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What’s new with PyTorch/XLA 2.5 | Google Cloud Blog
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class="Qwf2Db-MnozTc Qwf2Db-MnozTc-OWXEXe-MnozTc-qWD73c nRhiJb-BFbNVe-r8s4j-bMElCd dIsJJe" track-name="ai & machine learning"track-type="tag">AI & Machine Learning</div><div class="nRhiJb-ObfsIf"><div class="nRhiJb-kR0ZEf-OWXEXe-GV1x9e-R6PoUb"></div><div class="nRhiJb-kR0ZEf-OWXEXe-GV1x9e-EehZO nRhiJb-fmcmS-oXtfBe"><h1 class="Qwf2Db-MnozTc Qwf2Db-MnozTc-OWXEXe-MnozTc-ibL1re"><div class="Qwf2Db-MnozTc Qwf2Db-MnozTc-OWXEXe-MnozTc-ibL1re"><span class="FewWi"></span>PyTorch/XLA 2.5: vLLM support and an improved developer experience</div></h1></div></div><div class="nRhiJb-fmcmS-oXtfBe dEogG">November 1, 2024</div></div></section><div class="EKklye"><div class="nRhiJb-DARUcf ZWw7T"><div class="npzWPc"><div class="dzoHJ"><div class="nRhiJb-DX2B6 nRhiJb-DX2B6-OWXEXe-h30Snd"><div class="nRhiJb-j5y3u"><ul class="nRhiJb-Qijihe phRaUe" role="list"><li class="hpHPGf"><a class="nRhiJb-ARYxNe" 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khCp7b">Software Engineer</p></div><div class="nRhiJb-kR0ZEf-OWXEXe-GV1x9e-II5mzb kWSSdd"><h5 class="cHE8Ub Qwf2Db-MnozTc Qwf2Db-MnozTc-OWXEXe-MnozTc-qWD73c">Duncan Campbell</h5><p class="nRhiJb-qJTHM khCp7b">Developer Advocate</p></div></section></div></section><div class="nRhiJb-DARUcf"><div class="nRhiJb-ObfsIf nRhiJb-DbgRPb-wNfPc-ma6Yeb nRhiJb-DbgRPb-qWD73c-cGMI2b"><div class="nRhiJb-kR0ZEf-OWXEXe-GV1x9e-ibL1re dzoHJ"><div class="aLW4J" track-metadata-module="left rail"><h5 class="qZDXV Qwf2Db-MnozTc Qwf2Db-MnozTc-OWXEXe-MnozTc-wNfPc">Join us at Google Cloud Next</h5><p class="nRhiJb-cHYyed Mrfygc nRhiJb-BP2Omd-ma6Yeb-KE6vqe nRhiJb-DbgRPb-R6PoUb-cGMI2b">April 9-11 in Las Vegas</p><a class="nRhiJb-LgbsSe nRhiJb-LgbsSe-OWXEXe-CNusmb-o6Shpd " href="https://cloud.withgoogle.com/next?utm_source=cgc-blog&utm_medium=blog&utm_campaign=FY25-Q2-global-EXP106-physicalevent-er-next25-mc&utm_content=left-hand-rail-blog-cta&utm_term=-" track-metadata-module="left rail"track-metadata-module_headline="join us at google cloud next" track-name="register"track-type="button"track-metadata-position="cta"track-metadata-eventdetail="cloud.withgoogle.com/next?utm_source=cgc-blog&utm_medium=blog&utm_campaign=FY25-Q2-global-EXP106-physicalevent-er-next25-mc&utm_content=left-hand-rail-blog-cta&utm_term=-">Register </a></div></div><div class="OYL9D nRhiJb-kR0ZEf-OWXEXe-GV1x9e-OiUrBf" jsname="tx2NYc"><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><p><span style="vertical-align:baseline">Machine learning engineers are bullish on PyTorch/XLA, a Python package that uses the XLA deep learning compiler to connect the PyTorch deep learning framework and Cloud TPUs. And now, PyTorch/XLA 2.5 is here, along with a set of improvements to add support for vLLM and enhance the overall developer experience. Featured in this release are:</span></p> <ul> <li style="list-style-type:disc;vertical-align:baseline"> <p><span style="vertical-align:baseline">A </span><a href="https://github.com/pytorch/xla/issues/8000" rel="noopener" target="_blank"><span style="text-decoration:underline;vertical-align:baseline">clarified</span></a><span style="vertical-align:baseline"> proposal for deprecation of the older torch_xla API in favor of moving towards the existing PyTorch API, providing for a simplified developer experience. An example of this is the migration of existing Distributed API.</span></p> </li> <li style="list-style-type:disc;vertical-align:baseline"> <p><span style="vertical-align:baseline">A series of improvements to the torch_xla.compile function which improve the debugging experience for developers during the development process.</span></p> </li> <li style="list-style-type:disc;vertical-align:baseline"> <p><span style="vertical-align:baseline">Experimental support in vLLM for TPUs, allowing you to extend your existing deployments and while leveraging the same vLLM interface across your TPUs.