mirror of
https://github.com/kvcache-ai/ktransformers.git
synced 2026-04-20 06:18:59 +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
44 lines
1.3 KiB
Bash
44 lines
1.3 KiB
Bash
#!/bin/bash
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set -e
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# default backend
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DEV="cuda"
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# parse --dev argument
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while [[ "$#" -gt 0 ]]; do
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case $1 in
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--dev) DEV="$2"; shift ;;
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*) echo "Unknown parameter passed: $1"; exit 1 ;;
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esac
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shift
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done
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export DEV_BACKEND="$DEV"
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echo "Selected backend: $DEV_BACKEND"
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# clear build dirs
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rm -rf build
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rm -rf *.egg-info
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rm -rf csrc/build
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rm -rf csrc/ktransformers_ext/build
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rm -rf csrc/ktransformers_ext/cuda/build
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rm -rf csrc/ktransformers_ext/cuda/dist
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rm -rf csrc/ktransformers_ext/cuda/*.egg-info
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rm -rf ~/.ktransformers
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echo "Installing python dependencies from requirements.txt"
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pip install -r requirements-local_chat.txt
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pip install -r ktransformers/server/requirements.txt
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echo "Installing ktransformers"
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KTRANSFORMERS_FORCE_BUILD=TRUE pip install -v . --no-build-isolation
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if [[ "$DEV_BACKEND" == "cuda" ]]; then
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echo "Installing custom_flashinfer for CUDA backend"
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pip install third_party/custom_flashinfer/
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fi
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# SITE_PACKAGES=$(python -c "import site; print(site.getsitepackages()[0])")
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# echo "Copying thirdparty libs to $SITE_PACKAGES"
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# cp -a csrc/balance_serve/build/third_party/prometheus-cpp/lib/libprometheus-cpp-*.so* $SITE_PACKAGES/
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# patchelf --set-rpath '$ORIGIN' $SITE_PACKAGES/sched_ext.cpython*
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echo "Installation completed successfully"
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