Update metatensor

examples/programmatic/llpr/llpr.py

@@ -72,7 +72,7 @@
     get_system_with_neighbor_lists(system, requested_neighbor_lists)
     for system in qm9_systems
 ]
-dataset = Dataset({"system": qm9_systems, **targets})
+dataset = Dataset.from_dict({"system": qm9_systems, **targets})
 
 # We also load a single ethanol molecule on which we will compute properties.
 # This system is loaded without targets, as we are only interested in the LPR

pyproject.toml

@@ -10,9 +10,9 @@ authors = [{name = "metatrain developers"}]
 
 dependencies = [
     "ase < 3.23.0",
-    "metatensor-learn==0.2.2",
-    "metatensor-operations==0.2.1",
-    "metatensor-torch==0.5.3",
+    "metatensor-learn==0.2.3",
+    "metatensor-operations==0.2.3",
+    "metatensor-torch==0.5.4",
     "jsonschema",
     "omegaconf",
     "python-hostlist",

src/metatrain/cli/eval.py

@@ -296,7 +296,7 @@ def eval_model(
                     gradients=gradients,
                 )
 
-        eval_dataset = Dataset({"system": eval_systems, **eval_targets})
+        eval_dataset = Dataset.from_dict({"system": eval_systems, **eval_targets})
 
         # Evaluate the model
         try:

src/metatrain/cli/train.py

@@ -15,7 +15,13 @@
 
 from .. import PACKAGE_ROOT
 from ..utils.architectures import check_architecture_options, get_default_hypers
-from ..utils.data import DatasetInfo, TargetInfoDict, get_atomic_types, get_dataset
+from ..utils.data import (
+    DatasetInfo,
+    TargetInfoDict,
+    get_atomic_types,
+    get_dataset,
+    get_stats,
+)
 from ..utils.data.dataset import _train_test_random_split
 from ..utils.devices import pick_devices
 from ..utils.distributed.logging import is_main_process
@@ -290,7 +296,7 @@ def train_model(
         else:
             index = f" {i}"
         logger.info(
-            f"Training dataset{index}:\n    {train_dataset.get_stats(dataset_info)}"
+            f"Training dataset{index}:\n    {get_stats(train_dataset, dataset_info)}"
         )
 
     for i, val_dataset in enumerate(val_datasets):
@@ -299,15 +305,17 @@ def train_model(
         else:
             index = f" {i}"
         logger.info(
-            f"Validation dataset{index}:\n    {val_dataset.get_stats(dataset_info)}"
+            f"Validation dataset{index}:\n    {get_stats(val_dataset, dataset_info)}"
         )
 
     for i, test_dataset in enumerate(test_datasets):
         if len(test_datasets) == 1:
             index = ""
         else:
             index = f" {i}"
-        logger.info(f"Test dataset{index}:\n    {test_dataset.get_stats(dataset_info)}")
+        logger.info(
+            f"Test dataset{index}:\n    {get_stats(test_dataset, dataset_info)}"
+        )
 
     ###########################
     # SAVE EXPANDED OPTIONS ###

src/metatrain/experimental/alchemical_model/tests/test_regression.py

@@ -92,7 +92,7 @@ def test_regression_train():
         }
     }
     targets, target_info_dict = read_targets(OmegaConf.create(conf))
-    dataset = Dataset({"system": systems, "mtt::U0": targets["mtt::U0"]})
+    dataset = Dataset.from_dict({"system": systems, "mtt::U0": targets["mtt::U0"]})
 
     hypers = DEFAULT_HYPERS.copy()
 

