mirror of
https://github.com/nomic-ai/gpt4all.git
synced 2024-10-01 01:06:10 -04:00
820 lines
25 KiB
C++
820 lines
25 KiB
C++
#define GPTJ_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE
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#include "gptj_impl.h"
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#include "utils.h"
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#include "llmodel_shared.h"
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#include <cassert>
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#include <cinttypes>
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#include <cmath>
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#include <cstdio>
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#include <cstring>
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#include <map>
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#include <string>
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#include <vector>
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#include <iostream>
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#if defined(_WIN32) && defined(_MSC_VER)
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#define WIN32_LEAN_AND_MEAN
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#ifndef NOMINMAX
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#define NOMINMAX
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#endif
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#include <windows.h>
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#include <io.h>
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#include <stdio.h>
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#else
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#include <unistd.h>
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#endif
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#include <sstream>
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#include <unordered_set>
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#include <ggml.h>
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namespace {
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const char *modelType_ = "GPT-J";
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}
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// default hparams (GPT-J 6B)
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struct gptj_hparams {
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int32_t n_vocab = 50400;
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int32_t n_ctx = 2048;
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int32_t n_embd = 4096;
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int32_t n_head = 16;
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int32_t n_layer = 28;
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int32_t n_rot = 64;
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float norm_eps = 1e-5;
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};
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struct gptj_layer {
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// normalization
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struct ggml_tensor * ln_1_g;
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struct ggml_tensor * ln_1_b;
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// attention
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struct ggml_tensor * c_attn_q_proj_w;
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struct ggml_tensor * c_attn_k_proj_w;
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struct ggml_tensor * c_attn_v_proj_w;
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struct ggml_tensor * c_attn_proj_w;
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// ff
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struct ggml_tensor * c_mlp_fc_w;
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struct ggml_tensor * c_mlp_fc_b;
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struct ggml_tensor * c_mlp_proj_w;
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struct ggml_tensor * c_mlp_proj_b;
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};
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struct gptj_model {
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gptj_hparams hparams;
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// normalization
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struct ggml_tensor * ln_f_g;
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struct ggml_tensor * ln_f_b;
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struct ggml_tensor * wte; // position embedding
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struct ggml_tensor * lmh_g; // language model head
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struct ggml_tensor * lmh_b; // language model bias
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std::vector<gptj_layer> layers;
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// key + value memory
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struct llm_kv_cache kv_self;
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//
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struct ggml_context * ctx;
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std::map<std::string, struct ggml_tensor *> tensors;
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llm_buffer eval_buf;
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llm_buffer scr0_buf;
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llm_buffer scr1_buf;
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~gptj_model() {
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if (ctx) {
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ggml_free(ctx);
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}
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}
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};
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static bool kv_cache_init(
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const struct gptj_hparams & hparams,
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struct llm_kv_cache & cache,
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ggml_type wtype,
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int n_ctx) {
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const int n_embd = hparams.n_embd;
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const int n_layer = hparams.n_layer;
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const int64_t n_mem = (int64_t)n_layer*n_ctx;
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const int64_t n_elements = n_embd*n_mem;
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cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2_MiB);
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struct ggml_init_params params;
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params.mem_size = cache.buf.size;
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params.mem_buffer = cache.buf.addr;
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params.no_alloc = false;
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cache.ctx = ggml_init(params);
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if (!cache.ctx) {
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fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__);
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return false;
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}
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cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
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cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
