From 4388fd8bf64882e05637cf4c05e8c3c5aa04ff20 Mon Sep 17 00:00:00 2001 From: frostedoyster Date: Mon, 13 Jan 2025 10:37:28 +0100 Subject: [PATCH 01/10] Split last layers for different spherical irreps in `nanopet` --- src/metatrain/experimental/nanopet/model.py | 46 +++++++++++-------- .../nanopet/tests/test_functionality.py | 45 ++++++++++++++++++ .../nanopet/tests/test_regression.py | 24 +++++----- 3 files changed, 84 insertions(+), 31 deletions(-) diff --git a/src/metatrain/experimental/nanopet/model.py b/src/metatrain/experimental/nanopet/model.py index 97669cfc9..28291dced 100644 --- a/src/metatrain/experimental/nanopet/model.py +++ b/src/metatrain/experimental/nanopet/model.py @@ -119,7 +119,7 @@ def __init__(self, model_hypers: Dict, dataset_info: DatasetInfo) -> None: self.heads = torch.nn.ModuleDict() self.head_types = self.hypers["heads"] self.last_layers = torch.nn.ModuleDict() - self.output_shapes: Dict[str, List[List[int]]] = {} + self.output_shapes: Dict[str, Dict[str, List[int]]] = {} self.key_labels: Dict[str, Labels] = {} self.component_labels: Dict[str, List[List[Labels]]] = {} self.property_labels: Dict[str, List[Labels]] = {} @@ -436,12 +436,11 @@ def forward( for output_name, last_layer in self.last_layers.items(): if output_name in outputs: atomic_features = atomic_features_dict[output_name] - atomic_properties = last_layer(atomic_features) - split_atomic_properties_by_block = torch.split( - atomic_properties, - [manual_prod(shape) for shape in self.output_shapes[output_name]], - dim=-1, - ) + atomic_properties_by_block = [] + for last_layer_by_block in last_layer.values(): + atomic_properties_by_block.append( + last_layer_by_block(atomic_features) + ) blocks = [ TensorBlock( values=atomic_property.reshape([-1] + shape), @@ -450,8 +449,8 @@ def forward( properties=properties, ) for atomic_property, shape, components, properties in zip( - split_atomic_properties_by_block, - self.output_shapes[output_name], + atomic_properties_by_block, + self.output_shapes[output_name].values(), self.component_labels[output_name], self.property_labels[output_name], ) @@ -547,12 +546,16 @@ def export(self) -> MetatensorAtomisticModel: def _add_output(self, target_name: str, target_info: TargetInfo) -> None: - # one output shape for each tensor block - self.output_shapes[target_name] = [ - [len(comp.values) for comp in block.components] - + [len(block.properties.values)] - for block in target_info.layout.blocks() - ] + # one output shape for each tensor block, grouped by target (i.e. tensormap) + self.output_shapes[target_name] = {} + for key, block in target_info.layout.items(): + dict_key = target_name + for n, k in zip(key.names, key.values): + dict_key += f"_{n}_{int(k)}" + self.output_shapes[target_name][dict_key] = [ + len(comp.values) for comp in block.components + ] + [len(block.properties.values)] + self.outputs[target_name] = ModelOutput( quantity=target_info.quantity, unit=target_info.unit, @@ -576,10 +579,15 @@ def _add_output(self, target_name: str, target_info: TargetInfo) -> None: f"for target {target_name}" ) - self.last_layers[target_name] = torch.nn.Linear( - self.hypers["d_pet"], - sum(prod(shape) for shape in self.output_shapes[target_name]), - bias=False, + self.last_layers[target_name] = torch.nn.ModuleDict( + { + key: torch.nn.Linear( + self.hypers["d_pet"], + prod(shape), + bias=False, + ) + for key, shape in self.output_shapes[target_name].items() + } ) self.key_labels[target_name] = target_info.layout.keys diff --git a/src/metatrain/experimental/nanopet/tests/test_functionality.py b/src/metatrain/experimental/nanopet/tests/test_functionality.py index f886a8625..e114841f8 100644 --- a/src/metatrain/experimental/nanopet/tests/test_functionality.py +++ b/src/metatrain/experimental/nanopet/tests/test_functionality.py @@ -421,3 +421,48 @@ def test_spherical_output(per_atom): [system], {"spherical_tensor": model.outputs["spherical_tensor"]}, ) + + +@pytest.mark.parametrize("per_atom", [True, False]) +def test_spherical_output_multi_block(per_atom): + """Tests that the model can predict a spherical tensor output + with multiple irreps.""" + + dataset_info = DatasetInfo( + length_unit="Angstrom", + atomic_types=[1, 6, 7, 8], + targets={ + "spherical_tensor": get_generic_target_info( + { + "quantity": "spherical_tensor", + "unit": "", + "type": { + "spherical": { + "irreps": [ + {"o3_lambda": 2, "o3_sigma": 1}, + {"o3_lambda": 1, "o3_sigma": 1}, + {"o3_lambda": 0, "o3_sigma": 1}, + ] + } + }, + "num_subtargets": 100, + "per_atom": per_atom, + } + ) + }, + ) + + model = NanoPET(MODEL_HYPERS, dataset_info) + + system = System( + types=torch.tensor([6, 6]), + positions=torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 1.0]]), + cell=torch.zeros(3, 3), + pbc=torch.tensor([False, False, False]), + ) + system = get_system_with_neighbor_lists(system, model.requested_neighbor_lists()) + outputs = model( + [system], + {"spherical_tensor": model.outputs["spherical_tensor"]}, + ) + assert len(outputs["spherical_tensor"]) == 3 diff --git a/src/metatrain/experimental/nanopet/tests/test_regression.py b/src/metatrain/experimental/nanopet/tests/test_regression.py index 3c9bbba22..3c6eb9b4b 100644 --- a/src/metatrain/experimental/nanopet/tests/test_regression.py +++ b/src/metatrain/experimental/nanopet/tests/test_regression.py @@ -44,11 +44,11 @@ def test_regression_init(): expected_output = torch.tensor( [ - [0.146335646510], - [0.066624015570], - [0.077747359872], - [0.071314528584], - [0.013672338799], + [-0.031101370230], + [0.057578764856], + [0.023473259062], + [-0.040009934455], + [0.019827004522], ] ) @@ -113,16 +113,16 @@ def test_regression_train(): expected_output = torch.tensor( [ - [0.99948340654373168945], - [0.58770644664764404297], - [0.26674023270606994629], - [0.53543293476104736328], - [0.25562191009521484375], + [0.197110801935], + [0.129495143890], + [0.037004686892], + [0.096805259585], + [0.037616107613], ] ) # if you need to change the hardcoded values: - torch.set_printoptions(precision=12) - print(output["mtt::U0"].block().values) + # torch.set_printoptions(precision=12) + # print(output["mtt::U0"].block().values) torch.testing.assert_close(output["mtt::U0"].block().values, expected_output) From 0ee082f8d3d1d3d9456a007d5d7eaa635cbfdeb3 Mon Sep 17 00:00:00 2001 From: frostedoyster Date: Mon, 13 Jan 2025 17:41:37 +0100 Subject: [PATCH 02/10] Restructure scalars and vectors --- src/metatrain/experimental/soap_bpnn/model.py | 321 ++--- .../experimental/soap_bpnn/spherical.py | 79 ++ .../ethanol_reduced_100_spherical.xyz | 1100 +++++++++++++++++ tests/resources/options.yaml | 10 +- tests/resources/options_spherical.yaml | 25 + tests/resources/test.py | 11 + 6 files changed, 1389 insertions(+), 157 deletions(-) create mode 100644 src/metatrain/experimental/soap_bpnn/spherical.py create mode 100644 tests/resources/ethanol_reduced_100_spherical.xyz create mode 100644 tests/resources/options_spherical.yaml create mode 100644 tests/resources/test.py diff --git a/src/metatrain/experimental/soap_bpnn/model.py b/src/metatrain/experimental/soap_bpnn/model.py index e631efe47..a5995e056 100644 --- a/src/metatrain/experimental/soap_bpnn/model.py +++ b/src/metatrain/experimental/soap_bpnn/model.py @@ -1,4 +1,3 @@ -import copy from pathlib import Path from typing import Dict, List, Optional, Union @@ -22,6 +21,7 @@ from ...utils.additive import ZBL, CompositionModel from ...utils.dtype import dtype_to_str from ...utils.scaler import Scaler +from .spherical import ScalarBasis, TensorBasis, VectorBasis class Identity(torch.nn.Module): @@ -32,14 +32,6 @@ def forward(self, x: TensorMap) -> TensorMap: return x -class IdentityWithExtraArg(torch.nn.Module): - def __init__(self): - super().__init__() - - def forward(self, s: List[System], x: TensorMap) -> TensorMap: - return x - - class MLPMap(ModuleMap): def __init__(self, atomic_types: List[int], hypers: dict) -> None: # hardcoded for now, but could be a hyperparameter @@ -126,92 +118,6 @@ def __init__( self.activation_function = activation_function -class VectorFeaturizer(torch.nn.Module): - def __init__(self, atomic_types, num_features, soap_hypers) -> None: - super().__init__() - self.atomic_types = atomic_types - soap_vector_hypers = copy.deepcopy(soap_hypers) - soap_vector_hypers["max_angular"] = 1 - self.soap_calculator = rascaline.torch.SphericalExpansion( - radial_basis={"Gto": {}}, **soap_vector_hypers - ) - self.neighbors_species_labels = Labels( - names=["neighbor_type"], - values=torch.tensor(self.atomic_types).reshape(-1, 1), - ) - self.linear_layer = LinearMap( - Labels( - names=["o3_lambda", "o3_sigma", "center_type"], - values=torch.stack( - [ - torch.tensor([1] * len(self.atomic_types)), - torch.tensor([1] * len(self.atomic_types)), - torch.tensor(self.atomic_types), - ], - dim=1, - ), - ), - in_features=soap_vector_hypers["max_radial"] * len(self.atomic_types), - out_features=num_features, - bias=False, - out_properties=[ - Labels( - names=["property"], - values=torch.arange(num_features).reshape(-1, 1), - ) - for _ in self.atomic_types - ], - ) - - def forward(self, systems: List[System], scalar_features: TensorMap) -> TensorMap: - device = scalar_features.block(0).values.device - - spherical_expansion = self.soap_calculator(systems) - spherical_expansion = spherical_expansion.keys_to_properties( - self.neighbors_species_labels.to(device) - ) - - # drop all l=0 blocks - keys_to_drop_list: List[List[int]] = [] - for key in spherical_expansion.keys.values: - o3_lambda = int(key[0]) - o3_sigma = int(key[1]) - center_species = int(key[2]) - if o3_lambda == 0 and o3_sigma == 1: - keys_to_drop_list.append([o3_lambda, o3_sigma, center_species]) - keys_to_drop = Labels( - names=["o3_lambda", "o3_sigma", "center_type"], - values=torch.tensor(keys_to_drop_list, device=device), - ) - spherical_expansion = metatensor.torch.drop_blocks( - spherical_expansion, keys=keys_to_drop - ) - vector_features = self.linear_layer(spherical_expansion) - - overall_features = metatensor.torch.TensorMap( - keys=vector_features.keys, - blocks=[ - TensorBlock( - values=scalar_features.block( - {"center_type": int(ct)} - ).values.unsqueeze(1) - * vector_features.block({"center_type": int(ct)}).values - * 100.0, - samples=vector_features.block({"center_type": int(ct)}).samples, - components=vector_features.block( - {"center_type": int(ct)} - ).components, - properties=vector_features.block( - {"center_type": int(ct)} - ).properties, - ) - for ct in vector_features.keys.column("center_type") - ], - ) - - return overall_features - - class SoapBpnn(torch.nn.Module): __supported_devices__ = ["cuda", "cpu"] @@ -272,10 +178,16 @@ def __init__(self, model_hypers: Dict, dataset_info: DatasetInfo) -> None: ) self.outputs[ll_features_name] = ModelOutput(per_atom=True) - self.vector_featurizers = torch.nn.ModuleDict({}) + self.single_label = Labels.single() + + self.num_properties: Dict[str, Dict[str, int]] = {} # by target and block + self.basis_calculators = torch.nn.ModuleDict({}) self.heads = torch.nn.ModuleDict({}) self.head_types = self.hypers["heads"] self.last_layers = torch.nn.ModuleDict({}) + self.key_labels: Dict[str, Labels] = {} + self.component_labels: Dict[str, List[List[Labels]]] = {} + self.property_labels: Dict[str, List[Labels]] = {} for target_name, target in dataset_info.targets.items(): self._add_output(target_name, target) @@ -348,15 +260,36 @@ def forward( outputs: Dict[str, ModelOutput], selected_atoms: Optional[Labels] = None, ) -> Dict[str, TensorMap]: + device = systems[0].positions.device + if self.neighbors_species_labels.device != device: + self.neighbors_species_labels = self.neighbors_species_labels.to(device) + if self.center_type_labels.device != device: + self.center_type_labels = self.center_type_labels.to(device) + if self.single_label.values.device != device: + self.single_label = self.single_label.to(device) + self.key_labels = { + output_name: label.to(device) + for output_name, label in self.key_labels.items() + } + self.component_labels = { + output_name: [ + [labels.to(device) for labels in components_block] + for components_block in components_tmap + ] + for output_name, components_tmap in self.component_labels.items() + } + self.property_labels = { + output_name: [labels.to(device) for labels in properties_tmap] + for output_name, properties_tmap in self.property_labels.items() + } + # initialize the return dictionary return_dict: Dict[str, TensorMap] = {} soap_features = self.soap_calculator(systems, selected_samples=selected_atoms) device = soap_features.block(0).values.device - soap_features = soap_features.keys_to_properties( - self.neighbors_species_labels.to(device) - ) + soap_features = soap_features.keys_to_properties(self.neighbors_species_labels) soap_features = self.layernorm(soap_features) features = self.bpnn(soap_features) @@ -364,18 +297,14 @@ def forward( # output the hidden features, if requested: if "features" in outputs: features_options = outputs["features"] - out_features = features.keys_to_properties( - self.center_type_labels.to(device) - ) + out_features = features.keys_to_properties(self.center_type_labels) if not features_options.per_atom: out_features = metatensor.torch.sum_over_samples(out_features, ["atom"]) return_dict["features"] = _remove_center_type_from_properties(out_features) features_by_output: Dict[str, TensorMap] = {} - for output_name, vector_featurizer in self.vector_featurizers.items(): - features_by_output[output_name] = vector_featurizer(systems, features) for output_name, head in self.heads.items(): - features_by_output[output_name] = head(features_by_output[output_name]) + features_by_output[output_name] = head(features) # output the last-layer features for the outputs, if requested: for output_name in outputs.keys(): @@ -400,30 +329,92 @@ def forward( base_name = f"mtt::{base_name}" features_options = outputs[output_name] out_features = features_by_output[base_name].keys_to_properties( - self.center_type_labels.to(device) + self.center_type_labels ) if not features_options.per_atom: out_features = metatensor.torch.sum_over_samples(out_features, ["atom"]) return_dict[output_name] = _remove_center_type_from_properties(out_features) atomic_properties: Dict[str, TensorMap] = {} - for output_name, output_layer in self.last_layers.items(): + for output_name, output_layers in self.last_layers.items(): if output_name in outputs: - atomic_properties[output_name] = output_layer( - features_by_output[output_name] + blocks: List[TensorBlock] = [] + for (layer_key, output_layer), components, properties in zip( + output_layers.items(), + self.component_labels[output_name], + self.property_labels[output_name], + ): + invariant_coefficients = output_layer( + features_by_output[output_name] + ) + invariant_coefficients = invariant_coefficients.keys_to_samples( + "center_type" + ) + tensor_basis = torch.Tensor() + for ( + output_name_basis, + basis_calculators_by_block, + ) in self.basis_calculators.items(): + # need to loop again and do this due to torchscript + if output_name_basis == output_name: + for ( + basis_calculator_key, + basis_calculator, + ) in basis_calculators_by_block.items(): + if basis_calculator_key == layer_key: + tensor_basis = basis_calculator(systems) + if tensor_basis.shape == torch.Size([0]): # torch.Tensor() + # scalar + blocks.append( + TensorBlock( + invariant_coefficients.block().values, + invariant_coefficients.block().samples.remove( + "center_type" + ), + components, + properties, + ) + ) + else: + # multiply the invariant coefficients by the elements of the + # tensor basis + invariant_coefficients_tensor = ( + invariant_coefficients.block().values.reshape( + ( + invariant_coefficients.block().values.shape[0], + len(properties), + -1, + ) + ) + ) + # [sample, property, basis], [sample, component, property] to + # [sample. component, property] + atomic_property_tensor = torch.einsum( + "spb, scb -> scp", + invariant_coefficients_tensor, + tensor_basis, + ) + blocks.append( + TensorBlock( + atomic_property_tensor, + invariant_coefficients.block().samples.remove( + "center_type" + ), + components, + properties, + ) + ) + atomic_properties[output_name] = TensorMap( + self.key_labels[output_name], blocks ) for output_name, atomic_property in atomic_properties.items(): - atomic_property = atomic_property.keys_to_samples("center_type") if outputs[output_name].per_atom: - # this operation should just remove the center_type label - return_dict[output_name] = metatensor.torch.remove_dimension( - atomic_property, axis="samples", name="center_type" - ) + return_dict[output_name] = atomic_property else: # sum the atomic property to get the total property return_dict[output_name] = metatensor.torch.sum_over_samples( - atomic_property, ["atom", "center_type"] + atomic_property, ["atom"] ) if not self.training: @@ -491,21 +482,41 @@ def export(self) -> MetatensorAtomisticModel: def _add_output(self, target_name: str, target: TargetInfo) -> None: - # featurizers for non-scalars + # register bases of spherical tensors (ScalarBasis, VectorBasis, TensorBasis) + self.num_properties[target_name] = {} + self.basis_calculators[target_name] = torch.nn.ModuleDict({}) if target.is_scalar: - self.vector_featurizers[target_name] = IdentityWithExtraArg() + for key, block in target.layout.items(): + dict_key = target_name + for n, k in zip(key.names, key.values): + dict_key += f"_{n}_{int(k)}" + self.num_properties[target_name][dict_key] = len( + block.properties.values + ) + self.basis_calculators[target_name][dict_key] = ScalarBasis() elif target.is_spherical: - values_list: List[List[int]] = target.layout.keys.values.tolist() - if values_list != [[1, 1]]: - raise ValueError( - "SOAP-BPNN only supports spherical targets with " - "`o3_lambda=1` and `o3_sigma=1`, " + for key, block in target.layout.items(): + dict_key = target_name + for n, k in zip(key.names, key.values): + dict_key += f"_{n}_{int(k)}" + self.num_properties[target_name][dict_key] = len( + block.properties.values ) - self.vector_featurizers[target_name] = VectorFeaturizer( - atomic_types=self.atomic_types, - num_features=self.n_inputs_last_layer, - soap_hypers=self.hypers["soap"], - ) + o3_lambda = int(key[0]) + o3_sigma = int(key[1]) + if o3_lambda == 0 and o3_sigma == 1: + self.basis_calculators[target_name][dict_key] = ScalarBasis() + elif o3_lambda == 1 and o3_sigma == 1: + self.basis_calculators[target_name][dict_key] = VectorBasis( + self.atomic_types, self.hypers["soap"] + ) + elif o3_sigma == 1: + self.basis_calculators[target_name] = TensorBasis() + else: + raise ValueError( + f"Unsupported spherical tensor basis: {o3_lambda} {o3_sigma}, " + "pseudo-tensors are not supported yet." + ) else: raise ValueError("SOAP-BPNN only supports scalar and spherical targets.") @@ -539,33 +550,39 @@ def _add_output(self, target_name: str, target: TargetInfo) -> None: f"for target {target_name}" ) - # last linear layer - last_layer_arguments = { - "in_features": self.n_inputs_last_layer, - "out_features": len(target.layout.block().properties.values), - "bias": False, - "out_properties": [ - target.layout.block().properties for _ in self.atomic_types - ], - } - if target.is_scalar: - last_layer_arguments["in_keys"] = Labels( - "center_type", - values=torch.tensor(self.atomic_types).reshape(-1, 1), + # last linear layers, one per block + self.last_layers[target_name] = torch.nn.ModuleDict({}) + for key, block in target.layout.items(): + dict_key = target_name + for n, k in zip(key.names, key.values): + dict_key += f"_{n}_{int(k)}" + # the spherical tensor basis is made of 2*l+1 tensors, same as the number + # of components + out_properties = Labels.range( + "property", + len(block.properties.values) + * (1 if target.is_scalar else len(block.components[0])), ) - else: # spherical vector - last_layer_arguments["in_keys"] = Labels( - names=["o3_lambda", "o3_sigma", "center_type"], - values=torch.stack( - [ - torch.tensor([1] * len(self.atomic_types)), - torch.tensor([1] * len(self.atomic_types)), - torch.tensor(self.atomic_types), - ], - dim=1, + last_layer_arguments = { + "in_keys": Labels( + "center_type", + values=torch.tensor(self.atomic_types).reshape(-1, 1), ), - ) - self.last_layers[target_name] = LinearMap(**last_layer_arguments) + "in_features": self.n_inputs_last_layer, + "out_features": len(target.layout.block().properties.values) + * (1 if target.is_scalar else len(block.components[0])), + "bias": False, + "out_properties": [out_properties for _ in self.atomic_types], + } + self.last_layers[target_name][dict_key] = LinearMap(**last_layer_arguments) + + self.key_labels[target_name] = target.layout.keys + self.component_labels[target_name] = [ + block.components for block in target.layout.blocks() + ] + self.property_labels[target_name] = [ + block.properties for block in target.layout.blocks() + ] self.outputs[target_name] = ModelOutput( quantity=target.quantity, diff --git a/src/metatrain/experimental/soap_bpnn/spherical.py b/src/metatrain/experimental/soap_bpnn/spherical.py new file mode 100644 index 000000000..639b63726 --- /dev/null +++ b/src/metatrain/experimental/soap_bpnn/spherical.py @@ -0,0 +1,79 @@ +"""Modules to allow SOAP-BPNN to fit arbitrary spherical tensor targets.""" + +import copy +from typing import List + +import rascaline.torch +import torch +from metatensor.torch import Labels +from metatensor.torch.atomistic import System + + +class ScalarBasis(torch.nn.Module): + """ + A dummy module to trick torchscript (see model.py). + """ + + def __init__(self) -> None: + super().