mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-25 02:44:36 +00:00
Add support for ArcticForCausalLM (#7020)
* common : increase max number of experts to 128 * common : add tensor LLM_TENSOR_FFN_NORM_EXPS for normalization before MoE that runs in parallel to attention + ffn * gguf-py : add architecture-specific block mappings that override selected general block mappings * convert-hf : add model conversion support for ArcticForCausalLM * convert-hf : use added_tokens_decoder from tokenizer_config.json to redefine tokens from SentencePiece model (only for ArcticForCausalLM) * llama : add inference support for LLM_ARCH_ARCTIC --------- Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
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@ -2466,6 +2466,157 @@ class JinaBertV2Model(BertModel):
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self.gguf_writer.add_add_eos_token(True)
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self.gguf_writer.add_add_eos_token(True)
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@Model.register("ArcticForCausalLM")
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class ArcticModel(Model):
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model_arch = gguf.MODEL_ARCH.ARCTIC
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def set_vocab(self):
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# The reason for using a custom implementation here is that the
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# snowflake-arctic-instruct model redefined tokens 31998 and 31999 from
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# tokenizer.model and used them as BOS and EOS instead of adding new tokens.
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from sentencepiece import SentencePieceProcessor
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tokenizer_path = self.dir_model / 'tokenizer.model'
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if not tokenizer_path.is_file():
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logger.error(f'Error: Missing {tokenizer_path}')
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sys.exit(1)
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# Read the whole vocabulary from the tokenizer.model file
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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.UNKNOWN] * 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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# Use the added_tokens_decoder field from tokeniser_config.json as the source
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# of information about added/redefined tokens and modify them accordingly.
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tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
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if tokenizer_config_file.is_file():
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with open(tokenizer_config_file, "r", encoding="utf-8") as f:
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tokenizer_config_json = json.load(f)
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if "added_tokens_decoder" in tokenizer_config_json:
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added_tokens_decoder = tokenizer_config_json["added_tokens_decoder"]
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for token_id, token_json in added_tokens_decoder.items():
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token_id = int(token_id)
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if (token_id >= vocab_size):
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logger.debug(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
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continue
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token_content = token_json["content"]
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token_type = SentencePieceTokenTypes.USER_DEFINED
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token_score = -10000.0
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# Map unk_token to UNKNOWN, other special tokens to CONTROL
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# Set the score to 0.0 as in the original tokenizer.model
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if ("special" in token_json) and token_json["special"]:
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if token_content == tokenizer_config_json["unk_token"]:
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token_type = SentencePieceTokenTypes.UNKNOWN
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else:
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token_type = SentencePieceTokenTypes.CONTROL
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token_score = 0.0
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logger.info(f"Setting added token {token_id} to '{token_content}' (type: {token_type}, score: {token_score:.2f})")
