Dynamo Deep-Dive

TorchDynamo (or simply Dynamo) is the tracer within torch.compile, and it is, more often than not, the one to blame for those insane backtraces. However, we cannot blindly blame Dynamo for these errors. In order to provide the user with the flexibility it does, Dynamo is given the arduous task of understanding any Python program. In particular, Dynamo has to implement a good part of the Python programming language internally!

In this post, we will go over the internal design of Dynamo from the ground up. We will discuss the functionality it provides, and how it is implemented. By the end of this post, you will have a better understanding of what went wrong when you torch.compiled a PyTorch program and the compilation errored out, or succeeded but the speed-up was not what you expected.

A Gentle Introduction to Dynamo

Before getting our hands dirty with all the implementation details, let’s start by discussing what it is that Dynamo does.

Dynamo is a tracer. This means, given and function and inputs to it, it executes the function and records a linear sequence of instructions (without control flow) into a graph. For example, consider the following program:

import torch

def mse(x, y):
    z = (x - y) ** 2
    return z.sum()

x = torch.randn(200)
y = torch.randn(200)
mse(x, y)

If we save this program into the file and we run

TORCH_LOGS=graph_code python

we see the output that Dynamo traced

def forward(l_x_: torch.Tensor, l_y_: torch.Tensor):
    # File:, code: z = (x - y) ** 2
    sub = l_x_ - l_y_
    z = sub ** 2
    # File:, code: return z.sum()
    sum_1 = z.sum()
    return (sum_1,)

We call this a graph (or trace) of the function for the given inputs. This is represented via an FX graph. We will simply think of an FX graph as a container that stores a list of function calls.

The first thing we should notice is that the graph is a linear sequence of PyTorch operations. 1 Dynamo records all the PyTorch operations and stores them sequentially. For example, it split z = (x - y) ** 2 into its two constituting operations, sub = l_x_ - l_y_ and z = sub ** 2.

When we say that the trace is linear, we mean that there is no branching or any control flow. To see this, consider

import torch

def fn(x, n):
    y = x ** 2
    if n >= 0:
        return (n + 1) * y
        return y / n

x = torch.randn(200)
fn(x, 2)

which, when executed with TORCH_LOGS=graph_code, returns

def forward(l_x_: torch.Tensor):
    # File:, code: y = x ** 2
    y = l_x_ ** 2
    # File:, code: return (n + 1) * y
    mul = 3 * y
    return (mul,)

We see that Dynamo completely removed the if statement from the trace and just recorded the operations that were executed with the inputs.

As such, it should be clear that the trace of a function depends on the inputs. In particular, this means that the trace is not generated when we write @torch.compile, but when we execute the function fn(x, 2) with the actual arguments.

The other interesting thing to note here is that Dynamo removed the second argument to the function. Instead, it treated it as a constant and recorded the result of the operation n + 1 in the graph. This is another feature of Dynamo: Dynamo will treat as constant any non-tensor value… other than ints. Let’s see now how are ints special.

The last defining property of Dynamo is that it knows how to handle dynamic shapes. Symbolic shapes refer to Dynamo’s ability of tracing shapes, and more generally, integers, rather than leaving them as constants. This allows for avoiding recompilations and deploying generic models that work for any size in production. The main examples of places where dynamic shapes appear are the batch size, where we might train a model with a fixed batch size but then perform inference for an arbitrary batch size, or the variable sequence length that one encounters when processing text or audio.

We can see this by executing a few more times the example above

import torch

def fn(x, n):
    y = x ** 2
    if n >= 0:
        return (n + 1) * y
        return y / n

x = torch.randn(200)
fn(x, 2)
fn(x, 3)
fn(x, -2)

In this case, TORCH_LOGS=graph_code generates two more graphs

# Graph for n==2 omitted

def forward(self, l_x_: torch.Tensor, l_n_: torch.SymInt):
    # File:, code: y = x ** 2
    y = l_x_ ** 2

    # File:, code: return (n + 1) * y
    add = l_n_ + 1
    mul = add * y
    return (mul,)
def forward(self, l_x_: torch.Tensor, l_n_: torch.SymInt):
    # File:, code: y = x ** 2
    y = l_x_ ** 2

    # File:, code: return y / n
    truediv = y / l_n_
    return (truediv,)

Dynamo detected that one integer changed its value after the first call and started tracing it. We see that these graphs are generic, and trace the variable n symbolically via an object of type SymInt.

