flextrain微调模型示例代码报：load_state_dict unexpected_keys
ZhipuAI/ChatGLM-6B 的 flextrain 微调模型训练成功之后，跟着示例走，走到 snapshot_download 下来状态字典，里面的层名是类似：
transformer.layers.【层数】.attention.query_key_value.lora_A
transformer.layers.【层数】.attention.query_key_value.lora_B
然后进行 model.load_state_dict(state_dict) 的时候，上述的所有key都是 unexpected_keys。
看了一下swift model里的层名，只有：
transformer.layers.【层数】.attention.query_key_value.loramodule_default.lora_A
transformer.layers.【层数】.attention.query_key_value.loramodule_default.lora_B
不知道为什么FlexTrain出来的状态字典层名会和swift model的不一样。
求大神指教！
FlexTrain出来的状态字典：
OrderedDict([('transformer.layers.0.attention.query_key_value.lora_A',
tensor([[ 0.0092, -0.0156, -0.0047, ..., -0.0121, 0.0107, 0.0067],
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...,
[ 0.0041, 0.0074, 0.0130, ..., -0.0078, 0.0101, -0.0084],
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[ 0.0027, 0.0006, 0.0005, ..., -0.0086, -0.0156, -0.0050]],
dtype=torch.bfloat16)),
('transformer.layers.0.attention.query_key_value.lora_B',
tensor([[ 1.5640e-04, 7.3624e-04, -6.0654e-04, ..., 3.2043e-04,
1.4722e-05, -8.9169e-05],
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2.6703e-04, 1.7643e-04],
[ 1.5163e-04, -1.7738e-04, 1.5831e-04, ..., 1.8692e-04,
-9.1076e-05, 6.0654e-04],
...,
[ 3.8910e-04, -5.9891e-04, -6.7902e-04, ..., -3.2187e-05,
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[ 1.6479e-03, 4.8256e-04, -9.7275e-04, ..., -3.4904e-04,
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tensor([[-0.0060, 0.0018, -0.0060, ..., -0.0096, -0.0156, -0.0123],
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[-0.0140, -0.0038, -0.0036, ..., 0.0034, 0.0011, -0.0085]],
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('transformer.layers.1.attention.query_key_value.lora_B',
tensor([[ 1.3733e-04, -3.5858e-04, 8.4305e-04, ..., -6.2943e-04,
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('transformer.layers.3.attention.query_key_value.lora_A',
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('transformer.layers.3.attention.query_key_value.lora_B',
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('transformer.layers.4.attention.query_key_value.lora_B',
tensor([[ 4.5586e-04, 2.5368e-04, 5.9509e-04, ..., -8.6975e-04,
-2.3270e-04, 2.3174e-04],
[ 5.8365e-04, 4.8828e-04, 5.4169e-04, ..., -5.6458e-04,
1.7881e-05, 1.0986e-03],
[ 4.1962e-05, 2.8229e-04, -2.0027e-04, ..., 6.6376e-04,
-9.3937e-05, -2.0218e-04],
...,
[ 1.4954e-03, 1.0376e-03, 1.9836e-03, ..., -1.2207e-03,
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[ 1.0071e-03, 9.7275e-04, 1.1902e-03, ..., -1.5411e-03,
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[ 1.4648e-03, 1.1139e-03, 1.4801e-03, ..., -1.4343e-03,
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('transformer.layers.5.attention.query_key_value.lora_A',
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('transformer.layers.5.attention.query_key_value.lora_B',
