mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-31 08:51:55 +00:00
add gptneox gguf example
This commit is contained in:
klosax
812
gptneox-main.cpp
Normal file
812
gptneox-main.cpp
Normal file
@@ -0,0 +1,812 @@
|
||||
#include "ggml.h"
|
||||
|
||||
#include "gptneox-common.h"
|
||||
|
||||
#include <cassert>
|
||||
#include <cmath>
|
||||
#include <cstdio>
|
||||
#include <cstring>
|
||||
#include <cinttypes>
|
||||
#include <fstream>
|
||||
#include <map>
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
#if defined(_MSC_VER)
|
||||
#pragma warning(disable: 4244 4267) // possible loss of data
|
||||
#endif
|
||||
|
||||
// default hparams
|
||||
struct gpt_neox_hparams {
|
||||
size_t n_merges = 0;
|
||||
size_t n_vocab = 0;
|
||||
int32_t n_ctx = 0;
|
||||
int32_t n_embd = 0;
|
||||
int32_t n_head = 0;
|
||||
int32_t n_layer = 0;
|
||||
int32_t n_rot = 0; // rotary_pct * (n_embd / n_head)
|
||||
bool par_res = true;
|
||||
float norm_eps = 1e-5;
|
||||
};
|
||||
|
||||
struct gpt_neox_layer {
|
||||
// pre normalization
|
||||
struct ggml_tensor * ln_1_g;
|
||||
struct ggml_tensor * ln_1_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;
|
||||
|
||||
// post normalization
|
||||
struct ggml_tensor * ln_2_g;
|
||||
struct ggml_tensor * ln_2_b;
|
||||
|
||||
// ff
|
||||
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 gpt_neox_model {
|
||||
gpt_neox_hparams hparams;
|
||||
|
||||
// normalization
|
||||
struct ggml_tensor * ln_f_g;
|
||||
struct ggml_tensor * ln_f_b;
|
||||
|
||||
struct ggml_tensor * wte; // position embedding
|
||||
|
||||
struct ggml_tensor * lmh_g; // language model head
|
||||
|
||||
std::vector<gpt_neox_layer> layers;
|
||||
|
||||
// key + value memory
|
||||
struct ggml_tensor * memory_k;
|
||||
struct ggml_tensor * memory_v;
|
||||
|
||||
//
|
||||
struct gguf_context * ggufctx;
|
||||
struct ggml_context * ctx;
|
||||
struct ggml_context * kvctx;
|
||||
|
||||
std::map<std::string, struct ggml_tensor *> tensors;
|
||||
};
|
||||
|
||||
struct ggml_tensor * get_tensor_ex( struct ggml_context * ctx, std::string name){
|
||||
|
||||
struct ggml_tensor * cur = ggml_get_tensor(ctx, name.c_str());
|
||||
if( cur == NULL ) {
|
||||
fprintf(stdout, "%s: tensor '%s' not found!\n", __func__, name.c_str());
|
||||
} else {
|
||||
// fprintf(stdout, "%s: n_dims = %d, name = '%s'\n", __func__, cur->n_dims, cur->name);
|
||||
}
|
||||
|
||||
return cur;
|
||||
}
|
||||
|
||||
// load the model's weights from a file
|
||||
bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt_vocab & vocab) {
|
||||
printf("%s: loading model from '%s'..\n", __func__, fname.c_str());
|
||||
|
||||
model.ctx = NULL;
|
||||
|
||||
struct gguf_init_params ggufparams = {
|
||||
/*.no_alloc = */ false,
|
||||
/*.ctx = */ &model.ctx,
|
||||
};
|
||||
|
||||
auto & ggufctx = model.ggufctx;
|
||||
|
||||
ggufctx = gguf_init_from_file(fname.c_str(), ggufparams);
|
||||
|
||||
if (!ggufctx) {
|
||||
fprintf(stderr, "%s: gguf_init_from_file() failed\n", __func__);
|
||||
return false;
|
||||
}
|
||||
|
||||
fprintf(stdout, "%s: gguf version = %d\n", __func__, gguf_get_version(ggufctx));
|
||||
fprintf(stdout, "%s: gguf alignment = %zu\n", __func__, gguf_get_alignment(ggufctx));
|
||||