</span></p> </li> </ul> <p><span style="vertical-align:baseline">Let’s take a look at each of these enhancements.</span></p> <h3><strong style="vertical-align:baseline">Streamlining the torch_xla API</strong></h3> <p><span style="vertical-align:baseline">With PyTorch/XLA 2.5, we're taking a significant step towards making the API more consistent with upstream PyTorch. Our north star is to minimize the learning curve for developers already familiar with PyTorch, making it easier to use XLA devices. This means gradually phasing out and deprecating custom API calls for PyTorch/XLA for more mature functionality when possible, and then, migrating the API calls over to their PyTorch counterparts. Other features still remain within the existing Python module before migration.</span></p> <p><span style="vertical-align:baseline">In the spirit of a simpler developer experience for PyTorch/XLA, in this release we have migrated over to leveraging some existing PyTorch distributed API functions when running models on top of PyTorch/XLA. Historically, the calls for the distributed API were located under the torch_xla module; in this update we migrated most of them to torch.distributed.</span></p></span></section><section class="nRhiJb-DbgRPb-II5mzb-cGMI2b"><section><div class="kg99be" jscontroller="vkZm2d" jsaction="rcuQ6b:npT2md" data-text-to-copy="# With PyTorch/XLA 2.4 import torch_xla.core.xla_model as xm xm.all_reduce() # Supported after PyTorch/XLA 2.5 torch.distrbuted.all_reduce()"><div jsaction="click:VTVhdd" class="GaRnv"><svg class="" viewBox="0 0 20 20" role="presentation" aria-hidden="true" height=20 widdht=20><path d="M7.979 14.667c-.458 0-.84-.153-1.146-.459a1.53 1.53 0 0 1-.458-1.125V3.938c0-.459.153-.84.458-1.146.306-.306.688-.459 1.146-.459h6.625c.445 0 .823.156 1.136.469.312.313.468.691.468 1.136v9.145c0 .445-.156.82-.468 1.125a1.564 1.564 0 0 1-1.136.459H7.979Zm0-1.334h6.625c.083 0 .15-.024.198-.073a.239.239 0 0 0 .073-.177V3.938a.265.265 0 0 0-.073-.198.268.268 0 0 0-.198-.073H7.979a.268.268 0 0 0-.198.073.266.266 0 0 0-.073.198v9.145c0 .07.024.129.073.177a.267.267 0 0 0 .198.073ZM4.875 17.75a1.53 1.53 0 0 1-1.125-.458 1.532 1.532 0 0 1-.458-1.125V5.729h1.333v10.438c0 .07.024.128.073.177a.24.24 0 0 0 .177.073h7.937v1.333H4.875ZM7.708 3.667v9.666-9.666Z" fill="#1a73e8"/></svg></div><div jscontroller="M2lJBf" jsaction="rcuQ6b:npT2md" data-enable-content-editable-input-style="false" data-enable-line-wrapping data-read-only><div class="hg3Lgc qs41qe BEXrzd" data-loadingmessage="Loading..." jscontroller="GFartf" jsaction="animationend:kWijWc;dyRcpb:dyRcpb" data-active="true" jsname="aZ2wEe"><div class="eBrXtc" aria-live="polite" jsname="vyyg5">Loading...</div><div jsname="Hxlbvc" class="JdM54e"><div class="aopPX ZqnFk co39ub"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX fxjES Cn087"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX ZHXbZe hfsr6b"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX fDBOYb EjXFBf"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div></div></div><div dir="ltr"><textarea jsname="bqeLof" class="Fdco1c"># With PyTorch/XLA 2.4 import torch_xla.core.xla_model as xm xm.all_reduce() # Supported after PyTorch/XLA 2.5 torch.distrbuted.all_reduce()</textarea></div></div></div><p class="nRhiJb-cHYyed nRhiJb-DbgRPb-R6PoUb-ma6Yeb TxA6Xc"></p></section></section><section class="k6ctOd"><aside class="IwVNTb"><div class="ecJG2e nRhiJb-ObfsIf"><div class="nRhiJb-kR0ZEf-OWXEXe-GV1x9e-OiUrBf nRhiJb-snVHke-ibL1re-X66g3b nRhiJb-snVHke-R6PoUb-UFsB2c"><h4 class="Qwf2Db-MnozTc Qwf2Db-MnozTc-OWXEXe-MnozTc-qWD73c">$300 in free credit to try Google Cloud AI and ML</h4><p class="nRhiJb-qJTHM nRhiJb-DbgRPb-R6PoUb-ma6Yeb"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><p>Build and test your proof of concept with $300 in free credit for new customers. Plus, all customers get free monthly usage of 20+ products, including AI APIs.