注意
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(测试版) 使用FX构建一个简单的CPU性能分析器
创建于：2021年3月4日 | 最后更新：2024年1月16日 | 最后验证：未验证
作者: James Reed
在本教程中，我们将使用FX来执行以下操作：
以我们可以检查和收集有关代码结构和执行的统计信息的方式捕获PyTorch Python代码
构建一个小类，它将作为一个简单的性能“分析器”，从实际运行中收集模型每个部分的运行时统计信息。
在本教程中，我们将使用torchvision的ResNet18模型进行演示。
import torch
import torch.fx
import torchvision.models as models
rn18 = models.resnet18()
rn18.eval()
ResNet(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer2): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer3): Sequential(
(0): BasicBlock(
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer4): Sequential(
(0): BasicBlock(
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
(fc): Linear(in_features=512, out_features=1000, bias=True)
)
现在我们有了我们的模型，我们想要更深入地检查它的性能。也就是说，对于以下调用，模型的哪些部分耗时最长？
input = torch.randn(5, 3, 224, 224)
output = rn18(input)
回答这个问题的一个常见方法是浏览程序源代码，添加代码以在程序的不同点收集时间戳，并比较这些时间戳之间的差异，以查看时间戳之间的区域需要多长时间。
该技术当然适用于PyTorch代码，但如果我们不必复制模型代码并编辑它，尤其是我们未编写的代码（如这个torchvision模型），那将更好。相反，我们将使用FX来自动化这个“插装”过程，而无需修改任何源代码。
首先，让我们先导入一些必要的库（我们将在代码的后面部分使用所有这些库）。
import statistics, tabulate, time
from typing import Any, Dict, List
from torch.fx import Interpreter
注意
tabulate 是一个外部库，不是 PyTorch 的依赖项。我们将使用它来更轻松地可视化性能数据。请确保您已从您喜欢的 Python 包源安装了它。
使用符号追踪捕获模型
接下来，我们将使用FX的符号跟踪机制来捕获我们模型的定义，并将其存储在一个我们可以操作和检查的数据结构中。
traced_rn18 = torch.fx.symbolic_trace(rn18)
print(traced_rn18.graph)
graph():
%x : torch.Tensor [num_users=1] = placeholder[target=x]
%conv1 : [num_users=1] = call_module[target=conv1](args = (%x,), kwargs = {})
%bn1 : [num_users=1] = call_module[target=bn1](args = (%conv1,), kwargs = {})
%relu : [num_users=1] = call_module[target=relu](args = (%bn1,), kwargs = {})
%maxpool : [num_users=2] = call_module[target=maxpool](args = (%relu,), kwargs = {})
%layer1_0_conv1 : [num_users=1] = call_module[target=layer1.0.conv1](args = (%maxpool,), kwargs = {})
%layer1_0_bn1 : [num_users=1] = call_module[target=layer1.0.bn1](args = (%layer1_0_conv1,), kwargs = {})
%layer1_0_relu : [num_users=1] = call_module[target=layer1.0.relu](args = (%layer1_0_bn1,), kwargs = {})
%layer1_0_conv2 : [num_users=1] = call_module[target=layer1.0.conv2](args = (%layer1_0_relu,), kwargs = {})
%layer1_0_bn2 : [num_users=1] = call_module[target=layer1.0.bn2](args = (%layer1_0_conv2,), kwargs = {})
%add : [num_users=1] = call_function[target=operator.add](args = (%layer1_0_bn2, %maxpool), kwargs = {})
%layer1_0_relu_1 : [num_users=2] = call_module[target=layer1.0.relu](args = (%add,), kwargs = {})
%layer1_1_conv1 : [num_users=1] = call_module[target=layer1.1.conv1](args = (%layer1_0_relu_1,), kwargs = {})
%layer1_1_bn1 : [num_users=1] = call_module[target=layer1.1.bn1](args = (%layer1_1_conv1,), kwargs = {})
%layer1_1_relu : [num_users=1] = call_module[target=layer1.1.relu](args = (%layer1_1_bn1,), kwargs = {})
%layer1_1_conv2 : [num_users=1] = call_module[target=layer1.1.conv2](args = (%layer1_1_relu,), kwargs = {})
%layer1_1_bn2 : [num_users=1] = call_module[target=layer1.1.bn2](args = (%layer1_1_conv2,), kwargs = {})
%add_1 : [num_users=1] = call_function[target=operator.add](args = (%layer1_1_bn2, %layer1_0_relu_1), kwargs = {})
%layer1_1_relu_1 : [num_users=2] = call_module[target=layer1.1.relu](args = (%add_1,), kwargs = {})
