torch.fx

Overview

This feature is under a Beta release and its API may change.

FX is a toolkit for developers to use to transform nn.Module instances. FX consists of three main components: a symbolic tracer, an intermediate representation, and Python code generation. A demonstration of these components in action:

import torch
# Simple module for demonstration
class MyModule(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.param = torch.nn.Parameter(torch.rand(3, 4))
        self.linear = torch.nn.Linear(4, 5)

    def forward(self, x):
        return self.linear(x + self.param).clamp(min=0.0, max=1.0)

module = MyModule()

from torch.fx import symbolic_trace
# Symbolic tracing frontend - captures the semantics of the module
symbolic_traced : torch.fx.GraphModule = symbolic_trace(module)

# High-level intermediate representation (IR) - Graph representation
print(symbolic_traced.graph)
"""
graph(x):
    %param : [#users=1] = self.param
    %add_1 : [#users=1] = call_function[target=<built-in function add>](args = (%x, %param), kwargs = {})
    %linear_1 : [#users=1] = call_module[target=linear](args = (%add_1,), kwargs = {})
    %clamp_1 : [#users=1] = call_method[target=clamp](args = (%linear_1,), kwargs = {min: 0.0, max: 1.0})
    return clamp_1
"""

# Code generation - valid Python code
print(symbolic_traced.code)
"""
def forward(self, x):
    param = self.param
    add_1 = x + param;  x = param = None
    linear_1 = self.linear(add_1);  add_1 = None
    clamp_1 = linear_1.clamp(min = 0.0, max = 1.0);  linear_1 = None
    return clamp_1
"""

The symbolic tracer performs “symbolic execution” of the Python code. It feeds fake values, called Proxies, through the code. Operations on theses Proxies are recorded. More information about symbolic tracing can be found in the symbolic_trace() and Tracer documentation.

The intermediate representation is the container for the operations that were recorded during symbolic tracing. It consists of a list of Nodes that represent function inputs, callsites (to functions, methods, or torch.nn.Module instances), and return values. More information about the IR can be found in the documentation for Graph. The IR is the format on which transformations are applied.

Python code generation is what makes FX a Python-to-Python (or Module-to-Module) transformation toolkit. For each Graph IR, we can create valid Python code matching the Graph’s semantics. This functionality is wrapped up in GraphModule, which is a torch.nn.Module instance that holds a Graph as well as a forward method generated from the Graph.

Taken together, this pipeline of components (symbolic tracing → intermediate representation → transforms → Python code generation) constitutes the Python-to-Python transformation pipeline of FX. In addition, these components can be used separately. For example, symbolic tracing can be used in isolation to capture a form of the code for analysis (and not transformation) purposes. Code generation can be used for programmatically generating models, for example from a config file. There are many uses for FX!

Several example transformations can be found at the examples repository.

Writing Transformations

What is an FX transform? Essentially, it’s a function that looks like this.

import torch
import torch.fx

def transform(m: nn.Module,
              tracer_class : type = torch.fx.Tracer) -> torch.nn.Module:
    # Step 1: Acquire a Graph representing the code in `m`

    # NOTE: torch.fx.symbolic_trace is a wrapper around a call to
    # fx.Tracer.trace and constructing a GraphModule. We'll
    # split that out in our transform to allow the caller to
    # customize tracing behavior.
    graph : torch.fx.Graph = tracer_class().trace(m)

    # Step 2: Modify this Graph or create a new one
    graph = ...

    # Step 3: Construct a Module to return
    return torch.fx.GraphModule(m, graph)

Your transform will take in an torch.nn.Module, acquire a Graph from it, do some modifications, and return a new torch.nn.Module. You should think of the torch.nn.Module that your FX transform returns as identical to a regular torch.nn.Module – you can pass it to another FX transform, you can pass it to TorchScript, or you can run it. Ensuring that the inputs and outputs of your FX transform are a torch.nn.Module will allow for composability.

Note

It is also possible to modify an existing GraphModule instead of creating a new one, like so:

import torch
import torch.fx

def transform(m : nn.Module) -> nn.Module):
    gm : torch.fx.GraphModule = torch.fx.symbolic_trace(m)

    # Modify gm.graph
    # <...>

    # Recompile the forward() method of `gm` from its Graph
    gm.recompile()

    return gm

Note that you MUST call GraphModule.recompile() to bring the generated forward() method on the GraphModule in sync with the modified Graph.

Given that you’ve passed in a torch.nn.Module that has been traced into a Graph, there are now two primary approaches you can take to building a new Graph.

A Quick Primer on Graphs

Full treatment of the semantics of graphs can be found in the Graph documentation, but we are going to cover the basics here. A Graph is a data structure that represents a method on a GraphModule. The information that this requires is:

What are the inputs to the method?
What are the operations that run inside the method?
What is the output (i.e. return) value from the method?

All three of these concepts are represented with Node instances. Let’s see what we mean by that with a short example:

import torch
import torch.fx

class MyModule(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.param = torch.nn.Parameter(torch.rand(3, 4))
        self.linear = torch.nn.Linear(4, 5)

    def forward(self, x):
        return torch.topk(torch.sum(
            self.linear(x + self.linear.weight).relu(), dim=-1), 3)

m = MyModule()
gm = torch.fx.symbolic_trace(m)

gm.graph.print_tabular()

Here we define a module MyModule for demonstration purposes, instantiate it, symbolically trace it, then call the Graph.print_tabular() method to print out a table showing the nodes of this Graph:

opcode	name	target	args	kwargs
placeholder	x	x	()	{}
get_attr	linear_weight	linear.weight	()	{}
call_function	add_1	<built-in function add>	(x, linear_weight)	{}
call_module	linear_1	linear	(add_1,)	{}
call_method	relu_1	relu	(linear_1,)	{}
call_function	sum_1	<built-in method sum …>	(relu_1,)	{‘dim’: -1}
call_function	topk_1	<built-in method topk …>	(sum_1, 3)	{}
output	output	output	(topk_1,)	{}

We can use this information to answer the questions we posed above.

What are the inputs to the method? In FX, method inputs are specified via special placeholder nodes. In this case, we have a single placeholder node with a target of x, meaning we have a single (non-self) argument named x.
What are the operations within the method? The get_attr, call_function, call_module, and call_method nodes represent the operations in the method. A full treatment of the semantics of all of these can be found in the Node documentation.
What is the return value of the method? The return value in a Graph is specified by a special output node.

Given that we now know the basics of how code is represented in FX, we can now explore how we would edit a Graph.

Graph Manipulation

Direct Graph Manipulation

One approach to building this new Graph is to directly manipulate your old one. To aid in this, we can simply take the Graph we obtain from symbolic tracing and modify it. For example, let’s say we desire to replace torch.add() calls with torch.mul() calls.

import torch
import torch.fx

# Sample module
class M(torch.nn.Module):
    def forward(self, x, y):
        return torch.add(x, y)

def transform(m: torch.nn.Module,
              tracer_class : type = fx.Tracer) -> torch.nn.Module:
    graph : fx.Graph = tracer_class().trace(m)
    # FX represents its Graph as an ordered list of
    # nodes, so we can iterate through them.
    for node in graph.nodes:
        # Checks if we're calling a function (i.e:
        # torch.add)
        if node.op == 'call_function':
            # The target attribute is the function
            # that call_function calls.
            if node.target == torch.add:
                node.target = torch.mul

    graph.lint() # Does some checks to make sure the
                 # Graph is well-formed.

    return fx.GraphModule(m, graph)

We can also do more involved Graph rewrites, such as deleting or appending nodes. To aid in these transformations, FX has utility functions for transforming the graph that can be found in the Graph documentation. An example of using these APIs to append a torch.relu() call can be found below.

