From 67a055b09fcd707fef8e4936f1f24920239982f9 Mon Sep 17 00:00:00 2001
From: hippocookie <zesheng.zong@outlook.com>
Date: Tue, 9 Jul 2024 20:14:11 +0800
Subject: [PATCH] Translate recipes

---
 docs/.buildinfo                               |   2 +-
 docs/.doctrees/environment.pickle             | Bin 4208370 -> 4209322 bytes
 docs/.doctrees/recipes/amx.doctree            | Bin 29974 -> 25556 bytes
 .../recipes/compiling_optimizer.doctree       | Bin 12350 -> 11941 bytes
 .../inference_tuning_on_aws_graviton.doctree  | Bin 131694 -> 103498 bytes
 ...ser_defined_triton_kernel_tutorial.doctree | Bin 34779 -> 33559 bytes
 ...ile_user_defined_triton_kernel_tutorial.py |  95 ++--
 ..._user_defined_triton_kernel_tutorial.ipynb |  14 +-
 docs/_sources/recipes/amx.rst.txt             |  74 ++-
 .../recipes/compiling_optimizer.rst.txt       |  44 +-
 .../inference_tuning_on_aws_graviton.rst.txt  | 141 ++---
 ...ser_defined_triton_kernel_tutorial.rst.txt | 109 ++--
 docs/objects.inv                              | Bin 14348 -> 14499 bytes
 docs/recipes/amx.html                         |  97 ++--
 docs/recipes/compiling_optimizer.html         |  64 +--
 .../inference_tuning_on_aws_graviton.html     | 515 +++---------------
 docs/recipes/recipes/tuning_guide.html        |   4 +-
 docs/recipes/torch_compile_backend_ipex.html  |   4 +-
 ...e_user_defined_triton_kernel_tutorial.html | 127 ++---
 docs/searchindex.js                           |   2 +-
 recipes_source/amx.rst                        |  74 ++-
 recipes_source/compiling_optimizer.rst        |  44 +-
 .../inference_tuning_on_aws_graviton.rst      | 141 ++---
 ...ile_user_defined_triton_kernel_tutorial.py |  95 ++--
 24 files changed, 616 insertions(+), 1030 deletions(-)

diff --git a/docs/.buildinfo b/docs/.buildinfo
index ad50ab2..a2d394b 100644
--- a/docs/.buildinfo
+++ b/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
diff --git a/docs/.doctrees/environment.pickle b/docs/.doctrees/environment.pickle
index 82aef1b3ab5a971e59bce3fad8413e059d89212f..8ad1632e99ca942bd1ebe2101dca7719bd2c7d65 100644
GIT binary patch
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diff --git a/docs/.doctrees/recipes/compiling_optimizer.doctree b/docs/.doctrees/recipes/compiling_optimizer.doctree
index 2b3c130a28e3f85ddc76ab5039c66727eec27181..cdf78a7b89294b6aa9c89b4dd8ec29acb25f967b 100644
GIT binary patch
delta 3515
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delta 3808
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diff --git a/docs/.doctrees/recipes/inference_tuning_on_aws_graviton.doctree b/docs/.doctrees/recipes/inference_tuning_on_aws_graviton.doctree
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diff --git a/docs/.doctrees/recipes/torch_compile_user_defined_triton_kernel_tutorial.doctree b/docs/.doctrees/recipes/torch_compile_user_defined_triton_kernel_tutorial.doctree
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diff --git a/docs/_downloads/0ccffddcfee1f815c02241b985844376/torch_compile_user_defined_triton_kernel_tutorial.py b/docs/_downloads/0ccffddcfee1f815c02241b985844376/torch_compile_user_defined_triton_kernel_tutorial.py
index 68187d6..b5c0faf 100644
--- a/docs/_downloads/0ccffddcfee1f815c02241b985844376/torch_compile_user_defined_triton_kernel_tutorial.py
+++ b/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>`__
diff --git a/docs/_downloads/f827f181506a79226f4ffbcf7c9a5a50/torch_compile_user_defined_triton_kernel_tutorial.ipynb b/docs/_downloads/f827f181506a79226f4ffbcf7c9a5a50/torch_compile_user_defined_triton_kernel_tutorial.ipynb
index 7e7032b..abcbc36 100644
--- a/docs/_downloads/f827f181506a79226f4ffbcf7c9a5a50/torch_compile_user_defined_triton_kernel_tutorial.ipynb
+++ b/docs/_downloads/f827f181506a79226f4ffbcf7c9a5a50/torch_compile_user_defined_triton_kernel_tutorial.ipynb
@@ -15,14 +15,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Using User-Defined Triton Kernels with ``torch.compile``\n**Author:** [Oguz Ulgen](https://github.com/oulgen)\n"
+        "\n# \u4f7f\u7528 ``torch.compile`` \u548c\u7528\u6237\u81ea\u5b9a\u4e49\u7684 Triton \u5185\u6838\n**\u4f5c\u8005:** [Oguz Ulgen](https://github.com/oulgen)\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "User-defined Triton kernels can be used to optimize specific parts of your\nmodel's computation. These kernels are written in Triton's language, which is designed\nto make it easier to achieve peak hardware performance. By using user-defined Triton\nkernels with ``torch.compile``, you can integrate these optimized computations into\nyour PyTorch model, potentially achieving significant performance improvements.\n\nThis recipes demonstrates how you can use user-defined Triton kernels with ``torch.compile``.\n\n## Prerequisites\n\nBefore starting this recipe, make sure that you have the following:\n\n* Basic understanding of ``torch.compile`` and Triton. See:\n\n  * [torch.compiler API documentation](https://pytorch.org/docs/stable/torch.compiler.html#torch-compiler)_\n  * [Introduction to torch.compile](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html)_\n  * [Triton language documentation](https://triton-lang.org/main/index.html)_\n\n* PyTorch 2.3 or later\n* A GPU that supports Triton\n\n\n"
+        "\u7528\u6237\u81ea\u5b9a\u4e49\u7684 Triton \u5185\u6838\u53ef\u7528\u4e8e\u4f18\u5316\u6a21\u578b\u8ba1\u7b97\u7684\u7279\u5b9a\u90e8\u5206\u3002\u8fd9\u4e9b\u5185\u6838\u662f\u7528 Triton \u8bed\u8a00\u7f16\u5199\u7684,\n\u65e8\u5728\u66f4\u5bb9\u6613\u5b9e\u73b0\u786c\u4ef6\u7684\u5cf0\u503c\u6027\u80fd\u3002\u901a\u8fc7\u5728 ``torch.compile`` \u4e2d\u4f7f\u7528\u7528\u6237\u81ea\u5b9a\u4e49\u7684 Triton \u5185\u6838,\n\u60a8\u53ef\u4ee5\u5c06\u8fd9\u4e9b\u4f18\u5316\u8fc7\u7684\u8ba1\u7b97\u96c6\u6210\u5230 PyTorch \u6a21\u578b\u4e2d,\u4ece\u800c\u53ef\u80fd\u83b7\u5f97\u663e\u8457\u7684\u6027\u80fd\u63d0\u5347\u3002\n\n\u672c\u6559\u7a0b\u6f14\u793a\u4e86\u5982\u4f55\u5728 ``torch.compile`` \u4e2d\u4f7f\u7528\u7528\u6237\u81ea\u5b9a\u4e49\u7684 Triton \u5185\u6838\u3002\n\n## \u5148\u51b3\u6761\u4ef6\n\n\u5728\u5f00\u59cb\u672c\u6559\u7a0b\u4e4b\u524d,\u8bf7\u786e\u4fdd\u60a8\u5177\u5907\u4ee5\u4e0b\u6761\u4ef6:\n\n* \u5bf9 ``torch.compile`` \u548c Triton \u6709\u57fa\u672c\u7684\u4e86\u89e3\u3002\u53c2\u89c1:\n\n  * [torch.compiler API \u6587\u6863](https://pytorch.org/docs/stable/torch.compiler.html#torch-compiler)_\n  * [torch.compile \u4ecb\u7ecd](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html)_\n  * [Triton \u8bed\u8a00\u6587\u6863](https://triton-lang.org/main/index.html)_\n\n* PyTorch 2.3 \u6216\u66f4\u9ad8\u7248\u672c\n* \u652f\u6301 Triton \u7684 GPU\n\n\n"
       ]
     },
     {
@@ -40,7 +40,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Basic Usage\n\nIn this example, we will use a simple vector addition kernel from the Triton documentation\nwith ``torch.compile``.\nFor reference, see [Triton documentation](https://triton-lang.org/main/getting-started/tutorials/01-vector-add.html)_.\n\n\n"
+        "## \u57fa\u672c\u7528\u6cd5\n\n\u5728\u6b64\u793a\u4f8b\u4e2d,\u6211\u4eec\u5c06\u4f7f\u7528\u6765\u81ea Triton \u6587\u6863\u7684\u4e00\u4e2a\u7b80\u5355\u5411\u91cf\u52a0\u6cd5\u5185\u6838\u4e0e ``torch.compile``\u3002\n\u53c2\u8003 [Triton \u6587\u6863](https://triton-lang.org/main/getting-started/tutorials/01-vector-add.html)_\u3002\n\n\n"
       ]
     },
     {
@@ -51,14 +51,14 @@
       },
       "outputs": [],
       "source": [
-        "if not has_triton():\n    print(\"Skipping because triton is not supported on this device.\")\nelse:\n    import triton\n    from triton import language as tl\n\n    @triton.jit\n    def add_kernel(\n        in_ptr0,\n        in_ptr1,\n        out_ptr,\n        n_elements,\n        BLOCK_SIZE: \"tl.constexpr\",\n    ):\n        pid = tl.program_id(axis=0)\n        block_start = pid * BLOCK_SIZE\n        offsets = block_start + tl.arange(0, BLOCK_SIZE)\n        mask = offsets < n_elements\n        x = tl.load(in_ptr0 + offsets, mask=mask)\n        y = tl.load(in_ptr1 + offsets, mask=mask)\n        output = x + y\n        tl.store(out_ptr + offsets, output, mask=mask)\n\n    @torch.compile(fullgraph=True)\n    def add_fn(x, y):\n        output = torch.zeros_like(x)\n        n_elements = output.numel()\n        grid = lambda meta: (triton.cdiv(n_elements, meta[\"BLOCK_SIZE\"]),)\n        add_kernel[grid](x, y, output, n_elements, BLOCK_SIZE=4)\n        return output\n\n    x = torch.randn(4, device=\"cuda\")\n    y = torch.randn(4, device=\"cuda\")\n    out = add_fn(x, y)\n    print(f\"Vector addition of\\nX:\\t{x}\\nY:\\t{y}\\nis equal to\\n{out}\")"
+        "if not has_triton():\n    print(\"\u7531\u4e8e\u6b64\u8bbe\u5907\u4e0d\u652f\u6301 triton,\u56e0\u6b64\u8df3\u8fc7\u3002\")\nelse:\n    import triton\n    from triton import language as tl\n\n    @triton.jit\n    def add_kernel(\n        in_ptr0,\n        in_ptr1,\n        out_ptr,\n        n_elements,\n        BLOCK_SIZE: \"tl.constexpr\",\n    ):\n        pid = tl.program_id(axis=0)\n        block_start = pid * BLOCK_SIZE\n        offsets = block_start + tl.arange(0, BLOCK_SIZE)\n        mask = offsets < n_elements\n        x = tl.load(in_ptr0 + offsets, mask=mask)\n        y = tl.load(in_ptr1 + offsets, mask=mask)\n        output = x + y\n        tl.store(out_ptr + offsets, output, mask=mask)\n\n    @torch.compile(fullgraph=True)\n    def add_fn(x, y):\n        output = torch.zeros_like(x)\n        n_elements = output.numel()\n        grid = lambda meta: (triton.cdiv(n_elements, meta[\"BLOCK_SIZE\"]),)\n        add_kernel[grid](x, y, output, n_elements, BLOCK_SIZE=4)\n        return output\n\n    x = torch.randn(4, device=\"cuda\")\n    y = torch.randn(4, device=\"cuda\")\n    out = add_fn(x, y)\n    print(f\"\u5411\u91cf\u52a0\u6cd5\\nX:\\t{x}\\nY:\\t{y}\\n\u7ed3\u679c\u4e3a\\n{out}\")"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Advanced Usage\n\nTriton's autotune feature is a powerful tool that automatically optimizes the configuration\nparameters of your Triton kernels. It explores a range of possible configurations and\nselects the one that delivers the best performance for your specific use case.\n\nWhen used with ``torch.compile``, ``triton.autotune`` can help ensure that your PyTorch\nmodel is running as efficiently as possible. Here is an example of using ``torch.compile``\nand ``triton.autotune``.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>``torch.compile`` only supports configs and key arguments to ``triton.autotune``.</p></div>\n\n"
+        "## \u9ad8\u7ea7\u7528\u6cd5\n\nTriton \u7684\u81ea\u52a8\u8c03\u4f18\u529f\u80fd\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u5de5\u5177,\u53ef\u81ea\u52a8\u4f18\u5316 Triton \u5185\u6838\u7684\u914d\u7f6e\u53c2\u6570\u3002\n\u5b83\u63a2\u7d22\u4e00\u7cfb\u5217\u53ef\u80fd\u7684\u914d\u7f6e,\u5e76\u9009\u62e9\u4e3a\u60a8\u7684\u7279\u5b9a\u7528\u4f8b\u63d0\u4f9b\u6700\u4f73\u6027\u80fd\u7684\u914d\u7f6e\u3002\n\n\u4e0e ``torch.compile`` \u4e00\u8d77\u4f7f\u7528\u65f6, ``triton.autotune`` \u53ef\u4ee5\u5e2e\u52a9\u786e\u4fdd\u60a8\u7684 PyTorch \u6a21\u578b\u4ee5\u6700\u9ad8\u6548\u7684\u65b9\u5f0f\u8fd0\u884c\u3002\n\u4e0b\u9762\u662f\u4f7f\u7528 ``torch.compile`` \u548c ``triton.autotune`` \u7684\u793a\u4f8b\u3002\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>``torch.compile`` \u4ec5\u652f\u6301 ``triton.autotune`` \u7684\u914d\u7f6e\u548c\u5173\u952e\u53c2\u6570\u3002</p></div>\n\n"
       ]
     },
     {
@@ -69,14 +69,14 @@
       },
       "outputs": [],
       "source": [
-        "if not has_triton():\n    print(\"Skipping because triton is not supported on this device.\")\nelse:\n    import triton\n    from triton import language as tl\n\n    @triton.autotune(\n        configs=[\n            triton.Config({\"BLOCK_SIZE\": 4}, num_stages=3, num_warps=8),\n            triton.Config({\"BLOCK_SIZE\": 4}, num_stages=4, num_warps=4),\n            triton.Config({\"BLOCK_SIZE\": 2}, num_stages=3, num_warps=8),\n            triton.Config({\"BLOCK_SIZE\": 2}, num_stages=4, num_warps=4),\n        ],\n        key=[],\n    )\n    @triton.jit\n    def add_kernel_autotuned(\n        in_ptr0,\n        in_ptr1,\n        out_ptr,\n        n_elements,\n        BLOCK_SIZE: \"tl.constexpr\",\n    ):\n        pid = tl.program_id(axis=0)\n        block_start = pid * BLOCK_SIZE\n        offsets = block_start + tl.arange(0, BLOCK_SIZE)\n        mask = offsets < n_elements\n        x = tl.load(in_ptr0 + offsets, mask=mask)\n        y = tl.load(in_ptr1 + offsets, mask=mask)\n        output = x + y\n        tl.store(out_ptr + offsets, output, mask=mask)\n\n    @torch.compile(fullgraph=True)\n    def add_fn(x, y):\n        output = torch.zeros_like(x)\n        n_elements = output.numel()\n        grid = lambda meta: (triton.cdiv(n_elements, meta[\"BLOCK_SIZE\"]),)\n        add_kernel_autotuned[grid](x, y, output, n_elements)\n        return output\n\n    x = torch.randn(4, device=\"cuda\")\n    y = torch.randn(4, device=\"cuda\")\n    out = add_fn(x, y)\n    print(f\"Vector addition of\\nX:\\t{x}\\nY:\\t{y}\\nis equal to\\n{out}\")"
+        "if not has_triton():\n    print(\"\u7531\u4e8e\u6b64\u8bbe\u5907\u4e0d\u652f\u6301 triton,\u56e0\u6b64\u8df3\u8fc7\u3002\")\nelse:\n    import triton\n    from triton import language as tl\n\n    @triton.autotune(\n        configs=[\n            triton.Config({\"BLOCK_SIZE\": 4}, num_stages=3, num_warps=8),\n            triton.Config({\"BLOCK_SIZE\": 4}, num_stages=4, num_warps=4),\n            triton.Config({\"BLOCK_SIZE\": 2}, num_stages=3, num_warps=8),\n            triton.Config({\"BLOCK_SIZE\": 2}, num_stages=4, num_warps=4),\n        ],\n        key=[],\n    )\n    @triton.jit\n    def add_kernel_autotuned(\n        in_ptr0,\n        in_ptr1,\n        out_ptr,\n        n_elements,\n        BLOCK_SIZE: \"tl.constexpr\",\n    ):\n        pid = tl.program_id(axis=0)\n        block_start = pid * BLOCK_SIZE\n        offsets = block_start + tl.arange(0, BLOCK_SIZE)\n        mask = offsets < n_elements\n        x = tl.load(in_ptr0 + offsets, mask=mask)\n        y = tl.load(in_ptr1 + offsets, mask=mask)\n        output = x + y\n        tl.store(out_ptr + offsets, output, mask=mask)\n\n    @torch.compile(fullgraph=True)\n    def add_fn(x, y):\n        output = torch.zeros_like(x)\n        n_elements = output.numel()\n        grid = lambda meta: (triton.cdiv(n_elements, meta[\"BLOCK_SIZE\"]),)\n        add_kernel_autotuned[grid](x, y, output, n_elements)\n        return output\n\n    x = torch.randn(4, device=\"cuda\")\n    y = torch.randn(4, device=\"cuda\")\n    out = add_fn(x, y)\n    print(f\"\u5411\u91cf\u52a0\u6cd5\\nX:\\t{x}\\nY:\\t{y}\\n\u7ed3\u679c\u4e3a\\n{out}\")"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Composibility and Limitations\n\nAs of PyTorch 2.3, the support for user-defined Triton kernels in ``torch.compile``\nincludes dynamic shapes, ``torch.autograd.Function``, JIT inductor, and AOT inductor.\nYou can use these features together to build complex, high-performance models.\n\nHowever, there are certain limitations to be aware of:\n\n* **Tensor Subclasses:** Currently, there is no support for\n  tensor subclasses and other advanced features.\n* **Triton Features:** While ``triton.heuristics`` can be used either standalone or\n  before ``triton.autotune``, it cannot be used after ```triton.autotune``. This\n  implies that if ``triton.heuristics`` and ``triton.autotune`` are to be used\n  together, ``triton.heuristics`` must be used first.\n\n## Conclusion\nIn this recipe, we explored how to utilize user-defined Triton kernels\nwith ``torch.compile``. We delved into the basic usage of a simple\nvector addition kernel and advanced usage involving Triton's autotune\nfeature. We also discussed the composability of user-defined Triton\nkernels with other PyTorch features and highlighted some current limitations.\n\n## See Also\n\n* [Compiling the Optimizers](https://pytorch.org/tutorials/recipes/compiling_optimizer.html)_\n* [Implementing High-Performance Transformers with Scaled Dot Product Attention](https://pytorch.org/tutorials/intermediate/scaled_dot_product_attention_tutorial.html)_\n\n"
+        "## \u53ef\u7ec4\u5408\u6027\u548c\u9650\u5236\n\n\u4ece PyTorch 2.3 \u5f00\u59cb, ``torch.compile`` \u4e2d\u5bf9\u7528\u6237\u81ea\u5b9a\u4e49 Triton \u5185\u6838\u7684\u652f\u6301\u5305\u62ec\u52a8\u6001\u5f62\u72b6\u3001\n``torch.autograd.Function``\u3001JIT inductor \u548c AOT inductor\u3002\n\u60a8\u53ef\u4ee5\u5c06\u8fd9\u4e9b\u529f\u80fd\u7ec4\u5408\u5728\u4e00\u8d77\u6784\u5efa\u590d\u6742\u7684\u9ad8\u6027\u80fd\u6a21\u578b\u3002\n\n\u4f46\u662f,\u4e5f\u9700\u8981\u6ce8\u610f\u4e00\u4e9b\u9650\u5236:\n\n* **Tensor \u5b50\u7c7b:** \u76ee\u524d\u4e0d\u652f\u6301\u5f20\u91cf\u5b50\u7c7b\u548c\u5176\u4ed6\u9ad8\u7ea7\u529f\u80fd\u3002\n* **Triton \u529f\u80fd:** \u867d\u7136 ``triton.heuristics`` \u53ef\u4ee5\u5355\u72ec\u4f7f\u7528\u6216\u5728 ``triton.autotune`` \u4e4b\u524d\u4f7f\u7528,\n  \u4f46\u4e0d\u80fd\u5728 ``triton.autotune`` \u4e4b\u540e\u4f7f\u7528\u3002\u8fd9\u610f\u5473\u7740\u5982\u679c\u8981\u4e00\u8d77\u4f7f\u7528 ``triton.heuristics`` \u548c ``triton.autotune``,\n  \u5219\u5fc5\u987b\u5148\u4f7f\u7528 ``triton.heuristics``\u3002\n\n## \u7ed3\u8bba\n\u5728\u672c\u6559\u7a0b\u4e2d,\u6211\u4eec\u63a2\u8ba8\u4e86\u5982\u4f55\u5728 ``torch.compile`` \u4e2d\u4f7f\u7528\u7528\u6237\u81ea\u5b9a\u4e49\u7684 Triton \u5185\u6838\u3002\n\u6211\u4eec\u6df1\u5165\u7814\u7a76\u4e86\u4f7f\u7528\u7b80\u5355\u5411\u91cf\u52a0\u6cd5\u5185\u6838\u7684\u57fa\u672c\u7528\u6cd5,\u4ee5\u53ca\u6d89\u53ca Triton \u81ea\u52a8\u8c03\u4f18\u529f\u80fd\u7684\u9ad8\u7ea7\u7528\u6cd5\u3002\n\u6211\u4eec\u8fd8\u8ba8\u8bba\u4e86\u7528\u6237\u81ea\u5b9a\u4e49 Triton \u5185\u6838\u4e0e\u5176\u4ed6 PyTorch \u529f\u80fd\u7684\u53ef\u7ec4\u5408\u6027,\u5e76\u5f3a\u8c03\u4e86\u4e00\u4e9b\u5f53\u524d\u7684\u9650\u5236\u3002\n\n## \u53e6\u8bf7\u53c2\u9605\n\n* [\u7f16\u8bd1\u4f18\u5316\u5668](https://pytorch.org/tutorials/recipes/compiling_optimizer.html)_\n* [\u4f7f\u7528\u7f29\u653e\u70b9\u79ef\u6ce8\u610f\u529b\u5b9e\u73b0\u9ad8\u6027\u80fd Transformer](https://pytorch.org/tutorials/intermediate/scaled_dot_product_attention_tutorial.html)_\n\n"
       ]
     }
   ],
diff --git a/docs/_sources/recipes/amx.rst.txt b/docs/_sources/recipes/amx.rst.txt
index 459e7c5..e09b3e7 100644
--- a/docs/_sources/recipes/amx.rst.txt
+++ b/docs/_sources/recipes/amx.rst.txt
@@ -1,38 +1,31 @@
 ==============================================
-Leverage Intel® Advanced Matrix Extensions
+利用英特尔®高级矩阵扩展(Intel® Advanced Matrix Extensions)
 ==============================================
 
-Introduction
-============
+简介
+====
 
-Advanced Matrix Extensions (AMX), also known as Intel® Advanced Matrix Extensions (Intel® AMX), is an x86 extension,
-which introduce two new components: a 2-dimensional register file called 'tiles' and an accelerator of Tile Matrix Multiplication (TMUL) that is able to operate on those tiles.
-AMX is designed to work on matrices to accelerate deep-learning training and inference on the CPU and is ideal for workloads like natural-language processing, recommendation systems and image recognition.
+高级矩阵扩展(AMX)，也称为英特尔®高级矩阵扩展(Intel® AMX)，是一种x86扩展，引入了两个新组件:一个称为"tile"的二维寄存器文件和一个能够在这些tile上进行矩阵乘法(TMUL)的加速器。AMX旨在加速CPU上的深度学习训练和推理工作负载,非常适合自然语言处理、推荐系统和图像识别等工作负载。
 
-Intel advances AI capabilities with 4th Gen Intel® Xeon® Scalable processors and Intel® AMX, delivering 3x to 10x higher inference and training performance versus the previous generation, see `Accelerate AI Workloads with Intel® AMX`_.
-Compared to 3rd Gen Intel Xeon Scalable processors running Intel® Advanced Vector Extensions 512 Neural Network Instructions (Intel® AVX-512 VNNI),
-4th Gen Intel Xeon Scalable processors running Intel AMX can perform 2,048 INT8 operations per cycle, rather than 256 INT8 operations per cycle. They can also perform 1,024 BF16 operations per cycle, as compared to 64 FP32 operations per cycle, see page 4 of `Accelerate AI Workloads with Intel® AMX`_.
-For more detailed information of AMX, see `Intel® AMX Overview`_.
+英特尔通过第4代英特尔®至强®可扩展处理器和英特尔®AMX推进了AI能力,相比上一代产品,推理和训练性能提高了3倍至10倍,详见`使用Intel® AMX加速AI工作负载`_。与运行Intel®高级矢量扩展512神经网络指令(Intel® AVX-512 VNNI)的第3代英特尔至强可扩展处理器相比,运行Intel AMX的第4代英特尔至强可扩展处理器每周期可执行2,048个INT8操作,而不是256个INT8操作;它们还可以每周期执行1,024个BF16操作,而不是64个FP32操作,详见`使用Intel® AMX加速AI工作负载`_第4页。有关AMX的更多详细信息,请参阅`Intel® AMX概述`_。
 
-
-AMX in PyTorch
+PyTorch中的AMX
 ==============
 
-PyTorch leverages AMX for computing intensive operators with BFloat16 and quantization with INT8 by its backend oneDNN
-to get higher performance out-of-box on x86 CPUs with AMX support.
-For more detailed information of oneDNN, see `oneDNN`_.
+PyTorch通过其后端oneDNN利用AMX来计算BFloat16和INT8量化的计算密集型算子,从而在支持AMX的x86 CPU上获得更高的性能。
+有关oneDNN的更多详细信息,请参阅`oneDNN`_。
 
-The operation is fully handled by oneDNN according to the execution code path generated. For example, when a supported operation gets executed into oneDNN implementation on a hardware platform with AMX support, AMX instructions will be invoked automatically inside oneDNN.
-Since oneDNN is the default acceleration library for PyTorch CPU, no manual operations are required to enable the AMX support.
+操作完全由oneDNN根据生成的执行代码路径处理。例如,当支持的操作在支持AMX的硬件平台上执行到oneDNN实现时,AMX指令将在oneDNN内部自动调用。
+由于oneDNN是PyTorch CPU的默认加速库,因此无需手动操作即可启用AMX支持。
 
-Guidelines of leveraging AMX with workloads
+利用AMX加速工作负载的指南
 -------------------------------------------
 
-This section provides guidelines on how to leverage AMX with various workloads.
+本节提供了如何利用AMX加速各种工作负载的指南。
 
-- BFloat16 data type: 
+- BFloat16数据类型:
 
-  - Using ``torch.cpu.amp`` or ``torch.autocast("cpu")`` would utilize AMX acceleration for supported operators.
+  - 使用``torch.cpu.amp``或``torch.autocast("cpu")``将利用AMX加速支持的算子。
 
    ::
 
@@ -40,23 +33,23 @@ This section provides guidelines on how to leverage AMX with various workloads.
       with torch.cpu.amp.autocast():
          output = model(input)
 
-.. note:: Use ``torch.channels_last`` memory format to get better performance. 
+.. note:: 使用``torch.channels_last``内存格式可获得更好的性能。
 
-- Quantization:
+- 量化:
 
-  - Applying quantization would utilize AMX acceleration for supported operators.
+  - 应用量化将利用AMX加速支持的算子。
 
 - torch.compile:
 
-  - When the generated graph model runs into oneDNN implementations with the supported operators, AMX accelerations will be activated.
+  - 当生成的图模型运行到oneDNN实现的支持算子时,AMX加速将被激活。
 
-.. note:: When using PyTorch on CPUs that support AMX, the framework will automatically enable AMX usage by default. This means that PyTorch will attempt to leverage the AMX feature whenever possible to speed up matrix multiplication operations. However, it's important to note that the decision to dispatch to the AMX kernel ultimately depends on the internal optimization strategy of the oneDNN library and the quantization backend, which PyTorch relies on for performance enhancements. The specific details of how AMX utilization is handled internally by PyTorch and the oneDNN library may be subject to change with updates and improvements to the framework.
+.. note:: 在支持AMX的CPU上使用PyTorch时,框架将默认自动启用AMX使用。这意味着PyTorch将尽可能利用AMX功能来加速矩阵乘法操作。但是,重要的是要注意,是否调度到AMX内核最终取决于PyTorch所依赖的oneDNN库和量化后端的内部优化策略。PyTorch和oneDNN库内部如何处理AMX利用的具体细节可能会随着框架的更新和改进而发生变化。
 
 
-CPU operators that can leverage AMX:
+可利用AMX的CPU算子:
 ------------------------------------
 
-BF16 CPU ops that can leverage AMX:
+可利用AMX的BF16 CPU算子:
 
 - ``conv1d``
 - ``conv2d``
@@ -72,7 +65,7 @@ BF16 CPU ops that can leverage AMX:
 - ``linear``
 - ``matmul``
 
-Quantization CPU ops that can leverage AMX:
+可利用AMX的量化CPU算子:
 
 - ``conv1d``
 - ``conv2d``
@@ -84,10 +77,10 @@ Quantization CPU ops that can leverage AMX:
 
 
 
-Confirm AMX is being utilized
+确认AMX正在被利用
 ------------------------------
 
-Set environment variable ``export ONEDNN_VERBOSE=1``, or use ``torch.backends.mkldnn.verbose`` to enable oneDNN to dump verbose messages.
+设置环境变量``export ONEDNN_VERBOSE=1``或使用``torch.backends.mkldnn.verbose``以启用oneDNN转储详细消息。
 
 ::
 
@@ -95,7 +88,7 @@ Set environment variable ``export ONEDNN_VERBOSE=1``, or use ``torch.backends.mk
        with torch.cpu.amp.autocast():
            model(input)
 
-For example, get oneDNN verbose:
+例如,获取oneDNN详细输出:
 
 ::
 
@@ -111,24 +104,21 @@ For example, get oneDNN verbose:
    onednn_verbose,exec,cpu,matmul,brg:avx512_core_amx_int8,undef,src_s8::blocked:ab:f0 wei_s8:p:blocked:BA16a64b4a:f0 dst_s8::blocked:ab:f0,attr-scratchpad:user ,,1x30522:30522x768:1x768,7.66382
    ...
 
-If you get the verbose of ``avx512_core_amx_bf16`` for BFloat16 or ``avx512_core_amx_int8`` for quantization with INT8, it indicates that AMX is activated.
+如果你获得了``avx512_core_amx_bf16``的详细输出(用于BFloat16)或``avx512_core_amx_int8``(用于INT8量化),则表示AMX已被激活。
 
 
-Conclusion
+结论
 ----------
 
+在本教程中,我们简要介绍了AMX、如何在PyTorch中利用AMX来加速工作负载,以及如何确认AMX正在被利用。
 
-In this tutorial, we briefly introduced AMX, how to utilize AMX in PyTorch to accelerate workloads, and how to confirm that AMX is being utilized.
-
-With the improvements and updates of PyTorch and oneDNN, the utilization of AMX may be subject to change accordingly.
+随着PyTorch和oneDNN的改进和更新,AMX的利用情况可能会相应发生变化。
 
-As always, if you run into any problems or have any questions, you can use
-`forum <https://discuss.pytorch.org/>`_ or `GitHub issues
-<https://github.com/pytorch/pytorch/issues>`_ to get in touch. 
+如果您遇到任何问题或有任何疑问,您可以使用`论坛 <https://discuss.pytorch.org/>`_或`GitHub issues <https://github.com/pytorch/pytorch/issues>`_与我们联系。
 
 
-.. _Accelerate AI Workloads with Intel® AMX: https://www.intel.com/content/www/us/en/products/docs/accelerator-engines/advanced-matrix-extensions/ai-solution-brief.html
+.. _使用Intel® AMX加速AI工作负载: https://www.intel.com/content/www/us/en/products/docs/accelerator-engines/advanced-matrix-extensions/ai-solution-brief.html
 
-.. _Intel® AMX Overview: https://www.intel.com/content/www/us/en/products/docs/accelerator-engines/advanced-matrix-extensions/overview.html
+.. _Intel® AMX概述: https://www.intel.com/content/www/us/en/products/docs/accelerator-engines/advanced-matrix-extensions/overview.html
 
 .. _oneDNN: https://oneapi-src.github.io/oneDNN/index.html
diff --git a/docs/_sources/recipes/compiling_optimizer.rst.txt b/docs/_sources/recipes/compiling_optimizer.rst.txt
index 951495c..702dace 100644
--- a/docs/_sources/recipes/compiling_optimizer.rst.txt
+++ b/docs/_sources/recipes/compiling_optimizer.rst.txt
@@ -1,25 +1,21 @@
-(beta) Compiling the optimizer with torch.compile
+(beta) 使用 torch.compile 编译优化器
 ==========================================================================================
 
-**Author:** `Michael Lazos <https://github.com/mlazos>`_
+**作者:** `Michael Lazos <https://github.com/mlazos>`_
 
-The optimizer is a key algorithm for training any deep learning model.
-Since it is responsible for updating every model parameter, it can often
-become the bottleneck in training performance for large models. In this recipe, 
-we will apply ``torch.compile`` to the optimizer to observe the GPU performance 
-improvement.
+优化器是训练任何深度学习模型的关键算法。由于它负责更新每个模型参数,因此对于大型模型,它往往会成为训练性能的瓶颈。
+在本教程中,我们将在优化器使用 ``torch.compile`` ,提升在 GPU 上的性能。
 
 .. note::
 
-   This tutorial requires PyTorch 2.2.0 or later.
+   本教程需要 PyTorch 2.2.0 或更高版本。
 
-Model Setup
+模型设置
 ~~~~~~~~~~~~~~~~~~~~~
-For this example, we'll use a simple sequence of linear layers.
-Since we are only benchmarking the optimizer, the choice of model doesn't matter
-because optimizer performance is a function of the number of parameters.
+对于本例,我们将使用一个简单的线性层序列。由于我们只是对优化器进行基准测试,所选择的模型并不重要,
+因为优化器的性能与函数参数数量有关。
 
-Depending on what machine you are using, your exact results may vary.
+根据您使用的机器不同,结果可能会有所不同。
 
 .. code-block:: python
 
@@ -32,19 +28,17 @@ Depending on what machine you are using, your exact results may vary.
    output = model(input)
    output.sum().backward()
 
-Setting up and running the optimizer benchmark
+设置和运行优化器基准测试
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-In this example, we'll use the Adam optimizer
-and create a helper function to wrap the step()
-in ``torch.compile()``.
+在本例中,我们将使用 Adam 优化器,并创建一个辅助函数来将 step() 包装在 ``torch.compile()`` 中。
 
 .. note::
    
-   ``torch.compile`` is only supported on cuda devices with compute capability >= 7.0
+   ``torch.compile`` 仅支持device_capability >= 7.0 的 CUDA 设备
 
 .. code-block:: python
 
-   # exit cleanly if we are on a device that doesn't support torch.compile
+   # 如果我们在不支持 torch.compile 的设备上,则干净地退出
    if torch.cuda.get_device_capability() < (7, 0):
        print("Exiting because torch.compile is not supported on this device.")
        import sys
@@ -59,7 +53,7 @@ in ``torch.compile()``.
        opt.step()
    
    
-   # Let's define a helpful benchmarking function:
+   # 让我们定义一个有用的基准测试函数:
    import torch.utils.benchmark as benchmark
    
    
@@ -70,7 +64,7 @@ in ``torch.compile()``.
        return t0.blocked_autorange().mean * 1e6
 
 
-   # Warmup runs to compile the function
+   # 预热运行以编译函数
    for _ in range(5):
        fn()
    
@@ -82,13 +76,13 @@ in ``torch.compile()``.
    print(f"eager runtime: {eager_runtime}us")
    print(f"compiled runtime: {compiled_runtime}us")
 
-Sample Results:
+示例结果:
 
 * Eager runtime: 747.2437149845064us
 * Compiled runtime: 392.07384741178us
 
-See Also
+另请参阅
 ~~~~~~~~~
 
-* For an in-depth technical overview, see
-`Compiling the optimizer with PT2 <https://dev-discuss.pytorch.org/t/compiling-the-optimizer-with-pt2/1669>`__
+* 有关深入的技术概述,请参阅
+`使用 PT2 编译优化器 <https://dev-discuss.pytorch.org/t/compiling-the-optimizer-with-pt2/1669>`__
diff --git a/docs/_sources/recipes/inference_tuning_on_aws_graviton.rst.txt b/docs/_sources/recipes/inference_tuning_on_aws_graviton.rst.txt
index 08d3515..949fa30 100644
--- a/docs/_sources/recipes/inference_tuning_on_aws_graviton.rst.txt
+++ b/docs/_sources/recipes/inference_tuning_on_aws_graviton.rst.txt
@@ -1,38 +1,40 @@
-(Beta) PyTorch Inference Performance Tuning on AWS Graviton Processors
+
+
+(Beta) PyTorch在AWS Graviton处理器上的推理性能优化
 ======================================================================
 
-**Author**: `Sunita Nadampalli <https://github.com/snadampal>`_
+**作者**: `Sunita Nadampalli <https://github.com/snadampal>`_
 
-`AWS Graviton <https://aws.amazon.com/ec2/graviton/>`_ is a series of ARM-based processors designed by AWS. AWS Graviton3 processors are optimized for Machine Learning (ML) workloads, including support for ``bfloat16``, Scalable Vector Extension (SVE) and twice the Single Instruction Multiple Data (SIMD) bandwidth compared to Graviton2.
+`AWS Graviton <https://aws.amazon.com/ec2/graviton/>`_ 是一系列由AWS设计的基于ARM的处理器。AWS Graviton3处理器针对机器学习(ML)工作负载进行了优化,包括支持 ``bfloat16``、可扩展向量扩展(SVE)以及比Graviton2高两倍的单指令多数据(SIMD)带宽。
 
-PyTorch provides native reference ATen kernels for the machine learning operators like convolutions, matmul, relu, etc. These operators can be accelerated with platform specific kernel implementations from Basic Linear Algebra (BLAS) libraries. On AWS Graviton CPUs, MKLDNN with Arm Compute Library (`ACL <https://github.com/ARM-software/ComputeLibrary>`_) and `OpenBLAS <https://github.com/OpenMathLib/OpenBLAS>`_ libraries provide optimized implementations for a subset of the operators. Both these libraries are integrated into PyTorch with PyTorch 2.0 version.
+PyTorch为机器学习算子(如卷积、矩阵乘法、relu等)提供了原生参考ATen内核。这些算子可以通过来自基本线性代数(BLAS)库的特定于平台的内核实现进行加速。在AWS Graviton CPU上,MKLDNN与Arm Compute Library (`ACL <https://github.com/ARM-software/ComputeLibrary>`_) 和 `OpenBLAS <https://github.com/OpenMathLib/OpenBLAS>`_ 库为一部分算子提供了优化实现。从PyTorch 2.0版本开始,这两个库都集成到了PyTorch中。
 
-In this tutorial we will cover how to achieve the best inference performance for linear layer neural network on AWS Graviton3 CPUs (`AWS c7g instance <https://aws.amazon.com/ec2/instance-types/c7g/>`_) with ``bfloa16`` kernels and with the right backend selection.
+在本教程中,我们将介绍如何通过 ``bfloat16`` 内核和正确的后端选择,在AWS Graviton3 CPU (`AWS c7g实例 <https://aws.amazon.com/ec2/instance-types/c7g/>`_) 上实现线性层神经网络的最佳推理性能。
 
-Contents
+内容
 --------
-1. Basic Usage
-2. Speed up inference with Bfloat16 fast math kernels
-3. Improve inference performance with OpenBLAS for smaller batch dimensions
-4. Optimize memory allocation overhead with Linux Transparent huge pages
-5. Conclusion
+1. 基本用法
+2. 使用Bfloat16快速数学内核加速推理
+3. 对于较小的批次维度,使用OpenBLAS提高推理性能
+4. 使用Linux透明大页优化内存分配开销
+5. 总结
 
 .. note::
-   To successfully run this tutorial and reproduce the speedup numbers shown below, you need an instance from the Graviton3 family (``c7g/r7g/m7g``) of hardware. For this tutorial, we used the `c7g.xl (4vcpu) instance <https://aws.amazon.com/ec2/instance-types/c7g/>`_ .
+   要成功运行本教程并重现下面显示的加速数字,您需要来自Graviton3系列(``c7g/r7g/m7g``)的硬件实例。对于本教程,我们使用了 `c7g.xl (4vcpu)实例 <https://aws.amazon.com/ec2/instance-types/c7g/>`_ 。
 
-Basic Usage
+基本用法
 ---------------
 
-PyTorch natively supports AWS Graviton3 optimizations starting with PyTorch 2.0 version.
-Please refer to this `blog <https://pytorch.org/blog/optimized-pytorch-w-graviton/>`_ for more details on the optimizations.
+从PyTorch 2.0版本开始,PyTorch原生支持AWS Graviton3优化。
+更多详细信息请参阅此 `博客 <https://pytorch.org/blog/optimized-pytorch-w-graviton/>`_。
 
-1. Install PyTorch by running the following command:
+1. 运行以下命令安装PyTorch:
 
    .. code-block::
 
       python3 -m pip install torch
 
-2. We will start by importing the required dependencies and defining the device will run on:
+2. 我们将从导入所需的依赖项并定义将在其上运行的设备开始:
 
 .. code-block:: python
 
@@ -45,7 +47,7 @@ Please refer to this `blog <https://pytorch.org/blog/optimized-pytorch-w-gravito
     print(f"Using {device} device")
 
 
-3. Given linear layers are at the heart of several neural networks, including transformers, we take a linear layer for this demo. We define our neural network by subclassing ``nn.Module``, and initializing the layers in ``__init__``. We construct the network with a typical large language model parameters to match the real world scenario:
+3. 鉴于线性层是许多神经网络(包括Transformer)的核心,我们在此演示中使用线性层。我们通过子类化 ``nn.Module`` 并在 ``__init__`` 中初始化层来定义我们的神经网络。我们使用典型的大型语言模型参数构建网络,以匹配真实世界场景:
 
 .. code-block:: python
 
@@ -66,14 +68,14 @@ Please refer to this `blog <https://pytorch.org/blog/optimized-pytorch-w-gravito
         logits = self.linear_relu_stack(x)
         return logits
 
-4. Let's create an instance of ``MyNeuralNetwork``, and move it to the device:
+4. 让我们创建一个 ``MyNeuralNetwork`` 的实例,并将其移动到设备上:
 
 .. code-block:: python
 
     model = MyNeuralNetwork().to(device)
     print(model)
 
-Next, let's get the prediction probabilities by passing them through an instance of the ``nn.Softmax`` module:
+接下来,让我们通过将它们传递给 ``nn.Softmax`` 模块的实例来获取预测概率:
 
 .. code-block:: python
 
@@ -83,19 +85,19 @@ Next, let's get the prediction probabilities by passing them through an instance
     y_pred = pred_probab.argmax(1)
     print(f"Predicted class: {y_pred}")
 
-output:
+输出:
 
 .. code-block::
 
     Predicted class: tensor([2])
 
-Our network functionality is verified. Next, we will profile the performance. Lets' check two different scenarios: small and large batch dimensions.
+我们已验证了网络功能。接下来,我们将分析性能。让我们检查两种不同的情况:小批次维度和大批次维度。
 
-**Scenario 1:** A larger batch dimension, for example 256:
+**情况1:** 较大的批次维度,例如256:
 
 .. code-block:: python
 
-    # warm it up first and loop over multiple times to have enough execution time
+    # 首先进行预热,并循环多次以获得足够的执行时间
 
     X = torch.rand(256, 64, 64, device=device)
 
@@ -110,7 +112,7 @@ Our network functionality is verified. Next, we will profile the performance. Le
     print(prof.key_averages().table(sort_by="self_cpu_time_total"))
 
 
-Following is the profiler output with the default PyTorch configuration:
+使用默认PyTorch配置时的分析器输出如下:
 
 .. table::
    :widths: auto
@@ -130,17 +132,17 @@ Following is the profiler output with the default PyTorch configuration:
 **Self CPU time total:** 16.201s
 
 
-Speed up Inference with ``bfloat16`` Fast Math Kernels
+使用 ``bfloat16`` Fast Math Kernels加速推理
 ----------------------------------------------------------
 
-AWS Graviton3 processors support `bfloat16 MMLA instructions <https://developer.arm.com/documentation/ddi0596/2020-12/SVE-Instructions/BFMMLA--BFloat16-floating-point-matrix-multiply-accumulate->`_. Arm Compute Library (`ACL <https://github.com/ARM-software/ComputeLibrary>`_) provides optimized ``bfloat16`` General Matrix Multiplication (GEMM) kernels for AWS Graviton processors, and are integrated into PyTorch via MKLDNN backend starting with PyTorch 2.0.  The inference performance can be optimized with the fast math GEMM kernels. The fast math mode is not enabled by default because these kernels perform GEMM in ``bfloat16`` precision instead of ``float``, and hence results in a slight drop in the model inference accuracy. However, the accuracy drop is within the ``cosine similarity`` threshold defined for ``bfloat16`` backend in ``torchbench`` test suite, and hence acceptable for majority of the applications. To enable the fast math GEMM kernels, set the following environment variable:
+AWS Graviton3处理器支持 `bfloat16 MMLA指令 <https://developer.arm.com/documentation/ddi0596/2020-12/SVE-Instructions/BFMMLA--BFloat16-floating-point-matrix-multiply-accumulate->`_。Arm Compute Library (`ACL <https://github.com/ARM-software/ComputeLibrary>`_) 为AWS Graviton处理器提供了优化的 ``bfloat16`` 通用矩阵乘法(GEMM)内核,并从PyTorch 2.0版本开始通过MKLDNN后端集成到PyTorch中。可以使用快速数学GEMM内核优化推理性能。默认情况下不启用快速数学模式,因为这些内核以 ``bfloat16`` 精度而不是 ``float`` 执行GEMM,因此会导致模型推理精度略有下降。但是,精度下降在 ``torchbench`` 测试套件中为 ``bfloat16`` 后端定义的 ``余弦相似度`` 阈值范围内,因此对大多数应用程序来说是可以接受的。要启用快速数学GEMM内核,请设置以下环境变量:
 
 .. code-block:: bash
 
     $ export DNNL_DEFAULT_FPMATH_MODE=BF16
 
 
-When you run the above inference script, you should see the following profiler output with the MKLDNN fast math mode enabled:
+当您运行上述推理脚本时,应该会看到启用MKLDNN快速数学模式后的分析器输出:
 
 .. table::
    :widths: auto
@@ -160,16 +162,16 @@ When you run the above inference script, you should see the following profiler o
 **Self CPU time total:** 7.262s
 
 
-This is around ``2x (7.262s vs 16.201s)`` performance improvement with the ``bfloat16`` fastmath kernels. Next, let’s look at the smaller batch dimension scenario.
+这比默认配置快约 ``2倍 (7.262s vs 16.201s)``。接下来,让我们看看较小批次维度的情况。
 
-**Scenario 2:** A smaller batch dimension, for example, 32:
+**场景 2:** 较小的批量维度，例如 32:
 
 .. code-block:: python
 
     X = torch.rand(32, 64, 64, device=device)
     with torch.set_grad_enabled(False):
         for _ in range(50):
-            model(X) #Warmup
+            model(X) #预热
         with profile(activities=[ProfilerActivity.CPU]) as prof:
             with record_function("mymodel_inference"):
                 for _ in range(100):
@@ -178,13 +180,13 @@ This is around ``2x (7.262s vs 16.201s)`` performance improvement with the ``bfl
     print(prof.key_averages().table(sort_by="self_cpu_time_total"))
 
 
-You should see the following profiler output when the above script is run with the PyTorch default configuration:
+使用 PyTorch 默认配置运行上述脚本时，您应该会看到以下 profiler 输出:
 
 .. table::
    :widths: auto
 
    ======================  =============  ============  ============  ============  ============  ============
-                     Name    Self CPU %      Self CPU   CPU total %     CPU total   CPU time avg    # of Calls
+                     名称    自身 CPU %      自身 CPU   CPU 总计 %     CPU 总计   CPU 平均时间    调用次数
    ======================  =============  ============  ============  ============  ============  ============
            aten::addmm        95.51%         5.821s        97.04%        5.914s      19.713ms           300
        aten::clamp_min         2.33%      142.244ms         2.33%     142.244ms     711.220us           200
@@ -195,10 +197,10 @@ You should see the following profiler output when the above script is run with t
             aten::relu         0.03%        1.942ms         2.37%     144.186ms     720.930us           200
    ======================  =============  ============  ============  ============  ============  ============
 
-**Self CPU time total:** 6.094s
+**自身 CPU 总计:** 6.094s
 
 
-The following output is the profiler output when run with the MKLDNN fast math mode enabled:
+以下是启用 MKLDNN 快速数学模式时的 profiler 输出:
 
 .. code-block:: bash
 
@@ -208,7 +210,7 @@ The following output is the profiler output when run with the MKLDNN fast math m
    :widths: auto
 
    ======================  ============  ============  ============  ============  ============   =============
-                   Name     Self CPU %      Self CPU    CPU total %   CPU total    CPU time avg    # of Calls
+                   名称     自身 CPU %      自身 CPU    CPU 总计 %   CPU 总计    CPU 平均时间    调用次数
    ======================  ============  ============  ============  ============  ============   =============
            aten::addmm        93.31%        3.848s        95.66%        3.944s      13.148ms           300
        aten::clamp_min         3.43%     141.309ms         3.43%     141.309ms     706.545us           200
@@ -219,27 +221,29 @@ The following output is the profiler output when run with the MKLDNN fast math m
             aten::relu         0.05%       1.928ms         3.47%     143.237ms     716.185us           200
    ======================  ============  ============  ============  ============  ============   =============
 
-**Self CPU time total:** 4.123s
+**自身 CPU 总计:** 4.123s
 
-The MKLDNN fast math mode yields approximately a **1.47x  (4.123s vs 6.094s)**  performance improvement for smaller batch dimensions. Although this improvement is noteworthy, the overall performance still leaves room for improvement. This is because of the runtime overhead (weights reorders and kernel launch time) from oneDNN and ACL backend outweighing the compute benefits from the ACL GEMM kernels for the smaller batch compute.
+MKLDNN 快速数学模式为较小的批量维度提供了大约 **1.47x (4.123s vs 6.094s)** 的性能提升。
+尽管性能提升明显,但整体仍有提升空间。因为来自 oneDNN 和 ACL 后端的运行时开销(权重重排和内核启动时间)
+超过了 ACL GEMM 内核对较小批量计算的计算优势。
 
 
-Improve Inference Performance with OpenBLAS for Smaller Batch Dimensions
-------------------------------------------------------------------------
+使用 OpenBLAS 提高较小批量维度的推理性能
+----------------------------------------
 
-The inference performance for smaller batch dimensions can be improved by offloading the smaller shapes from MKLDNN to OpenBLAS backend. We are working on making the backend selection automatic, with robust heuristics, for the future releases. Till the heuristics are implemented, the smaller shapes can be offloaded to OpenBLAS by increasing the threshold for MKLDNN backend selection. In the following example, we use ``64`` as the threshold, so that input with ``batch dimension of 32`` is not dispatched to MKLDNN. Instead, it is dispatched to OpenBLAS.
+可以通过将较小的形状从 MKLDNN 卸载到 OpenBLAS 后端来提高较小批量维度的推理性能。我们正在努力为未来版本实现自动化的后端选择,并具有健壮的启发式算法。在实现启发式算法之前,可以通过增加 MKLDNN 后端选择的阈值将较小的形状卸载到 OpenBLAS。在以下示例中,我们使用 ``64`` 作为阈值,因此批量维度为 ``32`` 的输入不会分派到 MKLDNN。相反,它会被分派到 OpenBLAS。
 
 .. code-block:: bash
 
    $ export TORCH_MKLDNN_MATMUL_MIN_DIM=64
 
-Here is the profiler output with OpenBLAS backend:
+以下是使用 OpenBLAS 后端时的 profiler 输出:
 
 .. table::
    :widths: auto
 
    ======================  ============  ============  ============  =============  ============  =============
-                     Name    Self CPU %      Self CPU   CPU total %     CPU total   CPU time avg    # of Calls
+                     名称    自身 CPU %      自身 CPU   CPU 总计 %     CPU 总计   CPU 平均时间    调用次数
    ======================  ============  ============  ============  =============  ============  =============
            aten::addmm        96.25%        1.958s        97.51%        1.984s        6.612ms           300
        aten::clamp_min         1.28%      26.124ms         1.28%      26.124ms      130.620us           200
@@ -250,17 +254,18 @@ Here is the profiler output with OpenBLAS backend:
             aten::relu         0.06%       1.258ms         1.35%      27.382ms      136.910us           200
    ======================  ============  ============  ============  =============  ============  =============
 
-**Self CPU time total:** 2.034s
+**自身 CPU 总计:** 2.034s
 
 
-As you can see above, switching to OpenBLAS doubled the performance **(2.034s vs 4.123s)** compared to the default MKLDNN backend configuration. This becomes significant for even smaller batch dimensions, for example, for a batch dimension of 10:
+如您所见,切换到 OpenBLAS 将性能提高了一倍 **(2.034s vs 4.123s)** 与默认的 MKLDNN 后端配置相比。
+对于更小的批量维度,例如批量维度为 10,这一点更加显著:
 
 .. code-block:: python
 
     X = torch.rand(10, 64, 64, device=device)
     with torch.set_grad_enabled(False):
         for _ in range(50):
-            model(X) #Warmup
+            model(X) #预热
         with profile(activities=[ProfilerActivity.CPU]) as prof:
             with record_function("mymodel_inference"):
                 for _ in range(100):
@@ -269,13 +274,13 @@ As you can see above, switching to OpenBLAS doubled the performance **(2.034s vs
     print(prof.key_averages().table(sort_by="self_cpu_time_total"))
 
 
-The following is the profiler output with MKLDNN fast math mode:
+以下是启用 MKLDNN 快速数学模式时的 profiler 输出:
 
 .. table::
    :widths: auto
 
    ======================  ============  ============  ============  ============  =============  =============
-                     Name    Self CPU %      Self CPU   CPU total %     CPU total   CPU time avg    # of Calls
+                     名称    自身 CPU %      自身 CPU   CPU 总计 %     CPU 总计   CPU 平均时间    调用次数
    ======================  ============  ============  ============  ============  =============  =============
            aten::addmm        87.81%        3.613s        91.90%        3.781s      12.604ms           300
        aten::clamp_min         7.18%     295.437ms         7.18%     295.437ms       1.477ms           200
@@ -286,10 +291,10 @@ The following is the profiler output with MKLDNN fast math mode:
             aten::relu         0.05%       1.932ms         7.23%     297.369ms       1.487ms           200
    ======================  ============  ============  ============  ============  =============  =============
 
-**Self CPU time total:** 4.115s
+**自身 CPU 总计:** 4.115s
 
 
-and the following is the profiler output with the OpenBLAS backend:
+以下是使用 OpenBLAS 后端时的 profiler 输出:
 
 .. code-block:: bash
 
@@ -299,7 +304,7 @@ and the following is the profiler output with the OpenBLAS backend:
    :widths: auto
 
    ======================  =============  ============  ============  ============  =============  ============
-                   Name     Self CPU %      Self CPU     CPU total %   CPU total    CPU time avg    # of Calls
+                   名称     自身 CPU %      自身 CPU     CPU 总计 %   CPU 总计    CPU 平均时间    调用次数
    ======================  =============  ============  ============  ============  =============  ============
            aten::addmm        92.66%        1.179s        95.23%        1.211s         4.038ms           300
        aten::clamp_min         2.83%      36.060ms         2.83%      36.060ms       180.300us           200
@@ -310,29 +315,28 @@ and the following is the profiler output with the OpenBLAS backend:
             aten::relu         0.10%       1.285ms         2.94%      37.345ms       186.725us           200
    ======================  =============  ============  ============  ============  =============  ============
 
-**Self CPU time total:** 1.272s
-
+**自身 CPU 总计:** 1.272s
 
-Here we observed **3.2x (1.272s vs 4.115s)** performance improvement by tuning the backend thresholds appropriately.
+这里我们观察到通过适当调整后端阈值,**性能提高了3.2倍(1.272s vs 4.115s)**。
 
+使用 Linux Transparent Huge Pages (THP) 优化内存分配开销
+------------------------------------------------------
 
-Optimize Memory Allocation Overhead with Linux Transparent Huge Pages (THP)
----------------------------------------------------------------------------
-
-We also observed that for these larger networks, tensor memory allocations take significant portion of the inference latency. This can be optimized by enabling Linux transparent huge page allocations from PyTorch C10 memory allocator. Currently the feature is not enabled by default because it will increase the memory footprint marginally. Set the following environment variable to enable it:
+我们还观察到,对于这些较大的网络,张量内存分配占推理延迟的很大一部分。这可以通过从PyTorch C10内存分配器
+启用 THP 来优化。目前,该功能默认未启用,因为它会略微增加内存占用。设置以下环境变量以启用它:
 
 .. code-block:: bash
 
     $ export THP_MEM_ALLOC_ENABLE=1
 
-For the batch dimension of 256 and with MKLDNN fast math mode:
+对于批量维度为 256 且启用 MKLDNN Fast Math 模式:
 
 .. code-block:: python
 
     X = torch.rand(256, 64, 64, device=device)
     with torch.set_grad_enabled(False):
         for _ in range(50):
-            model(X) #Warmup
+            model(X) #预热
         with profile(activities=[ProfilerActivity.CPU]) as prof:
             with record_function("mymodel_inference"):
                 for _ in range(100):
@@ -340,14 +344,13 @@ For the batch dimension of 256 and with MKLDNN fast math mode:
 
     print(prof.key_averages().table(sort_by="self_cpu_time_total"))
 
-
-The following is the profiler output with THP memory allocations enabled:
+启用THP内存分配后,profiler的输出如下:
 
 .. table::
    :widths: auto
 
    ======================  ============  ============  ============  ============  ==============  ============
-                     Name   Self CPU %    Self CPU     CPU total %    CPU total     CPU time avg    # of Calls
+                     名称    自身CPU%      自身CPU       CPU总%        CPU总        CPU平均时间     调用次数
    ======================  ============  ============  ============  ============  ==============  ============
            aten::addmm        91.31%        6.115s        94.39%        6.321s      21.069ms           300
        aten::clamp_min         4.82%     322.568ms         4.82%     322.568ms       1.613ms           200
@@ -357,12 +360,14 @@ The following is the profiler output with THP memory allocations enabled:
             aten::relu         0.04%       2.547ms         4.85%     325.115ms       1.626ms           200
    ======================  ============  ============  ============  ============  ==============  ============
 
-**Self CPU time total:** 6.697s
-
-This is an additional **1.08x or 8% (6.697s vs 7.262s)** improvement on top of the already optimized MKLDNN fast math mode measured above.
+**自身CPU总时间:** 6.697s
 
+这比上面测量的已优化的 MKLDNN Fast Math 模式又提高了 **1.08倍或8%(6.697s vs 7.262s)**。
 
-Conclusion
+结论
 ------------
 
-In this tutorial, we covered PyTorch inference on AWS Graviton3 instances by covering the basic usage, demonstrating speedups with fast math kernels, comparing different backends for different batch dimensions, and how to optimize tensor memory allocation latencies with Linux transparent huge pages. The recommendation is to use MKLDNN backend with Bfloat16 fastmath mode and THP memory allocations for larger tensor shapes and to use OpenBLAS backend for smaller tensor shapes. We hope that you will give it a try!
+在本教程中,我们介绍了在AWS Graviton3实例上的PyTorch推理,包括基本用法、使用快速数学内核的加速、
+比较不同批量维度下不同后端的性能,以及如何使用Linux透明大页面优化张量内存分配延迟。
+对于较大的张量形状,建议使用MKLDNN后端和Bfloat16快速数学模式以及THP内存分配;对于较小的张量形状,
+建议使用OpenBLAS后端。希望您能尝试一下!
diff --git a/docs/_sources/recipes/torch_compile_user_defined_triton_kernel_tutorial.rst.txt b/docs/_sources/recipes/torch_compile_user_defined_triton_kernel_tutorial.rst.txt
index d7f9256..42b91d4 100644
--- a/docs/_sources/recipes/torch_compile_user_defined_triton_kernel_tutorial.rst.txt
+++ b/docs/_sources/recipes/torch_compile_user_defined_triton_kernel_tutorial.rst.txt
@@ -18,36 +18,34 @@
 .. _sphx_glr_recipes_torch_compile_user_defined_triton_kernel_tutorial.py:
 
 
-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>`_
 
-.. GENERATED FROM PYTHON SOURCE LINES 10-32
+.. GENERATED FROM PYTHON SOURCE LINES 10-30
 
-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
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 32-36
+.. GENERATED FROM PYTHON SOURCE LINES 30-34
 
 .. code-block:: default
 
@@ -56,23 +54,22 @@ Before starting this recipe, make sure that you have the following:
     from torch.utils._triton import has_triton
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 37-44
+.. GENERATED FROM PYTHON SOURCE LINES 35-41
 
-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>`__。
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 44-81
+.. GENERATED FROM PYTHON SOURCE LINES 41-78
 
 .. code-block:: default
 
 
     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
@@ -105,33 +102,31 @@ For reference, see `Triton documentation <https://triton-lang.org/main/getting-s
         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}")
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 82-96
+.. GENERATED FROM PYTHON SOURCE LINES 79-91
 
-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。
 
-.. GENERATED FROM PYTHON SOURCE LINES 96-142
+.. GENERATED FROM PYTHON SOURCE LINES 91-137
 
 .. code-block:: default
 
 
     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
@@ -173,40 +168,36 @@ and ``triton.autotune``.
         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}")
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 143-172
+.. GENERATED FROM PYTHON SOURCE LINES 138-163
 
-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>`__
 
 
 .. rst-class:: sphx-glr-timing
diff --git a/docs/objects.inv b/docs/objects.inv
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diff --git a/docs/recipes/amx.html b/docs/recipes/amx.html
index faece65..97dafba 100644
--- a/docs/recipes/amx.html
+++ b/docs/recipes/amx.html
@@ -9,7 +9,7 @@
   
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
   
-  <title>Leverage Intel® Advanced Matrix Extensions &mdash; PyTorch Tutorials 2.3.0 documentation</title>
+  <title>利用英特尔®高级矩阵扩展(Intel® Advanced Matrix Extensions) &mdash; PyTorch Tutorials 2.3.0 documentation</title>
   
 
   
@@ -531,7 +531,7 @@
       </li>
 
         
-      <li>Leverage Intel® Advanced Matrix Extensions</li>
+      <li>利用英特尔®高级矩阵扩展(Intel® Advanced Matrix Extensions)</li>
     
     
       <li class="pytorch-breadcrumbs-aside">
@@ -590,32 +590,26 @@
             <div role="main" class="main-content" itemscope="itemscope" itemtype="http://schema.org/Article">
              <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
               
