mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-14 14:59:52 +00:00
loader: refactor tensor weights storage (#9935)
* loader: refactor tensor weights storage * use sorted map, sort weights by layer --------- Co-authored-by: slaren <slarengh@gmail.com>
This commit is contained in:
Zhenwei Jin
GitHub
parent
0a683e8088
commit
ab3d71f97f
123
src/llama.cpp
123
src/llama.cpp
@ -4271,20 +4271,34 @@ struct llama_model_loader {
|
||||
|
||||
ggml_tensor * tensor;
|
||||
|
||||
llama_tensor_weight(const llama_file * file, uint16_t idx, const char * name, const struct gguf_context * gguf_ctx, ggml_tensor * tensor) : idx(idx), tensor(tensor) {
|
||||
const int tensor_idx = gguf_find_tensor(gguf_ctx, name);
|
||||
llama_tensor_weight(const llama_file * file, uint16_t idx, const struct gguf_context * gguf_ctx, ggml_tensor * tensor) : idx(idx), tensor(tensor) {
|
||||
const int tensor_idx = gguf_find_tensor(gguf_ctx, ggml_get_name(tensor));
|
||||
if (tensor_idx < 0) {
|
||||
throw std::runtime_error(format("tensor '%s' not found in the model", name));
|
||||
throw std::runtime_error(format("tensor '%s' not found in the model", ggml_get_name(tensor)));
|
||||
}
|
||||
|
||||
offs = gguf_get_data_offset(gguf_ctx) + gguf_get_tensor_offset(gguf_ctx, tensor_idx);
|
||||
if (offs + ggml_nbytes(tensor) < offs || offs + ggml_nbytes(tensor) > file->size) {
|
||||
throw std::runtime_error(format("tensor '%s' data is not within the file bounds, model is corrupted or incomplete", name));
|
||||
throw std::runtime_error(format("tensor '%s' data is not within the file bounds, model is corrupted or incomplete", ggml_get_name(tensor)));
|
||||
}
|
||||
}
|
||||
};
|
||||
std::vector<llama_tensor_weight> weights;
|
||||
|
||||
// custom comparator to sort weights more nicely by layer
|
||||
struct weight_name_comparer {
|
||||
bool operator()(const std::string & a, const std::string & b) const {
|
||||
int a_layer = -1;
|
||||
int b_layer = -1;
|
||||
sscanf(a.c_str(), "blk.%d.", &a_layer);
|
||||
sscanf(b.c_str(), "blk.%d.", &b_layer);
|
||||
if (a_layer != b_layer) {
|
||||
return a_layer < b_layer;
|
||||
}
|
||||
return a < b;
|
||||
}
|
||||
};
|
||||
|
||||
std::map<std::string, struct llama_tensor_weight, weight_name_comparer> weights_map;
|
||||
std::unordered_map<std::string, struct llama_model_kv_override> kv_overrides;
|
||||
|
||||
struct gguf_context * meta = NULL;
|
||||
@ -4326,7 +4340,14 @@ struct llama_model_loader {
|
||||
// For subsidiary files, `meta` tensor data offset must not be used,
|
||||
// so we build a unified tensors index for weights.
|
||||
for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
|
||||
weights.emplace_back(files.back().get(), 0, cur->name, meta, cur);
|
||||
std::string tensor_name = std::string(cur->name);
|
||||
// make sure there is no duplicated tensor names
|
||||
if (weights_map.find(tensor_name) != weights_map.end()) {
|
||||
throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", ggml_get_name(cur)));
|
||||
}
|
||||
n_elements += ggml_nelements(cur);
|
||||
n_bytes += ggml_nbytes(cur);
|
||||
weights_map.emplace(tensor_name, llama_tensor_weight(files.back().get(), 0, meta, cur));
|
||||
}
|
||||
uint16_t n_split = 0;
|
||||
get_key(llm_kv(LLM_KV_SPLIT_COUNT), n_split, false);
|
||||
@ -4366,7 +4387,14 @@ struct llama_model_loader {
|
||||
|
||||
// Save tensors data offset info of the shard.