src/metatrain/experimental/alchemical_model/utils/normalize.py

@@ -18,10 +18,10 @@ def get_average_number_of_atoms(
     """
     average_number_of_atoms = []
     for dataset in datasets:
-        dtype = dataset[0]["system"].positions.dtype
+        dtype = dataset[0].system.positions.dtype
         num_atoms = []
         for i in range(len(dataset)):
-            system = dataset[i]["system"]
+            system = dataset[i].system
             num_atoms.append(len(system))
         average_number_of_atoms.append(torch.mean(torch.tensor(num_atoms, dtype=dtype)))
     return torch.tensor(average_number_of_atoms)
@@ -39,9 +39,9 @@ def get_average_number_of_neighbors(
     average_number_of_neighbors = []
     for dataset in datasets:
         num_neighbor = []
-        dtype = dataset[0]["system"].positions.dtype
+        dtype = dataset[0].system.positions.dtype
         for i in range(len(dataset)):
-            system = dataset[i]["system"]
+            system = dataset[i].system
             known_neighbor_lists = system.known_neighbor_lists()
             if len(known_neighbor_lists) == 0:
                 raise ValueError(f"system {system} does not have a neighbor list")
@@ -94,4 +94,4 @@ def remove_composition_from_dataset(
         new_systems.append(system)
         new_properties.append(property)
 
-    return Dataset({"system": new_systems, property_name: new_properties})
+    return Dataset.from_dict({"system": new_systems, property_name: new_properties})

src/metatrain/experimental/gap/tests/test_errors.py

@@ -54,7 +54,9 @@ def test_ethanol_regression_train_and_invariance():
     }
 
     targets, _ = read_targets(OmegaConf.create(conf))
-    dataset = Dataset({"system": systems[:2], "energy": targets["energy"][:2]})
+    dataset = Dataset.from_dict(
+        {"system": systems[:2], "energy": targets["energy"][:2]}
+    )
 
     hypers = copy.deepcopy(DEFAULT_HYPERS)
     hypers["model"]["krr"]["num_sparse_points"] = 30

src/metatrain/experimental/gap/tests/test_regression.py

@@ -55,7 +55,7 @@ def test_regression_train_and_invariance():
         }
     }
     targets, _ = read_targets(OmegaConf.create(conf))
-    dataset = Dataset({"system": systems, "mtt::U0": targets["mtt::U0"]})
+    dataset = Dataset.from_dict({"system": systems, "mtt::U0": targets["mtt::U0"]})
 
     target_info_dict = TargetInfoDict()
     target_info_dict["mtt::U0"] = TargetInfo(quantity="energy", unit="eV")
@@ -132,7 +132,7 @@ def test_ethanol_regression_train_and_invariance():
     }
 
     targets, _ = read_targets(OmegaConf.create(conf))
-    dataset = Dataset({"system": systems, "energy": targets["energy"]})
+    dataset = Dataset.from_dict({"system": systems, "energy": targets["energy"]})
 
     hypers = copy.deepcopy(DEFAULT_HYPERS)
     hypers["model"]["krr"]["num_sparse_points"] = 900

src/metatrain/experimental/gap/tests/test_torchscript.py

@@ -36,7 +36,7 @@ def test_torchscript():
 
     # for system in systems:
     #    system.types = torch.ones(len(system.types), dtype=torch.int32)
-    dataset = Dataset({"system": systems, "mtt::U0": targets["mtt::U0"]})
+    dataset = Dataset.from_dict({"system": systems, "mtt::U0": targets["mtt::U0"]})
 
     hypers = DEFAULT_HYPERS.copy()
     gap = GAP(DEFAULT_HYPERS["model"], dataset_info)

src/metatrain/experimental/pet/tests/test_pet_compatibility.py

@@ -159,7 +159,7 @@ def test_predictions_compatibility(cutoff):
         "neighbors_pos": batch.neighbors_pos,
     }
 
-    pet = model._module.pet
+    pet = model.module.pet
 
     pet_prediction = pet.forward(batch_dict)
 

src/metatrain/experimental/soap_bpnn/tests/test_continue.py

@@ -44,7 +44,7 @@ def test_continue(monkeypatch, tmp_path):
         }
     }
     targets, _ = read_targets(OmegaConf.create(conf))
-    dataset = Dataset({"system": systems, "mtt::U0": targets["mtt::U0"]})
+    dataset = Dataset.from_dict({"system": systems, "mtt::U0": targets["mtt::U0"]})
 
     hypers = DEFAULT_HYPERS.copy()
     hypers["training"]["num_epochs"] = 0

src/metatrain/experimental/soap_bpnn/tests/test_regression.py

@@ -70,7 +70,7 @@ def test_regression_train():
         }
     }
     targets, target_info_dict = read_targets(OmegaConf.create(conf))
-    dataset = Dataset({"system": systems, "mtt::U0": targets["mtt::U0"]})
+    dataset = Dataset.from_dict({"system": systems, "mtt::U0": targets["mtt::U0"]})
 
     hypers = DEFAULT_HYPERS.copy()
     hypers["training"]["num_epochs"] = 2

src/metatrain/utils/data/__init__.py

@@ -7,7 +7,7 @@
     get_all_targets,
     collate_fn,
     check_datasets,
-    group_and_join,
+    get_stats,
 )
 from .readers import (  # noqa: F401
     read_energy,

src/metatrain/utils/data/dataset.py

@@ -3,10 +3,10 @@
 from collections import UserDict
 from typing import Any, Dict, List, Optional, Tuple, Union
 
-import metatensor.learn
 import numpy as np
-import torch
+from metatensor.learn.data import Dataset, group_and_join
 from metatensor.torch import TensorMap
+from torch.utils.data import Subset
 
 from ..external_naming import to_external_name
 from ..units import get_gradient_units
@@ -242,60 +242,7 @@ def union(self, other: "DatasetInfo") -> "DatasetInfo":
         return new
 
 
-class Dataset:
-    """A version of the `metatensor.learn.Dataset` class that allows for
-    the use of `mtt::` prefixes in the keys of the dictionary. See
-    https://github.com/lab-cosmo/metatensor/issues/621.
-
-    It is important to note that, instead of named tuples, this class
-    accepts and returns dictionaries.
-
-    :param dict: A dictionary with the data to be stored in the dataset.
-    """
-
-    def __init__(self, dict: Dict):
-
-        new_dict = {}
-        for key, value in dict.items():
-            key = key.replace("mtt::", "mtt_")
-            new_dict[key] = value
-
-        self.mts_learn_dataset = metatensor.learn.Dataset(**new_dict)
-
-    def __getitem__(self, idx: int) -> Dict:
-
-        mts_dataset_item = self.mts_learn_dataset[idx]._asdict()
-        new_dict = {}
-        for key, value in mts_dataset_item.items():
-            key = key.replace("mtt_", "mtt::")
-            new_dict[key] = value
-
-        return new_dict
-
-    def __len__(self) -> int:
-        return len(self.mts_learn_dataset)
-
-    def __iter__(self):
-        for i in range(len(self)):
-            yield self[i]
-
-    def get_stats(self, dataset_info: DatasetInfo) -> str:
-        return _get_dataset_stats(self, dataset_info)
-
-
-class Subset(torch.utils.data.Subset):
-    """
-    A version of `torch.utils.data.Subset` containing a `get_stats` method
-    allowing us to print information about atomistic datasets.
-    """
-
-    def get_stats(self, dataset_info: DatasetInfo) -> str:
-        return _get_dataset_stats(self, dataset_info)
-
-
-def _get_dataset_stats(
-    dataset: Union[Dataset, Subset], dataset_info: DatasetInfo
-) -> str:
+def get_stats(dataset: Union[Dataset, Subset], dataset_info: DatasetInfo) -> str:
     """Returns the statistics of a dataset or subset as a string."""
 
     dataset_len = len(dataset)
@@ -306,7 +253,7 @@ def _get_dataset_stats(
     # target_names will be used to store names of the targets,
     # along with their gradients
     target_names = []
-    for key, tensor_map in dataset[0].items():
+    for key, tensor_map in dataset[0]._asdict().items():
         if key == "system":
             continue
         target_names.append(key)
@@ -408,8 +355,8 @@ def get_all_targets(datasets: Union[Dataset, List[Dataset]]) -> List[str]:
     target_names = []
     for dataset in datasets:
         for sample in dataset:
-            sample.pop("system")  # system not needed
-            target_names += list(sample.keys())
+            # system not needed
+            target_names += [key for key in sample._asdict().keys() if key != "system"]
 
     return sorted(set(target_names))
 
