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llama : replace gguf_file_saver with new gguf write API
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@ggerganov
ggerganov committed Aug 15, 2023
1 parent 35177d7 commit ee57cbb
Showing 1 changed file with 15 additions and 162 deletions.
177 changes: 15 additions & 162 deletions gguf-llama.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -695,172 +695,14 @@ struct gguf_file_loader {

tensor.name = name;
tensor.size = ggml_nbytes(cur);
tensor.ggml_tensor = cur;

tensors_map.tensors.push_back(tensor);
tensors_map.name_to_idx[name] = tensors_map.tensors.size() - 1;
}
}
};

struct gguf_file_saver {
// TODO
// this implementation now assumes that the data section is of the same length as the unquantized model.
// this is needed to write tensor metadata and weights in a single pass by seeking to appropriate positions in the file.
// this may not be true when we add quantization version and change ftype description (currently it's string according to the specs,
// but better to have it as uint32).
// we need to calculate the delta in number of bytes written with a counter as a struct member.

gguf_context * ctx; // loaded gguf context (used to re-write the KV section (good enough for now))

gguf_file file;
size_t info_offset;
size_t tensor_offset;

gguf_file_saver(const char * fname, gguf_context * ctx) : ctx(ctx), file(fname, "wb") {
LLAMA_LOG_INFO("%s: saving model to %s\n", __func__, fname);

write_header();
write_kv();
}

void write_header() {
file.write_i32(GGUF_MAGIC);
file.write_i32(GGUF_VERSION);
file.write_i32(gguf_get_n_tensors(ctx));
file.write_i32(gguf_get_n_kv (ctx));
}

void write_kv_arr_i32(const std::string & key, enum gguf_type type, int i, int n_arr) {
std::vector<int32_t> data(n_arr);

for (int j = 0; j < n_arr; ++j) {
int32_t val = gguf_get_arr_i32(ctx, i, j);
data[j] = val;
}

file.write_arr<int32_t>(key, type, data);
}

void write_kv_arr_f32(const std::string & key, enum gguf_type type, int i, int n_arr) {
std::vector<float> data(n_arr);

for (int j = 0; j < n_arr; ++j) {
float val = gguf_get_arr_f32(ctx, i, j);
data[j] = val;
}

file.write_arr<float>(key, type, data);
}

void write_kv_arr_str(const std::string & key, enum gguf_type type, int i, int n_arr) {
std::vector<std::string> data(n_arr);

for (int j = 0; j < n_arr; ++j) {
std::string val = gguf_get_arr_str(ctx, i, j);
data[j] = val;
}

file.write_arr(key, type, data);
}

// re-write the key-value section from the loaded file
void write_kv() {
const int32_t n_kv = gguf_get_n_kv(ctx);
for (int i = 0; i < n_kv; ++i) {
const char * key = gguf_get_key(ctx, i);
LLAMA_LOG_INFO("%s: writing key '%s'\n", __func__, key);

if (strcmp(key, "general.quantization_version") == 0) {
file.write_val<uint32_t>("general.quantization_version", GGUF_TYPE_UINT32, GGML_QNT_VERSION);
} else {
const gguf_type vtype = gguf_get_kv_type(ctx, i);

switch (vtype) {
case GGUF_TYPE_BOOL: file.write_val<bool> (key, GGUF_TYPE_BOOL, gguf_get_val_bool(ctx, i)); break;
case GGUF_TYPE_FLOAT32: file.write_val<float> (key, GGUF_TYPE_FLOAT32, gguf_get_val_f32 (ctx, i)); break;
case GGUF_TYPE_INT16: file.write_val<int16_t> (key, GGUF_TYPE_INT16, gguf_get_val_i16 (ctx, i)); break;
case GGUF_TYPE_INT32: file.write_val<int32_t> (key, GGUF_TYPE_INT32, gguf_get_val_i32 (ctx, i)); break;
case GGUF_TYPE_INT8: file.write_val<int8_t> (key, GGUF_TYPE_INT8, gguf_get_val_i8 (ctx, i)); break;
case GGUF_TYPE_STRING: file.write_str (key, GGUF_TYPE_STRING, gguf_get_val_str (ctx, i)); break;
case GGUF_TYPE_UINT16: file.write_val<uint16_t>(key, GGUF_TYPE_UINT16, gguf_get_val_u16 (ctx, i)); break;
case GGUF_TYPE_UINT32: file.write_val<uint32_t>(key, GGUF_TYPE_UINT32, gguf_get_val_u32 (ctx, i)); break;
case GGUF_TYPE_UINT8: file.write_val<uint8_t> (key, GGUF_TYPE_UINT8, gguf_get_val_u8 (ctx, i)); break;
case GGUF_TYPE_ARRAY:
{
const gguf_type arr_type = gguf_get_arr_type(ctx, i);
const int n_arr = gguf_get_arr_n (ctx, i);

