#include "ggml/ggml.h"
|
#include "ggml/ggml-alloc.h"
|
#include "ggml/ggml-backend.h"
|
|
#ifdef GGML_USE_CUBLAS
|
#include "ggml-cuda.h"
|
#endif
|
|
#ifdef GGML_USE_METAL
|
#include "ggml-metal.h"
|
#endif
|
|
#include "common.h"
|
#include "common-ggml.h"
|
|
#include <cassert>
|
#include <cmath>
|
#include <cstdio>
|
#include <cstring>
|
#include <fstream>
|
#include <map>
|
#include <set>
|
#include <string>
|
#include <vector>
|
|
#if defined(_MSC_VER)
|
#pragma warning(disable: 4244 4267) // possible loss of data
|
#endif
|
|
#define GPT2_MAX_NODES 4096
|
|
static void ggml_log_callback_default(ggml_log_level level, const char * text, void * user_data) {
|
(void) level;
|
(void) user_data;
|
fputs(text, stderr);
|
fflush(stderr);
|
}
|
|
typedef int32_t gpt2_pos;
|
typedef int32_t gpt2_seq_id;
|
|
// default hparams (GPT-2 117M)
|
struct gpt2_hparams {
|
int32_t n_vocab = 50257;
|
int32_t n_ctx = 1024;
|
int32_t n_embd = 768;
|
int32_t n_head = 12;
|
int32_t n_layer = 12;
|
int32_t ftype = 1;
|
float eps = 1e-5f;
|
};
|
|
struct gpt2_layer {
|
// normalization
|
struct ggml_tensor * ln_1_g;
|
struct ggml_tensor * ln_1_b;
|
|
struct ggml_tensor * ln_2_g;
|
struct ggml_tensor * ln_2_b;
|
|
// attention
|
struct ggml_tensor * c_attn_attn_w;
|
struct ggml_tensor * c_attn_attn_b;
|
|
struct ggml_tensor * c_attn_proj_w;
|
struct ggml_tensor * c_attn_proj_b;
|
|
// mlp
|
struct ggml_tensor * c_mlp_fc_w;
|
struct ggml_tensor * c_mlp_fc_b;
|
|
struct ggml_tensor * c_mlp_proj_w;
|
struct ggml_tensor * c_mlp_proj_b;
|
};
|
|
struct gpt2_kv_cell {
|
gpt2_pos pos = -1;
|
gpt2_pos delta = 0;
|
|
std::set<gpt2_seq_id> seq_id;
|
|
bool has_seq_id(const gpt2_seq_id & id) const {
|
return seq_id.find(id) != seq_id.end();
|
}
|
};
|
|
struct gpt2_kv_cache {
|
// key + value memory
|
struct ggml_tensor * k;
|
struct ggml_tensor * v;
|
//
|
|
uint32_t head = 0;
|
uint32_t size = 0;
|
|
// computed before each graph build
|
uint32_t n = 0;
|
|
std::vector<gpt2_kv_cell> cells;
|
|
ggml_backend_buffer_t buffer;
|
};
|
|
struct gpt2_model {
|
gpt2_hparams hparams;
|
|
// normalization
|
struct ggml_tensor * ln_f_g;
|
struct ggml_tensor * ln_f_b;
|
|
struct ggml_tensor * wte; // position embedding
|
struct ggml_tensor * wpe; // token embedding
|
struct ggml_tensor * lm_head; // language model head
|
|
std::vector<gpt2_layer> layers;
|
|
gpt2_kv_cache kv_cache;
|
|
struct ggml_context * ctx;
|
|
ggml_backend_t backend = NULL;
|
|
ggml_backend_buffer_t buffer_w;
|
|
std::map<std::string, struct ggml_tensor *> tensors;
|
};
|
|
// Input data for gpt2_decode
|
// A gpt2_batch object can contain input about one or many sequences
|
// The provided arrays (i.e. token, embd, pos, etc.) must have size of n_tokens
|
//
|
// - token : the token ids of the input (used when embd is NULL)
|
// - embd : token embeddings (i.e. float vector of size n_embd) (used when token is NULL)
|
// - pos : the positions of the respective token in the sequence
|
// - seq_id : the sequence to which the respective token belongs
|
// - logits : if zero, the logits for the respective token will not be output
|
//
|
struct gpt2_batch {
|
int32_t n_tokens = -1;
|
|
gpt_vocab::id * token = {};
|
float * embd = {};
|
gpt2_pos * pos = {};
|
gpt2_seq_id * seq_id = {};
|
int8_t * logits = {};
|
};
|
|
// load the model's weights from a file
|
bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab & vocab, int n_ctx, int n_gpu_layers) {
|
printf("%s: loading model from '%s'\n", __func__, fname.c_str());
|
|
auto fin = std::ifstream(fname, std::ios::binary);
|
if (!fin) {
|
fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
|
return false;
|
}
|
|
// verify magic
|
{
|
uint32_t magic;
|
fin.read((char *) &magic, sizeof(magic));
|
if (magic != GGML_FILE_MAGIC) {
|
fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
|
return false;
|
}
|
}
|
|
// load hparams
|
{
|
auto & hparams = model.hparams;
|
|
fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
|
fin.read((char *) &hparams.n_ctx, sizeof(hparams.n_ctx));
|
fin.read((char *) &hparams.n_embd, sizeof(hparams.n_embd));
|
fin.read((char *) &hparams.n_head, sizeof(hparams.n_head));
|
fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
|
fin.read((char *) &hparams.ftype, sizeof(hparams.ftype));
