torch.fx¶
Overview¶
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 : [#users=1] = placeholder[target=x]
%param : [#users=1] = get_attr[target=param]
�d : [#users=1] = call_function[target=operator.add](args = (%x, %param), kwargs = {})
%linear : [#users=1] = call_module[target=linear](args = (�d,), kwargs = {})
%clamp : [#users=1] = call_method[target=clamp](args = (%linear,), kwargs = {min: 0.0, max: 1.0})
return clamp
"""
# Code generation - valid Python code
print(symbolic_traced.code)
"""
def forward(self, x):
param = self.param
add = x param; x = param = None
linear = self.linear(add); add = None
clamp = linear.clamp(min = 0.0, max = 1.0); linear = None
return clamp
"""
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 a 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 singleplaceholder
node with atarget
ofx
, meaning we have a single (non-self) argument named x.What are the operations within the method? The
get_attr
,call_function
,call_module
, andcall_method
nodes represent the operations in the method. A full treatment of the semantics of all of these can be found in theNode
documentation.What is the return value of the method? The return value in a
Graph
is specified by a specialoutput
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
Graph
s. 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 Graph
s 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 arguments.
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 = {}
tracer = torch.fx.proxy.GraphAppendingTracer(new_graph)
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], tracer) 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 Proxy
s
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. Note that while calling Proxy
we also
passed a tracer pointing to the underlying variable graph. This is done so
if in case the operations in graph are n-ary (e.g. add is a binary operator)
the call to Proxy
does not create multiple instances of a graph
tracer which can lead to unexpected runtime errors. We recommend this method
of using Proxy
especially when the underlying operators can not be
safely assumed to be unary.
A worked example of using Proxy
s for Graph
manipulation
can be found
here.
The Interpreter Pattern¶
A useful code organizational pattern in FX is to loop over all the Node
s
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 Proxy
s. 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 GraphModule
s, using
traditional debugging techniques like print
statements or pdb
is
not as straightforward. 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 = x y; x = y = None
return add
"""
# Print the internal Graph.
print(traced.graph)
# This print-out returns:
"""
graph():
%x : [#users=1] = placeholder[target=x]
%y : [#users=1] = placeholder[target=y]
�d : [#users=1] = call_function[target=operator.add](args = (%x, %y), kwargs = {})
return add
"""
# 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 <built-in function add> (x, y) {}
output output output (add,) {}
"""
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):
if x.sum() > 0:
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 x.sum() > 0:
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 x.sum()
,
which 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 binding concrete values to arguments
during symbolic tracing:
def f(x, flag):
if flag: return x
else: return x*2
fx.symbolic_trace(f) # Fails!
fx.symbolic_trace(f, concrete_args={'flag': True})
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 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
orzeros_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. One workaround is to wraptorch.randn
in atorch.fx.wrap
function and call that instead.
@torch.fx.wrap def torch_randn(x, shape): return torch.randn(shape) def f(x): return x torch_randn(x, 5) fx.symbolic_trace(f)
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.
Gotcha around
training
flag and submodulesWhen using functionals like
torch.nn.functional.dropout
, it will be common for the training argument to be passed in asself.training
. During FX tracing, this will likely be baked in as a constant value.
import torch import torch.fx class DropoutRepro(torch.nn.Module): def forward(self, x): return torch.nn.functional.dropout(x, training=self.training) traced = torch.fx.symbolic_trace(DropoutRepro()) print(traced.code) """ def forward(self, x): dropout = torch.nn.functional.dropout(x, p = 0.5, training = True, inplace = False); x = None return dropout """ traced.eval() x = torch.randn(5, 3) torch.testing.assert_close(traced(x), x) """ AssertionError: Tensor-likes are not close! Mismatched elements: 15 / 15 (100.0%) Greatest absolute difference: 1.6207983493804932 at index (0, 2) (up to 1e-05 allowed) Greatest relative difference: 1.0 at index (0, 0) (up to 0.0001 allowed) """
However, when the standard
nn.Dropout()
submodule is used, the training flag is encapsulated and–because of the preservation of thenn.Module
object model–can be changed.
class DropoutRepro2(torch.nn.Module): def __init__(self): super().__init__() self.drop = torch.nn.Dropout() def forward(self, x): return self.drop(x) traced = torch.fx.symbolic_trace(DropoutRepro2()) print(traced.code) """ def forward(self, x): drop = self.drop(x); x = None return drop """ traced.eval() x = torch.randn(5, 3) torch.testing.assert_close(traced(x), x)
Because of this difference, consider marking modules that interact with the
training
flag dynamically as leaf modules.