</p></span></p><a class="nRhiJb-LgbsSe nRhiJb-LgbsSe-OWXEXe-MNynB-o6Shpd LZTd1c" href="http://console.cloud.google.com/freetrial?redirectPath=/vertex-ai/" target="_blank" rel="noopener" track-name="start building for free"track-type="button"track-metadata-position="body"track-metadata-eventdetail="http://console.cloud.google.com/freetrial?redirectPath=/vertex-ai/"track-metadata-module="aside"track-metadata-module_headline="$300 in free credit to try google cloud ai and ml" track-name="start building for free"track-type="button"track-metadata-position="body"track-metadata-eventdetail="http://console.cloud.google.com/freetrial?redirectPath=/vertex-ai/">Start building for free </a></div></div></aside></section><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><h3><strong style="vertical-align:baseline">Improvement to ‘torch_xla.compile’</strong></h3> <p><span style="vertical-align:baseline">We’ve also added a few new compilation features to help you debug or notice potential issues within your model code. For example, a ‘full_graph’ mode emits an error message when there’s more than one compilation graph. This helps you discover potential issues caused by multiple compilation graphs early on (during compilation).</span></p> <p><span style="vertical-align:baseline">Additionally, you can now specify an expected number of recompilations for compiled functions. This can help you debug performance issues in which a function might be getting recompiled more times than expected, for example, when it has unexpected dynamism.</span></p> <p><span style="vertical-align:baseline">You can now also give compiled functions an understandable name instead of an automatically created one. By naming compiled targets, you gain more context when debugging messages, making it easier to figure out where the problem may be. Here’s an example of what that looks like in reality:</span></p></span></section><section class="nRhiJb-DbgRPb-II5mzb-cGMI2b"><section><div class="kg99be" jscontroller="vkZm2d" jsaction="rcuQ6b:npT2md" data-text-to-copy="# named code @torch_xla.compile def dummy_cos_sin_decored(self, tensor): return torch.cos(torch.sin(tensor)) # target dumped HLO renamed with named code function name ... module_0021.SyncTensorsGraph.4.hlo_module_config.txt module_0021.SyncTensorsGraph.4.target_arguments.txt module_0021.SyncTensorsGraph.4.tpu_comp_env.txt module_0024.dummy_cos_sin_decored.5.before_optimizations.txt module_0024.dummy_cos_sin_decored.5.execution_options.txt module_0024.dummy_cos_sin_decored.5.flagfile module_0024.dummy_cos_sin_decored.5.hlo_module_config.txt module_0024.dummy_cos_sin_decored.5.target_arguments.txt module_0024.dummy_cos_sin_decored.5.tpu_comp_env.txt ..."><div jsaction="click:VTVhdd" class="GaRnv"><svg class="" viewBox="0 0 20 20" role="presentation" aria-hidden="true" height=20 widdht=20><path d="M7.979 14.667c-.458 0-.84-.153-1.146-.459a1.53 1.53 0 0 1-.458-1.125V3.938c0-.459.153-.84.458-1.146.306-.306.688-.459 1.146-.459h6.625c.445 0 .823.156 1.136.469.312.313.468.691.468 1.136v9.145c0 .445-.156.82-.468 1.125a1.564 1.564 0 0 1-1.136.459H7.979Zm0-1.334h6.625c.083 0 .15-.024.198-.073a.239.239 0 0 0 .073-.177V3.938a.265.265 0 0 0-.073-.198.268.268 0 0 0-.198-.073H7.979a.268.268 0 0 0-.198.073.266.266 0 0 0-.073.198v9.145c0 .07.024.129.073.177a.267.267 0 0 0 .198.073ZM4.875 17.75a1.53 1.53 0 0 1-1.125-.458 1.532 1.532 0 0 1-.458-1.125V5.729h1.333v10.438c0 .07.024.128.073.177a.24.24 0 0 0 .177.073h7.937v1.333H4.875ZM7.708 3.667v9.666-9.666Z" fill="#1a73e8"/></svg></div><div jscontroller="M2lJBf" jsaction="rcuQ6b:npT2md" data-enable-content-editable-input-style="false" data-enable-line-wrapping data-read-only><div class="hg3Lgc qs41qe BEXrzd" data-loadingmessage="Loading..." jscontroller="GFartf" jsaction="animationend:kWijWc;dyRcpb:dyRcpb" data-active="true" jsname="aZ2wEe"><div class="eBrXtc" aria-live="polite" jsname="vyyg5">Loading...