kvcache-ai-ktransformers/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-multi-gpu-8.yaml

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

# === Rotary Embedding Replacement ===

# GPU 0: layers 0–7
- match:
    name: "^model\\.layers\\.([0-7])\\."
    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 8–15
- match:
    name: "^model\\.layers\\.(8|9|1[0-5])\\."
    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 16–23
- match:
    name: "^model\\.layers\\.(1[6-9]|2[0-3])\\."
    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 24–31
- match:
    name: "^model\\.layers\\.(2[4-9]|3[0-1])\\."
    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding
  replace:
    class: ktransformers.operators.RoPE.YarnRotaryEmbeddingV3
    kwargs:
      generate_device: "cuda:3"
      prefill_device: "cuda:3"

# GPU 4: layers 32–39
- match:
    name: "^model\\.layers\\.([3][2-9])\\."
    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding
  replace:
    class: ktransformers.operators.RoPE.YarnRotaryEmbeddingV3
    kwargs:
      generate_device: "cuda:4"
      prefill_device: "cuda:4"

# GPU 5: layers 40–47
- match:
    name: "^model\\.layers\\.(4[0-7])\\."
    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding
  replace:
    class: ktransformers.operators.RoPE.YarnRotaryEmbeddingV3
    kwargs:
      generate_device: "cuda:5"
      prefill_device: "cuda:5"

# GPU 6: layers 48–55
- match:
    name: "^model\\.layers\\.(4[8-9]|5[0-5])\\."
    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding
  replace:
    class: ktransformers.operators.RoPE.YarnRotaryEmbeddingV3
    kwargs:
      generate_device: "cuda:6"
      prefill_device: "cuda:6"

# GPU 7: layers 56–60
- match:
    name: "^model\\.layers\\.(5[6-9]|60)\\."
    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding
  replace:
    class: ktransformers.operators.RoPE.YarnRotaryEmbeddingV3
    kwargs:
      generate_device: "cuda:7"
      prefill_device: "cuda:7"


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

# GPU 0: layers 0–7
- match:
    name: "^model\\.layers\\.([0-7])\\.(?!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 8–15
- match:
    name: "^model\\.layers\\.(8|9|1[0-5])\\.(?!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 16–23
- match:
    name: "^model\\.layers\\.(1[6-9]|2[0-3])\\.(?!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 24–31
- match:
    name: "^model\\.layers\\.(2[4-9]|3[0-1])\\.(?!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"

# GPU 4: layers 32–39
- match:
    name: "^model\\.layers\\.(3[2-9])\\.(?!self_attn\\.kv_b_proj).*$"
    class: torch.nn.Linear
  replace:
    class: ktransformers.operators.linear.KTransformersLinear
    kwargs:
      generate_device: "cuda:4"
      prefill_device: "cuda:4"
      generate_op: "KLinearMarlin"
      prefill_op: "KLinearTorch"

# GPU 5: layers 40–47
- match:
    name: "^model\\.layers\\.(4[0-7])\\.(?!self_attn\\.kv_b_proj).*$"
    class: torch.nn.Linear
  replace:
    class: ktransformers.operators.linear.KTransformersLinear
    kwargs:
      generate_device: "cuda:5"
      prefill_device: "cuda:5"
      generate_op: "KLinearMarlin"
      prefill_op: "KLinearTorch"

# GPU 6: layers 48–55
- match:
    name: "^model\\.layers\\.(4[8-9]|5[0-5])\\.(?!self_attn\\.kv_b_proj).*$"
    class: torch.nn.Linear
  replace:
    class: ktransformers.operators.linear.KTransformersLinear
    kwargs:
      generate_device: "cuda:6"
      prefill_device: "cuda:6"
      generate_op: "KLinearMarlin"
      prefill_op: "KLinearTorch"

# GPU 7: layers 56–63
- match:
    name: "^model\\.layers\\.(5[6-9]|60)\\.(?!self_attn\\.kv_b_proj).*$"
    class: torch.nn.Linear
  replace:
    class: ktransformers.operators.linear.KTransformersLinear
    kwargs:
      generate_device: "cuda:7"
      prefill_device: "cuda:7"
      generate_op: "KLinearMarlin"
      prefill_op: "KLinearTorch"



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

# GPU 0: layers 0–7
- match:
    name: "^model\\.layers\\.([0-7])\\.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 8–15
- match:
    name: "^model\\.layers\\.(8|9|1[0-5])\\.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 16–23
- match:
    name: "^model\\.layers\\.(1[6-9]|2[0-3])\\.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 24–31
- match:
    name: "^model\\.layers\\.(2[4-9]|3[0-1])\\.mlp$"
    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
  replace:
    class: ktransformers.operators.experts.KDeepseekV3MoE
    kwargs:
      generate_device: "cuda:3"
      prefill_device: "cuda:3"

