ollama/llama/binding/binding.cpp

// MIT License

// Copyright (c) 2023 go-skynet authors

// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:

// The above copyright notice and this permission notice shall be included in
// all copies or substantial portions of the Software.

// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
// SOFTWARE.

#include "common.h"
#include "llama.h"

#include "binding.h"

#include <cassert>
#include <cinttypes>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <iostream>
#include <regex>
#include <sstream>
#include <string>
#include <vector>
#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__))
#include <signal.h>
#include <unistd.h>
#elif defined(_WIN32)
#define WIN32_LEAN_AND_MEAN
#define NOMINMAX
#include <signal.h>
#include <windows.h>
#endif

#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__)) ||          \
    defined(_WIN32)
void sigint_handler(int signo) {
  if (signo == SIGINT) {
    _exit(130);
  }
}
#endif

int eval(void *p, void *c, char *text) {
  gpt_params *params = (gpt_params *)params;
  llama_context *ctx = (llama_context *)ctx;

  auto n_past = 0;
  auto last_n_tokens_data = std::vector<llama_token>(params->repeat_last_n, 0);

  auto tokens = std::vector<llama_token>(params->n_ctx);
  auto n_prompt_tokens =
      llama_tokenize(ctx, text, tokens.data(), tokens.size(), true);

  if (n_prompt_tokens < 1) {
    fprintf(stderr, "%s : failed to tokenize prompt\n", __func__);
    return 1;
  }

  return llama_eval(ctx, tokens.data(), n_prompt_tokens, n_past,
                    params->n_threads);
}

int llama_predict(void *p, void *c, char *result, bool debug) {
  gpt_params *params = (gpt_params *)params;
  llama_context *ctx = (llama_context *)ctx;

  const int n_ctx = llama_n_ctx(ctx);

  if (params->seed <= 0) {
    params->seed = time(NULL);
  }

  std::mt19937 rng(params->seed);
  std::string path_session = params->path_prompt_cache;
  std::vector<llama_token> session_tokens;

  if (!path_session.empty()) {
    if (debug) {
      fprintf(stderr, "%s: attempting to load saved session from '%s'\n",
              __func__, path_session.c_str());
    }
    // fopen to check for existing session
    FILE *fp = std::fopen(path_session.c_str(), "rb");
    if (fp != NULL) {
      std::fclose(fp);

      session_tokens.resize(n_ctx);
      size_t n_token_count_out = 0;
      if (!llama_load_session_file(
              ctx, path_session.c_str(), session_tokens.data(),
              session_tokens.capacity(), &n_token_count_out)) {
        fprintf(stderr, "%s: error: failed to load session file '%s'\n",
                __func__, path_session.c_str());
        return 1;
      }
      session_tokens.resize(n_token_count_out);
      llama_set_rng_seed(ctx, params->seed);
      if (debug) {
        fprintf(stderr, "%s: loaded a session with prompt size of %d tokens\n",
                __func__, (int)session_tokens.size());
      }
    } else {
      if (debug) {
        fprintf(stderr, "%s: session file does not exist, will create\n",
                __func__);
      }
    }
  }

  std::vector<llama_token> embd_inp;
  if (!params->prompt.empty() || session_tokens.empty()) {
    // Add a space in front of the first character to match OG llama tokenizer
    // behavior
    params->prompt.insert(0, 1, ' ');

    embd_inp = ::llama_tokenize(ctx, params->prompt, true);
  } else {
    embd_inp = session_tokens;
  }

  // debug message about similarity of saved session, if applicable
  size_t n_matching_session_tokens = 0;
  if (session_tokens.size()) {
    for (llama_token id : session_tokens) {
      if (n_matching_session_tokens >= embd_inp.size() ||
          id != embd_inp[n_matching_session_tokens]) {
        break;
      }
      n_matching_session_tokens++;
    }
    if (debug) {
      if (params->prompt.empty() &&
          n_matching_session_tokens == embd_inp.size()) {
        fprintf(stderr, "%s: using full prompt from session file\n", __func__);
      } else if (n_matching_session_tokens >= embd_inp.size()) {
        fprintf(stderr, "%s: session file has exact match for prompt!\n",
                __func__);
      } else if (n_matching_session_tokens < (embd_inp.size() / 2)) {
        fprintf(stderr,
                "%s: warning: session file has low similarity to prompt (%zu / "
                "%zu tokens); will mostly be reevaluated\n",
                __func__, n_matching_session_tokens, embd_inp.size());
      } else {
        fprintf(stderr, "%s: session file matches %zu / %zu tokens of prompt\n",
                __func__, n_matching_session_tokens, embd_inp.size());
      }
    }
  }
  // if we will use the cache for the full prompt without reaching the end of
  // the cache, force reevaluation of the last token token to recalculate the
  // cached logits
  if (!embd_inp.empty() && n_matching_session_tokens == embd_inp.size() &&
      session_tokens.size() > embd_inp.size()) {
    session_tokens.resize(embd_inp.size() - 1);
  }
  // number of tokens to keep when resetting context
  if (params->n_keep < 0 || params->n_keep > (int)embd_inp.size()) {
    params->n_keep = (int)embd_inp.size();
  }

