mirror of
https://github.com/ggerganov/llama.cpp.git
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gguf-py, convert-hf : model conversion support for T5 and FLAN-T5 model variants (#5763)
* gguf-py : add T5 model architecture * gguf-py : add separate tensors for encoder and decoder * gguf-py : add new model header parameters: decoder_start_token_id, attention.relative_buckets_count, tokenizer.ggml.remove_extra_whitespaces, tokenizer.ggml.precompiled_charsmap * convert-hf : add model conversion support for T5ForConditionalGeneration and T5WithLMHeadModel --------- Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
This commit is contained in:
fairydreaming
GitHub
parent
95f57bb5d5
commit
de0d6a68ac
@ -80,7 +80,7 @@ class Model:
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if not self.is_safetensors:
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self.part_names = Model.get_model_part_names(self.dir_model, "pytorch_model", ".bin")
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self.hparams = Model.load_hparams(self.dir_model)
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self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"])
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self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer", "num_layers"])
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self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
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self.tensor_names = None
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if self.ftype == gguf.LlamaFileType.GUESSED:
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@ -2771,6 +2771,124 @@ class DeepseekV2Model(Model):
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raise ValueError(f"Unprocessed experts: {experts}")
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@Model.register("T5ForConditionalGeneration")
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@Model.register("T5WithLMHeadModel")
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class T5Model(Model):
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model_arch = gguf.MODEL_ARCH.T5
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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 / '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()
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sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
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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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assert sentencepiece_model.trainer_spec.model_type == 1 # UNIGRAM
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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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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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self.gguf_writer.add_name("T5")
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self.gguf_writer.add_context_length(self.hparams["n_positions"])
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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_decoder_start_token_id(self.hparams["decoder_start_token_id"])
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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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# Sometimes T5 and Flan-T5 based models contain "encoder.embed_tokens.weight" tensor or
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# "decoder.embed_tokens.weight" tensors that are duplicates of "shared.weight" tensor
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# To prevent errors caused by an unnecessary unmapped tensor, skip both of them and use only "shared.weight".
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if name == "decoder.embed_tokens.weight" or name == "encoder.embed_tokens.weight":
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logger.debug(f"Skipping 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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###### CONVERSION LOGIC ######
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@ -49,6 +49,7 @@ class Keys:
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EXPERT_WEIGHTS_SCALE = "{arch}.expert_weights_scale"
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POOLING_TYPE = "{arch}.pooling_type"
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LOGIT_SCALE = "{arch}.logit_scale"
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DECODER_START_TOKEN_ID = "{arch}.decoder_start_token_id"
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class Attention:
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HEAD_COUNT = "{arch}.attention.head_count"
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@ -62,6 +63,7 @@ class Keys:
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CAUSAL = "{arch}.attention.causal"
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Q_LORA_RANK = "{arch}.attention.q_lora_rank"
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KV_LORA_RANK = "{arch}.attention.kv_lora_rank"
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REL_BUCKETS_COUNT = "{arch}.attention.relative_buckets_count"
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class Rope:
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DIMENSION_COUNT = "{arch}.rope.dimension_count"
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@ -97,6 +99,8 @@ class Keys:
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ADD_BOS = "tokenizer.ggml.add_bos_token"
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ADD_EOS = "tokenizer.ggml.add_eos_token"
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ADD_PREFIX = "tokenizer.ggml.add_space_prefix"
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REMOVE_EXTRA_WS = "tokenizer.ggml.remove_extra_whitespaces"
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PRECOMPILED_CHARSMAP = "tokenizer.ggml.precompiled_charsmap"