# Specifies the insertion point. Any nodes added to the
# Graph within this scope will be inserted after `node`
with traced.graph.inserting_after(node):
    # Insert a new `call_function` node calling `torch.relu`
    new_node = traced.graph.call_function(
        torch.relu, args=(node,))

    # We want all places that used the value of `node` to
    # now use that value after the `relu` call we've added.
    # We use the `replace_all_uses_with` API to do this.
    node.replace_all_uses_with(new_node)

For simple transformations that only consist of substitutions, you can also make use of the subgraph rewriter.

Subgraph Rewriting With replace_pattern()

FX also provides another level of automation on top of direct graph manipulation. The replace_pattern() API is essentially a “find/replace” tool for editing Graphs. It allows you to specify a pattern and replacement function and it will trace through those functions, find instances of the group of operations in the pattern graph, and replace those instances with copies of the replacement graph. This can help to greatly automate tedious graph manipulation code, which can get unwieldy as the transformations get more complex.

Graph Manipulation Examples

Proxy/Retracing

Another way of manipulating Graphs is by reusing the Proxy machinery used in symbolic tracing. For example, let’s imagine that we wanted to write a transformation that decomposed PyTorch functions into smaller operations. It would transform every F.relu(x) call into (x > 0) * x. One possibility would be to perform the requisite graph rewriting to insert the comparison and multiplication after the F.relu, and then clean up the original F.relu. However, we can automate this process by using Proxy objects to automatically record operations into the Graph.

To use this method, we write the operations that we want inserted as regular PyTorch code and invoke that code with Proxy objects as arugments. These Proxy objects will capture the operations that are performed on them and append them to the Graph.

# Note that this decomposition rule can be read as regular Python
def relu_decomposition(x):
    return (x > 0) * x

decomposition_rules = {}
decomposition_rules[F.relu] = relu_decomposition

def decompose(model: torch.nn.Module,
              tracer_class : type = fx.Tracer) -> torch.nn.Module:
    """
    Decompose `model` into smaller constituent operations.
    Currently,this only supports decomposing ReLU into its
    mathematical definition: (x > 0) * x
    """
    graph : fx.Graph = tracer_class().trace(model)
    new_graph = fx.Graph()
    env = {}
    for node in graph.nodes:
        if node.op == 'call_function' and node.target in decomposition_rules:
            # By wrapping the arguments with proxies,
            # we can dispatch to the appropriate
            # decomposition rule and implicitly add it
            # to the Graph by symbolically tracing it.
            proxy_args = [
                fx.Proxy(env[x.name]) if isinstance(x, fx.Node) else x for x in node.args]
            output_proxy = decomposition_rules[node.target](*proxy_args)

            # Operations on `Proxy` always yield new `Proxy`s, and the
            # return value of our decomposition rule is no exception.
            # We need to extract the underlying `Node` from the `Proxy`
            # to use it in subsequent iterations of this transform.
            new_node = output_proxy.node
            env[node.name] = new_node
        else:
            # Default case: we don't have a decomposition rule for this
            # node, so just copy the node over into the new graph.
            new_node = new_graph.node_copy(node, lambda x: env[x.name])
            env[node.name] = new_node
    return fx.GraphModule(model, new_graph)

In addition to avoiding explicit graph manipulation, using Proxys also allows you to specify your rewrite rules as native Python code. For transformations that require a large amount of rewrite rules (such as vmap or grad), this can often improve readability and maintainability of the rules.

A worked example of using Proxys for Graph manipulation can be found here.

The Interpreter Pattern

A useful code organizational pattern in FX is to loop over all the Nodes in a Graph and execute them. This can be used for several things including runtime analysis of values flowing through the graph or transformation of the code via retracing with Proxys. For example, suppose we want to run a GraphModule and record the torch.Tensor shape and dtype properties on the nodes as we see them at runtime. That might look like:

import torch
import torch.fx
from torch.fx.node import Node

from typing import Dict

class ShapeProp:
    """
    Shape propagation. This class takes a `GraphModule`.
    Then, its `propagate` method executes the `GraphModule`
    node-by-node with the given arguments. As each operation
    executes, the ShapeProp class stores away the shape and
    element type for the output values of each operation on
    the `shape` and `dtype` attributes of the operation's
    `Node`.
    """
    def __init__(self, mod):
        self.mod = mod
        self.graph = mod.graph
        self.modules = dict(self.mod.named_modules())

    def propagate(self, *args):
        args_iter = iter(args)
        env : Dict[str, Node] = {}

        def load_arg(a):
            return torch.fx.graph.map_arg(a, lambda n: env[n.name])

        def fetch_attr(target : str):
            target_atoms = target.split('.')
            attr_itr = self.mod
            for i, atom in enumerate(target_atoms):
                if not hasattr(attr_itr, atom):
                    raise RuntimeError(f"Node referenced nonexistant target {'.'.join(target_atoms[:i])}")
                attr_itr = getattr(attr_itr, atom)
            return attr_itr

        for node in self.graph.nodes:
            if node.op == 'placeholder':
                result = next(args_iter)
            elif node.op == 'get_attr':
                result = fetch_attr(node.target)
            elif node.op == 'call_function':
                result = node.target(*load_arg(node.args), **load_arg(node.kwargs))
            elif node.op == 'call_method':
                self_obj, *args = load_arg(node.args)
                kwargs = load_arg(node.kwargs)
                result = getattr(self_obj, node.target)(*args, **kwargs)
            elif node.op == 'call_module':
                result = self.modules[node.target](*load_arg(node.args), **load_arg(node.kwargs))

            # This is the only code specific to shape propagation.
            # you can delete this `if` branch and this becomes
            # a generic GraphModule interpreter.
            if isinstance(result, torch.Tensor):
                node.shape = result.shape
                node.dtype = result.dtype

            env[node.name] = result

        return load_arg(self.graph.result)

As you can see, a full interpreter for FX is not that complicated but it can be very useful. To ease using this pattern, we provide the Interpreter class, which encompasses the above logic in a way that certain aspects of the interpreter’s execution can be overridden via method overrides.

In addition to executing operations, we can also generate a new Graph by feeding Proxy values through an interpreter. Similarly, we provide the Transformer class to encompass this pattern. Transformer behaves similarly to Interpreter, but instead of calling the run method to get a concrete output value from the Module, you would call the Transformer.transform() method to return a new GraphModule which was subject to any transformation rules you installed as overridden methods.

Examples of the Interpreter Pattern

Debugging

Introduction

Often in the course of authoring transformations, our code will not be quite right. In this case, we may need to do some debugging. The key is to work backwards: first, check the results of invoking the generated module to prove or disprove correctness. Then, inspect and debug the generated code. Then, debug the process of transformations that led to the generated code.

If you’re not familiar with debuggers, please see the auxiliary section Available Debuggers.