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return true;
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}
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// load the model's weights from a file path
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bool gptj_model_load(const std::string &fname, gptj_model & model, gpt_vocab & vocab, size_t * mem_req = nullptr) {
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printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
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if(mem_req != nullptr) {
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*mem_req = 0;
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}
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// create the ggml context
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struct gguf_init_params params = {
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/*.no_alloc = */ false,
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/*.ctx = */ &model.ctx,
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};
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gguf_context *ggufctx = gguf_init_from_file(fname.c_str(), params);
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if (!ggufctx) {
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fprintf(stderr, "%s: gguf_init_from_file() failed\n", __func__);
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return false;
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}
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// load hparams
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{
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auto & hparams = model.hparams;
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bool ok = false;
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int keyidx;
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do {
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keyidx = gguf_find_key(ggufctx, "gptj.context_length");
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if (keyidx == -1) { break; }
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hparams.n_ctx = gguf_get_val_u32(ggufctx, keyidx);
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keyidx = gguf_find_key(ggufctx, "gptj.embedding_length");
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if (keyidx == -1) { break; }
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hparams.n_embd = gguf_get_val_u32(ggufctx, keyidx);
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keyidx = gguf_find_key(ggufctx, "gptj.attention.head_count");
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if (keyidx == -1) { break; }
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hparams.n_head = gguf_get_val_u32(ggufctx, keyidx);
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keyidx = gguf_find_key(ggufctx, "gptj.block_count");
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if (keyidx == -1) { break; }
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hparams.n_layer = gguf_get_val_u32(ggufctx, keyidx);
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keyidx = gguf_find_key(ggufctx, "gptj.rope.dimension_count");
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if (keyidx == -1) { break; }
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hparams.n_rot = gguf_get_val_u32(ggufctx, keyidx);
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keyidx = gguf_find_key(ggufctx, "gptj.attention.layer_norm_epsilon");
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if (keyidx == -1) { break; }
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hparams.norm_eps = gguf_get_val_f32(ggufctx, keyidx);
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ok = true;
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} while (false);
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if (!ok) {
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fprintf(stderr, "%s: required hparam missing!\n", __func__);
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return false;
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}
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printf("%s: n_ctx = %d\n", __func__, hparams.n_ctx);
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printf("%s: n_embd = %d\n", __func__, hparams.n_embd);
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printf("%s: n_head = %d\n", __func__, hparams.n_head);
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printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
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printf("%s: n_rot = %d\n", __func__, hparams.n_rot);
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}
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// load vocab
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{
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auto & hparams = model.hparams;
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int keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.model");
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if (keyidx == -1) {
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fprintf(stderr, "%s: tokenizer model not found!\n", __func__);
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return false;
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}
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if (strcmp(gguf_get_val_str(ggufctx, keyidx), "gpt2") != 0) {
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fprintf(stderr, "%s: tokenizer model not supported!\n", __func__);
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return false;
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}
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int tokens_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.tokens");
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if (tokens_keyidx == -1) {
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fprintf(stderr, "%s: gpt2 tokenizer vocab not found!\n", __func__);
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return false;
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}
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hparams.n_vocab = gguf_get_arr_n(ggufctx, tokens_keyidx);
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printf("%s: gpt2 tokenizer vocab = %d\n", __func__, int(hparams.n_vocab));
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for (int i = 0; i < hparams.n_vocab; i++) {
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std::string word = gguf_get_arr_str(ggufctx, tokens_keyidx, i);
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vocab.token_to_id[word] = i;
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vocab.id_to_token[i] = word;
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}
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}
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auto & ctx = model.ctx;
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size_t ctx_size = ggml_get_mem_size(ctx);
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printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size / (1024.0 * 1024.0));
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if (mem_req != nullptr) {
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*mem_req = ctx_size;
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gguf_free(ggufctx);
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return false;
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}
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// prepare memory for the weights
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{
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const auto & hparams = model.hparams;
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model.layers.resize(hparams.n_layer);
