mirror of
https://github.com/microsoft/BitNet.git
synced 2026-05-03 19:30:32 +00:00
Junhui He
116 lines
4.1 KiB
Python
116 lines
4.1 KiB
Python
import re
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import torch
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from pathlib import Path
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from safetensors.torch import load_file
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from einops import rearrange
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from dataclasses import dataclass
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from typing import Optional
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transformer_configs = {
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"2B": dict(n_layer=30, n_head=20, dim=2560, vocab_size=128256, n_local_heads=5, intermediate_size=6912),
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}
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@dataclass
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class ModelArgs:
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block_size: int = 4096
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vocab_size: int = 32000
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n_layer: int = 32
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n_head: int = 32
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dim: int = 4096
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intermediate_size: int = None
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n_local_heads: int = -1
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head_dim: int = 64
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rope_base: float = 10000
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norm_eps: float = 1e-5
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def __post_init__(self):
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if self.n_local_heads == -1:
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self.n_local_heads = self.n_head
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if self.intermediate_size is None:
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hidden_dim = 4 * self.dim
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n_hidden = int(2 * hidden_dim / 3)
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self.intermediate_size = n_hidden + (256 - n_hidden % 256) if n_hidden % 256 else n_hidden
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self.head_dim = self.dim // self.n_head
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@classmethod
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def from_name(cls, name: str):
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if name in transformer_configs:
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return cls(**transformer_configs[name])
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config = [k for k in transformer_configs if k in name.upper() or k in name]
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assert len(config) == 1, f"Unknown model name: {name}"
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return cls(**transformer_configs[config[0]])
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def invert_convert_q(w: torch.Tensor, config: ModelArgs) -> torch.Tensor:
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return rearrange(w, '(h l d) i -> (h d l) i', h=config.n_head, l=2)
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def invert_convert_k(w: torch.Tensor, config: ModelArgs) -> torch.Tensor:
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return rearrange(w, '(h l d) i -> (h d l) i', h=config.n_local_heads, l=2)
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def convert_back(
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safetensors_path: str,
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output_file: str,
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model_name: Optional[str] = None,
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):
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st_dict = load_file(safetensors_path)
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cfg = ModelArgs.from_name(model_name)
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print(f"Using model configurations: {cfg}")
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recovered: dict = {}
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for layer in range(cfg.n_layer):
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base = f"model.layers.{layer}."
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wq = st_dict[f"{base}self_attn.q_proj.weight"]
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wk = st_dict[f"{base}self_attn.k_proj.weight"]
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wv = st_dict[f"{base}self_attn.v_proj.weight"]
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wq = invert_convert_q(wq, cfg)
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wk = invert_convert_k(wk, cfg)
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wqkv = torch.cat([wq, wk, wv], dim=0)
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recovered[f"layers.{layer}.attention.wqkv.weight"] = wqkv
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recovered[f"layers.{layer}.attention.wo.weight"] = st_dict[f"{base}self_attn.o_proj.weight"]
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recovered[f"layers.{layer}.attention_norm.weight"] = st_dict[f"{base}input_layernorm.weight"]
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recovered[f"layers.{layer}.ffn_norm.weight"] = st_dict[f"{base}post_attention_layernorm.weight"]
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recovered[f"layers.{layer}.attention.attn_sub_norm.weight"] = st_dict[f"{base}self_attn.attn_sub_norm.weight"]
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recovered[f"layers.{layer}.feed_forward.ffn_sub_norm.weight"] = st_dict[f"{base}mlp.ffn_sub_norm.weight"]
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gate = st_dict[f"{base}mlp.gate_proj.weight"]
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up = st_dict[f"{base}mlp.up_proj.weight"]
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w13 = torch.cat([gate, up], dim=0)
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recovered[f"layers.{layer}.feed_forward.w13.weight"] = w13
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recovered[f"layers.{layer}.feed_forward.w2.weight"] = st_dict[f"{base}mlp.down_proj.weight"]
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recovered["tok_embeddings.weight"] = st_dict["model.embed_tokens.weight"]
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recovered["output.weight"] = st_dict["model.embed_tokens.weight"]
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recovered["norm.weight"] = st_dict["model.norm.weight"]
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print(f"Saving to {output_file}")
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torch.save(recovered, output_file)
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser(description="Convert Safetensors back to Torch .pth checkpoint")
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parser.add_argument(
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"--safetensors_file", type=str, required=True,
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help="Path to input .safetensors file"
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)
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parser.add_argument(
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"--output", type=str, default="./checkpoints/model_state.pt",
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help="Path to output .pt file"
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)
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parser.add_argument(
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"--model_name", type=str, default="2B",
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help="Model configuration name to use (e.g. 2B)"
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)
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args = parser.parse_args()
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convert_back(
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safetensors_path=args.safetensors_file,
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output_file=args.output,
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model_name=args.model_name,
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) |