From 8c70a5ff25964f0a81e20d142a2f5ac5baff22fc Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 11 Oct 2023 21:25:33 +0300 Subject: [PATCH] batched : add bench tool (#3545) * batched : add bench tool * batched : minor fix table * batched-bench : add readme + n_kv_max is now configurable * batched-bench : init warm-up batch * batched-bench : pass custom set of PP, TG and PL * batched-bench : add mmq CLI arg --- .gitignore | 1 + Makefile | 13 +- examples/CMakeLists.txt | 1 + examples/batched-bench/CMakeLists.txt | 5 + examples/batched-bench/README.md | 51 +++++ examples/batched-bench/batched-bench.cpp | 251 +++++++++++++++++++++++ examples/batched/batched.cpp | 2 +- 7 files changed, 321 insertions(+), 3 deletions(-) create mode 100644 examples/batched-bench/CMakeLists.txt create mode 100644 examples/batched-bench/README.md create mode 100644 examples/batched-bench/batched-bench.cpp diff --git a/.gitignore b/.gitignore index 420e0d6d0..d288e66fc 100644 --- a/.gitignore +++ b/.gitignore @@ -55,6 +55,7 @@ models-mnt /server /simple /batched +/batched-bench /export-lora /finetune /speculative diff --git a/Makefile b/Makefile index 87e7bb604..571ad3bbe 100644 --- a/Makefile +++ b/Makefile @@ -1,8 +1,14 @@ # Define the default target now so that it is always the first target -BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot q8dot 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 infill benchmark-matmult parallel finetune export-lora tests/test-c.o +BUILD_TARGETS = \ + main quantize quantize-stats perplexity embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \ + simple batched batched-bench save-load-state server embd-input-test gguf llama-bench baby-llama beam-search \ + speculative infill benchmark-matmult parallel finetune export-lora tests/test-c.o # Binaries only useful for tests -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 tests/test-tokenizer-1-bpe +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 tests/test-tokenizer-1-bpe # Code coverage output files COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report @@ -557,6 +563,9 @@ simple: examples/simple/simple.cpp build-info.h ggml. batched: examples/batched/batched.cpp build-info.h ggml.o llama.o common.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) +batched-bench: examples/batched-bench/batched-bench.cpp build-info.h ggml.o llama.o common.o $(OBJS) + $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + quantize: examples/quantize/quantize.cpp build-info.h ggml.o llama.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index de4cf7a69..ab8459370 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -25,6 +25,7 @@ else() add_subdirectory(convert-llama2c-to-ggml) add_subdirectory(simple) add_subdirectory(batched) + add_subdirectory(batched-bench) add_subdirectory(speculative) add_subdirectory(parallel) add_subdirectory(embd-input) diff --git a/examples/batched-bench/CMakeLists.txt b/examples/batched-bench/CMakeLists.txt new file mode 100644 index 000000000..40a032c51 --- /dev/null +++ b/examples/batched-bench/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET batched-bench) +add_executable(${TARGET} batched-bench.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/batched-bench/README.md b/examples/batched-bench/README.md new file mode 100644 index 000000000..34b343f66 --- /dev/null +++ b/examples/batched-bench/README.md @@ -0,0 +1,51 @@ +# llama.cpp/example/batched-bench + +Benchmark the batched decoding performance of `llama.cpp` + +## Usage + +There are 2 modes of operation: + +- `prompt not shared` - each batch has a separate prompt of size `PP` (i.e. `N_KV = B*(PP + TG)`) +- `prompt is shared` - there is a common prompt of size `PP` used by all batches (i.e. `N_KV = PP + B*TG`) + +```bash +./batched-bench MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] [MMQ] + +# LLaMA 7B, F16, N_KV_MAX = 16384 (8GB), prompt not shared +./batched-bench ./models/llama-7b/ggml-model-f16.gguf 16384 0 99 + +# LLaMA 7B, Q8_0, N_KV_MAX = 16384 (8GB), prompt is shared +./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 