mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-28 08:31:25 +00:00
llama : remove beam search (#7736)
This commit is contained in:
Georgi Gerganov
@@ -15,7 +15,6 @@ else()
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add_subdirectory(baby-llama)
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add_subdirectory(batched)
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add_subdirectory(batched-bench)
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add_subdirectory(beam-search)
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add_subdirectory(benchmark)
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add_subdirectory(convert-llama2c-to-ggml)
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add_subdirectory(embedding)
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@@ -1,5 +0,0 @@
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set(TARGET beam-search)
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add_executable(${TARGET} beam-search.cpp)
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install(TARGETS ${TARGET} RUNTIME)
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target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
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target_compile_features(${TARGET} PRIVATE cxx_std_11)
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@@ -1,188 +0,0 @@
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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 <ctime>
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#include <fstream>
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#include <iostream>
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#include <string>
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#include <vector>
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#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
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#include <signal.h>
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#include <unistd.h>
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#elif defined (_WIN32)
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#define WIN32_LEAN_AND_MEAN
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#ifndef NOMINMAX
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# define NOMINMAX
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#endif
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#include <windows.h>
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#include <signal.h>
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#endif
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// Used for debugging to print out beam tokens.
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struct ostream_beam_view {
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llama_context * ctx;
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llama_beam_view beam_view;
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};
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static std::ostream & operator<<(std::ostream & os, const ostream_beam_view & obv) {
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os << "p(" << obv.beam_view.p << ") eob(" << std::boolalpha << obv.beam_view.eob << ") tokens(";
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for (size_t i = 0 ; i < obv.beam_view.n_tokens ; ++i) {
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os << llama_token_to_piece(obv.ctx, obv.beam_view.tokens[i]);
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}
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return os << ')';
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}
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// Put here anything you want back in beam_search_callback().
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struct beam_search_callback_data {
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llama_context * ctx;
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std::vector<llama_token> response;
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};
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// In this case, end-of-beam (eob) is equivalent to end-of-sentence (eos) but this need not always be the same.
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// For example, eob can be flagged due to maximum token length, stop words, etc.
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static bool is_at_eob(const beam_search_callback_data & callback_data, const llama_token * tokens, size_t n_tokens) {
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return n_tokens && llama_token_is_eog(llama_get_model(callback_data.ctx), tokens[n_tokens-1]);
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}
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// Function matching type llama_beam_search_callback_fn_t.
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// Custom callback example is called each time the beams lengths increase:
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// * Show progress by printing ',' following by number of convergent beam tokens if any.
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// * When all beams converge to a common prefix, they are made available in beams_state.beams[0].
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// This is also called when the stop condition is met.
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// Collect tokens into std::vector<llama_token> response which is pointed to by callback_data.
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static void beam_search_callback(void * callback_data_ptr, llama_beams_state beams_state) {
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auto& callback_data = *static_cast<beam_search_callback_data*>(callback_data_ptr);
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// Mark beams as EOS as needed.
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for (size_t i = 0 ; i < beams_state.n_beams ; ++i) {
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llama_beam_view& beam_view = beams_state.beam_views[i];
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if (!beam_view.eob && is_at_eob(callback_data, beam_view.tokens, beam_view.n_tokens)) {
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beam_view.eob = true;
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}
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}
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printf(","); // Show progress
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if (const size_t n = beams_state.common_prefix_length) {
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callback_data.response.resize(callback_data.response.size() + n);
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assert(0u < beams_state.n_beams);
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const llama_token * tokens = beams_state.beam_views[0].tokens;
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std::copy(tokens, tokens + n, callback_data.response.end() - n);
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printf("%zu", n);
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}
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fflush(stdout);
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#if 1 // DEBUG: print current beams for this iteration
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std::cout << "\n\nCurrent beams (last_call=" << beams_state.last_call << "):\n";
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for (size_t i = 0 ; i < beams_state.n_beams ; ++i) {
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std::cout << "beams["<<i<<"]: " << ostream_beam_view{callback_data.ctx,beams_state.beam_views[i]} << std::endl;
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}
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#endif
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}
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int main(int argc, char ** argv)
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{
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gpt_params params;
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//params.n_gpu_layers = 200;
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//---------------------------------
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// Print help :
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//---------------------------------
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if ( argc < 2 || argv[1][0] == '-' )
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{
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printf( "Usage: %s MODEL_PATH [BEAM_WIDTH=2] [PROMPT]\n" , argv[0] );
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return 1 ;
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}
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//---------------------------------
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// Load parameters :
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//---------------------------------
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params.model = argv[1];
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params.n_beams = 2 < argc ? std::stoi(argv[2]) : 2;
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if ( argc > 3 )
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{
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params.prompt = argv[3];
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}
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if ( params.prompt.empty() )
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{
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params.prompt = "### Request:\nHow many countries are there?\n\n### Response:\n";
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}
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//---------------------------------
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// Init LLM :
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//---------------------------------
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llama_backend_init();
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llama_numa_init(params.numa);
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llama_model * model;
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llama_context * ctx;
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std::tie(model, ctx) = llama_init_from_gpt_params( params );
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if ( model == NULL )
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{
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fprintf( stderr , "%s: error: unable to load model\n" , __func__ );
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return 1;
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}
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//---------------------------------
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// Tokenize the prompt :
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//---------------------------------
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std::vector<llama_token> tokens_list = llama_tokenize(ctx, params.prompt, true);
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const size_t max_context_size = llama_n_ctx( ctx );
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const size_t max_tokens_list_size = max_context_size - 4 ;
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if (tokens_list.size() > max_tokens_list_size)
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{
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fprintf( stderr , "%s: error: prompt too long (%zu tokens, max %zu)\n" ,
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__func__ , tokens_list.size() , max_tokens_list_size );
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return 1;
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}
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fprintf( stderr, "\n\n" );
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// Print the tokens from the prompt :
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for( auto id : tokens_list )
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{
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std::cout << llama_token_to_piece(ctx, id);
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}
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std::cout << std::flush;
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int n_past = 0;
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if (llama_decode(ctx, llama_batch_get_one(tokens_list.data(), tokens_list.size(), n_past, 0)))
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{
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fprintf(stderr, "%s : failed to eval prompt.\n" , __func__ );
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return 1;
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}
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n_past += tokens_list.size();
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beam_search_callback_data callback_data{ctx, {}};
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size_t const beam_width = static_cast<size_t>(params.n_beams);
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int const n_predict = 256;
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llama_beam_search(ctx, beam_search_callback, &callback_data, beam_width, n_past, n_predict);
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std::cout << "\n\n";
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for (llama_token const token_id : callback_data.response) {
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std::cout << llama_token_to_piece(ctx,token_id);
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}
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std::cout << std::endl;
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llama_free( ctx );
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llama_free_model( model );
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llama_backend_free();
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return 0;
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}
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