fprintf(stdout, "%s: gguf data offset = %zu\n", __func__, gguf_get_data_offset(ggufctx));
|
||||
|
||||
// print all kv
|
||||
if( false )
|
||||
{
|
||||
const int n_kv = gguf_get_n_kv(ggufctx);
|
||||
|
||||
fprintf(stdout, "%s: n_kv: %d\n", __func__, n_kv);
|
||||
|
||||
for (int i = 0; i < n_kv; ++i) {
|
||||
const char * key = gguf_get_key(ggufctx, i);
|
||||
|
||||
fprintf(stdout, "%s: kv[%d]: key = %s\n", __func__, i, key);
|
||||
}
|
||||
}
|
||||
|
||||
// print some standard metadata
|
||||
{
|
||||
int keyidx;
|
||||
|
||||
keyidx = gguf_find_key(ggufctx, "general.name");
|
||||
if (keyidx != -1) { fprintf(stdout, "%s: model name = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
keyidx = gguf_find_key(ggufctx, "general.description");
|
||||
if (keyidx != -1) { fprintf(stdout, "%s: model description = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
keyidx = gguf_find_key(ggufctx, "general.author");
|
||||
if (keyidx != -1) { fprintf(stdout, "%s: model author = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
keyidx = gguf_find_key(ggufctx, "general.license");
|
||||
if (keyidx != -1) { fprintf(stdout, "%s: model license = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
keyidx = gguf_find_key(ggufctx, "general.architecture");
|
||||
if (keyidx != -1) { fprintf(stdout, "%s: model architecture = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
|
||||
}
|
||||
|
||||
// check required metadata
|
||||
{
|
||||
int keyidx;
|
||||
|
||||
keyidx = gguf_find_key(ggufctx, "general.architecture");
|
||||
if (keyidx != -1) {
|
||||
if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "gptneox") != 0) {
|
||||
fprintf(stdout, "%s: model architecture not supported!\n", __func__);
|
||||
return false;
|
||||
}
|
||||
} else {
|
||||
fprintf(stdout, "%s: gguf model architecture not found!\n", __func__);
|
||||
return false;
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
// load hparams
|
||||
{
|
||||
auto & hparams = model.hparams;
|
||||
|
||||
bool ok = true;
|
||||
int keyidx;
|
||||
|
||||
if (ok) { keyidx = gguf_find_key(ggufctx, "gptneox.context_length");
|
||||
if (keyidx != -1) { hparams.n_ctx = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } }
|
||||
|
||||
if (ok) { keyidx = gguf_find_key(ggufctx, "gptneox.embedding_length");
|
||||
if (keyidx != -1) { hparams.n_embd = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } }
|
||||
|
||||
if (ok) { keyidx = gguf_find_key(ggufctx, "gptneox.attention.head_count");
|
||||
if (keyidx != -1) { hparams.n_head = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } }
|
||||
|
||||
if (ok) { keyidx = gguf_find_key(ggufctx, "gptneox.layer_count");
|
||||
if (keyidx != -1) { hparams.n_layer = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } }
|
||||
|
||||
if (ok) { keyidx = gguf_find_key(ggufctx, "gptneox.rope.dimension_count");
|
||||
if (keyidx != -1) { hparams.n_rot = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } }
|
||||
|
||||
if (ok) { keyidx = gguf_find_key(ggufctx, "gptneox.use_parallel_residual");
|
||||
if (keyidx != -1) { hparams.par_res = gguf_get_val_bool(ggufctx, keyidx); } else { ok = false; } }
|
||||
|
||||
if (ok) { keyidx = gguf_find_key(ggufctx, "gptneox.attention.layer_norm_epsilon");
|
||||
