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server : improve infill context reuse (ggml-org#9894)
ggml-ci
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‎examples/server/README.md

+6-4
Original file line numberDiff line numberDiff line change
@@ -524,10 +524,12 @@ Takes a prefix and a suffix and returns the predicted completion as stream.
524524

525525
- `input_prefix`: Set the prefix of the code to infill.
526526
- `input_suffix`: Set the suffix of the code to infill.
527-
- `prompt`: Added after the `FIM_MID` token
528-
- `extra_context`: Additional context inserted before the FIM prefix. See https://github.com/ggerganov/llama.cpp/pull/9874
527+
- `input_extra`: Additional context inserted before the FIM prefix.
528+
- `prompt`: Added after the `FIM_MID` token
529529

530-
It also accepts all the options of `/completion`.
530+
`input_extra` is array of `{"filename": string, "text": string}` objects.
531+
532+
The endpoint also accepts all the options of `/completion`.
531533

532534
If the model has `FIM_REPO` and `FIM_FILE_SEP` tokens, the [repo-level pattern](https://arxiv.org/pdf/2409.12186) is used:
533535

@@ -545,7 +547,7 @@ If the model has `FIM_REPO` and `FIM_FILE_SEP` tokens, the [repo-level pattern](
545547
If the tokens are missing, then the extra context is simply prefixed at the start:
546548

547549
```txt
548-
[extra_context]<FIM_PRE>[input_prefix]<FIM_SUF>[input_suffix]<FIM_MID>[prompt]
550+
[input_extra]<FIM_PRE>[input_prefix]<FIM_SUF>[input_suffix]<FIM_MID>[prompt]
549551
```
550552

551553
### **GET** `/props`: Get server global properties.

‎examples/server/server.cpp

+27-46
Original file line numberDiff line numberDiff line change
@@ -136,10 +136,6 @@ struct slot_params {
136136
int64_t t_max_predict_ms = -1; // if positive, limit the generation phase to this time limit
137137

138138
std::vector<std::string> antiprompt;
139-
140-
json input_prefix;
141-
json input_suffix;
142-
json extra_context;
143139
};
144140

145141
struct server_slot {
@@ -169,6 +165,10 @@ struct server_slot {
169165

170166
json prompt; // can be either a string, array of strings or array of token ids
171167

168+
json input_prefix;
169+
json input_suffix;
170+
json input_extra;
171+
172172
// when a task is submitted, we first tokenize the prompt and store it here
173173
std::vector<llama_token> prompt_tokens;
174174
std::vector<llama_token> extra_tokens;
@@ -910,12 +910,12 @@ struct server_context {
910910
}
911911

912912
// infill
913-
slot.params.input_prefix = json_value(data, "input_prefix", default_params.input_prefix);
914-
slot.params.input_suffix = json_value(data, "input_suffix", default_params.input_suffix);
915-
slot.params.extra_context = json_value(data, "extra_context", default_params.extra_context);
913+
slot.input_prefix = json_value(data, "input_prefix", json());
914+
slot.input_suffix = json_value(data, "input_suffix", json());
915+
slot.input_extra = json_value(data, "input_extra", json());
916916

917-
SLT_DBG(slot, "extra_context chunks: %d\n", (int) slot.params.extra_context.size());
918-
for (const auto & chunk : slot.params.extra_context) {
917+
SLT_DBG(slot, "extra_context chunks: %d\n", (int) slot.input_extra.size());
918+
for (const auto & chunk : slot.input_extra) {
919919
// { "text": string, "filename": string }
920920
if (!chunk.contains("text") || !chunk["text"].is_string()) {
921921
send_error(task, "extra_context chunk must contain a \"text\" field with a string value", ERROR_TYPE_INVALID_REQUEST);
@@ -932,7 +932,7 @@ struct server_context {
932932
}
933933

934934
// get prompt
935-
if (task.cmpl_type != SERVER_TASK_CMPL_TYPE_INFILL) {
935+
{
936936
const auto & prompt = data.find("prompt");
937937
if (prompt == data.end()) {
938938
send_error(task, "\"prompt\" must be provided", ERROR_TYPE_INVALID_REQUEST);
@@ -1958,6 +1958,8 @@ struct server_context {
19581958
} break;
19591959
case SERVER_TASK_CMPL_TYPE_INFILL:
19601960
{
1961+
// TODO: optimize this block by reducing memory allocations and movement
1962+
19611963
// use FIM repo-level pattern:
19621964
// ref: https://arxiv.org/pdf/2409.12186
19631965
//
@@ -1968,10 +1970,11 @@ struct server_context {
19681970
// extra chunk 1
19691971
// ...
19701972
// [FIM_SEP]filename
1971-
// [FIM_PRE]prefix[FIM_SUF]suffix[FIM_MID]
1973+
// [FIM_PRE]prefix[FIM_SUF]suffix[FIM_MID]prompt
19721974
//
1973-
auto prefix_tokens = tokenize(slot.params.input_prefix, false, false);
1974-
auto suffix_tokens = tokenize(slot.params.input_suffix, false, false);
1975+
auto tokens_prefix = tokenize(slot.input_prefix, false, false);
1976+
auto tokens_suffix = tokenize(slot.input_suffix, false, false);
1977+
auto tokens_prompt = tokenize(slot.prompt, false, false);
19751978

