Add a prototype of the heat flux wrapper

Author: GardevoirX

python/metatomic_torch/metatomic/torch/ase_calculator.py

@@ -90,6 +90,10 @@
     },
 }
 
+IMPLEMENTED_PROPERTIES = [
+    "heat_flux",
+]
+
 
 class MetatomicCalculator(ase.calculators.calculator.Calculator):
     """
@@ -284,9 +288,9 @@ def __init__(
             for name, output in additional_outputs.items():
                 assert isinstance(name, str)
                 assert isinstance(output, torch.ScriptObject)
-                assert "explicit_gradients_setter" in output._method_names(), (
-                    "outputs must be ModelOutput instances"
-                )
+                assert (
+                    "explicit_gradients_setter" in output._method_names()
+                ), "outputs must be ModelOutput instances"
 
             self._additional_output_requests = additional_outputs
 
@@ -309,7 +313,7 @@ def __init__(
 
         # We do our own check to verify if a property is implemented in `calculate()`,
         # so we pretend to be able to compute all properties ASE knows about.
-        self.implemented_properties = ALL_ASE_PROPERTIES
+        self.implemented_properties = ALL_ASE_PROPERTIES + IMPLEMENTED_PROPERTIES
 
         self.additional_outputs: Dict[str, TensorMap] = {}
         """
@@ -1002,9 +1006,11 @@ def _get_ase_input(
                 [torch.full((values.shape[0],), 0), torch.arange(values.shape[0])]
             ).T,
         ),
-        components=[Labels(["xyz"], torch.arange(values.shape[1]).reshape(-1, 1))]
-        if values.shape[1] != 1
-        else [],
+        components=(
+            [Labels(["xyz"], torch.arange(values.shape[1]).reshape(-1, 1))]
+            if values.shape[1] != 1
+            else []
+        ),
         properties=Labels(
             [
                 name if "::" not in name else name.split("::")[1],
@@ -1018,8 +1024,8 @@ def _get_ase_input(
     )
     tmap.set_info("quantity", option.quantity)
     tmap.set_info("unit", option.unit)
-    tmap.to(dtype=dtype, device=device)
-    return tmap
+
+    return tmap.to(dtype=dtype, device=device)
 
 
 def _ase_to_torch_data(atoms, dtype, device):