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model.wte = ggml_get_tensor(ctx, "token_embd.weight");
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model.ln_f_g = ggml_get_tensor(ctx, "output_norm.weight");
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model.ln_f_b = ggml_get_tensor(ctx, "output_norm.bias");
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model.lmh_g = ggml_get_tensor(ctx, "output.weight");
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model.lmh_b = ggml_get_tensor(ctx, "output.bias");
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auto name = [](int i, std::string n) {
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static std::string key;
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key = "blk." + std::to_string(i) + "." + n;
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return key.c_str();
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};
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for (int i = 0; i < hparams.n_layer; ++i) {
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auto & layer = model.layers[i];
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layer.ln_1_g = ggml_get_tensor(ctx, name(i, "attn_norm.weight"));
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layer.ln_1_b = ggml_get_tensor(ctx, name(i, "attn_norm.bias"));
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layer.c_attn_q_proj_w = ggml_get_tensor(ctx, name(i, "attn_q.weight"));
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layer.c_attn_k_proj_w = ggml_get_tensor(ctx, name(i, "attn_k.weight"));
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layer.c_attn_v_proj_w = ggml_get_tensor(ctx, name(i, "attn_v.weight"));
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layer.c_attn_proj_w = ggml_get_tensor(ctx, name(i, "attn_output.weight"));
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layer.c_mlp_fc_w = ggml_get_tensor(ctx, name(i, "ffn_up.weight"));
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layer.c_mlp_fc_b = ggml_get_tensor(ctx, name(i, "ffn_up.bias"));
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layer.c_mlp_proj_w = ggml_get_tensor(ctx, name(i, "ffn_down.weight"));
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layer.c_mlp_proj_b = ggml_get_tensor(ctx, name(i, "ffn_down.bias"));
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}
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}
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// key + value memory
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{
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const auto & hparams = model.hparams;
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if (!kv_cache_init(hparams, model.kv_self, GGML_TYPE_F16, model.hparams.n_ctx)) {
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fprintf(stderr, "%s: kv_cache_init() failed for self-attention cache\n", __func__);
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ggml_free(ctx);
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return false;
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}
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const size_t memory_size = ggml_nbytes(model.kv_self.k) + ggml_nbytes(model.kv_self.v);
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printf("%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
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}
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model.scr0_buf.resize(256u * 1024 * 1024);
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model.scr1_buf.resize(256u * 1024 * 1024);
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return true;
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}
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// evaluate the transformer
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//
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// - model: the model
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// - n_threads: number of threads to use
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// - n_past: the context size so far
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// - embd_inp: the embeddings of the tokens in the context
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// - embd_w: the predicted logits for the next token
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//
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// The GPT-J model requires about 16MB of memory per input token.
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//
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bool gptj_eval(
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gptj_model & model,
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const int n_threads,
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const int n_past,
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const std::vector<gpt_vocab::id> & embd_inp,
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std::vector<float> & embd_w,
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size_t & mem_per_token) {
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const int N = embd_inp.size();
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const auto & hparams = model.hparams;
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const int n_embd = hparams.n_embd;
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const int n_layer = hparams.n_layer;
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const int n_ctx = hparams.n_ctx;
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const int n_head = hparams.n_head;
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const int n_vocab = hparams.n_vocab;
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const int n_rot = hparams.n_rot;
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const size_t init_buf_size = 1024_MiB;
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if (!model.eval_buf.addr || model.eval_buf.size < init_buf_size)
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model.eval_buf.resize(init_buf_size);
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if (mem_per_token > 0 && mem_per_token*N > model.eval_buf.size) {
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const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead
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printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, model.eval_buf.size, buf_size_new);
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// reallocate
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model.eval_buf.resize(buf_size_new);
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if (model.eval_buf.addr == nullptr) {
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fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, model.eval_buf.size);
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return false;
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}
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}
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struct ggml_init_params params = {
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.mem_size = model.eval_buf.size,
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.mem_buffer = model.eval_buf.addr,
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.no_alloc = false
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};
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struct ggml_context * ctx0 = ggml_init(params);
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struct ggml_cgraph gf = {};
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struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
