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lookup : add prompt lookup decoding example (#4484)
* initial commit, going through initializations * main loop finished, starting to debug * BUG: generates gibberish/repeating tokens after a while * kv_cache management * Added colors to distinguish drafted tokens (--color). Updated README * lookup : fix token positions in the draft batch * lookup : use n_draft from CLI params * lookup : final touches --------- Co-authored-by: Leon Ericsson <leon.ericsson@icloud.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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LeonEricsson
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1
.gitignore
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vendored
@ -48,6 +48,7 @@ models-mnt
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/llama-bench
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/llava-cli
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/lookahead
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/lookup
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/main
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/metal
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/perplexity
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5
Makefile
5
Makefile
@ -2,7 +2,7 @@
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BUILD_TARGETS = \
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main quantize quantize-stats perplexity embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
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simple batched batched-bench save-load-state server gguf llama-bench libllava.a llava-cli baby-llama beam-search \
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speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead tests/test-c.o
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speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup tests/test-c.o
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# Binaries only useful for tests
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TEST_TARGETS = \
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@ -664,6 +664,9 @@ parallel: examples/parallel/parallel.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
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lookahead: examples/lookahead/lookahead.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
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$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
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lookup: examples/lookup/lookup.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
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$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
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ifdef LLAMA_METAL
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metal: examples/metal/metal.cpp ggml.o $(OBJS)
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$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
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@ -51,7 +51,7 @@ struct gpt_params {
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int32_t n_ctx = 512; // context size
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int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)
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int32_t n_keep = 0; // number of tokens to keep from initial prompt
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int32_t n_draft = 16; // number of tokens to draft during speculative decoding
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int32_t n_draft = 8; // number of tokens to draft during speculative decoding
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int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited)
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int32_t n_parallel = 1; // number of parallel sequences to decode
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int32_t n_sequences = 1; // number of sequences to decode
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@ -240,3 +240,4 @@ void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size = 80);
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// Dump the KV cache view showing individual sequences in each cell (long output).
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void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size = 40);
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@ -33,6 +33,7 @@ else()
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add_subdirectory(simple)
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add_subdirectory(speculative)
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add_subdirectory(lookahead)
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add_subdirectory(lookup)
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add_subdirectory(train-text-from-scratch)
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if (LLAMA_METAL)
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add_subdirectory(metal)
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5
examples/lookup/CMakeLists.txt
Normal file
5
examples/lookup/CMakeLists.txt
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@ -0,0 +1,5 @@
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set(TARGET lookup)
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add_executable(${TARGET} lookup.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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13
examples/lookup/README.md
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13
examples/lookup/README.md
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@ -0,0 +1,13 @@
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# llama.cpp/examples/lookup
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Demonstration of Prompt Lookup Decoding
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https://github.com/apoorvumang/prompt-lookup-decoding
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The key parameters for lookup decoding are `ngram_min`, `ngram_max` and `n_draft`. The first two determine the size of the ngrams to search for in the prompt for a match. The latter specifies how many subsequent tokens to draft if a match is found.
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More info:
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https://github.com/ggerganov/llama.cpp/pull/4484
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https://github.com/ggerganov/llama.cpp/issues/4226
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230
examples/lookup/lookup.cpp
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230
examples/lookup/lookup.cpp
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@ -0,0 +1,230 @@
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#include "common.h"
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#include "llama.h"
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#include <cmath>
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#include <cstdio>
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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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if (!gpt_params_parse(argc, argv, params)) {
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return 1;
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}
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// max/min n-grams size to search for in prompt
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const int ngram_max = 4;
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const int ngram_min = 1;
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// length of the candidate / draft sequence, if match is found
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const int n_draft = params.n_draft;
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const bool dump_kv_cache = params.dump_kv_cache;
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#ifndef LOG_DISABLE_LOGS
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log_set_target(log_filename_generator("lookup", "log"));
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LOG_TEE("Log start\n");
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log_dump_cmdline(argc, argv);
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#endif // LOG_DISABLE_LOGS
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// init llama.cpp
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llama_backend_init(params.numa);
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llama_model * model = NULL;
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llama_context * ctx = NULL;
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// load the model
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std::tie(model, ctx) = llama_init_from_gpt_params(params);
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// tokenize the prompt
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const bool add_bos = llama_should_add_bos_token(model);
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LOG("add_bos tgt: %d\n", add_bos);
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std::vector<llama_token> inp;
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inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
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const int max_context_size = llama_n_ctx(ctx);
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const int max_tokens_list_size = max_context_size - 4;
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if ((int) inp.size() > max_tokens_list_size) {
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fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.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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for (auto id : inp) {
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fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
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}
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fflush(stderr);