API Reference¶
- torch.fx.symbolic_trace(root, concrete_args=None)[source]¶
Symbolic tracing API
Given an
nn.Module
or function instanceroot
, this function will return aGraphModule
constructed by recording operations seen while tracing throughroot
.concrete_args
allows you to partially specialize your function, whether it’s to remove control flow or data structures.For example:
def f(a, b): if b == True: return a else: return a*2
FX can typically not trace through this due to the presence of control flow. However, we can use concrete_args to specialize on the value of b to trace through this:
f = fx.symbolic_trace(f, concrete_args={'b': False}) assert f(3, False) == 6
Note that although you can still pass in different values of b, they will be ignored.
We can also use concrete_args to eliminate data-structure handling from our function. This will use pytrees to flatten your input. To avoid overspecializing, pass in fx.PH for values that shouldn’t be specialized. For example:
def f(x): out = 0 for v in x.values(): out = v return out f = fx.symbolic_trace(f, concrete_args={'x': {'a': fx.PH, 'b': fx.PH, 'c': fx.PH}}) assert f({'a': 1, 'b': 2, 'c': 4}) == 7
- 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]]) – Inputs to be partially specialized
- Returns:
a Module created from the recorded operations from
root
.- Return type:
Note
Backwards-compatibility for this API is guaranteed.
- 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
Note
Backwards-compatibility for this API is guaranteed.
- class torch.fx.GraphModule(*args, **kwargs)[source]¶
GraphModule is an nn.Module generated from an fx.Graph. Graphmodule has a
graph
attribute, as well ascode
andforward
attributes generated from thatgraph
.Warning
When
graph
is reassigned,code
andforward
will be automatically regenerated. However, if you edit the contents of thegraph
without reassigning thegraph
attribute itself, you must callrecompile()
to update the generated code.Note
Backwards-compatibility for this API is guaranteed.
- __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 thatroot
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 withinroot
’s Module hierarchy into the GraphModule’s module hierarchy. In the case thatroot
is a dict, the qualified name found in a Node’starget
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 generationclass_name (str) –
name
denotes the name of this GraphModule for debugging purposes. If it’s unset, all error messages will report as originating fromGraphModule
. It may be helpful to set this toroot
’s original name or a name that makes sense within the context of your transform.
Note
Backwards-compatibility for this API is guaranteed.
- add_submodule(target, m)[source]¶
Adds the given submodule to
self
.This installs empty Modules where none exist yet if they are subpaths of
target
.- Parameters:
- Returns:
- Whether or not the submodule could be inserted. For
this method to return True, each object in the chain denoted by
target
must either a) not exist yet, or b) reference annn.Module
(not a parameter or other attribute)
- Return type:
Note
Backwards-compatibility for this API is guaranteed.
- delete_all_unused_submodules()[source]¶
Deletes all unused submodules from
self
.A Module is considered “used” if any one of the following is true: 1. It has children that are used 2. Its forward is called directly via a
call_module
node 3. It has a non-Module attribute that is used from aget_attr
nodeThis method can be called to clean up an
nn.Module
without manually callingdelete_submodule
on each unused submodule.Note
Backwards-compatibility for this API is guaranteed.
- delete_submodule(target)[source]¶
Deletes the given submodule from
self
.The module will not be deleted if
target
is not a valid target.- Parameters:
target (str) – The fully-qualified string name of the new submodule (See example in
nn.Module.get_submodule
for how to specify a fully-qualified string.)- Returns:
- Whether or not the target string referenced a
submodule we want to delete. A return value of
False
means that thetarget
was not a valid reference to a submodule.
- Return type:
Note
Backwards-compatibility for this API is guaranteed.
- print_readable(print_output=True)[source]¶
Return the Python code generated for current GraphModule and its children GraphModules
Warning
This API is experimental and is NOT backward-compatible.
- recompile()[source]¶
Recompile this GraphModule from its
graph
attribute. This should be called after editing the containedgraph
, otherwise the generated code of thisGraphModule
will be out of date.Note
Backwards-compatibility for this API is guaranteed.
