mirror of
https://github.com/kvcache-ai/ktransformers.git
synced 2026-05-03 06:01:35 +00:00
[Feature] Add SFT feature for KT
This commit is contained in:
JimmyPeilinLi
parent
b09e99fd87
commit
4421d48108
445 changed files with 112306 additions and 0 deletions
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@ -0,0 +1,388 @@
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- match:
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name: "^model.embed_tokens"
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replace:
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class: "default"
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kwargs:
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generate_device: "cpu"
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prefill_device: "cpu"
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# === Rotary Embedding Replacement ===
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# GPU 0: layers 0–14
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- match:
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name: "^model\\.layers\\.([0-9]|1[0-4])\\."
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class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding
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replace:
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class: ktransformers.operators.RoPE.YarnRotaryEmbeddingV3
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kwargs:
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generate_device: "cuda:0"
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prefill_device: "cuda:0"
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# GPU 1: layers 15–29
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- match:
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name: "^model\\.layers\\.(1[5-9]|2[0-9])\\."
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class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding
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replace:
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class: ktransformers.operators.RoPE.YarnRotaryEmbeddingV3
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kwargs:
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generate_device: "cuda:1"
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prefill_device: "cuda:1"
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# GPU 2: layers 30–44
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- match:
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name: "^model\\.layers\\.(3[0-9]|4[0-4])\\."
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class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding
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replace:
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class: ktransformers.operators.RoPE.YarnRotaryEmbeddingV3
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kwargs:
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generate_device: "cuda:2"
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prefill_device: "cuda:2"
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# GPU 3: layers 45–60
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- match:
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name: "^model\\.layers\\.(4[5-9]|5[0-9]|60)\\."
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class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding
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replace:
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class: ktransformers.operators.RoPE.YarnRotaryEmbeddingV3
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kwargs:
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generate_device: "cuda:3"
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prefill_device: "cuda:3"
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# === Linear Layers Replacement (excluding self_attn.kv_b_proj) ===
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# GPU 0: layers 0–14
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- match:
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name: "^model\\.layers\\.([0-9]|1[0-4])\\.(?!self_attn\\.kv_b_proj).*$"
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class: torch.nn.Linear
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replace:
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class: ktransformers.operators.linear.KTransformersLinear
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kwargs:
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generate_device: "cuda:0"
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prefill_device: "cuda:0"
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generate_op: "KLinearMarlin"
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prefill_op: "KLinearTorch"
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# GPU 1: layers 15–29
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- match:
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name: "^model\\.layers\\.(1[5-9]|2[0-9])\\.(?!self_attn\\.kv_b_proj).*$"
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class: torch.nn.Linear
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replace:
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class: ktransformers.operators.linear.KTransformersLinear
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kwargs:
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generate_device: "cuda:1"
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prefill_device: "cuda:1"
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generate_op: "KLinearMarlin"
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prefill_op: "KLinearTorch"
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# GPU 2: layers 30–44
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- match:
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name: "^model\\.layers\\.(3[0-9]|4[0-4])\\.(?!self_attn\\.kv_b_proj).*$"
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class: torch.nn.Linear
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replace:
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class: ktransformers.operators.linear.KTransformersLinear
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kwargs:
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generate_device: "cuda:2"
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prefill_device: "cuda:2"
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generate_op: "KLinearMarlin"
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prefill_op: "KLinearTorch"
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# GPU 3: layers 45–60
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- match:
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name: "^model\\.layers\\.(4[5-9]|5[0-9]|60)\\.(?!self_attn\\.kv_b_proj).*$"
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class: torch.nn.Linear
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replace:
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class: ktransformers.operators.linear.KTransformersLinear
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kwargs:
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generate_device: "cuda:3"
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prefill_device: "cuda:3"
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generate_op: "KLinearMarlin"
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prefill_op: "KLinearTorch"
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# === MLP (MoE) Replacement ===
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# GPU 0: layers 0–14
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- match:
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name: "^model\\.layers\\.([0-9]|1[0-4])\\.mlp$"
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class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
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replace:
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class: ktransformers.operators.experts.KDeepseekV3MoE
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kwargs:
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generate_device: "cuda:0"
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prefill_device: "cuda:0"
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# GPU 1: layers 15–29
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- match:
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name: "^model\\.layers\\.(1[5-9]|2[0-9])\\.mlp$"
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class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
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replace:
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class: ktransformers.operators.experts.KDeepseekV3MoE
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kwargs:
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generate_device: "cuda:1"
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prefill_device: "cuda:1"
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# GPU 2: layers 30–44
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- match:
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name: "^model\\.layers\\.(3[0-9]|4[0-4])\\.mlp$"
