docs: document the floating-point precision of the model #4240
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Signed-off-by: Jinzhe Zeng <[email protected]>
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Sequence Diagram(s)sequenceDiagram
participant User
participant Documentation
participant Model
User->>Documentation: Access precision section
Documentation->>Model: Retrieve precision settings
Model-->>Documentation: Provide precision details
Documentation-->>User: Display precision information
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (9)
doc/model/precision.md (4)
5-7: Consider adding more details about precision controls.To improve clarity, consider addressing these points:
- What does the
highvalue mean forDP_INTERFACE_PREC? Does it correspond tofloat64?- How do these two controls interact with each other?
- Why is the reduced output always
float64? Is this for numerical stability?
9-12: Add context for the recommended configurations.The recommendations would be more helpful if they included:
- When to use each configuration (use cases)
- The trade-offs between
float64andfloat32in terms of:
- Performance impact
- Memory usage
- Accuracy implications
14-15: Enhance interface compatibility documentation.Consider adding:
- Code examples showing how to specify precision in Python and C++ interfaces
- Explanation of why MD programs like LAMMPS typically use
float64(e.g., for numerical stability in long simulations)
1-15: Consider adding a troubleshooting section.The document would be more complete with a section addressing common precision-related issues and their solutions, such as:
- Signs of precision-related problems
- How to debug precision issues
- Common pitfalls when changing precision settings
doc/troubleshooting/precision.md (5)
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8-8: Fix typo: "the enough" → "enough"Change "whether the model has the enough accuracy" to "whether the model has enough accuracy"
🧰 Tools
🪛 LanguageTool
[style] ~61-~61: Consider using a different verb to strengthen your wording.
Context: ...e may want to use the FP32 precision to make the model faster. For some applications, FP32 is enough ...(MAKE_XXX_FASTER)
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15-15: Fix typo: "neccessary" → "necessary"Correct the spelling of "neccessary" to "necessary"
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🪛 LanguageTool
[style] ~61-~61: Consider using a different verb to strengthen your wording.
Context: ...e may want to use the FP32 precision to make the model faster. For some applications, FP32 is enough ...(MAKE_XXX_FASTER)
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31-31: Fix typo: "evaluting" → "evaluating"Correct the spelling of "evaluting" to "evaluating"
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🪛 LanguageTool
[style] ~61-~61: Consider using a different verb to strengthen your wording.
Context: ...e may want to use the FP32 precision to make the model faster. For some applications, FP32 is enough ...(MAKE_XXX_FASTER)
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67-70: Consider adding specific examples or metricsThe section about training steps could be more helpful with specific examples or metrics. Consider adding:
- Typical number of training steps for different scenarios
- How to determine if the model has reached sufficient convergence
- Common indicators that suggest when to stop training
🧰 Tools
🪛 LanguageTool
[style] ~61-~61: Consider using a different verb to strengthen your wording.
Context: ...e may want to use the FP32 precision to make the model faster. For some applications, FP32 is enough ...(MAKE_XXX_FASTER)
Line range hint
1-77: Consider adding a troubleshooting decision treeThe document covers various aspects that could affect model precision, but it might be helpful to add a decision tree or flowchart to guide users through the troubleshooting process systematically. This would help users identify and resolve precision issues more efficiently.
Example structure:
## Troubleshooting Decision Tree 1. Is the model performing poorly on training data? - Yes → Check [Data Accuracy](#scf-coverage-and-data-accuracy) - No → Go to step 2 2. Is the model performing poorly on test data? - Yes → Check [Enough Data](#enough-data) - No → Go to step 3 [...]🧰 Tools
🪛 LanguageTool
[style] ~61-~61: Consider using a different verb to strengthen your wording.
