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https://github.com/ikawrakow/ik_llama.cpp.git
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Adding IQ3_KS quants (#566)
* iq3_ks: basics * iq3_ks: CUDA dequantize * iq3_ks: CUDA mmvq * iq3_ks: mmq * iq3_ks: faster mmq * iq3_ks: Zen4 * iq3_ks: AVX2 convert to q8_k_r8 This gives usPP-512 = 360 t/s. * iq3_ks: AVX2 GEMM/GEMV * iq3_ks: NEON GEMM/GEMV * iq3_ks: NEON convert to q8_k_r8 This gives us PP-512 = 164 t/s. * iq3_ks: Metal dequantize * iq3_ks: Metal gemv - pathetic performance --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Kawrakow
@@ -1819,6 +1819,237 @@ void vec_dot_iq3_k_q8_k(int n, float * s, size_t bs, const void * vx, size_t bx,
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GGML_ABORT("not implemented");
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}
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//
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// ============================================== iq3_ks
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//
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namespace {
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static void quantize_row_iq3_ks_impl(const int super_block_size, const int block_size,
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int n_per_row, const float * x, char * cy,
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float * all_scales, float * weight,
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const int8_t * values,
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const float * quant_weights,
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const int ntry) {
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ggml_half * dptr = (ggml_half *)cy;
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block_iq3_ks * y = (block_iq3_ks *)(dptr + 1);
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const int8_t * shifted_values = values + 8;
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float amax_scale = 0;
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float max_scale = 0;
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for (int ibl = 0; ibl < n_per_row/super_block_size; ++ibl) {
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memset(&y[ibl], 0, sizeof(block_iq3_ks));
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const float * xbl = x + ibl*super_block_size;
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auto scales = all_scales + ibl*(super_block_size/block_size);
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float sigma2 = 0;
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for (int j = 0; j < super_block_size; ++j) sigma2 += xbl[j]*xbl[j];
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sigma2 *= 2.f/super_block_size;
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for (int ib = 0; ib < super_block_size/block_size; ++ib) {
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const float * xb = xbl + ib*block_size;
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if (quant_weights) {
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const float * qw = quant_weights + ibl*super_block_size + ib*block_size;
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for (int j = 0; j < block_size; ++j) weight[j] = qw[j] * sqrtf(sigma2 + xb[j]*xb[j]);
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} else {
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for (int j = 0; j < block_size; ++j) weight[j] = xb[j]*xb[j];
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}
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float amax = 0, max = 0;
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for (int j = 0; j < block_size; ++j) {
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float ax = fabsf(xb[j]);
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if (ax > amax) {
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amax = ax; max = xb[j];
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}
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}
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if (!amax) {
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scales[ib] = 0;
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continue;
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}
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float d = ntry > 0 ? -max/values[0] : max/values[0];
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float id = 1/d;
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float sumqx_p = 0, sumq2_p = 0;
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float sumqx_m = 0, sumq2_m = 0;
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for (int j = 0; j < block_size; ++j) {
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float w = weight[j];
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float al = id*xb[j];
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int l = best_index_iq3nl(values, al);
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float q = values[l];
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sumqx_p += w*q*xb[j];
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sumq2_p += w*q*q;
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l = best_index_iq3nl(values, -al);
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q = values[l];
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sumqx_m += w*q*xb[j];
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sumq2_m += w*q*q;
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}
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d = sumqx_p/sumq2_p;
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bool is_shifted = false;
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float best = d*sumqx_p;
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if (sumq2_m > 0 && sumqx_m*sumqx_m > best*sumq2_m) {
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d = sumqx_m/sumq2_m; best = d*sumqx_m;
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}
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for (int itry = -ntry; itry <= ntry; ++itry) {
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id = (itry + values[0])/max;
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sumqx_p = sumq2_p = 0;
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sumqx_m = sumq2_m = 0;
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for (int j = 0; j < block_size; ++j) {
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float w = weight[j];
