Adding IQ4_KSS: 4.0 bpw quants (#89)
* iq4_kss: WIP * iq4_kss: CUDA dequantize works So we can run perplexity. Sadly, the result does not look good on the bpw vs quantization error plot. * iq4_kss: slightly better quantization * iq4_kss: another small quantization improvement * iq4_kss: CUDA works TG-128 performance is very decent with 131 t/s for LLaMA-3.1-8B. In comparison, we have 123 t/s for q4_0 and 128 t/s for iq4_ks. I.e., the reduced model size more than offsets the additional bit fiddling required for iq4_kss. * iq4_kss: new bit arrangement - CUDA and Zen4 work Did not lose performance on CUDA. Zen4 is decent, but not great: PP-512(LLaMA-3.1-8B) = 163 t/s. TG-128 is of course better than other 4-bit quants due to smaller model size. We get 14.5 t/s @ 8 threads. * iq4_kss: ARM_NEON. Predictably very slow * iq4_kss: Metal PP is not too bad - just 10% slower than q4_0. But TG is 30% slower, i.e., predictably bad. * iq4_kss: somewhat faster Metal dot product 45.75 t/s -> 48.75 t/s. Still 22% slower than q4_0 * iq4_kss: AVX2 Bad, but better than I expected. PP-512(LLaMA-3.1-8B) = 167 t/s on the Ryzen-5950X. I.e., with 32 AVX2 threads we get the performance of 16 Zen4 threads. * iq4_kss: very slightly faster Metal dot product 48.7 t/s -> 49.3 t/s --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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@ -256,6 +256,8 @@ static void analyze_iq4ks(const char * name, int nrows, int n_per_row, const flo
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float mse0 = 0, mse = 0;
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auto compute = [&mutex, &counter, &mse0, &mse, values, row_size, nblock, nrows, n_per_row, chunk] () {
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std::vector<char> Q(row_size);
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float diff[4];
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float xv[4];
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float lmse0 = 0, lmse = 0;
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while (true) {
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std::unique_lock<std::mutex> lock(mutex);
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@ -282,25 +284,41 @@ static void analyze_iq4ks(const char * name, int nrows, int n_per_row, const flo
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for (int j = 0; j < 16; j += 2) {
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uint16_t v0 = *(const uint16_t *)(qs + j);
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int non = popcount(v0);
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float diff1 = xb[j+ 0] - dl*values[qs[j+0] & 0xf];
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float diff2 = xb[j+16] - dl*values[qs[j+0] >> 4];
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float diff3 = xb[j+ 1] - dl*values[qs[j+1] & 0xf];
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float diff4 = xb[j+17] - dl*values[qs[j+1] >> 4];
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lmse0 += diff1*diff1 + diff2*diff2 + diff3*diff3 + diff4*diff4;
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xv[0] = xb[j+ 0]; xv[1] = xb[j+16]; xv[2] = xb[j+ 1]; xv[3] = xb[j+17];
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diff[0] = xv[0] - dl*values[qs[j+0] & 0xf];
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diff[1] = xv[1] - dl*values[qs[j+0] >> 4];
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diff[2] = xv[2] - dl*values[qs[j+1] & 0xf];
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diff[3] = xv[3] - dl*values[qs[j+1] >> 4];
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float diff4 = diff[0]*diff[0] + diff[1]*diff[1] + diff[2]*diff[2] + diff[3]*diff[3];
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lmse0 += diff4;
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if (non%2 == 0) {
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lmse += diff1*diff1 + diff2*diff2 + diff3*diff3 + diff4*diff4;
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lmse += diff4;
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} else {
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float best = std::numeric_limits<float>::max();
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for (int k = 0; k < 16; k += 4) {
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uint16_t v = v0 ^ (1 << k);
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uint8_t v1 = v;
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uint8_t v2 = v >> 8;
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diff1 = xb[j+ 0] - dl*values[v1 & 0xf];
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diff2 = xb[j+16] - dl*values[v1 >> 4];
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diff3 = xb[j+ 1] - dl*values[v2 & 0xf];
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diff4 = xb[j+17] - dl*values[v2 >> 4];
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float score = diff1*diff1 + diff2*diff2 + diff3*diff3 + diff4*diff4;
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if (score < best) best = score;
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//for (int k = 0; k < 16; k += 4) {
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// uint16_t v = v0 ^ (1 << k);
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// uint8_t v1 = v;
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// uint8_t v2 = v >> 8;
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// diff1 = xb[j+ 0] - dl*values[v1 & 0xf];
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// diff2 = xb[j+16] - dl*values[v1 >> 4];
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// diff3 = xb[j+ 1] - dl*values[v2 & 0xf];
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// diff4 = xb[j+17] - dl*values[v2 >> 4];
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// float score = diff1*diff1 + diff2*diff2 + diff3*diff3 + diff4*diff4;
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// if (score < best) best = score;
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//}
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for (int k = 0; k < 4; ++k) {
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uint16_t v = (v0 >> 4*k) & 0xf;
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auto pc = popcount(v);
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if (v > 0 && popcount(v-1u) != pc) {
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float this_diff = xv[k] - dl*values[v-1u];
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float score = diff4 - diff[k]*diff[k] + this_diff*this_diff;
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if (score < best) best = score;
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}
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if (v < 15 && popcount(v + 1u) != pc) {
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float this_diff = xv[k] - dl*values[v+1u];
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float score = diff4 - diff[k]*diff[k] + this_diff*this_diff;
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if (score < best) best = score;
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}
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}
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lmse += best;
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}
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@ -44,6 +44,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
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{ "IQ4_NL", LLAMA_FTYPE_MOSTLY_IQ4_NL, " 4.50 bpw non-linear quantization", },
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{ "IQ4_XS", LLAMA_FTYPE_MOSTLY_IQ4_XS, " 4.25 bpw non-linear quantization", },
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{ "IQ4_KS", LLAMA_FTYPE_MOSTLY_IQ4_KS, " 4.25 bpw non-linear quantization", },
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{ "IQ4_KSS", LLAMA_FTYPE_MOSTLY_IQ4_KSS, " 4.0 bpw non-linear quantization", },
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{ "IQ2_K", LLAMA_FTYPE_MOSTLY_IQ2_K, " 2.375 bpw non-linear quantization",},
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{ "IQ2_KS", LLAMA_FTYPE_MOSTLY_IQ2_KS, " 2.1875 bpw non-linear quantization",},
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{ "IQ3_K", LLAMA_FTYPE_MOSTLY_IQ3_K, " 3.44 bpw non-linear quantization", },
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@ -405,6 +405,7 @@ extern "C" {
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GGML_TYPE_IQ1_TN = 143,
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GGML_TYPE_IQ4_KS = 144,
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GGML_TYPE_IQ2_KS = 145,
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GGML_TYPE_IQ4_KSS = 146,
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GGML_TYPE_COUNT,
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};
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@ -462,6 +463,7 @@ extern "C" {
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GGML_FTYPE_MOSTLY_IQ1_TN = 136, // except 1d tensors
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GGML_FTYPE_MOSTLY_IQ4_KS = 137, // except 1d tensors
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GGML_FTYPE_MOSTLY_IQ2_KS = 138, // except 1d tensors
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GGML_FTYPE_MOSTLY_IQ4_KSS = 139, // except 1d tensors
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};
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// available tensor operations:
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@ -447,6 +447,11 @@ typedef struct {
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} block_iq4_ks;
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static_assert(sizeof(block_iq4_ks) == QK_K/32 + QK_K/2, "wrong iq4_ks block size/padding");
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typedef struct {
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uint32_t qs[QK_K/8];
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} block_iq4_kss;
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static_assert(sizeof(block_iq4_kss) == QK_K/8*sizeof(uint32_t), "wrong iq4_kss block size/padding");
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typedef struct {
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ggml_half d;
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uint16_t extra;
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@ -2829,6 +2829,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
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case GGML_TYPE_IQ4_NL:
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case GGML_TYPE_IQ4_XS:
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case GGML_TYPE_IQ4_KS:
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case GGML_TYPE_IQ4_KSS:
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case GGML_TYPE_IQ2_K:
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case GGML_TYPE_IQ2_KS:
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case GGML_TYPE_IQ3_K:
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@ -543,6 +543,13 @@ struct ggml_cuda_type_traits<GGML_TYPE_IQ4_KS> {
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static constexpr int qi = QI4_XS;
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};
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template<>
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struct ggml_cuda_type_traits<GGML_TYPE_IQ4_KSS> {
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static constexpr int qk = QK_K;
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static constexpr int qr = QR4_XS;
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static constexpr int qi = QI4_XS;
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};
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template<>
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struct ggml_cuda_type_traits<GGML_TYPE_IQ5_K> {
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static constexpr int qk = QK_K;
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@ -638,6 +638,37 @@ static __global__ void dequantize_block_iq4_ks(const void * __restrict__ vx, dst
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}
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}
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template<typename dst_t>
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static __global__ void dequantize_block_iq4_kss(const void * __restrict__ vx, dst_t * __restrict__ yy, int64_t n_per_row, int64_t row_size) {
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int64_t ii = blockIdx.x;
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int64_t row = (QK_K * ii) / n_per_row;
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const char * cx = (const char *)vx + row * row_size;
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float scale = *(const float *)cx;
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const block_iq4_kss * x = (const block_iq4_kss *)(cx + sizeof(float));
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const int64_t i = ii - (row*n_per_row)/QK_K;
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const int64_t tid = threadIdx.x;
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const int64_t il = tid/8; // 0...3
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const int64_t ib = tid%8; // 0...7
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dst_t * y = yy + ii*QK_K + 32*ib + 4*il;
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const uint32_t * q4 = x[i].qs + 4*ib;
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uint32_t s32 = (q4[0] & 0x00010001) | ((q4[1] & 0x00010001) << 2) | ((q4[2] & 0x00010001) << 4) | ((q4[3] & 0x00010001) << 6);
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uint8_t ls = (s32 | (s32 >> 15)) & 0xff;
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const float d = scale * ((ls & 254) - 127);
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const int8_t * values = iq4k_values + ((ls & 1) << 4);
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uint32_t aux32[2];
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aux32[0] = q4[il] & 0xfffefffe;
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aux32[0] ^= (aux32[0] >> 1);
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aux32[1] = ((aux32[0] >> 4) & 0x0f0f0f0f);
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aux32[0] &= 0x0f0f0f0f;
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const uint8_t * aux8 = (const uint8_t *)aux32;
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for (int j = 0; j < 4; ++j) {
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y[j+ 0] = d * values[aux8[j+0]];
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y[j+16] = d * values[aux8[j+4]];
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}
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}
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template<typename dst_t>
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static __global__ void dequantize_block_iq4_k(const void * __restrict__ vx, dst_t * __restrict__ yy) {
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const int64_t i = blockIdx.x;
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@ -980,6 +1011,14 @@ static void dequantize_row_iq4_ks_cuda(const void * vx, dst_t * y, const int64_t
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dequantize_block_iq4_ks<<<nb, 32, 0, stream>>>(vx, y, n_per_row, row_size);
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}
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template<typename dst_t>
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static void dequantize_row_iq4_kss_cuda(const void * vx, dst_t * y, const int64_t nrows, const int64_t n_per_row, cudaStream_t stream) {
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const int64_t k = nrows * n_per_row;
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const int64_t row_size = ggml_row_size(GGML_TYPE_IQ4_KSS, n_per_row);
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const int nb = (k + QK_K - 1) / QK_K;
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dequantize_block_iq4_kss<<<nb, 32, 0, stream>>>(vx, y, n_per_row, row_size);
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}
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template<typename dst_t>
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static void dequantize_row_iq2_ks_cuda(const void * vx, dst_t * y, const int64_t nrows, const int64_t n_per_row, cudaStream_t stream) {
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const int64_t k = nrows * n_per_row;
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@ -1152,6 +1191,8 @@ to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
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return dequantize_row_iq4_xs_cuda;
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case GGML_TYPE_IQ4_KS:
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return dequantize_row_iq4_ks_cuda;
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case GGML_TYPE_IQ4_KSS:
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return dequantize_row_iq4_kss_cuda;
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case GGML_TYPE_IQ2_KS:
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return dequantize_row_iq2_ks_cuda;
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case GGML_TYPE_IQ2_K:
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@ -1225,6 +1266,8 @@ to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
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return dequantize_row_iq4_xs_cuda;
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case GGML_TYPE_IQ4_KS:
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return dequantize_row_iq4_ks_cuda;
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case GGML_TYPE_IQ4_KSS:
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return dequantize_row_iq4_kss_cuda;
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case GGML_TYPE_IQ2_KS:
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return dequantize_row_iq2_ks_cuda;
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case GGML_TYPE_IQ2_K:
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@ -239,6 +239,35 @@ __device__ __forceinline__ float vec_dot_iq4_ks_q8_1(
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return dl * __low2float(bq8_1[ib32].ds) * sumi;
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}
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#define VDR_IQ4_KSS_Q8_1_MMVQ 4
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#define VDR_IQ4_KSS_Q8_1_MMQ 4
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__device__ __forceinline__ float vec_dot_iq4_kss_q8_1(
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const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
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float scale = *(const float *)vbq;
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const block_iq4_kss * bq4 = (const block_iq4_kss *)((const char *)vbq + sizeof(float)) + kbx;
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const uint8_t * all_values = (const uint8_t *)iq4k_values;
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// iqs is 0...28
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const int ib32 = iqs/4; // Why iqs/4 ?