if (keyidx != -1) { hparams.norm_eps= gguf_get_val_f32(ggufctx, keyidx); } else { ok = false; } }
|
||||
|
||||
if (!ok) {
|
||||
fprintf(stderr, "%s: required hparam missing!\n", __func__);
|
||||
return false;
|
||||
}
|
||||
|
||||
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: n_rot = %d\n", __func__, hparams.n_rot);
|
||||
printf("%s: par_res = %d\n", __func__, hparams.par_res);
|
||||
printf("%s: norm_eps = %g\n", __func__, hparams.norm_eps);
|
||||
|
||||
}
|
||||
|
||||
// load vocab
|
||||
{
|
||||
|
||||
// TODO: implement a better bpe tokenizer, utilizing merges and handles unicode
|
||||
|
||||
auto & hparams = model.hparams;
|
||||
|
||||
int keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.model");
|
||||
|
||||
if (keyidx != -1) {
|
||||
if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "gpt2") != 0) {
|
||||
fprintf(stdout, "%s: tokenizer model not supported!\n", __func__);
|
||||
return false;
|
||||
}
|
||||
} else {
|
||||
fprintf(stdout, "%s: tokenizer model not found!\n", __func__);
|
||||
return false;
|
||||
}
|
||||
|
||||
|
||||
int tokens_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.tokens");
|
||||
|
||||
if (tokens_keyidx == -1) {
|
||||
fprintf(stdout, "%s: gpt2 tokenizer vocab not found!\n", __func__);
|
||||
return false;
|
||||
}
|
||||
|
||||
int merges_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.merges");
|
||||
|
||||
if (merges_keyidx == -1) {
|
||||
fprintf(stdout, "%s: gpt2 tokenizer merges not found!\n", __func__);
|
||||
return false;
|
||||
}
|
||||
|
||||
hparams.n_vocab = gguf_get_arr_n(ggufctx,tokens_keyidx);
|
||||
hparams.n_merges = gguf_get_arr_n(ggufctx,merges_keyidx);
|
||||
|
||||
fprintf(stdout, "%s: gpt2 tokenizer vocab = %zu\n", __func__, hparams.n_vocab);
|
||||
fprintf(stdout, "%s: gpt2 tokenizer merges = %zu\n", __func__, hparams.n_merges);
|
||||
|
||||
for (size_t i = 0; i < hparams.n_vocab; i++) {
|
||||
std::string word = gguf_get_arr_str(ggufctx, tokens_keyidx, i);
|
||||
|
||||
|
||||
// TEMP until a better bpe tokenizer is implemented
|
||||
word = replace(word, "Ġ", " ");
|
||||
word = replace(word, "Ċ", "\n");
|
||||
|
||||
|
||||
vocab.token_to_id[word] = i;
|
||||
vocab.id_to_token[i] = word;
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
|
||||
|
||||
auto & ctx = model.ctx;
|
||||
size_t ctx_size = ggml_get_mem_size(ctx);
|
||||
|
||||
printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
|
||||
|
||||
// print tensor info
|
||||
if( false )
|
||||
{
|
||||
const int n_tensors = gguf_get_n_tensors(ggufctx);
|
||||
|
||||
fprintf(stdout, "%s: n_tensors: %d\n", __func__, n_tensors);
|
||||
|
||||
for (int i = 0; i < n_tensors; ++i) {
|
||||
const char * name = gguf_get_tensor_name (ggufctx, i);
|
||||
const size_t offset = gguf_get_tensor_offset(ggufctx, i);
|
||||
|
||||
fprintf(stdout, "%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
// prepare memory for the weights
|
||||
{
|
||||
const int n_layer = model.hparams.n_layer;
|
||||
|
||||
model.layers.resize(n_layer);
|
||||
|
||||
model.wte = ggml_get_tensor(ctx, "gpt_neox.embed_in.weight");
|
||||
model.ln_f_g = ggml_get_tensor(ctx, "gpt_neox.final_layer_norm.weight");
|
||||
model.ln_f_b = ggml_get_tensor(ctx, "gpt_neox.final_layer_norm.bias");
|
||||
