wehub-resource-sync
ec436095dd
Book-CI / test (macos-latest) (push) Has been cancelled
Book-CI / test (ubuntu-latest) (push) Has been cancelled
Book-CI / test (windows-latest) (push) Has been cancelled
Release Fake Tag / publish (push) Has been cancelled
Deploy / deploy (macos-latest) (push) Has been cancelled
Deploy / deploy (ubuntu-latest) (push) Has been cancelled
Deploy / deploy (windows-latest) (push) Has been cancelled
Release to PyPI / Build & publish sglang-kt (push) Has been cancelled
Release to PyPI / Build kt-kernel (Python 3.11) (push) Has been cancelled
Release to PyPI / Build kt-kernel (Python 3.12) (push) Has been cancelled
Release to PyPI / Publish kt-kernel to PyPI (push) Has been cancelled
240 lines
8.2 KiB
YAML
240 lines
8.2 KiB
YAML
- match:
|
|
name: "^model.embed_tokens"
|
|
replace:
|
|
class: "default"
|
|
kwargs:
|
|
generate_device: "cpu"
|
|
prefill_device: "cpu"
|
|
|
|
- match:
|
|
name: "^model\\.layers\\.([0-9]|[1][0-4])\\."
|
|
class: ktransformers.models.modeling_deepseek.DeepseekV2YarnRotaryEmbedding
|
|
replace:
|
|
class: ktransformers.operators.RoPE.YarnRotaryEmbedding
|
|
kwargs:
|
|
generate_device: "cuda:0"
|
|
prefill_device: "cuda:0"
|
|
- match:
|
|
name: "^model\\.layers\\.([2][0-9]|[1][5-9])\\."
|
|
class: ktransformers.models.modeling_deepseek.DeepseekV2YarnRotaryEmbedding
|
|
replace:
|
|
class: ktransformers.operators.RoPE.YarnRotaryEmbedding
|
|
kwargs:
|
|
generate_device: "cuda:1"
|
|
prefill_device: "cuda:1"
|
|
- match:
|
|
name: "^model\\.layers\\.([3][0-9]|[4][0-4])\\."
|
|
class: ktransformers.models.modeling_deepseek.DeepseekV2YarnRotaryEmbedding
|
|
replace:
|
|
class: ktransformers.operators.RoPE.YarnRotaryEmbedding
|
|
kwargs:
|
|
generate_device: "cuda:2"
|
|
prefill_device: "cuda:2"
|
|
- match:
|
|
name: "^model\\.layers\\.([5][0-9]|[4][5-9])\\."
|
|
class: ktransformers.models.modeling_deepseek.DeepseekV2YarnRotaryEmbedding
|
|
replace:
|
|
class: ktransformers.operators.RoPE.YarnRotaryEmbedding
|
|
kwargs:
|
|
generate_device: "cuda:3"
|
|
prefill_device: "cuda:3"
|
|
|
|
- match:
|
|
name: "^model\\.layers\\.([0-9]|[1][0-4])\\.(?!self_attn\\.kv_b_proj).*$" # regular expression
|
|
class: torch.nn.Linear # only match modules matching name and class simultaneously
|
|
replace:
|
|
class: ktransformers.operators.linear.KTransformersLinear # optimized Kernel on quantized data types
|
|
kwargs:
|
|
generate_device: "cuda:0"
|
|
prefill_device: "cuda:0"
|
|
generate_op: "KLinearMarlin"
|
|
prefill_op: "KLinearTorch"
|
|
- match:
|
|
name: "^model\\.layers\\.([2][0-9]|[1][5-9])\\.(?!self_attn\\.kv_b_proj).*$" # regular expression
|
|
class: torch.nn.Linear # only match modules matching name and class simultaneously
|
|
replace:
|
|
class: ktransformers.operators.linear.KTransformersLinear # optimized Kernel on quantized data types
|
|
kwargs:
|
|
generate_device: "cuda:1"
|
|
prefill_device: "cuda:1"
|
|
generate_op: "KLinearMarlin"
|
|
prefill_op: "KLinearTorch"
|
|
- match:
|
|
