vbatts/llama.cpp
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Merge branch 'master' into compilade/refactor-kv-cache

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This commit is contained in:
Francis Couture-Harpin 2024-06-30 15:31:25 -04:00
commit 10c3c419e9
518 changed files with 78202 additions and 66427 deletions
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2
examples/retrieval/CMakeLists.txt
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@ -1,4 +1,4 @@
set(TARGET retrieval)
set(TARGET llama-retrieval)
add_executable(${TARGET} retrieval.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})

2
examples/retrieval/README.md
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@ -15,7 +15,7 @@ https://github.com/ggerganov/llama.cpp/pull/6193
`retrieval` example can be tested as follows:
```bash
make -j && ./retrieval --model ./models/bge-base-en-v1.5-f16.gguf --top-k 3 --context-file README.md --context-file License --chunk-size 100 --chunk-separator .
make -j && ./llama-retrieval --model ./models/bge-base-en-v1.5-f16.gguf --top-k 3 --context-file README.md --context-file License --chunk-size 100 --chunk-separator .
```
This chunks and embeds all given files and starts a loop requesting query inputs:

13
examples/retrieval/retrieval.cpp
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@ -73,9 +73,10 @@ static std::vector<chunk> chunk_file(const std::string & filename, int chunk_siz
return chunks;
}
static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, int seq_id) {
for (size_t i = 0; i < tokens.size(); i++) {
llama_batch_add(batch, tokens[i], i, { seq_id }, i == tokens.size() - 1);
static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, llama_seq_id seq_id) {
size_t n_tokens = tokens.size();
for (size_t i = 0; i < n_tokens; i++) {
llama_batch_add(batch, tokens[i], i, { seq_id }, true);
}
}
@ -160,6 +161,12 @@ int main(int argc, char ** argv) {
const int n_ctx_train = llama_n_ctx_train(model);
const int n_ctx = llama_n_ctx(ctx);
const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);
if (pooling_type == LLAMA_POOLING_TYPE_NONE) {
fprintf(stderr, "%s: error: pooling type NONE not supported\n", __func__);
return 1;
}
if (n_ctx > n_ctx_train) {
fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n",
__func__, n_ctx_train, n_ctx);