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memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));
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// wte
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struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.wte, embd);
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for (int il = 0; il < n_layer; ++il) {
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struct ggml_tensor * cur;
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ggml_set_scratch(ctx0, {0, model.scr0_buf.size, model.scr0_buf.addr, });
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// norm
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{
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cur = ggml_norm(ctx0, inpL, model.hparams.norm_eps);
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// cur = ln_1_g*cur + ln_1_b
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cur = ggml_add(ctx0,
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ggml_mul(ctx0,
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ggml_repeat(ctx0, model.layers[il].ln_1_g, cur),
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cur),
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ggml_repeat(ctx0, model.layers[il].ln_1_b, cur));
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}
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struct ggml_tensor * inpSA = cur;
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// self-attention
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{
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struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].c_attn_q_proj_w, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0);
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struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].c_attn_k_proj_w, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0);
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// store key and value to memory
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{
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struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_mul_mat(ctx0, model.layers[il].c_attn_v_proj_w, cur));
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struct ggml_tensor * k = ggml_view_1d(ctx0, model.kv_self.k, N*n_embd, (ggml_element_size(model.kv_self.k)*n_embd)*(il*n_ctx + n_past));
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struct ggml_tensor * v = ggml_view_2d(ctx0, model.kv_self.v, N, n_embd,
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( n_ctx)*ggml_element_size(model.kv_self.v),
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(il*n_ctx)*ggml_element_size(model.kv_self.v)*n_embd + n_past*ggml_element_size(model.kv_self.v));
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ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
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ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
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}
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// Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
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struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
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// K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
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struct ggml_tensor * K =
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ggml_permute(ctx0,
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ggml_reshape_3d(ctx0,
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ggml_view_1d(ctx0, model.kv_self.k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.kv_self.k)*n_embd),
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n_embd/n_head, n_head, n_past + N),
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0, 2, 1, 3);
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// K * Q
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struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
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// KQ_scaled = KQ / sqrt(n_embd/n_head)
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struct ggml_tensor * KQ_scaled =
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ggml_scale(ctx0,
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KQ,
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ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))
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);
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// KQ_masked = mask_past(KQ_scaled)
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struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
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// KQ = soft_max(KQ_masked)
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struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
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// V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
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struct ggml_tensor * V =
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ggml_view_3d(ctx0, model.kv_self.v,
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n_past + N, n_embd/n_head, n_head,
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n_ctx*ggml_element_size(model.kv_self.v),
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n_ctx*ggml_element_size(model.kv_self.v)*n_embd/n_head,
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il*n_ctx*ggml_element_size(model.kv_self.v)*n_embd);
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// KQV = transpose(V) * KQ_soft_max
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struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
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// KQV_merged = KQV.permute(0, 2, 1, 3)
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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 (no bias)
|
|
cur = ggml_mul_mat(ctx0,
|
|
model.layers[il].c_attn_proj_w,
|
|
cur);
|
|
}
|
|
|
|
struct ggml_tensor * inpFF = cur;
|
|
|
|
ggml_set_scratch(ctx0, {0, model.scr1_buf.size, model.scr1_buf.addr, });
|
|
// feed-forward network
|
|
// this is independent of the self-attention result, so it could be done in parallel to the self-attention
|
|
{
|
|
// note here we pass inpSA instead of cur
|
|
cur = ggml_mul_mat(ctx0,
|
|
model.layers[il].c_mlp_fc_w,
|
|
inpSA);
|
|
|
|
cur = ggml_add(ctx0,
|
|
ggml_repeat(ctx0, model.layers[il].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,
|
|
model.layers[il].c_mlp_proj_w,
|
|
cur);
|
|
|
|
cur = ggml_add(ctx0,
|
|
ggml_repeat(ctx0, model.layers[il].c_mlp_proj_b, cur),
|
|
cur);
|
|
}
|
|
|
|
// self-attention + FF
|
|
cur = ggml_add(ctx0, cur, inpFF);
|
|
|
|
// input for next layer
|
|
inpL = ggml_add(ctx0, cur, inpL);
|
|
}
|
|
|
|
ggml_set_scratch(ctx0, {0, model.scr0_buf.size, model.scr0_buf.addr, });
|
|
|
|
// norm
|
|
{
|
|
inpL = ggml_norm(ctx0, inpL, model.hparams.norm_eps);
|
|
|
|
// 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(ctx0, inpL);
|
|
|
|
ggml_build_forward_expand(&gf, inpL);
|
|
|
|
// run the computation
|
|
{
|
|
std::unique_ptr<uint8_t []> data;
|
|
auto plan = ggml_graph_plan(&gf, n_threads);
|
|
if (plan.work_size > 0) {
|
|
data.reset(new uint8_t[plan.work_size]);
|
|
plan.work_data = data.get();
|
|
}
|
|
ggml_graph_compute(&gf, &plan);
|
|
}
|
|
|
|
//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;
|
|
}
|
|
|
|
#define GPTJ_MAX_RNG_STATE 64*1024
|
|
|
|
size_t gptj_get_state_size(const gptj_model &model)
|
|
{
|
|
// we don't know size of rng until we actually serialize it. so reserve more than enough memory for its serialized state.
