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==================================== -This recipe explains how to use PyTorch profiler and measure the time and -memory consumption of the model's operators. +本教程解释了如何使用PyTorch profiler,并测量模型算子的时间和内存消耗。 -Introduction +简介 ------------ -PyTorch includes a simple profiler API that is useful when user needs -to determine the most expensive operators in the model. +当用户需要确定模型中最耗费资源的算子时,PyTorch包含一个简单的profiler API非常有用。 -In this recipe, we will use a simple Resnet model to demonstrate how to -use profiler to analyze model performance. +在本教程中,我们将使用一个简单的 Resnet 模型来演示如何使用profiler分析模型性能。 -Setup +环境设置 ----- -To install ``torch`` and ``torchvision`` use the following command: +要安装 ``torch`` 和 ``torchvision``,请使用以下命令: .. code-block:: sh @@ -23,88 +20,75 @@ """ - ###################################################################### -# Steps +# 具体步骤 # ----- # -# 1. Import all necessary libraries -# 2. Instantiate a simple Resnet model -# 3. Using profiler to analyze execution time -# 4. Using profiler to analyze memory consumption -# 5. Using tracing functionality -# 6. Examining stack traces -# 7. Using profiler to analyze long-running jobs +# 1. 导入所有必需的库 +# 2. 实例化一个简单的Resnet模型 +# 3. 使用profiler分析执行时间 +# 4. 使用profiler分析内存消耗 +# 5. 使用跟踪功能 +# 6. 检查堆栈跟踪 +# 7. 使用profiler分析长时间运行的作业 # -# 1. Import all necessary libraries +# 1. 导入依赖的库 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # -# In this recipe we will use ``torch``, ``torchvision.models`` -# and ``profiler`` modules: +# 在本教程中,我们将使用 ``torch``、``torchvision.models`` 和 ``profiler`` 模块: # import torch import torchvision.models as models -from torch.profiler import profile, record_function, ProfilerActivity +from torch.profiler import profile, ProfilerActivity, record_function ###################################################################### -# 2. Instantiate a simple Resnet model +# 2. 创建一个简单的 Resnet 模型 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # -# Let's create an instance of a Resnet model and prepare an input -# for it: +# 让我们创建一个 Resnet 模型实例,并为它准备一个输入: # model = models.resnet18() inputs = torch.randn(5, 3, 224, 224) ###################################################################### -# 3. Using profiler to analyze execution time +# 3. 使用profiler分析执行时间 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # -# PyTorch profiler is enabled through the context manager and accepts -# a number of parameters, some of the most useful are: +# PyTorch profiler通过上下文管理器启用,并接受多个参数,其中一些最有用的参数如下: # -# - ``activities`` - a list of activities to profile: -# - ``ProfilerActivity.CPU`` - PyTorch operators, TorchScript functions and -# user-defined code labels (see ``record_function`` below); -# - ``ProfilerActivity.CUDA`` - on-device CUDA kernels; -# - ``record_shapes`` - whether to record shapes of the operator inputs; -# - ``profile_memory`` - whether to report amount of memory consumed by -# model's Tensors; -# - ``use_cuda`` - whether to measure execution time of CUDA kernels. +# - ``activities`` - 要分析的活动列表: +# - ``ProfilerActivity.CPU`` - PyTorch算子、TorchScript函数和用户定义的代码标签(见下面的 ``record_function``); +# - ``ProfilerActivity.CUDA`` - 设备上的CUDA内核; +# - ``record_shapes`` - 是否记录算子输入的形状; +# - ``profile_memory`` - 是否报告模型张量所消耗的内存量; +# - ``use_cuda`` - 是否测量CUDA内核的执行时间。 # -# Note: when using CUDA, profiler also shows the runtime CUDA events -# occurring on the host. +# 注意:当使用CUDA时,profiler还会显示主机上发生的运行时CUDA事件。 ###################################################################### -# Let's see how we can use profiler to analyze the execution time: +# 让我们看看如何使用profiler分析执行时间: with profile(activities=[ProfilerActivity.CPU], record_shapes=True) as prof: with record_function("model_inference"): model(inputs) ###################################################################### -# Note that we can use ``record_function`` context manager to label -# arbitrary code ranges with user provided names -# (``model_inference`` is used as a label in the example above). +# 注意,我们可以使用 ``record_function`` 上下文管理器为任意代码范围添加用户提供的名称标签 +# (在上面的示例中使用 ``model_inference`` 作为标签)。 # -# Profiler allows one to check which operators were called during the -# execution of a code range wrapped with a profiler context manager. -# If multiple profiler ranges are active at the same time (e.g. in -# parallel PyTorch threads), each profiling context manager tracks only -# the operators of its corresponding range. -# Profiler also automatically profiles the asynchronous tasks launched -# with ``torch.jit._fork`` and (in case of a backward pass) -# the backward pass operators launched with ``backward()`` call. +# Profiler允许检查在使用profiler上下文管理器包装的代码范围内执行期间调用了哪些算子。 +# 如果同时存在多个活动的profiler范围(例如在并行PyTorch线程中),每个profiling上下文管理器只跟踪其对应范围的算子。 +# Profiler还会自动分析使用 ``torch.jit._fork`` 启动的异步任务,以及在反向传播过程中使用 ``backward()`` 调用启动的反向传播算子。 # -# Let's print out the stats for the execution above: +# 让我们打印出上述执行的统计信息: print(prof.key_averages().table(sort_by="cpu_time_total", row_limit=10)) ###################################################################### -# The output will look like (omitting some columns): +# 输出将如下所示(省略了一些列): # --------------------------------- ------------ ------------ ------------ ------------ # Name Self CPU CPU total CPU time avg # of Calls @@ -124,19 +108,24 @@ # ###################################################################### -# Here we see that, as expected, most of the time is spent in convolution (and specifically in ``mkldnn_convolution`` -# for PyTorch compiled with ``MKL-DNN`` support). -# Note the difference between self cpu time and cpu time - operators can call other operators, self cpu time excludes time -# spent in children operator calls, while total cpu time includes it. You can choose to sort by the self cpu time by passing -# ``sort_by="self_cpu_time_total"`` into the ``table`` call. # # To get a finer granularity of results and include operator input shapes, pass ``group_by_input_shape=True`` # (note: this requires running the profiler with ``record_shapes=True``): +# 这里我们可以看到,如预期的那样,大部分时间都花在了卷积上(对于使用 ``MKL-DNN`` 支持编译的PyTorch,特别是在 ``mkldnn_convolution`` 上)。 +# 注意自身cpu时间和cpu时间之间的区别 - 算子可以调用其他算子,自身cpu时间不包括在子算子调用中花费的时间,而总cpu时间包括了它。 +# 你可以通过将 ``sort_by="self_cpu_time_total"`` 传递给 ``table`` 调用来选择按自身cpu时间排序。 +# +# 要获得更细粒度的结果并包含算子输入形状,请传递 ``group_by_input_shape=True`` +# (注意:这需要使用 ``record_shapes=True`` 运行profiler): -print(prof.key_averages(group_by_input_shape=True).table(sort_by="cpu_time_total", row_limit=10)) +print( + prof.key_averages(group_by_input_shape=True).table( + sort_by="cpu_time_total", row_limit=10 + ) +) ######################################################################################## -# The output might look like this (omitting some columns): +# 输出可能如下所示(省略了一些列): # # .. code-block:: sh # @@ -158,26 +147,28 @@ # ###################################################################### -# Note the occurrence of ``aten::convolution`` twice with different input shapes. +# 注意 ``aten::convolution`` 出现了两次,具有不同的输入形状。 ###################################################################### -# Profiler can also be used to analyze performance of models executed on GPUs: +# Profiler也可用于分析在GPU上执行的模型的性能: + model = models.resnet18().cuda() inputs = torch.randn(5, 3, 224, 224).cuda() -with profile(activities=[ - ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True) as prof: +with profile( + activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True +) as prof: with record_function("model_inference"): model(inputs) print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10)) ###################################################################### -# (Note: the first use of CUDA profiling may bring an extra overhead.) +# (注意:第一次使用CUDA分析可能会带来额外的开销。) ###################################################################### -# The resulting table output (omitting some columns): +# 结果输出(省略了一些列): # # .. code-block:: sh # @@ -200,23 +191,22 @@ # ###################################################################### -# Note the occurrence of on-device kernels in the output (e.g. ``sgemm_32x32x32_NN``). +# 注意在输出中出现了设备上的内核(例如 ``sgemm_32x32x32_NN``)。 ###################################################################### -# 4. Using profiler to analyze memory consumption +# 4. 使用 profiler 分析内存消耗 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # -# PyTorch profiler can also show the amount of memory (used by the model's tensors) -# that was allocated (or released) during the execution of the model's operators. -# In the output below, 'self' memory corresponds to the memory allocated (released) -# by the operator, excluding the children calls to the other operators. -# To enable memory profiling functionality pass ``profile_memory=True``. +# PyTorch profiler还可以显示在执行模型算子期间分配(或释放)的内存量(由模型张量使用)。 +# 在下面的输出中,'self'内存对应于算子分配(释放)的内存,不包括对其他算子的子调用。 +# 要启用内存分析功能,请传递 ``profile_memory=True``。 model = models.resnet18() inputs = torch.randn(5, 3, 224, 224) -with profile(activities=[ProfilerActivity.CPU], - profile_memory=True, record_shapes=True) as prof: +with profile( + activities=[ProfilerActivity.CPU], profile_memory=True, record_shapes=True +) as prof: model(inputs) print(prof.key_averages().table(sort_by="self_cpu_memory_usage", row_limit=10)) @@ -241,7 +231,7 @@ print(prof.key_averages().table(sort_by="cpu_memory_usage", row_limit=10)) ############################################################################# -# The output might look like this (omitting some columns): +# 输出如下所示(省略了一些列): # # .. code-block:: sh # @@ -263,10 +253,10 @@ # ###################################################################### -# 5. Using tracing functionality +# 5. 使用跟踪功能 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # -# Profiling results can be outputted as a ``.json`` trace file: +# 可以将分析结果输出为 ``.json`` 跟踪文件: model = models.resnet18().cuda() inputs = torch.randn(5, 3, 224, 224).cuda() @@ -277,17 +267,16 @@ prof.export_chrome_trace("trace.json") ###################################################################### -# You can examine the sequence of profiled operators and CUDA kernels -# in Chrome trace viewer (``chrome://tracing``): +# 你可以在Chrome跟踪查看器(``chrome://tracing``)中检查分析的算子和CUDA内核序列: # # .. image:: ../../_static/img/trace_img.png # :scale: 25 % ###################################################################### -# 6. Examining stack traces +# 6. 检查堆栈跟踪 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # -# Profiler can be used to analyze Python and TorchScript stack traces: +# Profiler 可用于分析 Python 和 TorchScript 堆栈跟踪: with profile( activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], @@ -296,10 +285,14 @@ model(inputs) # Print aggregated stats -print(prof.key_averages(group_by_stack_n=5).table(sort_by="self_cuda_time_total", row_limit=2)) +print( + prof.key_averages(group_by_stack_n=5).table( + sort_by="self_cuda_time_total", row_limit=2 + ) +) ################################################################################# -# The output might look like this (omitting some columns): +# 输出如下所示(省略了一些列): # # .. code-block:: sh # @@ -322,83 +315,73 @@ # ###################################################################### -# Note the two convolutions and the two call sites in ``torchvision/models/resnet.py`` script. +# 注意在 ``torchvision/models/resnet.py`` 脚本中的两个卷积和两个调用位置。 # -# (Warning: stack tracing adds an extra profiling overhead.) +# (警告:堆栈跟踪会增加额外的分析开销。) ###################################################################### -# 7. Using profiler to analyze long-running jobs +# 7. 使用分析器分析长时间运行的作业 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # -# PyTorch profiler offers an additional API to handle long-running jobs -# (such as training loops). Tracing all of the execution can be -# slow and result in very large trace files. To avoid this, use optional -# arguments: +# PyTorch分析器提供了一个额外的API来处理长时间运行的作业 +# (例如训练循环)。跟踪所有执行可能会很慢,并导致非常大的跟踪文件。 +# 为了避免这种情况,可以使用可选参数: # -# - ``schedule`` - specifies a function that takes an integer argument (step number) -# as an input and returns an action for the profiler, the best way to use this parameter -# is to use ``torch.profiler.schedule`` helper function that can generate a schedule for you; -# - ``on_trace_ready`` - specifies a function that takes a reference to the profiler as -# an input and is called by the profiler each time the new trace is ready. +# - ``schedule`` - 指定一个函数,该函数以整数参数(步骤编号)作为输入, +# 并返回分析器的操作,使用此参数的最佳方式是使用 ``torch.profiler.schedule`` +# 帮助函数,它可以为您生成一个计划; +# - ``on_trace_ready`` - 指定一个函数,该函数以分析器的引用作为输入, +# 并在每次新的跟踪准备就绪时由分析器调用。 # -# To illustrate how the API works, let's first consider the following example with -# ``torch.profiler.schedule`` helper function: +# 为了说明该API的工作原理,让我们首先考虑以下使用 ``torch.profiler.schedule`` +# 帮助函数的示例: + from torch.profiler import schedule -my_schedule = schedule( - skip_first=10, - wait=5, - warmup=1, - active=3, - repeat=2) +my_schedule = schedule(skip_first=10, wait=5, warmup=1, active=3, repeat=2) ###################################################################### -# Profiler assumes that the long-running job is composed of steps, numbered -# starting from zero. The example above defines the following sequence of actions -# for the profiler: -# -# 1. Parameter ``skip_first`` tells profiler that it should ignore the first 10 steps -# (default value of ``skip_first`` is zero); -# 2. After the first ``skip_first`` steps, profiler starts executing profiler cycles; -# 3. Each cycle consists of three phases: -# -# - idling (``wait=5`` steps), during this phase profiler is not active; -# - warming up (``warmup=1`` steps), during this phase profiler starts tracing, but -# the results are discarded; this phase is used to discard the samples obtained by -# the profiler at the beginning of the trace since they are usually skewed by an extra -# overhead; -# - active tracing (``active=3`` steps), during this phase profiler traces and records data; -# 4. An optional ``repeat`` parameter specifies an upper bound on the number of cycles. -# By default (zero value), profiler will execute cycles as long as the job runs. +# 分析器假设长时间运行的作业由从零开始编号的步骤组成。 +# 上面的示例为分析器定义了以下操作序列: +# +# 1. 参数 ``skip_first`` 告诉分析器它应该忽略前10个步骤 +# (``skip_first`` 的默认值为零); +# 2. 在第一个 ``skip_first`` 步骤之后,分析器开始执行分析器周期; +# 3. 每个周期由三个阶段组成: +# +# - 空闲(``wait=5``步骤),在此阶段分析器不活动; +# - 预热(``warmup=1``步骤),在此阶段分析器开始跟踪,但结果被丢弃; +# 此阶段用于丢弃分析器在跟踪开始时获得的样本,因为它们通常由额外的开销扭曲; +# - 主动跟踪(``active=3``步骤),在此阶段分析器跟踪和记录数据; +# 4. 可选的 ``repeat`` 参数指定周期的上限。 +# 默认情况下(零值),分析器将尽可能长时间地执行周期。 ###################################################################### -# Thus, in the example above, profiler will skip the first 15 steps, spend the next step on the warm up, -# actively record the next 3 steps, skip another 5 steps, spend the next step on the warm up, actively -# record another 3 steps. Since the ``repeat=2`` parameter value is specified, the profiler will stop -# the recording after the first two cycles. +# 因此,在上面的示例中,分析器将跳过前15个步骤,在下一步进行预热, +# 在接下来的3个步骤中主动记录,再跳过另外5个步骤,在下一步进行预热, +# 在另外3个步骤中主动记录。由于指定了 ``repeat=2`` 参数值, +# 分析器将在前两个周期之后停止记录。 # -# At the end of each cycle profiler calls the specified ``on_trace_ready`` function and passes itself as -# an argument. This function is used to process the new trace - either by obtaining the table output or -# by saving the output on disk as a trace file. +# 在每个周期结束时,分析器调用指定的 ``on_trace_ready`` 函数并将自身作为参数传递。 +# 此函数用于处理新的跟踪 - 通过获取表输出或将输出保存到磁盘上的跟踪文件。 # -# To send the signal to the profiler that the next step has started, call ``prof.step()`` function. -# The current profiler step is stored in ``prof.step_num``. +# 要向分析器发送下一步已经开始的信号,请调用 ``prof.step()`` 函数。 +# 当前分析器步骤存储在 ``prof.step_num`` 中。 # -# The following example shows how to use all of the concepts above: +# 以下示例显示了如何使用上述所有概念: + def trace_handler(p): output = p.key_averages().table(sort_by="self_cuda_time_total", row_limit=10) print(output) p.export_chrome_trace("/tmp/trace_" + str(p.step_num) + ".json") + with profile( activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], - schedule=torch.profiler.schedule( - wait=1, - warmup=1, - active=2), - on_trace_ready=trace_handler + schedule=torch.profiler.schedule(wait=1, warmup=1, active=2), + on_trace_ready=trace_handler, ) as p: for idx in range(8): model(inputs) @@ -406,12 +389,12 @@ def trace_handler(p): ###################################################################### -# Learn More +# 了解更多 # ---------- # -# Take a look at the following recipes/tutorials to continue your learning: +# 查看以下教程以继续学习: # -# - `PyTorch Benchmark `_ -# - `PyTorch Profiler with TensorBoard `_ tutorial -# - `Visualizing models, data, and training with TensorBoard `_ tutorial +# - `PyTorch 基准测试 `_ +# - `使用 TensorBoard 的 PyTorch 分析器 `_ 教程 +# - `使用 TensorBoard 可视化模型、数据和训练 `_ 教程 # diff --git a/docs/_downloads/b2c9c15033f17c2bdf31c864f9d39c76/profiler_recipe.ipynb b/docs/_downloads/b2c9c15033f17c2bdf31c864f9d39c76/profiler_recipe.ipynb index 012d4a5..83a447f 100644 --- a/docs/_downloads/b2c9c15033f17c2bdf31c864f9d39c76/profiler_recipe.ipynb +++ b/docs/_downloads/b2c9c15033f17c2bdf31c864f9d39c76/profiler_recipe.ipynb @@ -15,14 +15,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n# PyTorch Profiler\nThis recipe explains how to use PyTorch profiler and measure the time and\nmemory consumption of the model's operators.\n\n## Introduction\nPyTorch includes a simple profiler API that is useful when user needs\nto determine the most expensive operators in the model.\n\nIn this recipe, we will use a simple Resnet model to demonstrate how to\nuse profiler to analyze model performance.\n\n## Setup\nTo install ``torch`` and ``torchvision`` use the following command:\n\n```sh\npip install torch torchvision\n```\n" + "\n# PyTorch Profiler\n\u672c\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u4f7f\u7528PyTorch profiler,\u5e76\u6d4b\u91cf\u6a21\u578b\u7b97\u5b50\u7684\u65f6\u95f4\u548c\u5185\u5b58\u6d88\u8017\u3002\n\n## \u7b80\u4ecb\n\u5f53\u7528\u6237\u9700\u8981\u786e\u5b9a\u6a21\u578b\u4e2d\u6700\u8017\u8d39\u8d44\u6e90\u7684\u7b97\u5b50\u65f6,PyTorch\u5305\u542b\u4e00\u4e2a\u7b80\u5355\u7684profiler API\u975e\u5e38\u6709\u7528\u3002\n\n\u5728\u672c\u6559\u7a0b\u4e2d,\u6211\u4eec\u5c06\u4f7f\u7528\u4e00\u4e2a\u7b80\u5355\u7684 Resnet \u6a21\u578b\u6765\u6f14\u793a\u5982\u4f55\u4f7f\u7528profiler\u5206\u6790\u6a21\u578b\u6027\u80fd\u3002\n\n## \u73af\u5883\u8bbe\u7f6e\n\u8981\u5b89\u88c5 ``torch`` \u548c ``torchvision``,\u8bf7\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4:\n\n```sh\npip install torch torchvision\n```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Steps\n\n1. Import all necessary libraries\n2. Instantiate a simple Resnet model\n3. Using profiler to analyze execution time\n4. Using profiler to analyze memory consumption\n5. Using tracing functionality\n6. Examining stack traces\n7. Using profiler to analyze long-running jobs\n\n### 1. Import all necessary libraries\n\nIn this recipe we will use ``torch``, ``torchvision.models``\nand ``profiler`` modules:\n\n\n" + "## \u5177\u4f53\u6b65\u9aa4\n\n1. \u5bfc\u5165\u6240\u6709\u5fc5\u9700\u7684\u5e93\n2. \u5b9e\u4f8b\u5316\u4e00\u4e2a\u7b80\u5355\u7684Resnet\u6a21\u578b\n3. \u4f7f\u7528profiler\u5206\u6790\u6267\u884c\u65f6\u95f4\n4. \u4f7f\u7528profiler\u5206\u6790\u5185\u5b58\u6d88\u8017\n5. \u4f7f\u7528\u8ddf\u8e2a\u529f\u80fd\n6. \u68c0\u67e5\u5806\u6808\u8ddf\u8e2a\n7. \u4f7f\u7528profiler\u5206\u6790\u957f\u65f6\u95f4\u8fd0\u884c\u7684\u4f5c\u4e1a\n\n### 1. \u5bfc\u5165\u4f9d\u8d56\u7684\u5e93\n\n\u5728\u672c\u6559\u7a0b\u4e2d,\u6211\u4eec\u5c06\u4f7f\u7528 ``torch``\u3001``torchvision.models`` \u548c ``profiler`` \u6a21\u5757:\n\n\n" ] }, { @@ -33,14 +33,14 @@ }, "outputs": [], "source": [ - "import torch\nimport torchvision.models as models\nfrom torch.profiler import profile, record_function, ProfilerActivity" + "import torch\nimport torchvision.models as models\nfrom torch.profiler import profile, ProfilerActivity, record_function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 2. Instantiate a simple Resnet model\n\nLet's create an instance of a Resnet model and prepare an input\nfor it:\n\n\n" + "### 2. \u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684 Resnet \u6a21\u578b\n\n\u8ba9\u6211\u4eec\u521b\u5efa\u4e00\u4e2a Resnet \u6a21\u578b\u5b9e\u4f8b,\u5e76\u4e3a\u5b83\u51c6\u5907\u4e00\u4e2a\u8f93\u5165:\n\n\n" ] }, { @@ -58,14 +58,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 3. Using profiler to analyze execution time\n\nPyTorch profiler is enabled through the context manager and accepts\na number of parameters, some of the most useful are:\n\n- ``activities`` - a list of activities to profile:\n - ``ProfilerActivity.CPU`` - PyTorch operators, TorchScript functions and\n user-defined code labels (see ``record_function`` below);\n - ``ProfilerActivity.CUDA`` - on-device CUDA kernels;\n- ``record_shapes`` - whether to record shapes of the operator inputs;\n- ``profile_memory`` - whether to report amount of memory consumed by\n model's Tensors;\n- ``use_cuda`` - whether to measure execution time of CUDA kernels.\n\nNote: when using CUDA, profiler also shows the runtime CUDA events\noccurring on the host.\n\n" + "### 3. \u4f7f\u7528profiler\u5206\u6790\u6267\u884c\u65f6\u95f4\n\nPyTorch profiler\u901a\u8fc7\u4e0a\u4e0b\u6587\u7ba1\u7406\u5668\u542f\u7528,\u5e76\u63a5\u53d7\u591a\u4e2a\u53c2\u6570,\u5176\u4e2d\u4e00\u4e9b\u6700\u6709\u7528\u7684\u53c2\u6570\u5982\u4e0b:\n\n- ``activities`` - \u8981\u5206\u6790\u7684\u6d3b\u52a8\u5217\u8868:\n - ``ProfilerActivity.CPU`` - PyTorch\u7b97\u5b50\u3001TorchScript\u51fd\u6570\u548c\u7528\u6237\u5b9a\u4e49\u7684\u4ee3\u7801\u6807\u7b7e(\u89c1\u4e0b\u9762\u7684 ``record_function``);\n - ``ProfilerActivity.CUDA`` - \u8bbe\u5907\u4e0a\u7684CUDA\u5185\u6838;\n- ``record_shapes`` - \u662f\u5426\u8bb0\u5f55\u7b97\u5b50\u8f93\u5165\u7684\u5f62\u72b6;\n- ``profile_memory`` - \u662f\u5426\u62a5\u544a\u6a21\u578b\u5f20\u91cf\u6240\u6d88\u8017\u7684\u5185\u5b58\u91cf;\n- ``use_cuda`` - \u662f\u5426\u6d4b\u91cfCUDA\u5185\u6838\u7684\u6267\u884c\u65f6\u95f4\u3002\n\n\u6ce8\u610f:\u5f53\u4f7f\u7528CUDA\u65f6,profiler\u8fd8\u4f1a\u663e\u793a\u4e3b\u673a\u4e0a\u53d1\u751f\u7684\u8fd0\u884c\u65f6CUDA\u4e8b\u4ef6\u3002\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's see how we can use profiler to analyze the execution time:\n\n" + "\u8ba9\u6211\u4eec\u770b\u770b\u5982\u4f55\u4f7f\u7528profiler\u5206\u6790\u6267\u884c\u65f6\u95f4:\n\n" ] }, { @@ -83,7 +83,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Note that we can use ``record_function`` context manager to label\narbitrary code ranges with user provided names\n(``model_inference`` is used as a label in the example above).\n\nProfiler allows one to check which operators were called during the\nexecution of a code range wrapped with a profiler context manager.\nIf multiple profiler ranges are active at the same time (e.g. in\nparallel PyTorch threads), each profiling context manager tracks only\nthe operators of its corresponding range.\nProfiler also automatically profiles the asynchronous tasks launched\nwith ``torch.jit._fork`` and (in case of a backward pass)\nthe backward pass operators launched with ``backward()`` call.\n\nLet's print out the stats for the execution above:\n\n" + "\u6ce8\u610f,\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 ``record_function`` \u4e0a\u4e0b\u6587\u7ba1\u7406\u5668\u4e3a\u4efb\u610f\u4ee3\u7801\u8303\u56f4\u6dfb\u52a0\u7528\u6237\u63d0\u4f9b\u7684\u540d\u79f0\u6807\u7b7e\n(\u5728\u4e0a\u9762\u7684\u793a\u4f8b\u4e2d\u4f7f\u7528 ``model_inference`` \u4f5c\u4e3a\u6807\u7b7e)\u3002\n\nProfiler\u5141\u8bb8\u68c0\u67e5\u5728\u4f7f\u7528profiler\u4e0a\u4e0b\u6587\u7ba1\u7406\u5668\u5305\u88c5\u7684\u4ee3\u7801\u8303\u56f4\u5185\u6267\u884c\u671f\u95f4\u8c03\u7528\u4e86\u54ea\u4e9b\u7b97\u5b50\u3002\n\u5982\u679c\u540c\u65f6\u5b58\u5728\u591a\u4e2a\u6d3b\u52a8\u7684profiler\u8303\u56f4(\u4f8b\u5982\u5728\u5e76\u884cPyTorch\u7ebf\u7a0b\u4e2d),\u6bcf\u4e2aprofiling\u4e0a\u4e0b\u6587\u7ba1\u7406\u5668\u53ea\u8ddf\u8e2a\u5176\u5bf9\u5e94\u8303\u56f4\u7684\u7b97\u5b50\u3002\nProfiler\u8fd8\u4f1a\u81ea\u52a8\u5206\u6790\u4f7f\u7528 ``torch.jit._fork`` \u542f\u52a8\u7684\u5f02\u6b65\u4efb\u52a1,\u4ee5\u53ca\u5728\u53cd\u5411\u4f20\u64ad\u8fc7\u7a0b\u4e2d\u4f7f\u7528 ``backward()`` \u8c03\u7528\u542f\u52a8\u7684\u53cd\u5411\u4f20\u64ad\u7b97\u5b50\u3002\n\n\u8ba9\u6211\u4eec\u6253\u5370\u51fa\u4e0a\u8ff0\u6267\u884c\u7684\u7edf\u8ba1\u4fe1\u606f:\n\n" ] }, { @@ -101,7 +101,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The output will look like (omitting some columns):\n\n" + "\u8f93\u51fa\u5c06\u5982\u4e0b\u6240\u793a(\u7701\u7565\u4e86\u4e00\u4e9b\u5217):\n\n" ] }, { @@ -119,7 +119,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Here we see that, as expected, most of the time is spent in convolution (and specifically in ``mkldnn_convolution``\nfor PyTorch compiled with ``MKL-DNN`` support).\nNote the difference between self cpu time and cpu time - operators can call other operators, self cpu time excludes time\nspent in children operator calls, while total cpu time includes it. You can choose to sort by the self cpu time by passing\n``sort_by=\"self_cpu_time_total\"`` into the ``table`` call.