mirror of https://github.com/nomic-ai/gpt4all
Scaffolding for the mpt <-> ggml project.
Adam Treat
1 year ago
parent
40b976436a
commit
159053be5a
@ -0,0 +1,571 @@
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#include "mpt.h"
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#include "llama.cpp/ggml.h"
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#include <cassert>
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#include <cmath>
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#include <cstdio>
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#include <cstring>
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#include <fstream>
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#include <map>
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#include <random>
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#include <string>
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#include <vector>
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#include <iostream>
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#include <unistd.h>
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#include <sstream>
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#include <thread>
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#include <unordered_set>
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static const size_t MB = 1024*1024;
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struct mpt_hparams {
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// FIXME: for mpt
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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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int32_t f16 = 1;
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};
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struct mpt_layer {
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// FIXME
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};
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struct mpt_buffer {
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uint8_t * addr = NULL;
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size_t size = 0;
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void resize(size_t size) {
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delete[] addr;
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addr = new uint8_t[size];
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this->size = size;
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}
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~mpt_buffer() {
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std::cout << "yes we are cleaning up" << std::endl;
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fflush(stdout);
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delete[] addr;
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}
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};
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struct mpt_kv_cache {
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struct ggml_tensor * k;
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struct ggml_tensor * v;
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struct ggml_context * ctx = NULL;
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mpt_buffer buf;
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int n; // number of tokens currently in the cache
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~mpt_kv_cache() {
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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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struct mpt_model {
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mpt_hparams hparams;
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struct mpt_kv_cache kv_self;
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struct ggml_context * ctx;
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std::map<std::string, struct ggml_tensor *> tensors;
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// FIXME
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mpt_buffer buf;
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~mpt_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 mpt_hparams & hparams,
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struct mpt_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) + 2u*MB);
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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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struct mpt_vocab {
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// FIXME
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};
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// load the model's weights from a stream
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bool mpt_model_load(const std::string &fname, std::istream &fin, mpt_model & model, mpt_vocab & vocab) {
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// FIXME
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return false;
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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, mpt_model & model, mpt_vocab & vocab) {
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auto fin = std::ifstream(fname, std::ios::binary);
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if (!fin) {
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fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
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return false;
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}
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bool loaded = mpt_model_load(fname, fin, model, vocab);
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fin.close();
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return loaded;
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}
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bool mpt_eval(
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mpt_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<int> & embd_inp,
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std::vector<float> & embd_w,
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size_t & mem_per_token) {
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// FIXME
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return false;
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}
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std::vector<int> mpt_tokenize(const mpt_vocab & vocab, const std::string & text) {
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// FIXME
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return std::vector<int>();
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}
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const std::string mpt_token_to_str(const mpt_vocab & vocab, int token) {
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// FIXME
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return std::string();
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}
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int mpt_sample_top_k_top_p(
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const mpt_vocab & vocab,
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const int32_t * last_n_tokens_data,
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int last_n_tokens_size,
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const std::vector<float> logits,
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int top_k,
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double top_p,
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double temp,
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float repeat_penalty,
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std::mt19937 & rng)
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{
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// FIXME
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return 0;
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}
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#define MPT_MAX_RNG_STATE 64*1024
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size_t mpt_get_state_size(const mpt_model &model)
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{
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// we don't know size of rng until we actually serialize it. so reserve more than enough memory for its serialized state.
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// for reference, std::mt19937(1337) serializes to 6701 bytes.