|
|
// for reference, std::mt19937(1337) serializes to 6701 bytes.
|
|
const size_t s_rng_size = sizeof(size_t);
|
|
const size_t s_rng = GPTJ_MAX_RNG_STATE;
|
|
const size_t s_kv_size = sizeof(size_t);
|
|
const size_t s_kv_ntok = sizeof(int);
|
|
const size_t s_kv = model.kv_self.buf.size;
|
|
const size_t s_total = (
|
|
+ s_rng_size
|
|
+ s_rng
|
|
+ s_kv_size
|
|
+ s_kv_ntok
|
|
+ s_kv
|
|
);
|
|
fflush(stdout);
|
|
return s_total;
|
|
}
|
|
|
|
size_t gptj_copy_state_data(const gptj_model &model, const std::mt19937 &rng, uint8_t *dest)
|
|
{
|
|
uint8_t * out = dest;
|
|
fflush(stdout);
|
|
// copy rng
|
|
{
|
|
std::stringstream rng_ss;
|
|
rng_ss << rng;
|
|
|
|
const size_t rng_size = rng_ss.str().size();
|
|
char rng_buf[GPTJ_MAX_RNG_STATE];
|
|
|
|
memset(&rng_buf[0], 0, GPTJ_MAX_RNG_STATE);
|
|
memcpy(&rng_buf[0], rng_ss.str().data(), rng_ss.str().size());
|
|
|
|
memcpy(out, &rng_size, sizeof(rng_size)); out += sizeof(rng_size);
|
|
memcpy(out, &rng_buf[0], GPTJ_MAX_RNG_STATE); out += GPTJ_MAX_RNG_STATE;
|
|
}
|
|
|
|
// copy kv cache
|
|
{
|
|
const size_t kv_size = model.kv_self.buf.size;
|
|
const int kv_ntok = model.kv_self.n;
|
|
|
|
memcpy(out, &kv_size, sizeof(kv_size)); out += sizeof(kv_size);
|
|
memcpy(out, &kv_ntok, sizeof(kv_ntok)); out += sizeof(kv_ntok);
|
|
|
|
if (kv_size) {
|
|
memcpy(out, model.kv_self.buf.addr, kv_size); out += kv_size;
|
|
}
|
|
}
|
|
|
|
const size_t written = out - dest;
|
|
assert(written == gptj_get_state_size(model));
|
|
fflush(stdout);
|
|
return written;
|
|
}
|
|
|
|
size_t gptj_set_state_data(gptj_model *model, std::mt19937 *rng, const uint8_t *src)
|
|
{
|
|
const uint8_t * in = src;
|
|
|
|
// set rng
|
|
{
|
|
size_t rng_size;
|
|
char rng_buf[GPTJ_MAX_RNG_STATE];
|
|
|
|
memcpy(&rng_size, in, sizeof(rng_size)); in += sizeof(rng_size);
|
|
memcpy(&rng_buf[0], in, GPTJ_MAX_RNG_STATE); in += GPTJ_MAX_RNG_STATE;
|
|
|
|
std::stringstream rng_ss;
|
|
rng_ss.str(std::string(&rng_buf[0], rng_size));
|
|
rng_ss >> *rng;
|
|
|
|
assert(rng_ss.fail() == false);
|
|
}
|
|
|
|
// set kv cache
|
|
{
|
|
size_t kv_size;
|
|
int kv_ntok;
|
|
|
|
memcpy(&kv_size, in, sizeof(kv_size)); in += sizeof(kv_size);
|
|
memcpy(&kv_ntok, in, sizeof(kv_ntok)); in += sizeof(kv_ntok);
|
|
|
|
if (kv_size) {
|
|
assert(model->kv_self.buf.size == kv_size);
|
|
|
|
void * k_data = model->kv_self.k->data; // remember data pointers
|
|
void * v_data = model->kv_self.v->data; // because their value is stored in buf and overwritten by memcpy
