docs(pt): examples for new dpa2 model #4138
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Warning Rate limit exceeded@iProzd has exceeded the limit for the number of commits or files that can be reviewed per hour. Please wait 14 minutes and 59 seconds before requesting another review. How to resolve this issue?After the wait time has elapsed, a review can be triggered using the We recommend that you space out your commits to avoid hitting the rate limit. How do rate limits work?CodeRabbit enforces hourly rate limits for each developer per organization. Our paid plans have higher rate limits than the trial, open-source and free plans. In all cases, we re-allow further reviews after a brief timeout. Please see our FAQ for further information. WalkthroughWalkthroughThe changes involve the introduction of a new README file and multiple JSON configuration files for the DPA2 model, which simulates water systems using deep learning. The README provides documentation on model configurations tailored for different precision and efficiency needs. The JSON files specify model parameters, including updates to interaction cutoffs, three-body interaction settings, and learning rate configurations. Additionally, the test file has been modified to include references to the new input files, enhancing the testing framework for the model. Changes
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Actionable comments posted: 0
Outside diff range and nitpick comments (4)
examples/water/dpa2_large/input_torch.json (3)
21-25: Monitor the computational cost with the addition of three-body interactions.The introduction of three-body interaction parameters (
three_body_sel,three_body_rcut,three_body_rcut_smth, anduse_three_body) suggests that the model will now incorporate three-body terms. While this can potentially improve the model's accuracy, it is important to keep an eye on the computational cost, as calculating three-body interactions can be computationally expensive.Consider benchmarking the model's performance with and without three-body interactions to ensure a favorable trade-off between accuracy and efficiency.
46-50: Monitor the training dynamics with the new update strategy and residual connections.Several new parameters related to the update strategy and residual connections have been introduced, such as
update_style,update_residual,update_residual_init,attn2_has_gate, anduse_sqrt_nnei. These changes suggest a residual update strategy with normalized initialization, a gating mechanism in the attention layer, and the use of the square root of the number of neighbors in certain calculations.These modifications have the potential to improve the model's training dynamics and convergence by allowing gradients to flow more easily, selectively attending to relevant information, and normalizing contributions from different sized neighborhoods. However, it is important to closely monitor the model's training progress and performance to ensure that these changes have the desired positive impact.
51-52: Evaluate the impact of the convolutional and MLP layers on the g1 output.The addition of the
g1_out_convandg1_out_mlpparameters suggests that a convolutional layer and a multi-layer perceptron will be applied to the output of the g1 component. These changes have the potential to enhance the model's ability to capture local spatial patterns and introduce additional non-linearity and flexibility.To ensure that these modifications have a positive impact on the model's performance, it is recommended to evaluate the model's accuracy and generalization ability with and without these additional layers. This will help to determine whether the increased complexity is justified by improved results.
examples/water/dpa2_medium/input_torch.json (1)
85-111: LGTM, but consider larger batch sizes for improved performance.The training parameters and data specifications are correctly defined, and the values seem reasonable. The training and validation data paths are correctly specified, and the settings for logging and saving model checkpoints are appropriate.
However, the batch sizes for both training and validation are set to 1, which may not be optimal for performance. Consider using larger batch sizes to improve training efficiency and resource utilization.
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Files selected for processing (4)
- examples/water/dpa2_large/input_torch.json (3 hunks)
- examples/water/dpa2_medium/input_torch.json (1 hunks)
- examples/water/dpa2_small/input_torch.json (1 hunks)
- source/tests/common/test_examples.py (1 hunks)
Additional comments not posted (14)
examples/water/dpa2_large/input_torch.json (3)
12-13: Verify the impact of the reducedrcutandrcut_smthvalues.The
rcutvalue has been reduced from 9.0 to 6.0, and thercut_smthvalue has been significantly reduced from 8.0 to 0.5. While these changes may improve computational efficiency, they could potentially impact the model's accuracy and introduce instabilities due to the sharper cutoff.Please ensure that these parameter changes have been thoroughly tested and validated to maintain the desired balance between efficiency and accuracy.
43-44: Verify the impact of disabling update mechanisms.The
update_g1_has_attnandupdate_g2_has_g1g1flags have been set to false, which suggests that the attention mechanism for g1 updates and the g1-g1 interaction for g2 updates will be disabled. While this may simplify the model and reduce computational cost, it is important to ensure that the model's performance and expressiveness are not negatively impacted by these changes.Please provide the rationale behind disabling these update mechanisms and confirm that the model's accuracy and ability to capture relevant interactions are maintained.
