mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-11 21:39:52 +00:00
4aa43fab56
* fix bug for norm_rms_eps missing * to align with the same order as convert.py for model write * fix: undo HF models permute tensor * update for flake8 lint
1721 lines
74 KiB
Python
Executable File
1721 lines
74 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 enum import IntEnum
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from pathlib import Path
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from typing import TYPE_CHECKING, Any, ContextManager, Iterator, 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 HfVocab
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# check for any of the given keys in the dictionary and return the value of the first key found
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def get_key_opts(d, keys):
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for k in keys:
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if k in d:
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return d[k]
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print(f"Could not find any of {keys}")
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sys.exit()
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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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class Model:
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def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: 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.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.model_arch = self._get_model_architecture()
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self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=False)
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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.hparams.get(
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"n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")),
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))
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if (n_ctx := self.hparams.get("max_position_embeddings")) is not None:
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self.gguf_writer.add_context_length(n_ctx)
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if (n_embd := self.hparams.get("hidden_size")) is not None:
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self.gguf_writer.add_embedding_length(n_embd)
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if (n_ff := self.hparams.get("intermediate_size")) is not None:
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self.gguf_writer.add_feed_forward_length(n_ff)
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if (n_head := self.hparams.get("num_attention_heads")) is not None:
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self.gguf_writer.add_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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if (n_rms_eps := self.hparams.get("rms_norm_eps")) is not None:
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self.gguf_writer.add_layer_norm_rms_eps(n_rms_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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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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self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True))
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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:
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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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@staticmethod
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def from_model_architecture(model_architecture):
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if model_architecture == "GPTNeoXForCausalLM":
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return GPTNeoXModel
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if model_architecture == "BloomForCausalLM":
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return BloomModel
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if model_architecture == "MPTForCausalLM":
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return MPTModel
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if model_architecture in ("BaichuanForCausalLM", "BaiChuanForCausalLM"):
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return BaichuanModel
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if model_architecture in ("FalconForCausalLM", "RWForCausalLM"):
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return FalconModel
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if model_architecture == "GPTBigCodeForCausalLM":
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return StarCoderModel
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if model_architecture == "GPTRefactForCausalLM":
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return RefactModel
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if model_architecture == "PersimmonForCausalLM":
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return PersimmonModel
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if model_architecture in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
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return StableLMModel
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if model_architecture == "QWenLMHeadModel":
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return QwenModel
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if model_architecture == "Qwen2ForCausalLM":
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return Model
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if model_architecture == "MixtralForCausalLM":
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return MixtralModel
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if model_architecture == "GPT2LMHeadModel":
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return GPT2Model
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if model_architecture == "PhiForCausalLM":
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return Phi2Model
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if model_architecture == "PlamoForCausalLM":
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return PlamoModel
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if model_architecture == "CodeShellForCausalLM":
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return CodeShellModel
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if model_architecture == "OrionForCausalLM":
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return OrionModel
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if model_architecture == "InternLM2ForCausalLM":
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return InternLM2Model
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if model_architecture == "MiniCPMForCausalLM":
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return MiniCPMModel
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return Model
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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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def _get_model_architecture(self) -> gguf.MODEL_ARCH:
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arch = self.hparams["architectures"][0]
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if arch == "GPTNeoXForCausalLM":
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return gguf.MODEL_ARCH.GPTNEOX
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if arch == "BloomForCausalLM":
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return gguf.MODEL_ARCH.BLOOM
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if arch == "MPTForCausalLM":
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return gguf.MODEL_ARCH.MPT
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if arch in ("BaichuanForCausalLM", "BaiChuanForCausalLM"):
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return gguf.MODEL_ARCH.BAICHUAN
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if arch in ("FalconForCausalLM", "RWForCausalLM"):
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return gguf.MODEL_ARCH.FALCON
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if arch == "GPTBigCodeForCausalLM":
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return gguf.MODEL_ARCH.STARCODER
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if arch == "GPTRefactForCausalLM":
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return gguf.MODEL_ARCH.REFACT
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if arch == "PersimmonForCausalLM":
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return gguf.MODEL_ARCH.PERSIMMON
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if arch in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
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return gguf.MODEL_ARCH.STABLELM
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if arch == "QWenLMHeadModel":
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return gguf.MODEL_ARCH.QWEN
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if arch == "Qwen2ForCausalLM":
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return gguf.MODEL_ARCH.QWEN2
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if arch == "MixtralForCausalLM":
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return gguf.MODEL_ARCH.LLAMA
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if arch == "GPT2LMHeadModel":
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return gguf.MODEL_ARCH.GPT2
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if arch == "PhiForCausalLM":
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return gguf.MODEL_ARCH.PHI2
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if arch == "PlamoForCausalLM":
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return gguf.MODEL_ARCH.PLAMO
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if arch == "CodeShellForCausalLM":
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return gguf.MODEL_ARCH.CODESHELL
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if arch == "OrionForCausalLM":
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return gguf.MODEL_ARCH.ORION
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if arch == "InternLM2ForCausalLM":
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return gguf.MODEL_ARCH.INTERNLM2
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if arch == "MiniCPMForCausalLM":
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return gguf.MODEL_ARCH.MINICPM
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raise NotImplementedError(f'Architecture "{arch}" not supported!')
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def _set_vocab_gpt2(self):
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dir_model = self.dir_model
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hparams = self.hparams
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tokens: list[bytearray] = []
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toktypes: list[int] = []
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(dir_model)
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vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
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assert max(tokenizer.vocab.values()) < vocab_size
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reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}
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added_vocab = tokenizer.get_added_vocab()
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for i in range(vocab_size):
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if i not in reverse_vocab:
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pad_token = f"[PAD{i}]".encode('utf-8')
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tokens.append(bytearray(pad_token))
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toktypes.append(gguf.TokenType.USER_DEFINED)
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elif reverse_vocab[i] in added_vocab:
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tokens.append(reverse_vocab[i])
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if tokenizer.added_tokens_decoder[i].special:
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toktypes.append(gguf.TokenType.CONTROL)
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else:
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toktypes.append(gguf.TokenType.USER_DEFINED)
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else:
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tokens.append(reverse_vocab[i])
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toktypes.append(gguf.TokenType.NORMAL)
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self.gguf_writer.add_tokenizer_model("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=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[bytearray] = []
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toktypes: list[int] = []
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(dir_model, 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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pad_token = f"[PAD{i}]".encode("utf-8")
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tokens.append(bytearray(pad_token))
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toktypes.append(gguf.TokenType.USER_DEFINED)
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elif reverse_vocab[i] in added_vocab:
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tokens.append(reverse_vocab[i])
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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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print(f'Error: Missing {tokenizer_path}', file=sys.stderr)
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sys.exit(1)
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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(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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tokens.append(key.encode("utf-8"))
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scores.append(-1000.0)
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toktypes.append(SentencePieceTokenTypes.USER_DEFINED)
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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_hf(self):
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path = self.dir_model
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added_tokens_path = self.dir_model
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vocab = HfVocab(
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path, added_tokens_path if added_tokens_path.exists() else None
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)
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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)
|
|
|
|
assert len(tokens) == vocab.vocab_size
|
|
|
|
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)
|
|
|
|
special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
|
|
special_vocab.add_to_gguf(self.gguf_writer)
|
|
|
|
|
|
class GPTNeoXModel(Model):
|
|
def set_gguf_parameters(self):
|
|
block_count = self.hparams["num_hidden_layers"]
|
|
|
|
self.gguf_writer.add_name(self.dir_model.name)
|
|
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(
|
|
int(self.hparams["rotary_pct"] * (self.hparams["hidden_size"] // self.hparams["num_attention_heads"])),
|
|
)
|
|
self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
|
|
self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True))
|
|
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])
|
|
|
|
|
|
class BloomModel(Model):
|
|
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}")
|
|
|
|
|
|
class MPTModel(Model):
|
|
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"])
|
|
self.gguf_writer.add_max_alibi_bias(self.hparams["attn_config"]["alibi_bias_max"])
|
|
|
|
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)
|
|
|
|
# note: MPT output is tied to (same as) wte in original model;
|
|
# for easier implementation in llama.cpp it's duplicated in GGUF, though :/
|
|
if new_name == "token_embd.weight":
|
|
self.gguf_writer.add_tensor("output.weight", data)
|
|
|
|
|
|
class OrionModel(Model):
|
|
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)
|
|
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)
|
|
|
|
|
|
class BaichuanModel(Model):
|
|
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, ...]
|
|
|
|
|
|
class FalconModel(Model):
|
|
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)
|
|
|
|
|
|
class StarCoderModel(Model):
|
|
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)
|
|
|
|
|
|
class RefactModel(Model):
|
|
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)
|
|
|
|
|
|
class PersimmonModel(Model):
|
|
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"])
|
|
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_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)
|
|
|
|
|
|
class StableLMModel(Model):
|
|
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"])
|
|
self.gguf_writer.add_rope_dimension_count(int(hparams["rope_pct"] * (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(1e-5)
|
|
|
|
|
|
class MixtralModel(Model):
|
|
def set_vocab(self):
|
|
self._set_vocab_sentencepiece()
|
|
|
|
|
|
class MiniCPMModel(Model):
|
|
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_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)
|
|
|
|
|
|
class QwenModel(Model):
|
|
@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)
|
|
|
|
|
|
class GPT2Model(Model):
|
|
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)
|
|
|
|
|
|
class Phi2Model(Model):
|
|
def set_gguf_parameters(self):
|
|
block_count = get_key_opts(self.hparams, ["num_hidden_layers", "n_layer"])
|
|
|
|
rot_pct = get_key_opts(self.hparams, ["partial_rotary_factor"])
|
|
n_embd = get_key_opts(self.hparams, ["hidden_size", "n_embd"])
|
|
n_head = get_key_opts(self.hparams, ["num_attention_heads", "n_head"])
|
|
|
|
self.gguf_writer.add_name("Phi2")
|
|
self.gguf_writer.add_context_length(get_key_opts(self.hparams, ["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(get_key_opts(self.hparams, ["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)
|
|
|
|
|
|
class PlamoModel(Model):
|
|
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)
|
|
|
|
|
|
class CodeShellModel(Model):
|
|
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}")
|
|
|
|
|
|
class InternLM2Model(Model):
|
|
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)
|
|
|
|
|
|
###### 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",
|
|
)
|
|
|
|
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)
|
|
|
|
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()
|