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HF_JSON = "tokenizer.huggingface.json"
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RWKV = "tokenizer.rwkv.world"
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CHAT_TEMPLATE = "tokenizer.chat_template"
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@ -150,6 +154,7 @@ class MODEL_ARCH(IntEnum):
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ARCTIC = auto()
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DEEPSEEK2 = auto()
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BITNET = auto()
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T5 = auto()
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class MODEL_TENSOR(IntEnum):
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@ -203,6 +208,34 @@ class MODEL_TENSOR(IntEnum):
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ATTN_KV_A_NORM = auto()
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FFN_SUB_NORM = auto()
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ATTN_SUB_NORM = auto()
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DEC_ATTN_NORM = auto()
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DEC_ATTN_Q = auto()
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DEC_ATTN_K = auto()
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DEC_ATTN_V = auto()
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DEC_ATTN_OUT = auto()
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DEC_ATTN_REL_B = auto()
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DEC_CROSS_ATTN_NORM = auto()
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DEC_CROSS_ATTN_Q = auto()
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DEC_CROSS_ATTN_K = auto()
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DEC_CROSS_ATTN_V = auto()
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DEC_CROSS_ATTN_OUT = auto()
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DEC_CROSS_ATTN_REL_B = auto()
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DEC_FFN_NORM = auto()
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DEC_FFN_GATE = auto()
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DEC_FFN_DOWN = auto()
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DEC_FFN_UP = auto()
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DEC_OUTPUT_NORM = auto()
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ENC_ATTN_NORM = auto()
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ENC_ATTN_Q = auto()
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ENC_ATTN_K = auto()
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ENC_ATTN_V = auto()
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ENC_ATTN_OUT = auto()
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ENC_ATTN_REL_B = auto()
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ENC_FFN_NORM = auto()
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ENC_FFN_GATE = auto()
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ENC_FFN_DOWN = auto()
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ENC_FFN_UP = auto()
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ENC_OUTPUT_NORM = auto()
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MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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@ -241,6 +274,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.ARCTIC: "arctic",
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MODEL_ARCH.DEEPSEEK2: "deepseek2",
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MODEL_ARCH.BITNET: "bitnet",
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MODEL_ARCH.T5: "t5",
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}
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TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
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@ -294,6 +328,34 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
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MODEL_TENSOR.ATTN_KV_A_NORM: "blk.{bid}.attn_kv_a_norm",
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MODEL_TENSOR.ATTN_SUB_NORM: "blk.{bid}.attn_sub_norm",
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MODEL_TENSOR.FFN_SUB_NORM: "blk.{bid}.ffn_sub_norm",
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MODEL_TENSOR.DEC_ATTN_NORM: "dec.blk.{bid}.attn_norm",
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MODEL_TENSOR.DEC_ATTN_Q: "dec.blk.{bid}.attn_q",
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MODEL_TENSOR.DEC_ATTN_K: "dec.blk.{bid}.attn_k",
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MODEL_TENSOR.DEC_ATTN_V: "dec.blk.{bid}.attn_v",
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MODEL_TENSOR.DEC_ATTN_OUT: "dec.blk.{bid}.attn_o",
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MODEL_TENSOR.DEC_ATTN_REL_B: "dec.blk.{bid}.attn_rel_b",
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MODEL_TENSOR.DEC_CROSS_ATTN_NORM: "dec.blk.{bid}.cross_attn_norm",
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MODEL_TENSOR.DEC_CROSS_ATTN_Q: "dec.blk.{bid}.cross_attn_q",
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MODEL_TENSOR.DEC_CROSS_ATTN_K: "dec.blk.{bid}.cross_attn_k",
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MODEL_TENSOR.DEC_CROSS_ATTN_V: "dec.blk.{bid}.cross_attn_v",
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MODEL_TENSOR.DEC_CROSS_ATTN_OUT: "dec.blk.{bid}.cross_attn_o",
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MODEL_TENSOR.DEC_CROSS_ATTN_REL_B: "dec.blk.{bid}.cross_attn_rel_b",
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MODEL_TENSOR.DEC_FFN_NORM: "dec.blk.{bid}.ffn_norm",
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MODEL_TENSOR.DEC_FFN_GATE: "dec.blk.{bid}.ffn_gate",
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MODEL_TENSOR.DEC_FFN_DOWN: "dec.blk.{bid}.ffn_down",
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MODEL_TENSOR.DEC_FFN_UP: "dec.blk.{bid}.ffn_up",
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MODEL_TENSOR.DEC_OUTPUT_NORM: "dec.output_norm",
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MODEL_TENSOR.ENC_ATTN_NORM: "enc.blk.{bid}.attn_norm",
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MODEL_TENSOR.ENC_ATTN_Q: "enc.blk.{bid}.attn_q",
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MODEL_TENSOR.ENC_ATTN_K: "enc.blk.{bid}.attn_k",
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MODEL_TENSOR.ENC_ATTN_V: "enc.blk.{bid}.attn_v",
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MODEL_TENSOR.ENC_ATTN_OUT: "enc.blk.{bid}.attn_o",
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MODEL_TENSOR.ENC_ATTN_REL_B: "enc.blk.{bid}.attn_rel_b",
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MODEL_TENSOR.ENC_FFN_NORM: "enc.blk.{bid}.ffn_norm",
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MODEL_TENSOR.ENC_FFN_GATE: "enc.blk.{bid}.ffn_gate",
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MODEL_TENSOR.ENC_FFN_DOWN: "enc.blk.{bid}.ffn_down",
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MODEL_TENSOR.ENC_FFN_UP: "enc.blk.{bid}.ffn_up",
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MODEL_TENSOR.ENC_OUTPUT_NORM: "enc.output_norm",
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}
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MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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@ -829,6 +891,38 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.ATTN_SUB_NORM,
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MODEL_TENSOR.FFN_SUB_NORM,
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],
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MODEL_ARCH.T5: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT,
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MODEL_TENSOR.DEC_ATTN_NORM,
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MODEL_TENSOR.DEC_ATTN_Q,
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MODEL_TENSOR.DEC_ATTN_K,
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MODEL_TENSOR.DEC_ATTN_V,
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MODEL_TENSOR.DEC_ATTN_OUT,
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MODEL_TENSOR.DEC_ATTN_REL_B,
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MODEL_TENSOR.DEC_CROSS_ATTN_NORM,
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MODEL_TENSOR.DEC_CROSS_ATTN_Q,
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MODEL_TENSOR.DEC_CROSS_ATTN_K,
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MODEL_TENSOR.DEC_CROSS_ATTN_V,
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MODEL_TENSOR.DEC_CROSS_ATTN_OUT,
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MODEL_TENSOR.DEC_CROSS_ATTN_REL_B,
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MODEL_TENSOR.DEC_FFN_NORM,
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MODEL_TENSOR.DEC_FFN_GATE,
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MODEL_TENSOR.DEC_FFN_DOWN,
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MODEL_TENSOR.DEC_FFN_UP,
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MODEL_TENSOR.DEC_OUTPUT_NORM,
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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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# TODO
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}
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@ -400,6 +400,9 @@ class GGUFWriter:
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def add_parallel_residual(self, use: bool) -> None:
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self.add_bool(Keys.LLM.USE_PARALLEL_RESIDUAL.format(arch=self.arch), use)
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def add_decoder_start_token_id(self, id: int) -> None:
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self.add_uint32(Keys.LLM.DECODER_START_TOKEN_ID.format(arch=self.arch), id)
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def add_head_count(self, count: int) -> None:
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self.add_uint32(Keys.Attention.HEAD_COUNT.format(arch=self.arch), count)
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@ -448,6 +451,9 @@ class GGUFWriter:
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def add_kv_lora_rank(self, length: int) -> None:
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self.add_uint32(Keys.Attention.KV_LORA_RANK.format(arch=self.arch), length)
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def add_relative_attn_buckets_count(self, value: int) -> None:
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self.add_uint32(Keys.Attention.REL_BUCKETS_COUNT.format(arch=self.arch), value)
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def add_pooling_type(self, value: PoolingType) -> None:
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self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value)
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@ -538,6 +544,12 @@ class GGUFWriter:
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def add_add_space_prefix(self, value: bool) -> None:
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self.add_bool(Keys.Tokenizer.ADD_PREFIX, value)
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def add_remove_extra_whitespaces(self, value: bool) -> None:
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self.add_bool(Keys.Tokenizer.REMOVE_EXTRA_WS, value)
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def add_precompiled_charsmap(self, charsmap: Sequence[bytes]) -> None:
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self.add_array(Keys.Tokenizer.PRECOMPILED_CHARSMAP, charsmap)
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def add_chat_template(self, value: str | Sequence[Mapping[str, str]]) -> None:
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if not isinstance(value, str):
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template_default = None
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@ -599,6 +611,9 @@ class GGUFWriter:
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kv_data += self._pack("Q", len(encoded_val))
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kv_data += encoded_val
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elif vtype == GGUFValueType.ARRAY and isinstance(val, Sequence) and val:
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if isinstance(val, bytes):
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ltype = GGUFValueType.UINT8
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else:
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ltype = GGUFValueType.get_type(val[0])
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if not all(GGUFValueType.get_type(i) is ltype for i in val[1:]):
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raise ValueError("All items in a GGUF array should be of the same type")
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@ -24,6 +24,7 @@ class TensorNameMap:
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"backbone.embedding", # mamba
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"backbone.embeddings", # mamba-hf
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"transformer.in_out_embed", # Grok
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"shared", # t5
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),
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# Token type embeddings
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@ -421,6 +422,120 @@ class TensorNameMap:
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MODEL_TENSOR.FFN_SUB_NORM: (
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"model.layers.{bid}.mlp.ffn_layernorm", # bitnet
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),
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MODEL_TENSOR.DEC_ATTN_NORM: (
|
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"decoder.block.{bid}.layer.0.layer_norm", # t5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.DEC_ATTN_Q: (
|
||||
"decoder.block.{bid}.layer.0.SelfAttention.q", # t5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.DEC_ATTN_K: (
|
||||
"decoder.block.{bid}.layer.0.SelfAttention.k", # t5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.DEC_ATTN_V: (
|
||||
"decoder.block.{bid}.layer.0.SelfAttention.v", # t5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.DEC_ATTN_OUT: (
|
||||
"decoder.block.{bid}.layer.0.SelfAttention.o", # t5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.DEC_ATTN_REL_B: (
|
||||
"decoder.block.{bid}.layer.0.SelfAttention.relative_attention_bias", # t5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.DEC_CROSS_ATTN_NORM: (
|
||||
"decoder.block.{bid}.layer.1.layer_norm", # t5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.DEC_CROSS_ATTN_Q: (
|
||||
"decoder.block.{bid}.layer.1.EncDecAttention.q", # t5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.DEC_CROSS_ATTN_K: (
|
||||
"decoder.block.{bid}.layer.1.EncDecAttention.k", # t5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.DEC_CROSS_ATTN_V: (
|
||||
"decoder.block.{bid}.layer.1.EncDecAttention.v", # t5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.DEC_CROSS_ATTN_OUT: (
|
||||
"decoder.block.{bid}.layer.1.EncDecAttention.o", # t5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.DEC_CROSS_ATTN_REL_B: (
|
||||
"decoder.block.{bid}.layer.1.EncDecAttention.relative_attention_bias", # t5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.DEC_FFN_NORM: (
|
||||
"decoder.block.{bid}.layer.2.layer_norm", # t5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.DEC_FFN_GATE: (
|
||||
"decoder.block.{bid}.layer.2.DenseReluDense.wi_0", # flan-t5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.DEC_FFN_UP: (
|
||||
"decoder.block.{bid}.layer.2.DenseReluDense.wi", # t5
|
||||
"decoder.block.{bid}.layer.2.DenseReluDense.wi_1", # flan-t5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.DEC_FFN_DOWN: (
|
||||
"decoder.block.{bid}.layer.2.DenseReluDense.wo", # t5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.DEC_OUTPUT_NORM: (
|
||||
"decoder.final_layer_norm", # t5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.ENC_ATTN_NORM: (
|
||||
"encoder.block.{bid}.layer.0.layer_norm", # t5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.ENC_ATTN_Q: (
|
||||
"encoder.block.{bid}.layer.0.SelfAttention.q", # t5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.ENC_ATTN_K: (
|
||||
"encoder.block.{bid}.layer.0.SelfAttention.k", # t5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.ENC_ATTN_V: (
|
||||
"encoder.block.{bid}.layer.0.SelfAttention.v", # t5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.ENC_ATTN_OUT: (
|
||||
"encoder.block.{bid}.layer.0.SelfAttention.o", # t5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.ENC_ATTN_REL_B: (
|
||||
"encoder.block.{bid}.layer.0.SelfAttention.relative_attention_bias", # t5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.ENC_FFN_NORM: (
|
||||
"encoder.block.{bid}.layer.1.layer_norm", # t5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.ENC_FFN_GATE: (
|
||||
"encoder.block.{bid}.layer.1.DenseReluDense.wi_0", # flan-t5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.ENC_FFN_UP: (
|
||||
"encoder.block.{bid}.layer.1.DenseReluDense.wi", # t5
|
||||
"encoder.block.{bid}.layer.1.DenseReluDense.wi_1", # flan-t5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.ENC_FFN_DOWN: (
|
||||
"encoder.block.{bid}.layer.1.DenseReluDense.wo", # t5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.ENC_OUTPUT_NORM: (
|
||||
"encoder.final_layer_norm", # t5
|
||||
),
|
||||
}
|
||||
|
||||
# architecture-specific block mappings
|
||||
|
||||
Loading…
Reference in New Issue
Block a user