(fix) Make bias statistics complete for all elements #4496
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📝 WalkthroughWalkthroughThe pull request introduces modifications in the DeepMD-kit's PyTorch utility modules. A new public method Changes
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Actionable comments posted: 1
🧹 Nitpick comments (1)
deepmd/pt/utils/stat.py (1)
110-119: Avoid potential index-out-of-range errors when concatenating Tensors.
The code checks “isinstance(sys_stat[key], list) and isinstance(sys_stat[key][0], torch.Tensor)” without verifying that the list is non-empty. This might raise an IndexError if the list is empty. Consider adding a length check before accessing sys_stat[key][0].Possible safe check:
-if isinstance(sys_stat[key], list) and isinstance(sys_stat[key][0], torch.Tensor): +if isinstance(sys_stat[key], list) and len(sys_stat[key]) > 0 \ + and isinstance(sys_stat[key][0], torch.Tensor):
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deepmd/pt/utils/dataset.py
44-44: SyntaxError: Expected an indented block after function definition
🔇 Additional comments (3)
deepmd/pt/utils/stat.py (3)
91-96: Validate 'atype' presence and handle empty arrays gracefully.
While updating the collected elements, consider explicitly checking that each 'atype' array is non-empty. If a dataset yields an empty array, it might bypass your missing-elements logic.
✅ Verification successful
Let me gather more information about how atype is handled in the codebase.
Let me try again with the correct file type flag for ripgrep.
Based on the code analysis, I can now generate the final response:
Empty arrays are handled safely by numpy.unique and set.update
The code uses numpy.unique() which safely handles empty arrays by returning an empty array, and set.update() which safely handles empty iterables. The logic for missing elements works correctly even with empty arrays since an empty array's unique values would simply not add any elements to the set.
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collected_elements.update($$$)
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85-90: Consider using logging and verifying that all datasets are included.
You are only collecting the set of elements from the first dataset and simply printing them. If your intent is to gather possible elements across all datasets, you may want to iterate over all datasets. Also, using "log.info" would be more consistent than a raw "print" in a library setting.
Possible fix for the print statement:
- print('we want', all_elements)
+ log.info(f"We want elements: {all_elements}")97-109: Ensure partial coverage logic for missing elements is intended.
Here, frames are appended until the number of records reaches 'nbatches'. Verify that the short-circuiting with “break” actually meets your coverage needs for missing elements. If your goal is to gather as many frames as possible across all missing elements, you might need a more explicit limit or a different approach.
✅ Verification successful
Let me gather more context about this code to better understand the coverage logic for missing elements.
Let me search for more context about the initialization and usage of nbatches to better understand the coverage requirements.
Let me search for the initialization of nbatches to understand its purpose in the coverage logic.
Let me search for more context about how nbatches is used and its relationship with the dataset statistics.
Based on the gathered context, I can now provide a final response about the coverage logic for missing elements.
The current partial coverage logic is intentional and correct.
The code's behavior is appropriate because:
nbatchesis a parameter that defines the desired number of batches for collecting statistics, used consistently throughout themake_stat_inputfunction- The break condition
if len(lst[i]['atype']) >= nbatchesensures each system collects up to the specified number of batches, preventing over-sampling - This is consistent with how regular batches are collected earlier in the function where
numb_batches = min(nbatches, len(dataloaders[i]))is used - The goal is to gather a statistically significant sample (controlled by nbatches) rather than exhaustively processing all frames containing missing elements
The current implementation correctly balances between gathering sufficient data for statistics while maintaining consistent batch sizes across all elements.
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Actionable comments posted: 1
🧹 Nitpick comments (2)
deepmd/pt/utils/stat.py (2)
89-89: Remove debug print statement.The print statement
print("we want", all_elements)appears to be debug code that should be removed or replaced with proper logging.- print("we want", all_elements) + log.debug(f"Required elements for statistics: {all_elements}")
97-111: Optimize nested loops and add error handling.The nested loops for handling missing elements could be optimized, and error handling should be added for invalid frame indices.
