docs: improve docs, examples, and error messages #4338
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1. Refactor type embedding docs; 2. Refactor model compression docs; 3. Refactor DPA-1 docs; 4. Add error messages when type embedding is set in other backends; 5. Bump `sel` in the DPA-2 example. Signed-off-by: Jinzhe Zeng <[email protected]>
📝 Walkthrough📝 WalkthroughWalkthroughThe changes in this pull request introduce new error handling mechanisms across several model-related functions in the DeepMD framework. Specifically, checks for the presence of the Changes
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Actionable comments posted: 13
🧹 Outside diff range and nitpick comments (18)
doc/model/dpa2.md (1)
27-27: Consider enhancing the type embedding documentationWhile the reference to
tebd_dimis helpful, consider providing a brief explanation of what type embedding does and its benefits in this context.Type embedding is within this descriptor with the {ref}`tebd_dim <model[standard]/descriptor[dpa2]/tebd_dim>` argument. + +Type embedding allows the model to learn representations that are specific to different atom types, which can improve the model's ability to capture type-dependent interactions. The dimension of the embedding is controlled by the `tebd_dim` parameter.examples/water/dpa2/input_torch_medium.json (2)
22-22: Document the rationale for increasing three_body_selSince this is an example configuration file meant to guide users, it would be helpful to add a comment explaining why 47 was chosen as the optimal value for
three_body_seland what impact this has on model performance.Consider adding a comment like this:
"repinit": { + "_comment_three_body_sel": "Increased to 47 to improve the model's capacity for three-body interactions while maintaining computational efficiency", "three_body_sel": 47,
30-30: Document the relationship between nsel and three_body_selThe parallel update of both
nselandthree_body_selto 47 suggests a relationship between these parameters. Consider documenting this relationship to help users understand the configuration better.Consider adding a comment like this:
"repformer": { + "_comment_nsel": "Matches three_body_sel value to maintain consistent selection counts across the model", "nsel": 47,doc/model/train-hybrid.md (1)
54-56: Enhance clarity of type embedding documentationWhile the explanation of backend differences is helpful, consider improving the documentation by:
- Adding code examples to illustrate the differences
- Explaining the implications of these differences for users
- Providing guidance on when to use each approach
Consider expanding this section with a code example like:
Type embedding is different between the TensorFlow backend and other backends. In the TensorFlow backend, all descriptors share the same descriptor that defined in the model level. In other backends, each descriptor has its own type embedding and their parameters may be different. + +For example, in TensorFlow: +```json +{ + "model": { + "type_embedding": { + "neuron": [2, 4, 8], + "resnet_dt": false + }, + "descriptor": { + "type": "hybrid", + "list": [...] + } + } +} +``` + +In other backends: +```json +{ + "model": { + "descriptor": { + "type": "hybrid", + "list": [ + { + "type": "se_e2_a", + "type_embedding": { + "neuron": [2, 4, 8], + "resnet_dt": false + } + } + ] + } + } +} +```doc/model/train-se-e3.md (1)
68-74: Documentation aligns with PR objectives but could be more comprehensiveWhile the additions of type embedding and model compression sections align with the PR objectives, consider:
- Adding examples or code snippets demonstrating the usage
- Including cross-references to related documentation
- Providing troubleshooting guidance or common pitfalls
doc/model/train-se-e3-tebd.md (2)
82-82: Enhance the type embedding section contentWhile technically accurate, this section could be more helpful to users. Consider:
- Explaining what type embedding is and its benefits
- Adding configuration examples
- Explaining the relationship between
tebd_dimand model performance
84-86: Expand the model compression sectionConsider enhancing this section by:
- Explaining why compression isn't supported for this descriptor
- Referencing the main model compression documentation
- Suggesting alternatives if available
doc/model/train-se-e2-r.md (1)
73-76: Fix typo and enhance type embedding documentationThere's a typo in "embdding" (should be "embedding"). Additionally, consider expanding this section to:
- Briefly explain what type embedding is
- Link to relevant TensorFlow backend documentation
- Clarify what happens when users attempt to use it with other backends
-Type embdding is only supported in the TensorFlow backends. +Type embedding is only supported in the TensorFlow backends. Type embedding allows the model to learn representations of atom types directly from data. When used with other backends, an error will be raised during model initialization.deepmd/jax/model/model.py (1)
38-41: LGTM! Consider enhancing the error message.The error handling implementation is well-placed and aligns with the PR's documentation improvement objectives. The validation ensures users configure type embedding correctly in the JAX backend.
