Atmorep Implementation with its tests #143

yuvraajnarula · 2025-03-11T19:06:42Z

Pull Request

Description

This PR introduces a comprehensive test suite for the AtmoRep model in test_atmorep.py. It includes unit tests for configuration, data preprocessing, model inference, normalization, training components, and robustness checks.

Issue Addressed #103

Key Features

AtmoRepConfig Tests
- Ensures correct initialization of configuration parameters.
- Validates parameter constraints and expected field structure.
Data Handling & Normalization
- Fixtures for generating dummy atmospheric data.
- Field normalizer tests for correct statistical transformations.
- Dataset loading and batch processing validation.
Model Inference & Forecasting
- Unit tests for model inference and batch inference mechanisms.
- Tests for integration with create_forecast.
- Evaluates ensemble forecast variations.
Training Components
- Validates loss function behavior with and without masks.
- Tests training loop initialization and checkpoint saving.
Performance & Robustness
- Memory usage and inference speed tests.
- Edge case handling (NaN inputs, zero inputs, and device transfers).

How Has This Been Tested?

Implemented unit tests covering all major functionalities.
Verified data transformations, normalization, and inference correctness.
Conducted training loop and checkpointing tests.
Ensured robustness through extreme input cases and device transfers.

=============================================================== 33 passed, 3 skipped, 2 warnings in 104.90s (0:01:44) ================================================================ 
tests/test_atmorep.py::TestAtmoRepConfig::test_config_initialization PASSED
tests/test_atmorep.py::TestAtmoRepConfig::test_config_custom_values PASSED
tests/test_atmorep.py::TestAtmoRepConfig::test_config_validation PASSED
tests/test_atmorep.py::TestModelOperations::test_model_loading_invalid_path PASSED
tests/test_atmorep.py::TestModelOperations::test_model_loading_valid_path PASSED
tests/test_atmorep.py::TestModelOperations::test_inference_output_shape PASSED
tests/test_atmorep.py::TestModelOperations::test_batch_inference_processing PASSED
tests/test_atmorep.py::TestModelOperations::test_forecasting_steps[1] PASSED
tests/test_atmorep.py::TestModelOperations::test_forecasting_steps[3] PASSED
tests/test_atmorep.py::TestDataHandling::test_dataset_initialization[True] PASSED
tests/test_atmorep.py::TestDataHandling::test_dataset_initialization[False] PASSED
tests/test_atmorep.py::TestDataHandling::test_dataset_getitem PASSED
tests/test_atmorep.py::TestDataHandling::test_normalization_field_validation PASSED
tests/test_atmorep.py::TestDataHandling::test_normalization_roundtrip PASSED
tests/test_atmorep.py::TestDataHandling::test_normalizer_stats_creation PASSED
tests/test_atmorep.py::TestTrainingComponents::test_loss_calculation_with_masks PASSED
tests/test_atmorep.py::TestTrainingComponents::test_loss_weighting PASSED
tests/test_atmorep.py::TestTrainingComponents::test_training_initialization PASSED
tests/test_atmorep.py::TestTrainingComponents::test_training_with_resume PASSED
tests/test_atmorep.py::TestTrainingComponents::test_checkpoint_saving PASSED
tests/test_atmorep.py::TestIntegration::test_inference_with_normalization PASSED
tests/test_atmorep.py::TestIntegration::test_full_forecast_pipeline PASSED
tests/test_atmorep.py::TestIntegration::test_model_training_epoch PASSED
tests/test_atmorep.py::TestModelArchitecture::test_model_initialization PASSED
tests/test_atmorep.py::TestModelArchitecture::test_model_with_masks PASSED
tests/test_atmorep.py::TestModelArchitecture::test_ensemble_forecast PASSED
tests/test_atmorep.py::TestModelArchitecture::test_model_training_mode PASSED
tests/test_atmorep.py::TestModelArchitecture::test_autoregressive_property PASSED
tests/test_atmorep.py::TestPerformanceAndScaling::test_memory_usage[spatial_size0] SKIPPED (CUDA not available)
tests/test_atmorep.py::TestPerformanceAndScaling::test_memory_usage[spatial_size1] SKIPPED (CUDA not available)
tests/test_atmorep.py::TestPerformanceAndScaling::test_inference_speed[1] Avg inference time for batch size 1: 1.6020 sec
PASSED
tests/test_atmorep.py::TestPerformanceAndScaling::test_inference_speed[2] Avg inference time for batch size 2: 2.7235 sec
PASSED
tests/test_atmorep.py::TestRobustness::test_zero_input PASSED
tests/test_atmorep.py::TestRobustness::test_nan_handling PASSED
tests/test_atmorep.py::TestRobustness::test_single_precision PASSED
tests/test_atmorep.py::TestRobustness::test_device_transfer SKIPPED (CUDA not available)