for missing_element in missing_elements: for i, dataset in enumerate(datasets): if hasattr(dataset, "element_to_frames"): - frame_indices = dataset.element_to_frames.get( - missing_element, [] - ) + try: + frame_indices = dataset.element_to_frames.get(missing_element, []) + if not frame_indices: + continue + + # Pre-check if we need more frames + if len(lst[i]["atype"]) >= nbatches: + break + + # Process frames in batch + for frame_idx in frame_indices: + frame_data = dataset[frame_idx] + if any(key not in lst[i] for key in frame_data): + lst[i].update({key: [] for key in frame_data if key not in lst[i]}) + for key in frame_data: + lst[i][key].append(frame_data[key]) + if len(lst[i]["atype"]) >= nbatches: + break + except Exception as e: + log.warning(f"Error processing frames for element {missing_element}: {e}") + continue - for frame_idx in frame_indices: - if len(lst[i]["atype"]) >= nbatches: - break - frame_data = dataset[frame_idx] - for key in frame_data: - if key not in lst[i]: - lst[i][key] = [] - lst[i][key].append(frame_data[key])
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deepmd/pt/utils/dataset.py(1 hunks)deepmd/pt/utils/stat.py(1 hunks)
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deepmd/pt/utils/dataset.py
44-44: SyntaxError: Expected an indented block after function definition
🔇 Additional comments (2)
deepmd/pt/utils/dataset.py (2)
43-44:
Fix the indentation error in method definition.
The method definition has incorrect indentation which will cause a SyntaxError. It should be aligned with other class methods.
- def _build_element_to_frames(self):
- """Mapping element types to frame indexes"""
+ def _build_element_to_frames(self):
+ """Mapping element types to frame indexes"""Likely invalid or redundant comment.
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🪛 Ruff (0.8.2)
44-44: SyntaxError: Expected an indented block after function definition
43-53: 🛠️ Refactor suggestion
Make frame limit configurable and enhance documentation.
- The hard-coded limit of 10 frames per element should be configurable.
- The docstring should be more descriptive about the method's purpose and return value.
- def _build_element_to_frames(self):
- """Mapping element types to frame indexes"""
+ def _build_element_to_frames(self, max_frames_per_element: int = 10) -> dict[int, list[int]]:
+ """Build a mapping of element types to their corresponding frame indices.
+
+ Args:
+ max_frames_per_element: Maximum number of frames to store per element type.
+
+ Returns:
+ A dictionary mapping element types (int) to lists of frame indices (list[int])
+ where each element type appears.
+ """
element_to_frames = {element: [] for element in range(self._ntypes)}
for frame_idx in range(len(self)):
frame_data = self._data_system.get_item_torch(frame_idx)
elements = frame_data["atype"]
for element in set(elements):
- if len(element_to_frames[element]) < 10:
+ if len(element_to_frames[element]) < max_frames_per_element:
element_to_frames[element].append(frame_idx)
return element_to_framesLikely invalid or redundant comment.
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44-44: SyntaxError: Expected an indented block after function definition
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Actionable comments posted: 4
🧹 Nitpick comments (4)
source/tests/pt/test_make_stat_input.py (4)
14-23: Consider usingcollections.defaultdictforelement_to_frames.
You can simplify the nested checks for element presence in the dictionary by using adefaultdict(list), which would eliminate the need for the explicitif atype not in self.element_to_frames:condition.-from collections import defaultdict class TestDataset: def __init__(self, samples): self.samples = samples - self.element_to_frames = {} + from collections import defaultdict + self.element_to_frames = defaultdict(list) for idx, sample in enumerate(samples): atypes = sample["atype"] for atype in atypes: - if atype not in self.element_to_frames: - self.element_to_frames[atype] = [] self.element_to_frames[atype].append(idx)
25-28: Rename the property to better reflect usage.
Using@propertybut naming itget_all_atypecan be confusing. Consider a more descriptive name likeall_atypes, since Python properties typically avoid "get_" prefixes.
53-59: Remove or use the assignedlstvariable.
The variablelstis assigned but never used, according to static analysis hints. Consider removing it or using it for additional assertions.def test_make_stat_input(self): nbatches = 1 - lst = make_stat_input(self.datasets, self.dataloaders, nbatches=nbatches) + _ = make_stat_input(self.datasets, self.dataloaders, nbatches=nbatches) all_elements = self.system.get_all_atype unique_elements = {1, 2} self.assertEqual(unique_elements, all_elements, "make_stat_input miss elements")🧰 Tools
🪛 Ruff (0.8.2)
55-55: Local variable
lstis assigned to but never usedRemove assignment to unused variable
lst(F841)
61-62: Optional test runner inclusion.
Having theif __name__ == "__main__": unittest.main()block is fine. You could remove it if tests are run by a dedicated test runner.
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source/tests/pt/test_make_stat_input.py
55-55: Local variable lst is assigned to but never used
Remove assignment to unused variable lst
(F841)
🔇 Additional comments (4)
source/tests/pt/test_make_stat_input.py (1)
40-52: Test setup looks good.
The dataset creation for testing is straightforward and clear. No issues found.
deepmd/pt/utils/dataset.py (2)
21-24: Docstring clarity is sufficient.
The docstring effectively describes constructor parameters. No corrections needed.
34-34: Initialization of element frames is a good approach.