|
|
|
|
memcpy(model->kv_self.buf.addr, in, kv_size); in += kv_size;
|
|
|
|
model->kv_self.k->data = k_data; // restore correct data pointers
|
|
model->kv_self.v->data = v_data;
|
|
|
|
}
|
|
|
|
model->kv_self.n = kv_ntok;
|
|
}
|
|
|
|
const size_t nread = in - src;
|
|
assert(nread == gptj_get_state_size(*model));
|
|
fflush(stdout);
|
|
return nread;
|
|
}
|
|
|
|
struct GPTJPrivate {
|
|
const std::string modelPath;
|
|
bool modelLoaded;
|
|
gpt_vocab vocab;
|
|
gptj_model *model = nullptr;
|
|
int64_t n_threads = 0;
|
|
size_t mem_per_token = 0;
|
|
std::mt19937 rng;
|
|
};
|
|
|
|
GPTJ::GPTJ()
|
|
: d_ptr(new GPTJPrivate) {
|
|
d_ptr->model = new gptj_model;
|
|
d_ptr->model->ctx = nullptr;
|
|
d_ptr->modelLoaded = false;
|
|
}
|
|
|
|
size_t GPTJ::requiredMem(const std::string &modelPath) {
|
|
gptj_model dummy_model;
|
|
gpt_vocab dummy_vocab;
|
|
size_t mem_req;
|
|
gptj_model_load(modelPath, dummy_model, dummy_vocab, &mem_req);
|
|
return mem_req;
|
|
}
|
|
|
|
bool GPTJ::loadModel(const std::string &modelPath) {
|
|
std::mt19937 rng(time(NULL));
|
|
d_ptr->rng = rng;
|
|
|
|
// load the model
|
|
if (!gptj_model_load(modelPath, *d_ptr->model, d_ptr->vocab)) {
|
|
std::cerr << "GPT-J ERROR: failed to load model from " << modelPath;
|
|
return false;
|
|
}
|
|
|
|
d_ptr->n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
|
|
d_ptr->modelLoaded = true;
|
|
fflush(stdout);
|
|
return true;
|
|
}
|
|
|
|
void GPTJ::setThreadCount(int32_t n_threads) {
|
|
d_ptr->n_threads = n_threads;
|
|
}
|
|
|
|
int32_t GPTJ::threadCount() const
|
|
{
|
|
return d_ptr->n_threads;
|
|
}
|
|
|
|
GPTJ::~GPTJ()
|
|
{
|
|
delete d_ptr->model;
|
|
}
|
|
|
|
bool GPTJ::isModelLoaded() const
|
|
{
|
|
return d_ptr->modelLoaded;
|
|
}
|
|
|
|
size_t GPTJ::stateSize() const
|
|
{
|
|
return gptj_get_state_size(*d_ptr->model);
|
|
}
|
|
|
|
size_t GPTJ::saveState(uint8_t *dest) const
|
|
{
|
|
return gptj_copy_state_data(*d_ptr->model, d_ptr->rng, dest);
|
|
}
|
|
|
|
size_t GPTJ::restoreState(const uint8_t *src)
|
|
{
|
|
return gptj_set_state_data(d_ptr->model, &d_ptr->rng, src);
|
|
}
|
|
|
|
std::vector<LLModel::Token> GPTJ::tokenize(PromptContext &, const std::string &str) const
|
|
{
|
|
return ::gpt_tokenize(d_ptr->vocab, str);
|
|
}
|
|
|
|
LLModel::Token GPTJ::sampleToken(PromptContext &promptCtx) const
|
|
{
|
|
const size_t n_prev_toks = std::min((size_t) promptCtx.repeat_last_n, promptCtx.tokens.size());
|