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tokens[token_id] = token_content.encode("utf-8")
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toktypes[token_id] = token_type
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scores[token_id] = token_score
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self.gguf_writer.add_tokenizer_model("llama")
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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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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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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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hparams = self.hparams
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self.gguf_writer.add_vocab_size(hparams["vocab_size"])
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self.gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"])
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_experts: list[dict[str, Tensor]] | None = None
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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n_head = self.hparams["num_attention_heads"]
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n_kv_head = self.hparams.get("num_key_value_heads")
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if name.endswith("q_proj.weight"):
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data_torch = LlamaModel.permute(data_torch, n_head, n_head)
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if name.endswith("k_proj.weight"):
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data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
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# process the experts separately
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if name.find("block_sparse_moe.experts") != -1:
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n_experts = self.hparams["num_local_experts"]
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assert bid is not None
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if self._experts is None:
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self._experts = [{} for _ in range(self.block_count)]
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self._experts[bid][name] = data_torch
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if len(self._experts[bid]) >= n_experts * 3:
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tensors: list[tuple[str, Tensor]] = []
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# merge the experts into a single 3d tensor
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for wid in ["w1", "w2", "w3"]:
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datas: list[Tensor] = []
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for xid in range(n_experts):
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ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight"
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datas.append(self._experts[bid][ename])
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del self._experts[bid][ename]
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data_torch = torch.stack(datas, dim=0)
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merged_name = f"layers.{bid}.feed_forward.experts.{wid}.weight"
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new_name = self.map_tensor_name(merged_name)
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tensors.append((new_name, data_torch))
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return tensors
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else:
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return []
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return [(self.map_tensor_name(name), data_torch)]
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def write_tensors(self):
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super().write_tensors()
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if self._experts is not None:
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# flatten `list[dict[str, Tensor]]` into `list[str]`
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experts = [k for d in self._experts for k in d.keys()]
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if len(experts) > 0:
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raise ValueError(f"Unprocessed experts: {experts}")
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###### CONVERSION LOGIC ######
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###### CONVERSION LOGIC ######
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@ -139,6 +139,7 @@ class MODEL_ARCH(IntEnum):
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COMMAND_R = auto()
