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Merge branch 'upstream' into concedo_experimental

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# Conflicts:
#	.devops/full-rocm.Dockerfile
#	.devops/llama-cli-rocm.Dockerfile
#	.devops/llama-server-rocm.Dockerfile
#	.github/workflows/build.yml
#	.github/workflows/python-type-check.yml
#	CMakeLists.txt
#	CONTRIBUTING.md
#	README.md
#	ci/run.sh
#	examples/embedding/embedding.cpp
#	examples/server/README.md
#	flake.lock
#	ggml/include/ggml.h
#	ggml/src/ggml.c
#	requirements/requirements-convert_legacy_llama.txt
#	scripts/sync-ggml.last
#	src/llama-vocab.cpp
#	src/llama.cpp
#	tests/test-backend-ops.cpp
#	tests/test-grad0.cpp
#	tests/test-tokenizer-0.cpp
This commit is contained in:
Concedo
2024-10-02 01:00:57 +08:00
parent a785a91e56 f1b8c42711
commit ce7f9c9a2c
39 changed files with 103400 additions and 102738 deletions
Download Patch File Download Diff File Expand all files Collapse all files
examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
+7 -7
View File
@@ -201,7 +201,7 @@ static void print_sample_weights(TransformerWeights *w){
//////////////////////////////////////// ggml structs and functions required to load models, configs and save the model.
struct llama_vocab {
struct my_llama_vocab {
using id = int32_t;
using token = std::string;
using ttype = llama_token_type;
@@ -525,7 +525,7 @@ static std::string llama_escape_whitespaces(const std::string & text) {
return out.str();
}
static void load_vocab(const char * filename, const Config * config, struct llama_vocab * vocab) {
static void load_vocab(const char * filename, const Config * config, struct my_llama_vocab * vocab) {
if (is_ggml_file(filename)) {
LOG_INF("%s: Loading vocabulary from gguf file %s\n", __func__, filename);
struct ggml_context * ctx_data = NULL;
@@ -583,13 +583,13 @@ static void load_vocab(const char * filename, const Config * config, struct llam
const int n_vocab = config->vocab_size;
/* uint32_t max_token_length = */ file.read_u32(); // unused
vocab->id_to_token.resize(n_vocab);
for (llama_vocab::id id=0; id<n_vocab; ++id) {
for (my_llama_vocab::id id=0; id<n_vocab; ++id) {
float_t score = file.read_f32();
uint32_t len = file.read_u32();
std::string text = file.read_string(len);
unsigned char byte_val;
llama_vocab::ttype type = LLAMA_TOKEN_TYPE_NORMAL;
my_llama_vocab::ttype type = LLAMA_TOKEN_TYPE_NORMAL;
if (id == UNKNOWN_TOKEN_ID) {
text = "<unk>";
type = LLAMA_TOKEN_TYPE_UNKNOWN;
@@ -631,7 +631,7 @@ static void convert_weights_ak_to_gg(struct ggml_tensor * gg_weights, const floa
}
static void save_as_llama_model(
struct llama_vocab * vocab, struct my_llama_model * model, TransformerWeights* w, const char * filename
struct my_llama_vocab * vocab, struct my_llama_model * model, TransformerWeights* w, const char * filename
) {
// convert AK weights into GG weights one by one.
