# SPDX-License-Identifier: LGPL-3.0-or-later
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
List,
Optional,
Tuple,
Union,
)
import numpy as np
import torch
from deepmd.dpmodel.output_def import (
ModelOutputDef,
OutputVariableCategory,
OutputVariableDef,
)
from deepmd.infer.deep_dipole import (
DeepDipole,
)
from deepmd.infer.deep_dos import (
DeepDOS,
)
from deepmd.infer.deep_eval import DeepEval as DeepEvalWrapper
from deepmd.infer.deep_eval import (
DeepEvalBackend,
)
from deepmd.infer.deep_polar import (
DeepGlobalPolar,
DeepPolar,
)
from deepmd.infer.deep_pot import (
DeepPot,
)
from deepmd.infer.deep_wfc import (
DeepWFC,
)
from deepmd.pt.model.model import (
get_model,
)
from deepmd.pt.train.wrapper import (
ModelWrapper,
)
from deepmd.pt.utils import (
env,
)
from deepmd.pt.utils.auto_batch_size import (
AutoBatchSize,
)
from deepmd.pt.utils.env import (
DEVICE,
GLOBAL_PT_FLOAT_PRECISION,
)
from deepmd.pt.utils.utils import (
to_torch_tensor,
)
if TYPE_CHECKING:
import ase.neighborlist
[docs]
class DeepEval(DeepEvalBackend):
"""PyTorch backend implementaion of DeepEval.
Parameters
----------
model_file : Path
The name of the frozen model file.
output_def : ModelOutputDef
The output definition of the model.
*args : list
Positional arguments.
auto_batch_size : bool or int or AutomaticBatchSize, default: False
If True, automatic batch size will be used. If int, it will be used
as the initial batch size.
neighbor_list : ase.neighborlist.NewPrimitiveNeighborList, optional
The ASE neighbor list class to produce the neighbor list. If None, the
neighbor list will be built natively in the model.
**kwargs : dict
Keyword arguments.
"""
def __init__(
self,
model_file: str,
output_def: ModelOutputDef,
*args: List[Any],
auto_batch_size: Union[bool, int, AutoBatchSize] = True,
neighbor_list: Optional["ase.neighborlist.NewPrimitiveNeighborList"] = None,
head: Optional[str] = None,
**kwargs: Dict[str, Any],
):
self.output_def = output_def
self.model_path = model_file
if str(self.model_path).endswith(".pt"):
state_dict = torch.load(model_file, map_location=env.DEVICE)
if "model" in state_dict:
state_dict = state_dict["model"]
self.input_param = state_dict["_extra_state"]["model_params"]
self.multi_task = "model_dict" in self.input_param
if self.multi_task:
model_keys = list(self.input_param["model_dict"].keys())
assert (
head is not None
), f"Head must be set for multitask model! Available heads are: {model_keys}"
assert (
head in model_keys
), f"No head named {head} in model! Available heads are: {model_keys}"
self.input_param = self.input_param["model_dict"][head]
state_dict_head = {"_extra_state": state_dict["_extra_state"]}
for item in state_dict:
if f"model.{head}." in item:
state_dict_head[
item.replace(f"model.{head}.", "model.Default.")
] = state_dict[item].clone()
state_dict = state_dict_head
self.input_param["resuming"] = True
model = get_model(self.input_param).to(DEVICE)
model = torch.jit.script(model)
self.dp = ModelWrapper(model)
self.dp.load_state_dict(state_dict)
elif str(self.model_path).endswith(".pth"):
model = torch.jit.load(model_file, map_location=env.DEVICE)
self.dp = ModelWrapper(model)
else:
raise ValueError("Unknown model file format!")