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COMMAND_R = auto()
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DBRX = auto()
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DBRX = auto()
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OLMO = auto()
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OLMO = auto()
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ARCTIC = auto()
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class MODEL_TENSOR(IntEnum):
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class MODEL_TENSOR(IntEnum):
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@ -167,6 +168,7 @@ class MODEL_TENSOR(IntEnum):
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FFN_DOWN = auto()
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FFN_DOWN = auto()
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FFN_UP = auto()
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FFN_UP = auto()
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FFN_ACT = auto()
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FFN_ACT = auto()
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FFN_NORM_EXP = auto()
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FFN_GATE_EXP = auto()
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FFN_GATE_EXP = auto()
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FFN_DOWN_EXP = auto()
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FFN_DOWN_EXP = auto()
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FFN_UP_EXP = auto()
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FFN_UP_EXP = auto()
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@ -218,6 +220,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.COMMAND_R: "command-r",
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MODEL_ARCH.COMMAND_R: "command-r",
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MODEL_ARCH.DBRX: "dbrx",
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MODEL_ARCH.DBRX: "dbrx",
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MODEL_ARCH.OLMO: "olmo",
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MODEL_ARCH.OLMO: "olmo",
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MODEL_ARCH.ARCTIC: "arctic",
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}
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}
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TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
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TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
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@ -251,6 +254,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
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MODEL_TENSOR.FFN_DOWN_SHEXP: "blk.{bid}.ffn_down_shexp",
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MODEL_TENSOR.FFN_DOWN_SHEXP: "blk.{bid}.ffn_down_shexp",
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MODEL_TENSOR.FFN_UP_SHEXP: "blk.{bid}.ffn_up_shexp",
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MODEL_TENSOR.FFN_UP_SHEXP: "blk.{bid}.ffn_up_shexp",
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MODEL_TENSOR.FFN_ACT: "blk.{bid}.ffn",
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MODEL_TENSOR.FFN_ACT: "blk.{bid}.ffn",
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MODEL_TENSOR.FFN_NORM_EXP: "blk.{bid}.ffn_norm_exps",
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MODEL_TENSOR.FFN_GATE_EXP: "blk.{bid}.ffn_gate_exps",
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MODEL_TENSOR.FFN_GATE_EXP: "blk.{bid}.ffn_gate_exps",
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MODEL_TENSOR.FFN_DOWN_EXP: "blk.{bid}.ffn_down_exps",
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MODEL_TENSOR.FFN_DOWN_EXP: "blk.{bid}.ffn_down_exps",
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MODEL_TENSOR.FFN_UP_EXP: "blk.{bid}.ffn_up_exps",
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MODEL_TENSOR.FFN_UP_EXP: "blk.{bid}.ffn_up_exps",
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@ -732,6 +736,27 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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MODEL_TENSOR.FFN_UP,
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],
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],
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MODEL_ARCH.ARCTIC: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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MODEL_TENSOR.OUTPUT,
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MODEL_TENSOR.ROPE_FREQS,
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MODEL_TENSOR.ATTN_NORM,
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MODEL_TENSOR.ATTN_Q,
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MODEL_TENSOR.ATTN_K,
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MODEL_TENSOR.ATTN_V,
