Note
Click here to download the full example code
(beta) Building a Simple CPU Performance Profiler with FX¶
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.
import torch
import torch.fx
import torchvision.models as models
rn18 = models.resnet18()
rn18.eval()
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).
import statistics, tabulate, time
from typing import Any, Dict, List
from torch.fx import 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)
Out:
graph():
%x : torch.Tensor [#users=1] = placeholder[target=x]
%conv1 : [#users=1] = call_module[target=conv1](args = (%x,), kwargs = {})
%bn1 : [#users=1] = call_module[target=bn1](args = (%conv1,), kwargs = {})
%relu : [#users=1] = call_module[target=relu](args = (%bn1,), kwargs = {})
%maxpool : [#users=2] = call_module[target=maxpool](args = (%relu,), kwargs = {})
%layer1_0_conv1 : [#users=1] = call_module[target=layer1.0.conv1](args = (%maxpool,), kwargs = {})
%layer1_0_bn1 : [#users=1] = call_module[target=layer1.0.bn1](args = (%layer1_0_conv1,), kwargs = {})
%layer1_0_relu : [#users=1] = call_module[target=layer1.0.relu](args = (%layer1_0_bn1,), kwargs = {})
%layer1_0_conv2 : [#users=1] = call_module[target=layer1.0.conv2](args = (%layer1_0_relu,), kwargs = {})
%layer1_0_bn2 : [#users=1] = call_module[target=layer1.0.bn2](args = (%layer1_0_conv2,), kwargs = {})
%add : [#users=1] = call_function[target=operator.add](args = (%layer1_0_bn2, %maxpool), kwargs = {})
%layer1_0_relu_1 : [#users=2] = call_module[target=layer1.0.relu](args = (%add,), kwargs = {})
%layer1_1_conv1 : [#users=1] = call_module[target=layer1.1.conv1](args = (%layer1_0_relu_1,), kwargs = {})
%layer1_1_bn1 : [#users=1] = call_module[target=layer1.1.bn1](args = (%layer1_1_conv1,), kwargs = {})
%layer1_1_relu : [#users=1] = call_module[target=layer1.1.relu](args = (%layer1_1_bn1,), kwargs = {})
%layer1_1_conv2 : [#users=1] = call_module[target=layer1.1.conv2](args = (%layer1_1_relu,), kwargs = {})
%layer1_1_bn2 : [#users=1] = call_module[target=layer1.1.bn2](args = (%layer1_1_conv2,), kwargs = {})
%add_1 : [#users=1] = call_function[target=operator.add](args = (%layer1_1_bn2, %layer1_0_relu_1), kwargs = {})
%layer1_1_relu_1 : [#users=2] = call_module[target=layer1.1.relu](args = (%add_1,), kwargs = {})
%layer2_0_conv1 : [#users=1] = call_module[target=layer2.0.conv1](args = (%layer1_1_relu_1,), kwargs = {})
%layer2_0_bn1 : [#users=1] = call_module[target=layer2.0.bn1](args = (%layer2_0_conv1,), kwargs = {})
%layer2_0_relu : [#users=1] = call_module[target=layer2.0.relu](args = (%layer2_0_bn1,), kwargs = {})
%layer2_0_conv2 : [#users=1] = call_module[target=layer2.0.conv2](args = (%layer2_0_relu,), kwargs = {})
%layer2_0_bn2 : [#users=1] = call_module[target=layer2.0.bn2](args = (%layer2_0_conv2,), kwargs = {})
%layer2_0_downsample_0 : [#users=1] = call_module[target=layer2.0.downsample.0](args = (%layer1_1_relu_1,), kwargs = {})
%layer2_0_downsample_1 : [#users=1] = call_module[target=layer2.0.downsample.1](args = (%layer2_0_downsample_0,), kwargs = {})
%add_2 : [#users=1] = call_function[target=operator.add](args = (%layer2_0_bn2, %layer2_0_downsample_1), kwargs = {})
%layer2_0_relu_1 : [#users=2] = call_module[target=layer2.0.relu](args = (%add_2,), kwargs = {})
%layer2_1_conv1 : [#users=1] = call_module[target=layer2.1.conv1](args = (%layer2_0_relu_1,), kwargs = {})
%layer2_1_bn1 : [#users=1] = call_module[target=layer2.1.bn1](args = (%layer2_1_conv1,), kwargs = {})
%layer2_1_relu : [#users=1] = call_module[target=layer2.1.relu](args = (%layer2_1_bn1,), kwargs = {})
%layer2_1_conv2 : [#users=1] = call_module[target=layer2.1.conv2](args = (%layer2_1_relu,), kwargs = {})
%layer2_1_bn2 : [#users=1] = call_module[target=layer2.1.bn2](args = (%layer2_1_conv2,), kwargs = {})
%add_3 : [#users=1] = call_function[target=operator.add](args = (%layer2_1_bn2, %layer2_0_relu_1), kwargs = {})
