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 !pull/420/head^2 master-a316a42
Georgi Gerganov
1 year ago
parent
ecbe466a36
commit
a316a425d0
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# dependencies
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find_package(Threads REQUIRED)
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# third-party
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# ...
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# common
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set(TARGET common)
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add_library(${TARGET} OBJECT
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common.h
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common.cpp
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)
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if (BUILD_SHARED_LIBS)
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set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
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endif()
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target_include_directories(${TARGET} PUBLIC .)
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target_compile_features(${TARGET} PUBLIC cxx_std_11)
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target_link_libraries(${TARGET} PRIVATE llama)
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# examples
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include_directories(${CMAKE_CURRENT_SOURCE_DIR})
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if (EMSCRIPTEN)
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else()
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add_subdirectory(main)
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add_subdirectory(quantize)
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add_subdirectory(perplexity)
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add_subdirectory(embedding)
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endif()
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@ -1,6 +1,6 @@
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#include "ggml.h"
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#include "common.h"
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#include "utils.h"
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#include "ggml.h"
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#include <cassert>
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#include <cstring>
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set(TARGET embedding)
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add_executable(${TARGET} embedding.cpp)
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target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
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target_compile_features(${TARGET} PRIVATE cxx_std_11)
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# embedding
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TODO
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#include "common.h"
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#include "llama.h"
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#include <cassert>
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#include <cinttypes>
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#include <cmath>
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#include <cstdio>
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#include <cstring>
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#include <fstream>
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#include <string>
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#include <vector>
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int main(int argc, char ** argv) {
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gpt_params params;
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params.model = "models/llama-7B/ggml-model.bin";
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if (gpt_params_parse(argc, argv, params) == false) {
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return 1;
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}
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params.embedding = true;
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if (params.n_ctx > 2048) {
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fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
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"expect poor results\n", __func__, params.n_ctx);
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}
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if (params.seed <= 0) {
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params.seed = time(NULL);
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}
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fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
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std::mt19937 rng(params.seed);
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if (params.random_prompt) {
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params.prompt = gpt_random_prompt(rng);
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}
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llama_context * ctx;
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// load the model
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{
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auto lparams = llama_context_default_params();
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lparams.n_ctx = params.n_ctx;
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lparams.n_parts = params.n_parts;
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lparams.seed = params.seed;
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lparams.f16_kv = params.memory_f16;
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lparams.logits_all = params.perplexity;
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lparams.use_mlock = params.use_mlock;
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lparams.embedding = params.embedding;
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ctx = llama_init_from_file(params.model.c_str(), lparams);
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if (ctx == NULL) {
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fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
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return 1;
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}
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}
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// print system information
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{
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fprintf(stderr, "\n");
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fprintf(stderr, "system_info: n_threads = %d / %d | %s\n",
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params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
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}
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int n_past = 0;
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// Add a space in front of the first character to match OG llama tokenizer behavior
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params.prompt.insert(0, 1, ' ');
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// tokenize the prompt
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auto embd_inp = ::llama_tokenize(ctx, params.prompt, true);
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// determine newline token
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auto llama_token_newline = ::llama_tokenize(ctx, "\n", false);
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if (params.verbose_prompt) {
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fprintf(stderr, "\n");
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fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
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fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
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for (int i = 0; i < (int) embd_inp.size(); i++) {
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fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_str(ctx, embd_inp[i]));
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}
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fprintf(stderr, "\n");
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}
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if (params.embedding){
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if (embd_inp.size() > 0) {
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if (llama_eval(ctx, embd_inp.data(), embd_inp.size(), n_past, params.n_threads)) {
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fprintf(stderr, "%s : failed to eval\n", __func__);
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return 1;
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}
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}
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const auto embeddings = llama_get_embeddings(ctx);
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// TODO: print / use the embeddings
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}
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llama_print_timings(ctx);
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llama_free(ctx);
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return 0;
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}
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set(TARGET main)
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add_executable(${TARGET} main.cpp)
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target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
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target_compile_features(${TARGET} PRIVATE cxx_std_11)
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# main
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TODO
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set(TARGET perplexity)
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add_executable(${TARGET} perplexity.cpp)
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target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
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target_compile_features(${TARGET} PRIVATE cxx_std_11)
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# perplexity
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TODO
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#include "common.h"
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#include "llama.h"
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#include <cassert>
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#include <cinttypes>
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#include <cmath>
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#include <cstdio>
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#include <cstring>
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#include <string>
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#include <vector>
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std::vector<double> softmax(const std::vector<float>& logits) {
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std::vector<double> probs(logits.size());
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float max_logit = logits[0];
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for (float v : logits) max_logit = std::max(max_logit, v);
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double sum_exp = 0.0;
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for (size_t i = 0; i < logits.size(); i++) {
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// Subtract the maximum logit value from the current logit value for numerical stability
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float logit = logits[i] - max_logit;
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double exp_logit = std::exp(logit);
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sum_exp += exp_logit;
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probs[i] = exp_logit;
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}
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for (size_t i = 0; i < probs.size(); i++) probs[i] /= sum_exp;
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return probs;
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}
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void perplexity(llama_context * ctx, const gpt_params & params) {
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// Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
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// Run `./main --perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
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// Output: `perplexity: 13.5106 [114/114]`
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auto tokens = ::llama_tokenize(ctx, params.prompt, true);
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int count = 0;
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double nll = 0.0;
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int seq_count = tokens.size() / params.n_ctx;
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fprintf(stderr, "%s : calculating perplexity over %d chunks\n", __func__, seq_count);
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for (int i = 0; i < seq_count; ++i) {
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int start = i * params.n_ctx;
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int end = start + params.n_ctx - 1;
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std::vector<llama_token> embd(tokens.begin() + start, tokens.begin() + end);
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auto start_t = std::chrono::high_resolution_clock::now();
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if (llama_eval(ctx, embd.data(), embd.size(), 0, params.n_threads)) {
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fprintf(stderr, "%s : failed to eval\n", __func__);
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return;
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}
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auto end_t = std::chrono::high_resolution_clock::now();
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if (i == 0) {
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double seconds = std::chrono::duration<double>(end_t - start_t).count();
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printf("%.2f seconds per pass - ETA %.2f hours\n", seconds, (seconds * seq_count) / (60.0*60.0));
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}
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// We get the logits for all the tokens in the context window (params.n_ctx)
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// from llama_eval above. Now, based on https://huggingface.co/docs/transformers/perplexity,
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// calculate the perplexity over the last half the window (so the model always has
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// some context to predict the token).
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//
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// We rely on the fact that attention in the forward pass only looks at previous
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// tokens here, so the logits returned for each token are an accurate representation
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// of what the model would have predicted at that point.
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//
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// Example, we have a context window of 512, we will compute perplexity for each of the
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// last 256 tokens. Then, we split the input up into context window size chunks to
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// process the entire prompt.
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auto logits = llama_get_logits(ctx);
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for (int j = params.n_ctx / 2; j < params.n_ctx - 1; ++j) {
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// Calculate probability of next token, given the previous ones.
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int n_vocab = llama_n_vocab(ctx);
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std::vector<float> tok_logits(
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logits + j * n_vocab,
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logits + (j + 1) * n_vocab);
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double prob = softmax(tok_logits)[tokens[start + j + 1]];
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nll += -std::log(prob);
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++count;
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}
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// perplexity is e^(average negative log-likelihood)
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printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
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fflush(stdout);
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}
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printf("\n");
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}
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int main(int argc, char ** argv) {
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gpt_params params;
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params.model = "models/llama-7B/ggml-model.bin";
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if (gpt_params_parse(argc, argv, params) == false) {
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return 1;
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}
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params.perplexity = true;
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if (params.n_ctx > 2048) {
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fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
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"expect poor results\n", __func__, params.n_ctx);
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}
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if (params.seed <= 0) {
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params.seed = time(NULL);
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}
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fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
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std::mt19937 rng(params.seed);
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if (params.random_prompt) {
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params.prompt = gpt_random_prompt(rng);
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}
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llama_context * ctx;
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// load the model
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{
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auto lparams = llama_context_default_params();
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lparams.n_ctx = params.n_ctx;
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lparams.n_parts = params.n_parts;
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lparams.seed = params.seed;
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lparams.f16_kv = params.memory_f16;
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lparams.logits_all = params.perplexity;
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lparams.use_mlock = params.use_mlock;
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lparams.embedding = params.embedding;
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ctx = llama_init_from_file(params.model.c_str(), lparams);
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if (ctx == NULL) {
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fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
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return 1;
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}
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}
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// print system information
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{
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fprintf(stderr, "\n");
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fprintf(stderr, "system_info: n_threads = %d / %d | %s\n",
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params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
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}
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perplexity(ctx, params);
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llama_print_timings(ctx);
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llama_free(ctx);
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return 0;
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}
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@ -0,0 +1,4 @@
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set(TARGET quantize)
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add_executable(${TARGET} quantize.cpp)
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target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT})
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target_compile_features(${TARGET} PRIVATE cxx_std_11)
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@ -0,0 +1,3 @@
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# quantize
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TODO
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