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

# === Rotary Embedding Replacement ===

# GPU 0: layers 0–14
- match:
    name: "^model\\.layers\\.([0-9]|1[0-4])\\."
    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding
  replace:
    class: ktransformers.operators.RoPE.YarnRotaryEmbeddingV3
    kwargs:
      generate_device: "cuda:0"
      prefill_device: "cuda:0"

# GPU 1: layers 15–29
- match:
    name: "^model\\.layers\\.(1[5-9]|2[0-9])\\."
    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding
  replace:
    class: ktransformers.operators.RoPE.YarnRotaryEmbeddingV3
    kwargs:
      generate_device: "cuda:1"
      prefill_device: "cuda:1"

# GPU 2: layers 30–44
- match:
    name: "^model\\.layers\\.(3[0-9]|4[0-4])\\."
    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding
  replace:
    class: ktransformers.operators.RoPE.YarnRotaryEmbeddingV3
    kwargs:
      generate_device: "cuda:2"
      prefill_device: "cuda:2"

# GPU 3: layers 45–60
- match:
    name: "^model\\.layers\\.(4[5-9]|5[0-9]|60)\\."
    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding
  replace:
    class: ktransformers.operators.RoPE.YarnRotaryEmbeddingV3
    kwargs:
      generate_device: "cuda:3"
      prefill_device: "cuda:3"

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

# GPU 0: layers 0–14
- match:
    name: "^model\\.layers\\.([0-9]|1[0-4])\\.(?!self_attn\\.kv_b_proj).*$"
    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"

# GPU 1: layers 15–29
- match:
    name: "^model\\.layers\\.(1[5-9]|2[0-9])\\.(?!self_attn\\.kv_b_proj).*$"
    class: torch.nn.Linear
  replace:
    class: ktransformers.operators.linear.KTransformersLinear
    kwargs:
      generate_device: "cuda:1"
      prefill_device: "cuda:1"
      generate_op: "KLinearMarlin"
      prefill_op: "KLinearTorch"

# GPU 2: layers 30–44
- match:
    name: "^model\\.layers\\.(3[0-9]|4[0-4])\\.(?!self_attn\\.kv_b_proj).*$"
    class: torch.nn.Linear
  replace:
    class: ktransformers.operators.linear.KTransformersLinear
    kwargs:
      generate_device: "cuda:2"
      prefill_device: "cuda:2"
      generate_op: "KLinearMarlin"
      prefill_op: "KLinearTorch"

# GPU 3: layers 45–60
- match:
    name: "^model\\.layers\\.(4[5-9]|5[0-9]|60)\\.(?!self_attn\\.kv_b_proj).*$"
    class: torch.nn.Linear
  replace:
    class: ktransformers.operators.linear.KTransformersLinear
    kwargs:
      generate_device: "cuda:3"
      prefill_device: "cuda:3"
      generate_op: "KLinearMarlin"
      prefill_op: "KLinearTorch"

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

# GPU 0: layers 0–14
- match:
    name: "^model\\.layers\\.([0-9]|1[0-4])\\.mlp$"
    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
  replace:
    class: ktransformers.operators.experts.KDeepseekV3MoE
    kwargs:
      generate_device: "cuda:0"
      prefill_device: "cuda:0"

# GPU 1: layers 15–29
- match:
    name: "^model\\.layers\\.(1[5-9]|2[0-9])\\.mlp$"
    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
  replace:
    class: ktransformers.operators.experts.KDeepseekV3MoE
    kwargs:
      generate_device: "cuda:1"
      prefill_device: "cuda:1"

# GPU 2: layers 30–44
- match:
    name: "^model\\.layers\\.(3[0-9]|4[0-4])\\.mlp$"
    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
  replace:
    class: ktransformers.operators.experts.KDeepseekV3MoE
    kwargs:
      generate_device: "cuda:2"
      prefill_device: "cuda:2"

# GPU 3: layers 45–60
- match:
    name: "^model\\.layers\\.(4[5-9]|5[0-9]|60)\\.mlp$"
    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
  replace:
    class: ktransformers.operators.experts.KDeepseekV3MoE
    kwargs:
      generate_device: "cuda:3"
      prefill_device: "cuda:3"

# === MLP Gate Replacement ===

# GPU 0: layers 0–14
- match:
    name: "^model\\.layers\\.([0-9]|1[0-4])\\.mlp\\.gate$"
    class: ktransformers.models.modeling_deepseek_v3.MoEGate
  replace:
    class: ktransformers.operators.gate.KMoEGate
    kwargs:
      generate_device: "cuda:0"
      prefill_device: "cuda:0"

# GPU 1: layers 15–29
- match:
    name: "^model\\.layers\\.(1[5-9]|2[0-9])\\.mlp\\.gate$"
    class: ktransformers.models.modeling_deepseek_v3.MoEGate
  replace:
    class: ktransformers.operators.gate.KMoEGate
    kwargs:
      generate_device: "cuda:1"
      prefill_device: "cuda:1"

# GPU 2: layers 30–44
- match:
    name: "^model\\.layers\\.(3[0-9]|4[0-4])\\.mlp\\.gate$"
    class: ktransformers.models.modeling_deepseek_v3.MoEGate
  replace:
    class: ktransformers.operators.gate.KMoEGate
    kwargs:
      generate_device: "cuda:2"
      prefill_device: "cuda:2"

