#include <inttypes.h>
#include <stdio.h>
#include <float.h>
+#include <limits.h>
// if C99 - static_assert is noop
// ref: https://stackoverflow.com/a/53923785/4039976
#elif defined(GGML_USE_CUBLAS)
#include <cublas_v2.h>
#include <cuda_runtime.h>
-#define CUDA_CHECK(err) \
- do { \
- cudaError_t err_ = (err); \
- if (err_ != cudaSuccess) { \
- printf("CUDA error %d at %s:%d: %s\n", err_, __FILE__, __LINE__, \
- cudaGetErrorString(err_)); \
- exit(1); \
- } \
+#include "ggml-cuda.h"
+
+#define CUDA_CHECK(err) \
+ do { \
+ cudaError_t err_ = (err); \
+ if (err_ != cudaSuccess) { \
+ printf("CUDA error %d at %s:%d: %s\n", err_, __FILE__, __LINE__, \
+ cudaGetErrorString(err_)); \
+ exit(1); \
+ } \
} while (0)
-#define CUBLAS_CHECK(err) \
- do { \
- cublasStatus_t err_ = (err); \
- if (err_ != CUBLAS_STATUS_SUCCESS) { \
- printf("cuBLAS error %d at %s:%d\n", err_, __FILE__, __LINE__); \
- exit(1); \
- } \
+#define CUBLAS_CHECK(err) \
+ do { \
+ cublasStatus_t err_ = (err); \
+ if (err_ != CUBLAS_STATUS_SUCCESS) { \
+ printf("cuBLAS error %d at %s:%d\n", err_, __FILE__, __LINE__); \
+ exit(1); \
+ } \
} while (0)
static cublasHandle_t cublasH = NULL;
CUBLAS_CHECK(cublasCreate(&cublasH));
CUDA_CHECK(cudaStreamCreateWithFlags(&cudaStream, cudaStreamNonBlocking));
+
CUBLAS_CHECK(cublasSetStream(cublasH, cudaStream));
// configure logging to stdout
// quantization
//
-// AVX routines provided by GH user Const-me
-// ref: https://github.com/ggerganov/ggml/pull/27#issuecomment-1464934600
+#if __AVX__ || __AVX2__ || __AVX512F__
+// Unpack 16 4-bit fields into 16 bytes
+// The output vector contains 16 bytes, each one in [ 0 .. 15 ] interval
+static inline __m128i bytes_from_nibbles_16(const uint8_t * rsi)
+{
+ // Load 8 bytes from memory
+ __m128i tmp = _mm_loadu_si64( ( const __m128i* )rsi );
+
+ // Expand bytes into uint16_t values
+ __m128i bytes = _mm_cvtepu8_epi16( tmp );
+
+ // Unpack values into individual bytes
+ const __m128i lowMask = _mm_set1_epi8( 0xF );
+ __m128i high = _mm_andnot_si128( lowMask, bytes );
+ __m128i low = _mm_and_si128( lowMask, bytes );
+ high = _mm_slli_epi16( high, 4 );
+ bytes = _mm_or_si128( low, high );
+ return bytes;
+}
+
#if __AVX2__ || __AVX512F__
// Unpack 32 4-bit fields into 32 bytes
// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval
-static inline __m256i bytesFromNibbles( const uint8_t* rsi )
+static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi)
{
// Load 16 bytes from memory
__m128i tmp = _mm_loadu_si128( ( const __m128i* )rsi );
__m128i r1 = _mm256_extracti128_si256( bytes, 1 );
return _mm_packus_epi16( r0, r1 );
}
-#elif __AVX__
-static inline __m128i bytesFromNibbles( const uint8_t* rsi )
-{
- // Load 8 bytes from memory
- __m128i tmp = _mm_loadu_si64( ( const __m128i* )rsi );
-
- // Expand bytes into uint16_t values
- __m128i bytes = _mm_cvtepu8_epi16( tmp );
-
- // Unpack values into individual bytes
- const __m128i lowMask = _mm_set1_epi8( 0xF );
- __m128i high = _mm_andnot_si128( lowMask, bytes );
- __m128i low = _mm_and_si128( lowMask, bytes );
- high = _mm_slli_epi16( high, 4 );
- bytes = _mm_or_si128( low, high );
- return bytes;
-}
-
+#else
static inline __m128i packNibbles( __m128i bytes1, __m128i bytes2 )
{
// Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh
return _mm_packus_epi16( bytes1, bytes2);
}
#endif
+#endif // __AVX__ || __AVX2__ || __AVX512F__
#if __ARM_NEON
float m; // min
uint8_t qs[QK4_1 / 2]; // nibbles / quants
} block_q4_1;
