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quantize: Handle user-defined quantization levels for additional tensors (#12511)

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* Add llama_model_quantize_params parameters

* Add new quantize parameters parsing and validation

* Update usage

* Add new parameters defaults

* Add new quantization parameters logic

* Add llama_model_quantize_params parameters

* Add new quantize parameters parsing and validation

* Update usage

* Add new parameters defaults

* Add new quantization parameters logic

* Minor refactoring as per the contributors' coding guidelines

* Update descriptions to match existing style

* Add llama_model_quantize_params parameters

* Add new quantize parameters parsing and validation

* Update usage

* Add new parameters defaults

* Add new quantization parameters logic

* Minor refactoring as per the contributors' guidelines

* Implement general --tensor-type instead of tensor-specific command option

* Fix implied type bug

* Restore missing #includes

* Add regex capability for tensor selection

* Refactor function name and update ALLOWED_TENSOR_TYPE

* Add missing #include

* Handle edge case when tensor name is cls.output

* Minor logging improvement
This commit is contained in:
Ed Addario
2025-04-13 19:29:28 +01:00
committed by GitHub
parent bc091a4dc5
commit 71e90e8813
3 changed files with 155 additions and 20 deletions
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35
src/llama-quant.cpp
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@ -10,6 +10,7 @@
#include <cinttypes>
#include <fstream>
#include <mutex>
#include <regex>
#include <thread>
#include <unordered_map>
@ -47,8 +48,14 @@ struct quantize_state_impl {
{}
};
// changes to this struct must be replicated in quantize.cpp
struct tensor_quantization {
std::string name;
ggml_type quant = GGML_TYPE_COUNT;
};
static void llama_tensor_dequantize_impl(
struct ggml_tensor * tensor, std::vector<no_init<float>> & output, std::vector<std::thread> & workers,
ggml_tensor * tensor, std::vector<no_init<float>> & output, std::vector<std::thread> & workers,
const size_t nelements, const int nthread
) {
if (output.size() < nelements) {
@ -536,7 +543,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
model.load_hparams(ml);
model.load_stats (ml);
struct quantize_state_impl qs(model, params);
quantize_state_impl qs(model, params);
if (params->only_copy) {
ftype = ml.ftype;
@ -661,7 +668,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
// populate the original tensors so we get an initial meta data
for (const auto * it : tensors) {
uint16_t i_split = params->keep_split ? it->idx : 0;
struct ggml_tensor * tensor = it->tensor;
ggml_tensor * tensor = it->tensor;
if (!ctx_outs[i_split]) {
ctx_outs[i_split].reset(gguf_init_empty());
}
@ -710,7 +717,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
new_ofstream(0);
for (const auto * it : tensors) {
const auto & weight = *it;
struct ggml_tensor * tensor = weight.tensor;
ggml_tensor * tensor = weight.tensor;
if (weight.idx != cur_split && params->keep_split) {
close_ofstream();
new_ofstream(weight.idx);
@ -776,7 +783,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
// do not quantize relative position bias (T5)
quantize &= name.find("attn_rel_b.weight") == std::string::npos;
enum ggml_type new_type;
ggml_type new_type;
void * new_data;
size_t new_size;
@ -786,6 +793,19 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
// get more optimal quantization type based on the tensor shape, layer, etc.
if (!params->pure && ggml_is_quantized(default_type)) {
new_type = llama_tensor_get_type(qs, new_type, tensor, ftype);
// unless the user specifies a type
if (params->tensor_types) {
const std::vector<tensor_quantization> & tensor_types = *static_cast<const std::vector<tensor_quantization> *>(params->tensor_types);
for (const auto & [tname, qtype] : tensor_types) {
if (std::regex pattern(tname); std::regex_search(tensor->name, pattern)) {
if (qtype != new_type) {
LLAMA_LOG_DEBUG("(overriding %s -> %s), ", ggml_type_name(new_type), ggml_type_name(qtype));
}
new_type = qtype;
break;
}
}
}
}
if (params->token_embedding_type < GGML_TYPE_COUNT && strcmp(tensor->name, "token_embd.weight") == 0) {
new_type = params->token_embedding_type;
@ -910,8 +930,8 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
// interface implementation
//
struct llama_model_quantize_params llama_model_quantize_default_params() {
struct llama_model_quantize_params result = {
llama_model_quantize_params llama_model_quantize_default_params() {
llama_model_quantize_params result = {
/*.nthread =*/ 0,
/*.ftype =*/ LLAMA_FTYPE_MOSTLY_Q5_1,
/*.output_tensor_type =*/ GGML_TYPE_COUNT,
@ -923,6 +943,7 @@ struct llama_model_quantize_params llama_model_quantize_default_params() {
/*.keep_split =*/ false,
/*.imatrix =*/ nullptr,
/*.kv_overrides =*/ nullptr,
/*.tensor_type =*/ nullptr,
};
return result;