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const int32_t * q8 = (const int *)bq8_1[ib32].qs;
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const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32;
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uint32_t s32 = (q4[0] & 0x00010001) | ((q4[1] & 0x00010001) << 2) | ((q4[2] & 0x00010001) << 4) | ((q4[3] & 0x00010001) << 6);
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uint8_t ls = (s32 | (s32 >> 15)) & 0xff;
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const float dl = scale * ((ls & 254) - 127);
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int v1, v2;
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int sumi = 0;
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for (int j = 0; j < 4; ++j) {
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uint32_t aux32 = q4[j] & 0xfffefffe;
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aux32 ^= (aux32 >> 1);
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get_int_from_table_16_shift(aux32, ls & 1, all_values, v1, v2);
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sumi = ggml_cuda_dp4a(v1, q8[j+0], sumi);
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sumi = ggml_cuda_dp4a(v2, q8[j+4], sumi);
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}
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return dl * __low2float(bq8_1[ib32].ds) * sumi;
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}
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#define VDR_IQ5_K_Q8_1_MMVQ 4
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#define VDR_IQ5_K_Q8_1_MMQ 4
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@ -703,6 +732,13 @@ void mul_mat_vec_iq4_ks_q8_1_cuda(
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iqk_mul_mat_vec_q_cuda<GGML_TYPE_IQ4_KS, VDR_IQ4_KS_Q8_1_MMVQ, vec_dot_iq4_ks_q8_1>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
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}
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void mul_mat_vec_iq4_kss_q8_1_cuda(
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const void * vx, const void * vy, float * dst,
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const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
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iqk_mul_mat_vec_q_cuda<GGML_TYPE_IQ4_KSS, VDR_IQ4_KSS_Q8_1_MMVQ, vec_dot_iq4_kss_q8_1>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
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}
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void mul_mat_vec_iq2_ks_q8_1_cuda(
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const void * vx, const void * vy, float * dst,
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const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
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@ -32,6 +32,10 @@ void mul_mat_vec_iq4_ks_q8_1_cuda(
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const void * vx, const void * vy, float * dst,
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const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream);
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void mul_mat_vec_iq4_kss_q8_1_cuda(
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const void * vx, const void * vy, float * dst,
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const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream);
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void mul_mat_vec_iq2_ks_q8_1_cuda(
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const void * vx, const void * vy, float * dst,
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const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream);
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@ -462,6 +462,9 @@ void ggml_cuda_op_mul_mat_vec_q(
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case GGML_TYPE_IQ4_KS:
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mul_mat_vec_iq4_ks_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
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break;
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case GGML_TYPE_IQ4_KSS:
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mul_mat_vec_iq4_kss_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
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break;
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case GGML_TYPE_IQ2_KS:
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mul_mat_vec_iq2_ks_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
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break;
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@ -107,6 +107,7 @@ enum ggml_metal_kernel_type {
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GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL,
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GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS,
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GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_KS,
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GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_KSS,
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GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_K,
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GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_KS,
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GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_K,
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@ -150,6 +151,7 @@ enum ggml_metal_kernel_type {
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GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_KS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_KSS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_K_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_KS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_K_F32,
|
||||
|
|
@ -187,6 +189,7 @@ enum ggml_metal_kernel_type {
|
|||
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_KS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_KSS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_K_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_KS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_K_F32,
|
||||
|
|
@ -221,6 +224,7 @@ enum ggml_metal_kernel_type {
|
|||
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_KS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_KSS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_K_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_KS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_K_F32,
|
||||
|
|
@ -255,6 +259,7 @@ enum ggml_metal_kernel_type {
|
|||
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_KS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_KSS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_K_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_KS_F32,
|
||||
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_K_F32,
|
||||
|
|
@ -650,6 +655,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(int n_cb) {
|
|||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL, get_rows_iq4_nl, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS, get_rows_iq4_xs, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_KS, get_rows_iq4_ks, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_KSS, get_rows_iq4_kss, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_K, get_rows_iq2_k, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_KS, get_rows_iq2_ks, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_K, get_rows_iq3_k, true);
|
||||
|
|
@ -693,6 +699,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(int n_cb) {
|
|||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, mul_mv_iq4_nl_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32, mul_mv_iq4_xs_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_KS_F32, mul_mv_iq4_ks_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_KSS_F32, mul_mv_iq4_kss_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_K_F32, mul_mv_iq2_k_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_KS_F32, mul_mv_iq2_ks_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_K_F32, mul_mv_iq3_k_f32, ctx->support_simdgroup_reduction);
|
||||
|
|
@ -730,6 +737,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(int n_cb) {
|
|||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, mul_mv_id_iq4_nl_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32, mul_mv_id_iq4_xs_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_KS_F32, mul_mv_id_iq4_ks_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_KSS_F32, mul_mv_id_iq4_kss_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_K_F32, mul_mv_id_iq2_k_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_KS_F32, mul_mv_id_iq2_ks_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_K_F32, mul_mv_id_iq3_k_f32, ctx->support_simdgroup_reduction);
|
||||
|
|
@ -764,6 +772,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(int n_cb) {
|