Checklist:

My code follows [OCF's coding style guidelines](https://github.com/openclimatefix/.github/blob/main/coding_style.md)
I have performed a self-review of my own code
I have made corresponding changes to the documentation
I have added tests that prove my fix is effective or that my feature works
I have checked my code and corrected any misspellings

@jacobbieker

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jacobbieker

Hi,

Thanks for working on this. I think this needs a lot of changes. A few general notes:

Don't put test logic in the actual library code. So don't have logic for MagicMocks and don't return dummy values, or add dummy values if something doesn't exist. That is the test code job to do, not the code in the library. This also applies to the other PRs you've opened.
Please don't just have a config as the argument to init, each class should take all the arguments it needs in the init spelled out as keyword arguments. The config can be nice to bundle them up, but shouldn't be the init arguments for nearly all classes or functions.
Model implementations shouldn't have their own datasets, unless really necessary. We are aiming to have these codes be as interoperable as possible, and reduce duplication where possible, so things like the ERA5Dataset can be removed, they have equivalents under data/ and work for any model in this repo. Same for the normalizer, there are simpler or equivalent normalizers that already exist in this repo, so those could be used instead, the implementation here isn't needed.
The CRPS, etc. are all useful, but those would be generally useful. My advice would be to split those metrics out into its own PR that adds them to a graph_weather/metrics/crps.py, etc. and tests them separately. They would be useful for all the models here!
All imports should be at the top of the file, you shouldn't need to import inside functions, and if that is necessary for tests to pass, then the code should be refactored so that is not the case, as it is saying that there is too much of a linking between test and library code.
Training script should also be quite general, unless the model has very specific needs, it should be able to be trained by the other training scripts.
As much as possible, there should be type hints for all the arguments and return types in the code, so it is easier to see what is expected in and out.

Please take the time to go through and address the comments. Also, please comment on issues or open one up if you want to add a model or a large PR like this one. I can help with scoping it or breaking it up so it is easier to review, and there is less wasted effort.

jacobbieker · 2025-03-11T22:02:27Z

graph_weather/models/atmorep/data/dataset.py

+from graph_weather.models.atmorep.config import AtmoRepConfig
+
+
+class ERA5Dataset(Dataset):


For this, we could probably just run from ARCO-ERA5, it might be easier. Ideally, this would also be a more separate PR for the dataset, under /data/ and be more general than AtmoRep-specific.

jacobbieker · 2025-03-11T22:03:44Z

graph_weather/models/atmorep/data/dataset.py

+        self.transform = transform
+
+        # Expect a data index file in the data directory
+        index_file = os.path.join(data_dir, "data_index.txt")


We shouldn't need a index file for dataset like this, the ARCO-ERA5 is quite simple to use and index into, so this shouldn't be necessary.

jacobbieker · 2025-03-11T22:04:56Z

graph_weather/models/atmorep/data/dataset.py

I would recommend removing this. An ERA5Dataset is/would be useful, but that would be a different PR, and more general than just to AtmoRep. Additionally, there is the ARCO-ERA5 dataset on GCP that is very simple to use and read from, so most of the functionality here could be removed.

jacobbieker · 2025-03-11T22:06:15Z

graph_weather/models/atmorep/data/normalizer.py

This is a more general normalizer than AtmoRep, and so shouldn't be in this repo. Additionally, generally, these classes and functions shouldn't make data for tests. Tests should handle that. I would remove this normalizer and file, as there are other normalizers already present.

jacobbieker · 2025-03-11T22:07:00Z

graph_weather/models/atmorep/data/normalizer.py

+        Returns:
+            dict: A dictionary of statistics for each field.
+        """
+        self.stats = {field: {"mean": 0.0, "std": 1.0} for field in self.config.input_fields}


Suggested change

self.stats = {field: {"mean": 0.0, "std": 1.0} for field in self.config.input_fields}

return NotImplementedError

If this isn't actually computing the mean/stddev then it should return a NotImplementedError, not some fake stats, as that can cause downstream errors.

jacobbieker · 2025-03-11T22:25:43Z

graph_weather/models/atmorep/utils/uncertainity.py

+
+
+class UncertaintyEstimator:
+    def __init__(self, config):