python/metatomic_torch/metatomic/torch/heat_flux.py

@@ -0,0 +1,298 @@
+import torch
+
+from torch.autograd.functional import jvp
+from typing import List, Dict, Optional
+from vesin.metatomic import compute_requested_neighbors
+
+
+from metatensor.torch import Labels, TensorBlock, TensorMap
+from metatomic.torch import (
+    AtomisticModel,
+    ModelEvaluationOptions,
+    ModelOutput,
+    System,
+)
+
+
+def wrap_positions(positions: torch.Tensor, cell: torch.Tensor) -> torch.Tensor:
+    fractional_positions = torch.einsum("iv,kv->ik", positions, cell.inverse())
+    fractional_positions -= torch.floor(fractional_positions)
+    wrapped_positions = torch.einsum("iv,kv->ik", fractional_positions, cell)
+
+    return wrapped_positions
+
+
+def check_collisions(
+    cell: torch.Tensor, positions: torch.Tensor, cutoff: float
+) -> tuple[torch.Tensor, torch.Tensor]:
+    inv_cell = cell.inverse()
+    norm_inv_cell = torch.linalg.norm(inv_cell, dim=1)
+    inv_cell /= norm_inv_cell[:, None]
+    norm_coords = torch.einsum("iv,kv->ik", positions, inv_cell)
+    cell_vec_lengths = torch.diag(cell @ inv_cell)
+    collisions = torch.hstack(
+        [norm_coords <= cutoff, norm_coords >= cell_vec_lengths - cutoff],
+    ).to(device=positions.device)
+
+    return collisions[:, [0, 3, 1, 4, 2, 5]], norm_coords
+
+
+def collisions_to_replicas(collisions: torch.Tensor) -> torch.Tensor:
+    """
+    Convert collisions to replicas.
+
+    collisions: [N, 6]: has collisions with (x_lo, x_hi, y_lo, y_hi, z_lo, z_hi)
+    """
+    origin = torch.full(
+        (len(collisions),), True, dtype=torch.bool, device=collisions.device
+    )
+    axs = torch.vstack([origin, collisions[:, 0], collisions[:, 1]])
+    ays = torch.vstack([origin, collisions[:, 2], collisions[:, 3]])
+    azs = torch.vstack([origin, collisions[:, 4], collisions[:, 5]])
+    # leverage broadcasting
+    outs = axs[:, None, None] & ays[None, :, None] & azs[None, None, :]
+    outs = torch.movedim(outs, -1, 0)
+    outs[:, 0, 0, 0] = False
+    return outs.to(device=collisions.device)
+
+
+def generate_replica_atoms(
+    types: torch.Tensor,
+    positions: torch.Tensor,
+    cell: torch.Tensor,
+    replicas: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    replicas = torch.argwhere(replicas)
+    replica_idx = replicas[:, 0]
+    replica_offsets = torch.tensor(
+        [0, 1, -1], device=positions.device, dtype=positions.dtype
+    )[replicas[:, 1:]]
+    replica_positions = positions[replica_idx]
+    replica_positions += torch.einsum("aA,iA->ia", cell, replica_offsets)
+
+    return replica_idx, types[replica_idx], replica_positions
+
+
+def unfold_system(metatomic_system: System, cutoff: float) -> System:
+    wrapped_positions = wrap_positions(
+        metatomic_system.positions, metatomic_system.cell
+    )
+    collisions, _ = check_collisions(
+        metatomic_system.cell, wrapped_positions, cutoff + 0.5
+    )
+    replicas = collisions_to_replicas(collisions)
+    replica_idx, replica_types, replica_positions = generate_replica_atoms(
+        metatomic_system.types, wrapped_positions, metatomic_system.cell, replicas
+    )
+    unfolded_types = torch.cat(
+        [
+            metatomic_system.types,
+            replica_types,
+        ]
+    )
+    unfolded_positions = torch.cat(
+        [
+            wrapped_positions,
+            replica_positions,
+        ]
+    )
+    unfolded_idx = torch.cat(
+        [
+            torch.arange(len(metatomic_system.types), device=metatomic_system.device),
+            replica_idx,
+        ]
+    )
+    unfolded_n_atoms = len(unfolded_types)
+    masses_block = metatomic_system.get_data("masses").block()
+    velocities_block = metatomic_system.get_data("velocities").block()
+    unfolded_masses = masses_block.values[unfolded_idx]
+    unfolded_velocities = velocities_block.values[unfolded_idx]
+    unfolded_masses_block = TensorBlock(
+        values=unfolded_masses,
+        samples=Labels(
+            ["atoms"],
+            torch.arange(unfolded_n_atoms, device=metatomic_system.device).reshape(
+                -1, 1
+            ),
+        ),
+        components=masses_block.components,
+        properties=masses_block.properties,
+    )
+    unfolded_velocities_block = TensorBlock(
+        values=unfolded_velocities,
+        samples=Labels(
+            ["atoms"],
+            torch.arange(unfolded_n_atoms, device=metatomic_system.device).reshape(
+                -1, 1
+            ),
+        ),
+        components=velocities_block.components,
+        properties=velocities_block.properties,
+    )
+    unfolded_system = System(
+        types=unfolded_types,
+        positions=unfolded_positions,
+        cell=torch.tensor(
+            [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
+            dtype=unfolded_positions.dtype,
+            device=metatomic_system.device,
+        ),
+        pbc=torch.tensor([False, False, False], device=metatomic_system.device),
+    )
+    unfolded_system.add_data(
+        "masses",
+        TensorMap(
+            Labels("_", torch.tensor([[0]], device=metatomic_system.device)),
+            [unfolded_masses_block],
+        ),
+    )
+    unfolded_system.add_data(
+        "velocities",
+        TensorMap(
+            Labels("_", torch.tensor([[0]], device=metatomic_system.device)),