</div><div jsname="Hxlbvc" class="JdM54e"><div class="aopPX ZqnFk co39ub"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX fxjES Cn087"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX ZHXbZe hfsr6b"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX fDBOYb EjXFBf"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div></div></div><div dir="ltr"><textarea jsname="bqeLof" class="Fdco1c"># named code @torch_xla.compile def dummy_cos_sin_decored(self, tensor): return torch.cos(torch.sin(tensor)) # target dumped HLO renamed with named code function name ... module_0021.SyncTensorsGraph.4.hlo_module_config.txt module_0021.SyncTensorsGraph.4.target_arguments.txt module_0021.SyncTensorsGraph.4.tpu_comp_env.txt module_0024.dummy_cos_sin_decored.5.before_optimizations.txt module_0024.dummy_cos_sin_decored.5.execution_options.txt module_0024.dummy_cos_sin_decored.5.flagfile module_0024.dummy_cos_sin_decored.5.hlo_module_config.txt module_0024.dummy_cos_sin_decored.5.target_arguments.txt module_0024.dummy_cos_sin_decored.5.tpu_comp_env.txt ...</textarea></div></div></div><p class="nRhiJb-cHYyed nRhiJb-DbgRPb-R6PoUb-ma6Yeb TxA6Xc"></p></section></section><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><p><span style="vertical-align:baseline">Looking at the above output you can see the original versus the named output generated from the same file; ‘SyncTensorsGraph’ is the automatically generated name. Below, you can see the renamed file related to the small code example above.</span></p> <h3><strong style="vertical-align:baseline">vLLM on TPU (experimental)</strong></h3> <p><span style="vertical-align:baseline">If you use </span><a href="https://docs.vllm.ai/en/latest/index.html" rel="noopener" target="_blank"><span style="text-decoration:underline;vertical-align:baseline">vLLM</span></a><span style="vertical-align:baseline"> to serve models on GPUs, you can now switch to TPU as a backend. vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. vLLM on TPU retains the same vLLM interface that developers love, including direct integration into </span><a href="https://huggingface.co/models" rel="noopener" target="_blank"><span style="text-decoration:underline;vertical-align:baseline">Hugging Face Model Hub</span></a><span style="vertical-align:baseline"> to simplify model experimentation on TPU. </span></p> <p><span style="vertical-align:baseline">Switching your vLLM endpoint to TPU is a matter of a few config changes. Aside from the TPU image, everything else remains the same: request payload, metrics used for autoscaling, load balancing, model source code, etc. For details, see the </span><a href="https://docs.vllm.ai/en/v0.5.5/getting_started/tpu-installation.html" rel="noopener" target="_blank"><span style="text-decoration:underline;vertical-align:baseline">installation guide</span></a><span style="vertical-align:baseline">. </span></p> <p><span style="vertical-align:baseline">Other vLLM features we've extended to TPU include </span><a href="https://jax.readthedocs.io/en/latest/pallas/index.html" rel="noopener" target="_blank"><span style="text-decoration:underline;vertical-align:baseline">Pallas</span></a><span style="vertical-align:baseline"> kernels such as paged attention, flash attention and performance optimizations in dynamo bridge, all which are now part of the PyTorch/XLA repository (</span><a href="https://github.com/pytorch/xla/blob/master/torch_xla/experimental/custom_kernel.py" rel="noopener" target="_blank"><span style="text-decoration:underline;vertical-align:baseline">code</span></a><span style="vertical-align:baseline">). While vLLM is available to PyTorch TPU users, this work is still ongoing, and we look forward to rolling out additional features and optimizations in future releases.</span></p> <h3><strong style="vertical-align:baseline">Start using PyTorch/XLA 2.5</strong></h3> <p><span style="vertical-align:baseline">You can start taking advantage of these latest features by downloading the latest release through your Python package manager. Or, if this is your first time hearing about PyTorch/XLA, check out the project’s </span><a href="https://github.com/pytorch/xla" rel="noopener" target="_blank"><span style="text-decoration:underline;vertical-align:baseline">Github page</span></a><span style="vertical-align:baseline"> for installation instructions and more detailed information.