\n\nTo get a finer granularity of results and include operator input shapes, pass ``group_by_input_shape=True``\n(note: this requires running the profiler with ``record_shapes=True``):\n\n" + "To get a finer granularity of results and include operator input shapes, pass ``group_by_input_shape=True``\n(note: this requires running the profiler with ``record_shapes=True``):\n\u8fd9\u91cc\u6211\u4eec\u53ef\u4ee5\u770b\u5230,\u5982\u9884\u671f\u7684\u90a3\u6837,\u5927\u90e8\u5206\u65f6\u95f4\u90fd\u82b1\u5728\u4e86\u5377\u79ef\u4e0a(\u5bf9\u4e8e\u4f7f\u7528 ``MKL-DNN`` \u652f\u6301\u7f16\u8bd1\u7684PyTorch,\u7279\u522b\u662f\u5728 ``mkldnn_convolution`` \u4e0a)\u3002\n\u6ce8\u610f\u81ea\u8eabcpu\u65f6\u95f4\u548ccpu\u65f6\u95f4\u4e4b\u95f4\u7684\u533a\u522b - \u7b97\u5b50\u53ef\u4ee5\u8c03\u7528\u5176\u4ed6\u7b97\u5b50,\u81ea\u8eabcpu\u65f6\u95f4\u4e0d\u5305\u62ec\u5728\u5b50\u7b97\u5b50\u8c03\u7528\u4e2d\u82b1\u8d39\u7684\u65f6\u95f4,\u800c\u603bcpu\u65f6\u95f4\u5305\u62ec\u4e86\u5b83\u3002\n\u4f60\u53ef\u4ee5\u901a\u8fc7\u5c06 ``sort_by=\"self_cpu_time_total\"`` \u4f20\u9012\u7ed9 ``table`` \u8c03\u7528\u6765\u9009\u62e9\u6309\u81ea\u8eabcpu\u65f6\u95f4\u6392\u5e8f\u3002\n\n\u8981\u83b7\u5f97\u66f4\u7ec6\u7c92\u5ea6\u7684\u7ed3\u679c\u5e76\u5305\u542b\u7b97\u5b50\u8f93\u5165\u5f62\u72b6,\u8bf7\u4f20\u9012 ``group_by_input_shape=True``\n(\u6ce8\u610f:\u8fd9\u9700\u8981\u4f7f\u7528 ``record_shapes=True`` \u8fd0\u884cprofiler):\n\n" ] }, { @@ -130,28 +130,28 @@ }, "outputs": [], "source": [ - "print(prof.key_averages(group_by_input_shape=True).table(sort_by=\"cpu_time_total\", row_limit=10))" + "print(\n prof.key_averages(group_by_input_shape=True).table(\n sort_by=\"cpu_time_total\", row_limit=10\n )\n)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The output might look like this (omitting some columns):\n\n```sh\n--------------------------------- ------------ -------------------------------------------\n Name CPU total Input Shapes\n--------------------------------- ------------ -------------------------------------------\n model_inference 57.503ms []\n aten::conv2d 8.008ms [5,64,56,56], [64,64,3,3], [], ..., []]\n aten::convolution 7.956ms [[5,64,56,56], [64,64,3,3], [], ..., []]\n aten::_convolution 7.909ms [[5,64,56,56], [64,64,3,3], [], ..., []]\n aten::mkldnn_convolution 7.834ms [[5,64,56,56], [64,64,3,3], [], ..., []]\n aten::conv2d 6.332ms [[5,512,7,7], [512,512,3,3], [], ..., []]\n aten::convolution 6.303ms [[5,512,7,7], [512,512,3,3], [], ..., []]\n aten::_convolution 6.273ms [[5,512,7,7], [512,512,3,3], [], ..., []]\n aten::mkldnn_convolution 6.233ms [[5,512,7,7], [512,512,3,3], [], ..., []]\n aten::conv2d 4.751ms [[5,256,14,14], [256,256,3,3], [], ..., []]\n--------------------------------- ------------ -------------------------------------------\nSelf CPU time total: 57.549ms\n```\n" + "\u8f93\u51fa\u53ef\u80fd\u5982\u4e0b\u6240\u793a(\u7701\u7565\u4e86\u4e00\u4e9b\u5217):\n\n```sh\n--------------------------------- ------------ -------------------------------------------\n Name CPU total Input Shapes\n--------------------------------- ------------ -------------------------------------------\n model_inference 57.503ms []\n aten::conv2d 8.008ms [5,64,56,56], [64,64,3,3], [], ..., []]\n aten::convolution 7.956ms [[5,64,56,56], [64,64,3,3], [], ..., []]\n aten::_convolution 7.909ms [[5,64,56,56], [64,64,3,3], [], ..., []]\n aten::mkldnn_convolution 7.834ms [[5,64,56,56], [64,64,3,3], [], ..., []]\n aten::conv2d 6.332ms [[5,512,7,7], [512,512,3,3], [], ..., []]\n aten::convolution 6.303ms [[5,512,7,7], [512,512,3,3], [], ..., []]\n aten::_convolution 6.273ms [[5,512,7,7], [512,512,3,3], [], ..., []]\n aten::mkldnn_convolution 6.233ms [[5,512,7,7], [512,512,3,3], [], ..., []]\n aten::conv2d 4.751ms [[5,256,14,14], [256,256,3,3], [], ..., []]\n--------------------------------- ------------ -------------------------------------------\nSelf CPU time total: 57.549ms\n```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Note the occurrence of ``aten::convolution`` twice with different input shapes.\n\n" + "\u6ce8\u610f ``aten::convolution`` \u51fa\u73b0\u4e86\u4e24\u6b21,\u5177\u6709\u4e0d\u540c\u7684\u8f93\u5165\u5f62\u72b6\u3002\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Profiler can also be used to analyze performance of models executed on GPUs:\n\n" + "Profiler\u4e5f\u53ef\u7528\u4e8e\u5206\u6790\u5728GPU\u4e0a\u6267\u884c\u7684\u6a21\u578b\u7684\u6027\u80fd:\n\n" ] }, { @@ -162,35 +162,35 @@ }, "outputs": [], "source": [ - "model = models.resnet18().cuda()\ninputs = torch.randn(5, 3, 224, 224).cuda()\n\nwith profile(activities=[\n ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True) as prof:\n with record_function(\"model_inference\"):\n model(inputs)\n\nprint(prof.key_averages().table(sort_by=\"cuda_time_total\", row_limit=10))" + "model = models.resnet18().cuda()\ninputs = torch.randn(5, 3, 224, 224).cuda()\n\nwith profile(\n activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True\n) as prof:\n with record_function(\"model_inference\"):\n model(inputs)\n\nprint(prof.key_averages().table(sort_by=\"cuda_time_total\", row_limit=10))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "(Note: the first use of CUDA profiling may bring an extra overhead.)\n\n" + "(\u6ce8\u610f:\u7b2c\u4e00\u6b21\u4f7f\u7528CUDA\u5206\u6790\u53ef\u80fd\u4f1a\u5e26\u6765\u989d\u5916\u7684\u5f00\u9500\u3002)\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The resulting table output (omitting some columns):\n\n```sh\n------------------------------------------------------- ------------ ------------\n Name Self CUDA CUDA total\n------------------------------------------------------- ------------ ------------\n model_inference 0.000us 11.666ms\n aten::conv2d 0.000us 10.484ms\n aten::convolution 0.000us 10.484ms\n aten::_convolution 0.000us 10.484ms\n aten::_convolution_nogroup 0.000us 10.484ms\n aten::thnn_conv2d 0.000us 10.484ms\n aten::thnn_conv2d_forward 10.484ms 10.484ms\nvoid at::native::im2col_kernel(long, float co... 3.844ms 3.844ms\n sgemm_32x32x32_NN 3.206ms 3.206ms\n sgemm_32x32x32_NN_vec 3.093ms 3.093ms\n------------------------------------------------------- ------------ ------------\nSelf CPU time total: 23.015ms\nSelf CUDA time total: 11.666ms\n```\n" + "\u7ed3\u679c\u8f93\u51fa(\u7701\u7565\u4e86\u4e00\u4e9b\u5217):\n\n```sh\n------------------------------------------------------- ------------ ------------\n Name Self CUDA CUDA total\n------------------------------------------------------- ------------ ------------\n model_inference 0.000us 11.666ms\n aten::conv2d 0.000us 10.484ms\n aten::convolution 0.000us 10.484ms\n aten::_convolution 0.000us 10.484ms\n aten::_convolution_nogroup 0.000us 10.484ms\n aten::thnn_conv2d 0.000us 10.484ms\n aten::thnn_conv2d_forward 10.484ms 10.484ms\nvoid at::native::im2col_kernel(long, float co... 3.844ms 3.844ms\n sgemm_32x32x32_NN 3.206ms 3.206ms\n sgemm_32x32x32_NN_vec 3.093ms 3.093ms\n------------------------------------------------------- ------------ ------------\nSelf CPU time total: 23.015ms\nSelf CUDA time total: 11.666ms\n```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Note the occurrence of on-device kernels in the output (e.g. ``sgemm_32x32x32_NN``).\n\n" + "\u6ce8\u610f\u5728\u8f93\u51fa\u4e2d\u51fa\u73b0\u4e86\u8bbe\u5907\u4e0a\u7684\u5185\u6838(\u4f8b\u5982 ``sgemm_32x32x32_NN``)\u3002\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 4. Using profiler to analyze memory consumption\n\nPyTorch profiler can also show the amount of memory (used by the model's tensors)\nthat was allocated (or released) during the execution of the model's operators.\nIn the output below, 'self' memory corresponds to the memory allocated (released)\nby the operator, excluding the children calls to the other operators.\nTo enable memory profiling functionality pass ``profile_memory=True``.\n\n" + "### 4. \u4f7f\u7528 profiler \u5206\u6790\u5185\u5b58\u6d88\u8017\n\nPyTorch profiler\u8fd8\u53ef\u4ee5\u663e\u793a\u5728\u6267\u884c\u6a21\u578b\u7b97\u5b50\u671f\u95f4\u5206\u914d(\u6216\u91ca\u653e)\u7684\u5185\u5b58\u91cf(\u7531\u6a21\u578b\u5f20\u91cf\u4f7f\u7528)\u3002\n\u5728\u4e0b\u9762\u7684\u8f93\u51fa\u4e2d,'self'\u5185\u5b58\u5bf9\u5e94\u4e8e\u7b97\u5b50\u5206\u914d(\u91ca\u653e)\u7684\u5185\u5b58,\u4e0d\u5305\u62ec\u5bf9\u5176\u4ed6\u7b97\u5b50\u7684\u5b50\u8c03\u7528\u3002\n\u8981\u542f\u7528\u5185\u5b58\u5206\u6790\u529f\u80fd,\u8bf7\u4f20\u9012 ``profile_memory=True``\u3002\n\n" ] }, { @@ -201,21 +201,21 @@ }, "outputs": [], "source": [ - "model = models.resnet18()\ninputs = torch.randn(5, 3, 224, 224)\n\nwith profile(activities=[ProfilerActivity.CPU],\n profile_memory=True, record_shapes=True) as prof:\n model(inputs)\n\nprint(prof.key_averages().table(sort_by=\"self_cpu_memory_usage\", row_limit=10))\n\n# (omitting some columns)\n# --------------------------------- ------------ ------------ ------------\n# Name CPU Mem Self CPU Mem # of Calls\n# --------------------------------- ------------ ------------ ------------\n# aten::empty 94.79 Mb 94.79 Mb 121\n# aten::max_pool2d_with_indices 11.48 Mb 11.48 Mb 1\n# aten::addmm 19.53 Kb 19.53 Kb 1\n# aten::empty_strided 572 b 572 b 25\n# aten::resize_ 240 b 240 b 6\n# aten::abs 480 b 240 b 4\n# aten::add 160 b 160 b 20\n# aten::masked_select 120 b 112 b 1\n# aten::ne 122 b 53 b 6\n# aten::eq 60 b 30 b 2\n# --------------------------------- ------------ ------------ ------------\n# Self CPU time total: 53.064ms\n\nprint(prof.key_averages().table(sort_by=\"cpu_memory_usage\", row_limit=10))" + "model = models.resnet18()\ninputs = torch.randn(5, 3, 224, 224)\n\nwith profile(\n activities=[ProfilerActivity.CPU], profile_memory=True, record_shapes=True\n) as prof:\n model(inputs)\n\nprint(prof.key_averages().table(sort_by=\"self_cpu_memory_usage\", row_limit=10))\n\n# (omitting some columns)\n# --------------------------------- ------------ ------------ ------------\n# Name CPU Mem Self CPU Mem # of Calls\n# --------------------------------- ------------ ------------ ------------\n# aten::empty 94.79 Mb 94.79 Mb 121\n# aten::max_pool2d_with_indices 11.48 Mb 11.48 Mb 1\n# aten::addmm 19.53 Kb 19.53 Kb 1\n# aten::empty_strided 572 b 572 b 25\n# aten::resize_ 240 b 240 b 6\n# aten::abs 480 b 240 b 4\n# aten::add 160 b 160 b 20\n# aten::masked_select 120 b 112 b 1\n# aten::ne 122 b 53 b 6\n# aten::eq 60 b 30 b 2\n# --------------------------------- ------------ ------------ ------------\n# Self CPU time total: 53.064ms\n\nprint(prof.key_averages().table(sort_by=\"cpu_memory_usage\", row_limit=10))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The output might look like this (omitting some columns):\n\n```sh\n--------------------------------- ------------ ------------ ------------\n Name CPU Mem Self CPU Mem # of Calls\n--------------------------------- ------------ ------------ ------------\n aten::empty 94.79 Mb 94.79 Mb 121\n aten::batch_norm 47.41 Mb 0 b 20\n aten::_batch_norm_impl_index 47.41 Mb 0 b 20\n aten::native_batch_norm 47.41 Mb 0 b 20\n aten::conv2d 47.37 Mb 0 b 20\n aten::convolution 47.37 Mb 0 b 20\n aten::_convolution 47.37 Mb 0 b 20\n aten::mkldnn_convolution 47.37 Mb 0 b 20\n aten::max_pool2d 11.48 Mb 0 b 1\n aten::max_pool2d_with_indices 11.48 Mb 11.48 Mb 1\n--------------------------------- ------------ ------------ ------------\nSelf CPU time total: 53.064ms\n```\n" + "\u8f93\u51fa\u5982\u4e0b\u6240\u793a(\u7701\u7565\u4e86\u4e00\u4e9b\u5217):\n\n```sh\n--------------------------------- ------------ ------------ ------------\n Name CPU Mem Self CPU Mem # of Calls\n--------------------------------- ------------ ------------ ------------\n aten::empty 94.79 Mb 94.79 Mb 121\n aten::batch_norm 47.41 Mb 0 b 20\n aten::_batch_norm_impl_index 47.41 Mb 0 b 20\n aten::native_batch_norm 47.41 Mb 0 b 20\n aten::conv2d 47.37 Mb 0 b 20\n aten::convolution 47.37 Mb 0 b 20\n aten::_convolution 47.37 Mb 0 b 20\n aten::mkldnn_convolution 47.37 Mb 0 b 20\n aten::max_pool2d 11.48 Mb 0 b 1\n aten::max_pool2d_with_indices 11.48 Mb 11.48 Mb 1\n--------------------------------- ------------ ------------ ------------\nSelf CPU time total: 53.064ms\n```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 5. Using tracing functionality\n\nProfiling results can be outputted as a ``.json`` trace file:\n\n" + "### 5. \u4f7f\u7528\u8ddf\u8e2a\u529f\u80fd\n\n\u53ef\u4ee5\u5c06\u5206\u6790\u7ed3\u679c\u8f93\u51fa\u4e3a ``.json`` \u8ddf\u8e2a\u6587\u4ef6:\n\n" ] }, { @@ -233,14 +233,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "You can examine the sequence of profiled operators and CUDA kernels\nin Chrome trace viewer (``chrome://tracing``):\n\n\n\n" + "\u4f60\u53ef\u4ee5\u5728Chrome\u8ddf\u8e2a\u67e5\u770b\u5668(``chrome://tracing``)\u4e2d\u68c0\u67e5\u5206\u6790\u7684\u7b97\u5b50\u548cCUDA\u5185\u6838\u5e8f\u5217:\n\n\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 6. Examining stack traces\n\nProfiler can be used to analyze Python and TorchScript stack traces:\n\n" + "### 6. \u68c0\u67e5\u5806\u6808\u8ddf\u8e2a\n\nProfiler \u53ef\u7528\u4e8e\u5206\u6790 Python \u548c TorchScript \u5806\u6808\u8ddf\u8e2a:\n\n" ] }, { @@ -251,28 +251,28 @@ }, "outputs": [], "source": [ - "with profile(\n activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],\n with_stack=True,\n) as prof:\n model(inputs)\n\n# Print aggregated stats\nprint(prof.key_averages(group_by_stack_n=5).table(sort_by=\"self_cuda_time_total\", row_limit=2))" + "with profile(\n activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],\n with_stack=True,\n) as prof:\n model(inputs)\n\n# Print aggregated stats\nprint(\n prof.key_averages(group_by_stack_n=5).table(\n sort_by=\"self_cuda_time_total\", row_limit=2\n )\n)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The output might look like this (omitting some columns):\n\n```sh\n------------------------- -----------------------------------------------------------\n Name Source Location\n------------------------- -----------------------------------------------------------\naten::thnn_conv2d_forward .../torch/nn/modules/conv.py(439): _conv_forward\n .../torch/nn/modules/conv.py(443): forward\n .../torch/nn/modules/module.py(1051): _call_impl\n .../site-packages/torchvision/models/resnet.py(63): forward\n .../torch/nn/modules/module.py(1051): _call_impl\naten::thnn_conv2d_forward .../torch/nn/modules/conv.py(439): _conv_forward\n .../torch/nn/modules/conv.py(443): forward\n .../torch/nn/modules/module.py(1051): _call_impl\n .../site-packages/torchvision/models/resnet.py(59): forward\n .../torch/nn/modules/module.py(1051): _call_impl\n------------------------- -----------------------------------------------------------\nSelf CPU time total: 34.016ms\nSelf CUDA time total: 11.659ms\n```\n" + "\u8f93\u51fa\u5982\u4e0b\u6240\u793a(\u7701\u7565\u4e86\u4e00\u4e9b\u5217):\n\n```sh\n------------------------- -----------------------------------------------------------\n Name Source Location\n------------------------- -----------------------------------------------------------\naten::thnn_conv2d_forward .../torch/nn/modules/conv.py(439): _conv_forward\n .../torch/nn/modules/conv.py(443): forward\n .../torch/nn/modules/module.py(1051): _call_impl\n .../site-packages/torchvision/models/resnet.py(63): forward\n .../torch/nn/modules/module.py(1051): _call_impl\naten::thnn_conv2d_forward .../torch/nn/modules/conv.py(439): _conv_forward\n .../torch/nn/modules/conv.py(443): forward\n .../torch/nn/modules/module.py(1051): _call_impl\n .../site-packages/torchvision/models/resnet.py(59): forward\n .../torch/nn/modules/module.py(1051): _call_impl\n------------------------- -----------------------------------------------------------\nSelf CPU time total: 34.016ms\nSelf CUDA time total: 11.659ms\n```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Note the two convolutions and the two call sites in ``torchvision/models/resnet.py`` script.\n\n(Warning: stack tracing adds an extra profiling overhead.)\n\n" + "\u6ce8\u610f\u5728 ``torchvision/models/resnet.py`` \u811a\u672c\u4e2d\u7684\u4e24\u4e2a\u5377\u79ef\u548c\u4e24\u4e2a\u8c03\u7528\u4f4d\u7f6e\u3002\n\n(\u8b66\u544a:\u5806\u6808\u8ddf\u8e2a\u4f1a\u589e\u52a0\u989d\u5916\u7684\u5206\u6790\u5f00\u9500\u3002)\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 7. Using profiler to analyze long-running jobs\n\nPyTorch profiler offers an additional API to handle long-running jobs\n(such as training loops). Tracing all of the execution can be\nslow and result in very large trace files. To avoid this, use optional\narguments:\n\n- ``schedule`` - specifies a function that takes an integer argument (step number)\n as an input and returns an action for the profiler, the best way to use this parameter\n is to use ``torch.profiler.schedule`` helper function that can generate a schedule for you;\n- ``on_trace_ready`` - specifies a function that takes a reference to the profiler as\n an input and is called by the profiler each time the new trace is ready.\n\nTo illustrate how the API works, let's first consider the following example with\n``torch.profiler.schedule`` helper function:\n\n" + "### 7. \u4f7f\u7528\u5206\u6790\u5668\u5206\u6790\u957f\u65f6\u95f4\u8fd0\u884c\u7684\u4f5c\u4e1a\n\nPyTorch\u5206\u6790\u5668\u63d0\u4f9b\u4e86\u4e00\u4e2a\u989d\u5916\u7684API\u6765\u5904\u7406\u957f\u65f6\u95f4\u8fd0\u884c\u7684\u4f5c\u4e1a\n(\u4f8b\u5982\u8bad\u7ec3\u5faa\u73af)\u3002\u8ddf\u8e2a\u6240\u6709\u6267\u884c\u53ef\u80fd\u4f1a\u5f88\u6162,\u5e76\u5bfc\u81f4\u975e\u5e38\u5927\u7684\u8ddf\u8e2a\u6587\u4ef6\u3002\n\u4e3a\u4e86\u907f\u514d\u8fd9\u79cd\u60c5\u51b5,\u53ef\u4ee5\u4f7f\u7528\u53ef\u9009\u53c2\u6570:\n\n- ``schedule`` - \u6307\u5b9a\u4e00\u4e2a\u51fd\u6570,\u8be5\u51fd\u6570\u4ee5\u6574\u6570\u53c2\u6570(\u6b65\u9aa4\u7f16\u53f7)\u4f5c\u4e3a\u8f93\u5165,\n \u5e76\u8fd4\u56de\u5206\u6790\u5668\u7684\u64cd\u4f5c,\u4f7f\u7528\u6b64\u53c2\u6570\u7684\u6700\u4f73\u65b9\u5f0f\u662f\u4f7f\u7528 ``torch.profiler.schedule``\n \u5e2e\u52a9\u51fd\u6570,\u5b83\u53ef\u4ee5\u4e3a\u60a8\u751f\u6210\u4e00\u4e2a\u8ba1\u5212;\n- ``on_trace_ready`` - \u6307\u5b9a\u4e00\u4e2a\u51fd\u6570,\u8be5\u51fd\u6570\u4ee5\u5206\u6790\u5668\u7684\u5f15\u7528\u4f5c\u4e3a\u8f93\u5165,\n \u5e76\u5728\u6bcf\u6b21\u65b0\u7684\u8ddf\u8e2a\u51c6\u5907\u5c31\u7eea\u65f6\u7531\u5206\u6790\u5668\u8c03\u7528\u3002\n\n\u4e3a\u4e86\u8bf4\u660e\u8be5API\u7684\u5de5\u4f5c\u539f\u7406,\u8ba9\u6211\u4eec\u9996\u5148\u8003\u8651\u4ee5\u4e0b\u4f7f\u7528 ``torch.profiler.schedule``\n\u5e2e\u52a9\u51fd\u6570\u7684\u793a\u4f8b:\n\n" ] }, { @@ -283,21 +283,21 @@ }, "outputs": [], "source": [ - "from torch.profiler import schedule\n\nmy_schedule = schedule(\n skip_first=10,\n wait=5,\n warmup=1,\n active=3,\n repeat=2)" + "from torch.profiler import schedule\n\nmy_schedule = schedule(skip_first=10, wait=5, warmup=1, active=3, repeat=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Profiler assumes that the long-running job is composed of steps, numbered\nstarting from zero. The example above defines the following sequence of actions\nfor the profiler:\n\n1. Parameter ``skip_first`` tells profiler that it should ignore the first 10 steps\n (default value of ``skip_first`` is zero);\n2. After the first ``skip_first`` steps, profiler starts executing profiler cycles;\n3. Each cycle consists of three phases:\n\n - idling (``wait=5`` steps), during this phase profiler is not active;\n - warming up (``warmup=1`` steps), during this phase profiler starts tracing, but\n the results are discarded; this phase is used to discard the samples obtained by\n the profiler at the beginning of the trace since they are usually skewed by an extra\n overhead;\n - active tracing (``active=3`` steps), during this phase profiler traces and records data;\n4. An optional ``repeat`` parameter specifies an upper bound on the number of cycles.\n By default (zero value), profiler will execute cycles as long as the job runs.\n\n" + "\u5206\u6790\u5668\u5047\u8bbe\u957f\u65f6\u95f4\u8fd0\u884c\u7684\u4f5c\u4e1a\u7531\u4ece\u96f6\u5f00\u59cb\u7f16\u53f7\u7684\u6b65\u9aa4\u7ec4\u6210\u3002\n\u4e0a\u9762\u7684\u793a\u4f8b\u4e3a\u5206\u6790\u5668\u5b9a\u4e49\u4e86\u4ee5\u4e0b\u64cd\u4f5c\u5e8f\u5217:\n\n1. \u53c2\u6570 ``skip_first`` \u544a\u8bc9\u5206\u6790\u5668\u5b83\u5e94\u8be5\u5ffd\u7565\u524d10\u4e2a\u6b65\u9aa4\n (``skip_first`` \u7684\u9ed8\u8ba4\u503c\u4e3a\u96f6);\n2. \u5728\u7b2c\u4e00\u4e2a ``skip_first`` \u6b65\u9aa4\u4e4b\u540e,\u5206\u6790\u5668\u5f00\u59cb\u6267\u884c\u5206\u6790\u5668\u5468\u671f;\n3. \u6bcf\u4e2a\u5468\u671f\u7531\u4e09\u4e2a\u9636\u6bb5\u7ec4\u6210:\n\n - \u7a7a\u95f2(``wait=5``\u6b65\u9aa4),\u5728\u6b64\u9636\u6bb5\u5206\u6790\u5668\u4e0d\u6d3b\u52a8;\n - \u9884\u70ed(``warmup=1``\u6b65\u9aa4),\u5728\u6b64\u9636\u6bb5\u5206\u6790\u5668\u5f00\u59cb\u8ddf\u8e2a,\u4f46\u7ed3\u679c\u88ab\u4e22\u5f03;\n \u6b64\u9636\u6bb5\u7528\u4e8e\u4e22\u5f03\u5206\u6790\u5668\u5728\u8ddf\u8e2a\u5f00\u59cb\u65f6\u83b7\u5f97\u7684\u6837\u672c,\u56e0\u4e3a\u5b83\u4eec\u901a\u5e38\u7531\u989d\u5916\u7684\u5f00\u9500\u626d\u66f2;\n - \u4e3b\u52a8\u8ddf\u8e2a(``active=3``\u6b65\u9aa4),\u5728\u6b64\u9636\u6bb5\u5206\u6790\u5668\u8ddf\u8e2a\u548c\u8bb0\u5f55\u6570\u636e;\n4. \u53ef\u9009\u7684 ``repeat`` \u53c2\u6570\u6307\u5b9a\u5468\u671f\u7684\u4e0a\u9650\u3002\n \u9ed8\u8ba4\u60c5\u51b5\u4e0b(\u96f6\u503c),\u5206\u6790\u5668\u5c06\u5c3d\u53ef\u80fd\u957f\u65f6\u95f4\u5730\u6267\u884c\u5468\u671f\u3002\n\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Thus, in the example above, profiler will skip the first 15 steps, spend the next step on the warm up,\nactively record the next 3 steps, skip another 5 steps, spend the next step on the warm up, actively\nrecord another 3 steps. Since the ``repeat=2`` parameter value is specified, the profiler will stop\nthe recording after the first two cycles.\n\nAt the end of each cycle profiler calls the specified ``on_trace_ready`` function and passes itself as\nan argument. This function is used to process the new trace - either by obtaining the table output or\nby saving the output on disk as a trace file.\n\nTo send the signal to the profiler that the next step has started, call ``prof.step()`` function.\nThe current profiler step is stored in ``prof.step_num``.\n\nThe following example shows how to use all of the concepts above:\n\n" + "\u56e0\u6b64,\u5728\u4e0a\u9762\u7684\u793a\u4f8b\u4e2d,\u5206\u6790\u5668\u5c06\u8df3\u8fc7\u524d15\u4e2a\u6b65\u9aa4,\u5728\u4e0b\u4e00\u6b65\u8fdb\u884c\u9884\u70ed,\n\u5728\u63a5\u4e0b\u6765\u76843\u4e2a\u6b65\u9aa4\u4e2d\u4e3b\u52a8\u8bb0\u5f55,\u518d\u8df3\u8fc7\u53e6\u59165\u4e2a\u6b65\u9aa4,\u5728\u4e0b\u4e00\u6b65\u8fdb\u884c\u9884\u70ed,\n\u5728\u53e6\u59163\u4e2a\u6b65\u9aa4\u4e2d\u4e3b\u52a8\u8bb0\u5f55\u3002\u7531\u4e8e\u6307\u5b9a\u4e86 ``repeat=2`` \u53c2\u6570\u503c,\n\u5206\u6790\u5668\u5c06\u5728\u524d\u4e24\u4e2a\u5468\u671f\u4e4b\u540e\u505c\u6b62\u8bb0\u5f55\u3002\n\n\u5728\u6bcf\u4e2a\u5468\u671f\u7ed3\u675f\u65f6,\u5206\u6790\u5668\u8c03\u7528\u6307\u5b9a\u7684 ``on_trace_ready`` \u51fd\u6570\u5e76\u5c06\u81ea\u8eab\u4f5c\u4e3a\u53c2\u6570\u4f20\u9012\u3002\n\u6b64\u51fd\u6570\u7528\u4e8e\u5904\u7406\u65b0\u7684\u8ddf\u8e2a - \u901a\u8fc7\u83b7\u53d6\u8868\u8f93\u51fa\u6216\u5c06\u8f93\u51fa\u4fdd\u5b58\u5230\u78c1\u76d8\u4e0a\u7684\u8ddf\u8e2a\u6587\u4ef6\u3002\n\n\u8981\u5411\u5206\u6790\u5668\u53d1\u9001\u4e0b\u4e00\u6b65\u5df2\u7ecf\u5f00\u59cb\u7684\u4fe1\u53f7,\u8bf7\u8c03\u7528 ``prof.step()`` \u51fd\u6570\u3002\n\u5f53\u524d\u5206\u6790\u5668\u6b65\u9aa4\u5b58\u50a8\u5728 ``prof.step_num`` \u4e2d\u3002\n\n\u4ee5\u4e0b\u793a\u4f8b\u663e\u793a\u4e86\u5982\u4f55\u4f7f\u7528\u4e0a\u8ff0\u6240\u6709\u6982\u5ff5:\n\n" ] }, { @@ -308,14 +308,14 @@ }, "outputs": [], "source": [ - "def trace_handler(p):\n output = p.key_averages().table(sort_by=\"self_cuda_time_total\", row_limit=10)\n print(output)\n p.export_chrome_trace(\"/tmp/trace_\" + str(p.step_num) + \".json\")\n\nwith profile(\n activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],\n schedule=torch.profiler.schedule(\n wait=1,\n warmup=1,\n active=2),\n on_trace_ready=trace_handler\n) as p:\n for idx in range(8):\n model(inputs)\n p.step()" + "def trace_handler(p):\n output = p.key_averages().table(sort_by=\"self_cuda_time_total\", row_limit=10)\n print(output)\n p.export_chrome_trace(\"/tmp/trace_\" + str(p.step_num) + \".json\")\n\n\nwith profile(\n activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],\n schedule=torch.profiler.schedule(wait=1, warmup=1, active=2),\n on_trace_ready=trace_handler,\n) as p:\n for idx in range(8):\n model(inputs)\n p.step()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Learn More\n\nTake a look at the following recipes/tutorials to continue your learning:\n\n- [PyTorch Benchmark](https://pytorch.org/tutorials/recipes/recipes/benchmark.html)\n- [PyTorch Profiler with TensorBoard](https://pytorch.org/tutorials/intermediate/tensorboard_profiler_tutorial.html) tutorial\n- [Visualizing models, data, and training with TensorBoard](https://pytorch.org/tutorials/intermediate/tensorboard_tutorial.html) tutorial\n\n\n" + "## \u4e86\u89e3\u66f4\u591a\n\n\u67e5\u770b\u4ee5\u4e0b\u6559\u7a0b\u4ee5\u7ee7\u7eed\u5b66\u4e60:\n\n- [PyTorch \u57fa\u51c6\u6d4b\u8bd5](https://pytorch.org/tutorials/recipes/recipes/benchmark.html)\n- [\u4f7f\u7528 TensorBoard \u7684 PyTorch \u5206\u6790\u5668](https://pytorch.org/tutorials/intermediate/tensorboard_profiler_tutorial.html) \u6559\u7a0b\n- [\u4f7f\u7528 TensorBoard \u53ef\u89c6\u5316\u6a21\u578b\u3001\u6570\u636e\u548c\u8bad\u7ec3](https://pytorch.org/tutorials/intermediate/tensorboard_tutorial.html) \u6559\u7a0b\n\n\n" ] } ], diff --git a/docs/_images/yi_jing_01_chien1.jpg b/docs/_images/yi_jing_01_chien1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..523dc2b8b868fa53e3b960d7017908dae486eecb GIT binary patch literal 6614 zcmdT|2{hDS`~Qx~k|idoBou?pNM*(nNoAcu8Zv_zWf_B1vK3(p4YFhyHKVLy7-J_} zsI-uM%bKzzlcfkDygKju@BDt}?|=U1_rB-+PVaNi_uPA*&vT#Wp7TBTexB#9_pHAM zgwT2@JpjZ30$y)4V7(vE20;J6aBy&detsYh@J7c027@<7VDQfwnB%9DKe*Vi+OYep zvR(^7cz$jQ41xe05D*vwTCW540w4ekfI%SOmuv*y%FD;IWdqd_0s!z9P982EP7dyW znrxt4JX^PLaznO@?!j5!yWjm*$&^ym_Jvn?|B;hdJwEJ^!nohul_IiJUiCuE(}E2z zJO1eXpJ9G#A%BSZFKr*d4+d>)4h#XbfSG~dQrXBEViB4yLzEFlK%u=UYL+qLqj%c; 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@@ -170,11 +170,11 @@
PyTorch -