|||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, mul_mm_iq4_nl_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32, mul_mm_iq4_xs_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_KS_F32, mul_mm_iq4_ks_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_KSS_F32, mul_mm_iq4_kss_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_K_F32, mul_mm_iq2_k_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_KS_F32, mul_mm_iq2_ks_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_K_F32, mul_mm_iq3_k_f32, ctx->support_simdgroup_mm);
|
||||
|
|
@ -798,6 +807,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(int n_cb) {
|
|||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, mul_mm_id_iq4_nl_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, mul_mm_id_iq4_xs_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_KS_F32, mul_mm_id_iq4_ks_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_KSS_F32, mul_mm_id_iq4_kss_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_K_F32, mul_mm_id_iq2_k_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_KS_F32, mul_mm_id_iq2_ks_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_K_F32, mul_mm_id_iq3_k_f32, ctx->support_simdgroup_mm);
|
||||
|
|
@ -1997,6 +2007,7 @@ static enum ggml_status ggml_metal_graph_compute(
|
|||
case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32 ].pipeline; break;
|
||||
case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32 ].pipeline; break;
|
||||
case GGML_TYPE_IQ4_KS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_KS_F32 ].pipeline; break;
|
||||
case GGML_TYPE_IQ4_KSS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_KSS_F32].pipeline; break;
|
||||
case GGML_TYPE_IQ2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_K_F32 ].pipeline; break;
|
||||
case GGML_TYPE_IQ2_KS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_KS_F32 ].pipeline; break;
|
||||
case GGML_TYPE_IQ3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_K_F32 ].pipeline; break;
|
||||
|
|
@ -2222,6 +2233,12 @@ static enum ggml_status ggml_metal_graph_compute(
|
|||
nth1 = 16;
|
||||
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_KS_F32].pipeline;
|
||||
} break;
|
||||
case GGML_TYPE_IQ4_KSS:
|
||||
{
|
||||
nth0 = 4;
|
||||
nth1 = 16;
|
||||
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_KSS_F32].pipeline;
|
||||
} break;
|
||||
case GGML_TYPE_IQ2_K:
|
||||
{
|
||||
nth0 = 4;
|
||||
|
|
@ -2309,7 +2326,8 @@ static enum ggml_status ggml_metal_graph_compute(
|
|||
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
|
||||
}
|
||||
else if (src0t == GGML_TYPE_IQ4_NL || src0t == GGML_TYPE_IQ4_XS || src0t == GGML_TYPE_IQ4_K ||
|
||||
src0t == GGML_TYPE_IQ5_K || src0t == GGML_TYPE_IQ6_K || src0t == GGML_TYPE_IQ4_KS) {
|
||||
src0t == GGML_TYPE_IQ5_K || src0t == GGML_TYPE_IQ6_K || src0t == GGML_TYPE_IQ4_KS||
|
||||
src0t == GGML_TYPE_IQ4_KSS) {
|
||||
const int mem_size = src0t == GGML_TYPE_IQ6_K ? 128*sizeof(float) : GGML_TYPE_IQ5_K ? 64*sizeof(float) : 32*sizeof(float);
|
||||
[encoder setThreadgroupMemoryLength:mem_size atIndex:0];
|
||||
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
|
||||
|
|
@ -2405,6 +2423,7 @@ static enum ggml_status ggml_metal_graph_compute(
|
|||
case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32 ].pipeline; break;
|
||||
case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32 ].pipeline; break;
|
||||
case GGML_TYPE_IQ4_KS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_KS_F32 ].pipeline; break;
|
||||
case GGML_TYPE_IQ4_KSS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_KSS_F32].pipeline; break;
|
||||
case GGML_TYPE_IQ2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_K_F32 ].pipeline; break;
|
||||
case GGML_TYPE_IQ2_KS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_KS_F32 ].pipeline; break;
|
||||
case GGML_TYPE_IQ3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_K_F32 ].pipeline; break;
|
||||
|
|
@ -2618,6 +2637,12 @@ static enum ggml_status ggml_metal_graph_compute(
|
|||
nth1 = 16;
|
||||
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_KS_F32].pipeline;
|
||||
} break;
|
||||
case GGML_TYPE_IQ4_KSS:
|
||||
{
|
||||
nth0 = 4;
|
||||
nth1 = 16;
|
||||
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_KSS_F32].pipeline;
|
||||
} break;
|
||||
case GGML_TYPE_IQ2_K:
|
||||
{
|
||||
nth0 = 4;
|
||||
|
|
@ -2716,7 +2741,8 @@ static enum ggml_status ggml_metal_graph_compute(
|
|||
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
|
||||
}
|
||||
else if (src0t == GGML_TYPE_IQ4_NL || src0t == GGML_TYPE_IQ4_XS || src0t == GGML_TYPE_IQ4_K ||
|
||||
src0t == GGML_TYPE_IQ5_K || src0t == GGML_TYPE_IQ6_K || src0t == GGML_TYPE_IQ4_KS) {
|
||||
src0t == GGML_TYPE_IQ5_K || src0t == GGML_TYPE_IQ6_K || src0t == GGML_TYPE_IQ4_KS||
|
||||
src0t == GGML_TYPE_IQ4_KSS) {
|
||||
const int mem_size = src0t == GGML_TYPE_IQ6_K ? 128*sizeof(float) : GGML_TYPE_IQ5_K ? 64*sizeof(float) : 32*sizeof(float);
|
||||
[encoder setThreadgroupMemoryLength:mem_size atIndex:0];
|
||||
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
|
||||
|
|
@ -2770,6 +2796,7 @@ static enum ggml_status ggml_metal_graph_compute(
|
|||
case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL ].pipeline; break;
|
||||
case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS ].pipeline; break;
|
||||
case GGML_TYPE_IQ4_KS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_KS ].pipeline; break;
|
||||
case GGML_TYPE_IQ4_KSS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_KSS].pipeline; break;
|
||||
case GGML_TYPE_IQ2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_K ].pipeline; break;
|
||||
case GGML_TYPE_IQ2_KS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_KS ].pipeline; break;
|
||||
case GGML_TYPE_IQ3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_K ].pipeline; break;
|
||||
|
|
|
|||
|
|
@ -6142,6 +6142,117 @@ void kernel_mul_mv_iq4_ks_f32_impl(
|
|||
}
|
||||
}
|
||||
|
||||
void kernel_mul_mv_iq4_kss_f32_impl(
|
||||
device const void * src0,
|
||||
device const float * src1,
|
||||
device float * dst,
|
||||
int64_t ne00,
|
||||
int64_t ne01,
|
||||
int64_t ne02,
|
||||
int64_t ne10,
|
||||
int64_t ne12,
|
||||
int64_t ne0,
|
||||
int64_t ne1,
|
||||
uint r2,
|
||||
uint r3,
|
||||
threadgroup int8_t * shared_values_i8,
|
||||
uint3 tgpig,
|
||||
uint tiisg,
|
||||
uint sgitg) {
|
||||
|
||||
threadgroup float * shared_values = (threadgroup float *)shared_values_i8;
|
||||
const int nb = ne00/QK_K;
|
||||
const int r0 = tgpig.x;
|
||||
const int r1 = tgpig.y;
|
||||
const int im = tgpig.z;
|
||||
const int first_row = (r0 * 2 + sgitg) * 2;
|
||||
|
||||
const uint i12 = im%ne12;
|
||||
const uint i13 = im/ne12;
|
||||
|
||||
const uint row_size = 4 + nb*sizeof(block_iq4_kss);
|
||||
const uint offset0 = (i12/r2)*ne01 + (i13/r3)*(ne01*ne02);
|
||||
device const char * cx = (device const char *)src0 + (first_row + offset0)*row_size;
|
||||
device const float * y = (device const float *)src1 + r1*ne10 + im*ne00*ne1;
|
||||
|
||||
const int ix = tiisg/16; // 0 or 1
|
||||
const int it = tiisg%16; // 0...15
|
||||
const int ib = it/2;
|
||||
const int il = it%2;
|
||||
|
||||
shared_values[tiisg] = kvalues_iq4k_f[tiisg];
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
float4 yl[4];
|
||||
float2 sumf = 0.f;
|
||||
float d[2];
|
||||
|
||||
device const float * yb = y + ix * QK_K + ib * 32 + il * 8;
|
||||
|
||||
uint32_t aux32;
|
||||
thread const uint8_t * q8 = (thread const uint8_t *)&aux32;
|
||||
|
||||
float4 qf1, qf2;
|
||||
|
||||
device const float * dptr = (device const float *)cx;
|
||||
d[0] = *dptr;
|
||||
device const uint32_t * qptr = (device const uint32_t *)(dptr + 1) + ix*(QK_K/8) + 4*ib;
|
||||
dptr += row_size/4;
|
||||
d[1] = *dptr;
|
||||
|
||||
for (int ibl = ix; ibl < nb; ibl += 2) {
|
||||
|
||||
device const float4 * y4 = (device const float4 *)yb;
|
||||
yl[0] = y4[0]; yl[1] = y4[4]; yl[2] = y4[1]; yl[3] = y4[5];
|
||||
|
||||
device const uint32_t * q4 = qptr;
|
||||
|
||||
for (int row = 0; row < 2; ++row) {
|
||||
|
||||
uint32_t s32 = (q4[0] & 0x00010001) | ((q4[1] & 0x00010001) << 2) | ((q4[2] & 0x00010001) << 4) | ((q4[3] & 0x00010001) << 6);
|
||||
int16_t ls = (s32 | (s32 >> 15)) & 0xff;
|
||||
|
||||
threadgroup const float * block_values = shared_values + ((ls & 1) << 4);
|
||||
const float scale = ((ls & 254) - 127);
|
||||
|
||||
float4 acc1 = {0.f}, acc2 = {0.f};
|
||||
|
||||
uint32_t v32 = q4[2*il+0] & 0xfffefffe;
|
||||
v32 ^= (v32 >> 1);
|
||||
aux32 = v32 & 0x0f0f0f0f;
|
||||
qf1 = {block_values[q8[0]], block_values[q8[1]], block_values[q8[2]], block_values[q8[3]]};
|
||||
acc1 += yl[0] * qf1;
|
||||
aux32 = (v32 >> 4) & 0x0f0f0f0f;
|
||||
qf2 = {block_values[q8[0]], block_values[q8[1]], block_values[q8[2]], block_values[q8[3]]};
|
||||
acc2 += yl[1] * qf2;
|
||||
|
||||
v32 = q4[2*il+1] & 0xfffefffe;
|
||||
v32 ^= (v32 >> 1);
|
||||
aux32 = v32 & 0x0f0f0f0f;
|
||||
qf1 = {block_values[q8[0]], block_values[q8[1]], block_values[q8[2]], block_values[q8[3]]};
|
||||
acc1 += yl[2] * qf1;
|
||||
aux32 = (v32 >> 4) & 0x0f0f0f0f;
|
||||
qf2 = {block_values[q8[0]], block_values[q8[1]], block_values[q8[2]], block_values[q8[3]]};
|
||||
acc2 += yl[3] * qf2;
|
||||
|
||||
acc1 += acc2;
|
||||
|
||||
sumf[row] += d[row] * scale * (acc1[0] + acc1[1] + acc1[2] + acc1[3]);
|
||||
|
||||
q4 += row_size/4;
|
||||
|
||||
}
|
||||
|
||||
yb += 2 * QK_K;
|
||||
qptr += 2 * (QK_K/8);
|
||||
}
|
||||
|
||||
sumf = simd_sum(sumf);
|
||||
if (tiisg < 2) {
|
||||
dst[r1*ne0 + im*ne0*ne1 + first_row + tiisg] = sumf[tiisg];