Checking Correctness of Modules

Because the output of most deep learning modules consists of floating point torch.Tensor instances, checking for equivalence between the results of two torch.nn.Module is not as straightforward as doing a simple equality check. To motivate this, let’s use an example:

import torch
import torch.fx
import torchvision.models as models

def transform(m : torch.nn.Module) -> torch.nn.Module:
    gm = torch.fx.symbolic_trace(m)

    # Imagine we're doing some transforms here
    # <...>

    gm.recompile()

    return gm

resnet18 = models.resnet18()
transformed_resnet18 = transform(resnet18)

input_image = torch.randn(5, 3, 224, 224)

assert resnet18(input_image) == transformed_resnet18(input_image)
"""
RuntimeError: Boolean value of Tensor with more than one value is ambiguous
"""

Here, we’ve tried to check equality of the values of two deep learning models with the == equality operator. However, this is not well- defined both due to the issue of that operator returning a tensor and not a bool, but also because comparison of floating point values should use a margin of error (or epsilon) to account for the non-commutativity of floating point operations (see here for more details). We can use torch.allclose() instead, which will give us an approximate comparison taking into account a relative and absolute tolerance threshold:

assert torch.allclose(resnet18(input_image), transformed_resnet18(input_image))

This is the first tool in our toolbox to check if transformed modules are behaving as we expect compared to a reference implementation.

Debugging the Generated Code

Because FX generates the forward() function on GraphModules, using traditional debugging techniques like print statements or pdb is not as straightfoward. Luckily, we have several techniques we can use for debugging the generated code.

Use `pdb`

Invoke pdb to step into the running program. Although the code that represents the Graph is not in any source file, we can still step into it manually using pdb when the forward pass is invoked.

import torch
import torch.fx
import torchvision.models as models

def my_pass(inp: torch.nn.Module, tracer_class : type = fx.Tracer) -> torch.nn.Module:
    graph = tracer_class().trace(inp)
    # Transformation logic here
    # <...>

    # Return new Module
    return fx.GraphModule(inp, graph)

my_module = models.resnet18()
my_module_transformed = my_pass(my_module)

input_value = torch.randn(5, 3, 224, 224)

# When this line is executed at runtime, we will be dropped into an
# interactive `pdb` prompt. We can use the `step` or `s` command to
# step into the execution of the next line
import pdb; pdb.set_trace()

my_module_transformed(input_value)

Print the Generated Code

If you’d like to run the same code multiple times, then it can be a bit tedious to step to the right code with pdb. In that case, one approach is to simply copy-paste the generated forward pass into your code and examine it from there.

# Assume that `traced` is a GraphModule that has undergone some
# number of transforms

# Copy this code for later
print(traced)
# Print the code generated from symbolic tracing. This outputs:
"""
def forward(self, y):
    x = self.x
    add_1 = x + y;  x = y = None
    return add_1
"""

# Subclass the original Module
class SubclassM(M):
    def __init__(self):
        super().__init__()

    # Paste the generated `forward` function (the one we printed and
    # copied above) here
    def forward(self, y):
        x = self.x
        add_1 = x + y;  x = y = None
        return add_1

# Create an instance of the original, untraced Module. Then, create an
# instance of the Module with the copied `forward` function. We can
# now compare the output of both the original and the traced version.
pre_trace = M()
post_trace = SubclassM()

Use the `to_folder` Function From `GraphModule`

GraphModule.to_folder() is a method in GraphModule that allows you to dump out the generated FX code to a folder. Although copying the forward pass into the code often suffices as in Print the Generated Code, it may be easier to examine modules and parameters using to_folder.

m = symbolic_trace(M())
m.to_folder("foo", "Bar")
from foo import Bar
y = Bar()

After running the above example, we can then look at the code within foo/module.py and modify it as desired (e.g. adding print statements or using pdb) to debug the generated code.

Debugging the Transformation

Now that we’ve identified that a transformation is creating incorrect code, it’s time to debug the transformation itself. First, we’ll check the Limitations of Symbolic Tracing section in the documentation. Once we verify that tracing is working as expected, the goal becomes figuring out what went wrong during our GraphModule transformation. There may be a quick answer in Writing Transformations, but, if not, there are several ways to examine our traced module:

# Sample Module
class M(torch.nn.Module):
    def forward(self, x, y):
        return x + y

# Create an instance of `M`
m = M()

# Symbolically trace an instance of `M` (returns a GraphModule). In
# this example, we'll only be discussing how to inspect a
# GraphModule, so we aren't showing any sample transforms for the
# sake of brevity.
traced = symbolic_trace(m)

# Print the code produced by tracing the module.
print(traced)
# The generated `forward` function is:
"""
def forward(self, x, y):
    add_1 = x + y;  x = y = None
    return add_1
"""

# Print the internal Graph.
print(traced.graph)
# This print-out returns:
"""
graph(x, y):
    %add_1 : [#users=1] = call_function[target=<built-in function add>](args = (%x, %y), kwargs = {})
    return add_1
"""

# Print a tabular representation of the internal Graph.
traced.graph.print_tabular()
# This gives us:
"""
opcode         name    target                   args      kwargs
-------------  ------  -----------------------  --------  --------
placeholder    x       x                        ()        {}
placeholder    y       y                        ()        {}
call_function  add_1   <built-in function add>  (x, y)    {}
"""

Using the utility functions above, we can compare our traced Module before and after we’ve applied our transformations. Sometimes, a simple visual comparison is enough to trace down a bug. If it’s still not clear what’s going wrong, a debugger like pdb can be a good next step.

Going off of the example above, consider the following code:

# Sample user-defined function
def transform_graph(module: torch.nn.Module, tracer_class : type = fx.Tracer) -> torch.nn.Module:
    # Get the Graph from our traced Module
    g = tracer_class().trace(module)

    """
    Transformations on `g` go here
    """

    return fx.GraphModule(module, g)

# Transform the Graph
transformed = transform_graph(traced)

# Print the new code after our transforms. Check to see if it was
# what we expected
print(transformed)

Using the above example, let’s say that the call to print(traced) showed us that there was an error in our transforms. We want to find what goes wrong using a debugger. We start a pdb session. We can see what’s happening during the transform by breaking on transform_graph(traced), then pressing s to “step into” the call to transform_graph(traced).

We may also have good luck by editing the print_tabular method to print different attributes of the Nodes in the Graph. (For example, we might want to see the Node’s input_nodes and users.)

Available Debuggers

The most common Python debugger is pdb. You can start your program in “debug mode” with pdb by typing python -m pdb FILENAME.py into the command line, where FILENAME is the name of the file you want to debug. After that, you can use the pdb debugger commands to move through your running program stepwise. It’s common to set a breakpoint (b LINE-NUMBER) when you start pdb, then call c to run the program until that point. This prevents you from having to step through each line of execution (using s or n) to get to the part of the code you want to examine. Alternatively, you can write import pdb; pdb.set_trace() before the line you want to break at. If you add pdb.set_trace(), your program will automatically start in debug mode when you run it. (In other words, you can just type python FILENAME.py into the command line instead of python -m pdb FILENAME.py.) Once you’re running your file in debug mode, you can step through the code and examine your program’s internal state using certain commands. There are many excellent tutorials on pdb online, including RealPython’s “Python Debugging With Pdb”.

IDEs like PyCharm or VSCode usually have a debugger built in. In your IDE, you can choose to either a) use pdb by pulling up a terminal window in your IDE (e.g. View → Terminal in VSCode), or b) use the built-in debugger (usually a graphical wrapper around pdb).

Limitations of Symbolic Tracing

FX uses a system of symbolic tracing (a.k.a symbolic execution) to capture the semantics of programs in a transformable/analyzable form. The system is tracing in that it executes the program (really a torch.nn.Module or function) to record operations. It is symbolic in that the data flowing through the program during this execution is not real data, but rather symbols (Proxy in FX parlance).