  // determine newline token
  auto llama_token_newline = ::llama_tokenize(ctx, "\n", false);

  // TODO: replace with ring-buffer
  std::vector<llama_token> last_n_tokens(n_ctx);
  std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);

  bool need_to_save_session =
      !path_session.empty() && n_matching_session_tokens < embd_inp.size();
  int n_past = 0;
  int n_remain = params->n_predict;
  int n_consumed = 0;
  int n_session_consumed = 0;

  std::vector<llama_token> embd;
  std::string res = "";

  // do one empty run to warm up the model
  {
    const std::vector<llama_token> tmp = {
        llama_token_bos(),
    };
    llama_eval(ctx, tmp.data(), tmp.size(), 0, params->n_threads);
    llama_reset_timings(ctx);
  }

  while (n_remain != 0) {
    // predict
    if (embd.size() > 0) {
      // infinite text generation via context swapping
      // if we run out of context:
      // - take the n_keep first tokens from the original prompt (via n_past)
      // - take half of the last (n_ctx - n_keep) tokens and recompute the
      // logits in batches
      if (n_past + (int)embd.size() > n_ctx) {
        const int n_left = n_past - params->n_keep;

        // always keep the first token - BOS
        n_past = std::max(1, params->n_keep);

        // insert n_left/2 tokens at the start of embd from last_n_tokens
        embd.insert(embd.begin(),
                    last_n_tokens.begin() + n_ctx - n_left / 2 - embd.size(),
                    last_n_tokens.end() - embd.size());

        // stop saving session if we run out of context
        path_session.clear();
      }

      // try to reuse a matching prefix from the loaded session instead of
      // re-eval (via n_past)
      if (n_session_consumed < (int)session_tokens.size()) {
        size_t i = 0;
        for (; i < embd.size(); i++) {
          if (embd[i] != session_tokens[n_session_consumed]) {
            session_tokens.resize(n_session_consumed);
            break;
          }

          n_past++;
          n_session_consumed++;

          if (n_session_consumed >= (int)session_tokens.size()) {
            ++i;
            break;
          }
        }
        if (i > 0) {
          embd.erase(embd.begin(), embd.begin() + i);
        }
      }

      // evaluate tokens in batches
      // embd is typically prepared beforehand to fit within a batch, but not
      // always
      for (int i = 0; i < (int)embd.size(); i += params->n_batch) {
        int n_eval = (int)embd.size() - i;
        if (n_eval > params->n_batch) {
          n_eval = params->n_batch;
        }

        if (llama_eval(ctx, &embd[i], n_eval, n_past, params->n_threads)) {
          fprintf(stderr, "%s : failed to eval\n", __func__);
          return 1;
        }

        n_past += n_eval;
      }

      if (embd.size() > 0 && !path_session.empty()) {
        session_tokens.insert(session_tokens.end(), embd.begin(), embd.end());
        n_session_consumed = session_tokens.size();
      }
    }

    embd.clear();

    if ((int)embd_inp.size() <= n_consumed) {
      // out of user input, sample next token
      const float temp = params->temp;
      const int32_t top_k =
          params->top_k <= 0 ? llama_n_vocab(ctx) : params->top_k;
      const float top_p = params->top_p;
      const float tfs_z = params->tfs_z;
      const float typical_p = params->typical_p;
      const int32_t repeat_last_n =
          params->repeat_last_n < 0 ? n_ctx : params->repeat_last_n;
      const float repeat_penalty = params->repeat_penalty;
      const float alpha_presence = params->presence_penalty;
      const float alpha_frequency = params->frequency_penalty;
      const int mirostat = params->mirostat;
      const float mirostat_tau = params->mirostat_tau;
      const float mirostat_eta = params->mirostat_eta;
      const bool penalize_nl = params->penalize_nl;

      // optionally save the session on first sample (for faster prompt loading
      // next time)
      if (!path_session.empty() && need_to_save_session &&
          !params->prompt_cache_ro) {
        need_to_save_session = false;
        llama_save_session_file(ctx, path_session.c_str(),
                                session_tokens.data(), session_tokens.size());
      }

      llama_token id = 0;