|
||||
}
|
||||
}
|
||||
|
||||
void kernel_mul_mv_iq2_k_f32_impl(
|
||||
device const void * src0,
|
||||
device const float * src1,
|
||||
|
|
@ -7098,6 +7209,35 @@ kernel void kernel_mul_mv_iq4_ks_f32(
|
|||
kernel_mul_mv_iq4_ks_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
|
||||
}
|
||||
|
||||
[[host_name("kernel_mul_mv_iq4_kss_f32")]]
|
||||
kernel void kernel_mul_mv_iq4_kss_f32(
|
||||
device const void * src0,
|
||||
device const float * src1,
|
||||
device float * dst,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
constant int64_t & ne02,
|
||||
constant uint64_t & nb00,
|
||||
constant uint64_t & nb01,
|
||||
constant uint64_t & nb02,
|
||||
constant int64_t & ne10,
|
||||
constant int64_t & ne11,
|
||||
constant int64_t & ne12,
|
||||
constant uint64_t & nb10,
|
||||
constant uint64_t & nb11,
|
||||
constant uint64_t & nb12,
|
||||
constant int64_t & ne0,
|
||||
constant int64_t & ne1,
|
||||
constant uint & r2,
|
||||
constant uint & r3,
|
||||
threadgroup int8_t * shared_values [[threadgroup(0)]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint tiisg[[thread_index_in_simdgroup]],
|
||||
uint sgitg[[simdgroup_index_in_threadgroup]]) {
|
||||
|
||||
kernel_mul_mv_iq4_kss_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
|
||||
}
|
||||
|
||||
[[host_name("kernel_mul_mv_iq4_k_f32")]]
|
||||
kernel void kernel_mul_mv_iq4_k_f32(
|
||||
device const void * src0,
|
||||
|
|
@ -7714,6 +7854,30 @@ void dequantize_iq4_ks(device const block_iq4_ks * xb, short il, thread type4x4
|
|||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq4_kss(device const block_iq4_kss * xb, short il, thread type4x4 & reg) {
|
||||
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
|
||||
const int ib32 = il/2;
|
||||
il = il%2;
|
||||
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
|
||||
device const uint32_t * q4 = (device const uint32_t *)xb->qs + 4*ib32;
|
||||
uint32_t s32 = (q4[0] & 0x00010001) | ((q4[1] & 0x00010001) << 2) | ((q4[2] & 0x00010001) << 4) | ((q4[3] & 0x00010001) << 6);
|
||||
uint8_t ls = (s32 | (s32 >> 15)) & 0xff;
|
||||
const half scale = (ls & 254) - 127;
|
||||
constant float * values = kvalues_iq4k_f + ((ls & 1) << 4);
|
||||
uint32_t aux32;
|
||||
thread const uint8_t * q8 = (thread const uint8_t *)&aux32;
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
aux32 = q4[i] & 0xfffefffe;
|
||||
aux32 ^= (aux32 >> 1);
|
||||
aux32 = (aux32 >> 4*il) & 0x0f0f0f0f;
|
||||
reg[i][0] = scale * values[q8[0]];
|
||||
reg[i][1] = scale * values[q8[1]];
|
||||
reg[i][2] = scale * values[q8[2]];
|
||||
reg[i][3] = scale * values[q8[3]];
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq2_k(device const block_iq2_k * xb, short il, thread type4x4 & reg) {
|
||||
// il is 0...15 for QK_K = 256
|
||||
|
|
@ -8378,6 +8542,7 @@ template [[host_name("kernel_get_rows_iq2_bn")]] kernel get_rows_q_t kernel_get
|
|||
template [[host_name("kernel_get_rows_iq1_tn")]] kernel get_rows_q_t kernel_get_rows_q2<DequantizerRS<float4x4, block_iq1_bn, half, 4, dequantize_iq1_bn>>;
|
||||
template [[host_name("kernel_get_rows_iq2_tn")]] kernel get_rows_q_t kernel_get_rows_q2<DequantizerRS<float4x4, block_iq2_tn, float, 16, dequantize_iq2_tn>>;
|
||||
template [[host_name("kernel_get_rows_iq4_ks")]] kernel get_rows_q_t kernel_get_rows_q2<DequantizerRS<float4x4, block_iq4_ks, float, 16, dequantize_iq4_ks>>;
|
||||
template [[host_name("kernel_get_rows_iq4_kss")]] kernel get_rows_q_t kernel_get_rows_q2<DequantizerRS<float4x4, block_iq4_kss,float, 16, dequantize_iq4_kss>>;
|
||||
template [[host_name("kernel_get_rows_iq2_ks")]] kernel get_rows_q_t kernel_get_rows_q2<DequantizerRS<float4x4, block_iq2_ks, half, 16, dequantize_iq2_ks>>;
|
||||
|
||||
//
|
||||
|
|
@ -8422,6 +8587,7 @@ template [[host_name("kernel_mul_mm_iq2_bn_f32")]] kernel mat_mm_t kernel_mul_m
|
|||
template [[host_name("kernel_mul_mm_iq1_tn_f32")]] kernel mat_mm_t kernel_mul_mm<half, simdgroup_half8x8, DequantizerRS<half4x4, block_iq1_bn, half, 4, dequantize_iq1_bn>>;
|
||||
template [[host_name("kernel_mul_mm_iq2_tn_f32")]] kernel mat_mm_t kernel_mul_mm<half, simdgroup_half8x8, DequantizerRS<half4x4, block_iq2_tn, float, 16, dequantize_iq2_tn>>;
|
||||
template [[host_name("kernel_mul_mm_iq4_ks_f32")]] kernel mat_mm_t kernel_mul_mm<half, simdgroup_half8x8, DequantizerRS<half4x4, block_iq4_ks, float, 16, dequantize_iq4_ks>>;
|
||||
template [[host_name("kernel_mul_mm_iq4_kss_f32")]] kernel mat_mm_t kernel_mul_mm<half, simdgroup_half8x8, DequantizerRS<half4x4, block_iq4_kss,float, 16, dequantize_iq4_kss>>;
|
||||
template [[host_name("kernel_mul_mm_iq2_ks_f32")]] kernel mat_mm_t kernel_mul_mm<half, simdgroup_half8x8, DequantizerRS<half4x4, block_iq2_ks, half, 16, dequantize_iq2_ks>>;
|
||||
|
||||
//
|
||||
|
|
@ -8463,6 +8629,7 @@ template [[host_name("kernel_mul_mm_id_iq6_k_f32")]] kernel mat_mm_id_t kernel
|
|||
template [[host_name("kernel_mul_mm_id_iq1_tn_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<DequantizerRS<half4x4, block_iq1_bn, half, 4, dequantize_iq1_bn>>;
|
||||
template [[host_name("kernel_mul_mm_id_iq2_tn_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<DequantizerRS<half4x4, block_iq2_tn, float, 16, dequantize_iq2_tn>>;
|
||||
template [[host_name("kernel_mul_mm_id_iq4_ks_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<DequantizerRS<half4x4, block_iq4_ks, float, 16, dequantize_iq4_ks>>;
|
||||
template [[host_name("kernel_mul_mm_id_iq4_kss_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<DequantizerRS<half4x4, block_iq4_kss,float, 16, dequantize_iq4_kss>>;
|
||||
template [[host_name("kernel_mul_mm_id_iq2_ks_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<DequantizerRS<half4x4, block_iq2_ks, half, 16, dequantize_iq2_ks>>;
|
||||
|
||||
//
|
||||
|
|
@ -8680,6 +8847,7 @@ template [[host_name("kernel_mul_mv_id_iq2_s_f32")]] kernel kernel_mul_mv_id_t
|
|||
template [[host_name("kernel_mul_mv_id_iq4_nl_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq4_nl_f32_impl>>;
|
||||
template [[host_name("kernel_mul_mv_id_iq4_xs_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq4_xs_f32_impl>>;
|
||||
template [[host_name("kernel_mul_mv_id_iq4_ks_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq4_ks_f32_impl>>;
|
||||
template [[host_name("kernel_mul_mv_id_iq4_kss_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq4_kss_f32_impl>>;
|
||||
template [[host_name("kernel_mul_mv_id_iq2_k_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq2_k_f32_impl>>;
|
||||
template [[host_name("kernel_mul_mv_id_iq2_ks_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq2_ks_f32_impl>>;
|
||||
template [[host_name("kernel_mul_mv_id_iq3_k_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq3_k_f32_impl>>;
|
||||
|
|
|
|||
|
|
@ -15197,6 +15197,7 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
|
|||
case GGML_TYPE_IQ2_TN: break;
|
||||
case GGML_TYPE_IQ1_TN: break;
|
||||
case GGML_TYPE_IQ4_KS: break;
|
||||
case GGML_TYPE_IQ4_KSS: break;
|
||||
case GGML_TYPE_Q4_0_4_4:
|
||||
case GGML_TYPE_Q4_0_4_8:
|
||||
{
|
||||
|
|
|
|||
|
|
@ -1100,6 +1100,19 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
|
|||
.nrows = 1,
|
||||
.row_meta_size = 4,
|
||||
},
|
||||
[GGML_TYPE_IQ4_KSS] = {
|
||||
.type_name = "iq4_kss",
|
||||
.blck_size = QK_K,
|
||||
.type_size = sizeof(block_iq4_kss),
|
||||
.is_quantized = true,
|
||||
.to_float = (ggml_to_float_t) dequantize_row_iq4_kss,
|
||||
.from_float = quantize_row_iq4_kss,
|
||||
.from_float_ref = (ggml_from_float_t)quantize_row_iq4_kss_ref,
|
||||
.vec_dot = vec_dot_iq4_kss_q8_k,
|
||||
.vec_dot_type = GGML_TYPE_Q8_K,
|
||||
.nrows = 1,
|
||||
.row_meta_size = 4,
|
||||
},
|
||||
[GGML_TYPE_Q8_K] = {
|
||||
.type_name = "q8_K",
|
||||
.blck_size = QK_K,
|
||||
|
|
@ -3918,6 +3931,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
|
|||
case GGML_FTYPE_MOSTLY_IQ4_NL: wtype = GGML_TYPE_IQ4_NL; break;
|
||||
case GGML_FTYPE_MOSTLY_IQ4_XS: wtype = GGML_TYPE_IQ4_XS; break;
|
||||
case GGML_FTYPE_MOSTLY_IQ4_KS: wtype = GGML_TYPE_IQ4_KS; break;
|
||||
case GGML_FTYPE_MOSTLY_IQ4_KSS: wtype = GGML_TYPE_IQ4_KSS; break;
|
||||
case GGML_FTYPE_MOSTLY_IQ2_K: wtype = GGML_TYPE_IQ2_K; break;
|
||||
case GGML_FTYPE_MOSTLY_IQ2_KS: wtype = GGML_TYPE_IQ2_KS; break;
|
||||
case GGML_FTYPE_MOSTLY_IQ3_K: wtype = GGML_TYPE_IQ3_K; break;
|
||||
|
|
@ -10419,6 +10433,7 @@ static void ggml_compute_forward_add(
|
|||
case GGML_TYPE_IQ4_NL:
|
||||
case GGML_TYPE_IQ4_XS:
|
||||
case GGML_TYPE_IQ4_KS:
|
||||
case GGML_TYPE_IQ4_KSS:
|
||||
case GGML_TYPE_IQ2_K:
|
||||
case GGML_TYPE_IQ2_KS:
|
||||
case GGML_TYPE_IQ3_K:
|
||||
|
|
@ -10809,6 +10824,7 @@ static void ggml_compute_forward_add1(
|
|||
case GGML_TYPE_IQ4_NL:
|
||||
case GGML_TYPE_IQ4_XS:
|
||||
case GGML_TYPE_IQ4_KS:
|
||||
case GGML_TYPE_IQ4_KSS:
|
||||
case GGML_TYPE_IQ2_K:
|
||||
case GGML_TYPE_IQ2_KS:
|
||||
case GGML_TYPE_IQ3_K:
|
||||
|
|
@ -10949,6 +10965,7 @@ static void ggml_compute_forward_acc(
|
|||
case GGML_TYPE_IQ4_NL:
|
||||
case GGML_TYPE_IQ4_XS:
|
||||
case GGML_TYPE_IQ4_KS:
|
||||
case GGML_TYPE_IQ4_KSS:
|
||||
case GGML_TYPE_IQ2_K:
|
||||
case GGML_TYPE_IQ2_KS:
|
||||
case GGML_TYPE_IQ3_K:
|
||||
|
|
@ -14135,6 +14152,7 @@ static void ggml_compute_forward_out_prod(
|
|||
case GGML_TYPE_IQ4_NL:
|
||||
case GGML_TYPE_IQ4_XS:
|
||||
