Bug: Swin Model Forward() Fails TorchScript Export Due To Optional Type Handling

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Introduction

The Swin model, a popular transformer-based architecture for image classification tasks, has been widely adopted in various applications. However, when attempting to export the Swin model using TorchScript, a common requirement for production deployments, a bug arises due to the improper handling of Optional types in the forward() method. This article delves into the issue, provides a detailed explanation of the problem, and offers a solution to resolve the bug.

Description

When trying to export a Swin model using TorchScript, the process fails because the forward() method in transformers/models/swin/modeling_swin.py doesn't properly handle Optional[torch.FloatTensor] type for pixel_values. This causes issues during scripting as TorchScript requires explicit handling of Optional types.

Error Message

The error message indicates that the Optional[Tensor] object has no attribute or method shape. This is because TorchScript requires explicit handling of Optional types, which is not provided in the current implementation.

RuntimeError: 'Optional[Tensor]' object has no attribute or method 'shape'.

Current Implementation

The forward() method in transformers/models/swin/modeling_swin.py is responsible for processing the input pixel_values. However, it fails to handle the Optional type properly, leading to the error.

def forward(self, pixel_values: Optional[torch.FloatTensor]) -> Tuple[torch.Tensor, Tuple[int]]:
    _, num_channels, height, width = pixel_values.shape  # Error occurs here
    pixel_values = self.maybe_pad(pixel_values, height, width)

Reproduction Steps

To reproduce the issue, follow these steps:

import torch
from transformers import SwinForImageClassification

class SwinWrapper(torch.nn.Module):
    def __init__(self, model):
        super().__init__()
        self.model = model

    def forward(self, x):
        return self.model(pixel_values=x).logits

model = SwinForImageClassification.from_pretrained("swin_model/")
wrapped_model = SwinWrapper(model)
# This fails:
scripted_model = torch.jit.script(wrapped_model)

Solution

To resolve the bug, add proper Optional type handling before accessing the shape:

def forward(self, pixel_values: Optional[torch.FloatTensor]) -> Tuple[torch.Tensor, Tuple[int]]:
    if pixel_values is None:
        raise ValueError("pixel_values cannot be None")
    _, num_channels, height, width = pixel_values.shape
    pixel_values = self.maybe_pad(pixel_values, height, width)

Additional Context

  • The issue specifically affects TorchScript export/scripting scenarios.
  • The error occurs because TorchScript requires explicit handling of Optional types.
  • This pattern should be checked in other model architectures that might be used with TorchScript.
  • Similar issues might exist in other models that use Optional types in their forward methods.

Impact

This bug prevents users from exporting Swin models to TorchScript format, which is a common requirement for production deployments. By resolving this issue, users can successfully export their Swin models and deploy them in production environments.

Conclusion

Q: What is the bug in the Swin model's forward() method?

A: The bug in the Swin model's forward() method arises due to the improper handling of Optional types. Specifically, the method fails to handle the Optional[torch.FloatTensor] type for pixel_values, leading to issues during scripting as TorchScript requires explicit handling of Optional types.

Q: What is the error message when trying to export the Swin model using TorchScript?

A: The error message indicates that the Optional[Tensor] object has no attribute or method shape. This is because TorchScript requires explicit handling of Optional types, which is not provided in the current implementation.

RuntimeError: 'Optional[Tensor]' object has no attribute or method 'shape'.

Q: How can I reproduce the issue?

A: To reproduce the issue, follow these steps:

import torch
from transformers import SwinForImageClassification

class SwinWrapper(torch.nn.Module):
    def __init__(self, model):
        super().__init__()
        self.model = model

    def forward(self, x):
        return self.model(pixel_values=x).logits

model = SwinForImageClassification.from_pretrained("swin_model/")
wrapped_model = SwinWrapper(model)
# This fails:
scripted_model = torch.jit.script(wrapped_model)

Q: What is the solution to resolve the bug?

A: To resolve the bug, add proper Optional type handling before accessing the shape:

def forward(self, pixel_values: Optional[torch.FloatTensor]) -> Tuple[torch.Tensor, Tuple[int]]:
    if pixel_values is None:
        raise ValueError("pixel_values cannot be None")
    _, num_channels, height, width = pixel_values.shape
    pixel_values = self.maybe_pad(pixel_values, height, width)

Q: Why is this bug specific to TorchScript export/scripting scenarios?

A: The bug is specific to TorchScript export/scripting scenarios because TorchScript requires explicit handling of Optional types. The current implementation of the Swin model's forward() method fails to provide this explicit handling, leading to issues during scripting.

Q: What other model architectures might be affected by this bug?

A: Similar issues might exist in other models that use Optional types in their forward methods. It is essential to check other model architectures that might be used with TorchScript to ensure they handle Optional types properly.

Q: What is the impact of this bug on users?

A: This bug prevents users from exporting Swin models to TorchScript format, which is a common requirement for production deployments. By resolving this issue, users can successfully export their Swin models and deploy them in production environments.

Q: How can users resolve this issue?

A: Users can resolve this issue by adding proper Optional type handling to the Swin model's forward() method, as shown in the solution above. This ensures that users can deploy their Swin models in production environments.