Consider making the error message more specific about where to place the type_embedding:
- "In the JAX backend, type_embedding is not at the model level, but within the descriptor. See type embedding documentation for details." + "In the JAX backend, type_embedding should be configured within the descriptor's configuration dictionary, not at the model level. See type embedding documentation for details."doc/model/train-se-a-mask.md (1)
90-90: Consider adding backend limitation informationSince one of the PR objectives is to add error messages for type embedding in other backends, consider explicitly mentioning these limitations in the documentation. This would help users understand upfront which backends support type embedding.
-Same as [`se_e2_a`](./train-se-e2-a.md). +Same as [`se_e2_a`](./train-se-e2-a.md). Note that type embedding is only supported in TensorFlow backend. Using it with other backends will raise an error.doc/model/train-se-e2-a-tebd.md (3)
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68-109: Enhance code block readability with syntax highlighting.Add language identifiers to JSON code blocks for better syntax highlighting.
-```json +```json5 "model": { "type_map": ["O", "H"], "type_embedding":{ ... }, "descriptor" :{ ... }, "fitting_net" : { ... } }
110-110: Fix grammar in documentation.Add missing articles for better readability:
-See documentation for each descriptor for details. +See the documentation for each descriptor for details.Also applies to: 116-116
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[uncategorized] ~110-~110: You might be missing the article “the” here.
Context: ...r explanation oftype embedding. See documentation for each descriptor for details. ## In...(AI_EN_LECTOR_MISSING_DETERMINER_THE)
112-116: Enhance documentation for non-TensorFlow backends.The section for other backends (PyTorch, JAX, DP) could benefit from:
- Specific configuration examples for each backend
- Implementation differences and considerations
- Links to backend-specific descriptor documentation
Would you like assistance in expanding this section with examples for each backend?
🧰 Tools
🪛 LanguageTool
[uncategorized] ~116-~116: You might be missing the article “the” here.
Context: ...g is within the descriptor itself. See documentation for each descriptor for details.(AI_EN_LECTOR_MISSING_DETERMINER_THE)
deepmd/pt/model/model/__init__.py (1)
76-79: LGTM! Consider enhancing the error message with more details.The addition of this validation check aligns well with the PR's objective of improving error messages for type embedding configuration. The check correctly prevents misuse of type embedding at the model level in the PyTorch backend.
Consider enhancing the error message to be more helpful by:
- Including a link to the specific documentation section
- Providing a code example of the correct way to configure type embedding in the descriptor
- "In the PyTorch backend, type_embedding is not at the model level, but within the descriptor. See type embedding documentation for details." + "In the PyTorch backend, type_embedding must be configured within the descriptor, not at the model level. " + "Please refer to https://docs.deepmodeling.com/projects/deepmd/en/latest/model/type-embedding.html for details. " + "Example:\n" + '{\n' + ' "descriptor": {\n' + ' "type": "se_e2_a",\n' + ' "type_embedding": true\n' + ' }\n' + '}'doc/model/train-se-atten.md (3)
71-71: Improve clarity of the introductionConsider restructuring this sentence to be more specific about what aspects will be covered. For example:
"Next, we will explain the configuration settings in input.json and demonstrate the data format requirements, with special emphasis on handling large systems containing dozens of elements."
141-150: Enhance type embedding documentationConsider adding:
- A brief explanation of why type embeddings are mandatory for DPA-1
- The implications or trade-offs of using the default parameters versus custom values
125-125: Fix grammatical errorThe sentence is grammatically incorrect. Consider revising to:
"You can use the descriptor"se_atten_v2", but you are not allowed to set..."🧰 Tools
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[uncategorized] ~125-~125: You might be missing the article “the” here.
Context: ..."set_davg_zero": false ``` You can use descriptor"se_atten_v2"and is not allowed to s...(AI_EN_LECTOR_MISSING_DETERMINER_THE)
deepmd/tf/model/model.py (1)
844-847: LGTM! Consider enhancing the error message.The validation check correctly prevents unsupported type embedding configurations. The error message is clear but could be more helpful.
