mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-23 21:17:54 +01:00
ggml : add new Q4_2 quantization (ARM only) (#1046)
* ggml : Q4_2 ARM * ggml : add ggml_is_quantized() * llama : update llama_type_name() with Q4_2 entry * ggml : speed-up q4_2 - 4 threads: ~100ms -> ~90ms - 8 threads: ~55ms -> ~50ms * ggml : optimize q4_2 using vmlaq_n_f32 + vmulq_n_f32
This commit is contained in:
Georgi Gerganov
GitHub
parent
50a8a2af97
commit
77a73403ca
@ -14,6 +14,7 @@ int main(int argc, char ** argv) {
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fprintf(stderr, "usage: %s model-f32.bin model-quant.bin type\n", argv[0]);
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fprintf(stderr, " type = %d - q4_0\n", LLAMA_FTYPE_MOSTLY_Q4_0);
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fprintf(stderr, " type = %d - q4_1\n", LLAMA_FTYPE_MOSTLY_Q4_1);
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fprintf(stderr, " type = %d - q4_2\n", LLAMA_FTYPE_MOSTLY_Q4_2);
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return 1;
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}
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282
ggml.c
282
ggml.c
@ -585,6 +585,13 @@ typedef struct {
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} block_q4_1;
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static_assert(sizeof(block_q4_1) == sizeof(float) * 2 + QK4_1 / 2, "wrong q4_1 block size/padding");
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#define QK4_2 16
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typedef struct {
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ggml_fp16_t d; // delta
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uint8_t qs[QK4_2 / 2]; // nibbles / quants
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} block_q4_2;
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static_assert(sizeof(block_q4_2) == sizeof(ggml_fp16_t) + QK4_2 / 2, "wrong q4_2 block size/padding");
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#define QK8_0 32
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typedef struct {
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float d; // delta
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@ -1045,6 +1052,49 @@ static void quantize_row_q4_1(const float * restrict x, void * restrict vy, int
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#endif
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}
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// reference implementation for deterministic creation of model files
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static void quantize_row_q4_2_reference(const float * restrict x, block_q4_2 * restrict y, int k) {
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assert(k % QK4_2 == 0);
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const int nb = k / QK4_2;
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for (int i = 0; i < nb; i++) {
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float amax = 0.0f; // absolute max
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for (int l = 0; l < QK4_2; l++) {
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const float v = x[i*QK4_2 + l];
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amax = MAX(amax, fabsf(v));
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}
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const float d = amax / ((1 << 3) - 1);
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const float id = d ? 1.0f/d : 0.0f;
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y[i].d = GGML_FP32_TO_FP16(d);
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for (int l = 0; l < QK4_2; l += 2) {
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const float v0 = x[i*QK4_2 + l + 0]*id;
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const float v1 = x[i*QK4_2 + l + 1]*id;
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const uint8_t vi0 = (uint8_t)(v0 + 8.5f);
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const uint8_t vi1 = (uint8_t)(v1 + 8.5f);
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assert(vi0 < 16);
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assert(vi1 < 16);
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y[i].qs[l/2] = vi0 | (vi1 << 4);
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}
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}
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}
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static void quantize_row_q4_2(const float * restrict x, void * restrict vy, int k) {
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assert(k % QK4_2 == 0);
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block_q4_2 * restrict y = vy;
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quantize_row_q4_2_reference(x, y, k);
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}
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// reference implementation for deterministic creation of model files
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static void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int k) {
