run.cc

/* Inference for Llama-2 Transformer model in pure C */

#include <stdio.h>
#include <stdlib.h>
#include <ctype.h>
#include <time.h>
#include <math.h>
#include <string.h>
#include <fcntl.h>
#include <unistd.h>
#include <sys/mman.h>

#include <fstream>
#include <iostream>
// ----------------------------------------------------------------------------
// Transformer model

typedef struct {
  int dim; // transformer dimension
  int hidden_dim; // for ffn layers
  int n_layers; // number of layers
  int n_heads; // number of query heads
  int n_kv_heads; // number of key/value heads (can be < query heads because of multiquery)
  int vocab_size; // vocabulary size, usually 256 (byte-level)
  int seq_len; // max sequence length
} Config;

typedef struct {
  // token embedding table
  float* token_embedding_table;    // (vocab_size, dim)
  // weights for rmsnorms
  float* rms_att_weight; // (layer, dim) rmsnorm weights
  float* rms_ffn_weight; // (layer, dim)
  // weights for matmuls. note dim == n_heads * head_size
  float* wq; // (layer, dim, n_heads * head_size)
  float* wk; // (layer, dim, n_kv_heads * head_size)
  float* wv; // (layer, dim, n_kv_heads * head_size)
  float* wo; // (layer, n_heads * head_size, dim)
  // weights for ffn
  float* w1; // (layer, hidden_dim, dim)
  float* w2; // (layer, dim, hidden_dim)
  float* w3; // (layer, hidden_dim, dim)
  // final rmsnorm
  float* rms_final_weight; // (dim,)
  // (optional) classifier weights for the logits, on the last layer
  float* wcls;
} TransformerWeights;

typedef struct {
  // current wave of activations
  float *x; // activation at current time stamp (dim,)
  float *xb; // same, but inside a residual branch (dim,)
  float *xb2; // an additional buffer just for convenience (dim,)
  float *hb; // buffer for hidden dimension in the ffn (hidden_dim,)
  float *hb2; // buffer for hidden dimension in the ffn (hidden_dim,)
  float *q; // query (dim,)
  float *k; // key (dim,)
  float *v; // value (dim,)
  float *att; // buffer for scores/attention values (n_heads, seq_len)
  float *logits; // output logits
  // kv cache
  float* key_cache;   // (layer, seq_len, dim)
  float* value_cache; // (layer, seq_len, dim)
} RunState;

typedef struct {
  Config config; // the hyperparameters of the architecture (the blueprint)
  TransformerWeights weights; // the weights of the model
  RunState state; // buffers for the "wave" of activations in the forward pass
  // some more state needed to properly clean up the memory mapping (sigh)
  int fd; // file descriptor for memory mapping
  float* data; // memory mapped data pointer
  ssize_t file_size; // size of the checkpoint file in bytes
} Transformer;

void malloc_run_state(RunState* s, Config* p) {
  // we calloc instead of malloc to keep valgrind happy
  int kv_dim = (p->dim * p->n_kv_heads) / p->n_heads;
  s->x = (float*)calloc(p->dim, sizeof(float));
  s->xb = (float*)calloc(p->dim, sizeof(float));
  s->xb2 = (float*)calloc(p->dim, sizeof(float));
  s->hb = (float*)calloc(p->hidden_dim, sizeof(float));
  s->hb2 = (float*)calloc(p->hidden_dim, sizeof(float));
  s->q = (float*)calloc(p->dim, sizeof(float));
  s->key_cache = (float*)calloc(p->n_layers * p->seq_len * kv_dim, sizeof(float));
  s->value_cache = (float*)calloc(p->n_layers * p->seq_len * kv_dim, sizeof(float));
  s->att = (float*)calloc(p->n_heads * p->seq_len, sizeof(float));
  s->logits = (float*)calloc(p->vocab_size, sizeof(float));
  // ensure all mallocs went fine
  if (!s->x || !s->xb || !s->xb2 || !s->hb || !s->hb2 || !s->q
      || !s->key_cache || !s->value_cache || !s->att || !s->logits) {
    fprintf(stderr, "malloc failed!\n");
    exit(EXIT_FAILURE);
  }
}

void free_run_state(RunState* s) {
  free(s->x);
  free(s->xb);
  free(s->xb2);
  free(s->hb);
  free(s->hb2);
  free(s->q);
  free(s->att);
  free(s->logits);
  free(s->key_cache);
  free(s->value_cache);
}

void memory_map_weights(TransformerWeights *w, Config* p, float* ptr, int shared_weights) {
  int head_size = p->dim / p->n_heads;
  // make sure the multiplications below are done in 64bit to fit the parameter counts of 13B+ models
  unsigned long long n_layers = p->n_layers;
  w->token_embedding_table = ptr;
  ptr += p->vocab_size * p->dim;
  w->rms_att_weight = ptr;
  ptr += n_layers * p->dim;
  w->wq = ptr;
  ptr += n_layers * p->dim * (p->n_heads * head_size);
  w->wk = ptr;
  ptr += n_layers * p->dim * (p->n_kv_heads * head_size);
  w->wv = ptr;
  ptr += n_layers * p->dim * (p->n_kv_heads * head_size);
  w->wo = ptr;
  ptr += n_layers * (p->n_heads * head_size) * p->dim;
  w->rms_ffn_weight = ptr;
  ptr += n_layers * p->dim;
  w->w1 = ptr;
  ptr += n_layers * p->dim * p->hidden_dim;
  w->w2 = ptr;
  ptr += n_layers * p->hidden_dim * p->dim;
  w->w3 = ptr;
  ptr += n_layers * p->dim * p->hidden_dim;
  w->rms_final_weight = ptr;
  ptr += p->dim;
  ptr += p->seq_len * head_size / 2; // skip what used to be freq_cis_real (for RoPE)
  ptr += p->seq_len * head_size / 2; // skip what used to be freq_cis_imag (for RoPE)
  w->wcls = shared_weights ? w->token_embedding_table : ptr;
}