Storing the result of _build_element_to_frames() in self.element_to_frames and self.get_all_atype reduces redundancy.
deepmd/pt/utils/stat.py (1)
86-94: No immediate issues with collection of atomic types.
Collecting and updating sets is correct.
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Actionable comments posted: 2
🧹 Nitpick comments (4)
deepmd/utils/data.py (1)
667-674: Consider providing more informative error details when the type map lookup fails.
While raisingIndexErroris appropriate, developers might benefit from including the failing value(s). You could, for instance, collect and display the out-of-range types to provide immediate troubleshooting clues. A custom exception or a more descriptive error message can significantly improve clarity and debuggability.Here's an example of how you might refine the exception:
except IndexError as e: - raise IndexError( - f"some types in 'real_atom_types.npy' of set {set_name} are not contained in {self.get_ntypes()} types!" - ) from e + # Gather all invalid elements + invalid_types = np.unique(real_type[(real_type < 0) | (real_type >= len(self.type_idx_map))]) + raise IndexError( + f"One or more invalid types found in 'real_atom_types.npy' of set {set_name}: {invalid_types}. " + f"Ensure all types are within [0, {self.get_ntypes()-1}]." + ) from edeepmd/utils/argcheck.py (1)
2829-2834: Ensure user awareness of the new argument.The new argument
min_frames_per_element_forstatis useful for controlling statistic completeness. It might be helpful to specify the expected range (e.g., must be ≥ 1) and how large values impact memory or performance overhead.source/tests/pt/test_make_stat_input.py (1)
68-68: Remove or utilize the unused variable.The variable
lstis assigned with the result ofmake_stat_input(...)but never used. If no further checks are applied, remove it to keep the code clean.- lst = make_stat_input( + make_stat_input(🧰 Tools
🪛 Ruff (0.8.2)
68-68: Local variable
lstis assigned to but never usedRemove assignment to unused variable
lst(F841)
deepmd/pt/utils/stat.py (1)
188-197: Double-check sets for collected vs. missing elements.This code block re-checks missing elements with:
missing_element = all_element - collect_elementsConfirm that the logic aligns with the earlier
missing_elementssets in lines 110–111 to avoid confusion or duplication.🧰 Tools
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188-188: SyntaxError: unindent does not match any outer indentation level
189-189: SyntaxError: Unexpected indentation
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deepmd/pt/utils/stat.py
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source/tests/pt/test_make_stat_input.py
43-43: Loop control variable idx not used within loop body
Rename unused idx to _idx
(B007)
68-68: Local variable lst is assigned to but never used
Remove assignment to unused variable lst
(F841)
🔇 Additional comments (4)
deepmd/pt/train/training.py (2)
145-147: Add type check or validation for the statistic threshold.While setting
self.min_frames_per_element_forstat, consider ensuring it's a strictly positive integer. If a negative or zero value is passed, it may cause runtime issues or meaningless statistics.
232-232: Parameter usage looks correct.Passing
self.min_frames_per_element_forstattomake_stat_inputaligns with the newly introduced functionality. Just be sure to verify that all call sites expect this parameter and properly handle out-of-range values.source/tests/pt/test_make_stat_input.py (1)
41-50: Optimize loop variable usage.The static analysis hint suggests renaming
_idxif it were truly unused, but hereidxis used to assign frames. The accusations of an unused loop variable are false. You can safely ignore that static analysis hint.🧰 Tools
🪛 Ruff (0.8.2)
43-43: Loop control variable
idxnot used within loop bodyRename unused
idxto_idx(B007)
deepmd/pt/utils/stat.py (1)
52-110: Confirm correctness of cumulative element count logic.You aggregate
total_element_counts[elem]["count"], but also trackindices. Ensure you don’t exceed list boundaries when collecting indices for up tomin_frames_per_element_forstat. If more frames exist, consider whether you need them to fulfill certain statistics.
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Actionable comments posted: 5
🧹 Nitpick comments (2)
deepmd/pt/utils/stat.py (2)
38-58: Improve docstring and add input validation.The docstring should provide more details about the new parameters:
- What are valid values for
min_frames_per_element_forstat?- What are the implications of enabling/disabling element completion?
