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Add support for encoder-only T5 models (#8900)
* gguf-py : add T5ENCODER model architecture * common : call llama_decode() during warmup only if the model has decoder * convert-hf : add T5EncoderModel * llama : add llama_model_has_decoder() API function * llama : split build_t5() into build_t5_encoder() and build_t5_decoder() * llama : add support for LLM_ARCH_T5ENCODER * llama-embedding : add support for LLAMA_POOLING_TYPE_NONE * llama-embedding : add support for encoder-only models --------- Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
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fairydreaming
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@ -2156,7 +2156,9 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
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tmp.clear();
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tmp.push_back(decoder_start_token_id);
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}
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if (llama_model_has_decoder(model)) {
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llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0));
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}
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llama_kv_cache_clear(lctx);
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llama_synchronize(lctx);
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llama_reset_timings(lctx);
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@ -3324,6 +3324,145 @@ class T5Model(Model):
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return [(self.map_tensor_name(name), data_torch)]
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@Model.register("T5EncoderModel")
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class T5EncoderModel(Model):
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model_arch = gguf.MODEL_ARCH.T5ENCODER
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.shared_token_embeddings_found = False
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def set_vocab(self):
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# to avoid TypeError: Descriptors cannot be created directly
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# exception when importing sentencepiece_model_pb2
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os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
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from sentencepiece import SentencePieceProcessor
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from sentencepiece import sentencepiece_model_pb2 as model
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tokenizer_path = self.dir_model / 'tokenizer.model'
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# many older models use spiece.model tokenizer model filename
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if not tokenizer_path.is_file():
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tokenizer_path = self.dir_model / 'spiece.model'
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if not tokenizer_path.is_file():
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raise FileNotFoundError(f"File not found: {tokenizer_path}")
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sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue]
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sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
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# some models like Pile-T5 family use BPE tokenizer instead of Unigram
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if sentencepiece_model.trainer_spec.model_type == 2: # BPE
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# assure the tokenizer model file name is correct
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assert tokenizer_path.name == 'tokenizer.model'
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return self._set_vocab_sentencepiece()
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else:
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assert sentencepiece_model.trainer_spec.model_type == 1 # UNIGRAM
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add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
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remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces
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precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap
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tokenizer = SentencePieceProcessor()
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tokenizer.LoadFromFile(str(tokenizer_path))
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vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
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tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
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scores: list[float] = [-10000.0] * vocab_size
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toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
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for token_id in range(tokenizer.vocab_size()):
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piece = tokenizer.IdToPiece(token_id)
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text = piece.encode("utf-8")
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score = tokenizer.GetScore(token_id)
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toktype = SentencePieceTokenTypes.NORMAL
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if tokenizer.IsUnknown(token_id):
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toktype = SentencePieceTokenTypes.UNKNOWN
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elif tokenizer.IsControl(token_id):
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toktype = SentencePieceTokenTypes.CONTROL
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elif tokenizer.IsUnused(token_id):
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toktype = SentencePieceTokenTypes.UNUSED
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elif tokenizer.IsByte(token_id):
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toktype = SentencePieceTokenTypes.BYTE
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tokens[token_id] = text
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scores[token_id] = score
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toktypes[token_id] = toktype
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added_tokens_file = self.dir_model / 'added_tokens.json'
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if added_tokens_file.is_file():
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with open(added_tokens_file, "r", encoding="utf-8") as f:
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added_tokens_json = json.load(f)
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for key in added_tokens_json:
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token_id = added_tokens_json[key]
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if token_id >= vocab_size:
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logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
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continue
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tokens[token_id] = key.encode("utf-8")
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scores[token_id] = -1000.0