如何使用 PyTorch 的全面指导

+

Explore the documentation for comprehensive guidance on how to use PyTorch

 PyTorch Domains -

更多关于特定领域库的信息

+

Read the PyTorch Domains documentation to learn more about domain-specific libraries
@@ -183,27 +183,27 @@
    • @@ -211,15 +211,15 @@
  • - 成为会员 + Become a Member
    • diff --git a/docs/advanced/coding_ddpg.html b/docs/advanced/coding_ddpg.html index 0c1aa0b..d06b0e3 100644 --- a/docs/advanced/coding_ddpg.html +++ b/docs/advanced/coding_ddpg.html @@ -87,28 +87,28 @@
  • @@ -117,28 +117,28 @@
  • @@ -151,12 +151,12 @@
    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -183,27 +183,27 @@
    • @@ -211,15 +211,15 @@
  • - 成为会员 + Become a Member
    • diff --git a/docs/advanced/cpp_autograd.html b/docs/advanced/cpp_autograd.html index b29a4f3..20ada33 100644 --- a/docs/advanced/cpp_autograd.html +++ b/docs/advanced/cpp_autograd.html @@ -89,28 +89,28 @@
  • @@ -119,28 +119,28 @@
  • @@ -153,12 +153,12 @@
    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
    • @@ -213,15 +213,15 @@
  • - 成为会员 + Become a Member
    • diff --git a/docs/advanced/cpp_cuda_graphs.html b/docs/advanced/cpp_cuda_graphs.html index 8b971c4..69fdee9 100644 --- a/docs/advanced/cpp_cuda_graphs.html +++ b/docs/advanced/cpp_cuda_graphs.html @@ -87,28 +87,28 @@
  • @@ -117,28 +117,28 @@
  • @@ -151,12 +151,12 @@
    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -183,27 +183,27 @@
    • @@ -211,15 +211,15 @@
  • - 成为会员 + Become a Member
    • diff --git a/docs/advanced/cpp_export.html b/docs/advanced/cpp_export.html index d67bbbb..e2419e9 100644 --- a/docs/advanced/cpp_export.html +++ b/docs/advanced/cpp_export.html @@ -89,28 +89,28 @@
  • @@ -119,28 +119,28 @@
  • @@ -153,12 +153,12 @@
    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
    • @@ -213,15 +213,15 @@
  • - 成为会员 + Become a Member
    • diff --git a/docs/advanced/cpp_extension.html b/docs/advanced/cpp_extension.html index 6742462..6b27970 100644 --- a/docs/advanced/cpp_extension.html +++ b/docs/advanced/cpp_extension.html @@ -89,28 +89,28 @@
  • @@ -119,28 +119,28 @@
  • @@ -153,12 +153,12 @@
    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
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  • - 成为会员 + Become a Member
    • diff --git a/docs/advanced/cpp_frontend.html b/docs/advanced/cpp_frontend.html index 3a4f398..bcc3461 100644 --- a/docs/advanced/cpp_frontend.html +++ b/docs/advanced/cpp_frontend.html @@ -89,28 +89,28 @@
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
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  • - 成为会员 + Become a Member
    • diff --git a/docs/advanced/ddp_pipeline.html b/docs/advanced/ddp_pipeline.html index 15ac085..60e6630 100644 --- a/docs/advanced/ddp_pipeline.html +++ b/docs/advanced/ddp_pipeline.html @@ -89,28 +89,28 @@
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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  • - 成为会员 + Become a Member
    • diff --git a/docs/advanced/dispatcher.html b/docs/advanced/dispatcher.html index faf50b8..025ea6c 100644 --- a/docs/advanced/dispatcher.html +++ b/docs/advanced/dispatcher.html @@ -89,28 +89,28 @@
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
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  • - 成为会员 + Become a Member
    • diff --git a/docs/advanced/dynamic_quantization_tutorial.html b/docs/advanced/dynamic_quantization_tutorial.html index a521cca..ea26b42 100644 --- a/docs/advanced/dynamic_quantization_tutorial.html +++ b/docs/advanced/dynamic_quantization_tutorial.html @@ -89,28 +89,28 @@
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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  • - 成为会员 + Become a Member
    • diff --git a/docs/advanced/extend_dispatcher.html b/docs/advanced/extend_dispatcher.html index 7ae5e73..fddd566 100644 --- a/docs/advanced/extend_dispatcher.html +++ b/docs/advanced/extend_dispatcher.html @@ -89,28 +89,28 @@
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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  • - 成为会员 + Become a Member
    • diff --git a/docs/advanced/generic_join.html b/docs/advanced/generic_join.html index 70bb811..50fc4e0 100644 --- a/docs/advanced/generic_join.html +++ b/docs/advanced/generic_join.html @@ -89,28 +89,28 @@
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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  • - 成为会员 + Become a Member
    • diff --git a/docs/advanced/neural_style_tutorial.html b/docs/advanced/neural_style_tutorial.html index 0cc0e9d..9d96c0c 100644 --- a/docs/advanced/neural_style_tutorial.html +++ b/docs/advanced/neural_style_tutorial.html @@ -87,28 +87,28 @@
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    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    • diff --git a/docs/advanced/numpy_extensions_tutorial.html b/docs/advanced/numpy_extensions_tutorial.html index 860ead4..eb726ce 100644 --- a/docs/advanced/numpy_extensions_tutorial.html +++ b/docs/advanced/numpy_extensions_tutorial.html @@ -87,28 +87,28 @@
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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  • - 成为会员 + Become a Member
    • diff --git a/docs/advanced/pendulum.html b/docs/advanced/pendulum.html index ecfd1b5..795324c 100644 --- a/docs/advanced/pendulum.html +++ b/docs/advanced/pendulum.html @@ -89,28 +89,28 @@
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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  • - 成为会员 + Become a Member
    • diff --git a/docs/advanced/privateuseone.html b/docs/advanced/privateuseone.html index 41eaf20..e179425 100644 --- a/docs/advanced/privateuseone.html +++ b/docs/advanced/privateuseone.html @@ -89,28 +89,28 @@
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    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
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  • - 成为会员 + Become a Member
    • diff --git a/docs/advanced/rpc_ddp_tutorial.html b/docs/advanced/rpc_ddp_tutorial.html index 82a3301..f17dd0e 100644 --- a/docs/advanced/rpc_ddp_tutorial.html +++ b/docs/advanced/rpc_ddp_tutorial.html @@ -89,28 +89,28 @@
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    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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  • - 成为会员 + Become a Member
    • diff --git a/docs/advanced/semi_structured_sparse.html b/docs/advanced/semi_structured_sparse.html index ffebe81..72ffc73 100644 --- a/docs/advanced/semi_structured_sparse.html +++ b/docs/advanced/semi_structured_sparse.html @@ -87,28 +87,28 @@
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    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -183,27 +183,27 @@
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    • diff --git a/docs/advanced/sharding.html b/docs/advanced/sharding.html index 601b69e..5f34e43 100644 --- a/docs/advanced/sharding.html +++ b/docs/advanced/sharding.html @@ -89,28 +89,28 @@
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    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
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  • - 成为会员 + Become a Member
    • diff --git a/docs/advanced/static_quantization_tutorial.html b/docs/advanced/static_quantization_tutorial.html index a3f8f7c..3ddea5d 100644 --- a/docs/advanced/static_quantization_tutorial.html +++ b/docs/advanced/static_quantization_tutorial.html @@ -89,28 +89,28 @@
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    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
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    • diff --git a/docs/advanced/super_resolution_with_onnxruntime.html b/docs/advanced/super_resolution_with_onnxruntime.html index 29125e5..58ed241 100644 --- a/docs/advanced/super_resolution_with_onnxruntime.html +++ b/docs/advanced/super_resolution_with_onnxruntime.html @@ -89,28 +89,28 @@
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    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
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    • diff --git a/docs/advanced/torch-script-parallelism.html b/docs/advanced/torch-script-parallelism.html index 3489b95..ac3b7d4 100644 --- a/docs/advanced/torch-script-parallelism.html +++ b/docs/advanced/torch-script-parallelism.html @@ -89,28 +89,28 @@
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    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
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    • diff --git a/docs/advanced/torch_script_custom_classes.html b/docs/advanced/torch_script_custom_classes.html index 93a687e..2d04197 100644 --- a/docs/advanced/torch_script_custom_classes.html +++ b/docs/advanced/torch_script_custom_classes.html @@ -89,28 +89,28 @@
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    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

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     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
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    • diff --git a/docs/advanced/torch_script_custom_ops.html b/docs/advanced/torch_script_custom_ops.html index 70c48a4..b9180a6 100644 --- a/docs/advanced/torch_script_custom_ops.html +++ b/docs/advanced/torch_script_custom_ops.html @@ -89,28 +89,28 @@
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    - PyTorch Edge -

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    • diff --git a/docs/advanced/usb_semisup_learn.html b/docs/advanced/usb_semisup_learn.html index 4c8db1a..06bd0e9 100644 --- a/docs/advanced/usb_semisup_learn.html +++ b/docs/advanced/usb_semisup_learn.html @@ -87,28 +87,28 @@
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    - PyTorch Edge -

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -183,27 +183,27 @@
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    • diff --git a/docs/beginner/Intro_to_TorchScript_tutorial.html b/docs/beginner/Intro_to_TorchScript_tutorial.html index 4546914..91a7986 100644 --- a/docs/beginner/Intro_to_TorchScript_tutorial.html +++ b/docs/beginner/Intro_to_TorchScript_tutorial.html @@ -89,28 +89,28 @@
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  • @@ -153,12 +153,12 @@
    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
    • @@ -213,15 +213,15 @@
  • - 成为会员 + Become a Member
    • diff --git a/docs/beginner/audio_data_augmentation_tutorial.html b/docs/beginner/audio_data_augmentation_tutorial.html index 78ff5c5..baf9a2f 100644 --- a/docs/beginner/audio_data_augmentation_tutorial.html +++ b/docs/beginner/audio_data_augmentation_tutorial.html @@ -89,28 +89,28 @@
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
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  • - 成为会员 + Become a Member
    • diff --git a/docs/beginner/audio_datasets_tutorial.html b/docs/beginner/audio_datasets_tutorial.html index b3c0cc8..374cd19 100644 --- a/docs/beginner/audio_datasets_tutorial.html +++ b/docs/beginner/audio_datasets_tutorial.html @@ -89,28 +89,28 @@
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    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
    • @@ -213,15 +213,15 @@
  • - 成为会员 + Become a Member
    • diff --git a/docs/beginner/audio_feature_augmentation_tutorial.html b/docs/beginner/audio_feature_augmentation_tutorial.html index e74e7b0..14b71b2 100644 --- a/docs/beginner/audio_feature_augmentation_tutorial.html +++ b/docs/beginner/audio_feature_augmentation_tutorial.html @@ -89,28 +89,28 @@
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    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
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  • - 成为会员 + Become a Member
    • diff --git a/docs/beginner/audio_feature_extractions_tutorial.html b/docs/beginner/audio_feature_extractions_tutorial.html index bb089f4..fae835b 100644 --- a/docs/beginner/audio_feature_extractions_tutorial.html +++ b/docs/beginner/audio_feature_extractions_tutorial.html @@ -89,28 +89,28 @@
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    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
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  • - 成为会员 + Become a Member
    • diff --git a/docs/beginner/audio_io_tutorial.html b/docs/beginner/audio_io_tutorial.html index eabc5e6..e54ca50 100644 --- a/docs/beginner/audio_io_tutorial.html +++ b/docs/beginner/audio_io_tutorial.html @@ -89,28 +89,28 @@
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    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
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  • - 成为会员 + Become a Member
    • diff --git a/docs/beginner/audio_resampling_tutorial.html b/docs/beginner/audio_resampling_tutorial.html index 049ca32..3dc3aa3 100644 --- a/docs/beginner/audio_resampling_tutorial.html +++ b/docs/beginner/audio_resampling_tutorial.html @@ -89,28 +89,28 @@
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    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
    • @@ -213,15 +213,15 @@
  • - 成为会员 + Become a Member
    • diff --git a/docs/beginner/basics/autogradqs_tutorial.html b/docs/beginner/basics/autogradqs_tutorial.html index 4b64b1e..ba657c9 100644 --- a/docs/beginner/basics/autogradqs_tutorial.html +++ b/docs/beginner/basics/autogradqs_tutorial.html @@ -89,28 +89,28 @@
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    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
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  • - 成为会员 + Become a Member
    • diff --git a/docs/beginner/basics/buildmodel_tutorial.html b/docs/beginner/basics/buildmodel_tutorial.html index e82a3d8..602c028 100644 --- a/docs/beginner/basics/buildmodel_tutorial.html +++ b/docs/beginner/basics/buildmodel_tutorial.html @@ -89,28 +89,28 @@
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    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
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  • - 成为会员 + Become a Member
    • diff --git a/docs/beginner/basics/data_tutorial.html b/docs/beginner/basics/data_tutorial.html index 838b631..f827d21 100644 --- a/docs/beginner/basics/data_tutorial.html +++ b/docs/beginner/basics/data_tutorial.html @@ -89,28 +89,28 @@
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
    • @@ -213,15 +213,15 @@
  • - 成为会员 + Become a Member
    • diff --git a/docs/beginner/basics/index.html b/docs/beginner/basics/index.html index 2df3712..164d54d 100644 --- a/docs/beginner/basics/index.html +++ b/docs/beginner/basics/index.html @@ -87,28 +87,28 @@
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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  • - 成为会员 + Become a Member
    • diff --git a/docs/beginner/basics/intro.html b/docs/beginner/basics/intro.html index 68674b7..bdefd7c 100644 --- a/docs/beginner/basics/intro.html +++ b/docs/beginner/basics/intro.html @@ -89,28 +89,28 @@
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    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
    • @@ -213,15 +213,15 @@
  • - 成为会员 + Become a Member
    • diff --git a/docs/beginner/basics/optimization_tutorial.html b/docs/beginner/basics/optimization_tutorial.html index 1eaf563..324518f 100644 --- a/docs/beginner/basics/optimization_tutorial.html +++ b/docs/beginner/basics/optimization_tutorial.html @@ -89,28 +89,28 @@
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    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
    • @@ -213,15 +213,15 @@
  • - 成为会员 + Become a Member
    • diff --git a/docs/beginner/basics/quickstart_tutorial.html b/docs/beginner/basics/quickstart_tutorial.html index bde130d..9c895f4 100644 --- a/docs/beginner/basics/quickstart_tutorial.html +++ b/docs/beginner/basics/quickstart_tutorial.html @@ -89,28 +89,28 @@
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    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
    • @@ -213,15 +213,15 @@
  • - 成为会员 + Become a Member
    • diff --git a/docs/beginner/basics/saveloadrun_tutorial.html b/docs/beginner/basics/saveloadrun_tutorial.html index 8b7a7db..afe36ed 100644 --- a/docs/beginner/basics/saveloadrun_tutorial.html +++ b/docs/beginner/basics/saveloadrun_tutorial.html @@ -89,28 +89,28 @@
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  • @@ -153,12 +153,12 @@
    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
    • @@ -213,15 +213,15 @@
  • - 成为会员 + Become a Member
    • diff --git a/docs/beginner/basics/tensorqs_tutorial.html b/docs/beginner/basics/tensorqs_tutorial.html index b21e1f3..814b3a3 100644 --- a/docs/beginner/basics/tensorqs_tutorial.html +++ b/docs/beginner/basics/tensorqs_tutorial.html @@ -89,28 +89,28 @@
  • @@ -119,28 +119,28 @@
  • @@ -153,12 +153,12 @@
    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
    • @@ -213,15 +213,15 @@
  • - 成为会员 + Become a Member
    • diff --git a/docs/beginner/basics/transforms_tutorial.html b/docs/beginner/basics/transforms_tutorial.html index f7e15cd..ff1bc75 100644 --- a/docs/beginner/basics/transforms_tutorial.html +++ b/docs/beginner/basics/transforms_tutorial.html @@ -89,28 +89,28 @@
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  • @@ -153,12 +153,12 @@
    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
    • @@ -213,15 +213,15 @@
  • - 成为会员 + Become a Member
    • diff --git a/docs/beginner/bettertransformer_tutorial.html b/docs/beginner/bettertransformer_tutorial.html index c48e0bc..7df6bf0 100644 --- a/docs/beginner/bettertransformer_tutorial.html +++ b/docs/beginner/bettertransformer_tutorial.html @@ -89,28 +89,28 @@
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  • @@ -153,12 +153,12 @@
    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
    • @@ -213,15 +213,15 @@
  • - 成为会员 + Become a Member
    • diff --git a/docs/beginner/blitz/autograd_tutorial.html b/docs/beginner/blitz/autograd_tutorial.html index d41d423..6fbc1cb 100644 --- a/docs/beginner/blitz/autograd_tutorial.html +++ b/docs/beginner/blitz/autograd_tutorial.html @@ -89,28 +89,28 @@
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  • @@ -153,12 +153,12 @@
    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
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  • - 成为会员 + Become a Member
    • diff --git a/docs/beginner/blitz/cifar10_tutorial.html b/docs/beginner/blitz/cifar10_tutorial.html index 3c34798..0bfb5e7 100644 --- a/docs/beginner/blitz/cifar10_tutorial.html +++ b/docs/beginner/blitz/cifar10_tutorial.html @@ -89,28 +89,28 @@
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  • @@ -153,12 +153,12 @@
    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    Build innovative and privacy-aware AI experiences for edge devices

