- match:
    name: "^model\\.layers\\..*\\.|^lm_head"
  replace:
    class: "default"
    kwargs:
      generate_device: "cuda"
      prefill_device: "cuda"
- match:
    class: ktransformers.models.modeling_deepseek.DeepseekV2YarnRotaryEmbedding
  replace:
    class: ktransformers.operators.RoPE.YarnRotaryEmbedding
- match:
    name: "^model\\.layers\\.(?!.*self_attn).*$"  # regular expression 
    class: torch.nn.Linear  # only match modules matching name and class simultaneously
  replace:
    class: ktransformers.operators.linear.KTransformerLinear  # optimized Kernel on quantized data types
    kwargs:
      generate_device: "cuda"
      prefill_device: "cuda"
      generate_op: "QuantizedLinearMarlin"
      prefill_op: "QuantizedLinearTorch"
- match:
    name: "^model\\.layers\\..*\\.mlp$"
    class: ktransformers.models.modeling_deepseek.DeepseekV2MoE
  replace:
    class: ktransformers.operators.experts.DeepseekV2MoEInjected     # mlp module with custom forward function
- match:
    name: "^model\\.layers\\..*\\.mlp\\.experts$"
  replace:
    class: ktransformers.operators.experts.KTransformersMLPExpert     # custom MoE Kernel with expert paralleism
    kwargs:
      prefill_device: "cuda"
      prefill_mlp_type: "MLPExpertsTorch"
      generate_device: "cpu"
      generate_mlp_type: "MLPCPUExperts"
      out_device: "cuda"
  recursive: False # don't recursively inject submodules of this module
- match:
    name: "^model\\.layers\\..*\\.self_attn$"
  replace:
    class: ktransformers.operators.attention.DeepseekV2AttentionInjected # optimized MLA implementation
- match:
    name: "^model$"
  replace:
    class: "ktransformers.operators.layer_wise_prefill.DeepseekV2ModelKTransformers"
    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"