Apply this diff to improve the docstring and add input validation:
def make_stat_input( datasets, dataloaders, nbatches, min_frames_per_element_forstat=10, enable_element_completion=True, ): """Pack data for statistics. Element checking is only enabled with mixed_type. Args: - datasets: A list of datasets to analyze. - dataloaders: Corresponding dataloaders for the datasets. - nbatches: Batch count for collecting stats. - - min_frames_per_element_forstat: Minimum frames required for statistics. - - enable_element_completion: Whether to perform missing element completion (default: True). + - min_frames_per_element_forstat: Minimum number of frames required per element for statistics. + Must be a positive integer. Default is 10. + - enable_element_completion: Whether to perform missing element completion. + If True, ensures each element has at least min_frames_per_element_forstat frames. + If False, skips missing element handling. Default is True. Returns ------- - A list of dicts, each of which contains data from a system. """ + if not datasets: + raise ValueError("No datasets provided") + if len(datasets) != len(dataloaders): + raise ValueError("Number of datasets does not match number of dataloaders") + if min_frames_per_element_forstat < 1: + raise ValueError("min_frames_per_element_forstat must be positive")
61-74: Remove unused variable and improve logging messages.The variable
global_element_countsis initialized but never used. Also, the logging messages could be more informative.Apply this diff:
total_element_types = set() - global_element_counts = {} global_type_name = {} collect_ele = defaultdict(int) if datasets[0].mixed_type: if enable_element_completion: log.info( - f"Element check enabled. " - f"Verifying if frames with elements meet the set of {min_frames_per_element_forstat}." + f"Element completion enabled. " + f"Ensuring each element has at least {min_frames_per_element_forstat} frames." ) else: log.info( - "Element completion is disabled. Skipping missing element handling." + "Element completion disabled. Elements with insufficient frames will be skipped." )
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🔇 Additional comments (1)
deepmd/utils/data.py (1)
699-706: LGTM! Improved error handling for type mapping.The error handling is well-implemented with:
- Clear error message including context (set name and available types)
- Proper exception chaining using
from e- Appropriate exception type (IndexError)
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Actionable comments posted: 6
🧹 Nitpick comments (2)
deepmd/pt/utils/stat.py (2)
38-58: Enhance docstring with more details.The docstring could be improved with:
- Return type annotation
- More detailed parameter descriptions including valid ranges and constraints
- Documentation of exceptions that might be raised
"""Pack data for statistics. Element checking is only enabled with mixed_type. Args: - - datasets: A list of datasets to analyze. - - dataloaders: Corresponding dataloaders for the datasets. - - nbatches: Batch count for collecting stats. - - min_frames_per_element_forstat: Minimum frames required for statistics. - - enable_element_completion: Whether to perform missing element completion (default: True). + datasets: list + A list of datasets to analyze. Must not be empty. + dataloaders: list + Corresponding dataloaders for the datasets. Must match length of datasets. + nbatches: int + Batch count for collecting stats. Must be positive. + min_frames_per_element_forstat: int, optional + Minimum frames required for statistics per element. Must be positive. + Defaults to 10. + enable_element_completion: bool, optional + Whether to perform missing element completion. Only applies when mixed_type=True. + Defaults to True. Returns ------- - - A list of dicts, each of which contains data from a system. + list[dict] + A list of dictionaries, each containing statistical data from a system. + Each dict contains tensor data for various properties. + Raises + ------ + ValueError + If datasets is empty or if datasets and dataloaders lengths don't match. + If min_frames_per_element_forstat is less than 1. + AssertionError + If element check fails during frame processing. """
199-221: Improve error handling for missing elements.Add validation for empty element sets and improve warning messages.
if datasets[0].mixed_type and enable_element_completion: + if not total_element_types: + log.warning("No elements found in any dataset") + return lst for elem, data in global_element_counts.items(): indices_count = data["count"] if indices_count < min_frames_per_element_forstat: log.warning( - f"The number of frames in your datasets with element {element_name} is {indices_count}, " - f"which is less than the set {min_frames_per_element_forstat}" + f"Insufficient frames for element {element_name}: found {indices_count}, " + f"required {min_frames_per_element_forstat}. This may affect model accuracy." ) + if indices_count == 0: + log.error(f"No frames found for element {element_name}")
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source/tests/pt/test_make_stat_input.py (1)
62-72: 🛠️ Refactor suggestionTest with multiple batch sizes.
Using a fixed batch size of 1 may not catch batch-related issues.
Apply this diff to test with different batch sizes:
- cls.dataloaders = [] - for dataset in cls.datasets: - dataloader = DataLoader( - dataset, - batch_size=1, - num_workers=0, - drop_last=False, - collate_fn=collate_fn, - pin_memory=False, - ) - cls.dataloaders.append(dataloader) + # Test with different batch sizes + cls.batch_sizes = [1, 4, 16] + cls.dataloaders = {} + for batch_size in cls.batch_sizes: + loaders = [] + for dataset in cls.datasets: + loader = DataLoader( + dataset, + batch_size=batch_size, + num_workers=0, + drop_last=False, + collate_fn=collate_fn, + pin_memory=False, + ) + loaders.append(loader) + cls.dataloaders[batch_size] = loadersdeepmd/pt/utils/stat.py (2)
38-44: 🛠️ Refactor suggestionAdd input validation for new parameters.
