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Unverified Commit f4bb374e authored by Azure's avatar Azure Committed by GitHub
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Merge pull request #254 from Azure-Tang/main 

[Update] Add V3/R1 8 gpu yaml example
parents 0a9c5992 95c81eaf
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ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-multi-gpu-8.yaml 0 → 100644
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- 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"
# For final modules (model.norm and lm_head), ensure they are on GPU 7 (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:7"
prefill_device: "cuda:7"
\ No newline at end of file
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