- Return type:
PythonCode
- to_folder(folder, module_name='FxModule')[source]¶
- Dumps out module to
folder
withmodule_name
so that it can be imported with
from <folder> import <module_name>
Args:
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
- module_name (str): Top-level name to use for the
Warning
This API is experimental and is NOT backward-compatible.
- Dumps out module to
- class torch.fx.Graph(owning_module=None, tracer_cls=None, tracer_extras=None)[source]¶
Graph
is the main data structure used in the FX Intermediate Representation. It consists of a series ofNode
s, each representing callsites (or other syntactic constructs). The list ofNode
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 �d_1 : [#users=1] = call_function[target=operator.add](args = (%x, %linear_weight), kwargs = {}) %linear_1 : [#users=1] = call_module[target=linear](args = (�d_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 seeNode
.Note
Backwards-compatibility for this API is guaranteed.
- __init__(owning_module=None, tracer_cls=None, tracer_extras=None)[source]¶
Construct an empty Graph.
Note
Backwards-compatibility for this API is guaranteed.
- call_function(the_function, args=None, kwargs=None, type_expr=None)[source]¶
Insert a
call_function
Node
into theGraph
. Acall_function
node represents a call to a Python callable, specified bythe_function
.- Parameters:
the_function (Callable[..., Any]) – The function to be called. Can be any PyTorch operator, Python function, or member of the
builtins
oroperator
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.- Return type:
Note
The same insertion point and type expression rules apply for this method as
Graph.create_node()
.Note
Backwards-compatibility for this API is guaranteed.
- call_method(method_name, args=None, kwargs=None, type_expr=None)[source]¶
Insert a
call_method
Node
into theGraph
. Acall_method
node represents a call to a given method on the 0th element ofargs
.- Parameters:
method_name (str) – The name of the method to apply to the self argument. For example, if args[0] is a
Node
representing aTensor
, then to callrelu()
on thatTensor
, passrelu
tomethod_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.- Return type:
Note
The same insertion point and type expression rules apply for this method as
Graph.create_node()
.Note
Backwards-compatibility for this API is guaranteed.
- call_module(module_name, args=None, kwargs=None, type_expr=None)[source]¶
Insert a
call_module
Node
into theGraph
. Acall_module
node represents a call to the forward() function of aModule
in theModule
hierarchy.- Parameters:
module_name (str) – The qualified name of the
Module
in theModule
hierarchy to be called. For example, if the tracedModule
has a submodule namedfoo
, which has a submodule namedbar
, the qualified namefoo.bar
should be passed asmodule_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.- Return type:
Note
The same insertion point and type expression rules apply for this method as
Graph.create_node()
.Note
Backwards-compatibility for this API is guaranteed.
- create_node(op, target, args=None, kwargs=None, name=None, type_expr=None)[source]¶
Create a
Node
and add it to theGraph
at the current insert-point. Note that the current insert-point can be set viaGraph.inserting_before()
andGraph.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.
- Return type:
Note
Backwards-compatibility for this API is guaranteed.
- eliminate_dead_code()[source]¶
Remove all dead code from the graph, based on each node’s number of users, and whether the nodes have any side effects. The graph must be topologically sorted before calling.
- Returns:
Whether the graph was changed as a result of the pass.
- Return type:
Example:
Before dead code is eliminated, a from a = x 1 below has no users and thus can be eliminated from the graph without having an effect.
def forward(self, x): a = x 1 return x self.attr_1
After dead code is eliminated, a = x 1 has been removed, and the rest of forward remains.
def forward(self, x): return x self.attr_1
Warning
Dead code elimination has some heuristics to avoid removing side-effectful nodes (see Node.is_impure) but in general coverage is very bad, so you should assume that this method is not sound to call unless you know that your FX graph consists entirely of functional operations.
Note
Backwards-compatibility for this API is guaranteed.
- erase_node(to_erase)[source]¶
Erases a
Node
from theGraph
. Throws an exception if there are still users of that node in theGraph
.- Parameters:
to_erase (Node) – The
Node
to erase from theGraph
.
Note
Backwards-compatibility for this API is guaranteed.
- get_attr(qualified_name, type_expr=None)[source]¶
Insert a
get_attr
node into the Graph. Aget_attr
Node
represents the fetch of an attribute from theModule
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 namedbar
, which has an attribute namedbaz
, the qualified namefoo.bar.baz
should be passed asqualified_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.- Return type:
Note
The same insertion point and type expression rules apply for this method as
Graph.create_node
.Note
Backwards-compatibility for this API is guaranteed.
