ikawrakow/ik_llama.cpp
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Iwan Kawrakow
2024-11-08 16:28:58 +02:00
parent 08503cec7d
commit 435eb9bdd3
1 changed files with 180 additions and 18 deletions
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198
ggml/src/iqk/iqk_quantize.cpp
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@@ -3164,7 +3164,8 @@ public:
const float * values() const { return m_values.data(); } const float * values() const { return m_values.data(); }
inline void find_best_match(float d, const float * xb, const float * weight, int * best_idx) const; inline void find_best_match(float d, const float * xb, const float * weight, int * best_idx) const;
inline float find_best_scale(const float * xb, const float * weight, const int * best_idx) const; inline void find_best_match(const float * xb, const float * weight, int * best_idx) const;
inline std::pair<float, float> find_best_scale(const float * xb, const float * weight, const int * best_idx) const;
static inline void set_values(uint32_t i, float * result, float scale) { static inline void set_values(uint32_t i, float * result, float scale) {
constexpr uint32_t ka = 89226354; constexpr uint32_t ka = 89226354;
@@ -3223,18 +3224,18 @@ QuantizerIQKT<block_size, group_size, num_bits, num_clusters>::QuantizerIQKT() {
} }
template <int block_size, int group_size, int num_bits, int num_clusters> template <int block_size, int group_size, int num_bits, int num_clusters>
float QuantizerIQKT<block_size, group_size, num_bits, num_clusters>::find_best_scale(const float * xb, const float * weight, const int * best_idx) const { std::pair<float, float> QuantizerIQKT<block_size, group_size, num_bits, num_clusters>::find_best_scale(
const float * xb, const float * weight, const int * best_idx) const {
float sumqx = 0, sumq2 = 0; float sumqx = 0, sumq2 = 0;
#ifdef z__AVX2__ #ifdef __AVX2__
// TODO: fix this for kGroupSize != 8
auto vqx = _mm256_setzero_ps(); auto vqx = _mm256_setzero_ps();
auto vq2 = _mm256_setzero_ps(); auto vq2 = _mm256_setzero_ps();
for (int l = 0; l < kNg; ++l) { for (int l = 0; l < kBlockSize; l += 8) {
auto vx = _mm256_loadu_ps(xb+8*l); auto vx = _mm256_loadu_ps(xb+l);
auto vw = _mm256_loadu_ps(weight+8*l); auto vw = _mm256_loadu_ps(weight+l);
auto vq = kGroupSize == 8 ? _mm256_loadu_ps(m_values.data() + kGroupSize*best_idx[l]) : auto vq = kGroupSize == 8 ? _mm256_loadu_ps(m_values.data() + kGroupSize*best_idx[l/kGroupSize]) :
_mm256_set_m128(_mm_loadu_ps(m_values.data() + kGroupSize*best_idx[l+1]), _mm256_set_m128(_mm_loadu_ps(m_values.data() + kGroupSize*best_idx[l/kGroupSize+1]),
_mm_loadu_ps(m_values.data() + kGroupSize*best_idx[l+0])); _mm_loadu_ps(m_values.data() + kGroupSize*best_idx[l/kGroupSize+0]));
auto vqw = _mm256_mul_ps(vq, vw); auto vqw = _mm256_mul_ps(vq, vw);
vqx = _mm256_fmadd_ps(vqw, vx, vqx); vqx = _mm256_fmadd_ps(vqw, vx, vqx);
vq2 = _mm256_fmadd_ps(vqw, vq, vq2); vq2 = _mm256_fmadd_ps(vqw, vq, vq2);
@@ -3252,7 +3253,149 @@ float QuantizerIQKT<block_size, group_size, num_bits, num_clusters>::find_best_s
} }
} }
#endif #endif
return sumq2 > 0 ? sumqx/sumq2 : 0.f; return sumq2 > 0 ? std::make_pair(sumqx/sumq2, sumqx*sumqx/sumq2) : std::make_pair(0.f, 0.f);