-static_assert(sizeof(block_q4_1) == sizeof(float) * 2 + QK4_1 / 2, "wrong q4_1 block size/padding");
+static_assert(sizeof(block_q4_1) == 2 * sizeof(float) + QK4_1 / 2, "wrong q4_1 block size/padding");
#define QK4_2 16
typedef struct {
} block_q4_2;
static_assert(sizeof(block_q4_2) == sizeof(ggml_fp16_t) + QK4_2 / 2, "wrong q4_2 block size/padding");
+#define QK4_3 16
+typedef struct {
+ ggml_fp16_t d; // delta
+ ggml_fp16_t m; // min
+ uint8_t qs[QK4_3 / 2]; // nibbles / quants
+} block_q4_3;
+static_assert(sizeof(block_q4_3) == 2 * sizeof(ggml_fp16_t) + QK4_3 / 2, "wrong q4_3 block size/padding");
+
#define QK8_0 32
typedef struct {
float d; // delta
}
}
+static inline int nearest_int(float fval) {
+ assert(fval <= 4194303.f);
+ float val = fval + 12582912.f;
+ int i; memcpy(&i, &val, sizeof(int));
+ return (i & 0x007fffff) - 0x00400000;
+}
+
+static float kquantize_q4_with_bounds(int n, int nmin, int nmax, const float * restrict X, int nCandidates,
+ const float * restrict candidates, int8_t * restrict L) {
+ assert (nmin >= INT8_MIN);
+ assert (nmax <= INT8_MAX);
+ float amax = 0;
+ for (int i=0; i<n; ++i) amax = MAX(amax, fabsf(X[i]));
+ if (!amax) { // all zero
+ for (int i=0; i<n; ++i) L[i] = 0;
+ return 1.f;
+ }
+ float best = 0, bestScale = 0;
+ for (int si=0; si<nCandidates; ++si) {
+ float iscale = candidates[si]/amax;
+ float sumlxP = 0; int suml2P = 0;
+ float sumlxM = 0; int suml2M = 0;
+ for (int i=0; i<n; ++i) {
+ int l = nearest_int(iscale*X[i]);
+ int lp = MAX(nmin, MIN(nmax, +l));
+ int lm = MAX(nmin, MIN(nmax, -l));
+ sumlxP += X[i]*lp; suml2P += lp*lp;
+ sumlxM += X[i]*lm; suml2M += lm*lm;
+ }
+ float sumlxP2 = sumlxP*sumlxP;
+ float sumlxM2 = sumlxM*sumlxM;
+ if (sumlxP2*suml2M > sumlxM2*suml2P) {
+ if (sumlxP2 > best*suml2P) {
+ best = sumlxP2/suml2P; bestScale = iscale;
+ }
+ } else {
+ if (sumlxM2 > best*suml2M) {
+ best = sumlxM2/suml2M; bestScale = -iscale;
+ }
+ }
+ }
+ float sumlx = 0; int suml2 = 0;
+ for (int i=0; i<n; ++i) {
+ int l = nearest_int(bestScale*X[i]);
+ l = MAX(nmin, MIN(nmax, l));
+ sumlx += X[i]*l; suml2 += l*l;
+ L[i] = l;
+ }
+ float scale = sumlx/suml2;
+ return scale;
+}
+
+static void quantize_row_q4_2_rmse(const float * restrict x, block_q4_2 * restrict y, int k) {
+#define CANDIDATE_COUNT 8
+ static const float candidates[CANDIDATE_COUNT] = { +8.7f, +8.3f, +8.1f, +7.8f, +7.3f, +7.0f, +6.3f, +5.7f };
+ assert(k % QK4_2 == 0);
+
+ int8_t L[QK4_2];
+
+ const int nb = k / QK4_2;
+
+ for (int i = 0; i < nb; i++) {
+ float scale = kquantize_q4_with_bounds(QK4_2, -8, 7, x, CANDIDATE_COUNT, candidates, L);
+ y[i].d = GGML_FP32_TO_FP16(scale);
+
+ for (int l = 0; l < QK4_2; l += 2) {
+ const uint8_t vi0 = (uint8_t)(L[l+0] + 8);
+ const uint8_t vi1 = (uint8_t)(L[l+1] + 8);
+
+ assert(vi0 < 16);
+ assert(vi1 < 16);
+
+ y[i].qs[l/2] = vi0 | (vi1 << 4);
+ }
+
+ x += QK4_2;
+ }
+}
+
static void quantize_row_q4_2(const float * restrict x, void * restrict vy, int k) {
assert(k % QK4_2 == 0);
block_q4_2 * restrict y = vy;
- quantize_row_q4_2_reference(x, y, k);
+ //quantize_row_q4_2_reference(x, y, k);
+ // This produces the exact same format, just better match to the input floats ("better" as measured by RMSE)
+ quantize_row_q4_2_rmse(x, y, k);
+}
+
+static void quantize_row_q4_3_reference(const float * restrict x, block_q4_3 * restrict y, int k) {
+ assert(k % QK4_3 == 0);
+ const int nb = k / QK4_3;
+
+ for (int i = 0; i < nb; i++) {
+ float min = FLT_MAX;
+ float max = -FLT_MAX;
+
+ for (int l = 0; l < QK4_3; l++) {
