Chore fill anemoi models (#1)

feat: Initial implementation of anemoi-models

Co-authored-by: Jesper Dramsch <[email protected]>

Co-authored-by: Simon Lang <[email protected]>

Co-authored-by: Matthew Chantry <[email protected]>

Co-authored-by: Mihai Alexe <[email protected]>

Co-authored-by: Florian Pinault <[email protected]>

Co-authored-by: Baudouin Raoult <[email protected]>

Co-authored-by: Steffen Tietsche <[email protected]>

Co-authored-by: Sara Hahner <[email protected]>

Co-authored-by: Mariana Clare <[email protected]>

pyproject.toml

@@ -1,4 +1,3 @@
-#!/usr/bin/env python
 # (C) Copyright 2024 ECMWF.
 #
 # This software is licensed under the terms of the Apache Licence Version 2.0
@@ -10,12 +9,13 @@
 # https://packaging.python.org/en/latest/guides/writing-pyproject-toml/
 
 [build-system]
-requires = ["setuptools>=60", "setuptools-scm>=8.0"]
+requires = ["setuptools>=61", "setuptools-scm>=8.0"]
+build-backend = "setuptools.build_meta"
 
 [project]
 description = "A package to hold various functions to support training of ML models."
 name = "anemoi-models"
-
+readme = "README.md"
 dynamic = ["version"]
 license = { file = "LICENSE" }
 requires-python = ">=3.9"
@@ -39,19 +39,34 @@ classifiers = [
     "Operating System :: OS Independent",
 ]
 
-dependencies = []
+dependencies = [
+    "torch==2.3",
+    "torch-geometric==2.4",
+    "einops==0.6.1",
+    "hydra-core==1.3",
+    "anemoi-datasets==0.2.1",
+    "anemoi-utils==0.1.9",
+]
 
 [project.optional-dependencies]
 
 
 docs = [
     # For building the documentation
-    "sphinx", "sphinx_rtd_theme", "nbsphinx", "pandoc", "sphinx_argparse"
 ]
 
 all = []
 
-dev = []
+tests = ["pytest", "hypothesis"]
+
+dev = [
+    "sphinx",
+    "sphinx_rtd_theme",
+    "nbsphinx",
+    "pandoc",
+    "pytest",
+    "hypothesis",
+]
 
 [project.urls]
 Homepage = "https://github.com/ecmwf/anemoi-models/"

src/anemoi/models/data_indices/collection.py

@@ -0,0 +1,74 @@
+# (C) Copyright 2024 ECMWF.
+#
+# This software is licensed under the terms of the Apache Licence Version 2.0
+# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0.
+# In applying this licence, ECMWF does not waive the privileges and immunities
+# granted to it by virtue of its status as an intergovernmental organisation
+# nor does it submit to any jurisdiction.
+#
+
+import operator
+
+import yaml
+from omegaconf import OmegaConf
+
+from anemoi.models.data_indices.index import BaseIndex
+from anemoi.models.data_indices.index import DataIndex
+from anemoi.models.data_indices.index import ModelIndex
+from anemoi.models.data_indices.tensor import BaseTensorIndex
+from anemoi.models.data_indices.tensor import InputTensorIndex
+from anemoi.models.data_indices.tensor import OutputTensorIndex
+
+
+class IndexCollection:
+    """Collection of data and model indices."""
+
+    def __init__(self, config, name_to_index) -> None:
+        self.config = OmegaConf.to_container(config, resolve=True)
+
+        self.forcing = [] if config.data.forcing is None else OmegaConf.to_container(config.data.forcing, resolve=True)
+        self.diagnostic = (
+            [] if config.data.diagnostic is None else OmegaConf.to_container(config.data.diagnostic, resolve=True)
+        )
+
+        assert set(self.diagnostic).isdisjoint(self.forcing), (
+            f"Diagnostic and forcing variables overlap: {set(self.diagnostic).intersection(self.forcing)}. ",
+            "Please drop them at a dataset-level to exclude them from the training data.",
+        )
+        self.name_to_index = dict(sorted(name_to_index.items(), key=operator.itemgetter(1)))
+        name_to_index_model_input = {
+            name: i for i, name in enumerate(key for key in self.name_to_index if key not in self.diagnostic)
+        }
+        name_to_index_model_output = {
+            name: i for i, name in enumerate(key for key in self.name_to_index if key not in self.forcing)
+        }
+
+        self.data = DataIndex(self.diagnostic, self.forcing, self.name_to_index)
+        self.model = ModelIndex(self.diagnostic, self.forcing, name_to_index_model_input, name_to_index_model_output)
+
+    def __repr__(self) -> str:
+        return f"IndexCollection(config={self.config}, name_to_index={self.name_to_index})"
+
+    def __eq__(self, other):
+        if not isinstance(other, IndexCollection):
+            # don't attempt to compare against unrelated types
+            return NotImplemented
+
+        return self.model == other.model and self.data == other.data
+
+    def __getitem__(self, key):
+        return getattr(self, key)
+
+    def todict(self):
+        return {
+            "data": self.data.todict(),
+            "model": self.model.todict(),
+        }
+
+    @staticmethod
+    def representer(dumper, data):
+        return dumper.represent_scalar(f"!{data.__class__.__name__}", repr(data))
+
+
+for cls in [BaseTensorIndex, InputTensorIndex, OutputTensorIndex, BaseIndex, DataIndex, ModelIndex, IndexCollection]:
+    yaml.add_representer(cls, cls.representer)