|
|
const int32_t qntvr = hparams.ftype / GGML_QNT_VERSION_FACTOR;
|
|
printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
|
printf("%s: n_ctx = %d\n", __func__, hparams.n_ctx);
|
printf("%s: n_embd = %d\n", __func__, hparams.n_embd);
|
printf("%s: n_head = %d\n", __func__, hparams.n_head);
|
printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
|
printf("%s: ftype = %d\n", __func__, hparams.ftype);
|
printf("%s: qntvr = %d\n", __func__, qntvr);
|
|
hparams.ftype %= GGML_QNT_VERSION_FACTOR;
|
}
|
|
// load vocab
|
{
|
int32_t n_vocab = 0;
|
fin.read((char *) &n_vocab, sizeof(n_vocab));
|
|
if (n_vocab != model.hparams.n_vocab) {
|
fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
|
__func__, fname.c_str(), n_vocab, model.hparams.n_vocab);
|
return false;
|
}
|
|
std::string word;
|
std::vector<char> buf(128);
|
|
for (int i = 0; i < n_vocab; i++) {
|
uint32_t len;
|
fin.read((char *) &len, sizeof(len));
|
|
buf.resize(len);
|
fin.read((char *) buf.data(), len);
|
word.assign(buf.data(), len);
|
|
vocab.token_to_id[word] = i;
|
vocab.id_to_token[i] = word;
|
}
|
}
|
|
// for the big tensors, we have the option to store the data in 16-bit floats or quantized
|
// in order to save memory and also to speed up the computation
|
ggml_type wtype = ggml_ftype_to_ggml_type((ggml_ftype) (model.hparams.ftype));
|
if (wtype == GGML_TYPE_COUNT) {
|
fprintf(stderr, "%s: invalid model file '%s' (bad ftype value %d)\n",
|
__func__, fname.c_str(), model.hparams.ftype);
|
return false;
|
}
|
|
auto & ctx = model.ctx;
|
|
size_t buffer_size = 0;
|
|
{
|
const auto & hparams = model.hparams;
|
|
const int n_embd = hparams.n_embd;
|
const int n_layer = hparams.n_layer;
|
const int n_ctx = hparams.n_ctx;
|
const int n_vocab = hparams.n_vocab;
|
|
buffer_size += ggml_row_size(GGML_TYPE_F32, n_embd); // ln_f_g
|
buffer_size += ggml_row_size(GGML_TYPE_F32, n_embd); // ln_f_b
|
|
buffer_size += ggml_row_size(wtype, n_vocab*n_embd); // wte
|
buffer_size += ggml_row_size(GGML_TYPE_F32, n_ctx*n_embd); // wpe
|
buffer_size += ggml_row_size(wtype, n_vocab*n_embd); // lm_head
|
|
buffer_size += n_layer*(ggml_row_size(GGML_TYPE_F32, n_embd)); // ln_1_g
|
buffer_size += n_layer*(ggml_row_size(GGML_TYPE_F32, n_embd)); // ln_1_b
|
|
buffer_size += n_layer*(ggml_row_size(GGML_TYPE_F32, n_embd)); // ln_2_g
|
buffer_size += n_layer*(ggml_row_size(GGML_TYPE_F32, n_embd)); // ln_2_b
|
|
buffer_size += n_layer*(ggml_row_size(wtype, 3*n_embd*n_embd)); // c_attn_attn_w
|
buffer_size += n_layer*(ggml_row_size(GGML_TYPE_F32, 3*n_embd)); // c_attn_attn_b
|
|
buffer_size += n_layer*(ggml_row_size(wtype, n_embd*n_embd)); // c_attn_proj_w
|
buffer_size += n_layer*(ggml_row_size(GGML_TYPE_F32, n_embd)); // c_attn_proj_b
|
|
buffer_size += n_layer*(ggml_row_size(wtype, 4*n_embd*n_embd)); // c_mlp_fc_w
|
buffer_size += n_layer*(ggml_row_size(GGML_TYPE_F32, 4*n_embd)); // c_mlp_fc_b
|
|
buffer_size += n_layer*(ggml_row_size(wtype, 4*n_embd*n_embd)); // c_mlp_proj_w
|
buffer_size += n_layer*(ggml_row_size(GGML_TYPE_F32, 4*n_embd)); // c_mlp_proj_b
|
|
buffer_size += (6 + 12*n_layer)*128; // alignment overhead
|
|
printf("%s: ggml tensor size = %d bytes\n", __func__, (int) sizeof(ggml_tensor));
|
printf("%s: backend buffer size = %6.2f MB\n", __func__, buffer_size/(1024.0*1024.0));
|
}
|
|
// create the ggml context
|
{
|
size_t n_tensors = 2 + 6 + 12*model.hparams.n_layer;
|
struct ggml_init_params params = {
|
/*.mem_size =*/ ggml_tensor_overhead() * n_tensors,
|
/*.mem_buffer =*/ NULL,
|
/*.no_alloc =*/ true,
|
};
|
|
model.ctx = ggml_init(params);
|
if (!model.ctx) {
|
fprintf(stderr, "%s: ggml_init() failed\n", __func__);
|
return false;
|
}
|
}
|
|
// initialize the backend
|
#ifdef GGML_USE_CUBLAS
|
if (n_gpu_layers > 0) {
|
fprintf(stderr, "%s: using CUDA backend\n", __func__);
|
model.backend = ggml_backend_cuda_init(0);
|
if (!model.backend) {
|
fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
|
}
|
}
|
#endif
|
|
#ifdef GGML_USE_METAL
|
if (n_gpu_layers > 0) {
|
fprintf(stderr, "%s: using Metal backend\n", __func__);
|
ggml_backend_metal_log_set_callback(ggml_log_callback_default, nullptr);
|
model.backend = ggml_backend_metal_init();
|
if (!model.backend) {
|
fprintf(stderr, "%s: ggml_backend_metal_init() failed\n", __func__);