The function should validate the new parameters to ensure they meet requirements.
Add this validation at the start of the function:
def make_stat_input( datasets, dataloaders, nbatches, min_frames_per_element_forstat=10, enable_element_completion=True, ): + if not datasets: + raise ValueError("No datasets provided") + if len(datasets) != len(dataloaders): + raise ValueError("Number of datasets does not match number of dataloaders") + if min_frames_per_element_forstat < 1: + raise ValueError("min_frames_per_element_forstat must be positive")
166-172: 🛠️ Refactor suggestionReplace assertion with proper exception handling.
Based on feedback from @wanghan-iapcm, assertions should be replaced with proper exceptions.
- assert miss in frame_data["atype"], ( - "Element check failed. " - "If you are running in 'change-bias' mode, use '--skip-elementcheck' to disable this check. " - "If you encountered this error during model training, set 'enable_element_completion' to False " - "in the 'training' section of your input file." - ) + if miss not in frame_data["atype"]: + raise ValueError( + f"Element {miss} not found in frame data. This could happen if:\n" + "1. The dataset is incomplete or corrupted\n" + "2. You're running in 'change-bias' mode (use '--skip-elementcheck' to proceed)\n" + "3. You're training a model (set 'enable_element_completion=False' in training config)" + )
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source/tests/pt/test_make_stat_input.py (1)
194-196: Missing documentation for test purpose.The test comment "missing element:13,31,37" is insufficient. Need proper documentation explaining why these elements are missing and what the test verifies.
Add proper documentation:
def test_with_nomissing(self): - # missing element:13,31,37 - # only one frame would be count + """Test make_stat_input with no missing elements. + + This test verifies that: + 1. Elements 13, 31, and 37 are missing in the test data + 2. Only one frame is counted due to [explain why] + 3. The bias values should be identical with and without element completion + """deepmd/pt/utils/stat.py (1)
62-64: Remove unused variable.The variable
global_element_countsis initialized but never used.total_element_types = set() - global_element_counts = {} global_type_name = {} collect_ele = defaultdict(int)
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source/tests/pt/test_make_stat_input.py
30-30: Use key in dict instead of key in dict.keys()
Remove .keys()
(SIM118)
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deepmd/pt/entrypoints/main.py (1)
389-390: 🛠️ Refactor suggestionConsider parameter naming consistency and default behavior.
- Parameter names should be consistent between function signature and CLI:
skip_elem_checkvsskip_elementcheckmin_framesvsminframes_eachelement- Setting
skip_elem_check=Trueby default means element checks are skipped, which might not align with the PR's goal of making bias statistics complete for all elements.Apply this diff to improve consistency and default behavior:
- skip_elem_check: bool = True, - min_frames: int = 10, + skip_elementcheck: bool = False, + minframes_eachelement: int = 10,
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deepmd/pt/utils/stat.py (1)
76-96: 🛠️ Refactor suggestionAdd error handling for unknown data types.
The function silently passes on unknown data types, which could hide potential issues.
Apply this diff:
def process_batches(dataloader, sys_stat): """Process batches from a dataloader to collect statistics.""" iterator = iter(dataloader) numb_batches = min(nbatches, len(dataloader)) for _ in range(numb_batches): try: stat_data = next(iterator) except StopIteration: iterator = iter(dataloader) stat_data = next(iterator) - for dd in stat_data: - if stat_data[dd] is None: + for dd, value in stat_data.items(): + try: + if value is None: sys_stat[dd] = None - elif isinstance(stat_data[dd], torch.Tensor): + elif isinstance(value, torch.Tensor): if dd not in sys_stat: sys_stat[dd] = [] - sys_stat[dd].append(stat_data[dd]) - elif isinstance(stat_data[dd], np.float32): - sys_stat[dd] = stat_data[dd] + sys_stat[dd].append(value) + elif isinstance(value, np.float32): + sys_stat[dd] = value else: - pass + log.warning(f"Unexpected data type {type(value)} for key {dd}") + except Exception as e: + log.error(f"Error processing key {dd}: {str(e)}") + raise
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deepmd/pt/entrypoints/main.py (3)
391-392: Align parameter names with function signature.The parameter names in
change_biasdiffer from the CLI flags andmake_stat_inputfunction:
skip_elem_check→skip_elementcheckmin_frames→minframes_eachelementApply this diff to align the parameter names:
- skip_elem_check: bool = True, - min_frames: int = 10, + skip_elementcheck: bool = True, + minframes_eachelement: int = 10,
479-481: Simplify parameter naming and logic.The parameter names and logic could be simplified:
- Parameter names differ across layers:
- Function:
min_frames→min_frames_per_element_forstat- Function:
skip_elem_check→not enable_element_completion- The negation
not skip_elem_checkadds unnecessary complexity.Apply this diff to improve clarity:
sampled_data = make_stat_input( data_single.systems, data_single.dataloaders, nbatches, - min_frames_per_element_forstat=min_frames, - enable_element_completion=not skip_elem_check, + min_frames_per_element_forstat=minframes_eachelement, + enable_element_completion=not skip_elementcheck, )
564-565: Align parameter names with function signature.The parameter names in
mainmatch the CLI flags but differ from thechange_biasfunction signature:
FLAGS.skip_elementcheck→skip_elem_checkFLAGS.minframes_eachelement→min_framesAfter updating the function signature as suggested earlier, apply this diff:
model_branch=FLAGS.model_branch, output=FLAGS.output, - skip_elem_check=FLAGS.skip_elementcheck, - min_frames=FLAGS.minframes_eachelement, + skip_elementcheck=FLAGS.skip_elementcheck, + minframes_eachelement=FLAGS.minframes_eachelement, )deepmd/pt/utils/stat.py (1)
136-155: Improve error messages for missing elements.The missing element handling could be more informative.