</span></p> <p><span style="vertical-align:baseline">For a full list of changes, check out the </span><a href="https://github.com/pytorch/xla/releases/tag/v2.5.0" rel="noopener" target="_blank"><span style="text-decoration:underline;vertical-align:baseline">release notes</span></a><span style="vertical-align:baseline">!</span></p></span></section><section class="kcBhad"><section class="Fabbec"><span class="WrMNjb">Posted in</span><ul class="FzXI4e"><li class="I4B51b"><a href="https://cloud.google.com/blog/products/ai-machine-learning" 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Bai","Software Engineer"],["Duncan Campbell","Developer Advocate"]],[null,"\u003cscript type\u003d\"application/ld+json\"\u003e{\"@context\":\"https://schema.org\",\"@type\":\"BlogPosting\",\"@id\":\"https://cloud.google.com/blog/products/ai-machine-learning/whats-new-with-pytorchxla-2-5\",\"headline\":\"What\u2019s new with PyTorch/XLA 2.5\",\"description\":\"The PyTorch/XLA 2.5 Python package includes a set of improvements to add support for vLLM and enhance the overall developer experience.\",\"image\":\"https://storage.googleapis.com/gweb-cloudblog-publish/images/01_-_AI__Machine_Learning_H1ZyZG8.max-2600x2600.jpg\",\"author\":[{\"@type\":\"Person\",\"name\":\"Manfei Bai\",\"url\":\"\"},{\"@type\":\"Person\",\"name\":\"Duncan Campbell\",\"url\":\"\"}],\"datePublished\":\"2024-10-31\",\"publisher\":{\"@type\":\"Organization\",\"name\":\"Google Cloud\",\"logo\":{\"@type\":\"ImageObject\",\"url\":\"https://www.gstatic.com/devrel-devsite/prod/v8bb8fa0afe9a8c3a776ebeb25d421bb443344d789b3607754dfabea418b8c4be/cloud/images/cloud-logo.svg\"}},\"url\":\"https://cloud.google.com/blog/products/ai-machine-learning/whats-new-with-pytorchxla-2-5\",\"keywords\":[\"AI \\u0026 Machine Learning\",\"Application Development\"],\"timeRequired\":\"PT4M\"}\u003c/script\u003e"],["PyTorch/XLA 2.5: vLLM support and an improved developer experience"],null,null,[[null,null,[null,[null,"\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eMachine learning engineers are bullish on PyTorch/XLA, a Python package that uses the XLA deep learning compiler to connect the PyTorch deep learning framework and Cloud TPUs. And now, PyTorch/XLA 2.5 is here, along with a set of improvements to add support for vLLM and enhance the overall developer experience. Featured in this release are:\u003c/span\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eA \u003c/span\u003e\u003ca href\u003d\"https://github.com/pytorch/xla/issues/8000\" rel\u003d\"noopener\" target\u003d\"_blank\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eclarified\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e proposal for deprecation of the older torch_xla API in favor of moving towards the existing PyTorch API, providing for a simplified developer experience. An example of this is the migration of existing Distributed API.\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eA series of improvements to the torch_xla.compile function which improve the debugging experience for developers during the development process.\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli style\u003d\"list-style-type:disc;vertical-align:baseline\"\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eExperimental support in vLLM for TPUs, allowing you to extend your existing deployments and while leveraging the same vLLM interface across your TPUs.\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eLet\u2019s take a look at each of these enhancements.