mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-11-28 11:14:18 +08:00
887c562d60
Co-authored-by: unknown <xiongchenhui@hisense.ad> Co-authored-by: Kingsley <kingsleydodonow@gmail.com>
80 lines
2.7 KiB
YAML
80 lines
2.7 KiB
YAML
- match:
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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"
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prefill_device: "cuda"
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- match:
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name: "^lm_head$" # regular expression
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class: torch.nn.Linear # only match modules matching name and class simultaneously
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replace:
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class: ktransformers.operators.linear.KTransformersLinear # optimized Kernel on quantized data types
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kwargs:
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generate_device: "cuda"
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prefill_device: "cuda"
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generate_op: "KLinearTorch"
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prefill_op: "KLinearTorch"
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# - match:
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# name: "^model\\.layers\\..*$" # 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: "KLinearTorch"
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# prefill_op: "KLinearTorch"
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- match:
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name: "^model\\.layers\\.(?!.*mlp\\.shared_expert_gate).*$" # regular expression
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class: torch.nn.Linear # only match modules matching name and class simultaneously
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replace:
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class: ktransformers.operators.linear.KTransformersLinear # optimized Kernel on quantized data types
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kwargs:
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generate_device: "cuda"
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prefill_device: "cuda"
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generate_op: "KLinearTorch"
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prefill_op: "KLinearTorch"
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- match:
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name: "^model\\.layers\\..*\\.mlp$"
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replace:
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class: ktransformers.operators.experts.KQwen3MoeSparseMoeBlock # 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$"
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replace:
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class: ktransformers.operators.experts.KTransformersExperts # custom MoE Kernel with expert paralleism
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kwargs:
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prefill_device: "cuda"
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prefill_op: "KExpertsTorch"
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generate_device: "cpu"
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generate_op: "KSFTExpertsCPU"
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out_device: "cuda"
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backend: "AMXInt8" # or "AMXBF16" or "AMXInt8"
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recursive: False # don't recursively inject submodules of this module
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- match:
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name: "^model\\.layers\\..*\\.self_attn$"
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replace:
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class: ktransformers.operators.attention.KQwen3MoeAttention # 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.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: "^model$"
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replace:
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class: "ktransformers.operators.models.KQwen3MoeModel"
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kwargs:
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per_layer_prefill_intput_threshold: 0 |