|
||||
for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
|
||||
weights.emplace_back(files.back().get(), idx, cur->name, ctx_gguf, cur);
|
||||
std::string tensor_name = std::string(cur->name);
|
||||
// make sure there is no duplicated tensor names
|
||||
if (weights_map.find(tensor_name) != weights_map.end()) {
|
||||
throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", ggml_get_name(cur)));
|
||||
}
|
||||
n_elements += ggml_nelements(cur);
|
||||
n_bytes += ggml_nbytes(cur);
|
||||
weights_map.emplace(tensor_name, llama_tensor_weight(files.back().get(), idx, ctx_gguf, cur));
|
||||
}
|
||||
|
||||
gguf_free(ctx_gguf);
|
||||
@ -4376,7 +4404,7 @@ struct llama_model_loader {
|
||||
|
||||
// sanity check
|
||||
{
|
||||
const int n_tensors_loaded = (int) weights.size();
|
||||
const int n_tensors_loaded = (int) weights_map.size();
|
||||
if (n_tensors != n_tensors_loaded) {
|
||||
throw std::runtime_error(format("corrupted model: %d tensors expected but %d found", n_tensors, n_tensors_loaded));
|
||||
}
|
||||
@ -4386,23 +4414,10 @@ struct llama_model_loader {
|
||||
}
|
||||
|
||||
n_kv = gguf_get_n_kv(meta);
|
||||
n_tensors = weights.size();
|
||||
n_tensors = weights_map.size();
|
||||
|
||||
fver = (enum llama_fver) gguf_get_version(meta);
|
||||
|
||||
std::set<std::string> tensor_names;
|
||||
for (auto & w : weights) {
|
||||
n_elements += ggml_nelements(w.tensor);
|
||||
n_bytes += ggml_nbytes(w.tensor);
|
||||
// make sure there is no duplicated tensor names
|
||||
const std::string name(w.tensor->name);
|
||||
auto found = tensor_names.find(name);
|
||||
if (found != tensor_names.end()) {
|
||||
throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", w.tensor->name));
|
||||
}
|
||||
tensor_names.insert(name);
|
||||
}
|
||||
|
||||
LLAMA_LOG_INFO("%s: loaded meta data with %d key-value pairs and %d tensors from %s (version %s)\n",
|
||||
__func__, n_kv, n_tensors, fname.c_str(), llama_file_version_name(fver));
|
||||
|
||||
@ -4414,8 +4429,10 @@ struct llama_model_loader {
|
||||
uint32_t n_type_max = 0;
|
||||
enum ggml_type type_max = GGML_TYPE_F32;
|
||||
|
||||
for (int i = 0; i < n_tensors; i++) {
|
||||
const ggml_tensor * tensor = weights.at(i).tensor;
|
||||
for (const auto & it : weights_map) {
|
||||
const llama_tensor_weight & w = it.second;
|
||||
const ggml_tensor * tensor = w.tensor;
|
||||
|
||||
enum ggml_type type = tensor->type;
|
||||
|
||||
n_type[type]++;
|
||||
@ -4426,8 +4443,8 @@ struct llama_model_loader {
|
||||
}
|
||||
|
||||
if (trace > 0) {
|
||||
const uint16_t sid = weights.at(i).idx;
|
||||
LLAMA_LOG_INFO("%s: - tensor %4d, split %2d: %32s %-8s [ %s ]\n", __func__, i, sid, ggml_get_name(tensor), ggml_type_name(type), llama_format_tensor_shape(tensor).c_str());
|
||||
const uint16_t sid = w.idx;
|
||||
LLAMA_LOG_INFO("%s: - tensor split %2d: %32s %-8s [ %s ]\n", __func__, sid, ggml_get_name(tensor), ggml_type_name(type), llama_format_tensor_shape(tensor).c_str());
|
||||
}
|
||||
}
|
||||
|
||||
@ -4691,21 +4708,13 @@ struct llama_model_loader {
|
||||
return llm_kv.arch;
|
||||
}
|
||||
|
||||
const char * get_tensor_name(int i) const {
|
||||
return weights.at(i).tensor->name;
|
||||
}
|
||||
|
||||
const llama_tensor_weight * get_weight(const char * name) const {
|
||||
for (const auto & weight : weights) {
|
||||
if (strcmp(name, weight.tensor->name) == 0) {
|
||||
return &weight;
|
||||
}
|
||||
}
|
||||
return nullptr;
|
||||
auto pos = weights_map.find(name);
|
||||
if (pos != weights_map.end()) {
|
||||
return &pos->second;