|
}
|
}
|
#endif
|
|
if (!model.backend) {
|
// fallback to CPU backend
|
fprintf(stderr, "%s: using CPU backend\n", __func__);
|
model.backend = ggml_backend_cpu_init();
|
}
|
|
if (!model.backend) {
|
fprintf(stderr, "%s: ggml_backend_cpu_init() failed\n", __func__);
|
return false;
|
}
|
|
// allocate weights buffer
|
model.buffer_w = ggml_backend_alloc_buffer(model.backend, buffer_size);
|
|
// prepare memory for the weights
|
{
|
const auto & hparams = model.hparams;
|
|
const int n_embd = hparams.n_embd;
|
const int n_layer = hparams.n_layer;
|
const int n_ctx = hparams.n_ctx;
|
const int n_vocab = hparams.n_vocab;
|
|
model.layers.resize(n_layer);
|
|
model.ln_f_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
model.ln_f_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
|
model.wte = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
|
model.wpe = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ctx);
|
model.lm_head = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
|
|
// map by name
|
model.tensors["model/ln_f/g"] = model.ln_f_g;
|
model.tensors["model/ln_f/b"] = model.ln_f_b;
|
|
model.tensors["model/wte"] = model.wte;
|
model.tensors["model/wpe"] = model.wpe;
|
model.tensors["model/lm_head"] = model.lm_head;
|
|
for (int i = 0; i < n_layer; ++i) {
|
auto & layer = model.layers[i];
|
|
layer.ln_1_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
layer.ln_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
|
layer.ln_2_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
layer.ln_2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
|
layer.c_attn_attn_w = ggml_new_tensor_2d(ctx, wtype, n_embd, 3*n_embd);
|
layer.c_attn_attn_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 3*n_embd);
|
|
layer.c_attn_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
|
layer.c_attn_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
|
layer.c_mlp_fc_w = ggml_new_tensor_2d(ctx, wtype, n_embd, 4*n_embd);
|
layer.c_mlp_fc_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_embd);
|
|
layer.c_mlp_proj_w = ggml_new_tensor_2d(ctx, wtype, 4*n_embd, n_embd);
|
layer.c_mlp_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
|
// map by name
|
model.tensors["model/h" + std::to_string(i) + "/ln_1/g"] = layer.ln_1_g;
|
model.tensors["model/h" + std::to_string(i) + "/ln_1/b"] = layer.ln_1_b;
|
|
model.tensors["model/h" + std::to_string(i) + "/ln_2/g"] = layer.ln_2_g;
|
model.tensors["model/h" + std::to_string(i) + "/ln_2/b"] = layer.ln_2_b;
|
|
model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/w"] = layer.c_attn_attn_w;
|
model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/b"] = layer.c_attn_attn_b;
|
|
model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/w"] = layer.c_attn_proj_w;
|
model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/b"] = layer.c_attn_proj_b;
|
|
model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/w"] = layer.c_mlp_fc_w;
|
model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/b"] = layer.c_mlp_fc_b;
|
|
model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/w"] = layer.c_mlp_proj_w;
|
model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/b"] = layer.c_mlp_proj_b;
|
}
|
}
|
|
// override the default training context with the user-provided
|
model.hparams.n_ctx = n_ctx;
|
|
// key + value memory
|
{
|
const auto & hparams = model.hparams;
|
|
const int n_embd = hparams.n_embd;
|
const int n_layer = hparams.n_layer;
|
const int n_ctx = hparams.n_ctx;
|
|
const int n_mem = n_layer*n_ctx;
|
const int n_elements = n_embd*n_mem;
|
|
model.kv_cache.k = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
|
model.kv_cache.v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
|
|
model.kv_cache.head = 0;
|
model.kv_cache.size = n_ctx;
|
|
model.kv_cache.cells.resize(n_ctx);
|
|
const size_t memory_size = ggml_nbytes(model.kv_cache.k) + ggml_nbytes(model.kv_cache.v);
|
|
printf("%s: memory size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
|
|
// create a backend buffer (can be in host or device memory)
|
model.kv_cache.buffer = ggml_backend_alloc_buffer(model.backend, memory_size + 256);
|
|
// allocate the tensors into the backend buffer
|
{
|
ggml_allocr * alloc = ggml_allocr_new_from_buffer(model.kv_cache.buffer);