case GGML_TYPE_IQ4_KS:
|
||||
case GGML_TYPE_IQ4_KSS:
|
||||
case GGML_TYPE_IQ2_K:
|
||||
case GGML_TYPE_IQ2_KS:
|
||||
case GGML_TYPE_IQ3_K:
|
||||
|
|
@ -14515,6 +14533,7 @@ static void ggml_compute_forward_set(
|
|||
case GGML_TYPE_IQ4_NL:
|
||||
case GGML_TYPE_IQ4_XS:
|
||||
case GGML_TYPE_IQ4_KS:
|
||||
case GGML_TYPE_IQ4_KSS:
|
||||
case GGML_TYPE_IQ2_K:
|
||||
case GGML_TYPE_IQ2_KS:
|
||||
case GGML_TYPE_IQ3_K:
|
||||
|
|
@ -14789,6 +14808,7 @@ static void ggml_compute_forward_get_rows(
|
|||
case GGML_TYPE_IQ4_NL:
|
||||
case GGML_TYPE_IQ4_XS:
|
||||
case GGML_TYPE_IQ4_KS:
|
||||
case GGML_TYPE_IQ4_KSS:
|
||||
case GGML_TYPE_IQ2_K:
|
||||
case GGML_TYPE_IQ2_KS:
|
||||
case GGML_TYPE_IQ3_K:
|
||||
|
|
@ -15390,6 +15410,7 @@ static void ggml_compute_forward_clamp(
|
|||
case GGML_TYPE_IQ4_NL:
|
||||
case GGML_TYPE_IQ4_XS:
|
||||
case GGML_TYPE_IQ4_KS:
|
||||
case GGML_TYPE_IQ4_KSS:
|
||||
case GGML_TYPE_IQ2_K:
|
||||
case GGML_TYPE_IQ2_KS:
|
||||
case GGML_TYPE_IQ3_K:
|
||||
|
|
@ -22208,6 +22229,7 @@ size_t ggml_quantize_chunk(
|
|||
case GGML_TYPE_IQ4_NL: result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
|
||||
case GGML_TYPE_IQ4_XS: result = quantize_iq4_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
|
||||
case GGML_TYPE_IQ4_KS: result = quantize_iq4_ks (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
|
||||
case GGML_TYPE_IQ4_KSS: result = quantize_iq4_kss(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
|
||||
case GGML_TYPE_IQ2_K: result = quantize_iq2_k (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
|
||||
case GGML_TYPE_IQ2_KS: result = quantize_iq2_ks (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
|
||||
case GGML_TYPE_IQ3_K: result = quantize_iq3_k (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
|
||||
|
|
|
|||
|
|
@ -1209,6 +1209,67 @@ struct DequantizerIQ4KS final : public BaseDequantizer<block_iq4_ks, true> {
|
|||
};
|
||||
};
|
||||
|
||||
struct DequantizerIQ4KSS final : public BaseDequantizer<block_iq4_kss, true> {
|
||||
DequantizerIQ4KSS(const void * vx, size_t bx) : BaseDequantizer(vx, bx), values(load_iq4nl_values_512()) {}
|
||||
template <typename Q8>
|
||||
inline void new_block(int i, const Q8& q8, __m256 * accm, __m512i * scales) {
|
||||
uint32_t aux32[2];
|
||||
auto b1 = _mm512_loadu_si512((const __m512i *)x[i].qs + 0);
|
||||
auto b2 = _mm512_loadu_si512((const __m512i *)x[i].qs + 1);
|
||||
auto bs1 = _mm512_and_si512(b1, mask15);
|
||||
bs1 = _mm512_xor_si512(bs1, _mm512_srli_epi16(bs1, 1));
|
||||
auto bs2 = _mm512_and_si512(b2, mask15);
|
||||
bs2 = _mm512_xor_si512(bs2, _mm512_srli_epi16(bs2, 1));
|
||||
bits.values[0] = _mm512_and_si512(bs1, bits.ml);
|
||||
bits.values[1] = _mm512_and_si512(_mm512_srli_epi16(bs1, 4), bits.ml);
|
||||
bits.values[2] = _mm512_and_si512(bs2, bits.ml);
|
||||
bits.values[3] = _mm512_and_si512(_mm512_srli_epi16(bs2, 4), bits.ml);
|
||||
auto tmp = _mm512_permutex2var_epi64(bits.values[0], permute1, bits.values[1]);
|
||||
bits.values[1] = _mm512_shuffle_epi8(values, _mm512_permutex2var_epi64(bits.values[0], permute2, bits.values[1]));
|
||||
bits.values[0] = _mm512_shuffle_epi8(values, tmp);
|
||||
tmp = _mm512_permutex2var_epi64(bits.values[2], permute1, bits.values[3]);
|
||||
bits.values[3] = _mm512_shuffle_epi8(values, _mm512_permutex2var_epi64(bits.values[2], permute2, bits.values[3]));
|
||||
bits.values[2] = _mm512_shuffle_epi8(values, tmp);
|
||||
//
|
||||
// Now the more difficult part - prepare the scales
|
||||
//
|
||||
aux32[0] = _mm512_cmpeq_epi16_mask(_mm512_and_si512(b1, mask1), mask1);
|
||||
aux32[1] = _mm512_cmpeq_epi16_mask(_mm512_and_si512(b2, mask1), mask1);
|
||||
|
||||
auto scales128 = _mm_cvtepu8_epi16(_mm_loadl_epi64((const __m128i *)aux32));
|
||||
auto m1 = _mm512_castsi512_si128(mask1);
|
||||
auto shifts = _mm_and_si128(_mm_cmpeq_epi16(_mm_and_si128(scales128, m1), m1), m4);
|
||||
scales128 = _mm_add_epi16(_mm_and_si128(scales128, mask), m127);
|
||||
auto scales_s = _mm_mullo_epi16(scales128, _mm_add_epi16(m128, shifts));
|
||||
s8k.accum_mins(scales_s, q8, i, d, accm);
|
||||
auto scales256 = MM256_SET_M128I(scales128, scales128);
|
||||
auto all_scales = _mm512_inserti32x8(_mm512_castsi256_si512(scales256), scales256, 1);
|
||||
scales[0] = _mm512_shuffle_epi8(all_scales, shuffles[0]);
|
||||
scales[1] = _mm512_shuffle_epi8(all_scales, shuffles[1]);
|
||||
scales[2] = _mm512_shuffle_epi8(all_scales, shuffles[2]);
|
||||
scales[3] = _mm512_shuffle_epi8(all_scales, shuffles[3]);
|
||||
}
|
||||
|
||||
Q4Bits bits;
|
||||
Scales8KBase s8k;
|
||||
const __m512i values;
|
||||
const __m512i mask15 = _mm512_set1_epi16(0xfffe);
|
||||
const __m512i mask1 = _mm512_set1_epi16(1);
|
||||
const __m512i permute1 = _mm512_set_epi64(11, 10, 3, 2, 9, 8, 1, 0);
|
||||
const __m512i permute2 = _mm512_set_epi64(15, 14, 7, 6, 13, 12, 5, 4);
|
||||
const __m128i mask = _mm_set1_epi16(254);
|
||||
const __m128i m127 = _mm_set1_epi16(-127);
|
||||
const __m128i m128 = _mm_set1_epi16(-128);
|
||||
const __m128i m4 = _mm_set1_epi16(4);
|
||||
const __m512i shuffles[4] = {
|
||||
_mm512_inserti32x8(_mm512_set1_epi16(0x0100), _mm256_set1_epi16(0x0302), 1),
|
||||
_mm512_inserti32x8(_mm512_set1_epi16(0x0504), _mm256_set1_epi16(0x0706), 1),
|
||||
_mm512_inserti32x8(_mm512_set1_epi16(0x0908), _mm256_set1_epi16(0x0b0a), 1),
|
||||
_mm512_inserti32x8(_mm512_set1_epi16(0x0d0c), _mm256_set1_epi16(0x0f0e), 1),
|
||||
};
|
||||
};
|
||||
|
||||
|
||||
template <typename Q8>
|
||||
inline void compute_block(int iy, int i, float d, const Q8& q8, const __m512i * values, const __m512i * scales, __m512 * accd) {
|
||||
const __m512i p1 = _mm512_dpbusd_epi32(_mm512_setzero_si512(), values[0], q8.load_quants64(iy, i, 0));
|
||||
|
|
@ -1821,8 +1882,54 @@ struct DequantizerIQ4KS final : public BaseDequantizer<block_iq4_ks, true> {
|
|||
const __m128i m128 = _mm_set1_epi16(-128);
|
||||
const __m128i m1 = _mm_set1_epi16(1);
|
||||
const __m128i m4 = _mm_set1_epi16(4);
|
||||
const __m256i shuff1 = _mm256_set_epi64x(0x0706070605040504, 0x0302030201000100, 0x0706070605040504, 0x0302030201000100);
|
||||
const __m256i shuff2 = _mm256_set_epi64x(0x0f0e0f0e0d0c0d0c, 0x0b0a0b0a09080908, 0x0f0e0f0e0d0c0d0c, 0x0b0a0b0a09080908);
|
||||
};
|
||||
|
||||
struct DequantizerIQ4KSS final : public BaseDequantizer<block_iq4_kss, true> {
|
||||
DequantizerIQ4KSS(const void * vx, size_t bx) : BaseDequantizer(vx, bx), values(load_iq4nl_values_256()) {}
|
||||
template <typename Q8>
|
||||
inline __m256i new_block(int i, const Q8& q8, __m256 * accd) {
|
||||
union { __m256i vec; uint16_t val[16]; } helper;
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
data[k] = _mm256_loadu_si256((const __m256i *)x[i].qs + k);
|
||||
auto p = _mm256_and_si256(_mm256_cmpeq_epi16(_mm256_and_si256(data[k], m1), m1), smask);
|
||||
p = _mm256_add_epi32(_mm256_unpackhi_epi64(p, p), p);
|
||||
p = _mm256_add_epi32(_mm256_shuffle_epi32(p, _MM_SHUFFLE(2, 3, 0, 1)), p);
|
||||
helper.vec = _mm256_hadd_epi16(p, p);
|
||||
aux[2*k+0] = helper.val[0];
|
||||
aux[2*k+1] = helper.val[8];
|
||||
data[k] = _mm256_and_si256(data[k], bmask);
|
||||
data[k] = _mm256_xor_si256(data[k], _mm256_srli_epi16(data[k], 1));
|
||||
}
|
||||
auto scales128 = _mm_loadu_si128((const __m128i *)aux);
|
||||
auto shifts = _mm_and_si128(_mm_cmpeq_epi16(_mm_and_si128(scales128, _mm256_castsi256_si128(m1)), _mm256_castsi256_si128(m1)), m4);
|
||||
scales128 = _mm_add_epi16(_mm_and_si128(scales128, mask), m127);
|
||||
auto scales_s = _mm_mullo_epi16(scales128, _mm_add_epi16(m128, shifts));
|
||||
s8k.accum_mins(scales_s, q8, i, d, accd);
|
||||
return MM256_SET_M128I(scales128, scales128);
|
||||
}
|
||||
inline void prepare(int, int j) {
|
||||
for (int k = 0; k < 2; ++k) {
|
||||
auto p1 = _mm256_castsi256_si128(data[2*j+k]);
|
||||
auto p2 = _mm256_extractf128_si256(data[2*j+k], 1);
|
||||
bits.values[2*k+0] = _mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(p1, 4), p1), bits.ml);
|
||||
bits.values[2*k+0] = _mm256_shuffle_epi8(values, bits.values[2*k+0]);
|
||||
bits.values[2*k+1] = _mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(p2, 4), p2), bits.ml);
|
||||
bits.values[2*k+1] = _mm256_shuffle_epi8(values, bits.values[2*k+1]);
|
||||
}
|
||||
}
|
||||
|
||||
Q4Bits bits;
|
||||
Scales8KBase s8k;
|
||||
const __m256i values;
|
||||
__m256i data[4];
|
||||
const __m256i smask = _mm256_set_epi64x(0x0080004000200010, 0x0008000400020001, 0x0080004000200010, 0x0008000400020001);
|
||||
const __m256i bmask = _mm256_set1_epi16(0xfffe);
|
||||
const __m128i mask = _mm_set1_epi16(254);
|
||||
const __m128i m127 = _mm_set1_epi16(-127);
|
||||
const __m128i m128 = _mm_set1_epi16(-128);
|
||||
const __m256i m1 = _mm256_set1_epi16(1);
|
||||
const __m128i m4 = _mm_set1_epi16(4);
|
||||
uint16_t aux[8];
|
||||
};
|
||||
|
||||
struct DequantizerIQ2KS final : public BaseDequantizer<block_iq2_ks, true, true> {
|
||||
|
|
@ -3848,7 +3955,8 @@ template <typename Dequantizer> void MulMat::set_functions(MulMat& m) {
|
|||
std::is_same_v<Dequantizer, DequantizerIQ4K> ||
|
||||
std::is_same_v<Dequantizer, DequantizerIQ3K> ||
|
||||
std::is_same_v<Dequantizer, DequantizerIQ4XS>||
|
||||
std::is_same_v<Dequantizer, DequantizerIQ4KS>) {
|
||||
std::is_same_v<Dequantizer, DequantizerIQ4KS>||
|
||||
std::is_same_v<Dequantizer, DequantizerIQ4KSS>) {