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MODEL_TENSOR.ATTN_OUT,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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MODEL_TENSOR.FFN_GATE_INP,
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MODEL_TENSOR.FFN_NORM,
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MODEL_TENSOR.FFN_GATE,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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MODEL_TENSOR.FFN_NORM_EXP,
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MODEL_TENSOR.FFN_GATE_EXP,
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MODEL_TENSOR.FFN_DOWN_EXP,
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MODEL_TENSOR.FFN_UP_EXP,
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],
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# TODO
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# TODO
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}
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}
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@ -244,6 +244,7 @@ class TensorNameMap:
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"encoder.layers.{bid}.mlp.fc11", # nomic-bert
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"encoder.layers.{bid}.mlp.fc11", # nomic-bert
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"model.layers.{bid}.mlp.c_fc", # starcoder2
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"model.layers.{bid}.mlp.c_fc", # starcoder2
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"encoder.layer.{bid}.mlp.gated_layers_v", # jina-bert-v2
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"encoder.layer.{bid}.mlp.gated_layers_v", # jina-bert-v2
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"model.layers.{bid}.residual_mlp.w3", # arctic
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),
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),
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MODEL_TENSOR.FFN_UP_EXP: (
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MODEL_TENSOR.FFN_UP_EXP: (
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@ -272,6 +273,7 @@ class TensorNameMap:
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"encoder.layers.{bid}.mlp.fc12", # nomic-bert
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"encoder.layers.{bid}.mlp.fc12", # nomic-bert
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"encoder.layer.{bid}.mlp.gated_layers_w", # jina-bert-v2
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"encoder.layer.{bid}.mlp.gated_layers_w", # jina-bert-v2
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"transformer.h.{bid}.mlp.linear_1", # refact
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"transformer.h.{bid}.mlp.linear_1", # refact
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"model.layers.{bid}.residual_mlp.w1", # arctic
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),
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),
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MODEL_TENSOR.FFN_GATE_EXP: (
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MODEL_TENSOR.FFN_GATE_EXP: (
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@ -306,6 +308,7 @@ class TensorNameMap:
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"encoder.layers.{bid}.mlp.fc2", # nomic-bert
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"encoder.layers.{bid}.mlp.fc2", # nomic-bert
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"model.layers.{bid}.mlp.c_proj", # starcoder2
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"model.layers.{bid}.mlp.c_proj", # starcoder2
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"encoder.layer.{bid}.mlp.wo", # jina-bert-v2
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"encoder.layer.{bid}.mlp.wo", # jina-bert-v2
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"model.layers.{bid}.residual_mlp.w2", # arctic
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),
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),
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MODEL_TENSOR.FFN_DOWN_EXP: (
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MODEL_TENSOR.FFN_DOWN_EXP: (
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@ -382,6 +385,18 @@ class TensorNameMap:
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),
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),
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}
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}
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# architecture-specific block mappings
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arch_block_mappings_cfg: dict[MODEL_ARCH, dict[MODEL_TENSOR, tuple[str, ...]]] = {
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MODEL_ARCH.ARCTIC: {
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MODEL_TENSOR.FFN_NORM: (
