- match:
    name: "^model.embed_tokens"
  replace:
    class: "default"
    kwargs:
        generate_device: "cpu"
        prefill_device: "cpu"

- match:
    name: "^model\\.layers\\.([0-9])\\."
    class: ktransformers.models.modeling_deepseek.DeepseekV2YarnRotaryEmbedding
  replace:
    class: ktransformers.operators.RoPE.YarnRotaryEmbedding
    kwargs:
      generate_device: "cuda:0"
      prefill_device: "cuda:0"
- match:
    name: "^model\\.layers\\.([1][0-9])\\."
    class: ktransformers.models.modeling_deepseek.DeepseekV2YarnRotaryEmbedding
  replace:
    class: ktransformers.operators.RoPE.YarnRotaryEmbedding
    kwargs:
      generate_device: "cuda:1"
      prefill_device: "cuda:1"
- match:
    name: "^model\\.layers\\.([2][0-9])\\."
    class: ktransformers.models.modeling_deepseek.DeepseekV2YarnRotaryEmbedding
  replace:
    class: ktransformers.operators.RoPE.YarnRotaryEmbedding
    kwargs:
      generate_device: "cuda:2"
      prefill_device: "cuda:2"
- match:
    name: "^model\\.layers\\.([345][0-9])\\."
    class: ktransformers.models.modeling_deepseek.DeepseekV2YarnRotaryEmbedding
  replace:
    class: ktransformers.operators.RoPE.YarnRotaryEmbedding
    kwargs:
      generate_device: "cuda:3"
      prefill_device: "cuda:3"

- match:
    name: "^model\\.layers\\.([0-9])\\.(?!self_attn).*$"  # regular expression 
    class: torch.nn.Linear  # only match modules matching name and class simultaneously
  replace:
    class: ktransformers.operators.linear.KTransformerLinear  # optimized Kernel on quantized data types
    kwargs:
      generate_device: "cuda:0"
      prefill_device: "cuda:0"
      generate_op: "QuantizedLinearMarlin"
      prefill_op: "QuantizedLinearTorch"
- match:
    name: "^model\\.layers\\.([1][0-9])\\.(?!self_attn).*$"  # 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:1"
      prefill_device: "cuda:1"
      generate_op: "QuantizedLinearMarlin"
      prefill_op: "QuantizedLinearTorch"
- match:
    name: "^model\\.layers\\.([2][0-9])\\.(?!self_attn).*$"  # regular expression 
    class: torch.nn.Linear  # only match modules matching name and class simultaneously
  replace:
    class: ktransformers.operators.linear.KTransformerLinear  # optimized Kernel on quantized data types
    kwargs:
      generate_device: "cuda:2"
      prefill_device: "cuda:2"
      generate_op: "QuantizedLinearMarlin"
      prefill_op: "QuantizedLinearTorch"
- match:
    name: "^model\\.layers\\.([345][0-9])\\.(?!self_attn).*$"  # 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:3"
      prefill_device: "cuda:3"
      generate_op: "QuantizedLinearMarlin"
      prefill_op: "QuantizedLinearTorch"

- match:
    name: "^model\\.layers\\.([0-9])\\.mlp$"
    class: ktransformers.models.modeling_deepseek.DeepseekV2MoE
  replace:
    class: ktransformers.operators.experts.DeepseekV2MoEInjected     # mlp module with custom forward function
    kwargs:
      generate_device: "cuda:0"
      prefill_device: "cuda:0"
- match:
    name: "^model\\.layers\\.([1][0-9])\\.mlp$"
    class: ktransformers.models.modeling_deepseek.DeepseekV2MoE
  replace:
    class: ktransformers.operators.experts.DeepseekV2MoEInjected     # mlp module with custom forward function
    kwargs:
      generate_device: "cuda:1"
      prefill_device: "cuda:1"
- match:
    name: "^model\\.layers\\.([2][0-9])\\.mlp$"
    class: ktransformers.models.modeling_deepseek.DeepseekV2MoE
  replace:
    class: ktransformers.operators.experts.DeepseekV2MoEInjected     # mlp module with custom forward function
    kwargs:
      generate_device: "cuda:2"
      prefill_device: "cuda:2"
- match:
    name: "^model\\.layers\\.([345][0-9])\\.mlp$"
    class: ktransformers.models.modeling_deepseek.DeepseekV2MoE
  replace:
    class: ktransformers.operators.experts.DeepseekV2MoEInjected     # mlp module with custom forward function
    kwargs:
      generate_device: "cuda:3"
      prefill_device: "cuda:3"

