Frequently Asked Questions

Author: Mark Saroufim

At a high level, the PyTorch 2.0 stack consists of a graph capture from Python code using dynamo and a backend compiler. In this example the backend compiler consists of backward graph tracing using AOTAutograd and graph lowering using TorchInductor. There are of course many more compilers available here but for this document we will focus on inductor as a motivating example.

Torchdynamo supports training, using AotAutograd to capture backwards:

1. the .forward() graph and optimizer.step() is captured by torchdynamo’s python evalframe frontend

2. for each segment of .forward() that torchdynamo captures, it uses AotAutograd to generate a backward graph segment

3. each pair of forward, backward graph are (optionally) min-cut partitioned to save the minimal state between forward/backward

4. the forward, backward pairs are wrapped in autograd.function modules 5. usercode calling.backward() still triggers eager’s autograd engine, which runs each ‘compiled backward’ graph as if it were one op, also running any non-compiled eager ops’ .backward() functions

Do you support Distributed code?

DDP has been tested and works, support for other distributed training libraries is under discussion.

The main reason why Distributed code is challenging with dynamo is because AOTAutograd unrolls both the forward and backward pass and provides 2 graphs for backends to optimize. This is a problem for distributed code because we’d like to ideally overlap communication operations with computations. Eager pytorch accomplishes this in different ways for DDP/FSDP- using autograd hooks, module hooks, and modifications/mutations of module states. In a naive application of dynamo, hooks that should run directly after an operation during backwards may be delayed until after the entire compiled region of backwards ops, due to how AOTAutograd compiled functions interact with dispatcher hooks.

The basic strategy for optimizing DDP with Dynamo is outlined in distributed.py where the main idea will be to graph break on DDP bucket boundaries.

When each node in DDP needs to synchronize its weights with the other nodes it organizes its gradients and parameters into buckets which reduces communication times and allows a node to broadcast a fraction of its gradients to other waiting nodes.

Graph breaks in distributed code mean you can expect dynamo and its backends to optimize the compute overhead of a distributed program but not its communication overhead. Graph-breaks may interfere with compilation speedups, if the reduced graph-size robs the compiler of fusion opportunities. However, there are diminishing returns with increasing graph size since most of the current compute optimizations are local fusions. So in practice this approach may be sufficient.

Do I still need to export whole graphs?

For the vast majority of models you probably don’t and you can use torch._dynamo() optimize as is but there are a few situations where full graphs are necessary and you can can ensure a full graph by simply running torch.dynamo(..., nopython=True) * Large scale training runs, think $250K+ that require pipeline parallelism and other advanced sharding strategies * Inference optimizers like TensorRT or AITemplate that rely on fusing much more aggressively than training optimizers * Mobile training or inference.

Future work will include tracing communication operations into graphs, coordinating these operations with compute optimizations, and optimizing the communication operations.

Why is my code crashing?

If your code ran just fine without dynamo and started to crash with it enabled then the most important first step is figuring out which part of the stack your failure occurred in so try running things in the below order and only try the next step if the previous step succeeded.

1. torch.compile(..., backend="eager") which only runs torchdynamo forward graph capture and then runs the captured graph with PyTorch. If this fails then there’s an issue with TorchDynamo.

2. torch.compile(..., backend="aot_eager") which runs torchdynamo to capture a forward graph, and then AOTAutograd to trace the backward graph without any additional backend compiler steps. PyTorch eager will then be used to run the forward and backward graphs. If this fails then there’s an issue with AOTAutograd.

3. torch.compile(..., backend="inductor") which runs torchdynamo to capture a forward graph, and then AOTAutograd to trace the backward graph with the TorchInductor compiler. If this fails then there’s an issue with TorchInductor

TorchDynamo Errors

If the error that is generated occurs with the "eager" backend, then torchdynamo is the most likely source of the error.

To debug these issues we recommend setting torch._dynamo.config.verbose=True to get a full stack trace to both the error in torchdynamo and the user code. In addition to this flag, you can also set the log_level of torchdynamo through torch._dynamo.config.log_level. The available levels are the following: - logging.DEBUG: Print every instruction that is encountered in addition to all below log levels - logging.INFO: Print each function that is compiled (original and modified bytecode) and the graph that is captured in addition to all below log levels - logging.WARNING (default): Print graph breaks in addition to all below log levels - logging.ERROR: Print errors only

If a model is sufficiently large, the logs can become overwhelming. If an error occurs deep within a model’s python code, it can be useful to execute only the frame in which the error occurs to enable easier debugging. There are 2 tools available to enable this:

• env TORCHDYNAMO_DEBUG_FUNCTION=<desired_function_name> will only run TorchDynamo on functions with that name.