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    Build innovative and privacy-aware AI experiences for edge devices

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    Build innovative and privacy-aware AI experiences for edge devices

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    • diff --git a/docs/beginner/colab.html b/docs/beginner/colab.html index b2195cc..b247711 100644 --- a/docs/beginner/colab.html +++ b/docs/beginner/colab.html @@ -87,28 +87,28 @@
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    Build innovative and privacy-aware AI experiences for edge devices

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
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  • - 成为会员 + Become a Member
    • diff --git a/docs/beginner/examples_nn/dynamic_net.html b/docs/beginner/examples_nn/dynamic_net.html index c1ca9ce..ee51057 100644 --- a/docs/beginner/examples_nn/dynamic_net.html +++ b/docs/beginner/examples_nn/dynamic_net.html @@ -89,28 +89,28 @@
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    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    • diff --git a/docs/beginner/examples_nn/index.html b/docs/beginner/examples_nn/index.html index f6b84e0..76c7de2 100644 --- a/docs/beginner/examples_nn/index.html +++ b/docs/beginner/examples_nn/index.html @@ -87,28 +87,28 @@
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -183,27 +183,27 @@
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    • diff --git a/docs/beginner/examples_nn/polynomial_module.html b/docs/beginner/examples_nn/polynomial_module.html index 9226132..686b024 100644 --- a/docs/beginner/examples_nn/polynomial_module.html +++ b/docs/beginner/examples_nn/polynomial_module.html @@ -89,28 +89,28 @@
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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
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    • diff --git a/docs/beginner/examples_nn/polynomial_nn.html b/docs/beginner/examples_nn/polynomial_nn.html index e8d3aab..6670996 100644 --- a/docs/beginner/examples_nn/polynomial_nn.html +++ b/docs/beginner/examples_nn/polynomial_nn.html @@ -89,28 +89,28 @@
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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
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    • diff --git a/docs/beginner/examples_nn/polynomial_optim.html b/docs/beginner/examples_nn/polynomial_optim.html index 6e39255..abd2187 100644 --- a/docs/beginner/examples_nn/polynomial_optim.html +++ b/docs/beginner/examples_nn/polynomial_optim.html @@ -89,28 +89,28 @@
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    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    • diff --git a/docs/beginner/examples_tensor/index.html b/docs/beginner/examples_tensor/index.html index b219d23..f51e9aa 100644 --- a/docs/beginner/examples_tensor/index.html +++ b/docs/beginner/examples_tensor/index.html @@ -87,28 +87,28 @@
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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    • diff --git a/docs/beginner/examples_tensor/polynomial_numpy.html b/docs/beginner/examples_tensor/polynomial_numpy.html index d5477ee..1f2c2ad 100644 --- a/docs/beginner/examples_tensor/polynomial_numpy.html +++ b/docs/beginner/examples_tensor/polynomial_numpy.html @@ -89,28 +89,28 @@
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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    • diff --git a/docs/beginner/examples_tensor/polynomial_tensor.html b/docs/beginner/examples_tensor/polynomial_tensor.html index 7564193..7b4eb4d 100644 --- a/docs/beginner/examples_tensor/polynomial_tensor.html +++ b/docs/beginner/examples_tensor/polynomial_tensor.html @@ -89,28 +89,28 @@
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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    • diff --git a/docs/beginner/fgsm_tutorial.html b/docs/beginner/fgsm_tutorial.html index 5ed5a83..b98d256 100644 --- a/docs/beginner/fgsm_tutorial.html +++ b/docs/beginner/fgsm_tutorial.html @@ -89,28 +89,28 @@
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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    • diff --git a/docs/beginner/finetuning_torchvision_models_tutorial.html b/docs/beginner/finetuning_torchvision_models_tutorial.html index 84580ba..e076bd0 100644 --- a/docs/beginner/finetuning_torchvision_models_tutorial.html +++ b/docs/beginner/finetuning_torchvision_models_tutorial.html @@ -87,28 +87,28 @@
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    为边缘设备构建创新的、注重隐私的人工智能体验

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    为边缘设备构建创新的、注重隐私的人工智能体验

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    • diff --git a/docs/beginner/hybrid_frontend/index.html b/docs/beginner/hybrid_frontend/index.html index 6809bc8..aaeb76e 100644 --- a/docs/beginner/hybrid_frontend/index.html +++ b/docs/beginner/hybrid_frontend/index.html @@ -87,28 +87,28 @@
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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  • - 成为会员 + Become a Member
    • diff --git a/docs/beginner/introyt.html b/docs/beginner/introyt.html index 5607da0..30d6480 100644 --- a/docs/beginner/introyt.html +++ b/docs/beginner/introyt.html @@ -89,28 +89,28 @@
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    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

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    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

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    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    为边缘设备构建创新的、注重隐私的人工智能体验

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
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    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    更多关于特定领域库的信息

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    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    为边缘设备构建创新的、注重隐私的人工智能体验

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    如何使用 PyTorch 的全面指导

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    • @@ -601,15 +601,15 @@

    Index

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -171,11 +171,11 @@
    PyTorch -

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    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
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    • diff --git a/docs/intermediate/fx_conv_bn_fuser.html b/docs/intermediate/fx_conv_bn_fuser.html index 5cab743..478b40a 100644 --- a/docs/intermediate/fx_conv_bn_fuser.html +++ b/docs/intermediate/fx_conv_bn_fuser.html @@ -89,28 +89,28 @@
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    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    • diff --git a/docs/intermediate/jacobians_hessians.html b/docs/intermediate/jacobians_hessians.html index 9b3d91d..71075fd 100644 --- a/docs/intermediate/jacobians_hessians.html +++ b/docs/intermediate/jacobians_hessians.html @@ -89,28 +89,28 @@
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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    • diff --git a/docs/intermediate/mario_rl_tutorial.html b/docs/intermediate/mario_rl_tutorial.html index 3a09930..298120e 100644 --- a/docs/intermediate/mario_rl_tutorial.html +++ b/docs/intermediate/mario_rl_tutorial.html @@ -89,28 +89,28 @@
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    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    • diff --git a/docs/intermediate/memory_format_tutorial.html b/docs/intermediate/memory_format_tutorial.html index 880a6d3..2ef557b 100644 --- a/docs/intermediate/memory_format_tutorial.html +++ b/docs/intermediate/memory_format_tutorial.html @@ -89,28 +89,28 @@
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    + About PyTorch Edge +

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    + About PyTorch Edge +

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    • diff --git a/docs/intermediate/model_parallel_tutorial.html b/docs/intermediate/model_parallel_tutorial.html index 598938e..c1b3638 100644 --- a/docs/intermediate/model_parallel_tutorial.html +++ b/docs/intermediate/model_parallel_tutorial.html @@ -89,28 +89,28 @@
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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -183,27 +183,27 @@
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    • diff --git a/docs/intermediate/nvfuser_intro_tutorial.html b/docs/intermediate/nvfuser_intro_tutorial.html index ff7231b..e376584 100644 --- a/docs/intermediate/nvfuser_intro_tutorial.html +++ b/docs/intermediate/nvfuser_intro_tutorial.html @@ -89,28 +89,28 @@
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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    • diff --git a/docs/intermediate/optimizer_step_in_backward_tutorial.html b/docs/intermediate/optimizer_step_in_backward_tutorial.html index a9a0529..2fa87ef 100644 --- a/docs/intermediate/optimizer_step_in_backward_tutorial.html +++ b/docs/intermediate/optimizer_step_in_backward_tutorial.html @@ -87,28 +87,28 @@
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    为边缘设备构建创新的、注重隐私的人工智能体验

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    @@ -172,11 +172,11 @@
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    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    更多关于特定领域库的信息

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    • diff --git a/docs/intermediate/rpc_tutorial.html b/docs/intermediate/rpc_tutorial.html index da45fa0..0ade493 100644 --- a/docs/intermediate/rpc_tutorial.html +++ b/docs/intermediate/rpc_tutorial.html @@ -89,28 +89,28 @@
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
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    为边缘设备构建创新的、注重隐私的人工智能体验

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     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    • diff --git a/docs/intermediate/seq2seq_translation_tutorial.html b/docs/intermediate/seq2seq_translation_tutorial.html index a810a67..906a564 100644 --- a/docs/intermediate/seq2seq_translation_tutorial.html +++ b/docs/intermediate/seq2seq_translation_tutorial.html @@ -89,28 +89,28 @@
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    为边缘设备构建创新的、注重隐私的人工智能体验

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    为边缘设备构建创新的、注重隐私的人工智能体验

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    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    为边缘设备构建创新的、注重隐私的人工智能体验

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    Build innovative and privacy-aware AI experiences for edge devices

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    为边缘设备构建创新的、注重隐私的人工智能体验

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

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    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

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    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    @@ -170,11 +170,11 @@
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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

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    @@ -170,11 +170,11 @@
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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    @@ -172,11 +172,11 @@
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    @@ -170,11 +170,11 @@
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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    @@ -170,11 +170,11 @@
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     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    @@ -183,27 +183,27 @@
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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
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    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    更多关于特定领域库的信息

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    @@ -170,11 +170,11 @@
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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    @@ -170,11 +170,11 @@
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -183,27 +183,27 @@
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    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    为边缘设备构建创新的、注重隐私的人工智能体验

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    为边缘设备构建创新的、注重隐私的人工智能体验

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    为边缘设备构建创新的、注重隐私的人工智能体验

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     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    为边缘设备构建创新的、注重隐私的人工智能体验

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -170,11 +170,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -183,27 +183,27 @@
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    • @@ -738,7 +738,7 @@
    Extension points in ``nn.Module`` for ``load_state_dict`` and tensor subclasses

    Extension points in nn.Module for load_state_dict and tensor subclasses

    Extension points in ``nn.Module`` for ``load_state_dict`` and tensor subclasses
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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    • @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

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    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    @@ -172,11 +172,11 @@
    PyTorch -

    如何使用 PyTorch 的全面指导

    +

    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

    更多关于特定领域库的信息

    +

    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
    • @@ -213,15 +213,15 @@
  • - 成为会员 + Become a Member
    • @@ -601,95 +601,80 @@

    PyTorch Profiler

    -

    This recipe explains how to use PyTorch profiler and measure the time and -memory consumption of the model’s operators.

    -
    -

    Introduction

    -

    PyTorch includes a simple profiler API that is useful when user needs -to determine the most expensive operators in the model.

    -

    In this recipe, we will use a simple Resnet model to demonstrate how to -use profiler to analyze model performance.

    +

    本教程解释了如何使用PyTorch profiler,并测量模型算子的时间和内存消耗。

    +
    +

    简介

    +

    当用户需要确定模型中最耗费资源的算子时,PyTorch包含一个简单的profiler API非常有用。

    +

    在本教程中,我们将使用一个简单的 Resnet 模型来演示如何使用profiler分析模型性能。

    -
    -

    Setup

    -

    To install torch and torchvision use the following command:

    +
    +

    环境设置

    +

    要安装 torch 和 torchvision,请使用以下命令:

    pip install torch torchvision
    -
    -

    Steps

    +
    +

    具体步骤

      -

    1. Import all necessary libraries

      2. -

    2. Instantiate a simple Resnet model

      3. -

    3. Using profiler to analyze execution time

      4. -

    4. Using profiler to analyze memory consumption

      5. -

    5. Using tracing functionality

      6. -

    6. Examining stack traces

      7. -

    7. Using profiler to analyze long-running jobs

      8. +

    8. 导入所有必需的库

      9. +

    9. 实例化一个简单的Resnet模型

      10. +

    10. 使用profiler分析执行时间

      11. +

    11. 使用profiler分析内存消耗

      12. +

    12. 使用跟踪功能

      13. +

    13. 检查堆栈跟踪

      14. +

    14. 使用profiler分析长时间运行的作业

    -
    -

    1. Import all necessary libraries

    -

    In this recipe we will use torch, torchvision.models -and profiler modules:

    +
    +

    1. 导入依赖的库

    +

    在本教程中,我们将使用 torch、torchvision.models 和 profiler 模块:

    import torch
 import torchvision.models as models
-from torch.profiler import profile, record_function, ProfilerActivity
+from torch.profiler import profile, ProfilerActivity, record_function
    -
    -

    2. Instantiate a simple Resnet model

    -

    Let’s create an instance of a Resnet model and prepare an input -for it:

    +
    +

    2. 创建一个简单的 Resnet 模型

    +

    让我们创建一个 Resnet 模型实例,并为它准备一个输入:

    model = models.resnet18()
 inputs = torch.randn(5, 3, 224, 224)
    -
    -

    3. Using profiler to analyze execution time

    -

    PyTorch profiler is enabled through the context manager and accepts -a number of parameters, some of the most useful are:

    +
    +

    3. 使用profiler分析执行时间

    +

    PyTorch profiler通过上下文管理器启用,并接受多个参数,其中一些最有用的参数如下:

    • -
      activities - a list of activities to profile:
        -

      • ProfilerActivity.CPU - PyTorch operators, TorchScript functions and -user-defined code labels (see record_function below);

        • -

      • ProfilerActivity.CUDA - on-device CUDA kernels;

        • +
        activities - 要分析的活动列表:
          +

        • ProfilerActivity.CPU - PyTorch算子、TorchScript函数和用户定义的代码标签(见下面的 record_function);

          • +

        • ProfilerActivity.CUDA - 设备上的CUDA内核;

      • -

    • record_shapes - whether to record shapes of the operator inputs;

      • -

    • profile_memory - whether to report amount of memory consumed by -model’s Tensors;

      • -

    • use_cuda - whether to measure execution time of CUDA kernels.

      • +

    • record_shapes - 是否记录算子输入的形状;

      • +

    • profile_memory - 是否报告模型张量所消耗的内存量;

      • +

    • use_cuda - 是否测量CUDA内核的执行时间。

    -

    Note: when using CUDA, profiler also shows the runtime CUDA events -occurring on the host.

    -

    Let’s see how we can use profiler to analyze the execution time:

    +

    注意:当使用CUDA时,profiler还会显示主机上发生的运行时CUDA事件。

    +

    让我们看看如何使用profiler分析执行时间:

    with profile(activities=[ProfilerActivity.CPU], record_shapes=True) as prof:
     with record_function("model_inference"):
         model(inputs)
    -

    Note that we can use record_function context manager to label -arbitrary code ranges with user provided names -(model_inference is used as a label in the example above).

    -

    Profiler allows one to check which operators were called during the -execution of a code range wrapped with a profiler context manager. -If multiple profiler ranges are active at the same time (e.g. in -parallel PyTorch threads), each profiling context manager tracks only -the operators of its corresponding range. -Profiler also automatically profiles the asynchronous tasks launched -with torch.jit._fork and (in case of a backward pass) -the backward pass operators launched with backward() call.

    -

    Let’s print out the stats for the execution above:

    +

    注意,我们可以使用 record_function 上下文管理器为任意代码范围添加用户提供的名称标签 +(在上面的示例中使用 model_inference 作为标签)。

    +

    Profiler允许检查在使用profiler上下文管理器包装的代码范围内执行期间调用了哪些算子。 +如果同时存在多个活动的profiler范围(例如在并行PyTorch线程中),每个profiling上下文管理器只跟踪其对应范围的算子。 +Profiler还会自动分析使用 torch.jit._fork 启动的异步任务,以及在反向传播过程中使用 backward() 调用启动的反向传播算子。

    +

    让我们打印出上述执行的统计信息:

    print(prof.key_averages().table(sort_by="cpu_time_total", row_limit=10))
    -

    The output will look like (omitting some columns):

    +

    输出将如下所示(省略了一些列):

    # ---------------------------------  ------------  ------------  ------------  ------------
 #                              Name      Self CPU     CPU total  CPU time avg    # of Calls
 # ---------------------------------  ------------  ------------  ------------  ------------
@@ -708,17 +693,21 @@ 
    3. Using profiler to analyze execution time#
    -

    Here we see that, as expected, most of the time is spent in convolution (and specifically in mkldnn_convolution -for PyTorch compiled with MKL-DNN support). -Note the difference between self cpu time and cpu time - operators can call other operators, self cpu time excludes time -spent in children operator calls, while total cpu time includes it. You can choose to sort by the self cpu time by passing -sort_by="self_cpu_time_total" into the table call.

    To get a finer granularity of results and include operator input shapes, pass group_by_input_shape=True -(note: this requires running the profiler with record_shapes=True):

    -
    print(prof.key_averages(group_by_input_shape=True).table(sort_by="cpu_time_total", row_limit=10))
+(note: this requires running the profiler with record_shapes=True):
+这里我们可以看到,如预期的那样,大部分时间都花在了卷积上(对于使用 MKL-DNN 支持编译的PyTorch,特别是在 mkldnn_convolution 上)。
+注意自身cpu时间和cpu时间之间的区别 - 算子可以调用其他算子,自身cpu时间不包括在子算子调用中花费的时间,而总cpu时间包括了它。
+你可以通过将 sort_by="self_cpu_time_total" 传递给 table 调用来选择按自身cpu时间排序。
    
+
    要获得更细粒度的结果并包含算子输入形状,请传递 group_by_input_shape=True
+(注意:这需要使用 record_shapes=True 运行profiler):
    
+
    print(
+    prof.key_averages(group_by_input_shape=True).table(
+        sort_by="cpu_time_total", row_limit=10
+    )
+)
 
    
 
    
-
    The output might look like this (omitting some columns):
    
+
    输出可能如下所示(省略了一些列):
    
 
    ---------------------------------  ------------  -------------------------------------------
                              Name     CPU total                                 Input Shapes
 ---------------------------------  ------------  -------------------------------------------
@@ -736,21 +725,22 @@ 
    3. Using profiler to analyze execution time CPU time total: 57.549ms
 
    
 
    
-
    Note the occurrence of aten::convolution twice with different input shapes.
    
-
    Profiler can also be used to analyze performance of models executed on GPUs:
    
+
    注意 aten::convolution 出现了两次,具有不同的输入形状。
    
+
    Profiler也可用于分析在GPU上执行的模型的性能:
    
 
    model = models.resnet18().cuda()
 inputs = torch.randn(5, 3, 224, 224).cuda()
 
-with profile(activities=[
-        ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True) as prof:
+with profile(
+    activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True
+) as prof:
     with record_function("model_inference"):
         model(inputs)
 
 print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
 
    
 
    
-
    (Note: the first use of CUDA profiling may bring an extra overhead.)
    
-
    The resulting table output (omitting some columns):
    
+
    (注意:第一次使用CUDA分析可能会带来额外的开销。)
    
+
    结果输出(省略了一些列):
    
 
    -------------------------------------------------------  ------------  ------------
                                                    Name     Self CUDA    CUDA total
 -------------------------------------------------------  ------------  ------------
@@ -769,20 +759,19 @@ 
    3. Using profiler to analyze execution time CUDA time total: 11.666ms
 
    
 
    
-
    Note the occurrence of on-device kernels in the output (e.g. sgemm_32x32x32_NN).
    
+
    注意在输出中出现了设备上的内核(例如 sgemm_32x32x32_NN)。
    -
    -

    4. Using profiler to analyze memory consumption

    -

    PyTorch profiler can also show the amount of memory (used by the model’s tensors) -that was allocated (or released) during the execution of the model’s operators. -In the output below, ‘self’ memory corresponds to the memory allocated (released) -by the operator, excluding the children calls to the other operators. -To enable memory profiling functionality pass profile_memory=True.

    +
    +

    4. 使用 profiler 分析内存消耗

    +

    PyTorch profiler还可以显示在执行模型算子期间分配(或释放)的内存量(由模型张量使用)。 +在下面的输出中,’self’内存对应于算子分配(释放)的内存,不包括对其他算子的子调用。 +要启用内存分析功能,请传递 profile_memory=True。

    model = models.resnet18()
 inputs = torch.randn(5, 3, 224, 224)
 
-with profile(activities=[ProfilerActivity.CPU],
-        profile_memory=True, record_shapes=True) as prof:
+with profile(
+    activities=[ProfilerActivity.CPU], profile_memory=True, record_shapes=True
+) as prof:
     model(inputs)
 
 print(prof.key_averages().table(sort_by="self_cpu_memory_usage", row_limit=10))
@@ -807,7 +796,7 @@ 
    4. Using profiler to analyze memory consumptionprint(prof.key_averages().table(sort_by="cpu_memory_usage", row_limit=10))
    -

    The output might look like this (omitting some columns):

    +

    输出如下所示(省略了一些列):

    ---------------------------------  ------------  ------------  ------------
                              Name       CPU Mem  Self CPU Mem    # of Calls
 ---------------------------------  ------------  ------------  ------------
@@ -826,9 +815,9 @@ 
    4. Using profiler to analyze memory consumption
-
    5. Using tracing functionality
    
-
    Profiling results can be outputted as a .json trace file:
    
+
-
    
-
    6. Examining stack traces
    
-
    Profiler can be used to analyze Python and TorchScript stack traces:
    
+
-
    
-
    7. Using profiler to analyze long-running jobs
    
-
    PyTorch profiler offers an additional API to handle long-running jobs
-(such as training loops). Tracing all of the execution can be
-slow and result in very large trace files. To avoid this, use optional
-arguments:
    
+
    
+
    7. 使用分析器分析长时间运行的作业
    
+
    PyTorch分析器提供了一个额外的API来处理长时间运行的作业
+(例如训练循环)。跟踪所有执行可能会很慢,并导致非常大的跟踪文件。
+为了避免这种情况,可以使用可选参数:
    
 
      
-
    • schedule - specifies a function that takes an integer argument (step number)
-as an input and returns an action for the profiler, the best way to use this parameter
-is to use torch.profiler.schedule helper function that can generate a schedule for you;
      • 
-
    • on_trace_ready - specifies a function that takes a reference to the profiler as
-an input and is called by the profiler each time the new trace is ready.
      • 
+
    • schedule - 指定一个函数,该函数以整数参数(步骤编号)作为输入,
+并返回分析器的操作,使用此参数的最佳方式是使用 torch.profiler.schedule
+帮助函数,它可以为您生成一个计划;
      • 
+
    • on_trace_ready - 指定一个函数,该函数以分析器的引用作为输入,
+并在每次新的跟踪准备就绪时由分析器调用。
      • 
 
    
-
    To illustrate how the API works, let’s first consider the following example with
-torch.profiler.schedule helper function:
    
+
    为了说明该API的工作原理,让我们首先考虑以下使用 torch.profiler.schedule
+帮助函数的示例:
    
 
    from torch.profiler import schedule
 
-my_schedule = schedule(
-    skip_first=10,
-    wait=5,
-    warmup=1,
-    active=3,
-    repeat=2)
+my_schedule = schedule(skip_first=10, wait=5, warmup=1, active=3, repeat=2)
 
    
 
    
-
    Profiler assumes that the long-running job is composed of steps, numbered
-starting from zero. The example above defines the following sequence of actions
-for the profiler:
    
+
    分析器假设长时间运行的作业由从零开始编号的步骤组成。
+上面的示例为分析器定义了以下操作序列:
    
 
      
-
    1. Parameter skip_first tells profiler that it should ignore the first 10 steps
-(default value of skip_first is zero);
      2. 
-
    2. After the first skip_first steps, profiler starts executing profiler cycles;
      3. 
-
    3. Each cycle consists of three phases:
      
+
    4. 参数 skip_first 告诉分析器它应该忽略前10个步骤
+(skip_first 的默认值为零);
      5. 
+
    5. 在第一个 skip_first 步骤之后,分析器开始执行分析器周期;
      6. 
+
    6. 每个周期由三个阶段组成:
      
 
        
-
      • idling (wait=5 steps), during this phase profiler is not active;
        • 
-
      • warming up (warmup=1 steps), during this phase profiler starts tracing, but
-the results are discarded; this phase is used to discard the samples obtained by
-the profiler at the beginning of the trace since they are usually skewed by an extra
-overhead;
        • 
-
      • active tracing (active=3 steps), during this phase profiler traces and records data;
        • 
+
      • 空闲(``wait=5``步骤),在此阶段分析器不活动;
        • 
+
      • 预热(``warmup=1``步骤),在此阶段分析器开始跟踪,但结果被丢弃;
+此阶段用于丢弃分析器在跟踪开始时获得的样本,因为它们通常由额外的开销扭曲;
        • 
+
      • 主动跟踪(``active=3``步骤),在此阶段分析器跟踪和记录数据;
        • 
 
      
 
      7. 
-
    7. An optional repeat parameter specifies an upper bound on the number of cycles.
-By default (zero value), profiler will execute cycles as long as the job runs.
      8. 
+
    8. 可选的 repeat 参数指定周期的上限。
+默认情况下(零值),分析器将尽可能长时间地执行周期。
      9. 
 
    
-
    Thus, in the example above, profiler will skip the first 15 steps, spend the next step on the warm up,
-actively record the next 3 steps, skip another 5 steps, spend the next step on the warm up, actively
-record another 3 steps. Since the repeat=2 parameter value is specified, the profiler will stop
-the recording after the first two cycles.
    
-
    At the end of each cycle profiler calls the specified on_trace_ready function and passes itself as
-an argument. This function is used to process the new trace - either by obtaining the table output or
-by saving the output on disk as a trace file.
    
-
    To send the signal to the profiler that the next step has started, call prof.step() function.
-The current profiler step is stored in prof.step_num.
    
