mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-07-02 05:15:47 +00:00
llama : add Jina Embeddings architecture (#6826)
* feat: first things to do * feat: create tensors for Jina architecture * fix: use other tensors * feat: embedding gets results * fix: fix usage of ALIBI * fix: clean prints * fix: do some cleanup unused vars * fix: revert changes to Makefile and CMakeLists * fix: revert some changes * fix: fix small detail * fix: fix convert formatting * fix: fix linting and editor * feat: set proper vocab settings * fix: JinaBertForMaskedLM registration * feat: support q_normalization and k_normalization in Jina arch * feat: handle gpt2 tokenizer with Jina architecture * feat: example comments in embedding * feat: rename Jina Bert to Jina Bert V2 * fix: add some changes as per review * feat: proper KQ_pos for Jina embeddings * feat: add capacity to load models ES and DE for Spanish * llama : fix pre-tokenizers * ggml : full ALiBi support * ggml : update ggml_soft_max_ext() CUDA, SYCL * ggml : ggml_flash_attn_ext() support ALiBi (CPU) * ggml : ggml_flash_attn_ext() support ALiBi (Metal) * ggml : fix warning * ggml : ggml_flash_attn_ext() support ALiBi (CUDA) ggml-ci * minor : clean-up * embedding : add warning about missing SEP --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Joan Fontanals
GitHub
190
llama.cpp
190
llama.cpp
@ -205,6 +205,7 @@ enum llm_arch {
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LLM_ARCH_REFACT,
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LLM_ARCH_BERT,
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LLM_ARCH_NOMIC_BERT,
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LLM_ARCH_JINA_BERT_V2,
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LLM_ARCH_BLOOM,
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LLM_ARCH_STABLELM,
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LLM_ARCH_QWEN,
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@ -228,39 +229,40 @@ enum llm_arch {
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};
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static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_LLAMA, "llama" },
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{ LLM_ARCH_FALCON, "falcon" },
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{ LLM_ARCH_GROK, "grok" },
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{ LLM_ARCH_GPT2, "gpt2" },
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{ LLM_ARCH_GPTJ, "gptj" },
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{ LLM_ARCH_GPTNEOX, "gptneox" },
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{ LLM_ARCH_MPT, "mpt" },
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{ LLM_ARCH_BAICHUAN, "baichuan" },
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{ LLM_ARCH_STARCODER, "starcoder" },
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{ LLM_ARCH_PERSIMMON, "persimmon" },
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{ LLM_ARCH_REFACT, "refact" },
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{ LLM_ARCH_BERT, "bert" },
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{ LLM_ARCH_NOMIC_BERT, "nomic-bert" },
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{ LLM_ARCH_BLOOM, "bloom" },
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{ LLM_ARCH_STABLELM, "stablelm" },
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{ LLM_ARCH_QWEN, "qwen" },
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{ LLM_ARCH_QWEN2, "qwen2" },
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{ LLM_ARCH_QWEN2MOE, "qwen2moe" },
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{ LLM_ARCH_PHI2, "phi2" },
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{ LLM_ARCH_PHI3, "phi3" },
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{ LLM_ARCH_PLAMO, "plamo" },
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{ LLM_ARCH_CODESHELL, "codeshell" },
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{ LLM_ARCH_ORION, "orion" },
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{ LLM_ARCH_INTERNLM2, "internlm2" },
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{ LLM_ARCH_MINICPM, "minicpm" },
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{ LLM_ARCH_GEMMA, "gemma" },
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{ LLM_ARCH_STARCODER2, "starcoder2" },
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{ LLM_ARCH_MAMBA, "mamba" },
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{ LLM_ARCH_XVERSE, "xverse" },
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{ LLM_ARCH_COMMAND_R, "command-r" },
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{ LLM_ARCH_DBRX, "dbrx" },
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{ LLM_ARCH_OLMO, "olmo" },
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{ LLM_ARCH_UNKNOWN, "(unknown)" },
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{ LLM_ARCH_LLAMA, "llama" },
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{ LLM_ARCH_FALCON, "falcon" },
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{ LLM_ARCH_GROK, "grok" },
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{ LLM_ARCH_GPT2, "gpt2" },
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{ LLM_ARCH_GPTJ, "gptj" },
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{ LLM_ARCH_GPTNEOX, "gptneox" },
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{ LLM_ARCH_MPT, "mpt" },
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{ LLM_ARCH_BAICHUAN, "baichuan" },
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{ LLM_ARCH_STARCODER, "starcoder" },
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{ LLM_ARCH_PERSIMMON, "persimmon" },
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{ LLM_ARCH_REFACT, "refact" },
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{ LLM_ARCH_BERT, "bert" },
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{ LLM_ARCH_NOMIC_BERT, "nomic-bert" },
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{ LLM_ARCH_JINA_BERT_V2, "jina-bert-v2" },
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{ LLM_ARCH_BLOOM, "bloom" },
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{ LLM_ARCH_STABLELM, "stablelm" },
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{ LLM_ARCH_QWEN, "qwen" },
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{ LLM_ARCH_QWEN2, "qwen2" },
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{ LLM_ARCH_QWEN2MOE, "qwen2moe" },
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{ LLM_ARCH_PHI2, "phi2" },
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{ LLM_ARCH_PHI3, "phi3" },
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{ LLM_ARCH_PLAMO, "plamo" },
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{ LLM_ARCH_CODESHELL, "codeshell" },
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{ LLM_ARCH_ORION, "orion" },
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{ LLM_ARCH_INTERNLM2, "internlm2" },
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{ LLM_ARCH_MINICPM, "minicpm" },
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{ LLM_ARCH_GEMMA, "gemma" },
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{ LLM_ARCH_STARCODER2, "starcoder2" },
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{ LLM_ARCH_MAMBA, "mamba" },
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{ LLM_ARCH_XVERSE, "xverse" },
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{ LLM_ARCH_COMMAND_R, "command-r" },
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{ LLM_ARCH_DBRX, "dbrx" },
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{ LLM_ARCH_OLMO, "olmo" },
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{ LLM_ARCH_UNKNOWN, "(unknown)" },
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};
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enum llm_kv {
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@ -691,6 +693,25 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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},
