[Bug] Error of matrix multiplication in unwrap_error_phase_closure.py

[lidar-nevermore]

I’m processing the stack of sentinel-1 bursts, and the program exits without reporting any errors.

It turns out that this is a bug of np.dot().

Bug occurs when running the following line in unwrap_error_phase_closure.py:

closure_pha = np.dot(C, unw)

In my application, the size of C is (4575, 2069), and the size of unw is (2069, 45120).

Here is the data

Finally, I get through this via pytorch:

C_torch= torch.from_numpy(C)
unw_torch=torch.from_numpy(unw)
closure_pha=torch.mm(C_torch,unw_torch).numpy()

I'm using numpy 1.26.4, and I have not test the other versions. Maybe switching a new version of numpy will work, too.

Hope this bug can be fixed soon.

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Potential solution

The plan to solve the bug involves addressing the potential memory constraints and inefficiencies associated with using np.dot() for large matrix operations. The user has already found success using PyTorch, which suggests that the issue may be related to how numpy handles large matrices. Therefore, the solution will involve either switching to PyTorch for matrix operations or using np.matmul() as a more robust alternative to np.dot(). Additionally, ensuring that the system has sufficient resources to handle large matrices is crucial.

What is causing this bug?

The bug is likely caused by memory allocation issues when performing large matrix multiplications with np.dot(). The resulting matrix from the operation closure_pha = np.dot(C, unw) is extremely large, with dimensions (4575, 45120), which can lead to high memory usage. If the system does not have enough memory to accommodate this operation, it may cause the program to exit unexpectedly without reporting an error. The user's success with PyTorch suggests that numpy's handling of large matrices might be less efficient or robust in this context.

Code

To implement the solution, we can either switch to using PyTorch for matrix multiplication or replace np.dot() with np.matmul(). Here are the code snippets for both approaches:

Using PyTorch

import torch

C_torch = torch.from_numpy(C)
unw_torch = torch.from_numpy(unw)
closure_pha = torch.mm(C_torch, unw_torch).numpy()

Using np.matmul()

closure_pha = np.matmul(C, unw)

Both approaches should be tested to determine which one performs better in terms of memory usage and execution time.

How to replicate the bug

1. Ensure you have numpy version 1.26.4 installed.
2. Prepare matrices C and unw with dimensions (4575, 2069) and (2069, 45120), respectively.

3. Attempt to perform matrix multiplication using np.dot() as follows:

   import numpy as np
   
   closure_pha = np.dot(C, unw)

4. Observe if the program exits unexpectedly without reporting an error.

If the system lacks sufficient memory, the bug should replicate, causing the program to exit.

Conclusion

The bug is likely due to memory constraints when using np.dot() for large matrix operations. Switching to PyTorch or using np.matmul() can provide more efficient handling of large matrices. Additionally, ensuring adequate system resources is essential for successful execution.

Click here to create a Pull Request with the proposed solution

Files used for this task:

Changes on src/mintpy/unwrap_error_phase_closure.py

## Bug Analysis Report ### Issue Summary The user reported a bug related to matrix multiplication in the `unwrap_error_phase_closure.py` file. The specific line causing the issue is: ```python closure_pha = np.dot(C, unw) ``` The user mentioned that the program exits without reporting any errors when processing a stack of Sentinel-1 bursts. The matrices involved have dimensions `C` of size (4575, 2069) and `unw` of size (2069, 45120). The user resolved the issue by switching to PyTorch for matrix multiplication. ### Analysis of the Code 1. **Matrix Multiplication with `np.dot()`**: - The `np.dot()` function is used for matrix multiplication. It is equivalent to matrix multiplication when both inputs are 2-D arrays. - The dimensions of `C` and `unw` are compatible for matrix multiplication, as the number of columns in `C` (2069) matches the number of rows in `unw` (2069). 2. **Potential Issues**: - **Memory Usage**: The resulting matrix `closure_pha` will have dimensions (4575, 45120), which is quite large. This could lead to high memory usage and potential memory allocation issues, especially if the system does not have sufficient resources. - **Numpy Version**: The user is using numpy version 1.26.4. While there are no known issues with `np.dot()` in this version, it's possible that specific system configurations or numpy build options could affect performance or behavior. 3. **User's Solution**: - The user switched to PyTorch for matrix multiplication, which resolved the issue. PyTorch may handle large matrix operations more efficiently due to its optimized backend and GPU support. ### Recommendations 1. **Switch to `np.matmul()`**: - Consider replacing `np.dot()` with `np.matmul()`, which is explicitly designed for matrix multiplication and might handle edge cases better. 2. **Use PyTorch**: - If performance and memory usage are concerns, integrating PyTorch for matrix operations, as the user did, could be a viable solution. PyTorch is optimized for large-scale computations and can leverage GPU acceleration. 3. **Check System Resources**: - Ensure that the system has sufficient memory to handle large matrix operations. Consider optimizing memory usage or processing data in smaller chunks if possible. 4. **Test with Different Numpy Versions**: - Test the code with different versions of numpy to identify if the issue is version-specific. ### Conclusion The issue seems to be related to the handling of large matrix operations, potentially due to memory constraints or specific numpy configurations. Switching to PyTorch or using `np.matmul()` could provide a more robust solution.

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