self.rcut = self.dp.model["Default"].get_rcut()
self.type_map = self.dp.model["Default"].get_type_map()
if isinstance(auto_batch_size, bool):
if auto_batch_size:
self.auto_batch_size = AutoBatchSize()
else:
self.auto_batch_size = None
elif isinstance(auto_batch_size, int):
self.auto_batch_size = AutoBatchSize(auto_batch_size)
elif isinstance(auto_batch_size, AutoBatchSize):
self.auto_batch_size = auto_batch_size
else:
raise TypeError("auto_batch_size should be bool, int, or AutoBatchSize")
self._has_spin = getattr(self.dp.model["Default"], "has_spin", False)
if callable(self._has_spin):
self._has_spin = self._has_spin()
[docs]
def get_rcut(self) -> float:
"""Get the cutoff radius of this model."""
return self.rcut
[docs]
def get_ntypes(self) -> int:
"""Get the number of atom types of this model."""
return len(self.type_map)
[docs]
def get_type_map(self) -> List[str]:
"""Get the type map (element name of the atom types) of this model."""
return self.type_map
[docs]
def get_dim_fparam(self) -> int:
"""Get the number (dimension) of frame parameters of this DP."""
return self.dp.model["Default"].get_dim_fparam()
[docs]
def get_dim_aparam(self) -> int:
"""Get the number (dimension) of atomic parameters of this DP."""
return self.dp.model["Default"].get_dim_aparam()
@property
[docs]
def model_type(self) -> "DeepEvalWrapper":
"""The the evaluator of the model type."""
model_output_type = self.dp.model["Default"].model_output_type()
if "energy" in model_output_type:
return DeepPot
elif "dos" in model_output_type:
return DeepDOS
elif "dipole" in model_output_type:
return DeepDipole
elif "polar" in model_output_type:
return DeepPolar
elif "global_polar" in model_output_type:
return DeepGlobalPolar
elif "wfc" in model_output_type:
return DeepWFC
else:
raise RuntimeError("Unknown model type")
[docs]
def get_sel_type(self) -> List[int]:
"""Get the selected atom types of this model.
Only atoms with selected atom types have atomic contribution
to the result of the model.
If returning an empty list, all atom types are selected.
"""
return self.dp.model["Default"].get_sel_type()
[docs]
def get_numb_dos(self) -> int:
"""Get the number of DOS."""
return self.dp.model["Default"].get_numb_dos()
[docs]
def get_has_efield(self):
"""Check if the model has efield."""
return False
[docs]
def get_ntypes_spin(self):
"""Get the number of spin atom types of this model. Only used in old implement."""
return 0
[docs]
def get_has_spin(self):
"""Check if the model has spin atom types."""
return self._has_spin
[docs]
def eval(
self,
coords: np.ndarray,
cells: np.ndarray,
atom_types: np.ndarray,
atomic: bool = False,
fparam: Optional[np.ndarray] = None,
aparam: Optional[np.ndarray] = None,
**kwargs: Dict[str, Any],
) -> Dict[str, np.ndarray]:
"""Evaluate the energy, force and virial by using this DP.
Parameters
----------
coords
The coordinates of atoms.
The array should be of size nframes x natoms x 3
cells
The cell of the region.
If None then non-PBC is assumed, otherwise using PBC.
The array should be of size nframes x 9
atom_types
The atom types
The list should contain natoms ints
atomic
Calculate the atomic energy and virial
fparam
The frame parameter.
The array can be of size :
- nframes x dim_fparam.
- dim_fparam. Then all frames are assumed to be provided with the same fparam.
aparam
The atomic parameter
The array can be of size :
- nframes x natoms x dim_aparam.
- natoms x dim_aparam. Then all frames are assumed to be provided with the same aparam.
- dim_aparam. Then all frames and atoms are provided with the same aparam.
**kwargs
Other parameters
Returns
-------
output_dict : dict
The output of the evaluation. The keys are the names of the output
variables, and the values are the corresponding output arrays.
