mirror of
https://github.com/deepseek-ai/DeepSeek-V3.git
synced 2025-02-23 06:08:58 -05:00
109 lines
3.8 KiB
Python
109 lines
3.8 KiB
Python
import os
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import shutil
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from argparse import ArgumentParser
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from glob import glob
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from pathlib import Path
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from tqdm import tqdm, trange
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import torch
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from safetensors.torch import safe_open, save_file
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mapping = {
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"embed_tokens": ("embed", 0),
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"input_layernorm": ("attn_norm", None),
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"post_attention_layernorm": ("ffn_norm", None),
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"q_proj": ("wq", 0),
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"q_a_proj": ("wq_a", None),
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"q_a_layernorm": ("q_norm", None),
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"q_b_proj": ("wq_b", 0),
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"kv_a_proj_with_mqa": ("wkv_a", None),
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"kv_a_layernorm": ("kv_norm", None),
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"kv_b_proj": ("wkv_b", 0),
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"o_proj": ("wo", 1),
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"gate": ("gate", None),
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"gate_proj": ("w1", 0),
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"down_proj": ("w2", 1),
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"up_proj": ("w3", 0),
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"norm": ("norm", None),
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"lm_head": ("head", 0),
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"scale": ("scale", None),
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}
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def main(hf_ckpt_path, save_path, n_experts, mp):
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"""
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Converts and saves model checkpoint files into a specified format.
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Args:
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hf_ckpt_path (str): Path to the directory containing the input checkpoint files.
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save_path (str): Path to the directory where the converted checkpoint files will be saved.
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n_experts (int): Total number of experts in the model.
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mp (int): Model parallelism factor.
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Returns:
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None
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"""
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assert mp > 0, "Model parallelism (mp) must be greater than 0"
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torch.set_num_threads(8)
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n_local_experts = n_experts // mp
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state_dicts = [{} for _ in range(mp)]
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hf_ckpt_path = Path(hf_ckpt_path)
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save_path = Path(save_path)
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save_path.mkdir(parents=True, exist_ok=True)
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for file_path in tqdm(hf_ckpt_path.glob("*.safetensors")):
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with safe_open(file_path, framework="pt", device="cpu") as f:
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for name in f.keys():
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if "model.layers.61" in name:
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continue
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param: torch.Tensor = f.get_tensor(name)
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if name.startswith("model."):
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name = name[len("model."):]
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name = (
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name.replace("self_attn", "attn")
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.replace("mlp", "ffn")
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.replace("weight_scale_inv", "scale")
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.replace("e_score_correction_bias", "bias")
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)
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key = name.split(".")[-2]
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assert key in mapping, f"Key {key} not found in mapping"
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new_key, dim = mapping[key]
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name = name.replace(key, new_key)
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if "experts" in name and "shared_experts" not in name:
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idx = int(name.split(".")[-3])
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target_index = idx // n_local_experts
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if target_index < mp:
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state_dicts[target_index][name] = param
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elif dim is not None:
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assert param.size(dim) % mp == 0, f"Dimension {dim} must be divisible by {mp}"
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shard_size = param.size(dim) // mp
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for i in range(mp):
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state_dicts[i][name] = param[:, i * shard_size : (i + 1) * shard_size] if dim == 1 else param[i * shard_size : (i + 1) * shard_size]
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for i in trange(mp):
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save_file(state_dicts[i], save_path / f"model{i}-mp{mp}.safetensors")
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for file_path in hf_ckpt_path.glob("*token*"):
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shutil.copyfile(file_path, save_path / file_path.name)
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if __name__ == "__main__":
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parser = ArgumentParser()
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parser.add_argument("--hf-ckpt-path", type=str, required=True)
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parser.add_argument("--save-path", type=str, required=True)
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parser.add_argument("--n-experts", type=int, required=True)
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parser.add_argument("--model-parallel", type=int, required=True)
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args = parser.parse_args()
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assert args.n_experts % args.model_parallel == 0, "Number of experts must be divisible by model parallelism"
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main(args.hf_ckpt_path, args.save_path, args.n_experts, args.model_parallel)
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