ikawrakow/ik_llama.cpp
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Add ability to hide imatrix details in llama-quantize (#329)

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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow
2025-04-14 19:41:31 +02:00
committed by GitHub
parent 0f7aa11b6d
commit 9be8812727
1 changed files with 26 additions and 5 deletions
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31
examples/quantize/quantize.cpp
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@@ -142,11 +142,12 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp
//
[[noreturn]]
static void usage(const char * executable) {
printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights] [--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--attn-q-type] [--attn-k-type] [--attn-v-type] [--attn-qkv-type] [--attn-output-type] [--ffn-gate-type] [--ffn-down-type] [--ffn-up-type] [--keep-split] [--override-kv] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable);
printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--hide-imatrix] [--include-weights] [--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--attn-q-type] [--attn-k-type] [--attn-v-type] [--attn-qkv-type] [--attn-output-type] [--ffn-gate-type] [--ffn-down-type] [--ffn-up-type] [--keep-split] [--override-kv] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable);
printf(" --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n");
printf(" --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n");
printf(" --pure: Disable k-quant mixtures and quantize all tensors to the same type\n");
printf(" --imatrix file_name: use data in file_name as importance matrix for quant optimizations\n");
printf(" --hide-imatrix: do not store imatrix details in the quantized model\n");
printf(" --include-weights tensor_name: use importance matrix for this/these tensor(s)\n");
printf(" --exclude-weights tensor_name: use importance matrix for this/these tensor(s)\n");
printf(" --output-tensor-type ggml_type: use this ggml_type for the output.weight tensor.\n");
@@ -337,6 +338,8 @@ int main(int argc, char ** argv) {
std::vector<std::string> repack_patterns;
bool hide_imatrix = false;
for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) {
if (strcmp(argv[arg_idx], "--leave-output-tensor") == 0) {
params.quantize_output_tensor = false;
@@ -429,6 +432,8 @@ int main(int argc, char ** argv) {
} else {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--hide-imatrix") == 0) {
hide_imatrix = true;
} else if (strcmp(argv[arg_idx], "--include-weights") == 0) {
if (arg_idx < argc-1) {
included_weights.emplace_back(argv[++arg_idx]);
@@ -469,7 +474,11 @@ int main(int argc, char ** argv) {
llama_model_kv_override kvo;
std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_FILE);
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
strncpy(kvo.val_str, imatrix_file.c_str(), 127);
if (hide_imatrix) {
strncpy(kvo.val_str, "top_secret", 127);
} else {
strncpy(kvo.val_str, imatrix_file.c_str(), 127);
}
kvo.val_str[127] = '\0';
kv_overrides.emplace_back(std::move(kvo));
}
@@ -477,7 +486,11 @@ int main(int argc, char ** argv) {
llama_model_kv_override kvo;
std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_DATASET);
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
strncpy(kvo.val_str, imatrix_dataset.c_str(), 127);
if (hide_imatrix) {
strncpy(kvo.val_str, "top_secret", 127);
} else {
strncpy(kvo.val_str, imatrix_dataset.c_str(), 127);
}
kvo.val_str[127] = '\0';
kv_overrides.emplace_back(std::move(kvo));
}
@@ -486,7 +499,11 @@ int main(int argc, char ** argv) {
llama_model_kv_override kvo;
std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_N_ENTRIES);
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
kvo.val_i64 = imatrix_data.size();
if (hide_imatrix) {
kvo.val_i64 = 0;
} else {
kvo.val_i64 = imatrix_data.size();
}
kv_overrides.emplace_back(std::move(kvo));
}
@@ -494,7 +511,11 @@ int main(int argc, char ** argv) {
llama_model_kv_override kvo;
std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_N_CHUNKS);
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
kvo.val_i64 = m_last_call;
if (hide_imatrix) {
kvo.val_i64 = 0;
} else {
kvo.val_i64 = m_last_call;
}
kv_overrides.emplace_back(std::move(kvo));
}
}