Resolving the RuntimeError in Pytorch Matmul: Understanding Float16 and Float32

Discover how to resolve the `RuntimeError: "addmm_impl_cpu_" not implemented for 'Half'` when using Pytorch's matmul, by understanding the importance of data types like float16 and float32.
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Tackling the Pytorch RuntimeError: Understanding Float Types and Matmul

When it comes to working with Pytorch for machine learning and data analysis, encountering errors can be part of the journey. One common issue that practitioners face is the RuntimeError: "addmm_impl_cpu_" not implemented for 'Half' while performing matrix multiplication (matmul). This error can be quite frustrating, especially when it hinders the progress of your project.

In this guide, we'll break down the cause of this error and offer a clear pathway toward its resolution. By understanding the underlying issues related to data types in Pytorch, we can ensure smooth execution of our code.

The Problem: Understanding the Error

The error arises during the execution of matrix multiplication when the data type of the operands involved is float16, commonly referred to as Half in Pytorch. The given traceback helps identify the origins of the problem:

However, if these tensors are in float16, Pytorch currently does not support this operation on the CPU, leading to the error message.

Example Scenario

Suppose inp1 and inp2 are defined as:

inp1 --> [1, 256]

inp2 --> [1, 256]

Given that the operation involves matrix multiplication between these two tensors, the float16 type is unsuitable for this operation on the CPU context.

The Solution: Switching to Float32

To resolve the error, the most effective and straightforward solution is to change the data type of the operands from float16 to float32. Here's how to do that:

Identify the Data Type: Confirm if your tensors (inp1 and inp2) are indeed in float16. You can print their data types using:

[[See Video to Reveal this Text or Code Snippet]]

Change Data Type: If they are in float16, convert them to float32 before performing any operations. You can do this with the following code:

[[See Video to Reveal this Text or Code Snippet]]

Perform Matmul: Now that the operands are in the correct format, you can safely carry out the matrix multiplication:

[[See Video to Reveal this Text or Code Snippet]]

Key Takeaways

Understand Data Types: Always be mindful of the data types you’re working with in Pytorch, as they can significantly affect performance and compatibility with various operations.

Use Float32 for CPU: While float16 can be useful for GPU operations and reducing memory usage, it’s best to stick with float32 for CPU computations to avoid such errors.

Conclusion

By simply changing the data type of the tensors involved in the matrix multiplication from float16 to float32, we can easily overcome the RuntimeError encountered in Pytorch. This not only ensures a smooth workflow but also helps improve the understanding of how tensor types interact within the Pytorch ecosystem.

Happy coding! If you run into more errors or have any questions, feel free to reach out or share your thoughts in the comments below.