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server : improve infill context reuse (#9894)

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Georgi Gerganov 2024-10-15 16:28:55 +03:00 committed by GitHub
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2 changed files with 33 additions and 50 deletions
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10
examples/server/README.md
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@ -524,10 +524,12 @@ Takes a prefix and a suffix and returns the predicted completion as stream.
- `input_prefix`: Set the prefix of the code to infill. - `input_prefix`: Set the prefix of the code to infill.
- `input_suffix`: Set the suffix of the code to infill. - `input_suffix`: Set the suffix of the code to infill.
- `prompt`: Added after the `FIM_MID` token - `input_extra`: Additional context inserted before the FIM prefix.
- `extra_context`: Additional context inserted before the FIM prefix. See https://github.com/ggerganov/llama.cpp/pull/9874 - `prompt`: Added after the `FIM_MID` token
It also accepts all the options of `/completion`. `input_extra` is array of `{"filename": string, "text": string}` objects.
The endpoint also accepts all the options of `/completion`.
If the model has `FIM_REPO` and `FIM_FILE_SEP` tokens, the [repo-level pattern](https://arxiv.org/pdf/2409.12186) is used: If the model has `FIM_REPO` and `FIM_FILE_SEP` tokens, the [repo-level pattern](https://arxiv.org/pdf/2409.12186) is used:
@ -545,7 +547,7 @@ If the model has `FIM_REPO` and `FIM_FILE_SEP` tokens, the [repo-level pattern](
If the tokens are missing, then the extra context is simply prefixed at the start: If the tokens are missing, then the extra context is simply prefixed at the start:
```txt ```txt
[extra_context]<FIM_PRE>[input_prefix]<FIM_SUF>[input_suffix]<FIM_MID>[prompt] [input_extra]<FIM_PRE>[input_prefix]<FIM_SUF>[input_suffix]<FIM_MID>[prompt]
``` ```
### **GET** `/props`: Get server global properties. ### **GET** `/props`: Get server global properties.

73
examples/server/server.cpp
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@ -136,10 +136,6 @@ struct slot_params {
int64_t t_max_predict_ms = -1; // if positive, limit the generation phase to this time limit int64_t t_max_predict_ms = -1; // if positive, limit the generation phase to this time limit
std::vector<std::string> antiprompt; std::vector<std::string> antiprompt;
json input_prefix;
json input_suffix;
json extra_context;
}; };
struct server_slot { struct server_slot {
@ -169,6 +165,10 @@ struct server_slot {
json prompt; // can be either a string, array of strings or array of token ids json prompt; // can be either a string, array of strings or array of token ids
json input_prefix;
json input_suffix;
json input_extra;
// when a task is submitted, we first tokenize the prompt and store it here // when a task is submitted, we first tokenize the prompt and store it here
std::vector<llama_token> prompt_tokens; std::vector<llama_token> prompt_tokens;
std::vector<llama_token> extra_tokens; std::vector<llama_token> extra_tokens;
@ -910,12 +910,12 @@ struct server_context {
} }
// infill // infill
slot.params.input_prefix = json_value(data, "input_prefix", default_params.input_prefix); slot.input_prefix = json_value(data, "input_prefix", json());
slot.params.input_suffix = json_value(data, "input_suffix", default_params.input_suffix); slot.input_suffix = json_value(data, "input_suffix", json());
slot.params.extra_context = json_value(data, "extra_context", default_params.extra_context); slot.input_extra = json_value(data, "input_extra", json());
SLT_DBG(slot, "extra_context chunks: %d\n", (int) slot.params.extra_context.size()); SLT_DBG(slot, "extra_context chunks: %d\n", (int) slot.input_extra.size());
for (const auto & chunk : slot.params.extra_context) { for (const auto & chunk : slot.input_extra) {
// { "text": string, "filename": string } // { "text": string, "filename": string }
if (!chunk.contains("text") || !chunk["text"].is_string()) { if (!chunk.contains("text") || !chunk["text"].is_string()) {
send_error(task, "extra_context chunk must contain a \"text\" field with a string value", ERROR_TYPE_INVALID_REQUEST); 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 {
} }
// get prompt // get prompt
if (task.cmpl_type != SERVER_TASK_CMPL_TYPE_INFILL) { {
const auto & prompt = data.find("prompt"); const auto & prompt = data.find("prompt");
if (prompt == data.end()) { if (prompt == data.end()) {
send_error(task, "\"prompt\" must be provided", ERROR_TYPE_INVALID_REQUEST); send_error(task, "\"prompt\" must be provided", ERROR_TYPE_INVALID_REQUEST);
@ -1958,6 +1958,8 @@ struct server_context {
} break; } break;
case SERVER_TASK_CMPL_TYPE_INFILL: case SERVER_TASK_CMPL_TYPE_INFILL:
{ {
// TODO: optimize this block by reducing memory allocations and movement
// use FIM repo-level pattern: // use FIM repo-level pattern:
// ref: https://arxiv.org/pdf/2409.12186 // ref: https://arxiv.org/pdf/2409.12186
// //
@ -1968,10 +1970,11 @@ struct server_context {
// extra chunk 1 // extra chunk 1
// ... // ...