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float al = id*xb[j];
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int l = best_index_iq3nl(values, al);
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float q = values[l];
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sumqx_p += w*q*xb[j];
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sumq2_p += w*q*q;
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l = best_index_iq3nl(values, -al);
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q = values[l];
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sumqx_m += w*q*xb[j];
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sumq2_m += w*q*q;
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}
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if (sumq2_p > 0 && sumqx_p*sumqx_p > best*sumq2_p) {
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d = sumqx_p/sumq2_p; best = d * sumqx_p; is_shifted = false;
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}
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if (sumq2_m > 0 && sumqx_m*sumqx_m > best*sumq2_m) {
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d = sumqx_m/sumq2_m; best = d * sumqx_m; is_shifted = false;
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}
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id = (itry + shifted_values[0])/max;
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sumqx_p = sumq2_p = 0;
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sumqx_m = sumq2_m = 0;
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for (int j = 0; j < block_size; ++j) {
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float w = weight[j];
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float al = id*xb[j];
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int l = best_index_iq3nl(shifted_values, al);
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float q = shifted_values[l];
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sumqx_p += w*q*xb[j];
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sumq2_p += w*q*q;
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l = best_index_iq3nl(shifted_values, -al);
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q = shifted_values[l];
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sumqx_m += w*q*xb[j];
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sumq2_m += w*q*q;
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}
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if (sumq2_p > 0 && sumqx_p*sumqx_p > best*sumq2_p) {
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d = sumqx_p/sumq2_p; best = d * sumqx_p; is_shifted = true;
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}
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if (sumq2_m > 0 && sumqx_m*sumqx_m > best*sumq2_m) {
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d = sumqx_m/sumq2_m; best = d * sumqx_m; is_shifted = true;
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}
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}
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if (is_shifted) y[ibl].extra |= (1 << (8 + ib));
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scales[ib] = d;
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float ascale = std::abs(d);
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if (ascale > amax_scale) {
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amax_scale = ascale; max_scale = d;
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}
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}
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}
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float d = -max_scale/16;
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*dptr = GGML_FP32_TO_FP16(d);
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if (!d) return;
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float id = d ? 1/d : 0.f;
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float sumqx = 0, sumq2 = 0;
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for (int ibl = 0; ibl < n_per_row/super_block_size; ++ibl) {
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const float * xbl = x + ibl*super_block_size;
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float sigma2 = 0;
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for (int j = 0; j < super_block_size; ++j) sigma2 += xbl[j]*xbl[j];
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sigma2 *= 2.f/super_block_size;
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auto scales = all_scales + (super_block_size/block_size)*ibl;
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for (int ib = 0; ib < super_block_size/block_size; ++ib) {
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const int8_t * block_values = (y[ibl].extra >> (8 + ib)) & 0x01 ? shifted_values : values;
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int l = nearest_int(id*scales[ib]);
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l = std::max(-16, std::min(15, l));
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uint8_t ul = l + 16;
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y[ibl].scales[ib%4] |= (ul & 0xf) << 4*(ib/4);
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y[ibl].extra |= (ul >> 4) << ib;
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float dl = d * l;
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float idl = dl ? 1/dl : 0.f;
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const float * xb = xbl + ib*block_size;
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if (quant_weights) {
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const float * qw = quant_weights + ibl*super_block_size + ib*block_size;
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for (int j = 0; j < block_size; ++j) weight[j] = qw[j] * sqrtf(sigma2 + xb[j]*xb[j]);
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} else {
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for (int j = 0; j < block_size; ++j) weight[j] = xb[j]*xb[j];
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}
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auto qs = y[ibl].qs + (ib/4)*block_size;
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auto qh = y[ibl].qh + (ib/8)*block_size;
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for (int j = 0; j < block_size; ++j) {
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uint8_t i = best_index_iq3nl(block_values, idl*xb[j]);