|
|
// this updates the pointers in the tensors to point to the correct location in the buffer
|
// this is necessary since the ggml_context is .no_alloc == true
|
// note that the buffer can actually be a device buffer, depending on the backend
|
ggml_allocr_alloc(alloc, model.kv_cache.k);
|
ggml_allocr_alloc(alloc, model.kv_cache.v);
|
|
ggml_allocr_free(alloc);
|
}
|
}
|
|
// load weights
|
{
|
ggml_allocr * alloc = ggml_allocr_new_from_buffer(model.buffer_w);
|
|
size_t total_size = 0;
|
|
bool has_lm_head = false;
|
|
std::vector<char> read_buf;
|
|
while (true) {
|
int32_t n_dims;
|
int32_t length;
|
int32_t ttype;
|
|
fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
|
fin.read(reinterpret_cast<char *>(&length), sizeof(length));
|
fin.read(reinterpret_cast<char *>(&ttype), sizeof(ttype));
|
|
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) == model.tensors.end()) {
|
fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.c_str());
|
return false;
|
}
|
|
auto tensor = model.tensors[name];
|
ggml_set_name(tensor, name.c_str());
|
if (ggml_nelements(tensor) != nelements) {
|
fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.c_str());
|
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.c_str(), (int) tensor->ne[0], (int) tensor->ne[1], ne[0], ne[1]);
|
return false;
|
}
|
|
// for debugging
|
if (0) {
|
printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.c_str(), ne[0], ne[1], ggml_type_name(ggml_type(ttype)), ggml_nbytes(tensor)/1024.0/1024.0, ggml_nbytes(tensor));
|
}
|
|
const size_t bpe = ggml_type_size(ggml_type(ttype));
|
|
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.c_str(), ggml_nbytes(tensor), nelements*bpe);
|
return false;
|
}
|
|
ggml_allocr_alloc(alloc, tensor);
|
|
if (ggml_backend_is_cpu (model.backend)
|
#ifdef GGML_USE_METAL
|
|| ggml_backend_is_metal(model.backend)
|
#endif
|
) {
|
// for the CPU and Metal backend, we can read directly into the tensor
|
fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
|
} else {
|
// read into a temporary buffer first, then copy to device memory
|
read_buf.resize(ggml_nbytes(tensor));
|
fin.read(read_buf.data(), ggml_nbytes(tensor));
|
ggml_backend_tensor_set(tensor, read_buf.data(), 0, ggml_nbytes(tensor));
|
}
|
|
// GPT-2 models share the WTE tensor as the LM head
|
if (name == "model/wte" && has_lm_head == false) {
|
//ggml_allocr_alloc(alloc, model.lm_head);
|
//ggml_backend_tensor_copy(tensor, model.lm_head);
|
model.lm_head = tensor;
|
}
|
|
if (name == "model/lm_head") {
|
has_lm_head = true;
|
}
|
|
total_size += ggml_nbytes(tensor);
|
}
|
|
ggml_allocr_free(alloc);
|
printf("%s: model size = %8.2f MB\n", __func__, total_size/1024.0/1024.0);
|
}
|
|
fin.close();
|
|
return true;
|
}
|
|
// build the computation graph
|
struct ggml_cgraph * gpt2_graph(
|
const gpt2_model & model,
|
struct ggml_allocr * allocr,
|
const gpt2_batch & batch) {
|
const auto & hparams = model.hparams;
|
|
const int n_embd = hparams.n_embd;
|
const int n_layer = hparams.n_layer;
|
const int n_ctx = hparams.n_ctx;
|
const int n_head = hparams.n_head;
|
|
const auto & kv_cache = model.kv_cache;
|
|
const int32_t n_tokens = batch.n_tokens;
|
const int32_t n_kv = ggml_allocr_is_measure(allocr) ? n_ctx : kv_cache.n;
|
const int32_t kv_head = ggml_allocr_is_measure(allocr) ? n_ctx - n_tokens : kv_cache.head;
|
|
// since we are using ggml-alloc, this buffer only needs enough space to hold the ggml_tensor and ggml_cgraph structs, but not the tensor data
|
static size_t buf_size = ggml_tensor_overhead()*GPT2_MAX_NODES + ggml_graph_overhead_custom(GPT2_MAX_NODES, false);
|
static std::vector<uint8_t> buf(buf_size);
|
|
struct ggml_init_params params = {
|
/*.mem_size =*/ buf_size,
|
/*.mem_buffer =*/ buf.data(),
|
/*.no_alloc =*/ true, // the tensors will be allocated later by ggml_allocr_alloc_graph()
|
};
|
|
struct ggml_context * ctx0 = ggml_init(params);
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, GPT2_MAX_NODES, false);
|
|
struct ggml_tensor * inpL;
|
if (batch.token) {
|
struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
|
ggml_allocr_alloc(allocr, inp_tokens);
|
if (!ggml_allocr_is_measure(allocr)) {
|
ggml_backend_tensor_set(inp_tokens, batch.token, 0, n_tokens*ggml_element_size(inp_tokens));