model.lmh_g = ggml_get_tensor(ctx, "embed_out.weight");
|
||||
|
||||
// map by name
|
||||
model.tensors["gpt_neox.embed_in.weight"] = model.wte;
|
||||
model.tensors["gpt_neox.final_layer_norm.weight"] = model.ln_f_g;
|
||||
model.tensors["gpt_neox.final_layer_norm.bias"] = model.ln_f_b;
|
||||
model.tensors["embed_out.weight"] = model.lmh_g;
|
||||
|
||||
for (int i = 0; i < n_layer; ++i) {
|
||||
auto & layer = model.layers[i];
|
||||
|
||||
layer.ln_1_g = get_tensor_ex(ctx, "gpt_neox.layers." + std::to_string(i) + ".input_layernorm.weight" );
|
||||
layer.ln_1_b = get_tensor_ex(ctx, "gpt_neox.layers." + std::to_string(i) + ".input_layernorm.bias" );
|
||||
|
||||
layer.c_attn_attn_w = get_tensor_ex(ctx, "gpt_neox.layers." + std::to_string(i) + ".attention.query_key_value.weight" );
|
||||
layer.c_attn_attn_b = get_tensor_ex(ctx, "gpt_neox.layers." + std::to_string(i) + ".attention.query_key_value.bias" );
|
||||
|
||||
layer.c_attn_proj_w = get_tensor_ex(ctx, "gpt_neox.layers." + std::to_string(i) + ".attention.dense.weight" );
|
||||
layer.c_attn_proj_b = get_tensor_ex(ctx, "gpt_neox.layers." + std::to_string(i) + ".attention.dense.bias" );
|
||||
|
||||
layer.ln_2_g = get_tensor_ex(ctx, "gpt_neox.layers." + std::to_string(i) + ".post_attention_layernorm.weight" );
|
||||
layer.ln_2_b = get_tensor_ex(ctx, "gpt_neox.layers." + std::to_string(i) + ".post_attention_layernorm.bias");
|
||||
|
||||
layer.c_mlp_fc_w = get_tensor_ex(ctx, "gpt_neox.layers." + std::to_string(i) + ".mlp.dense_h_to_4h.weight" );
|
||||
layer.c_mlp_fc_b = get_tensor_ex(ctx, "gpt_neox.layers." + std::to_string(i) + ".mlp.dense_h_to_4h.bias" );
|
||||
|
||||
layer.c_mlp_proj_w = get_tensor_ex(ctx, "gpt_neox.layers." + std::to_string(i) + ".mlp.dense_4h_to_h.weight" );
|
||||
layer.c_mlp_proj_b = get_tensor_ex(ctx, "gpt_neox.layers." + std::to_string(i) + ".mlp.dense_4h_to_h.bias" );
|
||||
|
||||
// map by name
|
||||
model.tensors["gpt_neox.layers." + std::to_string(i) + ".input_layernorm.weight"] = layer.ln_1_g;
|
||||
model.tensors["gpt_neox.layers." + std::to_string(i) + ".input_layernorm.bias"] = layer.ln_1_b;
|
||||
|
||||
model.tensors["gpt_neox.layers." + std::to_string(i) + ".attention.query_key_value.weight"] = layer.c_attn_attn_w;
|
||||
model.tensors["gpt_neox.layers." + std::to_string(i) + ".attention.query_key_value.bias"] = layer.c_attn_attn_b;
|
||||
|
||||
model.tensors["gpt_neox.layers." + std::to_string(i) + ".attention.dense.weight"] = layer.c_attn_proj_w;
|
||||
model.tensors["gpt_neox.layers." + std::to_string(i) + ".attention.dense.bias"] = layer.c_attn_proj_b;
|
||||
|
||||
model.tensors["gpt_neox.layers." + std::to_string(i) + ".post_attention_layernorm.weight"] = layer.ln_2_g;
|
||||
model.tensors["gpt_neox.layers." + std::to_string(i) + ".post_attention_layernorm.bias"] = layer.ln_2_b;
|
||||
|
||||
model.tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_h_to_4h.weight"] = layer.c_mlp_fc_w;
|
||||
model.tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_h_to_4h.bias"] = layer.c_mlp_fc_b;
|
||||
|
||||
model.tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_4h_to_h.weight"] = layer.c_mlp_proj_w;
|
||||
model.tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_4h_to_h.bias"] = layer.c_mlp_proj_b;
|
||||
}
|
||||
}