+ const float v = x[i*QK4_3 + l];
+ if (v < min) min = v;
+ if (v > max) max = v;
+ }
+
+ const float d = (max - min) / ((1 << 4) - 1);
+ const float id = d ? 1.0f/d : 0.0f;
+
+ y[i].d = GGML_FP32_TO_FP16(d);
+ y[i].m = GGML_FP32_TO_FP16(min);
+
+ for (int l = 0; l < QK4_3; l += 2) {
+ const float v0 = (x[i*QK4_3 + l + 0] - min)*id;
+ const float v1 = (x[i*QK4_3 + l + 1] - min)*id;
+
+ const uint8_t vi0 = (int) (v0 + 0.5f);
+ const uint8_t vi1 = (int) (v1 + 0.5f);
+
+ assert(vi0 < 16);
+ assert(vi1 < 16);
+
+ y[i].qs[l/2] = vi0 | (vi1 << 4);
+ }
+ }
+}
+
+static void quantize_row_q4_3(const float * restrict x, void * restrict vy, int k) {
+ assert(k % QK4_3 == 0);
+
+ block_q4_3 * restrict y = vy;
+
+ quantize_row_q4_3_reference(x, y, k);
}
// reference implementation for deterministic creation of model files
for (int l = 0; l < QK4_0; l += 32) {
// Load 32x4-bit integers into 32x8-bit integers
- __m256i vx8 = bytesFromNibbles(pp+l/2);
+ __m256i vx8 = bytes_from_nibbles_32(pp+l/2);
// Subtract 8 from the integers
vx8 = _mm256_sub_epi8(vx8, _mm256_set1_epi8(8));
for (int l = 0; l < QK4_1; l += 32) {
// Load 32x4-bit integers into 32x8-bit integers
- __m256i vx8 = bytesFromNibbles(pp+l/2);
+ __m256i vx8 = bytes_from_nibbles_32(pp+l/2);
// Convert to 16-bit int
const __m256i vx16_lo = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(vx8, 0));
}
}
+static void dequantize_row_q4_3(const void * restrict vx, float * restrict y, int k) {
+ assert(k % QK4_3 == 0);
+ const int nb = k / QK4_3;
+
+ const block_q4_3 * restrict x = vx;
+
+ for (int i = 0; i < nb; i++) {
+ const float d = GGML_FP16_TO_FP32(x[i].d);
+ const float m = GGML_FP16_TO_FP32(x[i].m);
+
+ const uint8_t * restrict pp = x[i].qs;
+
+ for (int l = 0; l < QK4_3; l += 2) {
+ const uint8_t vi = pp[l/2];
+
+ const int8_t vi0 = vi & 0xf;
+ const int8_t vi1 = vi >> 4;
+
+ const float v0 = vi0*d + m;
+ const float v1 = vi1*d + m;
+
+ y[i*QK4_3 + l + 0] = v0;
+ y[i*QK4_3 + l + 1] = v1;
+
+ assert(!isnan(y[i*QK4_3 + l + 0]));
+ assert(!isnan(y[i*QK4_3 + l + 1]));
+ }
+ }
+}
+
static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
static void ggml_vec_dot_q4_1_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
static void ggml_vec_dot_q4_2_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
+static void ggml_vec_dot_q4_3_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
static const quantize_fns_t quantize_fns[GGML_TYPE_COUNT] = {
[GGML_TYPE_Q4_0] = {
[GGML_TYPE_Q4_2] = {
.dequantize_row_q = dequantize_row_q4_2,
.quantize_row_q = quantize_row_q4_2,
- .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_2_reference,
+ .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_2_rmse, //quantize_row_q4_2_reference,
.quantize_row_q_dot = quantize_row_q8_0,
.vec_dot_q = ggml_vec_dot_q4_2_q8_0,
},
+ [GGML_TYPE_Q4_3] = {
+ .dequantize_row_q = dequantize_row_q4_3,
+ .quantize_row_q = quantize_row_q4_3,
+ .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_3_reference, // TODO: RMSE optimization
+ .quantize_row_q_dot = quantize_row_q8_0,
+ .vec_dot_q = ggml_vec_dot_q4_3_q8_0,
+ },
[GGML_TYPE_Q8_0] = {
.dequantize_row_q = NULL, // TODO
.quantize_row_q = quantize_row_q8_0,
/* Compute combined scale for the block */
const __m256 d = _mm256_mul_ps( _mm256_broadcast_ss( &x[i].d ), _mm256_broadcast_ss( &y[i].d ) );
- __m256i bx = bytesFromNibbles(x[i].qs);
+ __m256i bx = bytes_from_nibbles_32(x[i].qs);
// Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval.
