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fix: print dlerror if dlopen fails #4485

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merged 2 commits into from
Dec 22, 2024
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@njzjz njzjz commented Dec 22, 2024

xref: njzjz/deepmd-gnn#44

Summary by CodeRabbit

  • New Features

    • Enhanced error messages for library loading failures on non-Windows platforms.
    • Updated thread management environment variable checks for improved compatibility.
    • Added support for mixed types in tensor input handling, allowing for more flexible configurations.
  • Bug Fixes

    • Improved error reporting for dynamic library loading issues.

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Copilot wasn't able to review any files in this pull request.

Files not reviewed (2)
  • source/api_cc/src/common.cc: Language not supported
  • source/lib/src/gpu/cudart/cudart_stub.cc: Language not supported
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coderabbitai bot commented Dec 22, 2024

📝 Walkthrough

Walkthrough

The pull request introduces enhancements to error handling and compatibility in two source files: common.cc and cudart_stub.cc. The changes focus on improving error messages during library loading, extending environment variable checks for thread management, and adding more flexible tensor input handling. These modifications aim to provide more informative error reporting and better support for different runtime configurations in the deepmd framework.

Changes

File Change Summary
source/api_cc/src/common.cc - Enhanced _load_library_path error messages with dlerror() output
- Updated get_env_nthreads to check additional TensorFlow and OpenMP environment variables
- Extended session_input_tensors functions with new overloads for mixed type handling
source/lib/src/gpu/cudart/cudart_stub.cc - Added detailed error message using dlerror() for non-Windows platforms when library loading fails

Sequence Diagram

sequenceDiagram
    participant App as Application
    participant Common as Common Module
    participant Env as Environment
    
    App->>Common: Request thread configuration
    Common->>Env: Check environment variables
    Env-->>Common: Return thread settings
    alt No specific thread variables set
        Common->>App: Trigger warning about OMP_NUM_THREADS
    end
    
    App->>Common: Prepare input tensors
    Common->>Common: Process mixed type tensors
    Common-->>App: Return configured tensors
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Actionable comments posted: 0

🧹 Nitpick comments (2)
source/api_cc/src/common.cc (1)

393-399: Consider storing the dlerror() output immediately in a local variable.
dlerror() returns a thread-local error message; if other dynamic loading calls or errors occur in parallel, the message could change before usage. Storing it in a local std::string right after dlopen() ensures the reported error is correct, even in multi-threaded contexts.

source/lib/src/gpu/cudart/cudart_stub.cc (1)

28-31: Validate or cache dlerror() before further calls.
dlerror() should be captured in a local variable immediately after dlopen() to ensure correctness in concurrent scenarios. Although it’s typically safe due to thread-local storage, capturing it right away is best practice.

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Reviewing files that changed from the base of the PR and between c24498b and 40d2f54.

📒 Files selected for processing (2)
  • source/api_cc/src/common.cc (1 hunks)
  • source/lib/src/gpu/cudart/cudart_stub.cc (1 hunks)

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codecov bot commented Dec 22, 2024

Codecov Report

Attention: Patch coverage is 0% with 3 lines in your changes missing coverage. Please review.

Project coverage is 84.41%. Comparing base (c24498b) to head (40d2f54).
Report is 4 commits behind head on devel.

Files with missing lines Patch % Lines
source/api_cc/src/common.cc 0.00% 3 Missing ⚠️
Additional details and impacted files
@@            Coverage Diff             @@
##            devel    #4485      +/-   ##
==========================================
- Coverage   84.41%   84.41%   -0.01%     
==========================================
  Files         670      670              
  Lines       62149    62150       +1     
  Branches     3487     3487              
==========================================
- Hits        52465    52464       -1     
- Misses       8558     8559       +1     
- Partials     1126     1127       +1     

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@wanghan-iapcm wanghan-iapcm added this pull request to the merge queue Dec 22, 2024
Merged via the queue into deepmodeling:devel with commit cfe17a3 Dec 22, 2024
60 checks passed
njzjz added a commit to njzjz/deepmd-kit that referenced this pull request Dec 22, 2024
xref: njzjz/deepmd-gnn#44

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

- **New Features**
- Enhanced error messages for library loading failures on non-Windows
platforms.
- Updated thread management environment variable checks for improved
compatibility.
- Added support for mixed types in tensor input handling, allowing for
more flexible configurations.