# GPU 4: layers 32–39
- match:
    name: "^model\\.layers\\.(3[2-9])\\.mlp$"
    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
  replace:
    class: ktransformers.operators.experts.KDeepseekV3MoE
    kwargs:
      generate_device: "cuda:4"
      prefill_device: "cuda:4"

# GPU 5: layers 40–47
- match:
    name: "^model\\.layers\\.(4[0-7])\\.mlp$"
    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
  replace:
    class: ktransformers.operators.experts.KDeepseekV3MoE
    kwargs:
      generate_device: "cuda:5"
      prefill_device: "cuda:5"

# GPU 6: layers 48–55
- match:
    name: "^model\\.layers\\.(4[8-9]|5[0-5])\\.mlp$"
    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
  replace:
    class: ktransformers.operators.experts.KDeepseekV3MoE
    kwargs:
      generate_device: "cuda:6"
      prefill_device: "cuda:6"

# GPU 7: layers 56–60
- match:
    name: "^model\\.layers\\.(5[6-9]|60)\\.mlp$"
    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
  replace:
    class: ktransformers.operators.experts.KDeepseekV3MoE
    kwargs:
      generate_device: "cuda:7"
      prefill_device: "cuda:7"

# === MLP Gate Replacement ===

# GPU 0: layers 0–7
- match:
    name: "^model\\.layers\\.([0-7])\\.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 8–15
- match:
    name: "^model\\.layers\\.(8|9|1[0-5])\\.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 16–23
- match:
    name: "^model\\.layers\\.(1[6-9]|2[0-3])\\.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 24–31
- match:
    name: "^model\\.layers\\.(2[4-9]|3[0-1])\\.mlp\\.gate$"
    class: ktransformers.models.modeling_deepseek_v3.MoEGate
  replace:
    class: ktransformers.operators.gate.KMoEGate
    kwargs:
      generate_device: "cuda:3"
      prefill_device: "cuda:3"

# GPU 4: layers 32–39
- match:
    name: "^model\\.layers\\.(3[2-9])\\.mlp\\.gate$"
    class: ktransformers.models.modeling_deepseek_v3.MoEGate
  replace:
    class: ktransformers.operators.gate.KMoEGate
    kwargs:
      generate_device: "cuda:4"
      prefill_device: "cuda:4"

# GPU 5: layers 40–47
- match:
    name: "^model\\.layers\\.(4[0-7])\\.mlp\\.gate$"
    class: ktransformers.models.modeling_deepseek_v3.MoEGate
  replace:
    class: ktransformers.operators.gate.KMoEGate
    kwargs:
      generate_device: "cuda:5"
      prefill_device: "cuda:5"

# GPU 6: layers 48–55
- match:
    name: "^model\\.layers\\.(4[8-9]|5[0-5])\\.mlp\\.gate$"
    class: ktransformers.models.modeling_deepseek_v3.MoEGate
  replace:
    class: ktransformers.operators.gate.KMoEGate
    kwargs:
      generate_device: "cuda:6"
      prefill_device: "cuda:6"

# GPU 7: layers 56–60
- match:
    name: "^model\\.layers\\.(5[6-9]|60)\\.mlp\\.gate$"
    class: ktransformers.models.modeling_deepseek_v3.MoEGate
  replace:
    class: ktransformers.operators.gate.KMoEGate
    kwargs:
      generate_device: "cuda:7"
      prefill_device: "cuda:7"


# === MLP Experts Replacement ===
# replace with marlin expert. Open and modify layer-num as needed.
# Each layer of malin experts takes about 6GB of GPU memory.
# !!!Do remember 'close' cuda graph if you are using marlin expert.!!!
# !!!Loading marlin expert will take signifcant time.!!!

# GPU 0: layers 0–7
# - match:
#     name: "^model\\.layers\\.([0-7])\\.mlp\\.experts$" # inject experts in layer 0~4 as marlin expert
#   replace:
#     class: ktransformers.operators.experts.KTransformersExperts  
#     kwargs:
#       generate_device: "cuda:0"
#       generate_op:  "KExpertsMarlin"
#   recursive: False

# # GPU 1: layers 8–15
# - match:
#     name: "^model\\.layers\\.([8-9]|1[0-5)\\.mlp\\.experts$" # inject experts in layer 30~31 as marlin expert
#   replace:
#     class: ktransformers.operators.experts.KTransformersExperts
#     kwargs:
#       generate_device: "cuda:1"
#       generate_op:  "KExpertsMarlin"
#   recursive: False 

# # GPU 2: layers 16–23
# - match:
#     name: "^model\\.layers\\.(1[6-9]|2[0-3])\\.mlp\\.experts$" # inject experts in layer 0~4 as marlin expert
#   replace:
#     class: ktransformers.operators.experts.KTransformersExperts  
#     kwargs:
#       generate_device: "cuda:0" 
#       generate_op:  "KExpertsMarlin"
#   recursive: False

