add gptneox gguf example

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klosax 2023-07-30 16:59:26 +02:00 committed by GitHub
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#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;
}