__init__() + + def forward(self, systems: List[System]) -> torch.Tensor: + return torch.Tensor() + + +class VectorBasis(torch.nn.Module): + """ + This module creates a basis of 3 vectors for each atomic environment. + + In practice, this is done by contracting a l=1 spherical expansion. + """ + + def __init__(self, atomic_types, soap_hypers) -> None: + super().__init__() + self.atomic_types = atomic_types + soap_vector_hypers = copy.deepcopy(soap_hypers) + soap_vector_hypers["max_angular"] = 1 + self.soap_calculator = rascaline.torch.SphericalExpansion( + radial_basis={"Gto": {}}, **soap_vector_hypers + ) + self.neighbor_species_labels = Labels( + names=["neighbor_type"], + values=torch.tensor(self.atomic_types).reshape(-1, 1), + ) + self.contractor = torch.nn.Parameter( + torch.randn((soap_vector_hypers["max_radial"] * len(self.atomic_types), 3)) + ) + # this optimizable basis seems to work much better than the fixed one here: + # self.register_buffer( + # "contractor", + # torch.randn((soap_vector_hypers["max_radial"]*len(self.atomic_types), 3)), + # ) + + def forward(self, systems: List[System]) -> torch.Tensor: + device = systems[0].positions.device + if self.neighbor_species_labels.device != device: + self.neighbor_species_labels = self.neighbor_species_labels.to(device) + + spherical_expansion = self.soap_calculator(systems) + + # by calling these two in the same order that they are called in the main + # model, we should ensure that the order of the samples is the same + spherical_expansion = spherical_expansion.keys_to_properties( + self.neighbor_species_labels + ) + spherical_expansion = spherical_expansion.keys_to_samples("center_type") + + basis_vectors = ( + spherical_expansion.block({"o3_lambda": 1}).values @ self.contractor + ) + + return basis_vectors # [n_atoms, 3(yzx), 3] + + +class TensorBasis(torch.nn.Module): + """ + Creates a basis of spherical tensors for each atomic environment, starting from + a basis of 3 vectors. + """ + + pass diff --git a/tests/resources/ethanol_reduced_100_spherical.xyz b/tests/resources/ethanol_reduced_100_spherical.xyz new file mode 100644 index 000000000..8573e4bd7 --- /dev/null +++ b/tests/resources/ethanol_reduced_100_spherical.xyz @@ -0,0 +1,1100 @@ +9 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key: energy unit: eV -test_set: 0.5 +test_set: 0.1 validation_set: 0.1 diff --git a/tests/resources/options_spherical.yaml b/tests/resources/options_spherical.yaml new file mode 100644 index 000000000..312154100 --- /dev/null +++ b/tests/resources/options_spherical.yaml @@ -0,0 +1,25 @@ +seed: 42 + +architecture: + name: experimental.soap_bpnn + training: + batch_size: 8 + num_epochs: 1000 + +training_set: + systems: + read_from: ethanol_reduced_100_spherical.xyz + length_unit: angstrom + targets: + mtt::forces: + quantity: "" + key: forces_spherical + per_atom: True + unit: eV/A + type: + spherical: + irreps: + - {"o3_lambda": 1, "o3_sigma": 1} + +test_set: 0.1 +validation_set: 0.1 diff --git a/tests/resources/test.py b/tests/resources/test.py new file mode 100644 index 000000000..579a6f730 --- /dev/null +++ b/tests/resources/test.py @@ -0,0 +1,11 @@ +# y z x + +import ase.io + + +structures = ase.io.read("ethanol_reduced_100.xyz", index=":") + +for structure in structures: + structure.arrays["forces_spherical"] = structure.arrays["forces"][:, [1, 2, 0]] + +ase.io.write("ethanol_reduced_100_spherical.xyz", structures) From 4be6cd472128b8f6ba4f0f71cc0b1b84ac802c91 Mon Sep 17 00:00:00 2001 From: frostedoyster Date: Tue, 14 Jan 2025 17:43:09 +0100 Subject: [PATCH 03/10] Implementation of arbitrary tensors --- pyproject.toml | 1 + src/metatrain/experimental/soap_bpnn/model.py | 92 ++--- .../experimental/soap_bpnn/spherical.py | 340 ++++++++++++++++-- 3 files changed, 350 insertions(+), 83 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 0a8b0f3bf..8ee321160 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -60,6 +60,7 @@ build-backend = "setuptools.build_meta" [project.optional-dependencies] soap-bpnn = [ "rascaline-torch @ git+https://github.com/luthaf/rascaline@5326b6e#subdirectory=python/rascaline-torch", + "wigners", ] pet = [ "pet @ git+https://github.com/lab-cosmo/pet@5d40710", diff --git a/src/metatrain/experimental/soap_bpnn/model.py b/src/metatrain/experimental/soap_bpnn/model.py index a5995e056..714fc3c4b 100644 --- a/src/metatrain/experimental/soap_bpnn/model.py +++ b/src/metatrain/experimental/soap_bpnn/model.py @@ -21,7 +21,7 @@ from ...utils.additive import ZBL, CompositionModel from ...utils.dtype import dtype_to_str from ...utils.scaler import Scaler -from .spherical import ScalarBasis, TensorBasis, VectorBasis +from .spherical import TensorBasis class Identity(torch.nn.Module): @@ -350,7 +350,7 @@ def forward( invariant_coefficients = invariant_coefficients.keys_to_samples( "center_type" ) - tensor_basis = torch.Tensor() + tensor_basis = torch.Tensor([0]) for ( output_name_basis, basis_calculators_by_block, @@ -363,47 +363,37 @@ def forward( ) in basis_calculators_by_block.items(): if basis_calculator_key == layer_key: tensor_basis = basis_calculator(systems) - if tensor_basis.shape == torch.Size([0]): # torch.Tensor() - # scalar - blocks.append( - TensorBlock( - invariant_coefficients.block().values, - invariant_coefficients.block().samples.remove( - "center_type" - ), - components, - properties, + # multiply the invariant coefficients by the elements of the + # tensor basis + invariant_coefficients_tensor = ( + invariant_coefficients.block().values.reshape( + ( + invariant_coefficients.block().values.shape[0], + len(properties), + -1, ) ) - else: - # multiply the invariant coefficients by the elements of the - # tensor basis - invariant_coefficients_tensor = ( - invariant_coefficients.block().values.reshape( - ( - invariant_coefficients.block().values.shape[0], - len(properties), - -1, - ) - ) - ) - # [sample, property, basis], [sample, component, property] to - # [sample. component, property] - atomic_property_tensor = torch.einsum( - "spb, scb -> scp", - invariant_coefficients_tensor, - tensor_basis, - ) - blocks.append( - TensorBlock( - atomic_property_tensor, - invariant_coefficients.block().samples.remove( - "center_type" - ), - components, - properties, - ) + ) + # [sample, property, basis], [sample, component, property] to + # [sample. component, property] + atomic_property_tensor = torch.einsum( + "spb, scb -> scp", + invariant_coefficients_tensor, + tensor_basis, + ) + if len(components) == 0: + # "scalar", i.e. no components + atomic_property_tensor = atomic_property_tensor.squeeze(1) + blocks.append( + TensorBlock( + atomic_property_tensor, + invariant_coefficients.block().samples.remove( + "center_type" + ), + components, + properties, ) + ) atomic_properties[output_name] = TensorMap( self.key_labels[output_name], blocks ) @@ -482,7 +472,7 @@ def export(self) -> MetatensorAtomisticModel: def _add_output(self, target_name: str, target: TargetInfo) -> None: - # register bases of spherical tensors (ScalarBasis, VectorBasis, TensorBasis) + # register bases of spherical tensors (TensorBasis) self.num_properties[target_name] = {} self.basis_calculators[target_name] = torch.nn.ModuleDict({}) if target.is_scalar: @@ -493,7 +483,9 @@ def _add_output(self, target_name: str, target: TargetInfo) -> None: self.num_properties[target_name][dict_key] = len( block.properties.values ) - self.basis_calculators[target_name][dict_key] = ScalarBasis() + self.basis_calculators[target_name][dict_key] = TensorBasis( + self.atomic_types, self.hypers["soap"], o3_lambda=0, o3_sigma=1 + ) elif target.is_spherical: for key, block in target.layout.items(): dict_key = target_name @@ -504,19 +496,9 @@ def _add_output(self, target_name: str, target: TargetInfo) -> None: ) o3_lambda = int(key[0]) o3_sigma = int(key[1]) - if o3_lambda == 0 and o3_sigma == 1: - self.basis_calculators[target_name][dict_key] = ScalarBasis() - elif o3_lambda == 1 and o3_sigma == 1: - self.basis_calculators[target_name][dict_key] = VectorBasis( - self.atomic_types, self.hypers["soap"] - ) - elif o3_sigma == 1: - self.basis_calculators[target_name] = TensorBasis() - else: - raise ValueError( - f"Unsupported spherical tensor basis: {o3_lambda} {o3_sigma}, " - "pseudo-tensors are not supported yet." - ) + self.basis_calculators[target_name][dict_key] = TensorBasis( + self.atomic_types, self.hypers["soap"], o3_lambda, o3_sigma + ) else: raise ValueError("SOAP-BPNN only supports scalar and spherical targets.") diff --git a/src/metatrain/experimental/soap_bpnn/spherical.py b/src/metatrain/experimental/soap_bpnn/spherical.py index 639b63726..3b24e946d 100644 --- a/src/metatrain/experimental/soap_bpnn/spherical.py +++ b/src/metatrain/experimental/soap_bpnn/spherical.py @@ -3,22 +3,14 @@ import copy from typing import List +import metatensor.torch +import numpy as np import rascaline.torch import torch +import wigners from metatensor.torch import Labels from metatensor.torch.atomistic import System - - -class ScalarBasis(torch.nn.Module): - """ - A dummy module to trick torchscript (see model.py). - """ - - def __init__(self) -> None: - super().__init__() - - def forward(self, systems: List[System]) -> torch.Tensor: - return torch.Tensor() +from metatensor.torch.learn.nn import Linear as LinearMap class VectorBasis(torch.nn.Module): @@ -40,14 +32,24 @@ def __init__(self, atomic_types, soap_hypers) -> None: names=["neighbor_type"], values=torch.tensor(self.atomic_types).reshape(-1, 1), ) - self.contractor = torch.nn.Parameter( - torch.randn((soap_vector_hypers["max_radial"] * len(self.atomic_types), 3)) + self.contraction = LinearMap( + in_keys=Labels( + names=["o3_lambda", "o3_sigma", "center_type"], + values=torch.stack( + [ + torch.tensor([1] * len(self.atomic_types)), + torch.tensor([1] * len(self.atomic_types)), + torch.tensor(self.atomic_types), + ], + dim=1, + ), + ), + in_features=soap_vector_hypers["max_radial"] * len(self.atomic_types), + out_features=3, + bias=False, + out_properties=[Labels.range("basis", 3) for _ in self.atomic_types], ) - # this optimizable basis seems to work much better than the fixed one here: - # self.register_buffer( - # "contractor", - # torch.randn((soap_vector_hypers["max_radial"]*len(self.atomic_types), 3)), - # ) + # this optimizable basis seems to work much better than a fixed one def forward(self, systems: List[System]) -> torch.Tensor: device = systems[0].positions.device @@ -56,24 +58,306 @@ def forward(self, systems: List[System]) -> torch.Tensor: spherical_expansion = self.soap_calculator(systems) - # by calling these two in the same order that they are called in the main - # model, we should ensure that the order of the samples is the same + # by calling keys_to_samples and keys_to_properties in the same order as they + # are called in the main model, we should ensure that the order of the samples + # is the same spherical_expansion = spherical_expansion.keys_to_properties( self.neighbor_species_labels ) - spherical_expansion = spherical_expansion.keys_to_samples("center_type") - basis_vectors = ( - spherical_expansion.block({"o3_lambda": 1}).values @ self.contractor + # drop all l=0 blocks + keys_to_drop_list: List[List[int]] = [] + for key in spherical_expansion.keys.values: + o3_lambda = int(key[0]) + o3_sigma = int(key[1]) + center_species = int(key[2]) + if o3_lambda == 0 and o3_sigma == 1: + keys_to_drop_list.append([o3_lambda, o3_sigma, center_species]) + keys_to_drop = Labels( + names=["o3_lambda", "o3_sigma", "center_type"], + values=torch.tensor(keys_to_drop_list, device=device), + ) + spherical_expansion = metatensor.torch.drop_blocks( + spherical_expansion, keys=keys_to_drop ) - return basis_vectors # [n_atoms, 3(yzx), 3] + basis_vectors = self.contraction(spherical_expansion) + basis_vectors = basis_vectors.keys_to_samples("center_type") + + basis_vectors_as_tensor = basis_vectors.block({"o3_lambda": 1}).values + return basis_vectors_as_tensor # [n_atoms, 3(yzx), 3] class TensorBasis(torch.nn.Module): """ - Creates a basis of spherical tensors for each atomic environment, starting from - a basis of 3 vectors. + Creates a basis of spherical tensors for each atomic environment. Internally, it + uses one (for proper tensors) or two (for pseudotensors) VectorBasis objects to + build a basis of 3 vectors. """ - pass + def __init__(self, atomic_types, soap_hypers, o3_lambda, o3_sigma) -> None: + super().