1]; + sumq2[1] += weight[i]*x_p[1]*x_p[1]; + sumq2[2] += weight[i]*x_m[1]*x_m[1]; + sumq2[3] += weight[i]*x_m[1]*x_m[1]; + } else { + sumqx[0] += weight[i]*x_p[1]*xb[i]; + sumqx[2] += weight[i]*x_p[1]*xb[i]; + sumqx[1] += weight[i]*x_m[1]*xb[i]; + sumqx[3] += weight[i]*x_m[1]*xb[i]; + sumq2[0] += weight[i]*x_p[1]*x_p[1]; + sumq2[2] += weight[i]*x_p[1]*x_p[1]; + sumq2[1] += weight[i]*x_m[1]*x_m[1]; + sumq2[3] += weight[i]*x_m[1]*x_m[1]; + } + } + for (int j = i2; j < block_size; ++j) { + int i = idx[2*j]; + if (i < block_size/2) { + sumqx[0] += weight[i]*x_p[2]*xb[i]; + sumqx[1] += weight[i]*x_p[2]*xb[i]; + sumqx[2] += weight[i]*x_m[2]*xb[i]; + sumqx[3] += weight[i]*x_m[2]*xb[i]; + sumq2[0] += weight[i]*x_p[2]*x_p[2]; + sumq2[1] += weight[i]*x_p[2]*x_p[2]; + sumq2[2] += weight[i]*x_m[2]*x_m[2]; + sumq2[3] += weight[i]*x_m[2]*x_m[2]; + } else { + sumqx[0] += weight[i]*x_p[2]*xb[i]; + sumqx[2] += weight[i]*x_p[2]*xb[i]; + sumqx[1] += weight[i]*x_m[2]*xb[i]; + sumqx[3] += weight[i]*x_m[2]*xb[i]; + sumq2[0] += weight[i]*x_p[2]*x_p[2]; + sumq2[2] += weight[i]*x_p[2]*x_p[2]; + sumq2[1] += weight[i]*x_m[2]*x_m[2]; + sumq2[3] += weight[i]*x_m[2]*x_m[2]; + } + } + for (int k = 0; k < 4; ++k) { + if (sumq2[k] > 0 && sumqx[k]*sumqx[k] > best_score*sumq2[k]) { + scale = sumqx[k]/sumq2[k]; best_score = scale*sumqx[k]; + besti1 = i1; besti2 = i2; best_k = k; + } + } + } + } + GGML_ASSERT(besti1 >= 0 && besti2 >= 0 && best_k >= 0); + for (int j = 0; j < besti1; ++j) L[idx[2*j]] = 0; + for (int j = besti1; j < besti2; ++j) L[idx[2*j]] = 1; + for (int j = besti2; j < block_size; ++j) L[idx[2*j]] = 2; + if (scale < 0) { + for (int j = 0; j < block_size; ++j) L[j] = 2 - L[j]; + scale = -scale; + best_k = best_k == 0 ? 3 : best_k == 1 ? 2 : best_k == 2 ? 1 : 0; + } + const float * xx; + bool all_on_grid = true; + for (int k = 0; k < block_size/8; ++k) { + if (k == 0) xx = best_k < 2 ? x_p : x_m; + else xx = best_k%2 == 0 ? x_p : x_m; + uint16_t u = 0; + for (int j = 0; j < 8; ++j) u |= (L[8*k+j] << 2*j); + int grid_index = kmap_q2xs[u]; + if (grid_index < 0) { + all_on_grid = false; + const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1; + grid_index = iq1_find_best_neighbour2(neighbours, kgrid_q2xs, xb + 8*k, weight + 8*k, scale, xx, L + 8*k, NGRID_IQ1S); + GGML_ASSERT(grid_index >= 0); + } + index[k] = grid_index; + } + if (!all_on_grid) { + float sumqx_f = 0, sumq2_f = 0; + for (int k = 0; k < block_size/8; ++k) { + if (k == 0) xx = best_k < 2 ? x_p : x_m; + else xx = best_k%2 == 0 ? x_p : x_m; + const int8_t * pg = (const int8_t *)(kgrid_q2xs + index[k]); + for (int j = 0; j < 8; ++j) { + float w = weight[8*k + j]; + float q = xx[(pg[j] - 1)/2]; + sumqx_f += w*q*xb[8*k+j]; + sumq2_f += w*q*q; + } + } + if (sumqx_f > 0 && sumq2_f > 0) scale = sumqx_f/sumq2_f; + } + y[ibl].qs[2*ib + 0] = index[0] & 255; + y[ibl].qs[2*ib + 1] = index[1] & 255; + y[ibl].qh[ib] = (index[0] >> 8) | ((index[1] >> 8) << 4); + GGML_ASSERT(scale >= 0); + scales[ib] = scale; + shifts[ib] = best_k; + max_scale = MAX(max_scale, scale); + } + + if (!max_scale) { + continue; + } + + float d = max_scale/31; + y[ibl].d = GGML_FP32_TO_FP16(d*1.125f); // 1.125f is another fudge factor. Don't ask me why it is needed. + float id = 1/d; + for (int ib = 0; ib < QK_K/block_size; ib += 2) { + int l1 = nearest_int(0.5f*(id*scales[ib+0]-1)); + l1 = MAX(0, MIN(15, l1)); + int l2 = nearest_int(0.5f*(id*scales[ib+1]-1)); + l2 = MAX(0, MIN(15, l2)); + y[ibl].scales[ib/2] = l1 | (l2 << 4); + y[ibl].qh[ib+0] |= masks[shifts[ib+0]]; + y[ibl].qh[ib+1] |= masks[shifts[ib+1]]; + } + } +} + size_t quantize_iq1_m(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) { GGML_ASSERT(n_per_row%QK_K == 0); float scales[QK_K/IQ1M_BLOCK_SIZE]; float weight[IQ1M_BLOCK_SIZE]; int8_t L[IQ1M_BLOCK_SIZE]; - float sumx[IQ1M_BLOCK_SIZE+1]; - float sumw[IQ1M_BLOCK_SIZE+1]; float pairs[2*IQ1M_BLOCK_SIZE]; uint16_t index[IQ1M_BLOCK_SIZE/8]; int8_t shifts[QK_K/IQ1M_BLOCK_SIZE]; int nblock = n_per_row/QK_K; char * qrow = (char *)dst; for (int row = 0; row < nrow; ++row) { - quantize_row_iq1_impl(GGML_TYPE_IQ1_M, src, qrow, n_per_row, quant_weights, scales, weight, sumx, sumw, pairs, L, index, shifts); + quantize_row_iq1_m_impl(src, qrow, n_per_row, quant_weights, scales, weight, pairs, L, index, shifts); src += n_per_row; qrow += nblock*sizeof(block_iq1_m); }