// w->token_embedding_table -> model->tok_embeddings
@@ -671,7 +671,7 @@ static void save_as_llama_model(
std::vector<const char*> tokens;
std::vector<float> scores;
std::vector<llama_token_type> token_types;
for (const llama_vocab::token_data & token_data : vocab->id_to_token) {
for (const my_llama_vocab::token_data & token_data : vocab->id_to_token) {
tokens.push_back(token_data.text.c_str());
scores.push_back(token_data.score);
token_types.push_back(token_data.type);
@@ -905,7 +905,7 @@ int main(int argc, char ** argv) {
fclose(file);
}
struct llama_vocab vocab;
struct my_llama_vocab vocab;
load_vocab(params.fn_vocab_model, &config, &vocab);
struct my_llama_model model;
examples/cvector-generator/pca.hpp
-7
View File
@@ -204,13 +204,6 @@ static ggml_status compute_piter(
ggml_backend_cpu_set_n_threads(model.backend, params.n_threads);
}
// TODO: enable GPU support when support for GGML_OP_SQRT is added
//#ifdef GGML_USE_METAL
// if (ggml_backend_is_metal(model.backend)) {
// ggml_backend_metal_set_n_cb(model.backend, params.n_threads);
// }
//#endif
ggml_status res = ggml_backend_graph_compute(model.backend, gf);
if (res == GGML_STATUS_SUCCESS) {
auto extract_i = [](std::string prefix, std::string str) -> int {
examples/llava/clip.cpp
-6
View File
@@ -2535,12 +2535,6 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
ggml_backend_cpu_set_n_threads(ctx->backend, n_threads);
}
#ifdef GGML_USE_METAL
if (ggml_backend_is_metal(ctx->backend)) {
ggml_backend_metal_set_n_cb(ctx->backend, n_threads);
}
#endif
ggml_backend_graph_compute(ctx->backend, gf);
// the last node is the embedding tensor
examples/llava/convert_image_encoder_to_gguf.py
+2 -2
View File
@@ -274,7 +274,7 @@ fout.add_bool("clip.use_gelu", use_gelu)
if has_llava_projector:
model.vision_model.encoder.layers.pop(-1) # pyright: ignore[reportAttributeAccessIssue]
model.vision_model.encoder.layers.pop(-1)
projector = torch.load(args.llava_projector)
for name, data in projector.items():
name = get_tensor_name(name)
@@ -288,7 +288,7 @@ if has_llava_projector:
print("Projector tensors added\n")
state_dict = model.state_dict() # pyright: ignore[reportAttributeAccessIssue]
state_dict = model.state_dict()
for name, data in state_dict.items():
if should_skip_tensor(name, has_text_encoder, has_vision_encoder, has_llava_projector):
# we don't need this
examples/server/server.cpp
+199 -16
View File
@@ -93,6 +93,7 @@ enum server_task_type {
enum server_task_cmpl_type {
SERVER_TASK_CMPL_TYPE_NORMAL,
SERVER_TASK_CMPL_TYPE_EMBEDDING,
SERVER_TASK_CMPL_TYPE_RERANK,
SERVER_TASK_CMPL_TYPE_INFILL,
};
@@ -173,6 +174,7 @@ struct server_slot {
std::vector<completion_token_output> generated_token_probs;
server_task_cmpl_type cmpl_type = SERVER_TASK_CMPL_TYPE_NORMAL;
bool has_next_token = true;
bool truncated = false;
bool stopped_eos = false;
@@ -955,8 +957,17 @@ struct server_context {
slot.prompt = *prompt;
} else if (prompt->is_array() && prompt->size() == 1 && prompt->at(0).is_array()) {
slot.prompt = prompt->at(0);
} else if (prompt->is_array() && prompt->size() > 1) {
// array of strings
for (const auto & el : *prompt) {
if (!el.is_string()) {
send_error(task, "\"prompt\" must be a string, an array of strings or an array of integers", ERROR_TYPE_INVALID_REQUEST);
return false;
}
}
slot.prompt = *prompt;
} else {
send_error(task, "\"prompt\" must be a string or an array of integers", ERROR_TYPE_INVALID_REQUEST);
send_error(task, "\"prompt\" must be a string, an array of strings or an array of integers", ERROR_TYPE_INVALID_REQUEST);
return false;
}
}
@@ -1390,6 +1401,7 @@ struct server_context {
res.data = json {
{"embedding", std::vector<float>(n_embd, 0.0f)},
{"index", slot.index},
};
continue;
@@ -1408,6 +1420,44 @@ struct server_context {
queue_results.send(res);
}
void send_rerank(const server_slot & slot, const llama_batch & batch) {
server_task_result res;
res.id = slot.id_task;
