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llama : de-shadow libllama [no ci]

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This commit is contained in:
Georgi Gerganov
2025-01-12 13:22:16 +02:00
parent 32e7b9dc99
commit 82caffa74e
13 changed files with 181 additions and 179 deletions
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src/llama-quant.cpp
+19 -20
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@@ -423,8 +423,7 @@ static size_t llama_tensor_quantize_impl(enum ggml_type new_type, const float *
int64_t counter = 0;
size_t new_size = 0;
bool valid = true;
auto compute = [&mutex, &counter, &new_size, &valid, new_type, f32_data, new_data, chunk_size,
nrows, n_per_row, imatrix]() {
auto compute = [&mutex, &counter, &new_size, &valid, new_type, f32_data, new_data, chunk_size, nrows, n_per_row, imatrix]() {
const int64_t nrows_per_chunk = chunk_size / n_per_row;
size_t local_size = 0;
while (true) {
@@ -437,6 +436,7 @@ static size_t llama_tensor_quantize_impl(enum ggml_type new_type, const float *
break;
}
lock.unlock();
const int64_t this_nrow = std::min(nrows - first_row, nrows_per_chunk);
size_t this_size = ggml_quantize_chunk(new_type, f32_data, new_data, first_row * n_per_row, this_nrow, n_per_row, imatrix);
local_size += this_size;
@@ -445,7 +445,7 @@ static size_t llama_tensor_quantize_impl(enum ggml_type new_type, const float *
const size_t row_size = ggml_row_size(new_type, n_per_row);
void * this_data = (char *) new_data + first_row * row_size;
if (!ggml_validate_row_data(new_type, this_data, this_size)) {
std::unique_lock<std::mutex> lock(mutex);
lock.lock();
valid = false;
break;
}
@@ -589,15 +589,15 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
}
// make a list of weights
std::vector<const llama_model_loader::llama_tensor_weight *> tensors;
tensors.reserve(ml.weights_map.size());
std::vector<const llama_model_loader::llama_tensor_weight *> tensor_weights;
tensor_weights.reserve(ml.weights_map.size());
for (const auto & it : ml.weights_map) {
tensors.push_back(&it.second);
tensor_weights.push_back(&it.second);
}
// keep_split requires that the weights are sorted by split index
if (params->keep_split) {
std::sort(tensors.begin(), tensors.end(), [](const llama_model_loader::llama_tensor_weight * a, const llama_model_loader::llama_tensor_weight * b) {
std::sort(tensor_weights.begin(), tensor_weights.end(), [](const llama_model_loader::llama_tensor_weight * a, const llama_model_loader::llama_tensor_weight * b) {
if (a->idx == b->idx) {
return a->offs < b->offs;
}
@@ -605,8 +605,8 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
});
}
for (const auto * it : tensors) {
const struct ggml_tensor * tensor = it->tensor;
for (const auto * tw : tensor_weights) {
const ggml_tensor * tensor = tw->tensor;
const std::string name = ggml_get_name(tensor);
@@ -650,17 +650,17 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
// Assume split index is continuous
if (params->keep_split) {
for (const auto * it : tensors) {
n_split = std::max(uint16_t(it->idx + 1), n_split);
for (const auto * tw : tensor_weights) {
n_split = std::max(uint16_t(tw->idx + 1), n_split);
}
}
std::vector<gguf_context_ptr> ctx_outs(n_split);
ctx_outs[0] = std::move(ctx_out);
// 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;
// populate the original tensor_weights so we get an initial meta data
for (const auto * tw : tensor_weights) {
uint16_t i_split = params->keep_split ? tw->idx : 0;
ggml_tensor * tensor = tw->tensor;
if (!ctx_outs[i_split]) {
ctx_outs[i_split].reset(gguf_init_empty());
}
@@ -707,12 +707,11 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
const auto tn = LLM_TN(model.arch);
new_ofstream(0);
for (const auto * it : tensors) {
const auto & weight = *it;
struct ggml_tensor * tensor = weight.tensor;
if (weight.idx != cur_split && params->keep_split) {
for (const auto * tw : tensor_weights) {
ggml_tensor * tensor = tw->tensor;
if (tw->idx != cur_split && params->keep_split) {
close_ofstream();
new_ofstream(weight.idx);
new_ofstream(tw->idx);
}
const std::string name = ggml_get_name(tensor);