mirror of
https://github.com/kvcache-ai/ktransformers.git
synced 2026-04-28 11:49:51 +00:00
57d14d22bc
* 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
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915 B
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34 lines
No EOL
915 B
JSON
{
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"name": "Ktrans Dev Container",
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"privileged": true,
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"build": {
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"dockerfile": "Dockerfile",
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"context": "..",
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"args": {
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"http_proxy": "${env:http_proxy}",
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"https_proxy": "${env:https_proxy}",
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}
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},
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"runArgs": [
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"--network=host",
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"--gpus",
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"all"
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// "--gpu all"
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],
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"workspaceFolder": "/workspace",
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"workspaceMount": "source=${localWorkspaceFolder},target=/workspace,type=bind,consistency=cached",
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"mounts": [
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"source=/mnt/data,target=/mnt/incontainer,type=bind,consistency=cached"
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],
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"customizations": {
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"vscode": {
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"extensions": [
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],
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"settings": {
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"terminal.integrated.shell.linux": "/bin/bash",
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"cmake.configureOnOpen": true,
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"cmake.generator": "Ninja"
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}
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}
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}
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} |