%layer2_1_relu_1 : [#users=2] = call_module[target=layer2.1.relu](args = (%add_3,), kwargs = {})
%layer3_0_conv1 : [#users=1] = call_module[target=layer3.0.conv1](args = (%layer2_1_relu_1,), kwargs = {})
%layer3_0_bn1 : [#users=1] = call_module[target=layer3.0.bn1](args = (%layer3_0_conv1,), kwargs = {})
%layer3_0_relu : [#users=1] = call_module[target=layer3.0.relu](args = (%layer3_0_bn1,), kwargs = {})
%layer3_0_conv2 : [#users=1] = call_module[target=layer3.0.conv2](args = (%layer3_0_relu,), kwargs = {})
%layer3_0_bn2 : [#users=1] = call_module[target=layer3.0.bn2](args = (%layer3_0_conv2,), kwargs = {})
%layer3_0_downsample_0 : [#users=1] = call_module[target=layer3.0.downsample.0](args = (%layer2_1_relu_1,), kwargs = {})
%layer3_0_downsample_1 : [#users=1] = call_module[target=layer3.0.downsample.1](args = (%layer3_0_downsample_0,), kwargs = {})
%add_4 : [#users=1] = call_function[target=operator.add](args = (%layer3_0_bn2, %layer3_0_downsample_1), kwargs = {})
%layer3_0_relu_1 : [#users=2] = call_module[target=layer3.0.relu](args = (%add_4,), kwargs = {})
%layer3_1_conv1 : [#users=1] = call_module[target=layer3.1.conv1](args = (%layer3_0_relu_1,), kwargs = {})
%layer3_1_bn1 : [#users=1] = call_module[target=layer3.1.bn1](args = (%layer3_1_conv1,), kwargs = {})
%layer3_1_relu : [#users=1] = call_module[target=layer3.1.relu](args = (%layer3_1_bn1,), kwargs = {})
%layer3_1_conv2 : [#users=1] = call_module[target=layer3.1.conv2](args = (%layer3_1_relu,), kwargs = {})
%layer3_1_bn2 : [#users=1] = call_module[target=layer3.1.bn2](args = (%layer3_1_conv2,), kwargs = {})
%add_5 : [#users=1] = call_function[target=operator.add](args = (%layer3_1_bn2, %layer3_0_relu_1), kwargs = {})
%layer3_1_relu_1 : [#users=2] = call_module[target=layer3.1.relu](args = (%add_5,), kwargs = {})
%layer4_0_conv1 : [#users=1] = call_module[target=layer4.0.conv1](args = (%layer3_1_relu_1,), kwargs = {})
%layer4_0_bn1 : [#users=1] = call_module[target=layer4.0.bn1](args = (%layer4_0_conv1,), kwargs = {})
%layer4_0_relu : [#users=1] = call_module[target=layer4.0.relu](args = (%layer4_0_bn1,), kwargs = {})
%layer4_0_conv2 : [#users=1] = call_module[target=layer4.0.conv2](args = (%layer4_0_relu,), kwargs = {})
%layer4_0_bn2 : [#users=1] = call_module[target=layer4.0.bn2](args = (%layer4_0_conv2,), kwargs = {})
%layer4_0_downsample_0 : [#users=1] = call_module[target=layer4.0.downsample.0](args = (%layer3_1_relu_1,), kwargs = {})
%layer4_0_downsample_1 : [#users=1] = call_module[target=layer4.0.downsample.1](args = (%layer4_0_downsample_0,), kwargs = {})
%add_6 : [#users=1] = call_function[target=operator.add](args = (%layer4_0_bn2, %layer4_0_downsample_1), kwargs = {})
%layer4_0_relu_1 : [#users=2] = call_module[target=layer4.0.relu](args = (%add_6,), kwargs = {})
%layer4_1_conv1 : [#users=1] = call_module[target=layer4.1.conv1](args = (%layer4_0_relu_1,), kwargs = {})
%layer4_1_bn1 : [#users=1] = call_module[target=layer4.1.bn1](args = (%layer4_1_conv1,), kwargs = {})
%layer4_1_relu : [#users=1] = call_module[target=layer4.1.relu](args = (%layer4_1_bn1,), kwargs = {})
%layer4_1_conv2 : [#users=1] = call_module[target=layer4.1.conv2](args = (%layer4_1_relu,), kwargs = {})
%layer4_1_bn2 : [#users=1] = call_module[target=layer4.1.bn2](args = (%layer4_1_conv2,), kwargs = {})
%add_7 : [#users=1] = call_function[target=operator.add](args = (%layer4_1_bn2, %layer4_0_relu_1), kwargs = {})
%layer4_1_relu_1 : [#users=1] = call_module[target=layer4.1.relu](args = (%add_7,), kwargs = {})
%avgpool : [#users=1] = call_module[target=avgpool](args = (%layer4_1_relu_1,), kwargs = {})
%flatten : [#users=1] = call_function[target=torch.flatten](args = (%avgpool, 1), kwargs = {})
%fc : [#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:
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 entrypoint 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 runtim
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))
