Support xverse model convert to gguf format.
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willhe
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2 changed files with 158 additions and 0 deletions
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@ -201,6 +201,7 @@ class Model(ABC):
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try:
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return cls._model_classes[arch]
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except KeyError:
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print(f"{cls._model_classes}")
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raise NotImplementedError(f'Architecture {arch!r} not supported!') from None
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def _is_model_safetensors(self) -> bool:
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@ -763,7 +764,142 @@ class BaichuanModel(Model):
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r = weights.shape[0] // 3
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return weights[r * n_part:r * n_part + r, ...]
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@Model.register("XverseForCausalLM")
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class XverseModel(Model):
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model_arch = gguf.MODEL_ARCH.XVERSE
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def set_vocab(self):
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assert (self.dir_model / "tokenizer.json").is_file()
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dir_model = self.dir_model
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hparams = self.hparams
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tokens: list[bytearray] = []
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toktypes: list[int] = []
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(dir_model)
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vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
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assert max(tokenizer.vocab.values()) < vocab_size
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reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}
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added_vocab = tokenizer.get_added_vocab()
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for i in range(vocab_size):
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if i not in reverse_vocab:
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pad_token = f"[PAD{i}]".encode('utf-8')
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tokens.append(bytearray(pad_token))
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toktypes.append(gguf.TokenType.USER_DEFINED)
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elif reverse_vocab[i] in added_vocab:
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tokens.append(reverse_vocab[i])
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if tokenizer.added_tokens_decoder[i].special:
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toktypes.append(gguf.TokenType.CONTROL)
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else:
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toktypes.append(gguf.TokenType.USER_DEFINED)
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else:
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tokens.append(reverse_vocab[i])
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toktypes.append(gguf.TokenType.NORMAL)
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self.gguf_writer.add_tokenizer_model("xverse")
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self.gguf_writer.add_token_list(tokens)
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self.gguf_writer.add_token_types(toktypes)
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special_vocab = gguf.SpecialVocab(dir_model, load_merges=True)
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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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block_count = self.hparams["num_hidden_layers"]
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head_count = self.hparams["num_attention_heads"]
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head_count_kv = self.hparams.get("num_key_value_heads", head_count)
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hf_repo = self.hparams.get("_name_or_path", "")
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ctx_length = 0
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if "max_sequence_length" in self.hparams:
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ctx_length = self.hparams["max_sequence_length"]
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elif "max_position_embeddings" in self.hparams:
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ctx_length = self.hparams["max_position_embeddings"]
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elif "model_max_length" in self.hparams:
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ctx_length = self.hparams["model_max_length"]
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else:
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print("gguf: can not find ctx length parameter.")
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sys.exit()
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self.gguf_writer.add_name(self.dir_model.name)
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self.gguf_writer.add_source_hf_repo(hf_repo)
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self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
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self.gguf_writer.add_context_length(ctx_length)
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self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
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self.gguf_writer.add_block_count(block_count)
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self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
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self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
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self.gguf_writer.add_head_count(head_count)
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self.gguf_writer.add_head_count_kv(head_count_kv)
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self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
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if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
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if self.hparams["rope_scaling"].get("type") == "linear":
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self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
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self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
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def write_tensors(self):
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# Collect tensors from generator object
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model_kv = dict(self.get_tensors())
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block_count = self.hparams["num_hidden_layers"]
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head_count = self.hparams["num_attention_heads"]
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tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
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head_count_kv = self.hparams.get("num_key_value_heads", head_count)
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for name, data_torch in model_kv.items():
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# we don't need these
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if name.endswith(".rotary_emb.inv_freq"):
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continue
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old_dtype = data_torch.dtype
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# convert any unsupported data types to float32
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if data_torch.dtype not in (torch.float16, torch.float32):
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data_torch = data_torch.to(torch.float32)
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# HF models permute some of the tensors, so we need to undo that
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if name.endswith(("q_proj.weight")):
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data_torch = self._reverse_hf_permute(data_torch, head_count, head_count)
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if name.endswith(("k_proj.weight")):
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data_torch = self._reverse_hf_permute(data_torch, head_count, head_count_kv)
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data = data_torch.squeeze().numpy()
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# map tensor names
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new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
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if new_name is None:
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print(f"Can not map tensor {name!r}")
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sys.exit()
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n_dims = len(data.shape)
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data_dtype = data.dtype
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# if f32 desired, convert any float16 to float32
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if self.ftype == 0 and data_dtype == np.float16:
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data = data.astype(np.float32)
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# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
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if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
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data = data.astype(np.float32)
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# if f16 desired, convert any float32 2-dim weight tensors to float16
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if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
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data = data.astype(np.float16)
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print(f"{name} -> {new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
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self.gguf_writer.add_tensor(new_name, data)
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def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
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if n_kv_head is not None and n_head != n_kv_head:
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n_head //= n_kv_head
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return (
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weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
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.swapaxes(1, 2)
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.reshape(weights.shape)
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)
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@Model.register("FalconForCausalLM", "RWForCausalLM")
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class FalconModel(Model):
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model_arch = gguf.MODEL_ARCH.FALCON
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@ -120,6 +120,7 @@ class MODEL_ARCH(IntEnum):
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GEMMA = auto()
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STARCODER2 = auto()
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MAMBA = auto()
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XVERSE = auto()
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class MODEL_TENSOR(IntEnum):
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@ -186,6 +187,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.GEMMA: "gemma",
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MODEL_ARCH.STARCODER2: "starcoder2",
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MODEL_ARCH.MAMBA: "mamba",
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MODEL_ARCH.XVERSE: "xverse",
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}
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TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
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@ -578,6 +580,22 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.SSM_D,
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MODEL_TENSOR.SSM_OUT,
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],
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MODEL_ARCH.XVERSE: [
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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_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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],
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# TODO
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}
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@ -610,6 +628,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.ROPE_FREQS,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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],
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MODEL_ARCH.XVERSE: [
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MODEL_TENSOR.ROPE_FREQS,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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],
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}
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#
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