71-71: Monitor the training process with the increased learning rate.The
start_lrparameter in the learning rate configuration has been increased from 0.0002 to 0.001. This change suggests that the model will take larger optimization steps, especially in the early stages of training, which can potentially lead to faster convergence and improved training speed.However, it is important to keep a close eye on the training process to ensure that the increased learning rate does not cause instability or convergence issues. If the model exhibits unstable behavior or fails to converge, consider adjusting the learning rate or using a more adaptive optimization algorithm.
examples/water/dpa2_medium/input_torch.json (6)
3-7: LGTM!The model structure is correctly defined, and the type map accurately specifies the atom types for water molecules.
10-26: LGTM!The representation initialization (repinit) configuration is correctly defined, and the parameter values seem reasonable for a DPA2 model designed for water systems. The use of three-body interactions is appropriately enabled.
27-53: LGTM!The representation transformer (repformer) configuration is correctly defined, and the parameter values seem reasonable for a transformer-based architecture. The use of attention mechanisms and residual connections is appropriate for capturing long-range interactions in the water system.
56-65: LGTM!The fitting network configuration is correctly defined, and the neuron layout seems reasonable. The use of resnet_dt is appropriate for improving the training stability.
68-74: LGTM!The learning rate settings are correctly defined, and the use of an exponential decay schedule is appropriate for gradually reducing the learning rate during training. The start_lr, stop_lr, and decay_steps values seem reasonable.
75-84: LGTM!The loss function preferences are correctly defined, and the use of an energy-based loss function is appropriate for training a DPA2 model. The start and limit preferences for energy, force, and virial seem reasonable.
examples/water/dpa2_small/input_torch.json (4)
3-67: LGTM!The model configuration is well-structured and uses advanced techniques like three-body interactions, attention mechanisms, and residual connections to capture complex interactions in the data. The architecture should enhance the model's capacity to learn from the data effectively.
68-74: LGTM!The exponential decay strategy for the learning rate is a reasonable approach to gradually decrease the learning rate during training. The initial and final learning rates are within a reasonable range for deep learning models.
Please monitor the model's performance during training and consider adjusting the decay steps (currently set to 5000) if needed based on the total number of training steps and the model's convergence behavior.
75-84: LGTM!The loss function configuration provides flexibility in balancing the influence of energy, force, and virial predictions during training. The high starting preference for force predictions may help the model learn accurate force predictions early in the training process.
Please monitor the model's performance during training and consider adjusting the starting and limiting preferences for energy, force, and virial predictions if needed to optimize the model's overall performance.
85-111: LGTM!The training configuration provides a structured approach to manage the training and validation datasets, as well as operational parameters for training. The large number of training steps (1,000,000) should allow the model to learn complex patterns in the data.
Please consider the following suggestions during training:
Monitor the model's performance on the validation dataset to detect potential overfitting, given the large number of training steps. Consider implementing early stopping or adjusting the number of training steps if overfitting is observed.
The batch size of 1 may be appropriate for the specific problem but could be inefficient for larger datasets. If the training process is too slow, consider increasing the batch size to improve efficiency, while ensuring that the model's performance is not negatively impacted.
source/tests/common/test_examples.py (1)
55-57: LGTM!The addition of the new input files for the "dpa2" example is consistent with the PR objectives and expands the test coverage. The changes look good and do not introduce any issues.
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Actionable comments posted: 2
Outside diff range and nitpick comments (3)
examples/water/dpa2/input_torch_medium.json (2)
2-2: Consider removing redundant commentsThe
_commentfields with "that's all" appear multiple times and may not add meaningful information to the configuration file. Consider removing them or providing more descriptive comments to improve readability.Also applies to: 64-64, 66-66, 73-73, 83-83, 94-94, 101-101, 110-110
85-111: Set seeds consistently for reproducibilitySeeds are set in both
fitting_net("seed": 1) andtraining("seed": 10). For consistent reproducibility across all components, consider using the same seed value or clearly documenting the reasoning behind different seeds.examples/water/dpa2/input_torch_small.json (1)
2-2: Consider removing redundant_commententries for clarityThe repeated
_commententries with the value "that's all" may not be necessary and could be removed to improve readability.Also applies to: 64-64, 66-66, 73-73, 83-83, 94-94, 101-101, 110-110
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files selected for processing (5)
- examples/water/dpa2/README.md (1 hunks)
- examples/water/dpa2/input_torch_large.json (3 hunks)
- examples/water/dpa2/input_torch_medium.json (1 hunks)
- examples/water/dpa2/input_torch_small.json (1 hunks)
- source/tests/common/test_examples.py (1 hunks)
Files skipped from review as they are similar to previous changes (1)
- source/tests/common/test_examples.py
Additional context used
LanguageTool
examples/water/dpa2/README.md
[uncategorized] ~5-~5: Loose punctuation mark.