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"model.layers.{bid}.residual_layernorm",
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),
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MODEL_TENSOR.FFN_NORM_EXP: (
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"model.layers.{bid}.post_attention_layernorm",
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),
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},
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}
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mapping: dict[str, tuple[MODEL_TENSOR, str]]
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mapping: dict[str, tuple[MODEL_TENSOR, str]]
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def __init__(self, arch: MODEL_ARCH, n_blocks: int):
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def __init__(self, arch: MODEL_ARCH, n_blocks: int):
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@ -393,12 +408,14 @@ class TensorNameMap:
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self.mapping[tensor_name] = (tensor, tensor_name)
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self.mapping[tensor_name] = (tensor, tensor_name)
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for key in keys:
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for key in keys:
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self.mapping[key] = (tensor, tensor_name)
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self.mapping[key] = (tensor, tensor_name)
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if arch in self.arch_block_mappings_cfg:
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self.block_mappings_cfg.update(self.arch_block_mappings_cfg[arch])
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for bid in range(n_blocks):
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for bid in range(n_blocks):
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for tensor, keys in self.block_mappings_cfg.items():
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for tensor, keys in self.block_mappings_cfg.items():
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if tensor not in MODEL_TENSORS[arch]:
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if tensor not in MODEL_TENSORS[arch]:
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continue
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continue
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# TODO: make this configurable
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# TODO: make this configurable
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n_experts = 60
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n_experts = 128
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for xid in range(n_experts):
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for xid in range(n_experts):
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tensor_name = TENSOR_NAMES[tensor].format(bid = bid, xid = xid)
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tensor_name = TENSOR_NAMES[tensor].format(bid = bid, xid = xid)
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self.mapping[tensor_name] = (tensor, tensor_name)
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self.mapping[tensor_name] = (tensor, tensor_name)
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304
llama.cpp
304
llama.cpp
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#endif
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#endif
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#define LLAMA_MAX_NODES 8192
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#define LLAMA_MAX_NODES 8192
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#define LLAMA_MAX_EXPERTS 60
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#define LLAMA_MAX_EXPERTS 128
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//
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//
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// logging
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// logging
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@ -221,6 +221,7 @@ enum llm_arch {
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LLM_ARCH_COMMAND_R,
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LLM_ARCH_COMMAND_R,
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LLM_ARCH_DBRX,
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LLM_ARCH_DBRX,
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LLM_ARCH_OLMO,
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LLM_ARCH_OLMO,
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LLM_ARCH_ARCTIC,
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LLM_ARCH_UNKNOWN,
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LLM_ARCH_UNKNOWN,
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};
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};
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||||||