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const size_t s_rng_size = sizeof(size_t);
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const size_t s_rng = MPT_MAX_RNG_STATE;
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const size_t s_kv_size = sizeof(size_t);
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const size_t s_kv_ntok = sizeof(int);
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const size_t s_kv = model.kv_self.buf.size;
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const size_t s_total = (
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+ s_rng_size
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+ s_rng
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+ s_kv_size
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+ s_kv_ntok
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+ s_kv
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);
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fflush(stdout);
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return s_total;
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}
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size_t mpt_copy_state_data(const mpt_model &model, const std::mt19937 &rng, uint8_t *dest)
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{
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uint8_t * out = dest;
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fflush(stdout);
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// copy rng
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{
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std::stringstream rng_ss;
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rng_ss << rng;
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const size_t rng_size = rng_ss.str().size();
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char rng_buf[MPT_MAX_RNG_STATE];
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memset(&rng_buf[0], 0, MPT_MAX_RNG_STATE);
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memcpy(&rng_buf[0], rng_ss.str().data(), rng_ss.str().size());
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memcpy(out, &rng_size, sizeof(rng_size)); out += sizeof(rng_size);
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memcpy(out, &rng_buf[0], MPT_MAX_RNG_STATE); out += MPT_MAX_RNG_STATE;
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}
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// copy kv cache
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{
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const size_t kv_size = model.kv_self.buf.size;
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const int kv_ntok = model.kv_self.n;
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memcpy(out, &kv_size, sizeof(kv_size)); out += sizeof(kv_size);
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memcpy(out, &kv_ntok, sizeof(kv_ntok)); out += sizeof(kv_ntok);
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if (kv_size) {
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memcpy(out, model.kv_self.buf.addr, kv_size); out += kv_size;
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}
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}
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const size_t written = out - dest;
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const size_t expected = mpt_get_state_size(model);
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assert(written == expected);
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fflush(stdout);
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return written;
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}
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size_t mpt_set_state_data(mpt_model *model, std::mt19937 *rng, const uint8_t *src)
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{
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const uint8_t * in = src;
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// set rng
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{
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size_t rng_size;
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char rng_buf[MPT_MAX_RNG_STATE];
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memcpy(&rng_size, in, sizeof(rng_size)); in += sizeof(rng_size);
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memcpy(&rng_buf[0], in, MPT_MAX_RNG_STATE); in += MPT_MAX_RNG_STATE;
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std::stringstream rng_ss;
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rng_ss.str(std::string(&rng_buf[0], rng_size));
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rng_ss >> *rng;
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assert(rng_ss.fail() == false);
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}
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// set kv cache
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{
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size_t kv_size;
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int kv_ntok;
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memcpy(&kv_size, in, sizeof(kv_size)); in += sizeof(kv_size);
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memcpy(&kv_ntok, in, sizeof(kv_ntok)); in += sizeof(kv_ntok);
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if (kv_size) {
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assert(model->kv_self.buf.size == kv_size);
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void * k_data = model->kv_self.k->data; // remember data pointers
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void * v_data = model->kv_self.v->data; // because their value is stored in buf and overwritten by memcpy
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memcpy(model->kv_self.buf.addr, in, kv_size); in += kv_size;
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model->kv_self.k->data = k_data; // restore correct data pointers
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model->kv_self.v->data = v_data;
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}
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model->kv_self.n = kv_ntok;
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}
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const size_t nread = in - src;
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const size_t expected = mpt_get_state_size(*model);
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assert(nread == expected);
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fflush(stdout);
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return nread;
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}
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struct MPTPrivate {
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const std::string modelPath;
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bool modelLoaded;
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mpt_vocab vocab;
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mpt_model *model = nullptr;
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int64_t n_threads = 0;
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size_t mem_per_token = 0;
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std::mt19937 rng;
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};
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MPT::MPT()