Although symbolic tracing works for most neural net code, it has some limitations.

Dynamic Control Flow

The main limitation of symbolic tracing is it does not currently support dynamic control flow. That is, loops or if statements where the condition may depend on the input values of the program.

For example, let’s examine the following program:

def func_to_trace(x):
    dim0 = x.size[0]
    if dim0 == 3:
        return torch.relu(x)
    else:
        return torch.neg(x)

traced = torch.fx.symbolic_trace(func_to_trace)
"""
  <...>
  File "dyn.py", line 6, in func_to_trace
    if dim0 == 3:
  File "pytorch/torch/fx/proxy.py", line 155, in __bool__
    return self.tracer.to_bool(self)
  File "pytorch/torch/fx/proxy.py", line 85, in to_bool
    raise TraceError('symbolically traced variables cannot be used as inputs to control flow')
torch.fx.proxy.TraceError: symbolically traced variables cannot be used as inputs to control flow
"""

The condition to the if statement relies on the value of dim0, which eventually relies on the value of x, a function input. Since x can change (i.e. if you pass a new input tensor to the traced function), this is dynamic control flow. The traceback walks back up through your code to show you where this situation happens.

Static Control Flow

On the other hand, so-called static control flow is supported. Static control flow is loops or if statements whose value cannot change across invocations. Typically, in PyTorch programs, this control flow arises for code making decisions about a model’s architecture based on hyper-parameters. As a concrete example:

import torch
import torch.fx

class MyModule(torch.nn.Module):
    def __init__(self, do_activation : bool = False):
        super().__init__()
        self.do_activation = do_activation
        self.linear = torch.nn.Linear(512, 512)

    def forward(self, x):
        x = self.linear(x)
        # This if-statement is so-called static control flow.
        # Its condition does not depend on any input values
        if self.do_activation:
            x = torch.relu(x)
        return x

without_activation = MyModule(do_activation=False)
with_activation = MyModule(do_activation=True)

traced_without_activation = torch.fx.symbolic_trace(without_activation)
print(traced_without_activation.code)
"""
def forward(self, x):
    linear_1 = self.linear(x);  x = None
    return linear_1
"""

traced_with_activation = torch.fx.symbolic_trace(with_activation)
print(traced_with_activation.code)
"""
import torch
def forward(self, x):
    linear_1 = self.linear(x);  x = None
    relu_1 = torch.relu(linear_1);  linear_1 = None
    return relu_1
"""

The if-statement if self.do_activation does not depend on any function inputs, thus it is static. do_activation can be considered to be a hyper-parameter, and the traces of different instances of MyModule with different values for that parameter have different code. This is a valid pattern that is supported by symbolic tracing.

Many instances of dynamic control flow are semantically static control flow. These instances can be made to support symbolic tracing by removing the data dependencies on input values, for example by moving values to Module attributes or by passing constant values during symbolic tracing:

def f(x, flag):
    if flag: return x
    else: return x*2

fx.symbolic_trace(f) # Fails!

def wrapper(flag):
    return lambda x: f(x, flag)

new_f = wrapper(flag=True)
fx.symbolic_trace(new_f)

In the case of truly dynamic control flow, the sections of the program that contain this code can be traced as calls to the Method (see Customizing Tracing with the Tracer class) or function (see wrap()) rather than tracing through them.

Non-`torch` Functions

FX uses __torch_function__ as the mechanism by which it intercepts calls (see the technical overview for more information about this). Some functions, such as builtin Python functions or those in the math module, are things that are not covered by __torch_function__, but we would still like to capture them in symbolic tracing. For example:

import torch
import torch.fx
from math import sqrt

def normalize(x):
    """
    Normalize `x` by the size of the batch dimension
    """
    return x / sqrt(len(x))

# It's valid Python code
normalize(torch.rand(3, 4))

traced = torch.fx.symbolic_trace(normalize)
"""
  <...>
  File "sqrt.py", line 9, in normalize
    return x / sqrt(len(x))
  File "pytorch/torch/fx/proxy.py", line 161, in __len__
    raise RuntimeError("'len' is not supported in symbolic tracing by default. If you want "
RuntimeError: 'len' is not supported in symbolic tracing by default. If you want this call to be recorded, please call torch.fx.wrap('len') at module scope
"""

The error tells us that the built-in function len is not supported. We can make it so that functions like this are recorded in the trace as direct calls using the wrap() API:

torch.fx.wrap('len')
torch.fx.wrap('sqrt')

traced = torch.fx.symbolic_trace(normalize)

print(traced.code)
"""
import math
def forward(self, x):
    len_1 = len(x)
    sqrt_1 = math.sqrt(len_1);  len_1 = None
    truediv = x / sqrt_1;  x = sqrt_1 = None
    return truediv
"""

Customizing Tracing with the `Tracer` class

The Tracer class is the class that underlies the implementation of symbolic_trace. The behavior of tracing can be customized by subclassing Tracer, like so:

class MyCustomTracer(torch.fx.Tracer):
    # Inside here you can override various methods
    # to customize tracing. See the `Tracer` API
    # reference
    pass


# Let's use this custom tracer to trace through this module
class MyModule(torch.nn.Module):
    def forward(self, x):
        return torch.relu(x) + torch.ones(3, 4)

mod = MyModule()

traced_graph = MyCustomTracer().trace(mod)
# trace() returns a Graph. Let's wrap it up in a
# GraphModule to make it runnable
traced = torch.fx.GraphModule(mod, traced_graph)

Leaf Modules

Leaf Modules are the modules that appear as calls in the symbolic trace rather than being traced through. The default set of leaf modules is the set of standard torch.nn module instances. For example:

class MySpecialSubmodule(torch.nn.Module):
    def forward(self, x):
        return torch.neg(x)

class MyModule(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = torch.nn.Linear(3, 4)
        self.submod = MySpecialSubmodule()

    def forward(self, x):
        return self.submod(self.linear(x))

traced = torch.fx.symbolic_trace(MyModule())
print(traced.code)
# `linear` is preserved as a call, yet `submod` is traced though.
# This is because the default set of "Leaf Modules" includes all
# standard `torch.nn` modules.
"""
import torch
def forward(self, x):
    linear_1 = self.linear(x);  x = None
    neg_1 = torch.neg(linear_1);  linear_1 = None
    return neg_1
"""

The set of leaf modules can be customized by overriding Tracer.is_leaf_module().

Miscellanea

Tensor constructors (e.g. torch.zeros, torch.ones, torch.rand, torch.randn, torch.sparse_coo_tensor) are currently not traceable.
- The deterministic constructors (zeros, ones) can be used and the value they produce will be embedded in the trace as a constant. This is only problematic if the arguments to these constructors refers to dynamic input sizes. In this case, ones_like or zeros_like may be a viable substitute.
- Nondeterministic constructors (rand, randn) will have a single random value embedded in the trace. This is likely not the intended behavior.
- This behavior may be fixed in a future release.
Type annotations
- Python 3-style type annotations (e.g. func(x : torch.Tensor, y : int) -> torch.Tensor) are supported and will be preserved by symbolic tracing.
- Python 2-style comment type annotations # type: (torch.Tensor, int) -> torch.Tensor are not currently supported.
- Annotations on local names within a function are not currently supported.

API Reference

torch.fx.symbolic_trace(root, concrete_args=None) [source]

Symbolic tracing API

Given an nn.Module or function instance root, this function will return a GraphModule constructed by recording operations seen while tracing through root.