\u003c/span\u003e\u003c/p\u003e\n\u003ch3\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eStreamlining the torch_xla API\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eWith PyTorch/XLA 2.5, we're taking a significant step towards making the API more consistent with upstream PyTorch. Our north star is to minimize the learning curve for developers already familiar with PyTorch, making it easier to use XLA devices. This means gradually phasing out and deprecating custom API calls for PyTorch/XLA for more mature functionality when possible, and then, migrating the API calls over to their PyTorch counterparts. Other features still remain within the existing Python module before migration.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eIn the spirit of a simpler developer experience for PyTorch/XLA, in this release we have migrated over to leveraging some existing PyTorch distributed API functions when running models on top of PyTorch/XLA. Historically, the calls for the distributed API were located under the torch_xla module; in this update we migrated most of them to torch.distributed.\u003c/span\u003e\u003c/p\u003e"]]],[["# With PyTorch/XLA 2.4\r\nimport torch_xla.core.xla_model as xm\r\nxm.all_reduce()\r\n\r\n# Supported after PyTorch/XLA 2.5\r\ntorch.distrbuted.all_reduce()",null,[null,""]]],[null,["$300 in free credit to try Google Cloud AI and ML",null,"Start building for free","http://console.cloud.google.com/freetrial?redirectPath\u003d/vertex-ai/",[null,"\u003cp\u003eBuild and test your proof of concept with $300 in free credit for new customers. Plus, all customers get free monthly usage of 20+ products, including AI APIs.\u003c/p\u003e"]]],[null,null,[null,[null,"\u003ch3\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eImprovement to \u2018torch_xla.compile\u2019\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eWe\u2019ve also added a few new compilation features to help you debug or notice potential issues within your model code. For example, a \u2018full_graph\u2019 mode emits an error message when there\u2019s more than one compilation graph. This helps you discover potential issues caused by multiple compilation graphs early on (during compilation).\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eAdditionally, you can now specify an expected number of recompilations for compiled functions. This can help you debug performance issues in which a function might be getting recompiled more times than expected, for example, when it has unexpected dynamism.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eYou can now also give compiled functions an understandable name instead of an automatically created one. By naming compiled targets, you gain more context when debugging messages, making it easier to figure out where the problem may be. Here\u2019s an example of what that looks like in reality:\u003c/span\u003e\u003c/p\u003e"]]],[["# named code\r\n@torch_xla.compile\r\ndef dummy_cos_sin_decored(self, tensor):\r\n return torch.cos(torch.sin(tensor))\r\n\r\n# target dumped HLO renamed with named code function name \r\n...\r\nmodule_0021.SyncTensorsGraph.4.hlo_module_config.txt\r\nmodule_0021.SyncTensorsGraph.4.target_arguments.txt\r\nmodule_0021.SyncTensorsGraph.4.tpu_comp_env.txt\r\nmodule_0024.dummy_cos_sin_decored.5.before_optimizations.txt\r\nmodule_0024.dummy_cos_sin_decored.5.execution_options.txt\r\nmodule_0024.dummy_cos_sin_decored.5.flagfile\r\nmodule_0024.dummy_cos_sin_decored.5.hlo_module_config.txt\r\nmodule_0024.dummy_cos_sin_decored.5.target_arguments.txt\r\nmodule_0024.dummy_cos_sin_decored.5.tpu_comp_env.txt\r\n...",null,[null,""]]],[null,null,[null,[null,"\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eLooking at the above output you can see the original versus the named output generated from the same file; \u2018SyncTensorsGraph\u2019 is the automatically generated name. Below, you can see the renamed file related to the small code example above.