Support all LLaMA models + change Q4_0 quantization storage
This commit is contained in:
Georgi Gerganov
parent
5f2f970d51
commit
007a8f6f45
5 changed files with 404 additions and 205 deletions
311
main.cpp
311
main.cpp
|
|
@ -11,6 +11,14 @@
|
|||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
// determine number of model parts based on the dimension
|
||||
static const std::map<int, int> LLAMA_N_PARTS = {
|
||||
{ 4096, 1 },
|
||||
{ 5120, 2 },
|
||||
{ 6656, 4 },
|
||||
{ 8192, 8 },
|
||||
};
|
||||
|
||||
// default hparams (LLaMA 7B)
|
||||
struct llama_hparams {
|
||||
int32_t n_vocab = 32000;
|
||||
|
|
@ -82,6 +90,7 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
|
|||
}
|
||||
|
||||
int n_ff = 0;
|
||||
int n_parts = 0;
|
||||
|
||||
// load hparams
|
||||
{
|
||||
|
|
@ -99,6 +108,7 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
|
|||
hparams.n_ctx = n_ctx;
|
||||
|
||||
n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
|
||||
n_parts = LLAMA_N_PARTS.at(hparams.n_embd);
|
||||
|
||||
printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
|
||||
printf("%s: n_ctx = %d\n", __func__, hparams.n_ctx);
|
||||
|
|
@ -109,6 +119,7 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
|
|||
printf("%s: n_rot = %d\n", __func__, hparams.n_rot);
|
||||
printf("%s: f16 = %d\n", __func__, hparams.f16);
|
||||
printf("%s: n_ff = %d\n", __func__, n_ff);
|
||||
printf("%s: n_parts = %d\n", __func__, n_parts);
|
||||
}
|
||||
|
||||
// load vocab
|
||||
|
|
@ -220,7 +231,7 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
|
|||
|
||||
model.layers.resize(n_layer);
|
||||
|
||||
model.tok_embeddings = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
|
||||
model.tok_embeddings = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
|
||||
|
||||
model.norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
||||
model.output = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
|
||||
|
|
@ -234,14 +245,14 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
|
|||
for (int i = 0; i < n_layer; ++i) {
|
||||
auto & layer = model.layers[i];
|
||||
|
||||
layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
||||
layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
||||
|
||||
layer.wq = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
|
||||
layer.wk = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
|
||||
layer.wv = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
|
||||
layer.wo = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
|
||||
layer.wq = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
|
||||
layer.wk = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
|
||||
layer.wv = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
|
||||
layer.wo = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
|
||||
|
||||
layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
||||
layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
||||
|
||||
layer.w1 = ggml_new_tensor_2d(ctx, wtype, n_embd, n_ff);
|
||||
layer.w2 = ggml_new_tensor_2d(ctx, wtype, n_ff, n_embd);
|
||||
|
|
@ -282,95 +293,209 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
|
|||
printf("%s: memory_size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
|
||||
}
|
||||
|
||||
// load weights
|
||||
{
|
||||
int n_tensors = 0;
|
||||
size_t total_size = 0;
|
||||
|
||||
printf("%s: ", __func__);
|
||||
|
||||
while (true) {
|
||||
int32_t n_dims;
|
||||
int32_t length;
|
||||
int32_t ftype;
|
||||
|
||||
fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
|
||||
fin.read(reinterpret_cast<char *>(&length), sizeof(length));
|
||||
fin.read(reinterpret_cast<char *>(&ftype), sizeof(ftype));
|
||||
|
||||
if (fin.eof()) {
|
||||
break;
|
||||
}
|
||||
|
||||
int32_t nelements = 1;
|
||||
int32_t ne[2] = { 1, 1 };
|
||||
for (int i = 0; i < n_dims; ++i) {
|
||||
fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
|
||||
nelements *= ne[i];
|
||||
}
|
||||
|
||||
std::string name(length, 0);
|
||||
fin.read(&name[0], length);
|
||||
|
||||
if (model.tensors.find(name.data()) == model.tensors.end()) {
|
||||
fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
|
||||