Parameters

root (Union[torch.nn.Module, Callable]) – Module or function to be traced and converted into a Graph representation.
concrete_args (Optional[Dict[str, any]]) – Concrete arguments that should not be treated as Proxies.

Returns

a Module created from the recorded operations from root.

Return type

GraphModule

torch.fx.wrap(fn_or_name) [source]

This function can be called at module-level scope to register fn_or_name as a “leaf function”. A “leaf function” will be preserved as a CallFunction node in the FX trace instead of being traced through:

# foo/bar/baz.py
def my_custom_function(x, y):
    return x * x + y * y

torch.fx.wrap('my_custom_function')

def fn_to_be_traced(x, y):
    # When symbolic tracing, the below call to my_custom_function will be inserted into
    # the graph rather than tracing it.
    return my_custom_function(x, y)

This function can also equivalently be used as a decorator:

# foo/bar/baz.py
@torch.fx.wrap
def my_custom_function(x, y):
    return x * x + y * y

A wrapped function can be thought of a “leaf function”, analogous to the concept of “leaf modules”, that is, they are functions that are left as calls in the FX trace rather than traced through.

Parameters: fn_or_name (Union[str, Callable]) – The function or name of the global function to insert into the graph when it’s called

class torch.fx.GraphModule(root, graph, class_name='GraphModule') [source]

GraphModule is an nn.Module generated from an fx.Graph. Graphmodule has a graph attribute, as well as code and forward attributes generated from that graph.

Warning

When graph is reassigned, code and forward will be automatically regenerated. However, if you edit the contents of the graph without reassigning the graph attribute itself, you must call recompile() to update the generated code.

__init__(root, graph, class_name='GraphModule') [source]

Construct a GraphModule.

Parameters

root (Union[torch.nn.Module, Dict[str, Any]) – root can either be an nn.Module instance or a Dict mapping strings to any attribute type. In the case that root is a Module, any references to Module-based objects (via qualified name) in the Graph’s Nodes’ target field will be copied over from the respective place within root’s Module hierarchy into the GraphModule’s module hierarchy. In the case that root is a dict, the qualified name found in a Node’s target will be looked up directly in the dict’s keys. The object mapped to by the Dict will be copied over into the appropriate place within the GraphModule’s module hierarchy.
graph (Graph) – graph contains the nodes this GraphModule should use for code generation
name (str) – name denotes the name of this GraphModule for debugging purposes. If it’s unset, all error messages will report as originating from GraphModule. It may be helpful to set this to root’s original name or a name that makes sense within the context of your transform.

property code: Return the Python code generated from the Graph underlying this GraphModule.

property graph: Return the Graph underlying this GraphModule

recompile() [source]: Recompile this GraphModule from its graph attribute. This should be called after editing the contained graph, otherwise the generated code of this GraphModule will be out of date.

to_folder(folder, module_name='FxModule') [source]

Dumps out module to folder with module_name so that it can be imported with from <folder> import <module_name>

Parameters

folder (Union[str, os.PathLike]) – The folder to write the code out to
module_name (str) – Top-level name to use for the Module while writing out the code

class torch.fx.Graph [source]

Graph is the main data structure used in the FX Intermediate Representation. It consists of a series of Node s, each representing callsites (or other syntactic constructs). The list of Node s, taken together, constitute a valid Python function.

For example, the following code

import torch
import torch.fx

class MyModule(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.param = torch.nn.Parameter(torch.rand(3, 4))
        self.linear = torch.nn.Linear(4, 5)

    def forward(self, x):
        return torch.topk(torch.sum(self.linear(x + self.linear.weight).relu(), dim=-1), 3)

m = MyModule()
gm = torch.fx.symbolic_trace(m)

Will produce the following Graph:

print(gm.graph)

graph(x):
    %linear_weight : [#users=1] = self.linear.weight
    %add_1 : [#users=1] = call_function[target=operator.add](args = (%x, %linear_weight), kwargs = {})
    %linear_1 : [#users=1] = call_module[target=linear](args = (%add_1,), kwargs = {})
    %relu_1 : [#users=1] = call_method[target=relu](args = (%linear_1,), kwargs = {})
    %sum_1 : [#users=1] = call_function[target=torch.sum](args = (%relu_1,), kwargs = {dim: -1})
    %topk_1 : [#users=1] = call_function[target=torch.topk](args = (%sum_1, 3), kwargs = {})
    return topk_1

For the semantics of operations represented in the Graph, please see Node.

__init__() [source]: Construct an empty Graph.

call_function(the_function, args=None, kwargs=None, type_expr=None) [source]

Insert a call_function Node into the Graph. A call_function node represents a call to a Python callable, specified by the_function. the_function can be

Parameters

the_function (Callable[.., Any]) – The function to be called. Can be any PyTorch operator, Python function, or member of the builtins or operator namespaces.
args (Optional[Tuple[Argument, ..]]) – The positional arguments to be passed to the called function.
kwargs (Optional[Dict[str, Argument]]) – The keyword arguments to be passed to the called function
type_expr (Optional[Any]) – an optional type annotation representing the Python type the output of this node will have.

Returns

The newly created and inserted call_function node.

Note

The same insertion point and type expression rules apply for this method as Graph.create_node().

call_method(method_name, args=None, kwargs=None, type_expr=None) [source]

Insert a call_method Node into the Graph. A call_method node represents a call to a given method on the 0th element of args.

Parameters

method_name (str) – The name of the method to apply to the self argument. For example, if args[0] is a Node representing a Tensor, then to call relu() on that Tensor, pass relu to method_name.
args (Optional[Tuple[Argument, ..]]) – The positional arguments to be passed to the called method. Note that this should include a self argument.
kwargs (Optional[Dict[str, Argument]]) – The keyword arguments to be passed to the called method
type_expr (Optional[Any]) – an optional type annotation representing the Python type the output of this node will have.

Returns

The newly created and inserted call_method node.

Note

The same insertion point and type expression rules apply for this method as Graph.create_node().

call_module(module_name, args=None, kwargs=None, type_expr=None) [source]

Insert a call_module Node into the Graph. A call_module node represents a call to the forward() function of a Module in the Module hierarchy.

Parameters

module_name (str) – The qualified name of the Module in the Module hierarchy to be called. For example, if the traced Module has a submodule named foo, which has a submodule named bar, the qualified name foo.bar should be passed as module_name to call that module.
args (Optional[Tuple[Argument, ..]]) – The positional arguments to be passed to the called method. Note that this should not include a self argument.
kwargs (Optional[Dict[str, Argument]]) – The keyword arguments to be passed to the called method
type_expr (Optional[Any]) – an optional type annotation representing the Python type the output of this node will have.

Returns

The newly-created and inserted call_module node.

Note

The same insertion point and type expression rules apply for this method as Graph.create_node().

create_node(op, target, args=None, kwargs=None, name=None, type_expr=None) [source]

Create a Node and add it to the Graph at the current insert-point. Note that the current insert-point can be set via Graph.inserting_before() and Graph.inserting_after().

Parameters

op (str) – the opcode for this Node. One of ‘call_function’, ‘call_method’, ‘get_attr’, ‘call_module’, ‘placeholder’, or ‘output’. The semantics of these opcodes are described in the Graph docstring.
args (Optional[Tuple[Argument, ..]]) – is a tuple of arguments to this node.
kwargs (Optional[Dict[str, Argument]]) – the kwargs of this Node
name (Optional[str]) – an optional string name for the Node. This will influence the name of the value assigned to in the Python generated code.
type_expr (Optional[Any]) – an optional type annotation representing the Python type the output of this node will have.

