Guards Overview

From a UX perspective, TorchDynamo is very easy to use. The user invokes torchdynamo.optimize as an annotation:

def fn_foo(bar):

Where a complete example looks like this:

from typing import List
import torch
from torch import _dynamo as torchdynamo

def my_compiler(gm: torch.fx.GraphModule, example_inputs: List[torch.Tensor]):
    print("my_compiler() called with FX graph:")
    return gm.forward  # return a python callable

def toy_example(a, b):
    x = a / (torch.abs(a) + 1)
    if b.sum() < 0:
        b = b * -1
    return x * b

for _ in range(100):
    toy_example(torch.randn(10), torch.randn(10))

This allows TorchDynamo to capture the interpreted Python frames, grab any and all relevant information, and speed things up wherever it can. The speedup comes from a few places, and can be rather dependent on the backend (my_compiler in the example above) provided, but the one speedup that is important in this section is caching. Caching itself is not a direct speedup but a critical enablement that prevents recompilation. We dig a hole with dynamo, and caching allows us to get out. It enables us to hold perf neutrality while then enabling backends - the true source of our speedups.

With even a pass-through no-op backend provided:

def my_compiler(gm: torch.fx.GraphModule, example_inputs: List[torch.Tensor]):
    return gm.forward

We can see TorchDynamo speeding up Python execution even on regular Python, not just PyTorch.

Caching and Guards Overview

TorchDynamo operates through caching transformed (by TorchDynamo) user bytecode. When TorchDynamo receives a frame for evaluation, it checks if the objects referenced in the frame have changed in certain ways, and if not, TorchDynamo reads the previously transformed user bytecode to evaluate it. In this section, we will focus on how we can identify whether or not the objects referenced in the frame have changed. This is a critical piece of functionality in TorchDynamo, because it drives the entire invalidation lifecycle. This functionality is called guards.

At a very high level, the flow can be summarized like this:

  1. TorchDynamo receives a Python frame.

  2. It converts the frame (1) passing it through instruction translation.

  3. For the objects captured in (2), TorchDynamo creates tracking objects that are:

    • tracked on an output graph, which is an internal specialization of a torch.fx.Tracer

    • guards

  4. TorchDynamo processes the guard objects created in (3), turning them into a generated Python function, check_fn, associated with a piece of code.

  5. The check_fn is evaluated whenever we encounter this code a subsequent time - if a check_fn passes and evaluates to True, TorchDynamo identifies the code in the cache and the code encountered here as same, and can be safely used. If it fails and evaluates to False, TorchDynamo identifies the code in the cache as not valid, and can be thrown out in favor of a new entry, through recompilation or a graph break.

Python Frame Evaluation and PEP 523

The functionality of TorchDynamo is based on PEP 523.

TorchDynamo installs a frame evaluation function on Python by using _PyInterpreterState_SetEvalFrameFunc. TorchDynamo has a hook where Python can hand control back to us during evaluation.

The function we have installed is convert_frame or convert_frame_assert in the nopython=True case, but glossing over that nuance for now, let’s take a look at convert_frame_assert, as convert_frame proxies to it.

We can find the function in torch/_dynamo/ with a signature as follows:

def  convert_frame_assert(compiler_fn: Callable, one_graph=True):

This function wraps the entry point of where Python invokes TorchDynamo with a frame:

def  _convert_frame_assert(frame: types.FrameType, cache_size: int):

Here is what this function does:

  1. Checks if it has seen this code(see: f_code here) before and exits early if it did.

  2. Checks if the code is an unsupported case.

  3. Checks if the cache_size (second arg above) crosses the limit defined in the config, cache_size_limit. If it has, the function drops the frame and logs warnings. This helps to avoid constant recompilation of a frame as it generally means that the frame is hot in an unexpected way and caching it produces needless overhead, as it is likely to get evicted the next time it is encountered.

