ikawrakow/ik_llama.cpp
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Adding IQ1_TN - 1.6875 bpw for TriLM ternary models (#44)

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* Adding iq1_tn - 1.6875 bpw for TriLM ternary models

* iq1_tn: NEON

* iq1_tn: faster NEON

* iq2_bn: improve performance on NEON

We now get TG-128 = 100 t/s for Bitnet-3B-1.58b!

* iq1_tn: improve AVX2

PP-512 goes to 533 t/s up from 455.
TG-128 @ 2 threads goes to 16.6 t/s up from 14.2.
However, we seem to have a bottleneck somewhere as
TG saturates at 8 threads.

* iq1_tn: improve Zen4

PP-512 goes to 485 t/s up from 352. With FA we get 545 t/s up from 380.
TG-128 @ 1 thread goes to 12.4 t/s up from 10.4.
However, we seem to have a bottleneck somewhere as
TG saturates at 8 threads.

* iq2_bn: improve on Zen4

We now get PP-512 = 614 t/s up from 542 t/s

* iq2_bn: improve AVX2 implementation

We now get PP-512 = 753 t/s up from 680 t/s.

* Remove unnecessary barrier in ggml_compute_forward_mul_mat

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow
2024-09-09 14:56:34 +03:00
committed by GitHub
parent f2ef628e4e
commit 4a5d5e207d
10 changed files with 304 additions and 148 deletions
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10
src/llama.cpp
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@@ -3788,6 +3788,7 @@ struct llama_model_loader {
case GGML_TYPE_IQ1_M: ftype = LLAMA_FTYPE_MOSTLY_IQ1_M; break;
case GGML_TYPE_IQ1_BN: ftype = LLAMA_FTYPE_MOSTLY_IQ1_BN; break;
case GGML_TYPE_IQ2_BN: ftype = LLAMA_FTYPE_MOSTLY_IQ2_BN; break;
case GGML_TYPE_IQ1_TN: ftype = LLAMA_FTYPE_MOSTLY_IQ1_TN; break;
case GGML_TYPE_IQ2_TN: ftype = LLAMA_FTYPE_MOSTLY_IQ2_TN; break;
case GGML_TYPE_IQ4_NL: ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL; break;
case GGML_TYPE_IQ4_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS; break;
@@ -4497,8 +4498,9 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
case LLAMA_FTYPE_MOSTLY_IQ5_K: return "IQ5_K - 5.5 bpw";
case LLAMA_FTYPE_MOSTLY_IQ6_K: return "IQ6_K - 6.6 bpw";
case LLAMA_FTYPE_MOSTLY_IQ1_BN: return "IQ1_BN - 1.625 bpw Bitnet";
case LLAMA_FTYPE_MOSTLY_IQ1_TN: return "IQ1_TN - 1.6875 bpw TriLM";
case LLAMA_FTYPE_MOSTLY_IQ2_BN: return "IQ2_BN - 2.00 bpw Bitnet";
case LLAMA_FTYPE_MOSTLY_IQ2_TN: return "IQT_BN - 2.06 bpw TriLM";
case LLAMA_FTYPE_MOSTLY_IQ2_TN: return "IQ2_TN - 2.06 bpw TriLM";
case LLAMA_FTYPE_MOSTLY_IQ3_S: return "IQ3_S - 3.4375 bpw";
case LLAMA_FTYPE_MOSTLY_IQ3_M: return "IQ3_S mix - 3.66 bpw";
case LLAMA_FTYPE_MOSTLY_Q4_0_4_4: return "Q4_0_4_4";
@@ -15644,7 +15646,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_BN || ftype == LLAMA_FTYPE_MOSTLY_IQ2_BN) {
new_type = GGML_TYPE_IQ4_NL;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_TN) {
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_TN || ftype == LLAMA_FTYPE_MOSTLY_IQ2_TN) {
new_type = GGML_TYPE_Q4_K;
}
else if (new_type == GGML_TYPE_Q4_0_4_4 || new_type == GGML_TYPE_Q4_0_4_8 ||
@@ -15856,7 +15858,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
new_type == GGML_TYPE_IQ3_XXS || new_type == GGML_TYPE_IQ1_S || new_type == GGML_TYPE_IQ3_S ||
new_type == GGML_TYPE_IQ1_M || new_type == GGML_TYPE_IQ4_K || new_type == GGML_TYPE_IQ2_K ||
new_type == GGML_TYPE_IQ5_K || new_type == GGML_TYPE_IQ3_K || new_type == GGML_TYPE_IQ2_TN ||
new_type == GGML_TYPE_IQ6_K) {
new_type == GGML_TYPE_IQ6_K || new_type == GGML_TYPE_IQ1_TN) {
int nx = tensor->ne[0];
int ny = tensor->ne[1];
if (nx % QK_K != 0) {
@@ -15881,6 +15883,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ1_TN:
case GGML_TYPE_IQ2_TN:
case GGML_TYPE_Q2_K:
case GGML_TYPE_Q3_K:
@@ -15991,6 +15994,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
case LLAMA_FTYPE_MOSTLY_IQ1_M: default_type = GGML_TYPE_IQ1_M; break;
case LLAMA_FTYPE_MOSTLY_IQ1_BN: default_type = GGML_TYPE_IQ1_BN; break;
case LLAMA_FTYPE_MOSTLY_IQ2_BN: default_type = GGML_TYPE_IQ2_BN; break;
case LLAMA_FTYPE_MOSTLY_IQ1_TN: default_type = GGML_TYPE_IQ1_TN; break;
case LLAMA_FTYPE_MOSTLY_IQ2_TN: default_type = GGML_TYPE_IQ2_TN; break;
case LLAMA_FTYPE_MOSTLY_IQ4_NL: default_type = GGML_TYPE_IQ4_NL; break;
case LLAMA_FTYPE_MOSTLY_IQ4_XS: default_type = GGML_TYPE_IQ4_XS; break;