ikawrakow/ik_llama.cpp
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Q6_0_R4 (#122)

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* Adding q6_0_r4

We get PP-512(LLaMA-3.1-8B) = 257 t/s on a Ryzen-7950X.

* q6_0_r4: NEON

We get PP-512(LLaMA-3.1-8B) = 95 t/s on M2-Max.
In terms of ops, q6_0_r4 is identical to q5_0_r4
except for loading the high bits being
vld1q_u8_x2 instead of vld1q_u8. It is strange that
this can make a 5% difference in performance, especially
considering that this is amortized (re-used) over 8 columns
in the right matrix. Or am I running out of vector registers?

* Fix AVX2

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow
2024-12-03 14:48:26 +01:00
committed by GitHub
parent c5bf589367
commit f1f4eb988f
8 changed files with 334 additions and 2 deletions
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10
src/llama.cpp
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@@ -3852,6 +3852,7 @@ struct llama_model_loader {
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_Q6_0_R4: ftype = LLAMA_FTYPE_MOSTLY_Q6_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;
@@ -4560,6 +4561,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
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_Q6_0_R4: return "Q6_0_R4 - 6.5 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";
@@ -15783,6 +15785,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
else if (new_type == GGML_TYPE_Q5_0_R4) {
new_type = GGML_TYPE_Q5_0;
}
else if (new_type == GGML_TYPE_Q6_0_R4) {
new_type = GGML_TYPE_Q6_0;
}
else if (new_type == GGML_TYPE_Q8_0_R4) {
new_type = GGML_TYPE_Q8_0;
}
@@ -16180,6 +16185,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
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_Q6_0_R4: default_type = GGML_TYPE_Q6_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;
@@ -16546,8 +16552,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_Q4_0;
else chunk_size_multiplier = 4;
}
else if (new_type == GGML_TYPE_Q5_0_R4) {
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_Q5_0;
else if (new_type == GGML_TYPE_Q6_0_R4) {
if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_Q6_0;
else chunk_size_multiplier = 4;
}
else if (new_type == GGML_TYPE_Q8_0_R4) {