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: d_ptr(new MPTPrivate) {
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d_ptr->model = new mpt_model;
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d_ptr->modelLoaded = false;
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}
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bool MPT::loadModel(const std::string &modelPath) {
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std::mt19937 rng(time(NULL));
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d_ptr->rng = rng;
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auto fin = std::ifstream(modelPath, std::ios::binary);
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// load the model
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if (!mpt_model_load(modelPath, fin, *d_ptr->model, d_ptr->vocab)) {
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std::cerr << "GPT-J ERROR: failed to load model from " << modelPath;
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return false;
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}
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d_ptr->n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
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d_ptr->modelLoaded = true;
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fflush(stdout);
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return true;
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}
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void MPT::setThreadCount(int32_t n_threads) {
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d_ptr->n_threads = n_threads;
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}
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int32_t MPT::threadCount() {
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return d_ptr->n_threads;
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}
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MPT::~MPT()
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{
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delete d_ptr->model;
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}
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bool MPT::isModelLoaded() const
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{
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return d_ptr->modelLoaded;
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}
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size_t MPT::stateSize() const
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{
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return mpt_get_state_size(*d_ptr->model);
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}
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size_t MPT::saveState(uint8_t *dest) const
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{
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return mpt_copy_state_data(*d_ptr->model, d_ptr->rng, dest);
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}
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size_t MPT::restoreState(const uint8_t *src)
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{
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return mpt_set_state_data(d_ptr->model, &d_ptr->rng, src);
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}
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void MPT::prompt(const std::string &prompt,
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std::function<bool(int32_t)> promptCallback,
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std::function<bool(int32_t, const std::string&)> responseCallback,
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std::function<bool(bool)> recalculateCallback,
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PromptContext &promptCtx) {
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if (!isModelLoaded()) {
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std::cerr << "GPT-J ERROR: prompt won't work with an unloaded model!\n";
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return;
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}
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const int64_t t_main_start_us = ggml_time_us();
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int64_t t_sample_us = 0;
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int64_t t_predict_us = 0;
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int64_t t_prompt_us = 0;
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// tokenize the prompt
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std::vector<int> embd_inp = mpt_tokenize(d_ptr->vocab, prompt);
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// save the context size
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promptCtx.n_ctx = d_ptr->model->hparams.n_ctx;
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if ((int) embd_inp.size() > promptCtx.n_ctx - 4) {
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responseCallback(-1, "ERROR: The prompt size exceeds the context window size and cannot be processed.");
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std::cerr << "GPT-J ERROR: The prompt is" << embd_inp.size() <<
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"tokens and the context window is" << promptCtx.n_ctx << "!\n";
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return;
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}
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promptCtx.n_predict = std::min(promptCtx.n_predict, promptCtx.n_ctx - (int) embd_inp.size());
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promptCtx.n_past = std::min(promptCtx.n_past, promptCtx.n_ctx);
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// determine the required inference memory per token:
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static bool initialized = false;
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static std::vector<int> p_instruct;
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static std::vector<int> r_instruct;
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if (!initialized) {
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mpt_eval(*d_ptr->model, d_ptr->n_threads, 0, { 0, 1, 2, 3 }, promptCtx.logits,
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d_ptr->mem_per_token);
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initialized = true;
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}
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// process the prompt in batches
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size_t i = 0;
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const int64_t t_start_prompt_us = ggml_time_us();
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while (i < embd_inp.size()) {
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size_t batch_end = std::min(i + promptCtx.n_batch, embd_inp.size());
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std::vector<int> batch(embd_inp.begin() + i, embd_inp.begin() + batch_end);
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// Check if the context has run out...
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if (promptCtx.n_past + batch.size() > promptCtx.n_ctx) {
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const int32_t erasePoint = promptCtx.n_ctx * promptCtx.contextErase;
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// Erase the first percentage of context from the tokens...
