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Merge 5f95dccea8 into a5b57b08ce

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Georgi Gerganov 2024-09-22 17:55:48 +08:00 committed by GitHub
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9 changed files with 288 additions and 24 deletions
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7
common/arg.cpp
View File

@ -391,7 +391,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
[](gpt_params & params) {
params.verbose_prompt = true;
}
).set_examples({LLAMA_EXAMPLE_MAIN}));
));
add_opt(llama_arg(
{"--no-display-prompt"},
format("don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false"),
@ -1098,11 +1098,12 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
[](gpt_params & params, const std::string & value) {
/**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; }
else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; }
else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; }
else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; }
else if (value == "last") { params.pooling_type = LLAMA_POOLING_TYPE_LAST; }
else if (value == "rank") { params.pooling_type = LLAMA_POOLING_TYPE_RANK; }
else { throw std::invalid_argument("invalid value"); }
}
).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER}));
add_opt(llama_arg(
{"--attention"}, "{causal,non,causal}",
"attention type for embeddings, use model default if unspecified",

16
convert_hf_to_gguf.py
View File

@ -291,8 +291,13 @@ class Model:
bid = int(part)
break
for new_name, data in ((n, d.squeeze().numpy()) for n, d in self.modify_tensors(data_torch, name, bid)):
data: np.ndarray # type hint
for new_name, data_torch in (self.modify_tensors(data_torch, name, bid)):
data = data_torch.squeeze().numpy()
# if data ends up empty, it means data_torch was a scalar tensor -> restore
if len(data.shape) == 0:
data = data_torch.numpy()
n_dims = len(data.shape)
data_qtype: gguf.GGMLQuantizationType | bool = self.tensor_force_quant(name, new_name, bid, n_dims)
@ -2598,7 +2603,7 @@ class NomicBertModel(BertModel):
self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"])
@Model.register("XLMRobertaModel")
@Model.register("XLMRobertaModel", "XLMRobertaForSequenceClassification")
class XLMRobertaModel(BertModel):
model_arch = gguf.MODEL_ARCH.BERT
@ -2696,6 +2701,11 @@ class XLMRobertaModel(BertModel):
self.gguf_writer.add_add_eos_token(True)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# if name starts with "roberta.", remove the prefix
# e.g. https://huggingface.co/BAAI/bge-reranker-v2-m3/tree/main
if name.startswith("roberta."):
name = name[8:]
# position embeddings start at pad_token_id + 1, so just chop down the weight tensor
if name == "embeddings.position_embeddings.weight":
if self._position_offset is not None:

4
examples/embedding/embedding.cpp
View File

@ -234,6 +234,10 @@ int main(int argc, char ** argv) {
}
LOG("\n");
}
} else if (pooling_type == LLAMA_POOLING_TYPE_RANK) {
for (int j = 0; j < n_embd_count; j++) {
LOG("rank score %d: %8.3f\n", j, emb[j * n_embd]);
}
} else {
// print the first part of the embeddings or for a single prompt, the full embedding
for (int j = 0; j < n_prompts; j++) {