-  <div class="section" id="leverage-intel-advanced-matrix-extensions">
-<h1>Leverage Intel® Advanced Matrix Extensions<a class="headerlink" href="#leverage-intel-advanced-matrix-extensions" title="Permalink to this heading">¶</a></h1>
-<div class="section" id="introduction">
-<h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this heading">¶</a></h2>
-<p>Advanced Matrix Extensions (AMX), also known as Intel® Advanced Matrix Extensions (Intel® AMX), is an x86 extension,
-which introduce two new components: a 2-dimensional register file called ‘tiles’ and an accelerator of Tile Matrix Multiplication (TMUL) that is able to operate on those tiles.
-AMX is designed to work on matrices to accelerate deep-learning training and inference on the CPU and is ideal for workloads like natural-language processing, recommendation systems and image recognition.</p>
-<p>Intel advances AI capabilities with 4th Gen Intel® Xeon® Scalable processors and Intel® AMX, delivering 3x to 10x higher inference and training performance versus the previous generation, see <a class="reference external" href="https://www.intel.com/content/www/us/en/products/docs/accelerator-engines/advanced-matrix-extensions/ai-solution-brief.html">Accelerate AI Workloads with Intel® AMX</a>.
-Compared to 3rd Gen Intel Xeon Scalable processors running Intel® Advanced Vector Extensions 512 Neural Network Instructions (Intel® AVX-512 VNNI),
-4th Gen Intel Xeon Scalable processors running Intel AMX can perform 2,048 INT8 operations per cycle, rather than 256 INT8 operations per cycle. They can also perform 1,024 BF16 operations per cycle, as compared to 64 FP32 operations per cycle, see page 4 of <a class="reference external" href="https://www.intel.com/content/www/us/en/products/docs/accelerator-engines/advanced-matrix-extensions/ai-solution-brief.html">Accelerate AI Workloads with Intel® AMX</a>.
-For more detailed information of AMX, see <a class="reference external" href="https://www.intel.com/content/www/us/en/products/docs/accelerator-engines/advanced-matrix-extensions/overview.html">Intel® AMX Overview</a>.</p>
+  <div class="section" id="intel-advanced-matrix-extensions">
+<h1>利用英特尔®高级矩阵扩展(Intel® Advanced Matrix Extensions)<a class="headerlink" href="#intel-advanced-matrix-extensions" title="Permalink to this heading">¶</a></h1>
+<div class="section" id="id1">
+<h2>简介<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
+<p>高级矩阵扩展(AMX)，也称为英特尔®高级矩阵扩展(Intel® AMX)，是一种x86扩展，引入了两个新组件:一个称为”tile”的二维寄存器文件和一个能够在这些tile上进行矩阵乘法(TMUL)的加速器。AMX旨在加速CPU上的深度学习训练和推理工作负载,非常适合自然语言处理、推荐系统和图像识别等工作负载。</p>
+<p>英特尔通过第4代英特尔®至强®可扩展处理器和英特尔®AMX推进了AI能力,相比上一代产品,推理和训练性能提高了3倍至10倍,详见`使用Intel® AMX加速AI工作负载`_。与运行Intel®高级矢量扩展512神经网络指令(Intel® AVX-512 VNNI)的第3代英特尔至强可扩展处理器相比,运行Intel AMX的第4代英特尔至强可扩展处理器每周期可执行2,048个INT8操作,而不是256个INT8操作;它们还可以每周期执行1,024个BF16操作,而不是64个FP32操作,详见`使用Intel® AMX加速AI工作负载`_第4页。有关AMX的更多详细信息,请参阅`Intel® AMX概述`_。</p>
 </div>
-<div class="section" id="amx-in-pytorch">
-<h2>AMX in PyTorch<a class="headerlink" href="#amx-in-pytorch" title="Permalink to this heading">¶</a></h2>
-<p>PyTorch leverages AMX for computing intensive operators with BFloat16 and quantization with INT8 by its backend oneDNN
-to get higher performance out-of-box on x86 CPUs with AMX support.
-For more detailed information of oneDNN, see <a class="reference external" href="https://oneapi-src.github.io/oneDNN/index.html">oneDNN</a>.</p>
-<p>The operation is fully handled by oneDNN according to the execution code path generated. For example, when a supported operation gets executed into oneDNN implementation on a hardware platform with AMX support, AMX instructions will be invoked automatically inside oneDNN.
-Since oneDNN is the default acceleration library for PyTorch CPU, no manual operations are required to enable the AMX support.</p>
-<div class="section" id="guidelines-of-leveraging-amx-with-workloads">
-<h3>Guidelines of leveraging AMX with workloads<a class="headerlink" href="#guidelines-of-leveraging-amx-with-workloads" title="Permalink to this heading">¶</a></h3>
-<p>This section provides guidelines on how to leverage AMX with various workloads.</p>
+<div class="section" id="pytorchamx">
+<h2>PyTorch中的AMX<a class="headerlink" href="#pytorchamx" title="Permalink to this heading">¶</a></h2>
+<p>PyTorch通过其后端oneDNN利用AMX来计算BFloat16和INT8量化的计算密集型算子,从而在支持AMX的x86 CPU上获得更高的性能。
+有关oneDNN的更多详细信息,请参阅`oneDNN`_。</p>
+<p>操作完全由oneDNN根据生成的执行代码路径处理。例如,当支持的操作在支持AMX的硬件平台上执行到oneDNN实现时,AMX指令将在oneDNN内部自动调用。
+由于oneDNN是PyTorch CPU的默认加速库,因此无需手动操作即可启用AMX支持。</p>
+<div class="section" id="amx">
+<h3>利用AMX加速工作负载的指南<a class="headerlink" href="#amx" title="Permalink to this heading">¶</a></h3>
+<p>本节提供了如何利用AMX加速各种工作负载的指南。</p>
 <ul>
-<li><p>BFloat16 data type:</p>
+<li><p>BFloat16数据类型:</p>
 <ul class="simple">
-<li><p>Using <code class="docutils literal notranslate"><span class="pre">torch.cpu.amp</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.autocast(&quot;cpu&quot;)</span></code> would utilize AMX acceleration for supported operators.</p></li>
+<li><p>使用``torch.cpu.amp``或``torch.autocast(“cpu”)``将利用AMX加速支持的算子。</p></li>
 </ul>
 <blockquote>
 <div><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">channels_last</span><span class="p">)</span>
@@ -628,28 +622,28 @@ <h3>Guidelines of leveraging AMX with workloads<a class="headerlink" href="#guid
 </ul>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Use <code class="docutils literal notranslate"><span class="pre">torch.channels_last</span></code> memory format to get better performance.</p>
+<p>使用``torch.channels_last``内存格式可获得更好的性能。</p>
 </div>
 <ul class="simple">
-<li><p>Quantization:</p>
+<li><p>量化:</p>
 <ul>
-<li><p>Applying quantization would utilize AMX acceleration for supported operators.</p></li>
+<li><p>应用量化将利用AMX加速支持的算子。</p></li>
 </ul>
 </li>
 <li><p>torch.compile:</p>
 <ul>
-<li><p>When the generated graph model runs into oneDNN implementations with the supported operators, AMX accelerations will be activated.</p></li>
+<li><p>当生成的图模型运行到oneDNN实现的支持算子时,AMX加速将被激活。</p></li>
 </ul>
 </li>
 </ul>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>When using PyTorch on CPUs that support AMX, the framework will automatically enable AMX usage by default. This means that PyTorch will attempt to leverage the AMX feature whenever possible to speed up matrix multiplication operations. However, it’s important to note that the decision to dispatch to the AMX kernel ultimately depends on the internal optimization strategy of the oneDNN library and the quantization backend, which PyTorch relies on for performance enhancements. The specific details of how AMX utilization is handled internally by PyTorch and the oneDNN library may be subject to change with updates and improvements to the framework.</p>
+<p>在支持AMX的CPU上使用PyTorch时,框架将默认自动启用AMX使用。这意味着PyTorch将尽可能利用AMX功能来加速矩阵乘法操作。但是,重要的是要注意,是否调度到AMX内核最终取决于PyTorch所依赖的oneDNN库和量化后端的内部优化策略。PyTorch和oneDNN库内部如何处理AMX利用的具体细节可能会随着框架的更新和改进而发生变化。</p>
 </div>
 </div>
-<div class="section" id="cpu-operators-that-can-leverage-amx">
-<h3>CPU operators that can leverage AMX:<a class="headerlink" href="#cpu-operators-that-can-leverage-amx" title="Permalink to this heading">¶</a></h3>
-<p>BF16 CPU ops that can leverage AMX:</p>
+<div class="section" id="amxcpu">
+<h3>可利用AMX的CPU算子:<a class="headerlink" href="#amxcpu" title="Permalink to this heading">¶</a></h3>
+<p>可利用AMX的BF16 CPU算子:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">conv1d</span></code></p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">conv2d</span></code></p></li>
@@ -665,7 +659,7 @@ <h3>CPU operators that can leverage AMX:<a class="headerlink" href="#cpu-operato
 <li><p><code class="docutils literal notranslate"><span class="pre">linear</span></code></p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">matmul</span></code></p></li>
 </ul>
-<p>Quantization CPU ops that can leverage AMX:</p>
+<p>可利用AMX的量化CPU算子:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">conv1d</span></code></p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">conv2d</span></code></p></li>
@@ -676,15 +670,15 @@ <h3>CPU operators that can leverage AMX:<a class="headerlink" href="#cpu-operato
 <li><p><code class="docutils literal notranslate"><span class="pre">linear</span></code></p></li>
 </ul>
 </div>
-<div class="section" id="confirm-amx-is-being-utilized">
-<h3>Confirm AMX is being utilized<a class="headerlink" href="#confirm-amx-is-being-utilized" title="Permalink to this heading">¶</a></h3>
-<p>Set environment variable <code class="docutils literal notranslate"><span class="pre">export</span> <span class="pre">ONEDNN_VERBOSE=1</span></code>, or use <code class="docutils literal notranslate"><span class="pre">torch.backends.mkldnn.verbose</span></code> to enable oneDNN to dump verbose messages.</p>
+<div class="section" id="id2">
+<h3>确认AMX正在被利用<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h3>
+<p>设置环境变量``export ONEDNN_VERBOSE=1``或使用``torch.backends.mkldnn.verbose``以启用oneDNN转储详细消息。</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">mkldnn</span><span class="o">.</span><span class="n">verbose</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">mkldnn</span><span class="o">.</span><span class="n">VERBOSE_ON</span><span class="p">):</span>
     <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">cpu</span><span class="o">.</span><span class="n">amp</span><span class="o">.</span><span class="n">autocast</span><span class="p">():</span>
         <span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
 </pre></div>
 </div>
-<p>For example, get oneDNN verbose:</p>
+<p>例如,获取oneDNN详细输出:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">onednn_verbose</span><span class="p">,</span><span class="n">info</span><span class="p">,</span><span class="n">oneDNN</span> <span class="n">v2</span><span class="mf">.7.3</span> <span class="p">(</span><span class="n">commit</span> <span class="mi">6</span><span class="n">dbeffbae1f23cbbeae17adb7b5b13f1f37c080e</span><span class="p">)</span>
 <span class="n">onednn_verbose</span><span class="p">,</span><span class="n">info</span><span class="p">,</span><span class="n">cpu</span><span class="p">,</span><span class="n">runtime</span><span class="p">:</span><span class="n">OpenMP</span><span class="p">,</span><span class="n">nthr</span><span class="p">:</span><span class="mi">128</span>
 <span class="n">onednn_verbose</span><span class="p">,</span><span class="n">info</span><span class="p">,</span><span class="n">cpu</span><span class="p">,</span><span class="n">isa</span><span class="p">:</span><span class="n">Intel</span> <span class="n">AVX</span><span class="o">-</span><span class="mi">512</span> <span class="k">with</span> <span class="n">float16</span><span class="p">,</span> <span class="n">Intel</span> <span class="n">DL</span> <span class="n">Boost</span> <span class="ow">and</span> <span class="n">bfloat16</span> <span class="n">support</span> <span class="ow">and</span> <span class="n">Intel</span> <span class="n">AMX</span> <span class="k">with</span> <span class="n">bfloat16</span> <span class="ow">and</span> <span class="mi">8</span><span class="o">-</span><span class="n">bit</span> <span class="n">integer</span> <span class="n">support</span>
@@ -698,14 +692,13 @@ <h3>Confirm AMX is being utilized<a class="headerlink" href="#confirm-amx-is-bei
 <span class="o">...</span>
 </pre></div>
 </div>
-<p>If you get the verbose of <code class="docutils literal notranslate"><span class="pre">avx512_core_amx_bf16</span></code> for BFloat16 or <code class="docutils literal notranslate"><span class="pre">avx512_core_amx_int8</span></code> for quantization with INT8, it indicates that AMX is activated.</p>
+<p>如果你获得了``avx512_core_amx_bf16``的详细输出(用于BFloat16)或``avx512_core_amx_int8``(用于INT8量化),则表示AMX已被激活。</p>
 </div>
-<div class="section" id="conclusion">
-<h3>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this heading">¶</a></h3>
-<p>In this tutorial, we briefly introduced AMX, how to utilize AMX in PyTorch to accelerate workloads, and how to confirm that AMX is being utilized.</p>
-<p>With the improvements and updates of PyTorch and oneDNN, the utilization of AMX may be subject to change accordingly.</p>
-<p>As always, if you run into any problems or have any questions, you can use
-<a class="reference external" href="https://discuss.pytorch.org/">forum</a> or <a class="reference external" href="https://github.com/pytorch/pytorch/issues">GitHub issues</a> to get in touch.</p>
+<div class="section" id="id3">
+<h3>结论<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h3>
+<p>在本教程中,我们简要介绍了AMX、如何在PyTorch中利用AMX来加速工作负载,以及如何确认AMX正在被利用。</p>
+<p>随着PyTorch和oneDNN的改进和更新,AMX的利用情况可能会相应发生变化。</p>
+<p>如果您遇到任何问题或有任何疑问,您可以使用`论坛 &lt;<a class="reference external" href="https://discuss.pytorch.org/">https://discuss.pytorch.org/</a>&gt;`_或`GitHub issues &lt;<a class="reference external" href="https://github.com/pytorch/pytorch/issues">https://github.com/pytorch/pytorch/issues</a>&gt;`_与我们联系。</p>
 </div>
 </div>
 </div>
@@ -762,13 +755,13 @@ <h3>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
           <div class="pytorch-right-menu" id="pytorch-right-menu">
             <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
               <ul>
-<li><a class="reference internal" href="#">Leverage Intel® Advanced Matrix Extensions</a><ul>
-<li><a class="reference internal" href="#introduction">Introduction</a></li>
-<li><a class="reference internal" href="#amx-in-pytorch">AMX in PyTorch</a><ul>
-<li><a class="reference internal" href="#guidelines-of-leveraging-amx-with-workloads">Guidelines of leveraging AMX with workloads</a></li>
-<li><a class="reference internal" href="#cpu-operators-that-can-leverage-amx">CPU operators that can leverage AMX:</a></li>
-<li><a class="reference internal" href="#confirm-amx-is-being-utilized">Confirm AMX is being utilized</a></li>
-<li><a class="reference internal" href="#conclusion">Conclusion</a></li>
+<li><a class="reference internal" href="#">利用英特尔®高级矩阵扩展(Intel® Advanced Matrix Extensions)</a><ul>
+<li><a class="reference internal" href="#id1">简介</a></li>
+<li><a class="reference internal" href="#pytorchamx">PyTorch中的AMX</a><ul>
+<li><a class="reference internal" href="#amx">利用AMX加速工作负载的指南</a></li>
+<li><a class="reference internal" href="#amxcpu">可利用AMX的CPU算子:</a></li>
+<li><a class="reference internal" href="#id2">确认AMX正在被利用</a></li>
+<li><a class="reference internal" href="#id3">结论</a></li>
 </ul>
 </li>
 </ul>
diff --git a/docs/recipes/compiling_optimizer.html b/docs/recipes/compiling_optimizer.html
index 6561962..a8fd32d 100644
--- a/docs/recipes/compiling_optimizer.html
+++ b/docs/recipes/compiling_optimizer.html
@@ -9,7 +9,7 @@
   
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
   
-  <title>(beta) Compiling the optimizer with torch.compile &mdash; PyTorch Tutorials 2.3.0 documentation</title>
+  <title>(beta) 使用 torch.compile 编译优化器 &mdash; PyTorch Tutorials 2.3.0 documentation</title>
   
 
   
@@ -535,7 +535,7 @@
         
           <li><a href="recipes_index.html">PyTorch 示例</a> &gt;</li>
         
-      <li>(beta) Compiling the optimizer with torch.compile</li>
+      <li>(beta) 使用 torch.compile 编译优化器</li>
     
     
       <li class="pytorch-breadcrumbs-aside">
@@ -594,24 +594,20 @@
             <div role="main" class="main-content" itemscope="itemscope" itemtype="http://schema.org/Article">
              <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
               
-  <div class="section" id="beta-compiling-the-optimizer-with-torch-compile">
-<h1>(beta) Compiling the optimizer with torch.compile<a class="headerlink" href="#beta-compiling-the-optimizer-with-torch-compile" title="Permalink to this heading">¶</a></h1>
-<p><strong>Author:</strong> <a class="reference external" href="https://github.com/mlazos">Michael Lazos</a></p>
-<p>The optimizer is a key algorithm for training any deep learning model.
-Since it is responsible for updating every model parameter, it can often
-become the bottleneck in training performance for large models. In this recipe,
-we will apply <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> to the optimizer to observe the GPU performance
-improvement.</p>
+  <div class="section" id="beta-torch-compile">
+<h1>(beta) 使用 torch.compile 编译优化器<a class="headerlink" href="#beta-torch-compile" title="Permalink to this heading">¶</a></h1>
+<p><strong>作者:</strong> <a class="reference external" href="https://github.com/mlazos">Michael Lazos</a></p>
+<p>优化器是训练任何深度学习模型的关键算法。由于它负责更新每个模型参数,因此对于大型模型,它往往会成为训练性能的瓶颈。
+在本教程中,我们将在优化器使用 <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> ,提升在 GPU 上的性能。</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>This tutorial requires PyTorch 2.2.0 or later.</p>
+<p>本教程需要 PyTorch 2.2.0 或更高版本。</p>
 </div>
-<div class="section" id="model-setup">
-<h2>Model Setup<a class="headerlink" href="#model-setup" title="Permalink to this heading">¶</a></h2>
-<p>For this example, we’ll use a simple sequence of linear layers.
-Since we are only benchmarking the optimizer, the choice of model doesn’t matter
-because optimizer performance is a function of the number of parameters.</p>
-<p>Depending on what machine you are using, your exact results may vary.</p>
+<div class="section" id="id1">
+<h2>模型设置<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
+<p>对于本例,我们将使用一个简单的线性层序列。由于我们只是对优化器进行基准测试,所选择的模型并不重要,
+因为优化器的性能与函数参数数量有关。</p>
+<p>根据您使用的机器不同,结果可能会有所不同。</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
 
 <span class="n">model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
@@ -623,16 +619,14 @@ <h2>Model Setup<a class="headerlink" href="#model-setup" title="Permalink to thi
 </pre></div>
 </div>
 </div>
-<div class="section" id="setting-up-and-running-the-optimizer-benchmark">
-<h2>Setting up and running the optimizer benchmark<a class="headerlink" href="#setting-up-and-running-the-optimizer-benchmark" title="Permalink to this heading">¶</a></h2>
-<p>In this example, we’ll use the Adam optimizer
-and create a helper function to wrap the step()
-in <code class="docutils literal notranslate"><span class="pre">torch.compile()</span></code>.</p>
+<div class="section" id="id2">
+<h2>设置和运行优化器基准测试<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
+<p>在本例中,我们将使用 Adam 优化器,并创建一个辅助函数来将 step() 包装在 <code class="docutils literal notranslate"><span class="pre">torch.compile()</span></code> 中。</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p><code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> is only supported on cuda devices with compute capability &gt;= 7.0</p>
+<p><code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> 仅支持device_capability &gt;= 7.0 的 CUDA 设备</p>
 </div>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># exit cleanly if we are on a device that doesn&#39;t support torch.compile</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># 如果我们在不支持 torch.compile 的设备上,则干净地退出</span>
 <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">get_device_capability</span><span class="p">()</span> <span class="o">&lt;</span> <span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">0</span><span class="p">):</span>
     <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Exiting because torch.compile is not supported on this device.&quot;</span><span class="p">)</span>
     <span class="kn">import</span> <span class="nn">sys</span>
@@ -647,7 +641,7 @@ <h2>Setting up and running the optimizer benchmark<a class="headerlink" href="#s
     <span class="n">opt</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
 
 
-<span class="c1"># Let&#39;s define a helpful benchmarking function:</span>
+<span class="c1"># 让我们定义一个有用的基准测试函数:</span>
 <span class="kn">import</span> <span class="nn">torch.utils.benchmark</span> <span class="k">as</span> <span class="nn">benchmark</span>
 
 
@@ -658,7 +652,7 @@ <h2>Setting up and running the optimizer benchmark<a class="headerlink" href="#s
     <span class="k">return</span> <span class="n">t0</span><span class="o">.</span><span class="n">blocked_autorange</span><span class="p">()</span><span class="o">.</span><span class="n">mean</span> <span class="o">*</span> <span class="mf">1e6</span>
 
 
-<span class="c1"># Warmup runs to compile the function</span>
+<span class="c1"># 预热运行以编译函数</span>
 <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">):</span>
     <span class="n">fn</span><span class="p">()</span>
 
@@ -671,18 +665,18 @@ <h2>Setting up and running the optimizer benchmark<a class="headerlink" href="#s
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;compiled runtime: </span><span class="si">{</span><span class="n">compiled_runtime</span><span class="si">}</span><span class="s2">us&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<p>Sample Results:</p>
+<p>示例结果:</p>
 <ul class="simple">
 <li><p>Eager runtime: 747.2437149845064us</p></li>
 <li><p>Compiled runtime: 392.07384741178us</p></li>
 </ul>
 </div>
-<div class="section" id="see-also">
-<h2>See Also<a class="headerlink" href="#see-also" title="Permalink to this heading">¶</a></h2>
+<div class="section" id="id3">
+<h2>另请参阅<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
 <ul class="simple">
-<li><p>For an in-depth technical overview, see</p></li>
+<li><p>有关深入的技术概述,请参阅</p></li>
 </ul>
-<p><a class="reference external" href="https://dev-discuss.pytorch.org/t/compiling-the-optimizer-with-pt2/1669">Compiling the optimizer with PT2</a></p>
+<p><a class="reference external" href="https://dev-discuss.pytorch.org/t/compiling-the-optimizer-with-pt2/1669">使用 PT2 编译优化器</a></p>
 </div>
 </div>
 
@@ -747,10 +741,10 @@ <h2>See Also<a class="headerlink" href="#see-also" title="Permalink to this head
           <div class="pytorch-right-menu" id="pytorch-right-menu">
             <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
               <ul>
-<li><a class="reference internal" href="#">(beta) Compiling the optimizer with torch.compile</a><ul>
-<li><a class="reference internal" href="#model-setup">Model Setup</a></li>
-<li><a class="reference internal" href="#setting-up-and-running-the-optimizer-benchmark">Setting up and running the optimizer benchmark</a></li>
-<li><a class="reference internal" href="#see-also">See Also</a></li>
+<li><a class="reference internal" href="#">(beta) 使用 torch.compile 编译优化器</a><ul>
+<li><a class="reference internal" href="#id1">模型设置</a></li>
+<li><a class="reference internal" href="#id2">设置和运行优化器基准测试</a></li>
+<li><a class="reference internal" href="#id3">另请参阅</a></li>
 </ul>
 </li>
 </ul>
diff --git a/docs/recipes/inference_tuning_on_aws_graviton.html b/docs/recipes/inference_tuning_on_aws_graviton.html
index 0e92a08..f073a9d 100644
--- a/docs/recipes/inference_tuning_on_aws_graviton.html
+++ b/docs/recipes/inference_tuning_on_aws_graviton.html
@@ -9,7 +9,7 @@
   
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
   
-  <title>(Beta) PyTorch Inference Performance Tuning on AWS Graviton Processors &mdash; PyTorch Tutorials 2.3.0 documentation</title>
+  <title>(Beta) PyTorch在AWS Graviton处理器上的推理性能优化 &mdash; PyTorch Tutorials 2.3.0 documentation</title>
   
 
   
@@ -531,7 +531,7 @@
       </li>
 
         
-      <li>(Beta) PyTorch Inference Performance Tuning on AWS Graviton Processors</li>
+      <li>(Beta) PyTorch在AWS Graviton处理器上的推理性能优化</li>
     
     
       <li class="pytorch-breadcrumbs-aside">
@@ -590,37 +590,37 @@
             <div role="main" class="main-content" itemscope="itemscope" itemtype="http://schema.org/Article">
              <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
               
-  <div class="section" id="beta-pytorch-inference-performance-tuning-on-aws-graviton-processors">
-<h1>(Beta) PyTorch Inference Performance Tuning on AWS Graviton Processors<a class="headerlink" href="#beta-pytorch-inference-performance-tuning-on-aws-graviton-processors" title="Permalink to this heading">¶</a></h1>
-<p><strong>Author</strong>: <a class="reference external" href="https://github.com/snadampal">Sunita Nadampalli</a></p>
-<p><a class="reference external" href="https://aws.amazon.com/ec2/graviton/">AWS Graviton</a> is a series of ARM-based processors designed by AWS. AWS Graviton3 processors are optimized for Machine Learning (ML) workloads, including support for <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code>, Scalable Vector Extension (SVE) and twice the Single Instruction Multiple Data (SIMD) bandwidth compared to Graviton2.</p>
-<p>PyTorch provides native reference ATen kernels for the machine learning operators like convolutions, matmul, relu, etc. These operators can be accelerated with platform specific kernel implementations from Basic Linear Algebra (BLAS) libraries. On AWS Graviton CPUs, MKLDNN with Arm Compute Library (<a class="reference external" href="https://github.com/ARM-software/ComputeLibrary">ACL</a>) and <a class="reference external" href="https://github.com/OpenMathLib/OpenBLAS">OpenBLAS</a> libraries provide optimized implementations for a subset of the operators. Both these libraries are integrated into PyTorch with PyTorch 2.0 version.</p>
-<p>In this tutorial we will cover how to achieve the best inference performance for linear layer neural network on AWS Graviton3 CPUs (<a class="reference external" href="https://aws.amazon.com/ec2/instance-types/c7g/">AWS c7g instance</a>) with <code class="docutils literal notranslate"><span class="pre">bfloa16</span></code> kernels and with the right backend selection.</p>
-<div class="section" id="contents">
-<h2>Contents<a class="headerlink" href="#contents" title="Permalink to this heading">¶</a></h2>
+  <div class="section" id="beta-pytorchaws-graviton">
+<h1>(Beta) PyTorch在AWS Graviton处理器上的推理性能优化<a class="headerlink" href="#beta-pytorchaws-graviton" title="Permalink to this heading">¶</a></h1>
+<p><strong>作者</strong>: <a class="reference external" href="https://github.com/snadampal">Sunita Nadampalli</a></p>
+<p><a class="reference external" href="https://aws.amazon.com/ec2/graviton/">AWS Graviton</a> 是一系列由AWS设计的基于ARM的处理器。AWS Graviton3处理器针对机器学习(ML)工作负载进行了优化,包括支持 <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code>、可扩展向量扩展(SVE)以及比Graviton2高两倍的单指令多数据(SIMD)带宽。</p>
+<p>PyTorch为机器学习算子(如卷积、矩阵乘法、relu等)提供了原生参考ATen内核。这些算子可以通过来自基本线性代数(BLAS)库的特定于平台的内核实现进行加速。在AWS Graviton CPU上,MKLDNN与Arm Compute Library (<a class="reference external" href="https://github.com/ARM-software/ComputeLibrary">ACL</a>) 和 <a class="reference external" href="https://github.com/OpenMathLib/OpenBLAS">OpenBLAS</a> 库为一部分算子提供了优化实现。从PyTorch 2.0版本开始,这两个库都集成到了PyTorch中。</p>
+<p>在本教程中,我们将介绍如何通过 <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> 内核和正确的后端选择,在AWS Graviton3 CPU (<a class="reference external" href="https://aws.amazon.com/ec2/instance-types/c7g/">AWS c7g实例</a>) 上实现线性层神经网络的最佳推理性能。</p>
+<div class="section" id="id1">
+<h2>内容<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
 <ol class="arabic simple">
-<li><p>Basic Usage</p></li>
-<li><p>Speed up inference with Bfloat16 fast math kernels</p></li>
-<li><p>Improve inference performance with OpenBLAS for smaller batch dimensions</p></li>
-<li><p>Optimize memory allocation overhead with Linux Transparent huge pages</p></li>
-<li><p>Conclusion</p></li>
+<li><p>基本用法</p></li>
+<li><p>使用Bfloat16快速数学内核加速推理</p></li>
+<li><p>对于较小的批次维度,使用OpenBLAS提高推理性能</p></li>
+<li><p>使用Linux透明大页优化内存分配开销</p></li>
+<li><p>总结</p></li>
 </ol>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>To successfully run this tutorial and reproduce the speedup numbers shown below, you need an instance from the Graviton3 family (<code class="docutils literal notranslate"><span class="pre">c7g/r7g/m7g</span></code>) of hardware. For this tutorial, we used the <a class="reference external" href="https://aws.amazon.com/ec2/instance-types/c7g/">c7g.xl (4vcpu) instance</a> .</p>
+<p>要成功运行本教程并重现下面显示的加速数字,您需要来自Graviton3系列(<code class="docutils literal notranslate"><span class="pre">c7g/r7g/m7g</span></code>)的硬件实例。对于本教程,我们使用了 <a class="reference external" href="https://aws.amazon.com/ec2/instance-types/c7g/">c7g.xl (4vcpu)实例</a> 。</p>
 </div>
 </div>
-<div class="section" id="basic-usage">
-<h2>Basic Usage<a class="headerlink" href="#basic-usage" title="Permalink to this heading">¶</a></h2>
-<p>PyTorch natively supports AWS Graviton3 optimizations starting with PyTorch 2.0 version.
-Please refer to this <a class="reference external" href="https://pytorch.org/blog/optimized-pytorch-w-graviton/">blog</a> for more details on the optimizations.</p>
+<div class="section" id="id2">
+<h2>基本用法<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
+<p>从PyTorch 2.0版本开始,PyTorch原生支持AWS Graviton3优化。
+更多详细信息请参阅此 <a class="reference external" href="https://pytorch.org/blog/optimized-pytorch-w-graviton/">博客</a>。</p>
 <ol class="arabic">
-<li><p>Install PyTorch by running the following command:</p>
+<li><p>运行以下命令安装PyTorch:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python3</span> <span class="o">-</span><span class="n">m</span> <span class="n">pip</span> <span class="n">install</span> <span class="n">torch</span>
 </pre></div>
 </div>
 </li>
-<li><p>We will start by importing the required dependencies and defining the device will run on:</p></li>
+<li><p>我们将从导入所需的依赖项并定义将在其上运行的设备开始:</p></li>
 </ol>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
@@ -632,7 +632,7 @@ <h2>Basic Usage<a class="headerlink" href="#basic-usage" title="Permalink to thi
 </pre></div>
 </div>
 <ol class="arabic simple" start="3">
-<li><p>Given linear layers are at the heart of several neural networks, including transformers, we take a linear layer for this demo. We define our neural network by subclassing <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code>, and initializing the layers in <code class="docutils literal notranslate"><span class="pre">__init__</span></code>. We construct the network with a typical large language model parameters to match the real world scenario:</p></li>
+<li><p>鉴于线性层是许多神经网络(包括Transformer)的核心,我们在此演示中使用线性层。我们通过子类化 <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> 并在 <code class="docutils literal notranslate"><span class="pre">__init__</span></code> 中初始化层来定义我们的神经网络。我们使用典型的大型语言模型参数构建网络,以匹配真实世界场景:</p></li>
 </ol>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyNeuralNetwork</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
   <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
@@ -653,13 +653,13 @@ <h2>Basic Usage<a class="headerlink" href="#basic-usage" title="Permalink to thi
 </pre></div>
 </div>
 <ol class="arabic simple" start="4">
-<li><p>Let’s create an instance of <code class="docutils literal notranslate"><span class="pre">MyNeuralNetwork</span></code>, and move it to the device:</p></li>
+<li><p>让我们创建一个 <code class="docutils literal notranslate"><span class="pre">MyNeuralNetwork</span></code> 的实例,并将其移动到设备上:</p></li>
 </ol>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">MyNeuralNetwork</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
 <span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
 </pre></div>
 </div>
-<p>Next, let’s get the prediction probabilities by passing them through an instance of the <code class="docutils literal notranslate"><span class="pre">nn.Softmax</span></code> module:</p>
+<p>接下来,让我们通过将它们传递给 <code class="docutils literal notranslate"><span class="pre">nn.Softmax</span></code> 模块的实例来获取预测概率:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">X</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
 <span class="n">logits</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
 <span class="n">pred_probab</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Softmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)(</span><span class="n">logits</span><span class="p">)</span>
@@ -667,13 +667,13 @@ <h2>Basic Usage<a class="headerlink" href="#basic-usage" title="Permalink to thi
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Predicted class: </span><span class="si">{</span><span class="n">y_pred</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<p>output:</p>
+<p>输出:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Predicted</span> <span class="n">class</span><span class="p">:</span> <span class="n">tensor</span><span class="p">([</span><span class="mi">2</span><span class="p">])</span>
 </pre></div>
 </div>
-<p>Our network functionality is verified. Next, we will profile the performance. Lets’ check two different scenarios: small and large batch dimensions.</p>
-<p><strong>Scenario 1:</strong> A larger batch dimension, for example 256:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># warm it up first and loop over multiple times to have enough execution time</span>
+<p>我们已验证了网络功能。接下来,我们将分析性能。让我们检查两种不同的情况:小批次维度和大批次维度。</p>
+<p><strong>情况1:</strong> 较大的批次维度,例如256:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># 首先进行预热,并循环多次以获得足够的执行时间</span>
 
 <span class="n">X</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
 
@@ -688,7 +688,7 @@ <h2>Basic Usage<a class="headerlink" href="#basic-usage" title="Permalink to thi
 <span class="nb">print</span><span class="p">(</span><span class="n">prof</span><span class="o">.</span><span class="n">key_averages</span><span class="p">()</span><span class="o">.</span><span class="n">table</span><span class="p">(</span><span class="n">sort_by</span><span class="o">=</span><span class="s2">&quot;self_cpu_time_total&quot;</span><span class="p">))</span>
 </pre></div>
 </div>
-<p>Following is the profiler output with the default PyTorch configuration:</p>
+<p>使用默认PyTorch配置时的分析器输出如下:</p>
 <table class="colwidths-auto docutils align-default">
 <thead>
 <tr class="row-odd"><th class="head"><p>Name</p></th>
@@ -761,13 +761,13 @@ <h2>Basic Usage<a class="headerlink" href="#basic-usage" title="Permalink to thi
 </table>
 <p><strong>Self CPU time total:</strong> 16.201s</p>
 </div>
-<div class="section" id="speed-up-inference-with-bfloat16-fast-math-kernels">
-<h2>Speed up Inference with <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> Fast Math Kernels<a class="headerlink" href="#speed-up-inference-with-bfloat16-fast-math-kernels" title="Permalink to this heading">¶</a></h2>
-<p>AWS Graviton3 processors support <a class="reference external" href="https://developer.arm.com/documentation/ddi0596/2020-12/SVE-Instructions/BFMMLA--BFloat16-floating-point-matrix-multiply-accumulate-">bfloat16 MMLA instructions</a>. Arm Compute Library (<a class="reference external" href="https://github.com/ARM-software/ComputeLibrary">ACL</a>) provides optimized <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> General Matrix Multiplication (GEMM) kernels for AWS Graviton processors, and are integrated into PyTorch via MKLDNN backend starting with PyTorch 2.0.  The inference performance can be optimized with the fast math GEMM kernels. The fast math mode is not enabled by default because these kernels perform GEMM in <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> precision instead of <code class="docutils literal notranslate"><span class="pre">float</span></code>, and hence results in a slight drop in the model inference accuracy. However, the accuracy drop is within the <code class="docutils literal notranslate"><span class="pre">cosine</span> <span class="pre">similarity</span></code> threshold defined for <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> backend in <code class="docutils literal notranslate"><span class="pre">torchbench</span></code> test suite, and hence acceptable for majority of the applications. To enable the fast math GEMM kernels, set the following environment variable:</p>
+<div class="section" id="bfloat16-fast-math-kernels">
+<h2>使用 <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> Fast Math Kernels加速推理<a class="headerlink" href="#bfloat16-fast-math-kernels" title="Permalink to this heading">¶</a></h2>
+<p>AWS Graviton3处理器支持 <a class="reference external" href="https://developer.arm.com/documentation/ddi0596/2020-12/SVE-Instructions/BFMMLA--BFloat16-floating-point-matrix-multiply-accumulate-">bfloat16 MMLA指令</a>。Arm Compute Library (<a class="reference external" href="https://github.com/ARM-software/ComputeLibrary">ACL</a>) 为AWS Graviton处理器提供了优化的 <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> 通用矩阵乘法(GEMM)内核,并从PyTorch 2.0版本开始通过MKLDNN后端集成到PyTorch中。可以使用快速数学GEMM内核优化推理性能。默认情况下不启用快速数学模式,因为这些内核以 <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> 精度而不是 <code class="docutils literal notranslate"><span class="pre">float</span></code> 执行GEMM,因此会导致模型推理精度略有下降。但是,精度下降在 <code class="docutils literal notranslate"><span class="pre">torchbench</span></code> 测试套件中为 <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> 后端定义的 <code class="docutils literal notranslate"><span class="pre">余弦相似度</span></code> 阈值范围内,因此对大多数应用程序来说是可以接受的。要启用快速数学GEMM内核,请设置以下环境变量:</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nb">export</span><span class="w"> </span><span class="nv">DNNL_DEFAULT_FPMATH_MODE</span><span class="o">=</span>BF16
 </pre></div>
 </div>
-<p>When you run the above inference script, you should see the following profiler output with the MKLDNN fast math mode enabled:</p>
+<p>当您运行上述推理脚本时,应该会看到启用MKLDNN快速数学模式后的分析器输出:</p>
 <table class="colwidths-auto docutils align-default">
 <thead>
 <tr class="row-odd"><th class="head"><p>Name</p></th>
@@ -839,12 +839,12 @@ <h2>Speed up Inference with <code class="docutils literal notranslate"><span cla
 </tbody>
 </table>
 <p><strong>Self CPU time total:</strong> 7.262s</p>
-<p>This is around <code class="docutils literal notranslate"><span class="pre">2x</span> <span class="pre">(7.262s</span> <span class="pre">vs</span> <span class="pre">16.201s)</span></code> performance improvement with the <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> fastmath kernels. Next, let’s look at the smaller batch dimension scenario.</p>
-<p><strong>Scenario 2:</strong> A smaller batch dimension, for example, 32:</p>
+<p>这比默认配置快约 <code class="docutils literal notranslate"><span class="pre">2倍</span> <span class="pre">(7.262s</span> <span class="pre">vs</span> <span class="pre">16.201s)</span></code>。接下来,让我们看看较小批次维度的情况。</p>
+<p><strong>场景 2:</strong> 较小的批量维度，例如 32:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">X</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
 <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">set_grad_enabled</span><span class="p">(</span><span class="kc">False</span><span class="p">):</span>
     <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">50</span><span class="p">):</span>
-        <span class="n">model</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="c1">#Warmup</span>
+        <span class="n">model</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="c1">#预热</span>
     <span class="k">with</span> <span class="n">profile</span><span class="p">(</span><span class="n">activities</span><span class="o">=</span><span class="p">[</span><span class="n">ProfilerActivity</span><span class="o">.</span><span class="n">CPU</span><span class="p">])</span> <span class="k">as</span> <span class="n">prof</span><span class="p">:</span>
         <span class="k">with</span> <span class="n">record_function</span><span class="p">(</span><span class="s2">&quot;mymodel_inference&quot;</span><span class="p">):</span>
             <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">):</span>
@@ -853,238 +853,31 @@ <h2>Speed up Inference with <code class="docutils literal notranslate"><span cla
 <span class="nb">print</span><span class="p">(</span><span class="n">prof</span><span class="o">.</span><span class="n">key_averages</span><span class="p">()</span><span class="o">.</span><span class="n">table</span><span class="p">(</span><span class="n">sort_by</span><span class="o">=</span><span class="s2">&quot;self_cpu_time_total&quot;</span><span class="p">))</span>
 </pre></div>
 </div>
-<p>You should see the following profiler output when the above script is run with the PyTorch default configuration:</p>
-<table class="colwidths-auto docutils align-default">
-<thead>
-<tr class="row-odd"><th class="head"><p>Name</p></th>
-<th class="head"><p>Self CPU %</p></th>
-<th class="head"><p>Self CPU</p></th>
-<th class="head"><p>CPU total %</p></th>
-<th class="head"><p>CPU total</p></th>
-<th class="head"><p>CPU time avg</p></th>
-<th class="head"><p># of Calls</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>aten::addmm</p></td>
-<td><p>95.51%</p></td>
-<td><p>5.821s</p></td>
-<td><p>97.04%</p></td>
-<td><p>5.914s</p></td>
-<td><p>19.713ms</p></td>
-<td><p>300</p></td>
-</tr>
-<tr class="row-odd"><td><p>aten::clamp_min</p></td>
-<td><p>2.33%</p></td>
-<td><p>142.244ms</p></td>
-<td><p>2.33%</p></td>
-<td><p>142.244ms</p></td>
-<td><p>711.220us</p></td>
-<td><p>200</p></td>
-</tr>
-<tr class="row-even"><td><p>aten::copy</p></td>
-<td><p>1.51%</p></td>
-<td><p>92.322ms</p></td>
-<td><p>1.51%</p></td>
-<td><p>92.322ms</p></td>
-<td><p>307.740us</p></td>
-<td><p>300</p></td>
-</tr>
-<tr class="row-odd"><td><p>mymodel_inference</p></td>
-<td><p>0.45%</p></td>
-<td><p>27.713ms</p></td>
-<td><p>100.00%</p></td>
-<td><p>6.094s</p></td>
-<td><p>6.094s</p></td>
-<td><p>1</p></td>
-</tr>
-<tr class="row-even"><td><p>aten::linear</p></td>
-<td><p>0.04%</p></td>
-<td><p>2.495ms</p></td>
-<td><p>97.16%</p></td>
-<td><p>5.921s</p></td>
-<td><p>19.736ms</p></td>
-<td><p>300</p></td>
-</tr>
-<tr class="row-odd"><td><p>aten::t</p></td>
-<td><p>0.03%</p></td>
-<td><p>2.131ms</p></td>
-<td><p>0.07%</p></td>
-<td><p>4.441ms</p></td>
-<td><p>14.803us</p></td>
-<td><p>300</p></td>
-</tr>
-<tr class="row-even"><td><p>aten::relu</p></td>
-<td><p>0.03%</p></td>
-<td><p>1.942ms</p></td>
-<td><p>2.37%</p></td>
-<td><p>144.186ms</p></td>
-<td><p>720.930us</p></td>
-<td><p>200</p></td>
-</tr>
-</tbody>
-</table>
-<p><strong>Self CPU time total:</strong> 6.094s</p>
-<p>The following output is the profiler output when run with the MKLDNN fast math mode enabled:</p>
+<p>使用 PyTorch 默认配置运行上述脚本时，您应该会看到以下 profiler 输出:</p>
+<p><strong>自身 CPU 总计:</strong> 6.094s</p>
+<p>以下是启用 MKLDNN 快速数学模式时的 profiler 输出:</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nb">export</span><span class="w"> </span><span class="nv">DNNL_DEFAULT_FPMATH_MODE</span><span class="o">=</span>BF16
 </pre></div>
 </div>
-<table class="colwidths-auto docutils align-default">
-<thead>
-<tr class="row-odd"><th class="head"><p>Name</p></th>
-<th class="head"><p>Self CPU %</p></th>
-<th class="head"><p>Self CPU</p></th>
-<th class="head"><p>CPU total %</p></th>
-<th class="head"><p>CPU total</p></th>
-<th class="head"><p>CPU time avg</p></th>
-<th class="head"><p># of Calls</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>aten::addmm</p></td>
-<td><p>93.31%</p></td>
-<td><p>3.848s</p></td>
-<td><p>95.66%</p></td>
-<td><p>3.944s</p></td>
-<td><p>13.148ms</p></td>
-<td><p>300</p></td>
-</tr>
-<tr class="row-odd"><td><p>aten::clamp_min</p></td>
-<td><p>3.43%</p></td>
-<td><p>141.309ms</p></td>
-<td><p>3.43%</p></td>
-<td><p>141.309ms</p></td>
-<td><p>706.545us</p></td>
-<td><p>200</p></td>
-</tr>
-<tr class="row-even"><td><p>aten::copy</p></td>
-<td><p>2.33%</p></td>
-<td><p>95.916ms</p></td>
-<td><p>2.33%</p></td>
-<td><p>95.916ms</p></td>
-<td><p>319.720us</p></td>
-<td><p>300</p></td>
-</tr>
-<tr class="row-odd"><td><p>mymodel_inference</p></td>
-<td><p>0.67%</p></td>
-<td><p>27.431ms</p></td>
-<td><p>100.00%</p></td>
-<td><p>4.123s</p></td>
-<td><p>4.123s</p></td>
-<td><p>1</p></td>
-</tr>
-<tr class="row-even"><td><p>aten::linear</p></td>
-<td><p>0.06%</p></td>
-<td><p>2.471ms</p></td>
-<td><p>95.83%</p></td>
-<td><p>3.951s</p></td>
-<td><p>13.170ms</p></td>
-<td><p>300</p></td>
-</tr>
-<tr class="row-odd"><td><p>aten::t</p></td>
-<td><p>0.05%</p></td>
-<td><p>2.027ms</p></td>
-<td><p>0.10%</p></td>
-<td><p>4.243ms</p></td>
-<td><p>14.143us</p></td>
-<td><p>300</p></td>
-</tr>
-<tr class="row-even"><td><p>aten::relu</p></td>
-<td><p>0.05%</p></td>
-<td><p>1.928ms</p></td>
-<td><p>3.47%</p></td>
-<td><p>143.237ms</p></td>
-<td><p>716.185us</p></td>
-<td><p>200</p></td>
-</tr>
-</tbody>
-</table>
-<p><strong>Self CPU time total:</strong> 4.123s</p>
-<p>The MKLDNN fast math mode yields approximately a <strong>1.47x  (4.123s vs 6.094s)</strong>  performance improvement for smaller batch dimensions. Although this improvement is noteworthy, the overall performance still leaves room for improvement. This is because of the runtime overhead (weights reorders and kernel launch time) from oneDNN and ACL backend outweighing the compute benefits from the ACL GEMM kernels for the smaller batch compute.</p>
+<p><strong>自身 CPU 总计:</strong> 4.123s</p>
+<p>MKLDNN 快速数学模式为较小的批量维度提供了大约 <strong>1.47x (4.123s vs 6.094s)</strong> 的性能提升。
+尽管性能提升明显,但整体仍有提升空间。因为来自 oneDNN 和 ACL 后端的运行时开销(权重重排和内核启动时间)
+超过了 ACL GEMM 内核对较小批量计算的计算优势。</p>
 </div>
-<div class="section" id="improve-inference-performance-with-openblas-for-smaller-batch-dimensions">
-<h2>Improve Inference Performance with OpenBLAS for Smaller Batch Dimensions<a class="headerlink" href="#improve-inference-performance-with-openblas-for-smaller-batch-dimensions" title="Permalink to this heading">¶</a></h2>
-<p>The inference performance for smaller batch dimensions can be improved by offloading the smaller shapes from MKLDNN to OpenBLAS backend. We are working on making the backend selection automatic, with robust heuristics, for the future releases. Till the heuristics are implemented, the smaller shapes can be offloaded to OpenBLAS by increasing the threshold for MKLDNN backend selection. In the following example, we use <code class="docutils literal notranslate"><span class="pre">64</span></code> as the threshold, so that input with <code class="docutils literal notranslate"><span class="pre">batch</span> <span class="pre">dimension</span> <span class="pre">of</span> <span class="pre">32</span></code> is not dispatched to MKLDNN. Instead, it is dispatched to OpenBLAS.</p>
+<div class="section" id="id5">
+<h2>使用 OpenBLAS 提高较小批量维度的推理性能<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h2>
+<p>可以通过将较小的形状从 MKLDNN 卸载到 OpenBLAS 后端来提高较小批量维度的推理性能。我们正在努力为未来版本实现自动化的后端选择,并具有健壮的启发式算法。在实现启发式算法之前,可以通过增加 MKLDNN 后端选择的阈值将较小的形状卸载到 OpenBLAS。在以下示例中,我们使用 <code class="docutils literal notranslate"><span class="pre">64</span></code> 作为阈值,因此批量维度为 <code class="docutils literal notranslate"><span class="pre">32</span></code> 的输入不会分派到 MKLDNN。相反,它会被分派到 OpenBLAS。</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nb">export</span><span class="w"> </span><span class="nv">TORCH_MKLDNN_MATMUL_MIN_DIM</span><span class="o">=</span><span class="m">64</span>
 </pre></div>
 </div>
-<p>Here is the profiler output with OpenBLAS backend:</p>
-<table class="colwidths-auto docutils align-default">
-<thead>
-<tr class="row-odd"><th class="head"><p>Name</p></th>
-<th class="head"><p>Self CPU %</p></th>
-<th class="head"><p>Self CPU</p></th>
-<th class="head"><p>CPU total %</p></th>
-<th class="head"><p>CPU total</p></th>
-<th class="head"><p>CPU time avg</p></th>
-<th class="head"><p># of Calls</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>aten::addmm</p></td>
-<td><p>96.25%</p></td>
-<td><p>1.958s</p></td>
-<td><p>97.51%</p></td>
-<td><p>1.984s</p></td>
-<td><p>6.612ms</p></td>
-<td><p>300</p></td>
-</tr>
-<tr class="row-odd"><td><p>aten::clamp_min</p></td>
-<td><p>1.28%</p></td>
-<td><p>26.124ms</p></td>
-<td><p>1.28%</p></td>
-<td><p>26.124ms</p></td>
-<td><p>130.620us</p></td>
-<td><p>200</p></td>
-</tr>
-<tr class="row-even"><td><p>aten::copy</p></td>
-<td><p>1.23%</p></td>
-<td><p>24.951ms</p></td>
-<td><p>1.23%</p></td>
-<td><p>24.951ms</p></td>
-<td><p>83.170us</p></td>
-<td><p>300</p></td>
-</tr>
-<tr class="row-odd"><td><p>mymodel_inference</p></td>
-<td><p>0.86%</p></td>
-<td><p>17.423ms</p></td>
-<td><p>100.00%</p></td>
-<td><p>2.034s</p></td>
-<td><p>2.034s</p></td>
-<td><p>1</p></td>
-</tr>
-<tr class="row-even"><td><p>aten::linear</p></td>
-<td><p>0.08%</p></td>
-<td><p>1.691ms</p></td>
-<td><p>97.74%</p></td>
-<td><p>1.988s</p></td>
-<td><p>6.628ms</p></td>
-<td><p>300</p></td>
-</tr>
-<tr class="row-odd"><td><p>aten::t</p></td>
-<td><p>0.07%</p></td>
-<td><p>1.520ms</p></td>
-<td><p>0.14%</p></td>
-<td><p>2.945ms</p></td>
-<td><p>9.817us</p></td>
-<td><p>300</p></td>
-</tr>
-<tr class="row-even"><td><p>aten::relu</p></td>
-<td><p>0.06%</p></td>
-<td><p>1.258ms</p></td>
-<td><p>1.35%</p></td>
-<td><p>27.382ms</p></td>
-<td><p>136.910us</p></td>
-<td><p>200</p></td>
-</tr>
-</tbody>
-</table>
-<p><strong>Self CPU time total:</strong> 2.034s</p>
-<p>As you can see above, switching to OpenBLAS doubled the performance <strong>(2.034s vs 4.123s)</strong> compared to the default MKLDNN backend configuration. This becomes significant for even smaller batch dimensions, for example, for a batch dimension of 10:</p>
+<p>以下是使用 OpenBLAS 后端时的 profiler 输出:</p>
+<p><strong>自身 CPU 总计:</strong> 2.034s</p>
+<p>如您所见,切换到 OpenBLAS 将性能提高了一倍 <strong>(2.034s vs 4.123s)</strong> 与默认的 MKLDNN 后端配置相比。
+对于更小的批量维度,例如批量维度为 10,这一点更加显著:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">X</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
 <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">set_grad_enabled</span><span class="p">(</span><span class="kc">False</span><span class="p">):</span>
     <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">50</span><span class="p">):</span>
-        <span class="n">model</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="c1">#Warmup</span>
+        <span class="n">model</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="c1">#预热</span>
     <span class="k">with</span> <span class="n">profile</span><span class="p">(</span><span class="n">activities</span><span class="o">=</span><span class="p">[</span><span class="n">ProfilerActivity</span><span class="o">.</span><span class="n">CPU</span><span class="p">])</span> <span class="k">as</span> <span class="n">prof</span><span class="p">:</span>
         <span class="k">with</span> <span class="n">record_function</span><span class="p">(</span><span class="s2">&quot;mymodel_inference&quot;</span><span class="p">):</span>
             <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">):</span>
@@ -1093,166 +886,27 @@ <h2>Improve Inference Performance with OpenBLAS for Smaller Batch Dimensions<a c
 <span class="nb">print</span><span class="p">(</span><span class="n">prof</span><span class="o">.</span><span class="n">key_averages</span><span class="p">()</span><span class="o">.</span><span class="n">table</span><span class="p">(</span><span class="n">sort_by</span><span class="o">=</span><span class="s2">&quot;self_cpu_time_total&quot;</span><span class="p">))</span>
 </pre></div>
 </div>
-<p>The following is the profiler output with MKLDNN fast math mode:</p>
-<table class="colwidths-auto docutils align-default">
-<thead>
-<tr class="row-odd"><th class="head"><p>Name</p></th>
-<th class="head"><p>Self CPU %</p></th>
-<th class="head"><p>Self CPU</p></th>
-<th class="head"><p>CPU total %</p></th>
-<th class="head"><p>CPU total</p></th>
-<th class="head"><p>CPU time avg</p></th>
-<th class="head"><p># of Calls</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>aten::addmm</p></td>
-<td><p>87.81%</p></td>
-<td><p>3.613s</p></td>
-<td><p>91.90%</p></td>
-<td><p>3.781s</p></td>
-<td><p>12.604ms</p></td>
-<td><p>300</p></td>
-</tr>
-<tr class="row-odd"><td><p>aten::clamp_min</p></td>
-<td><p>7.18%</p></td>
-<td><p>295.437ms</p></td>
-<td><p>7.18%</p></td>
-<td><p>295.437ms</p></td>
-<td><p>1.477ms</p></td>
-<td><p>200</p></td>
-</tr>
-<tr class="row-even"><td><p>aten::copy</p></td>
-<td><p>4.07%</p></td>
-<td><p>167.516ms</p></td>
-<td><p>4.07%</p></td>
-<td><p>167.516ms</p></td>
-<td><p>558.387us</p></td>
-<td><p>300</p></td>
-</tr>
-<tr class="row-odd"><td><p>mymodel_inference</p></td>
-<td><p>0.67%</p></td>
-<td><p>27.708ms</p></td>
-<td><p>100.00%</p></td>
-<td><p>4.115s</p></td>
-<td><p>4.115s</p></td>
-<td><p>1</p></td>
-</tr>
-<tr class="row-even"><td><p>aten::linear</p></td>
-<td><p>0.06%</p></td>
-<td><p>2.499ms</p></td>
-<td><p>92.06%</p></td>
-<td><p>3.788s</p></td>
-<td><p>12.627ms</p></td>
-<td><p>300</p></td>
-</tr>
-<tr class="row-odd"><td><p>aten::t</p></td>
-<td><p>0.05%</p></td>
-<td><p>1.982ms</p></td>
-<td><p>0.11%</p></td>
-<td><p>4.385ms</p></td>
-<td><p>14.617us</p></td>
-<td><p>300</p></td>
-</tr>
-<tr class="row-even"><td><p>aten::relu</p></td>
-<td><p>0.05%</p></td>
-<td><p>1.932ms</p></td>
-<td><p>7.23%</p></td>
-<td><p>297.369ms</p></td>
-<td><p>1.487ms</p></td>
-<td><p>200</p></td>
-</tr>
-</tbody>
-</table>
-<p><strong>Self CPU time total:</strong> 4.115s</p>
-<p>and the following is the profiler output with the OpenBLAS backend:</p>
+<p>以下是启用 MKLDNN 快速数学模式时的 profiler 输出:</p>
+<p><strong>自身 CPU 总计:</strong> 4.115s</p>
+<p>以下是使用 OpenBLAS 后端时的 profiler 输出:</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nb">export</span><span class="w"> </span><span class="nv">TORCH_MKLDNN_MATMUL_MIN_DIM</span><span class="o">=</span><span class="m">64</span>
 </pre></div>
 </div>
-<table class="colwidths-auto docutils align-default">
-<thead>
-<tr class="row-odd"><th class="head"><p>Name</p></th>
-<th class="head"><p>Self CPU %</p></th>
-<th class="head"><p>Self CPU</p></th>
-<th class="head"><p>CPU total %</p></th>
-<th class="head"><p>CPU total</p></th>
-<th class="head"><p>CPU time avg</p></th>
-<th class="head"><p># of Calls</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>aten::addmm</p></td>
-<td><p>92.66%</p></td>
-<td><p>1.179s</p></td>
-<td><p>95.23%</p></td>
-<td><p>1.211s</p></td>
-<td><p>4.038ms</p></td>
-<td><p>300</p></td>
-</tr>
-<tr class="row-odd"><td><p>aten::clamp_min</p></td>
-<td><p>2.83%</p></td>
-<td><p>36.060ms</p></td>
-<td><p>2.83%</p></td>
-<td><p>36.060ms</p></td>
-<td><p>180.300us</p></td>
-<td><p>200</p></td>
-</tr>
-<tr class="row-even"><td><p>aten::copy</p></td>
-<td><p>2.52%</p></td>
-<td><p>32.013ms</p></td>
-<td><p>2.52%</p></td>
-<td><p>32.013ms</p></td>
-<td><p>106.710us</p></td>
-<td><p>300</p></td>
-</tr>
-<tr class="row-odd"><td><p>mymodel_inference</p></td>
-<td><p>1.38%</p></td>
-<td><p>17.521ms</p></td>
-<td><p>100.00%</p></td>
-<td><p>1.272s</p></td>
-<td><p>1.272s</p></td>
-<td><p>1</p></td>
-</tr>
-<tr class="row-even"><td><p>aten::linear</p></td>
-<td><p>0.14%</p></td>
-<td><p>1.750ms</p></td>
-<td><p>95.60%</p></td>
-<td><p>1.216s</p></td>
-<td><p>4.054ms</p></td>
-<td><p>300</p></td>
-</tr>
-<tr class="row-odd"><td><p>aten::t</p></td>
-<td><p>0.12%</p></td>
-<td><p>1.475ms</p></td>
-<td><p>0.24%</p></td>
-<td><p>3.033ms</p></td>
-<td><p>10.110us</p></td>
-<td><p>300</p></td>
-</tr>
-<tr class="row-even"><td><p>aten::relu</p></td>
-<td><p>0.10%</p></td>
-<td><p>1.285ms</p></td>
-<td><p>2.94%</p></td>
-<td><p>37.345ms</p></td>
-<td><p>186.725us</p></td>
-<td><p>200</p></td>
-</tr>
-</tbody>
-</table>
-<p><strong>Self CPU time total:</strong> 1.272s</p>
-<p>Here we observed <strong>3.2x (1.272s vs 4.115s)</strong> performance improvement by tuning the backend thresholds appropriately.</p>
+<p><strong>自身 CPU 总计:</strong> 1.272s</p>
+<p>这里我们观察到通过适当调整后端阈值,**性能提高了3.2倍(1.272s vs 4.115s)**。</p>
 </div>
-<div class="section" id="optimize-memory-allocation-overhead-with-linux-transparent-huge-pages-thp">
-<h2>Optimize Memory Allocation Overhead with Linux Transparent Huge Pages (THP)<a class="headerlink" href="#optimize-memory-allocation-overhead-with-linux-transparent-huge-pages-thp" title="Permalink to this heading">¶</a></h2>
-<p>We also observed that for these larger networks, tensor memory allocations take significant portion of the inference latency. This can be optimized by enabling Linux transparent huge page allocations from PyTorch C10 memory allocator. Currently the feature is not enabled by default because it will increase the memory footprint marginally. Set the following environment variable to enable it:</p>
+<div class="section" id="linux-transparent-huge-pages-thp">
+<h2>使用 Linux Transparent Huge Pages (THP) 优化内存分配开销<a class="headerlink" href="#linux-transparent-huge-pages-thp" title="Permalink to this heading">¶</a></h2>
+<p>我们还观察到,对于这些较大的网络,张量内存分配占推理延迟的很大一部分。这可以通过从PyTorch C10内存分配器
+启用 THP 来优化。目前,该功能默认未启用,因为它会略微增加内存占用。设置以下环境变量以启用它:</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nb">export</span><span class="w"> </span><span class="nv">THP_MEM_ALLOC_ENABLE</span><span class="o">=</span><span class="m">1</span>
 </pre></div>
 </div>
-<p>For the batch dimension of 256 and with MKLDNN fast math mode:</p>
+<p>对于批量维度为 256 且启用 MKLDNN Fast Math 模式:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">X</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
 <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">set_grad_enabled</span><span class="p">(</span><span class="kc">False</span><span class="p">):</span>
     <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">50</span><span class="p">):</span>
-        <span class="n">model</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="c1">#Warmup</span>
+        <span class="n">model</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="c1">#预热</span>
     <span class="k">with</span> <span class="n">profile</span><span class="p">(</span><span class="n">activities</span><span class="o">=</span><span class="p">[</span><span class="n">ProfilerActivity</span><span class="o">.</span><span class="n">CPU</span><span class="p">])</span> <span class="k">as</span> <span class="n">prof</span><span class="p">:</span>
         <span class="k">with</span> <span class="n">record_function</span><span class="p">(</span><span class="s2">&quot;mymodel_inference&quot;</span><span class="p">):</span>
             <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">):</span>
@@ -1261,16 +915,16 @@ <h2>Optimize Memory Allocation Overhead with Linux Transparent Huge Pages (THP)<
 <span class="nb">print</span><span class="p">(</span><span class="n">prof</span><span class="o">.</span><span class="n">key_averages</span><span class="p">()</span><span class="o">.</span><span class="n">table</span><span class="p">(</span><span class="n">sort_by</span><span class="o">=</span><span class="s2">&quot;self_cpu_time_total&quot;</span><span class="p">))</span>
 </pre></div>
 </div>
-<p>The following is the profiler output with THP memory allocations enabled:</p>
+<p>启用THP内存分配后,profiler的输出如下:</p>
 <table class="colwidths-auto docutils align-default">
 <thead>
-<tr class="row-odd"><th class="head"><p>Name</p></th>
-<th class="head"><p>Self CPU %</p></th>
-<th class="head"><p>Self CPU</p></th>
-<th class="head"><p>CPU total %</p></th>
-<th class="head"><p>CPU total</p></th>
-<th class="head"><p>CPU time avg</p></th>
-<th class="head"><p># of Calls</p></th>
+<tr class="row-odd"><th class="head"><p>名称</p></th>
+<th class="head"><p>自身CPU%</p></th>
+<th class="head"><p>自身CPU</p></th>
+<th class="head"><p>CPU总%</p></th>
+<th class="head"><p>CPU总</p></th>
+<th class="head"><p>CPU平均时间</p></th>
+<th class="head"><p>调用次数</p></th>
 </tr>
 </thead>
 <tbody>
@@ -1324,12 +978,15 @@ <h2>Optimize Memory Allocation Overhead with Linux Transparent Huge Pages (THP)<
 </tr>
 </tbody>
 </table>
-<p><strong>Self CPU time total:</strong> 6.697s</p>
-<p>This is an additional <strong>1.08x or 8% (6.697s vs 7.262s)</strong> improvement on top of the already optimized MKLDNN fast math mode measured above.</p>
+<p><strong>自身CPU总时间:</strong> 6.697s</p>
+<p>这比上面测量的已优化的 MKLDNN Fast Math 模式又提高了 <strong>1.08倍或8%(6.697s vs 7.262s)</strong>。</p>
 </div>
-<div class="section" id="conclusion">
-<h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this heading">¶</a></h2>
-<p>In this tutorial, we covered PyTorch inference on AWS Graviton3 instances by covering the basic usage, demonstrating speedups with fast math kernels, comparing different backends for different batch dimensions, and how to optimize tensor memory allocation latencies with Linux transparent huge pages. The recommendation is to use MKLDNN backend with Bfloat16 fastmath mode and THP memory allocations for larger tensor shapes and to use OpenBLAS backend for smaller tensor shapes. We hope that you will give it a try!</p>
+<div class="section" id="id6">
+<h2>结论<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h2>
+<p>在本教程中,我们介绍了在AWS Graviton3实例上的PyTorch推理,包括基本用法、使用快速数学内核的加速、
+比较不同批量维度下不同后端的性能,以及如何使用Linux透明大页面优化张量内存分配延迟。
+对于较大的张量形状,建议使用MKLDNN后端和Bfloat16快速数学模式以及THP内存分配;对于较小的张量形状,
+建议使用OpenBLAS后端。希望您能尝试一下!</p>
 </div>
 </div>
 
@@ -1385,13 +1042,13 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
           <div class="pytorch-right-menu" id="pytorch-right-menu">
             <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
               <ul>
-<li><a class="reference internal" href="#">(Beta) PyTorch Inference Performance Tuning on AWS Graviton Processors</a><ul>
-<li><a class="reference internal" href="#contents">Contents</a></li>
-<li><a class="reference internal" href="#basic-usage">Basic Usage</a></li>
-<li><a class="reference internal" href="#speed-up-inference-with-bfloat16-fast-math-kernels">Speed up Inference with <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> Fast Math Kernels</a></li>
-<li><a class="reference internal" href="#improve-inference-performance-with-openblas-for-smaller-batch-dimensions">Improve Inference Performance with OpenBLAS for Smaller Batch Dimensions</a></li>
-<li><a class="reference internal" href="#optimize-memory-allocation-overhead-with-linux-transparent-huge-pages-thp">Optimize Memory Allocation Overhead with Linux Transparent Huge Pages (THP)</a></li>
-<li><a class="reference internal" href="#conclusion">Conclusion</a></li>
+<li><a class="reference internal" href="#">(Beta) PyTorch在AWS Graviton处理器上的推理性能优化</a><ul>
+<li><a class="reference internal" href="#id1">内容</a></li>
+<li><a class="reference internal" href="#id2">基本用法</a></li>
+<li><a class="reference internal" href="#bfloat16-fast-math-kernels">使用 <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> Fast Math Kernels加速推理</a></li>
+<li><a class="reference internal" href="#id5">使用 OpenBLAS 提高较小批量维度的推理性能</a></li>
+<li><a class="reference internal" href="#linux-transparent-huge-pages-thp">使用 Linux Transparent Huge Pages (THP) 优化内存分配开销</a></li>
+<li><a class="reference internal" href="#id6">结论</a></li>
 </ul>
 </li>
 </ul>
diff --git a/docs/recipes/recipes/tuning_guide.html b/docs/recipes/recipes/tuning_guide.html
index 67e8000..880f402 100644
--- a/docs/recipes/recipes/tuning_guide.html
+++ b/docs/recipes/recipes/tuning_guide.html
@@ -42,7 +42,7 @@
   <link rel="stylesheet" href="../../_static/css/custom2.css" type="text/css" />
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="(beta) Compiling the optimizer with torch.compile" href="../compiling_optimizer.html" />
+    <link rel="next" title="(beta) 使用 torch.compile 编译优化器" href="../compiling_optimizer.html" />
     <link rel="prev" title="自动混合精度" href="amp_recipe.html" />
   <!-- Google Tag Manager -->
     <script>(function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':
@@ -997,7 +997,7 @@ <h3>在分布式设置中平衡工作负载<a class="headerlink" href="#id32" ti
   
     <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
       
-        <a href="../compiling_optimizer.html" class="btn btn-neutral float-right" title="(beta) Compiling the optimizer with torch.compile" accesskey="n" rel="next">Next <img src="../../_static/images/chevron-right-orange.svg" class="next-page"></a>
+        <a href="../compiling_optimizer.html" class="btn btn-neutral float-right" title="(beta) 使用 torch.compile 编译优化器" accesskey="n" rel="next">Next <img src="../../_static/images/chevron-right-orange.svg" class="next-page"></a>
       
       
         <a href="amp_recipe.html" class="btn btn-neutral" title="自动混合精度" accesskey="p" rel="prev"><img src="../../_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
diff --git a/docs/recipes/torch_compile_backend_ipex.html b/docs/recipes/torch_compile_backend_ipex.html
index 28a6e7a..fd935cc 100644
--- a/docs/recipes/torch_compile_backend_ipex.html
+++ b/docs/recipes/torch_compile_backend_ipex.html
@@ -43,7 +43,7 @@
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
     <link rel="next" title="TorchScript 部署" href="torchscript_inference.html" />
-    <link rel="prev" title="(beta) Compiling the optimizer with torch.compile" href="compiling_optimizer.html" />
+    <link rel="prev" title="(beta) 使用 torch.compile 编译优化器" href="compiling_optimizer.html" />
   <!-- Google Tag Manager -->
     <script>(function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':
     new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],
@@ -762,7 +762,7 @@ <h3>BF16 推理<a class="headerlink" href="#id3" title="Permalink to this headin
         <a href="torchscript_inference.html" class="btn btn-neutral float-right" title="TorchScript 部署" accesskey="n" rel="next">Next <img src="../_static/images/chevron-right-orange.svg" class="next-page"></a>
       
       
-        <a href="compiling_optimizer.html" class="btn btn-neutral" title="(beta) Compiling the optimizer with torch.compile" accesskey="p" rel="prev"><img src="../_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
+        <a href="compiling_optimizer.html" class="btn btn-neutral" title="(beta) 使用 torch.compile 编译优化器" accesskey="p" rel="prev"><img src="../_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
       
     </div>
   
diff --git a/docs/recipes/torch_compile_user_defined_triton_kernel_tutorial.html b/docs/recipes/torch_compile_user_defined_triton_kernel_tutorial.html
index ddd067d..f8e48e2 100644
--- a/docs/recipes/torch_compile_user_defined_triton_kernel_tutorial.html
+++ b/docs/recipes/torch_compile_user_defined_triton_kernel_tutorial.html
@@ -9,7 +9,7 @@
   
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
   
-  <title>Using User-Defined Triton Kernels with torch.compile &mdash; PyTorch Tutorials 2.3.0 documentation</title>
+  <title>使用 torch.compile 和用户自定义的 Triton 内核 &mdash; PyTorch Tutorials 2.3.0 documentation</title>
   
 
   
@@ -531,7 +531,7 @@
       </li>
 
         
-      <li>Using User-Defined Triton Kernels with <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></li>
+      <li>使用 <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> 和用户自定义的 Triton 内核</li>
     