%layer2_0_conv1 : [num_users=1] = call_module[target=layer2.0.conv1](args = (%layer1_1_relu_1,), kwargs = {})
%layer2_0_bn1 : [num_users=1] = call_module[target=layer2.0.bn1](args = (%layer2_0_conv1,), kwargs = {})
%layer2_0_relu : [num_users=1] = call_module[target=layer2.0.relu](args = (%layer2_0_bn1,), kwargs = {})
%layer2_0_conv2 : [num_users=1] = call_module[target=layer2.0.conv2](args = (%layer2_0_relu,), kwargs = {})
%layer2_0_bn2 : [num_users=1] = call_module[target=layer2.0.bn2](args = (%layer2_0_conv2,), kwargs = {})
%layer2_0_downsample_0 : [num_users=1] = call_module[target=layer2.0.downsample.0](args = (%layer1_1_relu_1,), kwargs = {})
%layer2_0_downsample_1 : [num_users=1] = call_module[target=layer2.0.downsample.1](args = (%layer2_0_downsample_0,), kwargs = {})
%add_2 : [num_users=1] = call_function[target=operator.add](args = (%layer2_0_bn2, %layer2_0_downsample_1), kwargs = {})
%layer2_0_relu_1 : [num_users=2] = call_module[target=layer2.0.relu](args = (%add_2,), kwargs = {})
%layer2_1_conv1 : [num_users=1] = call_module[target=layer2.1.conv1](args = (%layer2_0_relu_1,), kwargs = {})
%layer2_1_bn1 : [num_users=1] = call_module[target=layer2.1.bn1](args = (%layer2_1_conv1,), kwargs = {})
%layer2_1_relu : [num_users=1] = call_module[target=layer2.1.relu](args = (%layer2_1_bn1,), kwargs = {})
%layer2_1_conv2 : [num_users=1] = call_module[target=layer2.1.conv2](args = (%layer2_1_relu,), kwargs = {})
%layer2_1_bn2 : [num_users=1] = call_module[target=layer2.1.bn2](args = (%layer2_1_conv2,), kwargs = {})
%add_3 : [num_users=1] = call_function[target=operator.add](args = (%layer2_1_bn2, %layer2_0_relu_1), kwargs = {})
%layer2_1_relu_1 : [num_users=2] = call_module[target=layer2.1.relu](args = (%add_3,), kwargs = {})
%layer3_0_conv1 : [num_users=1] = call_module[target=layer3.0.conv1](args = (%layer2_1_relu_1,), kwargs = {})
%layer3_0_bn1 : [num_users=1] = call_module[target=layer3.0.bn1](args = (%layer3_0_conv1,), kwargs = {})
%layer3_0_relu : [num_users=1] = call_module[target=layer3.0.relu](args = (%layer3_0_bn1,), kwargs = {})
%layer3_0_conv2 : [num_users=1] = call_module[target=layer3.0.conv2](args = (%layer3_0_relu,), kwargs = {})
%layer3_0_bn2 : [num_users=1] = call_module[target=layer3.0.bn2](args = (%layer3_0_conv2,), kwargs = {})
%layer3_0_downsample_0 : [num_users=1] = call_module[target=layer3.0.downsample.0](args = (%layer2_1_relu_1,), kwargs = {})
%layer3_0_downsample_1 : [num_users=1] = call_module[target=layer3.0.downsample.1](args = (%layer3_0_downsample_0,), kwargs = {})
%add_4 : [num_users=1] = call_function[target=operator.add](args = (%layer3_0_bn2, %layer3_0_downsample_1), kwargs = {})
%layer3_0_relu_1 : [num_users=2] = call_module[target=layer3.0.relu](args = (%add_4,), kwargs = {})
%layer3_1_conv1 : [num_users=1] = call_module[target=layer3.1.conv1](args = (%layer3_0_relu_1,), kwargs = {})
%layer3_1_bn1 : [num_users=1] = call_module[target=layer3.1.bn1](args = (%layer3_1_conv1,), kwargs = {})
%layer3_1_relu : [num_users=1] = call_module[target=layer3.1.relu](args = (%layer3_1_bn1,), kwargs = {})
%layer3_1_conv2 : [num_users=1] = call_module[target=layer3.1.conv2](args = (%layer3_1_relu,), kwargs = {})