void read_checkpoint(char* checkpoint, Config* config, TransformerWeights* weights,
    int* fd, float** data, ssize_t* file_size) {
  FILE *file = fopen(checkpoint, "rb");
  if (!file) { fprintf(stderr, "Couldn't open file %s\n", checkpoint); exit(EXIT_FAILURE); }
  // read in the config header
  if (fread(config, sizeof(Config), 1, file) != 1) { exit(EXIT_FAILURE); }
  // negative vocab size is hacky way of signaling unshared weights. bit yikes.
  int shared_weights = config->vocab_size > 0 ? 1 : 0;
  config->vocab_size = abs(config->vocab_size);
  // figure out the file size
  fseek(file, 0, SEEK_END); // move file pointer to end of file
  *file_size = ftell(file); // get the file size, in bytes
  fclose(file);
  // memory map the Transformer weights into the data pointer
  *fd = open(checkpoint, O_RDONLY); // open in read only mode
  if (*fd == -1) { fprintf(stderr, "open failed!\n"); exit(EXIT_FAILURE); }
  *data = (float *)mmap(NULL, *file_size, PROT_READ, MAP_PRIVATE, *fd, 0);
  if (*data == MAP_FAILED) { fprintf(stderr, "mmap failed!\n"); exit(EXIT_FAILURE); }
  float* weights_ptr = *data + sizeof(Config)/sizeof(float);
  memory_map_weights(weights, config, weights_ptr, shared_weights);
}

void print_transformer(Transformer* t) {
  printf("---------Model Information----------\n");
  printf("dim: %d\n", t->config.dim);
  printf("hidden_dim: %d\n", t->config.hidden_dim);
  printf("n_layers: %d\n", t->config.n_layers);
  printf("n_heads: %d\n", t->config.n_heads);
  printf("n_kv_heads: %d\n", t->config.n_kv_heads);
  printf("vocab_size: %d\n", t->config.vocab_size);
  printf("seq_len: %d\n", t->config.seq_len);
  printf("weights_size: %lu MB\n", (t->file_size - sizeof(Config)) / (1024L*1024L));
  printf("------------------------------------\n");
}


void build_transformer(Transformer *t, char* checkpoint_path) {
  // read in the Config and the Weights from the checkpoint
  read_checkpoint(checkpoint_path, &t->config, &t->weights, &t->fd, &t->data, &t->file_size);
  // allocate the RunState buffers
  malloc_run_state(&t->state, &t->config);
  print_transformer(t);
}

void free_transformer(Transformer* t) {
  // close the memory mapping
  if (t->data != MAP_FAILED) { munmap(t->data, t->file_size); }
  if (t->fd != -1) { close(t->fd); }
  // free the RunState buffers
  free_run_state(&t->state);
}

// ----------------------------------------------------------------------------
// neural net blocks; the dynamics of the Transformer

void rmsnorm(float* o, float* x, float* weight, int size) {
  // calculate sum of squares
  float ss = 0.0f;
  for (int j = 0; j < size; j++) {
    ss += x[j] * x[j];
  }
  ss /= size;
  ss += 1e-5f;
  ss = 1.0f / sqrtf(ss);
  // normalize and scale
  for (int j = 0; j < size; j++) {
    o[j] = weight[j] * (ss * x[j]);
  }
}

void softmax(float* x, int size) {
  // find max value (for numerical stability)
  float max_val = x[0];
  for (int i = 1; i < size; i++) {
    if (x[i] > max_val) {
      max_val = x[i];
    }
  }
  // exp and sum
  float sum = 0.0f;
  for (int i = 0; i < size; i++) {
    x[i] = expf(x[i] - max_val);
    sum += x[i];
  }
  // normalize
  for (int i = 0; i < size; i++) {
    x[i] /= sum;
  }
}

void matmul(float* xout, float* x, float* w, int n, int d) {
  // W (d,n) @ x (n,) -> xout (d,)
  // by far the most amount of time is spent inside this little function
  int i;
  for (i = 0; i < d; i++) {
    float val = 0.0f;
    for (int j = 0; j < n; j++) {
      val += w[i * n + j] * x[j];
    }
    xout[i] = val;
  }
}

float* forward(Transformer* transformer, int token, int pos) {

  // a few convenience variables
  Config* p = &transformer->config;
  TransformerWeights* w = &transformer->weights;
  RunState* s = &transformer->state;
  float *x = s->x;
  int dim = p->dim;
  int kv_dim = (p->dim * p->n_kv_heads) / p->n_heads;
  int kv_mul = p->n_heads / p->n_kv_heads; // integer multiplier of the kv sharing in multiquery
  int hidden_dim =  p->hidden_dim;
  int head_size = dim / p->n_heads;

  // copy the token embedding into x
  float* content_row = w->token_embedding_table + token * dim;
  memcpy(x, content_row, dim*sizeof(*x));

  // forward all the layers
  for(unsigned long long l = 0; l < p->n_layers; l++) {

    // attention rmsnorm
    rmsnorm(s->xb, x, w->rms_att_weight + l*dim, dim);

    // key and value point to the kv cache
    int loff = l * p->seq_len * kv_dim; // kv cache layer offset for convenience
    s->k = s->key_cache + loff + pos * kv_dim;
    s->v = s->value_cache + loff + pos * kv_dim;

    // qkv matmuls for this position
    matmul(s->q, s->xb, w->wq + l*dim*dim, dim, dim);
    matmul(s->k, s->xb, w->wk + l*dim*kv_dim, dim, kv_dim);
    matmul(s->v, s->xb, w->wv + l*dim*kv_dim, dim, kv_dim);

    // RoPE relative positional encoding: complex-valued rotate q and k in each head
    for (int i = 0; i < dim; i+=2) {
      int head_dim = i % head_size;
      float freq = 1.0f / powf(10000.0f, head_dim / (float)head_size);
      float val = pos * freq;
      float fcr = cosf(val);
      float fci = sinf(val);
      int rotn = i < kv_dim ? 2 : 1; // how many vectors? 2 = q & k, 1 = q only
      for (int v = 0; v < rotn; v++) {
        float* vec = v == 0 ? s->q : s->k; // the vector to rotate (query or key)
        float v0 = vec[i];
        float v1 = vec[i+1];
        vec[i]   = v0 * fcr - v1 * fci;
        vec[i+1] = v0 * fci + v1 * fcr;
      }
    }

