Introduction to TorchScript

James Reed (jamesreed@fb.com), Michael Suo (suo@fb.com), rev2

This tutorial is an introduction to TorchScript, an intermediate representation of a PyTorch model (subclass of nn.Module) that can then be run in a high-performance environment such as C++.

In this tutorial we will cover:

1. The basics of model authoring in PyTorch, including:

• Modules
• Defining forward functions
• Composing modules into a hierarchy of modules

2. Specific methods for converting PyTorch modules to TorchScript, our high-performance deployment runtime

• Tracing an existing module
• Using scripting to directly compile a module
• How to compose both approaches
• Saving and loading TorchScript modules

We hope that after you complete this tutorial, you will proceed to go through the follow-on tutorial which will walk you through an example of actually calling a TorchScript model from C++.

import torch  # This is all you need to use both PyTorch and TorchScript!
print(torch.__version__)

Out:

1.8.1+cu102

Basics of PyTorch Model Authoring

Let’s start out be defining a simple Module. A Module is the basic unit of composition in PyTorch. It contains:

1. A constructor, which prepares the module for invocation
2. A set of Parameters and sub-Modules. These are initialized by the constructor and can be used by the module during invocation.
3. A forward function. This is the code that is run when the module is invoked.

Let’s examine a small example:

class MyCell(torch.nn.Module):
    def __init__(self):
        super(MyCell, self).__init__()

    def forward(self, x, h):
        new_h = torch.tanh(x + h)
        return new_h, new_h

my_cell = MyCell()
x = torch.rand(3, 4)
h = torch.rand(3, 4)
print(my_cell(x, h))

Out:

(tensor([[0.6475, 0.5528, 0.6704, 0.7645],
        [0.5550, 0.8042, 0.7031, 0.9115],
        [0.8567, 0.8573, 0.3531, 0.6721]]), tensor([[0.6475, 0.5528, 0.6704, 0.7645],
        [0.5550, 0.8042, 0.7031, 0.9115],
        [0.8567, 0.8573, 0.3531, 0.6721]]))

So we’ve:

1. Created a class that subclasses torch.nn.Module.
2. Defined a constructor. The constructor doesn’t do much, just calls the constructor for super.
3. Defined a forward function, which takes two inputs and returns two outputs. The actual contents of the forward function are not really important, but it’s sort of a fake RNN cell–that is–it’s a function that is applied on a loop.

We instantiated the module, and made x and h, which are just 3x4 matrices of random values. Then we invoked the cell with my_cell(x, h). This in turn calls our forward function.

Let’s do something a little more interesting:

class MyCell(torch.nn.Module):
    def __init__(self):
        super(MyCell, self).__init__()
        self.linear = torch.nn.Linear(4, 4)

    def forward(self, x, h):
        new_h = torch.tanh(self.linear(x) + h)
        return new_h, new_h

my_cell = MyCell()
print(my_cell)
print(my_cell(x, h))

Out:

MyCell(
  (linear): Linear(in_features=4, out_features=4, bias=True)
)
(tensor([[ 0.6410,  0.6691,  0.0640,  0.6950],
        [ 0.7906,  0.4525, -0.2827,  0.4677],
        [ 0.9168,  0.8085, -0.2076,  0.2824]], grad_fn=<TanhBackward>), tensor([[ 0.6410,  0.6691,  0.0640,  0.6950],
        [ 0.7906,  0.4525, -0.2827,  0.4677],
        [ 0.9168,  0.8085, -0.2076,  0.2824]], grad_fn=<TanhBackward>))

We’ve redefined our module MyCell, but this time we’ve added a self.linear attribute, and we invoke self.linear in the forward function.

What exactly is happening here? torch.nn.Linear is a Module from the PyTorch standard library. Just like MyCell, it can be invoked using the call syntax. We are building a hierarchy of Modules.

print on a Module will give a visual representation of the Module’s subclass hierarchy. In our example, we can see our Linear subclass and its parameters.

By composing Modules in this way, we can succintly and readably author models with reusable components.

You may have noticed grad_fn on the outputs. This is a detail of PyTorch’s method of automatic differentiation, called autograd. In short, this system allows us to compute derivatives through potentially complex programs. The design allows for a massive amount of flexibility in model authoring.