Returns

The newly-created and inserted node.

erase_node(to_erase) [source]

Erases a Node from the Graph. Throws an exception if there are still users of that node in the Graph.

Parameters: to_erase (Node) – The Node to erase from the Graph.

get_attr(qualified_name, type_expr=None) [source]

Insert a get_attr node into the Graph. A get_attr Node represents the fetch of an attribute from the Module hierarchy.

Parameters

qualified_name (str) – the fully-qualified name of the attribute to be retrieved. For example, if the traced Module has a submodule named foo, which has a submodule named bar, which has an attribute named baz, the qualified name foo.bar.baz should be passed as qualified_name.
type_expr (Optional[Any]) – an optional type annotation representing the Python type the output of this node will have.

Returns

The newly-created and inserted get_attr node.

Note

The same insertion point and type expression rules apply for this method as Graph.create_node.

graph_copy(g, val_map) [source]

Copy all nodes from a given graph into self.

Parameters

g (Graph) – The source graph from which to copy Nodes.
val_map (Dict[Node, Node]) – a dictionary that will be populated with a mapping from nodes in g to nodes in self. Note that val_map can be passed in with values in it already to override copying of certain values.

Returns

The value in self that is now equivalent to the output value in g, if g had an output node. None otherwise.

inserting_after(n=None) [source]

Set the point at which create_node and companion methods will insert into the graph. When used within a ‘with’ statement, this will temporary set the insert point and then restore it when the with statement exits:

with g.inserting_after(n):
    ... # inserting after node n
... # insert point restored to what it was previously
g.inserting_after(n) #  set the insert point permanently

Parameters: n (Optional[Node]) – The node before which to insert. If None this will insert after the beginning of the entire graph.
Returns: A resource manager that will restore the insert point on __exit__.

inserting_before(n=None) [source]

with g.inserting_before(n):
    ... # inserting before node n
... # insert point restored to what it was previously
g.inserting_before(n) #  set the insert point permanently

Parameters: n (Optional[Node]) – The node before which to insert. If None this will insert before the beginning of the entire graph.
Returns: A resource manager that will restore the insert point on __exit__.

lint(root=None) [source]

Runs various checks on this Graph to make sure it is well-formed. In particular: - Checks Nodes have correct ownership (owned by this graph) - Checks Nodes appear in topological order - If root is provided, checks that targets exist in root

Parameters: root (Optional[torch.nn.Module]) – The root module with which to check for targets. This is equivalent to the root argument that is passed when constructing a GraphModule.

node_copy(node, arg_transform=<function Graph.<lambda>>) [source]

Copy a node from one graph into another. arg_transform needs to transform arguments from the graph of node to the graph of self. Example:

# Copying all the nodes in `g` into `new_graph`
g : torch.fx.Graph = ...
new_graph = torch.fx.graph()
value_remap = {}
for node in g.nodes:
    value_remap[node] = new_graph.node_copy(node, lambda n : value_remap[n])

Parameters

node (Node) – The node to copy into self.
arg_transform (Callable[[Node], Argument]) – A function that transforms Node arguments in node’s args and kwargs into the equivalent argument in self. In the simplest case, this should retrieve a value out of a table mapping Nodes in the original graph to self.

property nodes

Get the list of Nodes that constitute this Graph.

Note that this Node list representation is a doubly-linked list. Mutations during iteration (e.g. delete a Node, add a Node) are safe.

Returns: A doubly-linked list of Nodes. Note that reversed can be called on this list to switch iteration order.

output(result, type_expr=None) [source]

Insert an output Node into the Graph. An output node represents a return statement in Python code. result is the value that should be returned.

Parameters

result (Argument) – The value to be returned.
type_expr (Optional[Any]) – an optional type annotation representing the Python type the output of this node will have.

Note

The same insertion point and type expression rules apply for this method as Graph.create_node.

placeholder(name, type_expr=None) [source]

Insert a placeholder node into the Graph. A placeholder represents a function input.

Parameters

name (str) – A name for the input value. This corresponds to the name of the positional argument to the function this Graph represents.
type_expr (Optional[Any]) – an optional type annotation representing the Python type the output of this node will have. This is needed in some cases for proper code generation (e.g. when the function is used subsequently in TorchScript compilation).

Note

The same insertion point and type expression rules apply for this method as Graph.create_node.

print_tabular() [source]: Prints the intermediate representation of the graph in tabular format.

python_code(root_module) [source]

Turn this Graph into valid Python code.

Parameters: root_module (str) – The name of the root module on which to look-up qualified name targets. This is usually ‘self’.
Returns: The string source code generated from this Graph.

class torch.fx.Node(graph, name, op, target, args, kwargs, type=None) [source]

Node is the data structure that represents individual operations within a Graph. For the most part, Nodes represent callsites to various entities, such as operators, methods, and Modules (some exceptions include nodes that specify function inputs and outputs). Each Node has a function specified by its op property. The Node semantics for each value of op are as follows:

placeholder represents a function input. The name attribute specifies the name this value will take on. target is similarly the name of the argument. args holds either: 1) nothing, or 2) a single argument denoting the default parameter of the function input. kwargs is don’t-care. Placeholders correspond to the function parameters (e.g. x) in the graph printout.
get_attr retrieves a parameter from the module hierarchy. name is similarly the name the result of the fetch is assigned to. target is the fully-qualified name of the parameter’s position in the module hierarchy. args and kwargs are don’t-care
call_function applies a free function to some values. name is similarly the name of the value to assign to. target is the function to be applied. args and kwargs represent the arguments to the function, following the Python calling convention
call_module applies a module in the module hierarchy’s forward() method to given arguments. name is as previous. target is the fully-qualified name of the module in the module hierarchy to call. args and kwargs represent the arguments to invoke the module on, including the self argument.
call_method calls a method on a value. name is as similar. target is the string name of the method to apply to the self argument. args and kwargs represent the arguments to invoke the module on, including the self argument
output contains the output of the traced function in its args[0] attribute. This corresponds to the “return” statement in the Graph printout.

property all_input_nodes

Return all Nodes that are inputs to this Node. This is equivalent to iterating over args and kwargs and only collecting the values that are Nodes.

Returns: List of Nodes that appear in the args and kwargs of this Node, in that order.

append(x) [source]

Insert x after this node in the list of nodes in the graph. Equvalent to self.next.prepend(x)

Parameters: x (Node) – The node to put after this node. Must be a member of the same graph.

property args

The tuple of arguments to this Node. The interpretation of arguments depends on the node’s opcode. See the Node docstring for more information.

Assignment to this property is allowed. All accounting of uses and users is updated automatically on assignment.

property kwargs

The dict of keyword arguments to this Node. The interpretation of arguments depends on the node’s opcode. See the Node docstring for more information.

Assignment to this property is allowed. All accounting of uses and users is updated automatically on assignment.

property next

Returns the next Node in the linked list of Nodes.

Returns: The next Node in the linked list of Nodes.

prepend(x) [source]

Insert x before this node in the list of nodes in the graph. Example:

Before: p -> self
        bx -> x -> ax
After:  p -> x -> self
        bx -> ax

Parameters: x (Node) – The node to put before this node. Must be a member of the same graph.

property prev

Returns the previous Node in the linked list of Nodes.