|
}
|
|
struct ggml_tensor * position = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
|
ggml_allocr_alloc(allocr, position);
|
if (!ggml_allocr_is_measure(allocr)) {
|
for (int i = 0; i < n_tokens; ++i) {
|
int32_t v = batch.pos[i];
|
ggml_backend_tensor_set(position, &v, i*sizeof(int32_t), sizeof(v));
|
}
|
}
|
|
// wte + wpe
|
inpL =
|
ggml_add(ctx0,
|
ggml_get_rows(ctx0, model.wte, inp_tokens),
|
ggml_get_rows(ctx0, model.wpe, position));
|
} else {
|
GGML_ASSERT(batch.embd);
|
|
inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
|
|
ggml_allocr_alloc(allocr, inpL);
|
if (!ggml_allocr_is_measure(allocr)) {
|
ggml_backend_tensor_set(inpL, batch.embd, 0, n_tokens * n_embd * ggml_element_size(inpL));
|
}
|
}
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
|
ggml_set_name(KQ_mask, "KQ_mask");
|
ggml_allocr_alloc(allocr, KQ_mask);
|
if (!ggml_allocr_is_measure(allocr)) {
|
std::vector<float> data_buf(n_kv*n_tokens);
|
const float neg_inf_v = -INFINITY;
|
|
for (int h = 0; h < 1; ++h) {
|
int h_offset = h*(n_kv*n_tokens);
|
for (int j = 0; j < n_tokens; ++j) {
|
const gpt2_pos pos = batch.pos[j];
|
const gpt2_seq_id seq_id = batch.seq_id[j];
|
|
for (int i = 0; i < n_kv; ++i) {
|
if (!kv_cache.cells[i].has_seq_id(seq_id) || kv_cache.cells[i].pos > pos) {
|
data_buf[h_offset + j*n_kv + i] = neg_inf_v;
|
}
|
}
|
}
|
}
|
|
ggml_backend_tensor_set(KQ_mask, data_buf.data(), 0, data_buf.size() * sizeof(float));
|
}
|
|
for (int il = 0; il < n_layer; ++il) {
|
struct ggml_tensor * cur;
|
|
// norm
|
{
|
// [ 768, N]
|
cur = ggml_norm(ctx0, inpL, hparams.eps);
|
|
// cur = ln_1_g*cur + ln_1_b
|
// [ 768, N]
|
cur = ggml_add(ctx0,
|
ggml_mul(ctx0,
|
cur,
|
model.layers[il].ln_1_g),
|
model.layers[il].ln_1_b);
|
}
|
|
// attn
|
// [2304, 768] - model.layers[il].c_attn_attn_w
|
// [2304, 1] - model.layers[il].c_attn_attn_b
|
// [ 768, n_tokens] - cur (in)
|
// [2304, n_tokens] - cur (out)
|
//
|
// cur = attn_w*cur + attn_b
|
// [2304, n_tokens]
|
{
|
cur = ggml_mul_mat(ctx0,
|
model.layers[il].c_attn_attn_w,
|
cur);
|
|
cur = ggml_add(ctx0,
|
cur,
|
model.layers[il].c_attn_attn_b);
|
}
|
|
// self-attention
|
{
|
struct ggml_tensor * Qcur = ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*n_embd);
|
struct ggml_tensor * Kcur = ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 1*sizeof(float)*n_embd);
|
struct ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 2*sizeof(float)*n_embd);
|
|
// store key and value to memory
|
if (n_tokens >= 1) {
|
struct ggml_tensor * k = ggml_view_1d(ctx0, model.kv_cache.k, n_tokens*n_embd, (ggml_element_size(model.kv_cache.k)*n_embd)*(il*n_ctx + kv_head));
|
struct ggml_tensor * v = ggml_view_1d(ctx0, model.kv_cache.v, n_tokens*n_embd, (ggml_element_size(model.kv_cache.v)*n_embd)*(il*n_ctx + kv_head));
|
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
|
}
|
|
// Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
|
// [64, N, 12]
|
struct ggml_tensor * Q =
|
ggml_permute(ctx0,
|
ggml_cpy(ctx0,
|
Qcur,
|
ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd/n_head, n_head, n_tokens)),
|
0, 2, 1, 3);
|
|
// K = Kmem.view(n_embd/n_head, n_head, n_kv).permute(0, 2, 1, 3)
|
// [64, n_kv, 12]
|
struct ggml_tensor * K =
|
ggml_permute(ctx0,
|
ggml_reshape_3d(ctx0,
|
ggml_view_1d(ctx0, model.kv_cache.k, n_kv*n_embd, il*n_ctx*ggml_element_size(model.kv_cache.k)*n_embd),
|
n_embd/n_head, n_head, n_kv),
|
0, 2, 1, 3);
|
|
// GG: flash attention
|
//struct ggml_tensor * V =
|
// ggml_cpy(ctx0,
|
// ggml_permute(ctx0,
|
// ggml_reshape_3d(ctx0,
|
// ggml_view_1d(ctx0, model.kv_cache.v, n_kv*n_embd, il*n_ctx*ggml_element_size(model.kv_cache.v)*n_embd),
|
// n_embd/n_head, n_head, n_kv),
|
// 1, 2, 0, 3),
|
// ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_embd/n_head, n_head));
|
|
//struct ggml_tensor * KQV = ggml_flash_attn(ctx0, Q, K, V, true);
|
|
// K * Q
|
// [n_kv, n_tokens, 12]
|
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
|
|
// KQ_scaled = KQ / sqrt(n_embd/n_head)
|
// [n_kv, n_tokens, 12]
|
struct ggml_tensor * KQ_scaled =
|
ggml_scale(ctx0,
|
KQ,
|
1.0f/sqrtf(float(n_embd)/n_head));
|
|
// KQ_masked = mask_past(KQ_scaled)
|
// [n_kv, n_tokens, 12]
|
struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask);