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},
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{
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LLM_ARCH_JINA_BERT_V2,
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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{ LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
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{ LLM_TENSOR_TOKEN_TYPES, "token_types" },
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{ LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" },
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{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
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{ LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" },
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{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
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{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
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{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
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{ LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" },
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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},
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},
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{
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LLM_ARCH_BLOOM,
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{
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@ -3778,6 +3799,12 @@ static void llm_load_hparams(
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// get hparams kv
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ml.get_key(LLM_KV_VOCAB_SIZE, hparams.n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab);
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// everything past this point is not vocab-related
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if (hparams.vocab_only) {
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return;
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}
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ml.get_key(LLM_KV_CONTEXT_LENGTH, hparams.n_ctx_train);
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ml.get_key(LLM_KV_EMBEDDING_LENGTH, hparams.n_embd);
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ml.get_key(LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff);
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@ -3961,6 +3988,19 @@ static void llm_load_hparams(
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model.type = e_model::MODEL_335M; break; // bge-large
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}
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} break;
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case LLM_ARCH_JINA_BERT_V2:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
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ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
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ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
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ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type);
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hparams.f_max_alibi_bias = 8.0f;
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switch (hparams.n_layer) {
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case 4: model.type = e_model::MODEL_33M; break; // jina-embeddings-small
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case 12: model.type = e_model::MODEL_137M; break; // jina-embeddings-base
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}
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} break;
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case LLM_ARCH_NOMIC_BERT:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
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@ -4382,7 +4422,9 @@ static void llm_load_vocab(
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tokenizer_pre == "starcoder") {
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vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_STARCODER;
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} else if (
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tokenizer_pre == "gpt-2") {
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tokenizer_pre == "gpt-2" ||
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tokenizer_pre == "jina-es" ||
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tokenizer_pre == "jina-de") {
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vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_GPT2;
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} else if (
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tokenizer_pre == "refact") {
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@ -5241,6 +5283,50 @@ static bool llm_load_tensors(
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layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd});
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}
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} break;
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case LLM_ARCH_JINA_BERT_V2:
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{
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model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // word_embeddings
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model.type_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}); //token_type_embeddings
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model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); // LayerNorm
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model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}); //LayerNorm bias
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for (int i = 0; i < n_layer; ++i) {
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ggml_context * ctx_layer = ctx_for_layer(i);
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ggml_context * ctx_split = ctx_for_layer_split(i);
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auto & layer = model.layers[i]; // JinaBertLayer
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layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
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layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
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layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, false);
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layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, false);
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layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
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layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
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layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, false);
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layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, false);
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layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
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layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
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layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); //output_dens
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layer.bo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); //output_dens
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layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}); //output_norm
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layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd});
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layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