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qs[j] |= ((i & 3) << 2*(ib%4));
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qh[j] |= ((i >> 2) << (ib%8));
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float w = weight[j];
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float q = block_values[i]*l;
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sumqx += w*q*xb[j];
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sumq2 += w*q*q;
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}
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}
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}
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if (sumq2 > 0) *dptr = GGML_FP32_TO_FP16(sumqx/sumq2);
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}
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}
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void quantize_row_iq3_ks_ref(const float * x, block_iq3_ks * y, int64_t k) {
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quantize_iq3_ks(x, (void *)y, 1, k, nullptr);
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}
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void quantize_row_iq3_ks(const float * x, void * y, int64_t k) {
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quantize_iq3_ks(x, (void *)y, 1, k, nullptr);
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}
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size_t quantize_iq3_ks(const float * src, void * dst, int64_t nrows, int64_t n_per_row, const float * imatrix) {
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constexpr int kBlockSize = 32;
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GGML_ASSERT(n_per_row%QK_K == 0);
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auto row_size = ggml_row_size(GGML_TYPE_IQ3_KS, n_per_row);
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char * qrow = (char *)dst;
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float weight[kBlockSize];
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std::vector<float> all_scales(n_per_row/kBlockSize);
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for (int64_t row = 0; row < nrows; ++row) {
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quantize_row_iq3_ks_impl(QK_K, kBlockSize, n_per_row, src, qrow, all_scales.data(), weight, iq3nl_values, imatrix, 5);
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src += n_per_row;
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qrow += row_size;
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}
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return nrows * row_size;
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}
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void dequantize_row_iq3_ks(const block_iq3_ks * x, float * y, int64_t k) {
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constexpr int kBlockSize = 32;
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static_assert(QK_K/kBlockSize == 8);
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GGML_ASSERT(k%QK_K == 0);
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const ggml_half * dptr = (const ggml_half *)x;
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float d = GGML_FP16_TO_FP32(*dptr);
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x = (const block_iq3_ks *)(dptr + 1);
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float dl[8];
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int nblock = k/QK_K;
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for (int ibl = 0; ibl < nblock; ++ibl) {
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for (int j = 0; j < 4; ++j) {
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int ls1 = (x[ibl].scales[j] & 0xf) | (((x[ibl].extra >> (j+0)) & 1) << 4);
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int ls2 = (x[ibl].scales[j] >> 4) | (((x[ibl].extra >> (j+4)) & 1) << 4);
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dl[j+0] = d*(ls1 - 16);
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dl[j+4] = d*(ls2 - 16);
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}
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auto qs = x[ibl].qs;
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auto qh = x[ibl].qh;
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for (int i128 = 0; i128 < QK_K/128; ++i128) {
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for (int ib = 0; ib < 4; ++ib) {
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const int8_t * values = iq3nl_values + ((x[ibl].extra >> (8 + (4*i128+ib)) & 1) << 3);
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for (int j = 0; j < kBlockSize; ++j) {
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y[j] = dl[4*i128 + ib] * values[((qs[j] >> 2*ib) & 3) | (((qh[j] >> (4*i128+ib)) & 1) << 2)];
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}
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y += kBlockSize;
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}
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qs += kBlockSize;
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}
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}
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}
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void vec_dot_iq3_ks_q8_k(int n, float * s, size_t bs, const void * vx, size_t bx, const void * vy, size_t by, int nrc) {
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#if GGML_USE_IQK_MULMAT
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if (iqk_mul_mat(1, 1, n, GGML_TYPE_IQ3_KS, vx, 0, GGML_TYPE_Q8_K, vy, 0, s, 0, 0, 1)) {
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return;
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}
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#endif
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GGML_ASSERT(n%QK_K == 0);
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GGML_ASSERT(nrc == 1);
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GGML_UNUSED(bs);
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GGML_UNUSED(bx);
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GGML_UNUSED(by);
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GGML_ABORT("Not implemented");
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}
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//
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// ============================================== iq4_K
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//
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