res.error = false;
res.stop = true;
for (int i = 0; i < batch.n_tokens; ++i) {
if (!batch.logits[i] || batch.seq_id[i][0] != slot.id + 1) {
continue;
}
const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
if (embd == NULL) {
embd = llama_get_embeddings_ith(ctx, i);
}
if (embd == NULL) {
SLT_ERR(slot, "failed to get embeddings, token = %d, seq_id = %d\n", batch.token[i], batch.seq_id[i][0]);
res.data = json {
{"index", slot.index},
{"score", -1e6},
};
continue;
}
res.data = json {
{"index", slot.index},
{"score", embd[0]},
};
}
SLT_DBG(slot, "sending rerank result, res = '%s'\n", res.data.dump().c_str());
queue_results.send(res);
}
//
// Functions to create new task(s) and receive result(s)
//
@@ -1443,13 +1493,27 @@ struct server_context {
// otherwise, it's a multiple-prompt task, we break it into smaller tasks
else if (prompt.is_array()) {
std::vector<json> prompts = prompt;
for (size_t i = 0; i < prompts.size(); i++) {
const auto & e = prompts[i];
if (e.is_string() || json_is_array_of_numbers(e)) {
data["index"] = i;
create_task(data, true, e);
} else {
throw std::runtime_error(error_msg);
if (cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
// prompts[0] is the question
// the rest are the answers/documents
SRV_DBG("creating rerank tasks, n_prompts = %d\n", (int) prompts.size() - 1);
for (size_t i = 1; i < prompts.size(); i++) {
json qd;
qd.push_back(prompts[0]);
qd.push_back(prompts[i]);
data["index"] = i - 1;
create_task(data, true, qd);
}
} else {
SRV_DBG("creating multi-prompt tasks, n_prompts = %d\n", (int) prompts.size());
for (size_t i = 0; i < prompts.size(); i++) {
const auto & e = prompts[i];
if (e.is_string() || json_is_array_of_numbers(e)) {
data["index"] = i;
create_task(data, true, e);
} else {
throw std::runtime_error(error_msg);
}
}
}
}
@@ -1493,7 +1557,9 @@ struct server_context {
return;
}
size_t idx = result.data["index"];
const size_t idx = result.data["index"];
GGML_ASSERT(idx < results.size() && "index out of range");
results[idx] = result;
}
result_handler(results);
@@ -1904,6 +1970,7 @@ struct server_context {
// track if this is an embedding or non-embedding batch
// if we've added sampled tokens above, we are in non-embedding mode
// -1: none, 0: non-embedding, 1: embedding
// TODO: make enum
int32_t batch_type = batch.n_tokens > 0 ? 0 : -1;
// next, batch any pending prompts without exceeding n_batch
@@ -1952,6 +2019,29 @@ struct server_context {
}
prompt_tokens = embd_inp;
} else if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
// require slot.prompt to be array of 2 strings
if (!slot.prompt.is_array() || slot.prompt.size() != 2) {
SLT_ERR(slot, "%s", "invalid prompt for rerank task\n");
slot.release();
send_error(slot, "invalid prompt for rerank task", ERROR_TYPE_INVALID_REQUEST);
continue;
}
// prompt: <s>query</s><s>doc</s>
prompt_tokens.clear();
prompt_tokens.push_back(llama_token_bos(model));
{
const auto part = tokenize(slot.prompt[0], false);
prompt_tokens.insert(prompt_tokens.end(), part.begin(), part.end());
}
prompt_tokens.push_back(llama_token_eos(model));
prompt_tokens.push_back(llama_token_bos(model));
{
const auto part = tokenize(slot.prompt[1], false);
prompt_tokens.insert(prompt_tokens.end(), part.begin(), part.end());
}
prompt_tokens.push_back(llama_token_eos(model));
} else {
prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt
}
@@ -1971,7 +2061,7 @@ struct server_context {
continue;
}
if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING) {
if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING || slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
// this prompt is too large to process - discard it
if (slot.n_prompt_tokens > n_ubatch) {
slot.release();
@@ -2049,7 +2139,8 @@ struct server_context {
slot.n_prompt_tokens_processed = 0;
}
if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING) {
// non-causal tasks require to fit the entire prompt in the physical batch
if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING || slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
// cannot fit the prompt in the current batch - will try next iter
if (batch.n_tokens + slot.n_prompt_tokens > n_batch) {
continue;
@@ -2057,7 +2148,10 @@ struct server_context {
}
// check that we are in the right batch_type, if not defer the slot
bool slot_type = slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING ? 1 : 0;
const bool slot_type =
slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING ||
slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK ? 1 : 0;
if (batch_type == -1) {
batch_type = slot_type;
} else if (batch_type != slot_type) {
@@ -2230,6 +2324,13 @@ struct server_context {
continue; // continue loop of slots
}
if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
send_rerank(slot, batch_view);
slot.release();
slot.i_batch = -1;
continue; // continue loop of slots
}
// prompt evaluated for next-token prediction
slot.state = SLOT_STATE_GENERATING;
} else if (slot.state != SLOT_STATE_GENERATING) {
@@ -2788,8 +2889,8 @@ int main(int argc, char ** argv) {
};
const auto handle_completions_generic = [&ctx_server, &res_error, &res_ok](server_task_cmpl_type cmpl_type, json & data, httplib::Response & res) {
if (ctx_server.params.embedding) {
res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
if (ctx_server.params.embedding || ctx_server.params.reranking) {
res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings` or `--reranking`", ERROR_TYPE_NOT_SUPPORTED));
return;
}
@@ -2849,8 +2950,8 @@ int main(int argc, char ** argv) {
// TODO: maybe merge this function with "handle_completions_generic"
const auto handle_chat_completions = [&ctx_server, &params, &res_error, &res_ok, verbose](const httplib::Request & req, httplib::Response & res) {
if (ctx_server.params.embedding) {
res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
if (ctx_server.params.embedding || ctx_server.params.reranking) {
res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings` or `--reranking`", ERROR_TYPE_NOT_SUPPORTED));
return;
}
@@ -2974,6 +3075,11 @@ int main(int argc, char ** argv) {
};
const auto handle_embeddings = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) {
// TODO: somehow clean up this checks in the future
if (!ctx_server.params.embedding || ctx_server.params.reranking) {
res_error(res, format_error_response("This server does not support embeddings. Start it with `--embeddings` and without `--reranking`", ERROR_TYPE_NOT_SUPPORTED));
return;
}
const json body = json::parse(req.body);
bool is_openai = false;
@@ -3024,6 +3130,79 @@ int main(int argc, char ** argv) {
res_ok(res, root);
};
const auto handle_rerank = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) {
if (!ctx_server.params.reranking) {
res_error(res, format_error_response("This server does not support reranking. Start it with `--reranking`", ERROR_TYPE_NOT_SUPPORTED));
return;
}
const json body = json::parse(req.body);
// TODO: implement
//int top_n = 1;
//if (body.count("top_n") != 1) {
// top_n = body.at("top_n");
//} else {
// res_error(res, format_error_response("\"top_n\" must be provided", ERROR_TYPE_INVALID_REQUEST));
// return;
//}
json query;
if (body.count("query") == 1) {
query = body.at("query");
if (!query.is_string()) {
res_error(res, format_error_response("\"query\" must be a string", ERROR_TYPE_INVALID_REQUEST));
return;
}
} else {
res_error(res, format_error_response("\"query\" must be provided", ERROR_TYPE_INVALID_REQUEST));
return;
}
std::vector<std::string> documents = json_value(body, "documents", std::vector<std::string>());
if (documents.empty()) {
res_error(res, format_error_response("\"documents\" must be a non-empty string array", ERROR_TYPE_INVALID_REQUEST));
return;
}
// construct prompt object: array of ["query", "doc0", "doc1", ...]
