mirror of
https://github.com/kvcache-ai/ktransformers.git
synced 2026-04-28 11:49:51 +00:00
Refactor: restructure repository to focus on kt-kernel and KT-SFT modulesq recon (#1581)
* refactor: move legacy code to archive/ directory - Moved ktransformers, csrc, third_party, merge_tensors to archive/ - Moved build scripts and configurations to archive/ - Kept kt-kernel, KT-SFT, doc, and README files in root - Preserved complete git history for all moved files * refactor: restructure repository to focus on kt-kernel and KT-SFT modules * fix README * fix README * fix README * fix README * docs: add performance benchmarks to kt-kernel section Add comprehensive performance data for kt-kernel to match KT-SFT's presentation: - AMX kernel optimization: 21.3 TFLOPS (3.9× faster than PyTorch) - Prefill phase: up to 20× speedup vs baseline - Decode phase: up to 4× speedup - NUMA optimization: up to 63% throughput improvement - Multi-GPU (8×L20): 227.85 tokens/s total throughput with DeepSeek-R1 FP8 Source: https://lmsys.org/blog/2025-10-22-KTransformers/ This provides users with concrete performance metrics for both core modules, making it easier to understand the capabilities of each component. * refactor: improve kt-kernel performance data with specific hardware and models Replace generic performance descriptions with concrete benchmarks: - Specify exact hardware: 8×L20 GPU + Xeon Gold 6454S, Single/Dual-socket Xeon + AMX - Include specific models: DeepSeek-R1-0528 (FP8), DeepSeek-V3 (671B) - Show detailed metrics: total throughput, output throughput, concurrency details - Match KT-SFT presentation style for consistency This provides users with actionable performance data they can use to evaluate hardware requirements and expected performance for their use cases. * fix README * docs: clean up performance table and improve formatting * add pic for README * refactor: simplify .gitmodules and backup legacy submodules - Remove 7 legacy submodules from root .gitmodules (archive/third_party/*) - Keep only 2 active submodules for kt-kernel (llama.cpp, pybind11) - Backup complete .gitmodules to archive/.gitmodules - Add documentation in archive/README.md for researchers who need legacy submodules This reduces initial clone size by ~500MB and avoids downloading unused dependencies. * refactor: move doc/ back to root directory Keep documentation in root for easier access and maintenance. * refactor: consolidate all images to doc/assets/ - Move kt-kernel/assets/heterogeneous_computing.png to doc/assets/ - Remove KT-SFT/assets/ (images already in doc/assets/) - Update KT-SFT/README.md image references to ../doc/assets/ - Eliminates ~7.9MB image duplication - Centralizes all documentation assets in one location * fix pic path for README
This commit is contained in:
Jiaqi Liao
GitHub
parent
8729435d85
commit
57d14d22bc
510 changed files with 711 additions and 334 deletions
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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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