|
||||
m.funcs[0] = mul_mat_iqX_k_q8_K_AVX512<Dequantizer, 1>;
|
||||
m.funcs[1] = mul_mat_iqX_k_q8_K_AVX512<Dequantizer, 2>;
|
||||
m.funcs[2] = mul_mat_iqX_k_q8_K_AVX512<Dequantizer, 3>;
|
||||
|
|
@ -4012,6 +4120,10 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& mm, int Ny) {
|
|||
assert (ne00 % QK_K == 0);
|
||||
MulMat::set_functions<DequantizerIQ4KS>(mm);
|
||||
break;
|
||||
case GGML_TYPE_IQ4_KSS:
|
||||
assert (ne00 % QK_K == 0);
|
||||
MulMat::set_functions<DequantizerIQ4KSS>(mm);
|
||||
break;
|
||||
case GGML_TYPE_IQ2_K:
|
||||
assert (ne00 % QK_K == 0);
|
||||
MulMat::set_functions<DequantizerIQ2K>(mm);
|
||||
|
|
@ -4945,6 +5057,63 @@ struct DequantizerIQ4KS final : public BaseDequantizer<block_iq4_ks, true> {
|
|||
const int16x8_t m127 = vdupq_n_s16(-127);
|
||||
};
|
||||
|
||||
struct DequantizerIQ4KSS final : public BaseDequantizer<block_iq4_kss, true> {
|
||||
|
||||
DequantizerIQ4KSS(const void * vx, size_t bx, int nrc) : BaseDequantizer(vx, bx, nrc), values(vld1q_s8_x2(iq4k_values)) {}
|
||||
|
||||
constexpr static int num_blocks() { return 8; }
|
||||
constexpr static bool should_scale_quants() { return false; }
|
||||
|
||||
template <typename Q8>
|
||||
inline int32x4x2_t new_block(int i, const Q8& q8, float32x4_t * acc) {
|
||||
(void)q8;
|
||||
(void)acc;
|
||||
auto q4bits_1 = vld1q_u16_x4((const uint16_t *)x[i].qs);
|
||||
q4bits_2 = vld1q_u16_x4((const uint16_t *)x[i].qs + 32);
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
aux[k+0] = vaddvq_s16(vshlq_s16(vandq_u16(q4bits_1.val[k], m1), shift));
|
||||
aux[k+4] = vaddvq_s16(vshlq_s16(vandq_u16(q4bits_2.val[k], m1), shift));
|
||||
q4bits_1.val[k] = vandq_u16(q4bits_1.val[k], bmask);
|
||||
q4bits_1.val[k] = veorq_u16(q4bits_1.val[k], vshrq_n_u16(q4bits_1.val[k], 1));
|
||||
q4bits_2.val[k] = vandq_u16(q4bits_2.val[k], bmask);
|
||||
q4bits_2.val[k] = veorq_u16(q4bits_2.val[k], vshrq_n_u16(q4bits_2.val[k], 1));
|
||||
}
|
||||
make_quants(q4bits_1, bits, aux);
|
||||
auto scales16 = vld1q_s16(aux);
|
||||
scales16 = vaddq_s16(vandq_s16(scales16, mask), m127);
|
||||
int32x4x2_t scales = {vmovl_s16(vget_low_s16(scales16)), vmovl_s16(vget_high_s16(scales16))};
|
||||
return scales;
|
||||
}
|
||||
inline void make_quants(uint16x8x4_t& q4bits, Q4bits& bits, const int16_t * aux) const {
|
||||
bits.b1.val[0] = vqtbl1q_s8(values.val[aux[0] & 1], vandq_u8(q4bits.val[0], bits.m4b));
|
||||
bits.b1.val[1] = vqtbl1q_s8(values.val[aux[0] & 1], vshrq_n_u8(q4bits.val[0], 4));
|
||||
bits.b1.val[2] = vqtbl1q_s8(values.val[aux[1] & 1], vandq_u8(q4bits.val[1], bits.m4b));
|
||||
bits.b1.val[3] = vqtbl1q_s8(values.val[aux[1] & 1], vshrq_n_u8(q4bits.val[1], 4));
|
||||
bits.b2.val[0] = vqtbl1q_s8(values.val[aux[2] & 1], vandq_u8(q4bits.val[2], bits.m4b));
|
||||
bits.b2.val[1] = vqtbl1q_s8(values.val[aux[2] & 1], vshrq_n_u8(q4bits.val[2], 4));
|
||||
bits.b2.val[2] = vqtbl1q_s8(values.val[aux[3] & 1], vandq_u8(q4bits.val[3], bits.m4b));
|
||||
bits.b2.val[3] = vqtbl1q_s8(values.val[aux[3] & 1], vshrq_n_u8(q4bits.val[3], 4));
|
||||
}
|
||||
inline void prepare([[maybe_unused]] int i, int j) {
|
||||
if (j == 0) return;
|
||||
make_quants(q4bits_2, bits, aux+4);
|
||||
}
|
||||
static int16x8_t load_shift() {
|
||||
static const int16_t k_shift[8] = {0, 1, 2, 3, 4, 5, 6, 7};
|
||||
return vld1q_s16(k_shift);
|
||||
}
|
||||
|
||||
Q4bits bits;
|
||||
const int8x16x2_t values;
|
||||
const uint16x8_t mask = vdupq_n_s16(254);
|
||||
const uint16x8_t bmask = vdupq_n_u16(0xfffe);
|
||||
const uint16x8_t m1 = vdupq_n_u16(1);
|
||||
const int16x8_t shift = load_shift();
|
||||
const int16x8_t m127 = vdupq_n_s16(-127);
|
||||
uint16x8x4_t q4bits_2;
|
||||
int16_t aux[8];
|
||||
};
|
||||
|
||||
struct DequantizerIQ2KS final : public BaseDequantizer<block_iq2_ks, true, true> {
|
||||
DequantizerIQ2KS(const void * vx, size_t bx, int nrc) : BaseDequantizer(vx, bx, nrc) {}
|
||||
|
||||
|
|
@ -6716,6 +6885,9 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& m, int /*Ny*/) {
|
|||
case GGML_TYPE_IQ4_KS:
|
||||
MulMat::set_functions<DequantizerIQ4KS>(m);
|
||||
break;
|
||||
case GGML_TYPE_IQ4_KSS:
|
||||
MulMat::set_functions<DequantizerIQ4KSS>(m);
|
||||
break;
|
||||
case GGML_TYPE_IQ2_KS:
|
||||
MulMat::set_functions<DequantizerIQ2KS>(m);
|
||||
break;
|
||||
|
|
|
|||
|
|
@ -20,6 +20,25 @@
|
|||
#include <array>
|
||||
#include <algorithm>
|
||||
#include <cstring>
|
||||
#include <mutex>
|
||||
|
||||
#if defined(_MSC_VER)
|
||||
#pragma warning(disable: 4244 4267) // possible loss of data
|
||||
#include <intrin.h>
|
||||
#include <ammintrin.h>
|
||||
#include <nmmintrin.h>
|
||||
#include <immintrin.h>
|
||||
#include <stdlib.h>
|
||||
inline int popcount(uint8_t x) { return __popcnt(x); }
|
||||
inline int popcount(uint16_t x) { return __popcnt(x); }
|
||||
inline int popcount(uint32_t x) { return __popcnt(x); }
|
||||
inline int popcount(uint64_t x) { return _mm_popcnt_u64(x); }
|
||||
#else
|
||||
constexpr int popcount(uint8_t x) { return __builtin_popcount(x); }
|
||||
constexpr int popcount(uint16_t x) { return __builtin_popcount(x); }
|
||||
constexpr int popcount(uint32_t x) { return __builtin_popcount(x); }
|
||||
constexpr int popcount(uint64_t x) { return __builtin_popcountll(x); }
|
||||
#endif
|
||||
|
||||
namespace {
|
||||
|
||||
|
|
@ -2811,3 +2830,432 @@ void vec_dot_iq4_ks_q8_k(int n, float * s, size_t bs, const void * vx, size_t b
|
|||
*s = sumf;
|
||||
}
|
||||
|
||||
namespace {
|
||||
const uint16_t * scramble_table() {
|
||||
static std::mutex mutex;
|
||||
static std::vector<uint16_t> table;
|
||||
std::lock_guard<std::mutex> lock(mutex);
|
||||
if (table.empty()) {
|
||||
table.resize(1 << 15);
|
||||
for (int i = 0; i < int(table.size()); ++i) {
|
||||
uint16_t val = i;
|
||||
int non = popcount(val);
|
||||
if (non%2) val |= (1 << 15);
|
||||
bool found = false;
|
||||
for (int j = 0; j < int(table.size()); ++j) {
|
||||
if ((j ^ (j << 1)) == val) {
|
||||
table[i] = j; found = true; break;
|
||||
}
|
||||
}
|
||||
if (!found) {
|
||||
printf("Oops: did not find for %d %u\n", i, val);
|
||||
exit(1);
|
||||
}
|
||||
}
|
||||
}
|
||||
return table.data();
|
||||
}
|
||||
uint16_t prune_iq4ks(uint16_t v, const int8_t * values, const float * x, const float * w, float dl) {
|
||||
if (popcount(v)%2 == 0) return v;
|
||||
float best_score = std::numeric_limits<float>::max();
|
||||
uint8_t q4[4];
|
||||
int jbest = -1;
|
||||
uint8_t bestq = 0;
|
||||
for (int j = 0; j < 4; ++j) {
|
||||
uint8_t q = (v >> 4*j) & 0xf;
|
||||
q4[j] = q;
|
||||
auto pc = popcount(q);
|
||||
float diff0 = dl*iq4k_values[q] - x[j];
|
||||
if (q > 0) {
|
||||
uint8_t qm = q - 1u;
|
||||
int pcm = popcount(qm);
|
||||
if (pcm == pc-1 || pcm == pc+1) {
|
||||
float diff1 = dl*values[qm] - x[j];
|
||||
float score = w[j]*(diff1*diff1 - diff0*diff0);
|
||||
if (score < best_score) {
|
||||
best_score = score; jbest = j; bestq = qm;
|
||||
}
|
||||
}
|
||||
}
|
||||
if (q < 15) {
|
||||
uint8_t qp = q + 1u;
|
||||
int pcp = popcount(qp);
|
||||
if (pcp == pc-1 || pcp == pc+1) {
|
||||
float diff1 = dl*values[qp] - x[j];
|
||||
float score = w[j]*(diff1*diff1 - diff0*diff0);
|
||||
if (score < best_score) {
|
||||
best_score = score; jbest = j; bestq = qp;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
GGML_ASSERT(jbest >= 0);
|
||||
q4[jbest] = bestq;
|
||||
return (q4[0] | (q4[1] << 4) | (q4[2] << 8) | (q4[3] << 12));
|
||||
}
|
||||
static void quantize_row_iq4_kss_impl(int n_per_row, const float * x, char * cy,
|
||||
float * all_scales, float * weight,
|
||||
const int8_t * values,
|
||||
const float * quant_weights,
|
||||
const uint16_t * table,
|
||||
const int ntry) {
|
||||
|
||||
constexpr int super_block_size = 256;
|
||||
constexpr int block_size = 32;
|
||||
|
||||
float * dptr = (float *)cy;
|
||||
*dptr = 0;
|
||||
block_iq4_kss * y = (block_iq4_kss *)(dptr + 1);
|
||||
|
||||
const int8_t * shifted_values = values + 16;
|
||||
|
||||
uint16_t vps[block_size/2], vms[block_size/2], vs[block_size/2];
|
||||
float xv[4], wv[4];
|
||||
|
||||
float amax_scale = 0;
|
||||
|
||||
for (int ibl = 0; ibl < n_per_row/super_block_size; ++ibl) {
|
||||
memset(&y[ibl], 0, sizeof(block_iq4_kss));
|
||||
const float * xbl = x + ibl*super_block_size;
|
||||
auto scales = all_scales + ibl*(super_block_size/block_size);
|
||||
float sigma2 = 0;
|
||||
for (int j = 0; j < super_block_size; ++j) sigma2 += xbl[j]*xbl[j];
|
||||
sigma2 *= 2.f/super_block_size;
|
||||
for (int ib = 0; ib < super_block_size/block_size; ++ib) {
|
||||
const float * xb = xbl + ib*block_size;
|
||||
if (quant_weights) {
|
||||
const float * qw = quant_weights + ibl*super_block_size + ib*block_size;
|
||||
for (int j = 0; j < block_size; ++j) weight[j] = qw[j] * sqrtf(sigma2 + xb[j]*xb[j]);
|
||||
} else {
|
||||
for (int j = 0; j < block_size; ++j) weight[j] = xb[j]*xb[j];
|
||||
}
|
||||
float amax = 0, max = 0;
|
||||
for (int j = 0; j < block_size; ++j) {
|
||||
float ax = fabsf(xb[j]);
|
||||
if (ax > amax) {
|
||||
amax = ax; max = xb[j];
|
||||
}
|
||||
}
|
||||
if (!amax) {
|
||||
scales[ib] = 0;
|
||||
continue;
|
||||
}
|
||||
float best = 0;
|
||||
bool is_shifted = false;
|
||||
float d = -max/iq4k_values[0];
|
||||
std::memset(vs, 0, block_size);
|
||||
for (int itry = -ntry; itry <= ntry; ++itry) {