# GPU 3: layers 45–60
- match:
    name: "^model\\.layers\\.(4[5-9]|5[0-9]|60)\\.mlp\\.gate$"
    class: ktransformers.models.modeling_deepseek_v3.MoEGate
  replace:
    class: ktransformers.operators.gate.KMoEGate
    kwargs:
      generate_device: "cuda:3"
      prefill_device: "cuda:3"

# === MLP Experts Replacement ===

# GPU 0: layers 0–14
- match:
    name: "^model\\.layers\\.([0-9]|1[0-4])\\.mlp\\.experts$"
  replace:
    class: ktransformers.operators.experts.KTransformersExperts
    kwargs:
      prefill_device: "cuda:0"
      prefill_op: "KExpertsTorch"
      generate_device: "cpu"
      generate_op: "KExpertsCPU"
      out_device: "cuda:0"
  recursive: False

# GPU 1: layers 15–29
- match:
    name: "^model\\.layers\\.(1[5-9]|2[0-9])\\.mlp\\.experts$"
  replace:
    class: ktransformers.operators.experts.KTransformersExperts
    kwargs:
      prefill_device: "cuda:1"
      prefill_op: "KExpertsTorch"
      generate_device: "cpu"
      generate_op: "KExpertsCPU"
      out_device: "cuda:1"
  recursive: False

# GPU 2: layers 30–44
- match:
    name: "^model\\.layers\\.(3[0-9]|4[0-4])\\.mlp\\.experts$"
  replace:
    class: ktransformers.operators.experts.KTransformersExperts
    kwargs:
      prefill_device: "cuda:2"
      prefill_op: "KExpertsTorch"
      generate_device: "cpu"
      generate_op: "KExpertsCPU"
      out_device: "cuda:2"
  recursive: False

# GPU 3: layers 45–60
- match:
    name: "^model\\.layers\\.(4[5-9]|5[0-9]|60)\\.mlp\\.experts$"
  replace:
    class: ktransformers.operators.experts.KTransformersExperts
    kwargs:
      prefill_device: "cuda:3"
      prefill_op: "KExpertsTorch"
      generate_device: "cpu"
      generate_op: "KExpertsCPU"
      out_device: "cuda:3"
  recursive: False

# === Self-Attention Replacement ===

# GPU 0: layers 0–14
- match:
    name: "^model\\.layers\\.([0-9]|1[0-4])\\.self_attn$"
  replace:
    class: ktransformers.operators.attention.KDeepseekV2Attention
    kwargs:
      generate_device: "cuda:0"
      prefill_device: "cuda:0"

# GPU 1: layers 15–29
- match:
    name: "^model\\.layers\\.(1[5-9]|2[0-9])\\.self_attn$"
  replace:
    class: ktransformers.operators.attention.KDeepseekV2Attention
    kwargs:
      generate_device: "cuda:1"
      prefill_device: "cuda:1"

# GPU 2: layers 30–44
- match:
    name: "^model\\.layers\\.(3[0-9]|4[0-4])\\.self_attn$"
  replace:
    class: ktransformers.operators.attention.KDeepseekV2Attention
    kwargs:
      generate_device: "cuda:2"
      prefill_device: "cuda:2"

# GPU 3: layers 45–60
- match:
    name: "^model\\.layers\\.(4[5-9]|5[0-9]|60)\\.self_attn$"
  replace:
    class: ktransformers.operators.attention.KDeepseekV2Attention
    kwargs:
      generate_device: "cuda:3"
      prefill_device: "cuda:3"

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

- match:
    name: "^model$"
  replace:
    class: "ktransformers.operators.models.KDeepseekV2Model"
    kwargs:
      per_layer_prefill_intput_threshold: 0 # 0 means close layer‐wise prefill
      transfer_map:
        15: "cuda:1" # Layers 15+ on GPU 1
        30: "cuda:2" # Layers 30+ on GPU 2
        45: "cuda:3" # Layers 45+ on GPU 3

# === Default Catch-All for Other Modules ===

# GPU 0: layers 0–14
- match:
    name: "^model\\.layers\\.([0-9]|1[0-4])\\."
  replace:
    class: "default"
    kwargs:
      generate_device: "cuda:0"
      prefill_device: "cuda:0"

# GPU 1: layers 15–29
- match:
    name: "^model\\.layers\\.(1[5-9]|2[0-9])\\."
  replace:
    class: "default"
    kwargs:
      generate_device: "cuda:1"
      prefill_device: "cuda:1"

# GPU 2: layers 30–44
- match:
    name: "^model\\.layers\\.(3[0-9]|4[0-4])\\."
  replace:
    class: "default"
    kwargs:
      generate_device: "cuda:2"
      prefill_device: "cuda:2"

# For final modules (model.norm and lm_head), ensure they are on GPU 3 (as in your original config)
- match:
    name: "(^model\\.layers\\.(4[5-9]|5[0-9]|60)\\.)|(^model\\.norm)|(^lm_head)"
  replace:
    class: "default"
    kwargs:
      generate_device: "cuda:3"
      prefill_device: "cuda:3"