|
|
// KQ = soft_max(KQ_masked)
|
// [n_kv, N, 12]
|
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
|
|
// V_trans = Vmem.view(n_embd/n_head, n_head, n_kv).permute(1, 2, 0, 3).contiguous()
|
// [n_kv, 64, 12]
|
struct ggml_tensor * V_trans =
|
ggml_cpy(ctx0,
|
ggml_permute(ctx0,
|
ggml_reshape_3d(ctx0,
|
ggml_view_1d(ctx0, model.kv_cache.v, n_kv*n_embd, il*n_ctx*ggml_element_size(model.kv_cache.v)*n_embd),
|
n_embd/n_head, n_head, n_kv),
|
1, 2, 0, 3),
|
ggml_new_tensor_3d(ctx0, model.kv_cache.v->type, n_kv, n_embd/n_head, n_head));
|
|
// KQV = transpose(V) * KQ_soft_max
|
// [64, n_tokens, 12]
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);
|
|
// KQV_merged = KQV.permute(0, 2, 1, 3)
|
// [64, 12, n_tokens]
|
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
|
|
// cur = KQV_merged.contiguous().view(n_embd, N)
|
// [768, n_tokens]
|
cur = ggml_cpy(ctx0,
|
KQV_merged,
|
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens));
|
}
|
|
// projection
|
// [ 768, 768] - model.layers[il].c_attn_proj_w
|
// [ 768, 1] - model.layers[il].c_attn_proj_b
|
// [ 768, N] - cur (in)
|
// [ 768, N] - cur (out)
|
//
|
// cur = proj_w*cur + proj_b
|
// [768, N]
|
{
|
cur = ggml_mul_mat(ctx0,
|
model.layers[il].c_attn_proj_w,
|
cur);
|
|
cur = ggml_add(ctx0,
|
cur,
|
model.layers[il].c_attn_proj_b);
|
}
|
|
// add the input
|
cur = ggml_add(ctx0, cur, inpL);
|
|
struct ggml_tensor * inpFF = cur;
|
|
// feed-forward network
|
{
|
// norm
|
{
|
cur = ggml_norm(ctx0, inpFF, hparams.eps);
|
|
// cur = ln_2_g*cur + ln_2_b
|
// [ 768, N]
|
cur = ggml_add(ctx0,
|
ggml_mul(ctx0,
|
cur,
|
model.layers[il].ln_2_g),
|
model.layers[il].ln_2_b);
|
}
|
|
// fully connected
|
// [3072, 768] - model.layers[il].c_mlp_fc_w
|
// [3072, 1] - model.layers[il].c_mlp_fc_b
|
// [ 768, N] - cur (in)
|
// [3072, N] - cur (out)
|
//
|
// cur = fc_w*cur + fc_b
|
// [3072, N]
|
cur = ggml_mul_mat(ctx0,
|
model.layers[il].c_mlp_fc_w,
|
cur);
|
|
cur = ggml_add(ctx0,
|
cur,
|
model.layers[il].c_mlp_fc_b);
|
|
// GELU activation
|
// [3072, N]
|
cur = ggml_gelu(ctx0, cur);
|
|
// projection
|
// [ 768, 3072] - model.layers[il].c_mlp_proj_w
|
// [ 768, 1] - model.layers[il].c_mlp_proj_b
|
// [3072, N] - cur (in)
|
// [ 768, N] - cur (out)
|
//
|
// cur = proj_w*cur + proj_b
|
// [768, N]
|
cur = ggml_mul_mat(ctx0,
|
model.layers[il].c_mlp_proj_w,
|
cur);
|
|
cur = ggml_add(ctx0,
|
cur,
|
model.layers[il].c_mlp_proj_b);
|
}
|
|
// input for next layer
|
inpL = ggml_add(ctx0, cur, inpFF);
|
}
|
|
// norm
|
{
|
// [ 768, N]
|
inpL = ggml_norm(ctx0, inpL, hparams.eps);
|
|
// inpL = ln_f_g*inpL + ln_f_b
|
// [ 768, N]
|
inpL = ggml_add(ctx0,
|
ggml_mul(ctx0,
|
inpL,
|
model.ln_f_g),
|
model.ln_f_b);
|
}
|
|
// inpL = WTE * inpL
|
// [ 768, 50257] - model.lm_head
|
// [ 768, N] - inpL
|
inpL = ggml_mul_mat(ctx0, model.lm_head, inpL);
|
|
// logits -> probs
|
//inpL = ggml_soft_max(ctx0, inpL);
|
|
ggml_build_forward_expand(gf, inpL);
|
|
ggml_free(ctx0);
|
|
return gf;
|
}
|
|
static void gpt2_kv_cache_seq_cp(
|
struct gpt2_kv_cache & cache,
|
gpt2_seq_id seq_id_src,
|
gpt2_seq_id seq_id_dst,
|
gpt2_pos p0,
|
gpt2_pos p1) {
|
if (p0 < 0) p0 = 0;
|
if (p1 < 0) p1 = std::numeric_limits<gpt2_pos>::max();
|
|
for (uint32_t i = 0; i < cache.size; ++i) {
|
if (cache.cells[i].has_seq_id(seq_id_src) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
|
cache.cells[i].seq_id.insert(seq_id_dst);
|
}
|
}
|
}
|
|
struct gpt2_batch gpt2_batch_init(int32_t n_tokens, int32_t embd) {
|
gpt2_batch batch;
|
|
if (embd) {
|
batch.embd = (float *) malloc(sizeof(float) * n_tokens * embd);
|
} else {
|
batch.token = (gpt_vocab::id *) malloc(sizeof(gpt_vocab::id) * n_tokens);
|
}
|
|
batch.pos = (gpt2_pos *) malloc(sizeof(gpt2_pos) * n_tokens);
|
batch.seq_id = (gpt2_seq_id *) malloc(sizeof(gpt2_seq_id) * n_tokens);
|
batch.logits = (int8_t *) malloc(sizeof(int8_t) * n_tokens);
|
|
return batch;
|
}
|
|
void gpt2_batch_free(struct gpt2_batch batch) {
|
if (batch.token) free(batch.token);
|
if (batch.embd) free(batch.embd);
|
if (batch.pos) free(batch.pos);
|
if (batch.seq_id) free(batch.seq_id);
|
if (batch.logits) free(batch.logits);
|
}
|
|
// Positive return values does not mean a fatal error, but rather a warning.