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toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
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if vocab_size > len(tokens):
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pad_count = vocab_size - len(tokens)
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logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
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for i in range(1, pad_count + 1):
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tokens.append(bytes(f"[PAD{i}]", encoding="utf-8"))
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scores.append(-1000.0)
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toktypes.append(SentencePieceTokenTypes.UNUSED)
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self.gguf_writer.add_tokenizer_model("t5")
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self.gguf_writer.add_tokenizer_pre("default")
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self.gguf_writer.add_token_list(tokens)
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self.gguf_writer.add_token_scores(scores)
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self.gguf_writer.add_token_types(toktypes)
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self.gguf_writer.add_add_space_prefix(add_prefix)
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self.gguf_writer.add_remove_extra_whitespaces(remove_whitespaces)
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if precompiled_charsmap:
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self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap)
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special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
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special_vocab.add_to_gguf(self.gguf_writer)
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self.gguf_writer.add_add_bos_token(False)
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self.gguf_writer.add_add_eos_token(True)
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def set_gguf_parameters(self):
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if (n_ctx := self.find_hparam(["n_positions"], optional=True)) is None:
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logger.warning("Couldn't find context length in config.json, assuming default value of 512")
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n_ctx = 512
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self.gguf_writer.add_context_length(n_ctx)
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self.gguf_writer.add_embedding_length(self.hparams["d_model"])
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self.gguf_writer.add_feed_forward_length(self.hparams["d_ff"])
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self.gguf_writer.add_block_count(self.hparams["num_layers"])
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self.gguf_writer.add_head_count(self.hparams["num_heads"])
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self.gguf_writer.add_key_length(self.hparams["d_kv"])
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self.gguf_writer.add_value_length(self.hparams["d_kv"])
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self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
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self.gguf_writer.add_relative_attn_buckets_count(self.hparams["relative_attention_num_buckets"])
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self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
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self.gguf_writer.add_file_type(self.ftype)
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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del bid # unused
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# T5 based models contain shared token embeddings tensors saved randomly as either "encoder.embed_tokens.weight",
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# "decoder.embed_tokens.weight" or "shared.weight" tensor. In some models there are even multiple of them stored
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# in the safetensors files. We use the first tensor from these three as the token embeddings for both encoder
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# and decoder and ignore the remaining ones.
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if name in ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "shared.weight"]:
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if not self.shared_token_embeddings_found:
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name = "shared.weight"
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self.shared_token_embeddings_found = True
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else:
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logger.debug(f"Skipping shared tensor {name!r} in safetensors so that convert can end normally.")
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return []
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return [(self.map_tensor_name(name), data_torch)]
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@Model.register("JAISLMHeadModel")
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class JaisModel(Model):
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model_arch = gguf.MODEL_ARCH.JAIS
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@ -31,25 +31,47 @@ static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & toke
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}
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static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd, int embd_norm) {
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const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);
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const struct llama_model * model = llama_get_model(ctx);
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// clear previous kv_cache values (irrelevant for embeddings)
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llama_kv_cache_clear(ctx);
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// run model
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fprintf(stderr, "%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq);
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if (llama_model_has_encoder(model) && !llama_model_has_decoder(model)) {
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// encoder-only model
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if (llama_encode(ctx, batch) < 0) {
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fprintf(stderr, "%s : failed to encode\n", __func__);
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}
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} else if (!llama_model_has_encoder(model) && llama_model_has_decoder(model)) {
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// decoder-only model
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if (llama_decode(ctx, batch) < 0) {
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fprintf(stderr, "%s : failed to decode\n", __func__);
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}
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}
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for (int i = 0; i < batch.n_tokens; i++) {
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if (!batch.logits[i]) {
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continue;
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}
