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.

update: I allocated 64GB of memory for mintpy, which should be enough to complete the matrix multiplication.

I'm not sure how numpy uses the allocated memory throughout the calculation.

If we cannot change the behavior of numpy.dot(), dynamically adjusting the data tiling strategy according to the number of triplets may be a possible solution.

update2:

I finally solved this issue by downgrading numpy to 1.25.0. Now mintpy works well with 16GB memory.

Oct 22 '24 18:10 lidar-nevermore

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Oct 22 '24 18:10 welcome[bot]

Potential solution

The solution to the bug involves addressing the compatibility issue with the specific version of NumPy that causes the matrix multiplication to fail silently. The immediate fix is to downgrade NumPy to version 1.25.0, which has been reported to work correctly. Additionally, implementing a workaround using PyTorch for matrix multiplication can provide a more robust solution, especially for handling large matrices. This approach ensures that the program can continue to function correctly without encountering the silent failure.

What is causing this bug?

The bug is likely caused by a regression or change in memory management in NumPy version 1.26.4, which affects how large matrix operations are handled. The user's experience suggests that this version of NumPy does not manage memory efficiently for the given matrix sizes, leading to a silent failure during the matrix multiplication operation. Downgrading to NumPy 1.25.0 resolves the issue, indicating that the problem is specific to the newer version.

Code

To implement the solution, the following changes should be made:

Update requirements.txt to specify the compatible version of NumPy:
```
numpy==1.25.0
```

Modify unwrap_error_phase_closure.py to use PyTorch for matrix multiplication as a workaround:

import torch

# Existing code
# closure_pha = np.dot(C, unw)

# Workaround using PyTorch
C_torch = torch.from_numpy(C)
unw_torch = torch.from_numpy(unw)
closure_pha = torch.mm(C_torch, unw_torch).numpy()

Update docs/FAQs.md to include information about the issue and the workaround:

### 3. Why does matrix multiplication fail with numpy.dot() in unwrap_error_phase_closure.py?

When processing large matrices, users have reported that `numpy.dot()` may fail silently without an error message. This issue was observed with numpy version 1.26.4. A workaround is to use PyTorch for matrix multiplication, which handles large matrices more robustly:

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

Alternatively, downgrading to numpy version 1.25.0 has been reported to resolve the issue. Ensure that your system has sufficient memory allocated for the operation.

How to replicate the bug

To replicate the bug, follow these steps:

Ensure that NumPy version 1.26.4 is installed in your environment.
Use the provided data to create matrices C and unw with dimensions (4575, 2069) and (2069, 45120), respectively.
Attempt to perform the matrix multiplication using numpy.dot() in unwrap_error_phase_closure.py:
```
closure_pha = np.dot(C, unw)
```
Observe that the program exits without reporting any errors, indicating the silent failure.

By following these steps, you should be able to replicate the issue and verify the effectiveness of the proposed solution.

Click here to create a Pull Request with the proposed solution

Files used for this task:

Changes on requirements.txt

To address the issue reported with the matrix multiplication in unwrap_error_phase_closure.py, the requirements.txt file should be updated to specify a compatible version of numpy that does not exhibit the bug. Based on the user's experience, downgrading to numpy version 1.25.0 resolved the issue. Therefore, the requirements.txt file should be modified to ensure compatibility by specifying this version.

Suggested Update for `requirements.txt`:

numpy==1.25.0

This change will help prevent the bug from occurring by ensuring that the environment uses a version of numpy that is known to work correctly with the user's application.

Changes on docs/FAQs.md

The file docs/FAQs.md is a documentation file that contains frequently asked questions about the MintPy software. It does not currently contain any information related to the matrix multiplication issue described in the bug report.

To address the issue, you should add a new entry in this FAQ file that describes the problem with numpy.dot() when performing matrix multiplication with large matrices, as reported by the user. You should also mention the workaround using PyTorch and the compatibility with different numpy versions, specifically noting that downgrading to numpy 1.25.0 resolved the issue for the user.

Here's a suggested addition to the FAQ:

### 3. Why does matrix multiplication fail with numpy.dot() in unwrap_error_phase_closure.py?

When processing large matrices, users have reported that `numpy.dot()` may fail silently without an error message. This issue was observed with numpy version 1.26.4. A workaround is to use PyTorch for matrix multiplication, which handles large matrices more robustly:

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

Alternatively, downgrading to numpy version 1.25.0 has been reported to resolve the issue. Ensure that your system has sufficient memory allocated for the operation.


This addition will help users encountering similar issues understand the problem and apply the suggested solutions.
  
</details>


<details>
  <summary>Changes on src/mintpy/unwrap_error_phase_closure.py</summary>
  
  ## Bug Analysis Report

### Overview
The reported bug involves a matrix multiplication operation using `numpy.dot()` in the `unwrap_error_phase_closure.py` file. The user experiences a program exit without error messages when processing large matrices. The issue seems to be related to memory management or compatibility with specific versions of NumPy.

### Code Analysis
The problematic line of code is:
```python
closure_pha = np.dot(C, unw)

C is a matrix with dimensions (4575, 2069).
unw is a matrix with dimensions (2069, 45120).

The matrix multiplication is valid in terms of dimensions, as the inner dimensions match (2069). The result, closure_pha, should have dimensions (4575, 45120).

Potential Causes

Memory Usage: The operation involves large matrices, resulting in a significant memory requirement. The user allocated 64GB of memory, which should be sufficient, but memory allocation issues might still occur depending on how NumPy handles memory.
NumPy Version: The user reports that downgrading NumPy to version 1.25.0 resolves the issue, suggesting a possible regression or bug in NumPy version 1.26.4.
NumPy Internal Changes: Changes in how NumPy handles large matrix operations or memory management between versions could be causing the issue.

Workaround

The user successfully bypassed the issue by using PyTorch for matrix multiplication:

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

This suggests that PyTorch handles the operation more efficiently or differently, avoiding the issue present in NumPy.

Recommendations

Implement a Workaround: Consider implementing a conditional workaround using PyTorch for matrix multiplication when large matrices are involved, or when a specific NumPy version is detected.
Version Compatibility: Specify a compatible version of NumPy in the requirements.txt file, such as 1.25.0, to avoid the issue until it is resolved in later versions.
Investigate NumPy Changes: Further investigation into changes between NumPy versions 1.25.0 and 1.26.4 could provide insights into the root cause of the issue.
Memory Management: Explore alternative strategies for handling large matrix operations, such as data tiling or chunking, to reduce memory usage.

Conclusion

The issue appears to be related to memory management or a regression in specific NumPy versions. Implementing a workaround using PyTorch and specifying compatible NumPy versions are immediate steps to mitigate the problem. Further investigation into NumPy's handling of large matrices is recommended for a long-term solution.

Oct 22 '24 18:10 codeautopilot[bot]

This seems like a numpy problem, please consider opening an issue on its repo instead. I will go ahead and close it since the mintpy code works with numpy-1.25.0 from your update.

Nov 15 '24 03:11 yunjunz

[Bug] Error of matrix multiplication in unwrap_error_phase_closure.py

Potential solution

What is causing this bug?

Code

How to replicate the bug

Suggested Update for requirements.txt:

Potential Causes

Workaround

Recommendations

Conclusion

Suggested Update for `requirements.txt`: