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llama : remove shards weight file support (#2000)

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* Remove multiple shards

* Remove multiple file loaders

* Remove llama_load_tensor_shard class

* Simplify load logic

* Remove dead code guess_n_parts function

* Remove vocab_only from constructor of llama_model_loader

* Remove alignment_prevents_mmap which is not more needed.

* Remove useless check
This commit is contained in:
Howard Su
2023-06-28 10:13:02 -07:00
committed by GitHub
parent 7f9753fa12
commit b922bc351b
1 changed files with 35 additions and 198 deletions
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233
llama.cpp
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@@ -364,96 +364,14 @@ static size_t llama_calc_tensor_size(const std::vector<uint32_t> & ne, enum ggml
return size / ggml_blck_size(type);
}
struct llama_load_tensor_shard {
std::vector<uint32_t> ne;
size_t size;
enum ggml_type type;
size_t file_idx;
size_t file_off;
void calc_size() {
size = llama_calc_tensor_size(ne, type);
}
};
enum llama_split_type {
SPLIT_NONE,
SPLIT_BY_COLUMNS,
SPLIT_BY_ROWS
};
struct llama_load_tensor {
std::vector<llama_load_tensor_shard> shards;
std::string name;
enum ggml_type type = GGML_TYPE_F32;
llama_split_type split_type = SPLIT_NONE;
std::vector<uint32_t> ne;
size_t file_off;
size_t size;
struct ggml_tensor * ggml_tensor = NULL;
uint8_t * data;
llama_load_tensor(const std::string & name) : name(name) {}
void calc_all() {
calc_type();
calc_split_type();
calc_ne();
calc_size();
}
void calc_type() {
const auto & first_shard = shards.at(0);
for (const auto & shard : shards) {
if (shard.type != first_shard.type) {
throw std::runtime_error(format("inconsistent tensor shard type in '%s'", name.c_str()));
}
}
type = first_shard.type;
}
void calc_split_type() {
if (shards.at(0).ne.size() == 1 || // 1D tensors are just duplicated in every file
shards.size() == 1) { // only one file?
split_type = SPLIT_NONE;
} else if (name.find("tok_embeddings.") == 0 ||
name.find(".attention.wo.weight") != std::string::npos ||
name.find(".feed_forward.w2.weight") != std::string::npos) {
split_type = SPLIT_BY_COLUMNS;
} else {
split_type = SPLIT_BY_ROWS;
}
}
void calc_ne() {
const auto & first_shard = shards.at(0);
for (const auto & shard : shards) {
if (shard.ne != first_shard.ne) {
throw std::runtime_error(format("inconsistent tensor shard shape in '%s': first was %s, other was %s",
name.c_str(), llama_format_tensor_shape(first_shard.ne).c_str(), llama_format_tensor_shape(shard.ne).c_str()));
}
}
ne = first_shard.ne;
LLAMA_ASSERT(shards.size() <= UINT32_MAX);
uint32_t n_shards = (uint32_t) shards.size();
switch (split_type) {
case SPLIT_NONE:
ne = first_shard.ne;
break;
case SPLIT_BY_COLUMNS:
ne = {checked_mul<uint32_t>(first_shard.ne[0], n_shards),
first_shard.ne[1]};
break;
case SPLIT_BY_ROWS:
ne = {first_shard.ne[0],
checked_mul<uint32_t>(first_shard.ne[1], n_shards)};
break;
}
}
void calc_size() {
size = llama_calc_tensor_size(ne, type);
}
};
struct llama_load_tensors_map {
@@ -476,13 +394,13 @@ struct llama_file_loader {
llama_hparams hparams;
llama_vocab vocab;
llama_file_loader(const char * fname, size_t file_idx, llama_load_tensors_map & tensors_map)
llama_file_loader(const char * fname, llama_load_tensors_map & tensors_map)
: file(fname, "rb") {
fprintf(stderr, "llama.cpp: loading model from %s\n", fname);
read_magic();
read_hparams();
read_vocab();
read_tensor_metadata(file_idx, tensors_map);
read_tensor_metadata(tensors_map);
}
void read_magic() {
uint32_t magic = file.read_u32();
@@ -539,19 +457,19 @@ struct llama_file_loader {
tok_score.score = score;
}
}
void read_tensor_metadata(size_t file_idx, llama_load_tensors_map & tensors_map) {
void read_tensor_metadata(llama_load_tensors_map & tensors_map) {
while (file.tell() < file.size) {
llama_load_tensor_shard shard;
llama_load_tensor tensor;
uint32_t n_dims = file.read_u32();
uint32_t name_len = file.read_u32();
shard.type = (enum ggml_type) file.read_u32();
shard.ne.resize(n_dims);
file.read_raw(shard.ne.data(), sizeof(shard.ne[0]) * n_dims);