Out:
Op type Op Average runtime (s) Pct total runtime
------------- --------------------- --------------------- -------------------
call_module conv1 0.00722051 8.99949
call_module maxpool 0.00558686 6.96335
call_module layer1_0_conv1 0.00517154 6.4457
call_module layer1_0_conv2 0.00450683 5.61722
call_module layer1_1_conv2 0.00428033 5.33491
call_module layer4_1_conv1 0.00381231 4.75159
call_module layer1_1_conv1 0.00372958 4.64847
call_module layer4_0_conv2 0.00367785 4.58399
call_module layer2_0_conv2 0.00346327 4.31655
call_module layer2_1_conv2 0.00340915 4.24909
call_module layer2_1_conv1 0.00335407 4.18045
call_module layer4_1_conv2 0.00320649 3.99651
call_module layer3_1_conv2 0.00294518 3.67082
call_module layer4_0_conv1 0.00287318 3.58108
call_module layer3_1_conv1 0.00271416 3.38287
call_module layer3_0_conv2 0.00270486 3.37128
call_module layer3_0_conv1 0.00233698 2.91276
call_module bn1 0.00232458 2.89731
call_module layer2_0_conv1 0.00211811 2.63997
call_module layer3_0_downsample_0 0.000958204 1.19429
call_module layer4_0_downsample_0 0.000771523 0.96161
call_module layer2_0_downsample_0 0.000762939 0.950912
call_function add 0.000671387 0.836803
call_function add_1 0.000524282 0.653455
call_function add_7 0.000395298 0.492691
call_module layer1_1_bn2 0.000331402 0.413052
call_function add_3 0.000299215 0.372936
call_module relu 0.000294685 0.36729
call_module layer1_0_bn2 0.000285149 0.355403
call_function add_4 0.000279665 0.348569
call_module fc 0.000271082 0.337871
call_module layer1_0_bn1 0.000215292 0.268336
call_module layer1_1_bn1 0.000197411 0.246049
call_function add_6 0.000172853 0.215441
call_function add_5 0.000171423 0.213658
call_module layer2_1_bn2 0.000170946 0.213064
call_module layer2_0_bn1 0.000169277 0.210984
call_module layer4_0_bn2 0.000153065 0.190777
call_module avgpool 0.00014472 0.180376
call_module layer2_1_bn1 0.000143528 0.17889
call_module layer3_0_bn2 0.000143051 0.178296
call_module layer4_1_bn1 0.000140905 0.175622
call_module layer2_0_bn2 0.000138998 0.173244
call_module layer2_0_downsample_1 0.000138283 0.172353
call_module layer3_1_bn1 0.000134468 0.167598
call_module layer3_0_bn1 0.000132799 0.165518
call_module layer1_0_relu 0.000119209 0.14858
call_function add_2 0.000117064 0.145906
call_module layer4_0_downsample_1 0.000115633 0.144123
call_module layer4_0_bn1 0.000108957 0.135802
call_module layer1_0_relu_1 0.000106335 0.132533
call_module layer3_0_downsample_1 0.000105858 0.131939
call_module layer2_0_relu_1 0.000105381 0.131345
call_module layer4_1_bn2 0.000105381 0.131345
call_module layer1_1_relu_1 0.000104666 0.130453
call_module layer3_1_bn2 0.000101566 0.12659
call_module layer1_1_relu 9.60827e-05 0.119755
call_module layer3_0_relu 9.34601e-05 0.116487
call_module layer3_1_relu 8.13007e-05 0.101332
call_module layer4_0_relu_1 7.82013e-05 0.0974685
call_module layer4_0_relu 7.67708e-05 0.0956855
call_module layer4_1_relu 7.58171e-05 0.0944969
call_module layer2_0_relu 7.41482e-05 0.0924168
call_module layer2_1_relu 6.86646e-05 0.0855821
call_module layer3_0_relu_1 6.67572e-05 0.0832048
call_module layer4_1_relu_1 6.58035e-05 0.0820162
call_module layer3_1_relu_1 6.38962e-05 0.0796389
call_module layer2_1_relu_1 6.1512e-05 0.0766673
call_function flatten 3.8147e-05 0.0475456
placeholder x 1.4782e-05 0.0184239
output output 1.35899e-05 0.0169381
There are two things we should call out here:
MaxPool2d takes up the most time. This is a known issue: https://github.com/pytorch/pytorch/issues/51393
BatchNorm2d also takes up significant time. We can continue this line of thinking and optimize this in the Conv-BN Fusion with FX tutorial.
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 exiciting 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 (https://github.com/pytorch/pytorch/issues) to provide any feedback you might have.
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