const __m256i off = _mm256_set1_epi8( 8 );
__m128i i32[2];
for (int j = 0; j < 2; ++j) {
// Load 8 bytes, and unpack 4 bit fields into bytes, making 16 bytes
- __m128i bx = bytesFromNibbles( x[i].qs + 8*j );
+ __m128i bx = bytes_from_nibbles_16(x[i].qs + 8*j);
__m128i by = _mm_loadu_si128((const __m128i *)(y[i].qs + 16*j));
// Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval.
const __m256 d1m0 = _mm256_mul_ps( d1v, m0v );
// Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes
- const __m256i bx = bytesFromNibbles( x[i].qs );
+ const __m256i bx = bytes_from_nibbles_32(x[i].qs);
const __m256i by = _mm256_loadu_si256( (const __m256i *)y[i].qs );
// Get absolute values of x vectors
const block_q4_2 * restrict x0_1 = &x[2*(i + 0) + 1];
const block_q4_2 * restrict x1_0 = &x[2*(i + 1) + 0];
const block_q4_2 * restrict x1_1 = &x[2*(i + 1) + 1];
+
const block_q8_0 * restrict y0 = &y[i + 0];
const block_q8_0 * restrict y1 = &y[i + 1];
}
sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
+#elif defined(__AVX2__)
+ // Initialize accumulator with zeros
+ __m256 acc = _mm256_setzero_ps();
+
+ // Main loop
+ for (int i = 0; i < nb; i++) {
+ /* Compute combined scale for the block */
+ const __m128 d0 = _mm_set1_ps(GGML_FP16_TO_FP32(x[2*i + 0].d));
+ const __m128 d1 = _mm_set1_ps(GGML_FP16_TO_FP32(x[2*i + 1].d));
+ const __m256 d = _mm256_mul_ps(_mm256_set_m128(d1, d0), _mm256_broadcast_ss(&y[i].d));
+
+ __m128i bx0 = bytes_from_nibbles_16(x[2*i + 0].qs);
+ __m128i bx1 = bytes_from_nibbles_16(x[2*i + 1].qs);
+ __m256i bx = _mm256_set_m128i(bx1, bx0);
+
+ // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval.
+ const __m256i off = _mm256_set1_epi8(8);
+ bx = _mm256_sub_epi8(bx, off);
+
+ __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
+
+ // Get absolute values of x vectors
+ const __m256i ax = _mm256_sign_epi8(bx, bx);
+ // Sign the values of the y vectors
+ const __m256i sy = _mm256_sign_epi8(by, bx);
+ // Perform multiplication and create 16-bit values
+ const __m256i dot = _mm256_maddubs_epi16(ax, sy);
+
+ const __m256i ones = _mm256_set1_epi16(1);
+ __m256i xy_q = _mm256_madd_epi16(ones, dot);
+
+ /* Convert to vectore of 8 int32_t to 8 floats */
+ __m256 q = _mm256_cvtepi32_ps(xy_q);
+
+ /* Multiply q with scale and accumulate */
+ acc = _mm256_fmadd_ps(d, q, acc);
+ }
+
+ // Return horizontal sum of the acc vector
+ __m128 res = _mm256_extractf128_ps(acc, 1);
+ res = _mm_add_ps(res, _mm256_castps256_ps128(acc));
+ res = _mm_add_ps(res, _mm_movehl_ps(res, res));
+ res = _mm_add_ss(res, _mm_movehdup_ps(res));
+
+ sumf = _mm_cvtss_f32(res);
#else
// scalar
for (int i = 0; i < nb; i++) {
*s = sumf;
}
+static void ggml_vec_dot_q4_3_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+ const int nb = n / QK8_0;
+
+ assert(n % QK8_0 == 0);
+ assert(nb % 2 == 0);
+ assert(QK8_0 == 2*QK4_2);
+
+ const block_q4_3 * restrict x = vx;
+ const block_q8_0 * restrict y = vy;
+
+ float sumf = 0.0;
+
+#if defined(__ARM_NEON)
+ float32x4_t sumv0 = vdupq_n_f32(0.0f);
+ float32x4_t sumv1 = vdupq_n_f32(0.0f);
+
+ for (int i = 0; i < nb; i += 2) {
+ const block_q4_3 * restrict x0_0 = &x[2*(i + 0) + 0];
+ const block_q4_3 * restrict x0_1 = &x[2*(i + 0) + 1];
+ const block_q4_3 * restrict x1_0 = &x[2*(i + 1) + 0];
+ const block_q4_3 * restrict x1_1 = &x[2*(i + 1) + 1];
+
+ const block_q8_0 * restrict y0 = &y[i + 0];
+ const block_q8_0 * restrict y1 = &y[i + 1];
+
+ const uint8x16_t m4b = vdupq_n_u8(0xf);
+
+ const float x0_0d = GGML_FP16_TO_FP32(x0_0->d);