- **Bug Fixes**
	- Improved error reporting for dynamic library loading issues.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
(cherry picked from commit cfe17a3)
njzjz added a commit that referenced this pull request Dec 23, 2024
xref: njzjz/deepmd-gnn#44

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

- **New Features**
- Enhanced error messages for library loading failures on non-Windows
platforms.
- Updated thread management environment variable checks for improved
compatibility.
- Added support for mixed types in tensor input handling, allowing for
more flexible configurations.

- **Bug Fixes**
	- Improved error reporting for dynamic library loading issues.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
(cherry picked from commit cfe17a3)
iProzd added a commit to iProzd/deepmd-kit that referenced this pull request Dec 24, 2024
* change property.npy to any name

* Init branch

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* change | to Union

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* change sub_var_name default to []

* Solve pre-commit

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* solve scanning github

* fix UT

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* delete useless file

* Solve some UT

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Solve precommit

* slove pre

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* Solve dptest UT, dpatomicmodel UT, code scannisang

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* Solve UT fail caused by task_dim and property_name

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* Fix UT

* Fix UT

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Fix UT

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Fix permutation error

* Add property bias UT

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* recover rcond doc

* recover blank

* Change code according  according to coderabbitai

* solve pre-commit

* Fix UT

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* change apply_bias doc

* update the version compatibility

* feat (tf/pt): add atomic weights to tensor loss (deepmodeling#4466)

Interfaces are of particular interest in many studies. However, the
configurations in the training set to represent the interface normally
also include large parts of the bulk material. As a result, the final
model would prefer the bulk information while the interfacial
information is less learnt. It is difficult to simply improve the
proportion of interfaces in the configurations since the electronic
structures of the interface might only be reasonable with a certain
thickness of bulk materials. Therefore, I wonder whether it is possible
to define weights for atomic quantities in loss functions. This allows
us to add higher weights for the atomic information for the regions of
interest and probably makes the model "more focused" on the region of
interest.
In this PR, I add the keyword `enable_atomic_weight` to the loss
function of the tensor model. In principle, it could be generalised to
any atomic quantity, e.g., atomic forces.
I would like to know the developers' comments/suggestions about this
feature. I can add support for other loss functions and finish unit
tests once we agree on this feature.

Best. 




<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

- **New Features**
- Introduced an optional parameter for atomic weights in loss
calculations, enhancing flexibility in the `TensorLoss` class.
- Added a suite of unit tests for the `TensorLoss` functionality,
ensuring consistency between TensorFlow and PyTorch implementations.

- **Bug Fixes**
- Updated logic for local loss calculations to ensure correct
application of atomic weights based on user input.

- **Documentation**
- Improved clarity of documentation for several function arguments,
including the addition of a new argument related to atomic weights.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

* delete sub_var_name

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for more information, see https://pre-commit.ci

* recover to property key

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* Fix conflict

* Fix UT

* Add document of property fitting

* Delete checkpoint

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* Add get_property_name to DeepEvalBackend

* pd: fix learning rate setting when resume (deepmodeling#4480)

"When resuming training, there is no need to add `self.start_step` to
the step count because Paddle uses `lr_sche.last_epoch` as the input for
`step`, which already records the `start_step` steps."

learning rate are correct after fixing


![22AD6874B74E437E9B133D75ABCC02FE](https://github.com/user-attachments/assets/1ad0ce71-6e1c-4de5-87dc-0daca1f6f038)



<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

- **New Features**
- Enhanced training process with improved optimizer configuration and
learning rate adjustments.
	- Refined logging of training and validation results for clarity.
- Improved model saving logic to preserve the latest state during
interruptions.
- Enhanced tensorboard logging for detailed tracking of training
metrics.

