ikawrakow/ik_llama.cpp
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Q5_0_R4 (#121)

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* Adding q5_0_r4

We get PP-512(LLaMA-3.1-8B) = 256.7 t/s on a Ryzen-7950X.
We even get TG-128 improvement to 11.7 t/s from 11.1 t/s.

* q5_0_r4: NEON

We get PP-512(LLaMA-3.1-8B) = 99.6 t/s on M2-Max,
up from 71.0 t/s for Q5_0. The difference to mainline llama.cpp
is no longer funny: they get 26.5 t/s for Q5_0.

For TG, we are nor able to fully saturate memory bandwidth
and arrive at 22.1 t/s @ 8 threads. Mainline llama.cpp gets
20.6 t/s for Q5_0.

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow
2024-12-03 12:59:22 +01:00
committed by GitHub
parent ccec00939a
commit c5bf589367
10 changed files with 383 additions and 21 deletions
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16
src/llama.cpp
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@@ -3851,6 +3851,7 @@ struct llama_model_loader {
case GGML_TYPE_IQ4_NL: ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL; break;
case GGML_TYPE_IQ4_NL_X4:ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL_X4;break;
case GGML_TYPE_Q4_0_R4: ftype = LLAMA_FTYPE_MOSTLY_Q4_0_R4; break;
case GGML_TYPE_Q5_0_R4: ftype = LLAMA_FTYPE_MOSTLY_Q5_0_R4; break;
case GGML_TYPE_Q8_0_R4: ftype = LLAMA_FTYPE_MOSTLY_Q8_0_R4; break;
case GGML_TYPE_IQ4_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS; break;
case GGML_TYPE_IQ4_KS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_KS; break;
@@ -4558,6 +4559,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
case LLAMA_FTYPE_MOSTLY_IQ4_NL: return "IQ4_NL - 4.5 bpw";
case LLAMA_FTYPE_MOSTLY_IQ4_NL_X4:return "IQ4_NL_X4 - 4.5 bpw";
case LLAMA_FTYPE_MOSTLY_Q4_0_R4: return "Q4_0_R4 - 4.5 bpw";
case LLAMA_FTYPE_MOSTLY_Q5_0_R4: return "Q5_0_R4 - 5.5 bpw";
case LLAMA_FTYPE_MOSTLY_Q8_0_R4: return "Q8_0_R4 - 8.5 bpw";
case LLAMA_FTYPE_MOSTLY_IQ4_XS: return "IQ4_XS - 4.25 bpw";
case LLAMA_FTYPE_MOSTLY_IQ4_KS: return "IQ4_KS - 4.25 bpw";
@@ -15778,6 +15780,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
else if (new_type == GGML_TYPE_Q4_0_R4) {
new_type = GGML_TYPE_Q4_0;
}
else if (new_type == GGML_TYPE_Q5_0_R4) {
new_type = GGML_TYPE_Q5_0;
}
else if (new_type == GGML_TYPE_Q8_0_R4) {
new_type = GGML_TYPE_Q8_0;
}
@@ -16174,6 +16179,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
case LLAMA_FTYPE_MOSTLY_IQ4_NL: default_type = GGML_TYPE_IQ4_NL; break;
case LLAMA_FTYPE_MOSTLY_IQ4_NL_X4:default_type = GGML_TYPE_IQ4_NL_X4;break;
case LLAMA_FTYPE_MOSTLY_Q4_0_R4: default_type = GGML_TYPE_Q4_0_R4; break;
case LLAMA_FTYPE_MOSTLY_Q5_0_R4: default_type = GGML_TYPE_Q5_0_R4; break;
case LLAMA_FTYPE_MOSTLY_Q8_0_R4: default_type = GGML_TYPE_Q8_0_R4; break;
case LLAMA_FTYPE_MOSTLY_IQ4_XS: default_type = GGML_TYPE_IQ4_XS; break;
case LLAMA_FTYPE_MOSTLY_IQ4_KS: default_type = GGML_TYPE_IQ4_KS; break;
@@ -16532,15 +16538,19 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
if (new_type == GGML_TYPE_Q4_0_8_8) chunk_size_multiplier = 8;
else if (new_type == GGML_TYPE_Q4_0_4_4 || new_type == GGML_TYPE_Q4_0_4_8) chunk_size_multiplier = 4;
}
if (new_type == GGML_TYPE_IQ4_NL_X4) {
else if (new_type == GGML_TYPE_IQ4_NL_X4) {
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_IQ4_NL;
else chunk_size_multiplier = 4;
}
if (new_type == GGML_TYPE_Q4_0_R4) {
else if (new_type == GGML_TYPE_Q4_0_R4) {
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_Q4_0;
else chunk_size_multiplier = 4;
}
if (new_type == GGML_TYPE_Q8_0_R4) {
else if (new_type == GGML_TYPE_Q5_0_R4) {
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_Q5_0;
else chunk_size_multiplier = 4;
}
else if (new_type == GGML_TYPE_Q8_0_R4) {
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_Q8_0;
else chunk_size_multiplier = 4;
}