Have this take the actual args, not the config

jacobbieker · 2025-03-11T22:26:06Z

graph_weather/models/atmorep/utils/uncertainity.py

+        return entropy
+
+
+class UncertaintyEstimator:


This is redundant to the one above, please remove it.

jacobbieker · 2025-03-11T22:27:38Z

graph_weather/models/atmorep/utils/uncertainity.py

+        return entropy.reshape(B, T, H, W)
+
+
+class CalibrationMetrics:


These aren't bad, but I would maybe generalize these rank histograms and CRPS to a more general losses/ or the losses.py file

jacobbieker · 2025-03-11T22:28:08Z

graph_weather/models/atmorep/utils/uncertainity.py

+        mapped_preds = torch.zeros_like(ensemble_preds)
+
+        # Process each location separately
+        for b in range(B):


Please do this vectorized or something, this won't scale at all.

jacobbieker · 2025-03-11T22:30:08Z

tests/test_atmorep.py

Please only use pytest for the testing, so don't use the unittest.mock or such. Also, the tests might be better if they are split across a few files, so maybe split the tests into model ones under tests/atmorep/test_model.py, loss ones under tests/atmorep/test_loss.py, etc.

jacobbieker · 2025-03-11T22:44:17Z

Finally, instead of tagging me in the description, please request my review when it's ready. And do check that the code you were doing passes the precommit tests.

…meters, and address reviewer feedback

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yuvraajnarula · 2025-03-17T13:16:39Z

Overview of the changes:

Modularization of Configuration: The code has been refactored to remove the monolithic AtmoRepConfig object. Instead, constructor arguments are now explicitly passed to classes in attention.py, decoder.py, transformer.py, field_transformer.py, and multiformer.py, improving flexibility and reducing reliance on a central config object.
Improved Documentation: Extensive updates to docstrings and type hints across the codebase have been made to improve clarity, readability, and maintainability. This includes detailed function-level and module-level docstrings for easier understanding of each component's purpose.
Test Suite Refactoring: The test suite has been reorganized into separate files focused on different aspects (model, loss, training, inference). The tests now use pytest fixtures and monkeypatching, eliminating the need for unittest.mock. The tests align with the refactored code to check the functionality of various modules (e.g., loss functions, training utilities, model inference).
Removal of Training Logic from Core Model: Training-specific components (such as DataParallelAtmoRep) have been removed or extracted, ensuring that the core codebase is focused on the model's functionality and not on training procedures.
Use of einops for Tensor Operations: The manual tensor reshaping using view and unsqueeze has been replaced by einops operations (rearrange and repeat), making tensor manipulations clearer and more expressive. This improves readability and consistency when handling tensor dimensions.

If the updated code aligns with your feedback, or if there are other areas you'd like me to prioritize or additional features to implement, please let me know. Otherwise, I will start working on the formatting for these files.

jacobbieker

Thank you for all this work. There are some changes I would like to see still, and you missed a few of the comments I made in the previous round. Just a suggestion, but if you go through and respond to the comments I make on the PR, it can help me see where the changes occured, as well as ensure they aren't missed.

Few other notes: I don't think we need the training script for this, thank you for the work making it and adding the tests, but I'm more thinking this repo should have a single train script that can train any of the models in this repo, rather than lots of individual training scripts for each model on its own. So if you could remove it that would be great. Same with the ERA5Dataset file, as there is already an ARCO-ERA5 dataset in the repo, that is more generic.
I also am not sure we need the sampler.py, I think that can be removed as its somewhat specific to NetCDF files, and I don't quite see the utility of it.

One final note in general. I'm very happy you are quite excited and dedicated to contribute to this repository. One thing that might also help is making smaller PRs. Rather than very large ones that include everything to do with model, training, etc. its easier for me to review and go over smaller ones. So maybe one only adding the unique layer/module from a paper, then a follow on PR that adds the encoder or decoder, and a another one that adds the processor, for example. Then a final one if needed for the dataset, possibly. It just makes it easier for me to review, faster for me to review, and gives more chances for feedback before you spend a lot time writing up a lot of code and opening the PR.

jacobbieker · 2025-03-19T21:15:06Z

graph_weather/models/atmorep/model/decoder.py

+        num_heads: int,
+        dropout: float = 0.1,
+        attention_dropout: float = 0.1,
+        transformer_block_cls=nn.Module,  # replace with your actual block class if needed


I would remove this bit or if set to a default, default to the one that works with the decoder. But the class is used in a quite specific way, so I would recommend removing this as a configuration option.

jacobbieker · 2025-03-19T21:16:10Z

graph_weather/models/atmorep/model/field_transformer.py

+        time_steps: int,
+        num_layers: int,
+        field_name: str = "unknown_field",
+        transformer_block_cls=nn.Module,  # replace with your actual block class


Same as for the decoder, its used in quite a specific way, so removing this as an option makes more sense.