- graph_copy(g, val_map, return_output_node=False)[source]¶
Copy all nodes from a given graph into
self
.- Parameters:
- Returns:
The value in
self
that is now equivalent to the output value ing
, ifg
had anoutput
node.None
otherwise.- Return type:
Optional[Union[Tuple[Any, …], List[Any], Dict[str, Any], slice, range, Node, str, int, float, bool, complex, dtype, Tensor, device, memory_format, layout]]
Note
Backwards-compatibility for this API is guaranteed.
- 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
Args:
- 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__
.
Note
Backwards-compatibility for this API is guaranteed.
- inserting_before(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_before(n): ... # inserting before node n ... # insert point restored to what it was previously g.inserting_before(n) # set the insert point permanently
Args:
- 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__
.
Note
Backwards-compatibility for this API is guaranteed.
- lint()[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 this Graph has an owning GraphModule, checks that targets exist in that GraphModule
Note
Backwards-compatibility for this API is guaranteed.
- 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’sargs
andkwargs
into the equivalent argument inself
. In the simplest case, this should retrieve a value out of a table mapping Nodes in the original graph toself
.
- Return type:
Note
Backwards-compatibility for this API is guaranteed.
- property nodes: _node_list¶
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.
- on_generate_code(make_transformer)[source]¶
Register a transformer function when python code is generated
- Args:
- make_transformer (Callable[[Optional[TransformCodeFunc]], TransformCodeFunc]):
a function that returns a code transformer to be registered. This function is called by on_generate_code to obtain the code transformer.
This function is also given as its input the currently registered code transformer (or None if nothing is registered), in case it is not desirable to overwrite it. This is useful to chain code transformers together.
- Returns:
a context manager that when used in a with statement, to automatically restore the previously registered code transformer.
Example:
gm: fx.GraphModule = ... # This is a code transformer we want to register. This code # transformer prepends a pdb import and trace statement at the very # beginning of the generated torch.fx code to allow for manual # debugging with the PDB library. def insert_pdb(body): return ["import pdb; pdb.set_trace()\n", *body] # Registers `insert_pdb`, and overwrites the current registered # code transformer (given by `_` to the lambda): gm.graph.on_generate_code( lambda _: insert_pdb ) # Or alternatively, registers a code transformer which first # runs `body` through existing registered transformer, then # through `insert_pdb`: gm.graph.on_generate_code( lambda current_trans: ( lambda body: insert_pdb( current_trans(body) if current_trans else body ) ) ) gm.recompile() gm(*inputs) # drops into pdb
This function can also be used as a context manager, with the benefit to automatically restores the previously registered code transformer:
# ... continue from previous example with gm.graph.on_generate_code(lambda _: insert_pdb): # do more stuff with `gm`... gm.recompile() gm(*inputs) # drops into pdb # now previous code transformer is restored (but `gm`'s code with pdb # remains - that means you can run `gm` with pdb here too, until you # run next `recompile()`).
Warning
This API is experimental and is NOT backward-compatible.
- output(result, type_expr=None)[source]¶
Insert an
output
Node
into theGraph
. Anoutput
node represents areturn
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
.Note
Backwards-compatibility for this API is guaranteed.
- placeholder(name, type_expr=None, default_value)[source]¶
Insert a
placeholder
node into the Graph. Aplaceholder
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).
default_value (Any) – The default value this function argument should take on. NOTE: to allow for None as a default value, inspect.Signature.empty should be passed as this argument to specify that the parameter does _not_ have a default value.
- Return type:
Note
The same insertion point and type expression rules apply for this method as
Graph.create_node
.Note
Backwards-compatibility for this API is guaranteed.
- print_tabular()[source]¶
Prints the intermediate representation of the graph in tabular format. Note that this API requires the
tabulate
module to be installed.Note
Backwards-compatibility for this API is guaranteed.
- process_inputs(*args)[source]¶
Processes args so that they can be passed to the FX graph.
Warning
This API is experimental and is NOT backward-compatible.
- python_code(root_module, *, verbose=False)[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:
src: the Python source code representing the object globals: a dictionary of global names in src -> the objects that they reference.
- Return type:
A PythonCode object, consisting of two fields
Note
Backwards-compatibility for this API is guaranteed.