switch (arr_type) {
case GGUF_TYPE_FLOAT32: write_kv_arr_f32(key, arr_type, i, n_arr); break;
case GGUF_TYPE_INT32: write_kv_arr_i32(key, arr_type, i, n_arr); break;
case GGUF_TYPE_STRING: write_kv_arr_str(key, arr_type, i, n_arr); break;
default:
throw std::runtime_error(format("cannot recognize array type for key %s\n", key));
}
} break;
default:
throw std::runtime_error(format("cannot recognize value type for key %s\n", key));
}
}
}

info_offset = file.tell();

GGML_ASSERT(gguf_get_data_offset(ctx) >= info_offset);

const size_t count = gguf_get_data_offset(ctx) - info_offset;

file.write_zeros(count);
file.seek(info_offset, SEEK_SET);
}

size_t write_tensor_info(gguf_load_tensor & tensor, enum ggml_type type) {
size_t total_written = 0;
file.seek(info_offset, SEEK_SET);
total_written += file.write_str(tensor.name);

int32_t n_dims = tensor.ne.size();
total_written += file.write_i32(n_dims);
for (int32_t i = 0; i < n_dims; ++i) {
total_written += file.write_i32(tensor.ne[i]);
}

total_written += file.write_i32(type);
total_written += file.write_u64(tensor_offset);
info_offset += total_written; // position to write info of the next tensor

file.seek(0, SEEK_END);

return total_written;
}

void write_tensor(gguf_load_tensor & tensor, enum ggml_type new_type, const void * new_data, size_t new_size) {
switch (new_type) {
case GGML_TYPE_F32:
case GGML_TYPE_F16:
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q5_0:
case GGML_TYPE_Q5_1:
case GGML_TYPE_Q8_0:
case GGML_TYPE_Q2_K:
case GGML_TYPE_Q3_K:
case GGML_TYPE_Q4_K:
case GGML_TYPE_Q5_K:
case GGML_TYPE_Q6_K:
break;
default: GGML_ASSERT(false);
}

write_tensor_info(tensor, new_type);
file.write_raw(new_data, new_size);
size_t padded_size = GGML_PAD(new_size, GGUF_DEFAULT_ALIGNMENT); // TODO: handle custom alignment
size_t pad = padded_size - new_size;
file.write_zeros(pad);
tensor_offset += padded_size; // offset of the next tensor
}
};

struct llama_model_loader {
std::unique_ptr<gguf_file_loader> file_loader;
gguf_load_tensors_map tensors_map;
Expand Down Expand Up @@ -897,7 +739,6 @@ struct llama_model_loader {
tensor = ggml_new_tensor_1d(ggml_ctx, lt.type, lt.ne.at(0));
}
ggml_set_name(tensor, lt.name.c_str());
GGML_ASSERT(lt.ggml_tensor == NULL); // if this fails, we called get_tensor twice on the same tensor

if (backend != GGML_BACKEND_CPU) {
ggml_set_no_alloc(ggml_ctx, use_mmap);
Expand Down Expand Up @@ -936,6 +777,8 @@ struct llama_model_loader {
file.seek(lt.file_off, SEEK_SET);
file.read_raw(lt.data, lt.size);
}

lt.ggml_tensor->data = lt.data; // TODO: not great
}

void load_all_data(llama_progress_callback progress_callback, void * progress_callback_user_data, gguf_mlock * lmlock) {
Expand Down Expand Up @@ -3245,7 +3088,12 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
}

std::unique_ptr<llama_model_loader> model_loader(new llama_model_loader(fname_inp, /*use_mmap*/ false));
gguf_file_saver file_saver(fname_out.c_str(), model_loader->file_loader->gguf_ctx);

struct gguf_context * ctx_out = gguf_init_empty();

// copy the KV pairs from the input file
gguf_set_kv(ctx_out, model_loader->file_loader->gguf_ctx);
gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION);

#ifdef GGML_USE_K_QUANTS
int n_attention_wv = 0;
Expand Down Expand Up @@ -3437,12 +3285,17 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
}
total_size_org += tensor.size;
total_size_new += new_size;
file_saver.write_tensor(tensor, new_type, new_data, new_size);

gguf_add_tensor(ctx_out, tensor.ggml_tensor);
}

gguf_write_to_file(ctx_out, fname_out.c_str());
gguf_free(ctx_out);

LLAMA_LOG_INFO("%s: model size = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0);
LLAMA_LOG_INFO("%s: quant size = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0);

// print histogram for all tensors
{
int64_t sum_all = 0;
for (size_t i = 0; i < hist_all.size(); i++) {
Expand Down

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