Dynamic Parallelism in TorchScript

In this tutorial, we introduce the syntax for doing dynamic inter-op parallelism in TorchScript. This parallelism has the following properties:

• dynamic - The number of parallel tasks created and their workload can depend on the control flow of the program.
• inter-op - The parallelism is concerned with running TorchScript program fragments in parallel. This is distinct from intra-op parallelism, which is concerned with splitting up individual operators and running subsets of the operator’s work in parallel.

Basic Syntax

The two important APIs for dynamic parallelism are:

• torch.jit.fork(fn : Callable[..., T], *args, **kwargs) -> torch.jit.Future[T]
• torch.jit.wait(fut : torch.jit.Future[T]) -> T

A good way to demonstrate how these work is by way of an example:

import torch

def foo(x):
    return torch.neg(x)

@torch.jit.script
def example(x):
    # Call `foo` using parallelism:
    # First, we "fork" off a task. This task will run `foo` with argument `x`
    future = torch.jit.fork(foo, x)

    # Call `foo` normally
    x_normal = foo(x)

    # Second, we "wait" on the task. Since the task may be running in
    # parallel, we have to "wait" for its result to become available.
    # Notice that by having lines of code between the "fork()" and "wait()"
    # call for a given Future, we can overlap computations so that they
    # run in parallel.
    x_parallel = torch.jit.wait(future)

    return x_normal, x_parallel

print(example(torch.ones(1))) # (-1., -1.)

fork() takes the callable fn and arguments to that callable args and kwargs and creates an asynchronous task for the execution of fn. fn can be a function, method, or Module instance. fork() returns a reference to the value of the result of this execution, called a Future. Because fork returns immediately after creating the async task, fn may not have been executed by the time the line of code after the fork() call is executed. Thus, wait() is used to wait for the async task to complete and return the value.

These constructs can be used to overlap the execution of statements within a function (shown in the worked example section) or be composed with other language constructs like loops:

import torch
from typing import List

def foo(x):
    return torch.neg(x)

@torch.jit.script
def example(x):
    futures : List[torch.jit.Future[torch.Tensor]] = []
    for _ in range(100):
        futures.append(torch.jit.fork(foo, x))

    results = []
    for future in futures:
        results.append(torch.jit.wait(future))

    return torch.sum(torch.stack(results))

print(example(torch.ones([])))

Note

When we initialized an empty list of Futures, we needed to add an explicit type annotation to futures. In TorchScript, empty containers default to assuming they contain Tensor values, so we annotate the list constructor # as being of type List[torch.jit.Future[torch.Tensor]]

This example uses fork() to launch 100 instances of the function foo, waits on the 100 tasks to complete, then sums the results, returning -100.0.

Applied Example: Ensemble of Bidirectional LSTMs

Let’s try to apply parallelism to a more realistic example and see what sort of performance we can get out of it. First, let’s define the baseline model: an ensemble of bidirectional LSTM layers.

import torch, time

# In RNN parlance, the dimensions we care about are:
# # of time-steps (T)
# Batch size (B)
# Hidden size/number of "channels" (C)
T, B, C = 50, 50, 1024

# A module that defines a single "bidirectional LSTM". This is simply two
# LSTMs applied to the same sequence, but one in reverse
class BidirectionalRecurrentLSTM(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.cell_f = torch.nn.LSTM(input_size=C, hidden_size=C)
        self.cell_b = torch.nn.LSTM(input_size=C, hidden_size=C)

    def forward(self, x : torch.Tensor) -> torch.Tensor:
        # Forward layer
        output_f, _ = self.cell_f(x)

        # Backward layer. Flip input in the time dimension (dim 0), apply the
        # layer, then flip the outputs in the time dimension
        x_rev = torch.flip(x, dims=[0])
        output_b, _ = self.cell_b(torch.flip(x, dims=[0]))
        output_b_rev = torch.flip(output_b, dims=[0])

        return torch.cat((output_f, output_b_rev), dim=2)


# An "ensemble" of `BidirectionalRecurrentLSTM` modules. The modules in the
# ensemble are run one-by-one on the same input then their results are
# stacked and summed together, returning the combined result.
class LSTMEnsemble(torch.nn.Module):
    def __init__(self, n_models):
        super().__init__()
        self.n_models = n_models
        self.models = torch.nn.ModuleList([
            BidirectionalRecurrentLSTM() for _ in range(self.n_models)])

    def forward(self, x : torch.Tensor) -> torch.Tensor:
        results = []
        for model in self.models:
            results.append(model(x))
        return torch.stack(results).sum(dim=0)