• env torch._dynamo.config.replay_record_enabled = True) which dumps an execution record when an error is encountered. This record can then be replayed to run only the frame where an error occurred.

TorchInductor Errors

With TorchInductor as the chosen backend, AOTAutograd is used to generate the backward graph from the forward graph captured by torchdynamo. It’s important to note that errors can occur during this tracing and also while TorchInductor lowers the forward and backward graphs to GPU code or C++.

A model can often consist of hundreds or thousands of FX nodes, so narrowing the exact nodes where this problem occurred can be very difficult which is why we highly recommend you use our minifier to create tiny reproducible examples of failures you’re seeing. We can minify errors that occur either at the AOTAutograd layer or Inductor layer which you should try in the following order.

1. env TORCHDYNAMO_REPRO_AFTER="aot" python your_model.py

2. env TORCHDYNAMO_REPRO_AFTER="dynamo" python your_model.py

Minifying your error is the quickest path to getting it fixed.

The minifier will actually create a repro.py for you at the location set by env TORCHDYNAMO_REPRO_DIR so make you have right access to that directory. You can then run python repro.py and confirm that you are getting the same error.

Note

For other compilers such as nvfuser, the process is similar but instead you would leverage env TORCHDYNAMO_REPRO_AFTER="dynamo" python your_model.py.

Why is compilation slow?

Dynamo Compilation

TorchDynamo has a builtin stats function for collecting and displaying the time spent in each compilation phase. These stats can be accessed by calling torch._dynamo.utils.compile_times() after executing torch._dynamo. By default, this returns a string representation of the compile times spent in each TorchDynamo function by name.

Inductor Compilation

TorchInductor has a builtin stats and trace function for displaying time spent in each compilation phase, output code, output graph visualization and IR dump. env TORCH_COMPILE_DEBUG=1 python repro.py. This is a debugging tool designed to make it easier to debug/understand the internals of TorchInductor with an output that will look something like this

Each file in that debug trace can be enabled/disabled via torch._inductor.config.trace.*. The profile and the diagram are both disabled by default since they are expensive to generate. See the example debug directory output for more examples.

Excessive Recompilation

When TorchDynamo compiles a function (or part of one), it makes certain assumptions about locals and globals in order to allow compiler optimizations, and expresses these assumptions as guards that check particular values at runtime. If any of these guards fail, Dynamo will recompile that function (or part) up to torch._dynamo.config.cache_size_limit times. If your program is hitting the cache limit, you will first need to determine which guard is failing and what part of your program is triggering it.

The recompilation profiler automates the process of setting TorchDynamo’s cache limit to 1 and running your program under an observation-only ‘compiler’ that records the causes of any guard failures. You should be sure to run your program for at least as long (as many iterations) as you were running when you ran into trouble, and the profiler will accumulate statistics over this duration.

from torch._dynamo.utils import CompileProfiler

prof = CompileProfiler()

def my_model():
    ...

profiler_model = torch.compile(my_model, backend=prof)
profiler_model()
print(prof.report())

Many of the reasons for graph breaks and excessive recompilation will be fixed with upcoming support for tracing dynamic tensor shapes, more careful choices for guards and better tuned heuristics.

Why are you recompiling in production?

In some cases, you may not want unexpected compiles after a program has warmed up. For example, if you are serving production traffic in a latency critical application. For this, TorchDynamo provides an alternate mode where prior compiled graphs are used, but no new ones are generated:

frozen_toy_example = dynamo.run(toy_example)
frozen_toy_example(torch.randn(10), torch.randn(10))

How are you speeding up my code?

There are 3 major ways to accelerate PyTorch code:

1. Kernel fusion via vertical fusions which fuse sequential operations to avoid excessive read/writes. For example, fuse 2 subsequent cosines means you can can do 1 read 1 write instead 2 reads 2 writes 2. Horizontal fusion: the simplest example being batching where a single matrix is multiplied with a batch of examples but the more general scenario is a grouped GEMM where a group of matrix multiplications are scheduled together

2. Out of order execution: A general optimization for compilers, by looking ahead at the exact data dependencies within a graph we can decide on the most opportune time to execute a node and which buffers can be reused

3. Automatic work placement: Similar of the out of order execution point, but by matching nodes of a graph to resources like physical hardware or memory we can design an appropriate schedule

The above are general principles for accelerating PyTorch code but different backends will each make different tradeoffs on what to optimize. For example Inductor first takes care of fusing whatever it can and only then generates Triton kernels. It can also

Triton in addition offers speedups because of automatic memory coalescing, memory management and scheduling within each Streaming Multiprocessor and has been designed to handle tiled computations.