|
||||
|
||||
// key + value memory
|
||||
{
|
||||
const auto & kvctx = model.kvctx;
|
||||
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 int64_t n_mem = n_layer*n_ctx;
|
||||
const int64_t n_elements = n_embd*n_mem;
|
||||
|
||||
// create the ggml context
|
||||
{
|
||||
struct ggml_init_params params = {
|
||||
/*.mem_size =*/ size_t(n_elements*4+ggml_tensor_overhead()*2),
|
||||
/*.mem_buffer =*/ NULL,
|
||||
/*.no_alloc =*/ false,
|
||||
};
|
||||
|
||||
model.kvctx = ggml_init(params);
|
||||
if (!model.kvctx) {
|
||||
fprintf(stderr, "%s: kv ggml_init() failed\n", __func__);
|
||||
return false;
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
|
||||
model.memory_k = ggml_new_tensor_1d(kvctx, GGML_TYPE_F16, n_elements);
|
||||
model.memory_v = ggml_new_tensor_1d(kvctx, GGML_TYPE_F16, n_elements);
|
||||
|
||||
const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
|
||||
|
||||
printf("%s: memory_size = %8.2f MB, n_mem = %" PRId64 "\n", __func__, memory_size/1024.0/1024.0, n_mem);
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
|
||||
// feed-forward network
|
||||
ggml_tensor * gpt_neox_ff(
|
||||
const gpt_neox_layer &layer,
|
||||
ggml_context * ctx0,
|
||||
ggml_tensor * inp) {
|
||||
ggml_tensor * cur = ggml_norm(ctx0, inp);
|
||||
|
||||
cur = ggml_add(ctx0,
|
||||
ggml_mul(ctx0,
|
||||
ggml_repeat(ctx0, layer.ln_2_g, cur),
|
||||
cur),
|
||||
ggml_repeat(ctx0, layer.ln_2_b, cur));
|
||||
|
||||
cur = ggml_mul_mat(ctx0,
|
||||
layer.c_mlp_fc_w,
|
||||
cur);
|
||||
|
||||
cur = ggml_add(ctx0,
|
||||
ggml_repeat(ctx0, layer.c_mlp_fc_b, cur),
|
||||
cur);
|
||||
|
||||
// GELU activation
|
||||
cur = ggml_gelu(ctx0, cur);
|
||||
|
||||
// projection
|
||||
// cur = proj_w*cur + proj_b
|
||||
cur = ggml_mul_mat(ctx0,
|
||||
layer.c_mlp_proj_w,
|
||||
cur);
|
||||
|
||||
cur = ggml_add(ctx0,
|
||||
ggml_repeat(ctx0, layer.c_mlp_proj_b, cur),
|
||||
cur);
|
||||
return cur;
|
||||
}
|
||||
|
||||
// evaluate the transformer
|
||||
//
|
||||
// - model: the model
|
||||
// - n_threads: number of threads to use
|
||||
// - n_past: the context size so far
|
||||
// - embd_inp: the embeddings of the tokens in the context
|
||||
// - embd_w: the predicted logits for the next token
|
||||
//
|
||||
bool gpt_neox_eval(
|
||||
const gpt_neox_model & model,
|
||||
const int n_threads,
|
||||
const int n_past,
|
||||
const std::vector<gpt_vocab::id> & embd_inp,
|
||||
std::vector<float> & embd_w,
|
||||
size_t & mem_per_token) {
|
||||
const int N = embd_inp.size();
|
||||
|
||||
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 int n_vocab = hparams.n_vocab;
|
||||
const int n_rot = hparams.n_rot;
|
||||
|
||||
static size_t buf_size = 256u*1024*1024;
|
||||
static void * buf = malloc(buf_size);
|
||||
|
||||
// use 2 scratch buffers
|
||||
// TODO: very hacky solution - reimplement in a more elegant way
|
||||
static size_t scr0_size = 256u*1024*1024;
|
||||
static void * scr0 = malloc(scr0_size);
|
||||
|
||||
static size_t scr1_size = 256u*1024*1024;
|
||||
static void * scr1 = malloc(scr1_size);
|
||||
|
||||
if (mem_per_token > 0 && mem_per_token*N > buf_size) {
|
||||
const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead
|
||||
//printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