|
||||
float id = (itry + values[0])/max;
|
||||
float sumqx_p = 0, sumq2_p = 0;
|
||||
float sumqx_m = 0, sumq2_m = 0;
|
||||
float this_d = 1/id;
|
||||
for (int k = 0; k < block_size/4; ++k) {
|
||||
xv[0] = xb[2*k+0]; xv[1] = xb[2*k+0+block_size/2]; xv[2] = xb[2*k+1]; xv[3] = xb[2*k+1+block_size/2];
|
||||
wv[0] = weight[2*k+0]; wv[1] = weight[2*k+0+block_size/2]; wv[2] = weight[2*k+1]; wv[3] = weight[2*k+1+block_size/2];
|
||||
uint16_t vp = 0, vm = 0;
|
||||
for (int j = 0; j < 4; ++j) {
|
||||
float al = id*xv[j];
|
||||
vp |= (best_index_iq4nl(values, al) << 4*j);
|
||||
vm |= (best_index_iq4nl(values, -al) << 4*j);
|
||||
}
|
||||
vp = prune_iq4ks(vp, values, xv, wv, this_d);
|
||||
vm = prune_iq4ks(vm, values, xv, wv, this_d);
|
||||
for (int j = 0; j < 4; ++j) {
|
||||
float w = wv[j];
|
||||
float q = values[(vp >> 4*j) & 0xf];
|
||||
sumqx_p += w*q*xv[j];
|
||||
sumq2_p += w*q*q;
|
||||
q = values[(vm >> 4*j) & 0xf];
|
||||
sumqx_m += w*q*xv[j];
|
||||
sumq2_m += w*q*q;
|
||||
}
|
||||
vps[k] = vp;
|
||||
vms[k] = vm;
|
||||
}
|
||||
bool copy_p = false, copy_m = false;
|
||||
if (sumq2_p > 0 && sumqx_p*sumqx_p > best*sumq2_p) {
|
||||
d = sumqx_p/sumq2_p; best = d * sumqx_p; is_shifted = false; copy_p = true;
|
||||
}
|
||||
if (sumq2_m > 0 && sumqx_m*sumqx_m > best*sumq2_m) {
|
||||
d = sumqx_m/sumq2_m; best = d * sumqx_m; is_shifted = false; copy_m = true;
|
||||
}
|
||||
if (copy_m) {
|
||||
std::memcpy(vs, vms, block_size);
|
||||
} else if (copy_p) {
|
||||
std::memcpy(vs, vps, block_size);
|
||||
}
|
||||
|
||||
id = (itry + shifted_values[0])/max;
|
||||
this_d = 1/id;
|
||||
sumqx_p = sumq2_p = 0;
|
||||
sumqx_m = sumq2_m = 0;
|
||||
for (int k = 0; k < block_size/4; ++k) {
|
||||
xv[0] = xb[2*k+0]; xv[1] = xb[2*k+0+block_size/2]; xv[2] = xb[2*k+1]; xv[3] = xb[2*k+1+block_size/2];
|
||||
wv[0] = weight[2*k+0]; wv[1] = weight[2*k+0+block_size/2]; wv[2] = weight[2*k+1]; wv[3] = weight[2*k+1+block_size/2];
|
||||
uint16_t vp = 0, vm = 0;
|
||||
for (int j = 0; j < 4; ++j) {
|
||||
float al = id*xv[j];
|
||||
vp |= (best_index_iq4nl(shifted_values, al) << 4*j);
|
||||
vm |= (best_index_iq4nl(shifted_values, -al) << 4*j);
|
||||
}
|
||||
vp = prune_iq4ks(vp, shifted_values, xv, wv, this_d);
|
||||
vm = prune_iq4ks(vm, shifted_values, xv, wv, this_d);
|
||||
for (int j = 0; j < 4; ++j) {
|
||||
float w = wv[j];
|
||||
float q = shifted_values[(vp >> 4*j) & 0xf];
|
||||
sumqx_p += w*q*xv[j];
|
||||
sumq2_p += w*q*q;
|
||||
q = shifted_values[(vm >> 4*j) & 0xf];
|
||||
sumqx_m += w*q*xv[j];
|
||||
sumq2_m += w*q*q;
|
||||
}
|
||||
vps[k] = vp;
|
||||
vms[k] = vm;
|
||||
}
|
||||
copy_p = copy_m = false;
|
||||
if (sumq2_p > 0 && sumqx_p*sumqx_p > best*sumq2_p) {
|
||||
d = sumqx_p/sumq2_p; best = d * sumqx_p; is_shifted = true; copy_p = true;
|
||||
}
|
||||
if (sumq2_m > 0 && sumqx_m*sumqx_m > best*sumq2_m) {
|
||||
d = sumqx_m/sumq2_m; best = d * sumqx_m; is_shifted = true; copy_m = true;
|
||||
}
|
||||
if (copy_m) {
|
||||
std::memcpy(vs, vms, block_size);
|
||||
} else if (copy_p) {
|
||||
std::memcpy(vs, vps, block_size);
|
||||
}
|
||||
}
|
||||
scales[ib] = d;
|
||||
amax_scale = std::max(amax_scale, std::abs(d));
|
||||
}
|
||||
}
|
||||
float d = amax_scale/127;
|
||||
*dptr = d;
|
||||
if (!d) return;
|
||||
float id = 1/d;
|
||||
float sumqx = 0, sumq2 = 0;
|
||||
for (int ibl = 0; ibl < n_per_row/super_block_size; ++ibl) {
|
||||
auto scales = all_scales + (super_block_size/block_size)*ibl;
|
||||
const float * xbl = x + ibl*super_block_size;
|
||||
float sigma2 = 0;
|
||||
for (int j = 0; j < super_block_size; ++j) sigma2 += xbl[j]*xbl[j];
|
||||
sigma2 *= 2.f/super_block_size;
|
||||
for (int ib = 0; ib < super_block_size/block_size; ++ib) {
|
||||
const float * xb = xbl + ib*block_size;
|
||||
if (quant_weights) {
|
||||
const float * qw = quant_weights + ibl*super_block_size + ib*block_size;
|
||||
for (int j = 0; j < block_size; ++j) weight[j] = qw[j] * sqrtf(sigma2 + xb[j]*xb[j]);
|
||||
} else {
|
||||
for (int j = 0; j < block_size; ++j) weight[j] = xb[j]*xb[j];
|
||||
}
|
||||
int l = nearest_int(0.5f*(id*scales[ib]+127.f));
|
||||
l = (std::max(0, std::min(127, l)) << 1) - 127;
|
||||
if (l) {
|
||||
float dl = d*l;
|
||||
float idl = 1/dl;
|
||||
float mse_p = 0, mse_m = 0;
|
||||
for (int k = 0; k < block_size/4; ++k) {
|
||||
xv[0] = xb[2*k+0]; xv[1] = xb[2*k+0+block_size/2]; xv[2] = xb[2*k+1]; xv[3] = xb[2*k+1+block_size/2];
|
||||
wv[0] = weight[2*k+0]; wv[1] = weight[2*k+0+block_size/2]; wv[2] = weight[2*k+1]; wv[3] = weight[2*k+1+block_size/2];
|
||||
uint16_t vp = 0, vm = 0;
|
||||
for (int j = 0; j < 4; ++j) {
|
||||
float al = idl*xv[j];
|
||||
vp |= (best_index_iq4nl( values, al) << 4*j);
|
||||
vm |= (best_index_iq4nl(shifted_values, al) << 4*j);
|
||||
}
|
||||
vp = prune_iq4ks(vp, values, xv, wv, dl);
|
||||
vm = prune_iq4ks(vm, shifted_values, xv, wv, dl);
|
||||
for (int j = 0; j < 4; ++j) {
|
||||
float w = wv[j];
|
||||
float q = values[(vp >> 4*j) & 0xf];
|
||||
mse_p += w*(xv[j] - dl*q)*(xv[j] - dl*q);
|
||||
q = shifted_values[(vm >> 4*j) & 0xf];
|
||||
mse_m += w*(xv[j] - dl*q)*(xv[j] - dl*q);
|
||||
}
|
||||
vps[k] = vp;
|
||||
vms[k] = vm;
|
||||
}
|
||||
const uint16_t * v = vps;
|
||||
const int8_t * block_values = values;
|
||||
if (mse_m < mse_p) {
|
||||
v = vms;
|
||||
block_values = values + 16;
|
||||
}
|
||||
for (int k = 0; k < block_size/4; ++k) {
|
||||
xv[0] = xb[2*k+0]; xv[1] = xb[2*k+0+block_size/2]; xv[2] = xb[2*k+1]; xv[3] = xb[2*k+1+block_size/2];
|
||||
wv[0] = weight[2*k+0]; wv[1] = weight[2*k+0+block_size/2]; wv[2] = weight[2*k+1]; wv[3] = weight[2*k+1+block_size/2];
|
||||
for (int j = 0; j < 4; ++j) {
|
||||
float q = block_values[(v[k] >> 4*j) & 0xf] * l;
|
||||
sumqx += wv[j]*q*xv[j];
|
||||
sumq2 += wv[j]*q*q;
|
||||
}
|
||||
}
|
||||
l += 127;
|
||||
if (mse_m < mse_p) l |= 1;
|
||||
uint16_t * q16 = (uint16_t *)y[ibl].qs + (block_size/4)*ib;
|
||||
for (int k = 0; k < block_size/4; ++k) {
|
||||
auto val = table[v[k] & 0x7fff];
|
||||
q16[k] = (val << 1) | ((l >> k) & 1);
|
||||
}
|
||||
} else {
|
||||
l += 127;
|
||||
uint16_t * q16 = (uint16_t *)y[ibl].qs + (block_size/4)*ib;
|
||||
for (int k = 0; k < block_size/4; ++k) {
|
||||
q16[k] = ((l >> k) & 1);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
if (sumq2 > 0) *dptr = sumqx/sumq2;
|
||||
}
|
||||
|
||||
void prune_iq4ks_to_iq4kss(int n_per_row, const uint16_t * table, const char * cx, const float * x, char *cy,
|
||||
const float * quant_weights, float * weight, float * all_scales) {
|
||||
constexpr int kBlockSize = 32;
|
||||
float xv[4], wv[4];
|
||||
uint16_t vps[kBlockSize/4];
|
||||
const float * dptr_ks = (const float *)cx;
|
||||
const float d_ks = *dptr_ks;
|
||||
const block_iq4_ks * iq4ks = (const block_iq4_ks *)(dptr_ks + 1);
|
||||
float * dptr = (float *)cy;
|
||||
*dptr = d_ks;
|
||||
block_iq4_kss * y = (block_iq4_kss *)(dptr + 1);
|
||||
int nblock = n_per_row/QK_K;
|
||||
float max_abs_scale = 0;
|
||||
for (int ibl = 0; ibl < nblock; ++ibl) {
|
||||
auto scales = all_scales + ibl*(QK_K/kBlockSize);
|
||||
const float * xbl = x + ibl*QK_K;
|
||||
float sigma2 = 0;
|
||||
for (int j = 0; j < QK_K; ++j) sigma2 += xbl[j]*xbl[j];
|
||||
sigma2 *= 2.f/QK_K;
|
||||
const uint16_t * q4 = (const uint16_t *)iq4ks[ibl].qs;
|
||||
for (int ib = 0; ib < QK_K/kBlockSize; ++ib) {
|
||||
const float * xb = xbl + ib*kBlockSize;
|
||||
if (quant_weights) {
|
||||
const float * qw = quant_weights + ibl*QK_K + ib*kBlockSize;
|
||||
for (int j = 0; j < kBlockSize; ++j) weight[j] = qw[j] * sqrtf(sigma2 + xb[j]*xb[j]);
|
||||
} else {
|
||||
for (int j = 0; j < kBlockSize; ++j) weight[j] = xb[j]*xb[j];
|
||||
}
|
||||
const int8_t * values = iq4k_values + ((iq4ks[ibl].scales[ib] & 1) << 4);
|
||||
float dl = d_ks * ((iq4ks[ibl].scales[ib] & 254) - 127);
|
||||
float sumqx = 0, sumq2 = 0;
|
||||
for (int k = 0; k < kBlockSize/4; ++k) {
|
||||
xv[0] = xb[2*k+0]; xv[1] = xb[2*k+kBlockSize/2]; xv[2] = xb[2*k+1]; xv[3] = xb[2*k+1+kBlockSize/2];
|
||||
wv[0] = weight[2*k+0]; wv[1] = weight[2*k+kBlockSize/2]; wv[2] = weight[2*k+1]; wv[3] = weight[2*k+1+kBlockSize/2];
|
||||
auto vp = prune_iq4ks(q4[k], values, xv, wv, dl);
|
||||
vps[k] = table[vp & 0x7fff];
|
||||
for (int j = 0; j < 4; ++j) {
|
||||
float q = values[(vp >> 4*j) & 0xf];
|
||||
sumqx += wv[j]*q*xv[j];
|
||||
sumq2 += wv[j]*q*q;
|
||||
}
|
||||
}
|
||||
for (int k = 0; k < kBlockSize/8; ++k) {
|
||||
y[ibl].qs[(kBlockSize/8)*ib + k] = vps[2*k+0] | (vps[2*k+1] << 15) | (((iq4ks[ibl].scales[ib] >> 2*k) & 3) << 30);
|
||||
//y[ibl].qs[(kBlockSize/8)*ib + k] = vps[2*k+0] | (vps[2*k+1] << 15);
|
||||
}
|
||||
scales[ib] = sumq2 > 0 ? sumqx/sumq2 : dl;
|
||||
max_abs_scale = std::max(max_abs_scale, scales[ib]);
|
||||
q4 += kBlockSize/4;
|
||||
}
|
||||
}
|
||||
//if (!max_abs_scale) return;
|
||||
//float d = max_abs_scale/127;
|
||||
//*dptr = d;
|
||||
//float id = 1/d;
|
||||
//for (int ibl = 0; ibl < nblock; ++ibl) {
|
||||
// auto scales = all_scales + ibl*(QK_K/kBlockSize);
|
||||
// for (int ib = 0; ib < QK_K/kBlockSize; ++ib) {
|
||||
// int l = nearest_int(0.5f*(id*scales[ib]+127.f));
|
||||
// l = std::max(0, std::min(127, l)) << 1;
|
||||
// l |= (iq4ks[ibl].scales[ib] & 1);
|
||||
// for (int k = 0; k < 4; ++k) {
|
||||
// //y[ibl].qs[4*ib+k] &= 0x3fffffff;