__init__() + + self.o3_lambda = o3_lambda + self.o3_sigma = o3_sigma + if self.o3_lambda > 0: + self.vector_basis = VectorBasis(atomic_types, soap_hypers) + else: + self.vector_basis = None # type: ignore + if self.o3_sigma == -1: + self.vector_basis_pseudotensor = VectorBasis(atomic_types, soap_hypers) + else: + self.vector_basis_pseudotensor = None # type: ignore + + if self.o3_lambda > 1: + try: + import sphericart.torch + except ImportError: + raise ImportError( + "To use spherical tensors with lambda > 1 with SOAP-BPNN, please " + "install the `sphericart-torch` package." + ) + self.spherical_hamonics_calculator = sphericart.torch.SphericalHarmonics( + l_max=self.o3_lambda + ) + self.cgs = { + f"{l1}_{l2}_{L}": torch.tensor(array) + for (l1, l2, L), array in get_cg_coefficients( + self.o3_lambda + )._cgs.items() + } + else: + self.spherical_hamonics_calculator = None + self.cgs = None # type: ignore + + def forward(self, systems: List[System]) -> torch.Tensor: + # transfer cg dict to device and dtype if needed + device = systems[0].positions.device + dtype = systems[0].positions.dtype + if self.cgs is not None: + for k, v in self.cgs.items(): + if v.device != device or v.dtype != dtype: + self.cgs[k] = v.to(device, dtype) + + if self.o3_lambda == 0: + basis = torch.ones( + (sum(len(system) for system in systems), 1, 1), + device=device, + dtype=dtype, + ) + elif self.o3_lambda == 1: + basis = self.vector_basis(systems) + elif self.o3_lambda == 2: + basis = torch.empty((len(systems), 5, 5), device=device, dtype=dtype) + vector_basis = self.vector_basis(systems) # [n_atoms, 3(yzx), 3] + vector_1_xyz = vector_basis[:, [2, 0, 1], 0] + vector_2_xyz = vector_basis[:, [2, 0, 1], 1] + basis[:, :, 0] = self.spherical_hamonics_calculator(vector_1_xyz)[:, 4:] + basis[:, :, 1] = self.spherical_hamonics_calculator(vector_2_xyz)[:, 4:] + vector_1_spherical = vector_basis[:, :, 0] + vector_2_spherical = vector_basis[:, :, 1] + vector_3_spherical = vector_basis[:, :, 2] + vector_1_spherical = vector_1_spherical / torch.sqrt( + torch.sum(torch.square(vector_1_spherical), dim=-1, keepdim=True) + ) + vector_2_spherical = vector_2_spherical / torch.sqrt( + torch.sum(torch.square(vector_2_spherical), dim=-1, keepdim=True) + ) + vector_3_spherical = vector_3_spherical / torch.sqrt( + torch.sum(torch.square(vector_3_spherical), dim=-1, keepdim=True) + ) + basis[:, :, 2] = cg_combine( + vector_1_spherical, vector_2_spherical, self.cgs["1_1_2"] + ) + basis[:, :, 3] = cg_combine( + vector_1_spherical, vector_3_spherical, self.cgs["1_1_2"] + ) + basis[:, :, 4] = cg_combine( + vector_2_spherical, vector_3_spherical, self.cgs["1_1_2"] + ) + elif self.o3_lambda > 2: + basis = torch.empty( + ( + sum(len(system) for system in systems), + 2 * self.o3_lambda + 1, + 2 * self.o3_lambda + 1, + ), + device=device, + dtype=dtype, + ) + vector_basis = self.vector_basis(systems) # [n_atoms, 3(yzx), 3] + vector_1_xyz = vector_basis[:, [2, 0, 1], 0] + vector_2_xyz = vector_basis[:, [2, 0, 1], 1] + vector_3_spherical = vector_basis[:, :, 2] + vector_3_spherical = vector_3_spherical / torch.sqrt( + torch.sum(torch.square(vector_3_spherical), dim=-1, keepdim=True) + ) + sh_1 = self.spherical_hamonics_calculator(vector_1_xyz) + sh_2 = self.spherical_hamonics_calculator(vector_2_xyz) + for lam in range(self.o3_lambda + 1): + basis[:, :, lam] = cg_combine( + sh_1[:, lam**2 : (lam + 1) ** 2], + sh_2[ + :, + (self.o3_lambda - lam) ** 2 : ((self.o3_lambda - lam) + 1) ** 2, + ], + self.cgs[ + str(lam) + + "_" + + str(self.o3_lambda - lam) + + "_" + + self.o3_lambda + ], + ) + for lam in range(self.o3_lambda): + basis[:, :, self.o3_lambda + lam] = cg_combine( + cg_combine( + sh_1[:, lam**2 : (lam + 1) ** 2], + sh_2[ + :, + (self.o3_lambda - lam - 1) + ** 2 : ((self.o3_lambda - lam - 1) + 1) + ** 2, + ], + self.cgs[ + str(lam) + + "_" + + str(self.o3_lambda - lam - 1) + + "_" + + self.o3_lambda + ], + ), + vector_3_spherical, + self.cgs[str(self.o3_lambda - 1) + "_1_" + self.o3_lambda], + ) + + if self.o3_sigma == 0: + # multiply by pseudotensor + vector_basis_pseudotensor = self.vector_basis_pseudotensor(systems) + vector_1_spherical = vector_basis_pseudotensor[:, :, 0] + vector_2_spherical = vector_basis_pseudotensor[:, :, 1] + vector_3_spherical = vector_basis_pseudotensor[:, :, 2] + vector_1_spherical = vector_1_spherical / torch.sqrt( + torch.sum(torch.square(vector_1_spherical), dim=-1, keepdim=True) + ) + vector_2_spherical = vector_2_spherical / torch.sqrt( + torch.sum(torch.square(vector_2_spherical), dim=-1, keepdim=True) + ) + vector_3_spherical = vector_3_spherical / torch.sqrt( + torch.sum(torch.square(vector_3_spherical), dim=-1, keepdim=True) + ) + pseudoscalar = cg_combine( + cg_combine(vector_1_spherical, vector_2_spherical, self.cgs["1_1_1"]), + vector_3_spherical, + self.cgs["1_1_0"], + ) + basis = basis * pseudoscalar.unsqueeze(1) + + return basis # [n_atoms, 2*o3_lambda+1, 2*o3_lambda+1] + + +def cg_combine(A, B, C): + return torch.einsum("im, in, mnp-> ip", A, B, C) + + +def get_cg_coefficients(l_max): + cg_object = ClebschGordanReal() + for l1 in range(l_max + 1): + for l2 in range(l_max + 1): + for L in range(abs(l1 - l2), min(l1 + l2, l_max) + 1): + cg_object._add(l1, l2, L) + return cg_object + + +class ClebschGordanReal: + + def __init__(self): + self._cgs = {} + + def _add(self, l1, l2, L): + # print(f"Adding new CGs with l1={l1}, l2={l2}, L={L}") + + if self._cgs is None: + raise ValueError("Trying to add CGs when not initialized... exiting") + + if (l1, l2, L) in self._cgs: + raise ValueError("Trying to add CGs that are already present... exiting") + + maxx = max(l1, max(l2, L)) + + # real-to-complex and complex-to-real transformations as matrices + r2c = {} + c2r = {} + for l in range(0, maxx + 1): # noqa: E741 + r2c[l] = _real2complex(l) + c2r[l] = np.conjugate(r2c[l]).T + + complex_cg = _complex_clebsch_gordan_matrix(l1, l2, L) + + real_cg = (r2c[l1].T @ complex_cg.reshape(2 * l1 + 1, -1)).reshape( + complex_cg.shape + ) + + real_cg = real_cg.swapaxes(0, 1) + real_cg = (r2c[l2].T @ real_cg.reshape(2 * l2 + 1, -1)).reshape(real_cg.shape) + real_cg = real_cg.swapaxes(0, 1) + + real_cg = real_cg @ c2r[L].T + + if (l1 + l2 + L) % 2 == 0: + rcg = np.real(real_cg) + else: + rcg = np.imag(real_cg) + + # Zero any possible (and very rare) near-zero elements + where_almost_zero = np.where( + np.logical_and(np.abs(rcg) > 0, np.abs(rcg) < 1e-14) + ) + if len(where_almost_zero[0] != 0): + print("INFO: Found almost-zero CG!") + for i0, i1, i2 in zip( + where_almost_zero[0], where_almost_zero[1], where_almost_zero[2] + ): + rcg[i0, i1, i2] = 0.0 + + self._cgs[(l1, l2, L)] = torch.tensor(rcg) + + def get(self, key): + if key in self._cgs: + return self._cgs[key] + else: + self._add(key[0], key[1], key[2]) + return self._cgs[key] + + +def _real2complex(L): + """ + Computes a matrix that can be used to convert from real to complex-valued + spherical harmonics(coefficients) of order L. + + It's meant to be applied to the left, ``real2complex @ [-L..L]``. + """ + result = np.zeros((2 * L + 1, 2 * L + 1), dtype=np.complex128) + + I_SQRT_2 = 1.0 / np.sqrt(2) + + for m in range(-L, L + 1): + if m < 0: + result[L - m, L + m] = I_SQRT_2 * 1j * (-1) ** m + result[L + m, L + m] = -I_SQRT_2 * 1j + + if m == 0: + result[L, L] = 1.0 + + if m > 0: + result[L + m, L + m] = I_SQRT_2 * (-1) ** m + result[L - m, L + m] = I_SQRT_2 + + return result + + +def _complex_clebsch_gordan_matrix(l1, l2, L): + if np.abs(l1 - l2) > L or np.abs(l1 + l2) < L: + return np.zeros((2 * l1 + 1, 2 * l2 + 1, 2 * L + 1), dtype=np.double) + else: + return wigners.clebsch_gordan_array(l1, l2, L) From 97dbfeca27d5a93ccfd9f941904fa5f97e36965d Mon Sep 17 00:00:00 2001 From: frostedoyster Date: Wed, 15 Jan 2025 18:19:08 +0100 Subject: [PATCH 04/10] Equivariance tests --- pyproject.toml | 3 +- src/metatrain/experimental/soap_bpnn/model.py | 2 +- .../experimental/soap_bpnn/spherical.py | 21 ++- .../soap_bpnn/tests/test_equivariance.py | 156 ++++++++++++++++++ .../soap_bpnn/tests/test_functionality.py | 42 +++++ .../soap_bpnn/tests/test_invariance.py | 40 ----- src/metatrain/utils/testing/__init__.py | 0 src/metatrain/utils/testing/equivariance.py | 121 ++++++++++++++ 8 files changed, 336 insertions(+), 49 deletions(-) create mode 100644 src/metatrain/experimental/soap_bpnn/tests/test_equivariance.py delete mode 100644 src/metatrain/experimental/soap_bpnn/tests/test_invariance.py create mode 100644 src/metatrain/utils/testing/__init__.py create mode 100644 src/metatrain/utils/testing/equivariance.py diff --git a/pyproject.toml b/pyproject.toml index 8ee321160..23c195ef9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -87,7 +87,8 @@ exclude_lines = [ ] omit = [ "*/site-packages/*", - "*/metatrain/experimental/*" + "*/metatrain/experimental/*", + "*/metatrain/utils/testing/*" ] [tool.coverage.paths] diff --git a/src/metatrain/experimental/soap_bpnn/model.py b/src/metatrain/experimental/soap_bpnn/model.py index 714fc3c4b..538bcdc00 100644 --- a/src/metatrain/experimental/soap_bpnn/model.py +++ b/src/metatrain/experimental/soap_bpnn/model.py @@ -551,7 +551,7 @@ def _add_output(self, target_name: str, target: TargetInfo) -> None: values=torch.tensor(self.atomic_types).reshape(-1, 