TorchScript export in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert a PyTorch model to TorchScript using tracing.

PyTorch

import torch
import torch.nn as nn

class SimpleModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(10, 5)

    def forward(self, x):
        return self.linear(x)

model = SimpleModel()
example_input = torch.randn(1, 10)
scripted_model = torch.jit.[1](model, example_input)

Drag options to blanks, or click blank then click option'

Atrace

Boptimize

Ccompile

Dscript

Attempts:

3 left

💡 Hint

Common Mistakes

Using torch.jit.script instead of torch.jit.trace for models with simple forward passes.

Not providing example inputs to the trace function.

Tracing a model with torch.jit.trace records the operations performed with example inputs, creating a TorchScript version.

2fill in blank

medium

Complete the code to save the TorchScript model to a file.

PyTorch

scripted_model.save([1])

Drag options to blanks, or click blank then click option'

Amodel

B'model.pt'

C'model'

Dmodel.pt

Attempts:

3 left

💡 Hint

Common Mistakes

Not using quotes around the filename string.

Using a filename without an extension.

The save method requires a string filename with quotes to save the model file.

3fill in blank

hard

Fix the error in loading a TorchScript model from a file.

PyTorch

loaded_model = torch.jit.[1]('model.pt')

Drag options to blanks, or click blank then click option'

Aload

Bscript

Ctrace

Dimport

Attempts:

3 left

💡 Hint

Common Mistakes

Using torch.jit.script or torch.jit.trace to load a model file.

Using an incorrect function name like import.

torch.jit.load loads a saved TorchScript model from a file.

4fill in blank

hard

Fill both blanks to create a TorchScript model using scripting and then save it.

PyTorch

scripted_model = torch.jit.[1](model)
scripted_model.[2]('scripted_model.pt')

Drag options to blanks, or click blank then click option'

Ascript

Bsave

Ctrace

Dload

Attempts:

3 left

💡 Hint

Common Mistakes

Using trace instead of script for models with control flow.

Using load instead of save to write the model.

torch.jit.script creates a scripted model, and save writes it to a file.

5fill in blank

hard

Fill all three blanks to load a TorchScript model, run it on input, and print the output shape.

PyTorch

loaded_model = torch.jit.[1]('scripted_model.pt')
input_tensor = torch.randn(2, 10)
output = loaded_model([2])
print(output.[3])

Drag options to blanks, or click blank then click option'

Aload

Binput_tensor

Cshape

Dscript

Attempts:

3 left

💡 Hint

Common Mistakes

Using script instead of load to load the model.

Passing wrong variable to the model call.

Printing output instead of output.shape.

Load the model with torch.jit.load, pass input_tensor to it, and print the output's shape attribute.

Practice

1. What is the main purpose of exporting a PyTorch model using TorchScript?

easy

A. To increase the training speed of the model

B. To save the model so it can run independently without Python

C. To convert the model into a TensorFlow format

D. To visualize the model architecture

Solution

1. Step 1: Understand TorchScript export purpose

   TorchScript export saves PyTorch models in a format that can run without Python, making deployment easier.

2. Step 2: Compare options with purpose

   Only To save the model so it can run independently without Python correctly states the main purpose: saving for standalone use without Python.

3. Final Answer:

   To save the model so it can run independently without Python -> Option B

4. Quick Check:

   TorchScript export = standalone model saving [OK]

Hint: TorchScript export = run model without Python [OK]

Common Mistakes:

• Thinking it speeds up training
• Confusing with TensorFlow conversion
• Assuming it is for visualization

2. Which of the following is the correct way to export a PyTorch model using scripting in TorchScript?

easy

A. torch.jit.trace(model, example_input)

B. torch.load('model.pt')

C. torch.save(model.state_dict(), 'model.pt')

D. torch.jit.script(model)

Solution

1. Step 1: Identify scripting syntax

   Using scripting to export a model requires torch.jit.script(model).

2. Step 2: Differentiate from tracing and saving

   torch.jit.trace(model, example_input) is tracing, torch.save(model.state_dict(), 'model.pt') saves weights only, torch.load('model.pt') loads a model, so only torch.jit.script(model) is correct for scripting export.

3. Final Answer:

   torch.jit.script(model) -> Option D

4. Quick Check:

   Scripting export uses torch.jit.script [OK]

Hint: Scripting export uses torch.jit.script(model) [OK]

Common Mistakes:

• Confusing scripting with tracing
• Using torch.save instead of torch.jit.script
• Trying to load instead of export

3. Given the code below, what will be the output of print(traced_model(torch.tensor([2.0])))?

import torch
class SimpleModel(torch.nn.Module):
    def forward(self, x):
        return x * 3

model = SimpleModel()
example_input = torch.tensor([1.0])
traced_model = torch.jit.trace(model, example_input)
print(traced_model(torch.tensor([2.0])))

medium

A. tensor([2.0])

B. tensor([3.0])

C. tensor([6.0])

D. RuntimeError

Solution

1. Step 1: Understand model behavior

   The model multiplies input by 3, so input 2.0 becomes 6.0.

2. Step 2: Check traced model output

   Tracing records the multiply by 3 operation, so traced_model(2.0) outputs tensor([6.0]).

3. Final Answer:

   tensor([6.0]) -> Option C

4. Quick Check:

   Input 2.0 * 3 = 6.0 [OK]

Hint: Model multiplies input by 3, so output is input*3 [OK]

Common Mistakes:

• Confusing input with output
• Expecting tracing to fail
• Thinking output is unchanged input

4. What is the error in the following code snippet when exporting a model with TorchScript scripting?

import torch
class MyModel(torch.nn.Module):
    def forward(self, x):
        if x.sum() > 0:
            return x * 2
        else:
            return x - 2

model = MyModel()
scripted_model = torch.jit.trace(model, torch.tensor([1.0]))

medium

A. Using torch.jit.trace instead of torch.jit.script for model with conditions

B. Missing example input tensor

C. Model class missing __init__ method

D. Incorrect tensor datatype

Solution

1. Step 1: Identify model features

   The model has a condition (if statement) in forward, which tracing cannot capture correctly.

2. Step 2: Understand TorchScript export methods

   Tracing works only for simple models without control flow; scripting is needed for conditions.

3. Final Answer:

   Using torch.jit.trace instead of torch.jit.script for model with conditions -> Option A

4. Quick Check:

   Model with conditions requires scripting, not tracing [OK]

Hint: Use scripting for models with if/else, not tracing [OK]

Common Mistakes:

• Using trace on models with control flow
• Assuming missing input tensor causes error
• Thinking __init__ is mandatory here

5. You want to export a PyTorch model that uses a loop and conditional statements inside its forward method. Which approach should you use to export it with TorchScript, and why?

hard

A. Use torch.jit.script because it supports control flow like loops and conditions

B. Use torch.jit.trace because it records operations for any model

C. Use torch.save to save the model weights only

D. Use torch.jit.trace with multiple example inputs to cover all paths

Solution

1. Step 1: Analyze model features

   The model has loops and conditions, which require TorchScript to understand control flow.

2. Step 2: Choose correct export method

   torch.jit.script compiles the model including control flow, while tracing cannot handle dynamic paths.

3. Final Answer:

   Use torch.jit.script because it supports control flow like loops and conditions -> Option A

4. Quick Check:

   Loops and conditions need scripting export [OK]

Hint: Loops and conditions require torch.jit.script export [OK]

Common Mistakes:

• Using tracing for models with dynamic control flow
• Saving weights only instead of full model
• Trying to cover all paths with tracing