Now let’s examine said flexibility:

class MyDecisionGate(torch.nn.Module):
    def forward(self, x):
        if x.sum() > 0:
            return x
        else:
            return -x

class MyCell(torch.nn.Module):
    def __init__(self):
        super(MyCell, self).__init__()
        self.dg = MyDecisionGate()
        self.linear = torch.nn.Linear(4, 4)

    def forward(self, x, h):
        new_h = torch.tanh(self.dg(self.linear(x)) + h)
        return new_h, new_h

my_cell = MyCell()
print(my_cell)
print(my_cell(x, h))

Out:

MyCell(
  (dg): MyDecisionGate()
  (linear): Linear(in_features=4, out_features=4, bias=True)
)
(tensor([[0.2264, 0.5256, 0.6205, 0.5346],
        [0.7090, 0.2887, 0.0348, 0.9279],
        [0.8096, 0.7650, 0.3442, 0.5109]], grad_fn=<TanhBackward>), tensor([[0.2264, 0.5256, 0.6205, 0.5346],
        [0.7090, 0.2887, 0.0348, 0.9279],
        [0.8096, 0.7650, 0.3442, 0.5109]], grad_fn=<TanhBackward>))

We’ve once again redefined our MyCell class, but here we’ve defined MyDecisionGate. This module utilizes control flow. Control flow consists of things like loops and if-statements.

Many frameworks take the approach of computing symbolic derivatives given a full program representation. However, in PyTorch, we use a gradient tape. We record operations as they occur, and replay them backwards in computing derivatives. In this way, the framework does not have to explicitly define derivatives for all constructs in the language.

How autograd works

Basics of TorchScript

Now let’s take our running example and see how we can apply TorchScript.

In short, TorchScript provides tools to capture the definition of your model, even in light of the flexible and dynamic nature of PyTorch. Let’s begin by examining what we call tracing.

Tracing Modules

class MyCell(torch.nn.Module):
    def __init__(self):
        super(MyCell, self).__init__()
        self.linear = torch.nn.Linear(4, 4)

    def forward(self, x, h):
        new_h = torch.tanh(self.linear(x) + h)
        return new_h, new_h

my_cell = MyCell()
x, h = torch.rand(3, 4), torch.rand(3, 4)
traced_cell = torch.jit.trace(my_cell, (x, h))
print(traced_cell)
traced_cell(x, h)

Out:

MyCell(
  original_name=MyCell
  (linear): Linear(original_name=Linear)
)

We’ve rewinded a bit and taken the second version of our MyCell class. As before, we’ve instantiated it, but this time, we’ve called torch.jit.trace, passed in the Module, and passed in example inputs the network might see.

What exactly has this done? It has invoked the Module, recorded the operations that occured when the Module was run, and created an instance of torch.jit.ScriptModule (of which TracedModule is an instance)

TorchScript records its definitions in an Intermediate Representation (or IR), commonly referred to in Deep learning as a graph. We can examine the graph with the .graph property:

print(traced_cell.graph)

Out:

graph(%self.1 : __torch__.MyCell,
      %input : Float(3, 4, strides=[4, 1], requires_grad=0, device=cpu),
      %h : Float(3, 4, strides=[4, 1], requires_grad=0, device=cpu)):
  %21 : __torch__.torch.nn.modules.linear.Linear = prim::GetAttr[name="linear"](%self.1)
  %23 : Tensor = prim::CallMethod[name="forward"](%21, %input)
  %14 : int = prim::Constant[value=1]() # /private/home/howardhuang/pytorch-projects/tutorials/beginner_source/Intro_to_TorchScript_tutorial.py:188:0
  %15 : Float(3, 4, strides=[4, 1], requires_grad=1, device=cpu) = aten::add(%23, %h, %14) # /private/home/howardhuang/pytorch-projects/tutorials/beginner_source/Intro_to_TorchScript_tutorial.py:188:0
  %16 : Float(3, 4, strides=[4, 1], requires_grad=1, device=cpu) = aten::tanh(%15) # /private/home/howardhuang/pytorch-projects/tutorials/beginner_source/Intro_to_TorchScript_tutorial.py:188:0
  %17 : (Float(3, 4, strides=[4, 1], requires_grad=1, device=cpu), Float(3, 4, strides=[4, 1], requires_grad=1, device=cpu)) = prim::TupleConstruct(%16, %16)
  return (%17)