- class torch.fx.Node(graph, name, op, target, args, kwargs, return_type=None)[source]¶
Node
is the data structure that represents individual operations within aGraph
. 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). EachNode
has a function specified by itsop
property. TheNode
semantics for each value ofop
are as follows:placeholder
represents a function input. Thename
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
andkwargs
are don’t-carecall_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
andkwargs
represent the arguments to the function, following the Python calling conventioncall_module
applies a module in the module hierarchy’sforward()
method to given arguments.name
is as previous.target
is the fully-qualified name of the module in the module hierarchy to call.args
andkwargs
represent the arguments to invoke the module on, excluding 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 theself
argument.args
andkwargs
represent the arguments to invoke the module on, including the self argumentoutput
contains the output of the traced function in itsargs[0]
attribute. This corresponds to the “return” statement in the Graph printout.
Note
Backwards-compatibility for this API is guaranteed.
- property all_input_nodes: List[Node]¶
Return all Nodes that are inputs to this Node. This is equivalent to iterating over
args
andkwargs
and only collecting the values that are Nodes.- Returns:
List of
Nodes
that appear in theargs
andkwargs
of thisNode
, in that order.
- append(x)[source]¶
Insert
x
after this node in the list of nodes in the graph. Equivalent toself.next.prepend(x)
- Parameters:
x (Node) – The node to put after this node. Must be a member of the same graph.
Note
Backwards-compatibility for this API is guaranteed.
- property args: Tuple[Optional[Union[Tuple[Any, ...], List[Any], Dict[str, Any], slice, range, Node, str, int, float, bool, complex, dtype, Tensor, device, memory_format, layout]], ...]¶
The tuple of arguments to this
Node
. The interpretation of arguments depends on the node’s opcode. See theNode
docstring for more information.Assignment to this property is allowed. All accounting of uses and users is updated automatically on assignment.
- format_node(placeholder_names=None, maybe_return_typename=None)[source]¶
Return a descriptive string representation of
self
.This method can be used with no arguments as a debugging utility.
This function is also used internally in the
__str__
method ofGraph
. Together, the strings inplaceholder_names
andmaybe_return_typename
make up the signature of the autogeneratedforward
function in this Graph’s surrounding GraphModule.placeholder_names
andmaybe_return_typename
should not be used otherwise.- Parameters:
placeholder_names (Optional[List[str]]) – A list that will store formatted strings representing the placeholders in the generated
forward
function. Internal use only.maybe_return_typename (Optional[List[str]]) – A single-element list that will store a formatted string representing the output of the generated
forward
function. Internal use only.
- Returns:
- If 1) we’re using
format_node
as an internal helper in the
__str__
method ofGraph
, and 2)self
is a placeholder Node, returnNone
. Otherwise, return a descriptive string representation of the current Node.
- If 1) we’re using
- Return type:
Note
Backwards-compatibility for this API is guaranteed.
- is_impure()[source]¶
Returns whether this op is impure, i.e. if its op is a placeholder or output, or if a call_function or call_module which is impure.
- Returns:
If the op is impure or not.
- Return type:
Warning
This API is experimental and is NOT backward-compatible.
- property kwargs: Dict[str, Optional[Union[Tuple[Any, ...], List[Any], Dict[str, Any], slice, range, Node, str, int, float, bool, complex, dtype, Tensor, device, memory_format, layout]]]¶
The dict of keyword arguments to this
Node
. The interpretation of arguments depends on the node’s opcode. See theNode
docstring for more information.Assignment to this property is allowed. All accounting of uses and users is updated automatically on assignment.
- property next: Node¶
Returns the next
Node
in the linked list of Nodes.- Returns:
The next
Node
in the linked list of Nodes.
- normalized_arguments(root, arg_types=None, kwarg_types=None, normalize_to_only_use_kwargs=False)[source]¶
Returns normalized arguments to Python targets. This means that args/kwargs will be matched up to the module/functional’s signature and return exclusively kwargs in positional order if normalize_to_only_use_kwargs is true. Also populates default values. Does not support positional-only parameters or varargs parameters.
Supports module calls.
May require arg_types and kwarg_types in order to disambiguate overloads.
- Parameters:
root (torch.nn.Module) – Module upon which to resolve module targets.
arg_types (Optional[Tuple[Any]]) – Tuple of arg types for the args
kwarg_types (Optional[Dict[str, Any]]) – Dict of arg types for the kwargs
normalize_to_only_use_kwargs (bool) – Whether to normalize to only use kwargs.