    // multihead attention. iterate over all heads
    int h;
    for (h = 0; h < p->n_heads; h++) {
      // get the query vector for this head
      float* q = s->q + h * head_size;
      // attention scores for this head
      float* att = s->att + h * p->seq_len;
      // iterate over all timesteps, including the current one
      for (int t = 0; t <= pos; t++) {
        // get the key vector for this head and at this timestep
        float* k = s->key_cache + loff + t * kv_dim + (h / kv_mul) * head_size;
        // calculate the attention score as the dot product of q and k
        float score = 0.0f;
        for (int i = 0; i < head_size; i++) {
          score += q[i] * k[i];
        }
        score /= sqrtf(head_size);
        // save the score to the attention buffer
        att[t] = score;
      }

      // softmax the scores to get attention weights, from 0..pos inclusively
      softmax(att, pos + 1);

      // weighted sum of the values, store back into xb
      float* xb = s->xb + h * head_size;
      memset(xb, 0, head_size * sizeof(float));
      for (int t = 0; t <= pos; t++) {
        // get the value vector for this head and at this timestep
        float* v = s->value_cache + loff + t * kv_dim + (h / kv_mul) * head_size;
        // get the attention weight for this timestep
        float a = att[t];
        // accumulate the weighted value into xb
        for (int i = 0; i < head_size; i++) {
          xb[i] += a * v[i];
        }
      }
    }

    // final matmul to get the output of the attention
    matmul(s->xb2, s->xb, w->wo + l*dim*dim, dim, dim);

    // residual connection back into x
    for (int i = 0; i < dim; i++) {
      x[i] += s->xb2[i];
    }

    // ffn rmsnorm
    rmsnorm(s->xb, x, w->rms_ffn_weight + l*dim, dim);

    // Now for FFN in PyTorch we have: self.w2(F.silu(self.w1(x)) * self.w3(x))
    // first calculate self.w1(x) and self.w3(x)
    matmul(s->hb, s->xb, w->w1 + l*dim*hidden_dim, dim, hidden_dim);
    matmul(s->hb2, s->xb, w->w3 + l*dim*hidden_dim, dim, hidden_dim);

    // SwiGLU non-linearity
    for (int i = 0; i < hidden_dim; i++) {
      float val = s->hb[i];
      // silu(x)=x*σ(x), where σ(x) is the logistic sigmoid
      val *= (1.0f / (1.0f + expf(-val)));
      // elementwise multiply with w3(x)
      val *= s->hb2[i];
      s->hb[i] = val;
    }

    // final matmul to get the output of the ffn
    matmul(s->xb, s->hb, w->w2 + l*dim*hidden_dim, hidden_dim, dim);

    // residual connection
    for (int i = 0; i < dim; i++) {
      x[i] += s->xb[i];
    }
  }

  // final rmsnorm
  rmsnorm(x, x, w->rms_final_weight, dim);

  // classifier into logits
  matmul(s->logits, x, w->wcls, p->dim, p->vocab_size);
  return s->logits;
}

// ----------------------------------------------------------------------------
// The Byte Pair Encoding (BPE) Tokenizer that translates strings <-> tokens

typedef struct {
  char *str;
  int id;
} TokenIndex;

typedef struct {
  char** vocab;
  float* vocab_scores;
  TokenIndex *sorted_vocab;
  int vocab_size;
  unsigned int max_token_length;
  unsigned char byte_pieces[512]; // stores all single-byte strings
} Tokenizer;

int compare_tokens(const void *a, const void *b) {
  return strcmp(((TokenIndex*)a)->str, ((TokenIndex*)b)->str);
}

void build_tokenizer(Tokenizer* t, char* tokenizer_path, int vocab_size) {
  // i should have written the vocab_size into the tokenizer file... sigh
  t->vocab_size = vocab_size;
  // malloc space to hold the scores and the strings
  t->vocab = (char**)malloc(vocab_size * sizeof(char*));
  t->vocab_scores = (float*)malloc(vocab_size * sizeof(float));
  t->sorted_vocab = NULL; // initialized lazily
  for (int i = 0; i < 256; i++) {
    t->byte_pieces[i * 2] = (unsigned char)i;
    t->byte_pieces[i * 2 + 1] = '\0';
  }
  // read in the file
  FILE *file = fopen(tokenizer_path, "rb");
  if (!file) { fprintf(stderr, "couldn't load %s\n", tokenizer_path); exit(EXIT_FAILURE); }
  if (fread(&t->max_token_length, sizeof(int), 1, file) != 1) { fprintf(stderr, "failed read\n"); exit(EXIT_FAILURE); }
  int len;
  for (int i = 0; i < vocab_size; i++) {
    if (fread(t->vocab_scores + i, sizeof(float), 1, file) != 1) { fprintf(stderr, "failed read\n"); exit(EXIT_FAILURE);}
    if (fread(&len, sizeof(int), 1, file) != 1) { fprintf(stderr, "failed read\n"); exit(EXIT_FAILURE); }
    t->vocab[i] = (char *)malloc(len + 1);
    if (fread(t->vocab[i], len, 1, file) != 1) { fprintf(stderr, "failed read\n"); exit(EXIT_FAILURE); }
    t->vocab[i][len] = '\0'; // add the string terminating token
  }
  fclose(file);
}

void free_tokenizer(Tokenizer* t) {
  for (int i = 0; i < t->vocab_size; i++) { free(t->vocab[i]); }
  free(t->vocab);
  free(t->vocab_scores);
  free(t->sorted_vocab);
}

char* decode(Tokenizer* t, int prev_token, int token) {
  char *piece = t->vocab[token];
  // following BOS (1) token, sentencepiece decoder strips any leading whitespace (see PR #89)
  if (prev_token == 1 && piece[0] == ' ') { piece++; }
  // careful, some tokens designate raw bytes, and look like e.g. '<0x01>'
  // parse this and convert and return the actual byte
  unsigned char byte_val;
  if (sscanf(piece, "<0x%02hhX>", &byte_val) == 1) {
    piece = (char*)t->byte_pieces + byte_val * 2;
  }
  return piece;
}

void safe_printf(char *piece) {
  // piece might be a raw byte token, and we only want to print printable chars or whitespace
  // because some of the other bytes can be various control codes, backspace, etc.
  if (piece == NULL) { return; }
  if (piece[0] == '\0') { return; }
  if (piece[1] == '\0') {
    unsigned char byte_val = piece[0];
    if (!(isprint(byte_val) || isspace(byte_val))) {
      return; // bad byte, don't print it
    }
  }
  printf("%s", piece);
}