      {
        auto logits = llama_get_logits(ctx);
        auto n_vocab = llama_n_vocab(ctx);

        // Apply params.logit_bias map
        for (auto it = params->logit_bias.begin();
             it != params->logit_bias.end(); it++) {
          logits[it->first] += it->second;
        }

        std::vector<llama_token_data> candidates;
        candidates.reserve(n_vocab);
        for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
          candidates.emplace_back(
              llama_token_data{token_id, logits[token_id], 0.0f});
        }

        llama_token_data_array candidates_p = {candidates.data(),
                                               candidates.size(), false};

        // Apply penalties
        float nl_logit = logits[llama_token_nl()];
        auto last_n_repeat =
            std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx);
        llama_sample_repetition_penalty(
            ctx, &candidates_p,
            last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
            last_n_repeat, repeat_penalty);
        llama_sample_frequency_and_presence_penalties(
            ctx, &candidates_p,
            last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
            last_n_repeat, alpha_frequency, alpha_presence);
        if (!penalize_nl) {
          logits[llama_token_nl()] = nl_logit;
        }

        if (temp <= 0) {
          // Greedy sampling
          id = llama_sample_token_greedy(ctx, &candidates_p);
        } else {
          if (mirostat == 1) {
            static float mirostat_mu = 2.0f * mirostat_tau;
            const int mirostat_m = 100;
            llama_sample_temperature(ctx, &candidates_p, temp);
            id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau,
                                             mirostat_eta, mirostat_m,
                                             &mirostat_mu);
          } else if (mirostat == 2) {
            static float mirostat_mu = 2.0f * mirostat_tau;
            llama_sample_temperature(ctx, &candidates_p, temp);
            id = llama_sample_token_mirostat_v2(
                ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
          } else {
            // Temperature sampling
            llama_sample_top_k(ctx, &candidates_p, top_k, 1);
            llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1);
            llama_sample_typical(ctx, &candidates_p, typical_p, 1);
            llama_sample_top_p(ctx, &candidates_p, top_p, 1);
            llama_sample_temperature(ctx, &candidates_p, temp);
            id = llama_sample_token(ctx, &candidates_p);
          }
        }

        last_n_tokens.erase(last_n_tokens.begin());
        last_n_tokens.push_back(id);
      }

      // add it to the context
      embd.push_back(id);

      // decrement remaining sampling budget
      --n_remain;

      // call the token callback, no need to check if one is actually
      // registered, that will be handled on the Go side.
      auto token_str = llama_token_to_str(ctx, id);
      if (!tokenCallback(ctx, (char *)token_str)) {
        break;
      }
    } else {
      // some user input remains from prompt or interaction, forward it to
      // processing
      while ((int)embd_inp.size() > n_consumed) {
        embd.push_back(embd_inp[n_consumed]);
        last_n_tokens.erase(last_n_tokens.begin());
        last_n_tokens.push_back(embd_inp[n_consumed]);
        ++n_consumed;
        if ((int)embd.size() >= params->n_batch) {
          break;
        }
      }
    }

    for (auto id : embd) {
      res += llama_token_to_str(ctx, id);
    }

    // check for stop prompt
    if (params->antiprompt.size()) {
      std::string last_output;
      for (auto id : last_n_tokens) {
        last_output += llama_token_to_str(ctx, id);
      }
      // Check if each of the reverse prompts appears at the end of the output.
      for (std::string &antiprompt : params->antiprompt) {
        // size_t extra_padding = params.interactive ? 0 : 2;
        size_t extra_padding = 2;
        size_t search_start_pos =
            last_output.length() >
                    static_cast<size_t>(antiprompt.length() + extra_padding)
                ? last_output.length() -
                      static_cast<size_t>(antiprompt.length() + extra_padding)
                : 0;

        if (last_output.find(antiprompt.c_str(), search_start_pos) !=
            std::string::npos) {
          goto end;
        }
      }
    }

