vbatts/llama.cpp
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iq1_m: checking pure iq1_m quantization

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It is pretty bad: PPL(LLaMA-v2-7B) = 34 if we quantize output.weight
with Q4_K.
This commit is contained in:
Iwan Kawrakow 2024-03-25 10:57:31 +02:00
parent abc1d4f951
commit dff85a804b
3 changed files with 16 additions and 6 deletions
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6
ggml-quants.c
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@ -11968,7 +11968,7 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy
const int * kmap_q2xs = iq2_data[gindex].map;
const uint16_t * kneighbors_q2xs = iq2_data[gindex].neighbours;
GGML_ASSERT(quant_weights && "missing quantization weights");
//GGML_ASSERT(quant_weights && "missing quantization weights");
GGML_ASSERT(kgrid_q2xs && "forgot to call ggml_quantize_init()?");
GGML_ASSERT(kmap_q2xs && "forgot to call ggml_quantize_init()?");
GGML_ASSERT(kneighbors_q2xs && "forgot to call ggml_quantize_init()?");
@ -12006,8 +12006,12 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy
for (int ib = 0; ib < QK_K/block_size; ++ib) {
const float * xb = xbl + block_size*ib;
if (quant_weights) {
const float * qw = quant_weights + QK_K*ibl + block_size*ib;
for (int i = 0; i < block_size; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
} else {
for (int i = 0; i < block_size; ++i) weight[i] = xb[i]*xb[i];
}
float max = fabsf(xb[0]);
for (int i = 1; i < block_size; ++i) max = MAX(max, fabsf(xb[i]));
if (!max) {

4
ggml.c
View file

@ -20353,8 +20353,8 @@ bool ggml_quantize_requires_imatrix(enum ggml_type type) {
return
type == GGML_TYPE_IQ2_XXS ||
type == GGML_TYPE_IQ2_XS ||
type == GGML_TYPE_IQ1_S ||
type == GGML_TYPE_IQ1_M;
type == GGML_TYPE_IQ1_S;// ||
//type == GGML_TYPE_IQ1_M;
}
size_t ggml_quantize_chunk(

8
llama.cpp
View file

@ -12919,6 +12919,12 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
if (!params->pure && ggml_is_quantized(default_type)) {
new_type = llama_tensor_get_type(qs, new_type, tensor, ftype);
}
else if (params->token_embedding_type < GGML_TYPE_COUNT && strcmp(tensor->name, "token_embd.weight") == 0) {
new_type = params->token_embedding_type;
}
else if (params->output_tensor_type < GGML_TYPE_COUNT && strcmp(tensor->name, "output.weight") == 0) {
new_type = params->output_tensor_type;
}
// If we've decided to quantize to the same type the tensor is already
// in then there's nothing to do.
@ -12951,7 +12957,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
new_type == GGML_TYPE_IQ2_XS ||
new_type == GGML_TYPE_IQ2_S ||
new_type == GGML_TYPE_IQ1_S ||
new_type == GGML_TYPE_IQ1_M ||
(new_type == GGML_TYPE_IQ1_M && strcmp(tensor->name, "token_embd.weight") && strcmp(tensor->name, "output.weight")) ||
(new_type == GGML_TYPE_Q2_K && params->ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && strcmp(tensor->name, "token_embd.weight") != 0)) && !imatrix) {
LLAMA_LOG_ERROR("\n\n============================================================\n");
LLAMA_LOG_ERROR("Missing importance matrix for tensor %s in a very low-bit quantization\n", tensor->name);