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assert(k % QK8_0 == 0);
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@ -1064,7 +1114,7 @@ static void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * r
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y[i].d = d;
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for (int l = 0; l < QK8_0; ++l) {
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const float v = x[i*QK8_0 + l]*id;
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const float v = x[i*QK8_0 + l]*id;
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y[i].qs[l] = roundf(v);
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}
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}
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@ -1420,8 +1470,39 @@ static void dequantize_row_q4_1(const void * restrict vx, float * restrict y, in
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#endif
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}
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static void dequantize_row_q4_2(const void * restrict vx, float * restrict y, int k) {
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assert(k % QK4_2 == 0);
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const int nb = k / QK4_2;
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const block_q4_2 * restrict x = vx;
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for (int i = 0; i < nb; i++) {
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const float d = GGML_FP16_TO_FP32(x[i].d);
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const uint8_t * restrict pp = x[i].qs;
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for (int l = 0; l < QK4_2; l += 2) {
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const uint8_t vi = pp[l/2];
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const int8_t vi0 = vi & 0xf;
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const int8_t vi1 = vi >> 4;
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const float v0 = (vi0 - 8)*d;
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const float v1 = (vi1 - 8)*d;
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y[i*QK4_2 + l + 0] = v0;
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y[i*QK4_2 + l + 1] = v1;
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assert(!isnan(y[i*QK4_2 + l + 0]));
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assert(!isnan(y[i*QK4_2 + l + 1]));
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}
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}
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}
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static void ggml_vec_dot_q4_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
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static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
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//static void ggml_vec_dot_q4_1_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
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static void ggml_vec_dot_q4_2_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
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static const quantize_fns_t quantize_fns[GGML_TYPE_COUNT] = {
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[GGML_TYPE_Q4_0] = {
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@ -1438,6 +1519,13 @@ static const quantize_fns_t quantize_fns[GGML_TYPE_COUNT] = {
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.quantize_row_q_dot = quantize_row_q4_1,
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.vec_dot_q = ggml_vec_dot_q4_1,
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},
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[GGML_TYPE_Q4_2] = {
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.dequantize_row_q = dequantize_row_q4_2,
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.quantize_row_q = quantize_row_q4_2,
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.quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_2_reference,
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.quantize_row_q_dot = quantize_row_q8_0,
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.vec_dot_q = ggml_vec_dot_q4_2_q8_0,
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},
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// TODO: GGML_TYPE_Q8_0
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};
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@ -2950,6 +3038,136 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
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*s = sumf;
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}
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static void ggml_vec_dot_q4_2_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
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const int nb = n / QK8_0;
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assert(n % QK8_0 == 0);
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assert(nb % 2 == 0);
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assert(QK8_0 == 2*QK4_2);