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class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
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replace:
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class: ktransformers.operators.experts.KDeepseekV3MoE
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kwargs:
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generate_device: "cuda:2"
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prefill_device: "cuda:2"
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# GPU 3: layers 45–60
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- match:
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name: "^model\\.layers\\.(4[5-9]|5[0-9]|60)\\.mlp$"
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class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
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replace:
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class: ktransformers.operators.experts.KDeepseekV3MoE
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kwargs:
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generate_device: "cuda:3"
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prefill_device: "cuda:3"
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# === MLP Gate Replacement ===
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# GPU 0: layers 0–14
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- match:
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name: "^model\\.layers\\.([0-9]|1[0-4])\\.mlp\\.gate$"
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class: ktransformers.models.modeling_deepseek_v3.MoEGate
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replace:
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class: ktransformers.operators.gate.KMoEGate
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kwargs:
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generate_device: "cuda:0"
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prefill_device: "cuda:0"
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# GPU 1: layers 15–29
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- match:
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name: "^model\\.layers\\.(1[5-9]|2[0-9])\\.mlp\\.gate$"
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class: ktransformers.models.modeling_deepseek_v3.MoEGate
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replace:
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class: ktransformers.operators.gate.KMoEGate
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kwargs:
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generate_device: "cuda:1"
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prefill_device: "cuda:1"
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# GPU 2: layers 30–44
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- match:
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name: "^model\\.layers\\.(3[0-9]|4[0-4])\\.mlp\\.gate$"
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class: ktransformers.models.modeling_deepseek_v3.MoEGate
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replace:
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class: ktransformers.operators.gate.KMoEGate
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kwargs:
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generate_device: "cuda:2"
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prefill_device: "cuda:2"
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# GPU 3: layers 45–60
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- match:
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name: "^model\\.layers\\.(4[5-9]|5[0-9]|60)\\.mlp\\.gate$"
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class: ktransformers.models.modeling_deepseek_v3.MoEGate
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replace:
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class: ktransformers.operators.gate.KMoEGate
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kwargs:
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generate_device: "cuda:3"
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prefill_device: "cuda:3"
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# === MLP Experts Replacement ===
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# replace with marlin expert. Open and modify layer-num as needed.
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# Each layer of malin experts takes about 6GB of GPU memory.
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# !!!Do remember 'close' cuda graph if you are using marlin expert.!!!
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# !!!KExpertsTorch is untested, we don't have enough VRAM.!!!
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# GPU 0: layers 3–4
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# - match:
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# name: "^model\\.layers\\.([3-4])\\.mlp\\.experts$"
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# replace:
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# class: ktransformers.operators.experts.KTransformersExperts
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# kwargs:
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# generate_device: "cuda:0"
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# generate_op: "KExpertsMarlin"
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# recursive: False
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# # GPU 1: layers 15–17
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# - match:
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# name: "^model\\.layers\\.(1[5-7])\\.mlp\\.experts$"
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# replace:
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# class: ktransformers.operators.experts.KTransformersExperts
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# kwargs:
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# generate_device: "cuda:1"
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# generate_op: "KExpertsMarlin"
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# recursive: False
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# # GPU 2: layers 30–32
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# - match:
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# name: "^model\\.layers\\.(3[0-2])\\.mlp\\.experts$"
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# replace:
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# class: ktransformers.operators.experts.KTransformersExperts
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# kwargs:
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# generate_device: "cuda:2"
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# generate_op: "KExpertsMarlin"
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# recursive: False
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# # GPU 3: layers 45–46
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# - match:
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# name: "^model\\.layers\\.(4[5-6])\\.mlp\\.experts$"
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# replace:
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# class: ktransformers.operators.experts.KTransformersExperts
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# kwargs:
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# generate_device: "cuda:3"
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# generate_op: "KExpertsMarlin"
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# recursive: False
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# === MLP Experts Replacement ===
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# GPU 0: layers 0–14
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- match:
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name: "^model\\.layers\\.([0-9]|1[0-4])\\.mlp\\.experts$"
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replace:
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class: ktransformers.operators.experts.KTransformersExperts
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kwargs:
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prefill_device: "cuda:0"
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prefill_op: "KExpertsTorch"
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generate_device: "cpu"
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generate_op: "KExpertsCPU"
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out_device: "cuda:0"
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recursive: False
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# GPU 1: layers 15–29
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- match:
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name: "^model\\.layers\\.(1[5-9]|2[0-9])\\.mlp\\.experts$"
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replace:
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class: ktransformers.operators.experts.KTransformersExperts
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kwargs:
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prefill_device: "cuda:1"
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prefill_op: "KExpertsTorch"
