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Apr 24, 2025
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blog edit #1995

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8 changes: 4 additions & 4 deletions _posts/2025-04-23-pytorch-2-7.md
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Expand Up @@ -41,13 +41,13 @@ This release is composed of 3262 commits from 457 contributors since PyTorch 2.6
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<td>FlexAttention LLM <span style="text-decoration:underline;">first token processing</span> on X86 CPUs
<td>FlexAttention LLM <span style="text-decoration:underline;">first token processing</span> on x86 CPUs
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<td>FlexAttention LLM <span style="text-decoration:underline;">throughput mode optimization</span> on X86 CPUs
<td>FlexAttention LLM <span style="text-decoration:underline;">throughput mode optimization</span> on x86 CPUs
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Expand Down Expand Up @@ -135,9 +135,9 @@ For more information regarding Intel GPU support, please refer to [Getting Start
See also the tutorials [here](https://pytorch.org/tutorials/prototype/inductor_windows.html) and [here](https://pytorch.org/tutorials/prototype/pt2e_quant_xpu_inductor.html).


### [Prototype] FlexAttention LLM first token processing on X86 CPUs
### [Prototype] FlexAttention LLM first token processing on x86 CPUs

FlexAttention X86 CPU support was first introduced in PyTorch 2.6, offering optimized implementations — such as PageAttention, which is critical for LLM inference—via the TorchInductor C++ backend. In PyTorch 2.7, more attention variants for first token processing of LLMs are supported. With this feature, users can have a smoother experience running FlexAttention on x86 CPUs, replacing specific *scaled_dot_product_attention* operators with a unified FlexAttention API, and benefiting from general support and good performance when using torch.compile.
FlexAttention x86 CPU support was first introduced in PyTorch 2.6, offering optimized implementations — such as PageAttention, which is critical for LLM inference—via the TorchInductor C++ backend. In PyTorch 2.7, more attention variants for first token processing of LLMs are supported. With this feature, users can have a smoother experience running FlexAttention on x86 CPUs, replacing specific *scaled_dot_product_attention* operators with a unified FlexAttention API, and benefiting from general support and good performance when using torch.compile.


### [Prototype] FlexAttention LLM throughput mode optimization
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