src/anemoi/models/data_indices/index.py

@@ -0,0 +1,93 @@
+# (C) Copyright 2024 ECMWF.
+#
+# This software is licensed under the terms of the Apache Licence Version 2.0
+# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0.
+# In applying this licence, ECMWF does not waive the privileges and immunities
+# granted to it by virtue of its status as an intergovernmental organisation
+# nor does it submit to any jurisdiction.
+#
+
+from anemoi.models.data_indices.tensor import InputTensorIndex
+from anemoi.models.data_indices.tensor import OutputTensorIndex
+
+
+class BaseIndex:
+    """Base class for data and model indices."""
+
+    def __init__(self) -> None:
+        self.input = NotImplementedError
+        self.output = NotImplementedError
+
+    def __eq__(self, other):
+        if not isinstance(other, BaseIndex):
+            # don't attempt to compare against unrelated types
+            return NotImplemented
+
+        return self.input == other.input and self.output == other.output
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}(input={self.input}, output={self.output})"
+
+    def __getitem__(self, key):
+        return getattr(self, key)
+
+    def todict(self):
+        return {
+            "input": self.input.todict(),
+            "output": self.output.todict(),
+        }
+
+    @staticmethod
+    def representer(dumper, data):
+        return dumper.represent_scalar(f"!{data.__class__.__name__}", repr(data))
+
+
+class DataIndex(BaseIndex):
+    """Indexing for data variables."""
+
+    def __init__(self, diagnostic, forcing, name_to_index) -> None:
+        self._diagnostic = diagnostic
+        self._forcing = forcing
+        self._name_to_index = name_to_index
+        self.input = InputTensorIndex(
+            includes=forcing,
+            excludes=diagnostic,
+            name_to_index=name_to_index,
+        )
+
+        self.output = OutputTensorIndex(
+            includes=diagnostic,
+            excludes=forcing,
+            name_to_index=name_to_index,
+        )
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}(diagnostic={self._input}, forcing={self._output}, name_to_index={self._name_to_index})"
+
+
+class ModelIndex(BaseIndex):
+    """Indexing for model variables."""
+
+    def __init__(self, diagnostic, forcing, name_to_index_model_input, name_to_index_model_output) -> None:
+        self._diagnostic = diagnostic
+        self._forcing = forcing
+        self._name_to_index_model_input = name_to_index_model_input
+        self._name_to_index_model_output = name_to_index_model_output
+        self.input = InputTensorIndex(
+            includes=forcing,
+            excludes=[],
+            name_to_index=name_to_index_model_input,
+        )
+
+        self.output = OutputTensorIndex(
+            includes=diagnostic,
+            excludes=[],
+            name_to_index=name_to_index_model_output,
+        )
+
+    def __repr__(self) -> str:
+        return (
+            f"{self.__class__.__name__}(diagnostic={self._input}, forcing={self._output}, "
+            f"name_to_index_model_input={self._name_to_index_model_input}, "
+            f"name_to_index_model_output={self._name_to_index_model_output})"
+        )