Returns: The previous Node in the linked list of Nodes.

replace_all_uses_with(replace_with) [source]

Replace all uses of self in the Graph with the Node replace_with.

Parameters: replace_with (Node) – The node to replace all uses of self with.
Returns: The list of Nodes on which this change was made.

class torch.fx.Tracer(autowrap_modules=(<module 'math' from '/home/matti/miniconda3/lib/python3.7/lib-dynload/math.cpython-37m-x86_64-linux-gnu.so'>, )) [source]

Tracer is the class that implements the symbolic tracing functionality of torch.fx.symbolic_trace. A call to symbolic_trace(m) is equivalent to Tracer().trace(m).

Tracer can be subclassed to override various behaviors of the tracing process. The different behaviors that can be overridden are described in the docstrings of the methods on this class.

call_module(m, forward, args, kwargs) [source]

Method that specifies the behavior of this Tracer when it encounters a call to an nn.Module instance.

By default, the behavior is to check if the called module is a leaf module via is_leaf_module. If it is, emit a call_module node referring to m in the Graph. Otherwise, call the Module normally, tracing through the operations in its forward function.

This method can be overridden to–for example–create nested traced GraphModules, or any other behavior you would want while tracing across Module boundaries. Module boundaries.

Parameters

m (Module) – The module for which a call is being emitted
forward (Callable) – The forward() method of the Module to be invoked
args (Tuple) – args of the module callsite
kwargs (Dict) – kwargs of the module callsite

Returns

The return value from the Module call. In the case that a call_module node was emitted, this is a Proxy value. Otherwise, it is whatever value was returned from the Module invocation.

create_arg(a) [source]

A method to specify the behavior of tracing when preparing values to be used as arguments to nodes in the Graph.

By default, the behavior includes:

Iterate through collection types (e.g. tuple, list, dict) and recursively call create_args on the elements.
Given a Proxy object, return a reference to the underlying IR Node
Given a non-Proxy Tensor object, emit IR for various cases:
- For a Parameter, emit a get_attr node referring to that Parameter
- For a non-Parameter Tensor, store the Tensor away in a special attribute referring to that attribute.

This method can be overridden to support more types.

Parameters: a (Any) – The value to be emitted as an Argument in the Graph.
Returns: The value a converted into the appropriate Argument

create_args_for_root(root_fn, is_module, concrete_args=None) [source]: Create placeholder nodes corresponding to the signature of the root Module. This method introspects root’s signature and emits those nodes accordingly, also supporting *args and **kwargs.

is_leaf_module(m, module_qualified_name) [source]

A method to specify whether a given nn.Module is a “leaf” module.

Leaf modules are the atomic units that appear in the IR, referenced by call_module calls. By default, Modules in the PyTorch standard library namespace (torch.nn) are leaf modules. All other modules are traced through and their constituent ops are recorded, unless specified otherwise via this parameter.

Parameters

m (Module) – The module being queried about
module_qualified_name (str) – The path to root of this module. For example, if you have a module hierarchy where submodule foo contains submodule bar, which contains submodule baz, that module will appear with the qualified name foo.bar.baz here.

path_of_module(mod) [source]

Helper method to find the qualified name of mod in the Module hierarchy of root. For example, if root has a submodule named foo, which has a submodule named bar, passing bar into this function will return the string “foo.bar”.

Parameters: mod (str) – The Module to retrieve the qualified name for.

trace(root, concrete_args=None) [source]

Trace root and return the corresponding FX Graph representation. root can either be an nn.Module instance or a Python callable.

Note that after this call, self.root may be different from the root passed in here. For example, when a free function is passed to trace(), we will create an nn.Module instance to use as the root and add embedded constants to.

Parameters: root (Union[Module, Callable]) – Either a Module or a function to be traced through.
Returns: A Graph representing the semantics of the passed-in root.

class torch.fx.Proxy(node, tracer=None) [source]

Proxy objects are Node wrappers that flow through the program during symbolic tracing and record all the operations (torch function calls, method calls, operators) that they touch into the growing FX Graph.

If you’re doing graph transforms, you can wrap your own Proxy method around a raw Node so that you can use the overloaded operators to add additional things to a Graph.

class torch.fx.Interpreter(module) [source]

An Interpreter executes an FX graph Node-by-Node. This pattern can be useful for many things, including writing code transformations as well as analysis passes.

Methods in the Interpreter class can be overridden to customize the behavior of execution. The map of overrideable methods in terms of call hierarchy:

run()
    +-- run_node
        +-- placeholder()
        +-- get_attr()
        +-- call_function()
        +-- call_method()
        +-- call_module()
        +-- output()

Example

Suppose we want to swap all instances of torch.neg with torch.sigmoid and vice versa (including their Tensor method equivalents). We could subclass Interpreter like so:

class NegSigmSwapInterpreter(Interpreter):
    def call_function(self, target : Target,
                      args : Tuple, kwargs : Dict) -> Any:
        if target == torch.sigmoid:
            return torch.neg(*args, **kwargs)
        return super().call_function(n)

    def call_method(self, target : Target,
                    args : Tuple, kwargs : Dict) -> Any:
        if target == 'neg':
            call_self, *args_tail = args
            return call_self.sigmoid(*args_tail, **kwargs)
        return super().call_method(n)

def fn(x):
    return torch.sigmoid(x).neg()

gm = torch.fx.symbolic_trace(fn)
input = torch.randn(3, 4)
result = NegSigmSwapInterpreter(gm).run(input)
torch.testing.assert_allclose(result, torch.neg(input).sigmoid())

Parameters: module (GraphModule) – The module to be executed

call_function(target, args, kwargs) [source]

Execute a call_function node and return the result.

Parameters

target (Target) – The call target for this node. See Node for details on semantics
args (Tuple) – Tuple of positional args for this invocation
kwargs (Dict) – Dict of keyword arguments for this invocation

Return: Any: The value returned by the function invocation

call_method(target, args, kwargs) [source]

Execute a call_method node and return the result.

Parameters

target (Target) – The call target for this node. See Node for details on semantics
args (Tuple) – Tuple of positional args for this invocation
kwargs (Dict) – Dict of keyword arguments for this invocation

Return: Any: The value returned by the method invocation

call_module(target, args, kwargs) [source]

Execute a call_module node and return the result.

Parameters

target (Target) – The call target for this node. See Node for details on semantics
args (Tuple) – Tuple of positional args for this invocation
kwargs (Dict) – Dict of keyword arguments for this invocation

Return: Any: The value returned by the module invocation

fetch_args_kwargs_from_env(n) [source]

Fetch the concrete values of args and kwargs of node n from the current execution environment.

Parameters: n (Node) – The node for which args and kwargs should be fetched.
Returns: args and kwargs with concrete values for n.
Return type: Tuple[Tuple, Dict]

fetch_attr(target) [source]

Fetch an attribute from the Module hierarchy of self.module.

Parameters: target (str) – The fully-qualfiied name of the attribute to fetch
Returns: The value of the attribute.
Return type: Any

get_attr(target, args, kwargs) [source]

Execute a get_attr node. Will retrieve an attribute value from the Module hierarchy of self.module.

Parameters

target (Target) – The call target for this node. See Node for details on semantics
args (Tuple) – Tuple of positional args for this invocation
kwargs (Dict) – Dict of keyword arguments for this invocation

Returns

The value of the attribute that was retrieved

Return type

Any

map_nodes_to_values(args, n) [source]

Recursively descend through args and look up the concrete value for each Node in the current execution environment.

