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examples : add example for batched decoding
This commit is contained in:
Georgi Gerganov
parent
d008733e6b
commit
a207561503
1
.gitignore
vendored
1
.gitignore
vendored
@ -51,6 +51,7 @@ models-mnt
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/save-load-state
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/server
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/simple
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/batched
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/speculative
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/parallel
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/train-text-from-scratch
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5
Makefile
5
Makefile
@ -1,5 +1,5 @@
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# Define the default target now so that it is always the first target
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BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple save-load-state server embd-input-test gguf llama-bench baby-llama beam-search speculative parallel tests/test-c.o
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BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple batched save-load-state server embd-input-test gguf llama-bench baby-llama beam-search speculative parallel tests/test-c.o
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# Binaries only useful for tests
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TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama tests/test-tokenizer-0-falcon tests/test-tokenizer-1-llama
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@ -519,6 +519,9 @@ main: examples/main/main.cpp build-info.h ggml.
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simple: examples/simple/simple.cpp build-info.h ggml.o llama.o common.o $(OBJS)
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$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
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batched: examples/batched/batched.cpp build-info.h ggml.o llama.o common.o $(OBJS)
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$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
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quantize: examples/quantize/quantize.cpp build-info.h ggml.o llama.o $(OBJS)
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$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
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@ -23,6 +23,7 @@ else()
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add_subdirectory(train-text-from-scratch)
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add_subdirectory(convert-llama2c-to-ggml)
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add_subdirectory(simple)
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add_subdirectory(batched)
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add_subdirectory(speculative)
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add_subdirectory(parallel)
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add_subdirectory(embd-input)
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5
examples/batched/CMakeLists.txt
Normal file
5
examples/batched/CMakeLists.txt
Normal file
@ -0,0 +1,5 @@
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set(TARGET batched)
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add_executable(${TARGET} batched.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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44
examples/batched/README.md
Normal file
44
examples/batched/README.md
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@ -0,0 +1,44 @@
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# llama.cpp/example/batched
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The example demonstrates batched generation from a given prompt
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```bash
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./batched ./models/llama-7b-v2/ggml-model-f16.gguf "Hello my name is" 4
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...
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main: n_len = 32, n_ctx = 2048, n_parallel = 4, n_kv_req = 113
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Hello my name is
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main: generating 4 sequences ...
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main: stream 0 finished
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main: stream 1 finished
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main: stream 2 finished
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main: stream 3 finished
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sequence 0:
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Hello my name is Shirley. I am a 25-year-old female who has been working for over 5 years as a b
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sequence 1:
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Hello my name is Renee and I'm a 32 year old female from the United States. I'm looking for a man between
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sequence 2:
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Hello my name is Diana. I am looking for a housekeeping job. I have experience with children and have my own transportation. I am
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sequence 3:
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Hello my name is Cody. I am a 3 year old neutered male. I am a very friendly cat. I am very playful and
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main: decoded 108 tokens in 3.57 s, speed: 30.26 t/s
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llama_print_timings: load time = 587.00 ms
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llama_print_timings: sample time = 2.56 ms / 112 runs ( 0.02 ms per token, 43664.72 tokens per second)
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llama_print_timings: prompt eval time = 4089.11 ms / 118 tokens ( 34.65 ms per token, 28.86 tokens per second)
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llama_print_timings: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)
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llama_print_timings: total time = 4156.04 ms
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```
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243
examples/batched/batched.cpp
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243
examples/batched/batched.cpp
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@ -0,0 +1,243 @@
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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 (argc == 1 || argv[1][0] == '-') {
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printf("usage: %s MODEL_PATH [PROMPT] [PARALLEL]\n" , argv[0]);
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return 1 ;
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}
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int n_parallel = 1;
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if (argc >= 2) {
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params.model = argv[1];
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}
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if (argc >= 3) {
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params.prompt = argv[2];
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}
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if (argc >= 4) {
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n_parallel = std::atoi(argv[3]);
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}
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if (params.prompt.empty()) {
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params.prompt = "Hello my name is";
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}
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// total length of the sequences including the prompt
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const int n_len = 32;
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// init LLM
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llama_backend_init(params.numa);
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llama_context_params ctx_params = llama_context_default_params();
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ctx_params.seed = 1234;
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ctx_params.n_ctx = 2048;
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llama_model * model = llama_load_model_from_file(params.model.c_str(), ctx_params);
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if (model == NULL) {
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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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llama_context * ctx = llama_new_context_with_model(model, ctx_params);
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if (ctx == NULL) {
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fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
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return 1;
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}
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// tokenize the prompt
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std::vector<llama_token> tokens_list;
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tokens_list = ::llama_tokenize(ctx, params.prompt, true);
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const int n_ctx = llama_n_ctx(ctx);
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const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size())*n_parallel;
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LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_parallel = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, n_parallel, n_kv_req);
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// make sure the KV cache is big enough to hold all the prompt and generated tokens
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if (n_kv_req > n_ctx) {
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LOG_TEE("%s: error: n_kv_req > n_ctx, the required KV cache size is not big enough\n", __func__);
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LOG_TEE("%s: either reduce n_parallel or increase n_ctx\n", __func__);
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return 1;
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}
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// print the prompt token-by-token
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fprintf(stderr, "\n");
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for (auto id : tokens_list) {
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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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// create a llama_batch with size 512
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// we use this object to submit token data for decoding
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llama_batch batch = llama_batch_init(512, 0);
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// evaluate the initial prompt
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batch.n_tokens = tokens_list.size();
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for (int32_t i = 0; i < batch.n_tokens; i++) {
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batch.token[i] = tokens_list[i];
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batch.pos[i] = i;
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batch.seq_id[i] = 0;
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batch.logits[i] = false;
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}
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// llama_decode will output logits only for the last token of the prompt
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batch.logits[batch.n_tokens - 1] = true;
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if (llama_decode(ctx, batch, params.n_threads) != 0) {
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LOG_TEE("%s: llama_decode() failed\n", __func__);
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return 1;
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}
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// assign the system KV cache to all parallel sequences
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// this way, the parallel sequences will "reuse" the prompt tokens without having to copy them
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for (int32_t i = 1; i < n_parallel; ++i) {
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llama_kv_cache_seq_cp(ctx, 0, i, 0, batch.n_tokens);
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}
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if (n_parallel > 1) {
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LOG_TEE("\n\n%s: generating %d sequences ...\n", __func__, n_parallel);
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}
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// main loop
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// we will store the parallel decoded sequences in this vector
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std::vector<std::string> streams(n_parallel);
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// remember the batch index of the last token for each parallel sequence
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// we need this to determine which logits to sample from
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std::vector<int32_t> i_batch(n_parallel, batch.n_tokens - 1);
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int n_cur = batch.n_tokens;
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int n_decode = 0;
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const auto t_main_start = ggml_time_us();
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while (n_cur <= n_len) {
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// evaluate the current batch with the transformer model
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if (llama_decode(ctx, batch, params.n_threads)) {
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fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
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return 1;
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}
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// prepare the next batch
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batch.n_tokens = 0;
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// sample the next token for each parallel sequence / stream
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for (int32_t i = 0; i < n_parallel; ++i) {
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if (i_batch[i] < 0) {
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// the stream has already finished
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continue;
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}
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auto n_vocab = llama_n_vocab(ctx);
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auto logits = llama_get_logits_ith(ctx, i_batch[i]);
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std::vector<llama_token_data> candidates;
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candidates.reserve(n_vocab);
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for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
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candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
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}
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llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
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const int top_k = 40;
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const float top_p = 0.9f;
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const float temp = 0.4f;
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llama_sample_top_k(ctx, &candidates_p, top_k, 1);
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llama_sample_top_p(ctx, &candidates_p, top_p, 1);
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llama_sample_temp (ctx, &candidates_p, temp);
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const llama_token new_token_id = llama_sample_token(ctx, &candidates_p);
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//const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
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// is it an end of stream? -> mark the stream as finished
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if (new_token_id == llama_token_eos(ctx) || n_cur == n_len) {
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i_batch[i] = -1;
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LOG_TEE("\n");
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if (n_parallel > 1) {
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LOG_TEE("%s: stream %d finished", __func__, i);
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}
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continue;
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}
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// if there is only one stream, we print immediately to stdout
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if (n_parallel == 1) {
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LOG_TEE("%s", llama_token_to_piece(ctx, new_token_id).c_str());
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fflush(stdout);
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}
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streams[i] += llama_token_to_piece(ctx, new_token_id);
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// push this new token for next evaluation
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batch.token [batch.n_tokens] = new_token_id;
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batch.pos [batch.n_tokens] = n_cur;
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batch.seq_id[batch.n_tokens] = i;
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batch.logits[batch.n_tokens] = true;
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i_batch[i] = batch.n_tokens;
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batch.n_tokens += 1;
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n_decode += 1;
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}
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// all streams are finished
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if (batch.n_tokens == 0) {
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break;
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}
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n_cur += 1;
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}
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LOG_TEE("\n");
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if (n_parallel > 1) {
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LOG_TEE("\n");
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for (int32_t i = 0; i < n_parallel; ++i) {
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LOG_TEE("sequence %d:\n\n%s%s\n\n", i, params.prompt.c_str(), streams[i].c_str());
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}
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}
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const auto t_main_end = ggml_time_us();
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LOG_TEE("%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n",
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__func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f));
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llama_print_timings(ctx);
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fprintf(stderr, "\n");
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llama_batch_free(batch);
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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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@ -1,12 +1,9 @@
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# llama.cpp/example/simple
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The purpose of this example is to demonstrate a minimal usage of llama.cpp for generating text with a given prompt.