return false;
|
||||
}
|
||||
|
||||
auto tensor = model.tensors[name.data()];
|
||||
if (ggml_nelements(tensor) != nelements) {
|
||||
fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
|
||||
return false;
|
||||
}
|
||||
|
||||
if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
|
||||
fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
|
||||
__func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
|
||||
return false;
|
||||
}
|
||||
|
||||
if (0) {
|
||||
static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
|
||||
printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ftype_str[ftype], ggml_nbytes(tensor)/1024.0/1024.0, ggml_nbytes(tensor));
|
||||
}
|
||||
|
||||
size_t bpe = 0;
|
||||
|
||||
switch (ftype) {
|
||||
case 0: bpe = ggml_type_size(GGML_TYPE_F32); break;
|
||||
case 1: bpe = ggml_type_size(GGML_TYPE_F16); break;
|
||||
case 2: bpe = ggml_type_size(GGML_TYPE_Q4_0); assert(ne[0] % 64 == 0); break;
|
||||
case 3: bpe = ggml_type_size(GGML_TYPE_Q4_1); assert(ne[0] % 64 == 0); break;
|
||||
default:
|
||||
{
|
||||
fprintf(stderr, "%s: unknown ftype %d in model file\n", __func__, ftype);
|
||||
return false;
|
||||
}
|
||||
};
|
||||
|
||||
if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {
|
||||
fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
|
||||
__func__, name.data(), ggml_nbytes(tensor), nelements*bpe);
|
||||
return false;
|
||||
}
|
||||
|
||||
fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
|
||||
|
||||
//printf("%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
|
||||
total_size += ggml_nbytes(tensor);
|
||||
if (++n_tensors % 8 == 0) {
|
||||
printf(".");
|
||||
fflush(stdout);
|
||||
}
|
||||
}
|
||||
|
||||
printf(" done\n");
|
||||
|
||||
printf("%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors);
|
||||
}
|
||||
const size_t file_offset = fin.tellg();
|
||||
|
||||
fin.close();
|
||||
|
||||
std::vector<uint8_t> tmp;
|
||||
|
||||
for (int i = 0; i < n_parts; ++i) {
|
||||
const int part_id = i;
|
||||
//const int part_id = n_parts - i - 1;
|
||||
|
||||
std::string fname_part = fname;
|
||||
if (i > 0) {
|
||||
fname_part += "." + std::to_string(i);
|
||||
}
|
||||
|
||||
printf("%s: loading model part %d/%d from '%s'\n", __func__, i+1, n_parts, fname_part.c_str());
|
||||
|
||||
fin = std::ifstream(fname_part, std::ios::binary);
|
||||
fin.seekg(file_offset);
|
||||
|
||||
// load weights
|
||||
{
|
||||
int n_tensors = 0;
|
||||
size_t total_size = 0;
|
||||
|
||||
printf("%s: ", __func__);
|
||||
|
||||
while (true) {
|
||||
int32_t n_dims;
|
||||
int32_t length;
|
||||
int32_t ftype;
|
||||
|
||||
fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
|
||||
fin.read(reinterpret_cast<char *>(&length), sizeof(length));
|
||||
fin.read(reinterpret_cast<char *>(&ftype), sizeof(ftype));
|
||||
|
||||
if (fin.eof()) {
|
||||
break;
|
||||
}
|
||||
|
||||
int32_t nelements = 1;
|
||||
int32_t ne[2] = { 1, 1 };
|
||||
for (int i = 0; i < n_dims; ++i) {
|
||||
fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
|
||||
nelements *= ne[i];
|
||||
}
|
||||
|
||||
std::string name(length, 0);
|
||||
fin.read(&name[0], length);
|
||||
|
||||
if (model.tensors.find(name.data()) == model.tensors.end()) {
|
||||
fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
|
||||
return false;
|
||||
}
|
||||
|
||||
// split_type = 0: split by columns
|
||||
// split_type = 1: split by rows
|
||||
int split_type = 0;
|
||||
|
||||
// split_type = 0:
|
||||
// regex:
|
||||
// - tok_embeddings.*
|
||||
// - layers.*.attention.wo.weight
|
||||
// - layers.*.feed_forward.w2.weight
|
||||
|
||||
// split_type = 1:
|
||||
// regex:
|
||||
// - output.*
|
||||
// - layers.*.attention.wq.weight
|
||||
// - layers.*.attention.wk.weight
|
||||
// - layers.*.attention.wv.weight
|
||||
// - layers.*.feed_forward.w1.weight
|
||||
// - layers.*.feed_forward.w3.weight
|
||||
if (name.find("tok_embeddings") != std::string::npos) {
|
||||
split_type = 0;
|
||||
} else if (name.find("layers") != std::string::npos) {
|
||||
if (name.find("attention.wo.weight") != std::string::npos) {
|
||||
split_type = 0;
|
||||
} else if (name.find("feed_forward.w2.weight") != std::string::npos) {