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[ 7.1716e-04, -3.3569e-04, 5.4932e-04, ..., -4.5598e-06,
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('transformer.layers.6.attention.query_key_value.lora_A',
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[ 1.2573e-02, 7.2327e-03, 1.3916e-02, ..., -1.5625e-02,
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[ 9.0942e-03, 1.4526e-02, -6.7139e-03, ..., 8.0566e-03,
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('transformer.layers.6.attention.query_key_value.lora_B',
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('transformer.layers.8.attention.query_key_value.lora_B',
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...,
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('transformer.layers.10.attention.query_key_value.lora_A',
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[-0.0085, 0.0007, 0.0074, ..., -0.0076, 0.0078, 0.0050],
...,
[-0.0038, 0.0011, 0.0150, ..., -0.0026, 0.0016, -0.0156],
[ 0.0013, 0.0148, -0.0107, ..., -0.0078, 0.0140, 0.0007],
[ 0.0027, 0.0121, -0.0115, ..., -0.0095, -0.0092, -0.0043]],
dtype=torch.bfloat16)),
('transformer.layers.25.attention.query_key_value.lora_B',
tensor([[ 5.8365e-04, 9.7656e-04, -2.5749e-04, ..., 4.3488e-04,
-8.8215e-05, 2.5177e-04],
[ 1.8539e-03, 1.3351e-03, -1.6708e-03, ..., 1.7776e-03,
1.8311e-03, 2.0142e-03],
[ 2.4109e-03, 2.3956e-03, -2.3956e-03, ..., 2.5787e-03,
2.4109e-03, 2.6550e-03],
...,
[ 6.9046e-04, -3.2997e-04, 7.5531e-04, ..., 7.6675e-04,
6.9809e-04, 6.7139e-04],
[ 1.3046e-03, 6.1035e-04, 1.3351e-03, ..., 1.3504e-03,
1.3275e-03, 9.8419e-04],
[-8.0109e-04, -2.1362e-03, 1.8921e-03, ..., -7.5817e-05,
-4.1199e-04, -2.0294e-03]], dtype=torch.bfloat16)),
('transformer.layers.26.attention.query_key_value.lora_A',
tensor([[-0.0004, 0.0095, 0.0145, ..., 0.0070, -0.0149, 0.0131],
[ 0.0023, 0.0054, -0.0156, ..., -0.0018, 0.0002, -0.0030],
[ 0.0116, -0.0014, 0.0027, ..., 0.0107, 0.0114, 0.0145],
...,
[ 0.0023, -0.0156, 0.0094, ..., 0.0147, -0.0014, 0.0056],
[ 0.0087, -0.0027, 0.0156, ..., 0.0094, 0.0022, -0.0072],
[-0.0023, 0.0080, 0.0020, ..., -0.0019, -0.0052, -0.0106]],
dtype=torch.bfloat16)),
('transformer.layers.26.attention.query_key_value.lora_B',
tensor([[-0.0008, -0.0021, 0.0011, ..., 0.0014, 0.0018, 0.0011],
[-0.0012, -0.0015, 0.0015, ..., 0.0012, 0.0009, 0.0014],
[ 0.0009, 0.0014, -0.0011, ..., -0.0010, -0.0016, -0.0011],
...,
[ 0.0010, 0.0026, 0.0014, ..., -0.0029, -0.0028, 0.0006],
[-0.0003, 0.0013, 0.0002, ..., -0.0009, -0.0010, 0.0002],
[-0.0025, -0.0016, 0.0024, ..., 0.0007, -0.0004, 0.0028]],
dtype=torch.bfloat16)),
('transformer.layers.27.attention.query_key_value.lora_A',
tensor([[ 0.0094, -0.0043, -0.0104, ..., -0.0098, 0.0050, 0.0079],
[ 0.0079, -0.0032, -0.0089, ..., 0.0106, 0.0081, 0.0093],
[ 0.0074, -0.0129, 0.0156, ..., -0.0090, -0.0151, 0.0056],
...,
[-0.0103, -0.0149, 0.0156, ..., -0.0110, 0.0004, 0.0029],