\u003c/span\u003e\u003c/p\u003e\n\u003ch3\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003evLLM on TPU (experimental)\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eIf you use \u003c/span\u003e\u003ca href\u003d\"https://docs.vllm.ai/en/latest/index.html\" rel\u003d\"noopener\" target\u003d\"_blank\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003evLLM\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e to serve models on GPUs, you can now switch to TPU as a backend. vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. vLLM on TPU retains the same vLLM interface that developers love, including direct integration into \u003c/span\u003e\u003ca href\u003d\"https://huggingface.co/models\" rel\u003d\"noopener\" target\u003d\"_blank\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eHugging Face Model Hub\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e to simplify model experimentation on TPU.\u00a0\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eSwitching your vLLM endpoint to TPU is a matter of a few config changes. Aside from the TPU image, everything else remains the same: request payload, metrics used for autoscaling, load balancing, model source code, etc. For details, see the \u003c/span\u003e\u003ca href\u003d\"https://docs.vllm.ai/en/v0.5.5/getting_started/tpu-installation.html\" rel\u003d\"noopener\" target\u003d\"_blank\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003einstallation guide\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e.\u00a0\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eOther vLLM features we've extended to TPU include \u003c/span\u003e\u003ca href\u003d\"https://jax.readthedocs.io/en/latest/pallas/index.html\" rel\u003d\"noopener\" target\u003d\"_blank\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003ePallas\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e kernels such as paged attention, flash attention and performance optimizations in dynamo bridge, all which are now part of the PyTorch/XLA repository (\u003c/span\u003e\u003ca href\u003d\"https://github.com/pytorch/xla/blob/master/torch_xla/experimental/custom_kernel.py\" rel\u003d\"noopener\" target\u003d\"_blank\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003ecode\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e). While vLLM is available to PyTorch TPU users, this work is still ongoing, and we look forward to rolling out additional features and optimizations in future releases.\u003c/span\u003e\u003c/p\u003e\n\u003ch3\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eStart using PyTorch/XLA 2.5\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eYou can start taking advantage of these latest features by downloading the latest release through your Python package manager. Or, if this is your first time hearing about PyTorch/XLA, check out the project\u2019s \u003c/span\u003e\u003ca href\u003d\"https://github.com/pytorch/xla\" rel\u003d\"noopener\" target\u003d\"_blank\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eGithub page\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e for installation instructions and more detailed information.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eFor a full list of changes, check out the \u003c/span\u003e\u003ca href\u003d\"https://github.com/pytorch/xla/releases/tag/v2.5.0\" rel\u003d\"noopener\" target\u003d\"_blank\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003erelease notes\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e!\u003c/span\u003e\u003c/p\u003e"]]]],[["Databases","Enhancing AlloyDB vector search with inline filtering and enterprise observability","AlloyDB vector search now has inline filtering, an enhancement to filtered vector search, and enterprise-grade observability and management tooling.",["https://storage.googleapis.com/gweb-cloudblog-publish/images/10_-_Databases.max-700x700.jpg","https://storage.googleapis.com/gweb-cloudblog-publish/images/10_-_Databases.max-400x400.jpg 324w, https://storage.googleapis.com/gweb-cloudblog-publish/images/10_-_Databases.max-700x700.jpg 648w"," 324px, 648px","https://storage.googleapis.com/gweb-cloudblog-publish/images/10_-_Databases.max-700x700.jpg"],null,6,null,"https://cloud.google.com/blog/products/databases/enhancing-alloydb-vector-search-with-inline-filtering-and-enterprise-observability",null,1,[["Sandy