vbatts/llama.cpp
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llama : use _impl suffix instead of _internal (#11060)

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ggml-ci
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Georgi Gerganov 2025-01-06 10:52:01 +02:00 • committed by GitHub
parent 46e3556e01
commit 5047dd3546
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2 changed files with 18 additions and 18 deletions
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20
src/llama-quant.cpp
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@ -22,7 +22,7 @@ static void zeros(std::ofstream & file, size_t n) {
}
}
struct quantize_state_internal {
struct quantize_state_impl {
const llama_model & model;
const llama_model_quantize_params * params;
@ -43,13 +43,13 @@ struct quantize_state_internal {
// used to figure out if a model shares tok_embd with the output weight
bool has_output = false;
quantize_state_internal(const llama_model & model, const llama_model_quantize_params * params)
quantize_state_impl(const llama_model & model, const llama_model_quantize_params * params)
: model(model)
, params(params)
{}
};
static void llama_tensor_dequantize_internal(
static void llama_tensor_dequantize_impl(
struct ggml_tensor * tensor, std::vector<no_init<float>> & output, std::vector<std::thread> & workers,
const size_t nelements, const int nthread
) {
@ -121,7 +121,7 @@ static void llama_tensor_dequantize_internal(
workers.clear();
}
static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) {
static ggml_type llama_tensor_get_type(quantize_state_impl & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) {
const std::string name = ggml_get_name(tensor);
// TODO: avoid hardcoded tensor names - use the TN_* constants
@ -410,7 +410,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
return new_type;
}
static size_t llama_tensor_quantize_internal(enum ggml_type new_type, const float * f32_data, void * new_data, const int64_t chunk_size, int64_t nrows, int64_t n_per_row, const float * imatrix, std::vector<std::thread> & workers, const int nthread) {
static size_t llama_tensor_quantize_impl(enum ggml_type new_type, const float * f32_data, void * new_data, const int64_t chunk_size, int64_t nrows, int64_t n_per_row, const float * imatrix, std::vector<std::thread> & workers, const int nthread) {
if (nthread < 2) {
// single-thread
size_t new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, imatrix);
@ -464,7 +464,7 @@ static size_t llama_tensor_quantize_internal(enum ggml_type new_type, const floa
return new_size;
}
static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
static void llama_model_quantize_impl(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
ggml_type default_type;
llama_ftype ftype = params->ftype;
@ -534,7 +534,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
llm_load_hparams(ml, model);
llm_load_stats (ml, model);
struct quantize_state_internal qs(model, params);
struct quantize_state_impl qs(model, params);
if (params->only_copy) {
ftype = model.ftype;
@ -837,7 +837,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
} else if (ggml_is_quantized(tensor->type) && !params->allow_requantize) {
throw std::runtime_error(format("requantizing from type %s is disabled", ggml_type_name(tensor->type)));
} else {
llama_tensor_dequantize_internal(tensor, f32_conv_buf, workers, nelements, nthread);
llama_tensor_dequantize_impl(tensor, f32_conv_buf, workers, nelements, nthread);
f32_data = (float *) f32_conv_buf.data();
}
@ -866,7 +866,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
void * new_data_03 = (char *)new_data + ggml_row_size(new_type, n_per_row) * i03 * nrows;
const float * imatrix_03 = imatrix ? imatrix + i03 * n_per_row : nullptr;
new_size += llama_tensor_quantize_internal(new_type, f32_data_03, new_data_03, chunk_size, nrows, n_per_row, imatrix_03, workers, nthread_use);
new_size += llama_tensor_quantize_impl(new_type, f32_data_03, new_data_03, chunk_size, nrows, n_per_row, imatrix_03, workers, nthread_use);
}
LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB\n", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0);
}
@ -919,7 +919,7 @@ uint32_t llama_model_quantize(
const char * fname_out,
const llama_model_quantize_params * params) {
try {
llama_model_quantize_internal(fname_inp, fname_out, params);
llama_model_quantize_impl(fname_inp, fname_out, params);
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: failed to quantize: %s\n", __func__, err.what());
return 1;