Context: ... complexity: -input_torch_small.json: Our smallest DPA2 model, optimized for ...(UNLIKELY_OPENING_PUNCTUATION)
[uncategorized] ~7-~7: Loose punctuation mark.
Context: ...r most users. -input_torch_large.json: Our most complex model with the highest...(UNLIKELY_OPENING_PUNCTUATION)
Additional comments not posted (12)
examples/water/dpa2/README.md (3)
1-8: LGTM!The introduction section provides a clear and concise overview of the available model configurations for training the DPA2 model. The descriptions of the small, medium, and large configurations are helpful in understanding their differences in terms of precision and efficiency.
Tools
LanguageTool
[uncategorized] ~5-~5: Loose punctuation mark.
Context: ... complexity: -input_torch_small.json: Our smallest DPA2 model, optimized for ...(UNLIKELY_OPENING_PUNCTUATION)
[uncategorized] ~7-~7: Loose punctuation mark.
Context: ...r most users. -input_torch_large.json: Our most complex model with the highest...(UNLIKELY_OPENING_PUNCTUATION)
9-15: Great work on the comparison table!The comparison table is well-structured and provides a clear overview of the differences between the small, medium, and large model configurations. The information in the table is consistent with the descriptions provided in the introduction section, and it enhances the usability and accessibility of the DPA2 model training process by aiding users in selecting the appropriate model based on their specific requirements.
5-5: Skipping static analysis hints.The flagged instances of "Loose punctuation mark" by LanguageTool are false positives. The use of backticks for code formatting is a common and accepted practice in Markdown and does not require any changes.
Also applies to: 7-7
Tools
LanguageTool
[uncategorized] ~5-~5: Loose punctuation mark.
Context: ... complexity: -input_torch_small.json: Our smallest DPA2 model, optimized for ...(UNLIKELY_OPENING_PUNCTUATION)
examples/water/dpa2/input_torch_large.json (5)
12-13: Verify the impact of reduced cutoff values on model performance.The reduced
rcutandrcut_smthvalues may improve computational efficiency. However, please ensure that the tighter cutoff does not adversely impact the model's accuracy or ability to capture important long-range interactions.To verify the impact, consider running experiments to compare the model's performance (e.g., accuracy, loss) with the original and reduced cutoff values on a representative dataset. Analyze the results to ensure that the reduced cutoff values do not lead to a significant degradation in performance.
21-25: Monitor the computational cost and memory usage with three-body interactions.The inclusion of three-body interactions through the new parameters
three_body_sel,three_body_rcut,three_body_rcut_smth, anduse_three_bodymay enhance the model's expressiveness and ability to capture complex interactions. This is a promising addition to the model.However, please keep an eye on the computational cost and memory usage, as the additional calculations involved in three-body interactions may increase the resource requirements.
To monitor the impact, consider measuring the training and inference time, as well as the memory usage, with and without three-body interactions enabled. If the computational cost or memory usage becomes prohibitive, explore techniques such as reducing the
three_body_selvalue or adjusting the cutoff parameters to strike a balance between model performance and resource efficiency.
43-44: Evaluate the impact of disabling attention mechanisms on model performance.The changes to disable the attention mechanism in the g1 update (
update_g1_has_attn = false) and the use of g1g1 features in the g2 update (update_g2_has_g1g1 = false) may simplify the model's architecture and reduce computational complexity. This simplification could be beneficial for efficiency.However, please assess the impact of these changes on the model's performance, as the attention mechanisms may have been important for capturing long-range dependencies and interactions.
To evaluate the impact, consider conducting experiments to compare the model's performance (e.g., accuracy, loss) with and without the attention mechanisms enabled. Analyze the results to determine if disabling the attention mechanisms leads to a significant degradation in performance. If the performance drop is acceptable given the computational benefits, the changes can be considered justified.
