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

# === Rotary Embedding Replacement ===

# GPU 0: layers 0–9
- match:
    name: "^model\\.layers\\.(0|[1-9])\\."
    class: ktransformers.models.modeling_deepseek.DeepseekV2YarnRotaryEmbedding
  replace:
    class: ktransformers.operators.RoPE.YarnRotaryEmbedding
    kwargs:
      generate_device: "cuda:0"
      prefill_device: "cuda:0"
# CPU: layers 10-29
- match:
    name: "^model\\.layers\\.([12][0-9])\\."
    class: ktransformers.models.modeling_deepseek.DeepseekV2YarnRotaryEmbedding
  replace:
    class: ktransformers.operators.RoPE.YarnRotaryEmbedding
    kwargs:
      generate_device: "cpu"
      prefill_device: "cpu"

# === Linear Layers Replacement (excluding self_attn) ===

# GPU 0: layers 0–9
- match:
    name: "^model\\.layers\\.(0|[1-9])\\.(?!self_attn).*$"  # regular expression
    class: torch.nn.Linear  # only match modules matching name and class simultaneously
  replace:
    class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
    kwargs:
      generate_device: "cuda:0"
      prefill_device: "cuda:0"
      generate_op: "KLinearMarlin"
      prefill_op: "KLinearTorch"
# CPU: layers 10-29
- match:
    name: "^model\\.layers\\.([12][0-9])\\.(?!self_attn).*$"  # 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: "cpu"
      prefill_device: "cpu"
      generate_op: "KLinearCPUInfer"
      prefill_op: "KLinearTorch"
      out_device: "cpu"

# === MLP (MoE) Replacement ===

# GPU 0: layers 0–9
- match:
    name: "^model\\.layers\\.(0|[1-9])\\.mlp$"
    class: ktransformers.models.modeling_deepseek.DeepseekV2MoE
  replace:
    class: ktransformers.operators.experts.KDeepseekV2MoE     # mlp module with custom forward function
    kwargs:
      generate_device: "cuda:0"
      prefill_device: "cuda:0"
# CPU: layers 10-29
- match:
    name: "^model\\.layers\\.([12][0-9])\\.mlp$"
    class: ktransformers.models.modeling_deepseek.DeepseekV2MoE
  replace:
    class: ktransformers.operators.experts.KDeepseekV2MoE     # mlp module with custom forward function
    kwargs:
      generate_device: "cpu"
      prefill_device: "cpu"

# === MLP Gate Replacement ===

# GPU 0: layers 0–9
- match:
    name: "^model\\.layers\\.(0|[1-9])\\.mlp\\.gate$"
    class: ktransformers.models.modeling_deepseek_v3.MoEGate
  replace:
    class: ktransformers.operators.gate.KMoEGate
    kwargs:
      generate_device: "cuda:0"
      prefill_device: "cuda:0"
# CPU: layers 10-29
- match:
    name: "^model\\.layers\\.([12][0-9])\\.mlp\\.gate$"
    class: ktransformers.models.modeling_deepseek_v3.MoEGate
  replace:
    class: ktransformers.operators.gate.KMoEGate
    kwargs:
      generate_device: "cpu"
      prefill_device: "cpu"

# === MLP Experts Replacement ===

# GPU 0: layers 0–9
- match:
    name: "^model\\.layers\\.(0|[1-9])\\.mlp\\.experts$"
  replace:
    class: ktransformers.operators.experts.KTransformersExperts     # custom MoE Kernel with expert paralleism
    kwargs:
      prefill_device: "cuda:0"
      prefill_op: "KExpertsTorch"
      generate_device: "cpu"
      generate_op:  "KExpertsCPU"
      out_device: "cuda:0"
  recursive: False # don't recursively inject submodules of this module
# CPU: layers 10-29
- match:
    name: "^model\\.layers\\.([12][0-9])\\.mlp\\.experts$"
  replace:
    class: ktransformers.operators.experts.KTransformersExperts     # custom MoE Kernel with expert paralleism
    kwargs:
      prefill_device: "cpu"
      prefill_op: "KExpertsTorch"
      generate_device: "cpu"
      generate_op:  "KExpertsCPU"
      out_device: "cpu"
  recursive: False # don't recursively inject submodules of this module

# === Self-Attention Replacement ===

# GPU 0: layers 0–9
- match:
    name: "^model\\.layers\\.(0|[1-9])\\.self_attn$"
  replace:
    class: ktransformers.operators.attention.KDeepseekV2Attention # optimized MLA implementation
    kwargs:
      generate_device: "cuda:0"
      prefill_device: "cuda:0"
# CPU: layers 10-29
- match:
    name: "^model\\.layers\\.([12][0-9])\\.self_attn$"
  replace:
    class: ktransformers.operators.attention.KDeepseekV2Attention # optimized MLA implementation
    kwargs:
      generate_device: "cpu"
      prefill_device: "cpu"

# === Overall Model Replacement with Transfer Map ===

- match:
    name: "^model$"
  replace:
    class: "ktransformers.operators.models.KDeepseekV2Model"
    kwargs:
      per_layer_prefill_intput_threshold: 0 # 0 is close layer wise prefill
      transfer_map:
        10: "cpu"

# === Default Catch-All for Other Modules ===#
# GPU 0: layers 0–9
- match:
    name: "^model\\.layers\\.(0|[1-9])\\."
  replace:
    class: "default"
    kwargs:
      generate_device: "cuda:0"
      prefill_device: "cuda:0"

#lmm_head on GPU 0
- match:
    name: "^lm_head"
    class: torch.nn.Linear
  replace:
    class: ktransformers.operators.linear.KTransformersLinear
    kwargs:
      generate_device: "cuda:0"
      prefill_device: "cuda:0"
      generate_op: "KLinearMarlin"
      prefill_op: "KLinearTorch"

# CPU: layers 10-29
- match:
    name: "(^model\\.layers\\.([12][0-9])\\.)|(model.norm)"
  replace:
    class: "default"
    kwargs:
      generate_device: "cpu"
      prefill_device: "cpu"