-
    The following example shows how to use all of the concepts above:
    
+
    因此,在上面的示例中,分析器将跳过前15个步骤,在下一步进行预热,
+在接下来的3个步骤中主动记录,再跳过另外5个步骤,在下一步进行预热,
+在另外3个步骤中主动记录。由于指定了 repeat=2 参数值,
+分析器将在前两个周期之后停止记录。
    
+
    在每个周期结束时,分析器调用指定的 on_trace_ready 函数并将自身作为参数传递。
+此函数用于处理新的跟踪 - 通过获取表输出或将输出保存到磁盘上的跟踪文件。
    
+
    要向分析器发送下一步已经开始的信号,请调用 prof.step() 函数。
+当前分析器步骤存储在 prof.step_num 中。
    
+
    以下示例显示了如何使用上述所有概念:
    
 
    def trace_handler(p):
     output = p.key_averages().table(sort_by="self_cuda_time_total", row_limit=10)
     print(output)
     p.export_chrome_trace("/tmp/trace_" + str(p.step_num) + ".json")
 
+
 with profile(
     activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
-    schedule=torch.profiler.schedule(
-        wait=1,
-        warmup=1,
-        active=2),
-    on_trace_ready=trace_handler
+    schedule=torch.profiler.schedule(wait=1, warmup=1, active=2),
+    on_trace_ready=trace_handler,
 ) as p:
     for idx in range(8):
         model(inputs)
@@ -952,13 +932,13 @@ 
    7. Using profiler to analyze long-running jobs
-
    Learn More
    
-
    Take a look at the following recipes/tutorials to continue your learning:
    
+
    
+
    了解更多
    
+
    查看以下教程以继续学习:
    
 
 
    Total running time of the script: ( 0 minutes  0.000 seconds)
    
   
@@ -170,11 +170,11 @@
               
    
                 
                   PyTorch
-                  
    如何使用 PyTorch 的全面指导
    
+                  
    Explore the documentation for comprehensive guidance on how to use PyTorch
    
                     
                 
                   PyTorch Domains
-                  
    更多关于特定领域库的信息
    
+                  
    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    
                     
               
    
             
    
@@ -183,27 +183,27 @@
           
  • 
             
    • 
@@ -211,15 +211,15 @@
           
  • 
             
  • 
             
    
               
-                成为会员
+                Become a Member
               
             
    
           
    • 
diff --git a/docs/recipes/recipes/save_load_across_devices.html b/docs/recipes/recipes/save_load_across_devices.html
index f11f78c..6f998c1 100644
--- a/docs/recipes/recipes/save_load_across_devices.html
+++ b/docs/recipes/recipes/save_load_across_devices.html
@@ -89,28 +89,28 @@
           
  • 
           
@@ -119,28 +119,28 @@
           
  • 
           
@@ -153,12 +153,12 @@
               
               
    
                 
-                  PyTorch Edge
-                  
    为边缘设备构建创新的、注重隐私的人工智能体验
    
+                  About PyTorch Edge
+                  
    Build innovative and privacy-aware AI experiences for edge devices
    
                     
                 
                   ExecuTorch
-                  
    在移动和边缘设备上实现端到端的设备内推理能力的解决方案
    
+                  
    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    
                     
               
    
             
    •   
@@ -172,11 +172,11 @@
               
    
                 
                   PyTorch
-                  
    如何使用 PyTorch 的全面指导
    
+                  
    Explore the documentation for comprehensive guidance on how to use PyTorch
    
                     
                 
                   PyTorch Domains
-                  
    更多关于特定领域库的信息
    
+                  
    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    
                     
               
    
             
    
@@ -185,27 +185,27 @@
           
  • 
             
    • 
@@ -213,15 +213,15 @@
           
  • 
             
  • 
             
    
               
-                成为会员
+                Become a Member
               
             
    
           
    • 
diff --git a/docs/recipes/recipes/saving_and_loading_a_general_checkpoint.html b/docs/recipes/recipes/saving_and_loading_a_general_checkpoint.html
index fc25b1e..23cc94c 100644
--- a/docs/recipes/recipes/saving_and_loading_a_general_checkpoint.html
+++ b/docs/recipes/recipes/saving_and_loading_a_general_checkpoint.html
@@ -89,28 +89,28 @@
           
  • 
           
@@ -119,28 +119,28 @@
           
  • 
           
@@ -153,12 +153,12 @@
               
               
    
                 
-                  PyTorch Edge
-                  
    为边缘设备构建创新的、注重隐私的人工智能体验
    
+                  About PyTorch Edge
+                  
    Build innovative and privacy-aware AI experiences for edge devices
    
                     
                 
                   ExecuTorch
-                  
    在移动和边缘设备上实现端到端的设备内推理能力的解决方案
    
+                  
    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
    
                     
               
    
             
    •   
@@ -172,11 +172,11 @@
               
    
                 
                   PyTorch
-                  
    如何使用 PyTorch 的全面指导
    
+                  
    Explore the documentation for comprehensive guidance on how to use PyTorch
    
                     
                 
                   PyTorch Domains
-                  
    更多关于特定领域库的信息
    
+                  
    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    
                     
               
    
             
    
@@ -185,27 +185,27 @@
           
  • 
             
    • 
@@ -213,15 +213,15 @@
           
  • 
             
  • 
             
    
               
-                成为会员
+                Become a Member
               
             
    
           
    • 
diff --git a/docs/recipes/recipes/saving_and_loading_models_for_inference.html b/docs/recipes/recipes/saving_and_loading_models_for_inference.html
index dc28346..8919892 100644
--- a/docs/recipes/recipes/saving_and_loading_models_for_inference.html
+++ b/docs/recipes/recipes/saving_and_loading_models_for_inference.html
@@ -89,28 +89,28 @@
           
  • 
           
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-                  PyTorch Edge
-                  
    为边缘设备构建创新的、注重隐私的人工智能体验
    
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    Build innovative and privacy-aware AI experiences for edge devices
    
                     
                 
                   ExecuTorch
-                  
    在移动和边缘设备上实现端到端的设备内推理能力的解决方案
    
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    •   
@@ -172,11 +172,11 @@
               
    
                 
                   PyTorch
-                  
    如何使用 PyTorch 的全面指导
    
+                  
    Explore the documentation for comprehensive guidance on how to use PyTorch
    
                     
                 
                   PyTorch Domains
-                  
    更多关于特定领域库的信息
    
+                  
    Read the PyTorch Domains documentation to learn more about domain-specific libraries
    @@ -185,27 +185,27 @@
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  • - 成为会员 + Become a Member
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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    Explore the documentation for comprehensive guidance on how to use PyTorch

     PyTorch Domains -

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    Explore the documentation for comprehensive guidance on how to use PyTorch

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    @@ -185,27 +185,27 @@
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  • - 成为会员 + Become a Member
    • @@ -903,7 +903,7 @@

    PyTorch Benchmark Timer 快速入门

    PyTorch Profiler

    -

    Learn how to use PyTorch's profiler to measure operators time and memory consumption

    +

    学习如何使用 PyTorch Profiler 来测量算子的时间和内存消耗。

    Basics

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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    为边缘设备构建创新的、注重隐私的人工智能体验

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    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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     PyTorch Domains -

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    Read the PyTorch Domains documentation to learn more about domain-specific libraries
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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

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    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    如何使用 PyTorch 的全面指导

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     PyTorch Domains -

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    为边缘设备构建创新的、注重隐私的人工智能体验

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    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
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    如何使用 PyTorch 的全面指导