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// try to get sequence embeddings - supported only when pooling_type is not NONE
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const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
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GGML_ASSERT(embd != NULL && "failed to get sequence embeddings");
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const float * embd = nullptr;
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int embd_pos = 0;
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float * out = output + batch.seq_id[i][0] * n_embd;
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if (pooling_type == LLAMA_POOLING_TYPE_NONE) {
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// try to get token embeddings
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embd = llama_get_embeddings_ith(ctx, i);
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embd_pos = i;
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GGML_ASSERT(embd != NULL && "failed to get token embeddings");
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} else {
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// try to get sequence embeddings - supported only when pooling_type is not NONE
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embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
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embd_pos = batch.seq_id[i][0];
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GGML_ASSERT(embd != NULL && "failed to get sequence embeddings");
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}
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float * out = output + embd_pos * n_embd;
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llama_embd_normalize(embd, out, n_embd, embd_norm);
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}
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}
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@ -93,8 +115,9 @@ int main(int argc, char ** argv) {
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const int n_ctx = llama_n_ctx(ctx);
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const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);
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if (pooling_type == LLAMA_POOLING_TYPE_NONE) {
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fprintf(stderr, "%s: error: pooling type NONE not supported\n", __func__);
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if (llama_model_has_encoder(model) && llama_model_has_decoder(model)) {
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fprintf(stderr, "%s: error: computing embeddings in encoder-decoder models is not supported\n", __func__);
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return 1;
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}
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@ -153,13 +176,23 @@ int main(int argc, char ** argv) {
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const int n_prompts = prompts.size();
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struct llama_batch batch = llama_batch_init(n_batch, 0, 1);
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// count number of embeddings
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int n_embd_count = 0;
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if (pooling_type == LLAMA_POOLING_TYPE_NONE) {
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for (int k = 0; k < n_prompts; k++) {
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n_embd_count += inputs[k].size();
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}
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} else {
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n_embd_count = n_prompts;
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}
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// allocate output
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const int n_embd = llama_n_embd(model);
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std::vector<float> embeddings(n_prompts * n_embd, 0);
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std::vector<float> embeddings(n_embd_count * n_embd, 0);
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float * emb = embeddings.data();
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// break into batches
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int p = 0; // number of prompts processed already
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int e = 0; // number of embeddings already stored
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int s = 0; // number of prompts in current batch
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for (int k = 0; k < n_prompts; k++) {
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// clamp to n_batch tokens
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@ -169,11 +202,11 @@ int main(int argc, char ** argv) {
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// encode if at capacity
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if (batch.n_tokens + n_toks > n_batch) {
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float * out = emb + p * n_embd;
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float * out = emb + e * n_embd;
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batch_decode(ctx, batch, out, s, n_embd, params.embd_normalize);
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llama_batch_clear(batch);
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p += s;
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e += pooling_type == LLAMA_POOLING_TYPE_NONE ? batch.n_tokens : s;
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s = 0;
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llama_batch_clear(batch);
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}
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// add to batch
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@ -182,12 +215,34 @@ int main(int argc, char ** argv) {
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}
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// final batch
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float * out = emb + p * n_embd;
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float * out = emb + e * n_embd;
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batch_decode(ctx, batch, out, s, n_embd, params.embd_normalize);
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if (params.embd_out.empty()) {
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// print the first part of the embeddings or for a single prompt, the full embedding
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fprintf(stdout, "\n");
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if (pooling_type == LLAMA_POOLING_TYPE_NONE) {
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for (int j = 0; j < n_embd_count; j++) {
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fprintf(stdout, "embedding %d: ", j);
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for (int i = 0; i < std::min(3, n_embd); i++) {
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if (params.embd_normalize == 0) {
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fprintf(stdout, "%6.0f ", emb[j * n_embd + i]);
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} else {
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fprintf(stdout, "%9.6f ", emb[j * n_embd + i]);
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}
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}
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fprintf(stdout, " ... ");
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for (int i = n_embd - 3; i < n_embd; i++) {
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if (params.embd_normalize == 0) {
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fprintf(stdout, "%6.0f ", emb[j * n_embd + i]);
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} else {
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fprintf(stdout, "%9.6f ", emb[j * n_embd + i]);
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}
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}
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fprintf(stdout, "\n");
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}
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} else {
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// print the first part of the embeddings or for a single prompt, the full embedding
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for (int j = 0; j < n_prompts; j++) {
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fprintf(stdout, "embedding %d: ", j);
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for (int i = 0; i < (n_prompts > 1 ? std::min(16, n_embd) : n_embd); i++) {
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@ -218,6 +273,7 @@ int main(int argc, char ** argv) {
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}
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}
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}
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}
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if (params.embd_out == "json" || params.embd_out == "json+" || params.embd_out == "array") {
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const bool notArray = params.embd_out != "array";
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@ -233,23 +289,23 @@ int main(int argc, char ** argv) {
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}
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fprintf(stdout, notArray ? "]\n }" : "]");
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j++;
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if (j < n_prompts) fprintf(stdout, notArray ? ",\n" : ","); else break;
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if (j < n_embd_count) fprintf(stdout, notArray ? ",\n" : ","); else break;
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}
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fprintf(stdout, notArray ? "\n ]" : "]\n");
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if (params.embd_out == "json+" && n_prompts > 1) {
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fprintf(stdout, ",\n \"cosineSimilarity\": [\n");
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for (int i = 0;;) { // at least two iteration (n_prompts > 1)
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for (int i = 0;;) { // at least two iteration (n_embd_count > 1)
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fprintf(stdout, " [");
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for (int j = 0;;) { // at least two iteration (n_prompts > 1)
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for (int j = 0;;) { // at least two iteration (n_embd_count > 1)
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float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
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fprintf(stdout, "%6.2f", sim);
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j++;
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if (j < n_prompts) fprintf(stdout, ", "); else break;
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if (j < n_embd_count) fprintf(stdout, ", "); else break;
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}
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fprintf(stdout, " ]");
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i++;
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if (i < n_prompts) fprintf(stdout, ",\n"); else break;
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if (i < n_embd_count) fprintf(stdout, ",\n"); else break;
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}
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fprintf(stdout, "\n ]");
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}
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@ -217,6 +217,7 @@ class MODEL_ARCH(IntEnum):
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CHATGLM = auto()
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BITNET = auto()
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T5 = auto()
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T5ENCODER = auto()
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JAIS = auto()
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@ -344,6 +345,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.CHATGLM: "chatglm",
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MODEL_ARCH.BITNET: "bitnet",
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MODEL_ARCH.T5: "t5",
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MODEL_ARCH.T5ENCODER: "t5encoder",
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MODEL_ARCH.JAIS: "jais",
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}
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@ -1036,6 +1038,21 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.ENC_FFN_UP,
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MODEL_TENSOR.ENC_OUTPUT_NORM,
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],
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MODEL_ARCH.T5ENCODER: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT,
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MODEL_TENSOR.ENC_ATTN_NORM,
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MODEL_TENSOR.ENC_ATTN_Q,
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MODEL_TENSOR.ENC_ATTN_K,
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MODEL_TENSOR.ENC_ATTN_V,
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MODEL_TENSOR.ENC_ATTN_OUT,
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MODEL_TENSOR.ENC_ATTN_REL_B,
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MODEL_TENSOR.ENC_FFN_NORM,
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MODEL_TENSOR.ENC_FFN_GATE,
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MODEL_TENSOR.ENC_FFN_DOWN,
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MODEL_TENSOR.ENC_FFN_UP,
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MODEL_TENSOR.ENC_OUTPUT_NORM,
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],
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MODEL_ARCH.JAIS: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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@ -504,6 +504,9 @@ extern "C" {
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// Returns true if the model contains an encoder that requires llama_encode() call
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LLAMA_API bool llama_model_has_encoder(const struct llama_model * model);
|
||||
|
||||
// Returns true if the model contains a decoder that requires llama_decode() call
|
||||
LLAMA_API bool llama_model_has_decoder(const struct llama_model * model);
|
||||
|
||||
// For encoder-decoder models, this function returns id of the token that must be provided
|
||||
// to the decoder to start generating output sequence. For other models, it returns -1.