json prompt;
prompt.push_back(query);
for (const auto & doc : documents) {
prompt.push_back(doc);
}
LOG_DBG("rerank prompt: %s\n", prompt.dump().c_str());
// create and queue the task
json responses = json::array();
bool error = false;
{
std::vector<server_task> tasks = ctx_server.create_tasks_cmpl({{"prompt", prompt}}, SERVER_TASK_CMPL_TYPE_RERANK);
ctx_server.queue_results.add_waiting_tasks(tasks);
ctx_server.queue_tasks.post(tasks);
// get the result
std::unordered_set<int> task_ids = server_task::get_list_id(tasks);
ctx_server.receive_cmpl_results(task_ids, [&](std::vector<server_task_result> & results) {
for (const auto & res : results) {
responses.push_back(res.data);
}
}, [&](const json & error_data) {
res_error(res, error_data);
error = true;
});
}
if (error) {
return;
}
// write JSON response
json root = format_response_rerank(body, responses);
res_ok(res, root);
};
const auto handle_lora_adapters_list = [&](const httplib::Request &, httplib::Response & res) {
json result = json::array();
for (size_t i = 0; i < ctx_server.loras.size(); ++i) {
@@ -3120,6 +3299,10 @@ int main(int argc, char ** argv) {
svr->Post("/embedding", handle_embeddings); // legacy
svr->Post("/embeddings", handle_embeddings);
svr->Post("/v1/embeddings", handle_embeddings);
svr->Post("/rerank", handle_rerank);
svr->Post("/reranking", handle_rerank);
svr->Post("/v1/rerank", handle_rerank);
svr->Post("/v1/reranking", handle_rerank);
svr->Post("/tokenize", handle_tokenize);
svr->Post("/detokenize", handle_detokenize);
// LoRA adapters hotswap
examples/server/tests/features/embeddings.feature
+1 -1
View File
@@ -15,7 +15,7 @@ Feature: llama.cpp server
And 128 as batch size
And 128 as ubatch size
And 512 KV cache size
And embeddings extraction
And enable embeddings endpoint
Then the server is starting
Then the server is healthy
examples/server/tests/features/rerank.feature
+42
View File
@@ -0,0 +1,42 @@
@llama.cpp
@rerank
Feature: llama.cpp server
Background: Server startup
Given a server listening on localhost:8080
And a model url https://huggingface.co/ggml-org/models/resolve/main/jina-reranker-v1-tiny-en/ggml-model-f16.gguf
And a model file jina-reranker-v1-tiny-en.gguf
And a model alias jina-reranker-v1-tiny-en
And 42 as server seed
And 2 slots
And 512 as batch size
And 512 as ubatch size
And 512 KV cache size
And enable reranking endpoint
Then the server is starting
Then the server is healthy
Scenario: Rerank
Given a rerank query:
"""
Machine learning is
"""
And a rerank document:
"""
A machine is a physical system that uses power to apply forces and control movement to perform an action. The term is commonly applied to artificial devices, such as those employing engines or motors, but also to natural biological macromolecules, such as molecular machines.
"""
And a rerank document:
"""
Learning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences. The ability to learn is possessed by humans, non-human animals, and some machines; there is also evidence for some kind of learning in certain plants.
"""
And a rerank document:
"""
Machine learning is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions.
"""
And a rerank document:
"""
Paris, capitale de la France, est une grande ville européenne et un centre mondial de l'art, de la mode, de la gastronomie et de la culture. Son paysage urbain du XIXe siècle est traversé par de larges boulevards et la Seine.