- match:
    name: "^model\\.layers\\.([0-9])\\.mlp\\.experts$"
  replace:
    class: ktransformers.operators.experts.KTransformersMLPExpert     # custom MoE Kernel with expert paralleism
    kwargs:
      prefill_device: "cuda:0"
      prefill_mlp_type: "MLPExpertsTorch"
      generate_device: "cpu"
      generate_mlp_type:  "MLPCPUExperts"
      out_device: "cuda:0"
  recursive: False # don't recursively inject submodules of this module
- match:
    name: "^model\\.layers\\.([1][0-9])\\.mlp\\.experts$"
  replace:
    class: ktransformers.operators.experts.KTransformersMLPExpert     # custom MoE Kernel with expert paralleism
    kwargs:
      prefill_device: "cuda:1"
      prefill_mlp_type: "MLPExpertsTorch"
      generate_device: "cpu"
      generate_mlp_type:  "MLPCPUExperts"
      out_device: "cuda:1"
  recursive: False # don't recursively inject submodules of this module
- match:
    name: "^model\\.layers\\.([2][0-9])\\.mlp\\.experts$"
  replace:
    class: ktransformers.operators.experts.KTransformersMLPExpert     # custom MoE Kernel with expert paralleism
    kwargs:
      prefill_device: "cuda:2"
      prefill_mlp_type: "MLPExpertsTorch"
      generate_device: "cpu"
      generate_mlp_type:  "MLPCPUExperts"
      out_device: "cuda:2"
  recursive: False # don't recursively inject submodules of this module
- match:
    name: "^model\\.layers\\.([345][0-9])\\.mlp\\.experts$"
  replace:
    class: ktransformers.operators.experts.KTransformersMLPExpert     # custom MoE Kernel with expert paralleism
    kwargs:
      prefill_device: "cuda:3"
      prefill_mlp_type: "MLPExpertsTorch"
      generate_device: "cpu"
      generate_mlp_type:  "MLPCPUExperts"
      out_device: "cuda:3"
  recursive: False # don't recursively inject submodules of this module

- match:
    name: "^model\\.layers\\.([0-9])\\.self_attn$"
  replace:
    class: ktransformers.operators.attention.DeepseekV2AttentionInjected # optimized MLA implementation
    kwargs:
      generate_device: "cuda:0"
      prefill_device: "cuda:0"
- match:
    name: "^model\\.layers\\.([1][0-9])\\.self_attn$"
  replace:
    class: ktransformers.operators.attention.DeepseekV2AttentionInjected # optimized MLA implementation
    kwargs:
      generate_device: "cuda:1"
      prefill_device: "cuda:1"
- match:
    name: "^model\\.layers\\.([2][0-9])\\.self_attn$"
  replace:
    class: ktransformers.operators.attention.DeepseekV2AttentionInjected # optimized MLA implementation
    kwargs:
      generate_device: "cuda:2"
      prefill_device: "cuda:2"
- match:
    name: "^model\\.layers\\.([345][0-9])\\.self_attn$"
  replace:
    class: ktransformers.operators.attention.DeepseekV2AttentionInjected # optimized MLA implementation
    kwargs:
      generate_device: "cuda:3"
      prefill_device: "cuda:3"

- match:
    name: "^model$"
  replace:
    class: "ktransformers.operators.layer_wise_prefill.DeepseekV2ModelKTransformers"
    kwargs:
      per_layer_prefill_intput_threshold: 0 # 0 is close layer wise prefill
      transfer_map: 
        10: "cuda:1"
        20: "cuda:2"
        30: "cuda:3"

- match:
    name: "^model\\.layers\\.([0-9])\\."
  replace:
    class: "default"
    kwargs:
      generate_device: "cuda:0"
      prefill_device: "cuda:0"
- match:
    name: "(^model\\.layers\\.([1][0-9])\\.)"
  replace:
    class: "default"
    kwargs:
      generate_device: "cuda:1"
      prefill_device: "cuda:1"
- match:
    name: "(^model\\.layers\\.([2][0-9])\\.)"
  replace:
    class: "default"
    kwargs:
      generate_device: "cuda:2"
      prefill_device: "cuda:2"
- match:
    name: "(^model\\.layers\\.([345][0-9])\\.)|(^model.norm)|(^lm_head)"
  replace:
    class: "default"
    kwargs:
      generate_device: "cuda:3"
      prefill_device: "cuda:3"