name: "^model\\.layers\\.([3][0-9]|[4][0-4])\\.(?!self_attn\\.kv_b_proj).*$" # regular expression
|
|
class: torch.nn.Linear # only match modules matching name and class simultaneously
|
|
replace:
|
|
class: ktransformers.operators.linear.KTransformersLinear # optimized Kernel on quantized data types
|
|
kwargs:
|
|
generate_device: "cuda:2"
|
|
prefill_device: "cuda:2"
|
|
generate_op: "KLinearMarlin"
|
|
prefill_op: "KLinearTorch"
|
|
- match:
|
|
name: "^model\\.layers\\.([5][0-9]|[4][5-9])\\.(?!self_attn\\.kv_b_proj).*$" # regular expression
|
|
class: torch.nn.Linear # only match modules matching name and class simultaneously
|
|
replace:
|
|
class: ktransformers.operators.linear.KTransformersLinear # optimized Kernel on quantized data types
|
|
kwargs:
|
|
generate_device: "cuda:3"
|
|
prefill_device: "cuda:3"
|
|
generate_op: "KLinearMarlin"
|
|
prefill_op: "KLinearTorch"
|
|
|
|
- match:
|
|
name: "^model\\.layers\\.([0-9]|[1][0-4])\\.mlp$"
|
|
class: ktransformers.models.modeling_deepseek.DeepseekV2MoE
|
|
replace:
|
|
class: ktransformers.operators.experts.KDeepseekV2MoE # mlp module with custom forward function
|
|
kwargs:
|
|
generate_device: "cuda:0"
|
|
prefill_device: "cuda:0"
|
|
- match:
|
|
name: "^model\\.layers\\.([2][0-9]|[1][5-9])\\.mlp$"
|
|
class: ktransformers.models.modeling_deepseek.DeepseekV2MoE
|
|
replace:
|
|
class: ktransformers.operators.experts.KDeepseekV2MoE # mlp module with custom forward function
|
|
kwargs:
|
|
generate_device: "cuda:1"
|
|
prefill_device: "cuda:1"
|
|
- match:
|
|
name: "^model\\.layers\\.([3][0-9]|[4][0-4])\\.mlp$"
|
|
class: ktransformers.models.modeling_deepseek.DeepseekV2MoE
|
|
replace:
|
|
class: ktransformers.operators.experts.KDeepseekV2MoE # mlp module with custom forward function
|
|
kwargs:
|
|
generate_device: "cuda:2"
|
|
prefill_device: "cuda:2"
|
|
- match:
|
|
name: "^model\\.layers\\.([5][0-9]|[4][5-9])\\.mlp$"
|
|
class: ktransformers.models.modeling_deepseek.DeepseekV2MoE
|
|
replace:
|
|
class: ktransformers.operators.experts.KDeepseekV2MoE # mlp module with custom forward function
|
|
kwargs:
|
|
generate_device: "cuda:3"
|
|
prefill_device: "cuda:3"
|
|
|
|
- match:
|
|
name: "^model\\.layers\\.([0-9]|[1][0-4])\\.mlp\\.experts$"
|
|
replace:
|
|
class: ktransformers.operators.experts.KTransformersExperts # custom MoE Kernel with expert paralleism
|
|
kwargs:
|
|
prefill_device: "cuda:0"
|
|
prefill_op: "KExpertsTorch"
|
|
generate_device: "cpu"
|
|
generate_op: "KExpertsCPU"
|
|
out_device: "cuda:0"
|
|
recursive: False # don't recursively inject submodules of this module
|
|
- match:
|
|
name: "^model\\.layers\\.([2][0-9]|[1][5-9])\\.mlp\\.experts$"
|
|
replace:
|
|
class: ktransformers.operators.experts.KTransformersExperts # custom MoE Kernel with expert paralleism
|
|
kwargs:
|
|
prefill_device: "cuda:1"
|
|
prefill_op: "KExpertsTorch"
|
|
generate_device: "cpu"
|
|
generate_op: "KExpertsCPU"
|
|
out_device: "cuda:1"
|
|
recursive: False # don't recursively inject submodules of this module