+ const float x0_1d = GGML_FP16_TO_FP32(x0_1->d);
+ const float x1_0d = GGML_FP16_TO_FP32(x1_0->d);
+ const float x1_1d = GGML_FP16_TO_FP32(x1_1->d);
+
+ const float x0_0m = GGML_FP16_TO_FP32(x0_0->m);
+ const float x0_1m = GGML_FP16_TO_FP32(x0_1->m);
+ const float x1_0m = GGML_FP16_TO_FP32(x1_0->m);
+ const float x1_1m = GGML_FP16_TO_FP32(x1_1->m);
+
+ const uint8x16_t v0_0 = vcombine_u8(vld1_u8(x0_0->qs), vld1_u8(x0_1->qs));
+ const uint8x16_t v0_1 = vcombine_u8(vld1_u8(x1_0->qs), vld1_u8(x1_1->qs));
+
+ // 4-bit -> 8-bit
+ const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b));
+ const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4));
+ const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b));
+ const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4));
+
+ // interleave
+ const int8x16_t v0_0lz = vzip1q_s8(v0_0l, v0_0h);
+ const int8x16_t v0_0hz = vzip2q_s8(v0_0l, v0_0h);
+ const int8x16_t v0_1lz = vzip1q_s8(v0_1l, v0_1h);
+ const int8x16_t v0_1hz = vzip2q_s8(v0_1l, v0_1h);
+
+ // load y
+ const int8x16_t v1_0l = vld1q_s8(y0->qs);
+ const int8x16_t v1_0h = vld1q_s8(y0->qs + 16);
+ const int8x16_t v1_1l = vld1q_s8(y1->qs);
+ const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
+
+ const int16x8_t sy0_0 = vaddq_s16(vmovl_s8(vget_low_s8(v1_0l)), vmovl_s8(vget_high_s8(v1_0l)));
+ const int16x8_t sy0_1 = vaddq_s16(vmovl_s8(vget_low_s8(v1_0h)), vmovl_s8(vget_high_s8(v1_0h)));
+
+ const int16x8_t sy1_0 = vaddq_s16(vmovl_s8(vget_low_s8(v1_1l)), vmovl_s8(vget_high_s8(v1_1l)));
+ const int16x8_t sy1_1 = vaddq_s16(vmovl_s8(vget_low_s8(v1_1h)), vmovl_s8(vget_high_s8(v1_1h)));
+
+ sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddl_s16(vget_low_s16(sy0_0), vget_high_s16(sy0_0))), x0_0m*y0->d);
+ sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddl_s16(vget_low_s16(sy0_1), vget_high_s16(sy0_1))), x0_1m*y0->d);
+ sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddl_s16(vget_low_s16(sy1_0), vget_high_s16(sy1_0))), x1_0m*y1->d);
+ sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddl_s16(vget_low_s16(sy1_1), vget_high_s16(sy1_1))), x1_1m*y1->d);
+
+#if defined(__ARM_FEATURE_DOTPROD)
+ sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vdotq_s32(vdupq_n_s32(0), v0_0lz, v1_0l)), x0_0d*y0->d);
+ sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vdotq_s32(vdupq_n_s32(0), v0_0hz, v1_0h)), x0_1d*y0->d);
+ sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vdotq_s32(vdupq_n_s32(0), v0_1lz, v1_1l)), x1_0d*y1->d);
+ sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vdotq_s32(vdupq_n_s32(0), v0_1hz, v1_1h)), x1_1d*y1->d);
+#else
+ const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0lz), vget_low_s8 (v1_0l));
+ const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lz), vget_high_s8(v1_0l));
+ const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hz), vget_low_s8 (v1_0h));
+ const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hz), vget_high_s8(v1_0h));
+
+ const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1lz), vget_low_s8 (v1_1l));
+ const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1lz), vget_high_s8(v1_1l));
+ const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hz), vget_low_s8 (v1_1h));
+ const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hz), vget_high_s8(v1_1h));
+
+ const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
+ const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
+ const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