|
||||
split_type = 0;
|
||||
} else {
|
||||
split_type = 1;
|
||||
}
|
||||
} else if (name.find("output") != std::string::npos) {
|
||||
split_type = 1;
|
||||
}
|
||||
|
||||
auto tensor = model.tensors[name.data()];
|
||||
|
||||
if (n_dims == 1) {
|
||||
if (ggml_nelements(tensor) != nelements) {
|
||||
fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
|
||||
return false;
|
||||
}
|
||||
} else {
|
||||
if (ggml_nelements(tensor)/n_parts != nelements) {
|
||||
fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
if (n_dims == 1) {
|
||||
if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
|
||||
fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
|
||||
__func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
|
||||
return false;
|
||||
}
|
||||
} else {
|
||||
if (split_type == 0) {
|
||||
if (tensor->ne[0]/n_parts != ne[0] || tensor->ne[1] != ne[1]) {
|
||||
fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
|
||||
__func__, name.data(), tensor->ne[0]/n_parts, tensor->ne[1], ne[0], ne[1]);
|
||||
return false;
|
||||
}
|
||||
} else {
|
||||
if (tensor->ne[0] != ne[0] || tensor->ne[1]/n_parts != ne[1]) {
|
||||
fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
|
||||
__func__, name.data(), tensor->ne[0], tensor->ne[1]/n_parts, ne[0], ne[1]);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (0) {
|
||||
static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
|
||||
printf("%24s - [%5d, %5d], type = %6s, split = %d\n", name.data(), ne[0], ne[1], ftype_str[ftype], split_type);
|
||||
}
|
||||
|
||||
size_t bpe = 0;
|
||||
|
||||
switch (ftype) {
|
||||
case 0: bpe = ggml_type_size(GGML_TYPE_F32); break;
|
||||
case 1: bpe = ggml_type_size(GGML_TYPE_F16); break;
|
||||
case 2: bpe = ggml_type_size(GGML_TYPE_Q4_0); assert(ne[0] % 64 == 0); break;
|
||||
case 3: bpe = ggml_type_size(GGML_TYPE_Q4_1); assert(ne[0] % 64 == 0); break;
|
||||
default:
|
||||
{
|
||||
fprintf(stderr, "%s: unknown ftype %d in model file\n", __func__, ftype);
|
||||
return false;
|
||||
}
|
||||
};
|
||||
|
||||
if (n_dims == 1 || n_parts == 1) {
|
||||
if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {
|
||||
fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
|
||||
__func__, name.data(), ggml_nbytes(tensor), nelements*bpe);
|
||||
return false;
|
||||
}
|
||||
|
||||
if (part_id == 0) {
|
||||
fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
|
||||
} else {
|
||||
fin.seekg(ggml_nbytes(tensor), std::ios::cur);
|
||||
}
|
||||
|
||||
total_size += ggml_nbytes(tensor);
|
||||
} else {
|
||||
if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)/n_parts) {
|
||||
fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
|
||||
__func__, name.data(), ggml_nbytes(tensor)/n_parts, nelements*bpe);
|
||||
return false;
|
||||
}
|
||||
|
||||
if (split_type == 0) {
|
||||
const int np0 = ne[0];
|
||||
|
||||
const size_t row_size = (tensor->ne[0]/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type);
|
||||
assert(row_size == tensor->nb[1]);
|
||||
|
||||
for (int i1 = 0; i1 < ne[1]; ++i1) {
|
||||
const size_t offset_row = i1*row_size;
|
||||
const size_t offset = offset_row + ((part_id*np0)/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type);
|
||||
fin.read(reinterpret_cast<char *>(tensor->data) + offset, row_size/n_parts);
|
||||
}
|
||||
} else {
|
||||
const int np1 = ne[1];
|
||||
|
||||
const size_t row_size = (tensor->ne[0]/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type);
|
||||
|
||||
for (int i1 = 0; i1 < ne[1]; ++i1) {
|
||||
const size_t offset_row = (i1 + part_id*np1)*row_size;
|
||||
fin.read(reinterpret_cast<char *>(tensor->data) + offset_row, row_size);
|
||||
}
|
||||
}
|
||||
|
||||
total_size += ggml_nbytes(tensor)/n_parts;
|
||||
}
|
||||
|
||||
//printf("%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
|
||||
if (++n_tensors % 8 == 0) {
|
||||
printf(".");
|
||||
fflush(stdout);
|
||||
}
|
||||
}
|
||||
|
||||
printf(" done\n");
|
||||
|
||||
printf("%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors);
|
||||
}
|
||||
|
||||
fin.close();
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue