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docs/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 2a6c4a48cd36af00ff44d2fe2c5e2d69
+config: d23dc79ff23e0b7c0eb8540e45ddc148
 tags: 645f666f9bcd5a90fca523b33c5a78b7

docs/_downloads/0ccffddcfee1f815c02241b985844376/torch_compile_user_defined_triton_kernel_tutorial.py

@@ -1,49 +1,46 @@
 # -*- coding: utf-8 -*-
 
 """
-Using User-Defined Triton Kernels with ``torch.compile``
+使用 ``torch.compile`` 和用户自定义的 Triton 内核
 =========================================================
-**Author:** `Oguz Ulgen <https://github.com/oulgen>`_
+**作者:** `Oguz Ulgen <https://github.com/oulgen>`_
 """
 
 ######################################################################
-# User-defined Triton kernels can be used to optimize specific parts of your
-# model's computation. These kernels are written in Triton's language, which is designed
-# to make it easier to achieve peak hardware performance. By using user-defined Triton
-# kernels with ``torch.compile``, you can integrate these optimized computations into
-# your PyTorch model, potentially achieving significant performance improvements.
+# 用户自定义的 Triton 内核可用于优化模型计算的特定部分。这些内核是用 Triton 语言编写的,
+# 旨在更容易实现硬件的峰值性能。通过在 ``torch.compile`` 中使用用户自定义的 Triton 内核,
+# 您可以将这些优化过的计算集成到 PyTorch 模型中,从而可能获得显著的性能提升。
 #
-# This recipes demonstrates how you can use user-defined Triton kernels with ``torch.compile``.
+# 本教程演示了如何在 ``torch.compile`` 中使用用户自定义的 Triton 内核。
 #
-# Prerequisites
+# 先决条件
 # -------------------
 #
-# Before starting this recipe, make sure that you have the following:
+# 在开始本教程之前,请确保您具备以下条件:
 #
-# * Basic understanding of ``torch.compile`` and Triton. See:
+# * 对 ``torch.compile`` 和 Triton 有基本的了解。参见:
 #
-#   * `torch.compiler API documentation <https://pytorch.org/docs/stable/torch.compiler.html#torch-compiler>`__
-#   * `Introduction to torch.compile <https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html>`__
-#   * `Triton language documentation <https://triton-lang.org/main/index.html>`__
+#   * `torch.compiler API 文档 <https://pytorch.org/docs/stable/torch.compiler.html#torch-compiler>`__
+#   * `torch.compile 介绍 <https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html>`__
+#   * `Triton 语言文档 <https://triton-lang.org/main/index.html>`__
 #
-# * PyTorch 2.3 or later
-# * A GPU that supports Triton
+# * PyTorch 2.3 或更高版本
+# * 支持 Triton 的 GPU
 #
 
 import torch
 from torch.utils._triton import has_triton
 
 ######################################################################
-# Basic Usage
+# 基本用法
 # --------------------
 #
-# In this example, we will use a simple vector addition kernel from the Triton documentation
-# with ``torch.compile``.
-# For reference, see `Triton documentation <https://triton-lang.org/main/getting-started/tutorials/01-vector-add.html>`__.
+# 在此示例中,我们将使用来自 Triton 文档的一个简单向量加法内核与 ``torch.compile``。
+# 参考 `Triton 文档 <https://triton-lang.org/main/getting-started/tutorials/01-vector-add.html>`__。
 #
 
 if not has_triton():
-    print("Skipping because triton is not supported on this device.")
+    print("由于此设备不支持 triton,因此跳过。")
 else:
     import triton
     from triton import language as tl
@@ -76,26 +73,24 @@ def add_fn(x, y):
     x = torch.randn(4, device="cuda")
     y = torch.randn(4, device="cuda")
     out = add_fn(x, y)
-    print(f"Vector addition of\nX:\t{x}\nY:\t{y}\nis equal to\n{out}")
+    print(f"向量加法\nX:\t{x}\nY:\t{y}\n结果为\n{out}")
 