196
examples/server/server.cpp
View File

@ -92,6 +92,7 @@ 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,
};
@ -172,6 +173,7 @@ 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;
@ -954,8 +956,17 @@ 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;
}
}
@ -1380,6 +1391,7 @@ struct server_context {
res.data = json {
{"embedding", std::vector<float>(n_embd, 0.0f)},
{"index", slot.index},
};
continue;
@ -1398,6 +1410,44 @@ struct server_context {
queue_results.send(res);
}
void send_rank(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},
{"rank", -1e6},
};
continue;
}
res.data = json {
{"index", slot.index},
{"rank", embd[0]},
};
}
SLT_DBG(slot, "sending rank, res = '%s'\n", res.data.dump().c_str());
queue_results.send(res);
}
//
// Functions to create new task(s) and receive result(s)
//
@ -1433,13 +1483,23 @@ 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) {
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 {
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);
}
}
}
}
@ -1483,7 +1543,9 @@ struct server_context {
break;
}
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);
@ -1934,6 +1996,29 @@ 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
}
@ -1953,7 +2038,7 @@ 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();
@ -2023,7 +2108,7 @@ struct server_context {
slot.n_prompt_tokens_processed = 0;
}
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) {
// cannot fit the prompt in the current batch - will try next iter
if (batch.n_tokens + slot.n_prompt_tokens > n_batch) {
continue;
@ -2031,7 +2116,10 @@ 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) {
@ -2204,6 +2292,13 @@ struct server_context {
continue; // continue loop of slots
}
if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
send_rank(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) {
@ -2994,6 +3089,82 @@ 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) {
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 {
exit(0);
res_error(res, format_error_response("\"query\" must be provided", ERROR_TYPE_INVALID_REQUEST));
return;
}
json documents;
if (body.count("documents") != 0) {
documents = body.at("documents");
if (!documents.is_array() || documents.size() == 0) {
res_error(res, format_error_response("\"documents\" must be a non-empty string array", ERROR_TYPE_INVALID_REQUEST));
return;
}
} else {
res_error(res, format_error_response("\"documents\" must be provided", 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) {
@ -3090,6 +3261,7 @@ 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("/v1/rerank", handle_rerank);
svr->Post("/tokenize", handle_tokenize);
svr->Post("/detokenize", handle_detokenize);
// LoRA adapters hotswap

25
examples/server/utils.hpp
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, "rank", 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();

6
gguf-py/gguf/constants.py
View File

@ -342,6 +342,8 @@ class MODEL_TENSOR(IntEnum):
ENC_FFN_DOWN = auto()
ENC_FFN_UP = auto()
ENC_OUTPUT_NORM = auto()
CLS = auto() # classifier
CLS_OUT = auto() # classifier output projection
MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
@ -499,6 +501,8 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
MODEL_TENSOR.ENC_FFN_DOWN: "enc.blk.{bid}.ffn_down",
MODEL_TENSOR.ENC_FFN_UP: "enc.blk.{bid}.ffn_up",
MODEL_TENSOR.ENC_OUTPUT_NORM: "enc.output_norm",
MODEL_TENSOR.CLS: "cls",
MODEL_TENSOR.CLS_OUT: "cls.output",
}
MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
@ -608,6 +612,8 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
MODEL_TENSOR.LAYER_OUT_NORM,
MODEL_TENSOR.CLS,
MODEL_TENSOR.CLS_OUT,
],
MODEL_ARCH.NOMIC_BERT: [
MODEL_TENSOR.TOKEN_EMBD,

8
gguf-py/gguf/tensor_mapping.py
View File

@ -677,6 +677,14 @@ class TensorNameMap:
MODEL_TENSOR.ENC_OUTPUT_NORM: (
"encoder.final_layer_norm", # t5
),
MODEL_TENSOR.CLS: (
"classifier.dense", # roberta
),
MODEL_TENSOR.CLS_OUT: (
"classifier.out_proj", # roberta
),
}
# architecture-specific block mappings

1
include/llama.h
View File

@ -192,6 +192,7 @@ extern "C" {
LLAMA_POOLING_TYPE_MEAN = 1,
LLAMA_POOLING_TYPE_CLS = 2,
LLAMA_POOLING_TYPE_LAST = 3,
LLAMA_POOLING_TYPE_RANK = 4,
};
enum llama_attention_type {