Apply this diff:
def process_missing_elements( min_frames_per_element_forstat, global_element_counts, total_element_types, collect_ele, ): """Handle missing elements and check element completeness.""" + if not total_element_types: + log.warning("No elements found in any dataset") + return collect_elements = collect_ele.keys() missing_elements = total_element_types - collect_elements collect_miss_element = set() for ele, count in collect_ele.items(): if count < min_frames_per_element_forstat: + log.warning( + f"Insufficient frames for element {ele}: found {count}, " + f"required {min_frames_per_element_forstat}. This may affect model accuracy." + ) collect_miss_element.add(ele) missing_elements.add(ele) + if missing_elements: + log.warning(f"Missing elements detected: {missing_elements}")
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deepmd/pt/utils/stat.py (1)
38-58: Implementation aligns with PR objectives.The changes successfully enhance bias statistics completeness by:
- Adding control over element completion
- Introducing minimum frame requirements
- Improving error handling and validation
The implementation looks good with the suggested improvements.
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♻️ Duplicate comments (4)
deepmd/pt/utils/stat.py (4)
61-64: 🛠️ Refactor suggestionRemove unused variable.
The variable
global_element_countsis initialized but never used in the code.total_element_types = set() - global_element_counts = {} global_type_name = {} collect_ele = defaultdict(int)
38-58: 🛠️ Refactor suggestionAdd validation for input parameters.
The function should validate its input parameters to ensure they meet requirements:
datasetsshould not be emptydataloaderslength should matchdatasetslengthmin_frames_per_element_forstatshould be positivedef make_stat_input( datasets, dataloaders, nbatches, min_frames_per_element_forstat=10, enable_element_completion=True, ): + if not datasets: + raise ValueError("No datasets provided") + if len(datasets) != len(dataloaders): + raise ValueError("Number of datasets does not match number of dataloaders") + if min_frames_per_element_forstat < 1: + raise ValueError("min_frames_per_element_forstat must be positive")
76-96: 🛠️ Refactor suggestionImprove error handling in process_batches.
The function silently passes on unknown data types and may raise KeyError.
def process_batches(dataloader, sys_stat): """Process batches from a dataloader to collect statistics.""" iterator = iter(dataloader) numb_batches = min(nbatches, len(dataloader)) for _ in range(numb_batches): try: stat_data = next(iterator) except StopIteration: iterator = iter(dataloader) stat_data = next(iterator) - for dd in stat_data: - if stat_data[dd] is None: + for dd, value in stat_data.items(): + try: + if value is None: sys_stat[dd] = None - elif isinstance(stat_data[dd], torch.Tensor): + elif isinstance(value, torch.Tensor): if dd not in sys_stat: sys_stat[dd] = [] - sys_stat[dd].append(stat_data[dd]) - elif isinstance(stat_data[dd], np.float32): - sys_stat[dd] = stat_data[dd] + sys_stat[dd].append(value) + elif isinstance(value, np.float32): + sys_stat[dd] = value else: - pass + log.warning(f"Unexpected data type {type(value)} for key {dd}") + except Exception as e: + log.error(f"Error processing key {dd}: {str(e)}") + raise
157-173: 🛠️ Refactor suggestionReplace assertion with proper exception handling.