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     PyTorch Domains -

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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, 213, 214, 215, 220, 221, 228, 231, 234, 237, 245, 251, 252, 254, 255, 258, 262, 263], "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, 231, 234, 238, 247, 252, 262], "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, 226, 231, 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, 231, 232, 245, 247, 253, 254, 256, 260, 262], "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, 231, 238, 247, 262], "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, 238, 251, 262], "pair": [1, 6, 14, 47, 49, 52, 116, 118, 128, 129, 137, 150, 154, 159, 160, 165, 168, 178, 179, 194, 199, 211, 226, 262], "maxim": [1, 14, 52, 73, 82, 97, 99, 126, 146, 160, 172, 176, 194, 247], "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, 219, 231, 239, 247, 258], "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, 229, 244, 254], "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, 210, 222, 230, 231, 232, 235, 237, 249, 252, 262], "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, 230, 231, 232, 239, 257, 262], "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, 226, 230, 235, 244, 247, 251, 253], "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, 226, 228, 230, 231, 237, 238, 244, 247, 258, 262], "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, 226, 228, 229, 230, 231, 234, 238, 244, 247, 254, 256, 257, 260, 262], "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, 231, 234, 247, 253, 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, 231, 237, 253, 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, 234, 238, 247, 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, 237, 238, 244], "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, 229, 234, 247, 252, 256], "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, 256], "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, 213, 219, 222, 224, 225, 231, 232, 234, 239, 244, 247, 252, 256], "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, 247, 262], "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, 230, 234, 237, 239, 244, 247, 251, 252, 258, 260, 262], "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, 226, 229, 245, 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, 229, 231, 233, 234, 237, 239, 240, 241, 242, 243, 244, 248, 249, 250, 252, 261, 262, 263], "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, 237, 244, 247, 253, 254, 257, 261], "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, 231, 234, 247], "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, 234, 247, 252, 254, 256, 258, 260], "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, 234, 258, 266], "illustr": [1, 19, 44, 47, 56, 116, 117, 124, 125, 126, 138, 160, 169, 171, 178, 191, 192, 195, 215, 226, 229, 230, 238, 244, 247], "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, 226, 229, 234, 237, 244, 247, 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, 230, 232, 237, 238, 239, 247], "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, 213, 214, 220, 222, 223, 224, 225, 226, 227, 228, 229, 231, 232, 247, 255, 256, 260], "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, 256, 258, 262, 263], "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, 231, 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, 229, 230, 231, 234, 235, 244, 245, 247, 252, 253, 255, 260], "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, 213, 218, 219, 234, 237, 247, 257, 263], "loss_modul": [1, 159], "whatev": [1, 8, 22, 23, 99, 101, 112, 195, 226], "convent": [1, 14, 52, 60, 112, 126, 136, 171, 216, 231], "receiv": [1, 4, 6, 14, 16, 55, 64, 87, 101, 111, 135, 159, 161, 162, 163, 172, 230, 247], "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, 230, 247, 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, 229, 230, 231, 233, 234, 237, 238, 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, 229, 238, 247, 262], "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, 226, 230, 234, 238, 245, 247, 252, 256, 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, 213, 220, 245, 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, 226, 230, 262], "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, 213, 215, 216, 226, 228, 230, 231, 244, 245, 263], "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, 226, 231, 257, 260], "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, 231, 234, 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, 231, 238, 247, 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, 234, 237, 245, 254, 262], "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, 234, 238, 247, 252, 262, 263], "metric": [1, 17, 87, 97, 109, 122, 137, 146, 168, 171, 177, 178, 201, 221, 226, 231, 245], "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, 251, 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, 226, 228, 234, 238, 244, 245, 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, 231, 235, 237, 251, 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, 226, 228, 238, 251, 262, 263], "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, 234, 237, 247, 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, 213, 215, 216, 219, 220, 221, 226, 237, 244, 245, 260, 266], "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], "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, 226, 231, 234, 237, 247, 254, 256, 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, 229, 230, 231, 247, 252, 260, 262, 263], "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, 213, 220, 223, 226, 229, 230, 231, 234, 238, 244, 247, 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, 213, 218, 219, 230, 231, 234, 247, 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, 234, 245, 260, 262, 266], "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, 229, 230, 231, 234, 237, 239, 245, 247, 251, 252, 254, 256, 262, 264], "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, 230, 234, 237, 247, 252, 254, 262], "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, 229, 235, 245, 247, 258, 260], "modal": [1, 60, 229], "complex": [1, 6, 23, 25, 50, 61, 67, 68, 97, 105, 112, 120, 123, 133, 150, 153, 161, 163, 169, 193, 203, 209, 234, 239, 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, 213, 214, 219, 230, 231, 235, 238, 247, 250, 262, 263], "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, 234, 262], "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, 226, 228, 229, 244, 245, 247, 253, 257, 262], "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, 231, 237, 247], "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, 214, 215, 216, 218, 219, 220, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 236, 237, 239, 240, 241, 242, 243, 244, 246, 247, 248, 249, 250, 251, 252, 254, 255, 262], "elementari": [1, 2, 234], "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, 226, 230, 231, 262], "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, 231, 247, 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, 230, 231, 245, 257, 260], "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, 226, 229, 230, 231, 234, 237, 238, 239, 244, 245, 247, 252, 255, 257, 258, 260, 262], "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, 226, 228, 230, 231, 232, 237, 244, 247, 254, 255], "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, 213, 216, 219, 223, 226, 231, 234, 238, 239, 245, 247, 251, 254, 258, 263], "solv": [1, 6, 14, 49, 51, 97, 103, 117, 118, 149, 153, 157, 159, 161, 163, 176, 191, 231, 237, 247], "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, 231, 238, 247], "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, 229, 251, 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, 231], "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, 226, 229, 231, 234, 237, 238, 244, 247, 252, 256, 258, 262, 263, 267], "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, 230, 231, 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, 226, 228, 229, 230, 231, 244, 256, 260, 262], "compat": [1, 4, 5, 6, 8, 11, 17, 50, 60, 94, 95, 101, 136, 147, 164, 173, 174, 182, 187, 202, 204, 216, 222, 256], "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, 230, 231, 234, 237, 239, 254, 256, 262], "fit": [1, 6, 7, 9, 10, 11, 12, 20, 24, 61, 87, 103, 122, 123, 124, 133, 148, 149, 163, 181, 230, 262], "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, 213, 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, 213, 216, 218, 220, 223, 224, 225, 227, 228, 229, 234, 244, 247, 251, 252], "strictli": [1, 159], "speak": [1, 8, 43, 125, 135, 149, 247], "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, 226, 230, 245, 247, 251, 256, 262], "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, 226, 228, 230, 231, 234, 237, 244, 247, 262], "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, 230, 247, 262], "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, 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134, 135, 147, 159, 162, 163, 168, 212, 216, 247], "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, 214, 215, 221, 228, 231, 237, 238, 247, 251, 255, 258, 261, 262], "offer": [1, 11, 14, 18, 42, 43, 53, 61, 99, 122, 124, 138, 141, 144, 145, 197, 214, 216, 229, 231, 238, 247], "multiasyncdatacollector": [1, 159], "rollout": [1, 136, 159], "asynchron": [1, 21, 61, 120, 121, 126, 134, 149, 155, 159, 163, 238], "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, 237], "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], "maximum": [1, 11, 49, 60, 82, 102, 113, 126, 128, 136, 137, 144, 159, 164, 165, 173, 174, 185, 194, 195, 213, 247], "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, 231, 244, 252, 263, 265], "termin": [1, 14, 23, 53, 60, 87, 159, 160, 162, 163, 171, 188, 206, 213, 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, 230, 234, 247, 260], "regist": [1, 22, 43, 47, 78, 108, 109, 112, 121, 122, 124, 133, 141, 152, 153, 159, 173, 174, 177, 207, 208, 216, 220, 226, 230, 239], "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, 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85, 87, 97, 111, 118, 123, 126, 127, 137, 138, 144, 145, 146, 152, 154, 162, 163, 165, 169, 177, 183, 185, 201, 219, 231, 238, 247, 250], "equal": [1, 4, 10, 95, 97, 115, 116, 125, 137, 147, 156, 159, 160, 171, 173, 174, 176, 192, 216, 247, 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], "sub": [1, 6, 20, 25, 49, 60, 109, 124, 143, 149, 159, 163, 165, 168, 176, 177, 185, 215, 246, 262], "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, 231, 241, 247], "dedic": [1, 10, 55, 60, 112, 134, 162, 163, 177, 199, 208, 223, 228, 229, 230, 258, 263], "frequenc": [1, 7, 83, 126, 223], "trainer": [1, 16, 17, 24, 55, 126, 131, 148, 161, 162, 163, 201, 214], "make_record": 1, "record_interv": 1, "load": [1, 5, 17, 18, 19, 20, 21, 23, 24, 34, 35, 38, 39, 42, 43, 47, 51, 52, 55, 73, 75, 87, 90, 96, 98, 104, 105, 110, 113, 116, 119, 121, 123, 125, 127, 139, 144, 147, 152, 159, 168, 169, 171, 174, 178, 181, 182, 184, 185, 186, 187, 194, 195, 201, 204, 206, 208, 213, 220, 221, 222, 224, 225, 230, 235, 240, 241, 242, 243, 244, 246, 248, 249, 251, 254], "recorder_obj": 1, "record_fram": 1, "1000": [1, 2, 7, 9, 17, 19, 43, 52, 79, 89, 94, 96, 117, 119, 122, 123, 125, 127, 134, 136, 144, 149, 159, 160, 169, 172, 176, 177, 187, 199, 201, 203, 212, 213, 226, 231, 237, 246], "policy_explor": 1, "everi": [1, 2, 6, 8, 10, 12, 14, 15, 17, 18, 19, 24, 43, 44, 47, 49, 51, 52, 60, 61, 87, 103, 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127, 128, 131, 132, 136, 152, 156, 160, 162, 169, 171, 175, 191, 201, 202], "dimens": [1, 5, 6, 7, 12, 14, 18, 20, 21, 44, 47, 48, 49, 60, 78, 79, 87, 92, 99, 101, 102, 103, 110, 115, 121, 124, 127, 129, 136, 138, 146, 147, 154, 156, 158, 159, 161, 173, 174, 175, 190, 193, 197, 198, 205, 213, 223, 230, 234, 239, 247], "feed": [1, 6, 12, 19, 44, 47, 49, 60, 97, 100, 102, 105, 112, 124, 127, 128, 133, 134, 146, 160, 165, 169, 176, 195, 214], "adapt": [1, 2, 14, 17, 24, 87, 105, 126, 201, 231], "divid": [1, 7, 8, 9, 10, 11, 12, 14, 15, 19, 85, 97, 99, 127, 134, 135, 149, 153, 163, 176, 177, 181], "yield": [1, 6, 11, 12, 16, 17, 19, 21, 104, 112, 115, 116, 118, 124, 125, 161, 163, 184, 192, 201, 219, 221, 241, 247], "regard": [1, 6, 11, 14, 23, 60, 112, 144, 145, 159, 168, 262], "25": [1, 7, 9, 22, 23, 73, 89, 90, 97, 109, 117, 123, 157, 162, 164, 171, 181, 184, 203, 219, 231, 233, 238], "balanc": [1, 6, 18, 133, 168, 226], "choic": [1, 4, 6, 8, 49, 87, 126, 128, 165, 210, 247], 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17, 42, 113, 124, 135, 137, 141, 143, 156, 160, 165, 185, 188, 200, 201, 207, 231, 234], "utd": [1, 136], "update_to_data": 1, "64": [1, 5, 6, 14, 15, 17, 18, 19, 20, 23, 34, 37, 38, 43, 49, 52, 60, 73, 87, 95, 97, 102, 104, 115, 119, 122, 123, 124, 126, 129, 134, 136, 138, 145, 146, 154, 158, 159, 161, 162, 164, 166, 173, 174, 175, 178, 184, 201, 203, 205, 207, 208, 219, 221, 231, 233, 238], "reproduc": [1, 12, 25, 52, 73, 97, 137, 144, 159, 163, 172, 185, 219, 231, 234, 247], "realiz": [1, 17, 201], "sever": [1, 2, 3, 10, 11, 15, 16, 19, 22, 25, 45, 47, 52, 53, 54, 61, 73, 78, 97, 99, 113, 122, 127, 132, 135, 142, 146, 149, 152, 153, 155, 157, 159, 161, 162, 168, 169, 172, 173, 174, 176, 177, 191, 200, 208, 219, 220, 223, 231, 252], "ve": [1, 5, 6, 7, 8, 17, 21, 22, 25, 45, 47, 50, 76, 78, 113, 121, 125, 129, 136, 142, 143, 145, 152, 154, 160, 162, 169, 176, 184, 189, 191, 192, 201, 213, 214, 231, 234, 257], "_must_": 1, "99": [1, 63, 64, 67, 68, 69, 71, 72, 111, 159, 160, 163], 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193, 214, 215, 247], "altern": [1, 6, 10, 23, 48, 49, 102, 113, 117, 123, 138, 143, 147, 155, 157, 159, 165, 168, 171, 172, 182, 184, 192, 199, 209, 244, 247, 255], "hack": [1, 137, 185, 189, 244], "find": [1, 2, 4, 5, 6, 8, 10, 17, 22, 23, 48, 52, 73, 82, 83, 85, 87, 98, 101, 121, 123, 127, 128, 135, 137, 141, 142, 144, 149, 153, 157, 160, 163, 165, 168, 173, 174, 183, 189, 190, 191, 192, 195, 197, 200, 201, 204, 208, 213, 221, 222, 223, 226, 229, 231, 245, 252], "despit": [1, 73, 133], "fact": [1, 3, 5, 6, 8, 12, 14, 19, 22, 23, 60, 73, 80, 97, 99, 103, 113, 125, 153, 159, 160, 169], "part": [1, 3, 5, 6, 7, 8, 9, 10, 13, 14, 15, 16, 20, 23, 42, 43, 45, 49, 52, 58, 59, 60, 73, 76, 87, 98, 100, 103, 113, 116, 118, 121, 122, 124, 125, 128, 130, 131, 134, 135, 136, 137, 139, 143, 144, 149, 152, 153, 159, 165, 173, 174, 181, 182, 183, 184, 186, 188, 189, 191, 196, 198, 204, 208, 209, 213, 216, 221, 222, 223, 226, 229, 230, 234, 244, 252, 254, 260, 261, 262], "thank": [1, 9, 19, 49, 104, 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169, 171, 175, 178, 181, 185, 200, 201, 209, 215, 216, 221], "rn": 1, "rs": [1, 165], "std": [1, 2, 4, 5, 6, 12, 15, 19, 22, 23, 51, 58, 59, 73, 90, 92, 95, 97, 117, 149, 155, 157, 158, 163, 166, 171, 182, 186, 187, 189, 197, 198, 204, 208, 220, 222, 229, 231, 256], "set_descript": [1, 14, 136, 159], "2f": [1, 7, 19, 97, 115, 119, 123, 127, 129, 156, 158, 161, 163, 176, 177, 182, 184, 197, 198, 203, 228, 231, 234], "grad": [1, 2, 6, 12, 13, 16, 32, 43, 47, 63, 64, 68, 73, 76, 78, 89, 101, 104, 111, 123, 125, 127, 128, 129, 130, 133, 135, 141, 145, 150, 152, 159, 161, 162, 163, 189, 205, 211, 216, 230, 250], "norm": [1, 2, 14, 17, 19, 52, 78, 89, 97, 110, 121, 124, 153, 156, 159, 171, 182, 195, 201], "shutdown": [1, 16, 134, 161, 162, 163, 212], "del": [1, 44, 129, 141, 152, 159, 184, 186, 237], "plot": [1, 6, 12, 14, 17, 51, 52, 73, 82, 89, 113, 117, 126, 136, 146, 149, 159, 160, 161, 166, 169, 171, 195, 236], "mention": [1, 5, 6, 10, 11, 15, 16, 52, 60, 73, 85, 95, 97, 98, 112, 123, 124, 135, 136, 160, 162, 168, 171, 185, 197, 226, 237, 244], "greater": [1, 11, 49, 52, 125, 176, 188, 206], "matplotlib": [1, 6, 12, 14, 34, 44, 51, 52, 57, 73, 75, 89, 90, 92, 94, 96, 104, 117, 127, 128, 136, 146, 149, 157, 159, 160, 165, 166, 169, 171, 178, 195, 236], "pyplot": [1, 6, 12, 14, 34, 44, 51, 52, 73, 75, 89, 90, 92, 94, 96, 104, 117, 127, 128, 136, 146, 149, 157, 159, 160, 165, 166, 169, 171, 178, 195, 236], "plt": [1, 6, 12, 14, 34, 44, 51, 52, 73, 75, 89, 90, 92, 94, 96, 117, 127, 128, 136, 146, 149, 157, 159, 160, 165, 166, 169, 171, 178, 195, 236], "zip": [1, 6, 17, 18, 44, 49, 50, 52, 104, 105, 108, 112, 116, 128, 137, 138, 150, 154, 160, 161, 163, 169, 171, 178, 181, 185, 201, 208, 216, 223, 230], "label": [1, 6, 17, 34, 40, 43, 44, 51, 52, 58, 59, 73, 75, 78, 87, 90, 92, 94, 96, 97, 99, 103, 109, 110, 113, 115, 117, 119, 122, 124, 127, 133, 134, 137, 146, 149, 157, 158, 161, 165, 168, 169, 178, 185, 201, 208, 213, 221, 226, 231, 234, 236, 238, 250, 251, 258, 262], "legend": [1, 52, 146, 262], "xlabel": [1, 14, 52, 73, 136, 160, 171], "ylabel": [1, 17, 52, 73, 160, 171], "tight_layout": [1, 51, 73, 149, 157], "concret": [1, 5, 6, 8, 11, 149, 159, 189, 200], "takeawai": [1, 114], "further": [1, 4, 5, 6, 8, 19, 21, 23, 47, 61, 73, 87, 97, 113, 116, 118, 124, 127, 142, 144, 149, 156, 159, 171, 172, 177, 184, 193, 195, 196, 197, 198, 199, 204, 216, 220, 222, 228, 237, 247, 252, 253], "dispatch": [1, 5, 15, 121, 155, 177, 207, 208, 219, 230], "distpatch": 1, "iql": 1, "flexibl": [1, 2, 5, 6, 23, 24, 25, 49, 60, 85, 105, 112, 115, 121, 122, 131, 199, 231, 240, 257], "minut": [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, 110, 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, 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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, 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], "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, 231, 246, 247, 254, 261, 266], "highli": [2, 5, 6, 10, 18, 49, 60, 87, 165, 175, 177, 205, 247], "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, 230, 235, 247, 251, 254], "explor": [2, 6, 8, 14, 21, 23, 45, 49, 73, 108, 121, 126, 130, 136, 144, 146, 152, 159, 164, 165, 229, 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, 247, 252, 257, 258, 260, 262], "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, 238, 245, 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], "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, 226, 230, 231, 234, 238, 257, 262], "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, 233, 234, 237, 238, 241, 244, 246, 247, 251, 252, 256, 258, 260], "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], "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, 237], "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, 213, 214, 230, 237, 244, 247, 252, 262, 263, 264], "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], "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, 247, 255, 256], "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], "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], "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, 247, 251], "explan": [2, 52, 61, 160, 169, 237], "arriv": [2, 6, 15, 23, 42, 113, 122, 133, 161, 208, 247], "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, 213, 216, 222, 229, 230, 231, 234, 238, 244, 245, 258, 260], "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, 213, 220, 226, 230, 231, 237, 247, 260, 262, 263, 264, 266], "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, 234, 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], "stop": [2, 4, 5, 23, 51, 58, 59, 76, 78, 87, 98, 101, 110, 126, 128, 135, 147, 152, 159, 161, 165, 168, 238], "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, 247, 266], "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], "eq": [2, 19, 23, 49, 60, 95, 123, 129, 162, 166, 173, 182, 197, 198, 221, 238, 262], "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], "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, 226, 230, 245, 260, 261, 262, 267], "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, 230], "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, 230, 235, 237, 239, 241, 242, 243, 244, 245, 248, 249, 250, 251, 253, 254, 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, 234, 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, 226, 229, 230, 231, 234, 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, 213, 214, 218, 220, 221, 228, 247, 252, 254, 255, 256, 257, 260, 262, 263, 267], "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], "subclass": [2, 5, 6, 14, 17, 25, 64, 67, 79, 111, 121, 126, 136, 146, 156, 162, 169, 178, 191, 193, 201, 219, 229, 230, 235, 251, 254, 256], "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, 229, 230, 245, 247, 262], "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, 230, 233, 234, 237, 238, 239, 240, 241, 242, 243, 247, 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, 213, 218, 219, 220, 224, 225, 226, 228, 229, 230, 231, 234, 237, 246, 247, 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], "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], "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, 230, 231, 238, 251, 252, 253, 255, 262, 266], "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, 244], "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, 229, 231, 244, 246, 247], "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, 226, 231, 234, 237, 238, 244], "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, 226, 230, 247], "easiest": [2, 5, 9, 23, 121, 139, 145, 157, 159, 228, 247], "tabl": [2, 16, 21, 89, 103, 109, 115, 121, 122, 135, 137, 143, 144, 162, 163, 164, 168, 173, 174, 175, 219, 231, 238, 266], "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, 230, 231, 234, 237, 238, 244, 245, 247, 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, 238], "fix": [2, 14, 17, 20, 23, 24, 49, 50, 51, 52, 97, 108, 113, 125, 157, 161, 173, 174, 184, 201, 226, 247, 262], "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], "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, 231, 232, 237, 247, 262], "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, 229, 239, 240, 241, 242, 243, 245, 248, 249, 250, 255, 260], "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, 253], "semant": [3, 6, 22, 49, 58, 59, 68, 95, 100, 102, 111, 135, 137, 191, 192, 193, 196, 205, 262], "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], "nvidia": [3, 5, 17, 50, 95, 129, 135, 137, 147, 172, 201, 215, 230, 247, 251, 257], "toolkit": [3, 23, 100, 142, 146, 245], "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, 238, 247, 262], "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, 231, 238, 247], "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, 229, 230, 231, 234, 247], "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, 234, 251, 252, 257], "appear": [3, 11, 14, 22, 25, 103, 226, 229, 234, 262], "heart": [3, 49, 113, 219, 263], "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, 226, 234, 238, 247, 263, 264], "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, 226, 228, 229, 230, 231, 232, 233, 234, 236, 237, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 254, 255, 256, 257, 260, 262], "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, 231, 238, 247, 251, 253, 256, 260], "boost": [3, 97, 99, 144, 145, 176, 184, 199, 207, 216, 220, 221, 247], "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, 230, 231, 234, 237, 238, 252, 254, 255, 258, 262, 263, 264], "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, 247, 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, 213, 221, 223, 230, 231, 239, 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, 244, 258], "captur": [3, 4, 6, 22, 23, 25, 52, 60, 107, 123, 141, 148, 158, 171, 172, 173, 174, 186, 194, 197, 198, 200, 231], "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, 231, 252, 262], "slightli": [3, 5, 14, 23, 122, 135, 136, 158, 165, 173, 174, 192, 231, 247], "prealloc": [3, 14], "reus": [3, 10, 65, 78, 111, 130, 137, 141, 153, 160, 176, 177, 185, 187, 247], "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, 230, 235, 238, 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, 238, 247], "side": [3, 20, 51, 52, 82, 103, 138, 147, 152, 154, 155, 160, 161, 166, 168, 188, 226, 260], "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, 238, 251], "cach": [3, 64, 111, 137, 144, 168, 176, 177, 184, 185, 247], "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], "num_warmup_it": 3, "success": [3, 6, 14, 23, 73, 101, 103, 126, 144, 165, 188, 204, 206, 226], "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, 230, 245, 251], "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, 231, 234], "0m44": 3, "287": [3, 177, 262], "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, 230, 234, 247, 262], "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], "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, 230, 238, 239, 247, 257, 260, 262], "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, 230, 231, 245, 247, 253, 254, 263], "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, 234, 261, 262], "heavi": [3, 6, 49, 97, 101, 223], "impact": [3, 12, 17, 87, 97, 136, 161, 164, 184, 201, 209, 229, 252, 258], "smaller": [3, 9, 51, 79, 97, 103, 123, 124, 157, 159, 161, 171, 203, 208, 223, 229, 231, 234, 237], "nevertheless": [3, 5, 19, 23, 147, 159], "primari": [4, 5, 6, 15, 142, 175, 176, 177, 186, 193, 206, 247], "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, 231, 247, 256], "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, 234, 246, 247, 252, 254, 256, 261, 262, 263], "suitabl": [4, 8, 131, 135, 139, 171, 199, 223], "prefer": [4, 6, 8, 53, 58, 145, 150, 155, 159, 176, 194, 230, 252], "eas": [4, 5, 85, 122, 177, 220, 237, 251], "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, 230, 231, 244, 249, 256, 260], "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, 213, 214, 215, 216, 219, 222, 223, 226, 229, 231, 247, 252, 255, 256, 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, 247, 262], "java": [4, 58, 177, 204, 222, 223], "rust": 4, "paragraph": [4, 6, 23, 263, 266], "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, 226, 228, 230, 238, 240, 244, 251, 258, 260], "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, 247], "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, 237, 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, 226, 230, 234, 237, 238, 260, 262, 263], "mechan": [4, 5, 6, 11, 14, 15, 24, 32, 49, 56, 60, 130, 143, 153, 165, 166, 168, 174, 177, 199, 216, 220, 221, 226], "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, 213, 220, 223, 226, 230, 231, 247, 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, 234, 238, 252], "explicit": [4, 6, 21, 23, 60, 139, 147, 163, 190, 200, 209, 226, 230, 262], "pars": [4, 5, 23, 49, 51, 103, 116, 122, 123, 126, 209, 231, 262], "subject": [4, 11, 14, 23, 42, 108, 113, 123, 141, 165, 173, 174, 187, 188, 193, 198, 204, 205, 206, 207, 212, 216, 222, 231], "constraint": [4, 6, 12, 17, 18, 60, 85, 98, 99, 121, 124, 126, 153, 159, 171, 197, 198, 200, 201, 230, 231, 244, 252], "impos": [4, 223, 232, 244], "guidanc": [4, 8, 176, 177, 195, 230], "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], "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, 247, 250, 252, 253, 262], "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, 231], "2698": 4, "0381": 4, "4023": 4, "3010": 4, "0448": 4, "slicebackward": 4, "circumst": [4, 5, 230], "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, 213, 223, 237, 247], "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, 226, 234, 247, 262], "accordingli": [4, 10, 12, 18, 22, 136, 149, 152, 161, 171, 188, 207, 260], "sai": [4, 5, 6, 24, 43, 51, 99, 101, 103, 113, 115, 125, 138, 145, 149, 152, 156, 168, 184, 200, 222, 234, 263], "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], "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], "sm": [4, 168], "exclud": [4, 8, 43, 238], "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, 247, 255, 262], "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, 247], "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, 237, 247], "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, 230, 238], "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, 238, 252], "hand": [4, 5, 6, 8, 14, 17, 18, 23, 61, 73, 98, 103, 128, 135, 139, 154, 172, 177, 190, 201, 234], "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, 213, 214, 219, 220, 226, 228, 234, 252, 255, 257, 258, 260, 262], "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, 226, 234, 260, 262], "realm": [4, 6], "cross": [4, 7, 8, 13, 20, 44, 52, 95, 118, 124, 126, 176, 247, 262], "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, 229, 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, 231, 237], "header": [4, 5, 6, 8, 22, 23, 143, 155, 188, 204, 208, 222, 225, 231, 260, 262, 263], "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": 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10, 15, 22, 144, 155, 186, 188, 208, 219, 220, 231, 246, 256], "ok": [4, 103, 161, 262], "relev": [4, 6, 14, 53, 98, 100, 103, 113, 114, 122, 124, 156, 171, 247], "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, 238, 247, 252], "proce": [4, 11, 25, 97, 99, 144, 157, 165, 234, 247], "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], "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, 230, 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, 213, 231, 262], "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], "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], "page": [4, 6, 10, 22, 23, 50, 54, 61, 109, 127, 139, 163, 168, 175, 199, 204, 207, 208, 209, 217, 220, 222, 247, 264, 266], "websit": [4, 6, 160, 226, 229], "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, 231, 234], "window": [4, 5, 6, 7, 20, 44, 51, 103, 105, 133, 162, 168, 178, 206, 213, 226, 262], "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, 230, 244, 260], "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, 213, 215, 219, 231, 239, 247, 260, 262], "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, 244, 254], "someth": [4, 5, 6, 11, 14, 19, 23, 25, 44, 87, 99, 101, 113, 116, 135, 144, 157, 158, 159, 165, 205, 231, 234, 262], "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, 226, 236, 245, 250, 252, 253, 260], "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, 213, 214, 219, 220, 222, 223, 226, 230, 238, 252, 253, 256], "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, 247], "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, 226, 231, 238, 246, 247], "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, 231, 262], "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], "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, 247], "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, 247], "awai": [4, 5, 6, 23, 47, 60, 98, 99, 101, 113, 143, 149, 159, 160, 161, 192, 234, 262], "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], "cppdoc": [4, 6], "pariti": 4, "manipul": [4, 60, 103, 143, 152, 182, 185, 213], "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, 226, 252, 255], "kcuda": [4, 6, 186], "live": [4, 6, 10, 15, 121, 125, 134, 162, 163, 192, 216, 262], "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, 238], "Of": [4, 14, 23, 97, 101, 125, 133, 135, 169, 190, 192, 226], "cours": [4, 6, 14, 17, 19, 23, 53, 97, 100, 101, 103, 104, 125, 133, 135, 169, 201, 213, 226], "did": [4, 6, 8, 19, 23, 25, 44, 52, 60, 68, 105, 111, 113, 135, 141, 153, 159, 162, 165, 176, 182, 231, 262], "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, 230, 245, 252], "insid": [4, 5, 6, 10, 16, 17, 18, 20, 22, 23, 45, 78, 108, 124, 168, 178, 195, 201, 205, 207, 223, 262], "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], "peter": 5, "goldsborough": 5, "plethora": 5, "relat": [5, 11, 14, 52, 60, 101, 103, 113, 124, 144, 153, 173, 174, 182, 231, 247, 262], "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, 247, 248, 249, 250, 251, 252, 256, 261, 262, 263], "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, 231, 254, 262], "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, 247], "express": [5, 13, 23, 95, 98, 107, 114, 124, 137, 173, 174, 191, 197, 198, 200, 209, 262], "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, 231, 247, 253, 257], "frequent": [5, 22, 23, 75, 82, 103, 123, 175, 176, 177, 191, 220], "expens": [5, 17, 97, 109, 163, 177, 184, 201, 238], "plausibl": 5, "address": [5, 12, 18, 22, 24, 82, 135, 161, 162, 174, 176, 197, 221, 244, 263], "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, 247, 251], "intend": [5, 6, 8, 12, 23, 97, 123, 124, 129, 144, 147, 164, 172, 173, 174, 189, 197, 212, 247], "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, 226, 227, 228, 230, 231, 234, 237, 245, 247, 251, 260, 262], "boilerpl": [5, 6, 19, 44, 99, 169, 189], "degre": [5, 64, 126, 165, 168, 192], "matter": [5, 134, 171, 189, 191, 210, 230], "organ": [5, 8, 14, 23, 51, 112, 127, 143, 149, 231, 257, 263], "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, 247], "rest": [5, 6, 14, 44, 117, 121, 136, 139, 143, 157, 159, 161, 162, 191, 198, 218, 234, 244, 262, 263], "chase": [5, 113], "someon": [5, 165], "fire": [5, 133], "dai": [5, 42, 103, 115, 116, 124, 244], "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, 234], "unit": [5, 6, 25, 49, 110, 122, 123, 145, 150, 156, 159, 160, 165, 171, 176, 177, 187, 247], "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, 244], "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, 234, 246, 247, 262, 263], "term": 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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, 226, 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, 229, 234, 260], "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, 231, 262], "room": [5, 97, 149, 219, 262], "obviou": [5, 113, 231], "knowledg": [5, 49, 52, 73, 99, 100, 114, 121, 144, 146, 262], "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, 226, 230, 234, 252, 256], "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, 231, 237, 238, 247, 251], "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, 239, 247, 254], "launch": [5, 6, 21, 53, 61, 115, 126, 132, 133, 149, 161, 162, 163, 164, 168, 176, 206, 219, 221, 231, 238, 247], "amount": [5, 19, 25, 73, 82, 112, 124, 133, 156, 172, 184, 223, 238, 247], "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, 230, 252], "furthermor": [5, 19, 97, 138, 145, 169, 176, 186, 200, 224, 225, 231], "interpret": [5, 6, 23, 25, 40, 56, 82, 85, 103, 121, 127, 128, 137, 165, 172, 173, 174, 186, 196, 216, 231, 235, 251, 262], "slow": [5, 6, 8, 42, 123, 148, 176, 228, 238, 247], "down": [5, 8, 10, 11, 16, 19, 42, 50, 82, 87, 99, 104, 123, 136, 144, 145, 146, 162, 166, 169, 176, 189, 247, 260], "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, 230, 262], "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, 