"""
# convert all of the input to numpy array
atom_types = np.array(atom_types, dtype=np.int32)
coords = np.array(coords)
if cells is not None:
cells = np.array(cells)
natoms, numb_test = self._get_natoms_and_nframes(
coords, atom_types, len(atom_types.shape) > 1
)
request_defs = self._get_request_defs(atomic)
if "spin" not in kwargs or kwargs["spin"] is None:
out = self._eval_func(self._eval_model, numb_test, natoms)(
coords, cells, atom_types, fparam, aparam, request_defs
)
else:
out = self._eval_func(self._eval_model_spin, numb_test, natoms)(
coords,
cells,
atom_types,
np.array(kwargs["spin"]),
fparam,
aparam,
request_defs,
)
return dict(
zip(
[x.name for x in request_defs],
out,
)
)
[docs]
def _get_request_defs(self, atomic: bool) -> List[OutputVariableDef]:
"""Get the requested output definitions.
When atomic is True, all output_def are requested.
When atomic is False, only energy (tensor), force, and virial
are requested.
Parameters
----------
atomic : bool
Whether to request the atomic output.
Returns
-------
list[OutputVariableDef]
The requested output definitions.
"""
if atomic:
return list(self.output_def.var_defs.values())
else:
return [
x
for x in self.output_def.var_defs.values()
if x.category
in (
OutputVariableCategory.OUT,
OutputVariableCategory.REDU,
OutputVariableCategory.DERV_R,
OutputVariableCategory.DERV_C_REDU,
)
]
[docs]
def _eval_func(self, inner_func: Callable, numb_test: int, natoms: int) -> Callable:
"""Wrapper method with auto batch size.
Parameters
----------
inner_func : Callable
the method to be wrapped
numb_test : int
number of tests
natoms : int
number of atoms
Returns
-------
Callable
the wrapper
"""
if self.auto_batch_size is not None:
def eval_func(*args, **kwargs):
return self.auto_batch_size.execute_all(
inner_func, numb_test, natoms, *args, **kwargs
)
else:
eval_func = inner_func
return eval_func
[docs]
def _get_natoms_and_nframes(
self,
coords: np.ndarray,
atom_types: np.ndarray,
mixed_type: bool = False,
) -> Tuple[int, int]:
if mixed_type:
natoms = len(atom_types[0])
else:
natoms = len(atom_types)
if natoms == 0:
assert coords.size == 0
else:
coords = np.reshape(np.array(coords), [-1, natoms * 3])
nframes = coords.shape[0]
return natoms, nframes
[docs]
def _eval_model(
self,
coords: np.ndarray,
cells: Optional[np.ndarray],
atom_types: np.ndarray,
fparam: Optional[np.ndarray],
aparam: Optional[np.ndarray],
request_defs: List[OutputVariableDef],
):
model = self.dp.to(DEVICE)
nframes = coords.shape[0]
if len(atom_types.shape) == 1:
natoms = len(atom_types)
atom_types = np.tile(atom_types, nframes).reshape(nframes, -1)
else:
natoms = len(atom_types[0])
coord_input = torch.tensor(
coords.reshape([-1, natoms, 3]),
dtype=GLOBAL_PT_FLOAT_PRECISION,
device=DEVICE,
)
type_input = torch.tensor(atom_types, dtype=torch.long, device=DEVICE)
if cells is not None:
box_input = torch.tensor(
cells.reshape([-1, 3, 3]),
dtype=GLOBAL_PT_FLOAT_PRECISION,
device=DEVICE,
)
else:
box_input = None
if fparam is not None:
fparam_input = to_torch_tensor(fparam.reshape(-1, self.get_dim_fparam()))
else:
fparam_input = None
if aparam is not None:
aparam_input = to_torch_tensor(