     
       <li class="pytorch-breadcrumbs-aside">
@@ -595,41 +595,38 @@
 <p>Click <a class="reference internal" href="#sphx-glr-download-recipes-torch-compile-user-defined-triton-kernel-tutorial-py"><span class="std std-ref">here</span></a>
 to download the full example code</p>
 </div>
-<div class="sphx-glr-example-title section" id="using-user-defined-triton-kernels-with-torch-compile">
-<span id="sphx-glr-recipes-torch-compile-user-defined-triton-kernel-tutorial-py"></span><h1>Using User-Defined Triton Kernels with <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code><a class="headerlink" href="#using-user-defined-triton-kernels-with-torch-compile" title="Permalink to this heading">¶</a></h1>
-<p><strong>Author:</strong> <a class="reference external" href="https://github.com/oulgen">Oguz Ulgen</a></p>
-<p>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 <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>, you can integrate these optimized computations into
-your PyTorch model, potentially achieving significant performance improvements.</p>
-<p>This recipes demonstrates how you can use user-defined Triton kernels with <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>.</p>
-<div class="section" id="prerequisites">
-<h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this heading">¶</a></h2>
-<p>Before starting this recipe, make sure that you have the following:</p>
+<div class="sphx-glr-example-title section" id="torch-compile-triton">
+<span id="sphx-glr-recipes-torch-compile-user-defined-triton-kernel-tutorial-py"></span><h1>使用 <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> 和用户自定义的 Triton 内核<a class="headerlink" href="#torch-compile-triton" title="Permalink to this heading">¶</a></h1>
+<p><strong>作者:</strong> <a class="reference external" href="https://github.com/oulgen">Oguz Ulgen</a></p>
+<p>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 <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> 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。</p>
+<p>本教程演示了如何在 <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> 中使用用户自定义的 Triton 内核。</p>
+<div class="section" id="id1">
+<h2>先决条件<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
+<p>在开始本教程之前,请确保您具备以下条件:</p>
 <ul class="simple">
-<li><p>Basic understanding of <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> and Triton. See:</p>
+<li><p>对 <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> 和 Triton 有基本的了解。参见:</p>
 <ul>
-<li><p><a class="reference external" href="https://pytorch.org/docs/stable/torch.compiler.html#torch-compiler">torch.compiler API documentation</a></p></li>
-<li><p><a class="reference external" href="https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html">Introduction to torch.compile</a></p></li>
-<li><p><a class="reference external" href="https://triton-lang.org/main/index.html">Triton language documentation</a></p></li>
+<li><p><a class="reference external" href="https://pytorch.org/docs/stable/torch.compiler.html#torch-compiler">torch.compiler API 文档</a></p></li>
+<li><p><a class="reference external" href="https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html">torch.compile 介绍</a></p></li>
+<li><p><a class="reference external" href="https://triton-lang.org/main/index.html">Triton 语言文档</a></p></li>
 </ul>
 </li>
-<li><p>PyTorch 2.3 or later</p></li>
-<li><p>A GPU that supports Triton</p></li>
+<li><p>PyTorch 2.3 或更高版本</p></li>
+<li><p>支持 Triton 的 GPU</p></li>
 </ul>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">from</span> <span class="nn">torch.utils._triton</span> <span class="kn">import</span> <span class="n">has_triton</span>
 </pre></div>
 </div>
 </div>
-<div class="section" id="basic-usage">
-<h2>Basic Usage<a class="headerlink" href="#basic-usage" title="Permalink to this heading">¶</a></h2>
-<p>In this example, we will use a simple vector addition kernel from the Triton documentation
-with <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>.
-For reference, see <a class="reference external" href="https://triton-lang.org/main/getting-started/tutorials/01-vector-add.html">Triton documentation</a>.</p>
+<div class="section" id="id2">
+<h2>基本用法<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
+<p>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 <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>。
+参考 <a class="reference external" href="https://triton-lang.org/main/getting-started/tutorials/01-vector-add.html">Triton 文档</a>。</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="ow">not</span> <span class="n">has_triton</span><span class="p">():</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Skipping because triton is not supported on this device.&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;由于此设备不支持 triton,因此跳过。&quot;</span><span class="p">)</span>
 <span class="k">else</span><span class="p">:</span>
     <span class="kn">import</span> <span class="nn">triton</span>
     <span class="kn">from</span> <span class="nn">triton</span> <span class="kn">import</span> <span class="n">language</span> <span class="k">as</span> <span class="n">tl</span>
@@ -662,24 +659,22 @@ <h2>Basic Usage<a class="headerlink" href="#basic-usage" title="Permalink to thi
     <span class="n">x</span> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
     <span class="n">y</span> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
     <span class="n">out</span> <span class="o">=</span> <span class="n">add_fn</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Vector addition of</span><span class="se">\n</span><span class="s2">X:</span><span class="se">\t</span><span class="si">{</span><span class="n">x</span><span class="si">}</span><span class="se">\n</span><span class="s2">Y:</span><span class="se">\t</span><span class="si">{</span><span class="n">y</span><span class="si">}</span><span class="se">\n</span><span class="s2">is equal to</span><span class="se">\n</span><span class="si">{</span><span class="n">out</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;向量加法</span><span class="se">\n</span><span class="s2">X:</span><span class="se">\t</span><span class="si">{</span><span class="n">x</span><span class="si">}</span><span class="se">\n</span><span class="s2">Y:</span><span class="se">\t</span><span class="si">{</span><span class="n">y</span><span class="si">}</span><span class="se">\n</span><span class="s2">结果为</span><span class="se">\n</span><span class="si">{</span><span class="n">out</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
-<div class="section" id="advanced-usage">
-<h2>Advanced Usage<a class="headerlink" href="#advanced-usage" title="Permalink to this heading">¶</a></h2>
-<p>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.</p>
-<p>When used with <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>, <code class="docutils literal notranslate"><span class="pre">triton.autotune</span></code> can help ensure that your PyTorch
-model is running as efficiently as possible. Here is an example of using <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>
-and <code class="docutils literal notranslate"><span class="pre">triton.autotune</span></code>.</p>
+<div class="section" id="id3">
+<h2>高级用法<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
+<p>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。</p>
+<p>与 <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> 一起使用时, <code class="docutils literal notranslate"><span class="pre">triton.autotune</span></code> 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 <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> 和 <code class="docutils literal notranslate"><span class="pre">triton.autotune</span></code> 的示例。</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p><code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> only supports configs and key arguments to <code class="docutils literal notranslate"><span class="pre">triton.autotune</span></code>.</p>
+<p><code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> 仅支持 <code class="docutils literal notranslate"><span class="pre">triton.autotune</span></code> 的配置和关键参数。</p>
 </div>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="ow">not</span> <span class="n">has_triton</span><span class="p">():</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Skipping because triton is not supported on this device.&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;由于此设备不支持 triton,因此跳过。&quot;</span><span class="p">)</span>
 <span class="k">else</span><span class="p">:</span>
     <span class="kn">import</span> <span class="nn">triton</span>
     <span class="kn">from</span> <span class="nn">triton</span> <span class="kn">import</span> <span class="n">language</span> <span class="k">as</span> <span class="n">tl</span>
@@ -721,38 +716,34 @@ <h2>Advanced Usage<a class="headerlink" href="#advanced-usage" title="Permalink
     <span class="n">x</span> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
     <span class="n">y</span> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.randn.html#torch.randn" title="torch.randn" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
     <span class="n">out</span> <span class="o">=</span> <span class="n">add_fn</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Vector addition of</span><span class="se">\n</span><span class="s2">X:</span><span class="se">\t</span><span class="si">{</span><span class="n">x</span><span class="si">}</span><span class="se">\n</span><span class="s2">Y:</span><span class="se">\t</span><span class="si">{</span><span class="n">y</span><span class="si">}</span><span class="se">\n</span><span class="s2">is equal to</span><span class="se">\n</span><span class="si">{</span><span class="n">out</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;向量加法</span><span class="se">\n</span><span class="s2">X:</span><span class="se">\t</span><span class="si">{</span><span class="n">x</span><span class="si">}</span><span class="se">\n</span><span class="s2">Y:</span><span class="se">\t</span><span class="si">{</span><span class="n">y</span><span class="si">}</span><span class="se">\n</span><span class="s2">结果为</span><span class="se">\n</span><span class="si">{</span><span class="n">out</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
-<div class="section" id="composibility-and-limitations">
-<h2>Composibility and Limitations<a class="headerlink" href="#composibility-and-limitations" title="Permalink to this heading">¶</a></h2>
-<p>As of PyTorch 2.3, the support for user-defined Triton kernels in <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>
-includes dynamic shapes, <code class="docutils literal notranslate"><span class="pre">torch.autograd.Function</span></code>, JIT inductor, and AOT inductor.
-You can use these features together to build complex, high-performance models.</p>
-<p>However, there are certain limitations to be aware of:</p>
+<div class="section" id="id4">
+<h2>可组合性和限制<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
+<p>从 PyTorch 2.3 开始, <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> 中对用户自定义 Triton 内核的支持包括动态形状、
+<code class="docutils literal notranslate"><span class="pre">torch.autograd.Function</span></code>、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。</p>
+<p>但是,也需要注意一些限制:</p>
 <ul class="simple">
-<li><p><strong>Tensor Subclasses:</strong> Currently, there is no support for
-tensor subclasses and other advanced features.</p></li>
-<li><p><strong>Triton Features:</strong> While <code class="docutils literal notranslate"><span class="pre">triton.heuristics</span></code> can be used either standalone or
-before <code class="docutils literal notranslate"><span class="pre">triton.autotune</span></code>, it cannot be used after <code class="docutils literal notranslate"><span class="pre">`triton.autotune</span></code>. This
-implies that if <code class="docutils literal notranslate"><span class="pre">triton.heuristics</span></code> and <code class="docutils literal notranslate"><span class="pre">triton.autotune</span></code> are to be used
-together, <code class="docutils literal notranslate"><span class="pre">triton.heuristics</span></code> must be used first.</p></li>
+<li><p><strong>Tensor 子类:</strong> 目前不支持张量子类和其他高级功能。</p></li>
+<li><p><strong>Triton 功能:</strong> 虽然 <code class="docutils literal notranslate"><span class="pre">triton.heuristics</span></code> 可以单独使用或在 <code class="docutils literal notranslate"><span class="pre">triton.autotune</span></code> 之前使用,
+但不能在 <code class="docutils literal notranslate"><span class="pre">triton.autotune</span></code> 之后使用。这意味着如果要一起使用 <code class="docutils literal notranslate"><span class="pre">triton.heuristics</span></code> 和 <code class="docutils literal notranslate"><span class="pre">triton.autotune</span></code>,
+则必须先使用 <code class="docutils literal notranslate"><span class="pre">triton.heuristics</span></code>。</p></li>
 </ul>
 </div>
-<div class="section" id="conclusion">
-<h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this heading">¶</a></h2>
-<p>In this recipe, we explored how to utilize user-defined Triton kernels
-with <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>. 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.</p>
+<div class="section" id="id5">
+<h2>结论<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h2>
+<p>在本教程中,我们探讨了如何在 <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> 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。</p>
 </div>
-<div class="section" id="see-also">
-<h2>See Also<a class="headerlink" href="#see-also" title="Permalink to this heading">¶</a></h2>
+<div class="section" id="id6">
+<h2>另请参阅<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h2>
 <ul class="simple">
-<li><p><a class="reference external" href="https://pytorch.org/tutorials/recipes/compiling_optimizer.html">Compiling the Optimizers</a></p></li>
-<li><p><a class="reference external" href="https://pytorch.org/tutorials/intermediate/scaled_dot_product_attention_tutorial.html">Implementing High-Performance Transformers with Scaled Dot Product Attention</a></p></li>
+<li><p><a class="reference external" href="https://pytorch.org/tutorials/recipes/compiling_optimizer.html">编译优化器</a></p></li>
+<li><p><a class="reference external" href="https://pytorch.org/tutorials/intermediate/scaled_dot_product_attention_tutorial.html">使用缩放点积注意力实现高性能 Transformer</a></p></li>
 </ul>
 <p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.000 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-recipes-torch-compile-user-defined-triton-kernel-tutorial-py">
@@ -819,13 +810,13 @@ <h2>See Also<a class="headerlink" href="#see-also" title="Permalink to this head
           <div class="pytorch-right-menu" id="pytorch-right-menu">
             <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
               <ul>
-<li><a class="reference internal" href="#">Using User-Defined Triton Kernels with <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></a><ul>
-<li><a class="reference internal" href="#prerequisites">Prerequisites</a></li>
-<li><a class="reference internal" href="#basic-usage">Basic Usage</a></li>
-<li><a class="reference internal" href="#advanced-usage">Advanced Usage</a></li>
-<li><a class="reference internal" href="#composibility-and-limitations">Composibility and Limitations</a></li>
-<li><a class="reference internal" href="#conclusion">Conclusion</a></li>
-<li><a class="reference internal" href="#see-also">See Also</a></li>
+<li><a class="reference internal" href="#">使用 <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> 和用户自定义的 Triton 内核</a><ul>
+<li><a class="reference internal" href="#id1">先决条件</a></li>
+<li><a class="reference internal" href="#id2">基本用法</a></li>
+<li><a class="reference internal" href="#id3">高级用法</a></li>
+<li><a class="reference internal" href="#id4">可组合性和限制</a></li>
+<li><a class="reference internal" href="#id5">结论</a></li>
+<li><a class="reference internal" href="#id6">另请参阅</a></li>
 </ul>
 </li>
 </ul>
diff --git a/docs/searchindex.js b/docs/searchindex.js
index 9a2424d..f4ee843 100644
--- a/docs/searchindex.js
+++ b/docs/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["advanced/ONNXLive", "advanced/coding_ddpg", "advanced/cpp_autograd", "advanced/cpp_cuda_graphs", "advanced/cpp_export", "advanced/cpp_extension", "advanced/cpp_frontend", "advanced/ddp_pipeline", "advanced/dispatcher", "advanced/dynamic_quantization_tutorial", "advanced/extend_dispatcher", "advanced/generic_join", "advanced/neural_style_tutorial", "advanced/numpy_extensions_tutorial", "advanced/pendulum", "advanced/privateuseone", "advanced/rpc_ddp_tutorial", "advanced/semi_structured_sparse", "advanced/sharding", "advanced/static_quantization_tutorial", "advanced/super_resolution_with_onnxruntime", "advanced/torch-script-parallelism", "advanced/torch_script_custom_classes", "advanced/torch_script_custom_ops", "advanced/usb_semisup_learn", "beginner/Intro_to_TorchScript_tutorial", "beginner/audio_data_augmentation_tutorial", "beginner/audio_datasets_tutorial", "beginner/audio_feature_augmentation_tutorial", "beginner/audio_feature_extractions_tutorial", "beginner/audio_io_tutorial", "beginner/audio_resampling_tutorial", "beginner/basics/autogradqs_tutorial", "beginner/basics/buildmodel_tutorial", "beginner/basics/data_tutorial", "beginner/basics/index", "beginner/basics/intro", "beginner/basics/optimization_tutorial", "beginner/basics/quickstart_tutorial", "beginner/basics/saveloadrun_tutorial", "beginner/basics/tensorqs_tutorial", "beginner/basics/transforms_tutorial", "beginner/bettertransformer_tutorial", "beginner/blitz/autograd_tutorial", "beginner/blitz/cifar10_tutorial", "beginner/blitz/data_parallel_tutorial", "beginner/blitz/index", "beginner/blitz/neural_networks_tutorial", "beginner/blitz/tensor_tutorial", "beginner/chatbot_tutorial", "beginner/colab", "beginner/data_loading_tutorial", "beginner/dcgan_faces_tutorial", "beginner/ddp_series_fault_tolerance", "beginner/ddp_series_intro", "beginner/ddp_series_multigpu", "beginner/ddp_series_theory", "beginner/deep_learning_60min_blitz", "beginner/deeplabv3_on_android", 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Distillation \u6559\u7a0b", "Advanced: Making Dynamic Decisions and the Bi-LSTM CRF", "Deep Learning with PyTorch", "Deep Learning for NLP with Pytorch", "Introduction to PyTorch", "Sequence Models and Long Short-Term Memory Networks", "Word Embeddings: Encoding Lexical Semantics", "<cite>torch.nn</cite> \u5177\u4f53\u662f\u4ec0\u4e48?", "Export a PyTorch model to ONNX", "ONNX", "ONNX \u4ecb\u7ecd", "Extending the ONNX Registry", "PyTorch \u6a21\u578b\u5206\u6790", "PyTorch Cheat Sheet", "\u8ddf\u7740\u793a\u4f8b\u5b66\u4e60 PyTorch", "Saving and Loading Models", "T5-Base Model for Summarization, Sentiment Classification, and Translation", "Template Tutorial", "torchtext \u6587\u672c\u5206\u7c7b", "Torchtext \u9884\u5904\u7406\u81ea\u5b9a\u4e49\u6587\u672c\u6570\u636e\u96c6", "\u8ba1\u7b97\u673a\u89c6\u89c9\u8fc1\u79fb\u5b66\u4e60\u6559\u7a0b", "\u6570\u636e\u83b7\u53d6\u548c\u5904\u7406", "\u4f18\u5316\u89c6\u89c9 Transformer \u6a21\u578b", "Distributed and Parallel Training Tutorials", "\u6b22\u8fce\u6765\u5230 PyTorch \u6559\u7a0b", "Advanced Model Training with Fully Sharded Data Parallel (FSDP)", "Getting Started with Fully Sharded Data Parallel(FSDP)", "Large Scale Transformer model training with Tensor Parallel (TP)", "Hooks for autograd saved tensors", "Multi-Objective NAS with Ax", "\u4ece\u96f6\u5f00\u59cb\u7684\u81ea\u7136\u8bed\u8a00\u5904\u7406\uff1a\u5b57\u7b26\u7ea7 RNN \u8fdb\u884c\u59d3\u540d\u5206\u7c7b", "\u4ece\u96f6\u5f00\u59cb\u7684\u81ea\u7136\u8bed\u8a00\u5904\u7406\uff1a\u5b57\u7b26\u7ea7 RNN \u751f\u6210\u59d3\u540d", "Fusing Convolution and Batch Norm using Custom Function", "Double Backward with Custom Functions", "Training \u201creal-world\u201d models with DDP", "Multinode Training", "Getting Started with Distributed Data Parallel", "Distributed Pipeline Parallelism Using RPC", "Writing Distributed Applications with PyTorch", "Recurrent DQN: Training recurrent policies", "(beta) Dynamic Quantization on BERT", "Model ensembling", "API \u5b9a\u4e49", "Wav2Vec2 \u5f3a\u5236\u5bf9\u9f50", "Forward-mode Automatic Differentiation (Beta)", "(beta) Building a Convolution/Batch Norm fuser in FX", "(beta) Building a Simple CPU Performance Profiler with FX", "Inductor CPU backend debugging and profiling", "Jacobians, Hessians, hvp, vhp, and more: composing function transforms", "\u8bad\u7ec3\u4e00\u4e2a\u9a6c\u91cc\u5965\u6e38\u620f\u7684 RL Agent", "(beta) Channels Last Memory Format in PyTorch", "&lt;no title&gt;", "Single-Machine Model Parallel Best Practices", "Neural Tangent Kernels", "Getting Started - Accelerate Your Scripts with nvFuser", "How to save memory by fusing the optimizer step into the backward pass", "Parametrizations Tutorial", "Per-sample-gradients", "Customize Process Group Backends Using Cpp Extensions", "Pruning Tutorial", "(beta) Quantized Transfer Learning for Computer Vision Tutorial", "\u5728 Raspberry Pi 4 \u4e0a\u8fdb\u884c\u5b9e\u65f6\u63a8\u7406 (30 fps!)", "\u4f7f\u7528 TorchRL \u5f3a\u5316\u5b66\u4e60 (PPO) \u6559\u7a0b", "\u5f3a\u5316\u5b66\u4e60 (DQN) \u6559\u7a0b", "Implementing Batch RPC Processing Using Asynchronous Executions", "Implementing a Parameter Server Using Distributed RPC Framework", "Getting Started with Distributed RPC Framework", "(Beta) Implementing High-Performance Transformers with Scaled Dot Product Attention (SDPA)", "\u4ece\u96f6\u5f00\u59cb\u7684\u81ea\u7136\u8bed\u8a00\u5904\u7406\uff1a\u5e8f\u5217\u5230\u5e8f\u5217\u7f51\u7edc\u548c\u6ce8\u610f\u529b\u673a\u5236\u8fdb\u884c\u7ffb\u8bd1", "Spatial Transformer Networks \u6559\u7a0b", "Wav2Vec2 \u8fdb\u884c\u8bed\u97f3\u8bc6\u522b", "PyTorch Profiler With TensorBoard", "TensorBoard \u53ef\u89c6\u5316\u6a21\u578b\u3001\u6570\u636e\u548c\u8bad\u7ec3", "Tacotron2 \u6587\u672c\u8f6c\u8bed\u97f3", "PyTorch \u548c TIAToolbox \u8fdb\u884c\u5168\u5207\u7247\u56fe\u50cf\u5206\u7c7b", "Introduction to <code class=\"docutils literal notranslate\"><span class=\"pre\">torch.compile</span></code>", "torch.export Nightly Tutorial", "torch.export Tutorial", "Introduction to TorchRec", "Grokking PyTorch Intel CPU performance from first principles", "Grokking PyTorch Intel CPU performance from first principles (Part 2)", "TorchVision \u5bf9\u8c61\u68c0\u6d4b\u5fae\u8c03\u6559\u7a0b", "(prototype) PyTorch BackendConfig Tutorial", "Profiling PyTorch RPC-Based Workloads", "(prototype) FX Graph Mode Post Training Dynamic Quantization", "(prototype) FX Graph Mode Post Training Static Quantization", "(prototype) FX Graph Mode Quantization User Guide", "(prototype) GPU Quantization with TorchAO", "(prototype) Graph Mode Dynamic Quantization on BERT", "Inductor C++ Wrapper Tutorial", "(Prototype) Convert Mobilenetv2 to Core ML", "(Prototype) Use iOS GPU in PyTorch", "(Prototype) Efficiently writing \u201csparse\u201d semantics for Adagrad with MaskedTensor", "(Prototype) MaskedTensor Advanced Semantics", "(Prototype) MaskedTensor Overview", "(Prototype) MaskedTensor Sparsity", "Getting Started with Nested Tensors", "(Beta) Convert MobileNetV2 to NNAPI", "PyTorch Numeric Suite Tutorial", "PyTorch Prototype Recipes", "(prototype) PyTorch 2 Export Post Training Quantization", "(prototype) PyTorch 2 Export Quantization-Aware Training (QAT)", "PyTorch 2 Export Quantization with X86 Backend through Inductor", "How to Write a <code class=\"docutils literal notranslate\"><span class=\"pre\">Quantizer</span></code> for PyTorch 2 Export Quantization", "(prototype) Accelerating BERT with semi-structured (2:4) sparsity", "Skipping Module Parameter Initialization", "Model Freezing in TorchScript", "(prototype) Tracing-based Selective Build Mobile Interpreter in Android and iOS", "torch.vmap", "PyTorch Vulkan Backend User Workflow", "Leverage Intel\u00ae Advanced Matrix Extensions", "Making Native Android Application that uses PyTorch prebuilt libraries", "(beta) Bundling inputs to PyTorch Models", "(beta) Compiling the optimizer with torch.compile", "(beta) Running the compiled optimizer with an LR Scheduler", "Direct Device-to-Device Communication with TensorPipe CUDA RPC", "\u4f7f\u7528 Flask \u8fdb\u884c\u90e8\u7f72", "Getting Started with Distributed Checkpoint (DCP)", "Getting Started with DeviceMesh", "Distributed Optimizer with TorchScript support", "Profiling PyTorch RPC-Based Workloads", "Fuse Modules Recipe", "(Beta) PyTorch Inference Performance Tuning on AWS Graviton Processors", "Intel\u00ae Extension for PyTorch*", "Ease-of-use quantization for PyTorch with Intel\u00ae Neural Compressor", "(beta) Efficient mobile interpreter in Android and iOS", "Pytorch Mobile Performance Recipes", "Model Preparation for Android Recipe", "Model Preparation for iOS Recipe", "\u4f7f\u7528 Instrumentation and Tracing Technology (ITT) API \u5206\u6790 PyTorch \u5de5\u4f5c\u8d1f\u8f7d", "Summary of PyTorch Mobile Recipes", "Quantization Recipe", "\u4f7f\u7528 Captum \u8fdb\u884c\u6a21\u578b\u53ef\u89e3\u91ca\u6027", "\u81ea\u52a8\u6df7\u5408\u7cbe\u5ea6", "PyTorch Benchmark", "Changing default device", "PyTorch \u521b\u5efa\u795e\u7ecf\u7f51\u7edc", "\u52a8\u6001\u91cf\u5316", "PyTorch Recipes", "PyTorch \u52a0\u8f7d\u6570\u636e", "\u4ece\u68c0\u67e5\u70b9\u52a0\u8f7d <code class=\"docutils literal notranslate\"><span class=\"pre\">nn.Module</span></code> \u7684\u6280\u5de7", "PyTorch Profiler", "\u5728PyTorch\u4e2d\u63a8\u7406\u5f62\u72b6", "PyTorch \u4e2d\u8de8\u8bbe\u5907\u4fdd\u5b58\u548c\u52a0\u8f7d\u6a21\u578b", "PyTorch \u4fdd\u5b58\u548c\u52a0\u8f7d\u901a\u7528\u68c0\u67e5\u70b9", "PyTorch \u4fdd\u5b58\u548c\u52a0\u8f7d\u6a21\u578b", "PyTorch \u5728\u4e00\u4e2a\u6587\u4ef6\u4e2d\u4fdd\u5b58\u548c\u52a0\u8f7d\u591a\u4e2a\u6a21\u578b", "\u5728 <code class=\"docutils literal notranslate\"><span class=\"pre\">nn.Module</span></code> \u4e2d\u4e3a <code class=\"docutils literal notranslate\"><span class=\"pre\">load_state_dict</span></code> \u548c\u5f20\u91cf\u5b50\u7c7b\u63d0\u4f9b\u6269\u5c55\u70b9", "\u5982\u4f55\u5728PyTorch\u4e2d\u4f7f\u7528TensorBoard", "Timer\u5feb\u901f\u5165\u95e8", "\u6027\u80fd\u8c03\u4f18\u6307\u5357", "PyTorch \u4f7f\u7528\u4e0d\u540c\u6a21\u578b\u7684\u53c2\u6570\u5bf9\u6a21\u578b\u8fdb\u884c\u70ed\u542f\u52a8", "PyTorch \u4e2d state_dict \u662f\u4ec0\u4e48", "\u4ecb\u7ecd", "PyTorch \u793a\u4f8b", "Script and Optimize for Mobile Recipe", "Intel\u00ae PyTorch* \u6269\u5c55\u540e\u7aef", "Using User-Defined Triton Kernels with <code class=\"docutils literal notranslate\"><span class=\"pre\">torch.compile</span></code>", "(Beta) \u4f7f\u7528 TORCH_LOGS python API \u4e0e torch.compile", "TorchScript \u90e8\u7f72", "Deploying a PyTorch Stable Diffusion model as a Vertex AI Endpoint", "Shard Optimizer States with ZeroRedundancyOptimizer", "Changelog", "Configuration", "<span class=\"section-number\">5. </span><code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">test_py_module</span></code>", "<span class=\"section-number\">3. </span>Paragraph Level Markup", "<span class=\"section-number\">4. </span>Lists &amp; Tables", "<span class=\"section-number\">1. </span>Long Sticky Nav", "<span class=\"section-number\">1. </span>Structural Elements", "&lt;no title&gt;", "Installation", "Changelog", "Configuration", "<span class=\"section-number\">5. </span><code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">test_py_module</span></code>", "<span class=\"section-number\">3. </span>Paragraph Level Markup", "<span class=\"section-number\">4. </span>Lists &amp; Tables", "<span class=\"section-number\">1. </span>Long Sticky Nav", "<span class=\"section-number\">1. </span>Structural Elements", "&lt;no title&gt;", "Installation"], "terms": {"thi": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 40, 43, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 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19, 21, 22, 23, 24, 32, 43, 44, 47, 49, 51, 52, 58, 59, 60, 63, 64, 67, 73, 76, 78, 79, 85, 87, 97, 98, 99, 100, 101, 102, 103, 108, 111, 112, 116, 124, 125, 126, 127, 135, 136, 137, 138, 141, 143, 145, 147, 148, 150, 153, 156, 157, 158, 159, 162, 164, 165, 166, 171, 173, 174, 175, 178, 181, 182, 183, 189, 191, 195, 196, 197, 198, 200, 205, 208, 221, 222, 223, 228, 240], "iphon": [0, 187, 223], "linux": [0, 5, 6, 18, 20, 22, 23, 105, 124, 133, 135, 158, 168, 176, 177, 178, 194, 206, 208, 220], "howev": [0, 1, 5, 6, 8, 10, 12, 14, 15, 17, 20, 22, 23, 25, 45, 47, 49, 51, 52, 60, 61, 73, 76, 85, 87, 97, 98, 113, 117, 124, 125, 129, 130, 134, 135, 136, 138, 139, 143, 147, 149, 152, 155, 156, 157, 160, 161, 162, 163, 164, 165, 169, 171, 172, 173, 174, 176, 179, 183, 186, 190, 191, 193, 198, 200, 201, 204, 205, 207, 214, 219, 222, 223, 254, 260, 261, 269, 270], "itself": [0, 5, 7, 11, 23, 32, 43, 60, 61, 82, 85, 97, 101, 102, 108, 112, 113, 124, 125, 127, 130, 135, 141, 142, 146, 159, 162, 163, 165, 195, 216], "mac": [0, 20, 137, 206, 225], "For": [0, 1, 2, 4, 5, 6, 7, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 32, 43, 44, 45, 47, 48, 49, 50, 51, 52, 53, 55, 58, 59, 60, 61, 63, 64, 65, 68, 73, 75, 78, 79, 80, 82, 83, 87, 97, 98, 99, 100, 102, 103, 105, 108, 111, 112, 113, 114, 115, 116, 119, 121, 122, 123, 124, 125, 126, 127, 128, 129, 132, 133, 134, 135, 136, 137, 138, 139, 142, 143, 144, 146, 147, 149, 153, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 168, 171, 172, 173, 174, 175, 176, 177, 178, 179, 181, 182, 184, 185, 186, 188, 189, 190, 191, 192, 193, 195, 197, 198, 199, 200, 201, 202, 206, 207, 208, 209, 210, 211, 214, 215, 216, 218, 219, 220, 221, 223, 227, 228, 252, 254, 257, 258, 260, 262, 263, 269, 271, 272], "publish": [0, 208], "http": [0, 4, 6, 7, 18, 19, 20, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 37, 38, 40, 45, 46, 49, 58, 59, 60, 74, 75, 77, 78, 84, 90, 91, 94, 100, 104, 105, 106, 108, 113, 115, 118, 119, 120, 122, 123, 127, 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160, 161, 162, 163, 165, 171, 172, 173, 174, 176, 177, 178, 182, 185, 191, 192, 196, 198, 205, 207, 209, 214, 215, 216, 223, 225], "like": [0, 1, 3, 4, 5, 6, 8, 9, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 25, 43, 44, 45, 47, 48, 49, 51, 52, 53, 58, 59, 60, 61, 65, 67, 68, 75, 78, 79, 80, 82, 85, 87, 97, 98, 99, 101, 102, 103, 105, 108, 109, 110, 111, 112, 113, 114, 116, 117, 120, 121, 123, 124, 125, 126, 127, 128, 130, 132, 133, 135, 136, 137, 138, 139, 141, 142, 143, 144, 145, 147, 150, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 168, 169, 171, 173, 174, 175, 176, 177, 178, 179, 181, 182, 183, 185, 186, 187, 190, 191, 192, 194, 196, 197, 198, 199, 200, 201, 205, 206, 207, 208, 209, 214, 215, 216, 218, 219, 220, 221, 222, 225, 232, 257, 261, 262, 270, 271], "differ": [0, 1, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 32, 47, 48, 49, 51, 52, 58, 59, 60, 61, 73, 80, 81, 82, 83, 85, 87, 92, 97, 98, 99, 103, 105, 115, 116, 119, 121, 122, 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182, 183, 184, 196, 197, 199, 204, 205, 219, 220, 221, 222, 223, 262, 263, 271, 272], "own": [0, 5, 6, 7, 8, 10, 11, 14, 16, 17, 18, 23, 25, 49, 52, 58, 59, 64, 73, 87, 103, 108, 109, 111, 113, 121, 122, 123, 128, 129, 133, 139, 144, 149, 152, 153, 156, 158, 159, 162, 163, 165, 171, 172, 176, 178, 179, 184, 194, 195, 197, 198, 200, 201, 209, 216, 262, 271], "pleas": [0, 2, 4, 5, 6, 7, 9, 10, 19, 20, 22, 23, 44, 45, 47, 51, 61, 75, 87, 108, 113, 115, 118, 122, 123, 124, 133, 137, 138, 141, 142, 143, 144, 147, 149, 152, 154, 155, 157, 161, 162, 163, 168, 171, 172, 173, 174, 175, 176, 178, 179, 183, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 197, 198, 199, 200, 204, 205, 209, 214, 215, 219, 220, 221, 222, 232, 252, 257, 262, 271], "know": [0, 1, 5, 6, 8, 11, 18, 19, 22, 23, 43, 49, 73, 95, 99, 101, 103, 108, 113, 125, 127, 128, 130, 135, 136, 138, 144, 146, 147, 149, 150, 154, 159, 160, 161, 165, 169, 173, 174, 176, 177, 178, 179, 182, 190, 195, 221, 260, 262, 269, 271], 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65, 67, 71, 72, 76, 87, 89, 92, 95, 99, 103, 111, 113, 115, 122, 124, 126, 127, 128, 129, 133, 135, 137, 145, 146, 149, 152, 165, 168, 171, 175, 185, 192, 193, 198, 205, 208, 215, 220, 250, 262, 263, 271, 272], "hear": [0, 142, 143], "think": [0, 6, 22, 23, 44, 52, 68, 78, 98, 99, 101, 102, 103, 113, 143, 145, 159, 162, 164, 192, 205, 262, 271], "click": [1, 7, 9, 12, 13, 14, 17, 20, 21, 24, 25, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 47, 48, 49, 50, 51, 52, 60, 63, 64, 65, 67, 68, 69, 71, 72, 73, 75, 76, 78, 79, 80, 85, 87, 89, 90, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 107, 108, 109, 112, 113, 114, 115, 116, 117, 118, 119, 125, 126, 127, 128, 129, 136, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 152, 153, 154, 156, 159, 160, 164, 165, 166, 168, 169, 172, 174, 178, 181, 184, 188, 189, 190, 191, 192, 193, 195, 203, 204, 205, 208, 211, 225, 229, 230, 231, 232, 233, 234, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 254, 255], "here": [1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 47, 48, 49, 50, 51, 52, 53, 55, 58, 59, 60, 63, 64, 65, 67, 68, 69, 71, 72, 73, 75, 76, 78, 79, 80, 82, 85, 87, 89, 90, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 107, 108, 109, 111, 112, 113, 114, 115, 116, 117, 118, 119, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 135, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 152, 153, 154, 156, 157, 159, 160, 162, 163, 164, 165, 166, 168, 169, 172, 173, 174, 175, 176, 178, 179, 181, 182, 183, 184, 185, 187, 188, 189, 190, 191, 192, 193, 195, 197, 198, 199, 200, 201, 203, 204, 205, 208, 209, 211, 214, 216, 218, 219, 220, 221, 222, 223, 225, 228, 229, 230, 231, 232, 233, 234, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 252, 254, 255, 260, 262, 263, 269, 271, 272], "download": [1, 4, 6, 7, 9, 12, 13, 14, 17, 19, 20, 24, 25, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 47, 48, 49, 50, 51, 52, 58, 59, 60, 63, 64, 65, 67, 68, 69, 71, 72, 73, 75, 76, 78, 79, 80, 82, 85, 87, 89, 90, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 107, 108, 109, 112, 113, 114, 115, 116, 117, 118, 119, 122, 123, 125, 126, 127, 128, 129, 135, 136, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 152, 153, 154, 156, 157, 158, 159, 160, 162, 164, 165, 166, 168, 169, 172, 174, 178, 182, 184, 187, 188, 189, 190, 191, 192, 193, 195, 197, 198, 203, 204, 205, 206, 208, 211, 220, 221, 229, 230, 231, 232, 233, 234, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 253, 254, 255, 266, 275], "full": [1, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 17, 19, 20, 22, 24, 25, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 47, 48, 49, 50, 51, 52, 60, 63, 64, 65, 67, 68, 69, 71, 72, 73, 75, 76, 78, 79, 80, 85, 89, 90, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 107, 108, 109, 111, 112, 113, 114, 115, 116, 117, 118, 119, 122, 123, 125, 126, 127, 128, 129, 131, 134, 136, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 152, 153, 154, 155, 156, 159, 160, 161, 164, 165, 166, 168, 172, 174, 178, 181, 182, 184, 189, 190, 191, 192, 193, 194, 195, 198, 203, 204, 205, 208, 209, 211, 222, 228, 229, 230, 231, 232, 233, 234, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 254, 255], "author": [1, 5, 7, 9, 11, 12, 13, 14, 16, 17, 19, 24, 36, 42, 45, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 73, 81, 82, 83, 85, 86, 97, 98, 99, 101, 102, 103, 104, 107, 108, 109, 111, 112, 113, 114, 116, 117, 122, 123, 124, 126, 127, 128, 131, 132, 133, 134, 135, 136, 137, 139, 142, 143, 144, 146, 147, 149, 153, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 172, 173, 174, 176, 177, 179, 181, 182, 183, 184, 185, 186, 187, 188, 197, 198, 199, 200, 201, 204, 205, 206, 208, 209, 210, 211, 214, 215, 219, 222, 254, 263, 272], "vincent": [1, 14, 136, 159], "moen": [1, 14, 136, 159], "separ": [1, 5, 6, 7, 8, 20, 22, 23, 25, 49, 52, 85, 97, 109, 124, 126, 138, 144, 146, 153, 157, 162, 165, 171, 178, 179, 181, 182, 189, 193, 197, 200, 216, 255], "rl": [1, 61, 121, 159, 160, 161], "algorithm": [1, 5, 6, 10, 11, 12, 49, 52, 56, 69, 82, 87, 98, 99, 100, 101, 111, 118, 122, 124, 126, 129, 135, 136, 146, 155, 159, 162, 166, 210, 211, 216], "variou": [1, 8, 15, 47, 48, 49, 50, 60, 83, 85, 102, 109, 112, 116, 126, 143, 145, 156, 159, 162, 163, 171, 184, 191, 193, 207], "piec": [1, 5, 8, 14, 59, 85, 158, 159, 163, 171, 175, 177, 178, 179, 188, 213], "assembl": [1, 8, 49, 134], "collect": [1, 4, 6, 11, 14, 15, 17, 18, 19, 21, 42, 43, 44, 45, 49, 55, 60, 61, 73, 75, 79, 97, 99, 103, 121, 122, 123, 124, 133, 134, 136, 143, 146, 149, 155, 160, 163, 175, 177, 201, 214, 215, 230], "final": [1, 6, 9, 10, 11, 12, 14, 15, 16, 17, 19, 20, 21, 22, 23, 24, 42, 43, 47, 49, 52, 58, 60, 73, 75, 85, 87, 97, 102, 105, 107, 108, 109, 112, 113, 117, 118, 119, 122, 123, 124, 126, 127, 128, 129, 130, 134, 135, 139, 143, 144, 145, 146, 150, 152, 154, 156, 157, 159, 160, 161, 162, 163, 165, 168, 169, 175, 176, 177, 179, 184, 185, 187, 188, 189, 192, 197, 198, 199, 200, 201, 203, 204, 208, 216, 221], "function": [1, 3, 4, 5, 6, 8, 9, 10, 13, 14, 16, 17, 18, 20, 21, 22, 23, 24, 25, 32, 38, 42, 43, 48, 49, 51, 53, 55, 59, 60, 61, 62, 65, 67, 68, 69, 75, 76, 79, 80, 82, 83, 89, 90, 92, 93, 94, 95, 96, 101, 102, 103, 104, 105, 107, 108, 109, 111, 112, 113, 116, 117, 118, 120, 121, 122, 123, 124, 125, 126, 127, 128, 134, 135, 136, 138, 142, 143, 144, 146, 147, 148, 149, 152, 153, 155, 160, 161, 162, 163, 164, 165, 166, 168, 169, 171, 172, 173, 174, 176, 177, 178, 179, 181, 185, 186, 187, 189, 190, 191, 192, 193, 194, 195, 200, 201, 202, 203, 205, 206, 208, 209, 210, 211, 216, 219, 220, 221, 223, 230, 231, 232, 233, 239, 246, 249, 250, 252, 254, 256, 258], "trainabl": [1, 6, 68, 97, 99, 157], "paramet": [1, 4, 5, 7, 9, 10, 11, 12, 14, 15, 16, 17, 19, 20, 22, 24, 25, 32, 33, 35, 37, 38, 43, 44, 47, 48, 49, 51, 52, 61, 65, 67, 68, 69, 73, 75, 85, 87, 89, 92, 94, 96, 97, 98, 99, 101, 102, 103, 104, 109, 110, 111, 115, 116, 117, 118, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 131, 133, 134, 135, 136, 137, 138, 139, 141, 142, 144, 145, 146, 147, 148, 149, 150, 152, 153, 154, 157, 160, 163, 164, 165, 166, 168, 169, 171, 172, 173, 174, 176, 177, 178, 182, 184, 189, 195, 196, 197, 201, 203, 210, 211, 212, 214, 216, 219, 220, 221, 228, 230, 235, 237, 241, 242, 243, 244, 245, 249, 250, 252, 253, 254, 258, 266, 275], "tutori": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 35, 43, 44, 45, 46, 51, 52, 53, 55, 56, 58, 59, 60, 61, 73, 74, 75, 77, 79, 81, 82, 84, 86, 87, 91, 97, 98, 100, 101, 104, 105, 106, 107, 108, 112, 113, 115, 116, 117, 118, 119, 121, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 149, 150, 151, 152, 154, 155, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 175, 177, 178, 180, 181, 182, 184, 185, 187, 188, 189, 190, 191, 192, 193, 194, 196, 197, 198, 199, 200, 201, 203, 204, 205, 206, 207, 208, 209, 210, 211, 214, 215, 217, 219, 222, 223, 228, 229, 234, 235, 237, 247, 251, 252, 257, 258], "guid": [1, 17, 58, 59, 61, 82, 85, 123, 135, 147, 158, 168, 173, 174, 176, 184, 196, 201, 229, 261, 270], "ground": [1, 14, 44, 73, 178], "aim": [1, 6, 61, 75, 97, 100, 152, 160, 163, 192, 221], "focus": [1, 3, 20, 97, 100, 149, 155, 165, 221], "rel": [1, 5, 6, 7, 117, 119, 125, 126, 137, 145, 149, 150, 163, 165, 176, 186, 197, 221], "straightforward": [1, 5, 6, 16, 17, 49, 60, 97, 98, 144, 200], "determinist": [1, 11, 14, 136, 148, 160], "gradient": [1, 6, 7, 10, 11, 13, 14, 16, 25, 37, 42, 43, 44, 47, 49, 52, 56, 61, 63, 64, 65, 67, 68, 69, 71, 72, 78, 87, 97, 98, 99, 101, 102, 103, 104, 110, 111, 115, 117, 121, 122, 123, 124, 125, 127, 129, 130, 131, 133, 135, 141, 145, 146, 149, 152, 156, 157, 159, 160, 161, 162, 163, 169, 171, 189, 205, 214, 216, 229, 235, 258], "simpl": [1, 3, 4, 5, 6, 8, 12, 15, 16, 17, 19, 21, 22, 23, 24, 25, 47, 49, 51, 54, 56, 61, 67, 73, 79, 85, 87, 97, 107, 112, 116, 120, 121, 123, 125, 126, 130, 135, 138, 139, 144, 145, 150, 154, 156, 159, 161, 162, 163, 164, 166, 168, 172, 182, 185, 199, 201, 205, 207, 210, 211, 214, 215, 220, 221, 228, 252, 254, 258, 262, 263, 271, 272], "continu": [1, 5, 17, 20, 21, 49, 53, 60, 73, 85, 87, 97, 102, 113, 116, 121, 124, 128, 131, 135, 142, 143, 146, 157, 159, 163, 165, 168, 176, 187, 188, 189, 191, 192, 198, 199, 200, 201, 204, 222, 252, 262, 271], "control": [1, 4, 8, 10, 14, 21, 23, 25, 34, 43, 60, 61, 66, 83, 85, 97, 110, 111, 113, 114, 122, 125, 126, 134, 135, 141, 153, 159, 160, 161, 172, 183, 197, 208, 252], "It": [1, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 42, 43, 44, 45, 47, 49, 51, 52, 60, 61, 73, 74, 75, 76, 82, 87, 95, 97, 98, 99, 100, 101, 103, 108, 112, 113, 115, 117, 122, 123, 124, 126, 127, 128, 129, 130, 131, 133, 134, 135, 136, 138, 140, 141, 143, 144, 145, 146, 149, 150, 152, 153, 154, 156, 157, 160, 161, 163, 165, 166, 167, 168, 170, 171, 173, 174, 177, 178, 179, 193, 200, 201, 202, 203, 204, 205, 208, 212, 213, 214, 215, 216, 222, 223, 224, 225, 228, 232, 254, 260, 262, 269, 271], "consist": [1, 3, 6, 7, 14, 15, 16, 22, 24, 25, 43, 75, 97, 99, 118, 124, 131, 142, 143, 146, 150, 152, 159, 164, 165, 168, 173, 174, 177, 179, 191, 192, 199, 200, 208, 211, 262, 271], "parametr": [1, 2, 17, 121, 159, 201], "action": [1, 19, 58, 59, 101, 113, 122, 123, 146, 156, 159, 160, 161, 162, 163, 168, 182, 189, 190, 191, 192, 197, 198, 204, 208, 251, 262, 271], "pair": [1, 6, 14, 47, 49, 52, 116, 118, 128, 129, 137, 150, 154, 159, 160, 165, 168, 178, 179, 194, 199, 211, 262, 271], "maxim": [1, 14, 52, 73, 82, 97, 99, 126, 146, 160, 172, 176, 194], 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192, 196, 197, 198, 205, 206, 208, 223, 224, 225, 232, 257, 262, 271], "custom": [1, 4, 6, 8, 11, 17, 49, 52, 64, 65, 66, 79, 90, 109, 111, 121, 126, 136, 146, 159, 162, 171, 172, 177, 179, 183, 188, 195, 197, 199, 200, 201, 202, 204, 220, 221, 235], "its": [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 16, 17, 18, 20, 21, 22, 23, 25, 43, 47, 49, 51, 52, 53, 55, 60, 61, 68, 69, 73, 75, 80, 82, 95, 97, 99, 101, 102, 103, 105, 107, 109, 111, 112, 113, 115, 122, 123, 124, 125, 126, 127, 128, 135, 136, 137, 138, 139, 141, 143, 144, 146, 147, 149, 152, 153, 156, 159, 160, 161, 162, 163, 165, 168, 169, 172, 173, 174, 175, 176, 184, 188, 191, 192, 193, 197, 200, 201, 202, 204, 206, 207, 208, 209, 214, 215, 218, 220, 222, 228, 258, 262, 271], "includ": [1, 2, 3, 4, 5, 6, 8, 14, 15, 16, 22, 23, 24, 25, 42, 48, 49, 53, 55, 58, 59, 60, 61, 69, 73, 75, 82, 85, 87, 97, 98, 105, 107, 109, 113, 115, 124, 126, 127, 128, 130, 131, 133, 135, 136, 144, 146, 147, 155, 156, 159, 162, 165, 168, 169, 172, 173, 174, 175, 176, 177, 178, 182, 184, 188, 189, 191, 194, 199, 200, 204, 206, 208, 212, 214, 216, 219, 220, 221, 222, 224, 225, 228, 231, 238, 254, 256, 257, 260, 262, 269, 271], "design": [1, 5, 6, 14, 17, 24, 25, 52, 56, 61, 73, 85, 103, 128, 133, 136, 159, 160, 161, 163, 164, 168, 169, 177, 179, 189, 190, 191, 195, 197, 200, 201, 205, 207, 219, 254, 257], "effici": [1, 5, 7, 10, 12, 15, 17, 20, 23, 25, 42, 49, 51, 73, 82, 95, 97, 101, 103, 115, 119, 122, 123, 124, 126, 127, 135, 136, 145, 150, 156, 159, 163, 164, 168, 171, 175, 177, 186, 187, 192, 193, 194, 201, 204, 254], "store": [1, 4, 5, 6, 16, 17, 19, 20, 22, 23, 40, 43, 48, 51, 53, 60, 68, 87, 95, 98, 99, 101, 103, 105, 111, 125, 126, 128, 135, 136, 141, 143, 144, 146, 147, 153, 155, 156, 159, 160, 161, 162, 163, 165, 182, 184, 192, 197, 198, 201, 204, 216, 252, 254, 257], "trajectori": [1, 14, 61, 136, 159], "transit": [1, 14, 60, 85, 86, 98, 136, 160, 200], "assum": [1, 2, 4, 6, 8, 10, 12, 14, 15, 19, 21, 22, 43, 44, 51, 54, 60, 73, 97, 98, 100, 102, 116, 124, 125, 127, 128, 135, 136, 139, 153, 156, 159, 162, 164, 165, 173, 174, 175, 178, 191, 192, 193, 199, 200, 223], "complet": [1, 4, 5, 6, 15, 21, 25, 49, 76, 78, 85, 87, 98, 99, 101, 113, 117, 119, 122, 124, 126, 130, 135, 156, 157, 158, 159, 160, 162, 165, 171, 172, 177, 178, 184, 191, 192, 225, 228, 252], "ppo": [1, 121], "compon": [1, 5, 6, 8, 10, 14, 20, 25, 52, 61, 85, 97, 101, 112, 113, 115, 119, 121, 126, 136, 142, 146, 159, 163, 166, 168, 172, 173, 174, 177, 193, 207], "depend": [1, 5, 6, 7, 8, 11, 14, 21, 22, 23, 42, 47, 50, 52, 60, 73, 82, 85, 97, 98, 102, 110, 118, 119, 121, 124, 126, 129, 130, 135, 136, 137, 139, 141, 142, 143, 145, 146, 149, 155, 158, 159, 162, 168, 172, 173, 174, 181, 182, 183, 184, 188, 191, 196, 197, 198, 204, 206, 207, 208, 210, 219, 222, 224, 225, 232, 252], "tensordict": [1, 14, 136, 146, 159], "nn": [1, 2, 4, 5, 6, 7, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 25, 32, 37, 38, 39, 42, 43, 44, 45, 47, 49, 52, 55, 60, 65, 66, 69, 73, 75, 77, 79, 87, 89, 90, 92, 94, 96, 97, 98, 99, 102, 103, 105, 107, 108, 109, 110, 115, 117, 118, 119, 121, 122, 123, 124, 125, 127, 128, 129, 133, 134, 136, 137, 138, 141, 142, 143, 145, 146, 147, 148, 149, 150, 153, 154, 157, 159, 160, 161, 162, 163, 164, 165, 166, 168, 169, 171, 172, 173, 174, 177, 179, 181, 182, 183, 185, 190, 193, 194, 195, 197, 198, 199, 200, 201, 202, 203, 209, 210, 211, 212, 214, 215, 218, 219, 220, 221, 223, 226, 228, 230, 232, 233, 234, 235, 238, 239, 241, 242, 243, 245, 247, 248, 249, 250, 251, 252, 253, 256, 258], "tensordictmodul": [1, 14, 136, 159], "although": [1, 12, 16, 43, 49, 60, 61, 98, 99, 103, 105, 108, 115, 119, 125, 146, 149, 153, 157, 162, 172, 173, 174, 176, 182, 203, 219, 262, 271], "suffici": [1, 6, 49, 52, 97, 98, 117, 131, 133, 152], "transpar": [1, 12, 42, 99, 136, 162, 206, 220], "understood": [1, 4, 113], "without": [1, 4, 5, 6, 8, 9, 10, 14, 17, 20, 23, 32, 42, 49, 53, 55, 60, 73, 78, 97, 98, 107, 112, 113, 116, 123, 124, 125, 128, 129, 135, 137, 138, 141, 143, 145, 146, 147, 152, 154, 155, 156, 157, 158, 159, 160, 161, 164, 165, 168, 171, 176, 177, 189, 191, 192, 193, 194, 199, 200, 201, 203, 208, 209, 211, 215, 220, 227, 228, 252, 258, 260, 262, 269, 271], "understand": [1, 2, 4, 6, 15, 23, 43, 44, 52, 57, 58, 59, 82, 85, 91, 98, 99, 101, 108, 117, 121, 125, 126, 127, 128, 130, 135, 137, 141, 143, 144, 149, 157, 165, 171, 173, 174, 176, 190, 195, 199, 200, 208, 215, 249, 254], "class": [1, 2, 4, 5, 6, 7, 8, 9, 10, 12, 13, 15, 16, 19, 20, 21, 23, 24, 25, 33, 34, 37, 38, 42, 44, 45, 47, 49, 52, 53, 58, 59, 60, 64, 65, 67, 73, 75, 76, 78, 79, 83, 85, 87, 89, 90, 92, 93, 94, 96, 98, 99, 100, 102, 103, 104, 105, 108, 109, 110, 111, 112, 115, 117, 118, 119, 121, 122, 123, 125, 126, 127, 128, 129, 130, 131, 133, 134, 135, 136, 137, 138, 139, 141, 142, 143, 144, 146, 147, 148, 149, 150, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 169, 171, 172, 173, 174, 177, 178, 179, 181, 182, 183, 189, 190, 191, 193, 194, 195, 197, 198, 199, 200, 202, 203, 208, 209, 212, 213, 214, 215, 216, 218, 219, 221, 223, 226, 233, 234, 237, 239, 240, 241, 242, 243, 244, 248, 249, 250, 252, 261, 262, 263, 270, 271, 272], "sota": [1, 75, 113, 119], "implement": [1, 2, 3, 4, 5, 6, 7, 10, 11, 12, 13, 14, 15, 17, 20, 24, 42, 43, 45, 47, 49, 51, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, 69, 76, 79, 85, 103, 108, 111, 115, 120, 121, 124, 125, 126, 127, 130, 133, 134, 135, 136, 138, 139, 141, 144, 145, 146, 147, 149, 150, 154, 156, 159, 160, 163, 168, 173, 174, 178, 179, 186, 190, 192, 193, 194, 196, 197, 200, 201, 204, 206, 207, 208, 216, 219, 220, 221, 222, 224, 231, 254, 257, 261, 270], "rather": [1, 13, 23, 25, 49, 52, 69, 73, 85, 97, 103, 112, 121, 128, 129, 143, 144, 149, 153, 154, 159, 171, 184, 188, 189, 207, 223], "high": [1, 2, 5, 6, 14, 15, 19, 23, 25, 42, 44, 49, 52, 53, 55, 57, 60, 82, 85, 99, 103, 105, 109, 112, 121, 122, 123, 124, 126, 127, 129, 135, 139, 146, 149, 159, 168, 169, 171, 176, 177, 186, 192, 195, 196, 197, 199, 212, 216, 252, 254, 258, 260, 269], "level": [1, 2, 5, 6, 17, 19, 20, 23, 25, 44, 49, 53, 55, 57, 68, 79, 100, 105, 115, 122, 123, 124, 126, 127, 128, 131, 133, 135, 137, 141, 142, 143, 144, 147, 149, 164, 165, 168, 171, 173, 174, 176, 177, 182, 185, 195, 196, 197, 199, 201, 209, 212, 215, 216, 221, 223, 227, 258, 266, 275], "illustr": [1, 19, 44, 47, 56, 116, 117, 124, 125, 126, 138, 160, 169, 171, 178, 191, 192, 195, 215], "librari": [1, 3, 4, 5, 6, 8, 12, 14, 18, 20, 22, 23, 25, 42, 44, 50, 51, 57, 61, 75, 87, 107, 108, 113, 115, 118, 121, 126, 129, 130, 137, 139, 143, 155, 158, 159, 163, 168, 173, 174, 177, 194, 204, 206, 207, 215, 219, 220, 222, 223, 226, 227, 228, 249, 251], "featur": [1, 4, 6, 10, 11, 12, 14, 17, 19, 22, 23, 34, 49, 50, 51, 52, 58, 59, 60, 61, 82, 83, 85, 90, 94, 95, 97, 98, 103, 108, 113, 121, 123, 125, 136, 137, 144, 145, 146, 149, 152, 155, 158, 159, 163, 164, 169, 172, 173, 174, 175, 176, 177, 178, 185, 186, 187, 188, 192, 193, 196, 199, 201, 204, 205, 206, 207, 208, 212, 216, 219, 251, 252, 254], "context": [1, 2, 5, 8, 14, 16, 17, 43, 49, 60, 61, 64, 73, 103, 109, 111, 120, 124, 134, 141, 153, 159, 162, 163, 164, 165, 168, 177, 186, 199, 201, 206, 208, 212, 232], "bash": [1, 18, 20, 146, 160, 226], "pip3": [1, 18, 50, 122, 136, 159, 160, 168, 175, 184, 187, 188], "mujoco": [1, 136, 159], "glfw": 1, "tqdm": [1, 14, 17, 122, 136, 137, 159, 185, 201], "avail": [1, 2, 3, 5, 6, 10, 12, 14, 15, 17, 18, 19, 20, 21, 22, 23, 40, 42, 43, 44, 48, 50, 51, 52, 53, 58, 59, 73, 80, 87, 97, 101, 105, 113, 115, 119, 122, 125, 135, 136, 139, 141, 146, 147, 156, 157, 158, 159, 160, 163, 164, 165, 168, 171, 175, 176, 177, 178, 181, 182, 187, 188, 196, 197, 198, 199, 201, 205, 212, 214, 220, 222, 223, 224, 225, 227, 228, 232, 260, 269], "is_fork": [1, 136, 159], "multiprocess": [1, 6, 7, 11, 14, 34, 51, 53, 55, 56, 110, 122, 123, 133, 134, 135, 136, 159, 162, 163, 212, 214, 216, 258], "get_start_method": [1, 136, 159], "fork": [1, 21, 136, 159, 160], "is_avail": [1, 5, 6, 12, 20, 33, 38, 40, 42, 44, 45, 48, 49, 52, 63, 73, 80, 87, 89, 95, 97, 104, 110, 111, 115, 117, 118, 129, 136, 146, 147, 155, 156, 157, 159, 160, 162, 164, 165, 166, 172, 178, 193, 230], "els": [1, 4, 5, 8, 9, 11, 12, 14, 16, 17, 18, 19, 20, 23, 25, 33, 38, 42, 44, 45, 47, 49, 51, 52, 58, 59, 60, 63, 73, 87, 94, 95, 96, 97, 103, 104, 105, 108, 110, 111, 115, 116, 117, 118, 122, 127, 128, 129, 134, 135, 136, 137, 142, 146, 147, 150, 155, 156, 157, 159, 160, 161, 162, 163, 164, 165, 166, 169, 171, 172, 178, 181, 182, 185, 186, 193, 195, 197, 198, 201, 208, 209, 212, 215, 216, 218, 222, 230, 231, 244, 246, 252, 254, 255, 258, 262, 263, 271, 272], "cpu": [1, 3, 5, 6, 8, 9, 10, 11, 12, 14, 15, 18, 19, 20, 23, 33, 38, 42, 43, 44, 45, 48, 49, 52, 60, 63, 64, 72, 73, 80, 82, 83, 87, 89, 90, 95, 97, 99, 104, 105, 108, 109, 110, 111, 115, 117, 118, 121, 123, 124, 129, 133, 134, 135, 136, 137, 146, 147, 150, 153, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 168, 171, 175, 178, 181, 182, 185, 186, 187, 188, 193, 194, 195, 197, 198, 199, 202, 206, 212, 216, 219, 223, 226, 229, 230, 232, 234, 237, 238, 240, 244, 251, 252, 253], "collector_devic": 1, "chang": [1, 2, 5, 6, 10, 11, 12, 14, 19, 21, 22, 23, 24, 40, 43, 48, 50, 51, 52, 53, 55, 58, 59, 61, 76, 78, 79, 80, 82, 83, 85, 87, 95, 97, 98, 100, 101, 102, 105, 108, 112, 116, 121, 123, 124, 126, 131, 132, 135, 136, 137, 139, 141, 144, 145, 146, 149, 152, 153, 155, 156, 157, 161, 168, 171, 172, 173, 174, 177, 181, 182, 184, 186, 187, 188, 191, 193, 197, 198, 200, 204, 206, 207, 208, 211, 212, 214, 216, 220, 221, 222, 235, 252, 260, 269], "seri": [1, 6, 15, 23, 52, 53, 54, 55, 56, 82, 91, 120, 121, 127, 128, 131, 132, 139, 143, 156, 159, 191, 219], "reusabl": [1, 6, 25], "swappabl": 1, "signatur": [1, 5, 8, 10, 14, 15, 23, 108, 135, 153, 162, 173, 174, 252], "characterist": [1, 14, 43, 143, 145, 146, 158, 164], "copi": [1, 5, 6, 12, 18, 22, 23, 44, 45, 50, 55, 58, 61, 73, 82, 97, 109, 110, 112, 114, 117, 123, 125, 129, 133, 135, 136, 137, 138, 141, 142, 143, 146, 149, 153, 157, 162, 168, 171, 181, 182, 183, 188, 194, 198, 199, 204, 206, 208, 212, 218, 219, 234, 257, 263, 272], "loss_modul": [1, 159], "whatev": [1, 8, 22, 23, 99, 101, 112, 195], "convent": [1, 14, 52, 60, 112, 126, 136, 171, 216], "receiv": [1, 4, 6, 14, 16, 55, 64, 87, 101, 111, 135, 159, 161, 162, 163, 172], "necessari": [1, 4, 5, 6, 7, 8, 10, 12, 15, 16, 18, 19, 23, 24, 44, 52, 53, 55, 60, 85, 87, 98, 112, 113, 122, 123, 124, 129, 133, 146, 149, 159, 161, 162, 163, 168, 173, 174, 177, 179, 182, 185, 191, 193, 195, 197, 198, 199, 249], "return": [1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 25, 33, 34, 37, 38, 40, 44, 45, 47, 49, 51, 52, 59, 60, 64, 65, 67, 68, 73, 75, 78, 79, 80, 82, 85, 87, 89, 90, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103, 104, 105, 108, 109, 110, 111, 112, 113, 115, 116, 117, 118, 122, 123, 124, 126, 127, 128, 129, 130, 133, 134, 135, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 168, 169, 171, 172, 173, 174, 176, 177, 178, 179, 181, 182, 183, 184, 185, 186, 187, 189, 191, 193, 194, 195, 197, 198, 199, 200, 201, 203, 205, 206, 208, 209, 210, 212, 213, 214, 215, 218, 219, 220, 221, 222, 223, 226, 230, 231, 233, 234, 237, 239, 240, 241, 242, 243, 244, 246, 247, 248, 249, 250, 252, 254, 255, 256, 257], "replay_buff": [1, 159], "sampl": [1, 6, 8, 44, 47, 48, 49, 51, 52, 55, 60, 61, 75, 78, 79, 87, 92, 97, 99, 105, 110, 115, 116, 117, 118, 121, 123, 127, 129, 135, 136, 137, 139, 145, 146, 152, 153, 157, 159, 