void append_str(char *piece, std::string& str) {
  // piece might be a raw byte token, and we only want to print printable chars or whitespace
  // because some of the other bytes can be various control codes, backspace, etc.
  if (piece == NULL) { return; }
  if (piece[0] == '\0') { return; }
  if (piece[1] == '\0') {
    unsigned char byte_val = piece[0];
    if (!(isprint(byte_val) || isspace(byte_val))) {
      return; // bad byte, don't print it
    }
  }
  //printf("%s", piece);
  str += piece;
}



int str_lookup(char *str, TokenIndex *sorted_vocab, int vocab_size) {
  // efficiently find the perfect match for str in vocab, return its index or -1 if not found
  TokenIndex tok = { .str = str }; // acts as the key to search for
  TokenIndex *res = (TokenIndex *)bsearch(&tok, sorted_vocab, vocab_size, sizeof(TokenIndex), compare_tokens);
  return res != NULL ? res->id : -1;
}

void encode(Tokenizer* t, char *text, int8_t bos, int8_t eos, int *tokens, int *n_tokens) {
  // encode the string text (input) into an upper-bound preallocated tokens[] array
  // bos != 0 means prepend the BOS token (=1), eos != 0 means append the EOS token (=2)
  if (text == NULL) { fprintf(stderr, "cannot encode NULL text\n"); exit(EXIT_FAILURE); }

  if (t->sorted_vocab == NULL) {
    // lazily malloc and sort the vocabulary
    t->sorted_vocab = (TokenIndex *)malloc(t->vocab_size * sizeof(TokenIndex));
    for (int i = 0; i < t->vocab_size; i++) {
      t->sorted_vocab[i].str = t->vocab[i];
      t->sorted_vocab[i].id = i;
    }
    qsort(t->sorted_vocab, t->vocab_size, sizeof(TokenIndex), compare_tokens);
  }

  // create a temporary buffer that will store merge candidates of always two consecutive tokens
  // *2 for concat, +1 for null terminator +2 for UTF8 (in case max_token_length is 1)
  char* str_buffer = (char*)malloc((t->max_token_length*2 +1 +2) * sizeof(char));
  size_t str_len = 0;

  // start at 0 tokens
  *n_tokens = 0;

  // add optional BOS (=1) token, if desired
  if (bos) tokens[(*n_tokens)++] = 1;

  // add_dummy_prefix is true by default
  // so prepend a dummy prefix token to the input string, but only if text != ""
  // TODO: pretty sure this isn't correct in the general case but I don't have the
  // energy to read more of the sentencepiece code to figure out what it's doing
  if (text[0] != '\0') {
    int dummy_prefix = str_lookup((char *)" ", t->sorted_vocab, t->vocab_size);
    tokens[(*n_tokens)++] = dummy_prefix;
  }

  // Okay UTF-8 time. This will get messy. Here is the reference from Wikipedia:
  // Code point ↔ UTF-8 conversion
  // First code point	Last code point	Byte 1	Byte 2	Byte 3	Byte 4
  // U+0000	U+007F	    0xxxxxxx
  // U+0080	U+07FF	    110xxxxx	10xxxxxx
  // U+0800	U+FFFF	    1110xxxx	10xxxxxx	10xxxxxx
  // U+10000	U+10FFFF    11110xxx	10xxxxxx	10xxxxxx	10xxxxxx

  // process the raw (UTF-8) byte sequence of the input string
  for (char *c = text; *c != '\0'; c++) {

    // reset buffer if the current byte is ASCII or a leading byte
    // 0xC0 is 11000000, so (*c & 0xC0) keeps the first 2 bits and zeros the rest
    // 0x80 is 10000000
    // in UTF-8, all continuation bytes start with "10" in first two bits
    // so in English this is: "if this byte is not a continuation byte"
    if ((*c & 0xC0) != 0x80) {
      // this byte must be either a leading byte (11...) or an ASCII char (0x...)
      // => reset our location, as we're starting a new UTF-8 codepoint
      str_len = 0;
    }

    // append the current byte to the buffer
    str_buffer[str_len++] = *c; // ++ is post-increment, incremented after this line
    str_buffer[str_len] = '\0';

    // while the next character is a continuation byte, continue appending
    // but if there are too many of them, just stop to avoid overruning str_buffer size.
    if ((*(c+1) & 0xC0) == 0x80 && str_len < 4) {
      continue;
    }

    // ok c+1 is not a continuation byte, so we've read in a full codepoint
    int id = str_lookup(str_buffer, t->sorted_vocab, t->vocab_size);

    if (id != -1) {
      // we found this codepoint in vocab, add it as a token
      tokens[(*n_tokens)++] = id;
    } else {
      // byte_fallback encoding: just encode each byte as a token
      // +3 is here because the first 3 vocab elements are <unk>, <s>, </s>
      // so the individual bytes only start at index 3
      for (int i=0; i < str_len; i++) {
        tokens[(*n_tokens)++] = (unsigned char)str_buffer[i] + 3;
      }
    }
    str_len = 0; // protect against a sequence of stray UTF8 continuation bytes
  }

  // merge the best consecutive pair each iteration, according the scores in vocab_scores
  while (1) {
    float best_score = -1e10;
    int best_id = -1;
    int best_idx = -1;

    for (int i=0; i < (*n_tokens-1); i++) {
      // check if we can merge the pair (tokens[i], tokens[i+1])
      sprintf(str_buffer, "%s%s", t->vocab[tokens[i]], t->vocab[tokens[i+1]]);
      int id = str_lookup(str_buffer, t->sorted_vocab, t->vocab_size);
      if (id != -1 && t->vocab_scores[id] > best_score) {
        // this merge pair exists in vocab! record its score and position
        best_score = t->vocab_scores[id];
        best_id = id;
        best_idx = i;
      }
    }

    if (best_idx == -1) {
      break; // we couldn't find any more pairs to merge, so we're done
    }

    // merge the consecutive pair (best_idx, best_idx+1) into new token best_id
    tokens[best_idx] = best_id;
    // delete token at position best_idx+1, shift the entire sequence back 1
    for (int i = best_idx+1; i < (*n_tokens-1); i++) {
      tokens[i] = tokens[i+1];
    }
    (*n_tokens)--; // token length decreased
  }