+ const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
+
+ sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(pl0), x0_0d*y0->d);
+ sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(ph0), x0_1d*y0->d);
+ sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(pl1), x1_0d*y1->d);
+ sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(ph1), x1_1d*y1->d);
+#endif
+ }
+
+ sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
+#else
+ // scalar
+ for (int i = 0; i < nb; i++) {
+ const uint8_t * restrict x0 = x[2*i + 0].qs;
+ const uint8_t * restrict x1 = x[2*i + 1].qs;
+ const int8_t * restrict y0 = y[i].qs;
+
+ const float d0 = GGML_FP16_TO_FP32(x[2*i + 0].d);
+ const float m0 = GGML_FP16_TO_FP32(x[2*i + 0].m);
+ const float d1 = GGML_FP16_TO_FP32(x[2*i + 1].d);
+ const float m1 = GGML_FP16_TO_FP32(x[2*i + 1].m);
+
+ int sy_0 = 0;
+ int sy_1 = 0;
+
+ int sxy_0 = 0;
+ int sxy_1 = 0;
+
+ for (int j = 0; j < QK8_0/4; j++) {
+ const uint8_t v0 = x0[j];
+ const uint8_t v1 = x1[j];
+
+ const int x0_0 = v0 & 0xf;
+ const int x1_0 = v0 >> 4;
+
+ const int x0_1 = v1 & 0xf;
+ const int x1_1 = v1 >> 4;
+
+ const int y0_0 = y0[2*j + 0];
+ const int y1_0 = y0[2*j + 1];
+
+ const int y0_1 = y0[2*(j + QK8_0/4) + 0];
+ const int y1_1 = y0[2*(j + QK8_0/4) + 1];
+
+ sy_0 += y0_0 + y1_0;
+ sy_1 += y0_1 + y1_1;
+
+ sxy_0 += x0_0*y0_0 + x1_0*y1_0;
+ sxy_1 += x0_1*y0_1 + x1_1*y1_1;
+ }
+
+ sumf += (d0*sxy_0 + m0*sy_0)*y[i].d;
+ sumf += (d1*sxy_1 + m1*sy_1)*y[i].d;
+ }
+#endif
+
+ *s = sumf;
+}
+
+
// compute GGML_VEC_DOT_UNROLL dot products at once
// xs - x row stride in bytes
inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * restrict s, void * restrict xv, ggml_fp16_t * restrict y) {
[GGML_TYPE_Q4_0] = QK4_0,
[GGML_TYPE_Q4_1] = QK4_1,
[GGML_TYPE_Q4_2] = QK4_2,
+ [GGML_TYPE_Q4_3] = QK4_3,
[GGML_TYPE_Q8_0] = QK8_0,
[GGML_TYPE_I8] = 1,
[GGML_TYPE_I16] = 1,
[GGML_TYPE_I32] = 1,
};
-static_assert(GGML_TYPE_COUNT == 9, "GGML_BLCK_SIZE is outdated");
+static_assert(GGML_TYPE_COUNT == 10, "GGML_BLCK_SIZE is outdated");
static const size_t GGML_TYPE_SIZE[GGML_TYPE_COUNT] = {
[GGML_TYPE_F32] = sizeof(float),
[GGML_TYPE_Q4_0] = sizeof(block_q4_0),
[GGML_TYPE_Q4_1] = sizeof(block_q4_1),
[GGML_TYPE_Q4_2] = sizeof(block_q4_2),
+ [GGML_TYPE_Q4_3] = sizeof(block_q4_3),
[GGML_TYPE_Q8_0] = sizeof(block_q8_0),
[GGML_TYPE_I8] = sizeof(int8_t),
[GGML_TYPE_I16] = sizeof(int16_t),
[GGML_TYPE_I32] = sizeof(int32_t),
};
-static_assert(GGML_TYPE_COUNT == 9, "GGML_TYPE_SIZE is outdated");
+static_assert(GGML_TYPE_COUNT == 10, "GGML_TYPE_SIZE is outdated");
static const char * GGML_TYPE_NAME[GGML_TYPE_COUNT] = {
[GGML_TYPE_Q4_0] = "q4_0",
[GGML_TYPE_Q4_1] = "q4_1",
[GGML_TYPE_Q4_2] = "q4_2",
+ [GGML_TYPE_Q4_3] = "q4_3",
[GGML_TYPE_Q8_0] = "q8_0",
[GGML_TYPE_I8] = "i8",
[GGML_TYPE_I16] = "i16",
[GGML_TYPE_I32] = "i32",
};
-static_assert(GGML_TYPE_COUNT == 9, "GGML_TYPE_NAME is outdated");
+static_assert(GGML_TYPE_COUNT == 10, "GGML_TYPE_NAME is outdated");
static bool GGML_IS_QUANTIZED[GGML_TYPE_COUNT] = {
[GGML_TYPE_F32] = false,
[GGML_TYPE_Q4_0] = true,
[GGML_TYPE_Q4_1] = true,
[GGML_TYPE_Q4_2] = true,
+ [GGML_TYPE_Q4_3] = true,
[GGML_TYPE_Q8_0] = true,
[GGML_TYPE_I8] = false,
[GGML_TYPE_I16] = false,
[GGML_TYPE_I32] = false,
};
-static_assert(GGML_TYPE_COUNT == 9, "GGML_IS_QUANTIZED is outdated");
+static_assert(GGML_TYPE_COUNT == 10, "GGML_IS_QUANTIZED is outdated");
static const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
"NONE",
(t0->ne[3] == t1->ne[3]);
}