%layer3_1_bn2 : [num_users=1] = call_module[target=layer3.1.bn2](args = (%layer3_1_conv2,), kwargs = {})
%add_5 : [num_users=1] = call_function[target=operator.add](args = (%layer3_1_bn2, %layer3_0_relu_1), kwargs = {})
%layer3_1_relu_1 : [num_users=2] = call_module[target=layer3.1.relu](args = (%add_5,), kwargs = {})
%layer4_0_conv1 : [num_users=1] = call_module[target=layer4.0.conv1](args = (%layer3_1_relu_1,), kwargs = {})
%layer4_0_bn1 : [num_users=1] = call_module[target=layer4.0.bn1](args = (%layer4_0_conv1,), kwargs = {})
%layer4_0_relu : [num_users=1] = call_module[target=layer4.0.relu](args = (%layer4_0_bn1,), kwargs = {})
%layer4_0_conv2 : [num_users=1] = call_module[target=layer4.0.conv2](args = (%layer4_0_relu,), kwargs = {})
%layer4_0_bn2 : [num_users=1] = call_module[target=layer4.0.bn2](args = (%layer4_0_conv2,), kwargs = {})
%layer4_0_downsample_0 : [num_users=1] = call_module[target=layer4.0.downsample.0](args = (%layer3_1_relu_1,), kwargs = {})
%layer4_0_downsample_1 : [num_users=1] = call_module[target=layer4.0.downsample.1](args = (%layer4_0_downsample_0,), kwargs = {})
%add_6 : [num_users=1] = call_function[target=operator.add](args = (%layer4_0_bn2, %layer4_0_downsample_1), kwargs = {})
%layer4_0_relu_1 : [num_users=2] = call_module[target=layer4.0.relu](args = (%add_6,), kwargs = {})
%layer4_1_conv1 : [num_users=1] = call_module[target=layer4.1.conv1](args = (%layer4_0_relu_1,), kwargs = {})
%layer4_1_bn1 : [num_users=1] = call_module[target=layer4.1.bn1](args = (%layer4_1_conv1,), kwargs = {})
%layer4_1_relu : [num_users=1] = call_module[target=layer4.1.relu](args = (%layer4_1_bn1,), kwargs = {})
%layer4_1_conv2 : [num_users=1] = call_module[target=layer4.1.conv2](args = (%layer4_1_relu,), kwargs = {})
%layer4_1_bn2 : [num_users=1] = call_module[target=layer4.1.bn2](args = (%layer4_1_conv2,), kwargs = {})
%add_7 : [num_users=1] = call_function[target=operator.add](args = (%layer4_1_bn2, %layer4_0_relu_1), kwargs = {})
%layer4_1_relu_1 : [num_users=1] = call_module[target=layer4.1.relu](args = (%add_7,), kwargs = {})
%avgpool : [num_users=1] = call_module[target=avgpool](args = (%layer4_1_relu_1,), kwargs = {})
%flatten : [num_users=1] = call_function[target=torch.flatten](args = (%avgpool, 1), kwargs = {})
%fc : [num_users=1] = call_module[target=fc](args = (%flatten,), kwargs = {})
return fc
这为我们提供了ResNet18模型的图形表示。一个图形由一系列相互连接的节点组成。每个节点代表Python代码中的一个调用点（无论是函数、模块还是方法），而边（在每个节点上表示为args和kwargs）表示在这些调用点之间传递的值。有关图形表示和FX其他API的更多信息，请参阅FX文档https://pytorch.org/docs/master/fx.html。
创建一个性能分析解释器
接下来，我们将创建一个继承自torch.fx.Interpreter的类。
虽然symbolic_trace生成的GraphModule编译了Python代码，
当您调用GraphModule时会运行该代码，但另一种运行GraphModule的方法是
逐个执行Graph中的每个Node。这就是Interpreter提供的功能：
它逐个节点地解释图形。
通过继承Interpreter，我们可以覆盖各种功能并安装我们想要的性能分析行为。目标是拥有一个对象，我们可以向其传递模型，调用模型一次或多次，然后获取有关模型及其每个部分在这些运行中所花费时间的统计信息。
让我们定义我们的 ProfilingInterpreter 类：
class ProfilingInterpreter(Interpreter):
def __init__(self, mod : torch.nn.Module):
# Rather than have the user symbolically trace their model,
# we're going to do it in the constructor. As a result, the
# user can pass in any ``Module`` without having to worry about
# symbolic tracing APIs
gm = torch.fx.symbolic_trace(mod)
super().__init__(gm)
