{"product_id":"mlir-for-machine-learning-compilers-anik-rao-9798272164148","title":"Mlir for Machine Learning Compilers: TENSORFLOW, PYTORCH, AND HARDWARE ACCELERATION: Optimize inference and training with dialect design, graph transf","description":"\u003cp\u003e\u003cb\u003eBuild production grade ML compilers with MLIR, from TensorFlow and PyTorch graphs to fast GPU, CPU, and embedded executables\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eMachine learning teams struggle to turn research models into efficient binaries across diverse hardware. Toolchains are fragmented, passes are opaque, and small changes can break performance or correctness.\u003c\/p\u003e\u003cp\u003eThis book gives you a clear path. You get a practical workflow that starts with readable IR, enforces graph invariants with strong verifiers, and lowers to portable or vendor specific code that you can ship with confidence.\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eDesign solid operators using ODS and traits, add verifiers and builders that keep graphs legal, and attach interfaces that unlock tiling, fusion, and bufferization\u003c\/li\u003e\n\u003cli\u003eImport TensorFlow with StableHLO and VHLO, use the TFLite and TF bridges, and keep portability with TOSA when you need framework neutral flows\u003c\/li\u003e\n\u003cli\u003eCapture PyTorch programs with Torch MLIR, decompose to arith tensor and linalg, and manage distinct training and inference paths without forking pipelines\u003c\/li\u003e\n\u003cli\u003eApply shape reasoning with the Shape dialect, handle static and dynamic ranks, and wire in inference that feeds downstream transforms\u003c\/li\u003e\n\u003cli\u003eRun post training quantization with the Quant dialect, carry scales and zero points correctly, and build calibration aware dequant pipelines\u003c\/li\u003e\n\u003cli\u003eBufferize tensors with One Shot Bufferize, control function boundaries, model effects precisely, and validate lifetimes with ownership based deallocation\u003c\/li\u003e\n\u003cli\u003eTune memory with MemRef layout maps, alignment and packing, and pick layouts that suit accelerators without losing legality\u003c\/li\u003e\n\u003cli\u003eGenerate GPU code with GPU and NVGPU dialects, target NVVM or ROCDL, and use vector and tensor core paths that map to real intrinsics\u003c\/li\u003e\n\u003cli\u003eTarget SPIR V for Vulkan environments with capability gating, or generate portable C and C++ for microcontrollers with EmitC\u003c\/li\u003e\n\u003cli\u003eJIT with ExecutionEngine and JitRunner, or use IREE end to end for compilation and runtime on mobile, desktop, and server\u003c\/li\u003e\n\u003cli\u003eDrive performance with tiling fusion and vectorization in Linalg and Vector, add autotuning hooks, and apply the Sparse Tensor dialect for structured sparsity\u003c\/li\u003e\n\u003cli\u003eProfile with remarks counters and traces, then lock down stability with lit and FileCheck, mlir reduce, bytecode, and dialect versioning\u003c\/li\u003e\n\u003cli\u003eWork through complete case studies, TensorFlow ResNet to CUDA with NVGPU and NVVM, PyTorch Transformer to ROCm with ROCDL, quantized MobileNet to EmitC for Cortex M, and sparse attention to SPIR V for Vulkan\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eThis is a code heavy guide with labeled MLIR Python C++ Shell and TableGen listings, you can copy pipelines and schedules directly into your builds to stand up real projects.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eGrab your copy today\u003c\/b\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Anik Rao\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9798272164148\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Independently Published\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 10\/29\/2025\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 252\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 0.98lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 10.00h x 7.00w x 0.53d","brand":"Anik Rao","offers":[{"title":"Paperback","offer_id":47931161542911,"sku":"9798272164148","price":39.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0662\/2982\/9887\/files\/img_f23004d3-cdf4-4794-9532-d68dcc80b507.jpg?v=1766346868","url":"https:\/\/www.whiterainbookhouse.com\/products\/mlir-for-machine-learning-compilers-anik-rao-9798272164148","provider":"WR Book House","version":"1.0","type":"link"}