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‎_posts/2023-03-29-experience-power.md

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PyTorch 2.0 represents a significant step forward for the PyTorch machine learning framework. The stable release of PyTorch 2.0 brings new features that unlock even higher performance, while remaining backward compatible with prior releases and retaining the Pythonic focus which has helped to make PyTorch so enthusiastically adopted by the AI/ML community. AMD has long been a strong proponent of PyTorch, and we are delighted that PyTorch 2.0 stable release includes support for AMD Instinct™ and Radeon™ GPUs that are supported by the ROCm™ software platform.
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Along with the stable PyTorch 2.0 release, the Beta includes torch.compile underpinned by TorchInductor with support for AMD Instinct and Radeon GPUs through OpenAI Triton deep learning compiler. Through TorchInductor, developers can now generate low level code using Triton that are portable and performant to hand-written kernels on native hardware centric kernel programming models.
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Along with the stable PyTorch 2.0 release, PyTorch 2.0 beta introduces torch.compile underpinned by TorchInductor with support for AMD Instinct and Radeon GPUs through OpenAI Triton deep learning compiler. Through TorchInductor, developers can now generate low level code using Triton that are portable and performant to hand-written kernels on native hardware centric kernel programming models.
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Compilers like Triton can optimize the code generated by machine learning frameworks such as PyTorch for multiple AI accelerators including AMD Instinct GPU accelerator by leveraging hardware-specific features of the AMD CDNA™ GPU architecture. This makes it easy for developers and users to switch seamlessly from any HW to AMD Instinct GPU accelerators and get great out of the box performance.
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‎docs/master/_dynamo.html

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<span class="kn">import</span> <span class="nn">contextlib</span>
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<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">callback</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
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<span class="k">def</span> <span class="nf">first_real_inst_idx</span><span class="p">(</span><span class="n">code</span><span class="p">):</span>
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