# We are going to store away two things here:
#
# 1. A list of total runtimes for ``mod``. In other words, we are
# storing away the time ``mod(...)`` took each time this
# interpreter is called.
self.total_runtime_sec : List[float] = []
# 2. A map from ``Node`` to a list of times (in seconds) that
# node took to run. This can be seen as similar to (1) but
# for specific sub-parts of the model.
self.runtimes_sec : Dict[torch.fx.Node, List[float]] = {}
######################################################################
# Next, let's override our first method: ``run()``. ``Interpreter``'s ``run``
# method is the top-level entry point for execution of the model. We will
# want to intercept this so that we can record the total runtime of the
# model.
def run(self, *args) -> Any:
# Record the time we started running the model
t_start = time.time()
# Run the model by delegating back into Interpreter.run()
return_val = super().run(*args)
# Record the time we finished running the model
t_end = time.time()
# Store the total elapsed time this model execution took in the
# ``ProfilingInterpreter``
self.total_runtime_sec.append(t_end - t_start)
return return_val
######################################################################
# Now, let's override ``run_node``. ``Interpreter`` calls ``run_node`` each
# time it executes a single node. We will intercept this so that we
# can measure and record the time taken for each individual call in
# the model.
def run_node(self, n : torch.fx.Node) -> Any:
# Record the time we started running the op
t_start = time.time()
# Run the op by delegating back into Interpreter.run_node()
return_val = super().run_node(n)
# Record the time we finished running the op
t_end = time.time()
# If we don't have an entry for this node in our runtimes_sec
# data structure, add one with an empty list value.
self.runtimes_sec.setdefault(n, [])
# Record the total elapsed time for this single invocation
# in the runtimes_sec data structure
self.runtimes_sec[n].append(t_end - t_start)
return return_val
######################################################################
# Finally, we are going to define a method (one which doesn't override
# any ``Interpreter`` method) that provides us a nice, organized view of
# the data we have collected.
def summary(self, should_sort : bool = False) -> str:
# Build up a list of summary information for each node
node_summaries : List[List[Any]] = []
# Calculate the mean runtime for the whole network. Because the
# network may have been called multiple times during profiling,
# we need to summarize the runtimes. We choose to use the
# arithmetic mean for this.
mean_total_runtime = statistics.mean(self.total_runtime_sec)
# For each node, record summary statistics
for node, runtimes in self.runtimes_sec.items():
# Similarly, compute the mean runtime for ``node``
mean_runtime = statistics.mean(runtimes)
# For easier understanding, we also compute the percentage
# time each node took with respect to the whole network.
pct_total = mean_runtime / mean_total_runtime * 100
# Record the node's type, name of the node, mean runtime, and
# percent runtime.
node_summaries.append(
[node.op, str(node), mean_runtime, pct_total])
# One of the most important questions to answer when doing performance
# profiling is "Which op(s) took the longest?". We can make this easy
# to see by providing sorting functionality in our summary view
if should_sort:
node_summaries.sort(key=lambda s: s[2], reverse=True)
# Use the ``tabulate`` library to create a well-formatted table
# presenting our summary information
headers : List[str] = [
'Op type', 'Op', 'Average runtime (s)', 'Pct total runtime'
]
return tabulate.tabulate(node_summaries, headers=headers)
注意
我们使用Python的time.time函数来获取挂钟时间戳并进行比较。这不是测量性能的最准确方法，只能给我们一个初步的近似值。我们仅在本教程中为了演示目的使用这种简单技术。
研究ResNet18的性能
我们现在可以使用ProfilingInterpreter来检查我们的ResNet18模型的性能特征；