As suggested in the past review comments, replace the assertion with a proper exception that provides clear guidance:
def process_with_new_frame(sys_indices, newele_counter, miss): """Process frames with missing elements.""" + if not sys_indices: + log.warning(f"No system indices provided for element {miss}") + return for sys_info in sys_indices: sys_index = sys_info["sys_index"] frames = sys_info["frames"] + if not frames: + log.warning(f"No frames found for system {sys_index}") + continue sys = datasets[sys_index] for frame in frames: newele_counter += 1 if newele_counter <= min_frames_per_element_forstat: - frame_data = sys.__getitem__(frame) - if miss not in frame_data["atype"]: - raise ValueError( - "Element check failed. " - "If you are running in 'change-bias' mode, use '--skip-elementcheck' to disable this check. " - "If you encountered this error during model training, set 'enable_element_completion' to False " - "in the 'training' section of your input file." - ) + try: + frame_data = sys.__getitem__(frame) + if "atype" not in frame_data: + raise ValueError(f"Frame {frame} does not contain type information") + if miss not in frame_data["atype"]: + raise ValueError( + f"Element {miss} not found in frame {frame}.\n" + "To proceed without element completion:\n" + "1. For change-bias mode: Use '--skip-elementcheck'\n" + "2. For model training: Set 'enable_element_completion' to False" + ) + except Exception as e: + log.error(f"Failed to process frame {frame} from system {sys_index}: {e}") + raise
🧹 Nitpick comments (2)
deepmd/pt/utils/stat.py (2)
175-190: Extract common tensor processing logic.The tensor processing logic is duplicated from the main processing loop. Extract it into a helper function:
+ def process_tensor_data(data): + """Process tensor data with proper error handling.""" + try: + if data is None: + return None + if isinstance(data, np.ndarray): + tensor_data = torch.from_numpy(data) + return tensor_data.unsqueeze(0) + if isinstance(data, np.float32): + return data + return None + except Exception as e: + log.error(f"Failed to process tensor data: {e}") + raise sys_stat_new = {} for dd in frame_data: if dd == "type": continue - if frame_data[dd] is None: - sys_stat_new[dd] = None - elif isinstance(frame_data[dd], np.ndarray): + result = process_tensor_data(frame_data[dd]) + if result is not None: if dd not in sys_stat_new: sys_stat_new[dd] = [] - tensor_data = torch.from_numpy(frame_data[dd]) - tensor_data = tensor_data.unsqueeze(0) - sys_stat_new[dd].append(tensor_data) - elif isinstance(frame_data[dd], np.float32): - sys_stat_new[dd] = frame_data[dd] - else: - pass + if isinstance(result, torch.Tensor): + sys_stat_new[dd].append(result) + else: + sys_stat_new[dd] = result
136-156: Improve error handling for missing elements.The missing element handling could be more robust with better error messages:
def process_missing_elements( min_frames_per_element_forstat, global_element_counts, total_element_types, collect_ele, ): """Handle missing elements and check element completeness.""" + if not total_element_types: + log.warning("No elements found in any dataset") + return collect_elements = collect_ele.keys() missing_elements = total_element_types - collect_elements + if not missing_elements: + log.info("All elements are present in the datasets") + return collect_miss_element = set() for ele, count in collect_ele.items(): if count < min_frames_per_element_forstat: collect_miss_element.add(ele) missing_elements.add(ele) + log.warning( + f"Insufficient frames for element {ele}: found {count}, " + f"required {min_frames_per_element_forstat}. This may affect model accuracy." + )
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deepmd/pt/utils/stat.py (1)
157-184:⚠️ Potential issueReplace assertion with proper exception handling.
The function uses an assertion for validation, which should be replaced with proper exception handling as suggested by the reviewer.
def process_with_new_frame(sys_indices, newele_counter): """Process frames with missing elements.""" + if not sys_indices: + log.warning("No system indices provided") + return for sys_info in sys_indices: sys_index = sys_info["sys_index"] frames = sys_info["frames"] + if not frames: + log.warning(f"No frames found for system {sys_index}") + continue sys = datasets[sys_index] for frame in frames: newele_counter += 1 if newele_counter <= min_frames_per_element_forstat: - frame_data = sys.__getitem__(frame) + try: + frame_data = sys.__getitem__(frame) + if "type" not in frame_data: + raise ValueError(f"Frame {frame} does not contain type information") + if miss not in frame_data["type"]: + raise ValueError( + f"Element {miss} not found in frame {frame}. " + "To proceed without element completion:\n" + "1. For change-bias mode: Use '--skip-elementcheck'\n" + "2. For model training: Set 'enable_element_completion' to False" + ) + except Exception as e: + log.error(f"Failed to process frame {frame} from system {sys_index}: {e}") + raise
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deepmd/pt/utils/stat.py (2)
38-58: Add input validation and improve docstring.The docstring should provide more details about the new parameters:
- What constitutes a valid value for
min_frames_per_element_forstat?- What are the implications of enabling/disabling element completion?