[ 0.0045, 0.0028, 0.0110, ..., 0.0033, 0.0052, -0.0103],
[-0.0146, -0.0019, -0.0031, ..., -0.0052, -0.0129, 0.0065]],
dtype=torch.bfloat16)),
('transformer.layers.27.attention.query_key_value.lora_B',
tensor([[-5.1117e-04, 1.0223e-03, -1.5926e-04, ..., 8.4686e-04,
-1.4496e-03, 7.0190e-04],
[ 4.9353e-05, 3.8338e-04, 2.1744e-04, ..., 1.3123e-03,
-1.2894e-03, 1.2398e-04],
[-7.9727e-04, 9.0027e-04, -1.8082e-03, ..., -1.8082e-03,
3.7956e-04, -1.3962e-03],
...,
[ 3.4943e-03, -4.9133e-03, 5.3406e-03, ..., 4.4861e-03,
2.1057e-03, 5.4321e-03],
[ 1.2054e-03, -1.1139e-03, 1.6327e-03, ..., 1.9836e-03,
-9.5367e-04, 3.2501e-03],
[ 1.3885e-03, -7.4387e-04, 1.4572e-03, ..., 2.0142e-03,
-9.4986e-04, 3.2806e-03]], dtype=torch.bfloat16))])
ChatGlm6b模型
SwiftModel(
(model): ChatGLMForConditionalGeneration(
(transformer): ChatGLMModel(
(word_embeddings): Embedding(130528, 4096)
(layers): ModuleList(
(0-27): 28 x GLMBlock(
(input_layernorm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)
(attention): SelfAttention(
(rotary_emb): RotaryEmbedding()
(query_key_value): Linear(
in_features=4096, out_features=12288, bias=True
(loramodule_default): Linear(
in_features=4096, out_features=12288, bias=True
(lora_dropout): Dropout(p=0.05, inplace=False)
)
)
(dense): Linear(in_features=4096, out_features=4096, bias=True)
)
(post_attention_layernorm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)
(mlp): GLU(
(dense_h_to_4h): Linear(in_features=4096, out_features=16384, bias=True)
(dense_4h_to_h): Linear(in_features=16384, out_features=4096, bias=True)
)
)
)
(final_layernorm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)
)
(lm_head): Linear(in_features=4096, out_features=130528, bias=False)
)
)
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根据错误描述来看可能是 state_dict 里有的 layer 名称跟 Swift model 不一样导致的问题，请核实你使用的 model 是否与 FlexTrain 对应版本完全相同，并且是否加载了正确的 state_dict 文件。
例如：检查 state_dict 中的层名是否为 transformer.layers.<层数>.attentions.<子模块>.query_key_value.lora_A 或 transformer.layers.<层数>.attentions.query_key_value.lora_B 等。
2023-11-27 15:56:00赞同 1 展开评论 打赏 -
面对过去，不要迷离；面对未来，不必彷徨；活在今天，你只要把自己完全展示给别人看。"load_state_dict unexpected_keys"这个错误是因为您正在尝试加载的状态字典中包含了一些不在当前模型中的层，这些层可能是在预训练过程中定义的。
为了解决这个问题，您可以在载入状态字典之前，使用torch.nn.Module.load_state_dict()函数的strict=False参数，这将忽略掉与当前模型中不存在的层相对应的关键字。
例如：model.load_state_dict(state_dict, strict=False)2023-11-24 13:54:01赞同 展开评论 打赏 -
北京阿里云ACE会长出现 "load_state_dict unexpected_keys" 错误通常是因为在加载状态字典时，模型的层或模块名称发生了变化，导致加载时出现了不匹配的情况。
在这种情况下，你可以尝试使用 load_state_dict 函数的 strict 参数来加载状态字典。当 strict 参数设置为 True 时，如果模型的层或模块名称与状态字典中的名称不匹配，load_state_dict 函数会自动忽略这些不匹配的项，只加载匹配的项。这样，模型将使用已存在的层和模块来恢复状态，而不会尝试创建新的层或模块。
示例代码如下：model.load_state_dict(state_dict, strict=True)
CopyCopy你提到的 "ZhipuAI/ChatGLM-6B" 模型可能是基于清华大学 KEG 实验室和智谱AI共同训练的 GLM-130B 模型进行微调的。由于模型结构的差异，可能需要对状态字典进行一些调整，以便正确地将其加载到微调后的模型中。
2023-11-24 08:41:45赞同 展开评论 打赏
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