- Returns:
Returns NamedTuple ArgsKwargsPair, or None if not successful.
- Return type:
Optional[ArgsKwargsPair]
Warning
This API is experimental and is NOT backward-compatible.
- 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.
Note
Backwards-compatibility for this API is guaranteed.
- property prev: Node¶
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, delete_user_cb=<function Node.<lambda>>, *, propagate_meta=False)[source]¶
Replace all uses of
self
in the Graph with the Nodereplace_with
.- Parameters:
replace_with (Node) – The node to replace all uses of
self
with.delete_user_cb (Callable) – Callback that is called to determine whether a given user of the self node should be removed.
propagate_meta (bool) – Whether or not to copy all properties on the .meta field of the original node onto the replacement node. For safety, this is only valid to do if the replacement node doesn’t already have an existing .meta field.
- Returns:
The list of Nodes on which this change was made.
- Return type:
Note
Backwards-compatibility for this API is guaranteed.
- replace_input_with(old_input, new_input)[source]¶
Loop through input nodes of
self
, and replace all instances ofold_input
withnew_input
.- Parameters:
Note
Backwards-compatibility for this API is guaranteed.
- property stack_trace: Optional[str]¶
Return the Python stack trace that was recorded during tracing, if any. This property is usually populated by Tracer.create_proxy. To record stack traces during tracing for debug purposes, set record_stack_traces = True on the Tracer instance.
- update_arg(idx, arg)[source]¶
Update an existing positional argument to contain the new value
arg
. After calling,self.args[idx] == arg
.- Parameters:
idx (int) – The index into
self.args
of the element to updatearg (Argument) – The new argument value to write into
args
Note
Backwards-compatibility for this API is guaranteed.
- update_kwarg(key, arg)[source]¶
Update an existing keyword argument to contain the new value
arg
. After calling,self.kwargs[key] == arg
.- Parameters:
key (str) – The key in
self.kwargs
of the element to updatearg (Argument) – The new argument value to write into
kwargs
Note
Backwards-compatibility for this API is guaranteed.
- class torch.fx.Tracer(autowrap_modules=(math,), autowrap_functions=())[source]¶
Tracer
is the class that implements the symbolic tracing functionality oftorch.fx.symbolic_trace
. A call tosymbolic_trace(m)
is equivalent toTracer().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.
Note
Backwards-compatibility for this API is guaranteed.
- call_module(m, forward, args, kwargs)[source]¶
Method that specifies the behavior of this
Tracer
when it encounters a call to annn.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 acall_module
node referring tom
in theGraph
. Otherwise, call theModule
normally, tracing through the operations in itsforward
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.- Parameters:
m (Module) – The module for which a call is being emitted
forward (Callable) – The forward() method of the
Module
to be invokedargs (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 aProxy
value. Otherwise, it is whatever value was returned from theModule
invocation.- Return type:
Note
Backwards-compatibility for this API is guaranteed.
- 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 ParameterFor 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 theGraph
.- Returns:
The value
a
converted into the appropriateArgument
- Return type:
Optional[Union[Tuple[Any, …], List[Any], Dict[str, Any], slice, range, Node, str, int, float, bool, complex, dtype, Tensor, device, memory_format, layout]]
Note
Backwards-compatibility for this API is guaranteed.
- create_args_for_root(root_fn, is_module, concrete_args=None)[source]¶
Create
placeholder
nodes corresponding to the signature of theroot
Module. This method introspects root’s signature and emits those nodes accordingly, also supporting*args
and**kwargs
.Warning
This API is experimental and is NOT backward-compatible.
- create_node(kind, target, args, kwargs, name=None, type_expr=None)¶
Inserts a graph node given target, args, kwargs, and name.
This method can be overridden to do extra checking, validation, or modification of values used in node creation. For example, one might want to disallow in-place operations from being recorded.
Note
Backwards-compatibility for this API is guaranteed.
- Return type:
- create_proxy(kind, target, args, kwargs, name=None, type_expr=None, proxy_factory_fn=None)¶
Create a Node from the given arguments, then return the Node wrapped in a Proxy object.
If kind = ‘placeholder’, then we’re creating a Node that represents the parameter of a function. If we need to encode a default parameter, we use the
args
tuple.args
is otherwise empty forplaceholder
Nodes.Note
Backwards-compatibility for this API is guaranteed.