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The example demonstrates single-batch as well as parallel generation.
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## Single-batch generation
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```bash
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./simple ./models/llama-7b-v2/ggml-model-f16.gguf "Hello my name is" 1
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./simple ./models/llama-7b-v2/ggml-model-f16.gguf "Hello my name is"
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...
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@ -22,46 +19,3 @@ llama_print_timings: prompt eval time = 655.63 ms / 10 tokens ( 65.56 ms
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llama_print_timings: eval time = 2180.97 ms / 27 runs ( 80.78 ms per token, 12.38 tokens per second)
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llama_print_timings: total time = 2891.13 ms
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```
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## Parallel generation
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```bash
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./simple ./models/llama-7b-v2/ggml-model-f16.gguf "Hello my name is" 4
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...
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main: n_len = 32, n_ctx = 2048, n_parallel = 4, n_kv_req = 113
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Hello my name is
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main: generating 4 sequences ...
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main: stream 0 finished
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main: stream 1 finished
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main: stream 2 finished
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main: stream 3 finished
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sequence 0:
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Hello my name is Shirley. I am a 25-year-old female who has been working for over 5 years as a b
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sequence 1:
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Hello my name is Renee and I'm a 32 year old female from the United States. I'm looking for a man between
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sequence 2:
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Hello my name is Diana. I am looking for a housekeeping job. I have experience with children and have my own transportation. I am
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sequence 3:
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Hello my name is Cody. I am a 3 year old neutered male. I am a very friendly cat. I am very playful and
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main: decoded 108 tokens in 3.57 s, speed: 30.26 t/s
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llama_print_timings: load time = 587.00 ms
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llama_print_timings: sample time = 2.56 ms / 112 runs ( 0.02 ms per token, 43664.72 tokens per second)
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llama_print_timings: prompt eval time = 4089.11 ms / 118 tokens ( 34.65 ms per token, 28.86 tokens per second)
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llama_print_timings: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)
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llama_print_timings: total time = 4156.04 ms
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```
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@ -10,12 +10,10 @@ int main(int argc, char ** argv) {
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gpt_params params;
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if (argc == 1 || argv[1][0] == '-') {
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printf("usage: %s MODEL_PATH [PROMPT] [PARALLEL]\n" , argv[0]);
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printf("usage: %s MODEL_PATH [PROMPT]\n" , argv[0]);
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return 1 ;
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}
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int n_parallel = 1;
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if (argc >= 2) {
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params.model = argv[1];
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}
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@ -24,15 +22,11 @@ int main(int argc, char ** argv) {
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params.prompt = argv[2];
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}
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if (argc >= 4) {
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n_parallel = std::atoi(argv[3]);
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}
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if (params.prompt.empty()) {
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params.prompt = "Hello my name is";
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}
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// total length of the sequences including the prompt
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// total length of the sequence including the prompt
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const int n_len = 32;