However, this is a very low-level representation and most of the information contained in the graph is not useful for end users. Instead, we can use the .code property to give a Python-syntax interpretation of the code:

print(traced_cell.code)

Out:

def forward(self,
    input: Tensor,
    h: Tensor) -> Tuple[Tensor, Tensor]:
  _0 = torch.add((self.linear).forward(input, ), h, alpha=1)
  _1 = torch.tanh(_0)
  return (_1, _1)

So why did we do all this? There are several reasons:

1. TorchScript code can be invoked in its own interpreter, which is basically a restricted Python interpreter. This interpreter does not acquire the Global Interpreter Lock, and so many requests can be processed on the same instance simultaneously.
2. This format allows us to save the whole model to disk and load it into another environment, such as in a server written in a language other than Python
3. TorchScript gives us a representation in which we can do compiler optimizations on the code to provide more efficient execution
4. TorchScript allows us to interface with many backend/device runtimes that require a broader view of the program than individual operators.

We can see that invoking traced_cell produces the same results as the Python module:

print(my_cell(x, h))
print(traced_cell(x, h))

Out:

(tensor([[0.5175, 0.7063, 0.3240, 0.9168],
        [0.4317, 0.6472, 0.3432, 0.7272],
        [0.7614, 0.6960, 0.1853, 0.7272]], grad_fn=<TanhBackward>), tensor([[0.5175, 0.7063, 0.3240, 0.9168],
        [0.4317, 0.6472, 0.3432, 0.7272],
        [0.7614, 0.6960, 0.1853, 0.7272]], grad_fn=<TanhBackward>))
(tensor([[0.5175, 0.7063, 0.3240, 0.9168],
        [0.4317, 0.6472, 0.3432, 0.7272],
        [0.7614, 0.6960, 0.1853, 0.7272]], grad_fn=<TanhBackward>), tensor([[0.5175, 0.7063, 0.3240, 0.9168],
        [0.4317, 0.6472, 0.3432, 0.7272],
        [0.7614, 0.6960, 0.1853, 0.7272]], grad_fn=<TanhBackward>))

Using Scripting to Convert Modules

There’s a reason we used version two of our module, and not the one with the control-flow-laden submodule. Let’s examine that now:

class MyDecisionGate(torch.nn.Module):
    def forward(self, x):
        if x.sum() > 0:
            return x
        else:
            return -x

class MyCell(torch.nn.Module):
    def __init__(self, dg):
        super(MyCell, self).__init__()
        self.dg = dg
        self.linear = torch.nn.Linear(4, 4)

    def forward(self, x, h):
        new_h = torch.tanh(self.dg(self.linear(x)) + h)
        return new_h, new_h

my_cell = MyCell(MyDecisionGate())
traced_cell = torch.jit.trace(my_cell, (x, h))

print(traced_cell.dg.code)
print(traced_cell.code)

Out:

def forward(self,
    argument_1: Tensor) -> None:
  return None

def forward(self,
    input: Tensor,
    h: Tensor) -> Tuple[Tensor, Tensor]:
  _0 = self.dg
  _1 = (self.linear).forward(input, )
  _2 = (_0).forward(_1, )
  _3 = torch.tanh(torch.add(_1, h, alpha=1))
  return (_3, _3)

Looking at the .code output, we can see that the if-else branch is nowhere to be found! Why? Tracing does exactly what we said it would: run the code, record the operations that happen and construct a ScriptModule that does exactly that. Unfortunately, things like control flow are erased.

