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

@@ -55,6 +55,7 @@ models-mnt
 /server
 /simple
 /batched
+/batched-bench
 /export-lora
 /finetune
 /speculative

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)
 

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)

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)

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] <PP> <TG> <PL>
+
+# 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 |

examples/batched-bench/batched-bench.cpp

@@ -0,0 +1,251 @@
+#include "common.h"
+#include "llama.h"
+
+#include <algorithm>
+#include <cmath>
+#include <cstdio>
+#include <string>
+#include <vector>
+
+// mutates the input string
+static std::vector<int> parse_list(char * p) {
+ std::vector<int> 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] <PP> <TG> <PL>\n" , argv[0]);
+ printf(" <PP>, <TG> 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<int> n_pp = { 128, 256, 512, 1024, 2048, 3584, 7680, };
+ std::vector<int> n_tg = { 128, 256, };
+ std::vector<int> n_pl = { 1, 2, 4, 8, 16, 32, };
+ //std::vector<int> 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;
+}

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);