16384 1 99 + +# custom set of batches +./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 2048 0 999 0 128,256,512 128,256 1,2,4,8,16,32 +``` + +## Sample results + +- `PP` - prompt tokens per batch +- `TG` - generated tokens per batch +- `B` - number of batches +- `N_KV` - required KV cache size +- `T_PP` - prompt processing time (i.e. time to first token) +- `S_PP` - prompt processing speed (`(B*PP)/T_PP` or `PP/T_PP`) +- `T_TG` - time to generate all batches +- `S_TG` - text generation speed (`(B*TG)/T_TG`) +- `T` - total time +- `S` - total speed (i.e. all tokens / total time) + +| PP | TG | B | N_KV | T_PP s | S_PP t/s | T_TG s | S_TG t/s | T s | S t/s | +|-------|--------|------|--------|----------|----------|----------|----------|----------|----------| +| 128 | 128 | 1 | 256 | 0.108 | 1186.64 | 3.079 | 41.57 | 3.187 | 80.32 | +| 128 | 128 | 2 | 512 | 0.198 | 1295.19 | 5.029 | 50.90 | 5.227 | 97.95 | +| 128 | 128 | 4 | 1024 | 0.373 | 1373.96 | 6.878 | 74.44 | 7.251 | 141.23 | +| 128 | 128 | 8 | 2048 | 0.751 | 1363.27 | 7.344 | 139.43 | 8.095 | 252.99 | +| 128 | 128 | 16 | 4096 | 1.570 | 1304.68 | 8.455 | 242.23 | 10.024 | 408.60 | +| 128 | 128 | 32 | 8192 | 3.408 | 1201.73 | 8.801 | 465.40 | 12.209 | 670.96 | +| 128 | 256 | 1 | 384 | 0.107 | 1196.70 | 6.329 | 40.45 | 6.436 | 59.67 | +| 128 | 256 | 2 | 768 | 0.194 | 1317.45 | 10.239 | 50.00 | 10.433 | 73.61 | +| 128 | 256 | 4 | 1536 | 0.366 | 1399.03 | 13.960 | 73.35 | 14.326 | 107.22 | +| 128 | 256 | 8 | 3072 | 0.751 | 1363.92 | 15.110 | 135.54 | 15.861 | 193.69 | +| 128 | 256 | 16 | 6144 | 1.569 | 1304.93 | 18.073 | 226.64 | 19.642 | 312.80 | +| 128 | 256 | 32 | 12288 | 3.409 | 1201.35 | 19.223 | 426.15 | 22.633 | 542.93 | diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp new file mode 100644 index 000000000..3e1e0716d --- /dev/null +++ b/examples/batched-bench/batched-bench.cpp @@ -0,0 +1,251 @@ +#include "common.h" +#include "llama.h" + +#include +#include +#include +#include +#include + +// mutates the input string +static std::vector parse_list(char * p) { + std::vector ret; + + char * q = p; + + while (*p) { + if (*p == ',') { + *p = '\0'; + ret.push_back(std::atoi(q)); + q = p + 1; + } + + ++p; + } + + ret.push_back(std::atoi(q)); + + return ret; +} + +int main(int argc, char ** argv) { + gpt_params params; + + if (argc == 1 || argv[1][0] == '-') { + printf("usage: %s MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] [MMQ] \n" , argv[0]); + printf(" , and PL are comma-separated lists of numbers without spaces\n\n"); + printf(" example: %s ggml-model-f16.gguf 2048 0 999 0 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]); + return 1 ; + } + + int n_kv_max = 2048; + int is_pp_shared = 0; + int n_gpu_layers = 0; + int mmq = 0; + + std::vector n_pp = { 128, 256, 512, 1024, 2048, 3584, 7680, }; + std::vector n_tg = { 128, 256, }; + std::vector n_pl = { 1, 2, 4, 8, 16, 32, }; + //std::vector n_pl = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 32, }; + + if (argc >= 2) { + params.model = argv[1]; + } + + if (argc >= 3) { + n_kv_max = std::atoi(argv[2]); + } + + if (argc >= 4) { + is_pp_shared = std::atoi(argv[3]); + } + + if (argc >= 5) { + n_gpu_layers = std::atoi(argv[4]); + } + + if (argc >= 6) { + mmq = std::atoi(argv[5]); + } + + if (argc >= 7) { + n_pp = parse_list(argv[6]); + } + + if (argc >= 8) { + n_tg = parse_list(argv[7]); + } + + if (argc >= 9) { + n_pl = parse_list(argv[8]); + } + + // init LLM + + llama_backend_init(params.numa); + + // initialize the model + + llama_model_params model_params = llama_model_default_params(); + + model_params.n_gpu_layers = n_gpu_layers; + + llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params); + + if (model == NULL) { + fprintf(stderr , "%s: error: unable to load model\n" , __func__); + return 1; + } + + llama_context_params ctx_params = llama_context_default_params(); + + ctx_params.seed = 1234; + ctx_params.n_ctx = n_kv_max; + ctx_params.n_batch = 512; + ctx_params.mul_mat_q = mmq; + + ctx_params.n_threads = params.n_threads; + ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; + + llama_context * ctx = llama_new_context_with_model(model, ctx_params); + + if (ctx == NULL) { + fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__); + return 1; + } + + llama_batch batch = llama_batch_init(n_kv_max, 0); + + // decode in batches of ctx_params.n_batch tokens + auto decode_helper = [](llama_context * ctx, llama_batch & batch, int32_t n_batch) { + for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) { + const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i)); + + llama_batch batch_view = { + n_tokens, + batch.token + i, + nullptr, + batch.pos + i, + batch.seq_id + i, + batch.logits + i, + 0, 0, 0, // unused + }; + + const int ret = llama_decode(ctx, batch_view); + if (ret != 0) { + LOG_TEE("failed to decode the batch, n_batch = %d, ret = %d\n", n_batch, ret); + return false; + } + } + + return true; + }; + + // warm up + { + batch.n_tokens = 16; + + for (int i = 0; i < batch.n_tokens; ++i) { + batch.token[i] = 0; + batch.pos[i] = i; + batch.seq_id[i] = 0; + batch.logits[i] = false; + } + + if (!decode_helper(ctx, batch, ctx_params.n_batch)) { + LOG_TEE("%s: llama_decode() failed\n", __func__); + return 1; + } + } + + LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "B", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s", "T s", "S t/s"); + LOG_TEE("|%6s-|-%6s-|-%4s-|-%6s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|\n", "------", "------", "----", "------", "--------", "--------", "--------", "--------", "--------", "--------"); + + for ( int i_pp = 0; i_pp < (int) n_pp.size(); ++i_pp) { + for ( int i_tg = 0; i_tg < (int) n_tg.size(); ++i_tg) { + for (int i_pl = 0; i_pl < (int) n_pl.size(); ++i_pl) { + const int pp = n_pp[i_pp]; + const int tg = n_tg[i_tg]; + const int pl = n_pl[i_pl]; + + const int n_ctx_req = is_pp_shared ? pp + pl*tg : pl*(pp + tg); + + if (n_ctx_req > n_kv_max) { + continue; + } + + batch.n_tokens = is_pp_shared ? pp : pl*pp; + + for (int i = 0; i < batch.n_tokens; ++i) { + batch.token[i] = 0; + batch.pos[i] = i; + batch.seq_id[i] = 0; + batch.logits[i] = false; + } + batch.logits[batch.n_tokens - 1] = true; + + const auto t_pp_start = ggml_time_us(); + + llama_kv_cache_tokens_rm(ctx, -1, -1); + + if (!decode_helper(ctx, batch, ctx_params.n_batch)) { + LOG_TEE("%s: llama_decode() failed\n", __func__); + return 1; + } + + if (is_pp_shared) { + for (int32_t i = 1; i < pl; ++i) { + llama_kv_cache_seq_cp(ctx, 0, i, 0, pp); + } + } + + const auto t_pp_end = ggml_time_us(); + + const auto t_tg_start = ggml_time_us(); + + for (int i = 0; i < tg; ++i) { + batch.n_tokens = pl; + + for (int j = 0; j < pl; ++j) { + batch.token[j] = 0; + batch.pos[j] = pp + i; + batch.seq_id[j] = j; + batch.logits[j] = true; + } + + if (!decode_helper(ctx, batch, ctx_params.n_batch)) { + LOG_TEE("%s: llama_decode() failed\n", __func__); + return 1; + } + } + + const auto t_tg_end = ggml_time_us(); + + const int32_t n_kv = n_ctx_req; + + const float t_pp = (t_pp_end - t_pp_start) / 1000000.0f; + const float t_tg = (t_tg_end - t_tg_start) / 1000000.0f; + const float t = t_pp + t_tg; + + const float speed_pp = is_pp_shared ? pp / t_pp : pl*pp / t_pp; + const float speed_tg = pl*tg / t_tg; + const float speed = n_kv / t; + + LOG_TEE("|%6d | %6d | %4d | %6d | %8.3f | %8.2f | %8.3f | %8.2f | %8.3f | %8.2f |\n", pp, tg, pl, n_kv, t_pp, speed_pp, t_tg, speed_tg, t, speed); + } + } + } + + llama_print_timings(ctx); + + llama_batch_free(batch); + + llama_free(ctx); + llama_free_model(model); + + llama_backend_free(); + + fprintf(stderr, "\n\n"); + + return 0; +} diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp index 688ef2213..a88e022d6 100644 --- a/examples/batched/batched.cpp +++ b/examples/batched/batched.cpp @@ -66,7 +66,7 @@ int main(int argc, char ** argv) { ctx_params.seed = 1234; ctx_params.n_ctx = n_kv_req; ctx_params.n_batch = std::max(n_len, n_parallel); - ctx_params.n_threads = params.n_threads; + ctx_params.n_threads = params.n_threads; ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; llama_context * ctx = llama_new_context_with_model(model, ctx_params);