}
template <int block_size, int group_size, int num_bits, int num_clusters>
void QuantizerIQKT<block_size, group_size, num_bits, num_clusters>::find_best_match(const float * xb, const float * weight, int * best_idx) const {
int ncluster = m_clusters.size()/kGroupSize;
#ifdef __AVX2__
if constexpr (kGroupSize == 8) {
__m256 sqx[8];
const __m256i add_idx = _mm256_set_epi32(7, 6, 5, 4, 3, 2, 1, 0);
float sx[8];
int index[8];
for (int l = 0; l < kNg; ++l) {
auto xl = xb + 8*l;
auto wl = weight + 8*l;
auto vx = _mm256_loadu_ps(xl);
auto vw = _mm256_loadu_ps(wl);
auto vbest = _mm256_set1_ps(0.f);
auto best_index = _mm256_set1_epi32(-1);
float best = 0; int jbest = -1;
for (int j = 0; j < ncluster; j += 8) {
auto idx = _mm256_add_epi32(_mm256_set1_epi32(j), add_idx);
for (int i = 0; i < 8; ++i) {
auto vq = _mm256_loadu_ps(m_clusters.data() + kGroupSize*(j+i));
auto sumqx = _mm256_mul_ps(vw, _mm256_mul_ps(vx, vq));
auto sumq2 = hsum_float_8(_mm256_mul_ps(vw, _mm256_mul_ps(vq, vq)));
sqx[i] = _mm256_mul_ps(_mm256_set1_ps(sumq2 > 0 ? 1/sumq2 : 0), _mm256_mul_ps(sumqx, sumqx));
}
auto score = hsum_float_8x8(sqx);
auto mask = _mm256_cmp_ps(score, vbest, _CMP_GT_OQ);
best_index = _mm256_or_si256(_mm256_and_si256(_mm256_castps_si256(mask), idx),
_mm256_andnot_si256(_mm256_castps_si256(mask), best_index));
vbest = _mm256_max_ps(vbest, score);
}
_mm256_store_ps(sx, vbest);
_mm256_store_si256((__m256i *)index, best_index);
for (int i = 0; i < 8; ++i) {
if (sx[i] > best) { best = sx[i]; jbest = index[i]; }
}
auto& points = m_in_cluster[jbest];
GGML_ASSERT(!points.empty() && points.size()%8 == 0);
int jbest_cluster = jbest;
vbest = _mm256_set1_ps(0.f);
best_index = _mm256_set1_epi32(-1);
best = 0; jbest = -1;
for (int j = 0; j < int(points.size()); j += 8) {
auto idx = _mm256_loadu_si256((const __m256i*)(points.data() + j));
for (int i = 0; i < 8; ++i) {
auto vq = _mm256_loadu_ps(m_values.data() + kGroupSize*points[j+i]);
auto sumqx = _mm256_mul_ps(vw, _mm256_mul_ps(vx, vq));
auto sumq2 = hsum_float_8(_mm256_mul_ps(vw, _mm256_mul_ps(vq, vq)));
sqx[i] = _mm256_mul_ps(_mm256_set1_ps(sumq2 > 0 ? 1/sumq2 : 0), _mm256_mul_ps(sumqx, sumqx));
}
auto score = hsum_float_8x8(sqx);
auto mask = _mm256_cmp_ps(score, vbest, _CMP_GT_OQ);
best_index = _mm256_or_si256(_mm256_and_si256(_mm256_castps_si256(mask), idx),
_mm256_andnot_si256(_mm256_castps_si256(mask), best_index));
vbest = _mm256_max_ps(vbest, score);
}
_mm256_store_ps(sx, vbest);
_mm256_store_si256((__m256i *)index, best_index);
for (int i = 0; i < 8; ++i) {
if (sx[i] > best) { best = sx[i]; jbest = index[i]; }
}
if (jbest < 0) {
fprintf(stderr, "Oops: jbest = %d for cluster %d with %d points\n", jbest, jbest_cluster, int(points.size()));
GGML_ASSERT(false);
}
best_idx[l] = jbest;
}
} else {
__m128 sqx[4];
const __m128i add_idx = _mm_set_epi32(3, 2, 1, 0);
float sx[4];
int index[4];
for (int l = 0; l < kNg; ++l) {
auto xl = xb + 4*l;
auto wl = weight + 4*l;
auto vx = _mm_loadu_ps(xl);
auto sumx2 = hsum_float_4(_mm_mul_ps(vx, vx));
if (!sumx2) {
best_idx[l] = 0; continue;
}
auto vw = _mm_loadu_ps(wl);
auto vbest = _mm_set1_ps(0);
auto best_index = _mm_set1_epi32(-1);
