- match:
    class: ktransformers.models.modeling_qwen2_moe.Qwen2MoeRotaryEmbedding
  replace:
    class: ktransformers.operators.RoPE.RotaryEmbedding
    kwargs:
      generate_device: "cuda"
      prefill_device: "cuda"

- match:
    name: "^lm_head$"  # 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\\..*$"  # regular expression 
#     class: torch.nn.Linear  # only match modules matching name and class simultaneously
#   replace:
#     class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
#     kwargs:
#       generate_device: "cuda"
#       prefill_device: "cuda"
#       generate_op: "VLinearMarlin"
#       prefill_op: "KLinearTorch"
- match:
    name: "^model\\.layers\\.(?!.*mlp\\.shared_expert_gate).*$"  # 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: "VLinearMarlin"
      prefill_op: "KLinearTorch"
- match:
    name: "^model\\.layers\\..*\\.mlp$"
    class: ktransformers.models.modeling_qwen2_moe.Qwen2MoeSparseMoeBlock
  replace:
    class: ktransformers.operators.experts.KQwen2MoeSparseMoeBlockV2     # mlp module with custom forward function
    kwargs:
      generate_device: "cuda"
      prefill_device: "cuda"

- match:
    name: "^model\\.layers\\..*\\.mlp\\.experts$"
  replace:
    class: ktransformers.operators.experts.KTransformersExpertsV2     # custom MoE Kernel with expert paralleism
    kwargs:
      prefill_device: "cuda"
      prefill_op: "KExpertsTorch"
      generate_device: "cpu"
      generate_op: "KExpertsCPU"
      out_device: "cuda"
  recursive: False # don't recursively inject submodules of this module
- match:
    name: "^model\\.layers\\..*\\.self_attn$"
  replace:
    class: ktransformers.operators.balance_serve_attention.KQwen2MoeAttention # optimized MLA implementation
    kwargs:
      generate_device: "cuda"
      prefill_device: "cuda"
- 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:
    class: ktransformers.models.modeling_qwen2_moe.Qwen2MoeRMSNorm
  replace:
    class: ktransformers.operators.layernorm.KQwen2MoeRMSNorm
    kwargs:
      generate_device: "cuda"
      prefill_device: "cuda"

- match:
    class: ktransformers.models.modeling_qwen2_moe.Qwen2MoeMLP
  replace:
    class:  ktransformers.operators.mlp.KQwen2MoeMLP
    kwargs:
      generate_device: "cuda"
      prefill_device: "cuda"