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ggml-quants : faster 1.625 bpw AVX2 vec_dot
Not using a lookup table anymore makes it match q4_0 speed. * gguf-py : fix formatting * llama : remove spaces on empty line
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@ -11371,40 +11371,68 @@ void ggml_vec_dot_q1_3_q8_0(int n, float * restrict s, size_t bs, const void * r
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__m256 accumf = _mm256_setzero_ps();
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for (int i = 0; i < nb; ++i) {
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{
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__m256i x0 = _mm256_set_epi32(q1_3_grid[x[i].q[7]], q1_3_grid[x[i].q[6]],
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q1_3_grid[x[i].q[5]], q1_3_grid[x[i].q[4]],
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q1_3_grid[x[i].q[3]], q1_3_grid[x[i].q[2]],
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q1_3_grid[x[i].q[1]], q1_3_grid[x[i].q[0]]);
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__m256i y0 = _mm256_lddqu_si256((const __m256i_u *) (y[2*i].qs));
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__m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(y[2*i].d));
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__m256 q = mul_sum_i8_pairs_float(x0, y0);
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accumf = _mm256_fmadd_ps(d, q, accumf);
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}
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// __m128i x12b = _mm_maskload_epi32((const int32_t *) x[i].q, _mm_set_epi32(0, -1, -1, -1));
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// __m128i x12b = _mm_insert_epi8(x12a, x[i].qs[0], 12);
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// WARNING: reading 3 bytes further than necessary. It's faster than the above on my CPU, though.
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__m128i x12b = _mm_loadu_si128((const __m128i_u *) x[i].q);
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__m256i x12 = MM256_SET_M128I(x12b, x12b);
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{
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__m256i x1 = _mm256_castsi128_si256(_mm_set_epi32(q1_3_grid[x[i].q[11]], q1_3_grid[x[i].q[10]],
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q1_3_grid[x[i].q[9]], q1_3_grid[x[i].q[8]]));
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__m256i x2 = _mm256_cvtepu8_epi16(_mm_maskload_epi32((const int32_t *) x[i].q, _mm_set_epi32(0, -1, -1, -1)));
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__m256i x0l = _mm256_shuffle_epi8(x12, _mm256_set_epi8(5, -1, 5, -1, 5, -1, 5, -1,
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4, -1, 4, -1, 4, -1, 4, -1,
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1, -1, 1, -1, 1, -1, 1, -1,
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0, -1, 0, -1, 0, -1, 0, -1));
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__m256i x0h = _mm256_shuffle_epi8(x12, _mm256_set_epi8(7, -1, 7, -1, 7, -1, 7, -1,
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6, -1, 6, -1, 6, -1, 6, -1,
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3, -1, 3, -1, 3, -1, 3, -1,
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2, -1, 2, -1, 2, -1, 2, -1));
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__m256i x1l = _mm256_shuffle_epi8(x12, _mm256_set_epi8(7, -1, 6, -1, 5, -1, 4, -1,
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3, -1, 2, -1, 1, -1, 0, -1,
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9, -1, 9, -1, 9, -1, 9, -1,
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8, -1, 8, -1, 8, -1, 8, -1));
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__m256i x1h = _mm256_shuffle_epi8(x12, _mm256_set_epi8(12, -1, 12, -1, 12, -1, 12, -1,
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11, -1, 10, -1, 9, -1, 8, -1,
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11, -1, 11, -1, 11, -1, 11, -1,
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10, -1, 10, -1, 10, -1, 10, -1));
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const __m256i shift0 = _mm256_set_epi16(3, 9, 27, 81,
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3, 9, 27, 81,
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3, 9, 27, 81,
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3, 9, 27, 81);
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const __m256i shift1l = _mm256_set_epi16(1, 1, 1, 1,
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1, 1, 1, 1,
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3, 9, 27, 81,
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3, 9, 27, 81);
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const __m256i shift1h = _mm256_set_epi16(3, 9, 27, 81,
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1, 1, 1, 1,
