Extending the modified mapper

Oct 13, 2025 by magicalceylontours.com 24 views

Introduction

In the context of distributed tensor operations, the ReplicateTensorToMesh mapper plays a crucial role in replicating tensors across multiple devices. However, when dealing with borrowed storage tensors, the current implementation of this mapper can lead to unexpected behavior. Specifically, if the input tensor is of borrowed storage, the resulting tensors will also be of borrowed storage. This can cause issues when using the aggregate_as_tensor API, which does not handle borrowed storage and will throw a 'std::get device index fail' error.

Problem Statement

The primary issue at hand is that the ReplicateTensorToMesh mapper does not return an owned storage tensor when the input tensor is of borrowed storage. This is in contrast to the ShardTensorTo2dMesh mapper, which will return an owned storage tensor even if the input is borrowed. This inconsistency can lead to problems when working with borrowed storage tensors, particularly when using the aggregate_as_tensor API.

Proposed Solution

To address this issue, we propose modifying the ReplicateTensorToMesh mapper to return an owned storage tensor when the input tensor is of borrowed storage. This can be achieved by introducing a hack similar to the one provided in the original report. The modified mapper would create a new tensor by chunking the input tensor and then concatenating the chunks. This approach ensures that the resulting tensor is an owned storage tensor, regardless of the storage type of the input tensor.

Code Implementation

The modified ReplicateTensorToMesh mapper can be implemented as follows:

std::vector<Tensor> map(const Tensor& tensor) const override {
    std::vector<Tensor> tensors;
    tensors.reserve(num_devices_);

    for (int i = 0; i < num_devices_; i++) {
      auto tapsTensor = experimental::xtensor::chunk(tensor, 1, 0);
      Tensor another = experimental::xtensor::concat(tapsTensor, 0);
      tensors.push_back(another);
    }
    return tensors;
}

This implementation creates a new tensor by chunking the input tensor and then concatenating the chunks. The resulting tensor is an owned storage tensor, which can be safely used with the aggregate_as_tensor API.

Benefits and Advantages

The proposed solution offers several benefits and advantages:

Consistency: The modified ReplicateTensorToMesh mapper ensures that the resulting tensor is an owned storage tensor, regardless of the storage type of the input tensor. This consistency is essential when working with borrowed storage tensors.
Reliability: By returning an owned storage tensor, the modified mapper eliminates the risk of 'std::get device index fail' errors when using the aggregate_as_tensor API.
Flexibility: The modified mapper provides more flexibility when working with borrowed storage tensors, allowing users to perform operations that were previously not possible.

Conclusion

In conclusion, the proposed solution addresses the issue of inconsistent storage types in the ReplicateTensorToMesh mapper. By returning an owned storage tensor when the input tensor is of borrowed storage, the modified mapper ensures consistency, reliability, and flexibility when working with borrowed storage tensors. We recommend implementing the modified mapper to resolve this issue and improve the overall usability of the ReplicateTensorToMesh mapper.

Future Work

Future work may involve:

Extending the modified mapper: Exploring ways to extend the modified mapper to handle other storage types, such as shared storage.
Improving performance: Optimizing the modified mapper to improve performance when working with large tensors.
Enhancing usability: Providing additional documentation and examples to help users understand and utilize the modified mapper effectively.

References

[Original Report](link to original report)
[API Documentation](link to API documentation)
[Related Issues](link to related issues)
Q&A: Ensuring Owned Storage Tensors in ReplicateTensorToMesh Mapper ====================================================================

Introduction

In our previous article, we discussed the issue of inconsistent storage types in the ReplicateTensorToMesh mapper. We proposed a solution to modify the mapper to return an owned storage tensor when the input tensor is of borrowed storage. In this Q&A article, we will address some of the most frequently asked questions related to this issue and provide additional insights to help users understand and utilize the modified mapper effectively.

Q: What is the primary issue with the current implementation of ReplicateTensorToMesh mapper?

A: The primary issue is that the current implementation does not return an owned storage tensor when the input tensor is of borrowed storage. This can lead to problems when using the aggregate_as_tensor API, which does not handle borrowed storage and will throw a 'std::get device index fail' error.

Q: How does the modified ReplicateTensorToMesh mapper address this issue?

A: The modified mapper creates a new tensor by chunking the input tensor and then concatenating the chunks. This approach ensures that the resulting tensor is an owned storage tensor, regardless of the storage type of the input tensor.

Q: What are the benefits of using the modified ReplicateTensorToMesh mapper?

A: The modified mapper offers several benefits, including:

Consistency: The modified mapper ensures that the resulting tensor is an owned storage tensor, regardless of the storage type of the input tensor.
Reliability: By returning an owned storage tensor, the modified mapper eliminates the risk of 'std::get device index fail' errors when using the aggregate_as_tensor API.
Flexibility: The modified mapper provides more flexibility when working with borrowed storage tensors, allowing users to perform operations that were previously not possible.

Q: How does the modified ReplicateTensorToMesh mapper compare to the ShardTensorTo2dMesh mapper?

A: The ShardTensorTo2dMesh mapper will return an owned storage tensor even if the input is borrowed. However, the ReplicateTensorToMesh mapper differs from ShardTensorTo2dMesh in that it will return a borrowed storage tensor if the input is borrowed. The modified ReplicateTensorToMesh mapper addresses this issue by returning an owned storage tensor even if the input is borrowed.

Q: What are the potential use cases for the modified ReplicateTensorToMesh mapper?

A: The modified mapper can be used in a variety of scenarios, including:

Distributed tensor operations: The modified mapper can be used to replicate tensors across multiple devices, ensuring that the resulting tensors are owned storage tensors.
Machine learning: The modified mapper can be used in machine learning applications where borrowed storage tensors are used extensively.
Scientific computing: The modified mapper can be used in scientific computing applications where large tensors are used to represent complex data.

Q: How can users implement the modified ReplicateTensorToMesh mapper in their code?

A: Users can implement the modified mapper by modifying the existing ReplicateTensorToMesh mapper to create a new tensor by chunking the input tensor and then concatenating the chunks. The resulting tensor will be an owned storage tensor, which can be safely used with the aggregate_as_tensor API.

Q: What are the potential future developments for the modified ReplicateTensorToMesh mapper?

A: Future developments may include:

Extending the modified mapper: Exploring ways to extend the modified mapper to handle other storage types, such as shared storage.
Improving performance: Optimizing the modified mapper to improve performance when working with large tensors.
Enhancing usability: Providing additional documentation and examples to help users understand and utilize the modified mapper effectively.

Conclusion

In conclusion, the modified ReplicateTensorToMesh mapper addresses the issue of inconsistent storage types in the original mapper. By returning an owned storage tensor when the input tensor is of borrowed storage, the modified mapper ensures consistency, reliability, and flexibility when working with borrowed storage tensors. We hope this Q&A article has provided valuable insights and information to help users understand and utilize the modified mapper effectively.