|
|
- match:
|
|
name: "^model\\.layers\\.([3][0-9]|[4][0-4])\\.mlp\\.experts$"
|
|
replace:
|
|
class: ktransformers.operators.experts.KTransformersExperts # custom MoE Kernel with expert paralleism
|
|
kwargs:
|
|
prefill_device: "cuda:2"
|
|
prefill_op: "KExpertsTorch"
|
|
generate_device: "cpu"
|
|
generate_op: "KExpertsCPU"
|
|
out_device: "cuda:2"
|
|
recursive: False # don't recursively inject submodules of this module
|
|
- match:
|
|
name: "^model\\.layers\\.([5][0-9]|[4][5-9])\\.mlp\\.experts$"
|
|
replace:
|
|
class: ktransformers.operators.experts.KTransformersExperts # custom MoE Kernel with expert paralleism
|
|
kwargs:
|
|
prefill_device: "cuda:3"
|
|
prefill_op: "KExpertsTorch"
|
|
generate_device: "cpu"
|
|
generate_op: "KExpertsCPU"
|
|
out_device: "cuda:3"
|
|
recursive: False # don't recursively inject submodules of this module
|
|
|
|
- match:
|
|
name: "^model\\.layers\\.([0-9]|[1][0-4])\\.self_attn$"
|
|
replace:
|
|
class: ktransformers.operators.attention.KDeepseekV2Attention # optimized MLA implementation
|
|
kwargs:
|
|
generate_device: "cuda:0"
|
|
prefill_device: "cuda:0"
|
|
- match:
|
|
name: "^model\\.layers\\.([2][0-9]|[1][5-9])\\.self_attn$"
|
|
replace:
|
|
class: ktransformers.operators.attention.KDeepseekV2Attention # optimized MLA implementation
|
|
kwargs:
|
|
generate_device: "cuda:1"
|
|
prefill_device: "cuda:1"
|
|
- match:
|
|
name: "^model\\.layers\\.([3][0-9]|[4][0-4])\\.self_attn$"
|
|
replace:
|
|
class: ktransformers.operators.attention.KDeepseekV2Attention # optimized MLA implementation
|
|
kwargs:
|
|
generate_device: "cuda:2"
|
|
prefill_device: "cuda:2"
|
|
- match:
|
|
name: "^model\\.layers\\.([5][0-9]|[4][5-9])\\.self_attn$"
|
|
replace:
|
|
class: ktransformers.operators.attention.KDeepseekV2Attention # optimized MLA implementation
|
|
kwargs:
|
|
generate_device: "cuda:3"
|
|
prefill_device: "cuda:3"
|
|
|
|
- match:
|
|
name: "^model$"
|
|
replace:
|
|
class: "ktransformers.operators.models.KDeepseekV2Model"
|
|
kwargs:
|
|
per_layer_prefill_intput_threshold: 0 # 0 is close layer wise prefill
|
|
transfer_map:
|
|
15: "cuda:1"
|
|
30: "cuda:2"
|
|
45: "cuda:3"
|
|
|
|
- match:
|
|
name: "^model\\.layers\\.([0-9]|[1][0-4])\\."
|
|
replace:
|
|
class: "default"
|
|
kwargs:
|
|
generate_device: "cuda:0"
|
|
prefill_device: "cuda:0"
|
|
- match:
|
|
name: "(^model\\.layers\\.([2][0-9]|[1][5-9])\\.)"
|
|
replace:
|
|
class: "default"
|
|
kwargs:
|
|
generate_device: "cuda:1"
|
|
prefill_device: "cuda:1"
|
|
- match:
|
|
name: "(^model\\.layers\\.([3][0-9]|[4][0-4])\\.)"
|
|
replace:
|
|
class: "default"
|
|
kwargs:
|
|
generate_device: "cuda:2"
|
|
prefill_device: "cuda:2"
|
|
|
|
- match:
|
|
name: "^lm_head"
|
|
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"
|
|
|
|
- match:
|
|
name: "(^model\\.layers\\.([5][0-9]|[4][5-9])\\.)|(^model.norm)"
|
|
replace:
|
|
class: "default"
|
|
kwargs:
|
|
generate_device: "cuda:3"
|
|
prefill_device: "cuda:3" |