torch.fx

Overview

This feature is experimental and its stability is not currently guaranteed. Proceed at your own risk

FX is a toolkit for capturing and transforming functional PyTorch programs. It consists of GraphModule and a corresponding intermediate representation (IR). When GraphModule is constructed with an nn.Module instance as its argument, GraphModule will trace through the computation of that Module’s forward method symbolically and record those operations in the FX intermediate representation.

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)

The Intermediate Representation centers around a 5-opcode format:

print(gm.graph)

graph(x):
    %linear_weight : [#users=1] = self.linear.weight
    %add_1 : [#users=1] = call_function[target=<built-in function 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=<built-in method sum of type object at 0x7ff2da9dc300>](args = (%relu_1,), kwargs = {dim: -1}) # noqa: B950
    %topk_1 : [#users=1] = call_function[target=<built-in method topk of type object at 0x7ff2da9dc300>](args = (%sum_1, 3), kwargs = {}) # noqa: B950
    return topk_1

The Node semantics 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.

GraphModule automatically generates Python code for the operations it symbolically observed:

print(gm.code)

import torch
def forward(self, x):
    linear_weight = self.linear.weight
    add_1 = x + linear_weight
    x = linear_weight = None
    linear_1 = self.linear(add_1)
    add_1 = None
    relu_1 = linear_1.relu()
    linear_1 = None
    sum_1 = torch.sum(relu_1, dim = -1)
    relu_1 = None
    topk_1 = torch.topk(sum_1, 3)
    sum_1 = None
    return topk_1
    topk_1 = None

Because this code is valid PyTorch code, the resulting GraphModule can be used in any context another nn.Module can be used, including in TorchScript tracing/compilation.

API Reference

torch.fx.symbolic_trace(root)

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.

Returns

a Module created from the recorded operations from root.

Return type

GraphModule

class torch.fx.GraphModule(root, graph)

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)

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

property code

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

property graph

Return the Graph underlying this GraphModule

recompile()

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')

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

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=<built-in function 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=<built-in method sum of type object at 0x7ff2da9dc300>](args = (%relu_1,), kwargs = {dim: -1}) # noqa: B950
    %topk_1 : [#users=1] = call_function[target=<built-in method topk of type object at 0x7ff2da9dc300>](args = (%sum_1, 3), kwargs = {}) # noqa: B950
    return topk_1

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

__init__()

Construct an empty Graph.

call_function(the_function, args=None, kwargs=None, type_expr=None)

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)

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)

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)

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)

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)

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)

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)

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)

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

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)

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>>)

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)

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)

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.

python_code(root_module)

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)

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)

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)

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)

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

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)

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)

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:

1. Iterate through collection types (e.g. tuple, list, dict) and recursively call create_args on the elements.

2. Given a Proxy object, return a reference to the underlying IR Node

3. 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)

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)

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)

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)

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

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.