From dcc7d2cee2f810ffd4f95fe3b6d905b6e45f2efe Mon Sep 17 00:00:00 2001 From: Erich Focht Date: Mon, 25 Sep 2023 21:39:11 +0200 Subject: [PATCH 1/2] Added export method to save matrix data in weights as bfloat16 while saving the rest as float32. The `--version` value must be set to -1 (preliminary). Added runbf16.c demonstrator how to run the bfloat16 matrix multiply using float32 arithmetics. Speed is ok'ish, not great, but space is halved. --- Makefile | 4 + export.py | 66 ++++ runbf16.c | 997 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 1067 insertions(+) create mode 100644 runbf16.c diff --git a/Makefile b/Makefile index 11c35c87..208d6245 100644 --- a/Makefile +++ b/Makefile @@ -7,6 +7,10 @@ CC = gcc run: run.c $(CC) -O3 -o run run.c -lm +.PHONY: runbf16 +runbf16: runbf16.c + $(CC) -O3 -o runbf16 runbf16.c -lm -fopenmp + # useful for a debug build, can then e.g. analyze with valgrind, example: # $ valgrind --leak-check=full ./run out/model.bin -n 3 rundebug: run.c diff --git a/export.py b/export.py index 4143f70f..d57e298b 100644 --- a/export.py +++ b/export.py @@ -37,6 +37,16 @@ def serialize_fp32(file, tensor): b = struct.pack(f'{len(d)}f', *d) file.write(b) +def serialize_bf16(file, tensor): + """ writes one bf16 tensor to file that is open in wb mode """ + d = tensor.detach().cpu().view(-1) + if d.dtype == torch.bfloat16: + d = d.view(torch.int16).numpy() + else: + d = d.to(torch.bfloat16).numpy() + b = struct.pack(f'{len(d)}h', *d) + file.write(b) + def serialize_int8(file, tensor): """ writes one int8 tensor to file that is open in wb mode """ d = tensor.detach().cpu().view(-1).numpy().astype(np.int8) @@ -126,6 +136,60 @@ def legacy_export(model, filepath): out_file.close() print(f"wrote {filepath}") +def legacy_export_bf16(model, filepath): + """ Original export of llama2.c bin files, i.e. version v0 """ + out_file = open(filepath, 'wb') + + # first write out the header + hidden_dim = model.layers[0].feed_forward.w1.weight.shape[0] + p = model.params + shared_classifier = torch.equal(model.tok_embeddings.weight, model.output.weight) + # legacy format uses negative/positive vocab size as a shared classifier flag + if not shared_classifier: + p.vocab_size = -p.vocab_size + n_kv_heads = p.n_heads if p.n_kv_heads is None else p.n_kv_heads + header = struct.pack('iiiiiii', p.dim, hidden_dim, p.n_layers, p.n_heads, + n_kv_heads, p.vocab_size, p.max_seq_len) + out_file.write(header) + + # next write out the embedding weights + serialize_fp32(out_file, model.tok_embeddings.weight) + + # now all the layers + # attention weights + for layer in model.layers: + serialize_fp32(out_file, layer.attention_norm.weight) + for layer in model.layers: + serialize_bf16(out_file, layer.attention.wq.weight) + for layer in model.layers: + serialize_bf16(out_file, layer.attention.wk.weight) + for layer in model.layers: + serialize_bf16(out_file, layer.attention.wv.weight) + for layer in model.layers: + serialize_bf16(out_file, layer.attention.wo.weight) + # ffn weights + for layer in model.layers: + serialize_fp32(out_file, layer.ffn_norm.weight) + for layer in model.layers: + serialize_bf16(out_file, layer.feed_forward.w1.weight) + for layer in model.layers: + serialize_bf16(out_file, layer.feed_forward.w2.weight) + for layer in model.layers: + serialize_bf16(out_file, layer.feed_forward.w3.weight) + # final rmsnorm + serialize_fp32(out_file, model.norm.weight) + # freqs_cis + serialize_fp32(out_file, model.freqs_cos[:p.max_seq_len]) + serialize_fp32(out_file, model.freqs_sin[:p.max_seq_len]) + + # final classifier weights + if not shared_classifier: + serialize_bf16(out_file, model.output.weight) + + # write to binary file + out_file.close() + print(f"wrote {filepath}") + # ----------------------------------------------------------------------------- # new version @@ -408,6 +472,8 @@ def permute_reverse(w, n_heads=config.n_heads, dim1=config.dim, dim2=config.dim) def model_export(model, filepath, version): if version == 0: legacy_export(model, filepath) + elif version == -1: + legacy_export_bf16(model, filepath) elif version == 1: version1_export(model, filepath) elif version == 2: diff --git a/runbf16.c b/runbf16.c new file mode 100644 index 00000000..91c6d58f --- /dev/null +++ b/runbf16.c @@ -0,0 +1,997 @@ +/* Inference for Llama-2 Transformer model in pure C */ + +#include +#include +#include +#include +#include +#include +#include +#if defined _WIN32 + #include "win.h" +#else + #include + #include +#endif + +#define matmul matmul_bf16 +typedef unsigned short bf16; +// ---------------------------------------------------------------------------- +// 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 + bf16* wq; // (layer, dim, n_heads * head_size) + bf16* wk; // (layer, dim, n_kv_heads * head_size) + bf16* wv; // (layer, dim, n_kv_heads * head_size) + bf16* wo; // (layer, n_heads * head_size, dim) + // weights for ffn + bf16* w1; // (layer, hidden_dim, dim) + bf16* w2; // (layer, dim, hidden_dim) + bf16* w3; // (layer, hidden_dim, dim) + // final rmsnorm + float* rms_final_weight; // (dim,) + // (optional) classifier weights for the logits, on the last layer + bf16* 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 = calloc(p->dim, sizeof(float)); + s->xb = calloc(p->dim, sizeof(float)); + s->xb2 = calloc(p->dim, sizeof(float)); + s->hb = calloc(p->hidden_dim, sizeof(float)); + s->hb2 = calloc(p->hidden_dim, sizeof(float)); + s->q = calloc(p->dim, sizeof(float)); + s->k = calloc(kv_dim, sizeof(float)); + s->v = calloc(kv_dim, sizeof(float)); + s->att = calloc(p->n_heads * p->seq_len, sizeof(float)); + s->logits = calloc(p->vocab_size, sizeof(float)); + s->key_cache = calloc(p->n_layers * p->seq_len * kv_dim, sizeof(float)); + s->value_cache = calloc(p->n_layers * p->seq_len * kv_dim, sizeof(float)); + // ensure all mallocs went fine + if (!s->x || !s->xb || !s->xb2 || !s->hb || !s->hb2 || !s->q + || !s->k || !s->v || !s->att || !s->logits || !s->key_cache + || !s->value_cache) { + 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->k); + free(s->v); + 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 = (bf16 *)ptr; + ptr += (n_layers * p->dim * (p->n_heads * head_size)) / 2; + w->wk = (bf16 *)ptr; + ptr += (n_layers * p->dim * (p->n_kv_heads * head_size)) / 2; + w->wv = (bf16 *)ptr; + ptr += (n_layers * p->dim * (p->n_kv_heads * head_size)) / 2; + w->wo = (bf16 *)ptr; + ptr += (n_layers * (p->n_heads * head_size) * p->dim) / 2; + w->rms_ffn_weight = ptr; + ptr += n_layers * p->dim; + w->w1 = (bf16 *)ptr; + ptr += (n_layers * p->dim * p->hidden_dim) / 2; + w->w2 = (bf16 *)ptr; + ptr += (n_layers * p->hidden_dim * p->dim) / 2; + w->w3 = (bf16 *)ptr; + ptr += (n_layers * p->dim * p->hidden_dim) / 2; + 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 = (bf16 *)(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 = 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 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); +} + +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_bf16(float* xout, float* x, bf16* 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; + #define STRIPESZ 64 + union lfloat { + unsigned long l[STRIPESZ/2]; + float f[STRIPESZ]; + }; + #pragma omp parallel for private(i) + for (i = 0; i < d; i++) { + float val = 0.0f; + union lfloat uf; + for (int j = 0; j < n; j+=STRIPESZ) { + for (int k = 0; k < STRIPESZ && (j+k) < n; k+=2) { + unsigned long wi = *(unsigned int *)(w + i * n + j + k); + unsigned long wv = (wi & 0xffff0000) << 32; + unsigned