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authorKawrakow <48489457+ikawrakow@users.noreply.github.com>2024-02-24 16:23:52 +0200
committerGitHub <noreply@github.com>2024-02-24 16:23:52 +0200
commit4c4cb30736582cacb1a164a9d4bc8e17b1014be7 (patch)
tree5f953370b3124531d9cbb9b9d5cfdb264ddf60bc /ggml.c
parent525213d2f5da1eaf4b922b6b792cb52b2c613368 (diff)
IQ3_S: a much better alternative to Q3_K (#5676)
* iq4_nl: squash commits for easier rebase * Basics (quantize, dequantize) * CUDA dequantize and dot product * Slightly faster CUDA dot product (120 t/s) * Switch to 6-bit scales * Scalar dot product * AVX2 dot product * ARM_NEON dot product * Works on metal, but still slow * Slightly better Metal dot product * Another small Metal improvement * Metal dot product is getting there * Faster CUDA dot product * Add 1/8 ffn_down layers as Q5_K when no imatrix has been provided * Report the actual bpw * Add _xs mix that is 4.05 bpw for non-MoE models * Remove IQ4_XS for now, slightly adjust kvalues_iq4nl * AVX2 dot product uses Q8_0 instead of Q8_K * Add to test-backend-ops * Minor fix * Also use use Q5_K for attn_output in MoE models * Fixes after merging latest master * Switching to blocks of 32 * AVX2 for blocks of 32 * Scaler dot product for blocks of 32 * ARM_NEON dot product for blocks of 32 * Metal kernels for blocks of 32 * Slightly faster Metal kernels * Resurrecting iq3_xs After all the experimentation, nothing was better than this. * Minor PPL improvement via a block scale fudge factor * Minor improvement via 3 neighbours * iq3_xs: working scalar and AVX2 dot products * iq3_xs: ARM_NEON dot product - works but extremely slow (10 t/s) * iq3_xs: working Metal implementation * Adding IQ3_M - IQ3_XS mix with mostly Q4_K * iiq3_xs: a 3.4375 bpw variant * iq3_xs: make CUDA work for new version * iq3_xs: make scalar and AVX2 work for new version * iq3_s: make ARM_NEON work with new version * iq3_xs: make new version work on metal Performance is very similar to Q3_K_S * iq3_xs: tiny Metal speed improvement * iq3_xs: tiny Metal speed improvement * Fix stupid warning * Q3_K_XS now uses a mix of IQ3_XS and IQ3_XXS * iq3_xs: rename to iq3_s * iq3_s: make tests pass * Move Q3_K_XS mix to 3.25 bpw * Attempt to fix failing tests * Another attempt to fix the Windows builds * Attempt to fix ROCm * ROCm again * iq3_s: partial fix for QK_K = 64 * iq3_s: make it work on metal for QK_K = 64 Pleasent surprise: the coding was super-block size independent, so all it took was to delete some QK_K == 256 guards. * Will this fix ROCm? --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Diffstat (limited to 'ggml.c')
-rw-r--r--ggml.c31
1 files changed, 31 insertions, 0 deletions
diff --git a/ggml.c b/ggml.c
index d710fe70..c09a3cad 100644
--- a/ggml.c
+++ b/ggml.c
@@ -678,6 +678,18 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
},
+ [GGML_TYPE_IQ3_S] = {
+ .type_name = "iq3_s",
+ .blck_size = QK_K,
+ .type_size = sizeof(block_iq3_s),
+ .is_quantized = true,
+ .to_float = (ggml_to_float_t) dequantize_row_iq3_s,
+ .from_float = quantize_row_iq3_s,
+ .from_float_reference = (ggml_from_float_t)quantize_row_iq3_s_reference,
+ .vec_dot = ggml_vec_dot_iq3_s_q8_K,
+ .vec_dot_type = GGML_TYPE_Q8_K,
+ .nrows = 1,
+ },
[GGML_TYPE_IQ1_S] = {
.type_name = "iq1_s",
.blck_size = QK_K,
@@ -2304,6 +2316,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
case GGML_FTYPE_MOSTLY_IQ3_XXS: wtype = GGML_TYPE_IQ3_XXS; break;
case GGML_FTYPE_MOSTLY_IQ1_S: wtype = GGML_TYPE_IQ1_S; break;
case GGML_FTYPE_MOSTLY_IQ4_NL: wtype = GGML_TYPE_IQ4_NL; break;
+ case GGML_FTYPE_MOSTLY_IQ3_S: wtype = GGML_TYPE_IQ3_S; break;
case GGML_FTYPE_UNKNOWN: wtype = GGML_TYPE_COUNT; break;
case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16: wtype = GGML_TYPE_COUNT; break;
}
@@ -7738,6 +7751,7 @@ static void ggml_compute_forward_add(
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
+ case GGML_TYPE_IQ3_S:
{
ggml_compute_forward_add_q_f32(params, dst);
} break;
@@ -8017,6 +8031,7 @@ static void ggml_compute_forward_add1(
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
+ case GGML_TYPE_IQ3_S:
{
ggml_compute_forward_add1_q_f32(params, dst);
} break;
@@ -8141,6 +8156,7 @@ static void ggml_compute_forward_acc(
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
+ case GGML_TYPE_IQ3_S:
default:
{
GGML_ASSERT(false);
@@ -11039,6 +11055,7 @@ static void ggml_compute_forward_out_prod(
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
+ case GGML_TYPE_IQ3_S:
{
ggml_compute_forward_out_prod_q_f32(params, dst);
} break;
@@ -11227,6 +11244,7 @@ static void ggml_compute_forward_set(
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
+ case GGML_TYPE_IQ3_S:
default:
{
GGML_ASSERT(false);
@@ -11429,6 +11447,7 @@ static void ggml_compute_forward_get_rows(
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
+ case GGML_TYPE_IQ3_S:
{
ggml_compute_forward_get_rows_q(params, dst);
} break;
@@ -12129,6 +12148,7 @@ static void ggml_compute_forward_alibi(
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
+ case GGML_TYPE_IQ3_S:
case GGML_TYPE_Q8_K:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
@@ -12212,6 +12232,7 @@ static void ggml_compute_forward_clamp(
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
+ case GGML_TYPE_IQ3_S:
case GGML_TYPE_Q8_K:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
@@ -19463,6 +19484,7 @@ void ggml_quantize_init(enum ggml_type type) {
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ1_S: iq2xs_init_impl(type); break;
case GGML_TYPE_IQ3_XXS: iq3xs_init_impl(256); break;
+ case GGML_TYPE_IQ3_S: iq3xs_init_impl(512); break;
default: // nothing
break;
}
@@ -19737,6 +19759,15 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i
result = quantize_iq3_xxs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
GGML_ASSERT(result == row_size * nrows);
} break;
+ case GGML_TYPE_IQ3_S:
+ {
+ GGML_ASSERT(start % QK_K == 0);
+ GGML_ASSERT(start % n_per_row == 0);
+ size_t start_row = start / n_per_row;
+ size_t row_size = ggml_row_size(type, n_per_row);
+ result = quantize_iq3_s(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
+ GGML_ASSERT(result == row_size * nrows);
+ } break;
case GGML_TYPE_IQ1_S:
{
GGML_ASSERT(start % QK_K == 0);