TorchScript for production 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 torchvision.models as models

model = models.resnet18(pretrained=True)
model.eval()
example_input = torch.randn(1, 3, 224, 224)
scripted_model = torch.jit.[1](model, example_input)

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

Atrace

Bscript

Ccompile

Doptimize

Attempts:

3 left

💡 Hint

Common Mistakes

Using torch.jit.script instead of torch.jit.trace for models with fixed control flow.

Trying to call compile which is not a torch.jit method.

Tracing records the operations of the model with example inputs to create a TorchScript version.

2fill in blank

medium

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

PyTorch

scripted_model = torch.jit.trace(model, torch.randn(1, 3, 224, 224))
scripted_model.[1]('resnet18_scripted.pt')

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

Asave

Bwrite

Cexport

Ddump

Attempts:

3 left

💡 Hint

Common Mistakes

Using export or write which are not TorchScript methods.

Trying to use dump which is not a valid method here.

The save() method writes the scripted model to disk for later use.

3fill in blank

hard

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

PyTorch

import torch

scripted_model = torch.jit.[1]('resnet18_scripted.pt')

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

Aread

Bload

Copen

Dimport

Attempts:

3 left

💡 Hint

Common Mistakes

Using open or read which are Python file functions but not for TorchScript models.

Using import which is not a torch.jit method.

torch.jit.load() loads a saved TorchScript model from disk.

4fill in blank

hard

Fill both blanks to define a TorchScript function and save it.

PyTorch

import torch

@torch.jit.[1]
def add_tensors(x, y):
    return x + y

scripted_fn = add_tensors
scripted_fn.[2]('add_tensors.pt')

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

Ascript

Bsave

Ctrace

Dexport

Attempts:

3 left

💡 Hint

Common Mistakes

Using trace decorator which does not exist.

Using export instead of save to write the file.

Use @torch.jit.script to compile the function and save() to write it to a file.

5fill in blank

hard

Fill all three blanks to create a scripted model, run inference, and print the output shape.

PyTorch

import torch
import torchvision.models as models

model = models.mobilenet_v2(pretrained=True)
model.eval()
example_input = torch.randn(1, 3, 224, 224)
scripted_model = torch.jit.[1](model, example_input)
output = scripted_model([2])
print(output.[3])

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

Atrace

Bexample_input

Cshape

Dscript

Attempts:

3 left

💡 Hint

Common Mistakes

Using script instead of trace for this model.

Passing wrong input variable to the model.

Printing output instead of output.shape.

Trace the model, pass example_input to get output, then print output.shape to see the tensor size.

Practice

1. What is the main purpose of using TorchScript in PyTorch?

easy

A. To increase the size of the model for better accuracy

B. To visualize model training progress

C. To convert PyTorch models into a format optimized for production use

D. To automatically generate training data

Solution

1. Step 1: Understand TorchScript's role

   TorchScript is designed to convert PyTorch models into a form that can run faster and be deployed easily in production environments.

2. Step 2: Compare options

   Options A, B, and D describe unrelated tasks like visualization, model size increase, or data generation, which are not the purpose of TorchScript.

3. Final Answer:

   To convert PyTorch models into a format optimized for production use -> Option C

4. Quick Check:

   TorchScript purpose = convert for production [OK]

Hint: TorchScript = production-ready model format [OK]

Common Mistakes:

• Confusing TorchScript with visualization tools
• Thinking TorchScript generates data
• Assuming TorchScript increases model size

2. Which of the following is the correct way to save a TorchScript model after scripting?

easy

A. torch.save(model, 'model.pt')

B. torchscript.save_model(model, 'model.pt')

C. model.save('model.pt')

D. scripted_model.save('model.pt')

Solution

1. Step 1: Recall TorchScript saving method

   After scripting a model, the resulting scripted model object has a save() method to save it to disk.

2. Step 2: Evaluate options

   torch.save(model, 'model.pt') uses torch.save which is for regular PyTorch models, not scripted ones. model.save('model.pt') is invalid as model.save() does not exist. torchscript.save_model(model, 'model.pt') is not a valid PyTorch API.

3. Final Answer:

   scripted_model.save('model.pt') -> Option D

4. Quick Check:

   Use scripted_model.save() to save TorchScript [OK]

Hint: Use scripted_model.save() to save TorchScript models [OK]

Common Mistakes:

• Using torch.save() instead of scripted_model.save()
• Calling save() on the original model
• Assuming torchscript.save_model() exists

3. Given the code below, what will be the output printed?

import torch
class MyModule(torch.nn.Module):
    def forward(self, x):
        return x * 2

model = MyModule()
scripted = torch.jit.script(model)
print(scripted(torch.tensor(3)))

medium

A. tensor(6)

B. tensor(3)

C. 6

D. Error: scripting failed

Solution

1. Step 1: Understand the model's forward method

   The model multiplies input by 2, so input 3 becomes 6.

2. Step 2: Check scripting and output

   The model is scripted successfully, so calling scripted(torch.tensor(3)) returns tensor(6).

3. Final Answer:

   tensor(6) -> Option A

4. Quick Check:

   Input 3 * 2 = tensor(6) [OK]

Hint: Model doubles input; output = input * 2 [OK]

Common Mistakes:

• Confusing tensor(6) with integer 6
• Expecting error from scripting simple model
• Ignoring tensor wrapper in output

4. What is the error in the following code snippet when trying to trace a model?

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

model = MyModel()
example_input = torch.tensor([1.0, -1.0])
traced = torch.jit.trace(model, example_input)

medium

A. Syntax error in model definition

B. Tracing fails because the model has data-dependent control flow

C. example_input must be a scalar tensor

D. No error, tracing works fine

Solution

1. Step 1: Identify model control flow

   The model uses an if-statement depending on input data (x.sum() > 0), which is data-dependent control flow.

2. Step 2: Understand tracing limitation

   Tracing records operations from example input but cannot capture data-dependent branches, so it will not handle this correctly.

3. Final Answer:

   Tracing fails because the model has data-dependent control flow -> Option B

4. Quick Check:

   Tracing can't handle data-dependent control flow [OK]

Hint: Tracing fails if model has if/else based on input data [OK]

Common Mistakes:

• Assuming tracing works with all control flow
• Thinking example_input shape causes error
• Ignoring data-dependent branching

5. You want to deploy a PyTorch model with dynamic control flow and save it for production. Which approach is best to create a TorchScript model that handles this correctly?

hard

A. Use torch.jit.script() to convert the model

B. Rewrite the model to remove all control flow before tracing

C. Save the original PyTorch model with torch.save()

D. Use torch.jit.trace() with a fixed example input

Solution

1. Step 1: Identify model characteristics

   The model has dynamic control flow, meaning decisions depend on input data during execution.

2. Step 2: Choose correct TorchScript method

   torch.jit.script() analyzes the model code and supports dynamic control flow, unlike tracing which only records fixed operations.

3. Step 3: Evaluate other options

   Use torch.jit.trace() with a fixed example input (tracing) fails with dynamic control flow. Save the original PyTorch model with torch.save() saves the model but not in optimized TorchScript form. Rewrite the model to remove all control flow before tracing is impractical and changes model logic.

4. Final Answer:

   Use torch.jit.script() to convert the model -> Option A

5. Quick Check:

   Dynamic control flow needs scripting, not tracing [OK]

Hint: Use scripting for models with dynamic control flow [OK]

Common Mistakes:

• Using tracing for dynamic control flow models
• Saving without TorchScript conversion
• Trying to remove control flow instead of scripting