Repository: huggingface/transformers
Pull request: Ensure e_score_correction_bias dtype of DeepSeek-V3/R1 is FP32

# What does this PR do?

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Fixes # (issue)

Hi, we find that below loading method cannot keep the dtype of e_score_correction_bias in safetensors, so raised this PR to fix it. 

Please let me know if this is not the expected change. Thanks.

https://github.com/intel/auto-round/issues/845
```python
from transformers import AutoModelForCausalLM
model_name = "deepseek-ai/DeepSeek-V3.1-Terminus"

model = AutoModelForCausalLM.from_pretrained(
        model_name,torch_dtype="auto"
)

print(model.model.layers[3].mlp.gate.weight) # BF16
print(model.model.layers[3].mlp.gate.e_score_correction_bias) # FP32
```

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@CyrilVallez @ArthurZucker 

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Changed files:
filename: src/transformers/models/deepseek_v3/modeling_deepseek_v3.py
patch: @@ -548,6 +548,7 @@ class DeepseekV3PreTrainedModel(PreTrainedModel):
         "hidden_states": DeepseekV3DecoderLayer,
         "attentions": DeepseekV3Attention,
     }
+    _keep_in_fp32_modules_strict = ["e_score_correction_bias"]
 
     @torch.no_grad()
     def _init_weights(self, module):

filename: src/transformers/models/deepseek_v3/modular_deepseek_v3.py
patch: @@ -304,6 +304,7 @@ def __init__(self, config: DeepseekV3Config, layer_idx: int):
 
 class DeepseekV3PreTrainedModel(LlamaPreTrainedModel):
     _can_compile_fullgraph = False
+    _keep_in_fp32_modules_strict = ["e_score_correction_bias"]
 
     @torch.no_grad()
     def _init_weights(self, module):

filename: src/transformers/models/dots1/modeling_dots1.py
patch: @@ -469,6 +469,7 @@ class Dots1PreTrainedModel(PreTrainedModel):
         "hidden_states": Dots1DecoderLayer,
         "attentions": Dots1Attention,
     }
+    _keep_in_fp32_modules_strict = ["e_score_correction_bias"]
 
     @torch.no_grad()
     def _init_weights(self, module):

filename: src/transformers/models/glm4_moe/modeling_glm4_moe.py
patch: @@ -492,6 +492,7 @@ class Glm4MoePreTrainedModel(PreTrainedModel):
         "hidden_states": Glm4MoeDecoderLayer,
         "attentions": Glm4MoeAttention,
     }
+    _keep_in_fp32_modules_strict = ["e_score_correction_bias"]
 
     @torch.no_grad()
     def _init_weights(self, module):

filename: src/transformers/models/glm4v_moe/modeling_glm4v_moe.py
patch: @@ -594,6 +594,7 @@ class Glm4vMoePreTrainedModel(PreTrainedModel):
         "attentions": Glm4vMoeTextAttention,
         "router_logits": OutputRecorder(nn.Linear, layer_name="mlp.gate", index=0),
     }
+    _keep_in_fp32_modules_strict = ["e_score_correction_bias"]
     input_modalities = ("text", "image", "video")
 
     @torch.no_grad()