|
return gpt_sample_top_k_top_p(d_ptr->model->hparams.n_vocab,
|
|
promptCtx.tokens.data() + promptCtx.tokens.size() - n_prev_toks,
|
|
n_prev_toks,
|
|
promptCtx.logits,
|
|
promptCtx.top_k, promptCtx.top_p, promptCtx.temp,
|
|
promptCtx.repeat_penalty,
|
|
d_ptr->rng);
|
|
}
|
|
|
|
std::string GPTJ::tokenToString(Token id) const
|
|
{
|
|
return d_ptr->vocab.id_to_token[id];
|
|
}
|
|
|
|
bool GPTJ::evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens) const
|
|
{
|
|
// determine the required inference memory per token:
|
|
static bool initialized = false;
|
|
if (!initialized) {
|
|
gptj_eval(*d_ptr->model, d_ptr->n_threads, 0, { 0, 1, 2, 3 }, ctx.logits,
|
|
d_ptr->mem_per_token);
|
|
initialized = true;
|
|
}
|
|
|
|
return gptj_eval(*d_ptr->model, d_ptr->n_threads, ctx.n_past, tokens, ctx.logits, d_ptr->mem_per_token);
|
|
}
|
|
|
|
int32_t GPTJ::contextLength() const
|
|
{
|
|
return d_ptr->model->hparams.n_ctx;
|
|
}
|
|
|
|
const std::vector<LLModel::Token> &GPTJ::endTokens() const
|
|
{
|
|
static const std::vector<LLModel::Token> fres = {50256};
|
|
return fres;
|
|
}
|
|
|
|
std::string get_arch_name(gguf_context *ctx_gguf) {
|
|
std::string arch_name;
|
|
const int kid = gguf_find_key(ctx_gguf, "general.architecture");
|
|
enum gguf_type ktype = gguf_get_kv_type(ctx_gguf, kid);
|
|
if (ktype != GGUF_TYPE_STRING) {
|
|
throw std::runtime_error("ERROR: Can't get general architecture from gguf file.");
|
|
}
|
|
return gguf_get_val_str(ctx_gguf, kid);
|
|
}
|
|
|
|
#if defined(_WIN32)
|
|
#define DLL_EXPORT __declspec(dllexport)
|
|
#else
|
|
#define DLL_EXPORT __attribute__ ((visibility ("default")))
|
|
#endif
|
|
|
|
extern "C" {
|
|
DLL_EXPORT bool is_g4a_backend_model_implementation() {
|
|
return true;
|
|
}
|
|
|
|
DLL_EXPORT const char *get_model_type() {
|
|
return modelType_;
|
|
}
|
|
|
|
DLL_EXPORT const char *get_build_variant() {
|
|
return GGML_BUILD_VARIANT;
|
|
}
|
|
|
|
DLL_EXPORT bool magic_match(const char * fname) {
|
|
struct ggml_context * ctx_meta = NULL;
|
|
struct gguf_init_params params = {
|
|
/*.no_alloc = */ true,
|
|
/*.ctx = */ &ctx_meta,
|
|
};
|
|
gguf_context *ctx_gguf = gguf_init_from_file(fname, params);
|
|
if (!ctx_gguf)
|
|
return false;
|
|
|
|
bool isValid = gguf_get_version(ctx_gguf) <= 3;
|
|
isValid = isValid && get_arch_name(ctx_gguf) == "gptj";
|
|
|
|
gguf_free(ctx_gguf);
|
|
return isValid;
|
|
}
|
|
|
|
DLL_EXPORT LLModel *construct() {
|
|
return new GPTJ;
|
|
}
|
|
}
|