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const block_q4_2 * restrict x = vx;
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const block_q8_0 * restrict y = vy;
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float sumf = 0.0;
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#if defined(__ARM_NEON)
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float32x4_t sumv0 = vdupq_n_f32(0.0f);
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float32x4_t sumv1 = vdupq_n_f32(0.0f);
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for (int i = 0; i < nb; i += 2) {
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const block_q4_2 * restrict x0_0 = &x[2*(i + 0) + 0];
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const block_q4_2 * restrict x0_1 = &x[2*(i + 0) + 1];
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const block_q4_2 * restrict x1_0 = &x[2*(i + 1) + 0];
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const block_q4_2 * restrict x1_1 = &x[2*(i + 1) + 1];
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const block_q8_0 * restrict y0 = &y[i + 0];
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const block_q8_0 * restrict y1 = &y[i + 1];
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const uint8x16_t m4b = vdupq_n_u8(0xf);
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const int8x16_t s8b = vdupq_n_s8(0x8);
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const uint8x16_t v0_0 = vcombine_u8(vld1_u8(x0_0->qs), vld1_u8(x0_1->qs));
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const uint8x16_t v0_1 = vcombine_u8(vld1_u8(x1_0->qs), vld1_u8(x1_1->qs));
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// 4-bit -> 8-bit
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const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b));
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const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4));
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const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b));
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const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4));
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// sub 8
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const int8x16_t v0_0ls = vsubq_s8(v0_0l, s8b);
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const int8x16_t v0_0hs = vsubq_s8(v0_0h, s8b);
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const int8x16_t v0_1ls = vsubq_s8(v0_1l, s8b);
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const int8x16_t v0_1hs = vsubq_s8(v0_1h, s8b);
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// interleave
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const int8x16_t v0_0lz = vzip1q_s8(v0_0ls, v0_0hs);
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const int8x16_t v0_0hz = vzip2q_s8(v0_0ls, v0_0hs);
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const int8x16_t v0_1lz = vzip1q_s8(v0_1ls, v0_1hs);
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const int8x16_t v0_1hz = vzip2q_s8(v0_1ls, v0_1hs);
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// load y
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const int8x16_t v1_0l = vld1q_s8(y0->qs);
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const int8x16_t v1_0h = vld1q_s8(y0->qs + 16);
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const int8x16_t v1_1l = vld1q_s8(y1->qs);
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const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
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#if defined(__ARM_FEATURE_DOTPROD)
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sumv0 = vmlaq_n_f32(sumv0, vaddq_f32(
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vmulq_n_f32(vcvtq_f32_s32(vdotq_s32(vdupq_n_s32(0), v0_0lz, v1_0l)), GGML_FP16_TO_FP32(x0_0->d)),
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vmulq_n_f32(vcvtq_f32_s32(vdotq_s32(vdupq_n_s32(0), v0_0hz, v1_0h)), GGML_FP16_TO_FP32(x0_1->d))), y0->d);
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sumv1 = vmlaq_n_f32(sumv1, vaddq_f32(
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vmulq_n_f32(vcvtq_f32_s32(vdotq_s32(vdupq_n_s32(0), v0_1lz, v1_1l)), GGML_FP16_TO_FP32(x1_0->d)),
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vmulq_n_f32(vcvtq_f32_s32(vdotq_s32(vdupq_n_s32(0), v0_1hz, v1_1h)), GGML_FP16_TO_FP32(x1_1->d))), y1->d);
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#else
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const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0lz), vget_low_s8 (v1_0l));
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const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lz), vget_high_s8(v1_0l));
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const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hz), vget_low_s8 (v1_0h));
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const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hz), vget_high_s8(v1_0h));