|
||||
|
||||
// reallocate
|
||||
buf_size = buf_size_new;
|
||||
buf = realloc(buf, buf_size);
|
||||
if (buf == nullptr) {
|
||||
fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
struct ggml_init_params params = {
|
||||
/*.mem_size =*/ buf_size,
|
||||
/*.mem_buffer =*/ buf,
|
||||
/*.no_alloc =*/ false,
|
||||
};
|
||||
|
||||
struct ggml_context * ctx0 = ggml_init(params);
|
||||
struct ggml_cgraph gf = {};
|
||||
|
||||
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
|
||||
memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));
|
||||
|
||||
|
||||
// wte
|
||||
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.wte, embd);
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
struct ggml_tensor * cur;
|
||||
|
||||
ggml_set_scratch(ctx0, { 0, scr0_size, scr0, });
|
||||
|
||||
// self-attention
|
||||
{
|
||||
{
|
||||
cur = ggml_norm(ctx0, inpL);
|
||||
|
||||
cur = ggml_add(ctx0,
|
||||
ggml_mul(ctx0,
|
||||
ggml_repeat(ctx0, model.layers[il].ln_1_g, cur),
|
||||
cur),
|
||||
ggml_repeat(ctx0, model.layers[il].ln_1_b, cur));
|
||||
}
|
||||
|
||||
// compute QKV
|
||||
{
|
||||
|
||||
cur = ggml_mul_mat(ctx0,
|
||||
model.layers[il].c_attn_attn_w,
|
||||
cur);
|
||||
|
||||
cur = ggml_add(ctx0,
|
||||
ggml_repeat(ctx0, model.layers[il].c_attn_attn_b, cur),
|
||||
cur);
|
||||
}
|
||||
|
||||
struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd/n_head, n_head, N, cur->nb[1]/n_head, cur->nb[1], 0*sizeof(float)*n_embd/n_head));
|
||||
struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd/n_head, n_head, N, cur->nb[1]/n_head, cur->nb[1], 1*sizeof(float)*n_embd/n_head));
|
||||
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd/n_head, n_head, N, cur->nb[1]/n_head, cur->nb[1], 2*sizeof(float)*n_embd/n_head));
|
||||
|
||||
// using mode = 2 for GPT-NeoX mode
|
||||
Qcur = ggml_rope_inplace(ctx0, Qcur, n_past, n_rot, 2, 0);
|
||||
Kcur = ggml_rope_inplace(ctx0, Kcur, n_past, n_rot, 2, 0);
|
||||
|
||||
// store key and value to memory
|
||||
{
|
||||
Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd, N));
|
||||
|
||||
struct ggml_tensor * k = ggml_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past));
|
||||
struct ggml_tensor * v = ggml_view_2d(ctx0, model.memory_v, N, n_embd,
|
||||
( n_ctx)*ggml_element_size(model.memory_v),
|
||||
(il*n_ctx)*ggml_element_size(model.memory_v)*n_embd + n_past*ggml_element_size(model.memory_v));
|
||||
|
||||
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)
|
||||
struct ggml_tensor * Q =
|
||||
ggml_permute(ctx0,
|
||||
Qcur,
|
||||
0, 2, 1, 3);
|
||||
|
||||
// K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
|
||||
struct ggml_tensor * K =
|
||||
ggml_permute(ctx0,
|
||||
ggml_reshape_3d(ctx0,
|
||||
ggml_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_k)*n_embd),
|
||||
n_embd/n_head, n_head, n_past + N),
|
||||
0, 2, 1, 3);
|
||||
|
||||
// K * Q
|
||||
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
|
||||
|
||||
// KQ_scaled = KQ / sqrt(n_embd/n_head)
|
||||
struct ggml_tensor * KQ_scaled =
|
||||
ggml_scale_inplace(ctx0,
|
||||
KQ,
|
||||
ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))
|
||||
);
|
||||
|
||||
// KQ_masked = mask_past(KQ_scaled)
|
||||
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