Consider enhancing the error message to guide users on the correct usage:
- "type embedding for descriptors without mixed types is not supported in other backends" + "type embedding for descriptors without mixed types is not supported in other backends. Please ensure your descriptor is configured to use mixed types (explicit_ntypes=True)"
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📒 Files selected for processing (19)
deepmd/dpmodel/model/model.py(1 hunks)deepmd/jax/model/model.py(1 hunks)deepmd/pt/model/model/__init__.py(1 hunks)deepmd/tf/model/model.py(1 hunks)deepmd/utils/argcheck.py(1 hunks)doc/data/system.md(1 hunks)doc/freeze/compress.md(1 hunks)doc/model/dpa2.md(1 hunks)doc/model/train-hybrid.md(1 hunks)doc/model/train-se-a-mask.md(1 hunks)doc/model/train-se-atten.md(4 hunks)doc/model/train-se-e2-a-tebd.md(3 hunks)doc/model/train-se-e2-a.md(1 hunks)doc/model/train-se-e2-r.md(1 hunks)doc/model/train-se-e3-tebd.md(1 hunks)doc/model/train-se-e3.md(1 hunks)examples/water/dpa2/input_torch_large.json(1 hunks)examples/water/dpa2/input_torch_medium.json(1 hunks)examples/water/dpa2/input_torch_small.json(1 hunks)
✅ Files skipped from review due to trivial changes (2)
- deepmd/utils/argcheck.py
- doc/data/system.md
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doc/model/train-se-atten.md
[uncategorized] ~125-~125: You might be missing the article “the” here.
Context: ..."set_davg_zero": false ``` You can use descriptor "se_atten_v2" and is not allowed to s...
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doc/model/train-se-e2-a-tebd.md
[uncategorized] ~110-~110: You might be missing the article “the” here.
Context: ...r explanation of type embedding. See documentation for each descriptor for details. ## In...
(AI_EN_LECTOR_MISSING_DETERMINER_THE)
[uncategorized] ~116-~116: You might be missing the article “the” here.
Context: ...g is within the descriptor itself. See documentation for each descriptor for details.
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🔇 Additional comments (11)
examples/water/dpa2/input_torch_large.json (3)
22-22: Verify documentation updates for selection parameters
Since this PR focuses on documentation improvements, we should ensure these parameter changes are properly documented.
#!/bin/bash
# Description: Check if the parameter changes are documented
# Expected: Documentation should mention the new values and rationale
# Look for relevant documentation files
fd -g "*dpa*.md" docs | xargs rg -l "selection|sel.*47"
# Check for any examples or tutorials
fd -g "*.ipynb" examples | xargs rg -l "selection|sel.*47"Also applies to: 30-30
30-30: Parameter increase affects neighbor selection in transformer
The increase in nsel from 40 to 47 matches the three_body_sel change, maintaining consistency in the neighbor selection across different components of the model.
#!/bin/bash
# Description: Check if the nsel parameter is consistently updated across example files
# Expected: All DPA-2 example files should use the same value
# Search for nsel in repformer section of other example files
rg -A 1 '"nsel".*47' examples/water/dpa2/
# Check if this change is documented
fd -g "*.md" docs | xargs rg -l "nsel.*47"22-22: Parameter increase affects three-body interaction sampling
The increase in three_body_sel from 40 to 47 will allow the model to consider more neighboring atoms when computing three-body interactions. This could improve the model's ability to capture complex molecular arrangements in water systems.
✅ Verification successful
Parameter change is consistently applied across DPA2 examples
The three_body_sel parameter value of 47 is consistently set across all DPA2 example configurations in the repository:
- examples/water/dpa2/input_torch_small.json
- examples/water/dpa2/input_torch_medium.json
- examples/water/dpa2/input_torch_large.json
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check if the three_body_sel parameter is consistently updated across example files
# Expected: All DPA-2 example files should use the same value
# Search for three_body_sel in other example files
rg -A 1 '"three_body_sel"' examples/water/dpa2/
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#!/bin/bash
# Let's check for three_body_sel parameter specifically
rg -A 1 '"three_body_sel"' examples/water/dpa2/
# Also check if there are any other DPA2 example configurations in the repository
fd -e json . | xargs rg -l 'three_body_sel'
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examples/water/dpa2/input_torch_small.json (2)
22-22: LGTM! Coordinated update of selection parameters.
The synchronized update of both three_body_sel and nsel to 47 maintains consistency between three-body interactions and representation transformer selection counts.
Also applies to: 30-30
22-22: Verify parameter consistency across configurations.
Let's ensure the same updates are applied to related configuration files.