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const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1lz), vget_low_s8 (v1_1l));
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const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1lz), vget_high_s8(v1_1l));
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const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hz), vget_low_s8 (v1_1h));
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const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hz), vget_high_s8(v1_1h));
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const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
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const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
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const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
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const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
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sumv0 = vmlaq_n_f32(sumv0, vaddq_f32(
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vmulq_n_f32(vcvtq_f32_s32(pl0), GGML_FP16_TO_FP32(x0_0->d)),
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vmulq_n_f32(vcvtq_f32_s32(ph0), GGML_FP16_TO_FP32(x0_1->d))), y0->d);
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sumv1 = vmlaq_n_f32(sumv1, vaddq_f32(
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vmulq_n_f32(vcvtq_f32_s32(pl1), GGML_FP16_TO_FP32(x1_0->d)),
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vmulq_n_f32(vcvtq_f32_s32(ph1), GGML_FP16_TO_FP32(x1_1->d))), y1->d);
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#endif
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}
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sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
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#else
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// scalar
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for (int i = 0; i < nb; i++) {
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const uint8_t * restrict x0 = x[2*i + 0].qs;
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const uint8_t * restrict x1 = x[2*i + 1].qs;
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const int8_t * restrict y0 = y[i].qs;
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const float d0 = GGML_FP16_TO_FP32(x[2*i + 0].d);
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const float d1 = GGML_FP16_TO_FP32(x[2*i + 1].d);
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int sumi_0 = 0;
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int sumi_1 = 0;
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for (int j = 0; j < QK8_0/4; j++) {
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const uint8_t v0 = x0[j];
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const uint8_t v1 = x1[j];
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const int i0_0 = (int8_t) (v0 & 0xf) - 8;
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const int i1_0 = (int8_t) (v0 >> 4) - 8;
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const int i0_1 = (int8_t) (v1 & 0xf) - 8;
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const int i1_1 = (int8_t) (v1 >> 4) - 8;
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const int i2_0 = y0[2*j + 0];
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const int i3_0 = y0[2*j + 1];
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const int i2_1 = y0[2*(j + QK8_0/4) + 0];
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const int i3_1 = y0[2*(j + QK8_0/4) + 1];
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sumi_0 += i0_0*i2_0 + i1_0*i3_0;
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sumi_1 += i0_1*i2_1 + i1_1*i3_1;
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}
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sumf += (d0 * y[i].d) * sumi_0;
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sumf += (d1 * y[i].d) * sumi_1;
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}
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#endif
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*s = sumf;
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}
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// compute GGML_VEC_DOT_UNROLL dot products at once
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// xs - x row stride in bytes
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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) {
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@ -3196,24 +3414,26 @@ static const int GGML_BLCK_SIZE[GGML_TYPE_COUNT] = {
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[GGML_TYPE_F16] = 1,
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[GGML_TYPE_Q4_0] = QK4_0,
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[GGML_TYPE_Q4_1] = QK4_1,
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[GGML_TYPE_Q4_2] = QK4_2,
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[GGML_TYPE_Q8_0] = QK8_0,
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[GGML_TYPE_I8] = 1,
