mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-10-30 14:40:16 +01:00
4fbd8098e6
This commit adds special token metadata for Fill-In-the-Middle (FIM)/Infill to the GGUF model. The motivation for this is that currently there is support for CodeLlama but other models exist now like CodeGemma, but the different models use different token ids for the special tokens and this commit allows for supporting multiple models. Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
2542 lines
110 KiB
Python
Executable File
2542 lines
110 KiB
Python
Executable File
#!/usr/bin/env python3
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from __future__ import annotations
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import argparse
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import contextlib
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import json
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import os
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import re
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import sys
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from abc import ABC, abstractmethod
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from enum import IntEnum
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from pathlib import Path
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from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterator, Sequence, TypeVar, cast
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import numpy as np
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import torch
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if TYPE_CHECKING:
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from torch import Tensor
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if 'NO_LOCAL_GGUF' not in os.environ:
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sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
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import gguf
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from convert import LlamaHfVocab, permute
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###### MODEL DEFINITIONS ######
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class SentencePieceTokenTypes(IntEnum):
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NORMAL = 1
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UNKNOWN = 2
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CONTROL = 3
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USER_DEFINED = 4
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UNUSED = 5
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BYTE = 6
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AnyModel = TypeVar("AnyModel", bound="type[Model]")
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class Model(ABC):
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_model_classes: dict[str, type[Model]] = {}
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def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool, use_temp_file: bool):
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self.dir_model = dir_model
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self.ftype = ftype
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self.fname_out = fname_out
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self.is_big_endian = is_big_endian
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self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE
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self.use_temp_file = use_temp_file
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self.is_safetensors = self._is_model_safetensors()
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self.num_parts = Model.count_model_parts(self.dir_model, ".safetensors" if self.is_safetensors else ".bin")
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self.part_names = self._get_part_names()
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self.hparams = Model.load_hparams(self.dir_model)
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self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file)
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self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"])
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@property
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@abstractmethod
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def model_arch(self) -> gguf.MODEL_ARCH:
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pass
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def find_hparam(self, keys: Sequence[str], optional: bool = False) -> Any:
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key = next((k for k in keys if k in self.hparams), None)
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if key is not None:
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return self.hparams[key]
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if optional:
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return None
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raise KeyError(f"could not find any of: {keys}")
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def set_vocab(self):
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self._set_vocab_gpt2()
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def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
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for part_name in self.part_names:
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print(f"gguf: loading model part '{part_name}'")
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ctx: ContextManager[Any]
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if self.is_safetensors:
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from safetensors import safe_open
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ctx = cast(ContextManager[Any], safe_open(self.dir_model / part_name, framework="pt", device="cpu"))
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else:
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ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", mmap=True, weights_only=True))
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with ctx as model_part:
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for name in model_part.keys():
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data = model_part.get_tensor(name) if self.is_safetensors else model_part[name]
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yield name, data
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def set_gguf_parameters(self):
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self.gguf_writer.add_name(self.dir_model.name)
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self.gguf_writer.add_block_count(self.block_count)
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if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx"], optional=True)) is not None:
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self.gguf_writer.add_context_length(n_ctx)
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print(f"gguf: context length = {n_ctx}")
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n_embd = self.find_hparam(["hidden_size", "n_embd"])
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self.gguf_writer.add_embedding_length(n_embd)
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print(f"gguf: embedding length = {n_embd}")
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if (n_ff := self.find_hparam(["intermediate_size", "n_inner"], optional=True)) is not None:
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self.gguf_writer.add_feed_forward_length(n_ff)
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print(f"gguf: feed forward length = {n_ff}")
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n_head = self.find_hparam(["num_attention_heads", "n_head"])
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self.gguf_writer.add_head_count(n_head)
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print(f"gguf: head count = {n_head}")
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if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None:
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self.gguf_writer.add_head_count_kv(n_head_kv)
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print(f"gguf: key-value head count = {n_head_kv}")
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if (rope_theta := self.hparams.get("rope_theta")) is not None:
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self.gguf_writer.add_rope_freq_base(rope_theta)
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print(f"gguf: rope theta = {rope_theta}")
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if (f_rms_eps := self.hparams.get("rms_norm_eps")) is not None:
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self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps)
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print(f"gguf: rms norm epsilon = {f_rms_eps}")
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if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"], optional=True)) is not None:
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self.gguf_writer.add_layer_norm_eps(f_norm_eps)
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print(f"gguf: layer norm epsilon = {f_norm_eps}")
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if (n_experts := self.hparams.get("num_local_experts")) is not None:
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self.gguf_writer.add_expert_count(n_experts)
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print(f"gguf: expert count = {n_experts}")
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if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None:
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self.gguf_writer.add_expert_used_count(n_experts_used)
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print(f"gguf: experts used count = {n_experts_used}")
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self.gguf_writer.add_file_type(self.ftype)
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print(f"gguf: file type = {self.ftype}")
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def write_tensors(self):
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block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
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tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
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for name, data_torch in self.get_tensors():
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# we don't need these
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if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.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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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 or new_name.endswith("_norm.weight")):
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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"{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 write(self):
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self.write_tensors()
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self.gguf_writer.write_header_to_file()
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self.gguf_writer.write_kv_data_to_file()
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self.gguf_writer.write_tensors_to_file()
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self.gguf_writer.close()
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def write_vocab(self):
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self.gguf_writer.write_header_to_file()
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self.gguf_writer.write_kv_data_to_file()
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self.gguf_writer.close()
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@staticmethod
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def count_model_parts(dir_model: Path, prefix: str) -> int:
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num_parts = 0
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for filename in os.listdir(dir_model):
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if filename.endswith(prefix):
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num_parts += 1
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return num_parts
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@staticmethod
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def load_hparams(dir_model):
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with open(dir_model / "config.json", "r", encoding="utf-8") as f:
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return json.load(f)
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@classmethod
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def register(cls, *names: str) -> Callable[[AnyModel], AnyModel]:
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assert names
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def func(modelcls: type[Model]):
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for name in names:
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cls._model_classes[name] = modelcls
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return modelcls
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return func
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@classmethod
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def from_model_architecture(cls, arch):
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try:
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return cls._model_classes[arch]
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except KeyError:
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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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return Model.count_model_parts(self.dir_model, ".safetensors") > 0
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def _get_part_names(self):
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if self.is_safetensors:
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if self.num_parts == 1: # there's only one .safetensors file
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return ("model.safetensors",)
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return (f"model-{n:05}-of-{self.num_parts:05}.safetensors" for n in range(1, self.num_parts + 1))
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if self.num_parts == 1: # there's only one .bin file
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return ("pytorch_model.bin",)
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return (f"pytorch_model-{n:05}-of-{self.num_parts:05}.bin" for n in range(1, self.num_parts + 1))
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# used for GPT-2 BPE and WordPiece vocabs
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def get_basic_vocab(self) -> tuple[list[str], list[int]]:
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tokens: list[str] = []
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toktypes: list[int] = []
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
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vocab_size = self.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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tokens.append(f"[PAD{i}]")
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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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return tokens, toktypes
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def _set_vocab_gpt2(self) -> None:
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tokens, toktypes = self.get_basic_vocab()
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self.gguf_writer.add_tokenizer_model("gpt2")
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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(self.dir_model, load_merges=True)
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special_vocab.add_to_gguf(self.gguf_writer)
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def _set_vocab_qwen(self):
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dir_model = self.dir_model
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hparams = self.hparams
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tokens: list[str] = []
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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, trust_remote_code=True)
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vocab_size = hparams["vocab_size"]
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assert max(tokenizer.get_vocab().values()) < vocab_size
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merges = []
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vocab = {}
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mergeable_ranks = tokenizer.mergeable_ranks
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for token, rank in mergeable_ranks.items():
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vocab[QwenModel.token_bytes_to_string(token)] = rank
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if len(token) == 1:
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continue
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merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
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assert len(merged) == 2
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merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
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# for this kind of tokenizer, added_vocab is not a subset of vocab, so they need to be combined
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added_vocab = tokenizer.special_tokens
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reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in (vocab | added_vocab).items()}
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for i in range(vocab_size):
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if i not in reverse_vocab:
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tokens.append(f"[PAD{i}]")
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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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toktypes.append(gguf.TokenType.CONTROL)
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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("gpt2")
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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=False)
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special_vocab.merges = merges
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# only add special tokens when they were not already loaded from config.json
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if len(special_vocab.special_token_ids) == 0:
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special_vocab._set_special_token("bos", tokenizer.special_tokens["<|endoftext|>"])
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special_vocab._set_special_token("eos", tokenizer.special_tokens["<|endoftext|>"])
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# this one is usually not in config.json anyway
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special_vocab._set_special_token("unk", tokenizer.special_tokens["<|endoftext|>"])
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special_vocab.add_to_gguf(self.gguf_writer)
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def _set_vocab_sentencepiece(self):
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from sentencepiece import SentencePieceProcessor
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tokenizer_path = self.dir_model / 'tokenizer.model'
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tokens: list[bytes] = []
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scores: list[float] = []
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toktypes: list[int] = []
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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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tokenizer = SentencePieceProcessor(str(tokenizer_path))
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vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
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for token_id in range(tokenizer.vocab_size()):
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piece = tokenizer.id_to_piece(token_id)
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text = piece.encode("utf-8")
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score = tokenizer.get_score(token_id)
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toktype = SentencePieceTokenTypes.NORMAL
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if tokenizer.is_unknown(token_id):
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toktype = SentencePieceTokenTypes.UNKNOWN
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elif tokenizer.is_control(token_id):
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toktype = SentencePieceTokenTypes.CONTROL
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elif tokenizer.is_unused(token_id):
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toktype = SentencePieceTokenTypes.UNUSED
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elif tokenizer.is_byte(token_id):
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toktype = SentencePieceTokenTypes.BYTE
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tokens.append(text)
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scores.append(score)
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toktypes.append(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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key = key.encode("utf-8")
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if key not in tokens:
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tokens.append(key)
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scores.append(-1000.0)
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toktypes.append(SentencePieceTokenTypes.USER_DEFINED)
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assert len(tokens) == vocab_size
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self.gguf_writer.add_tokenizer_model("llama")
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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_vocab_llama_hf(self):
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vocab = LlamaHfVocab(self.dir_model)
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tokens = []
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scores = []
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toktypes = []
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for text, score, toktype in vocab.all_tokens():
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tokens.append(text)
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scores.append(score)
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toktypes.append(toktype)
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assert len(tokens) == vocab.vocab_size
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self.gguf_writer.add_tokenizer_model("llama")
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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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@Model.register("GPTNeoXForCausalLM")
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class GPTNeoXModel(Model):
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model_arch = gguf.MODEL_ARCH.GPTNEOX
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def set_gguf_parameters(self):
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block_count = self.hparams["num_hidden_layers"]
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self.gguf_writer.add_name(self.dir_model.name)
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self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
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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(
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int(self.hparams["rotary_pct"] * (self.hparams["hidden_size"] // self.hparams["num_attention_heads"])),
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)
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self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
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self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True))
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self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])
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@Model.register("BloomForCausalLM")
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|
class BloomModel(Model):
|
|
model_arch = gguf.MODEL_ARCH.BLOOM
|
|
|
|
def set_gguf_parameters(self):
|
|
self.gguf_writer.add_name("Bloom")
|
|
n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
|
|
n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
|
|
self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed))
|
|
self.gguf_writer.add_embedding_length(n_embed)
|
|
self.gguf_writer.add_feed_forward_length(4 * n_embed)
|
|
self.gguf_writer.add_block_count(self.hparams["n_layer"])
|
|
self.gguf_writer.add_head_count(n_head)
|
|
self.gguf_writer.add_head_count_kv(n_head)
|
|
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
|
|
self.gguf_writer.add_file_type(self.ftype)
|
|
|
|
def write_tensors(self):
|
|
block_count = self.hparams["n_layer"]
|
|
tensors = dict(self.get_tensors())
|
|
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
|
has_lm_head = True
|
|
n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
|
|
n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
|
|
|
|
for name, data_torch in tensors.items():
|
|
if "lm_head.weight" not in tensors.keys() and "output.weight" not in tensors.keys():
|
|
has_lm_head = False
|
|
|
|
name = re.sub(r'transformer\.', '', name)
|
|
|
|
old_dtype = data_torch.dtype
|
|
|
|
# convert any unsupported data types to float32
|
|
if data_torch.dtype not in (torch.float16, torch.float32):
|
|
data_torch = data_torch.to(torch.float32)
|
|
|
|
data = data_torch.squeeze().numpy()
|
|
|
|
if re.match(r"h\.\d+\.self_attention\.query_key_value\.weight", name):
|
|
# Map bloom-style qkv_linear to gpt-style qkv_linear
|
|
# bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252 # noqa
|
|
# gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312 # noqa
|
|
qkv_weights = data.reshape((n_head, 3, n_embed // n_head, n_embed))
|
|
data = np.concatenate(
|
|
(
|
|
qkv_weights[:, 0, :, :].reshape((-1, n_embed)),
|
|
qkv_weights[:, 1, :, :].reshape((-1, n_embed)),
|
|
qkv_weights[:, 2, :, :].reshape((-1, n_embed)),
|
|
),
|
|
axis=0,
|
|
)
|
|
print("re-format attention.linear_qkv.weight")
|
|
elif re.match(r"h\.\d+\.self_attention\.query_key_value\.bias", name):
|
|
qkv_bias = data.reshape((n_head, 3, n_embed // n_head))
|
|
data = np.concatenate(
|
|
(
|
|
qkv_bias[:, 0, :].reshape((n_embed,)),
|
|
qkv_bias[:, 1, :].reshape((n_embed,)),
|
|
qkv_bias[:, 2, :].reshape((n_embed,)),
|
|
),
|
|
axis=0,
|
|
)
|
|
print("re-format attention.linear_qkv.bias")
|
|
|
|
# map tensor names
|
|
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
|
if new_name is None:
|
|
print(f"Can not map tensor {name!r}")
|
|
sys.exit()
|
|
|
|
n_dims = len(data.shape)
|
|
data_dtype = data.dtype
|
|
|
|
# if f32 desired, convert any float16 to float32
|
|
if self.ftype == 0 and data_dtype == np.float16:
|
|
data = data.astype(np.float32)
|
|
|
|
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
|
|
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
|
|
data = data.astype(np.float32)
|
|
|
|
# if f16 desired, convert any float32 2-dim weight tensors to float16
|
|
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
|
data = data.astype(np.float16)
|
|
|
|
print(f"=> {new_name}, shape = {data.shape}, {old_dtype} --> {data.dtype}")
|
|
|
|
self.gguf_writer.add_tensor(new_name, data)
|
|
|
|
if not has_lm_head and name == "word_embeddings.weight":
|
|
self.gguf_writer.add_tensor("output.weight", data)
|
|
print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}")
|
|
|
|
|
|
@Model.register("MPTForCausalLM")
|
|
class MPTModel(Model):
|
|
model_arch = gguf.MODEL_ARCH.MPT
|
|
|
|
def set_vocab(self):
|
|
try:
|
|
self._set_vocab_gpt2()
|
|
except Exception:
|
|
# Fallback for SEA-LION model
|
|
self._set_vocab_sentencepiece()
|
|
self.gguf_writer.add_add_bos_token(False)
|
|
self.gguf_writer.add_pad_token_id(3)
|
|
self.gguf_writer.add_eos_token_id(1)
|
|
self.gguf_writer.add_unk_token_id(0)
|
|
|
|
def set_gguf_parameters(self):
|
|
block_count = self.hparams["n_layers"]
|
|
self.gguf_writer.add_name(self.dir_model.name)
|
|
self.gguf_writer.add_context_length(self.hparams["max_seq_len"])
|
|
self.gguf_writer.add_embedding_length(self.hparams["d_model"])
|
|
self.gguf_writer.add_block_count(block_count)
|
|
self.gguf_writer.add_feed_forward_length(4 * self.hparams["d_model"])
|
|
self.gguf_writer.add_head_count(self.hparams["n_heads"])
|
|
if kv_n_heads := self.hparams["attn_config"].get("kv_n_heads"):
|
|
self.gguf_writer.add_head_count_kv(kv_n_heads)
|
|
self.gguf_writer.add_layer_norm_eps(1e-5)
|
|
if self.hparams["attn_config"]["clip_qkv"] is not None:
|
|
self.gguf_writer.add_clamp_kqv(self.hparams["attn_config"]["clip_qkv"])
|
|
if self.hparams["attn_config"]["alibi"]:
|
|
self.gguf_writer.add_max_alibi_bias(self.hparams["attn_config"]["alibi_bias_max"])
|
|
else:
|
|
self.gguf_writer.add_max_alibi_bias(0.0)
|
|
|
|
def write_tensors(self):
|
|
block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers"))
|
|
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
|
for name, data_torch in self.get_tensors():
|
|
# we don't need these
|
|
if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")):
|
|
continue
|
|
|
|
old_dtype = data_torch.dtype
|
|
|
|
# convert any unsupported data types to float32
|
|
if data_torch.dtype not in (torch.float16, torch.float32):
|
|
data_torch = data_torch.to(torch.float32)
|
|
|
|
data = data_torch.squeeze().numpy()
|
|
|
|
# map tensor names
|
|
if "scales" in name:
|
|
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias", ".scales"))
|
|
if new_name is not None:
|
|
new_name = new_name.replace("scales", "act.scales")
|
|
else:
|
|
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
|
if new_name is None:
|
|
print(f"Can not map tensor {name!r}")
|
|
sys.exit()
|
|
|
|
n_dims = len(data.shape)
|
|
data_dtype = data.dtype
|
|
|
|
# if f32 desired, convert any float16 to float32
|
|
if self.ftype == 0 and data_dtype == np.float16:
|
|
data = data.astype(np.float32)
|
|
|
|
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
|
|
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
|
|
data = data.astype(np.float32)
|
|
|
|
# if f16 desired, convert any float32 2-dim weight tensors to float16
|
|
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
|
data = data.astype(np.float16)
|
|
|
|
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
|
|
|
self.gguf_writer.add_tensor(new_name, data)
|
|
|
|
|
|
@Model.register("OrionForCausalLM")
|
|
class OrionModel(Model):
|
|
model_arch = gguf.MODEL_ARCH.ORION
|
|
|
|
def set_vocab(self):
|
|
self._set_vocab_sentencepiece()
|
|
|
|
def set_gguf_parameters(self):
|
|
block_count = self.hparams["num_hidden_layers"]
|
|
head_count = self.hparams["num_attention_heads"]
|
|
head_count_kv = self.hparams.get("num_key_value_heads", head_count)
|
|
hf_repo = self.hparams.get("_name_or_path", "")
|
|
|
|
ctx_length = 0
|
|
if "max_sequence_length" in self.hparams:
|
|
ctx_length = self.hparams["max_sequence_length"]
|
|
elif "max_position_embeddings" in self.hparams:
|
|
ctx_length = self.hparams["max_position_embeddings"]
|
|
elif "model_max_length" in self.hparams:
|
|
ctx_length = self.hparams["model_max_length"]
|
|
else:
|
|
print("gguf: can not find ctx length parameter.")
|
|
sys.exit()
|
|
|
|
self.gguf_writer.add_file_type(self.ftype)
|
|
self.gguf_writer.add_name(self.dir_model.name)
|
|
self.gguf_writer.add_source_hf_repo(hf_repo)
|
|
self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
|
|
self.gguf_writer.add_context_length(ctx_length)
|
|
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
|
|
self.gguf_writer.add_block_count(block_count)
|
|
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
|
|
self.gguf_writer.add_head_count(head_count)
|
|
self.gguf_writer.add_head_count_kv(head_count_kv)
|
|
# note: config provides rms norm but it is actually layer norm
|
|
# ref: https://huggingface.co/OrionStarAI/Orion-14B-Chat/blob/276a17221ce42beb45f66fac657a41540e71f4f5/modeling_orion.py#L570-L571
|
|
self.gguf_writer.add_layer_norm_eps(self.hparams["rms_norm_eps"])
|
|
|
|
def write_tensors(self):
|
|
# Collect tensors from generator object
|
|
model_kv = dict(self.get_tensors())
|
|
block_count = self.hparams["num_hidden_layers"]
|
|
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
|
|
|
for name, data_torch in model_kv.items():
|
|
# we don't need these
|
|
if name.endswith(".rotary_emb.inv_freq"):
|
|
continue
|
|
|
|
old_dtype = data_torch.dtype
|
|
|
|
# convert any unsupported data types to float32
|
|
if data_torch.dtype not in (torch.float16, torch.float32):
|
|
data_torch = data_torch.to(torch.float32)
|
|
|
|
data = data_torch.squeeze().numpy()
|
|
|
|
# map tensor names
|
|
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
|
if new_name is None:
|
|
print(f"Can not map tensor {name!r}")
|
|
sys.exit()
|
|
|
|
n_dims = len(data.shape)
|
|
data_dtype = data.dtype
|
|
|
|
# if f32 desired, convert any float16 to float32
|
|
if self.ftype == 0 and data_dtype == np.float16:
|
|
data = data.astype(np.float32)
|
|
|
|
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
|
|
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
|
|
data = data.astype(np.float32)
|
|
|
|
# if f16 desired, convert any float32 2-dim weight tensors to float16
|
|
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
|
data = data.astype(np.float16)
|
|
|
|
print(f"{name} -> {new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
|
self.gguf_writer.add_tensor(new_name, data)
|
|
|
|
|
|
@Model.register("BaichuanForCausalLM", "BaiChuanForCausalLM")
|
|
class BaichuanModel(Model):
|
|
model_arch = gguf.MODEL_ARCH.BAICHUAN
|
|
|
|
def set_vocab(self):
|
|
self._set_vocab_sentencepiece()
|
|
|
|
def set_gguf_parameters(self):
|
|
block_count = self.hparams["num_hidden_layers"]
|
|
head_count = self.hparams["num_attention_heads"]
|
|
head_count_kv = self.hparams.get("num_key_value_heads", head_count)
|
|
hf_repo = self.hparams.get("_name_or_path", "")
|
|
|
|
ctx_length = 0
|
|
if "max_sequence_length" in self.hparams:
|
|
ctx_length = self.hparams["max_sequence_length"]
|
|
elif "max_position_embeddings" in self.hparams:
|
|
ctx_length = self.hparams["max_position_embeddings"]
|
|
elif "model_max_length" in self.hparams:
|
|
ctx_length = self.hparams["model_max_length"]
|
|
else:
|
|
print("gguf: can not find ctx length parameter.")
|
|
sys.exit()
|
|
|
|
self.gguf_writer.add_name(self.dir_model.name)
|
|
self.gguf_writer.add_source_hf_repo(hf_repo)
|
|
self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
|
|
self.gguf_writer.add_context_length(ctx_length)
|
|
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
|
|
self.gguf_writer.add_block_count(block_count)
|
|
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
|
|
self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
|
|
self.gguf_writer.add_head_count(head_count)
|
|
self.gguf_writer.add_head_count_kv(head_count_kv)
|
|
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
|
|
|
|
if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
|
|
if self.hparams["rope_scaling"].get("type") == "linear":
|
|
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
|
|
self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
|
|
|
|
def write_tensors(self):
|
|
# Collect tensors from generator object
|
|
model_kv = dict(self.get_tensors())
|
|
block_count = self.hparams["num_hidden_layers"]
|
|
head_count = self.hparams["num_attention_heads"]
|
|
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
|
head_count_kv = self.hparams.get("num_key_value_heads", head_count)
|
|
|
|
for i in range(block_count):
|
|
if (w := model_kv.get(f"model.layers.{i}.self_attn.W_pack.weight")) is not None:
|
|
print(f"Unpacking and permuting layer {i}")
|
|
model_kv[f"model.layers.{i}.self_attn.q_proj.weight"] = \
|
|
self._reverse_hf_permute_part(w, 0, head_count, head_count)
|
|
model_kv[f"model.layers.{i}.self_attn.k_proj.weight"] = \
|
|
self._reverse_hf_permute_part(w, 1, head_count, head_count_kv)
|
|
model_kv[f"model.layers.{i}.self_attn.v_proj.weight"] = \
|
|
self._reverse_hf_part(w, 2)
|
|
del model_kv[f"model.layers.{i}.self_attn.W_pack.weight"]
|
|
|
|
for name, data_torch in model_kv.items():
|
|
# we don't need these
|
|
if name.endswith(".rotary_emb.inv_freq"):
|
|
continue
|
|
|
|
old_dtype = data_torch.dtype
|
|
|
|
# convert any unsupported data types to float32
|
|
if data_torch.dtype not in (torch.float16, torch.float32):
|
|
data_torch = data_torch.to(torch.float32)
|
|
|
|
data = data_torch.squeeze().numpy()
|
|
|
|
# map tensor names
|
|
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
|
if new_name is None:
|
|
print(f"Can not map tensor {name!r}")
|
|
sys.exit()
|
|
|
|
n_dims = len(data.shape)
|
|
data_dtype = data.dtype
|
|
|
|
# if f32 desired, convert any float16 to float32
|
|
if self.ftype == 0 and data_dtype == np.float16:
|
|
data = data.astype(np.float32)
|
|
|
|
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
|
|
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
|
|
data = data.astype(np.float32)
|
|
|
|
# if f16 desired, convert any float32 2-dim weight tensors to float16
|
|
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
|
data = data.astype(np.float16)
|
|
|
|
print(f"{name} -> {new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
|
self.gguf_writer.add_tensor(new_name, data)
|
|
|
|
def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
|
|
if n_kv_head is not None and n_head != n_kv_head:
|
|
n_head //= n_kv_head
|
|
|
|
return (
|
|
weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
|
|
.swapaxes(1, 2)
|
|
.reshape(weights.shape)
|
|
)
|
|
|
|
def _reverse_hf_permute_part(
|
|
self, weights: Tensor, n_part: int, n_head: int, n_head_kv: int | None = None,
|
|
) -> Tensor:
|
|
r = weights.shape[0] // 3
|
|
return self._reverse_hf_permute(weights[r * n_part:r * n_part + r, ...], n_head, n_head_kv)
|
|
|
|
def _reverse_hf_part(self, weights: Tensor, n_part: int) -> Tensor:
|
|
r = weights.shape[0] // 3
|
|
return weights[r * n_part:r * n_part + r, ...]
|
|
|
|
|
|
@Model.register("XverseForCausalLM")
|
|
class XverseModel(Model):
|
|
model_arch = gguf.MODEL_ARCH.XVERSE
|
|
|
|
def set_vocab(self):
|
|
assert (self.dir_model / "tokenizer.json").is_file()
|
|
dir_model = self.dir_model
|
|
hparams = self.hparams
|
|
|
|
tokens: list[bytearray] = []
|
|
toktypes: list[int] = []
|
|
|
|
from transformers import AutoTokenizer
|
|
tokenizer = AutoTokenizer.from_pretrained(dir_model)
|
|
vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
|
|
assert max(tokenizer.vocab.values()) < vocab_size
|
|
|
|
reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}
|
|
added_vocab = tokenizer.get_added_vocab()
|
|
|
|
for token_id in range(vocab_size):
|
|
token_text = reverse_vocab[token_id].encode('utf-8')
|
|
# replace "\x00" to string with length > 0
|
|
if token_text == b"\x00":
|
|
toktype = gguf.TokenType.BYTE # special
|
|
token_text = f"<{token_text}>".encode('utf-8')
|
|
elif re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text):
|
|
toktype = gguf.TokenType.BYTE # special
|
|
elif reverse_vocab[token_id] in added_vocab:
|
|
if tokenizer.added_tokens_decoder[token_id].special:
|
|
toktype = gguf.TokenType.CONTROL
|
|
else:
|
|
toktype = gguf.TokenType.USER_DEFINED
|
|
else:
|
|
toktype = gguf.TokenType.NORMAL
|
|
|
|
tokens.append(token_text)
|
|
toktypes.append(toktype)
|
|
|
|
self.gguf_writer.add_tokenizer_model("llama")
|
|
self.gguf_writer.add_token_list(tokens)
|
|
self.gguf_writer.add_token_types(toktypes)
|
|
|
|
special_vocab = gguf.SpecialVocab(dir_model, n_vocab=len(tokens))
|
|
special_vocab.add_to_gguf(self.gguf_writer)
|
|
|
|
def set_gguf_parameters(self):
|
|
block_count = self.hparams["num_hidden_layers"]
|
|
head_count = self.hparams["num_attention_heads"]
|
|
head_count_kv = self.hparams.get("num_key_value_heads", head_count)
|
|
hf_repo = self.hparams.get("_name_or_path", "")
|
|
|
|
ctx_length = 0
|
|
if "max_sequence_length" in self.hparams:
|
|
ctx_length = self.hparams["max_sequence_length"]
|
|
elif "max_position_embeddings" in self.hparams:
|
|
ctx_length = self.hparams["max_position_embeddings"]
|
|
elif "model_max_length" in self.hparams:
|
|
ctx_length = self.hparams["model_max_length"]
|
|
else:
|
|
print("gguf: can not find ctx length parameter.")
|
|
sys.exit()
|
|
|
|
self.gguf_writer.add_name(self.dir_model.name)
|
|
self.gguf_writer.add_source_hf_repo(hf_repo)
|
|
self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
|
|
self.gguf_writer.add_context_length(ctx_length)
|
|
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
|
|
self.gguf_writer.add_block_count(block_count)
|
|
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
|
|
self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
|
|
self.gguf_writer.add_head_count(head_count)
|
|
self.gguf_writer.add_head_count_kv(head_count_kv)
|
|
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
|
|
|
|
if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
|
|
if self.hparams["rope_scaling"].get("type") == "linear":
|
|
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
|
|
self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
|
|
|
|
def write_tensors(self):
|
|
# Collect tensors from generator object
|
|
model_kv = dict(self.get_tensors())
|
|
block_count = self.hparams["num_hidden_layers"]
|
|
head_count = self.hparams["num_attention_heads"]
|
|
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
|
head_count_kv = self.hparams.get("num_key_value_heads", head_count)
|
|
|
|
for name, data_torch in model_kv.items():
|
|
# we don't need these
|
|
if name.endswith(".rotary_emb.inv_freq"):
|
|
continue
|
|
|
|
old_dtype = data_torch.dtype
|
|
|
|
# convert any unsupported data types to float32
|
|
if data_torch.dtype not in (torch.float16, torch.float32):
|
|
data_torch = data_torch.to(torch.float32)
|
|
|
|
# HF models permute some of the tensors, so we need to undo that
|
|
if name.endswith(("q_proj.weight")):
|
|
data_torch = self._reverse_hf_permute(data_torch, head_count, head_count)
|
|
if name.endswith(("k_proj.weight")):
|
|
data_torch = self._reverse_hf_permute(data_torch, head_count, head_count_kv)
|
|
|
|
data = data_torch.squeeze().numpy()
|
|
|
|
# map tensor names
|
|
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
|
if new_name is None:
|
|
print(f"Can not map tensor {name!r}")
|
|
sys.exit()
|
|
|
|
n_dims = len(data.shape)
|
|
data_dtype = data.dtype
|
|
|
|
# if f32 desired, convert any float16 to float32
|
|
if self.ftype == 0 and data_dtype == np.float16:
|
|
data = data.astype(np.float32)
|
|
|
|
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
|
|
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
|
|
data = data.astype(np.float32)
|
|
|
|
# if f16 desired, convert any float32 2-dim weight tensors to float16
|
|
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
|
data = data.astype(np.float16)
|
|
|
|
print(f"{name} -> {new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
|
self.gguf_writer.add_tensor(new_name, data)
|
|
|
|
def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
|
|
if n_kv_head is not None and n_head != n_kv_head:
|
|
n_head //= n_kv_head
|
|
|
|
return (
|
|
weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
|
|
.swapaxes(1, 2)
|
|
.reshape(weights.shape)
|
|
)
|
|
|
|
|
|
@Model.register("FalconForCausalLM", "RWForCausalLM")
|
|
class FalconModel(Model):
|
|
model_arch = gguf.MODEL_ARCH.FALCON
|
|
|
|
def set_gguf_parameters(self):
|
|
block_count = self.hparams.get("num_hidden_layers")
|
|
if block_count is None:
|
|
block_count = self.hparams["n_layer"] # old name
|
|
|
|
n_head = self.hparams.get("num_attention_heads")
|
|
if n_head is None:
|
|
n_head = self.hparams["n_head"] # old name
|
|
|
|
n_head_kv = self.hparams.get("num_kv_heads")
|
|
if n_head_kv is None:
|
|
n_head_kv = self.hparams.get("n_head_kv", 1) # old name
|
|
|
|
self.gguf_writer.add_name("Falcon")
|
|
self.gguf_writer.add_context_length(2048) # not in config.json
|
|
self.gguf_writer.add_tensor_data_layout("jploski") # qkv tensor transform
|
|
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
|
|
self.gguf_writer.add_feed_forward_length(4 * self.hparams["hidden_size"])
|
|
self.gguf_writer.add_block_count(block_count)
|
|
self.gguf_writer.add_head_count(n_head)
|
|
self.gguf_writer.add_head_count_kv(n_head_kv)
|
|
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
|
|
self.gguf_writer.add_file_type(self.ftype)
|
|
|
|
def write_tensors(self):
|
|
block_count = self.hparams.get("num_hidden_layers")
|
|
if block_count is None:
|
|
block_count = self.hparams["n_layer"] # old name
|
|
|
|
n_head = self.hparams.get("num_attention_heads")
|
|
if n_head is None:
|
|
n_head = self.hparams["n_head"] # old name
|
|
|
|
n_head_kv = self.hparams.get("num_kv_heads")
|
|
if n_head_kv is None:
|
|
n_head_kv = self.hparams.get("n_head_kv", 1) # old name
|
|
|
|
head_dim = self.hparams["hidden_size"] // n_head
|
|
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
|
|
|
for name, data_torch in self.get_tensors():
|
|
old_dtype = data_torch.dtype
|
|
|
|
# convert any unsupported data types to float32
|
|
if data_torch.dtype not in (torch.float16, torch.float32):
|
|
data_torch = data_torch.to(torch.float32)
|
|
|
|
# QKV tensor transform
|
|
# The original query_key_value tensor contains n_head_kv "kv groups",
|
|
# each consisting of n_head/n_head_kv query weights followed by one key
|
|
# and one value weight (shared by all query heads in the kv group).
|
|
# This layout makes it a big pain to work with in GGML.
|
|
# So we rearrange them here,, so that we have n_head query weights
|
|
# followed by n_head_kv key weights followed by n_head_kv value weights,
|
|
# in contiguous fashion.
|
|
# ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py
|
|
|
|
if "query_key_value" in name:
|
|
qkv = data_torch.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head)
|
|
q = qkv[:, :-2].reshape(n_head * head_dim, head_dim * n_head)
|
|
k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head)
|
|
v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head)
|
|
data_torch = torch.cat((q, k, v)).reshape_as(data_torch)
|
|
|
|
data = data_torch.squeeze().numpy()
|
|
|
|
# map tensor names
|
|
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
|
if new_name is None:
|
|
print(f"Can not map tensor {name!r}")
|
|
sys.exit()
|
|
|
|
n_dims = len(data.shape)
|
|
data_dtype = data.dtype
|
|
|
|
# if f32 desired, convert any float16 to float32
|
|
if self.ftype == 0 and data_dtype == np.float16:
|
|
data = data.astype(np.float32)
|
|
|
|
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
|
|
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
|
|
data = data.astype(np.float32)
|
|
|
|
# if f16 desired, convert any float32 2-dim weight tensors to float16
|
|
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
|
data = data.astype(np.float16)
|
|
|
|
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
|
|
|
self.gguf_writer.add_tensor(new_name, data)
|
|
|
|
|
|
@Model.register("GPTBigCodeForCausalLM")
|
|
class StarCoderModel(Model):
|
|
model_arch = gguf.MODEL_ARCH.STARCODER
|
|
|
|
def set_gguf_parameters(self):
|
|
block_count = self.hparams["n_layer"]
|
|
|
|
self.gguf_writer.add_name("StarCoder")
|
|
self.gguf_writer.add_context_length(self.hparams["n_positions"])
|
|
self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
|
|
self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
|
|
self.gguf_writer.add_block_count(block_count)
|
|
self.gguf_writer.add_head_count(self.hparams["n_head"])
|
|
self.gguf_writer.add_head_count_kv(1)
|
|
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
|
|
self.gguf_writer.add_file_type(self.ftype)
|
|
|
|
|
|
@Model.register("GPTRefactForCausalLM")
|
|
class RefactModel(Model):
|
|
model_arch = gguf.MODEL_ARCH.REFACT
|
|
|
|
def set_gguf_parameters(self):
|
|
hidden_dim = self.hparams["n_embd"]
|
|
inner_dim = 4 * hidden_dim
|
|
hidden_dim = int(2 * inner_dim / 3)
|
|
multiple_of = 256
|
|
ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
|
|
|
|
block_count = self.hparams["n_layer"]
|
|
|
|
self.gguf_writer.add_name("Refact")
|
|
# refact uses Alibi. So this is from config.json which might be used by training.
|
|
self.gguf_writer.add_context_length(self.hparams["n_positions"])
|
|
self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
|
|
|
|
self.gguf_writer.add_feed_forward_length(ff_dim)
|
|
self.gguf_writer.add_block_count(block_count)
|
|
self.gguf_writer.add_head_count(self.hparams["n_head"])
|
|
self.gguf_writer.add_head_count_kv(1)
|
|
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
|
|
self.gguf_writer.add_file_type(self.ftype)
|
|
|
|
def write_tensors(self):
|
|
hidden_dim = self.hparams["n_embd"]
|
|
inner_dim = 4 * hidden_dim
|
|
hidden_dim = int(2 * inner_dim / 3)
|
|
multiple_of = 256
|
|
ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
|
|
n_head = self.hparams["n_head"]
|
|
n_head_kv = 1
|
|
head_dim = self.hparams["n_embd"] // n_head
|
|
block_count = self.hparams["n_layer"]
|
|
|
|
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
|
|
|
tensors = dict(self.get_tensors())
|
|
for i in range(block_count):
|
|
if (w := tensors.get(f"transformer.h.{i}.attn.kv.weight")) is not None:
|
|
tensors[f"model.layers.{i}.self_attn.k_proj.weight"] = w[:n_head_kv * head_dim]
|
|
tensors[f"model.layers.{i}.self_attn.v_proj.weight"] = w[n_head_kv * head_dim:]
|
|
del tensors[f"transformer.h.{i}.attn.kv.weight"]
|
|
if (w := tensors.get(f"transformer.h.{i}.attn.q.weight")) is not None:
|
|
tensors[f"model.layers.{i}.self_attn.q_proj.weight"] = w
|
|
del tensors[f"transformer.h.{i}.attn.q.weight"]
|
|
if (w := tensors.get(f"transformer.h.{i}.mlp.gate_up_proj.weight")) is not None:
|
|
tensors[f"model.layers.{i}.mlp.gate_proj.weight"] = w[:ff_dim]
|
|
tensors[f"model.layers.{i}.mlp.up_proj.weight"] = w[ff_dim:]
|
|
del tensors[f"transformer.h.{i}.mlp.gate_up_proj.weight"]
|
|
|
|
for name, data_torch in tensors.items():
|
|
old_dtype = data_torch.dtype
|
|
|
|
# convert any unsupported data types to float32
|
|
if data_torch.dtype not in (torch.float16, torch.float32):
|
|
data_torch = data_torch.to(torch.float32)
|
|
|
|
data = data_torch.squeeze().numpy()
|
|
|
|
# map tensor names
|
|
new_name = tensor_map.get_name(name, try_suffixes=(".weight",))
|
|
if new_name is None:
|
|
print(f"Can not map tensor {name!r}")
|
|
sys.exit()
|
|
|
|
n_dims = len(data.shape)
|
|
data_dtype = data.dtype
|
|
|
|
# if f32 desired, convert any float16 to float32
|
|
if self.ftype == 0 and data_dtype == np.float16:
|
|
data = data.astype(np.float32)
|
|
|
|
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
|
|
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
|
|
data = data.astype(np.float32)
|
|
|
|
# if f16 desired, convert any float32 2-dim weight tensors to float16
|
|
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
|
data = data.astype(np.float16)
|
|
|
|
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
|
|
|
self.gguf_writer.add_tensor(new_name, data)
|
|
|
|
|
|
@Model.register("PersimmonForCausalLM")
|
|
class PersimmonModel(Model):
|
|
model_arch = gguf.MODEL_ARCH.PERSIMMON
|
|
|
|
def set_gguf_parameters(self):
|
|
block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers"))
|
|
head_count = self.hparams["num_attention_heads"]
|
|
head_count_kv = head_count
|
|
hidden_size = self.hparams["hidden_size"]
|
|
|
|
self.gguf_writer.add_name('persimmon-8b-chat')
|
|
self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
|
|
self.gguf_writer.add_embedding_length(hidden_size)
|
|
self.gguf_writer.add_block_count(block_count)
|
|
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
|
|
|
|
# NOTE: not sure about this change - why does the model not have a rope dimension count when it is smaller
|
|
# than the head size?
|
|
# ref: https://github.com/ggerganov/llama.cpp/pull/4889
|
|
# self.gguf_writer.add_rope_dimension_count(hidden_size // head_count)
|
|
self.gguf_writer.add_rope_dimension_count(hidden_size // head_count // 2)
|
|
|
|
self.gguf_writer.add_head_count(head_count)
|
|
self.gguf_writer.add_head_count_kv(head_count_kv)
|
|
self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"])
|
|
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])
|
|
|
|
def set_vocab(self):
|
|
self._set_vocab_sentencepiece()
|
|
# self.gguf_writer.add_bos_token_id(71013)
|
|
# self.gguf_writer.add_eos_token_id(71013)
|
|
|
|
def write_tensors(self):
|
|
block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers"))
|
|
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
|
|
|
for name, data_torch in self.get_tensors():
|
|
if name.endswith(".self_attention.rotary_emb.inv_freq"):
|
|
continue
|
|
old_dtype = data_torch.dtype
|
|
# TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?)
|
|
data = data_torch.to(torch.float32).squeeze().numpy()
|
|
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
|
if new_name is None:
|
|
print(f"Can not map tensor {name!r}")
|
|
sys.exit()
|
|
n_dims = len(data.shape)
|
|
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
|
self.gguf_writer.add_tensor(new_name, data)
|
|
|
|
|
|
@Model.register("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM")
|
|
class StableLMModel(Model):
|
|
model_arch = gguf.MODEL_ARCH.STABLELM
|
|
|
|
def set_vocab(self):
|
|
if (self.dir_model / "tokenizer.json").is_file():
|
|
self._set_vocab_gpt2()
|
|
else:
|
|
# StableLM 2 1.6B uses a vocab in a similar format to Qwen's vocab
|
|
self._set_vocab_qwen()
|
|
|
|
def set_gguf_parameters(self):
|
|
hparams = self.hparams
|
|
block_count = hparams["num_hidden_layers"]
|
|
|
|
self.gguf_writer.add_name(self.dir_model.name)
|
|
self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
|
|
self.gguf_writer.add_embedding_length(hparams["hidden_size"])
|
|
self.gguf_writer.add_block_count(block_count)
|
|
self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
|
|
rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"])
|
|
self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"])))
|
|
self.gguf_writer.add_head_count(hparams["num_attention_heads"])
|
|
self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
|
|
self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"]))
|
|
|
|
|
|
@Model.register("LlamaForCausalLM", "MistralForCausalLM", "MixtralForCausalLM")
|
|
class LlamaModel(Model):
|
|
model_arch = gguf.MODEL_ARCH.LLAMA
|
|
|
|
def set_vocab(self):
|
|
try:
|
|
self. _set_vocab_sentencepiece()
|
|
except FileNotFoundError:
|
|
self._set_vocab_llama_hf()
|
|
|
|
special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
|
|
special_token_types = ['prefix', 'suffix', 'middle', 'eot'])
|
|
special_vocab._set_special_token("prefix", 32007)
|
|
special_vocab._set_special_token("suffix", 32008)
|
|
special_vocab._set_special_token("middle", 32009)
|
|
special_vocab._set_special_token("eot", 32010)
|
|
special_vocab.add_to_gguf(self.gguf_writer)
|
|
|
|
def set_gguf_parameters(self):
|
|
super().set_gguf_parameters()
|
|
hparams = self.hparams
|
|
self.gguf_writer.add_vocab_size(hparams["vocab_size"])
|
|
self.gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"])
|
|
|
|
# Same as super class, but permuting q_proj, k_proj
|
|
def write_tensors(self):
|
|
block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
|
|
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
|
n_head = self.hparams.get("num_attention_heads")
|
|
n_kv_head = self.hparams.get("num_key_value_heads")
|
|
n_experts = self.hparams.get("num_local_experts")
|
|
experts = dict()
|
|
for name, data_torch in self.get_tensors():
|
|
# we don't need these
|
|
if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")):
|
|
continue
|
|
|
|
old_dtype = data_torch.dtype
|
|
|
|
# convert any unsupported data types to float32
|
|
if data_torch.dtype not in (torch.float16, torch.float32):
|
|
data_torch = data_torch.to(torch.float32)
|
|
|
|
data = data_torch.numpy()
|
|
|
|
if name.endswith("q_proj.weight"):
|
|
data = permute(data, n_head, n_head)
|
|
if name.endswith("k_proj.weight"):
|
|
data = permute(data, n_head, n_kv_head)
|
|
|
|
data = data.squeeze()
|
|
|
|
# process the experts separately
|
|
if name.find("block_sparse_moe.experts") != -1:
|
|
experts[name] = data
|
|
if len(experts) >= n_experts:
|
|
# merge the experts into a single 3d tensor
|
|
for bid in range(block_count):
|
|
for wid in range(1, 4):
|
|
full = True
|
|
for xid in range(n_experts):
|
|
ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.w{wid}.weight"
|
|
if ename not in experts:
|
|
full = False
|
|
break
|
|
if not full:
|
|
continue
|
|
|
|
datas = []
|
|
for xid in range(n_experts):
|
|
ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.w{wid}.weight"
|
|
datas.append(experts[ename])
|
|
del experts[ename]
|
|
|
|
data = np.stack(datas, axis=0)
|
|
data_dtype = data.dtype
|
|
|
|
if self.ftype == 0 and data_dtype == np.float16:
|
|
data = data.astype(np.float32)
|
|
|
|
if self.ftype == 1 and data_dtype == np.float32:
|
|
data = data.astype(np.float16)
|
|
|
|
merged_name = f"layers.{bid}.feed_forward.experts.w{wid}.weight"
|
|
|
|
new_name = tensor_map.get_name(merged_name, try_suffixes=(".weight", ".bias"))
|
|
if new_name is None:
|
|
print(f"Can not map tensor {name!r}")
|
|
sys.exit()
|
|
|
|
print(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}")
|
|
|
|
self.gguf_writer.add_tensor(new_name, data)
|
|
continue
|
|
|
|
# map tensor names
|
|
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
|
if new_name is None:
|
|
print(f"Can not map tensor {name!r}")
|
|
sys.exit()
|
|
|
|
n_dims = len(data.shape)
|
|
data_dtype = data.dtype
|
|
|
|
# if f32 desired, convert any float16 to float32
|
|
if self.ftype == 0 and data_dtype == np.float16:
|
|
data = data.astype(np.float32)
|
|
|
|
# 1d tensors need to be converted to float32
|
|
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
|
|
data = data.astype(np.float32)
|
|
|
|
# if f16 desired, convert any float32 2-dim weight tensors to float16
|
|
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
|
data = data.astype(np.float16)
|
|
|
|
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
|
|
|
self.gguf_writer.add_tensor(new_name, data)
|
|
|
|
if len(experts) > 0:
|
|
raise ValueError(f"Unprocessed experts: {experts.keys()}")
|
|
|
|
|
|
@Model.register("GrokForCausalLM")
|
|
class GrokModel(Model):
|
|
model_arch = gguf.MODEL_ARCH.GROK
|
|
|
|
def set_vocab(self):
|
|
self._set_vocab_sentencepiece()
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
|
|
def set_gguf_parameters(self):
|
|
super().set_gguf_parameters()
|
|
self.gguf_writer.add_name("Grok")
|
|
|
|
def write_tensors(self):
|
|
block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
|
|
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
|
n_experts = self.hparams.get("num_local_experts")
|
|
experts = dict()
|
|
for name, data_torch in self.get_tensors():
|
|
# we don't need these
|
|
if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")):
|
|
continue
|
|
|
|
old_dtype = data_torch.dtype
|
|
|
|
# convert any unsupported data types to float32
|
|
if data_torch.dtype not in (torch.float16, torch.float32):
|
|
data_torch = data_torch.to(torch.float32)
|
|
|
|
data = data_torch.squeeze().numpy()
|
|
|
|
# process the experts separately
|
|
if name.find(".moe.") != -1:
|
|
experts[name] = data
|
|
if len(experts) >= n_experts:
|
|
# merge the experts into a single 3d tensor
|
|
for bid in range(block_count):
|
|
for wid in ["linear", "linear_1", "linear_v"]:
|
|
full = True
|
|
for xid in range(n_experts):
|
|
ename = f"transformer.decoder_layer.{bid}.moe.{xid}.{wid}.weight"
|
|
if ename not in experts:
|
|
full = False
|
|
break
|
|
if not full:
|
|
continue
|
|
|
|
datas = []
|
|
for xid in range(n_experts):
|
|
ename = f"transformer.decoder_layer.{bid}.moe.{xid}.{wid}.weight"
|
|
datas.append(experts[ename])
|
|
del experts[ename]
|
|
|
|
data = np.stack(datas, axis=0)
|
|
data_dtype = data.dtype
|
|
|
|
if self.ftype == 0 and data_dtype == np.float16:
|
|
data = data.astype(np.float32)
|
|
|
|
if self.ftype == 1 and data_dtype == np.float32:
|
|
data = data.astype(np.float16)
|
|
|
|
merged_name = f"transformer.decoder_layer.{bid}.moe.{wid}.weight"
|
|
|
|
new_name = tensor_map.get_name(merged_name, try_suffixes=(".weight", ".bias"))
|
|
if new_name is None:
|
|
print(f"Can not map tensor {name!r}")
|
|
sys.exit()
|
|
|
|
print(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}")
|
|
|
|
self.gguf_writer.add_tensor(new_name, data)
|
|
continue
|
|
|
|
# map tensor names
|
|
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
|
if new_name is None:
|
|
print(f"Can not map tensor {name!r}")
|
|
sys.exit()
|
|
|
|
n_dims = len(data.shape)
|
|
data_dtype = data.dtype
|
|
|
|
# if f32 desired, convert any float16 to float32
|
|
if self.ftype == 0 and data_dtype == np.float16:
|
|
data = data.astype(np.float32)
|
|
|
|
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
|
|
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
|
|
data = data.astype(np.float32)
|
|
|
|
# if f16 desired, convert any float32 2-dim weight tensors to float16
|
|
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
|
data = data.astype(np.float16)
|
|
|
|
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
|
|
|
self.gguf_writer.add_tensor(new_name, data)
|
|
|
|
|
|
@Model.register("DbrxForCausalLM")
|
|
class DbrxModel(Model):
|
|
model_arch = gguf.MODEL_ARCH.DBRX
|
|
|
|
def set_gguf_parameters(self):
|
|
ffn_config = self.hparams["ffn_config"]
|
|
attn_config = self.hparams["attn_config"]
|
|
self.gguf_writer.add_name(self.hparams["model_type"])
|
|
self.gguf_writer.add_block_count(self.hparams["n_layers"])
|
|
|
|
self.gguf_writer.add_context_length(self.hparams["max_seq_len"])
|
|
self.gguf_writer.add_embedding_length(self.hparams["d_model"])
|
|
self.gguf_writer.add_feed_forward_length(ffn_config["ffn_hidden_size"])
|
|
|
|
self.gguf_writer.add_head_count(self.hparams["n_heads"])
|
|
self.gguf_writer.add_head_count_kv(attn_config["kv_n_heads"])
|
|
|
|
self.gguf_writer.add_rope_freq_base(attn_config["rope_theta"])
|
|
|
|
self.gguf_writer.add_clamp_kqv(attn_config["clip_qkv"])
|
|
self.gguf_writer.add_file_type(self.ftype)
|
|
|
|
self.gguf_writer.add_expert_count(ffn_config["moe_num_experts"])
|
|
self.gguf_writer.add_expert_used_count(ffn_config["moe_top_k"])
|
|
|
|
self.gguf_writer.add_layer_norm_eps(1e-5)
|
|
|
|
self.gguf_writer.add_file_type(self.ftype)
|
|
print(f"gguf: file type = {self.ftype}")
|
|
|
|
def write_tensors(self):
|
|
block_count = self.hparams.get("n_layers")
|
|
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
|
for name, data_torch in self.get_tensors():
|
|
n_expert = self.hparams["ffn_config"]["moe_num_experts"]
|
|
n_ff = self.hparams["ffn_config"]["ffn_hidden_size"]
|
|
n_embd = self.hparams["d_model"]
|
|
|
|
# Specific behavior for experts tensors: suffix .weight, view as 3D and transpose
|
|
# original implementation expects (n_expert, n_ff, n_embd) for all experts weights
|
|
# But llama.cpp moe graph works differently
|
|
# AND the dimensions in ggml are typically in the reverse order of the pytorch dimensions
|
|
# so (n_expert, n_ff, n_embd) in pytorch is {n_embd, n_ff, n_expert} in ggml_tensor
|
|
exp_tensor_names = {"ffn.experts.mlp.w1": None, # LLM_TENSOR_FFN_GATE_EXPS ggml_tensor->ne{n_embd, n_ff, n_expert}
|
|
"ffn.experts.mlp.w2": (0, 2, 1), # LLM_TENSOR_FFN_DOWN_EXPS ggml_tensor->ne{n_ff, n_embd, n_expert}
|
|
"ffn.experts.mlp.v1": None} # LLM_TENSOR_FFN_UP_EXPS ggml_tensor->ne{n_embd, n_ff, n_expert}
|
|
experts = False
|
|
for exp_tensor_name in exp_tensor_names.keys():
|
|
if name.find(exp_tensor_name) != -1 and name.find(".weight") == -1:
|
|
experts = True
|
|
data_torch = data_torch.view(n_expert, n_ff, n_embd)
|
|
if (permute_tensor := exp_tensor_names[exp_tensor_name]) is not None:
|
|
data_torch = data_torch.permute(*permute_tensor)
|
|
break
|
|
|
|
old_dtype = data_torch.dtype
|
|
|
|
# convert any unsupported data types to float32
|
|
if data_torch.dtype not in (torch.float16, torch.float32):
|
|
data_torch = data_torch.to(torch.float32)
|
|
|
|
data = data_torch.squeeze().numpy()
|
|
|
|
# map tensor names
|
|
# In MoE models the ffn tensors are typically most of the model weights,
|
|
# and need to be quantizable. Quantize expects tensor names to be suffixed by .weight.
|
|
# Every other model has the weight names ending in .weight,
|
|
# let's assume that is the convention which is not the case for dbrx:
|
|
# https://huggingface.co/databricks/dbrx-instruct/blob/main/model.safetensors.index.json#L15
|
|
new_name = tensor_map.get_name(name if not experts else name + ".weight", try_suffixes=(".weight",))
|
|
if new_name is None:
|
|
print(f"Can not map tensor {name!r}")
|
|
sys.exit()
|
|
|
|
n_dims = len(data.shape)
|
|
data_dtype = data.dtype
|
|
|
|
# Most of the codebase that takes in 1D tensors only handles F32 tensors
|
|
# and most of the outputs tensors are F32.
|
|
if data_dtype != np.float32 and n_dims == 1:
|
|
print(f"Can not map tensor {name!r}: all 1D tensors must be F32")
|
|
sys.exit()
|
|
|
|
# if f32 desired, convert any float16 to float32
|
|
if self.ftype == 0 and data_dtype == np.float16:
|
|
data = data.astype(np.float32)
|
|
|
|
# if f16 desired, convert any float32 2-dim weight tensors to float16
|
|
if self.ftype == 1 and data_dtype == np.float32 and n_dims > 1:
|
|
data = data.astype(np.float16)
|
|
|
|
print(f"{new_name}, n_dims = {n_dims}, shape = {data.shape}, {old_dtype} --> {data.dtype}")
|
|
|
|
self.gguf_writer.add_tensor(new_name, data)
|
|
|
|
|
|
@Model.register("MiniCPMForCausalLM")
|
|
class MiniCPMModel(Model):
|
|
model_arch = gguf.MODEL_ARCH.MINICPM
|
|
|
|
def set_gguf_parameters(self):
|
|
block_count = self.hparams["num_hidden_layers"]
|
|
self.gguf_writer.add_name("MiniCPM")
|
|
self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
|
|
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
|
|
self.gguf_writer.add_block_count(block_count)
|
|
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
|
|
self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
|
|
self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
|
|
self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"])
|
|
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
|
|
self.gguf_writer.add_file_type(self.ftype)
|
|
|
|
def set_vocab(self):
|
|
self._set_vocab_llama_hf()
|
|
|
|
def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
|
|
if n_kv_head is not None and n_head != n_kv_head:
|
|
n_head //= n_kv_head
|
|
|
|
return (
|
|
weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
|
|
.swapaxes(1, 2)
|
|
.reshape(weights.shape)
|
|
)
|
|
|
|
def write_tensors(self):
|
|
block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
|
|
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
|
n_head = self.hparams.get("num_attention_heads")
|
|
n_kv_head = self.hparams.get("num_key_value_heads")
|
|
for name, data_torch in self.get_tensors():
|
|
# we don't need these
|
|
if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")):
|
|
continue
|
|
|
|
old_dtype = data_torch.dtype
|
|
|
|
# convert any unsupported data types to float32
|
|
if data_torch.dtype not in (torch.float16, torch.float32):
|
|
data_torch = data_torch.to(torch.float32)
|
|
|
|
# HF models permute some of the tensors, so we need to undo that
|
|
if name.endswith(("q_proj.weight")):
|
|
data_torch = self._reverse_hf_permute(data_torch, n_head, n_head)
|
|
if name.endswith(("k_proj.weight")):
|
|
data_torch = self._reverse_hf_permute(data_torch, n_head, n_kv_head)
|
|
|
|
data = data_torch.squeeze().numpy()
|
|
|
|
# map tensor names
|
|
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
|
if new_name is None:
|
|
print(f"Can not map tensor {name!r}")
|
|
sys.exit()
|
|
|
|
n_dims = len(data.shape)
|
|
data_dtype = data.dtype
|
|
|
|
# if f32 desired, convert any float16 to float32
|
|
if self.ftype == 0 and data_dtype == np.float16:
|
|
data = data.astype(np.float32)
|
|
|
|
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
|
|
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
|
|
data = data.astype(np.float32)
|
|
|
|
# if f16 desired, convert any float32 2-dim weight tensors to float16
|
|
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
|
data = data.astype(np.float16)
|
|
|
|
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
|
|
|
self.gguf_writer.add_tensor(new_name, data)
|
|
|
|
|
|
@Model.register("QWenLMHeadModel")
|
|
class QwenModel(Model):
|
|
model_arch = gguf.MODEL_ARCH.QWEN
|
|
|
|
@staticmethod
|
|
def token_bytes_to_string(b):
|
|
from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode
|
|
byte_encoder = bytes_to_unicode()
|
|
return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')])
|
|
|
|
@staticmethod
|
|
def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: int | None = None) -> list[bytes]:
|
|
parts = [bytes([b]) for b in token]
|
|
while True:
|
|
min_idx = None
|
|
min_rank = None
|
|
for i, pair in enumerate(zip(parts[:-1], parts[1:])):
|
|
rank = mergeable_ranks.get(pair[0] + pair[1])
|
|
if rank is not None and (min_rank is None or rank < min_rank):
|
|
min_idx = i
|
|
min_rank = rank
|
|
if min_rank is None or (max_rank is not None and min_rank >= max_rank):
|
|
break
|
|
assert min_idx is not None
|
|
parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx + 1]] + parts[min_idx + 2:]
|
|
return parts
|
|
|
|
def set_vocab(self):
|
|
self._set_vocab_qwen()
|
|
|
|
def set_gguf_parameters(self):
|
|
self.gguf_writer.add_name("Qwen")
|
|
self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
|
|
self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"])
|
|
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
|
|
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
|
|
self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"])
|
|
self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
|
|
self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
|
|
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
|
|
|
|
def write_tensors(self):
|
|
block_count = self.hparams["num_hidden_layers"]
|
|
model_kv = dict(self.get_tensors())
|
|
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
|
for name, data_torch in model_kv.items():
|
|
# we don't need these
|
|
if name.endswith(".rotary_emb.inv_freq"):
|
|
continue
|
|
|
|
old_dtype = data_torch.dtype
|
|
|
|
# convert any unsupported data types to float32
|
|
if data_torch.dtype not in (torch.float16, torch.float32):
|
|
data_torch = data_torch.to(torch.float32)
|
|
|
|
data = data_torch.squeeze().numpy()
|
|
|
|
# map tensor names
|
|
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
|
if new_name is None:
|
|
print(f"Can not map tensor {name!r}")
|
|
sys.exit()
|
|
|
|
n_dims = len(data.shape)
|
|
data_dtype = data.dtype
|
|
|
|
# if f32 desired, convert any float16 to float32
|
|
if self.ftype == 0 and data_dtype == np.float16:
|
|
data = data.astype(np.float32)
|
|
|
|
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
|
|
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
|
|
data = data.astype(np.float32)
|
|
|
|
# if f16 desired, convert any float32 2-dim weight tensors to float16
|
|
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
|
data = data.astype(np.float16)
|
|
|
|
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
|
self.gguf_writer.add_tensor(new_name, data)
|
|
|
|
|
|
@Model.register("Qwen2ForCausalLM")
|
|
class Qwen2Model(Model):
|
|
model_arch = gguf.MODEL_ARCH.QWEN2
|
|
|
|
|
|
@Model.register("GPT2LMHeadModel")
|
|
class GPT2Model(Model):
|
|
model_arch = gguf.MODEL_ARCH.GPT2
|
|
|
|
def set_gguf_parameters(self):
|
|
self.gguf_writer.add_name(self.dir_model.name)
|
|
self.gguf_writer.add_block_count(self.hparams["n_layer"])
|
|
self.gguf_writer.add_context_length(self.hparams["n_ctx"])
|
|
self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
|
|
self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
|
|
self.gguf_writer.add_head_count(self.hparams["n_head"])
|
|
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
|
|
self.gguf_writer.add_file_type(self.ftype)
|
|
|
|
def write_tensors(self):
|
|
block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
|
|
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
|
|
|
for name, data_torch in self.get_tensors():
|
|
# we don't need these
|
|
if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq", ".attn.bias", ".attn.masked_bias")):
|
|
continue
|
|
|
|
if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_proj.weight")):
|
|
data_torch = data_torch.transpose(1, 0)
|
|
|
|
old_dtype = data_torch.dtype
|
|
|
|
# convert any unsupported data types to float32
|
|
if data_torch.dtype not in (torch.float16, torch.float32):
|
|
data_torch = data_torch.to(torch.float32)
|
|
|
|
data = data_torch.squeeze().numpy()
|
|
|
|
# map tensor names
|
|
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
|
if new_name is None:
|
|
print(f"Can not map tensor {name!r}")
|
|
sys.exit()
|
|
|
|
n_dims = len(data.shape)
|
|
data_dtype = data.dtype
|
|
|
|
# if f32 desired, convert any float16 to float32
|
|
if self.ftype == 0 and data_dtype == np.float16:
|
|
data = data.astype(np.float32)
|
|
|
|
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
|
|
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
|
|
data = data.astype(np.float32)
|
|
|
|
# if f16 desired, convert any float32 2-dim weight tensors to float16
|
|
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
|
data = data.astype(np.float16)
|
|
|
|
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
|
|
|
self.gguf_writer.add_tensor(new_name, data)
|
|
|
|
# note: GPT2 output is tied to (same as) wte in original model
|
|
if new_name == "token_embd.weight":
|
|
print(f"output.weight, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
|
self.gguf_writer.add_tensor("output.weight", data)
|
|
|
|
|
|
@Model.register("PhiForCausalLM")
|
|
class Phi2Model(Model):
|
|
model_arch = gguf.MODEL_ARCH.PHI2
|
|
|
|
def set_gguf_parameters(self):
|
|
block_count = self.find_hparam(["num_hidden_layers", "n_layer"])
|
|
|
|
rot_pct = self.find_hparam(["partial_rotary_factor"])
|
|
n_embd = self.find_hparam(["hidden_size", "n_embd"])
|
|
n_head = self.find_hparam(["num_attention_heads", "n_head"])
|
|
|
|
self.gguf_writer.add_name("Phi2")
|
|
self.gguf_writer.add_context_length(self.find_hparam(["n_positions", "max_position_embeddings"]))
|
|
|
|
self.gguf_writer.add_embedding_length(n_embd)
|
|
self.gguf_writer.add_feed_forward_length(4 * n_embd)
|
|
self.gguf_writer.add_block_count(block_count)
|
|
self.gguf_writer.add_head_count(n_head)
|
|
self.gguf_writer.add_head_count_kv(n_head)
|
|
self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_epsilon", "layer_norm_eps"]))
|
|
self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)
|
|
self.gguf_writer.add_file_type(self.ftype)
|
|
self.gguf_writer.add_add_bos_token(False)
|
|
|
|
|
|
@Model.register("PlamoForCausalLM")
|
|
class PlamoModel(Model):
|
|
model_arch = gguf.MODEL_ARCH.PLAMO
|
|
|
|
def set_vocab(self):
|
|
self._set_vocab_sentencepiece()
|
|
|
|
def set_gguf_parameters(self):
|
|
hparams = self.hparams
|
|
block_count = hparams["num_hidden_layers"]
|
|
|
|
self.gguf_writer.add_name("PLaMo")
|
|
self.gguf_writer.add_context_length(4096) # not in config.json
|
|
self.gguf_writer.add_embedding_length(hparams["hidden_size"])
|
|
self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
|
|
self.gguf_writer.add_block_count(block_count)
|
|
self.gguf_writer.add_head_count(hparams["num_attention_heads"])
|
|
self.gguf_writer.add_head_count_kv(5) # hparams["num_key_value_heads"]) is wrong
|
|
self.gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
|
|
|
|
def shuffle_attn_q_weight(self, data_torch):
|
|
assert data_torch.size() == (5120, 5120)
|
|
data_torch = data_torch.reshape(8, 5, 128, 5120)
|
|
data_torch = torch.permute(data_torch, (1, 0, 2, 3))
|
|
data_torch = torch.reshape(data_torch, (5120, 5120))
|
|
return data_torch
|
|
|
|
def shuffle_attn_output_weight(self, data_torch):
|
|
assert data_torch.size() == (5120, 5120)
|
|
data_torch = data_torch.reshape(5120, 8, 5, 128)
|
|
data_torch = torch.permute(data_torch, (0, 2, 1, 3))
|
|
data_torch = torch.reshape(data_torch, (5120, 5120))
|
|
return data_torch
|
|
|
|
def write_tensors(self):
|
|
block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers"))
|
|
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
|
|
|
for name, data_torch in self.get_tensors():
|
|
if "self_attn.rotary_emb.inv_freq" in name:
|
|
continue
|
|
|
|
# map tensor names
|
|
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
|
if new_name is None:
|
|
print(f"Can not map tensor {name!r}")
|
|
sys.exit()
|
|
|
|
# shuffle for broadcasting of gqa in ggml_mul_mat
|
|
if new_name.endswith("attn_q.weight"):
|
|
data_torch = self.shuffle_attn_q_weight(data_torch)
|
|
elif new_name.endswith("attn_output.weight"):
|
|
data_torch = self.shuffle_attn_output_weight(data_torch)
|
|
|
|
old_dtype = data_torch.dtype
|
|
|
|
# convert any unsupported data types to float32
|
|
if data_torch.dtype not in (torch.float16, torch.float32):
|
|
data_torch = data_torch.to(torch.float32)
|
|
|
|
data = data_torch.squeeze().numpy()
|
|
|
|
n_dims = len(data.shape)
|
|
data_dtype = data.dtype
|
|
|
|
# if f32 desired, convert any float16 to float32
|
|
if self.ftype == 0 and data_dtype == np.float16:
|
|
data = data.astype(np.float32)
|
|
|
|
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
|
|
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
|
|
data = data.astype(np.float32)
|
|
|
|
# if f16 desired, convert any float32 2-dim weight tensors to float16
|
|
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
|
data = data.astype(np.float16)
|
|
|
|
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
|
|
|
self.gguf_writer.add_tensor(new_name, data)
|
|
|
|
|
|
@Model.register("CodeShellForCausalLM")
|
|
class CodeShellModel(Model):
|
|
model_arch = gguf.MODEL_ARCH.CODESHELL
|
|
|
|
def set_gguf_parameters(self):
|
|
block_count = self.hparams["n_layer"]
|
|
|
|
self.gguf_writer.add_name("CodeShell")
|
|
self.gguf_writer.add_context_length(self.hparams["n_positions"])
|
|
self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
|
|
self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
|
|
self.gguf_writer.add_block_count(block_count)
|
|
self.gguf_writer.add_head_count(self.hparams["n_head"])
|
|
self.gguf_writer.add_head_count_kv(self.hparams["num_query_groups"])
|
|
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
|
|
self.gguf_writer.add_file_type(self.ftype)
|
|
self.gguf_writer.add_rope_freq_base(10000.0)
|
|
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
|
|
self.gguf_writer.add_rope_scaling_factor(1.0)
|
|
|
|
def write_tensors(self):
|
|
block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
|
|
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
|
tensors = dict(self.get_tensors())
|
|
has_lm_head = "lm_head.weight" in tensors.keys() or "output.weight" in tensors.keys()
|
|
for name, data_torch in tensors.items():
|
|
# we don't need these
|
|
if name.endswith((".attn.rotary_emb.inv_freq")):
|
|
continue
|
|
|
|
old_dtype = data_torch.dtype
|
|
|
|
# convert any unsupported data types to float32
|
|
if data_torch.dtype not in (torch.float16, torch.float32):
|
|
data_torch = data_torch.to(torch.float32)
|
|
|
|
data = data_torch.squeeze().numpy()
|
|
|
|
# map tensor names
|
|
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
|
if new_name is None:
|
|
print(f"Can not map tensor {name!r}")
|
|
sys.exit()
|
|
|
|
n_dims = len(data.shape)
|
|
data_dtype = data.dtype
|
|
|
|
# if f32 desired, convert any float16 to float32
|
|
if self.ftype == 0 and data_dtype == np.float16:
|
|
data = data.astype(np.float32)
|
|
|
|
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
|
|
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
|
|
data = data.astype(np.float32)
|
|
|
|
# if f16 desired, convert any float32 2-dim weight tensors to float16
|
|
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
|
data = data.astype(np.float16)
|
|
|
|
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
|
|
|
self.gguf_writer.add_tensor(new_name, data)
|
|
|
|
if not has_lm_head and name == "transformer.wte.weight":
|
|
self.gguf_writer.add_tensor("output.weight", data)
|
|
print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}")
|
|
|
|
|
|
@Model.register("InternLM2ForCausalLM")
|
|
class InternLM2Model(Model):
|
|
model_arch = gguf.MODEL_ARCH.INTERNLM2
|
|
|
|
def set_vocab(self):
|
|
# (TODO): Is there a better way?
|
|
# Copy from _set_vocab_sentencepiece, The only difference is that we will treat the character
|
|
# \x00 specially and convert it into an emoji character to prevent it from being mistakenly
|
|
# recognized as an empty string in C++.
|
|
from sentencepiece import SentencePieceProcessor
|
|
from sentencepiece import sentencepiece_model_pb2 as model
|
|
|
|
tokenizer_path = self.dir_model / 'tokenizer.model'
|
|
|
|
tokens: list[bytes] = []
|
|
scores: list[float] = []
|
|
toktypes: list[int] = []
|
|
|
|
if not tokenizer_path.is_file():
|
|
print(f'Error: Missing {tokenizer_path}', file=sys.stderr)
|
|
sys.exit(1)
|
|
|
|
sentencepiece_model = model.ModelProto()
|
|
sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
|
|
add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
|
|
|
|
tokenizer = SentencePieceProcessor(str(tokenizer_path))
|
|
vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
|
|
|
|
for token_id in range(vocab_size):
|
|
piece = tokenizer.id_to_piece(token_id)
|
|
text = piece.encode("utf-8")
|
|
score = tokenizer.get_score(token_id)
|
|
if text == b"\x00":
|
|
# (TODO): fixme
|
|
# Hack here and replace the \x00 characters.
|
|
print(f"InternLM2 convert token '{text}' to '🐉'!")
|
|
text = "🐉"
|
|
|
|
toktype = SentencePieceTokenTypes.NORMAL
|
|
if tokenizer.is_unknown(token_id):
|
|
toktype = SentencePieceTokenTypes.UNKNOWN
|
|
elif tokenizer.is_control(token_id):
|
|
toktype = SentencePieceTokenTypes.CONTROL
|
|
elif tokenizer.is_unused(token_id):
|
|
toktype = SentencePieceTokenTypes.UNUSED
|
|
elif tokenizer.is_byte(token_id):
|
|
toktype = SentencePieceTokenTypes.BYTE
|
|
|
|
tokens.append(text)
|
|
scores.append(score)
|
|
toktypes.append(toktype)
|
|
|
|
added_tokens_file = self.dir_model / 'added_tokens.json'
|
|
if added_tokens_file.is_file():
|
|
with open(added_tokens_file, "r", encoding="utf-8") as f:
|
|
added_tokens_json = json.load(f)
|
|
|
|
for key in added_tokens_json:
|
|
tokens.append(key.encode("utf-8"))
|
|
scores.append(-1000.0)
|
|
toktypes.append(SentencePieceTokenTypes.USER_DEFINED)
|
|
|
|
self.gguf_writer.add_tokenizer_model("llama")
|
|
self.gguf_writer.add_token_list(tokens)
|
|
self.gguf_writer.add_token_scores(scores)
|
|
self.gguf_writer.add_token_types(toktypes)
|
|
self.gguf_writer.add_add_space_prefix(add_prefix)
|
|
|
|
special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
|
|
old_eos = special_vocab.special_token_ids["eos"]
|
|
if "chat" in os.path.basename(self.dir_model.absolute()):
|
|
# For the chat model, we replace the eos with '<|im_end|>'.
|
|
special_vocab.special_token_ids["eos"] = self._try_get_sft_eos(tokenizer)
|
|
print(f"Replace eos:{old_eos} with a special token:{special_vocab.special_token_ids['eos']} \
|
|
in chat mode so that the conversation can end normally.")
|
|
|
|
special_vocab.add_to_gguf(self.gguf_writer)
|
|
|
|
def _try_get_sft_eos(self, tokenizer):
|
|
unused_145_list = tokenizer.encode('[UNUSED_TOKEN_145]')
|
|
im_end_list = tokenizer.encode('<|im_end|>')
|
|
assert (len(unused_145_list) == 1) ^ (len(im_end_list) == 1)
|
|
if len(unused_145_list) == 1:
|
|
eos_token = unused_145_list[0]
|
|
if len(im_end_list) == 1:
|
|
eos_token = im_end_list[0]
|
|
return eos_token
|
|
|
|
def _hf_permute_qk(self, weights, n_head: int, n_head_kv: int):
|
|
if n_head_kv is not None and n_head != n_head_kv:
|
|
n_head = n_head_kv
|
|
return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
|
|
.swapaxes(1, 2)
|
|
.reshape(weights.shape))
|
|
|
|
def set_gguf_parameters(self):
|
|
self.gguf_writer.add_name("InternLM2")
|
|
self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
|
|
self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"])
|
|
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
|
|
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
|
|
self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"])
|
|
self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
|
|
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
|
|
self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"])
|
|
|
|
def post_write_tensors(self, tensor_map, name, data_torch):
|
|
old_dtype = data_torch.dtype
|
|
|
|
# convert any unsupported data types to float32
|
|
if data_torch.dtype not in (torch.float16, torch.float32):
|
|
data_torch = data_torch.to(torch.float32)
|
|
|
|
data = data_torch.squeeze().numpy()
|
|
|
|
# map tensor names
|
|
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
|
if new_name is None:
|
|
print(f"Can not map tensor {name!r}")
|
|
sys.exit()
|
|
|
|
n_dims = len(data.shape)
|
|
data_dtype = data.dtype
|
|
|
|
# if f32 desired, convert any float16 to float32
|
|
if self.ftype == 0 and data_dtype == np.float16:
|
|
data = data.astype(np.float32)
|
|
|
|
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
|
|
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
|
|
data = data.astype(np.float32)
|
|
|
|
# if f16 desired, convert any float32 2-dim weight tensors to float16
|
|
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
|
data = data.astype(np.float16)
|
|
|
|
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
|
self.gguf_writer.add_tensor(new_name, data)
|
|
|
|
def write_tensors(self):
|
|
from einops import rearrange
|
|
|
|
num_heads = self.hparams.get("num_attention_heads")
|
|
num_kv_heads = self.hparams.get("num_key_value_heads")
|
|
hidden_size = self.hparams.get("hidden_size")
|
|
q_per_kv = num_heads // num_kv_heads
|
|
head_dim = hidden_size // num_heads
|
|
num_groups = num_heads // q_per_kv
|
|
|
|
block_count = self.hparams["num_hidden_layers"]
|
|
model_kv = dict(self.get_tensors())
|
|
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
|
qkv_pattern = r"model\.layers\.(\d+)\.attention\.wqkv"
|
|
for name, data_torch in model_kv.items():
|
|
# we don't need these
|
|
if name.endswith(".rotary_emb.inv_freq"):
|
|
continue
|
|
|
|
if re.match(qkv_pattern, name):
|
|
bid = re.findall(qkv_pattern, name)[0]
|
|
qkv = data_torch
|
|
qkv = rearrange(qkv.T, " o (g n i) ->o g n i", g=num_groups, n=q_per_kv + 2, i=head_dim)
|
|
q, k, v = qkv[..., : q_per_kv, :], qkv[..., q_per_kv: q_per_kv + 1, :], qkv[..., q_per_kv + 1: q_per_kv + 2, :]
|
|
# The model weights of q and k equire additional reshape.
|
|
q = self._hf_permute_qk(rearrange(q, " o g n i -> o (g n i)").T, num_heads, num_heads)
|
|
k = self._hf_permute_qk(rearrange(k, " o g n i -> o (g n i)").T, num_heads, num_kv_heads)
|
|
v = rearrange(v, " o g n i -> o (g n i)").T
|
|
self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wq.weight", q)
|
|
self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wk.weight", k)
|
|
self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wv.weight", v)
|
|
else:
|
|
self.post_write_tensors(tensor_map, name, data_torch)
|
|
|
|
|
|
@Model.register("BertModel", "CamembertModel")
|
|
class BertModel(Model):
|
|
model_arch = gguf.MODEL_ARCH.BERT
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
self.vocab_size = None
|
|
|
|
def set_gguf_parameters(self):
|
|
super().set_gguf_parameters()
|
|
self.gguf_writer.add_causal_attention(False)
|
|
|
|
# get pooling path
|
|
pooling_path = None
|
|
module_path = self.dir_model / "modules.json"
|
|
if module_path.is_file():
|
|
with open(module_path, encoding="utf-8") as f:
|
|
modules = json.load(f)
|
|
for mod in modules:
|
|
if mod["type"] == "sentence_transformers.models.Pooling":
|
|
pooling_path = mod["path"]
|
|
break
|
|
|
|
# get pooling type
|
|
if pooling_path is not None:
|
|
with open(self.dir_model / pooling_path / "config.json", encoding="utf-8") as f:
|
|
pooling = json.load(f)
|
|
if pooling["pooling_mode_mean_tokens"]:
|
|
pooling_type = gguf.PoolingType.MEAN
|
|
elif pooling["pooling_mode_cls_token"]:
|
|
pooling_type = gguf.PoolingType.CLS
|
|
else:
|
|
raise NotImplementedError("Only MEAN and CLS pooling types supported")
|
|
self.gguf_writer.add_pooling_type(pooling_type)
|
|
|
|
def set_vocab(self):
|
|
tokens, toktypes = self.get_basic_vocab()
|
|
self.vocab_size = len(tokens)
|
|
|
|
# we need this to validate the size of the token_type embeddings
|
|
# though currently we are passing all zeros to the token_type embeddings
|
|
self.gguf_writer.add_token_type_count(2) # "Sequence A" or "Sequence B"
|
|
|
|
# convert to phantom space vocab
|
|
def phantom(tok):
|
|
if tok.startswith("[") and tok.endswith("]"):
|
|
return tok
|
|
if tok.startswith("##"):
|
|
return tok[2:]
|
|
return "\u2581" + tok
|
|
tokens = list(map(phantom, tokens))
|
|
|
|
# add vocab to gguf
|
|
self.gguf_writer.add_tokenizer_model("bert")
|
|
self.gguf_writer.add_token_list(tokens)
|
|
self.gguf_writer.add_token_types(toktypes)
|
|
|
|
# handle special tokens
|
|
special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
|
|
special_vocab.add_to_gguf(self.gguf_writer)
|
|
|
|
def write_tensors(self):
|
|
tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
|
|
tensors = dict(self.get_tensors())
|
|
for name, data_torch in tensors.items():
|
|
# we are only using BERT for embeddings so we don't need the pooling layer
|
|
if name in ("embeddings.position_ids", "pooler.dense.weight", "pooler.dense.bias"):
|
|
continue # we don't need these
|
|
|
|
# map tensor names
|
|
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
|
if new_name is None:
|
|
print(f"Can not map tensor {name!r}")
|
|
sys.exit()
|
|
|
|
data = data_torch.squeeze().numpy()
|
|
n_dims = len(data.shape)
|
|
new_dtype: type[np.floating[Any]]
|
|
|
|
if (
|
|
self.ftype == 1 and name.endswith(".weight") and n_dims == 2
|
|
and name != "embeddings.token_type_embeddings.weight" # not used with get_rows, must be F32
|
|
):
|
|
# if f16 desired, convert any float32 2-dim weight tensors to float16
|
|
new_dtype = np.float16
|
|
else:
|
|
# if f32 desired, convert any float16 to float32
|
|
new_dtype = np.float32
|
|
|
|
print(f"{new_name}, n_dims = {n_dims}, {data_torch.dtype} --> {new_dtype}")
|
|
|
|
if data.dtype != new_dtype:
|
|
data = data.astype(new_dtype)
|
|
|
|
self.gguf_writer.add_tensor(new_name, data)
|
|
|
|
|
|
@Model.register("NomicBertModel")
|
|
class NomicBertModel(BertModel):
|
|
model_arch = gguf.MODEL_ARCH.NOMIC_BERT
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
|
|
# the HF config claims n_ctx=8192, but it uses RoPE scaling
|
|
self.hparams["n_ctx"] = 2048
|
|
|
|
# SwigLU activation
|
|
assert self.hparams["activation_function"] == "swiglu"
|
|
# this doesn't do anything in the HF version
|
|
assert self.hparams["causal"] is False
|
|
# no bias tensors
|
|
assert self.hparams["qkv_proj_bias"] is False
|
|
assert self.hparams["mlp_fc1_bias"] is False
|
|
assert self.hparams["mlp_fc2_bias"] is False
|
|
# norm at end of layer
|
|
assert self.hparams["prenorm"] is False
|
|
# standard RoPE
|
|
assert self.hparams["rotary_emb_fraction"] == 1.0
|
|
assert self.hparams["rotary_emb_interleaved"] is False
|
|
assert self.hparams["rotary_emb_scale_base"] is None
|
|
|
|
def set_gguf_parameters(self):
|
|
super().set_gguf_parameters()
|
|
self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"])
|
|
|
|
|
|
@Model.register("GemmaForCausalLM")
|
|
class GemmaModel(Model):
|
|
model_arch = gguf.MODEL_ARCH.GEMMA
|
|
|
|
def set_vocab(self):
|
|
self._set_vocab_sentencepiece()
|
|
special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
|
|
special_token_types = ['prefix', 'suffix', 'middle', 'eot'])
|
|
special_vocab._set_special_token("prefix", 67)
|
|
special_vocab._set_special_token("suffix", 69)
|
|
special_vocab._set_special_token("middle", 68)
|
|
special_vocab._set_special_token("eot", 70)
|
|
special_vocab.add_to_gguf(self.gguf_writer)
|
|
|
|
def set_gguf_parameters(self):
|
|
hparams = self.hparams
|
|
block_count = hparams["num_hidden_layers"]
|
|
|
|
self.gguf_writer.add_name(self.dir_model.name)
|
|
self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
|
|
self.gguf_writer.add_embedding_length(hparams["hidden_size"])