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layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
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layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
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layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
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layer.layer_out_norm = ml.create_tensor(ctx_split, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
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layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd});
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}
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} break;
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case LLM_ARCH_BLOOM:
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{
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model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
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@ -6317,7 +6403,7 @@ static struct ggml_tensor * llm_build_ffn(
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llm_ffn_gate_type type_gate,
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const llm_build_cb & cb,
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int il) {
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struct ggml_tensor * tmp = ggml_mul_mat(ctx, up, cur);
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struct ggml_tensor * tmp = up ? ggml_mul_mat(ctx, up, cur) : cur;
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cb(tmp, "ffn_up", il);
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if (up_b) {
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@ -8118,8 +8204,11 @@ struct llm_build_context {
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struct ggml_tensor * cur;
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struct ggml_tensor * inpL;
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struct ggml_tensor * inp_pos = nullptr;
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struct ggml_tensor * inp_pos = build_inp_pos();
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if (model.arch != LLM_ARCH_JINA_BERT_V2) {
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inp_pos = build_inp_pos();
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}
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struct ggml_tensor * inp_mean = build_inp_mean();
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struct ggml_tensor * inp_cls = build_inp_cls();
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@ -8150,13 +8239,26 @@ struct llm_build_context {
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struct ggml_tensor * Vcur;
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// self-attention
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if (model.arch == LLM_ARCH_BERT) {
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if (model.arch == LLM_ARCH_BERT || model.arch == LLM_ARCH_JINA_BERT_V2) {
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Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), model.layers[il].bq);
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cb(Qcur, "Qcur", il);
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if (model.layers[il].attn_q_norm) {
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Qcur = llm_build_norm(ctx0, Qcur, hparams,
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model.layers[il].attn_q_norm,
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model.layers[il].attn_q_norm_b,
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LLM_NORM, cb, il);
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}
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Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), model.layers[il].bk);
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cb(Kcur, "Kcur", il);
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if (model.layers[il].attn_k_norm) {
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Kcur = llm_build_norm(ctx0, Kcur, hparams,
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model.layers[il].attn_k_norm,
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model.layers[il].attn_k_norm_b,
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LLM_NORM, cb, il);
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}
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Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, cur), model.layers[il].bv);
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cb(Vcur, "Vcur", il);
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@ -8247,6 +8349,13 @@ struct llm_build_context {
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model.layers[il].ffn_down, model.layers[il].ffn_down_b,
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NULL,
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LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
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} else if (model.arch == LLM_ARCH_JINA_BERT_V2) {
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cur = llm_build_ffn(ctx0, cur,
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model.layers[il].ffn_up, NULL,
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model.layers[il].ffn_gate, NULL,
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model.layers[il].ffn_down, model.layers[il].ffn_down_b,
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NULL,
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LLM_FFN_GELU, LLM_FFN_PAR, cb, il);
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} else {
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cur = llm_build_ffn(ctx0, cur,
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model.layers[il].ffn_up, NULL,
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@ -10769,6 +10878,7 @@ static struct ggml_cgraph * llama_build_graph(
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result = llm.build_refact();
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} break;
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case LLM_ARCH_BERT:
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case LLM_ARCH_JINA_BERT_V2:
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case LLM_ARCH_NOMIC_BERT:
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{
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result = llm.build_bert();
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@ -12695,7 +12805,10 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
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}
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}
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GGML_ASSERT(vocab.special_add_eos != 1);
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if (add_special && vocab.special_add_eos == 1) {
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GGML_ASSERT(vocab.special_add_eos != -1);
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output.push_back(vocab.special_eos_id);
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}
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} break;
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case LLAMA_VOCAB_TYPE_WPM:
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{
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@ -15746,6 +15859,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
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case LLM_ARCH_REFACT:
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case LLM_ARCH_BLOOM:
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case LLM_ARCH_MAMBA:
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case LLM_ARCH_JINA_BERT_V2:
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return LLAMA_ROPE_TYPE_NONE;
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// use what we call a normal RoPE, operating on pairs of consecutive head values
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