    // end of text token
    if (!embd.empty() && embd.back() == llama_token_eos()) {
      break;
    }
  }

  if (!path_session.empty() && params->prompt_cache_all &&
      !params->prompt_cache_ro) {
    if (debug) {
      fprintf(stderr, "\n%s: saving final output to session file '%s'\n",
              __func__, path_session.c_str());
    }
    llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(),
                            session_tokens.size());
  }

end:
#if defined(_WIN32)
  signal(SIGINT, SIG_DFL);
#endif

  if (debug) {
    llama_print_timings(ctx);
    llama_reset_timings(ctx);
  }

  strcpy(result, res.c_str());
  return 0;
}

void llama_binding_free_model(void *ctx) { llama_free((llama_context *)ctx); }
void llama_free_params(void *params) { delete (gpt_params *)params; }

void *llama_allocate_params(
    const char *prompt, int seed, int threads, int tokens, int top_k,
    float top_p, float temp, float repeat_penalty, int repeat_last_n,
    bool ignore_eos, bool memory_f16, int n_batch, int n_keep,
    const char **antiprompt, int antiprompt_count, float tfs_z, float typical_p,
    float frequency_penalty, float presence_penalty, int mirostat,
    float mirostat_eta, float mirostat_tau, bool penalize_nl,
    const char *logit_bias, const char *session_file, bool prompt_cache_all,
    bool mlock, bool mmap, const char *maingpu, const char *tensorsplit,
    bool prompt_cache_ro) {
  gpt_params *params = new gpt_params;
  params->seed = seed;
  params->n_threads = threads;
  params->n_predict = tokens;
  params->repeat_last_n = repeat_last_n;
  params->prompt_cache_ro = prompt_cache_ro;
  params->top_k = top_k;
  params->top_p = top_p;
  params->memory_f16 = memory_f16;
  params->temp = temp;
  params->use_mmap = mmap;
  params->use_mlock = mlock;
  params->repeat_penalty = repeat_penalty;
  params->n_batch = n_batch;
  params->n_keep = n_keep;
  if (maingpu[0] != '\0') {
    params->main_gpu = std::stoi(maingpu);
  }

  if (tensorsplit[0] != '\0') {
    std::string arg_next = tensorsplit;
    // split string by , and /
    const std::regex regex{R"([,/]+)"};
    std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
    std::vector<std::string> split_arg{it, {}};
    GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);

    for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) {
      if (i < split_arg.size()) {
        params->tensor_split[i] = std::stof(split_arg[i]);
      } else {
        params->tensor_split[i] = 0.0f;
      }
    }
  }

  params->prompt_cache_all = prompt_cache_all;
  params->path_prompt_cache = session_file;

  if (ignore_eos) {
    params->logit_bias[llama_token_eos()] = -INFINITY;
  }

  if (antiprompt_count > 0) {
    for (int i = 0; i < antiprompt_count; i++) {
      params->antiprompt.push_back(std::string(antiprompt[i]));
    }
  }

  params->tfs_z = tfs_z;
  params->typical_p = typical_p;
  params->presence_penalty = presence_penalty;
  params->mirostat = mirostat;
  params->mirostat_eta = mirostat_eta;
  params->mirostat_tau = mirostat_tau;
  params->penalize_nl = penalize_nl;

  std::stringstream ss(logit_bias);
  llama_token key;
  char sign;
  std::string value_str;
  if (ss >> key && ss >> sign && std::getline(ss, value_str) &&
      (sign == '+' || sign == '-')) {
    params->logit_bias[key] =
        std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
  }
  params->frequency_penalty = frequency_penalty;
  params->prompt = prompt;

  return params;
}

void *load_model(const char *fname, int n_ctx, int n_seed, bool memory_f16,
                 bool mlock, bool embeddings, bool mmap, bool low_vram,
                 bool vocab_only, int n_gpu_layers, int n_batch,
                 const char *maingpu, const char *tensorsplit, bool numa) {
  auto lparams = llama_context_default_params();

  lparams.n_ctx = n_ctx;
  lparams.seed = n_seed;
  lparams.f16_kv = memory_f16;
  lparams.embedding = embeddings;
  lparams.use_mlock = mlock;
  lparams.n_gpu_layers = n_gpu_layers;
  lparams.use_mmap = mmap;
  lparams.low_vram = low_vram;
  lparams.vocab_only = vocab_only;

  if (maingpu[0] != '\0') {
    lparams.main_gpu = std::stoi(maingpu);
  }

  if (tensorsplit[0] != '\0') {
    std::string arg_next = tensorsplit;
    // split string by , and /
    const std::regex regex{R"([,/]+)"};
    std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
    std::vector<std::string> split_arg{it, {}};
    GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);

    for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) {
      if (i < split_arg.size()) {
        lparams.tensor_split[i] = std::stof(split_arg[i]);
      } else {
        lparams.tensor_split[i] = 0.0f;
      }
    }
  }

  lparams.n_batch = n_batch;

  llama_init_backend(numa);

  struct llama_model *model = llama_load_model_from_file(fname, lparams);
  if (!model) {
    return nullptr;
  }

  return llama_new_context_with_model(model, lparams);
}