|
||||
|
||||
// KQ = soft_max(KQ_masked)
|
||||
struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
|
||||
|
||||
// V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
|
||||
struct ggml_tensor * V =
|
||||
ggml_view_3d(ctx0, model.memory_v,
|
||||
n_past + N, n_embd/n_head, n_head,
|
||||
n_ctx*ggml_element_size(model.memory_v),
|
||||
n_ctx*ggml_element_size(model.memory_v)*n_embd/n_head,
|
||||
il*n_ctx*ggml_element_size(model.memory_v)*n_embd);
|
||||
|
||||
// KQV = transpose(V) * KQ_soft_max
|
||||
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
|
||||
|
||||
// KQV_merged = KQV.permute(0, 2, 1, 3)
|
||||
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
|
||||
|
||||
// cur = KQV_merged.contiguous().view(n_embd, N)
|
||||
cur = ggml_cpy(ctx0,
|
||||
KQV_merged,
|
||||
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
|
||||
|
||||
// projection
|
||||
{
|
||||
cur = ggml_mul_mat(ctx0,
|
||||
model.layers[il].c_attn_proj_w,
|
||||
cur);
|
||||
|
||||
cur = ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].c_attn_proj_b, cur), cur);
|
||||
}
|
||||
}
|
||||
|
||||
ggml_set_scratch(ctx0, { 0, scr1_size, scr1, });
|
||||
|
||||
if (hparams.par_res == 0) {
|
||||
struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpL);
|
||||
|
||||
cur = gpt_neox_ff(model.layers[il], ctx0, inpFF);
|
||||
|
||||
// input for next layer
|
||||
inpL = ggml_add(ctx0, cur, inpFF);
|
||||
} else {
|
||||
struct ggml_tensor * inpFF = cur;
|
||||
|
||||
// this is independent of the self-attention result, so it could be done in parallel to the self-attention
|
||||
// note here we pass inpL instead of cur
|
||||
cur = gpt_neox_ff(model.layers[il], ctx0, inpL);
|
||||
|
||||
// layer input + FF
|
||||
cur = ggml_add(ctx0, cur, inpFF);
|
||||
|
||||
// input for next layer
|
||||
inpL = ggml_add(ctx0, cur, inpL);
|
||||
}
|
||||
}
|
||||
|
||||
ggml_set_scratch(ctx0, { 0, scr0_size, scr0, });
|
||||
|
||||
// norm
|
||||
{
|
||||
inpL = ggml_norm(ctx0, inpL);
|
||||
|
||||
// inpL = ln_f_g*inpL + ln_f_b
|
||||
inpL = ggml_add(ctx0,
|
||||
ggml_mul(ctx0,
|
||||
ggml_repeat(ctx0, model.ln_f_g, inpL),
|
||||
inpL),
|
||||
ggml_repeat(ctx0, model.ln_f_b, inpL));
|
||||
}
|
||||
|
||||
ggml_set_scratch(ctx0, { 0, 0, nullptr, });
|
||||
|
||||
// lm_head
|
||||
{
|
||||
inpL = ggml_mul_mat(ctx0, model.lmh_g, inpL);
|
||||
|
||||
//inpL = ggml_add(ctx0,
|
||||
// ggml_repeat(ctx0, model.lmh_b, inpL),
|
||||
// inpL);
|
||||
}
|
||||
|
||||
// logits -> probs
|
||||
//inpL = ggml_soft_max_inplace(ctx0, inpL);
|
||||
|
||||
// run the computation
|
||||
ggml_build_forward_expand(&gf, inpL);
|
||||
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
|
||||
|
||||
//if (n_past%100 == 0) {
|
||||
// ggml_graph_print (&gf);
|
||||
// ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot");
|
||||
//}
|
||||
|
||||
//embd_w.resize(n_vocab*N);
|
||||
//memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);
|
||||
|
||||
// return result for just the last token
|
||||
embd_w.resize(n_vocab);
|
||||
memcpy(embd_w.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
|
||||
|
||||
if (mem_per_token == 0) {
|
||||
mem_per_token = ggml_used_mem(ctx0)/N;
|
||||
}
|
||||