  // add optional EOS (=2) token, if desired
  if (eos) tokens[(*n_tokens)++] = 2;

  free(str_buffer);
}

// ----------------------------------------------------------------------------
// The Sampler, which takes logits and returns a sampled token
// sampling can be done in a few ways: greedy argmax, sampling, top-p sampling

typedef struct {
  float prob;
  int index;
} ProbIndex; // struct used when sorting probabilities during top-p sampling

typedef struct {
  int vocab_size;
  ProbIndex* probindex; // buffer used in top-p sampling
  float temperature;
  float topp;
  unsigned long long rng_state;
} Sampler;

int sample_argmax(float* probabilities, int n) {
  // return the index that has the highest probability
  int max_i = 0;
  float max_p = probabilities[0];
  for (int i = 1; i < n; i++) {
    if (probabilities[i] > max_p) {
      max_i = i;
      max_p = probabilities[i];
    }
  }
  return max_i;
}

int sample_mult(float* probabilities, int n, float coin) {
  // sample index from probabilities (they must sum to 1!)
  // coin is a random number in [0, 1), usually from random_f32()
  float cdf = 0.0f;
  for (int i = 0; i < n; i++) {
    cdf += probabilities[i];
    if (coin < cdf) {
      return i;
    }
  }
  return n - 1; // in case of rounding errors
}

int compare(const void* a, const void* b) {
  ProbIndex* a_ = (ProbIndex*) a;
  ProbIndex* b_ = (ProbIndex*) b;
  if (a_->prob > b_->prob) return -1;
  if (a_->prob < b_->prob) return 1;
  return 0;
}

int sample_topp(float* probabilities, int n, float topp, ProbIndex* probindex, float coin) {
  // top-p sampling (or "nucleus sampling") samples from the smallest set of
  // tokens that exceed probability topp. This way we never sample tokens that
  // have very low probabilities and are less likely to go "off the rails".
  // coin is a random number in [0, 1), usually from random_f32()

  int n0 = 0;
  // quicksort indices in descending order of probabilities
  // values smaller than (1 - topp) / (n - 1) cannot be part of the result
  // so for efficiency we crop these out as candidates before sorting
  const float cutoff = (1.0f - topp) / (n - 1);
  for (int i = 0; i < n; i++) {
    if (probabilities[i] >= cutoff) {
      probindex[n0].index = i;
      probindex[n0].prob = probabilities[i];
      n0++;
    }
  }
  qsort(probindex, n0, sizeof(ProbIndex), compare);

  // truncate the list where cumulative probability exceeds topp
  float cumulative_prob = 0.0f;
  int last_idx = n0 - 1; // in case of rounding errors consider all elements
  for (int i = 0; i < n0; i++) {
    cumulative_prob += probindex[i].prob;
    if (cumulative_prob > topp) {
      last_idx = i;
      break; // we've exceeded topp by including last_idx
    }
  }

  // sample from the truncated list
  float r = coin * cumulative_prob;
  float cdf = 0.0f;
  for (int i = 0; i <= last_idx; i++) {
    cdf += probindex[i].prob;
    if (r < cdf) {
      return probindex[i].index;
    }
  }
  return probindex[last_idx].index; // in case of rounding errors
}

void build_sampler(Sampler* sampler, int vocab_size, float temperature, float topp, unsigned long long rng_seed) {
  sampler->vocab_size = vocab_size;
  sampler->temperature = temperature;
  sampler->topp = topp;
  sampler->rng_state = rng_seed;
  // buffer only used with nucleus sampling; may not need but it's ~small
  sampler->probindex = (ProbIndex *)malloc(sampler->vocab_size * sizeof(ProbIndex));
}

void free_sampler(Sampler* sampler) {
  free(sampler->probindex);
}

unsigned int random_u32(unsigned long long *state) {
  // xorshift rng: https://en.wikipedia.org/wiki/Xorshift#xorshift.2A
  *state ^= *state >> 12;
  *state ^= *state << 25;
  *state ^= *state >> 27;
  return (*state * 0x2545F4914F6CDD1Dull) >> 32;
}
float random_f32(unsigned long long *state) { // random float32 in [0,1)
  return (random_u32(state) >> 8) / 16777216.0f;
}

int sample(Sampler* sampler, float* logits) {
  // sample the token given the logits and some hyperparameters
  int next;
  if (sampler->temperature == 0.0f) {
    // greedy argmax sampling: take the token with the highest probability
    next = sample_argmax(logits, sampler->vocab_size);
  } else {
    // apply the temperature to the logits
    for (int q=0; q<sampler->vocab_size; q++) { logits[q] /= sampler->temperature; }
    // apply softmax to the logits to get the probabilities for next token
    softmax(logits, sampler->vocab_size);
    // flip a (float) coin (this is our source of entropy for sampling)
    float coin = random_f32(&sampler->rng_state);
    // we sample from this distribution to get the next token
    if (sampler->topp <= 0 || sampler->topp >= 1) {
      // simply sample from the predicted probability distribution
      next = sample_mult(logits, sampler->vocab_size, coin);
    } else {
      // top-p (nucleus) sampling, clamping the least likely tokens to zero
      next = sample_topp(logits, sampler->vocab_size, sampler->topp, sampler->probindex, coin);
    }
  }
  return next;
}

int sample_greedy(Sampler* sampler, float* logits) {
  // sample the token given the logits and some hyperparameters
  int next;
  // greedy argmax sampling: take the token with the highest probability
  next = sample_argmax(logits, sampler->vocab_size);
  return next;
}

int sample_determin(const Sampler* sampler, float* logits, unsigned long long* rng_states, int idx) {
  // sample the token given the logits and some hyperparameters
  int next;
  float temperature = 1.0f;
  // apply the temperature to the logits
  for (int q=0; q<sampler->vocab_size; q++) { logits[q] /= temperature; }
  // apply softmax to the logits to get the probabilities for next token
  softmax(logits, sampler->vocab_size);
  // flip a (float) coin (this is our source of entropy for sampling)
  float coin = random_f32(&rng_states[idx]);

  next = sample_mult(logits, sampler->vocab_size, coin);
  return next;
}

typedef struct {
  int num_reqs;		// number of reqeusts;
  int max_token_len;  // maximum size of token
  int max_seq_len;	// maximum number of sequence
  char* str_reqs;		// buffer for request strings
  char* str_gens;		// buffer for generated strings
} Requests;