Suggested change

transformer_block_cls=nn.Module, # replace with your actual block class

jacobbieker · 2025-03-19T21:17:49Z

graph_weather/models/atmorep/model/multiformer.py

+    after the regular transformer blocks to better capture temporal/spatial dependencies.
+
+    Args:
+        All the same args as MultiFormer, plus any needed for SpatioTemporalAttention.


I would still copy the args over here, duplication in these kinds of docstrings is okay so that the documentation is right next to the code it is describing.

jacobbieker · 2025-03-19T21:19:48Z

graph_weather/models/atmorep/training/loss.py

+        """
+        Args:
+            predictions (dict): Dict of field predictions, each with shape
+                [E, B, T, H, W] or [B, T, H, W].


Suggested change

[E, B, T, H, W] or [B, T, H, W].

[Ensemble, Batch, Time, Height, Width] or [Batch, Time, Height, Width].

For these in the docstrings, I think it is helpful to write out what the dimension ordering means, just to be a bit clearer. If you could do that on the other docstrings that would be great!

jacobbieker · 2025-03-19T21:21:07Z

graph_weather/models/atmorep/training/train.py

+    # Ensure the model has parameters; if it fails, let it break.
+    params = list(model.parameters())
+    if len(params) == 0:
+        # If for some reason the model has no parameters, register a dummy parameter.


This falls under the test logic, if there is no parameters, you want the script to fail, not continue working but training something that you don't know what it is.

jacobbieker · 2025-03-19T21:26:38Z

graph_weather/data/sampler.py

+        self.logger = logging.getLogger("HierarchicalSampler")
+        self.logger.setLevel(logging.INFO)
+
+    def _get_available_time_segments(self):


This isn't actually checking available times in the dataset, just generating a list of years and months.

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yuvraajnarula · 2025-03-20T04:47:02Z

Thank you so much for your detailed feedback! I appreciate you taking the time to review my work and provide such specific guidance.

I apologize for missing some of your earlier comments — that was an oversight on my part. If you could point me to any specific ones that I missed, I’ll make sure to address them as soon as possible.

As per your suggestion, I’ve removed the training script, ERA5Dataset file, sampler.py removing test_loss.py and test_training.py. I now understand your vision for a more streamlined codebase with a single training script, rather than multiple model-specific implementations.

Your advice on submitting smaller, more focused PRs makes perfect sense, and I’ll adopt this approach going forward. Breaking down contributions into more manageable pieces will certainly make the review process smoother for everyone involved.

I’m excited about contributing to this project and learning from your expertise. I’m committed to aligning my workflow with the team’s practices and improving my contributions.

Is there anything else you would suggest I prioritize in addressing the current PR, aside from the files mentioned?

Thanks again for your valuable guidance!

yuvraajnarula · 2025-04-10T17:40:43Z

Hi @jacobbieker,
I hope you’re doing well! It’s been a while since we last discussed this PR, and I genuinely value your insights. I would love to hear your thoughts on the latest version whenever you have a moment. Thank you!

yuvraajnarula and others added 2 commits March 12, 2025 00:31

Atmorep Implementation with its tests

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yuvraajnarula and others added 8 commits March 15, 2025 00:14

Refactor code to remove monolithic config usage, accept explicit para…

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Splitting test for inference, loss, model, and training modules

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yuvraajnarula requested a review from jacobbieker March 17, 2025 13:16

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yuvraajnarula requested a review from jacobbieker March 20, 2025 04:47

		from graph_weather.models.atmorep.config import AtmoRepConfig


		class ERA5Dataset(Dataset):

	self.stats = {field: {"mean": 0.0, "std": 1.0} for field in self.config.input_fields}
	return NotImplementedError

	[E, B, T, H, W] or [B, T, H, W].
	[Ensemble, Batch, Time, Height, Width] or [Batch, Time, Height, Width].

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Atmorep Implementation with its tests #143

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Atmorep Implementation with its tests #143

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yuvraajnarula commented Mar 11, 2025

Pull Request

Description

Key Features

How Has This Been Tested?

Checklist:

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jacobbieker commented Mar 11, 2025

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yuvraajnarula commented Mar 17, 2025

Overview of the changes:

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yuvraajnarula commented Mar 20, 2025

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yuvraajnarula commented Apr 10, 2025

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