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std::cerr << "GPTJ: reached the end of the context window so resizing\n";
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promptCtx.tokens.erase(promptCtx.tokens.begin(), promptCtx.tokens.begin() + erasePoint);
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promptCtx.n_past = promptCtx.tokens.size();
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recalculateContext(promptCtx, recalculateCallback);
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assert(promptCtx.n_past + batch.size() <= promptCtx.n_ctx);
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}
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if (!mpt_eval(*d_ptr->model, d_ptr->n_threads, promptCtx.n_past, batch, promptCtx.logits,
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d_ptr->mem_per_token)) {
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std::cerr << "GPT-J ERROR: Failed to process prompt\n";
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return;
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}
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size_t tokens = batch_end - i;
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for (size_t t = 0; t < tokens; ++t) {
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if (promptCtx.tokens.size() == promptCtx.n_ctx)
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promptCtx.tokens.erase(promptCtx.tokens.begin());
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promptCtx.tokens.push_back(batch.at(t));
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if (!promptCallback(batch.at(t)))
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return;
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}
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promptCtx.n_past += batch.size();
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i = batch_end;
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}
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t_prompt_us += ggml_time_us() - t_start_prompt_us;
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int p_instructFound = 0;
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int r_instructFound = 0;
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std::string cachedResponse;
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std::vector<int> cachedTokens;
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std::unordered_set<std::string> reversePrompts
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= { "### Instruction", "### Prompt", "### Response", "### Human", "### Assistant" };
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// predict next tokens
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int32_t totalPredictions = 0;
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for (int i = 0; i < promptCtx.n_predict; i++) {
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// sample next token
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const int n_vocab = d_ptr->model->hparams.n_vocab;
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int id = 0;
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{
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const int64_t t_start_sample_us = ggml_time_us();
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id = mpt_sample_top_k_top_p(d_ptr->vocab,
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promptCtx.tokens.data() + promptCtx.n_ctx - promptCtx.n_ctx,
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promptCtx.n_ctx,
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promptCtx.logits,
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promptCtx.top_k, promptCtx.top_p, promptCtx.temp,
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promptCtx.repeat_penalty,
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d_ptr->rng);
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t_sample_us += ggml_time_us() - t_start_sample_us;
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}
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// Check if the context has run out...
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if (promptCtx.n_past + 1 > promptCtx.n_ctx) {
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const int32_t erasePoint = promptCtx.n_ctx * promptCtx.contextErase;
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// Erase the first percentage of context from the tokens...
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std::cerr << "GPTJ: reached the end of the context window so resizing\n";
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promptCtx.tokens.erase(promptCtx.tokens.begin(), promptCtx.tokens.begin() + erasePoint);
|
||||