void build_requests(Requests* reqs, int num_reqs, int max_token_len, int max_seq_len) {
  reqs->num_reqs = num_reqs;
  reqs->max_token_len = max_token_len;
  reqs->max_seq_len = max_seq_len;
  reqs->str_reqs = (char*)calloc(num_reqs * max_token_len * max_seq_len + 1, sizeof(char));
  reqs->str_gens = (char*)calloc(num_reqs * max_token_len * max_seq_len + 1, sizeof(char));
  printf("requests size = %lu B\n", ((num_reqs * max_token_len * max_seq_len * sizeof(char) +1) * 2));
}

void free_requests(Requests* reqs) {
  free(reqs->str_reqs);
  free(reqs->str_gens);
}

char* get_str_req_ptr(Requests* reqs, int idx) {
  return reqs->str_reqs + idx * reqs->max_token_len * reqs->max_seq_len;
}

char* get_str_gen_ptr(Requests* reqs, int idx) {
  return reqs->str_gens + idx * reqs->max_token_len * reqs->max_seq_len;
}


int read_inputfile(const char* input_filename, int max_token_len, int max_seq_len, Requests* reqs) {
  std::string filename = input_filename;
  int num_reqs= 0;

  printf("max_token_len: %d, max_seq_len: %d\n", max_token_len, max_seq_len);

  std::ifstream openFile(filename.c_str());
  if (openFile.is_open() ) {
    std::string line;

    // Read the number of Requests
    std::getline(openFile, line);
    num_reqs = atoi(line.c_str());

    build_requests(reqs, num_reqs, max_token_len, max_seq_len);

    int idx = 0;
    while(std::getline(openFile, line)) {
      memcpy(get_str_req_ptr(reqs, idx), line.c_str(), line.size());
      idx++;
      if(idx >= num_reqs) break;
    }
    openFile.close();
  }
  else {
    fprintf(stderr, "cannot open the file: %s\n", input_filename);
    exit(EXIT_FAILURE);
  }

  return 0;
}

int write_outputfile(const char* output_filename, Requests* reqs) {
  std::string filename = output_filename;

  // write File
  std::ofstream writeFile(filename.c_str());
  if( writeFile.is_open() ){
    writeFile << reqs->num_reqs << "\n";
    for(int i = 0; i < reqs->num_reqs; i++) {
      writeFile << get_str_gen_ptr(reqs, i) << "\n";
    }
    writeFile.close();
  }
  else {
    fprintf(stderr, "cannot write the file: %s\n", output_filename);
    exit(EXIT_FAILURE);
  }

  return 0;
}



// ----------------------------------------------------------------------------
// utilities: time

long time_in_ms() {
  // return time in milliseconds, for benchmarking the model speed
  struct timespec time;
  clock_gettime(CLOCK_REALTIME, &time);
  return time.tv_sec * 1000 + time.tv_nsec / 1000000;
}

// ----------------------------------------------------------------------------
// generation loop

void generate(Transformer *transformer, Tokenizer *tokenizer, Sampler *sampler, char *prompt, int steps) {
  char *empty_prompt = (char*)"";
  if (prompt == NULL) { prompt = empty_prompt; }

  // encode the (string) prompt into tokens sequence
  int num_prompt_tokens = 0;
  int* prompt_tokens = (int*)malloc((strlen(prompt)+3) * sizeof(int)); // +3 for '\0', ?BOS, ?EOS
  encode(tokenizer, prompt, 1, 0, prompt_tokens, &num_prompt_tokens);
  if (num_prompt_tokens < 1) {
    fprintf(stderr, "something is wrong, expected at least 1 prompt token\n");
    exit(EXIT_FAILURE);
  }

  // start the main loop
  long start = 0;  // used to time our code, only initialized after first iteration
  int next;        // will store the next token in the sequence
  int token = prompt_tokens[0]; // kick off with the first token in the prompt
  int pos = 0;     // position in the sequence
  while (pos < steps) {
    //printf("\npos=%d/steps=%d\n", pos, steps);

    // forward the transformer to get logits for the next token
    float* logits = forward(transformer, token, pos);

    // advance the state machine
    if (pos < num_prompt_tokens - 1) {
      // if we are still processing the input prompt, force the next prompt token
      next = prompt_tokens[pos + 1];
    } else {
      // otherwise sample the next token from the logits
      next = sample(sampler, logits);
    }
    pos++;

    // data-dependent terminating condition: the BOS (=1) token delimits sequences
    if (next == 1) { break;
    }

    // print the token as string, decode it with the Tokenizer object
    char* piece = decode(tokenizer, token, next);
    safe_printf(piece); // same as printf("%s", piece), but skips "unsafe" bytes
    fflush(stdout);
    token = next;

    // init the timer here because the first iteration can be slower
    if (start == 0) { start = time_in_ms(); }

  }
  printf("\n");

  // report achieved tok/s (pos-1 because the timer starts after first iteration)
  if (pos > 1) {
    long end = time_in_ms();
    fprintf(stderr, "achieved tok/s: %f\n", (pos-1) / (double)(end-start)*1000);
  }

  free(prompt_tokens);
}

void read_stdin(const char* guide, char* buffer, size_t bufsize) {
  // read a line from stdin, up to but not including \n
  printf("%s", guide);
  if (fgets(buffer, bufsize, stdin) != NULL) {
    size_t len = strlen(buffer);
    if (len > 0 && buffer[len - 1] == '\n') {
      buffer[len - 1] = '\0'; // strip newline
    }
  }
}

// ----------------------------------------------------------------------------
// chat loop
// I manually inspected the tokens for a few chat conversations compared to
// python reference and that seemed ok, but this was not thoroughly tested and
// is not safely implemented, it's more a proof of concept atm.

void chat(Transformer *transformer, Tokenizer *tokenizer, Sampler *sampler,
    char *cli_user_prompt, char *cli_system_prompt, int steps) {

  // buffers for reading the system prompt and user prompt from stdin
  // you'll notice they are soomewhat haphazardly and unsafely set atm
  char system_prompt[512];
  char user_prompt[512];
  char rendered_prompt[1152];
  int num_prompt_tokens = 0;
  int* prompt_tokens = (int*)malloc(1152 * sizeof(int));
  int user_idx;