If after these calls we call fn(x, 4), Dynamo would not recompile, but rather reuse the graph that was already traced.

To summarize: 1. Dynamo is a Python tracer 2. Given some inputs, it returns an FX graph with the PyTorch functions that were executed 3. It can also trace integers if it detects that they changed between calls 4. It specializes any other value that is not a tensor or a scalar

Of course, Dynamo does many more things, like figuring out when it needs to retrace, rewriting the bytecode of the function, implementing graph breaks… To keep the introduction short, we will incrementally discuss all these in the sequel.

PEP 523: Adding a frame evaluation API to CPython

Imagine now that we are given the task to implement Dynamo. Where would we even start? Rather conveniently for us, PEP 523 was released with Python 3.6. This PEP was designed to allow third parties to create JIT compilers for Python. Let’s see how.

A note on CPython: CPython is internally implemented as a stack machine. A Python program is compiled into bytecodes that then are executed by this interpreter. To learn more about these bytecodes, see the dis module from the standard library. See also the developer docs for an introduction to CPython’s interpreter. We will assume that the reader is familiar with the notion of a stack machine.

PEP 523 exposes an API where a user can add a custom per-function interpreter. Then, CPython will use this interpreter rather than its own to execute the function. In order to be able to execute the function, on entry, CPython provides the custom interpreter with things like - The bytecode of the function - The value of the arguments of the function (i.e., the local variables) and their names - The value of the global variables and their names - The builtin functions like abs or print

You can see all the fields here. 2

In summary, CPython provides the user’s interpreter with all the information necessary to execute the function. 3

With this API, we can implement a tracer by implementing an interpreter that runs the code and records in a graph all the PyTorch operations that occur during this execution. This is exactly what Dynamo does.

Dynamo uses this CPython API to parse all these objects and packs them into a Python structure. After it has done so… it goes back from C to python. Other than for this piece of code that communicates with CPython, Dynamo is fully implemented in Python.

It should be clear that it is the decorator @torch.compile’s job to install the necessary scaffolding that will pass the bytecode, the args, global variables and so on to Dynamo when the function is called. Again, @torch.compile does not actually compile anything.

Implementing CPython in Python

So, we are back in the Python world. We have the bytecode of a function, and all the context necessary to execute it. In particular, we have landed at _convert_frame_assert. This is the function that the decorator torch.compile returns! We get to this function from _dynamo.optimize. The decorator torch.compile is just a nice API around _dynamo.optimize.

Before getting into implementing a Python interpreter, we want to define an IR. In particular, we want to wrap all the local and global variables in our own internal classes. This allows us to better track these objects and group together objects that can be treated in the same way to the eyes of Dynamo.

The parent class of the internal class structure is VariableTracker and represents the different objects that Dynamo understands. For example, ListVariable, represents a list object, and keeps internally a list of VariableTrackers. Another example of VariableTracker is ConstantVariable. ConstantVariable wraps all the objects considered constant by Dynamo. We also have special subclasses for objects that require special attention, like TensorVariable. All these internal classes are defined in the torch/_dynamo/variables folder.

Python objects are wrapped into their corresponding VariableTracker class in VariableBuilder._wrap. This function is just a very long chain of elifs that tries to recursively pattern-match the Python inputs into the appropriate type of VariableTracker.

Debugging tip. When we get unexpected results from dynamo, it is sometimes caused by the builder. If the logic of the builder is wrong, sometimes Dynamo may wrap a variable in the incorrect VariableTracker type, and this may cause issues later on. It is rather useful to have a look at the VariableTracker types that appear in the errors, and the VariableTracker method that throws the exception when you encounter a Dynamo error. In particular, sometimes we find that an object is tracked as a UserDefinedObjectVariable (this is Dynamo’s catch-all class), when it should have been tracked as something more specific. In these cases, the SourceBuilder.__call__ logic is often to blame.