# # GPU 3: layers 24–31
# - match:
#     name: "^model\\.layers\\.(2[4-9]|3[0-1])\\.mlp\\.experts$" # inject experts in layer 30~31 as marlin expert
#   replace:
#     class: ktransformers.operators.experts.KTransformersExperts
#     kwargs:
#       generate_device: "cuda:1"
#       generate_op:  "KExpertsMarlin"
#   recursive: False 

# # GPU 4: layers 32–39
# - match:
#     name: "^model\\.layers\\.(3[2-9])\\.mlp\\.experts$" # inject experts in layer 0~4 as marlin expert
#   replace:
#     class: ktransformers.operators.experts.KTransformersExperts  
#     kwargs:
#       generate_device: "cuda:0" 
#       generate_op:  "KExpertsMarlin"
#   recursive: False

# # GPU 5: layers 40–47
# - match:
#     name: "^model\\.layers\\.(4[0-7])\\.mlp\\.experts$" # inject experts in layer 30~31 as marlin expert
#   replace:
#     class: ktransformers.operators.experts.KTransformersExperts
#     kwargs:
#       generate_device: "cuda:1"
#       generate_op:  "KExpertsMarlin"
#   recursive: False 

# # GPU 6: layers 48–55
# - match:
#     name: "^model\\.layers\\.(4[8-9]|5[0-5])\\.mlp\\.experts$" # inject experts in layer 0~4 as marlin expert
#   replace:
#     class: ktransformers.operators.experts.KTransformersExperts  
#     kwargs:
#       generate_device: "cuda:0"
#       generate_op:  "KExpertsMarlin"
#   recursive: False

# # GPU 7: layers 56–60
# - match:
#     name: "^model\\.layers\\.(5[6-9]|60)\\.mlp\\.experts$" # inject experts in layer 30~31 as marlin expert
#   replace:
#     class: ktransformers.operators.experts.KTransformersExperts
#     kwargs:
#       generate_device: "cuda:1"
#       generate_op:  "KExpertsMarlin"
#   recursive: False 


# === MLP Experts Replacement ===

# GPU 0: layers 0–7
- match:
    name: "^model\\.layers\\.([0-7])\\.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 8–15
- match:
    name: "^model\\.layers\\.(8|9|1[0-5])\\.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 16–23
- match:
    name: "^model\\.layers\\.(1[6-9]|2[0-3])\\.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 24–31
- match:
    name: "^model\\.layers\\.(2[4-9]|3[0-1])\\.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

# GPU 4: layers 32–39
- match:
    name: "^model\\.layers\\.(3[2-9])\\.mlp\\.experts$"
  replace:
    class: ktransformers.operators.experts.KTransformersExperts
    kwargs:
      prefill_device: "cuda:4"
      prefill_op: "KExpertsTorch"
      generate_device: "cpu"
      generate_op: "KExpertsCPU"
      out_device: "cuda:4"
  recursive: False

# GPU 5: layers 40–47
- match:
    name: "^model\\.layers\\.(4[0-7])\\.mlp\\.experts$"
  replace:
    class: ktransformers.operators.experts.KTransformersExperts
    kwargs:
      prefill_device: "cuda:5"
      prefill_op: "KExpertsTorch"
      generate_device: "cpu"
      generate_op: "KExpertsCPU"
      out_device: "cuda:5"
  recursive: False

# GPU 6: layers 48–55
- match:
    name: "^model\\.layers\\.(4[8-9]|5[0-5])\\.mlp\\.experts$"
  replace:
    class: ktransformers.operators.experts.KTransformersExperts
    kwargs:
      prefill_device: "cuda:6"
      prefill_op: "KExpertsTorch"
      generate_device: "cpu"
      generate_op: "KExpertsCPU"
      out_device: "cuda:6"
  recursive: False

# GPU 7: layers 56–60
- match:
    name: "^model\\.layers\\.(5[6-9]|60)\\.mlp\\.experts$"
  replace:
    class: ktransformers.operators.experts.KTransformersExperts
    kwargs:
      prefill_device: "cuda:7"
      prefill_op: "KExpertsTorch"
      generate_device: "cpu"
      generate_op: "KExpertsCPU"
      out_device: "cuda:7"
  recursive: False


# === Self-Attention Replacement ===

# GPU 0: layers 0–7
- match:
    name: "^model\\.layers\\.([0-7])\\.self_attn$"
  replace:
    class: ktransformers.operators.attention.KDeepseekV2Attention
    kwargs:
      generate_device: "cuda:0"
      prefill_device: "cuda:0"