-static inline bool ggml_is_quantized(enum ggml_type type) {
+bool ggml_is_quantized(enum ggml_type type) {
return GGML_IS_QUANTIZED[type];
}
i10 += ne00 * ir0;
while (i10 >= ne0) {
i10 -= ne0;
- i11++;
if (++i11 == ne1) {
i11 = 0;
if (++i12 == ne2) {
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q4_2:
+ case GGML_TYPE_Q4_3:
{
ggml_compute_forward_add_q_f32(params, src0, src1, dst);
} break;
// copy data to host
CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, cudaStream));
- CUDA_CHECK(cudaStreamSynchronize(cudaStream));
#else
// zT = y * xT
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
}
}
#if defined(GGML_USE_CUBLAS)
+ CUDA_CHECK(cudaStreamSynchronize(cudaStream));
CUDA_CHECK(cudaFree(d_X));
CUDA_CHECK(cudaFree(d_Y));
CUDA_CHECK(cudaFree(d_D));
// copy data to host
CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, cudaStream));
- CUDA_CHECK(cudaStreamSynchronize(cudaStream));
#else
const float * x = wdata;
const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
}
#if defined(GGML_USE_CUBLAS)
+ CUDA_CHECK(cudaStreamSynchronize(cudaStream));
CUDA_CHECK(cudaFree(d_X));
CUDA_CHECK(cudaFree(d_Y));
CUDA_CHECK(cudaFree(d_D));
return;
}
- float * const wdata = params->wdata;
- dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q;
-
#if defined(GGML_USE_CUBLAS)
float *d_X = NULL;
float *d_Y = NULL;
float *d_D = NULL;
+ float *d_Q = NULL;
const float alpha = 1.0f;
const float beta = 0.0f;
const int x_ne = ne01 * ne10;
CUDA_CHECK(cudaMalloc((void **)(&d_X), sizeof(float) * x_ne));
CUDA_CHECK(cudaMalloc((void **)(&d_Y), sizeof(float) * y_ne));
CUDA_CHECK(cudaMalloc((void **)(&d_D), sizeof(float) * d_ne));
+ CUDA_CHECK(cudaMalloc((void **)(&d_Q), GGML_TYPE_SIZE[type] * x_ne / GGML_BLCK_SIZE[type]));
+
+ void (*dequantize_row_q_cuda)(const void * x, float * y, int k, cudaStream_t stream) = NULL;
+ if (type == GGML_TYPE_Q4_0) {
+ dequantize_row_q_cuda = dequantize_row_q4_0_cuda;
+ }
+ else if (type == GGML_TYPE_Q4_1) {
+ dequantize_row_q_cuda = dequantize_row_q4_1_cuda;
+ }
+ else if (type == GGML_TYPE_Q4_2) {
+ dequantize_row_q_cuda = dequantize_row_q4_2_cuda;
+ }
+ else {
+ GGML_ASSERT(false);
+ }
+#else
+ float * const wdata = params->wdata;
+ dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q;
#endif
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
+ const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
+
+ float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
+
+#if defined(GGML_USE_CUBLAS)
+ // copy and dequantize on device
+ CUDA_CHECK(
+ cudaMemcpyAsync(d_Q, (char *) src0->data + i03*nb03 + i02*nb02,
+ GGML_TYPE_SIZE[type] * x_ne / GGML_BLCK_SIZE[type], cudaMemcpyHostToDevice, cudaStream));
+
+ dequantize_row_q_cuda(d_Q, d_X, ne01 * ne00, cudaStream);
+ CUDA_CHECK(cudaGetLastError());
+#else
{
size_t id = 0;
for (int64_t i01 = 0; i01 < ne01; ++i01) {
id += ne00;
}
}
-
const float * x = wdata;
- const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
+#endif
- float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
#if defined(GGML_USE_CUBLAS)
// copy data to device
- CUDA_CHECK(cudaMemcpyAsync(d_X, x, sizeof(float) * x_ne, cudaMemcpyHostToDevice, cudaStream));
CUDA_CHECK(cudaMemcpyAsync(d_Y, y, sizeof(float) * y_ne, cudaMemcpyHostToDevice, cudaStream));
// compute
// copy data to host
CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, cudaStream));
- CUDA_CHECK(cudaStreamSynchronize(cudaStream));
#else
// zT = y * xT
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
}
#if defined(GGML_USE_CUBLAS)