|
||||
// y[ibl].qs[4*ib+k] |= (((l >> 2*k) & 3) << 30);
|
||||
// }
|
||||
// }
|
||||
//}
|
||||
}
|
||||
}
|
||||
|
||||
size_t quantize_iq4_kss(const float * src, void * dst, int64_t nrows, int64_t n_per_row, const float * imatrix) {
|
||||
constexpr int kBlockSize = 32; //128;
|
||||
GGML_ASSERT(n_per_row%QK_K == 0);
|
||||
auto row_size = ggml_row_size(GGML_TYPE_IQ4_KSS, n_per_row);
|
||||
auto row_size_ks = ggml_row_size(GGML_TYPE_IQ4_KS, n_per_row);
|
||||
std::vector<char> work(row_size_ks);
|
||||
std::vector<float> all_scales(n_per_row/kBlockSize);
|
||||
float weight[kBlockSize];
|
||||
auto qrow = (char *)dst;
|
||||
auto table = scramble_table();
|
||||
for (int row = 0; row < nrows; ++row) {
|
||||
quantize_row_iq4_kss_impl(n_per_row, src, qrow, all_scales.data(), weight, iq4k_values, imatrix, table, 7);
|
||||
src += n_per_row;
|
||||
qrow += row_size;
|
||||
}
|
||||
return nrows * row_size;
|
||||
}
|
||||
|
||||
void quantize_row_iq4_kss_ref(const float * x, block_iq4_kss * y, int64_t k) {
|
||||
quantize_iq4_kss(x, y, 1, k, nullptr);
|
||||
}
|
||||
|
||||
void quantize_row_iq4_kss(const float * x, void * y, int64_t k) {
|
||||
quantize_iq4_kss(x, (block_iq4_kss *)y, 1, k, nullptr);
|
||||
}
|
||||
|
||||
void dequantize_row_iq4_kss(const block_iq4_kss * x, float * y, int64_t k) {
|
||||
const float * dptr = (const float *)x;
|
||||
const float d = *dptr;
|
||||
x = (const block_iq4_kss *)(dptr + 1);
|
||||
uint16_t aux16[8];
|
||||
const uint8_t * aux8 = (const uint8_t *)aux16;
|
||||
for (int ibl = 0; ibl < k/QK_K; ++ibl) {
|
||||
auto qs = (const uint16_t *)x[ibl].qs;
|
||||
for (int ib = 0; ib < QK_K/32; ++ib) {
|
||||
//uint8_t ls = ((qs[0] >> 30) | ((qs[1] >> 28) & 0x0c) | ((qs[2] >> 26) & 0x30) | ((qs[3] >> 24) & 0xc0));
|
||||
//const int8_t * values = iq4k_values + ((ls & 1) << 4);
|
||||
//const float dl = d * ((ls & 254) - 127);
|
||||
//for (int k = 0; k < 4; ++k) {
|
||||
// uint16_t vl = qs[k] & 0x7fff;
|
||||
// vl ^= (vl << 1);
|
||||
// uint16_t vh = (qs[k] >> 15) & 0x7fff;
|
||||
// vh ^= (vh << 1);
|
||||
// for (int j = 0; j < 4; ++j) {
|
||||
// y[4*k + j + 0] = dl*values[(vl >> 4*j) & 0xf];
|
||||
// y[4*k + j + 16] = dl*values[(vh >> 4*j) & 0xf];
|
||||
// }
|
||||
//}
|
||||
int16_t ls = 0;
|
||||
for (int k = 0; k < 8; ++k) {
|
||||
aux16[k] = qs[k] & 0xfffe;
|
||||
aux16[k] ^= (aux16[k] >> 1);
|
||||
ls |= (qs[k] & 1) << k;
|
||||
}
|
||||
const int8_t * values = iq4k_values + ((ls & 1) << 4);
|
||||
float dl = d * ((ls & 254) - 127);
|
||||
for (int j = 0; j < 16; ++j) {
|
||||
y[j+ 0] = dl * values[aux8[j] & 0xf];
|
||||
y[j+16] = dl * values[aux8[j] >> 4];
|
||||
}
|
||||
y += 32;
|
||||
qs += 8;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void vec_dot_iq4_kss_q8_k(int n, float * s, size_t bs, const void * vx, size_t bx, const void * vy, size_t by, int nrc) {
|
||||
#if GGML_USE_IQK_MULMAT
|
||||
if (iqk_mul_mat(1, 1, n, GGML_TYPE_IQ4_KSS, vx, 0, GGML_TYPE_Q8_K, vy, 0, s, 0, 0, 1)) {
|
||||
return;
|
||||
}
|
||||
#endif
|
||||
GGML_ASSERT(n%QK_K == 0);
|
||||
GGML_ASSERT(nrc == 1);
|
||||
GGML_UNUSED(bs);
|
||||
GGML_UNUSED(bx);
|
||||
GGML_UNUSED(by);
|
||||
}
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -61,6 +61,12 @@ size_t quantize_iq4_ks(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst
|
|||
void dequantize_row_iq4_ks(const block_iq4_ks * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
|
||||
void vec_dot_iq4_ks_q8_k(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
|
||||
|
||||
void quantize_row_iq4_kss_ref(const float * GGML_RESTRICT x, block_iq4_kss * GGML_RESTRICT y, int64_t k);
|
||||
void quantize_row_iq4_kss(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
|
||||
size_t quantize_iq4_kss(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
|
||||
void dequantize_row_iq4_kss(const block_iq4_kss * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
|
||||
void vec_dot_iq4_kss_q8_k(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
|
||||
|
||||
void quantize_row_iq2_ks_ref(const float * GGML_RESTRICT x, block_iq2_ks * GGML_RESTRICT y, int64_t k);
|
||||
void quantize_row_iq2_ks(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
|
||||
size_t quantize_iq2_ks(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
|
||||
|
|
|
|||
|
|
@ -180,6 +180,7 @@ extern "C" {
|
|||
LLAMA_FTYPE_MOSTLY_IQ4_KS = 145, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_IQ3_KL = 146, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_IQ2_KS = 147, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_IQ4_KSS = 148, // except 1d tensors
|
||||
|
||||
LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
|
||||
};
|
||||
|
|
|
|||
|
|
@ -3795,6 +3795,7 @@ struct llama_model_loader {
|
|||
case GGML_TYPE_IQ4_NL: ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL; break;
|
||||
case GGML_TYPE_IQ4_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS; break;
|
||||
case GGML_TYPE_IQ4_KS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_KS; break;
|
||||
case GGML_TYPE_IQ4_KSS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_KSS; break;
|
||||
case GGML_TYPE_IQ2_K: ftype = LLAMA_FTYPE_MOSTLY_IQ2_K; break;
|
||||
case GGML_TYPE_IQ3_K: ftype = LLAMA_FTYPE_MOSTLY_IQ3_K; break;
|
||||
case GGML_TYPE_IQ4_K: ftype = LLAMA_FTYPE_MOSTLY_IQ4_K; break;
|
||||
|
|
@ -4498,6 +4499,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
|
|||
case LLAMA_FTYPE_MOSTLY_IQ4_NL: return "IQ4_NL - 4.5 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_XS: return "IQ4_XS - 4.25 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_KS: return "IQ4_KS - 4.25 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_KSS: return "IQ4_KSS - 4.0 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ2_K: return "IQ2_K - 2.375 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ3_K: return "IQ3_K - 3.4325 bpw";
|
||||
case LLAMA_FTYPE_MOSTLY_IQ3_KL: return "IQ3_KL - 4 bpw";
|
||||
|
|
@ -15651,7 +15653,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
ftype == LLAMA_FTYPE_MOSTLY_IQ2_KS) {
|
||||
new_type = !qs.has_output ? GGML_TYPE_IQ4_K : GGML_TYPE_Q5_K;
|
||||
}
|
||||
else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_S || ftype == LLAMA_FTYPE_MOSTLY_IQ3_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_KS) && !qs.has_output) {
|
||||
else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_S || ftype == LLAMA_FTYPE_MOSTLY_IQ3_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ4_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_KSS) && !qs.has_output) {
|
||||
new_type = GGML_TYPE_IQ5_K;
|
||||
}
|
||||
else if (new_type != GGML_TYPE_Q8_0 && new_type != GGML_TYPE_IQ6_K) {
|
||||
|
|
@ -15742,7 +15745,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
|
||||
else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_KS) && qs.model.hparams.n_gqa() >= 2) {
|
||||
else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ4_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_KSS) && qs.model.hparams.n_gqa() >= 2) {
|
||||
new_type = GGML_TYPE_IQ5_K;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ4_K && qs.model.hparams.n_gqa() >= 2) {
|
||||
|
|
@ -15822,7 +15826,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
if (use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
|
||||
}
|
||||
}
|
||||
else if (i_layer < n_layer/8 && (ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_KS) && !qs.has_imatrix) {
|
||||
else if (i_layer < n_layer/8 && (ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS ||
|
||||
ftype == LLAMA_FTYPE_MOSTLY_IQ4_KS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_KSS) && !qs.has_imatrix) {
|
||||
new_type = GGML_TYPE_Q5_K;
|
||||
}
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
|
||||
|
|
@ -15910,7 +15915,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
new_type == GGML_TYPE_IQ1_M || new_type == GGML_TYPE_IQ4_K || new_type == GGML_TYPE_IQ2_K ||
|
||||
new_type == GGML_TYPE_IQ5_K || new_type == GGML_TYPE_IQ3_K || new_type == GGML_TYPE_IQ2_TN ||
|
||||
new_type == GGML_TYPE_IQ6_K || new_type == GGML_TYPE_IQ1_TN || new_type == GGML_TYPE_IQ4_KS ||
|
||||
new_type == GGML_TYPE_IQ2_KS) {
|
||||
new_type == GGML_TYPE_IQ2_KS || new_type == GGML_TYPE_IQ4_KSS) {
|
||||
int nx = tensor->ne[0];
|
||||
int ny = tensor->ne[1];
|
||||
if (nx % QK_K != 0) {
|
||||
|
|
@ -15942,6 +15947,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_IQ2_K:
|
||||
case GGML_TYPE_IQ3_K:
|
||||
case GGML_TYPE_IQ4_KSS:
|
||||
case GGML_TYPE_IQ4_KS:
|
||||
case GGML_TYPE_IQ4_XS: new_type = GGML_TYPE_IQ4_NL; break;
|
||||
case GGML_TYPE_IQ4_K:
|
||||
|
|
@ -16055,6 +16061,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
case LLAMA_FTYPE_MOSTLY_IQ4_NL: default_type = GGML_TYPE_IQ4_NL; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_XS: default_type = GGML_TYPE_IQ4_XS; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_KS: default_type = GGML_TYPE_IQ4_KS; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ4_KSS: default_type = GGML_TYPE_IQ4_KSS; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ2_K: default_type = GGML_TYPE_IQ2_K; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ3_K: default_type = GGML_TYPE_IQ3_K; break;
|
||||
case LLAMA_FTYPE_MOSTLY_IQ3_KL: default_type = GGML_TYPE_IQ3_K; break;
|
||||
|
|
|
|||
Loading…
Reference in New Issue