(beta) Building a Simple CPU Performance Profiler with FX#

Created On: Mar 04, 2021 | Last Updated: Jul 14, 2025 | Last Verified: Not Verified

Author: James Reed

In this tutorial, we are going to use FX to do the following:

Capture PyTorch Python code in a way that we can inspect and gather statistics about the structure and execution of the code
Build out a small class that will serve as a simple performance "profiler", collecting runtime statistics about each part of the model from actual runs.

For this tutorial, we are going to use the torchvision ResNet18 model for demonstration purposes.

importtorch
importtorch.fx
importtorchvision.modelsasmodels
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)
)

Now that we have our model, we want to inspect deeper into its performance. That is, for the following invocation, which parts of the model are taking the longest?

input = torch.randn (5, 3, 224, 224)
output = rn18(input)

A common way of answering that question is to go through the program source, add code that collects timestamps at various points in the program, and compare the difference between those timestamps to see how long the regions between the timestamps take.

That technique is certainly applicable to PyTorch code, however it would be nicer if we didn’t have to copy over model code and edit it, especially code we haven’t written (like this torchvision model). Instead, we are going to use FX to automate this "instrumentation" process without needing to modify any source.

First, let’s get some imports out of the way (we will be using all of these later in the code).

importstatistics,tabulate,time
fromtypingimport Any, Dict, List
fromtorch.fximport Interpreter

Note

tabulate is an external library that is not a dependency of PyTorch. We will be using it to more easily visualize performance data. Please make sure you’ve installed it from your favorite Python package source.

Capturing the Model with Symbolic Tracing#

Next, we are going to use FX’s symbolic tracing mechanism to capture the definition of our model in a data structure we can manipulate and examine.

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

This gives us a Graph representation of the ResNet18 model. A Graph consists of a series of Nodes connected to each other. Each Node represents a call-site in the Python code (whether to a function, a module, or a method) and the edges (represented as args and kwargs on each node) represent the values passed between these call-sites. More information about the Graph representation and the rest of FX’s APIs ca be found at the FX documentation https://pytorch.org/docs/master/fx.html.

Creating a Profiling Interpreter#

Next, we are going to create a class that inherits from torch.fx.Interpreter. Though the GraphModule that symbolic_trace produces compiles Python code that is run when you call a GraphModule, an alternative way to run a GraphModule is by executing each Node in the Graph one by one. That is the functionality that Interpreter provides: It interprets the graph node- by-node.

By inheriting from Interpreter, we can override various functionality and install the profiling behavior we want. The goal is to have an object to which we can pass a model, invoke the model 1 or more times, then get statistics about how long the model and each part of the model took during those runs.

Let’s define our ProfilingInterpreter class:

classProfilingInterpreter(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.
 defrun(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.
 defrun_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.
 defsummary(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)

Note

We use Python’s time.time function to pull wall clock timestamps and compare them. This is not the most accurate way to measure performance, and will only give us a first- order approximation. We use this simple technique only for the purpose of demonstration in this tutorial.

Investigating the Performance of ResNet18#

We can now use ProfilingInterpreter to inspect the performance characteristics of our ResNet18 model;

interp = ProfilingInterpreter (rn18)
interp.run (input)
print(interp.summary(True))