Debugging tip. When running a program with TORCH_LOGS=dynamo, one of the artifacts that are printed out is lines of the form

TRACE LOAD_GLOBAL y [TorchInGraphFunctionVariable(<built-in method any>), TensorVariable()]

This is the bytecode for the original program and the state of the stack at that point. This is very useful to find where an object was not traced into the right VariableTracker.

Ok, so we have an IR for our tracer, now we just need to reimplement CPython’s stack machine. This is implemented by InstructorTranslatorBase in

InstructionTranslatorBase has about 200 methods, implementing almost all of Python bytecodes. As an example, we can see the implementation of BUILD_LIST

def BUILD_LIST(self, inst):
    items = self.popn(inst.argval)
    self.push(ListVariable(items, mutable_local=MutableLocal()))

This is the bytecode generated by constructions like l = [2, 3, 4]. In this case, since there are three elements, the generated bytecode is BUILD_LIST 3. This means that we pop the top 3 elements of the stack and push a new list object to the top of the stack formed by these three elements.

Generating the Output Graph

With a way to symbolically execute Python code, we are set to extract the PyTorch operations that happen during the symbolic execution of a program given some inputs. This is implemented in Dynamo via the OutputGraph object. The OutputGraph object is bound to an `InstructionTranslator object and it tracks all the data necessary to create the FX graph which will be returned by Dynamo.

All the inputs and intermediary elements of the FX graph are fx.Nodes. In Dynamo, fx.Nodes are wrapped in fx.Proxys. fx.Proxys are used to build the FX graph. In particular, they record every PyTorch operation performed on them into the graph. You can can create a new operation to be added to the graph by calling create_proxy. Then, we can add it to the graph through the function wrap_fx_proxy.

A graph stores operations on tensors… and operations on symbolic integers. We will discuss symbolic integers later on, but first we will discuss how Dynamo addresses a rather important correctness issue.

Making Dynamo Sound: Guards

At this point, we have a way to trace programs completely disregarding control flow. And for that, we have reimplemented all of CPython… If this sounds like a bit of an overkill, that is because it is. torch.jit.trace already implements this without all this machinery, so what gives?

The issue with torch.jit.trace, as it is warned in its docs, is that it just works if the traced program is not data dependent. In other words, it will just work if the program itself is linear. This means writing our program without using if-elses, for-while loops, exceptions. Even more, none of the libraries that we use can use any control flow! All in all, not using control flow in a language as dynamic as Python is, in fact, a huge constraint.

JAX solves this problem by always retracing and caching the graph after retracing. Dynamo, on the other hand, uses guards to avoid retracing the whole program every time.

A guard is an assumption (a boolean expression on an input) made in order to specialize a frame for one set of example inputs. Reusing the graph is only valid if these assumptions hold on the new inputs.

For example, any constant input to a function, like a string, installs a guard stating that that input should be of type str and equal to the string we passed. Running

import torch

def fn(a, b):
    return a * len(b)

fn(torch.arange(10), "Hello")

with TORCH_LOGS=guards prints (among other guards)

___check_type_id(L['b'], 94334122025024)
L['b'] == 'Hello'

This reads as “the local variable b should have a specific type (str in this case, represented by the constant 9433...) and its value should be 'Hello'”. If we then execute the function again passing a different argument

import torch

def fn(a, b):
    return a * len(b)

fn(torch.arange(10), "Hello")
fn(torch.arange(10), "Hi")

we can see the guard that failed by running TORCH_LOGS=recompiles

Recompiling function fn in
triggered by the following guard failure(s):
     - L['b'] == 'Hello'

Guards are accumulated while the inputs to the function are wrapped in the builder and during the execution of the program. We will show many more examples of guards in the next section, but first let us discuss sources.

A source tracks how to reconstruct a variable from the original local or global variables present when entering the current frame. In particular, it tracks the original local and global objects and any of the objects they contain. In

def foo(x: Tensor, y: List[Tensor]):
    a = x * y[0]
    return a * x

x and y have LocalSource as their source, and y[0] has GetItemSource, which stores a LocalSource inside. On the other hand, a will not have a source as it is an intermediate variable that only exists within the fx graph.

All these are defined in torch/_dynamo/ We can see the guard generated by GetItemSource in the following example:

import torch

def fn(x, l):
    return x * len(l[0])

fn(torch.randn(8), ["Hi", "Hello"])

generates the following guards

___check_type_id(L['l'], 94439025877664)
len(L['l']) == 2
___check_type_id(L['l'][0], 94439025840192)
L['l'][0] == 'Hi'
___check_type_id(L['l'][1], 94439025840192)
L['l'][1] == 'Hello'

Here, we see the code generated by GetItemSource ([0] and [1]) wrapping a LocalSource (L['l']).

At this point, with sources and guards, we are able to implement a caching system to avoid recompilation without having to retrace every time. We will discuss a bit more in detail this caching system in the sequel.

The attentive reader will have noticed that this does not explain yet why we need to have such fine control over the Python interpreter as to having to reimplement it. The examples of guards that we have shown depend on the input objects, so we could still compute these before executing the function. In other words, we could implement this guard system on top of torch.jit.trace and get the same functionality with much less effort… Enter symbolic shapes.

Symbolic Shapes

Another point we discussed in the introduction is that Dynamo knows how to trace integers. In order to implement this, we use a symbolic class torch.SymInt that acts like an int but it records all the operations performed on it in the output FX graph. 4 We already saw this class in the introduction when introducing symbolic integer tracing.

Let us now discuss the three properties that define symbolic shape tracing in Dynamo, and how to implement them.

Static by default

Dynamo assumes that every integer, let that be an input or the shape of a tensor, is static by default. In other words, no integers will be traced on the first execution of a function. Then, only if it detects that an integer or a shape changed value during the execution, it will trace it and generate a graph generic on that variable.

We already saw this behavior in the introduction using integers. Let us now look at an example using shapes of tensors.

import torch

def fn(a, b):
    return a.shape[0] * a * b

fn(torch.randn(4, 3), torch.randn(4, 3))
fn(torch.randn(8, 3), torch.randn(8, 3))

Running this program with TORCH_LOGS=graph_code we see that these two calls are traced as

def forward(self, l_a_: torch.Tensor, l_b_: torch.Tensor):
    mul = 4 * l_a_
    mul_1 = mul * l_b_
    return (mul_1,)

def forward(self, s0: torch.SymInt, l_a_: torch.Tensor, l_b_: torch.Tensor):
    size = l_a_.size()
    getitem = size[0]
    mul = getitem * l_a_
    mul_1 = mul * l_b_
    return (mul_1,)

In the first graph the shape is traced as a constant, but once it changes, it traces it symbolically using a SymInts. In general, a simpler way to see the shapes of the intermediary values is by running the program with TORCH_LOGS=graph_sizes

===== __compiled_fn_1 =====
l_a_: (s0, 3)
l_a_ (concrete): (8, 3)
l_b_: (s0, 3)
l_b_ (concrete): (8, 3)
mul: (s0, 3)
mul (concrete): (8, 3)
mul_1: (s0, 3)
mul_1 (concrete): (8, 3)

where we can see that the first dimension of the two tensor args is dynamic, given that it is represented by the s0 variable.

We can find how Dynamo implements this by running TORCH_LOGS=guards

# Guards first call
check_tensor(L['a'], torch.float32, device=None, requires_grad=False, size=[4, 3], stride=[3, 1])
check_tensor(L['b'], torch.float32, device=None, requires_grad=False, size=[4, 3], stride=[3, 1])

# Guards second call
check_tensor(L['a'], torch.float32, device=None, requires_grad=False, size=[None, 3], stride=[3, 1])
check_tensor(L['b'], torch.float32, device=None, requires_grad=False, size=[None, 3], stride=[3, 1])

L['b'].size()[0] == L['a'].size()[0]
2 <= L['a'].size()[0]

We see that on the first call, the guards check that the tensors have some fixed sizes and strides. These guards fail in the second execution, so it retraces. Since it was an int guard that failed, in this second iteration it traces this int symbolically and it installs more general guards on this more generic kernel.

Compilation performance tip. If you know that a dimension will vary in size, you can mark it as dynamic by calling torch._dynamo.mark_dynamic before calling torch.compile. This will avoid the first compilation with a static shape. There are other useful utility functions like maybe_mark_dynamic or mark_static. You can also have all integers and shapes traced by calling torch.compile(dynamic=True). This is mostly useful for debugging purposes.

