mirror of
https://github.com/kvcache-ai/ktransformers.git
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5ec33d046d
use marlin for lm_head, lm_head only calc last token for prefill extend context window to 19K for DeepSeek-V3/R1 within 24GB VRAM
122 lines
3.9 KiB
YAML
122 lines
3.9 KiB
YAML
- match:
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name: "^model\\.layers\\.([012])\\."
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class: ktransformers.models.modeling_qwen2_moe.Qwen2MoeRotaryEmbedding
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replace:
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class: ktransformers.operators.RoPE.RotaryEmbedding
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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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- match:
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name: "^model\\.layers\\.([012])$" # regular expression
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class: torch.nn.Linear # only match modules matching name and class simultaneously
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replace:
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class: ktransformers.operators.linear.KTransformersLinear # optimized Kernel on quantized data types
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kwargs:
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generate_device: "cuda:0"
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prefill_device: "cuda:0"
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generate_op: "KLinearMarlin"
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prefill_op: "KLinearTorch"
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- match:
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name: "^model\\.layers\\.([012])\\.mlp$"
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class: ktransformers.models.modeling_qwen2_moe.Qwen2MoeSparseMoeBlock
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replace:
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class: ktransformers.operators.experts.KQwen2MoeSparseMoeBlock # mlp module with custom forward function
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- match:
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name: "^model\\.layers\\.([012])\\.mlp\\.experts$"
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replace:
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class: ktransformers.operators.experts.KTransformersExperts # custom MoE Kernel with expert paralleism
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# device: "cpu" # which devices to load this module when initializing
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kwargs:
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prefill_device: "cuda:0"
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prefill_op: "KExpertsTorch"
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generate_device: "cpu"
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generate_op: "KExpertsCPU"
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out_device: "cuda:0"
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recursive: False # don't recursively inject submodules of this module
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- match:
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name: "^model\\.layers\\.([12][0-9]|[3-9])\\."
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class: ktransformers.models.modeling_qwen2_moe.Qwen2MoeRotaryEmbedding
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replace:
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class: ktransformers.operators.RoPE.RotaryEmbedding
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kwargs:
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generate_device: "cuda:1"
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prefill_device: "cuda:1"
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- match:
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name: "^model\\.layers\\.([12][0-9]|[3-9])$" # regular expression
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class: torch.nn.Linear # only match modules matching name and class simultaneously
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replace:
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class: ktransformers.operators.linear.KTransformersLinear # optimized Kernel on quantized data types
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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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- match:
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name: "^model\\.layers\\.([12][0-9]|[3-9])\\.mlp$"
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class: ktransformers.models.modeling_qwen2_moe.Qwen2MoeSparseMoeBlock
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replace:
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class: ktransformers.operators.experts.KQwen2MoeSparseMoeBlock # mlp module with custom forward function
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- match:
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name: "^model\\.layers\\.([12][0-9]|[3-9])\\.mlp\\.experts$"
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replace:
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class: ktransformers.operators.experts.KTransformersExperts # custom MoE Kernel with expert paralleism
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# device: "cpu" # which devices to load this module when initializing
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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 # don't recursively inject submodules of this module
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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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- 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: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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- match:
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name: "(^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:1"
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prefill_device: "cuda:1"
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- match:
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name: "^model$"
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replace:
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class: "ktransformers.operators.models.KQwen2MoeModel"
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kwargs:
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per_layer_prefill_intput_threshold: 0 # 0 is close layer wise prefill
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transfer_map:
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3: "cuda:1"
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- match:
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name: "^model\\.layers\\.([012])\\."
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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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- match:
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name: "^model\\.layers\\.([12][0-9]|[3-9])\\."
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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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