19761979
slot.extra_tokens.clear();
19771980
if (llama_token_fim_rep(model) != LLAMA_TOKEN_NULL) {
@@ -1981,7 +1984,7 @@ struct server_context {
19811984
slot.extra_tokens.insert(slot.extra_tokens.end(), k_fim_repo.begin(), k_fim_repo.end());
19821985
}
19831986

1984-
for (const auto & chunk : slot.params.extra_context) {
1987+
for (const auto & chunk : slot.input_extra) {
19851988
// { "text": string, "filename": string }
19861989
const std::string text = chunk.value("text", "");
19871990
const std::string filename = chunk.value("filename", "tmp");
@@ -2012,20 +2015,21 @@ struct server_context {
20122015
}
20132016

20142017
// for now pick FIM context to fit in a batch (ratio prefix:suffix = 3:1, TODO: configurable?)
2015-
const int n_suffix_take = std::min<int>(suffix_tokens.size(), (n_batch)/4);
2016-
const int n_prefix_take = std::min<int>(prefix_tokens.size(), (n_batch - 3) - n_suffix_take);
2018+
const int n_suffix_take = std::min<int>(tokens_suffix.size(), (n_batch/4));
2019+
const int n_prefix_take = std::min<int>(tokens_prefix.size(), 3*(n_batch/4) - 3);
20172020

20182021
// fill the rest of the context with extra chunks
20192022
const int n_extra_take = std::min<int>(std::max<int>(0, slot.n_ctx - (n_batch) - 2*slot.n_predict), slot.extra_tokens.size());
20202023

2021-
prefix_tokens.erase(prefix_tokens.begin(), prefix_tokens.begin() + prefix_tokens.size() - n_prefix_take);
2022-
suffix_tokens.resize(n_suffix_take);
2024+
tokens_prefix.erase(tokens_prefix.begin(), tokens_prefix.begin() + tokens_prefix.size() - n_prefix_take);
2025+
tokens_suffix.resize(n_suffix_take);
20232026

2024-
prefix_tokens.insert(prefix_tokens.begin(), llama_token_fim_pre(model));
2025-
suffix_tokens.insert(suffix_tokens.begin(), llama_token_fim_suf(model));
2027+
tokens_prefix.insert(tokens_prefix.begin(), llama_token_fim_pre(model));
2028+
tokens_prefix.insert(tokens_prefix.end(), tokens_prompt.begin(), tokens_prompt.end());
2029+
tokens_suffix.insert(tokens_suffix.begin(), llama_token_fim_suf(model));
20262030

2027-
auto embd_inp = params.spm_infill ? suffix_tokens : prefix_tokens;
2028-
auto embd_end = params.spm_infill ? prefix_tokens : suffix_tokens;
2031+
auto embd_inp = params.spm_infill ? tokens_suffix : tokens_prefix;
2032+
auto embd_end = params.spm_infill ? tokens_prefix : tokens_suffix;
20292033

20302034
if (llama_add_bos_token(model)) {
20312035
embd_inp.insert(embd_inp.begin(), llama_token_bos(model));
@@ -2140,40 +2144,17 @@ struct server_context {
21402144

21412145
while (head_c < slot.cache_tokens.size() &&
21422146
head_p < prompt_tokens.size()) {
2143-
if (llama_token_is_control(model, slot.cache_tokens[head_c]) &&
2144-
slot.cache_tokens[head_c] != llama_token_fim_rep(model) &&
2145-
slot.cache_tokens[head_c] != llama_token_fim_sep(model)) {
2146-
break;
2147-
}
2148-
2149-
if (llama_token_is_control(model, prompt_tokens[head_p]) &&
2150-
prompt_tokens[head_p] != llama_token_fim_rep(model) &&
2151-
prompt_tokens[head_p] != llama_token_fim_sep(model)) {
2152-
break;
2153-
}
21542147

21552148
size_t n_match = 0;
2156-
21572149
while (head_c + n_match < slot.cache_tokens.size() &&
21582150
head_p + n_match < prompt_tokens.size() &&
21592151
slot.cache_tokens[head_c + n_match] == prompt_tokens[head_p + n_match]) {
2160-
if (llama_token_is_control(model, slot.cache_tokens[head_c + n_match]) &&
2161-
slot.cache_tokens[head_c + n_match] != llama_token_fim_rep(model) &&
2162-
slot.cache_tokens[head_c + n_match] != llama_token_fim_sep(model)) {
2163-
break;
2164-
}
2165-
2166-
if (llama_token_is_control(model, prompt_tokens[head_p + n_match]) &&
2167-
prompt_tokens[head_p + n_match] != llama_token_fim_rep(model) &&
2168-
prompt_tokens[head_p + n_match] != llama_token_fim_sep(model)) {
2169-
break;
2170-
}
21712152

21722153
n_match++;
21732154
}
21742155

21752156
if (n_match >= (size_t) params.n_cache_reuse) {
2176-
SLT_DBG(slot, "reusing chunk with size %zu, shifting KV cache [%zu, %zu) -> [%zu, %zu)\n", n_match, head_c, head_c + n_match, head_p, head_p + n_match);
2157+
SLT_INF(slot, "reusing chunk with size %zu, shifting KV cache [%zu, %zu) -> [%zu, %zu)\n", n_match, head_c, head_c + n_match, head_p, head_p + n_match);
21772158
//for (size_t i = head_p; i < head_p + n_match; i++) {
21782159
// SLT_DBG(slot, "cache token %3zu: %6d '%s'\n", i, prompt_tokens[i], common_token_to_piece(ctx, prompt_tokens[i]).c_str());
21792160
//}

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