float best = 0; int jbest = -1;
for (int j = 0; j < ncluster; j += 4) {
auto idx = _mm_add_epi32(_mm_set1_epi32(j), add_idx);
for (int i = 0; i < 4; ++i) {
auto vq = _mm_loadu_ps(m_clusters.data() + kGroupSize*(j+i));
auto sumqx = _mm_mul_ps(vw, _mm_mul_ps(vx, vq));
auto sumq2 = hsum_float_4(_mm_mul_ps(vw, _mm_mul_ps(vq, vq)));
sqx[i] = _mm_mul_ps(_mm_set1_ps(sumq2 > 0 ? 1/sumq2 : 0), _mm_mul_ps(sumqx, sumqx));
}
auto score = hsum_float_4x4(sqx);
auto mask = _mm_cmp_ps(score, vbest, _CMP_GT_OQ);
best_index = _mm_or_si128(_mm_and_si128(_mm_castps_si128(mask), idx),
_mm_andnot_si128(_mm_castps_si128(mask), best_index));
vbest = _mm_max_ps(vbest, score);
}
_mm_store_ps(sx, vbest);
_mm_store_si128((__m128i *)index, best_index);
for (int i = 0; i < 4; ++i) {
if (sx[i] > best) { best = sx[i]; jbest = index[i]; }
}
GGML_ASSERT(jbest >= 0 && jbest <= int(m_in_cluster.size()));
auto& points = m_in_cluster[jbest];
GGML_ASSERT(!points.empty() && points.size()%4 == 0);
int jbest_cluster = jbest;
vbest = _mm_set1_ps(0);
best_index = _mm_set1_epi32(-1);
best = 0; jbest = -1;
for (int j = 0; j < int(points.size()); j += 4) {
auto idx = _mm_loadu_si128((const __m128i*)(points.data() + j));
for (int i = 0; i < 4; ++i) {
auto vq = _mm_loadu_ps(m_values.data() + kGroupSize*points[j+i]);
auto sumqx = _mm_mul_ps(vw, _mm_mul_ps(vx, vq));
auto sumq2 = hsum_float_4(_mm_mul_ps(vw, _mm_mul_ps(vq, vq)));
sqx[i] = _mm_mul_ps(_mm_set1_ps(sumq2 > 0 ? 1/sumq2 : 0), _mm_mul_ps(sumqx, sumqx));
}
auto score = hsum_float_4x4(sqx);
auto mask = _mm_cmp_ps(score, vbest, _CMP_GT_OQ);
best_index = _mm_or_si128(_mm_and_si128(_mm_castps_si128(mask), idx),
_mm_andnot_si128(_mm_castps_si128(mask), best_index));
vbest = _mm_max_ps(vbest, score);
}
_mm_store_ps(sx, vbest);
_mm_store_si128((__m128i *)index, best_index);
for (int i = 0; i < 4; ++i) {
if (sx[i] > best) { best = sx[i]; jbest = index[i]; }
}
if (jbest < 0) {
fprintf(stderr, "Oops: jbest = %d for cluster %d with %d points\n", jbest, jbest_cluster, int(points.size()));
GGML_ASSERT(false);
}
best_idx[l] = jbest;
}
}
#else
// TODO
std::memset(best_idx, 0, kNg*sizeof(int));
#endif
} }
template <int block_size, int group_size, int num_bits, int num_clusters> template <int block_size, int group_size, int num_bits, int num_clusters>
@@ -3329,6 +3472,7 @@ void QuantizerIQKT<block_size, group_size, num_bits, num_clusters>::find_best_ma
} else { } else {
__m128 sqx[4]; __m128 sqx[4];
const __m128i add_idx = _mm_set_epi32(3, 2, 1, 0); const __m128i add_idx = _mm_set_epi32(3, 2, 1, 0);
const __m128 sign_bit = _mm_set1_ps(-0.f);
float sx[4]; float sx[4];
int index[4]; int index[4];
auto vid = _mm_set1_ps(id); auto vid = _mm_set1_ps(id);
@@ -3345,7 +3489,9 @@ void QuantizerIQKT<block_size, group_size, num_bits, num_clusters>::find_best_ma
for (int i = 0; i < 4; ++i) { for (int i = 0; i < 4; ++i) {
auto vq = _mm_loadu_ps(m_clusters.data() + kGroupSize*(j+i)); auto vq = _mm_loadu_ps(m_clusters.data() + kGroupSize*(j+i));
auto vdiff = _mm_sub_ps(vq, vx); auto vdiff = _mm_sub_ps(vq, vx);
sqx[i] = _mm_mul_ps(vw, _mm_mul_ps(vdiff, vdiff)); //sqx[i] = _mm_mul_ps(vw, _mm_mul_ps(vdiff, vdiff));
vdiff = _mm_andnot_ps(sign_bit, vdiff);
