batched-bench : add readme + n_kv_max is now configurable

Author: ggerganov

.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
@@ -554,6 +560,9 @@ 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/batched-bench/README.md

@@ -0,0 +1,48 @@
+# 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]
+
+# 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
+```
+
+## 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

@@ -11,10 +11,11 @@ int main(int argc, char ** argv) {
     gpt_params params;
 
     if (argc == 1 || argv[1][0] == '-') {
-        printf("usage: %s MODEL_PATH [IS_PP_SHARED] [NGL]\n" , argv[0]);
+        printf("usage: %s MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL]\n" , argv[0]);
         return 1 ;
     }
 
+    int n_kv_max     = 2048;
     int is_pp_shared = 0;
     int n_gpu_layers = 0;
 
@@ -23,18 +24,20 @@ int main(int argc, char ** argv) {
     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, };
 
-    const int32_t n_ctx_max = 16*1024;
-
     if (argc >= 2) {
         params.model = argv[1];
     }
 
     if (argc >= 3) {
-        is_pp_shared = std::atoi(argv[2]);
+        n_kv_max = std::atoi(argv[2]);
     }
 
     if (argc >= 4) {
-        n_gpu_layers = std::atoi(argv[3]);
+        is_pp_shared = std::atoi(argv[3]);
+    }
+
+    if (argc >= 5) {
+        n_gpu_layers = std::atoi(argv[4]);
     }
 
     // init LLM
@@ -56,8 +59,8 @@ int main(int argc, char ** argv) {
 
     llama_context_params ctx_params = llama_context_default_params();
 
-    ctx_params.seed  = 1234;
-    ctx_params.n_ctx = n_ctx_max;
+    ctx_params.seed    = 1234;
+    ctx_params.n_ctx   = n_kv_max;
     ctx_params.n_batch = 512;
     ctx_params.n_threads       = params.n_threads;
     ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
@@ -69,7 +72,7 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    llama_batch batch = llama_batch_init(n_ctx_max, 0);
+    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) {
@@ -88,7 +91,7 @@ int main(int argc, char ** argv) {
 
             const int ret = llama_decode(ctx, batch_view);
             if (ret != 0) {
-                LOG_TEE("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret);
+                LOG_TEE("failed to decode the batch, n_batch = %d, ret = %d\n", n_batch, ret);
                 return false;
             }
         }
@@ -117,7 +120,7 @@ int main(int argc, char ** argv) {
 
                 const int n_ctx_req = is_pp_shared ? pp + pl*tg : pl*(pp + tg);
 
-                if (n_ctx_req > n_ctx_max) {
+                if (n_ctx_req > n_kv_max) {
                     continue;
                 }