1), ), "in_features": self.n_inputs_last_layer, - "out_features": len(target.layout.block().properties.values) + "out_features": len(block.properties.values) * (1 if target.is_scalar else len(block.components[0])), "bias": False, "out_properties": [out_properties for _ in self.atomic_types], diff --git a/src/metatrain/experimental/soap_bpnn/spherical.py b/src/metatrain/experimental/soap_bpnn/spherical.py index 3b24e946d..edbc75dee 100644 --- a/src/metatrain/experimental/soap_bpnn/spherical.py +++ b/src/metatrain/experimental/soap_bpnn/spherical.py @@ -120,14 +120,17 @@ def __init__(self, atomic_types, soap_hypers, o3_lambda, o3_sigma) -> None: self.spherical_hamonics_calculator = sphericart.torch.SphericalHarmonics( l_max=self.o3_lambda ) + else: + self.spherical_hamonics_calculator = None + + if self.o3_lambda > 1 or self.o3_sigma == -1: self.cgs = { f"{l1}_{l2}_{L}": torch.tensor(array) for (l1, l2, L), array in get_cg_coefficients( - self.o3_lambda + max(self.o3_lambda, 1) # need at least 1 for pseudoscalar case )._cgs.items() } else: - self.spherical_hamonics_calculator = None self.cgs = None # type: ignore def forward(self, systems: List[System]) -> torch.Tensor: @@ -148,7 +151,11 @@ def forward(self, systems: List[System]) -> torch.Tensor: elif self.o3_lambda == 1: basis = self.vector_basis(systems) elif self.o3_lambda == 2: - basis = torch.empty((len(systems), 5, 5), device=device, dtype=dtype) + basis = torch.empty( + (sum(len(system) for system in systems), 5, 5), + device=device, + dtype=dtype, + ) vector_basis = self.vector_basis(systems) # [n_atoms, 3(yzx), 3] vector_1_xyz = vector_basis[:, [2, 0, 1], 0] vector_2_xyz = vector_basis[:, [2, 0, 1], 1] @@ -206,7 +213,7 @@ def forward(self, systems: List[System]) -> torch.Tensor: + "_" + str(self.o3_lambda - lam) + "_" - + self.o3_lambda + + str(self.o3_lambda) ], ) for lam in range(self.o3_lambda): @@ -224,14 +231,14 @@ def forward(self, systems: List[System]) -> torch.Tensor: + "_" + str(self.o3_lambda - lam - 1) + "_" - + self.o3_lambda + + str(self.o3_lambda - 1) ], ), vector_3_spherical, - self.cgs[str(self.o3_lambda - 1) + "_1_" + self.o3_lambda], + self.cgs[str(self.o3_lambda - 1) + "_1_" + str(self.o3_lambda)], ) - if self.o3_sigma == 0: + if self.o3_sigma == -1: # multiply by pseudotensor vector_basis_pseudotensor = self.vector_basis_pseudotensor(systems) vector_1_spherical = vector_basis_pseudotensor[:, :, 0] diff --git a/src/metatrain/experimental/soap_bpnn/tests/test_equivariance.py b/src/metatrain/experimental/soap_bpnn/tests/test_equivariance.py new file mode 100644 index 000000000..75773e12a --- /dev/null +++ b/src/metatrain/experimental/soap_bpnn/tests/test_equivariance.py @@ -0,0 +1,156 @@ +import copy + +import ase.io +import numpy as np +import pytest +import torch +from metatensor.torch.atomistic import System, systems_to_torch + +from metatrain.experimental.soap_bpnn import SoapBpnn +from metatrain.utils.data import DatasetInfo +from metatrain.utils.data.target_info import ( + get_energy_target_info, + get_generic_target_info, +) +from metatrain.utils.testing.equivariance import ( + get_random_rotation, + rotate_spherical_tensor, + rotate_system, +) + +from . import DATASET_PATH, MODEL_HYPERS + + +def test_rotational_invariance(): + """Tests that the model is rotationally invariant for a scalar target.""" + + dataset_info = DatasetInfo( + length_unit="Angstrom", + atomic_types=[1, 6, 7, 8], + targets={"energy": get_energy_target_info({"unit": "eV"})}, + ) + model = SoapBpnn(MODEL_HYPERS, dataset_info) + + system = ase.io.read(DATASET_PATH) + original_system = copy.deepcopy(system) + system.rotate(48, "y") + + original_output = model( + [systems_to_torch(original_system)], + {"energy": model.outputs["energy"]}, + ) + rotated_output = model( + [systems_to_torch(system)], + {"energy": model.outputs["energy"]}, + ) + + torch.testing.assert_close( + original_output["energy"].block().values, + rotated_output["energy"].block().values, + ) + + +@pytest.mark.parametrize("o3_lambda", [0, 1, 2, 3, 4]) +@pytest.mark.parametrize("o3_sigma", [-1, 1]) +def test_equivariance_rotations(o3_lambda, o3_sigma): + """Tests that the model is rotationally equivariant when predicting + spherical tensors.""" + + dataset_info = DatasetInfo( + length_unit="Angstrom", + atomic_types=[1, 6, 7, 8], + targets={ + "spherical_target": get_generic_target_info( + { + "quantity": "", + "unit": "", + "type": { + "spherical": { + "irreps": [{"o3_lambda": o3_lambda, "o3_sigma": o3_sigma}] + } + }, + "num_subtargets": 100, + "per_atom": False, + } + ) + }, + ) + model = SoapBpnn(MODEL_HYPERS, dataset_info) + + system = ase.io.read(DATASET_PATH) + original_system = systems_to_torch(system) + rotation = get_random_rotation() + rotated_system = rotate_system(original_system, rotation) + + original_output = model( + [original_system], + {"spherical_target": model.outputs["spherical_target"]}, + ) + rotated_output = model( + [rotated_system], + {"spherical_target": model.outputs["spherical_target"]}, + ) + + np.testing.assert_allclose( + rotate_spherical_tensor( + original_output["spherical_target"].block().values.detach().numpy(), + rotation, + ), + rotated_output["spherical_target"].block().values.detach().numpy(), + atol=1e-5, + rtol=1e-5, + ) + + +@pytest.mark.parametrize("o3_lambda", [0, 1, 2, 3, 4]) +@pytest.mark.parametrize("o3_sigma", [-1, 1]) +def test_equivariance_inversion(o3_lambda, o3_sigma): + """Tests that the model is equivariant with respect to inversions.""" + + dataset_info = DatasetInfo( + length_unit="Angstrom", + atomic_types=[1, 6, 7, 8], + targets={ + "spherical_target": get_generic_target_info( + { + "quantity": "", + "unit": "", + "type": { + "spherical": { + "irreps": [{"o3_lambda": o3_lambda, "o3_sigma": o3_sigma}] + } + }, + "num_subtargets": 100, + "per_atom": False, + } + ) + }, + ) + model = SoapBpnn(MODEL_HYPERS, dataset_info) + + system = ase.io.read(DATASET_PATH) + original_system = systems_to_torch(system) + inverted_system = System( + positions=original_system.positions * (-1), + cell=original_system.cell * (-1), + types=original_system.types, + pbc=original_system.pbc, + ) + + original_output = model( + [original_system], + {"spherical_target": model.outputs["spherical_target"]}, + ) + inverted_output = model( + [inverted_system], + {"spherical_target": model.outputs["spherical_target"]}, + ) + + torch.testing.assert_close( + original_output["spherical_target"].block().values + * (-1) ** o3_lambda + * (-1 if o3_sigma == -1 else 1), + inverted_output["spherical_target"].block().values, + atol=1e-5, + rtol=1e-5, + ) diff --git a/src/metatrain/experimental/soap_bpnn/tests/test_functionality.py b/src/metatrain/experimental/soap_bpnn/tests/test_functionality.py index 8d8315ec6..47befda4c 100644 --- a/src/metatrain/experimental/soap_bpnn/tests/test_functionality.py +++ b/src/metatrain/experimental/soap_bpnn/tests/test_functionality.py @@ -307,3 +307,45 @@ def test_vector_output(per_atom): [system], {"force": model.outputs["forces"]}, ) + + +@pytest.mark.parametrize("per_atom", [True, False]) +def test_spherical_outputs(per_atom): + """Tests that the model can predict a spherical target with multiple blocks.""" + + dataset_info = DatasetInfo( + length_unit="Angstrom", + atomic_types=[1, 6, 7, 8], + targets={ + "spherical_target": get_generic_target_info( + { + "quantity": "", + "unit": "", + "type": { + "spherical": { + "irreps": [ + {"o3_lambda": 0, "o3_sigma": 1}, + {"o3_lambda": 2, "o3_sigma": 1}, + ] + } + }, + "num_subtargets": 100, + "per_atom": per_atom, + } + ) + }, + ) + + model = SoapBpnn(MODEL_HYPERS, dataset_info) + + system = System( + types=torch.tensor([6, 6]), + positions=torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 1.0]]), + cell=torch.zeros(3, 3), + pbc=torch.tensor([False, False, False]), + ) + outputs = model( + [system], + {"spherical_target": model.outputs["spherical_target"]}, + ) + assert len(outputs["spherical_target"]) == 2 diff --git a/src/metatrain/experimental/soap_bpnn/tests/test_invariance.py b/src/metatrain/experimental/soap_bpnn/tests/test_invariance.py deleted file mode 100644 index 28a8e8007..000000000 --- a/src/metatrain/experimental/soap_bpnn/tests/test_invariance.py +++ /dev/null @@ -1,40 +0,0 @@ -import copy - -import ase.io -import torch -from metatensor.torch.atomistic import systems_to_torch - -from metatrain.experimental.soap_bpnn import SoapBpnn -from metatrain.utils.data import DatasetInfo -from metatrain.utils.data.target_info import get_energy_target_info - -from . import DATASET_PATH, MODEL_HYPERS - - -def test_rotational_invariance(): - """Tests that the model is rotationally invariant.""" - - dataset_info = DatasetInfo( - length_unit="Angstrom", - atomic_types=[1, 6, 7, 8], - targets={"energy": get_energy_target_info({"unit": "eV"})}, - ) - model = SoapBpnn(MODEL_HYPERS, dataset_info) - - system = ase.io.read(DATASET_PATH) - original_system = copy.deepcopy(system) - system.rotate(48, "y") - - original_output = model( - [systems_to_torch(original_system)], - {"energy": model.outputs["energy"]}, - ) - rotated_output = model( - [systems_to_torch(system)], - {"energy": model.outputs["energy"]}, - ) - - torch.testing.assert_close( - original_output["energy"].block().values, - rotated_output["energy"].block().values, - ) diff --git a/src/metatrain/utils/testing/__init__.py b/src/metatrain/utils/testing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/metatrain/utils/testing/equivariance.py b/src/metatrain/utils/testing/equivariance.py new file mode 100644 index 000000000..20839eb8b --- /dev/null +++ b/src/metatrain/utils/testing/equivariance.py @@ -0,0 +1,121 @@ +import numpy as np +import spherical +import torch +from metatensor.torch.atomistic import System +from scipy.spatial.transform import Rotation + + +def get_random_rotation() -> Rotation: + return Rotation.random() + + +def rotate_system(system: System, rotation: Rotation): + rotated_positions = ( + system.positions.detach().cpu().numpy() @ np.array(rotation.as_matrix()).T + ) + rotated_cell = system.cell.detach().cpu().numpy() @ np.array(rotation.as_matrix()).T + return System( + positions=torch.tensor( + rotated_positions, device=system.device, dtype=system.positions.dtype + ), + cell=torch.tensor(rotated_cell, device=system.device, dtype=system.cell.dtype), + types=system.types, + pbc=system.pbc, + ) + + +def rotate_spherical_tensor(spherical_tensor: np.ndarray, rotation: Rotation): + # the spherical tensor is a tensor of shape (n_samples, 2*l+1, n_properties) + L = (spherical_tensor.shape[1] - 1) // 2 + rotated_spherical_tensor = ( + spherical_tensor.swapaxes(-1, -2) @ calculate_wigner_D(rotation, L).T + ).swapaxes(-1, -2) + return rotated_spherical_tensor + + +def calculate_wigner_D(rotation, L): + + # We initialize the Wigner calculator from the quaternionic library... + wigner = spherical.Wigner(L) + # ...and we also