合并权重问题

[red-tie]

在合并7b-hf与奖励模型，sft模型，policy 模型的时候都报错。 Naive integrity check failed. This could imply that some of the checkpoint files are corrupted.

每个模型我都下载两遍，模型本身应该没问题，应该是哪出了问题。

[overal-well-off-society]

(moss) dk@user-SYS-4029GP-TRT2:~/Downloads/MOSS-RLHF-main$ python merge_weight_en.py recover --path_raw /data/dk_downloads/llama-7b-hf --path_diff /data/dk_downloads/sft_model/diff --path_tuned ./models/moss-rlhf-sft-model-7B-en/recover --model_type sft [2023-10-07 21:07:27,031] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect) Loading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████| 33/33 [00:11<00:00, 2.88it/s] 0%| | 0/291 [00:00<?, ?it/s]model.embed_tokens.weight model.layers.0.self_attn.q_proj.weight model.layers.0.self_attn.k_proj.weight model.layers.0.self_attn.v_proj.weight model.layers.0.self_attn.o_proj.weight model.layers.0.mlp.gate_proj.weight model.layers.0.mlp.up_proj.weight model.layers.0.mlp.down_proj.weight model.layers.0.input_layernorm.weight model.layers.0.post_attention_layernorm.weight model.layers.1.self_attn.q_proj.weight model.layers.1.self_attn.k_proj.weight model.layers.1.self_attn.v_proj.weight model.layers.1.self_attn.o_proj.weight model.layers.1.mlp.gate_proj.weight model.layers.1.mlp.up_proj.weight model.layers.1.mlp.down_proj.weight model.layers.1.input_layernorm.weight model.layers.1.post_attention_layernorm.weight model.layers.2.self_attn.q_proj.weight model.layers.2.self_attn.k_proj.weight model.layers.2.self_attn.v_proj.weight model.layers.2.self_attn.o_proj.weight model.layers.2.mlp.gate_proj.weight model.layers.2.mlp.up_proj.weight model.layers.2.mlp.down_proj.weight model.layers.2.input_layernorm.weight model.layers.2.post_attention_layernorm.weight model.layers.3.self_attn.q_proj.weight model.layers.3.self_attn.k_proj.weight model.layers.3.self_attn.v_proj.weight model.layers.3.self_attn.o_proj.weight model.layers.3.mlp.gate_proj.weight model.layers.3.mlp.up_proj.weight model.layers.3.mlp.down_proj.weight model.layers.3.input_layernorm.weight model.layers.3.post_attention_layernorm.weight model.layers.4.self_attn.q_proj.weight model.layers.4.self_attn.k_proj.weight model.layers.4.self_attn.v_proj.weight model.layers.4.self_attn.o_proj.weight model.layers.4.mlp.gate_proj.weight model.layers.4.mlp.up_proj.weight model.layers.4.mlp.down_proj.weight model.layers.4.input_layernorm.weight model.layers.4.post_attention_layernorm.weight model.layers.5.self_attn.q_proj.weight model.layers.5.self_attn.k_proj.weight model.layers.5.self_attn.v_proj.weight model.layers.5.self_attn.o_proj.weight model.layers.5.mlp.gate_proj.weight model.layers.5.mlp.up_proj.weight model.layers.5.mlp.down_proj.weight model.layers.5.input_layernorm.weight model.layers.5.post_attention_layernorm.weight model.layers.6.self_attn.q_proj.weight model.layers.6.self_attn.k_proj.weight model.layers.6.self_attn.v_proj.weight model.layers.6.self_attn.o_proj.weight model.layers.6.mlp.gate_proj.weight model.layers.6.mlp.up_proj.weight model.layers.6.mlp.down_proj.weight model.layers.6.input_layernorm.weight 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model.layers.29.self_attn.k_proj.weight model.layers.29.self_attn.v_proj.weight model.layers.29.self_attn.o_proj.weight model.layers.29.mlp.gate_proj.weight model.layers.29.mlp.up_proj.weight model.layers.29.mlp.down_proj.weight model.layers.29.input_layernorm.weight model.layers.29.post_attention_layernorm.weight model.layers.30.self_attn.q_proj.weight model.layers.30.self_attn.k_proj.weight model.layers.30.self_attn.v_proj.weight model.layers.30.self_attn.o_proj.weight model.layers.30.mlp.gate_proj.weight model.layers.30.mlp.up_proj.weight model.layers.30.mlp.down_proj.weight model.layers.30.input_layernorm.weight model.layers.30.post_attention_layernorm.weight model.layers.31.self_attn.q_proj.weight model.layers.31.self_attn.k_proj.weight model.layers.31.self_attn.v_proj.weight model.layers.31.self_attn.o_proj.weight model.layers.31.mlp.gate_proj.weight model.layers.31.mlp.up_proj.weight model.layers.31.mlp.down_proj.weight model.layers.31.input_layernorm.weight model.layers.31.post_attention_layernorm.weight model.norm.weight lm_head.weight 100%|███████████████████████████████████████████████████████████████████████████████████████████████| 291/291 [00:00<00:00, 315141.35it/s] 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 291/291 [00:01<00:00, 215.24it/s] Traceback (most recent call last): File "merge_weight_en.py", line 181, in fire.Fire(main) File "/home/dk/anaconda3/envs/moss/lib/python3.8/site-packages/fire/core.py", line 141, in Fire component_trace = _Fire(component, args, parsed_flag_args, context, name) File "/home/dk/anaconda3/envs/moss/lib/python3.8/site-packages/fire/core.py", line 475, in _Fire component, remaining_args = _CallAndUpdateTrace( File "/home/dk/anaconda3/envs/moss/lib/python3.8/site-packages/fire/core.py", line 691, in _CallAndUpdateTrace component = fn(varargs, kwargs) File "merge_weight_en.py", line 177, in main globals()[task](kwargs) File "/home/dk/anaconda3/envs/moss/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context return func(args, **kwargs) File "merge_weight_en.py", line 165, in recover assert torch.allclose( AssertionError: Naive integrity check failed. This could imply that some of the checkpoint files are corrupted.

[red-tie]

上面是报错详细信息

[yuanhuachao]

+1，我也遇到了

[red-tie]

已经解决了，把requirements里面的包装一下就可以。命令行运行pip install -r requirements, 里面那个flash-attn可能会报错，我直接把那个删了，希望后续不会有问题。。。

[Ablustrund]

您好，这个之前有同学遇到过，确实可能是包的版本问题。本质上是加载模型时候有一些层被加载（例如旋转位置编码），但是在merge的时候不需要用到这些层。 debug的方式查看diff中有哪些层，是否和原始的llama相对应。

[Ablustrund]

已经解决了，把requirements里面的包装一下就可以。命令行运行pip install -r requirements, 里面那个flash-attn可能会报错，我直接把那个删了，希望后续不会有问题。。。

flash-attn需要外网环境，我们这里安装时也不太稳定，多安装几次会成功。

[BeyondTheStarry]

pip install -r requirements, 我也安装了requirement，但是仍然报错，错误提示一模一样。