diff options
| -rw-r--r-- | examples/perplexity/perplexity.cpp | 36 |
1 files changed, 21 insertions, 15 deletions
diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index b62f00d..38e3643 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -27,20 +27,27 @@ void perplexity(llama_context * ctx, const gpt_params & params) { int count = 0; int seq_count = tokens.size() / params.n_ctx; + int n_vocab = llama_n_vocab(ctx); double nll = 0.0; - - fprintf(stderr, "%s : calculating perplexity over %d chunks\n", __func__, seq_count); + fprintf(stderr, "%s : calculating perplexity over %d chunks, batch_size=%d\n", __func__, seq_count, params.n_batch); for (int i = 0; i < seq_count; ++i) { int start = i * params.n_ctx; - int end = start + params.n_ctx - 1; // TODO: this is not optimal, e.g. it makes the batch 511 instead of 512 - // it is better to always be power of 2 for better performance - std::vector<llama_token> embd(tokens.begin() + start, tokens.begin() + end); + int end = start + params.n_ctx; + + 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(); - if (llama_eval(ctx, embd.data(), embd.size(), 0, params.n_threads)) { - fprintf(stderr, "%s : failed to eval\n", __func__); - return; + 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)) { + fprintf(stderr, "%s : failed to eval\n", __func__); + return; + } + 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(); if (i == 0) { @@ -59,15 +66,12 @@ void perplexity(llama_context * ctx, const gpt_params & params) { // Example, we have a context window of 512, we will compute perplexity for each of the // last 256 tokens. Then, we split the input up into context window size chunks to // process the entire prompt. - - auto logits = llama_get_logits(ctx); - for (int j = params.n_ctx / 2; j < params.n_ctx - 1; ++j) { + 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. - int n_vocab = llama_n_vocab(ctx); std::vector<float> tok_logits( - logits + j * n_vocab, - logits + (j + 1) * n_vocab); - const float prob = softmax(tok_logits)[tokens[start + j + 1]]; + logits.begin() + j * n_vocab, + logits.begin() + (j + 1) * n_vocab); + float prob = softmax(tok_logits)[tokens[start + j + 1]]; nll += -std::log(prob); ++count; } @@ -82,11 +86,13 @@ int main(int argc, char ** argv) { gpt_params params; params.model = "models/llama-7B/ggml-model.bin"; + params.n_batch = 512; if (gpt_params_parse(argc, argv, params) == false) { return 1; } params.perplexity = true; + params.n_batch = std::min(params.n_batch, params.n_ctx); if (params.n_ctx > 2048) { fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);" |