initialize the transformation matrix from complex to real + complex_to_real_transform = complex_to_real_spherical_harmonics_transform(L) + + # Obtaining the quaternion associated with the rotation by means of the scipy + # libtary + quaternion_scipy = rotation.as_quat() + # Change convention to be consistent with the one of the quaternionic library + quaternion_quaternionic = scipy_quaternion_to_quaternionic(quaternion_scipy) + # applying the quaternion to the Wigner D to obtain the actual values + wigners_R = wigner.D(quaternion_quaternionic) + + # We now extract the values of the Wigner D matrices and transform them to real + wigner_D_matrix_complex = np.zeros((2 * L + 1, 2 * L + 1), dtype=np.complex128) + for m in range(-L, L + 1): + for mp in range(-L, L + 1): + # This is the procedure that gives the correct indexing of the Wigner + # D matrices, note that the quaternionic library uses a convention such + # that the resulting matrix is the complex conjugate of the one that we + # expect from the rotation, and so we take the complex conjugation + wigner_D_matrix_complex[m + L, mp + L] = ( + wigners_R[wigner.Dindex(L, m, mp)] + ).conj() + + # We finally transform everything in the real representation... + wigner_D_matrix = ( + complex_to_real_transform.conj() + @ wigner_D_matrix_complex + @ complex_to_real_transform.T + ) + # ...and check that we do not have imaginary contributions + assert np.allclose(wigner_D_matrix.imag, 0.0) # check that the matrix is real + + return wigner_D_matrix.real + + +def complex_to_real_spherical_harmonics_transform(ell: int): + # Generates the transformation matrix from complex spherical harmonics + # to real spherical harmonics for a given l. + # Returns a transformation matrix of shape ((2l+1), (2l+1)). + + if ell < 0 or not isinstance(ell, int): + raise ValueError("l must be a non-negative integer.") + + # The size of the transformation matrix is (2l+1) x (2l+1) + size = 2 * ell + 1 + U = np.zeros((size, size), dtype=complex) + + for m in range(-ell, ell + 1): + m_index = m + ell # Index in the matrix + if m > 0: + # Real part of Y_{l}^{m} + U[m_index, ell + m] = 1 / np.sqrt(2) * (-1) ** m + U[m_index, ell - m] = 1 / np.sqrt(2) + elif m < 0: + # Imaginary part of Y_{l}^{|m|} + U[m_index, ell + abs(m)] = -1j / np.sqrt(2) * (-1) ** m + U[m_index, ell - abs(m)] = 1j / np.sqrt(2) + else: # m == 0 + # Y_{l}^{0} remains unchanged + U[m_index, ell] = 1 + + return U + + +def scipy_quaternion_to_quaternionic(q_scipy): + # This function convert a quaternion obtained from the scipy library to the format + # used by the quaternionic library. + # Note: 'xyzw' is the format used by scipy.spatial.transform.Rotation + # while 'wxyz' is the format used by quaternionic. + qx, qy, qz, qw = q_scipy + q_quaternion = np.array([qw, qx, qy, qz]) + return q_quaternion + + +def rotation_matrix_conversion_order(rotation_matrix): + # This function is used to convert a rotation matrix from the format (y, z, x) + # to (x, y, z). + # Note: 'xyz' is the format used by scipy.spatial.transform.Rotation + # while 'yzx' is the format used by the spherical harmonics. + converted_matrix = rotation_matrix[[2, 0, 1], :] + converted_matrix = converted_matrix[:, [2, 0, 1]] + return converted_matrix From 326fe0e6a986e8ec408eb21406aa21c13c697440 Mon Sep 17 00:00:00 2001 From: frostedoyster Date: Wed, 15 Jan 2025 19:55:42 +0100 Subject: [PATCH 05/10] Torchscript tests --- src/metatrain/experimental/soap_bpnn/model.py | 12 +++-- .../experimental/soap_bpnn/spherical.py | 50 ++++++++++++------- .../soap_bpnn/tests/test_torchscript.py | 47 ++++++++++++++++- tests/resources/options_spherical.yaml | 25 ---------- tox.ini | 1 + 5 files changed, 87 insertions(+), 48 deletions(-) delete mode 100644 tests/resources/options_spherical.yaml diff --git a/src/metatrain/experimental/soap_bpnn/model.py b/src/metatrain/experimental/soap_bpnn/model.py index 538bcdc00..b3198e9bf 100644 --- a/src/metatrain/experimental/soap_bpnn/model.py +++ b/src/metatrain/experimental/soap_bpnn/model.py @@ -123,6 +123,8 @@ class SoapBpnn(torch.nn.Module): __supported_devices__ = ["cuda", "cpu"] __supported_dtypes__ = [torch.float64, torch.float32] + component_labels: Dict[str, List[List[Labels]]] # torchscript needs this + def __init__(self, model_hypers: Dict, dataset_info: DatasetInfo) -> None: super().__init__() self.hypers = model_hypers @@ -339,18 +341,18 @@ def forward( for output_name, output_layers in self.last_layers.items(): if output_name in outputs: blocks: List[TensorBlock] = [] - for (layer_key, output_layer), components, properties in zip( - output_layers.items(), - self.component_labels[output_name], - self.property_labels[output_name], + for layer_idx, (layer_key, output_layer) in enumerate( + output_layers.items() ): + components = self.component_labels[output_name][layer_idx] + properties = self.property_labels[output_name][layer_idx] invariant_coefficients = output_layer( features_by_output[output_name] ) invariant_coefficients = invariant_coefficients.keys_to_samples( "center_type" ) - tensor_basis = torch.Tensor([0]) + tensor_basis = torch.tensor(0) for ( output_name_basis, basis_calculators_by_block, diff --git a/src/metatrain/experimental/soap_bpnn/spherical.py b/src/metatrain/experimental/soap_bpnn/spherical.py index edbc75dee..5c7627a7d 100644 --- a/src/metatrain/experimental/soap_bpnn/spherical.py +++ b/src/metatrain/experimental/soap_bpnn/spherical.py @@ -1,7 +1,7 @@ """Modules to allow SOAP-BPNN to fit arbitrary spherical tensor targets.""" import copy -from typing import List +from typing import Dict, List import metatensor.torch import numpy as np @@ -95,6 +95,8 @@ class TensorBasis(torch.nn.Module): build a basis of 3 vectors. """ + cgs: Dict[str, torch.Tensor] # torchscript needs this + def __init__(self, atomic_types, soap_hypers, o3_lambda, o3_sigma) -> None: super().__init__() @@ -103,11 +105,11 @@ def __init__(self, atomic_types, soap_hypers, o3_lambda, o3_sigma) -> None: if self.o3_lambda > 0: self.vector_basis = VectorBasis(atomic_types, soap_hypers) else: - self.vector_basis = None # type: ignore + self.vector_basis = FakeVectorBasis() # needed to make torchscript work if self.o3_sigma == -1: self.vector_basis_pseudotensor = VectorBasis(atomic_types, soap_hypers) else: - self.vector_basis_pseudotensor = None # type: ignore + self.vector_basis_pseudotensor = FakeVectorBasis() # make torchscript work if self.o3_lambda > 1: try: @@ -121,26 +123,27 @@ def __init__(self, atomic_types, soap_hypers, o3_lambda, o3_sigma) -> None: l_max=self.o3_lambda ) else: - self.spherical_hamonics_calculator = None + # needed to make torchscript work + self.spherical_hamonics_calculator = torch.nn.Identity() if self.o3_lambda > 1 or self.o3_sigma == -1: self.cgs = { - f"{l1}_{l2}_{L}": torch.tensor(array) - for (l1, l2, L), array in get_cg_coefficients( + f"{l1}_{l2}_{L}": cg_tensor + for (l1, l2, L), cg_tensor in get_cg_coefficients( max(self.o3_lambda, 1) # need at least 1 for pseudoscalar case )._cgs.items() } else: - self.cgs = None # type: ignore + # needed to make torchscript work + self.cgs = {} # type: ignore def forward(self, systems: List[System]) -> torch.Tensor: # transfer cg dict to device and dtype if needed device = systems[0].positions.device dtype = systems[0].positions.dtype - if self.cgs is not None: - for k, v in self.cgs.items(): - if v.device != device or v.dtype != dtype: - self.cgs[k] = v.to(device, dtype) + for k, v in self.cgs.items(): + if v.device != device or v.dtype != dtype: + self.cgs[k] = v.to(device, dtype) if self.o3_lambda == 0: basis = torch.ones( @@ -182,7 +185,7 @@ def forward(self, systems: List[System]) -> torch.Tensor: basis[:, :, 4] = cg_combine( vector_2_spherical, vector_3_spherical, self.cgs["1_1_2"] ) - elif self.o3_lambda > 2: + else: # self.o3_lambda > 2 basis = torch.empty( ( sum(len(system) for system in systems), @@ -203,10 +206,12 @@ def forward(self, systems: List[System]) -> torch.Tensor: sh_2 = self.spherical_hamonics_calculator(vector_2_xyz) for lam in range(self.o3_lambda + 1): basis[:, :, lam] = cg_combine( - sh_1[:, lam**2 : (lam + 1) ** 2], + sh_1[:, lam * lam : (lam + 1) * (lam + 1)], sh_2[ :, - (self.o3_lambda - lam) ** 2 : ((self.o3_lambda - lam) + 1) ** 2, + (self.o3_lambda - lam) + * (self.o3_lambda - lam) : ((self.o3_lambda - lam) + 1) + * ((self.o3_lambda - lam) + 1), ], self.cgs[ str(lam) @@ -219,12 +224,14 @@ def forward(self, systems: List[System]) -> torch.Tensor: for lam in range(self.o3_lambda): basis[:, :, self.o3_lambda + lam] = cg_combine( cg_combine( - sh_1[:, lam**2 : (lam + 1) ** 2], + sh_1[:, lam * lam : (lam + 1) * (lam + 1)], sh_2[ :, (self.o3_lambda - lam - 1) - ** 2 : ((self.o3_lambda - lam - 1) + 1) - ** 2, + * (self.o3_lambda - lam - 1) : ( + (self.o3_lambda - lam - 1) + 1 + ) + * ((self.o3_lambda - lam - 1) + 1), ], self.cgs[ str(lam) @@ -368,3 +375,12 @@ def _complex_clebsch_gordan_matrix(l1, l2, L): return np.zeros((2 * l1 + 1, 2 * l2 + 1, 2 * L + 1), dtype=np.double) else: return wigners.clebsch_gordan_array(l1, l2, L) + + +class FakeVectorBasis(torch.nn.Module): + # fake class to make torchscript work + # def __init__(self, *args, **kwargs): + # super().__init__(*args, **kwargs) + + def forward(self, systems: List[System]) -> torch.Tensor: + return torch.tensor(0) diff --git a/src/metatrain/experimental/soap_bpnn/tests/test_torchscript.py b/src/metatrain/experimental/soap_bpnn/tests/test_torchscript.py index 2e16ba26a..95e17e354 100644 --- a/src/metatrain/experimental/soap_bpnn/tests/test_torchscript.py +++ b/src/metatrain/experimental/soap_bpnn/tests/test_torchscript.py @@ -1,11 +1,15 @@ import copy +import pytest import torch from metatensor.torch.atomistic import System from metatrain.experimental.soap_bpnn import SoapBpnn from metatrain.utils.data import DatasetInfo -from metatrain.utils.data.target_info import get_energy_target_info +from metatrain.utils.data.target_info import ( + get_energy_target_info, + get_generic_target_info, +) from . import MODEL_HYPERS @@ -62,6 +66,47 @@ def test_torchscript_with_identity(): ) +@pytest.mark.parametrize("o3_lambda", [0, 1, 2, 3]) +@pytest.mark.parametrize("o3_sigma", [-1, 1]) +def test_torchscript_spherical(o3_lambda, o3_sigma): + """Tests that the spherical modules can be jitted.""" + + dataset_info = DatasetInfo( + length_unit="Angstrom", + atomic_types=[1, 6, 7, 8], + targets={ + "spherical_target": get_generic_target_info( + { + "quantity": "", + "unit": "", + "type": { + "spherical": { + "irreps": [{"o3_lambda": o3_lambda, "o3_sigma": o3_sigma}] + } + }, + "num_subtargets": 100, + "per_atom": False, + } + ) + }, + ) + model = SoapBpnn(MODEL_HYPERS, dataset_info) + model = torch.jit.script(model) + + system = System( + types=torch.tensor([6, 1, 8, 7]), + positions=torch.tensor( + [[0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 