- getattr(attr, attr_val, parameter_proxy_cache)[source]¶
Method that specifies the behavior of this
Tracer
when we call getattr on a call to annn.Module
instance.By default, the behavior is to return a proxy value for the attribute. It also stores the proxy value in the
parameter_proxy_cache
, so that future calls will reuse the proxy rather than creating a new one.This method can be overridden to –for example– not return proxies when querying parameters.
- Parameters:
- Returns:
The return value from the getattr call.
Warning
This API is experimental and is NOT backward-compatible.
- 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:
- Return type:
Note
Backwards-compatibility for this API is guaranteed.
- iter(obj)¶
- Called when a proxy object is being iterated over, such as
when used in control flow. Normally we don’t know what to do because we don’t know the value of the proxy, but a custom tracer can attach more information to the graph node using create_node and can choose to return an iterator.
Note
Backwards-compatibility for this API is guaranteed.
- Return type:
- keys(obj)¶
- Called when a proxy object is has the keys() method called.
This is what happens when ** is called on a proxy. This should return an iterator it ** is suppose to work in your custom tracer.
Note
Backwards-compatibility for this API is guaranteed.
- Return type:
- path_of_module(mod)[source]¶
Helper method to find the qualified name of
mod
in the Module hierarchy ofroot
. For example, ifroot
has a submodule namedfoo
, which has a submodule namedbar
, passingbar
into this function will return the string “foo.bar”.Note
Backwards-compatibility for this API is guaranteed.
- to_bool(obj)¶
- Called when a proxy object is being converted to a boolean, such as
when used in control flow. Normally we don’t know what to do because we don’t know the value of the proxy, but a custom tracer can attach more information to the graph node using create_node and can choose to return a value.
Note
Backwards-compatibility for this API is guaranteed.
- Return type:
- trace(root, concrete_args=None)[source]¶
Trace
root
and return the corresponding FXGraph
representation.root
can either be annn.Module
instance or a Python callable.Note that after this call,
self.root
may be different from theroot
passed in here. For example, when a free function is passed totrace()
, we will create annn.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. Backwards-compatibility for this parameter is guaranteed.concrete_args (Optional[Dict[str, any]]) – Concrete arguments that should not be treated as Proxies. This parameter is experimental and its backwards-compatibility is NOT guaranteed.
- Returns:
A
Graph
representing the semantics of the passed-inroot
.- Return type:
Note
Backwards-compatibility for this API is guaranteed.
- class torch.fx.Proxy(node, tracer=None)[source]¶
Proxy
objects areNode
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 rawNode
so that you can use the overloaded operators to add additional things to aGraph
.Proxy
objects cannot be iterated. In other words, the symbolic tracer will throw an error if aProxy
is used in a loop or as an*args
/**kwargs
function argument.There are two main ways around this: 1. Factor out the untraceable logic into a top-level function and use
fx.wrap
on it. 2. If the control flow is static (i.e. the loop trip count is based on some hyperparameter), the code can be kept in its original position and refactored into something like:for i in range(self.some_hyperparameter): indexed_item = proxied_value[i]
For a more detailed description into the Proxy internals, check out the “Proxy” section in torch/fx/OVERVIEW.md
Note
Backwards-compatibility for this API is guaranteed.
- class torch.fx.Interpreter(module, garbage_collect_values=True)[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
withtorch.sigmoid
and vice versa (including theirTensor
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_close(result, torch.neg(input).sigmoid())
- Parameters:
module (GraphModule) – The module to be executed
garbage_collect_values (bool) – Whether to delete values after their last use within the Module’s execution. This ensures optimal memory usage during execution. This can be disabled to, for example, examine all of the intermediate values in the execution by looking at the
Interpreter.env
attribute.
Note
Backwards-compatibility for this API is guaranteed.
- 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 type:
- Return
Any: The value returned by the function invocation
Note
Backwards-compatibility for this API is guaranteed.
- 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 type:
- Return
Any: The value returned by the method invocation
Note
Backwards-compatibility for this API is guaranteed.
- 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 type:
- Return
Any: The value returned by the module invocation
Note
Backwards-compatibility for this API is guaranteed.
- fetch_args_kwargs_from_env(n)[source]¶
Fetch the concrete values of
args
andkwargs
of noden
from the current execution environment.- Parameters:
n (Node) – The node for which
args
andkwargs
should be fetched.- Returns:
args
andkwargs
with concrete values forn
.- Return type:
Tuple[Tuple, Dict]
Note
Backwards-compatibility for this API is guaranteed.