|
|
||||||
@ -257,6 +258,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
|
|||||||
{ LLM_ARCH_COMMAND_R, "command-r" },
|
{ LLM_ARCH_COMMAND_R, "command-r" },
|
||||||
{ LLM_ARCH_DBRX, "dbrx" },
|
{ LLM_ARCH_DBRX, "dbrx" },
|
||||||
{ LLM_ARCH_OLMO, "olmo" },
|
{ LLM_ARCH_OLMO, "olmo" },
|
||||||
|
{ LLM_ARCH_ARCTIC, "arctic" },
|
||||||
{ LLM_ARCH_UNKNOWN, "(unknown)" },
|
{ LLM_ARCH_UNKNOWN, "(unknown)" },
|
||||||
};
|
};
|
||||||
|
|
||||||
@ -455,6 +457,7 @@ enum llm_tensor {
|
|||||||
LLM_TENSOR_FFN_DOWN_EXP, // split experts for backward compatibility
|
LLM_TENSOR_FFN_DOWN_EXP, // split experts for backward compatibility
|
||||||
LLM_TENSOR_FFN_GATE_EXP,
|
LLM_TENSOR_FFN_GATE_EXP,
|
||||||
LLM_TENSOR_FFN_UP_EXP,
|
LLM_TENSOR_FFN_UP_EXP,
|
||||||
|
LLM_TENSOR_FFN_NORM_EXPS,
|
||||||
LLM_TENSOR_FFN_DOWN_EXPS, // merged experts
|
LLM_TENSOR_FFN_DOWN_EXPS, // merged experts
|
||||||
LLM_TENSOR_FFN_GATE_EXPS,
|
LLM_TENSOR_FFN_GATE_EXPS,
|
||||||
LLM_TENSOR_FFN_UP_EXPS,
|
LLM_TENSOR_FFN_UP_EXPS,
|
||||||
@ -1032,6 +1035,28 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
|
|||||||
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
||||||
},
|
},
|
||||||
},
|
},
|
||||||
|
{
|
||||||
|
LLM_ARCH_ARCTIC,
|
||||||
|
{
|
||||||
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
||||||
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
||||||
|
{ LLM_TENSOR_OUTPUT, "output" },
|
||||||
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
||||||
|
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
|
||||||
|
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
|
||||||
|
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
|
||||||
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
||||||
|
{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
|
||||||
|
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
|
||||||
|
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
|
||||||
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
||||||
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
||||||
|
{ LLM_TENSOR_FFN_NORM_EXPS, "blk.%d.ffn_norm_exps" },
|
||||||
|
{ LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
|
||||||
|
{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
|
||||||
|
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
|
||||||
|
},
|
||||||
|
},
|
||||||
{
|
{
|
||||||
LLM_ARCH_UNKNOWN,
|
LLM_ARCH_UNKNOWN,
|
||||||
{
|
{
|
||||||
@ -1732,6 +1757,7 @@ enum e_model {
|
|||||||
MODEL_8x7B,
|
MODEL_8x7B,
|
||||||
MODEL_8x22B,
|
MODEL_8x22B,
|
||||||
MODEL_16x12B,
|
MODEL_16x12B,
|
||||||
|
MODEL_10B_128x3_66B,
|
||||||
};
|
};
|
||||||
|
|
||||||
static const size_t kiB = 1024;
|
static const size_t kiB = 1024;
|
||||||
@ -1907,6 +1933,7 @@ struct llama_layer {
|
|||||||
struct ggml_tensor * ffn_norm_b;
|
struct ggml_tensor * ffn_norm_b;
|
||||||
struct ggml_tensor * layer_out_norm;
|
struct ggml_tensor * layer_out_norm;
|
||||||
struct ggml_tensor * layer_out_norm_b;
|
struct ggml_tensor * layer_out_norm_b;
|
||||||
|
struct ggml_tensor * ffn_norm_exps;
|
||||||
|
|
||||||
// ff
|
// ff
|
||||||
struct ggml_tensor * ffn_gate; // w1
|
struct ggml_tensor * ffn_gate; // w1
|
||||||
@ -3781,47 +3808,48 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
|
|||||||
|
|
||||||
static const char * llama_model_type_name(e_model type) {
|
static const char * llama_model_type_name(e_model type) {
|
||||||
switch (type) {
|
switch (type) {
|
||||||
case MODEL_14M: return "14M";
|
case MODEL_14M: return "14M";
|
||||||
case MODEL_17M: return "17M";
|
case MODEL_17M: return "17M";
|
||||||
case MODEL_22M: return "22M";
|
case MODEL_22M: return "22M";
|
||||||
case MODEL_33M: return "33M";
|
case MODEL_33M: return "33M";
|
||||||
case MODEL_70M: return "70M";
|
case MODEL_70M: return "70M";
|
||||||
case MODEL_109M: return "109M";
|
case MODEL_109M: return "109M";
|
||||||
case MODEL_137M: return "137M";
|
case MODEL_137M: return "137M";
|
||||||
case MODEL_160M: return "160M";
|
case MODEL_160M: return "160M";
|
||||||
case MODEL_335M: return "335M";
|
case MODEL_335M: return "335M";
|
||||||
case MODEL_410M: return "410M";
|
case MODEL_410M: return "410M";
|
||||||
case MODEL_0_5B: return "0.5B";
|
case MODEL_0_5B: return "0.5B";
|
||||||
case MODEL_1B: return "1B";
|
case MODEL_1B: return "1B";
|
||||||
case MODEL_1_4B: return "1.4B";
|
case MODEL_1_4B: return "1.4B";
|
||||||
case MODEL_2B: return "2B";
|
case MODEL_2B: return "2B";
|
||||||
case MODEL_2_8B: return "2.8B";
|
case MODEL_2_8B: return "2.8B";
|
||||||
case MODEL_3B: return "3B";
|
case MODEL_3B: return "3B";
|
||||||
case MODEL_4B: return "4B";
|
case MODEL_4B: return "4B";
|
||||||
case MODEL_6_9B: return "6.9B";
|
case MODEL_6_9B: return "6.9B";
|
||||||
case MODEL_7B: return "7B";
|
case MODEL_7B: return "7B";
|
||||||
case MODEL_8B: return "8B";
|
case MODEL_8B: return "8B";
|
||||||
case MODEL_12B: return "12B";
|