|
||||
LLAMA_API llama_token llama_model_decoder_start_token(const struct llama_model * model);
|
||||
|
||||
172
src/llama.cpp
172
src/llama.cpp
@ -208,6 +208,7 @@ enum llm_arch {
|
||||
LLM_ARCH_CHATGLM,
|
||||
LLM_ARCH_BITNET,
|
||||
LLM_ARCH_T5,
|
||||
LLM_ARCH_T5ENCODER,
|
||||
LLM_ARCH_JAIS,
|
||||
LLM_ARCH_UNKNOWN,
|
||||
};
|
||||
@ -252,6 +253,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
|
||||
{ LLM_ARCH_CHATGLM, "chatglm" },
|
||||
{ LLM_ARCH_BITNET, "bitnet" },
|
||||
{ LLM_ARCH_T5, "t5" },
|
||||
{ LLM_ARCH_T5ENCODER, "t5encoder" },
|
||||
{ LLM_ARCH_JAIS, "jais" },
|
||||
{ LLM_ARCH_UNKNOWN, "(unknown)" },
|
||||
};
|
||||
@ -1261,6 +1263,24 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
|
||||
{ LLM_TENSOR_ENC_FFN_UP, "enc.blk.%d.ffn_up" },
|
||||
},
|
||||
},
|
||||
{
|
||||
LLM_ARCH_T5ENCODER,
|
||||
{
|
||||
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
||||
{ LLM_TENSOR_OUTPUT, "output" },
|
||||
{ LLM_TENSOR_ENC_OUTPUT_NORM, "enc.output_norm" },
|
||||
{ LLM_TENSOR_ENC_ATTN_NORM, "enc.blk.%d.attn_norm" },
|
||||
{ LLM_TENSOR_ENC_ATTN_Q, "enc.blk.%d.attn_q" },
|
||||
{ LLM_TENSOR_ENC_ATTN_K, "enc.blk.%d.attn_k" },
|
||||
{ LLM_TENSOR_ENC_ATTN_V, "enc.blk.%d.attn_v" },
|
||||
{ LLM_TENSOR_ENC_ATTN_OUT, "enc.blk.%d.attn_o" },
|
||||
{ LLM_TENSOR_ENC_ATTN_REL_B, "enc.blk.%d.attn_rel_b" },
|
||||
{ LLM_TENSOR_ENC_FFN_NORM, "enc.blk.%d.ffn_norm" },
|
||||
{ LLM_TENSOR_ENC_FFN_GATE, "enc.blk.%d.ffn_gate" },
|
||||
{ LLM_TENSOR_ENC_FFN_DOWN, "enc.blk.%d.ffn_down" },
|
||||
{ LLM_TENSOR_ENC_FFN_UP, "enc.blk.%d.ffn_up" },
|
||||
},
|
||||
},
|
||||
{
|
||||
LLM_ARCH_JAIS,
|
||||
{
|
||||
@ -5187,6 +5207,12 @@ static void llm_load_hparams(
|
||||
default: model.type = e_model::MODEL_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_T5ENCODER:
|
||||
{
|
||||
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
||||
ml.get_key(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, hparams.n_rel_attn_bkts);
|
||||
model.type = e_model::MODEL_UNKNOWN;
|
||||
} break;
|
||||
case LLM_ARCH_JAIS:
|
||||
{
|
||||
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
||||
@ -7421,6 +7447,42 @@ static bool llm_load_tensors(
|
||||
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_FFN_UP, "weight", i), {n_embd, n_ff});
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_T5ENCODER:
|
||||
{
|
||||
const auto n_rel_attn_bkts = hparams.n_rel_attn_bkts;
|
||||
|
||||
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
||||
|
||||
// output
|
||||
{
|
||||
model.output_norm_enc = ml.create_tensor(ctx_output, tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd});
|
||||
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
||||
// if output is NULL, init from the input tok embed
|
||||
if (model.output == NULL) {
|
||||
model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
|
||||
}
|
||||
}
|
||||
|
||||
for (int i = 0; i < n_layer; ++i) {
|
||||
ggml_context * ctx_layer = ctx_for_layer(i);
|
||||
ggml_context * ctx_split = ctx_for_layer_split(i);
|
||||
|
||||
auto & layer = model.layers[i];
|
||||
|
||||
layer.attn_norm_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_ATTN_NORM, "weight", i), {n_embd});
|
||||
layer.attn_rel_b_enc = ml.create_tensor(ctx_input, tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
||||
|
||||
layer.wq_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa});
|
||||
layer.wk_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
|
||||
layer.wv_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
|
||||
layer.wo_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd});
|
||||
|
||||
layer.ffn_norm_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd});
|
||||
layer.ffn_gate_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd, n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
||||
layer.ffn_down_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), { n_ff, n_embd});
|
||||
layer.ffn_up_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_UP, "weight", i), {n_embd, n_ff});
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_JAIS:
|
||||
{
|
||||
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
||||
@ -13135,7 +13197,7 @@ struct llm_build_context {
|
||||
return gf;
|
||||
}
|
||||
|
||||
struct ggml_cgraph * build_t5() {
|
||||
struct ggml_cgraph * build_t5_encoder() {
|
||||
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
|
||||
|
||||
// mutable variable, needed during the last layer of the computation to skip unused tokens
|
||||
@ -13150,7 +13212,7 @@ struct llm_build_context {
|
||||
|
||||
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
||||
|
||||
if (lctx.is_encoding) {
|
||||
GGML_ASSERT(lctx.is_encoding);
|
||||
struct ggml_tensor * pos_bucket_enc = llm_build_pos_bucket(false);
|
||||
|
||||
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
||||
@ -13261,7 +13323,28 @@ struct llm_build_context {
|
||||
model.output_norm_enc, NULL,
|
||||
LLM_NORM_RMS, cb, -1);
|
||||
cb(cur, "result_norm", -1);
|
||||
} else {
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
return gf;
|
||||
}
|
||||
|
||||
struct ggml_cgraph * build_t5_decoder() {
|
||||
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
|
||||
|
||||
// mutable variable, needed during the last layer of the computation to skip unused tokens
|
||||
int32_t n_tokens = this->n_tokens;
|
||||
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * inpL;
|
||||
|
||||
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
||||
|
||||
GGML_ASSERT(!lctx.is_encoding);
|
||||
GGML_ASSERT(n_outputs_enc > 0 && "call llama_encode() first");
|
||||
|
||||
struct ggml_tensor * embd_enc = llm_build_inp_embd_enc();
|
||||
@ -13445,7 +13528,6 @@ struct llm_build_context {
|
||||
// lm_head
|
||||
cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
|
||||
cb(cur, "result_output", -1);
|
||||
}
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
@ -13898,7 +13980,15 @@ static struct ggml_cgraph * llama_build_graph(
|
||||
} break;
|
||||
case LLM_ARCH_T5:
|
||||
{
|
||||
result = llm.build_t5();
|
||||
if (lctx.is_encoding) {
|
||||
result = llm.build_t5_encoder();
|
||||
} else {
|
||||
result = llm.build_t5_decoder();
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_T5ENCODER:
|
||||
{
|
||||
result = llm.build_t5_encoder();
|
||||
} break;
|
||||
case LLM_ARCH_JAIS:
|
||||
{
|
||||
@ -14346,7 +14436,7 @@ static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) {
|
||||
|
||||
// TODO: use a per-batch flag for logits presence instead
|
||||
const bool has_logits = !cparams.embeddings;
|
||||
const bool has_embd = lctx.is_encoding || (cparams.embeddings && (cparams.pooling_type == LLAMA_POOLING_TYPE_NONE));
|
||||
const bool has_embd = cparams.embeddings && (cparams.pooling_type == LLAMA_POOLING_TYPE_NONE);
|
||||
|
||||
const size_t logits_size = has_logits ? n_vocab*n_outputs_max : 0;
|
||||
const size_t embd_size = has_embd ? n_embd*n_outputs_max : 0;
|
||||
@ -14829,9 +14919,24 @@ static int llama_encode_internal(
|
||||
ggml_cgraph * gf = llama_build_graph(lctx, batch, false);
|
||||
|
||||
// the output embeddings after the final encoder normalization
|
||||
struct ggml_tensor * embd = gf->nodes[gf->n_nodes - 1];
|
||||
struct ggml_tensor * embd = nullptr;
|
||||
|
||||
GGML_ASSERT(strcmp(embd->name, "result_norm") == 0);
|
||||
// there are two cases here
|
||||
if (llama_model_has_decoder(&lctx.model)) {
|
||||
// first case is an encoder-decoder T5 model where embeddings are passed to decoder
|
||||
embd = gf->nodes[gf->n_nodes - 1];
|
||||