tensor.type = (enum ggml_type) file.read_u32();
tensor.ne.resize(n_dims);
file.read_raw(tensor.ne.data(), sizeof(tensor.ne[0]) * n_dims);
std::string name = file.read_string(name_len);
if (n_dims < 1 || n_dims > 2) {
throw std::runtime_error(format("llama.cpp: tensor '%s' should not be %u-dimensional", name.c_str(), n_dims));
}
switch (shard.type) {
switch (tensor.type) {
case GGML_TYPE_F32:
case GGML_TYPE_F16:
case GGML_TYPE_Q4_0:
@@ -566,30 +484,20 @@ struct llama_file_loader {
case GGML_TYPE_Q6_K:
break;
default: {
throw std::runtime_error(format("unrecognized tensor type %u\n", shard.type));
throw std::runtime_error(format("unrecognized tensor type %u\n", tensor.type));
}
}
if (file_version >= LLAMA_FILE_VERSION_GGJT_V1) {
// skip to the next multiple of 32 bytes
file.seek(-static_cast<ptrdiff_t>(file.tell()) & 31, SEEK_CUR);
}
shard.file_idx = file_idx;
shard.file_off = file.tell();
// skip to the next multiple of 32 bytes
file.seek(-static_cast<ptrdiff_t>(file.tell()) & 31, SEEK_CUR);
shard.calc_size();
file.seek(shard.size, SEEK_CUR);
tensor.file_off = file.tell();
tensor.name = name;
tensor.size = llama_calc_tensor_size(tensor.ne, tensor.type);
file.seek(tensor.size, SEEK_CUR);
auto it = tensors_map.name_to_idx.find(name);
size_t idx;
if (it != tensors_map.name_to_idx.end()) {
idx = it->second;
} else {
tensors_map.tensors.emplace_back(name);
idx = tensors_map.tensors.size() - 1;
tensors_map.name_to_idx.emplace(name, idx);
}
tensors_map.tensors.at(idx).shards.push_back(shard);
tensors_map.tensors.push_back(tensor);
tensors_map.name_to_idx[name] = tensors_map.tensors.size() - 1;
}
}
};
@@ -659,56 +567,19 @@ struct llama_file_saver {
};
struct llama_model_loader {
std::vector<std::unique_ptr<llama_file_loader>> file_loaders;
std::unique_ptr<llama_file_loader> file_loader;
llama_load_tensors_map tensors_map;
bool use_mmap;
size_t num_ggml_tensors_created = 0;
struct ggml_context * ggml_ctx = NULL;
std::unique_ptr<llama_mmap> mapping;
llama_model_loader(const std::string & fname_base, bool use_mmap, bool vocab_only) {
auto * first_file = new llama_file_loader(fname_base.c_str(), 0, tensors_map);
file_loaders.emplace_back(first_file);
uint32_t n_parts = vocab_only ? 1 : guess_n_parts();
for (uint32_t i = 1; i < n_parts; i++) {
std::string fname = fname_base + "." + std::to_string(i);
auto * ith_file = new llama_file_loader(fname.c_str(), i, tensors_map);
file_loaders.emplace_back(ith_file);
if (ith_file->hparams != first_file->hparams) {
throw std::runtime_error(format("llama.cpp: hparams inconsistent between files"));
}
}
llama_model_loader(const std::string & fname_base, bool use_mmap) {
file_loader = std::unique_ptr<llama_file_loader>(new llama_file_loader(fname_base.c_str(), tensors_map));
if (!llama_mmap::SUPPORTED) {
use_mmap = false;
}
if (use_mmap && alignment_prevents_mmap()) {
fprintf(stderr, "llama.cpp: can't use mmap because tensors are not aligned; convert to new format to avoid this\n");
use_mmap = false;
}
this->use_mmap = use_mmap;
for (llama_load_tensor & lt : tensors_map.tensors) {
lt.calc_all();
}
}
bool alignment_prevents_mmap() {
for (const llama_load_tensor & lt : tensors_map.tensors) {
for (const llama_load_tensor_shard & shard : lt.shards) {
if (shard.file_off & 3) {
return true;
}
}
}
return false;
}
uint32_t guess_n_parts() const {
auto it = tensors_map.name_to_idx.find("tok_embeddings.weight");
if (it == tensors_map.name_to_idx.end()) {
throw std::runtime_error(std::string("missing tok_embeddings.weight"));
}
const llama_load_tensor & lt = tensors_map.tensors.at(it->second);
return file_loaders.at(0)->hparams.n_embd / lt.shards.at(0).ne.at(0);
}
void calc_sizes(size_t * ctx_size_p, size_t * mmapped_size_p) const {
@@ -774,7 +645,7 @@ struct llama_model_loader {
}
if (use_mmap) {
mapping.reset(new llama_mmap(&file_loaders.at(0)->file, prefetch_size, ggml_is_numa()));
mapping.reset(new llama_mmap(&file_loader->file, prefetch_size, ggml_is_numa()));
if (lmlock) {
lmlock->init(mapping->addr);
}
@@ -830,45 +701,13 @@ struct llama_model_loader {
void load_data_for(llama_load_tensor & lt) {