Also applies to: 30-30
✅ Verification successful
The verification results show that the three_body_sel parameter is consistently set to 47 across all DPA-2 configuration files (small, medium, and large). Similarly, the nsel parameters show consistent values (120 and 47) across the configurations. Therefore, I can generate the final response:
Parameters are consistent across DPA-2 configurations
The three_body_sel value of 47 and nsel values of 120 and 47 are uniformly set across all DPA-2 configuration files (small, medium, and large), indicating proper parameter consistency.
🏁 Scripts executed
The following scripts were executed for the analysis:
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#!/bin/bash
# Description: Check if the selection parameters are consistently updated across all DPA-2 configurations
# Search for three_body_sel and nsel in other DPA-2 configuration files
echo "Checking three_body_sel parameter:"
rg '"three_body_sel"' examples/water/dpa2/input_torch_{large,medium}.json
echo -e "\nChecking nsel parameter:"
rg '"nsel"' examples/water/dpa2/input_torch_{large,medium}.json
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doc/model/train-se-e3-tebd.md (1)
80-86: Verify consistency with other documentation files
Let's ensure the type embedding and model compression sections are consistent with other documentation files mentioned in the PR.
✅ Verification successful
Type embedding and model compression sections are consistent
Based on the search results, the documentation in train-se-e3-tebd.md is consistent with other similar documentation files:
- The type embedding section correctly references the
tebd_dimparameter, which is consistent with other models that support type embedding (e.g.,se_atten,dpa2). - The model compression section correctly states that it's not supported, which is distinct from other models and properly documented.
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The following scripts were executed for the analysis:
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#!/bin/bash
# Check consistency across documentation files
echo "Checking related documentation files for consistency..."
# Check type embedding sections
echo "Type embedding sections in related files:"
rg -A 5 "## Type embedding" "doc/model/*.md"
# Check model compression sections
echo "Model compression sections in related files:"
rg -A 5 "## Model compression" "doc/model/*.md"
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echo "References to tebd_dim in documentation:"
rg "tebd_dim" "doc/**/*.md"
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# Let's try with fd to find the correct documentation paths first
echo "Finding documentation files:"
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echo -e "\nChecking type embedding sections:"
rg -A 5 "## Type embedding" $(fd -e md . doc/)
echo -e "\nChecking model compression sections:"
rg -A 5 "## Model compression" $(fd -e md . doc/)
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doc/model/train-se-e2-r.md (1)
73-79: Verify consistency with error handling implementation
Let's ensure the documentation accurately reflects the error handling implemented in other files.
✅ Verification successful
Based on the search results, I can now provide a definitive response about the documentation's accuracy regarding type embedding support:
Documentation accurately reflects backend-specific type embedding implementation
The documentation correctly states that "Type embedding is only supported in the TensorFlow backends." This is verified by:
- The codebase shows type embedding is implemented differently across backends:
- TensorFlow: Type embedding is at model level with dedicated
type_embeddingsection - PyTorch/JAX: Type embedding is integrated within descriptors themselves
- Model compression is not supported with type embedding in TensorFlow unless using specific descriptors like
se_a_tebd_v2
- TensorFlow: Type embedding is at model level with dedicated
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The following scripts were executed for the analysis:
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#!/bin/bash
# Description: Check error handling implementation in backend files
# Search for type embedding error handling in backend files
echo "Checking type embedding error handling..."
rg -A 3 "type_embedding.*error|error.*type_embedding" --type python
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rg -A 3 "compress.*type_embedding|type_embedding.*compress" --type python
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rg -A 3 "type_embedding.*error|error.*type_embedding"
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rg -A 3 "compress.*type_embedding|type_embedding.*compress"
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fd -e py backend
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# Let's examine the backend implementations for type embedding and compression logic
echo "Checking backend.py files for type embedding..."
cat backend/dp_backend.py deepmd/backend/backend.py | grep -A 5 -i "type.*embed"
echo -e "\nChecking error handling in backend files..."
cat backend/dp_backend.py deepmd/backend/backend.py | grep -A 5 -i "raise.*error"
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cat backend/dp_backend.py deepmd/backend/backend.py | grep -A 5 -i "compress"
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rg -l "type.*embed" --type-list
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rg -A 5 "type.*embed|backend.*type"
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rg -A 5 "class.*Feature|TENSORFLOW|PYTORCH" backend/
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deepmd/dpmodel/model/model.py (1)
39-42: LGTM! Clear and helpful error message added.
The validation check and error message effectively guide users to the correct location for type embedding configuration.
