Feat(pt): Support fitting_net statistics. #4504
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📝 WalkthroughWalkthroughThe pull request introduces a new method Changes
Sequence DiagramsequenceDiagram
participant AM as Atomic Model
participant FN as Fitting Network
participant Sampler as Data Sampler
AM->>Sampler: Prepare sampled data
AM->>FN: compute_input_stats(sampled_data)
FN-->>FN: Calculate mean and std dev
FN-->>AM: Statistics computed
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Actionable comments posted: 1
🧹 Nitpick comments (2)
deepmd/pt/model/task/fitting.py (2)
437-440: Use a ternary operator for compactness.
Ruff suggests replacing theif callable(...)block with a ternary operator. This is a minor readability enhancement.- if callable(merged): - sampled = merged() - else: - sampled = merged + sampled = merged() if callable(merged) else merged🧰 Tools
🪛 Ruff (0.8.2)
437-440: Use ternary operator
sampled = merged() if callable(merged) else mergedinstead ofif-else-blockReplace
if-else-block withsampled = merged() if callable(merged) else merged(SIM108)
457-457: Implementaparamstatistics.
The TODO suggests you plan to handleaparamsimilarly tofparam. Let me know if you’d like help implementing that.
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📒 Files selected for processing (2)
deepmd/pt/model/atomic_model/dp_atomic_model.py(1 hunks)deepmd/pt/model/task/fitting.py(3 hunks)
🧰 Additional context used
🪛 Ruff (0.8.2)
deepmd/pt/model/task/fitting.py
437-440: Use ternary operator sampled = merged() if callable(merged) else merged instead of if-else-block
Replace if-else-block with sampled = merged() if callable(merged) else merged
(SIM108)
🔇 Additional comments (3)
deepmd/pt/model/atomic_model/dp_atomic_model.py (1)
301-301: Consider verifying that fitting_net is defined before usage.
If fitting_net were ever None, invoking compute_input_stats would raise an exception. A quick check ensures safe execution.
deepmd/pt/model/task/fitting.py (2)
7-7: New import statements look good.
Thanks for adding Callable, Union, and DPPath; these additions enable clearer type hints and better modularity.
Also applies to: 43-45
416-436: Comprehensive documentation.
The docstring clearly explains the purpose and usage of compute_input_stats. This addition aligns with the PR objective to compute input statistics for fitting parameters.
| if self.numb_fparam > 0: | ||
| cat_data = torch.cat([frame["fparam"] for frame in sampled], dim=0) | ||
| cat_data = torch.reshape(cat_data, [-1, self.numb_fparam]) | ||
| fparam_avg = torch.mean(cat_data, axis=0) | ||
| fparam_std = torch.std(cat_data, axis=0) | ||
| fparam_inv_std = 1.0 / fparam_std | ||
| self.fparam_avg.copy_( | ||
| torch.tensor(fparam_avg, device=env.DEVICE, dtype=self.fparam_avg.dtype) | ||
| ) | ||
| self.fparam_inv_std.copy_( | ||
| torch.tensor( | ||
| fparam_inv_std, device=env.DEVICE, dtype=self.fparam_inv_std.dtype | ||
| ) | ||
| ) | ||
| # TODO: stat aparam |
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🛠️ Refactor suggestion
Handle potential zero or near-zero standard deviation.
Currently, the code divides by fparam_std, potentially leading to inf or NaN values if std == 0. Consider adding a small epsilon or performing a check to avoid division by zero.
fparam_std = torch.std(cat_data, axis=0)
+epsilon = 1e-12
+fparam_std = torch.where(fparam_std < epsilon, torch.tensor(epsilon, dtype=fparam_std.dtype, device=fparam_std.device), fparam_std)
fparam_inv_std = 1.0 / fparam_std📝 Committable suggestion
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| if self.numb_fparam > 0: | |
| cat_data = torch.cat([frame["fparam"] for frame in sampled], dim=0) | |
| cat_data = torch.reshape(cat_data, [-1, self.numb_fparam]) | |
| fparam_avg = torch.mean(cat_data, axis=0) | |
| fparam_std = torch.std(cat_data, axis=0) | |
| fparam_inv_std = 1.0 / fparam_std | |
| self.fparam_avg.copy_( | |
| torch.tensor(fparam_avg, device=env.DEVICE, dtype=self.fparam_avg.dtype) | |
| ) | |
| self.fparam_inv_std.copy_( | |
| torch.tensor( | |
| fparam_inv_std, device=env.DEVICE, dtype=self.fparam_inv_std.dtype | |
| ) | |
| ) | |
| # TODO: stat aparam | |
| if self.numb_fparam > 0: | |
| cat_data = torch.cat([frame["fparam"] for frame in sampled], dim=0) | |
| cat_data = torch.reshape(cat_data, [-1, self.numb_fparam]) | |
| fparam_avg = torch.mean(cat_data, axis=0) | |
| fparam_std = torch.std(cat_data, axis=0) | |
| epsilon = 1e-12 | |
| fparam_std = torch.where(fparam_std < epsilon, torch.tensor(epsilon, dtype=fparam_std.dtype, device=fparam_std.device), fparam_std) | |
| fparam_inv_std = 1.0 / fparam_std | |
| self.fparam_avg.copy_( | |
| torch.tensor(fparam_avg, device=env.DEVICE, dtype=self.fparam_avg.dtype) | |
| ) | |
| self.fparam_inv_std.copy_( | |
| torch.tensor( | |
| fparam_inv_std, device=env.DEVICE, dtype=self.fparam_inv_std.dtype | |
| ) | |
| ) | |
| # TODO: stat aparam |
Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## devel #4504 +/- ##
=======================================
Coverage 84.59% 84.59%
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Files 675 675
Lines 63574 63589 +15
Branches 3490 3486 -4
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+ Hits 53778 53791 +13
- Misses 8670 8672 +2
Partials 1126 1126 ☔ View full report in Codecov by Sentry. |
Support fitting_net statistics to calculate the mean value and standard deviation of
fparam/aparam. So thatfparam/aparamcan be normalized automatically before concatenating to descriptor.Summary by CodeRabbit
New Features
Bug Fixes