if (use_mmap) {
LLAMA_ASSERT(lt.shards.size() == 1);
lt.data = (uint8_t *) mapping->addr + lt.shards.at(0).file_off;
} else if (lt.split_type == SPLIT_NONE) {
llama_file & file = file_loaders.at(lt.shards.at(0).file_idx)->file;
file.seek(lt.shards.at(0).file_off, SEEK_SET);
lt.data = (uint8_t *) mapping->addr + lt.file_off;
} else {
llama_file & file = file_loader->file;
file.seek(lt.file_off, SEEK_SET);
file.read_raw(lt.data, lt.size);
} else if (lt.split_type == SPLIT_BY_ROWS) {
size_t offset = 0;
for (llama_load_tensor_shard & shard : lt.shards) {
llama_file & file = file_loaders.at(shard.file_idx)->file;
file.seek(shard.file_off, SEEK_SET);
file.read_raw(lt.data + offset, shard.size);
offset += shard.size;
}
LLAMA_ASSERT(offset == lt.size);
} else if (lt.split_type == SPLIT_BY_COLUMNS) {
// Let's load the data into temporary buffers to ensure the OS performs large loads.
std::vector<llama_buffer> tmp_bufs(lt.shards.size());
for (size_t i = 0; i < lt.shards.size(); i++) {
llama_load_tensor_shard & shard = lt.shards.at(i);
llama_file & file = file_loaders.at(shard.file_idx)->file;
file.seek(shard.file_off, SEEK_SET);
tmp_bufs.at(i).resize(shard.size);
file.read_raw(tmp_bufs.at(i).addr, shard.size);
}
// Then reshape.
size_t num_rows = lt.ne.at(1);
size_t per_shard_row_size = lt.shards.at(0).size / num_rows;
size_t out_offset = 0;
for (size_t row = 0; row < num_rows; row++) {
for (llama_buffer & tmp_buf : tmp_bufs) {
memcpy(lt.data + out_offset,
tmp_buf.addr + row * per_shard_row_size,
per_shard_row_size);
out_offset += per_shard_row_size;
}
}
LLAMA_ASSERT(out_offset == lt.size);
}
if (0) {
print_checksum(lt);
}
@@ -1067,12 +906,12 @@ static void llama_model_load_internal(
model.t_start_us = ggml_time_us();
std::unique_ptr<llama_model_loader> ml(new llama_model_loader(fname, use_mmap, vocab_only));
std::unique_ptr<llama_model_loader> ml(new llama_model_loader(fname, use_mmap));
vocab = std::move(ml->file_loaders.at(0)->vocab);
model.hparams = ml->file_loaders.at(0)->hparams;
vocab = std::move(ml->file_loader->vocab);
model.hparams = ml->file_loader->hparams;
model.n_gpu_layers = n_gpu_layers;
llama_file_version file_version = ml->file_loaders.at(0)->file_version;
llama_file_version file_version = ml->file_loader->file_version;
auto & hparams = model.hparams;
{
@@ -1106,7 +945,6 @@ static void llama_model_load_internal(
fprintf(stderr, "%s: n_rot = %u\n", __func__, hparams.n_rot);
fprintf(stderr, "%s: ftype = %u (%s)\n", __func__, hparams.ftype, llama_ftype_name(hparams.ftype));
fprintf(stderr, "%s: n_ff = %u\n", __func__, n_ff);
fprintf(stderr, "%s: n_parts = %zu\n", __func__, ml->file_loaders.size());
fprintf(stderr, "%s: model size = %s\n", __func__, llama_model_type_name(model.type));
}
@@ -2461,9 +2299,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
nthread = std::thread::hardware_concurrency();
}
std::unique_ptr<llama_model_loader> model_loader(new llama_model_loader(fname_inp, /*use_mmap*/ false,
/*vocab_only*/ false));
llama_file_saver file_saver(fname_out.c_str(), model_loader->file_loaders.at(0).get(), params->ftype);
std::unique_ptr<llama_model_loader> model_loader(new llama_model_loader(fname_inp, /*use_mmap*/ false));
llama_file_saver file_saver(fname_out.c_str(), model_loader->file_loader.get(), params->ftype);
#ifdef GGML_USE_K_QUANTS
int n_attention_wv = 0;
@@ -2897,7 +2734,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
llama_buffer base_buf;
if (path_base_model) {
fprintf(stderr, "%s: loading base model from '%s'\n", __func__, path_base_model);
model_loader.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*vocab_only*/ false));
model_loader.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true));
size_t ctx_size;
size_t mmapped_size;
@@ -2915,7 +2752,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
// maybe this should in llama_model_loader
if (model_loader->use_mmap) {
model_loader->mapping.reset(new llama_mmap(&model_loader->file_loaders.at(0)->file, /* prefetch */ 0, ggml_is_numa()));
model_loader->mapping.reset(new llama_mmap(&model_loader->file_loader->file, /* prefetch */ 0, ggml_is_numa()));
}
}