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generate_device: "cpu"
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generate_op: "KExpertsCPU"
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out_device: "cuda:1"
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recursive: False
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# GPU 2: layers 30–44
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- match:
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name: "^model\\.layers\\.(3[0-9]|4[0-4])\\.mlp\\.experts$"
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replace:
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class: ktransformers.operators.experts.KTransformersExperts
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kwargs:
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prefill_device: "cuda:2"
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prefill_op: "KExpertsTorch"
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generate_device: "cpu"
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generate_op: "KExpertsCPU"
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out_device: "cuda:2"
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recursive: False
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# GPU 3: layers 45–60
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- match:
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name: "^model\\.layers\\.(4[5-9]|5[0-9]|60)\\.mlp\\.experts$"
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replace:
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class: ktransformers.operators.experts.KTransformersExperts
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kwargs:
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prefill_device: "cuda:3"
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prefill_op: "KExpertsTorch"
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generate_device: "cpu"
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generate_op: "KExpertsCPU"
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out_device: "cuda:3"
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recursive: False
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# === Self-Attention Replacement ===
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# GPU 0: layers 0–14
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- match:
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name: "^model\\.layers\\.([0-9]|1[0-4])\\.self_attn$"
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replace:
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class: ktransformers.operators.attention.KDeepseekV2Attention
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kwargs:
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generate_device: "cuda:0"
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prefill_device: "cuda:0"
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absorb_for_prefill: False
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# GPU 1: layers 15–29
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- match:
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name: "^model\\.layers\\.(1[5-9]|2[0-9])\\.self_attn$"
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replace:
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class: ktransformers.operators.attention.KDeepseekV2Attention
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kwargs:
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generate_device: "cuda:1"
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prefill_device: "cuda:1"
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absorb_for_prefill: False
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# GPU 2: layers 30–44
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- match:
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name: "^model\\.layers\\.(3[0-9]|4[0-4])\\.self_attn$"
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replace:
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class: ktransformers.operators.attention.KDeepseekV2Attention
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kwargs:
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generate_device: "cuda:2"
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prefill_device: "cuda:2"
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absorb_for_prefill: False
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# GPU 3: layers 45–60
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- match:
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name: "^model\\.layers\\.(4[5-9]|5[0-9]|60)\\.self_attn$"
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replace:
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class: ktransformers.operators.attention.KDeepseekV2Attention
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kwargs:
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generate_device: "cuda:3"
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prefill_device: "cuda:3"
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absorb_for_prefill: False
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# === Overall Model Replacement with Transfer Map ===
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- match:
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name: "^model$"
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replace:
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class: "ktransformers.operators.models.KDeepseekV2Model"
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kwargs:
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per_layer_prefill_intput_threshold: 0 # 0 means close layer‐wise prefill
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transfer_map:
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15: "cuda:1" # Layers 15+ on GPU 1
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30: "cuda:2" # Layers 30+ on GPU 2
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45: "cuda:3" # Layers 45+ on GPU 3
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# === Default Catch-All for Other Modules ===
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# GPU 0: layers 0–14
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- match:
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name: "^model\\.layers\\.([0-9]|1[0-4])\\."
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replace:
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class: "default"
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kwargs:
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generate_device: "cuda:0"
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prefill_device: "cuda:0"
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# GPU 1: layers 15–29
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- match:
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name: "^model\\.layers\\.(1[5-9]|2[0-9])\\."
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replace:
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class: "default"
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kwargs:
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generate_device: "cuda:1"
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prefill_device: "cuda:1"
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# GPU 2: layers 30–44
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- match:
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name: "^model\\.layers\\.(3[0-9]|4[0-4])\\."
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replace:
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class: "default"
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kwargs:
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generate_device: "cuda:2"
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prefill_device: "cuda:2"
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- match:
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name: "^lm_head"
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class: torch.nn.Linear
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replace:
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class: ktransformers.operators.linear.KTransformersLinear
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kwargs:
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generate_device: "cuda:3"
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prefill_device: "cuda:3"
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generate_op: "KLinearMarlin"
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prefill_op: "KLinearTorch"
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# For final modules (model.norm), ensure they are on GPU 3 (as in your original config)
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- match:
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name: "(^model\\.layers\\.(4[5-9]|5[0-9]|60)\\.)|(^model\\.norm)"
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replace:
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class: "default"
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kwargs:
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generate_device: "cuda:3"
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prefill_device: "cuda:3"
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