|
// 0 - success
|
// < 0 - error
|
int gpt2_decode(
|
struct gpt2_model & model,
|
struct ggml_allocr * allocr,
|
struct gpt2_batch batch,
|
int n_threads,
|
std::vector<float> & logits) {
|
const int32_t n_tokens = batch.n_tokens;
|
const auto & hparams = model.hparams;
|
const int n_vocab = hparams.n_vocab;
|
|
if (n_tokens == 0) {
|
printf("%s: n_tokens == 0", __func__);
|
return -1;
|
}
|
|
GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd));
|
|
auto & cache = model.kv_cache;
|
|
for (int i = 0; i < n_tokens; i++) {
|
cache.cells[cache.head + i].pos = batch.pos[i];
|
cache.cells[cache.head + i].seq_id.insert(batch.seq_id[i]);
|
}
|
|
cache.n = cache.head + n_tokens;
|
|
// reset the allocator to free all the memory allocated during the previous inference
|
ggml_allocr_reset(allocr);
|
|
struct ggml_cgraph * gf = gpt2_graph(model, allocr, batch);
|
|
// allocate tensors
|
ggml_allocr_alloc_graph(allocr, gf);
|
|
// run the computation
|
if (ggml_backend_is_cpu(model.backend)) {
|
ggml_backend_cpu_set_n_threads(model.backend, n_threads);
|
}
|
#ifdef GGML_USE_METAL
|
if (ggml_backend_is_metal(model.backend)) {
|
ggml_backend_metal_set_n_cb(model.backend, n_threads);
|
}
|
#endif
|
ggml_backend_graph_compute(model.backend, gf);
|
|
//if (n_past%100 == 0) {
|
// ggml_graph_print (&gf);
|
// ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot");
|
//}
|
|
// in this case, the output tensor is the last one in the graph
|
struct ggml_tensor * inpL = gf->nodes[gf->n_nodes - 1];
|
|
if (batch.logits) {
|
// return logits for all tokens
|
logits.resize(n_vocab*n_tokens);
|
for (int32_t i = 0; i < n_tokens; i++) {
|
if (batch.logits[i] == 0) {
|
continue;
|
}
|
ggml_backend_tensor_get(inpL, logits.data() + n_vocab*i, n_vocab*i*sizeof(float), sizeof(float)*n_vocab);
|
}
|
} else {
|
// return result just for the last token
|
logits.resize(n_vocab);
|
ggml_backend_tensor_get(inpL, logits.data(), (n_vocab*(n_tokens-1))*sizeof(float), sizeof(float)*n_vocab);
|
}
|
|
// update the kv ring buffer
|
cache.head += n_tokens;
|
|
// ensure kv cache head points to a valid index.
|
if (cache.head >= cache.size) {
|
printf("%s: cache.head >= cache.size\n", __func__);
|
return -2;
|
}
|
|
return 0;
|
}
|
|
int main(int argc, char ** argv) {
|
ggml_time_init();
|
|
const int64_t t_main_start_us = ggml_time_us();
|
|
gpt_params params;
|
|
if (gpt_params_parse(argc, argv, params) == false) {
|
return 1;
|
}
|
|
if (params.seed < 0) {
|
params.seed = time(NULL);
|
}
|
|
printf("%s: seed = %d\n", __func__, params.seed);
|
|
std::mt19937 rng(params.seed);
|
if (params.prompt.empty()) {
|
params.prompt = gpt_random_prompt(rng);
|
}
|
|
int64_t t_load_us = 0;
|
|
gpt_vocab vocab;
|
gpt2_model model;
|
|
// load the model
|
{
|
const int64_t t_start_us = ggml_time_us();
|
|
if (!gpt2_model_load(params.model, model, vocab, params.n_ctx, params.n_gpu_layers)) {
|
fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
|
return 1;
|
}
|
|
t_load_us = ggml_time_us() - t_start_us;
|
|
test_gpt_tokenizer(vocab, params.token_test);
|
}
|
|
// tokenize the prompt
|
std::vector<gpt_vocab::id> embd_inp = ::gpt_tokenize(vocab, params.prompt);
|
|
// keep this buffer alive while evaluating the model
|
ggml_backend_buffer_t buf_compute;
|
|
const int n_parallel = params.n_parallel;
|
const int n_batch_max = std::max(embd_inp.size(), (size_t)n_parallel);
|
|
// create a gpt2_batch
|
// we use this object to submit token data for decoding
|
gpt2_batch batch = gpt2_batch_init(n_batch_max, 0);
|
|
// prepare required memory and allocate the compute buffer
|
struct ggml_allocr * allocr = NULL;
|
{
|
// create an allocator to measure the memory usage
|
allocr = ggml_allocr_new_measure_from_backend(model.backend);
|
|
batch.n_tokens = n_batch_max;
|
|
// create the worst case graph for memory usage estimation
|
struct ggml_cgraph * gf = gpt2_graph(model, allocr, batch);
|
|
// compute the required memory
|
size_t mem_size = ggml_allocr_alloc_graph(allocr, gf);
|
|
// recreate the allocator with the required memory
|
ggml_allocr_free(allocr);
|
buf_compute = ggml_backend_alloc_buffer(model.backend, mem_size);
|
allocr = ggml_allocr_new_from_buffer(buf_compute);
|
|
fprintf(stderr, "%s: compute buffer size: %.2f MB\n", __func__, mem_size/1024.0/1024.0);