//printf("used_mem = %zu\n", ggml_used_mem(ctx0));
|
||||
|
||||
ggml_free(ctx0);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
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;
|
||||
gpt_neox_model model;
|
||||
|
||||
// load the model
|
||||
{
|
||||
const int64_t t_start_us = ggml_time_us();
|
||||
|
||||
if (!gpt_neox_model_load(params.model, model, vocab)) {
|
||||
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;
|
||||
|
||||
}
|
||||
|
||||
int n_past = 0;
|
||||
|
||||
int64_t t_sample_us = 0;
|
||||
int64_t t_predict_us = 0;
|
||||
|
||||
std::vector<float> logits;
|
||||
|
||||
// tokenize the prompt
|
||||
std::vector<gpt_vocab::id> embd_inp = ::gpt_tokenize(vocab, params.prompt);
|
||||
|
||||
params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size());
|
||||
|
||||
printf("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
|
||||
for (int i = 0; i < embd_inp.size(); i++) {
|
||||
printf("%s: token[%d] = %6d, %s\n", __func__, i, embd_inp[i], vocab.id_to_token.at(embd_inp[i]).c_str());
|
||||
}
|
||||
printf("\n");
|
||||
|
||||
std::vector<gpt_vocab::id> embd;
|
||||
|
||||
// determine the required inference memory per token:
|
||||
size_t mem_per_token = 0;
|
||||
gpt_neox_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);
|
||||
|
||||
for (int i = embd.size(); i < embd_inp.size() + params.n_predict; i++) {
|
||||
// predict
|
||||
if (embd.size() > 0) {
|
||||
const int64_t t_start_us = ggml_time_us();
|
||||
|
||||
if (!gpt_neox_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) {
|
||||
printf("Failed to predict\n");
|
||||
return 1;
|
||||
}
|
||||
|
||||
t_predict_us += ggml_time_us() - t_start_us;
|
||||
}
|
||||
|
||||
n_past += embd.size();
|
||||
embd.clear();
|
||||
|
||||
if (i >= embd_inp.size()) {
|
||||
// sample next token
|
||||
const int top_k = params.top_k;
|
||||
const float top_p = params.top_p;
|
||||
const float temp = params.temp;
|
||||
|
||||
const int n_vocab = model.hparams.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.data() + (logits.size() - n_vocab), top_k, top_p, temp, rng);
|
||||
|
||||
t_sample_us += ggml_time_us() - t_start_sample_us;
|
||||
}
|
||||
|
||||
// add it to the context
|
||||
embd.push_back(id);
|
||||
} else {
|
||||
// if here, it means we are still processing the input prompt
|
||||
for (int k = i; k < embd_inp.size(); k++) {
|
||||
embd.push_back(embd_inp[k]);
|
||||
if (embd.size() > params.n_batch) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
i += embd.size() - 1;
|
||||
}
|
||||
|
||||
// display text
|
||||
for (auto id : embd) {
|
||||
printf("%s", vocab.id_to_token[id].c_str());
|
||||
}
|
||||
fflush(stdout);
|
||||
|
||||
// end of text token
|
||||
if (embd.back() == 0) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
// report timing
|
||||
{
|
||||
const int64_t t_main_end_us = ggml_time_us();
|
||||
|
||||
printf("\n\n");
|
||||
printf("%s: mem per token = %8zu bytes\n", __func__, mem_per_token);
|
||||
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 / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/n_past);
|
||||
printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
|
||||
}
|
||||
|
||||
ggml_free(model.ctx);
|
||||
|
||||
return 0;
|
||||
}
|
||||
Reference in New Issue
Block a user