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// init LLM
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@ -64,9 +58,9 @@ int main(int argc, char ** argv) {
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tokens_list = ::llama_tokenize(ctx, params.prompt, true);
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const int n_ctx = llama_n_ctx(ctx);
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const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size())*n_parallel;
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const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size());
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LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_parallel = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, n_parallel, n_kv_req);
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LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, n_kv_req);
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// make sure the KV cache is big enough to hold all the prompt and generated tokens
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if (n_kv_req > n_ctx) {
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@ -108,25 +102,8 @@ int main(int argc, char ** argv) {
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return 1;
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}
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// assign the system KV cache to all parallel sequences
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// this way, the parallel sequences will "reuse" the prompt tokens without having to copy them
|
||||
for (int32_t i = 1; i < n_parallel; ++i) {
|
||||
llama_kv_cache_seq_cp(ctx, 0, i, 0, batch.n_tokens);
|
||||
}
|
||||
|
||||
if (n_parallel > 1) {
|
||||
LOG_TEE("\n\n%s: generating %d sequences ...\n", __func__, n_parallel);
|
||||
}
|
||||
|
||||
// main loop
|
||||
|
||||
// we will store the parallel decoded sequences in this vector
|
||||
std::vector<std::string> streams(n_parallel);
|
||||
|
||||
// remember the batch index of the last token for each parallel sequence
|
||||
// we need this to determine which logits to sample from
|
||||
std::vector<int32_t> i_batch(n_parallel, batch.n_tokens - 1);
|
||||
|
||||
int n_cur = batch.n_tokens;
|
||||
int n_decode = 0;
|
||||
|
||||
@ -139,18 +116,10 @@ int main(int argc, char ** argv) {
|
||||
return 1;
|
||||
}
|
||||
|
||||
// prepare the next batch
|
||||
batch.n_tokens = 0;
|
||||
|
||||
// sample the next token for each parallel sequence / stream
|
||||
for (int32_t i = 0; i < n_parallel; ++i) {
|
||||
if (i_batch[i] < 0) {
|
||||
// the stream has already finished
|
||||
continue;
|
||||
}
|
||||
|
||||
// sample the next token
|
||||
{
|
||||
auto n_vocab = llama_n_vocab(ctx);
|
||||
auto logits = llama_get_logits_ith(ctx, i_batch[i]);
|
||||
auto logits = llama_get_logits_ith(ctx, batch.n_tokens - 1);
|
||||
|
||||
std::vector<llama_token_data> candidates;
|
||||
candidates.reserve(n_vocab);
|
||||
@ -161,68 +130,38 @@ int main(int argc, char ** argv) {
|
||||
|
||||
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
|
||||
|
||||
const int top_k = 40;
|
||||
const float top_p = 0.9f;
|
||||
const float temp = 0.4f;
|
||||
// sample the most likely token
|
||||
const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
|
||||
|
||||
llama_sample_top_k(ctx, &candidates_p, top_k, 1);
|
||||
llama_sample_top_p(ctx, &candidates_p, top_p, 1);
|
||||
llama_sample_temp (ctx, &candidates_p, temp);
|
||||
|
||||
const llama_token new_token_id = llama_sample_token(ctx, &candidates_p);
|
||||
|
||||
//const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
|
||||
|
||||
// is it an end of stream? -> mark the stream as finished
|
||||
// is it an end of stream?
|
||||
if (new_token_id == llama_token_eos(ctx) || n_cur == n_len) {
|
||||
i_batch[i] = -1;
|
||||
LOG_TEE("\n");
|
||||
if (n_parallel > 1) {
|
||||
LOG_TEE("%s: stream %d finished", __func__, i);
|
||||
}
|
||||
|
||||
continue;
|
||||
break;
|
||||
}
|
||||
|
||||
// if there is only one stream, we print immediately to stdout
|
||||
if (n_parallel == 1) {
|
||||
LOG_TEE("%s", llama_token_to_piece(ctx, new_token_id).c_str());
|
||||
fflush(stdout);
|
||||
}
|
||||
LOG_TEE("%s", llama_token_to_piece(ctx, new_token_id).c_str());
|
||||
fflush(stdout);
|
||||
|
||||
streams[i] += llama_token_to_piece(ctx, new_token_id);
|
||||
// prepare the next batch
|
||||
batch.n_tokens = 0;
|
||||
|
||||
// push this new token for next evaluation
|
||||
batch.token [batch.n_tokens] = new_token_id;
|
||||
batch.pos [batch.n_tokens] = n_cur;
|
||||
batch.seq_id[batch.n_tokens] = i;
|
||||
batch.seq_id[batch.n_tokens] = 0;
|
||||
batch.logits[batch.n_tokens] = true;
|
||||
|
||||
i_batch[i] = batch.n_tokens;
|
||||
|
||||
batch.n_tokens += 1;
|
||||
|
||||
n_decode += 1;
|
||||
}
|
||||
|
||||
// all streams are finished
|
||||
if (batch.n_tokens == 0) {
|
||||
break;
|
||||
}
|
||||
|
||||
n_cur += 1;
|
||||
}
|
||||
|
||||
LOG_TEE("\n");
|
||||
|
||||
if (n_parallel > 1) {
|
||||
LOG_TEE("\n");
|
||||
|
||||
for (int32_t i = 0; i < n_parallel; ++i) {
|
||||
LOG_TEE("sequence %d:\n\n%s%s\n\n", i, params.prompt.c_str(), streams[i].c_str());
|
||||
}
|
||||
}
|
||||
|
||||
const auto t_main_end = ggml_time_us();
|
||||
|
||||
LOG_TEE("%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n",
|
||||
|
||||
Loading…
Reference in New Issue
Block a user