- match:
    class: ktransformers.models.modeling_qwen2_moe.Qwen2MoeRotaryEmbedding
  replace:
    class: ktransformers.operators.RoPE.RotaryEmbedding
    kwargs:
      generate_device: "cuda"
      prefill_device: "cuda"
- match:
    name: "^model\\.layers\\..*$"  # regular expression 
    class: torch.nn.Linear  # only match modules matching name and class simultaneously
  replace:
    class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
    kwargs:
      generate_device: "cuda"
      prefill_device: "cuda"
      generate_op: "KLinearMarlin"
      prefill_op: "KLinearTorch"
- match:
    name: "^model\\.layers\\..*\\.mlp$"
    class: ktransformers.models.modeling_qwen2_moe.Qwen2MoeSparseMoeBlock
  replace:
    class: ktransformers.operators.experts.KQwen2MoeSparseMoeBlock     # mlp module with custom forward function
    kwargs:
      generate_device: "cuda"
      prefill_device: "cuda"
- match:
    name: "^model\\.layers\\..*\\.mlp\\.experts$"
  replace:
    class: ktransformers.operators.experts.KTransformersExperts     # custom MoE Kernel with expert paralleism
    # device: "cpu"   # which devices to load this module when initializing
    kwargs:
      prefill_device: "cuda"
      prefill_op: "KExpertsTorch"
      generate_device: "cpu"
      generate_op:  "KExpertsCPU"
      out_device: "cuda"
  recursive: False # don't recursively inject submodules of this module
- match:
    name: "^model$"
  replace:
    class: "ktransformers.operators.models.KQwen2MoeModel"
    kwargs:
      per_layer_prefill_intput_threshold: 0 # 0 is close layer wise prefill
- match:
    name: "^model.embed_tokens"
  replace:
    class: "default"
    kwargs:
        generate_device: "cpu"
        prefill_device: "cpu"
- match:
    name: "^model\\.layers\\..*\\."
  replace:
    class: "default"
    kwargs:
      generate_device: "cuda"
      prefill_device: "cuda"