46-52: The residual-based update mechanism and additional graph operations may improve model performance.The introduction of a residual-based update mechanism through the
update_style,update_residual, andupdate_residual_initparameters may improve the model's performance by facilitating better gradient flow and allowing for deeper architectures. The normalized initialization and the use of square root of the number of neighbors (use_sqrt_nnei = true) may help stabilize the training process.Additionally, the inclusion of convolutional and MLP operations in the g1 output (
g1_out_conv = true,g1_out_mlp = true) may enhance the model's expressiveness and ability to capture local and global features.These changes seem promising for improving the model's performance and stability.
71-71: Monitor the training process closely and adjust the learning rate if necessary.The increase in the initial learning rate (
start_lr = 0.001) may accelerate the training process by allowing larger updates to the model's parameters. This change has the potential to speed up convergence.However, please keep a close eye on the training process, as an excessively high learning rate can cause the training to diverge or oscillate, leading to unstable or suboptimal results.
To ensure stable training, monitor the loss curves and validation metrics during the training process. If you observe signs of divergence or instability, such as rapidly increasing loss values or erratic validation performance, consider reducing the learning rate or employing learning rate scheduling techniques to adapt the learning rate during training.
examples/water/dpa2/input_torch_medium.json (3)
85-92: Verify the dataset pathsEnsure that the paths specified in
training_data("../data/data_0","../data/data_1","../data/data_2") andvalidation_data("../data/data_3") are correct and that the datasets are accessible. Incorrect paths could lead to runtime errors during training.Also applies to: 96-102
70-72: Confirm learning rate decay parametersThe learning rate decays exponentially from
start_lrof0.001tostop_lrof3.51e-08overdecay_stepsof5000. Verify that this decay schedule aligns with your training objectives and that the learning rate does not become too small too quickly, which could hinder the training process.
77-80: Check loss function weighting preferencesThe
start_pref_eincreases from0.02to alimit_pref_eof1, whilestart_pref_fdecreases from1000tolimit_pref_fof1. Ensure that these weightings for energy and force components in the loss function are set intentionally to balance their contributions during training.examples/water/dpa2/input_torch_small.json (1)
104-104: Verify ifwarmup_stepsshould be greater than zeroSetting
warmup_stepsto0means there will be no gradual increase in the learning rate at the start of training. Verify if this is intentional or if a warmup period is desired to improve training stability.Run the following script to check
warmup_stepssettings in other configuration files:Verification successful
To gather more information about the intentional use of
warmup_steps: 0, I'll search for any comments or documentation related to this setting.
Setting
warmup_stepsto 0 is a valid configurationThe codebase supports warmup steps, but setting
warmup_stepsto 0 is an intentional and valid option. The training implementation indeepmd/pt/train/training.pyis designed to handle both cases where warmup steps are used and where they are not used.Key points:
- The default value for
warmup_stepsis 0 in the training parameters.- The code includes checks to ensure training is valid with or without warmup steps.
- This configuration is consistent across multiple JSON files in the project.
While using warmup steps can potentially improve training stability in some cases, it's not mandatory. If you believe your specific use case would benefit from a warmup period, you can adjust the
warmup_stepsvalue. However, the current setting of 0 is a standard configuration in this project and doesn't necessarily indicate an issue.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Find other JSON configuration files and check their `warmup_steps` settings. # Search for `warmup_steps` settings in JSON files. rg --type json '"warmup_steps"'Length of output: 421
Script:
#!/bin/bash # Search for comments or documentation related to warmup_steps rg -i "warmup.?steps" --type md --type py --type js --type ts # Search for training or model configuration documentation rg -i "training.?config|model.?config" --type mdLength of output: 1118
Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## devel #4138 +/- ##
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+ Coverage 83.02% 83.12% +0.09%
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Files 532 533 +1
Lines 52198 52244 +46
Branches 3031 3030 -1
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+ Hits 43339 43429 +90
+ Misses 7913 7866 -47
- Partials 946 949 +3 ☔ View full report in Codecov by Sentry. |
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small: 3 layers; w three-body; wo g2 attn;
medium: 6 layers; w three-body; w g2 attn;
large: 12 layers; w three-body; w g2 attn;
Summary by CodeRabbit
New Features
Bug Fixes
Documentation