- **Bug Fixes**
- Corrected lambda function for learning rate scheduler to reference
warmup steps accurately.

- **Chores**
- Streamlined data loading and handling for efficient training across
different tasks.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

* docs: update deepmd-gnn URL (deepmodeling#4482)

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

- **Documentation**
- Updated guidelines for creating and integrating new models in the
DeePMD-kit framework.
- Added new sections on descriptors, fitting networks, and model
requirements.
	- Enhanced unit testing section with instructions for regression tests.
- Updated URL for the DeePMD-GNN plugin to reflect new repository
location.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

Signed-off-by: Jinzhe Zeng <[email protected]>

* docs: update DPA-2 citation (deepmodeling#4483)

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

- **New Features**
- Updated references in the bibliography for the DPA-2 model to include
a new article entry for 2024.
	- Added a new reference for an attention-based descriptor.
  
- **Bug Fixes**
- Corrected reference links in documentation to point to updated DOI
links instead of arXiv.

- **Documentation**
- Revised entries in the credits and model documentation to reflect the
latest citations and details.
- Enhanced clarity and detail in fine-tuning documentation for
TensorFlow and PyTorch implementations.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Jinzhe Zeng <[email protected]>

* docs: fix a minor typo on the title of `install-from-c-library.md` (deepmodeling#4484)

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

- **Documentation**
- Updated formatting of the installation guide for the pre-compiled C
library.
- Icons for TensorFlow and JAX are now displayed together in the header.
	- Retained all installation instructions and compatibility notes.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

Signed-off-by: Jinzhe Zeng <[email protected]>

* fix: print dlerror if dlopen fails (deepmodeling#4485)

xref: njzjz/deepmd-gnn#44

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

- **New Features**
- Enhanced error messages for library loading failures on non-Windows
platforms.
- Updated thread management environment variable checks for improved
compatibility.
- Added support for mixed types in tensor input handling, allowing for
more flexible configurations.

- **Bug Fixes**
	- Improved error reporting for dynamic library loading issues.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

* change doc to py

* Add out_bias out_std doc

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* Fix UT

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* Delete key 'property' completely

* Fix UT

* Fix dptest UT

* pd: fix oom error (deepmodeling#4493)

Paddle use `MemoryError` rather than `RuntimeError` used in pytorch, now
I can test DPA-1 and DPA-2 in 16G V100...

![image](https://github.com/user-attachments/assets/42ead773-bf26-4195-8f67-404b151371de)

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

- **Bug Fixes**
- Improved detection of out-of-memory (OOM) errors to enhance
application stability.
- Ensured cached memory is cleared upon OOM errors, preventing potential
memory leaks.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

* pd: add missing `dp.eval()` in pd backend (deepmodeling#4488)

Switch to eval mode when evaluating model, otherwise `self.training`
will be `True`, backward graph will be created and cause OOM

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

- **New Features**
- Enhanced model evaluation state management to ensure correct behavior
during evaluation.

- **Bug Fixes**
- Improved type consistency in the `normalize_coord` function for better
computational accuracy.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

* [pre-commit.ci] pre-commit autoupdate (deepmodeling#4497)

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updates:
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v0.8.4](astral-sh/ruff-pre-commit@v0.8.3...v0.8.4)
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---------

Signed-off-by: Jinzhe Zeng <[email protected]>
Co-authored-by: root <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Chenqqian Zhang <[email protected]>
Co-authored-by: Jia-Xin Zhu <[email protected]>
Co-authored-by: HydrogenSulfate <[email protected]>
Co-authored-by: Jinzhe Zeng <[email protected]>
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