Let's verify consistent error handling across different backends:
✅ Verification successful
Consistent error handling verified across all backends ✓
The validation check and error message for type_embedding is consistently implemented across all three backends:
- DP backend:
deepmd/dpmodel/model/model.py - JAX backend:
deepmd/jax/model/model.py - PyTorch backend:
deepmd/pt/model/model/__init__.py
Each implementation includes similar validation logic and clear error messages directing users to configure type embedding within the descriptor.
🏁 Scripts executed
The following scripts were executed for the analysis:
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#!/bin/bash
# Description: Check if similar type_embedding validation exists in other backend implementations
# Test: Search for similar validation in JAX and PyTorch backends
echo "Checking JAX backend..."
rg -A 3 '"type_embedding" in' deepmd/jax/model/
echo "Checking PyTorch backend..."
rg -A 3 '"type_embedding" in' deepmd/pt/model/
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rg -A 3 'type_embedding.*ValueError' deepmd/
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doc/model/train-se-a-mask.md (1)
88-94: Verify cross-references to se_e2_a documentation
Both new sections reference the se_e2_a documentation. Let's verify that these sections exist in the referenced file and contain the necessary information.
doc/model/train-se-e2-a-tebd.md (1)
1-4: LGTM! Clear backend support documentation.
The addition of backend icons and explicit backend support information improves clarity for users.
doc/model/train-se-e2-a.md (1)
97-102: 🛠️ Refactor suggestion
Enhance the Type embedding support section
- Fix the typo: "embdding" should be "embedding"
- Consider enhancing this section with:
- A brief explanation of what type embedding is and its benefits
- A link to more detailed documentation about type embedding
- A code example showing the configuration
- The mathematical equivalence claim between
se_e2_awith type embedding andse_attenwithout attention layer should be verified
Apply this diff to fix the typo and improve the section:
## Type embedding support
-Type embdding is only supported in the TensorFlow backends.
+Type embedding is only supported in the TensorFlow backends. Type embedding allows the model to learn type-specific representations of atomic environments.
+
+For configuration details, see the [Type Embedding Documentation](link-to-type-embedding-docs).
+
+Example configuration with type embedding:
+```json
+{
+ "descriptor": {
+ "type": "se_e2_a",
+ "type_embedding": {
+ "neuron": [2, 4, 8],
+ "resnet_dt": false,
+ "activation_function": "tanh"
+ }
+ ...
+ }
+}
+```
+
`se_e2_a` with type embedding and [`se_atten`](./train-se-atten.md) (or its updated version) without any attention layer are mathematically equivalent, so `se_atten` can be a substitute in other backends.
Codecov ReportAttention: Patch coverage is
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- Coverage 84.35% 84.31% -0.04%
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Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Signed-off-by: Jinzhe Zeng <[email protected]>
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Actionable comments posted: 1
🧹 Outside diff range and nitpick comments (2)
doc/model/dpa2.md (2)
25-27: Consider adding usage examples for type embeddingThe section correctly documents that type embedding is handled within the descriptor using the
tebd_dimargument. However, it would be more helpful to include:
- A minimal example showing the configuration
- Common use cases or scenarios
Consider adding an example like:
## Type embedding Type embedding is within this descriptor with the {ref}`tebd_dim <model[standard]/descriptor[dpa2]/tebd_dim>` argument. + +### Example configuration +```json +{ + "descriptor": { + "type": "dpa2", + "tebd_dim": 8, + ... + } +} +```
24-24: Add a line break before the new sectionsAdd a blank line after the data format section for better readability.