How can we faithfully represent this module in TorchScript? We provide a script compiler, which does direct analysis of your Python source code to transform it into TorchScript. Let’s convert MyDecisionGate using the script compiler:

scripted_gate = torch.jit.script(MyDecisionGate())

my_cell = MyCell(scripted_gate)
scripted_cell = torch.jit.script(my_cell)

print(scripted_gate.code)
print(scripted_cell.code)

Out:

def forward(self,
    x: Tensor) -> Tensor:
  _0 = bool(torch.gt(torch.sum(x, dtype=None), 0))
  if _0:
    _1 = x
  else:
    _1 = torch.neg(x)
  return _1

def forward(self,
    x: Tensor,
    h: Tensor) -> Tuple[Tensor, Tensor]:
  _0 = (self.dg).forward((self.linear).forward(x, ), )
  new_h = torch.tanh(torch.add(_0, h, alpha=1))
  return (new_h, new_h)

Hooray! We’ve now faithfully captured the behavior of our program in TorchScript. Let’s now try running the program:

# New inputs
x, h = torch.rand(3, 4), torch.rand(3, 4)
traced_cell(x, h)

Mixing Scripting and Tracing

Some situations call for using tracing rather than scripting (e.g. a module has many architectural decisions that are made based on constant Python values that we would like to not appear in TorchScript). In this case, scripting can be composed with tracing: torch.jit.script will inline the code for a traced module, and tracing will inline the code for a scripted module.

An example of the first case:

class MyRNNLoop(torch.nn.Module):
    def __init__(self):
        super(MyRNNLoop, self).__init__()
        self.cell = torch.jit.trace(MyCell(scripted_gate), (x, h))

    def forward(self, xs):
        h, y = torch.zeros(3, 4), torch.zeros(3, 4)
        for i in range(xs.size(0)):
            y, h = self.cell(xs[i], h)
        return y, h

rnn_loop = torch.jit.script(MyRNNLoop())
print(rnn_loop.code)

Out:

def forward(self,
    xs: Tensor) -> Tuple[Tensor, Tensor]:
  h = torch.zeros([3, 4], dtype=None, layout=None, device=None, pin_memory=None)
  y = torch.zeros([3, 4], dtype=None, layout=None, device=None, pin_memory=None)
  y0 = y
  h0 = h
  for i in range(torch.size(xs, 0)):
    _0 = (self.cell).forward(torch.select(xs, 0, i), h0, )
    y1, h1, = _0
    y0, h0 = y1, h1
  return (y0, h0)

And an example of the second case:

class WrapRNN(torch.nn.Module):
    def __init__(self):
        super(WrapRNN, self).__init__()
        self.loop = torch.jit.script(MyRNNLoop())

    def forward(self, xs):
        y, h = self.loop(xs)
        return torch.relu(y)

traced = torch.jit.trace(WrapRNN(), (torch.rand(10, 3, 4)))
print(traced.code)

Out:

def forward(self,
    argument_1: Tensor) -> Tensor:
  _0, y, = (self.loop).forward(argument_1, )
  return torch.relu(y)

This way, scripting and tracing can be used when the situation calls for each of them and used together.

Saving and Loading models

We provide APIs to save and load TorchScript modules to/from disk in an archive format. This format includes code, parameters, attributes, and debug information, meaning that the archive is a freestanding representation of the model that can be loaded in an entirely separate process. Let’s save and load our wrapped RNN module:

traced.save('wrapped_rnn.pt')

loaded = torch.jit.load('wrapped_rnn.pt')

print(loaded)
print(loaded.code)

Out:

RecursiveScriptModule(
  original_name=WrapRNN
  (loop): RecursiveScriptModule(
    original_name=MyRNNLoop
    (cell): RecursiveScriptModule(
      original_name=MyCell
      (dg): RecursiveScriptModule(original_name=MyDecisionGate)
      (linear): RecursiveScriptModule(original_name=Linear)
    )
  )
)
def forward(self,
    argument_1: Tensor) -> Tensor:
  _0, y, = (self.loop).forward(argument_1, )
  return torch.relu(y)

As you can see, serialization preserves the module hierarchy and the code we’ve been examining throughout. The model can also be loaded, for example, into C++ for python-free execution.

Further Reading

We’ve completed our tutorial! For a more involved demonstration, check out the NeurIPS demo for converting machine translation models using TorchScript: https://colab.research.google.com/drive/1HiICg6jRkBnr5hvK2-VnMi88Vi9pUzEJ