src/anemoi/models/data_indices/tensor.py

@@ -0,0 +1,114 @@
+# (C) Copyright 2024 ECMWF.
+#
+# This software is licensed under the terms of the Apache Licence Version 2.0
+# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0.
+# In applying this licence, ECMWF does not waive the privileges and immunities
+# granted to it by virtue of its status as an intergovernmental organisation
+# nor does it submit to any jurisdiction.
+#
+
+import torch
+
+
+class BaseTensorIndex:
+    """Indexing for variables in index as Tensor."""
+
+    def __init__(self, *, includes: list[str], excludes: list[str], name_to_index: dict[str, int]) -> None:
+        """Initialize indexing tensors from includes and excludes using name_to_index.
+
+        Parameters
+        ----------
+        includes : list[str]
+            Variables to include in the indexing that are exclusive to this indexing.
+            e.g. Forcing variables for the input indexing, diagnostic variables for the output indexing
+        excludes : list[str]
+            Variables to exclude from the indexing.
+            e.g. Diagnostic variables for the input indexing, forcing variables for the output indexing
+        name_to_index : dict[str, int]
+            Dictionary mapping variable names to their index in the Tensor.
+        """
+        self.includes = includes
+        self.excludes = excludes
+        self.name_to_index = name_to_index
+
+        assert set(self.excludes).issubset(
+            self.name_to_index.keys(),
+        ), f"Data indexing has invalid entries {[var for var in self.excludes if var not in self.name_to_index]}, not in dataset."
+        assert set(self.includes).issubset(
+            self.name_to_index.keys(),
+        ), f"Data indexing has invalid entries {[var for var in self.includes if var not in self.name_to_index]}, not in dataset."
+
+        self.full = self._build_idx_from_excludes()
+        self._only = self._build_idx_from_includes()
+        self._removed = self._build_idx_from_includes(self.excludes)
+        self.prognostic = self._build_idx_prognostic()
+        self.diagnostic = NotImplementedError
+        self.forcing = NotImplementedError
+
+    def __len__(self) -> int:
+        return len(self.full)
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}(includes={self.includes}, excludes={self.excludes}, name_to_index={self.name_to_index})"
+
+    def __eq__(self, other):
+        if not isinstance(other, BaseTensorIndex):
+            # don't attempt to compare against unrelated types
+            return NotImplemented
+
+        return (
+            torch.allclose(self.full, other.full)
+            and torch.allclose(self._only, other._only)
+            and torch.allclose(self._removed, other._removed)
+            and torch.allclose(self.prognostic, other.prognostic)
+            and torch.allclose(self.diagnostic, other.diagnostic)
+            and torch.allclose(self.forcing, other.forcing)
+            and self.includes == other.includes
+            and self.excludes == other.excludes
+        )
+
+    def __getitem__(self, key):
+        return getattr(self, key)
+
+    def todict(self):
+        return {
+            "full": self.full,
+            "prognostic": self.prognostic,
+            "diagnostic": self.diagnostic,
+            "forcing": self.forcing,
+        }
+
+    @staticmethod
+    def representer(dumper, data):
+        return dumper.represent_scalar(f"!{data.__class__.__name__}", repr(data))
+
+    def _build_idx_from_excludes(self, excludes=None) -> "torch.Tensor[int]":
+        if excludes is None:
+            excludes = self.excludes
+        return torch.Tensor(sorted(i for name, i in self.name_to_index.items() if name not in excludes)).to(torch.int)
+
+    def _build_idx_from_includes(self, includes=None) -> "torch.Tensor[int]":
+        if includes is None:
+            includes = self.includes
+        return torch.Tensor(sorted(self.name_to_index[name] for name in includes)).to(torch.int)
+
+    def _build_idx_prognostic(self) -> "torch.Tensor[int]":
+        return self._build_idx_from_excludes(self.includes + self.excludes)
+
+
+class InputTensorIndex(BaseTensorIndex):
+    """Indexing for input variables."""
+
+    def __init__(self, *, includes: list[str], excludes: list[str], name_to_index: dict[str, int]) -> None:
+        super().__init__(includes=includes, excludes=excludes, name_to_index=name_to_index)
+        self.forcing = self._only
+        self.diagnostic = self._removed
+
+
+class OutputTensorIndex(BaseTensorIndex):
+    """Indexing for output variables."""
+
+    def __init__(self, *, includes: list[str], excludes: list[str], name_to_index: dict[str, int]) -> None:
+        super().__init__(includes=includes, excludes=excludes, name_to_index=name_to_index)
+        self.forcing = self._removed
+        self.diagnostic = self._only