160, 161, 163, 171, 173, 174, 178, 182, 185, 193, 194, 197, 199, 205, 209, 210, 225, 226, 262, 271], "loss_dict": 1, "instanc": [1, 4, 5, 6, 7, 11, 12, 14, 21, 22, 23, 25, 45, 53, 54, 55, 58, 59, 60, 78, 82, 87, 97, 98, 99, 102, 103, 122, 123, 125, 126, 131, 132, 133, 134, 136, 144, 146, 157, 159, 161, 162, 163, 165, 172, 182, 183, 197, 199, 200, 202, 219, 220, 252, 257, 258], "written": [1, 4, 5, 6, 8, 10, 14, 22, 23, 25, 85, 100, 130, 136, 143, 150, 153, 154, 157, 164, 168, 171, 184, 189, 220, 254], "under": [1, 4, 5, 8, 14, 18, 19, 23, 47, 49, 50, 52, 54, 56, 97, 99, 109, 113, 115, 124, 125, 135, 137, 139, 145, 146, 153, 156, 163, 168, 169, 177, 178, 179, 187, 188, 190, 192, 204, 212, 214, 216, 221, 222, 225, 262, 271], "loss_": 1, "smth": 1, "where": [1, 3, 4, 6, 7, 8, 11, 12, 13, 14, 16, 17, 18, 20, 21, 22, 23, 24, 32, 47, 49, 51, 60, 61, 64, 68, 75, 78, 79, 83, 85, 87, 97, 98, 99, 101, 102, 103, 110, 113, 122, 124, 126, 127, 128, 130, 132, 133, 134, 135, 137, 138, 139, 141, 144, 147, 149, 150, 152, 153, 154, 158, 159, 160, 161, 162, 163, 164, 165, 169, 172, 174, 175, 177, 178, 179, 182, 184, 187, 189, 192, 193, 194, 195, 200, 201, 204, 208, 215, 216, 226, 228, 263, 272], "string": [1, 8, 15, 22, 23, 49, 51, 58, 59, 60, 65, 67, 105, 111, 115, 116, 118, 126, 127, 128, 136, 139, 156, 159, 165, 171, 182, 208, 209, 257, 260, 269], "describ": [1, 4, 5, 6, 8, 10, 14, 15, 16, 19, 20, 21, 22, 23, 48, 49, 52, 58, 59, 61, 73, 97, 105, 114, 120, 135, 150, 159, 160, 163, 168, 171, 173, 174, 176, 196, 197, 198, 202, 215, 252], "addit": [1, 2, 5, 7, 8, 11, 15, 17, 19, 50, 60, 73, 75, 97, 102, 105, 108, 109, 113, 122, 124, 125, 133, 135, 137, 138, 139, 142, 144, 147, 149, 156, 161, 162, 165, 169, 172, 173, 174, 176, 185, 189, 190, 191, 192, 197, 200, 201, 206, 208, 216, 218, 219, 220, 254], "kei": [1, 6, 8, 11, 14, 15, 17, 49, 58, 75, 82, 90, 100, 103, 105, 109, 112, 114, 115, 116, 119, 122, 126, 136, 137, 139, 143, 146, 156, 158, 159, 160, 161, 164, 165, 168, 169, 171, 173, 174, 175, 177, 185, 193, 194, 195, 201, 209, 210, 211, 220, 237, 254, 262, 271], "mai": [1, 4, 5, 6, 8, 10, 11, 12, 14, 15, 17, 19, 21, 22, 23, 25, 42, 49, 50, 52, 58, 59, 60, 68, 73, 85, 95, 99, 112, 113, 116, 123, 124, 125, 126, 129, 130, 136, 137, 138, 139, 141, 143, 144, 145, 150, 152, 153, 158, 159, 162, 165, 168, 171, 172, 173, 174, 176, 177, 179, 181, 182, 185, 188, 191, 193, 197, 198, 199, 200, 201, 202, 207, 208, 210, 218, 228, 231, 252, 262, 263, 271, 272], "metric": [1, 17, 87, 97, 109, 122, 137, 146, 168, 171, 177, 178, 201, 221], "log": [1, 7, 14, 18, 49, 50, 52, 53, 58, 73, 97, 98, 99, 102, 103, 104, 118, 123, 126, 129, 132, 137, 148, 158, 159, 161, 163, 166, 168, 169, 171, 173, 174, 177, 185, 195, 208, 211, 221, 255], "dure": [1, 3, 7, 8, 12, 14, 16, 18, 19, 25, 32, 37, 49, 52, 60, 61, 63, 64, 76, 78, 85, 97, 99, 103, 108, 111, 112, 113, 118, 121, 122, 123, 124, 125, 128, 129, 130, 131, 133, 136, 142, 143, 144, 149, 150, 153, 157, 158, 159, 160, 161, 163, 168, 172, 176, 177, 178, 185, 196, 198, 202, 206, 214, 216, 220, 223, 224, 225, 228, 252], "reason": [1, 5, 6, 8, 14, 15, 17, 23, 25, 52, 78, 82, 97, 99, 102, 112, 117, 125, 129, 135, 144, 149, 157, 159, 164, 165, 184, 191, 201, 214, 223, 252], "independ": [1, 7, 23, 49, 60, 79, 103, 108, 110, 145, 146, 150, 162, 189], "user": [1, 3, 5, 14, 17, 18, 19, 22, 24, 25, 44, 49, 50, 60, 76, 79, 82, 83, 85, 97, 101, 108, 110, 113, 114, 115, 122, 124, 128, 133, 137, 139, 142, 143, 144, 147, 161, 163, 164, 165, 166, 168, 171, 173, 174, 175, 176, 177, 178, 179, 182, 185, 187, 189, 190, 191, 192, 195, 196, 197, 198, 199, 200, 201, 204, 207, 212, 215, 216, 220, 221, 228, 262, 263, 271, 272], "sum": [1, 2, 4, 5, 7, 11, 13, 14, 16, 18, 19, 21, 25, 37, 38, 40, 43, 44, 49, 52, 60, 63, 64, 65, 67, 68, 69, 71, 72, 76, 78, 82, 87, 89, 92, 97, 98, 99, 101, 103, 104, 109, 111, 115, 117, 122, 123, 125, 127, 128, 129, 130, 135, 136, 145, 146, 150, 152, 153, 156, 157, 159, 160, 161, 162, 163, 166, 168, 172, 173, 174, 175, 182, 189, 190, 191, 192, 197, 198, 210, 211, 212, 214, 221, 231, 252, 258], "done": [1, 4, 5, 6, 8, 10, 14, 16, 17, 19, 20, 21, 22, 23, 25, 37, 38, 49, 54, 58, 59, 82, 85, 97, 98, 99, 108, 113, 115, 122, 123, 124, 125, 128, 129, 135, 136, 138, 143, 144, 146, 147, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 169, 171, 173, 174, 184, 185, 194, 201, 202, 208, 216, 220, 223, 256, 257], "via": [1, 3, 5, 6, 7, 16, 17, 18, 20, 22, 23, 54, 55, 58, 59, 73, 85, 97, 121, 122, 123, 124, 126, 135, 136, 139, 145, 153, 158, 159, 164, 169, 171, 172, 176, 177, 178, 188, 191, 201, 212, 215, 216, 219, 220, 221, 260, 266, 269, 275], "loss_val": [1, 136, 159], "item": [1, 2, 6, 7, 9, 10, 11, 12, 14, 15, 34, 37, 38, 40, 44, 49, 52, 60, 63, 64, 65, 67, 68, 69, 72, 73, 87, 90, 92, 94, 95, 96, 97, 98, 101, 103, 104, 109, 111, 112, 114, 115, 117, 118, 119, 122, 123, 127, 128, 129, 135, 136, 137, 139, 141, 143, 146, 147, 157, 158, 159, 160, 161, 162, 163, 165, 166, 169, 171, 178, 179, 181, 193, 209, 213, 218, 221, 230, 234, 247, 250, 261, 263, 270, 272], "startswith": [1, 83, 147, 165, 246], "parent": [1, 14, 104, 115, 142, 146, 183, 185], "As": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 19, 20, 21, 22, 23, 25, 43, 49, 50, 52, 58, 59, 60, 61, 73, 85, 87, 97, 103, 105, 108, 112, 116, 118, 122, 123, 124, 125, 126, 127, 133, 135, 136, 137, 141, 142, 143, 144, 145, 146, 149, 152, 153, 156, 157, 159, 160, 161, 162, 163, 164, 168, 171, 174, 175, 176, 177, 178, 179, 182, 184, 185, 187, 188, 192, 193, 195, 197, 200, 204, 207, 208, 212, 219, 221, 222, 254, 258], "mani": [1, 2, 4, 5, 6, 10, 14, 15, 17, 18, 23, 25, 49, 51, 52, 60, 61, 65, 69, 73, 82, 97, 99, 100, 101, 104, 105, 107, 111, 113, 122, 124, 126, 127, 129, 135, 137, 138, 145, 147, 149, 150, 154, 157, 159, 161, 162, 165, 173, 174, 176, 177, 191, 194, 201, 204, 205, 220, 221, 252, 260, 262, 263, 269, 271, 272], "expect": [1, 4, 5, 6, 10, 11, 14, 20, 22, 23, 32, 45, 47, 49, 51, 58, 59, 60, 61, 73, 85, 87, 97, 101, 102, 103, 112, 113, 117, 119, 126, 129, 133, 134, 136, 145, 146, 152, 153, 156, 158, 159, 160, 161, 162, 164, 171, 172, 173, 174, 176, 178, 179, 182, 187, 188, 194, 195, 197, 199, 200, 204, 205, 220, 223, 258], "similar": [1, 3, 5, 8, 10, 11, 14, 15, 19, 22, 23, 48, 49, 58, 59, 61, 82, 83, 97, 98, 103, 108, 116, 124, 130, 134, 135, 136, 139, 143, 149, 153, 159, 161, 162, 163, 164, 165, 168, 169, 171, 176, 178, 179, 182, 185, 189, 190, 191, 192, 193, 198, 199, 218, 219, 258], "structur": [1, 4, 5, 6, 8, 9, 14, 18, 19, 20, 21, 22, 23, 33, 48, 49, 52, 53, 60, 61, 78, 85, 97, 98, 102, 105, 110, 112, 121, 131, 136, 138, 143, 146, 147, 149, 153, 154, 156, 159, 163, 169, 171, 172, 178, 192, 194, 196, 197, 205, 208, 260, 262, 266, 269, 271, 275], "make": [1, 4, 5, 6, 8, 10, 12, 14, 18, 19, 22, 23, 43, 44, 45, 47, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 68, 69, 73, 87, 97, 99, 100, 101, 102, 103, 108, 111, 112, 113, 114, 115, 117, 118, 121, 122, 123, 124, 126, 127, 128, 129, 133, 135, 136, 137, 139, 142, 143, 144, 145, 146, 149, 152, 153, 155, 156, 157, 159, 160, 161, 162, 163, 164, 165, 169, 171, 172, 173, 174, 176, 178, 182, 184, 185, 188, 189, 190, 191, 192, 193, 194, 195, 197, 200, 205, 212, 213, 214, 215, 216, 218, 219, 220, 223, 227, 228, 252, 254, 256, 262, 264, 271, 273], "possibl": [1, 2, 4, 5, 6, 8, 10, 14, 15, 17, 22, 23, 52, 60, 61, 75, 98, 101, 108, 119, 125, 129, 130, 136, 138, 141, 143, 145, 146, 149, 157, 158, 159, 161, 162, 165, 178, 182, 185, 187, 193, 197, 198, 199, 200, 201, 202, 204, 207, 216, 220, 221, 223, 252, 254, 262, 271], "across": [1, 5, 7, 8, 9, 11, 14, 16, 18, 20, 24, 49, 52, 54, 55, 56, 61, 82, 97, 105, 115, 120, 122, 123, 124, 131, 132, 133, 134, 135, 138, 146, 149, 156, 162, 163, 175, 176, 181, 211, 214, 215, 235, 258, 260, 269], "modal": [1, 60], "complex": [1, 6, 23, 25, 50, 61, 67, 68, 97, 105, 112, 120, 123, 133, 150, 153, 161, 163, 169, 193, 203, 209, 254], "multipl": [1, 5, 8, 10, 11, 14, 16, 17, 18, 19, 20, 23, 40, 45, 48, 49, 53, 54, 55, 56, 61, 65, 78, 79, 81, 82, 87, 97, 101, 110, 120, 123, 124, 125, 126, 127, 128, 133, 134, 135, 138, 139, 143, 144, 146, 149, 158, 159, 161, 162, 163, 165, 168, 169, 171, 173, 174, 175, 176, 177, 182, 184, 193, 199, 200, 201, 207, 214, 219, 235, 250, 262, 263, 271, 272], "entri": [1, 4, 11, 14, 23, 53, 75, 98, 101, 103, 109, 110, 112, 115, 131, 136, 143, 144, 156, 159, 161, 164, 168, 173, 174, 191, 192, 193, 195, 212], "word": [1, 6, 7, 10, 11, 14, 42, 44, 49, 60, 73, 79, 82, 97, 98, 100, 102, 112, 115, 116, 118, 121, 127, 128, 135, 137, 143, 152, 153, 156, 163, 165, 176, 181, 190, 192, 193, 195, 199, 262, 271], "oblivi": [1, 159], "type": [1, 4, 5, 6, 8, 9, 10, 14, 18, 19, 20, 21, 22, 23, 37, 38, 40, 42, 48, 49, 50, 51, 52, 60, 61, 73, 78, 80, 82, 85, 95, 101, 105, 108, 113, 118, 120, 122, 123, 124, 126, 134, 137, 138, 139, 142, 143, 144, 147, 148, 154, 155, 156, 159, 161, 162, 163, 164, 168, 171, 172, 173, 174, 175, 177, 179, 181, 185, 187, 189, 194, 197, 199, 200, 202, 204, 207, 208, 209, 212, 213, 214, 216, 220, 221, 222, 223, 228, 244, 257, 262, 271], "being": [1, 3, 4, 5, 6, 10, 12, 14, 17, 20, 21, 23, 42, 47, 49, 52, 58, 59, 60, 76, 80, 82, 97, 98, 99, 101, 103, 105, 110, 113, 117, 122, 124, 126, 129, 135, 136, 142, 153, 156, 159, 160, 162, 177, 185, 188, 190, 191, 193, 195, 199, 201, 202, 220], "run": [1, 2, 3, 4, 5, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 21, 23, 24, 25, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 47, 48, 51, 52, 56, 57, 61, 63, 64, 65, 67, 68, 69, 71, 72, 75, 76, 78, 79, 80, 82, 87, 89, 90, 92, 93, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 107, 108, 109, 111, 112, 113, 114, 116, 117, 118, 119, 121, 122, 123, 124, 125, 128, 129, 130, 131, 132, 134, 135, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 169, 172, 173, 175, 176, 177, 178, 181, 182, 183, 184, 186, 187, 188, 189, 190, 191, 192, 193, 195, 196, 197, 198, 199, 200, 201, 203, 204, 205, 206, 207, 208, 209, 213, 214, 215, 216, 218, 219, 220, 222, 223, 225, 227, 228, 229, 230, 231, 232, 233, 234, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 252, 254, 255, 262, 271], "elementari": [1, 2], "those": [1, 4, 5, 6, 10, 11, 14, 17, 42, 43, 61, 79, 87, 98, 103, 113, 115, 116, 124, 125, 127, 135, 138, 143, 152, 153, 155, 156, 163, 165, 169, 171, 173, 174, 177, 182, 184, 188, 190, 201, 202, 204, 205, 206, 207, 221, 223, 262, 271], "keep": [1, 6, 7, 10, 11, 14, 23, 43, 49, 51, 52, 60, 61, 73, 82, 85, 95, 97, 99, 101, 102, 108, 112, 116, 119, 121, 122, 123, 124, 125, 127, 128, 132, 133, 136, 142, 144, 150, 157, 159, 163, 165, 177, 181, 182, 197, 208, 218, 257, 258], "didact": [1, 135], "displai": [1, 2, 5, 6, 12, 14, 34, 44, 52, 58, 75, 108, 109, 117, 129, 139, 157, 160, 165, 168, 212, 257, 260, 269], "each": [1, 2, 5, 6, 7, 8, 10, 11, 12, 14, 16, 17, 18, 19, 21, 23, 24, 25, 34, 43, 44, 45, 48, 49, 51, 52, 53, 55, 56, 58, 59, 60, 61, 65, 68, 73, 75, 76, 79, 82, 83, 85, 87, 97, 98, 99, 102, 103, 107, 108, 109, 111, 112, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 126, 127, 128, 130, 131, 132, 133, 134, 135, 136, 138, 141, 142, 143, 145, 146, 147, 149, 150, 152, 153, 154, 156, 157, 159, 160, 161, 162, 163, 164, 165, 166, 168, 169, 171, 173, 174, 175, 176, 177, 178, 185, 186, 189, 192, 193, 195, 197, 198, 200, 201, 208, 209, 211, 212, 215, 216, 220, 221, 231, 252, 257, 258, 260, 262, 269, 271], "popul": [1, 14, 22, 43, 49, 58, 59, 87, 122, 136, 146, 159, 161, 211, 216], "later": [1, 3, 4, 5, 6, 11, 23, 47, 49, 52, 60, 73, 78, 87, 97, 101, 102, 112, 113, 123, 124, 127, 128, 129, 130, 134, 135, 138, 141, 142, 143, 144, 145, 146, 150, 154, 159, 160, 163, 164, 165, 169, 171, 173, 174, 182, 189, 197, 198, 210, 211, 223, 228, 232, 254], "stage": [1, 7, 14, 16, 148, 186, 188, 206, 212], "start": [1, 4, 5, 6, 9, 11, 14, 16, 17, 18, 19, 23, 24, 25, 43, 44, 49, 50, 52, 53, 54, 55, 59, 60, 61, 73, 87, 97, 98, 100, 101, 105, 113, 116, 120, 121, 122, 124, 125, 126, 127, 128, 129, 134, 135, 137, 139, 143, 144, 145, 146, 148, 149, 152, 153, 157, 158, 160, 161, 162, 165, 168, 169, 171, 172, 173, 176, 177, 178, 182, 184, 185, 187, 191, 195, 197, 198, 199, 200, 201, 203, 208, 212, 216, 219, 223, 239, 254, 258, 263, 272], "solv": [1, 6, 14, 49, 51, 97, 103, 117, 118, 149, 153, 157, 159, 161, 163, 176, 191], "task": [1, 6, 7, 13, 14, 17, 21, 24, 49, 58, 59, 60, 75, 97, 98, 103, 109, 113, 116, 117, 118, 119, 120, 121, 123, 136, 137, 153, 157, 159, 160, 165, 166, 171, 178, 185, 201, 204, 208], "strategi": [1, 5, 17, 18, 24, 52, 82, 113, 121, 128, 135, 144, 145, 149, 154, 161, 162, 201, 207, 215, 216, 221], "predict": [1, 9, 17, 19, 20, 33, 37, 38, 43, 44, 49, 52, 60, 63, 64, 65, 67, 68, 69, 71, 72, 73, 87, 89, 90, 92, 97, 98, 102, 103, 104, 111, 113, 115, 116, 118, 121, 124, 126, 127, 128, 137, 138, 145, 146, 149, 154, 160, 165, 169, 178, 181, 182, 197, 198, 201, 213, 219, 256, 257], "henc": [1, 14, 17, 43, 48, 61, 78, 80, 82, 113, 123, 125, 133, 134, 147, 149, 150, 155, 159, 161, 163, 176, 201, 219, 220], "two": [1, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 23, 24, 25, 40, 42, 43, 48, 49, 50, 52, 57, 58, 59, 60, 73, 75, 83, 85, 87, 92, 95, 97, 98, 99, 101, 102, 103, 105, 107, 108, 109, 116, 117, 119, 122, 123, 126, 127, 129, 130, 132, 133, 134, 135, 136, 139, 141, 143, 144, 145, 146, 147, 149, 150, 153, 154, 155, 156, 157, 159, 160, 161, 163, 164, 165, 171, 175, 176, 177, 178, 179, 183, 184, 186, 190, 192, 193, 195, 197, 198, 199, 200, 201, 202, 203, 207, 212, 215, 216, 219, 221, 224, 225, 229, 252, 258, 262, 263, 267, 271, 272, 276], "constructor": [1, 6, 10, 11, 12, 21, 22, 23, 25, 60, 65, 67, 69, 78, 85, 111, 116, 122, 123, 133, 134, 143, 155, 156, 159, 161, 163, 192, 202, 252], "both": [1, 2, 4, 5, 6, 7, 8, 10, 11, 12, 14, 16, 19, 20, 21, 22, 23, 24, 25, 42, 49, 51, 52, 58, 59, 60, 61, 73, 82, 85, 97, 103, 109, 113, 116, 118, 122, 124, 126, 127, 129, 132, 133, 134, 135, 141, 142, 144, 145, 147, 149, 150, 156, 157, 159, 161, 162, 163, 164, 165, 173, 174, 175, 176, 177, 178, 179, 182, 184, 185, 186, 189, 192, 194, 195, 197, 199, 200, 209, 212, 215, 219, 220, 221, 223, 228, 231, 260, 262, 269, 271], "compat": [1, 4, 5, 6, 8, 11, 17, 50, 60, 94, 95, 101, 136, 147, 164, 173, 174, 182, 187, 202, 204, 216, 222], "comput": [1, 3, 5, 6, 8, 11, 12, 13, 16, 17, 19, 20, 21, 23, 24, 25, 32, 37, 38, 40, 44, 47, 48, 49, 52, 53, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, 69, 71, 72, 73, 75, 76, 78, 79, 83, 85, 95, 97, 98, 99, 100, 102, 105, 107, 110, 111, 115, 117, 119, 120, 121, 122, 123, 124, 125, 126, 129, 130, 131, 132, 133, 135, 136, 137, 139, 141, 142, 143, 144, 146, 148, 149, 152, 153, 154, 156, 158, 159, 160, 162, 164, 165, 168, 171, 172, 173, 174, 176, 177, 178, 182, 184, 187, 188, 193, 194, 195, 196, 197, 198, 199, 201, 202, 205, 206, 207, 208, 210, 211, 216, 219, 223, 228, 231, 254, 262, 271], "fit": [1, 6, 7, 9, 10, 11, 12, 20, 24, 61, 87, 103, 122, 123, 124, 133, 148, 149, 163, 181, 262, 271], "crucial": [1, 2, 12, 14, 23, 82, 101, 136, 159, 223], "convert_to_funct": 1, "extract": [1, 5, 20, 49, 52, 58, 59, 73, 97, 116, 117, 127, 128, 137, 141, 144, 154, 157, 159, 165, 172, 173, 174, 178, 208, 212, 216], "convert": [1, 5, 9, 10, 12, 14, 19, 20, 22, 23, 44, 49, 51, 52, 55, 73, 75, 95, 97, 105, 107, 110, 112, 113, 115, 116, 118, 119, 121, 127, 128, 137, 139, 157, 158, 159, 160, 161, 162, 166, 169, 177, 178, 181, 183, 184, 185, 188, 189, 190, 192, 193, 196, 199, 200, 209, 216, 218, 220, 223, 224, 225, 227, 228, 252], "strictli": [1, 159], "speak": [1, 8, 43, 125, 135, 149], "perfectli": [1, 14, 65, 78, 111], "encourag": [1, 6, 19, 139, 160, 165, 171], "usag": [1, 3, 4, 11, 13, 15, 21, 23, 37, 60, 82, 109, 116, 121, 123, 125, 135, 136, 144, 145, 159, 161, 163, 164, 168, 177, 184, 185, 188, 193, 194, 195, 199, 207, 220, 256, 262, 271], "doe": [1, 2, 5, 6, 8, 13, 14, 15, 19, 22, 23, 25, 43, 47, 60, 61, 73, 79, 80, 85, 97, 98, 99, 101, 103, 105, 108, 112, 113, 117, 122, 123, 130, 133, 134, 135, 136, 139, 142, 145, 146, 147, 149, 152, 153, 158, 159, 160, 162, 163, 164, 165, 168, 169, 172, 173, 174, 176, 178, 182, 183, 184, 190, 191, 192, 197, 199, 202, 203, 205, 208, 216, 223, 225, 228, 231, 262, 271], "often": [1, 4, 5, 6, 10, 14, 17, 49, 73, 87, 97, 99, 101, 103, 112, 113, 124, 125, 126, 128, 146, 153, 177, 193, 201, 203, 210, 216, 262, 271], "same": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 17, 19, 20, 21, 22, 23, 25, 32, 40, 43, 44, 47, 49, 51, 52, 53, 55, 58, 59, 60, 65, 73, 78, 80, 85, 87, 95, 97, 98, 99, 101, 102, 103, 105, 108, 109, 111, 112, 118, 119, 122, 123, 124, 125, 127, 129, 132, 133, 135, 137, 138, 139, 141, 144, 145, 146, 147, 149, 152, 153, 154, 155, 156, 158, 159, 160, 161, 162, 163, 164, 165, 166, 168, 169, 171, 172, 173, 174, 176, 177, 179, 181, 182, 185, 186, 189, 190, 191, 192, 193, 194, 197, 198, 199, 200, 201, 208, 214, 218, 222, 223, 228, 231, 252, 256, 258, 260, 262, 269, 271], "usual": [1, 5, 6, 8, 19, 23, 43, 58, 59, 60, 61, 99, 100, 102, 103, 113, 117, 124, 125, 128, 129, 130, 133, 135, 136, 144, 147, 152, 156, 157, 159, 166, 195, 205, 260, 269], "former": [1, 5, 61, 79, 127, 128, 165], "lag": [1, 159], "absolut": [1, 6, 7, 10, 82, 99, 126, 156, 160, 208], "dilut": 1, "averag": [1, 3, 19, 49, 52, 61, 82, 87, 97, 113, 115, 123, 127, 128, 129, 135, 137, 143, 146, 154, 159, 160, 163, 165, 166, 168, 176, 177, 182, 197, 198], "associ": [1, 5, 6, 8, 10, 17, 50, 82, 130, 141, 142, 156, 164, 171, 190, 201, 202], "One": [1, 2, 4, 5, 6, 7, 10, 11, 15, 21, 23, 49, 51, 60, 61, 73, 79, 82, 97, 98, 99, 101, 122, 123, 124, 125, 128, 133, 135, 137, 138, 142, 143, 149, 152, 153, 166, 169, 172, 177, 178, 191, 195, 200, 205, 209, 221, 223, 262, 263, 271, 272], "advantag": [1, 3, 6, 14, 17, 23, 49, 60, 85, 95, 98, 107, 120, 122, 125, 135, 136, 153, 159, 172, 177, 182, 185, 192, 201, 209, 220, 222, 257], "match": [1, 4, 5, 10, 14, 17, 19, 20, 22, 49, 51, 58, 59, 60, 61, 68, 75, 76, 92, 97, 105, 108, 111, 112, 113, 134, 137, 138, 142, 144, 147, 149, 152, 154, 159, 162, 172, 173, 174, 182, 185, 190, 192, 195, 197, 201, 219, 220], "exactli": [1, 5, 7, 8, 10, 12, 17, 25, 43, 51, 52, 60, 78, 80, 101, 103, 105, 136, 144, 153, 174, 185, 201], "configur": [1, 4, 5, 6, 14, 18, 19, 20, 22, 23, 24, 42, 49, 50, 60, 61, 82, 113, 122, 124, 131, 133, 142, 144, 149, 152, 157, 159, 162, 168, 171, 176, 183, 184, 199, 200, 208, 212, 219, 220, 221, 225, 226, 254, 266, 275], "pessimist": [1, 159], "bound": [1, 23, 49, 112, 126, 144, 159, 160, 168, 173, 174, 176, 178, 184], "pai": [1, 10, 45, 49, 60, 115], "attent": [1, 7, 10, 42, 45, 49, 115, 118, 119, 121, 124, 136, 166, 184, 185, 193, 252, 254], "create_target_param": 1, "keyword": [1, 5, 156, 159, 171], "argument": [1, 2, 4, 5, 6, 8, 14, 21, 22, 23, 32, 43, 44, 48, 51, 55, 60, 69, 76, 78, 82, 89, 97, 99, 102, 103, 109, 111, 112, 115, 122, 123, 126, 127, 128, 132, 133, 135, 136, 138, 144, 145, 154, 155, 156, 159, 161, 162, 163, 164, 168, 171, 172, 173, 174, 179, 188, 191, 194, 199, 205, 206, 208, 209, 212, 222, 223, 254, 262, 263, 271, 272], "below": [1, 2, 4, 6, 10, 11, 12, 14, 16, 17, 18, 19, 20, 23, 24, 34, 43, 45, 48, 49, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 79, 82, 85, 97, 98, 99, 101, 102, 103, 105, 107, 108, 113, 114, 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176, 177, 179, 181, 182, 183, 184, 186, 192, 197, 200, 201, 218, 220, 221, 228, 252, 257], "paper": [1, 5, 12, 16, 17, 52, 58, 59, 61, 73, 97, 113, 114, 118, 119, 124, 137, 159, 162, 164, 165, 166, 193, 201], "found": [1, 4, 5, 6, 10, 16, 22, 23, 25, 42, 49, 50, 60, 79, 97, 113, 115, 116, 118, 126, 128, 133, 134, 135, 136, 137, 142, 143, 147, 160, 162, 163, 164, 171, 173, 174, 181, 182, 183, 185, 193, 206, 220, 222, 262, 271], "ddpgmlpactor": 1, "ddpgmlpqnet": 1, "materi": [1, 61, 123, 124, 145, 189, 202, 208], "achiev": [1, 3, 6, 14, 17, 19, 24, 32, 44, 49, 56, 82, 87, 97, 108, 119, 121, 125, 129, 135, 136, 137, 144, 149, 152, 153, 156, 157, 159, 160, 168, 176, 179, 182, 184, 185, 198, 199, 201, 203, 212, 219, 222, 254], "practic": [1, 5, 6, 11, 20, 23, 37, 47, 49, 51, 52, 58, 59, 60, 61, 97, 99, 114, 117, 121, 124, 125, 126, 127, 131, 134, 136, 137, 144, 153, 156, 159, 173, 174, 175, 177, 189, 190, 193, 195, 232], "fake": [1, 6, 12, 17, 19, 25, 47, 52, 78, 152, 157, 193, 197, 198, 200, 201, 228], "spec": [1, 108, 126, 136, 159, 163, 179, 200, 231], "ornsteinuhlenbeckprocesswrapp": 1, "probabilisticactor": [1, 159], "tanhdelta": 1, "make_ddpg_actor": 1, "proof_environ": 1, "out_featur": [1, 33, 109, 123, 136, 179, 201], "action_spec": [1, 14, 136, 159], "shape": [1, 4, 12, 17, 23, 34, 38, 40, 42, 43, 47, 48, 49, 51, 52, 58, 59, 60, 63, 68, 76, 85, 90, 92, 95, 97, 98, 101, 103, 104, 109, 111, 118, 122, 124, 127, 128, 136, 137, 138, 141, 142, 145, 146, 147, 150, 152, 154, 156, 159, 168, 171, 178, 184, 185, 187, 192, 193, 195, 197, 201, 202, 209, 219, 226, 231, 238, 239, 244, 254], "actor_net": [1, 159], "action_dim": [1, 146], "distribution_class": [1, 159], "q_net": 1, "qnet": 1, "initi": [1, 6, 8, 11, 14, 16, 18, 19, 20, 21, 22, 23, 25, 37, 43, 49, 55, 60, 64, 71, 72, 73, 80, 97, 98, 99, 102, 103, 109, 111, 112, 117, 122, 123, 124, 127, 128, 129, 136, 138, 142, 144, 147, 148, 152, 156, 157, 158, 160, 161, 162, 163, 165, 166, 168, 173, 174, 175, 189, 192, 196, 198, 203, 206, 214, 219, 223, 231, 262, 271], "reset": [1, 19, 117, 128, 136, 146, 159, 160, 161, 163, 165, 168, 172, 173, 174, 182, 186, 195, 197, 198, 221, 255], "suggest": [1, 4, 12, 52, 86, 97, 128, 137, 144, 145, 146, 147, 150, 157, 168, 171, 173, 174, 175], "nois": [1, 6, 12, 52, 73, 148, 195], "reach": [1, 10, 17, 52, 60, 61, 97, 133, 135, 137, 146, 147, 149, 159, 163, 173, 174, 201, 221], "minimum": [1, 49, 82, 159, 163, 173, 174, 177, 191], "annealing_fram": 1, "1_000_000": [1, 96, 136], "actor_model_explor": 1, "annealing_num_step": [1, 136], "share_memori": 1, "iter": [1, 3, 4, 6, 11, 12, 14, 16, 24, 34, 42, 43, 44, 47, 52, 53, 55, 60, 75, 85, 87, 92, 94, 96, 98, 103, 105, 112, 113, 116, 117, 118, 126, 127, 128, 131, 133, 134, 136, 142, 144, 149, 157, 159, 160, 161, 162, 163, 166, 168, 169, 172, 178, 181, 182, 194, 197, 198, 203, 211, 218, 223, 245, 258], "tight": [1, 105, 107, 108], "per": [1, 5, 6, 8, 11, 19, 61, 87, 97, 103, 121, 122, 126, 127, 128, 129, 133, 135, 136, 137, 145, 147, 152, 156, 159, 163, 164, 165, 168, 169, 171, 175, 176, 177, 178, 185, 189, 194, 205, 207, 212, 214, 216, 218, 221, 223, 231, 246, 258], "sync": [1, 7, 10, 11, 16, 55, 121, 122, 123, 142, 146, 188, 194, 257], "natur": [1, 5, 6, 17, 18, 23, 24, 25, 45, 61, 73, 75, 97, 107, 116, 119, 126, 127, 135, 136, 137, 162, 171, 191, 193, 197, 200, 201, 207, 262, 271], "resourc": [1, 53, 58, 59, 61, 73, 87, 105, 119, 123, 133, 135, 152, 159, 168, 171, 176, 216, 223, 236], "alloc": [1, 6, 18, 21, 22, 23, 48, 55, 59, 129, 135, 152, 168, 175, 176, 193, 202, 214, 223, 258], "gpu": [1, 3, 4, 7, 12, 17, 18, 19, 20, 24, 33, 38, 40, 42, 43, 47, 48, 49, 50, 52, 53, 54, 56, 57, 60, 61, 64, 72, 73, 77, 80, 81, 82, 83, 88, 92, 96, 97, 99, 105, 111, 114, 117, 120, 121, 122, 123, 124, 125, 131, 132, 133, 134, 135, 136, 137, 138, 144, 147, 148, 149, 150, 152, 154, 157, 159, 160, 162, 163, 164, 171, 172, 175, 177, 178, 185, 186, 196, 201, 206, 207, 210, 214, 216, 223, 230, 231, 234, 240, 251, 252, 254, 257], "worker": [1, 6, 7, 11, 16, 51, 52, 61, 115, 120, 122, 123, 134, 135, 147, 159, 162, 163, 168, 212, 216], "syncdatacollector": [1, 136, 159], "process": [1, 4, 5, 6, 11, 12, 14, 15, 16, 17, 18, 20, 22, 23, 24, 25, 42, 47, 49, 50, 51, 52, 56, 60, 61, 73, 82, 85, 97, 103, 105, 110, 112, 113, 114, 116, 118, 119, 120, 121, 122, 123, 125, 126, 127, 128, 131, 132, 135, 136, 137, 143, 144, 146, 147, 149, 154, 158, 160, 162, 163, 164, 165, 168, 171, 173, 174, 175, 176, 177, 182, 184, 185, 187, 188, 193, 195, 196, 201, 203, 204, 207, 208, 212, 213, 214, 215, 221, 228, 258, 261, 262, 270, 271], "offer": [1, 11, 14, 18, 42, 43, 53, 61, 99, 122, 124, 138, 141, 144, 145, 197, 214, 216], "multiasyncdatacollector": [1, 159], "rollout": [1, 136, 159], "asynchron": [1, 21, 61, 120, 121, 126, 134, 149, 155, 159, 163], "manner": [1, 5, 8, 14, 19, 61, 159, 171, 216], "therebi": [1, 186, 189, 193], "decoupl": [1, 61, 153, 197], "factori": [1, 6, 101, 115, 190, 191, 232], "empti": [1, 5, 6, 8, 14, 19, 21, 23, 49, 80, 95, 108, 128, 129, 143, 144, 147, 153, 158, 165, 168, 171, 173, 174, 176, 185, 191, 193, 202, 206, 238, 246, 263, 272], "maximum": [1, 11, 49, 60, 82, 102, 113, 126, 128, 136, 137, 144, 159, 164, 165, 173, 174, 185, 194, 195], "non": [1, 2, 3, 5, 8, 11, 14, 19, 22, 49, 51, 53, 54, 56, 60, 82, 85, 97, 98, 100, 103, 112, 113, 119, 122, 126, 129, 130, 134, 135, 136, 137, 139, 141, 145, 147, 150, 156, 157, 160, 161, 164, 165, 168, 172, 173, 176, 182, 184, 185, 189, 199, 202, 214, 228, 252, 263, 265, 272, 274], "termin": [1, 14, 23, 53, 60, 87, 159, 160, 162, 163, 171, 188, 206, 225], "effect": [1, 5, 6, 8, 9, 11, 23, 55, 73, 82, 97, 103, 108, 124, 127, 128, 138, 152, 154, 156, 160, 164, 165, 171, 176, 177, 191, 199, 200, 205, 260, 269], "regist": [1, 22, 43, 47, 78, 108, 109, 112, 121, 122, 124, 133, 141, 152, 153, 159, 173, 174, 177, 207, 208, 216, 220], "new": [1, 2, 4, 5, 6, 8, 9, 12, 13, 14, 22, 23, 24, 25, 31, 42, 43, 45, 48, 49, 50, 52, 55, 60, 62, 73, 75, 79, 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144, 145, 146, 152, 154, 162, 163, 165, 169, 177, 183, 185, 201, 219, 250], "equal": [1, 4, 10, 95, 97, 115, 116, 125, 137, 147, 156, 159, 160, 171, 173, 174, 176, 192, 216, 254], "length": [1, 7, 12, 14, 17, 20, 42, 45, 49, 52, 60, 102, 103, 105, 113, 115, 116, 124, 128, 136, 137, 146, 159, 164, 165, 175, 185, 191, 193, 196, 201, 205, 208, 216, 251, 263, 272], "sub": [1, 6, 20, 25, 49, 60, 109, 124, 143, 149, 159, 163, 165, 168, 176, 177, 185, 215, 246, 262, 271], "traj_len": [1, 136], "200": [1, 6, 9, 90, 93, 136, 147, 163, 184, 194, 219], "init_random_fram": 1, "num_collector": 1, "explorationtyp": [1, 136, 159], "reset_at_each_it": 1, "split_traj": [1, 159], "exploration_typ": 1, "assess": 1, "mode": [1, 4, 7, 9, 12, 13, 16, 20, 37, 42, 43, 49, 51, 52, 55, 60, 73, 79, 82, 85, 86, 87, 97, 112, 115, 116, 117, 121, 122, 129, 130, 134, 136, 139, 142, 144, 146, 147, 150, 157, 161, 164, 165, 166, 169, 171, 172, 174, 177, 179, 184, 187, 188, 194, 195, 196, 198, 199, 200, 216, 219, 221, 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190, 191, 192, 193, 195, 203, 205, 211, 229, 230, 231, 232, 233, 234, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 254, 255], "notebook": [1, 7, 9, 12, 13, 14, 17, 18, 20, 23, 24, 25, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 47, 48, 49, 50, 51, 52, 60, 63, 64, 65, 67, 68, 69, 71, 72, 73, 75, 76, 78, 79, 80, 82, 83, 85, 87, 89, 90, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 107, 108, 109, 112, 113, 114, 115, 116, 117, 118, 119, 125, 126, 127, 128, 129, 136, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 152, 153, 154, 156, 159, 160, 164, 165, 166, 168, 169, 171, 172, 174, 178, 181, 184, 189, 190, 191, 192, 193, 195, 203, 205, 211, 229, 230, 231, 232, 233, 234, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 254, 255, 257], "ipynb": [1, 7, 9, 12, 13, 14, 17, 20, 24, 25, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 47, 48, 49, 51, 52, 60, 63, 64, 65, 67, 68, 69, 71, 72, 73, 75, 76, 78, 79, 80, 85, 87, 89, 90, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 107, 108, 109, 112, 113, 114, 115, 116, 117, 118, 119, 125, 126, 127, 128, 129, 136, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 152, 153, 154, 156, 159, 160, 164, 165, 166, 168, 172, 174, 178, 181, 184, 189, 190, 191, 192, 193, 195, 203, 205, 211, 229, 230, 231, 232, 233, 234, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 254, 255], "galleri": [1, 7, 9, 12, 13, 14, 17, 20, 24, 25, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 47, 48, 49, 51, 52, 60, 63, 64, 65, 67, 68, 69, 71, 72, 73, 75, 76, 78, 79, 80, 85, 87, 89, 90, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 107, 108, 109, 112, 113, 114, 115, 116, 117, 118, 119, 125, 126, 127, 128, 129, 136, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 152, 153, 154, 156, 159, 160, 164, 165, 166, 168, 172, 174, 178, 181, 184, 189, 190, 191, 192, 193, 195, 203, 205, 211, 229, 230, 231, 232, 233, 234, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 254, 255], "sphinx": [1, 7, 9, 12, 13, 14, 17, 20, 24, 25, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 47, 48, 49, 51, 52, 60, 63, 64, 65, 67, 68, 69, 71, 72, 73, 75, 76, 78, 79, 80, 85, 87, 89, 90, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 107, 108, 109, 112, 113, 114, 115, 116, 117, 118, 119, 125, 126, 127, 128, 129, 136, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 152, 153, 154, 156, 159, 160, 164, 165, 166, 168, 172, 174, 178, 181, 184, 189, 190, 191, 192, 193, 195, 203, 205, 211, 229, 230, 231, 232, 233, 234, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 254, 255, 260, 261, 262, 263, 267, 269, 270, 271, 272, 276], "build": [2, 6, 7, 12, 14, 19, 20, 25, 35, 47, 49, 60, 61, 65, 68, 69, 75, 76, 78, 81, 87, 91, 95, 98, 103, 109, 111, 113, 114, 115, 118, 120, 121, 127, 128, 134, 135, 136, 137, 139, 141, 152, 154, 159, 160, 161, 162, 163, 174, 175, 181, 182, 185, 187, 188, 193, 194, 196, 197, 199, 205, 220, 223, 224, 225, 246, 254, 261, 266, 270, 275], "highli": [2, 5, 6, 10, 18, 49, 60, 87, 165, 175, 177, 205], "dynam": [2, 4, 5, 6, 8, 12, 14, 15, 19, 20, 22, 23, 25, 43, 53, 61, 65, 97, 100, 107, 111, 112, 119, 121, 147, 156, 159, 179, 182, 183, 184, 196, 197, 198, 199, 208, 220, 221, 222, 235, 254], "explor": [2, 6, 8, 14, 21, 23, 45, 49, 73, 108, 121, 126, 130, 136, 144, 146, 152, 159, 164, 165, 254], "note": [2, 4, 5, 6, 7, 8, 9, 12, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 43, 44, 45, 47, 49, 50, 52, 61, 73, 85, 99, 100, 101, 102, 103, 108, 109, 112, 113, 116, 117, 122, 123, 124, 125, 126, 129, 130, 132, 133, 134, 135, 137, 138, 141, 142, 144, 145, 147, 149, 152, 154, 155, 156, 157, 161, 162, 163, 168, 169, 171, 172, 173, 174, 175, 176, 177, 178, 179, 181, 182, 183, 185, 187, 188, 189, 190, 191, 192, 193, 197, 198, 199, 201, 202, 203, 204, 205, 207, 212, 214, 215, 216, 222, 238, 252, 257, 258, 260, 262, 269, 271], "differenti": [2, 5, 6, 14, 18, 25, 35, 40, 46, 47, 57, 76, 121, 136, 154, 160, 166, 191], "requires_grad": [2, 6, 7, 8, 12, 13, 20, 32, 37, 43, 47, 63, 64, 68, 73, 76, 89, 95, 101, 104, 105, 108, 110, 111, 117, 125, 129, 130, 141, 146, 147, 157, 178, 191, 201, 205, 237, 244, 250], "track": [2, 5, 7, 8, 9, 14, 43, 52, 63, 82, 99, 101, 110, 111, 117, 122, 127, 128, 132, 136, 142, 157, 163, 165, 168, 208, 257], "auto": [2, 3, 5, 6, 8, 10, 12, 22, 55, 59, 122, 123, 144, 155, 186, 187, 188, 206, 208, 220, 221, 231, 246, 262, 263, 271, 272], "cout": [2, 4, 6, 22, 23, 187, 256], "endl": [2, 6, 22, 23, 187, 208], "cpufloattyp": [2, 4, 6, 23, 208], "wa": [2, 3, 4, 5, 11, 17, 20, 22, 23, 25, 42, 44, 49, 51, 52, 58, 59, 60, 61, 73, 76, 79, 95, 97, 98, 99, 101, 108, 112, 113, 115, 116, 123, 124, 126, 133, 135, 146, 150, 152, 153, 154, 156, 158, 159, 160, 163, 164, 165, 169, 176, 177, 184, 188, 191, 192, 198, 201, 208, 223, 231, 257, 262, 271], "result": [2, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 17, 19, 20, 21, 23, 24, 25, 43, 44, 51, 58, 59, 60, 63, 64, 65, 67, 68, 69, 71, 72, 76, 78, 82, 83, 87, 97, 101, 107, 108, 111, 112, 114, 116, 119, 122, 124, 125, 128, 133, 135, 136, 137, 141, 142, 143, 144, 145, 146, 147, 149, 150, 154, 155, 156, 157, 158, 160, 161, 162, 163, 164, 169, 171, 172, 173, 174, 175, 176, 177, 178, 179, 182, 184, 188, 190, 191, 192, 193, 195, 197, 198, 199, 200, 202, 203, 204, 205, 206, 208, 209, 210, 212, 218, 219, 221, 222, 223, 228, 231, 233, 238, 241, 246, 252, 256, 258, 260, 269], "grad_fn": [2, 4, 25, 32, 43, 47, 76, 89, 101, 125, 130, 173, 185], "addbackward1": 2, "z": [2, 5, 7, 23, 32, 43, 49, 52, 60, 76, 80, 85, 89, 92, 95, 101, 147, 165, 174, 191, 203, 208, 255, 263, 272], "27": [2, 7, 51, 144, 163, 176, 184, 219, 228, 231], "mulbackward1": 2, "meanbackward0": 2, "requires_grad_": [2, 12, 32, 76, 101, 104, 145], "flag": [2, 5, 14, 23, 43, 73, 76, 101, 137, 150, 153, 165, 174, 176, 185, 196, 198, 204], "place": [2, 5, 6, 11, 12, 14, 18, 22, 23, 43, 45, 48, 49, 52, 76, 78, 85, 99, 101, 108, 113, 116, 118, 122, 126, 129, 133, 135, 138, 148, 149, 152, 154, 156, 157, 159, 160, 165, 171, 172, 175, 182, 189, 197, 198, 199, 205, 208, 212, 214, 252, 262, 263, 264, 271, 272, 273], "randn": [2, 5, 6, 12, 13, 20, 23, 32, 45, 47, 52, 63, 65, 67, 71, 72, 76, 78, 80, 89, 97, 98, 99, 101, 102, 104, 105, 108, 110, 111, 125, 133, 134, 138, 141, 142, 143, 144, 145, 149, 150, 154, 161, 163, 164, 172, 173, 174, 184, 186, 191, 193, 197, 198, 199, 205, 208, 212, 230, 231, 232, 234, 238, 239, 245, 254, 258], "b": [2, 5, 6, 7, 12, 18, 21, 23, 32, 43, 47, 63, 64, 65, 67, 71, 72, 76, 80, 83, 89, 92, 93, 95, 98, 99, 102, 103, 104, 109, 110, 111, 125, 127, 128, 129, 142, 144, 145, 147, 149, 158, 160, 172, 174, 191, 193, 194, 203, 209, 231, 238, 246, 263, 272], "sumbackward0": 2, "backprop": [2, 43, 71, 72, 76, 98, 101, 111, 127, 146], "scalar": [2, 5, 14, 15, 23, 32, 43, 49, 52, 60, 63, 76, 101, 111, 169, 197, 206], "equival": [2, 4, 5, 11, 13, 17, 22, 23, 32, 43, 99, 137, 141, 154, 160, 162, 171, 173, 174, 185, 186, 189, 191, 193, 198, 199, 200, 201], "print": [2, 4, 5, 6, 7, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 25, 32, 33, 34, 37, 38, 40, 42, 43, 44, 45, 47, 48, 49, 51, 52, 53, 58, 59, 63, 64, 65, 67, 68, 69, 71, 72, 73, 75, 76, 78, 80, 85, 87, 89, 90, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 107, 108, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 122, 123, 125, 127, 128, 129, 132, 133, 134, 135, 136, 137, 138, 139, 142, 143, 144, 145, 146, 147, 148, 150, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 169, 172, 173, 174, 175, 176, 177, 178, 179, 181, 182, 184, 185, 188, 189, 190, 191, 192, 193, 194, 195, 197, 198, 201, 203, 206, 208, 209, 210, 211, 212, 214, 215, 218, 219, 220, 221, 223, 228, 230, 231, 232, 233, 234, 236, 237, 238, 239, 240, 241, 242, 244, 246, 247, 249, 250, 252, 254, 255, 256, 258, 262, 271], "dx": [2, 64, 76, 99, 111, 130, 174], "got": [2, 15, 19, 55, 98, 99, 101, 113, 133, 146, 147, 155, 162, 188, 197, 200, 213, 262, 271], "matrix": [2, 5, 6, 12, 17, 23, 32, 40, 43, 48, 82, 92, 98, 99, 101, 103, 109, 110, 124, 127, 128, 145, 150, 153, 164, 165, 166, 171, 173, 174, 176, 177, 192, 193, 199, 201, 205, 219, 220, 221, 226], "explan": [2, 52, 61, 160, 169], "arriv": [2, 6, 15, 23, 42, 113, 122, 133, 161, 208], "valu": [2, 4, 5, 6, 7, 9, 12, 14, 18, 19, 20, 21, 22, 23, 25, 32, 33, 40, 41, 43, 47, 48, 49, 50, 55, 58, 59, 60, 61, 63, 68, 73, 80, 82, 85, 87, 92, 97, 98, 99, 101, 102, 103, 105, 108, 111, 113, 115, 124, 125, 126, 127, 129, 132, 133, 135, 137, 138, 139, 141, 143, 146, 149, 150, 154, 155, 156, 160, 161, 162, 163, 164, 165, 168, 171, 172, 173, 174, 175, 182, 183, 185, 187, 189, 190, 191, 192, 193, 195, 196, 197, 198, 200, 203, 204, 208, 209, 211, 216, 222, 258, 260, 269], "section": [2, 4, 5, 6, 7, 8, 11, 15, 18, 19, 21, 23, 43, 44, 47, 50, 51, 52, 73, 95, 97, 98, 99, 102, 103, 108, 113, 116, 118, 125, 129, 135, 139, 141, 144, 146, 150, 157, 160, 161, 163, 168, 171, 173, 176, 177, 178, 179, 182, 188, 190, 191, 200, 202, 207, 211, 220, 260, 262, 263, 264, 266, 269, 271, 272, 273, 275], "jacobian": [2, 43, 89, 121, 141, 205], "product": [2, 3, 4, 6, 12, 40, 42, 43, 48, 60, 61, 85, 99, 105, 113, 121, 122, 135, 139, 141, 165, 175, 176, 177, 185, 199, 200, 205, 209, 231, 251, 254, 257], "1021": 2, "4020": 2, "314": 2, "6695": 2, "613": [2, 219], "4944": [2, 208], "0001": [2, 19, 49, 87, 89, 118, 144, 221], "kfloat": [2, 3, 59, 186, 188, 206], "102": 2, "4000": [2, 49, 60, 92, 246], "1024": [2, 5, 18, 21, 42, 82, 97, 129, 147, 164, 184, 199, 208, 210, 211, 231, 239], "0000": [2, 23, 173, 201, 208, 263, 272], "stop": [2, 4, 5, 23, 51, 58, 59, 76, 78, 87, 98, 101, 110, 126, 128, 135, 147, 152, 159, 161, 165, 168], "histori": [2, 9, 47, 48, 101, 110, 113, 117, 128, 146, 156, 157, 165, 181], "nogradguard": [2, 256], "block": [2, 5, 6, 7, 8, 10, 12, 16, 17, 19, 22, 23, 47, 49, 75, 76, 82, 90, 101, 115, 116, 123, 124, 134, 135, 136, 144, 157, 161, 162, 163, 164, 168, 171, 184, 201, 207, 208, 212, 266, 275], "no_grad": [2, 7, 9, 12, 17, 19, 32, 37, 38, 42, 43, 44, 52, 58, 59, 63, 64, 68, 76, 87, 89, 92, 96, 97, 98, 99, 101, 102, 104, 110, 111, 115, 117, 122, 123, 127, 128, 129, 136, 137, 144, 146, 157, 158, 159, 160, 162, 165, 166, 169, 172, 174, 177, 178, 181, 182, 184, 185, 194, 197, 198, 199, 202, 216, 220, 247, 253, 256], "Or": [2, 21, 23, 152, 163, 179, 198, 206, 208, 262, 271], "eq": [2, 19, 23, 49, 60, 95, 123, 129, 162, 166, 173, 182, 197, 198, 221, 238, 262, 271], "bool": [2, 11, 14, 15, 17, 23, 95, 109, 118, 137, 143, 146, 155, 159, 160, 164, 171, 179, 185, 190, 192, 201, 208, 252, 260, 269], "is_leaf": 2, "detach_": [2, 163], "register_hook": 2, "retain_grad": 2, "doc": [2, 4, 6, 32, 33, 34, 37, 38, 40, 60, 69, 94, 104, 109, 111, 132, 135, 142, 143, 161, 163, 171, 174, 181, 193, 205, 230, 237, 260, 261, 262, 267, 269, 270, 271, 276], "calcul": [2, 12, 17, 43, 44, 49, 52, 56, 60, 73, 82, 85, 87, 97, 110, 127, 128, 137, 143, 146, 160, 161, 163, 164, 165, 171, 177, 182, 191, 193, 197, 200, 201, 215, 221], "penalti": [2, 153, 158], "h": [2, 4, 5, 6, 7, 8, 9, 10, 12, 22, 23, 25, 38, 49, 51, 96, 124, 129, 137, 144, 146, 147, 155, 178, 181, 185, 188, 208, 213, 220, 222, 225, 246, 256], "model": [2, 3, 5, 8, 11, 14, 16, 22, 23, 24, 33, 35, 37, 38, 39, 42, 43, 44, 47, 48, 52, 53, 54, 56, 61, 65, 67, 68, 69, 75, 78, 86, 87, 89, 90, 91, 93, 95, 96, 98, 99, 100, 101, 104, 106, 107, 108, 109, 110, 111, 116, 118, 119, 120, 121, 123, 126, 127, 128, 129, 132, 135, 139, 141, 142, 144, 145, 148, 152, 153, 154, 158, 159, 160, 161, 162, 163, 164, 172, 173, 174, 176, 177, 183, 184, 186, 193, 196, 199, 200, 201, 204, 205, 207, 212, 213, 214, 215, 216, 219, 222, 227, 228, 229, 230, 234, 235, 238, 241, 242, 243, 245, 248, 249, 250, 251, 253, 254, 256, 258], "linear": [2, 5, 6, 7, 9, 11, 16, 17, 19, 25, 37, 38, 43, 44, 45, 47, 48, 49, 60, 68, 69, 73, 78, 79, 87, 89, 92, 93, 94, 96, 97, 98, 100, 102, 103, 105, 109, 110, 111, 112, 115, 117, 118, 119, 123, 124, 125, 127, 128, 129, 133, 134, 137, 138, 141, 144, 145, 146, 148, 149, 150, 153, 154, 156, 157, 160, 161, 162, 163, 164, 165, 166, 169, 172, 173, 174, 177, 179, 181, 182, 184, 185, 189, 193, 195, 197, 198, 199, 200, 201, 202, 203, 205, 207, 209, 210, 211, 212, 214, 215, 218, 219, 220, 221, 223, 226, 228, 230, 232, 233, 234, 237, 239, 240, 241, 242, 243, 244, 245, 248, 249, 250, 252, 258], "loss": [2, 3, 5, 6, 7, 9, 11, 14, 16, 17, 19, 32, 38, 43, 48, 63, 64, 65, 67, 68, 69, 71, 72, 73, 75, 78, 87, 89, 92, 94, 96, 99, 102, 103, 104, 111, 112, 115, 117, 118, 121, 122, 123, 125, 127, 129, 134, 135, 146, 147, 148, 149, 152, 154, 157, 160, 162, 163, 165, 166, 168, 169, 172, 178, 181, 182, 188, 191, 197, 198, 201, 216, 220, 221, 230, 241, 245, 250, 253, 258], "target": [2, 3, 4, 6, 9, 12, 14, 16, 18, 19, 22, 23, 44, 47, 49, 55, 60, 73, 78, 90, 94, 97, 98, 99, 102, 103, 104, 113, 116, 118, 123, 127, 128, 129, 134, 135, 136, 138, 142, 144, 152, 154, 155, 158, 160, 161, 162, 163, 165, 166, 169, 171, 172, 173, 174, 178, 179, 181, 182, 188, 197, 198, 199, 200, 204, 206, 208, 220, 221, 222, 225, 229, 230, 253, 256], "mseloss": [2, 12, 37, 47, 65, 67, 68, 69, 78, 97, 110, 111, 133, 134, 149, 161, 214, 230, 245, 258], "grad_output": [2, 8, 10, 13, 64, 76, 78, 111], "ones_lik": [2, 32, 40, 48, 95, 142, 191], "create_graph": [2, 130], "gradient_penalti": 2, "dim": [2, 4, 5, 11, 14, 21, 33, 40, 41, 45, 48, 49, 60, 73, 90, 92, 93, 94, 96, 97, 99, 102, 103, 104, 110, 115, 118, 123, 127, 128, 129, 134, 144, 147, 148, 149, 154, 156, 158, 159, 161, 162, 163, 164, 165, 166, 169, 171, 173, 174, 190, 191, 192, 193, 203, 206, 219, 221, 233, 256], "combined_loss": 2, "1042": 2, "0638": 2, "0103": 2, "0723": 2, "2543": 2, "1222": 2, "0071": 2, "0814": 2, "1683": 2, "1052": 2, "0355": 2, "document": [2, 4, 5, 6, 20, 47, 52, 60, 61, 79, 82, 85, 87, 101, 112, 113, 117, 121, 133, 135, 136, 139, 141, 143, 144, 157, 162, 163, 164, 168, 171, 172, 173, 174, 176, 177, 178, 179, 191, 197, 199, 205, 206, 209, 214, 218, 220, 221, 228, 252, 254, 257, 260, 262, 263, 267, 269, 271, 272, 276], "link": [2, 4, 5, 6, 10, 12, 22, 23, 52, 58, 59, 82, 105, 108, 114, 116, 118, 135, 139, 141, 191, 204, 206, 208, 220, 260, 261, 266, 269, 270, 275], "subclass": [2, 5, 6, 14, 17, 25, 64, 67, 79, 111, 121, 126, 136, 146, 156, 162, 169, 178, 191, 193, 201, 219, 254], "encod": [2, 7, 9, 14, 17, 42, 47, 48, 75, 76, 100, 104, 113, 118, 122, 126, 127, 128, 136, 153, 159, 163, 171, 178, 181, 184, 185, 195, 200, 201, 208, 230, 252], "method": [2, 4, 5, 6, 7, 8, 10, 11, 12, 14, 16, 17, 19, 21, 23, 25, 44, 47, 49, 51, 55, 58, 59, 60, 64, 65, 67, 73, 79, 83, 85, 90, 95, 97, 99, 101, 111, 112, 113, 115, 120, 121, 126, 130, 133, 136, 137, 139, 141, 142, 143, 144, 145, 146, 149, 153, 154, 155, 156, 157, 159, 160, 161, 162, 169, 171, 172, 173, 174, 176, 182, 183, 189, 197, 198, 200, 201, 203, 208, 209, 213, 221, 223, 224, 225, 228, 262, 271], "forward": [2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 19, 20, 22, 23, 25, 33, 37, 38, 43, 44, 45, 47, 49, 52, 56, 58, 59, 60, 61, 63, 64, 65, 67, 68, 69, 71, 72, 73, 75, 76, 79, 85, 87, 89, 92, 93, 94, 95, 96, 97, 98, 99, 100, 102, 103, 104, 105, 108, 111, 112, 115, 117, 118, 121, 122, 123, 124, 125, 127, 128, 129, 130, 131, 133, 134, 135, 138, 139, 142, 146, 148, 149, 150, 152, 153, 154, 156, 157, 160, 161, 162, 163, 164, 165, 166, 168, 169, 171, 172, 173, 174, 177, 179, 181, 182, 183, 185, 187, 188, 193, 194, 195, 197, 198, 199, 203, 204, 206, 208, 209, 211, 212, 213, 214, 215, 218, 219, 220, 221, 226, 228, 233, 234, 237, 238, 239, 240, 241, 242, 243, 248, 249, 250, 252, 256, 258], "detail": [2, 5, 6, 8, 10, 12, 15, 16, 20, 22, 23, 25, 43, 44, 45, 52, 53, 58, 59, 60, 61, 69, 73, 82, 83, 85, 111, 113, 116, 119, 123, 124, 126, 128, 129, 131, 133, 142, 144, 149, 150, 152, 157, 160, 163, 164, 168, 169, 172, 173, 174, 177, 179, 185, 188, 189, 190, 191, 192, 194, 198, 199, 205, 207, 208, 211, 218, 219, 220, 224, 225, 228, 231, 246, 252, 257], "namespac": [2, 6, 8, 10, 22, 23, 108, 137, 155, 179, 185, 193, 208, 256], "inherit": [2, 11, 15, 22, 51, 60, 85, 99, 143, 146, 149, 159, 171, 178, 191, 193, 195, 199, 216], "linearfunct": 2, "public": [2, 8, 10, 15, 155, 208, 263, 272], "static": [2, 8, 10, 14, 21, 58, 59, 60, 107, 121, 137, 141, 142, 155, 173, 174, 181, 183, 184, 185, 196, 197, 198, 199, 200, 208, 221, 247, 260, 269], "option": [2, 5, 6, 8, 10, 11, 14, 23, 44, 46, 49, 51, 53, 60, 61, 82, 87, 97, 109, 120, 121, 126, 131, 134, 136, 138, 144, 147, 149, 156, 158, 171, 173, 174, 182, 183, 184, 185, 193, 194, 197, 198, 199, 200, 204, 209, 212, 216, 218, 227, 252, 262, 266, 271, 275], "autogradcontext": [2, 8, 10], "ctx": [2, 5, 8, 10, 13, 18, 64, 111, 129, 130, 141, 212], "save_for_backward": [2, 5, 13, 64, 111, 129, 130], "mm": [2, 5, 12, 59, 110, 137, 185, 186, 188, 194, 197, 206, 207, 222, 225], "expand_a": [2, 19, 182, 197, 198], "tensor_list": [2, 8, 10, 135], "get_saved_vari": 2, "grad_input": [2, 13, 78, 129, 130], "grad_weight": 2, "grad_bia": [2, 13], "Then": [2, 12, 15, 17, 20, 22, 24, 25, 44, 45, 52, 58, 59, 61, 73, 85, 98, 99, 102, 103, 114, 121, 123, 133, 134, 149, 152, 155, 156, 159, 160, 161, 163, 165, 168, 173, 174, 188, 195, 200, 201, 212, 215, 222, 224, 225, 228], "5314": 2, "2807": 2, "4864": 2, "7608": 2, "9101": [2, 173], "0073": 2, "mulconst": [2, 78], "object": [2, 4, 5, 6, 7, 9, 10, 11, 14, 19, 20, 22, 23, 43, 49, 51, 52, 60, 61, 64, 68, 69, 75, 95, 97, 101, 110, 111, 112, 116, 117, 118, 121, 125, 129, 135, 136, 141, 142, 143, 145, 154, 155, 159, 160, 161, 162, 163, 164, 168, 169, 171, 173, 174, 177, 181, 182, 193, 197, 198, 200, 216, 220, 221, 223, 225, 231, 246], "stash": [2, 64, 111], "saved_data": 2, "were": [2, 3, 5, 6, 9, 12, 17, 18, 23, 32, 52, 60, 83, 85, 97, 99, 101, 103, 113, 114, 126, 132, 133, 138, 147, 153, 159, 160, 162, 164, 165, 173, 174, 176, 189, 201, 204, 205], "todoubl": 2, "On": [2, 4, 5, 6, 8, 17, 19, 21, 22, 23, 115, 117, 122, 133, 135, 137, 147, 153, 156, 161, 162, 172, 177, 178, 201, 203, 208, 219], "easiest": [2, 5, 9, 23, 121, 139, 145, 157, 159, 228], "tabl": [2, 16, 21, 89, 103, 109, 115, 121, 122, 135, 137, 143, 144, 162, 163, 164, 168, 173, 174, 175, 219, 238, 266, 275], "set_data": 2, "output_nr": 2, "after": [2, 3, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 19, 20, 21, 23, 24, 25, 32, 33, 34, 43, 45, 47, 49, 52, 53, 58, 59, 60, 61, 63, 64, 76, 79, 87, 97, 98, 99, 102, 103, 105, 110, 111, 112, 113, 115, 116, 122, 124, 127, 128, 129, 132, 133, 134, 135, 136, 137, 138, 142, 144, 146, 147, 149, 152, 153, 155, 156, 157, 158, 159, 160, 162, 163, 165, 166, 168, 169, 173, 174, 176, 178, 179, 181, 182, 183, 185, 187, 188, 195, 197, 198, 199, 200, 201, 202, 203, 204, 206, 208, 213, 214, 223, 224, 225, 228, 231, 252, 254, 257, 258], "bug": [2, 5, 10, 23, 108, 144, 186], "report": [2, 10, 17, 23, 52, 73, 87, 98, 121, 137, 144, 161, 163, 164, 186, 201], "fix": [2, 14, 17, 20, 23, 24, 49, 50, 51, 52, 97, 108, 113, 125, 157, 161, 173, 174, 184, 201, 262, 271], "soon": [2, 5, 52, 58, 59, 122, 147, 152, 198, 220], "overview": [2, 5, 6, 11, 53, 55, 61, 113, 119, 120, 121, 127, 128, 133, 134, 135, 142, 155, 159, 161, 162, 163, 165, 168, 189, 190, 192, 196, 207, 210, 227, 257], "alwai": [2, 3, 4, 6, 9, 14, 16, 18, 19, 22, 23, 49, 52, 99, 102, 103, 108, 113, 124, 125, 129, 135, 136, 137, 139, 158, 159, 160, 161, 163, 173, 178, 185, 187, 188, 189, 195, 204, 207, 222, 252, 262, 271], "problem": [2, 4, 6, 11, 14, 15, 18, 22, 23, 49, 51, 52, 61, 98, 100, 103, 115, 117, 126, 136, 142, 144, 145, 149, 153, 157, 159, 161, 163, 168, 172, 176, 189, 191, 207, 232, 262, 271], "question": [2, 4, 5, 6, 8, 10, 17, 22, 23, 49, 75, 122, 135, 137, 143, 165, 183, 190, 200, 201, 207, 231], "forum": [2, 4, 5, 6, 22, 23, 44, 79, 110, 142, 143, 183, 207], "view": [3, 7, 9, 10, 11, 12, 14, 15, 16, 19, 25, 47, 49, 50, 52, 53, 55, 56, 61, 73, 78, 82, 92, 93, 94, 96, 98, 99, 101, 102, 103, 104, 105, 110, 112, 118, 123, 124, 126, 127, 131, 132, 133, 134, 135, 141, 142, 143, 144, 149, 150, 155, 156, 160, 161, 162, 163, 164, 165, 166, 169, 173, 174, 181, 182, 183, 193, 197, 198, 206, 211, 214, 215, 226, 240, 241, 242, 243, 245, 248, 249, 250, 260, 269], "prerequisit": [3, 7, 53, 55, 56, 100, 108, 114, 124, 131, 132, 133, 134, 135, 136, 155, 161, 162, 163, 171, 197, 214, 215], "frontend": [3, 10, 84, 110, 121, 177, 186, 187, 193, 199, 220, 221], "semant": [3, 6, 22, 49, 58, 59, 68, 95, 100, 102, 111, 135, 137, 191, 192, 193, 196, 205, 262, 271], "11": [3, 5, 6, 7, 11, 17, 18, 23, 59, 61, 95, 104, 109, 122, 123, 141, 158, 163, 171, 172, 173, 174, 175, 194, 204, 208, 215, 219, 225, 227, 231, 238, 256, 262, 266, 271, 275], "nvidia": [3, 5, 17, 50, 95, 129, 135, 137, 147, 172, 201, 215, 230, 247, 251, 257], "toolkit": [3, 23, 100, 142, 146], "releas": [3, 4, 6, 10, 17, 20, 23, 24, 42, 50, 105, 108, 109, 112, 122, 123, 125, 139, 142, 152, 162, 164, 168, 199, 201, 204, 208, 212, 219, 220, 221, 262, 271], "greatli": [3, 6, 49, 160], "overhead": [3, 5, 6, 10, 17, 56, 82, 109, 122, 123, 124, 133, 145, 147, 149, 158, 161, 163, 164, 168, 172, 176, 177, 184, 186, 193, 199, 201], "increas": [3, 5, 6, 18, 19, 20, 24, 44, 73, 82, 83, 87, 97, 122, 123, 124, 126, 128, 131, 134, 142, 152, 158, 168, 182, 184, 193, 194, 197, 209, 219], "mostli": [3, 10, 19, 85, 97, 116, 127, 163, 165, 179, 197, 198, 199], "deploy": [3, 4, 25, 42, 60, 97, 112, 126, 177, 186, 199, 204, 220, 227, 228, 252, 257], "appear": [3, 11, 14, 22, 25, 103, 262, 271], "heart": [3, 49, 113, 219, 263, 272], "veri": [3, 4, 5, 6, 8, 12, 14, 15, 18, 19, 21, 22, 23, 24, 25, 45, 47, 48, 49, 58, 59, 60, 61, 65, 73, 75, 76, 85, 99, 101, 113, 115, 117, 123, 124, 125, 127, 134, 135, 149, 152, 153, 157, 160, 161, 163, 164, 165, 166, 168, 169, 176, 178, 182, 189, 191, 195, 198, 205, 263, 264, 272, 273], "time": [3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 17, 19, 20, 21, 23, 24, 25, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 47, 48, 49, 50, 51, 52, 58, 59, 60, 61, 63, 64, 65, 67, 68, 69, 71, 72, 73, 75, 76, 78, 79, 80, 83, 85, 87, 89, 90, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 107, 108, 111, 112, 113, 114, 115, 116, 117, 118, 119, 121, 122, 123, 125, 126, 127, 128, 129, 130, 133, 134, 135, 136, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 152, 153, 154, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 168, 169, 171, 172, 173, 174, 176, 177, 178, 181, 182, 184, 185, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 201, 203, 204, 205, 206, 211, 212, 214, 219, 228, 229, 230, 231, 232, 233, 234, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 254, 255, 257, 260, 262, 269, 271], "compil": [3, 4, 6, 8, 10, 17, 21, 22, 25, 60, 85, 98, 108, 121, 135, 143, 173, 174, 175, 184, 186, 193, 199, 204, 207, 216, 220, 223, 251, 253, 256, 260, 269], "boost": [3, 97, 99, 144, 145, 176, 184, 199, 207, 216, 220, 221], "demonstr": [3, 7, 9, 14, 16, 17, 20, 21, 22, 25, 42, 43, 50, 57, 61, 75, 82, 85, 108, 113, 120, 121, 122, 123, 124, 125, 127, 129, 130, 133, 134, 137, 138, 141, 142, 143, 144, 150, 155, 159, 161, 162, 163, 164, 168, 171, 173, 174, 177, 179, 184, 185, 186, 187, 188, 191, 193, 195, 198, 201, 202, 203, 204, 211, 214, 215, 218, 219, 221, 222, 224, 225, 228, 252, 254, 258, 262, 263, 264, 271, 272, 273], "mnist": [3, 6, 34, 44, 47, 73, 78, 92, 94, 96, 119, 120, 121, 123, 126, 129, 135, 138, 148, 154, 162, 166, 169, 221, 233], "libtorch": [3, 6, 22, 23, 187, 204, 206, 208, 220, 222, 225, 251, 256], "counterpart": [3, 17, 108, 134, 144, 195, 201, 215, 220, 252], "depict": 3, "chunk": [3, 5, 7, 55, 135, 152, 164], "batch": [3, 5, 6, 9, 12, 16, 17, 19, 21, 34, 37, 38, 39, 42, 44, 45, 47, 49, 51, 52, 53, 55, 56, 60, 61, 73, 75, 78, 79, 82, 87, 90, 92, 94, 97, 102, 104, 110, 112, 113, 117, 118, 120, 121, 122, 123, 124, 125, 126, 127, 131, 134, 135, 136, 137, 138, 139, 146, 147, 148, 149, 150, 152, 154, 157, 158, 159, 160, 162, 163, 164, 166, 168, 169, 171, 172, 173, 174, 175, 177, 178, 181, 182, 184, 185, 191, 193, 196, 198, 201, 204, 205, 221, 223, 231, 241, 242, 243], "data_load": [3, 6, 19, 178, 182, 197, 198, 199, 236], "nll_loss": [3, 73, 123, 129, 135, 148, 154, 162, 166, 221], "updat": [3, 6, 10, 11, 12, 13, 14, 16, 17, 19, 21, 23, 42, 43, 44, 49, 51, 52, 61, 63, 64, 65, 67, 68, 69, 71, 72, 75, 82, 97, 98, 99, 102, 103, 110, 111, 112, 117, 122, 123, 126, 129, 136, 137, 139, 147, 152, 159, 160, 162, 163, 168, 178, 182, 185, 189, 197, 198, 199, 200, 201, 204, 207, 210, 214, 216, 218, 221, 222, 228, 230, 258], "captur": [3, 4, 6, 22, 23, 25, 52, 60, 107, 123, 141, 148, 158, 171, 172, 173, 174, 186, 194, 197, 198, 200], "But": [3, 6, 8, 10, 20, 42, 44, 45, 52, 73, 78, 101, 103, 116, 125, 147, 152, 153, 154, 160, 173, 174, 176, 178, 182, 185, 189, 192, 200, 205, 218, 221, 223, 228, 252, 262, 271], "slightli": [3, 5, 14, 23, 122, 135, 136, 158, 165, 173, 174, 192], "prealloc": [3, 14], "reus": [3, 10, 65, 78, 111, 130, 137, 141, 153, 160, 176, 177, 185, 187], "tensoropt": [3, 186], "floatcuda": 3, "dtype": [3, 7, 8, 9, 10, 13, 14, 15, 38, 40, 41, 48, 49, 51, 52, 60, 63, 64, 72, 78, 80, 85, 89, 92, 95, 98, 101, 102, 103, 109, 111, 115, 119, 127, 129, 130, 137, 141, 144, 146, 147, 150, 160, 164, 165, 166, 173, 174, 175, 178, 179, 185, 186, 189, 190, 191, 192, 193, 195, 197, 199, 200, 206, 209, 218, 220, 223, 228, 230, 234, 237, 244, 247, 252, 253], "longcuda": 3, "klong": 3, "zero": [3, 6, 7, 11, 16, 17, 19, 25, 32, 40, 41, 44, 47, 48, 49, 60, 63, 64, 65, 67, 68, 69, 73, 78, 87, 92, 95, 98, 99, 103, 104, 110, 111, 117, 118, 122, 123, 127, 128, 134, 135, 136, 141, 144, 149, 150, 153, 155, 156, 157, 160, 161, 163, 165, 169, 178, 181, 185, 189, 191, 192, 194, 200, 201, 209, 221, 223, 235, 246, 247, 252, 255, 258], "ktrainbatchs": 3, "28": [3, 6, 7, 17, 33, 34, 37, 38, 47, 78, 93, 94, 104, 138, 148, 154, 169, 176, 201, 203, 204, 208, 219, 221, 223, 231, 233, 246], "training_step": [3, 148], "void": [3, 5, 6, 15, 22, 23, 59, 144, 155, 186, 188, 208, 231, 238, 246], "cudagraph": 3, "cudastream": 3, "capturestream": 3, "getstreamfrompool": 3, "setcurrentcudastream": 3, "capture_begin": 3, "capture_end": 3, "warm": [3, 21, 70, 103, 109, 168, 172, 176, 177, 193, 203, 219, 231], "side": [3, 20, 51, 52, 82, 103, 138, 147, 152, 154, 155, 160, 161, 166, 168, 188, 260, 269], "prepar": [3, 11, 17, 19, 25, 44, 51, 52, 58, 59, 68, 69, 102, 103, 111, 112, 116, 134, 137, 138, 152, 155, 159, 161, 181, 185, 193, 195, 196, 199, 200, 201, 204, 209, 212, 218, 222, 227, 228], "cach": [3, 64, 111, 137, 144, 168, 176, 177, 184, 185], "cubla": [3, 231], "cudnn": [3, 5, 78, 117, 129, 136, 147, 150, 230], "warmupstream": 3, "int": [3, 4, 5, 6, 9, 11, 14, 18, 19, 22, 23, 24, 51, 53, 55, 58, 59, 60, 75, 85, 87, 98, 109, 115, 118, 122, 123, 126, 135, 137, 144, 146, 148, 155, 156, 161, 162, 163, 164, 168, 172, 173, 174, 178, 181, 185, 188, 193, 206, 208, 209, 213, 215, 220, 223, 252, 256, 260, 269], "num_warmup_it": 3, "success": [3, 6, 14, 23, 73, 101, 103, 126, 144, 165, 188, 204, 206], "replai": [3, 14, 25, 76, 146], "spin": [3, 60, 176], "ordinari": [3, 194], "epoch": [3, 6, 7, 9, 16, 19, 24, 37, 38, 44, 52, 53, 55, 75, 87, 92, 94, 96, 97, 98, 99, 102, 103, 104, 112, 115, 117, 118, 122, 123, 126, 129, 135, 147, 148, 152, 157, 159, 163, 165, 166, 169, 178, 198, 221, 230, 241, 245, 250], "59584": 3, "60000": [3, 135], "3921": 3, "2051": 3, "accuraci": [3, 9, 17, 19, 20, 24, 37, 38, 44, 92, 97, 104, 115, 119, 120, 121, 122, 123, 126, 129, 148, 150, 156, 157, 158, 162, 166, 169, 171, 177, 182, 185, 194, 195, 198, 199, 201, 218, 219, 227, 228], "938": [3, 6, 147], "1826": 3, "1273": 3, "960": 3, "1796": 3, "1012": [3, 147], "968": 3, "1603": 3, "0869": 3, "973": 3, "2315": 3, "0736": 3, "978": 3, "0511": [3, 185], "0704": 3, "977": [3, 147, 219], "0802": 3, "0654": 3, "979": 3, "0774": 3, "0604": 3, "980": [3, 176], "0669": 3, "0544": 3, "984": [3, 219], "0219": 3, "0517": 3, "983": 3, "real": [3, 6, 14, 20, 32, 52, 54, 58, 97, 98, 99, 100, 103, 121, 123, 126, 127, 128, 135, 136, 149, 152, 155, 158, 160, 165, 172, 176, 191, 193, 197, 200, 218, 219], "0m44": 3, "287": [3, 177, 262, 271], "018": 3, "0m1": 3, "116": [3, 185], "produc": [3, 4, 5, 6, 11, 22, 23, 25, 60, 68, 97, 111, 113, 115, 126, 136, 138, 141, 143, 145, 147, 149, 159, 160, 165, 171, 173, 174, 179, 182, 183, 185, 197, 198, 199, 206, 214, 262, 271], "4092": 3, "2037": 3, "2039": 3, "1274": 3, "961": 3, "1779": 3, "1017": 3, "1559": 3, "0871": 3, "972": 3, "2240": 3, "0735": [3, 201], "0520": 3, "0710": 3, "0935": 3, "0666": [3, 23], "0744": 3, "0603": 3, "981": 3, "0762": 3, "0547": 3, "0207": 3, "0525": [3, 208], "0m6": 3, "952": [3, 144], "0m7": 3, "048": [3, 207], "0m0": 3, "619": 3, "gain": [3, 5, 17, 82, 145, 154, 168, 176, 201], "six": [3, 159, 166, 262, 271], "kind": [3, 5, 6, 19, 23, 44, 47, 49, 58, 59, 73, 98, 102, 112, 119, 121, 137, 173, 177, 197, 252], "larg": [3, 5, 6, 7, 12, 16, 17, 18, 23, 42, 49, 87, 97, 102, 103, 105, 117, 120, 121, 122, 123, 126, 129, 133, 135, 138, 139, 149, 150, 159, 160, 162, 163, 164, 171, 172, 176, 177, 178, 191, 199, 201, 202, 210, 219, 223, 257, 260, 262, 269, 271], "improv": [3, 5, 6, 8, 10, 17, 19, 21, 24, 42, 49, 56, 97, 108, 120, 121, 122, 123, 124, 129, 142, 145, 147, 149, 153, 154, 157, 160, 164, 165, 171, 172, 176, 177, 184, 185, 194, 197, 200, 201, 204, 207, 210, 212, 216, 218, 220, 222, 254, 263, 272], "due": [3, 5, 6, 17, 18, 22, 52, 58, 60, 82, 85, 108, 122, 123, 124, 133, 152, 153, 157, 162, 172, 176, 182, 184, 191, 200, 201, 202, 211, 216, 221, 261, 262, 270, 271], "heavi": [3, 6, 49, 97, 101, 223], "impact": [3, 12, 17, 87, 97, 136, 161, 164, 184, 201, 209, 252, 258], "smaller": [3, 9, 51, 79, 97, 103, 123, 124, 157, 159, 161, 171, 203, 208, 223], "nevertheless": [3, 5, 19, 23, 147, 159], "primari": [4, 5, 6, 15, 142, 175, 176, 177, 186, 193, 206], "program": [4, 5, 18, 21, 22, 23, 25, 60, 61, 98, 100, 101, 103, 121, 124, 127, 143, 162, 172, 173, 177, 197, 198, 199, 200, 214], "languag": [4, 5, 6, 7, 21, 23, 24, 25, 44, 49, 58, 59, 60, 78, 79, 98, 100, 102, 116, 118, 119, 121, 124, 126, 127, 128, 137, 163, 165, 173, 174, 181, 186, 195, 207, 219, 246, 252, 254, 261, 262, 263, 270, 271, 272], "suitabl": [4, 8, 131, 135, 139, 171, 199, 223], "prefer": [4, 6, 8, 53, 58, 145, 150, 155, 159, 176, 194, 252], "eas": [4, 5, 85, 122, 177, 220], "situat": [4, 15, 23, 25, 37, 129, 130, 133, 135, 178, 184, 195], "properti": [4, 5, 6, 11, 14, 21, 23, 25, 32, 40, 48, 101, 103, 126, 147, 153, 165, 173, 174, 176, 177, 190, 208, 220, 249, 256, 260, 269], "unfavor": 4, "environ": [4, 5, 6, 7, 11, 17, 18, 22, 25, 42, 55, 61, 82, 85, 105, 112, 114, 115, 121, 122, 123, 124, 132, 133, 134, 135, 144, 148, 149, 155, 160, 161, 162, 163, 172, 173, 174, 175, 176, 204, 206, 207, 208, 212, 214, 215, 216, 219, 222, 223, 231, 252, 258], "latter": [4, 5, 6, 60, 61, 126, 161, 198], "land": [4, 23, 113, 146, 191, 220, 258], "latenc": [4, 6, 17, 121, 124, 126, 132, 144, 158, 172, 176, 177, 187, 194, 201, 219, 223], "strict": [4, 112, 171, 190, 220, 248], "bind": [4, 6, 10, 23, 121, 176, 177, 262, 271], "java": [4, 58, 177, 204, 222, 223], "rust": 4, "paragraph": [4, 6, 23, 263, 266, 272, 275], "outlin": [4, 5, 6, 23, 227], "pure": [4, 5, 6, 10, 23, 47, 121, 127, 130, 138, 154, 178, 186, 199], "journei": [4, 6, 52, 137], "enabl": [4, 5, 6, 8, 11, 14, 15, 16, 18, 19, 23, 24, 42, 47, 55, 56, 58, 59, 60, 61, 75, 82, 97, 107, 112, 122, 123, 124, 126, 129, 130, 133, 135, 137, 144, 147, 152, 156, 158, 159, 168, 169, 171, 175, 176, 177, 179, 183, 184, 189, 191, 193, 195, 196, 199, 204, 207, 214, 216, 219, 220, 224, 225, 228, 230, 240, 258, 260, 269], "vanilla": [4, 5, 6, 23, 49, 65, 99, 111, 127, 171, 189, 258], "eager": [4, 10, 23, 60, 85, 86, 121, 144, 147, 164, 172, 174, 181, 195, 197, 198, 199, 200, 210, 221], "discuss": [4, 5, 6, 8, 10, 15, 16, 23, 44, 73, 79, 101, 102, 116, 122, 123, 134, 142, 143, 144, 149, 150, 172, 176, 177, 183, 189, 190, 191, 192, 228, 254], "littl": [4, 17, 25, 52, 61, 97, 99, 113, 136, 159, 161, 163, 164, 166, 168, 201], "effort": [4, 14, 49, 51, 52, 108, 182, 195, 196], "next": [4, 5, 6, 8, 9, 11, 12, 14, 15, 16, 17, 19, 20, 22, 23, 34, 42, 43, 49, 51, 53, 55, 56, 58, 59, 60, 75, 78, 80, 82, 85, 92, 94, 96, 97, 98, 99, 102, 103, 105, 112, 113, 115, 117, 121, 122, 123, 124, 125, 127, 128, 129, 130, 132, 136, 137, 138, 139, 143, 144, 146, 149, 150, 152, 154, 157, 160, 161, 162, 163, 165, 166, 168, 169, 177, 178, 181, 182, 184, 187, 188, 191, 192, 195, 197, 198, 199, 201, 203, 208, 216, 219, 220, 222, 223, 260, 262, 263, 269, 271, 272], "mechan": [4, 5, 6, 11, 14, 15, 24, 32, 49, 56, 60, 130, 143, 153, 165, 166, 168, 174, 177, 199, 216, 220, 221], "evalu": [4, 6, 9, 12, 17, 19, 20, 24, 37, 73, 97, 105, 107, 112, 118, 145, 150, 159, 160, 162, 169, 172, 173, 174, 178, 181, 198, 201, 221, 241], "onc": [4, 5, 6, 8, 10, 11, 14, 16, 17, 20, 21, 22, 23, 25, 51, 52, 56, 60, 82, 97, 98, 102, 105, 113, 131, 136, 139, 147, 148, 152, 153, 156, 158, 159, 160, 162, 165, 168, 169, 177, 184, 185, 188, 193, 195, 201, 220, 223, 231, 257], "record": [4, 6, 8, 19, 20, 23, 25, 43, 49, 60, 112, 121, 122, 123, 127, 129, 130, 143, 146, 152, 159, 160, 162, 163, 172, 174, 204, 221, 252], "explicit": [4, 6, 21, 23, 60, 139, 147, 163, 190, 200, 209, 262, 271], "pars": [4, 5, 23, 49, 51, 103, 116, 122, 123, 126, 209, 262, 271], "subject": [4, 11, 14, 23, 42, 108, 113, 123, 141, 165, 173, 174, 187, 188, 193, 198, 204, 205, 206, 207, 212, 216, 222], "constraint": [4, 6, 12, 17, 18, 60, 85, 98, 99, 121, 124, 126, 153, 159, 171, 197, 198, 200, 201, 252], "impos": [4, 223, 232], "guidanc": [4, 8, 176, 177, 195], "offici": [4, 82, 108, 113, 115, 135, 160, 172, 174, 181, 199, 218, 220, 252], "jit": [4, 6, 8, 15, 19, 21, 22, 25, 49, 58, 59, 60, 85, 112, 119, 137, 142, 147, 172, 177, 182, 185, 187, 188, 194, 197, 198, 203, 204, 206, 207, 208, 209, 216, 218, 220, 222, 223, 224, 225, 231, 238, 247, 252, 254, 256], "scriptmodul": [4, 22, 23, 25, 85, 203, 222, 252, 256], "embed": [4, 7, 9, 16, 21, 23, 49, 60, 75, 79, 93, 98, 100, 102, 110, 112, 115, 118, 121, 122, 124, 137, 162, 163, 165, 169, 175, 181, 188, 193, 195, 241, 262, 271], "resnet18": [4, 43, 90, 117, 147, 157, 158, 168, 171, 182, 195, 197, 198, 199, 229, 238, 256], "normal": [4, 6, 8, 11, 12, 19, 20, 21, 37, 39, 49, 51, 52, 58, 59, 60, 65, 73, 80, 85, 87, 90, 92, 94, 96, 97, 98, 99, 102, 103, 111, 112, 117, 119, 123, 127, 128, 129, 135, 136, 139, 146, 148, 153, 157, 158, 161, 162, 164, 165, 166, 168, 169, 171, 172, 177, 182, 184, 190, 197, 198, 204, 209, 213, 216, 220, 221, 223, 224, 225, 228, 229, 241, 242, 243, 250, 252, 253, 262, 271], "rand": [4, 5, 6, 14, 15, 17, 21, 23, 25, 33, 40, 43, 48, 85, 89, 92, 93, 95, 96, 109, 114, 129, 130, 144, 146, 152, 153, 164, 176, 177, 178, 179, 187, 195, 197, 198, 201, 203, 206, 210, 211, 214, 219, 220, 223, 224, 225, 226, 233, 239, 247, 252, 253, 256], "224": [4, 12, 19, 20, 51, 58, 59, 75, 90, 97, 117, 119, 139, 142, 143, 152, 157, 158, 166, 168, 171, 176, 177, 182, 187, 188, 194, 197, 198, 199, 204, 206, 213, 218, 220, 223, 224, 225, 229, 238, 247, 252, 253, 256], "traced_script_modul": [4, 223], "ident": [4, 6, 17, 55, 85, 124, 132, 142, 150, 157, 166, 169, 185, 192, 194, 201, 218], "2698": 4, "0381": 4, "4023": 4, "3010": 4, "0448": 4, "slicebackward": 4, "circumst": [4, 5], "emploi": [4, 97, 165, 168], "particular": [4, 5, 6, 8, 11, 23, 42, 44, 51, 60, 82, 83, 87, 103, 124, 126, 127, 135, 136, 138, 139, 150, 154, 162, 171, 173, 174, 178, 179, 189, 190, 192, 193, 223], "form": [4, 6, 9, 12, 15, 17, 23, 47, 49, 52, 60, 98, 105, 110, 113, 116, 121, 125, 128, 139, 144, 165, 171, 174, 184, 193, 201, 202, 213, 214, 262, 271], "accordingli": [4, 10, 12, 18, 22, 136, 149, 152, 161, 171, 188, 207, 260, 269], "sai": [4, 5, 6, 24, 43, 51, 99, 101, 103, 113, 115, 125, 138, 145, 149, 152, 156, 168, 184, 200, 222, 263, 272], "mymodul": [4, 6, 109, 172, 173, 174, 202, 212], "__init__": [4, 5, 6, 7, 9, 11, 12, 13, 14, 16, 19, 20, 21, 22, 25, 33, 37, 38, 44, 45, 47, 49, 51, 52, 53, 60, 65, 67, 73, 78, 79, 85, 87, 89, 92, 93, 94, 96, 97, 98, 99, 102, 103, 104, 105, 109, 111, 112, 115, 118, 123, 125, 127, 128, 129, 133, 134, 135, 138, 142, 143, 146, 148, 149, 150, 153, 154, 156, 160, 161, 162, 163, 164, 165, 166, 169, 171, 172, 173, 174, 177, 178, 179, 181, 182, 183, 193, 194, 195, 197, 198, 199, 202, 203, 209, 212, 214, 215, 216, 218, 219, 221, 223, 226, 228, 233, 234, 237, 239, 240, 241, 242, 243, 244, 248, 249, 250, 252, 262, 271], "n": [4, 5, 6, 7, 9, 12, 17, 19, 22, 23, 32, 33, 37, 38, 40, 43, 47, 48, 49, 51, 59, 60, 75, 82, 85, 89, 90, 93, 94, 95, 97, 104, 110, 113, 115, 118, 122, 123, 127, 129, 133, 135, 136, 137, 143, 145, 146, 147, 150, 153, 156, 160, 161, 162, 163, 165, 166, 169, 178, 182, 184, 185, 189, 190, 191, 192, 193, 197, 198, 200, 201, 205, 208, 213, 220, 230, 231, 236, 244, 247, 254, 256], "mv": [4, 110], "my_modul": 4, "20": [4, 6, 7, 9, 13, 16, 17, 19, 23, 33, 58, 59, 61, 78, 79, 82, 85, 87, 93, 95, 109, 123, 126, 128, 133, 135, 136, 142, 144, 146, 147, 149, 150, 152, 156, 161, 163, 166, 168, 173, 174, 177, 184, 187, 192, 195, 198, 201, 209, 221, 223, 231, 232, 234, 238, 246, 258, 266, 275], "sm": [4, 168], "exclud": [4, 8, 43], "doesn": [4, 7, 8, 10, 12, 13, 17, 25, 58, 99, 101, 103, 113, 125, 143, 145, 147, 156, 171, 172, 176, 179, 183, 184, 189, 195, 198, 200, 201, 205, 208, 210, 211, 228, 262, 271], "yet": [4, 6, 10, 11, 18, 23, 50, 73, 102, 107, 108, 113, 135, 162, 165, 175, 179, 185, 193, 198, 199, 216, 220, 224, 225], "could": [4, 5, 6, 8, 10, 11, 23, 52, 60, 87, 97, 98, 99, 101, 102, 103, 105, 109, 122, 123, 124, 125, 127, 128, 129, 135, 139, 147, 149, 152, 159, 160, 161, 162, 163, 165, 168, 169, 171, 176, 177, 178, 179, 189, 191, 197, 199, 200, 205, 214, 215, 216, 220, 221], "ignor": [4, 19, 49, 51, 97, 102, 103, 112, 142, 148, 155, 159, 171, 178, 179, 182, 187, 189, 190, 191, 192, 193, 197, 198, 218], "readi": [4, 6, 9, 10, 16, 22, 23, 42, 49, 58, 59, 60, 98, 99, 102, 103, 122, 134, 135, 150, 155, 159, 161, 162, 163, 175, 178, 187, 194, 197, 198, 199, 208, 213, 223, 224, 225, 228, 252], "hand": [4, 5, 6, 8, 14, 17, 18, 23, 61, 73, 98, 103, 128, 135, 139, 154, 172, 177, 190, 201], "shown": [4, 6, 8, 17, 19, 20, 21, 52, 58, 59, 113, 116, 124, 126, 137, 144, 146, 157, 160, 161, 163, 164, 168, 171, 172, 176, 177, 183, 188, 190, 191, 192, 195, 198, 200, 201, 214, 219, 220, 228, 252, 257, 258, 260, 262, 269, 271], "filenam": [4, 6, 49, 104, 109, 116, 127, 128, 171, 230], "traced_resnet_model": 4, "pt": [4, 6, 22, 23, 25, 53, 58, 59, 75, 112, 117, 119, 122, 123, 137, 188, 194, 204, 206, 208, 218, 220, 221, 222, 223, 224, 225, 228, 240, 241, 242, 243, 248, 256], "my_module_model": 4, "left": [4, 17, 32, 43, 47, 49, 51, 52, 64, 85, 89, 99, 103, 111, 112, 113, 135, 137, 146, 150, 159, 160, 162, 164, 168, 169, 200, 201, 260, 262, 269, 271], "realm": [4, 6], "cross": [4, 7, 8, 13, 20, 44, 52, 95, 118, 124, 126, 176, 262, 271], "sphere": 4, "distribut": [4, 5, 6, 14, 15, 19, 24, 52, 54, 73, 75, 79, 80, 87, 97, 99, 103, 108, 113, 121, 122, 123, 124, 126, 131, 132, 137, 147, 149, 152, 155, 159, 161, 168, 176, 185, 193, 196, 202, 208, 212, 215, 223, 231, 251, 258], "encompass": 4, "share": [4, 5, 6, 10, 11, 18, 22, 23, 48, 55, 66, 78, 80, 87, 97, 101, 108, 110, 113, 121, 122, 125, 133, 135, 136, 146, 159, 161, 162, 163, 173, 174, 176, 195, 208, 220], "header": [4, 5, 6, 8, 22, 23, 143, 155, 188, 204, 208, 222, 225, 260, 262, 263, 269, 271, 272], "cmake": [4, 6, 188, 206, 220, 256], "futur": [4, 7, 18, 21, 22, 42, 49, 58, 59, 109, 110, 118, 123, 134, 137, 141, 146, 152, 155, 157, 160, 161, 162, 163, 173, 174, 179, 181, 187, 188, 192, 197, 198, 199, 200, 204, 208, 219, 222, 252], "begin": [4, 5, 6, 7, 11, 12, 17, 19, 22, 23, 25, 32, 43, 49, 50, 52, 55, 58, 59, 73, 85, 89, 102, 103, 108, 113, 115, 116, 122, 124, 137, 142, 152, 157, 160, 162, 168, 169, 191, 193, 201, 223, 228, 262, 271], "iostream": [4, 5, 6, 22, 23, 220], "argc": [4, 22, 23, 220, 256], "const": [4, 5, 6, 8, 10, 15, 22, 23, 59, 144, 155, 186, 208, 220, 222, 231, 246, 256], "char": [4, 22, 23, 59, 144, 208, 220, 256], "cerr": [4, 22, 23, 220, 256], "app": [4, 23, 105, 119, 121, 139, 194, 204, 213, 220, 222, 227, 228, 252], "try": [4, 6, 12, 14, 15, 17, 19, 21, 22, 23, 25, 42, 44, 47, 48, 49, 52, 53, 58, 59, 60, 61, 73, 79, 97, 98, 99, 100, 101, 104, 105, 109, 116, 125, 126, 127, 128, 129, 136, 142, 144, 146, 147, 149, 150, 152, 155, 156, 159, 160, 164, 165, 168, 172, 173, 174, 176, 182, 184, 187, 190, 197, 201, 203, 208, 213, 216, 219, 220, 222, 256, 262, 263, 271, 272], "deseri": [4, 6, 23, 112, 182, 197, 198], "catch": [4, 8, 11, 22, 58, 208, 220, 222, 256], "c10": [4, 8, 10, 15, 22, 144, 155, 186, 188, 208, 219, 220, 231, 246, 256], "ok": [4, 103, 161, 262, 271], "relev": [4, 6, 14, 53, 98, 100, 103, 113, 114, 122, 124, 156, 171], "accept": [4, 5, 20, 67, 78, 87, 97, 102, 111, 115, 116, 124, 126, 141, 145, 150, 154, 159, 162, 168, 171, 179, 200, 202, 205, 212, 219, 252], "proce": [4, 11, 25, 97, 99, 144, 157, 165], "examin": [4, 11, 22, 25, 58, 59, 82, 97, 143], "moment": [4, 6, 11, 173, 179, 192, 206, 223], "cpp": [4, 5, 6, 8, 22, 23, 120, 121, 144, 187, 196, 199, 208, 220, 246, 256], "cmakelist": [4, 6, 22, 23, 208, 220, 256], "txt": [4, 5, 6, 9, 22, 23, 49, 75, 116, 127, 128, 137, 144, 158, 165, 185, 208, 220, 256], "cmake_minimum_requir": [4, 6, 22, 23, 208, 220, 256], "fatal_error": [4, 6, 22, 23, 208, 220, 256], "custom_op": [4, 108, 173, 174, 256], "find_packag": [4, 6, 22, 23, 220, 256], "add_execut": [4, 6, 22, 23, 220, 256], "target_link_librari": [4, 6, 22, 23, 208, 220, 256], "torch_librari": [4, 6, 8, 22, 23, 220, 256], "set_properti": [4, 6, 220, 256], "cxx_standard": [4, 6, 220, 256], "14": [4, 6, 22, 23, 24, 47, 73, 92, 123, 144, 171, 176, 208, 219, 220, 221, 228, 231, 238, 266, 275], "last": [4, 6, 11, 12, 14, 19, 23, 40, 43, 49, 52, 53, 59, 60, 73, 83, 85, 87, 99, 102, 105, 113, 117, 121, 124, 125, 127, 128, 135, 136, 142, 144, 148, 149, 152, 157, 159, 160, 161, 163, 164, 165, 169, 176, 178, 188, 189, 192, 193, 218, 220, 222, 228, 247, 252], "thing": [4, 5, 6, 8, 15, 21, 22, 23, 25, 43, 44, 47, 49, 58, 59, 85, 87, 97, 98, 99, 101, 102, 103, 113, 116, 124, 125, 126, 129, 130, 131, 132, 135, 136, 139, 143, 144, 147, 148, 153, 158, 159, 166, 177, 182, 184, 195, 197, 208, 262, 271], "grab": [4, 6, 52, 158, 163], "latest": [4, 6, 10, 14, 20, 87, 107, 108, 112, 121, 137, 157, 158, 159, 162, 165, 166, 171, 172, 208, 257, 260, 269], "stabl": [4, 20, 26, 27, 28, 29, 30, 32, 33, 34, 37, 38, 40, 94, 98, 113, 140, 158, 167, 168, 170, 181, 221, 223, 230, 233, 251, 260, 269], "page": [4, 6, 10, 22, 23, 50, 54, 61, 109, 127, 139, 163, 168, 175, 199, 204, 207, 208, 209, 217, 220, 222, 264, 266, 273, 275], "websit": [4, 6, 160], "unzip": [4, 6, 19, 50, 171, 178, 181, 182, 197, 198, 208], "archiv": [4, 5, 6, 25, 147, 257], "against": [4, 22, 23, 44, 60, 81, 105, 135, 147, 159, 212, 220], "window": [4, 5, 6, 7, 20, 44, 51, 103, 105, 133, 162, 168, 178, 206, 262, 271], "debug": [4, 6, 8, 19, 25, 58, 59, 60, 78, 98, 121, 125, 173, 174, 186, 195, 196, 231, 255], "abi": [4, 5, 6, 22, 23, 204, 206, 208, 220], "plan": [4, 6, 10, 18, 60, 112, 122, 124, 171, 175, 182, 187, 192, 198, 206, 208, 224], "correct": [4, 5, 6, 8, 10, 11, 12, 13, 19, 37, 38, 43, 44, 47, 49, 60, 64, 73, 85, 87, 92, 97, 98, 99, 102, 111, 122, 123, 125, 127, 129, 133, 136, 144, 147, 153, 156, 159, 161, 162, 165, 166, 168, 169, 182, 193, 197, 198, 215, 221, 260, 269], "laid": 4, "within": [4, 5, 7, 14, 18, 21, 23, 61, 85, 103, 105, 109, 110, 124, 130, 137, 142, 144, 153, 156, 160, 162, 171, 176, 177, 185, 186, 192, 193, 195, 199, 206, 208, 215, 219, 260, 262, 269, 271], "mkdir": [4, 6, 23, 104, 146, 168, 171, 181, 194, 208], "dcmake_prefix_path": [4, 6, 22, 23, 220, 256], "config": [4, 6, 10, 17, 20, 24, 87, 123, 126, 137, 144, 158, 176, 177, 179, 184, 185, 186, 197, 199, 201, 220, 221, 254], "someth": [4, 5, 6, 11, 14, 19, 23, 25, 44, 87, 99, 101, 113, 116, 135, 144, 157, 158, 159, 165, 205, 262, 271], "root": [4, 5, 6, 14, 22, 23, 34, 37, 38, 41, 43, 44, 51, 52, 87, 92, 97, 98, 110, 129, 136, 144, 162, 163, 166, 168, 178, 188, 204, 213, 220, 223, 236, 250, 252, 253, 260, 269], "4b5a67132e81": 4, "identif": [4, 6, 22, 23, 220], "gnu": [4, 5, 6, 22, 23, 220, 247], "cxx": [4, 6, 22, 23, 204, 206, 208, 220], "check": [4, 5, 6, 7, 8, 13, 14, 15, 19, 20, 22, 23, 25, 42, 43, 44, 45, 49, 50, 52, 55, 58, 59, 60, 73, 75, 85, 97, 98, 101, 104, 105, 108, 109, 110, 115, 116, 122, 126, 133, 135, 136, 139, 141, 142, 144, 146, 147, 153, 154, 156, 158, 159, 162, 171, 172, 173, 174, 176, 178, 188, 192, 193, 198, 200, 206, 208, 214, 219, 220, 222, 223, 252], "usr": [4, 6, 18, 22, 23, 135, 194, 220], "cc": [4, 6, 22, 23, 43, 108, 118, 204, 206, 220], "detect": [4, 6, 11, 12, 18, 22, 23, 52, 75, 121, 139, 158, 168, 172, 220], "info": [4, 5, 6, 22, 23, 82, 118, 132, 135, 137, 146, 160, 171, 173, 174, 175, 185, 207, 220, 221, 228], "pthread": [4, 5, 6, 22, 23, 208, 220], "pthread_creat": [4, 6, 22, 23, 220], "thread": [4, 5, 6, 8, 9, 21, 22, 23, 52, 61, 109, 133, 134, 137, 149, 158, 161, 162, 163, 176, 177, 181, 182, 194, 216, 220, 231, 246], "scan": [4, 6, 22, 23, 171], "50": [4, 6, 7, 12, 16, 17, 19, 21, 22, 23, 24, 49, 52, 53, 58, 78, 92, 136, 144, 147, 156, 160, 163, 166, 177, 178, 182, 185, 191, 197, 199, 201, 203, 219, 221, 223, 228, 230, 247], "cmakefil": [4, 6, 22, 23], "dir": [4, 6, 22, 23, 82, 126, 147, 148, 204, 208, 223, 246], "o": [4, 5, 6, 7, 17, 22, 23, 90, 97, 98, 108, 128, 137, 150, 152, 171, 201, 262, 271], "100": [4, 6, 9, 14, 16, 17, 19, 21, 22, 23, 37, 38, 44, 45, 48, 49, 52, 63, 64, 67, 68, 69, 71, 72, 80, 89, 92, 93, 94, 97, 99, 111, 119, 123, 125, 127, 128, 129, 133, 138, 143, 144, 145, 146, 147, 149, 154, 156, 158, 159, 160, 163, 165, 166, 169, 171, 172, 173, 174, 176, 177, 182, 187, 191, 195, 197, 198, 201, 215, 219, 221, 231, 234, 246, 257], "suppli": [4, 6, 101, 147, 158, 262, 271], "binari": [4, 6, 20, 22, 23, 49, 52, 105, 135, 147, 156, 172, 178, 188, 190, 196, 199, 204, 208, 212, 218, 220, 222, 223, 231], "incompat": [4, 173, 174, 197], "1d": [4, 68, 93, 111, 205], "4d": [4, 47, 78, 147, 200], "path_to_model": 4, "successfulli": [4, 6, 22, 50, 58, 59, 60, 105, 119, 126, 135, 144, 162, 191, 194, 206, 219, 225, 227, 241, 256], "coupl": [4, 14, 49, 103, 122, 124, 130, 136, 138, 152, 169, 183, 203], "awai": [4, 5, 6, 23, 47, 60, 98, 99, 101, 113, 143, 149, 159, 160, 161, 192, 262, 271], "ivalu": [4, 23, 58, 144, 155, 206, 208, 220, 223, 256], "push_back": [4, 22, 23, 220, 256], "totensor": [4, 12, 19, 20, 23, 34, 37, 38, 44, 51, 52, 58, 59, 73, 75, 87, 90, 92, 94, 96, 97, 116, 117, 119, 123, 129, 135, 139, 148, 157, 158, 162, 166, 168, 169, 171, 182, 187, 188, 197, 198, 204, 206, 213, 220, 221, 223, 229, 250, 253, 256], "slice": [4, 5, 48, 80, 102, 127, 150, 156, 193, 206], "eras": [4, 25], "org": [4, 6, 26, 27, 28, 29, 30, 32, 33, 34, 35, 37, 38, 40, 42, 45, 46, 49, 58, 59, 74, 77, 84, 91, 93, 94, 96, 100, 104, 106, 113, 120, 122, 127, 128, 137, 140, 141, 142, 143, 152, 153, 157, 158, 165, 167, 168, 170, 172, 174, 181, 184, 187, 188, 190, 192, 194, 196, 203, 204, 205, 206, 208, 213, 221, 222, 223, 224, 226, 230, 233, 234, 235, 236, 237, 256, 260, 262, 269, 271], "cppdoc": [4, 6], "pariti": 4, "manipul": [4, 60, 103, 143, 152, 182, 185], "five": [4, 9, 65, 95, 111, 113], "ideal": [4, 6, 14, 58, 59, 97, 149, 165, 177, 197, 207], "variabl": [4, 5, 6, 7, 8, 12, 20, 22, 23, 42, 49, 60, 69, 76, 82, 87, 98, 99, 101, 111, 114, 127, 132, 135, 144, 161, 164, 173, 174, 176, 184, 191, 193, 205, 206, 207, 208, 219, 222, 252], "kcuda": [4, 6, 186], "live": [4, 6, 10, 15, 121, 125, 134, 162, 163, 192, 216, 262, 271], "hopefulli": [4, 6, 50, 51, 73, 85, 99, 112], "equip": [4, 5, 130, 136, 189], "concept": [4, 6, 11, 22, 55, 100, 101, 114, 121, 126, 146, 161, 164, 165, 186, 197, 199, 200], "Of": [4, 14, 23, 97, 101, 125, 133, 135, 169, 190, 192], "cours": [4, 6, 14, 17, 19, 23, 53, 97, 100, 101, 103, 104, 125, 133, 135, 169, 201], "did": [4, 6, 8, 19, 23, 25, 44, 52, 60, 68, 105, 111, 113, 135, 141, 153, 159, 162, 165, 176, 182, 262, 271], "cover": [4, 5, 14, 15, 16, 18, 22, 25, 47, 58, 59, 100, 108, 113, 114, 119, 121, 122, 126, 135, 155, 159, 162, 163, 169, 172, 173, 174, 175, 191, 193, 197, 200, 212, 219, 220, 252], "insid": [4, 5, 6, 10, 16, 17, 18, 20, 22, 23, 45, 78, 108, 124, 168, 178, 195, 201, 205, 207, 223, 262, 271], "shortli": [4, 161], "html": [4, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 40, 45, 46, 52, 57, 74, 77, 84, 90, 91, 94, 100, 104, 106, 113, 114, 118, 120, 122, 137, 140, 142, 143, 157, 167, 170, 171, 174, 181, 187, 188, 190, 192, 203, 204, 230, 233, 234, 235, 237, 262, 271], "peter": 5, "goldsborough": 5, "plethora": 5, "relat": [5, 11, 14, 52, 60, 101, 103, 113, 124, 144, 153, 173, 174, 182, 262, 271], "algebra": [5, 14, 48, 99, 219], "wrangl": 5, "novel": 5, "research": [5, 6, 17, 19, 23, 25, 49, 52, 60, 73, 75, 85, 99, 114, 115, 135, 137, 150, 154, 156, 171, 181, 201, 216], "modul": [5, 7, 9, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 33, 37, 38, 39, 42, 44, 45, 47, 49, 52, 53, 55, 58, 59, 65, 66, 68, 73, 78, 79, 87, 89, 92, 94, 95, 96, 97, 98, 99, 102, 103, 105, 107, 108, 109, 110, 112, 115, 116, 117, 118, 121, 122, 123, 124, 125, 127, 128, 129, 133, 135, 138, 142, 143, 144, 146, 147, 150, 152, 154, 155, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 169, 171, 172, 173, 174, 175, 176, 177, 179, 181, 182, 183, 184, 185, 186, 187, 188, 193, 194, 196, 197, 198, 199, 200, 201, 203, 206, 208, 209, 212, 214, 215, 219, 220, 221, 222, 223, 226, 227, 228, 231, 233, 234, 235, 238, 239, 240, 241, 242, 243, 248, 249, 250, 251, 252, 256, 261, 262, 263, 270, 271, 272], "power": [5, 6, 23, 43, 49, 57, 65, 73, 75, 87, 97, 99, 121, 123, 126, 135, 144, 146, 150, 155, 156, 158, 165, 171, 184, 187, 192, 194, 227, 254, 262, 271], "spare": [5, 6], "deriv": [5, 6, 10, 17, 23, 25, 43, 48, 76, 78, 99, 101, 102, 103, 110, 125, 129, 130, 141, 145, 195, 201], "express": [5, 13, 23, 95, 98, 107, 114, 124, 137, 173, 174, 191, 197, 198, 200, 209, 262, 271], "better": [5, 6, 14, 17, 19, 21, 44, 52, 56, 61, 79, 97, 103, 109, 113, 115, 121, 126, 127, 128, 139, 144, 145, 146, 152, 154, 157, 159, 160, 165, 168, 169, 171, 173, 174, 176, 177, 182, 184, 189, 190, 195, 197, 198, 199, 200, 201, 207, 223, 257], "frequent": [5, 22, 23, 75, 82, 103, 123, 175, 176, 177, 191, 220], "expens": [5, 17, 97, 109, 163, 177, 184, 201], "plausibl": 5, "address": [5, 12, 18, 22, 24, 82, 135, 161, 162, 174, 176, 197, 221, 263, 272], "nativ": [5, 6, 8, 15, 23, 42, 55, 87, 107, 108, 119, 121, 122, 124, 136, 137, 163, 176, 177, 179, 184, 189, 192, 197, 216, 219, 220, 223, 238, 246], "intend": [5, 6, 8, 12, 23, 97, 123, 124, 129, 144, 147, 164, 172, 173, 174, 189, 197, 212], "much": [5, 6, 8, 10, 12, 14, 17, 19, 23, 25, 52, 56, 73, 97, 98, 99, 103, 112, 119, 122, 125, 126, 142, 145, 146, 152, 158, 159, 162, 163, 165, 168, 169, 172, 176, 184, 189, 192, 200, 201, 218, 227, 228, 260, 262, 269, 271], "boilerpl": [5, 6, 19, 44, 99, 169, 189], "degre": [5, 64, 126, 165, 168, 192], "matter": [5, 134, 171, 189, 191, 210], "organ": [5, 8, 14, 23, 51, 112, 127, 143, 149, 257, 263, 272], "tackl": [5, 109], "decid": [5, 6, 8, 10, 17, 18, 52, 87, 160, 175, 192, 196, 201], "contribut": [5, 52, 61, 65, 82, 111, 137, 147, 168, 176], "upstream": [5, 220], "rest": [5, 6, 14, 44, 117, 121, 136, 139, 143, 157, 159, 161, 162, 191, 198, 218, 262, 263, 271, 272], "chase": [5, 113], "someon": [5, 165], "fire": [5, 133], "dai": [5, 42, 103, 115, 116, 124], "head": [5, 7, 17, 21, 42, 75, 83, 109, 113, 124, 126, 133, 157, 164, 178, 193, 201], "straight": [5, 6, 23, 139, 165], "recurr": [5, 9, 44, 49, 60, 79, 81, 98, 102, 110, 121, 127, 128, 153, 165, 181, 195], "unit": [5, 6, 25, 49, 110, 122, 123, 145, 150, 156, 159, 160, 165, 171, 176, 177, 187], "superior": 5, "lstm": [5, 44, 49, 78, 79, 93, 100, 110, 119, 121, 127, 128, 163, 181, 183, 195, 228, 234, 251], "lack": [5, 15, 82, 189], "forget": [5, 6, 112, 157, 172, 188], "gate": [5, 49], "exponenti": [5, 49, 99, 101, 153, 160, 184], "elu": [5, 110], "never": [5, 7, 98, 99, 100, 103, 115, 125, 156, 166], "lltm": 5, "long": [5, 6, 7, 9, 10, 20, 23, 49, 50, 60, 78, 80, 82, 87, 98, 99, 100, 101, 103, 113, 118, 122, 125, 127, 128, 136, 137, 143, 144, 149, 153, 160, 163, 165, 168, 178, 185, 186, 195, 197, 208, 223, 231, 238, 246, 262, 263, 271, 272], "term": [5, 6, 15, 52, 73, 99, 100, 101, 109, 122, 124, 150, 156, 159, 174, 184, 192, 197, 198, 199, 200, 202, 262, 263, 271, 272], "signific": [5, 6, 9, 19, 42, 52, 58, 59, 82, 122, 129, 137, 143, 144, 145, 147, 152, 159, 164, 168, 172, 177, 203, 214, 215, 219, 221, 223, 254, 262, 271], "lstmcell": 5, "cell": [5, 21, 23, 25, 50, 60, 75, 80, 109, 159, 160, 164, 171, 184, 263, 272], "plain": [5, 6, 23, 49, 127, 128, 165, 178], "input_featur": 5, "state_s": 5, "candid": [5, 144], "reset_paramet": [5, 129], "stdv": 5, "math": [5, 7, 10, 49, 63, 64, 65, 67, 68, 69, 71, 72, 85, 89, 95, 101, 104, 110, 111, 118, 127, 128, 129, 150, 158, 160, 164, 165, 266, 275], "sqrt": [5, 7, 85, 104, 118, 129, 189, 202], "uniform_": [5, 6, 7, 9, 115, 163, 181, 195, 202], "old_h": 5, "old_cel": 5, "cat": [5, 7, 9, 20, 21, 40, 44, 48, 49, 60, 78, 90, 92, 98, 101, 102, 110, 115, 118, 128, 134, 139, 144, 149, 160, 163, 165, 169, 178, 181, 200, 229, 250], "gate_weight": 5, "split": [5, 7, 8, 9, 18, 19, 20, 21, 45, 49, 52, 60, 79, 85, 87, 98, 99, 102, 103, 113, 118, 121, 127, 128, 133, 134, 137, 142, 149, 159, 162, 163, 165, 178, 181, 182, 185, 193, 197, 198, 212, 246], "input_g": 5, "sigmoid": [5, 6, 52, 93, 110, 179, 200, 247], "output_g": 5, "tanh": [5, 6, 14, 25, 49, 52, 60, 93, 99, 110, 127, 145, 159, 165, 247], "candidate_cel": 5, "new_cel": 5, "hidden": [5, 7, 9, 21, 49, 60, 78, 97, 98, 102, 124, 126, 127, 128, 136, 142, 148, 163, 164, 165, 181, 195, 197, 234, 260, 269], "new_h": [5, 25, 51], "rnn": [5, 9, 21, 25, 45, 49, 60, 61, 78, 79, 93, 110, 118, 121, 134, 136, 153, 162, 165, 181, 195, 199, 247], "new_c": 5, "intel": [5, 121, 135, 144, 147, 199, 206, 251], "mkl": [5, 144, 238], "nnpack": 5, "why": [5, 6, 8, 11, 14, 25, 44, 52, 85, 97, 99, 103, 112, 114, 130, 133, 144, 152, 165, 172, 173, 174, 190, 192, 262, 271], "room": [5, 97, 149, 219, 262, 271], "obviou": [5, 113], "knowledg": [5, 49, 52, 73, 99, 100, 114, 121, 144, 146, 262, 271], "execut": [5, 6, 7, 8, 11, 15, 16, 20, 21, 22, 23, 25, 42, 43, 45, 50, 60, 61, 76, 78, 82, 98, 108, 120, 121, 125, 126, 134, 135, 136, 143, 144, 147, 149, 155, 159, 160, 162, 163, 164, 172, 173, 174, 176, 177, 182, 183, 185, 186, 187, 194, 203, 204, 206, 207, 208, 212, 219, 223, 224, 225, 230, 252], "kernel": [5, 6, 8, 13, 17, 18, 23, 47, 83, 108, 110, 121, 138, 144, 147, 149, 153, 154, 156, 164, 165, 168, 172, 176, 177, 184, 186, 199, 201, 207, 216], "involv": [5, 8, 9, 15, 16, 17, 19, 23, 25, 50, 60, 85, 98, 101, 105, 112, 120, 125, 127, 132, 139, 146, 152, 163, 165, 173, 174, 182, 184, 193, 201, 254], "launch": [5, 6, 21, 53, 61, 115, 126, 132, 133, 149, 161, 162, 163, 164, 168, 176, 206, 219, 221, 226], "amount": [5, 19, 25, 73, 82, 112, 124, 133, 156, 172, 184, 223], "becom": [5, 6, 11, 21, 24, 52, 61, 73, 75, 78, 85, 97, 124, 130, 145, 147, 168, 169, 176, 186, 193, 197, 210, 216, 219, 252], "furthermor": [5, 19, 97, 138, 145, 169, 176, 186, 200, 224, 225], "interpret": [5, 6, 23, 25, 40, 56, 82, 85, 103, 121, 127, 128, 137, 165, 172, 173, 174, 186, 196, 216, 229, 235, 251, 262, 271], "slow": [5, 6, 8, 42, 123, 148, 176, 228], "down": [5, 8, 10, 11, 16, 19, 42, 50, 82, 87, 99, 104, 123, 136, 144, 145, 146, 162, 166, 169, 176, 189, 260, 269], "therefor": [5, 6, 9, 15, 19, 49, 51, 60, 97, 108, 112, 113, 115, 120, 133, 134, 147, 150, 155, 156, 162, 163, 173, 174, 176, 191, 192, 200, 206, 223, 262, 271], "rewrit": [5, 21, 45, 60, 107, 129, 153, 173, 174, 200, 205, 206, 252], "fuse": [5, 17, 19, 121, 144, 157, 158, 176, 177, 179, 181, 182, 184, 194, 198, 201, 206, 227, 247, 252], "group": [5, 7, 11, 16, 18, 19, 24, 49, 61, 83, 109, 113, 120, 121, 122, 123, 128, 129, 131, 133, 134, 135, 144, 168, 175, 178, 214, 215, 216, 258, 262, 263, 271, 272], "profit": 5, "fewer": [5, 11, 129, 145], "visibl": [5, 22, 23, 44, 87, 171, 182], "aten": [5, 8, 10, 15, 23, 42, 109, 144, 168, 173, 174, 177, 182, 185, 186, 188, 197, 198, 199, 219, 220, 226, 238, 244, 246], "translat": [5, 23, 25, 49, 60, 105, 107, 116, 118, 150, 165, 187, 191, 252], "benefit": [5, 6, 9, 17, 18, 42, 43, 85, 87, 119, 122, 141, 147, 152, 157, 164, 176, 184, 197, 201, 216, 219, 220, 257], "massiv": [5, 25, 44, 101, 103, 220], "parallel": [5, 6, 11, 16, 18, 44, 46, 49, 51, 52, 55, 73, 79, 87, 112, 121, 126, 131, 135, 137, 144, 150, 159, 161, 162, 163, 175, 176, 214, 215, 216, 230, 240, 247, 258], "ahead": [5, 22, 152, 169, 173, 174, 179, 188, 214], "cpp_extens": [5, 10, 23, 155, 208, 231], "setup": [5, 6, 7, 10, 16, 19, 22, 42, 52, 53, 55, 122, 123, 126, 133, 148, 149, 152, 155, 158, 163, 184, 188, 191, 192, 204, 205, 206, 214, 215, 231, 246], "lltm_cpp": 5, "ext_modul": [5, 10, 23, 155], "cppextens": [5, 10, 23, 155], "cmdclass": [5, 10, 23, 155], "build_ext": [5, 10, 23, 155], "buildextens": [5, 10, 23, 155], "conveni": [5, 8, 14, 22, 23, 44, 47, 49, 101, 125, 127, 128, 138, 145, 155, 159, 190, 192, 220, 252], "wrapper": [5, 6, 8, 16, 55, 112, 122, 123, 130, 136, 146, 159, 171, 196, 199, 240], "include_dir": [5, 10, 155], "include_path": 5, "manag": [5, 22, 43, 49, 61, 108, 109, 113, 120, 124, 131, 132, 133, 149, 153, 164, 168, 177, 214, 215, 216, 232, 257], "And": [5, 6, 10, 22, 23, 24, 25, 52, 101, 103, 105, 113, 130, 144, 145, 147, 150, 158, 168, 169, 172, 173, 174, 176, 177, 195, 199, 200, 213], "overal": [5, 19, 42, 49, 122, 123, 135, 149, 160, 171, 197, 200, 219, 228], "d_sigmoid": 5, "bit": [5, 12, 15, 23, 25, 51, 68, 95, 109, 113, 117, 136, 148, 158, 159, 160, 165, 184, 189, 197, 199, 207, 221, 228], "pybind11": [5, 8, 22, 23, 155, 231], "datatyp": [5, 23, 40, 48, 109, 220], "Its": [5, 97, 99, 193, 262, 271], "inspect": [5, 23, 78, 97, 108, 122, 126, 143, 164, 166, 172, 173, 174, 182, 185, 216], "notic": [5, 8, 14, 21, 22, 23, 25, 32, 42, 43, 44, 52, 60, 73, 85, 97, 99, 112, 130, 135, 144, 146, 149, 153, 154, 157, 159, 168, 172, 173, 174, 176, 177, 188, 189, 191, 195], "dispos": 5, "nvcc": 5, "workaround": [5, 7, 23, 79, 85, 130, 141], "logic": [5, 6, 11, 17, 23, 85, 98, 123, 126, 132, 134, 156, 162, 163, 171, 177, 183, 201, 202, 214, 216], "sigmoidalphablendforwardcuda": 5, "port": [5, 16, 135, 162], "entir": [5, 6, 14, 16, 18, 19, 25, 47, 49, 53, 60, 78, 97, 99, 102, 117, 121, 122, 123, 127, 129, 134, 142, 149, 152, 154, 156, 157, 159, 163, 165, 176, 182, 189, 190, 191, 194, 197, 198, 208, 214, 262, 271], "lltm_forward": 5, "addmm": [5, 6, 109, 144, 173, 188, 197, 206, 207, 219, 238], "transpos": [5, 6, 7, 12, 40, 44, 48, 49, 51, 52, 60, 90, 92, 94, 96, 110, 117, 118, 129, 144, 146, 153, 157, 160, 164, 166, 169, 173, 174, 193, 206, 229], "respect": [5, 16, 32, 34, 43, 49, 51, 52, 63, 64, 68, 69, 71, 72, 99, 101, 111, 114, 115, 124, 125, 134, 136, 141, 143, 144, 145, 154, 159, 163, 165, 168, 173, 174, 176, 177, 198, 212, 249], "ultim": [5, 19, 49, 52, 60, 85, 189, 207], "plop": [5, 23], "autograd": [5, 12, 13, 15, 16, 21, 25, 32, 40, 42, 46, 47, 57, 59, 61, 62, 68, 69, 77, 78, 81, 91, 93, 98, 100, 101, 104, 109, 110, 119, 121, 127, 128, 129, 130, 133, 134, 144, 145, 150, 154, 160, 161, 162, 165, 177, 191, 200, 205, 208, 212, 216, 226, 230, 247, 254], "nice": [5, 12, 49, 80, 143, 152, 154, 159], "dig": [5, 99, 103, 164], "deeper": [5, 11, 12, 95, 97, 99, 143, 144, 164, 177, 211], "interest": [5, 6, 10, 14, 17, 20, 23, 25, 44, 49, 51, 58, 59, 78, 87, 97, 99, 105, 107, 108, 113, 117, 122, 126, 145, 152, 153, 157, 159, 166, 173, 174, 176, 178, 201, 205, 262, 271], "alex": 5, "grave": 5, "thesi": 5, "d_tanh": 5, "relu": [5, 6, 12, 19, 20, 23, 25, 37, 38, 44, 47, 52, 73, 78, 87, 89, 92, 93, 94, 96, 97, 99, 103, 104, 105, 110, 112, 123, 133, 134, 138, 144, 146, 148, 149, 150, 154, 156, 157, 158, 160, 161, 162, 163, 165, 166, 169, 172, 173, 174, 177, 181, 182, 200, 203, 205, 214, 215, 218, 219, 220, 221, 223, 230, 233, 239, 240, 241, 242, 243, 247, 248, 249, 250, 252], "exp": [5, 7, 9, 65, 89, 98, 99, 104, 111, 118, 125, 130, 141, 160, 191], "d_elu": 5, "mask": [5, 17, 58, 75, 90, 109, 118, 121, 136, 153, 156, 160, 164, 171, 178, 184, 189, 190, 192, 193, 196, 201, 220, 254], "type_a": [5, 118], "lltm_backward": 5, "grad_h": 5, "grad_cel": 5, "d_output_g": 5, "d_tanh_new_cel": 5, "d_new_cel": 5, "d_old_cel": 5, "d_candidate_cel": 5, "d_input_g": 5, "d_gate": 5, "d_weight": 5, "d_bia": 5, "keepdim": [5, 13, 19, 73, 123, 129, 162, 166, 182, 197, 198, 221], "d_x": [5, 52], "d_old_h": 5, "d_input": 5, "span": [5, 17, 75, 98, 133, 149, 168, 201, 263, 272], "four": [5, 7, 14, 18, 22, 61, 67, 85, 94, 95, 108, 111, 115, 119, 122, 134, 135, 149, 169, 223, 228, 257, 262, 263, 271, 272], "pybind11_modul": [5, 155], "torch_extension_nam": [5, 155], "macro": [5, 6, 8, 15, 23], "maintain": [5, 10, 14, 22, 43, 49, 61, 73, 97, 102, 108, 121, 131, 135, 146, 160, 163, 176, 177, 194, 258], "mismatch": [5, 61, 97], "nasti": 5, "hard": [5, 6, 8, 15, 21, 99, 126, 156], "At": [5, 6, 8, 14, 15, 17, 20, 23, 43, 47, 49, 50, 85, 87, 102, 116, 122, 123, 124, 136, 141, 146, 157, 159, 160, 161, 164, 165, 168, 189, 192, 197, 201, 206, 223, 261, 270], "point": [5, 6, 8, 10, 11, 14, 17, 18, 19, 20, 22, 23, 43, 47, 49, 50, 51, 52, 53, 58, 60, 82, 85, 97, 98, 100, 101, 102, 103, 110, 123, 124, 125, 126, 130, 131, 133, 143, 146, 149, 150, 157, 159, 161, 165, 169, 171, 173, 174, 181, 182, 184, 185, 189, 191, 192, 194, 197, 200, 201, 208, 221, 223, 228, 260, 261, 262, 269, 270, 271], "bdist_egg": 5, "egg_info": [5, 23], "egg": [5, 23], "pkg": [5, 23, 257], "dependency_link": [5, 23], "top": [5, 6, 8, 17, 19, 20, 22, 23, 38, 50, 51, 52, 82, 83, 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50, 52, 58, 59, 142, 143, 165, 187, 224, 225, 227, 238, 246, 257, 262, 271], "csrc": [5, 22, 23, 155, 188, 222], "th": [5, 14, 23, 51, 99, 103, 135, 146], "thc": [5, 23], "7m": [5, 23], "dtorch_api_include_extension_h": [5, 23], "dtorch_extension_nam": [5, 23], "d_glibcxx_use_cxx11_abi": [5, 23], "cc1plu": [5, 23], "warn": [5, 19, 23, 42, 51, 137, 144, 148, 159, 164, 171, 172, 173, 174, 182, 185, 187, 189, 190, 191, 192, 197, 198, 216, 231, 252], "valid": [5, 7, 9, 13, 17, 20, 23, 24, 49, 87, 94, 97, 104, 107, 109, 112, 113, 115, 117, 118, 122, 123, 126, 129, 130, 148, 155, 157, 171, 178, 181, 182, 190, 191, 200, 201, 204, 222], "objc": [5, 23], "l": [5, 7, 12, 14, 20, 23, 43, 49, 51, 52, 89, 99, 108, 110, 127, 128, 159, 160, 165, 173, 193, 194, 231], "rpath": [5, 23], "cpython": [5, 23], "37m": [5, 23], "stub": [5, 157, 209, 223, 263, 272], "loader": [5, 6, 12, 24, 38, 44, 79, 159, 162, 178, 222, 231], "byte": [5, 137, 139, 174, 181, 208, 230], "37": [5, 7, 17, 109, 158, 163, 177, 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176, 177, 184, 195, 199, 203, 212, 219, 223, 231, 246], "durat": [5, 83, 155, 160, 168, 177, 262, 271], "32": [5, 14, 17, 18, 19, 20, 21, 23, 24, 47, 52, 55, 73, 87, 92, 93, 97, 102, 105, 109, 123, 126, 129, 136, 137, 144, 145, 146, 147, 150, 154, 158, 162, 163, 164, 165, 166, 168, 171, 173, 174, 176, 177, 178, 198, 203, 219, 221, 228, 231, 233, 239, 247], "128": [5, 6, 12, 22, 51, 52, 55, 73, 87, 97, 103, 109, 118, 123, 124, 126, 127, 128, 129, 134, 135, 136, 137, 138, 144, 149, 154, 158, 160, 161, 162, 163, 165, 172, 178, 185, 200, 203, 207, 212, 220, 230, 231, 232, 233, 246, 253], "100000": [5, 58, 59, 127, 128, 137, 146, 231], "3f": [5, 9, 17, 19, 44, 87, 92, 115, 118, 146, 164, 178, 181, 193, 198, 201, 230, 250], "wrote": [5, 23, 139, 172, 178, 262, 271], "post": [5, 6, 11, 20, 49, 58, 59, 97, 121, 122, 123, 126, 137, 139, 147, 149, 166, 176, 177, 183, 185, 193, 196, 198, 200, 213, 221], "my": [5, 21, 42, 50, 98, 103, 191, 198, 203, 262, 271], "machin": [5, 6, 18, 20, 21, 25, 44, 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60, 97, 112, 117, 119, 121, 147, 150, 156, 157, 166, 176, 177, 182, 199, 200, 206, 207, 219, 220, 223, 226, 238, 252], "harder": [5, 97, 184, 185], "ourselv": [5, 6, 49, 76, 129, 159], "grid": [5, 47, 51, 117, 149, 157, 166, 169, 186, 254], "fill": [5, 6, 14, 80, 103, 127, 136, 141, 176, 190, 191, 208, 223], "matric": [5, 12, 17, 23, 25, 48, 101, 145, 153, 201, 207], "2048": [5, 18, 97, 129, 145], "heard": 5, "introductori": [5, 79], "fairli": [5, 97, 113, 135, 152, 160], "ever": [5, 6, 23, 125, 173, 174], "__global__": 5, "__restrict__": 5, "size_t": 5, "column": [5, 7, 18, 23, 40, 80, 99, 101, 109, 119, 124, 127, 144, 145, 150, 160, 168, 171, 190, 191, 192, 193, 231, 238, 263, 272], "blockidx": 5, "blockdim": 5, "threadidx": 5, "index": [5, 6, 15, 34, 38, 41, 44, 45, 48, 49, 51, 58, 59, 60, 73, 83, 98, 99, 101, 102, 103, 109, 115, 116, 118, 119, 123, 125, 126, 127, 128, 129, 135, 139, 153, 160, 161, 165, 166, 168, 171, 172, 176, 184, 193, 205, 213, 260, 266, 269, 275], "gates_row": 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67, 68, 69, 71, 72, 73, 75, 76, 78, 79, 80, 85, 87, 89, 90, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 107, 108, 109, 112, 113, 114, 115, 116, 117, 118, 119, 125, 126, 127, 128, 129, 136, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 152, 153, 154, 156, 159, 160, 164, 165, 166, 168, 169, 172, 174, 178, 181, 184, 188, 189, 190, 191, 192, 193, 195, 203, 204, 205, 208, 211, 225, 229, 230, 231, 232, 233, 234, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 255, 256], "here": [1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 47, 48, 49, 50, 51, 52, 53, 55, 58, 59, 60, 63, 64, 65, 67, 68, 69, 71, 72, 73, 75, 76, 78, 79, 80, 82, 85, 87, 89, 90, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 107, 108, 109, 111, 112, 113, 114, 115, 116, 117, 118, 119, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 135, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 152, 153, 154, 156, 157, 159, 160, 162, 163, 164, 165, 166, 168, 169, 172, 173, 174, 175, 176, 178, 179, 181, 182, 183, 184, 185, 187, 188, 189, 190, 191, 192, 193, 195, 197, 198, 199, 200, 201, 203, 204, 205, 208, 209, 211, 214, 216, 218, 220, 221, 222, 223, 225, 228, 229, 230, 231, 232, 233, 234, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 252, 253, 255, 256, 261, 263, 264, 270, 272, 273], "download": [1, 4, 6, 7, 9, 12, 13, 14, 17, 19, 20, 24, 25, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 47, 48, 49, 50, 51, 52, 58, 59, 60, 63, 64, 65, 67, 68, 69, 71, 72, 73, 75, 76, 78, 79, 80, 82, 85, 87, 89, 90, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 107, 108, 109, 112, 113, 114, 115, 116, 117, 118, 119, 122, 123, 125, 126, 127, 128, 129, 135, 136, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 152, 153, 154, 156, 157, 158, 159, 160, 162, 164, 165, 166, 168, 169, 172, 174, 178, 182, 184, 187, 188, 189, 190, 191, 192, 193, 195, 197, 198, 203, 204, 205, 206, 208, 211, 220, 221, 229, 230, 231, 232, 233, 234, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 254, 255, 256, 267, 276], "full": [1, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 17, 19, 20, 22, 24, 25, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 47, 48, 49, 50, 51, 52, 60, 63, 64, 65, 67, 68, 69, 71, 72, 73, 75, 76, 78, 79, 80, 85, 89, 90, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 107, 108, 109, 111, 112, 113, 114, 115, 116, 117, 118, 119, 122, 123, 125, 126, 127, 128, 129, 131, 134, 136, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 152, 153, 154, 155, 156, 159, 160, 161, 164, 165, 166, 168, 172, 174, 178, 181, 182, 184, 189, 190, 191, 192, 193, 194, 195, 198, 203, 204, 205, 208, 209, 211, 222, 228, 229, 230, 231, 232, 233, 234, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 255, 256], "author": [1, 5, 7, 9, 11, 12, 13, 14, 16, 17, 19, 24, 36, 42, 45, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 73, 81, 82, 83, 85, 86, 97, 98, 99, 101, 102, 103, 104, 107, 108, 109, 111, 112, 113, 114, 116, 117, 122, 123, 124, 126, 127, 128, 131, 132, 133, 134, 135, 136, 137, 139, 142, 143, 144, 146, 147, 149, 153, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 172, 173, 174, 176, 177, 179, 181, 182, 183, 184, 185, 186, 187, 188, 197, 198, 199, 200, 201, 204, 205, 206, 208, 209, 211, 214, 215, 222, 264, 273], "vincent": [1, 14, 136, 159], "moen": [1, 14, 136, 159], "separ": [1, 5, 6, 7, 8, 20, 22, 23, 25, 49, 52, 85, 97, 109, 124, 126, 138, 144, 146, 153, 157, 162, 165, 171, 178, 179, 181, 182, 189, 193, 197, 200, 216, 256], "rl": [1, 61, 121, 159, 160, 161], "algorithm": [1, 5, 6, 10, 11, 12, 49, 52, 56, 69, 82, 87, 98, 99, 100, 101, 111, 118, 122, 124, 126, 129, 135, 136, 146, 155, 159, 162, 166, 211, 216], "variou": [1, 8, 15, 47, 48, 49, 50, 60, 83, 85, 102, 109, 112, 116, 126, 143, 145, 156, 159, 162, 163, 171, 184, 191, 193], "piec": [1, 5, 8, 14, 59, 85, 158, 159, 163, 171, 175, 177, 178, 179, 188, 213], "assembl": [1, 8, 49, 134], "collect": [1, 4, 6, 11, 14, 15, 17, 18, 19, 21, 42, 43, 44, 45, 49, 55, 60, 61, 73, 75, 79, 97, 99, 103, 121, 122, 123, 124, 133, 134, 136, 143, 146, 149, 155, 160, 163, 175, 177, 201, 214, 215, 230], "final": [1, 6, 9, 10, 11, 12, 14, 15, 16, 17, 19, 20, 21, 22, 23, 24, 42, 43, 47, 49, 52, 58, 60, 73, 75, 85, 87, 97, 102, 105, 107, 108, 109, 112, 113, 117, 118, 119, 122, 123, 124, 126, 127, 128, 129, 130, 134, 135, 139, 143, 144, 145, 146, 150, 152, 154, 156, 157, 159, 160, 161, 162, 163, 165, 168, 169, 175, 176, 177, 179, 184, 185, 187, 188, 189, 192, 197, 198, 199, 200, 201, 203, 204, 208, 216, 221], "function": [1, 3, 4, 5, 6, 8, 9, 10, 13, 14, 16, 17, 18, 20, 21, 22, 23, 24, 25, 32, 38, 42, 43, 48, 49, 51, 53, 55, 59, 60, 61, 62, 65, 67, 68, 69, 75, 76, 79, 80, 82, 83, 89, 90, 92, 93, 94, 95, 96, 101, 102, 103, 104, 105, 107, 108, 109, 111, 112, 113, 116, 117, 118, 120, 121, 122, 123, 124, 125, 126, 127, 128, 134, 135, 136, 138, 142, 143, 144, 146, 147, 148, 149, 152, 153, 155, 160, 161, 162, 163, 164, 165, 166, 168, 169, 171, 172, 173, 174, 176, 177, 178, 179, 181, 185, 186, 187, 189, 190, 191, 192, 193, 194, 195, 200, 201, 202, 203, 205, 206, 208, 209, 211, 216, 220, 221, 223, 230, 231, 232, 233, 239, 246, 249, 250, 252, 255, 257, 259], "trainabl": [1, 6, 68, 97, 99, 157], "paramet": [1, 4, 5, 7, 9, 10, 11, 12, 14, 15, 16, 17, 19, 20, 22, 24, 25, 32, 33, 35, 37, 38, 43, 44, 47, 48, 49, 51, 52, 61, 65, 67, 68, 69, 73, 75, 85, 87, 89, 92, 94, 96, 97, 98, 99, 101, 102, 103, 104, 109, 110, 111, 115, 116, 117, 118, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 131, 133, 134, 135, 136, 137, 138, 139, 141, 142, 144, 145, 146, 147, 148, 149, 150, 152, 153, 154, 157, 160, 163, 164, 165, 166, 168, 169, 171, 172, 173, 174, 176, 177, 178, 182, 184, 189, 195, 196, 197, 201, 203, 210, 211, 212, 214, 216, 220, 221, 228, 230, 235, 237, 241, 242, 243, 244, 245, 249, 250, 252, 254, 259, 267, 276], "tutori": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 35, 43, 44, 45, 46, 51, 52, 53, 55, 56, 58, 59, 60, 61, 73, 74, 75, 77, 79, 81, 82, 84, 86, 87, 91, 97, 98, 100, 101, 104, 105, 106, 107, 108, 112, 113, 115, 116, 117, 118, 119, 121, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 149, 150, 151, 152, 154, 155, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 175, 177, 178, 180, 181, 182, 184, 185, 187, 188, 189, 190, 191, 192, 193, 194, 196, 197, 198, 199, 200, 201, 203, 204, 205, 206, 208, 209, 211, 214, 215, 217, 222, 223, 228, 229, 234, 235, 237, 247, 251, 252, 253, 258, 259], "guid": [1, 17, 58, 59, 61, 82, 85, 123, 135, 147, 158, 168, 173, 174, 176, 184, 196, 201, 229, 262, 271], "ground": [1, 14, 44, 73, 178], "aim": [1, 6, 61, 75, 97, 100, 152, 160, 163, 192, 221], "focus": [1, 3, 20, 97, 100, 149, 155, 165, 221], "rel": [1, 5, 6, 7, 117, 119, 125, 126, 137, 145, 149, 150, 163, 165, 176, 186, 197, 221], "straightforward": [1, 5, 6, 16, 17, 49, 60, 97, 98, 144, 200], "determinist": [1, 11, 14, 136, 148, 160], "gradient": [1, 6, 7, 10, 11, 13, 14, 16, 25, 37, 42, 43, 44, 47, 49, 52, 56, 61, 63, 64, 65, 67, 68, 69, 71, 72, 78, 87, 97, 98, 99, 101, 102, 103, 104, 110, 111, 115, 117, 121, 122, 123, 124, 125, 127, 129, 130, 131, 133, 135, 141, 145, 146, 149, 152, 156, 157, 159, 160, 161, 162, 163, 169, 171, 189, 205, 214, 216, 229, 235, 259], "simpl": [1, 3, 4, 5, 6, 8, 12, 15, 16, 17, 19, 21, 22, 23, 24, 25, 47, 49, 51, 54, 56, 61, 67, 73, 79, 85, 87, 97, 107, 112, 116, 120, 121, 123, 125, 126, 130, 135, 138, 139, 144, 145, 150, 154, 156, 159, 161, 162, 163, 164, 166, 168, 172, 182, 185, 199, 201, 205, 207, 211, 214, 215, 220, 221, 228, 252, 259, 263, 264, 272, 273], "continu": [1, 5, 17, 20, 21, 49, 53, 60, 73, 85, 87, 97, 102, 113, 116, 121, 124, 128, 131, 135, 142, 143, 146, 157, 159, 163, 165, 168, 176, 187, 188, 189, 191, 192, 198, 199, 200, 201, 204, 222, 252, 263, 272], "control": [1, 4, 8, 10, 14, 21, 23, 25, 34, 43, 60, 61, 66, 83, 85, 97, 110, 111, 113, 114, 122, 125, 126, 134, 135, 141, 153, 159, 160, 161, 172, 183, 197, 208, 252], "It": [1, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 42, 43, 44, 45, 47, 49, 51, 52, 60, 61, 73, 74, 75, 76, 82, 87, 95, 97, 98, 99, 100, 101, 103, 108, 112, 113, 115, 117, 122, 123, 124, 126, 127, 128, 129, 130, 131, 133, 134, 135, 136, 138, 140, 141, 143, 144, 145, 146, 149, 150, 152, 153, 154, 156, 157, 160, 161, 163, 165, 166, 167, 168, 170, 171, 173, 174, 177, 178, 179, 193, 200, 201, 202, 203, 204, 205, 208, 212, 213, 214, 215, 216, 222, 223, 224, 225, 228, 232, 261, 263, 270, 272], "consist": [1, 3, 6, 7, 14, 15, 16, 22, 24, 25, 43, 75, 97, 99, 118, 124, 131, 142, 143, 146, 150, 152, 159, 164, 165, 168, 173, 174, 177, 179, 191, 192, 199, 200, 208, 211, 263, 272], "parametr": [1, 2, 17, 121, 159, 201], "action": [1, 19, 58, 59, 101, 113, 122, 123, 146, 156, 159, 160, 161, 162, 163, 168, 182, 189, 190, 191, 192, 197, 198, 204, 208, 251, 263, 272], "pair": [1, 6, 14, 47, 49, 52, 116, 118, 128, 129, 137, 150, 154, 159, 160, 165, 168, 178, 179, 194, 199, 211, 263, 272], "maxim": [1, 14, 52, 73, 82, 97, 99, 126, 146, 160, 172, 176, 194], "given": [1, 6, 8, 10, 12, 14, 17, 20, 21, 22, 23, 25, 32, 43, 48, 49, 51, 52, 60, 61, 73, 76, 78, 82, 85, 97, 98, 100, 101, 103, 112, 116, 122, 127, 128, 133, 135, 138, 141, 142, 145, 146, 147, 154, 156, 159, 160, 162, 163, 165, 172, 173, 174, 177, 178, 192, 195, 200, 201, 216, 231, 259], "certain": [1, 4, 5, 6, 10, 11, 15, 49, 55, 60, 101, 113, 120, 122, 124, 125, 129, 141, 145, 147, 159, 164, 188, 189, 192, 193, 194, 198], "what": [1, 2, 3, 5, 8, 14, 18, 19, 20, 21, 22, 23, 25, 43, 45, 46, 53, 54, 55, 58, 59, 61, 73, 78, 86, 87, 98, 99, 101, 102, 103, 114, 121, 124, 125, 126, 128, 131, 132, 135, 136, 142, 146, 150, 152, 156, 159, 160, 161, 164, 169, 171, 173, 178, 187, 191, 195, 197, 200, 222, 226, 232, 235, 249, 252, 257, 263, 272], "write": [1, 4, 8, 9, 10, 21, 22, 23, 44, 49, 58, 59, 60, 61, 64, 75, 98, 99, 100, 101, 104, 116, 117, 121, 125, 126, 130, 131, 133, 136, 137, 139, 141, 142, 144, 146, 147, 149, 150, 153, 155, 159, 162, 163, 165, 168, 171, 172, 185, 188, 192, 196, 197, 198, 205, 206, 208, 223, 224, 225, 232, 258, 263, 272], "custom": [1, 4, 6, 8, 11, 17, 49, 52, 64, 65, 66, 79, 90, 109, 111, 121, 126, 136, 146, 159, 162, 171, 172, 177, 179, 183, 188, 195, 197, 199, 200, 201, 202, 204, 220, 221, 235], "its": [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 16, 17, 18, 20, 21, 22, 23, 25, 43, 47, 49, 51, 52, 53, 55, 60, 61, 68, 69, 73, 75, 80, 82, 95, 97, 99, 101, 102, 103, 105, 107, 109, 111, 112, 113, 115, 122, 123, 124, 125, 126, 127, 128, 135, 136, 137, 138, 139, 141, 143, 144, 146, 147, 149, 152, 153, 156, 159, 160, 161, 162, 163, 165, 168, 169, 172, 173, 174, 175, 176, 184, 188, 191, 192, 193, 197, 200, 201, 202, 204, 206, 208, 209, 214, 215, 218, 220, 222, 228, 259, 263, 272], "includ": [1, 2, 3, 4, 5, 6, 8, 14, 15, 16, 22, 23, 24, 25, 42, 48, 49, 53, 55, 58, 59, 60, 61, 69, 73, 75, 82, 85, 87, 97, 98, 105, 107, 109, 113, 115, 124, 126, 127, 128, 130, 131, 133, 135, 136, 144, 146, 147, 155, 156, 159, 162, 165, 168, 169, 172, 173, 174, 175, 176, 177, 178, 182, 184, 188, 189, 191, 194, 199, 200, 204, 206, 208, 212, 214, 216, 220, 221, 222, 224, 225, 228, 231, 238, 257, 258, 261, 263, 270, 272], "design": [1, 5, 6, 14, 17, 24, 25, 52, 56, 61, 73, 85, 103, 128, 133, 136, 159, 160, 161, 163, 164, 168, 169, 177, 179, 189, 190, 191, 195, 197, 200, 201, 205, 258], "effici": [1, 5, 7, 10, 12, 15, 17, 20, 23, 25, 42, 49, 51, 73, 82, 95, 97, 101, 103, 115, 119, 122, 123, 124, 126, 127, 135, 136, 145, 150, 156, 159, 163, 164, 168, 171, 175, 177, 186, 187, 192, 193, 194, 201, 204], "store": [1, 4, 5, 6, 16, 17, 19, 20, 22, 23, 40, 43, 48, 51, 53, 60, 68, 87, 95, 98, 99, 101, 103, 105, 111, 125, 126, 128, 135, 136, 141, 143, 144, 146, 147, 153, 155, 156, 159, 160, 161, 162, 163, 165, 182, 184, 192, 197, 198, 201, 204, 216, 252, 255, 258], "trajectori": [1, 14, 61, 136, 159], "transit": [1, 14, 60, 85, 86, 98, 136, 160, 200], "assum": [1, 2, 4, 6, 8, 10, 12, 14, 15, 19, 21, 22, 43, 44, 51, 54, 60, 73, 97, 98, 100, 102, 116, 124, 125, 127, 128, 135, 136, 139, 153, 156, 159, 162, 164, 165, 173, 174, 175, 178, 191, 192, 193, 199, 200, 223], "complet": [1, 4, 5, 6, 15, 21, 25, 49, 76, 78, 85, 87, 98, 99, 101, 113, 117, 119, 122, 124, 126, 130, 135, 156, 157, 158, 159, 160, 162, 165, 171, 172, 177, 178, 184, 191, 192, 225, 228, 252], "ppo": [1, 121], "compon": [1, 5, 6, 8, 10, 14, 20, 25, 52, 61, 85, 97, 101, 112, 113, 115, 119, 121, 126, 136, 142, 146, 159, 163, 166, 168, 172, 173, 174, 177, 193], "depend": [1, 5, 6, 7, 8, 11, 14, 21, 22, 23, 42, 47, 50, 52, 60, 73, 82, 85, 97, 98, 102, 110, 118, 119, 121, 124, 126, 129, 130, 135, 136, 137, 139, 141, 142, 143, 145, 146, 149, 155, 158, 159, 162, 168, 172, 173, 174, 181, 182, 183, 184, 188, 191, 196, 197, 198, 204, 206, 208, 222, 224, 225, 232, 252], "tensordict": [1, 14, 136, 146, 159], "nn": [1, 2, 4, 5, 6, 7, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 25, 32, 37, 38, 39, 42, 43, 44, 45, 47, 49, 52, 55, 60, 65, 66, 69, 73, 75, 77, 79, 87, 89, 90, 92, 94, 96, 97, 98, 99, 102, 103, 105, 107, 108, 109, 110, 115, 117, 118, 119, 121, 122, 123, 124, 125, 127, 128, 129, 133, 134, 136, 137, 138, 141, 142, 143, 145, 146, 147, 148, 149, 150, 153, 154, 157, 159, 160, 161, 162, 163, 164, 165, 166, 168, 169, 171, 172, 173, 174, 177, 179, 181, 182, 183, 185, 190, 193, 194, 195, 197, 198, 199, 200, 201, 202, 203, 209, 210, 211, 212, 214, 215, 218, 219, 220, 221, 223, 226, 228, 230, 232, 233, 234, 235, 238, 239, 241, 242, 243, 245, 247, 248, 249, 250, 251, 252, 254, 257, 259], "tensordictmodul": [1, 14, 136, 159], "although": [1, 12, 16, 43, 49, 60, 61, 98, 99, 103, 105, 108, 115, 119, 125, 146, 149, 153, 157, 162, 172, 173, 174, 176, 182, 203, 263, 272], "suffici": [1, 6, 49, 52, 97, 98, 117, 131, 133, 152], "transpar": [1, 12, 42, 99, 136, 162, 206, 220], "understood": [1, 4, 113], "without": [1, 4, 5, 6, 8, 9, 10, 14, 17, 20, 23, 32, 42, 49, 53, 55, 60, 73, 78, 97, 98, 107, 112, 113, 116, 123, 124, 125, 128, 129, 135, 137, 138, 141, 143, 145, 146, 147, 152, 154, 155, 156, 157, 158, 159, 160, 161, 164, 165, 168, 171, 176, 177, 189, 191, 192, 193, 194, 199, 200, 201, 203, 208, 209, 211, 215, 220, 227, 228, 252, 259, 261, 263, 270, 272], "understand": [1, 2, 4, 6, 15, 23, 43, 44, 52, 57, 58, 59, 82, 85, 91, 98, 99, 101, 108, 117, 121, 125, 126, 127, 128, 130, 135, 137, 141, 143, 144, 149, 157, 165, 171, 173, 174, 176, 190, 195, 199, 200, 208, 215, 249], "class": [1, 2, 4, 5, 6, 7, 8, 9, 10, 12, 13, 15, 16, 19, 20, 21, 23, 24, 25, 33, 34, 37, 38, 42, 44, 45, 47, 49, 52, 53, 58, 59, 60, 64, 65, 67, 73, 75, 76, 78, 79, 83, 85, 87, 89, 90, 92, 93, 94, 96, 98, 99, 100, 102, 103, 104, 105, 108, 109, 110, 111, 112, 115, 117, 118, 119, 121, 122, 123, 125, 126, 127, 128, 129, 130, 131, 133, 134, 135, 136, 137, 138, 139, 141, 142, 143, 144, 146, 147, 148, 149, 150, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 169, 171, 172, 173, 174, 177, 178, 179, 181, 182, 183, 189, 190, 191, 193, 194, 195, 197, 198, 199, 200, 202, 203, 208, 209, 212, 213, 214, 215, 216, 218, 219, 221, 223, 226, 233, 234, 237, 239, 240, 241, 242, 243, 244, 248, 249, 250, 252, 262, 263, 264, 271, 272, 273], "sota": [1, 75, 113, 119], "implement": [1, 2, 3, 4, 5, 6, 7, 10, 11, 12, 13, 14, 15, 17, 20, 24, 42, 43, 45, 47, 49, 51, 55, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, 69, 76, 79, 85, 103, 108, 111, 115, 120, 121, 124, 125, 126, 127, 130, 133, 134, 135, 136, 138, 139, 141, 144, 145, 146, 147, 149, 150, 154, 156, 159, 160, 163, 168, 173, 174, 178, 179, 186, 190, 192, 193, 194, 196, 197, 200, 201, 204, 206, 207, 208, 216, 220, 221, 222, 224, 231, 258, 262, 271], "rather": [1, 13, 23, 25, 49, 52, 69, 73, 85, 97, 103, 112, 121, 128, 129, 143, 144, 149, 153, 154, 159, 171, 184, 188, 189, 223], "high": [1, 2, 5, 6, 14, 15, 19, 23, 25, 42, 44, 49, 52, 53, 55, 57, 60, 82, 85, 99, 103, 105, 109, 112, 121, 122, 123, 124, 126, 127, 129, 135, 139, 146, 149, 159, 168, 169, 171, 176, 177, 186, 192, 195, 196, 197, 199, 212, 216, 252, 259, 261, 270], "level": [1, 2, 5, 6, 17, 19, 20, 23, 25, 44, 49, 53, 55, 57, 68, 79, 100, 105, 115, 122, 123, 124, 126, 127, 128, 131, 133, 135, 137, 141, 142, 143, 144, 147, 149, 164, 165, 168, 171, 173, 174, 176, 177, 182, 185, 195, 196, 197, 199, 201, 209, 212, 215, 216, 221, 223, 227, 259, 267, 276], "illustr": [1, 19, 44, 47, 56, 116, 117, 124, 125, 126, 138, 160, 169, 171, 178, 191, 192, 195, 215], "librari": [1, 3, 4, 5, 6, 8, 12, 14, 18, 20, 22, 23, 25, 42, 44, 50, 51, 57, 61, 75, 87, 107, 108, 113, 115, 118, 121, 126, 129, 130, 137, 139, 143, 155, 158, 159, 163, 168, 173, 174, 177, 194, 204, 206, 215, 219, 220, 222, 223, 226, 227, 228, 249, 251], "featur": [1, 4, 6, 10, 11, 12, 14, 17, 19, 22, 23, 34, 49, 50, 51, 52, 58, 59, 60, 61, 82, 83, 85, 90, 94, 95, 97, 98, 103, 108, 113, 121, 123, 125, 136, 137, 144, 145, 146, 149, 152, 155, 158, 159, 163, 164, 169, 172, 173, 174, 175, 176, 177, 178, 185, 186, 187, 188, 192, 193, 196, 199, 201, 204, 205, 206, 208, 212, 216, 251, 252, 253], "context": [1, 2, 5, 8, 14, 16, 17, 43, 49, 60, 61, 64, 73, 103, 109, 111, 120, 124, 134, 141, 153, 159, 162, 163, 164, 165, 168, 177, 186, 199, 201, 206, 208, 212, 232], "bash": [1, 18, 20, 146, 160, 226], "pip3": [1, 18, 50, 122, 136, 159, 160, 168, 175, 184, 187, 188], "mujoco": [1, 136, 159], "glfw": 1, "tqdm": [1, 14, 17, 122, 136, 137, 159, 185, 201], "avail": [1, 2, 3, 5, 6, 10, 12, 14, 15, 17, 18, 19, 20, 21, 22, 23, 40, 42, 43, 44, 48, 50, 51, 52, 53, 58, 59, 73, 80, 87, 97, 101, 105, 113, 115, 119, 122, 125, 135, 136, 139, 141, 146, 147, 156, 157, 158, 159, 160, 163, 164, 165, 168, 171, 175, 176, 177, 178, 181, 182, 187, 188, 196, 197, 198, 199, 201, 205, 212, 214, 220, 222, 223, 224, 225, 227, 228, 232, 261, 270], "is_fork": [1, 136, 159], "multiprocess": [1, 6, 7, 11, 14, 34, 51, 53, 55, 56, 110, 122, 123, 133, 134, 135, 136, 159, 162, 163, 212, 214, 216, 259], "get_start_method": [1, 136, 159], "fork": [1, 21, 136, 159, 160], "is_avail": [1, 5, 6, 12, 20, 33, 38, 40, 42, 44, 45, 48, 49, 52, 63, 73, 80, 87, 89, 95, 97, 104, 110, 111, 115, 117, 118, 129, 136, 146, 147, 155, 156, 157, 159, 160, 162, 164, 165, 166, 172, 178, 193, 230], "els": [1, 4, 5, 8, 9, 11, 12, 14, 16, 17, 18, 19, 20, 23, 25, 33, 38, 42, 44, 45, 47, 49, 51, 52, 58, 59, 60, 63, 73, 87, 94, 95, 96, 97, 103, 104, 105, 108, 110, 111, 115, 116, 117, 118, 122, 127, 128, 129, 134, 135, 136, 137, 142, 146, 147, 150, 155, 156, 157, 159, 160, 161, 162, 163, 164, 165, 166, 169, 171, 172, 178, 181, 182, 185, 186, 193, 195, 197, 198, 201, 208, 209, 212, 215, 216, 218, 222, 230, 231, 244, 246, 252, 255, 256, 259, 263, 264, 272, 273], "cpu": [1, 3, 5, 6, 8, 9, 10, 11, 12, 14, 15, 18, 19, 20, 23, 33, 38, 42, 43, 44, 45, 48, 49, 52, 60, 63, 64, 72, 73, 80, 82, 83, 87, 89, 90, 95, 97, 99, 104, 105, 108, 109, 110, 111, 115, 117, 118, 121, 123, 124, 129, 133, 134, 135, 136, 137, 146, 147, 150, 153, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 168, 171, 175, 178, 181, 182, 185, 186, 187, 188, 193, 194, 195, 197, 198, 199, 202, 206, 207, 212, 216, 219, 223, 226, 229, 230, 232, 234, 237, 238, 240, 244, 251, 252, 253, 254], "collector_devic": 1, "chang": [1, 2, 5, 6, 10, 11, 12, 14, 19, 21, 22, 23, 24, 40, 43, 48, 50, 51, 52, 53, 55, 58, 59, 61, 76, 78, 79, 80, 82, 83, 85, 87, 95, 97, 98, 100, 101, 102, 105, 108, 112, 116, 121, 123, 124, 126, 131, 132, 135, 136, 137, 139, 141, 144, 145, 146, 149, 152, 153, 155, 156, 157, 161, 168, 171, 172, 173, 174, 177, 181, 182, 184, 186, 187, 188, 191, 193, 197, 198, 200, 204, 206, 208, 211, 212, 214, 216, 220, 221, 222, 235, 252, 261, 270], "seri": [1, 6, 15, 23, 52, 53, 54, 55, 56, 82, 91, 120, 121, 127, 128, 131, 132, 139, 143, 156, 159, 191], "reusabl": [1, 6, 25], "swappabl": 1, "signatur": [1, 5, 8, 10, 14, 15, 23, 108, 135, 153, 162, 173, 174, 252], "characterist": [1, 14, 43, 143, 145, 146, 158, 164], "copi": [1, 5, 6, 12, 18, 22, 23, 44, 45, 50, 55, 58, 61, 73, 82, 97, 109, 110, 112, 114, 117, 123, 125, 129, 133, 135, 136, 137, 138, 141, 142, 143, 146, 149, 153, 157, 162, 168, 171, 181, 182, 183, 188, 194, 198, 199, 204, 206, 208, 212, 218, 219, 234, 253, 258, 264, 273], "loss_modul": [1, 159], "whatev": [1, 8, 22, 23, 99, 101, 112, 195], "convent": [1, 14, 52, 60, 112, 126, 136, 171, 216], "receiv": [1, 4, 6, 14, 16, 55, 64, 87, 101, 111, 135, 159, 161, 162, 163, 172], "necessari": [1, 4, 5, 6, 7, 8, 10, 12, 15, 16, 18, 19, 23, 24, 44, 52, 53, 55, 60, 85, 87, 98, 112, 113, 122, 123, 124, 129, 133, 146, 149, 159, 161, 162, 163, 168, 173, 174, 177, 179, 182, 185, 191, 193, 195, 197, 198, 199, 249], "return": [1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 25, 33, 34, 37, 38, 40, 44, 45, 47, 49, 51, 52, 59, 60, 64, 65, 67, 68, 73, 75, 78, 79, 80, 82, 85, 87, 89, 90, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103, 104, 105, 108, 109, 110, 111, 112, 113, 115, 116, 117, 118, 122, 123, 124, 126, 127, 128, 129, 130, 133, 134, 135, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 168, 169, 171, 172, 173, 174, 176, 177, 178, 179, 181, 182, 183, 184, 185, 186, 187, 189, 191, 193, 194, 195, 197, 198, 199, 200, 201, 203, 205, 206, 208, 209, 210, 212, 213, 214, 215, 218, 219, 220, 221, 222, 223, 226, 230, 231, 233, 234, 237, 239, 240, 241, 242, 243, 244, 246, 247, 248, 249, 250, 252, 255, 256, 257, 258], "replay_buff": [1, 159], "sampl": [1, 6, 8, 44, 47, 48, 49, 51, 52, 55, 60, 61, 75, 78, 79, 87, 92, 97, 99, 105, 110, 115, 116, 117, 118, 121, 123, 127, 129, 135, 136, 137, 139, 145, 146, 152, 153, 157, 159, 160, 161, 163, 171, 173, 174, 178, 182, 185, 193, 194, 197, 199, 205, 209, 225, 226, 263, 272], "loss_dict": 1, "instanc": [1, 4, 5, 6, 7, 11, 12, 14, 21, 22, 23, 25, 45, 53, 54, 55, 58, 59, 60, 78, 82, 87, 97, 98, 99, 102, 103, 122, 123, 125, 126, 131, 132, 133, 134, 136, 144, 146, 157, 159, 161, 162, 163, 165, 172, 182, 183, 197, 199, 200, 202, 220, 252, 253, 258, 259], "written": [1, 4, 5, 6, 8, 10, 14, 22, 23, 25, 85, 100, 130, 136, 143, 150, 153, 154, 157, 164, 168, 171, 184, 189, 220], "under": [1, 4, 5, 8, 14, 18, 19, 23, 47, 49, 50, 52, 54, 56, 97, 99, 109, 113, 115, 124, 125, 135, 137, 139, 145, 146, 153, 156, 163, 168, 169, 177, 178, 179, 187, 188, 190, 192, 204, 212, 214, 216, 221, 222, 225, 263, 272], "loss_": 1, "smth": 1, "where": [1, 3, 4, 6, 7, 8, 11, 12, 13, 14, 16, 17, 18, 20, 21, 22, 23, 24, 32, 47, 49, 51, 60, 61, 64, 68, 75, 78, 79, 83, 85, 87, 97, 98, 99, 101, 102, 103, 110, 113, 122, 124, 126, 127, 128, 130, 132, 133, 134, 135, 137, 138, 139, 141, 144, 147, 149, 150, 152, 153, 154, 158, 159, 160, 161, 162, 163, 164, 165, 169, 172, 174, 175, 177, 178, 179, 182, 184, 187, 189, 192, 193, 194, 195, 200, 201, 204, 208, 215, 216, 226, 228, 264, 273], "string": [1, 8, 15, 22, 23, 49, 51, 58, 59, 60, 65, 67, 105, 111, 115, 116, 118, 126, 127, 128, 136, 139, 156, 159, 165, 171, 182, 208, 209, 258, 261, 270], "describ": [1, 4, 5, 6, 8, 10, 14, 15, 16, 19, 20, 21, 22, 23, 48, 49, 52, 58, 59, 61, 73, 97, 105, 114, 120, 135, 150, 159, 160, 163, 168, 171, 173, 174, 176, 196, 197, 198, 202, 215, 252], "addit": [1, 2, 5, 7, 8, 11, 15, 17, 19, 50, 60, 73, 75, 97, 102, 105, 108, 109, 113, 122, 124, 125, 133, 135, 137, 138, 139, 142, 144, 147, 149, 156, 161, 162, 165, 169, 172, 173, 174, 176, 185, 189, 190, 191, 192, 197, 200, 201, 206, 208, 216, 218, 220, 253], "kei": [1, 6, 8, 11, 14, 15, 17, 49, 58, 75, 82, 90, 100, 103, 105, 109, 112, 114, 115, 116, 119, 122, 126, 136, 137, 139, 143, 146, 156, 158, 159, 160, 161, 164, 165, 168, 169, 171, 173, 174, 175, 177, 185, 193, 194, 195, 201, 209, 211, 220, 237, 255, 263, 272], "mai": [1, 4, 5, 6, 8, 10, 11, 12, 14, 15, 17, 19, 21, 22, 23, 25, 42, 49, 50, 52, 58, 59, 60, 68, 73, 85, 95, 99, 112, 113, 116, 123, 124, 125, 126, 129, 130, 136, 137, 138, 139, 141, 143, 144, 145, 150, 152, 153, 158, 159, 162, 165, 168, 171, 172, 173, 174, 176, 177, 179, 181, 182, 185, 188, 191, 193, 197, 198, 199, 200, 201, 202, 208, 218, 228, 231, 252, 263, 264, 272, 273], "metric": [1, 17, 87, 97, 109, 122, 137, 146, 168, 171, 177, 178, 201, 221], "log": [1, 7, 14, 18, 49, 50, 52, 53, 58, 73, 97, 98, 99, 102, 103, 104, 118, 123, 126, 129, 132, 137, 148, 158, 159, 161, 163, 166, 168, 169, 171, 173, 174, 177, 185, 195, 208, 211, 221, 256], "dure": [1, 3, 7, 8, 12, 14, 16, 18, 19, 25, 32, 37, 49, 52, 60, 61, 63, 64, 76, 78, 85, 97, 99, 103, 108, 111, 112, 113, 118, 121, 122, 123, 124, 125, 128, 129, 130, 131, 133, 136, 142, 143, 144, 149, 150, 153, 157, 158, 159, 160, 161, 163, 168, 172, 176, 177, 178, 185, 196, 198, 202, 206, 214, 216, 220, 223, 224, 225, 228, 252], "reason": [1, 5, 6, 8, 14, 15, 17, 23, 25, 52, 78, 82, 97, 99, 102, 112, 117, 125, 129, 135, 144, 149, 157, 159, 164, 165, 184, 191, 201, 214, 223, 252], "independ": [1, 7, 23, 49, 60, 79, 103, 108, 110, 145, 146, 150, 162, 189], "user": [1, 3, 5, 14, 17, 18, 19, 22, 24, 25, 44, 49, 50, 60, 76, 79, 82, 83, 85, 97, 101, 108, 110, 113, 114, 115, 122, 124, 128, 133, 137, 139, 142, 143, 144, 147, 161, 163, 164, 165, 166, 168, 171, 173, 174, 175, 176, 177, 178, 179, 182, 185, 187, 189, 190, 191, 192, 195, 196, 197, 198, 199, 200, 201, 204, 207, 212, 215, 216, 220, 221, 228, 263, 264, 272, 273], "sum": [1, 2, 4, 5, 7, 11, 13, 14, 16, 18, 19, 21, 25, 37, 38, 40, 43, 44, 49, 52, 60, 63, 64, 65, 67, 68, 69, 71, 72, 76, 78, 82, 87, 89, 92, 97, 98, 99, 101, 103, 104, 109, 111, 115, 117, 122, 123, 125, 127, 128, 129, 130, 135, 136, 145, 146, 150, 152, 153, 156, 157, 159, 160, 161, 162, 163, 166, 168, 172, 173, 174, 175, 182, 189, 190, 191, 192, 197, 198, 210, 211, 212, 214, 221, 231, 252, 259], "done": [1, 4, 5, 6, 8, 10, 14, 16, 17, 19, 20, 21, 22, 23, 25, 37, 38, 49, 54, 58, 59, 82, 85, 97, 98, 99, 108, 113, 115, 122, 123, 124, 125, 128, 129, 135, 136, 138, 143, 144, 146, 147, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 169, 171, 173, 174, 184, 185, 194, 201, 202, 208, 216, 220, 223, 257, 258], "via": [1, 3, 5, 6, 7, 16, 17, 18, 20, 22, 23, 54, 55, 58, 59, 73, 85, 97, 121, 122, 123, 124, 126, 135, 136, 139, 145, 153, 158, 159, 164, 169, 171, 172, 176, 177, 178, 188, 191, 201, 212, 215, 216, 220, 221, 261, 267, 270, 276], "loss_val": [1, 136, 159], "item": [1, 2, 6, 7, 9, 10, 11, 12, 14, 15, 34, 37, 38, 40, 44, 49, 52, 60, 63, 64, 65, 67, 68, 69, 72, 73, 87, 90, 92, 94, 95, 96, 97, 98, 101, 103, 104, 109, 111, 112, 114, 115, 117, 118, 119, 122, 123, 127, 128, 129, 135, 136, 137, 139, 141, 143, 146, 147, 157, 158, 159, 160, 161, 162, 163, 165, 166, 169, 171, 178, 179, 181, 193, 209, 213, 218, 221, 230, 234, 247, 250, 262, 264, 271, 273], "startswith": [1, 83, 147, 165, 246], "parent": [1, 14, 104, 115, 142, 146, 183, 185], "As": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 19, 20, 21, 22, 23, 25, 43, 49, 50, 52, 58, 59, 60, 61, 73, 85, 87, 97, 103, 105, 108, 112, 116, 118, 122, 123, 124, 125, 126, 127, 133, 135, 136, 137, 141, 142, 143, 144, 145, 146, 149, 152, 153, 156, 157, 159, 160, 161, 162, 163, 164, 168, 171, 174, 175, 176, 177, 178, 179, 182, 184, 185, 187, 188, 192, 193, 195, 197, 200, 204, 208, 212, 221, 222, 259], "mani": [1, 2, 4, 5, 6, 10, 14, 15, 17, 18, 23, 25, 49, 51, 52, 60, 61, 65, 69, 73, 82, 97, 99, 100, 101, 104, 105, 107, 111, 113, 122, 124, 126, 127, 129, 135, 137, 138, 145, 147, 149, 150, 154, 157, 159, 161, 162, 165, 173, 174, 176, 177, 191, 194, 201, 204, 205, 220, 221, 252, 261, 263, 264, 270, 272, 273], "expect": [1, 4, 5, 6, 10, 11, 14, 20, 22, 23, 32, 45, 47, 49, 51, 58, 59, 60, 61, 73, 85, 87, 97, 101, 102, 103, 112, 113, 117, 119, 126, 129, 133, 134, 136, 145, 146, 152, 153, 156, 158, 159, 160, 161, 162, 164, 171, 172, 173, 174, 176, 178, 179, 182, 187, 188, 194, 195, 197, 199, 200, 204, 205, 220, 223, 259], "similar": [1, 3, 5, 8, 10, 11, 14, 15, 19, 22, 23, 48, 49, 58, 59, 61, 82, 83, 97, 98, 103, 108, 116, 124, 130, 134, 135, 136, 139, 143, 149, 153, 159, 161, 162, 163, 164, 165, 168, 169, 171, 176, 178, 179, 182, 185, 189, 190, 191, 192, 193, 198, 199, 218, 259], "structur": [1, 4, 5, 6, 8, 9, 14, 18, 19, 20, 21, 22, 23, 33, 48, 49, 52, 53, 60, 61, 78, 85, 97, 98, 102, 105, 110, 112, 121, 131, 136, 138, 143, 146, 147, 149, 153, 154, 156, 159, 163, 169, 171, 172, 178, 192, 194, 196, 197, 205, 208, 261, 263, 267, 270, 272, 276], "make": [1, 4, 5, 6, 8, 10, 12, 14, 18, 19, 22, 23, 43, 44, 45, 47, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, 61, 68, 69, 73, 87, 97, 99, 100, 101, 102, 103, 108, 111, 112, 113, 114, 115, 117, 118, 121, 122, 123, 124, 126, 127, 128, 129, 133, 135, 136, 137, 139, 142, 143, 144, 145, 146, 149, 152, 153, 155, 156, 157, 159, 160, 161, 162, 163, 164, 165, 169, 171, 172, 173, 174, 176, 178, 182, 184, 185, 188, 189, 190, 191, 192, 193, 194, 195, 197, 200, 205, 212, 213, 214, 215, 216, 218, 220, 223, 227, 228, 252, 257, 263, 265, 272, 274], "possibl": [1, 2, 4, 5, 6, 8, 10, 14, 15, 17, 22, 23, 52, 60, 61, 75, 98, 101, 108, 119, 125, 129, 130, 136, 138, 141, 143, 145, 146, 149, 157, 158, 159, 161, 162, 165, 178, 182, 185, 187, 193, 197, 198, 199, 200, 201, 202, 204, 216, 220, 221, 223, 252, 263, 272], "across": [1, 5, 7, 8, 9, 11, 14, 16, 18, 20, 24, 49, 52, 54, 55, 56, 61, 82, 97, 105, 115, 120, 122, 123, 124, 131, 132, 133, 134, 135, 138, 146, 149, 156, 162, 163, 175, 176, 181, 211, 214, 215, 235, 259, 261, 270], "modal": [1, 60], "complex": [1, 6, 23, 25, 50, 61, 67, 68, 97, 105, 112, 120, 123, 133, 150, 153, 161, 163, 169, 193, 203, 209], "multipl": [1, 5, 8, 10, 11, 14, 16, 17, 18, 19, 20, 23, 40, 45, 48, 49, 53, 54, 55, 56, 61, 65, 78, 79, 81, 82, 87, 97, 101, 110, 120, 123, 124, 125, 126, 127, 128, 133, 134, 135, 138, 139, 143, 144, 146, 149, 158, 159, 161, 162, 163, 165, 168, 169, 171, 173, 174, 175, 176, 177, 182, 184, 193, 199, 200, 201, 214, 235, 250, 263, 264, 272, 273], "entri": [1, 4, 11, 14, 23, 53, 75, 98, 101, 103, 109, 110, 112, 115, 131, 136, 143, 144, 156, 159, 161, 164, 168, 173, 174, 191, 192, 193, 195, 212], "word": [1, 6, 7, 10, 11, 14, 42, 44, 49, 60, 73, 79, 82, 97, 98, 100, 102, 112, 115, 116, 118, 121, 127, 128, 135, 137, 143, 152, 153, 156, 163, 165, 176, 181, 190, 192, 193, 195, 199, 263, 272], "oblivi": [1, 159], "type": [1, 4, 5, 6, 8, 9, 10, 14, 18, 19, 20, 21, 22, 23, 37, 38, 40, 42, 48, 49, 50, 51, 52, 60, 61, 73, 78, 80, 82, 85, 95, 101, 105, 108, 113, 118, 120, 122, 123, 124, 126, 134, 137, 138, 139, 142, 143, 144, 147, 148, 154, 155, 156, 159, 161, 162, 163, 164, 168, 171, 172, 173, 174, 175, 177, 179, 181, 185, 187, 189, 194, 197, 199, 200, 202, 204, 208, 209, 212, 213, 214, 216, 220, 221, 222, 223, 228, 244, 258, 263, 272], "being": 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157, 158, 160, 161, 162, 165, 168, 169, 171, 172, 173, 176, 177, 178, 182, 184, 185, 187, 191, 195, 197, 198, 199, 200, 201, 203, 208, 212, 216, 223, 239, 259, 264, 273], "solv": [1, 6, 14, 49, 51, 97, 103, 117, 118, 149, 153, 157, 159, 161, 163, 176, 191], "task": [1, 6, 7, 13, 14, 17, 21, 24, 49, 58, 59, 60, 75, 97, 98, 103, 109, 113, 116, 117, 118, 119, 120, 121, 123, 136, 137, 153, 157, 159, 160, 165, 166, 171, 178, 185, 201, 204, 208], "strategi": [1, 5, 17, 18, 24, 52, 82, 113, 121, 128, 135, 144, 145, 149, 154, 161, 162, 201, 215, 216, 221], "predict": [1, 9, 17, 19, 20, 33, 37, 38, 43, 44, 49, 52, 60, 63, 64, 65, 67, 68, 69, 71, 72, 73, 87, 89, 90, 92, 97, 98, 102, 103, 104, 111, 113, 115, 116, 118, 121, 124, 126, 127, 128, 137, 138, 145, 146, 149, 154, 160, 165, 169, 178, 181, 182, 197, 198, 201, 213, 219, 257, 258], "henc": [1, 14, 17, 43, 48, 61, 78, 80, 82, 113, 123, 125, 133, 134, 147, 149, 150, 155, 159, 161, 163, 176, 201, 220], "two": [1, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 23, 24, 25, 40, 42, 43, 48, 49, 50, 52, 57, 58, 59, 60, 73, 75, 83, 85, 87, 92, 95, 97, 98, 99, 101, 102, 103, 105, 107, 108, 109, 116, 117, 119, 122, 123, 126, 127, 129, 130, 132, 133, 134, 135, 136, 139, 141, 143, 144, 145, 146, 147, 149, 150, 153, 154, 155, 156, 157, 159, 160, 161, 163, 164, 165, 171, 175, 176, 177, 178, 179, 183, 184, 186, 190, 192, 193, 195, 197, 198, 199, 200, 201, 202, 203, 212, 215, 216, 221, 224, 225, 229, 252, 259, 263, 264, 268, 272, 273, 277], "constructor": [1, 6, 10, 11, 12, 21, 22, 23, 25, 60, 65, 67, 69, 78, 85, 111, 116, 122, 123, 133, 134, 143, 155, 156, 159, 161, 163, 192, 202, 252], "both": [1, 2, 4, 5, 6, 7, 8, 10, 11, 12, 14, 16, 19, 20, 21, 22, 23, 24, 25, 42, 49, 51, 52, 58, 59, 60, 61, 73, 82, 85, 97, 103, 109, 113, 116, 118, 122, 124, 126, 127, 129, 132, 133, 134, 135, 141, 142, 144, 145, 147, 149, 150, 156, 157, 159, 161, 162, 163, 164, 165, 173, 174, 175, 176, 177, 178, 179, 182, 184, 185, 186, 189, 192, 194, 195, 197, 199, 200, 209, 212, 215, 220, 221, 223, 228, 231, 261, 263, 270, 272], "compat": [1, 4, 5, 6, 8, 11, 17, 50, 60, 94, 95, 101, 136, 147, 164, 173, 174, 182, 187, 202, 204, 216, 222], "comput": [1, 3, 5, 6, 8, 11, 12, 13, 16, 17, 19, 20, 21, 23, 24, 25, 32, 37, 38, 40, 44, 47, 48, 49, 52, 53, 57, 58, 59, 60, 61, 63, 64, 65, 67, 68, 69, 71, 72, 73, 75, 76, 78, 79, 83, 85, 95, 97, 98, 99, 100, 102, 105, 107, 110, 111, 115, 117, 119, 120, 121, 122, 123, 124, 125, 126, 129, 130, 131, 132, 133, 135, 136, 137, 139, 141, 142, 143, 144, 146, 148, 149, 152, 153, 154, 156, 158, 159, 160, 162, 164, 165, 168, 171, 172, 173, 174, 176, 177, 178, 182, 184, 187, 188, 193, 194, 195, 196, 197, 198, 199, 201, 202, 205, 206, 208, 211, 216, 219, 223, 228, 231, 263, 272], "fit": [1, 6, 7, 9, 10, 11, 12, 20, 24, 61, 87, 103, 122, 123, 124, 133, 148, 149, 163, 181, 263, 272], "crucial": [1, 2, 12, 14, 23, 82, 101, 136, 159, 223], "convert_to_funct": 1, "extract": [1, 5, 20, 49, 52, 58, 59, 73, 97, 116, 117, 127, 128, 137, 141, 144, 154, 157, 159, 165, 172, 173, 174, 178, 208, 212, 216], "convert": [1, 5, 9, 10, 12, 14, 19, 20, 22, 23, 44, 49, 51, 52, 55, 73, 75, 95, 97, 105, 107, 110, 112, 113, 115, 116, 118, 119, 121, 127, 128, 137, 139, 157, 158, 159, 160, 161, 162, 166, 169, 177, 178, 181, 183, 184, 185, 188, 189, 190, 192, 193, 196, 199, 200, 209, 216, 218, 220, 223, 224, 225, 227, 228, 252], "strictli": [1, 159], "speak": [1, 8, 43, 125, 135, 149], "perfectli": [1, 14, 65, 78, 111], "encourag": [1, 6, 19, 139, 160, 165, 171], "usag": [1, 3, 4, 11, 13, 15, 21, 23, 37, 60, 82, 109, 116, 121, 123, 125, 135, 136, 144, 145, 159, 161, 163, 164, 168, 177, 184, 185, 188, 193, 194, 195, 199, 220, 253, 257, 263, 272], "doe": [1, 2, 5, 6, 8, 13, 14, 15, 19, 22, 23, 25, 43, 47, 60, 61, 73, 79, 80, 85, 97, 98, 99, 101, 103, 105, 108, 112, 113, 117, 122, 123, 130, 133, 134, 135, 136, 139, 142, 145, 146, 147, 149, 152, 153, 158, 159, 160, 162, 163, 164, 165, 168, 169, 172, 173, 174, 176, 178, 182, 183, 184, 190, 191, 192, 197, 199, 202, 203, 205, 208, 216, 223, 225, 228, 231, 263, 272], "often": [1, 4, 5, 6, 10, 14, 17, 49, 73, 87, 97, 99, 101, 103, 112, 113, 124, 125, 126, 128, 146, 153, 177, 193, 201, 203, 216, 263, 272], "same": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 17, 19, 20, 21, 22, 23, 25, 32, 40, 43, 44, 47, 49, 51, 52, 53, 55, 58, 59, 60, 65, 73, 78, 80, 85, 87, 95, 97, 98, 99, 101, 102, 103, 105, 108, 109, 111, 112, 118, 119, 122, 123, 124, 125, 127, 129, 132, 133, 135, 137, 138, 139, 141, 144, 145, 146, 147, 149, 152, 153, 154, 155, 156, 158, 159, 160, 161, 162, 163, 164, 165, 166, 168, 169, 171, 172, 173, 174, 176, 177, 179, 181, 182, 185, 186, 189, 190, 191, 192, 193, 194, 197, 198, 199, 200, 201, 208, 214, 218, 222, 223, 228, 231, 252, 257, 259, 261, 263, 270, 272], "usual": [1, 5, 6, 8, 19, 23, 43, 58, 59, 60, 61, 99, 100, 102, 103, 113, 117, 124, 125, 128, 129, 130, 133, 135, 136, 144, 147, 152, 156, 157, 159, 166, 195, 205, 261, 270], "former": [1, 5, 61, 79, 127, 128, 165], "lag": [1, 159], "absolut": [1, 6, 7, 10, 82, 99, 126, 156, 160, 208], "dilut": 1, "averag": [1, 3, 19, 49, 52, 61, 82, 87, 97, 113, 115, 123, 127, 128, 129, 135, 137, 143, 146, 154, 159, 160, 163, 165, 166, 168, 176, 177, 182, 197, 198], "associ": [1, 5, 6, 8, 10, 17, 50, 82, 130, 141, 142, 156, 164, 171, 190, 201, 202], "One": [1, 2, 4, 5, 6, 7, 10, 11, 15, 21, 23, 49, 51, 60, 61, 73, 79, 82, 97, 98, 99, 101, 122, 123, 124, 125, 128, 133, 135, 137, 138, 142, 143, 149, 152, 153, 166, 169, 172, 177, 178, 191, 195, 200, 205, 209, 221, 223, 263, 264, 272, 273], "advantag": [1, 3, 6, 14, 17, 23, 49, 60, 85, 95, 98, 107, 120, 122, 125, 135, 136, 153, 159, 172, 177, 182, 185, 192, 201, 209, 220, 222, 258], "match": [1, 4, 5, 10, 14, 17, 19, 20, 22, 49, 51, 58, 59, 60, 61, 68, 75, 76, 92, 97, 105, 108, 111, 112, 113, 134, 137, 138, 142, 144, 147, 149, 152, 154, 159, 162, 172, 173, 174, 182, 185, 190, 192, 195, 197, 201, 220], "exactli": [1, 5, 7, 8, 10, 12, 17, 25, 43, 51, 52, 60, 78, 80, 101, 103, 105, 136, 144, 153, 174, 185, 201], "configur": [1, 4, 5, 6, 14, 18, 19, 20, 22, 23, 24, 42, 49, 50, 60, 61, 82, 113, 122, 124, 131, 133, 142, 144, 149, 152, 157, 159, 162, 168, 171, 176, 183, 184, 199, 200, 208, 212, 220, 221, 225, 226, 267, 276], "pessimist": [1, 159], "bound": [1, 23, 49, 112, 126, 144, 159, 160, 168, 173, 174, 176, 178, 184], "pai": [1, 10, 45, 49, 60, 115], "attent": [1, 7, 10, 42, 45, 49, 115, 118, 119, 121, 124, 136, 166, 184, 185, 193, 252], "create_target_param": 1, "keyword": [1, 5, 156, 159, 171], "argument": [1, 2, 4, 5, 6, 8, 14, 21, 22, 23, 32, 43, 44, 48, 51, 55, 60, 69, 76, 78, 82, 89, 97, 99, 102, 103, 109, 111, 112, 115, 122, 123, 126, 127, 128, 132, 133, 135, 136, 138, 144, 145, 154, 155, 156, 159, 161, 162, 163, 164, 168, 171, 172, 173, 174, 179, 188, 191, 194, 199, 205, 206, 208, 209, 212, 222, 223, 263, 264, 272, 273], "below": [1, 2, 4, 6, 10, 11, 12, 14, 16, 17, 18, 19, 20, 23, 24, 34, 43, 45, 48, 49, 51, 52, 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228, 230, 232, 244, 246, 247, 248, 250, 252, 253, 254, 259, 261, 262, 263, 264, 270, 271, 272, 273], "target_actor_network_param": 1, "attribut": [1, 6, 11, 14, 22, 25, 43, 47, 53, 60, 73, 76, 79, 82, 85, 90, 103, 108, 116, 125, 134, 136, 141, 147, 148, 153, 156, 173, 174, 176, 182, 185, 193, 194, 196, 199, 203, 207, 229, 263, 272], "access": [1, 5, 6, 7, 10, 12, 14, 17, 19, 23, 50, 60, 68, 73, 78, 79, 87, 97, 102, 111, 112, 118, 119, 122, 125, 131, 135, 142, 153, 158, 160, 162, 171, 173, 174, 177, 185, 187, 189, 190, 192, 194, 201, 208, 209, 215, 218, 252, 261, 270], "detach": [1, 2, 6, 9, 11, 12, 13, 20, 32, 52, 73, 89, 90, 95, 101, 105, 108, 137, 150, 154, 165, 181, 185, 229, 244], "def": [1, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 25, 33, 34, 37, 38, 44, 45, 47, 49, 51, 52, 53, 55, 60, 64, 65, 67, 73, 75, 78, 79, 85, 87, 89, 90, 92, 93, 94, 96, 97, 98, 99, 102, 103, 104, 105, 108, 109, 111, 112, 113, 115, 116, 117, 118, 122, 123, 124, 125, 126, 127, 128, 129, 130, 133, 134, 135, 137, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 152, 153, 154, 155, 156, 157, 160, 161, 162, 163, 164, 165, 166, 168, 169, 171, 172, 173, 174, 177, 178, 179, 181, 182, 183, 184, 185, 186, 187, 189, 193, 194, 195, 197, 198, 199, 200, 201, 202, 203, 205, 208, 209, 210, 211, 212, 213, 214, 215, 216, 218, 219, 221, 223, 226, 228, 230, 231, 233, 234, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 252, 255, 256, 259, 263, 272], "_init": 1, "self": [1, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 19, 20, 21, 22, 24, 25, 33, 34, 37, 38, 44, 45, 47, 49, 51, 52, 53, 55, 60, 65, 67, 73, 78, 79, 85, 87, 89, 92, 93, 94, 96, 97, 98, 99, 102, 103, 104, 105, 108, 109, 111, 112, 115, 118, 123, 124, 125, 127, 128, 129, 133, 134, 135, 138, 142, 143, 144, 146, 148, 149, 150, 153, 154, 156, 160, 161, 162, 163, 165, 166, 168, 169, 171, 172, 173, 174, 177, 178, 179, 181, 182, 183, 185, 193, 194, 195, 197, 198, 199, 202, 203, 209, 212, 214, 215, 216, 218, 219, 221, 222, 223, 226, 228, 233, 234, 237, 238, 239, 240, 241, 242, 243, 244, 248, 249, 250, 252, 253], "actor_network": [1, 159], "value_network": [1, 159], "none": [1, 7, 11, 12, 14, 15, 17, 18, 19, 20, 24, 34, 49, 51, 60, 63, 64, 76, 79, 87, 89, 90, 97, 104, 105, 108, 111, 113, 115, 117, 118, 119, 122, 123, 129, 134, 135, 137, 138, 141, 142, 144, 145, 146, 147, 148, 150, 152, 154, 157, 160, 162, 164, 165, 171, 173, 174, 175, 178, 179, 182, 185, 194, 201, 202, 207, 209, 213, 215, 216, 230, 244, 245, 252, 261, 263, 270, 272], "super": [1, 4, 5, 6, 7, 9, 11, 12, 13, 14, 16, 18, 19, 20, 21, 22, 25, 33, 37, 38, 44, 45, 47, 49, 52, 59, 60, 65, 67, 73, 78, 79, 85, 87, 89, 92, 93, 94, 96, 97, 98, 99, 102, 103, 104, 105, 109, 111, 112, 115, 118, 123, 125, 127, 128, 129, 133, 134, 138, 142, 143, 146, 148, 149, 150, 153, 154, 156, 160, 161, 162, 163, 164, 165, 166, 169, 171, 172, 173, 174, 177, 179, 181, 193, 194, 195, 197, 198, 199, 202, 203, 208, 209, 212, 214, 215, 218, 219, 221, 222, 223, 226, 233, 234, 237, 239, 240, 241, 242, 243, 248, 249, 250], "true": [1, 2, 4, 5, 6, 7, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20, 22, 23, 24, 32, 34, 37, 38, 41, 42, 43, 44, 45, 47, 49, 51, 52, 55, 58, 59, 60, 63, 64, 68, 73, 76, 82, 87, 89, 90, 92, 94, 95, 96, 97, 98, 101, 104, 108, 109, 110, 111, 113, 115, 116, 117, 118, 119, 122, 123, 124, 125, 126, 127, 129, 130, 133, 134, 135, 136, 137, 139, 141, 143, 144, 146, 147, 148, 149, 153, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 168, 169, 171, 172, 173, 174, 176, 177, 178, 179, 182, 184, 185, 186, 187, 188, 190, 191, 192, 193, 194, 197, 198, 199, 200, 201, 203, 204, 205, 206, 208, 211, 212, 213, 214, 218, 220, 221, 222, 223, 224, 225, 228, 229, 230, 231, 236, 238, 244, 250, 252, 254, 255, 256, 257, 258, 259, 261, 264, 270, 273], "compare_against": 1, "list": [1, 5, 6, 8, 11, 12, 13, 14, 15, 16, 17, 19, 20, 21, 24, 44, 47, 48, 49, 50, 52, 60, 61, 68, 73, 87, 90, 101, 102, 103, 105, 107, 108, 110, 111, 113, 115, 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11, 16, 55, 121, 122, 123, 142, 146, 188, 194, 258], "natur": [1, 5, 6, 17, 18, 23, 24, 25, 45, 61, 73, 75, 97, 107, 116, 119, 126, 127, 135, 136, 137, 162, 171, 191, 193, 197, 200, 201, 263, 272], "resourc": [1, 53, 58, 59, 61, 73, 87, 105, 119, 123, 133, 135, 152, 159, 168, 171, 176, 216, 223, 236], "alloc": [1, 6, 18, 21, 22, 23, 48, 55, 59, 129, 135, 152, 168, 175, 176, 193, 202, 214, 223, 259], "gpu": [1, 3, 4, 7, 12, 17, 18, 19, 20, 24, 33, 38, 40, 42, 43, 47, 48, 49, 50, 52, 53, 54, 56, 57, 60, 61, 64, 72, 73, 77, 80, 81, 82, 83, 88, 92, 96, 97, 99, 105, 111, 114, 117, 120, 121, 122, 123, 124, 125, 131, 132, 133, 134, 135, 136, 137, 138, 144, 147, 148, 149, 150, 152, 154, 157, 159, 160, 162, 163, 164, 171, 172, 175, 177, 178, 185, 186, 196, 201, 206, 207, 210, 214, 216, 223, 230, 231, 234, 240, 251, 252, 255, 258], "worker": [1, 6, 7, 11, 16, 51, 52, 61, 115, 120, 122, 123, 134, 135, 147, 159, 162, 163, 168, 212, 216], "syncdatacollector": [1, 136, 159], "process": [1, 4, 5, 6, 11, 12, 14, 15, 16, 17, 18, 20, 22, 23, 24, 25, 42, 47, 49, 50, 51, 52, 56, 60, 61, 73, 82, 85, 97, 103, 105, 110, 112, 113, 114, 116, 118, 119, 120, 121, 122, 123, 125, 126, 127, 128, 131, 132, 135, 136, 137, 143, 144, 146, 147, 149, 154, 158, 160, 162, 163, 164, 165, 168, 171, 173, 174, 175, 176, 177, 182, 184, 185, 187, 188, 193, 195, 196, 201, 203, 204, 208, 212, 213, 214, 215, 221, 228, 259, 262, 263, 271, 272], "offer": [1, 11, 14, 18, 42, 43, 53, 61, 99, 122, 124, 138, 141, 144, 145, 197, 214, 216], "multiasyncdatacollector": [1, 159], "rollout": [1, 136, 159], "asynchron": [1, 21, 61, 120, 121, 126, 134, 149, 155, 159, 163], "manner": [1, 5, 8, 14, 19, 61, 159, 171, 216], "therebi": [1, 186, 189, 193], "decoupl": [1, 61, 153, 197], "factori": [1, 6, 101, 115, 190, 191, 232], "empti": [1, 5, 6, 8, 14, 19, 21, 23, 49, 80, 95, 108, 128, 129, 143, 144, 147, 153, 158, 165, 168, 171, 173, 174, 176, 185, 191, 193, 202, 206, 238, 246, 264, 273], "maximum": [1, 11, 49, 60, 82, 102, 113, 126, 128, 136, 137, 144, 159, 164, 165, 173, 174, 185, 194, 195], "non": [1, 2, 3, 5, 8, 11, 14, 19, 22, 49, 51, 53, 54, 56, 60, 82, 85, 97, 98, 100, 103, 112, 113, 119, 122, 126, 129, 130, 134, 135, 136, 137, 139, 141, 145, 147, 150, 156, 157, 160, 161, 164, 165, 168, 172, 173, 176, 182, 184, 185, 189, 199, 202, 214, 228, 252, 264, 266, 273, 275], "termin": [1, 14, 23, 53, 60, 87, 159, 160, 162, 163, 171, 188, 206, 225], "effect": [1, 5, 6, 8, 9, 11, 23, 55, 73, 82, 97, 103, 108, 124, 127, 128, 138, 152, 154, 156, 160, 164, 165, 171, 176, 177, 191, 199, 200, 205, 261, 270], "regist": [1, 22, 43, 47, 78, 108, 109, 112, 121, 122, 124, 133, 141, 152, 153, 159, 173, 174, 177, 208, 216, 220], "new": [1, 2, 4, 5, 6, 8, 9, 12, 13, 14, 22, 23, 24, 25, 31, 42, 43, 45, 48, 49, 50, 52, 55, 60, 62, 73, 75, 79, 80, 82, 85, 97, 99, 102, 103, 108, 109, 110, 112, 113, 117, 121, 122, 123, 125, 126, 127, 136, 137, 141, 142, 144, 146, 149, 152, 153, 154, 155, 156, 157, 159, 160, 161, 164, 165, 168, 171, 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65, 97, 100, 107, 111, 112, 119, 121, 147, 156, 159, 179, 182, 183, 184, 196, 197, 198, 199, 208, 220, 221, 222, 235], "explor": [2, 6, 8, 14, 21, 23, 45, 49, 73, 108, 121, 126, 130, 136, 144, 146, 152, 159, 164, 165], "note": [2, 4, 5, 6, 7, 8, 9, 12, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 43, 44, 45, 47, 49, 50, 52, 61, 73, 85, 99, 100, 101, 102, 103, 108, 109, 112, 113, 116, 117, 122, 123, 124, 125, 126, 129, 130, 132, 133, 134, 135, 137, 138, 141, 142, 144, 145, 147, 149, 152, 154, 155, 156, 157, 161, 162, 163, 168, 169, 171, 172, 173, 174, 175, 176, 177, 178, 179, 181, 182, 183, 185, 187, 188, 189, 190, 191, 192, 193, 197, 198, 199, 201, 202, 203, 204, 205, 212, 214, 215, 216, 222, 238, 252, 258, 259, 261, 263, 270, 272], "differenti": [2, 5, 6, 14, 18, 25, 35, 40, 46, 47, 57, 76, 121, 136, 154, 160, 166, 191], "requires_grad": [2, 6, 7, 8, 12, 13, 20, 32, 37, 43, 47, 63, 64, 68, 73, 76, 89, 95, 101, 104, 105, 108, 110, 111, 117, 125, 129, 130, 141, 146, 147, 157, 178, 191, 201, 205, 237, 244, 250], "track": [2, 5, 7, 8, 9, 14, 43, 52, 63, 82, 99, 101, 110, 111, 117, 122, 127, 128, 132, 136, 142, 157, 163, 165, 168, 208, 258], "auto": [2, 3, 5, 6, 8, 10, 12, 22, 55, 59, 122, 123, 144, 155, 186, 187, 188, 206, 208, 220, 221, 231, 246, 263, 264, 272, 273], "cout": [2, 4, 6, 22, 23, 187, 257], "endl": [2, 6, 22, 23, 187, 208], "cpufloattyp": [2, 4, 6, 23, 208], "wa": [2, 3, 4, 5, 11, 17, 20, 22, 23, 25, 42, 44, 49, 51, 52, 58, 59, 60, 61, 73, 76, 79, 95, 97, 98, 99, 101, 108, 112, 113, 115, 116, 123, 124, 126, 133, 135, 146, 150, 152, 153, 154, 156, 158, 159, 160, 163, 164, 165, 169, 176, 177, 184, 188, 191, 192, 198, 201, 208, 223, 231, 258, 263, 272], "result": [2, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 17, 19, 20, 21, 23, 24, 25, 43, 44, 51, 58, 59, 60, 63, 64, 65, 67, 68, 69, 71, 72, 76, 78, 82, 83, 87, 97, 101, 107, 108, 111, 112, 114, 116, 119, 122, 124, 125, 128, 133, 135, 136, 137, 141, 142, 143, 144, 145, 146, 147, 149, 150, 154, 155, 156, 157, 158, 160, 161, 162, 163, 164, 169, 171, 172, 173, 174, 175, 176, 177, 178, 179, 182, 184, 188, 190, 191, 192, 193, 195, 197, 198, 199, 200, 202, 203, 204, 205, 206, 208, 209, 212, 218, 221, 222, 223, 228, 231, 233, 238, 241, 246, 252, 257, 259, 261, 270], "grad_fn": [2, 4, 25, 32, 43, 47, 76, 89, 101, 125, 130, 173, 185], "addbackward1": 2, "z": [2, 5, 7, 23, 32, 43, 49, 52, 60, 76, 80, 85, 89, 92, 95, 101, 147, 165, 174, 191, 203, 208, 256, 264, 273], "27": [2, 7, 51, 144, 163, 176, 184, 228, 231], "mulbackward1": 2, "meanbackward0": 2, "requires_grad_": [2, 12, 32, 76, 101, 104, 145], "flag": [2, 5, 14, 23, 43, 73, 76, 101, 137, 150, 153, 165, 174, 176, 185, 196, 198, 204], "place": [2, 5, 6, 11, 12, 14, 18, 22, 23, 43, 45, 48, 49, 52, 76, 78, 85, 99, 101, 108, 113, 116, 118, 122, 126, 129, 133, 135, 138, 148, 149, 152, 154, 156, 157, 159, 160, 165, 171, 172, 175, 182, 189, 197, 198, 199, 205, 208, 212, 214, 252, 263, 264, 265, 272, 273, 274], "randn": [2, 5, 6, 12, 13, 20, 23, 32, 45, 47, 52, 63, 65, 67, 71, 72, 76, 78, 80, 89, 97, 98, 99, 101, 102, 104, 105, 108, 110, 111, 125, 133, 134, 138, 141, 142, 143, 144, 145, 149, 150, 154, 161, 163, 164, 172, 173, 174, 184, 186, 191, 193, 197, 198, 199, 205, 208, 212, 230, 231, 232, 234, 238, 239, 245, 255, 259], "b": [2, 5, 6, 7, 12, 18, 21, 23, 32, 43, 47, 63, 64, 65, 67, 71, 72, 76, 80, 83, 89, 92, 93, 95, 98, 99, 102, 103, 104, 109, 110, 111, 125, 127, 128, 129, 142, 144, 145, 147, 149, 158, 160, 172, 174, 191, 193, 194, 203, 209, 231, 238, 246, 264, 273], "sumbackward0": 2, "backprop": [2, 43, 71, 72, 76, 98, 101, 111, 127, 146], "scalar": [2, 5, 14, 15, 23, 32, 43, 49, 52, 60, 63, 76, 101, 111, 169, 197, 206], "equival": [2, 4, 5, 11, 13, 17, 22, 23, 32, 43, 99, 137, 141, 154, 160, 162, 171, 173, 174, 185, 186, 189, 191, 193, 198, 199, 200, 201], "print": [2, 4, 5, 6, 7, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 25, 32, 33, 34, 37, 38, 40, 42, 43, 44, 45, 47, 48, 49, 51, 52, 53, 58, 59, 63, 64, 65, 67, 68, 69, 71, 72, 73, 75, 76, 78, 80, 85, 87, 89, 90, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 107, 108, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 122, 123, 125, 127, 128, 129, 132, 133, 134, 135, 136, 137, 138, 139, 142, 143, 144, 145, 146, 147, 148, 150, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 169, 172, 173, 174, 175, 176, 177, 178, 179, 181, 182, 184, 185, 188, 189, 190, 191, 192, 193, 194, 195, 197, 198, 201, 203, 206, 208, 209, 210, 211, 212, 214, 215, 218, 219, 220, 221, 223, 228, 230, 231, 232, 233, 234, 236, 237, 238, 239, 240, 241, 242, 244, 246, 247, 249, 250, 252, 253, 255, 256, 257, 259, 263, 272], "dx": [2, 64, 76, 99, 111, 130, 174], "got": [2, 15, 19, 55, 98, 99, 101, 113, 133, 146, 147, 155, 162, 188, 197, 200, 213, 263, 272], "matrix": [2, 5, 6, 12, 17, 23, 32, 40, 43, 48, 82, 92, 98, 99, 101, 103, 109, 110, 124, 127, 128, 145, 150, 153, 164, 165, 166, 171, 173, 174, 176, 177, 192, 193, 199, 201, 205, 220, 221, 226], "explan": [2, 52, 61, 160, 169], "arriv": [2, 6, 15, 23, 42, 113, 122, 133, 161, 208], "valu": [2, 4, 5, 6, 7, 9, 12, 14, 18, 19, 20, 21, 22, 23, 25, 32, 33, 40, 41, 43, 47, 48, 49, 50, 55, 58, 59, 60, 61, 63, 68, 73, 80, 82, 85, 87, 92, 97, 98, 99, 101, 102, 103, 105, 108, 111, 113, 115, 124, 125, 126, 127, 129, 132, 133, 135, 137, 138, 139, 141, 143, 146, 149, 150, 154, 155, 156, 160, 161, 162, 163, 164, 165, 168, 171, 172, 173, 174, 175, 182, 183, 185, 187, 189, 190, 191, 192, 193, 195, 196, 197, 198, 200, 203, 204, 208, 209, 211, 216, 222, 259, 261, 270], "section": [2, 4, 5, 6, 7, 8, 11, 15, 18, 19, 21, 23, 43, 44, 47, 50, 51, 52, 73, 95, 97, 98, 99, 102, 103, 108, 113, 116, 118, 125, 129, 135, 139, 141, 144, 146, 150, 157, 160, 161, 163, 168, 171, 173, 176, 177, 178, 179, 182, 188, 190, 191, 200, 202, 211, 220, 261, 263, 264, 265, 267, 270, 272, 273, 274, 276], "jacobian": [2, 43, 89, 121, 141, 205], "product": [2, 3, 4, 6, 12, 40, 42, 43, 48, 60, 61, 85, 99, 105, 113, 121, 122, 135, 139, 141, 165, 175, 176, 177, 185, 199, 200, 205, 209, 231, 251, 258], "1021": 2, "4020": 2, "314": 2, "6695": 2, "613": 2, "4944": [2, 208], "0001": [2, 19, 49, 87, 89, 118, 144, 221], "kfloat": [2, 3, 59, 186, 188, 206], "102": 2, "4000": [2, 49, 60, 92, 246], "1024": [2, 5, 18, 21, 42, 82, 97, 129, 147, 164, 184, 199, 208, 210, 211, 231, 239], "0000": [2, 23, 173, 201, 208, 264, 273], "stop": [2, 4, 5, 23, 51, 58, 59, 76, 78, 87, 98, 101, 110, 126, 128, 135, 147, 152, 159, 161, 165, 168], "histori": [2, 9, 47, 48, 101, 110, 113, 117, 128, 146, 156, 157, 165, 181], "nogradguard": [2, 257], "block": [2, 5, 6, 7, 8, 10, 12, 16, 17, 19, 22, 23, 47, 49, 75, 76, 82, 90, 101, 115, 116, 123, 124, 134, 135, 136, 144, 157, 161, 162, 163, 164, 168, 171, 184, 201, 207, 208, 212, 267, 276], "no_grad": [2, 7, 9, 12, 17, 19, 32, 37, 38, 42, 43, 44, 52, 58, 59, 63, 64, 68, 76, 87, 89, 92, 96, 97, 98, 99, 101, 102, 104, 110, 111, 115, 117, 122, 123, 127, 128, 129, 136, 137, 144, 146, 157, 158, 159, 160, 162, 165, 166, 169, 172, 174, 177, 178, 181, 182, 184, 185, 194, 197, 198, 199, 202, 216, 220, 247, 254, 257], "Or": [2, 21, 23, 152, 163, 179, 198, 206, 208, 263, 272], "eq": [2, 19, 23, 49, 60, 95, 123, 129, 162, 166, 173, 182, 197, 198, 221, 238, 263, 272], "bool": [2, 11, 14, 15, 17, 23, 95, 109, 118, 137, 143, 146, 155, 159, 160, 164, 171, 179, 185, 190, 192, 201, 208, 252, 261, 270], "is_leaf": 2, "detach_": [2, 163], "register_hook": 2, "retain_grad": 2, "doc": [2, 4, 6, 32, 33, 34, 37, 38, 40, 60, 69, 94, 104, 109, 111, 132, 135, 142, 143, 161, 163, 171, 174, 181, 193, 205, 230, 237, 261, 262, 263, 268, 270, 271, 272, 277], "calcul": [2, 12, 17, 43, 44, 49, 52, 56, 60, 73, 82, 85, 87, 97, 110, 127, 128, 137, 143, 146, 160, 161, 163, 164, 165, 171, 177, 182, 191, 193, 197, 200, 201, 215, 221], "penalti": [2, 153, 158], "h": [2, 4, 5, 6, 7, 8, 9, 10, 12, 22, 23, 25, 38, 49, 51, 96, 124, 129, 137, 144, 146, 147, 155, 178, 181, 185, 188, 208, 213, 220, 222, 225, 246, 257], "model": [2, 3, 5, 8, 11, 14, 16, 22, 23, 24, 33, 35, 37, 38, 39, 42, 43, 44, 47, 48, 52, 53, 54, 56, 61, 65, 67, 68, 69, 75, 78, 86, 87, 89, 90, 91, 93, 95, 96, 98, 99, 100, 101, 104, 106, 107, 108, 109, 110, 111, 116, 118, 119, 120, 121, 123, 126, 127, 128, 129, 132, 135, 139, 141, 142, 144, 145, 148, 152, 153, 154, 158, 159, 160, 161, 162, 163, 164, 172, 173, 174, 176, 177, 183, 184, 186, 193, 196, 199, 200, 201, 204, 205, 207, 210, 212, 213, 214, 215, 216, 219, 222, 227, 228, 229, 230, 234, 235, 238, 241, 242, 243, 245, 248, 249, 250, 251, 253, 254, 257, 259], "linear": [2, 5, 6, 7, 9, 11, 16, 17, 19, 25, 37, 38, 43, 44, 45, 47, 48, 49, 60, 68, 69, 73, 78, 79, 87, 89, 92, 93, 94, 96, 97, 98, 100, 102, 103, 105, 109, 110, 111, 112, 115, 117, 118, 119, 123, 124, 125, 127, 128, 129, 133, 134, 137, 138, 141, 144, 145, 146, 148, 149, 150, 153, 154, 156, 157, 160, 161, 162, 163, 164, 165, 166, 169, 172, 173, 174, 177, 179, 181, 182, 184, 185, 189, 193, 195, 197, 198, 199, 200, 201, 202, 203, 205, 207, 209, 210, 211, 212, 214, 215, 218, 219, 220, 221, 223, 226, 228, 230, 232, 233, 234, 237, 239, 240, 241, 242, 243, 244, 245, 248, 249, 250, 252, 253, 259], "loss": [2, 3, 5, 6, 7, 9, 11, 14, 16, 17, 19, 32, 38, 43, 48, 63, 64, 65, 67, 68, 69, 71, 72, 73, 75, 78, 87, 89, 92, 94, 96, 99, 102, 103, 104, 111, 112, 115, 117, 118, 121, 122, 123, 125, 127, 129, 134, 135, 146, 147, 148, 149, 152, 154, 157, 160, 162, 163, 165, 166, 168, 169, 172, 178, 181, 182, 188, 191, 197, 198, 201, 216, 220, 221, 230, 241, 245, 250, 254, 259], "target": [2, 3, 4, 6, 9, 12, 14, 16, 18, 19, 22, 23, 44, 47, 49, 55, 60, 73, 78, 90, 94, 97, 98, 99, 102, 103, 104, 113, 116, 118, 123, 127, 128, 129, 134, 135, 136, 138, 142, 144, 152, 154, 155, 158, 160, 161, 162, 163, 165, 166, 169, 171, 172, 173, 174, 178, 179, 181, 182, 188, 197, 198, 199, 200, 204, 206, 208, 220, 221, 222, 225, 229, 230, 254, 257], "mseloss": [2, 12, 37, 47, 65, 67, 68, 69, 78, 97, 110, 111, 133, 134, 149, 161, 214, 230, 245, 259], "grad_output": [2, 8, 10, 13, 64, 76, 78, 111], "ones_lik": [2, 32, 40, 48, 95, 142, 191], "create_graph": [2, 130], "gradient_penalti": 2, "dim": [2, 4, 5, 11, 14, 21, 33, 40, 41, 45, 48, 49, 60, 73, 90, 92, 93, 94, 96, 97, 99, 102, 103, 104, 110, 115, 118, 123, 127, 128, 129, 134, 144, 147, 148, 149, 154, 156, 158, 159, 161, 162, 163, 164, 165, 166, 169, 171, 173, 174, 190, 191, 192, 193, 203, 206, 219, 221, 233, 257], "combined_loss": 2, "1042": 2, "0638": 2, "0103": 2, "0723": 2, "2543": 2, "1222": 2, "0071": 2, "0814": 2, "1683": 2, "1052": 2, "0355": 2, "document": [2, 4, 5, 6, 20, 47, 52, 60, 61, 79, 82, 85, 87, 101, 112, 113, 117, 121, 133, 135, 136, 139, 141, 143, 144, 157, 162, 163, 164, 168, 171, 172, 173, 174, 176, 177, 178, 179, 191, 197, 199, 205, 206, 209, 214, 218, 220, 221, 228, 252, 258, 261, 263, 264, 268, 270, 272, 273, 277], "link": [2, 4, 5, 6, 10, 12, 22, 23, 52, 58, 59, 82, 105, 108, 114, 116, 118, 135, 139, 141, 191, 204, 206, 208, 220, 261, 262, 267, 270, 271, 276], "subclass": [2, 5, 6, 14, 17, 25, 64, 67, 79, 111, 121, 126, 136, 146, 156, 162, 169, 178, 191, 193, 201], "encod": [2, 7, 9, 14, 17, 42, 47, 48, 75, 76, 100, 104, 113, 118, 122, 126, 127, 128, 136, 153, 159, 163, 171, 178, 181, 184, 185, 195, 200, 201, 208, 230, 252], "method": [2, 4, 5, 6, 7, 8, 10, 11, 12, 14, 16, 17, 19, 21, 23, 25, 44, 47, 49, 51, 55, 58, 59, 60, 64, 65, 67, 73, 79, 83, 85, 90, 95, 97, 99, 101, 111, 112, 113, 115, 120, 121, 126, 130, 133, 136, 137, 139, 141, 142, 143, 144, 145, 146, 149, 153, 154, 155, 156, 157, 159, 160, 161, 162, 169, 171, 172, 173, 174, 176, 182, 183, 189, 197, 198, 200, 201, 203, 208, 209, 213, 221, 223, 224, 225, 228, 263, 272], "forward": [2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 19, 20, 22, 23, 25, 33, 37, 38, 43, 44, 45, 47, 49, 52, 56, 58, 59, 60, 61, 63, 64, 65, 67, 68, 69, 71, 72, 73, 75, 76, 79, 85, 87, 89, 92, 93, 94, 95, 96, 97, 98, 99, 100, 102, 103, 104, 105, 108, 111, 112, 115, 117, 118, 121, 122, 123, 124, 125, 127, 128, 129, 130, 131, 133, 134, 135, 138, 139, 142, 146, 148, 149, 150, 152, 153, 154, 156, 157, 160, 161, 162, 163, 164, 165, 166, 168, 169, 171, 172, 173, 174, 177, 179, 181, 182, 183, 185, 187, 188, 193, 194, 195, 197, 198, 199, 203, 204, 206, 208, 209, 211, 212, 213, 214, 215, 218, 219, 220, 221, 226, 228, 233, 234, 237, 238, 239, 240, 241, 242, 243, 248, 249, 250, 252, 257, 259], "detail": [2, 5, 6, 8, 10, 12, 15, 16, 20, 22, 23, 25, 43, 44, 45, 52, 53, 58, 59, 60, 61, 69, 73, 82, 83, 85, 111, 113, 116, 119, 123, 124, 126, 128, 129, 131, 133, 142, 144, 149, 150, 152, 157, 160, 163, 164, 168, 169, 172, 173, 174, 177, 179, 185, 188, 189, 190, 191, 192, 194, 198, 199, 205, 208, 211, 218, 220, 224, 225, 228, 231, 246, 252, 258], "namespac": [2, 6, 8, 10, 22, 23, 108, 137, 155, 179, 185, 193, 208, 257], "inherit": [2, 11, 15, 22, 51, 60, 85, 99, 143, 146, 149, 159, 171, 178, 191, 193, 195, 199, 216], "linearfunct": 2, "public": [2, 8, 10, 15, 155, 208, 264, 273], "static": [2, 8, 10, 14, 21, 58, 59, 60, 107, 121, 137, 141, 142, 155, 173, 174, 181, 183, 184, 185, 196, 197, 198, 199, 200, 208, 221, 247, 261, 270], "option": [2, 5, 6, 8, 10, 11, 14, 23, 44, 46, 49, 51, 53, 60, 61, 82, 87, 97, 109, 120, 121, 126, 131, 134, 136, 138, 144, 147, 149, 156, 158, 171, 173, 174, 182, 183, 184, 185, 193, 194, 197, 198, 199, 200, 204, 209, 212, 216, 218, 227, 252, 263, 267, 272, 276], "autogradcontext": [2, 8, 10], "ctx": [2, 5, 8, 10, 13, 18, 64, 111, 129, 130, 141, 212], "save_for_backward": [2, 5, 13, 64, 111, 129, 130], "mm": [2, 5, 12, 59, 110, 137, 185, 186, 188, 194, 197, 206, 207, 222, 225], "expand_a": [2, 19, 182, 197, 198], "tensor_list": [2, 8, 10, 135], "get_saved_vari": 2, "grad_input": [2, 13, 78, 129, 130], "grad_weight": 2, "grad_bia": [2, 13], "Then": [2, 12, 15, 17, 20, 22, 24, 25, 44, 45, 52, 58, 59, 61, 73, 85, 98, 99, 102, 103, 114, 121, 123, 133, 134, 149, 152, 155, 156, 159, 160, 161, 163, 165, 168, 173, 174, 188, 195, 200, 201, 212, 215, 222, 224, 225, 228], "5314": 2, "2807": 2, "4864": 2, "7608": 2, "9101": [2, 173], "0073": 2, "mulconst": [2, 78], "object": [2, 4, 5, 6, 7, 9, 10, 11, 14, 19, 20, 22, 23, 43, 49, 51, 52, 60, 61, 64, 68, 69, 75, 95, 97, 101, 110, 111, 112, 116, 117, 118, 121, 125, 129, 135, 136, 141, 142, 143, 145, 154, 155, 159, 160, 161, 162, 163, 164, 168, 169, 171, 173, 174, 177, 181, 182, 193, 197, 198, 200, 216, 220, 221, 223, 225, 231, 246], "stash": [2, 64, 111], "saved_data": 2, "were": [2, 3, 5, 6, 9, 12, 17, 18, 23, 32, 52, 60, 83, 85, 97, 99, 101, 103, 113, 114, 126, 132, 133, 138, 147, 153, 159, 160, 162, 164, 165, 173, 174, 176, 189, 201, 204, 205], "todoubl": 2, "On": [2, 4, 5, 6, 8, 17, 19, 21, 22, 23, 115, 117, 122, 133, 135, 137, 147, 153, 156, 161, 162, 172, 177, 178, 201, 203, 208], "easiest": [2, 5, 9, 23, 121, 139, 145, 157, 159, 228], "tabl": [2, 16, 21, 89, 103, 109, 115, 121, 122, 135, 137, 143, 144, 162, 163, 164, 168, 173, 174, 175, 219, 238, 253, 267, 276], "set_data": 2, "output_nr": 2, "after": [2, 3, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 19, 20, 21, 23, 24, 25, 32, 33, 34, 43, 45, 47, 49, 52, 53, 58, 59, 60, 61, 63, 64, 76, 79, 87, 97, 98, 99, 102, 103, 105, 110, 111, 112, 113, 115, 116, 122, 124, 127, 128, 129, 132, 133, 134, 135, 136, 137, 138, 142, 144, 146, 147, 149, 152, 153, 155, 156, 157, 158, 159, 160, 162, 163, 165, 166, 168, 169, 173, 174, 176, 178, 179, 181, 182, 183, 185, 187, 188, 195, 197, 198, 199, 200, 201, 202, 203, 204, 206, 208, 213, 214, 223, 224, 225, 228, 231, 252, 258, 259], "bug": [2, 5, 10, 23, 108, 144, 186], "report": [2, 10, 17, 23, 52, 73, 87, 98, 121, 137, 144, 161, 163, 164, 186, 201], "fix": [2, 14, 17, 20, 23, 24, 49, 50, 51, 52, 97, 108, 113, 125, 157, 161, 173, 174, 184, 201, 263, 272], "soon": [2, 5, 52, 58, 59, 122, 147, 152, 198, 220], "overview": [2, 5, 6, 11, 53, 55, 61, 113, 119, 120, 121, 127, 128, 133, 134, 135, 142, 155, 159, 161, 162, 163, 165, 168, 189, 190, 192, 196, 227, 258], "alwai": [2, 3, 4, 6, 9, 14, 16, 18, 19, 22, 23, 49, 52, 99, 102, 103, 108, 113, 124, 125, 129, 135, 136, 137, 139, 158, 159, 160, 161, 163, 173, 178, 185, 187, 188, 189, 195, 204, 222, 252, 263, 272], "problem": [2, 4, 6, 11, 14, 15, 18, 22, 23, 49, 51, 52, 61, 98, 100, 103, 115, 117, 126, 136, 142, 144, 145, 149, 153, 157, 159, 161, 163, 168, 172, 176, 189, 191, 232, 263, 272], "question": [2, 4, 5, 6, 8, 10, 17, 22, 23, 49, 75, 122, 135, 137, 143, 165, 183, 190, 200, 201, 231], "forum": [2, 4, 5, 6, 22, 23, 44, 79, 110, 142, 143, 183], "view": [3, 7, 9, 10, 11, 12, 14, 15, 16, 19, 25, 47, 49, 50, 52, 53, 55, 56, 61, 73, 78, 82, 92, 93, 94, 96, 98, 99, 101, 102, 103, 104, 105, 110, 112, 118, 123, 124, 126, 127, 131, 132, 133, 134, 135, 141, 142, 143, 144, 149, 150, 155, 156, 160, 161, 162, 163, 164, 165, 166, 169, 173, 174, 181, 182, 183, 193, 197, 198, 206, 211, 214, 215, 226, 240, 241, 242, 243, 245, 248, 249, 250, 261, 270], "prerequisit": [3, 7, 53, 55, 56, 100, 108, 114, 124, 131, 132, 133, 134, 135, 136, 155, 161, 162, 163, 171, 197, 214, 215], "frontend": [3, 10, 84, 110, 121, 177, 186, 187, 193, 199, 220, 221], "semant": [3, 6, 22, 49, 58, 59, 68, 95, 100, 102, 111, 135, 137, 191, 192, 193, 196, 205, 263, 272], "11": [3, 5, 6, 7, 11, 17, 18, 23, 59, 61, 95, 104, 109, 122, 123, 141, 158, 163, 171, 172, 173, 174, 175, 194, 204, 208, 215, 219, 225, 227, 231, 238, 257, 263, 267, 272, 276], "nvidia": [3, 5, 17, 50, 95, 129, 135, 137, 147, 172, 201, 215, 230, 247, 251, 258], "toolkit": [3, 23, 100, 142, 146], "releas": [3, 4, 6, 10, 17, 20, 23, 24, 42, 50, 105, 108, 109, 112, 122, 123, 125, 139, 142, 152, 162, 164, 168, 199, 201, 204, 208, 212, 220, 221, 263, 272], "greatli": [3, 6, 49, 160], "overhead": [3, 5, 6, 10, 17, 56, 82, 109, 122, 123, 124, 133, 145, 147, 149, 158, 161, 163, 164, 168, 172, 176, 177, 184, 186, 193, 199, 201], "increas": [3, 5, 6, 18, 19, 20, 24, 44, 73, 82, 83, 87, 97, 122, 123, 124, 126, 128, 131, 134, 142, 152, 158, 168, 182, 184, 193, 194, 197, 209, 253], "mostli": [3, 10, 19, 85, 97, 116, 127, 163, 165, 179, 197, 198, 199], "deploy": [3, 4, 25, 42, 60, 97, 112, 126, 177, 186, 199, 204, 220, 227, 228, 252, 258], "appear": [3, 11, 14, 22, 25, 103, 263, 272], "heart": [3, 49, 113, 264, 273], "veri": [3, 4, 5, 6, 8, 12, 14, 15, 18, 19, 21, 22, 23, 24, 25, 45, 47, 48, 49, 58, 59, 60, 61, 65, 73, 75, 76, 85, 99, 101, 113, 115, 117, 123, 124, 125, 127, 134, 135, 149, 152, 153, 157, 160, 161, 163, 164, 165, 166, 168, 169, 176, 178, 182, 189, 191, 195, 198, 205, 264, 265, 273, 274], "time": [3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 17, 19, 20, 21, 23, 24, 25, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 47, 48, 49, 50, 51, 52, 58, 59, 60, 61, 63, 64, 65, 67, 68, 69, 71, 72, 73, 75, 76, 78, 79, 80, 83, 85, 87, 89, 90, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 107, 108, 111, 112, 113, 114, 115, 116, 117, 118, 119, 121, 122, 123, 125, 126, 127, 128, 129, 130, 133, 134, 135, 136, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 152, 153, 154, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 168, 169, 171, 172, 173, 174, 176, 177, 178, 181, 182, 184, 185, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 201, 203, 204, 205, 206, 211, 212, 214, 219, 228, 229, 230, 231, 232, 233, 234, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 253, 255, 256, 258, 261, 263, 270, 272], "compil": [3, 4, 6, 8, 10, 17, 21, 22, 25, 60, 85, 98, 108, 121, 135, 143, 173, 174, 175, 184, 186, 193, 199, 204, 207, 216, 220, 223, 251, 254, 257, 261, 270], "boost": [3, 97, 99, 144, 145, 176, 184, 199, 207, 216, 220, 221], "demonstr": [3, 7, 9, 14, 16, 17, 20, 21, 22, 25, 42, 43, 50, 57, 61, 75, 82, 85, 108, 113, 120, 121, 122, 123, 124, 125, 127, 129, 130, 133, 134, 137, 138, 141, 142, 143, 144, 150, 155, 159, 161, 162, 163, 164, 168, 171, 173, 174, 177, 179, 184, 185, 186, 187, 188, 191, 193, 195, 198, 201, 202, 203, 204, 211, 214, 215, 218, 221, 222, 224, 225, 228, 252, 253, 259, 263, 264, 265, 272, 273, 274], "mnist": [3, 6, 34, 44, 47, 73, 78, 92, 94, 96, 119, 120, 121, 123, 126, 129, 135, 138, 148, 154, 162, 166, 169, 221, 233], "libtorch": [3, 6, 22, 23, 187, 204, 206, 208, 220, 222, 225, 251, 257], "counterpart": [3, 17, 108, 134, 144, 195, 201, 215, 220, 252], "depict": 3, "chunk": [3, 5, 7, 55, 135, 152, 164], "batch": [3, 5, 6, 9, 12, 16, 17, 19, 21, 34, 37, 38, 39, 42, 44, 45, 47, 49, 51, 52, 53, 55, 56, 60, 61, 73, 75, 78, 79, 82, 87, 90, 92, 94, 97, 102, 104, 110, 112, 113, 117, 118, 120, 121, 122, 123, 124, 125, 126, 127, 131, 134, 135, 136, 137, 138, 139, 146, 147, 148, 149, 150, 152, 154, 157, 158, 159, 160, 162, 163, 164, 166, 168, 169, 171, 172, 173, 174, 175, 177, 178, 181, 182, 184, 185, 191, 193, 196, 198, 201, 204, 205, 221, 223, 231, 241, 242, 243, 253], "data_load": [3, 6, 19, 178, 182, 197, 198, 199, 236], "nll_loss": [3, 73, 123, 129, 135, 148, 154, 162, 166, 221], "updat": [3, 6, 10, 11, 12, 13, 14, 16, 17, 19, 21, 23, 42, 43, 44, 49, 51, 52, 61, 63, 64, 65, 67, 68, 69, 71, 72, 75, 82, 97, 98, 99, 102, 103, 110, 111, 112, 117, 122, 123, 126, 129, 136, 137, 139, 147, 152, 159, 160, 162, 163, 168, 178, 182, 185, 189, 197, 198, 199, 200, 201, 204, 214, 216, 218, 221, 222, 228, 230, 259], "captur": [3, 4, 6, 22, 23, 25, 52, 60, 107, 123, 141, 148, 158, 171, 172, 173, 174, 186, 194, 197, 198, 200], "But": [3, 6, 8, 10, 20, 42, 44, 45, 52, 73, 78, 101, 103, 116, 125, 147, 152, 153, 154, 160, 173, 174, 176, 178, 182, 185, 189, 192, 200, 205, 218, 221, 223, 228, 252, 263, 272], "slightli": [3, 5, 14, 23, 122, 135, 136, 158, 165, 173, 174, 192], "prealloc": [3, 14], "reus": [3, 10, 65, 78, 111, 130, 137, 141, 153, 160, 176, 177, 185, 187], "tensoropt": [3, 186], "floatcuda": 3, "dtype": [3, 7, 8, 9, 10, 13, 14, 15, 38, 40, 41, 48, 49, 51, 52, 60, 63, 64, 72, 78, 80, 85, 89, 92, 95, 98, 101, 102, 103, 109, 111, 115, 119, 127, 129, 130, 137, 141, 144, 146, 147, 150, 160, 164, 165, 166, 173, 174, 175, 178, 179, 185, 186, 189, 190, 191, 192, 193, 195, 197, 199, 200, 206, 209, 218, 220, 223, 228, 230, 234, 237, 244, 247, 252, 254], "longcuda": 3, "klong": 3, "zero": [3, 6, 7, 11, 16, 17, 19, 25, 32, 40, 41, 44, 47, 48, 49, 60, 63, 64, 65, 67, 68, 69, 73, 78, 87, 92, 95, 98, 99, 103, 104, 110, 111, 117, 118, 122, 123, 127, 128, 134, 135, 136, 141, 144, 149, 150, 153, 155, 156, 157, 160, 161, 163, 165, 169, 178, 181, 185, 189, 191, 192, 194, 200, 201, 209, 221, 223, 235, 246, 247, 252, 256, 259], "ktrainbatchs": 3, "28": [3, 6, 7, 17, 33, 34, 37, 38, 47, 78, 93, 94, 104, 138, 148, 154, 169, 176, 201, 203, 204, 208, 221, 223, 231, 233, 246], "training_step": [3, 148], "void": [3, 5, 6, 15, 22, 23, 59, 144, 155, 186, 188, 208, 231, 238, 246], "cudagraph": 3, "cudastream": 3, "capturestream": 3, "getstreamfrompool": 3, "setcurrentcudastream": 3, "capture_begin": 3, "capture_end": 3, "warm": [3, 21, 70, 103, 109, 168, 172, 176, 177, 193, 203, 231], "side": [3, 20, 51, 52, 82, 103, 138, 147, 152, 154, 155, 160, 161, 166, 168, 188, 261, 270], "prepar": [3, 11, 17, 19, 25, 44, 51, 52, 58, 59, 68, 69, 102, 103, 111, 112, 116, 134, 137, 138, 152, 155, 159, 161, 181, 185, 193, 195, 196, 199, 200, 201, 204, 209, 212, 218, 222, 227, 228], "cach": [3, 64, 111, 137, 144, 168, 176, 177, 184, 185], "cubla": [3, 231], "cudnn": [3, 5, 78, 117, 129, 136, 147, 150, 230], "warmupstream": 3, "int": [3, 4, 5, 6, 9, 11, 14, 18, 19, 22, 23, 24, 51, 53, 55, 58, 59, 60, 75, 85, 87, 98, 109, 115, 118, 122, 123, 126, 135, 137, 144, 146, 148, 155, 156, 161, 162, 163, 164, 168, 172, 173, 174, 178, 181, 185, 188, 193, 206, 208, 209, 213, 215, 220, 223, 252, 257, 261, 270], "num_warmup_it": 3, "success": [3, 6, 14, 23, 73, 101, 103, 126, 144, 165, 188, 204, 206], "replai": [3, 14, 25, 76, 146], "spin": [3, 60, 176], "ordinari": [3, 194], "epoch": [3, 6, 7, 9, 16, 19, 24, 37, 38, 44, 52, 53, 55, 75, 87, 92, 94, 96, 97, 98, 99, 102, 103, 104, 112, 115, 117, 118, 122, 123, 126, 129, 135, 147, 148, 152, 157, 159, 163, 165, 166, 169, 178, 198, 221, 230, 241, 245, 250], "59584": 3, "60000": [3, 135], "3921": 3, "2051": 3, "accuraci": [3, 9, 17, 19, 20, 24, 37, 38, 44, 92, 97, 104, 115, 119, 120, 121, 122, 123, 126, 129, 148, 150, 156, 157, 158, 162, 166, 169, 171, 177, 182, 185, 194, 195, 198, 199, 201, 218, 227, 228], "938": [3, 6, 147], "1826": 3, "1273": 3, "960": 3, "1796": 3, "1012": [3, 147], "968": 3, "1603": 3, "0869": 3, "973": 3, "2315": 3, "0736": 3, "978": 3, "0511": [3, 185], "0704": 3, "977": [3, 147, 219], "0802": 3, "0654": 3, "979": 3, "0774": 3, "0604": 3, "980": [3, 176], "0669": 3, "0544": 3, "984": 3, "0219": 3, "0517": 3, "983": 3, "real": [3, 6, 14, 20, 32, 52, 54, 58, 97, 98, 99, 100, 103, 121, 123, 126, 127, 128, 135, 136, 149, 152, 155, 158, 160, 165, 172, 176, 191, 193, 197, 200, 218], "0m44": 3, "287": [3, 177, 263, 272], "018": 3, "0m1": 3, "116": [3, 185], "produc": [3, 4, 5, 6, 11, 22, 23, 25, 60, 68, 97, 111, 113, 115, 126, 136, 138, 141, 143, 145, 147, 149, 159, 160, 165, 171, 173, 174, 179, 182, 183, 185, 197, 198, 199, 206, 214, 263, 272], "4092": 3, "2037": 3, "2039": 3, "1274": 3, "961": 3, "1779": 3, "1017": 3, "1559": 3, "0871": 3, "972": 3, "2240": 3, "0735": [3, 201], "0520": 3, "0710": 3, "0935": 3, "0666": [3, 23], "0744": 3, "0603": 3, "981": 3, "0762": 3, "0547": 3, "0207": 3, "0525": [3, 208], "0m6": 3, "952": [3, 144], "0m7": 3, "048": 3, "0m0": 3, "619": 3, "gain": [3, 5, 17, 82, 145, 154, 168, 176, 201], "six": [3, 159, 166, 263, 272], "kind": [3, 5, 6, 19, 23, 44, 47, 49, 58, 59, 73, 98, 102, 112, 119, 121, 137, 173, 177, 197, 252], "larg": [3, 5, 6, 7, 12, 16, 17, 18, 23, 42, 49, 87, 97, 102, 103, 105, 117, 120, 121, 122, 123, 126, 129, 133, 135, 138, 139, 149, 150, 159, 160, 162, 163, 164, 171, 172, 176, 177, 178, 191, 199, 201, 202, 223, 258, 261, 263, 270, 272], "improv": [3, 5, 6, 8, 10, 17, 19, 21, 24, 42, 49, 56, 97, 108, 120, 121, 122, 123, 124, 129, 142, 145, 147, 149, 153, 154, 157, 160, 164, 165, 171, 172, 176, 177, 184, 185, 194, 197, 200, 201, 204, 212, 216, 218, 220, 222, 253, 264, 273], "due": [3, 5, 6, 17, 18, 22, 52, 58, 60, 82, 85, 108, 122, 123, 124, 133, 152, 153, 157, 162, 172, 176, 182, 184, 191, 200, 201, 202, 211, 216, 221, 262, 263, 271, 272], "heavi": [3, 6, 49, 97, 101, 223], "impact": [3, 12, 17, 87, 97, 136, 161, 164, 184, 201, 209, 252, 259], "smaller": [3, 9, 51, 79, 97, 103, 123, 124, 157, 159, 161, 171, 203, 208, 223, 253], "nevertheless": [3, 5, 19, 23, 147, 159], "primari": [4, 5, 6, 15, 142, 175, 176, 177, 186, 193, 206], "program": [4, 5, 18, 21, 22, 23, 25, 60, 61, 98, 100, 101, 103, 121, 124, 127, 143, 162, 172, 173, 177, 197, 198, 199, 200, 214], "languag": [4, 5, 6, 7, 21, 23, 24, 25, 44, 49, 58, 59, 60, 78, 79, 98, 100, 102, 116, 118, 119, 121, 124, 126, 127, 128, 137, 163, 165, 173, 174, 181, 186, 195, 246, 252, 255, 262, 263, 264, 271, 272, 273], "suitabl": [4, 8, 131, 135, 139, 171, 199, 223], "prefer": [4, 6, 8, 53, 58, 145, 150, 155, 159, 176, 194, 252], "eas": [4, 5, 85, 122, 177, 220], "situat": [4, 15, 23, 25, 37, 129, 130, 133, 135, 178, 184, 195], "properti": [4, 5, 6, 11, 14, 21, 23, 25, 32, 40, 48, 101, 103, 126, 147, 153, 165, 173, 174, 176, 177, 190, 208, 220, 249, 257, 261, 270], "unfavor": 4, "environ": [4, 5, 6, 7, 11, 17, 18, 22, 25, 42, 55, 61, 82, 85, 105, 112, 114, 115, 121, 122, 123, 124, 132, 133, 134, 135, 144, 148, 149, 155, 160, 161, 162, 163, 172, 173, 174, 175, 176, 204, 206, 208, 212, 214, 215, 216, 222, 223, 231, 252, 253, 259], "latter": [4, 5, 6, 60, 61, 126, 161, 198], "land": [4, 23, 113, 146, 191, 220, 259], "latenc": [4, 6, 17, 121, 124, 126, 132, 144, 158, 172, 176, 177, 187, 194, 201, 223, 253], "strict": [4, 112, 171, 190, 220, 248], "bind": [4, 6, 10, 23, 121, 176, 177, 263, 272], "java": [4, 58, 177, 204, 222, 223], "rust": 4, "paragraph": [4, 6, 23, 264, 267, 273, 276], "outlin": [4, 5, 6, 23, 227], "pure": [4, 5, 6, 10, 23, 47, 121, 127, 130, 138, 154, 178, 186, 199], "journei": [4, 6, 52, 137], "enabl": [4, 5, 6, 8, 11, 14, 15, 16, 18, 19, 23, 24, 42, 47, 55, 56, 58, 59, 60, 61, 75, 82, 97, 107, 112, 122, 123, 124, 126, 129, 130, 133, 135, 137, 144, 147, 152, 156, 158, 159, 168, 169, 171, 175, 176, 177, 179, 183, 184, 189, 191, 193, 195, 196, 199, 204, 214, 216, 220, 224, 225, 228, 230, 240, 253, 259, 261, 270], "vanilla": [4, 5, 6, 23, 49, 65, 99, 111, 127, 171, 189, 259], "eager": [4, 10, 23, 60, 85, 86, 121, 144, 147, 164, 172, 174, 181, 195, 197, 198, 199, 200, 210, 221], "discuss": [4, 5, 6, 8, 10, 15, 16, 23, 44, 73, 79, 101, 102, 116, 122, 123, 134, 142, 143, 144, 149, 150, 172, 176, 177, 183, 189, 190, 191, 192, 207, 228], "littl": [4, 17, 25, 52, 61, 97, 99, 113, 136, 159, 161, 163, 164, 166, 168, 201], "effort": [4, 14, 49, 51, 52, 108, 182, 195, 196], "next": [4, 5, 6, 8, 9, 11, 12, 14, 15, 16, 17, 19, 20, 22, 23, 34, 42, 43, 49, 51, 53, 55, 56, 58, 59, 60, 75, 78, 80, 82, 85, 92, 94, 96, 97, 98, 99, 102, 103, 105, 112, 113, 115, 117, 121, 122, 123, 124, 125, 127, 128, 129, 130, 132, 136, 137, 138, 139, 143, 144, 146, 149, 150, 152, 154, 157, 160, 161, 162, 163, 165, 166, 168, 169, 177, 178, 181, 182, 184, 187, 188, 191, 192, 195, 197, 198, 199, 201, 203, 208, 216, 220, 222, 223, 261, 263, 264, 270, 272, 273], "mechan": [4, 5, 6, 11, 14, 15, 24, 32, 49, 56, 60, 130, 143, 153, 165, 166, 168, 174, 177, 199, 216, 220, 221], "evalu": [4, 6, 9, 12, 17, 19, 20, 24, 37, 73, 97, 105, 107, 112, 118, 145, 150, 159, 160, 162, 169, 172, 173, 174, 178, 181, 198, 201, 221, 241], "onc": [4, 5, 6, 8, 10, 11, 14, 16, 17, 20, 21, 22, 23, 25, 51, 52, 56, 60, 82, 97, 98, 102, 105, 113, 131, 136, 139, 147, 148, 152, 153, 156, 158, 159, 160, 162, 165, 168, 169, 177, 184, 185, 188, 193, 195, 201, 220, 223, 231, 258], "record": [4, 6, 8, 19, 20, 23, 25, 43, 49, 60, 112, 121, 122, 123, 127, 129, 130, 143, 146, 152, 159, 160, 162, 163, 172, 174, 204, 221, 252], "explicit": [4, 6, 21, 23, 60, 139, 147, 163, 190, 200, 209, 263, 272], "pars": [4, 5, 23, 49, 51, 103, 116, 122, 123, 126, 209, 263, 272], "subject": [4, 11, 14, 23, 42, 108, 113, 123, 141, 165, 173, 174, 187, 188, 193, 198, 204, 205, 206, 212, 216, 222], "constraint": [4, 6, 12, 17, 18, 60, 85, 98, 99, 121, 124, 126, 153, 159, 171, 197, 198, 200, 201, 252], "impos": [4, 223, 232], "guidanc": [4, 8, 176, 177, 195], "offici": [4, 82, 108, 113, 115, 135, 160, 172, 174, 181, 199, 218, 220, 252], "jit": [4, 6, 8, 15, 19, 21, 22, 25, 49, 58, 59, 60, 85, 112, 119, 137, 142, 147, 172, 177, 182, 185, 187, 188, 194, 197, 198, 203, 204, 206, 207, 208, 209, 216, 218, 220, 222, 223, 224, 225, 231, 238, 247, 252, 255, 257], "scriptmodul": [4, 22, 23, 25, 85, 203, 222, 252, 257], "embed": [4, 7, 9, 16, 21, 23, 49, 60, 75, 79, 93, 98, 100, 102, 110, 112, 115, 118, 121, 122, 124, 137, 162, 163, 165, 169, 175, 181, 188, 193, 195, 241, 263, 272], "resnet18": [4, 43, 90, 117, 147, 157, 158, 168, 171, 182, 195, 197, 198, 199, 229, 238, 257], "normal": [4, 6, 8, 11, 12, 19, 20, 21, 37, 39, 49, 51, 52, 58, 59, 60, 65, 73, 80, 85, 87, 90, 92, 94, 96, 97, 98, 99, 102, 103, 111, 112, 117, 119, 123, 127, 128, 129, 135, 136, 139, 146, 148, 153, 157, 158, 161, 162, 164, 165, 166, 168, 169, 171, 172, 177, 182, 184, 190, 197, 198, 204, 209, 213, 216, 220, 221, 223, 224, 225, 228, 229, 241, 242, 243, 250, 252, 254, 263, 272], "rand": [4, 5, 6, 14, 15, 17, 21, 23, 25, 33, 40, 43, 48, 85, 89, 92, 93, 95, 96, 109, 114, 129, 130, 144, 146, 152, 153, 164, 176, 177, 178, 179, 187, 195, 197, 198, 201, 203, 206, 210, 211, 214, 219, 220, 223, 224, 225, 226, 233, 239, 247, 252, 253, 254, 257], "224": [4, 12, 19, 20, 51, 58, 59, 75, 90, 97, 117, 119, 139, 142, 143, 152, 157, 158, 166, 168, 171, 176, 177, 182, 187, 188, 194, 197, 198, 199, 204, 206, 213, 218, 220, 223, 224, 225, 229, 238, 247, 252, 254, 257], "traced_script_modul": [4, 223], "ident": [4, 6, 17, 55, 85, 124, 132, 142, 150, 157, 166, 169, 185, 192, 194, 201, 218], "2698": 4, "0381": 4, "4023": 4, "3010": 4, "0448": 4, "slicebackward": 4, "circumst": [4, 5], "emploi": [4, 97, 165, 168], "particular": [4, 5, 6, 8, 11, 23, 42, 44, 51, 60, 82, 83, 87, 103, 124, 126, 127, 135, 136, 138, 139, 150, 154, 162, 171, 173, 174, 178, 179, 189, 190, 192, 193, 223], "form": [4, 6, 9, 12, 15, 17, 23, 47, 49, 52, 60, 98, 105, 110, 113, 116, 121, 125, 128, 139, 144, 165, 171, 174, 184, 193, 201, 202, 213, 214, 263, 272], "accordingli": [4, 10, 12, 18, 22, 136, 149, 152, 161, 171, 188, 261, 270], "sai": [4, 5, 6, 24, 43, 51, 99, 101, 103, 113, 115, 125, 138, 145, 149, 152, 156, 168, 184, 200, 222, 264, 273], "mymodul": [4, 6, 109, 172, 173, 174, 202, 212], "__init__": [4, 5, 6, 7, 9, 11, 12, 13, 14, 16, 19, 20, 21, 22, 25, 33, 37, 38, 44, 45, 47, 49, 51, 52, 53, 60, 65, 67, 73, 78, 79, 85, 87, 89, 92, 93, 94, 96, 97, 98, 99, 102, 103, 104, 105, 109, 111, 112, 115, 118, 123, 125, 127, 128, 129, 133, 134, 135, 138, 142, 143, 146, 148, 149, 150, 153, 154, 156, 160, 161, 162, 163, 164, 165, 166, 169, 171, 172, 173, 174, 177, 178, 179, 181, 182, 183, 193, 194, 195, 197, 198, 199, 202, 203, 209, 212, 214, 215, 216, 218, 219, 221, 223, 226, 228, 233, 234, 237, 239, 240, 241, 242, 243, 244, 248, 249, 250, 252, 263, 272], "n": [4, 5, 6, 7, 9, 12, 17, 19, 22, 23, 32, 33, 37, 38, 40, 43, 47, 48, 49, 51, 59, 60, 75, 82, 85, 89, 90, 93, 94, 95, 97, 104, 110, 113, 115, 118, 122, 123, 127, 129, 133, 135, 136, 137, 143, 145, 146, 147, 150, 153, 156, 160, 161, 162, 163, 165, 166, 169, 178, 182, 184, 185, 189, 190, 191, 192, 193, 197, 198, 200, 201, 205, 208, 213, 220, 230, 231, 236, 244, 247, 255, 257], "mv": [4, 110], "my_modul": 4, "20": [4, 6, 7, 9, 13, 16, 17, 19, 23, 33, 58, 59, 61, 78, 79, 82, 85, 87, 93, 95, 109, 123, 126, 128, 133, 135, 136, 142, 144, 146, 147, 149, 150, 152, 156, 161, 163, 166, 168, 173, 174, 177, 184, 187, 192, 195, 198, 201, 209, 221, 223, 231, 232, 234, 238, 246, 259, 267, 276], "sm": [4, 168], "exclud": [4, 8, 43], "doesn": [4, 7, 8, 10, 12, 13, 17, 25, 58, 99, 101, 103, 113, 125, 143, 145, 147, 156, 171, 172, 176, 179, 183, 184, 189, 195, 198, 200, 201, 205, 208, 211, 228, 263, 272], "yet": [4, 6, 10, 11, 18, 23, 50, 73, 102, 107, 108, 113, 135, 162, 165, 175, 179, 185, 193, 198, 199, 216, 220, 224, 225], "could": [4, 5, 6, 8, 10, 11, 23, 52, 60, 87, 97, 98, 99, 101, 102, 103, 105, 109, 122, 123, 124, 125, 127, 128, 129, 135, 139, 147, 149, 152, 159, 160, 161, 162, 163, 165, 168, 169, 171, 176, 177, 178, 179, 189, 191, 197, 199, 200, 205, 214, 215, 216, 220, 221], "ignor": [4, 19, 49, 51, 97, 102, 103, 112, 142, 148, 155, 159, 171, 178, 179, 182, 187, 189, 190, 191, 192, 193, 197, 198, 218], "readi": [4, 6, 9, 10, 16, 22, 23, 42, 49, 58, 59, 60, 98, 99, 102, 103, 122, 134, 135, 150, 155, 159, 161, 162, 163, 175, 178, 187, 194, 197, 198, 199, 208, 213, 223, 224, 225, 228, 252], "hand": [4, 5, 6, 8, 14, 17, 18, 23, 61, 73, 98, 103, 128, 135, 139, 154, 172, 177, 190, 201], "shown": [4, 6, 8, 17, 19, 20, 21, 52, 58, 59, 113, 116, 124, 126, 137, 144, 146, 157, 160, 161, 163, 164, 168, 171, 172, 176, 177, 183, 188, 190, 191, 192, 195, 198, 200, 201, 214, 220, 228, 252, 258, 259, 261, 263, 270, 272], "filenam": [4, 6, 49, 104, 109, 116, 127, 128, 171, 230], "traced_resnet_model": 4, "pt": [4, 6, 22, 23, 25, 53, 58, 59, 75, 112, 117, 119, 122, 123, 137, 188, 194, 204, 206, 208, 218, 220, 221, 222, 223, 224, 225, 228, 240, 241, 242, 243, 248, 257], "my_module_model": 4, "left": [4, 17, 32, 43, 47, 49, 51, 52, 64, 85, 89, 99, 103, 111, 112, 113, 135, 137, 146, 150, 159, 160, 162, 164, 168, 169, 200, 201, 261, 263, 270, 272], "realm": [4, 6], "cross": [4, 7, 8, 13, 20, 44, 52, 95, 118, 124, 126, 176, 263, 272], "sphere": 4, "distribut": [4, 5, 6, 14, 15, 19, 24, 52, 54, 73, 75, 79, 80, 87, 97, 99, 103, 108, 113, 121, 122, 123, 124, 126, 131, 132, 137, 147, 149, 152, 155, 159, 161, 168, 176, 185, 193, 196, 202, 208, 212, 215, 223, 231, 251, 259], "encompass": 4, "share": [4, 5, 6, 10, 11, 18, 22, 23, 48, 55, 66, 78, 80, 87, 97, 101, 108, 110, 113, 121, 122, 125, 133, 135, 136, 146, 159, 161, 162, 163, 173, 174, 176, 195, 208, 220], "header": [4, 5, 6, 8, 22, 23, 143, 155, 188, 204, 208, 222, 225, 261, 263, 264, 270, 272, 273], "cmake": [4, 6, 188, 206, 220, 257], "futur": [4, 7, 18, 21, 22, 42, 49, 58, 59, 109, 110, 118, 123, 134, 137, 141, 146, 152, 155, 157, 160, 161, 162, 163, 173, 174, 179, 181, 187, 188, 192, 197, 198, 199, 200, 204, 208, 222, 252], "begin": [4, 5, 6, 7, 11, 12, 17, 19, 22, 23, 25, 32, 43, 49, 50, 52, 55, 58, 59, 73, 85, 89, 102, 103, 108, 113, 115, 116, 122, 124, 137, 142, 152, 157, 160, 162, 168, 169, 191, 193, 201, 223, 228, 263, 272], "iostream": [4, 5, 6, 22, 23, 220], "argc": [4, 22, 23, 220, 257], "const": [4, 5, 6, 8, 10, 15, 22, 23, 59, 144, 155, 186, 208, 220, 222, 231, 246, 257], "char": [4, 22, 23, 59, 144, 208, 220, 257], "cerr": [4, 22, 23, 220, 257], "app": [4, 23, 105, 119, 121, 139, 194, 204, 213, 220, 222, 227, 228, 252], "try": [4, 6, 12, 14, 15, 17, 19, 21, 22, 23, 25, 42, 44, 47, 48, 49, 52, 53, 58, 59, 60, 61, 73, 79, 97, 98, 99, 100, 101, 104, 105, 109, 116, 125, 126, 127, 128, 129, 136, 142, 144, 146, 147, 149, 150, 152, 155, 156, 159, 160, 164, 165, 168, 172, 173, 174, 176, 182, 184, 187, 190, 197, 201, 203, 208, 213, 216, 220, 222, 253, 257, 263, 264, 272, 273], "deseri": [4, 6, 23, 112, 182, 197, 198], "catch": [4, 8, 11, 22, 58, 208, 220, 222, 257], "c10": [4, 8, 10, 15, 22, 144, 155, 186, 188, 208, 220, 231, 246, 253, 257], "ok": [4, 103, 161, 263, 272], "relev": [4, 6, 14, 53, 98, 100, 103, 113, 114, 122, 124, 156, 171], "accept": [4, 5, 20, 67, 78, 87, 97, 102, 111, 115, 116, 124, 126, 141, 145, 150, 154, 159, 162, 168, 171, 179, 200, 202, 205, 212, 252], "proce": [4, 11, 25, 97, 99, 144, 157, 165], "examin": [4, 11, 22, 25, 58, 59, 82, 97, 143], "moment": [4, 6, 11, 173, 179, 192, 206, 223], "cpp": [4, 5, 6, 8, 22, 23, 120, 121, 144, 187, 196, 199, 208, 220, 246, 257], "cmakelist": [4, 6, 22, 23, 208, 220, 257], "txt": [4, 5, 6, 9, 22, 23, 49, 75, 116, 127, 128, 137, 144, 158, 165, 185, 208, 220, 257], "cmake_minimum_requir": [4, 6, 22, 23, 208, 220, 257], "fatal_error": [4, 6, 22, 23, 208, 220, 257], "custom_op": [4, 108, 173, 174, 257], "find_packag": [4, 6, 22, 23, 220, 257], "add_execut": [4, 6, 22, 23, 220, 257], "target_link_librari": [4, 6, 22, 23, 208, 220, 257], "torch_librari": [4, 6, 8, 22, 23, 220, 257], "set_properti": [4, 6, 220, 257], "cxx_standard": [4, 6, 220, 257], "14": [4, 6, 22, 23, 24, 47, 73, 92, 123, 144, 171, 176, 208, 220, 221, 228, 231, 238, 267, 276], "last": [4, 6, 11, 12, 14, 19, 23, 40, 43, 49, 52, 53, 59, 60, 73, 83, 85, 87, 99, 102, 105, 113, 117, 121, 124, 125, 127, 128, 135, 136, 142, 144, 148, 149, 152, 157, 159, 160, 161, 163, 164, 165, 169, 176, 178, 188, 189, 192, 193, 218, 220, 222, 228, 247, 252], "thing": [4, 5, 6, 8, 15, 21, 22, 23, 25, 43, 44, 47, 49, 58, 59, 85, 87, 97, 98, 99, 101, 102, 103, 113, 116, 124, 125, 126, 129, 130, 131, 132, 135, 136, 139, 143, 144, 147, 148, 153, 158, 159, 166, 177, 182, 184, 195, 197, 208, 263, 272], "grab": [4, 6, 52, 158, 163], "latest": [4, 6, 10, 14, 20, 87, 107, 108, 112, 121, 137, 157, 158, 159, 162, 165, 166, 171, 172, 208, 258, 261, 270], "stabl": [4, 20, 26, 27, 28, 29, 30, 32, 33, 34, 37, 38, 40, 94, 98, 113, 140, 158, 167, 168, 170, 181, 221, 223, 230, 233, 251, 261, 270], "page": [4, 6, 10, 22, 23, 50, 54, 61, 109, 127, 139, 163, 168, 175, 199, 204, 208, 209, 217, 220, 222, 265, 267, 274, 276], "websit": [4, 6, 160], "unzip": [4, 6, 19, 50, 171, 178, 181, 182, 197, 198, 208], "archiv": [4, 5, 6, 25, 147, 258], "against": [4, 22, 23, 44, 60, 81, 105, 135, 147, 159, 212, 220], "window": [4, 5, 6, 7, 20, 44, 51, 103, 105, 133, 162, 168, 178, 206, 263, 272], "debug": [4, 6, 8, 19, 25, 58, 59, 60, 78, 98, 121, 125, 173, 174, 186, 195, 196, 231, 256], "abi": [4, 5, 6, 22, 23, 204, 206, 208, 220], "plan": [4, 6, 10, 18, 60, 112, 122, 124, 171, 175, 182, 187, 192, 198, 206, 208, 224], "correct": [4, 5, 6, 8, 10, 11, 12, 13, 19, 37, 38, 43, 44, 47, 49, 60, 64, 73, 85, 87, 92, 97, 98, 99, 102, 111, 122, 123, 125, 127, 129, 133, 136, 144, 147, 153, 156, 159, 161, 162, 165, 166, 168, 169, 182, 193, 197, 198, 215, 221, 261, 270], "laid": 4, "within": [4, 5, 7, 14, 18, 21, 23, 61, 85, 103, 105, 109, 110, 124, 130, 137, 142, 144, 153, 156, 160, 162, 171, 176, 177, 185, 186, 192, 193, 195, 199, 206, 208, 215, 261, 263, 270, 272], "mkdir": [4, 6, 23, 104, 146, 168, 171, 181, 194, 208], "dcmake_prefix_path": [4, 6, 22, 23, 220, 257], "config": [4, 6, 10, 17, 20, 24, 87, 123, 126, 137, 144, 158, 176, 177, 179, 184, 185, 186, 197, 199, 201, 220, 221, 255], "someth": [4, 5, 6, 11, 14, 19, 23, 25, 44, 87, 99, 101, 113, 116, 135, 144, 157, 158, 159, 165, 205, 263, 272], "root": [4, 5, 6, 14, 22, 23, 34, 37, 38, 41, 43, 44, 51, 52, 87, 92, 97, 98, 110, 129, 136, 144, 162, 163, 166, 168, 178, 188, 204, 213, 220, 223, 236, 250, 252, 254, 261, 270], "4b5a67132e81": 4, "identif": [4, 6, 22, 23, 220], "gnu": [4, 5, 6, 22, 23, 220, 247], "cxx": [4, 6, 22, 23, 204, 206, 208, 220], "check": [4, 5, 6, 7, 8, 13, 14, 15, 19, 20, 22, 23, 25, 42, 43, 44, 45, 49, 50, 52, 55, 58, 59, 60, 73, 75, 85, 97, 98, 101, 104, 105, 108, 109, 110, 115, 116, 122, 126, 133, 135, 136, 139, 141, 142, 144, 146, 147, 153, 154, 156, 158, 159, 162, 171, 172, 173, 174, 176, 178, 188, 192, 193, 198, 200, 206, 208, 214, 220, 222, 223, 252], "usr": [4, 6, 18, 22, 23, 135, 194, 220], "cc": [4, 6, 22, 23, 43, 108, 118, 204, 206, 220], "detect": [4, 6, 11, 12, 18, 22, 23, 52, 75, 121, 139, 158, 168, 172, 220], "info": [4, 5, 6, 22, 23, 82, 118, 132, 135, 137, 146, 160, 171, 173, 174, 175, 185, 207, 220, 221, 228], "pthread": [4, 5, 6, 22, 23, 208, 220], "pthread_creat": [4, 6, 22, 23, 220], "thread": [4, 5, 6, 8, 9, 21, 22, 23, 52, 61, 109, 133, 134, 137, 149, 158, 161, 162, 163, 176, 177, 181, 182, 194, 216, 220, 231, 246], "scan": [4, 6, 22, 23, 171], "50": [4, 6, 7, 12, 16, 17, 19, 21, 22, 23, 24, 49, 52, 53, 58, 78, 92, 136, 144, 147, 156, 160, 163, 166, 177, 178, 182, 185, 191, 197, 199, 201, 203, 219, 221, 223, 228, 230, 247, 253], "cmakefil": [4, 6, 22, 23], "dir": [4, 6, 22, 23, 82, 126, 147, 148, 204, 208, 223, 246], "o": [4, 5, 6, 7, 17, 22, 23, 90, 97, 98, 108, 128, 137, 150, 152, 171, 201, 263, 272], "100": [4, 6, 9, 14, 16, 17, 19, 21, 22, 23, 37, 38, 44, 45, 48, 49, 52, 63, 64, 67, 68, 69, 71, 72, 80, 89, 92, 93, 94, 97, 99, 111, 119, 123, 125, 127, 128, 129, 133, 138, 143, 144, 145, 146, 147, 149, 154, 156, 158, 159, 160, 163, 165, 166, 169, 171, 172, 173, 174, 176, 177, 182, 187, 191, 195, 197, 198, 201, 215, 219, 221, 231, 234, 246, 253, 258], "suppli": [4, 6, 101, 147, 158, 263, 272], "binari": [4, 6, 20, 22, 23, 49, 52, 105, 135, 147, 156, 172, 178, 188, 190, 196, 199, 204, 208, 212, 218, 220, 222, 223, 231], "incompat": [4, 173, 174, 197], "1d": [4, 68, 93, 111, 205], "4d": [4, 47, 78, 147, 200], "path_to_model": 4, "successfulli": [4, 6, 22, 50, 58, 59, 60, 105, 119, 126, 135, 144, 162, 191, 194, 206, 225, 227, 241, 257], "coupl": [4, 14, 49, 103, 122, 124, 130, 136, 138, 152, 169, 183, 203], "awai": [4, 5, 6, 23, 47, 60, 98, 99, 101, 113, 143, 149, 159, 160, 161, 192, 263, 272], "ivalu": [4, 23, 58, 144, 155, 206, 208, 220, 223, 257], "push_back": [4, 22, 23, 220, 257], "totensor": [4, 12, 19, 20, 23, 34, 37, 38, 44, 51, 52, 58, 59, 73, 75, 87, 90, 92, 94, 96, 97, 116, 117, 119, 123, 129, 135, 139, 148, 157, 158, 162, 166, 168, 169, 171, 182, 187, 188, 197, 198, 204, 206, 213, 220, 221, 223, 229, 250, 254, 257], "slice": [4, 5, 48, 80, 102, 127, 150, 156, 193, 206], "eras": [4, 25], "org": [4, 6, 26, 27, 28, 29, 30, 32, 33, 34, 35, 37, 38, 40, 42, 45, 46, 49, 58, 59, 74, 77, 84, 91, 93, 94, 96, 100, 104, 106, 113, 120, 122, 127, 128, 137, 140, 141, 142, 143, 152, 153, 157, 158, 165, 167, 168, 170, 172, 174, 181, 184, 187, 188, 190, 192, 194, 196, 203, 204, 205, 206, 207, 208, 213, 221, 222, 223, 224, 226, 230, 233, 234, 235, 236, 237, 257, 261, 263, 270, 272], "cppdoc": [4, 6], "pariti": 4, "manipul": [4, 60, 103, 143, 152, 182, 185], "five": [4, 9, 65, 95, 111, 113], "ideal": [4, 6, 14, 58, 59, 97, 149, 165, 177, 197], "variabl": [4, 5, 6, 7, 8, 12, 20, 22, 23, 42, 49, 60, 69, 76, 82, 87, 98, 99, 101, 111, 114, 127, 132, 135, 144, 161, 164, 173, 174, 176, 184, 191, 193, 205, 206, 208, 222, 252, 253], "kcuda": [4, 6, 186], "live": [4, 6, 10, 15, 121, 125, 134, 162, 163, 192, 216, 263, 272], "hopefulli": [4, 6, 50, 51, 73, 85, 99, 112], "equip": [4, 5, 130, 136, 189], "concept": [4, 6, 11, 22, 55, 100, 101, 114, 121, 126, 146, 161, 164, 165, 186, 197, 199, 200], "Of": [4, 14, 23, 97, 101, 125, 133, 135, 169, 190, 192], "cours": [4, 6, 14, 17, 19, 23, 53, 97, 100, 101, 103, 104, 125, 133, 135, 169, 201], "did": [4, 6, 8, 19, 23, 25, 44, 52, 60, 68, 105, 111, 113, 135, 141, 153, 159, 162, 165, 176, 182, 263, 272], "cover": [4, 5, 14, 15, 16, 18, 22, 25, 47, 58, 59, 100, 108, 113, 114, 119, 121, 122, 126, 135, 155, 159, 162, 163, 169, 172, 173, 174, 175, 191, 193, 197, 200, 212, 220, 252, 253], "insid": [4, 5, 6, 10, 16, 17, 18, 20, 22, 23, 45, 78, 108, 124, 168, 178, 195, 201, 205, 223, 263, 272], "shortli": [4, 161], "html": [4, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 40, 45, 46, 52, 57, 74, 77, 84, 90, 91, 94, 100, 104, 106, 113, 114, 118, 120, 122, 137, 140, 142, 143, 157, 167, 170, 171, 174, 181, 187, 188, 190, 192, 203, 204, 230, 233, 234, 235, 237, 263, 272], "peter": 5, "goldsborough": 5, "plethora": 5, "relat": [5, 11, 14, 52, 60, 101, 103, 113, 124, 144, 153, 173, 174, 182, 263, 272], "algebra": [5, 14, 48, 99], "wrangl": 5, "novel": 5, "research": [5, 6, 17, 19, 23, 25, 49, 52, 60, 73, 75, 85, 99, 114, 115, 135, 137, 150, 154, 156, 171, 181, 201, 216], "modul": [5, 7, 9, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 33, 37, 38, 39, 42, 44, 45, 47, 49, 52, 53, 55, 58, 59, 65, 66, 68, 73, 78, 79, 87, 89, 92, 94, 95, 96, 97, 98, 99, 102, 103, 105, 107, 108, 109, 110, 112, 115, 116, 117, 118, 121, 122, 123, 124, 125, 127, 128, 129, 133, 135, 138, 142, 143, 144, 146, 147, 150, 152, 154, 155, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 169, 171, 172, 173, 174, 175, 176, 177, 179, 181, 182, 183, 184, 185, 186, 187, 188, 193, 194, 196, 197, 198, 199, 200, 201, 203, 206, 208, 209, 212, 214, 215, 219, 220, 221, 222, 223, 226, 227, 228, 231, 233, 234, 235, 238, 239, 240, 241, 242, 243, 248, 249, 250, 251, 252, 257, 262, 263, 264, 271, 272, 273], "power": [5, 6, 23, 43, 49, 57, 65, 73, 75, 87, 97, 99, 121, 123, 126, 135, 144, 146, 150, 155, 156, 158, 165, 171, 184, 187, 192, 194, 227, 263, 272], "spare": [5, 6], "deriv": [5, 6, 10, 17, 23, 25, 43, 48, 76, 78, 99, 101, 102, 103, 110, 125, 129, 130, 141, 145, 195, 201], "express": [5, 13, 23, 95, 98, 107, 114, 124, 137, 173, 174, 191, 197, 198, 200, 209, 263, 272], "better": [5, 6, 14, 17, 19, 21, 44, 52, 56, 61, 79, 97, 103, 109, 113, 115, 121, 126, 127, 128, 139, 144, 145, 146, 152, 154, 157, 159, 160, 165, 168, 169, 171, 173, 174, 176, 177, 182, 184, 189, 190, 195, 197, 198, 199, 200, 201, 223, 258], "frequent": [5, 22, 23, 75, 82, 103, 123, 175, 176, 177, 191, 220], "expens": [5, 17, 97, 109, 163, 177, 184, 201], "plausibl": 5, "address": [5, 12, 18, 22, 24, 82, 135, 161, 162, 174, 176, 197, 221, 264, 273], "nativ": [5, 6, 8, 15, 23, 42, 55, 87, 107, 108, 119, 121, 122, 124, 136, 137, 163, 176, 177, 179, 184, 189, 192, 197, 216, 220, 223, 238, 246], "intend": [5, 6, 8, 12, 23, 97, 123, 124, 129, 144, 147, 164, 172, 173, 174, 189, 197, 212], "much": [5, 6, 8, 10, 12, 14, 17, 19, 23, 25, 52, 56, 73, 97, 98, 99, 103, 112, 119, 122, 125, 126, 142, 145, 146, 152, 158, 159, 162, 163, 165, 168, 169, 172, 176, 184, 189, 192, 200, 201, 218, 227, 228, 261, 263, 270, 272], "boilerpl": [5, 6, 19, 44, 99, 169, 189], "degre": [5, 64, 126, 165, 168, 192], "matter": [5, 134, 171, 189, 191], "organ": [5, 8, 14, 23, 51, 112, 127, 143, 149, 258, 264, 273], "tackl": [5, 109], "decid": [5, 6, 8, 10, 17, 18, 52, 87, 160, 175, 192, 196, 201], "contribut": [5, 52, 61, 65, 82, 111, 137, 147, 168, 176], "upstream": [5, 220], "rest": [5, 6, 14, 44, 117, 121, 136, 139, 143, 157, 159, 161, 162, 191, 198, 218, 263, 264, 272, 273], "chase": [5, 113], "someon": [5, 165], "fire": [5, 133], "dai": [5, 42, 103, 115, 116, 124], "head": [5, 7, 17, 21, 42, 75, 83, 109, 113, 124, 126, 133, 157, 164, 178, 193, 201], "straight": [5, 6, 23, 139, 165], "recurr": [5, 9, 44, 49, 60, 79, 81, 98, 102, 110, 121, 127, 128, 153, 165, 181, 195], "unit": [5, 6, 25, 49, 110, 122, 123, 145, 150, 156, 159, 160, 165, 171, 176, 177, 187], "superior": 5, "lstm": [5, 44, 49, 78, 79, 93, 100, 110, 119, 121, 127, 128, 163, 181, 183, 195, 228, 234, 251], "lack": [5, 15, 82, 189], "forget": [5, 6, 112, 157, 172, 188], "gate": [5, 49], "exponenti": [5, 49, 99, 101, 153, 160, 184], "elu": [5, 110], "never": [5, 7, 98, 99, 100, 103, 115, 125, 156, 166], "lltm": 5, "long": [5, 6, 7, 9, 10, 20, 23, 49, 50, 60, 78, 80, 82, 87, 98, 99, 100, 101, 103, 113, 118, 122, 125, 127, 128, 136, 137, 143, 144, 149, 153, 160, 163, 165, 168, 178, 185, 186, 195, 197, 208, 223, 231, 238, 246, 263, 264, 272, 273], "term": [5, 6, 15, 52, 73, 99, 100, 101, 109, 122, 124, 150, 156, 159, 174, 184, 192, 197, 198, 199, 200, 202, 263, 264, 272, 273], "signific": [5, 6, 9, 19, 42, 52, 58, 59, 82, 122, 129, 137, 143, 144, 145, 147, 152, 159, 164, 168, 172, 177, 203, 214, 215, 221, 223, 253, 263, 272], "lstmcell": 5, "cell": [5, 21, 23, 25, 50, 60, 75, 80, 109, 159, 160, 164, 171, 184, 264, 273], "plain": [5, 6, 23, 49, 127, 128, 165, 178], "input_featur": 5, "state_s": 5, "candid": [5, 144], "reset_paramet": [5, 129], "stdv": 5, "math": [5, 7, 10, 49, 63, 64, 65, 67, 68, 69, 71, 72, 85, 89, 95, 101, 104, 110, 111, 118, 127, 128, 129, 150, 158, 160, 164, 165, 253, 267, 276], "sqrt": [5, 7, 85, 104, 118, 129, 189, 202], "uniform_": [5, 6, 7, 9, 115, 163, 181, 195, 202], "old_h": 5, "old_cel": 5, "cat": [5, 7, 9, 20, 21, 40, 44, 48, 49, 60, 78, 90, 92, 98, 101, 102, 110, 115, 118, 128, 134, 139, 144, 149, 160, 163, 165, 169, 178, 181, 200, 229, 250], "gate_weight": 5, "split": [5, 7, 8, 9, 18, 19, 20, 21, 45, 49, 52, 60, 79, 85, 87, 98, 99, 102, 103, 113, 118, 121, 127, 128, 133, 134, 137, 142, 149, 159, 162, 163, 165, 178, 181, 182, 185, 193, 197, 198, 212, 246], "input_g": 5, "sigmoid": [5, 6, 52, 93, 110, 179, 200, 247], "output_g": 5, "tanh": [5, 6, 14, 25, 49, 52, 60, 93, 99, 110, 127, 145, 159, 165, 247], "candidate_cel": 5, "new_cel": 5, "hidden": [5, 7, 9, 21, 49, 60, 78, 97, 98, 102, 124, 126, 127, 128, 136, 142, 148, 163, 164, 165, 181, 195, 197, 234, 261, 270], "new_h": [5, 25, 51], "rnn": [5, 9, 21, 25, 45, 49, 60, 61, 78, 79, 93, 110, 118, 121, 134, 136, 153, 162, 165, 181, 195, 199, 247], "new_c": 5, "intel": [5, 121, 135, 144, 147, 199, 206, 251], "mkl": [5, 144, 238], "nnpack": 5, "why": [5, 6, 8, 11, 14, 25, 44, 52, 85, 97, 99, 103, 112, 114, 130, 133, 144, 152, 165, 172, 173, 174, 190, 192, 263, 272], "room": [5, 97, 149, 263, 272], "obviou": [5, 113], "knowledg": [5, 49, 52, 73, 99, 100, 114, 121, 144, 146, 263, 272], "execut": [5, 6, 7, 8, 11, 15, 16, 20, 21, 22, 23, 25, 42, 43, 45, 50, 60, 61, 76, 78, 82, 98, 108, 120, 121, 125, 126, 134, 135, 136, 143, 144, 147, 149, 155, 159, 160, 162, 163, 164, 172, 173, 174, 176, 177, 182, 183, 185, 186, 187, 194, 203, 204, 206, 208, 212, 223, 224, 225, 230, 252], "kernel": [5, 6, 8, 13, 17, 18, 23, 47, 83, 108, 110, 121, 138, 144, 147, 149, 153, 154, 156, 164, 165, 168, 172, 176, 177, 184, 186, 199, 201, 216, 253], "involv": [5, 8, 9, 15, 16, 17, 19, 23, 25, 50, 60, 85, 98, 101, 105, 112, 120, 125, 127, 132, 139, 146, 152, 163, 165, 173, 174, 182, 184, 193, 201], "launch": [5, 6, 21, 53, 61, 115, 126, 132, 133, 149, 161, 162, 163, 164, 168, 176, 206, 221, 226], "amount": [5, 19, 25, 73, 82, 112, 124, 133, 156, 172, 184, 223], "becom": [5, 6, 11, 21, 24, 52, 61, 73, 75, 78, 85, 97, 124, 130, 145, 147, 168, 169, 176, 186, 193, 197, 216, 252], "furthermor": [5, 19, 97, 138, 145, 169, 176, 186, 200, 224, 225], "interpret": [5, 6, 23, 25, 40, 56, 82, 85, 103, 121, 127, 128, 137, 165, 172, 173, 174, 186, 196, 216, 229, 235, 251, 263, 272], "slow": [5, 6, 8, 42, 123, 148, 176, 228], "down": [5, 8, 10, 11, 16, 19, 42, 50, 82, 87, 99, 104, 123, 136, 144, 145, 146, 162, 166, 169, 176, 189, 261, 270], "therefor": [5, 6, 9, 15, 19, 49, 51, 60, 97, 108, 112, 113, 115, 120, 133, 134, 147, 150, 155, 156, 162, 163, 173, 174, 176, 191, 192, 200, 206, 223, 263, 272], "rewrit": [5, 21, 45, 60, 107, 129, 153, 173, 174, 200, 205, 206, 252], "fuse": [5, 17, 19, 121, 144, 157, 158, 176, 177, 179, 181, 182, 184, 194, 198, 201, 206, 227, 247, 252], "group": [5, 7, 11, 16, 18, 19, 24, 49, 61, 83, 109, 113, 120, 121, 122, 123, 128, 129, 131, 133, 134, 135, 144, 168, 175, 178, 214, 215, 216, 259, 263, 264, 272, 273], "profit": 5, "fewer": [5, 11, 129, 145], "visibl": [5, 22, 23, 44, 87, 171, 182], "aten": [5, 8, 10, 15, 23, 42, 109, 144, 168, 173, 174, 177, 182, 185, 186, 188, 197, 198, 199, 219, 220, 226, 238, 244, 246, 253], "translat": [5, 23, 25, 49, 60, 105, 107, 116, 118, 150, 165, 187, 191, 252], "benefit": [5, 6, 9, 17, 18, 42, 43, 85, 87, 119, 122, 141, 147, 152, 157, 164, 176, 184, 197, 201, 216, 220, 258], "massiv": [5, 25, 44, 101, 103, 220], "parallel": [5, 6, 11, 16, 18, 44, 46, 49, 51, 52, 55, 73, 79, 87, 112, 121, 126, 131, 135, 137, 144, 150, 159, 161, 162, 163, 175, 176, 214, 215, 216, 230, 240, 247, 259], "ahead": [5, 22, 152, 169, 173, 174, 179, 188, 214], "cpp_extens": [5, 10, 23, 155, 208, 231], "setup": [5, 6, 7, 10, 16, 19, 22, 42, 52, 53, 55, 122, 123, 126, 133, 148, 149, 152, 155, 158, 163, 184, 188, 191, 192, 204, 205, 206, 214, 215, 231, 246], "lltm_cpp": 5, "ext_modul": [5, 10, 23, 155], "cppextens": [5, 10, 23, 155], "cmdclass": [5, 10, 23, 155], "build_ext": [5, 10, 23, 155], "buildextens": [5, 10, 23, 155], "conveni": [5, 8, 14, 22, 23, 44, 47, 49, 101, 125, 127, 128, 138, 145, 155, 159, 190, 192, 220, 252], "wrapper": [5, 6, 8, 16, 55, 112, 122, 123, 130, 136, 146, 159, 171, 196, 199, 240], "include_dir": [5, 10, 155], "include_path": 5, "manag": [5, 22, 43, 49, 61, 108, 109, 113, 120, 124, 131, 132, 133, 149, 153, 164, 168, 177, 214, 215, 216, 232, 258], "And": [5, 6, 10, 22, 23, 24, 25, 52, 101, 103, 105, 113, 130, 144, 145, 147, 150, 158, 168, 169, 172, 173, 174, 176, 177, 195, 199, 200, 213], "overal": [5, 19, 42, 49, 122, 123, 135, 149, 160, 171, 197, 200, 228], "d_sigmoid": 5, "bit": [5, 12, 15, 23, 25, 51, 68, 95, 109, 113, 117, 136, 148, 158, 159, 160, 165, 184, 189, 197, 199, 207, 221, 228], "pybind11": [5, 8, 22, 23, 155, 231], "datatyp": [5, 23, 40, 48, 109, 220], "Its": [5, 97, 99, 193, 263, 272], "inspect": [5, 23, 78, 97, 108, 122, 126, 143, 164, 166, 172, 173, 174, 182, 185, 216], "notic": [5, 8, 14, 21, 22, 23, 25, 32, 42, 43, 44, 52, 60, 73, 85, 97, 99, 112, 130, 135, 144, 146, 149, 153, 154, 157, 159, 168, 172, 173, 174, 176, 177, 188, 189, 191, 195], "dispos": 5, "nvcc": 5, "workaround": [5, 7, 23, 79, 85, 130, 141], "logic": [5, 6, 11, 17, 23, 85, 98, 123, 126, 132, 134, 156, 162, 163, 171, 177, 183, 201, 202, 214, 216], "sigmoidalphablendforwardcuda": 5, "port": [5, 16, 135, 162], "entir": [5, 6, 14, 16, 18, 19, 25, 47, 49, 53, 60, 78, 97, 99, 102, 117, 121, 122, 123, 127, 129, 134, 142, 149, 152, 154, 156, 157, 159, 163, 165, 176, 182, 189, 190, 191, 194, 197, 198, 208, 214, 263, 272], "lltm_forward": 5, "addmm": [5, 6, 109, 144, 173, 188, 197, 206, 207, 219, 238, 253], "transpos": [5, 6, 7, 12, 40, 44, 48, 49, 51, 52, 60, 90, 92, 94, 96, 110, 117, 118, 129, 144, 146, 153, 157, 160, 164, 166, 169, 173, 174, 193, 206, 229], "respect": [5, 16, 32, 34, 43, 49, 51, 52, 63, 64, 68, 69, 71, 72, 99, 101, 111, 114, 115, 124, 125, 134, 136, 141, 143, 144, 145, 154, 159, 163, 165, 168, 173, 174, 176, 177, 198, 212, 249], "ultim": [5, 19, 49, 52, 60, 85, 189], "plop": [5, 23], "autograd": [5, 12, 13, 15, 16, 21, 25, 32, 40, 42, 46, 47, 57, 59, 61, 62, 68, 69, 77, 78, 81, 91, 93, 98, 100, 101, 104, 109, 110, 119, 121, 127, 128, 129, 130, 133, 134, 144, 145, 150, 154, 160, 161, 162, 165, 177, 191, 200, 205, 208, 212, 216, 226, 230, 247, 255], "nice": [5, 12, 49, 80, 143, 152, 154, 159], "dig": [5, 99, 103, 164], "deeper": [5, 11, 12, 95, 97, 99, 143, 144, 164, 177, 211], "interest": [5, 6, 10, 14, 17, 20, 23, 25, 44, 49, 51, 58, 59, 78, 87, 97, 99, 105, 107, 108, 113, 117, 122, 126, 145, 152, 153, 157, 159, 166, 173, 174, 176, 178, 201, 205, 263, 272], "alex": 5, "grave": 5, "thesi": 5, "d_tanh": 5, "relu": [5, 6, 12, 19, 20, 23, 25, 37, 38, 44, 47, 52, 73, 78, 87, 89, 92, 93, 94, 96, 97, 99, 103, 104, 105, 110, 112, 123, 133, 134, 138, 144, 146, 148, 149, 150, 154, 156, 157, 158, 160, 161, 162, 163, 165, 166, 169, 172, 173, 174, 177, 181, 182, 200, 203, 205, 214, 215, 218, 219, 220, 221, 223, 230, 233, 239, 240, 241, 242, 243, 247, 248, 249, 250, 252, 253], "exp": [5, 7, 9, 65, 89, 98, 99, 104, 111, 118, 125, 130, 141, 160, 191], "d_elu": 5, "mask": [5, 17, 58, 75, 90, 109, 118, 121, 136, 153, 156, 160, 164, 171, 178, 184, 189, 190, 192, 193, 196, 201, 220, 255], "type_a": [5, 118], "lltm_backward": 5, "grad_h": 5, "grad_cel": 5, "d_output_g": 5, "d_tanh_new_cel": 5, "d_new_cel": 5, "d_old_cel": 5, "d_candidate_cel": 5, "d_input_g": 5, "d_gate": 5, "d_weight": 5, "d_bia": 5, "keepdim": [5, 13, 19, 73, 123, 129, 162, 166, 182, 197, 198, 221], "d_x": [5, 52], "d_old_h": 5, "d_input": 5, "span": [5, 17, 75, 98, 133, 149, 168, 201, 264, 273], "four": [5, 7, 14, 18, 22, 61, 67, 85, 94, 95, 108, 111, 115, 119, 122, 134, 135, 149, 169, 223, 228, 258, 263, 264, 272, 273], "pybind11_modul": [5, 155], "torch_extension_nam": [5, 155], "macro": [5, 6, 8, 15, 23], "maintain": [5, 10, 14, 22, 43, 49, 61, 73, 97, 102, 108, 121, 131, 135, 146, 160, 163, 176, 177, 194, 259], "mismatch": [5, 61, 97], "nasti": 5, "hard": [5, 6, 8, 15, 21, 99, 126, 156], "At": [5, 6, 8, 14, 15, 17, 20, 23, 43, 47, 49, 50, 85, 87, 102, 116, 122, 123, 124, 136, 141, 146, 157, 159, 160, 161, 164, 165, 168, 189, 192, 197, 201, 206, 223, 262, 271], "point": [5, 6, 8, 10, 11, 14, 17, 18, 19, 20, 22, 23, 43, 47, 49, 50, 51, 52, 53, 58, 60, 82, 85, 97, 98, 100, 101, 102, 103, 110, 123, 124, 125, 126, 130, 131, 133, 143, 146, 149, 150, 157, 159, 161, 165, 169, 171, 173, 174, 181, 182, 184, 185, 189, 191, 192, 194, 197, 200, 201, 208, 221, 223, 228, 261, 262, 263, 270, 271, 272], "bdist_egg": 5, "egg_info": [5, 23], "egg": [5, 23], "pkg": [5, 23, 258], "dependency_link": [5, 23], "top": [5, 6, 8, 17, 19, 20, 22, 23, 38, 50, 51, 52, 82, 83, 94, 96, 97, 115, 124, 127, 135, 139, 143, 157, 158, 163, 164, 168, 169, 176, 178, 182, 197, 198, 199, 201, 209, 227, 253, 257, 261, 265, 270, 274], "top_level": [5, 23], "manifest": [5, 23, 191, 194], "bdist": 5, "x86_64": [5, 18, 23, 204, 208], "install_lib": 5, "temp": [5, 9, 19, 23, 125, 137, 181, 182, 185, 197, 198, 234], "gcc": [5, 23, 108, 144], "miniconda": [5, 18, 23], "compiler_compat": [5, 23], "wl": [5, 22, 23], "sysroot": [5, 23], "wsign": [5, 23], "dndebug": [5, 23], "g": [5, 6, 7, 8, 10, 11, 12, 14, 18, 23, 25, 42, 43, 49, 51, 52, 60, 61, 79, 87, 89, 97, 99, 100, 103, 108, 110, 117, 121, 123, 126, 127, 128, 133, 135, 137, 138, 144, 152, 154, 155, 159, 161, 163, 165, 168, 173, 174, 176, 179, 182, 185, 186, 192, 196, 200, 205, 206, 215, 258, 263, 272], "fwrapv": [5, 23], "o3": [5, 23, 231], "wall": [5, 23, 98, 143, 246], "wstrict": [5, 23], "prototyp": [5, 10, 11, 15, 23, 61, 113, 173, 174, 186, 193, 194, 200, 205, 206, 212], "fpic": [5, 23, 108], "site": [5, 18, 22, 23, 50, 52, 58, 59, 142, 143, 165, 187, 224, 225, 227, 238, 246, 258, 263, 272], "csrc": [5, 22, 23, 155, 188, 222], "th": [5, 14, 23, 51, 99, 103, 135, 146], "thc": [5, 23], "7m": [5, 23], "dtorch_api_include_extension_h": [5, 23], "dtorch_extension_nam": [5, 23], "d_glibcxx_use_cxx11_abi": [5, 23], "cc1plu": [5, 23], "warn": [5, 19, 23, 42, 51, 137, 144, 148, 159, 164, 171, 172, 173, 174, 182, 185, 187, 189, 190, 191, 192, 197, 198, 216, 231, 252], "valid": [5, 7, 9, 13, 17, 20, 23, 24, 49, 87, 94, 97, 104, 107, 109, 112, 113, 115, 117, 118, 122, 123, 126, 129, 130, 148, 155, 157, 171, 178, 181, 182, 190, 191, 200, 201, 204, 222], "objc": [5, 23], "l": [5, 7, 12, 14, 20, 23, 43, 49, 51, 52, 89, 99, 108, 110, 127, 128, 159, 160, 165, 173, 193, 194, 231], "rpath": [5, 23], "cpython": [5, 23], "37m": [5, 23], "stub": [5, 157, 209, 223, 264, 273], "loader": [5, 6, 12, 24, 38, 44, 79, 159, 162, 178, 222, 231], "byte": [5, 137, 139, 174, 181, 208, 230], "37": [5, 7, 17, 109, 158, 163, 177, 201, 238], "pyc": 5, "native_lib": 5, "zip_saf": 5, "analyz": [5, 19, 23, 60, 82, 121, 152, 159, 172, 174, 185], "__pycache__": 5, "__file__": [5, 155, 231], "dist": [5, 7, 11, 16, 18, 110, 122, 123, 133, 135, 153, 155, 162, 175, 214, 215, 259], "py3": 5, "remov": [5, 9, 12, 17, 19, 49, 52, 53, 59, 60, 83, 109, 110, 114, 116, 125, 133, 137, 142, 144, 152, 158, 164, 165, 171, 172, 173, 174, 178, 181, 182, 183, 184, 185, 189, 190, 193, 194, 197, 198, 201, 204, 216, 228, 231, 234, 246, 252, 261, 270], "everyth": [5, 43, 87, 97, 99, 108, 126, 127, 130, 136, 139, 157, 158, 159, 160, 161, 165, 169, 187, 188, 212, 223, 246], "finish": [5, 6, 16, 23, 44, 45, 58, 87, 92, 94, 115, 133, 134, 135, 143, 149, 161, 163, 169, 188, 199, 204, 212, 218, 223, 250], "ubuntu": [5, 6, 168, 208], "16": [5, 7, 16, 17, 19, 23, 24, 44, 47, 52, 87, 89, 92, 93, 94, 96, 97, 104, 105, 112, 126, 133, 136, 141, 145, 147, 156, 157, 158, 163, 164, 169, 171, 172, 173, 174, 177, 178, 184, 187, 191, 194, 201, 204, 208, 214, 219, 231, 239, 240, 241, 242, 243, 248, 249, 250, 267, 276], "04": [5, 7, 118, 168, 219, 231, 253], "recent": [5, 49, 75, 102, 109, 113, 115, 124, 135, 137, 144, 150, 153, 157, 160, 168, 208], "maco": [5, 6, 105, 135, 171], "clang": [5, 204, 206], "worst": [5, 115, 137], "resolv": [5, 23, 97, 142, 147, 177, 191], "symbol": [5, 25, 115, 118, 142, 173, 174, 182, 200, 231, 246, 263, 272], "linker": [5, 23, 204], "pycapsul": [5, 23], "builtin": [5, 155], "arg0": 5, "arg1": [5, 162], "arg2": [5, 162], "arg3": 5, "arg4": 5, "citizen": [5, 23, 189, 191], "lltmfunction": 5, "staticmethod": [5, 13, 14, 64, 111, 129, 130, 141, 161, 171, 244], "contigu": [5, 7, 8, 9, 14, 147, 181, 194, 199, 209, 218, 223], "saved_tensor": [5, 13, 64, 111, 129, 130], "benchmark": [5, 15, 17, 24, 42, 109, 117, 121, 126, 137, 138, 144, 145, 154, 158, 164, 172, 176, 177, 184, 187, 193, 199, 201, 210, 220, 221, 235, 246, 247, 251], "measur": [5, 12, 21, 82, 97, 103, 123, 137, 143, 145, 149, 159, 160, 164, 166, 172, 176, 177, 184, 195, 199, 203, 212, 223, 231, 246, 253], "durat": [5, 83, 155, 160, 168, 177, 263, 272], "32": [5, 14, 17, 18, 19, 20, 21, 23, 24, 47, 52, 55, 73, 87, 92, 93, 97, 102, 105, 109, 123, 126, 129, 136, 137, 144, 145, 146, 147, 150, 154, 158, 162, 163, 164, 165, 166, 168, 171, 173, 174, 176, 177, 178, 198, 203, 219, 221, 228, 231, 233, 239, 247], "128": [5, 6, 12, 22, 51, 52, 55, 73, 87, 97, 103, 109, 118, 123, 124, 126, 127, 128, 129, 134, 135, 136, 137, 138, 144, 149, 154, 158, 160, 161, 162, 163, 165, 172, 178, 185, 200, 203, 207, 212, 220, 230, 231, 232, 233, 246, 254], "100000": [5, 58, 59, 127, 128, 137, 146, 231], "3f": [5, 9, 17, 19, 44, 87, 92, 115, 118, 146, 164, 178, 181, 193, 198, 201, 230, 250], "wrote": [5, 23, 139, 172, 178, 263, 272], "post": [5, 6, 11, 20, 49, 58, 59, 97, 121, 122, 123, 126, 137, 139, 147, 149, 166, 176, 177, 183, 185, 193, 196, 198, 200, 213, 221], "my": [5, 21, 42, 50, 98, 103, 191, 198, 203, 263, 272], "machin": [5, 6, 18, 20, 21, 25, 44, 49, 50, 51, 53, 54, 55, 56, 58, 59, 60, 61, 73, 87, 105, 107, 116, 118, 121, 122, 123, 126, 131, 132, 133, 134, 135, 143, 154, 158, 162, 163, 164, 165, 176, 178, 185, 194, 198, 203, 258], "506": 5, "480": [5, 238], "444": 5, "694": 5, "349": [5, 92], "335": [5, 147, 163, 259], "443": [5, 163, 238], "523": 5, "speedup": [5, 17, 21, 42, 44, 121, 138, 144, 149, 154, 177, 181, 182, 184, 193, 201, 223, 253], "30": [5, 6, 7, 14, 17, 19, 45, 82, 99, 115, 121, 122, 147, 156, 161, 163, 182, 192, 197, 201, 231, 232, 238], "albeit": [5, 14, 228], "major": [5, 10, 11, 19, 103, 117, 144, 152, 164, 172, 176, 177, 192, 216, 259], "particularli": [5, 13, 17, 53, 153, 165, 201], "engin": [5, 6, 14, 20, 43, 61, 87, 107, 119, 123, 158, 163, 171, 174, 178, 187, 205, 207, 220, 228, 261, 270], "wonder": [5, 99, 152], "abstract": [5, 11, 14, 51, 87, 95, 100, 103, 110, 113, 124, 126, 135, 142, 155, 159, 215, 264, 273], "correspondingli": 5, "big": [5, 42, 52, 98, 103, 128, 129, 138, 152, 159, 165, 171, 194], "win": [5, 115, 152, 185], "No": [5, 6, 49, 53, 60, 99, 144, 148, 179, 204, 211], "cuda_devic": 5, "creation": [5, 6, 10, 192, 202, 208], "assert": [5, 9, 11, 12, 17, 18, 19, 22, 51, 94, 95, 98, 105, 108, 125, 129, 133, 138, 141, 142, 144, 145, 150, 153, 154, 162, 164, 169, 172, 181, 193, 194, 200, 201, 205, 208, 209, 210, 230, 231, 244], "synchron": [5, 11, 16, 55, 56, 61, 82, 133, 135, 149, 159, 161, 162, 168, 172, 176, 177, 184, 193, 212, 230, 259], "1e6": [5, 9, 19, 137, 164, 181, 182, 185, 197, 198, 210, 228, 231, 259], "1e5": 5, "again": [5, 6, 9, 21, 25, 44, 50, 60, 78, 97, 98, 102, 103, 108, 113, 116, 119, 125, 129, 135, 136, 152, 161, 163, 165, 168, 171, 172, 176, 184, 197, 200, 223, 263, 272], "187": [5, 231], "719": 5, "410": [5, 147], "815": 5, "149": 5, "802": [5, 144], "393": [5, 177], "458": [5, 144], "That": [5, 6, 17, 23, 43, 44, 45, 49, 99, 101, 102, 103, 105, 108, 116, 124, 127, 134, 141, 143, 145, 147, 149, 150, 152, 159, 164, 168, 178, 189, 190, 192, 201, 223, 224, 263, 272], "great": [5, 49, 60, 105, 112, 113, 191, 197, 263, 272], "pull": [5, 7, 21, 143, 173, 174], "dive": [5, 6, 11, 23, 133, 144, 157], "elabor": [5, 6, 124, 144, 161], "fly": [5, 14, 23, 51, 98, 115, 159, 228], "background": [5, 6, 23, 58, 59, 73, 113, 158, 169, 171, 178, 263, 272], "tmp": [5, 23, 126, 129, 144, 171, 186, 218, 223, 228, 238], "torch_extens": 5, "emit": [5, 6, 98], "ninja": 5, "verbos": [5, 23, 132, 171, 177, 207, 208, 264, 273], "complic": [5, 14, 98, 99, 103, 126, 177, 197, 205, 209, 215, 252], "techniqu": [5, 9, 16, 17, 19, 21, 49, 60, 97, 98, 103, 107, 121, 124, 129, 130, 131, 143, 149, 153, 156, 157, 163, 171, 177, 184, 189, 193, 201, 203, 204, 228], "fine": [5, 6, 17, 19, 49, 51, 98, 113, 120, 125, 134, 135, 144, 157, 158, 168, 185, 188, 189, 201, 247], "system": [5, 6, 8, 10, 12, 14, 15, 22, 23, 25, 55, 76, 97, 121, 126, 135, 153, 158, 159, 161, 175, 176, 177, 178, 206, 208], "increment": [5, 11, 12, 60, 85, 101, 135, 146, 160], "thu": [5, 6, 8, 10, 19, 20, 21, 23, 49, 85, 87, 97, 108, 122, 138, 142, 152, 165, 177, 197, 202, 208, 216, 252, 263, 272], "didn": [5, 8, 22, 76, 105, 143, 161, 181, 205, 263, 272], "prospect": 5, "pointwis": [5, 8, 142, 147, 199], "declar": [5, 6, 13, 23, 60, 73, 78, 115, 208, 223, 252], "cu": 5, "ensur": [5, 8, 9, 10, 11, 12, 14, 15, 16, 19, 22, 37, 49, 53, 56, 60, 64, 97, 109, 111, 112, 115, 116, 123, 132, 135, 141, 159, 160, 162, 164, 171, 176, 178, 186, 194, 198, 202, 231, 241], "lltm_cuda": 5, 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162, 171, 179, 185, 190, 198, 241, 264, 273], "explicitli": [5, 6, 18, 25, 43, 48, 52, 53, 60, 87, 101, 124, 132, 136, 147, 163, 164, 166, 173, 176, 177, 182, 192, 193, 200], "zeros_lik": [5, 14, 95, 142, 161, 216, 255], "dim3": 5, "at_dispatch_floating_typ": 5, "lltm_forward_cuda": 5, "lltm_cuda_forward_kernel": 5, "indic": [5, 6, 10, 11, 14, 16, 23, 49, 60, 63, 64, 82, 98, 99, 102, 103, 108, 109, 110, 111, 115, 116, 118, 126, 127, 136, 137, 144, 156, 159, 160, 162, 168, 169, 171, 176, 177, 178, 185, 189, 191, 192, 195, 200, 257, 261, 263, 270, 272], "runtim": [5, 8, 14, 18, 21, 23, 25, 40, 50, 60, 82, 85, 107, 109, 121, 124, 129, 143, 152, 168, 172, 176, 177, 181, 184, 185, 197, 206, 207, 210], "back": [5, 6, 8, 10, 14, 15, 19, 20, 22, 23, 44, 47, 51, 58, 59, 60, 73, 80, 87, 97, 98, 101, 105, 109, 113, 125, 127, 139, 143, 147, 149, 152, 154, 159, 161, 162, 163, 165, 171, 176, 188, 189, 193, 194, 263, 272], "determin": [5, 6, 8, 11, 17, 19, 48, 49, 97, 98, 101, 102, 103, 105, 124, 126, 138, 142, 152, 153, 154, 156, 160, 172, 182, 193, 201], "conceptu": [5, 6, 43, 49, 60, 177], "scalartyp": 5, "messag": [5, 49, 108, 135, 137, 171, 173, 174, 185, 208, 222, 225, 252], "alia": [5, 10, 64, 111, 173, 174], "retriev": [5, 6, 7, 14, 16, 21, 49, 125, 126, 146, 159, 161, 162, 177, 209], "at_dispatch_all_typ": 5, "sens": [5, 8, 12, 14, 97, 103, 113, 126, 138, 169, 263, 272], "routin": [5, 6, 23], "convolut": [5, 6, 8, 12, 13, 20, 47, 52, 60, 97, 112, 117, 119, 121, 147, 150, 156, 157, 166, 176, 177, 182, 199, 200, 206, 207, 220, 223, 226, 238, 252], "harder": [5, 97, 184, 185], "ourselv": [5, 6, 49, 76, 129, 159], "grid": [5, 47, 51, 117, 149, 157, 166, 169, 186, 255], "fill": [5, 6, 14, 80, 103, 127, 136, 141, 176, 190, 191, 208, 223], "matric": [5, 12, 17, 23, 25, 48, 101, 145, 153, 201], "2048": [5, 18, 97, 129, 145], "heard": 5, "introductori": [5, 79], "fairli": [5, 97, 113, 135, 152, 160], "ever": [5, 6, 23, 125, 173, 174], "__global__": 5, "__restrict__": 5, 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195, 197, 205, 252, 263, 272], "inde": [5, 14, 58, 59, 97, 129, 145, 159, 164, 172, 191, 192], "agnost": [5, 60, 110, 232], "ineffici": [5, 82, 176, 193], "readabl": [5, 25, 51, 98, 110, 128, 139, 168, 213], "especi": [5, 17, 19, 49, 52, 60, 73, 113, 122, 133, 143, 150, 152, 173, 174, 177, 184, 190, 199, 201, 221, 223, 228], "dimension": [5, 47, 48, 49, 52, 60, 97, 100, 101, 102, 103, 113, 124, 147, 156, 164, 165, 169, 171, 192, 215, 223], "stride": [5, 6, 19, 52, 90, 97, 104, 113, 123, 129, 134, 144, 146, 147, 166, 171, 177, 179, 192, 218, 229, 237, 244], "row": [5, 18, 23, 34, 40, 51, 73, 80, 99, 101, 102, 103, 116, 124, 126, 127, 145, 157, 160, 161, 176, 177, 190, 192, 205, 208, 231, 264, 273], "arithmet": [5, 19, 143, 185], "fortun": [5, 6, 10, 15, 23, 87, 135, 136], "expos": [5, 6, 8, 22, 23, 108, 113, 121, 139, 163, 181, 197, 198, 206, 208], "foo": [5, 21, 22, 141, 142, 153, 162, 172, 174, 182, 197, 202, 209, 246, 263, 264, 272, 273], "12": [5, 7, 23, 42, 58, 59, 92, 101, 109, 122, 123, 144, 149, 161, 169, 173, 178, 179, 184, 190, 193, 200, 201, 204, 208, 221, 222, 225, 227, 231, 258, 263, 267, 272, 276], "hold": [5, 14, 16, 18, 47, 60, 63, 64, 65, 67, 68, 69, 76, 78, 87, 98, 111, 122, 123, 132, 134, 139, 152, 160, 161, 163], "foo_a": 5, "packed_accessor64": 5, "packed_accessor32": 5, "pack": [5, 49, 60, 78, 115, 144, 159, 161, 163, 193, 208, 212, 223, 252], "integ": [5, 6, 8, 60, 97, 99, 101, 103, 113, 115, 126, 146, 156, 173, 174, 178, 184, 192, 197, 198, 199, 207, 228, 266, 275], "fundament": [5, 49, 91, 101, 103, 146, 190, 198, 214], "packedtensoraccessor32": 5, "restrictptrtrait": 5, "decompos": [5, 10, 17, 123, 149, 173, 174, 197, 201], "packedaccessor32": 5, "variant": [5, 49, 60, 124, 129, 158, 164], "int32_t": 5, "packedaccessor64": 5, "slower": [5, 17, 56, 133, 145, 149, 158, 160, 172, 176, 178, 184, 186, 193, 201], "host": [5, 7, 16, 18, 54, 82, 98, 122, 123, 124, 133, 134, 135, 149, 162, 163, 168, 215, 216], "reshap": [5, 6, 9, 12, 19, 51, 95, 103, 104, 110, 118, 142, 159, 181, 182, 188, 189, 190, 191, 193, 197, 198, 206, 221, 231], "pattern": [5, 17, 21, 103, 124, 135, 142, 144, 153, 162, 177, 182, 183, 185, 189, 191, 197, 201, 202, 215, 220], "lltm_cuda_backward_kernel": 5, "lltm_backward_cuda": 5, "d_gate_weight": 5, "cudaextens": [5, 155], "hassl": [5, 6], "entail": 5, "simpler": [5, 78, 124, 129, 141, 153, 197, 205], "fastest": [5, 149, 164], "129": [5, 109, 187], "431": 5, "304": [5, 49, 177], "641": [5, 147], "faq": [5, 22, 23], "sit": [6, 42, 105, 107, 108, 149, 153, 262, 264, 266, 271, 273, 275], "atop": 6, "substanti": [6, 126], "codebas": [6, 10, 14], "foundat": [6, 159, 171], "underli": [6, 8, 14, 23, 48, 73, 80, 95, 97, 112, 126, 138, 144, 149, 154, 162, 182, 191, 192, 193, 215, 216], "popular": [6, 68, 73, 75, 97, 111, 126, 136, 137, 177, 184, 220, 221, 262, 271], "stochast": [6, 7, 47, 52, 65, 104, 111, 115, 135, 159, 160], "descent": [6, 7, 43, 47, 63, 64, 65, 68, 72, 104, 110, 111, 115, 135, 184], "digit": 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13, 44, 98, 101, 102, 103, 139, 148, 158, 181, 182, 184, 263, 264, 272, 273], "serv": [6, 37, 57, 61, 85, 97, 102, 121, 127, 133, 139, 143, 146, 149, 155, 162, 163, 176, 177, 191, 212, 213, 258], "web": [6, 105, 213, 251, 263, 272], "server": [6, 16, 25, 61, 119, 120, 121, 127, 133, 149, 155, 163, 177, 179, 185, 194, 214, 216, 220, 228], "3d": [6, 7, 75, 93, 101, 102, 115, 171, 197, 200], "graphic": [6, 164, 206], "photo": [6, 229], "softwar": [6, 137, 149, 155, 168, 176, 206, 213, 263, 272], "integr": [6, 10, 14, 23, 78, 87, 109, 121, 126, 139, 169, 176, 177, 179, 199, 200, 206, 229], "remain": [6, 7, 87, 97, 119, 135, 142, 152, 156, 165, 179, 184, 191, 193, 195, 199, 209], "forth": [6, 149, 176, 264, 273], "retain": [6, 40, 48, 76, 80, 112], "intuit": [6, 52, 73, 78, 99, 108, 112, 144, 149, 165, 171, 190], "tradit": [6, 42, 52, 97, 99, 107, 145, 162], "compet": [6, 113, 115, 119, 126, 176, 177], "complement": 6, "alik": 6, "love": [6, 113], "simplic": [6, 73, 122, 129, 159, 160, 215, 222], "core": [6, 8, 10, 11, 42, 45, 60, 76, 98, 99, 100, 104, 108, 112, 115, 121, 124, 126, 135, 136, 144, 147, 158, 168, 173, 174, 177, 194, 196, 197, 204, 222, 246, 247], "principl": [6, 8, 102, 103, 121, 126], "curiou": [6, 138, 152, 154, 211], "tri": [6, 18, 52, 98, 103, 113, 160, 206], "experienc": [6, 85], "ask": [6, 17, 22, 23, 60, 103, 128, 136, 159, 201, 209], "rememb": [6, 44, 52, 59, 73, 76, 98, 99, 102, 109, 112, 139, 145, 146, 152, 165], "dot": [6, 14, 32, 49, 52, 60, 102, 103, 121, 145, 165, 176, 177, 205, 231], "colon": [6, 171, 263, 272], "minim": [6, 10, 12, 17, 23, 52, 61, 63, 64, 67, 68, 69, 73, 82, 99, 103, 121, 122, 126, 132, 137, 144, 160, 172, 182, 185, 189, 199, 201, 204, 209], "verifi": [6, 20, 58, 59, 85, 108, 114, 116, 119, 130, 138, 141, 142, 145, 147, 156, 158, 176, 177, 178, 206, 220], "too": [6, 10, 14, 19, 44, 64, 68, 82, 87, 97, 98, 103, 109, 111, 124, 127, 133, 139, 149, 152, 156, 160, 161, 163, 165, 189, 228, 261, 263, 264, 270, 272, 273], "cu90": 6, "url": [6, 19, 104, 118, 168, 172, 184, 208, 222, 236, 261, 270], "wget": [6, 18, 75, 178, 181, 184, 208], "nightli": [6, 18, 75, 122, 137, 141, 172, 175, 178, 184, 187, 188, 196, 197, 199, 204, 205, 208, 221], "dep": 6, "tini": [6, 123, 149, 152], "three": [6, 10, 12, 14, 15, 51, 52, 61, 64, 73, 82, 85, 92, 95, 98, 108, 112, 113, 115, 118, 121, 124, 128, 135, 147, 153, 155, 159, 163, 164, 165, 166, 168, 169, 175, 176, 177, 184, 192, 199, 228, 263, 264, 272, 273], "ey": [6, 20, 23, 32, 103, 145, 150, 153, 205, 208, 263, 272], "fledg": 6, "visual": [6, 14, 25, 43, 52, 73, 75, 82, 83, 90, 116, 121, 122, 125, 126, 130, 143, 153, 159, 168, 169, 171, 176, 177, 186, 191, 229, 251], "studio": [6, 58, 204, 222, 224, 227], "qmake": 6, "makefil": 6, "comfort": 6, "box": [6, 8, 10, 11, 17, 73, 113, 126, 130, 135, 146, 158, 161, 168, 176, 177, 178, 199, 201, 205, 220, 225, 263, 272], "cmake_prefix_path": [6, 22, 23], "invok": [6, 8, 22, 23, 25, 97, 133, 134, 141, 143, 144, 155, 162, 163, 177, 186, 188, 191, 199, 203], "agre": [6, 23, 137, 259], "break": [6, 8, 10, 11, 12, 19, 38, 49, 51, 60, 75, 82, 112, 113, 116, 118, 128, 139, 146, 152, 156, 160, 161, 163, 165, 168, 172, 189, 236, 263, 272], "unexpect": [6, 51, 191, 195, 200], "pwd": [6, 208, 226], "fa350df05ecf": 6, "home": [6, 48, 113, 189, 194, 204, 206, 209, 217, 222], "enter": [6, 49, 60, 122, 133, 135], "ran": [6, 103, 159, 162, 164, 173, 174, 204, 231], "me": [6, 17, 42, 49, 99, 152, 165, 201, 263, 264, 272, 273], "extens": [6, 7, 15, 22, 23, 52, 112, 121, 127, 130, 135, 171, 176, 186, 189, 191, 197, 199, 221, 251], "besid": [6, 23, 103, 153, 161, 221, 263, 272], "encapsul": [6, 47, 152, 161], "buffer": [6, 14, 44, 47, 59, 69, 76, 110, 111, 112, 122, 129, 133, 138, 141, 146, 150, 152, 153, 154, 156, 160, 173, 174, 177, 188, 202, 206, 208, 223], "nest": [6, 14, 43, 110, 142, 159, 161, 163, 164, 196, 263, 264, 272, 273], "similarli": [6, 9, 12, 22, 51, 60, 80, 83, 113, 116, 125, 126, 134, 143, 144, 145, 152, 179, 192, 214], "w": [6, 7, 9, 12, 32, 38, 43, 47, 49, 51, 73, 76, 98, 102, 103, 122, 125, 129, 137, 146, 147, 163, 168, 178, 184, 185, 187, 222], "struct": [6, 15, 22, 208, 252], "int64_t": [6, 8, 23], "register_paramet": [6, 202], "reflect": [6, 8, 23, 48, 95, 136, 159, 195], "magic": [6, 99, 128, 135, 145], "behind": [6, 8, 17, 23, 45, 82, 97, 99, 129, 160, 179, 190, 192, 196, 201, 202, 263, 272], "scene": [6, 23, 58, 59, 113, 202], "another_bia": 6, "recurs": [6, 16, 44, 60, 110, 202, 218], "0808": 6, "8613": 6, "2017": [6, 73, 104, 118], "5206": 6, "5353": 6, "3740": 6, "0976": 6, "4786": 6, "4928": 6, "1434": 6, "4713": 6, "1735": 6, "3293": 6, "3467": 6, "3858": 6, "1980": 6, "1986": 6, "1975": 6, "4278": 6, "1831": 6, "2709": 6, "3730": 6, "4307": 6, "3236": 6, "0629": 6, "2038": 6, "4638": 6, "2023": [6, 171, 173], "1230": 6, "0516": 6, "aptli": [6, 13], "register_modul": 6, "dropout": [6, 7, 9, 12, 19, 20, 37, 39, 49, 60, 73, 93, 96, 97, 104, 110, 112, 118, 123, 124, 126, 128, 129, 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diff --git a/recipes_source/amx.rst b/recipes_source/amx.rst
index 459e7c5..e09b3e7 100644
--- a/recipes_source/amx.rst
+++ b/recipes_source/amx.rst
@@ -1,38 +1,31 @@
 ==============================================
-Leverage Intel® Advanced Matrix Extensions
+利用英特尔®高级矩阵扩展(Intel® Advanced Matrix Extensions)
 ==============================================
 
-Introduction
-============
+简介
+====
 
-Advanced Matrix Extensions (AMX), also known as Intel® Advanced Matrix Extensions (Intel® AMX), is an x86 extension,
-which introduce two new components: a 2-dimensional register file called 'tiles' and an accelerator of Tile Matrix Multiplication (TMUL) that is able to operate on those tiles.
-AMX is designed to work on matrices to accelerate deep-learning training and inference on the CPU and is ideal for workloads like natural-language processing, recommendation systems and image recognition.
+高级矩阵扩展(AMX)，也称为英特尔®高级矩阵扩展(Intel® AMX)，是一种x86扩展，引入了两个新组件:一个称为"tile"的二维寄存器文件和一个能够在这些tile上进行矩阵乘法(TMUL)的加速器。AMX旨在加速CPU上的深度学习训练和推理工作负载,非常适合自然语言处理、推荐系统和图像识别等工作负载。
 
-Intel advances AI capabilities with 4th Gen Intel® Xeon® Scalable processors and Intel® AMX, delivering 3x to 10x higher inference and training performance versus the previous generation, see `Accelerate AI Workloads with Intel® AMX`_.
-Compared to 3rd Gen Intel Xeon Scalable processors running Intel® Advanced Vector Extensions 512 Neural Network Instructions (Intel® AVX-512 VNNI),
-4th Gen Intel Xeon Scalable processors running Intel AMX can perform 2,048 INT8 operations per cycle, rather than 256 INT8 operations per cycle. They can also perform 1,024 BF16 operations per cycle, as compared to 64 FP32 operations per cycle, see page 4 of `Accelerate AI Workloads with Intel® AMX`_.
-For more detailed information of AMX, see `Intel® AMX Overview`_.
+英特尔通过第4代英特尔®至强®可扩展处理器和英特尔®AMX推进了AI能力,相比上一代产品,推理和训练性能提高了3倍至10倍,详见`使用Intel® AMX加速AI工作负载`_。与运行Intel®高级矢量扩展512神经网络指令(Intel® AVX-512 VNNI)的第3代英特尔至强可扩展处理器相比,运行Intel AMX的第4代英特尔至强可扩展处理器每周期可执行2,048个INT8操作,而不是256个INT8操作;它们还可以每周期执行1,024个BF16操作,而不是64个FP32操作,详见`使用Intel® AMX加速AI工作负载`_第4页。有关AMX的更多详细信息,请参阅`Intel® AMX概述`_。
 
-
-AMX in PyTorch
+PyTorch中的AMX
 ==============
 
-PyTorch leverages AMX for computing intensive operators with BFloat16 and quantization with INT8 by its backend oneDNN
-to get higher performance out-of-box on x86 CPUs with AMX support.
-For more detailed information of oneDNN, see `oneDNN`_.
+PyTorch通过其后端oneDNN利用AMX来计算BFloat16和INT8量化的计算密集型算子,从而在支持AMX的x86 CPU上获得更高的性能。
+有关oneDNN的更多详细信息,请参阅`oneDNN`_。
 
-The operation is fully handled by oneDNN according to the execution code path generated. For example, when a supported operation gets executed into oneDNN implementation on a hardware platform with AMX support, AMX instructions will be invoked automatically inside oneDNN.
-Since oneDNN is the default acceleration library for PyTorch CPU, no manual operations are required to enable the AMX support.
+操作完全由oneDNN根据生成的执行代码路径处理。例如,当支持的操作在支持AMX的硬件平台上执行到oneDNN实现时,AMX指令将在oneDNN内部自动调用。
+由于oneDNN是PyTorch CPU的默认加速库,因此无需手动操作即可启用AMX支持。
 
-Guidelines of leveraging AMX with workloads
+利用AMX加速工作负载的指南
 -------------------------------------------
 
-This section provides guidelines on how to leverage AMX with various workloads.
+本节提供了如何利用AMX加速各种工作负载的指南。
 
-- BFloat16 data type: 
+- BFloat16数据类型:
 
-  - Using ``torch.cpu.amp`` or ``torch.autocast("cpu")`` would utilize AMX acceleration for supported operators.
+  - 使用``torch.cpu.amp``或``torch.autocast("cpu")``将利用AMX加速支持的算子。
 
    ::
 
@@ -40,23 +33,23 @@ This section provides guidelines on how to leverage AMX with various workloads.
       with torch.cpu.amp.autocast():
          output = model(input)
 
-.. note:: Use ``torch.channels_last`` memory format to get better performance. 
+.. note:: 使用``torch.channels_last``内存格式可获得更好的性能。
 
-- Quantization:
+- 量化:
 
-  - Applying quantization would utilize AMX acceleration for supported operators.
+  - 应用量化将利用AMX加速支持的算子。
 
 - torch.compile:
 
-  - When the generated graph model runs into oneDNN implementations with the supported operators, AMX accelerations will be activated.
+  - 当生成的图模型运行到oneDNN实现的支持算子时,AMX加速将被激活。
 
-.. note:: When using PyTorch on CPUs that support AMX, the framework will automatically enable AMX usage by default. This means that PyTorch will attempt to leverage the AMX feature whenever possible to speed up matrix multiplication operations. However, it's important to note that the decision to dispatch to the AMX kernel ultimately depends on the internal optimization strategy of the oneDNN library and the quantization backend, which PyTorch relies on for performance enhancements. The specific details of how AMX utilization is handled internally by PyTorch and the oneDNN library may be subject to change with updates and improvements to the framework.
+.. note:: 在支持AMX的CPU上使用PyTorch时,框架将默认自动启用AMX使用。这意味着PyTorch将尽可能利用AMX功能来加速矩阵乘法操作。但是,重要的是要注意,是否调度到AMX内核最终取决于PyTorch所依赖的oneDNN库和量化后端的内部优化策略。PyTorch和oneDNN库内部如何处理AMX利用的具体细节可能会随着框架的更新和改进而发生变化。
 
 
-CPU operators that can leverage AMX:
+可利用AMX的CPU算子:
 ------------------------------------
 
-BF16 CPU ops that can leverage AMX:
+可利用AMX的BF16 CPU算子:
 
 - ``conv1d``
 - ``conv2d``
@@ -72,7 +65,7 @@ BF16 CPU ops that can leverage AMX:
 - ``linear``
 - ``matmul``
 
-Quantization CPU ops that can leverage AMX:
+可利用AMX的量化CPU算子:
 
 - ``conv1d``
 - ``conv2d``
@@ -84,10 +77,10 @@ Quantization CPU ops that can leverage AMX:
 
 
 
-Confirm AMX is being utilized
+确认AMX正在被利用
 ------------------------------
 
-Set environment variable ``export ONEDNN_VERBOSE=1``, or use ``torch.backends.mkldnn.verbose`` to enable oneDNN to dump verbose messages.
+设置环境变量``export ONEDNN_VERBOSE=1``或使用``torch.backends.mkldnn.verbose``以启用oneDNN转储详细消息。
 
 ::
 
@@ -95,7 +88,7 @@ Set environment variable ``export ONEDNN_VERBOSE=1``, or use ``torch.backends.mk
        with torch.cpu.amp.autocast():
            model(input)
 
-For example, get oneDNN verbose:
+例如,获取oneDNN详细输出:
 
 ::
 
@@ -111,24 +104,21 @@ For example, get oneDNN verbose:
    onednn_verbose,exec,cpu,matmul,brg:avx512_core_amx_int8,undef,src_s8::blocked:ab:f0 wei_s8:p:blocked:BA16a64b4a:f0 dst_s8::blocked:ab:f0,attr-scratchpad:user ,,1x30522:30522x768:1x768,7.66382
    ...
 
-If you get the verbose of ``avx512_core_amx_bf16`` for BFloat16 or ``avx512_core_amx_int8`` for quantization with INT8, it indicates that AMX is activated.
+如果你获得了``avx512_core_amx_bf16``的详细输出(用于BFloat16)或``avx512_core_amx_int8``(用于INT8量化),则表示AMX已被激活。
 
 
-Conclusion
+结论
 ----------
 
+在本教程中,我们简要介绍了AMX、如何在PyTorch中利用AMX来加速工作负载,以及如何确认AMX正在被利用。
 
-In this tutorial, we briefly introduced AMX, how to utilize AMX in PyTorch to accelerate workloads, and how to confirm that AMX is being utilized.
-
-With the improvements and updates of PyTorch and oneDNN, the utilization of AMX may be subject to change accordingly.
+随着PyTorch和oneDNN的改进和更新,AMX的利用情况可能会相应发生变化。
 
-As always, if you run into any problems or have any questions, you can use
-`forum <https://discuss.pytorch.org/>`_ or `GitHub issues
-<https://github.com/pytorch/pytorch/issues>`_ to get in touch. 
+如果您遇到任何问题或有任何疑问,您可以使用`论坛 <https://discuss.pytorch.org/>`_或`GitHub issues <https://github.com/pytorch/pytorch/issues>`_与我们联系。
 
 
-.. _Accelerate AI Workloads with Intel® AMX: https://www.intel.com/content/www/us/en/products/docs/accelerator-engines/advanced-matrix-extensions/ai-solution-brief.html
+.. _使用Intel® AMX加速AI工作负载: https://www.intel.com/content/www/us/en/products/docs/accelerator-engines/advanced-matrix-extensions/ai-solution-brief.html
 
-.. _Intel® AMX Overview: https://www.intel.com/content/www/us/en/products/docs/accelerator-engines/advanced-matrix-extensions/overview.html
+.. _Intel® AMX概述: https://www.intel.com/content/www/us/en/products/docs/accelerator-engines/advanced-matrix-extensions/overview.html
 
 .. _oneDNN: https://oneapi-src.github.io/oneDNN/index.html
diff --git a/recipes_source/compiling_optimizer.rst b/recipes_source/compiling_optimizer.rst
index 951495c..702dace 100644
--- a/recipes_source/compiling_optimizer.rst
+++ b/recipes_source/compiling_optimizer.rst
@@ -1,25 +1,21 @@
-(beta) Compiling the optimizer with torch.compile
+(beta) 使用 torch.compile 编译优化器
 ==========================================================================================
 
-**Author:** `Michael Lazos <https://github.com/mlazos>`_
+**作者:** `Michael Lazos <https://github.com/mlazos>`_
 
-The optimizer is a key algorithm for training any deep learning model.
-Since it is responsible for updating every model parameter, it can often
-become the bottleneck in training performance for large models. In this recipe, 
-we will apply ``torch.compile`` to the optimizer to observe the GPU performance 
-improvement.
+优化器是训练任何深度学习模型的关键算法。由于它负责更新每个模型参数,因此对于大型模型,它往往会成为训练性能的瓶颈。
+在本教程中,我们将在优化器使用 ``torch.compile`` ,提升在 GPU 上的性能。
 
 .. note::
 
-   This tutorial requires PyTorch 2.2.0 or later.
+   本教程需要 PyTorch 2.2.0 或更高版本。
 
-Model Setup
+模型设置
 ~~~~~~~~~~~~~~~~~~~~~
-For this example, we'll use a simple sequence of linear layers.
-Since we are only benchmarking the optimizer, the choice of model doesn't matter
-because optimizer performance is a function of the number of parameters.
+对于本例,我们将使用一个简单的线性层序列。由于我们只是对优化器进行基准测试,所选择的模型并不重要,
+因为优化器的性能与函数参数数量有关。
 
-Depending on what machine you are using, your exact results may vary.
+根据您使用的机器不同,结果可能会有所不同。
 
 .. code-block:: python
 
@@ -32,19 +28,17 @@ Depending on what machine you are using, your exact results may vary.
    output = model(input)
    output.sum().backward()
 
-Setting up and running the optimizer benchmark
+设置和运行优化器基准测试
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-In this example, we'll use the Adam optimizer
-and create a helper function to wrap the step()
-in ``torch.compile()``.
+在本例中,我们将使用 Adam 优化器,并创建一个辅助函数来将 step() 包装在 ``torch.compile()`` 中。
 
 .. note::
    
-   ``torch.compile`` is only supported on cuda devices with compute capability >= 7.0
+   ``torch.compile`` 仅支持device_capability >= 7.0 的 CUDA 设备
 
 .. code-block:: python
 
-   # exit cleanly if we are on a device that doesn't support torch.compile
+   # 如果我们在不支持 torch.compile 的设备上,则干净地退出
    if torch.cuda.get_device_capability() < (7, 0):
        print("Exiting because torch.compile is not supported on this device.")
        import sys
@@ -59,7 +53,7 @@ in ``torch.compile()``.
        opt.step()
    
    
-   # Let's define a helpful benchmarking function:
+   # 让我们定义一个有用的基准测试函数:
    import torch.utils.benchmark as benchmark
    
    
@@ -70,7 +64,7 @@ in ``torch.compile()``.
        return t0.blocked_autorange().mean * 1e6
 
 
-   # Warmup runs to compile the function
+   # 预热运行以编译函数
    for _ in range(5):
        fn()
    
@@ -82,13 +76,13 @@ in ``torch.compile()``.
    print(f"eager runtime: {eager_runtime}us")
    print(f"compiled runtime: {compiled_runtime}us")
 
-Sample Results:
+示例结果:
 
 * Eager runtime: 747.2437149845064us
 * Compiled runtime: 392.07384741178us
 
-See Also
+另请参阅
 ~~~~~~~~~
 
-* For an in-depth technical overview, see
-`Compiling the optimizer with PT2 <https://dev-discuss.pytorch.org/t/compiling-the-optimizer-with-pt2/1669>`__
+* 有关深入的技术概述,请参阅
+`使用 PT2 编译优化器 <https://dev-discuss.pytorch.org/t/compiling-the-optimizer-with-pt2/1669>`__
diff --git a/recipes_source/inference_tuning_on_aws_graviton.rst b/recipes_source/inference_tuning_on_aws_graviton.rst
index 08d3515..949fa30 100644
--- a/recipes_source/inference_tuning_on_aws_graviton.rst
+++ b/recipes_source/inference_tuning_on_aws_graviton.rst
@@ -1,38 +1,40 @@
-(Beta) PyTorch Inference Performance Tuning on AWS Graviton Processors
+
+
+(Beta) PyTorch在AWS Graviton处理器上的推理性能优化
 ======================================================================
 
-**Author**: `Sunita Nadampalli <https://github.com/snadampal>`_
+**作者**: `Sunita Nadampalli <https://github.com/snadampal>`_
 
-`AWS Graviton <https://aws.amazon.com/ec2/graviton/>`_ is a series of ARM-based processors designed by AWS. AWS Graviton3 processors are optimized for Machine Learning (ML) workloads, including support for ``bfloat16``, Scalable Vector Extension (SVE) and twice the Single Instruction Multiple Data (SIMD) bandwidth compared to Graviton2.
+`AWS Graviton <https://aws.amazon.com/ec2/graviton/>`_ 是一系列由AWS设计的基于ARM的处理器。AWS Graviton3处理器针对机器学习(ML)工作负载进行了优化,包括支持 ``bfloat16``、可扩展向量扩展(SVE)以及比Graviton2高两倍的单指令多数据(SIMD)带宽。
 
-PyTorch provides native reference ATen kernels for the machine learning operators like convolutions, matmul, relu, etc. These operators can be accelerated with platform specific kernel implementations from Basic Linear Algebra (BLAS) libraries. On AWS Graviton CPUs, MKLDNN with Arm Compute Library (`ACL <https://github.com/ARM-software/ComputeLibrary>`_) and `OpenBLAS <https://github.com/OpenMathLib/OpenBLAS>`_ libraries provide optimized implementations for a subset of the operators. Both these libraries are integrated into PyTorch with PyTorch 2.0 version.
+PyTorch为机器学习算子(如卷积、矩阵乘法、relu等)提供了原生参考ATen内核。这些算子可以通过来自基本线性代数(BLAS)库的特定于平台的内核实现进行加速。在AWS Graviton CPU上,MKLDNN与Arm Compute Library (`ACL <https://github.com/ARM-software/ComputeLibrary>`_) 和 `OpenBLAS <https://github.com/OpenMathLib/OpenBLAS>`_ 库为一部分算子提供了优化实现。从PyTorch 2.0版本开始,这两个库都集成到了PyTorch中。
 
-In this tutorial we will cover how to achieve the best inference performance for linear layer neural network on AWS Graviton3 CPUs (`AWS c7g instance <https://aws.amazon.com/ec2/instance-types/c7g/>`_) with ``bfloa16`` kernels and with the right backend selection.
+在本教程中,我们将介绍如何通过 ``bfloat16`` 内核和正确的后端选择,在AWS Graviton3 CPU (`AWS c7g实例 <https://aws.amazon.com/ec2/instance-types/c7g/>`_) 上实现线性层神经网络的最佳推理性能。
 
-Contents
+内容
 --------
-1. Basic Usage
-2. Speed up inference with Bfloat16 fast math kernels
-3. Improve inference performance with OpenBLAS for smaller batch dimensions
-4. Optimize memory allocation overhead with Linux Transparent huge pages
-5. Conclusion
+1. 基本用法
+2. 使用Bfloat16快速数学内核加速推理
+3. 对于较小的批次维度,使用OpenBLAS提高推理性能
+4. 使用Linux透明大页优化内存分配开销
+5. 总结
 
 .. note::
-   To successfully run this tutorial and reproduce the speedup numbers shown below, you need an instance from the Graviton3 family (``c7g/r7g/m7g``) of hardware. For this tutorial, we used the `c7g.xl (4vcpu) instance <https://aws.amazon.com/ec2/instance-types/c7g/>`_ .
+   要成功运行本教程并重现下面显示的加速数字,您需要来自Graviton3系列(``c7g/r7g/m7g``)的硬件实例。对于本教程,我们使用了 `c7g.xl (4vcpu)实例 <https://aws.amazon.com/ec2/instance-types/c7g/>`_ 。
 
-Basic Usage
+基本用法
 ---------------
 
-PyTorch natively supports AWS Graviton3 optimizations starting with PyTorch 2.0 version.
-Please refer to this `blog <https://pytorch.org/blog/optimized-pytorch-w-graviton/>`_ for more details on the optimizations.
+从PyTorch 2.0版本开始,PyTorch原生支持AWS Graviton3优化。
+更多详细信息请参阅此 `博客 <https://pytorch.org/blog/optimized-pytorch-w-graviton/>`_。
 
-1. Install PyTorch by running the following command:
+1. 运行以下命令安装PyTorch:
 
    .. code-block::
 
       python3 -m pip install torch
 
-2. We will start by importing the required dependencies and defining the device will run on:
+2. 我们将从导入所需的依赖项并定义将在其上运行的设备开始:
 
 .. code-block:: python
 
@@ -45,7 +47,7 @@ Please refer to this `blog <https://pytorch.org/blog/optimized-pytorch-w-gravito
     print(f"Using {device} device")
 
 
-3. Given linear layers are at the heart of several neural networks, including transformers, we take a linear layer for this demo. We define our neural network by subclassing ``nn.Module``, and initializing the layers in ``__init__``. We construct the network with a typical large language model parameters to match the real world scenario:
+3. 鉴于线性层是许多神经网络(包括Transformer)的核心,我们在此演示中使用线性层。我们通过子类化 ``nn.Module`` 并在 ``__init__`` 中初始化层来定义我们的神经网络。我们使用典型的大型语言模型参数构建网络,以匹配真实世界场景:
 
 .. code-block:: python
 
@@ -66,14 +68,14 @@ Please refer to this `blog <https://pytorch.org/blog/optimized-pytorch-w-gravito
         logits = self.linear_relu_stack(x)
         return logits
 
-4. Let's create an instance of ``MyNeuralNetwork``, and move it to the device:
+4. 让我们创建一个 ``MyNeuralNetwork`` 的实例,并将其移动到设备上:
 
 .. code-block:: python
 
     model = MyNeuralNetwork().to(device)
     print(model)
 
-Next, let's get the prediction probabilities by passing them through an instance of the ``nn.Softmax`` module:
+接下来,让我们通过将它们传递给 ``nn.Softmax`` 模块的实例来获取预测概率:
 
 .. code-block:: python
 
@@ -83,19 +85,19 @@ Next, let's get the prediction probabilities by passing them through an instance
     y_pred = pred_probab.argmax(1)
     print(f"Predicted class: {y_pred}")
 
-output:
+输出:
 
 .. code-block::
 
     Predicted class: tensor([2])
 
-Our network functionality is verified. Next, we will profile the performance. Lets' check two different scenarios: small and large batch dimensions.
+我们已验证了网络功能。接下来,我们将分析性能。让我们检查两种不同的情况:小批次维度和大批次维度。
 
-**Scenario 1:** A larger batch dimension, for example 256:
+**情况1:** 较大的批次维度,例如256:
 
 .. code-block:: python
 
-    # warm it up first and loop over multiple times to have enough execution time
+    # 首先进行预热,并循环多次以获得足够的执行时间
 
     X = torch.rand(256, 64, 64, device=device)
 
@@ -110,7 +112,7 @@ Our network functionality is verified. Next, we will profile the performance. Le
     print(prof.key_averages().table(sort_by="self_cpu_time_total"))
 
 
-Following is the profiler output with the default PyTorch configuration:
+使用默认PyTorch配置时的分析器输出如下:
 
 .. table::
    :widths: auto
@@ -130,17 +132,17 @@ Following is the profiler output with the default PyTorch configuration:
 **Self CPU time total:** 16.201s
 
 
-Speed up Inference with ``bfloat16`` Fast Math Kernels
+使用 ``bfloat16`` Fast Math Kernels加速推理
 ----------------------------------------------------------
 
-AWS Graviton3 processors support `bfloat16 MMLA instructions <https://developer.arm.com/documentation/ddi0596/2020-12/SVE-Instructions/BFMMLA--BFloat16-floating-point-matrix-multiply-accumulate->`_. Arm Compute Library (`ACL <https://github.com/ARM-software/ComputeLibrary>`_) provides optimized ``bfloat16`` General Matrix Multiplication (GEMM) kernels for AWS Graviton processors, and are integrated into PyTorch via MKLDNN backend starting with PyTorch 2.0.  The inference performance can be optimized with the fast math GEMM kernels. The fast math mode is not enabled by default because these kernels perform GEMM in ``bfloat16`` precision instead of ``float``, and hence results in a slight drop in the model inference accuracy. However, the accuracy drop is within the ``cosine similarity`` threshold defined for ``bfloat16`` backend in ``torchbench`` test suite, and hence acceptable for majority of the applications. To enable the fast math GEMM kernels, set the following environment variable:
+AWS Graviton3处理器支持 `bfloat16 MMLA指令 <https://developer.arm.com/documentation/ddi0596/2020-12/SVE-Instructions/BFMMLA--BFloat16-floating-point-matrix-multiply-accumulate->`_。Arm Compute Library (`ACL <https://github.com/ARM-software/ComputeLibrary>`_) 为AWS Graviton处理器提供了优化的 ``bfloat16`` 通用矩阵乘法(GEMM)内核,并从PyTorch 2.0版本开始通过MKLDNN后端集成到PyTorch中。可以使用快速数学GEMM内核优化推理性能。默认情况下不启用快速数学模式,因为这些内核以 ``bfloat16`` 精度而不是 ``float`` 执行GEMM,因此会导致模型推理精度略有下降。但是,精度下降在 ``torchbench`` 测试套件中为 ``bfloat16`` 后端定义的 ``余弦相似度`` 阈值范围内,因此对大多数应用程序来说是可以接受的。要启用快速数学GEMM内核,请设置以下环境变量:
 
 .. code-block:: bash
 
     $ export DNNL_DEFAULT_FPMATH_MODE=BF16
 
 
-When you run the above inference script, you should see the following profiler output with the MKLDNN fast math mode enabled:
+当您运行上述推理脚本时,应该会看到启用MKLDNN快速数学模式后的分析器输出:
 
 .. table::
    :widths: auto
@@ -160,16 +162,16 @@ When you run the above inference script, you should see the following profiler o
 **Self CPU time total:** 7.262s
 
 
-This is around ``2x (7.262s vs 16.201s)`` performance improvement with the ``bfloat16`` fastmath kernels. Next, let’s look at the smaller batch dimension scenario.
+这比默认配置快约 ``2倍 (7.262s vs 16.201s)``。接下来,让我们看看较小批次维度的情况。
 
-**Scenario 2:** A smaller batch dimension, for example, 32:
+**场景 2:** 较小的批量维度，例如 32:
 
 .. code-block:: python
 
     X = torch.rand(32, 64, 64, device=device)
     with torch.set_grad_enabled(False):
         for _ in range(50):
-            model(X) #Warmup
+            model(X) #预热
         with profile(activities=[ProfilerActivity.CPU]) as prof:
             with record_function("mymodel_inference"):
                 for _ in range(100):
@@ -178,13 +180,13 @@ This is around ``2x (7.262s vs 16.201s)`` performance improvement with the ``bfl
     print(prof.key_averages().table(sort_by="self_cpu_time_total"))
 
 
-You should see the following profiler output when the above script is run with the PyTorch default configuration:
+使用 PyTorch 默认配置运行上述脚本时，您应该会看到以下 profiler 输出:
 
 .. table::
    :widths: auto
 
    ======================  =============  ============  ============  ============  ============  ============
-                     Name    Self CPU %      Self CPU   CPU total %     CPU total   CPU time avg    # of Calls
+                     名称    自身 CPU %      自身 CPU   CPU 总计 %     CPU 总计   CPU 平均时间    调用次数
    ======================  =============  ============  ============  ============  ============  ============
            aten::addmm        95.51%         5.821s        97.04%        5.914s      19.713ms           300
        aten::clamp_min         2.33%      142.244ms         2.33%     142.244ms     711.220us           200
@@ -195,10 +197,10 @@ You should see the following profiler output when the above script is run with t
             aten::relu         0.03%        1.942ms         2.37%     144.186ms     720.930us           200
    ======================  =============  ============  ============  ============  ============  ============
 
-**Self CPU time total:** 6.094s
+**自身 CPU 总计:** 6.094s
 
 
-The following output is the profiler output when run with the MKLDNN fast math mode enabled:
+以下是启用 MKLDNN 快速数学模式时的 profiler 输出:
 
 .. code-block:: bash
 
@@ -208,7 +210,7 @@ The following output is the profiler output when run with the MKLDNN fast math m
    :widths: auto
 
    ======================  ============  ============  ============  ============  ============   =============
-                   Name     Self CPU %      Self CPU    CPU total %   CPU total    CPU time avg    # of Calls
+                   名称     自身 CPU %      自身 CPU    CPU 总计 %   CPU 总计    CPU 平均时间    调用次数
    ======================  ============  ============  ============  ============  ============   =============
            aten::addmm        93.31%        3.848s        95.66%        3.944s      13.148ms           300
        aten::clamp_min         3.43%     141.309ms         3.43%     141.309ms     706.545us           200
@@ -219,27 +221,29 @@ The following output is the profiler output when run with the MKLDNN fast math m
             aten::relu         0.05%       1.928ms         3.47%     143.237ms     716.185us           200
    ======================  ============  ============  ============  ============  ============   =============
 
-**Self CPU time total:** 4.123s
+**自身 CPU 总计:** 4.123s
 
-The MKLDNN fast math mode yields approximately a **1.47x  (4.123s vs 6.094s)**  performance improvement for smaller batch dimensions. Although this improvement is noteworthy, the overall performance still leaves room for improvement. This is because of the runtime overhead (weights reorders and kernel launch time) from oneDNN and ACL backend outweighing the compute benefits from the ACL GEMM kernels for the smaller batch compute.
+MKLDNN 快速数学模式为较小的批量维度提供了大约 **1.47x (4.123s vs 6.094s)** 的性能提升。
+尽管性能提升明显,但整体仍有提升空间。因为来自 oneDNN 和 ACL 后端的运行时开销(权重重排和内核启动时间)
+超过了 ACL GEMM 内核对较小批量计算的计算优势。
 
 
-Improve Inference Performance with OpenBLAS for Smaller Batch Dimensions
-------------------------------------------------------------------------
+使用 OpenBLAS 提高较小批量维度的推理性能
+----------------------------------------
 
-The inference performance for smaller batch dimensions can be improved by offloading the smaller shapes from MKLDNN to OpenBLAS backend. We are working on making the backend selection automatic, with robust heuristics, for the future releases. Till the heuristics are implemented, the smaller shapes can be offloaded to OpenBLAS by increasing the threshold for MKLDNN backend selection. In the following example, we use ``64`` as the threshold, so that input with ``batch dimension of 32`` is not dispatched to MKLDNN. Instead, it is dispatched to OpenBLAS.
+可以通过将较小的形状从 MKLDNN 卸载到 OpenBLAS 后端来提高较小批量维度的推理性能。我们正在努力为未来版本实现自动化的后端选择,并具有健壮的启发式算法。在实现启发式算法之前,可以通过增加 MKLDNN 后端选择的阈值将较小的形状卸载到 OpenBLAS。在以下示例中,我们使用 ``64`` 作为阈值,因此批量维度为 ``32`` 的输入不会分派到 MKLDNN。相反,它会被分派到 OpenBLAS。
 
 .. code-block:: bash
 
    $ export TORCH_MKLDNN_MATMUL_MIN_DIM=64
 
-Here is the profiler output with OpenBLAS backend:
+以下是使用 OpenBLAS 后端时的 profiler 输出:
 
 .. table::
    :widths: auto
 
    ======================  ============  ============  ============  =============  ============  =============
-                     Name    Self CPU %      Self CPU   CPU total %     CPU total   CPU time avg    # of Calls
+                     名称    自身 CPU %      自身 CPU   CPU 总计 %     CPU 总计   CPU 平均时间    调用次数
    ======================  ============  ============  ============  =============  ============  =============
            aten::addmm        96.25%        1.958s        97.51%        1.984s        6.612ms           300
        aten::clamp_min         1.28%      26.124ms         1.28%      26.124ms      130.620us           200
@@ -250,17 +254,18 @@ Here is the profiler output with OpenBLAS backend:
             aten::relu         0.06%       1.258ms         1.35%      27.382ms      136.910us           200
    ======================  ============  ============  ============  =============  ============  =============
 
-**Self CPU time total:** 2.034s
+**自身 CPU 总计:** 2.034s
 
 
-As you can see above, switching to OpenBLAS doubled the performance **(2.034s vs 4.123s)** compared to the default MKLDNN backend configuration. This becomes significant for even smaller batch dimensions, for example, for a batch dimension of 10:
+如您所见,切换到 OpenBLAS 将性能提高了一倍 **(2.034s vs 4.123s)** 与默认的 MKLDNN 后端配置相比。
+对于更小的批量维度,例如批量维度为 10,这一点更加显著:
 
 .. code-block:: python
 
     X = torch.rand(10, 64, 64, device=device)
     with torch.set_grad_enabled(False):
         for _ in range(50):
-            model(X) #Warmup
+            model(X) #预热
         with profile(activities=[ProfilerActivity.CPU]) as prof:
             with record_function("mymodel_inference"):
                 for _ in range(100):
@@ -269,13 +274,13 @@ As you can see above, switching to OpenBLAS doubled the performance **(2.034s vs
     print(prof.key_averages().table(sort_by="self_cpu_time_total"))
 
 
-The following is the profiler output with MKLDNN fast math mode:
+以下是启用 MKLDNN 快速数学模式时的 profiler 输出:
 
 .. table::
    :widths: auto
 
    ======================  ============  ============  ============  ============  =============  =============
-                     Name    Self CPU %      Self CPU   CPU total %     CPU total   CPU time avg    # of Calls
+                     名称    自身 CPU %      自身 CPU   CPU 总计 %     CPU 总计   CPU 平均时间    调用次数
    ======================  ============  ============  ============  ============  =============  =============
            aten::addmm        87.81%        3.613s        91.90%        3.781s      12.604ms           300
        aten::clamp_min         7.18%     295.437ms         7.18%     295.437ms       1.477ms           200
@@ -286,10 +291,10 @@ The following is the profiler output with MKLDNN fast math mode:
             aten::relu         0.05%       1.932ms         7.23%     297.369ms       1.487ms           200
    ======================  ============  ============  ============  ============  =============  =============
 
-**Self CPU time total:** 4.115s
+**自身 CPU 总计:** 4.115s
 
 
-and the following is the profiler output with the OpenBLAS backend:
+以下是使用 OpenBLAS 后端时的 profiler 输出:
 
 .. code-block:: bash
 
@@ -299,7 +304,7 @@ and the following is the profiler output with the OpenBLAS backend:
    :widths: auto
 
    ======================  =============  ============  ============  ============  =============  ============
-                   Name     Self CPU %      Self CPU     CPU total %   CPU total    CPU time avg    # of Calls
+                   名称     自身 CPU %      自身 CPU     CPU 总计 %   CPU 总计    CPU 平均时间    调用次数
    ======================  =============  ============  ============  ============  =============  ============
            aten::addmm        92.66%        1.179s        95.23%        1.211s         4.038ms           300
        aten::clamp_min         2.83%      36.060ms         2.83%      36.060ms       180.300us           200
@@ -310,29 +315,28 @@ and the following is the profiler output with the OpenBLAS backend:
             aten::relu         0.10%       1.285ms         2.94%      37.345ms       186.725us           200
    ======================  =============  ============  ============  ============  =============  ============
 
-**Self CPU time total:** 1.272s
-
+**自身 CPU 总计:** 1.272s
 
-Here we observed **3.2x (1.272s vs 4.115s)** performance improvement by tuning the backend thresholds appropriately.
+这里我们观察到通过适当调整后端阈值,**性能提高了3.2倍(1.272s vs 4.115s)**。
 
+使用 Linux Transparent Huge Pages (THP) 优化内存分配开销
+------------------------------------------------------
 
-Optimize Memory Allocation Overhead with Linux Transparent Huge Pages (THP)
----------------------------------------------------------------------------
-
-We also observed that for these larger networks, tensor memory allocations take significant portion of the inference latency. This can be optimized by enabling Linux transparent huge page allocations from PyTorch C10 memory allocator. Currently the feature is not enabled by default because it will increase the memory footprint marginally. Set the following environment variable to enable it:
+我们还观察到,对于这些较大的网络,张量内存分配占推理延迟的很大一部分。这可以通过从PyTorch C10内存分配器
+启用 THP 来优化。目前,该功能默认未启用,因为它会略微增加内存占用。设置以下环境变量以启用它:
 
 .. code-block:: bash
 
     $ export THP_MEM_ALLOC_ENABLE=1
 
-For the batch dimension of 256 and with MKLDNN fast math mode:
+对于批量维度为 256 且启用 MKLDNN Fast Math 模式:
 
 .. code-block:: python
 
     X = torch.rand(256, 64, 64, device=device)
     with torch.set_grad_enabled(False):
         for _ in range(50):
-            model(X) #Warmup
+            model(X) #预热
         with profile(activities=[ProfilerActivity.CPU]) as prof:
             with record_function("mymodel_inference"):
                 for _ in range(100):
@@ -340,14 +344,13 @@ For the batch dimension of 256 and with MKLDNN fast math mode:
 
     print(prof.key_averages().table(sort_by="self_cpu_time_total"))
 
-
-The following is the profiler output with THP memory allocations enabled:
+启用THP内存分配后,profiler的输出如下:
 
 .. table::
    :widths: auto
 
    ======================  ============  ============  ============  ============  ==============  ============
-                     Name   Self CPU %    Self CPU     CPU total %    CPU total     CPU time avg    # of Calls
+                     名称    自身CPU%      自身CPU       CPU总%        CPU总        CPU平均时间     调用次数
    ======================  ============  ============  ============  ============  ==============  ============
            aten::addmm        91.31%        6.115s        94.39%        6.321s      21.069ms           300
        aten::clamp_min         4.82%     322.568ms         4.82%     322.568ms       1.613ms           200
@@ -357,12 +360,14 @@ The following is the profiler output with THP memory allocations enabled:
             aten::relu         0.04%       2.547ms         4.85%     325.115ms       1.626ms           200
    ======================  ============  ============  ============  ============  ==============  ============
 
-**Self CPU time total:** 6.697s
-
-This is an additional **1.08x or 8% (6.697s vs 7.262s)** improvement on top of the already optimized MKLDNN fast math mode measured above.
+**自身CPU总时间:** 6.697s
 
+这比上面测量的已优化的 MKLDNN Fast Math 模式又提高了 **1.08倍或8%(6.697s vs 7.262s)**。
 
-Conclusion
+结论
 ------------
 
-In this tutorial, we covered PyTorch inference on AWS Graviton3 instances by covering the basic usage, demonstrating speedups with fast math kernels, comparing different backends for different batch dimensions, and how to optimize tensor memory allocation latencies with Linux transparent huge pages. The recommendation is to use MKLDNN backend with Bfloat16 fastmath mode and THP memory allocations for larger tensor shapes and to use OpenBLAS backend for smaller tensor shapes. We hope that you will give it a try!
+在本教程中,我们介绍了在AWS Graviton3实例上的PyTorch推理,包括基本用法、使用快速数学内核的加速、
+比较不同批量维度下不同后端的性能,以及如何使用Linux透明大页面优化张量内存分配延迟。
+对于较大的张量形状,建议使用MKLDNN后端和Bfloat16快速数学模式以及THP内存分配;对于较小的张量形状,
+建议使用OpenBLAS后端。希望您能尝试一下!
diff --git a/recipes_source/torch_compile_user_defined_triton_kernel_tutorial.py b/recipes_source/torch_compile_user_defined_triton_kernel_tutorial.py
index 68187d6..b5c0faf 100644
--- a/recipes_source/torch_compile_user_defined_triton_kernel_tutorial.py
+++ b/recipes_source/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>`__