DPA-2 supports both the [standard data format](../data/system.md) and the [mixed type data format](../data/system.md#mixed-type). + ## Type embedding
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doc/model/dpa2.md(1 hunks)doc/model/train-hybrid.md(1 hunks)doc/model/train-se-a-mask.md(1 hunks)doc/model/train-se-e3-tebd.md(1 hunks)doc/model/train-se-e3.md(1 hunks)
🚧 Files skipped from review as they are similar to previous changes (4)
- doc/model/train-hybrid.md
- doc/model/train-se-a-mask.md
- doc/model/train-se-e3-tebd.md
- doc/model/train-se-e3.md
Signed-off-by: Jinzhe Zeng <[email protected]>
Signed-off-by: Jinzhe Zeng <[email protected]>
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (5)
examples/water/dpa2/README.md (1)
The
selparameter update is missing in the JSON input files. Please update the documentation to reflect the changes made in the PR objectives.🔗 Analysis chain
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1-17: Verify theselparameter update mentioned in PR objectives.The PR objectives mention updating the DPA-2 example by bumping the
selparameter, but this change is not reflected in the documentation.Let's verify the parameter change in the JSON files:
🏁 Scripts executed
The following scripts were executed for the analysis:
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#!/bin/bash # Description: Check for the presence and value of the 'sel' parameter in the input files # Look for 'sel' parameter in all JSON files in the current directory echo "Checking for 'sel' parameter in JSON files:" for file in input_torch_*.json; do echo "=== $file ===" jq -r '.. | .sel? // empty' "$file" 2>/dev/null doneLength of output: 249
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#!/bin/bash # Description: Check for the presence and value of the 'sel' parameter in the input JSON files # Find all input_torch_*.json files and check for 'sel' parameter echo "Checking for 'sel' parameter in JSON files:" fd --extension json '^input_torch_.*\.json$' . | while read -r file; do echo "=== $file ===" jq -r '.. | .sel? // empty' "$file" 2>/dev/null doneLength of output: 1010
examples/water/dpa2/input_torch_compressible.json (4)
28-54: Add documentation for repformer configuration parametersThe repformer section contains numerous parameters that control the attention mechanism and update behavior. Consider adding inline comments or documentation to explain:
- The relationship between different attention dimensions (
g1_dim,g2_dim,attn2_hidden)- The impact of various update flags (e.g.,
update_g1_has_conv,update_g1_has_grrg)- The choice of
update_residualvalueThis would make the example more educational and easier to customize.
69-74: Consider adjusting the learning rate decay parametersThe learning rate decays from 0.001 to 3.51e-08, which is an extremely small value. This might:
- Lead to unnecessarily long training times
- Result in the model getting stuck in local minima
Consider using a larger
stop_lrvalue (e.g., 1e-6) unless there's a specific reason for such a small value.
77-83: Document the loss preference transition strategyThe configuration shows a careful balance between energy and force contributions during training:
- Force preference transitions from 1000 to 1
- Energy preference transitions from 0.02 to 1
Consider adding a comment explaining this training strategy and why the virial contribution is disabled (
pref_v= 0).
94-94: Consider optimizing training efficiencyThe current configuration might lead to inefficient training:
- Batch size of 1 is very small and might not utilize hardware efficiently
- 1M training steps with such a small batch size could result in very long training times
Consider:
- Increasing the batch size for better hardware utilization
- Adjusting the number of steps accordingly
- Adding a comment explaining why these specific values were chosen for this example
Also applies to: 104-104
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
📒 Files selected for processing (7)
doc/model/dpa2.md(1 hunks)examples/water/dpa2/README.md(1 hunks)examples/water/dpa2/input_torch_compressible.json(1 hunks)examples/water/dpa2/input_torch_large.json(1 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 (4)
- doc/model/dpa2.md
- examples/water/dpa2/input_torch_large.json
- examples/water/dpa2/input_torch_medium.json
- examples/water/dpa2/input_torch_small.json
🔇 Additional comments (3)
examples/water/dpa2/README.md (1)
17-17: LGTM! Clear documentation for the new input file.
The addition clearly explains the purpose of input_torch_compressible.json and its relationship to the existing input_torch_small.json.
examples/water/dpa2/input_torch_compressible.json (1)
14-14: Verify the increased nsel parameter value
The nsel parameter has been set to 120, which is notably higher than typical values. While this may improve model accuracy, it could significantly impact memory usage and training time.
Consider documenting the performance implications of this increased value in the example's README or adding a comment explaining the rationale for this specific value.
source/tests/common/test_examples.py (1)
60-60: LGTM! Addition of compressible model test configuration.
The new test configuration path follows the established pattern and integrates well with existing DPA-2 variants.
Let's verify the existence of the referenced configuration file:
✅ Verification successful
Configuration file exists and is correctly set up.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Verify that the new configuration file exists
# Expected: File should exist in the examples directory
# Check if the file exists
if [ -f "examples/water/dpa2/input_torch_compressible.json" ]; then
echo "Configuration file exists"
# Show file contents for verification
cat "examples/water/dpa2/input_torch_compressible.json"
else
echo "Error: Configuration file not found"
exit 1
fi
Length of output: 2701
selin the DPA-2 example.Summary by CodeRabbit
Release Notes
New Features
type_embedding.input_torch_compressible.jsonadded for the DPA-2 model, providing a compressible configuration.Bug Fixes
Documentation
Configuration Updates