# GPU 1: layers 8–15
- match:
    name: "^model\\.layers\\.(8|9|1[0-5])\\.self_attn$"
  replace:
    class: ktransformers.operators.attention.KDeepseekV2Attention
    kwargs:
      generate_device: "cuda:1"
      prefill_device: "cuda:1"

# GPU 2: layers 16–23
- match:
    name: "^model\\.layers\\.(1[6-9]|2[0-3])\\.self_attn$"
  replace:
    class: ktransformers.operators.attention.KDeepseekV2Attention
    kwargs:
      generate_device: "cuda:2"
      prefill_device: "cuda:2"

# GPU 3: layers 24–31
- match:
    name: "^model\\.layers\\.(2[4-9]|3[0-1])\\.self_attn$"
  replace:
    class: ktransformers.operators.attention.KDeepseekV2Attention
    kwargs:
      generate_device: "cuda:3"
      prefill_device: "cuda:3"

# GPU 4: layers 32–39
- match:
    name: "^model\\.layers\\.(3[2-9])\\.self_attn$"
  replace:
    class: ktransformers.operators.attention.KDeepseekV2Attention
    kwargs:
      generate_device: "cuda:4"
      prefill_device: "cuda:4"

# GPU 5: layers 40–47
- match:
    name: "^model\\.layers\\.(4[0-7])\\.self_attn$"
  replace:
    class: ktransformers.operators.attention.KDeepseekV2Attention
    kwargs:
      generate_device: "cuda:5"
      prefill_device: "cuda:5"

# GPU 6: layers 48–55
- match:
    name: "^model\\.layers\\.(4[8-9]|5[0-5])\\.self_attn$"
  replace:
    class: ktransformers.operators.attention.KDeepseekV2Attention
    kwargs:
      generate_device: "cuda:6"
      prefill_device: "cuda:6"

# GPU 7: layers 56–60
- match:
    name: "^model\\.layers\\.(5[6-9]|60)\\.self_attn$"
  replace:
    class: ktransformers.operators.attention.KDeepseekV2Attention
    kwargs:
      generate_device: "cuda:7"
      prefill_device: "cuda:7"

# === 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:
        8: "cuda:1"
        16: "cuda:2"
        24: "cuda:3"
        32: "cuda:4"
        40: "cuda:5"
        48: "cuda:6"
        56: "cuda:7"

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

# GPU 0: layers 0–7
- match:
    name: "^model\\.layers\\.([0-7])\\."
  replace:
    class: "default"
    kwargs:
      generate_device: "cuda:0"
      prefill_device: "cuda:0"

# GPU 1: layers 8–15
- match:
    name: "^model\\.layers\\.(8|9|1[0-5])\\."
  replace:
    class: "default"
    kwargs:
      generate_device: "cuda:1"
      prefill_device: "cuda:1"

# GPU 2: layers 16–23
- match:
    name: "^model\\.layers\\.(1[6-9]|2[0-3])\\."
  replace:
    class: "default"
    kwargs:
      generate_device: "cuda:2"
      prefill_device: "cuda:2"

# GPU 3: layers 24–31
- match:
    name: "^model\\.layers\\.(2[4-9]|3[0-1])\\."
  replace:
    class: "default"
    kwargs:
      generate_device: "cuda:3"
      prefill_device: "cuda:3"

# GPU 4: layers 32–39
- match:
    name: "^model\\.layers\\.(3[2-9])\\."
  replace:
    class: "default"
    kwargs:
      generate_device: "cuda:4"
      prefill_device: "cuda:4"

# GPU 5: layers 40–47
- match:
    name: "^model\\.layers\\.(4[0-7])\\."
  replace:
    class: "default"
    kwargs:
      generate_device: "cuda:5"
      prefill_device: "cuda:5"

# GPU 6: layers 48–55
- match:
    name: "^model\\.layers\\.(4[8-9]|5[0-5])\\."
  replace:
    class: "default"
    kwargs:
      generate_device: "cuda:6"
      prefill_device: "cuda:6"

# GPU 7: layers 56–63
- match:
    name: "^model\\.layers\\.(5[6-9]|60)\\."
  replace:
    class: "default"
    kwargs:
      generate_device: "cuda:7"
      prefill_device: "cuda:7"

- match:
    name: "^lm_head"
    class: torch.nn.Linear
  replace:
    class: ktransformers.operators.linear.KTransformersLinear
    kwargs:
      generate_device: "cuda:7"
      prefill_device: "cuda:7"
      generate_op: "KLinearMarlin"
      prefill_op: "KLinearTorch"

# For final modules (model.norm), ensure they are on GPU 7 (as in your original config)
- match:
    name: "(^model\\.layers\\.(4[5-9]|5[0-9]|60)\\.)|(^model\\.norm)"
  replace:
    class: "default"
    kwargs:
      generate_device: "cuda:7"
      prefill_device: "cuda:7"