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authorGeorgi Gerganov <ggerganov@gmail.com>2023-03-25 20:26:40 +0200
committerGeorgi Gerganov <ggerganov@gmail.com>2023-03-25 20:26:40 +0200
commita316a425d04027453dc0fd45f003b647c12f66f9 (patch)
treeb33d7c55741f10f1cc84f489df05e1fad96f0417 /examples/perplexity
parentecbe466a364876927994e2f1ec14f4d82301d201 (diff)
Overhaul the examples structure
- main -> examples - utils -> examples (renamed to "common") - quantize -> examples - separate tools for "perplexity" and "embedding" Hope I didn't break something !
Diffstat (limited to 'examples/perplexity')
-rw-r--r--examples/perplexity/CMakeLists.txt4
-rw-r--r--examples/perplexity/README.md3
-rw-r--r--examples/perplexity/perplexity.cpp146
3 files changed, 153 insertions, 0 deletions
diff --git a/examples/perplexity/CMakeLists.txt b/examples/perplexity/CMakeLists.txt
new file mode 100644
index 0000000..5836df8
--- /dev/null
+++ b/examples/perplexity/CMakeLists.txt
@@ -0,0 +1,4 @@
+set(TARGET perplexity)
+add_executable(${TARGET} perplexity.cpp)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/examples/perplexity/README.md b/examples/perplexity/README.md
new file mode 100644
index 0000000..a932275
--- /dev/null
+++ b/examples/perplexity/README.md
@@ -0,0 +1,3 @@
+# perplexity
+
+TODO
diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp
new file mode 100644
index 0000000..f0266a0
--- /dev/null
+++ b/examples/perplexity/perplexity.cpp
@@ -0,0 +1,146 @@
+#include "common.h"
+#include "llama.h"
+
+#include <cassert>
+#include <cinttypes>
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <string>
+#include <vector>
+
+std::vector<double> softmax(const std::vector<float>& logits) {
+ std::vector<double> probs(logits.size());
+ float max_logit = logits[0];
+ for (float v : logits) max_logit = std::max(max_logit, v);
+ double sum_exp = 0.0;
+ for (size_t i = 0; i < logits.size(); i++) {
+ // Subtract the maximum logit value from the current logit value for numerical stability
+ float logit = logits[i] - max_logit;
+ double exp_logit = std::exp(logit);
+ sum_exp += exp_logit;
+ probs[i] = exp_logit;
+ }
+ for (size_t i = 0; i < probs.size(); i++) probs[i] /= sum_exp;
+ return probs;
+}
+
+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 `./main --perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
+ // Output: `perplexity: 13.5106 [114/114]`
+ auto tokens = ::llama_tokenize(ctx, params.prompt, true);
+
+ int count = 0;
+ double nll = 0.0;
+ int seq_count = tokens.size() / params.n_ctx;
+
+ fprintf(stderr, "%s : calculating perplexity over %d chunks\n", __func__, seq_count);
+
+ for (int i = 0; i < seq_count; ++i) {
+ int start = i * params.n_ctx;
+ int end = start + params.n_ctx - 1;
+ std::vector<llama_token> embd(tokens.begin() + start, tokens.begin() + end);
+ 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;
+ }
+ auto end_t = std::chrono::high_resolution_clock::now();
+ if (i == 0) {
+ double seconds = std::chrono::duration<double>(end_t - start_t).count();
+ printf("%.2f seconds per pass - ETA %.2f hours\n", seconds, (seconds * seq_count) / (60.0*60.0));
+ }
+ // 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
+ // some context to predict the token).
+ //
+ // We rely on the fact that attention in the forward pass only looks at previous
+ // tokens here, so the logits returned for each token are an accurate representation
+ // of what the model would have predicted at that point.
+ //
+ // 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) {
+ // 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);
+ double prob = softmax(tok_logits)[tokens[start + j + 1]];
+ nll += -std::log(prob);
+ ++count;
+ }
+ // perplexity is e^(average negative log-likelihood)
+ printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
+ fflush(stdout);
+ }
+ printf("\n");
+}
+
+int main(int argc, char ** argv) {
+ gpt_params params;
+ params.model = "models/llama-7B/ggml-model.bin";
+
+ if (gpt_params_parse(argc, argv, params) == false) {
+ return 1;
+ }
+
+ params.perplexity = true;
+
+ if (params.n_ctx > 2048) {
+ fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
+ "expect poor results\n", __func__, params.n_ctx);
+ }
+
+ if (params.seed <= 0) {
+ params.seed = time(NULL);
+ }
+
+ fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
+
+ std::mt19937 rng(params.seed);
+ if (params.random_prompt) {
+ params.prompt = gpt_random_prompt(rng);
+ }
+
+ llama_context * ctx;
+
+ // load the model
+ {
+ auto lparams = llama_context_default_params();
+
+ lparams.n_ctx = params.n_ctx;
+ lparams.n_parts = params.n_parts;
+ lparams.seed = params.seed;
+ lparams.f16_kv = params.memory_f16;
+ lparams.logits_all = params.perplexity;
+ lparams.use_mlock = params.use_mlock;
+ lparams.embedding = params.embedding;
+
+ ctx = llama_init_from_file(params.model.c_str(), lparams);
+
+ if (ctx == NULL) {
+ fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
+ return 1;
+ }
+ }
+
+ // print system information
+ {
+ fprintf(stderr, "\n");
+ fprintf(stderr, "system_info: n_threads = %d / %d | %s\n",
+ params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
+ }
+
+ perplexity(ctx, params);
+
+ llama_print_timings(ctx);
+ llama_free(ctx);
+
+ return 0;
+}