  4. Passes the frame, alongside a function that creates an InstructionTranslator through bytecode transformation, via transform_code_object. A few crucial things happen under the hood here:

    1. New code is produced through transform_code_object.

    2. An FX tracer named output is produced through InstructionTranslator. This can be a bit confusing, as InstructionTranslator is not an fx tracer, but its stored in a variable named tracer, and its output is an fx tracer.

    3. The function produces guards and stores them on output above.

    4. The function produces output_instructions and stores them on output above.

    5. The function maps the newly produced transformed code to the initial code it read off the frame. This mapping is worth remembering, we will refer to it much later on below where we cover guard failures.

  5. Using the transformed code from 4.1 and the guards from 4.3, the function produces a GuardedCode.

Now that we have learned about frame evaluation, let’s review InstructionTranslator, and see how it turns the frame we handed it over into TorchDynamo internal types.


InstructionTranslator does a lot! We won’t cover the details of everything it does, but most importantly for this document, it produces a mapping of symbolic_locals which maintains a mapping from the frame’s f_locals to TorchDynamo internal Variable objects (more on these in a moment. symbolic_locals is filled via traversing the frame’s locals:

self.symbolic_locals = collections.OrderedDict(
    (k, VariableBuilder(self, LocalSource(k))(f_locals[k]))
    for k in vars
    if k in f_locals

The important component here is the invocation of a call into VariableBuilder. VariableBuilder’s call implementation proxies into a function called _wrap, which in turn both constructs instances of VariableTracker and calls make_guards on them. More on that later.

This mapping, in turn, is critical as each Variable has associated guards, which are then passed to self.output, the instance of OutputGraph, an fx tracer, mentioned in 4.2 of the section above. If you recall, this OutputGraph, stored in a variable called output is where our guards are stored before being passed on to become GuardedCode

How does InstructionTranslator do this? At the heart of it, there is a loop that is pumped, which drives a function step.

step is just that - a single processing step, taking exactly one instruction and doing something with it.


These are real instructions processed by TorchDynamo’s transform_code_object, and it is pretty cool.


This section purposely skips the details of dis.get_instructions.

For the example above, here is a snippet of a what a few Instruction's may look like:

Instruction(opcode=124, opname='LOAD_FAST', arg=0, argval='b', offset=32, starts_line=8, is_jump_target=True, target=None)
Instruction(opcode=100, opname='LOAD_CONST', arg=3, argval=-1, offset=34, starts_line=None, is_jump_target=False, target=None)
Instruction(opcode=20, opname='BINARY_MULTIPLY', arg=None, argval=None, offset=36, starts_line=None, is_jump_target=False, target=None)

This is the core functionality of this function. Take a look at the opname, and then take a look at this little snippet from inside step;

if not hasattr(self, inst.opname):
    unimplemented(f"missing: {inst.opname}")
getattr(self, inst.opname)(inst)

As we can see, the function checks if the current class, the InstructionTranslator has an attribute set matching the operator name (for example, LOAD_CONST). If it does, the function invokes it, passing the whole instruction object in. If it does not, the function drops the frame as unimplemented.

For the LOAD_CONST example, we can see that we do indeed support it, with a relatively straightforward definition:

def LOAD_CONST(self, inst):

We can see that this function creates a new instance of the class ConstantVariable , with a value, in our example case, -1, and then pushes it onto the stack.

There are dozens of such methods - see for all of them. Generally, we implement as many matching methods to Python bytecode instructions as possible.

Across both the logic downstream of step and the logic from invoking VariableBuilder - we now have a lot of VariableTrackers and of course, we’ve spoken about creating guards quiet a bit. Let’s dig into what Variables are, and get a little closer to understanding guards.


A ConstantVariable is an instance of VariableTracker. VariableTracker represents a tracked Python local or stack value.