sqx[i] = _mm_mul_ps(vw, _mm_mul_ps(vdiff, _mm_mul_ps(vdiff, vdiff)));
} }
auto score = hsum_float_4x4(sqx); auto score = hsum_float_4x4(sqx);
auto mask = _mm_cmp_ps(score, vbest, _CMP_LT_OQ); auto mask = _mm_cmp_ps(score, vbest, _CMP_LT_OQ);
@@ -3369,7 +3515,9 @@ void QuantizerIQKT<block_size, group_size, num_bits, num_clusters>::find_best_ma
for (int i = 0; i < 4; ++i) { for (int i = 0; i < 4; ++i) {
auto vq = _mm_loadu_ps(m_values.data() + kGroupSize*points[j+i]); auto vq = _mm_loadu_ps(m_values.data() + kGroupSize*points[j+i]);
auto vdiff = _mm_sub_ps(vq, vx); auto vdiff = _mm_sub_ps(vq, vx);
sqx[i] = _mm_mul_ps(vw, _mm_mul_ps(vdiff, vdiff)); //sqx[i] = _mm_mul_ps(vw, _mm_mul_ps(vdiff, vdiff));
vdiff = _mm_andnot_ps(sign_bit, vdiff);
sqx[i] = _mm_mul_ps(vw, _mm_mul_ps(vdiff, _mm_mul_ps(vdiff, vdiff)));
} }
auto score = hsum_float_4x4(sqx); auto score = hsum_float_4x4(sqx);
auto mask = _mm_cmp_ps(score, vbest, _CMP_LT_OQ); auto mask = _mm_cmp_ps(score, vbest, _CMP_LT_OQ);
@@ -3589,7 +3737,8 @@ void quantize_row_iq2_kt_impl(const float * x, void * vy, int n_per_row, const f
} }
float d = amax/96.f; float d = amax/96.f;
quantizer.find_best_match(d, xb, weight, best_idx); quantizer.find_best_match(d, xb, weight, best_idx);
scales[ib] = quantizer.find_best_scale(xb, weight, best_idx); auto pair = quantizer.find_best_scale(xb, weight, best_idx);
scales[ib] = pair.first;
for (int j = 0; j < Q::kNg; ++j) qs[j] = best_idx[j]; for (int j = 0; j < Q::kNg; ++j) qs[j] = best_idx[j];
qs += Q::kNg; qs += Q::kNg;
@@ -3665,7 +3814,8 @@ void quantize_row_iq2_kt_impl(const float * x, void * vy, int n_per_row, const f
const float * xb = xbl + Q::kBlockSize*ib; const float * xb = xbl + Q::kBlockSize*ib;
const float * weight = all_weights + ibl*Q::kSuperBlockSize + ib*Q::kBlockSize; const float * weight = all_weights + ibl*Q::kSuperBlockSize + ib*Q::kBlockSize;
for (int j = 0; j < Q::kNg; ++j) best_idx[j] = qs[ib*Q::kNg+j]; for (int j = 0; j < Q::kNg; ++j) best_idx[j] = qs[ib*Q::kNg+j];
scales[ib] = quantizer.find_best_scale(xb, weight, best_idx); auto pair = quantizer.find_best_scale(xb, weight, best_idx);
scales[ib] = pair.first;
} }
} }
float id = d ? 1/d : 0.f; float id = d ? 1/d : 0.f;
@@ -3809,9 +3959,21 @@ void quantize_row_iq3_kt_impl(const float * x, void * vy, int n_per_row, const f
float ax = std::abs(xb[j]); float ax = std::abs(xb[j]);
amax = std::max(amax, ax); amax = std::max(amax, ax);
} }
float d = amax/96.f; scales[ib] = 0;
quantizer.find_best_match(d, xb, weight, best_idx); if (!amax) continue;
scales[ib] = quantizer.find_best_scale(xb, weight, best_idx); float best = 0;
for (int itry = -5; itry <= 5; ++itry) {
quantizer.find_best_match(amax/(96.f + 4.f*itry), xb, weight, best_idx);
auto [d, score] = quantizer.find_best_scale(xb, weight, best_idx);
if (score > best) {
best = score;
scales[ib] = d;
}
}
//float d = amax/96.f;
//quantizer.find_best_match(d, xb, weight, best_idx);
////quantizer.find_best_match(xb, weight, best_idx);
//scales[ib] = quantizer.find_best_scale(xb, weight, best_idx);
for (int j = 0; j < Q::kNg; ++j) { for (int j = 0; j < Q::kNg; ++j) {
int jj = ib*Q::kNg + j; int jj = ib*Q::kNg + j;