.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "intermediate/fx_profiling_tutorial.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_intermediate_fx_profiling_tutorial.py: (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: 1) Capture PyTorch Python code in a way that we can inspect and gather statistics about the structure and execution of the code 2) 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. .. GENERATED FROM PYTHON SOURCE LINES 18-20 For this tutorial, we are going to use the torchvision ResNet18 model for demonstration purposes. .. GENERATED FROM PYTHON SOURCE LINES 20-28 .. code-block:: default import torch import torch.fx import torchvision.models as models rn18 = models.resnet18() rn18.eval() .. rst-class:: sphx-glr-script-out .. code-block:: none 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) ) .. GENERATED FROM PYTHON SOURCE LINES 29-32 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? .. GENERATED FROM PYTHON SOURCE LINES 32-35 .. code-block:: default input = torch.randn(5, 3, 224, 224) output = rn18(input) .. GENERATED FROM PYTHON SOURCE LINES 36-46 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. .. GENERATED FROM PYTHON SOURCE LINES 48-50 First, let's get some imports out of the way (we will be using all of these later in the code). .. GENERATED FROM PYTHON SOURCE LINES 50-55 .. code-block:: default import statistics, tabulate, time from typing import Any, Dict, List from torch.fx import Interpreter .. GENERATED FROM PYTHON SOURCE LINES 56-60 .. 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. .. GENERATED FROM PYTHON SOURCE LINES 62-67 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. .. GENERATED FROM PYTHON SOURCE LINES 67-71 .. code-block:: default traced_rn18 = torch.fx.symbolic_trace(rn18) print(traced_rn18.graph) .. rst-class:: sphx-glr-script-out .. code-block:: none 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 .. GENERATED FROM PYTHON SOURCE LINES 72-79 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. .. GENERATED FROM PYTHON SOURCE LINES 82-97 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: .. GENERATED FROM PYTHON SOURCE LINES 97-196 .. code-block:: default 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) .. GENERATED FROM PYTHON SOURCE LINES 197-203 .. 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. .. GENERATED FROM PYTHON SOURCE LINES 205-209 Investigating the Performance of ResNet18 ----------------------------------------- We can now use ``ProfilingInterpreter`` to inspect the performance characteristics of our ResNet18 model; .. GENERATED FROM PYTHON SOURCE LINES 209-214 .. code-block:: default interp = ProfilingInterpreter(rn18) interp.run(input) print(interp.summary(True)) .. rst-class:: sphx-glr-script-out .. code-block:: none Op type Op Average runtime (s) Pct total runtime ------------- --------------------- --------------------- ------------------- call_module maxpool 0.00738263 12.2137 call_module conv1 0.00569725 9.42543 call_module layer1_0_conv1 0.00370312 6.12637 call_module layer1_1_conv1 0.00350189 5.79347 call_module layer1_0_conv2 0.0034368 5.68578 call_module layer4_0_conv2 0.00305367 5.05193 call_module layer4_1_conv1 0.00300813 4.97659 call_module layer4_1_conv2 0.00281096 4.65039 call_module layer1_1_conv2 0.00270438 4.47408 call_module layer3_0_conv2 0.00215507 3.5653 call_module layer3_1_conv1 0.00212455 3.51481 call_module layer2_1_conv1 0.00211716 3.50259 call_module layer2_0_conv2 0.00208473 3.44894 call_module layer3_1_conv2 0.0020206 3.34284 call_module layer4_0_conv1 0.00200081 3.3101 call_module layer2_1_conv2 0.00193548 3.20203 call_module layer2_0_conv1 0.00148559 2.45773 call_module layer3_0_conv1 0.00133681 2.2116 call_module bn1 0.00107312 1.77535 call_module layer2_0_downsample_0 0.000623226 1.03105 call_module layer4_0_downsample_0 0.000453472 0.750216 call_module layer3_0_downsample_0 0.000452518 0.748638 call_function add 0.000341415 0.564831 call_module relu 0.000286341 0.473717 call_module fc 0.00022912 0.379052 call_function add_1 0.000200272 0.331326 call_module layer1_0_bn1 0.000195742 0.323831 call_module layer1_0_bn2 0.000164509 0.27216 call_module layer1_1_bn1 0.000158548 0.262299 call_module layer1_1_bn2 0.000150442 0.248889 call_module layer4_0_bn2 0.000138998 0.229956 call_module layer4_1_bn2 0.000135183 0.223645 call_module layer2_0_bn1 0.0001266 0.209445 call_module layer4_1_bn1 0.000124693 0.20629 call_module avgpool 0.000121593 0.201162 call_function add_2 0.000115395 0.190907 call_module layer2_0_downsample_1 0.000107288 0.177496 call_module layer2_0_bn2 0.000104427 0.172763 call_module layer2_1_bn1 0.000101805 0.168424 call_module layer1_0_relu 0.000100374 0.166057 call_module layer2_1_bn2 9.91821e-05 0.164085 call_module layer4_0_bn1 9.17912e-05 0.151858 call_module layer4_0_downsample_1 8.9407e-05 0.147913 call_function add_3 8.91685e-05 0.147519 call_module layer3_0_downsample_1 8.89301e-05 0.147124 call_module layer1_0_relu_1 8.84533e-05 0.146335 call_module layer3_0_bn1 8.72612e-05 0.144363 call_module layer3_0_bn2 8.67844e-05 0.143574 call_module layer3_1_bn1 8.44002e-05 0.13963 call_module layer3_1_bn2 8.36849e-05 0.138447 call_module layer1_1_relu_1 8.17776e-05 0.135291 call_module layer1_1_relu 7.70092e-05 0.127403 call_function add_6 6.38962e-05 0.105709 call_module layer4_1_relu 6.27041e-05 0.103736 call_module layer2_0_relu_1 6.24657e-05 0.103342 call_function add_5 6.12736e-05 0.10137 call_function add_4 5.96046e-05 0.0986088 call_module layer4_0_relu_1 5.96046e-05 0.0986088 call_function add_7 5.67436e-05 0.0938756 call_module layer2_0_relu 5.29289e-05 0.0875646 call_module layer4_0_relu 5.17368e-05 0.0855925 call_module layer2_1_relu_1 5.126e-05 0.0848036 call_module layer4_1_relu_1 5.05447e-05 0.0836203 call_module layer2_1_relu 4.72069e-05 0.0780982 call_module layer3_1_relu 4.64916e-05 0.0769149 call_module layer3_0_relu_1 4.45843e-05 0.0737594 call_module layer3_1_relu_1 4.24385e-05 0.0702095 call_module layer3_0_relu 4.19617e-05 0.0694206 call_function flatten 3.00407e-05 0.0496988 placeholder x 1.88351e-05 0.0311604 output output 1.40667e-05 0.0232717 .. GENERATED FROM PYTHON SOURCE LINES 215-237 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 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 (https://github.com/pytorch/pytorch/issues) to provide any feedback you might have. .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.336 seconds) .. _sphx_glr_download_intermediate_fx_profiling_tutorial.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: fx_profiling_tutorial.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: fx_profiling_tutorial.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_