+            [unfolded_velocities_block],
+        ),
+    )
+    return unfolded_system.to(metatomic_system.dtype, metatomic_system.device)
+
+
+class HeatFluxWrapper(torch.nn.Module):
+
+    def __init__(self, model: AtomisticModel):
+        super().__init__()
+
+        self._model = model
+        # TODO: throw error if the simulation cell is smaller than double the interaction range
+        self._interaction_range = model.capabilities().interaction_range
+
+        self._requested_inputs = {
+            "masses": ModelOutput(quantity="mass", unit="u", per_atom=True),
+            "velocities": ModelOutput(quantity="velocity", unit="A/fs", per_atom=True),
+        }
+
+        hf_output = ModelOutput(
+            quantity="heat_flux",
+            unit="",
+            explicit_gradients=[],
+            per_atom=False,
+        )
+        outputs = self._model.capabilities().outputs.copy()
+        outputs["extra::heat_flux"] = hf_output
+        self._model.capabilities().outputs["extra::heat_flux"] = hf_output
+
+        energies_output = ModelOutput(
+            quantity="energy", unit=outputs["energy"].unit, per_atom=True
+        )
+        self._unfolded_run_options = ModelEvaluationOptions(
+            length_unit=self._model.capabilities().length_unit,
+            outputs={"energy": energies_output},
+            selected_atoms=None,
+        )
+
+    def requested_inputs(self) -> Dict[str, ModelOutput]:
+        return self._requested_inputs
+
+    def barycenter_and_atomic_energies(self, system: System, n_atoms: int):
+        atomic_e = self._model([system], self._unfolded_run_options, False)["energy"][
+            0
+        ].values.flatten()
+        total_e = atomic_e[:n_atoms].sum()
+        r_aux = system.positions.detach()
+        barycenter = torch.einsum("i,ik->k", atomic_e[:n_atoms], r_aux[:n_atoms])
+
+        return barycenter, atomic_e, total_e
+
+    def calc_unfolded_heat_flux(self, system: System) -> torch.Tensor:
+        n_atoms = len(system.positions)
+        unfolded_system = unfold_system(system, self._interaction_range).to("cpu")
+        compute_requested_neighbors(
+            unfolded_system, self._unfolded_run_options.length_unit, model=self._model
+        )
+        unfolded_system = unfolded_system.to(system.device)
+        velocities: torch.Tensor = (
+            unfolded_system.get_data("velocities").block().values.reshape(-1, 3)
+        )
+        masses: torch.Tensor = (
+            unfolded_system.get_data("masses").block().values.reshape(-1)
+        )
+        barycenter, atomic_e, total_e = self.barycenter_and_atomic_energies(
+            unfolded_system, n_atoms
+        )
+
+        term1 = torch.zeros(
+            (3), device=system.positions.device, dtype=system.positions.dtype
+        )
+        for i in range(3):
+            grad_i = torch.autograd.grad(
+                [barycenter[i]],
+                [unfolded_system.positions],
+                retain_graph=True,
+                create_graph=False,
+            )[0]
+            grad_i = torch.jit._unwrap_optional(grad_i)
+            term1[i] = (grad_i * velocities).sum()
+
+        go = torch.jit.annotate(
+            Optional[List[Optional[torch.Tensor]]], [torch.ones_like(total_e)]
+        )
+        grads = torch.autograd.grad(
+            [total_e],
+            [unfolded_system.positions],
+            grad_outputs=go,
+        )[0]
+        grads = torch.jit._unwrap_optional(grads)
+        term2 = (
+            unfolded_system.positions * (grads * velocities).sum(dim=1, keepdim=True)
+        ).sum(dim=0)
+
+        hf_pot = term1 - term2
+
+        hf_conv = (
+            (
+                atomic_e[:n_atoms]
+                + 0.5
+                * masses[:n_atoms]
+                * torch.linalg.norm(velocities[:n_atoms], dim=1) ** 2
+                * 103.6427  # u*A^2/fs^2 to eV
+            )[:, None]
+            * velocities[:n_atoms]
+        ).sum(dim=0)
+
+        return hf_pot + hf_conv
+
+    def forward(
+        self,
+        systems: List[System],
+        outputs: Dict[str, ModelOutput],
+        selected_atoms: Optional[Labels],
+    ) -> Dict[str, TensorMap]:
+
+        run_options = ModelEvaluationOptions(
+            length_unit=self._model.capabilities().length_unit,
+            outputs=outputs,
+            selected_atoms=None,
+        )
+        results = self._model(systems, run_options, False)
+
+        if "extra::heat_flux" not in outputs:
+            return results
+
+        device = systems[0].device
+        heat_fluxes: List[torch.Tensor] = []
+        for system in systems:
+            heat_fluxes.append(self.calc_unfolded_heat_flux(system))
+
+        samples = Labels(
+            ["system"], torch.arange(len(systems), device=device).reshape(-1, 1)
+        )
+
+        hf_block = TensorBlock(
+            values=torch.vstack(heat_fluxes).reshape(-1, 3, 1).to(device=device),
+            samples=samples,
+            components=[Labels(["xyz"], torch.arange(3, device=device).reshape(-1, 1))],
+            properties=Labels(["heat_flux"], torch.tensor([[0]], device=device)),
+        )
+        results["extra::heat_flux"] = TensorMap(
+            Labels("_", torch.tensor([[0]], device=device)), [hf_block]
+        )
+        return results