|
||||
}
|
||||
|
||||
const llama_tensor_weight * get_weight(int i) const {
|
||||
return get_weight(get_tensor_name(i));
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
const llama_tensor_weight & require_weight(const char * name) const {
|
||||
@ -4732,10 +4741,6 @@ struct llama_model_loader {
|
||||
return tensor;
|
||||
}
|
||||
|
||||
struct ggml_tensor * get_tensor_meta(int i) const {
|
||||
return get_tensor_meta(get_tensor_name(i));
|
||||
}
|
||||
|
||||
const struct ggml_tensor * check_tensor_dims(const std::string & name, const std::vector<int64_t> & ne, bool required) const {
|
||||
const struct ggml_tensor * cur = get_tensor_meta(name.c_str());
|
||||
|
||||
@ -4842,8 +4847,8 @@ struct llama_model_loader {
|
||||
}
|
||||
|
||||
// compute the total size of all tensors for progress reporting
|
||||
for (auto & w : weights) {
|
||||
size_data += ggml_nbytes(w.tensor);
|
||||
for (const auto & it : weights_map) {
|
||||
size_data += ggml_nbytes(it.second.tensor);
|
||||
}
|
||||
}
|
||||
|
||||
@ -18598,10 +18603,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
}
|
||||
}
|
||||
|
||||
for (int i = 0; i < ml.n_tensors; ++i) {
|
||||
const struct ggml_tensor * meta = ml.get_tensor_meta(i);
|
||||
for (const auto & it : ml.weights_map) {
|
||||
const struct ggml_tensor * tensor = it.second.tensor;
|
||||
|
||||
const std::string name = ggml_get_name(meta);
|
||||
const std::string name = ggml_get_name(tensor);
|
||||
|
||||
// TODO: avoid hardcoded tensor names - use the TN_* constants
|
||||
if (name.find("attn_v.weight") != std::string::npos ||
|
||||
@ -18639,20 +18644,22 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
std::vector<no_init<float>> f32_conv_buf;
|
||||
|
||||
uint16_t n_split = 1;
|
||||
const auto & weights_map = ml.weights_map;
|
||||
|
||||
// Assume split index is continuous
|
||||
if (params->keep_split) {
|
||||
for (int i = 0; i < ml.n_tensors; ++i) {
|
||||
n_split = std::max(uint16_t(ml.get_weight(i)->idx+1), n_split);
|
||||
for (const auto & it : weights_map) {
|
||||
n_split = std::max(uint16_t(it.second.idx + 1), n_split);
|
||||
}
|
||||
|
||||
}
|
||||
std::vector<gguf_context*> ctx_outs(n_split, NULL);
|
||||
ctx_outs[0] = ctx_out;
|
||||
|
||||
// populate the original tensors so we get an initial meta data
|
||||
for (int i = 0; i < ml.n_tensors; ++i) {
|
||||
auto weight = ml.get_weight(i);
|
||||
uint16_t i_split = params->keep_split ? weight->idx : 0;
|
||||
struct ggml_tensor * tensor = weight->tensor;
|
||||
for (const auto & it : weights_map) {
|
||||
uint16_t i_split = params->keep_split ? it.second.idx : 0;
|
||||
struct ggml_tensor * tensor = it.second.tensor;
|
||||
if (ctx_outs[i_split] == NULL) {
|
||||
ctx_outs[i_split] = gguf_init_empty();
|
||||
}
|
||||
@ -18699,12 +18706,12 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
|
||||
const auto tn = LLM_TN(model.arch);
|
||||
new_ofstream(0);
|
||||
for (int i = 0; i < ml.n_tensors; ++i) {
|
||||
auto weight = ml.get_weight(i);
|
||||
struct ggml_tensor * tensor = weight->tensor;
|
||||
if (weight->idx != cur_split && params->keep_split) {
|
||||
for (const auto & it : weights_map) {
|
||||
const auto & weight = it.second;
|
||||
struct ggml_tensor * tensor = weight.tensor;
|
||||
if (weight.idx != cur_split && params->keep_split) {
|
||||
close_ofstream();
|
||||
new_ofstream(weight->idx);
|
||||
new_ofstream(weight.idx);
|
||||
}
|
||||
|
||||
const std::string name = ggml_get_name(tensor);
|
||||
|
||||
Loading…
Reference in New Issue
Block a user