注意
跳转到结尾 下载完整的示例代码。
(beta) 使用 FX 构建一个简单的 CPU 性能分析器#
创建日期：2021 年 3 月 4 日 | 最后更新：2025 年 7 月 14 日 | 最后验证：未验证
作者: James Reed
在本教程中，我们将使用 FX 来完成以下操作
以一种我们可以检查和收集代码结构和执行统计信息的方式捕获 PyTorch Python 代码
构建一个小的类，它将充当一个简单的性能“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。
对于本教程，我们将使用 torchvision 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)
)
现在我们有了模型，我们想更深入地检查它的性能。也就是说，对于以下调用，模型的哪些部分花费的时间最长？
input = torch.randn(5, 3, 224, 224)
output = rn18(input)
回答该问题的一种常见方法是遍历程序源代码，添加在程序各个点收集时间戳的代码，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。
这种技术当然适用于 PyTorch 代码，但是如果我们不需要复制模型代码并对其进行编辑，尤其是我们未编写的代码（例如这个 torchvision 模型），那就更好了。相反，我们将使用 FX 来自动执行此“插桩”过程，而无需修改任何源代码。
首先，让我们先导入一些内容（我们将在后面的代码中使用所有这些）。
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。请确保已从您最喜欢的 Python 包源安装它。
使用符号跟踪捕获模型#
接下来，我们将使用 FX 的符号跟踪机制来捕获模型定义，并将其放入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 模型的图表示。图由一系列节点连接在一起组成。每个节点代表 Python 代码中的一个调用站点（无论是函数、模块还是方法），而边（表示为每个节点上的 args 和 kwargs）表示在这些调用站点之间传递的值。有关图表示和 FX 的其余 API 的更多信息，请参见 FX 文档 https://pytorch.ac.cn/docs/stable/fx.html。
创建分析解释器#
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 编译了在调用 GraphModule 时运行的 Python 代码，但运行 GraphModule 的另一种方法是逐个执行 Graph 中的每个 Node。这就是 Interpreter 提供的功能：它逐节点解释图。
通过继承 Interpreter，我们可以覆盖各种功能并安装我们想要的分析行为。目标是创建一个对象，我们可以将模型传递给它，调用模型 1 次或多次，然后获取有关模型及其每个部分在这些运行期间花费的时间的统计信息。
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 函数来提取挂钟时间戳并进行比较。这不是衡量性能的最准确方法，只会给我们提供一阶近似。我们仅出于本教程的演示目的才使用这种简单技术。
调查 ResNet18 的性能#
现在我们可以使用 ProfilingInterpreter 来检查 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 0.0047183 8.42285
call_module conv1 0.00443172 7.91127
call_module layer4_0_conv2 0.00308084 5.49975
call_module layer4_1_conv1 0.00290632 5.18821
call_module layer4_1_conv2 0.00289464 5.16735
call_module layer1_0_conv1 0.00283766 5.06563
call_module layer1_0_conv2 0.00264406 4.72003
call_module layer2_1_conv1 0.00246239 4.39572
call_module layer1_1_conv2 0.00244856 4.37103
call_module layer3_1_conv1 0.00239992 4.28421
call_module layer1_1_conv1 0.00224876 4.01437
call_module layer3_0_conv2 0.00220203 3.93095
call_module layer3_1_conv2 0.00216937 3.87264
call_module layer2_0_conv2 0.00201201 3.59174
call_module layer2_1_conv2 0.0020051 3.57939
call_module layer4_0_conv1 0.00185251 3.307
call_module layer2_0_conv1 0.00162482 2.90054
call_module layer3_0_conv1 0.00142622 2.54601
call_module bn1 0.00137639 2.45706
call_module layer2_0_downsample_0 0.00097847 1.74671
call_module layer4_0_downsample_0 0.000461102 0.823133
call_function add 0.000457764 0.817174
call_module layer3_0_downsample_0 0.000441074 0.787381
call_function add_1 0.000394583 0.704387
call_module relu 0.000341892 0.610327
call_module layer1_0_bn2 0.00029254 0.522225
call_function add_3 0.000222445 0.397096
call_module fc 0.000195742 0.349427
call_module layer2_1_bn1 0.000193596 0.345597
call_module layer1_0_bn1 0.000177622 0.317081
call_module layer1_1_bn1 0.000152349 0.271966
call_module layer1_1_bn2 0.000148058 0.264305
call_module layer4_1_bn2 0.000134945 0.240896
call_module layer2_0_downsample_1 0.000132322 0.236214
call_module layer2_0_bn1 0.00012207 0.217913
call_module avgpool 0.000119925 0.214083
call_module layer3_1_bn1 0.000115395 0.205996
call_module layer4_1_bn1 0.000115156 0.20557
call_module layer3_1_bn2 0.000111818 0.199612
call_module layer4_0_bn2 0.000109673 0.195781
call_module layer1_0_relu 9.63211e-05 0.171947
call_module layer1_1_relu_1 9.10759e-05 0.162584
call_module layer1_0_relu_1 8.96454e-05 0.16003
call_module layer2_1_bn2 8.13007e-05 0.145134
call_module layer2_0_bn2 8.08239e-05 0.144282
call_function add_2 8.08239e-05 0.144282
call_function add_5 7.86781e-05 0.140452
call_module layer4_0_downsample_1 7.65324e-05 0.136621
call_module layer1_1_relu 7.53403e-05 0.134493
call_module layer4_0_bn1 7.48634e-05 0.133642
call_module layer3_0_downsample_1 7.12872e-05 0.127258
call_module layer3_0_bn1 7.05719e-05 0.125981
call_function add_7 6.91414e-05 0.123427
call_module layer3_0_bn2 6.86646e-05 0.122576
call_module layer4_1_relu 6.41346e-05 0.11449
call_function add_6 6.17504e-05 0.110233
call_function add_4 5.65052e-05 0.10087
call_module layer2_0_relu 5.48363e-05 0.0978907
call_module layer2_0_relu_1 5.05447e-05 0.0902297
call_module layer4_0_relu 4.98295e-05 0.0889528
call_module layer4_1_relu_1 4.93526e-05 0.0881016
call_module layer2_1_relu 4.88758e-05 0.0872504
call_module layer2_1_relu_1 4.69685e-05 0.0838455
call_module layer4_0_relu_1 4.62532e-05 0.0825687
call_module layer3_0_relu_1 4.19617e-05 0.0749076
call_module layer3_1_relu 4.02927e-05 0.0719284
call_module layer3_0_relu 3.83854e-05 0.0685235
call_module layer3_1_relu_1 3.52859e-05 0.0629905
placeholder x 2.69413e-05 0.0480941
call_function flatten 2.67029e-05 0.0476685
output output 1.12057e-05 0.0200037
这里有两点需要说明
MaxPool2d花费的时间最多。这是一个已知问题：pytorch/pytorch#51393
结论#
如我们所见，使用 FX，我们可以轻松地将 PyTorch 程序（即使我们没有源代码！）捕获到机器可解释的格式中，并将其用于分析，例如我们在此处进行的性能分析。FX 为使用 PyTorch 程序打开了一个令人兴奋的可能性世界。
最后，由于 FX 仍处于 beta 版本，我们很高兴听到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 (pytorch/pytorch#issues) 提供您可能有的任何反馈。
脚本总运行时间： (0 分钟 0.306 秒)