 ######################################################################
-# Advanced Usage
+# 高级用法
 # -------------------------------------------------------------------
 #
-# Triton's autotune feature is a powerful tool that automatically optimizes the configuration
-# parameters of your Triton kernels. It explores a range of possible configurations and
-# selects the one that delivers the best performance for your specific use case.
+# Triton 的自动调优功能是一个强大的工具,可自动优化 Triton 内核的配置参数。
+# 它探索一系列可能的配置,并选择为您的特定用例提供最佳性能的配置。
 #
-# When used with ``torch.compile``, ``triton.autotune`` can help ensure that your PyTorch
-# model is running as efficiently as possible. Here is an example of using ``torch.compile``
-# and ``triton.autotune``.
+# 与 ``torch.compile`` 一起使用时, ``triton.autotune`` 可以帮助确保您的 PyTorch 模型以最高效的方式运行。
+# 下面是使用 ``torch.compile`` 和 ``triton.autotune`` 的示例。
 #
 # .. note::
 #
-#   ``torch.compile`` only supports configs and key arguments to ``triton.autotune``.
+#   ``torch.compile`` 仅支持 ``triton.autotune`` 的配置和关键参数。
 
 if not has_triton():
-    print("Skipping because triton is not supported on this device.")
+    print("由于此设备不支持 triton,因此跳过。")
 else:
     import triton
     from triton import language as tl
@@ -137,35 +132,31 @@ def add_fn(x, y):
     x = torch.randn(4, device="cuda")
     y = torch.randn(4, device="cuda")
     out = add_fn(x, y)
-    print(f"Vector addition of\nX:\t{x}\nY:\t{y}\nis equal to\n{out}")
+    print(f"向量加法\nX:\t{x}\nY:\t{y}\n结果为\n{out}")
 
 ######################################################################
-# Composibility and Limitations
+# 可组合性和限制
 # --------------------------------------------------------------------
 #
-# As of PyTorch 2.3, the support for user-defined Triton kernels in ``torch.compile``
-# includes dynamic shapes, ``torch.autograd.Function``, JIT inductor, and AOT inductor.
-# You can use these features together to build complex, high-performance models.
+# 从 PyTorch 2.3 开始, ``torch.compile`` 中对用户自定义 Triton 内核的支持包括动态形状、
+# ``torch.autograd.Function``、JIT inductor 和 AOT inductor。
+# 您可以将这些功能组合在一起构建复杂的高性能模型。
 #
-# However, there are certain limitations to be aware of:
+# 但是,也需要注意一些限制:
 #
-# * **Tensor Subclasses:** Currently, there is no support for
-#   tensor subclasses and other advanced features.
-# * **Triton Features:** While ``triton.heuristics`` can be used either standalone or
-#   before ``triton.autotune``, it cannot be used after ```triton.autotune``. This
-#   implies that if ``triton.heuristics`` and ``triton.autotune`` are to be used
-#   together, ``triton.heuristics`` must be used first.
+# * **Tensor 子类:** 目前不支持张量子类和其他高级功能。
+# * **Triton 功能:** 虽然 ``triton.heuristics`` 可以单独使用或在 ``triton.autotune`` 之前使用,
+#   但不能在 ``triton.autotune`` 之后使用。这意味着如果要一起使用 ``triton.heuristics`` 和 ``triton.autotune``,
+#   则必须先使用 ``triton.heuristics``。
 #
-# Conclusion
+# 结论
 # -----------
-# In this recipe, we explored how to utilize user-defined Triton kernels
-# with ``torch.compile``. We delved into the basic usage of a simple
-# vector addition kernel and advanced usage involving Triton's autotune
-# feature. We also discussed the composability of user-defined Triton
-# kernels with other PyTorch features and highlighted some current limitations.
+# 在本教程中,我们探讨了如何在 ``torch.compile`` 中使用用户自定义的 Triton 内核。
+# 我们深入研究了使用简单向量加法内核的基本用法,以及涉及 Triton 自动调优功能的高级用法。
+# 我们还讨论了用户自定义 Triton 内核与其他 PyTorch 功能的可组合性,并强调了一些当前的限制。
 #
-# See Also
+# 另请参阅
 # ---------
 #
-# * `Compiling the Optimizers <https://pytorch.org/tutorials/recipes/compiling_optimizer.html>`__
-# * `Implementing High-Performance Transformers with Scaled Dot Product Attention <https://pytorch.org/tutorials/intermediate/scaled_dot_product_attention_tutorial.html>`__
+# * `编译优化器 <https://pytorch.org/tutorials/recipes/compiling_optimizer.html>`__
+# * `使用缩放点积注意力实现高性能 Transformer <https://pytorch.org/tutorials/intermediate/scaled_dot_product_attention_tutorial.html>`__