MindSpore 2.6.0 Features Unveiled: Graph Compilation & Combined Quantization
🚀 Hey devs! Ready for more MindSpore 2.6.0 magic? 🚀
In the last blog, we kicked off the "𝗠𝗶𝗻𝗱𝗦𝗽𝗼𝗿𝗲 𝟮.𝟲.𝟬 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝗨𝗻𝘃𝗲𝗶𝗹𝗲𝗱" series by diving deep into 𝗱𝗿𝗼𝗽𝗹𝗲𝘀𝘀 𝗠𝗼𝗘 𝗺𝗼𝗱𝗲𝗹 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴. We showed you how we made the feature happen through intelligent optimizations like Morph custom parallelism, hierarchical communication, and forward-backward communication overlapping. This innovation turbocharges DeepSeek and other model deployments on large-scale clusters. 🌌
😎 Now, for the next chapter in our full-stack DeepSeek support—prepare to discover how MindSpore 2.6.0 helps you supercharge inference throughput and halve NPU memory for your models! Let's dive in! 👇
Supercharging Inference Throughput & Halving NPU Memory
Get ready to experience next-level inference performance! MindSpore 2.6.0 delivers a massive boost in throughput and significantly optimizes NPU memory usage for models like DeepSeek-V3/R1. How? Through advanced graph compilation and smart combined quantization.
Smarter Inference, Faster Results
We've implemented a more efficient inference network based on the unique network structure and multi-head latent attention (MLA) of DeepSeek-V3/R1, focusing on innovations:
⚡ Fast Graph Generation
MindSpore's just-in-time (JIT) compilation automatically transforms your Python classes or functions into highly optimized computational graphs. With support for Abstract Syntax Tree (AST), bytecode, and trace modes, our JIT compilation covers nearly all Python syntax, ready for any scenario you throw at it.
🧠 Intelligent Operator Fusion
Forget slow operations! MindSpore automatically spots and merges multiple foundational operators into single, advanced operators within your graph. This operator fusion dramatically cuts down host communication overhead and slashes device compute latency. The result? Your models run much faster.
🧩 Dynamic Shape Support
Building models with dynamic shapes? We've got you covered. Our three-level pipeline handles shape inference, data tiling, and execution dispatch with incredible efficiency. This smart pipelining seamlessly overlaps computations between your host and device, supercharging performance even when shapes change on the fly.
Combined Quantization: Cost-Cutting Deployment
Want to deploy models like DeepSeek-R1 without breaking the bank on compute resources? Look no further! MindSpore 2.6.0 introduces the 𝗠𝗶𝗻𝗱𝗦𝗽𝗼𝗿𝗲 𝗚𝗼𝗹𝗱𝗲𝗻 𝗦𝘁𝗶𝗰𝗸 🪄 model compression tool!
This powerhouse lets you easily create and verify multiple combined quantization solutions without ever touching or modifying your original model script. Shrink your memory footprint and accelerate inference, hassle-free!
For more info about MindSpore Golden Stick, dive into its resources:
🔗 GitHub repository: https://github.com/mindspore-ai/golden-stick
🔗 Website documentation: https://www.mindspore.cn/golden_stick/docs/en/master/index.html
And just like that, we've pulled back the curtain on how MindSpore 2.6.0 delivers incredible inference throughput and NPU memory optimization! Our secret? 🌟 Intelligent graph generation, operator fusion, dynamic shape wizardry, and the powerhouse MindSpore Golden Stick for model compression.
But the journey continues! 🤩 Next up in our "MindSpore 2.6.0 Features Unveiled" series, we'll unlock advanced Reinforcement Learning (𝗥𝗟) support, including Group Relative Policy Optimization (𝗚𝗥𝗣𝗢), and showcase training APIs designed to accelerate your model development. 🌠 Expect even more of our capabilities for DeepSeek-R1/V3 deployments!
Explore more MindSpore 2.6.0 goodness:
🔗 Release Notes → https://www.mindspore.cn/docs/en/r2.6.0/RELEASE.html
🔗 Download → https://www.mindspore.cn/versions/en
🔗 DeepSeek-V3 implementation in MindSpore Transformers (GitHub): https://github.com/mindspore-lab/mindformers/tree/dev/research/deepseek3