aparam.reshape(-1, natoms, self.get_dim_aparam())
)
else:
aparam_input = None
do_atomic_virial = any(
x.category == OutputVariableCategory.DERV_C for x in request_defs
)
batch_output = model(
coord_input,
type_input,
box=box_input,
do_atomic_virial=do_atomic_virial,
fparam=fparam_input,
aparam=aparam_input,
)
if isinstance(batch_output, tuple):
batch_output = batch_output[0]
results = []
for odef in request_defs:
pt_name = self._OUTDEF_DP2BACKEND[odef.name]
if pt_name in batch_output:
shape = self._get_output_shape(odef, nframes, natoms)
out = batch_output[pt_name].reshape(shape).detach().cpu().numpy()
results.append(out)
else:
shape = self._get_output_shape(odef, nframes, natoms)
results.append(np.full(np.abs(shape), np.nan)) # this is kinda hacky
return tuple(results)
[docs]
def _eval_model_spin(
self,
coords: np.ndarray,
cells: Optional[np.ndarray],
atom_types: np.ndarray,
spins: np.ndarray,
fparam: Optional[np.ndarray],
aparam: Optional[np.ndarray],
request_defs: List[OutputVariableDef],
):
model = self.dp.to(DEVICE)
nframes = coords.shape[0]
if len(atom_types.shape) == 1:
natoms = len(atom_types)
atom_types = np.tile(atom_types, nframes).reshape(nframes, -1)
else:
natoms = len(atom_types[0])
coord_input = torch.tensor(
coords.reshape([-1, natoms, 3]),
dtype=GLOBAL_PT_FLOAT_PRECISION,
device=DEVICE,
)
type_input = torch.tensor(atom_types, dtype=torch.long, device=DEVICE)
spin_input = torch.tensor(
spins.reshape([-1, natoms, 3]),
dtype=GLOBAL_PT_FLOAT_PRECISION,
device=DEVICE,
)
if cells is not None:
box_input = torch.tensor(
cells.reshape([-1, 3, 3]),
dtype=GLOBAL_PT_FLOAT_PRECISION,
device=DEVICE,
)
else:
box_input = None
if fparam is not None:
fparam_input = to_torch_tensor(fparam.reshape(-1, self.get_dim_fparam()))
else:
fparam_input = None
if aparam is not None:
aparam_input = to_torch_tensor(
aparam.reshape(-1, natoms, self.get_dim_aparam())
)
else:
aparam_input = None
do_atomic_virial = any(
x.category == OutputVariableCategory.DERV_C_REDU for x in request_defs
)
batch_output = model(
coord_input,
type_input,
spin=spin_input,
box=box_input,
do_atomic_virial=do_atomic_virial,
fparam=fparam_input,
aparam=aparam_input,
)
if isinstance(batch_output, tuple):
batch_output = batch_output[0]
results = []
for odef in request_defs:
pt_name = self._OUTDEF_DP2BACKEND[odef.name]
if pt_name in batch_output:
shape = self._get_output_shape(odef, nframes, natoms)
out = batch_output[pt_name].reshape(shape).detach().cpu().numpy()
results.append(out)
else:
shape = self._get_output_shape(odef, nframes, natoms)
results.append(np.full(np.abs(shape), np.nan)) # this is kinda hacky
return tuple(results)
[docs]
def _get_output_shape(self, odef, nframes, natoms):
if odef.category == OutputVariableCategory.DERV_C_REDU:
# virial
return [nframes, *odef.shape[:-1], 9]
elif odef.category == OutputVariableCategory.REDU:
# energy
return [nframes, *odef.shape, 1]
elif odef.category == OutputVariableCategory.DERV_C:
# atom_virial
return [nframes, *odef.shape[:-1], natoms, 9]
elif odef.category == OutputVariableCategory.DERV_R:
# force
return [nframes, *odef.shape[:-1], natoms, 3]
elif odef.category == OutputVariableCategory.OUT:
# atom_energy, atom_tensor
# Something wrong here?
# return [nframes, *shape, natoms, 1]
return [nframes, natoms, *odef.shape, 1]
else:
raise RuntimeError("unknown category")
# For tests only
[docs]
def eval_model(
model,
coords: Union[np.ndarray, torch.Tensor],
cells: Optional[Union[np.ndarray, torch.Tensor]],
atom_types: Union[np.ndarray, torch.Tensor, List[int]],
spins: Optional[Union[np.ndarray, torch.Tensor]] = None,
atomic: bool = False,
infer_batch_size: int = 2,
denoise: bool = False,
):
model = model.to(DEVICE)
energy_out = []
atomic_energy_out = []
force_out = []
force_mag_out = []
virial_out = []
atomic_virial_out = []
updated_coord_out = []
logits_out = []
err_msg = (
f"All inputs should be the same format, "
f"but found {type(coords)}, {type(cells)}, {type(atom_types)} instead! "
)
return_tensor = True
if isinstance(coords, torch.Tensor):
if cells is not None:
assert isinstance(cells, torch.Tensor), err_msg
if spins is not None:
assert isinstance(spins, torch.Tensor), err_msg
assert isinstance(atom_types, torch.Tensor) or isinstance(atom_types, list)
atom_types = torch.tensor(atom_types, dtype=torch.long, device=DEVICE)
elif isinstance(coords, np.ndarray):
if cells is not None:
assert isinstance(cells, np.ndarray), err_msg
if spins is not None:
assert isinstance(spins, np.ndarray), err_msg
assert isinstance(atom_types, np.ndarray) or isinstance(atom_types, list)
atom_types = np.array(atom_types, dtype=np.int32)
return_tensor = False
nframes = coords.shape[0]
if len(atom_types.shape) == 1:
natoms = len(atom_types)
if isinstance(atom_types, torch.Tensor):
atom_types = torch.tile(atom_types.unsqueeze(0), [nframes, 1]).reshape(
nframes, -1
)
else:
atom_types = np.tile(atom_types, nframes).reshape(nframes, -1)
else:
natoms = len(atom_types[0])
coord_input = torch.tensor(
coords.reshape([-1, natoms, 3]), dtype=GLOBAL_PT_FLOAT_PRECISION, device=DEVICE
)
spin_input = None
if spins is not None:
spin_input = torch.tensor(
spins.reshape([-1, natoms, 3]),
dtype=GLOBAL_PT_FLOAT_PRECISION,
device=DEVICE,
)
has_spin = getattr(model, "has_spin", False)
if callable(has_spin):
has_spin = has_spin()
type_input = torch.tensor(atom_types, dtype=torch.long, device=DEVICE)
box_input = None
if cells is None:
pbc = False
else:
pbc = True
box_input = torch.tensor(
cells.reshape([-1, 3, 3]), dtype=GLOBAL_PT_FLOAT_PRECISION, device=DEVICE
)
num_iter = int((nframes + infer_batch_size - 1) / infer_batch_size)
for ii in range(num_iter):
batch_coord = coord_input[ii * infer_batch_size : (ii + 1) * infer_batch_size]
batch_atype = type_input[ii * infer_batch_size : (ii + 1) * infer_batch_size]
batch_box = None
batch_spin = None
if spin_input is not None:
batch_spin = spin_input[ii * infer_batch_size : (ii + 1) * infer_batch_size]
if pbc:
batch_box = box_input[ii * infer_batch_size : (ii + 1) * infer_batch_size]
input_dict = {
"coord": batch_coord,
"atype": batch_atype,
"box": batch_box,
"do_atomic_virial": atomic,
}
if has_spin:
input_dict["spin"] = batch_spin
batch_output = model(**input_dict)
if isinstance(batch_output, tuple):
batch_output = batch_output[0]
if not return_tensor:
if "energy" in batch_output:
energy_out.append(batch_output["energy"].detach().cpu().numpy())
if "atom_energy" in batch_output:
atomic_energy_out.append(
batch_output["atom_energy"].detach().cpu().numpy()
)
if "force" in batch_output:
force_out.append(batch_output["force"].detach().cpu().numpy())
if "force_mag" in batch_output:
force_mag_out.append(batch_output["force_mag"].detach().cpu().numpy())
if "virial" in batch_output:
virial_out.append(batch_output["virial"].detach().cpu().numpy())
if "atom_virial" in batch_output:
atomic_virial_out.append(
batch_output["atom_virial"].detach().cpu().numpy()
)
if "updated_coord" in batch_output:
updated_coord_out.append(
batch_output["updated_coord"].detach().cpu().numpy()
)
if "logits" in batch_output:
logits_out.append(batch_output["logits"].detach().cpu().numpy())
else:
if "energy" in batch_output:
energy_out.append(batch_output["energy"])
if "atom_energy" in batch_output:
atomic_energy_out.append(batch_output["atom_energy"])
if "force" in batch_output:
force_out.append(batch_output["force"])
if "force_mag" in batch_output:
force_mag_out.append(batch_output["force_mag"])
if "virial" in batch_output:
virial_out.append(batch_output["virial"])
if "atom_virial" in batch_output:
atomic_virial_out.append(batch_output["atom_virial"])
if "updated_coord" in batch_output:
updated_coord_out.append(batch_output["updated_coord"])
if "logits" in batch_output:
logits_out.append(batch_output["logits"])
if not return_tensor:
energy_out = (
np.concatenate(energy_out) if energy_out else np.zeros([nframes, 1])
)
atomic_energy_out = (
np.concatenate(atomic_energy_out)
if atomic_energy_out
else np.zeros([nframes, natoms, 1])
)
force_out = (
np.concatenate(force_out) if force_out else np.zeros([nframes, natoms, 3])
)
force_mag_out = (
np.concatenate(force_mag_out)
if force_mag_out
else np.zeros([nframes, natoms, 3])
)
virial_out = (
np.concatenate(virial_out) if virial_out else np.zeros([nframes, 3, 3])
)
atomic_virial_out = (
np.concatenate(atomic_virial_out)
if atomic_virial_out
else np.zeros([nframes, natoms, 3, 3])
)
updated_coord_out = (
np.concatenate(updated_coord_out) if updated_coord_out else None
)
logits_out = np.concatenate(logits_out) if logits_out else None
else:
energy_out = (
torch.cat(energy_out)
if energy_out
else torch.zeros(
[nframes, 1], dtype=GLOBAL_PT_FLOAT_PRECISION, device=DEVICE
)
)
atomic_energy_out = (
torch.cat(atomic_energy_out)
if atomic_energy_out
else torch.zeros(
[nframes, natoms, 1], dtype=GLOBAL_PT_FLOAT_PRECISION, device=DEVICE
)
)
force_out = (
torch.cat(force_out)
if force_out
else torch.zeros(
[nframes, natoms, 3], dtype=GLOBAL_PT_FLOAT_PRECISION, device=DEVICE
)
)
force_mag_out = (
torch.cat(force_mag_out)
if force_mag_out
else torch.zeros(
[nframes, natoms, 3], dtype=GLOBAL_PT_FLOAT_PRECISION, device=DEVICE
)
)
virial_out = (
torch.cat(virial_out)
if virial_out
else torch.zeros(
[nframes, 3, 3], dtype=GLOBAL_PT_FLOAT_PRECISION, device=DEVICE
)
)
atomic_virial_out = (
torch.cat(atomic_virial_out)
if atomic_virial_out
else torch.zeros(
[nframes, natoms, 3, 3], dtype=GLOBAL_PT_FLOAT_PRECISION, device=DEVICE
)
)
updated_coord_out = torch.cat(updated_coord_out) if updated_coord_out else None
logits_out = torch.cat(logits_out) if logits_out else None
if denoise:
return updated_coord_out, logits_out
else:
results_dict = {
"energy": energy_out,
"force": force_out,
"virial": virial_out,
}
if has_spin:
results_dict["force_mag"] = force_mag_out
if atomic:
results_dict["atom_energy"] = atomic_energy_out
results_dict["atom_virial"] = atomic_virial_out
return results_dict