case MODEL_12B: return "12B";
|
||||||
case MODEL_13B: return "13B";
|
case MODEL_13B: return "13B";
|
||||||
case MODEL_14B: return "14B";
|
case MODEL_14B: return "14B";
|
||||||
case MODEL_15B: return "15B";
|
case MODEL_15B: return "15B";
|
||||||
case MODEL_20B: return "20B";
|
case MODEL_20B: return "20B";
|
||||||
case MODEL_30B: return "30B";
|
case MODEL_30B: return "30B";
|
||||||
case MODEL_34B: return "34B";
|
case MODEL_34B: return "34B";
|
||||||
case MODEL_35B: return "35B";
|
case MODEL_35B: return "35B";
|
||||||
case MODEL_40B: return "40B";
|
case MODEL_40B: return "40B";
|
||||||
case MODEL_65B: return "65B";
|
case MODEL_65B: return "65B";
|
||||||
case MODEL_70B: return "70B";
|
case MODEL_70B: return "70B";
|
||||||
case MODEL_314B: return "314B";
|
case MODEL_314B: return "314B";
|
||||||
case MODEL_SMALL: return "0.1B";
|
case MODEL_SMALL: return "0.1B";
|
||||||
case MODEL_MEDIUM: return "0.4B";
|
case MODEL_MEDIUM: return "0.4B";
|
||||||
case MODEL_LARGE: return "0.8B";
|
case MODEL_LARGE: return "0.8B";
|
||||||
case MODEL_XL: return "1.5B";
|
case MODEL_XL: return "1.5B";
|
||||||
case MODEL_A2_7B: return "A2.7B";
|
case MODEL_A2_7B: return "A2.7B";
|
||||||
case MODEL_8x7B: return "8x7B";
|
case MODEL_8x7B: return "8x7B";
|
||||||
case MODEL_8x22B: return "8x22B";
|
case MODEL_8x22B: return "8x22B";
|
||||||
case MODEL_16x12B: return "16x12B";
|
case MODEL_16x12B: return "16x12B";
|
||||||
default: return "?B";
|
case MODEL_10B_128x3_66B: return "10B+128x3.66B";
|
||||||
|
default: return "?B";
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
@ -4343,6 +4371,19 @@ static void llm_load_hparams(
|
|||||||
default: model.type = e_model::MODEL_UNKNOWN;
|
default: model.type = e_model::MODEL_UNKNOWN;
|
||||||
}
|
}
|
||||||
} break;
|
} break;
|
||||||
|
case LLM_ARCH_ARCTIC:
|
||||||
|
{
|
||||||
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
||||||
|
|
||||||
|
if (hparams.n_expert == 128) {
|
||||||
|
switch (hparams.n_layer) {
|
||||||
|
case 35: model.type = e_model::MODEL_10B_128x3_66B; break;
|
||||||
|
default: model.type = e_model::MODEL_UNKNOWN;
|
||||||
|
}
|
||||||
|
} else {
|
||||||
|
model.type = e_model::MODEL_UNKNOWN;
|
||||||
|
}
|
||||||
|
} break;
|
||||||
default: (void)0;
|
default: (void)0;
|
||||||
}
|
}
|
||||||
|
|
||||||
@ -6129,6 +6170,46 @@ static bool llm_load_tensors(
|
|||||||
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
|
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
|
||||||
}
|
}
|
||||||
} break;
|
} break;
|
||||||
|
case LLM_ARCH_ARCTIC:
|
||||||
|
{
|
||||||
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
||||||
|
|
||||||
|
// output
|
||||||
|
{
|
||||||
|
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_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 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
||||||
|
|
||||||
|
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
||||||
|
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
||||||
|
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
||||||
|
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
||||||
|
|
||||||
|
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
||||||
|
|
||||||
|
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_embd});
|
||||||
|
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_embd, n_embd});
|
||||||
|
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_embd});
|
||||||
|
|
||||||
|
layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
|
||||||
|
layer.ffn_norm_exps = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM_EXPS, "weight", i), {n_embd});
|
||||||
|
layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, false);
|
||||||
|
layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert});
|
||||||
|
layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert});
|
||||||
|
}
|
||||||
|
} break;
|
||||||
default:
|
default:
|
||||||
throw std::runtime_error("unknown architecture");
|
throw std::runtime_error("unknown architecture");
|
||||||
}
|
}
|
||||||
@ -10790,6 +10871,140 @@ struct llm_build_context {
|
|||||||
|
|
||||||
return gf;
|
return gf;
|
||||||
}
|
}
|
||||||
|
|
||||||
|
struct ggml_cgraph * build_arctic() {
|
||||||
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, 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;
|
||||||
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||||
|
GGML_ASSERT(n_embd_head == hparams.n_rot);
|
||||||
|
|
||||||
|
struct ggml_tensor * cur;
|
||||||
|
struct ggml_tensor * inpL;
|
||||||
|
|
||||||
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
||||||
|
|
||||||
|
// inp_pos - contains the positions
|
||||||
|
struct ggml_tensor * inp_pos = build_inp_pos();
|
||||||
|
|
||||||
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
||||||
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
||||||
|
|
||||||
|
for (int il = 0; il < n_layer; ++il) {
|
||||||
|