2.0], [0.0, 0.0, 3.0]] + ), + cell=torch.zeros(3, 3), + pbc=torch.tensor([False, False, False]), + ) + model( + [system], + {"spherical_target": model.outputs["spherical_target"]}, + ) + + def test_torchscript_save_load(): """Tests that the model can be jitted and saved.""" diff --git a/tests/resources/options_spherical.yaml b/tests/resources/options_spherical.yaml deleted file mode 100644 index 312154100..000000000 --- a/tests/resources/options_spherical.yaml +++ /dev/null @@ -1,25 +0,0 @@ -seed: 42 - -architecture: - name: experimental.soap_bpnn - training: - batch_size: 8 - num_epochs: 1000 - -training_set: - systems: - read_from: ethanol_reduced_100_spherical.xyz - length_unit: angstrom - targets: - mtt::forces: - quantity: "" - key: forces_spherical - per_atom: True - unit: eV/A - type: - spherical: - irreps: - - {"o3_lambda": 1, "o3_sigma": 1} - -test_set: 0.1 -validation_set: 0.1 diff --git a/tox.ini b/tox.ini index 24c7e05b9..545591d79 100644 --- a/tox.ini +++ b/tox.ini @@ -96,6 +96,7 @@ description = Run SOAP-BPNN tests with pytest passenv = * deps = pytest + sphericart-torch # tensor targets extras = soap-bpnn changedir = src/metatrain/experimental/soap_bpnn/tests/ commands = From 56ab294217d8ede76e3fded218cd76e1ce39bc2d Mon Sep 17 00:00:00 2001 From: frostedoyster Date: Wed, 15 Jan 2025 20:02:56 +0100 Subject: [PATCH 06/10] Fix everything --- .../fitting-generic-targets.rst | 2 +- src/metatrain/experimental/soap_bpnn/model.py | 6 +- .../experimental/soap_bpnn/spherical.py | 45 +- .../soap_bpnn/tests/test_regression.py | 20 +- .../ethanol_reduced_100_spherical.xyz | 1100 ----------------- tests/resources/options.yaml | 10 +- tests/resources/test.py | 11 - tox.ini | 1 + 8 files changed, 51 insertions(+), 1144 deletions(-) delete mode 100644 tests/resources/ethanol_reduced_100_spherical.xyz delete mode 100644 tests/resources/test.py diff --git a/docs/src/advanced-concepts/fitting-generic-targets.rst b/docs/src/advanced-concepts/fitting-generic-targets.rst index cd20707d2..0754e4eb3 100644 --- a/docs/src/advanced-concepts/fitting-generic-targets.rst +++ b/docs/src/advanced-concepts/fitting-generic-targets.rst @@ -21,7 +21,7 @@ capabilities of the architectures in metatrain. * - SOAP-BPNN - Energy, forces, stress/virial - Yes - - Only with ``o3_lambda=1, o3_sigma=1`` + - Yes - No * - GAP - Energy, forces diff --git a/src/metatrain/experimental/soap_bpnn/model.py b/src/metatrain/experimental/soap_bpnn/model.py index b3198e9bf..12aa9e332 100644 --- a/src/metatrain/experimental/soap_bpnn/model.py +++ b/src/metatrain/experimental/soap_bpnn/model.py @@ -364,7 +364,9 @@ def forward( basis_calculator, ) in basis_calculators_by_block.items(): if basis_calculator_key == layer_key: - tensor_basis = basis_calculator(systems) + tensor_basis = basis_calculator( + systems, selected_atoms + ) # multiply the invariant coefficients by the elements of the # tensor basis invariant_coefficients_tensor = ( @@ -372,7 +374,7 @@ def forward( ( invariant_coefficients.block().values.shape[0], len(properties), - -1, + tensor_basis.shape[2], ) ) ) diff --git a/src/metatrain/experimental/soap_bpnn/spherical.py b/src/metatrain/experimental/soap_bpnn/spherical.py index 5c7627a7d..23236dfb7 100644 --- a/src/metatrain/experimental/soap_bpnn/spherical.py +++ b/src/metatrain/experimental/soap_bpnn/spherical.py @@ -1,7 +1,7 @@ """Modules to allow SOAP-BPNN to fit arbitrary spherical tensor targets.""" import copy -from typing import Dict, List +from typing import Dict, List, Optional import metatensor.torch import numpy as np @@ -51,12 +51,16 @@ def __init__(self, atomic_types, soap_hypers) -> None: ) # this optimizable basis seems to work much better than a fixed one - def forward(self, systems: List[System]) -> torch.Tensor: + def forward( + self, systems: List[System], selected_atoms: Optional[Labels] + ) -> torch.Tensor: device = systems[0].positions.device if self.neighbor_species_labels.device != device: self.neighbor_species_labels = self.neighbor_species_labels.to(device) - spherical_expansion = self.soap_calculator(systems) + spherical_expansion = self.soap_calculator( + systems, selected_samples=selected_atoms + ) # by calling keys_to_samples and keys_to_properties in the same order as they # are called in the main model, we should ensure that the order of the samples @@ -137,7 +141,9 @@ def __init__(self, atomic_types, soap_hypers, o3_lambda, o3_sigma) -> None: # needed to make torchscript work self.cgs = {} # type: ignore - def forward(self, systems: List[System]) -> torch.Tensor: + def forward( + self, systems: List[System], selected_atoms: Optional[Labels] + ) -> torch.Tensor: # transfer cg dict to device and dtype if needed device = systems[0].positions.device dtype = systems[0].positions.dtype @@ -145,21 +151,27 @@ def forward(self, systems: List[System]) -> torch.Tensor: if v.device != device or v.dtype != dtype: self.cgs[k] = v.to(device, dtype) + if selected_atoms is None: + num_atoms = sum(len(system) for system in systems) + else: + num_atoms = len(selected_atoms) + if self.o3_lambda == 0: basis = torch.ones( - (sum(len(system) for system in systems), 1, 1), + (num_atoms, 1, 1), device=device, dtype=dtype, ) elif self.o3_lambda == 1: - basis = self.vector_basis(systems) + basis = self.vector_basis(systems, selected_atoms) elif self.o3_lambda == 2: basis = torch.empty( - (sum(len(system) for system in systems), 5, 5), + (num_atoms, 5, 5), device=device, dtype=dtype, ) - vector_basis = self.vector_basis(systems) # [n_atoms, 3(yzx), 3] + vector_basis = self.vector_basis(systems, selected_atoms) + # vector_basis is [n_atoms, 3(yzx), 3] vector_1_xyz = vector_basis[:, [2, 0, 1], 0] vector_2_xyz = vector_basis[:, [2, 0, 1], 1] basis[:, :, 0] = self.spherical_hamonics_calculator(vector_1_xyz)[:, 4:] @@ -188,14 +200,15 @@ def forward(self, systems: List[System]) -> torch.Tensor: else: # self.o3_lambda > 2 basis = torch.empty( ( - sum(len(system) for system in systems), + num_atoms, 2 * self.o3_lambda + 1, 2 * self.o3_lambda + 1, ), device=device, dtype=dtype, ) - vector_basis = self.vector_basis(systems) # [n_atoms, 3(yzx), 3] + vector_basis = self.vector_basis(systems, selected_atoms) + # vector_basis is [n_atoms, 3(yzx), 3] vector_1_xyz = vector_basis[:, [2, 0, 1], 0] vector_2_xyz = vector_basis[:, [2, 0, 1], 1] vector_3_spherical = vector_basis[:, :, 2] @@ -222,7 +235,7 @@ def forward(self, systems: List[System]) -> torch.Tensor: ], ) for lam in range(self.o3_lambda): - basis[:, :, self.o3_lambda + lam] = cg_combine( + basis[:, :, self.o3_lambda + 1 + lam] = cg_combine( cg_combine( sh_1[:, lam * lam : (lam + 1) * (lam + 1)], sh_2[ @@ -247,7 +260,9 @@ def forward(self, systems: List[System]) -> torch.Tensor: if self.o3_sigma == -1: # multiply by pseudotensor - vector_basis_pseudotensor = self.vector_basis_pseudotensor(systems) + vector_basis_pseudotensor = self.vector_basis_pseudotensor( + systems, selected_atoms + ) vector_1_spherical = vector_basis_pseudotensor[:, :, 0] vector_2_spherical = vector_basis_pseudotensor[:, :, 1] vector_3_spherical = vector_basis_pseudotensor[:, :, 2] @@ -379,8 +394,8 @@ def _complex_clebsch_gordan_matrix(l1, l2, L): class FakeVectorBasis(torch.nn.Module): # fake class to make torchscript work - # def __init__(self, *args, **kwargs): - # super().__init__(*args, **kwargs) - def forward(self, systems: List[System]) -> torch.Tensor: + def forward( + self, systems: List[System], selected_atoms: Optional[Labels] + ) -> torch.Tensor: return torch.tensor(0) diff --git a/src/metatrain/experimental/soap_bpnn/tests/test_regression.py b/src/metatrain/experimental/soap_bpnn/tests/test_regression.py index f0d333621..fdb425bfe 100644 --- a/src/metatrain/experimental/soap_bpnn/tests/test_regression.py +++ b/src/metatrain/experimental/soap_bpnn/tests/test_regression.py @@ -41,11 +41,11 @@ def test_regression_init(): expected_output = torch.tensor( [ - [-0.066850379109], - [-0.012763320468], - [-0.076015546918], - [0.041823804379], - [-0.022180110216], + [-0.129940971732], + [0.120985284448], + [0.011349373497], + [-0.254746466875], + [-0.125208795071], ] ) @@ -108,11 +108,11 @@ def test_regression_train(): expected_output = torch.tensor( [ - [0.052166312933], - [-0.290258288383], - [0.722498893738], - [0.444654256105], - [0.069842025638], + [0.081161700189], + [0.346309483051], + [0.907268881798], + [0.426068425179], + [0.185527727008], ] ) diff --git a/tests/resources/ethanol_reduced_100_spherical.xyz b/tests/resources/ethanol_reduced_100_spherical.xyz deleted file mode 100644 index 8573e4bd7..000000000 --- a/tests/resources/ethanol_reduced_100_spherical.xyz +++ /dev/null @@ -1,1100 +0,0 @@ -9 -Properties=species:S:1:pos:R:3:forces:R:3:forces_spherical:R:3 energy=-97079.92222178938 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1.71603574 -0.16213206 0.17209863 -4.03453555 0.92875165 6.97874185 0.92875165 6.97874185 -4.03453555 -C -0.19971546 -0.52890905 0.02669811 -50.32240078 27.67883959 2.32694968 27.67883959 2.32694968 -50.32240078 -C -0.94799848 0.79560327 0.48240569 35.17158268 -63.98953145 63.45314783 -63.98953145 63.45314783 35.17158268 -O 1.01797936 -0.25663016 -0.49599471 55.70955734 10.69667622 -28.12359719 10.69667622 -28.12359719 55.70955734 -9 -Properties=species:S:1:pos:R:3:forces:R:3:forces_spherical:R:3 energy=-97078.09991335492 pbc="F F F" -H 1.01598992 0.08086265 -1.15841812 10.79205655 4.49420129 17.18137656 4.49420129 17.18137656 10.79205655 -H 1.21715646 0.75364502 0.62585141 3.15019409 0.12272536 -9.91066067 0.12272536 -9.91066067 3.15019409 -H -1.48522286 0.55315340 -1.07258549 14.25175704 -6.10499214 -0.00458676 -6.10499214 -0.00458676 14.25175704 -H -1.29128171 0.91950758 0.57906134 -19.58378952 19.34400465 36.11595040 19.34400465 36.11595040 -19.58378952 -H -0.57219152 2.05329630 -0.80743259 -6.05223970 -11.95308564 23.69844033 -11.95308564 23.69844033 -6.05223970 -H 0.90730026 -1.55654698 0.90237933 -6.18990641 14.73482378 -10.98446550 14.73482378 -10.98446550 -6.18990641 -C 0.58286502 0.23090702 -0.12661968 -55.38788915 -38.42851489 -7.32868357 -38.42851489 -7.32868357 -55.38788915 -C -0.78169642 1.03511562 -0.34571802 43.72805824 -17.01660910 -39.52317357 -17.01660910 -39.52317357 43.72805824 -O 0.16231922 -1.12713673 0.41324323 15.29175768 34.80744668 -9.24419722 34.80744668 -9.24419722 15.29175768 diff --git a/tests/resources/options.yaml b/tests/resources/options.yaml index 5c341257c..977c68e9f 100644 --- a/tests/resources/options.yaml +++ b/tests/resources/options.yaml @@ -3,17 +3,17 @@ seed: 42 architecture: name: experimental.soap_bpnn training: - batch_size: 8 - num_epochs: 100 + batch_size: 2 + num_epochs: 1 training_set: systems: - read_from: ethanol_reduced_100.xyz + read_from: qm9_reduced_100.xyz length_unit: angstrom targets: energy: - key: energy + key: U0 unit: eV -test_set: 0.1 +test_set: 0.5 validation_set: 0.1 diff --git a/tests/resources/test.py b/tests/resources/test.py deleted file mode 100644 index 579a6f730..000000000 --- a/tests/resources/test.py +++ /dev/null @@ -1,11 +0,0 @@ -# y z x - -import ase.io - - -structures = ase.io.read("ethanol_reduced_100.xyz", index=":") - -for structure in structures: - structure.arrays["forces_spherical"] = structure.arrays["forces"][:, [1, 2, 0]] - -ase.io.write("ethanol_reduced_100_spherical.xyz", structures) diff --git a/tox.ini b/tox.ini index 545591d79..2ae1f9999 100644 --- a/tox.ini +++ b/tox.ini @@ -97,6 +97,7 @@ passenv = * deps = pytest sphericart-torch # tensor targets + spherical # tensor target tests extras = soap-bpnn changedir = src/metatrain/experimental/soap_bpnn/tests/ commands = From d2dc9b4bca85bb8b48edacbb42d949ac8d6fa6fe Mon Sep 17 00:00:00 2001 From: frostedoyster Date: Thu, 16 Jan 2025 19:51:20 +0100 Subject: [PATCH 07/10] Simplify `drop` code --- .../getting-started/custom_dataset_conf.rst | 3 +- src/metatrain/cli/train.py | 30 +++++++++- .../experimental/soap_bpnn/spherical.py | 18 ++---- src/metatrain/utils/data/dataset.py | 59 ++++++++++++++++++- tests/cli/test_train_model.py | 5 ++ 5 files changed, 98 insertions(+), 17 deletions(-) diff --git a/docs/src/getting-started/custom_dataset_conf.rst b/docs/src/getting-started/custom_dataset_conf.rst index 9d9c16c9e..d887c1cff 100644 --- a/docs/src/getting-started/custom_dataset_conf.rst +++ b/docs/src/getting-started/custom_dataset_conf.rst @@ -26,7 +26,8 @@ Below is the simplest form of these sections: validation_set: 0.1 This configuration parses all information from ``dataset.xyz``, with 20% of the training -set randomly selected for testing and validation (10% each). +set randomly selected for testing and validation (10% each). The selected indices for +the training, validation and test subset will be available in the ``outputs`` directory. Expanded Configuration Format ----------------------------- diff --git a/src/metatrain/cli/train.py b/src/metatrain/cli/train.py index c96fddd0b..5a06ac14e 100644 --- a/src/metatrain/cli/train.py +++ b/src/metatrain/cli/train.py @@ -26,7 +26,7 @@ get_dataset, get_stats, ) -from ..utils.data.dataset import _train_test_random_split +from ..utils.data.dataset import _save_indices, _train_test_random_split from ..utils.devices import pick_devices from ..utils.distributed.logging import is_main_process from ..utils.errors import ArchitectureError @@ -250,6 +250,8 @@ def train_model( logger.info("Setting up validation set") val_datasets = [] + train_indices = [] + val_indices = [] if isinstance(options["validation_set"], float): val_size = options["validation_set"] train_size -= val_size @@ -260,9 +262,10 @@ def train_model( train_size=train_size, test_size=val_size, ) - train_datasets[i_dataset] = train_dataset_new val_datasets.append(val_dataset) + train_indices.append(train_dataset_new.indices) + val_indices.append(val_dataset.indices) else: options["validation_set"] = expand_dataset_config(options["validation_set"]) @@ -281,6 +284,8 @@ def train_model( for valid_options in options["validation_set"]: dataset, _ = get_dataset(valid_options) val_datasets.append(dataset) + train_indices.append(None) + val_indices.append(None) ############################ # SET UP TEST SET ########## @@ -288,6 +293,7 @@ def train_model( logger.info("Setting up test set") test_datasets = [] + test_indices = [] if isinstance(options["test_set"], float): test_size = options["test_set"] train_size -= test_size @@ -301,6 +307,18 @@ def train_model( train_datasets[i_dataset] = train_dataset_new test_datasets.append(test_dataset) + there_was_no_validation_split = train_indices[i_dataset] is None + new_train_indices = ( + train_dataset_new.indices + if there_was_no_validation_split + else [train_indices[i_dataset][i] for i in train_dataset_new.indices] + ) + test_indices.append( + test_dataset.indices + if there_was_no_validation_split + else [train_indices[i_dataset][i] for i in test_dataset.indices] + ) + train_indices[i_dataset] = new_train_indices else: options["test_set"] = expand_dataset_config(options["test_set"]) @@ -319,6 +337,14 @@ def train_model( for test_options in options["test_set"]: dataset, _ = get_dataset(test_options) test_datasets.append(dataset) + test_indices.append(None) + + ############################################ + # SAVE TRAIN, VALIDATION, TEST INDICES ##### + ############################################ + + if is_main_process(): + _save_indices(train_indices, val_indices, test_indices, checkpoint_dir) ########################### # CREATE DATASET_INFO ##### diff --git a/src/metatrain/experimental/soap_bpnn/spherical.py b/src/metatrain/experimental/soap_bpnn/spherical.py index 23236dfb7..fe2cfc60b 100644 --- a/src/metatrain/experimental/soap_bpnn/spherical.py +++ b/src/metatrain/experimental/soap_bpnn/spherical.py @@ -69,20 +69,12 @@ def forward( self.neighbor_species_labels ) - # drop all l=0 blocks - keys_to_drop_list: List[List[int]] = [] - for key in spherical_expansion.keys.values: - o3_lambda = int(key[0]) - o3_sigma = int(key[1]) - center_species = int(key[2]) - if o3_lambda == 0 and o3_sigma == 1: - keys_to_drop_list.append([o3_lambda, o3_sigma, center_species]) - keys_to_drop = Labels( - names=["o3_lambda", "o3_sigma", "center_type"], - values=torch.tensor(keys_to_drop_list, device=device), - ) + # drop all L=0 blocks spherical_expansion = metatensor.torch.drop_blocks( - spherical_expansion, keys=keys_to_drop + spherical_expansion, + keys=Labels( + ["o3_lambda", "o3_sigma"], torch.tensor([[0, 1]], device=device) + ), ) basis_vectors = self.contraction(spherical_expansion) diff --git a/src/metatrain/utils/data/dataset.py b/src/metatrain/utils/data/dataset.py index 4a0a9d779..3e25e395a 100644 --- a/src/metatrain/utils/data/dataset.py +++ b/src/metatrain/utils/data/dataset.py @@ -1,6 +1,8 @@ import math +import os import warnings -from typing import Any, Dict, List, Tuple, Union +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple, Union import numpy as np from metatensor.learn.data import Dataset, group_and_join @@ -339,3 +341,58 @@ def _train_test_random_split( Subset(train_dataset, train_indices), Subset(train_dataset, test_indices), ] + + +def _save_indices( + train_indices: List[Optional[List[int]]], + val_indices: List[Optional[List[int]]], + test_indices: List[Optional[List[int]]], + checkpoint_dir: Union[str, Path], +) -> None: + # Save the indices of the training, validation, and test sets to the checkpoint + # directory. This is useful for plotting errors and similar. + + # case 1: all indices are None (i.e. all datasets were user-provided explicitly) + if all(indices is None for indices in train_indices): + return + + # case 2: there is only one dataset + elif len(train_indices) == 1: # val and test are the same length + os.mkdir(os.path.join(checkpoint_dir, "indices/")) + if train_indices is not None: + np.save( + os.path.join(checkpoint_dir, "indices/training.npy"), + train_indices[0], + ) + if val_indices is not None: + np.save( + os.path.join(checkpoint_dir, "indices/validation.npy"), + val_indices[0], + ) + if test_indices is not None: + np.save( + os.path.join(checkpoint_dir, "indices/test.npy"), + test_indices[0], + ) + + # case 3: there are multiple datasets + else: + os.mkdir(os.path.join(checkpoint_dir, "indices/")) + for i, (train, val, test) in enumerate( + zip(train_indices, val_indices, test_indices) + ): + if train is not None: + np.save( + os.path.join(checkpoint_dir, f"indices/training_{i}.npy"), + train, + ) + if val is not None: + np.save( + os.path.join(checkpoint_dir, f"indices/validation_{i}.npy"), + val, + ) + if test is not None: + np.save( + os.path.join(checkpoint_dir, f"indices/test_{i}.npy"), + test, + ) diff --git a/tests/cli/test_train_model.py b/tests/cli/test_train_model.py index 97f3c5ddd..3ed48a485 100644 --- a/tests/cli/test_train_model.py +++ b/tests/cli/test_train_model.py @@ -79,6 +79,11 @@ def test_train(capfd, monkeypatch, tmp_path, output): extensions_glob = glob.glob("extensions/") assert len(extensions_glob) == 1 + # Test if training indices are saved + for subset in ["training", "validation", "test"]: + subset_glob = glob.glob(f"outputs/*/*/indices/{subset}.npy") + assert len(subset_glob) == 1 + # Open the log file and check if the logging is correct with open(log_glob[0]) as f: file_log = f.read() From da1e80e2a095353588da96e550aeafa9f7fc50bb Mon Sep 17 00:00:00 2001 From: Filippo Bigi <98903385+frostedoyster@users.noreply.github.com> Date: Fri, 17 Jan 2025 21:46:45 +0100 Subject: [PATCH 08/10] Update src/metatrain/experimental/soap_bpnn/spherical.py --- src/metatrain/experimental/soap_bpnn/spherical.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/metatrain/experimental/soap_bpnn/spherical.py b/src/metatrain/experimental/soap_bpnn/spherical.py index fe2cfc60b..9c72c1791 100644 --- a/src/metatrain/experimental/soap_bpnn/spherical.py +++ b/src/metatrain/experimental/soap_bpnn/spherical.py @@ -251,7 +251,7 @@ def forward( ) if self.o3_sigma == -1: - # multiply by pseudotensor + # multiply by pseudoscalar vector_basis_pseudotensor = self.vector_basis_pseudotensor( systems, selected_atoms ) From 974bc08573dd2703a20b9ec7ea1eb9cc6b213eaf Mon Sep 17 00:00:00 2001 From: frostedoyster Date: Fri, 24 Jan 2025 15:02:34 +0100 Subject: [PATCH 09/10] Normalize L=1 consistently with the others --- src/metatrain/experimental/soap_bpnn/spherical.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/metatrain/experimental/soap_bpnn/spherical.py b/src/metatrain/experimental/soap_bpnn/spherical.py index 9c72c1791..d4e29178b 100644 --- a/src/metatrain/experimental/soap_bpnn/spherical.py +++ b/src/metatrain/experimental/soap_bpnn/spherical.py @@ -156,6 +156,9 @@ def forward( ) elif self.o3_lambda == 1: basis = self.vector_basis(systems, selected_atoms) + basis = basis / torch.sqrt( + torch.sum(torch.square(basis), dim=-1, keepdim=True) + ) elif self.o3_lambda == 2: basis = torch.empty( (num_atoms, 5, 5), From 6f0daecd10833bd02add2a344ccbc74f111ef3d2 Mon Sep 17 00:00:00 2001 From: frostedoyster Date: Mon, 27 Jan 2025 20:11:55 +0100 Subject: [PATCH 10/10] Fix bug --- src/metatrain/experimental/soap_bpnn/spherical.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/metatrain/experimental/soap_bpnn/spherical.py b/src/metatrain/experimental/soap_bpnn/spherical.py index d4e29178b..cd3585b03 100644 --- a/src/metatrain/experimental/soap_bpnn/spherical.py +++ b/src/metatrain/experimental/soap_bpnn/spherical.py @@ -157,7 +157,7 @@ def forward( elif self.o3_lambda == 1: basis = self.vector_basis(systems, selected_atoms) basis = basis / torch.sqrt( - torch.sum(torch.square(basis), dim=-1, keepdim=True) + torch.sum(torch.square(basis), dim=1, keepdim=True) ) elif self.o3_lambda == 2: basis = torch.empty(