When it comes to representing an object inside TorchDynamo, a VariableTracker does exactly what it says - it tracks a given variable. It is an extremely flexible class, but there are a few points to keep in mind:

  • It manages the guard relationship around the underlying object through:

    • make_guard

    • replace_guards

    • add_guard(s)

    • propagate - propagate(*vars: List[List["VariableTracker"]]) - Perhaps the most important of all, in that it combines guards from all the provided VariableTracker instances passed in. It visits the guards and combines the guards from these onto itself.

  • It acts as a proxy on behalf of the underlying object, implementing methods for the rest of TorchDynamo to get information about the tracked object:

    • call_method

    • call_function

    • python_type

    • as_proxy

    • is/as_python_proxy

  • It stores the variable source of type Source, from torchdynamo/ This source type is a relatively self contained class that helps us organize and bookkeep where the original source came from, and helps provide convenience methods for things like getting the name, and importantly for us, producing guards.

And this class (VariableTracker) is built around subclassing, somewhere between a full Abstract Base Class and fully fleshed out class - it leaves many methods raising NotImplementedError - with reliance on subclasses. See torchdynamo/variables/ for all subclasses to fulfill contracts and custom behaviors.

Knowing what we know now, we can see an example of how an instruction from dis, BUILD_TUPLE:

BUILD_TUPLE(count) Creates a tuple consuming count items from the stack, and pushes the resulting tuple onto the stack.

In our case, our signature will be a little different due to the way we create Instruction objects, but the gist of it will be the same. Instead of passing in count, we pass in an object with a little extra bookkeeping, and of course, we deal with turning regular old python objects into TorchDynamo notions:

def BUILD_TUPLE(self, inst):
    items = self.popn(inst.argval)
    options = VariableTracker.propagate(items)
    self.push(TupleVariable(items, **options))

Here is what this code does:

  1. The function reads argval, which in this case, is analogous to counts in the pydoc for the equivalent instruction.

  2. The function popn the items, in this case, the signature is def  popn(self, n: int) -> List[TensorVariable]: this hints at an underlying contract - we are returning TensorVariables. If we take a closer look at and InstructionTranslatorBase/InstructionTranslatorwe see that the only thing pushed onto and popped from our stack are VariableTrackers.

  1. The function calls VariableTracker.propagate. This takes the guards from every single item popped off the stack in 2, and recursively traverses it and combines all the guards into options: py  return {      "guards": guards,  }

  2. The function then makes a new instance of a VariableTracker, TupleVariableout of the items and options. This then allows us to install all the appropriate guards from the items that make up the new TupleVariable


Where did the first guards come from? Propagation is a good technique, but we need something created before it can be propagated. VariableBuilder calls make_guards as it creates VariableTracker instances, from f_locals. This in turn calls into the source, to have it create guards.

After all this, bytecode translation is done and we are one step closer to producing GuardedCode. We now understand how locals become VariableTrackers, how instructions are handled, and where guards are called on for creation. Before we can go into seeing how code and guards are combined into a GuardedCode object, we need to dig a little bit into those make_guard and source.make_guard calls above. We can then understand, what was going on when we made guards alongside, and on, VariableTracker instances.

Making Guards

Guards are just Python objects, of the class Guard. Let’s look at them in more detail.

Looking at the definition of the dataclass (and therefore, ctor signature), we see that it has a name, a source, and a create function.

class Guard:
    name: str
    source: GuardSource
    create_fn: Callable

The name should be the name of the variable.

The source here is an enum indicating what kind of source the guard belongs to.


Not to be confused with Source and the other types in, as stored on VariableTracker.

create_fn provides the main functionality to transition from a simple dataclass to actually producing valid Python code to be invoked for knowing whether or not things have changed in between invocations, and whether we can safely read from the code cache or not.

The most common code paths for getting an instance of a guard are through make_guards on VariableTracker. make_guards -> source.make_guard -> return Guard(, self.guard_source(), fn)

Or, in a concrete example:

elif istype(value, range):
    guards = self.make_guards(GuardBuilder.EQUALS_MATCH)
    return RangeVariable(value=value, guards=guards)

Since source was set at the construction time of this VariableTracker, all that was needed here was to provide the fn, GuardBuilder.EQUALS_MATCH to the create_fn field.

This create_fn must be a method on GuardBuilder. The reason for this becomes apparent in our next step. Once we have all the guards created for a frame, we move on to CheckFunctionManager and compile_check_fn.

Before the convert_frame function can produce a GuardedCode, it needs to run the CheckFunctionManager, with all the guards, to produce a check_fn which will then, in turn get passed in alongside the code into GuardedCode. This is the same check_fn that we store in our cache entry, and the same one we run to know whether or not to retrieve the code stored alongside. For reference, here is that code:

static CacheEntry *create_cache_entry(CacheEntry *next,
                                      PyObject *guarded_code) {
  CacheEntry *e = (CacheEntry *)malloc(sizeof(CacheEntry));
  e->check_fn = PyObject_GetAttrString(guarded_code, "check_fn");
  e->code = (PyCodeObject *)PyObject_GetAttrString(guarded_code, "code");
  e->next = next;
  return e;

We now know how a check_fn function is used, and who makes it, and what it is composed of, but what we do not yet know is how. How does a list of Guard objects become a function we can run later on?

First, we iterate these guards:

for guard in sorted(guards or [], key=Guard.sort_key):
    if not config.guard_nn_modules and guard.is_nn_module():
    guard.create(local_builder, global_builder)

Calling guard.create runs that create_fn we set on the Guard class above (don’t confuse it with the check_fn we are working on producing, the names are similar, so it can get a little confusing). In our example above, our create_fn is GuardBuilder.EQUALS_MATCH. So we are now invoking it, passing in the self, the guard itself, in.

The signature is: def EQUALS_MATCH(self, guard: Guard):

And internally to that function, we can use the name on the guard to get back our original object, querying it for data and type information, which in turn gets us to the most important bit: appending code.

At its simplest, EQUALS_MATCH appends just one line of code: self.code.append(f"{ref} == {val!r}"). Where ref is the name of the variable, and val is the value. It might produce code like this:

y == 2

This is a basic example. But if we append a few other kinds of GuardBuilder functions and then combine them all with and in between each statement (as we do), we might get something like this:

___guarded_code.valid and ___check_type_id(y, 94367738391392) and y == 2 and ___check_tensors(x)

Here is what this code performs:

  1. A check for .valid

  2. A type ID check

  3. A value check

  4. A tensor check

This becomes the heart of the code our check_fn, which in turn is evaluated the next time we encounter this code. It will then check:

  1. Is this code still valid?

  2. If (1), Does y still have a type of 94367738391392?

  3. If (2), is y still 2?

  4. If (3), let’s check on if tensor x changed in some specific ways.

If all of these are still true, then we can use the code cached alongside this check_fn.


For a deeper dive for how and where this happens you can read static PyCodeObject *lookup(CacheEntry *e, PyObject *f_locals) { of _eval_frame.c.

If not, then, we can move on to recompiling the code anew, and storing that in the cache alongside this code, and a whole new check_fn, again to be checked on yet another subsequent frame.

There are lots of other such functions on GuardBuilder which get coalesced into, at times massive, strings which then get evaluated as Python code and stored into check_fn. The example above illustrates of a simple case. To understand this functionality better, read the other functions on GuardBuilder, or better yet, dump the code variable in compile_check_fn to see what is getting produced, especially on larger, real models.


In this section, we have reviewed:

  • The role of .valid and invalidation around weak references (and potentially soon to be NN Moduleinvalidations).

  • How the C++ side of guard functions (___check_type_id, ___check_tensors, etc) operate.

  • What happens when guards fail.

  • What happens if we produce invalid guard code.

We covered how user provided code wrapped in a TorchDynamo context goes on to get traced and tracked internally, organized into VariableTrackers Sources and subsequently Guards, and how those Guards in turn guide cache entry selection and invalidation when handing Python code.


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