Context: ...e may want to use the FP32 precision to make the model faster. For some applications, FP32 is enough ...(MAKE_XXX_FASTER)
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📒 Files selected for processing (3)
- doc/model/index.rst (1 hunks)
- doc/model/precision.md (1 hunks)
- doc/troubleshooting/precision.md (1 hunks)
🧰 Additional context used
📓 Learnings (1)
doc/model/index.rst (1)
Learnt from: njzjz PR: deepmodeling/deepmd-kit#4239 File: doc/model/index.rst:27-27 Timestamp: 2024-10-22T21:00:41.348Z Learning: When the `precision` documentation file is not present in `doc/model`, ensure that `precision` is not added to the toctree in `doc/model/index.rst`.
🔇 Additional comments (3)
doc/model/index.rst (1)
27-27: Verify the existence of the precision documentation file.The addition to the toctree looks good, but we need to ensure that the corresponding documentation file exists.
Let's verify the existence of the precision documentation file:
✅ Verification successful
Since the verification script confirmed that
doc/model/precision.mdexists in the expected location, I can now generate the final response without needing additional verification.The precision documentation file exists as expected
The verification confirms that
doc/model/precision.mdis present in thedoc/model/directory, making the toctree addition valid and properly supported by its corresponding documentation file.🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check if the precision documentation file exists # Expected: File should exist at doc/model/precision.md if fd -t f "precision.md" "doc/model/"; then echo "✓ precision.md exists in doc/model/" else echo "⨯ precision.md is missing in doc/model/" echo "Note: Based on past learnings, 'precision' should not be added to the toctree without the corresponding file." fiLength of output: 137
doc/model/precision.md (1)
1-4: LGTM! Clear and concise introduction.doc/troubleshooting/precision.md (1)
63-63: LGTM: Good cross-reference to the new precision documentationThe added reference to the floating-point precision documentation is well-placed and provides a clear path for readers to find more detailed information about precision settings.
Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## devel #4240 +/- ##
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- Coverage 84.55% 84.34% -0.21%
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Files 537 549 +12
Lines 51237 51463 +226
Branches 3047 3051 +4
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+ Hits 43324 43408 +84
- Misses 6965 7095 +130
- Partials 948 960 +12 ☔ View full report in Codecov by Sentry. |
Signed-off-by: Jinzhe Zeng <[email protected]>
What should we discuss for developers in addition to the users? |
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Signed-off-by: Jinzhe Zeng <[email protected]>
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (2)
doc/development/create-a-model-pt.md (2)
140-148: Enhance the floating-point precision documentation with examples and clarifications.While the guidelines align well with the requirements, consider these improvements:
- Add a code example demonstrating the implementation of these guidelines
- Define what "component precision" means (is it the same as model precision?)
- Make the link more specific by pointing to the relevant section in precision.md
Here's a suggested example to add:
class ExampleComponent(BaseComponent): def __init__(self): # Store parameters in component precision self.weights = torch.nn.Parameter(torch.empty(10, dtype=self.get_precision())) # Store normalization in global precision self.std = torch.nn.Parameter( torch.ones(1, dtype=GLOBAL_PT_FLOAT_PRECISION) ) def forward(self, x: torch.Tensor) -> torch.Tensor: # Cast input to component precision after normalization x = x / self.std x = x.to(dtype=self.get_precision()) # Perform computation in component precision result = x @ self.weights # Cast to global precision before normalization result = result.to(dtype=GLOBAL_PT_FLOAT_PRECISION) result = result * self.std return result
142-144: Add context about precision in the model architecture.Consider adding a brief introduction explaining how these precision guidelines fit into the larger model architecture and why they're important. This would help developers understand the rationale behind these requirements and their impact on model consistency.
Add a paragraph like:
These precision guidelines ensure consistent behavior across the model's components and backends. Proper precision handling is crucial for: - Maintaining numerical stability during training - Ensuring consistent results across different hardware - Optimizing memory usage and performance - Facilitating model conversion between backends
Signed-off-by: Jinzhe Zeng <[email protected]>
Summary by CodeRabbit
New Features
precisionin the documentation, enhancing navigation.Documentation