struct ggml_tensor * inpSA = inpL;
|
||||||
|
|
||||||
|
// norm
|
||||||
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
||||||
|
model.layers[il].attn_norm, NULL,
|
||||||
|
LLM_NORM_RMS, cb, il);
|
||||||
|
cb(cur, "attn_norm", il);
|
||||||
|
|
||||||
|
// self-attention
|
||||||
|
{
|
||||||
|
// compute Q and K and RoPE them
|
||||||
|
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
||||||
|
cb(Qcur, "Qcur", il);
|
||||||
|
|
||||||
|
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
||||||
|
cb(Kcur, "Kcur", il);
|
||||||
|
|
||||||
|
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
||||||
|
cb(Vcur, "Vcur", il);
|
||||||
|
|
||||||
|
Qcur = ggml_rope_ext(
|
||||||
|
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
|
||||||
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
||||||
|
ext_factor, attn_factor, beta_fast, beta_slow
|
||||||
|
);
|
||||||
|
cb(Qcur, "Qcur", il);
|
||||||
|
|
||||||
|
Kcur = ggml_rope_ext(
|
||||||
|
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
|
||||||
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
||||||
|
ext_factor, attn_factor, beta_fast, beta_slow
|
||||||
|
);
|
||||||
|
cb(Kcur, "Kcur", il);
|
||||||
|
|
||||||
|
cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
|
||||||
|
model.layers[il].wo, NULL,
|
||||||
|
Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
||||||
|
}
|
||||||
|
|
||||||
|
if (il == n_layer - 1) {
|
||||||
|
// skip computing output for unused tokens
|
||||||
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||||
|
n_tokens = n_outputs;
|
||||||
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||||
|
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
||||||
|
}
|
||||||
|
|
||||||
|
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
||||||
|
cb(ffn_inp, "ffn_inp", il);
|
||||||
|
|
||||||
|
// feed-forward network
|
||||||
|
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
||||||
|
model.layers[il].ffn_norm, NULL,
|
||||||
|
LLM_NORM_RMS, cb, il);
|
||||||
|
cb(cur, "ffn_norm", il);
|
||||||
|
|
||||||
|
cur = llm_build_ffn(ctx0, cur,
|
||||||
|
model.layers[il].ffn_up, NULL,
|
||||||
|
model.layers[il].ffn_gate, NULL,
|
||||||
|
model.layers[il].ffn_down, NULL,
|
||||||
|
NULL,
|
||||||
|
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
|
||||||
|
cb(cur, "ffn_out", il);
|
||||||
|
|
||||||
|
struct ggml_tensor * ffn_out = ggml_add(ctx0, cur, ffn_inp);
|
||||||
|
cb(ffn_out, "ffn_out", il);
|
||||||
|
|
||||||
|
// MoE
|
||||||
|
cur = llm_build_norm(ctx0, inpSA, hparams,
|
||||||
|
model.layers[il].ffn_norm_exps, NULL,
|
||||||
|
LLM_NORM_RMS, cb, il);
|
||||||
|
cb(cur, "ffn_norm_exps", il);
|
||||||
|
|
||||||
|
cur = llm_build_moe_ffn(ctx0, cur,
|
||||||
|
model.layers[il].ffn_gate_inp,
|
||||||
|
model.layers[il].ffn_up_exps,
|
||||||
|
model.layers[il].ffn_gate_exps,
|
||||||
|
model.layers[il].ffn_down_exps,
|
||||||
|
n_expert, n_expert_used,
|
||||||
|
LLM_FFN_SILU, true,
|
||||||
|
cb, il);
|
||||||
|
cb(cur, "ffn_moe_out", il);
|
||||||
|
|
||||||
|
cur = ggml_add(ctx0, cur, ffn_out);
|
||||||
|
cb(cur, "ffn_out", il);
|
||||||
|
|
||||||
|
ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
|
||||||
|
if (layer_dir != nullptr) {
|
||||||
|
cur = ggml_add(ctx0, cur, layer_dir);
|
||||||
|
}
|
||||||
|
cb(cur, "l_out", il);
|
||||||
|
|
||||||
|
// input for next layer
|
||||||
|
inpL = cur;
|
||||||
|
}
|
||||||
|
|
||||||
|
cur = inpL;
|
||||||
|
|
||||||
|
cur = llm_build_norm(ctx0, cur, hparams,
|
||||||
|
model.output_norm, NULL,
|
||||||
|
LLM_NORM_RMS, cb, -1);
|
||||||
|
cb(cur, "result_norm", -1);
|
||||||
|
|
||||||
|
// lm_head
|
||||||
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
||||||
|
cb(cur, "result_output", -1);
|
||||||
|
|
||||||
|
ggml_build_forward_expand(gf, cur);
|
||||||
|
|
||||||
|
return gf;
|
||||||
|
}
|
||||||
};
|
};
|
||||||
|
|
||||||
static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
|
static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
|
||||||
@ -11004,6 +11219,10 @@ static struct ggml_cgraph * llama_build_graph(
|
|||||||
{
|
{
|
||||||
result = llm.build_gptneox();
|
result = llm.build_gptneox();
|
||||||
} break;
|
} break;
|
||||||
|
case LLM_ARCH_ARCTIC:
|
||||||
|
{
|
||||||
|
result = llm.build_arctic();
|
||||||
|
} break;
|
||||||
default:
|
default:
|
||||||
GGML_ASSERT(false);
|
GGML_ASSERT(false);
|
||||||
}
|
}
|
||||||
@ -16015,6 +16234,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
|
|||||||
case LLM_ARCH_XVERSE:
|
case LLM_ARCH_XVERSE:
|
||||||
case LLM_ARCH_COMMAND_R:
|
case LLM_ARCH_COMMAND_R:
|
||||||
case LLM_ARCH_OLMO:
|
case LLM_ARCH_OLMO:
|
||||||
|
case LLM_ARCH_ARCTIC:
|
||||||
return LLAMA_ROPE_TYPE_NORM;
|
return LLAMA_ROPE_TYPE_NORM;
|
||||||
|
|
||||||
// the pairs of head values are offset by n_rot/2
|
// the pairs of head values are offset by n_rot/2
|
||||||
|
Loading…
Reference in New Issue
Block a user