llama.cpp/examples/batched/README.md

# llama.cpp/example/batched

The example demonstrates batched generation from a given prompt

```bash
./llama-batched -m ./models/llama-7b-v2/ggml-model-f16.gguf -p "Hello my name is" -np 4

...

main: n_len = 32, n_ctx = 2048, n_parallel = 4, n_kv_req = 113

 Hello my name is

main: generating 4 sequences ...

main: stream 0 finished
main: stream 1 finished
main: stream 2 finished
main: stream 3 finished

sequence 0:

Hello my name is Shirley. I am a 25-year-old female who has been working for over 5 years as a b

sequence 1:

Hello my name is Renee and I'm a 32 year old female from the United States. I'm looking for a man between

sequence 2:

Hello my name is Diana. I am looking for a housekeeping job. I have experience with children and have my own transportation. I am

sequence 3:

Hello my name is Cody. I am a 3 year old neutered male. I am a very friendly cat. I am very playful and

main: decoded 108 tokens in 3.57 s, speed: 30.26 t/s

llama_print_timings:        load time =   587.00 ms
llama_print_timings:      sample time =     2.56 ms /   112 runs   (    0.02 ms per token, 43664.72 tokens per second)
llama_print_timings: prompt eval time =  4089.11 ms /   118 tokens (   34.65 ms per token,    28.86 tokens per second)
llama_print_timings:        eval time =     0.00 ms /     1 runs   (    0.00 ms per token,      inf tokens per second)
llama_print_timings:       total time =  4156.04 ms
```
llama : custom attention mask + parallel decoding + no context swaps (#3228) * tests : verify that RoPE is "additive" * llama : replace ggml_diag_mask_inf with ggml_add (custom -inf mask) * ggml : ggml_rope now takes a vector with positions instead of n_past * metal : add rope_f16 kernel + optimize cpy kernels * llama : unified KV cache + batch inference API * llama : add new llama_decode() API that works with llama_batch * llama : add cell_max heuristic for more efficient kv_cache * llama : extend llama_kv_cache API * llama : more robust cell_max heuristic + wip shift * metal : disable concurrency optimization * llama : add llama_kv_cache_shift_seq + no more context swaps * llama : apply K-cache roping for Falcon and Baichuan * speculative : fix KV cache management * parallel : example for serving multiple users in parallel * parallel : disable hot-plug to avoid cache fragmentation * fixes : speculative KV cache + llama worst-case graph * llama : extend batch API to select which logits to output * llama : fix worst case graph build * ggml-cuda : update rope implementation for parallel decoding (#3254) * ggml-cuda : update rope implementation for parallel decoding * better solution for p0 computation * fix rope * simpler rope implementation --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * make : add parallel to build + fix static functions in llama.cpp * simple : fix token counting * parallel : various improvements * llama : fix cell_max logic + rename functions * parallel : try smaller batches when the KV cache is fragmented * parallel : fix sequence termination criteria * llama : silence errors KV cache errors * parallel : remove new line from prompt * parallel : process system prompt once + configurable paramters + llama API * parallel : remove question with short answers * parallel : count cache misses * parallel : print misses on each request * parallel : minor * llama : fix n_kv to never become 0 * parallel : rename hot-plug to continuous-batching * llama : improve llama_batch API + simplify parallel example * simple : add parallel decoding support * simple : improve comments + free batch * ggml-cuda : add rope f16, restore performance with parallel decoding (#3272) * ggml-cuda : add rope f16, restore performance * offload KQ_mask with all models * fix rope shift --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * llama : disable MPI for now ggml-ci * train : make KQ_pos memory buffer permanent via dummy scale op * ggml : revert change to ggml_cpy, add ggml_cont_Nd instead (#3275) ggml-ci * parallel : fix bug (extra BOS) + smaller token_prev array * parallel : fix cases where the input prompts can overflow the batch * parallel : add disabled experimental batch chunking in powers of two * llama : llama.h formatting + comments * simple : add README.md * llama : fix kv cache heuristic when context is less than 32 * parallel : fix crash when `-n -1` * llama : simplify returns if/else branches * metal : use mm kernels for batch size > 2 * examples : utilize new llama_get_logits_ith() * examples : add example for batched decoding * examples : do not eval prompt 2 times (close #3348) * server : clear the KV cache beyond n_past before llama_decode * server : avoid context swaps by shifting the KV cache --------- Co-authored-by: slaren <slarengh@gmail.com> 2023-09-28 16:04:36 +00:00			`# llama.cpp/example/batched`