251, 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, 231, 258, 262, 263], "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, 213, 247, 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, 230, 234, 247, 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, 238, 240, 258], "ahead": [5, 22, 152, 169, 173, 174, 179, 188, 214, 234, 247, 256], "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, 246, 251], "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, 213, 220, 230, 231, 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, 230, 231, 232, 237, 238, 239, 247, 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, 231], "overal": [5, 19, 42, 49, 122, 123, 135, 149, 160, 171, 197, 200, 219, 228, 231, 247], "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, 231, 234], "pybind11": [5, 8, 22, 23, 155, 231], "datatyp": [5, 23, 40, 48, 109, 220, 230, 234, 247], "Its": [5, 97, 99, 193, 262], "inspect": [5, 23, 78, 97, 108, 122, 126, 143, 164, 166, 172, 173, 174, 182, 185, 216, 231], "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, 256], "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, 213], "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, 230, 237, 239, 247, 262], "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, 244, 247, 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, 213, 216, 226, 230, 247, 254, 256], "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, 256], "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, 231, 234, 262], "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, 226, 263], "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], "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, 230, 258], "mismatch": [5, 61, 97], "nasti": [5, 244], "hard": [5, 6, 8, 15, 21, 99, 126, 156, 231], "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, 238, 244, 261], "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, 234, 235, 245, 247, 251, 260, 261, 262], "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, 94, 96, 97, 115, 124, 127, 135, 139, 143, 157, 158, 163, 164, 168, 169, 176, 178, 182, 197, 198, 199, 201, 209, 219, 226, 227, 229, 256, 260, 264], "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, 231, 238, 247, 257, 262], "fwrapv": [5, 23], "o3": [5, 23, 231], "wall": [5, 23, 98, 143, 231, 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, 213, 224, 225, 227, 238, 246, 257, 262], "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, 238, 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, 231], "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], "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, 219, 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, 258], "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, 260], "everyth": [5, 43, 87, 97, 99, 108, 126, 127, 130, 136, 139, 157, 158, 159, 160, 161, 165, 169, 187, 188, 212, 213, 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, 226, 247, 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, 266], "04": [5, 7, 118, 168, 219, 231], "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, 262], "linker": [5, 23, 204], "pycapsul": [5, 23], "builtin": [5, 155, 231], "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, 247], "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, 220, 221, 235, 238, 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, 219, 223, 230, 231, 238, 246, 251], "durat": [5, 83, 155, 160, 168, 177, 262], "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], "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, 221, 229], "my": [5, 21, 42, 50, 98, 103, 191, 198, 203, 262], "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, 210, 219, 226, 245, 247, 257], "506": 5, "480": [5, 238], "444": 5, "694": 5, "349": [5, 92], "335": [5, 147, 163, 258], "443": [5, 163, 238], "523": 5, "speedup": [5, 17, 21, 42, 44, 121, 138, 144, 149, 154, 177, 181, 182, 184, 193, 201, 219, 223, 247], "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, 219, 258], "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, 260], "wonder": [5, 99, 152], "abstract": [5, 11, 14, 51, 87, 95, 100, 103, 110, 113, 124, 126, 135, 142, 155, 159, 215, 263], "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, 237], "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, 226, 230, 231, 258], "1e6": [5, 9, 19, 137, 164, 181, 182, 185, 197, 198, 210, 228, 231, 258], "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, 231, 262], "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, 234, 251, 262], "great": [5, 49, 60, 105, 112, 113, 191, 197, 231, 262], "pull": [5, 7, 21, 143, 173, 174, 213], "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, 262], "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, 263], "complic": [5, 14, 98, 99, 103, 126, 177, 197, 205, 209, 215, 230, 231, 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, 234, 247], "fine": [5, 6, 17, 19, 49, 51, 98, 113, 120, 125, 134, 135, 144, 157, 158, 168, 185, 188, 189, 201, 229, 230, 231, 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, 207, 208, 213, 247], "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, 226, 231, 238, 247, 252, 262], "didn": [5, 8, 22, 76, 105, 143, 161, 181, 205, 262], "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, 234, 241, 244, 254, 256], "lltm_cuda": 5, "lltm_cuda_forward": 5, "lltm_cuda_backward": 5, "check_cuda": 5, "torch_check": [5, 8], "is_cuda": [5, 147], "check_contigu": 5, "is_contigu": [5, 147, 231, 246], "check_input": 5, "lltm_cuda_kernel": 5, "cannot": [5, 6, 11, 14, 16, 18, 22, 23, 49, 60, 61, 82, 108, 112, 113, 130, 133, 135, 136, 147, 149, 157, 159, 173, 174, 184, 195, 203, 205, 254, 261], "peek": [5, 211], "cuda_runtim": 5, "templat": [5, 8, 22, 23, 59, 135, 144, 208, 209, 221, 260], "typenam": [5, 208], "scalar_t": [5, 144], "__device__": 5, "__forceinline__": 5, "specif": [5, 6, 8, 9, 10, 11, 12, 17, 18, 19, 22, 23, 25, 44, 55, 58, 59, 73, 82, 87, 99, 100, 101, 105, 107, 108, 110, 112, 114, 122, 123, 124, 127, 128, 129, 133, 135, 136, 137, 143, 144, 148, 149, 156, 157, 159, 161, 162, 163, 164, 165, 168, 169, 173, 174, 176, 177, 178, 179, 185, 187, 190, 193, 199, 200, 201, 204, 206, 207, 212, 214, 219, 220, 226, 229, 234, 238, 244, 251, 252, 254, 258, 262], "fmax": 5, "fmin": 5, "d_relu": 5, "wish": [5, 6, 23, 52, 60, 73, 95, 112, 113, 150, 154, 162, 171, 179, 185, 190, 198, 230, 241, 263], "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, 226, 247], "zeros_lik": [5, 14, 95, 142, 161, 216, 254], "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, 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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, 234, 238, 265], "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, 229], "host": [5, 7, 16, 18, 54, 82, 98, 122, 123, 124, 133, 134, 135, 149, 162, 163, 168, 215, 216, 238, 247], "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, 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"humor": 6, "sweep": [6, 164], "philosophi": [6, 113], "ecosystem": [6, 108], "descript": [6, 50, 61, 122, 123, 144, 148, 159, 161, 162, 163, 164, 171, 181, 231, 247, 255, 257, 263], "embark": 6, "excit": [6, 22, 23, 143, 152], "team": [6, 108, 115, 126, 137, 160, 171], "job": [6, 45, 52, 53, 54, 82, 97, 126, 131, 132, 133, 135, 223], "reinforc": [6, 14, 61, 121, 146, 159, 160, 161, 162], "game": [6, 44, 52, 79, 146], "tractabl": [6, 98], "multithread": [6, 43, 56, 109, 216, 226, 231], "lock": [6, 14, 25, 56, 134, 135, 161, 162, 177, 216, 261], "gil": [6, 56, 61, 133, 216], "scalabl": [6, 126, 189, 207, 219, 220, 247], "shortcom": [6, 191], "neuroevolut": 6, "owner": [6, 161, 162, 163], "anyth": [6, 13, 44, 98, 101, 102, 103, 139, 148, 158, 181, 182, 184, 226, 234, 244, 245, 262, 263], "serv": [6, 37, 57, 61, 85, 97, 102, 121, 127, 133, 139, 143, 146, 149, 155, 162, 163, 176, 177, 191, 212, 213, 230, 231, 247, 257], "web": [6, 105, 213, 251, 262], "server": [6, 16, 25, 61, 119, 120, 121, 127, 133, 149, 155, 163, 177, 179, 185, 194, 213, 214, 216, 220, 228, 251], "3d": [6, 7, 75, 93, 101, 102, 115, 171, 197, 200, 247], "graphic": [6, 164, 206], "photo": [6, 229], "softwar": [6, 137, 149, 155, 168, 176, 206, 213, 262], "integr": [6, 10, 14, 23, 78, 87, 109, 121, 126, 139, 169, 176, 177, 179, 199, 200, 206, 219, 226, 229, 244, 254], "remain": [6, 7, 87, 97, 119, 135, 142, 152, 156, 165, 179, 184, 191, 193, 195, 199, 209, 247], "forth": [6, 149, 176, 263], "retain": [6, 40, 48, 76, 80, 112], "intuit": [6, 52, 73, 78, 99, 108, 112, 144, 149, 165, 171, 190, 226, 244], "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, 231], "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, 226, 230, 246, 247], "principl": [6, 8, 102, 103, 121, 126], 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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, 226, 228, 238, 256, 262, 263], "ey": [6, 20, 23, 32, 103, 145, 150, 153, 205, 208, 262], "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, 238, 245, 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, 207, 220, 225, 226, 229, 262], "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, 207, 226, 244, 247, 253], "agre": [6, 23, 137, 258], "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, 262], "unexpect": [6, 51, 191, 195, 200, 247], "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, 262, 263], "extens": [6, 7, 15, 22, 23, 52, 112, 121, 127, 130, 135, 171, 176, 186, 189, 191, 197, 199, 219, 221, 231, 235, 251], "besid": [6, 23, 103, 153, 161, 221, 229, 247, 262], "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, 237], "nest": [6, 14, 43, 110, 142, 159, 161, 163, 164, 196, 262, 263], "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": 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20, 21, 23, 37, 38, 43, 44, 47, 49, 52, 60, 61, 65, 67, 69, 73, 82, 85, 87, 89, 92, 94, 96, 97, 98, 99, 102, 103, 104, 105, 108, 110, 111, 115, 117, 118, 119, 120, 121, 122, 123, 127, 128, 129, 131, 133, 135, 136, 137, 139, 142, 144, 146, 149, 150, 153, 157, 160, 161, 162, 163, 164, 165, 166, 169, 171, 172, 173, 174, 176, 177, 178, 179, 181, 182, 184, 185, 188, 193, 194, 195, 197, 198, 199, 200, 201, 202, 204, 206, 208, 209, 210, 211, 214, 216, 220, 221, 222, 223, 226, 228, 230, 238, 244, 245, 247, 250, 252, 253, 257, 258], "we": [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, 32, 34, 39, 40, 42, 43, 44, 45, 47, 48, 49, 50, 51, 52, 55, 58, 59, 60, 63, 64, 65, 67, 68, 69, 73, 75, 76, 78, 79, 82, 83, 85, 87, 95, 97, 98, 99, 101, 102, 103, 105, 108, 109, 111, 113, 115, 116, 117, 118, 119, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 152, 153, 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199, 203, 204, 205, 208, 211, 213, 215, 216, 220, 221, 222, 223, 226, 229, 230, 231, 232, 233, 234, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 254, 256, 262, 263, 271, 272], "3": [0, 1, 2, 3, 5, 6, 7, 13, 14, 16, 17, 18, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 40, 42, 43, 48, 49, 51, 52, 60, 63, 64, 65, 67, 68, 69, 71, 72, 73, 74, 75, 76, 78, 80, 87, 89, 90, 92, 93, 95, 96, 97, 98, 99, 101, 102, 103, 104, 108, 109, 110, 111, 112, 113, 114, 115, 116, 118, 119, 121, 122, 123, 124, 127, 129, 130, 135, 136, 138, 139, 140, 142, 143, 144, 146, 147, 149, 150, 151, 152, 153, 154, 156, 157, 158, 159, 161, 162, 163, 164, 166, 167, 170, 171, 172, 173, 174, 175, 177, 178, 183, 184, 187, 188, 189, 190, 191, 192, 193, 194, 197, 198, 201, 202, 203, 204, 205, 206, 207, 208, 211, 213, 215, 219, 220, 221, 222, 226, 227, 229, 230, 237, 239, 244, 247, 253, 254, 256, 262, 263, 266, 271, 272, 275], "6": [0, 3, 6, 7, 11, 13, 19, 23, 34, 36, 43, 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7, 8, 10, 12, 14, 20, 22, 23, 25, 42, 80, 105, 107, 108, 115, 116, 119, 122, 123, 125, 126, 127, 129, 135, 136, 137, 138, 141, 145, 147, 153, 155, 156, 157, 158, 159, 166, 168, 169, 171, 175, 178, 183, 184, 185, 187, 188, 189, 197, 198, 200, 203, 204, 206, 208, 218, 219, 220, 221, 222, 223, 228, 230, 231, 234, 237, 256, 257, 260, 263, 269, 272], "should": [0, 1, 2, 4, 5, 6, 7, 8, 10, 11, 14, 15, 16, 18, 19, 20, 21, 22, 23, 32, 42, 43, 44, 49, 50, 51, 52, 53, 55, 58, 59, 60, 69, 73, 78, 82, 85, 87, 97, 98, 99, 100, 102, 103, 111, 117, 119, 121, 122, 125, 126, 127, 130, 133, 135, 136, 138, 139, 143, 146, 147, 150, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 165, 169, 171, 172, 173, 174, 175, 176, 177, 178, 182, 184, 187, 188, 189, 190, 191, 197, 200, 205, 206, 208, 213, 214, 218, 219, 222, 223, 226, 230, 231, 245, 247, 252, 256, 260, 262, 265, 269, 271, 274], "well": [0, 1, 3, 4, 5, 6, 8, 10, 11, 19, 20, 22, 23, 42, 44, 48, 49, 53, 60, 67, 82, 85, 87, 97, 99, 101, 105, 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124, 125, 126, 127, 128, 129, 134, 135, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 152, 153, 154, 155, 156, 159, 160, 161, 163, 164, 165, 166, 168, 171, 172, 174, 177, 178, 181, 184, 186, 187, 189, 190, 191, 192, 193, 194, 195, 196, 199, 203, 204, 205, 206, 211, 218, 220, 221, 222, 223, 229, 230, 231, 232, 233, 234, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 254, 255, 257], "bin": [0, 4, 6, 22, 23, 92, 135, 186, 194, 195, 204, 208, 218, 220, 223, 226], "activ": [0, 5, 6, 9, 10, 12, 14, 15, 17, 19, 47, 52, 82, 93, 97, 99, 104, 122, 124, 131, 135, 137, 144, 145, 152, 156, 158, 164, 168, 177, 179, 182, 185, 186, 187, 195, 199, 200, 201, 205, 207, 208, 219, 220, 221, 226, 228, 229, 234, 238, 247, 256, 262, 271], "need": [0, 1, 3, 4, 5, 6, 7, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 32, 34, 43, 44, 45, 47, 49, 50, 51, 52, 53, 54, 55, 58, 59, 60, 61, 63, 64, 67, 75, 76, 79, 82, 83, 87, 97, 98, 99, 101, 102, 103, 105, 108, 111, 112, 115, 116, 117, 118, 119, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 135, 136, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 152, 153, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 168, 171, 172, 173, 174, 175, 177, 178, 179, 182, 184, 185, 187, 188, 189, 193, 194, 195, 197, 198, 199, 200, 201, 202, 204, 206, 208, 209, 212, 213, 214, 215, 216, 219, 220, 221, 222, 223, 224, 225, 226, 228, 230, 231, 244, 245, 247, 252, 256, 262, 271], "instal": [0, 1, 5, 6, 7, 8, 17, 20, 23, 24, 50, 51, 53, 55, 57, 58, 87, 90, 94, 107, 115, 116, 118, 119, 122, 123, 124, 127, 128, 131, 132, 135, 136, 139, 141, 143, 146, 155, 158, 159, 160, 161, 165, 168, 171, 172, 178, 184, 185, 187, 188, 194, 204, 206, 208, 213, 219, 222, 223, 224, 225, 226, 227, 229, 231, 233, 236, 238, 240, 241, 242, 243, 246, 248, 249, 250, 256, 266, 275], "pip": [0, 17, 20, 24, 50, 75, 82, 90, 94, 105, 107, 115, 118, 119, 137, 139, 146, 157, 158, 160, 168, 171, 172, 178, 184, 194, 206, 219, 221, 223, 229, 231, 233, 236, 238, 240, 241, 242, 243, 245, 248, 249, 250], "torchvis": [0, 4, 10, 12, 19, 20, 33, 34, 37, 38, 39, 41, 43, 44, 50, 52, 57, 58, 59, 73, 75, 87, 90, 92, 94, 96, 97, 110, 117, 119, 121, 122, 123, 129, 134, 137, 139, 142, 143, 146, 148, 149, 152, 157, 158, 161, 162, 166, 168, 169, 171, 172, 176, 177, 182, 184, 187, 188, 194, 195, 197, 198, 199, 200, 204, 206, 213, 220, 221, 223, 224, 225, 227, 228, 229, 233, 236, 238, 245, 247, 250, 253, 256], "xcode": [0, 59, 188, 204, 222, 223, 225, 227], "want": [0, 1, 2, 4, 5, 6, 8, 9, 10, 12, 14, 15, 16, 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, 226, 228, 230, 231, 234, 237, 240, 244], 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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, 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, 210, 211, 214, 215, 219, 222, 230, 237, 244, 247, 254, 255, 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, 231, 247, 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, 229, 247], "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, 234], "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, 226, 230, 247], "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, 213, 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, 213, 216, 219, 220, 221, 223, 226, 230, 232, 233, 234, 239, 244, 246, 249, 250, 252, 254, 255, 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, 226, 228, 230, 234, 235, 237, 239, 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, 213, 214, 215, 217, 219, 222, 223, 228, 229, 230, 234, 235, 237, 244, 245, 247, 251, 252, 255, 256, 257, 258], "guid": [1, 17, 58, 59, 61, 82, 85, 123, 135, 147, 158, 168, 173, 174, 176, 184, 196, 201, 213, 226, 229, 231, 235, 251, 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, 234], "straightforward": [1, 5, 6, 16, 17, 49, 60, 97, 98, 144, 200, 234], "determinist": [1, 11, 14, 136, 148, 160, 247], "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, 213, 214, 215, 220, 221, 228, 231, 234, 237, 245, 251, 252, 254, 255, 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, 231, 234, 247, 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, 226, 231, 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, 231, 232, 245, 247, 253, 254, 256, 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, 231, 247, 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, 226, 262, 271], "maxim": [1, 14, 52, 73, 82, 97, 99, 126, 146, 160, 172, 176, 194, 247], "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, 219, 231, 239, 247, 258], "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, 229, 244, 254], "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, 210, 222, 230, 231, 232, 235, 237, 249, 252, 262, 271], "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, 230, 231, 232, 239, 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, 226, 230, 235, 244, 247, 251, 253], "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, 226, 228, 230, 231, 237, 244, 247, 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, 226, 228, 229, 230, 231, 234, 238, 244, 247, 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, 231, 234, 247, 253, 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, 231, 237, 253, 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, 234, 247, 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, 237, 244], "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, 229, 234, 247, 252, 256], "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, 256], "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, 213, 219, 222, 224, 225, 231, 232, 234, 239, 244, 247, 252, 256], "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, 247, 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, 230, 234, 237, 239, 244, 247, 251, 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, 226, 229, 245, 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, 229, 231, 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, 237, 244, 247, 253, 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, 231, 234, 247], "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, 234, 247, 252, 254, 256, 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, 234, 258, 266, 275], "illustr": [1, 19, 44, 47, 56, 116, 117, 124, 125, 126, 138, 160, 169, 171, 178, 191, 192, 195, 215, 226, 229, 230, 244, 247], "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, 226, 229, 234, 237, 244, 247, 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, 230, 232, 237, 239, 247], "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, 213, 214, 220, 222, 223, 224, 225, 226, 227, 228, 229, 231, 232, 247, 255, 256, 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, 256, 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, 231, 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, 229, 230, 231, 234, 235, 244, 245, 247, 252, 253, 255, 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, 213, 218, 219, 234, 237, 247, 257, 263, 272], "loss_modul": [1, 159], "whatev": [1, 8, 22, 23, 99, 101, 112, 195, 226], "convent": [1, 14, 52, 60, 112, 126, 136, 171, 216, 231], "receiv": [1, 4, 6, 14, 16, 55, 64, 87, 101, 111, 135, 159, 161, 162, 163, 172, 230, 247], "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, 230, 247, 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, 229, 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, 229, 247, 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, 226, 230, 234, 245, 247, 252, 256, 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, 213, 220, 245, 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, 226, 230, 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, 213, 215, 216, 226, 228, 230, 231, 244, 245, 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, 226, 231, 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, 231, 234, 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, 231, 247, 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, 234, 237, 245, 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, 234, 247, 252, 262, 263, 271, 272], "metric": [1, 17, 87, 97, 109, 122, 137, 146, 168, 171, 177, 178, 201, 221, 226, 231, 245], "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, 251, 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, 226, 228, 234, 244, 245, 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, 231, 235, 237, 251, 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, 226, 228, 251, 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, 234, 237, 247, 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, 213, 215, 216, 219, 220, 221, 226, 237, 244, 245, 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, 226, 231, 234, 237, 247, 254, 256, 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, 229, 230, 231, 247, 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, 213, 220, 223, 226, 229, 230, 231, 234, 244, 247, 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, 213, 218, 219, 230, 231, 234, 247, 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, 234, 245, 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, 229, 230, 231, 234, 237, 239, 245, 247, 251, 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, 230, 234, 237, 247, 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, 229, 235, 245, 247, 258, 260, 269], "modal": [1, 60, 229], "complex": [1, 6, 23, 25, 50, 61, 67, 68, 97, 105, 112, 120, 123, 133, 150, 153, 161, 163, 169, 193, 203, 209, 234, 239, 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, 213, 214, 219, 230, 231, 235, 247, 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, 234, 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, 226, 228, 229, 244, 245, 247, 253, 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, 231, 237, 247], "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, 214, 215, 216, 218, 219, 220, 222, 223, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 236, 237, 238, 239, 240, 241, 242, 243, 244, 246, 247, 248, 249, 250, 251, 252, 254, 255, 262, 271], "elementari": [1, 2, 234], "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, 226, 230, 231, 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, 231, 247, 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, 230, 231, 245, 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, 226, 229, 230, 231, 234, 237, 239, 244, 245, 247, 252, 255, 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, 226, 228, 230, 231, 232, 237, 244, 247, 254, 255], "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, 213, 216, 219, 223, 226, 231, 234, 239, 245, 247, 251, 254, 258, 263, 272], "solv": [1, 6, 14, 49, 51, 97, 103, 117, 118, 149, 153, 157, 159, 161, 163, 176, 191, 231, 237, 247], "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, 231, 247], "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, 229, 251, 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, 231], "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, 226, 229, 231, 234, 237, 244, 247, 252, 256, 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, 230, 231, 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, 226, 228, 229, 230, 231, 244, 256, 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, 256], "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, 230, 231, 234, 237, 239, 254, 256, 262, 271], "fit": [1, 6, 7, 9, 10, 11, 12, 20, 24, 61, 87, 103, 122, 123, 124, 133, 148, 149, 163, 181, 230, 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, 213, 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, 213, 216, 218, 220, 223, 224, 225, 227, 228, 229, 234, 244, 247, 251, 252], "strictli": [1, 159], "speak": [1, 8, 43, 125, 135, 149, 247], "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, 226, 230, 245, 247, 251, 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, 226, 228, 230, 231, 234, 237, 244, 247, 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, 230, 247, 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, 213, 214, 218, 222, 223, 228, 230, 231, 234, 244, 247, 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, 230, 237, 247, 260, 269], "former": [1, 5, 61, 79, 127, 128, 165], "lag": [1, 159], "absolut": [1, 6, 7, 10, 82, 99, 126, 156, 160, 208, 234], "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, 231, 247], "associ": [1, 5, 6, 8, 10, 17, 50, 82, 130, 141, 142, 156, 164, 171, 190, 201, 202, 244, 247], "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, 231, 239, 244, 247, 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, 226, 234, 247, 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, 230, 239], "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, 254, 266, 275], "pessimist": [1, 159], "bound": [1, 23, 49, 112, 126, 144, 159, 160, 168, 173, 174, 176, 178, 184, 230, 231, 247], "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, 237, 244], "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, 230, 231, 237, 244, 245, 247, 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, 116, 118, 119, 124, 126, 129, 131, 132, 133, 134, 135, 136, 137, 141, 144, 147, 149, 152, 155, 157, 159, 160, 161, 162, 163, 164, 168, 169, 171, 172, 173, 174, 175, 176, 177, 178, 184, 185, 186, 188, 190, 191, 192, 193, 195, 202, 203, 204, 211, 212, 213, 214, 216, 218, 219, 221, 222, 223, 225, 226, 228, 230, 231, 234, 237, 247, 252, 253, 258, 262, 271], "tell": [1, 18, 23, 42, 69, 87, 103, 111, 126, 127, 136, 138, 141, 152, 160, 161, 163, 165, 175, 187, 188, 209, 231, 262, 271], "fals": [1, 2, 6, 7, 10, 11, 12, 14, 19, 20, 23, 24, 34, 37, 38, 42, 43, 44, 49, 52, 55, 59, 60, 63, 64, 73, 82, 83, 87, 89, 92, 94, 96, 97, 101, 110, 111, 112, 115, 116, 117, 119, 122, 123, 124, 125, 126, 129, 134, 137, 141, 143, 144, 146, 147, 148, 150, 152, 153, 157, 158, 159, 160, 161, 162, 164, 165, 166, 169, 171, 172, 173, 174, 176, 177, 178, 179, 182, 184, 185, 186, 190, 191, 192, 194, 195, 197, 198, 200, 201, 206, 208, 210, 211, 218, 219, 220, 221, 223, 228, 230, 232, 244, 246, 247, 248, 250, 252, 253, 258, 260, 261, 262, 263, 269, 270, 271, 272], "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, 230, 251, 262, 271], "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, 260, 269], "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, 254, 255, 258, 262, 271], "_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], "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, 260, 262, 269, 271], "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, 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164, 165, 171, 172, 173, 174, 177, 182, 185, 189, 191, 193, 203, 205, 207, 214, 220, 229, 237, 257], "specifi": [1, 4, 5, 6, 7, 8, 9, 11, 17, 19, 20, 22, 23, 24, 34, 39, 51, 52, 59, 76, 82, 87, 101, 110, 113, 116, 122, 123, 124, 126, 130, 134, 136, 137, 138, 141, 155, 156, 159, 162, 163, 168, 171, 173, 174, 179, 189, 190, 191, 192, 193, 196, 197, 198, 200, 201, 204, 205, 206, 208, 209, 212, 221, 222, 228, 237, 242, 245, 247, 252, 256, 257, 260, 262, 269, 271], "scenario": [1, 4, 6, 14, 61, 105, 107, 108, 112, 117, 125, 160, 163, 199, 219, 244], "tensordictsequenti": [1, 136], "valueoper": [1, 159], "automat": [1, 2, 5, 6, 8, 10, 14, 15, 22, 25, 35, 40, 43, 45, 46, 47, 48, 53, 57, 61, 76, 80, 98, 114, 121, 122, 123, 124, 125, 126, 130, 137, 139, 142, 144, 147, 149, 159, 160, 163, 166, 168, 171, 176, 177, 182, 185, 207, 209, 212, 214, 216, 219, 220, 221, 226, 232, 235, 237, 247, 251, 252, 254, 262, 271], "state_valu": 1, "built": [1, 4, 5, 6, 7, 8, 22, 23, 47, 49, 59, 60, 61, 99, 108, 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154, 156, 159, 168, 171, 178, 184, 185, 187, 192, 193, 195, 197, 201, 202, 209, 213, 219, 226, 231, 234, 235, 238, 244, 247, 251, 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, 213, 214, 219, 223, 226, 229, 231, 234, 237, 244, 256, 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, 234], "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, 231], "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, 226, 230, 234, 245, 247, 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, 237, 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, 231, 236, 247, 253], "alloc": [1, 6, 18, 21, 22, 23, 48, 55, 59, 129, 135, 152, 168, 175, 176, 193, 202, 214, 223, 237, 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, 247], "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, 214, 215, 221, 228, 231, 237, 247, 251, 255, 258, 261, 262, 270, 271], "offer": [1, 11, 14, 18, 42, 43, 53, 61, 99, 122, 124, 138, 141, 144, 145, 197, 214, 216, 229, 231, 247], "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, 237], "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, 213, 247], "non": [1, 2, 3, 5, 8, 11, 14, 19, 22, 49, 51, 53, 54, 56, 60, 82, 85, 97, 98, 100, 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208, 213, 216, 222, 223, 224, 225, 226, 234, 244, 247, 251, 257, 262, 271], "infer": [1, 3, 6, 8, 9, 10, 19, 20, 21, 22, 23, 42, 48, 49, 60, 61, 97, 101, 103, 105, 113, 121, 125, 126, 129, 139, 142, 144, 158, 159, 161, 171, 172, 177, 178, 185, 187, 188, 193, 194, 196, 197, 198, 199, 201, 203, 206, 207, 209, 214, 221, 223, 224, 225, 226, 228, 234, 235, 241, 251], "around": [1, 5, 6, 11, 23, 24, 49, 55, 58, 59, 75, 97, 117, 128, 138, 144, 145, 150, 154, 157, 165, 168, 169, 173, 174, 182, 189, 190, 194, 197, 219, 234, 239, 262, 265, 271, 274], "1m": [1, 136, 159], "10_000": [1, 97], "outer": [1, 6, 122, 145], "loop": [1, 3, 4, 5, 7, 8, 11, 16, 17, 19, 21, 25, 44, 47, 49, 51, 52, 59, 60, 65, 73, 75, 78, 85, 87, 97, 111, 118, 123, 126, 127, 137, 138, 144, 145, 146, 152, 154, 162, 163, 165, 169, 177, 183, 185, 201, 219, 231, 247, 250], "equal": [1, 4, 10, 95, 97, 115, 116, 125, 137, 147, 156, 159, 160, 171, 173, 174, 176, 192, 216, 247, 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, 231, 241, 247], "dedic": [1, 10, 55, 60, 112, 134, 162, 163, 177, 199, 208, 223, 228, 229, 230, 258, 263, 272], "frequenc": [1, 7, 83, 126, 223], "trainer": [1, 16, 17, 24, 55, 126, 131, 148, 161, 162, 163, 201, 214], 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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, 231, 246, 247, 254, 261, 266, 270, 275], "highli": [2, 5, 6, 10, 18, 49, 60, 87, 165, 175, 177, 205, 247], "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, 230, 235, 247, 251, 254], "explor": [2, 6, 8, 14, 21, 23, 45, 49, 73, 108, 121, 126, 130, 136, 144, 146, 152, 159, 164, 165, 229, 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, 247, 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, 245, 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, 226, 230, 231, 234, 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, 233, 234, 237, 238, 241, 244, 246, 247, 251, 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, 237], "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, 213, 214, 230, 237, 244, 247, 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, 247, 255, 256], "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, 247, 251], "explan": [2, 52, 61, 160, 169, 237], "arriv": [2, 6, 15, 23, 42, 113, 122, 133, 161, 208, 247], "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, 213, 216, 222, 229, 230, 231, 234, 244, 245, 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, 213, 220, 226, 230, 231, 237, 247, 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, 234, 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, 247, 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, 226, 230, 245, 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, 230], "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, 230, 235, 237, 238, 239, 241, 242, 243, 244, 245, 248, 249, 250, 251, 253, 254, 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, 234, 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, 226, 229, 230, 231, 234, 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, 213, 214, 218, 220, 221, 228, 247, 252, 254, 255, 256, 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, 229, 230, 235, 251, 254, 256], "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, 229, 230, 245, 247, 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, 230, 233, 234, 237, 238, 239, 240, 241, 242, 243, 247, 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, 213, 218, 219, 220, 224, 225, 226, 228, 229, 230, 231, 234, 237, 246, 247, 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, 230, 231, 251, 252, 253, 255, 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, 244], "5314": 2, "2807": 2, "4864": 2, "7608": 2, "9101": [2, 173], "0073": 2, 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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, 230, 231, 234, 237, 244, 245, 247, 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, 226, 247, 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, 231, 232, 237, 247, 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, 229, 239, 240, 241, 242, 243, 245, 248, 249, 250, 255, 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, 253], "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, 245], "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, 247, 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, 231, 247], "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, 229, 230, 231, 234, 247], "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, 234, 251, 252, 257], "appear": [3, 11, 14, 22, 25, 103, 226, 229, 234, 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, 226, 234, 247, 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, 226, 228, 229, 230, 231, 232, 233, 234, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 254, 255, 256, 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, 231, 247, 251, 253, 256, 260, 269], "boost": [3, 97, 99, 144, 145, 176, 184, 199, 207, 216, 220, 221, 247], "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, 230, 231, 234, 237, 252, 254, 255, 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, 247, 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, 213, 221, 223, 230, 231, 239, 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, 244, 258], "captur": [3, 4, 6, 22, 23, 25, 52, 60, 107, 123, 141, 148, 158, 171, 172, 173, 174, 186, 194, 197, 198, 200, 231], "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, 