0, 1 are always specialized

Regardless of whether we mark a dimension as dynamic, or we have traced an integer as dynamic, if we pass an input where that dimension is 0 or 1, Dynamo will trace it as non-dynamic and it will generate a specific graph for it. This is the reason why in the example above we find guards of the form 2 <= L['a'].size()[0].

There are several reasons for this choice. There are two particularly important - A tensor is empty if and only if any of its dimensions is zero - A tensor can only be contiguous if one of the strides is one

Duck shaping

Dynamo performs what we call “duck shaping”. If two dynamic integers have the same value at trace time, we will assume that they are equal and guard on it. Effectively, this means that rather than having two symbols s0, s1 in the example above, we just unified them to s0 and had the guard L['b'].size()[0] == L['a'].size()[0]. This enables performing fusions within the compiler while being able to generate kernels that are generic enough.

Guards on symbolic ints

We now understand how symbolic shapes are implemented at a high level and the properties they have. Now, why is that symbolic shapes forced us through the tricky route of getting control of the CPython interpreter? Consider the following example:

import torch

def fn(a):
    if a.shape[0] * 2 < 16:
        return a
        return a + 1


This code has a guard of the form 2*L['a'].size()[0] >= 16. This is a non-trivial guard in terms of the inputs of the function, but it is registered in the middle of the execution of the program. Even more so, we cannot know this guard is needed until we see the if statement conditional on a SymNodeVariable argument. Such conditions are invisible to torch.jit.trace and require deep analysis of the python code.

Debugging tip Running this code with TORCH_LOGS=dynamo tells us where this guard was added

eval 2*s0 >= 16 [guard added] at in fn (_dynamo/variables/ in evaluate_expr)

Placing a breakpoint there and looking at the backtrace is rather useful to understand where a guard came from.

Making Dynamo Complete: Graph Breaks

With all the tools we have discussed, we have a tracer that can trace PyTorch operations on tensors and integers and has a caching system that knows when it can reuse a previously traced graph and when it needs to retrace. All this executing arbitrary Python code!

There is just one small issue with this. The statement “executing arbitrary Python code” is perhaps a bit too general. Dynamo implements a good part of Python, but does it implement the more complex parts, like coroutines or async? Does it implement the whole Python standard library? NumPy also has a Python API. Does torch.compile also understand NumPy? and Django? 5

Python’s ecosystem is massive, and a good part of it is written in other more performant languages like C++ or Rust, and it just exposes Python bindings. There is no hope in Dynamo tracing through Python objects that are implemented in C++. What can a tracer do when it finds an operation that it does not understand?

The usual way machine learning tracers handle this issue is by informing the user that the operation they choked on and giving up tracing altogether. This would pose a real usability issue in the case of PyTorch, where its users are used to the flexibility it gives them. As a real-world example the doctr_det_predictor model uses NumPy and the cv2 library to postprocess the model’s result.

Here is another place where having access to CPython is interesting. Rather than erroring out, Dynamo can let CPython run that problematic code! To do this, Dynamo generates at trace time one graph with all the operations before the problematic code, and one with all the operations after. 6 Then, at runtime, it will delegate to CPython to execute the first graph, then the problematic code, and then the second graph. This process of stopping the tracing and generating multiple graphs is called a graph break.

A small confession: I lied all throughout the introduction and the first sections. Dynamo does not generate one graph, but multiple graphs! For all practical purposes, starting retracing after a second graph can be thought of as starting tracing a new function. The new graph after the graph break will have its own guards, its new set of local variables, and so on.

To discuss how to implement graph breaks, we need to first revisit how Dynamo interacts with CPython. Using PEP 523, CPython allows a user to use their own frame evaluation mechanism. What we had not discussed is that CPython also exposes its own frame evaluation for others to use. Dynamo leverages this to let the fast CPython interpreter run the compiled code. For a function without graph breaks, the whole tracing / execution process of a program that calls the function 2 times with the same arguments looks like this:

  1. In the first call to the function

    1. Dynamo traces the function into an FX graph

      1. The FX graph is compiled by the compiler (Inductor) into efficient low-level code… but that’s a story for another day

    2. It rewrites the bytecode of the function so that it simply calls the compiled function

    3. It gives CPython this new bytecode and asks it to run it [here]

  2. In the second call to the function

    1. It checks the guards from the first call against the new arguments [here]. Since they are the same arguments as before, they pass

    2. It asks CPython to run the bytecode associated to those guards [here]

This process on its own looks overly complicated. Why generate new bytecode and ask CPython to run it rather than simply creating a C++ binding to the compiled function and executing it? Well, this pattern allows us to implement graph breaks! The bytecode generated by a graph break has the following structure:

  1. Bytecode that executes the first graph

  2. Bytecode that leaves the stack as it would be if CPython would have executed the first graph. It also replays any modifications to local or global variables that would be visible at this point

  3. The bytecode that made Dynamo graph break

  4. Bytecode that executes the second graph

Let us see this in a simple example

import torch

def fn(a):
    b = a + 2
    return b + a


Running this with TORCH_LOGS=bytecode shows us the initial bytecode and the modified bytecode

 0 LOAD_GLOBAL              1 (__compiled_fn_0)
 2 LOAD_FAST                0 (a)
 4 CALL_FUNCTION            1
 6 STORE_FAST               3 (graph_out_0)
 8 LOAD_GLOBAL              0 (print)
10 LOAD_CONST               2 ('Hi')
12 LOAD_FAST                3 (graph_out_0)
14 LOAD_CONST               3 (0)
18 STORE_FAST               1 (b)

20 CALL_FUNCTION            1
22 LOAD_GLOBAL              2 (__resume_at_14_1)
26 LOAD_FAST                0 (a)
28 LOAD_FAST                1 (b)
30 CALL_FUNCTION            3

MODIFIED BYTECODE resume_in_fn line 6
 0 LOAD_GLOBAL              1 (__compiled_fn_2)
 2 LOAD_FAST                2 (b)
 4 LOAD_FAST                1 (a)
 6 CALL_FUNCTION            2
 8 UNPACK_SEQUENCE          1

We can see that the modified bytecode is split into two functions, fn, the original function, and a function called resume_in_fn. This second function is a function created by Dynamo to implement the execution of the program starting at the graph break. This is often called a continuation function. This continuation function simply calls the second compiled function with the right arguments. The code for the initial function is rewritten implementing the strategy that we described before

  • L0-4. Call the compiled function (a + 2).

  • L6. Store its result in a local variable called graph_out_0. graph_out_0 is a tuple

  • L8-18. Leave the stack as it would be at the point of the graph break

  • L20. Execute the code that caused the graph break

  • L22-32. Call the compiled continuation function (a + b)

The code generation of the stack in Dynamo is delegated to VariableTracker subclasses. Every VariableTracker object in Dynamo has a reconstruct method that generates the necessary bytecode to create the python object it represents on the stack.

Debugging tip. Graph breaks hamper performance, and as such, it is best to avoid them. Running a program with TORCH_LOGS=graph_breaks is a great way to find how many graph breaks did our program hit. The information it returns is in terms of VariableTracker objects, so the debugging tips above are sometimes also helpful to figure out what caused that graph break.


Dynamo is a complex piece of software. Once you sign up to implement a CPython interpreter you know you are in for a ride. That being said, we hope that this post helps demystify it a bit.

Dynamo is (mostly) implemented in Python. We left plenty of links to the pieces of the code that we discussed. We hope that reading those pieces of code and grepping for the places that call them, or putting breakpoints on them and looking at the call stack helps understanding the rest of the code base.

Of course, the best way to learn how a piece of software works is by extending it. In this case, the best way is to have a look at the open dynamo issues on github. Many of them require very minor changes in the code, once you find where you need to make those changes.



In the literature, this is called a Directed Acyclical Graph (DAG).


All this binding code lives in torch/csrc/dynamo/eval_frame.c.


In CPython lingo, the set of all these objects are called a frame.


There are also SymBool and SymFloat classes. The latter one is not used all that much at the time of this writing.


Interestingly enough, it does understand NumPy code! Have a look at this blogpost and the docs. Now, this is just possible because we reimplemented NumPy using PyTorch. Good luck implementing Django in PyTorch though…


Assuming there is just one piece of problematic code. If there are more, Dynamo can split the code into as many graphs as it needs.


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