			`The example demonstrates batched generation from a given prompt`

			```bash
`build`: rename main → llama-cli, server → llama-server, llava-cli → llama-llava-cli, etc... (#7809) * `main`/`server`: rename to `llama` / `llama-server` for consistency w/ homebrew * server: update refs -> llama-server gitignore llama-server * server: simplify nix package * main: update refs -> llama fix examples/main ref * main/server: fix targets * update more names * Update build.yml * rm accidentally checked in bins * update straggling refs * Update .gitignore * Update server-llm.sh * main: target name -> llama-cli * Prefix all example bins w/ llama- * fix main refs * rename {main->llama}-cmake-pkg binary * prefix more cmake targets w/ llama- * add/fix gbnf-validator subfolder to cmake * sort cmake example subdirs * rm bin files * fix llama-lookup-* Makefile rules * gitignore /llama-* * rename Dockerfiles * rename llama\|main -> llama-cli; consistent RPM bin prefixes * fix some missing -cli suffixes * rename dockerfile w/ llama-cli * rename(make): llama-baby-llama * update dockerfile refs * more llama-cli(.exe) * fix test-eval-callback * rename: llama-cli-cmake-pkg(.exe) * address gbnf-validator unused fread warning (switched to C++ / ifstream) * add two missing llama- prefixes * Updating docs for eval-callback binary to use new `llama-` prefix. * Updating a few lingering doc references for rename of main to llama-cli * Updating `run-with-preset.py` to use new binary names. Updating docs around `perplexity` binary rename. * Updating documentation references for lookup-merge and export-lora * Updating two small `main` references missed earlier in the finetune docs. * Update apps.nix * update grammar/README.md w/ new llama-* names * update llama-rpc-server bin name + doc * Revert "update llama-rpc-server bin name + doc" This reverts commit e474ef1df481fd8936cd7d098e3065d7de378930. * add hot topic notice to README.md * Update README.md * Update README.md * rename gguf-split & quantize bins refs in **/tests.sh --------- Co-authored-by: HanClinto <hanclinto@gmail.com> 2024-06-12 23:41:52 +00:00			`./llama-batched -m ./models/llama-7b-v2/ggml-model-f16.gguf -p "Hello my name is" -np 4`
llama : custom attention mask + parallel decoding + no context swaps (#3228) * tests : verify that RoPE is "additive" * llama : replace ggml_diag_mask_inf with ggml_add (custom -inf mask) * ggml : ggml_rope now takes a vector with positions instead of n_past * metal : add rope_f16 kernel + optimize cpy kernels * llama : unified KV cache + batch inference API * llama : add new llama_decode() API that works with llama_batch * llama : add cell_max heuristic for more efficient kv_cache * llama : extend llama_kv_cache API * llama : more robust cell_max heuristic + wip shift * metal : disable concurrency optimization * llama : add llama_kv_cache_shift_seq + no more context swaps * llama : apply K-cache roping for Falcon and Baichuan * speculative : fix KV cache management * parallel : example for serving multiple users in parallel * parallel : disable hot-plug to avoid cache fragmentation * fixes : speculative KV cache + llama worst-case graph * llama : extend batch API to select which logits to output * llama : fix worst case graph build * ggml-cuda : update rope implementation for parallel decoding (#3254) * ggml-cuda : update rope implementation for parallel decoding * better solution for p0 computation * fix rope * simpler rope implementation --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * make : add parallel to build + fix static functions in llama.cpp * simple : fix token counting * parallel : various improvements * llama : fix cell_max logic + rename functions * parallel : try smaller batches when the KV cache is fragmented * parallel : fix sequence termination criteria * llama : silence errors KV cache errors * parallel : remove new line from prompt * parallel : process system prompt once + configurable paramters + llama API * parallel : remove question with short answers * parallel : count cache misses * parallel : print misses on each request * parallel : minor * llama : fix n_kv to never become 0 * parallel : rename hot-plug to continuous-batching * llama : improve llama_batch API + simplify parallel example * simple : add parallel decoding support * simple : improve comments + free batch * ggml-cuda : add rope f16, restore performance with parallel decoding (#3272) * ggml-cuda : add rope f16, restore performance * offload KQ_mask with all models * fix rope shift --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * llama : disable MPI for now ggml-ci * train : make KQ_pos memory buffer permanent via dummy scale op * ggml : revert change to ggml_cpy, add ggml_cont_Nd instead (#3275) ggml-ci * parallel : fix bug (extra BOS) + smaller token_prev array * parallel : fix cases where the input prompts can overflow the batch * parallel : add disabled experimental batch chunking in powers of two * llama : llama.h formatting + comments * simple : add README.md * llama : fix kv cache heuristic when context is less than 32 * parallel : fix crash when `-n -1` * llama : simplify returns if/else branches * metal : use mm kernels for batch size > 2 * examples : utilize new llama_get_logits_ith() * examples : add example for batched decoding * examples : do not eval prompt 2 times (close #3348) * server : clear the KV cache beyond n_past before llama_decode * server : avoid context swaps by shifting the KV cache --------- Co-authored-by: slaren <slarengh@gmail.com> 2023-09-28 16:04:36 +00:00
			`...`

			`main: n_len = 32, n_ctx = 2048, n_parallel = 4, n_kv_req = 113`

			`Hello my name is`

			`main: generating 4 sequences ...`

			`main: stream 0 finished`
			`main: stream 1 finished`
			`main: stream 2 finished`
			`main: stream 3 finished`

			`sequence 0:`

			`Hello my name is Shirley. I am a 25-year-old female who has been working for over 5 years as a b`

			`sequence 1:`

			`Hello my name is Renee and I'm a 32 year old female from the United States. I'm looking for a man between`

			`sequence 2:`

			`Hello my name is Diana. I am looking for a housekeeping job. I have experience with children and have my own transportation. I am`

			`sequence 3:`

			`Hello my name is Cody. I am a 3 year old neutered male. I am a very friendly cat. I am very playful and`

			`main: decoded 108 tokens in 3.57 s, speed: 30.26 t/s`

			`llama_print_timings: load time = 587.00 ms`
			`llama_print_timings: sample time = 2.56 ms / 112 runs ( 0.02 ms per token, 43664.72 tokens per second)`
			`llama_print_timings: prompt eval time = 4089.11 ms / 118 tokens ( 34.65 ms per token, 28.86 tokens per second)`
			`llama_print_timings: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)`
			`llama_print_timings: total time = 4156.04 ms`
			```