+ CUDA_CHECK(cudaStreamSynchronize(cudaStream));
CUDA_CHECK(cudaFree(d_X));
CUDA_CHECK(cudaFree(d_Y));
CUDA_CHECK(cudaFree(d_D));
+ CUDA_CHECK(cudaFree(d_Q));
#endif
//printf("CBLAS = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q4_2:
+ case GGML_TYPE_Q4_3:
case GGML_TYPE_Q8_0:
{
ggml_compute_forward_mul_mat_q_f32(params, src0, src1, dst);
GGML_ASSERT(false);
} break;
}
-
-#if 0
- if (src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_Q4_1) {
- static int first = 8;
- printf("src0: ne0 = %5d, ne1 = %5d, ne2 = %5d\n", src0->ne[0], src0->ne[1], src0->ne[2]);
- printf("src1: ne0 = %5d, ne1 = %5d, ne2 = %5d\n", src1->ne[0], src1->ne[1], src1->ne[2]);
- printf("dst: ne0 = %5d, ne1 = %5d, ne2 = %5d\n", dst->ne[0], dst->ne[1], dst->ne[2]);
- if (first) {
- --first;
- } else {
- for (int k = 0; k < dst->ne[1]; ++k) {
- for (int j = 0; j < dst->ne[0]/16; ++j) {
- for (int i = 0; i < 16; ++i) {
- printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]);
- }
- printf("\n");
- }
- printf("\n");
- }
- printf("\n");
- exit(0);
- }
- } else {
- printf("aaaa src0: ne0 = %5d, ne1 = %5d, ne2 = %5d\n", src0->ne[0], src0->ne[1], src0->ne[2]);
- printf("aaaa src1: ne0 = %5d, ne1 = %5d, ne2 = %5d\n", src1->ne[0], src1->ne[1], src1->ne[2]);
- printf("aaaa dst: ne0 = %5d, ne1 = %5d, ne2 = %5d\n", dst->ne[0], dst->ne[1], dst->ne[2]);
- }
-#endif
}
// ggml_compute_forward_scale
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q4_2:
+ case GGML_TYPE_Q4_3:
case GGML_TYPE_Q8_0:
{
ggml_compute_forward_get_rows_q(params, src0, src1, dst);
for (int j = 0; j < n; j += k) {
block_q4_2 * restrict y = (block_q4_2 *)dst + j/QK4_2;
- quantize_row_q4_2_reference(src + j, y, k);
+ //quantize_row_q4_2_reference(src + j, y, k);
+ quantize_row_q4_2_rmse(src + j, y, k);
for (int i = 0; i < nb; i++) {
for (int l = 0; l < QK4_2; l += 2) {
return (n/QK4_2*sizeof(block_q4_2));
}
+size_t ggml_quantize_q4_3(const float * src, void * dst, int n, int k, int64_t * hist) {
+ assert(k % QK4_3 == 0);
+ const int nb = k / QK4_3;
+
+ for (int j = 0; j < n; j += k) {
+ block_q4_3 * restrict y = (block_q4_3 *)dst + j/QK4_3;
+
+ quantize_row_q4_3_reference(src + j, y, k);
+
+ for (int i = 0; i < nb; i++) {
+ for (int l = 0; l < QK4_3; l += 2) {
+ const uint8_t vi0 = y[i].qs[l/2] & 0xF;
+ const uint8_t vi1 = y[i].qs[l/2] >> 4;
+
+ hist[vi0]++;
+ hist[vi1]++;
+ }
+ }
+ }
+
+ return (n/QK4_3*sizeof(block_q4_3));
+}
+
+size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist) {
+ size_t result = 0;
+ switch (type) {
+ case GGML_TYPE_Q4_0:
+ {
+ GGML_ASSERT(start % QK4_0 == 0);
+ block_q4_0 * block = (block_q4_0*)dst + start / QK4_0;
+ result = ggml_quantize_q4_0(src + start, block, n, n, hist);
+ } break;
+ case GGML_TYPE_Q4_1:
+ {
+ GGML_ASSERT(start % QK4_1 == 0);
+ block_q4_1 * block = (block_q4_1*)dst + start / QK4_1;
+ result = ggml_quantize_q4_1(src + start, block, n, n, hist);
+ } break;
+ case GGML_TYPE_Q4_2:
+ {
+ GGML_ASSERT(start % QK4_2 == 0);
+ block_q4_2 * block = (block_q4_2*)dst + start / QK4_2;
+ result = ggml_quantize_q4_2(src + start, block, n, n, hist);
+ } break;
+ case GGML_TYPE_Q4_3:
+ {
+ GGML_ASSERT(start % QK4_3 == 0);
+ block_q4_3 * block = (block_q4_3*)dst + start / QK4_3;
+ result = ggml_quantize_q4_3(src + start, block, n, n, hist);
+ } break;
+ default:
+ assert(false);
+ }
+ return result;
+}
+
////////////////////////////////////////////////////////////////////////////////
int ggml_cpu_has_avx(void) {
overview / pkg / ggml / sources / ggml / commitdiff