GGML_ASSERT(strcmp(embd->name, "result_norm") == 0 && "missing result_output tensor");
|
||||
} else {
|
||||
// second case is an encoder-only T5 model
|
||||
if (cparams.embeddings) {
|
||||
// only output embeddings if required
|
||||
embd = gf->nodes[gf->n_nodes - 1];
|
||||
if (strcmp(embd->name, "result_embd_pooled") != 0) {
|
||||
embd = gf->nodes[gf->n_nodes - 2];
|
||||
}
|
||||
GGML_ASSERT(strcmp(embd->name, "result_embd_pooled") == 0 && "missing embeddings tensor");
|
||||
}
|
||||
}
|
||||
|
||||
ggml_backend_sched_alloc_graph(lctx.sched, gf);
|
||||
|
||||
@ -14844,9 +14949,7 @@ static int llama_encode_internal(
|
||||
ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(lctx.sched, embd);
|
||||
GGML_ASSERT(backend_embd != nullptr);
|
||||
|
||||
// extract token embeddings
|
||||
GGML_ASSERT(lctx.embd != nullptr);
|
||||
|
||||
if (llama_model_has_decoder(&lctx.model)) {
|
||||
lctx.embd_enc.resize(n_tokens*n_embd);
|
||||
float * embd_out = lctx.embd_enc.data();
|
||||
|
||||
@ -14860,6 +14963,42 @@ static int llama_encode_internal(
|
||||
lctx.seq_ids_enc[i].insert(seq_id);
|
||||
}
|
||||
}
|
||||
} else {
|
||||
GGML_ASSERT(lctx.embd != nullptr);
|
||||
|
||||
switch (cparams.pooling_type) {
|
||||
case LLAMA_POOLING_TYPE_NONE:
|
||||
{
|
||||
// extract token embeddings
|
||||
GGML_ASSERT(lctx.embd != nullptr);
|
||||
float * embd_out = lctx.embd;
|
||||
|
||||
GGML_ASSERT(n_tokens*n_embd <= (int64_t) lctx.embd_size);
|
||||
ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_tokens*n_embd*sizeof(float));
|
||||
} break;
|
||||
case LLAMA_POOLING_TYPE_MEAN:
|
||||
case LLAMA_POOLING_TYPE_CLS:
|
||||
case LLAMA_POOLING_TYPE_LAST:
|
||||
{
|
||||
// extract sequence embeddings
|
||||
auto & embd_seq_out = lctx.embd_seq;
|
||||
embd_seq_out.clear();
|
||||
|
||||
for (uint32_t i = 0; i < n_tokens; i++) {
|
||||
const llama_seq_id seq_id = batch.seq_id[i][0];
|
||||
if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
|
||||
continue;
|
||||
}
|
||||
embd_seq_out[seq_id].resize(n_embd);
|
||||
ggml_backend_tensor_get_async(backend_embd, embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float));
|
||||
}
|
||||
} break;
|
||||
case LLAMA_POOLING_TYPE_UNSPECIFIED:
|
||||
{
|
||||
GGML_ABORT("unknown pooling type");
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Reset state for the next token before backend sync, to allow the CPU activities in the reset to
|
||||
@ -16567,6 +16706,8 @@ struct llama_context * llama_new_context_with_model(
|
||||
|
||||
ctx->sampling.rng = std::mt19937(params.seed);
|
||||
ctx->logits_all = params.logits_all;
|
||||
// build worst-case graph for encoder if a model contains encoder
|
||||
ctx->is_encoding = llama_model_has_encoder(model);
|
||||
|
||||
uint32_t kv_size = cparams.n_ctx;
|
||||
ggml_type type_k = params.type_k;
|
||||
@ -16881,6 +17022,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
|
||||
case LLM_ARCH_MAMBA:
|
||||
case LLM_ARCH_JINA_BERT_V2:
|
||||
case LLM_ARCH_T5:
|
||||
case LLM_ARCH_T5ENCODER:
|
||||
case LLM_ARCH_JAIS:
|
||||
return LLAMA_ROPE_TYPE_NONE;
|
||||
|
||||
@ -17029,10 +17171,18 @@ struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const ch
|
||||
bool llama_model_has_encoder(const struct llama_model * model) {
|
||||
switch (model->arch) {
|
||||
case LLM_ARCH_T5: return true;
|
||||
case LLM_ARCH_T5ENCODER: return true;
|
||||
default: return false;
|
||||
}
|
||||
}
|
||||
|
||||
bool llama_model_has_decoder(const struct llama_model * model) {
|
||||
switch (model->arch) {
|
||||
case LLM_ARCH_T5ENCODER: return false;
|
||||
default: return true;
|
||||
}
|
||||
}
|
||||
|
||||
llama_token llama_model_decoder_start_token(const struct llama_model * model) {
|
||||
return model->hparams.dec_start_token_id;
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user