[Bug]Qwen3-Coder-30B-A3B GGUF Model Expert Operators Compatibility Issues - KExpertsMarlin KeyError and KExpertsTorch NoneType Error

### Checklist

- [x] 1. I have searched related issues but cannot get the expected help.
- [x] 2. The bug has not been fixed in the latest version.
- [x] 3. Please note that if the bug-related issue you submitted lacks corresponding environment info and a minimal reproducible demo, it will be challenging for us to reproduce and resolve the issue, reducing the likelihood of receiving feedback.
- [ ] 4. If the issue you raised is not a bug but a question, please raise a discussion at https://github.com/kvcache-ai/ktransformers/discussions. Otherwise, it will be closed.
- [x] 5. To help the community, I will use Chinese/English or attach an Chinese/English translation if using another language. Non-Chinese/English content without translation may be closed.

### Describe the bug

@jinmmd @jizhilong @sayap @sammcj @twobob @
**中文描述 / Chinese Description:**
在使用KTransformers部署Unsloth的Qwen3-Coder-30B-A3B-Instruct GGUF模型时遇到两个相关的专家操作符兼容性问题：

1. **KExpertsMarlin权重命名不匹配**: 配置前24个专家使用`KExpertsMarlin`操作符时出现`KeyError: 'model.layers.0.mlp.experts.ffn_up_exps.weight'`错误
2. **KExpertsTorch NoneType错误**: 改用`KExpertsTorch`操作符后出现`TypeError: 'NoneType' object is not subscriptable`错误

根本原因是GGUF格式模型的权重命名格式与专家操作符期望的格式不匹配：
- GGUF格式使用: `blk.{layer_id}.ffn_*_exps.weight`
- 操作符期望: `model.layers.{layer_id}.mlp.experts.ffn_*_exps.weight`

**English Description:**
Encountered two related expert operator compatibility issues when deploying Unsloth's Qwen3-Coder-30B-A3B-Instruct GGUF model with KTransformers:

1. **KExpertsMarlin weight naming mismatch**: `KeyError: 'model.layers.0.mlp.experts.ffn_up_exps.weight'` when configuring first 24 experts to use `KExpertsMarlin` operator
2. **KExpertsTorch NoneType error**: `TypeError: 'NoneType' object is not subscriptable` when switching to `KExpertsTorch` operator

Root cause is the mismatch between GGUF model weight naming format and expert operators' expected format:
- GGUF format uses: `blk.{layer_id}.ffn_*_exps.weight`
- Operators expect: `model.layers.{layer_id}.mlp.experts.ffn_*_exps.weight`

### Reproduction

**模型信息 / Model Information:**
- **Model**: Unsloth Qwen3-Coder-30B-A3B-Instruct (GGUF Q4_K_M quantized)
- **Source**: Hugging Face model converted to GGUF format
- **Architecture**: Qwen3MoeForCausalLM (MoE model with 128 experts, 8 experts per token)

**复现步骤 / Reproduction Steps:**

1. **下载模型 / Download Model:**
```bash
# Download Qwen3-Coder-30B-A3B-Instruct GGUF model
# Place in: /root/ktransformers_models/qwen3-coder-30b/quantized_Q4_K_M/
# Original config in: /root/ktransformers_models/qwen3-coder-30b/original_config/
```

2. **配置优化规则 / Configure Optimization Rules:**
```yaml
# optimize_config.yaml - First attempt with KExpertsMarlin
- match:
name: "^model\\.layers\\.(0|[1-9]|1[0-9]|2[0-3])\\.mlp\\.experts$"
replace:
class: ktransformers.operators.experts.KTransformersExpertsV2
kwargs:
generate_device: "cuda"
generate_op: "KExpertsMarlin" # This causes KeyError
prefill_device: "cuda"
prefill_op: "KExpertsTorch"
```

3. **启动命令 / Launch Command:**
```bash
#!/bin/bash
source /opt/miniconda3/etc/profile.d/conda.sh
conda activate kt

python3 /opt/kt/ktransformers/server/main.py \
--model_path "/root/ktransformers_models/qwen3-coder-30b/original_config" \
--gguf_path "/root/ktransformers_models/qwen3-coder-30b/quantized_Q4_K_M" \
--architectures Qwen3MoeForCausalLM \
--optimize_config_path "/mnt/d/code/kT/deployment_Kt/output/optimize_config.yaml" \
--cpu_infer 18 \
--max_batch_size 4 \
--backend_type balance_serve \
--port 8000 \
--chunk_size 1024 \
--cache_lens 16384 \
--max_new_tokens 4096
```

4. **错误1 - KExpertsMarlin / Error 1 - KExpertsMarlin:**
KeyError: 'model.layers.0.mlp.experts.ffn_up_exps.weight'

5. **修改配置使用KExpertsTorch / Modified config to use KExpertsTorch:**
```yaml
# Changed generate_op to KExpertsTorch
generate_op: "KExpertsTorch" # This causes NoneType error
```

6. **错误2 - KExpertsTorch / Error 2 - KExpertsTorch:**
TypeError: 'NoneType' object is not subscriptable
File "ktransformers/util/custom_loader.py", line 415, in load_expert_tensor
data = data[offset: offset + block_size * blocks_per_experts]

**权重命名分析 / Weight Naming Analysis:**
使用gguf库检查发现实际权重命名为：
```python
# Actual GGUF weights:
blk.0.ffn_gate_exps.weight
blk.0.ffn_up_exps.weight
blk.0.ffn_down_exps.weight

# Expected by operators:
model.layers.0.mlp.experts.ffn_gate_exps.weight
model.layers.0.mlp.experts.ffn_up_exps.weight
model.layers.0.mlp.experts.ffn_down_exps.weight
```

### Environment

**系统环境 / System Environment:**
- **OS**: WSL Ubuntu 24.04.1 LTS
- **Python**: 3.12.3
- **Conda Environment**: kt
- **CUDA**: Available (GPU 0)

**KTransformers配置 / KTransformers Configuration:**
- **Installation**: Source installation in `/opt/kt/`
- **Version**: Latest from main branch
- **Backend**: balance_serve
- **Device Configuration**: CUDA GPU + CPU hybrid

**模型配置 / Model Configuration:**
- **Model Path**: `/root/ktransformers_models/qwen3-coder-30b/original_config`
- **GGUF Path**: `/root/ktransformers_models/qwen3-coder-30b/quantized_Q4_K_M`
- **Model Type**: Qwen3MoeForCausalLM
- **Quantization**: Q4_K_M
- **Total Layers**: 24
- **Total Experts**: 128
- **Experts Per Token**: 8

**硬件配置 / Hardware Configuration:**
- **CPU**: 18 cores allocated for CPU inference
- **GPU**: CUDA-capable GPU for first 24 experts
- **Memory**: Sufficient for model loading

Read ktransformers/util/custom_loader.py around line 415 and trace the expert operator path selected by optimize_config.yaml from server/main.py. Compare the GGUF names listed in the issue with the operator lookups; done means both KExpertsMarlin and KExpertsTorch load the listed expert tensors without the reported KeyError or NoneType failure.

Written by the indexing model from the issue text.