Op type Op Average runtime (s) Pct total runtime
------------- --------------------- --------------------- -------------------
call_module maxpool 0.00458145 8.15724
call_module conv1 0.00455642 8.11266
call_module layer4_0_conv2 0.00312781 5.56905
call_module layer1_0_conv1 0.00304317 5.41835
call_module layer4_1_conv1 0.00294089 5.23624
call_module layer4_1_conv2 0.00291324 5.18699
call_module layer1_0_conv2 0.00278592 4.96031
call_module layer1_1_conv2 0.00265574 4.72853
call_module layer1_1_conv1 0.0025475 4.53581
call_module layer2_0_conv2 0.00242448 4.31676
call_module layer2_1_conv2 0.00233269 4.15333
call_module layer3_1_conv2 0.00219154 3.90202
call_module layer2_1_conv1 0.00214982 3.82774
call_module layer3_0_conv2 0.00213575 3.80269
call_module layer3_1_conv1 0.00207639 3.69699
call_module layer4_0_conv1 0.00192642 3.42998
call_module bn1 0.00134301 2.39122
call_module layer3_0_conv1 0.00125623 2.2367
call_module layer2_0_conv1 0.00122809 2.18661
call_module layer2_0_downsample_0 0.000622749 1.1088
call_module layer3_0_downsample_0 0.000458956 0.817167
call_module layer4_0_downsample_0 0.000439644 0.782782
call_function add 0.000433683 0.77217
call_function add_1 0.000389099 0.692788
call_module layer1_0_bn1 0.000309706 0.551428
call_module layer1_1_bn2 0.00030756 0.547608
call_module layer1_0_bn2 0.000283003 0.503884
call_module relu 0.000269175 0.479263
call_function add_3 0.000226974 0.404126
call_module fc 0.000194788 0.346818
call_module layer2_1_bn2 0.00017643 0.314132
call_module layer2_0_bn1 0.000175953 0.313283
call_module layer1_1_bn1 0.000154257 0.274653
call_module layer1_0_relu 0.000147343 0.262342
call_module layer2_0_downsample_1 0.000122786 0.218619
call_module avgpool 0.000118732 0.211402
call_module layer3_1_bn1 0.000112057 0.199516
call_module layer3_1_bn2 0.000111341 0.198243
call_module layer3_0_bn2 9.05991e-05 0.161311
call_module layer1_0_relu_1 8.70228e-05 0.154943
call_module layer4_0_bn2 8.63075e-05 0.15367
call_module layer2_0_bn2 8.58307e-05 0.152821
call_module layer4_1_bn2 8.55923e-05 0.152396
call_module layer4_1_bn1 8.27312e-05 0.147302
call_module layer1_1_relu_1 8.13007e-05 0.144755
call_module layer4_0_downsample_1 8.05855e-05 0.143482
call_module layer2_1_bn1 7.98702e-05 0.142208
call_function add_2 7.72476e-05 0.137539
call_function add_5 7.4625e-05 0.132869
call_module layer1_1_relu 7.24792e-05 0.129049
call_module layer4_0_bn1 7.24792e-05 0.129049
call_module layer3_0_downsample_1 7.10487e-05 0.126502
call_module layer3_0_bn1 6.62804e-05 0.118012
call_function add_7 6.46114e-05 0.11504
call_function add_6 5.79357e-05 0.103154
call_function add_4 5.62668e-05 0.100183
call_module layer4_1_relu 5.17368e-05 0.092117
call_module layer4_0_relu 4.8399e-05 0.086174
call_module layer2_0_relu_1 4.673e-05 0.0832024
call_module layer2_1_relu_1 4.62532e-05 0.0823534
call_module layer2_0_relu 4.29153e-05 0.0764104
call_module layer4_0_relu_1 4.24385e-05 0.0755614
call_module layer2_1_relu 4.1008e-05 0.0730144
call_module layer4_1_relu_1 3.95775e-05 0.0704674
call_module layer3_1_relu 3.62396e-05 0.0645243
call_module layer3_0_relu 3.60012e-05 0.0640998
call_module layer3_0_relu_1 3.55244e-05 0.0632508
call_module layer3_1_relu_1 3.38554e-05 0.0602793
call_function flatten 2.59876e-05 0.0462707
placeholder x 2.28882e-05 0.0407522
output output 9.29832e-06 0.0165556

There are two things we should call out here:

MaxPool2d takes up the most time. This is a known issue: pytorch/pytorch#51393

Conclusion#

As we can see, using FX we can easily capture PyTorch programs (even ones we don’t have the source code for!) in a machine-interpretable format and use that for analysis, such as the performance analysis we’ve done here. FX opens up an exciting world of possibilities for working with PyTorch programs.

Finally, since FX is still in beta, we would be happy to hear any feedback you have about using it. Please feel free to use the PyTorch Forums (https://discuss.pytorch.org/) and the issue tracker (pytorch/pytorch#issues) to provide any feedback you might have.

Total running time of the script: (0 minutes 0.313 seconds)

Download Jupyter notebook: fx_profiling_tutorial.ipynb

Download Python source code: fx_profiling_tutorial.py

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