  // start the main loop
  int8_t user_turn = 1; // user starts
  int next;        // will store the next token in the sequence
  int token;       // stores the current token to feed into the transformer
  int prev_token;
  int pos = 0;     // position in the sequence
  while (pos < steps) {

    // when it is the user's turn to contribute tokens to the dialog...
    if (user_turn) {
      // get the (optional) system prompt at position 0
      if (pos == 0) {
        // at position 0, the user can also contribute a system prompt
        if (cli_system_prompt == NULL) {
          // system prompt was not passed in, attempt to get it from stdin
          read_stdin("Enter system prompt (optional): ", system_prompt, sizeof(system_prompt));
        } else {
          // system prompt was passed in, use it
          strcpy(system_prompt, cli_system_prompt);
        }
      }
      // get the user prompt
      if (pos == 0 && cli_user_prompt != NULL) {
        // user prompt for position 0 was passed in, use it
        strcpy(user_prompt, cli_user_prompt);
      } else {
        // otherwise get user prompt from stdin
        read_stdin("User: ", user_prompt, sizeof(user_prompt));
      }
      // render user/system prompts into the Llama 2 Chat schema
      if (pos == 0 && system_prompt[0] != '\0') {
        char system_template[] = "[INST] <<SYS>>\n%s\n<</SYS>>\n\n%s [/INST]";
        sprintf(rendered_prompt, system_template, system_prompt, user_prompt);
      } else {
        char user_template[] = "[INST] %s [/INST]";
        sprintf(rendered_prompt, user_template, user_prompt);
      }
      // encode the rendered prompt into tokens
      encode(tokenizer, rendered_prompt, 1, 0, prompt_tokens, &num_prompt_tokens);
      user_idx = 0; // reset the user index
      user_turn = 0;
      printf("Assistant: ");
    }

    // determine the token to pass into the transformer next
    if (user_idx < num_prompt_tokens) {
      // if we are still processing the input prompt, force the next prompt token
      token = prompt_tokens[user_idx++];
    } else {
      // otherwise use the next token sampled from previous turn
      token = next;
    }
    // EOS (=2) token ends the Assistant turn
    if (token == 2) { user_turn = 1; }

    // forward the transformer to get logits for the next token
    float* logits = forward(transformer, token, pos);
    next = sample(sampler, logits);
    pos++;

    if (user_idx >= num_prompt_tokens && next != 2) {
      // the Assistant is responding, so print its output
      char* piece = decode(tokenizer, token, next);
      safe_printf(piece); // same as printf("%s", piece), but skips "unsafe" bytes
      fflush(stdout);
    }
    if (next == 2) { printf("\n"); }
  }
  printf("\n");
  free(prompt_tokens);
}

// ----------------------------------------------------------------------------
// You should parallelize and optimize from this function exploiting multiple GPUs
//
int test(Transformer *transformer, Tokenizer *tokenizer, Requests * requests, int batch=1) {
  // Count the number of the generated tokens
  int gen_cnt = 0;

  // Avoid randomness to generate tokens for batch input
  // Each input request has its Sampler each
  Sampler samplers[requests->num_reqs];
  for(int idx = 0; idx < requests->num_reqs; idx++) {
    build_sampler(&samplers[idx], transformer->config.vocab_size, 1.0f, 0.9f, 314028);
  }

  // Loop for the multiple requests
  for(int idx = 0; idx < requests->num_reqs; idx++) {
    std::string gen_str = "";
    char* prompt = get_str_req_ptr(requests, idx);
    int* prompt_tokens = (int*)malloc((strlen(prompt)+3) * sizeof(int)); // +3 for '\0', ?BOS, ?EOS

    // encode the (string) prompt into tokens sequence
    int num_prompt_tokens = 0;
    encode(tokenizer, prompt, 1, 0, prompt_tokens, &num_prompt_tokens);
    if (num_prompt_tokens < 1) {
      fprintf(stderr, "something is wrong, expected at least 1 prompt token\n");
      exit(EXIT_FAILURE);
    }

    // start the main loop
    long start = 0;  // used to time our code, only initialized after first iteration
    int next;        // will store the next token in the sequence
    int token = prompt_tokens[0]; // kick off with the first token in the prompt
    int pos = 0;     // position in the sequence
    int steps = requests->max_seq_len; // max sequence length
    while (pos < steps) {
      // forward the transformer to get logits for the next token
      float* logits = forward(transformer, token, pos);

      // advance the state machine
      if (pos < num_prompt_tokens - 1) {
        // if we are still processing the input prompt, force the next prompt token
        next = prompt_tokens[pos + 1];
      } else {
        // otherwise sample the next token from the logits
        next = sample(&samplers[idx], logits);
        //next = sample_greedy(sampler, logits);
        //next = sample_determin(sampler, logits, rng_states, idx);
      }
      pos++;

      // data-dependent terminating condition: the BOS (=1) token delimits sequences
      if (next == 1) { 
        break;
      }

      // print the token as string, decode it with the Tokenizer object
      char* piece = decode(tokenizer, token, next);

      // You don't need to print every tokens are generated.
      // {
      safe_printf(piece); // same as printf("%s", piece), but skips "unsafe" bytes
      fflush(stdout);
      // }

      // gen_str += piece;

      append_str(piece, gen_str);

      token = next;

      // init the timer here because the first iteration can be slower
      // this timer is not important
      if (start == 0) { start = time_in_ms(); }

    }
    printf("\n");

    gen_str += "\n";
    strcpy(get_str_gen_ptr(requests, idx), gen_str.c_str());
    free(prompt_tokens);

    // report achieved tok/s (pos-1 because the timer starts after first iteration)
    if (pos > 1) {
      long end = time_in_ms();
      fprintf(stderr, "achieved tok/s: %f\n", (pos-1) / (double)(end-start)*1000);
      gen_cnt += pos-1;
    }
  }

  for(int idx = 0; idx < requests->num_reqs; idx++) {
    free_sampler(&samplers[idx]);
  }
  return gen_cnt;
}