promptCtx.n_past = promptCtx.tokens.size();
|
||||
recalculateContext(promptCtx, recalculateCallback);
|
||||
assert(promptCtx.n_past + 1 <= promptCtx.n_ctx);
|
||||
}
|
||||
|
||||
const int64_t t_start_predict_us = ggml_time_us();
|
||||
if (!mpt_eval(*d_ptr->model, d_ptr->n_threads, promptCtx.n_past, { id }, promptCtx.logits,
|
||||
d_ptr->mem_per_token)) {
|
||||
std::cerr << "GPT-J ERROR: Failed to predict next token\n";
|
||||
return;
|
||||
}
|
||||
t_predict_us += ggml_time_us() - t_start_predict_us;
|
||||
|
||||
promptCtx.n_past += 1;
|
||||
// display text
|
||||
++totalPredictions;
|
||||
|
||||
if (id == 50256 /*end of text*/)
|
||||
goto stop_generating;
|
||||
|
||||
const std::string str = mpt_token_to_str(d_ptr->vocab, id);
|
||||
|
||||
// Check if the provided str is part of our reverse prompts
|
||||
bool foundPartialReversePrompt = false;
|
||||
const std::string completed = cachedResponse + str;
|
||||
if (reversePrompts.find(completed) != reversePrompts.end()) {
|
||||
goto stop_generating;
|
||||
}
|
||||
|
||||
// Check if it partially matches our reverse prompts and if so, cache
|
||||
for (auto s : reversePrompts) {
|
||||
if (s.compare(0, completed.size(), completed) == 0) {
|
||||
foundPartialReversePrompt = true;
|
||||
cachedResponse = completed;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
// Regardless the token gets added to our cache
|
||||
cachedTokens.push_back(id);
|
||||
|
||||
// Continue if we have found a partial match
|
||||
if (foundPartialReversePrompt)
|
||||
continue;
|
||||
|
||||
// Empty the cache
|
||||
for (auto t : cachedTokens) {
|
||||
if (promptCtx.tokens.size() == promptCtx.n_ctx)
|
||||
promptCtx.tokens.erase(promptCtx.tokens.begin());
|
||||
promptCtx.tokens.push_back(t);
|
||||
if (!responseCallback(t, mpt_token_to_str(d_ptr->vocab, t)))
|
||||
goto stop_generating;
|
||||
}
|
||||
cachedTokens.clear();
|
||||
}
|
||||
|
||||
stop_generating:
|
||||
|
||||
#if 0
|
||||
// report timing
|
||||
{
|
||||
const int64_t t_main_end_us = ggml_time_us();
|
||||
|
||||
std::cout << "GPT-J INFO: mem per token = " << mem_per_token << " bytes\n";
|
||||
std::cout << "GPT-J INFO: sample time = " << t_sample_us/1000.0f << " ms\n";
|
||||
std::cout << "GPT-J INFO: prompt time = " << t_prompt_us/1000.0f << " ms\n";
|
||||
std::cout << "GPT-J INFO: predict time = " << t_predict_us/1000.0f << " ms / " << t_predict_us/1000.0f/totalPredictions << " ms per token\n";
|
||||
std::cout << "GPT-J INFO: total time = " << (t_main_end_us - t_main_start_us)/1000.0f << " ms\n";
|
||||
fflush(stdout);
|
||||
}
|
||||
#endif
|
||||
|
||||
return;
|
||||
}
|
||||
|
||||
void MPT::recalculateContext(PromptContext &promptCtx, std::function<bool(bool)> recalculate)
|
||||
{
|
||||
size_t i = 0;
|
||||
promptCtx.n_past = 0;
|
||||
while (i < promptCtx.tokens.size()) {
|
||||
size_t batch_end = std::min(i + promptCtx.n_batch, promptCtx.tokens.size());
|
||||
std::vector<int> batch(promptCtx.tokens.begin() + i, promptCtx.tokens.begin() + batch_end);
|
||||
|
||||
assert(promptCtx.n_past + batch.size() <= promptCtx.n_ctx);
|
||||
|
||||
if (!mpt_eval(*d_ptr->model, d_ptr->n_threads, promptCtx.n_past, batch, promptCtx.logits,
|
||||
d_ptr->mem_per_token)) {
|
||||
std::cerr << "GPTJ ERROR: Failed to process prompt\n";
|
||||
goto stop_generating;
|
||||
}
|
||||
promptCtx.n_past += batch.size();
|
||||
if (!recalculate(true))
|
||||
goto stop_generating;
|
||||
i = batch_end;
|
||||
}
|
||||
assert(promptCtx.n_past == promptCtx.tokens.size());
|
||||
|
||||
stop_generating:
|
||||
recalculate(false);
|
||||
}
|
||||
@ -0,0 +1,36 @@
|
||||
#ifndef MPT_H
|
||||
#define MPT_H
|
||||
|
||||
#include <string>
|
||||
#include <functional>
|
||||
#include <vector>
|
||||
#include "llmodel.h"
|
||||
|
||||
class MPTPrivate;
|
||||
class MPT : public LLModel {
|
||||
public:
|
||||
MPT();
|
||||
~MPT();
|
||||
|
||||
bool loadModel(const std::string &modelPath) override;
|
||||
bool isModelLoaded() const override;
|
||||
size_t stateSize() const override;
|
||||
size_t saveState(uint8_t *dest) const override;
|
||||
size_t restoreState(const uint8_t *src) override;
|
||||
void prompt(const std::string &prompt,
|
||||
std::function<bool(int32_t)> promptCallback,
|
||||
std::function<bool(int32_t, const std::string&)> responseCallback,
|
||||
std::function<bool(bool)> recalculateCallback,
|
||||
PromptContext &ctx) override;
|
||||
void setThreadCount(int32_t n_threads) override;
|
||||
int32_t threadCount() override;
|
||||
|
||||
protected:
|
||||
void recalculateContext(PromptContext &promptCtx,
|
||||
std::function<bool(bool)> recalculate) override;
|
||||
|
||||
private:
|
||||
MPTPrivate *d_ptr;
|
||||
};
|
||||
|
||||
#endif // MPT_H
|
||||
Loading…
Reference in New Issue