|
}
|
|
int64_t t_sample_us = 0;
|
int64_t t_predict_us = 0;
|
|
std::vector<float> logits;
|
|
// evaluate the initial prompt
|
batch.n_tokens = embd_inp.size();
|
|
for (int32_t i = 0; i < batch.n_tokens; i++) {
|
batch.token[i] = embd_inp[i];
|
batch.pos[i] = i;
|
batch.seq_id[i] = 0;
|
batch.logits[i] = false;
|
}
|
|
// gpt2_decode will output logits only for the last token of the prompt
|
batch.logits[batch.n_tokens - 1] = true;
|
|
if (gpt2_decode(model, allocr, batch, params.n_threads, logits) != 0) {
|
printf("%s: gpt2_decode() failed\n", __func__);
|
return 1;
|
}
|
|
// assign the system KV cache to all parallel sequences
|
// this way, the parallel sequences will "reuse" the prompt tokens without having to copy them
|
for (int32_t i = 1; i < n_parallel; ++i) {
|
gpt2_kv_cache_seq_cp(model.kv_cache, 0, i, 0, batch.n_tokens);
|
}
|
|
if (n_parallel > 1) {
|
printf("\n\n%s: generating %d sequences ...\n", __func__, n_parallel);
|
}
|
|
params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size());
|
|
printf("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
|
printf("%s: number of tokens in prompt = %zu, first 8 tokens: ", __func__, embd_inp.size());
|
for (int i = 0; i < std::min(8, (int) embd_inp.size()); i++) {
|
printf("%d ", embd_inp[i]);
|
}
|
printf("\n\n");
|
|
std::vector<gpt_vocab::token> streams(n_parallel);
|
|
// remember the batch index of the last token for each parallel sequence
|
// we need this to determine which logits to sample from
|
std::vector<int32_t> i_batch(n_parallel, batch.n_tokens - 1);
|
|
int n_cur = batch.n_tokens;
|
int n_len = batch.n_tokens + params.n_predict;
|
int n_decoded = 0;
|
|
const int n_vocab = model.hparams.n_vocab;
|
const int top_k = params.top_k;
|
const float top_p = params.top_p;
|
const float temp = params.temp;
|
|
while (n_cur < n_len) {
|
batch.n_tokens = 0;
|
|
for (int32_t i = 0; i < n_parallel; ++i) {
|
if (i_batch[i] < 0) {
|
// the stream has already finished
|
continue;
|
}
|
|
auto * logits_i = logits.data() + i_batch[i]*n_vocab;
|
|
gpt_vocab::id id = 0;
|
{
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
id = gpt_sample_top_k_top_p(vocab, logits_i, top_k, top_p, temp, rng);
|
|
t_sample_us += ggml_time_us() - t_start_sample_us;
|
}
|
|
// is it an end of stream? -> mark the stream as finished
|
if ((!params.ignore_eos && id == 50256) || n_cur == n_len - 1) {
|
i_batch[i] = -1;
|
printf("\n");
|
if (n_parallel > 1) {
|
printf("%s: stream %d finished at n_cur = %d", __func__, i, n_cur);
|
}
|
|
continue;
|
}
|
|
auto& token = vocab.id_to_token[id];
|
if (n_parallel == 1) {
|
printf("%s", token.c_str());
|
fflush(stdout);
|
}
|
|
streams[i] += token;
|
|
// push this new token for next evaluation
|
batch.token [batch.n_tokens] = id;
|
batch.pos [batch.n_tokens] = n_cur;
|
batch.seq_id[batch.n_tokens] = i;
|
batch.logits[batch.n_tokens] = true;
|
|
i_batch[i] = batch.n_tokens;
|
|
batch.n_tokens += 1;
|
|
n_decoded += 1;
|
}
|
|
// all streams are finished
|
if (batch.n_tokens == 0) {
|
break;
|
}
|
|
n_cur += 1;
|
|
{
|
const int64_t t_start_us = ggml_time_us();
|
|
// evaluate the current batch with the transformer model
|
int ret_code = gpt2_decode(model, allocr, batch, params.n_threads, logits);
|
if (ret_code != 0) {
|
fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, ret_code);
|
return 1;
|
}
|
|
t_predict_us += ggml_time_us() - t_start_us;
|
}
|
}
|
|
if (n_parallel > 1) {
|
printf("\n");
|
|
for (int32_t i = 0; i < n_parallel; ++i) {
|
printf("sequence %d:\n\n%s%s\n\n", i, params.prompt.c_str(), streams[i].c_str());
|
}
|
}
|
|
// report timing
|
{
|
const int64_t t_main_end_us = ggml_time_us();
|
|
printf("\n\n");
|
printf("%s: n_decoded = %8d\n", __func__, n_decoded);
|
printf("%s: load time = %8.2f ms\n", __func__, t_load_us/1000.0f);
|
printf("%s: sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f);
|
printf("%s: predict time = %8.2f ms\n", __func__, t_predict_us/1000.0f);
|
printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
|
}
|
|
gpt2_batch_free(batch);
|
ggml_free(model.ctx);
|
|
ggml_backend_buffer_free(model.buffer_w);
|
ggml_backend_buffer_free(model.kv_cache.buffer);
|
ggml_backend_buffer_free(buf_compute);
|
ggml_backend_free(model.backend);
|
|
return 0;
|
}
|
