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+#!/usr/bin/env python3
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+
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+from __future__ import annotations
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+
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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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+
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+import numpy as np
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+import torch
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+
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+if TYPE_CHECKING:
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+ from torch import Tensor
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+
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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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+
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+
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+###### MODEL DEFINITIONS ######
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+
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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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+
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+
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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)
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+
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+ def set_vocab(self):
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+ self._set_vocab_gpt2()
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+
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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(self.dir_model / part_name, map_location="cpu"))
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+
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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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+
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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_head")) is not None:
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+ self.gguf_writer.add_head_count(n_head)
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+ self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True))
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+
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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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+
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+ old_dtype = data_torch.dtype
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+
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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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+
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+ data = data_torch.squeeze().numpy()
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+
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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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+
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+ n_dims = len(data.shape)
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+ data_dtype = data.dtype
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+
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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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+
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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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+
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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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+
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+ print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
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+
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+ self.gguf_writer.add_tensor(new_name, data)
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+
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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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+
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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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+
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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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+
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+ return num_parts
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+
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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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+
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+ @staticmethod
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+ def from_model_architecture(model_architecture):
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+ if model_architecture == "StableLMEpochForCausalLM":
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+ return StableLMModel
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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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+ return Model
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+
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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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+
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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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+
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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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+
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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 == "FalconForCausalLM":
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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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+
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+ raise NotImplementedError(f'Architecture "{arch}" not supported!')
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+
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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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+
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+ from transformers import AutoTokenizer # type: ignore[attr-defined]
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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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+
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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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+
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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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+
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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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+
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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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+
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+ def _set_vocab_sentencepiece(self):
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+ from sentencepiece import SentencePieceProcessor
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+
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+ tokenizer_path = self.dir_model / 'tokenizer.model'
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+
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+class StableLMModel(Model):
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+ def set_gguf_parameters(self):
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+ super().set_gguf_parameters()
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+ self.gguf_writer.add_rope_dimension_count(
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+ int(self.hparams["rope_pct"] * (self.hparams["hidden_size"] // self.hparams["num_attention_heads"])),
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+ )
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+ self.gguf_writer.add_layer_norm_eps(1e-5)
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+
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+
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+class GPTNeoXModel(Model):
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+ def set_gguf_parameters(self):
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+ block_count = self.hparams["num_hidden_layers"]
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+
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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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+
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+
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+class BloomModel(Model):
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+ def set_gguf_parameters(self):
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+ self.gguf_writer.add_name("Bloom")
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+ n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
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+ n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
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+ self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed))
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+ self.gguf_writer.add_embedding_length(n_embed)
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+ self.gguf_writer.add_feed_forward_length(4 * n_embed)
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+ self.gguf_writer.add_block_count(self.hparams["n_layer"])
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+ self.gguf_writer.add_head_count(n_head)
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+ self.gguf_writer.add_head_count_kv(n_head)
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+ self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
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+ self.gguf_writer.add_file_type(self.ftype)
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+
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+ def write_tensors(self):
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+ block_count = self.hparams["n_layer"]
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+ tensors = dict(self.get_tensors())
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+ tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
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+ has_lm_head = True
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+ n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
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+ n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
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+
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|
|
+ 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
|
|
|
+ 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 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_embedding_length(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(hidden_size // head_count)
|
|
|
+ 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)
|
|
|
+
|
|
|
+
|
|
|
+###### 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(
|
|
|
+ "--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()
|
|
|
+
|
|
|
+
|
|
|
+args = parse_args()
|
|
|
+
|
|
|
+dir_model = args.model
|
|
|
+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)
|
|
|
+
|
|
|
+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}'")
|
Galunid