231, 252, 262, 271], "slightli": [3, 5, 14, 23, 122, 135, 136, 158, 165, 173, 174, 192, 231, 247], "prealloc": [3, 14], "reus": [3, 10, 65, 78, 111, 130, 137, 141, 153, 160, 176, 177, 185, 187, 247], "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, 230, 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, 247], "side": [3, 20, 51, 52, 82, 103, 138, 147, 152, 154, 155, 160, 161, 166, 168, 188, 226, 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, 251], "cach": [3, 64, 111, 137, 144, 168, 176, 177, 184, 185, 247], "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, 226], "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, 230, 245, 251], "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, 231, 234], "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, 230, 234, 247, 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, 230, 239, 247, 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, 230, 231, 245, 247, 253, 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, 234, 261, 262, 270, 271], "heavi": [3, 6, 49, 97, 101, 223], "impact": [3, 12, 17, 87, 97, 136, 161, 164, 184, 201, 209, 229, 252, 258], "smaller": [3, 9, 51, 79, 97, 103, 123, 124, 157, 159, 161, 171, 203, 208, 223, 229, 231, 234, 237], "nevertheless": [3, 5, 19, 23, 147, 159], "primari": [4, 5, 6, 15, 142, 175, 176, 177, 186, 193, 206, 247], "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, 231, 247, 256], "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, 234, 246, 247, 252, 254, 256, 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, 230, 252], "eas": [4, 5, 85, 122, 177, 220, 237, 251], "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, 230, 231, 244, 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, 213, 214, 215, 216, 219, 222, 223, 226, 229, 231, 247, 252, 255, 256, 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, 247, 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, 226, 228, 230, 240, 244, 251, 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, 247], "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, 237, 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, 226, 230, 234, 237, 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, 226], "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, 213, 220, 223, 226, 230, 231, 247, 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, 234, 252], "explicit": [4, 6, 21, 23, 60, 139, 147, 163, 190, 200, 209, 226, 230, 262, 271], "pars": [4, 5, 23, 49, 51, 103, 116, 122, 123, 126, 209, 231, 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, 231], "constraint": [4, 6, 12, 17, 18, 60, 85, 98, 99, 121, 124, 126, 153, 159, 171, 197, 198, 200, 201, 230, 231, 244, 252], "impos": [4, 223, 232, 244], "guidanc": [4, 8, 176, 177, 195, 230], "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, 247, 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, 231], "2698": 4, "0381": 4, "4023": 4, "3010": 4, "0448": 4, "slicebackward": 4, "circumst": [4, 5, 230], "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, 213, 223, 237, 247], "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, 226, 234, 247, 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, 234, 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, 247, 255, 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, 247], "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, 237, 247], "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, 230], "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, 234], "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, 213, 214, 219, 220, 226, 228, 234, 252, 255, 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, 226, 234, 260, 262, 269, 271], "realm": [4, 6], "cross": [4, 7, 8, 13, 20, 44, 52, 95, 118, 124, 126, 176, 247, 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, 229, 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, 231, 237], "header": [4, 5, 6, 8, 22, 23, 143, 155, 188, 204, 208, 222, 225, 231, 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, 230, 231, 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, 220, 222, 227, 228, 251, 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, 230, 231, 234, 244, 245, 256, 262, 263, 271, 272], "deseri": [4, 6, 23, 112, 182, 197, 198, 256], "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, 247], "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, 247, 252], "proce": [4, 11, 25, 97, 99, 144, 157, 165, 234, 247], "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, 230, 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, 213, 231, 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, 247, 264, 266, 273, 275], "websit": [4, 6, 160, 226, 229], "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, 231, 234], "window": [4, 5, 6, 7, 20, 44, 51, 103, 105, 133, 162, 168, 178, 206, 213, 226, 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, 230, 244, 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, 213, 215, 219, 231, 239, 247, 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, 244, 254], "someth": [4, 5, 6, 11, 14, 19, 23, 25, 44, 87, 99, 101, 113, 116, 135, 144, 157, 158, 159, 165, 205, 231, 234, 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, 226, 236, 245, 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, 213, 214, 219, 220, 222, 223, 226, 230, 252, 253, 256], "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, 247], "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, 226, 231, 246, 247], "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, 231, 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, 247], "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, 247], "awai": [4, 5, 6, 23, 47, 60, 98, 99, 101, 113, 143, 149, 159, 160, 161, 192, 234, 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, 213], "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, 226, 252, 255], "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, 226], "cours": [4, 6, 14, 17, 19, 23, 53, 97, 100, 101, 103, 104, 125, 133, 135, 169, 201, 213, 226], "did": [4, 6, 8, 19, 23, 25, 44, 52, 60, 68, 105, 111, 113, 135, 141, 153, 159, 162, 165, 176, 182, 231, 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, 230, 245, 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, 231, 247, 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, 247, 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, 231, 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, 247], "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, 231, 247, 253, 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, 244, 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, 247, 251], "intend": [5, 6, 8, 12, 23, 97, 123, 124, 129, 144, 147, 164, 172, 173, 174, 189, 197, 212, 247], "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, 226, 227, 228, 230, 231, 234, 237, 245, 247, 251, 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, 230], "organ": [5, 8, 14, 23, 51, 112, 127, 143, 149, 231, 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, 247], "rest": [5, 6, 14, 44, 117, 121, 136, 139, 143, 157, 159, 161, 162, 191, 198, 218, 234, 244, 262, 263, 271, 272], "chase": [5, 113], "someon": [5, 165], "fire": [5, 133], "dai": [5, 42, 103, 115, 116, 124, 244], "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, 234], "unit": [5, 6, 25, 49, 110, 122, 123, 145, 150, 156, 159, 160, 165, 171, 176, 177, 187, 247], "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, 244], "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, 234, 238, 246, 247, 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, 234, 239, 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, 230, 231, 234, 254, 262, 271], "lstmcell": 5, "cell": [5, 21, 23, 25, 50, 60, 75, 80, 109, 159, 160, 164, 171, 184, 234, 247, 263, 272], "plain": [5, 6, 23, 49, 127, 128, 165, 178, 244], "input_featur": 5, "state_s": 5, "candid": [5, 144, 247], "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, 234, 247, 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, 226, 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, 229, 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, 231, 262, 271], "room": [5, 97, 149, 219, 262, 271], "obviou": [5, 113, 231], "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, 226, 230, 234, 252, 256], "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, 231, 237, 247, 251], "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, 239, 247, 254], "launch": [5, 6, 21, 53, 61, 115, 126, 132, 133, 149, 161, 162, 163, 164, 168, 176, 206, 219, 221, 231, 247], "amount": [5, 19, 25, 73, 82, 112, 124, 133, 156, 172, 184, 223, 247], "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, 230, 252], "furthermor": [5, 19, 97, 138, 145, 169, 176, 186, 200, 224, 225, 231], "interpret": [5, 6, 23, 25, 40, 56, 82, 85, 103, 121, 127, 128, 137, 165, 172, 173, 174, 186, 196, 216, 231, 235, 251, 262, 271], "slow": [5, 6, 8, 42, 123, 148, 176, 228, 247], "down": [5, 8, 10, 11, 16, 19, 42, 50, 82, 87, 99, 104, 123, 136, 144, 145, 146, 162, 166, 169, 176, 189, 247, 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, 230, 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, 251, 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, 231, 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, 213, 247, 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, 230, 234, 247, 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, 258], "ahead": [5, 22, 152, 169, 173, 174, 179, 188, 214, 234, 247, 256], "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, 246, 251], "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, 213, 220, 230, 231, 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, 230, 231, 232, 237, 239, 247, 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, 231], "overal": [5, 19, 42, 49, 122, 123, 135, 149, 160, 171, 197, 200, 219, 228, 231, 247], "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, 231, 234], "pybind11": [5, 8, 22, 23, 155, 231], "datatyp": [5, 23, 40, 48, 109, 220, 230, 234, 247], "Its": [5, 97, 99, 193, 262, 271], "inspect": [5, 23, 78, 97, 108, 122, 126, 143, 164, 166, 172, 173, 174, 182, 185, 216, 231], "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, 256], "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, 213], "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, 230, 237, 239, 247, 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, 244, 247, 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, 213, 216, 226, 230, 247, 254, 256], "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, 256], "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, 231, 234, 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, 226, 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, 230, 258], "mismatch": [5, 61, 97], "nasti": [5, 244], "hard": [5, 6, 8, 15, 21, 99, 126, 156, 231], "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, 244, 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, 234, 235, 245, 247, 251, 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, 94, 96, 97, 115, 124, 127, 135, 139, 143, 157, 158, 163, 164, 168, 169, 176, 178, 182, 197, 198, 199, 201, 209, 219, 226, 227, 229, 256, 260, 264, 269, 273], "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, 231, 247, 257, 262, 271], "fwrapv": [5, 23], "o3": [5, 23, 231], "wall": [5, 23, 98, 143, 231, 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, 213, 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, 231], "objc": [5, 23], 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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, 221, 229], "my": [5, 21, 42, 50, 98, 103, 191, 198, 203, 262, 271], "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, 210, 219, 226, 245, 247, 257], "506": 5, "480": [5, 238], "444": 5, "694": 5, "349": [5, 92], "335": [5, 147, 163, 258], "443": [5, 163, 238], "523": 5, "speedup": [5, 17, 21, 42, 44, 121, 138, 144, 149, 154, 177, 181, 182, 184, 193, 201, 219, 223, 247], "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, 219, 258], "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, 260, 269], "wonder": [5, 99, 152], "abstract": [5, 11, 14, 51, 87, 95, 100, 103, 110, 113, 124, 126, 135, 142, 155, 159, 215, 263, 272], "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, 237], "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, 226, 230, 231, 258], "1e6": [5, 9, 19, 137, 164, 181, 182, 185, 197, 198, 210, 228, 231, 258], "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, 231, 262, 271], "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, 234, 251, 262, 271], "great": [5, 49, 60, 105, 112, 113, 191, 197, 231, 262, 271], "pull": [5, 7, 21, 143, 173, 174, 213], "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, 262, 271], "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, 263, 272], "complic": [5, 14, 98, 99, 103, 126, 177, 197, 205, 209, 215, 230, 231, 252], "techniqu": [5, 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188, 189, 193, 194, 213, 234, 244, 247, 262, 271], "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, 231, 234, 239, 247], "conceptu": [5, 6, 43, 49, 60, 177], "scalartyp": 5, "messag": [5, 49, 108, 135, 137, 171, 173, 174, 185, 207, 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, 226], "at_dispatch_all_typ": 5, "sens": [5, 8, 12, 14, 97, 103, 113, 126, 138, 169, 262, 271], "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, 219, 220, 223, 226, 230, 238, 239, 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": 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182, 203, 218, 223, 227, 228, 230, 231, 234, 247, 251], "right": [5, 6, 8, 10, 12, 14, 20, 23, 32, 43, 48, 49, 52, 64, 82, 89, 97, 99, 101, 103, 111, 113, 135, 137, 146, 150, 152, 157, 159, 160, 161, 164, 165, 168, 171, 178, 185, 195, 197, 205, 219, 226, 234, 252, 262, 271], "inde": [5, 14, 58, 59, 97, 129, 145, 159, 164, 172, 191, 192, 231, 247], "agnost": [5, 60, 110, 232], "ineffici": [5, 82, 176, 193], "readabl": [5, 25, 51, 98, 110, 128, 139, 168, 213, 231], "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, 207, 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, 226, 231, 263, 272], "arithmet": [5, 19, 143, 185, 234], "fortun": [5, 6, 10, 15, 23, 87, 135, 136, 231], "expos": [5, 6, 8, 22, 23, 108, 113, 121, 139, 163, 181, 197, 198, 206, 208, 213, 247], "foo": [5, 21, 22, 141, 142, 153, 162, 172, 174, 182, 197, 202, 209, 246, 262, 263, 271, 272], "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, 219, 221, 222, 225, 227, 231, 257, 262, 266, 271, 275], "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, 237, 244, 247], "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, 234, 265, 274], "fundament": [5, 49, 91, 101, 103, 146, 190, 198, 214], "packedtensoraccessor32": 5, "restrictptrtrait": 5, "decompos": [5, 10, 17, 123, 149, 173, 174, 197, 201], 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    +
    .. only:: html diff --git a/docs/_sources/recipes/recipes/profiler_recipe.rst.txt b/docs/_sources/recipes/recipes/profiler_recipe.rst.txt index 4db78c2..bdfbba7 100644 --- a/docs/_sources/recipes/recipes/profiler_recipe.rst.txt +++ b/docs/_sources/recipes/recipes/profiler_recipe.rst.txt @@ -20,66 +20,61 @@ PyTorch Profiler ==================================== -This recipe explains how to use PyTorch profiler and measure the time and -memory consumption of the model's operators. +本教程解释了如何使用PyTorch profiler,并测量模型算子的时间和内存消耗。 -Introduction +简介 ------------ -PyTorch includes a simple profiler API that is useful when user needs -to determine the most expensive operators in the model. +当用户需要确定模型中最耗费资源的算子时,PyTorch包含一个简单的profiler API非常有用。 -In this recipe, we will use a simple Resnet model to demonstrate how to -use profiler to analyze model performance. +在本教程中,我们将使用一个简单的 Resnet 模型来演示如何使用profiler分析模型性能。 -Setup +环境设置 ----- -To install ``torch`` and ``torchvision`` use the following command: +要安装 ``torch`` 和 ``torchvision``,请使用以下命令: .. code-block:: sh pip install torch torchvision -.. GENERATED FROM PYTHON SOURCE LINES 28-45 +.. GENERATED FROM PYTHON SOURCE LINES 24-40 -Steps +具体步骤 ----- -1. Import all necessary libraries -2. Instantiate a simple Resnet model -3. Using profiler to analyze execution time -4. Using profiler to analyze memory consumption -5. Using tracing functionality -6. Examining stack traces -7. Using profiler to analyze long-running jobs +1. 导入所有必需的库 +2. 实例化一个简单的Resnet模型 +3. 使用profiler分析执行时间 +4. 使用profiler分析内存消耗 +5. 使用跟踪功能 +6. 检查堆栈跟踪 +7. 使用profiler分析长时间运行的作业 -1. Import all necessary libraries +1. 导入依赖的库 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -In this recipe we will use ``torch``, ``torchvision.models`` -and ``profiler`` modules: +在本教程中,我们将使用 ``torch``、``torchvision.models`` 和 ``profiler`` 模块: -.. GENERATED FROM PYTHON SOURCE LINES 45-51 +.. GENERATED FROM PYTHON SOURCE LINES 40-46 .. code-block:: default import torch import torchvision.models as models - from torch.profiler import profile, record_function, ProfilerActivity + from torch.profiler import profile, ProfilerActivity, record_function -.. GENERATED FROM PYTHON SOURCE LINES 52-58 +.. GENERATED FROM PYTHON SOURCE LINES 47-52 -2. Instantiate a simple Resnet model +2. 创建一个简单的 Resnet 模型 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Let's create an instance of a Resnet model and prepare an input -for it: +让我们创建一个 Resnet 模型实例,并为它准备一个输入: -.. GENERATED FROM PYTHON SOURCE LINES 58-62 +.. GENERATED FROM PYTHON SOURCE LINES 52-56 .. code-block:: default @@ -88,31 +83,27 @@ for it: inputs = torch.randn(5, 3, 224, 224) -.. GENERATED FROM PYTHON SOURCE LINES 63-80 +.. GENERATED FROM PYTHON SOURCE LINES 57-70 -3. Using profiler to analyze execution time +3. 使用profiler分析执行时间 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -PyTorch profiler is enabled through the context manager and accepts -a number of parameters, some of the most useful are: +PyTorch profiler通过上下文管理器启用,并接受多个参数,其中一些最有用的参数如下: -- ``activities`` - a list of activities to profile: - - ``ProfilerActivity.CPU`` - PyTorch operators, TorchScript functions and - user-defined code labels (see ``record_function`` below); - - ``ProfilerActivity.CUDA`` - on-device CUDA kernels; -- ``record_shapes`` - whether to record shapes of the operator inputs; -- ``profile_memory`` - whether to report amount of memory consumed by - model's Tensors; -- ``use_cuda`` - whether to measure execution time of CUDA kernels. +- ``activities`` - 要分析的活动列表: + - ``ProfilerActivity.CPU`` - PyTorch算子、TorchScript函数和用户定义的代码标签(见下面的 ``record_function``); + - ``ProfilerActivity.CUDA`` - 设备上的CUDA内核; +- ``record_shapes`` - 是否记录算子输入的形状; +- ``profile_memory`` - 是否报告模型张量所消耗的内存量; +- ``use_cuda`` - 是否测量CUDA内核的执行时间。 -Note: when using CUDA, profiler also shows the runtime CUDA events -occurring on the host. +注意:当使用CUDA时,profiler还会显示主机上发生的运行时CUDA事件。 -.. GENERATED FROM PYTHON SOURCE LINES 82-83 +.. GENERATED FROM PYTHON SOURCE LINES 72-73 -Let's see how we can use profiler to analyze the execution time: +让我们看看如何使用profiler分析执行时间: -.. GENERATED FROM PYTHON SOURCE LINES 83-88 +.. GENERATED FROM PYTHON SOURCE LINES 73-78 .. code-block:: default @@ -122,24 +113,18 @@ Let's see how we can use profiler to analyze the execution time: model(inputs) -.. GENERATED FROM PYTHON SOURCE LINES 89-103 +.. GENERATED FROM PYTHON SOURCE LINES 79-87 -Note that we can use ``record_function`` context manager to label -arbitrary code ranges with user provided names -(``model_inference`` is used as a label in the example above). +注意,我们可以使用 ``record_function`` 上下文管理器为任意代码范围添加用户提供的名称标签 +(在上面的示例中使用 ``model_inference`` 作为标签)。 -Profiler allows one to check which operators were called during the -execution of a code range wrapped with a profiler context manager. -If multiple profiler ranges are active at the same time (e.g. in -parallel PyTorch threads), each profiling context manager tracks only -the operators of its corresponding range. -Profiler also automatically profiles the asynchronous tasks launched -with ``torch.jit._fork`` and (in case of a backward pass) -the backward pass operators launched with ``backward()`` call. +Profiler允许检查在使用profiler上下文管理器包装的代码范围内执行期间调用了哪些算子。 +如果同时存在多个活动的profiler范围(例如在并行PyTorch线程中),每个profiling上下文管理器只跟踪其对应范围的算子。 +Profiler还会自动分析使用 ``torch.jit._fork`` 启动的异步任务,以及在反向传播过程中使用 ``backward()`` 调用启动的反向传播算子。 -Let's print out the stats for the execution above: +让我们打印出上述执行的统计信息: -.. GENERATED FROM PYTHON SOURCE LINES 103-106 +.. GENERATED FROM PYTHON SOURCE LINES 87-90 .. code-block:: default @@ -147,11 +132,11 @@ Let's print out the stats for the execution above: print(prof.key_averages().table(sort_by="cpu_time_total", row_limit=10)) -.. GENERATED FROM PYTHON SOURCE LINES 107-108 +.. GENERATED FROM PYTHON SOURCE LINES 91-92 -The output will look like (omitting some columns): +输出将如下所示(省略了一些列): -.. GENERATED FROM PYTHON SOURCE LINES 108-126 +.. GENERATED FROM PYTHON SOURCE LINES 92-110 .. code-block:: default @@ -174,28 +159,32 @@ The output will look like (omitting some columns): # -.. GENERATED FROM PYTHON SOURCE LINES 127-135 - -Here we see that, as expected, most of the time is spent in convolution (and specifically in ``mkldnn_convolution`` -for PyTorch compiled with ``MKL-DNN`` support). -Note the difference between self cpu time and cpu time - operators can call other operators, self cpu time excludes time -spent in children operator calls, while total cpu time includes it. You can choose to sort by the self cpu time by passing -``sort_by="self_cpu_time_total"`` into the ``table`` call. +.. GENERATED FROM PYTHON SOURCE LINES 111-119 To get a finer granularity of results and include operator input shapes, pass ``group_by_input_shape=True`` (note: this requires running the profiler with ``record_shapes=True``): +这里我们可以看到,如预期的那样,大部分时间都花在了卷积上(对于使用 ``MKL-DNN`` 支持编译的PyTorch,特别是在 ``mkldnn_convolution`` 上)。 +注意自身cpu时间和cpu时间之间的区别 - 算子可以调用其他算子,自身cpu时间不包括在子算子调用中花费的时间,而总cpu时间包括了它。 +你可以通过将 ``sort_by="self_cpu_time_total"`` 传递给 ``table`` 调用来选择按自身cpu时间排序。 + +要获得更细粒度的结果并包含算子输入形状,请传递 ``group_by_input_shape=True`` +(注意:这需要使用 ``record_shapes=True`` 运行profiler): -.. GENERATED FROM PYTHON SOURCE LINES 135-138 +.. GENERATED FROM PYTHON SOURCE LINES 120-127 .. code-block:: default - print(prof.key_averages(group_by_input_shape=True).table(sort_by="cpu_time_total", row_limit=10)) + print( + prof.key_averages(group_by_input_shape=True).table( + sort_by="cpu_time_total", row_limit=10 + ) + ) -.. GENERATED FROM PYTHON SOURCE LINES 139-159 +.. GENERATED FROM PYTHON SOURCE LINES 128-148 -The output might look like this (omitting some columns): +输出可能如下所示(省略了一些列): .. code-block:: sh @@ -216,37 +205,39 @@ The output might look like this (omitting some columns): Self CPU time total: 57.549ms -.. GENERATED FROM PYTHON SOURCE LINES 161-162 +.. GENERATED FROM PYTHON SOURCE LINES 150-151 -Note the occurrence of ``aten::convolution`` twice with different input shapes. +注意 ``aten::convolution`` 出现了两次,具有不同的输入形状。 -.. GENERATED FROM PYTHON SOURCE LINES 164-165 +.. GENERATED FROM PYTHON SOURCE LINES 153-154 -Profiler can also be used to analyze performance of models executed on GPUs: +Profiler也可用于分析在GPU上执行的模型的性能: -.. GENERATED FROM PYTHON SOURCE LINES 165-176 +.. GENERATED FROM PYTHON SOURCE LINES 154-167 .. code-block:: default + model = models.resnet18().cuda() inputs = torch.randn(5, 3, 224, 224).cuda() - with profile(activities=[ - ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True) as prof: + with profile( + activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True + ) as prof: with record_function("model_inference"): model(inputs) print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10)) -.. GENERATED FROM PYTHON SOURCE LINES 177-178 +.. GENERATED FROM PYTHON SOURCE LINES 168-169 -(Note: the first use of CUDA profiling may bring an extra overhead.) +(注意:第一次使用CUDA分析可能会带来额外的开销。) -.. GENERATED FROM PYTHON SOURCE LINES 180-201 +.. GENERATED FROM PYTHON SOURCE LINES 171-192 -The resulting table output (omitting some columns): +结果输出(省略了一些列): .. code-block:: sh @@ -268,22 +259,20 @@ The resulting table output (omitting some columns): Self CUDA time total: 11.666ms -.. GENERATED FROM PYTHON SOURCE LINES 203-204 +.. GENERATED FROM PYTHON SOURCE LINES 194-195 -Note the occurrence of on-device kernels in the output (e.g. ``sgemm_32x32x32_NN``). +注意在输出中出现了设备上的内核(例如 ``sgemm_32x32x32_NN``)。 -.. GENERATED FROM PYTHON SOURCE LINES 206-214 +.. GENERATED FROM PYTHON SOURCE LINES 197-203 -4. Using profiler to analyze memory consumption +4. 使用 profiler 分析内存消耗 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -PyTorch profiler can also show the amount of memory (used by the model's tensors) -that was allocated (or released) during the execution of the model's operators. -In the output below, 'self' memory corresponds to the memory allocated (released) -by the operator, excluding the children calls to the other operators. -To enable memory profiling functionality pass ``profile_memory=True``. +PyTorch profiler还可以显示在执行模型算子期间分配(或释放)的内存量(由模型张量使用)。 +在下面的输出中,'self'内存对应于算子分配(释放)的内存,不包括对其他算子的子调用。 +要启用内存分析功能,请传递 ``profile_memory=True``。 -.. GENERATED FROM PYTHON SOURCE LINES 214-243 +.. GENERATED FROM PYTHON SOURCE LINES 203-233 .. code-block:: default @@ -291,8 +280,9 @@ To enable memory profiling functionality pass ``profile_memory=True``. model = models.resnet18() inputs = torch.randn(5, 3, 224, 224) - with profile(activities=[ProfilerActivity.CPU], - profile_memory=True, record_shapes=True) as prof: + with profile( + activities=[ProfilerActivity.CPU], profile_memory=True, record_shapes=True + ) as prof: model(inputs) print(prof.key_averages().table(sort_by="self_cpu_memory_usage", row_limit=10)) @@ -317,9 +307,9 @@ To enable memory profiling functionality pass ``profile_memory=True``. print(prof.key_averages().table(sort_by="cpu_memory_usage", row_limit=10)) -.. GENERATED FROM PYTHON SOURCE LINES 244-264 +.. GENERATED FROM PYTHON SOURCE LINES 234-254 -The output might look like this (omitting some columns): +输出如下所示(省略了一些列): .. code-block:: sh @@ -340,14 +330,14 @@ The output might look like this (omitting some columns): Self CPU time total: 53.064ms -.. GENERATED FROM PYTHON SOURCE LINES 266-270 +.. GENERATED FROM PYTHON SOURCE LINES 256-260 -5. Using tracing functionality +5. 使用跟踪功能 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Profiling results can be outputted as a ``.json`` trace file: +可以将分析结果输出为 ``.json`` 跟踪文件: -.. GENERATED FROM PYTHON SOURCE LINES 270-279 +.. GENERATED FROM PYTHON SOURCE LINES 260-269 .. code-block:: default @@ -361,22 +351,21 @@ Profiling results can be outputted as a ``.json`` trace file: prof.export_chrome_trace("trace.json") -.. GENERATED FROM PYTHON SOURCE LINES 280-285 +.. GENERATED FROM PYTHON SOURCE LINES 270-274 -You can examine the sequence of profiled operators and CUDA kernels -in Chrome trace viewer (``chrome://tracing``): +你可以在Chrome跟踪查看器(``chrome://tracing``)中检查分析的算子和CUDA内核序列: .. image:: ../../_static/img/trace_img.png :scale: 25 % -.. GENERATED FROM PYTHON SOURCE LINES 287-291 +.. GENERATED FROM PYTHON SOURCE LINES 276-280 -6. Examining stack traces +6. 检查堆栈跟踪 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Profiler can be used to analyze Python and TorchScript stack traces: +Profiler 可用于分析 Python 和 TorchScript 堆栈跟踪: -.. GENERATED FROM PYTHON SOURCE LINES 291-301 +.. GENERATED FROM PYTHON SOURCE LINES 280-294 .. code-block:: default @@ -388,12 +377,16 @@ Profiler can be used to analyze Python and TorchScript stack traces: model(inputs) # Print aggregated stats - print(prof.key_averages(group_by_stack_n=5).table(sort_by="self_cuda_time_total", row_limit=2)) + print( + prof.key_averages(group_by_stack_n=5).table( + sort_by="self_cuda_time_total", row_limit=2 + ) + ) -.. GENERATED FROM PYTHON SOURCE LINES 302-323 +.. GENERATED FROM PYTHON SOURCE LINES 295-316 -The output might look like this (omitting some columns): +输出如下所示(省略了一些列): .. code-block:: sh @@ -415,99 +408,89 @@ The output might look like this (omitting some columns): Self CUDA time total: 11.659ms -.. GENERATED FROM PYTHON SOURCE LINES 325-328 +.. GENERATED FROM PYTHON SOURCE LINES 318-321 -Note the two convolutions and the two call sites in ``torchvision/models/resnet.py`` script. +注意在 ``torchvision/models/resnet.py`` 脚本中的两个卷积和两个调用位置。 -(Warning: stack tracing adds an extra profiling overhead.) +(警告:堆栈跟踪会增加额外的分析开销。) -.. GENERATED FROM PYTHON SOURCE LINES 330-346 +.. GENERATED FROM PYTHON SOURCE LINES 323-338 -7. Using profiler to analyze long-running jobs +7. 使用分析器分析长时间运行的作业 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -PyTorch profiler offers an additional API to handle long-running jobs -(such as training loops). Tracing all of the execution can be -slow and result in very large trace files. To avoid this, use optional -arguments: +PyTorch分析器提供了一个额外的API来处理长时间运行的作业 +(例如训练循环)。跟踪所有执行可能会很慢,并导致非常大的跟踪文件。 +为了避免这种情况,可以使用可选参数: -- ``schedule`` - specifies a function that takes an integer argument (step number) - as an input and returns an action for the profiler, the best way to use this parameter - is to use ``torch.profiler.schedule`` helper function that can generate a schedule for you; -- ``on_trace_ready`` - specifies a function that takes a reference to the profiler as - an input and is called by the profiler each time the new trace is ready. +- ``schedule`` - 指定一个函数,该函数以整数参数(步骤编号)作为输入, + 并返回分析器的操作,使用此参数的最佳方式是使用 ``torch.profiler.schedule`` + 帮助函数,它可以为您生成一个计划; +- ``on_trace_ready`` - 指定一个函数,该函数以分析器的引用作为输入, + 并在每次新的跟踪准备就绪时由分析器调用。 -To illustrate how the API works, let's first consider the following example with -``torch.profiler.schedule`` helper function: +为了说明该API的工作原理,让我们首先考虑以下使用 ``torch.profiler.schedule`` +帮助函数的示例: -.. GENERATED FROM PYTHON SOURCE LINES 346-356 +.. GENERATED FROM PYTHON SOURCE LINES 338-344 .. code-block:: default + from torch.profiler import schedule - my_schedule = schedule( - skip_first=10, - wait=5, - warmup=1, - active=3, - repeat=2) + my_schedule = schedule(skip_first=10, wait=5, warmup=1, active=3, repeat=2) -.. GENERATED FROM PYTHON SOURCE LINES 357-374 +.. GENERATED FROM PYTHON SOURCE LINES 345-359 -Profiler assumes that the long-running job is composed of steps, numbered -starting from zero. The example above defines the following sequence of actions -for the profiler: +分析器假设长时间运行的作业由从零开始编号的步骤组成。 +上面的示例为分析器定义了以下操作序列: -1. Parameter ``skip_first`` tells profiler that it should ignore the first 10 steps - (default value of ``skip_first`` is zero); -2. After the first ``skip_first`` steps, profiler starts executing profiler cycles; -3. Each cycle consists of three phases: +1. 参数 ``skip_first`` 告诉分析器它应该忽略前10个步骤 + (``skip_first`` 的默认值为零); +2. 在第一个 ``skip_first`` 步骤之后,分析器开始执行分析器周期; +3. 每个周期由三个阶段组成: - - idling (``wait=5`` steps), during this phase profiler is not active; - - warming up (``warmup=1`` steps), during this phase profiler starts tracing, but - the results are discarded; this phase is used to discard the samples obtained by - the profiler at the beginning of the trace since they are usually skewed by an extra - overhead; - - active tracing (``active=3`` steps), during this phase profiler traces and records data; -4. An optional ``repeat`` parameter specifies an upper bound on the number of cycles. - By default (zero value), profiler will execute cycles as long as the job runs. + - 空闲(``wait=5``步骤),在此阶段分析器不活动; + - 预热(``warmup=1``步骤),在此阶段分析器开始跟踪,但结果被丢弃; + 此阶段用于丢弃分析器在跟踪开始时获得的样本,因为它们通常由额外的开销扭曲; + - 主动跟踪(``active=3``步骤),在此阶段分析器跟踪和记录数据; +4. 可选的 ``repeat`` 参数指定周期的上限。 + 默认情况下(零值),分析器将尽可能长时间地执行周期。 -.. GENERATED FROM PYTHON SOURCE LINES 376-389 +.. GENERATED FROM PYTHON SOURCE LINES 361-373 -Thus, in the example above, profiler will skip the first 15 steps, spend the next step on the warm up, -actively record the next 3 steps, skip another 5 steps, spend the next step on the warm up, actively -record another 3 steps. Since the ``repeat=2`` parameter value is specified, the profiler will stop -the recording after the first two cycles. +因此,在上面的示例中,分析器将跳过前15个步骤,在下一步进行预热, +在接下来的3个步骤中主动记录,再跳过另外5个步骤,在下一步进行预热, +在另外3个步骤中主动记录。由于指定了 ``repeat=2`` 参数值, +分析器将在前两个周期之后停止记录。 -At the end of each cycle profiler calls the specified ``on_trace_ready`` function and passes itself as -an argument. This function is used to process the new trace - either by obtaining the table output or -by saving the output on disk as a trace file. +在每个周期结束时,分析器调用指定的 ``on_trace_ready`` 函数并将自身作为参数传递。 +此函数用于处理新的跟踪 - 通过获取表输出或将输出保存到磁盘上的跟踪文件。 -To send the signal to the profiler that the next step has started, call ``prof.step()`` function. -The current profiler step is stored in ``prof.step_num``. +要向分析器发送下一步已经开始的信号,请调用 ``prof.step()`` 函数。 +当前分析器步骤存储在 ``prof.step_num`` 中。 -The following example shows how to use all of the concepts above: +以下示例显示了如何使用上述所有概念: -.. GENERATED FROM PYTHON SOURCE LINES 389-408 +.. GENERATED FROM PYTHON SOURCE LINES 373-391 .. code-block:: default + def trace_handler(p): output = p.key_averages().table(sort_by="self_cuda_time_total", row_limit=10) print(output) p.export_chrome_trace("/tmp/trace_" + str(p.step_num) + ".json") + with profile( activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], - schedule=torch.profiler.schedule( - wait=1, - warmup=1, - active=2), - on_trace_ready=trace_handler + schedule=torch.profiler.schedule(wait=1, warmup=1, active=2), + on_trace_ready=trace_handler, ) as p: for idx in range(8): model(inputs) @@ -515,16 +498,16 @@ The following example shows how to use all of the concepts above: -.. GENERATED FROM PYTHON SOURCE LINES 409-418 +.. GENERATED FROM PYTHON SOURCE LINES 392-401 -Learn More +了解更多 ---------- -Take a look at the following recipes/tutorials to continue your learning: +查看以下教程以继续学习: -- `PyTorch Benchmark `_ -- `PyTorch Profiler with TensorBoard `_ tutorial -- `Visualizing models, data, and training with TensorBoard `_ tutorial +- `PyTorch 基准测试 `_ +- `使用 TensorBoard 的 PyTorch 分析器 `_ 教程 +- `使用 TensorBoard 可视化模型、数据和训练 `_ 教程 diff --git a/docs/_sources/recipes/recipes_index.rst.txt b/docs/_sources/recipes/recipes_index.rst.txt index 289e119..b19ac42 100644 --- a/docs/_sources/recipes/recipes_index.rst.txt +++ b/docs/_sources/recipes/recipes_index.rst.txt @@ -117,7 +117,7 @@ Recipes are bite-sized, actionable examples of how to use specific PyTorch featu .. customcarditem:: :header: PyTorch Profiler - :card_description: Learn how to use PyTorch's profiler to measure operators time and memory consumption + :card_description: 学习如何使用 PyTorch Profiler 来测量算子的时间和内存消耗。 :image: 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  • @@ -117,28 +117,28 @@
  • @@ -151,12 +151,12 @@
    - PyTorch Edge -

    为边缘设备构建创新的、注重隐私的人工智能体验

    + About PyTorch Edge +

    Build innovative and privacy-aware AI experiences for edge devices

     ExecuTorch -

    在移动和边缘设备上实现端到端的设备内推理能力的解决方案

    +

    End-to-end solution for enabling on-device inference capabilities across mobile and edge devices

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