interp = ProfilingInterpreter(rn18)
interp.run(input)
print(interp.summary(True))
Op type Op Average runtime (s) Pct total runtime
------------- --------------------- --------------------- -------------------
call_module maxpool 0.00571299 8.807
call_module conv1 0.0048542 7.48312
call_module layer4_0_conv2 0.00380135 5.86006
call_module layer1_1_conv2 0.00335789 5.17644
call_module layer4_1_conv1 0.0030942 4.76994
call_module layer4_1_conv2 0.00308824 4.76075
call_module layer1_0_conv1 0.00308061 4.74899
call_module layer1_0_conv2 0.0030272 4.66666
call_module layer2_1_conv2 0.00298619 4.60344
call_module layer1_1_conv1 0.00297284 4.58286
call_module layer3_1_conv2 0.00284076 4.37924
call_module layer2_1_conv1 0.00279307 4.30573
call_module layer3_1_conv1 0.00268817 4.14402
call_module layer3_0_conv2 0.00266528 4.10873
call_module layer2_0_conv2 0.00240898 3.71363
call_module layer4_0_conv1 0.00221109 3.40857
call_module layer3_0_conv1 0.00173402 2.67312
call_module bn1 0.00161076 2.4831
call_module layer2_0_conv1 0.00146675 2.26111
call_module layer2_0_downsample_0 0.00102091 1.57381
call_module layer4_0_downsample_0 0.000473499 0.729935
call_module layer3_0_downsample_0 0.000461102 0.710823
call_function add_1 0.000446558 0.688403
call_function add 0.000440359 0.678847
call_module relu 0.000230074 0.354676
call_module layer1_1_bn2 0.000178814 0.275655
call_module fc 0.000176668 0.272347
call_module layer1_0_bn1 0.000176191 0.271612
call_module layer1_0_bn2 0.000160694 0.247722
call_module layer1_1_bn1 0.000154495 0.238166
call_module avgpool 0.000152826 0.235593
call_module layer2_0_downsample_1 0.000143051 0.220524
call_module layer2_1_bn2 0.00014019 0.216114
call_module layer3_1_bn1 0.000132799 0.20472
call_module layer2_0_bn2 0.000131607 0.202882
call_module layer3_1_bn2 0.0001297 0.199942
call_module layer2_1_bn1 0.000128269 0.197737
call_module layer2_0_bn1 0.000127554 0.196634
call_module layer4_0_bn2 0.000125647 0.193694
call_module layer3_0_bn1 0.000121593 0.187446
call_module layer3_0_bn2 0.000118732 0.183035
call_module layer3_0_downsample_1 0.00011754 0.181197
call_module layer4_1_bn1 0.000117064 0.180462
call_module layer4_0_downsample_1 0.000116587 0.179727
call_module layer4_0_bn1 0.000115871 0.178625
call_module layer4_1_bn2 0.000115871 0.178625
call_module layer1_0_relu 0.000108719 0.167598
call_module layer1_1_relu_1 0.000100374 0.154734
call_module layer1_0_relu_1 9.84669e-05 0.151794
call_module layer1_1_relu 9.48906e-05 0.146281
call_function add_2 8.4877e-05 0.130844
call_function add_3 8.29697e-05 0.127904
call_module layer4_1_relu 8.27312e-05 0.127536
call_module layer4_0_relu 8.17776e-05 0.126066
call_module layer2_1_relu 8.05855e-05 0.124229
call_module layer2_0_relu 7.9155e-05 0.122023
call_module layer2_0_relu_1 7.86781e-05 0.121288
call_module layer2_1_relu_1 7.84397e-05 0.120921
call_module layer3_0_relu 7.70092e-05 0.118716
call_module layer3_1_relu 7.51019e-05 0.115775
call_function add_6 7.51019e-05 0.115775
call_module layer3_0_relu_1 7.22408e-05 0.111365
call_function add_5 7.12872e-05 0.109895
call_module layer4_0_relu_1 7.12872e-05 0.109895
call_module layer3_1_relu_1 7.03335e-05 0.108424
call_function add_7 6.98566e-05 0.107689
call_module layer4_1_relu_1 6.96182e-05 0.107322
call_function add_4 6.93798e-05 0.106954
call_function flatten 4.8399e-05 0.0746107
placeholder x 2.6226e-05 0.0404294
output output 1.83582e-05 0.0283006
这里有两件事情我们需要指出：
MaxPool2d占用了最多的时间。这是一个已知问题： https://github.com/pytorch/pytorch/issues/51393BatchNorm2d 也占用了大量时间。我们可以继续这种思路，并在 Conv-BN Fusion with FX 教程中优化这一点。
结论
正如我们所看到的，使用FX我们可以轻松地以机器可解释的格式捕获PyTorch程序（即使我们没有源代码！），并将其用于分析，例如我们在这里进行的性能分析。FX为处理PyTorch程序打开了一个充满可能性的激动人心的世界。
最后，由于FX仍处于测试阶段，我们非常乐意听取您使用它的任何反馈。请随时使用PyTorch论坛（https://discuss.pytorch.org/）和问题跟踪器（https://github.com/pytorch/pytorch/issues）提供您可能有的任何反馈。
脚本总运行时间： ( 0 分钟 0.425 秒)