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const int n_input = inp.size();
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const auto t_enc_start = ggml_time_us();
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llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1, 0, 0));
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llama_decode(ctx, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0));
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const auto t_enc_end = ggml_time_us();
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int n_predict = 0;
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int n_drafted = 0;
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int n_accept = 0;
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int n_past = inp.size();
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bool has_eos = false;
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struct llama_sampling_context * ctx_sampling = llama_sampling_init(params.sparams);
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std::vector<llama_token> draft;
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llama_batch batch_tgt = llama_batch_init(params.n_ctx, 0, 1);
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// debug
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struct llama_kv_cache_view kvc_view = llama_kv_cache_view_init(ctx, 1);
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const auto t_dec_start = ggml_time_us();
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while (true) {
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// debug
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if (dump_kv_cache) {
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llama_kv_cache_view_update(ctx, &kvc_view);
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dump_kv_cache_view_seqs(kvc_view, 40);
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}
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// print current draft sequence
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LOG("drafted %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, draft).c_str());
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int i_dft = 0;
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while (true) {
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// sample from the target model
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llama_token id = llama_sampling_sample(ctx_sampling, ctx, NULL, i_dft);
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llama_sampling_accept(ctx_sampling, ctx, id, true);
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const std::string token_str = llama_token_to_piece(ctx, id);
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if (!params.use_color) {
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printf("%s", token_str.c_str());
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}
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if (id == llama_token_eos(model)) {
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has_eos = true;
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}
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++n_predict;
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// check if the target token matches the draft
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if (i_dft < (int) draft.size() && id == draft[i_dft]) {
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LOG("the sampled target token matches the %dth drafted token (%d, '%s') - accepted\n", i_dft, id, token_str.c_str());
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++n_accept;
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++n_past;
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++i_dft;
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inp.push_back(id);
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if (params.use_color) {
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// color accepted draft token
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printf("\033[34m%s\033[0m", token_str.c_str());
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fflush(stdout);
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}
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continue;
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}
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if (params.use_color) {
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printf("%s", token_str.c_str());
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}
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fflush(stdout);
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LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", id, token_str.c_str());
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draft.clear();
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draft.push_back(id);
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inp.push_back(id);
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break;
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}
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if ((params.n_predict > 0 && n_predict > params.n_predict) || has_eos) {
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break;
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}
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// KV cache management
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// clean the cache of draft tokens that weren't accepted
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llama_kv_cache_seq_rm(ctx, 0, n_past, -1);
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llama_batch_clear(batch_tgt);
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llama_batch_add(batch_tgt, draft[0], n_past, { 0 }, true);
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// generate n_pred tokens through prompt lookup
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auto prompt_lookup = [&]() -> void {
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int inp_size = inp.size();
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for (int ngram_size = ngram_max ; ngram_size > ngram_min; --ngram_size){
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const llama_token * ngram = &inp[inp_size - ngram_size];
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for (int i = 0; i <= (int) inp_size - (ngram_size * 2); ++i) {
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bool match = true;
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for (int j = 0; j < ngram_size; ++j) {
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if (inp[i + j] != ngram[j]) {
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match = false;
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break;
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}
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}
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if (match) {
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const int startIdx = i + ngram_size;
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const int endIdx = startIdx + n_draft;
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if (endIdx < inp_size) {
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for (int j = startIdx; j < endIdx; ++j) {
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LOG(" - draft candidate %d: %d\n", j, inp[j]);
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draft.push_back(inp[j]);
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llama_batch_add(batch_tgt, inp[j], n_past + (j - startIdx) + 1, { 0 }, true);
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++n_drafted;
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}
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return;
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}
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}
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}
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}
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return;
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};
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prompt_lookup();
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llama_decode(ctx, batch_tgt);
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++n_past;
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draft.erase(draft.begin());
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}
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auto t_dec_end = ggml_time_us();
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LOG_TEE("\n\n");
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LOG_TEE("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input, (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f));
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LOG_TEE("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict / ((t_dec_end - t_dec_start) / 1e6f));
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LOG_TEE("\n");
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LOG_TEE("n_draft = %d\n", n_draft);
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LOG_TEE("n_predict = %d\n", n_predict);
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LOG_TEE("n_drafted = %d\n", n_drafted);
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LOG_TEE("n_accept = %d\n", n_accept);
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LOG_TEE("accept = %.3f%%\n", 100.0f * n_accept / n_drafted);
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LOG_TEE("\ntarget:\n");
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llama_print_timings(ctx);
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llama_sampling_free(ctx_sampling);
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llama_batch_free(batch_tgt);
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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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fprintf(stderr, "\n\n");
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return 0;
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}
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