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
77 lines
1.6 KiB
TOML
77 lines
1.6 KiB
TOML
[build-system]
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requires = [
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"setuptools",
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"torch >= 2.3.0",
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"ninja",
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"packaging",
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"cpufeature"
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]
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build-backend = "setuptools.build_meta"
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[project]
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name = "ktransformers"
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dynamic = ["version"]
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dependencies = [
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"torch >= 2.3.0",
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"transformers",
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"fastapi >= 0.111.0",
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"uvicorn >= 0.30.1",
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"langchain >= 0.2.0",
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"blessed >= 1.20.0",
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"accelerate >= 0.31.0",
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"sentencepiece >= 0.1.97",
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"setuptools",
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"ninja",
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"wheel",
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"colorlog",
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"build",
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"fire",
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"protobuf",
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]
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requires-python = ">=3.10"
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authors = [
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{name = "KVCache.AI", email = "zhang.mingxing@outlook.com"}
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]
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maintainers = [
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{name = "james0zan", email = "zhang.mingxing@outlook.com"},
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{name = "awake", email = "awake@approaching.ai"},
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{name = "unicorn chan", email = "nl@approaching.ai"}
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]
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description = "KTransformers, pronounced as Quick Transformers, is designed to enhance your Transformers experience with advanced kernel optimizations and placement/parallelism strategies."
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readme = "README.md"
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license = {file = "LICENSE"}
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keywords = ["ktransformers", "llm"]
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classifiers = [
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"Development Status :: 4 - Beta",
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"Programming Language :: Python :: 3.10",
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"Programming Language :: Python :: 3.11",
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"Programming Language :: Python :: 3.12"
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]
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[project.urls]
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Homepage = "https://kvcache.ai"
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Repository = "https://github.com/kvcache-ai/ktransformers.git"
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Issues = "https://github.com/kvcache-ai/ktransformers/issues"
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[project.scripts]
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ktransformers = "ktransformers.server.main:main"
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[tool.setuptools.packages.find]
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where = ["./", ]
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include = ["ktransformers","ktransformers.*"]
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[tool.black]
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line-length = 120
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preview = true
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unstable = true
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