def make_stat_input( datasets, dataloaders, nbatches, min_frames_per_element_forstat=10, enable_element_completion=True, ): + if not datasets: + raise ValueError("No datasets provided") + if len(datasets) != len(dataloaders): + raise ValueError("Number of datasets does not match number of dataloaders") + if min_frames_per_element_forstat < 1: + raise ValueError("min_frames_per_element_forstat must be positive") """Pack data for statistics. Element checking is only enabled with mixed_type. Args: - datasets: A list of datasets to analyze. - dataloaders: Corresponding dataloaders for the datasets. - nbatches: Batch count for collecting stats. - - min_frames_per_element_forstat: Minimum frames required for statistics. - - enable_element_completion: Whether to perform missing element completion (default: True). + - min_frames_per_element_forstat: Minimum number of frames required per element for accurate statistics. + Must be a positive integer. Default: 10. + - enable_element_completion: If True, attempts to complete missing elements by searching for frames + containing those elements. If False, skips this process. Default: True. Returns ------- - A list of dicts, each of which contains data from a system. """
61-74: Remove unused variable and improve logging messages.The variable
global_element_countsis initialized but never used. Also, the logging messages could be more specific.total_element_types = set() - global_element_counts = {} global_type_name = {} collect_ele = defaultdict(int) if datasets[0].mixed_type: if enable_element_completion: log.info( - f"Element check enabled. " - f"Verifying if frames with elements meet the set of {min_frames_per_element_forstat}." + f"Element completion enabled. " + f"Each element requires at least {min_frames_per_element_forstat} frames for statistics." ) else: log.info( - "Element completion is disabled. Skipping missing element handling." + "Element completion disabled. Statistics may be incomplete for elements with insufficient frames." )
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source/tests/pt/test_make_stat_input.py (3)
62-72:⚠️ Potential issueTest with multiple batch sizes.
Using a fixed batch size of 1 may not catch batch-related issues.
- cls.dataloaders = [] - for dataset in cls.datasets: - dataloader = DataLoader( - dataset, - batch_size=1, - num_workers=0, - drop_last=False, - collate_fn=collate_fn, - pin_memory=False, - ) - cls.dataloaders.append(dataloader) + # Test with different batch sizes + cls.batch_sizes = [1, 4, 16] + cls.dataloaders = {} + for batch_size in cls.batch_sizes: + loaders = [] + for dataset in cls.datasets: + loader = DataLoader( + dataset, + batch_size=batch_size, + num_workers=0, + drop_last=False, + collate_fn=collate_fn, + pin_memory=False, + ) + loaders.append(loader) + cls.dataloaders[batch_size] = loaders
171-172:⚠️ Potential issueUse self.real_ntypes instead of hard-coded ntypes value.
The hard-coded
ntypes=57should use the derived value from the dataset.- bias_ori, _ = compute_output_stats(lst_ori, ntypes=57) - bias_all, _ = compute_output_stats(lst_all, ntypes=57) + bias_ori, _ = compute_output_stats(lst_ori, ntypes=self.real_ntypes) + bias_all, _ = compute_output_stats(lst_all, ntypes=self.real_ntypes)
192-193: 🛠️ Refactor suggestionDocument missing elements and improve test method.
The test method needs better documentation of missing elements and their impact.
- # missing element:13,31,37 - # only one frame would be count + """Test make_stat_input with element completion enabled/disabled. + + Missing elements in test data: + - Element 13: Missing due to [reason] + - Element 31: Missing due to [reason] + - Element 37: Missing due to [reason] + + When element completion is disabled: + - Only one frame is counted because [explain why] + - Missing elements should have zero bias + """
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source/tests/pt/test_make_stat_input.py (1)
25-44: Optimize dictionary key lookup in collate function.The dictionary key lookup in the loop can be optimized.
- for key in batch[0].keys(): + for key in batch[0]:🧰 Tools
🪛 Ruff (0.8.2)
30-30: Use
key in dictinstead ofkey in dict.keys()Remove
.keys()(SIM118)
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source/tests/pt/test_make_stat_input.py(1 hunks)
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🪛 Ruff (0.8.2)
source/tests/pt/test_make_stat_input.py
30-30: Use key in dict instead of key in dict.keys()
Remove .keys()
(SIM118)
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Summary by CodeRabbit
New Features
Bug Fixes
Tests
make_stat_inputfunction to ensure accurate processing of atomic types.