"""
When reranking request
Then reranking results are returned
Then reranking highest score is index 2 and lowest score is index 3
examples/server/tests/features/steps/steps.py
+53 -1
View File
@@ -68,6 +68,7 @@ def step_server_config(context, server_fqdn: str, server_port: str):
context.server_api_key = None
context.server_continuous_batching = False
context.server_embeddings = False
context.server_reranking = False
context.server_metrics = False
context.server_process = None
context.seed = None
@@ -83,6 +84,10 @@ def step_server_config(context, server_fqdn: str, server_port: str):
context.concurrent_tasks = []
context.prompts = []
context.reranking_query = None
context.reranking_documents = []
context.reranking_results = None
@step('a model file {hf_file} from HF repo {hf_repo}')
def step_download_hf_model(context, hf_file: str, hf_repo: str):
@@ -172,10 +177,13 @@ def step_server_continuous_batching(context):
context.server_continuous_batching = True
@step('embeddings extraction')
@step('enable embeddings endpoint')
def step_server_embeddings(context):
context.server_embeddings = True
@step('enable reranking endpoint')
def step_server_reranking(context):
context.server_reranking = True
@step('prometheus compatible metrics exposed')
def step_server_metrics(context):
@@ -452,6 +460,14 @@ def step_impl(context, n_ga_w):
def step_prompt_passkey(context):
context.prompt_passkey = context_text(context)
@step('a rerank query')
def step_set_rerank_query(context):
context.reranking_query = context_text(context)
context.reranking_documents = []
@step('a rerank document')
def step_set_rerank_document(context):
context.reranking_documents.append(context_text(context))
@step('{n_prompts:d} fixed prompts')
def step_fixed_prompts(context, n_prompts):
@@ -619,6 +635,22 @@ async def step_compute_embedding(context):
context.embeddings = await request_embedding(context_text(context), None, base_url=context.base_url)
@step('reranking request')
@async_run_until_complete
async def step_compute_reranking(context):
async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session:
async with session.post(f'{context.base_url}/reranking',
json={
"query": context.reranking_query,
"documents": context.reranking_documents,
}) as response:
if response.status == 200:
response_json = await response.json()
context.reranking_results = response_json['results']
else:
context.reranking_results = response.status
@step('all embeddings are the same')
@async_run_until_complete
async def step_all_embeddings_are_the_same(context):
@@ -704,6 +736,24 @@ async def all_embeddings_are_generated(context):
for i in range(n_embedding_requests):
assert_embeddings(context.tasks_result.pop().pop())
@step('reranking results are returned')
def reranking_results_are_returned(context):
assert len(context.reranking_results) == len(context.reranking_documents)
@step('reranking highest score is index {idx_high:d} and lowest score is index {idx_low:d}')
def reranking_results_are_returned(context, idx_high: int, idx_low: int):
max_score, max_idx = 0, 0
min_score, min_idx = 0, 0
for res in context.reranking_results:
if max_score < res['relevance_score']:
max_score = res['relevance_score']
max_idx = res['index']
if min_score > res['relevance_score']:
min_score = res['relevance_score']
min_idx = res['index']
print(context.reranking_results)
assert max_idx == idx_high
assert min_idx == idx_low
@step('adding special tokens')
def step_tokenize_set_add_special(context):
@@ -1362,6 +1412,8 @@ def start_server_background(context):
server_args.append('--cont-batching')
if context.server_embeddings:
server_args.append('--embedding')
if context.server_reranking:
server_args.append('--reranking')
if context.server_metrics:
server_args.append('--metrics')
if context.model_alias:
examples/server/tests/requirements.txt
+1 -1
View File
@@ -1,6 +1,6 @@
aiohttp~=3.9.3
behave~=1.2.6
huggingface_hub~=0.20.3
huggingface_hub~=0.23.2
numpy~=1.26.4
openai~=1.30.3
prometheus-client~=0.20.0
examples/server/utils.hpp
+24 -1
View File
@@ -537,7 +537,7 @@ static json format_embeddings_response_oaicompat(const json & request, const jso
json res = json {
{"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
{"object", "list"},
{"usage", json {
{"usage", json { // TODO: fill
{"prompt_tokens", 0},
{"total_tokens", 0}
}},
@@ -547,6 +547,29 @@ static json format_embeddings_response_oaicompat(const json & request, const jso
return res;
}
static json format_response_rerank(const json & request, const json & ranks) {
json data = json::array();
int i = 0;
for (const auto & rank : ranks) {
data.push_back(json{
{"index", i++},
{"relevance_score", json_value(rank, "score", 0.0)},
});
}
json res = json {
{"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
{"object", "list"},
{"usage", json { // TODO: fill
{"prompt_tokens", 0},
{"total_tokens", 0}
}},
{"results", data}
};
return res;
}
static bool is_valid_utf8(const std::string & str) {
const unsigned char* bytes = reinterpret_cast<const unsigned char*>(str.data());
const unsigned char* end = bytes + str.length();