Parameters

args (Argument) – Data structure within which to look up concrete values
n (Node) – Node to which args belongs. This is only used for error reporting.

output(target, args, kwargs) [source]

Execute an output node. This really just retrieves the value referenced by the output node and returns it.

Parameters

target (Target) – The call target for this node. See Node for details on semantics
args (Tuple) – Tuple of positional args for this invocation
kwargs (Dict) – Dict of keyword arguments for this invocation

Returns

The return value referenced by the output node

Return type

Any

placeholder(target, args, kwargs) [source]

Execute a placeholder node. Note that this is stateful: Interpreter maintains an internal iterator over arguments passed to run and this method returns next() on that iterator.

Parameters

target (Target) – The call target for this node. See Node for details on semantics
args (Tuple) – Tuple of positional args for this invocation
kwargs (Dict) – Dict of keyword arguments for this invocation

Returns

The argument value that was retrieved.

Return type

Any

run(*args, initial_env=None) [source]

Run module via interpretation and return the result.

Parameters

*args – The arguments to the Module to run, in positional order
initial_env (Optional[Dict[Node, Any]]) – An optional starting environment for execution. This is a dict mapping Node to any value. This can be used, for example, to pre-populate results for certain Nodes so as to do only partial evaluation within the interpreter.

Returns

The value returned from executing the Module

Return type

Any

run_node(n) [source]

Run a specific node n and return the result. Calls into placeholder, get_attr, call_function, call_method, call_module, or output depending on node.op

Parameters: n (Node) – The Node to execute
Returns: The result of executing n
Return type: Any

class torch.fx.Transformer(module) [source]

Transformer is a special type of interpreter that produces a new Module. It exposes a transform() method that returns the transformed Module. Transformer does not require arguments to run, as Interpreter does. Transformer works entirely symbolically.

Example

Suppose we want to swap all instances of torch.neg with torch.sigmoid and vice versa (including their Tensor method equivalents). We could subclass Transformer like so:

class NegSigmSwapXformer(Transformer):
    def call_function(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Any:
        if target == torch.sigmoid:
            return torch.neg(*args, **kwargs)
        return super().call_function(n)

    def call_method(self, target : 'Target', args : Tuple[Argument, ...], kwargs : Dict[str, Any]) -> Any:
        if target == 'neg':
            call_self, *args_tail = args
            return call_self.sigmoid(*args_tail, **kwargs)
        return super().call_method(n)

def fn(x):
    return torch.sigmoid(x).neg()

gm = torch.fx.symbolic_trace(fn)

transformed : torch.nn.Module = NegSigmSwapXformer(gm).transform()
input = torch.randn(3, 4)
torch.testing.assert_allclose(transformed(input), torch.neg(input).sigmoid())

Parameters: module (GraphModule) – The Module to be transformed.

get_attr(target, args, kwargs) [source]

Execute a get_attr node. In Transformer, this is overridden to insert a new get_attr node into the output graph.

Parameters

target (Target) – The call target for this node. See Node for details on semantics
args (Tuple) – Tuple of positional args for this invocation
kwargs (Dict) – Dict of keyword arguments for this invocation

placeholder(target, args, kwargs) [source]

Execute a placeholder node. In Transformer, this is overridden to insert a new placeholder into the output graph.

Parameters

target (Target) – The call target for this node. See Node for details on semantics
args (Tuple) – Tuple of positional args for this invocation
kwargs (Dict) – Dict of keyword arguments for this invocation

transform() [source]: Transform self.module and return the transformed GraphModule.

torch.fx.replace_pattern(gm, pattern, replacement) [source]

Matches all possible non-overlapping sets of operators and their data dependencies (pattern) in the Graph of a GraphModule (gm), then replaces each of these matched subgraphs with another subgraph (replacement).

Parameters

gm – The GraphModule that wraps the Graph to operate on
pattern – The subgraph to match in gm for replacement
replacement – The subgraph to replace pattern with

Returns

A list of Match objects representing the places in the original graph that pattern was matched to. The list is empty if there are no matches. Match is defined as:

class Match(NamedTuple):
    # Node from which the match was found
    anchor: Node
    # Maps nodes in the pattern subgraph to nodes in the larger graph
    nodes_map: Dict[Node, Node]

Return type

List[Match]

Examples:

import torch
from torch.fx import symbolic_trace, subgraph_rewriter

class M(torch.nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x, w1, w2):
        m1 = torch.cat([w1, w2]).sum()
        m2 = torch.cat([w1, w2]).sum()
        return x + torch.max(m1) + torch.max(m2)

def pattern(w1, w2):
    return torch.cat([w1, w2]).sum()

def replacement(w1, w2):
    return torch.stack([w1, w2])

traced_module = symbolic_trace(M())

subgraph_rewriter.replace_pattern(traced_module, pattern, replacement)

The above code will first match pattern in the forward method of traced_module. Pattern-matching is done based on use-def relationships, not node names. For example, if you had p = torch.cat([a, b]) in pattern, you could match m = torch.cat([a, b]) in the original forward function, despite the variable names being different (p vs m).

The return statement in pattern is matched based on its value only; it may or may not match to the return statement in the larger graph. In other words, the pattern doesn’t have to extend to the end of the larger graph.

When the pattern is matched, it will be removed from the larger function and replaced by replacement. If there are multiple matches for pattern in the larger function, each non-overlapping match will be replaced. In the case of a match overlap, the first found match in the set of overlapping matches will be replaced. (“First” here being defined as the first in a topological ordering of the Nodes’ use-def relationships. In most cases, the first Node is the parameter that appears directly after self, while the last Node is whatever the function returns.)

One important thing to note is that the parameters of the pattern Callable must be used in the Callable itself, and the parameters of the replacement Callable must match the pattern. The first rule is why, in the above code block, the forward function has parameters x, w1, w2, but the pattern function only has parameters w1, w2. pattern doesn’t use x, so it shouldn’t specify x as a parameter. As an example of the second rule, consider replacing

def pattern(x, y):
    return torch.neg(x) + torch.relu(y)

with

def replacement(x, y):
    return torch.relu(x)

In this case, replacement needs the same number of parameters as pattern (both x and y), even though the parameter y isn’t used in replacement.

After calling subgraph_rewriter.replace_pattern, the generated Python code looks like this:

def forward(self, x, w1, w2):
    stack_1 = torch.stack([w1, w2])
    sum_1 = stack_1.sum()
    stack_2 = torch.stack([w1, w2])
    sum_2 = stack_2.sum()
    max_1 = torch.max(sum_1)
    add_1 = x + max_1
    max_2 = torch.max(sum_2)
    add_2 = add_1 + max_2
    return add_2

torch.fx

Overview

Writing Transformations

A Quick Primer on Graphs

Graph Manipulation

Direct Graph Manipulation

Subgraph Rewriting With replace_pattern()

Graph Manipulation Examples

Proxy/Retracing

The Interpreter Pattern

Examples of the Interpreter Pattern

Debugging

Introduction

Checking Correctness of Modules

Debugging the Generated Code

Use pdb

Print the Generated Code

Use the to_folder Function From GraphModule

Debugging the Transformation

Available Debuggers

Limitations of Symbolic Tracing

Dynamic Control Flow

Static Control Flow

Non-torch Functions

Customizing Tracing with the Tracer class

Leaf Modules

Miscellanea

API Reference

Example

Example

Use `pdb`

Use the `to_folder` Function From `GraphModule`

Non-`torch` Functions

Customizing Tracing with the `Tracer` class