Ghai","Group Product Manager"],["Tabatha Lewis-Simo","Product Manager, AlloyDB"]],null,"56102"],["AI \u0026 Machine Learning","Announcing Claude 3.7 Sonnet, Anthropic\u2019s first hybrid reasoning model, is available on Vertex AI","Claude 3.7 Sonnet, Anthropic\u2019s most intelligent model to date and the first hybrid reasoning model on the market, is available on Vertex AI. Get started today.",["https://storage.googleapis.com/gweb-cloudblog-publish/images/claude_3_7_sonnet.max-700x700.jpg","https://storage.googleapis.com/gweb-cloudblog-publish/images/claude_3_7_sonnet.max-400x400.jpg 324w, https://storage.googleapis.com/gweb-cloudblog-publish/images/claude_3_7_sonnet.max-700x700.jpg 648w"," 324px, 648px","https://storage.googleapis.com/gweb-cloudblog-publish/images/claude_3_7_sonnet.max-700x700.jpg"],null,6,null,"https://cloud.google.com/blog/products/ai-machine-learning/anthropics-claude-3-7-sonnet-is-available-on-vertex-ai",null,1,[["Nenshad Bardoliwalla","Director, Product Management, Vertex AI"]],null,"56099"],["AI \u0026 Machine Learning","Optimizing image generation pipelines on Google Cloud: A practical guide","Learn about three things you can optimize to improve your image generation pipelines: hardware, inference code, or inference pipelines.",["https://storage.googleapis.com/gweb-cloudblog-publish/images/echo.max-700x700.jpg","https://storage.googleapis.com/gweb-cloudblog-publish/images/echo.max-400x400.jpg 324w, https://storage.googleapis.com/gweb-cloudblog-publish/images/echo.max-700x700.jpg 648w"," 324px, 648px","https://storage.googleapis.com/gweb-cloudblog-publish/images/echo.max-700x700.jpg"],null,5,null,"https://cloud.google.com/blog/products/ai-machine-learning/guide-to-optimizing-image-generation-pipelines",null,1,[["Gopala Dhar","AI Engineering Lead, Google Cloud Consulting"],["Kartik Chaudhary","Lead AI/ML Engineer, Google Cloud Consulting"]],null,"56089"],["DevOps \u0026 SRE","An SRE\u2019s guide to optimizing ML systems with MLOps pipelines","As AI and ML become more prevalent, administrators can use Site Reliability Engineering (SRE) techniques to manage the ML infrastructure and software.",["https://storage.googleapis.com/gweb-cloudblog-publish/images/12_-_DevOps__SRE_qBRZDbA.max-700x700.jpg","https://storage.googleapis.com/gweb-cloudblog-publish/images/12_-_DevOps__SRE_qBRZDbA.max-400x400.jpg 324w, https://storage.googleapis.com/gweb-cloudblog-publish/images/12_-_DevOps__SRE_qBRZDbA.max-700x700.jpg 648w"," 324px, 648px","https://storage.googleapis.com/gweb-cloudblog-publish/images/12_-_DevOps__SRE_qBRZDbA.max-700x700.jpg"],null,5,null,"https://cloud.google.com/blog/products/devops-sre/applying-sre-principles-to-your-mlops-pipelines",null,1,[["Max Saltonstall","Developer Relations Engineer, Google"],["Salim Virji","Technical Program Manager, Google"]],null,"56073"]],null,"AI \u0026 Machine Learning",["Join us at Google Cloud Next","April 9-11 in Las Vegas","Register","https://cloud.withgoogle.com/next?utm_source\u003dcgc-blog\u0026utm_medium\u003dblog\u0026utm_campaign\u003dFY25-Q2-global-EXP106-physicalevent-er-next25-mc\u0026utm_content\u003dleft-hand-rail-blog-cta\u0026utm_term\u003d-"],[["AI \u0026 Machine Learning","https://cloud.google.com/blog/products/ai-machine-learning","ai-machine-learning"],["Application Development","https://cloud.google.com/blog/products/application-development","application-development"]],null,null,4], sideChannel: {}});</script><script id="wiz_jd" nonce="y9ByX4Bk91kZCDaHbmt4jA">if (window['_wjdc']) {const wjd = {}; window['_wjdc'](wjd); delete window['_wjdc'];}</script><script aria-hidden="true" id="WIZ-footer" nonce="y9ByX4Bk91kZCDaHbmt4jA">window.wiz_progress&&window.wiz_progress(); window.stopScanForCss&&window.stopScanForCss(); ccTick('bl');</script></body></html><footer id="ZCHFDb"><footer class="nRhiJb-RWrDld nRhiJb-yePe5c QJnbF" jscontroller="NsSboe" track-metadata-module="footer"><h3 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