// [FIM_SEP]filename // [FIM_SEP]filename
// [FIM_PRE]prefix[FIM_SUF]suffix[FIM_MID] // [FIM_PRE]prefix[FIM_SUF]suffix[FIM_MID]prompt
// //
auto prefix_tokens = tokenize(slot.params.input_prefix, false, false); auto tokens_prefix = tokenize(slot.input_prefix, false, false);
auto suffix_tokens = tokenize(slot.params.input_suffix, false, false); auto tokens_suffix = tokenize(slot.input_suffix, false, false);
auto tokens_prompt = tokenize(slot.prompt, false, false);
slot.extra_tokens.clear(); slot.extra_tokens.clear();
if (llama_token_fim_rep(model) != LLAMA_TOKEN_NULL) { if (llama_token_fim_rep(model) != LLAMA_TOKEN_NULL) {
@ -1981,7 +1984,7 @@ struct server_context {
slot.extra_tokens.insert(slot.extra_tokens.end(), k_fim_repo.begin(), k_fim_repo.end()); slot.extra_tokens.insert(slot.extra_tokens.end(), k_fim_repo.begin(), k_fim_repo.end());
} }
for (const auto & chunk : slot.params.extra_context) { for (const auto & chunk : slot.input_extra) {
// { "text": string, "filename": string } // { "text": string, "filename": string }
const std::string text = chunk.value("text", ""); const std::string text = chunk.value("text", "");
const std::string filename = chunk.value("filename", "tmp"); const std::string filename = chunk.value("filename", "tmp");
@ -2012,20 +2015,21 @@ struct server_context {
} }
// for now pick FIM context to fit in a batch (ratio prefix:suffix = 3:1, TODO: configurable?) // for now pick FIM context to fit in a batch (ratio prefix:suffix = 3:1, TODO: configurable?)
const int n_suffix_take = std::min<int>(suffix_tokens.size(), (n_batch)/4); const int n_suffix_take = std::min<int>(tokens_suffix.size(), (n_batch/4));
const int n_prefix_take = std::min<int>(prefix_tokens.size(), (n_batch - 3) - n_suffix_take); const int n_prefix_take = std::min<int>(tokens_prefix.size(), 3*(n_batch/4) - 3);
// fill the rest of the context with extra chunks // fill the rest of the context with extra chunks
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()); 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());
prefix_tokens.erase(prefix_tokens.begin(), prefix_tokens.begin() + prefix_tokens.size() - n_prefix_take); tokens_prefix.erase(tokens_prefix.begin(), tokens_prefix.begin() + tokens_prefix.size() - n_prefix_take);
suffix_tokens.resize(n_suffix_take); tokens_suffix.resize(n_suffix_take);
prefix_tokens.insert(prefix_tokens.begin(), llama_token_fim_pre(model)); tokens_prefix.insert(tokens_prefix.begin(), llama_token_fim_pre(model));
suffix_tokens.insert(suffix_tokens.begin(), llama_token_fim_suf(model)); tokens_prefix.insert(tokens_prefix.end(), tokens_prompt.begin(), tokens_prompt.end());
tokens_suffix.insert(tokens_suffix.begin(), llama_token_fim_suf(model));
auto embd_inp = params.spm_infill ? suffix_tokens : prefix_tokens; auto embd_inp = params.spm_infill ? tokens_suffix : tokens_prefix;
auto embd_end = params.spm_infill ? prefix_tokens : suffix_tokens; auto embd_end = params.spm_infill ? tokens_prefix : tokens_suffix;
if (llama_add_bos_token(model)) { if (llama_add_bos_token(model)) {
embd_inp.insert(embd_inp.begin(), llama_token_bos(model)); embd_inp.insert(embd_inp.begin(), llama_token_bos(model));
@ -2140,40 +2144,17 @@ struct server_context {
while (head_c < slot.cache_tokens.size() && while (head_c < slot.cache_tokens.size() &&
head_p < prompt_tokens.size()) { head_p < prompt_tokens.size()) {
if (llama_token_is_control(model, slot.cache_tokens[head_c]) &&
slot.cache_tokens[head_c] != llama_token_fim_rep(model) &&
slot.cache_tokens[head_c] != llama_token_fim_sep(model)) {
break;
}
if (llama_token_is_control(model, prompt_tokens[head_p]) &&
prompt_tokens[head_p] != llama_token_fim_rep(model) &&
prompt_tokens[head_p] != llama_token_fim_sep(model)) {
break;
}
size_t n_match = 0; size_t n_match = 0;
while (head_c + n_match < slot.cache_tokens.size() && while (head_c + n_match < slot.cache_tokens.size() &&
head_p + n_match < prompt_tokens.size() && head_p + n_match < prompt_tokens.size() &&
slot.cache_tokens[head_c + n_match] == prompt_tokens[head_p + n_match]) { slot.cache_tokens[head_c + n_match] == prompt_tokens[head_p + n_match]) {
if (llama_token_is_control(model, slot.cache_tokens[head_c + n_match]) &&
slot.cache_tokens[head_c + n_match] != llama_token_fim_rep(model) &&
slot.cache_tokens[head_c + n_match] != llama_token_fim_sep(model)) {
break;
}
if (llama_token_is_control(model, prompt_tokens[head_p + n_match]) &&
prompt_tokens[head_p + n_match] != llama_token_fim_rep(model) &&
prompt_tokens[head_p + n_match] != llama_token_fim_sep(model)) {
break;
}
n_match++; n_match++;
} }
if (n_match >= (size_t) params.n_cache_reuse) { if (n_match >= (size_t) params.n_cache_reuse) {
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); 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);
//for (size_t i = head_p; i < head_p + n_match; i++) { //for (size_t i = head_p; i < head_p + n_match; i++) {
// SLT_DBG(slot, "cache token %3zu: %6d '%s'\n", i, prompt_tokens[i], common_token_to_piece(ctx, prompt_tokens[i]).c_str()); // SLT_DBG(slot, "cache token %3zu: %6d '%s'\n", i, prompt_tokens[i], common_token_to_piece(ctx, prompt_tokens[i]).c_str());
//} //}