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https://github.com/kvcache-ai/ktransformers.git
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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
32 lines
1.0 KiB
Makefile
32 lines
1.0 KiB
Makefile
flake_find:
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cd ktransformers && flake8 | grep -Eo '[A-Z][0-9]{3}' | sort | uniq| paste -sd ',' -
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format:
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@cd ktransformers && black .
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@black setup.py
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dev_install:
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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 ktransformers/ktransformers_ext/build
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rm -rf ktransformers/ktransformers_ext/cuda/build
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rm -rf ktransformers/ktransformers_ext/cuda/dist
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rm -rf ktransformers/ktransformers_ext/cuda/*.egg-info
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# install 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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echo "Installing ktransformers"
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KTRANSFORMERS_FORCE_BUILD=TRUE pip install -e . -v --no-build-isolation
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echo "Installation completed successfully"
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clean:
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rm -rf build
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rm -rf *.egg-info
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rm -rf ktransformers/ktransformers_ext/build
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rm -rf ktransformers/ktransformers_ext/cuda/build
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rm -rf ktransformers/ktransformers_ext/cuda/dist
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rm -rf ktransformers/ktransformers_ext/cuda/*.egg-info
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install_numa:
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USE_NUMA=1 make dev_install
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install_no_numa:
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env -u USE_NUMA make dev_install
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