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3, 9, 27, 81,
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3, 9, 27, 81);
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x0l = _mm256_mullo_epi16(x0l, shift0);
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x0h = _mm256_mullo_epi16(x0h, shift0);
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x1l = _mm256_mullo_epi16(x1l, shift1l);
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x1h = _mm256_mullo_epi16(x1h, shift1h);
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x0l = _mm256_mulhi_epu16(x0l, _mm256_set1_epi16(3));
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x0h = _mm256_mulhi_epu16(x0h, _mm256_set1_epi16(3));
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x1l = _mm256_mulhi_epu16(x1l, _mm256_set1_epi16(3));
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x1h = _mm256_mulhi_epu16(x1h, _mm256_set1_epi16(3));
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x0l = _mm256_sub_epi16(x0l, _mm256_set1_epi16(1));
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x0h = _mm256_sub_epi16(x0h, _mm256_set1_epi16(1));
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x1l = _mm256_sub_epi16(x1l, _mm256_set1_epi16(1));
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x1h = _mm256_sub_epi16(x1h, _mm256_set1_epi16(1));
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__m256i x0 = _mm256_packs_epi16(x0l, x0h);
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__m256i x1 = _mm256_packs_epi16(x1l, x1h);
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__m256i y0 = _mm256_lddqu_si256((const __m256i_u *) (y[2*i + 0].qs));
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__m256i y1 = _mm256_lddqu_si256((const __m256i_u *) (y[2*i + 1].qs));
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x2 = _mm256_mulhi_epu16(x2, _mm256_set1_epi16(3 << 8));
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x2 = _mm256_sub_epi16(x2, _mm256_set1_epi16(1));
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__m256 d0 = _mm256_set1_ps(GGML_FP16_TO_FP32(y[2*i].d));
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__m256 d1 = _mm256_set1_ps(GGML_FP16_TO_FP32(y[2*i + 1].d));
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// TODO: reduce shuffling
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x2 = _mm256_packs_epi16(x2, _mm256_setzero_si256());
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x2 = _mm256_permute4x64_epi64(x2, _MM_SHUFFLE(3, 1, 2, 0));
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__m128i x2_l = _mm_insert_epi32(_mm256_castsi256_si128(x2), q1_3_grid[x[i].qs[0]], 3);
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x1 = _mm256_inserti128_si256(x1, x2_l, 1);
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__m256 q0 = mul_sum_i8_pairs_float(x0, y0);
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__m256 q1 = mul_sum_i8_pairs_float(x1, y1);
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__m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(y[2*i + 1].d));
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__m256 q = mul_sum_i8_pairs_float(x1, y1);
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accumf = _mm256_fmadd_ps(d, q, accumf);
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accumf = _mm256_fmadd_ps(d0, q0, accumf);
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accumf = _mm256_fmadd_ps(d1, q1, accumf);
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}
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}
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@ -148,9 +148,9 @@ def __quantize_q1_3_rows(n: np.ndarray) -> np.ndarray:
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q48 = np.sum(q48 * pow3.reshape((1, 1, 4)), axis=2, keepdims=True).reshape((n_blocks, 12))
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q4 = np.sum(q4 * pow3.reshape((1, 4)), axis=1, keepdims=True)
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q48 = q48 + (q12 * 81)
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q = np.concatenate([q48, q4], axis=1);
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q = np.concatenate([q48, q4], axis=1)
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q = ((q.astype(np.uint16) * 256) // 243).astype(np.uint8)
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q = np.where(q != 0, q + 1, 0);
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q = np.where(q != 0, q + 1, 0)
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return q.reshape(__quantize_q1_3_shape_change(shape))
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@ -170,4 +170,3 @@ def quantize_q1_3(data: np.ndarray):
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return __quantize_q1_3_lazy(data)
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else:
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return __quantize_q1_3_array(data)
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