mirror of
https://github.com/kvcache-ai/ktransformers.git
synced 2025-09-03 19:20:04 +00:00
68 lines
No EOL
2.2 KiB
YAML
68 lines
No EOL
2.2 KiB
YAML
- match:
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class: ktransformers.models.modeling_deepseek.DeepseekV2YarnRotaryEmbedding
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replace:
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class: ktransformers.operators.RoPE.YarnRotaryEmbedding
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kwargs:
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generate_device: "cuda"
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prefill_device: "cuda"
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- match:
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name: "^model\\.layers\\.(?!.*self_attn\\.kv_b_proj).*$" # regular expression
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class: torch.nn.Linear # only match modules matching name and class simultaneously
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replace:
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class: ktransformers.operators.linear.KTransformersLinear # optimized Kernel on quantized data types
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kwargs:
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generate_device: "cuda"
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prefill_device: "cuda"
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generate_op: "KLinearMarlin"
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prefill_op: "KLinearTorch"
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- match:
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name: "^lm_head"
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class: torch.nn.Linear
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replace:
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class: ktransformers.operators.linear.KTransformersLinear
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kwargs:
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generate_device: "cuda"
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prefill_device: "cuda"
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generate_op: "KLinearMarlin"
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prefill_op: "KLinearTorch"
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- match:
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name: "^model\\.layers\\..*\\.mlp$"
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class: ktransformers.models.modeling_deepseek.DeepseekV2MoE
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replace:
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class: ktransformers.operators.experts.KDeepseekV2MoE # mlp module with custom forward function
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kwargs:
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generate_device: "cuda"
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prefill_device: "cuda"
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- match:
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name: "^model\\.layers\\..*\\.mlp\\.experts$"
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replace:
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class: ktransformers.operators.experts.KTransformersExperts # custom MoE Kernel with expert paralleism
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kwargs:
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prefill_device: "cuda"
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prefill_op: "KExpertsTorch"
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generate_device: "cpu"
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generate_op: "KExpertsCPU"
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out_device: "cuda"
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recursive: False # don't recursively inject submodules of this module
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- match:
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name: "^model\\.layers\\..*\\.self_attn$"
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replace:
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class: ktransformers.operators.attention.KDeepseekV2Attention # optimized MLA implementation
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kwargs:
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generate_device: "cuda"
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prefill_device: "cuda"
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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 is close layer wise prefill
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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" |