vbatts/llama.cpp
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llama : custom attention mask + parallel decoding + no context swaps (#3228)

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* 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>
This commit is contained in:
Georgi Gerganov 2023-09-28 19:04:36 +03:00 • committed by GitHub
parent 45855b3f1c
commit ec893798b7
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35 changed files with 2700 additions and 673 deletions
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16
examples/save-load-state/save-load-state.cpp
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@ -35,11 +35,11 @@ int main(int argc, char ** argv) {
auto last_n_tokens_data = std::vector<llama_token>(params.repeat_last_n, 0);
// init
auto model = llama_load_model_from_file(params.model.c_str(), lparams);
auto * model = llama_load_model_from_file(params.model.c_str(), lparams);
if (model == nullptr) {
return 1;
}
auto ctx = llama_new_context_with_model(model, lparams);
auto * ctx = llama_new_context_with_model(model, lparams);
if (ctx == nullptr) {
llama_free_model(model);
return 1;
@ -54,7 +54,7 @@ int main(int argc, char ** argv) {
}
// evaluate prompt
llama_eval(ctx, tokens.data(), n_prompt_tokens, n_past, params.n_threads);
llama_decode(ctx, llama_batch_get_one(tokens.data(), n_prompt_tokens, n_past, 0), params.n_threads);
last_n_tokens_data.insert(last_n_tokens_data.end(), tokens.data(), tokens.data() + n_prompt_tokens);
n_past += n_prompt_tokens;
@ -78,7 +78,7 @@ int main(int argc, char ** argv) {
printf("\n%s", params.prompt.c_str());
for (auto i = 0; i < params.n_predict; i++) {
auto logits = llama_get_logits(ctx);
auto * logits = llama_get_logits(ctx);
auto n_vocab = llama_n_vocab(ctx);
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
@ -91,7 +91,7 @@ int main(int argc, char ** argv) {
last_n_tokens_data.push_back(next_token);
printf("%s", next_token_str.c_str());
if (llama_eval(ctx, &next_token, 1, n_past, params.n_threads)) {
if (llama_decode(ctx, llama_batch_get_one(&next_token, 1, n_past, 0), params.n_threads)) {
fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
llama_free(ctx);
llama_free_model(model);
@ -106,7 +106,7 @@ int main(int argc, char ** argv) {
llama_free(ctx);
// make new context
auto ctx2 = llama_new_context_with_model(model, lparams);
auto * ctx2 = llama_new_context_with_model(model, lparams);
// Load state (rng, logits, embedding and kv_cache) from file
{
@ -138,7 +138,7 @@ int main(int argc, char ** argv) {
// second run
for (auto i = 0; i < params.n_predict; i++) {
auto logits = llama_get_logits(ctx2);
auto * logits = llama_get_logits(ctx2);
auto n_vocab = llama_n_vocab(ctx2);
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
@ -151,7 +151,7 @@ int main(int argc, char ** argv) {
last_n_tokens_data.push_back(next_token);
printf("%s", next_token_str.c_str());
if (llama_eval(ctx2, &next_token, 1, n_past, params.n_threads)) {
if (llama_decode(ctx, llama_batch_get_one(&next_token, 1, n_past, 0), params.n_threads)) {
fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
llama_free(ctx2);
llama_free_model(model);