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[GGML_TYPE_I16] = 1,
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[GGML_TYPE_I32] = 1,
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};
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static_assert(GGML_TYPE_COUNT == 8, "GGML_BLCK_SIZE is outdated");
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static_assert(GGML_TYPE_COUNT == 9, "GGML_BLCK_SIZE is outdated");
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static const size_t GGML_TYPE_SIZE[GGML_TYPE_COUNT] = {
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[GGML_TYPE_F32] = sizeof(float),
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[GGML_TYPE_F16] = sizeof(ggml_fp16_t),
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[GGML_TYPE_Q4_0] = sizeof(block_q4_0),
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[GGML_TYPE_Q4_1] = sizeof(block_q4_1),
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[GGML_TYPE_Q4_2] = sizeof(block_q4_2),
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[GGML_TYPE_Q8_0] = sizeof(block_q8_0),
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[GGML_TYPE_I8] = sizeof(int8_t),
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[GGML_TYPE_I16] = sizeof(int16_t),
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[GGML_TYPE_I32] = sizeof(int32_t),
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};
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static_assert(GGML_TYPE_COUNT == 8, "GGML_TYPE_SIZE is outdated");
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static_assert(GGML_TYPE_COUNT == 9, "GGML_TYPE_SIZE is outdated");
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static const char * GGML_TYPE_NAME[GGML_TYPE_COUNT] = {
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@ -3221,12 +3441,26 @@ static const char * GGML_TYPE_NAME[GGML_TYPE_COUNT] = {
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[GGML_TYPE_F16] = "f16",
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[GGML_TYPE_Q4_0] = "q4_0",
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[GGML_TYPE_Q4_1] = "q4_1",
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[GGML_TYPE_Q4_2] = "q4_2",
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[GGML_TYPE_Q8_0] = "q8_0",
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[GGML_TYPE_I8] = "i8",
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[GGML_TYPE_I16] = "i16",
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[GGML_TYPE_I32] = "i32",
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};
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static_assert(GGML_TYPE_COUNT == 8, "GGML_TYPE_NAME is outdated");
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static_assert(GGML_TYPE_COUNT == 9, "GGML_TYPE_NAME is outdated");
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static bool GGML_IS_QUANTIZED[GGML_TYPE_COUNT] = {
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[GGML_TYPE_F32] = false,
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[GGML_TYPE_F16] = false,
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[GGML_TYPE_Q4_0] = true,
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[GGML_TYPE_Q4_1] = true,
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[GGML_TYPE_Q4_2] = true,
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[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 const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
|
||||
"NONE",
|
||||
@ -3488,6 +3722,10 @@ static inline bool ggml_can_mul_mat(const struct ggml_tensor * t0, const struct
|
||||
(t0->ne[3] == t1->ne[3]);
|
||||
}
|
||||
|
||||
static inline bool ggml_is_quantized(enum ggml_type type) {
|
||||
return GGML_IS_QUANTIZED[type];
|
||||
}
|
||||
|
||||
static inline bool ggml_is_transposed(const struct ggml_tensor * tensor) {
|
||||
return tensor->nb[0] > tensor->nb[1];
|
||||
}
|
||||
@ -5609,7 +5847,7 @@ static void ggml_compute_forward_dup_f16(
|
||||
}
|
||||
}
|
||||
}
|
||||
} else if (dst->type == GGML_TYPE_Q4_0 || dst->type == GGML_TYPE_Q4_1) {
|
||||
} else if (ggml_is_quantized(dst->type)) {
|
||||
quantize_row_q_t const quantize_row_q = quantize_fns[dst->type].quantize_row_q;
|
||||
size_t id = 0;
|
||||
uint8_t * dst_ptr = (uint8_t *) dst->data;
|
||||
@ -5821,7 +6059,7 @@ static void ggml_compute_forward_dup_f32(
|
||||
}
|
||||
}
|
||||
}
|
||||
} else if (dst->type == GGML_TYPE_Q4_0 || dst->type == GGML_TYPE_Q4_1) {
|
||||
} else if (ggml_is_quantized(dst->type)) {
|
||||
quantize_row_q_t const quantize_row_q = quantize_fns[dst->type].quantize_row_q;
|
||||
size_t id = 0;
|
||||
uint8_t * dst_ptr = (uint8_t *) dst->data;
|
||||
@ -6184,7 +6422,7 @@ static void ggml_compute_forward_add_q_f32(
|
||||
GGML_ASSERT(nb1 <= nb2);
|
||||
GGML_ASSERT(nb2 <= nb3);
|
||||
|
||||
GGML_ASSERT(src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1);
|
||||
GGML_ASSERT(ggml_is_quantized(src0->type));
|
||||
GGML_ASSERT(dst->type == src0->type);
|
||||
GGML_ASSERT(src1->type == GGML_TYPE_F32);
|
||||
|
||||
@ -6254,6 +6492,7 @@ static void ggml_compute_forward_add(
|
||||
} break;
|
||||
case GGML_TYPE_Q4_0:
|
||||
case GGML_TYPE_Q4_1:
|
||||
case GGML_TYPE_Q4_2:
|
||||
{
|
||||
ggml_compute_forward_add_q_f32(params, src0, src1, dst);
|
||||
} break;
|
||||
@ -7732,6 +7971,7 @@ static void ggml_compute_forward_mul_mat(
|
||||
switch (src0->type) {
|
||||
case GGML_TYPE_Q4_0:
|
||||
case GGML_TYPE_Q4_1:
|
||||
case GGML_TYPE_Q4_2:
|
||||
case GGML_TYPE_Q8_0:
|
||||
{
|
||||
ggml_compute_forward_mul_mat_q_f32(params, src0, src1, dst);
|
||||
@ -7987,6 +8227,7 @@ static void ggml_compute_forward_get_rows(
|
||||
switch (src0->type) {
|
||||
case GGML_TYPE_Q4_0:
|
||||
case GGML_TYPE_Q4_1:
|
||||
case GGML_TYPE_Q4_2:
|
||||
case GGML_TYPE_Q8_0:
|
||||
{
|
||||
ggml_compute_forward_get_rows_q(params, src0, src1, dst);
|
||||
@ -10398,7 +10639,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
|
||||
node->n_tasks = 1;
|
||||
|
||||
size_t cur = 0;
|
||||
if (node->type == GGML_TYPE_Q4_0 || node->type == GGML_TYPE_Q4_1) {
|
||||
if (ggml_is_quantized(node->type)) {
|
||||
cur = GGML_TYPE_SIZE[GGML_TYPE_F32] * node->ne[0];
|
||||
}
|
||||
|
||||
@ -10410,7 +10651,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
|
||||
|
||||
size_t cur = 0;
|
||||
|
||||
if (node->src0->type == GGML_TYPE_Q4_0 || node->src0->type == GGML_TYPE_Q4_1) {
|
||||
if (ggml_is_quantized(node->src0->type)) {
|
||||
cur = GGML_TYPE_SIZE[GGML_TYPE_F32] * node->src0->ne[0] * n_threads;
|
||||
}
|
||||
|
||||
@ -11702,6 +11943,29 @@ size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t *
|
||||
return (n/QK4_1*sizeof(block_q4_1));
|
||||
}
|
||||
|
||||
size_t ggml_quantize_q4_2(const float * src, void * dst, int n, int k, int64_t * hist) {
|
||||
assert(k % QK4_2 == 0);
|
||||
const int nb = k / QK4_2;
|
||||
|
||||
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);
|
||||
|
||||
for (int i = 0; i < nb; i++) {
|
||||
for (int l = 0; l < QK4_2; 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_2*sizeof(block_q4_2));
|
||||
}
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
int ggml_cpu_has_avx(void) {
|
||||
|
||||
4
ggml.h
4
ggml.h
@ -204,7 +204,8 @@ enum ggml_type {
|
||||
GGML_TYPE_F16 = 1,
|
||||
GGML_TYPE_Q4_0 = 2,
|
||||
GGML_TYPE_Q4_1 = 3,
|
||||
GGML_TYPE_Q8_0 = 4,
|
||||
GGML_TYPE_Q4_2 = 4,
|
||||
GGML_TYPE_Q8_0 = 5,
|
||||
GGML_TYPE_I8,
|
||||
GGML_TYPE_I16,
|
||||
GGML_TYPE_I32,
|
||||
@ -806,6 +807,7 @@ enum ggml_opt_result ggml_opt(
|
||||
|
||||
size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
size_t ggml_quantize_q4_2(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
|
||||
//
|
||||
// system info
|
||||
|
||||
10
llama.cpp
10
llama.cpp
@ -478,6 +478,7 @@ struct llama_file_loader {
|
||||
case GGML_TYPE_F16:
|
||||
case GGML_TYPE_Q4_0:
|
||||
case GGML_TYPE_Q4_1:
|
||||
case GGML_TYPE_Q4_2:
|
||||
break;
|
||||
default: {
|
||||
throw format("unrecognized tensor type %u\n", shard.type);
|
||||
@ -550,6 +551,7 @@ struct llama_file_saver {
|
||||
case GGML_TYPE_F16:
|
||||
case GGML_TYPE_Q4_0:
|
||||
case GGML_TYPE_Q4_1:
|
||||
case GGML_TYPE_Q4_2:
|
||||
break;
|
||||
default: LLAMA_ASSERT(false);
|
||||
}
|
||||
@ -838,6 +840,7 @@ static const char *llama_ftype_name(enum llama_ftype ftype) {
|
||||
case LLAMA_FTYPE_MOSTLY_Q4_1: return "mostly Q4_1";
|
||||
case LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16:
|
||||
return "mostly Q4_1, some F16";
|
||||
case LLAMA_FTYPE_MOSTLY_Q4_2: return "mostly Q4_2";
|
||||
default: return "unknown, may not work";
|
||||
}
|
||||
}
|
||||
@ -1571,6 +1574,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
switch (ftype) {
|
||||
case LLAMA_FTYPE_MOSTLY_Q4_0: quantized_type = GGML_TYPE_Q4_0; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q4_1: quantized_type = GGML_TYPE_Q4_1; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q4_2: quantized_type = GGML_TYPE_Q4_2; break;
|
||||
default: throw format("invalid output file type %d\n", ftype);
|
||||
};
|
||||
|
||||
@ -1644,6 +1648,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
{
|
||||
new_size = ggml_quantize_q4_1(f32_data, new_data, nelements, (int) tensor.ne.at(0), hist_cur.data());
|
||||
} break;
|
||||
case GGML_TYPE_Q4_2:
|
||||
{
|
||||
new_size = ggml_quantize_q4_2(f32_data, new_data, nelements, (int) tensor.ne.at(0), hist_cur.data());
|
||||
} break;
|
||||
default:
|
||||
LLAMA_ASSERT(false);
|
||||
}
|
||||
@ -1955,7 +1963,7 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char *
|
||||
base_t = dest_t;
|
||||
}
|
||||
|
||||
if (base_t->type == GGML_TYPE_Q4_0 || base_t->type == GGML_TYPE_Q4_1) {
|
||||
if (base_t->type == GGML_TYPE_Q4_0 || base_t->type == GGML_TYPE_Q4_1 || base_t->type == GGML_TYPE_Q4_2) {
|
||||
if (!warned) {
|
||||
fprintf(stderr, "%s: warning: using a lora adapter with a quantized model may result in poor quality, "
|
||||
"use a f16 or f32 base model with --lora-base\n", __func__);
|
||||
|
||||
1
llama.h
1
llama.h
@ -72,6 +72,7 @@ extern "C" {
|
||||
LLAMA_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
|
||||
LLAMA_FTYPE_MOSTLY_Q4_2 = 5, // except 1d tensors
|
||||
};
|
||||
|
||||
LLAMA_API struct llama_context_params llama_context_default_params();
|
||||
|
||||
Loading…
Reference in New Issue
Block a user