|
|
self.gguf_writer.add_block_count(block_count)
|
|
self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
|
|
self.gguf_writer.add_head_count(hparams["num_attention_heads"])
|
|
self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"])
|
|
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
|
|
self.gguf_writer.add_key_length(hparams["head_dim"])
|
|
self.gguf_writer.add_value_length(hparams["head_dim"])
|
|
self.gguf_writer.add_file_type(self.ftype)
|
|
|
|
def write_tensors(self):
|
|
block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
|
|
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
|
|
|
for name, data_torch in self.get_tensors():
|
|
old_dtype = data_torch.dtype
|
|
|
|
# convert any unsupported data types to float32
|
|
if data_torch.dtype not in (torch.float16, torch.float32):
|
|
data_torch = data_torch.to(torch.float32)
|
|
|
|
# ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89
|
|
if name.endswith("norm.weight"):
|
|
data_torch = data_torch + 1
|
|
data = data_torch.squeeze().numpy()
|
|
|
|
# map tensor names
|
|
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
|
if new_name is None:
|
|
print(f"Can not map tensor {name!r}")
|
|
sys.exit()
|
|
|
|
n_dims = len(data.shape)
|
|
data_dtype = data.dtype
|
|
|
|
data = data.astype(np.float32)
|
|
|
|
# if f16 desired, convert any float32 2-dim weight tensors to float16
|
|
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
|
data = data.astype(np.float16)
|
|
|
|
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
|
|
|
self.gguf_writer.add_tensor(new_name, data)
|
|
|
|
|
|
@Model.register("Starcoder2ForCausalLM")
|
|
class StarCoder2Model(Model):
|
|
model_arch = gguf.MODEL_ARCH.STARCODER2
|
|
|
|
|
|
@Model.register("MambaForCausalLM", "MambaLMHeadModel")
|
|
class MambaModel(Model):
|
|
model_arch = gguf.MODEL_ARCH.MAMBA
|
|
|
|
def set_vocab(self):
|
|
vocab_size = self.hparams["vocab_size"]
|
|
# Round vocab size to next multiple of 8
|
|
pad_vocab = self.hparams.get("pad_vocab_size_multiple", 8)
|
|
# pad using ceiling division
|
|
# ref: https://stackoverflow.com/a/17511341/22827863
|
|
vocab_size = -(vocab_size // -pad_vocab) * pad_vocab
|
|
self.hparams["vocab_size"] = vocab_size
|
|
|
|
if (self.dir_model / "tokenizer.json").is_file():
|
|
self._set_vocab_gpt2()
|
|
else:
|
|
# Use the GPT-NeoX tokenizer when no tokenizer files are present
|
|
tokenizer_path = Path(sys.path[0]) / "models" / "ggml-vocab-gpt-neox.gguf"
|
|
print(f"Using tokenizer from '{os.path.relpath(tokenizer_path, os.getcwd())}'")
|
|
neox_reader = gguf.GGUFReader(tokenizer_path, "r")
|
|
|
|
field = neox_reader.get_field(gguf.Keys.Tokenizer.MODEL)
|
|
self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1]))
|
|
field = neox_reader.get_field(gguf.Keys.Tokenizer.LIST)
|
|
self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size])
|
|
field = neox_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE)
|
|
self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size])
|
|
field = neox_reader.get_field(gguf.Keys.Tokenizer.MERGES)
|
|
self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data])
|
|
field = neox_reader.get_field(gguf.Keys.Tokenizer.BOS_ID)
|
|
self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0])
|
|
field = neox_reader.get_field(gguf.Keys.Tokenizer.EOS_ID)
|
|
self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0])
|
|
field = neox_reader.get_field(gguf.Keys.Tokenizer.UNK_ID)
|
|
self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0])
|
|
|
|
def set_gguf_parameters(self):
|
|
d_model = self.find_hparam(["hidden_size", "d_model"])
|
|
d_conv = self.find_hparam(["conv_kernel", "d_conv"], optional=True) or 4
|
|
d_inner = self.find_hparam(["intermediate_size", "d_inner"], optional=True) or 2 * d_model
|
|
d_state = self.find_hparam(["state_size", "d_state"], optional=True) or 16
|
|
# ceiling division
|
|
# ref: https://stackoverflow.com/a/17511341/22827863
|
|
# ref: https://github.com/state-spaces/mamba/blob/ce59daea3a090d011d6476c6e5b97f6d58ddad8b/mamba_ssm/modules/mamba_simple.py#L58
|
|
dt_rank = self.find_hparam(["time_step_rank", "dt_rank"], optional=True) or -(d_model // -16)
|
|
rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5
|
|
|
|
# Fail early for models which don't have a block expansion factor of 2
|
|
assert d_inner == 2 * d_model
|
|
|
|
self.gguf_writer.add_name(self.dir_model.name)
|
|
self.gguf_writer.add_context_length(2**20) # arbitrary value; for those who use the default
|
|
self.gguf_writer.add_embedding_length(d_model)
|
|
self.gguf_writer.add_feed_forward_length(0) # unused, but seemingly required when loading
|
|
self.gguf_writer.add_head_count(0) # unused, but seemingly required when loading
|
|
self.gguf_writer.add_block_count(self.hparams["n_layer"])
|
|
self.gguf_writer.add_ssm_conv_kernel(d_conv)
|
|
self.gguf_writer.add_ssm_inner_size(d_inner)
|
|
self.gguf_writer.add_ssm_state_size(d_state)
|
|
self.gguf_writer.add_ssm_time_step_rank(dt_rank)
|
|
self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
|
|
self.gguf_writer.add_file_type(self.ftype)
|
|
|
|
def write_tensors(self):
|
|
block_count = self.hparams["n_layer"]
|
|
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
|
|
|
tok_embd = None
|
|
tok_embd_name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.TOKEN_EMBD] + ".weight"
|
|
output_name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.OUTPUT] + ".weight"
|
|
|
|
for name, data_torch in self.get_tensors():
|
|
old_dtype = data_torch.dtype
|
|
|
|
# convert any unsupported data types to float32
|
|
if data_torch.dtype not in (torch.float16, torch.float32):
|
|
data_torch = data_torch.to(torch.float32)
|
|
|
|
# map tensor names
|
|
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
|
if new_name is None:
|
|
print(f"Can not map tensor {name!r}")
|
|
sys.exit()
|
|
|
|
if name.endswith(".A_log"):
|
|
print("A_log --> A ==> " + new_name)
|
|
data_torch = -torch.exp(data_torch)
|
|
|
|
# assuming token_embd.weight is seen before output.weight
|
|
if tok_embd is not None and new_name == output_name:
|
|
if torch.equal(tok_embd, data_torch):
|
|
print(f"{output_name} is equivalent to {tok_embd_name}, omitting")
|
|
continue
|
|
if new_name == tok_embd_name:
|
|
tok_embd = data_torch
|
|
|
|
data = data_torch.squeeze().numpy()
|
|
|
|
n_dims = len(data.shape)
|
|
data_dtype = data.dtype
|
|
|
|
# if f32 desired, convert any float16 to float32
|
|
if self.ftype == 0 and data_dtype == np.float16:
|
|
data = data.astype(np.float32)
|
|
|
|
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
|
|
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
|
|
data = data.astype(np.float32)
|
|
|
|
# if f16 desired, convert big float32 2-dim weight tensors to float16
|
|
new_weight_name = new_name[:-len(".weight")] if new_name.endswith(".weight") else ""
|
|
if self.ftype == 1 and data_dtype == np.float32 and new_weight_name.endswith((".ssm_in", ".ssm_out", "token_embd", "output")) and n_dims == 2:
|
|
data = data.astype(np.float16)
|
|
|
|
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
|
|
|
self.gguf_writer.add_tensor(new_name, data)
|
|
|
|
|
|
@Model.register("CohereForCausalLM")
|
|
class CommandR2Model(Model):
|
|
model_arch = gguf.MODEL_ARCH.COMMAND_R
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
|
|
# max_position_embeddings = 8192 in config.json but model was actually
|
|
# trained on 128k context length
|
|
self.hparams["max_position_embeddings"] = self.hparams["model_max_length"]
|
|
|
|
def set_gguf_parameters(self):
|
|
super().set_gguf_parameters()
|
|
self.gguf_writer.add_logit_scale(self.hparams["logit_scale"])
|
|
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
|
|
|
|
|
|
###### CONVERSION LOGIC ######
|
|
|
|
|
|
def parse_args() -> argparse.Namespace:
|
|
parser = argparse.ArgumentParser(
|
|
description="Convert a huggingface model to a GGML compatible file")
|
|
parser.add_argument(
|
|
"--vocab-only", action="store_true",
|
|
help="extract only the vocab",
|
|
)
|
|
parser.add_argument(
|
|
"--awq-path", type=Path, default=None,
|
|
help="Path to scale awq cache file")
|
|
parser.add_argument(
|
|
"--outfile", type=Path,
|
|
help="path to write to; default: based on input",
|
|
)
|
|
parser.add_argument(
|
|
"--outtype", type=str, choices=["f32", "f16"], default="f16",
|
|
help="output format - use f32 for float32, f16 for float16",
|
|
)
|
|
parser.add_argument("--bigendian", action="store_true", help="model is executed on big endian machine")
|
|
parser.add_argument(
|
|
"model", type=Path,
|
|
help="directory containing model file",
|
|
)
|
|
parser.add_argument("--use-temp-file", action="store_true", help="use the tempfile library while processing (helpful when running out of memory, process killed)")
|
|
|
|
return parser.parse_args()
|
|
|
|
|
|
def main() -> None:
|
|
args = parse_args()
|
|
|
|
dir_model = args.model
|
|
|
|
if args.awq_path:
|
|
sys.path.insert(1, str(Path(__file__).parent / 'awq-py'))
|
|
from awq.apply_awq import add_scale_weights # type: ignore[import-not-found]
|
|
tmp_model_path = args.model / "weighted_model"
|
|
dir_model = tmp_model_path
|
|
if tmp_model_path.is_dir():
|
|
print(f"{tmp_model_path} exists as a weighted model.")
|
|
else:
|
|
tmp_model_path.mkdir(parents=True, exist_ok=True)
|
|
print("Saving new weighted model ...")
|
|
add_scale_weights(str(args.model), str(args.awq_path), str(tmp_model_path))
|
|
print(f"Saved weighted model at {tmp_model_path}.")
|
|
|
|
if not dir_model.is_dir():
|
|
print(f'Error: {args.model} is not a directory', file=sys.stderr)
|
|
sys.exit(1)
|
|
|
|
ftype_map = {
|
|
"f32": gguf.GGMLQuantizationType.F32,
|
|
"f16": gguf.GGMLQuantizationType.F16,
|
|
}
|
|
|
|
if args.outfile is not None:
|
|
fname_out = args.outfile
|
|
else:
|
|
# output in the same directory as the model by default
|
|
fname_out = dir_model / f'ggml-model-{args.outtype}.gguf'
|
|
|
|
print(f"Loading model: {dir_model.name}")
|
|
|
|
hparams = Model.load_hparams(dir_model)
|
|
|
|
with torch.inference_mode():
|
|
model_class = Model.from_model_architecture(hparams["architectures"][0])
|
|
model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian, args.use_temp_file)
|
|
|
|
print("Set model parameters")
|
|
model_instance.set_gguf_parameters()
|
|
|
|
print("Set model tokenizer")
|
|
model_instance.set_vocab()
|
|
|
|
if args.vocab_only:
|
|
print(f"Exporting model vocab to '{fname_out}'")
|
|
model_instance.write_vocab()
|
|
else:
|
|
print(f"Exporting model to '{fname_out}'")
|
|
model_instance.write()
|
|
|
|
print(f"Model successfully exported to '{fname_out}'")
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|