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Make more ML improvements

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- Fix UX issues with llama.com
- Do housekeeping on libm code
- Add more vectorization to GGML
- Get GGJT quantizer programs working well
- Have the quantizer keep the output layer as f16c
- Prefetching improves performance 15% if you use fewer threads
This commit is contained in:
Justine Tunney 2023-05-16 08:07:23 -07:00
parent 80db9de173
commit e7eb0b3070
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GPG key ID: BE714B4575D6E328
46 changed files with 340 additions and 289 deletions
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17
third_party/ggml/ggml.c vendored
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@ -1784,9 +1784,7 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
// Initialize accumulator with zeros
__m256 acc = _mm256_setzero_ps();
//
// Main loop
//
#define WORK(I) \
/* Compute combined scale for the block */ \
const __m256 d = _mm256_mul_ps( _mm256_broadcast_ss( &x[I].d ), _mm256_broadcast_ss( &y[I].d ) ); \
@ -2702,9 +2700,15 @@ inline static void ggml_vec_silu_f32(const int n, float * y, const float * x) {
inline static void ggml_vec_sum_f32(const int n, float * s, const float * x) {
#ifndef GGML_USE_ACCELERATE
ggml_float sum = 0.0;
for (int i = 0; i < n; ++i) {
sum += (ggml_float)x[i];
int i = 0;
ggml_float sum = 0;
#if __AVX__ || __AVX2__ || __AVX512F__
for (; i + 8 <= n; i += 8) {
sum += hsum_float_8(_mm256_loadu_ps(x + i));
}
#endif
for (; i < n; ++i) {
sum += x[i];
}
*s = sum;
#else
@ -2802,6 +2806,7 @@ const char *const ggjt_v2_type_name[GGML_TYPE_COUNT] = {
[GGML_TYPE_F16] = "f16",
[GGML_TYPE_Q4_0] = "q4_0",
[GGML_TYPE_Q4_1] = "q4_1",
[GGML_TYPE_Q4_2] = "q4_2",
[GGML_TYPE_Q5_0] = "q5_0",
[GGML_TYPE_Q5_1] = "q5_1",
[GGML_TYPE_Q8_0] = "q8_0",
@ -8113,7 +8118,7 @@ static void ggml_compute_forward_alibi_f32(
assert(ne1 + n_past == ne0); (void) n_past;
// add alibi to src0 (KQ_scaled)
const int n_heads_log2_floor = 1 << _bsr(n_head);
const int n_heads_log2_floor = 1 << _bsr(n_head); // [jart]
const float m0 = exp2f(-8.0f / n_heads_log2_floor);
const float m1 = exp2f(-4.0f / n_heads_log2_floor);