# For a head-to-head comparison to what we're going to do with fork/wait, let's
# instantiate the model and compile it with TorchScript
ens = torch.jit.script(LSTMEnsemble(n_models=4))

# Normally you would pull this input out of an embedding table, but for the
# purpose of this demo let's just use random data.
x = torch.rand(T, B, C)

# Let's run the model once to warm up things like the memory allocator
ens(x)

x = torch.rand(T, B, C)

# Let's see how fast it runs!
s = time.time()
ens(x)
print('Inference took', time.time() - s, ' seconds')

On my machine, this network runs in 2.05 seconds. We can do a lot better!

Parallelizing Forward and Backward Layers

A very simple thing we can do is parallelize the forward and backward layers within BidirectionalRecurrentLSTM. For this, the structure of the computation is static, so we don’t actually even need any loops. Let’s rewrite the forward method of BidirectionalRecurrentLSTM like so:

def forward(self, x : torch.Tensor) -> torch.Tensor:
    # Forward layer - fork() so this can run in parallel to the backward
    # layer
    future_f = torch.jit.fork(self.cell_f, x)

    # Backward layer. Flip input in the time dimension (dim 0), apply the
    # layer, then flip the outputs in the time dimension
    x_rev = torch.flip(x, dims=[0])
    output_b, _ = self.cell_b(torch.flip(x, dims=[0]))
    output_b_rev = torch.flip(output_b, dims=[0])

    # Retrieve the output from the forward layer. Note this needs to happen
    # *after* the stuff we want to parallelize with
    output_f, _ = torch.jit.wait(future_f)

    return torch.cat((output_f, output_b_rev), dim=2)

In this example, forward() delegates execution of cell_f to another thread, while it continues to execute cell_b. This causes the execution of both the cells to be overlapped with each other.

Running the script again with this simple modification yields a runtime of 1.71 seconds for an improvement of 17%!

Aside: Visualizing Parallelism

We’re not done optimizing our model but it’s worth introducing the tooling we have for visualizing performance. One important tool is the PyTorch profiler.

Let’s use the profiler along with the Chrome trace export functionality to visualize the performance of our parallelized model:

with torch.autograd.profiler.profile() as prof:
    ens(x)
prof.export_chrome_trace('parallel.json')

This snippet of code will write out a file named parallel.json. If you navigate Google Chrome to chrome://tracing, click the Load button, and load in that JSON file, you should see a timeline like the following:

The horizontal axis of the timeline represents time and the vertical axis represents threads of execution. As we can see, we are running two lstm instances at a time. This is the result of our hard work parallelizing the bidirectional layers!

Parallelizing Models in the Ensemble

You may have noticed that there is a further parallelization opportunity in our code: we can also run the models contained in LSTMEnsemble in parallel with each other. The way to do that is simple enough, this is how we should change the forward method of LSTMEnsemble:

def forward(self, x : torch.Tensor) -> torch.Tensor:
    # Launch tasks for each model
    futures : List[torch.jit.Future[torch.Tensor]] = []
    for model in self.models:
        futures.append(torch.jit.fork(model, x))

    # Collect the results from the launched tasks
    results : List[torch.Tensor] = []
    for future in futures:
        results.append(torch.jit.wait(future))

    return torch.stack(results).sum(dim=0)

Or, if you value brevity, we can use list comprehensions:

def forward(self, x : torch.Tensor) -> torch.Tensor:
    futures = [torch.jit.fork(model, x) for model in self.models]
    results = [torch.jit.wait(fut) for fut in futures]
    return torch.stack(results).sum(dim=0)

Like described in the intro, we’ve used loops to fork off tasks for each of the models in our ensemble. We’ve then used another loop to wait for all of the tasks to be completed. This provides even more overlap of computation.

With this small update, the script runs in 1.4 seconds, for a total speedup of 32%! Pretty good for two lines of code.

We can also use the Chrome tracer again to see where’s going on:

We can now see that all LSTM instances are being run fully in parallel.

Conclusion

In this tutorial, we learned about fork() and wait(), the basic APIs for doing dynamic, inter-op parallelism in TorchScript. We saw a few typical usage patterns for using these functions to parallelize the execution of functions, methods, or Modules in TorchScript code. Finally, we worked through an example of optimizing a model using this technique and explored the performance measurement and visualization tooling available in PyTorch.