@@ -422,6 +369,7 @@ def collate_fn(batch: List[Dict[str, Any]]) -> Tuple[List, Dict[str, TensorMap]]
     """
 
     collated_targets = group_and_join(batch)
+    collated_targets = collated_targets._asdict()
     systems = collated_targets.pop("system")
     return systems, collated_targets
 
@@ -441,15 +389,15 @@ def check_datasets(train_datasets: List[Dataset], val_datasets: List[Dataset]):
         or targets that are not present in the training set
     """
     # Check that system `dtypes` are consistent within datasets
-    desired_dtype = train_datasets[0][0]["system"].positions.dtype
+    desired_dtype = train_datasets[0][0].system.positions.dtype
     msg = f"`dtype` between datasets is inconsistent, found {desired_dtype} and "
     for train_dataset in train_datasets:
-        actual_dtype = train_dataset[0]["system"].positions.dtype
+        actual_dtype = train_dataset[0].system.positions.dtype
         if actual_dtype != desired_dtype:
             raise TypeError(f"{msg}{actual_dtype} found in `train_datasets`")
 
     for val_dataset in val_datasets:
-        actual_dtype = val_dataset[0]["system"].positions.dtype
+        actual_dtype = val_dataset[0].system.positions.dtype
         if actual_dtype != desired_dtype:
             raise TypeError(f"{msg}{actual_dtype} found in `val_datasets`")
 
@@ -515,33 +463,3 @@ def _train_test_random_split(
         Subset(train_dataset, train_indices),
         Subset(train_dataset, test_indices),
     ]
-
-
-def group_and_join(
-    batch: List[Dict[str, Any]],
-) -> Dict[str, Any]:
-    """
-    Same as metatenor.learn.data.group_and_join, but joins dicts and not named tuples.
-
-    :param batch: A list of dictionaries, each containing the data for a single sample.
-
-    :returns: A single dictionary with the data fields joined together among all
-        samples.
-    """
-    data: List[Union[TensorMap, torch.Tensor]] = []
-    names = batch[0].keys()
-    for name, f in zip(names, zip(*(item.values() for item in batch))):
-        if name == "sample_id":  # special case, keep as is
-            data.append(f)
-            continue
-
-        if isinstance(f[0], torch.ScriptObject) and f[0]._has_method(
-            "keys_to_properties"
-        ):  # inferred metatensor.torch.TensorMap type
-            data.append(metatensor.torch.join(f, axis="samples"))
-        elif isinstance(f[0], torch.Tensor):  # torch.Tensor type
-            data.append(torch.vstack(f))
-        else:  # otherwise just keep as a list
-            data.append(f)
-
-    return {name: value for name, value in zip(names, data)}

src/metatrain/utils/data/extract_targets.py

@@ -28,6 +28,7 @@ def get_targets_dict(
     targets_dict = {}
     for dataset in datasets:
         targets = next(iter(dataset))
+        targets = targets._asdict()
         targets.pop("system")  # system not needed
 
         # targets is now a dictionary of TensorMaps

src/metatrain/utils/data/get_dataset.py

@@ -27,6 +27,6 @@ def get_dataset(options: DictConfig) -> Tuple[Dataset, TargetInfoDict]:
         reader=options["systems"]["reader"],
     )
     targets, target_info_dictionary = read_targets(conf=options["targets"])
-    dataset = Dataset({"system": systems, **targets})
+    dataset = Dataset.from_dict({"system": systems, **targets})
 
     return dataset, target_info_dictionary

src/metatrain/utils/llpr.py

@@ -31,7 +31,7 @@ def __init__(
         super().__init__()
 
         self.model = model
-        self.ll_feat_size = self.model._module.last_layer_feature_size
+        self.ll_feat_size = self.model.module.last_layer_feature_size
 
         # update capabilities: now we have additional outputs for the uncertainty
         old_capabilities = self.model.capabilities()