- fetch_attr(target)[source]¶
Fetch an attribute from the
Module
hierarchy ofself.module
.- Parameters:
target (str) – The fully-qualified name of the attribute to fetch
- Returns:
The value of the attribute.
- Return type:
Any
Note
Backwards-compatibility for this API is guaranteed.
- get_attr(target, args, kwargs)[source]¶
Execute a
get_attr
node. Will retrieve an attribute value from theModule
hierarchy ofself.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
Note
Backwards-compatibility for this API is guaranteed.
- map_nodes_to_values(args, n)[source]¶
Recursively descend through
args
and look up the concrete value for eachNode
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.
- Return type:
Optional[Union[Tuple[Any, …], List[Any], Dict[str, Any], slice, range, Node, str, int, float, bool, complex, dtype, Tensor, device, memory_format, layout]]
Note
Backwards-compatibility for this API is guaranteed.
- output(target, args, kwargs)[source]¶
Execute an
output
node. This really just retrieves the value referenced by theoutput
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
Note
Backwards-compatibility for this API is guaranteed.
- placeholder(target, args, kwargs)[source]¶
Execute a
placeholder
node. Note that this is stateful:Interpreter
maintains an internal iterator over arguments passed torun
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
Note
Backwards-compatibility for this API is guaranteed.
- run(*args, initial_env=None, enable_io_processing=True)[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.
enable_io_processing (bool) – If true, we process the inputs and outputs with graph’s process_inputs and process_outputs function first before using them.
- Returns:
The value returned from executing the Module
- Return type:
Any
Note
Backwards-compatibility for this API is guaranteed.
- 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 onnode.op
- Parameters:
n (Node) – The Node to execute
- Returns:
The result of executing
n
- Return type:
Any
Note
Backwards-compatibility for this API is guaranteed.
- class torch.fx.Transformer(module)[source]¶
Transformer
is a special type of interpreter that produces a newModule
. It exposes atransform()
method that returns the transformedModule
.Transformer
does not require arguments to run, asInterpreter
does.Transformer
works entirely symbolically.Example
Suppose we want to swap all instances of
torch.neg
withtorch.sigmoid
and vice versa (including theirTensor
method equivalents). We could subclassTransformer
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_close(transformed(input), torch.neg(input).sigmoid())
- Parameters:
module (GraphModule) – The
Module
to be transformed.
Note
Backwards-compatibility for this API is guaranteed.
- call_function(target, args, kwargs)[source]¶
Note
Backwards-compatibility for this API is guaranteed.
- Return type:
- call_module(target, args, kwargs)[source]¶
Note
Backwards-compatibility for this API is guaranteed.
- Return type:
- get_attr(target, args, kwargs)[source]¶
Execute a
get_attr
node. InTransformer
, this is overridden to insert a newget_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
- Return type:
Note
Backwards-compatibility for this API is guaranteed.
- placeholder(target, args, kwargs)[source]¶
Execute a
placeholder
node. InTransformer
, this is overridden to insert a newplaceholder
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
- Return type:
Note
Backwards-compatibility for this API is guaranteed.
- 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 (GraphModule) – The GraphModule that wraps the Graph to operate on
pattern (Union[Callable, GraphModule]) – The subgraph to match in
gm
for replacementreplacement (Union[Callable, GraphModule]) – The subgraph to replace
pattern
with
- Returns:
A list of
Match
objects representing the places in the original graph thatpattern
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 theforward
method oftraced_module
. Pattern-matching is done based on use-def relationships, not node names. For example, if you hadp = torch.cat([a, b])
inpattern
, you could matchm = torch.cat([a, b])
in the originalforward
function, despite the variable names being different (p
vsm
).The
return
statement inpattern
is matched based on its value only; it may or may not match to thereturn
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 forpattern
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 afterself
, 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 thereplacement
Callable must match the pattern. The first rule is why, in the above code block, theforward
function has parametersx, w1, w2
, but thepattern
function only has parametersw1, w2
.pattern
doesn’t usex
, so it shouldn’t specifyx
as a parameter. As an example of the second rule, consider replacingdef 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 aspattern
(bothx
andy
), even though the parametery
isn’t used inreplacement
.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
Note
Backwards-compatibility for this API is guaranteed.