However, regardless of the backend you use it’s best to use a benchmark and see approach so try out the PyTorch profiler, visually inspect the generated kernels and try to see what’s going on for yourself.

Why am I not seeing speedups?

Graph Breaks

The main reason you won’t see the speedups you’d like to by using dynamo is excessive graph breaks. So what’s a graph break?

Given a program like:

def some_fun(x):
    ...

torch.compile(some_fun)(x)
...

Torchdynamo will attempt to compile all of the torch/tensor operations within some_fun() into a single FX graph, but it may fail to capture everything into one graph.

Some graph break reasons are insurmountable to TorchDynamo like calling into a C extension other than torch is invisible to torchdynamo, and could do arbitrary things without TorchDynamo being able to introduce necessary guards to ensure that the compiled program would be safe to reuse.

To maximize performance, it’s important to have as few graph breaks as possible.

Identifying the cause of a graph break

To identify all graph breaks in a program and the associated reasons for the breaks, torch._dynamo.explain can be used. This tool runs TorchDynamo on the supplied function and aggregates the graph breaks that are encountered. Here is an example usage:

import torch
import torch._dynamo as dynamo
def toy_example(a, b):
    x = a / (torch.abs(a) + 1)
    print("woo")
    if b.sum() < 0:
        b = b * -1
    return x * b
explanation, out_guards, graphs, ops_per_graph = dynamo.explain(toy_example, torch.randn(10), torch.randn(10))
print(explanation)
"""
Dynamo produced 3 graphs, with 2 graph break and 6 ops.
 Break reasons:
1. call_function BuiltinVariable(print) [ConstantVariable(str)] {}
   File "t2.py", line 16, in toy_example
    print("woo")

2. generic_jump
   File "t2.py", line 17, in toy_example
    if b.sum() < 0:
 """

To throw an error on the first graph break encountered you can use disable python fallback by using nopython=True, this should be familiar if you’ve worked with export based compilers.

def toy_example(a, b):
   ...

torch.compile(toy_example, fullgraph=True, backend=<compiler>)

Why didn’t my code recompile when I changed it?

If you went ahead and enabled dynamic shapes via env TORCHDYNAMO_DYNAMIC_SHAPES=1 python model.py then your code won’t recompile on shape changes. We’ve added support for dynamic shapes which avoids recompilations in the case when shapes vary by less than a factor of 2. This is especially useful in scenarios like varying image sizes in CV or variable sequence length in NLP. In inference scenarios it’s often not possible to know what a batch size will be beforehand because you take what you can get from different client apps.

In general, TorchDynamo tries very hard not to recompile things unnecessarily so if for example torchdynamo finds 3 graphs and your change only modified one graph then only that graph will recompile. So another tip to avoid potentially slow compilation times is to warmup a model by compiling it once after which subsequent compilations will be much faster. Cold start compile times is still a metric we track visibly.

Why am I getting incorrect results?

Accuracy issues can also be minified if you set the environment variable TORCHDYNAMO_REPRO_LEVEL=4, it operates with a similar git bisect model and a full repro might be something like TORCHDYNAMO_REPRO_AFTER="aot" TORCHDYNAMO_REPRO_LEVEL=4 the reason we need this is downstream compilers will codegen code whether it’s Triton code or the C++ backend, the numerics from those downstream compilers can be different in subtle ways yet have dramatic impact on your training stability. So the accuracy debugger is very useful for us to detect bugs in our codegen or with a backend compiler.

If you’d like to ensure that random number generation is the same across both torch and triton then you can enable torch._inductor.config.fallback_random = True

Why am I getting OOMs?

Dynamo is still an alpha product so there’s a few sources of OOMs and if you’re seeing an OOM try disabling the following configurations in this order and then open an issue on GitHub so we can solve the root problem 1. If you’re using dynamic shapes try disabling them, we’ve disabled them by default: env TORCHDYNAMO_DYNAMIC_SHAPES=0 python model.py 2. CUDA graphs with Triton are enabled by default in inductor but removing them may alleviate some OOM issues: torch._inductor.config.triton.cudagraphs = False.