// ----------------------------------------------------------------------------
// CLI, include only if not testing
#ifndef TESTING

void error_usage() {
  fprintf(stderr, "Usage:   run <checkpoint> [options]\n");
  fprintf(stderr, "Example: run model.bin -n 256 -i \"Once upon a time\"\n");
  fprintf(stderr, "Example: run model.bin -m test -f <input_filename> -o <output_filename>\n");
  fprintf(stderr, "Options:\n");
  fprintf(stderr, "  -t <float>  temperature in [0,inf], default 1.0 (ignore the arg for test mode)\n");
  fprintf(stderr, "  -p <float>  p value in top-p (nucleus) sampling in [0,1] default 0.9 (ignore the arg for test mode)\n");
  fprintf(stderr, "  -s <int>    random seed, default time(NULL) (ignore the arg for test mode)\n");
  fprintf(stderr, "  -n <int>    number of steps to run for, default 256. 0 = max_seq_len (for test mode steps = max_seq_len)\n");
  fprintf(stderr, "  -i <string> input prompt (ignore the arg for test mode)\n");
  fprintf(stderr, "  -z <string> optional path to custom tokenizer\n");
  fprintf(stderr, "  -m <string> mode: generate|chat|test, default: generate\n");
  fprintf(stderr, "  -y <string> (optional) system prompt in chat mode\n");
  fprintf(stderr, "  -f <string> (only for test mode) input filename\n");
  fprintf(stderr, "  -o <string> (only for test mode) output filename\n");
  fprintf(stderr, "  -b <string> batch size\n");

  exit(EXIT_FAILURE);
}



int main(int argc, char *argv[]) {

  // default parameters
  char *checkpoint_path = NULL;  // e.g. out/model.bin
  char *tokenizer_path = (char*)"./assets/tokenizer.bin";
  float temperature = 1.0f;   // 0.0 = greedy deterministic. 1.0 = original. don't set higher
  float topp = 0.9f;          // top-p in nucleus sampling. 1.0 = off. 0.9 works well, but slower
  int steps = 256;            // number of steps to run for
  char *prompt = NULL;        // prompt string
  unsigned long long rng_seed = 0; // seed rng with time by default
  char *mode = (char*)"generate";    // generate|chat|test
  char *system_prompt = NULL; // the (optional) system prompt to use in chat mode
  char *input_filename = NULL; // Input Filename
  char *output_filename = NULL; // Output Filename
  int batch = 1;

  // poor man's C argparse so we can override the defaults above from the command line
  if (argc >= 2) { checkpoint_path = argv[1]; } else { error_usage(); }
  for (int i = 2; i < argc; i+=2) {
    // do some basic validation
    if (i + 1 >= argc) { error_usage(); } // must have arg after flag
    if (argv[i][0] != '-') { error_usage(); } // must start with dash
    if (strlen(argv[i]) != 2) { error_usage(); } // must be -x (one dash, one letter)
    // read in the args
    if (argv[i][1] == 't') { temperature = atof(argv[i + 1]); }
    else if (argv[i][1] == 'p') { topp = atof(argv[i + 1]); }
    else if (argv[i][1] == 's') { rng_seed = atoi(argv[i + 1]); }
    else if (argv[i][1] == 'n') { steps = atoi(argv[i + 1]); }
    else if (argv[i][1] == 'i') { prompt = argv[i + 1]; }
    else if (argv[i][1] == 'z') { tokenizer_path = argv[i + 1]; }
    else if (argv[i][1] == 'm') { mode = argv[i + 1]; }
    else if (argv[i][1] == 'y') { system_prompt = argv[i + 1]; }
    else if (argv[i][1] == 'f') { input_filename = argv[i + 1]; }
    else if (argv[i][1] == 'o') { output_filename = argv[i + 1]; }
    else if (argv[i][1] == 'b') { batch = atoi(argv[i + 1]); }
    else { error_usage(); }
  }

  // parameter validation/overrides
  if (rng_seed <= 0) rng_seed = (unsigned int)time(NULL);
  if (temperature < 0.0) temperature = 0.0;
  if (topp < 0.0 || 1.0 < topp) topp = 0.9;
  if (steps < 0) steps = 0;

  // build the Transformer via the model .bin file
  Transformer transformer;
  build_transformer(&transformer, checkpoint_path);
  if (steps == 0 || steps > transformer.config.seq_len) steps = transformer.config.seq_len; // ovrerride to ~max length

  // build the Tokenizer via the tokenizer .bin file
  Tokenizer tokenizer;
  build_tokenizer(&tokenizer, tokenizer_path, transformer.config.vocab_size);

  // build the Sampler
  Sampler sampler;
  build_sampler(&sampler, transformer.config.vocab_size, temperature, topp, rng_seed);

  Requests requests;

  // run!
  if (strcmp(mode, "generate") == 0) {
    generate(&transformer, &tokenizer, &sampler, prompt, steps);
  } 
  else if (strcmp(mode, "chat") == 0) {
    //chat(&transformer, &tokenizer, &sampler, prompt, system_prompt, steps);
  } 
  else if  (strcmp(mode, "test") == 0) {
    int num_reqs;
    steps = transformer.config.seq_len;
    if(input_filename == NULL || output_filename == NULL) {
      error_usage();
    }
    if(EXIT_FAILURE == read_inputfile(input_filename, tokenizer.max_token_length, steps, &requests)) {
      fprintf(stderr, "cannot read input file: %s\n", input_filename);
      exit(EXIT_FAILURE);
    }

    // Don't modify this parts for evaluation
    // {
    long start, end;
    start = time_in_ms();
    int num_gen_tokens = test(&transformer, &tokenizer, &requests, batch);
    end = time_in_ms();

    // Your goal is to achieve best throughput(=reduce elapsed time)! 
    fprintf(stdout, "elapsed time(s): %f, achieved throughput(tok/s): %f\n", (double)(end-start)/1000, (num_gen_tokens) / (double)(end-start)*1000);
    //}

    if(EXIT_FAILURE == write_outputfile(output_filename, &requests)) {
      fprintf(stderr, "cannot write output file: %s\n", input_filename);
      exit(EXIT_FAILURE);
    }

    free_requests(&requests);

  } else {
    fprintf(stderr, "unknown mode: %s\n", mode);
    error_usage();
  }

  // memory and file handles cleanup
  free_sampler(&sampler);
  free_tokenizer(&tokenizer);
  free_transformer(&transformer);
  return 0;
}
#endif