long wr = (wi & 0x0000ffff) << 16; + uf.l[k / 2] = wv | wr; + } + for (int k = 0; k < STRIPESZ && (j+k) < n; k++) { + val += uf.f[k] * x[j + k]; + } + } + 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); + + // 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; + } + } + + // save key,value at this time step (pos) to our kv cache + int loff = l * p->seq_len * kv_dim; // kv cache layer offset for convenience + float* key_cache_row = s->key_cache + loff + pos * kv_dim; + float* value_cache_row = s->value_cache + loff + pos * kv_dim; + memcpy(key_cache_row, s->k, kv_dim * sizeof(*key_cache_row)); + memcpy(value_cache_row, s->v, kv_dim * sizeof(*value_cache_row)); + + // multihead attention. iterate over all heads + int h; + #pragma omp parallel for private(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); +} + +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 = 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 = 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 = 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(" ", 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 , , + // 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 = 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; qvocab_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; +} + +// ---------------------------------------------------------------------------- +// 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 = ""; + 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) { + + // 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] <>\n%s\n<>\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); +} + + +// ---------------------------------------------------------------------------- +// CLI, include only if not testing +#ifndef TESTING + +void error_usage() { + fprintf(stderr, "Usage: run [options]\n"); + fprintf(stderr, "Example: run model.bin -n 256 -i \"Once upon a time\"\n"); + fprintf(stderr, "Options:\n"); + fprintf(stderr, " -t temperature in [0,inf], default 1.0\n"); + fprintf(stderr, " -p p value in top-p (nucleus) sampling in [0,1] default 0.9\n"); + fprintf(stderr, " -s random seed, default time(NULL)\n"); + fprintf(stderr, " -n number of steps to run for, default 256. 0 = max_seq_len\n"); + fprintf(stderr, " -i input prompt\n"); + fprintf(stderr, " -z optional path to custom tokenizer\n"); + fprintf(stderr, " -m mode: generate|chat, default: generate\n"); + fprintf(stderr, " -y (optional) system prompt in chat mode\n"); + exit(EXIT_FAILURE); +} + +int main(int argc, char *argv[]) { + + // default parameters + char *checkpoint_path = NULL; // e.g. out/model.bin + char *tokenizer_path = "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 = "generate"; // generate|chat + char *system_prompt = NULL; // the (optional) system prompt to use in chat mode + + // 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 { 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); + + // 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 { + 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 From 6ade3a9b1d726831e2ae7249422613fe0082edd4 Mon Sep 17 00:00:00 2001 From: Erich Focht Date: Mon, 25 Sep 2023 23:18:34 +0200 Subject: [PATCH 2/2] Untested case for serialize_bf16 when source is not coming in bfloat16 format: attempt to convert into torch.bfloat16, then view it as torch.int16. --- export.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/export.py b/export.py index d57e298b..5bbc335a 100644 --- a/export.py +++ b/export.py @@ -43,7 +43,7 @@ def serialize_bf16(file, tensor): if d.dtype == torch.bfloat16: d = d.view(torch.int16).numpy() else: - d = d.to(torch.bfloat16).numpy() + d = d.to(torch.bfloat16).view(torch.int16).numpy() b = struct.pack(f'{len(d)}h', *d) file.write(b)