diff options
Diffstat (limited to 'examples')
| -rw-r--r-- | examples/common.cpp | 4 | ||||
| -rw-r--r-- | examples/main/main.cpp | 4 | ||||
| -rw-r--r-- | examples/perplexity/perplexity.cpp | 70 |
3 files changed, 51 insertions, 27 deletions
diff --git a/examples/common.cpp b/examples/common.cpp index f1c3bae..6af4402 100644 --- a/examples/common.cpp +++ b/examples/common.cpp @@ -438,8 +438,8 @@ std::string gpt_random_prompt(std::mt19937 & rng) { // TODO: not great allocating this every time std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::string & text, bool add_bos) { // initialize to prompt numer of chars, since n_tokens <= n_prompt_chars - std::vector<llama_token> res(text.size() + (int)add_bos); - int n = llama_tokenize(ctx, text.c_str(), res.data(), res.size(), add_bos); + std::vector<llama_token> res(text.size() + (int) add_bos); + const int n = llama_tokenize(ctx, text.c_str(), res.data(), res.size(), add_bos); assert(n >= 0); res.resize(n); diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 5ac151e..045093c 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -313,7 +313,8 @@ int main(int argc, char ** argv) { if (n_past + (int) embd.size() > n_ctx) { const int n_left = n_past - params.n_keep; - 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()); @@ -331,7 +332,6 @@ int main(int argc, char ** argv) { } // try to reuse a matching prefix from the loaded session instead of re-eval (via n_past) - // REVIEW if (n_session_consumed < (int) session_tokens.size()) { size_t i = 0; for ( ; i < embd.size(); i++) { diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 299a199..9212dee 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -25,46 +25,68 @@ void perplexity(llama_context * ctx, const gpt_params & params) { // Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research // Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw` // Output: `perplexity: 13.5106 [114/114]` + // BOS tokens will be added for each chunk before eval auto tokens = ::llama_tokenize(ctx, params.prompt, true); - int count = 0; - int seq_count = tokens.size() / params.n_ctx; - int n_vocab = llama_n_vocab(ctx); + int count = 0; + + const int n_chunk = tokens.size() / params.n_ctx; + const int n_vocab = llama_n_vocab(ctx); + const int n_batch = params.n_batch; double nll = 0.0; - fprintf(stderr, "%s : calculating perplexity over %d chunks, batch_size=%d\n", __func__, seq_count, params.n_batch); + fprintf(stderr, "%s: calculating perplexity over %d chunks, batch_size=%d\n", __func__, n_chunk, n_batch); + + for (int i = 0; i < n_chunk; ++i) { + const int start = i * params.n_ctx; + const int end = start + params.n_ctx; - for (int i = 0; i < seq_count; ++i) { - int start = i * params.n_ctx; - int end = start + params.n_ctx; + const int num_batches = (params.n_ctx + n_batch - 1) / n_batch; std::vector<float> logits; - int num_batches = (params.n_ctx + params.n_batch - 1) / params.n_batch; - auto start_t = std::chrono::high_resolution_clock::now(); + + const auto t_start = std::chrono::high_resolution_clock::now(); + for (int j = 0; j < num_batches; ++j) { - int batch_start = start + j * params.n_batch; - int batch_size = std::min(end - batch_start, params.n_batch); - if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * params.n_batch, params.n_threads)) { + const int batch_start = start + j * n_batch; + const int batch_size = std::min(end - batch_start, n_batch); + + // save original token and restore it after eval + const auto token_org = tokens[batch_start]; + + // add BOS token for the first batch of each chunk + if (j == 0) { + tokens[batch_start] = llama_token_bos(); + } + + if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * n_batch, params.n_threads)) { fprintf(stderr, "%s : failed to eval\n", __func__); return; } - auto batch_logits = llama_get_logits(ctx); + + // restore the original token in case it was set to BOS + tokens[batch_start] = token_org; + + const auto batch_logits = llama_get_logits(ctx); logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab); } - auto end_t = std::chrono::high_resolution_clock::now(); + + const auto t_end = std::chrono::high_resolution_clock::now(); + if (i == 0) { - const float seconds = std::chrono::duration<float>(end_t - start_t).count(); - printf("%.2f seconds per pass - ETA ", seconds); - int total_seconds = (int)(seconds * seq_count); + const float t_total = std::chrono::duration<float>(t_end - t_start).count(); + fprintf(stderr, "%s: %.2f seconds per pass - ETA ", __func__, t_total); + int total_seconds = (int)(t_total * n_chunk); if (total_seconds >= 60*60) { - printf("%d hours ", total_seconds / (60*60)); + fprintf(stderr, "%d hours ", total_seconds / (60*60)); total_seconds = total_seconds % (60*60); } - printf("%d minutes\n", total_seconds / 60); + fprintf(stderr, "%d minutes\n", total_seconds / 60); } + // We get the logits for all the tokens in the context window (params.n_ctx) // from llama_eval above. Now, based on https://huggingface.co/docs/transformers/perplexity, - // calculate the perplexity over the last half the window (so the model always has + // calculate the perplexity over the last half of the window (so the model always has // some context to predict the token). // // We rely on the fact that attention in the forward pass only looks at previous @@ -76,10 +98,12 @@ void perplexity(llama_context * ctx, const gpt_params & params) { // process the entire prompt. for (int j = std::min(512, params.n_ctx / 2); j < params.n_ctx - 1; ++j) { // Calculate probability of next token, given the previous ones. - std::vector<float> tok_logits( - logits.begin() + j * n_vocab, + const std::vector<float> tok_logits( + logits.begin() + (j + 0) * n_vocab, logits.begin() + (j + 1) * n_vocab); - float prob = softmax(tok_logits)[tokens[start + j + 1]]; + + const float prob = softmax(tok_logits)[tokens[start + j + 1]]; + nll += -std::log(prob); ++count; } |