49
src/llama.cpp
View File

@ -600,6 +600,8 @@ enum llm_tensor {
LLM_TENSOR_ENC_FFN_DOWN,
LLM_TENSOR_ENC_FFN_UP,
LLM_TENSOR_ENC_OUTPUT_NORM,
LLM_TENSOR_CLS,
LLM_TENSOR_CLS_OUT,
};
static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
@ -787,6 +789,8 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{ LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_CLS, "cls" },
{ LLM_TENSOR_CLS_OUT, "cls.output" },
},
},
{
@ -2861,6 +2865,12 @@ struct llama_model {
struct ggml_tensor * output_b;
struct ggml_tensor * output_norm_enc;
// classifier
struct ggml_tensor * cls;
struct ggml_tensor * cls_b;
struct ggml_tensor * cls_out;
struct ggml_tensor * cls_out_b;
std::vector<llama_layer> layers;
llama_split_mode split_mode;
@ -7284,6 +7294,12 @@ static bool llm_load_tensors(
if (model.arch == LLM_ARCH_BERT) {
model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train});
model.cls = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
model.cls_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS, "bias"), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
model.cls_out = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
model.cls_out_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS_OUT, "bias"), {1}, llama_model_loader::TENSOR_NOT_REQUIRED);
}
model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd});
@ -10111,6 +10127,10 @@ struct llm_build_context {
struct ggml_tensor * cur;
switch (pooling_type) {
case LLAMA_POOLING_TYPE_NONE:
{
cur = inp;
} break;
case LLAMA_POOLING_TYPE_MEAN:
{
struct ggml_tensor * inp_mean = build_inp_mean();
@ -10122,9 +10142,24 @@ struct llm_build_context {
struct ggml_tensor * inp_cls = build_inp_cls();
cur = ggml_get_rows(ctx0, inp, inp_cls);
} break;
case LLAMA_POOLING_TYPE_NONE:
case LLAMA_POOLING_TYPE_RANK:
{
cur = inp;
struct ggml_tensor * inp_cls = build_inp_cls();
inp = ggml_get_rows(ctx0, inp, inp_cls);
// classification head
// https://github.com/huggingface/transformers/blob/5af7d41e49bbfc8319f462eb45253dcb3863dfb7/src/transformers/models/roberta/modeling_roberta.py#L1566
GGML_ASSERT(model.cls != nullptr);
GGML_ASSERT(model.cls_b != nullptr);
GGML_ASSERT(model.cls_out != nullptr);
GGML_ASSERT(model.cls_out_b != nullptr);
cur = ggml_add (ctx0, ggml_mul_mat(ctx0, model.cls, inp), model.cls_b);
cur = ggml_tanh(ctx0, cur);
cur = ggml_add (ctx0, ggml_mul_mat(ctx0, model.cls_out, cur), model.cls_out_b);
// broadcast across the embedding size to make it compatible with the llama_get_embeddings API
cur = ggml_repeat(ctx0, cur, inp);
} break;
default:
{
@ -11353,8 +11388,8 @@ struct llm_build_context {
inpL = cur;
}
// final output
cur = inpL;
cb(cur, "result_embd", -1);
ggml_build_forward_expand(gf, cur);
@ -16331,7 +16366,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
}
}
if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_CLS) {
if (cparams.embeddings && (
cparams.pooling_type == LLAMA_POOLING_TYPE_CLS ||
cparams.pooling_type == LLAMA_POOLING_TYPE_RANK)) {
const int64_t n_tokens = batch.n_tokens;
const int64_t n_seq_tokens = batch.n_seq_tokens;
const int64_t n_seqs = batch.n_seqs;
@ -16346,7 +16383,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
const llama_seq_id seq_id = batch.seq_id[s][0];
// TODO: adapt limits to n_seqs when batch.equal_seqs is true
GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS");
GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS or RANK");
for (int i = 0; i < n_seq_tokens; ++i) {
const llama_pos pos = batch.pos[s*n_seq_tokens + i];
@ -16873,6 +16910,7 @@ static int llama_decode_internal(
case LLAMA_POOLING_TYPE_MEAN:
case LLAMA_POOLING_TYPE_CLS:
case LLAMA_POOLING_TYPE_LAST:
case LLAMA_POOLING_TYPE_RANK:
{
// extract sequence embeddings (cleared before processing each batch)
auto & embd_seq_out = lctx.embd_seq;
@ -17076,6 +17114,7 @@ static int llama_encode_internal(
case LLAMA_POOLING_TYPE_MEAN:
case LLAMA_POOLING_TYPE_CLS:
case LLAMA_POOLING_TYPE_LAST:
case LLAMA_POOLING_TYPE_RANK:
{
// extract sequence embeddings
auto & embd_seq_out = lctx.embd_seq;